ft_eng_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  import stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kurma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import os\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "import featuretools as ft\n",
    "from joblib import dump, load\n",
    "from imblearn.combine import SMOTETomek\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, minmax_scale, scale, power_transform, quantile_transform, normalize \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import make_scorer, log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from  sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import make_pipeline,Pipeline\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "from tensorflow.keras import layers,models,metrics,callbacks,losses\n",
    "from tensorflow.keras.activations import relu,sigmoid,swish,tanh,selu\n",
    "from tensorflow.keras.optimizers import SGD,Adam,RMSprop,Adadelta,Nadam\n",
    "from tensorflow.keras.models import Model, Sequential,load_model\n",
    "import tensorflow as tf\n",
    "\n",
    "RANDOM_SEED = 777\n",
    "np.random.seed(RANDOM_SEED)\n",
    "random.seed(RANDOM_SEED)\n",
    "os.environ['PYTHONHASHSEED'] = str(RANDOM_SEED)\n",
    "    \n",
    "import warnings\n",
    "warnings.filterwarnings(\n",
    "    action='ignore',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.read_csv('data/metadata.csv')\n",
    "sup_metadata = pd.read_csv('data/supplemental_metadata.csv')\n",
    "train_labels = pd.read_csv('data/train_labels.csv', index_col=\"sample_id\")\n",
    "val_labels = pd.read_csv('data/val_labels.csv',index_col='sample_id')\n",
    "targets = pd.read_csv('targets.csv',index_col='sample_id')\n",
    "submission_format = pd.read_csv('data/submission_format.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('data/test_df.csv',index_col=0,header=[0,1])\n",
    "train_df = pd.read_csv('data/train_df.csv',index_col=0,header=[0,1])\n",
    "val_df = pd.read_csv('data/val_df.csv',index_col=0,header=[0,1])\n",
    "\n",
    "val_df.index = metadata[metadata.split == 'val'].sample_id\n",
    "\n",
    "val_df.sort_index(inplace=True,axis=1)\n",
    "test_df.sort_index(inplace=True,axis=1)\n",
    "train_df.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 57.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_df_5 = pd.read_csv('train/train_df_5.csv',index_col=0,header=[0,1])\n",
    "val_df_5 = pd.read_csv('train/val_df_5.csv',index_col=0,header=[0,1])\n",
    "test_df_5 = pd.read_csv('test/test_df_5.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_df_5.sort_index(inplace=True,axis=1)\n",
    "val_df_5.sort_index(inplace=True,axis=1)\n",
    "train_df_5.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.76 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_set_50 = pd.read_csv('train/train_val_50.csv',index_col=0,header=[0,1])\n",
    "test_df_50 = pd.read_csv('test/test_df_50.csv',index_col=0,header=[0,1])\n",
    "val_df_50 = train_set_50[:val_df.shape[0]]\n",
    "\n",
    "test_df_50.sort_index(inplace=True,axis=1)\n",
    "train_set_50.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_200 = pd.read_csv('train/train_df_200.csv',index_col=0,header=[0,1])\n",
    "val_df_200 = pd.read_csv('train/val_df_200.csv',index_col=0,header=[0,1])\n",
    "test_df_200 = pd.read_csv('test/test_df_200.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_df_200.sort_index(inplace=True,axis=1)\n",
    "val_df_200.sort_index(inplace=True,axis=1)\n",
    "train_df_200.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_500 = pd.read_csv('train/train_df_500.csv',index_col=0,header=[0,1])\n",
    "val_df_500 = pd.read_csv('train/val_df_500.csv',index_col=0,header=[0,1])\n",
    "test_df_500 = pd.read_csv('test/test_df_500.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_df_500.sort_index(inplace=True,axis=1)\n",
    "val_df_500.sort_index(inplace=True,axis=1)\n",
    "train_df_500.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((766, 400), (766, 1000), (766, 1600), (1059, 4200), (766, 42000))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_500.shape,train_df_200.shape, train_df.shape, train_set_50.shape, train_df_5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_5 = pd.concat([train_df_5,val_df_5],axis=0)\n",
    "# train_set_50 = pd.concat([train_set_50,val_df_50],axis=0)\n",
    "train_set_100 = pd.concat([train_df,val_df],axis=0)\n",
    "train_set_200 = pd.concat([train_df_200,val_df_200],axis=0)\n",
    "train_set_500 = pd.concat([train_df_500,val_df_500],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set_5 = pd.concat([val_df_5,test_df_5],axis=0)\n",
    "test_set_50 = pd.concat([val_df_50,test_df_50],axis=0)\n",
    "test_set_100 = pd.concat([val_df,test_df],axis=0)\n",
    "test_set_200 = pd.concat([val_df_200,test_df_200],axis=0)\n",
    "test_set_500 = pd.concat([val_df_500,test_df_500],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 754,  755,  756,  757,  758,  759,  760,  761,  762,  763,  764,\n",
       "             765, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515,\n",
       "            1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526,\n",
       "            1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537,\n",
       "            1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548,\n",
       "            1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559,\n",
       "            1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sam_id = metadata[metadata.instrument_type == 'sam_testbed'].index\n",
    "sam_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples:  766\n",
      "Number of validation samples:  293\n",
      "Number of testing samples:  511\n"
     ]
    }
   ],
   "source": [
    "train_files = metadata[metadata[\"split\"] == \"train\"][\"features_path\"].to_dict()\n",
    "val_files = metadata[metadata[\"split\"] == \"val\"][\"features_path\"].to_dict()\n",
    "test_files = metadata[metadata[\"split\"] == \"test\"][\"features_path\"].to_dict()\n",
    "\n",
    "print(\"Number of training samples: \", len(train_files))\n",
    "print(\"Number of validation samples: \", len(val_files))\n",
    "print(\"Number of testing samples: \", len(test_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_frac_and_He(df):\n",
    "    \"\"\"\n",
    "    Drops fractional m/z values, m/z values > 100, and carrier gas m/z\n",
    "\n",
    "    Args:\n",
    "        df: a dataframe representing a single sample, containing m/z values\n",
    "\n",
    "    Returns:\n",
    "        The dataframe without fractional an carrier gas m/z\n",
    "    \"\"\"\n",
    "\n",
    "    # drop fractional m/z values\n",
    "    df = df[df[\"m/z\"].transform(round) == df[\"m/z\"]]\n",
    "    assert df[\"m/z\"].apply(float.is_integer).all(), \"not all m/z are integers\"\n",
    "\n",
    "    # drop m/z values greater than 99\n",
    "    df = df[df[\"m/z\"] < 100]\n",
    "\n",
    "    # drop carrier gas\n",
    "    df = df[df[\"m/z\"] != 4]\n",
    "    \n",
    "    return df\n",
    "\n",
    "def remove_background_abundance(df):\n",
    "    \"\"\"\n",
    "    Subtracts minimum abundance value\n",
    "\n",
    "    Args:\n",
    "        df: dataframe with 'm/z' and 'abundance' columns\n",
    "\n",
    "    Returns:\n",
    "        dataframe with minimum abundance subtracted for all observations\n",
    "    \"\"\"\n",
    "\n",
    "    df[\"abundance_minsub\"] = df.groupby([\"m/z\"])[\"abundance\"].transform(\n",
    "        lambda x: (x - x.min())\n",
    "    )\n",
    "\n",
    "    return df\n",
    "    \n",
    "def scale_abun(df):\n",
    "    \"\"\"\n",
    "    Scale abundance from 0-100 according to the min and max values across entire sample\n",
    "\n",
    "    Args:\n",
    "        df: dataframe containing abundances and m/z\n",
    "\n",
    "    Returns:\n",
    "        dataframe with additional column of scaled abundances\n",
    "    \"\"\"\n",
    "    # v1 =  naive qt transformation (uniform distribution)\n",
    "    # v2 =  min-max scaling + qt transformation\n",
    "    # v3 = qt transformation based on groupby mz\n",
    "    \n",
    "    print('original')\n",
    "    sns.kdeplot(df[\"abundance_minsub\"])\n",
    "    plt.show()\n",
    "\n",
    "    # df[\"abun_minsub_scaled\"] = quantile_transform(df[\"abundance_minsub\"].astype(float).values.reshape(-1,1),output_distribution='uniform',random_state=RANDOM_SEED)\n",
    "    print('minmax')\n",
    "    df[\"abun_minsub_scaled\"] = minmax_scale(df[\"abundance_minsub\"].astype(float))\n",
    "    for i in range(100):\n",
    "        try:\n",
    "            \n",
    "            df['abun_minsub_scaled'] = quantile_transform(\n",
    "                temp.groupby('m/z')['abundance'].get_group(i).values.reshape(-1,1),\n",
    "                output_distribution='uniform',\n",
    "                random_state=RANDOM_SEED\n",
    "                )\n",
    "        except:\n",
    "            print(f'no mz --> {i}')\n",
    "            pass\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_sample(n):\n",
    "    if n < len(train_files):\n",
    "        idx = metadata.loc[n,'sample_id']\n",
    "        print(idx)\n",
    "        df = pd.read_csv('data/train_features/'+idx+'.csv')\n",
    "    else:\n",
    "        idx = metadata.loc[n,'sample_id']\n",
    "        print(idx)\n",
    "        df = pd.read_csv('data/test_features/'+idx+'.csv')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S0760\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>temp</th>\n",
       "      <th>m/z</th>\n",
       "      <th>abundance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>647.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>235.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>294.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.000</td>\n",
       "      <td>81.0</td>\n",
       "      <td>470.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>176.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89786</th>\n",
       "      <td>2494.24</td>\n",
       "      <td>1100.556</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89787</th>\n",
       "      <td>2494.26</td>\n",
       "      <td>1100.544</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89788</th>\n",
       "      <td>2494.28</td>\n",
       "      <td>1100.532</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89789</th>\n",
       "      <td>2494.30</td>\n",
       "      <td>1100.520</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89790</th>\n",
       "      <td>2494.32</td>\n",
       "      <td>1100.508</td>\n",
       "      <td>169.0</td>\n",
       "      <td>529.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89791 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time      temp    m/z  abundance\n",
       "0         0.00     0.000   78.0      647.1\n",
       "1         0.02     0.000   79.0      235.3\n",
       "2         0.04     0.000   80.0      294.1\n",
       "3         0.06     0.000   81.0      470.6\n",
       "4         0.26     0.000   82.0      176.5\n",
       "...        ...       ...    ...        ...\n",
       "89786  2494.24  1100.556  165.0        0.0\n",
       "89787  2494.26  1100.544  166.0        0.0\n",
       "89788  2494.28  1100.532  167.0        0.0\n",
       "89789  2494.30  1100.520  168.0        0.0\n",
       "89790  2494.32  1100.508  169.0      529.4\n",
       "\n",
       "[89791 rows x 4 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = load_sample(760)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "S0039\n"
     ]
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(40):\n",
    "    temp = load_sample(i)\n",
    "    plt.plot(quantile_transform(temp.groupby('m/z')['abundance'].get_group(i).values.reshape(-1,1),output_distribution='uniform',random_state=RANDOM_SEED))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess function\n",
    "def preprocess_sample(df):\n",
    "    df = drop_frac_and_He(df)\n",
    "    df = remove_background_abundance(df)\n",
    "    df = scale_abun(df)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2100, 2)\n"
     ]
    },
    {
     "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>temp_bin</th>\n",
       "      <th>m/z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-100, 0]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(-100, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(-100, 0]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(-100, 0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(-100, 0]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp_bin  m/z\n",
       "0  (-100, 0]    0\n",
       "1  (-100, 0]    1\n",
       "2  (-100, 0]    2\n",
       "3  (-100, 0]    3\n",
       "4  (-100, 0]    4"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series of temperature bins\n",
    "temprange = pd.interval_range(start=-100, end=2000, freq=100)\n",
    "temprange\n",
    "\n",
    "# Make dataframe with rows that are combinations of all temperature bins\n",
    "# and all m/z values\n",
    "allcombs = list(itertools.product(temprange, [*range(0, 100)]))\n",
    "\n",
    "allcombs_df = pd.DataFrame(allcombs, columns=[\"temp_bin\", \"m/z\"])\n",
    "print(allcombs_df.shape)\n",
    "allcombs_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def abun_per_tempbin(df):\n",
    "\n",
    "    \"\"\"\n",
    "    Transforms dataset to take the preprocessed max abundance for each\n",
    "    temperature range for each m/z value\n",
    "\n",
    "    Args:\n",
    "        df: dataframe to transform\n",
    "\n",
    "    Returns:\n",
    "        transformed dataframe\n",
    "    \"\"\"\n",
    "    \n",
    "    # Bin temperatures\n",
    "    df[\"temp_bin\"] = pd.cut(df[\"temp\"], bins=temprange)\n",
    "\n",
    "    # Combine with a list of all temp bin-m/z value combinations\n",
    "    df = pd.merge(allcombs_df, df, on=[\"temp_bin\", \"m/z\"], how=\"left\")\n",
    "\n",
    "    # Aggregate to temperature bin level to find max\n",
    "    df = df.groupby([\"temp_bin\", \"m/z\"]).max(\"abun_minsub_scaled\").reset_index()\n",
    "\n",
    "    # Fill in 0 for abundance values without information\n",
    "    df = df.replace(np.nan, 0)\n",
    "\n",
    "    # Reshape so each row is a single sample\n",
    "    df = df.pivot_table(columns=[\"m/z\", \"temp_bin\"], values=[\"abun_minsub_scaled\"])\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sam_2_com(df):\n",
    "    df['abundance'] = df['abundance']*1.60217646 * 10**-19\n",
    "    df['time'] = minmax_scale(df['time'],feature_range=(0,1800))\n",
    "    df['temp'] = minmax_scale(df['temp'],feature_range=(0,1200))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/766 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of train files:  766\n",
      "original\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minmax\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qt\n"
     ]
    },
    {
     "data": {
      "image/png": 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7OZEDcx/aDm9spw+wkUPjq85rPv6Ax12uIyExCoh1gRxV91xu/PwPPIKXPP3RE3vuObMWCqcBnx+b3ws8cXyBJDuAHc3s15J8akq1TcoW4L+GLuI42YfZYB9mwyB9eGlzWaZvW+iGWQuFRVXVTmDn0HUsV5LdVbV96DqOh32YDfZhNqyGPoybtR3NdwBnjM2f3rRJkqZg1kLhw8C2JI9Isgm4CNg1cE2StGbM1PBRVR1K8mLg74H1wJur6hMDlzVpK3boa4x9mA32YTashj60Uv0eaiNJWkFmbfhIkjQgQ0GS1DIUerLY6TqSbE5yVXP7h5JsHaDMY+rQh5ckuTXJzUmuS7Lgsc9D6XralCQ/maSSzNyhhV36kOTC5n/xiSRvm3aNi+nwWjozyQeSfLR5PZ03RJ0LSfLmJPuS3LLA7Uny2qZ/Nyc5Z9o1TkxVeZnwhdFO8v8EHglsAv4NOOuoZV4EvL6Zvgi4aui6l9GHpwInNtO/uBL70Cx3EnA9cAOwfei6l/F/2AZ8FHhoM//woeteRh92Ar/YTJ8F3D503UfV94PAOcAtC9x+HvC3jL56/STgQ0PXvNyLWwr9aE/XUVUHgbnTdYy7ALiimX4XcG5m4cQnRyzah6r6QFV9vZm9gdH3SmZJl/8DwO8Cvw/83zSL66hLH14I/GlV/TdAVe2bco2L6dKHAr6pmX4w8IUp1reoqroe+MoxFrkAuLJGbgAekuTU6VQ3WYZCP+Y7XcdpCy1TVYeAu4Bvnkp13XTpw7hLGH1SmiWL9qHZzD+jqv5mmoUtQZf/w6OARyX51yQ3JHnG1KrrpksfLgeem2QvcC3wS9MpbWKW+n6ZWTP1PQWtTEmeC2wHfmjoWpYiyTrg1cDzBy7leG1gNIT0FEZba9cneUxV/c+QRS3Rc4C3VNWrknwf8NYk311Vh4cubK1xS6EfXU7X0S6TZAOjTeYvT6W6bjqdciTJjwC/CZxfVQemVFtXi/XhJOC7gX9KcjujseBdM7azucv/YS+wq6rurarPAP/BKCRmRZc+XAJcDVBVHwROYHSiuZVi1Zyix1DoR5fTdewCLm6mfwr4x2r2WM2IRfuQ5HuANzAKhFkbx4ZF+lBVd1XVlqraWlVbGe0XOb+qdg9T7ry6vJbew2grgSRbGA0nfXqKNS6mSx8+B5wLkOQ7GYXC/qlWeXx2Ac9rjkJ6EnBXVd05dFHL4fBRD2qB03Uk+R1gd1XtAt7EaBN5D6MdWBcNV/EDdezDHwDfCLyz2Uf+uao6f7Cij9KxDzOtYx/+Hnh6kluB+4Bfr6qZ2ers2IeXAm9M8quMdjo/f5Y+JCV5O6Pg3dLs93gFsBGgql7PaD/IecAe4Osc/cMpK4inuZAktRw+kiS1DAVJUstQkCS1DAVJUstQkCS1DAWpR0men+Rbh65D6spQkPr1fMBQ0Irhl9ekCUnym4y+pb6P0cnRbmJ0Tqi/THIP8H1Vdc+AJUqLMhSkCUjyvYy+lX42o/fVRxiFwm7g12bs1BnSggwFaTKeDFwz9/sSSWb+FBrSfNynIElqGQrSZFwPPCvJg5KcBDyzaf8qo1N0SyuCw0fSBFTVR5Jcxej3h/cxOl00wFuA17ujWSuFZ0mVepDkcuBrVfWHQ9ciLYXDR5KkllsKkqSWWwqSpJahIElqGQqSpJahIElqGQqSpNb/A8dQq6Vos7H3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qt\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/766 [00:01<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "# Assembling preprocessed and transformed training set\n",
    "\n",
    "train_features_dict = {}\n",
    "print(\"Total number of train files: \", len(train_files))\n",
    "\n",
    "for i, (sample_id, filepath) in enumerate(tqdm(train_files.items())):\n",
    "\n",
    "    # Load training sample\n",
    "    temp = pd.read_csv('data/' + filepath)\n",
    "\n",
    "    # map sam samples to com samples\n",
    "    if sample_id in sam_id:\n",
    "        temp = sam_2_com(temp)\n",
    "    \n",
    "    # Preprocessing training sample\n",
    "    train_sample_pp = preprocess_sample(temp)\n",
    "\n",
    "    # Feature engineering\n",
    "    train_sample_fe = abun_per_tempbin(train_sample_pp).reset_index(drop=True)\n",
    "    train_features_dict[sample_id] = train_sample_fe\n",
    "    break\n",
    "\n",
    "train_features = pd.concat(\n",
    "    train_features_dict, names=[\"sample_id\", \"dummy_index\"]\n",
    ").reset_index(level=\"dummy_index\", drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assembling preprocessed and transformed valing set\n",
    "\n",
    "val_features_dict = {}\n",
    "print(\"Total number of val files: \", len(val_files))\n",
    "\n",
    "for i, (sample_id, filepath) in enumerate(tqdm(val_files.items())):\n",
    "\n",
    "    # Load valing sample\n",
    "    temp = pd.read_csv('data/' + filepath)\n",
    "\n",
    "    # Preprocessing valing sample\n",
    "    val_sample_pp = preprocess_sample(temp)\n",
    "\n",
    "    # Feature engineering\n",
    "    val_sample_fe = abun_per_tempbin(val_sample_pp).reset_index(drop=True)\n",
    "    val_features_dict[sample_id] = val_sample_fe\n",
    "\n",
    "val_features = pd.concat(\n",
    "    val_features_dict, names=[\"sample_id\", \"dummy_index\"]\n",
    ").reset_index(level=\"dummy_index\", drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assembling preprocessed and transformed testing set\n",
    "\n",
    "test_features_dict = {}\n",
    "print(\"Total number of test files: \", len(test_files))\n",
    "\n",
    "for i, (sample_id, filepath) in enumerate(tqdm(test_files.items())):\n",
    "\n",
    "    # Load testing sample\n",
    "    temp = pd.read_csv('data/' + filepath)\n",
    "\n",
    "    # map sam samples to com samples\n",
    "    if sample_id in sam_id:\n",
    "        temp = sam_2_com(temp)\n",
    "\n",
    "    # Preprocessing testing sample\n",
    "    test_sample_pp = preprocess_sample(temp)\n",
    "\n",
    "    # Feature engineering\n",
    "    test_sample_fe = abun_per_tempbin(test_sample_pp).reset_index(drop=True)\n",
    "    test_features_dict[sample_id] = test_sample_fe\n",
    "\n",
    "test_features = pd.concat(\n",
    "    test_features_dict, names=[\"sample_id\", \"dummy_index\"]\n",
    ").reset_index(level=\"dummy_index\", drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# total abundance\n",
    "- this varies coz of min max scaling..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- get mz is here....."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mz(mz,df):\n",
    "    ''' returns list of columns for given mz'''\n",
    "    cols_list = []\n",
    "    for col in df.columns:\n",
    "        if col[0] == str(mz):\n",
    "            cols_list.append(col)\n",
    "    return cols_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_total_abundance(df):\n",
    "    ''' returns total abundance for each sample'''\n",
    "    tot_abund_df = pd.DataFrame(index=df.index)\n",
    "    tot_abund_df['tot_abund'] = df.sum(axis=1)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        tot_abund_df[f\"tot_abund_{mz}\"] = df[get_mz(mz,df)].sum(axis=1)\n",
    "    \n",
    "    print('tot_abund_df.shape',tot_abund_df.shape)\n",
    "    return tot_abund_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 276.96it/s]\n",
      " 25%|██▌       | 25/100 [00:00<00:00, 245.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (1059, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 247.55it/s]\n",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (1059, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 59.21it/s]\n",
      " 32%|███▏      | 32/100 [00:00<00:00, 318.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (1059, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 322.24it/s]\n",
      " 83%|████████▎ | 83/100 [00:00<00:00, 416.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (1059, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 361.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (1059, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "tot_abund_200 = get_total_abundance(train_set_200)\n",
