IMG_net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat\n",
      "Download Progress: 14\n",
      "Download Progress: 28\n",
      "Download Progress: 42\n",
      "Download Progress: 57\n",
      "Download Progress: 71\n",
      "Download Progress: 85\n",
      "Download Progress: 100\n",
      "Download Complete\n"
     ]
    }
   ],
   "source": [
    "# import the custom GDrive downloader library:\n",
    "from URL_download_2 import gdown\n",
    "\n",
    "# provide file ID:\n",
    "file_id = '1YPr_K8xSuKghONozyjnVThe47EsiDEZj'\n",
    "\n",
    "# instantiate class (create a class instance):\n",
    "G = gdown()\n",
    "\n",
    "# Download data from Google Drive as necessary:\n",
    "G.download_file(file_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat' #0.77\n",
    "# file = 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat' #0.77\n",
    "# file = 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, small.mat'\n",
    "# file = 'Merged123-143-202_ICA(-Eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat' #0.56\n",
    "# file = 'Merged123_75_134_ICA(-eyes)AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat'\n",
    "SHUFFLE = False\n",
    "BATCH_SIZE = 20\n",
    "TEST_TRAIN = 0.2\n",
    "DS_FREQ = 1\n",
    "n_bins = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(450, 67, 67, 65)\n",
      "(450, 1)\n",
      "450\n"
     ]
    }
   ],
   "source": [
    "# get the Dataset:\n",
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def downsample_frequency_domain(n_bins):\n",
    "    print('Downsampling the frequency domain from', X.shape[3],'to', n_bins)\n",
    "    xx = np.zeros([X.shape[0],X.shape[1],X.shape[2],n_bins])\n",
    "    print(xx.shape)\n",
    "    a = np.round(np.linspace(0,X.shape[3],n_bins+1))\n",
    "    a = a.astype('uint8')\n",
    "    print(a)\n",
    "    for i in range(n_bins):\n",
    "        xx[:,:,:,i] = np.mean(X[:,:,:,a[i]:a[i+1]], axis=3)   \n",
    "        print(i)\n",
    "    print(xx.shape)\n",
    "    return xx\n",
    "\n",
    "def parse_array(in_array):\n",
    "    new_array = []\n",
    "    for i in range(len(in_array)):\n",
    "        if str(in_array[i]).find(\"left\")>0:\n",
    "            new_array.append(\"left\")\n",
    "        if str(in_array[i]).find(\"right\")>0:\n",
    "            new_array.append(\"right\")\n",
    "        if (str(in_array[i]).find(\"left\")<0 and \n",
    "            str(in_array[i]).find(\"right\")<0):\n",
    "            print(i)\n",
    "            new_array.append(\"unknown\")\n",
    "    return new_array\n",
    "\n",
    "def find_right(Q, label):\n",
    "    idx = []\n",
    "    for i in range(len(Q)):\n",
    "        if Q[i] == label:\n",
    "            idx.append(i)\n",
    "    return(idx)\n",
    "\n",
    "path = '/home/amplifier/home/DATASETS/' + file\n",
    "mat_contents = sio.loadmat(path)\n",
    "X = mat_contents['X']\n",
    "Y = mat_contents['Z']\n",
    "# Z = mat_contents['Z']\n",
    "Q = parse_array(mat_contents['Q'])\n",
    "\n",
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "print(len(Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downsampling the frequency domain from 65 to 4\n",
      "(450, 67, 67, 4)\n",
      "[ 0 16 32 49 65]\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "(450, 67, 67, 4)\n",
      "Original data shape: (450, 67, 67, 4)\n",
      "Original labels shape: (450, 1)\n",
      "Original data type: float64\n",
      "Original data type: float64\n",
      "y_test.shape before (90, 1)\n",
      "y_test.shape after (90, 2)\n",
      "Normalized data type: float16\n",
      "test input shape (90, 67, 67, 4) Nomralized MEAN: 0.093018 min 0.0 max 0.74805\n",
      "train input shape (360, 67, 67, 4) Nomralized MEAN: 0.093384 min -0.036926 max 1.5732\n",
      "test labels shape (90, 2) Nomralized MEAN: 0.5 min 0.0 max 1.0\n",
      "train labels shape (360, 2) Nomralized MEAN: 0.5 min 0.0 max 1.0\n"
     ]
    }
   ],
   "source": [
    "if DS_FREQ == 1:\n",
    "    X = downsample_frequency_domain(n_bins)\n",
    "\n",
    "if X.shape[1]<X.shape[2]:\n",
    "    X = np.transpose(X,[0,2,1])\n",
    "\n",
    "if Y.shape[1] > Y.shape[0]:\n",
    "    Y = Y.T\n",
    "    \n",
    "print('Original data shape:', X.shape)\n",
    "print('Original labels shape:', Y.shape)\n",
    "\n",
    "# verify that the model REALLY finds a mapping between the input and the labels. If we get\n",
    "# our accuracy by chance, then we should get the same accuracy on a permuted dataset:\n",
    "# Y = np.random.permutation(Y)\n",
    "\n",
    "\n",
    "# winsize = mat_contents['winsize']\n",
    "# stepsize = mat_contents['stepsize']\n",
    "# trial_len = mat_contents['trial_len']\n",
    "# low_cutoff = mat_contents['low_cutoff']\n",
    "# high_cutoff = mat_contents['high_cutoff']\n",
    "# source = mat_contents['filein']\n",
    "\n",
    "x_train, x_test, y_train, y_test, q_train, q_test = train_test_split(X, Y, Q, test_size=TEST_TRAIN, shuffle=SHUFFLE)\n",
    "print('Original data type:', x_train.dtype)\n",
    "print('Original data type:', x_train.dtype)\n",
    "\n",
    "# convert to float64 for numerical stability:\n",
    "x_train = x_train.astype('float64')\n",
    "y_train = y_train.astype('float64')\n",
    "x_test = x_test.astype('float64')\n",
    "y_test = y_test.astype('float64')\n",
    "print('y_test.shape before', y_test.shape)\n",
    "\n",
    "# one hot encode the labels:\n",
    "onehot_encoder = preprocessing.OneHotEncoder(sparse=False)\n",
    "y_train = onehot_encoder.fit_transform(y_train)\n",
    "y_test = onehot_encoder.fit_transform(y_test)\n",
    "print('y_test.shape after', y_test.shape)\n",
    "\n",
    "# convert to float16 to save space:\n",
    "x_train = x_train.astype('float16')\n",
    "y_train = y_train.astype('float16')\n",
    "x_test = x_test.astype('float16')\n",
    "y_test = y_test.astype('float16')\n",
    "print('Normalized data type:', x_train.dtype)\n",
    "\n",
    "leng = X.shape[1] # if you work in the FD, this is the height of the sample time-frequency image, othewise EEG channels\n",
    "chan = X.shape[2] # if you work in the FD, this is the width of the sample time-frequency image, othewise time samples of EEG signal\n",
    "\n",
    "if len(X.shape)==3:\n",
    "    streams = 1 # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "if len(X.shape)==4:\n",
    "    streams = X.shape[3] # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "\n",
    "\n",
    "print('test input shape', x_test.shape, \"Nomralized MEAN:\", np.mean(x_test), \"min\", np.min(x_test),\"max\", np.max(x_test))\n",
    "print('train input shape', x_train.shape, \"Nomralized MEAN:\", np.mean(x_train), \"min\", np.min(x_train),\"max\", np.max(x_train))\n",
    "\n",
    "print('test labels shape', y_test.shape, \"Nomralized MEAN:\", np.mean(y_test), \"min\", np.min(y_test),\"max\", np.max(y_test))\n",
    "print('train labels shape', y_train.shape, \"Nomralized MEAN:\", np.mean(y_train), \"min\", np.min(y_train),\"max\", np.max(y_train))\n",
    "\n",
    "# print('Window length', winsize)\n",
    "# print('Step size:', stepsize)\n",
    "# print('Trial length:', trial_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 67, 67, 4)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 67, 67, 40)        1480      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 67, 67, 40)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 67, 67, 40)        14440     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 34, 34, 40)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 34, 34, 40)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 34, 34, 80)        28880     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 34, 34, 80)        57680     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 34, 34, 80)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 34, 34, 160)       115360    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 17, 17, 160)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 46240)             0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 46240)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               4624100   \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2)                 202       \n",
