ECG Autoencoder Convolutional 5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "import tensorflow as tf \n",
    "tf.set_random_seed(22)\n",
    "from keras.backend.tensorflow_backend import set_session \n",
    "config = tf.ConfigProto() \n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.8\n",
    "set_session(tf.Session(config = config))\n",
    "import pandas as pd\n",
    "import wfdb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Good beats from Physionet beats annotation guidlines\n",
    "good_beats = ['N', 'L', 'R', 'B', 'A', 'a', 'J', 'S', 'V', 'r',\n",
    "        'F', 'e', 'j', 'n', 'E', '/', 'f', 'Q', '?']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Beat extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "twoch_df = pd.DataFrame()\n",
    "firstch_df = pd.DataFrame()\n",
    "\n",
    "# 120 samples to the left and right from the beat label \n",
    "half_qrs=120\n",
    "\n",
    "for filename in os.listdir('mitdb'):\n",
    "    # Exclusion of some datasets\n",
    "    if filename.endswith(\".dat\") and not filename.startswith(('102','104','107','217')):\n",
    "        ann = wfdb.rdann('mitdb/' + filename.strip('.dat'), 'atr')\n",
    "        record = wfdb.rdsamp('mitdb/' + filename.strip('.dat'))\n",
    "        \n",
    "        # We want just the signals\n",
    "        data = record[0]\n",
    "        \n",
    "        # Prepare containers\n",
    "        signals, classes = [], []\n",
    "        firstch_signals = []\n",
    "        firstch_class = []\n",
    "        \n",
    "        # Beat extraction\n",
    "        for it, beat in enumerate(ann.symbol):\n",
    "            if beat in good_beats:\n",
    "                \n",
    "                sta = ann.sample[it] - half_qrs\n",
    "                end = ann.sample[it] + half_qrs\n",
    "                qrs = data[sta : end, :]\n",
    "            \n",
    "                # This may happen at the edges\n",
    "                if len(qrs) != 2 * half_qrs: continue\n",
    "                \n",
    "                # Extracting only first channel signals and classes \n",
    "                firstch_beat_class = '{}_{}'.format(record[1]['sig_name'][0], beat)\n",
    "                firstch_signals.append(qrs[:,0].reshape(240,))\n",
    "                firstch_class.append(firstch_beat_class)\n",
    "            \n",
    "                # Keep the channel type in the class name (both channels)\n",
    "                for ch in range(2):\n",
    "                    beat_class = '{}_{}'.format(record[1]['sig_name'][ch], beat)\n",
    "                    signals.append(qrs[:, ch].reshape(240,))\n",
    "                    classes.append(beat_class)\n",
    "                \n",
    "        new_frame = pd.DataFrame({'qrs_data' : signals,\n",
    "                              'qrs_type' : classes})\n",
    "        \n",
    "        new_frame_firstch = pd.DataFrame({'qrs_data' : firstch_signals,\n",
    "                              'qrs_type' : firstch_class})\n",
    "        # Cumulate\n",
    "        twoch_df = twoch_df.append(new_frame)\n",
    "        firstch_df = firstch_df.append(new_frame_firstch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparation of datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "training = pd.DataFrame()\n",
    "\n",
    "# We extract 120 samples to the left and right from the beat label. Total: 240 samples per beat\n",
    "half_qrs=120\n",
    "\n",
    "for filename in os.listdir('mitdb'):\n",
    "    if filename.endswith(\".dat\"):\n",
    "        ann = wfdb.rdann('mitdb/' + filename.strip('.dat'), 'atr')\n",
    "        record = wfdb.rdsamp('mitdb/' + filename.strip('.dat'))\n",
    "        \n",
    "        # Signals are stored in position 0 of record\n",
    "        data = record[0]\n",
    "        \n",
    "        # Prepare containers\n",
    "        signals, classes = [], []\n",
    "        \n",
    "        # Beat extraction\n",
    "        for it, beat in enumerate(ann.symbol):\n",
    "            if beat in good_beats:\n",
    "                \n",
    "                sta = ann.sample[it] - half_qrs\n",
    "                end = ann.sample[it] + half_qrs\n",
    "                qrs = data[sta : end, :]\n",
    "            \n",
    "                # This may happen at the edges\n",
    "                if len(qrs) != 2 * half_qrs: continue\n",
    "                \n",
    "                # Extracting only first channel signals and classes \n",
    "                beat_class = '{}_{}'.format(record[1]['sig_name'][0], beat)\n",
    "                signals.append(qrs[:,0].reshape(240,))\n",
    "                classes.append(beat_class)\n",
    "                \n",
    "        frame = pd.DataFrame({'beat' : signals,\n",
    "                              'label' : classes})\n",
    "\n",
    "        # Append\n",
    "        training = training.append(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109459, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLII_N    72945\n",
       "MLII_L     8071\n",
       "MLII_R     7257\n",
       "MLII_V     7080\n",
       "MLII_/     3619\n",
       "V5_/       3407\n",
       "MLII_A     2536\n",
       "V5_N       2082\n",
       "MLII_F      798\n",
       "V5_f        722\n",
       "MLII_f      260\n",
       "MLII_j      229\n",
       "MLII_a      150\n",
       "MLII_E      106\n",
       "MLII_J       81\n",
       "V5_V         49\n",
       "V5_Q         18\n",
       "MLII_e       16\n",
       "MLII_Q       15\n",