    "tot_abund_50 = get_total_abundance(train_set_50)\n",
    "tot_abund_5 = get_total_abundance(train_set_5)\n",
    "tot_abund_100 = get_total_abundance(train_set_100)\n",
    "tot_abund_500 = get_total_abundance(train_set_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 61.26it/s]\n",
      " 27%|██▋       | 27/100 [00:00<00:00, 265.47it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (804, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 259.84it/s]\n",
      " 34%|███▍      | 34/100 [00:00<00:00, 337.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (804, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 321.84it/s]\n",
      " 35%|███▌      | 35/100 [00:00<00:00, 343.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (804, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 328.50it/s]\n",
      " 83%|████████▎ | 83/100 [00:00<00:00, 411.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (804, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 366.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tot_abund_df.shape (804, 101)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "tot_abund_5_test = get_total_abundance(test_set_5)\n",
    "tot_abund_50_test = get_total_abundance(test_set_50)\n",
    "tot_abund_100_test = get_total_abundance(test_set_100)\n",
    "tot_abund_200_test = get_total_abundance(test_set_200)\n",
    "tot_abund_500_test = get_total_abundance(test_set_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_tempbins(mz):\n",
    "#     col_list = []\n",
    "#     for col in train_df.columns:\n",
    "#         if col[0] == str(mz):\n",
    "#             col_list.append(col)\n",
    "#     return col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop if all values are zero\n",
    "def check_col(df):\n",
    "    col_with_zeros = []\n",
    "    for id,col in enumerate(tqdm(df.columns)):\n",
    "        if df[col].nunique() == 1:\n",
    "            col_with_zeros.append(id)\n",
    "    return col_with_zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set_200.loc[:5,get_mz(0,train_set_200)].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set_500.loc[:50,get_mz(0,train_set_500)].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set_50.loc[:5,get_mz(0,train_set_50)].plot(legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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515GYDyCl7ACGhRCzLs7++Szw5r/depVK9bHzZlETa7WnOBA+g1UZ8y8tr6qqwlZXh3FoCCVpEW2DXrJnxSEDHgrfep306TOISc8EYNrqDfi9Hsr3X3g28NRrMtEAvVUmOsaMQe4/QODig+PjYjdhEgpLzc08G7WWCDlA3YnXrnS3r0r/MgAuntO/E3gbqAa2SSkrhRA/FUL89Z6tTwARQoha4C7g0lRRIUQj8ABwuxCi9V0ziL4OPA7UAnXA7g+nSyqV6mrl9PoZqdpHhBihLi6B+OgVl9bl5+czrqUFYQqiK3kqiiKZsCCe0r27cNtHmHXNTZfKRiWnkjxxMqVv70QJBAhOCyEt0kR9wzDGzZ9B6/PR+uSFB8MbDJFERi4jz/08e9Lm0SXD8J1++kp3/ap0WWMAUspdUsosKWWGlPIXF5f9REq5/eJrt5TyeillppRyppSy/l11U6WU4VJKm5QyUUpZdXF5oZRy4sVt3vnPfjGoVKpPhr2VXayWR+jXBpE1djIajQGA1tZW+mpriWpsQpc0h0YPJIwNxRaqpfCt10mdMp24MWPfs61pqzcy0tdDTcFJAKauTEYB/J4sumJjGX7xJaT3wqme+PjrSfEXkWqD7bYVZA2fwtXbdEX7fjW66geBVSrVJ8fOolpWaIs4GJXLgoy/PfQlPz+fzKZmhKIwmLUMu93HhPkJlO3bhWt4iFnX3vx320qfnktITOylKaExs+OJN2upPTOAc/kqdMPD9G2/MFAcET4fkzGWJZoTPJ64Bq2Q1O979O+2+WmjBoBKpboiukfc2Br2YMHDQHIaFksKAMPDw1RWVJDV2IQ2Mpu2kCjMwQaSxgVxesdrJE+cQsLYcX+3PY1Gy7SV62k/V0VnXQ1Cp2HyzBh8AYkxZTlDwcF0PfYYUkqE0BIXdx3TnM/QFpvCSTGZqNptoChXejdcVdQAUKlUV8T20nY2aI7Tpo9i7pS/XflbWFhIdHs7+sEBAmOW09bhYPycOKoO78M5NMjsD/j2/1cTFy9DbzJf+hWQsjKVSJ2guWiEjtwZaJqacFx8VkB83PWEMMxcSx8vRqwmOtBNV9mne+hRDQCVSnVF7C+qYr62nOOxUxkbtwS4OPWzsJDJnV0IYxBd43KRwNhZURRsf5XE8RNJHD/x0jY8zcN0/b6YwPCFm74ZLVYmLlrGuRNHsQ/0ows3MS4jGKcrgGnW9biNRtoffgQAszmR8LA5zPW+wutpi+mXNgaPPXHF98PVRA0AlUr1kavpGiGzZx86FDRjc9Fo9ABUVFSg9PURWlOLJnkODT0eUiZG0Fh2BMdAP7Ov/cx7tjP8TjO+Dgf24+2Xlk1btQ5FCVC278K3+cwVKQRpoP+sjvrssQROncJT3wBAXPz1ZPv2ExJsZJdpCel9hwmMfHrvMKAGgEql+si9XtLGJu1xzppTWTbtVuDCDeHy8/OZ0NmFUAKMzFqPc8THuDmxFLz5CgnZ40maMOnSNnxdDjznBwgA9lMdKJ4AAGFxCaRPy+XMO7vx+3yYx0WQFWZksN8DC24koNHQ/cSFb/pRkSsw6oJZqi/nibg16PHTdPDxK74/rhZqAKhUqo+Uokjyi4vI0dRQkTiZUOuFmwC0tLTQ0d5O2rkatJFjadaGEBRuYqSnCHtfL7Ouvfk99/8fOdpGADht9yM9ARyFnZfWTV+9EefQIOdOHEFoBGMXJWIS4B+IozE1lZHt2/EPDKDVGomN3cgM19NUJ2ZTLLOwVLwAn9JZ6GoAqFSqj1RBYz+zHIcAiJ323qmfiX39aAf7UWasp71xmHFzYyh482XixowlZdLUS2UDI16cxd2c8fdRHXqCVsXFyNE2pHLhD3fypClEJCZTvGs7UkqC8+LIMGnobXUxMGs1wudjYOtW4MJD42NlM1OtDl4NXU2stxl7zbErtj+uJmoAqFSqj9TrRa1s0h2jKHgcc7I3AzA0NERVVRVTWjsQBhudWTloNAJBLSO9Pcx+37d/+8l2pCKp0JcT0Pk4Lc+iDHpwVfYCF+75M331Brob62g7W4k22ED2lEj0AgymqXTExdL77HMoXi9BtmyCgyYzX9nN1pSljEgzXYc+ndcEqAGgUqk+Mm5fgNry44wR7bSl5aHR6AA4ffo0JpcL2/lqtNmLqK0dJnVKBKV7thGTPobUqTmXtqF4A4ycaOdsYACPMQABP32mXgaln5Ejf7ur/Lj5izDZgi5NCQ2dm0CqQUNfg5f6SbNgcJDht3YCF34FTPW8jgwPZrduPontb4Nr8MrtmKuEGgAqleojs7+6m5XKMbxCx6Q5XwbA5/NRVFRETt8QKAojizbjcfixhXYz1N3F7Otues+3f2dJN7gDlGmqaA6J5Jl5axi0BXFKqcHXYsfTNAyA3mhi0tKV1BacYqi7C2NGKGNizQjAlLiUwZAQep58AiklMTHrsGg0LDE18FzMWox46DqxZTR20ahSA0ClUn1kXi9qYIP2BEURk0mLmQBAeXk5LqeT+LJytDHZ1I8YCI0xc+74S0SnZpA+feal+lKRDB9upVmxM2JyUZw6BpfBQnlsCm3GFtwo7/kVMHXFWhBQuncnQiOInJtAol5gb9Jyduxk/LV1OE+eRKcLIiZ6NXnu5zmdPJFKJQWl6OkrvXtGnRoAKpXqI9Hv8OKsPUasGMCdfeGun3+d+pnldCOG+1CW3kBXwzCRiS6GujqY9b5v/+6z/Sj9bgpFNa2hUXSGxhAquzmbmI7dYqHA34Srsg9/nwuA4MgoxuTNpfzA2/jcbiw5MWRadAQCEiVrNW6zmd4nnwIgPv5G0pUy0oIU3ghaTZzzPL7Wkiu/o0aRGgAqleoj8daZdjaI49g1ZmbM+ToATU1NdHV1Mf5sI8Jgoz1pElq9htbKN4hKTiUzJ+892xg63EK/4qbfOMSZhDSClH6+zy9QhIaKmGTqDLUoSEaO/e15UtNXb8DjcFB55ABaq56YadHEGjUEekM5nzkW57FjeGpqCAnJwWpJZ7E4ynMJy3BJAx0HPl1PC1MDQKVSfSTeOF3NGl0BZXEzsVlCgAtTP0MVibG2An3eSmpL+4hKlgx21DPr2psQmr/9SfK2juBvGuEkNXQFh9ESlcB6sZ1Yfzc58hRVSRk4zQZKA504TneiOH0AxGdlE5M+hpLd25GKgjUvlky9wOeW9GQuR9Hp6H/22QtPC4u7nlz3FuyxkbwtZhHZsAO8jlHZX6NBDQCVSvWha+h1ENV5imCc2KZduJ3D4OAgZ8+eJbd9AKTC0MJN+DwBBlrfISIxmTEz57xnG8NHWnFIH52mTsoSUjFJO/P8BxkovptN4nU8ehPVMUlUaavBL7HnX7gwTAjB9DUb6G9vpelMCYaUYGISbESYtRh9qdSlpDL45nb8fX3Exl1DqHAw29LFtsg1WKSD4aKXr/j+Gi1qAKhUqg/d68VNbNQep08fyqRpFx76fvr0aZCSiJISdCkTON+gEBQuGOosY9Y1N77n279/0IOrvJdTsoE+Wwj1samsZhfmtlzWTR0mrNdPtnKGiqR0nGbB+cAAw0dbkf4Lt3ceO3se1tAwindvRwiBbVYcGQK8dmjIXAReLwNbX8RoiCQycgmzPa9wMDmHOiUO56mnRmOXjQo1AFQq1YdKSsnO0xUs0ZZSk7IYjVaP1+ulqKiI6W4Bjl6U9bfQ12bH6ygiPD6RrNnz3rONkWOt+GSAJmMzZXEp6KWbpYE9LDDpsJ36L8Z0j2eTeB27KYjzUfGUiEpw+nGWXrixm1anZ8ryNTSUFtHX1oJlWjRxVi1BFh0mwyQ6EhPp37IFxeMhPu4GJgYOExGm5XXTCmKHSpHdZ0dj111xagCoVKoPVXHzINOdpzHhI2HWhbn/Z86cwe12k1ZSjTAF0WzORKuHoc7jzLrmRjQa7aX6ituP/VQnhbKFQYuV8/FpLGEfET3jMddceJj7WPcxkga7SJT1nElKZ8jspk1xMHSomb8+XXbyslVodTpK9ryFxqTDOi2GDA34R3RUZ85GGRhgeMcOwsPnYzFEsURXynNxK/FKLT1HHrvyO24UqAGgUqk+VNvyK9ikPU6rOY6kjNmXpn4m6C1om8sxLttAXUkfGlFPWFwUY+cseE99x+lOpD/AeV0dZ+JSEEKyWtnBHIOFgKLwM9+tGO2NpHdOYxOvM2ALpzEyjtNUofS68dQMAmANDSN77kKqDu/H7bBjnRlLohZMJi3YcrFHRdH39NMXnxZ2LTNcz9KVEMd+mYu1+hXwe0Zh711ZagCoVKoPjdevkF9Rx2xNFT1jN4AQNDQ00NPTw9SzHSAVenPXEvAr2HuOkbf5RjTav337lwHJ4KFWymUnwxYjZxPTmcdh4gfT0Fe+xov+RTwZWEWtEk+W+zTjRs4SITs5k5BGj2mQIcXDwIHGS9ubtnoDPo+bigN7MSQGYUoKIsOmA7uNMxk5eGvrcBw7Rnz89cTTymTbCK+FrcYaGMRbuWMU9uCVpQaASqX60ByobmVpoAANksxZXwAuTP00G0wEVRVhGDuFc1VutNo+gqN0ZM9d+J76rspecHgp156nPDYFv0bLOvkms4UVv5Q85N/IuskJ/MW/AevwOZI7c9nAdjrDYmgPi+aUPE+g0Y6v88JUzpi0DBLHTaTk7Z0oSgBbXhzJ3gA6vYbhsDz8QUH0P/U0ZnMyYWGzmR/Yya7EWbTKyE/F08LUAFCpVB+arfmlbNKdoD5kLEGx2fT393Pu3Dly+yXS0YNv/W0MdbtwDeeTt+kGtDrdpbpSSgbeaaZO9jFkhsqkdHI5RYY9Am3Fm2z1LyYsLpXfXD+F/foFtChRjHGWk+vMxyoHOZOQQqupA7f00/d27aXtTl+9geGeLuoK8zFPicJo0ZERbcbgjqIyczKOEydwnztPfNwNTPftRB9l5HXtEqJ7TsBA4yjsxSvnsgJACLFKCHFOCFErhLj3A9YbhRAvXVyfL4RIfde6719cfk4IsfJdy78jhKgUQlQIIbYKIUwfSo9UKtWoGHL5aK/vYJJowDfpRuDC1E+NRkN8yRmEJYgGXwJCeLEGDzJ+weL31Pc2DSO7nRRpzlMVk4xXb2KjfI0ZviB8UvBn/0Z+tXkSJr2WO5eN45HAOkKHyonvyGENu2iKSqInOIwCpQFv9RCBYS8AGTPyCI6Kpnj3djQGLdbpMSQ7vAghaI+eizQY6H/mGaKiVmLTGVlkrGVrzGoCUjB88pM9JfRfBoAQQgs8BKwGxgM3CyHGv6/YF4EBKWUm8CBw38W644GbgAnAKuDPQgitECIB+CaQK6WcCGgvllOpVB9Tr50uYr3mBAE0ZMz4DB6Ph+LiYrJ14dBejmH9dTSc6cfnOkPepmvR6vTvqd+/r4kOOcSA2UN5cgYTZCkTXEZ0lW/xgn8J0QlpZEebKS0t5TMzEtmtX0q3DCXDUcd870EM0kVZfBr1hkaklHTuqgBAo9EydeU6Wqsq6G6sx5oXiwVISw7C6EuiKXsiwzt2IPuHiY3dwGz38zQkJXNYmYKmdAsE/KOwN6+My/kFMBOolVLWSym9wIvAxveV2Qg8c/H1K8BSceGOThuBF6WUHillA1B7cXsAOsAshNABFqAdlUr1sbWtsIGNupM0xMxAFxJHWVkZHo+HsSWNIAN0ZS5FSjCaW5iwaNl76vp7XfjrhsjXnOdsdCJOo5VNvEaOMwSv1PBn/wb+55pJbN/yDA/tfYjig/v52tIJPOZfQ/RgIfHtk1jKPmrjkum32ShT2vCWDhHwXPjjPWnxCnRGI8W7tqOPsWJICybNHwBFw7mYuUi/n4GtW4mPu4EMWUlysJ83bKuweXtQavaNwt68Mi4nABKAlne9b7247APLSCn9wBAQ8Y/qSinbgN8CzUAHMCSl3PtB/7gQ4stCiEIhRGFPT89lNFelUl1pjT29mHq6SKEL87SbURSFgoICYqwRGOtPY5iSQ1XZCAFfI7M2rUKnf9+3/wPNDOKg2zjMmaQM0uQ5prtd6Kv3sMW/jKTkNCKFg6cChzgVc4pHzr3BxgnhbNevYlBaSR7pYLl/NwJJeXwqVbrz6NHR/MZpAEw2GxMWLOXs8UM4hwax5cVhs/tISLGhDWQwMHY8Ay9sxapLIzhoIos4xBsJc+mRIQwe++Q+NH5UBoGFEGFc+HWQBsQDViHErR9UVkr5qJQyV0qZGxUVdSWbqVKpLtMLxw+zUXscj8ZAwtRrqa+vp7e3lyn1LqSjG9fyz+C2K+j19UxcvPw9dRWnD3dpD6dEHbVRCQxbQtjMa+QMheCRWh72r+eXmyfx4nN/odV44Qrd8oizHHjzVb68dBJP+VeRPHCCuI4xzOUI1QmpDNgM1Ci9eIuGCfgu/AqYtno9Ab+fsnd2Y54YicaqZ4xNj0bRUxI3i8DgIENvbic+7gZmel8mEGvjVbmIkNaDMNxxxffplXA5AdAGJL3rfeLFZR9Y5uIpnRCg75/UXQY0SCl7pJQ+4DXgvXeCUqlUHwuKorCj3MU63Slak5eCKZj8/HwsBjOR58+gCQqmusuGVOzMXD8TncHwnvqDR9twK25a9d2UJaYTJ5uZ4+zCcG4/z/uXkZqahr7tPK/FVCEBa9hyAko/23z5zIuD1/RrsUsTCYMDrFG249caqIhLpURTiVVj4fy2AwBEJCSROjWHsr27UAhgzY0huH2EsFgTfk023uQU+p95huiotYRr3MyydPJy5Gq0BPAUPT8Ke/ajdzkBcBoYI4RIE0IYuDBYu/19ZbYDn7v4+jrggLxwPfZ24KaLs4TSgDFAARdO/cwSQlgujhUsBar/791RqVRXWkFNMWNdTUQyTGTOzfT19VFTU8NEZxiBjlJ062+ku8WHEDVMXr7yPXWlX2HkeBsFooGmyFj6giPYxOvkDIfgQccj/vX8avNEnt7zPH2iBnNQLjsX3wGGZM5bqtn15it8dslUng8sJ2PgKMmd0UyTBVQmptFnVuhQRvAW2/G6nMCFKaGOwQHOnzyGdWYsQgompoSgC1goTZ+Lt6EBz8lSoqNXMdu7jaqkDE4ExuM7/TQoyijs3Y/WvwyAi+f07wTe5sIf6W1SykohxE+FEBsuFnsCiBBC1AJ3AfderFsJbAOqgD3AHVLKgJQynwuDxcVA+cV2PPqh9kylUl0RL5wqZoP2BHZdECHjVlFQUIBGaEivrgclQE3QJKSUTF2Wgd5gfE9de1EXAa+XOn0rpQnphMsuljjPYTx/hGf9y8nKSGfw6D72pdQhhZkfjF9KRFAG1ybPh8Ag+6JryKSTl/Xr8UsdUb0+1sk3cOvNnI1NoUBUEqGPomzrhat6UydPIyw+keLd29GGmzBmhRHRPoIpWMuAbiIyIoL+p58hPu4GJiknCAsXvG5Yjs3ZCo1HR2P3fqQuawxASrlLSpklpcyQUv7i4rKfSCm3X3ztllJeL6XMlFLOlFLWv6vuLy7WGyul3P2u5f8ppcyWUk6UUt4mpfzk33hDpfqEcbmHOF5nZaW2kL6sdbj9kpKSEjIM8YjmUxhyZlBfE0DQzMz1q95TV0pJ374mSkULbWGRdIbHsIE3yRkMxS31POJfz09Xj+Gp2gM4lEZiQnPYNPZ6AH6Udxd6YwrNVLH/1F42z5nEi4FFZA8cJrNbzxhZxZmkNDpNdoYVD4EyNyN9vQiNhumr1tNZV0NHzVlsebHIER9Tp8ag94dQM3kJzlOnMHUEEWROZLGuiNcTFjIorYyc+ORdGaxeCaxSqf7X3iraxVx/FVbcxOXeQmlpKV6vl4l1dpSRLromrUQqBrJmRKA3vfdaT9f5AYTdQ5WugdKEdGxykFWOIsy1J3gmsIJJWRk0vvQ0JxNrULSh/DrnBi5clgQ6nYHvTrwFoQxzLKWdiP5KXtJvRgChXTo28AYj5mDqYpI4STUJpjQKtmwDYPzCJRgtVop3bceUHYE22EC8w4fWCA26CQizmYFnniU+7gbyPC9gjw/l9cA8zHW7wNl/pXfxR0oNAJVK9b8ipeSVkm426o8zaI5BlzKbgoICYk0RWJpL0AQHU9luAjnMgs8s/7v6PTsbqBLtdIQE0xyVwFreYkZ/ME5p5DH/Wu7NCeZp3Xl8gQ7GReaSm7DoPfVvmXgzoaY0erzlFHSVsGBKFm8E5jJ54Ajje0eIl82UJabRYuzGix9x1k93Yz0Gk5mJS1ZwPv84IwO9WGfGEqgbZPzMaPS+KLpmLGVo1y6itAtIFJ1MDBrm9bDV6KSPQOnWK7R3rww1AFQq1f9Ke08hde0RLBBn8Ey4htq6evr7+xnfG4S/rRj3grV4vWEkjNVitFjeU9fbYUd0OyjT1lEan45JOtlgP4Kl4TRPB1YwbVwmFS88QXnEeRRdLL/P+xoAzuJiGm+5FV/HhWmZv5r7PTSKg+Px/Ria83lefw1GfNjaLWzgDfqCImiKiqdA1pEeNJljzz6DlJJpK9eBhLK9O7HOiAUNjLWaQCMpN08Bvx/Hy28TEbGYeYEdFCZkU6Jk4sp/Ci4+b+CTQA0AlUr1v/Jq/iFWiUL0BIjO+Qz5+flY9WYSmmoh4OeMLwFkgEW3Lvi7up1v1VMvuukMMlIXm8QKdpPXZ8UhTTzuX8udkUNsyRxECfSyKGEWCWFZSJ+Pjp/8BFdRET2/+x0A8xLnkmLNxuEsoiakjfFpKexS8pg+cIycwUbCZA9liWnU6ZvRCIGx1UBjaREh0TFk5OZxZv/bKCaJKTuCQHkvyZODwROLc+YCBl56ibiwjeT638YQbeQVsQTbUA20nr7Ce/qjowaASqX6t/l8g2w/a2Gj/gS9oWPo1cZSV1dHtjuOQPtJmDCZIVccYXFeQqND3lM3MOJF1g1QpK2lNCEdHX6us+/G1lTMU4GV5I3PoODtV6kzVSIMKfxm9ncAGNi6FW9tHeacHIbe3I6rohKA+5f8Co30cMw2grH9GM9pryFIuDE0h7KWHbSHxdIWHkmZ0sLY0DyOPP80SiBAzpqNuO0jVB87hG1WHIrDx4zxsYCgNHI2ytAQ2iP9hBiCmW88xxsxS7FLE56CT84N4tQAUKlU/7YzNW/g7rcwg3MYptxAwenTaIWGrHYHgb52qiMnIzQm5lw7/e/qdu6qp1MM0mGB83EpLGY/s3tNjEgzTwbWcNtgMa/kukEZ4ZbMpViM4fj7++n545+wzp1L0iMPow0Pp/u++5BSMjY8k+mhuUjHCWqz/ERGxLA/MI0ZA/nMGSnDLO2UJqZTqavBqDFgHbRScWgfCeMmEJWaTvGu7RgyQtCGm9BU9xOeqsPpjEOOm8TAs88RG7OZ2Z4X6E+MYEdgNprK18A9PAp7/cOnBoBKpfq3SCl5tegsGzQnADCM20hpaSkZxKLrK0LYbLQF0jBZfaRMjH1PXcUbwFvazWlNHWcS0kEIbh55g6Dmcp4MrGbOmCSONJbQQQVGcxZ3T/86AD0P/g7F5SLmB99Ha7MR9Y07cZ4+jf3gQQB+seSnCBngtNJPqP8UT2s3Ey7saBvCWcVuGqIS6AgJolb2MCFyMcdfeh6fx8301Rvoa22mpeoMtrxYvA3DzFmcjkbqqByzEl9TM2G1SWRRRWKYn9etK9Erbqh49cru9I+IGgAqlerfMjiYz77GbDYZTtAbm0txfR8+n4/xQyF4awtozcxBo4tl6ooMLlzo/ze9h1oYwk6r2UV1fCpzOMq8HhiWFp4MrGZ91VvsyrWD9PC9Sdei1RpxVVYy+MorhN9yC8aMDABCr78eQ3o63b/+DdLnIzEokRWxy9E7jlGWFYowhnEyMJ68gRIWuY6ix0dZYjrFooogYcXmCeH09tfInrMAc3AIxbu3Y8mJAa0gtMuLMcJPpz0eEReP44XdhIXmsZD9HI+bSLWShDP/k3EaSA0AlUr1bzlWsYNIp5Ms2Ypt6o0Xpn5qwwkdOA9+H+cNY9FoFCYuSH1PPalIhg41U6ippzw+DZ/OwK0j2whuPcsT/tUsTAjlQNAIA74KIoImcV32zUgp6frFL9GGhRF5x9cvbUvodET/x914GxsZ2HZhfv/35t+DFjjnaiI+ppwn2ESsGEBXG84iDnA+NpmOYD0dcoipSWso3PEaLvswU5avpr74NMP2XswTI3EUdzF9WQoaxUjTzOtwnj5N9MhcZvlehzgL2+QSLD1l0Fl+Bff6R0MNAJVKddm83l62V/rYqD1OQOhoMU9mcHCQ8c44fI1HsccnoZjHk5kbjdGse0/dgcJOfNJNg7GfysQ0cmQBi7s8DEkrT8s1LDr9EgezukFo+fXMryCEYPitnbiKi4m+6ztog4Pfsz3bokVY8vLo/dNDBEZGiLZEc0PadRgdxzkUn8aILohSJZ0Z/RUs8+5FCkF5QjoFVBLmD8YqQjixbQtTlq9Bo9FSsmcHtrw4pDtAli0YzB5qXckIqw2xvY4oXYAZtnZ2RCzDg55A4TN83KkBoFKpLltr26uc6Mhhg/4kQykLOVF6DpvGTLLDjr+jhYrwKQihZ8rS1L+r2/lmDSWaRqriUnHrzdw+vIWQ9loe969moVVhz7Qg3N4qMsKnk5e4AMXhoPs3v8E0YQIh11wDgLu6mtZvfgt/Xx9CCGK+dw+BwUH6HnkEgK/PvAMDevqHC4me2MrjykaSRQ/GGhuzOEFVQgqtNh9D0kVe1rVUHHoH1/AQY2fPo/LQO8gYHbpoC46CLsbMCgOvmb7FNzHy9j6iNcuZ432Z9oRodgdmoJS9BD7Xldz9Hzo1AFQq1WWRUuGd8hNM9LYQL/sQGWtoaGhgnCceBk/jNxpwhM0kMtFCdMp7v60PVPaiD/g4p++kPDGdcbKc5Z2DDEorz8lVzCjfTkFME1Jj4Y/z7gGg99HH8Hd3E/PDHyI0GmQgQMcPf8TI3r103/8AAKbx4wnZuJH+Z5/D29pGmCmML4y7DaPrNPusY+k2BnNWSSKnt5ZV/p34tEYq49PIp4oQezAh1kiObHmK6as34HW5qDr8Dra8WHwtI8yekYWi9VBFNigKtiNapshThERreFW3HL1vGKref2Pkjxc1AFQq1WXpHzjB3sYxbNQfx6szc7IvBK3QMjYQhqvkGDUx4xHaCCYvSf67ug0vVHBG28y52ETsJhtfGn6G0M5GHvWvZYGvn53LIwl4a5kVN5fk0DF4m5vpf/JJgjesxzJ9GgCD27bhrqqiPl7L0Guv4SorAyDq298CjYaeBx8E4PapX8KCGWVoL6bpHh4PrGOMaMdWo2WSLKU8MZUG0yA+6WP25JtpLCvGbR8hPmscJXvewjQ1EqHX4C/tI2qcFq/djHvxtThfP0CobgwLNac5HJtDoxKD92N+TYAaACqV6rLUN71EVdc41mjzcWasorjiHBn+GEzKWfD56Iqfh8GkJTM35j31uir6CPErVGqbKUtMJ1XWsra9g35pY4uykvGdJ6gwn0NoQ/nd/J9cqHPfr0GvJ/q7dwPg7++n68EHqUrV8l83wXCwjs6f/gwZCKCPjSX887czvHMnrjNnsBlsfGXqlzG6z3BYk0xraBiNSgxTu5tZG3gDl8HKufhUTnMeW5eFsJgEDm95immr1jHY1UHT2VLMU6JwlnazYN1EFOGjKmQmysgIkWUZzPZtw5dg5cXAYgxtJ6G39oofiw+LGgAqlepf8ni62VfdzTxZTqh00GaZht/vZ0IgEWfR23SHRKKYs8meE4feoL1UT0pJ7fNlnNW1cz4mjkFrKF8depLQ7lYe869jQV8dO1aFIHytbE5fic0Yiv3Ycez79xP51a+ij4kGoPuBBwjY7Ty+DLQBHU8tUnBXVjL46oX5+BFf/BLayEi67vs1UkpunnALoZogLIOvMjAxjMcCa5kgmomod5MmaylLSuWcvg2UAPNzb6G3uRGvy4UtIpLiXdsvDAZ7FYI7wBjvYqDfTCBnAcqbVSTTzbjQYXYEL8ePFln88R0MVgNApVL9S+0dL7O/fT4bDSdwmcLZV+sjToYRE+wk0NZKQ8oCkIIJ8xPeU6/2VAcxioYybSNlienEyjY2ttfTJ4N4QVlOoqGRRlmOXh/Nj/O+h/T56PrlL9EnJxN++4WHDLpKSxl65VV25oIQNq47lEBhhpZzydoLwTA4iNZmJeob38BVVMTIvn2YdWa+nnsnOm8NVdJMTWwsHTKc8R09rJNvMGQOpTY2iTOyCV2NlrjMsZx45QUmLVlBc0UZw7IPfYINR34HuSvTAUltxhr8rW1EN0xjnv9NGhPieCcwHX/xFvB7R+Go/N+pAaBSqf4pKQNU12+nuy+aZRQznLCEwRE7E3yJuGoP4NVq8cbOJyErlPA466V6Pm+AxlfKadZ3URsRTk9wJHcMP05YTweP+tcxv62CPQs0aAK93DnpFnRaPf1btuCtryfm3nvRGAwXBn5/+lMGgjS8OlvDgqJw9EYTS8/E8dhy8A8N0/OHPwIQeu01GMdk0n3//Uivl+uyridWF0Fo/wvUjMnmicBqcqglqbGLGNlOaVI65dpadL4Ac+fcimOgH7/Xi85gpPjilFBfp5PsyEQCwf209dhQUrIwvj3CDGU/+lgjL7MEvbsPzu/+R7vvqqYGgEql+qf6+o5wqCmJlZpCjPg4bU/EhpmMmBhcJw9Tlzwdn9fAhAXv/fZftKuBZI2BYk09pUnphMlerm2poFcG86J/CcGpQ3R7Sgg2J/P5iZ/H39dH758ewjp/PrbFiwAYfPllPFXVPLMEZjUmkh41kesWfJ/k4XB8Ogt7pwsGXnwRd3X1xYvD/gNfUzMDL76IXqvnjrxvQ6CVvkAPhYkZ9MkgxrSOsI7t9ARHUh8dTS1dBE67ycqbS+met8icOZvqIwch3YAwanGf7mHs3EikhPa8W/GdqSW6PYS5pmoORefRISM+ttcEqAGgUqn+qbb2FznUuZiNhhPYbYkUdsF4XwL+oXxEQMGevQ5zkJ70qVGX6gz1OOnYf54e/QC1YVbaw2K5Y+gxwvu6ecS/jnnt5eyf5EKjjPDfM7+FEILuBx9EcbuJ+f69CCHwDwzQcf/9VKQIapKtpDWZmBc9F1vDkyyfeDMLiiPYNk/gMAs6f/4LpJRY58/HOmcOvQ/9mcDQEOvS15FijCeiZwtnUnN4JrCC2bKasS3nCZaDlCZlUCyqMbj9zJp9LQG/n4DPi9/npeLoO1imR+Ms7yEvbwpeYx+1A+EoweGEHY1gtncbzgQb2wIL0dQfgMGWUTxK/ztqAKhUqn/I7W6nvKkCOSSZIytptU5Hh47x4ekM7HmVrrBY7O5Ixs2NR6v725+TY9tqSDcaKNHWU5qYjk0Oc3NLIT0yhG2+RYiZfhyuQhKCx7EsdQWu8gqGXn2N8Ntuw5ieDkD3/Q+gOOw8tUzDwtMRLJl8G+HuBwjRbyF25E2mRM9mTEcEzy6UuIqKGH7rLYQQRN/zHwSGh+l9+BF0Gh13zvoOftGL4itnX2IOw9JCWrOX1bxFa3gc9RFBdMpBRt7pZ+rKtdQWnCI2M4vSvTsx50aDX6I77yJqvAbFJ+hf9iUCx+qY0HeW+Eg/bxiXIQFKnh+dg/R/oAaASqX6h9rbX+Zw+xzWaU+hReFAdwSZ/liMwT3o+wfonXYtEpgwL/5SncbyXoYqmvHqnZwP1tAYlcgdg48T0d/Lw/71zO2u5FhsN0gfD8z/8cX7/fwCbXg4kV+/8OQvV1kZQ6+8wq5cQZwnjglBU0l0lGLSliLDMwjSvcbMkDSm1oRzaqyO+ngNXff9moDdjik7m5BrNjPw/PN4W1pYkbKCsbZ0IrtfoDQ1h+eVpcwNVJDTVYRJOilNyiBfVKC3e8mZvgaDxYxUFOz9fTQ2lWJIDcZR0MmcpdPw6Yc4505BEVqi8lNYKPdxPj6Zo4FJ+IueBSUwSkfqf0cNAJVK9YEUxU9r20uc7JrLJuMJ+oIy6ZahTDKl0bH7aTw6HcOWSaRMiCA40gxAwKdwbFsNY8wGSnSNlCamY5Qubms5SrcM5RXPfEYWSfzOQiZHz2B85CSGd+zAVVpK9F13oQ0KQgYC1P/k+/TbYM8sCxMrbcyJyiNU/zhDUTO5XfNzAsZQwvx/YNnEW5lXHsVjywWB3l56//wXAKK++S3Q6eh+4AE0QsM3876LR2cnxHmQl+OX4pYGkuoVlrKX+qhEakO0DAsXvW+2MGvzjXTV12INDad494Upof5eF/FKGJrYQTxOGFl6O7qDfcy1v4WMN/OSshidvR3qDozmIfu3XVYACCFWCSHOCSFqhRD3fsB6oxDipYvr84UQqe9a9/2Ly88JIVa+a3moEOIVIcRZIUS1EGL2h9IjlUr1oejrO0hlt5UoVy9TlDrKPWnEB8KJzg7FUH2OzimrcdkDTHzX4G/p/ma83d0YDAHOWt3UxiTz9cEniBzo5y/+9cwaOU+xpREhdDw4/+cE7A66f/NbTJMmEbJ5EwA9L76A5lwDzy3RMKM8nKXjbiXM9xBCq/CFwc9yuFXyiPX/YdCcJ8mZT54yFZfJwoEpgv5nnsFTV4c+JpqIL3yBkd17cJaUMD9hPpNDxxPc9yrVaeN4SVnIfO8ZFvQfQUOAsqQM8qlCO+IlO2MewVExIKDj/FmGzANoLDqcBZ3kLMrCr3VSa56K4nCTnO8nN6iNg+Fz6ScYWfTxGgz+lwEghNACDwGrgfHAzUKI8e8r9kVgQEqZCTwI3Hex7njgJmACsAr488XtAfwe2COlzAamANX/9+6oVKoPS1vbCxzoWs1G3XEUBEXeVCaKZBoPPIlWSkayVmMLM5I8MQIA+4Cbwl2NjLHqKdM1UZqYgVb6+ULzPrpkKK+759K52IvGVcqKlGXE2GLpe+QR/D09xP7wBwiNBv/AAO33/4aKZEF/TDQzNbkkuKsxawt4M/zzlNkjuH1CPL9pn0xHxBxC9M8xL24uc85E8sICDU79hdNJUkoivvgFdFFRdN/3awC+nXc3LoOHhL7XeTJuIwqChBrJfA5zLi6Zc1YnPuGn57V65t/8ORwD/Wj1ekr27cCSG4Orqo8pmRPwBLczNAiOvPXYDumY697GUHwQL/vnI8/tBnv3KB61f8/l/AKYCdRKKeullF7gRWDj+8psBP4afa8AS8WFJ0FsBF6UUnqklA1ALTBTCBECLACeAJBSeqWUg//n3qhUqg+Fy9VCZ89JSjvGsEl/gjZjJsgo0idmwInjDCRl090hmTA/Ho3mwkNfjr9ai/ANEWrQUmke5FxcMl8dfJqowUH+7N/INE0L56lGq7Hy37N/jLepif6nnyZk0ybMU6cCUPbTu9G6fTy/TM+MslBmRc4hzPgog2GTuatlLt+MjCTy+ADrQkL4Qt/NoFUI9z7M6uSbSOkOZet8cJw4yci+fWgsFqK+9U1cpaWMvP02M2JnkBc9A51zLzVpabwm5zPffYblw2+jCC3lSZkUcg457CXJmk1c5lg0Gi1nTxxBZJlAAaVimMzcKBStl6bkFdDtYHZpIUExOl7RLEUj/VD6wigeuX/P5QRAAvDu+U2tF5d9YBkppR8YAiL+Sd00oAd4SghRIoR4XAhhRaVSXRXa2l+itHci4/31pCqdFHtSmRBIpPncDmxuLyNzP4tGIxg398Lgb9u5AWoLu8m0QLWuhbLEdBQ0fLnpLTpkGG+6Z9M8awid5zyfHXcjNoONrv+5D6HXE3XXhYe+t+UfwrL7BLtzBQk9EaxK+yxh8hGEcPLFwduZHxJCWcDHA5tCCXMLejxRvBZ8G2btKdL9HSxuy+boRB3N0YLOX/0KxeUiZPNmjGPH0v3b+1G8Xr6V+x2cBj/jmp7moZgb0BEg8ZyfHAqoTEih0tSDRqPQ82YtCz7zeXweN1JRqCjaj3FMKI6CTmbOnIHT3EpXjwZXZi7hh2ws0J3kXGwGp2U2gaJnQMrRPHyXbbQGgXXAdOAvUsppgAP4u7EFACHEl4UQhUKIwp6enivZRpXqU0lRfHR0vMzBns1sNJzAJ/ScJ5sJGdnY92zHZ7TQMhxB2tRIrCFGlIDCkZfOYwlyEa21cMbYRVVCKl8efI7o4SEe8m9iQmg37Z5iTPowvjHtTuxHj2I/eJDIO76OPjqagN/HuR/eTb8NjueEsdwxhzhfLRbNEd4MvoXznngShI7dORacRg2v5pq51RTM99vnMxSURZj+UZanbGJGdQRPLNcQ6Oik77HHEFot0ff8B77WVga2vMCkqEksTliIQ1dAU0ocO+Rs5rsqWOvcgUdnoiIxjXKa8Dn8hAyEMmbmHIRGQ+neXZhzoggMeQgfNhGSJkEToH3ajWhrnaw8/xK+BAsv+BajHaiHpuOjfRgvy+UEQBuQ9K73iReXfWAZIYQOCAH6/kndVqBVSpl/cfkrXAiEvyOlfFRKmSulzI2KivqgIiqV6kPU0/sO/Q4ndZ1BbNCepEamkuJPpcNZRnT/MMOLb8Pj9F8a/C0/3EZ/u4N4jYc6Qxtl8al4NQa+3vQabTKCt9x51E7oQudr5bvT70AXkHT98lcYUlIIv+02AN7+/d3EtTp4fomW3IooZkTMIcz0FwaDsri7cwk3R4azdaqJFKOBh1IjGbBp2ZOoZY0plG84Po+GPiI9W7nWdA19wWaOjhf0PP443pYWbHPnYp0/n96//AX/wAB35nwTt14y9exD/DH6Zix4Sa12ME5WcCYpjTJdAyYN9O9tYt61F9rnto/Q2FeOJsiAs6CTGbOm4zS109xrwROZTMaBQbLChthvm4ddWOFjMhh8OQFwGhgjhEgTQhi4MKj7/qcgbAc+d/H1dcABKaW8uPymi7OE0oAxQIGUshNoEUKMvVhnKVD1f+yLSqX6ELS1vUBB7wrmUEGkMkQ5Y5kcnUX79ufQSklHWC6hMRYSxobhHPZSsL2emBSIDQRTqm+nIimNL/RvJWZ4iD/7N5KZ0MeI8wTh5nhuGHs9/c9vwdvQQMwPvo8wGKisO0nUc3upSBaMmMPYHPM5wsRTaOQgXxn+AkvCwtmapkVr0PJVew29e/+DG0yS0nQTikFLjSONI6Gbsep2MkFnZEl1Cs8v1uDBT9evfgVA9H/cjWK30/fww2SFZbEqdSXtYTV0xIXytpLLPEc16z1v4DDYqEhIppYO3K4A2rMBpq5YC0DBjlewzojBfX6AcYljCIT1IpF0zfsCplLJyq6X6IsL5VXfHJSqN8E1MJqH8bL8ywC4eE7/TuBtLszU2SalrBRC/FQIseFisSeACCFELXAXF0/nSCkrgW1c+OO+B7hDSvnXKyW+AWwRQpwBpgK//NB6pVKp/leczgYGBk5woHMhG4wncQkTzkAOTmMP8Z19uCbNpbvDy4T58QghOPlGHX6fgtXZR4exizNxibh0Zr7Z9BKtMoKd3jzOp7WgDfTxs9k/Qunto/ehh7AuXIBt4UKcPien//NbmLzw8hIzm3sXEaO0YNXsZYf1Wur9aZxPMtEXouVHwQqm7t9g6m5iyrmfMd6o561cK+sMNr7RsRaPKYYw7Z+4Pu7zxAwE8cocgf3AQexHjmDKyiL02mvpf2Er3qYm7ph2J36dYHL1H/lD1M0E4WZsVTdJspGy5DSKNOewagVDR1qYufwadAYDAx1tDIcNAeAr7Wdyzjg85m4aHNH49EEsPnACXYKRl5TFaAIeOLNtlI/mv3ZZYwBSyl1SyiwpZYaU8hcXl/1ESrn94mu3lPJ6KWWmlHKmlLL+XXV/cbHeWCnl7nctL714ameylHKTlPLqj0uV6hOurf1FelzRDPT4Wc1pqmQm4y1ZVO59iiCPj94p16LVa8ieHUdnwxBnT3QwdqaVSGcoJbpmypNTua3/FWJHBnnIv4n49F4C9sOkhWSzIGk+3Q/+DsXrJebeC0N+j7/4PWYUjrA7V5DeEU9e6DzCzQ8xaEnlP3rWkDYmgrNxer4ZbEVT+Z90n7DRWxFOoLqfDS3bkQYNb403sUQTwY/9t6MXjcR493Lj0Cr2TdPSHg5tP/8ZitdL1De/gUavp/u395MSnMLGzE2cS+hiyKbhiDKJ2SO1rPe/zoAljIq4aNrFAA6fxHtygLxrbgTg2I4tmLLDcZzuJHd6Dg5zCwGfpGfhFwk5CYtlEdVR46gkA1n09FU/GKxeCaxSqQBQFA8dHa9yfOBWlmmKsUoX9coUTKEB4tq7CNjCaOwxMyYnGqNZx9EXz2MJMeBurWfEOMCZ2GiGDUHc1fg8rTKSPf5ZNMTUoVGc3Dfvp7jOnGHotdcI/+xtGNPS2Fu3h/Qn3mHIBgXZIXzW+iVCdc+hCXTxtZHPMyU9jiMpelZhILbyTwQa+/EO6PEFhTHcaCOy7U0+K/poiNXTF2WkYHAqFcELCNa/wBzLOHLrY3lyuQaluZX+p59GFxVFxP/7EiP79uEsKuKrU76K0GrJqH+MP0TeTDgOplfXESG7KUtOp4BKLBpwFHUyJWclBrOF9nNVMNaAYvdh69aQNCYaxTpMozYLxatj86EteOLNbPEtQnRXQXvxaB/Wf0oNAJVKBUB399t4vQMcbspgo+kkgwQRoplHwYkXiBt2Mrz0dnwehQkLEqg+0UF30whTl4UT3BNKma6RM8lpfKbvTeLsg/zBv5mwrE609sPMjJ1NdvhYOn/xC7RRkUR+7Wt0Ojo58OcfkNEJWxbruKZrMZGyA5vYwU7zehpskzmRYWSME1b2HcLiPk5/VSiBmHDGbYgnEBRE1+kIsmr+mwVmPQemmJlvsfLVzhsJ6PSEiz/zVdNXaIk0UTBG0PXQn/B1dhJ+++3oYmLouu/XxFpiuCH7Rkoz7CjuIU4rWcwcbGJ94A06gqMpj7Rg17hwILC/08q8mz4LwOH9z6ENM+I41UFubi7DxgbcLklf3vWkH+olPtLDLv08PMJ01Q8GqwGgUqkAaGvfSqsrD83IIIuVEs7KscSEBxHe1oYIKDTps4lMshESbebk63XEZYbQUVaKNDspjQqhzxzG3Y1P06REs0/m0RpcjUYq/HTuTxjavh132Rmi7/ouWMz89+7vcu1+F5XJoOgTWGBZTLjtIYaN8dxjv5b+yaHYvJK7jQMY/E/RejQOxWIic0MnNtsrxK3RoGiN9J8ys7jm94TqtLw53cJM4vmjuAWTpoREXyWbWnJ5ZpkGf8BH669+gcZsJurb38Z95gzDu3fzpUlfwqA1EjL8Mg+F30C0HGZuTRk2OUxZSianqMSIxHN+gOy0OZisNhrLS9BNDsZTP0RmRAqGUD/aIC9NEXPRDgg+W/w0vbERbPfnoZS/Ah77aB/af0gNAJVKhcNRy+BgAe/0XsdqXQE6AozIBRSceY0Mpw/39GX09/qZMD+B0zsa8Dh95K6JwtAcxBldE2Up6dzQt5N4xwB/CmzCPLYNg/M46zLWE0so3fffj2nKZEI2buCJiieY8HIJZg+8Os/M15WvEmLchs7XzB3Oz6PPTcKlE/xEb8DR/XM6j4WhBAQpG92EaOo4yzjiQ09hmxGEz65DV3GOW+1l9IToqMmwsLdvMa3WiYQaH+cG03qCXcG8OUuD++13cOQXELJxA8Zx4+i5/wHCNUHcOuE2ijL9mLpaqFBSmdbbzkr5Fo0RCZwJUfDhxanXMLynkbk33gZScrx0G2gF7qIecnJz6NfWMmKHvnELmLvvNEq8ia3+xWh8Dqh8bbQP7z+kBoBKpaKtbSsBaaSgwcIm40m6CScsaDLajmaMg8N0jV2D3qQlIsFKxZE2Ji5IoHL/YawWSUmEnk5bJN9rfIJGJZp9mjx6TcXoNAb+I/cu+h7+C4GeXmJ/+EPO9JXz9s4/sfiMZNcMwSL7EqJFPzZeYbdhOcXZC+gK1vLVfi3ett/ib3Lh7jEQvUQSEVzNS8qN/H4gm3oySJ5YjEwNZ7gxiMSzD7PZFOB0lomssCC+0Xc74CBMPsXXnZ/lrRkaukOg4b9+CIEAMd+7B197OwPPPcftE27Hqrfi1h/mseBrSVT6WVF/HIN0U5qSSbGoQRNQ8HU4GBOZi9Fipa7sNKQbcBR1MX3KVHymPvQ2SWvmZszNCpt7T1AWMpFGTRKy+NnRPrz/kBoAKtWnXCDgpqPzNeq9txLl6SQvUEWzMp3K2n2MkzoCwZE09ZjImhnDydfrMFr1TFwYTuCciSp9M2UpGVzTu4cERz9/9F+DGNuM0V3CFyfcjqVjkL5nniXkmmsIZKdz76F7+PI+GLRCZVo0azSriQh+CLs+gm+HfoW+eBPL630ke3YQLEroKQ0naIpCQkY1h1jCof5WQof28Kh9LgEhSFvaRCA4iO7CUGaU/4QMvY6duVbSlAxe1G3Cqn2HaX4/MzrSeGaZBm1DK93PP4d11ixsixbR+/AjWJ0Kt0/8PEUZCrbaSmplPBM7e1ks36EmJoli6xBaJYA32MDw3ibyNtwAUnK6eSfS5Uff6CNrbBYOUzP9I3oG48Zzzdsv4Y638JxnIaL1NHRdnZc5qQGgUn3KdXfvxO8fZkfLTNYbTgEgTUsZ6m0gtKWD/oWfJeCXBIWb6KgdYtbGdAp37CHSYqUo1E9rSAw/aHyUBiWWffpcXNqjmHVBfGnyF+n+n/vQGAxEf+fb/DL/l4w73kpyu4+XFur5hu8OQqxvoPPUcqv5uwxnRzKu1ctmcyshQdto2p+MKdFPal4dVUxg63AC3z50iqf/6COz4nUe9d1KqK6V+OUupNaAvcDPtW2v4DFpOD3Zxuu91zBgTCTM+Ge+Lb/M2QQ9ZamCrj/8Dn9v74WLw1wuev/0ELeOv5UwQygNiWd5zrSejEAn61r2AVCakkGFaMTn8RMY8pAVmovOaKT+bBEOmx1H/oXB4CFtC3qzoH3aZ4g7M0SGvpc3mI9f6OEq/RWgBoBK9SnX1rYV9NlUNXnZpD9Ou0ygtrWOCaZgpM9Hsy6LmLRgzhxsJToliJRJVobLJLXGZsqSM9jcs49EZz+/91+Df2wjem8t3835NoHjBdgPHybyjjvYM5LPoYrt3HZEQ2UyxFnmkijdBClbec62maKJeUQPBrjDFyBg+C2th2PQGr2kr2yliygecm/ks8e2Ed82jtfWXMf/OxRMd/N+3pLriYssx5prxDeiJ6j4IDeLHmoSDFgTQrl38MvoZBsxge3c0rOKJ1doEB4fNb/6L4wZGYTecD0DL72ErqWbL03+f5QnKViqimmRkYxrHWC2PEp1XAqnjW2YPX5kgg374TZyl164IXLJwAG8zSMkW2IJCw+BiAG6nGHYbfF85fCz9MTEsE/OQJ55EXzuUT7Sf08NAJXqU2zEfpah4RIKRz5LltJEdqCZAc082nvPktg9gGP6KoYGApgsepxDXubflEX+G28Rb4mh0OagPiKBHzQ9Qp0SxzuGaUj5DpHmWK5JXX/hfj9paTg2LeTnp37OHfmh6F1+ds4K5hbf9USG/YUOQzQ/GPt1DAp8uc6PQ/NbPE0BvH1a0td34tFquV/5BgsLHye3MoXSadNQ9AZO5c7nf17tYXe/n7OMI3XSGURGKMONQYw7/XNmGrXsn2rBrJnMfv0ygvSvstk9AQOh7JyhgZ37sZcUE3XnnWiMRrrvv58bs28k2hzN6Ul9vKJZzkR/E5vbd+LTGihJTqNWdOC0e5GeAFm2GQiNhrbWarq9LTgLOsnNzaXDW4VWL2ifcSNTTlRjCpM8512EcA3A2bdG+3D/HTUAVKpPsba2rWg0BnbUxrDReJIAgoa+YLKi41FaWujMXInBrKO5uo/sOXGERAraTzpoM7VSmpLOpp79JDr7+L3/Gjxjz6Pzt/OjvHsZ3vIC3qYmIu+9h++f+jEZbQrT8vvYnavhs/JrhAbtBudZVmc+QMCi56Z8B2bbW4QZz9JdFEnK6k50VjcPiv8gufpFbjhq5vTMmYR7DWxw5aDoDJSOW8SPXz3Ao67FuISRtAW1yFAbA8U2VlX9FotOw6EZQbzQ8zncWiuRpj9zt/3LvDpH0G+Dsz/6LtrQUCK+/GXs+/fjLyrjq1O/yrkYP5rqMrplKGObB5kqi6hMTCVfV4tp0I1+fASe4j4m560A4Iz9MI7ibqaMn4RGL7Em+mgnE58I4bPVuzltnEy3NhaKnh7dg/0B1ABQqT6l/H4HnZ1vgGUzbe0jbNIco5tsznedI9unwRcaS2uvEaNZh96oY/amDAre3E6KLY1CSz81UYn8oPFhapR49psmofe/TWboWBaYJtH70J+xLVrEc8EVlHeV8c2DZgas0Jc5hXF+E0HKM3wl4z/pioxlTaGD7JAWYpNepX5vOgnzuwiOHeYx8XVcrWXc/Xo3J2bNIyhgYJ0ym0gRwlLvREaCg3EEzWXloa38JfBFbPpe4pcMIHV6Avkd3GI/TWeYjv6sKH5p/zoGWcUUz1lmD07iuSUarHWdNGx5gvDPfRZdXBzd9/2ajekbSLIlsX+Whx3++czwnWdz15s49RaKkpLo1AwwOOBGaATjrbMA6Btqo3GgHM45mDBhAq2ecgBap61j9f59eGOMPONeAI1Hoa9uFI/431MDQKX6lOrqfotAwM7uzpXMEOeIV/poc2aSmJZB4FQ+ffNuQ1EkI/1uZq5LQ6PxUnO4mwFTB8XJqWzoOUSSq5c/+K/BM6YSrTLIj2f9gJ4HH0T6fHR/aR2PlT/Gd9omEVzfzavzjHzJ/XkiIx/luejV7ExcyMwaNwu9XoJSH6D5cDLhY3qIHNvPa1xH+bCZB584zLE5izFhYJ0vDwM6BgIKSUQz3ZtCe0ICOc1piLP7eU1cT0xULbYZWrwjBuKOPs9qvY9T2Sb6TLMp0c0kxPgMdw5vpCRDz9kEweDv/4jichH9nW/jrqzEtettvjb1azSE+bDXVTEorYyv7yNTnqM8OZV8zVkMbXZMebEodS7Gjp2LRqujfOgoQyeayc3NxaOMEJ6mo92Wh96hYXHPGV4OLERBCyXPj/Zhfw81AFSqT6m2tq1YLFnsqfKxwXwKH3ryW4aYHhqD4vHSRDpanYbweCuTFiVQ+NZ20iwTKDR3cz42kR80PcJ5JYF3LNmYfHuZFTeHcZ1aht54A+utN3Fv4+/IEnFMf6OaymTIC/8cUSFHKRFu7s38Jmm9ftacdSOS/oi7RYNO6yJhVg8nlFls9y7k4T8/ybHZS9FoTazx5WIRRk5rJf89X8NAwM8UJZ1UbxjV4ybwrb0ODvdqOMMUUidUoM0IYrjRxuxT/0miVse+GUE8OfANpPSSpH+e2wc38fgqDUann9M//y7B69ZhmjCB7gd/x6q4JWSEZrB7vmCvawbzfRVc0/s6Q8YQCuIjGNE4GOhzobHpmRQ8DyXgx+kdour8EaJlCDExMQxo6/D7NbSNXcRtR16nJzyG45rpyNItEPCN9qG/RA0AlepTaHi4nJGRcoYNt+EadLBOnqTDl01Y2hjEsRMMT1+Lw64Q8CssuDELn8dFxf56PNZeipISWdt7lCRXD7/3X4svowQh3Xwv9246f/4LdFFR/HlqN73OXr5XnIDW4eFEXioL3MkMad7k1gn/g8UN1x4dQZtwgFDbWQZrgklZ0k5NII3HxFf4zZN/oGTyPPxGC6s8UwmWFhpRqEzW8b1CeCEJ/FKyWJlKmM9I8dRZ3LdlP4+51zBMMGnzz0OEFXuR4LrmLTjNGiomx/An552YOcq1QzakJYJ3pmoJ3nWS7jMFRH/vHvwdHQw9t4VvTP0GbTYvLR3N2KWJSXWdxMtWylLSKBDVUN2HbXEi2l7IjM3FbA2ieugU/UdqmTFjBl3DTUSlWWiLX0Zsaz/Zvhaedi9A2LugZu9oH/5L1ABQqT6F2tq3otGY2FqTyQJtGaHSTmmXnrwpuXhra2lNXgxAxvQoEsaGUbxzB0mmaZSY2jkbn8z3Gx/lnJLIvqAMTN4DrM/YQOShCtzl5TTfuoi3OvfzPds1WPac4O0cLbfJr2KJfYbPZ/8Iu8bKrYdGCA7rJHnCizQfTSdtRQsDMoT7tT/mK9ufoSt2LE5rEEs9E4ggmH6p8Ha6nlsbA8R4JZ/r0vFShIKQgjWBmejRU5W9kP/3xnP8UX4dk36ExIVdSJ0Oy5FirlPaqU42UhK2lFoxkQjzo9zVdysvLAK76cKAsGXGDGxLl9L36KMssE5hQsQEds3TctA+mSXuUjb2v0GPNZKT0UYCeBnoc6OLNDMlfCFuhx2/9HH66HYmZo7DYDAQCO/B4zPRnjyDW0rf5qh2OkO6yKvqBnGf+ACQUrKlegv7m/aPdlNUqquC3z9CV9cOIqPWc6yqj83mE7ilhZGoyZhKyvGEJdI1aEJoYN71Y/C6nJS8XYHO5uB0Qiyr+46T5O7hQf91yJTj6ISGb4z5PN0PPIBm0jh+YN3NrOiZZD91lEEL6MeuJTWkkrtjFlNpy+T6YjdxAR9xOQ/QcjKN5HkNKAYNv9L/NzNKTxLuMTAYFsZ8TxbxMgK3VNiWYeC2pgCKpp04wx1oxAjrR/QcMAUwY2CVbxpekwWdYTIJRQd4UdxKZFQLIbPAO2Jg3N77mawVHJhu5c/O76PxdzErcIxc1zReWKQhurafwmfuJ/ru76J4PPQ+9BDfmPYNuk0eygd78aMjp6aJMNlHWWo6JZpaXKfaCF6Vgs6lY0xYDpHRSdQNldBz5CxTpkyhtqOU8AQLrZlrmF5eTpjBzgve+cjafTD0/qfqjo5PfAD4pZ+d9Tv5/rHvc67/3Gg3R6UadZ2d2wkEnFS5NmH0OFjuL6R2OIa81RsZ2b2b9mk3ATB1WTK2MBPFe3YSY8ilwthCdWIy9zY+SrWSxDthCZh8+dw2/lY0z7xGoK+Ph5f40OuM3NM5HWNNCzvnhbLJPYsno/vZEb2YmTVuxjR6CBr/JI52DRHpHRjDvDygvQt9l4vFlbV0xcaQ50klXcajAM8na7m+LYBODpGi+z7dfXbSNXehx83UgI5q/EQSwnxvNn2REaysEhR3GjhNHsnZVRiybTiazKwpvg+9TnA4N5an3N/GKrbzjd48TkzQ0xCjIfCnp/CFWgm78UYGt71MjiuGnJgc3p6t5fDwOFa4i1g/+CatIXEcC3NjUBSGBr0YUoKZFLGAwb4utFodJ3ZuJTc3l4ASICjNizMQTk/0BDbXHuUF3wKEVKB0y+h+CC76xAeAXqPnd/PvJ1hn45sHvkmfq2+0m6RSjRopJW3tLxBkm8BTxX5W6Asx4qPFlE14czsBr58mJQWdQUPe+nS8bhdFu04TFBTgVFw4K/pPkuTu4Xf+a9HGH8Sss3B70HL6n32WloVZvGNp5GcT/gPHHx+hMgkWhX6V0rGn+U3yrSS0DbGixIU+vpDQyGJEQCE4ycGz/huo84zlS/v20JaUyBRPPONlGgLByzEalvVBqM9Nku777C3OoP+AhYo6M6ma72KRfsL1OnoCPjKUOCZ642hMS+cHb+bzlGcjvUSSNqsKbZQZJX+QGweO0RapY2fiSlqUbDJMT3PLwDoeXSUIsisc/NkdRN55BxqzmZ7f3s83p32Tfr2HU14HAsg7X4NFOihNzaBCNNG/t5aQtWnoAnqybDmkJE+mdfAsjvIGkpOTqestwRZupHncKlYXH2VEF0aZYRoUPweKMtofh09+AEivF/fd/8kfqnLoc/dx16G78F1Fo/Aq1ZU0PFyK3X6W0KibONs4xDWG44z4bWRsvJPBl7bRPXYVChqmr0xBq9dQtm83odocakytVCWncE/j41QqKbwTEYHBX8EdU76K47d/RBr1/PfkWq7Pup6YZ/ehc/k4NyuH4FgP345fR+xQD58tlGitg6TOfJz+ulgix/VzZCSXfYbr+Nb2l2hPTiLbG0WOMhaN0LA3RDDOJUh2BYjT/xdHzkSQWtOK2wCixEd7m4t47b0EKwoBow63EmBmIJt4XzBnsyfz7Ze38Qe+hU7vIWFhO+i1xO3ZziLh5MQEMw/wE7S+Om4ZtuMMj+DYRC2peyo4W5dP5Fe/gv3QIcY2eJmbMJc9uYIjI2NY4ypk1fBb1EUkciyoB6tH4nD6MU+KZFzYLAb6OzDrgjj68jPk5uYwMDhA4lQLwyINlzmRZZ0FPGafB0PNUH9wtD8On/wAEAYDhuQUxEtv8buB1RR3F/Pz/J8jr/JndapUH4W2tq1otVbeap5KlDLInEA5DUoqiQYLnpoa6sLnojNoyF2dis/j5vSOo0QGmTkRY2H54CmSPD086N+MIWYvkeYo1ncl4Dh6lFfn64iIT+dO40rkjv28M93AUsM13JGWitnvYU2RAZ3XT8Ls39NXk0Ds1FYa+xJ4NOh7fHHv6wzGRpHqC2NOYCIaoaHEINFrYeqIJEr/GwqrIP5sF0cmCL71VUFPqKSvwIqzt4Mw/U8JV2DIoEECywPTCVKM9MdlM+3YMZ4VXyQ8opOwuQreET2z9/6SaKFhz4xonvd8lwj9Vr7RvYZnloJXJ2j47x9h/cyN6OPj6brv13xj8tcZ0Xg5KPwY8TH/XCVa/BSmplOn7aTj1QpCVqaiETrSdRMZN2YevYMtiM4uLBYLXd6zGC1aGsYvY3PZQQ76p+LUhVwVN4j7xAcAQMz378W2aBERf3mNewMreK3mNbZUXx3n4FSqK8XnG6Kr+y1iYzawJb+VtaZ8tEiCFt/B4MuvMBKcjMcQwqSFCQiN4Mw7ezDJybSamqlOTuLuxicpV1I5GG1BF2jirsl30n/fbxmIsbJ9qo/75v6Kuh/ew5AVQrJu4lc5enp0IUwrHCKhJ0BI9nb8bicRY1oYGQzip+G/ZHnJSfRGDfGBYJYEpgLQJBTqggVLBiBE9xhV57sJr+jn1FjBO2s0fNZn5Hc3CDwGHzUnozEMVaI1/Z4IRdCvAT1aVvtyEVoTmUNG6tusHGMeSZlnMU+04q+Da88+w5BVy/NZy2jzZLJC7Gd8YBLb5gsyaxzs3vIzor57F57qahKO17M8ZTn7JkmOOtNZ5yxgsf0dzsUkc9TUjGVYwe1XsM2KIz1oMg7XEKGGaE689DxTJ0+mpu48Y2ZF0W+agtFnZNbQOV5X5iPP7gRH76h+Jj4VASC0WhLu/y3GsWPJ+dMhrhMz+E3hbzjRdmK0m6ZSXTGdna+jKB5E0HX09fu4VnuMvkAYSTPWMLRzF7WpmxACpq1Iwef1ULD9HeKCozkWpWXhcBGJnh7u92/GFLGHtJAMZh3pxdfczEOLXHxz5l3Y9pzC1tDNwXlxFE+cTr45iZWVp5jdHowurJ6I9D2YQ0bwe3T8zPB9Mls7yBzuJkqxscI3HUUJMCRgf7SG63rBqn2TpvoyzGV2ijIEu9dqWXAqEcfxOFa0RnL/DQJTwEnxsVRinIewBz1HuBQMohCEmWW+ydiDgrj1ZCVbPNfSTjypuZXo4wzYjp5lvaueM2kmfmH9LzTeUr7Vm8o703W0h2uJfOwt+nMzME2eTM/vfsfXs7+IS/jZpdcSJFwsPVuEIrTkpybTqR2kcUsxwUuTkTpBlDOWqWOWY7f3YxrsRUqJx9aJRidoGLuczWcP8ZRzPkLxQdnWUf1MfCoCAEBjtZL0lz+jtdm4+cl6pmqSufvI3TQONY5201Sqj5yUkta2rQQHT+GB/Q5SRQeTAnUEsjcxtH0HAb/CYPgY0qdHYw4yUL5/L/jG0WtuoSolie82PkWZks7ReIlG6eWe1C/Q+/DDFGVpCZo/n5ti19LzwINUJQncE+/i5fAYPtO0g6ymSaBxkzL3IQJeE1p9gMeHb0HxR7CwroJgaWaVL4dAwIdfaNkWreELXWDUHKO3ZReU+KhMEby1Tse8U4kodjOZY2bia7GxoDeS+68VhDuHOHZ0HOOc22gL20UoGpxSIU4JI8+bQW90NF/buYvfi7tBr5A4vx30Gsa/8RhjZIA9ORE857+H8boXuWZwEQ+vgchhyYFf3kn0Pf+Bv6uLkNcOsz5jPe+M9XPEncIGxylmOo5TFZ/KcV0Npq4AfgnBixJJsI5BEQHizBmU7d5ORnIyZ6qKyZ4VS0/oTBIHhgka8FBnmnDhNNAono6+rAAQQqwSQpwTQtQKIe79gPVGIcRLF9fnCyFS37Xu+xeXnxNCrHxfPa0QokQI8ZHeJ/Wv5/v1sbEkPfwXlOERfvCqBotPyzcOfINh7/BH+c+rVKNucKgQp7OWhPibOdQ4xEbzKaSEiDXfoeuZrTQlrUCiYfKiRPxeLwVv7iQpOJ1j4X7mOUpI9Pbw28AGzMF7yYnJJWXLEfweN6+vDuHnc39O2X9+F4M7wImVN/FkRjzL+k5gqBiL2QlxM57C69FiCnZwsGY2lWGL2VR6DLMwstqbS8DvQacxsjVSwxd6JDpRiavjGdynoSYe3lirZ/apBITHSmR8KrU1BaSmTiPQbCPHEclDazUk9PVx6MRkZjv+QmVkPhahwSclE5QUxnijGIqMZcGRfB4XXyMkrIeI+QECIxpWH3kARafhkckLqXdk8FVXJz2xoeRn6ch9p40DzhKCli+n7/En+HLCDSgCXreYCBd21p47jkdr4lhyLHatg3PP5hOyKAW/zoex38DU+CX43C6sQz3Y7XZsaT4kWhrTFrOx6RiP2OdD73loPjVqn4t/GQBCCC3wELAaGA/cLIQY/75iXwQGpJSZwIPAfRfrjgduAiYAq4A/X9zeX30LqP6/duKfCUjJPedbebi5GwDT+PEk/Pa3KOdqefBICm1DLdxz+B78iv+jbIZKNara2l5ApwuisnUiHqfCtfIIzrCJDFf3INoa6EpdSFichbjMECoO7sPnzsBubaMqNYG7Gp+hRMngVIIDIe1817gO+46d7JgJ31r/K/RnmzDvO8UbC1N4a+oaMpxN5BY3kmiPwJqUjyG0FmvYADVl6WxL/wrXFR3EiI413hykz4VFZ+PNEMENgxIDrci+PzCQr6UpBl5ba2Tm6QR0gSBsoRH0djUxZlYejY0lpKZMhaYgMojg6SUaEtp7OVI4gRUjP6Mg6hwGjQYFybzABKL8VsIUHfYGC/vlUhLTz2OdZsZYPcQ1rftpitHzXzE/wuI9zte6Z/LkCokUAvtv/4j+zi8gfT4MT77KNWOu4VCKlyPeBDaMnGKio4SK5DROaWrQN3oJCLAtTSLcGIfGZiA9aApNBScINuiprCkhdUownXHzyGmpoXggFa/WOqqDwZfzC2AmUCulrJdSeoEXgY3vK7MR+Ov1za8AS4UQ4uLyF6WUHillA1B7cXsIIRKBtcDj//du/HN9bh//VdfOC+0XrgEIWrKYmHvvRXesiD+czeV4+3HuL7z/o26GSjUqvN5+urv3EBu7id8cbWCSpp5k2YV53peo/91TDIRk4BJWJi5IIOD3c+qNN0kMmcTJUAezXWdI8PbwG7kWk/Ugy5OWIR98jH4b6D9/M/Pj5lJx9x10hJt5dd2PQLr5adlDSPtKtKZBYqY+hzlomK7KSB5OvotNZccwB2ClZwoarxubPpTDFpjvVrAqgxiG/oeu4xo6IiSvrDEzvSgeowhBZzLjdA0yN8VL+gtbyJ0WRmNTKalJU9A2BRNii+DNmYLo+gFOV4xh4/DdHI1uRys0aNCw0p+DGSOLauvZ7thEo5JE6tRKjIkakt7eT657kIMTQ3iUn7BZvkOCJpPX52iZet7HC3vvJ/wzNzP46qt8wbIMrVbHSyFBxIoBNpw7iF1v42BiEOgClD15mIiFmTh1djQDkgmhc9FqdYSO9NHY2EhGXjQBLLQmzmN1SyH7tPOh8nVwDY7KZ+NyAiABaHnX+9aLyz6wjJTSDwwBEf+i7u+Ae4B/ejWEEOLLQohCIURhT0/PZTT3fQKSZbv7mOgQ3H2uhe3dgwCEf/Y2wm65hYg3jvPDjlyer36e12te//e3r1Jd5To6X0NKL+G2jdQMwTXmkyhCR7dmNsaKo7RNvBadQcPYWXFUHd6P25mI39pFZUoc3258lmIlk8KEHjT4ub0pGf35Jt5ZF883597D2Ud/R0j7MP955z10Wyw8UfkjjvZ9FYNXkrzgQfRGD0NNNl4Qn2NO8zmCvW6W+sZj9inYdGFU6iFJKER6PVgcv6D1iKQ3ROGVVRYml8Ri0YUSIIDGDHnaNqyv1qEZEUQ9U0juJAuNLWWkJEzG2BSMJzaSIxMEQZUOztUlsmn4To5E9COEwISeVb7pKAYjNx8/wh+5B79WQ+L8DrRGyfy3fk+QlDyak0PlyFjuHgplex70BmuZsqWQ6lXj0NhsBP70FDeNvYljsS6OBGLZNHySVGcN5anpFIk6dOd9+Pw+LItisWiD0AebyQ6fxUD9eXQuBw1dVUSmKDSnLmFxQxHP9uaA3wUVr4zKZ2NUBoGFEOuAbill0b8qK6V8VEqZK6XMjYqK+rf/La1Ow/jcGNbu7iPTLbijqokDfRfO+cd8/16sCxcw5bnT3DSYzU9P/ZTiruJ/+99Qqa5WUkra2rYSEpLDozt7EN4AGwPHYMxKKn//OlJo6TenMGZGDDo9nHr9VeKDZ1AU3E+ut4J4Xy+/Eiswmk9yU+IGfH9+ivOJGj7zzYfRDDlwPvIkv7nlRurisvlZ7R843zaDcGc4UVO2oQvqwd1v5FD9EiKlhpiRQRZ4swjzGDBrg+nQgM+ikOaQBHl+TcMhF0PWAK+ssDGuPIYgUwQur5PghDByW89gPdpHe2wsOzasx2UyE7WlnNxxGprazpASPwlbUzBt6ZGUpgqUIoXu1hBWur/B0dARAEIVK4u8E/CZTKw4XshfNN8kKLifyEUBdH1eNhU/S1+wlh9lfJsMZz6rR6bz6GqIG4T8J35J8Je/gOPoUW4dmYhJZ+b58DCSRTfXnNtLvymMd2I1WHV6Tj65i4SlU+mXXeBQyDLlYA0KJWS4h9KSEqatyMYvQhkKn0xGQxsd5jGjdoO4ywmANiDpXe8TLy77wDJCCB0QAvT9k7pzgQ1CiEYunFJaIoT4yJ6UMHVZMvPXprNxVz/xHvhCRQP5g3aETkfC/Q9gHDOGa5+tJ8ceyXcOfYd2e/tH1RSV6ooaGDiJy9VIfOxNvNrrZrauknCGaTUuJ7jiHTrHrcPvh4kLEqg6egDnSDRaaz9lKTF8q2kLp5UsziS0YtQayNlVh2nEh/jOlxgTPoZj3/kCxybN4u25G7m59xALmssYcazAHFVFUMZRpBvOHJ9Id/xEUvs6yfOnkuANQSuMuDRaasIkU4c0BPn+QM3BbpxGP68sC2ZMdRShlmiGnf0kTkxiyqljmKrd1KSnc3ThArr0sby9aiUjtiCiXqxhRpafpvYzJMVOJKQ5mIqJkTRGSwZOmVC6JHncQ6HFhRCCFCWaqb5kLFLBVg27lFXEJdURNENPdHEti7qrKcq08oDpP/mWu4G6ZCtnUvWsPDTC1qh69ElJOH/3F27LvoWT4Q4OE8XmweNEutopS0+lQtOEpVbD4OAg+nnhaNGhNeiZkrwMb38vgd5ORujCGuGkdsxq1jae5Gn7POg8A+2lV/zzcTkBcBoYI4RIE0IYuDCou/19ZbYDn7v4+jrggLww9WY7cNPFWUJpwBigQEr5fSllopQy9eL2Dkgpb/0Q+vMP5a5JZe7SZK7d1U+YF249U0/5iBOtzUrSw39Ba7Hy3Zc8mIc8fPPAN3H6nB9lc1SqK6KtfSs6XSitxdH027VcZzyONARRsFvB6minK3k+0SlBRCRYOPXay0TaZlNu62a6v4o4Xy+/0C3CYCzlFvMC4naXUD03gfVrvk3b4Xfo7/Txm89+hRnebv6n8me8NvJj9Bo3MfMeRiv91O5LpSxrBdldLUwOxDPGE4dX8aHTmTgSqbCwX4M18Cw1B2rxa3y8ujiEtNpIwm2xDDp6GJuXSfab+9B3KJRMmkzhjJmE2VPJ6JuK3p/G3qXLGAgLI/LlJmaku2jpLCcpZgLhLcEcz4lkwBqg4Wg40d09JFp+yjm9F4DpgQyS/eGM624nv28ZtYE0UiZVY0pRmL7jBWJdHp6fnsWZkUn8v8EsHlmtoJWCkKd34PjSZjznz3NdTTjBhmCeioxkjKaNa8/tosMaw94IBxHaII688AJjVs2n1Xse/AoJjjQiE1Kw9HZwuiCfqcsS8OpikKZEOmpNBLQmKL7yvwL+ZQBcPKd/J/A2F2bsbJNSVgohfiqE2HCx2BNAhBCiFrgLuPdi3UpgG1AF7AHukFIGPvxuXJ68jenMmZfI9bsGMPkkN5XVU+t0o4+NJfEvf0EM2fnNzjCaes7zw2M/RJGjf7Mmlep/y+PtpadnL3Gxm/l9ZRvGgJfV8jRd5kWE1R1nKGocQ04dExYkcPb4YewDwZhsDopTIriz+QVOKVmci68hxBBC2tMH8RoEi3/2KDIQoODXv+LHX/suYTLAE4VfYZv9q1g9wSQv/xVGnYvmA/EUJ6xkXE8rY5QopnjSGPT2EaIP+//svXecHVX9//+cmdvr3rt3e2/J7mZ3s+m9UEIPRXpHRUAURREVFUUECwqCSgeRJtJ7gARCSN/UzSbZkmzv7e7tfcrvjw0BBBRR8fP7wuvxmMfcu/ecmXNmz7xf57zb4bV0mRPHJczqKg6u3YaqpXh2WToFPem4bdlMhEeoW1BMyV/eQAzCpnkLaKmeRrp/Kpa4nXDuaqyhEpyJKlYvPZKxjAw8zw8xtyBI38g+CjKq8Aw4Wb3AgyomaVqfS914M0nPnxgWVQQEjkjV4lItHL2/kb8kv0lCMFCweBijMcXKN+8ioRe4YdpFrAh0Yjfl8upcPQtbNP7S9yzG+umE7ryPS8suYKc9wttiOqf7NmKPTbCrtJgOaZiMnnQ6OjsRZ9tQNRVREJldfRJaIoa3uYn0sgz0ljAHK07k+M4GthgXw95nIBn5TMfIJ7IBaJq2StO0KZqmlWmadvOhv/1U07SXDn2Oa5p2pqZp5ZqmzdU0rfN9dW8+VG+qpmmvfcS112madtJ/qkP/CIIgsOiMchbOyeHM13woSYWzGzvojycx10wj79bfoTvQw+3rS3irZw1377n7s2jWF/gC/xUMDT6NpsnofIvZpVo5ytCISY2x/WAtOeO7GJt5JgazjrIZHrY+9yRptkV02IaYoTWTk/Jyk2EhOn0Hx/S5mHowhvDVc0jPLeXtm6/jjtO/Tshq48HOWwlFM/BHlpMz7wHM9hEGt2bSIi6kMBYkDxcLEpUMRbvINhew2pVipVeHUdtKx9urEFNJnl+SQc5gGum2bCbCw8ya6SHvvrcgJfDmEUfQV1RCunc6FiHIX2bcwWNFr7Kp8mH0cQ850TremHcEwznZeF4ZZ272BH3j+8n3VOEZcvLiIg+mRJyt75RwzOjb7Mv/KyE09IKO41IzMaHniB17uVu7Cos1gOeoFGmDYxzTuoaOHCM3u37E90Imnlqk4LdKnPD8IFtOKUceG+PYLXHSTencl5lBrdjFaQdW0Z2WzxrHKFlkseuNe6k89igOBnehoZHW76SwdgZG7xDbtmxk6kKBqKUEt6Ln1bZCSARh/wuf6Rj53EQCvwtBEFh2zhQW1WRy5mo//rjMWY0djCVT2I88kqwf/gB3wwF+truMe/bcw+vdr/+vm/wFvsC/DE1TGRj8G2lp81j9Vg/RkMA5pk3EhHTEkRgp9Ayksqicn03nzk0Exw3YbRo7Cuxc2fskm9VKunJbyJTcLHmunVBeGjOuuA5fTycPmAtpLSnn5tgOZg2t5cXAdbhL38FZtJ3xFhddA9OwGk24BQdHx+roCjZRbKtiqyXJUQEJA210r38CKZ7khYWZZIw6JoV/fIy5JRKZDzWgCBKvHXMsPk8eHu9MBOc+/jT9LmozpvPr9HsYyx7jlZp7QTFRFJ3Ja3VH0Z+Xh+cNP3Nd4/T79pOfPhXPmJPnFnlw+aNs3lTOJcNP8krxayQ0FSsmVqTqMakKJXvCvKScRHZ2N45FEtM2rKfMO8ZLtbn0BWexOF7Gn48Vyfdq7Nv8IuJRSwj+5VGuzD+HfeYoqw1pnD6xAWM8yrayAoZ1PvIGq9m9bydKlYSsJtHiCvPnnIqgqrS/8ya1y+ci6mK0l55A9cFOfOaizzwm4HNHAACCKHDERVUsLk/nzLUBBmIJztnTgT8l47roIlznnUv1Gwe4pD2P6zdeT7O3+X/d5C/wBf4lTExsIB7vJ11/Mk9JRtK0MItSu2kLLaAksJ2x6SejqlC1KIetzz2Jw7KIAWs/08VWsuQJbrTMQJSGWLbJT7Yfpt7wGwS9np8+8lfenrOEkwZ3c+7un/K30HdwuYbJnPk3AoN2uneWk8oqxCZaOSk2gwOBbVQ4ZtGmT1KXEDFqw/RvfgBdKMZL87Nx+ey4rZmEtCALzBOkv3CAuNHIqyecQNyeRbq3nuGCN3ig4m+c776UuZvOpvuVBJd03oAzw8Hfav+IKiYoS87ktcpj6C3Ix/N2iNnGCfoD+8lzTcHlc/LcwnTShyNs3V7KNcP38EjeVhRNI1N1sDBVSWYkyFDXdNpS5RRVtmEpT3Hca/ejlxV+PeNEzvPq2F2u42C+kbPWKTxYMwayzMJXe8ix5nBfRhZzxVZOPLCKA+lFrLX0kaWUMNj2AKXLlrPXtwEA/X4om78YyTtC4/Yd5E8fxu+uYVrQy0vjs6FvK4y2fmbj5HNJAACiKHD0l6tZmpfG6euCtIXjXNDUSVRVyfrRj7AuXcIJz/Uzv8/Et9Z+i/HY/zZr3xf4Av8KBgaeQK930/amQk/SwYmW7UiajF+ug/4uBjzzya1IY7xnF74RBZvNSkO+icv6nmadWk1/1n5yo2ZWbkwiLluAc8lS7v3bUzyz8ARqO3fyq/GH6IrXEJFKyV38R2IhI11vFxMqqsYkGDktOoODvgYqHHMYFRUyBAGrEmZk+92I3hAvz83FEbSSZskgaZWZP9FO2sZxAnY7r514Aqo+G4+vgu3Vj/Fa7ga+p/8VtjemYTTrWXJ2BaGRJEfv+Boz3HN4uO4PyAYvFUo9b5WfQG9BAZlbAszQQgxG9pPnqsAWTuPFuek4u+PsaSrgh77f8XD6fgCmKrlUpXKZNtTLq74LiGlmChaO4BT9nLT5CUbTdPwu6xtcEizmTyfI6GWN0jfbGDlhNqHnX+Rqx6kcMMRYZXJy1vg7CAmFjSXZhHUxcvoWsfvA20Sz4yS1OPJolGXHnocgijS+/Byzjq0DQaOrcAXRPTFUUQ+7H/3MxsnnlgAAJEnk2EtrOMLj4NRNIXYFo3x1bzdJUSTvttswlpVx5TNR7P0+vv32t0koif91k7/AF/iniCeGGfeuJdt1Gq+HAygRjXOkrfjkPApUL76c6YTjEtOW5LDl6b9iMy3EZ+ulTt9KpuzjF/ZKBNHP6W+GMaCj5Cc/p2lkjF878yke7OEmawdObxtr4peRv/QOZE2m87Vi/Hl16AU9p8dm0O3fRaF9OklRQjFpuJMyE013wvA4q2bnYo2YSDOlI2Xrmde0C1trjEFPJmuOOxZRySUz7OKZ+rsZSJvgWu8dhDdYKK3P4MwfzqbuiALO+P4sdHqJqRtWcIrlPB6q+yMxazdFWi3rS06hPy+PnF0T1EXiDMZbyU0rw5BI4/V6N6YWmZ42N99I/pLnzZNxqvPlKeQoTma1dvBw9GuYTGHSj05ScqCF6R372DjVTSyyAuxpvD3TwrL9Go9Y9yA47FQ/sZ1iexH3ZGSxRGriqPY3ac4uYaOxi/T4NHTyM2TMmUfj+FoAUht9lC5ahuYdob89QHr5foZzFjB3uJN94rzJDKHyZyNrPtcEACDpRY6/vJajrVZO2h5hnS/Elc09aBYrBffeg85i5cYXTPR07eHGLTd+sZHMF/g/j8HBp9E0hbGGXDYZs8jGS11qL177ClIb3mSk9lTMdj1qqh3fSAyrPZOtOXBp/3O8oU1jNGM/lf0aS/ZrZHzlq0Syc7igYR+mZIrzOl6lvu0Bng5/h8L5D6MzT9D9Ril+z3REycBpsXqGA62km0ow66wM2xTyYiLBtnuRe/p4bWYupoQRh8mFtdjMrDUbMI4otBWWsvHIZRjjBaRpMe6qv5MCcwXnN/+EQLvC4rMqOPZr0zCYdQCk59k487rZZJU4cWys5FL5BzxafS/+tL3kilVsLT+Tocws8ppHqfEmGFE7yHGWoGku1lW7UHaJRDslVppuYrPgRxIkjkrW4VBNTNkX4NXYiWRn9GFfBkesfYa0YJh7Z8zjvLF6Hl6eIGLScfaaBBuOziK2ZSvXyEfRrYvzksXJuaNrSckSbxa7kPQalpZj6Pe9wZhxmBRJkh0BVpz5ZTS9gYanH6dmuQsEHSM5i9mxyw5RL7S++pmMlc89AQDoDBInfL2OYzFxbGOUV8cCXNPWh3TIPVQfjHHrKg+vt77Iw/v/NxF7X+ALfBKoqszg4N9wORexryvAWNTC6dYGAOzWEmKqkeFUBpULstn65CPYTQuJWXupNbXhkf38Mq0IQYty6ZsCYmYGaZd9jQvX72LCbOey5+7gJFcX7dFZmGv2Yslop2djCV5DLZrexMr4dALhPnSihUxTFm12mfKwnkj3IyQOtrG6Pg+jbMCud+IpNlH73DvowrCzajqNC2ZjiZQgOw5yV9UjrFQvYN7WsxA1kdOumcn0IwuYTC/2Hsw2AydfXU/N0jzU3S6+6f0VL1U8TX/GJtxCBbuqLmLM5abg4BDVvXFGdAPkOIuJ6d00VKTh22rB2eOnPP1XdMhRTIKB41L12FIqckshrfEplFQcxFER5eQ1fyFqgIcLzmNWvJxHVkgUjCm0BttJ5XooePgtqhxTuMeTwZHiDhYdXM/+/FIadN24w3PISNuEvb6GPePrAIi/MUj+vCUkJ8ZITDgw5e6nq+goKjt7COlzPzNj8OeCAHp67mV09I1/WMZg0nHSVdM5MaZnWXOcJ4cn+Fn7AKZp1eT97rc4Oke58S0Pv99xK+v7139GLf8CX+Bfg3fiHRKJYbTuetbaTWhxldOVjYRttUTefIfRulPQAKt9AO9oCIOtgG05SS4ZeJGXmMaEq5UjmjQKh2Syv/8DftYzxg7RwLefeJDqBRlkjR9kb345zuKtDO3OZThcg2q2siJZQzLuJ5jwUWKbyn6bzLSQnvjwC4Sat7OmNg89eiw6O3m5GlOf2oSQgo2zFtBeV4kzWEJ34Ts8k7eWr/t+QeaOOnKnuDjrx3PILnV+bH8lSWTZeVNZdu4Ukt16Luu6mZ1FW2nOfx2zUExj/eX4bE4KegaoORBgyOoj21GIz5LO3kIn3ZvcVA0cJJp1NxNyEqdq4ehULZ5IiF3dxxORbRTOGyY/1cvi3WtpybdgTJ3P+iqNvmwL568XeWxegmRHB9eM1DMoJXnBbuf8kTeJKkZWFRhJ0xtINR6PZnuLTuUgiqAQb5ngyJPPQTGa2fzUM1TMHgXRRMRdx9auysn9gn3d//Xx8v88AahqitGxNezddyXd3Xf9QxWO0azj5G/Vc+qEwPz2BPf3j3Nr9wj2o44i8/vfp3T3CN/cmsb313+fDn/HZ9iLL/AFPhkGBp7AYMjgwB4jLUIOFVI/pXQj5BxNrO0AA65ZFFS52f3MQzhMC1BtQ9RYWkhXAvzGnY05nuLLG/WYZ83i5RnzeGjEz+lvrSJPbmbR0Eussp1CxrRV+No9dPfXo9icLEpVoZc1+v17me5eSLtZZlpYR3JiHRONb/J2dR56nQ6TaKfcGqTkxUY0BN5efAQD5cVkBHN5p+oZ2iyjXNl5C9pBB3NXlnDSN6djthk+0L9wuI329ltIJj/olFGzLJ+Tv10PMR2n778Wf4aPLaVPIWm57JnzbXxmJ/n9g9Q3DjHkSpHpyGPYmU57poO967M5enQ9ze7nSagyeYqbualyCkYneGX0bAyGGOnHxJm7823yhgd4vracE71f4o4TkhiTCsXtUUaneHA9+gaz7dO4Nz2DY6XN1LfvZG9xCU36PtIDi3C5OrHPzKbV3wAaSBv9uKfPIRkMoIacqO4hOoqPQdjjRUWE3f+17DiH8f88AYiinpkzHicr62Q6Om+lufkalH9gzDXZ9Jzy7RmcMaAxoyfJ77qHua9vFPclF5N2ztksXjfOUbtVrlp7Ff64/7PryBf4Av8EsdgAXu86rJElNIsBgkEdZxm3ogkSgaYw3pxZxJIS7vR+vIEwkmUKuzJDXDD4Cn8Vawg62rmowYgxnKTv2h/ywwN91Lbv49w3/sqUWj3Nhlk4Z64mPOymrWUWsjOdGXI5adhpHX6LuZknMmhQKIuJyOE9jOx4nvVTc9EbJAyinbpkH3nrupAlHWuOOobx3Fwyo1Yeq/0LrlQZZ+y9FjFu4OSr6plzYgmi+J7KR9M0+vofYfuOU+nquZeGbSvx+T64kUreVBdnXjcbh8vMoj3nkmHKZfWUh1BxsXf+NfjMbnKGhpi99QCD6UbSHTn0pacz6LCxbUMeZ4YeY4tlGyoatUohZUoW+Z1x3vIdR7ZrANvRCivXPI6kpHit5HiwlLC1zsHyvQp/q5hA8Xr5dkshY2KKZ+02Lhx+jYBm48UcmSydneDOY8ks3ERTaj8aGrF94yxZcgKy1UHTqgYKpqxFMbjQmQroCE6fJADlv7tPyf/zBAAgSUamVd9Gael3GR55kV27zyeR/Hi3TovDwKnfnsHZHTLVQyl+2j7I34YnyP7JT7AuWcL5qyKk7xvge+98j5Sa+gx78gW+wMdjcOhJAAa257LDmo8gK3yJTaj5SwmuXsvItJOxphk4uPpRnMZ5SPYRqmz7cSsh/pDuJM+rcuS2ONELL+aKkIY74Ofmu29n6LgpFCT68c3sIxWx07Z7BilnJlOVAvKFbJp6nmFh1umEJY3spIoW76Z/26NsrshGZxEx6F3MHWwjfc8EEaOZ1cccR9CVhZ0491U9xorx85m99xQy8u2c/eM5FFS7P9CvZHKcPU1fo7H15zwTyua6QSdvBmDHrgvp6voT2vtStjg8Zk7//iyKaz2UNy9mdvIIXqq8j5RgYu/8a/HasskcHWXhxl0MpTtxOjLpzPLgM1jZsqmAc5Vf06B1ICCwJFWFR3Wga8mgLVRJeUk7mSXjHLP+WQbTDVh13+CB5QmSRj2n79Czq9aC6ek1HGWq54H0dE6QNjKlo5k9pcV06EfInFiIoI/hmpukJ9EGGribQVdaSSoeQwgZSdgidBYfR88uAUJD0P7mf3XMfC4IACYjgEuKv0FtzZ2Ew61s334qodDHb0Zmcxn50tUzOH9fkvIxmWta+1jlC5P3+9swlZXzgxcEBvZu5ZZtt3yGvfgCX+CjoaopBgefxirM4kAySZfqYZa+nXR1hKCvnAg2RlNuMtN7mIgnECw17M8Y59zh17lfX0PE2sv3NqSRTHNz7REnEY8nuOGumxnzyCzS72B3jQNQad8ynag1lyIlk0qhlB1djzE363RESYeVFELKR9e2+2koSUdnEzAb3Cxs3o21L47X5mLNcccSt3iI2Xt5On89X+68gazOKqYfVcCp18zE5jJ9oF9e73oatp3IuoHN/HYsg02+CaaaMnluPMy9/iy2HbydxsYvf0AlZDDpOP7yWmafUIy7u5TjvRfyYuWDxCWF/bO/x6izmPRxL8vWb2Q0PQOL3UNbjoegYmFbQx4nmn5Ic3IcHRLHJOuwK3q6WxcQSjgpmjNIjW831Qf2sH5KJpXxr/C3IwzkjSTYn55AVlJcusWCT1B42mniwqFXGRXdvOAJkSelM964goLiRnbpJjPhJ1p9LKpZRtLpoXPLAM6CVcSseVjidkLRjP96grjPDQG8i8zM45g162+Axs5dZzE2tvpjyzrSzZx+9QwubIyT71O4Yl83G5IqBffcjcFq56YXzLy26wmeanvqs+vAF/gCH4Hx8bUkk6OE9pTTZxCI+TUutDagSWZ8Gw4yUnMyCBpDO54hzTQHnc1HhW0faUqI+9JNLOzQkdM2wW0/uonWeIqvvPoXpvQNIMx30laZhc7qZ2DDXCZM+WQrTmZr1WwYeJIZ6Sfg1NtRpQS6VIqDO+5iR74TnQPsejcLG3Zi9KkMpuex7pgj0HRpHMjbwT6jjwubf4o5nMZxl9Ww+MwKJOk9caSqCQ4cvJn1u77Mg6MqD45JZEk2HhsJ8Nj+LfwiomMgluTWUSer+rfT0LASn2/b4fqCKDDv5FKOuXQaZp+LU3uv5I3yJwkb/TTPuJrB9GrSJgIcve4t/OmZ6O0uWvIzCEZt7G90U2v/AYOpCGZNz7HJOqwxla2dx6OXUniOjbJi0/OkhYJsqljEjvIyxjx2ztwq8nq9hvTGBs5QZ/KQy81K3dvkd3ayqzyfEb2fXG8twaSdjJkHGRSGQISifjtyZj6aIKD3jpIwKHQVHktbWz4ceAOCQ/+1cfO5IwAAh72GObOfx2opp2nvlXT33PuxxuG0TAtnXjWDi7dH8YQUvtzURaPVScFdd2MNy/ziJQu/2/hLtg9v/4x78QW+wHsYGHwCneCha8LDbnMJBk3meHUzccdSogc7GXTW47Z24kdGsNbTmT7AmaNruN00DUU/wtfXm/nrBZey2uLknKb9nPr6W+yf6yGrYhTVM8HI1mX06vNJV60sU+pZFV7LNGM9ueYcwvo4JlmiZfed7M4yoXdrpIsO5q3fgS6u0ZVfwaajFqIXbKwtXoM1UMVxLZfhTndw1nVzKJuZ+YG+RCLtbNv+JZ5pfZhbRpzsjSh8S03jr83b0Yfs/Dx1CUtHxnm2q5MaycnfJiTuGUnyzo7zDzl6vKcSqpidxZeunYVNb+WkjsvZnv8OPmsfbbVfpzd7NjZ/hGPWrSbmyga7k/35GfjGHYy2KFhttxJWUqSrNpYnqzCP62gYOIIsxxBpR8Y58c0nCJkFVPs3+dPxYIyncMdNRCwiZ66JEkbhqTQjFw2+Qq8hh1dc4+STx2jzMWRmdbM77U1QQemPsKBwDrInh7G2BEr2evzuKlIDelJRDRof/6+Nm88lAQAYjZnMnPkEWZkn0tFxC80t16KqH20cdudYOfsb9Vy8OYo1onBeYwedJWXk/vYWsnvCXPO6nmvWfoe+UN9H1v8CX+C/iWi0h4mJDWhdtQyIAfriDo6wNGNIBvC3WxnPnUs8JRDqehWXeRaCOUyJowmbGubRdJFzd9vYmjuFBxceyTHeMMc991uCVoGyhUP485KM71/GwVQOdtXICnkWj5vamBoxMNUxDb8+jj1pZN/eO9mbLqD3aOREdcx8Zw+iCq0V9WxbNAOzJvJi4SqWdl9ATc9yKhdkc/oPZpGWZTncD03T6B/4K69tOYVbe/p5YsJIhd7NU31DXNjdxm9T53Nc8pc8qx3NssRt7EnO4oG2nVwTk2iNqdwyauf5fbfTuOcrJJPew9fNKLRz5nVzyCpIY2nHOfS7ehl0NtNR+WU6C5ZhDiQ4dt3rqM5MFJuDfQWZjPSnY+hpxW96DkVTKVEymC4XoXRn0zFezZTCg5TkdLFw59s0F9pJOb5JU7WbeftivFanIezax9dCM3nMmcZK3Zt4uvrZUp5F1JigaCSLwVAludO20yn1IxglpnoziTkyMFit2IZ3kBRUBvKPpL+/YjI1hPrfSU3/uSUAAEkyMW3a7ZSWXM3w8PPs2n3Bh9zL3oUn3865V0znoo1hxKjCWbvbGVu0hMxrr6Vub5hT3orwrbXfIpwMf8a9+AKfdwwOPgmIjPRXMmBKRw3JfNWxHUVyE9i4h6HKEzFq7YR0Mjr7HIZdPZw2vpZfWquxx/1M6czg11/5JnWCwMrXH6JgJMHAaXoC5RqBvhm0jhVg1gyckJrDvdkhpna2MdO9FJ8+iTNppLHtHprtSfSZCmWjCWq2HQRgT90C9syaiqiLsSZzF2e1/pDMYBFHXFjJURdXozdIh/uQSvnY03QF9+34Ob8e0tGb1PPDmIm/tGxnNF7K8sTveJrj+NPZ09l544mcM6uIb6Su4orkdzllyMvfevvJE6z82WvkzvbtvLP1pA+ohCwOA6d+ZwaVC3Oo6V+OoNdx0LONnrKzOFB6IoaQzDHrVqNPcyNbHewtyKDzQA6FY8/Rpe1CQGRmqoRCxcNgywwCkXRKZ/WxuOct8kb62DWlhicW16HqdCzusjLoFjjy5X4UDZ5y6bhw4BXaLQW8aRsiRytnoH0xFkuQzuLnSCWTCAGZ2dYqxMJyoiMSAfdORrJmM3rAjDrWA90b/itj53NNAHDIOFxyFTU1fyQUamb7ji8RCn90Nr6sYgcXfLWOCzeESERlztzdTuzc80g7+2xO2BSn6J12rttwHYr6P9vz5gt8zqCqSQaHnkbyTuWAEqVJV4qVGHPCmwjGZxISXUzIacjjq3FZ6klJcQrSdmPQojznVrl4Vy4//eq3STMa+O6avRTt3EzXHAn3vAjRiRKaO2vQIXGiPJv7SyRKd69iUeYphPUKaUk9Ozr/zAFjCEO2TE3bBKX7BlEEkYZ5R9JaVUjQOkKbLsapLd8m3ebi9O/PpnpR7gf6MDGxmRc2HMf1zRt53m9gjs7Dc13dnDg0yHeS3+T85A85uq6CTT87gRNnFGLQifz4zLm89I1FtOpnsCxxK+3xmfz1QBOXxnVsj0jc3BflqS0X0N1992GVkKQXOfLCShafWUGut5KcRCn7st5hoPAEmqeehT6icvTatdhdVmSrk30FmTTtL6Q29ms6UwNIgsSyZDVpip3W5uVIgkrWsT5OWPskGjID+Rfz/JIssodDNJUZkXqH+HZ/DU85HKzUv4Gte4y15S4MRoEpQzK9wePILd7DXsseJJeRmmg+ftWIPcuDc2QjChojmYvx9bv/a8bgzz0BvIuszBOYNfMJNFVm586zGBt/6yPL5ZancclFNZy/PsxENMXZjR3ovv8DrIsWcdkbKuMb1/LH3X/8jFv/BT6vGBtbTSo1QaS3noSgMRrVcUraXkQ5jn9PhOHqlWipbqL6BGbHAoKuDk6eWMfPbFWUDcd5YuFX8Ke5uHH/OOGW+zGaNYznxknG09i/fzEqAiekZvF0ngF741OsyDgLRSdgS4ls7XmETnEcfa5M/Y4RsnuDJCUDG5YeQ09xBt2uLnT+Shb2nEZpXSZn/WguGQX2w21X1SQtB37Fb975Cr/sj+JTLPzCr3JX2w62J+exNHEbbdYFrPrWUm46bwEWgw5FSRAMNqGqSWoKXLz105M4e1YR30xdxTeT3+WiwTEeHhzBqpm5c8zIbbvuYNvuSw6rhARBYPpRBZx01XTcchaVvvnsyX6b4Zwl7K25BCkGS97cRKZbQrbYac7LZNP+Cmr4LkPJIHokjknVQdRB68FlZNlGyVkyxtEbXqI/w8SW6d/A73awfK/Gvnyof7kVY1LkaZfAef2raHEWs948QKZST+vBCjRNIDb1OQKpMFIS6inFXjUbNaQxZmmkP3cxfQdz0JpfhujEf3z8fEEA74PDUcfsOc9hsZTS1HQ5PT33faRxOL/SzVfOrebcDSF6IwnO2deD7Xe/w1hSyg9fkHh93QO80vnK/6AHn2Mo8uRuSt0bIRX/X7fmM0P/wF8R4mm0BK10WMshKnOJuYFYsoBw1zCD1mqE4Fu4bbUENZkc1w5U4rzhTGC0XMre8kp+LuoxtW5maucIQ9+R0SQdzU3HkdDgmNR0tjhVhgbf5DTzSZgkA3pZZlPfY/Rqw+jzU8zbMEC6N07caOPto45hNNtJi7OTqp6VFPtqWXh6OcddXoPxUCI3gGi0i2c2nszVOx5jVVDPUYKbFzvbmTOR4Nzkj/mxfDk/OHEGr/3oRCpznYdyHD3D1q1Hs33HaWzctJDWtp8RDTfyozPm8tI3F9Gir2dZ4jaGotN5uqOVMxISb4f0XN+6i2c3nojfv+Pw/Qur0znzh3PwuNKoG1lGc+YWxjLqaay/EjEhMGf1Too9KWSLjfZMN2+2TqVQ+iEhJYlF0XNUqhb/aC4DgzVMzWunzrKTyvYm9pQXcc9Jx2GKJ8DuRh+McU1rKc/b7ZxoeA19d5BVZWbSDQbmjA/SHrqMdE8/u2wvYyhxUisXMjwWJbM8C8f4JlRBY8g6m0Dhd8Fg+4+PH+H/T9ktZ8+ere3YseOfF/w3oSgxmlu+z+joKnKyT6ey8heIovFD5Tp3j/Gnl1p5crGNmU4rj2dZGD7nHLxamB9dJPKH0x+mNqP2v97ezz2GmuClb8LQnsnvkgHyZkPxIihaCPlzwfiff3n+29A0jVTKRyIxTCIxQjwxRCIxcugYJpEYJhI5iHbgKDYO5/JKxjK0gSF2GL/JcM8CmvsKaC5aRDL0JEV5X2fE1szF0q1c6yyiV1jIzrqLuTwe4px3AnQ3/BDTGXHUqRotu05jPGLjiFQNXlI8kOXjRx1uii15pJQIm4aeZSQ1hL4wwaI3BjEnVcJWD+uOWEzMpqfd6GNu30osNhPHX1ZHbnnaB/rU1f83/rDjl6wNQrpk4sfD4xwR8vOgcgK3yqezotTFTRcsIs1iQNM0xsZW09F5K9FoB3Z7LXl55+Kb2MzY+BpUNYHZVEh29imkZ5zMn14b5c+7xjla3MGv9Q+y1yLz09xc/GqS450yl06/itLiKxCEyblvIiaz+oF99O6foMvVRKG/grTgELN23Y4mqXQeV07ruAkxFiVdVVhaYgS+Bai060fZamhh+vQ3MVp87Hm1ij8ffTWSZuEnj/2W+r372V4hMqtT5dor9NQLQdThL/Hkoi9x05aDZAfsPJ3Vz8rqhxD1Cp7Om8kbTKNNGECbJ7Dn8UcIO+eQLc9gduBpZj376VNDCIKwU9O02R/6+xcE8NHQNJWurj/S1f0HnM7Z1NXehcGQ/qFyB7YPc8fqgzy3wMZSl537xAiDF19Md4bG7Ze4eeTUv5FlzfpM2vy5g5yAd26BTbeD2Q3H/WpyltSzEXo2w2AjaAqIOsipnySDokVQOB/Maf/TpqtqimRy7INCPf7e53himGRyBFVN/l1NAYMhA6MxC5Mxm4m9Edraa+kXJR5JVfMd59tcNXEfB1aVsGPG9/FF38RucxEzLSer7M/Mi7zDSYXHMJr7PRb3d3GT18WOlvsozd1FZLlK9/6j6fPmsCA1BWMsxQ/qbfykYZQZ9hoSSoCNQy8wnhxCV5Rg2apBDIqGz1XAuuVzSJhEgikbFeNzyJ3i5NhLa7E43svlk0oFeG7HN7inczfjsshpKQvXDrTRpxRyTeoKwoY8fn/JImaXeACYmNhER8fvCIaasFjKKCu9hoyMYw5nBZXlEGNjqxkafgGfbwug4XDUExJP4YYXbfgTKW7RP8g8/XZuzC3kTb1KiUHhG6XTOWLGnRgM7kP/C42tz3ewe00vY7ZeHLE00kJh5u64BUGQ6T2+hH3jZoRYAovZxFxPGVnSCUSFJDt1nXRbDzJz5qv4kjYa3p7D3066lOoeL3fc9j386VYc4xP0zszlx0eOcnt/iosz72auvpebW7LZLb7IgZoFLM69geGeelbYfkFi5zhrsw5gjL9NX1MIk+3LTOl6jSMfvwl9ViafBp9rAogGA5jtjg+lk/0kGBl5heaW72MwZDC97j5stqkfKtOyeZBbN3TyylwbJ3qc/G7wIMNXX01DlcTrl1TzlxMexqQzfcTVv8CnRt92ePEbMN4G08+FY39J0htBcjqRHI7JMokw9DVMkkHPZhjYAUoSECC7ZpIM3iUFq+c/1jRZDn9glv6uQH//3yZ10h9890TRgNGYfejIeu9syEaTrSQCApGJJKExL4HhEaJjXtyBUtbaO+mYspwNTRG2Zf8K/YEYbdsz2T79fJKhv1FW8HVGjG2cb/gNF2fPYWfRD8kZ9/GoycXEti4SsV+QPE3B21VHc9906lPF5IZFvjXbzpe3tXOidT5RZYINQy8QSI4iFsY4YtUwBlVjOGsqG5bUktSJGIMVuKO5zDq+iLkrSz+Qy6dv7B1+tfG7bAgmyRUN3DAwzIxYgt/KZ/NXZQXfWFbMFcfUoZNEgsEm2jt+i8+3GaMxh9KSq8nOPhVR1PFxiCeGGRl5meHhFwmHW1A0A2/1nc8zrXWsEHfxK/0DbLKp3JyZQVJQONNj5vIFd+FyzTl8jbatQ7z1WAthyQeqgiskMH/7LxG1OEMrimj0mSChILrTmWVZSJm+irCQYq1+D4q7laraN2kfKOG1sZU0zFzGlzZs5qq//pHdVU5mtAS48csmMu1B5KFzeX3RMdy2qQ8xLPK4y8uX8p/BVTiA3HcN0zqn0a+MYzo5ztt/eJykfiqq5Ti+9uslZDo/nRz53BKApqo8/uNr0BkMHPWVK8goKvmX7xsMNrGn6XIUJULNtDvweI74UJm96/q5ZVcva2ZYOCfLxU+2vMnYb3/HcwtFAhefyG+W/uZTEdAX+DskI7D2Jth6NzjyYOXtpBx1jNzyELLfjRoeRWAAY4UHy4wZmGfUYygpmXz2qRgM7Jwkg+6N0LcN5NjkdT1TJ8mgePHk2ZH7oVtrmkoyNXFYqCfi7wn4RGKY+KGzonzYFVinc2J6n2A3GLIwSpno8WBQPBCzER9NEfP6SUyESAajyOE4akyBpIoeA3rRhEE0ohdN6EQ9AOt1LXToRnjKs5T00U7WiVfTtaWO3baT6DX14rQYiOqPJr/sfnLl3ZxZexcGxcFj+3bgCdSyOXU1OcfHiI1ms+PAUUyV86gMO/hZSZDaIT9fYwFx2cuG4ecJJycQ8yMc8foIelWjq2gG2+eVk9BMZEzUYzaYOP6r0ymqeW+lrKoyT+68ljvb1hBUBM6LCHx7rJedyjR+mPoaxVnZ3PqV5WQ5TEQi7XR03sbY2Bvo9W6Ki68kL/c8JOnD6td/hHC4jeHhFxkeeZGOcYGH9l3ARMjFrcZ7qdU18uPcfLbpNKaZFH4w41LqK64+rBIa7grwyl2NRCIxonof7pCF+dt/gU6OMHxkEY0BHVpKQM7OY7Z+GZW6fIIkeNW4g+zi7eQWNdKytZz7y79FwJHNbXf+idL+JsJClEi2je+dFePmAYGvZ/6JI9QOrjuYQ6PuSdYVnciZpT8lFHYzw3gXho1B9ld4mfDfy+A2iWT2JVx1w0kfSpXxSfG5JoC9b69hwxMPkwiHqT/2RBaedT4m67+mE47Hh2jaezmhUDMV5ddRUPCVDwn0xjd7ubl1gA3TzFye5+HyR+8j8PTT3HWiSN3FV/O1uq/9S/f8An+Hznfg5W9N5kmfcynasp8w/tjLRBqC6LKmg5gETQeaiBr3keptQO5vQBDCmOqnTxJCfT3m2lpEqxXk5KTdoGcjWvdG6N2KcCiOI2VPJ5qRS9Btx+eQCEkBEskxNO3vk/+JGHQeDFImBiEDg+ZBr6SjT7nRJd3oYi50UQdaVEIOxZEjSeSkjCqrKGgogoKMioKKjIIiTH5OoZAkSQqZpKCQ1BSSqkoKkAVQJBAMMKqNkFY3i1u3C/wq63VO6/grB9cWsX7ut0hEnqai6DJGxU7OMv+GeTN+Q8BSza0P3MG8GZfwWupPVM1sIh61sqN5JQWpHGZHcnjAsIsxVzY3TNSSkr1sGH6OeCqAkBNi+ZsTGBSZ5spF7K3LR01kkBWoIq3AyMorZuJINx9+MgO+vfxs3eU0BEOUIHLTwBCFCT0/TV3MFmEOt5w7lyNq8ojHB+nsuoOhoeeQJAuFhV+lsOAr6HT/nt1G01R8/gYGBl/k4a1hXmhfwbG6bdyse5CX7XpuT09DL2p8tbCCixc8eFglFPYleOmuXUz0RQkYR3FHHMzffhPGhJ+hpUU0hQQUVUciv5R63RzKhTziQoyXDduorVuLyTHMtjdncd9R3yF3Isl9N13FQFEaJR3D/OkMI3JemNTgxWydv5A/bvLijYd5xB7nHPM75MzdwcTgqdQfOImBaAJ/cZC+vSYQzJz260Xkp1n+Sa8/Gv8WAQiCcBxwByABD2ia9uu/+90IPALMArzA2ZqmdR/67Trgq4ACfEvTtDcEQSg4VD6LyXXwfZqm3fHP2vHv2ABi4RCbnnyMpjWvYbLbWXreJUxbdhSC+MkdoRQlyv7maxkbe52cnDOpnHojovjBfOXbV3VyY98o26eY+H5hBqff/FPCW7fwi7MFLvvyHzmy8MhP1f7PNeIBWH39pC+0uxRO/hPhAYnxB95G8swFUcQ230Paymloika8xUtk9yiJA75DWpYgqaEdJPauQYt6QRSgyIFSYSZRrBItDJF0BQANe1ghLZA6dMgY5Ekf8rjehN+UiVefj1crwZ/IQ45bSaXMKBrIgorCIWEuvCfQ338ogoYqfPoJl4CApNOh1+sxGPTodDpMJhNvZ85i85ZB9mdeT3Crjr0Tc9ifrcNp1IgZj6W45D5eya3h+dyzuPqJBzl/6hK2pzqxT70fBInt+0/FHc9lcayUVwMv8/rcpdx+IBtB8bNh+FkUOYqQ6WPpuhA6OUXjjGV0lOejDxfjiGVTuTST5WdWI+kn3yVN0/jr7hv54/5niKsaXw0kuWJihJeVxfwydT6nzCzh2tPmImo+urvvnvRkEiAv7wKKi674SFvbvwtFibO1+Q1+/OI4vrCNO+y3U8gBfpCdSatOxzy7nhuW3E5+xlIA5KTCmkf20bnDS9AwjjNmZ+6OX2OJjTI0v4h9UQ1Z0BMtqKDCWEWpUEpCDbDeuo3ZM18jLEu81LiSNxZ/iZVb27j60ZvozdBhkVN8+6tw7bCB72fczsrEQS7vzGS/+QmedH2JL9tfIzReRmx4HkpKQBQ1BFsrSf8AF/7ialyftQ1AEAQJOACsAPqB7cC5mqY1v6/MlUCdpmlXCIJwDnCapmlnC4JQDTwBzAVygTeBKUAmkKNp2i5BEOzATuDU91/zo/CfMAKPdHWw9s/3MHighZyKqRz1la+TVVr+ietrmkpn1+10d99JWtpcamveMya9i80vtHPDhJemEiM3FqRz5LevIDjYw88vMfLbi55gimvKv9WHzxXaXodXvgPhYVjwTZJVX2X09qdRk+WIVg/6bJW0C+fy131d/GldK7KqYdbFseiiZIkxFiomZiUyKExM6vh9Yj/h8A7o2oatx4suOTn+kxaJQI4NX4aLMXcGo45sUpjwMEERAxTTTxH92IkCEMZCL3n0kU+fmseI4gJVA1UBTUFTZXQ6HQajEaPJjMliwWy1odNbQDOhxPVEAwqRiRRyXANEBEQcLguuLDueHAeeAgdZBU6sDjN6vR5Jkj70eBRNY+qta5keO8BT8es48GIeG+u/TjD1OiUFF+EXRjCUbuZ7lT/gmC1r+NnOPfTVHYV/2s3YrbB77wkYwkUcHa/incEneGL5sdxwwIU7FWbD8DMIiozoHGPRljCSqtIwfznDWaVYguXoBSMrLqph6tycw+3pD7Tzo7e/xu7AOFWqyq8GhzEm07gudSl+ew1/uPQIClwavb0P0tv3ZxQlRm7OGZSUXIXJ9GG1238asqJyy9Nv8+fGCMfqG7jZcB+POsw84HTg1GlcU72Sk2f8EkEQ0TSNnW90s/WFTmL6ENa4iVm7b8MR7mNgVgEtcUjqDEQLp5BhLKRaqmVC6aczfSt1tWsYH8zgT/JVdBVW8qt7/kL5aBPO0RGeXKGnuyZGov9rdM2o4aebVAYSSeKyAx0ioj6K3t1LWaKcLEEkccazeMd2cuQxGz804fyk+HcIYAFwg6Zpxx76fh2Apmm/el+ZNw6V2SIIgg4YBjKAH76/7PvL/d09XgT+pGnamn/Ulv+UF5CmaTSvX8v6xx8iGgxQd9SxLD7nIsx2xye+xvDwS7S0/gCjIZu66fdhs1Z84Prrnz7ITxN+2vIN3JZpY+bXLmJMC3L713O4/6wncZvc/+DqX4CIF17/Aex9GjKnoZ3we8ZfayOyM47OU4Wgi+M6r463k3F+9tJOxqNGVKcezaqDlIaQUkFWEWQNUirZCqxAz9HoKUdCRmOHJtMYGyM8up9ybydVvh5yw5OpQGRRos+dS3dGHr2uTAbsTqKCSrEyzHSpkyrdABWmUdJ0k4SQEs1EnVXIuXPQVSzHVLaUoE9jrC/EeG+Isb4w4wNh5MRklLioE0jPtZFRYMNTYCej0E56ng298cNC/h/hhZ5xvn13Aw8UvMDshlW0HJhFQ3kpVl0U1bISreoVbq7+Kplj7Tx846+InncdzRU3UpCeonXvMuK+qRwfn86WwSd5dXotK4Ml1AdjbBh5BoMmojMNMm9HFE3Us3HxYsLWGqzhYgzpGmd8cwHuHCsAqqbycOOt3Ln3EdA0vjkR4Ty/j78ox/Ogcio/OHUWJ8/KZ3Dwcbp77iaV8pGZcTylpd/Bai3718aGpsG/aU9r7fdyxb3v4EuluM18FxmGZn6Y6aFPp+Mop57vzvkR+TmnIIp6uprGWHX/HpJKEoOsY8aeP+AKdDBQl0ebLJLQ6YkWTsVqyWa+MJtmrQWt8G1KSnfTtauUX1X+FEtKx4O/uIaRTB1ub4y7Tq1n4dgM5EQlOg10qGBq4Q1nMd8qfga5cBOBvsvJ2m6jnd0EQsN87c57sdhz/nnnPgL/DgGcARynadqlh75fCMzTNO2b7yuz71CZ/kPfO4B5wA3AVk3THjv09weB1zRNe+Z9dYuB9UCNpmnBj7j/ZcBlAIWFhbN6enr+hW7/YySiETY//Vd2v/4yRouVxedcRO1RxyCKn+wlDAQaadp7OYoSp6bmDjzpyw//pmkaa55o42dCiJ4sPXfZYcpXL+JghsJLV8/inhMeRC/p/2N9+X8Gmgb7noXXvg/xICz9HmEWMf7IFiTPHBBU7EuyOFiZyfef2UiX34hqkRCnmDjPHWduQS1xnURUEoiLENU0oopKKCUTiCXxBaPYRqLMHEqx0KuRlRKICxobzSpvmDRa5SDlg51MG+6garSbqd4ejId2ZRozO2l2F9PqKqI5vZhOZy5Zoo85Yhtzda3MF1soZTJ1bwQTjcJUdovT2KurocdShcVmxe0wkpVuJifDgsdqJN2ix23SY9VLWEQRiyRhkUTMovCJnAaOf2oHbbuGaHN/l95nJbbmncOwfgf5eWcxaE7y0JISAig8+rPrSVtyOuunP05VXpielnpGR2ZxYnIm24dfpNGtx5i1mDMHomwefQGLYMak9jBzb4iE0cn6ZYtQtdmYEulkTjdx6lfmHSarTn8H1719Bc3BYWalUtw0PEowmccPUpdRXl7Nz89bRNj3Ep1dd5BIDON2Laas7BocjrpPPi5ifmh5CZqegt6tYM+GtCJwFYPr0Pnd77bMT0QQiqrxu6c2cn+jn2PE7fzM+AD3uw086bCTrVO5KEPH3KLTyM4+BTlSwbN/aCDhVxFVlbp99+OZ2M9AZTYHBD1xvZ5UYQ06i5ujldms1e2ioOZV0lyDvNFwNI8uvIwFzV6+tKmboL0IBJGYYZxhSaOt1sINbQK7hF08Qh7nyj7M5pfxd1hRFRGLqqfUamXZnfdislo/+TN7H/5PEoAgCDbgHeBmTdOe+2ed+G/FAYz1drP2oXvob95HVmk5R375CnKnVH6iuvH4IHuaLiccbqWi4kcU5F9y+MXVVI1VjzbzU2uMEbeO+1M+ir99JZuqBPqvOZOfLbrhC8+g9yM4BK9+F9pWQe5MkvNvZPThbahyBaLZhT5XJbyylmtf2sCuIR2aQUQrs7AyM8jPFx9Nus1ELBQkMDpMYHSEwMgwgbERAqMjBEdHCI6Poirv5WkSBJGCzCqK7bVkaLnoVD2qXkMuNqHVZ6JOySAqy8RbWkntaoSmJizN+zCPjwKQkvR0ZxRxwFNMa2YJzRnFSEaFGWozc5T9zNFaqKQXgISmZ7dWToNaRYNayW61nBiTHh2aAOhENL1w6CyCTkRnECe3UzRIGI0SZqMOi0mHzaTDZtLjMOl5ZdVBjtM18/uBm2hbV8bbdQsxCX4U1yn8dYWOAbOby564gZMOaLxxcZhZpaOMdZXQ0buMExOzaJx4h95EN3vmnMZ3WoNsG3sFp5SGI9rOtFY/IXsO7yw7EmOsHlE1MOeMQuYdOQVBEEipKR7c8yfu3fsQRk3he+MBTgzGuV0+gzf1R3P7V44k09RAZ+dtRKOdOBzTKSv9Hm73wk82HlJxOPjGpNA/uHrShdddChXHQMwHvp5Jh4Dw8Afr6cyTpPCRBFEERvsHirf1e7ni3nX4Uil+q78fs20f12ek45NEjnMqHGlPYrMU4naczvaXpxLsltC0FNOaHyN7bAeD5Zm06YzEDXp0BTPRWd0sSVazyrqJ2pkvIqp67h7+PQ3ldi56c4iZ7Tsp6FrH9ecNc3zcyR2eX3Jt2xbyursYppeEKCGpKtn+MAUTIVyRJJInm/JXnkVKS/uXX6vJsf5/TAUkCIIeeAV4Q9O02z5JJ/6bgWCaptG2eT3vPPogYd8E05YfzdLzLsHiTPundSeNw9cwNraa3NyzmTrlhsO6OlXVeP4v+/i5K0EwTccD/c3k3fwLnl0oUHLtjzm/6vz/Sn/+fwVNm0x5+8ZPQEmgLb2O8SY30SYFyV2OoI8inVnLz7bvYXW7giYKyMU2lub4+PW85WRadOx+/WV2v/EqsYD/A5c2O5w4M7NwZmRNnrOycWZk48zMwu7xIOkmV2GaohI/6CfWOEqs2YuWVBFseuR8O16jxNBEgvH+MGFfAkPCjzPQSUayj7RwF8axLoRDqwR9QcGkp9GMeiz19RjzPagD20h1bICezRjH9yFoKqqgY8heTbttOvuMNTRKVYymjETiMpG4TCwhk0goJJIKqvKP39EXCx8n45VNNGgn0OnoIz/3dP48s4CmPDf1++/gt3fv5IVv5jJ/SifBcQ/7W47luOQsOgMt9Hs38NaS0/h2S5T946tx67PIGG+mtNvHeHoxW+atxBKdgmJJcMZVC8grmVRdtnhbuO6dq+kIDbIsEeeGkXFak1X8XL6EM5fP5bRZQbq6biUU2ofVWkFZ6XfxeFb88wmPqkxmvmx6enLGnwiCNRNqToe6MyF35odn96kY+HvfIwT/ofO735OhD5a3pH9wxeAqQnEWc9+mEe5oMXCUuIsfGv/M7RkmVlstlJkMXFZYhDHWhKaKePdfznjLDBDilB94icKBdxgodHPQYiVmMGLNOJ5MXRkFlgTvpK+hrv51YqNT+ZHx28TMDu7/9c8xhgdJGBOMpplJkIbXbkYTBNIicUxxAYcln4yS/YiBEKY9VvSJBKWvv4mx8NMFlf47BKBj0gh8FDDApBH4PE3T9r+vzDeA2vcZgb+kadpZgiBMA/7Ke0bgt4AKQAUeBiY0Tbv6k3bis4gETsaibH3uSXa++gJ6o4mFZ11A/TEnIH6EAe790DSVzs7b6O65m7S0edTV3ole7wJAUVSeenAvN2bLpOw6Hti6mqyH/8zdJ+k497v3sSB3wX+1T/+n4euGl78NneugaDGhjK/ifaEHyT0DkDEtzeZPUS+P7QmgqBJyvpW6Aj+31M+j1Glm56sv0Lh6HQrVYC9EJ5lw2p04Myyk56Xhyrbj8JhxeEzYXKYPBCe9C03V8I9GD+nrw3h7goiDEbI0lUydgCgIRASIuM1IFS7SK114CuyYrJPkoSYSxPc3E2tsJLZ7N9HG3Shjk7YEwWLBXFs7SQgzZmCuLEUKtkHPpsljYBeoKRBEyK49FJy2CAoXgHXSGyaeUgjGUwRjKQIxmWAsdfi7koxzweqlHHw+nTUzj0GPn83zL2DNtAyOGHiUC+9bxa7j8pg3c4BYwkjTnhM5Ij4LXyhI2+gz7KxZwMpRE4Ojb5NlLCS3r5GcIR8D+dU0TTsDYyILsSDKJd8+BrPNQFyOc9fuP/Jw86M4NIUfj/pYEIFfyBcwnrGMG87OZmLoNnz+rZhMeZSWfJvs7FOZ9CX5GGgaDDVOCv19z07O6A12qFo5KfSLl4L08UFg/xCadmi10PXRBBHoA/W9jdc1QWJYddGrppMneNljj3Kzx01SFLhyykkcmV3MyNjL9O/JYHjXeUjGIIU9b1HSso7e3BnszxLR1Cj2tJMpcjqR9Ak6Cl+kvGIbYxuruXb+9cxo28clzz3IgMtOUq9D0hRGbIVU2HLJSpUQjz1OotfEjMEDaIJGqMzMaPaRHHPHjRg/axXQoconALcz6Qb6Z03TbhYE4UZgh6ZpLwmCYAIeBWYAE8A5mqZ1Hqr7Y+ArgAxcrWnaa4IgLAY2AHuZJAOAH2matuoftePTEkA8Hsdk+tcCKLwDfax96F569zaSUVjMkV+5gvyqmn9ab2j4BVparsNkymZ63f1YrZMeRkpK5ZEHmvhloYreInHPsw/iensNt11g44ZvPEORo+hf7tf/r6EqsO1+eOvnIEgkZ36P0dUaqjYFwWDHUKDxUomVW7f2E0/pUTJNFJVE+VVVFdM9Tna8/Bx7394O+jqSNh1xdy/+cglFkZBCIvpxAUsggTEuokuakBQbOtGG2WLDbLWj15vREEkkIBJReVczJErgzjDiyTHjybOQnmHGGlZJHQiQ7JmcSRoK7FjqMzDXZSDZP+yVoWkaqYHBw4QQa2wk3trKuzcxlJa+t0qYVonB6EXo3TJJCP3bQT6UzC6j6r18RkWLJvXef4fU2nsJP3QD23sWsDcnSWTml7l7TikrxteRtfEBMgU7tUfG0AwyjY0nMC88GyVkZaf3z4ynecjXF6IMbSXPXEbRwW2kef0crFpEV+6pSIqZ7GUip5+9HEEU2Dmyk+s3XkdfeIgTo1GuG/OyLrWAu7WzufbMajKEPzM2vga93k1J8TfIyzv3I3NoHcZEJ+x9ZlLF4z0Ion5SvVN3Jkw5DvTmj6/7n4IiQ2jwAysG1dfDUHsThtgwGUKAYUni+gw3W81mlsTi/ETJJCrPpGmkhqGJMtAEska3Mq3lMUYzMtmfbialk5jTNUJu7SVsyzOg1D5Jhqebp/ZdwEvTT2PFOy+yZE8DjtgIfzktStr+C1keaGPprn0IqShjFgft1QspPMJLwjCM0HYcy667HOv/ggD+r+DTEsADDzyApmnMmzeP6upqdLpPNpvQNI32bVt4+5H7CY2PUbV4OUsv+Ao21z/24AkEdrGn6QpUNUFtzR9JT5/0LU4lFR64fw+3lIHTqOOPd92E+eA+7vpGIXdc/DR2g/0fXvf/GYwdmEze1teAVnIUY+MriB20IaUVIxgi7J+Xxfd3dOGPGVHTDKSXpfhZWS5LczLY/uKztGxqAeN04vYIibwRuqaks8U2l4Dg+tCtTGoMqxLBkkxgiiUxheJYIgFsoQDWcBxbTMGaAGtCwpwUMacUzKkklngUWyyANTqBJe7FmPAhmdLQ589BVzAPyZGPpqmo/nYU7z7U4AEEUUXQ60CvR9DpEXS6yUOvB0FAiUZRQyHUQADF70dLTub5EfR6dJmZ6HJy0OdkYc7SMEqD6JOd6ONdCNrkTnWKMRvFUYWSNg0lfTrY8hBf+CrDG2VeKz4Sb7qNB049n4LYEMcfuAZLk56pJ9gwpPvYs2cFVb55WCP5bA08hJoKE/dMxTm0l2JLJcX7N2AOhtg591QC1iNQxBQLLilk7pwawskwt++6nSfbniRLVbhxZJzimI2fpi6hZPo8Tpr2JuOjzyNJVooKL6Wg4MsfH8QVHoX9z08K/YFD73LRYqg9A6pPAcu/5h2naRqKpiCr8nuHNnlOqakP/v1jfvv7+u/+NuTz8/yWFmxqgB8nGui1hLnN5UJSzCzpPJfSiamY4q0oYgEpvZvyjucp6F9LxCCxZUoesiSSTFM41nEZ26wd5M7+G4IQ50fxnzKWVkbZgZ+xcF8vS5pVyodAFmFvuZm+qUU47dOZ4s/GjQu3PhsBAfkcFyUzPl1yyc8tAaiqyvbt22loaGBiYgKbzcacOXOYNWsWNtsnizRMJeJse+Fptr/0LKJOz8IzzmXG8Scj/QMiicUGaNp7GeHwAaZU/IT8/IsQBIFkXObOBxr5/RSJHL3IbTd9h1RkmBd/MJ/fnXI/0if0QPr/JZQUbP4DrPsNGCyEMq9kYrMJ0T0dtAS+GU6u7OyjP2RGteqwlsE1hRZW5uex/cVnad8+gGarIWYdJTrVx/7CEnYYZpMSDFSG9rJo70EMsQBBl0w83ULMYSVhsRDRWwkIToI4CeAkLHy0u6+kyFiiYayxMJZYGEssgjUawxpPYkmoWONJbPEk9mSMXFVHiSmfHGMRJsmOqskkUt2k4u2Q6EenqkgqSLIGsoz27pFKockyaioJ8QRqIjFJBIoyqa74EDQMjhTW7CTWzCSWjASScbJcKiIhx0R2bq1iXXUOT559DTFR5NG9F/N0n8aS+lzsxd20NC8jZ2QxOZFidgVfIRk+wHhGEZ6xHsrN0yhpWgvxFJsXfw1FrCJum+C8a5eTk+Vhff96btx8A6OxMc4KRbh63MfT8gpWW1dy5TF9pIKPIAgC+fkXUVx0xWG15weQCEHrq5NCv3PdZIK+rNpJoV97Bjjz/+nQGQgPsKF/A+v719M03kRSSR4W1P8tmFJWysZrqBquIz0+lTLjDorT7+WGLDPNJgNL9mmcs15l3GNlIPdcRKkeVd7KkZueoCtbojUzDwEBk8HG8tzzWZu+jqkzXmYw6OYX5t+SMz7OXb+5HtViJVY+C1vmcpJCEkk0kK7PRhAEfPI4O/V7OOjcwxXnfZeCwiWfqi+fWwJ4F6qq0tHRQUNDA+3t7UiSRE1NDfPmzSM395MFoPiGB3n7L/fRtXsH7rwCjvzy5RTV1n9seVmOsL/5u4yPv0le3nlMqfgpoqgnEZO59YFG7qqSKAV++6PLGbSH6LrxYr676Iefqn//5zG0B178Jgw3kcw9ntHmuWhiNejNyDky30v52ek1oRlE9GV6vpavcG5+ETtffJbuvQE0ewUxRy/eGo1d2dW0StUYtASzR7ZzUlOU5XIlBmlS+Khakqg8gjcxjjfhxS8PE7P0ImVG0WWo6NI0NIdE0qwjKDgJkEZAc+KVs5hQPAQ1FyHBSViyEtEZUT8iWlxQVczxKJZYmLR4iizZSL5qIyMl4YgnicYHGFTa6Bb3oTp86B0GzA4HDnMaDqMDh8GB0+jEaXDiMDpwGpw4U3ps7UMY9nXA3lYS+/ajhidTU4hOJ6bKSoxTKjDlWjFZxtEFmhh5rYOXpJk8fNqZ9GXnc3Pj3TQm1jHfOo2M2r10ds7E3HMUlaFidobbCYVeJ2RLwxH2U2WqoXD3GqI6Bw3zrkTU0kkUDPLNa88mooa5ZfstvNL5CoWKwi9HxjDGsrhJuZijlxgoNj+AqibIyTmDkuKrMJn+zj9dTkLHW5NCv+21yZxLzsJJgV93FmRW/cPhklJTNI42sr5/PRv6N9AR6ACgwF7A3Oy52A12dKJu8hB0733+qO+HDr2o/9jfdKIOXTRFfE83gzu9DA3qmJDdIIiY4l4yxhqxBtoYM8aore1mXXGI+9McuA1Wfrn8Dmalz+TxPzxNpCMHXayPubvvwmdNsbc0E0RYWuFGip7PlsK/UV65mb27F/LrmddwbvMw5x0cxiCZSddPyiGvPMJufRN9tkZEnCipEhQkfvD9a7FY/gepIP6v4NMSgCarCLr3XuLx8XEaGhpobGwklUpRUFDAvHnzqKqq+shoy79Hx84G3v7LfQRGR5gyfzHLLvwqDk/GR99bU+no+B09vffici2gtuZP6PVpxMMpfvmXRh6o0lGbSvHra7/GjooUGb+5mVMrTvuX+/h/Fqk4rL8FNt6OZvIwFr+I2EgZkiMf1RDizrQ4T44Z0EQRodjI6XlhLsstYs/LLzBwUEVxZhPL6qWvysZW12xGhWxcipel3Y2c2epgqlSBIEi04+MxDOgQmQpUo1CCAQvv6ejjih9fYpSJxDj+5CghBlA8g5CponNLWBwyDmsIkylyuI6s6BiL5jAWLyKoFJEwlqFai0manPiUFGPxJF5VJSCKhPQGUvqPju0wJRKYY0Es8RjmRBRDMowgB9C0CRTBiyL4UUQ/SclHQhdFEMAimSj3m5k2JFHWp1DQE8U1Mtk2VRKJF2cTH/Fx00WXsbt2Pmfs6OUE5QqG1SMomr+VoaFy4q0nsMBXxDtJHaHIQ6hoGOQktYZp5O9+nWFPLfsrLwBBwDR3gksvOYc3ut/gl1tvJpjw8+VAiK9MhLhPPo2houkcW/EYojZOZuYJlJV+F4vlfckVVRX6tk4K/eYXJo2vZjdMO21S6OdPpu34OIzHxtk4sJH1/evZMriFcCqMTtQxK2sWS/OWsiR/CcWO4v+I67QSDBJvbiG+fz9j+3roHRQYlooIHbLFWeOj5JnGKSoxkj27DPO0asSMTH7717d4cH+M48QGLrA/xC8yHPTo9dT1u6nbZ8XmmAEsQ6cq1O++HUmK0VDgRNVkjogXEZh+DN3T7iE7p4NHRm9jdWYhd++IUTwyQJO+kTHbTqxYmEhVkBTMKJrAgOokpmrc8e3zyMz8jL2A/i/h0xLAmh89ggUjhXUl5J48A8ky+ZLG43F2797Ntm3b8Pl8OBwO5syZw8yZM/+psUVOJtn+8rNse/5pEAXmf+kcZp14KrqPEQBDQ8/R0vpjTKbcQ8bhUqLBJD97tJHHqvTMDYb4xY+v5OUFAif+8lHqM+v/5X7+n0PftkMpmw8QNJ+Or2MWkrsOTY3ylifCjX49iiai5Zs4qsDPVZ4cWlatYrTPRCrNSrhkmAMV2TSY5xITrJTE2zm2rZezB0qxS+kkifE6cZ7XQzzXhzc/GwENYzSCFEmhhVUcET15ETOFspFyJCo0gQIkpHdzymtJAskx/Ikx/MlRwsoQcUsXclYAOd2I3mHAaU1gNwcxGmOHu5ZKGYhGXCTCmZAqxG6oIi93Lp6KClJpevq8YXr2DzM2GiEog98gMmpQGdalGNerhEwG4qaPNnLq5BSWeARLMoZZiWJUIujUEGhBhKQX5/gIBYMj1HQHaKhZyUtHncT83kGu6LyVdq2EyqXrCQQyGG46hSO9xbyTSicWfxglPoEAzJAqydq9mgNTTmEw5whSuhDlp5qYs6Cem7bexLr+dUxJKvxqdBRfoox79F/i+DkbSDe04HYvoazsezjs73OIGNk/GbG995lJrxqdGSpPnBT6ZUfCxwQ8qprK/vH9rB+YnOXv9046FmaaM1mSv4QleUuYnzsfq/7TGT7fhezzEW9unjz2NxNrbmZiQmMsYzpjnnqi1snVi9sco3iKhfJl5WRUF3zs9fa2dHDbnY9TFmrj9MzdPFcY5W8OO4WpdK4duoz0ESvb4jqSgo6qtkex+pvYOrUYRadjRd7FdJm9KHPuQjSo/Ej5PQlJ46od9zGRKESTTGgajKh2ArKVTKmPiGjk0twR5l9x56d+Bp9bApBlmT/c8iOCSRMO1UqJkkGezkHJsho8y6YgSCKqqnLw4EEaGhro7OxEkiTq6uqYN28e2dkf9rx4PwKjI6x75AHat2/BlZPLEZdcTkn9rI8s6/fvoGnv19E0mZqaP5LuXkzYl+CHTzTyTJWRZYMDXH/TtTxyqo3v/PhFcmyfLuz7f45kBN76BTTcQ1IsYHT0fDTjTJD0dFsm+IasIyjrUDJNzC7y8R2Xi65Vb+L1uUk6ZSZqQjTll7NHNx0RjZn+Jr7UInNUsAJR0NHDBI+iZ2+6n2CBHW96LkX+IWZM9CKJAiGDgYDBxITBitfowG9yIGsiQlRBiMoYQzJlwRQVEY2KuEa5KlKBiP19rorhlB9/chR/coyYMogsdRF29RPN0pAdDqxmiTRzDJslgF7/3qYtyaSJWMhDMpyNFCvGKVaRZa4mXbShm4gjD03O4IUsC/E8G90Wge6An4FAgNF4FJ+aJGSEqFEiYtITMRuJmk1ETVbUj1md1vjGOO2dDhKWPdQs20BC1tO16zSWBqayx2cgojQQi+9DQ2COWoKzbRuNdZcSsZUStQxw5Fen0anv4NZtt5KSI3xzIsCpgRS3K2dgnZZgZuZbOJ0zKC/7Hi7X/Mmb+vtg3zOTrpuj+0GQJoV97ZmTwv9jdmALJoNsHtzMhv4NbBzYyER8AgGBuow6luYvZWn+Uqa6pn7qWb48Pv4+Yb+f+P5mUoODaAj4nWV4SxYzljaNGBYEAXJKbJTNyaW03vMPUy0ryRR9mxoZ2rKf1GCYNH0mTkMmkiBhFjfQbL2fF/0m5uwVqO3RSOntNM+8HLfNSo5hcmX69vATqJrKkoIz2Z92EM/sR2mJ13CL9UcUjw8xa/9e/IY43Uo2s3KDnBocJpS/hpjdiMMYYsHc7Tj/hXQ178fnlgAA3ll3DLIygn+kgL6RPPyBLDyqk2I5nXybm9JTZmOvzkEQBEZHR2loaGDPnj3IskxRURHz5s1j6tSp/1A91N24k7V/uRff0CDlc+az/KKv4fyI5Vos1s+epq8RjXZQUXE9BfkXEvTGuPqZvayaYuDYthau+cONPHpZCT//xjNY9J9O5/c/Q+c6eOlbqN4exqIXk0gsQbRlE9bG+K5BpTllRk0zUF4S4rs2IyOrNxCIZRFNDzA0XWB7xnR6xWKsaojlw/u4qC2TsmQmKWKsJcrTBoGRwhQjOYXkJALMHD1IvteHlkphMEyqe5LJv99FC/QGIwarBcFoJqXXEdNJhHUCQZ2EFx1jsgkxZiY/pKM0IjAlpjFV1shBj3hIGKXUxORqITlKUBklpesnaWojmh7E77aiMzlwGFWc5gg2awBJes9AmYjZiIcyUUM5pIWLyY6V4ojkIyhGpDQDxmInpqludBlmkqJAKCoT8sYJjscIjsXwjgQY8U0wnggRNslEDAphE+h0AmVKCMPEAaYv3grGEG27TmZ+cB6Dnf30W4wkoi+BIDA/lo04MkJTzVdJ6cyE0w9y5Jdnc8+Be9gxtoP6uMwvx0bZl5jB6owZHDl1FRlphZSVXoPHcxRCzDep2ml6Gno3T3Ysf+6k0J92Gtg+rAbVNI12f/ukLn9gA42jjSiagtPoZFHuIpbkL2FR7iJcpo8wHv8DaJqGPDpKfP/7hH1zM/LIyOEyuqJSgpVLGLVXMRi2E4+DpBMpqHJROiOD4joPZttHuPGmVFLDEZIDYcLto0Q6xtBHdYiHJgiKqKDLsWAty2DM28HGJ//KtN5G7MkYwdw8xqoWUGipxy1NZu4ciw/TpwwStzzLQJ8DQbSyLP9C9uS9SsG01fw5cAVvpa3g0ugDLDe9hiYIiIc2CwpoTlqFatqo5Mqic5hX9skTV74fn1sCUFWVo158E6PBxzzjWmaKW7EqGhOjBQyO5RPwZZGjeiiU0yjMyab0rAWYsu1Eo9HD6qFAIIDT6WTu3LnMmDHjYw0xcirFrlUvsvXZv6GpKnNPPZPZJ38JveGDvtCyHGb//u8w7l1LXt4FTKm4nuB4kite2se6MgOnbdvMl5/4I6/9cAnXn30PovDJU1b/zxDzw5rrYdcjBMMz8AXPRnLXoMhB7tQHeEpzolp1ZJfGucqcIvLmbsKqh0jROB3VaTTY5xAQ0shJDbKyp4/zO8uxagYG8fIYEluzYgznZ+PWJakd7aTEO4aYnBT6lZWVVJTkk/Q2YzRbsLnz0NuySKREQqEQoVCIYDD4gc/hcBhVVT/QBUEQsFqt2Ox2dCYTqt5AWJGIRwSksIQrIlEUkyjRLFiEQxHEmkZY9uFPjuFVfASkcWLmPmK2HpJpMqrJhNlgwmlK4bAEsVgCiKJ6qC7IMRdSOBt7uBBruIhULI24LCBYAjjsBkxmF0azG4PNg9HhQbQ6SAgC4YRCMJSiq/cpGpv8zJrfgNHdRWvTccwbX8FA+1v0OxaQDD2Ohsb8oIOw7KGj9GRkXYxwXhuuFRYebHkQnZLie14vi0N67tStpLS2iamZTO7ElX40woHVk0K//c3JoDXPFKg9a9Kg6/7wBkvRVJRtw9vY0L+BDQMbGIpM5keqdFeyJG8JS/OXUuup/cQeb5qqkurvn9TZt7QQb2km3tyCMj7+7j8OQ2kppupqpMppeG0V9Pst9LQESMUV9EaJ4tp0SuozKKpJx2B6z3tPSykkhyKkBsIk+8OkBsOkRqKTmV2BhBLDlxxGTQN3bQn5S+rRWQVCq1bhf/ZZ4nuaUDwlTFQtwuWsxqXzoGkaw6keBqRd2M0b6EssY4hSEkYvYiqCuacNizGNxTlnsL/qHjLy2rhJ+zk9QgnXKL9iVMqijSr6KCSDEWppopY9HDnjSTJdny5e6HNNAIvfeJwBsZSEwYqoqZQn+pinf5vZ4hbccpTx0QJGxgsI+XIoVDPJU+yUVpVSePocRJOOtrY2Ghoa6O7uRqfTMX36dObNm0dm5kfn5g55x3nn0Qdp27IBZ2YWyy++jLJZcz+wrNU0hfaO39Lbez9u1yJqav5IcFTiK6ubaSg2cMGaVZy49lE6bvkaly797r/13P7raHsNXvkOyRE/o4Gvo9kXgyCwQR3gBr2bhEHEUapyhdkPb7cRlRz4qr00lxay0ziLlGCgNtLKue0KK4YLUbQ4G4UgfzVLHCg0Y3LoqfL1MHVsCF0iiU6nY8qUKRRl2wn3badnTxO+3tihpDrvQTIqGO1gduqxuMxYXU7sHjd2TxbOjDyMtiySSSPxuJ5wWD1MEO8njFgs9qHu6nQ67FYbBtGElJQwRDUcSQPpmhWrZsKKEZ2sEUp5GZaDjAhh/AYvAUcPsi2IwaBgMoo4DOA0R7FaAlgsQYRDewVoqgBRD1owj3A4hyHZQ0CyETep6O