      "=================================================================\n",
      "Total params: 4,853,682\n",
      "Trainable params: 4,852,962\n",
      "Non-trainable params: 720\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# make sure you don't hog all the video memory\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.Session(config=config)\n",
    "from keras import backend as K\n",
    "K.set_session(sess)\n",
    "###################################\n",
    "\n",
    "def activation():\n",
    "    return ELU(alpha=1.0) # Activation('relu') #\n",
    "\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, BatchNormalization, Dropout, ELU\n",
    "from keras.models import Model\n",
    "from keras.callbacks import TensorBoard, EarlyStopping\n",
    "\n",
    "sh = x_train.shape\n",
    "\n",
    "\n",
    "input_img = Input(shape=(sh[1], sh[2], sh[3]))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(40, (3, 3), activation='relu', padding='same')(input_img)\n",
    "# x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = BatchNormalization()(x)\n",
    "# x = Dropout(0.2)(x)\n",
    "x = Conv2D(40, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = BatchNormalization()(x)\n",
    "# x = Dropout(0.3)(x)\n",
    "x = Conv2D(80, (3, 3), activation='relu', padding='same')(x)\n",
    "x = Conv2D(80, (3, 3), activation='relu', padding='same')(x)\n",
    "x = BatchNormalization()(x)\n",
    "# x = Dropout(0.5)(x)\n",
    "x = Conv2D(160, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.8)(x)\n",
    "x = Dense(100, activation='relu')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.8)(x)\n",
    "x = Dense(100, activation='relu')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dense(2, activation='softmax')(x)\n",
    "\n",
    "model = Model(input_img, x)\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 360 samples, validate on 90 samples\n",
      "Epoch 1/100\n",
      " - 3s - loss: 1.0360 - acc: 0.4972 - val_loss: 2.6648 - val_acc: 0.5000\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.50000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.9415 - acc: 0.5000 - val_loss: 1.1147 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.50000 to 0.53333, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.7982 - acc: 0.5444 - val_loss: 0.5866 - val_acc: 0.6444\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.53333 to 0.64444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.7511 - acc: 0.6028 - val_loss: 0.6066 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.64444 to 0.65556, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.7178 - acc: 0.6083 - val_loss: 0.5462 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.65556 to 0.77778, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.6978 - acc: 0.6056 - val_loss: 0.5588 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.77778 to 0.77778, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.6813 - acc: 0.6333 - val_loss: 0.6834 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00007: val_acc did not improve\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.6506 - acc: 0.6556 - val_loss: 0.7791 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00008: val_acc did not improve\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.6324 - acc: 0.6500 - val_loss: 1.0109 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00009: val_acc did not improve\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.6367 - acc: 0.6583 - val_loss: 0.6663 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00010: val_acc did not improve\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.6157 - acc: 0.6750 - val_loss: 0.9117 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00011: val_acc did not improve\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.5867 - acc: 0.7056 - val_loss: 0.7924 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00012: val_acc did not improve\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.6201 - acc: 0.6917 - val_loss: 0.6050 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00013: val_acc did not improve\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.5990 - acc: 0.6889 - val_loss: 0.4748 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.77778 to 0.80000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.6132 - acc: 0.6917 - val_loss: 0.5138 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00015: val_acc did not improve\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.5628 - acc: 0.7000 - val_loss: 0.5936 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00016: val_acc did not improve\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.5906 - acc: 0.6861 - val_loss: 0.6004 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00017: val_acc did not improve\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.6088 - acc: 0.6722 - val_loss: 0.5179 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00018: val_acc did not improve\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.5856 - acc: 0.6972 - val_loss: 0.5408 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00019: val_acc did not improve\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.5706 - acc: 0.7167 - val_loss: 0.6132 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00020: val_acc did not improve\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.5835 - acc: 0.6944 - val_loss: 0.5861 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00021: val_acc did not improve\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.5894 - acc: 0.7167 - val_loss: 0.5348 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00022: val_acc did not improve\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.5717 - acc: 0.7194 - val_loss: 0.5072 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00023: val_acc did not improve\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.5241 - acc: 0.7111 - val_loss: 0.5397 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00024: val_acc did not improve\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.5278 - acc: 0.7472 - val_loss: 0.4264 - val_acc: 0.8333\n",
      "\n",
      "Epoch 00025: val_acc improved from 0.80000 to 0.83333, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.5506 - acc: 0.7500 - val_loss: 0.4124 - val_acc: 0.8222\n",
      "\n",
      "Epoch 00026: val_acc did not improve\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.5054 - acc: 0.7528 - val_loss: 0.6923 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00027: val_acc did not improve\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.5328 - acc: 0.7472 - val_loss: 0.5268 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00028: val_acc did not improve\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.5101 - acc: 0.7500 - val_loss: 0.5199 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00029: val_acc did not improve\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.5459 - acc: 0.7417 - val_loss: 0.5354 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00030: val_acc did not improve\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.5197 - acc: 0.7528 - val_loss: 0.5027 - val_acc: 0.8222\n",
      "\n",
      "Epoch 00031: val_acc did not improve\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.5080 - acc: 0.7444 - val_loss: 0.6475 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00032: val_acc did not improve\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.4969 - acc: 0.7528 - val_loss: 0.5273 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00033: val_acc did not improve\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.4532 - acc: 0.7944 - val_loss: 0.5581 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00034: val_acc did not improve\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.4646 - acc: 0.7861 - val_loss: 0.4368 - val_acc: 0.8444\n",
      "\n",
