ensembling_images_and_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features and images ensembling and fit generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook are going see how to ensemble features with images in a classification problem.\n",
    "\n",
    "The main idea is to show how to :\n",
    "* ensemble CNN with other architectures to train a network using images and features together.\n",
    "* implement a fit generator with a network with multiple inputs.\n",
    "\n",
    "The dataset consists on features a and images of leaves from 100 different plants and the task consist of the Leaf Classification contest of [Kaggle](https://www.kaggle.com/c/leaf-classification):\n",
    "> The objective of this playground competition is to use binary leaf images and extracted features, including shape, margin & texture, to accurately identify 99 species of plants. Leaves, due to their volume, prevalence, and unique characteristics, are an effective means of differentiating plant species. They also provide a fun introduction to applying techniques that involve image-based features.\n",
    "\n",
    "The dataset loading code was taken from https://www.kaggle.com/abhmul/keras-convnet-lb-0-0052-w-visualization as well as some code.\n",
    "\n",
    "So, to brief the idea is to generate a leaf classifier as a excuse to make an hybrid DNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading the training data...\n",
      "Training data loaded!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.preprocessing.image import img_to_array, load_img\n",
    "\n",
    "# A large amount of the data loading code is based on najeebkhan's kernel\n",
    "# Check it out at https://www.kaggle.com/najeebkhan/leaf-classification/neural-network-through-keras\n",
    "root = './data'\n",
    "np.random.seed(2016)\n",
    "split_random_state = 7\n",
    "split = .8\n",
    "image_max_size = 512\n",
    "\n",
    "\n",
    "def load_numeric_training(standardize=False):\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted features for the training data\n",
    "    and returns a tuple of the image ids, the data, and the labels\n",
    "    \"\"\"\n",
    "    # Read data from the CSV file\n",
    "    data = pd.read_csv(os.path.join(root, 'train.csv'))\n",
    "    ID = data.pop('id')\n",
    "\n",
    "    # Since the labels are textual, so we encode them categorically\n",
    "    y = data.pop('species')\n",
    "    y = LabelEncoder().fit(y).transform(y)\n",
    "    # standardize the data by setting the mean to 0 and std to 1\n",
    "    X = StandardScaler().fit(data).transform(data) if standardize else data.values\n",
    "\n",
    "    return ID, X, y\n",
    "\n",
    "\n",
    "def load_numeric_test(scaler, standardize=False):\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted features for the test data\n",
    "    and returns a tuple of the image ids, the data\n",
    "    \"\"\"\n",
    "    test = pd.read_csv(os.path.join(root, 'test.csv'))\n",
    "    ID = test.pop('id')\n",
    "    # standardize the data by setting the mean to 0 and std to 1\n",
    "    test = StandardScaler().fit(test).transform(test) if standardize else test.values\n",
    "    return ID, test\n",
    "\n",
    "\n",
    "def resize_img(img, max_dim=image_max_size):\n",
    "    \"\"\"\n",
    "    Resize the image to so the maximum side is of size max_dim\n",
    "    Returns a new image of the right size\n",
    "    \"\"\"\n",
    "    # Get the axis with the larger dimension\n",
    "    max_ax = max((0, 1), key=lambda i: img.size[i])\n",
    "    # Scale both axes so the image's largest dimension is max_dim\n",
    "    scale = max_dim / float(img.size[max_ax])\n",
    "    return img.resize((int(img.size[0] * scale), int(img.size[1] * scale)))\n",
    "\n",
    "\n",
    "def load_image_data(ids, max_dim=image_max_size, center=True):\n",
    "    \"\"\"\n",
    "    Takes as input an array of image ids and loads the images as numpy\n",
    "    arrays with the images resized so the longest side is max-dim length.\n",
    "    If center is True, then will place the image in the center of\n",
    "    the output array, otherwise it will be placed at the top-left corner.\n",
    "    \"\"\"\n",
    "    # Initialize the output array\n",
    "    # NOTE: Theano users comment line below and\n",
    "    X = np.empty((len(ids), max_dim, max_dim, 1))\n",
    "    # X = np.empty((len(ids), 1, max_dim, max_dim)) # uncomment this\n",
    "    for i, idee in enumerate(ids):\n",
    "        # Turn the image into an array\n",
    "        x = resize_img(load_img(os.path.join(root, 'images', str(idee) + '.jpg'), grayscale=True), max_dim=max_dim)\n",
    "        x = img_to_array(x)\n",
    "        # Get the corners of the bounding box for the image\n",
    "        # NOTE: Theano users comment the two lines below and\n",
    "        length = x.shape[0]\n",
    "        width = x.shape[1]\n",
    "        # length = x.shape[1] # uncomment this\n",
    "        # width = x.shape[2] # uncomment this\n",
    "        if center:\n",
    "            h1 = int((max_dim - length) / 2)\n",
    "            h2 = h1 + length\n",
    "            w1 = int((max_dim - width) / 2)\n",
    "            w2 = w1 + width\n",
    "        else:\n",
    "            h1, w1 = 0, 0\n",
    "            h2, w2 = (length, width)\n",
    "        # Insert into image matrix\n",
    "        # NOTE: Theano users comment line below and\n",
    "        X[i, h1:h2, w1:w2, 0:1] = x\n",
    "        # X[i, 0:1, h1:h2, w1:w2] = x  # uncomment this\n",
    "    # Scale the array values so they are between 0 and 1\n",
    "    return np.around(X / 255.0)\n",
    "\n",
    "\n",
    "def load_train_data(split=split, random_state=None):\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted feature and image training data and\n",
    "    splits them into training and cross-validation.\n",
    "    Returns one tuple for the training data and one for the validation\n",
    "    data. Each tuple is in the order pre-extracted features, images,\n",
    "    and labels.\n",
    "    \"\"\"\n",
    "    # Load the pre-extracted features\n",
    "    ID, X_num_tr, y = load_numeric_training()\n",
    "    # Load the image data\n",
    "    X_img_tr = load_image_data(ID)\n",
    "    # Split them into validation and cross-validation\n",
    "    sss = StratifiedShuffleSplit(n_splits=1, train_size=split, random_state=random_state)\n",
    "    train_ind, test_ind = next(sss.split(X_num_tr, y))\n",
    "    X_num_val, X_img_val, y_val = X_num_tr[test_ind], X_img_tr[test_ind], y[test_ind]\n",
    "    X_num_tr, X_img_tr, y_tr = X_num_tr[train_ind], X_img_tr[train_ind], y[train_ind]\n",
    "    return (X_num_tr, X_img_tr, y_tr), (X_num_val, X_img_val, y_val)\n",
    "\n",
    "\n",
    "def load_test_data():\n",
    "    \"\"\"\n",
    "    Loads the pre-extracted feature and image test data.\n",
    "    Returns a tuple in the order ids, pre-extracted features,\n",
    "    and images.\n",
    "    \"\"\"\n",
    "    # Load the pre-extracted features\n",
    "    ID, X_num_te = load_numeric_test()\n",
    "    # Load the image data\n",
    "    X_img_te = load_image_data(ID)\n",
    "    return ID, X_num_te, X_img_te\n",
    "\n",
    "print('Loading the training data...')\n",
    "(X_num_tr, X_img_tr, y_tr), (X_num_val, X_img_val, y_val) = load_train_data(random_state=split_random_state)\n",
    "y_tr_cat = to_categorical(y_tr)\n",
    "y_val_cat = to_categorical(y_val)\n",
    "print('Training data loaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some inspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number of categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n"
     ]
    }
   ],
   "source": [
    "n_cats = len(np.unique(y_tr))\n",
    "print(n_cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(792, 192)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_num_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(792, 512, 512, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_img_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the dataset consist of 792 samples of a vector of 192 features and a image.\n",
    "\n",
    "Here we resized the images from the original size, to 512x512 pixels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DNN using only features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we build a deep feed forward NN using only the features.\n",
    "\n",
    "We implement some callbacks such as Early Stopping and Reduce LR on plateau to avoid overfitting and to increase the accuracy of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import Sequential\n",
    "from keras import Model\n",
    "from keras.layers import ReLU, Dense, Conv2D, Softmax, Conv1D, Flatten, Dropout, Input, GlobalMaxPooling2D, MaxPooling2D, Convolution2D, Concatenate, merge\n",
    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_model_seq():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(64, input_shape = (192,), activation = 'relu', name='dense_first_layer'))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(128,activation = 'relu', name = 'dense_for_ens'))\n",
    "    model.add(Dense(n_cats,activation = 'softmax', name = 'out_layer'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_model():\n",
    "\n",
    "    feat_input = keras.engine.input_layer.Input(shape = (192,), name = 'features_input')\n",
    "\n",
    "#    feat_input = Input(shape = (192,), name = 'features')\n",
    "    x = Dense(64, activation = 'relu', name='dense_first_layer')(feat_input)\n",
    "    x = Dropout(0.3)(x)\n",
    "    x = Dense(128,activation = 'relu', name = 'dense_for_ens')(x)\n",
    "    out = Dense(n_cats, activation = 'softmax', name = 'out_layer')(x)\n",
    "\n",
    "    model = Model(inputs=feat_input, outputs=out)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "features_input (InputLayer)  (None, 192)               0         \n",
      "_________________________________________________________________\n",
      "dense_first_layer (Dense)    (None, 64)                12352     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_for_ens (Dense)        (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "out_layer (Dense)            (None, 99)                12771     \n",
      "=================================================================\n",
      "Total params: 33,443\n",
      "Trainable params: 33,443\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# model \n",
    "# model = Sequential()\n",
    "# model.add(Dense(150, input_shape = (192,), activation = 'relu'))\n",
    "# model.add(Dropout(0.4))\n",
    "# model.add(Dense(120, activation = 'relu'))\n",
    "# model.add(Dropout(0.1))\n",
    "# model.add(Dense(110, activation = 'relu'))\n",
    "# model.add(Dense(105, activation = 'relu'))\n",
    "# model.add(Dense(100, activation = 'relu'))\n",
    "# model.add(Dense(n_cats,activation = 'softmax', name = 'out_layer'))\n",
    "\n",
    "model = features_model()\n",
    "\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, \n",
    "                        patience=5, verbose=1, mode='auto', \n",
    "                        min_delta=0.01,  min_lr=0)\n",
    "erl = EarlyStopping(monitor='val_loss',\n",
    "                        patience=10, verbose=1, mode='auto', \n",
    "                        min_delta=0.01)\n",
    "\n",
    "callbacks = [rlr,erl]\n",
    "\n",
    "model.compile(optimizer='adam', loss= 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 792 samples, validate on 99 samples\n",
      "Epoch 1/100\n",
      "792/792 [==============================] - 1s 2ms/step - loss: 4.5890 - acc: 0.0290 - val_loss: 4.5596 - val_acc: 0.1414\n",
      "Epoch 2/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 4.5075 - acc: 0.0997 - val_loss: 4.3680 - val_acc: 0.2222\n",
      "Epoch 3/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 4.1629 - acc: 0.0947 - val_loss: 3.7549 - val_acc: 0.2525\n",
      "Epoch 4/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 3.4854 - acc: 0.1692 - val_loss: 3.0672 - val_acc: 0.3131\n",
      "Epoch 5/100\n",
      "792/792 [==============================] - 0s 290us/step - loss: 2.9516 - acc: 0.2652 - val_loss: 2.5953 - val_acc: 0.5253\n",
      "Epoch 6/100\n",
      "792/792 [==============================] - 0s 284us/step - loss: 2.5662 - acc: 0.3258 - val_loss: 2.2841 - val_acc: 0.5758\n",
      "Epoch 7/100\n",
      "792/792 [==============================] - 0s 290us/step - loss: 2.3006 - acc: 0.3687 - val_loss: 2.0062 - val_acc: 0.5960\n",
      "Epoch 8/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 2.0436 - acc: 0.4381 - val_loss: 1.8068 - val_acc: 0.6364\n",
      "Epoch 9/100\n",
      "792/792 [==============================] - 0s 291us/step - loss: 1.8820 - acc: 0.4722 - val_loss: 1.6705 - val_acc: 0.6263\n",
      "Epoch 10/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 1.7195 - acc: 0.5253 - val_loss: 1.4960 - val_acc: 0.6970\n",
      "Epoch 11/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 1.5855 - acc: 0.5619 - val_loss: 1.3674 - val_acc: 0.6869\n",
      "Epoch 12/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 1.4664 - acc: 0.5694 - val_loss: 1.2655 - val_acc: 0.7778\n",
      "Epoch 13/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 1.3556 - acc: 0.6187 - val_loss: 1.1701 - val_acc: 0.7172\n",
      "Epoch 14/100\n",
      "792/792 [==============================] - 0s 296us/step - loss: 1.2966 - acc: 0.6288 - val_loss: 1.1050 - val_acc: 0.7273\n",
      "Epoch 15/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 1.2054 - acc: 0.6528 - val_loss: 1.0004 - val_acc: 0.8182\n",
      "Epoch 16/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 1.1533 - acc: 0.6692 - val_loss: 0.9361 - val_acc: 0.8182\n",
      "Epoch 17/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 1.0900 - acc: 0.6667 - val_loss: 0.9061 - val_acc: 0.7677\n",
      "Epoch 18/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 1.0512 - acc: 0.6881 - val_loss: 0.8773 - val_acc: 0.7576\n",
      "Epoch 19/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 1.0131 - acc: 0.6982 - val_loss: 0.8116 - val_acc: 0.8384\n",
      "Epoch 20/100\n",
      "792/792 [==============================] - 0s 293us/step - loss: 0.9477 - acc: 0.7311 - val_loss: 0.7934 - val_acc: 0.8081\n",
      "Epoch 21/100\n",
      "792/792 [==============================] - 0s 285us/step - loss: 0.8811 - acc: 0.7412 - val_loss: 0.7479 - val_acc: 0.8182\n",
      "Epoch 22/100\n",
      "792/792 [==============================] - 0s 290us/step - loss: 0.8614 - acc: 0.7348 - val_loss: 0.7326 - val_acc: 0.8384\n",
      "Epoch 23/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.8316 - acc: 0.7652 - val_loss: 0.6887 - val_acc: 0.8586\n",
      "Epoch 24/100\n",
      "792/792 [==============================] - 0s 291us/step - loss: 0.7898 - acc: 0.7576 - val_loss: 0.6578 - val_acc: 0.7980\n",
      "Epoch 25/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.7253 - acc: 0.7942 - val_loss: 0.6281 - val_acc: 0.8889\n",
      "Epoch 26/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 0.7563 - acc: 0.7778 - val_loss: 0.5873 - val_acc: 0.8788\n",
      "Epoch 27/100\n",
      "792/792 [==============================] - 0s 294us/step - loss: 0.6901 - acc: 0.7904 - val_loss: 0.6060 - val_acc: 0.8283\n",
      "Epoch 28/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.6836 - acc: 0.8056 - val_loss: 0.5561 - val_acc: 0.8586\n",
      "Epoch 29/100\n",
      "792/792 [==============================] - 0s 284us/step - loss: 0.6311 - acc: 0.8295 - val_loss: 0.5441 - val_acc: 0.8586\n",
      "Epoch 30/100\n",
      "792/792 [==============================] - 0s 285us/step - loss: 0.6238 - acc: 0.8081 - val_loss: 0.5143 - val_acc: 0.8889\n",
      "Epoch 31/100\n",
      "792/792 [==============================] - 0s 292us/step - loss: 0.6170 - acc: 0.8194 - val_loss: 0.5094 - val_acc: 0.8788\n",
      "Epoch 32/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 0.6164 - acc: 0.8043 - val_loss: 0.5382 - val_acc: 0.8384\n",
      "Epoch 33/100\n",
      "792/792 [==============================] - 0s 285us/step - loss: 0.5528 - acc: 0.8384 - val_loss: 0.4818 - val_acc: 0.8788\n",
      "Epoch 34/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 0.5738 - acc: 0.8220 - val_loss: 0.4994 - val_acc: 0.8687\n",
      "Epoch 35/100\n",
      "792/792 [==============================] - 0s 290us/step - loss: 0.5361 - acc: 0.8384 - val_loss: 0.4638 - val_acc: 0.9091\n",
      "Epoch 36/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.5078 - acc: 0.8573 - val_loss: 0.4377 - val_acc: 0.8990\n",
      "Epoch 37/100\n",
      "792/792 [==============================] - 0s 284us/step - loss: 0.5013 - acc: 0.8548 - val_loss: 0.4446 - val_acc: 0.8687\n",
      "Epoch 38/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.4869 - acc: 0.8422 - val_loss: 0.4531 - val_acc: 0.8990\n",
      "Epoch 39/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 0.4710 - acc: 0.8737 - val_loss: 0.4295 - val_acc: 0.8889\n",
      "Epoch 40/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 0.4506 - acc: 0.8649 - val_loss: 0.4347 - val_acc: 0.8889\n",