1jWE1eHGoCMaZycHcVc6e2YC/axoGWJcwaOJV14ZewhpYTjz4DmsrsCTPDaYsY99QRN47in9JCS/Y+2sIHWBhNceP4KGtTS+gosbCkvIvSoivIi2cj7nseWl+BZBjsOYfSMZwF2XUfSsfQF+o7PMvfPrSdpJrErDOzIGcBS/OXsjhv8SfaG1tLJkl0dBBvaT0s7BOtbYe9oZAkjGVlmKqrMU2bhmlaNeSX0tMeo7NxjL7mCRRZxWTTUzLdQ2l9BvmVLnR6CTWpTAr4gTDJQ4c8Fn0vFNUkEBYCDIy1MRbpR3EolC1dSPWyI7Gne4jtbsT/zDMEX3sN2ZxFrHIRNncNdl06qqYykuxhRNxBpnE9I1I2m5gNohG7MYTdPk66LYjTMQzKBO2vFpJpKqTCcyz9c24hatVxrfQHDFqCk3ie6cIectQ+wtFCWpVi+lIl3HXK5RiN/9pK6V18bgkAgOcuY1BL8WjaVF5NZtKvFRM1TwacFCSGmatbzxxxMzmpcbxjBYyMFxL35VGkZJEn2JmyZDrZR1UzMjZCQ0MDe/fuRZZlSkpKmDdvHlOmTEH8CO+G3n17WPvQvXj7eymZMZsjLrkMV/YHXU4HB5+hte0nmM35TK+7n7A3nQvXtbIn38DXn3uS2pYXsN99KysqTvhUz+y/isg4vPYD1D3PMDZ2MgnDaYiWDPpjvVxjtNOv12EqEjjfPEba+i4iVh0jMxPszqmiTVeFQYuzdKKVrxzMYErAwRjjPCEIvJEjkcqwUBEdomqsD1M8gSiKlJWVkeeUCfXupn9fJ1Hv5Jsbddk4kF5Nm6EENLArIRypEGnJII5EAHsihDUewpD6oGpIE5nMJGpTMThULE4BR5oBl9uM0+PB4cnGaE5HUSY3foknDEQjEuEwhEKRD6wqZPnD/ugGTYdVM06SgmbErBmQFIV4KsW4qtEPBM0+cPRh1oWwGJLYDSpuUxKLJYDVGsBkCh2WtaoqEgu5SfhzSAUKSQVKsHjacVe9Tk/3dKa2X8Dj5j3MarbiVzeDlqJuwk5v3unETWkEHR0crN7GLnEnVkXjeu8YZaE0HncuZEZNC3PcS8keSyLufxkio2B0QvXJk0K/aNFkmPQhpJQUO0d3HvbN7w52A1DsKD5swJ2VNQuD9PH565VwhERb63sz+9YWkgfb0VKTu64JZjOmqVMxVVdhrKrCVFmFcUoFotFI2Bens3GczsYxBg/60VQNm8tIaX0GpTMyyMq3oYxESQ68J/DlsejhbZhFmx5Dng3SdQxNtLNv7zoG+1vRGYxMmb+ImiNWkF85DcXnI/DiS3iffpJUUCJZOh9z5nRseheqpjKc7MIn7qDI+A7dlgreTlRjtibJShvA4RjF6RzGbJ5MGS6rOqLeMhKjFWwRC8lo2EqFfTqezExS83/PJpZwv+FKCuJ9CCmBUWMG8fdpD3ZPLyDH/ek2y/n8EoCmTXqitK2adEtDIJg7nadz5/CsVEa7UkLQkgWCQEbKx2xxE3PEzZQk+5gYz2d0rAjZX0CxnEm+OY2qlfPRl7vZtWsX27dvJxgM4nK5mDt3LvX19ZjNH/TsUGSZ3a+/zJZn/oqSSjF75enMO/VM9O9LTeHzb2fv3ivRNIXamj8RDUzjgi0HaM3R8/1HHsQZWsfCe56kylP97z/E/wTel7I50O3AL38dyV1NPDHBb4Qkq40WpHw9p1mHyN/cRzQjRdcME9vcMybdONUJTh7q44K2QhxJhe3CBH+xGenIMVEi+Kge78EWjSEIAsWFeWSafIR6WxhuHSEVFdAEGPdk02yvpEsoJKROeolk2mV0Ro2wLJBMQEoGJSUCk+SsV5PY5RB2OXzoCB0+2+QwNiVyWPf6LmI6ExGDlajJQtxkJmnRo1j0CDY9BocRq8OI2yyRZpBw6UQckg6TJkJKIhUTCQY1gr4owUCAaDJOSkgR08WIC0lUJFD16GUjlrgRS8KCOenAoDhBMqLqY5jMfnRpfWj2AYzWCaxWPxaLH5MperiNIyMlZO/9GmusQbJad6AkRxG0JGWhQgYKTgMhQVfOdrYXr8creDk2FOf7Xh8vcwSW0lGOtdhw9fcj+LpBMsCUYydVPBXHgP69cToWHWPDwAY29G9gy9AWIqkIelHPnOw5LM1fypK8JRQ6Cj9yyMhjY8RbWz+gxkn19B7+XXK7MVVVYaqqnBT2VdXoCwuIRZT3bCHjcULeGOP9YUYPpeVwZVsoq0mnKM+KJaUenuHL47H3hL3dgCHPhj7PhiHPhi7bTG9nE/vWvUnnru2oikLOlEpqlq9g6oIlGIxGIps3M/jEIyT2j6IWzMGcNR2LPg1FUxhJdhFiO6XGd2g2l7LbVIDD5ScrbRCHYwyDYTKVR0S2ciBZQbehjF26OfRQgjMZ5MRdUeSkk6iniZJtbzLDtYJYUT/2+ie5Wf45zbpppEcjTPFHqQ5JzAzoqQ4LFH+9BHPePw+a+yh8fgkAeP7559FUheq0BIXJA5gHtyD0bwdNJWl08kbJEh63z2CvMgWfNR9VFLErEWYJDcwWtjA12U5gLJux8SJEXylFspuijGymnLmI7sAgDQ0N9Pb2otfrqa+vZ+7cuWRkfNAgFvZNsP7xh2jZ8Db29AyWX3wpFXMXHlYLxWJ9h4zDnUyZcgOp6HGct7uDnnQd1z94JzFnExfdtgqP2fMfeZafGsFBeOW7JHe/w+jEV9HcR6JpCi8nR/i9xYWcpWeFY4TK7QP4S0McqMpim3U2McFCebKb83oSHN+VQ1gb52lJ45UsExnmEFMD/bgPLfMLsl24pWFCPV2Md4XQZAFZL9GbXkqbsYxeckmKRhz6KHPMISp9CcxBN4Jw6JmrSQQ1jKCGQA2DGgItgqCFgBAaETTCaGIYTYhObtUoCqiCgIqEqkmg6dA0EVQJQRURVBFRlRD+Ts2koaFJGqpORdGrJI0qSaOMbJRQDFYUyYFedmOKpWFOujGlXOhVN6IwSVoiYBLBhIJVGsdqHMNo8qGzBBEsYRRrCNkSQZVkBH8ZSW8ZoZidCZ2XmL0H9DGmjC5jryjT61tN0WgcUZXJVuYxkbkIQRjm7YqXaHPvJ10W+IV3BGckmzGHifm6cSz+cUCAkiWTQr9qJZjTAFBUhb3jew8L/ZaJFgCyLFmHBf68nHkfcFTQVJVUX9+kkG+enNXHW1oOJ8cD0OfnTwr76ioonUoqs5SIaibkTRD0vifoQ944cuo9O40AOO063GkShR4bbr2EMBGfFPaHIDkMhwX95NmO5JhchXj7+9i3bg3N69cSDfixONOoXnokNctXkJ5fQLyvlwOP3oOysR0xfTrWzOmY9E4UTWY40UmcbRRb32F/ehZ9zjScaT7cjpHDxn6FDHymenYJ1bwZK2WAPHTIeJQR0iQ/KQyMkP2BSPSy3h5WrnmE5Rlfor/2ZYzF+/hx6l50spnfbxkgpPnxihEmtACnXHEupfkfTbD/DJ9rAlj6i5dJxONk46NInKDQqaeiwEONeZzcWAum/k0IoUFUYFtOLX/JPpJt1DJqKUbW6TGqCaazi9nCVmoSzUS8HsbHijBPTCFfSaO8vATL8lJ27tnFvn37UBSFsrIy5s2bR3l5+QfUQ/0t+1j753sY6+2msLaeIy+5nPT8SZ9jWQ6xb//VeL3ryM+/CDV1Oee39jPqFLn5rt8xVO/juz9+8R8uq/9r0DTY9QjKqusZ75pPwn4+otlNW6iHH1kcDKebWOwcp25vD+PTozQVl9Kkr0NEY37kAJcctFM3YqFJGOVBu5mIK055fJisoB80jVy3Dqc2RLBnkOBgChCImC20Oyvo1BUxqMvGJMaZbvRRF5SxR9IRhEM2GDWBIg+gygOAhiBaEAQLiBYEwYwgWkCwfHxGSjWOoMYQtRiCGkfS4oiHzjotjlEMohkCxExeoqYQYQniqgVZNiEkDOjjErqkhqikENT4hy4viSYMehMmo4TJDHqbjN6eRJcWQ5cWRnIEEIxhBFH9UF1Z1pNMmiePhAlhXEdWt4HMUCUWYz2CNZ9Ex3O0hnfSYXehVyQs5pXErcVEzXt5ZtqTRA0RzgpEuHIiREywkadNxgOQM31S6NecDo5Jl+NAIsCmgU2sH1jPpoFN+BN+REGkPqP+sGpnimtyP4DD+vrDs/oWEq2tqJFDgXQ6HVL5VNTy6aTyp5BIyyOmdxEKKgS9cULeOMnYpPAUALMIdpMOl0OPzSSiSTFiaphAys+Y7MOvhYmSRBM0JE3EKphIt6SRl5lDQWkReZVF2DKdH3h+iWiUti0b2LduDUMHWhFEkdKZc6k5YgUl9bOIJ8I0PnMf6upmnGIl1sw6jHo7iioznGhHNm7AlbWV3nQbkTQJh92HKGhoGsTjGYR102i2zOYFuZpRnAhoWIkhaDJhrGiHxpxeTeKJ+cgKhsmORKkO6pk24qE3KKJThklGXmZx7ul0L7idPoeLm6WfU93bztENqxHkFPqUxld+9ydcni/cQP/lerU3vEEo/p6OVkTDLibJIEiRNEGZTWFmjo5qwyDZ4f3oh7YjKEnazU4eLD6JdwwLGbSUETeYkTSFam0fs4UG6hJ7kb02vKPF2CcqyROclMycyrAryfYd2wmHw7jdbubNm8f06dMPZyRVFYU9a1ax6cnHSCXizDzhFBacfg4Gs2XSONz+G3r7HsTtXoKmXc/FPX4CFvjNH25m7EsZXPXlez7bjWQmuuDlbxPY2k9AvRLRNZVQdIxfiQrr0h3Md/ip7ztA/0zYkVU76caphTnB282FrZnYwmGe0yfY5IZ8xskNehE0lWxbFGtqlEDPBAn/5K3G7Bm0W0rplgoJG81USV5mRmRcUQ+COGlE1NQkqjIA8gCaXqE9O58WawbDqgFJ0DBLKjZBwUGSNC2OS4uQLkewpWJIgookgKQKiIqEoOqQVR2KYkBRzKiyFUGxISofF3ykISIgCRo6KYVBn0BviKAzBpDMfgTTOKpuFFkYR1GDKIkUqYieZEhPMqwnFdajyn9nLxI1RLMABh2ywUhUshASHSSTRooio5SHBsn1ebF5o/DuZFfQkJwqkfRsJpQIjY5srCkdmvsSJE1kWstDiIk2JlxQaI6TbUlitMtI2Q7MSy5AnHUuZExB0zQO+A6wYWBSl79nbA+qpuIyulict5gl+UtYmLsQW0oi8a4Kp3XSQJtob0eVVeImN3FnHkphJcnMEmKWDKKClXBEIB5OHV7lmAUBq17EadNjN4pYJAFRVYjIQQLJED4xwpgQxC9EiAqJD9j0VQ0iRjtBs5ukwYYjMoErMoaIPMkeh6DX67Hb7dhMRtTABP4D+yEUJD03j5ojVlC95AhC+gQNG55G9/I+8qJlONy1GPQ2UmqSIXE7qmc9xswD+F06ROukPUJRJEIhD95oIU3GuWywzWDkkOuqoKlo7/PU86hjZCVGSQ9FyRnXUe0zMjeShkezISAQ16KMq920a4OEwnFUZSGK6sOUeJtZxUczuug3PCucySvGU1i55kkqu3pQBRunXHoxU45c9Kle4c81ASQmWhhIZrO6dYJ1B0ZpGQoRiKXeV0LDLCi4hAgFop8aS5BlWWEqxD4y/I3oAt2MixJ/yVvOa85j6bFUEzZPZu4sVduZI2xlenIP+jE9vrES0ryV5BpcWGbk0DzaRX9/PwaDgRkzZjB37lzS0ycNOdGAnw1PPMy+t9dgc7lZeuFXqVy4FEEQGBx8ita26zGbi1DFX3PpiEZcr/GrP1wP3zmGc1Z85z/xSP8xVAW23Ufihd8xNnIOWsYKVCXBs/FR/uj2MNceojq6j/bpTrY5ZxEQ0siThzh70M8pbR461CGesguIxgD5ES+SmiDDMI4p7iPQG0ONgyKK9NkL6DIUMWjKIl8KMzMm44llIEiTQXialkSVB0HzMeq0s9ftoke0EEgIaIc2ddcEEKwiqKAlNIS/20RFEwCDiGAC0aAi6RT0koJV1UhXRbJTenKTOjIQSBdCuIQJjNIEqhQjJSZJoZJSBRRVhyIbUZJWlIQdJeFAjtvRlI9JeywmQR9D0SVJ6WQSkkJCl0ARwmiEUJUIYjKMMRLEFgwiJWVkPkzuBkVFJ+hQdA4Seg9JKRuj4EJSfMjxjeiETCTn2Vijwww4dqIpg8waG2Tq2BjJoA7+bmGS9LjwefQctIfpcMQZdoGlpIxpdUexxFZP8YhGsrWNWHML/oP9hCYSxM0eYqZ0Eo6cydm8IY2ErMckCpgFAbMIZknAYdFh04uYBAG9oiKlVJLI+IUIfjGCT4gwoYvgE8JED2U6fRcJSY/fbCdkchG2pBMw2/CaTXhtehTp77y8FI0sv0yuL0FWJEBWxIslOoIiJdCEDxKDpJPAoGEIRSgIOyjVTyFb9BC3dTFm3YLq2YOW7kU5ZPJIpIz0hiroSFbTqZXRbi1m3OZCEcXDHlA6LUUefZSonWRG/WT7NEqHPJQHSsjUXIiIKJqMLzGCNzGINzGENzFAVA6iCSKaZCRgcBEXc5GN9WhqgFnxXRRX5xOd9QDXy7+lX8jDsXmQZFyg4XuzyfT848DUj8PnmgDCt+ZhjkSIORykPAWQVUsqcyE75dm8diDB7l4fA/7Yu66/wOQmzQ4hRo4YZLZpiBPSB6mgmzTfHuJylKddlTybdQYHLfX4bJN6+RxtgNn/X3vvHmZJchV2/k5EPu+9dW9VdVV1V797enp6NA/NaCRNIwQyCIEE0uqBDUhCGNmAzFpgY+P1Iq+NYT/trlh7jdfLGpAB2TJeAQbxWSCvsISEBbJeM6MZzbvn1e+ud933zVdE7B95a7q6p3umNXRPr7ry9335RWbeyLwn8uY9J+LEiQi+wh3Z/UwsF/SXb2B69WYmmxP0dvo89swTWGs5dOgQR44c4eDBg4gIZ594nD/9rV9l8ekn2f2y23j93/xJZvfuZ339yzz40PtxzpHo/4Wfas8BBb/wqx9g3y/9A77t1u+9Qk/2Iiw/jvn997PylRnS1nuQcIKHOsf5J60pbo777Jl8hEdu2s290V0U4nNn8gTveVpxx3H4ZLjKE42cnckKoe0xJQt4/T690wViIfFCnqnv52S4k1qouX1kmcvmELUDROFcjjOLDMOCJycbPFSLWS40+cid+0/HivpEwu7mWW6ZfpS7J77KnF4GwDjFSrGNU8kuFpIdLCUzrKVTdJIm/aTOKAnJE68Mt9yEEyDUuKjciBRhlBFHCfVgRCMYUvcSAmfxneBJgNJ1nNfEs01UESH9AumM8LopQTclGubEiSUqFIHReC5ECMsFYC7EWbxihDJDrPQpSMjJwA7B9nC2h7NdnO1RLq9R4qu9qObbmV5/iOXpQ3ica704LL7qMq1OMW3PUM+W8IZrZP0RdHpEnQFxkm3KL2R+nTSaZlDbQdrci63vgMYcfm0bkVY0Qk1dCyHgbTK0DkdCTica0YlS2t6INj1W0g6j/JwFMiL0ohqdaIJBNEWv1qRdq7Ncq9HfFKOvrWPHsGDfIOfA0LB/oNk/gKnM8uSE5qEWPNISHm0GjMZzfUW5Y+d6wY7Vgu3tIdv7q8TFKsYbknujCwyDQ6mCIBxhQst6NMGp+jxPNvazXJ8h9c+5Wn2XMcciN/EYt9kH2NFrM71YZ2rlDvb27yagrAAs5eucsIuckg4LjFjB0WeSPk0GboKh8hkqzUBBdpFG/Hxyln84OE3t1adZufFhfs79Mo32iLc8cIYf/ME7efVtzz+J3qXY0gbgp3/3f+KW3gnu7j/F4f7TNIqyHe2ApBaSTu/AzB5mofYqPtN9OZ85XeeplSHDbNP6sThqZGxXPV4TPMU7Wk9ys32MuPcUn5nYxUfn3s5DE69luTmPFcWkW+OVfJU7868xszQgW7qRidWDyHTIKbvOYDhkZmaGI0eO8PKXvxzf93jos5/mzz/270iHA17xxrfwmh94N1ZWeeDr72M0OkZb/SN+dnQ7cZryP/y7n+N1v/5hDs4evlKPt8Tk8IV/Sft3/5Cu+QnU5I2s9xf4kAe1epf4xlM8sPswj3s3E7iE13ef5Icfa9DpnOULtYKaXWfCLtIwi5j1jGKl9Gt3gibH4j24sM6BPGau2IHo+bHCLzCuzWJD83jD51FfM0yA8eN3nhA2DdtbK9w0+QSvat3LQe9p8jN1hsdrZGcU/mJBs53jFQ6kXFBdcIgISspBXlpcOReZOEQcVoQcTeE8MnxSfHLrkzmf3HkYq7HlXXBQ1tqknIjNaYXzBKs1zpNnjz1t0Nrgk+O5At8UbOt1mOr1qA1GBIMCkyuSaIZRNMuovq2sVQeT5F4D44UYHWF0iNEBRge48fKioUAkBdprY9U6uWuTS0IczhCZgmEwYsYO2VXkLMVN8kZM182y1nW4YZ0wr4H1yd250Pe6WqWpFtBmnTRLSNMEBl0m24vsXj1JlJ2roTvloRqz6Kl50p276c/P0Z9u0a57tG3GSneNJD2n6BM/pB3XGcR1hvEEnXiC5doEy7XaeTOsNlPL3oHhwMBwYAj7BpZ9A8vukcNzGTAiUyltGbDozrDOInNqFzvsFE0XIi7m6fo0j7QiHmlpHm5pjk4oivHqb63UcmC9YNeaYftaRmu9j287FGGPzBtg9PA8w+CAQinyQOHHQ+bqxwn1kGKwE9W5lWKwh4G1dEjo0qMvGQmW3FmUFISSE5ITUKah5DTImfQKWr5hMrRMRRC4lLOry4gx7A2eZKgTDqw1WTKzdPK3M/yO/8yXpvfzb9T7+dlnfp3vf+Pf4eCuygB8w9fd/rHvg+wkRrUw/k4iaTFvFDdlQ44Mlnht7xn2J+eGkGe+Jm1N0Z88yMPcyWd7L+OzKztYHFg213cCDDukzbd6T/D2xoPcVdzHQ1rzmzNv4qtT383Z1l5y7RO7IXdyL3fk97NzeRV19iCqu5t+w7E26hKGIXfddRd33303kaf5wu/+ex74zKeoNVu87of/Bje95lU8/MjPsLr2eU7xt/gnxRuY7g35yT/6ed75ax9nMpq8Eo8Xzj5A8m//HstPfDvMvh6TD/m9ZJVTM8v0b8v46rY7WVbbmbarvG3pFN/6qONhVumqPtvscYJ0lWQFVN/ggMVojmHYYs61mLb7Ud4uEI1zhqGXcKouPB4LT1mhyM+5cvQEbGt1ONA6zp3N+3nF8Oskx0LSkz7eoqO5kjOzlhNn536NbgyntwnZuAIpgLiN1JX7zx4L2gqCoFx5vJFu5FGlxmc8Nutc9M/GV573uRtf4/CtxbcWz5TrAeDKMFQnghNhIyQVHLhnTQtuw1jVprH1OTozO+lPTdOdaLDYrHOmEbAQK1Yin35Uox/G9KIaoyBCnMU3Bs8U+LZMA2OJDNSMo2aERiFMGMWE0TQLzUShiAtDkBf4eY5XGHTh0LlDG1AF4Ap6akBHj+hJQqqGWDUiDxxWl+UwouiFEYNanWG9Rj+usxq3WKhN0t8Uw+4Zx65hqdhvGKf7B5a9Q0szSxEzJM8HjLJ1BskCw+Fpkt5Z1KBDbZQRJxnxBVN8jIKAbj1mfSJkvanxmlPM12eZD2apBS2ONbfxUGuGB5vTPNyKeKauxr8BzA8NN3UKDqxbdq0VzKxlJHZAV/XJvB6F3yf3Rli1aXyHc/hSUGfANtbZySIHOMluzhJgzpPNATkwUIqBEgaiGCphqBQDEbpa01WKngg9pehv5FMKU3gEnUl+vP2jrH3nr/Irwfu5T93F/+1O8/3f9bbL+y9fwJY2AJ/+0L9BjOaM3+GUv8rx4DRnvDMsBKskKsWJj9NzTEiDnQUcTgfcPVzidd0TbDPlS2dEGEw0WI7387XiVv6ifxOf7+9lzZwLgdNY5mWdI/oob43uY7fcz0dmvoPPT34fJ6cPMvJiPJdzG1/njuJr7F1cwj99gCTZxrqMcM5x+PBhjhw5Qs0ZPvuRX+PsE48zf9PNvP69P0HX/kdOnvwIR91f5YPuXexa6fDX7/kX/OT//nE89SLXTAXIE8yn/1eW/+A4eeuHwKtxf/cEf77/BM/c0uKrjVeSSI1D+THecmKVyWPrnNFtpuwz2OGAYtGhc0MhmnY4TUMmacphVHCwVPhYOkHBsdjxhAcnC4vZaP7GionJAXsnTvNqcy8vW/866pRDnxUmlg0zqwXRpu6adg1OzQhntilOzcCJWcfJWaFXexGd4he++y/Usb6RXUqDMb8GB89aDp6N2b2+nWa6gzTawbC2g359nmzTqE0jBZ1omUG0hA4HRJLScorQ1hkGU6zENZZrPkuRZjUOnlXwgzA6r4MRoJYMaQ1XqSWL4FYRNLg6VtUodI1CRxReRO4FZJ5Prj0K7V10XQMAZQ2Twz5Twz5Tw+54v8fksI92ZaVn5Id0a3WSWsQojmjHNZZq21iMW9hN8m1Lxsp9rOT3DSz7+obZ/gjSDm6wjBksYLqnoX0G1TsLRfocmQa+TzeKaNd81mOPTk3Rrjs6DUMaGqa7irkOzHYN27sZs/2EwJwfRdUPI3q1OnkjJG4GRNNznN15gMfn9vDw9BwPtWY5HZdOf3GOAwPLrR3Lze2Cg23DXNswMBmrMqSj+gxUn8Trk3tDnDqn8MvXqMC6FON6ZLZNapfI7TqW585LdSECaCeIU2A9nA2YGhZM2F18197v4NRrPsp9vdfwav9NvPUtb33B+130O7ayATj60x/C9/eg/VmUnD9Qa+h1WfHbnAnWOemvcMo/y1l/mcVglRVvHacaNKXOrgJuTgYcGSxwZ9JmR2FQQCesccrbzdeLw3wpOch92T5OujlAkLFBeLU6ynf493J0W8hnp9/EsW230AmbiLMc4nHuLL7G/rPL6JM76RUhBZa5uTnuvvtuvM4q/+13Psqo1+WON3wvB18f88zxD/E18wb+D/lxDp5e5b2rv8+P/b1fe3EP9cSXaf9fH6I7+H5Ucz+nByf5/Vue4oEDe3kwuB2F5dWjx/jWp1axKyeI7ElGHYu3nKKcI1M+Sk8R6YPo8BWIirA4VkPDsdDxjLOcUZZcwPlCfSLlFh7j9uF97F99huZiysSSY2bVEG5S9GuNskZ/akY4OVsq/dPboFcTpo1hb16wJy3YPTDs7Fpm2prptZgwV6AcG5VtkdLdLsqV6bh6LxuuIOXKSrmAUY6BJww0rIYRR1t7eKq5h040xXx7yM3H19i+ZNCjBgM3Tz+eZ1DbQR5sCs1zOXhDwjijFXYwQcFKGLIU1VmIY5Zjj+XIpx0F9KLaeX5mAGUtzdGA5mCdeLiINQsk3hJDfwXLGtqsomxGSJ1AJvFpjl1UBjA4CqAAV6BcgXIG5Qq0s0RZjUYxSy1rUS8aBEVEYD08Kwil26MT1xnGEUktpB/HrNVanKnNMPDODQgLjGPvWMnvHyv5PX3Djs6IoLcO/QVU5wSqcwI3WMYOV7C2oBeFtKOQTuzTrmnadaFTt3QbBZ1GTqeZ0I8do1AhTuMXiqAQ/FwRFx61wqNmhNAKQ8/R8w29oGAQ5qR+QVhYZrqO2Y4rjUPbMdvWzHUcM12Df0GUbS/2WZqd4tiNBzm1/zBP7dzPwzN7WAvLSp1vLYd6pVG4pWO4tVOWM7WONZexTJ81VbYaRnpA6g1wsulLXGlY/NwQDzNa7T7bVlaYWVum2VnFszkvRHv7fswPvozBgc+QrryTt/71f3QZf+rnsqUNwMf+uzcj2YhaklJ3PnVvkjicIgym8evbUM0ZVG0G5U+fFytuKFj31zgTrHPaX+FMsMSCv8JZf4XFoI2nYVfhuCXp84rhKgfzjP15ARLwjNrJQ+YGvpbdwMN2H0fdblJ8ZulwlzpKc2KRR+du4eT8zSzGZTz7bnecO/Ovsf+ZNnqxSeoUURhxxx23484c57HPfIqw0eDIu76Fgf9v+Xx+N/9a/ha3PrPAT089wtu+/2cv/6GkfZKP/QLLfz6FzL6OM+4sv33bM3xx/mZO6n3UXY9vW3+Ym556DL99lnTZUeuWo0+tivH1HnR4B+LtAhGWQssxbTkhltOepcCwR53lZfnXOdR7kj1rS8wspcysOoJNreqVCTg9I5zaBqdmhZNjRe/7lj2JYfewVPDb2zC76jGz5BO1NW5k4IIanx3X4NVF3mkHWOVTeDUKLxqnMaOwwYkd8xzfvoOT2+foT8L+3iluPr3EzLrDZS36ep5BNE8xHvAkQJ0E3xswmMrpTDjWopClOGa5FrAS+axGAd0oxF4w4VlQ5LSGA1qDDrXBCn56lozT9IMFetEqkRkQExGoOoFqEOiQSeWYcxnz1rCDnNgGYENMWidPJyiKEGMCrPUxeVCOHbCa3EKOxVDgJMWosjY/CGM6cY00ChhGEeu1Jkv1aVbi5rMuEoDto3ENfnhOyc/2CibbPdxwFdc7Szo4SS85zpLq0qmVBnoQOwaxJQ0NaWDJPYURwTcavxD8QhHmmrBQhIUQK0fkWULt8EODDg1eZNCRwQvH6aZ97VtMobCZwmQKk2lsXu7bQlFYKJyQWMUIoY+iZyHLHSSGoJ9T7xq2dQ1zHVcaiA5oV74ni9MzPLrvBh46dJDHDhzkmZ03MArKimNc5BzuDritY7m17XNrV5hPShegw7HiRizSZ5Uy4qmvByR6gNvwJbqNN8gh1gcX4WydgiaZjhnpmDQQEt+Ra8t33vNf2UOTuZ27eXTHg7znH/zzy/+Pb2JLG4B/9VM/Q2o3qoMWbIZzOaZIUabAK3LCNKU+zJi0AU3dou5PUgumCMNp/No0XrwN5Z+/aHsiQxa9VU6Gy5wNV1jwV1nwV+j6bSJWOZx1OJSNOJAV7MkNiZ3lUbOPh8x+HnX7eMTuI0ezPzjJYLLO2vxuFmZ34rRmm13iW9bvZc/xHvR8QLhh716K40+w9tiD7Lptnt1/5Sj/Ob+Nj6if4JWPHefnX+lx5O43v+DzMA//Fxb/1X/CNN7Go1Mr/D8vW+a/Td1JV1rsNGc4sngP2x58muhMnzhNcIBS21DhLWj/RtCTLPmWE8pyWhWY/AS7zVEOjo6xv3OWXSt9tq8Z/E1u0eVmWYs/NbORCp2WZVth2dWzzK875tYUs0uK7WeF+vD899KK0IkbrNRaLNW2s1SbpxtNM4imSIIJCi/CE4hdTmQzIpsTugLPlp2x4gztiYDlVsTyZJ2VVo0JV3Dz0jI3LbRpDHwKu42+vwOrw9JTHwlZnNKvpaw1Nat1n+V6wEocsBr59MMLBuQ5Rz1LaI6GtAZ9WoM1wsECKj9Nqk6RRku4GGq0mKRJizot8Zk0Qj3zUWmDPG1RJE1MVsdmNSSP0ZlHkKf4xRCdD1Cuj9ghTvpYNSINLXkMWV0zDEMyPyRXmnatQTeqkQQBvWiC1fokSxOTpP45d2GtGNfmx4p+V69gtpcy2Rlgkja9fIVOscCaO0NHlih0hhOFOIW2Cr8QglzhWYUo+xxl7UUXKPRnP7NlGhYXDYiCclI8k8WYtHYu7DabwBYxSo8Qf4D2Bih/hPJGKD9F+xkqKF7QmwdgcikNR64oivGWKWwGLgU1gqBvibrQczt4ZuIGnpq6gaNzB3l6+35yr5wRdmLUZ9/6Ejf0BhzuOW7v1dmXtaiPI4Mslp4kLNHb1GoYMFTDcx3PDhIbsu5C1lyNnovo2ZihjVAolFO8S/+//OyHfuWFC3YRtrQB+OV3vQtrexf5RKGUj1IeSnsorRFPI1rjlMbgU+BjnQOX4duCRuExbWNa1JmQCWrBZGkkomnUJj+8dZaurLOkVzgdrXAiXmE5WMWpJSbtKbabdQ4UOc0spJ/u5il7gEftXo66XayFE6TbJljfPkM+WWO2WOXbz9zH7NkhYoQJP0JWTiCrx7jtB0Z8sn4XvyM/wmsffJxffsdt7N1z68UfxKjN0i/9PMOV1/D5QwM+fiPcE99BIT63pA9x8+P3s++Lj+M5B3iIvx/PP4j4+1n2I9puDfInmeo/wN7+Sfas95hfM3ibKuKLrfMV/cokON8yN4Adq465ZWHHCuxYh3oCVgUkwQTrjTmWa9tpx1MMogapH2I8XfrbpcB3Gb5J8IoBftHHly6B7iE6ofChiAJSP8aKR45P6kWMdMxQN0nNBGoU0hrl1DJBrI9RdUzokweaQQgjH4a+ZhB6DHyPQeiTK41RCicKoxTiHJ41BEVBaFLiPKGZjajlQ2KXEWLQyke7GF1EaBPiGR9d+HjGI8gKomyIdj2sdMopKWSAIwXJEF2Uo4E9wXoKO/bdF9onH29m/I5ZhNzzSDyfTtygF8b0wzqdWpO1Rot27ZyrU5xj58g9W5uf746Y7XaZWl8i6C8wyNv083X6RZvMJoBDB3ZTDbxAagYVbyhxixda/NDiBRY/LPDCAu2f3xG6GZt7FGmASUNMGlKkPibxMZmHSTU2UZhU4VKFTQWbSjmeA0s5uKNM3UbnuSgcCicah8aKh8PD4mPxUZ5CfMHzBfENOihQflYaiHGqgxQVJuggQ/s5yi9QnkX7BuVbdGAvaZwKPE6wj6e5kafcQZ5yhzijdj/bXzOTLXHD4Gn2j46zb7TAvrRNzTaI7HYaxQzNYgopYgbG0TGWdVuw4hLaasBQbRq04YTchbQl5JBq8zP/9Bcv+Yyfjy1tAD7xhjcSZ3WSICL1Q1LPI/c0mSfkCowaN5PJcC4FN+Jcj9+FCCIBSvlordGeRmmF8hSxbhDpSWJpElGnTkxdatS9CULv/BWScpvQZoVlvcKZcIXVaBnrL1BjgZZbgrxBP93NyfwGnvL38uD0YbpTkxwuznLX8hPUhjkKR9xps++me/j04TfwR/IOvuuBB/j1n3grjfr508Z2/vijnP7kMn/06ohP7p7lqH8TgUt4Zecebv+LL9E61Qapo/2DqOAGUt2E/BiT3YfYu/Qou1b66PEjscDi1DlFv9yCNABlYGoQMtutMdWv0xw1ESboRw36YUyiPRCDp4YEuof1ckxgGUU+SS2gCD2KQFMowegCKzlOcrAZnkkIsgRtUvwixSsKVJGj8wJlyugVTOnLHrv0KeN8xr+ajKN8Ns4/W0WUZ39T5xo46iB1nNSBOo4ajjqGGCsxuRIsI4xkWBmhGKHdCK0zlLagynBRq0qlZJSmUKUSL5SHFUXueWTaI/P8Z9Nce2SeR658Cu2NO25LpZ95G5tH6vkknkfmPXdai4m8VPJ7B4advRGz3TZT7SUmRmcwskri90jCNjYeIVGCihJ0mOIHOWFgCQKLHxr851F8zoFJNEWqz08v2DfpOB2fd0YhuHFIrtu0f24rfywZ/yTnTpYRVGULvuxwFsTZcxsOca40Es6NA6zK6RrKTbDPbhsxW5eDQzyLFxf4tQIvLg2i8i3KsyjPIbrsR1LakYc+pxt7OVE7wPFwP8f8G1jR5eh1cZZ5TnOQJ7mBJznIk+zlGP6m8RxYhRQxqojBhJjCJzMemVEkRpEP38173vfDlyn7+fylDICIvAn4PwEN/IZz7kMXfB4CHwVeCawCP+ScOzb+7APAj1FGdf8d59yfXM49L8aLNQA/9YH3QT7CyzP8PCXIc8IsI8pyoiynlhfEaU49K4hyIbA1cDWcrpEEcWk0/PIPmOtNRkMMVnKsS8ElnIuufs4TxFM16v40E/4UDb9Fw2/S8CaoeRPU1ARazl83dVR06doV1mSZnr9IGi6QBG06yrCUt1jxb0FJne1JF4DdOx/jcwfv4rPqe3jzQ1/mw+//cbTSJKeP8ef//CN88sgcfzp7iGU1x7Rd4cjpL3Loz+4jSloo/yAeTab6q+xaOsr0+pP4xQArsDBW9GenhF7sU/ghWk8Q2xY6aEIYkkWQBwZDjpgUr0jQeYZnMrwsw8sKdG7QhUEVDmVKYyEFiNVoq9EuRDsfLQGaAI2P1SHWjzF+ROGH4y2g8MaK0Rdyv4xuKbTGiOCsQlmFMmo81YNCGYdxltwlFOQ4Cnw3xCOhHAGQoynQY9XgRONEY5UuFbjWZN75CvtCxZ3pcbSNN1b2G0pbjxW3Lmvr2fOsKvfs2+IcNWOo24K6KaiZgtjm1ExGZDNimxKZlMilRDZhplhmzi7QUGs4f4iEQyQY4IUDtH/pKBRbKGzm41IPm3m4TJdpoSBXuFwgB8kcLjfYHGxe9qo7JYiScWe6xipv7BrycON+dY0lkoKY0iUXu5SaS4llRKBSPGVRymC1YLQw8jSZ0uWzVT4D3aAnTfrUGdoaqY1ITEhhPGb9VW7KT7JnsEqdLjhHjkeOx4iYnppiIJMMZIKBaTKyNYY2JjMhmXEYU7qCcRm+SVGkKJehXYYv5Z20c4grO6QFD+c0Dh9nddnScB5mvBVWYf0MF4wQnaC8FFEJA9/ndGsnZyZ2crq1l5PT+xhEpStZ24Id/dPs6R9n7+gZ9qdPs8OcIfBywkDwQoUej1wXv+DUU+/mR3/6Ay+s8C72Tr1YAyBlr+hR4LuBU8BXgXc55x7ZlOdvAy93zv2kiLwTeIdz7odE5BbgY8DdwE7gM8BN48ue954X48UagE998I1MFX0SCcnwyQjKQT/ij18anwKPzHmMCBi6kEQ0qXIUJsXaFClSdJGjsxQ/T/HznCArY5PjPCfKNUERoVw0HukZYsXHKE2uNYWCQjuMlIrSkp1nNEJVo+FPUvda43SShj9N3WtR043z5v4xrmBo+gxdjzXWeDpYZTGwNKYW+fztt/Ml9Vr+6hOf4+Bql3tv2cEXJm4nkRoH8yd5xVNfZuboItoDVIpXWLQTrAvKP69EoDRGPKwKyDeUodWgFbhyMJVgELFjpWnxxKKwqHHqKYdgUMqhsYjY8UpYFhGHGg/GKje7KS1riGWEjqWc3M2dOzcO8Bex4ygf++zALsSVAfwCTpX7hRIS5ZOokFSFJONtYz+ViERFJFJuqUSMJCaRiJFEJMSMxudfCHGWmBExw/E2Oi+tXXCudpF8NYaEJJddR92gyGLyIiIrAhKnGDnNAMVAK6wnaOWINLR0TmQTIpMhxkMkJlA1akGTwJsh8LcTBNsIwhZB2CKMWkTxJGHcwvdbeN4ESl18kfdLkfYG9M4s019cZrC4TO/scZKVU5j+AqSr+HQIpI+vBoSqIFAFgTKE2hDpfHzOosfvB4AVj1TFJC5kYEJ6mU8nCVgbBbRTj6TQjIxPYjy4xNP0xBBpQ6gtgQJfKTytSzeeCtB+DS9s4EdN/PoUfmMGv7kNXZ/Em5jEb00TTjQIaho/cCA5ziSkwx7DtTUGC8v0Tpyit7BO0k3IM0MusD4dcXJukrPTE5ycmObYxDwjr3TZRfmIvcsn2bN4gvmFU8wunaHW65IrzSt+5N1851tf4haAiLwG+AXn3BvHxx8AcM79b5vy/Mk4zxdFxAMWgFng5zbn3cg3vux573kxXqwBeMcf/wbrwcQLZ7wu2RjJtGkf2BjhJBc9N84rPDfP/89wyLPFcigSNpR2qVYvW3G7tNxsUtawbUZkU2KbEY1r3qHNCU1OaHMCWxCaDH/jnCk7m8fODcx4BLEpzSJGBCOlvFYcBodTjkIcVhxWbNmqVA6rLFYKLBYnBVYZLEU5KElyrBQYyRFVgLO0dMG8l7BLD5jUFus8hiama1qsmynW82nWimlWilmW81ly98LP5KVDcOJhxceJR5yk1Ad96sMe9UGH5mCV6d4i0bAL1uJLaQgSe75iF3F4Hihfga+xfoAJQrIgIg1iRkGdQVinHzbphk3WoyaJjil7UwziLDiDYMDZcd/DS4ODsXvP4auCImyyVN9BoUtDO521uaP7GB+88/UcPHTT89/sElzKAFzO6KFdwMlNx6eAI5fK45wrRKQDbBuf/9IF1+4a77/QPTcEfx/wPoC9e1/cXNhGFAWXt/7o9cemEaznzWUvm+zBWMVv/vx58j57v0vsu0vmuTDfxe+9uU7iNsnhLrw/55m0chSxKYhMQc30wK6XzXhrUc6irB3HxZfx9sqWvmPlBKfKDkUrqlwpTGCkNAOJsCq6cLXJTY+m9BMr71zrZyPV5x2b53x+8fQi+SgV38a+cuPUlC2rbtLiATPJfy2m6BRNDN9YDf3a4hCXo10ZE5/70J70aU9OA9M4OVQO1MQjzDJqwwGNUZdRUCMLIrIgIPPLlraI/QYUeYJnnjt197VAgLAoCIvx0bBHY71HGkSlCzoIuT++kX37b7ji3/2XGD760uCc+zDwYShbAC/mHp9489+8ojJVVFRUXA9coq//PE4DezYd7x6fu2iesQuoRdkZfKlrL+eeFRUVFRVXkcsxAF8FDonIAREJgHcCn7ggzyeAHx3v/zXgs67sXPgE8E4RCUXkAHAI+Mpl3rOioqKi4irygi6gsU//p4A/oQzZ/C3n3MMi8j8D9zjnPgH8JvDvReRJYI1SoTPO93vAI5QTmL/fOWcALnbPK1+8ioqKiopLsSUGglVUVFRsZS4VBXQ5LqCKioqKiuuQygBUVFRUbFEqA1BRUVGxRakMQEVFRcUW5ZuqE1hEloHjL/LyGWDlCorzzUBV5q3BVivzVisv/OXLvM85N3vhyW8qA/CXQUTuuVgv+PVMVeatwVYr81YrL1y9MlcuoIqKiootSmUAKioqKrYoW8kAfPhaC3ANqMq8NdhqZd5q5YWrVOYt0wdQUVFRUXE+W6kFUFFRUVGxicoAVFRUVGxRrnsDICJvEpHHReRJEfm5ay3PS4GI/JaILInIQ9dalpcCEdkjIp8TkUdE5GER+bvXWqarjYhEIvIVEXlgXOZfvNYyvVSIiBaRr4nIH19rWV4KROSYiDwoIveLyBWdDfO67gO4nAXtr0dE5HVAH/ioc+62ay3P1UZE5oF559x9IjIB3Au8/Xr+nUVEgLpzri8iPvAXwN91zn3pBS79pkdE/j7wKqDpnHvLtZbnaiMix4BXOeeu+OC3670FcDfwpHPuaedcBvwO8LZrLNNVxzn3ecp1GbYEzrmzzrn7xvs94FHOrT19XeJK+uNDf7xdv7W5MSKyG3gz8BvXWpbrgevdAFxsQfvrWjFsdURkP/AK4MvXWJSrztgVcj+wBHzaOXfdlxn4l8A/hEuu9n494oD/IiL3isj7ruSNr3cDULGFEJEG8AfAzzjnutdanquNc8445+6kXFP7bhG5rt19IvIWYMk5d++1luUl5tucc3cB3wu8f+zivSJc7wagWnx+izD2g/8B8B+ccx+/1vK8lDjn2sDngDddY1GuNq8F3jr2if8O8HoR+e1rK9LVxzl3epwuAX9I6dq+IlzvBqBafH4LMO4Q/U3gUefcv7jW8rwUiMisiEyO92PKQIfHrqlQVxnn3Aecc7udc/sp/8ufdc695xqLdVURkfo4sAERqQPfA1yx6L7r2gA45wpgY/H5R4Hf2wqLz4vIx4AvAodF5JSI/Ni1lukq81rgRyhrhPePt++71kJdZeaBz4nI1ykrOp92zm2JsMgtxnbgL0TkAeArwCedc5+6Uje/rsNAKyoqKiouzXXdAqioqKiouDSVAaioqKjYolQGoKKiomKLUhmAioqKii1KZQAqKioqtiiVAaioqKjYolQGoKLiCiIifyYir3oR1/2GiNxykfPvFZFfuTLSVVScj3etBaioqADn3I9faxkqth5VC6Diumc8nP6T48VTHhKRHxKRnxeRr46PPzyeTmKjBv/LInKPiDwqIq8WkY+LyBMi8sFxnv0i8piI/Idxnt8XkdpFvvd7ROSLInKfiPzH8WR1l5Lx2ZaDiPwNETkqIl+hHOVcUXFVqAxAxVbgTcAZ59wd4wVyPgX8inPu1ePjGNi8sEjmnHsV8GvAfwLeD9wGvFdEto3zHAb+tXPuZUAX+Nubv1BEZoB/DLxhPJPjPcDffyFBx4vb/CKl4v824DluoYqKK0VlACq2Ag8C3y0ivyQi3+6c6wDfKSJfFpEHgdcDt27K/4lN1z08XnAmBZ7m3OyyJ51zXxjv/zalst7Mt1Aq7y+M5+z/UWDfZch6BPgz59zyeBGj3/2GSlpR8Q1Q9QFUXPc4546KyF3A9wEfFJE/pazVv8o5d1JEfgGINl2SjlO7aX/jeOM/c+EkWhceC+UEbe+6AkWoqLgqVC2AiuseEdkJDJ1zvw38M+Cu8UcrY7/8X3sRt90rIq8Z77+bck3ezXwJeK2I3DiWoS4iN13Gfb8M/BUR2TZe4+AHXoRsFRWXRdUCqNgK3A78MxGxQA7898DbKedVX6CcTvkb5XHK1Zl+C3gE+NXNHzrnlkXkvcDHRCQcn/7HwNHnu6lz7uy4RfJFoA3clb/PUAAAAGtJREFU/yJkq6i4LKrpoCsqvkHG6w7/8bgDuaLim5bKBVRRUVGxRalaABUVLyEi8ofAgQtO/4/OuT+5FvJUbG0qA1BRUVGxRalcQBUVFRVblMoAVFRUVGxRKgNQUVFRsUWpDEBFRUXFFuX/A1Jt25z+fC20AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set_5.loc[:5,get_mz(0,train_set_5)].plot(legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grads(df,bin_size=5):\n",
    "    ''' returns gradients for each mz'''\n",
    "    grad_df = pd.DataFrame(index=df.index)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        df_mz = df.loc[:,get_mz(mz,df)]\n",
    "        # df_mz_col1 = df_mz.iloc[:,0].values\n",
    "        df_mz = df_mz.diff(axis=1)\n",
    "        # df_mz.iloc[:,0] = df_mz_col1\n",
    "        df_mz.dropna(axis='columns',inplace=True)\n",
    "        # df_mz = df_mz/bin_size\n",
    "        df_mz.columns = [col[0]+ '_' + col[1] + '_' + 'grad' for col in df_mz.columns]\n",
    "        grad_df = pd.concat([grad_df,df_mz],axis=1)\n",
    "\n",
    "    print('grad_df.shape',grad_df.shape)\n",
    "\n",
    "    return  grad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 157.79it/s]\n",
      " 10%|█         | 10/100 [00:00<00:00, 96.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (1059, 900)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 58.83it/s]\n",
      "  3%|▎         | 3/100 [00:00<00:03, 28.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (1059, 4100)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:10<00:00,  9.91it/s]\n",
