Prosup_Classifier_data_prep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prosup  Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from Data_preparation_Library import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm\n",
    "from time import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import load_model\n",
    "from random import choice\n",
    "from scipy import signal\n",
    "from pickle import load\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "file = 'Subjects/Nikhil/computed_Bulb.csv'\n",
    "emg_labels = ['EMG1','EMG2','EMG3','EMG4','EMG5','EMG6','EMG7','EMG8']\n",
    "angle_lables = ['Pronation_Angle','Flexion_Angle',  'Radial_Angle','Elbow_Joint_Angle']\n",
    "df = pd.read_csv(file,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "b,a = signal.butter(3, 3,fs=2000)\n",
    "for labels in emg_labels:\n",
    "    df[labels] = signal.lfilter(b, a,abs(df[labels]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "b,a = signal.butter(3, 3,fs=2000)\n",
    "for labels in angle_lables:\n",
    "    df[labels] = signal.lfilter(b, a,df[labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Pronation_Angle'].plot(figsize=(16,5),title=file,grid=True)#,xticks=np.linspace(0,len(df),5)\n",
    "df = df[72500:82500]#[6500:85000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -> 20.512820512820515 %\n",
      "1 -> 34.35897435897436 %\n",
      "2 -> 45.12820512820513 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((195, 250, 8), (195, 3))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = prep_data_prosup(df,250,interval=50,Normalize=True,plot=True,angle_thresh=0.001,rms=False)\n",
    "X.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EMG1 - 0.9999999999999999\n",
      "EMG2 - 1.0\n",
      "EMG3 - 1.0\n",
      "EMG4 - 1.0\n",
      "EMG5 - 1.0\n",
      "EMG6 - 1.0\n",
      "EMG7 - 1.0\n",
      "EMG8 - 0.9999999999999999\n"
     ]
    }
   ],
   "source": [
    "Xn = X.reshape(X.shape[0]*X.shape[1],8)\n",
    "for i in range(len(emg_labels)):\n",
    "    print(emg_labels[i],'-',Xn[:,i].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File Name Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = []\n",
    "subjects = ['Fazil','Nikhil','Abhishek','Jai', 'Deepti', 'Nirmayee','Ingy','Varun'] #'Fazil/day_04_14_21' ,'Varun'\n",
    "# subjects = ['Varun'] #'Fazil/day_04_14_21' ,'Varun'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Subjects/Fazil/computed_Bulb.csv',\n",
       " 'Subjects/Fazil/computed_Bulb01.csv',\n",
       " 'Subjects/Fazil/computed_Bulb02.csv',\n",
       " 'Subjects/Fazil/computed_Screw.csv',\n",
       " 'Subjects/Fazil/computed_Screw01.csv',\n",
       " 'Subjects/Fazil/computed_Screw02.csv',\n",
       " 'Subjects/Nikhil/computed_Bulb.csv',\n",
       " 'Subjects/Nikhil/computed_Bulb01.csv',\n",
       " 'Subjects/Nikhil/computed_Bulb02.csv',\n",
       " 'Subjects/Nikhil/computed_Screw.csv',\n",
       " 'Subjects/Nikhil/computed_Screw01.csv',\n",
       " 'Subjects/Nikhil/computed_Screw02.csv',\n",
       " 'Subjects/Abhishek/computed_Bulb.csv',\n",
       " 'Subjects/Abhishek/computed_Bulb01.csv',\n",
       " 'Subjects/Abhishek/computed_Bulb02.csv',\n",
       " 'Subjects/Abhishek/computed_Screw.csv',\n",
       " 'Subjects/Abhishek/computed_Screw01.csv',\n",
       " 'Subjects/Abhishek/computed_Screw02.csv',\n",
       " 'Subjects/Jai/computed_Bulb.csv',\n",
       " 'Subjects/Jai/computed_Bulb01.csv',\n",
       " 'Subjects/Jai/computed_Bulb02.csv',\n",
       " 'Subjects/Jai/computed_Screw.csv',\n",
       " 'Subjects/Jai/computed_Screw01.csv',\n",
       " 'Subjects/Jai/computed_Screw02.csv',\n",
       " 'Subjects/Deepti/computed_Bulb.csv',\n",
       " 'Subjects/Deepti/computed_Bulb01.csv',\n",
       " 'Subjects/Deepti/computed_Bulb02.csv',\n",
       " 'Subjects/Deepti/computed_Screw.csv',\n",
       " 'Subjects/Deepti/computed_Screw01.csv',\n",
       " 'Subjects/Deepti/computed_Screw02.csv',\n",
       " 'Subjects/Nirmayee/computed_Bulb.csv',\n",
       " 'Subjects/Nirmayee/computed_Bulb01.csv',\n",
       " 'Subjects/Nirmayee/computed_Bulb02.csv',\n",
       " 'Subjects/Nirmayee/computed_Screw.csv',\n",
       " 'Subjects/Nirmayee/computed_Screw01.csv',\n",
       " 'Subjects/Nirmayee/computed_Screw02.csv',\n",
       " 'Subjects/Ingy/computed_Bulb.csv',\n",
       " 'Subjects/Ingy/computed_Bulb01.csv',\n",
       " 'Subjects/Ingy/computed_Bulb02.csv',\n",
       " 'Subjects/Ingy/computed_Screw.csv',\n",
       " 'Subjects/Ingy/computed_Screw01.csv',\n",
       " 'Subjects/Ingy/computed_Screw02.csv',\n",
       " 'Subjects/Varun/computed_Bulb.csv',\n",
       " 'Subjects/Varun/computed_Bulb01.csv',\n",
       " 'Subjects/Varun/computed_Bulb02.csv',\n",
       " 'Subjects/Varun/computed_Screw.csv',\n",
       " 'Subjects/Varun/computed_Screw01.csv',\n",
       " 'Subjects/Varun/computed_Screw02.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for subj in subjects:\n",
    "    path = 'Subjects/'+subj\n",
    "    trial_names = ['Bulb','Screw'] # 'Bulb','Hammer' ,'Cup',\n",
    "#     trial_names = ['Bulb','Hammer' ,'Cup','Screw']\n",
    "    for exp in trial_names:\n",
    "        for i in range(3):\n",
    "            if(i==0):\n",
    "                files.append(path+'/computed_'+exp+'.csv')\n",
    "            else:\n",
    "                files.append(path+'/computed_'+exp+'0'+str(i)+'.csv')\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = []\n",
    "for file in files:\n",
    "    files_df.append(pd.read_csv(file,index_col=0))#[6500:85000])#[4500:85000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_df(files_df,order=1,cf=50,fs=2000):\n",
    "    emg_labels = ['EMG1','EMG2','EMG3','EMG4','EMG5','EMG6','EMG7','EMG8']\n",
    "    angle_lables = ['Pronation_Angle','Flexion_Angle',  'Radial_Angle','Elbow_Joint_Angle']\n",
    "    \n",
    "    for i in range(len(files_df)):\n",
    "        b,a = signal.butter(3, 3,fs=fs)\n",
    "        emg_df = pd.DataFrame(columns=emg_labels)\n",
    "        for labels in emg_labels:\n",
    "            emg_df[labels] = signal.lfilter(b, a,abs(files_df[i][labels]))\n",
    "        files_df[i][emg_labels] = np.array(emg_df)\n",
    "        \n",
    "        b,a = signal.butter(3, 3,fs=2000)\n",
    "        for labels in angle_lables:\n",
    "            files_df[i][labels] = signal.lfilter(b, a,files_df[i][labels])\n",
    "            \n",
    "    return files_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "emg_labels = ['EMG1','EMG2','EMG3','EMG4','EMG5','EMG6','EMG7','EMG8']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = filter_df(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Test Reps Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_rep_files = ['Subjects/Nikhil/computed_Bulb.csv',\n",