      "Epoch 41/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.4475 - acc: 0.8598 - val_loss: 0.4173 - val_acc: 0.8788\n",
      "Epoch 42/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.4192 - acc: 0.8737 - val_loss: 0.4037 - val_acc: 0.9091\n",
      "Epoch 43/100\n",
      "792/792 [==============================] - 0s 284us/step - loss: 0.4217 - acc: 0.8725 - val_loss: 0.4188 - val_acc: 0.8889\n",
      "Epoch 44/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.4040 - acc: 0.8775 - val_loss: 0.3679 - val_acc: 0.8889\n",
      "Epoch 45/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 0.4357 - acc: 0.8510 - val_loss: 0.4128 - val_acc: 0.8889\n",
      "Epoch 46/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.3781 - acc: 0.9015 - val_loss: 0.3929 - val_acc: 0.8586\n",
      "Epoch 47/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 0.3856 - acc: 0.8902 - val_loss: 0.4007 - val_acc: 0.8788\n",
      "Epoch 48/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.3989 - acc: 0.8864 - val_loss: 0.3993 - val_acc: 0.8788\n",
      "Epoch 49/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.3707 - acc: 0.8914 - val_loss: 0.3882 - val_acc: 0.8990\n",
      "\n",
      "Epoch 00049: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 50/100\n",
      "792/792 [==============================] - 0s 292us/step - loss: 0.3309 - acc: 0.9003 - val_loss: 0.3786 - val_acc: 0.8990\n",
      "Epoch 51/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2998 - acc: 0.9205 - val_loss: 0.3609 - val_acc: 0.8889\n",
      "Epoch 52/100\n",
      "792/792 [==============================] - 0s 289us/step - loss: 0.3065 - acc: 0.9154 - val_loss: 0.3534 - val_acc: 0.8990\n",
      "Epoch 53/100\n",
      "792/792 [==============================] - 0s 291us/step - loss: 0.3064 - acc: 0.9179 - val_loss: 0.3553 - val_acc: 0.8990\n",
      "Epoch 54/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.2971 - acc: 0.9242 - val_loss: 0.3574 - val_acc: 0.9091\n",
      "Epoch 55/100\n",
      "792/792 [==============================] - 0s 295us/step - loss: 0.3095 - acc: 0.9167 - val_loss: 0.3589 - val_acc: 0.8990\n",
      "Epoch 56/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2817 - acc: 0.9268 - val_loss: 0.3544 - val_acc: 0.8990\n",
      "Epoch 57/100\n",
      "792/792 [==============================] - 0s 284us/step - loss: 0.2972 - acc: 0.9116 - val_loss: 0.3488 - val_acc: 0.8990\n",
      "\n",
      "Epoch 00057: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 58/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2657 - acc: 0.9369 - val_loss: 0.3308 - val_acc: 0.8990\n",
      "Epoch 59/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2625 - acc: 0.9356 - val_loss: 0.3274 - val_acc: 0.8990\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 60/100\n",
      "792/792 [==============================] - 0s 291us/step - loss: 0.2893 - acc: 0.9116 - val_loss: 0.3249 - val_acc: 0.9091\n",
      "Epoch 61/100\n",
      "792/792 [==============================] - 0s 285us/step - loss: 0.2988 - acc: 0.9154 - val_loss: 0.3209 - val_acc: 0.9091\n",
      "Epoch 62/100\n",
      "792/792 [==============================] - 0s 290us/step - loss: 0.2707 - acc: 0.9318 - val_loss: 0.3247 - val_acc: 0.9091\n",
      "Epoch 63/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 0.2681 - acc: 0.9255 - val_loss: 0.3303 - val_acc: 0.9091\n",
      "\n",
      "Epoch 00063: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 64/100\n",
      "792/792 [==============================] - 0s 285us/step - loss: 0.2410 - acc: 0.9482 - val_loss: 0.3242 - val_acc: 0.9091\n",
      "Epoch 65/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2605 - acc: 0.9293 - val_loss: 0.3207 - val_acc: 0.9091\n",
      "Epoch 66/100\n",
      "792/792 [==============================] - 0s 285us/step - loss: 0.2595 - acc: 0.9407 - val_loss: 0.3181 - val_acc: 0.9192\n",
      "Epoch 67/100\n",
      "792/792 [==============================] - 0s 290us/step - loss: 0.2437 - acc: 0.9356 - val_loss: 0.3227 - val_acc: 0.8990\n",
      "Epoch 68/100\n",
      "792/792 [==============================] - 0s 285us/step - loss: 0.2438 - acc: 0.9331 - val_loss: 0.3214 - val_acc: 0.9091\n",
      "Epoch 69/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2649 - acc: 0.9293 - val_loss: 0.3205 - val_acc: 0.8889\n",
      "Epoch 70/100\n",
      "792/792 [==============================] - 0s 286us/step - loss: 0.2590 - acc: 0.9369 - val_loss: 0.3157 - val_acc: 0.8889\n",
      "\n",
      "Epoch 00070: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "Epoch 71/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.2638 - acc: 0.9255 - val_loss: 0.3137 - val_acc: 0.8990\n",
      "Epoch 72/100\n",
      "792/792 [==============================] - 0s 287us/step - loss: 0.2382 - acc: 0.9343 - val_loss: 0.3181 - val_acc: 0.8990\n",
      "Epoch 73/100\n",
      "792/792 [==============================] - 0s 295us/step - loss: 0.2521 - acc: 0.9356 - val_loss: 0.3172 - val_acc: 0.8889\n",
      "Epoch 74/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2591 - acc: 0.9280 - val_loss: 0.3175 - val_acc: 0.8990\n",
      "Epoch 75/100\n",
      "792/792 [==============================] - 0s 288us/step - loss: 0.2642 - acc: 0.9280 - val_loss: 0.3147 - val_acc: 0.8990\n",
      "\n",
      "Epoch 00075: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "Epoch 00075: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f6af25d2a58>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_num_tr, y_tr_cat, batch_size = 10, epochs = 100, validation_data=(X_num_val, y_val_cat), callbacks=[rlr, erl])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit generator first implementation\n",
    "\n",
    "Here we implement a generator of keras following [a very *detailed example of how to use data generators with Keras\n",
    "*](https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly) and a nice discussion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "class DataGenerator(keras.utils.Sequence):\n",
    "    'Generates data for Keras'\n",
    "    def __init__(self, features, targets, \n",
    "                 n_classes =99, batch_size=32,  shuffle=True):\n",
    "        'Initialization'\n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        self.list_IDs = np.arange(self.n_vals)\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        \n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        X, y = self.__data_generation(list_IDs_temp)\n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __data_generation(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "\n",
    "#        # Generate data\n",
    "#        for i, ID in enumerate(list_IDs_temp):\n",
    "#            # Store sample\n",
    "#            X[i,] = np.load('data/' + ID + '.npy')\n",
    "#\n",
    "#            # Store class\n",
    "#            y[i] = self.labels[ID]\n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we implement the generator for both, the training and the validation set. Note that at this point the generator only shuffles the data, but does not do nothing complicated.\n",
    "\n",
    "Later we are going to use the generator for more complex things."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'batch_size': 10,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True}\n",
    "\n",
    "training_generator = DataGenerator(X_num_tr,y_tr, **params)\n",
    "validation_generator = DataGenerator(X_num_val,y_val, **params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "features_input (InputLayer)  (None, 192)               0         \n",
      "_________________________________________________________________\n",
      "dense_first_layer (Dense)    (None, 64)                12352     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_for_ens (Dense)        (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "out_layer (Dense)            (None, 99)                12771     \n",
      "=================================================================\n",
      "Total params: 33,443\n",
      "Trainable params: 33,443\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = features_model()\n",
    "\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, \n",
    "                        patience=5, verbose=1, mode='auto', \n",
    "                        min_delta=0.01,  min_lr=0)\n",
    "erl = EarlyStopping(monitor='val_loss',\n",
    "                        patience=10, verbose=1, mode='auto', \n",
    "                        min_delta=0.01)\n",
    "\n",
    "model.compile(optimizer='adam', loss= 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "79/79 [==============================] - 1s 7ms/step - loss: 4.5886 - acc: 0.0304 - val_loss: 4.5573 - val_acc: 0.1222\n",
      "Epoch 2/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 4.4956 - acc: 0.0861 - val_loss: 4.3327 - val_acc: 0.1778\n",
      "Epoch 3/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 4.1163 - acc: 0.0924 - val_loss: 3.7071 - val_acc: 0.2000\n",
      "Epoch 4/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 3.4492 - acc: 0.1671 - val_loss: 3.0140 - val_acc: 0.2889\n",
      "Epoch 5/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 2.8924 - acc: 0.2582 - val_loss: 2.5404 - val_acc: 0.4667\n",
      "Epoch 6/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 2.5174 - acc: 0.3152 - val_loss: 2.2061 - val_acc: 0.5333\n",
      "Epoch 7/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 2.2653 - acc: 0.3785 - val_loss: 2.0238 - val_acc: 0.5333\n",
      "Epoch 8/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 2.0720 - acc: 0.4152 - val_loss: 1.8803 - val_acc: 0.5778\n",
      "Epoch 9/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.8596 - acc: 0.4658 - val_loss: 1.6734 - val_acc: 0.6222\n",
      "Epoch 10/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.7761 - acc: 0.4734 - val_loss: 1.5635 - val_acc: 0.6667\n",
      "Epoch 11/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.6316 - acc: 0.5266 - val_loss: 1.4548 - val_acc: 0.6333\n",
      "Epoch 12/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.5464 - acc: 0.5557 - val_loss: 1.4079 - val_acc: 0.6889\n",
      "Epoch 13/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.4559 - acc: 0.5684 - val_loss: 1.2231 - val_acc: 0.7667\n",
      "Epoch 14/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.3699 - acc: 0.5924 - val_loss: 1.1760 - val_acc: 0.7556\n",
      "Epoch 15/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.2864 - acc: 0.6101 - val_loss: 1.1053 - val_acc: 0.7667\n",
      "Epoch 16/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.2505 - acc: 0.6392 - val_loss: 1.0392 - val_acc: 0.7444\n",
      "Epoch 17/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.1684 - acc: 0.6658 - val_loss: 0.9978 - val_acc: 0.8111\n",
      "Epoch 18/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.1371 - acc: 0.6646 - val_loss: 0.9283 - val_acc: 0.7667\n",
      "Epoch 19/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 1.0548 - acc: 0.6911 - val_loss: 0.9893 - val_acc: 0.7778\n",
      "Epoch 20/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 1.0286 - acc: 0.6987 - val_loss: 0.8910 - val_acc: 0.8111\n",
      "Epoch 21/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.9714 - acc: 0.7139 - val_loss: 0.8214 - val_acc: 0.8333\n",
      "Epoch 22/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.9563 - acc: 0.7152 - val_loss: 0.8598 - val_acc: 0.8333\n",
      "Epoch 23/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.8970 - acc: 0.7316 - val_loss: 0.8594 - val_acc: 0.8333\n",
      "Epoch 24/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.8466 - acc: 0.7570 - val_loss: 0.8290 - val_acc: 0.8444\n",
      "Epoch 25/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.8418 - acc: 0.7481 - val_loss: 0.7683 - val_acc: 0.8667\n",
      "Epoch 26/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.7883 - acc: 0.7709 - val_loss: 0.7817 - val_acc: 0.8000\n",
      "Epoch 27/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.8055 - acc: 0.7544 - val_loss: 0.6716 - val_acc: 0.8333\n",
      "Epoch 28/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.7418 - acc: 0.7785 - val_loss: 0.7229 - val_acc: 0.8667\n",
      "Epoch 29/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.7470 - acc: 0.7696 - val_loss: 0.7013 - val_acc: 0.8889\n",
      "Epoch 30/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.7045 - acc: 0.7924 - val_loss: 0.6914 - val_acc: 0.8111\n",
      "Epoch 31/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.6687 - acc: 0.7911 - val_loss: 0.6361 - val_acc: 0.8333\n",
      "Epoch 32/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.6817 - acc: 0.7835 - val_loss: 0.6462 - val_acc: 0.8444\n",
      "Epoch 33/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.6456 - acc: 0.8063 - val_loss: 0.6101 - val_acc: 0.8778\n",
      "Epoch 34/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.6596 - acc: 0.8025 - val_loss: 0.5681 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5934 - acc: 0.8215 - val_loss: 0.6010 - val_acc: 0.8778\n",
      "Epoch 36/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.6154 - acc: 0.8038 - val_loss: 0.5457 - val_acc: 0.8778\n",
      "Epoch 37/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5488 - acc: 0.8304 - val_loss: 0.5716 - val_acc: 0.9000\n",
      "Epoch 38/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.5460 - acc: 0.8329 - val_loss: 0.6133 - val_acc: 0.8444\n",
      "Epoch 39/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5425 - acc: 0.8354 - val_loss: 0.4949 - val_acc: 0.8667\n",
      "Epoch 40/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.5000 - acc: 0.8405 - val_loss: 0.4868 - val_acc: 0.9111\n",
      "Epoch 41/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.5107 - acc: 0.8405 - val_loss: 0.5027 - val_acc: 0.9222\n",
      "Epoch 42/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.5275 - acc: 0.8291 - val_loss: 0.4402 - val_acc: 0.9111\n",
      "Epoch 43/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.4415 - acc: 0.8734 - val_loss: 0.5034 - val_acc: 0.9000\n",
      "Epoch 44/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.5015 - acc: 0.8430 - val_loss: 0.4717 - val_acc: 0.8889\n",
      "Epoch 45/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4207 - acc: 0.8823 - val_loss: 0.5068 - val_acc: 0.9000\n",
      "Epoch 46/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.4241 - acc: 0.8696 - val_loss: 0.4428 - val_acc: 0.8778\n",
      "Epoch 47/100\n",
      "79/79 [==============================] - 0s 5ms/step - loss: 0.4313 - acc: 0.8671 - val_loss: 0.4885 - val_acc: 0.9000\n",
      "\n",
      "Epoch 00047: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 48/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.3799 - acc: 0.8823 - val_loss: 0.4843 - val_acc: 0.8889\n",
      "Epoch 49/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.3894 - acc: 0.8785 - val_loss: 0.4498 - val_acc: 0.9000\n",
      "Epoch 50/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.3789 - acc: 0.8835 - val_loss: 0.4430 - val_acc: 0.9111\n",
      "Epoch 51/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.3741 - acc: 0.8848 - val_loss: 0.4659 - val_acc: 0.8889\n",
      "Epoch 52/100\n",
      "79/79 [==============================] - 0s 4ms/step - loss: 0.3409 - acc: 0.9127 - val_loss: 0.4411 - val_acc: 0.9111\n",
      "\n",
      "Epoch 00052: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 00052: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f69e02665c0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(training_generator, \n",
    "                   validation_data=validation_generator,\n",
    "                   epochs=100, callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train using images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we are going to use the images to train the classifier.\n",
    "\n",
    "First we use the same generator as before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using Custom Data generator for the images set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True}\n",
    "\n",