       "V5_A         10\n",
       "V5_F          4\n",
       "MLII_S        2\n",
       "V5_J          2\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLII_N    72945\n",
       "MLII_L     8071\n",
       "MLII_R     7257\n",
       "MLII_V     7080\n",
       "MLII_A     2536\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exclude all classes except N, L, R, V, A\n",
    "\n",
    "training = training[training['label']!='V5_J']\n",
    "training = training[training['label']!='MLII_S']\n",
    "training = training[training['label']!='V5_F']\n",
    "training = training[training['label']!='V5_A']\n",
    "training = training[training['label']!='MLII_Q']\n",
    "training = training[training['label']!='MLII_e']\n",
    "training = training[training['label']!='V5_Q']\n",
    "training = training[training['label']!='V5_V']\n",
    "training = training[training['label']!='MLII_J']\n",
    "training = training[training['label']!='MLII_E']\n",
    "training = training[training['label']!='MLII_a']\n",
    "training = training[training['label']!='MLII_j']\n",
    "training = training[training['label']!='MLII_f']\n",
    "training = training[training['label']!='V5_f']\n",
    "training = training[training['label']!='MLII_F']\n",
    "training = training[training['label']!='V5_N']\n",
    "training = training[training['label']!='V5_/']\n",
    "training = training[training['label']!='MLII_/']\n",
    "\n",
    "\n",
    "\n",
    "training['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_labels = training['label'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We take 2000 beats from each class for train autoencoders' datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoenc_train = pd.DataFrame()\n",
    "firstch_classes = pd.DataFrame(firstch_df['qrs_type'].value_counts())\n",
    "\n",
    "for row in firstch_classes.itertuples():\n",
    "    if row[0] in ['MLII_N', 'MLII_L', 'MLII_R', 'MLII_V', 'MLII_A']:\n",
    "        autoenc_train = autoenc_train.append(firstch_df[firstch_df['qrs_type']==row[0]].iloc[:2000,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoenc_train_labels = autoenc_train['qrs_type'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoenc_train = np.vstack(autoenc_train['qrs_data']).reshape(10000,240,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.layers import Layer, Input,Conv1D,MaxPooling1D,UpSampling1D, Flatten, Dense, Reshape\n",
    "from keras.models import Model\n",
    "from keras.optimizers import RMSprop, Nadam\n",
    "import keras.models as models\n",
    "from keras import backend as K\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers import Dropout\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from IPython.display import SVG\n",
    "from keras.utils import plot_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "import random\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "batch_size = 25\n",
    "epochs = 20\n",
    "input_sig = Input(shape = (240,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_autoencoder(input_sig):\n",
    "    \n",
    "    # Part 1: Encoder\n",
    "    conv1 = Conv1D(30, 3, activation='relu', padding='same')(input_sig)\n",
    "    pool1 = MaxPooling1D(2)(conv1)\n",
    "    conv2 = Conv1D(60, 3, activation='relu', padding='same')(pool1)\n",
    "    pool2 = MaxPooling1D(2)(conv2)\n",
    "    flat = Flatten()(pool2)\n",
    "    dense1 = Dense(25)(flat)\n",
    "    dense2 = Dense(5)(dense1) #15\n",
    "                         \n",
    "    # Part 2: Decoder\n",
    "    dense3 = Dense(25)(dense2)\n",
    "    dense4 = Dense(60*60)(dense3)\n",
    "    reshape = Reshape((60,60))(dense4)\n",
    "    up1 = UpSampling1D(2)(reshape)\n",
    "    conv5 = Conv1D(30, 3, activation='relu', padding='same')(up1)\n",
    "    up2 = UpSampling1D(2)(conv5) \n",
    "    decoded = Conv1D(1, 3, activation='relu', padding='same')(up2)\n",
    "    return decoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/miguel/.local/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "autoencoder = Model(input_sig, conv_autoencoder(input_sig))\n",
    "autoencoder.compile(loss='mean_squared_error', optimizer = Nadam())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 240, 1)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 240, 30)           120       \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 120, 30)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 120, 60)           5460      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 60, 60)            0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 3600)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 25)                90025     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 5)                 130       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 25)                150       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 3600)              93600     \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 60, 60)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_1 (UpSampling1 (None, 120, 60)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 120, 30)           5430      \n",
      "_________________________________________________________________\n",
      "up_sampling1d_2 (UpSampling1 (None, 240, 30)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 240, 1)            91        \n",
      "=================================================================\n",
      "Total params: 195,006\n",
      "Trainable params: 195,006\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X,valid_X,train_ground,valid_ground = train_test_split(autoenc_train,\n",
    "                                                             autoenc_train, \n",
    "                                                             test_size=0.3, \n",
    "                                                             random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/miguel/.local/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 7000 samples, validate on 3000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 6s 840us/step - loss: 0.2344 - val_loss: 0.2375\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 5s 722us/step - loss: 0.2298 - val_loss: 0.2370\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 5s 726us/step - loss: 0.2301 - val_loss: 0.2386\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 5s 728us/step - loss: 0.2293 - val_loss: 0.2362\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 5s 730us/step - loss: 0.2287 - val_loss: 0.2362\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 5s 728us/step - loss: 0.2286 - val_loss: 0.2361\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 5s 738us/step - loss: 0.2285 - val_loss: 0.2364\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 5s 734us/step - loss: 0.2287 - val_loss: 0.2361\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 6s 811us/step - loss: 0.2283 - val_loss: 0.2358\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 6s 820us/step - loss: 0.2283 - val_loss: 0.2358\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 6s 838us/step - loss: 0.2284 - val_loss: 0.2364\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 5s 784us/step - loss: 0.2283 - val_loss: 0.2363\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 5s 755us/step - loss: 0.2283 - val_loss: 0.2363\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 5s 749us/step - loss: 0.2282 - val_loss: 0.2359\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 5s 758us/step - loss: 0.2282 - val_loss: 0.2358\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 5s 760us/step - loss: 0.2283 - val_loss: 0.2357\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 5s 762us/step - loss: 0.2282 - val_loss: 0.2357\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 5s 780us/step - loss: 0.2282 - val_loss: 0.2357\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 6s 792us/step - loss: 0.2288 - val_loss: 0.2362\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 5s 766us/step - loss: 0.2284 - val_loss: 0.2360\n"
     ]
    }
   ],
   "source": [
    "autoencoder_train = autoencoder.fit(train_X, train_ground, batch_size=batch_size,epochs=epochs,verbose=1,\n",
    "                                    validation_data=(valid_X, valid_ground))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = autoencoder_train.history['loss']\n",
    "val_loss = autoencoder_train.history['val_loss']\n",
    "plt_epochs = range(epochs)\n",
    "plt.figure()\n",
    "plt.plot(plt_epochs, loss, 'ko', label='Training loss')\n",
    "plt.plot(plt_epochs, val_loss, 'k', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test how it is the output of a segment of every type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation N\n",
      " [[1.         0.95464132]\n",
      " [0.95464132 1.        ]]\n",
      "Correlation L\n",
      " [[1.         0.86027107]\n",
      " [0.86027107 1.        ]]\n",
      "Correlation R\n",
      " [[1.         0.83137971]\n",
      " [0.83137971 1.        ]]\n",
      "Correlation V\n",
      " [[1.         0.88784933]\n",
      " [0.88784933 1.        ]]\n",
      "Correlation A\n",
      " [[1.         0.71652885]\n",
      " [0.71652885 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hQU91CttQcYV1qNTUvYOyKo1mGNNgcxBnVS6hHmMKALZ6xqXEcLCodnAWqNH0g55SUh8CNuvBNxqNj8ZWu6pmttvA2dp5FKcHT6qqrY5xKam8feg8rQ4nESbj4C1Wo+kj3YqClHITgBBiHPBtIMv/OVLK1YGfqdH8+9Jgc89S6K7vEfjEorWe8SlZuCQUVTczYURs4OM1mmFAbyevvQ78GfgXoPsdaT7VNLY6SIq2+IlCFyU87dxH0QCcrWjSoqAZ1vRWFGxSysdCuhKNJkxosDkYmxzd/dQ1/+22OrLcoqCDzZrhTm9F4VEhxE+A9wBvu0cp5b6QrEqjGcb4pq5VqQ09uo/qiI80kxJj4UxF4+AsUqMJkt6KwnTgC8Bl+NxH0v2zRvOposFmV7MUmqvVhsgu3Ecmi+p/ZFMWxZS0OI6U6IbCmuFNb0XhRmC8u4BNo/nU0uZw0epwKUuhJl9tTMjs+gnuVhcAMzLiefKTs9jsTqxmnYGkGZ70tqL5CNDnhnhCiJVCiJNCiDwhxENdHHOTEOKYEOKoEOKFvp5DoxlMGlv9OqRWn4W4dDB30wosIs4bkJ6enoDDJTl+QVsLmuFLby2FBOCEEGI37WMKXaakCiGMwOOoeQvFwG4hxJtSymN+x+QA/wUsklLWuCe8aTTDFu/UNatZiULS+O6fYI3zuo9mZKjYw+GSOmZnJoZ0nRpNsPRWFH4SxGvPB/KklGcBhBAvoZrpHfM75i7gcSllDYCUsjyI82g0g0a9TTXD81oKk1Z1/wR3UzyAtHgrKTEWDhXXhXqZGk3Q9NTmQkjFJz0dE2BXOlDk93MxsKDDMRPdr7EVMAI/lVK+G+AcdwN3A2RmduO/1WhCjMd9FG+wQVNFLyyFeKg/D4AQgunp8Rwq1u0uNMOXnmIKHwkhvi2EaHclFkJYhBCXCSH+CnyxH+c3ATnAcuAW4OlAw3yklE9JKedKKeempqb243QaTf/wzFJIblMXehLHdf8Eqy+mADBrTCKnyxupa7GHaokaTb/oSRRWAk7gRSHEeXdA+BxwGnUR/z8p5XNdPLcENYPBQ4Z7mz/FwJtSSruU8hxwCiUSGs2wxDNLIa7FbQT3ZCn4uY8A5mUlIiXsL6wJ1RI1mn7R05Adm5TyCSnlImAssAKYLaUcK6W8S0q5v5un7wZyhBDjhBAWYA3wZodjXkdZCQghUlDupLPBvRWNJvR4LIWYpkK1IaknSyEe7M3gVGIyKzMBo0GwJ1+LgmZ40pchO3Yp5QUpZa8colJKB/AtYANqMM86KeVRIcTPhBCerKUNQJUQ4hjwEfCglLKqb29Boxk8PKIQ0VAI0akQ0UMfI29VcwMAURYTU0fHsaegOpTL1GiCprfZR0EhpXwHeKfDth/7/V8C97v/aTTDngabA4vRgKmpDGLTen6CRfU8oq0JopIAmDs2iRd2FWB3ujAbe31fptEMCvo3UqPpA42tdjWbubEMYkb2/ARPYZvdN5/5orEJ2OwuTpY2hGiVGk3wdCsKQojHhRCLBmsxGs1wxztLobEcYnsjClHq0d7s3ZTjbp2tO6ZqhiM9WQqngIeFEPlCiN8IIWYPxqI0muFKo81BrMUATeVBWwpjk5VQaFHQDEd6yj56VEp5CbAMqAKeFUKcEEL8RAgxcVBWqNEMIxpsDtIszeBy9FIUOlsKVrOR9IRILQqaYUmvYgpSygIp5a+llLNR9QnXozKKNJpPFQ2tDkab3LGAIC0FgHEp0ZzVoqAZhvRKFIQQJiHEtUKItcB64CTwuZCuLFxoa2pXsdoOpwPeug9K9g7umjQho8FmJ83g/r6DtBRAicK5ikYCd