      " 42%|████▏     | 42/100 [00:00<00:00, 207.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (1059, 41900)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 191.83it/s]\n",
      " 18%|█▊        | 18/100 [00:00<00:00, 175.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (1059, 300)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 139.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (1059, 1500)\n",
      "Wall time: 13.7 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grad_200 = get_grads(train_set_200,200)\n",
    "grad_50 = get_grads(train_set_50,50)\n",
    "grad_5 = get_grads(train_set_5,5)\n",
    "grad_500 = get_grads(train_set_500,500)\n",
    "grad_100 = get_grads(train_set_100,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 150.56it/s]\n",
      " 15%|█▌        | 15/100 [00:00<00:00, 142.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (804, 900)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 86.16it/s]\n",
      "  4%|▍         | 4/100 [00:00<00:02, 38.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (804, 4100)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:08<00:00, 12.48it/s]\n",
      " 39%|███▉      | 39/100 [00:00<00:00, 192.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (804, 41900)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 134.08it/s]\n",
      " 13%|█▎        | 13/100 [00:00<00:00, 128.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (804, 300)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 103.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grad_df.shape (804, 1500)\n",
      "Wall time: 11.6 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grad_200_test = get_grads(test_set_200,200)\n",
    "grad_50_test = get_grads(test_set_50,50)\n",
    "grad_5_test = get_grads(test_set_5,5)\n",
    "grad_500_test = get_grads(test_set_500,500)\n",
    "grad_100_test = get_grads(test_set_100,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# add metadata\n",
    "- mean,varience of all mzs\n",
    "- mean,varience of a tempbin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean_std(df):\n",
    "    ''' returns mean and std for each mz'''\n",
    "    mean_std_df = pd.DataFrame(index=df.index)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        df_mz = df.loc[:,get_mz(mz,df)]\n",
    "        df_mz[f'{mz}_mean'] = df_mz.mean(axis=1)\n",
    "        df_mz[f'{mz}_std'] = df_mz.std(axis=1)\n",
    "        mean_std_df = pd.concat([mean_std_df,df_mz],axis=1)\n",
    "    print('mean_std_df.shape',mean_std_df.shape)\n",
    "    return mean_std_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:14<00:00,  7.13it/s]\n",
      "  9%|▉         | 9/100 [00:00<00:01, 84.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (1059, 42200)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 45.49it/s]\n",
      " 15%|█▌        | 15/100 [00:00<00:00, 138.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (1059, 4400)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 101.91it/s]\n",
      " 18%|█▊        | 18/100 [00:00<00:00, 171.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (1059, 1800)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 129.91it/s]\n",
      " 16%|█▌        | 16/100 [00:00<00:00, 154.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (1059, 1200)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 139.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (1059, 600)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "mean_std_5 = get_mean_std(train_set_5)\n",
    "mean_std_50 = get_mean_std(train_set_50)\n",
    "mean_std_100 = get_mean_std(train_set_100)\n",
    "mean_std_200 = get_mean_std(train_set_200)\n",
    "mean_std_500 = get_mean_std(train_set_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:07<00:00, 12.81it/s]\n",
      " 28%|██▊       | 28/100 [00:00<00:00, 133.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (804, 42200)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 68.70it/s]\n",
      " 26%|██▌       | 26/100 [00:00<00:00, 125.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (804, 4400)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 124.84it/s]\n",
      " 21%|██        | 21/100 [00:00<00:00, 202.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (804, 1800)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 152.41it/s]\n",
      " 23%|██▎       | 23/100 [00:00<00:00, 113.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (804, 1200)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 117.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_std_df.shape (804, 600)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "mean_std_5_test = get_mean_std(test_set_5)\n",
    "mean_std_50_test = get_mean_std(test_set_50)\n",
    "mean_std_100_test = get_mean_std(test_set_100)\n",
    "mean_std_200_test = get_mean_std(test_set_200)\n",
    "mean_std_500_test = get_mean_std(test_set_500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**next up**\n",
    "- quantile transform, guassian rank transform , feature tools and nns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_SEED)\n",
    "log_loss_scorer = make_scorer(log_loss, needs_proba=True)\n",
    "phy = 'phyllosilicate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, penalty='l1', random_state=777, solver='liblinear')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_clf = LogisticRegression(penalty='l1',C=1, solver='liblinear',random_state=RANDOM_SEED)\n",
    "logreg_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_imp_coefs(clf,df,target_col):\n",
    "    \"\"\"\n",
    "    Returns the df with imp cols based on logreg coefs...\n",
    "    \"\"\"\n",
    "    clf.fit(df,targets[target_col])\n",
    "    coefs = clf.coef_\n",
    "    coefs = pd.Series(coefs.ravel())\n",
    "    coefs = coefs.where(coefs != 0.0).dropna().sort_values(ascending=False).index\n",
    "    # imp_fts_5[target_col] = coefs\n",
    "    \n",
    "    cv_score = cross_val_score(clf,df,targets[target_col],cv=skf,scoring=log_loss_scorer)\n",
    "    print(cv_score,cv_score.mean())\n",
    "    # print(target_col)\n",
    "    # print(coefs)\n",
    "    # print(\"------------------------------------\")\n",
    "    \n",
    "    return df.iloc[:,coefs],coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_500 = pd.concat([train_set_500,grad_500,tot_abund_500,mean_std_500],axis=1)\n",
    "# X_200 = pd.concat([train_set_200,grad_200,tot_abund_200,mean_std_200],axis=1)\n",
    "# X_50 = pd.concat([train_set_50,grad_50,tot_abund_50,mean_std_50],axis=1)\n",
    "# X_5 = pd.concat([train_set_5,grad_5,tot_abund_5,mean_std_5],axis=1)\n",
    "# X_5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_imp_coefs_for_tar(clf,df_set,tar):\n",
    "    imp_fts = dict()\n",
    "    \n",
    "    imp_df_org,imp_fts['org_set'] = get_imp_coefs(clf,df_set[0],tar)\n",
    "    imp_df_tot_abund,imp_fts['tot_abund_set'] = get_imp_coefs(clf,df_set[1],tar)\n",
    "    imp_df_grad,imp_fts['grad_set'] = get_imp_coefs(clf,df_set[2],tar)\n",
    "    imp_df_mean_std,imp_fts['mean_std_set'] = get_imp_coefs(clf,df_set[3],tar)\n",
    "    \n",
    "    print(imp_df_org.shape,imp_df_tot_abund.shape,imp_df_grad.shape,imp_df_mean_std.shape)\n",
    "    imp_df = pd.concat([imp_df_org,imp_df_tot_abund,imp_df_grad,imp_df_mean_std],axis=1)\n",
    "    print(imp_df.shape)\n",
    "\n",
    "    return imp_df,imp_fts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_200' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14316\\3518008206.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimp_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_imp_coefs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogreg_clf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX_200\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mphy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mimp_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_200' is not defined"
     ]
    }
   ],
   "source": [
    "imp_df = get_imp_coefs(logreg_clf,X_200,phy)\n",
    "imp_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.39902645 0.4469758  0.54718085 0.43083517 0.39331084] 0.44346581996003975\n",
      "phyllosilicate\n",
      "Int64Index([1120,  884,  458,  450, 1119,  455, 1122,  451, 1133, 1132,  423,\n",
      "            1096,  445,  448,  459,  456, 1665,  518, 1117, 1118, 1672,  919,\n",
      "            1121,  920, 1123, 1126,  428, 1125,  422,  449, 1668,   76, 1676,\n",
      "             434, 1099,  910,  914, 1002, 1106,  461],\n",
      "           dtype='int64')\n",
      "------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1059, 40)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_df = get_imp_coefs(logreg_clf,train_set_50,phy)\n",
    "imp_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.49192296 0.54731078 0.57861353 0.58395914 0.49831128] 0.5400235382526797\n",
      "phyllosilicate\n",
      "Int64Index([2, 33, 3, 19, 18, 36, 30, 15, 45, 29, 0, 1, 42, 21, 49, 4], dtype='int64')\n",
      "------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1059, 16)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_df_tot_abund = get_imp_coefs(logreg_clf,tot_abund_50,phy)\n",
    "imp_df_tot_abund.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.39000504 0.44701419 0.53933929 0.40139343 0.40729768] 0.43700992630546764\n",
      "phyllosilicate\n",
      "Int64Index([ 767, 1326,   55,  765,  762,  752, 1337,  763, 1338, 1336, 1834,\n",
      "            1346, 1842,  740,  764, 1162,  773, 1344,   85,  739,  769, 1343,\n",
      "             741,  770, 1832, 1179, 1341,  777, 1340, 1150, 1342, 1316,  774,\n",
      "             776,  778],\n",
      "           dtype='int64')\n",
      "------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1059, 35)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_df_grad = get_imp_coefs(logreg_clf,grad_50,phy)\n",
    "imp_df_grad.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.39905379 0.4469948  0.54719403 0.43083807 0.3933131 ] 0.44347875936491193\n",
      "phyllosilicate\n",
      "Int64Index([1436, 1234,  830,  822, 1435,  827, 1438,  823, 1449, 1448,  795,\n",
      "            1412,  817,  820,  831,  828, 1963,  102, 1433, 1434, 1970, 1269,\n",
      "            1437, 1270, 1439, 1442,  800, 1441,  794,  821, 1966,   78,  806,\n",
      "            1974, 1415, 1260, 1264,  168, 1422,  833],\n",
      "           dtype='int64')\n",
      "------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1059, 40)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_df_mean_std = get_imp_coefs(logreg_clf,mean_std_50,phy)\n",
    "imp_df_mean_std.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1059, 131)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_imp_50 = pd.concat([imp_df,imp_df_tot_abund,imp_df_grad,imp_df_mean_std],axis=1)\n",
    "x_imp_50.shape\n",
    "# x.to_csv('phy_x5_imp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.34849224, 0.42750634, 0.50378198, 0.40485506, 0.38616757]),\n",
       " 0.4141606368342388,\n",
       " 0.05175449242858923)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_cv = cross_val_score(logreg_clf,X_50,targets[phy],cv=skf,scoring=log_loss_scorer)\n",
    "logreg_cv, logreg_cv.mean(), logreg_cv.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_clf = CatBoostClassifier(random_seed=RANDOM_SEED,verbose=999,eval_metric='Logloss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_clf = XGBClassifier(booster='gblinear',random_state=RANDOM_SEED,verbosity=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([       nan,        nan, 0.46583628,        nan,        nan]), nan)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xgb_cv = cross_val_score(xgb_clf,imp_df.values,targets[phy],cv=skf,scoring=log_loss_scorer)\n",
    "# xgb_cv, xgb_cv.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.009597\n",
      "0:\tlearn: 0.6859790\ttotal: 178ms\tremaining: 2m 57s\n",
      "999:\tlearn: 0.0586682\ttotal: 21s\tremaining: 0us\n",
      "Learning rate set to 0.009597\n",
      "0:\tlearn: 0.6850536\ttotal: 21.2ms\tremaining: 21.2s\n",
      "999:\tlearn: 0.0562640\ttotal: 20.4s\tremaining: 0us\n",
      "Learning rate set to 0.009597\n",
      "0:\tlearn: 0.6851798\ttotal: 20.5ms\tremaining: 20.5s\n",
      "999:\tlearn: 0.0473869\ttotal: 23.8s\tremaining: 0us\n",
      "Learning rate set to 0.009597\n",
      "0:\tlearn: 0.6862154\ttotal: 51.2ms\tremaining: 51.1s\n",
      "999:\tlearn: 0.0481319\ttotal: 20.8s\tremaining: 0us\n",
      "Learning rate set to 0.009602\n",
      "0:\tlearn: 0.6858241\ttotal: 20ms\tremaining: 19.9s\n",
      "999:\tlearn: 0.0585132\ttotal: 19.8s\tremaining: 0us\n",
      "Wall time: 1min 47s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.15715623, 0.19957427, 0.30083273, 0.2696438 , 0.18046425]),\n",
       " 0.22153425492556167,\n",
       " 0.054619214043050285)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "cb_cv = cross_val_score(cb_clf,x_imp_50.values,targets[phy],cv=skf,scoring=log_loss_scorer)\n",
    "cb_cv, cb_cv.mean() ,cb_cv.std()\n",
    "# c =0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_fts_phy = get_imp_coefs_for_tar(logreg_clf,[train_set_500,grad_500,tot_abund_500,mean_std_500],phy)\n",
    "imp_fts_phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "calb_clf = CalibratedClassifierCV(cb_clf,cv=skf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf_10 = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccv = cross_val_score(calb_clf,x_imp_50.values,targets[phy],cv=skf_10,scoring=log_loss_scorer,n_jobs=-1,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccv, ccv.mean(), ccv.std()\n",
    "# this is something we definitely wanted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  seems like grads do improve the score...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  selecting fts each on additional datasets is best strategy\n",
    "\n",
    "# c=5 l1 penality\n",
    "# .25 from cb_clf on imp fts of x_500\n",
    "# on whole x_500 directly fit on cb_clf (no imp fts) 0.26...\n",
    "\n",
    "\n",
    "# .23 from cb_clf on individual imp fts of x_500 (73 fts)\n",
    "\n",
    "# x_50 (131 fts) C =1\n",
    "# (array([0.15715623, 0.19957427, 0.30083273, 0.2696438 , 0.18046425]),\n",
    "#  0.22153425492556167,\n",
    "#  0.054619214043050285)\n",
    "# c = 5\n",
    "# (array([0.16005222, 0.20716925, 0.30667596, 0.2675076 , 0.17654597]),\n",
    "#  0.22359020020625892,\n",
    "#  0.05541094180104098)\n",
    "\n",
    "# X_5 (321) c = 5\n",
    "# (array([0.2062453 , 0.23841005, 0.33270952, 0.28029341, 0.21285483]),\n",
    "#  0.2541026221564036,\n",
    "#  0.04712316089806101)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get oof_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "777"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NFOLDS = 5\n",
    "ntrain = 1059\n",
    "ntest = 804\n",
    "RANDOM_SEED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_set_5 = [train_set_5,tot_abund_5,grad_5,mean_std_5]\n",
    "df_set_50 = [train_set_50,tot_abund_50,grad_50,mean_std_50]\n",
    "df_set_100 = [train_set_100,tot_abund_100,grad_100,mean_std_100]\n",
    "df_set_200 = [train_set_200,tot_abund_200,grad_200,mean_std_200]\n",
    "df_set_500 = [train_set_500,tot_abund_500,grad_500,mean_std_500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_set_5 = [train_set_5,tot_abund_5,grad_5,mean_std_5]\n",
    "# df_set_50 = [train_set_50,tot_abund_50,grad_50,mean_std_50]\n",
    "# df_set_100 = [train_set_100,tot_abund_100,grad_100,mean_std_100]\n",
    "# df_set_200 = [train_set_200,tot_abund_200,grad_200,mean_std_200]\n",
    "# df_set_500 = [train_set_500,tot_abund_500,grad_500,mean_std_500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_oof(clf, x_train, y_train, x_test):\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "    cv_loss = []\n",
    "\n",
    "    for i, (train_index, test_index) in enumerate(skf.split(x_train,y_train)):\n",
    "        x_tr = x_train.iloc[train_index]\n",
    "        y_tr = y_train.iloc[train_index]\n",
    "        x_te = x_train.iloc[test_index]\n",
    "        y_te = y_train.iloc[test_index]\n",
    "\n",
    "        clf.fit(x_tr.values, y_tr)\n",
    "\n",
    "        oof_train[test_index] = clf.predict_proba(x_te.values)[:,1]\n",
    "        fold_loss = log_loss(y_te, clf.predict_proba(x_te.values)[:,1])\n",
    "        cv_loss.append(fold_loss)\n",
    "        print(f\"logloss for fold {i}: {fold_loss}\")\n",
    "\n",
    "        oof_test_skf[i, :] = clf.predict_proba(x_test.values)[:,1]\n",
    "\n",
    "    print(\"CV ccv loss \",np.array(cv_loss).mean(),\"CV ccv std\", np.array(cv_loss).std())\n",
    "    \n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_df_50_v1 = dict() # contains all imp fts dfs concatenated for each target\n",
    "imp_fts_50_v1 = dict() # contains imp fts for each df target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "oof_train_df = pd.DataFrame(index=metadata[metadata.split != 'test'].sample_id, columns=targets.columns)\n",
    "oof_test_df = pd.DataFrame(index=metadata[metadata.split != 'train'].sample_id, columns=targets.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_50.sort_index(axis=1,inplace=True)\n",
    "test_set_50.sort_index(axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target: basalt\n",
      "logreg cv score:\n",
      "[0.28982238 0.33623706 0.31127075 0.27422727 0.28018212] 0.2983479139845212\n",
      "[0.2876918  0.33739336 0.30120381 0.30952967 0.31358065] 0.30987985636536014\n",
      "[0.28640171 0.33066183 0.30311909 0.28130432 0.29090339] 0.29847806687968764\n",
      "[0.29001505 0.33637952 0.31144797 0.27426195 0.28017955] 0.29845681046689415\n",
      "(1059, 34) (1059, 13) (1059, 25) (1059, 34)\n",
      "(1059, 106)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'imp_df_50_v1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_1664\\2596746213.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'logreg cv score:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mimp_df_50_v1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mimp_fts_50_v1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_imp_coefs_for_tar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogreg_clf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdf_set_50\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# cv logreg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimp_df_50_v1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'imp_df_50_v1' is not defined"
     ]
    }
   ],
   "source": [
    "agg_loss = []\n",
    "for target in targets.columns:\n",
    "    print('target:',target)\n",
    "    # select the best features for each target\n",
    "    logreg_clf = LogisticRegression(penalty='l1',C=1, solver='liblinear',random_state=RANDOM_SEED)\n",
    "    print('logreg cv score:')\n",
    "    \n",
    "    imp_df_50_v1[target],imp_fts_50_v1[target] = get_imp_coefs_for_tar(logreg_clf,df_set_50,target) # cv logreg\n",
    "\n",
    "    X = imp_df_50_v1[target]\n",
    "\n",
    "    X_test = pd.concat([\n",
    "        test_set_50.iloc[:,imp_fts_50_v1[target]['org_set']],\n",
    "        tot_abund_50_test.iloc[:,imp_fts_50_v1[target]['tot_abund_set']],\n",
    "        grad_50_test.iloc[:,imp_fts_50_v1[target]['grad_set']],\n",
    "        mean_std_50_test.iloc[:,imp_fts_50_v1[target]['mean_std_set']]\n",
    "    ],axis=1)\n",
    "\n",
    "    # save df to csv\n",
    "    X.to_csv('x50_imp_v1_'+target+'.csv')\n",
    "    print(\"Caliberated catboost logloss:\")\n",
    "    cb_clf = CatBoostClassifier(random_seed=RANDOM_SEED,verbose=False,eval_metric='Logloss')\n",
    "    calb_clf = CalibratedClassifierCV(cb_clf,cv=skf,method='sigmoid')   # calibrating this way is calibrating on less data\n",
    "\n",
    "    oof_train, oof_test = get_oof(calb_clf,X,targets[target],X_test)\n",
    "\n",
    "\n",
    "    oof_train_df[target] = oof_train\n",
    "    oof_test_df[target] = oof_test\n",
    "    print('----------------------------------------------------')\n",
    "    break\n",
    "\n",
    "print('AGG Loss:',agg_loss)\n",
    "print(\"AGG Loss mean\",np.array(agg_loss).mean())\n",
    "# save oofs to csv\n",
    "oof_train_df.to_csv('oof_train_x50_calb_cb_clf_v2.csv')\n",
    "oof_test_df.to_csv('oof_test_x50_calb_cb_clf_v2.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>tot_abund</th>\n",
       "      <th>tot_abund_0</th>\n",
       "      <th>tot_abund_1</th>\n",
       "      <th>tot_abund_2</th>\n",
       "      <th>tot_abund_3</th>\n",
       "      <th>tot_abund_4</th>\n",
       "      <th>tot_abund_5</th>\n",
       "      <th>tot_abund_6</th>\n",
       "      <th>tot_abund_7</th>\n",
       "      <th>tot_abund_8</th>\n",
       "      <th>...</th>\n",
       "      <th>tot_abund_90</th>\n",
       "      <th>tot_abund_91</th>\n",
       "      <th>tot_abund_92</th>\n",
       "      <th>tot_abund_93</th>\n",
       "      <th>tot_abund_94</th>\n",
       "      <th>tot_abund_95</th>\n",
       "      <th>tot_abund_96</th>\n",
       "      <th>tot_abund_97</th>\n",
       "      <th>tot_abund_98</th>\n",
       "      <th>tot_abund_99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.512686</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>0.010775</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.447116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020585</td>\n",
       "      <td>0.169831</td>\n",
       "      <td>0.016767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.060739</td>\n",
       "      <td>0.014823</td>\n",
       "      <td>0.023462</td>\n",
       "      <td>0.005976</td>\n",
       "      <td>0.005905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.362475</td>\n",
       "      <td>0.016349</td>\n",
       "      <td>0.023742</td>\n",
       "      <td>0.014225</td>\n",
       "      <td>0.014009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001093</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.916496</td>\n",
       "      <td>0.079048</td>\n",
       "      <td>0.110982</td>\n",
       "      <td>0.041565</td>\n",
       "      <td>0.037638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>42.669656</td>\n",
       "      <td>2.546106</td>\n",
       "      <td>3.085556</td>\n",
       "      <td>1.847891</td>\n",
       "      <td>2.099487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.403305</td>\n",
       "      <td>0.043969</td>\n",
       "      <td>0.010030</td>\n",
       "      <td>0.023578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006560</td>\n",
       "      <td>0.007559</td>\n",
       "      <td>0.006983</td>\n",
       "      <td>0.007164</td>\n",
       "      <td>0.006506</td>\n",
       "      <td>0.006547</td>\n",
       "      <td>0.006663</td>\n",
       "      <td>0.006935</td>\n",
       "      <td>0.006263</td>\n",
       "      <td>0.006317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>23.785018</td>\n",
       "      <td>0.732379</td>\n",
       "      <td>0.957667</td>\n",
       "      <td>0.514923</td>\n",
       "      <td>0.715647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163587</td>\n",
       "      <td>0.017113</td>\n",
       "      <td>0.005302</td>\n",
       "      <td>0.008326</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>0.002803</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.002629</td>\n",
       "      <td>0.002780</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.002935</td>\n",
       "      <td>0.002876</td>\n",
       "      <td>0.002858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>6.851450</td>\n",
       "      <td>0.011521</td>\n",
       "      <td>0.015770</td>\n",
       "      <td>0.006859</td>\n",
       "      <td>0.005334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>6.270355</td>\n",
       "      <td>0.009567</td>\n",
       "      <td>0.013110</td>\n",
       "      <td>0.011906</td>\n",
       "      <td>0.004096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>6.403682</td>\n",
       "      <td>0.151762</td>\n",
       "      <td>0.231224</td>\n",
       "      <td>0.123294</td>\n",
       "      <td>0.080553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013915</td>\n",
       "      <td>0.001623</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           tot_abund  tot_abund_0  tot_abund_1  tot_abund_2  tot_abund_3  \\\n",
       "sample_id                                                                  \n",
       "0           3.512686     0.012171     0.010775     0.001742     0.001357   \n",
       "1           2.447116     0.000000     0.020585     0.169831     0.016767   \n",
       "2           6.060739     0.014823     0.023462     0.005976     0.005905   \n",
       "3           6.362475     0.016349     0.023742     0.014225     0.014009   \n",
       "4           6.916496     0.079048     0.110982     0.041565     0.037638   \n",
       "...              ...          ...          ...          ...          ...   \n",
       "1054       42.669656     2.546106     3.085556     1.847891     2.099487   \n",
       "1055       23.785018     0.732379     0.957667     0.514923     0.715647   \n",
       "1056        6.851450     0.011521     0.015770     0.006859     0.005334   \n",
       "1057        6.270355     0.009567     0.013110     0.011906     0.004096   \n",
       "1058        6.403682     0.151762     0.231224     0.123294     0.080553   \n",
       "\n",
       "           tot_abund_4  tot_abund_5  tot_abund_6  tot_abund_7  tot_abund_8  \\\n",
       "sample_id                                                                    \n",
       "0                  0.0     0.000176     0.000041     0.000032     0.000046   \n",
       "1                  0.0     0.000371     0.000326     0.000236     0.000245   \n",
       "2                  0.0     0.000429     0.000060     0.000038     0.000074   \n",
       "3                  0.0     0.001093     0.000243     0.000184     0.000291   \n",
       "4                  0.0     0.004453     0.000378     0.000145     0.000253   \n",
       "...                ...          ...          ...          ...          ...   \n",
       "1054               0.0     0.403305     0.043969     0.010030     0.023578   \n",
       "1055               0.0     0.163587     0.017113     0.005302     0.008326   \n",
       "1056               0.0     0.000554     0.000146     0.000080     0.000144   \n",
       "1057               0.0     0.000427     0.000101     0.000072     0.000140   \n",
       "1058               0.0     0.013915     0.001623     0.000385     0.000867   \n",
       "\n",
       "           ...  tot_abund_90  tot_abund_91  tot_abund_92  tot_abund_93  \\\n",
       "sample_id  ...                                                           \n",
       "0          ...      0.000024      0.000029      0.000022      0.000022   \n",
       "1          ...      0.000000      0.000000      0.000000      0.000000   \n",
       "2          ...      0.000019      0.000018      0.000019      0.000020   \n",
       "3          ...      0.000088      0.000092      0.000089      0.000088   \n",
       "4          ...      0.000085      0.000079      0.000083      0.000069   \n",
       "...        ...           ...           ...           ...           ...   \n",
       "1054       ...      0.006560      0.007559      0.006983      0.007164   \n",
       "1055       ...      0.002886      0.003094      0.002803      0.002700   \n",
       "1056       ...      0.000043      0.000057      0.000058      0.000050   \n",
       "1057       ...      0.000034      0.000065      0.000046      0.000043   \n",
       "1058       ...      0.000254      0.000437      0.000274      0.000266   \n",
       "\n",
       "           tot_abund_94  tot_abund_95  tot_abund_96  tot_abund_97  \\\n",
       "sample_id                                                           \n",
       "0              0.000028      0.000023      0.000030      0.000025   \n",
       "1              0.000000      0.000000      0.000000      0.000000   \n",
       "2              0.000019      0.000019      0.000020      0.000021   \n",
       "3              0.000087      0.000101      0.000082      0.000092   \n",
       "4              0.000085      0.000075      0.000091      0.000082   \n",
       "...                 ...           ...           ...           ...   \n",
       "1054           0.006506      0.006547      0.006663      0.006935   \n",
       "1055           0.002629      0.002780      0.002575      0.002935   \n",
       "1056           0.000043      0.000043      0.000048      0.000052   \n",
       "1057           0.000038      0.000037      0.000037      0.000036   \n",
       "1058           0.000266      0.000282      0.000221      0.000268   \n",
       "\n",
       "           tot_abund_98  tot_abund_99  \n",
       "sample_id                              \n",
       "0              0.000023      0.000024  \n",
       "1              0.000000      0.000000  \n",
       "2              0.000020      0.000020  \n",
       "3              0.000080      0.000087  \n",
       "4              0.000089      0.000079  \n",
       "...                 ...           ...  \n",
       "1054           0.006263      0.006317  \n",
       "1055           0.002876      0.002858  \n",
       "1056           0.000045      0.000046  \n",
       "1057           0.000033      0.000032  \n",
       "1058           0.000274      0.000237  \n",
       "\n",
       "[1059 rows x 101 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_set_50[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14925846459526557"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss= [0.08568977930924544,0.18725364626183266,0.19295663545431943,0.19295663545431943,0.1485500258847397,0.017557044164855446,0.2219487168441076,0.17024561438735059,0.11826058953609235,0.1571659586557931]\n",
    "np.array(loss).mean()\n",
    "# second run on df_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14447899264164712"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_loss =  np.array([ 0.15796721870217575,0.11778220324912292,0.16957356591882863,0.017403201778143515,0.14871534853419863,0.224095137993568,0.19258528888448911,0.18639302435617577,0.0857959443581221])\n",
    "agg_loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['imp_df_50_v1.joblib']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(imp_df_50_v1,'imp_df_50_v1.joblib')\n",
    "dump(imp_fts_50_v1,'imp_fts_50_v1.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>sample_id</th>\n",
       "      <th>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S0766</td>\n",
       "      <td>0.046021</td>\n",
       "      <td>0.051149</td>\n",
       "      <td>0.040366</td>\n",
       "      <td>0.041744</td>\n",
       "      <td>0.008425</td>\n",
       "      <td>0.037048</td>\n",
       "      <td>0.041023</td>\n",
       "      <td>0.071723</td>\n",
       "      <td>0.882801</td>\n",
       "      <td>0.019296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S0767</td>\n",
       "      <td>0.040726</td>\n",
       "      <td>0.271604</td>\n",
       "      <td>0.040183</td>\n",
       "      <td>0.044695</td>\n",
       "      <td>0.008428</td>\n",
       "      <td>0.037894</td>\n",
       "      <td>0.041578</td>\n",
       "      <td>0.051584</td>\n",
       "      <td>0.221860</td>\n",
       "      <td>0.019344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0768</td>\n",
       "      <td>0.046001</td>\n",
       "      <td>0.038898</td>\n",
       "      <td>0.036841</td>\n",
       "      <td>0.042707</td>\n",
       "      <td>0.008422</td>\n",
       "      <td>0.039486</td>\n",
       "      <td>0.042513</td>\n",
       "      <td>0.060938</td>\n",
       "      <td>0.636716</td>\n",
       "      <td>0.019290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S0769</td>\n",
       "      <td>0.045113</td>\n",
       "      <td>0.069279</td>\n",
       "      <td>0.036869</td>\n",
       "      <td>0.044737</td>\n",
       "      <td>0.008425</td>\n",
       "      <td>0.036945</td>\n",
       "      <td>0.041433</td>\n",
       "      <td>0.047845</td>\n",
       "      <td>0.510866</td>\n",
       "      <td>0.019358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S0770</td>\n",
       "      <td>0.041444</td>\n",
       "      <td>0.038590</td>\n",
       "      <td>0.040734</td>\n",
       "      <td>0.045782</td>\n",
       "      <td>0.008421</td>\n",
       "      <td>0.044046</td>\n",
       "      <td>0.042510</td>\n",
       "      <td>0.049528</td>\n",
       "      <td>0.210017</td>\n",
       "      <td>0.019300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>S1565</td>\n",
       "      <td>0.055514</td>\n",
       "      <td>0.394514</td>\n",
       "      <td>0.040030</td>\n",
       "      <td>0.046140</td>\n",
       "      <td>0.008509</td>\n",
       "      <td>0.039658</td>\n",
       "      <td>0.044581</td>\n",
       "      <td>0.218110</td>\n",
       "      <td>0.187004</td>\n",
       "      <td>0.019493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>S1566</td>\n",
       "      <td>0.041948</td>\n",
       "      <td>0.076168</td>\n",
       "      <td>0.037433</td>\n",
       "      <td>0.049264</td>\n",
       "      <td>0.008422</td>\n",
       "      <td>0.036838</td>\n",
       "      <td>0.044158</td>\n",
       "      <td>0.060370</td>\n",
       "      <td>0.267933</td>\n",
       "      <td>0.019425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>S1567</td>\n",
       "      <td>0.041673</td>\n",
       "      <td>0.337179</td>\n",
       "      <td>0.042402</td>\n",
       "      <td>0.043181</td>\n",
       "      <td>0.008424</td>\n",
       "      <td>0.037141</td>\n",
       "      <td>0.049654</td>\n",
       "      <td>0.059765</td>\n",
       "      <td>0.406512</td>\n",
       "      <td>0.019403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>S1568</td>\n",
       "      <td>0.040859</td>\n",
       "      <td>0.339863</td>\n",
       "      <td>0.040581</td>\n",
       "      <td>0.043569</td>\n",
       "      <td>0.008497</td>\n",
       "      <td>0.037318</td>\n",
       "      <td>0.055851</td>\n",
       "      <td>0.051528</td>\n",
       "      <td>0.272265</td>\n",
       "      <td>0.019722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>S1569</td>\n",
       "      <td>0.068883</td>\n",
       "      <td>0.442015</td>\n",
       "      <td>0.043177</td>\n",
       "      <td>0.046143</td>\n",
       "      <td>0.008549</td>\n",
       "      <td>0.042985</td>\n",
       "      <td>0.044788</td>\n",
       "      <td>0.235675</td>\n",
       "      <td>0.238412</td>\n",
       "      <td>0.019522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>804 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sample_id    basalt  carbonate  chloride  iron_oxide   oxalate  \\\n",
       "0       S0766  0.046021   0.051149  0.040366    0.041744  0.008425   \n",
       "1       S0767  0.040726   0.271604  0.040183    0.044695  0.008428   \n",
       "2       S0768  0.046001   0.038898  0.036841    0.042707  0.008422   \n",
       "3       S0769  0.045113   0.069279  0.036869    0.044737  0.008425   \n",
       "4       S0770  0.041444   0.038590  0.040734    0.045782  0.008421   \n",
       "..        ...       ...        ...       ...         ...       ...   \n",
       "799     S1565  0.055514   0.394514  0.040030    0.046140  0.008509   \n",
       "800     S1566  0.041948   0.076168  0.037433    0.049264  0.008422   \n",
       "801     S1567  0.041673   0.337179  0.042402    0.043181  0.008424   \n",
       "802     S1568  0.040859   0.339863  0.040581    0.043569  0.008497   \n",
       "803     S1569  0.068883   0.442015  0.043177    0.046143  0.008549   \n",
       "\n",
       "     oxychlorine  phyllosilicate  silicate   sulfate   sulfide  \n",
       "0       0.037048        0.041023  0.071723  0.882801  0.019296  \n",
       "1       0.037894        0.041578  0.051584  0.221860  0.019344  \n",
       "2       0.039486        0.042513  0.060938  0.636716  0.019290  \n",
       "3       0.036945        0.041433  0.047845  0.510866  0.019358  \n",
       "4       0.044046        0.042510  0.049528  0.210017  0.019300  \n",
       "..           ...             ...       ...       ...       ...  \n",
       "799     0.039658        0.044581  0.218110  0.187004  0.019493  \n",
       "800     0.036838        0.044158  0.060370  0.267933  0.019425  \n",
       "801     0.037141        0.049654  0.059765  0.406512  0.019403  \n",
       "802     0.037318        0.055851  0.051528  0.272265  0.019722  \n",
       "803     0.042985        0.044788  0.235675  0.238412  0.019522  \n",
       "\n",
       "[804 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_test_50 = pd.read_csv('oof_test_x50_calb_cb_clf_v1.csv')\n",
    "oof_test_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.041826</td>\n",
       "      <td>0.035228</td>\n",
       "      <td>0.039655</td>\n",
       "      <td>0.041868</td>\n",
       "      <td>0.008435</td>\n",
       "      <td>0.036793</td>\n",
       "      <td>0.041465</td>\n",
       "      <td>0.054703</td>\n",
       "      <td>0.911435</td>\n",
       "      <td>0.019366</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.038260</td>\n",
       "      <td>0.564670</td>\n",
       "      <td>0.038677</td>\n",
       "      <td>0.049636</td>\n",
       "      <td>0.008446</td>\n",
       "      <td>0.037306</td>\n",
       "      <td>0.043245</td>\n",
       "      <td>0.046429</td>\n",
       "      <td>0.074672</td>\n",
       "      <td>0.019422</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.052762</td>\n",
       "      <td>0.030196</td>\n",
       "      <td>0.039787</td>\n",
       "      <td>0.043186</td>\n",
       "      <td>0.008426</td>\n",
       "      <td>0.053301</td>\n",
       "      <td>0.046294</td>\n",
       "      <td>0.050413</td>\n",
       "      <td>0.309814</td>\n",
       "      <td>0.019356</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.042086</td>\n",
       "      <td>0.068271</td>\n",
       "      <td>0.037248</td>\n",
       "      <td>0.053948</td>\n",
       "      <td>0.008447</td>\n",
       "      <td>0.036221</td>\n",
       "      <td>0.042740</td>\n",
       "      <td>0.044824</td>\n",
       "      <td>0.413024</td>\n",
       "      <td>0.019502</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.041382</td>\n",
       "      <td>0.030146</td>\n",
       "      <td>0.051489</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>0.008428</td>\n",
       "      <td>0.100341</td>\n",
       "      <td>0.068726</td>\n",
       "      <td>0.047090</td>\n",
       "      <td>0.076600</td>\n",
       "      <td>0.019380</td>\n",
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       "      <th>799</th>\n",
       "      <td>0.050517</td>\n",
       "      <td>0.454062</td>\n",
       "      <td>0.039511</td>\n",
       "      <td>0.050144</td>\n",
       "      <td>0.008985</td>\n",
       "      <td>0.042707</td>\n",
       "      <td>0.045898</td>\n",
       "      <td>0.067008</td>\n",
       "      <td>0.050607</td>\n",
       "      <td>0.019705</td>\n",
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       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>0.040518</td>\n",
       "      <td>0.037004</td>\n",
       "      <td>0.038390</td>\n",
       "      <td>0.055501</td>\n",
       "      <td>0.008420</td>\n",
       "      <td>0.036445</td>\n",
       "      <td>0.049802</td>\n",
       "      <td>0.046124</td>\n",
       "      <td>0.124842</td>\n",
       "      <td>0.019522</td>\n",
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       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>0.038913</td>\n",
       "      <td>0.241122</td>\n",
       "      <td>0.043798</td>\n",
       "      <td>0.042732</td>\n",
       "      <td>0.008433</td>\n",
       "      <td>0.036478</td>\n",
       "      <td>0.073431</td>\n",
       "      <td>0.046757</td>\n",
       "      <td>0.218515</td>\n",
       "      <td>0.019526</td>\n",
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       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.171087</td>\n",
       "      <td>0.038036</td>\n",
       "      <td>0.044006</td>\n",
       "      <td>0.008641</td>\n",
       "      <td>0.036630</td>\n",
       "      <td>0.059932</td>\n",
       "      <td>0.043291</td>\n",
       "      <td>0.098676</td>\n",
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       "      <td>0.009460</td>\n",
       "      <td>0.054454</td>\n",
       "      <td>0.046884</td>\n",
       "      <td>0.082803</td>\n",
       "      <td>0.056435</td>\n",
       "      <td>0.020753</td>\n",
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      "text/plain": [
       "       basalt  carbonate  chloride  iron_oxide   oxalate  oxychlorine  \\\n",
       "0    0.041826   0.035228  0.039655    0.041868  0.008435     0.036793   \n",
       "1    0.038260   0.564670  0.038677    0.049636  0.008446     0.037306   \n",
       "2    0.052762   0.030196  0.039787    0.043186  0.008426     0.053301   \n",
       "3    0.042086   0.068271  0.037248    0.053948  0.008447     0.036221   \n",
       "4    0.041382   0.030146  0.051489    0.077498  0.008428     0.100341   \n",
       "..        ...        ...       ...         ...       ...          ...   \n",
       "799  0.050517   0.454062  0.039511    0.050144  0.008985     0.042707   \n",
       "800  0.040518   0.037004  0.038390    0.055501  0.008420     0.036445   \n",
       "801  0.038913   0.241122  0.043798    0.042732  0.008433     0.036478   \n",
       "802  0.038563   0.171087  0.038036    0.044006  0.008641     0.036630   \n",
       "803  0.071801   0.566403  0.043885    0.054898  0.009460     0.054454   \n",
       "\n",
       "     phyllosilicate  silicate   sulfate   sulfide  \n",
       "0          0.041465  0.054703  0.911435  0.019366  \n",
       "1          0.043245  0.046429  0.074672  0.019422  \n",
       "2          0.046294  0.050413  0.309814  0.019356  \n",
       "3          0.042740  0.044824  0.413024  0.019502  \n",
       "4          0.068726  0.047090  0.076600  0.019380  \n",
       "..              ...       ...       ...       ...  \n",
       "799        0.045898  0.067008  0.050607  0.019705  \n",
       "800        0.049802  0.046124  0.124842  0.019522  \n",
       "801        0.073431  0.046757  0.218515  0.019526  \n",
       "802        0.059932  0.043291  0.098676  0.020097  \n",
       "803        0.046884  0.082803  0.056435  0.020753  \n",
       "\n",
       "[804 rows x 10 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_test_50_v2 = pd.read_csv('oof_test_x50_calb_cb_clf_v2.csv')\n",
    "oof_test_50_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "      <th>sample_id</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.004105</td>\n",