      "Epoch 00035: val_acc improved from 0.83333 to 0.84444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.4268 - acc: 0.8056 - val_loss: 0.5803 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00036: val_acc did not improve\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.4597 - acc: 0.8111 - val_loss: 0.4228 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00037: val_acc did not improve\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.4426 - acc: 0.7972 - val_loss: 0.4593 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00038: val_acc did not improve\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.4340 - acc: 0.8000 - val_loss: 0.5965 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00039: val_acc did not improve\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.4636 - acc: 0.7611 - val_loss: 0.4510 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00040: val_acc did not improve\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.4384 - acc: 0.8000 - val_loss: 0.4939 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00041: val_acc did not improve\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.3937 - acc: 0.8167 - val_loss: 0.4685 - val_acc: 0.8222\n",
      "\n",
      "Epoch 00042: val_acc did not improve\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.3716 - acc: 0.8333 - val_loss: 0.4821 - val_acc: 0.8222\n",
      "\n",
      "Epoch 00043: val_acc did not improve\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.4252 - acc: 0.7944 - val_loss: 0.6886 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00044: val_acc did not improve\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.4219 - acc: 0.8167 - val_loss: 0.6210 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00045: val_acc did not improve\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.4337 - acc: 0.7944 - val_loss: 0.4792 - val_acc: 0.8444\n",
      "\n",
      "Epoch 00046: val_acc improved from 0.84444 to 0.84444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.4003 - acc: 0.8222 - val_loss: 0.5276 - val_acc: 0.8333\n",
      "\n",
      "Epoch 00047: val_acc did not improve\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.3960 - acc: 0.8278 - val_loss: 0.7236 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00048: val_acc did not improve\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.4031 - acc: 0.8306 - val_loss: 0.6760 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00049: val_acc did not improve\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.3300 - acc: 0.8722 - val_loss: 0.5169 - val_acc: 0.8222\n",
      "\n",
      "Epoch 00050: val_acc did not improve\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.3853 - acc: 0.8250 - val_loss: 0.5215 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00051: val_acc did not improve\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.3028 - acc: 0.8694 - val_loss: 0.5330 - val_acc: 0.8111\n",
      "\n",
      "Epoch 00052: val_acc did not improve\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.3592 - acc: 0.8444 - val_loss: 0.5400 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00053: val_acc did not improve\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.3203 - acc: 0.8500 - val_loss: 0.6513 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00054: val_acc did not improve\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.3002 - acc: 0.8917 - val_loss: 0.5166 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00055: val_acc did not improve\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.2926 - acc: 0.8722 - val_loss: 0.5268 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00056: val_acc did not improve\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.3249 - acc: 0.8750 - val_loss: 0.5968 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00057: val_acc did not improve\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.3562 - acc: 0.8528 - val_loss: 2.1743 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00058: val_acc did not improve\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.3174 - acc: 0.8722 - val_loss: 1.0511 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00059: val_acc did not improve\n",
      "Epoch 60/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 0s - loss: 0.3409 - acc: 0.8667 - val_loss: 0.6790 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00060: val_acc did not improve\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.2902 - acc: 0.8778 - val_loss: 0.6685 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00061: val_acc did not improve\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.3682 - acc: 0.8500 - val_loss: 0.8626 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00062: val_acc did not improve\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.2754 - acc: 0.8917 - val_loss: 0.6941 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00063: val_acc did not improve\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.2390 - acc: 0.9000 - val_loss: 0.6808 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00064: val_acc did not improve\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.2300 - acc: 0.9167 - val_loss: 0.5950 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00065: val_acc did not improve\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.2308 - acc: 0.9333 - val_loss: 0.5221 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00066: val_acc did not improve\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.2364 - acc: 0.9000 - val_loss: 0.7331 - val_acc: 0.8222\n",
      "\n",
      "Epoch 00067: val_acc did not improve\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.2463 - acc: 0.8806 - val_loss: 0.9573 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00068: val_acc did not improve\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.2638 - acc: 0.9056 - val_loss: 0.6676 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00069: val_acc did not improve\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.3046 - acc: 0.8944 - val_loss: 0.6583 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00070: val_acc did not improve\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.2453 - acc: 0.9000 - val_loss: 0.8153 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00071: val_acc did not improve\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.1924 - acc: 0.9194 - val_loss: 0.7804 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00072: val_acc did not improve\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.1649 - acc: 0.9389 - val_loss: 0.9164 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00073: val_acc did not improve\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.1987 - acc: 0.9250 - val_loss: 0.9787 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00074: val_acc did not improve\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.2611 - acc: 0.9083 - val_loss: 0.7594 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00075: val_acc did not improve\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.2310 - acc: 0.9083 - val_loss: 0.8184 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00076: val_acc did not improve\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=50, mode='min')\n",
    "checkpointer = ModelCheckpoint(filepath='/home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5',\n",
    "                               verbose=1,\n",
    "                               monitor='val_acc',\n",
    "                               save_best_only=True)\n",
    "\n",
    "train_history = model.fit(x_train, y_train,\n",
    "                epochs=100,\n",
    "                batch_size=BATCH_SIZE,\n",
    "                verbose=2,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, y_test),\n",
    "                callbacks=[checkpointer, early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff65b836d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(np.array(history.losses))\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "loss = train_history.history['loss']\n",
    "val_loss = train_history.history['val_loss']\n",
    "plt.plot(loss)\n",
    "plt.plot(val_loss)\n",
    "plt.legend(['loss', 'val_loss'])\n",
    "plt.title('AdaDelta')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 2)\n",
      "(90, 2)\n",