4jRaIaOngLNVwghnkVVHt8FvA1kSynXSCnfGIwFDnveug+euybwvpNvw55n4cOfDe6aNCGjsdVBqnCX6sT0oqmvpWtRqLc5qG7SI0o0w4ueLIX/ArYBU6SUq6WUL0gptc3rz/kDUHoIKk933rfjj+rx7MdQfmJQl6UZeKSUNNgcpOARhX64j1L1zGbN8KSnQPNlUspnPK2tNR1wOaHmnPr/8X+133fhEBRuhyXfBWME7Hpq8NfXgROl9dy/7gA2u3OolxKWtNidOF2SRFc1WGIgIqbnJ3XhPhqfokThbIUWBc3wQhev9Ye6InC6zf8Tb7Xfl/e+erz4G5C1GIp3D+7aOiCl5EevH+HVfSV8fLJiSNcSrngG7MQ5qnvnOgIwWkAYOlkKGYlRxFpN7C3Q91ua4YUWhf5Qlacec65UweQTb/v2FWyH1MkQnQKJWVBbOCRL9LDhaCm73U3Y3j1yYUjXEq7Uu0Uh2lHdO9cRgBDKWuggCkaDYFF2ClvyKnWwWTOs0KLQH6rcDV1X/RrS58I/7lRuI5cTinZC5iVqf+JYsNV2naUUYh55/xTfWLuPiSNj+NzsdD48Xk6rQ7uQ+kpts7IKo9sqey8KoOIKbZ3dRItzUiipbdGpqZphhRaF/lCVB5ZYNWjl1peVK+n0Big7qnroj12ojktwzyjqh7Vgszv55tp9rPjtx/zirWOd9p8qa+DWp3dQ3mBrt/1sRSOPfXiaVdPS+MfXFnLtrNE0tDq0CykIPJlClrZab3O7XhHAUgBYmqMGRm0+pb8LzfBBi0J/qMqD5GzlIohOUQNXSg+rADP4LIWEseqxpiCo00gp+a9XD/P24QskRFl4Zss5tuVVtjvm528dY9uZKtbuaC88L+wsxGQQ/GR1LvFRZhZlp5CRGMljH57G5dJui75Q09wGSIxtDb6W2L3BHNUp0AyQmRzF2OQoNmqB1gwjtCj0lYJtqjZBSqg+o0TBw6jpShTObYL4TEhwD57ziEJHS6GtGfK39HjKXeeqeW1/CfddPpG1X11AekIkv1p/wntR33y6gs2nK4mJMPHS7kLsThfna1v4w8bT/GNvMVdNG8WIWCsAFpOBB66cxNHz9bx58Hy/P45PE9VNdiKwI1x2NWazt5gjA1oKAKtnjmbz6QqKazqLhkYzFGhR6CtHX1cFaSfehpp8GDnNt2/UdKg+C2c+ggkrfNujklQKY62fpeBywj++CM99BhpKuz3lJ6cqMBoEX16chdVs5DsrJnC4pI79RSpw/Ndt+YyMi+DXN8ygrL6VJz85wxf+vJOH3ztFS5uTLy9qPx1s9czRTBgRwws7Bzj4XVsED09U7//fkJrmNpJNbvdcny2FwKKwZn4mAnhx19AmImg0HrQo9JV695jpt7+rHnOv8+0bNUM92psg5wrfdiFUXMHfUtjyCJx+T/2/B1HYklfJRZkJxFrNAKyanobFaGD94VIabHY2na7kM9NHc9XUkVwyPpmH3ztFQVUzL919MSd/sZI5YxPbvZ7BILh8ykj2FdbQ2Oro80fQJafeVXMG1n9Ptfj4N6O6qY2MSPf7ssb3/onmyIDuI4D0hEgumzyCl3cX0dKmg/+aoUeLQl/xiEJjqRKBju4jAIMZxi1r/7yEse1F4eDLEOkOVja3jw/4U93UxuGSOhZPSPVui7OaWZyTwvojpWw8UU6bw8Wq6aMwGQ2s/eoCfnfzTJ66Yw4Xj09GCBHwdZfmpOBwSXaeVdNP9+RXU2+z9+4z6IqzH6tCvcqTsP9v/XutYUhtcxtpVnddSp8sha5FAeCuJeOpbGzjL9vO9XOFGk3/0aLQV+pKwORuXTD1s+33xaZBVIrKOupY7ZqQqQLNUkL9eag6DbnuUdVNXY+l3ppXiZSwZGJKu+2rpo2ipLaF/91wkhGxEczJVNaAwSD47OwMLpvcfcrknKxErGYDm09XUlDVxI1PbucHrx3p+f13hcsJ+Zthxo1KHA+8GPxrDVOqm9oYFeEWhT7FFAIHmj0sGJ/M5VNG8MePzlAzzHsh2exOnt50lqrG1qFeiiZEaFHoC45WNVxlzp0w63aY/YX2+4XA9tk/80zcN/jZvzqkjcaNhrYGdXE4t0ltm+puNNvctSjsOFtFTISJGent3RVXTh3FxJEx2OxO7lyUhcEQ2CLoigiTkQXjktl4opzntuUjJfzr4HkOFdf26XW8XDio6jDGXwpTVqsK7sby4F5rmFLTbCfV4r4YRsT2/omWrmMKHh68ajINrQ5e2l3UjxWGnn/uLeaX7xzna8/vpc3hGurlaEKAFoW+0OCuBB45Fa5/HGJS2+3+4FgZK15x8YudTp7deo7yer+agahk9dhcpUQhMkm1vxDGbt1HewtqmJ2ZgKnDgPf4SDPv3beMPT+8gm8snxDU27n94rEUVjfzl635LMlJISnawm/fO9X3F5ISdjwBCBi3FCZdDUg4uT6odQ0rpIStj8Gup6luaiNlgAPNHiaNiuXi8Um8sKugX6nC7x65wIL//oC6ln66AgMgpeTvOwpIjrawp6CGJz85M+Dn0Aw9WhT6Qp07nhCf3mnXkZI6vvq3PcREmPjZdVMBFSD24hYFZ2MVzSc/4kz0bD48WYmMSoamwKJQ12znZFkD87L6UCjVB67IHcm3L1OCcs+ybO5cmMUnpyrI72uF7db/g8P/gEu/r3oCjZyq3GUn3wnBqgeZf90L7/8Iuel/qWuxk2h0i0KfU1KblcB0w20LxlJU3cInp4OvW/jTJ2cpq29lT3510K/RFfsKazlR2sB3r5zE7MwEPtFFd/+WhFQUhBArhRAnhRB5QoiHujnuBiGEFELMDeV6+o0nyBzXWRT+vqOASLORdfdcwu0LxpIUbWHLaX9RUBf2/173EVEt53njQiJf+eseituiaKot63wuKdlXUI2UMLdD9tBAcv8VE9n60GUsmpDCzfPGYDSIvqVHupyw/XGYcDksfVBtEwJyroJzm8E58Hesg0bFSdj3V0jIRDSWkUot8YZgLIVIkC5f88QuuGrqKNLirfz4jSOdYwsNpT0OazpSUseBIuX+8/S5GkjeO1qKxWhg9azRzMtK4lBxne64+29IyERBCGEEHgdWAbnALUKI3ADHxQL3AjtDtZZASCk5X9vSt8Cenyg4XZIvP7ebH7x2mOMX6nnjwHlWzxxNfKQZg0GwaEIKm/2anbVFJAAQWavmLnxz9UJ+em0u59uiOXHmHO8fK+OJj/O48U/b2H76As6HJ1Gz43mMBsGszIQBfe9Un1XxEUAIQXq8FXb8iZFtxVwxZSTr9hS19xc77VDYxddTuB2aKmDWbUoMPGQtUqm5Fw4N7NoHk0Mvqw6nK34CwBRDAbE0q5oTg7H3r+Npnx2g/5E/FpOBx2+7iLK6Vr714j4cThfNbe4U2A3fhz9fqYS2C17eXYTVbCBnRAx7CwbeUjhYXMuUtFhiIkzMHZtIm9PFkZKB6+d1tqKRpb/5iIW/+lA3bRxCQmkpzAfypJRnpZRtwEvAdQGO+znwa8AWYF/IeGbzORb+z0bm//cHXZrBpXU2TpTW+zbUlaj89IgYNp2uYOOJctbuLGTVo5tpsTu57eJM76FLclKoaGjlsPuPZu1hdUFYk6UeI+LTuHPROGZOymakqZFvvbCP37x7koPFdXz7zx9hbCpDnN3ItPR4oiym/r3ZQ//wDfkpPwG/nwP/N93n86/Kg3e/Bxt/zufnZFDTbGfbGT8rZ+9z8OyVgQcJHXsTTFbVKdafTHffp4Kt/Vt7kBTXNFPR0I8MGZcLDq1TgfMJlwOQKwqIprlvVgJ0OWgnEBdlJvLLz05ja14VN/xxG9N+soE/bTwOpz8AlwPW3dHlwKYDRbXMHZvE8kmpHCwa2Lt4l0typKSeGRnqBsVT+zKQFsn2s1UUVjdjMAh+9MZRnyB+CjhV1tC/39cBJJSikA74p1IUu7d5EUJcBIyRUr5NNwgh7hZC7BFC7KmoGBg/5u78akbHW0lPiOSXbx/D2SG49/z2fC59+GNW/34re/OrYfsTKuXS7Tpau6OQlBgL73xnCb/5/Az+cuc87x8MKFeA1WzgxV3qI3j3dDNODGS0uXPR3V02rfEjGW1qIjHKwvysJHZ9fwW//owSl8viS3nkppn9e6MXDsKrX4WPfqF+3v+8Cm5HxKq7Tynh1Aa17+Q7LM4wEhNh4t0jfgV1eR+qx6IO1oKUarhQ9orOKbixIyEp29cHqiOOth597MHicLq46U/bueb3m9sH+/vChf1qXsaMmyEygeaodHINBUTBy2pOAAAgAElEQVS6GvsWTwC/QTs9iwLAjXPH8JXF4zhUUseEETF8/MFb0FoHV/xMzWd47mqozGv3HJdLklfeSM7IGOZmJdHmdHlvSALR1Orgjx+fobCqmfeOlvLh8QAuTD/OVTXR2OpgeobKgkuOiWB8avSAWiSnyxqJshh5dM0sKhpaeWbzOVodTp7fUdD/GpphjNMlueWpHdz85PZOCQK786t5effgVrsPWaBZCGEAHgG+29OxUsqnpJRzpZRzU1NTezq8V5wobWD22ET+c+VkTpU18pnHNvPQK4eoaGjljQMl/OiNo8wfl8SoeCvPP/80bPgvqDgBo6bz0YlyNp4o46a5Y8gdHcdNc8dw6eT2Q1fiI82snjmaNw6UUFpnY29RA62mOKh0Z/d4hrREp2BorWXjfQt54a4FJERZWDFO9SmKbzpHdnyQX1H9Bdj1NLz3Q/Xzuc3qonTwRZi0Chbdq9xIJftUJXJkIjjbsJ58ncsmj2DD0VIcTpeqTPb0Z+o4KKiuGBrOQ/algdcwdqHqFeXyc0WVn4A3vgW/yoDNDwf33npg44lyztfZKG9o5Zsv7AtuXoGnLfroWerH2ElMFflYnU1BWAqBp691xw8/M4V9P7yCN765mBvjjmGXRvIyb4IvvaNSfw++0O7483UttNid5IyIZX5WEhEmA2t3BG7A6HJJ7nv5AL9+9wTLH/6Iu5/fy70vHei2nfrhYiUwMzJ8qdELxiWx81y1+j0ZAM5UNDJhRAxzxiaxcuoonvzkDA9vOMmPXj/CwxtODsg5hiNHSuqoamrjbGUTd/1tDyW1LVQ2tnL/ugPc+KftfO+Vw50aYIaSUIpCCTDG7+cM9zYPscA04GMhRD5wMfDmYASbG2x2CqubyU2LY9W0UdyzLJuUmAhe2VfMvF9+wL0vHWB+VhJP3zGXZ+6Yw1cdL1FqGInt/rM8m/o9vvTcbrJSorlzYVa357ltwVia25x856X9OFxSZSA53HeuHlFwZyVFOep9aact7loB6VJtuINh22PwzgMq/TV9jprn8OHPVUrsRXeoWgKjBXb+Ud3NX3QHjJgKh19h1bRR1DTb2XS6As7vU/UVJisUdwh0etbmae/RkbEL1Xkr3O6OXU/DEwtUplJ8Omx5FJoH3ve9dmcho+Ks/L/VU9mdX8Ouc4HPIaXkp28e5fZndnYWjvpi9Rg3GoA8MY5xohRLc3kQlkLv3UceRGM5iUf+QqRs4TrLbvYZpvK1dSexJ4xTFlhF+4vk6fJGACaMiCEx2sKXF4/j9QPnA/r8X95TxHvHyvjOihxunpfJzXPH0NjqaJ8Y0YFDxXVYzQYmpPoswkUTUmiwOTg0QHGF02VKFAAeXDkJm8PF05vPYTEaeGFn4b/tPOvN7myzH12Ty+HiOhb9z0bm/uID/nXwPF9fns3oeGu7BpihJpSisBvIEUKME0JYgDXAm56dUso6KWWKlDJLSpkF7ABWSyn3hHBNgPLfAUweFYsQgodWTebvX13Au/+xlPsun8h/rpzEk1+Yg8VkYGLTHqaJs/y29TruezOf332Qx9KJqay/dwkj4qzdnmdGRjyrZ45m17lqIs1GrHHuqmRrPJgi1P+j3dv8axVsfgVkH/wUnrumxyBlJwq2qsE/N6+Fm/+utu14XDXwy74MIhNg4kp1gXY5YPI1MOEyKNnDZTlxZCRG8tv3TlF24F0kAmbfDuVHobXRd46yw+pxxJTAa8iYrx5L9qiOsB//CsYuhvuOwZoXoK0Rtv2+b++rBwqrmtl0uoI188dw45wxxFlNrO2i8d+zW/N5bls+W/Iq2dlROOpKICLeW6T2SWM6BiERlSf6VrgGfpZCH77Dgy/C+v+EPy7EVF+EZel9nKloUplhqZM6iUJemfpectwX1XuWZZMQZebRDzvHgT46Uc7Y5CjuuzyHX31uOj+/fhqxVhPvHO66B9fB4lqmjo5vVy+zKDsFIWDzqf7fxdbb7JTW27yikJ0aw63zMzEaBM99aR4Wk4E/bMzr4VXCk82nK5k6Oo6vLB7H+/cv5YErJ3Lf5RNZf+8SvrdyMvdfOYnDJXW8dXhwgu8hEwUppQP4FrABOA6sk1IeFUL8TAixOlTn7Q6PmXv8glsU0trf8WWnxnDv5Tl8Y/kEEqMtauORVyAijqzld7L+SClNbQ5+9JkpRJh6zj4RQvDrG2Ywa0wCl+eOxOARAP+pXVHubf61Cp4JbQYTFGxRsYytj/b+jdrqVQvvCStgyjXqbnekuy/Tqt/4MmdW/g9c/ye4ayOMma/mPzjbiCg76G2vfWDXJ5yRozkRe4myXA6+CHa3tVN2VPV06urOOTkbrAnK7XRgrbJSLvsBRCcrIZn8GRXjcA1cQPSFXYUYhGDNvEwiLUZumJPB+iMXqOzQlqHN4eKR906ydGJqYOGoL/HWozTY7LxTlebbF8JAsxfPqNfaApj9BWYt/ywLxiXxyPun2FAej/TLIAPIK28kJcbi/b2NjzRzy/xMPjxexoU633mllOwtqGHu2CRvXyyLycAVuSN5/1hpwKZ8NruTw8V1nRorJkZbmJEez5a8/sf58so9ouYT3J+unsrHDyxn4YQUPndROm8dOu+dfvfvQlOrg32FNSxxD1zKSIziW5flcO/lOUxwfxafnZ3O5FGxPLzh5KBUkYc0piClfEdKOVFKmS2l/KV724+llG8GOHZ5KK2E94+VcfGvPqS8wcbxC/XEWU2Mju/+Th+nXbXInrSKb1yey5cXjeP+KyaSM7L3d4qRFiOvfH0hj62Z5ZvW1U4U3JXOTX5/WB5RyLlK3VlPvkaJQp2/960binapC7hn8hvA0gdgxY9VqqiH+HSYdYtyL4FvKFDhdlbPHM11s0YzLbaJatMIvvaxCac1UbmknrhYnaPsaPvW4R0RAjLmQuEO5c4as8B3DoBpn1Pvu3BH795XD7Q6nKzbU8SKySMY5f5uPz8nA7tTsvFE+5Yb+wtraGpzcuv8TD4/ZwzvHrnQPj25rtibVLC/sJZyGY8tSrmSgg40t/VhZkLVGfV53fYKXP2/CCH4f9dNJTMpig3lCQjpVMe4OV3e4L3L9nDr/EwkeJMdAM5VNlHV1Ma8rPYX+DXzMmlodfC9Vw51cqUdKamjzekKWC+zOCeFfYW11DW3D5DW2+x9Svf2iYLvPRgNgjFJ6rO7bcFYWh0u/rm3OODzd56t4uYnt7N+kO6mB4qDRbXYnZKLx3ddoGo0KG9GYXUza3cGN6irL3xqKpqzU6OpbGzj5V1F7M6vZnJaXJcdRL3kb4aWGpiyGiEEP742l29dltPncxsNQp3LIwAxfkHpxLEqF77Sr72ErVb5+9eshTvfgqt+qQRq15O9O2HBVmVlZMzzbZt6PSzpIaYflQSpk6FgOwaD4NE1s0k31jI5ZyLVzkg+Z/0zrZ//u7qz/+u16m52VDeiAMqFVXlKdYhd9B/taxlyrlRdVY93ukcIio3Hy6luauPWBb7U4Ny0OFJiItjcwV++Ja8So0FwSXYyK6aMwO6UHPdPP/azFPYU1GAQYBzjDndF9KFtNoDZffPh6IOlUH0GUnIg53KvpTF5VBxvfmsxU2cqt1xtgXLftTqcnCprbHeXDTAmKYplE1NZt7vI64/e404hndtBFOaPS+KBKyfx5sHzndxInrTTjpYCwKppaThdktf2+y7W6/YUMftn7zP75+/z9KaztLQ5vYHqrjhV2oDFZPCKQEempMUxZ2wiL+ws7CRaewuqufmpHew8V81P3gyvVFZPPMY/czEQyyamctPcDNITIkO+pk+NKIxPjWHRhGR+vzGPU2WNrJk3pucn7fkLmKPbD8zpD15R8LMULNEwIrd9taqtTrldhFD/ErOUG2jvX9Xd5pmP4IU1Xbsjiner4K8luu9rzLxYpZ66XOri31hG3IhMHrt1NgdLbTxdPhnu/kgJm3Splhbd4RGm1MkqhuFPRKz6bI//q32GUpB8fLKCWKuJxRN8HWWFECzJSWFrXmW7QN2m05XMzIgnPtLMuBT1OXkDmfYW5eqKywDgk5PlTB0djznTLQpBp6T2Mj3WVq/mUiRlB9x9xZKFuKTg+GGVDfbukVIaWx1cObVzZ9zPzk6ntN7mHci0K7+axCgz2akxnY79+rJskqMtndJT9+RXMz41muSYiE7PmZYez8yMeP6+s5BX9hbzy7eP8YPXDjN3bCILs5P53Qen+OJfdnHtH7bwpb/s4jfvnvDG9PzZV1jDjPR4jN00drxtQSZnK5vYfqZ9A8lX95UQZTHy3JfmUd7QyrNbwqcF+eHiOjISI0nyuKu7QAjBbz4/kyunjgr5mj41ogDKBG1zupiSFsf1szq3qqAmH7b8n7pAnVyv7mAX/4fPJ9xfAlkKAKNnK1Hw3AHZ6joPcVlwj7IgXr8HXr4dTq3vXDfgofqsCkYGQ/pcaK2HmnOqy6l0QWwal04awaIJyby4qwhnZDLcuk5lMGUt6f71xsyD+DGqL5IhwK/b9M+ru3LPwKEgkVKyJa+ShdnJnZoHLslJobqpjWMXlCVQ12zncHEti91+3FFxVqxmA+cq3KJQ7x5TGp/OkZI6DhbXccNF6TD6IrW9rzEFUx8thWq3Wyg5cKPDzJEpVJrTqCk4zMGiWv6+o4Cs5CgWZad0OvayySOwGA28c7iU3fnVvHGghEsnjQhoJQeqxHe6JHsLa5g3tmv3xm0LxpJX3sh3/3GQ57blM3lUHE9+YQ6//Ox02hwudp2r5vpZo9lfVMsTH5/hf9a3L76z2Z0cLqljTlb37Vyunp5GQpS5XfzH6ZJsOFrKpZNGsHzSCJZPSuX5Hf1rKjiYHCqpZWYPVsJg089S2fDiityR3HBRBrdfnNm51bSjVV1sSw9D2kyV+TEiV7k8BopAlgIon/7+55UoJY0LLAqZl6hW3YfWKVGxN6sagPHLO7+P+vO+udB9ZaS7E0n5MW86JrEqyHr7grF8fe0+Pj5ZzoopU+Dm5wFobHVQWNVMcoyFkR0zsqzxcF83cxqmrFZ35Nt+D5NWdn1cD5ytbKKktoWvL+98d+2xHDadrmBaejy786txSViYrb4Pg0GQlRztsxTqPOmo6azdWYDVbOCzF2WAZQxc+kOYeFXfFucNNPfSUvDECpIDWwoACaOzGVNUznVPbMUl4ftXTw7YPj3WamZJTgqv7y/h1X3FZCRG8ZNru7buFuek8ObB85wsa2DyqDg2naqgttnOskld1wddO3M0u/KrWTwhhetmjfYKTkKUhZ9cm4vN7uKupeMB+MVbx/jr9nzqbXbi3JMEPX717oQHwGo2cuOcDP6yNZ/qpjaSoi3sya+msrGNVdPVHfT1s9L5+GQF+4tqmNPD6w01NXX1xNUcY/qCVUO9lHZ8qiwFs9HAb2+ayezMAHckn/xGCYLRAm/9h/KBL/8vMHVv1vWJpHGAgJQOd/Hp7jtQjwuppValjPojBFz3B3ioAL65S7mHCrZ1PkdtESCVyykYUqeoNZYd9Y0JjVOicHnuSFJiInh1f/uA99ee38PVj21m2f9+1PeWzUYzXPx1lWV14WBwawZvjv2SnM53yyPirMzIiGfDUeUW2V1QjdkomDXG9xmPT/UTBXePq7aYNN44cJ5rZ6ieVhhNsOxBX8JAbzEY1TS+3haveUQhaXyXh1hiU5kcb+eeZdl8Z0UOty3o+ibgmplpVDW1kZkUxbN3ziM+ytzlsZ7Pz5Nm+vcdBaTGRnBFbtdDmyItRh6+cSbXz07vZIF84ZIsryCAGiVrd0o2HvcF/vcUdB2z6MjKaaNwuKS39mT9kVIiTAYunaSs78umjPCOqvWntrmNTcOsq2vl5md4w/Ij5iQPr2rtT5UodInTDnv/AlOuVW0NavJV5smkqwf2PKmT4MEzkDGn/fYRue7iMHfyVSBLwYM5Ug1tGbtIxQ4cHfql1OSrx2BFwRKlLkZlR3xulFhlMZiNBi4en8SBQl8dRXmDjW1nqpiflYTN7urk7+0V7gl0u7Z+wN6CvvfSqWho5clPzjBhRAxjkwPHUVZNS+NgUS0ltS3sza9heno8VrMvrXhcSjSF1c3YnS5vlte+6iia25xcNRB+XHOUr3CxJypPKeupO7dlVBLm1hr+c+Vk7r9iItERXRv9189K57VvLOTVbyzyxk+6Ii0+kqmj43h681k+PF7GRyfLuXnuGMzGgblUzB6TwMi4CNb7Nbzbk1/tLbrriWnp8VhMBvbkV+NySd49Usqyiane9+8/qtbhdCGl5B97irjst59wx7O7gvr9ChVNF05jEi6mmLuf0T7YaFEAFbhtroKZt8KsW9W2uV9Wd4YDTXRy521GsyooO/iCynbqThQ8jF2oLjIl+9pvr81Xj4lBuo9ABY/LjqmhQsLoK7BDFeSV1LZ4xzG+d7QMKeHH1+YSbTF6qzP7ROxopMHE7gMH+fEbR/rUlkJKybdf3Ed1cxv/d/OsLo9bNU1d2N84UMKh4jrmdphRMS4lBodLUlzToortksbz8blGTAbBxdkBvrO+Yrb2rk6hrVnFV7IWd39cVLKyKHtR4yGEYHZmYrdBXH8evnEmja0OvvLXPSRFR7Rr9NhfDAbBqmlpfHyygqZWB3ani935Nb2eGRJhMjIrI4E9BTUcKK6ltN7mdR15WDNvDCW1LfznK4e4+ckdPPjPQ2QlR7ktiL6lrB47X8+qRzdz1e828adPzqibhgFCut2UMY35A/aaA4EWBYDD61S2z4TL1cX2C6/Bwu8M7hou/YHKOtn8iC/7qDuyFqmA5zsPQKtfNkdNvkrzjOnH3e3IqSpYXXUGYke1axM9PV2ty9Ns7d0jpYxPiWbq6DguyU5uP1iotxhNNEWMJF1UcPR8PQd7SF/0590jpew4W82PrsllWnrXQpqVEs2UtDge35gXMOfecwedX14L+Vth3DK25FVwUWYiMd3chfcak7V3lsKxN1Sg/6I7uj8uMgmQvpYoA8iUNBUo/vrybD68fxlp8QObBrly2ihaHS4+PlnBgaJaGlsdLA3g9uuKOVmJHCmp47V9JZiNghVT2ru2rpw6ii8vGser+0o4Vd7Ar2+Yzj/vWcgStwXRl5uONw6UkFfeQHyUmf9Zf4IfvxFk25kAWFvcFkKg7sNDiBYFp0NlGuWu9sUPsi8b2FhCbxg1TWXi7HoKXPaeLYXIRLjxOSg/Dh/+zLe9pkBZCYEyfXrLiFxAwrlPvEFmD9PS4xBCpdLVtdjZfraKq6aNcqd+plJQ1UxhVR+KtNwUulIYZ6omymLkz1vO9Sp7pM3h4jcbTpIzIoab5/acYvybG2aQlhCJ1WzoZClkpypRqM3bCW0NNIxexJGS+oAxiqDwTF/rjrZm9f0nZbcvPAyE/3jXELAkJ5XvrZzcbfwhWOZlJakOw0cusPl0JQYBCwNkTnX9/EQcLsnzOwpYPmmEN2Dtz/evnsyja2ax8bvLuXmeSixZOW0UJbUt3XaP7cim05XMHZvEuq9dwlVTR/bKEj5V1sCjH5xu929/YXu3lc3uJNHhfq2q4dW+41OVfRSQypOqB8/YHsz1wWD6jaoXEfQsCqBy/LMW+2IRoCyFYDOPPGReos7fUqMsBT9irWbGp0RzqKSO7WeqcLqkN8i3yJ3ls+1MJZnJvXc51DXbOd4czxXW49w2N5OnN5/jQm0Lf/7iPF7alc+Os1Vkj4jjoVWT26Wb/vRfRzlX2cRf7pzXKQ01ENMz4nnnO0uobW7rlBeeEGVhXEo04tz7AHzUOhkoZOnEgenKq0ShG0uhqRKevUpdIFb/oX2RXyCi3JZOy8A3FAw1RoPgyqmjeH1/CSdLG5iRkdAn8ZmblURWchTzspL4/tWB+26ZjAau65B2frnboth8urLHYjHA2/3gwatUYshFmYlsOFrmzXwKRHFNM2ue2kF1h2ruV/cX89F3l3szxPJKa8jFbeUNM0tBi8L5/epx9OyhXQeoofemSJXP3htRABW8PvCiqnEQQvXKGbOgf+uISYXbX4Pnr1dVtR2YkZHA5tMVJEVZiLYYme2eDJedGq3SBAtqWDO/96Lw6v5i6mQKsfZKvn9VNhNHxvL91w5z7R+28LOGnzDFEsUdp76NRHWSBHjn8AVe2FnIPcuyO7Ut7w6LydBlI8M5YxMZfXQ3ctR03jxtY3S8tV2r6H5hiuzefbT3L0oQbn+1d8WSIbYUQs0XLh7L+sMXyCtv9M4J7y1xVjMfP9hFu/ZuSIy2kJ4QycnSzsVzgdjqdoUuddezeGZJHCquZfkk3++cyyV5aXcRj3+UR2VjKxaTgQ/uX8Z4t0vyzYPn+Y+XD7D1TKW3x1F+/lmmCYkjMhVTTb6aLzLY3oku0O6j8wfUeMUuCoUGFXOkEgbonJLaFSkTVWvrhlLVhtpWF3zmkT8Zc+DegyrW0YHVM0dT2djGur1FXJKd7M1MEUIwZ2xiwAyP02UNvLK3mFf2FvPO4Qve3v1SStbuLMSQmIlAIupLuHHuGH5x/TQKq5uYb8pjqXM7/z21hD9vOcc3X9hHdVMb/9hTREZipPcubiCYl5VIhjxPTewkNp2qZOW0tJ5bofSW7gLNLhfse159972tno90u79C0Hp8QHnvh/BILjy7Urlq3UxJi2Pjd5fz0KrJPbagH0impMVy/EJ9u21Hz9cFbI2xLa+KhCgzU0erYsXp7phVx5YdL+4u5PuvHWZ0gpVb5mfy/FcWMGFEDAaDUIH16aNIirawdoev6K6iWKUdG7KXgXT6sgaHAdpSOL8f0mb1zwc/kEy8Ck5v8P3R94TnTr7ypK9yNmXiwKyli3z85ZNSvQNW/FtKgLqwvn+sjIqGVlJjVVuEY+frueGP22jxGw85PjWan183jQabnbzyRqYvn6Gap9cWQdJ4bp6XyeR4B1EvqNqBW6qfoOryF/n9R/nUt9jZcbaKOxdm9TqjhuZqFZjvJqNsTkYsI6nhtQsW2pwurp4+gC0FTJFg72K6Wf4mZeGt+HHvXy9YS+HketXkMSoJFt/f+5uPYKg6A9sfVxXthdtVHYpfOnZitIV7lnVdoBcKJo+K46OTFdjsTqxmIydK67nm91tYOXUUf7y9far4sQv1TE+P97p8Yq1mxqdGd5of8f6xMsanRLPua5cEvImIMKmiu2e2nKOs3sbIOCsN5aqxnWH8cjjyT6g6DakD9HfbT4bJlXCIcNpVwdrorlMZB53Zt8MNf1ZV1b3BUwhXedrXVC+Ay2cgEULwo2tyyU2L69SLxVNF6hnTuDu/mrv+tof4SDPvfGcJmx68lGfumIvTJbntmZ3c8/d9jEuJ5pLZ7u+g1nc3NTPKfRc8+3ZEzTm+PfIo37psAptPV2J3SlZNbx8E75LWBnhsNmz6TbeHZUfUYhSSXTXRTEuP46JARY7BYo7sus3FuU0q9XfyNb1/PUu0KrTsa0zh3YfgyKuw7Q/wx4W977wbDFsfVUV7a9aqn/M3he5cvWRKWhxO9+hSgF+vP4GUqghun18w2OF0cbqskdwO7fVnpMdzsKjWm8HU6nCy42wVSyemdmtV3rogE6dL8vLuIqSUOGrdVfPZl6nHCr/WH1Kq2eeekavN1aqTgXNwGv19ukWh/Bg4W4dHPMGDKUJlIfXWbRE7CiyxShAqT6l01ISByyvvimnp8bxz7xJGd+jaOC09jgiTgSc+PsN31x3kxj+pGc3PfHEuuaPjyEyO4vLckWz4DzVM5Dsrcnj7O4uxJo9R3WLr/MZ6V7sbm138TUjOga2P8tXFWaTGRjAqzsqs3vaMOf4v1Tfq0MvdzoUW7rzxpXMv4pWvLwzYNiJozNauA82euhRz4FhHQDxdd/tiKdRfUG6Ky34AX35XVW4fXtf75/cFW72avTHrVhg1XTVEPLc58LH//Aq8/AVfBX0ImZymOskev1DP/sIaPjpZwXdW5JASE8Hv3vd1Kj5b2USb0+U93sPSiamUN7Sy/az63PcW1GCzuzpZzB0ZmxzNkpwUXtxVyIU6G4n2ctqM0aoTb3ymyiL0sOfPsO4L8Pg8eGo5/H4OvHqX+v0dBD7dolDobig3Zv7QrqM/CKEsg4qT6s4ieUK7uoLBJsJk5Defn0FBVTNvHCjhnmXZvH//0k41BFazkW9dlsP9V0wkymJSQbb4Me3vmKrPAkJVWC/8NpQeIqr8AM9+cR5P3H5R7y/anj+mmvzuW2m4BemapQt6NUSpT5i6sRRsdX3vvApuUehDhW6huy1K5iXqd37UDDi1oe/n7Q1nPwZnm8qoA9U4sXCHss79Kdqt3CfH34Rnrui8f4DJSo7GajZworSBNw+ex2IycNeScdw6fwxb8yq9RZmeuMPkUe2/l6unpxEf6WvKt/lIAfEGW68KHG9bMJYLdTae2nSWUaIaZ4zb0h0xRRWLghLO9Q9B9gq3ey9RpSfHZfgyE0PMp1sUCraqC9Eg3FmHlNRJyuqpOB5y11FvuG5WOp88uJyPHlCBxChLL0NXGXM7pNeeU+1GzFZfy5HCbUzPiPe5dhrKuq/qrb8AZz+BeXcpF82x17s+1q8R3oDTXaDZVt/3zqugLhh9sRQKtqtW8J6Z2hOvUp12QxGsPv2eek+eG66sxWoc6fkD7Y/b/ntlJX3uaagrhFPv9u71myqDarduNAhmZiTw7pFS3j1SytKcVGKtZlZOS8Ml4b1jKu5zorQBs1F0ajHuacq34Ugptzy1g6l7vs8rsb/tVYHjpZNTiY0w8cKuQkaKGkwJ7t+zEVOUlZ/3Iaz9vLoJuuEZWPEjVUi7Zi3MXKPqhhq6iEsNIJ9eUZBSNZTrqUgoHJj8GTXBrCZ/4ILM/SQhytLlwJQuyZivXBqei3P1WXcTQVSabOI4NfHNQ9kxeGSyym45/X7g1zyzEZAw90vqDvnsx12fv7YQokf0zY3TWzy9jwK5r1rre5+C7E9Uct9iCoU7VCtzT7A95yrVGv3Mxr6d9zkdoqQAABe2SURBVMJBFUAuPxF4v5TqAjd+uWrhAr6mj2V+HXMby5Vrb86XYOrnVKHkvud7Pn9DGfxuqupq3NvOs358Z0UOJbUtXKizedufTEmLJSs5infcbTCOX6gnOzUGi6nzJfKLC7OYlh5PbYudhTEXmNB61N2IsnsiTEZWTBlBm8NFmqEOc7zHUshVBauv3q1uSL70Tuckjxk3qe/q6Gt9fr995dMrClVnoKn830MUJn3G3d2UYWEpBM0Y90Aez4XfXxRA3XUW7/ZdWD1/IMKgWm8HominuuCmToGkrO791nVFkNCL4UvB4J2pEOAi1pteV4GISuq9pdDWrC7IYy72bUu/CKJT1YXZQ08tIE5/AE8uhQ3fh5dugbamzsdcOAgN59VkPQ9xGcqF5l+9W7JXXegmrVJCNetWyHu/5wtsyR71OZ58G966r/tjA7BoQgpLJ6ZiMRq8BW1CCFZNT2NrXiXfXXeQHWermDo68HcyJimK17+5iPXfXkRSm/v36eT6Xp175bQ0QJJCLcS623OMcP/tNlfConvb9RrzkjpJueIC7RtgPr2iULBVPY5d1P1x4YDBAMv+ExC9z1oajoyc7u4Wu1vdDTZVKOvAQ8Y8NZGs1j2n9vi/1Pc35RrldgqUnVG0U1kgBoPqB9VY3rW7qbZIuRNDgXemQgAXUrDuo6hkVXXei6Z4yvqS7Wc0GIyQe52KK7Q2qjv//81WF35/agpUcZXLBR/8VNXB3PS8Eu33ftheSKSED/+fej/+XYYNBnVu/+rdkn1K0EdNVz/PuVO5+LY8Evg9uJxK3M4fUM+b91U49JIvS6cP/N/Ns3j5axe3q6T++vJsrp+Vziv7ipmXlcQDV/VgdTeWqUQVUALVC5ZNTGVcVBtm7L7+ZCkT1fu2xvtiMIG44RmVhBJiPr2iUH5s+BStDQTTPgcPnAp+4tpwwGRRmWB5H6ghRwazamfuwVOpXbRbXVwqjqv9YxYof3XZ4fav11KjAteZ7ufFjFSFQoHurtua1IUz1JZCIFEI1n0Ul67utD0tzrvDk9UVn9F++7QbVAD8xFvw+tfVZ3PiLd/+pip4fD5s+Z2Kx5QdVgWNuatV8H/Ps/C+X33F0deUO+qyH3buCJw8QeXjezi/T1lwnrGxCZkw54uw72++zDMPVWfgj4vg6UtVbVHqZFj2kMq22/zbnt9/B5KiLZ3mqsRZzTxy8yx2/+By/vbl+T03AvSkT4+YCvlbfHMwuiHSYuStL7mvOR5LwWx1z1B/QLWuH2I+vaJQV6zuCgeqYnU40HHMZzgy/251B3rsdWVK+7vDRuRCRLy66HgCxpM/o2IFoHzm/hSpGcZeMfH8EXZ0IV04BA9PVHd9qZMH9v148Mxp7ug+crlUHUUw2Uee9uh+tR1d4onTdBSFMRcrcXn96+oiHT3CZ0WDCvw6bHD0Vdj3V9VXa5r7bvXyn6kW89seU/G5uhJ4+34l7HO/0nkNKTk+q0NKZSmkd0gHX/KAumve8UffNilVy5WqPCXyZz5