    "images_training_generator = DataGenerator(X_img_tr,y_tr, **params)\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 512, 512, 1)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABkAAAAZACAYAAAAhDI6nAAAgAElEQVR4Xuzd7bEdSbIdWLwRg1JQiTFKMFKOBLRRYqQYMaZp6CaqANTFvTs/3cNj8dczlp+TGcsDuf1kNKr+61//+te/vvl/BAgQIECAAAECBAgQIECAAAECBAgQIECAAIFBAv/lAGRQNy2FAAECBAgQIECAAAECBAgQIECAAAECBAgQ+LeAAxAbgQABAgQIECBAgAABAgQIECBAgAABAgQIEBgn4ABkXEstiAABAgQIECBAgAABAgQIECBAgAABAgQIEHAAYg8QIECAAAECBAgQIECAAAECBAgQIECAAAEC4wQcgIxrqQURIECAAAECBAgQIECAAAECBAgQIECAAAECDkDsAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCcgAOQcS21IAIECBAgQIAAAQIECBAgQIAAAQIECBAgQMABiD1AgAABAgQIECBAgAABAgQIECBAgAABAgQIjBNwADKupRZEgAABAgQIECBAgAABAgQIECBAgAABAgQIOACxBwgQIECAAAECBAgQIECAAAECBAgQIECAAIFxAg5AxrXUgggQIECAAAECBAgQIECAAAECBAgQIECAAAEHIPYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgME7AAci4lloQAQIECBAgQIAAAQIECBAgQIAAAQIECBAg4ADEHiBAgAABAgQIECBAgAABAgQIECBAgAABAgTGCTgAGddSCyJAgAABAgQIECBAgAABAgQIECBAgAABAgQcgNgDBAgQIECAAAECBAgQIECAAAECBAgQIECAwDgBByDjWmpBBAgQIECAAAECBAgQIECAAAECBAgQIECAgAMQe4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYJ+AAZFxLLYgAAQIECBAgQIAAAQIECBAgQIAAAQIECBBwAGIPECBAgAABAgQIECBAgAABAgQIECBAgAABAuMEHICMa6kFESBAgAABAgQIECBAgAABAgQIECBAgAABAg5A7AECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnIADkHEttSACBAgQIECAAAECBAgQIECAAAECBAgQIEDAAYg9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIwTcAAyrqUWRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDgAsQcIECBAgAABAgQIECBAgAABAgQIECBAgACBcQIOQMa11IIIECBAgAABAgQIECBAgAABAgQIECBAgAABByD2AAECBAgQIECAAAECBAgQIECAAAECBAgQIDBOwAHIuJZaEAECBAgQIECAAAECBAgQIECAAAECBAgQIOAAxB4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExgk4ABnXUgsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEHIDYAwQIECBAgAABAgQIECBAgAABAgQIECBAgMA4AQcg41pqQQQIECBAgAABAgQIECBAgAABAgQIECBAgIADEHuAAAECBAgQIECAAAECBAgQIECAAAECBAgQGCfgAGRcSy2IAAECBAgQIECAAAECBAgQIECAAAECBAgQcABiDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLjBByAjGupBREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIOQOwBAgQIECBAgAABAgQIECBAgAABAgQIECBAYJyAA5BxLbUgAgQIECBAgAABAgQIECBAgAABAgQIECBAwAGIPUCAAAECBAgQIECAAAECBAgQIECAAAECBAiME3AAMq6lFkSAAAECBAgQIECAAAECBAgQIECAAAECBAg4ALEHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXECDkDGtdSCCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQcg9gABAgQIECBAgAABAgQIECBAgAABAgQIECAwTsAByLiWWhABAgQIECBAgAABAgQIECBAgAABAgQIECDgAMQeIECAAAECBAgQIECAAAECBAgQIECAAAECBMYJOAAZ11ILIkCAAAECBAgQIECAAAECBAgQIECAAAECBByA2AMECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAOAEHIONaakEECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAxB7gAABAgQIECBAgAABAgQIECBAgAABAgQIEBgn4ABkXEstiAABAgQIECBAgAABAgQIECBAgAABAgQIEHAAYg8QIECAAAECBAgQIECAAAECBAgQIECAAAEC4wQcgIxrqQURIECAAAECBAgQIECAAAECBAgQIECAAAECDkDsAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCcgAOQcS21IAIECBAgQIAAAQIECBAgQIAAAQIECBAgQMABiD1AgAABAgQIECBAgAABAgQIECBAgAABAgQIjBNwADKupRZEgAABAgQIECBAgAABAgQIECBAgAABAgQIOACxBwgQIECAAAECBAgQIECAAAECBAgQIECAAIFxAg5AxrXUgggQIECAAAECBAgQIECAAAECBAgQIECAAAEHIPYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgME7AAci4lloQAQIECBAgQIAAAQIECBAgQIAAAQIECBAg4ADEHiBAgAABAgQIECBAgAABAgQIECBAgAABAgTGCTgAGddSCyJAgAABAgQIECBAgAABAgQIECBAgAABAgQcgNgDBAgQIECAAAECBAgQIECAAAECBAgQIECAwDgBByDjWmpBBAgQIECAAAECBAgQIECAAAECBAgQIECAgAMQe4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYJ+AAZFxLLYgAAQIECBAgQIAAAQIECBAgQIAAAQIECBBwAGIPECBAgAABAgQIECBAgAABAgQIECBAgAABAuMEHICMa6kFESBAgAABAgQIECBAgAABAgQIECBAgAABAg5A7AECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnIADkHEttSACBAgQIECAAAECBAgQIECAAAECBAgQIEDAAYg9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIwTcAAyrqUWRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDgAsQcIECBAgAABAgQIECBAgAABAgQIECBAgACBcQIOQMa11IIIECBAgAABAgQIECBAgAABAgQIECBAgAABByD2AAECBAgQIECAAAECBAgQIECAAAECBAgQIDBOwAHIuJZaEAECBAgQIECAAAECBAgQIECAAAECBAgQIOAAxB4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExgk4ABnXUgsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEHIDYAwQIECBAgAABAgQIECBAgAABAgQIECBAgMA4AQcg41pqQQQIECBAgAABAgQIECBAgAABAgQIECBAgIADEHuAAAECBAgQIECAAAECBAgQIECAAAECBAgQGCfgAGRcSy2IAAECBAgQIECAAAECBAgQIECAAAECBAgQcABiDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLjBByAjGupBREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIOQOwBAgQIECBAgAABAgQIECBAgAABAgQIECBAYJyAA5BxLbUgAgQIECBAgAABAgQIECBAgAABAgQIECBAwAGIPUCAAAECBAgQIECAAAECBAgQIECAAAECBAiME3AAMq6lFkSAAAECBAgQIECAAAECBAgQIECAAAECBAg4ALEHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXECDkDGtdSCCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQcg9gABAgQIECBAgAABAgQIECBAgAABAgQIECAwTsAByLiWWhABAgQIECBAgAABAgQIECBAgAABAgQIECDgAMQeIECAAAECBAgQIECAAAECBAgQIECAAAECBMYJOAAZ11ILIkCAAAECBAgQIECAAAECBAgQIECAAAECBByA2AMECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAOAEHIONaakEECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAxB7gAABAgQIECBAgAABAgQIECBAgAABAgQIEBgn4ABkXEstiAABAgQIECBAgAABAgQIECBAgAABAgQIEHAAYg8QIECAAAECBAgQIECAAAECBAgQIECAAAEC4wQcgIxrqQURIECAAAECBAgQIECAAAECBAgQIECAAAECDkDsAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCcgAOQcS21IAIECBAgQIAAAQIECBAgQIAAAQIECBAgQMABiD1AgAABAgQIECBAgAABAgQIECBAgAABAgQIjBNwADKupRZEgAABAgQIECBAgAABAgQIECBAgAABAgQIOACxBwgQIECAAAECBAgQIECAAAECBAgQIECAAIFxAg5AxrXUgggQIECAAAECBAgQIECAAAECBAgQIECAAAEHIPYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgME7AAci4lloQAQIECBAgQIAAAQIECBAgQIAAAQIECBAg4ADEHiBAgAABAgQIECBAgAABAgQIECBAgAABAgTGCTgAGddSCyJAgAABAgQIECBAgAABAgQIECBAgAABAgQcgNgDBAgQIECAAAECBAgQIECAAAECBAgQIECAwDgBByDjWmpBBAgQIECAAAECBAgQIECAAAECBAgQIECAgAMQe4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYJ+AAZFxLLYgAAQIECBAgQIAAAQIECBAgQIAAAQIECBBwAGIPECBAgAABAgQIECBAgAABAgQIECBAgAABAuMEHICMa6kFESBAgAABAgQIECBAgAABAgQIECBAgAABAg5A7AECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnIADkHEttSACBAgQIECAAAECBAgQIECAAAECBAgQIEDAAYg9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIwTcAAyrqUWRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDgAsQcIECBAgAABAgQIECBAgAABAgQIECBAgACBcQIOQMa11IIIECBAgAABAgQIECBAgAABAgQIECBAgAABByD2AAECBAgQIECAAAECBAgQIECAAAECBAgQIDBOwAHIuJZaEAECBAgQIECAAAECBAgQIECAAAECBAgQIOAAxB4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExgk4ABnXUgsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEHIDYAwQIECBAgAABAgQIECBAgAABAgQIECBAgMA4AQcg41pqQQQIECBAgAABAgQIECBAgAABAgQIECBAgIADEHuAAAECBAgQIECAAAECBAgQIECAAAECBAgQGCfgAGRcSy2IAAECBAgQIECAAAECBAgQIECAAAECBAgQcABiDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLjBByAjGupBREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIOQOwBAgQIECBAgAABAgQIECBAgAABAgQIECBAYJyAA5BxLbUgAgQIECBAgAABAgQIECBAgAABAgQIECBAwAGIPUCAAAECBAgQIECAAAECBAgQIECAAAECBAiME3AAMq6lFkSAAAECBAgQIECAAAECBAgQIECAAAECBAg4ALEHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXECDkDGtdSCCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQcg9gABAgQIECBAgAABAgQIECBAgAABAgQIECAwTsAByLiWWhABAgQIECBAgAABAgQIECBAgAABAgQIECDgAMQeIECAAAECBAgQIECAAAECBAgQIECAAAECBMYJOAAZ11ILIkCAAAECBAgQIECAAAECBAgQIECAAAECBByA2AMECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAOAEHIONaakEECBAgQIAAAQIECBAgQIAAAQIECBAgQICAAxB7gAABAgQIECBAgAABAgQIECBAgAABAgQIEBgn4ABkXEstiAABAgQIECBAgAABAgQIECBAgAABAgQIEHAAYg8QIECAAAECBAgQIECAAAECBAgQIECAAAEC4wQcgIxrqQURIECAAAECBAgQIECAAAECBAgQIECAAAECDkDsAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCcgAOQcS21IAIECBAgQIAAAQIECBAgQIAAAQIECBAgQMABiD1AgAABAgQIECBAgAABAgQIECBAgAABAgQIjBNwADKupRZEgAABAgQIECBAgAABAgQIECBAgAABAgQIOACxBwgQIECAAAECBAgQIECAAAECBAgQIECAAIFxAg5AxrXUgggQIECAAAECBAgQIECAAAECBAgQIECAAAEHIPYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgME7AAci4lloQAQIECBAgQIAAAQIECBAgQIAAAQIECBAg4ADEHiBAgAABAgQIECBAgAABAgQIECBAgAABAgTGCTgAGddSCyJAgAABAgQIECBAgAABAgQIECBAgAABAgQcgNgDBAgQIECAAAECBAgQIECAAAECBAgQIECAwDgBByDjWmpBBAgQIECAAAECBAgQIECAAAECBAgQIECAgAMQe4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYJ+AAZFxLLYgAAQIECBAgQIAAAQIECBAgQIAAAQIECBBwAGIPECBAgAABAgQIECBAgAABAgQIECBAgAABAuMEHICMa6kFESBAgAABAgQIECBAgAABAgQIECBAgAABAg5A7AECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnIADkHEttSACBAgQIECAAAECBAgQIECAAAECBAgQIEDAAYg9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIwTcAAyrqUWRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDgAsQcIECBAgAABAgQIECBAgAABAgQIECBAgACBcQIOQMa11IIIECBAgAABAgQIECBAgAABAgQIECBAgAABByD2AAECBAgQIECAAAECBAgQIECAAAECBAgQIDBOwAHIuJZaEAECBAgQIECAAAECBAgQIECAAAECBAgQIOAAxB4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExgk4ABnXUgsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEHIDYAwQIECBAgAABAgQIECBAgAABAgQIECBAgMA4AQcg41pqQQQIECBAgAABAgQIECBAgAABAgQIECBAgIADEHuAAAECBAgQIECAAAECBAgQIECAAAECBAgQGCfgAGRcSy2IAAECBAgQIECAAAECBAgQIECAAAECBAgQcABiDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLjBByAjGupBREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIOQOwBAgQIECBAgAABAgQIECBAgAABAgQIECBAYJyAA5BxLbUgAgQIECBAgAABAgQIECBAgAABAgQIECBAwAGIPUCAAAECBAgQIECAAAECBAgQIECAAAECBAiME3AAMq6lFkSAAAECBAgQIECAAAECBAgQIECAAAECBAg4ALEHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXECDkDGtdSCCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQcg9gABAgQIECBAgAABAgQIECBAgAABAgQIECAwTsAByLiWWhABAgQIECBAgAABAgQIECBAgAABAgQIECDgAMQeIECAAAECBAgQIECAAAECBAgQIECAAAECBMYJOAAZ11ILIkCAAAECBAgQIECAAAECBAgQIECAAAECBByA2AMEvhD4P/+P/4sRAQIECDQX+H/+//+7+R32uT251qcX7oQAAQJ/EpBr2d6QaZmTKgIECFQKyLRKfdf+LuAAxD4g4ADEHiBAgMDyAobqvIVeFuVWKgkQIFAlINcyeZmWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApIUCAQLGAoTpvgFzLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79oOQOwBAoGAoTpAUkKAAIFiAUN13gC5llupJECAQJWAXMvkZVrmpIoAAQKVAjKtUt+1HYDYAwQCAUN1gKSEAAECxQKG6rwBci23UkmAAIEqAbmWycu0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJCQECBIoFDNV5A+RabqWSAAECVQJyLZOXaZmTKgIECFQKyLRKfdd2AGIPEAgEDNUBkhICBAgUCxiq8wbItdxKJQECBKoE5FomL9MyJ1UECBCoFJBplfqu7QDEHiAQCBiqAyQlBAgQKBYwVOcNkGu5lUoCBAhUCci1TF6mZU6qCBAgUCkg0yr1XdsBiD1AIBAwVAdISggQIFAsYKjOGyDXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkJQQIECgWMBQnTdAruVWKgkQIFAlINcyeZmWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApIUCAQLGAoTpvgFzLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79oOQOwBAoGAoTpAUkKAAIFiAUN13gC5llupJECAQJWAXMvkZVrmpIoAAQKVAjKtUt+1HYDYAwQCAUN1gKSEAAECxQKG6rwBci23UkmAAIEqAbmWycu0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJCQECBIoFDNV5A+RabqWSAAECVQJyLZOXaZmTKgIECFQKyLRKfdd2AGIPEAgEDNUBkhICBAgUCxiq8wbItdxKJQECBKoE5FomL9MyJ1UECBCoFJBplfqu7QDEHiAQCBiqAyQlBAgQKBYwVOcNkGu5lUoCBAhUCci1TF6mZU6qCBAgUCkg0yr1XdsBiD1AIBAwVAdISggQIFAsYKjOGyDXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkJQQIECgWMBQnTdAruVWKgkQIFAlINcyeZmWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApIUCAQLGAoTpvgFzLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79oOQOwBAoGAoTpAUkKAAIFiAUN13gC5llupJECAQJWAXMvkZVrmpIoAAQKVAjKtUt+1HYDYAwQCAUN1gKRkrMD//P/+31/W9j/+238fu1YLW1vAUJ33T67lVioJECBQJSDXMnmZljmpIkCAQKWATKvUd20HIPYAgUDAUB0gKRkp8Pvhx++LdBgysu3LLspQnbdOruVWKgkQIFAlINcyeZmWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApGSnw1QHI90U7BBnZ+iUXZajO2ybXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkJSME0gOP34s2iHIuPYvuSBDdd42uZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLftR2A2AMEAgFDdYCkZJTAkcOP7wt3ADKq/csuxlCdt06u5VYqCRAgUCUg1zJ5mZY5qSJAgEClgEyr1HdtByD2AIFAwFAdICkZJXD0AOTH4h2EjNoGyy3GUJ23TK7lVioJECBQJSDXMnmZljmpIkCAQKWATKvUd20HIPYAgUDAUB0gKRkjcPbw4zuAA5Ax22DJhRiq87bJtdxKJQECBKoE5FomL9MyJ1UECBCoFJBplfqu7QDEHiAQCBiqAyQlywtcOfj4efEOQZbfCssuwFCdt06u5VYqCRAgUCUg1zJ5mZY5qSJAgEClgEyr1HdtByD2AIFAwFAdIClZXuCuA5DvEA5Blt8OSy7AUJ23Ta7lVioJECBQJSDXMnmZljmpIkCAQKWATKvUd20HIPYAgUDAUB0gKVlW4M6Dj58RHIIsuyWWvXFDdd46uZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLftR2A2AMEAgFDdYCkZFkBByDLts6N/yZgqM63hFzLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79oOQOwBAoGAoTpAUrKkwFOHHz8w/C2QJbfFsjdtqM5bJ9dyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlCwl8PTBx88YDkGW2hpL36yhOm+fXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2g5A7AECgYChOkBSspSAA5Cl2uVmQwFDdQj17ds3uZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLftR2A2AMEAgFDdYCkZAmBNw8+fgfxN0GW2CJL36ShOm+fXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2g5A7AECgYChOkBSsoSAA5Al2uQmTwoYqnM4uZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLftR2A2AMEAgFDdYCkpL1A5eHHDxx/C6T9Nln6Bg3VefvkWm6lkgABAlUCci2Tl2mZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKS1gIdDj++AzkAab1Nlr85Q3XeQrmWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37UdgNgDBAIBQ3WApKS1QJcDEIcgrbfJ8jdnqM5bKNdyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlLQU6HTw8TOQvwnScrssf1OG6ryFci23UkmAAIEqAbmWycu0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJSTuBrocfP6AcgrTbMsvfkKE6b6Fcy61UEiBAoG1k3cUAACAASURBVEpArmXyMi1zUkWAAIFKAZlWqe/aDkDsAQKBgKE6QFLSTqD7Ach3MIcg7bbN0jdkqM7bJ9dyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlLQRWOHg42cshyBtts7yN2Kozlso13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpCUtBFY7QDkO5xDkDbbZ+kbMVTn7ZNruZVKAgQIVAnItUxepmVOqggQIFApINMq9V3bAYg9QCAQMFQHSErKBVY8+PgdzUFI+TZa+gYM1Xn75FpupZIAAQJVAnItk5dpmZMqAgQIVArItEp913YAYg8QCAQM1QGSklKBCYcfPwAdgpRupaUvbqjO2yfXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkJSUCjgAKeV38SYChuq8EXItt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvms7ALEHCAQChuoASUmZwKTDjx+I/hZI2XZa+sKG6rx9ci23UkmAAIEqAbmWycu0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJSYnAxMMPhyAlW2nERQ3VeRvlWm6lkgABAlUCci2Tl2mZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKS1wUmH344BHl9O424oKE6b6Ncy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/aDkDsAQKBgKE6QFLyqsAOhx8OQV7dUiMuZqjO2yjXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkJS8JrDT4YdDkNe21YgLGarzNsq13EolAQIEqgTkWiYv0zInVQQIEKgUkGmV+q7tAMQeIBAIGKoDJCWvCOx4+OEQ5JWtNeIihuq8jXItt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvms7ALEHCAQChuoAScnjAjsffjgEeXx7jbiAoTpvo1zLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79oOQOwBAoGAoTpAUvK4gAOQ/xD/j//23x+3doE1BQzVed/kWm6lkgABAlUCci2Tl2mZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKSRwUcfvzK6xDk0e227JcbqvPWybXcSiUBAgSqBORaJi/TMidVBAgQqBSQaZX6ru0AxB4gEAgYqgMkJY8JOPz4mNYhyGNbbtkvNlTnrZNruZVKAgQIVAnItUxepmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEoeE3AA4gDksc017IsN1XlD5VpupZIAAQJVAnItk5dpmZMqAgQIVArItEp913YAYg8QCAQM1QGSkkcEHH78mdXfAHlkyy39pYbqvH1yLbdSSYAAgSoBuZbJy7TMSRUBAgQqBWRapb5rOwCxBwgEAobqAEnJ7QIOP74mdQjytdFOFYbqvNtyLbdSSYAAgSoBuZbJy7TMSRUBAgQqBWRapb5rOwCxBwgEAobqAEnJrQIOPzJOByCZ0y5Vhuq803Itt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvms7ALEHCAQChuoAScltAg4/jlM6CDluNvEThuq8q3Itt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvms7ALEHCAQChuoAScktAg4/zjE6ADnnNu1Thuq8o3Itt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvms7ALEHCAQChuoAScktAg5AzjE6ADnnNu1Thuq8o3Itt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvms7ALEHCAQChuoAScklAQcfl/j+/WGHINcNV/8GQ3XeQbmWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37UdgNgDBAIBQ3WApOSSgAOQS3wOQK7zjfgGQ3XeRrmWW61W+Vme/Dgo/l7z/f/+ufbnf/b7mn8+YP7xGYfOq+0M97uigFzLuibTMidVBAgQqBSQaZX6ru0AxB4gEAgYqgMkJacFHH6cpvvHB72Qu89yxW8yVOddk2u5VefKnw8jumaJ53LnHeTeugvItaxDMi1zUkWAAIFKAZlWqe/aDkDsAQKBgKE6QFJyWqDrS6vTCyr+oJdtxQ0ovLyhOseXa7lV18pVssMzuesOcl8rCMi1rEsyLXNSRYAAgUoBmVap79oOQOwBAoGAoTpAUnJaYJWXWKcX+PIHvWx7GbzR5QzVeTPkWm7VofKjf2VVh/s6cw/+dVpn1HxmVwG5lnVepmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEpOCTj8OMX26YccgNxvuso3GqrzTsm13KqycqeM8Oyu3Gmu3VVArmWdkWmZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKSUwI7vdw6BXTyQ16knYRb/GOG6ryBci23qqzcLSM8uyt3m2t3FJBrWVdkWuakigABApUCMq1S37UdgNgDBAIBQ3WApOSwwG4vtg4DXfyAF2kXARf8uKE6b5pcy62eqPzo+f/jmSUbMnHP+MxJ1doCci3rn0zLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpCUHBLwgusQ16liL8dOsS39IUN13j65lltdrfzxvP/+TJr03/K46nLH5z3n71D0HZ0F5FrWHZmWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApiQUcfsRUlwq9GLvEt+SHDdV52+RabnW20rP+rNz5z3nun7fzyZ4Cci3ri0zLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpCUxAJeisVUlwu9DLtMuNQXGKrzdsm13OpopWf8UbH76z377zf1jTUCci1zl2mZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKSSMCLsYjptiIvwW6jXOKLDNV5m+RabnWk0jP+iNZztZ79z9n65ncF5FrmLdMyJ1UECBCoFJBplfqu7QDEHiAQCBiqAyQlkYCXYxHTrUVehN3K2frLDNV5e+RabpVUerYnSnU1cqDO3pWvCci1zE+mZU6qCBAgUCkg0yr1XdsBiD1AIBAwVAdISiIBL8kipluLvPi6lbP1lxmq8/bItdwqqfRsT5Rqa2RBrb+rnxOQa5mbTMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkJR8KeAF2ZdEjxR46fUIa8svNVTnbZFrudWfKj3TrxtWf4N8qO6A638lINe+EvrPP5dpmZMqAgQIVArItEp913YAYg8QCAQM1QGSkk8FvCir3SBectX6v3V1Q3UuLddyq48qPdOv+XX5tGzo0gn38ScBuZbtDZmWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApcQDSfA940dW8QTfcnqE6R5RruZUDkGtWK3xaPqzQpT3vUa5lfZdpmZMqAgQIVArItEp913YAYg8QCAQM1QGSEgcgzfeAF1zNG3TD7Rmqc0S5llv9qPS3Po6brfoJebFq5+bdt1zLeirTMidVBAgQqBSQaZX6ru0AxB4gEAgYqgMkJQ5Amu8BL7SaN+iG2zNU54hyLbdyAHLcasInZMaELq6/BrmW9VCmZU6qCBAgUCkg0yr1XdsBiD1AIBAwVAdISv4o4H813GNzeJnVow9P3oWhOteVa7nV90rP8WNe06rlx7SOrrMeuZb1SqZlTqoIECBQKSDTKvVd2wGIPUAgEDBUB0hKPhTw0qzXxvASq1c/7r4bQ3UuKtdyK8/x3Gp6pQyZ3uF+65NrWU9kWuakigABApUCMq1S37UdgNgDBAIBQ3WApMQByCJ7wAusRRp14jYN1TmaXMusHH5kTrtVyZHdOl63XrmW2cu0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJiQOQRfaAF1eLNOrEbRqqczS5llk5AMmcdqySJTt2/f01y7XMXKZlTqoIECBQKSDTKvVd2wGIPUAgEDBUB0hKHIAssge8tFqkUSdu01Cdo8m1r60cfnxttHuFPNl9Bzy/frmWGcu0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJyT8EvDzruym8tOrbmyt3ZqjO9eTax1ae2/keUvmrgFyxI54QkGuZqkzLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpCU/CLgJVrvDeFFVe/+nL07Q3UuJ9ccgOS7RWUqIFtSKXWpgFzLpGRa5qSKAAEClQIyrVLftR2A2AMEAgFDdYCk5C8Bhx/9N4OXVP17dOYODdW5mlz7p5Vnd75/VGYCsiZzUvVnAbmW7Q6ZljmpIkCAQKWATKvUd20HIPYAgUDAUB0gKXEAstAe8FJqoWYduFVDdY4l1361cviR7x2VuYCsya1Ufiwg17KdIdMyJ1UECBCoFJBplfqu7QDEHiAQCBiqAyQlDkAW2gNeSi3UrAO3aqjOseSaA5B8t6i8IiBvruj5rFzL9oBMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlPxbwP+KeJ2N4KXUOr1K79RQnUp9+ybX/rby3M73jcrrArLnuuFO3yDXsm7LtMxJFQECBCoFZFqlvms7ALEHCAQChuoASYkDkMX2gJdQizUsuF1DdYD0v0vkmgOQfLeofEJABj2hOu875VrWU5mWOakiQIBApYBMq9R3bQcg9gCBQMBQHSAp8bc/FtwDXkAt2LRPbtlQnfdTrvkbe/luUfmkgBx6Unf975ZrWQ9lWuakigABApUCMq1S37UdgNgDBAIBQ3WApMQByIJ7wIunBZvmAOSWpu2ea/61V7dsI19yk4Asugly4Nd4WZQ1dfdMy5RUESBAoFZAptX6u/q3b//1r3/9618gCBD4s4Ch2u74SsDLtK+Eev5zL5169uXsXRmqc7ndc80zO98rKt8TkEnvWa9yJbmWdWr3TMuUVBEgQKBWQKbV+ru6AxB7gMCXAobqL4m2LvAibd32e9m0bu8+unNDdd7PXXPN8zrfIyrrBWRUfQ+q70CuZR3YNdMyHVUECBDoISDTevRh57vwN0B27r61RwKG6ohp2yIv1NZtvZdL6/bOAci13u2aa57X1/aNT78rIKPe9e54NS+Lsq7smmmZjioCBAj0EJBpPfqw8104ANm5+9YeCRiqI6Yti7xMW7/tXjCt38MfKzBU573cMdc8r/P9obKngLzq2Zcn70quZbo7Zlomo4oAAQJ9BGRan17seicOQHbtvHXHAobqmGq7Qi/U1m+5F0rr99AByPEe7pZrntXH94hP9BSQWT378tRdeVmUye6WaZmKKgIECPQSkGm9+rHj3TgA2bHr1nxIwFB9iGurYi/V1m+3l0nr99AByPEe7pZrntXH94hP9BWQW317