    "                  'Subjects/Abhishek/computed_Bulb.csv',\n",
    "                 'Subjects/Jai/computed_Bulb.csv']\n",
    "tr_range = [30000,70000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_df = []\n",
    "for file in files:\n",
    "    if file in test_rep_files:\n",
    "        df = pd.read_csv(file,index_col=0)\n",
    "        df = df.drop(df.index[tr_range[0]:tr_range[1]])\n",
    "    else:\n",
    "        df = pd.read_csv(file,index_col=0)\n",
    "    files_df.append(df)\n",
    "files_df = filter_df(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Only Removed Reps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\The Stuffs\\Awear\\Final_Project\\wrist_data_collection_AWEAR\\Data_preparation_Library.py:675: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  files_df[i][emg_labels] = np.array(emg_df)\n",
      "c:\\users\\moham\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1736: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value[:, i].tolist())\n"
     ]
    }
   ],
   "source": [
    "files_df = []\n",
    "files = test_rep_files\n",
    "for file in files:\n",
    "    df = pd.read_csv(file,index_col=0)\n",
    "    files_df.append(df[tr_range[0]:tr_range[1]])\n",
    "files_df = filter_df(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Augumentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gausian Noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcee83e2dbdd46de9f121ca7ede6ed97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=22.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_df_gausian = []\n",
    "for df in tqdm(files_df):\n",
    "    snr = choice(np.linspace(60,90,5))\n",
    "    emg_array = np.array(add__Gausian_noise(df[emg_labels],snr))\n",
    "    df[emg_labels] = emg_array\n",
    "    files_df_gausian.append(df)\n",
    "\n",
    "files_df = files_df+files_df_gausian\n",
    "len(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rearrange "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['EMG8', 'EMG1', 'EMG2', 'EMG3', 'EMG4', 'EMG5', 'EMG6', 'EMG7'],\n",
       " ['EMG2', 'EMG3', 'EMG4', 'EMG5', 'EMG6', 'EMG7', 'EMG8', 'EMG1']]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import deque\n",
    "rearr_labels = []\n",
    "labels = deque(emg_labels)\n",
    "labels.rotate(1)\n",
    "rearr_labels.append(list(labels))\n",
    "labels.rotate(-2)\n",
    "rearr_labels.append(list(labels))\n",
    "rearr_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4513317eeb9c4b5b89857e5d7fef30ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=48.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_df_rearrange = []\n",
    "for df in tqdm(files_df):\n",
    "    for lb in rearr_labels:\n",
    "        df[emg_labels] = df[lb]\n",
    "        files_df_rearrange.append(df) \n",
    "    \n",
    "files_df = files_df+files_df_rearrange\n",
    "len(files_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Prep Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 250\n",
    "interval = 25\n",
    "Normalize = True\n",
    "rms = False\n",
    "angle_thresh = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "636ed80df4924c8bba980b493a49225f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=48.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(90789, 250, 8) (90789, 2)\n"
     ]
    }
   ],
   "source": [
    "X_prosup, y_prosup = multiple_prep_data_prosup_bin(files_df,\n",
    "                                      window,\n",
    "                                      interval,\n",
    "                                      Normalize,\n",
    "                                      rms,\n",
    "                                      angle_thresh)\n",
    "print(X_prosup.shape,y_prosup.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard Normalization Parameter Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_prosup.reshape(X_prosup.shape[0]*X_prosup.shape[1],8))\n",
    "dump(scaler, open('standard_scaler_master.pkl', 'wb')) # standard_scaler_filtered_master"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TW Normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "‘‘time window normalization’’ (i.e., subtracting to\n",
    "each time window the mean and dividing it by the standard\n",
    "deviation) and ‘‘normalization based on training data’’ (i.e.,\n",
    "subtracting to all the time windows the training data mean and\n",
    "dividing them by the training data standard deviation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def variance(data, ddof=0):\n",
    "    n = len(data)\n",
    "    mean = sum(data) / n\n",
    "    return sum((x - mean) ** 2 for x in data) / (n - ddof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = np.zeros(X_prosup[0].shape)\n",
    "for x in X_prosup:\n",
    "    X_mean = X_mean + x\n",
    "X_mean = X_mean/X_prosup.shape[0]\n",
    "X_std = np.sqrt(variance(X_prosup))\n",
    "np.save('mean_std',np.array([X_mean,X_std]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = np.load('mean_std_master.npy')[0]\n",
    "X_std = np.load('mean_std_master.npy')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e57c984ad93f412e9eb66aa81aed4725",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1170.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "X = []\n",
    "for x in tqdm(X_prosup):\n",
    "    X.append((x-X_mean)/X_std)\n",
    "X_prosup = np.array(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(89881, 250, 8)\n",
      "(908, 250, 8)\n",
      "(89881, 2)\n",
      "(908, 2)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_prosup, y_prosup,test_size=0.01)\n",
    "for data in [X_train, X_test, y_train, y_test]:\n",
    "    print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_train',X_train)\n",
    "np.save('prepared_data/X_test',X_test)\n",
    "np.save('prepared_data/y_train',y_train)\n",
    "np.save('prepared_data/y_test',y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/test_X',X_prosup)\n",
    "np.save('prepared_data/test_y',y_prosup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count(y):\n",
    "    s0 = 0\n",
    "    s1 = 0\n",
    "    s2 = 0\n",
    "    for arr in y:\n",
    "        if arr[0]==1:\n",
    "            s0 = s0+1\n",
    "        if arr[1]==1:\n",
    "            s1 = s1+1\n",
    "#         if arr[2]==1:\n",
    "#             s2 = s2+1\n",
    "    ls = [s0,s1]\n",
    "    for i in range(2):\n",