       "      <td>0.017981</td>\n",
       "      <td>0.009874</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.004325</td>\n",
       "      <td>0.036534</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.034601</td>\n",
       "      <td>0.045297</td>\n",
       "      <td>0.609227</td>\n",
       "      <td>0.091435</td>\n",
       "      <td>0.001141</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0768</td>\n",
       "      <td>0.484653</td>\n",
       "      <td>0.146311</td>\n",
       "      <td>0.002644</td>\n",
       "      <td>0.081566</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.017212</td>\n",
       "      <td>0.394838</td>\n",
       "      <td>0.623719</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.000775</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S0769</td>\n",
       "      <td>0.020934</td>\n",
       "      <td>0.012400</td>\n",
       "      <td>0.068250</td>\n",
       "      <td>0.019692</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.879791</td>\n",
       "      <td>0.100869</td>\n",
       "      <td>0.010727</td>\n",
       "      <td>0.871765</td>\n",
       "      <td>0.002046</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S0770</td>\n",
       "      <td>0.022837</td>\n",
       "      <td>0.010769</td>\n",
       "      <td>0.014176</td>\n",
       "      <td>0.226716</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.980885</td>\n",
       "      <td>0.899091</td>\n",
       "      <td>0.009366</td>\n",
       "      <td>0.014347</td>\n",
       "      <td>0.000715</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>799</th>\n",
       "      <td>S1565</td>\n",
       "      <td>0.067185</td>\n",
       "      <td>0.155509</td>\n",
       "      <td>0.015313</td>\n",
       "      <td>0.142559</td>\n",
       "      <td>0.029369</td>\n",
       "      <td>0.019302</td>\n",
       "      <td>0.022436</td>\n",
       "      <td>0.014338</td>\n",
       "      <td>0.007562</td>\n",
       "      <td>0.054753</td>\n",
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       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>S1566</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>0.020832</td>\n",
       "      <td>0.013878</td>\n",
       "      <td>0.056196</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.019664</td>\n",
       "      <td>0.102973</td>\n",
       "      <td>0.025733</td>\n",
       "      <td>0.074486</td>\n",
       "      <td>0.001506</td>\n",
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       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>S1567</td>\n",
       "      <td>0.005550</td>\n",
       "      <td>0.099935</td>\n",
       "      <td>0.042125</td>\n",
       "      <td>0.012218</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.014931</td>\n",
       "      <td>0.277126</td>\n",
       "      <td>0.014189</td>\n",
       "      <td>0.181096</td>\n",
       "      <td>0.004653</td>\n",
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       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>S1568</td>\n",
       "      <td>0.012997</td>\n",
       "      <td>0.088101</td>\n",
       "      <td>0.009234</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.013429</td>\n",
       "      <td>0.021371</td>\n",
       "      <td>0.024821</td>\n",
       "      <td>0.009997</td>\n",
       "      <td>0.020158</td>\n",
       "      <td>0.020348</td>\n",
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       "      <th>803</th>\n",
       "      <td>S1569</td>\n",
       "      <td>0.232156</td>\n",
       "      <td>0.201649</td>\n",
       "      <td>0.017136</td>\n",
       "      <td>0.170331</td>\n",
       "      <td>0.052704</td>\n",
       "      <td>0.027394</td>\n",
       "      <td>0.040634</td>\n",
       "      <td>0.033435</td>\n",
       "      <td>0.008782</td>\n",
       "      <td>0.232007</td>\n",
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       "<p>804 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "    sample_id    basalt  carbonate  chloride  iron_oxide   oxalate  \\\n",
       "0       S0766  0.008515   0.004105  0.017981    0.009874  0.001412   \n",
       "1       S0767  0.077800   0.035944  0.004325    0.036534  0.000102   \n",
       "2       S0768  0.484653   0.146311  0.002644    0.081566  0.002529   \n",
       "3       S0769  0.020934   0.012400  0.068250    0.019692  0.000095   \n",
       "4       S0770  0.022837   0.010769  0.014176    0.226716  0.000148   \n",
       "..        ...       ...        ...       ...         ...       ...   \n",
       "799     S1565  0.067185   0.155509  0.015313    0.142559  0.029369   \n",
       "800     S1566  0.035084   0.020832  0.013878    0.056196  0.000100   \n",
       "801     S1567  0.005550   0.099935  0.042125    0.012218  0.000646   \n",
       "802     S1568  0.012997   0.088101  0.009234    0.011730  0.013429   \n",
       "803     S1569  0.232156   0.201649  0.017136    0.170331  0.052704   \n",
       "\n",
       "     oxychlorine  phyllosilicate  silicate   sulfate   sulfide  \n",
       "0       0.008454        0.019390  0.271978  0.005496  0.002052  \n",
       "1       0.034601        0.045297  0.609227  0.091435  0.001141  \n",
       "2       0.017212        0.394838  0.623719  0.043436  0.000775  \n",
       "3       0.879791        0.100869  0.010727  0.871765  0.002046  \n",
       "4       0.980885        0.899091  0.009366  0.014347  0.000715  \n",
       "..           ...             ...       ...       ...       ...  \n",
       "799     0.019302        0.022436  0.014338  0.007562  0.054753  \n",
       "800     0.019664        0.102973  0.025733  0.074486  0.001506  \n",
       "801     0.014931        0.277126  0.014189  0.181096  0.004653  \n",
       "802     0.021371        0.024821  0.009997  0.020158  0.020348  \n",
       "803     0.027394        0.040634  0.033435  0.008782  0.232007  \n",
       "\n",
       "[804 rows x 11 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_preds = pd.read_csv('submissions\\catboost_preds_on_test_imp_fts-2.csv')\n",
    "cb_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>sample_id</th>\n",
       "      <th>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S0000</td>\n",
       "      <td>0.046069</td>\n",
       "      <td>0.031305</td>\n",
       "      <td>0.036531</td>\n",
       "      <td>0.042597</td>\n",
       "      <td>0.008983</td>\n",
       "      <td>0.040069</td>\n",
       "      <td>0.039597</td>\n",
       "      <td>0.043380</td>\n",
       "      <td>0.966699</td>\n",
       "      <td>0.018704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S0001</td>\n",
       "      <td>0.038418</td>\n",
       "      <td>0.975501</td>\n",
       "      <td>0.041802</td>\n",
       "      <td>0.062605</td>\n",
       "      <td>0.009007</td>\n",
       "      <td>0.033487</td>\n",
       "      <td>0.040268</td>\n",
       "      <td>0.050186</td>\n",
       "      <td>0.041741</td>\n",
       "      <td>0.018795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0002</td>\n",
       "      <td>0.084273</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>0.055014</td>\n",
       "      <td>0.041782</td>\n",
       "      <td>0.008971</td>\n",
       "      <td>0.961506</td>\n",
       "      <td>0.044605</td>\n",
       "      <td>0.036082</td>\n",
       "      <td>0.049080</td>\n",
       "      <td>0.020378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S0003</td>\n",
       "      <td>0.041053</td>\n",
       "      <td>0.227361</td>\n",
       "      <td>0.033505</td>\n",
       "      <td>0.054467</td>\n",
       "      <td>0.008756</td>\n",
       "      <td>0.034686</td>\n",
       "      <td>0.043093</td>\n",
       "      <td>0.036110</td>\n",
       "      <td>0.447207</td>\n",
       "      <td>0.020161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S0004</td>\n",
       "      <td>0.040080</td>\n",
       "      <td>0.027870</td>\n",
       "      <td>0.042560</td>\n",
       "      <td>0.583049</td>\n",
       "      <td>0.008045</td>\n",
       "      <td>0.974037</td>\n",
       "      <td>0.902420</td>\n",
       "      <td>0.044392</td>\n",
       "      <td>0.029145</td>\n",
       "      <td>0.017490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>S1054</td>\n",
       "      <td>0.045401</td>\n",
       "      <td>0.023425</td>\n",
       "      <td>0.092599</td>\n",
       "      <td>0.042936</td>\n",
       "      <td>0.008085</td>\n",
       "      <td>0.033533</td>\n",
       "      <td>0.052842</td>\n",
       "      <td>0.066822</td>\n",
       "      <td>0.038885</td>\n",
       "      <td>0.018756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>S1055</td>\n",
       "      <td>0.032899</td>\n",
       "      <td>0.025028</td>\n",
       "      <td>0.043735</td>\n",
       "      <td>0.060109</td>\n",
       "      <td>0.008752</td>\n",
       "      <td>0.038740</td>\n",
       "      <td>0.041931</td>\n",
       "      <td>0.898605</td>\n",
       "      <td>0.026602</td>\n",
       "      <td>0.017609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>S1056</td>\n",
       "      <td>0.030648</td>\n",
       "      <td>0.028253</td>\n",
       "      <td>0.037729</td>\n",
       "      <td>0.041349</td>\n",
       "      <td>0.909968</td>\n",
       "      <td>0.033899</td>\n",
       "      <td>0.037275</td>\n",
       "      <td>0.032015</td>\n",
       "      <td>0.037941</td>\n",
       "      <td>0.018764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>S1057</td>\n",
       "      <td>0.037680</td>\n",
       "      <td>0.787315</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>0.073169</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.039862</td>\n",
       "      <td>0.063518</td>\n",
       "      <td>0.045303</td>\n",
       "      <td>0.029741</td>\n",
       "      <td>0.017601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>S1058</td>\n",
       "      <td>0.045029</td>\n",
       "      <td>0.028267</td>\n",
       "      <td>0.037596</td>\n",
       "      <td>0.045693</td>\n",
       "      <td>0.008042</td>\n",
       "      <td>0.033738</td>\n",
       "      <td>0.964900</td>\n",
       "      <td>0.035284</td>\n",
       "      <td>0.027143</td>\n",
       "      <td>0.020408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample_id    basalt  carbonate  chloride  iron_oxide   oxalate  \\\n",
       "0        S0000  0.046069   0.031305  0.036531    0.042597  0.008983   \n",
       "1        S0001  0.038418   0.975501  0.041802    0.062605  0.009007   \n",
       "2        S0002  0.084273   0.025317  0.055014    0.041782  0.008971   \n",
       "3        S0003  0.041053   0.227361  0.033505    0.054467  0.008756   \n",
       "4        S0004  0.040080   0.027870  0.042560    0.583049  0.008045   \n",
       "...        ...       ...        ...       ...         ...       ...   \n",
       "1054     S1054  0.045401   0.023425  0.092599    0.042936  0.008085   \n",
       "1055     S1055  0.032899   0.025028  0.043735    0.060109  0.008752   \n",
       "1056     S1056  0.030648   0.028253  0.037729    0.041349  0.909968   \n",
       "1057     S1057  0.037680   0.787315  0.042295    0.073169  0.008100   \n",
       "1058     S1058  0.045029   0.028267  0.037596    0.045693  0.008042   \n",
       "\n",
       "      oxychlorine  phyllosilicate  silicate   sulfate   sulfide  \n",
       "0        0.040069        0.039597  0.043380  0.966699  0.018704  \n",
       "1        0.033487        0.040268  0.050186  0.041741  0.018795  \n",
       "2        0.961506        0.044605  0.036082  0.049080  0.020378  \n",
       "3        0.034686        0.043093  0.036110  0.447207  0.020161  \n",
       "4        0.974037        0.902420  0.044392  0.029145  0.017490  \n",
       "...           ...             ...       ...       ...       ...  \n",
       "1054     0.033533        0.052842  0.066822  0.038885  0.018756  \n",
       "1055     0.038740        0.041931  0.898605  0.026602  0.017609  \n",
       "1056     0.033899        0.037275  0.032015  0.037941  0.018764  \n",
       "1057     0.039862        0.063518  0.045303  0.029741  0.017601  \n",
       "1058     0.033738        0.964900  0.035284  0.027143  0.020408  \n",
       "\n",
       "[1059 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_train_50 = pd.read_csv('oof_train_x50_calb_cb_clf_v1.csv')\n",
    "oof_train_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_clb = LogisticRegression(random_state=RANDOM_SEED,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "clb_cv = cross_val_score(lr_clb,oof_train_50[['basalt']],targets['basalt'],cv=skf,scoring=log_loss_scorer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.14385143, 0.16268594, 0.16267129, 0.19863805, 0.19790215]),\n",
       " 0.1731497707682477)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clb_cv, clb_cv.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "calib = CalibratedClassifierCV(base_estimator=cb_clf,cv='prefit',ensemble=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccv_1 =  cross_val_score(calib, oof_train_50[['basalt']], targets['basalt'],cv=skf, scoring=log_loss_scorer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([nan, nan, nan, nan, nan]), nan)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccv_1, ccv_1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.010558\n",
      "0:\tlearn: 0.6743554\ttotal: 156ms\tremaining: 2m 36s\n",
      "999:\tlearn: 0.1178114\ttotal: 1.66s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x2838951ce50>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_clf.fit(oof_train_50[['basalt']],targets['basalt'])\n",
    "cb_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = oof_train_50.drop(index=metadata[metadata.split == 'val'].index,axis=0)\n",
    "X = X.drop('sample_id',axis=1)\n",
    "Y = targets.drop(index=metadata[metadata.split == 'val'].sample_id,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.046069</td>\n",
       "      <td>0.031305</td>\n",
       "      <td>0.036531</td>\n",
       "      <td>0.042597</td>\n",
       "      <td>0.008983</td>\n",
       "      <td>0.040069</td>\n",
       "      <td>0.039597</td>\n",
       "      <td>0.043380</td>\n",
       "      <td>0.966699</td>\n",
       "      <td>0.018704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.038418</td>\n",
       "      <td>0.975501</td>\n",
       "      <td>0.041802</td>\n",
       "      <td>0.062605</td>\n",
       "      <td>0.009007</td>\n",
       "      <td>0.033487</td>\n",
       "      <td>0.040268</td>\n",
       "      <td>0.050186</td>\n",
       "      <td>0.041741</td>\n",
       "      <td>0.018795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.084273</td>\n",
       "      <td>0.025317</td>\n",
       "      <td>0.055014</td>\n",
       "      <td>0.041782</td>\n",
       "      <td>0.008971</td>\n",
       "      <td>0.961506</td>\n",
       "      <td>0.044605</td>\n",
       "      <td>0.036082</td>\n",
       "      <td>0.049080</td>\n",
       "      <td>0.020378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.041053</td>\n",
       "      <td>0.227361</td>\n",
       "      <td>0.033505</td>\n",
       "      <td>0.054467</td>\n",
       "      <td>0.008756</td>\n",
       "      <td>0.034686</td>\n",
       "      <td>0.043093</td>\n",
       "      <td>0.036110</td>\n",
       "      <td>0.447207</td>\n",
       "      <td>0.020161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.040080</td>\n",
       "      <td>0.027870</td>\n",
       "      <td>0.042560</td>\n",
       "      <td>0.583049</td>\n",
       "      <td>0.008045</td>\n",
       "      <td>0.974037</td>\n",
       "      <td>0.902420</td>\n",
       "      <td>0.044392</td>\n",
       "      <td>0.029145</td>\n",
       "      <td>0.017490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>0.040897</td>\n",
       "      <td>0.030983</td>\n",
       "      <td>0.032553</td>\n",
       "      <td>0.052167</td>\n",
       "      <td>0.008221</td>\n",
       "      <td>0.042143</td>\n",
       "      <td>0.131683</td>\n",
       "      <td>0.033124</td>\n",
       "      <td>0.092498</td>\n",
       "      <td>0.018871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>0.033147</td>\n",
       "      <td>0.486420</td>\n",
       "      <td>0.044994</td>\n",
       "      <td>0.040181</td>\n",
       "      <td>0.008093</td>\n",
       "      <td>0.034986</td>\n",
       "      <td>0.040852</td>\n",
       "      <td>0.044504</td>\n",
       "      <td>0.198451</td>\n",
       "      <td>0.020984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>0.039143</td>\n",
       "      <td>0.082237</td>\n",
       "      <td>0.036185</td>\n",
       "      <td>0.050952</td>\n",
       "      <td>0.008078</td>\n",
       "      <td>0.032136</td>\n",
       "      <td>0.056739</td>\n",
       "      <td>0.033697</td>\n",
       "      <td>0.061231</td>\n",
       "      <td>0.023405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.039292</td>\n",
       "      <td>0.114992</td>\n",
       "      <td>0.037282</td>\n",
       "      <td>0.045582</td>\n",
       "      <td>0.008113</td>\n",
       "      <td>0.033680</td>\n",
       "      <td>0.059778</td>\n",
       "      <td>0.045394</td>\n",
       "      <td>0.033325</td>\n",
       "      <td>0.023074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.040082</td>\n",
       "      <td>0.030646</td>\n",
       "      <td>0.038503</td>\n",
       "      <td>0.060716</td>\n",
       "      <td>0.008089</td>\n",
       "      <td>0.032643</td>\n",
       "      <td>0.058103</td>\n",
       "      <td>0.045451</td>\n",
       "      <td>0.122933</td>\n",
       "      <td>0.020599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>766 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       basalt  carbonate  chloride  iron_oxide   oxalate  oxychlorine  \\\n",
       "0    0.046069   0.031305  0.036531    0.042597  0.008983     0.040069   \n",
       "1    0.038418   0.975501  0.041802    0.062605  0.009007     0.033487   \n",
       "2    0.084273   0.025317  0.055014    0.041782  0.008971     0.961506   \n",
       "3    0.041053   0.227361  0.033505    0.054467  0.008756     0.034686   \n",
       "4    0.040080   0.027870  0.042560    0.583049  0.008045     0.974037   \n",
       "..        ...        ...       ...         ...       ...          ...   \n",
       "761  0.040897   0.030983  0.032553    0.052167  0.008221     0.042143   \n",
       "762  0.033147   0.486420  0.044994    0.040181  0.008093     0.034986   \n",
       "763  0.039143   0.082237  0.036185    0.050952  0.008078     0.032136   \n",
       "764  0.039292   0.114992  0.037282    0.045582  0.008113     0.033680   \n",
       "765  0.040082   0.030646  0.038503    0.060716  0.008089     0.032643   \n",
       "\n",
       "     phyllosilicate  silicate   sulfate   sulfide  \n",
       "0          0.039597  0.043380  0.966699  0.018704  \n",
       "1          0.040268  0.050186  0.041741  0.018795  \n",
       "2          0.044605  0.036082  0.049080  0.020378  \n",
       "3          0.043093  0.036110  0.447207  0.020161  \n",
       "4          0.902420  0.044392  0.029145  0.017490  \n",
       "..              ...       ...       ...       ...  \n",
       "761        0.131683  0.033124  0.092498  0.018871  \n",
       "762        0.040852  0.044504  0.198451  0.020984  \n",
       "763        0.056739  0.033697  0.061231  0.023405  \n",
       "764        0.059778  0.045394  0.033325  0.023074  \n",
       "765        0.058103  0.045451  0.122933  0.020599  \n",
       "\n",
       "[766 rows x 10 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.11647724404099095]\n",
      "[0.11647724404099095, 0.15715604682311565]\n",
      "[0.11647724404099095, 0.15715604682311565, 0.10983927782767743]\n",
      "[0.11647724404099095, 0.15715604682311565, 0.10983927782767743, 0.137331038931166]\n",
      "[0.11647724404099095, 0.15715604682311565, 0.10983927782767743, 0.137331038931166, 0.12377210449094868]\n"
     ]
    }
   ],
   "source": [
    "loss_x = []\n",
    "for i, (train_index, test_index) in enumerate(skf.split(X[['basalt']],Y['basalt'])):\n",
    "        x_tr = X[['basalt']].iloc[train_index]\n",
    "        y_tr = Y['basalt'].iloc[train_index]\n",
    "        x_te = X[['basalt']].iloc[test_index]\n",
    "        y_te = Y['basalt'].iloc[test_index]\n",
    "\n",
    "        calib.fit(x_tr.values,y_tr.values)\n",
    "        loss  = log_loss(y_te,calib.predict_proba(x_te))\n",
    "        loss_x.append(loss)\n",
    "        print(loss_x)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12891514242277974"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(loss_x).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01977934, 0.02042402, 0.09062856, ..., 0.0182138 , 0.01929143,\n",
       "       0.01967246])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calib.fit(oof_train_50[['basalt']], targets['basalt'])\n",
    "calib.predict_proba(oof_train_50[['basalt']])[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_temp_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "518a807dccee1bb2cf00c0cea9388abbe4210a3c385c01718c979ef7759eaf87"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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