      "Accuracy on the test data: 0.844444444444\n",
      "Accuracy on the training data: 0.733333333333\n"
     ]
    }
   ],
   "source": [
    "model.load_weights('/home/amplifier/home/NEW_DL/weights/IMG_weights_TD_1D.h5')\n",
    "# let's predict a TRAINING sammple:\n",
    "decoded_train = model.predict(x_train)\n",
    "decoded_test = model.predict(x_test)\n",
    "print(decoded_train.shape)\n",
    "print(decoded_test.shape)\n",
    "\n",
    "# now test sample by sample\n",
    "cor_test = []\n",
    "L = x_test.shape\n",
    "for sample_no in range(x_test.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_test[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_test[sample_no,0] < y_test[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) and (y_test[sample_no,0] > y_test[sample_no,1]))):\n",
    "        cor_test.append(1)\n",
    "    else:\n",
    "        cor_test.append(0)\n",
    "\n",
    "cor_train = []\n",
    "L = x_train.shape\n",
    "for sample_no in range(x_train.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_train[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_train[sample_no,0] < y_train[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) and (y_train[sample_no,0] > y_train[sample_no,1]))):\n",
    "        cor_train.append(1)\n",
    "    else:\n",
    "        cor_train.append(0)\n",
    "\n",
    "# Report accuracies\n",
    "print('Accuracy on the test data:', np.mean(cor_test))\n",
    "print('Accuracy on the training data:', np.mean(cor_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample: 0 Prediction: [[ 0.50938272  0.49061728]] Ground Truth [ 0.  1.] right\n",
      "Sample: 1 Prediction: [[ 0.49962109  0.50037891]] Ground Truth [ 0.  1.] right\n",
      "Sample: 2 Prediction: [[ 0.43155926  0.56844074]] Ground Truth [ 0.  1.] right\n",
      "Sample: 3 Prediction: [[ 0.48422608  0.51577395]] Ground Truth [ 0.  1.] right\n",
      "Sample: 4 Prediction: [[ 0.43632904  0.56367093]] Ground Truth [ 0.  1.] right\n",
      "Sample: 5 Prediction: [[ 0.37916195  0.62083805]] Ground Truth [ 0.  1.] right\n",
      "Sample: 6 Prediction: [[ 0.44813746  0.55186248]] Ground Truth [ 0.  1.] right\n",
      "Sample: 7 Prediction: [[ 0.47883537  0.52116466]] Ground Truth [ 0.  1.] right\n",
      "Sample: 8 Prediction: [[ 0.46625942  0.53374058]] Ground Truth [ 0.  1.] right\n",
      "Sample: 9 Prediction: [[ 0.53044748  0.46955255]] Ground Truth [ 0.  1.] right\n",
      "Sample: 10 Prediction: [[ 0.46571931  0.53428072]] Ground Truth [ 0.  1.] right\n",
      "Sample: 11 Prediction: [[ 0.439127    0.56087303]] Ground Truth [ 0.  1.] right\n",
      "Sample: 12 Prediction: [[ 0.36003366  0.63996631]] Ground Truth [ 0.  1.] right\n",
      "Sample: 13 Prediction: [[ 0.35769185  0.64230818]] Ground Truth [ 0.  1.] right\n",
      "Sample: 14 Prediction: [[ 0.45832348  0.54167658]] Ground Truth [ 0.  1.] right\n",
      "Sample: 15 Prediction: [[ 0.41259092  0.58740908]] Ground Truth [ 0.  1.] right\n",
      "Sample: 16 Prediction: [[ 0.48727542  0.51272458]] Ground Truth [ 0.  1.] right\n",
      "Sample: 17 Prediction: [[ 0.52363002  0.47637001]] Ground Truth [ 0.  1.] right\n",
      "Sample: 18 Prediction: [[ 0.42498943  0.57501054]] Ground Truth [ 0.  1.] right\n",
      "Sample: 19 Prediction: [[ 0.34988686  0.65011311]] Ground Truth [ 0.  1.] right\n",
      "Sample: 20 Prediction: [[ 0.3998037  0.6001963]] Ground Truth [ 0.  1.] right\n",
      "Sample: 21 Prediction: [[ 0.41542014  0.58457983]] Ground Truth [ 0.  1.] right\n",
      "Sample: 22 Prediction: [[ 0.42133415  0.57866591]] Ground Truth [ 0.  1.] right\n",
      "Sample: 23 Prediction: [[ 0.4149211  0.5850789]] Ground Truth [ 0.  1.] right\n",
      "Sample: 24 Prediction: [[ 0.5123437   0.48765627]] Ground Truth [ 1.  0.] left\n",
      "Sample: 25 Prediction: [[ 0.50912887  0.49087116]] Ground Truth [ 1.  0.] left\n",
      "Sample: 26 Prediction: [[ 0.55807155  0.44192842]] Ground Truth [ 1.  0.] left\n",
      "Sample: 27 Prediction: [[ 0.53726166  0.46273836]] Ground Truth [ 1.  0.] left\n",
      "Sample: 28 Prediction: [[ 0.57804066  0.42195931]] Ground Truth [ 1.  0.] left\n",
      "Sample: 29 Prediction: [[ 0.52871192  0.47128806]] Ground Truth [ 1.  0.] left\n",
      "Sample: 30 Prediction: [[ 0.37328213  0.62671781]] Ground Truth [ 1.  0.] left\n",
      "Sample: 31 Prediction: [[ 0.41378248  0.58621746]] Ground Truth [ 1.  0.] left\n",
      "Sample: 32 Prediction: [[ 0.45302674  0.54697323]] Ground Truth [ 1.  0.] left\n",
      "Sample: 33 Prediction: [[ 0.56819153  0.4318085 ]] Ground Truth [ 1.  0.] left\n",
      "Sample: 34 Prediction: [[ 0.38499737  0.61500263]] Ground Truth [ 1.  0.] left\n",
      "Sample: 35 Prediction: [[ 0.44601497  0.55398506]] Ground Truth [ 1.  0.] left\n",
      "Sample: 36 Prediction: [[ 0.41127026  0.58872968]] Ground Truth [ 1.  0.] left\n",
      "Sample: 37 Prediction: [[ 0.36233178  0.63766819]] Ground Truth [ 1.  0.] left\n",
      "Sample: 38 Prediction: [[ 0.44718796  0.55281198]] Ground Truth [ 1.  0.] left\n",
      "Sample: 39 Prediction: [[ 0.35083205  0.64916795]] Ground Truth [ 1.  0.] left\n",
      "Sample: 40 Prediction: [[ 0.3362793   0.66372073]] Ground Truth [ 1.  0.] left\n",
      "Sample: 41 Prediction: [[ 0.45405793  0.54594207]] Ground Truth [ 1.  0.] left\n",
      "Sample: 42 Prediction: [[ 0.39579812  0.60420185]] Ground Truth [ 1.  0.] left\n",
      "Sample: 43 Prediction: [[ 0.36299768  0.63700229]] Ground Truth [ 1.  0.] left\n",
      "Sample: 44 Prediction: [[ 0.38466996  0.61532998]] Ground Truth [ 1.  0.] left\n",
      "Sample: 45 Prediction: [[ 0.35491559  0.64508438]] Ground Truth [ 1.  0.] left\n",
      "Sample: 46 Prediction: [[ 0.35007179  0.64992821]] Ground Truth [ 1.  0.] left\n",
      "Sample: 47 Prediction: [[ 0.43062288  0.56937712]] Ground Truth [ 1.  0.] left\n",
      "Sample: 48 Prediction: [[ 0.42124432  0.57875568]] Ground Truth [ 1.  0.] left\n",
      "Sample: 49 Prediction: [[ 0.38603544  0.61396456]] Ground Truth [ 1.  0.] left\n",
      "Sample: 50 Prediction: [[ 0.39947286  0.60052711]] Ground Truth [ 1.  0.] left\n",
      "Sample: 51 Prediction: [[ 0.40129197  0.59870797]] Ground Truth [ 1.  0.] left\n",
      "Sample: 52 Prediction: [[ 0.37313452  0.62686545]] Ground Truth [ 1.  0.] left\n",
      "Sample: 53 Prediction: [[ 0.64520705  0.35479298]] Ground Truth [ 0.  1.] right\n",
      "Sample: 54 Prediction: [[ 0.54383326  0.45616677]] Ground Truth [ 0.  1.] right\n",
      "Sample: 55 Prediction: [[ 0.49241483  0.50758517]] Ground Truth [ 0.  1.] right\n",
      "Sample: 56 Prediction: [[ 0.43868446  0.56131554]] Ground Truth [ 0.  1.] right\n",
      "Sample: 57 Prediction: [[ 0.42332467  0.57667536]] Ground Truth [ 0.  1.] right\n",
      "Sample: 58 Prediction: [[ 0.48587775  0.51412225]] Ground Truth [ 0.  1.] right\n",
      "Sample: 59 Prediction: [[ 0.47284964  0.52715033]] Ground Truth [ 0.  1.] right\n",
      "Sample: 60 Prediction: [[ 0.42045248  0.57954752]] Ground Truth [ 0.  1.] right\n",
      "Sample: 61 Prediction: [[ 0.4467755  0.5532245]] Ground Truth [ 0.  1.] right\n",
      "Sample: 62 Prediction: [[ 0.47769442  0.52230561]] Ground Truth [ 0.  1.] right\n",
      "Sample: 63 Prediction: [[ 0.44702756  0.55297244]] Ground Truth [ 0.  1.] right\n",
      "Sample: 64 Prediction: [[ 0.45787048  0.54212958]] Ground Truth [ 0.  1.] right\n",
      "Sample: 65 Prediction: [[ 0.51396316  0.48603684]] Ground Truth [ 0.  1.] right\n",
      "Sample: 66 Prediction: [[ 0.53650987  0.46349013]] Ground Truth [ 0.  1.] right\n",
      "Sample: 67 Prediction: [[ 0.49229661  0.50770336]] Ground Truth [ 0.  1.] right\n",
      "Sample: 68 Prediction: [[ 0.41908091  0.58091903]] Ground Truth [ 0.  1.] right\n",
      "Sample: 69 Prediction: [[ 0.43274003  0.56725991]] Ground Truth [ 0.  1.] right\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9ec65b99b0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ec6755080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sample_no in range(70):\n",
    "    pred = model.predict(np.expand_dims(x_test[sample_no],0))\n",
    "    print('Sample:', sample_no, 'Prediction:', pred, 'Ground Truth', y_test[sample_no], q_test[sample_no])\n",