    "        print(i,'-',ls[i],'[',ls[i]*100/len(y),'%]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 - 53969 [ 25.88292280540209 %]\n",
      "1 - 79223 [ 37.99445595457336 %]\n",
      "2 - 75320 [ 36.12262124002456 %]\n",
      "None\n",
      "0 - 13627 [ 26.14142111724985 %]\n",
      "1 - 19873 [ 38.12346531614487 %]\n",
      "2 - 18628 [ 35.73511356660528 %]\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(count(y_train))\n",
    "print(count(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('best_model_current.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = np.load('mean_std_master_norm.npy')[0]\n",
    "X_std = np.load('mean_std_master_norm.npy')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2af730fe34114aabb9357992d8c9da4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1170.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "X = []\n",
    "for x in tqdm(X_prosup):\n",
    "    X.append((x-X_mean)/X_std)\n",
    "X_prosup = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = np.load('prepared_data/test_X.npy')\n",
    "test_y = np.load('prepared_data/test_y.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 - 3735 [ 56 %]\n",
      "1 - 2904 [ 44 %]\n"
     ]
    }
   ],
   "source": [
    "count(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6639, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "208/208 [==============================] - 5s 11ms/step - loss: 0.0517 - accuracy: 0.9855\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.05170975252985954, 0.985539972782135]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "yt = []\n",
    "for e in test_y:\n",
    "    if e[0] == 1:\n",
    "        yt.append('Pronation')\n",
    "    else:\n",
    "        yt.append('Supination')\n",
    "yp = []\n",
    "pred = model.predict(test_X)\n",
    "for e in pred:\n",
    "    if e[0] == 1:\n",
    "        yp.append('Pronation')\n",
    "    else:\n",
    "        yp.append('Supination')\n",
    "\n",
    "matrix = confusion_matrix(yt, yp,labels=['Pronation','Supination'])\n",
    "\n",
    "group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
    "\n",
    "group_counts = ['{0:0.0f}'.format(value) for value in\n",
    "                matrix.flatten()]\n",
    "group_percentages = ['{0:.2%}'.format(value) for value in\n",
    "                     matrix.flatten()/np.sum(matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "sns.heatmap(matrix, annot=labels, fmt='', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 250, 8)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, 250, 32)      256         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D)    (None, 250, 8)       0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 250, 32)      40960       conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 250, 32)      20480       conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 250, 32)      10240       conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 250, 32)      256         max_pooling1d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 250, 128)     0           conv1d_1[0][0]                   \n",
      "                                                                 conv1d_2[0][0]                   \n",
      "                                                                 conv1d_3[0][0]                   \n",
      "                                                                 conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 250, 128)     512         concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 250, 128)     0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 250, 32)      4096        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 250, 128)     0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 250, 32)      40960       conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 250, 32)      20480       conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 250, 32)      10240       conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 250, 32)      4096        max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 250, 128)     0           conv1d_6[0][0]                   \n",
      "                                                                 conv1d_7[0][0]                   \n",
      "                                                                 conv1d_8[0][0]                   \n",
      "                                                                 conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 250, 128)     512         concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 250, 128)     0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 250, 32)      4096        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 250, 128)     0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 250, 32)      40960       conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_12 (Conv1D)              (None, 250, 32)      20480       conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_13 (Conv1D)              (None, 250, 32)      10240       conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_14 (Conv1D)              (None, 250, 32)      4096        max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 250, 128)     0           conv1d_11[0][0]                  \n",
      "                                                                 conv1d_12[0][0]                  \n",
      "                                                                 conv1d_13[0][0]                  \n",
      "                                                                 conv1d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_15 (Conv1D)              (None, 250, 128)     1024        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 250, 128)     512         concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 250, 128)     512         conv1d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 250, 128)     0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 250, 128)     0           batch_normalization_3[0][0]      \n",
      "                                                                 activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 250, 128)     0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_16 (Conv1D)              (None, 250, 32)      4096        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 250, 128)     0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_17 (Conv1D)              (None, 250, 32)      40960       conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_18 (Conv1D)              (None, 250, 32)      20480       conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_19 (Conv1D)              (None, 250, 32)      10240       conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_20 (Conv1D)              (None, 250, 32)      4096        max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 250, 128)     0           conv1d_17[0][0]                  \n",