UBacxqTDvK8pa6BjA7gepsRG9q2T3WKxX/VIJ22tf61UtQXSbu5Owfyfjzz8Liwa5M3MXfIpFoajzH4hm6Jn2OfjK+7D4Plj6QPt9RpMSgRNvq35PmQvVdxif7q6a7SAKxbvVBSbdXanq+SNsLG9/3JFX1IXvjjdh1m2heV/mLiyF1npABuc+8jQ+9FycuqOuWLlcOnS9xWBQd/W518FNf4NLvqkyYTyf0Qm3W6TihArSz1zjq/43GODKX6o271sfg1e+qi74n3smcOV4co67pcM5JWQt1b651x7i0mDy1Sr90tHmO3dtIVz138qiki5fwenSB1Vc5F/3dnajSQkvrIFND/f8+QRDjftzz7wYPvOI+n07+GLPz/NkEHVoNjlcCKkoCCFWCiFOCiHyhBAPBdh/vxDimBDikBDiQyFEP9t79oG6Yi0Kw5X0i+DynwaePDbtc9Bap4bbz7zZt33MgvaZSaBchMnZPveEx1Jo7GApFGxTF6fxy0JnOZrc76WjpdDq7sMTjPsoPgMQvotTd9QVK0EwBuhGOutW1YY99zpVTwDKHdLWrKwy/0rrGTe3f64lSsUCTq1XdRDXPgopXbhkPdsrT6sLKPiyktqt5zYlGKfdNRSeorecK2D6Ter/noLTyAS48hdw4YBKg/Unf7Na18afK5fkQFObr1yS5kjlhkudAruf7jlY3+Ae9PNpEwUhhBF4HFgF5AK3CCFyOxy2H5grpZwB/BPovg/BQGFvUb5TLQrhx7hl6s7UGAG51/u2j56lMl6a/Ib8VJxoH2OJCeA+amtWbpOsECcceC2FDjMVbB5RCMJSMEWoC32v3Ee9tIzTZqpY2+5nlCvI0aJcemMXKTedf6Daw/y71cjWK38BM7oJlCZ7+nSdggMvKLfVyOmdjxt/qfqutv1e/a3mb1JVv4lZyoJc8kB7CyP3eiWqx95QPzvtygW5+8/qdyV1CvzrPlWP8fjF7dOX+1MsV1Pgs9aEUK6sCwc7T0PsSGOZ6kLguVkZZoTSUpgP5Ekpz0op2/j/7d15lFT1lcDx74WWRqabVWiQRVoFFRMU0m6jCEZ0MIvtRBNxGz3jMjE6HsfMxBxNolHPiUTHxHM0mTFjJqtB0YlpRybRGAwZ5hDBUWSRGMQNBoSwKGizpe/8cV9Vv6quqn5d3a9eLfdzTp+qevW6+nbRvFu/7f5gPtAaPkFVF6lq6n/JUqA0V+lUvZe4Zpq4+NQNgDNugVk3ZxZzSy3I2vyK3R7YaxeG8BjBQQfbmMTu0AKgDcug40D8s9BSYwrZ8+r3BMXViuk+AhtXiNp9FCUp9K+Ds75u00VX/My6Z5pPh4t+BhfnKbMwZCzctMYGngsZONhm2rzwPRsXmH557m6m/nVw1p3W8vvJ+dZSaJ5hF94hY21RV/j76gbYtOy1C63L6cUfWK2rNU9aLbGZX7KFcU9ea5MTltxvCxq/cwrcNQq2vtY1hih2vp257e3UCy2hLvl24e/btbmz1VqG4kwKY4HwhOMNwbF8rgRyTvYVkWtEZLmILN+6tYg9gLOlZ2LEsHLVxe+kv4MZX8w8lprWuClICtvWWd9z9sBxY1NmUnhrifW1x13qJL1OIdeYAsV1H4HN2NnxFjx3p20jmktHhy0KjNoyPuEquH65je18/Ct2MR44pHCMUbvdzvuuTTWW/jD9svznHXchfOYh2LzK6lY1zyz8useeZ92K65+3Qef6ITDtMjjlepsaO3CoDZpLP+tWWnC5tSq1w7qeemr7eruODA+1nAYOtskRr7ZZ0tm91RZWrnoi83t3v9u77XJjVhYDzSJyKdAC3JPreVV9SFVbVLVl5Mg+2Akr30wMV7kGDbeWX6qlkCowlp0UGpoySwX88Rnriij2ohxVep1Cdkuht0nhMHh/A/zuXpsemssHW60OUU9axkPHx5Mox7XAZ/8dzpkHgw8tfO7Uz1kL5OIF3c/PP3yWjdusX2TTXkd/FFofsIHrgwZ2zv//+FctIe15H+Y+YseK2cvg17dbov/YFZnH//IG6+Z6+iZYdJcl48X/nDnOsGtz2Y4nQLz7KWwEwn+F44JjGURkNnArMFNV98YYT6f3NgBi/aCueoyeaqXQwQoESr+u61AammxFLNhitf97yQa145ZOClljCqmWQm+6j1JSCTHb+zHWdCpGeO1Jd+obYPLZ3Z9XV2/7nG9aYQPZU1oznz/1BhtkP/kL1l148DBbQd94aNc1EYW8vdRWc298EWbdYkkn7KCB0Pog/KjVWquNY2DLahu4b55h0493bSrrpBBnS2EZMElEmkVkADAXaAufICLTgH8FzlXVLTleIx7vbbB/lDLZ/s71kdEftQvCvg9skHlYc9c6Ro2jraXwzjKbew9wdA8uUsXKV+ZiT7BPbzEDzWDF08DKSKR+97Cdb8NTNwJS2Qsboxg91Va2t2/vWu5l6ASY8w37e5j5JTjxajs+bKJNkY1q9c/tg8esW6yrKJeJp8Gnvm0J55LHbeX54m9aN97ie+1v4NjPFPUrlkJsSUFVDwDXA78CXgUeU9XVInKHiJwbnHYP0AAsEJGXRaQtz8v1LV+jUJ3GnQCofSrb+CI0Hdv1nCHjrV//4dm2EnfUlPxTKPtSvjIXe963hFFXX9zrTjgFLppvC6jQzLr8AL+7z5LF3EdyzxyqJmOO6yw7MSJiDbDhzT3rPmrfYR8sZt1cuHDi9Mus62v0R2wF+BuL4efXwNLvwHEXZ+xAV25i3Y5TVRcCC7OOfS10f3acPz+njg7L9OG6LK46NM+wbpjn7rC+3DNv63rOtEvtA8Gf91lLITytNU75Wgp7i6x7lCJiBeVSaxXeWmJdVKm9vne8AU1TbEFYtQvX/YpaGHJYs3Xn7G/PvS4mW/tOG7SOIjX43vK3Vl9q5QJL4md9Pdr3J6R29mje+Ta89BPrz2zfXh3VUV2munrbI2DlAlvHcNQ5Xc+pb7ACemAL4UpFJNinOcc6hb4Y5B46wV7n2dsAhat+Y59Gd77TOTOr2o06xuplSb/oe6QMm2i3O97srFZaSPsOG4/oCRFbHLjjTUvQZa4sZh+VxKYV8Nt5VtURPClUq2OCnsnJZxffTx+XXFtyFrvrWjaRYK2G2oXx5Z/ajJdaWrlfV28X9uGHR999MFWaPWoX0p6dmetjohowqCISAtRSS+GoT9iqyPXP2wBQ6hOCqy5Hzrauk5OuTTqSrrJbCquesMVzE0/rm9c/8zbrCnm1zba4PO1G62Ov9J0Fe2LO3bZhTVSp0uxRZyAV01KoMLWTFPr1txkHz37VWgnVVB3VdRowCC5/qvvzkjDyKJvSqGqfOJ+42vrBz76rb14/tUlRfaN1oS172B7X0sr9npYrGTTcFrpt777sNao9G1OoULXTfQQ2I2DoYVYL3rlSO/qTdvH502uwbb1VDD39n/p+VtDEGbYX84r59jiuPSKqgYjNPgtv/pPP/g+tFVLlLYXaSgoHD4MbX+m6sMW5Ujj6k3a79j8758bH0Y3Zv842FkpVg62VMYVijZiUuU1oPu077LaYMYUKUltJwbkkDT7U9nZY+3S8SQE6C/wNaKz67o5eO+RIm8K8d3fh89qDhYbeUnDO9ZkjzrTSGptXWVG0uLZfTCWFoVW2u2AcDgn2Yu6utZBqKVR5kvWk4FwpjT/RKnP+8Zl4Z8CNnW4L5mppkLlYqdXP3SWFPbXRUqid2UfOlYPU1qD7P+ycIx+Hunqb1TQsxp9RLYYfDkj3g83p7qPqbil4UnCulAYNt8qt29bFf8FOFX1zhR000NZybCuQFNb8onMzoyrvPvKk4FypjTshSAoTk47EpYw4wjbOyWXXu/DY30D/AbYXQ31jaWMrMR9TcK7UxrXYbZzdR65nGsfA7jzV+3dtsts/77OuoyofuPeWgnOlNvVC2zB+bEvSkbiUhiZLCh0d0C/rs3I4WVT5IDN4S8G50qtvhJOv7XrxcclpaLLVyqkZRmHhPb2rfDwBPCk45xw0jLLbcAJISR07eLjtolblvPvIOecamux21+au+yrs3mJF8y78cdUPMoMnBeec60wKuQabd79rLYm+KnFe5rz7yDnnCnYfbelMGjXAk4JzztU32iZI+cYUUkmjBnhScM45Ebvw5+w+8paCc87Vnoamri2FfR/Avl3eUugrIjJHRP4gIutE5Ms5nq8XkUeD538vIhPjjMc55/LK1VJIPfaWQu+JSH/gQeAcYApwkYhMyTrtSmCHqh4JfAuYF1c8zjlXUOPori2FGkwKcU5JPRFYp6rrAURkPtAKrAmd0wrcHtx/HHhARERVNca4nHOuq4YmaN8OD57UeWzfB8FzI5OJKQFxJoWxwDuhxxuAk/Kdo6oHROQ9YATwp/BJInINcA3AhAkT4orXOVfLprTC1rXQcSDz+JGzYVR2J0f1qojFa6r6EPAQQEtLi7cinHN9b+RRcMH3k44icXEONG8EwnsBjguO5TxHROqAIcC2GGNyzjlXQJxJYRkwSUSaRWQAMBdoyzqnDbg8uH8B8BsfT3DOueTE1n0UjBFcD/wK6A98X1VXi8gdwHJVbQMeBn4sIuuA7VjicM45l5BYxxRUdSGwMOvY10L39wCfjTMG55xz0fmKZuecc2meFJxzzqV5UnDOOZfmScE551yaVNoMUBHZCrxV5LcfQtZq6QrhcZeWx11alRh3JcZ8mKp2W6+j4pJCb4jIclVtSTqOnvK4S8vjLq1KjLsSY47Ku4+cc86leVJwzjmXVmtJ4aGkAyiSx11aHndpVWLclRhzJDU1puCcc66wWmspOOecK8CTgnPOubSqTAoiMkdE/iAi60TkyzmerxeRR4Pnfy8iE0sfZVcR4j5dRP5XRA6IyAVJxJhLhLhvEpE1IvKKiDwnIoclEWe2CHF/XkRWisjLIvLfOfYYT0R3cYfOO19EVEQSnzoZ4b2+QkS2Bu/1yyJyVRJxZovyXovI54K/79Ui8kipY+xzqlpVX1iZ7teBw4EBwApgStY5XwD+Jbg/F3i0QuKeCEwFfgRckHTMPYj7DGBQcP/aCnq/B4funwv8shLiDs5rBBYDS4GWco8ZuAJ4IOn3t4i4JwEvAcOCx6OSjru3X9XYUjgRWKeq61V1HzAfaM06pxX4YXD/ceBMEZESxphLt3Gr6puq+grQkUSAeUSJe5Gqfhg8XIrtwpe0KHG/H3r4F0A5zMqI8vcNcCcwD9hTyuDyiBpzuYkS99XAg6q6A0BVt5Q4xj5XjUlhLPBO6PGG4FjOc1T1APAeMKIk0eUXJe5y1NO4rwT+K9aIookUt4hcJyKvA98EbihRbIV0G7eITAfGq+rTpQysgKh/I+cHXYyPi8j4HM+XWpS4JwOTRWSJiCwVkTkliy4m1ZgUXJkSkUuBFuCepGOJSlUfVNUjgJuBryQdT3dEpB9wH/DFpGPpoaeAiao6FXiWzpZ8uavDupBmARcB3xORoYlG1EvVmBQ2AuFPGeOCYznPEZE6YAiwrSTR5Rcl7nIUKW4RmQ3cCpyrqntLFFshPX2/5wPnxRpRNN3F3Qh8BHheRN4ETgbaEh5s7va9VtVtob+LfwM+VqLYConyN7IBaFPV/ar6BvAaliQqV9KDGn39hWXu9UAznYNDx2adcx2ZA82PVULcoXN/QPkMNEd5v6dhA3aTko63h3FPCt3/NLa3eNnHnXX+8yQ/0BzlvR4Tuv/XwNJKeK+BOcAPg/uHYN1NI5KOvVe/d9IBxPSP+QksY78O3BocuwP7lAowEFgArANeAA5POuaIcZ+AfTL5AGvZrE465ohx/xp4F3g5+GpLOuaIcd8PrA5iXlTo4ltOcWedm3hSiPhefyN4r1cE7/XRScccMW7BuuvWACuBuUnH3NsvL3PhnHMurRrHFJxzzhXJk4Jzzrk0TwrOOefSPCk455xL86TgnHMuzZOCq1kiMiJUlXOziGwMPf6fmH7mNBF5uMDzI0Xkl3H8bOeiqEs6AOeSoqrbgOMBROR2YLeq3hvzj70FuKtATFtFZJOInKqqS2KOxbkuvKXgXA4isju4nSUivxWRX4jIehG5W0QuEZEXgr0WjgjOGykiT4jIsuDr1Byv2QhMVdUVweOZoZbJS8HzAE8Cl5ToV3UugycF57p3HPB54BjgMmCyqp6I1ej5++Cc+4FvqeoJwPnBc9lagFWhx/8IXKeqxwMzgPbg+PLgsXMl591HznVvmapuAgjKaD8THF+JbSAEMBuYEtqWY7CINKjq7tDrjAG2hh4vAe4TkZ8C/6GqG4LjW4BD+/7XcK57nhSc6164qmtH6HEHnf+H+gEnq2qhTW3asbpbAKjq3SLyNFZfZ4mI/JWqrg3Oac/zGs7FyruPnOsbz9DZlYSIHJ/jnFeBI0PnHKGqK1V1HrAMODp4ajKZ3UzOlYwnBef6xg1AS7Bz2BpsDCJD0AoYEhpQvlFEVonIK8B+OnekOwMol13TXI3xKqnOlZCI/AOwS1VzDUSnzlkMtGqw769zpeQtBedK67tkjlFkEJGRwH2eEFxSvKXgnHMuzVsKzjnn0jwpOOecS/Ok4JxzLs2TgnPOuTRPCs4559L+H6ywwl6o2FZjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Input vs Output of the autoencoder\n",
    "convout1_f = K.function([autoencoder.input], [autoencoder.layers[14].output])\n",
    "C1 = convout1_f([autoenc_train[autoenc_train_labels=='MLII_N']])[0] #N, L, R, V, A\n",
    "C1 = np.squeeze(C1[0])\n",
    "C1.shape\n",
    "C2 = convout1_f([autoenc_train[autoenc_train_labels=='MLII_L']+1.5])[0] #N, L, R, V, A\n",
    "C2 = np.squeeze(C2[0])\n",
    "C2.shape\n",
    "C3 = convout1_f([autoenc_train[autoenc_train_labels=='MLII_R']+1])[0] #N, L, R, V, A\n",
    "C3 = np.squeeze(C3[0])\n",
    "C3.shape\n",
    "C4 = convout1_f([autoenc_train[autoenc_train_labels=='MLII_V']+2])[0] #N, L, R, V, A\n",
    "C4 = np.squeeze(C4[0])\n",
    "C4.shape\n",
    "C5 = convout1_f([autoenc_train[autoenc_train_labels=='MLII_A']+0.5])[0] #N, L, R, V, A\n",
    "C5 = np.squeeze(C5[0])\n",
    "C5.shape\n",
    "\n",
    "# Build X vector\n",
    "x=np.arange(240)\n",
    "x=x/360\n",
    "\n",
    "# Plot\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title(\"N\")\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('V (mV)')\n",
    "C01 = autoenc_train[autoenc_train_labels=='MLII_N'][0]\n",
    "C01 = C01.flatten()\n",
    "ax1.plot(x, C01)\n",
    "ax1.plot(x, C1)\n",
    "corrcoefN = np.corrcoef(C01,C1)\n",
    "print('Correlation N\\n', corrcoefN)\n",
    "\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_title(\"L\")\n",
    "ax2.set_xlabel('Time (s)')\n",
    "ax2.set_ylabel('V (mV)')\n",
    "C02 = autoenc_train[autoenc_train_labels=='MLII_L'][0]+1.5\n",
    "C02 = C02.flatten()\n",
    "ax2.plot(x, C02)\n",
    "ax2.plot(x, C2)\n",
    "corrcoefL = np.corrcoef(C02,C2)\n",
    "print('Correlation L\\n', corrcoefL)\n",
    "\n",
    "\n",
    "fig3, ax3 = plt.subplots()\n",
    "ax3.set_title(\"R\")\n",
    "ax3.set_xlabel('Time (s)')\n",
    "ax3.set_ylabel('V (mV)')\n",
    "C03 = autoenc_train[autoenc_train_labels=='MLII_R'][0]+1\n",
    "C03 = C03.flatten()\n",
    "ax3.plot(x, C03)\n",
    "ax3.plot(x, C3)\n",
    "corrcoefR = np.corrcoef(C03,C3)\n",
    "print('Correlation R\\n', corrcoefR)\n",
    "\n",
    "fig4, ax4 = plt.subplots()\n",
    "ax4.set_title(\"V\")\n",
    "ax4.set_xlabel('Time (s)')\n",
    "ax4.set_ylabel('V (mV)')\n",
    "C04 = autoenc_train[autoenc_train_labels=='MLII_V'][0]+2\n",
    "C04 = C04.flatten()\n",
    "ax4.plot(x, C04)\n",
    "ax4.plot(x, C4)\n",
    "corrcoefV = np.corrcoef(C04,C4)\n",
    "print('Correlation V\\n', corrcoefV)\n",
    "\n",
    "fig5, ax5 = plt.subplots()\n",
    "ax5.set_title(\"A\")\n",
    "ax5.set_xlabel('Time (s)')\n",
    "ax5.set_ylabel('V (mV)')\n",
    "C05 = autoenc_train[autoenc_train_labels=='MLII_A'][0]+0.5\n",
    "C05 = C05.flatten()\n",
    "ax5.plot(x, C05)\n",
    "ax5.plot(x, C5)\n",
    "corrcoefA = np.corrcoef(C05,C5)\n",
    "print('Correlation A\\n', corrcoefA)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_encoding_layers():\n",
    "    return [\n",
    "     Conv1D(30, 3, activation='relu', padding='same'),\n",
    "     MaxPooling1D(2),\n",
    "     Conv1D(60, 3, activation='relu', padding='same'),\n",
    "     MaxPooling1D(2),\n",
    "     Flatten(),\n",
    "     Dense(25),\n",
    "     Dense(5), #15\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build an encoder-only by copying the weights\n",
    "encoder = models.Sequential()\n",
    "encoder.add(Layer(input_shape=(240,1)))\n",
    "encoding_layers = create_encoding_layers()\n",
    "for l in encoding_layers:\n",
    "    encoder.add(l)\n",
    "encode = K.function([encoder.input], [encoder.output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_repr = encode([autoenc_train])[0] #it was not train, but it was test, changed to avoid error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,15))\n",
    "\n",
    "for i,label in enumerate(np.unique(autoenc_train_labels)): #it was not train, but it was test, changed to avoid error\n",
    "    plt.subplot(4, 3, i + 1)\n",
    "    plt.title(label)\n",
    "    encodings = train_repr[autoenc_train_labels == label] #it was not train, but it was test, changed to avoid error\n",
    "    \n",
    "    means = np.mean(encodings, axis=0)\n",
    "    stds = np.std(encodings, axis=0)\n",
    "    \n",
    "    bar_centers = np.arange(train_repr.shape[1])\n",
    "    plt.bar(bar_centers, means, width=0.8, align='center', yerr=stds, alpha=0.5)\n",
    "    plt.xticks(bar_centers, bar_centers)\n",
    "    plt.xlim((-0.5, bar_centers[-1] + 0.5))"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}