    "sample_no = 2\n",
    "\n",
    "plt.imshow(x_test[sample_no,:,:,2].astype('float64'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Topographies ')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vOQr/KS4fhsQhbiV8rscM5qMM4dOxWxzihs+uqqbbT0pWnjOS6sD+ssphoefp0QEzdtcY1h5ka+YQei+rPYcUoqsFSi1ub49MuTsgtTgFqcV2uzDl2p3jYtHY8xYotaA+gLILKwcK3C4ivHhoinM0oZ29019TirNGG9kFjiG2WIdyEghDHptnoHxpMfn3+8empUwckj3bFcU4S9gpcV3mERYlGCy7wAz6pm56v9852fr7xGHSvuxi0+d7g1KilJr0KFA6riP8/BeRm4iR9fiORIgXQvzVCi82jdSC57xhur8nCrVGd6ph1PkhDB/mnuJNdEExMsY37axjSqf/8xyB8op1CVIq6rfcr80xrB/HCXo8jSPrYInHPvSOBOP+ZcxzD4lC7a8rlSNJiGQX+0oNjKQR143cPT0rBJZx4L4nOb8EoTsFYNuMf8zWN++8odsF4f22HDvf+XVQhfCR4IfMVTiEeHOGP6THay7np9C+98L8ZrrEsatAkQ9CCCGEEEIIIYQ4KHr5IIQQQgghhBBCiIOilw9CCCGEEEIIIYQ4KFeW88HlKsQirMVB28wF6MQpl0O9wDwP8yw0UfM5skYsfW2o0R7O/NLmM5X9jNU/g41b6eeraMDiriytwKyDYx1b4YEOu4ecD2zxZWRanfP3RBZ/RuYY5FeYqxxlS5lu+lgkXTd6K9ZeeRo4zkHgtBnOE4IWrIuS7L9KsP8q37ge9GGk6aGdBq9CSxQTUrssx8lE7tdNGujzLmvl0U7z9nZtyt3b5JwP2w1Yge5sfcMOLTRBy93axoIacJb9mmN7WMqO8GSVrC+NxJkewTVhzoeeHwc8rxYk1mO97fR0FNkQz4V1zph7oanyeTeDtVNd95DzAbTiS3qQqB1H3fjYri+3IczdsKNcExvYx3Ip2TwPj2LOB8zlUJfd5N/5GIP6eCzHbcvThA+jnCzTeR54LKDKc32RVxrZ82IOiAHaMecl2icZg/m+wbxuDo3umVPfxP4bxdrp5ro5vwL2T9zGPC5cB+cQwfnE5IbgHDeOTW41ykmC7S7ITzLTJjG0TcTtRzDnw6KeHgu4v+M97Njuep+2G3nScn/9KkMV9GPMVxZYbUaY/A2ca87YCQdWm7htLDT9crOJ1j5zcwvOxXkGfKrX8Rb2wZuD7YnXY173p3WayX9h6rvENe1BcYAxQ5EPQgghhBBCCCGEOCh6+SCEEEIIIYQQQoiDsrfs4qW/++1m340au0y0BkbUYOgRSxIWaKEJYe1La5u0hFArDLtiy0MT1eTIHVKy4fWjUGCIAYrCn7yQYQ5rKSEcqIfzjsKHIWyqLvM2h//v2hz+15AkA8/VQojnOLLUkWSQy5z5hlG4konPREvOoNFwGNLIKnPqInwbwyJxWNN0FSO7P7hnVSDHwba2ruyNwv2jKod/fuPP2zDrL3z8+oRTP/OpJ8x+i7ocvHX8WhSOcR9qoTCGvN5trZzi1i5baqLU4u75ypTbbnLofXeOFpoUogiWmsYyk9qvaXsjiz7n2FzZBasp4BKLK45QDO2xghDAHsPGwUOVW/XccOB9bON6srj0QqNRWpFSSqsyt4Wqw7Bu25etpR7IqSj8G8972ud2d68j6Q/so1yI93H78f/r2JS7/UdfTteBd794w+yjTGJVYji9vdeeBW9Kdr6ugjkf22SPc03QF4yUKpL1Ycgvh+EaWSP1Xmd+HPUfYyk4+ZFRObSe47nRW8tx3XOtDCO4v84BpQvcP7EfotSCZReRHeYSHsomGFF3IGPF+lY0tqCcKpLtemvGSI42V8T5yk8+b/af+jMvzfzkg832//gGs78epq2Qmc70IbtO7vvp/hT2rbkYC00+NkxvX8Ia0pXdROMdLslJnmHHRbAFHq1bnEVIKK3wj0VrJPdY9HPC/PiLjgXjGJYLiqFUrxh5l8I2SvVqLgbjxMy1Dw6lA9u4BlOax0t/z/72f/4vfXreBwlFPgghhBBCCCGEEOKg6OWDEEIIIYQQQgghDsr+bhccdYihdkGciwkhHIUN4ZXlOBSUWaRkXSxQarFa+LKLJYS/c2i8xz5Z1V+vjkjWMYcowzGGRu16GzJmpAGtPS+GCSLjQDVwxcCM2+N0z5korMeEA8FH2Fkiak8m1a7vKjCbfRIaw3NcsOyixDZo7+gK9jF8GCUY1w1uly08+BrCZlsKuy2DoQoz12+7XO5OY0PZXz7PYel3zvKxzZl1OOjPcx3FFrLJN7YdlhgeF0grTOj13JDCmfBHsI5wlDNyqJljcmQhAyGFo6+B12QcMsg9BMYvDHHnMW9wZBccdo7HmtqeC10izqsc/oz9MCXOSO8/ICu7gO3SD/fdQtj1WWfb4Fmf909bKwtCqcUG2vuuP7x51VsBu1Z4Ugt2saqgBzQUQo0h+hhq3VJ4sde+2O3CyP+87OwRUd+PQpn7oKCRSQb1YxR2FNZtqvalFVftaDFXglE5UpqU2O0CJRjc3wPXCWdE7ehGoZRjU+S+OnbgmJYPnVN7r/tpd5fxHOnf90fN32Jd2zWUJ8HiOQPvKd+zDvp4OfM3xF56x2AOniv32Mv5gjDjXXDMHuC52jkWjGkjZwVHTjH6it534WULdptAkuAei9ZI0foO6+Pm0+FYDb8R2S0Jh350PmEXNVyPQbnRUGrkPlc7br8einwQQgghhBBCCCHEQdHLByGEEEIIIYQQQhwUvXwQQgghhBBCCCHEQbk6kSjqWUZ+K0CkIa5BwwJ5HjDHQ0opLVeY5wFskyjnw8KxQIy0u3Ot2/bFs1QqSWs4NzeEZzu3IP1vXeZ91kOiPsy15EkpDWg1VJqCtpyp3NlOgc0NvRJDGS7r7hGjeQwFcTO1TUFbwHtWObabKaW0xJwPpOXE3A5H1e5im+3+Ulqk68KGbAO9PlAWQY4Hei6odT9vc/13NmRfCJaa27Ncbji358I8D+UOdJ6sq8P9yE7zKsYU7KNz+1RgFWW0fxX+fU/dH36MrQCNntE/F9orm5wPpK/H/S7INYH6Xc6HsIMcEJhDYUEP2dNR94GKGnOXRPVhHU3POSnyPveZncnzABbKVMd1+c8C6/A9rTzfawNpbBsnz1GUXyRa1mBbLkps5IH9nekLft2zGY0zzrn3HY5Mbph540R0qj7ItYF9127b59bBMc69gGA+CLTMXI/yMPha/sZYOkNeFzK2RFvPpZMLJiWbD2bRT7fplFJqwRrd5Ejq7bW2sCi76rwbDxuPLTezynF/b2EMrakt4B3dtTCfRLmQIgvJuXj9LrB/H9nf7nHayPrapFoz236+Bjf/w6hyu1sYi0o4wHl3nPs7PpWzlmKrTViPWLvT6fNQ1aN98/2jvBGwXur5txquM2GeGU1nXjKPyE40WksegOuyPhFCCCGEEEIIIcQDil4+CCGEEEIIIYQQ4qDsL7vg8Fwv1n7kOwfbFYWUOFILlFmklNLxCkLUawhdq8iiyAmhi0JmTbnLxJ1A0cqEklNoFBxD2yS2CfPKsWSiM7KL/C5pOfj3gkOJTfhjB6GFdE0mzAfCHwcO8TGhTLAzajMYkwSbpb3vuF8U/L5s+hkVUfssp/8cVDeuH5936YdZo70mW2gelbkdH4Ps4hj+/tVa513UQwDKIlKybTEKFcVwPrbr3HV5/2wLFoXn1r6wOYdzn+fPlBuyLANpD0bARnZG9mKn/3z/Q/4hGwI3BMfg7xxGh20bxteBw/LwWOmEF77O9bqWgnMtsAru53BNYDE1LFh2UU5udxS63S39Yw20GZw3IolbG5zLm1N4PMBx3pNgpEQ2oZ1vT4vHOpqvbkxe0cMHW23WznjLYe1mnqN+UkHIOs+pHsbmbiRjgra7CKzNBmdtNOrHQZivZ40brbVCW755nzGHAsu/IhoKHGtwbuNbkIkuYRBGK+WUUtpWeT+y5CxBd2NtcUnSEwzeFXwOJR681to456qSLx9CqSV+d95HqcVlpLpXYRv/MHGz3pp9e2/yfeM2g2PrsiILbnjmKLvYbu3zYtXhBfwI5v68cPrkKHLf9En/+e8jyRlJsD0L4uhz0RoBmzLdQBzWCzgWKKQsfKOc9f/YahP262CNFIHfxVwv3U9cB5trstXhTyaUWvDaFCUp0eMObTgPPGYo8kEIIYQQQgghhBAHRS8fhBBCCCGEEEIIcVD2l12QZMLN6hqEAhY1hY3V07ILdLRIKaVjdLio83YUWsZhsgiG0w5GxhBke6VzYVZSzDo7qgNVCPAZlogsjEtCDjuMQkQ7kz2aM5/7Ib4YylhFIUUzM38jI/mDOTh9bKiCbPnshOHEfxYUC2pOhdFfFI5us9867h4ppdKE/ubtJUl/1o6jxf39fAylFmPZxUm6Lpw3JLuYGdqFbdtkmk4pbVtwBtjk+o3MIqVUnOXPlecgrSAHlQKkFmUXhAo6iacvlSXYCZse1YGhgs52SiS1gNs01PbiBy+McK7Mgi8SszC3dD/74B5idfhd4PqGluQUXT7ZELliGOmCrWMLbheLwAnJ1AHzREth4rt22j2hpvFgUU07MEXwHNI581VH3/+6yC6icPVVgW4XVp6J8sdqsHV0FYRXd/lz64rqQMcseHbdgurz+j/PZcPMvhZIMnzZxbz6RkZQTkj2WE4xb6yOst97a60os76VoNr7jnPoWZ/D5Bua2Hu4iaZd0IC0CO5hA0X5c965UJ5R0RrPyHMDqW7lSAYiacWjJrNgbi6s28UicB1BsN2ckOwC7ym2401j1yMtyDCGBjovTxFGauwsJlKyv5mwf7JUO5jHCtM2kotnkhARulh4vwvZqcJztEgktTASDL4Q/BBs833H+nC9RMVw3MVyRcUXCJdA38u9nZ5kl+obO1BAjT3+ne/ndDnGrLlG84Jz3itCkQ9CCCGEEEIIIYQ4KHr5IIQQQgghhBBCiIOilw9CCCGEEEIIIYQ4KHvnfCgo54PNAeDrQzAHANeBmspFDTZEC6vDXFW+RhNB2xyUB7GGdjC5EkCT6NY8rgO1iJhTYVoJfB/UZY0sGkG/uoLtkZ0Y1IHXtC2s3h2vaUN2VYWpI/+ddZhYP+qwWefkadk4/4PRUQW5IQbQaHHqjgHen6G0iXVto2t87e+cuwT3YbskfR0+71WN7dHmJ7lR5/wNbLWJuR1WJeZ/sDZR14lt67e9SPeLFrAtNYLdLtfZwnba2t5X7NBCE3R/NISUjj3SSPbmaAy5XKSrM1ZHJt8IlYOvYhyreByup49xzgeT5wHr4EeAH2MPMfwY3M9xDg3H9imwHutBKz/KV4HjAWw3dH092gYv7Q1tummbY9a1e3NDQzrfrp3WsrMut4J7jfMdnzfqF4OTeyfqPw8zrF82+m2YG9fUkRew3wX/Z9nW+VmeLlbm2M0V5BHo/DqaItfRYb8b9ZmZzyjQ2I5yNlyWuZace+p8TR4S13cwpc7R0KdElt/Yn+ialrBuwpxKT9b2Oe5gAMU8DPxEo/Uazt5Yx44+hccaGJB3gx0zML8Ars8uZfHuwHV4de6j8X8YeLw+N/uYGwZtTXk9jc/urLNtCNdoCNvENk3e38K60+R/SMnq/gtnPk70mwmqKGnNjPucTwhzuYW5W2a2Pc9Ck3PBmXW32aYKMQ8D54zCHFywthhZnzt5d3j90Ls7hPmtCvk0+Png7yf64YH5X7zfICO83BWMk5/ufhXFdLnotJHN+hXMC4wiH4QQQgghhBBCCHFQ9PJBCCGEEEIIIYQQB2V/2QXZZM6NxLBhQxTKDlKLJW6TXRmGudcQdjcKLWMfuq/CtpsY8tfNtH8ahS5BHXiM7ZWQ0rFaSimlBXwOQ8PYagqP4ffnUNUG7gUfM6HFEELWNmRXtcv7ww7uYRROhCFKZE9m7lMQgmxCmehUViYyLcFIacKW57XPjMLWIdTKsX5NKaUVSIGOwO71pCY7TZBW3Kys/dNxleUVKMFYO+F914GtY0nIjCQ/0MZaqqODfRPayI5IuB84W4XHTIXTfx4NGxgCyOFxnm0myy5MOZRTUDlPasEhdcY6KpA8BbILtMDCcMhyZ8uVRu6CldPJjJwETmsdz1IPYZk9dEsOcW/hINtwtjVY7wXyKhtCDvMEtUG8JgyvLApbroXnYMI3WXaB28F4bf4++dfrDdoVclj0ushjKltUrwu06M7PnC0aW2cNcZv60wbWKyjJYdnB4Mk6LxFq79pcc8jzzDrnWmh618DX0xs7TfrcTFkr9kO0sY0s01GeymH3T1f3Lrat3MGHlwxYFtdTm95KXE/71eQ2WoHy53Cb2yCuSXtc4wTSipG02Cl3XaVaN3itBeurE5C14jiQkl1PN7V9Dk/Vxxfb2NZGVsiw/yr8vdnaybrH9TU+oJE8GUL+0U6TZRe4nqZjnq1zJPGJ2gn2a1OOfwvgvrHMpDVCO72dUkoVrh9geT2SXaD1JK6XWv6OsEaq/Gs36geUS9b2xCiDK7gOlN0EXa2IJClYzvNJHa050UI0OnEgufVkF1eEIh+EEEIIIYQQQghxUPTyQQghhBBCCCGEEAdlb9kFh57MDRvEzK0cNlRX0w4CJwsbyn4Moe3oCtH2FAoLsTdNkMfYJGDHMFsK8cMQ/1H0C0hD6j3C2lgKgaGgxhWDYo1Q1lFhhBNdO4adcqjVFqQWOwgZbSlMbNjkcugcMAo1wvBpkFoMlKYbQ8Yx9LMmSY8JmebwrxrudYf3Pc2DmwWGYNd4Tfa+r6F9Ynu8Wdtwvxv1tLSC96NQwOsEuwR4jMJ1MaSUss73xp2BFEUNAAAgAElEQVQCnRWicDOom4phMzVR13MlGCP1F4b58THnOkZuF9N1jGRDuF8627w/N/s19fPCkVpU57YcJKFPGBnPijTr4gGfIRkHJiFHKURPqdtxt+fw9wW0J5RaBfcC6+hJkpaa6fs0qg3HaO9ZUbmryi79sBKFBuM8WVG8KsrXeEzF8Rbn0N6RWaRkpZHsTnVvlxvlZpHHuIbkOSjxxHktkplFIc+hjMGRmc2Grwm2CxPiz/1uek5OKaUeXGESbtP1ddAfWlhDNNTvOrhPKEk9qa1j1JP16cX2UyDBeJycpRZGgmOv6RQWLKdDllCcDdYV4W53dLF9r1vnciS7OAM9GR7b9XaOxP3WyC5sW8U133jtiutaf417XWDnG+z/N8sNbFt5jhkXqFGe1vkZ4djCMpkduMnh/b1brE05VIgOUag9rknhNxJKlVNKqYbfIGO3CxzjgsUPYKQ6fTA+9X5/96QWI0cLmD+rLUkyoIuCUjmxOtmTXZAqKuHkiuulkTOP+b3n/z7BNUNHa1OzH83j6KqG1zG6pEAmYS7KfmpWOV5zetd7RRKM6znyCCGEEEIIIYQQ4oFBLx+EEEIIIYQQQghxUPTyQQghhBBCCCGEEAflUjkfmvev07/8rz6UUkppMTjehQTJ/K22n3RJaF94Y5nFPU8srS7riUXeR6vN886Ke1AftwEdVqjrNVpGe8zozklHY45dsX0R54Pw6OD7tqTl28L3P2tJe7gFveEmlxvOrJatOs37oXYb9VbLwBoKy6F+LdBUjSzpjJ4eRd70DJyUH6yZT47t3oLtXivISQKi9qPKCtEwr8Mx6Uu9PA9ryg3xH/2LX7rY/qn3jb/Dg872vUfpN/7zj93f2TOdhcnLEtk5RXV4+RWobWDfLkwOCarPOS3baeIrXpaU+1abnMsBt502T+cy20FeC3uiQLPZshZzOs9DfWarQNe7ssF7S+fGtAkwM3VL0odCV8Tn03HaBMwHsSLdPOjNOxxv+HU8PnTUqDd0L8B62LP1TYmet8n5wOVmWmAFvPT3vj2llNL2R3523gceIJYfKNO7//6NlFJKzWDHTdRYs94aQc0253xYwkNagD68Yx29kwOippwPaxj3T5ucAwDn3fv707aRLWuFe3/e9PKcjHLhGE056rxNMZtTYnCLpdkNDzXgO3tNBfYT6ENRn0HNdrOy9/N2N/38axpcboBY/IkqD1AnNCc3Kc+9fN9Ph3whd/us37/TWS3/7e4YtnP+hzPyDN7CIHcOx7aUuwxzPmxasOcctS3INcJ28nCf+qBt/e4/ur++bz75f6eHjcc+1KXv+od3U0o2F0xKto/jNtua38Q8XJQ34qlk83m9xq3Vsdm/0+b2gM+Ic7ecwtzCtvaIlydvnCcub3vWmq8HXqP5TTPK+QC5ZjBHQUvjJawZvBxRKVFeKNslzXqi3sD3t8tkP+cDWXVjvhq02mRL7249fS/49yM+B1774W+IvpxnM4yEPyX3+ZkZfGaU48ErS39/6Uc/nnf+4v8y+1IU+SCEEEIIIYQQQoiDopcPQgghhBBCCCGEOCiXkl0s/sUmvevP/UpKKaUv/8Q3uuUwLIUtKdEOZkWh7Ci1eGad7ZDevrpryj0FtkkYXnW7teFPSO+EE6WUUuOE7j0o4LWzZSiGnWIY32lr7Z9e3eXwv9tbGyZ4fp7jjfrTHNZX37HnWpxi2FT+O4dPY8h0DyHTLcVF49NvMHSJnk8BoUtDZA1lbGMobN37DJUrQP6BtkYLsjVag+wCJRiRnSaH+HlSCw4R/i/f/82w94XRV3jQWf3meXrfX/hsSimlL/6Db3HLRXIoLzT4/rE0eWwURlZiuJ0p6J43mXbJx6a3R9WFsoth8tgomtyTF3F3mGmhaaUr6P9IF4+hkht7rIZ9DI1c3LPnXcCxaufLLoyrJ4Ra01CWSpSCoEUVhYZ2nn1VSqlfwP0Eu97R/cTb2eL4R7ILlKFFsgsjn4H2OLNd8DWatkbP+7kf+rmUUkqvDqSDeQjY/XqffveF+2uAb/j0kTmG89wWGsqmtLLLLogxxXXDGrRgGJJ/v47p+YYtr1Fud1zn7bPWXhNKHhsIr9+2dimGssmG5BRNAdKNLpgPUZ5R4PqHypnxc9pCb1wOz0PzNYRel2SbhzZ6uM3u0tiUjQTryNbXwvh0C9VTdOlreCarEmU2tuDTdV53stXiBmQXKLW43Z2Ycnfh2D0YvM4p/htlwri963zZBUoruM2g1GLHFq8ou0DbTWo/X/dv/3JKKaV/OUxLDB5k7vxKlX7qwzdTSil9z6++bI710I/7mf9zHZfK7eGkyOs1tG5NKaVnlnn/7iq3BZZgoeXpFhp8T2tc83sqkF0gkfTbk1bcv6ZpK2CWXRhbe5Ra8FjQTI8FFUkrItnm4hSk+uewvaW1OwzJOGd2K38c62EOrmid0cO1dyjTpD5T1PnEI0kGPC+0TB0WdvEzYGuL1g972G5HlvOmvkjeGUjfn//en7/Y/p1LXJciH4QQQgghhBBCCHFQ9PJBCCGEEEIIIYQQB0UvH4QQQgghhBBCCHFQLpXzATlaWpGekduDJoQtChdgUXVjYYU/T4Kl5teub11sv3Nxy5R7prY5IF7jy+VN93pRN4fbKVktFmotI83ooelBtIR5HarBaoXwGNo1YY6HlFJ6ZZN1ibdP7bHmXv4c5nlY3rHffwG3HS1vWIPvaTRZd43vvrqEz4B0Y2iFx1V4EqjRqZyCZFWIuqwK8jywLewS8jwsS9+6aQX7bLVp7J8SWr/Ns7F9GBnYigmP7aFnSylZq00zEFE5kzfBzyHh1hFdXpDzIbTQ9PJBVFzOyfMw1x4p0nkbO0170zDPA+oyU/LzPCzv2pMtT3PfqWDcKMjLGPWXmCumpGtqzMdC76hcqmN9KOZb8OswUkdwXqtIy47H0IUxagsD5sahmdjmhqBjTvscXP/Uhxuer3Gew7GS8+2c9VnEO7LaBKtw1Paz9aK3BliU1obvGLzibld5fj0lITHmCsL8Dwuy7kRLxaLwc1JZOzw66IynrPM2uR1wjg4sjbH/FGw7i9puzhMDtrsVbu+ofzq2eS3lfGg2+d7sunyvX6FLR+vBEp73ltrW25d3Lra5zSCY/+EeWW3e66bzPNxpbDm0YdxAzoeRPWs7baHZdpzXwbdat/r9aV3/dYIteHcw2G4gT8xZYfvnEtZhlbdmJEZ2nVXOl3FzkbfvLOzzx9we2CdbmhfxWBlYaOLHOnr++JsMj3VsoTl47cTWN2DeA8dOMyWbGwmH52hcWJxRzijI+bC8B2uJrb0XZQO54WBOb49GCbQutnrI94TWmiml1ME41kH+B7ZFNXk46Pngb4hhkduWnT1S6nFtimPwaAGRpgn68YBJfmbmLRtxgGFCkQ9CCCGEEEIIIYQ4KHr5IIQQQgghhBBCiIOyt+zieGFDjTCUDUPc1hXZC0LYIYYkpWQtalBq8XVLa5vzjiqHxmFYZEXebXf7HOb0SpVlBzWFtaM9SmQT2if/mP3c5cPH2RqnTxiGlcN8WrLaxLDBu2DrdJtkF7fP873YnFnLp/I017m4m8+7vG2vcXUbZAjnGD5ty3UQMt1gJCyHBpkwYQgFYytFqI/D0d1woChkDqP0yUITQ6hqOLaqbaAUtmsM9+XQXwzj5TBOa7WZt5eR187DThvEb2F7mCsnGNXhWweN7Cs9MOQXQ42DJhVZHlrZBX0O22nl/D0l3/aIXx97F8nhe6hkAllDwRaSEHpYR7KL7H6cVndsn1rcy32n3IAtVUd2UzWMAUf+1DSUJWzj33l8wQ+RTMKx5Ro1OyhnZRe2XGXGOdim59M7UovCOjKa7zKMJBmOPes1/VcC2hCmZNcaNcgV7lL4eyR5Ww/TY+9xQQ8WQHnGsqD5APZXsH2b5Blo84jXXjbk8xZgpRa5sfXU/jvPQpyHCE92MbLQhBBqtOClMWOubR6OGUbGmVIqW2jjUD3LLrZbGLsgNHxT2jXOV8ob+fpgfdpSp0HJxGO1XZ+ixMezOE8ppVOo4x5IayLb1bMmb4+sNsE2sw1C4dECkO1UjbwisFq8LqAFb0q2796Bv5e0eC2TL2vA529+d1CHwnHHWvDateF5na+xwWdOMflsB/sa/JsBLRU7XpOn6WfO8gxsJx1KEwOrTSPHovUdDn8VjBOj+RP6f02yC5Ra4FqiOiXpP8gucN4td3YswMm2BRvOdmSfPS27GElQArfKJdhwogSjot8dbT1d/0BjMD4HVy432ndkya+Ht5a+oiHjmi5XhBBCCCGEEEII8aCglw9CCCGEEEIIIYQ4KHvLLk4ohAjDl+pAdoFhSBzWhlliMUzypLDnWjvhT1HIFNIH8alzw9CKmdIKLsdhXh4m1Ao2m96G5GFIqg3js2Fnm13eHza2DgynriEUcnnHXvv6VXBkgPAnvu0dZJctMaNvQeFKkGkWs85iWDVXPyzoZJUTgk5h6wXs43ZJMo7lCsLkVrndPQ5OLCml9MQi7z8OqXpvVrZcKLuA8LwKvuXcNvJQwuFhXnfjvwfP1g8J8/voEISRYagbOjJwKOu8awicKqJjPL5Ex7zrGJztlGyoNYZTcwj1zFDJxTmM/6dWNlTfyR8sz6EP0A0dltPTUb+g8OIl9F/MtE3hqsaBgp+Jdwv5tsNXQUUVqatMtn4vU39KKRVGagH3nZRWRp5Bw4GRZOBQfk3/lcBh6DXcxBrmvwVlrsf1BM/5KNHEcZlDqJfwYKxMzjYAXK+syizxZFeMylmj8PXhfkfH0PHASkZtna75yUiCNR2Sz+5UntRi5IKDmetP6X6CE87yLoZT2w5QgTzLyC5O7BhRghMErht4zNgu89roK1WWYPAtQlnDkzTnH1XT7hcNPR9ck90DOc0pSWtOQWpx3oAbW2u/YwuyCxsKT+spsxNlv3cPXRvY7eIMXEciFzvs/yzjPkm5z3cw2C5GEqxpSS7/FkKHG3QFRNeSlJL7LEcGVjPlNMbRguXeGPIfhP9bh6xAdgFf2czHPH/C2qLeDnQM5ArnILu4RwuSHZ4MxzFbX7fK36va5e2y4XERtqMlF3ysZnfHanq872pbDp85SnA6agsdSGTxWD9aWKIsZpj+OxNJnQM5875c0+WKEEIIIYQQQgghHhT08kEIIYQQQgghhBAHRS8fhBBCCCGEEEIIcVD2zvmwrq1+qQZ9FGqZloHVVGRJ2YC/2J3eWmihJh61Xbe6Y1PuHlhvoQ5vR3kTUGNj9VDu5Y0oHbvOufQkpEHNJ14HW0Phd9m2gW6wQSsfX5eFlnH1xrfMq29Bvg4SERZgNYW5HOq1PW/r6Ml7csYZ4HqHigRHRgufr7egXA5lPW1zs1za9nljnS/k6aPsE/bM+p4p945lNmx6W333YvuJynqL3SyzbnRBFq9oqYltmnXH14mCLZtcLRkLy3zNGdulXkBOcyaXCFotBlrpYabub9+cD65tZqi/C67DnNjZpn2UtnK6EdRpck4FHNrLBrSyW9vOMc9DcWbz/HgUR5CzhscX5zkUo3KBTarTZKLvb75vy2PedB2j9C143+F6WXsbPTuUM2P7nG0l+5DB+RBaMzfiNllNYj4oehA4Fq+d7fvktrsBe87jgaw7g7wRCF5vZNeI8/qms8cqaMxV6a87Zq9DsBi2XbbaNDrv/PeSUiHgXF5Rd0eLbszzsLht72d5igsC+L7nZEla5DWfsc07prXG3Xw/d6s8ttxaWEtyXMe11KHQKhHXu9zu8NlhHi7M8ZBSSmeQhwtzcjU7+7x7z/JwX5vMPdanDxucJwUpYdBkS85NmfdHdcDtxrXbksYMzOWFv3dWlNgHc+OZ6T14PlEuB6w9ahmmu9N6DPdNDgkqZ/LEoNUmDZ84ZuDX53LRsQLm2qKBg41dkBQbSiTxGgvKEwM5IPBWj26ts+YqS5pLIM/D0YJ+79bTv3dHcxXaBMNvt21jr30L+Y7M86E8aGafn91cvDXnFY0finwQQgghhBBCCCHEQdHLByGEEEIIIYQQQhyUvWUXbDtUQjBPDTE0VRCiwaEnaIfzSpftqtg255UuWyXtQJ7xleamKffyLpe7vcsSjDMKf0N7pc4JO0rJhkOxZGSfwBaUWnR0rtKpka02jUxk7lVwhO/ci8dwpR3GY9N9MtYuaXo7UUiyEwaeUootb1DughaaNYVGLSBMDkKjUGaRkpVavPPo9sX2e9avmHLvXr6cP1NnScbN0saZetKKlPzw3Ossu+Bw9wLazlAF39t5zilZiY05Rg17gHEJJRgDtV+zb8Jc/csLZRGetCIlaxuK7rpsJ+rVx98RP4bf44rskVyiaGCjY8F7QVZxFcjfang+NYUogkWlsaSs+XlPb09e42uXOv3n1y2I9YdOqF6IO8k4vPE/JXsLjQTjGg8bCK41cExdlWx518K2Xa+gffdNKHfCnqzAbsjlzgayhnQGB167bCGsG693QSHZZbDW8CSeozXJ3FDZPWRcKJ8bKcSgXbOFLIY8ly2EnTe2oAmhhvDqkp5PtckyjGqX73W1ZcvgvN1tQYKxtUvg00V+PhWFV+Nai+XEyAYkvps2b29JCosh1TuQWuD1pZRSAnu90E0ev3JoR43zwvUcNHgt7CnS2CYT99lqk2Wzr4E2uymxJW/e5vWfJzvn3x1zLTQNM6Ub43NNb48mTE+2Gcg7o/UTVj+aq2FeHxbwJElOYWQYXb6ooSa7ylWuA6VaHSm6erD0TiuQiJFUe73Mz/hkYaUfR5CeYFn5Y8YOpHWnVf59yhIcHIPQahMtOPcmWrfikCGrTSGEEEIIIYQQQjwM6OWDEEIIIYQQQgghDsqVyS48ysRhI/l9B2d4xhDFe22OgXm1PDHlMEQRM9Leaa0rxld2+XN3m3xs27HbBYT4BmFNQUQmlfNDIW127n7y7yml1MPnWJLhgWFidUWZdUF20NZ0TaBCaSH5c3Nsr6m5mUMIC3AW4Szz7QlkDD6C0KAlZdbFhPYYPjwKTXe2aR/D76uKZRc55Ol4lUOjHl9ZmcQzqyyh+Lr1qxfb71l9xZR7V52PodRinC0901Fc0yjD/aMAZ941Mgnn7ynRc6bwRZTbeM4XyWZs7jGSdSS7cMJc54a88mM1bhcUGu3JLiK3C4Rj7TGk0vydrwkzJafJ7ZRsv6TE4KnDcWOVP1jdsAULCLWuUE5BsosOxo3mMXAnukHjNWSyb48gDNEO/0aSMXKCwPuJiaGpmEkajdPVSCYHmazxOVKFg3NeftyDcR1I87ieEdQjcE5dBLILDIc+odDomxAO/QS0w+OSGjmwAdlF6u1D6SBce+OEXd+/dnA1GukLoVzy1xCe1GIkzwDZAMoV2DHKZLJHZwWSwQ1mzMjHxrI1/Iw5lHo4t5FWLe1acFjD4FJXbjmUWplzRWZJ8B0xdDkl6xK2aX0pTFv6/7dDaSyuNbctjWMgtehBMpJ2tu4CQ6qNrM6eN8xw781BVxVD/YDBvztwH+UTIwey4BiONQuYKXhd50ktIgm6USeEsgu3ijRXPzVbumE+NLNc4Bhh1hk0H+PaolvZSlrTR/O40JOcojoh3cRX2T1uZfabp6BPPpnP1Txmv2R3A6Tax/mZ3jyycwn+hnhsaX9PnDgOOSzB2cHiogycdFoYT3Yg2+L14pVLMA8gz1LkgxBCCCGEEEIIIQ6KXj4IIYQQQgghhBDioOjlgxBCCCGEEEIIIQ7K3jkf6plC1FEuAxABNZTzAcu2sF2PLGqmcyWcd1avedZmrU8DWpm5mifW0eA+2zDVqK+cqY9BPU87EkfOqsKcdw1WLtva6l+PIM9Bc2IFV22br2MHusSCdINDlZ9XcxPKdfb79mCFh/rs5gbplyC/RLcCzfSC9Eu1o1dMVv+P2v+6Jr0e5MBYw71hjdZTy9OL7bfVdy+231HfMuWeqXK546Av4GPc0TNuILfAo5L/YaRtn60lnM6NkFJKBfQBzPUxsinCRA/YPUg72MP4MnQoWgyekbG/C46Ncj445fwzGQLn2dChNqEdbo19zxZD+6mipf57Mm23N5R2fOnWuZJyB/pL+pLtOt8MzDfTnFA5GFPw+nor7Uy9GTeSj7EGDOw6g7w0qIcvwTaThwZ3ahjZFQa6bDf/h/P3hxzWx2LOp1Xp51fAfbbaXMODOC5zw1kVfs6HDgarZZCvwX6GxhbY78w25QOChxzNDSb/A61JKsyvgGPfYMt5mvKBrh3nYWN/R6tI7IdsX4drjRLXZIXtvBXY4WGf6dZ2bNk9nk/ujQt8jZgbgfsj5oDYUY4GXNd1Qc4HtG7HOlqqD5/JAPelYNu81nn+vD7F58i21ZwD4uJD039+2BnZZKKtrcnr4Fttok16StZOF/M3VLSI4T6f//5g5Pyy66K51p3zjo2cNp18UjxXm3tD5+rAXrOCdUFJ1rU4eGEumObEVrh9Ku9vnoI8Lk/ZtrB6Iv82ePLm2cX200dnptwTy/OL7ccW9vfE0ml3DOY/xHuxo9/ImzrvVxXMVdHz2bedeR+7ovwPinwQQgghhBBCCCHEQdHLByGEEEIIIYQQQhyUvWUXX/i4DWP88P8DIYTmnYYNf+ohdpUlGdt++liT2CcN68MQFZIT9NPvVjgc21homvBpWw6lFqPIagx/DOJfvXDK0bWWuOnbaaEk5RhsXThUFeUZ/L3uwv4OQlD7BVmhQvhSfY5hxvbSMcrHWujYct0awqRW09sppTQs4Lss7PdCe83SkWCklNICjq1AnrJke7Yq30O0ahuH7YKMI4hqwk911C4ap5n8tfd+h1/hQ86zf+VFs//Fv/nCxbYJ3ec+dAUhiqaPBpac2PkG+Ewk1ypCWYgvpygCScYcRrIVE74IYbgUuu9LMsj+EnfY2g3CutFGF6VWKaVU7SCkGCvk8EqoA8cKttC0Ei24HrIQHtlrmoNwGXAzyEExofPi0Di2fonCunf+2FigJGPu4x5JMqaLveNvfGpmhQ8Xp3/sy2b/yRdvXGxb2022xss3vwrmZJRTjNrkMH2zO+p4PfSbzbCY3L6/nxsKykQ5vHbX4Voosv9GKSjbrYF8DGSIIxtfoINj/M2NEgjWK92ooC9VM6HX9XQ4dUoplW2+N956IiW7Jtk9hqHWFAoPaw2UmRXUj4tgvO+c9SQ/HyyH1ng9y1jNIDxTCoxzZBhqzR+cPvb8939m1nkfNn7mI3bS+O5fzuu6Clp2ZH+5o98dG5hQcPtub891CpqCM5jIWvp9gu3G206JpVAz1yAzj42l5dPbo8nKWGhCn2EbX5R0LrFyWx0Ofy0tqO1859eB872xAaexoH0MxsLHc7t48jErp3jmJEurn17nbZRZpJTSSZV/J7DdM85JKAViaQ6Ww9++PC+cw7h4r/R/7zhD1exxIeK5f/8X5hV8HRT5IIQQQgghhBBCiIOilw9CCCGEEEIIIYQ4KHvLLkYVYQgIxAmNJBNBaAeGQPUzQ0AiTEhmhWEt+4Vw4/XVgduFCe8OQqGs7MTWhzIMrJtdRlaQ+RulFjcpPPHGIocGnYA8I6WUXllmccCtoxxCdnbDhpNtznJzKTaQdXbH8U+wjSGT3NowezZmZ65JWoFhknQMw43MduBUgkRhd0g3CkefjleqKLwdw3MXLLuA/f66pp1+HUwYKd4e7qNwcBiFr4JrSI/tITgvRu9RyFoJfdHKFea1lVEoI8ouwjDHfWQXHKKJYf0QykntFx1C+tLphymloQIJyoLa9grkBSBJYKkBDm3ooDNwmDiGaMJY0S+p3BLtKWY6WjA4jMA1FeR20WN2fhg2Sxpfy20uV+F1bKkc9nNwCWIphWkKNBkOUMcVJZ5+qCid+ZWlhkhH4ytK3rYDyDM4hB76E5bb0H0/BTnFaZ9Dre+SZuge7N+DkGx26tqZ7OfkOuGsX0aSTMf5py3IxQH6UANONWyygHfXrkLo+jAku/LHDHSxKflk+FVQqsFuPE54dXtMfeYI1klreN7kioXjcSS7iNaQbTddbvTccDcYg905UswmGhuQBvrxhhpbA/0G++St/tiUe6XLsrA7be7vp521eNh24NQSyKyGqA2ZcrjOsMe8T3G4Pu6jBIvrQzkq6ta57aLrBH6GfwtEciLjMhWsC/o1jHdrcDQ5spLpp0+yI8UzJ/cutt95dMeUe9sqH3u8ylKLBS1wIkmf1+445cDCcVna0o26V+c5Y7WA+aiiz3sLg5FM11wsXSTWN13dG0GRD0IIIYQQQgghhDgoevkghBBCCCGEEEKIg6KXD0IIIYQQQgghhDgoV5bzAa1CMM9DRVZVJmcBv/pAKS9ojyI9fBdooFz7GvrW5R62VphDIiVro2LyX0Q5H4ztJukwh2ldK4PWLic1WEOWzVTxlFJK95bW8/KZ9dHF9qvHefv2zSNT7u42f+50m/Vr2629oT3cT3z8ozsLf/DsTvlzI5tUY6/p6/o83VxD9ke4vxnydxzp/yChQId2OnTeJYjlehJcrUE5G9mzXmt6Z5tvB+Z54JwP2M87P48K6xbz36m9gW5xH2urkd4ysO+9+pwP08dGVkw9HoP7R1a2A9hjDZ0dsHvIj8A2crYS2I60g3DfMbdLxXleHC17eC96XzeP32OgnA9DC/ptuBfl1tZXYcf3GlpK5l6UcA0FJzlCW0N2OcP8J49mqphZGLvuwc5RaJV3OuRxuOttPiT7mXzfz2i+PsM8Dz3MoZ3Vg99u87G7DeRXan09eNPNsxCvaP7DnC+Yh2RBNsOYywDr2NGYAWldUudsp5RSX0Of4dwouGya53ZMOSRoHAPd9wDbqPlOKaV6mffrBczXdC/4HppzRTmJ8JBzLMwFhHPOKMcLXgR8nse7wO6ZCgYHryeeLr+hfozrvI59WLEcWOi+0t4wx77S3LzYvtXk/n+vsevuTYs5H2A9yfMs5knCOYPak213tKZjJp4AACAASURBVE6eubbw8tX1bAOO/dPka7PFcFlA6eoMmAJhWPg5nsrj/HtnfWzH6seOcy6Hp4+ybeZTq1NT7mtWdy+237m8fbH9ttrmfHisyvVhHrDxXALWyjTgca44jxX8XsOcEtw+T2GeuFNDPhHKXdOU+RrN8itYp43s7Y2fqvuxvVHkgxBCCCGEEEIIIQ6KXj4IIYQQQgghhBDioFyZ7OIzH8vhId/6i6ifoIJBqN1cqQWCsouWbE08S64N2VphyHs7+O9jsI6KzmXtMIPQPSO1cGQhfO0YFlnb+BcM17kJYUJoDcPlmoUN5UH7r7uraSuwlKwd2Ku7HE52e2vlGfeaHBq0g9CyLgiXb41d4nyrIS9Unf+KoaU7lFZ0tguc9cvJbQytSimlBtpJb+QTPmsKx8Z294Pv+cPBJ68vz/7VFy+2v/Q3XsgHqAtBZHQayBKoN/v+E/AsL6Mo+UjKM7eOUHbhf8zFhNHt6bXo9anIym0sa5gOAR3bd4FMIrgmI2uDMMKxxA3swOAaOgpHbyB8taXQdTP2wnjDIa9dC/ZqKMGobX1DyYKr+3CYYwEuXSNLUixnQrz9cm/773/WP3hN+Z3vyGG0T/1i/juHqO4GlNDZ8RutMUsIc2+CuXsDY/7d3sok7vQwh8J8ijKLlFK63eT9ey3IGBtbH0otImkpwqUqZ/0zAvpXXaF1N0vE8v7OhGRTX4A1ClprpmTcZS0c1o2h3CjHonIoV6hhzFgu7ZiBY8hceeZljnmE4yJKxkD+wVbSVGMuxwPDzKng+b/06XkFrxE/+aEnL7b/7K9++WKbbWw3CULoaczYwZiBa+bbne3jX95lGcarO+jvO5JdNLm+Hc4zNAd57WHk3BrYxPZGggUyBqrTrCeCdcvgWFwP/IsSbTJRBsn9HcYMlkytwSrzqZt57H/niZVJvPfk5Yvtd61ezZ+p7plyb6/vTh57urS+2Ogsfgpri1d6a5/8cmdlNwjOSSjdWAYalBP28QbOwa711jK3rVuVvSYzGeCtZktvbFvcGGD/ub989WOGIh+EEEIIIYQQQghxUPTyQQghhBBCCCGEEAflymQXyAJTGnOEG7zuKCnGN3KaMFVgyB+G3Vb2Xcqqz19vAaEsHE64KfKxAp0aghB/rgOlFmUkuxjmOWsgRoJBsXUo90BpxTGFEN0kGQbyRJUzw55C1u4tZW5FGcIri5PJ7ZRsqBlm8WZniS1IMnZw3zEELSWWZHDW+jQJPzvMJoxZhjnL+CmEwp6B7GQUgjfzvd0iyJi8eFQdLjyC8DBzu9mRAN0a8ADLXPBgicX88OLSkWrw/lXkDw+dcVDiMLOOSJJhxzK/vqgODN/EEG+WSSxhPojqw2u3MrbOLbeDMR7Hk/vH8jjCjgGeXIPHDRyLGtjeVZzVGrbhXCWZJ5Q7CJsOM9LnzdF0omHjApwbmp4ldCBrIBeLhdG/5M3t4Es3rUOGnTes24UvXcRs5Si12FDbZQkREq1DvGuPxgWvXLJf0b2GhmQMPcg6h0Bmi1MjSyE8Rwq+dhx3aii3rFu3nFkzUifE+84yUVx7zB1bcdQZyFnDczEY6KYN3lpjdG8fPReLNwq7EeAalY/h+hdlFy/vbNg9SquMo01j54wt7Lcwt/Qsu3Cund1O+gL7Cf92AaemoP270iL+M8omKr+ckQbhMXKwqla5vx8d2bH6yeP82+XdN25dbD934w9MuefXv3+x/XWLLMF4urS/fR6H9chNkIzdKKwzEfJyketokr2+0z7/7sLfkinZ+4tSC/49toAVRA/tbkFrH/xN8geL3O5W5HZxalzA8t9HMlDjPsQ6nnRQFPkghBBCCCGEEEKIg6KXD0IIIYQQQgghhDgoevkghBBCCCGEEEKIg3KQnA8vfjTrXl74XGOOYZ6HkZ0mun5EWmPMr5D8+jaQswDLzbWuakl36WojiT6w69wrz0Nw3gqEfxV8R7YCnX0MtJebwuomUSeLFkX9grRn8B3xejm/gveZruS8DqBfGt33aZtAtuts4T0b6rjPW7Jga6etNlHTm5LNAdEMkF+DHm8Ff1gUVnf+Pe/+tiQyz/6wY7uZkrmvoy7UYgIHsCzj3BBw+4tAOI95Hkonr0FKvoXmZezaIrvdOUR2eFU53a9H5aLxBXMvUB04juCxJXlILtHmLrrvzpjKOXRwfA3niR6vyWoi51oedwsYNyCXwynpt89gd2hgbNxyfpLp7RFBzocn/6dHz17T45e/Nd+cFz5nlzM4Rt8lezQE57J1adcr+Byw3Z3SfODlStpSHoodWDsbO01aa0RjgSm5h9Uu9xmzD/14wXrwRe7XkYVk34N+mb6X9znOE4P5G9D+k8dgHHeMhWBwX8y6jnLB4Pqio8ncy/MwO7cO3U/MZYHrlYKT8JQ4t5iLteWCpvAo2mt6/PgHn7nYRtvNlFJqwCtyQ76RqLdHC907rR0L7jWQNwzyOqC1ZkoptWjdDHkexpbWE18ipdFa09Zhj2GdaE0+Wo/00UILTo1tEg+UfrkC2ntFOR9W6zzuPn60McfeAZaa7zrKOR++fvmyKfeuOttrvqMCO2a6puMir+tXRX4mFeVna8DfvcPfrcF9qagTYp4HzMN3s7Tf8bEiH8Oxa13Y+ejWIueleHyR7WM5xw1aEIchBkE+iPd/8rDrDEU+CCGEEEIIIYQQ4qDo5YMQQgghhBBCCCEOykFkFwiH2ZYQhtIXUWihLxNA+5HI1hLLYahMM3CIYzlZrkw2JG8kE5lxjEN0Zssu4PvPtfHEMMEdhYw1QzVZ7jKw9ZDHfHkKhH/NvAYOcYxC3L1yGAq6o7BLtOfDkFm+n9hmovuJUoua2pMI4GbeB8cwUhClFkUQ8o7hZpF0wbFy43Ke/W9K1rJtFFKJ2zPb8mybNyckOaWUFs6xBckTrOUl2eFB2Uh2YWQdM3s6jqcsk2vd8doPyWbJiHveYLzeQsg8l9suIJweQh5ZWmH2gwjX0oRDvs5Fi5SSlVmmZO3wOBwWwfF7PdgwV1x7oDwjluGhnbY/Z2K/K1ji6X7K4kkco2Nz1yA8ZqBo0pN3pRSPd97napJd4PiEkgweg+aOwd7YOle2dr+OySpGeBI8vp/GPtmftkhqOnPtJjveWfCaFscCtu4160FnOyU7T+zAQrcjC00jk4C/z21n43LQ72hd2/fYT+aNLtE4UZgJCja5f6KdLvTj1cquEW6ss+zgibW1oXxyeXax/bbFvYtttqtEWcPa2N3637cFi8tzsmO+2+drfKVHm2U7z1hrTF+qfgLXhzKLlFK6CXI/q2I/M+WeqPL+SZ3rWNL4iZKuDiUynOkAx6A3eZ2hyAchhBBCCCGEEEIcFL18EEIIIYQQQgghxEHRywchhBBCCCGEEEIclIPnfPjUR6294nd+PluMRAr4q8j50IPgaAU65BVpktsSNZq+zqkF3Q/neJhrmecdi3IjRKBmDTVq24IsJNGSM7hnmNtgSxpatJ68B7ZD5519xlu4T5hTga1LPaux/hK3opiph/RsEdm6a+5z8PI8VPT3Ep7Pn37Xt8yqW6T07F990ewb600rb7M6tt47YC1bEc6hUDp5HliXzG3Ho4VxiIz8Ug99IMr/EOV5QEqjvwQrO875AN8F8zywJaWX1yElq7/GOsZ5fmbmeUC7XdjGcff+PuRsgTFvdN6EOteZgkbq1i3mygHrzrFWHE88rYcd7eOz4lsE+0/8A1lrzuGzf8juf+sv5nmJ24aXH2k50uyi3RrmjWI9OOR86P2VDbZD7GtsL93NXE8gXA7bKPYZ7se4F+ZrcvKmcDk7l/vXFM3DlZNrhvMmzJ6v97A4Z1vvtyz1Cl67mST8jzz/A7LWnMNPfPBps/+v/sotp6Rda8/tn3bdScfQ0hutmjlPkFs7gTnU+EOQh6WDCxml3XPtZP1rMla7vJbCXFDLPN6tF3acPV7kldGNhc2H8FidfzMelzkvA9tQIh1cRsPfaWihXD52RjftFqwtboFVM+cWwrlk9FsVFqsnRb72BZXzogBiW8/ABh3zk1X+88FFyPv+2pu7zlDkgxBCCCGEEEIIIQ6KXj4IIYQQQgghhBDioBxcdsHMtVqrZtpLImxr1Tk2Vxyq54UaXkYK4ckuOHwa5RrmXuwpu8Bwym2ZH2fVWykE2n/x/cT7huU4vAhlF6dtll3caa3t2Fmby226XAdaEKW0X2jpKErMCZPkchiuuYKQr6Pahm6tq7yP8pwoHHcBzxGtNaf2xZ5gkx3FAA6T5Tj00ES8B9ZmKK/A7RXLLsrpMHxuyzX00SJoDy1KMPa0lPWkFgu69lUFbRvbOZWLbH49C82542Y7Gq+npRa7kezCkcmxXWUw13jXGFlt4nU0ZJs2QFgrhriOLsELm+a2KnvNNwxKA9lSD+e5bZnnqFVp5wMc573Pp2TnRj6G1E6fZBlnE8gVvVY9tvjFcQfHSFufCdlFyRmvE2CgNOskqg+vIpI77LvWOiR2jmAZ1zSxzDZvd3SfemNJmia3R/tGgkHn1ZjxhokseSsjXfbbLv52MfLGanosSYnbAstF0+SxkbQiWE+b+WkfApmh6TMszayn78WqtutpXIefVNbyEtfhbGWJ4O+YM7Q77u19x3VBA1d/Rr937gwrOIZzSSSFsPdp6aQI4DrsdeTvcas/MuVO4TqiecblDTaDq0SRD0IIIYQQQgghhDgoevkghBBCCCGEEEKIg/Kmyy5++iM5jOQTnz9/w/VhqA2HsnAoo8fsTOjmvH7dGBoVuWLYbOdzM1pTqDK8P2KZBIKhRnxNKE/ZomNGb5sHulqcgrQCZRYppbQBeQVKLTCUNKXI+YP/kJ9PdJ9M9uzShj9hyNcaQr5OKLPujTqHfEWZdXF/AZfEMovv/tqPudcr5oPuF8b5IiUT5u4orVJKLLXAtsJOECDRMbILG/KHoYNRiD+G61el7VMYvrmDNttSWD+G7Jpw5cjFIrh2lBctjaOFDVHkrMzIPi4WOPZwSDbKKVCSEY21+4Zre3W2FMqIYxY69/BY1uHzmo5iv7/fO9tU7sm/L4eLN8oXPp7b+HOfsdLAtpyWK65KO4d6sgueQ3HuDWUXxu0i98m5GfNTsmPBXBcHE64d9BnPjSIlOxYYCQGNuUZ2SvW7185OGlCuxWtyHIuYuWNGdC9GhgF4bOb6bwhC4Xv4joPZZjkFSrr8C3z+B+Vw8Ub5yQ89ebH9Xb981xxboCsO9N2jyq4NNxWMBbU/FuzaXB/O9+FvC9wetSeYWztf4jOS6+yD4+JR0rq7Mm5hvgwU7yevQTx3vp4Gns2Q73s55M80waJwh26BNG7jmN6ZdYutz6QICLRPRt5OnRevcQPt7OXuhil3u8u/n1H6Pvpt5Uk/acx4/yffunWGIh+EEEIIIYQQQghxUPTyQQghhBBCCCGEEAdFLx+EEEIIIYQQQghxUN70nA8I5n9Iyc8BwXkOPB+yyGpzrpXjvhy6fu88qJNGK6CzweZhwM+x7diun87RwDkfULtuLDRbWw7rQJuwSK1prArpWIWaskCDjtr9irScqM8/gbwOjy83ptzNOu/frPL2cWlzQ6xBC7wG0dv3vOvb3OsTV8Ozf+VFs//Fv/0deSfKAWByJfg5H9B6DnMljGxZa8yb4FtAYR/l/CgL0DduWuhTJeUUwH4Efblm7aST52FdsaVsPrYK9JZz6YOx1svz0LKF5hscr6O8G6M5xDkv2xriWIYa3aaxY97Qgq1hV8C2PRc2TxzKnv7vlOPhkHzx2+z4/Q2fzmsP1Pqy7ndhrNJ8O138HB7j+Qr7Vw3tbjns1+8wN8K+axCT/wZzUgTWgCbXChXrCtA2s73k3HwIjo3xOMfLG/OX5LwWqIcfpZ6CslGuDc9es+99jb7R70c5H6Dc8z+gHA+H5Kc+fNPs4+8TzAe2q+xc0FAesddg69ottPG562Sv/6Rk2xrnjGqddji29Zxu11GqFZvzwX7H0smtVQdrLs4z5c3rO87RADkfcFjYBL8ZcF3A9WFOCc7zgET2rMguQf4oPhfUf6dfX2yPcj60xxfbp5CDbxvmfMjbz/3Qz8261jcDRT4IIYQQQgghhBDioOjlgxBCCCGEEEIIIQ7KWyq7YDxLFcYLoeWQSRPum4Kw4MifbyZzQ/JMCCGE63AY0lz7T/yO28BqE61cdr0vk0BpBZfD0C0M8dr18yw0GRNCieFZVA5r47BLL6xrQeHjGGZ+A8LiUGaRUkqPVzm07iZsn4SyC99OSRyeAe2doCsXZPuEIYGeBVRKVqKD28cg10nJyndQusB9F+0b2fISLWuN3IPC6LiPTX2G68frPaFrR3uw2rETZHicjCys5taxD+b+7lmfJ7XAsfD+fr7v2xasQBv7PIYWQu07DKGmE+/nDCquGJzbrGSI2jjMI1UQ4m/XF0GILsxXyz0lTnPpHAkBjxl4HSi1mCslawv/+3IYN4aDe/bBKaU0gNYytNOFbhjKrmbbrvvSGo+x5eH0WnMU4m7C39Pk9v0/ONviTeW4ymtAbE/8u8Nb/7OkcdVPy5P3bXe4RmDrRbSJ7qDd9YEsKrT8dI6x7MJIoY0smmQXaFdJx1yrTRpnWcpwUd8QyC5QWkFjRCS1mIu5JpTmBTaht7osrXi1PTHl7rRZkoEy3Y6e45XYqR4YRT4IIYQQQgghhBDioOjlgxBCCCGEEEIIIQ7KAyW7+OffnENM/vgvnbrljIuFyaT+xt0ubMhP5R4rE0s3IMTRSAj8ODkM8eNMuPXsbM9wDRgKRmHaW8epgvdNeNYwLySLQ7DCMEmH6DOVcy9SsiFZeM84uz+GoD9Wb2Gb3C7A4eJmmbdPChu2vobr+Lfe/YJ77eLwPP+DOev3S//Dxy+2y5raSjUtc+Ks7ijZwXZ0RG3qBNoRZr/mNophmRzKjP0ew583HUuePNkFuV3APkotjirbfq1MxO97GA7a0LEq4XjgVmGw46sdX6KwacRzE5jraJESfa8oXBWlFrDddzSfGLeL/OdoKHz73/6Uf1AclN974e7F9te+CFntSWqIcyq2Tw4N7p01CePVURe0npgpycD6Wp4bh2npAq81Fo7UYq4spHTGppTG667OkW72QUcpQNYYreKidcIh4bHPdfQgFwuzbjLSCv9byuHireN/+9ATF9vf/ct3Lra7yp930LnhvLTr7sZI/zAkf95vFe5bO0c+nZKVEzZmjT+vf3ZR2wXYPaZCV7Hgd8zc3zidkUmwDHT652xV+PczklagNKIMJXdGGO4ew3PxtZ72WX6Lsou73dqWa1cX27hG5OeDPP99n3GPvZUo8kEIIYQQQgghhBAHRS8fhBBCCCGEEEIIcVD08kEIIYQQQgghhBAH5YHK+YDMzf+AsFZqbp4Hj1CXRH5IqCGfe97IXsZqoAK9kcnDABqykcYZtMtky4J5HlD/PPd7VKV/fXNNKD0LUq6fbcLQJhC1+yN9foU5H7KF5uP1mSnn2Wsel7a+7/v6PzrxLcRbzfN/8ecvtn/zxz5qjqG95qLObWVR+basUc4HzJuA2wuyrsRxaVFY3SeWXZVZ97etSHuO/RzqG+m3Qad9VE5bgU7tT9WdUkoN9GAe85KTvoHzzZjubKqnvuxcR0+azbm2ZKYO1rYO09pWHhuNPhZsAoeO3tt7dnh03nf9F8rz8KDh5n9IyTRRnIerQJc912ob80FxzgOe5+wlOXrrkUfj9HmjObQ2c62f88HkwrhEfgXTNczH/LUb6pm7kvJLYa6tmeuVKF9VOJ7MzHnlfce4XKDZ/v4HU7P9KPNPP/zYxTbmf0jJ5iGz87tdP2AuqN7kAwjyEAR57BpYn3BeN8zfsqum1/sp2fnP5HwI+knUdnHtbnPL+Hm2IktjhC1Ovc/1M+uLYGtMcwyfHR0z6wwY4876lSl3Cvv3IM/DvY7KdXmNaH6r0brlvX/+c+71Pigo8kEIIYQQQgghhBAHRS8fhBBCCCGEEEIIcVAeWNkFghKMlFL6xOfPnZL7YcIpi/w+hkOaMfyXQxJtWLT/TsfYdUbWM47t3DgscDoMaxROZSQZ88QQUThlJJNgu53L1sf3vXTsNFOy4VtHJkTeWgveqLKE4nGUXVS2LaG95jHILj75DbLTfNjg0LM/+CcfuNjGEMBVZSUIywolFLm9cdjkGvYxvHLhSBq43P39HB6JUogtW/45oY3cp+x1wHcs7DVVjpSLradKsMri6EWWQ+QPcZ3T0rCRXTF8F/wM24x640hkecf3z1ptwjbJKVBq0aPsgqytCtyHc73nP5XM4mECJRgpWRkGhvizTMKTRkb2r5F1J56L2zvaYZZpnkVfdC6UK/Lc6xHJE4pgXVOYctOfiYhkEub5XKIOt1zwOW/79Y7NAaWD4sEHJRgppfRnfuXVi22cj1kmgPu4XmcLSW8e43I4T67o98kWpBY7WFvsOv6dUE0e43UBzp9zZRe4zlpWvuV49DsBx1O+pp0j8u5mWpdGVIENeFQ/Wmri8z4l2QVKLc5AWnFO8hnzTOAZvP3f+HX3Gh5UFPkghBBCCCGEEEKIg6KXD0IIIYQQQgghhDgoD4XsgvnpjxxdbH/n5zdByWk4rKdyQv47CiuuCwy79MP/+plZpyPpAso6qjAMaToLfgSfK5I8eGA4Fd9Pr47ItSPOxg0ZcymcDENQUXZxXPqyixtVbjOPlSy7yPs/8r6PudcrHj4wNG33z95zsb2suE1NSzK4XXpSi3Xhyy54TPHCMo9LCr10+vbIGcZx0GG5hweHhnqOFqPrgDGPXTFwjMKwznEGbTwByB0CSdpcR54oNBrDFzlrdO+Fl/JQBpf4/k/+7KxrEg8+rhPGaEp+Y//HGc3xOM8FciecN2e7bNE45kk8mUhC4sFyirqaNw7ZddK8cxkJxryPjO5ZJNWy5WaeAAiWhem5f+8XLl+heCD5yQ89ebH9Z3/1yxfbGIJ/fx9kF6UvccC5vzOuGCTjgDrq3vazFUgytnBsW7LbBbjiVflcu85eexu4biA41uBaPXK7iGQXKHEYuV3AemcHVVQ0Gsx100CiT6D8hdc0+Mw3Q5ZQnPVLUw73cZtlF7h+Wv3J3wqv+UFHkQ9CCCGEEEIIIYQ4KHr5IIQQQgghhBBCiIOilw9CCCGEEEIIIYQ4KA9lzgfkZz6SLUr+8Oeszt/LqVANvubR6qRJywSvalj/aDTZMzWKVtdorynK8+DBeiP/vJRToZynIfVyQ4zrc+xEA21plOMCtWILOhfaH2LOh5uVzQWCOSAwrwPaaaaU0n/97IfcaxTXh+Wf+O28/TNvM8dQf2jbHttkds62zfkQW0dNW2fto6++f65pdWKkc8TzsqVUFVht4ngY5XxAWy68F2yhaXI7BLkm+s63ITTlgnw4rn3ZKDcEbOM1Ubnn/vLPudchrgdu/oeUTA6IqE3uw6g+51yu9W2K515vHTJa48zMh4D18VxuxpOUx0zOyTLbetO5vrnwZ6KzevWP6phptfn+v/DZ179A8VDz4x985mL7z//675ljXv4GttDEY32U8wH215TLYdPn3AGYD2LVcx2Q5wEsOTlHw9ycD95vBlyr3z8G6/+0X04akysDL4l+76Xijf3PfZSryuR8sHVjnodtP72dkn0+eN95jbT9xO/vccUPJop8EEIIIYQQQgghxEHRywchhBBCCCGEEEIclIdedoF86qPWvuSFz+XQHhMyyBaanq0VvZrBcOKWQovnSh6QSFoxN9TIq4+toIz9Jb9ywu8882t40go+F4Zx8vf1yrG0Ap/PqrQh7ety2l6TrTZvVllq8RhIMv7Oc88n8Whz9zu/Yvaf/nS28vWkFSn5VpbcH5aB9ebc/sbhfB7VTKtcr+4mVX5BugQjX4tO68gzuD4MMcR7Wwbyr7nDZGSpF4VJ9/30see/7zPzTiyuJSjBSOn1bDgzniRjNN+bZkhhyOj4Cn13nzXD/frmjRmzrbwD22y7TkIbW1tHYaQgw+Tf98X0/ejYnnIKj/f+u5+/9GfE9eHHPvC1Zv97X/rNi220ZIzsGlGSEcku+BiuT1CSsaHwf5RINrDW3vb2p2LvyC54jMAxKVrH4/6inGfHy/IUM/5hx+auyjIMh9753zyvxeY+E7zXfN/x/m7B1vTlP/LqrGt9GFHkgxBCCCGEEEIIIQ6KXj4IIYQQQgghhBDioFwr2QXz4kcXk3//2C/aYLsFZF02r2MoOgezsZcUuuOFIUVcdVZsDDXi0KUGQpprDuOs5l2HdQXxnTpQNhG5WNROqDqHZNmQMZsld+XILk7IxYJD3oTw+K1vP5/8+yc+P8/tgmUWiyCT82wuH+UbgqGCZUJpFI0NGA89U1rBbkK43/S+rAPlcBV8YRatGEnGnpmrbZZ8+DuN3QqVFnNgGcZrvPvFG3vVZ0KIg75fwdiyj/TzKuB1DF47yyRwDVBWcO2934/3kVrs43yxbx18de/6c7/yhs8trj8/+vx7J//+/S/9htnvUl7jogSDQ/x3wTFcg6DTAktJG5Bk4FzNa3LrwHH53zt83lXkKlYGstU98OQUjCd15XsbyS7wXkeymF/7VvyO92Zd38OOIh+EEEIIIYQQQghxUPTyQQghhBBCCCGEEAdFLx+EEEIIIYQQQghxUK51zgePz/4hu485INz8D8lqm0o62F91/oaZeNZVI9ut4DXTXK2o0Ws6Fjpcrg6sCtGGK7I0xDwPK8r5sC4w50PO8/A/f+DdzrcQYj9++iNHZv97fvXsYhtzOYw0i5ADokqsgZ6XAwLzMszVLCKsX8RxDu01u9AnkHbnWm3idRT+GIX3DXPo8PjSdNPff2wZOE+XinkepNcWV8nvvmD1u1//cycX21HOJ7vWmLm2uIKUD1eRN6IIrDaxr5k+We6XC2efq7X5Xnw7TcbLDfPOf/PX9rgKIab5b59/n9n/oS/9+sV2X198EQAABbFJREFUM2COFzsPGlvHwea7w3wQOM9G1pDbYjpfAZ8b+xDbX3o5rjgnXbT+xzWT3Z43ZvB9wjVXZGGO3yX6jlEuB+8Y/wZ9FFHkgxBCCCGEEEIIIQ6KXj4IIYQQQgghhBDioDySsgvGhsDksJ6P/aK1eOm9kMHkh+hcNT2FCWHoZiT9YGvQqM5cN1nmObKLsdVmDpvypBW8b7bJWmcNYessu/hH3/T2yWsX4tD8xAefnvz79770m2Z/iZayifsU2FzODa9O3esXSSl1EJS8oCHJCzfk65tLP9PysgvKodWmGVNGYZPT49BluPGnsp3ZfmaIQlye3/mO08m/oxwjpfk23PvIM/or9u3l8+KcP3T2XBXKKwJ7TeSQ66nL8LZ//aW3+hLEI8jfevYDk3//4S9Zuc8OwvoXg10joAwDw/9RjsHHInkG/mYwEoyZY8tIcln60lS8jrG08vJ4ax+WU3hSC/69hPtNb+/nz3xkvfd1XncU+SCEEEIIIYQQQoiDopcPQgghhBBCCCGEOCh6+SCEEEIIIYQQQoiDopwPAWM7FMwHYXVJ5QD7IHu6ar1it4cWNKWU6uAyULOEmqpId2p1WL5GC7fZVhDzN2A5tM9MKaUf/+Az7nUI8aDxo8+/1z3GOk3MsRDZbs7NB2HyPODfg7wJ9jz7vY/GfA3hNaGdJulDcRzBbb7Wudr27l/5vYvt5axPCPHW4OWCSGmcDwK5itwQ++SAsH3Vjlsl1FexhWZvCl5QBJaXJq/VzPXUvuuux//0l/b6nBBvNn/92W9yj/E6o4Kcb1FuCC8fBPenBo7h/O7lj2Oi3A281ilNXiz/98lc6803Skfj5T/98GNvynmvG4p8EEIIIYQQQgghxEHRywchhBBCCCGEEEIcFMku9iSSZHzbZyG0OAj/Y2uXOdQUWuTWfwVqjyisCY+xhSaGj3sSjJSs7MKzLRTiOhGFSv7Hv/FZ99jckMLSGVM4lLFz6vP+Pll2mJZTjOuEsEyw2lyQZSjacKKMo03W5gvHnpf/yKuzr1eIh5FIkvENnz5yj1k5pS9dMDIMIxlNbrlIqmFtuEniAfu4lmnZ5s6t26d3pBp8DG12hbiOROuMH/rSr19sNyS78CQZ3Uh2AceMDeUb/+ERScmidVAkW0U8q81ROfhe+n1y9SjyQQghhBBCCCGEEAdFLx+EEEIIIYQQQghxUCS7OACf+Vj1+oVSSi98rnn9QsQoZOhqzTRM6BJnxfaOLcrWloNjygQrxDx+5H0fm1XuP/vNz7jHKnDdMVmZeZzAcaSA/jvYKQFlGOyEsXSkFiwnQ3kFyi7GDhz5XJ//Fhx7tnQG3hfi0eS3vv18Vjl0zIgkCQhnpO8hJNtzprlfLrnHjPwD/j6SkxaYQR+2J6/0Pkd/8reCo0KIlFL6W89+YFa5H/hidn7hudqTXbA8oz/g/7fLYDSIzovrjB/7wNde6TWJ+SjyQQghhBBCCCGEEAdFLx+EEEIIIYQQQghxUPTyQQghhBBCCCGEEAdFOR/eQl786OJNO9d3fn5zsf0zH1kHJeflqxBCvDX8J+/9tjftXD/8pV+72P47zz3/pp1XCHF1RHadV83J//nMxfb5H/vyXnWUzrYQ4s3h7z737Jt2rv/gC799sf0/fuN73rTzircOjetCCCGEEEIIIYQ4KHr5IIQQQgghhBBCiINSDMPw+qVeK1wUX04p/fbrFhRCHIr3DMPwzOsXe3DQuCHEW4rGDCHEZdCYIYS4LLPHjUu9fBBCCCGEEEIIIYS4LJJdCCGEEEIIIYQQ4qDo5YMQQgghhBBCCCEOil4+CCGEEEIIIYQQ4qDo5YMQQgghhBBCCCEOil4+CCGEEEIIIYQQ4qDo5YMQQgghhBBCCCEOil4+CCGEEEIIIYQQ4qDo5YMQQgghhBBCCCEOil4+CCGEEEIIIYQQ4qD8/waG1NRXRFjlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ec67004e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_nos = [2, 30]\n",
    "I = 2\n",
    "J = x_test.shape[3]\n",
    "c = 0\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        axs[c].imshow(x_test[sample_nos[i],:,:,j].astype('float64'))\n",
    "        axs[c].get_xaxis().set_visible(False)\n",
    "        axs[c].get_yaxis().set_visible(False)        \n",
    "        axs[c].set_title('Sample number ' + str(sample_nos[i]) + ' (' + q_test[sample_nos[i]] + '), Freq. band ' + str(j+1))\n",
    "        c = c + 1\n",
    "plt.suptitle('Topographies ', fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Topographies Averaged within Classes ')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ec63c6860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "avg_right = np.zeros([67,67, x_test.shape[3]])\n",
    "avg_left = np.zeros([67,67, x_test.shape[3]])\n",
    "\n",
    "for b in range(x_test.shape[3]):\n",
    "    avg_right[:,:,b] = np.mean(x_test[np.array(find_right(q_test, label='right')),:,:,b],axis=0)\n",
    "    avg_left[:,:,b] = np.mean(x_test[np.array(find_right(q_test, label='left')),:,:,b],axis=0)\n",
    "\n",
    "I = x_test.shape[3]\n",
    "J = 2\n",
    "\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "\n",
    "c = 0\n",
    "for b in range(x_test.shape[3]):\n",
    "    axs[c].imshow(avg_left[:,:,b].astype('float64'))\n",
    "    axs[c].set_title('Average Left trials')\n",
    "    c = c + 1\n",
    "    axs[c].imshow(avg_right[:,:,b].astype('float64'))\n",
    "    axs[c].set_title('Average Right trials')\n",
    "    c = c + 1\n",
    "#         axs[c].get_xaxis().set_visible(False)\n",
    "#         axs[c].get_yaxis().set_visible(False)        \n",
    "#         axs[c].set_title('Sample number ' + str(sample_nos[i]) + ' (' + q_test[sample_nos[i]] + '), Freq. band ' + str(j+1))\n",
    "plt.tight_layout()\n",
    "plt.suptitle('Topographies Averaged within Classes ', fontsize=26)"
   ]
  },
  {
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}