      "                                                                 conv1d_18[0][0]                  \n",
      "                                                                 conv1d_19[0][0]                  \n",
      "                                                                 conv1d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 250, 128)     512         concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 250, 128)     0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_21 (Conv1D)              (None, 250, 32)      4096        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 250, 128)     0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_22 (Conv1D)              (None, 250, 32)      40960       conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_23 (Conv1D)              (None, 250, 32)      20480       conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_24 (Conv1D)              (None, 250, 32)      10240       conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_25 (Conv1D)              (None, 250, 32)      4096        max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 250, 128)     0           conv1d_22[0][0]                  \n",
      "                                                                 conv1d_23[0][0]                  \n",
      "                                                                 conv1d_24[0][0]                  \n",
      "                                                                 conv1d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 250, 128)     512         concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 250, 128)     0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d (Globa (None, 128)          0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 2)            258         global_average_pooling1d[0][0]   \n",
      "==================================================================================================\n",
      "Total params: 396,034\n",
      "Trainable params: 394,498\n",
      "Non-trainable params: 1,536\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "metrics_df = pd.read_csv('df_metrics.csv')\n",
    "best_model_df = pd.read_csv('df_best_model.csv')\n",
    "history_df = pd.read_csv('history.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>precision</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>recall</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977353</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>0.979682</td>\n",
       "      <td>24035.79844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision  accuracy    recall     duration\n",
       "0   0.977353  0.978112  0.979682  24035.79844"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Loss'}>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ygeeSJGn09fVFNnV0J8FlmCDTOBB2kuDTso2paEUFeUxLr4upLi9i/9oKppX3VzgbOmpTzKSi/DH+pMoZfX3QtALWPp0e5Xk6CTzrn4e+9MymkA/T9oNZR8Lh50PtgVCzECbPhfT/dB9vDEIZstdeew1MgZs7dy7PPPPMVscMnb42WAiBn/3sZ8O+9s1vfnNEbbfccstWbddee+3A44MOOojHHx++mMTtt9++VVtPTw8XXngh+fl+MUqStKt6+yKb2recbjZ0+tngEZyN7V30DlPlLC8wEFqqy4tYNKuK6kHVzarLk8f9r1sIQMNqW7/llLaGp5Jpbl2tm4+p2jsJOvufATUHJYFn2n5QUJy9fo8Cg9AYu//++/noRz+6Vfvb3va2ERVUGCsf//jHueuuu4YNV5IkTWSdPb1sbOseKCDQPy1t6FqbDenws7G9i7iNQmgVJZurme09tZTD956SDjObR3KmlRdTPWiNjcZQjElASDVDZwt0Nqcf999aBj1vgVTTlsdBMnWscFJyKyrb/LiwdNBtEhQNejz0tS3eWwp5I/if1P3reAZPaVv7FLSt23zMpKlQexAc9o4k7NQelExzK6kcnd9njjEIjbGjjz56uyNDueJzn/tctrsgSdKoizHS1tWbrn7WmYSZ1i1HbjYOGcFp7Rx+KlpegCmlm9fQ7F9bzpTSzdPPpqYDTf/rU0qLKCoYn1OKxo0Yob0RmldtDiwDoaZpmCAzzGN2VM47QHEFFFcmAaK4AkqnwZR5EPKguwO625L7jg3JfVc7dLcnj3u3v3xiWPnFgwLTkBAV8mHDi7Bx2ebj+9fx7H8G1By4+VZeM67W9GSaQUiSJO0x+voizanuzXvYDISaTja0dbOhrXOLUNO4neIBRfl5W1RFm1Ndmjwv3bqAQHVZEVWTCi3nnA2dLbBxebKAf+j9phVbTvkaKr94c3gpTt9Pnb/5cUnlMI8rt3xPUfnurZHp690civrvBwel7vZBt470621bHj/49d4umHkEHPbOPWIdz2jaI4JQjNE5sGMsbmuMX5KkDOrp7WNje/fAOpoNQwNO+5Zln7e1vgagrCifqen1NLWVJSycUUl1WRFThpR57i8kUFaU798XuaA7BU2vpMPNsi1DzsblySjLYEXlMHkOTJkL805OHlfNhkmTB43cVCWPc2HNS15+ul8V2e7JhDPug1BJSQmNjY1UV1f7ZTVGYow0NjZSUlKS7a5IksaxVHcva5pSrN7UwapNHaweuKVY09RBY1sXm9q3vRdf1aTCgfAyp7qUI+ZMTo/gbDkFrf9WUmjxn5zU25NMXdu0YvhRnZY1Wx6fXwST905uMw6DKXPSwWdOMvJROnVCT/fSyI37IDR79mxWrlzJunXrdnxwhqVSqQkbBkpKSpg9e3a2uyFJylExRta3dg2Em1XpgLN6Uwerm5K29a1dW7wnBKipKGbm5EkcUFeRjMykp6VNHTxaU1bElNJCCvKd6pOTYkymbnW1bi4c0Ln58d7L/wa33LQ57DSvgr5B665CHlTOToLNPq8eFHLS9+V1TvNSRoz7IFRYWDjsnjljYcmSJRx++OFZ+dmSJGVTqrt3YPRm9aYOVm4xotPB6qbUVmtvJhXmM2vKJGZOnsRBMyuZWZU8njVlErMmT6K2ssTiAdkSI/R0bg4uAyGmJR1i0gUEtmhvGaatFbpaIA6/7gpgPkB5bTKiM/tVMOXNW4adqtmQXzhmH10T17gPQpIkKTNijLR39bKpo5uN6WlpG9u7uPvlbu5seXKL4NPYNvxozqzJk1g0q4ozDqpj5uRJ6VsJsyZPompSodPYs6GvD1pWQ+ML6duLyX3z6s0Bp7Nly1GZbQl5UFSxeU1LcXlyXzlz+Pb+QgNF5QPtf33keU5+zRmj/7mlHTAISZK0B+rp7WNTRzeb2rvZ1N7FxnSo6X+8qb2LjW39ben7ju5tVlArffkVZqWDzaJZVcyaXDIQdBzNyRHtGwaFncGh50Xo6dh8XGEpVO+TjMj0B5WB8DI0uPSHmfTzwtLdXn/Tl79iNz+olBkGIUmSclxvX6SxtZO1Lck+N1sHmUFt6VDTktr2/90vyAtMLk3W2UwpLWLv6lIO3auKKaVFA+0Dr5cV8fxjD3LWaYsdzckFXW2w4aWtR3caX4COjZuPC/lJ1bTqfWHeKUnwmbZf8rxihsUEJAxCkiRlVVdPH2tbUtQ3pVjTlKKhOblPnndQ35RibUsnPdsoCV1RXMDkssKBEDN3Wln6ceHA/eDQM7m0kPLigp0KNasLgyFoLPV2JxXUhhvdaV615bEVM5OQc+A5Scjpv02Z4zobaQcMQpIkjZKOrl7qm5NQU9/cMSjgbA4861s7Gbo126TCfGZMLmFGVQnH7lPNjKoS6qomUVOxuWLa5NJkA89CK6ftvrZGWPoTePjHsOFFyCtMQkReQfq+EPILkrLN/Y+HPWaY94zkuBC2DD4bl225XqekCqr3g7knpYPOPsn91PnJlDVJu8QgJEnSToox0tLZQ0NTaotwU9/cMehxatg9cKomFTKjqoTayhIOnFFJXVXJQNCpqyyhrqqEypKdG7HRLujrg2V/hYd+BM/8Fnq7YPbRcNy/JCGkrycZmenrTva56etOPx/cnn7e3bGD4wa3b2NfpIISmLoP1BwIC9+w5eiO++JIo8IgJElSWl9fZGN7F2tbkvU461o6WduSYm1zJ+taO1nXnDxf19JJW1fvVu+fVl5EXVUJs6eU8qq5U6mrKqGusj/oJLfSIv/Tm1UtDfDo9fDwdbDxZSiZDEe9B464AGoPHP2fHyP09SbBqz8kxT4orXZvHGmM+W0sSdrjdfb0sm4g2GwOOesGhZy1zZ2sbx1+LU55cQE1FcVMqyhm0awqaipKqKsqpq5qUhJyKkuoqSymuCB/9D5ESz00rYTpByTVuzRyfb3w4l/goWvhuT8mozVzToRT/wMWng2FY7g5egjpaXb+CSZlm/8WSpLGrdbOnnQxgdQWQWfoSM5wU9RCgOqyIqZXlFBTUcz+tRXUVBRTU1GctFX2Py7OzihOTyes+Du8cHvyR3zDE/09T9aIzDgU6g5J7mccmkyf0paaVsIj1ye3plegdBocexkc8S6Ytm+2eycpywxCkqScE2OkOdWzReW0NenH/Wty6ptStHRuXSK6qCBvIMDMn17GsfOrmZ4OODWVxUwvT0JOdVkRBblUaCDGpCrYi7cn4WfZXdDdniym3/tYOO1TyXqRhqeg/jF45X544heb31+116BgdEjyuHLmxFtb0tsDz9+arP154bZk2tn8U+EfPgsHvA4KirLdQ0k5wiAkSRpTMUaaOroHAs3qQUGnflDYaR+yBicEmF5ezIyqEuZPL+OEfacNFB0YHHIqJ42jQgOpZnj5r5vDz6blSfvU+XDYO2Df1ySVwgZXBlt49ubH7RtgzdIkGK1ZCmseg2d/D6Sn95VOS0LR4NGjKfP2zLUoG5cl634euR5a66G8Dk78ABx+Pkydl+3eScpBBiFJUsbEGNnQ1rU51DSnqE8HmzWbkkpqa5o6SHX3bfG+vEB63U0JB9RVcMr+NQMFBmZUlTBjclI6etyXiu7rg/qlm6e7vXJfsl6lqBzmnQzH/2sSfqbOH9n5SqfCPqcmt36drck0ujXpcFS/FO755uZyzEUVm0eM+kPStAPG55qVni549nfJ6M9Ld0DIg31PhyO/BvudMT4/k6Qx4zeEJGnE2rt6WL2pg1WbUqze1MGaQY9XpwNPV8+WISc/LwyUhT5wZiWvWVBDXVUJMydPGgg608uLc2uaWia1rk1CT3/4aV+ftNcdAsdfngSf2UdnbspWcXkylW7vYze39XTC2qe3HDl66Fro6Uhezy+G2oMGjR4dmlRQK5yUmT5l2voX4OEfwaP/l/w+K2fD4ivg8HdC1exs907SOGEQkiQB0NsXWdfSyapNHUmwSd8GB52hRQfyAtRWJqHm4FlVnHFQXTKCk94XZ0ZVCdPKi8nPGydT1TKhpysZ6Xnhz8mUt/rHk/ay6Uno2ec1yQhOec3Y9amgGGYeltz69fUmm3euWbp5et2Tv0oCEkDITyrU1R3C3KYIJc8kJZ5Lp6Zv1cmtsHRs1iF1p+DpXyejP8v/lvTvgNfCke+GfV4NeaNYsU/SHskgJEkTREsqWZezZdDZ/Ly+KbVV6eiKkgJmTZ7EzMmTOGLOZGZOnjTwvH99zrifrpYJG15KRnz6ixx0tUJeAex1LLzmP5PwU3dIbq3NyUsHnekHwCFvTdpihE0rBq07egxevpM5LfWw/KfDnye/eOuANGlQUBquvahs5OFp7dNJ+HnsRujYCJPnJL/Tw94BFXWZ+V1ImpAMQpI0zvX09rGhvYv1LV00tiV74dz9Uhd/3vQ4q9OjOas2ddCS2rLCWkFeoLayhFmTJ3HUnCnMTAecgaAzuYTKksIsfaoc19kCL9+1ucjBxpeT9ilz4ZC3bS5yUFKZ1W7utBBgypzkduAbBprvvON2Fh99GHRsgPbG9G3Q444Nm5/XP5Fu28hA0Yah8osHhaMpQ0JT+nFXWzL1beX9SeW8ha9Pyl7POyW3AqWkccsgJEk5qLOnl8bWLta3JsFmfUsX61o7t2xLP9/Q3kUc5u/NyaVrmFk1idlTSjlm3tSBoNMfdqZXTLApa7sjxmRk4oXb4PnbYMW90NcNhWUw7yQ47n3J9KzqfbLd09ER8qGsOrmx38je09cLqabhg9PQ8NTw5PDhqXpf+Icr4dC3Q9m00fhkkiYwg5AkjZG2zp5BIaZrIOAMDjXrW5MNQIeO3vQrK8pnWkUx08qLmVtdxlFzpzKtvJjp5UVUlyft08qLeG7pA5x52qnDnkMjlGqGl5Yk4eeF26F5VdJecxAcdxnsexrsdUyy/kZby8vfPC1uV8JTbzfULJx4+yBJGjMGIUnKgJ7ePtY0pXhlQzsrNrSzPH2/amPHQMjp6O4d9r1VkwqZVl7EtPJiFs6s5OTyZLPP/sDT/9q08mImFY1sQfiKAv943GkxJiMTL9wGz/8ZXrk3KTldXAnzF8Pif0/CT+XMbPd0z7VFeJKk0WUQkqQRakl1s2JDO69saGd5YxJ0+p+v3NixRaGBgrzA7CmTmDVlEnOrpyRBpmJzwJmeDjZTy4ooKnC9Q9Z0bNpy1KdlTdJee3B6T5/TYa+jId+1UpK0pzEISVJab1+kvjnFisZ2VmxoSwedjuS+sY2NQ0pHTy4tZO+ppRw0q4qzDp7B3lNL2XtqKXtNLWVGVcmeuy/OeBZjUg3t+XTweeU+iL1QXJWUtN7v9KTCW+WMbPdUkjTKDEKSJpTWzp6BEZ3+aWz9t5Ub2+nu3Tyqk58XmDV5EntPLeXMRTOYU126RdipmuQowbjQsRFevCPZ1+eFP0NrQ9I+41A48QPJdLfZr4J8/5MoSROJ3/qS9hjdvX00NKeob0qxpmnz/ZqmDlY3pVi5oZ3Gtq4t3lNRUsCc6lIWzqjgHw6qZc7UsoGwM2Oye+SMWF8fxL7cCBN9fVC/NFnn88JtsPKBpG8lk5PKbv2jPhW12e6pJCmLcuC/WJK0Y109ScjpDzaDQ07/43WtnVuVkZ5UmM+MySXMqCrh9ANr2Wtq6RYjO5NLi7LzgUZDT2dS4rnhSWa/8iD87ZGk8lZvd1Lqubc7Wfw/8LwHers2Px56zPZe63/e/zj2JX3IK0w2yywqT+6Ly7d8PvB46PP+Y4dpH0lVtvYN8OJfNo/6tK1L2mceDid9OAk/M4/IjaAmScoJI/ovQgghH7gSeDdQAfwReF+MsWEbx5+ZPn4B8DLwuRjjTwa9/jrgt8O8da8Y48qd+QCSxr/Onl4amjpZ09SRDjcp6gc9XtOUYn1r51bvKyvKZ8bkScyoKuGAugrqqpLHyW0SdVUlVJYUEPbE8rut66Dh8WTzyvrHoeEJWP9cElSAfQFeHHR8XkESUvKLkjCQV5gUAMgrSN8XJu35RcnjgqIkhGx1TOGWjwe/FvKgpwM6W5PNMLv679ug/RXoatn8vLt95J91aLjqvxVXJPcbl8Gqh5IwNmlqspnpvqcloz7l0zP4S5ck7UlG+r/GPgW8C7gAaASuAn4BnDj0wBDCCcDvgf8B3gkcAXw3hFAQY7wufdgi4BHgrCFvX7uT/Zc0DqS6e1mxoZ2X17exvLGNVzYkIae+uYM1m1JbTVeDZMpaf6A5aGYldemAU1c1iZlVJdRVlVBRMgHW6PT2wIYXk7DTH3jqH9+8zgWgYibULYL9z4S6g6HuYO569DlOOvnUzWEl18JgX28ShoYLTV2tg54Pbm+DzkFhatMryeul1XDyR9OjPocnJZglSdqBHQahEEIR8H7g8hjjbem284CXQwjHxxjvGfKWjwD3xBj/Lf38mRDCPOAzwOAg9HiMsT4Dn0FSDugPO8vWt7GssY2X17ezvLGNZevbWNOc2mLKWmVJATPTIzkHz5qcDjhbjuSUF0/AKUyppmQfm/on0qM9jydT3XpSyet5BTB9QbLOpXZREn5qD4ay6q1O1VuwCopKx/gD7IS8/GREp7gi2z2RJE1QI/lL4zCS6XBL+htijMtCCMuAk4ChQWg/4NdD2h4B5oYQ9o4xriAJQj/dtS5LypZUdy+vpEd2ljW2sawxCT7LG9tZ3dSxRdiZUlrI3GllHDO/mrnVZcydVprcV5dRVToBRnK2J0bYtDwdeJ7YPNqzafnmYyZNSUZ3jnpPEnjqDoZpByRT1iRJ0m4bSRCanb5fNaR9NbDXMMcP1z43fV8TQlhFsnboyBDCUmA68ADw0RjjsyPptKTR0x92+kPOy41t6ZGd4cPOnOoyjp43lTnVpcybVmbYGaq7IxnVGZjW9kQy6tPZlD4gQPU+yZSuI86HukOS0Z7Kmbk3nU2SpD1IiENLLA09IIR3Aj+KMeYPaf8L8FKM8aIh7RcAPwDeAfySZPTnl8A84ARgPfAs8BuSggpFwCdI1hItijGuHXK+S4BLAGpra4+88cYbd+mDjobW1lbKy8uz3Q3tIcbyeurpi6zviKxp66OhLdLQ3pfc2iIbUpHB3wplhVBbmkdtaaC2LG/gcU1pHuVF/qG+LYVdTcx/6UfU1d9BIKmo1ptXQmv5HFrL59NaPpe2snm0ls+hL78k4z/f7ydlkteTMsnrSZm0revp1FNPfSjGeNT23juSEaEOIC9d7KBnUHsx0Db04Bjjj0MIc4BrgZ8AK4CvAN8CmmKMz4UQpgEbY0zqrYYQzk0fdz7w1SHnuwa4BuCoo46KixcvHkGXx8aSJUvIpf5ofBuN62lTexcvrmvjxXWtvDRw38ryxg56+jbHnapJhcydVslJe5Uyp7osGdmZVsbc6j2svPRY6OuDh38Ef/5UspD/Ve+BuSdC3cHkT5lHVV4eVWPQDb+flEleT8okrydl0u5cTyMJQq+k72cMegwwk62nywEQY/xsCOHzQA2wBjgb6AWWp19vHHJ8ewjhJYafaidpO3p6+1i5sWNI2EnuB1djK8wPzK0uY9+acs44qI7508uZP72M+dPKDDuZsmYp/PYDSSnnOSfC674KNQuy3StJkjSMkQShpUALcApwPUAIYS7Jup+/Dj04hPAvwL7pqnGr023nkFSSa00/vg6YH2Ncl369Atgf+N7ufBhpT9bU0c1L61p5cV1b+j4JPMsa2+ju3Ty6U11WxPzpZZx+YC3zp5exz/Ry9plezuwpkyjIz8viJ9iDpZrgL/8FD3wvKeX8pu/CIW9zjY8kSTlsh0EoxtgZQrgK+EoIYT3JXj9XAXfGGO9Nl9eeCmyIMXYBzwD/HUJ4EPgb8HaS/YROT5/yTqAZuC6E8NF0Hz5HsnboOqQJrLcvsio9uvPiFqGnbYsNRQvyAntXl7LP9HJevbBmIOzsM93RnTEVIzx+E/zpP6B1bTIN7tWfSCq+SZKknDbSjTo+ARSSjAgVAn8E3pd+7XjgDuBUYEmM8c8hhEtJNmGdCTwBnB1jvBMgxrgxhHAa8CWSktwFwG3Aq2OMqQx8JinnxRhZ3ZTi6dXNPFPfzNP1LSx9qZ21f/4jXT19A8dNLi1Mws6C6cxPh53508vYe2ophY7uZNe65+D3H4KX/5pUfHv7jTDriGz3SpIkjdCIglC6SMKH0rehry0BwpC27wPf3875niZZNyTt8dq7eniuoZWn1zTzzJok9Dyzppnm1ObaI3tNnUT1pDzOOmIO+0wvGwg9U8sc3ck5Xe1w11fg7v+BwtJkHdCR/5RsECpJksaNCbh1uzQ6Yoys3NjBM/UtSeipb+bpNS0sa2wb2HunrCifA+oqOPvQmSyYUcmBMyrYv7aCipLCdNWThdn9EKMp1QQv/gWevw16OuHgN8O+p0H+ONpv6Nk/wO8/Ck0r4JDz4B8+C+U12e6VJEnaBQYhaRe0dfbwbEM68Kxp4Zn65L6lc/Moz5zqUhbWVfLGw2ayoK6SA2dUMnvKJPLyJsgC+hhh/XPw3K3w/J9gxd+hrwdKqiCvAJ64CUqnJYHo0PNgxmG5W1xg43L447/Ds7+H6Qvg3b9LSmJLkqRxyyAkbUdfXzLK83R98xahZ/mG9oFRnvLiAhbUVXDO4bNYMKOCBXWVLKiroKx4Av7r1Z2CZX+D529NAtCm5Ul7zYFw3L/A/mfA7KOBCC/8GZb+BB78X7jv6iRgHHoeHPxWqJqV1Y8xoKcL/v4tuPNLSUg77dNw7GVQ4JRFSZLGuwn4l5o0vL6+yLMNLTy0fGN6alsLz9a30Joe5QkB5laXsXBGJeceMZsFdRUsTI/yhFwdyRgLTSvToz63wct3Qnc7FEyCeSfDCZfDfmfA5GG2CDvgtcmtYyM8+StYemOyCemfP52899C3w8KzoThLu4+//Ff43YeSUa0Fr4czvzD855AkSeOSQUgTVoyR5xpaufelRv7+YiP3vdzIxvZuACpKClhYV8m5R8xi4YxkhOeAugpKi/xXht4eWPlAetTnT7D2yaR98t5w2DuSUZ+5J0LhpJGdb9IUOOrC5Nb4Ijz2s2Sk6OZ/ht99EBa+IRkpmnfy2BQkaGmAP30CHv8ZTJ4D//iz5DNJkqQ9in/VacKIMfLC2nTweamR+17aQGNbFwCzJk/i1QtqOW6fao6ZN9VRnqHaNyRT2Z67FV68PRnFCfmw93Fw+meSUZ/pB+z+Gp/qfeDUK2Dxv8OKe5NA9OTN8NiNUDEDDnlrMlJUMwpFJfp64YEfwF8+Cz0pOPkjcOIHoag08z9LkiRlnUFIe6wYIy+uaxsUfBpZ35oEnxlVJZyy/3SO3aea4+ZXs9dU/9jdQozQ8MTmQgcrH4DYlxQ32P9M2O8fYJ9Xw6TJo/PzQ4A5xyW3134JnvtDMnXunm/B3d+AGYcmgWjRm6F8+u7/vJUPwe8+AGuWwrxTkpLY0/bb/fNKkqScZRDSHiPGyMvr27j3pQ38/aVG7n2pkXUtnQDUVhZz4r7TOG6fao6dX83eU0sd8Rmqqw1eujOZ8vb8bdC8KmmfcSic9OFketjMIyBvjDdyLSyBg96U3FrXJdXmlv4kqeJ263/AfqcnU+f2f21y7M7o2Ai3fwYe/CGU18Kb/xcOOjd3q9dJkqSMMQhp3IoxsryxfWDE596XGmloToJPTUUxx82vHgg+c6sNPsPauCwZ9Xnu1qTaW28nFJXD/MXJ9LR9T4fKGdnu5Wbl0+HYS5Nbw1PJlLnHfgbP/RGKq2DRm5L9ffY+dvthJsYkTP3p/4OODcn5Fl8BJZVj91kkSVJWGYQ0bsQYeWVDxxbBZ01TCoBp5cUcO3/qQPCZP63M4LM9Xe1Jhbb7v5s8n7oPvOo9yZS3OcdDQXFWuzcitQcm65Ne88mkWt3Snyah6KFrYcrcJBAd+jaYOn/L9zU8lVSDW3EPzH4VvO5XMOOQbHwCSZKURQYh5bSVG9v5+4uN3PvSBu59qZFVmzoAqC4r4tj51ek1PlPZZ3q5wWekVj4Ev7oEGl+Aoy+BY/45KVIwXuXlJ+uV9nk1dH4Vnv5NMtpz5xfhzi/AXscmU+f2+4dkv6J7r4LiCjj7f+Dw88d+qp8kScoJBiHlnA1tXfz8wVe48YFXeHl9GwBTSgs5dn417z1lPsfOr2a/GoPPTuvtTjYGveurSQW2C25JpsDtSYrL4bC3J7emlelS3DfCb/9t8zGHn59sjFpWnbVuSpKk7DMIKSfEGHnklU1c//fl/PbxNXT19HH03Km867g5HLtPNfvXVJCXZ/DZZWufhl+9N6mKdujb4bVfhJKqbPdqdFXNhpM+CCd+AFY/khSAmL8Y9j4m2z2TJEk5wCCkrGrv6uHXj67munuX8+TqZsqLCzjvVXvxjmPmcEBdRba7N/719SVTwW7/TDJa8tbr4MA3ZLtXYysEmHVEcpMkSUozCCkrXljbyvX3LucXD6+kJdXDgroKrjxnEeccPovyYi/LjNi4HG6+DJb/DQ44C87+BpTXZLtXkiRJOcG/ODVmunv7uO2pBq6/dzn3vNhIYX7grINncP6xczhyzhTX/GRKjPDI9fDHK5Lnb/w2HPYO98aRJEkaxCCkUVfflOIn96/gJ/evYG1LJ7MmT+IjZxzA2161F9PKx0GZ5vGkdS38+nJ47g8w50Q45yqYMifbvZIkSco5BiGNihgj97zYyHV/X85tTzfQFyOn7D+dzx87h8UH1JCfq4UPGl+El5bA/mcki+3Hk6d+nVRH62yFMz4Hx1xqaWhJkqRtMAgpo5rau7np4ZXccN9yXlrXxpTSQi46aR7vOHoOe1eXZrt729bXC/d+B/7yWehJwe/zkn1njnw37Hs65Ofwvyodm+APH4PHboQZh8KbroGaBdnulSRJUk7L4b/uNJ48vrKJ6+9dzi1LV5Hq7uOIvSfztbceylkHz6CkMD/b3du+9c/DLe+DV+5Ligqc9GF49vfwyHXw3B+TPXcOPx+OOB8m753t3m7pxTuSvrfUwykfg5M/AvmF2e6VJElSzjMIaZelunv57WNruO7e5Sx9ZROTCvN50+GzeMcxc1g0axzsUdPXm5SW/suVUFAC534PDn5LUlRg9pGw+N/huVvhoWvhr19Obvu+Jhkl2v/M7AaOrnb486fg/u9C9X7wntuSPkuSJGlEDELaacvWt3HDfcv5+UMr2dTezT7Ty/jU2Qdy7pGzqSwZJ6MRQ0eBXv/fUFG35TH5hbDw9clt04qkEtvD18FP3wnltUkltiMugKnzxrbvKx+CX10CjS/AMf8Mr/kkFOXwtENJkqQcZBDSiPT09vGXZ9Zy/X0r+Otz6yjIC5xxUB3vPHYOx86fOn5KX29vFGh7Ju8Np34cTv4ovPDnZJTo7q/D374G80+FI98FB7wOCopGr++93XDnl+Curyah7YJbYP7i0ft5kiRJezCDkHboj0+s4TO/eYrVTSnqKkv4wGn7c97Re1FbWZLtru2c9c8nG4yuvH/bo0A7kl8AB5yZ3JpWJaNEj1wHP383lE6Dw98BR7wLqvfJbN/XPpOMAq1ZCoe+Hc78AkyanNmfIUmSNIEYhLRdDy3fwOU/eZT9asv5z7MP4rSFNRTkj7OSzLs6CrQjVbNg8cfg5A/Di39JRonu+Rbc/Q2Ye1Kylmjh2VCwG3sl9fUlfb/9M1BcDm+9Dg58w+71W5IkSQYhbdvKje2897qHmDm5hBsuOobJpaM47Wu0ZGIUaEfy8mG/05NbS316LdGP4BfvgUlT4bB/TEaJpu+/c+fduDzp+/K/wf6vhTf8D5TXZLbvkiRJE5RBSMNq6+zhoh89SGdPHzde8qrxF4JGaxRoRyrqkhGiEz8IL92RBKL7roa/fwv2Pj4ZJTrwDVA4advniDEJU3+8Inn+xm8nhRnGyzosSZKkccAgpK309UU+8NNHea6hhWv/6Wj2rSnPdpd2zrrn4JbLYOUDSQGD1/83VNSObR/y8pJS2/u+BlrXwqP/l0yd+9Ul8IePwqHnJaNEtQdu+b7WtfCb9yf7GM05Ec65CqbMGdu+S5IkTQAGIW3lK396lj891cAnzz6Qk/efnu3ujFxfL/z928koUOGksRsF2pHyGjjx3+D4y2HZXUkgeuAHyUjRXsckgeigNzFt3d/hqguhsxXO+Bwcc2kSqCRJkpRxBiFt4VePrOSqJS/y9qP34t3Hz812d0YuF0aBdiQvD+afktza1sPSnySh6JbL4PcfZlF3O8w4FN50DdQsyHZvJUmS9mgGIQ14eMVGPvaLxzlm3lQ+/YZF42NvoFwdBdqRsmlw/L/Ccf8Cy++BpT/hpU0w/53/nWzkKkmSpFFlEBIAqzZ1cMmPH6KusoSr33kkRQXjYErWeBgF2pEQYO4JMPcEVixZwnxDkCRJ0pgwCIn2rh4u/tGDdHb38pOLj2FKWY5XiOvrTaqw/eW/oKgUzv0+HPzm3B8FkiRJUs4wCE1wfX2RD/50Kc/UN/ODd7+K/Worst2l7dsTRoEkSZKUdQahCe6///wcf3yynk+8biGnHpDDm3U6CiRJkqQMMghNYLc8uopv/uUF3nrUbN5z4rxsd2fbHAWSJElShhmEJqhHX9nER256jKPnTuXKcw7OzQpxjgJJkiRplBiEJqA1TR1c/OMHqako5jvvPCL3KsTFCC/dAbd/FlY/7CiQJEmSMs4gNMF0dPVy8Y8fpL2zh+vfcwLV5cXZ7tJmMcKLt8OSL8LK+6FylqNAkiRJGhUGoQmkry/y4Z8v5cnVzXz/gqM4oC5HKsTFCC/8GZZ8AVY9CJWz4XVfhcPPh4IcCmqSJEnaYxiEJpBv3P48v3t8DR8/awGvWZgD08xihOf/BHd+EVY9BFV7JVPgDnuHAUiSJEmjyiA0Qfz2sdV84/bn+X9HzObik+ZntzMxwnO3wp1fgNWPwOS94exvwKH/CAU5vpmrJEmS9ggGoQngsZWb+NDPlnLUnCl87txF2asQFyM8+4dkBGjNozB5Drzhm3Do2yG/MDt9kiRJ0oRkENrD1TeluPjHDzKtvJirzz+S4oL8se9EjPDM75IAVP8YTJkHb/w2HPI2A5AkSZKywiC0B0t193LJdQ/SkurhF5cez7SxrhDX1wfP/Bbu/BI0PJ4EoHO+Awe/FfK99CRJkpQ9/jW6h4ox8pGbHuPxVU1cc/5RLJxROXY/vK8Pnv41/PXL0PAETN0HzrkaDn6LAUiSJEk5wb9K91Df/MsL/Gbpaj565gGcfuAYVYjr64Onbk4C0NqnoHo/eNM1sOj/GYAkSZKUU/zrdA/0h8fX8LXbnuPcw2dx6Sn7jP4P7OtNAtCdX4J1z8C0/ZONUBedC3lZWJMkSZIk7YBBaA/zxKomPvCzRzl878l87tyDR7dCXF8vPPmrJACtfxamL4D/9wM46E0GIEmSJOU0g9AeZG1ziot+9CBTS4u45vyjKCkcpTDS1wtP/CKZArf+OZi+EN78QzjwHMjLG52fKUmSJGWQQWgPkeru5eLrHqKpo5ubLj2O6RWjUCGutweeuCkJQI0vQM1B8JYfwcI3GIAkSZI0rhiE9gAxRj72i8dY+somrn7nkRw0syqzP6C3Bx7/eRKANrwItQfDW6+DBa83AEmSJGlcMgjtAa5a8iK3PLqaD//D/py5qC5zJ+7phEf/D+7+OmxcBnUHw9tugAPOMgBJkiRpXDMIjXN/fKKeL9/6LG88bCbvO3XfzJy0qw0e+hHc801oWQ0zj4AzPpcEoNEsviBJkiSNEYPQOPbk6iY+8NNHOXSvyXzx/x2y+xXiOjbBA9+De78D7Y0w9yQ45yqYv9gAJEmSpD2KQWicWtuS4uIfPcjk0kK+d/6Ru1chrm093HsV3P896GyG/f4BTvow7H1M5josSZIk5RCD0DiU6u7lvdc9xIb2Lm765+OpqSzZtRM1rYK/fwseuha6O+DAN8BJH4IZh2a0v5IkSVKuMQiNMzFGPv7Lx3lkxSauescRLJq1CxXiNrwEf/t6Uggh9sEhb4MTPwDT9894fyVJkqRcNKLSXyGE/BDC50MIa0IIrSGEm0IItds5/swQwoPpYx8PIbx9yOulIYRrQgjrQwibQgjfCyGU7+6HmQiuvvMlfvnIKj5w2v6cdfCMnXvz2qfhFxfDN4+EpTfCERfA5Y/Am75jCJIkSdKEMtIRoU8B7wIuABqBq4BfACcOPTCEcALwe+B/gHcCRwDfDSEUxBivSx/2XeBI4PVAIfC/6bZ37OoHmQiWN7bxpVuf4fWHzODy1+xEhbjVj8BfvwLP/BYKy+DYy+D4f4WKDJbaliRJksaRHQahEEIR8H7g8hjjbem284CXQwjHxxjvGfKWjwD3xBj/Lf38mRDCPOAzwHUhhFnAPwKviTHemz7fRcAdIYSPxhhXZeKD7YleWNtKjHDhifNGViFu2d1w11fhxduhpApO+Rgc889QOnX0OytJkiTlsJGMCB0GVABL+htijMtCCMuAk4ChQWg/4NdD2h4B5oYQ9gaOBfqAuwe9fjfQSzLC9NMR936CqW9OAVC3veIIMcILt8NdX4EVf4ey6XDap+Co90BJ5dh0VJIkScpxIwlCs9P3Q0dqVgN7DXP8cO1z0/c16fOtjTF2978YY+wJIazdxvmU1tCUIgSYXlG89Yt9ffDMb5IRoDVLoXI2vPZLcPj5UFQ69p2VJEmScthIglAp0Dc4uKR1AsMNTVwH/CCE8Gvgl8Ai4MPp14rS50sN875tnU9pDc2dTCsvpjB/UI2L3h544ia462uw/lmYug+84VtJJbiCoux1VpIkScphIwlCHUBeuthBz6D2YqBt6MExxh+HEOYA1wI/AVYAXwG+BTSlzzfMkMbw5wshXAJcAlBbW8uSJUtG0OWx0draOqb9eWpZirIQWbJkCaGvm7r629l7xS+ZlGqgtWwOKxZ+iLU1J0BzPvxt6IxF5bqxvp60Z/N6UiZ5PSmTvJ6USbtzPY0kCL2Svp8x6DHATLaeLgdAjPGzIYTPk0yFWwOcTbIGaHn6HDUhhPwYYy9ACKEgfexW54sxXgNcA3DUUUfFxYsXj6DLY2PJkiWMZX++8OhfObA6sLhoabIRassamHUknPR1yvc/kwPz8jhwzHqjTBvr60l7Nq8nZZLXkzLJ60mZtDvX00iC0FKgBTgFuB4ghDCXZN3PX4ceHEL4F2DfdNW41em2c0gqybWGEO5O/9zjgL+l33YiyZ5Gdw89nzbrbVrNF1s/DssbYe5J8KarYd4pMJIKcpIkSZIG7DAIxRg7QwhXAV8JIawH1pLsI3RnjPHedHntqcCGGGMX8Azw3yGEB0mCzttJ9hM6PX2+VSGEn5GsI7oQCMD3gOssnb1tqe5eTu1aQkVhI1zwa5h/Sra7JEmSJI1bI91Q9RMkG59en77/I/C+9GvHA3cApwJLYox/DiFcSrIJ60zgCeDsGOOdg853EfBNko1Xe4CbgH/bnQ+yp2toTvG6/PvYULWIqYYgSZIkabeMKAiliyR8KH0b+toSklGdwW3fB76/nfO1Av+UvmkENq58nsPyXuKl+R/D7VAlSZKk3ZO340OUCwqfS/aoDQe+Mcs9kSRJksY/g9A4MX3FH1naN5/qvfbPdlckSZKkcc8gNB5sXE5Ny5PcxrFUFI90WZckSZKkbTEIjQdP3QLAQ+WLCZbKliRJknabQWg8eOpmXijYjzh572z3RJIkSdojGIRy3aYVsOoh/sSx1FWWZLs3kiRJ0h7BIJTr0tPibuo4ktoqg5AkSZKUCQahXPfkzfTUHspLvTWOCEmSJEkZYhDKZZtWwKoHaZzzWgCDkCRJkpQhBqFclp4W92LNaQBOjZMkSZIyxCCUy568GWYcyrLeWsARIUmSJClTDEK5atMrsOpBOPAc6ptThADTK4qz3StJkiRpj2AQylXpaXEcdA4NTSmmlRdTmO8/LkmSJCkT/Ms6Vz11M9QdAlPnU9+cclqcJEmSlEEGoVzUtBJWPgAHnQNAQ3OKWoOQJEmSlDEGoVzUPy3uwHMAkhGhKtcHSZIkSZliEMpFT94MdQdD9T6kunvZ1N7t1DhJkiQpgwxCuaZpJay8Hw56E5BMiwOoMQhJkiRJGWMQyjVP/Tq5758W15QEIUeEJEmSpMwxCOWaJ381MC0OkvVBAHVVBiFJkiQpUwxCuaR/Wlx6NAhgbXMngFXjJEmSpAwyCOWS/mlx6fVBkIwITSrMp7KkIEudkiRJkvY8BqFc8tTNULt5Whz0l84uIYSQvX5JkiRJexiDUK5oWgWv3AcHvXGL5oamFLWV7iEkSZIkZZJBKFc83V8t7k1bNNc3p6wYJ0mSJGWYQShXPHkz1C6CafsONMUYWdvcSa0V4yRJkqSMMgjlgubV8Mq9W1SLA9jQ1kVXb58jQpIkSVKGGYRywUC1uHO2aB7YQ8ggJEmSJGWUQSgXPHVzelrcfls0N6SDkFPjJEmSpMwyCGVb82pY8fetpsUB1Dclm6k6IiRJkiRllkEo27YxLQ6SqXEhwPQKy2dLkiRJmWQQyranboaag7aaFgfJHkLVZcUU5vuPSZIkScok/8LOpuY1sOLeYUeDIL2HUJWjQZIkSVKmGYSy6elfA3HY9UGQFEtwfZAkSZKUeQahbHryZqg5EKbvP+zLDc0pag1CkiRJUsYZhLKlpX6b1eIAUt29bGzvdkRIkiRJGgUGoWx5Kj0tbhvrg9Y2J6Wz3UNIkiRJyjyDULY8dTNMXwjTDxj25fr0ZqqOCEmSJEmZZxDKhpZ6WH4PHPSmbR4yEIQcEZIkSZIyziCUDTuYFgfJHkKAxRIkSZKkUWAQyoYdTIuDZERoUmE+lSUFY9cvSZIkaYIwCI21gWlx52z3sGQz1RJCCGPTL0mSJGkCMQiNtad/w/Y2Ue3X0JSitrJ4TLokSZIkTTQGobH25M0wfQHULNjuYfVupipJkiSNGoPQWGppgOV373A0KMbI2uZOS2dLkiRJo8QgNJae3nG1OIANbV109fY5IiRJkiSNEoPQWHrqFph2ANQs3O5hDc2dgHsISZIkSaPFIDRWWtcm0+J2MBoE0NDsHkKSJEnSaDIIjZWnfw2xDw560w4PrU8HIUeEJEmSpNFhEBorT948omlxAPVNKUKAmgrLZ0uSJEmjwSA0FnZiWhwkU+Oqy4opzPcfjyRJkjQa/Et7LPRPi9tB2ex+9c0p6qocDZIkSZJGi0FoLDx5M0zbf0TT4iCZGuceQpIkSdLoMQiNttZ1mzdRDWFEb2loTlkxTpIkSRpFBqHRNlAt7pwRHZ7q7mVje7cjQpIkSdIoMgiNtqduhur9oObAER2+Nr2Zaq2lsyVJkqRRYxAaTa3rYNnfktGgEU6LG9hDyBEhSZIkadQYhEbTM7/ZqWpxsDkIuUZIkiRJGj0GodH05M1QvS/UHjTitzQ0OSIkSZIkjbYRBaEQQn4I4fMhhDUhhNYQwk0hhNrtHP/qEML9IYS2EMKLIYSPhrB5blgI4XUhhDjMbXYmPlROaF0Hy+6Cg9404mlxkIwIlRTmUTmpYBQ7J0mSJE1sIx0R+hTwLuAC4GRgNvCL4Q4MIewL/DZ9Oxj4GPBJ4LJBhy0CHgFmDLmt3tkPkLN2YVocJKWz6ypLCDsRniRJkiTtnB0OO4QQioD3A5fHGG9Lt50HvBxCOD7GeM+Qt5wJdMQYP5N+/lII4a3AGcC3022LgMdjjPWZ+BA5aRemxYF7CEmSJEljYSQjQocBFcCS/oYY4zJgGXDSMMevA6aGEN4eQsgLISwiGUV6cNAxi4Cnd6nH40Hb+mRa3E5sotqvvjlFnaWzJUmSpFE1kiDUv25n1ZD21cBewxz/C+AHwA1AF/A4cCdwJSTrjYAFwJEhhKUhhNUhhFtCCAfsQv9z09O/2alNVPvFGGlo7rRQgiRJkjTKRrIivxToizF2D2nvBIb7i30yMAf4EvBTknVCXydZJ/RJYJ/0+4qBi4Ei4BPAXSGERTHGtYNPFkK4BLgEoLa2liVLloygy2OjtbV12P4csvSHlEyayf1Pr4dntn59W1q6Il09fbSsXcmSJQ2Z66jGhW1dT9Ku8HpSJnk9KZO8npRJu3M9jSQIdQB5IYSCGGPPoPZioG2Y478I9MYY/z39/JEQQgFwdQjhf2KMz4UQpgEbY4x9ACGEc4EVwPnAVwefLMZ4DXANwFFHHRUXL1488k83ypYsWcJW/WlrhDufgBP/jcWnnrpT53tqdTP85S5OPHIRiw+ekbmOalwY9nqSdpHXkzLJ60mZ5PWkTNqd62kkU+NeSd8P/ct8JltPlwM4li3XAwHcBxQCewPEGBv7Q1D6eTvwEsNPtRtfnvkNxN6drhYHSaEEcDNVSZIkabSNJAgtBVqAU/obQghzgbnAX4c5fiVwyJC2RUAf8GII4ZwQQksIYfqg81UA+wNP7kznc9KTN8PU+VB38E6/tT4dhCyWIEmSJI2uHQahGGMncBXwlRDCmSGEI4AbgTtjjPeGEIpCCHXpMtsA3wBeH0L4RAhhfgjh9cDXgKtijM0khROagetCCIekz/dzYD1wXeY/4hhqa4SX/7pL1eIA6ptShAA1FcWZ75skSZKkASPdUPUTJFXgrgfuAJYDb06/djywJn1PjPH3wLnAOcBjJIUSvgt8MP36RuA0oJukJPcSkrVGr44xpnbr02Rb/7S4g960S29vaE5RXVZMYf5I/7FIkiRJ2hUjKZZAukjCh9K3oa8tAcKQtpuBm7dzvqeBs0fezXFiN6bFQTI1rrbS0SBJkiRptDn0kCm7OS0Okqlx7iEkSZIkjT6DUKY889v0tLhzdvkUDc0pai2UIEmSJI06g1CmPHUzTJkHdUML5o1MZ08vG9u7HRGSJEmSxoBBKBPaN8BLdyajQbs4LW5tcyeAQUiSJEkaAwahTOifFrcLm6j2699DyKlxkiRJ0ugzCGXCkzfDlLkw49BdPkV9U3ozVUeEJEmSpFFnENpd7Rvg5Tt3q1ocJIUSwCAkSZIkjQWD0O565nfQ17Nb1eIgGREqKcyjctKItnaSJEmStBsMQrvryV/B5Dkw47DdOk19c7KHUNiNUSVJkiRJI2MQ2g0F3S3JtLiD3rRb0+IgvYeQ0+IkSZKkMWEQ2g3T1t+XkWlxkB4RsmKcJEmSNCYMQrth+rq7MzItLsZIQ3OnhRIkSZKkMWIQ2lXtG5iycelubaLab2N7N109fU6NkyRJksaIQWhXPft78nZzE9V+/XsIGYQkSZKksWEQ2lVP3kxHSQ3MPHy3TzWwh1BV8W6fS5IkSdKOuWnNrtrrGFYyh/0yUO66Pwg5IiRJkiSNDUeEdtUpH2HV7LMzcqr6dBCqqTAISZIkSWPBIJQDGppTTCsvoqjAfxySJEnSWPAv7xxQ3+RmqpIkSdJYMgjlgHr3EJIkSZLGlEEoBzQ0p6itMghJkiRJY8UglGWdPb1saOtyREiSJEkaQwahLFvb3AlgEJIkSZLGkEEoy/pLZzs1TpIkSRo7BqEsq29KgpAjQpIkSdLYMQhlWUOzQUiSJEkaawahLKtvSlFckEflpIJsd0WSJEmaMAxCWVbfnKKuqoQQQra7IkmSJE0YBqEsa2hOUeu0OEmSJGlMGYSyrKG50/VBkiRJ0hgzCGVRjHFgapwkSZKksWMQyqJN7d109fQ5NU6SJEkaYwahLKq3dLYkSZKUFQahLBoIQlXFWe6JJEmSNLEYhLKooSkJQk6NkyRJksaWQSiL+keEaioMQpIkSdJYMghlUUNzimnlRRQV+I9BkiRJGkv+BZ5F9U1upipJkiRlg0Eoi+rdTFWSJEnKCoNQFjU0p6gxCEmSJEljziCUJZ09vWxo63JESJIkScoCg1CWrG3uBNxDSJIkScoGg1CWNDS7h5AkSZKULQahLOnfQ6iuyiAkSZIkjTWDUJbUN6WDkCNCkiRJ0pgzCGVJQ3OK4oI8qiYVZrsrkiRJ0oRjEMqS+uZO6qpKCCFkuyuSJEnShGMQypKGppSFEiRJkqQsMQhlSX1zyvVBkiRJUpYYhLIgxpgEISvGSZIkSVlhEMqCTe3ddPX0OTVOkiRJyhKDUBYM7CFkEJIkSZKywiCUBZs3Uy3Ock8kSZKkickglAUN6c1UayocEZIkSZKywSCUBf0jQq4RkiRJkrLDIJQFDc2dVJcVUVTgr1+SJEnKBv8Sz4KGZjdTlSRJkrLJIJQF9U3uISRJkiRl04iCUAghP4Tw+RDCmhBCawjhphBC7XaOf3UI4f4QQlsI4cUQwkdDCGHQ66UhhGtCCOtDCJtCCN8LIZRn4gONB44ISZIkSdk10hGhTwHvAi4ATgZmA78Y7sAQwr7Ab9O3g4GPAZ8ELht02HeBE4HXA2cDi9Nte7zOnl4a27rcQ0iSJEnKoh0GoRBCEfB+4OMxxttijA8D5wEnhBCOH+YtZwIdMcbPxBhfijHeBPwOOCN9vlnAPwKXxRjvjTHeBVwEvD392h5tbXMn4B5CkiRJUjaNZEToMKACWNLfEGNcBiwDThrm+HXA1BDC20MIeSGERSSjSA+mXz8B6APuHvSeu4FeklGiPVqDpbMlSZKkrBtJEJqdvl81pH01sNcwx/8C+AFwA9AFPA7cCVw56HxrY4zd/W+IMfYAa7dxvj1K/x5CFkuQJEmSsqdgBMeUAn2Dg0taJzDcX/OTgTnAl4CfkqwT+jrJOqFPps+XGuZ9w54vhHAJcAlAbW0tS5YsGUGXx0Zra+tO9+fuZcmv8YXHHqL+mbCDozWR7Mr1JG2L15MyyetJmeT1pEzanetpJEGoA8gLIRSkR276FQNtwxz/RaA3xvjv6eePhBAKgKtDCP+TPt9wC2SGPV+M8RrgGoCjjjoqLl68eARdHhtLlixhZ/tz9++eoviF5bzu9MUMKqQn7dL1JG2L15MyyetJmeT1pEzanetpJFPjXknfzxjSPpOtp8sBHMvm9UD97gMKgb3T56sJIeT3v5gOSjXbON8epb65k7qqEkOQJEmSlEUjCUJLgRbglP6GEMJcYC7w12GOXwkcMqRtEUmBhBdJCiMUAMcNev3EdF/uZg/X0JSitsL1QZIkSVI27XBqXIyxM4RwFfCVEMJ6kqIGVwF3xhjvTZfXngpsiDF2Ad8AfhtC+ATwf8CBwNeAq2KMzUBzCOFnwA9CCBcCAfgecF2McQKMCKU4dK/J2e6GJEmSNKGNdEPVT5BUgbseuANYDrw5/drxwJr0PTHG3wPnAucAj5EUSvgu8MFB57sIuAf4PXAL8Bfg0l3+FONEjJH65hR1le4hJEmSJGXTSIol9Je3/lD6NvS1JSSjOoPbbgZu3s75WoF/St8mjKaObrp6+txDSJIkScqykY4IKQPcQ0iSJEnKDQahMVTflA5CjghJkiRJWWUQGkMN6REhp8ZJkiRJ2WUQGkP1TZ2AQUiSJEnKNoPQGKpvTlFdVkRRgb92SZIkKZv8i3wMNTSnHA2SJEmScoBBaAzVN6WsGCdJkiTlAIPQGHJESJIkScoNBqEx0tnTS2Nbl6WzJUmSpBxgEBoja5uTinF1VcVZ7okkSZIkg9AY6d9DqMYRIUmSJCnrDEJjpD4dhJwaJ0mSJGWfQWiMNPRPjTMISZIkSVlnEBojDc0pigrymFxamO2uSJIkSROeQWiM1DelqKssIYSQ7a5IkiRJE55BaIzUN6ecFidJkiTlCIPQGGloTlFbZRCSJEmScoFBaAzEGNNT49xDSJIkScoFBqEx0NTRTWdPH7VOjZMkSZJygkFoDAzsIeTUOEmSJCknGITGQH2Tm6lKkiRJucQgNAYa0iNCTo2TJEmScoNBaAzUN3UCUGOxBEmSJCknGITGQH1ziqllRRQX5Ge7K5IkSZIwCI2JhuaU0+IkSZKkHGIQGgMNze4hJEmSJOUSg9AYaGhOWTpbkiRJyiEGoVHW1dPH+tYup8ZJkiRJOcQgNMrWtriHkCRJkpRrDEKjbGAPIafGSZIkSTnDIDTK+vcQckRIkiRJyh0GoVFW3+zUOEmSJCnXGIRGWUNziqKCPCaXFma7K5IkSZLSDEKjrL4pRV1lCSGEbHdFkiRJUppBaJTVN6ecFidJkiTlGIPQKGtoTlkxTpIkScoxBqFRFGOkvilFbUVxtrsiSZIkaRCD0Chq6uims6ePOkeEJEmSpJxiEBpF/aWza10jJEmSJOUUg9AoamhOb6bqiJAkSZKUUwxCo6ihyc1UJUmSpFxkEBpF/VPjaiotliBJkiTlEoPQKKpvTjG1rIjigvxsd0WSJEnSIAahUdTQlLJQgiRJkpSDDEKjqL45RZ3T4iRJkqScYxAaRQ3NKSvGSZIkSTnIIDRKunr6WN/a5dQ4SZIkKQcZhEbJ2hZLZ0uSJEm5yiA0ShrSpbNrnRonSZIk5RyD0Cipb+oEHBGSJEmScpFBaJT0b6bqGiFJkiQp9xiERklDc4qigjymlBZmuyuSJEmShjAIjZKG5hS1lcWEELLdFUmSJElDGIRGSX1TyvVBkiRJUo4yCI2SZETIICRJkiTlIoPQKIgxUt/siJAkSZKUqwxCo6C5o4dUdx917iEkSZIk5SSD0CiwdLYkSZKU20YUhEII+SGEz4cQ1oQQWkMIN4UQardx7JIQQtzG7eT0Ma/bxuuzM/nhsqU/CDkiJEmSJOWmghEe9yngXcAFQCNwFfAL4MRhjj0XKBr0PA/4LdAM3JNuWwQ8Apw15L1rR9ifnNbQlA5CjghJkiRJOWmHQSiEUAS8H7g8xnhbuu084OUQwvExxnsGHx9j3DDk/R8D5gMLYow96eZFwOMxxvoMfIac0z8iVFNZnOWeSJIkSRrOSKbGHQZUAEv6G2KMy4BlwEnbe2MIoQ74BPDxIaFnEfD0TvV0HKlvTjG1rIjigvxsd0WSJEnSMEYShPrX7awa0r4a2GsH7/0YyXS37/Y3hBDygQXAkSGEpSGE1SGEW0IIB4ywzzmvock9hCRJkqRcNpI1QqVAX4yxe0h7J7DNv/ZDCBXAhcBHY4y9g17aJ/2+YuBikvVEnwDuCiEsijGuHXKeS4BLAGpra1myZMkIujw2Wltbh+3PC6s7qCoKOdVX5b5tXU/SrvB6UiZ5PSmTvJ6USbtzPY0kCHUAeSGEgkFrfCAJMm3bed8b0+e/YXBjjPG5EMI0YGOMsQ8ghHAusAI4H/jqkOOvAa4BOOqoo+LixYtH0OWxsWTJEobrT9vfbuO4ebUsXnzI2HdK49a2ridpV3g9KZO8npRJXk/KpN25nkYyNe6V9P2MIe0z2Xq63GBvBH4bY2wd+kKMsbE/BKWftwMvseOpdjmvu7eP9a1dTo2TJEmScthIgtBSoAU4pb8hhDAXmAv8dTvvOxH4y9DGEMI5IYSWEML0QW0VwP7AkyPqdQ5b29IJuIeQJEmSlMt2GIRijJ0k+wZ9JYRwZgjhCOBG4M4Y470hhKIQQl26zDYAIYQZQB3w+DCnvJNkT6HrQgiHpM/3c2A9cN3uf6TsqncPIUmSJCnnjWRECJJiBjcA1wN3AMuBN6dfOx5Yk77v1z+NrnHoiWKMG4HTgG6SktxLSNYavTrGmNqp3ueghvQeQk6NkyRJknLXSIolkC6S8KH0behrS4AwpO3hoW1DXn8aOHtnOjpeDIwIOTVOkiRJylkjHRHSCDU0pygqyGNKaWG2uyJJkiRpGwxCGVbfnKK2spgQtjkgJkmSJCnLDEIZVt+UslCCJEmSlOMMQhnW0JyyUIIkSZKU4wxCGRRjpL7ZESFJkiQp1xmEMqi5o4dUd58V4yRJkqQcZxDKoPr0HkI1jghJkiRJOc0glEH9QcipcZIkSVJuMwhlUINBSJIkSRoXDEIZ1NDUPzWuOMs9kSRJkrQ9BqEMqm9OMaW0kJLC/Gx3RZIkSdJ2GIQyyD2EJEmSpPHBIJRB9c0pS2dLkiRJ44BBKIPqmzotlCBJkiSNAwahDOnu7aOxrdOpcZIkSdI4YBDKkLUtncSIU+MkSZKkccAglCH1Te4hJEmSJI0XBqEM6d9M1alxkiRJUu4zCGXIwIiQU+MkSZKknGcQypCG5hRF+XlMKS3MdlckSZIk7YBBKEPqm1PUVBYTQsh2VyRJkiTtgEEoQ+qbUhZKkCRJksYJg1CGrG3ppNb1QZIkSdK4YBDKgBijI0KSJEnSOGIQyoDmVA8d3b0GIUmSJGmcMAhlwMAeQk6NkyRJksYFg1AGDOwh5IiQJEmSNC4YhDKgvtkgJEmSJI0nBqEMaEiPCNVUFme5J5IkSZJGwiCUAfXNKaaUFlJSmJ/trkiSJEkaAYNQBjQ0p6h1WpwkSZI0bhiEMqC+OUWdFeMkSZKkccMglAH1TZ0WSpAkSZLGEYPQburu7aOxrZMag5AkSZI0bhiEdtPalk5itHS2JEmSNJ4YhHZTQ/8eQlWWzpYkSZLGC4PQburfQ8iqcZIkSdL4YRDaTfX9I0IGIUmSJGncMAjtpvrmFEX5eUwtK8p2VyRJkiSNkEFoNzU0paipLCaEkO2uSJIkSRohg9Buqm9OOS1OkiRJGmcMQrupobmT2iqDkCRJkjSeGIR2Q4yR+iZHhCRJkqTxxiC0G9p7oKO71yAkSZIkjTMGod2wKRUBnBonSZIkjTMGod2wsbMPcA8hSZIkabwxCO2GjekRIYOQJEmSNL4YhHbDxs4kCNVUFme5J5IkSZJ2hkFoN2xKRSaXFlJSmJ/trkiSJEnaCQah3bCxMzotTpIkSRqHDEK7YWMqUmsQkiRJksYdg9BucERIkiRJGp8MQruou7eP5s7oHkKSJEnSOGQQ2kXrWjqJWDpbkiRJGo8MQruovjkFQF2VpbMlSZKk8cYgtIsampIgZLEESZIkafwxCO2igREhg5AkSZI07hiEdlF9c4qCAFPLirLdFUmSJEk7aURBKISQH0L4fAhhTQihNYRwUwihdhvHLgkhxG3cTk4fUxpCuCaEsD6EsCmE8L0QQnkmP9hoa2hKMbkkEELIdlckSZIk7aSRjgh9CngXcAFwMjAb+MU2jj0XmDHoNgt4BLgTuCd9zHeBE4HXA2cDi9Nt40Z9c4opxYYgSZIkaTwq2NEBIYQi4P3A5THG29Jt5wEvhxCOjzHeM/j4GOOGIe//GDAfWBBj7AkhzAL+EXhNjPHe9DEXAXeEED4aY1yViQ822mZPKaWqtznb3ZAkSZK0C0YyInQYUAEs6W+IMS4DlgEnbe+NIYQ64BPAx2OM9enmE4A+4O5Bh94N9JKMEo0LX3nLobx9oaWzJUmSpPFoJEFodvp+6EjNamCvHbz3Y8Batpz2NhtYG2Ps7m+IMfakj9vR+SRJkiRpt40kCJUCfYODS1onsM3a0SGECuBC4Esxxt4h50sN85btnk+SJEmSMmWHa4SADiAvhFCQHrnpVwy0bed9b0yf/4ZhzjfcnLJhzxdCuAS4BKC2tpYlS5aMoMtjo7W1Naf6o/HN60mZ5PWkTPJ6UiZ5PSmTdud6GkkQeiV9P2PQY4CZbD1dbrA3Ar+NMbYOc76aEEJ+/0hRCKEAqBnufDHGa4BrAI466qi4ePHiEXR5bCxZsoRc6o/GN68nZZLXkzLJ60mZ5PWkTNqd62kkU+OWAi3AKf0NIYS5wFzgr9t534nAX4Zpv5skgB035Ng8tiygIEmSJEmjYocjQjHGzhDCVcBXQgjrSYoaXAXcGWO8N11eeyqwIcbYBRBCmAHUAY8Pc75VIYSfAT8IIVwIBOB7wHXjpXS2JEmSpPFtpBuqfoJkrc/1wB3AcuDN6deOB9ak7/vNSN83buN8F5Fsrvp74BaSkaNLR9xrSZIkSdoNI1kj1F/e+kPp29DXlpCM6gxue3ho25DXW4F/St8kSZIkaUyNdERIkiRJkvYYBiFJkiRJE45BSJIkSdKEYxCSJEmSNOEYhCRJkiRNOAYhSZIkSROOQUiSJEnShGMQkiRJkjThGIQkSZIkTTgGIUmSJEkTTogxZrsPIxZCWAcsz3Y/BpkGrM92J7TH8HpSJnk9KZO8npRJXk/KpG1dT3NijNO398ZxFYRyTQjhwRjjUdnuh/YMXk/KJK8nZZLXkzLJ60mZtDvXk1PjJEmSJE04BiFJkiRJE45BaPdck+0OaI/i9aRM8npSJnk9KZO8npRJu3w9uUZIkiRJ0oTjiJAkSZKkCccgtJNCCPkhhM+HENaEEFpDCDeFEGqz3S+NTyGEg0IIcZjbidnum8aXEMJ3QwjfH9L2DyGER0MIHSGEx0IIr81W/zS+bON6emCY76rvb+scmthCCLUhhB+l/17aFEK4NYSwaNDrfj9pxEZwPe3S95NBaOd9CngXcAFwMjAb+EU2O6RxbRFJ7fsZQ273ZbNTGj9C4jPAJUPaDwR+DfwcOBy4Bbg5hHDQ2PdS48V2rqcALATewZbfVR8c804q54UQ8oBfAfsDbwSOB5qA20MI1X4/aWeM4Hra5e8n1wjthBBCEckfrZfHGK9Nt80FXgZOiDHek73eaTwKIXwWODnGeEq2+6LxJ4QwH/gBSaBuB26LMV6Ufu27wAExxsWDjr8DeD7GeMkwp9MEt4PraR/gBWB+jPHl7PVS40EI4XDgYeDAGOPT6bZiYANwKXACfj9phEZwPd3NLn4/OSK0cw4DKoAl/Q0xxmXAMuCkbHRI494i4Olsd0Lj1nHAS8DBJP9DZrCTGPRdlbYEv6u0bdu7nhYBHcDyse6UxqUVwOuBZwe19QEBmILfT9o5O7qedvn7ySC0c2an71cNaV8N7DXGfdGeYREwJ4RwbwihPoTw5xDC0dnulMaHGOMNMcb3xBjrh3l5Nn5XaSfs4HpaBGwCbgghrA4hPB5C+GB6yoq0hRhjY4zxdzHGvkHNlwMlwJ/w+0k7YQTX0y5/P/kFtnNKgb4YY/eQ9k6SfxjSiIUQJgHzgSrgI8AbSP5DcGcIYWE2+6Y9QimQGtLmd5V21UFAOXArcAbwbeDTwCez2SmNDyGENwCfB76Wntrk95N22TDX0y5/PxWMYj/3RB1AXgihIMbYM6i9GGjLUp80TsUYO0IIU4DOGGMnQAjh3cCRwGXAv2axexr/Oki+mwbzu0q76gKgPMa4Kf388RBCFfAfIYRPRRccaxvS/137HnAj8NF0s99P2iXbuJ52+fvJEaGd80r6fsaQ9plsPcQr7VCMsbk/BKWf9wFP4vQA7b5X8LtKGRJj7Bn0R0a/x0nWzVaNfY80HoQQ/gP4IXA1cMGgqU1+P2mnbet62p3vJ4PQzlkKtAADFb7SVePmAn/NTpc0XoUQjgwhNIcQjhjUlk9SlOPJrHVMe4q/Mei7Ku1U/K7SLkivY/z6kOajgNXD/AEiEUL4KHAl8J8xxn8d8n/l/X7STtne9bQ7309OjdsJMcbOEMJVwFdCCOuBtcBVwJ0xxnuz2zuNQ0tJKg5eE0J4H9AKfAyYBnwji/3SnuGbwEMhhE8DPwH+ETiGpNSotLN+CXwmhPAwSanaxSTfV+/PZqeUm0IIhwCfA/4X+F4IoW7Qyy34/aSdMILraZe/nwxCO+8TQCFwffr+j8D7stojjUsxxp70TtpfAn4DlJH8C3xyjHFtVjuncS/G+HgI4U0k19fHgGeAs/v3YJB20peBHpL/Bu5NUs72AzHGHe7crgnpPCAfuDB9G+z/izFe6feTdsJ2ryfgv9jF7yc3VJUkSZI04bhGSJIkSdKEYxCSJEmSNOEYhCRJkiRNOAYhSZIkSROOQUiSJEnShGMQkiRJkjThGIQkSZIkTTgGIUmSJEkTjkFIkiRJ0oTz/wNzkrBjOJoFpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history_df[['accuracy','val_accuracy']].plot(figsize=(14,8),title='Accuracy',grid=True,fontsize=15)\n",
    "history_df[['loss','val_loss']].plot(figsize=(14,8),title='Loss',grid=True,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "with open('loop.csv','w') as f1:\n",
    "    writer=csv.writer(f1, delimiter='\\t',lineterminator='\\n',)\n",
    "    for i in range(0,10):\n",
    "       row = [i]\n",
    "       writer.writerow(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load('prepared_data/X_train.npy')\n",
    "X_test = np.load('prepared_data/X_test.npy')\n",
    "y_train = np.load('prepared_data/y_train.npy')\n",
    "y_test = np.load('prepared_data/y_test.npy')\n",
    "\n",
    "test_X = np.load('prepared_data/test_X.npy')\n",
    "test_y = np.load('prepared_data/test_y.npy')\n",
    "\n",
    "model = load_model('best_model_current.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count(y):\n",
    "    s0 = 0\n",
    "    s1 = 0\n",
    "    for arr in y:\n",
    "        if arr[0]==1:\n",
    "            s0 = s0+1\n",
    "        if arr[1]==1:\n",
    "            s1 = s1+1\n",
    "    ls = [s0,s1]\n",
    "    for i in range(len(ls)):\n",
    "        print(i,'-',ls[i],'[',round(ls[i]*100/len(y)),'%]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 - 247469 [ 66 %]\n",
      "1 - 127147 [ 34 %]\n",
      "Train Data None\n",
      "0 - 61852 [ 66 %]\n",
      "1 - 31802 [ 34 %]\n",
      "Val Data None\n",
      "0 - 3735 [ 56 %]\n",
      "1 - 2904 [ 44 %]\n",
      "Test Data None\n"
     ]
    }
   ],
   "source": [
    "print('Train Data',count(y_train))\n",
    "print('Val Data',count(y_test))\n",
    "print('Test Data',count(test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from random import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_list1 = list(test_X)\n",
    "test_list2 = list(test_y)\n",
    "\n",
    "temp = list(zip(test_list1, test_list2))\n",
    "shuffle(temp)\n",
    "res1, res2 = zip(*temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.vstack((X_train,np.array(res1[:1200])))\n",
    "y = np.vstack((y_train,np.array(res2[:1200])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_train1',X)\n",
    "np.save('prepared_data/y_train1',y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
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