c/edeVmUie6WaZmKKgIECPQSkGm9+rHj3TgA2bHr1nxIwFB9iGubYi/U5rTay6QZvTRU533cLdc8r/O9oXIdAdm1Tq/O3qlcy+R2y7RMRRUBAgR6Cci0Xv3Y8W4cgOzYdWs+JGCoPsS1TbEXanNa7SXSjF4aqvM+7pRrntX5vlC5poAMW7NvyV3LtUTp27edMi0TUUWAAIF+AjKtX092uyMHILt13HoPCxiqD5ON/4AXarNa7OXRjH4aqvM+7pBrntP5flA5Q0CWzejjz6uQa1lPd8i0TEIVAQIE+grItL692eXOHIDs0mnrPC1gqD5NN/aDXqzNaq2XRjP6aajO+7hDrnlO5/tB5RwBeTanl99XIteyfu6QaZmEKgIECPQVkGl9e7PLnTkA2aXT1nlawFB9mm7kB71Um9dWL4xm9NRQnfdxh1zzrM73g8qZArJt/b7KtayHO2RaJqGKAAECfQVkWt/e7HJnDkB26bR1nhYwVJ+mG/lBL9XmtdVLohk9NVTnfZyea57T+V5QOV9Axq3bY7mW9W56pmUKqggQINBbQKb17s8Od+cAZIcuW+MlAUP1Jb5RH/ZSbVQ7/1qMl0Mz+mqozvs4Pdc8q/O9oHK+gIxbt8dyLevd9EzLFFT9EDgyA3g+2jcE3hOQae9Zu9LHAg5A7AwCXwgYqm2R7wJHhmli6wn4AbRez36/Y0N13sPJueZZne8DlXsJyLn1+i3Xsp5NzrRMYF7V9yxPn1lXcj+9xjxhKyLwvoBMe9/cFX8VcABiRxBwAGIPBAJXhuvg65UUC/gBVNyAGy5vqM4RJ78s8qzO94HK/QRk3Vo9l2tZvyZnWiawdtWfcvuz59UdWX/393u+rr0P3f3zAjLteWNX+FzAAYgdQsABiD0QCNwxaAeXUVIk4EdLEfyNlzVU55iTXxZ5Vuf7QOW+AjJvjd7LtaxPkzMtE1irKs3pj55T6WdTkd+v8fT3p/eljsBEAZk2satrrckByFr9crcFAobqAvSGl7x7IG64xK1vycug9dtvqM57ODnXPKvzfaBybwG517//ci3r0eRMywR6V53N5Z+fUWe/4yuZ79d46ru/X9tz9qsO+Oc7Cci0nbrdc60OQHr2xV01EjBUN2pG0a08ORgXLcllfxPwA2X9LWGozns4Ndc8q/M9oJKAl3P994Bcy3o0NdOy1fetuprJP2bzq99TJeS3RZW863YVkGldO7PPfTkA2afXVnpSwFB9Em7Ix1Yduofwv7oMP1Re5b79YobqnHRqrnle53tAJYEfArKv716Qa1lvpmZatvqeVfLY3/7ouTPdVaWATKvUd+3vAg5A7AMCXwgYqvfeIgb4ffrvJdDavTZU5/2bmmue1/keUEngZwH513M/yLWsL1MzLVv9s1Wf5eob/42OZ1f33LcfeaZ+ZHzk88+twjcTuFdApt3r6duOCzgAOW7mE5sJGKo3a/hPy/Uybb/e+8Gxbs8N1XnvpuWaZ3Xee5UEPhOQgb32h1zL+jEt07JVP1eVZOob/32O51b4zjc//R9Y/74Kz+x3eukq9wjItHscfct5AQcg5+18chMBQ/Umjf5tmcnwv6fM7FX7IbFufw3Vee+m5Zrndd57lQQcgKyzB+Ra1qtpmZat+pmqNE8dgHzt//R/w8Rvlq97oKKXgEzr1Y8d78YByI5dt+ZDAobqQ1xjitMfAGMWbCH/FvBjYt2NYKjOezcp1zyr876rJPCVgAz8Sujdfy7XMu9JmZat+P4qWXq/6ZPf6Fn9pK7vfkpApj0l63tTAQcgqZS6bQUM1fu13o+A/Xr+Y8V+UKzbe0N13rspueZZnfdcJYEzAjLxjNp9n5FrmeWUTMtWe1+VDL3P8u1v8mx+W9z17hCQaXco+o4rAg5Aruj57BYChuot2vzLIv0g2K/nDkDW77mhOu/hlFzzrM57rpLAGQEv2c6o3fcZuZZZTsm0bLX3VMnPexyrvsWzuUreda8IyLQrej57h4ADkDsUfcdoAUP16Pb+Y3F+EOzV799X6wfFuv03VOe9m5Jrntd5z1USOCsgF8/KXf+cXMsMp2RattrzVTLzvF23T370XP5Tfz3Du3Vv3/uRafv2vsvKHYB06YT7aCtgqG7bmkduzI+DR1iX+VI/EpZp1T9u1FCd925CrnlW5/1WSeCqgGy8Knju83Itc5uQadlKr1XJzWt+nT599j+w7lneqYv73YtM26/n3VbsAKRbR9xPOwFDdbuWPHZDfhg8RrvMF/thsEyrHIBcaNXqueZZfaH5PkrggoCMvIB34qNeFmVoq2datsrzVTLzvN0On/Rc36HLPdYo03r0Yee7cACyc/etPRIwVEdMI4r8QBjRxsuL8EPgMmHJFxiqc/bVc82zOu+1SgJ3C8jIu0X//H1yLbNePdOyVZ6rkpfn3Hb8lGf7jl1/d80y7V1vV/ungAMQu4LAFwKG6n22iB8J+/T6Tys1/K+7BwzVee9WzzXP6rzXKgk8ISArn1D953fKtcx59UzLVnm8SlYeN9v9E57tu++AZ9cv05719e1fCzgA+dpIxeYChuo9NoAfCXv0OVml4T9R6ldjqM57snKueVbnfVZJ4EkBWfmk7n++W65lxitnWrbC41Wy8rjZzp/wPN+5+++tXaa9Z+1KHws4ALEzCHwhYKiev0X8SJjf43SFfgCkUv3qDNV5T1bONc/rvM8qCTwtIDOfFZZrme/KmZat8Ouq79l49j+M/fW3q5gs4Dk+ubu91ibTevVjx7txALJj1635kICh+hDXksVeqC3Ztkdu2o+AR1hf+VJDdc68Yq55Tuf9VUngbQHZ+Yy4XMtcV8y0bGWfV8nFOxR9h+e3PfCWgEx7S9p1/iTgAMTeIPCFwK5D9U4bww+Inbr9+Vr9CFh3Lxiq896tmGue03l/VRJ4W0B2PiMu1zLXFTMtW9mfq2TiVUGf/yHg+W0vvCUg096Sdh0HIPYAgZMCOw7VJ6mW/JgfEEu27bGb9iPgMdrHv9hQnROvmGue1Xl/VRKoEpCh98rLtcxzxUzLVvZxlTy8ouezvwt4btsTbwnItLekXccBiD1A4KTAbkP1SaZlP+ZHxLKte+TG/Qh4hPWVLzVU58wr5ppndd5flQQqBeToffpyLbNcMdOylf1aJQfPqPnMHQKe63co+g6ZZg9UC/hXYFV3wPXbC+wyVLdvxAM36IfEA6gDvtKQv2YTDdV531bMNc/rvL8qCVQLyNF7OiDXMscVMy1bmQOQM04+84yA5/ozrjt9q0zbqds91+oApGdf3FUjgV2G6kbkr92KF2qvUS91IQP+Uu3662YN1XnfVsw1z+u8vyoJdBGQp9c6IdcyvxUzLVvZ31Uy8KiY+icFPNuf1J373TJtbm9XWZkDkFU65T7LBHYYqstwCy/sh0QhfvNLG+qbN+gPt2eozvu2Yq55Zuf9VUmgk4BMPd8NuZbZrZhp2cq+fZN9qZS6KgHP+Cr59a4r09br2bQ7dgAyraPWc7vA5KH6dqyFvtAPioWa9fKtGuRfBr/pcobqHHLFXPPMzvurkkAnAZl6vhtyLbNbMdOylTkASZ3U1Qp4ztf6r3J1mbZKp+bepwOQub21spsEJg/VNxEt+TVepi3Ztldu2hD/CvPtFzFU56Qr5ppndt5flQQ6CsjW412Ra5nZipn21cpk3ldC/nknAc/3Tt3oey8yrW9vdrkzByC7dNo6TwtMHKpPYwz5oB8VQxr50DIM8Q/BPvy1huoceMVc89zO+6uSQFcB+XqsM3It81ox0z5bmbzL+q6qj4Bne59edL4Tmda5O3vcmwOQPfpslRcEpg3VFyjGfNQPizGtfGQhhvhHWB//UkN1Trxirnlu5/1VSaC7gJzNOiTXMqcVM+1PK5N1Wc9V9RDwLO/Rh1XuQqat0qm59+kAZG5vrewmgUlD9U0kS3+NHxZLt++VmzfMv8J8+0UM1Tnpirnm2Z33VyWBFQRk7dddkmtfG32vWDHTPlqZnMv6raqPgOd4n16scCcybYUuzb5HByCz+2t1NwhMGapvoBjxFX5cjGjjo4swzD/K+9iXG6pz2hVzzbM7769KAqsIyNvPOyXXsp28YqY5AMl6q6q3gGd47/50uzuZ1q0j+92PA5D9em7FBwWmDNUHlz223Eu0sa29bWGG+dsoX/0iQ3XOvWKueXbn/VVJYCUBmfvnbsm1bCevmGk/r0y+ZX1W1U/A87tfTzrfkUzr3J097s0ByB59tsoLAqsP1ReWPvKjfmSMbOutizLM38r52pcZqnPq1XLNczvvrUoCqwrI3n92Tq5lu3m1THP4kfVV1doCnulr9++Ju5dpT6j6ziMCDkCOaKndUmDloXrLhn2yaC/R7IhEwMCeKPWrMVTnPVkt1zy7896qJLCygPz9tXtyLdvNq2Xaj1XJtqy/quYIeMbP6eWZlci0M2o+c6eAA5A7NX3XSIFVh+qRzbiwKD8yLuBt9lHD+ZoNN1TnfVst1zy/896qJLC6gAz+u4NyLdvNq2Xa91XJtay3qmYKeM7P7OtXq5JpXwn5508LOAB5Wtj3Ly+w4lC9PPoDC/BD4wHUoV9pKF+zsYbqvG+r5Zrnd95blQSmCMjib9/kWrabZVrmpIpAVwHP+66dufe+ZNq9nr7tuIADkONmPrGZwGpD9WbtiZfrBVpMtX2hIXzNLWCozvu2Wq55fue9VUlgksDueSzXst28UqbJs6ynqvYT2P15v0PHZdoOXe69Rgcgvfvj7hoIrDRUN+BqeQt+bLRsS9ubMoC3bc2nN2aozvu2Wq55hue9VUlgosCuuSzXst28UqbJs6ynqvYS2PUZv1eX/a3G3frdcb0OQDp2xT21ElhpqG4F1+hm/Nho1IwFbsUQvkCTPrhFL4ryvq2Ya57jeX9VEpgqsFs+y7VsJ6+SaXIs66eqvQR2e67v1d1fVyvTdu5+j7U7AOnRB3fRWGCVoboxYfmt+cFR3oKlbsAgvlS7/rpZQ3XetxVzzXM8769KAlMFdstnuZbt5BUyTYZlvVS1l8Buz/S9uvvP1cq03XdA/fodgNT3wB00F1hhqG5OWH57fnSUt2CpGzCML9UuByAn2rVqrnmWn2i2jxAYKrBDVntZlG3ezpkmt7IeqtpPYIdn+H5d/XzFMs2OqBZwAFLdAddvL9B5qG6P1+QG/fho0ohFbsNAvkijfrtNQ3Xet1VzzbM877FKAjsITM9ruZbt4s6ZJreyHqraT2D683u/jn69Ypn2tZGKZwUcgDzr69sHCHQeqgfwvrIEPz5eYR5zEQP5mq00VOd9WzXXPMvzHqsksJvAxOyWa9ku7pxpcivroar9BCY+s/fr4rEVy7RjXqrvF3AAcr+pbxwm0HmoHkb92HL8+HiMduQXG8jXbKuhOu/birnmOZ73VyWBnQUmZbhcy3Zy10yTW1n/VBH4XWDSc1x3/xaQaXZDtYADkOoOuH57ga5DdXu4RjfoB0ijZixwK4buBZr0wS0aqvO+rZhrnuN5f1US2F1gSo7LtWwnd8w0mZX1ThWBPwlMeY7rsAMQe6CPgAOQPr1wJ00FOg7VTana3pYfIW1b0/LGDNwt2/LlTXlR9CXRXwUr5prneN5flQQI/FNgxWyXa9lO7pZp8irrmyoCRwRWGj8jzgAAIABJREFUfIYfWd8OtTJthy73XqMDkN79cXcNBLoN1Q1IlrsFP0SWa1n5DRuyy1tw+AYM1TnZirnmOZ73VyUBAp8LrJLxci3byd0yTV5lfVNF4KjAKs/uo+vapV6m7dLpvut0ANK3N+6siUC3oboJy1K34YfIUu1qcbMG7BZtOHQThuqca7Vc8wzPe6uSAIFjAp3zXq5lveyUafIq65kqAmcFOj+zz65pl8/JtF063XedDkD69sadNRHoNFQ3IVnuNvwYWa5l5TdsuC5vweEbMFTnZKvlmmd43luVBAjcI9BhDpBrWS87ZZq8ynqmisCdAh2e13euZ+p3ybSpnV1nXQ5A1umVOy0S6DRUFxEsf1k/RpZv4esLMEi/Tn75gobqnHC1XPMMz3urkgCBewUq5wG5lvWyU6bJq6xnqgjcKVD5nL5zHdO/S6ZN73D/9TkA6d8jd1gs0GmoLqZY9vJ+jCzbutIbN0yX8h++uKE6J1sx1zzH8/6qJEDgGYG35wK5lvWxS6bJqaxfqgjcJfD2M/mu+971e2Tarp3vs24HIH164U6aCnQZqpvyLHFbfpAs0aZ2N2mobteST2/IUJ33a8Vc8xzP+6uSAIF3BJ6eE+Ra1scOmSajsl6pInCXwNPP37vu0/f8LSDT7IZqAQcg1R1w/fYCHYbq9kiNb9APksbNaX5rBuvmDfrt9gzVeb9WzDXP8ry/KgkQeF/giZlBrmV97JBpMirrlSoCdwk88cy96958z8cCMs3OqBZwAFLdAddvL9BhqG6P1PgG/SBp3Jzmt2awbt4gByCnG7RirnmWn263DxIg8JLA3XODl0VZ4zpkmozKeqWKwF0Cdz9v77ov3/NnAZlmd1QLOACp7oDrtxfoMFS3R2p8g36QNG5O81szWDdvkAOQ0w1aMdc8y0+32wcJEHhZ4K75wcuirHEdMk1GZb1SReBOgbuetXfek+9yAGIP9BVwANK3N+6siUCHoboJxZK34QfJkm1rcdOG6hZtiG/Ci6KY6tuqueZ5nvdYJQEC9QJX5wi5lvWwQ6bJp6xXqgg8LXD1ufv0/e38/TJt5+73WLsDkB59cBeNBToM1Y152t+aHyTtW9T2Bg3QbVvz4Y0ZqvN+rZprnud5j1USINBD4MosIdeyHnbINPmU9UoVgbcFrjyD377X6deTadM73H99DkD698gdFgt0GKqLCZa+vB8kS7ev7OYNy2X0py9sqM7pVs41z/S8zyoJELhf4KP54Ptz6U///9/v4OxMIdey/nXINNmU9UoVgQqBs8/ginudfE2ZNrm7a6zNAcgafXKXhQIdhurC5S9/aT9Ilm9hyQIMyiXsly5qqM75rubaR8/Vt/7MeKbnfVZJgMAxgbeeY+ldybVM6mqmZVf5vEo23aHoOwg8K9DtGf/savt9u0zr15Pd7sgByG4dt97DAh2G6sM37QN/CfhBYjOcETAgn1Gr/YyhOve/mmtHn6t3/nk6eu1cRSUBAjsK3Pl8uttPrmWiVzPt96v8nDPp/pBNWa9UEagUSP88V97j5GvLtMndXWNtDkDW6JO7LBS4e6guXMqWl/aDZMu2X160Afky4etfYKjOya/m2hvP1d//DL5xzVxQJQECqwisnOdyLdtlVzPtx1XkTOatisDqAivnwsr2Mm3l7s24dwcgM/poFQ8K3DVUP3iLvvoTAT9mbI+jAobio2I96g3VeR+u5prnam6tkgCBdwUmZbhcy/bO1UxzAJI5qyIwSWBSVqzSF5m2Sqfm3qcDkLm9tbKbBO4aqm+6HV9zQsDLuhNoG3/EQLxm8w3Ved/uyjXP1txcJQECzwpMzG65lu0ZmZY5qSJA4J8CE7Oja59lWtfO7HNfDkD26bWVnhS4a6g+eXkfu0HAS7obEDf6CoPwms02VOd9eyPXPHfzfqgkQOC8wOTMlmvZvrgr0+RW5q2KwDSByTnSqVcyrVM39rwXByB79t2qDwjcNVQfuKTSmwX8oLkZdPjXGYLXbLChOu9bVa55Fuc9UkmAwNcC0/Narn29B75X3J1psipzV0VgZYHp+dGxNzKtY1f2uicHIHv122pPCNw9VJ+4BR+5KOCHzEXAjT5uGF632YbqvHcr5Jrndt5PlQR2Edgto+VatrPfzjT5lPVFFYHOArvlSYdeyLQOXdj7HhyA7N1/qw8E3h6qg1tSclDAD5WDYBuXG4bXbb6hOu/dirnmOZ73VyWBiQI75rNcy3by25kmj7K+qCKwgsCO2VLVF5lWJe+6PwQcgNgLBL4QeHuo1pD7BfxQud906jcagtftrKE6792KueY5nvdXJYEpArtnslzLdvJbmSaHsn6oIrC6wO7Z81T/ZNpTsr43FXAAkkqp21bgraF6W+AXFu4HywvIQy5h4F23kYbqvHcr5prneN5flQRWF5DF/+mgXMt28luZJoeyfqgiMEVAFt3bSZl2r6dvOy7gAOS4mU9sJvDWUL0Z66vL9YPlVe5lL2bIXbZ1XhQdbN1queYZfrDBygksLCCL/26el0XZRn4r02RR1g9VBKYJyKV7OirT7nH0LecFHICct/PJTQTeGqo34SxZph8sJezLXdRwu1zLfrlhQ3Xev9VyzTM8761KAisLyOFfuyfXst38VqbJoqwfqghMFJBP17sq064b+oZrAg5Arvn59AYCbw3VG1CWLdEPljL6ZS5sqF2mVX+8UUN13sOVcs3zO++rSgIrC8jhf3ZPrmU7+q1Mk0dZP1QRmCwgq853V6adt/PJewQcgNzj6FsGC7w1VA8mLF+aHyzlLWh/A4bZ9i368gYN1V8S/VWwUq55fud9VUlgVQEZ/HHn5Fq2o9/KNHmU9UMVgR0E5NbxLsu042Y+ca+AA5B7PX3bQIG3huqBdG2W5AdLm1a0vRFDbNvWxDdmqI6pvq2Sa57deU9VElhVQP7+uXNyLdvVb2WaTMr6oYrADgKy63iXZdpxM5+4V8AByL2evm2gwFtD9UC6Nkvyg6VNK9rdiOG1XUtO35ChOqdbIdc8t/N+qiSwqoAM/rxzci3b2W9lmlzK+qGKwA4C8ut4l2XacTOfuFfAAci9nr5toMBbQ/VAujZL8oOlTSva3YjhtV1LTt+QoTqnWyHXPLfzfqoksKKA/P26a3Lta6PvFW9lmlzK+qGKwA4CMux4l2XacTOfuFfAAci9nr5toMBbQ/VAujZL8oOlTSta3YjBtVU7Lt+MoTon7J5rntl5L1USWFFA/mZdk2uZ01uZJpuyfqgisJuATMs6LtMyJ1XPCTgAec7WNw8ReGuoHsLVchl+sLRsS/lNGVbLW3DrDRiqc87uueaZnfdSJYHVBGRv3jG5llm9lWmyKeuHKgIE/haQeX9byDR/MqoFHIBUd8D12wu8NVS3h1j4Bv1gWbh5D926YfQh2MKvNVTn+J1zzfM676NKAqsJyN5jHZNrmddbmSafsn6oIkDgVwHZ9x8PmeZPRrWAA5DqDrh+e4G3hur2EAvfoB8sCzfvgVs3hD6A2uArDdV5E7rnmmd23kuVBFYRkL3HOyXXMrM3M00+ZT1RRYDAxwI7Z6FM86eiWsABSHUHXL+9wJtDdXuMRW/Qj5VFG/fQbe88eD5E2uJrDdV5G7rnmmd23kuVBFYRkL3HOyXXMrO3M01GZX1RRYCAQ5CfBWSaPxHVAg5Aqjvg+u0F3h6q24MseIN+qCzYtIdu2QuYh2AbfK2hOm9C51zzvM77qJLAKgKy91yn5Frm9namyamsL6oIEPinwM55KNP8iagWcABS3QHXby/w9lDdHmTRG/RjZdHG3XTbOw+bNxG2/xpDdd6izrnmWZ33USWBFQTk7/kuybXM7u1Mk1NZX1QRIOAA5GcBmeZPRLWAA5DqDrh+e4G3h+r2IIveoB8rizbuhtv28uUGxAW+wlCdN6lzrnlW531USaC7gPy91iG5lvm9nWlyKuuLKgIEMoFdslKmZftB1XMCDkCes/XNQwTeHqqHsLVchh8sLdvy6E3tMlA+irjIlxuq80Z1zTXP6LyHKgl0F5C/1zsk1zLDqkyTWVl/VBEgkAtMzk6Zlu8Dlc8IOAB5xtW3DhKoGqoHEbZZih8qbVrx6o1MHiRfhWx+MUN13qCuueYZnfdQJYHOAnL3nu7ItcyxKtNkVtYfVQQIHBOYmqEy7dg+UH2/gAOQ+0194zCBqqF6GGOL5fih0qINJTcxdZAswWx6UUN13piuueYZnfdQJYHOAjL3nu7ItcyxMtPkVtYjVQQInBeYkqky7fwe8Ml7BByA3OPoWwYLVA7Vg1lLluZHSgl7i4tOGRxbYDa9CUN13piuueYZnfdQJYGOArL23q7ItcyzOtNkV9YnVQQIXBdYOWdl2vX++4ZrAg5Arvn59AYC1UP1BsSvLdEPlNeoW15o5YGxJWizmzJU5w3pmmue0XkPVRLoKCBn7+2KXMs8O2Sa/Mp6pYoAgXsEVsxbmXZP733LeQEHIOftfHITgQ5D9SbUryzTD5RXmNteZMVhsS1msxszVOcN6Zprns95D1US6CQgW5/phlzLXDtkmvzKeqWKAIHnBLpnsUx7rve+ORNwAJI5qdpYoMNQvTH/7Uv3A+V20qW+sPtguBRms5s1VOcN6Zprns95D1US6CAgU5/tglzLfLtkmgzL+qWKAIHnBDrnskx7ru++ORNwAJI5qdpYoMtQvXELblu6Hya3US79RZ0Hw6Vhi2/eUJ03oGuueUbnPVRJoFpAlj7fAbmWGXfLNFmW9U0VAQLvCHTJa5n2Tr9d5c8CDkDsDgJfCHQbqjXsvIAfJOftJn2yyxA4ybTDWgzVeRe65ppndN5DlQSqBWTp8x2Qa5lxt0yTZVnfVBEg8J5Ah8yWae/125U+FnAAYmcQcACyzR7wg2SbVn+60A4DoE7cL2Cozk27vSz6+c49p/M+qiRQJSBH35GXa5lz10yTZ1n/VBEg8J5AZX7LtPf67EoOQOwBAqcEug7Vpxaz+Yf8ENl8A/y0/MrhTxeeETBU567dc82zOu+lSgJvCsjON7W/fZNrmXfnTJNnWQ9VESBQI/Bmrsu0mh676t8C/gaI3UDgC4HOQ7XmHRPwI+SY1/TqNwe+6ZYd1meozrvQPdc8q/NeqiTwpoDcfFPbAUiqLdNSKXUECBD4p8Bb2e63mt1XLeAApLoDrt9eoPtQ3R6wyQ16odakEY1u461hr9GSR9+KoTpvb/dc87zOe6mSwFsCMvMt6b+vI9cy8+6Z9n0Vci3rpSoCBHoIPJH5Mq1Hb3e+CwcgO3ff2iOBFYbqaCGbF/nhsfkG+GD5Twx2lOsEDNW5ffdc87zOe6mSwBsC8vIN5X9eQ65l7t0z7ccqZFvWT1UECNQLPJH7Mq2+r7vfgQOQ3XeA9X8psMpQ/eVCFPhfX9kDvwg8MdghrhMwVOf23XPNS6K8lyoJvCEgL99QdgByVrl7pjkAOdtZnyNAoFrgzvz3W626m67vAMQeIPCFwCpDtUZ+LuCFmh3yu8CdAx3degFDdd6D7rnmeZ33UiWBJwXk5JO6X3+3XPva6HtF90z7fRUyLuurKgK7CnzP3o7PiaszgUzbdUf3WbcDkD69cCdNBVYbqpsylt9WxyGiHMUNfLs6yCHsI2CoznvRPdc8r/NeqiTwlIB8fEo2/165lll1z7SPViHnst6qIrCiwEf5eeTP/I/PH/nMG05X5wKZ9kaXXOMzAQcg9geBLwRWHKo19Z8C3QYIPeohcHWQ67EKd/FdwFCd74PuueZ5nfdSJYEnBGTjE6rHv1OuZWbdM+1Pq5B1WX9VEVhJoHt+Js+dp9Yg01bayTPv1QHIzL5a1Y0Cqw7VNxKM+Kok7Ecs1CIOCTw14B26CcW3CBiqc8buueZ5nfdSJYG7BeTi3aLnv0+uZXbdM+2zVci7rMeqCKwgID8/75JMW2EXz75HByCz+2t1NwisPFTfsPwxX+EHxphW3roQg+qtnKVfZqjO+bvnmud13kuVBO4WkIt3i57/PrmW2XXPNAcgWR9VEVhVQG5mnZNpmZOq5wQcgDxn65uHCKw8VA9pwW3L8FLtNsoxX2RgHdNK/wqsA63snmue1QeaqZTATQLy8CbIG7/Gy6IMs3umJauQe4mSGgLvCMjDZ5xl2jOuvjUXcACSW6ncVGDCUL1p6/6xbD8u7ITfBQy4c/aEoTrvZfdc86zOe6mSwF0C8vAuyfu+R65llt0zLVvFt2+yL5VSR+BZAXn4jK9Me8bVt+YCDkByK5WbCkwZqjdt3y/L9sPCLvhIwJA7Y18YqvM+ds81z+q8lyoJ3CUgC++SvO975Fpm2T3TslU4AEmd1BF4QkAGPqH663fKtOeNXeFzAQcgdgiBLwSmDNUa7YeFPfCxgIF3xs4wVOd97J5rDkDyXqokcIeAHLxD8f7vkGuZafdMy1bxd5UMPCqmnsBxAbl33OzqJ2TaVUGfvyrgAOSqoM+PF5g2VI9v2CcL9INi5+7/ee0G4Bn7wlCd93GFXPO8zvupksAVARl4Re/Zz8q1zHeFTMtW8p8q+XdESy2BjwVkW7+dIdP69WS3O3IAslvHrfewwLSh+jDAsA/4UTGsoReXYzi+CNjo44bqvBkr5Jpndd5PlQSuCMjBK3rPflauZb4rZFq2kl+r5OAZNZ8h8B8B2dZvJ8i0fj3Z7Y4cgOzWces9LDB1qD4MMeQDfkwMaeRNyzAc3wTZ4GsM1XkTVsk1z+u8pyoJnBGQgWfU3vuMXMusV8m0bDUOQc44+QyB3wXkW789IdP69WS3O3IAslvHrfewwOSh+jDGgA94oTagiTcuwXB8I2bxVxmq8waskmue13lPVRI4IyADz6i99xm5llmvkmnZav5ZJQvPyvnczgLyrV/3ZVq/nux2Rw5Aduu49R4WmD5UHwZZ/AN+RCzewJtv33B8M2jh1xmqc/yVcs0zO++rSgJHBOTfEa2aWrmWua+UadmKvj4E+f7nVz6e1fS56QLyrWeHZVrPvux0Vw5Aduq2tZ4S2GGoPgWz6If8WFi0cQ/dtgH5IdiCrzVU5+gr5Zpndt5XlQRSAdmXStXWybXMf6VMy1aUV8nI3Eplf4GPsumzPS7L+vf05zuUaWv1a+LdOgCZ2FVrulVg56H6VshGX+bHQqNmFN+Kwbm4ATde3lCdY66Ua57XeV9VEkgE5F6i1KNGrmV9WCnTshXlVTIyt1LZX0A+9e/RlTuUaVf0fPYOAQcgdyj6jtECOw/VkxvrB8Pk7uZrM2jnVt0rDdV5h1bLNc/rvLcqCXwmIPPW2h9yLevXapmWrSqvkpG5lcreAjKqd3+u3p1Muyro81cFHIBcFfT58QK7D9VTG+zHwtTOHluXQfuYV+dqQ3XendVyzfM6761KAg5A5uwBuZb1crVMy1Z1rEpOHvNS3VfAb7O+vbl6ZzLtqqDPXxVwAHJV0OfHCxiqZ7bYD4WZfT26KkP2UbG+9YbqvDer5Zrndd5blQQcgMzZA3It6+VqmZat6liVnDzmpbqvgN9mfXtz9c5k2lVBn78q4ADkqqDPjxcwVM9ssR8KM/t6dFWG7KNifesN1XlvVsw1z+y8vyoJ/C4g69bcE3It69uKmZat7FiVnDzmpbqngLzq2Zc77kqm3aHoO64IOAC5ouezWwgYque22Q+Fub09sjKD9hGtvrWG6rw3K+aa53XeX5UEHIDM2ANyLevjipmWrex4law8buYT1wS+/466Y9/5PXatDyt8Wqat0KXZ9+gAZHZ/re4GAUP1DYhNv+KOYa3p0tzWAQED9wGsxqWG6rw5K+aa53XeX5UEfgjIt7X3glzL+rdipmUrO14lK4+b+cQ1gR85c3bvyalr/it9Wqat1K2Z9+oAZGZfrepGAUP1jZgNv+rssNZwKW7ppIDB+yRcs48ZqvOGrJprntd5j1US+PmllJxbcz/Itaxvq2ZatrpzVfLynJtPHReQL8fNdv2ETNu1833W7QCkTy/cSVMBQ3XTxtx0W34g3AS58NcY3Bdu3k+3bqjO+7hyrnlm531Wua+AXJvRe7mW9XHlTMtWeL5KZp6388lMQN5kTqq+fZNpdkG1gAOQ6g64fnsBQ3X7Fl2+QT8OLhMu/QUG96Xb99fNG6rzPq6ea57Zea9V7ikg12b0Xa5lfVw907JVXq+SndcNfcPHAjLHzkgEZFqipOZJAQcgT+r67hEChuoRbfx0EX4QzO/xZys0tM/ov6E67+OUXPPsznuuch8BmTan13It6+WUTMtWe75KZp6388nPBeSOHZIIyLRESc2TAg5AntT13SMEDNUj2ugAZH4bT6/Q0H6artUHDdV5O6bkmpc5ec9V7iEgz2b1Wa5l/ZySadlqr1fJzuuGE77h6n+8/GcD2TNhRzy/Bpn2vLErfC7gAMQOIfCFgKF6jy3ix8Aeff5olYb2Gb03VOd9nJRrnt1531XOFpBl8/or17KeTsq0bMX3Vn2Woz8/V+Ttve7V3yYzqjuw3/Vl2n4977ZiByDdOuJ+2gkYqtu15JEbMtQ/wrrEl/oBsESbvrxJQ/WXRH8VTMo1z+687yrnCsixmb2Va1lfJ2VatuJnqn7P04+eKzL3GfuKb5UbFep7X1Om7d3/Dqt3ANKhC+6htYChunV7brs5A/1tlMt9kR8Ay7Xswxs2VOd9nJRrnt1531XOFZBjM3sr17K+Tsq0bMX1VbK3vgdX70BuXBX0+aMCMu2omPq7BRyA3C3q+8YJGKrHtfTDBRnk9+jzR6v0A2BG7w3VeR+n5Zrnd957lfMEZNi8nv5YkVzLejst07JV11bJ3Rp//92OGndXvUdApt3j6FvOCzgAOW/nk5sIGKo3afS3b98M8/v0+sdKvTia03NDdd7Labnm2Z33XuUcAfk1p5d/Wolcy3o8LdOyVfeouit/vz/P7vquHjLP3MWd/1oyGfJMj3zrnwVkmt1RLeAApLoDrt9ewFDdvkW33aDB+zbKZb7I8L9Mq768UUP1l0R/FUzLNc/uvPcq1xeQW+v3MF2BXMukpmVatupeVVdz2H9sPeun53/mpKqngEzr2Zed7soByE7dttZTAobqU2xLfujq8L7koje/aT8k5mwAQ3Xey2m55tmd917l+gJya/0epiuQa5nUtEzLVt2z6mwe//5cO/s9icrKf9vE8z/psJquAjKta2f2uS8HIPv02kpPChiqT8It+LEnh+0FOba4ZT8k5rTZUJ33cmKueX7n/Ve5roDMWrd3Z+5crmVqEzMtW/kaVUk+3/mvdvpK5c2/bXLnf7Pj+7pkwFfd9c87C8i0zt3Z494cgOzRZ6u8IGCovoC32EeTAX2xJbndLwT8kJizRQzVeS8n5prnd95/lWsKyKs1+3blruVapjcx07KV71F1d75XHbb86NaV9ciBPfb81FXKtKmdXWddDkDW6ZU7LRIwVBfBF1z2ykBacLsueVHAj4iLgM0+bqjOGzI11zzD8z2gcg0BObVGn566S7mWyU7NtGz1e1VdzfmvnqlXv/+jbpy55lef2avrVjtFQKZN6eS663AAsm7v3PlLAobql6AbXOaJobfBstzCHwT8uJi1NQzVeT+n5ppneL4HVPYWkE+9+/PW3cm1THpqpmWr37PqbN4nz9az3/2nTiTX3LOLVr2bgEzbreP91usApF9P3FEzAUN1s4Y8fDt3D70P366vvyDgB8kFvIYfNVTnTZmaa57f+R5Q2VtAPvXuz1t3J9cy6amZlq1e1Q+BZAY482xNvvezLpy5pq4SmCgg0yZ2da01OQBZq1/utkDAUF2AXnzJq4Nu8e27fCjgB0kItUiZoTpv1NRc8+zO94DK3gLyqXd/3ro7uZZJT820bPWqCBAgsIaATFujT5Pv0gHI5O5a2y0ChupbGJf6Ei/RlmrX6Zv1guk0XcsPGqrztkzONc/vfB+o7CUgk3r1o8PdyLWsC5MzLRNQRYAAgf4CMq1/j6bfoQOQ6R22vssChurLhMt9gRdoy7Xs8A170XSYrP0HDNV5i6bnmmd4vhdU1gvIo/oedL0DuZZ1ZnqmZQqqCBAg0FtApvXuzw535wBkhy5b4yUBQ/UlviU/7OXZkm07dNNeOB3iWqLYUJ23aYdc8xzP94PKWgF5VOvf+epyLevODpmWSagiQIBAXwGZ1rc3u9yZA5BdOm2dpwUM1afplv6gl2dLt+/Lm/fC6Uui5QoM1XnLdsg1z/B8P6isEZBDNe4rXVWuZd3aIdMyCVUECBDoKyDT+vZmlztzALJLp63ztICh+jTd0h/08mzp9n168146zeytoTrv6y655jme7wmV7wrIoXe9V72aXMs6t0umZRqqCBAg0FNApvXsy0535QBkp25b6ykBQ/UptuU/5MXZ8i384wK8eJrZW0N13tedcs2zPN8XKt8TkEPvWa98JbmWdW+nTMtEVBEgQKCfgEzr15Pd7sgByG4dt97DAobqw2SjPuDl2ah2fvPSaVY/f16NoTrv7W655jme7w2V9wvInftNd/lGuZZ1erdMy1RUESBAoJeATOvVjx3vxgHIjl235kMChupDXOOKvTib1VIvomb10wHIuX7ulmue4+f2iU/dIyB37nHc8Vu8LMq6vlumZSqqCBAg0EtApvXqx4534wBkx65b8yEBQ/UhrpHFXp7NaKuXUDP6+KdVGKrz/u6Ya57j+f5Qea+A7LnXc6dvk2tZt3fMtExGFQECBPoIyLQ+vdj1ThyA7Np5644FDNUx1ehCL8/Wbq8XUGv3L7l7Q3Wi9J+aHXPNMzzfHyrvE5A991nu+E1yLev6jpmWyagiQIBAHwGZ1qcXu96JA5BdO2/dsYChOqYaXejl2drt9RJq7f4ld2+oTpT2PQD5vnLP8XyPqLwuIHeuG+7+DXIt2wF+q2VOqggQIFApINMq9V37u4ADEPuAwBcChmpb5IeAl2fr7gUvotbtXXrnhupUas+/AeIAJN8fKs/JkcfoAAAgAElEQVQLyJrzdj75TwG5lu0Kv9UyJ1UECBCoFJBplfqu7QDEHiAQCBiqA6RNShyArNtoL6XW7V1654bqVGrfAxCHIPkeUXlcQM4cN/OJzwXkWrZD/FbLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpA2KnEIsl6zvZRar2dn7thQnavJNf86rHy3qEwE5EyipOaogFzLxGRa5qSKAAEClQIyrVLftR2A2AMEAgFDdYC0UYkDkLWa7aXUWv26creG6lxPrjkAyXeLykRA1iRKao4KyLVMTKZlTqoIECBQKSDTKvVd2wGIPUAgEDBUB0iblTgEWafhXkqt06urd2qozgXl2t9Wnuf5vlH5sYCcsTOeEpBrmaxMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QNivxwmydhnsxtU6vrt6poToXlGv/tPJcz/fP7pVyZfcd8N765VpmLdMyJ1UECBCoFJBplfqu7QDEHiAQCBiqA6TNSrwoW6PhXlKt0ae77tJQnUvKNQcg+W5R+buAbLEn3hKQa5m0TMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkDYtcRDSu/FeUvXuz913Z6jOReXax1ae6fke2rVSruza+Zp1y7XMXaZlTqoIECBQKSDTKvVd2wGIPUAgEDBUB0iblnhZ1rfxXlL17c1Td2aozmXlmgOQfLeo/CEgV+yFtwXkWiYu0zInVQQIEKgUkGmV+q7tAMQeIBAIGKoDpI1LHIL0bL4XVT378uRdGapzXbn2ZyvP9Hwf7VApS3boct81yrWsNzItc1JFgACBSgGZVqnv2g5A7AECgYChOkBS8s1Ls16bwEurXv14424M1bmyXPvcyvM830tTK2XI1M6utS65lvVLpmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEocgDTaA15cNWrGi7diqM6x5Vpm5SAkc5pYJUcmdnW9Ncm1rGcyLXNSRYAAgUoBmVap79oOQOwBAoGAoTpAUvKXgBdmtZvBS6ta/8qrG6pzfbmWW3mm51ZTKuXIlE6uvw65lvVQpmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEp+EfDCrG5DeHFVZ199ZUN13gG5llt9r/RMP+a1crUMWbl78+5drmU9lWmZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKSDwW8NHt3Y3hx9a53t6sZqvOOyLXc6kel5/lxs9U+IUNW69j8+5VrWY9lWuakigABApUCMq1S37UdgNgDBAIBQ3WApORLAS/PviS6XODl1WXCpb/AUJ23T67lVj9Xeo6fc+v6KZnRtTPu64eAXMv2gkzLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpCURAJenkVMp4q8yDrFNupDhuq8nXItt/q90nP8vF2XT8qLLp1wH18JyLWvhP7zz2Va5qSKAAEClQIyrVLftR2A2AMEAgFDdYCkJBLw4ixiOlzkZdZhspEfMFTnbZVrudVnlZ7p9zi++S3y4k1t17oqINcyQZmWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApiQW8MIup4kIvtGKq0YWG6ry9ci23Sio91xOlHjXyokcf3EUmINcyJ5mWOakiQIBApYBMq9R3bQcg9gCBQMBQHSApOSTgZdkhrk+Lvcy6z3L1bzJU5x2Ua7lVUumZnijV1siKWn9XPycg1zI3mZY5qSJAgEClgEyr1HdtByD2AIFAwFAdICk5JOBl2SGuPxZ7oXWP45RvMVTnnZRruVVa6bmeSr1XJyPes3alZwTkWuYq0zInVQQIEKgUkGmV+q7tAMQeIBAIGKoDJCWHBLwoO8TlAOQervHfYqjOWyzXcqsjlZ7tR7Seq3Xw8Zytb35XQK5l3jItc1JFgACBSgGZVqnv2g5A7AECgYChOkBScljAi7LDZL98wAuua34TP22ozrsq13KrI5We60e07q2VCfd6+rYeAnIt64NMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlBwW8KLsMJkDkGtk4z9tqM5bLNdyqzOVnu9n1M5/xuHHeTuf7C0g17L+yLTMSRUBAgQqBWRapb5rOwCxBwgEAobqAEnJKQEvyU6x/ftDXnidt5v6SUN13lm5llvdUelZf4fix98hC56z9c31AnIt64FMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlJwW8GLsOJ0XXsfNdviEoTrvslzLre6s9Ly/U9NB+L2avq2jgFzLuiLTMidVBAgQqBSQaZX6ru0AxB4gEAgYqgMkJacFvBA7Rufw45jXTtWG6rzbci23urvSM/+6qBy4bugb1hCQa1mfZFrmpIoAAQKVAjKtUt+1HYDYAwQCAUN1gKTksoCXYhmhF1+Z045Vhuq863Itt3q60rP/Y2HP+qd3nu9fQUCuZV2SaZmTKgIECFQKyLRKfdd2AGIPEAgEDNUBkpJbBLwI+5rRS7GvjXatMFTnnZdrudUblZ79vyp7zr+x61xjBQG5lnVJpmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEpuE/Ai7GNKL8Ru22Jjv8hQnbdWruVWb1bu+vz3fH9zl7nWSgJyLeuWTMucVBEgQKBSQKZV6ru2AxB7gEAgYKgOkJTcKrDrS7DPEL0gu3WLjfwyQ3XeVrmWW71ducvz3zP97Z3leisKyLWsazItc1JFgACBSgGZVqnv2g5A7AECgYChOkBScqvALi/AUjQvylKpvesM1Xn/5VpuVVH5cwb8/PxbORs8xyt2kmuuLiDXsg7KtMxJFQECBCoFZFqlvms7ALEHCAQChuoASckjAiu/7LoTxIuzOzXnfpehOu+tXMutVqrsmBme3yvtIPfaTUCuZR2RaZmTKgIECFQKyLRKfdd2AGIPEAgEDNUBkpLHBDq+0HpssR98sZdnb2qvfS1Ddd4/uZZbrVTZKS88u1faOe61q4Bcyzoj0zInVQQIEKgUkGmV+q7tAMQeIBAIGKoDJCWPCnR6qfXoQn/7ci/Q3tRe/1qG6ryHci23mlqZ5Ipn8NTuW9cqAnIt65RMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlDwukLysevwmXr6Al28vgy9+OUN13kC5llvtUPlRvnj+7tB5a+wuINeyDsm0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJyWsCOxyEePH22nYadSFDdd5OuZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLftR2A2AMEAgFDdYCk5FWByYcgDj9e3UqjLmaoztsp13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpCUvC4w9RDEAcjrW2nMBQ3VeSvlWm6lkgABAlUCci2Tl2mZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKSMoFpByEOQMq20vIXNlTnLZRruZVKAgQIVAnItUxepmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEpKBaYcgjj8KN1Gy1/cUJ23UK7lVioJECBQJSDXMnmZljmpIkCAQKWATKvUd20HIPYAgUDAUB0gKSkXWP0QxOFH+RZa/gYM1XkL5VpupZIAAQJVAnItk5dpmZMqAgQIVArItEp913YAYg8QCAQM1QGSknKBlQ9AHH6Ub58RN2Coztso13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVeq7tgMQe4BAIGCoDpCUtBBY8RDE4UeLrTPiJgzVeRvlWm6lkgABAlUCci2Tl2mZkyoCBAhUCsi0Sn3XdgBiDxAIBAzVAZKSNgKrHYI4AGmzdZa/EUN13kK5llupJECAQJWAXMvkZVrmpIoAAQKVAjKtUt+1HYDYAwQCAUN1gKSklcAqhyAOP1ptm+VvxlCdt1Cu5VYqCRAgUCUg1zJ5mZY5qSJAgEClgEyr1HdtByD2AIFAwFAdIClpKdD5IMThR8sts/RNGarz9sm13EolAQIEqgTkWiYv0zInVQQIEKgUkGmV+q7tAMQeIBAIGKoDJCVtBboegjgAabtllr0xQ3XeOrmWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37UdgNgDBAIBQ3WApKS1QLdDEIcfrbfLsjdnqM5bJ9dyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlCwh0OUgxAHIEttluZs0VOctk2u5lUoCBAhUCci1TF6mZU6qCBAgUCkg0yr1XdsBiD1AIBAwVAdISpYQcACyRJvc5EkBQ3UOJ9dyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqu7YDEHuAQCBgqA6QlCwlUHUQ4m9+LLVNlrtZQ3XeMrmWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37UdgNgDBAIBQ3WApGQpAQcgS7XLzYYChuoQ6tu3b3Itt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvms7ALEHCAQChuoASclyAhWHIP4GyHLbZKkbNlTn7ZJruZVKAgQIVAnItUxepmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEqWFHjzEMThx5JbZKmbNlTn7ZJruZVKAgQIVAnItUxepmVOqggQIFApINMq9V3bAYg9QCAQMFQHSEqWFHAAsmTb3PQfBAzV+daQa7mVSgIECFQJyLVMXqZlTqoIECBQKSDTKvVd2wGIPUAgEDBUB0hKlhZ48iDE3/xYemssdfOG6rxdci23UkmAAIEqAbmWycu0zEkVAQIEKgVkWqW+azsAsQcIBAKG6gBJyfICTx2COABZfmssswBDdd4quZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLftR2A2AMEAgFDdYCkZHmBJw5AHH4svy2WWoChOm+XXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2g5A7AECgYChOkBSMkLg7kMQByAjtsUyizBU562Sa7mVSgIECFQJyLVMXqZlTqoIECBQKSDTKvVd2wGIPUAgEDBUB0hKRgjceQDi8GPEllhqEYbqvF1yLbdSSYAAgSoBuZbJy7TMSRUBAgQqBWRapb5rOwCxBwgEAobqAEnJKIH/xd4dXleuK+uh1XUYjsJJvOEIHKUj8HASjsJhvO2xTl+dVqsl8SMJEAVg+m/XIolZWPxKCz77tjgIcQCy1JaYYjGG6rxNci23UkmAAIFRAnItk5dpmZMqAgQIjBSQaSP13dsBiD1AIBAwVAdISpYTuHsI4gBkuS1RfkGG6rxFci23UkmAAIFRAnItk5dpmZMqAgQIjBSQaSP13dsBiD1AIBAwVAdISpYUuHMI4gBkyS1RelGG6rw9ci23UkmAAIFRAnItk5dpmZMqAgQIjBSQaSP13dsBiD1AIBAwVAdISpYUuHoA4vBjye1QflGG6rxFci23UkmAAIFRAnItk5dpmZMqAgQIjBSQaSP13dsBiD1AIBAwVAdISpYVuHII4gBk2e1QemGG6rw9ci23UkmAAIFRAnItk5dpmZMqAgQIjBSQaSP13dsBiD1AIBAwVAdISpYXSA9CHH4svxXKLtBQnbdGruVWKgkQIDBKQK5l8jItc1JFgACBkQIybaS+ezsAsQcIBAKG6gBJyfICDkCWb/H0CzRU5y2Ua7mVSgIECIwSkGuZvEzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBSsoXA0SGI//XHFtug7CIN1Xlr5FpupZIAAQKjBORaJi/TMidVBAgQGCkg00bqu7cDEHuAQCBgqA6QlGwh4ABkizZPu0hDdd46uZZbqSRAgMAoAbmWycu0zEkVAQIERgrItJH67u0AxB4gEAgYqgMkJdsI/HQI4n8Bss02KLlQQ3XeFrmWW6kkQIDAKAG5lsnLtMxJFQECBEYKyLSR+u7tAMQeIBAIGKoDJCXbCDgA2abV0y3UUJ23TK7lVioJECAwSkCuZfIyLXNSRYAAgZECMm2kvns7ALEHCAQChuoASck2Ag5Atmn1dAs1VOctk2u5lUoCBAiMEpBrmbxMy5xUESBAYKSATBup794OQOwBAoGAoTpAUrKVwHeHIP4TWFttg3KLNVTnLZFruZVKAgQIjBKQa5m8TMucVBEgQGCkgEwbqe/eDkDsAQKBgKE6QFKylYADkK3aPc1iDdV5q+RabqWSAAECowTkWiYv0zInVQQIEBgpINNG6ru3AxB7gEAgYKgOkJRsJ/DVIYj/Bch226DUgg3VeTvkWm6lkgABAqME5FomL9MyJ1UECBAYKSDTRuq7twMQe4BAIGCoDpCUbCnw+RDEAciW26DMog3VeSvkWm6lkgABAqME5FomL9MyJ1UECBAYKSDTRuq7twMQe4BAIGCoDpCUbCngAGTLtpddtKE6b41cy61UEiBAYJSAXMvkZVrmpIoAAQIjBWTaSH33dgBiDxAIBAzVAZKSLQUcgGzZ9rKLNlTnrZFruZVKAgQIjBKQa5m8TMucVBEgQGCkgEwbqe/eDkDsAQKBgKE6QFKypcDHAxD/+astt0CpRRuq83bItdxKJQECBEYJyLVMXqZlTqoIECAwUkCmjdR3bwcg9gCBQMBQHSApIUCAwGABQ3XeALmWW6kkQIDAKAG5lsnLtMxJFQECBEYKyLSR+u7tAMQeIBAIGKoDJCUECBAYLGCozhsg13IrlQQIEBglINcyeZmWOakiQIDASAGZNlLfvR2A2AMEAgFDdYCkhAABAoMFDNV5A+RabqWSAAECowTkWiYv0zInVQQIEBgpINNG6ru3AxB7gEAgYKgOkJQQIEBgsIChOm+AXMutVBIgQGCUgFzL5GVa5qSKAAECIwVk2kh993YAYg8QCAQM1QGSEgIECAwWMFTnDZBruZVKAgQIjBKQa5m8TMucVBEgQGCkgEwbqe/eDkDsAQKBgKE6QFJCgACBwQKG6rwBci23UkmAAIFRAnItk5dpmZMqAgQIjBSQaSP13dsBiD1AIBAwVAdISggQIDBYwFCdN0Cu5VYqCRAgMEpArmXyMi1zUkWAAIGRAjJtpL57OwCxBwgEAobqAEkJAQIEBgsYqvMGyLXcSiUBAgRGCci1TF6mZU6qCBAgMFJApo3Ud28HIPYAgUDAUB0gKSFAgMBgAUN13gC5llupJECAwCgBuZbJy7TMSRUBAgRGCsi0kfru7QDEHiAQCBiqAyQlBAgQGCxgqM4bINdyK5UECBAYJSDXMnmZljmpIkCAwEgBmTZS370dgNgDBAIBQ3WApIQAAQKDBQzVeQPkWm6lkgABAqME5FomL9MyJ1UECBAYKSDTRuq7twMQe4BAIGCoDpCUECBAYLCAoTpvgFzLrVQSIEBglIBcy+RlWuakigABAiMFZNpIffd2AGIPEAgEDNUBkhICBAgMFjBU5w2Qa7mVSgIECIwSkGuZvEzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBSQoAAgcEChuq8AXItt1JJgACBUQJyLZOXaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSEoIECAwWMBQnTdAruVWKgkQIDBKQK5l8jItc1JFgACBkQIybaS+ezsAsQcIBAKG6gBJCQECBAYLGKrzBsi13EolAQIERgnItUxepmVOqggQIDBSQKaN1HdvByD2AIFAwFAdICkhQIDAYAFDdd4AuZZbqSRAgMAoAbmWycu0zEkVAQIERgrItJH67u0AxB4gEAgYqgMkJQQIEBgsYKjOGyDXciuVBAgQGCUg1zJ5mZY5qSJAgMBIAZk2Ut+9HYDYAwQCAUN1gKSEAAECgwUM1XkD5FpupZIAAQKjBORaJi/TMidVBAgQGCkg00bqu7cDEHuAQCBgqA6QlBAgQGCwgKE6b4Bcy61UEiBAYJSAXMvkZVrmpIoAAQIjBWTaSH33dgBiDxAIBAzVAZISAgQIDBYwVOcNkGu5lUoCBAiMEpBrmbxMy5xUESBAYKSATBup794OQOwBAgQIECBAgAABAgQIECBAgAABAgQIECBAYEmB//jnn3/+WXJlFkWAAAECBAgQIECAAAECBAgQIECAAAECBAhsK+AAZNvWWzgBAgQIECBAgAABAgQIECBAgAABAgQIEFhXwAHIur21MgIECBAgQIAAAQIECBAgQIAAAQIECBAgsK2AA5BtW2/hBAgQIECAAAECBAgQIECAAAECBAgQIEBgXQEHIOv21soIECBAgAABAgQIECBAgAABAgQIECBAgMC2Ag5Atm29hRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgXUFHICs21srI0CAAAECBAgQIECAAAECBAgQIECAAAEC2wo4ANm29RZOgAABAgQIECBAgAABAgQIECBAgAABAgTWFXAAsm5vrYwAAQIECBAgQIAAAQIECBAgQIAAAQIECGwr4ABk29ZbOAECBAgQIECAAAECBAgQIECAAAECBAgQWFfAAci6vbUyAgQIECBAgAABAgQIECBAgAABAgQIECCwrYADkG1bb+EECBAgQIAAAQIECBAgQIAAAQIECBAgQGBdAQcg6/bWyggQIECAAAECBAgQIECAAAECBAgQIECAwLYCDkC2bb2FEyBAgAABAgQIECBAgAABAgQIECBAgACBdQUcgKzbWysjQIAAAQIECBAgQIAAAQIECBAgQIAAAQLbCjgA2bb1Fk6AAAECBAgQIECAAAECBAgQIECAAAECBNYVcACybm+tjAABAgQIECBAgAABAgQIECBAgAABAgQIbCvgAGTb1ls4AQIECBAgQIAAAQIECBAgQIAAAQIECBBYV8AByLq9tTICBAgQIECAAAECBAgQIECAAAECBAgQILCtgAOQbVtv4QQIECBAgAABAgQIECBAgAABAgQIECBAYF0BByDr9tbKCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAtgIOQLZtvYUTIECAAAECBAgQIECAAAECBAgQIECAAIF1BRyArNtbKyNAgAABAgQIECBAgAABAgQIECBAgAABAtsKOADZtvUWToAAAQIECBAgQIAAAQIECBAgQIAAAQIE1hVwALJub62MAAECBAgQIECAAAECBAgQIECAAAECBAhsK+AAZNvWWzgBAgQIECBAgAABAgQIECBAgAABAgQIEFhXwAHIur21MgIECBAgQIAAAQIECBAgQIAAAQIECBAgsK2AA5BtW2/hBAgQIECAAAECBAgQIECAAAECBAgQIEBgXQEHIOv21soIECBAgAABAgQIECBAgAABAgQIECBAgMC2Ag5Atm29hRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgXUFHICs21srI0CAAAECBAgQIECAAAECBAgQIECAAAEC2wo4ANm29RZOgAABAgQIECBAgAABAgQIECBAgAABAgTWFXAAsm5vrYwAAQIECBAgQIAAAQIECBAgQIAAAQIECGwr4ABk29ZbOAECBAgQIECAAAECBAgQIECAAAECBAgQWFfAAci6vbUyAgQIECBAgAABAgQIECBAgAABAgQIECCwrYADkG1bb+EECBAgQIAAAQIECBAgQIAAAQIECBAgQGBdAQcg6/bWyggQIECAAAECBAgQIECAAAECBAgQIECAwLYCDkC2bb2FEyBAgAABAgQIECBAgAABAgQIECBAgACBdQUcgKzbWysjQIAAAQIECBAgQIAAAQIECBAgQIAAAQLbCjgA2bb1Fk6AAAECBAgQIECAAAECBAgQIECAAAECBNYVcACybm+tjAABAgQIECBAgAABAgQIECBAgAABAgQIbCvgAGTb1ls4AQIECBAgQIAAAQIECBAgQIAAAQIECBBYV8AByLq9tTICBAgQIECAAAECBAgQIECAAAECBAgQILCtgAOQbVtv4QQIECBAgAABAgQIECBAgAABAgQIECBAYF0BByDr9tbKCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAtgIOQLZtvYUTIECAAAECBAgQIECAAAECBAgQIECAAIF1BRyArNtbKyNAgAABAgQIECBAgAABAgQIECBAgAABAtsKOADZtvUWToAAAQIECBAgQIAAAQIECBAgQIAAAQIE1hVwALJub62MAAECBAgQIECAAAECBAgQIECAAAECBAhsK+AAZNvWWzgBAgQIECBAgAABAgQIECBAgAABAgQIEFhXwAHIur21MgIECBAgQIAAAQIECBAgQIAAAQIECBAgsK2AA5BtW2/hBAgQIECAAAECBAgQIECAAAECBAgQIEBgXQEHIOv21soIECBAgAABAgQIECBAgAABAgQIECBAgMC2Ag5Atm29hRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgXUFHICs21srI0CAAAECBAgQIECAAAECBAgQIECAAAEC2wo4ANm29RZOgAABAgQIECBAgAABAgQIECBAgAABAgTWFXAAsm5vrYwAAQIECBAgQIAAAQIECBAgQIAAAQIECGwr4ABk29ZbOAECBAgQIECAAAECBAgQIECAAAECBAgQWFfAAci6vbUyAgQIECBAgAABAgQIECBAgAABAgQIECCwrYADkG1bb+EECBAgQIAAAQIECBAgQIAAAQIECBAgQGBdAQcg6/bWyggQIECAAAECBAgQIECAAAECBAgQIECAwLYCDkC2bb2FEyBAgAABAgQIECBAgAABAgQIECBAgACBdQUcgKzbWysjQIAAAQIECBAgQIAAAQIECBAgQIAAAQLbCjgA2bb1Fk6AAAECBAgQIECAAAECBAgQIECAAAECBNYVcACybm+tjAABAgQIECBAgAABAgQIECBAgAABAgQIbCvgAGTb1ls4AQIECBAgQIAAAQIECBAgQIAAAQIECBBYV8AByLq9tTICBAgQIECAAAECBAgQIECAAAECBAgQILCtgAOQbVtv4QQIECBAgAABAgQIECBAgAABAgQIECBAYF0BByDr9tbKCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAtgIOQLZtvYUTIHzldn0AACAASURBVECAAAECBAgQIECAAAECBAgQIECAAIF1BRyArNtbKyNAgAABAgQIECBAgAABAgQIECBAgAABAtsKOADZtvUWToAAAQIECBAgQIAAAQIECBAgQIAAAQIE1hVwALJub62skcD/91/+R6MruQwBAgQI9BL43////+x16eWuK9eWa6kFESCwoIBcy5oq0zInVQQIEBgpINNG6rv3S8ABiH1A4EDAUG2LECBAoL6AoTrvkVzLrVQSIEBglIBcy+RlWuakigABAiMFZNpIffd2AGIPEAgEDNUBkhICBAgMFjBU5w2Qa7mVSgIECIwSkGuZvEzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBSQoAAgcEChuq8AXItt1JJgACBUQJyLZOXaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSEoIECAwWMBQnTdAruVWKgkQIDBKQK5l8jItc1JFgACBkQIybaS+ezsAsQcIBAKG6gBJCQECBAYLGKrzBsi13EolAQIERgnItUxepmVOqggQIDBSQKaN1HdvByD2AIFAwFAdICkhQIDAYAFDdd4AuZZbqSRAgMAoAbmWycu0zEkVAQIERgrItJH67u0AxB4gEAgYqgMkJQQIEBgsYKjOGyDXciuVBAgQGCUg1zJ5mZY5qSJAgMBIAZk2Ut+9HYDYAwQCAUN1gKRkGoH/9X//z9t//6//bZrn9aAEUgFDdSr19ibXciuVBAgQGCUg1zJ5mZY5qSJAgMBIAZk2Ut+9HYDYAwQCAUN1gKRkGgEHINO0yoOeFDBU52ByLbdSSYAAgVECci2Tl2mZkyoCBAiMFJBpI/Xd2wGIPUAgEDBUB0hKphB4HX68/z//K5ApWuYhTwgYqnMsuZZbqSRAgMAoAbmWycu0zEkVAQIERgrItJH67u0AxB4gEAgYqgMkJVMIvB+AOPyYol0e8qSAoToHk2u5lUoCBAiMEpBrmbxMy5xUESBAYKSATBup794OQOwBAoGAoTpAUjKFgP8FyBRt8pAXBQzVOZxcy61UEiBAYJSAXMvkZVrmpIoAAQIjBWTaSH33dgBiDxAIBAzVAZKSKQQcgEzRJg95UcBQncPJtdxKJQECBEYJyLVMXqZlTqoIECAwUkCmjdR3bwcg9gCBQMBQHSApKS/w8fDj9bD+M1jlW+YBTwoYqnMwuZZbqSRAgMAoAbmWycu0zEkVAQIERgrItJH67u0AxB4gEAgYqgMkJaUFPh9+vD+sQ5DSbfNwJwUM1TmYXMutVBIgQGCUgFzL5GVa5qSKAAECIwVk2kh993YAYg8QCAQM1QGSktICDkBKt8fDNRIwVOeQci23UkmAAIFRAnItk5dpmZMqAgQIjBSQaSP13dsBiD1AIBAwVAdISkoLfHcA8npo/yuQ0q3zcCcEDNU5llzLrVQSIEBglIBcy+RlWuakigABAiMFZNpIffd2AGIPEAgEDNUBkpKyAj8dfjgAKds2D3ZBwFCdo8m13EolAQIERgnItUxepmVOqggQIDBSQKaN1HdvByD2AIFAwFAdICkpK+AApGxrPFhjAUN1DirXciuVBAgQGCUg1zJ5mZY5qSJAgMBIAZk2Ut+9HYDYAwQCAUN1gKSkpMDR4cfrof0nsEq2zkNdEDBU52hyLbdSSYAAgVECci2Tl2mZkyoCBAiMFJBpI/Xd2wGIPUAgEDBUB0hKSgo4ACnZFg/VScBQncPKtdxKJQECBEYJyLVMXqZlTqoIECAwUkCmjdR3bwcg9gCBQMBQHSApKSeQHH68P7T/FUi59nmgCwKG6hxNruVWKgkQIDBKQK5l8jItc1JFgACBkQIybaS+ezsAsQcIBAKG6gBJSTmBMwcgr4d3CFKuhR7opIChOgeTa7mVSgIECIwSkGuZvEzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBSUk7AAUi5lnigzgKG6hxYruVWKgkQIDBKQK5l8jItc1JFgACBkQIybaS+ezsAsQcIBAKG6gBJSSmBs4cf7w/vfwVSqo0e5qSAoToHk2u5lUoCBAiMEpBrmbxMy5xUESBAYKSATBup794OQOwBAoGAoTpAUlJG4Orhx8cFOAgp004PckLAUJ1jybXcSiUBAgRGCci1TF6mZU6qCBAgMFJApo3Ud28HIPYAgUDAUB0gKSkj4ACkTCs8yMMChuocXK7lVioJECAwSkCuZfIyLXNSRYAAgZECMm2kvns7ALEHCAQChuoASUkZAQcgZVrhQR4WMFTn4HItt1JJgACBUQJyLZOXaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSEpKCLQ4/HhfiP8MVomWeogTAobqHEuu5VYqCRAgMEpArmXyMi1zUkWAAIGRAjJtpL57OwCxBwgEAobqAEnJcIGWhx+vxTgAGd5SD3BSwFCdg8m13EolAQIERgnItUxepmVOqggQIDBSQKaN1HdvByD2AIFAwFAdICkZLuAAZHgLPMBgAUN13gC5llupJECAwCgBuZbJy7TMSRUBAgRGCsi0kfru7QDEHiAQCBiqAyQlQwVaH368L8b/CmRoW938pIChOgeTa7mVSgIECIwSkGuZvEzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBSMlTAAchQfjcvImCozhsh13IrlQQIEBglINcyeZmWOakiQIDASAGZNlLfvR2A2AMEAgFDdYC0Qcn7IUPF/1WEA5ANNqAlHgoYqg+J/l0g13IrlQQIEBglINcyeZmWOakiQIDASAGZNlLfvR2A2AMEAgFDdYC0QYkDkA2abIlTCxiq8/bJtdxKJQECBEYJyLVMXqZlTqoIECAwUkCmjdR3bwcg9gCBQMBQHSBtUPLxf2VR7X8F4n8BssEGtMRDAUP1IdG/C+RabqWSAAECowTkWiYv0zInVQQIEBgpINNG6ru3AxB7gEAgYKgOkDYo+eqQocJBSK/Dj1dLK6xvg61liY0EDNU5pFzLrVTWF3jloLyq3ydPeF5ArmVmMi1zUkWAAIGRAjJtpL57OwCxBwgEAobqAGmDku8OGkb/6OIAZIPNZ4mRgKE6YvpXkVzLrVTWF3AAUr9HnvCagFzL3GRa5qSKAAECIwVk2kh993YAYg8QCAQM1QHSJiU/HTaMOAjpefjxaumINW2ylSyzg4ChOkeVa7mVyvoClf9vdNXX84SVBeRa1h2ZljmpIkCAwEgBmTZS370dgNgDBAIBQ3WAtEnJ0YHD0wcGR89zty1Pr+fu8/r83gKG6rz/ci23UllfwAFI/R55wmsCci1zk2mZkyoCBAiMFJBpI/Xd2wGIPUAgEDBUB0iblCQHDk8eGiTPc6c1T67lznP6LIGXgKE63wdyLbdSWV/AAUj9HnnCawJyLXOTaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSJuUnD1w6HmAcPZZrrSo5/NfeR6fIfCTgKE63x9yLbdSWV/gYx7Krfr98oS5gFzLrGRa5qSKAAECIwVk2kh993YAYg8QCAQM1QHSJiVXDh16/Rhz5VnOtqnXs599DvUEEgFDdaL0q0au5VYq6ws4AKnfI094TUCuZW4yLXNSRYAAgZECMm2kvns7ALEHCAQChuoAaYOSFgcOrQ4UWjxL2rJWz5zeTx2BqwKG6lxOruVWKmsLfJWHcqt2zzxdLiDXMiuZljmpIkCAwEgBmTZS370dgNgDBAIBQ3WAtElJy4OHqz/QtHyGpG1XnzO5thoCLQUM1bmmXMutVNYX+JyLcqt+zzxhJiDXMieZljmpIkCAwEgBmTZS370dgNgDBAIBQ3WAtElJ68OHKz/StH6Go9Zdecaja/p3Aj0EDNW5qlzLrVTWF3AAUr9HnvCagFzL3GRa5qSKAAECIwVk2kh993YAYg8QCAQM1QHSJiU9Dx+Sg4ae9/+uhclzbdJ+yywuYKjOGyTXciuV9QUcgNTvkSe8JiDXMjeZljmpIkCAwEgBmTZS370dgNgDBAIBQ3WAtFFJz0OInw4bet73qH1VD0HeTao+35Grf28rYKjOPeVabqWyvoADkPo98oTXBORa5ibTMidVBAgQGCkg00bqu7cDEHuAQCBgqA6QNip56iDi/Uf9p+73UwurHDB8Z1Hl+Tb6GpRcqqE6b4tcy61U1hdwAFK/R57wmoBcy9xkWuakigABAiMFZNpIffd2AGIPEAgEDNUB0mYlFQ4lniQffcBw5D36+Z7shXt9L2CozneHXMutVNYXcABSv0ee8JqAXMvcZFrmpIoAAQIjBWTaSH33dgBiDxAIBAzVAdJmJUc/yK/IMeKQIXEe8Vwr9neFNRmq8y7KtdxKZW2Br3JCLtTumafLBeRaZiXTMidVBAgQGCkg00bqu7cDEHuAQCBgqA6QNitJfphfjeTpH5RS46efa7W+rrQeQ3XeTbmWW6msLeA/jVi7P57unoBcy/xkWuakigABAiMFZNpIffd2AGIPEAgEDNUB0oYl6Q/0q9A8ddBw1vWp51qljyuvw1Cdd1eu5VYqaws4AKndH093T0CuZX4yLXNSRYAAgZECMm2kvns7ALEHCAQChuoAacOSsz/Uz07U+6Dhqmfv55q9bzs9v6E677Zcy61U1hfwn8Gq3yNPeE1ArmVuMi1zUkWAAIGRAjJtpL57OwCxBwgEAobqAGnDkqs/2M9K1fOg4Y5lz+eatVe7PrehOu+8XMutVNYXcABSv0ee8JqAXMvcZFrmpIoAAQIjBWTaSH33dgBiDxAIBAzVAdKGJXd+tJ+Vq+VhQwu/ls8za088928BQ3W+G+RabqWyvoD/DFb9HnnCawJyLXOTaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSBuWtPgBfza2VgcOrexaPc9sffC8XwsYqvOdIddyK5W1BX7KExlRu3ee7lhArh0bvSpkWuakigABAiMFZNpIffd2AGIPEAgEDNUB0oYlrX7En4muxY9JrdxaPMtM9p71WMBQfWz0XiHXciuVtQUcgNTuj6e7JyDXMj+ZljmpIkCAwEgBmTZS370dgNgDBAIBQ3WAtGFJqx/yZ6M7e/DQy+nsc8zm7HnPCxiqczO5lluprC3gAKR2fzzdPQG5lvnJtMxJFQECBEYKyLSR+u7tAMQeIBAIGKoDpA1Lev2wX53yzMFDT6Mzz1Hd1PO1ETBU545yLbdSWVvgKGdkRe3+ebqfBeRatkNkWuakigABAiMFZNpIffd2AGIPEAgEDNUB0qYlRz+8rMpy9IPSEy5Hz7CqvXV9L2CozneHXMutVNYWOMobWVG7f57OAUiLPSDTWii6BgECBPoK+Futr6+rHwv8xz///PPPcZkKAvsKGKr37f3Ryo9+eDn6/Oz//vmHpSc9/Kg1++5p//yG6txUruVWKmsLHOWOrKjdP0/nAKTFHpBpLRRdgwABAn0F/K3W19fVjwUcgBwbqdhcwFC9+Qb4YflHP7yQ6yfgR61+trNe2VCdd06u5VYqawskOSwvavfQ030vINey3SHTMidVBAgQGCkg00bqu/dLwAGIfUDgQMBQbYt8J5D88EKvj4AftPq4znxVQ3XePbmWW6msK5BmsLyo20NP9rOAXMt2iEzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkDauCT9AWZjoi5L94NWF9apL2qoztsn13IrlXUFzuSvzKjbR0/2vYBcy3aHTMucVBEgQGCkgEwbqe/eDkDsAQKBgKE6QNq85MyPMJtTNV2+H7Sack5/MUN13kK5lluprCtwJnvlRd0+ejIHIHf3gEy7K+jzBAgQ6C/gb7X+xu7ws4D/BJYdQuBAwFBtixwJnPkR5uha/v2cgB+1znmtXG2ozrsr13IrlTUFzuaurKjZR0/1s4Bcy3aITMucVBEgQGCkgEwbqe/eLwEHIPYBAQcg9kADgbM/xjS4pUu8vb35Ucs2eBcwVOd7wY9FuZXKmgJXMlde1Oylp/peQK5lu0OmZU6qCBAgMFJApo3Ud28HIPYAgUDAUB0gKXm78mMMtvsCftC6b7jKFQzVeSflWm6lsp7A1byVF/V66Yl+FpBr2Q6RaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSEr+JXD1Rxl81wT8mHXNbdVPGarzzsq13EplPYGrWSsz6vXSEzkAabEHZFoLRdcgQIBAXwF/q/X1dfVjAf8JrGMjFZsLGKo33wAnl3/1h5mTt9m+3A9Z22+BvwAM1fmekGu5lcp6AndyVnbU66cn+l5ArmW7Q6ZlTqoIECAwUkCmjdR375eAAxD7gMCBgKHaFjkjcOeHmTP32b3Wj1i774C/12+ozveEXMutVNYTuJOzsqNePz2RA5C7e0Cm3RX0eQIECPQX8Ldaf2N3+FnAAYgdQsABiD3QWODOjzONH2XZy/kRa9nWXl6YoTqn82NRbqWylsDdfJUdtfrpaX4WkGvZDpFpmZMqAgQIjBSQaSP13fsl4ADEPiDgAMQeaCxw9weaxo+z3OX8gLVcS5ssyFCdM/qxKLdSWUegRbbKjzr99CTHAnLt2OhVIdMyJ1UECBAYKSDTRuq7twMQe4BAIGCoDpCU/CXQ4ocarH8L+PHKrvhOwFCd7w25lluprCPQIldlSJ1+epJjAbl2bOQAJDNSRYAAgdECMm10B9zf/wLEHiBwIOCHIlvkikCLH2qu3Hflz/jhauXu3l+boTo3lGu5lcoaAi0zVZbU6KmnOBaQa8dGDkAyI1UECBAYLSDTRnfA/R2A2AMEHIDYAx0EWv5Y0+HxprykH62mbNtjD22ozqkdgORWKmsItM5UeVKjr57iZwG5lu0QmZY5qSJAgMBIAZk2Ut+9XwIOQOwDAg5A7IFOAq1/sOn0mOUv64eq8i0q8YCG6rwNfizKrVSOF+iVpbJlfG89gQOQFntAprVQdA0CBAj0FfC3Wl9fVz8WcABybKRicwFD9eYb4Mbye/1oc+ORpvyoH6mmbNvjD22ozsnlWm6lcrxAryyVLeN76wkcgLTYAzKthaJrEKgh8FXmy+savbn7FP5Wuyvo83cFHIDcFfT55QUM1cu3uNsCe/1o0+2Bi17Y0Fu0McUey1CdN0Su5VYqxwv0ylLZMr63nsABSIs9INNaKLoGgTECZzNedo/pU4u7+luthaJr3BFwAHJHz2e3EDBUb9HmLos8O9B1eYjJL2rInbyBDz6+oTrHlmu5lcrxAr2yVL6M760ncADSYg/ItBaKrkHgWYG72S7Dn+1Xi7v5W62FomvcEXAAckfPZ7cQMFRv0eYui7w72HV5qMkuaridrGEDH9dQnePLtdxK5TiB3hkqX8b11p0zAbmWOcm0zEkVgSoCrfJdjlfpaPYcMi1zUtVPwAFIP1tXXkTAUL1IIwcto9WAN+jxh97WUDuUf7qbG6rzlsm13ErlGIEns1PWjOmxux4LyLVjo1eFTMucVBEYKdAr12X4yK6eu7dMO+elur2AA5D2pq64mICherGGPrycXsPew8sYcjsD7RD2aW9qqM5bJ9dyK5VjBJ7MTlkzpsfueiwg146NHIBkRqoIjBTonelyfGR383vLtNxKZR8BByB9XF11IQE/FC3UzEFL6T30DVpW19saZLvyLnlxQ3XeVrmWW6kcI/BkbsqbMT1212MBuXZs5AAkM1JF4IzATxl8JjOfyvIzz3TGQW1bAZnW1tPVzgs4ADlv5hObCfihaLOGd1juU8Nfh0cfdkmD7DD6aW9sqM5bJ9dyK5VjBJ7MTXkzpsfueiwg146NHIBkRqoIJAJp9ia5mV4rea6jmuR5jq7h3/sLyLT+xu7ws4ADEDuEwIGAH4pskbsCTw6Ad5+1wucNsRW6MN8zGKrznsm13Erl8wIjMlPuPN9ndzwWkGvHRg5AMiNVBI4EzmbvT7l59lpHz3b07zL8SKjGv8u0Gn3Y+SkcgOzcfWuPBPxQFDEpOhB4ehCctSEG2Fk7N/65DdV5D+RabqXyWYHRWSmDnu23u/0sINeyHSLTMidVBL4SGJ27Lboiu1so9r+GTOtv7A4/CzgAsUMIHAgYqm2RFgIrDJctHI6uYYA9EvLv3wkYqvO9IddyK5XPCVTISRn0XL/d6VhArh0bvSpkWuakioADEHtgpIBMG6nv3i8BByD2AQEHIPbAQwIVftx5aKmXb+PHp8t023/QUJ1vAT8W5VYqnxGoko8y6Jl+u0smINcyJ5mWOaki8FGgSu626IrsbqHY/xoyrb+xO/ws4ADEDiHgAMQeeEhgpUGzB5nhtYfqPtc0VOe99mNRbqXyGYEq+SiHnum3u2QCci1zkmmZkyoCDkDsgZECMm2kvnu/BByA2AcEHIDYAw8JVPmB56Hlnr6NH55Ok/nABwFDdb4d/FiUW6l8RqBSPsqiZ3ruLscCcu3Y6FUh0zInVQTeBSplbouuyO0Wiv2vIdP6G7vDzwIOQOwQAg5A7IEHBVYbOFvRGVxbSe57HUN13ns/FuVWKvsLVMxFmdS/7+5wLCDXjo0cgGRGqgi8BCrm7d3OyOu7gs99XqY9Z+1OXws4ALEzCDgAsQceFlhx+LxLaHi9K+jzhup8DzgAya1U9heomIkyqX/f3eFYQK4dGzkAyYxUEaiYtXe7IqvvCj77eZn2rLe7/S3gAMSuIOAAxB54WGDFAfQqocH1qpzPfRYwVOd7wgFIbqWyn0DlLJRN/fruyrmAXMusZFrmpGpfgcp5e6crsvqO3vOflWnPm7vjnwIOQOwIAg5A7IEBAqsOomcpDa5nxdR/J2CozveGH4tyK5X9BCrnoGzq13dXzgXkWmYl0zInVfsKVM7bO12R1Xf0nv+sTHve3B0dgNgDBE4JGKpPcSkOBVYdRMPl/7vM4HpWTL0DkPt7QK7dN3SF+wLVc1A+3e+xK9wT8GNR5ifTMidVewpUz9o7XZHTd/Se/6xMe97cHR2A2AMETgkYqk9xKQ4FVh5GQ4I3Q2sqpS4RMFQnSr9q5FpupbKPwCwZKKf69N9VMwG5ljnJtMxJ1W+BMxk0cw6cWeds+2Pmvsxm3ep5ZVorSde5KuA/gXVVzue2ETBUb9PqRxe68kCaQhpcUyl1iYChOlFyAJIrqewpMEsGyqmeu8C1jwTk2pGQTMuEVH0UOJs/M+fA2bXOtFNm7stMzi2fVaa11HStKwIOQK6o+cxWAg5Atmr3o4tdeSg9gjS0Hgn597MChupcTK7lVirbC8yUfbKqff9dMReQa5mVTMucdq5qkTsz5UGL9VbeLzP1orLj088m054Wd7/PAg5A7AkCBwKGalukl8Dqw+l3bobWXjtq7+saqvP+y7XcSmVbgdlyT1617b+rnROQa5mXTMucdqxqnTkzZELrNVfbNzP0oJpZleeRaVU6se9zOADZt/dWHgoYqkMoZacFVh9QHYCc3hI+cEPAUJ3jybXcSmU7gRkzzw8t7frvSucF5FpmJtMypx2rWufODJnQes3V9s0MPahmVuV5ZFqVTuz7HA5A9u29lYcChuoQStklgdWH1K9QDK6XtooPHQgYqvMtItdyK5XtBGbNO5nVbg+40jkBuZZ5ybTMaaeqXnkzQx70WnuV/TNDD6pYVXsOmVatI/s9jwOQ/XpuxScFDNUnwZSfElh9SHUAcmo7KL4hYKjO8eRabqWyjcDsWecHlzb7wFXOCci1zEumZU67VPXMm+pZ0HPtFfZPdf8KRpWfQaZV7s4ez+YAZI8+W+UNAUP1DTwfPRRYfVD9DGBwPdwSCi4KGKpzOLmWW6lsIzB71smuNvvAVc4JyLXMS6ZlTqtXPZEzlbPgifWP3EOV7Ue6zHRvmTZTt9Z8Vgcga/bVqhoKGKobYrrUlwKrD6wfF2149SXoJWCozmXlWm6lso3A7Dknu9rsA1c5JyDXMi+ZljmtXPVUxlTOgqcMRu2jyvajTGa7r0ybrWPrPa8DkPV6akWNBQzVjUFd7i+B1QdWByA2/RMChupcWa7lVirvC6yScX58ub8XXOGcgFzLvGRa5rRi1dP5UjUHnnZ4ei9VdT9yOOrLrOs6Wvd3/y7Trsr5XCsBByCtJF1nWQFD9bKtLbOwo+GozIM2eJDdBr0GZC4RChiqQ6i3tze5llupvCewUr7Jr3t7wafPC8i1zEymZU6rVY3Il4o5MMLhyb1U0fyn9d/px2xrPbsPZNpZMfWtBRyAtBZ1veUEDNXLtbTcgu4MSuUW88MDrT7UzdSLj8/6vv9m74+hOt+Bci23UnlPYLV8m/09ea+bPv20gFzLxGVa5rRS1ahsqZYBoxye3EvVzL9be6tezLLeK3tApl1R85mWAg5AWmq61pIChuol21puUa2GpnIL+/BAKw90ld0/P9t3e232/hiq810o13IrldcFVsy12d+T17vpkyME5FqmLtMyp1WqRmdLpRwYbdF7T1Wy/mqtvf2rr/9s/2XaWTH1rQUcgLQWdb3lBAzVy7W05IJ6D1AVFr3aEFfBNHmGZG+t0BtDdbIbftXItdxK5TWB5L1z7cpjP7XCu3KsoLufEZBrmZZMy5xWqBqdLZUyYLTFE/upkvfH9T5lX3X9V3sv067K+VwrAQcgrSRdZ1kBQ/WyrS21sKcGqZGLXm2Iu2vZ+j89dWcPrdAbQ3W+I+VabqXymsCd99G1Oz7zqRXelc9IuUsLAbmWKcq0zGn2qgq5UiUDKlj03k9VrEccfLzfs6LBnb7LtDt6PttCwAFIC0XXWFrAUL10e8ssziBbphXdH+SrXt8dcO/sn7v37g4W3sBQHUL5X4DkUCovC9x5J12+6QMfXOV9+QCVWzQQkGsZor/VMqeZq6pkSpUMqOLRa09VcX5f30jvahZ3ei7T7uj5bAsBByAtFF1jaQFD9dLtLbO4kYPVEwgrDW93vVr+3+BosW9W6Y2hOt+Zci23UnlNoMW76dqd+35qlfdlXyVXbyUg1zJJmZY5zVxVJVOqZEAVj157qoqzA5C2HZZpbT1d7byAEwZ3HgAAIABJREFUA5DzZj6xmYCherOGD1quQXYQ/EO3rdzfan9kXG2JoTqXk2u5lcprApXfeddW9OtTq7wv7xj47HMCci2zlmmZ06xVlfKkQgZU8uixpyoYVzj4eH+GSh53+y3T7gr6/F0BByB3BX1+eQFD9fItHr5Ag+zwFnR7gOq9NVR3a33pC8u10u2Z/uGqv/fuAK/0zrzj4LPPCPixKHOWaZnTjFXV8mR0BlTz6LGnRht/XFMF70oed/st0+4K+vxdAQcgdwV9fnkBQ/XyLR6+wArDVU+ElQa3xGmWfq7WF0N1sjt/1ci13ErleYFZ3oHnV/brE6u9O686+Fx/AbmWGcu0zGm2qopZMvL9X9Gj5Z4aaVvt0OPj81RxadFrmdZC0TXuCDgAuaPns1sIGKq3aPPQRRpoh/I3vflMvVxpoH410VCdb2W5llupPCcw0zvw3Mp+V6/27rzq4HP9BeRaZizTMqeZqqpmyaj3f1WPlntqlK3Dj5Zd/PlaMu05a3f6WsABiJ1B4EDAUG2L9BZYfaitMND27uHr+rP1cbW+GKrzXS7XciuVucBs78B8ZX9WrvbuvOrgc/0F5FpmLNMyp1mqKmfJqPd/ZZMW+2qUa+XDj9ezVXBp0d/3a8i0lpqudUXAAcgVNZ/ZSsBQvVW7hyx29aF2xQHufaPM2rvVBupXPwzV+etLruVWKnOBWd+H+QodhFy18rlrAnItc5NpmdMsVVWzZNTsXNWj1X4a5TrD33KjbVr12AFIa0nXuyrgAOSqnM9tI2Co3qbVQxa6+lD7EXWlIW7mvq3Uh4/7yw9F+StMruVWKnOBmd+L+Sp/V676Lr1i4TN9BORa5irTMqcZqirnyIh3fmWPVvtphOsMhx+vZxxp06q//lbrIemaVwUcgFyV87ltBAzV27R6yEJ3GGzfYWcf4lbp1ex9+O6L6oei/BUm13IrlbnAKu/IdMWrvkvT9avrLyDXMmOZljlVr6qeIU+/86t73N1PT3t+fN4ZbEf63O2tv9V6CbruXQEHIHcFfX55AUP18i0eusAZBrBWQLMOciv1aNYeJHvQD0WJ0q8auZZbqcwFVnpXJqte+X2arF9NfwG5lhnLtMypelX1DHnynV/dosVeetLT4UeLjt2/hky7b+gK9wQcgNzz8+kNBAzVGzR5wBJ3GGy/Yh017F5p8Yo9msn/bM8M1bmYXMutVGYCK74vj1a+8vv0aO3+/RkBuZY5y7TMqXJV9Qx58n1f3aLFPnrSc7bDj9fzjvJp0dufriHTegu7/pGAA5AjIf++vYChuu8WeA15q4b8T3I7DLezHoCs3JuVv2uG6vxdLddyK5WZwMrvze8EVn6fZl1X1VtArmXCMi1zqlxVPUOeet9Xd2i1h57yfH/emVyftmnV0+Q6Mi1RUtNTwAFIT13XXkLAUN23je8Dycph/5XgTINY6x1Quder96Wy/d19ZqjOBeVabqUyE1j93ekQJNsHqtoKyLXMU6ZlTpWrqmfIU/NzdYcWe+gpyxkPP17P/LRPi56m15BpqZS6XgIOQHrJuu4yAobqfq08GvJWHgCO1t5PffyVK/Z1l35UtG+1Iw3VuaRcy61UZgK7vEO/0lj5vZp1X1UvAbmWycq0zKlyVeUMeeIdX3n9rfbNE44fn3VG06eNWvU2vY5MS6XU9RJwANJL1nWXETBU92nlmaFktWHgzNr76I+/aqWe7taPSvYtd6KhOteUa7mVykxgt/foR5VV36lZ51X1FJBrma5My5yqVlXPjyfe8dUN7u6dJwzfn3FWyyeN7vbz6udl2lU5n2sl4ACklaTrLCsw+1Bd7f/GRouhZOYBocX6V/myjerjzj0YZf7EnjVU58qz51q+UpW9BXZ+n77brvxe7b1/XP9nAbmW7RCZljn1qPqYAVfehTNkyJV1nbGeweDMer6q7W34uufsjk8Y3e3j3c/LtLuCPn9XwAHIXUGfX15g9qH67mDaqsGth5IZh4TWBq16M/I6I/q4ax9GWD+5twzVufbsuZavVGVvgV3fpx9dV3+39t5Drv+9gFzLdodMy5xaVn337j/7PqyeIWfXc9a4+vrPrsfhxzWx3vvs2lO1/5RMa2/qiucEHICc81K9ocAqQ/VPA1YSulUHtOTZq2zbqoYjfZ7s3+7+T1qP2FOG6lx9lVzLV6yyl8Du79WX6+rv1l57x3WPBeTasdGrQqZlTi2qknf+mXdicr0Wz331GmfWcvYe1dd+dj0jDj9WMOy5x1r0sOU1ZFpLTde6IuAA5Iqaz2wlsMpQvcKA8NPGqz48rO5/56XQu3e72/f2vdP7lp81VOeaq+RavmKVvQR2f7++u+7ynu21j1z3awG5lu0MmZY53a06875P34lnrnn3+c9+Pl3D2eu+11de+9U1ffxcT79V7Hoatehh62vItNairndWwAHIWTH12wmsNFSvMix8twmrDhGru7d4KfTq3e72vVxb9Lz1NQzVuehKuZavWmUPgd3fsQ5Aeuwq13wXkGvZXpBpmdOdqrPv+nT+PHvdO2s4+9l0DWevW3nNZ9cy4m/yVfx67a9WPexxHZnWQ9U1zwg4ADmjpXZLgRWH6lUGh6MNWWGw2MX6qBfJv7fqF/Nf2q08k95VqDFU511YMdfy1atsKeB9u+f7tuUecq3vBeRatjtkWuZ0p+rKuz6ZQ69c98460s8mz55e61VXdZ1n1pDWtrZ7v+9Khr2M0h6NqpNpo+Td913AAYi9QOBAYNWheqUh4qcWjhwwdjFu+RK50y/ef3bijmXLnj51LUN1Lr1qruUCKlsJeO86AGm1l1znbwG5lu0KmZY5Xa26857/aRa9c92ra0k/13KGrrzO1ONMHbuftVr6nOlLhVqZVqELez+DA5C9+2/1gcCqQ/VOw9ioQWMn4+CrdKok7Rnjr1lTv1NNKV5sqM4btGqu5QIqWwl4BzsAabWXXMcByNU9INOuymWfu/Oe3/0A5I5d1p1aVa3//ljRr7VRrR3w89P4W22mbq35rA5A1uyrVTUUWH2oXnGwSNvfawDZ2TS1V9dPoNe+7vfEba5sqM4dV8+1XELlHQFZ91tv1/funf3js8cCcu3Y6FUh0zKns1Wt3vGf34+trnt2PWl9q/d59XWmHmldC7eVzVr4pL2oWifTqnZmn+dyALJPr630osAOQ/XKw0ba9lZDCctUXF0vgVZ7udfz9bquoTqX3SHXcg2VVwXk3Z9yu757r+4fnzsWkGvHRg5AMqOzVS3f7zMdgLR4j7e0O9u3UfXcjuVbGB3fpXaFTKvdnx2ezgHIDl22xlsCu/xQtOOw9tPGODuk8Lv1NfPhRgJn922j25a4jKE6b0OSa1+903beX7nuPpVyzwHIPrt9zErlWuaeZFp2JVUvgZ3f7XfnnF3t7ritbnbHZrU3kkxbraPzrccByHw988QPC+w2VK8+hJzdPunQwu2srPoeAul+7XHv0dc0VOcdOMq15H22817LpdeuTPbJ2gIOQHbq74i1yrVM/SjTsquo2v3w47X+O7PNrpl41WwXr6s+K76RZNqKXZ1rTQ5A5uqXpx0gsNtQvcswcnYrfTe88Dorqb6XwO4DtqE631lJrqXvtt33Xa6+XmW6R9Zb+dcr8l3YpdPPrVOuZdZJpmVX2rfK+/ze4cfOh0dXsm+X/XbFZuW3kExbubtzrM0ByBx98pQDBXYbqncZSAZuKbcm0FzAgP32ZqjOt9WZXDuTCfZh3oMVKs/sjRXWm6zBdyBRUpMKyLVM6kymZVdcv8r7++8en31/Mzx3aLSb19n9tP5bx99qO/S4+hodgFTvkOcbLrDjUL3bgDJ8k3kAAhcFDNe/4fxQlG+is7l2JhPsybwPs1ee2RezrzV9fvs/lVKXCMi1ROnt7WymZVddu8r7+94BCL/ffknu7eaVmKz9hvl6dTJtx67XWrMDkFr98DQFBXYcqncbUgpuO49E4FDAcP0nkaH6cMv8u+BOrp3JB3s078lslWf2wWxru/u89v1dQZ9/F5Br2V64k2nZHeav8s4+7mHy7ub4veNnv12tkn10vBvXrJBpa/Z1plU5AJmpW551iMCuQ/WuQ8uQTeamBE4KGK7/BjNU55vobq6l+WCf5j2ZrTLdA7Otq8Xz2vctFF3jJSDXsn1wN9Oyu8xb5X2d9+6n9zfH3HHXSvn/c+dl2q7fjDrrdgBSpxeepKjArkP1a8h7hbhhr+jG9FjbChiuv269oTr/SrTItbPZYN/m/Zmh8mz/Z1hTq2e011tJuo5cy/ZAi0zL7jRHlffzHH3ylOsJ9Mr/K9/pXs9yp2sy7Y6ez7YQcADSQtE1lhYwVL85BFl6h1vcLAIVB9lKdobqvButcu3sH2T2cN6jypVn+155LT2ezT7vobrnNeVa1vdWmZbdrXaV93Pt/ni6tQVa5//d73Pr57nbPZl2V9Dn7wo4ALkr6PPLCxiqf7X4bgAvv1EskEBngWpDbOflnr68oTona5lrZ7PBPs77VLXybM+rrqPXc9njvWT3u65cy3reMtOyO9as8m6u2RdPtYdAq+zv8T1u9Wx3OynT7gr6/F0BByB3BX1+eQFD9e8W9wjk5TeQBRK4KVBlaL25jO4fN1TnxK1z7Uo22Nd5v6pVXul3tTX0fB57u6fuXteWa1m/W2dadtdaVd7LtfrhafYTuJP9T3x/7zxfq27KtFaSrnNVwAHIVTmf20bAUP1nq58I6G02l4USCAQqDKzBYw4vMVTnLeiRa1eywd7Oe1ap8kqvKz1/72exr3sL73N9uZb1ukemZXceX+V9PL4HnoDA1dx/8vt79RlbdlemtdR0rSsCDkCuqPnMVgI7D9XfNfrJsN5qs1ksgf8UqDCkztYMQ3XesZ65dicf7Pu8hyMr7/R45HM/dW/7+Cnp9e8j17Ie98y07AnGVHkXj3F3VwJfCaTZP+p7mz5fz+7KtJ66rp0IOABJlNRsLbDrUH3U9FHhffRc/p3ACgIVhtTZHA3Vecd65tqdbLDv8x6OrLzT45HP/dS97eOnpNe/j1zLetwz07IneLbKO/hZb3cjkAr8lP+jv7cVZhOZlu4kdb0EHID0knXdZQR2G6rPNG50kJ95VrUEZhKoMKTO5PV6VkN13rGeudYqF3wH8n4+Xdmqx08/95P3s3+f1F73XnIt623PTMue4Nkq7+Bnvd2NwBmBj/lf6btaYS6RaWd2ktoeAg5Aeqi65lICuw3VZ5tXKdjPPrt6AhUFKgyoFV2OnslQfST0+9+fyLVW2eD7kPf1qcpWvX3qeUfcx74dob7ePeVa1tMnMi17kn5V3rv9bF2ZwA4CFeYSmbbDTqu9Rgcgtfvj6QoI7DBU32E2kN/R81kCfwpUGE5n7YmhOu/cE7nWOht8N/L+9qxs3deezzry2vbrSP117i3Xsl4+kWnZk/Sp8t7t4+qqBHYSqDCXyLSddlzNtToAqdkXT1VIYPWhugW1wbyFomsQeHurMJzO2gdDdd65p3KtZzb4ruT9blnZs6ctn3P0tezP0R1Y4/5yLevjU5mWPU27Ku/bdpauRGB3gQpziUzbfReOX78DkPE98ATFBVYdqluyG9BbarrWzgIVhtNZ/Q3VeeeezLWe+eD7kve8VWXPfrZ6xgrXsTcrdGH+Z5BrWQ+fzLTsie5Vec/e8/NpAgT+Fqgwl8g0O3O0gAOQ0R1w//ICqw3VPcAN6j1UXXNHgQrD6azuhuq8c0/n2hMZ4buT9/9O5RO9vPN8VT5rP1bpxNzPIdey/j2dadlTXavyjr3m5lMECHwvUGUmkWl26WgBByCjO+D+5QVWGqp7YhvYe+q69i4CVQbUGb0N1XnXns61p/PB9yjfC1cqn+7nlWes8Bn7sEIX5n4GuZb17+lMy57qXJX36jkv1QQIZAKVZhGZlvVMVT8BByD9bF15EYEVhuonWmFwf0LZPVYWqDSgzuhsqM679nSujcoH36l8T5ypHNXPM89Yodb+q9CFuZ9BrmX9ezrTsqfKqrxPMydVBAhcE6g0i8i0az30qXYCDkDaWbrSogIzD9VPtsQA/6S2e60oUGlAndHXUJ13bUSuVcgI37F8j/xUWaGXbVbS9yr2W1/fHa4u17Iuj8i07Mm+rvIOvaPnswQInBGoNIvItDOdU9tDwAFID1XXXEpgtqF6FL5hfpS8+64iUGlAndHUUJ13bVSuVcoJ37d8v3yurNTH66vo/0l7rL/x6neQa1mHR2Va9nS/q7w7z4qpJ0DgrkClWUSm3e2mz98VcAByV9DnlxeYZage3QhD/egOuP/MApWG01kdDdV550bmWrWs8N3L9817ZbUenl/BM5+wt55xXvkuci3r7shM++kJvSuz/qkiQOA5gZGziUx7rs/u9LWAAxA7g8CBQNWhulrjDPnVOuJ5ZhIYOYzO5PTTsxqq806OzrWKeeE7mO+fiv3Ln/65SnvqOetV7yTXss6OzrSvntJ7MuudKgIE6gj0nltkWp1e7/okDkB27bx1xwIVh+r44R8sNOg/iO1Wywn0HjiXA/tiQYbqvMsVcq16ZvhOfr+fqvcu/yb0rbSH+vrucHW5lnW5Qqa9P6n3Y9YzVQQI1BXoNb/ItLo93+XJHIDs0mnrvCxQaai+vIjOHzTsdwZ2+eUFeg2ay8N9WKChOu92hVybNTd8V9/eZu1d/g1pU2mvtHHc+SpyLeu+TMucVBEgQOCKQKt5RqZd0feZlgIOQFpqutaSAhWG6sqwfgip3B3PNotAq8FylvX2eE5Dda5aIddmz46dv7Oz9y7/ptyr3HmP3JPz6XcBuZbtBZmWOakiQIDAHYG7c41Mu6Pvsy0EHIC0UHSNpQUqDNVVgf0IUrUznmtGgbtD5YxrbvnMhupcs1KurZQju3yHV+pZ/q05X7nLfjgv4xOpgFzLpEZmmvdh1iNVBAisJXBlxpFpa+2BGVfjAGTGrnnmRwVGDtWPLvTkzQz8J8GUEwgErgyTwWW3KDFU522ulGsrZcku39+VepZ/a65V7rInrun41JGAXDsS+vXvozLNuzDrjyoCBNYTuDLfyLT19sFsK3IAMlvHPO/jAqOG6scXeuKGBv4TWEoJXBC4MlReuM1SHzFU5+2slGur5smq3+FV+5V/e85VrroPzimoviog1zK5UZnmfZj1RxUBAusJXJlvZNp6+2C2FTkAma1jnvdxgVFD9eMLDW9o2A+hlBFoJHBlwGx066kuY6jO21Up13bJlFW+x7v0K/82/Vy5St9bebjOOQG5lnmdybTP77Ar31HvwawvqggQWF/gzDtUpq2/H6qv0AFI9Q55vuECZ4bq4Q/74AMY/h/EdisCb29vZwbMHcEM1XnXq+XaLnny+g6/1jrzd3mXXuXfJgcgraxc528BuZbtijOZlr7DfnpPp9fInl4VAQIE5hU4M9PKtHn7vMqTOwBZpZPW0U3gzFDd7SEKX9gfAYWb49GWFTgzbC6L8Glhhuq80xVzbdcsmfG7vGuv8m/Y78oZ+3tlnT0/c3Ro+HE/fuVdeb8e7Q+5lu2sNNMq74VspaoIECBQT+Aoy96fWKbV691uT+QAZLeOW+9pgXSoPn3hhT7gD4qFmmkp0wmkQ+d0Czv5wIbqHKxqru2YJbN9f3fsUf7N+rNytt5eXeeVz13dR++mVz9/5Vl7fuZoj8i1TP9Mpq2ydzIZVQQIEOgvcJRlDkD698AdMgEHIJmTqo0FzgzVGzP96z/r4f8RIDBGIB08xzzdM3f1Q1HuXDXXds6RWb7DO/co/4b9qpylp2fXdbfeHsr3h1zLdtuVTLMPM1tVBAgQOBJI5x2ZdiTp33sLOADpLez60wtcGaqnX/TNBfij4iagjxO4KZAOojdvU+rjhuq8HdVzbfcMqfz93b03+bcs/5H77DUr1X/+z0/ZH1l30u+4XMs8e2aaPZ31QBUBAnsKpHn20pFpe+6RSqt2AFKpG56lpEDPobrkghs+lD8aGmK6FIELAmeG0guXL/URQ3Xejsq5Jjdq/3CuP/n37FW58jvYXji3F96rz+wJuZYZP5Vp9nzWD1UECOwjINP26fUKK3UAskIXraGrwFNDdddFDL64PxgGN8DttxM4M4yuguOHoryT1XNNZvzuZbXvst7k37MVDkD0+1y/v6q+8x2Wa5n/iEzz3ch6o4oAgXUFzuabTFt3L8yyMgcgs3TKcw4TGDFUD1tsxxv7Q6EjrksT+CBwdhhdBc9QnXdyhlyTGd/3c+R3XF/y79nMByD6fK7Pn6tbfUflWtaHUZnme5L1RxUBAusJXMk5mbbePphtRQ5AZuuY531cYNRQ/fhCH7qhPxYegnabLQWuDKOrQBmq807OkmvyotYhiH7k37H3yorvZH0838efPtGzx3It69XITPN9ynqkigCBdQSu5p5MW2cPzLoSByCzds5zPyYwcqh+bJEP3sgfCg9iu9VWAleH0VWQDNV5J2fKNZnxc1+f/N7rRf4dq3oAoofnezjq8ON1X7mW9Wt0pvleZX1SRYDA/AJ35k6ZNn//Z1+BA5DZO+j5uwuMHqq7L3DQDfyxMAjebZcUuDOMrgJiqM47OUuuyYmsp098//Ui68Xnqid6c/RkenckdP7fn+qrXMt6UyHTfM+yXqkiQGBegbvZJ9Pm7f0qT+4AZJVOWkc3gQpDdbfFDb6wPxYGN8Dtpxe4O4hOD/BhAYbqvJsz5ZqcyPv6quz1TtCHc334WN26Jx978X5t/bnen/STrfuY3FeuJUpvb5UyzXcx65kqAgTmEWiVfzJtnp6v+qQOQFbtrHU1E6g0VDdbVMEL+YOhYFM8UnmBVgNp+YUGD2ioDpD+s2SmXJMNeV+f+tH92hPt+6mW72nfhzH7qGUPz6xArmValTLNdzTrmSoCBOoLtM4+mVa/56s/oQOQ1TtsfbcFKg3Vtxcz6QX8MTFp4zx2F4HWw2iXhxxwUUN1jj5jrsmBvL+fK1u8M/g/48/5unPrT7b43tx9JrmWCVbMNN/lrHeqCBCoJdAz+2RarV7v+DQOQHbsujWfEqg4VJ9awELF/phYqJmWclqg50B6+mEKfsBQnTdlxlzz/s/7+1XlnfcH+3v2r08n/pzvO7e6QtKvVvf66TpyLVOummm+01n/VBEgMFbgqcyTaWP77O5vbw5A7AICBwJVh+pdG+ePiV07v/e6nxpMZ1Y2VOfdmzHXvPvz/v5UeeVdwr6N/ceDEKbtTFtf6cp3pPUzvF9PrmWyVTPN9zzrnyoCBMYIPJ13Mm1Mn931t4ADELuBgAOQqfeAPy6mbp+HPxB4ejCduSGG6rx7VX8s+mkF3vV5f69Ufveu4X5F02dmFKiYt3It20nVM817NOujKgIE+ghUyTeZ1qe/rpoLOADJrVRuKlB9qN60LX8s2x8WdsFqAlUG1ZlcDdV5t2bNNe/6vMdXKz+/e5hflfS5mQSqZq5cy3bRDJnmXZr1UhUBAu0EqmWbTGvXW1e6JuAA5JqbT20kMMNQvVE7DpfqD4xDIgUTCFQbWCcgezNU512aOde84/M+qyRA4Figct7KteP+vSpmyjQZlvVUFQEC1wRk2jU3n9pDwAHIHn22yhsCMw3VN5a51Ef9cbFUO7dcTOXhtWpD/FCUd2b2XPOOz3utkgCB7wWqZ61cy3bvbJkmw7K+qiJA4LxA5VyTaef76RNtBRyAtPV0tQUFZhuqF2zBpSX54+ISmw89KFB5QH2QodmtDNU55ey55v2e91olAQIOQFbfAzNmmhxbfVdaH4ExApX/vvS32pg94a6/BRyA2A0EDgRmHKo19beAPzDshgoClYfRCj4tnsFQnSuukGve7Xm/VRIg8KfALJks17KdO2umybGsv6oIEMgFKuebTMv7qLKPgAOQPq6uupDArEP1Qi24vRR/YNwmdIGLApWH0ItLKvsxQ3XemlVyzbs977lKAgR+CcyUy3It27WzZ5osy/qsigCBTKBqzsm0rH+q+gk4AOln68qLCMw+VC/ShmbL8EdGM8rtL1R1uNy1MYbqvPOr5Jr3ed5zlQQIOABZdQ+skGnybNXdaV0Enheo+jeqv9We3wvu+KeAAxA7gsCBwApDtSb/KeCPDDviqkDVgfLqelb6nKE67+YqueZdnvdcJQECDkBW3QMybdXOWhcBAlcFKv7N6m+1q930uVYCDkBaSbrOsgKrDNXLNujiwvxwdhFu049VHCI3bcW3yzZU5ztilVzzHs97rpIAgbn+81evfsm1bNeukmmv1cq1rOeqCBD4WaDi364yza4dLeAAZHQH3L+8wEpDdXnsQQ/oj41B8EVvW3FgLEpV6rEM1Xk7Vsk17+685yoJ7C4wY7bLtWzXrpJpn1cr47L+qyJA4GuBarkn0+zU0QIOQEZ3wP3LC6w6VJeHf/AB/YHxIHbxW1UbFItzlXo8Q3XejpVyzfs777tKArsKzJrtci3bsStlmkOQrOeqCBA4FqiWfTLtuGcq+go4AOnr6+oLCKw8VC/QnuZL8GNac9JyF6w2DJYDmvSBDNV541bLNe/tvPcqCewkMHvey7Vst66WaV+tWs5le0EVgZYCSYZU/m4mz9/S6+haMu1IyL/3FnAA0lvY9acX2GGonr5JjRdQeZBpvNQtLldt+NsCfcAiDdU5+mq55p2d914lgV0EVsh+uZbt1tUy7adVV8+71/eu+jNmu0rVzgJX86Pa3r+6jl69l2m9ZF03FXAAkkqp21Zgp6F62yZ/s/BqQ4z+/CxQbcjTr2cFDNW594q55n2d918lgR0EVpgJ5Fq2U1fMtGTlFXPv4/eu4vMlrmoI9MiPEd+HHuu4sztk2h09n20h4ACkhaJrLC2w61C9dFNPLG7EsHLi8ZS+vb1VG+40ZYyAoTp3XzXXvK/zPaCSwMoCq8wFci3bpatmWrb6t1L/i4vP3z25nHZRXSWBJzKk93fjiTWc7ZlMOyumvrWAA5DWoq6YW69PAAAgAElEQVS3nMDuQ/VyDb2woN4DyoVH8hEHH/bAJwFDdb4lVs417+t8H6gksKpAxR9+rljLtUxt5UzLBOocgnz13ZPLaRfVVREYkSEtvycjnj/pnUxLlNT0FHAA0lPXtZcQMFQv0cYmi2g5mDR5oA0vUnWg27AV5ZZsqM5bsnKueU/n+0AlgdUEVpsR5Fq2Q1fOtEzgd9XoDPzpOzj62c5aqt9boHqefPw+VX/W950k0/b+TlVYvQOQCl3wDKUFDNWl2zPk4QzwQ9j/ddNZBrxxQvve2VCd9371XPOOzveCSgIrCaw2I8i1bHeunmmZQo1DEAcgZ7ulvrLAapky2lqmje6A+zsAsQcIHAgYqm2R7wT8yPbM3jB8PuM8+10M1XkHV8417+V8H6gksJLAirOCXMt26MqZlgn8XTUyC4++iyOf7aqnz+0rcLSf95U5v3KZdt7MJ9oKOABp6+lqCwoYqhdsasMlGeIbYn5xKUNnX9+Vrm6ozru5eq55L+d7QSWBVQRWnBfkWrY7V8+0TOHPqpE5mHwXRz7fFU+f2Vcg2c/76pxbuUw756W6vYADkPamrriYgKF6sYZ2Wo5Bvj2sgbO96cpXNFTn3d0h17yT8/2gksAKAivODHIt25k7ZFomUeMQJP0uyukrXfWZEQLpnh7xbDPdU6bN1K01n9UByJp9taqGAobqhpgbXMowf7/Jhsz7hjtewVCdd32XXPM+zveESgKzCqw8M8i1bFfukmmZxt9VI7Iw/V6OeLarjjt/7nM/d+xbuqd33idHa5dpR0L+vbeAA5Dewq4/vYChevoWPr6AHYfCVsiGy1aS+13HUJ33fKdc8z7O94VKAjMKrDw3yLVsR+6UaZnI+EOQs99LWX21s/0/5/+w/W/js/u6f3fmuoNMm6tfKz6tA5AVu2pNTQUM1U05t7qYYT5vt4Eyt1L5tYChOt8Zu+Wad3G+N1QSmEVgh7lBrmW7cbdMy1S+rnoqD89+P596rjt2O3827ecOfUwtdt4v361dptkVowUcgIzugPuXFzBUl29R+QfcYRi80wSD5B09n30XMFTne2G3XPMOzveGSgKzCOwwO8i1bDfulmmZylwHIK+nldV3Otv3s2fetzv08YxH387MdXWZNle/VnxaByArdtWamgoYqptybnmxHQbBq401QF6V87nPAobqfE/smGvew/n+UEmgusAus4Ncy3bijpmWyYw7BLnyHZXTd7ra97Nn+7lDL8+a9O3QHFeXaXP0aeWndACycnetrYmAoboJo4v4/9n0xx4wNPpKtBYwVOeiu+baDn+Q57tAJYH5BHabHeRatkd3zbRMZ8whyNXvqpy+09W+n9XTv32vmvTtVN2ry7S6vdnlyRyA7NJp67wsYKi+TOeDnwQM9b9ADIu+Gj0EDNW56s655j2c7xOVBKoJ7DY/yLVsB+6caZnQ84cgd76rcvpOV/t9Vk+/tr3j0q9bNa8s02r2ZaencgCyU7et9ZKAofoSmw99I7D7UG9I9NXoJWCozmXlmv/WeL5bVBKoIbDj/CDXsr0n0zKnr6p6/V1y9/va67muS/nku4DeOgi5+m2QaVflfK6VgAOQVpKus6yAoXrZ1g5b2K5D/d2BeVjD3HgKAUN13ia55gAk3y0qCYwX2HV+kGvZ3pNpmdN3VT3+Lrn7ne3xTPeUsk9/XPesazha6d3evl9/RZ9WNkc9mPXfZdqsnVvnuR2ArNNLK+kkYKjuBLv5ZVcc+r5rqWFw883+0PIN1Tm0XPtttdO7ON8hKgmMFzA7vL3JtWwfyrTM6aiqZR62+P62fJ6jtbf495/WPNtaEo8WPf54nxWMWpskfZipRqbN1K01n9UByJp9taqGAobqhpgu9YfACoPeUUsNgkdC/r2VgKE6l5RrDkDy3aKSwBgB84MDkHTnybRU6ue6ln+XtPr+tnymNkrfX+VozTOtJbU6WnN6nfe6FYxam5w1rFzvb7XK3dnj2RyA7NFnq7whYKi+geejhwIrDHo/LdIQeLgFFDQSMFTnkHLNAUi+W1QSeF7A7PDLXK5le0+mZU5JVau/S1p+h1s9U7L+OzXpmmdZT2KRrjm51sea2Y16uZx1rFYv06p1ZL/ncQCyX8+t+KSAofokmPLTArMPed8t2PB3eiv4wA0BQ3WOJ9f+tFr1HZzvCJUEagmYHxyAnNmRMu2M1nFti0xs/R1u8UzHK79fka57lvWkIum60+utcBjS0+SKY4XP+FutQhf2fgYHIHv33+oDAUN1gKTklsBqQ/ALw9B3a0v48AUBQ3WOJtf+tlrxPZzvCJUE6giYH373Qq5l+1KmZU5nq+7kYuvv8Z1nObvuO/Vn1j3LmhKPM+tOrvdVzWxeT5hctRz1OZk2St593wUcgNgLBA4EDNW2yFMCsw12P7kY+p7aNe7zLmCozveCXPveaqX3cL4jVBIYL2Bu+LsHci3blzItczpbdScPe3yf7zzP2bVfrb+y7hnWlXpcWX967fe62byeMDlrOKpepo2Sd18HIPYAgVDAUB1CKbstMNtA5wDkdstdoKGAoTrHlGs/W630Ls53hUoCYwX8SOQA5OoOlGlX5Y4/dzUPe32frz7P8UrbVFxdd/V1pTpX159e/1U3m9UTJmf8Rtb6W22kvnu/BPwvQOwDAgcChmpb5CmB2QY6ByBP7Qz3SQQM1YnSrxq55gAk3y0qCfQX8APR18ZyLdt7Mi1zulp15e+TXt/pK89ydd1XP3d17TOsLTG5uv7k2h9rZvF6yuOs34h6mTZC3T0/CjgAsR8IOACxBwoJzDLMHZEZ9o6E/HtrAUN1LurHosxqlfdxtlpVBJ4XMCv8bC7Xsj0p0zKnO1Vn8rD39/rMs9xZ89XP3l1/9fUdudxd/9H1P//7DF5Pm5w1fKpepj0l7T7fCTgAsTcIOACxB4oJzDDIHZEZ9I6E/HtrAUN1LurHomOrFd7Dx6tUQWCsgFnBAUiLHSjTWihm1zjKxie+00fPkK2kX1ULg+prPNJrYXB0j4//Xt3raY8zdk/W+lvtSW33+krAAYh9QcABiD1QTKD6EJdwGfQSJTUtBQzVuaYfizKrFd7F2UpVERgjYFZwANJi58m0For5NX7Kxqe+09XzuZVD9XX+tGtaGeQ781dlVbNRHmf9etb7W62nrmsnAg5AEiU1WwsYqrdu/5DFVx3czmAY8s5oqW0hYKjOFeVabrXC+zhfrUoCzwmYE46t5dqx0atCpmVOras+5+OT3+nq2dzSovpav9tXLQ2u7N1qbqM9rhi2/oxMay3qemcFHICcFVO/nYCheruWD19wtYHtCogh74qaz9wRMFTnenItt3pVrvBOPrdi1QT6CZgPclu5llnJtMxptarq2dzqXVd9nT/tq1YGV/duJbvRFlcNW35OprXUdK0rAg5Arqj5zFYChuqt2l1isZWGtTsgBr07ej57VsBQnYvJtdzqvXKV9/L5lfsEgTYCZoLzjnItM5NpmdOKVZWzufU7r/JaKx+CvJ6til3rPTHbd1qmzdax9Z7XAch6PbWixgKG6sagLncoUGVIO3zQg4Ldh7y7fj5/TsBQnXvJtdzqY+Uq7+Zrq/cpAvcEzATn/eRaZibTMqcVq6rncsv3XvW1OgTJvmEt90R2xzpVMq1OL3Z9Egcgu3beumMBQ3VMpbCRwMwD7meCnYe8RtvBZUIBQ3UI5b+XnkN9U7nSO/o2hgsQOBAwB1zfInIts/O3Wua0alXlTG79/qu81uoHIB+fb6Rj6z0x0/daps3UrTWf1QHImn21qoYChuqGmC4VCYwcyqIHPFG085B3gklpAwFDdY4o13KrnypXele3EXEVAn8KmAHu7Qi5lvnJtMxp1arqWdzjPVh9zV/ttR4Od/b0KMNqDncMz35Wpp0VU99awAFIa1HXW07AUL1cS8svaNRA1gNm5yGvh6drfi9gqM53h1zLrRyAtLFylb0EZH+bfsu1zFGmZU6rVlX/u6nH+7D6mmc4AHk94wjHHvthlu+2TJulU+s+pwOQdXtrZY0EDNWNIF0mFhgxjMUPd7Fw52HvIpmPnRQwVOdgci23Oqpc8X19tGb/TuAnAXnfbn/ItcxSpmVOK1dVzuJe78TKa/5ur/WyaLW3nzCtbtDK8qvryLSeuq6dCDgASZTUbC1gqN66/UMW/8TwNWJhOw98I7x3u6ehOu+4XMutkspV39nJ2tUQ+Cwg69vtCbmWWcq0zGnlquo53Ou9WH3ds+ZDT9dee2GG77dMm6FLaz+jA5C1+2t1DQQM1Q0QXeKUQM+h69SDdCjeeejrwOmSHwQM1fl2kGu51ZnKld/dZxzU7icg2/v0XK5lrjItc1q9qnIG935HVl77V/uut0frvd7Cd7Y1tzZ8XU+m9VB1zTMCDkDOaKndUsBQvWXbhy66xZA1dAEHNzcAVu7OvM9mqM57J9dyqzOVq7+7z1io3UdApvfrtVzLbGVa5rR6VeUM7v2erLz27/Zdb5PW+/2u8Wzrbe3nAKSHqGueFXAAclZM/XYChurtWj50wXeHq6EPf+LmhsATWEojAT8URUz/KpJrudWVyl3e41dsfGYdATnev5dyLTOWaZnT6lWVs/ep92Vlg8/77ymTJ/b9V+4rra+VoUxrJek6VwUcgFyV87ltBAzV27R6+EJnGlpbYRkOW0m6jqE63wNyLbe6Urnju/yKk8/MKyC7n+mdXMucZVrmtENV5fx96r1Z2eDjHnzKY4d9P8saZdosnVr3OR2ArNtbK2skYKhuBOkyhwKzDKyHCzlZYAA+Cab8SwFDdb4x5FpudaVy13f5FSufmU9AZj/XM7mWWcu0zGmHqsr5+9S7s7LB5z34lMkOe3+GNcq0Gbq09jM6AFm7v1bXQMBQ3QDRJSKBmQbWaEE3iwzFNwE3+7ihOm+4XMut7lR6p9/R89lKAvJ4TDfkWuYu0zKnXaqqZu/T79GqDg5Bdvkm/r1OmbZv76us3AFIlU54jrIChuqyrVnqwWYZUkeiP/2Hw8i1uvd5AUN1bibXcqu7ld7tdwV9voKA/B3TBbmWucu0zGmnqorZO+I9WtHBAchO38Q/1yrT9u19lZU7AKnSCc9RVsBQXbY1yzzYDMPpbNgj/siYzWi15zVU5x2Va7lVq0rv+VaSrvOkgCx9Uvvve8m1zF+mZU47VVXO3Kffq5Ut3vfk0yY7fRcqrVWmVerGns/iAGTPvlv1CQFD9QkspZcEZhhMLy2swIcM1AWa8NAjGKpzaLmWW7Ws9K5vqelaTwjI0CeUv7+HXMv8ZVrmtFNV5bx9+r1a2cIByE7fyrc3mbZXvyuu1gFIxa54plIChupS7VjuYWYYSldBf/oPjlXcZlmHoTrvlFzLrVpWet+31HStJwTk5hPKDkDuKsu0u4Lrfb5y3o54r1b2eO2+ESbr7fr6K/K3Wv0erf6EDkBW77D13RYwVN8mdIEfBKoPpLs0z+A9f6cN1XkP5Vpu1aPSe7+Hqmu2EpCHrSTvX0euZYYyLXPasapi3o56x1a0+LgnR7ns+L0YtWaZNkrefd8FHIDYCwQOBAzVtkgvgeqDaK91V72uwbtqZ7LnMlRnTq8quZZb9aj07u+h6pqtBGRhK8n715FrmaFMy5x2rKqYtyPfsRU93vflSJcdvxsj1izTRqi750cBByD2AwEHIPbAAIHKA+gAjpK3NIiXbMu3D2Wozvvlx6LcqmelHOip69pnBWTeWbH+9XItM5ZpmdOuVRWzduT7tqKHQ5A9vp0ybY8+V16lA5DK3fFsJQQM1SXasNRDVB48l4JutJiRf6Q0WsIWlzFU522Wa7lVz0pZ0FPXtc8IyLkzWs/VyrXMWqZlTjtXVcvb0e/cah4OQPb4dsq0PfpceZUOQCp3x7OVEDBUl2jDUg9RdehcCrnhYkb/kdJwKUtfylCdt1eu5VY9K2VBT13XTgVkXCr1fJ1cy8xlWua0e1W1zB397q3m8dqfo012/470Xr9M6y3s+kcCDkCOhPz79gKG6n23wJnB8GhgO3OtfcXrr/yoz/VXsO4TGqrz3sq13Kp3pWzoLez6PwnItNr7Q65l/ZFpmZOqXwJVcrfC+7eKxce9WcHFd6WPgEzr4+qquYADkNxK5aYChup9Gl9xCNxHf66VGs7r9ctQnfdEruVWvSvlTm9h1/9OQI7V3xtyLeuRTMucVDkA+WoPVJtDZNO631SZtm5vZ1mZA5BZOuU5hwkYqofRP3LjakPfI4t2k0sCBvJLbI99yFCdU8u13OqpSln0lLT7vAvItPp7Qa5lPZJpmZOq3wIVMrfSO7iCx8f9WcnG96adgExrZ+lK1wQcgFxz86mNBAzVaza72qC3pvJaqzKM1+6noTrvj1zLrZ6qlElPSbvPS0CezbEP5FrWJ5mWOan6U2B07lZ7D4/2cACy/jdUpq3f4+ordABSvUOeb7iAoXp4C5o+QKXhrunCXKyLQLU/TroscpGLGqrzRsq13OqJSrn0hLJ7vAvItXn2glzLeiXTMidVfwuMzN+K7+KRHg5A1v+GyrT1e1x9hQ5AqnfI8w0XMFQPb0HTB6gy2DVdlIt1Eaj4h0mXhS5yUUN13ki5llv1rpRJvYVd/7OAbJtnT8i1rFcyLXNS5QDkaA9UmUnk1FGn5vx3mTZn31Z6agcgK3XTWroIGKq7sA69aJXhbiiCm38rYOiec3MYqvO+ybXcqlelHOol67pHAjLuSKjOv8u1rBcyLXNS5QAk2QNV5hNZlXRrrhqZNle/VnxaByArdtWamgoYqptylrlYleGuDIgH+ZeAYXvejWCoznsn13KrHpXyp4eqa14RkHlX1J77jFzLrGVa5qTqe4FRuVz1HTzK42OHqtr4Hl0XkGnX7XyyjYADkDaOrrKwgKF64ea+vb1VGPDWFp5ndQbteXr11ZMaqvP+ybXcqkel3Omh6ppXBOTeFbXnPiPXMmuZljmpcgByZg+MnlXk05luzVEr0+bo08pP6QBk5e5aWxMBQ3UTxvIXGT3klQda9AEN1+s01lCd91Ku5VYtK+VMS03Xai0gD1uL3r+eXMsMZVrmpOpngaczepZ37tMu712axcf3KheQabmVyj4CDkD6uLrqQgKG6oWaebCUUQPePsK1VmqwrtWPu09jqM4F5Vpu1aJStrRQdI0nBOTiE8r5PeRaZiXTMidVtQ5AXk8zwzt31Awzg43v1DkBmXbOS3V7AQcg7U1dcTEBQ/ViDf1hOaMGvH2E66zUUF2nF62exFCdS8q13OpupVy5K+jzTwvIx6fFv7+fXMt6IdMyJ1XHAk9n9kzv26dtZjkgOt5VKt4FZJq9MFrAAcjoDrh/eQFDdfkWNX/AEQNe80W44B8CM/2BoXXXBAzVuZtcy63uVMqSO3o+W0FAdo7tglzL/GVa5qQqE3gyu2d7xz5p4wAk268zVcm0mbq15rM6AFmzr1bVUMBQ3RBzkks9PdxNwjLlY872h8WUyEUe2lCdN0Ku5VZXK+XIVTmfqyQgQ8d2Q65l/jItc1KVCTyZ3zO+Y/lk+0jV3wIyza4YLeAAZHQH3L+8gKG6fIu6POCTw12XBWx60Rn/kNi0Vc2XbajOSeVabnW2UnacFVM/g4BsHdMluZa5y7TMSdU5gSfyfNZ36xM2r27N6nNup+1TLdP26XXVlToAqdoZz1VGwFBdphWPP8hTw93jC1vshobjxRp6cTmG6hxOruVWZyplxhkttTMKyNtnuybXMm+ZljmpOifwRKbP/k7tbTS7z7kdt361TFu/x9VX6ACkeoc833ABQ/XwFgx7gN5D3bCFTXpjQ/CkjXvosQ3VObRcy62SSlmRKKlZRUAWP9dJuZZZy7TMSdV5gd75Pvv7lM/5PbXzJ2Tazt2vsXYHIDX64CkKCxiqCzen86P1Huo6P/70l5/9j4LpGzDZAgzVecPkWm51VCknjoT8+6oCMrp/Z+VaZizTMidV1wR65fxK79AeRiv5XNt5631Kpq3X09lW5ABkto553scFDNWPk5e5YY9hrsziCj6IQbdgUyZ6JEN13iy5llv9VCkj2ji6yrwCcrtv7+Ra5ivTMidV1wV65P2K789WTivaXN9963xSpq3Ty1lX4gBk1s557scEDNWPUZe9UathruwCBz6YAXcg/mK3NlTnDZVrudXnSnlw3c4n1xWQ5X16K9cyV5mWOam6L9ByBtjhvXnFaweX+ztxzivItDn7ttJTOwBZqZvW0kXAUN2FdbqLXhngplvkQw9ssH0IerPbGKrzhsu13MoByHUrn9xPQL637blcyzxlWuak6r5Ay78Hd3tfHtnt5nF/N853BZk2X89We2IHIKt11HqaCxiqm5NOe8GjwW3ahT344IbbB7E3u5WhOm+4XMutPlbKgGtuPrWXgJxv12+5llnKtMxJVRuBVrOAd2WbfrjKPAIybZ5erfqkDkBW7ax1NRMwVDejXOZCrQbfZUCChRjyAyQltwQM1TmfXMut3iu998+b+cS+AjK/Te/lWuYo0zInVe0Frs4G3pHte+GK9QVkWv0erf6EDkBW77D13RYwVN8mXPICVwfeJTF+WJQBf7eOj1uvoTq3l2u5lXd9bqWSwLuA7G+zF+Ra5ijTMidVfQTOzgnej3364Kr1BWRa/R6t/oQOQFbvsPXdFjBU3yZc+gJnh96lMT4tzoC/U7fHr9VQnfdArh1bebcfG6kgcCRgDjgS+vnf5VrmJ9MyJ1XPCHw3P3gfPuPvLnUFZFrd3uzyZA5Adum0dV4WMFRfptvmg34o+7vVhvxttn+ZhRqq81bItZ+tvNPzvaSSwJGAeeBI6Pt/l2uZnUzLnFQ9L/A+T3gPPm/vjvUEZFq9nuz2RA5Aduu49Z4WMFSfJtvyA34w+9V2A/6W27/Eog3VeRvk2tdW3uP5HlJJ4IyA2eCM1u9auZa5ybTMSRUBAgRGCsi0kfru/RJwAGIfEDgQMFTbIqnA7j+e+YEj3SnqeggYqnNVufa31e7v73z3qCRwTcCMcN5NrmVmMi1zUkWAAIGRAjJtpL57OwCxBwgEAobqAEnJvwV2/hHNjxu+CCMFDNW5vlxzAJLvFpUE2gqYFXJPuZZZybTMSRUBAgRGCsi0kfru7QDEHiAQCBiqAyQlfwjsdgjixwxfgAoChuq8C3LtT6vd3tn5TlFJoI+AuSFzlWuZk0zLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBS8pfATj+o+SHDF6CCgKE674Jc+22107s63yEqCfQXMDscG8u1Y6NXhUzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBS8qXA6j+s+fHCxq8kYKjOuyHX3t5Wfz/nu0ElgXEC5oif7eVatjdlWuakigABAiMFZNpIffd2AGIPEAgEDNUBkpJvBVb+kc0PFzZ+JQFDdd4NueYAJN8tKgn0EzBHOABpsbtkWgtF1yBAgEBfAX+r9fV19WOB//jnn3/+OS5TQWBfAUP1vr1vtfIVD0H8aNFqd7hOKwFDdS65e66t+E7Ou79u5cdc0uM5+myWcADSYqfunmktDF2DAAECvQX8rdZb2PWPBByAHAn59+0FDNXbb4EmACv9GOMHiyZbwkUaCxiqc9Cdc22ld3He8T0qz2aTvVBnX5ztXZ0n7/skci3z3TnTMiFVBAgQGC8g08b3YPcncACy+w6w/kMBQ/UhkYJQYIUfW/xIETZb2eMChuqcfNdcW+EdnHd5jcqfMudzP6/mk31RY69c7V+Np+/zFHItc9010zIdVQQIEKghINNq9GHnp3AAsnP3rT0SMFRHTIoCgRV+ZPEDRdBoJUMEDNU5+465tsL7N+/wHJUt8uRjX1tc76OcPfP8Pmrdw+dX0PaOci3z3DHTMhlVBAgQqCMg0+r0YtcncQCya+etOxYwVMdUCgOB2X9Q8eNE0GQlQwQM1Tn7brk2+3s37+xclS3ypOcByEvT3nl+T7XYF88/dZ87yrXMdbdMy1RUESBAoJaATKvVjx2fxgHIjl235lMChupTXIoDgZl/UPHDRNBgJUMEDNU5+265NvM7N+/qPJUtc6T3AchHVfvomT3Wcn8888T97iLXMtvdMi1TUUWAAIFaAjKtVj92fBoHIDt23ZpPCRiqT3EpDgVm/CHFjxJhc5UNETBU5+y75NqM79m8i/NV9siQVv93QM5o2ldntM7X9tgn55+ixifkWtaHXTIt01BFgACBmgIyrWZfdnoqByA7ddtaLwkYqi+x+dCBwIw/oPhRwrauLGCozruzQ67N+I7NOzhPZe/cGHEA8lnfXmu7H3vvmbZP2/dqci3z3SHTMglVBAgQqCsg0+r2ZpcncwCyS6et87KAofoynQ8udgjiRwlburKAoTrvzuq55gfpfC/0qHwyKyocgHw0tPfa7agn91G7p257JbmWea6eaZmCKgIECNQWkGm1+7PD0zkA2aHL1nhLwFB9i8+HFzoE8WOE7VxZwFCdd2flXPMDdL4PelU+mRXVDkBepvZgm5315D5q88TtryLXMtOVMy0TUEWAAIH6AjKtfo9Wf0IHIKt32PpuCxiqbxO6wCKHIH6MsJUrCxiq8+6snGt+fM73QevKURlR8RDEQUi73TVqX7VbwfUrybXMbuVMywRUESBAoL6ATKvfo9Wf0AHI6h22vtsChurbhC7gAMQeINBdwFCdE6+aaw4/8j3QsrLCD9Qfe1/heT762pf3d1u1nt5fUXYFuZY5rZpp2epVESBAYA4BmTZHn1Z+SgcgK3fX2poIGKqbMLqIQxB7gEBXAUN1zrtirvmROe9/i8pdf5C+a2efXhfccc/JtWy/rJhp2cpVESBAYB4BmTZPr1Z9Ugcgq3bWupoJGKqbUbrQAocgO/4AYePOIWCozvu0Wq75UTnv/dVK7/6rcsefs3+PjV4VO+5BuZbtjdUyLVu1KgIECMwlINPm6teKT+sAZMWuWvO3W/4AACAASURBVFNTAUN1U04X+0Fghh9BdvwBwqadQ8BQnfdppVyb4b2Zd6ZGpff8832wjzPz3famXMv2xUqZlq1YFQECBOYTkGnz9Wy1J3YAslpHrae5gKG6OakLfiNQ/QeQ3X54sFHnEjBU5/1aJdeqvzPzjoyv9H4f34OjJ7Dffwvtsl/l2tG34te/r5Jp2WpVESBAYE4BmTZn31Z6agcgK3XTWroIGKq7sLroZIcgu/zYYGPOK2Coznu3Qq75MTjv98fK17u88v/B8Gur2utT9v6vfu8wl8i17Lu9QqZlK1VFgACBeQVk2ry9W+XJHYCs0knr6CZgqO5G68ITHYLs8EODDTm3gKE679/sueYH4LzXu/xQfE5k3erPh1srf1d2mEvkWvZdnT3TslWqIkCAwNwCMm3u/q3w9A5AVuiiNXQVMFR35XXxLwSq/WCxw48MNuL8AobqvIez51q1d2Quf63SO/iam09lArN/n1b+fsi1bA/PnmnZKlURIEBgbgGZNnf/Vnh6ByArdNEaugoYqrvyunjxA5CVf1iw+dYSMFTn/Zw512b/sTbp0vt/psr7N9FS00Ngpu/Zyt8TuZbt7pkzLVuhKgIECMwvINPm7+HsK3AAMnsHPX93AUN1d2I3KHoIsvKPCjbdegKG6rynM+baTD/IftcJ79R8j6qsL/D6Tn7+vykz8qlX/H7JtWxHzZhp2cpUESBAYB0BmbZOL2ddiQOQWTvnuR8TMFQ/Ru1GnwRG/+C34o8JNtm6AobqvLcz5tro92Gu+7vy/R36/kPxlWv4DIGZBEZ/T1ebW+RatvtnzLRsZaoIECCwjoBMW6eXs67EAcisnfPcjwkYqh+jdqMvBEb+mLDaDwk22NoChuq8v7Pl2sj3YK769q//3/D+HwECfwo8/f1d6Xso17Jv02yZlq1KFQECBNYSkGlr9XPG1TgAmbFrnvlRAUP1o9xu9kng6R8O3m+/0g8INtUeAobqvM8z5dpT70DvvHz/qCTQW+Du936V77Ncy3baTJmWrUgVAQIE1hOQaev1dLYVOQCZrWOe93EBQ/Xj5G44+BBklR8ObKS9BAzVeb9nyrW7P4QeqXjfHQn5dwJjBO7+3xhZ4bst17K9N1OmZStSRYAAgfUEZNp6PZ1tRQ5AZuuY531cwFD9OLkbfiHQ+0fAj7dc4UcDm2g/AUN13vMZcq3lO887Ld8bKglUFjj7Xpj9uy/Xst04Q6ZlK1FFgACBdQVk2rq9nWVlDkBm6ZTnHCZgqB5G78YfBM7+0X8Hb/YfDO6s3WfnFTBU572rnmst3nfeY/l+UElgRoH0PTHzu0CuZTuzeqZlq1BFgACBtQVk2tr9nWF1DkBm6JJnHCpgqB7K7+afBNI/+K/CzfxDwdU1+9waAobqvI9Vc+3O+827K++/SgKrCnz3Dpn1/SDXsp1aNdOyp1dFgACBPQRk2h59rrxKByCVu+PZSggYqku0wUP8p8CdHwiPEGf9geBoXf59DwFDdd7nqrl29f3m3ZX3XiWB1QW+eo/M+o6Qa9lurZpp2dOrIkCAwB4CMm2PPldepQOQyt3xbCUEDNUl2uAhOh+AzPrjgI1B4F3AUJ3vhYq5dvbwwzsr77dKArsJrHIIIteynVsx07InV0WAAIF9BGTaPr2uulIHIFU747nKCBiqy7TCg7y9vZ39kTBF82NiKqWuqoChOu9MtVw7817zrsr7rJIAgd9z04zvDrmW7eBqmZY9tSoCBAjsJSDT9up3xdU6AKnYFc9USsBQXaodHqbDIciMPwrYCAQ+Cxiq8z1RKdfSww/vqby/KgkQWENArmV9rJRp2ROrIkCAwH4CMm2/nldbsQOQah3xPOUEDNXlWuKBGh6C+FHRdlpFwFCdd7JSrh0dgHhH5X1VSYDAWgJyLetnpUzLnlgVAQIE9hOQafv1vNqKHYBU64jnKSdgqC7XEg/kAMQeIPCXgKE63xSVcu27AxAHH3k/VRIgsKaAXMv6WinTsidWRYAAgf0EZNp+Pa+2Ygcg1TriecoJGKrLtcQD/afA0f/P6SMoPzAeCfn3mQQM1Xm3KuXaKv/HinN9lQQIEMgE5FrmVCnTsidWRYAAgf0EZNp+Pa+2Ygcg1TriecoJGKrLtcQDNTgAcfhhG60mYKjOO1op194PQLyT8v6pJEBgDwG5lvW5UqZlT6yKAAEC+wnItP16Xm3FDkCqdcTzlBMwVJdriQe6eQDih0ZbaEUBQ3Xe1Uq59joA8U7Ke6eSAIF9BORa1utKmZY9sSoCBAjsJyDT9ut5tRU7AKnWEc9TTsBQXa4lHujGAYgfGm2fVQUM1Xln5VpupZIAAQKjBORaJi/TMidVBAgQGCkg00bqu/dLwAGIfUDgQMBQbYtUFTj7fwPE4UfVTnquFgKG6lxRruVWKgkQIDBKQK5l8jItc1JFgACBkQIybaS+ezsAsQcIBAKG6gBJyRABByBD2N20qIChOm+MXMutVBIgQGCUgFzL5GVa5qSKAAECIwVk2kh993YAYg8QCAQM1QGSkmECZw5B/C9AhrXJjR8QMFTnyHItt1JJgACBUQJyLZOXaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSEqGCTgAGUbvxsUEDNV5Q+RabqWSAAECowTkWiYv0zInVQQIEBgpINNG6ru3AxB7gEAgYKgOkJQMFUgOQfyvP4a2yM0fEDBU58hyLbdSSYAAgVECci2Tl2mZkyoCBAiMFJBpI/Xd2wGIPUAgEDBUB0hKhgocHYA4/BjaHjd/SMBQnUPLtdxKJQECBEYJyLVMXqZlTqoIECAwUkCmjdR3bwcg9gCBQMBQHSApGSrgAGQov5sXETBU542Qa7mVSgIECIwSkGuZvEzLnFQRIEBgpIBMG6nv3g5A7AECgYChOkBSMlTAAchQfjcvImCozhsh13IrlQQIEBglINcyeZmWOakiQIDASAGZNlLfvR2A2AMEAgFDdYCkZLjAT4cg/hNYw9vjAR4QMFTnyHItt1JJgACBUQJyLZOXaZmTKgIECIwUkGkj9d3bAYg9QCAQMFQHSEqGC3x3AOLwY3hrPMBDAobqHFqu5VYqCRAgMEpArmXyMi1zUkWAAIGRAjJtpL57OwCxBwgEAobqAEnJcIGvDkAcfgxviwd4UMBQnWPLtdxKJQECBEYJyLVMXqZlTqoIECAwUkCmjdR3bwcg9gCBQMBQHSApGS7gAGR4CzzAYAFDdd4AuZZbqSRAgMAoAbmWycu0zEkVAQIERgrItJH67u0AxB4gEAgYqgMkJcMFHIAMb4EHGCxgqM4bINdyK5UECBAYJSDXMnmZljmpIkCAwEgBmTZS370dgNgDBAIBQ3WApKSEwMdDEP/5qxIt8RAPChiqc2y5llupJECAwCgBuZbJy7TMSRUBAgRGCsi0kfru7QDEHiAQCBiqAyQlJQQcgJRog4cYJGCozuHlWm6lkgABAqME/h9793pdSdJcBxSUGbSCTmjRAlkpC7TkhKyQGRotsAkOGgP0PVWVmZGP/bujHrkjByfqJj9JrmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFIyhYADkCna4CWKBAzVObxcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFIyhYADkCna4CWKBAzVObxcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFIyhYADkCna4CWKBAzVObxcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFIyhYADkCna4CWKBAzVObxcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFIyhYADkCna4CWKBAzVObxcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFIyhYADkCna4CWKBAzVObxcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFIyhcDHAci//+u/TfE+XoLASAFDda4t13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVep7tgMQe4BAIGCoDpCUTCHgAGSKNniJIgFDdQ4v13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVep7tgMQe4BAIGCoDpCUTCHgAGSKNniJIgFDdQ4v13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVep7tgMQe4BAIGCoDpCUlAv4//+jvAVeoFjAUJ03QK7lVioJECBQJSDXMnmZljmpIkCAQKWATKvU92wHIPYAgUDAUB0gKSkXcABS3gIvUCxgqM4bINdyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqe7YDEHuAQCBgqA6QlBAgQKBYwFCdN0Cu5VYqCRAgUCUg1zJ5mZY5qSJAgEClgEyr1PdsByD2AIFAwFAdICkhQIBAsYChOm+AXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2Q5A7AECgYChOkBSQoAAgWIBQ3XeALmWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37MdgNgDBAIBQ3WApIQAAQLFAobqvAFyLbdSSYAAgSoBuZbJy7TMSRUBAgQqBWRapb5nOwCxBwgEAobqAEkJAQIEigUM1XkD5FpupZIAAQJVAnItk5dpmZMqAgQIVArItEp9z3YAYg8QCAQM1QGSEgIECBQLGKrzBsi13EolAQIEqgTkWiYv0zInVQQIEKgUkGmV+p7tAMQeIBAIGKoDJCUECBAoFjBU5w2Qa7mVSgIECFQJyLVMXqZlTqoIECBQKSDTKvU92wGIPUAgEDBUB0hKCBAgUCxgqM4bINdyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqe7YDEHuAQCBgqA6QlBAgQKBYwFCdN0Cu5VYqCRAgUCUg1zJ5mZY5qSJAgEClgEyr1PdsByD2AIFAwFAdICkhQIBAsYChOm+AXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2Q5A7AECgYChOkBSQoAAgWIBQ3XeALmWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37MdgNgDBAgQIECAAAECBAgQIECAAAECBAgQIECAwJYC//LXX3/9teXKLIoAAQIECBAgQIAAAQIECBAgQIAAAQIECBA4VsAByLGtt3ACBAgQIECAAAECBAgQIECAAAECBAgQILCvgAOQfXtrZQQIECBAgAABAgQIECBAgAABAgQIECBA4FgBByDHtt7CCRAgQIAAAQIECBAgQIAAAQIECBAgQIDAvgIOQPbtrZURIECAAAECBAgQIECAAAECBAgQIECAAIFjBRyAHNt6CydAgAABAgQIECBAgAABAgQIECBAgAABAvsKOADZt7dWRoAAAQIECBAgQIAAAQIECBAgQIAAAQIEjhVwAHJs6y2cAAECBAgQIECAAAECBAgQIECAAAECBAjsK+AAZN/eWhkBAgQIECBAgAABAgQIECBAgAABAgQIEDhWwAHIsa23cAIECBAgQIAAAQIECBAgQIAAAQIECBAgsK+AA5B9e2tlBAgQIECAAAECBAgQIECAAAECBAgQIEDgWAEHIMe23sIJECBAgAABAgQIECBAgAABAgQIECBAgMC+Ag5A9u2tlREgQIAAAQIECBAgQIAAAQIECBAgQIAAgWMFHIAc23oLJ0CAAAECBAgQIECAAAECBAgQIECAAAEC+wo4ANm3t1ZGgAABAgQIECBAgAABAgQIECBAgAABAgSOFXAAcmzrLZwAAQIECBAgQIAAAQIECBAgQIAAAQIECOwr4ABk395aGQECBAgQIECAAAECBAgQIECAAAECBAgQOFbAAcixrbdwAgQIECBAgAABAgQIECBAgAABAgQIECCwr4ADkH17a2UECBAgQIAAAQIECBAgQIAAAQIECBAgQOBYAQcgx7bewgkQIECAAAECBAgQIECAAAECBAgQIECAwL4CDkD27a2VESBAgAABAgQIECBAgAABAgQIECBAgACBYwUcgBzbegsnQIAAAQIECBAgQIAAAQIECBAgQIAAAQL7CjgA2be3VkaAAAECBAgQIECAAAECBAgQIECAAAECBI4VcABybOstnAABAgQIECBAgAABAgQIECBAgAABAgQI7CvgAGTf3loZAQIECBAgQIAAAQIECBAgQIAAAQIECBA4VsAByLGtt3ACBAgQIECAAAECBAgQIECAAAECBAgQILCvgAOQfXtrZQQIECBAgAABAgQIECBAgAABAgQIECBA4FgBByDHtt7CCRAgQIAAAQIECBAgQIAAAQIECBAgQIDAvgIOQPbtrZURIECAAAECBAgQIECAAAECBAgQIECAAIFjBRyAHNt6CydAgAABAgQIECBAgAABAgQIECBAgAABAvsKOADZt7dWRoAAAQIECBAgQIAAAQIECBAgQIAAAQIEjhVwAHJs6y2cAAECBAgQIECAAAECBAgQIECAAAECBAjsK+AAZN/eWhkBAgQIECBAgAABAgQIECBAgAABAgQIEDhWwAHIsa23cAIECBAgQIAAAQIECBAgQIAAAQIECBAgsK+AA5B9e2tlBAgQIECAAAECBAgQIECAAAECBAgQIEDgWAEHIMe23sIJECBAgAABAgQIECBAgAABAgQIECBAgMC+Ag5A9u2tlREgQIAAAQIECBAgQIAAAQIECBAgQIAAgWMFHIAc23oLJ0CAAAECBAgQIECAAAECBAgQIECAAAEC+wo4ANm3t1ZGgAABAgQIECBAgAABAgQIECBAgAABAgSOFXAAcmzrLZwAAQIECBAgQIAAAQIECBAgQIAAAQIECOwr4ABk395aGQECBAgQIECAAAECBAgQIECAAAECBAgQOFbAAcixrbdwAgQIECBAgAABAgQIECBAgAABAgQIECCwr4ADkH17a2UECBAgQIAAAQIECBAgQIAAAQIECBAgQOBYAQcgx7bewgkQIECAAAECBAgQIECAAAECBAgQIECAwL4CDkD27a2VESBAgAABAgQIECBAgAABAgQIECBAgACBYwUcgBzbegsnQIAAAQIECBAgQIAAAQIECBAgQIAAAQL7CjgA2be3VkaAAAECBAgQIECAAAECBAgQIECAAAECBI4VcABybOstnAABAgQIECBAgAABAgQIECBAgAABAgQI7CvgAGTf3loZAQIECBAgQIAAAQIECBAgQIAAAQIECBA4VsAByLGtt3ACBAgQIECAAAECBAgQIECAAAECBAgQILCvgAOQfXtrZQQIECBAgAABAgQIECBAgAABAgQIECBA4FgBByDHtt7CCRAgQIAAAQIECBAgQIAAAQIECBAgQIDAvgIOQPbtrZURIECAAAECBAgQIECAAAECBAgQIECAAIFjBRyAHNt6CydAgAABAgQIECBAgAABAgQIECBAgAABAvsKOADZt7dWRoAAAQIECBAgQIAAAQIECBAgQIAAAQIEjhVwAHJs6y2cAAECBAgQIECAAAECBAgQIECAAAECBAjsK+AAZN/eWhkBAgQIECBAgAABAgQIECBAgAABAgQIEDhWwAHIsa23cAIECBAgQIAAAQIECBAgQIAAAQIECBAgsK+AA5B9e2tlBAgQIECAAAECBAgQIECAAAECBAgQIEDgWAEHIMe23sIJECBAgAABAgQIECBAgAABAgQIECBAgMC+Ag5A9u2tlREgQIAAAQIECBAgQIAAAQIECBAgQIAAgWMFHIAc23oLJ0CAAAECBAgQIECAAAECBAgQIECAAAEC+wo4ANm3t1ZGgAABAgQIECBAgAABAgQIECBAgAABAgSOFXAAcmzrLZwAAQIECBAgQIAAAQIECBAgQIAAAQIECOwr4ABk395aGQECBAgQIECAAAECBAgQIECAAAECBAgQOFbAAcixrbdwAgQIECBAgAABAgQIECBAgAABAgQIECCwr4ADkH17a2UECBAgQIAAAQIECBAgQIAAAQIECBAgQOBYAQcgx7bewgkQIECAAAECBAgQIECAAAECBAgQIECAwL4CDkD27a2VESBAgAABAgQIECBAgAABAgQIECBAgACBYwUcgBzbegsnQIAAAQIECBAgQIAAAQIECBAgQIAAAQL7CjgA2be3VkaAAAECBAgQIECAAAECBAgQIECAAAECBI4VcABybOstnAABAgQIECBAgAABAgQIECBAgAABAgQI7CvgAGTf3lpZI4H//t/+R6M7uQ0BAgQI9BL43//vf/a69Xb3lWvbtdSCCBDYUECuZU2VaZmTKgIECFQKyLRKfc9+F3AAYh8QeCFgqLZFCBAgML+AoTrvkVzLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79kOQOwBAoGAoTpAUkKAAIFiAUN13gC5llupJECAQJWAXMvkZVrmpIoAAQKVAjKtUt+zHYDYAwQCAUN1gKSEAAECxQKG6rwBci23UkmAAIEqAbmWycu0zEkVAQIEKgVkWqW+ZzsAsQcIBAKG6gBJCQECBIoFDNV5A+RabqWSAAECVQJyLZOXaZmTKgIECFQKyLRKfc92AGIPEAgEDNUBkhICBAgUCxiq8wbItdxKJQECBKoE5FomL9MyJ1UECBCoFJBplfqe7QDEHiAQCBiqAyQlBAgQKBYwVOcNkGu5lUoCBAhUCci1TF6mZU6qCBAgUCkg0yr1PdsBiD1AIBAwVAdISggQIFAsYKjOGyDXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6nu2AxB7gEAgYKgOkJQQIECgWMBQnTdAruVWKgkQIFAlINcyeZmWOakiQIBApYBMq9T3bAcg9gCBQMBQHSApIUCAQLGAoTpvgFzLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79kOQOwBAoGAoTpAUkJgU4H/9X//z3+t7N//9d82XeUeyzJU532Ua7mVSgIECFQJyLVMXqZlTqoIECBQKSDTKvU92wGIPUAgEDBUB0hKCGwo8Pnw4315DkDmbrKhOu+PXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2Q5A7AECgYChOkBSQmBxga+HHT8txyHIvI02VOe9kWu5lUoCBAhUCci1TF6mZU6qCBAgUCkg0yr1PdsBiD1AIBAwVAdISggsKJAeenxdmkOQOZttqM77ItdyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqe7YDEHuAQCBgqA6QlBBYTODu4cfHMh2CzNdwQ3XeE7mWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37MdgNgDBAIBQ3WApITA5AJPDzy+Ls8ByHwNN1TnPZFruZVKAgQIVAnItUxepmVOqggQIFApINMq9T3bAYg9QCAQMFQHSEoITCrQ+uDjY5kOQOZruKE674lcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFJCYBKBXgceX5fnAGSShn96DUN13hO5llupJECAQJWAXMvkZVrmpIoAAQKVAjKtUt+zHYDYAwQCAUN1gKSEwAQCow4/3pfqAGSChn95BUN13hO5llupJECAQJWAXMvkZVrmpIoAAQKVAjKtUt+zHYDYAwQCAUN1gKSEQKHAyIOPz8t0CFLY9G8ebajO+yHXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6nu2AxB7gEAgYKgOkJQQGCRQddjx3fIcgAxqevgYQ3UI9fb2JtdyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqe7YDEHuAQCBgqA6QOpa8/+Dth+aOwAvdeqbDjw82e3OeDWSoznsh13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVep7tgMQe4BAIGCoDpA6lPz0Y/f7D86f/80P0B3wJ7nljAceX2nsv0k2y9vbm6E674Vcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QOpQkh6AfDzaD9EdmlB4yxUOP9557LvCTfLl0YbqvBdyLbdSSYAAgSoBuZbJy7TMSRUBAgQqBWRapb5nOwCxBwgEAobqAKlDyd0fwP0g3aEZA295t+8DX/G3R9lvVfL/fK6hOu+FXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2Q5A7AECgYChOkBqXNLrR3A/VjduVKPb9ep3o9d7eRv76iXRkAJDdc4s13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVep7tgMQe4BAIGCoDpAal4z8QdyP142bF95uZI/DV7pdZg/dpmt6oaE655RruZVKAgQIVAnItUxepmVOqggQIFApINMq9T3bAYg9QCAQMFQHSA1LZvhh3A/aDRv66VYz9LbPyvz/BdLL9cp9DdW5llzLrVQSIECgSkCuZfIyLXNSRYAAgUoBmVap79kOQOwBAoGAoTpAalgy24/kDkOeNXe2fj5bzc9X2ye9ZPP7GqpzK7mWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37MdgNgDBAIBQ3WA1KhklR/L/dj9uuGr9PL1SrIKeyJz6lllqM515VpupZIAAQJVAnItk5dpmZMqAgQIVArItEp9z3YAYg8QCAQM1QFSo5LVfjT3o/c/G79aDxtt3f+4jf3QUvP6vQzVuZlcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QGpUsvKP56f/+L1y7xptXwcgrSBv3sdQncPJtdxKJQECBKoE5FomL9MyJ1UECBCoFJBplfqe7QDEHiAQCBiqA6RGJbv9iL7zochuvWqxhXfudwuf3vcwVOfCci23UkmAAIEqAbmWycu0zEkVAQIEKgVkWqW+ZzsAsQcIBAKG6gCpUcnuP6rv8AP57j16spV36O+T9Vdfa6jOOyDXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6nu2AxB7f7/5IgAAIABJREFUgEAgYKgOkBqVnPjj+uw/mp/Yk7vbefZe3l3XKtcZqvNOybXcSiUBAgSqBORaJi/TMidVcwm0/MbyDTJXb73N9wIyzc6oFviXv/7666/ql/B8AjMLGKrHdaflIDjurds8acbB9eR+3OnqjD28s45VrzFU552Ta7mVSgIECFQJyLVMXqZlTqrmEmj5neUbZK7eehsHIPbAnAIOQObsi7eaSMBQPaYZLYfAMW/c7ynVQ6xe3Ottdd/uvfU+V/mhKO+lXMutVBIgQKBKQK5l8jItc1J1TeDr91CrOb/3d1ar97ympZrAawGZ9tpIRV8BByB9fd19AwFD9Zgm9h4Gx6yi71N6DLTc2/asR4/avuG+dzNU572Va7mVSgIECFQJyLVMXqZlTqp+CfT+9vn6LdD7eVf76lvlqpj6VgIyrZWk+9wVcAByV851xwgYqse0erbhcMyqnz/lzhDL+rn7T3e4049+b3PWnQ3Veb/lWm6lkgABAlUCci2Tl2mZ0+lVo75/Pn8LjHrm3d76brkr57o7AjLtjpprWgo4AGmp6V5bChiq+7d19uGwv0C/J7wPtnz7+X53Zx8TY70/nmaozt3lWm6lkgABAlUCci2Tl2mZ04lVvoHadd33TTvLU+8k007t/DzrdgAyTy+8yaQChur+jTGc9jf2hHECPhDGWX9+kqE6d5druZVKAgQIVAnItUxepmVOJ1X5tuzXbd85/Wx3v7NM273D86/PAcj8PfKGxQKG6v4NMKT2N/aEcQI+DMZZOwC5Zy3X7rm5igABAiMF/FiUacu0zOmUKt+V4zrtm2ec9Q5Pkmk7dHHtNTgAWbt/3n6AgKG6P7JBtb+xJ4wT8DEwztoByD1ruXbPzVUECBAYKeDHokxbpmVOO1f5lpyju76B5ujDrG8h02btzDnv5QDknF5b6U0BQ/VNuAuXGVovYCmdXsDwX9MiQ3XuLtdyK5UECBCoEpBrmbxMy5x2q/L9OGdHfQfN2ZcZ3kqmzdCFs9/BAcjZ/bf6QMBQHSA9LDHAPgR0+XQChv/xLTFU5+ZyLbdSSYAAgSoBuZbJy7TMaeUq34rrds830bq9a/3mMq21qPtdFXAAclVM/XEChur+LTfU9jf2hLEChv2x3u9PM1Tn5nItt1JJgACBKgG5lsnLtMxp1Srfiat27u/39l20fg9brECmtVB0jycCDkCe6Ln2CAFDdf82G2z7G3vCeAHD/lhzQ3XuLddyK5UECBCoEpBrmbxMy5xWq/J9uFrHXr+vb6PXRjtXyLSdu7vG2hyArNEnb1koYKjui2+47evr7nUChvyx9obq3Fuu5VYqCRAgUCUg1zJ5mZY5rVLl23CVTj17T99Jz/xWvFqmrdi1vd7ZAche/bSaDgKG6g6on25pyO3r6+51Agb7sfaG6txbruVWKgkQIFAlINcyeZmWOc1e5Ztw9g71eT/fS31cZ7yrTJuxK2e9kwOQs/pttTcEDNU30C5cYti9gKV0OQFD/biWGapza7mWW6kkQIBAlYBcy+RlWuY0Y5XvwBm7UvtOvp1q/Xs+Xab11HXvRMABSKKk5mgBQ3W/9ht6+9m68xwChvhxfTBU59ZyLbdSSYAAgSoBuZbJy7TMabYq34GzdWSe9/H9NE8vWr6JTGup6V53BByA3FFzzVEChuo+7Tb09nF117kEDPDj+mGozq3lWm6lkgABAlUCci2Tl2mZ0wxVvv9m6MKa7+Cbas2+fX5rmbZ+D1dfgQOQ1Tvo/bsLGKr7EBuA+7i661wChvVx/TBU59ZyLbdSSYAAgSoBuZbJy7TMqbrKt191B9Z/vu+qtXso09bu3w5v7wBkhy5aQ1cBQ3UfXkNwH1d3nUvAoD6uH4bq3Fqu5VYqCRAgUCUg1zJ5mZY5VVX55quS3/O5vq3W7atMW7d3u7y5A5BdOmkd3QQM1X1oDcN9XN11PgGD+pieGKpzZ7mWW6kkQIBAlYBcy+RlWuY0ssp33kjtc5/lG2ut3su0tfq149s6ANmxq9bUVMBQ3ZTzv25mMO7j6q7zCRjOx/TEUJ07y7XcSiUBAgSqBORaJi/TMqeRVb7zRmqf/SzfWev0X6at06td39QByK6dta5mAobqZpS/3chg3MfVXecTMJiP6YmhOneWa7mVSgIECFQJyLVMXqZlTiOqfN+NUPaM7wR8b82/L2Ta/D3a/Q0dgOzeYet7LGCofkz47Q0MyH1c3XVOAUN5/74YqnNjuZZbqSRAgECVgFzL5GVa5jSiyvfdCGXP+EnA99bce0Omzd2fE97OAcgJXbbGRwKG6kd8P15sQO7j6q5zChjI+/fFUJ0by7XcSiUBAgSqBORaJi/TMqfeVb7tegu7/ysB31uvhGr/XabV+nv625sDELuAwAsBQ3WfLWJI7uPqrnMKGMj798VQnRvLtdxKJQECBKoE5FomL9Myp55Vvut66rr3VQHfXVfFxtTLtDHOnvKzgAMQu4OAA5CSPWBQLmH30CIBg3h/eEN1buzHotxKJQECBKoE5FomL9Myp15Vvul6ybrvEwHfXk/0+lwr0/q4umsu4AAkt1J5qIChun3jDcrtTd1xbgFDeP/+GKpzY7mWW6kkQIBAlYBcy+RlWubUq8p3XS9Z920h4BushWKbe8i0No7ucl/AAch9O1ceImCobt9og3J7U3ecW8Dw3b8/hurcWK7lVioJECBQJSDXMnmZljn1qPJN10PVPVsK+AZrqfnsXjLtmZ+rnws4AHlu6A6bCxiq2zfYsNze1B3nFjB89++PoTo3lmu5lUoCBAhUCci1TF6mZU6tq3zPtRZ1v14CvsN6yV67r0y75qW6vYADkPam7riZgKG6fUMNzO1N3XF+AcN33x4ZqnNfuZZbqSRAgECVgFzL5GVa5tSiyjdcC0X3qBLwLVYl/+u5Mq3W39Pf3hyA2AUEXggYqttuEYNzW093W0fA0N23V4bq3Feu5VYqCRAgUCUg1zJ5mZY5Pany/fZEz7WzCfgmq+mITKtx99S/BRyA2A0EHIAM3QMG6KHcHjaRgGG7bzMM1bmvH4tyK5UECBCoEpBrmbxMy5zuVvl2uyvnutkFfJuN7ZBMG+vtaf8UcABiVxBwADJ0Dxiih3J72EQChuy+zTBU575+LMqtVBIgQKBKQK5l8jItc7pa5Zvtqpj6FQV8n43rmkwbZ+1J3ws4ALEzCDgAGboHDNNDuT1sMgFDdr+GGKpzWz8W5VYqCRAgUCUg1zJ5mZY5XanyvXZFS+0OAr7R+ndRpvU39oQ/CzgAsUMIOAAZugcM1EO5PWxCAQN2n6YYqnNXPxblVioJECBQJSDXMnmZljmlVb7VUil1uwn4RuvbUZnW19fdXws4AHltpOJwAUN12w1gqG7r6W7rCRiu+/TMUJ27yrXcSiUBAgSqBORaJi/TMqe0yrdaKqVuVwHfan06K9P6uLprLuAAJLdSeaiAobpt4w3VbT3dbT0BQ3Wfnhmqc1e5llupJECAQJWAXMvkZVrmlFb5Vkul1O0s4HutfXdlWntTd7wm4ADkmpfqAwUM1W2bbqhu6+lu6wkYqPv0zFCdu8q13EolAQIEqgTkWiYv0zKnpMp3WqKk5hQB32xtOy3T2nq623UBByDXzVxxmIChum3DDdZtPd1tPQHDdJ+eGapzV7mWW6kkQIBAlYBcy+RlWub0qso32ish/36qgG+3Np2XaW0c3eW+gAOQ+3auPETAUN220Ybrtp7utp6AIbpPzwzVuatcy61UEiBAoEpArmXyMi1z+lOV77Pnhu6wr4Bvtza9lWltHN3lvoADkPt2rjxEwFDdttEG7Lae7raegCG6T88M1bmrXMutVBIgQKBKQK5l8jItc/qpyrfZMz9XnyHg++15n2Xac0N3eCbgAOSZn6sPEDBUt2uyAbudpTutLWCIbt8/Q3VuKtdyK5UECBCoEpBrmbxMy5y+q/Jtdt/OlWcK+Ia733eZdt/OlW0EHIC0cXSXjQUM1e2aa8huZ+lOawsYntv3z1Cdm8q13EolAQIEqgTkWiYv0zKnr1W+y+65uYrAu4Bvuev7QKZdN3NFWwEHIG093W1DAUN1u6YatNtZutPaAobm9v0zVOemci23UrmHQMX84e/8HnunchVyLdOXaZnT56qKv4nX39IVBOYWkPPX+iPTrnmpbi/gAKS9qTtuJmCobtdQw3Y7S3daW8DA3L5/hurcVK7lVirXEFh1vpAFa+yvqreUa5m8TMucPqpW/Xt5bZWqCYwRkOO5s0zLrVT2EXAA0sfVXTcSMFS3a6aBu52lO60tYFhu3z9DdW4q13IrlXMK7DxPyIc591zFW8m1TF2mZU7vVTv/7cwVVBJoKyC3M0+Zljmp6ifgAKSfrTtvImCobtNIA3cbR3fZR8Cw3LaXhurcU67lVirHC5gX/mwuO8bvyaonyrVMXqZlTv62Zk6qCNwVkM9/lpNpd3eW61oJOABpJek+2woYqtu01tDdxtFd9hEwJLftpaE695RruZXKvgJmg2e+cuSZ3+xXy7WsQzLttZO/ta+NVBB4KiCTHYA83UOu7yvgAKSvr7tvIGCobtNEg3cbR3fZR8CQ3LaXfijKPeVabqWyjYAZoI1jehf5kkrNXSfXsv7ItD87+fub7SNVBFoIyN+fFWVaix3mHk8EHIA80XPtEQKG6jZtNny3cXSXfQQMyG17aajOPeVabqXyvoDcv2/X8kpZ01Jz7L3kWuYt0xyAZDtFFYExAnL3e2eZNmb/ecrPAg5A7A4CLwQM1W22iB9C2ji6yz4ChuO2vTRU555yLbdS+bOAXF93d8ifNXon17I+yTR/p7OdoorAOAE5+09rmTZu/3nS9wIOQOwMAg5AhuwBP5QMYfaQhQQMxm2bZajOPf1YlFup/F1Alu+1I+TQ3P2Ua1l/ZNr3Tv5eZ/tHFYFeAjL2d1mZ1munuW8q4AAklVJ3rIChuk3rDeFtHN1lHwFDcdteGqpzT7mWW6n8W0CO77sb5NGcvZVrWV9k2j+d/L3O9o4qAr0F5OvfwjKt925z/1cCDkBeCfn34wUM1W22gEG8jaO77CNgIG7bS0N17inXcquTK+X2yd1/e5NR9f2Xa1kPZNrvTv52Z/tGFYGRAjL17U2mjdxxnvWdgAMQ+4LACwFDdZstYhhv4+guewkYhtv101CdW8q13OrESnl9Yte/X7OMqt0Lci3zl2l/O/n7ne0ZVQRGC8hTByCj95zn/VPAAYhdQcAByJA9YCAfwuwhiwkYhts1zA9FuaUfi3Krkyrl9EndvrdWmXXP7e5Vci2Tk2m/nPwNz/aLKgKVAifnqEyr3Hme/S7gAMQ+IOAAZMgeMJQPYfaQxQROHoJbt8pQnYv6sSi3OqFSPp/Q5XZrlFvtLF/dSa69Evr17zLN4Ue2U1QRmEPg1ByVaXPsv5PfwgHIyd239kjAUB0xvSzyA8tLIgUHCpw6APdotaE6V5VrudXOlXJ55+72X5v86m8s1zJjmeYAJNspqgjMIXBqfsq0OfbfyW/hAOTk7lt7JGCojpheFvmh5SWRggMFTh2Ae7TaUJ2ryrXcardKWbxbR+daj0xr2w+5lnmenGn+pmd7RBWB2QROzEuZNtsuPO99HICc13Mrvihw8lB9keqP5Qb0lprutYvAicNvr94ZqnNZuZZb7VQph3fq5txrkW1t+iPXMsdTM83f9Gx/qCIwq8BpWSnTZt2J57yXA5Bzem2lNwVOHapvcv14mSG9taj77SBw2uDbs2eG6lxXruVWq1fK3tU7uP77y7n7PZRrmd2pmebve7Y/VBGYVeC0fJRps+7Ec97LAcg5vbbSmwKnDtU3uRyAtIZzv60FTht8ezbTUJ3ryrXcatVKP4yt2rn931vu5T2Wa5nVqZnm73y2P1QRmF3glFyUabPvxP3fzwHI/j22wocCpw7VD9n+cbkhvbWo++0gcMrAO6JXhupcWa7lVitWytsVu3beO8u/1z2Xa6+N3itOzDR/57O9oYrAKgInZKJMW2U37vueDkD27a2VNRI4cahuRPfbbQzqPVTdcweBEwbeEX0yVOfKci23WqlSzq7ULe/6ISADf94Lci377+S0TPO3PtsXqgisJrB7Hsq01Xbkfu/rAGS/nlpRY4HThurGfP91O8N6L1n3XV1g92F3VH8M1bm0XMutVqmUsat0ynv+JCAL/ykj17L/Xk7LNH/vs32hisCKAjtnoUxbcUfu9c4OQPbqp9V0EDhtqO5A+B+3NKz3knXf1QV2HnRH9sZQnWvLtdxq1kqZOmtnvFcrAdn49ibXst10Uqb525/tCVUEVhbYNf9k2sq7co93dwCyRx+toqPASUN1R0YHID1x3XtpgV2H3NFNMVTn4nItt5qx0g9gM3bFO7UWkI0OQNI9dUqm+duf7gh1BNYX2DEDfautvy9XX4EDkNU76P27C5wyVPeGNLT3Fnb/VQV2HHAremGoztXlWm41W6Usna0j3qe3wMkZKdey3XVCpvnbn+0FVQR2Etgt/2TaTrtzzbU4AFmzb956oMAJQ/UIToP7CGXPWFFgt+G2qgeG6lxeruVWs1TK0Fk64T0qBU7LS7mW7bbdM83f/2wfqCKwm8BumSfTdtuh663HAch6PfPGgwV2H6pHcRreR0l7zmoCuw23Vf6G6lxeruVW1ZWys7oDnj+rwAnZKdey3bdzpsmAbA+oIrCrwE5ZJ9N23aXrrMsByDq98qZFAjsP1SNJDfAjtT1rJYGdBttKd0N1ri/XcqvKSrlZqe/ZqwjsnKFyLduFu2aaDMj6r4rA7gK75JxM232nzr8+ByDz98gbFgvsOlSPZjXEjxb3vFUEdhlqq70N1XkH5FpuVVUpM6vkPXdFgV1zVK5lu3HHTJMBWe9VEThBYJeMk2kn7Na51+gAZO7+eLsJBHYcqitYDfIV6p45u8AuA+0MzobqvAtyLbcaXSkrR4t73m4CO+WqXMt2546ZJguy3qsicILALrkm007YrXOv0QHI3P3xdhMI7DhUV7Aa5CvUPXN2gV0G2hmcDdV5F+RabjWyUk6O1PasnQV2yVa5lu3S3TJNFmR9V0XgJIEdck2mnbRj51yrA5A5++KtJhLYbaiuojXMV8l77swCOwyzs/gaqvNOyLXcalSljBwl7TmnCOyQr3It2627ZZo8yPquisBJAjLtpG5bay8BByC9ZN13G4Hdhuqqxhjmq+Q9d2aBHYbZWXz9UJR3Qq7lVr0rZWNvYfc/WWD1jJVr2e7dKdNkQtZzVQROFJBpJ3bdmlsKOABpqeleWwrsNFRXNshAX6nv2bMKrD7IzuTqh6K8G3Itt+pdKRt7C7v/yQKrZ6xcy3bvLpkmD7J+qyJwqoBMO7Xz1t1KwAFIK0n32VZgl6G6ukGG+uoOeP6MAqsPsjOZ+qEo74Zcy616VMrDHqruSeDPAivmrVzLdvUumSYbsn6rInCywIpZ9tEvmXbyzp1j7Q5A5uiDt5hYYJehuprYUF/dAc+fUWDlIXY2T0N13hG5llv1qJSHPVTdk4ADkFP3wA6ZJhdO3b3WTeCawMrfjr7VrvVadXsBByDtTd1xM4EdhuoZWmKwn6EL3mE2gZWH2NksDdV5R+RabtWjUh72UHVPAq8FVstcufa6p+8Vq2eaTMj6rIoAgV8Cq2XZR99kmh1cLeAApLoDnj+9wOpD9SzAhvtZOuE9ZhJYdYCdydBQfb0bcu26WYsr5GALRfcg8Fxglez1Y1HW65UzTS5kPVZFgMDvAqvk2Oe3lml2cbWAA5DqDnj+9AIrD9Uz4RrwZ+qGd5lFYMXhdRa7r+9hqM47I9dyqxaV8q+FonsQaCuwQv7Ktaznq2aabMj6q4oAgX8KrJBhvtXs3NkEHIDM1hHvM53AqkP1bJCG/Nk64n1mEVhxgJ3F7vN7+KEo74pcy62eVsq+p4KuJ9BPYPb8lWtZ71fMNNmQ9VYVAQLfC8yeX9+9tUyzm6sFHIBUd8DzpxdYcaieEdWgP2NXvNMMAisOsDO4fX0HQ3XeFbmWWz2tlH1PBV1PoJ/A7Pkr17Ler5ZpciHrqyoCBP4sMHuG+Vazg2cTcAAyW0e8z3QCqw3V0wH+5wsZ9mftjPeqFlhteK32+un5fijKOyPXcqu7lTLvrpzrCIwVmDmD5Vq2F1bKNNmQ9VQVAQKvBWbOr+/eXqa97qmKvgIOQPr6uvsGAisN1TNzG/hn7o53qxRYbXittPrTsw3VeWfkWm51p1Le3VFzDYE6gVlzWK5le2KVTJMNWT9VESCQCcyaXT+9vUzL+qqqn4ADkH627ryJwCpD9ezchv7ZO+T9qgRWG16rnF4911D9Sujvf5drudXVSll3VUw9gXqBWXNYrmV7Y5VMkw9ZP1URIJALzJpf361ApuV9VdlHwAFIH1d33UhglaF6dnJD/+wd8n5VAisNrlVGyXMN1YnSrxq5lltdqZRzV7TUEphLYMYslmvZHpk902RD1kdVBAjcE5gxvxyA3Oulq/oKOADp6+vuGwjMPlSvQmz4X6VT3nO0wCpD62iXq8/zQ1EuJtdyq6RSviVKagjMLzBbHsu1bM/MnmkyIuujKgIE7gnMll0/rUKm3euvq9oJOABpZ+lOmwrMPlSvwm74X6VT3nO0wCpD62iXq88zVOdici23SirlW6KkhsAaAjNlslzL9sysmSYbsv6pIkDgucBM2eUA5Hk/3aGPgAOQPq7uupHArEP1asQ+AlbrmPcdJbDCwDrK4slz/FCU68m13OpVpWx7JeTfCawlMFMmy7Vs78yaafIh658qAgTaCMyUX9+tSKa16bO73BdwAHLfzpWHCMw6VK/G7yNgtY5531ECsw+roxyePsdQnQvKtdzqT5VyrY2juxCYTWCWXJZr2c6YNdNkRNY/VQQItBGYJbt+Wo1Ma9Nnd7kv4ADkvp0rDxGYdahejd9HwGod876jBWYfWkd7XH2eoToXk2u51XeV8uyZn6sJrCAwQybLtWynzJZpMiLrmyoCBNoKzJBbf1qRTGvbb3e7LuAA5LqZKw4TmG2oXpXfx8CqnfPeowRmH1pHOdx9jqE6l5NruZUDkGdWriawqsAMmSzXst0zU6b53sl6pooAgT4CM2TXTyuTaX167q65gAOQ3ErloQIzDdUrt8AHwcrd8+6jBGYeWkcZ3H2OoTqXk2u51ddKWXbfzpUEVhOozmS5lu2YmTJNRmQ9U0WAQB+B6tz606pkWp+eu2su4AAkt1J5qMBMQ/XKLfBBsHL3vPsogZmH1lEGd59jqM7l5Fpu9blSjt1zcxWBVQWqM1muZTtnpkyTE1nPVBEg0E+gOrt+WplM69dzd84EHIBkTqoOFphpqF65DT4IVu6edx8pMOvQOtLgzrMM1bmaXMutPipl2HUzVxDYQaAyk+VatoNmyTQ5kfVLFQECfQUqc+tPK5Npffvu7q8FHIC8NlJxuMAsQ/XqbfBRsHoHvf9IgVkH15EGV59lqM7F5Fpu9V4pv655qSawm0BVJsu1bCfNkmmyIuuXKgIE+gpUZdarVcm0V0L+vbeAA5Dewu6/vMAsQ/XqkD4KVu+g9x8pMOvgOtLg6rMM1bmYXMutHIBcs1JNYEeBqkyWa9lumiHTfOdkvVJFgEB/garMerUymfZKyL/3FnAA0lvY/ZcXmGGoXh7R/wXtDi20hsECsw6vgxnixxmqY6o3uZZZ+UErc1JF4ASBikyWa9nOmiHT5EXWK1UECPQXqMirZFUyLVFS01PAAUhPXffeQmCGoXoHSB8GO3TRGkYLzDrAjnZInmeoTpR+1ci111Yy67WRCgInCVTksVzLdtgMmSYzsl6pIkBgjEBFZr1amUx7JeTfews4AOkt7P7LC8wwVK+O6KNg9Q56/0qBGQfYSo+fnm2ozrsi1/5sJbPyvaSSwEkCo/NYrmW7qzrTZEbWJ1UECIwTGJ1XycpkWqKkpqeAA5Ceuu69hUD1UL06oo+C1Tvo/WcQmHGIncHl8zsYqvOOyLWfrWRWvo9UEjhNYHQWy7Vsh1VmmszIeqSKAIGxAqPzKlmdTEuU1PQUcADSU9e9txCoHKp3APRhsEMXrWEGgRkH2RlcPt7BUJ13Q67900pW5ftHJYGTBUZmsVzLdlplpsmOrEeqCBAYKzAyq9KVybRUSl0vAQcgvWTddxuByqF6dUQfBat30PvPKDDjQDuDk6E674Jc+91KVuV7RyUBAm9vo3JYrmW7rSrTZEfWH1UECNQIjMqqdHUyLZVS10vAAUgvWffdRqBqqF4d0EfB6h30/jMLzDbQzmBlqM67INccgOS7RSUBAl8FRmWwXMv2XlWm+dbJ+qOKAIEagVFZla5OpqVS6noJOADpJeu+2whUDdWrA/ooWL2D3n92gdmG2movQ3XeAbn2t5WsyveNSgIEfgmMyl+5lu24ikyTHVlvVBEgUCcwKqvSFcq0VEpdLwEHIL1k3XcbgYqhenU8HwWrd9D7ryQw23BbZWeozuXl2tubnMr3i0oCBL4X6J2/ci3beaMzTX5kfVFFgECtQO+Muro6mXZVTH1rAQcgrUXdbzuB0UP16oA+ClbvoPdfVWC2IXe0o6E6F5drDkDy3aKSAIGfBHrnrlzL9t7oTPOtk/VFFQEC9QK9c+rKCmXaFS21PQQcgPRQdc+tBEYP1Svj+SBYuXvefSeBmYbdUa6G6lz69FyTVfleUUmAwJ8FeuatXMt23+hMkyFZX1QRIFAv0DOjrq5Opl0VU99awAFIa1H3205g9FC9MqAPgpW75913E5hp4B1ha6jOlU/ONTmV7xOVBAi8FuiZtXLttf97xehMkyNZX1QRIFAv0DOjrq5Opl0VU99awAFIa1H3205g9FC9IqAPgRW75p1PFJhpCG7tb6jORU/MNTmV7w+VBAhcF+iRr3It68PITJMlWU9UESAwh0CPbLq7Mpl2V851rQQcgLSSdJ/2nO01AAAgAElEQVRtBUYO1asi+hhYtXPe+2SBmQbiFn0wVOeKp+WajMr3hkoCBO4J9MhUuZb1YmSmyZOsJ6oIEJhDoEc23V2ZTLsr57pWAg5AWkm6z7YCI4fqFRF9CKzYNe9M4HuBmYbkqz0yVOdip+WanMr3hkoCBO4J9MhPuZb1YmSmyZOsJ6oIEJhDoEc23V2ZTLsr57pWAg5AWkm6z7YCI4fq1RB9BKzWMe9LoJ3ATAP1+6oM1XlvT8k1GZXvCZUECDwT6JGJci3ryahMkylZP1QRIDCPQI9surs6mXZXznWtBByAtJJ0n20FRg3VKwL6EFixa96ZQHuBV8P1x9+KV3VP3sxQneudkGvyKd8PKgkQaCfQMufkWtaXUZkmV7J+qCJAYB6Blpn0dFUy7amg658KOAB5Kuj67QVGDdUrQfoAWKlb3pXAWgJ3B3VDdd7nnXNNPuX7QCUBAn0E7ubY17eRa1l/RmWafMn6oYoAgXkEWuVRixXJtBaK7vFEwAHIEz3XHiEwaqheBdPwv0qnvCeBdQXuDOuG6rzfu+aafMr3gEoCBPoJ3Mmw795GrmU9GpVpMibrhyoCBOYRaJVHLVYk01oouscTAQcgT/Rce4TAqKF6BUyD/wpd8o4E1he4M6wbqvO+75hr8invv0oCBPoK3MkwByD3ezIi02TM/f64kgCBWoFWmfR0Fb7Vngq6/qmAA5Cngq7fXmDEUD07oqF/9g55PwJ7CdwZ1A3V+R7YJddkU95zlQQIjBO4k2EOQO73Z0SmyZv7/XElAQK1Aq0y6ekqfKs9FXT9UwEHIE8FXb+9wIihenZEQ//sHfJ+BPYSuDOoG6rzPbBDrsmlvN8qCRAYK3AnwxyA3O9R70yTN/d740oCBOYQaJVLT1bjW+2JnmtbCDgAaaHoHlsL9B6qZ8Uz7M/aGe9FYH+BO0O6oTrfF6vnmnzKe62SAIEagTs59vVN5VrWu96ZJnOyPqgiQGBegRaZ9HR1Mu2poOufCjgAeSro+u0Feg/VMwIa9GfsincicI7AnSHdUJ3vj1VzTTblPVZJgECtwJ0ccwByr2e9M0323OuLqwgQmEegRSY9XY1vtaeCrn8q4ADkqaDrtxfoPVTPBmjIn60j3ofAeQJ3hnRDdb5PVsw12ZT3VyUBAnMI3Mmyz28u17I+9s40+ZP1QRUBAnMLPM2kp6uTaU8FXf9UwAHIU0HXby/Qe6ieAdBgP0MXvAMBAh8CdwZ0Q3W+f1bKNfmU91UlAQJzCdzJMgcg13vYM9Nk0PV+uIIAgbkFnmbT3dX5Vrsr57pWAg5AWkm6z7YCPYfqajRDfXUHPJ8Aga8Cd4dyQ3W+l1bINfmU91MlAQJzCtzNs4/VyLWsrz0zTRZlPVBFgMA6Ak+z6e5KZdpdOde1EnAA0krSfbYV6DlUj0QzwI/U9iwCBO4K3B3KDdW5+NVc+y4/7vbpT28pp/IeqiRAYA2BJ38r5VrW46uZlt31V5VcuqKllgCBlQSe5NOddcq0O2quaSngAKSlpnttKdBzqB4FZngfJe05BAg8Fbg7jBuqc/mruXYlQ+7278oz8pWqJECAQK3A3b+J728t17LeXc207K4OQK44qSVAYD2BJ/l0Z7Uy7Y6aa1oKOABpqeleWwr0HKpbgvnxqKWmexEgUCHwZBA3VOcdu5prPfLlvdc97psrqCRAgMAYgbvZJtey/lzNtOyu/tcfqZM6AgTWFLibTXdXK9PuyrmulYADkFaS7rOtQK+h+imYH46eCrqeAIHZBJ4M4obqvJt3ck3m5L4qCRAg8FngbrbJtWwf3cm05M5yL1FSQ4DAygJ38+nOmmXaHTXXtBRwANJS0722FOg1VCdYBu9ESQ0BArsIPBnCDdX5Lniaa7Ipt1ZJgACBu9km17K98zTTfnqKrMv8VREgsK7A3Xy6s2KZdkfNNS0FHIC01HSvLQV6DNUG6i23ikURIPBA4OkAbqjO8Xvk2teny7m8HyoJENhb4G6+ybVsX/TKNDmW+asiQGBtgbsZdXXVMu2qmPrWAg5AWou633YCLYZqA/R228KCCBBoLPB0+DZU5w1pkWv5035VysGrYuoJENhF4G6+ybVsB/TINJmV2asiQGB9gbsZdXXlMu2qmPrWAg5AWou633YCd4dqg/N2W8GCCBDoKPB0+DZU5825m2v5E65VystrXqoJEFhP4E7GybWszz0yTS5l9qoIEFhf4E4+3Vm1TLuj5pqWAg5AWmq615YCd4dqg/OW28GiCBDoJPB0+DZU5425m2v5E65XyszrZq4gQGAdgTsZJ9ey/vbINJmU2asiQGAPgTsZdXXlMu2qmPrWAg5AWou633YCd4dqg/N2W8GCCBDoKPB08DZU5825m2v5E+5Vys17bq4iQGB+gTsZJ9eyvvbINHmU2asiQGAPgTsZdXXlMu2qmPrWAg5AWou633YCd4dqg/N2W8GCCBDoKPB08DZU5825m2v5E+5Xys77dq4kQGBegTsZJ9eyfvbINFmU2asiQGAfgTs5dWX1Mu2KltoeAg5Aeqi651YCT4Zqw/NWW8FiCBDoJNBi4DZU5815kmv5U+5Xys77dq4kQGBOgTs5J9eyXvbINDmU2asiQGAfgTs5dWX1Mu2KltoeAg5Aeqi651YCT4dqA/RW28FiCBDoINBi4DZU5415mmv5k55Vys9nfq4mQGAOgbsZJ9ey/vXINPmT2asiQGAvgbt5lSjItERJTU8BByA9dd17C4GnQ7UBeottYBEECHQSaDVoG6rzBj3NtfxJzytl6HNDdyBAoFbgbs7JtaxvPTJN9mT2qggQ2Evgbl4lCjItUVLTU8ABSE9d995C4OlQbYDeYhtYBAECnQRaDdqG6rxBT3Mtf9LzShn63NAdCBCoF7iTdXIt61uPTJM9mb0qAgT2EriTVamATEul1PUScADSS9Z9txF4OlQboLfZChZCgEAHgVaDtqE6b87TXMuf1K5SlrazdCcCBMYL3Mk6uZb1qUemyZzMXhUBAnsJ3MmqVECmpVLqegk4AOkl677bCLQYqg3R22wHCyFAoLFAq0HbUJ03pkWu5U9rUylH2zi6CwECNQJ3sk6uZb3qkWkyJ7NXRYDAXgJ3sioVkGmplLpeAg5Aesm67zYCrYZqg/Q2W8JCCBBoKNBq0DZU501plWv5E9tUytE2ju5CgMB4gTtZJ9eyPvXINHmT2asiQGA/gTt5lSjItERJTU8BByA9dd17C4FWQ7VBeovtYBEECDQUaDlgG6rzxrTKtfyJ7SplaTtLdyJAYJzAnbyTa1l/emSarMnsVREgsJ/AnbxKFGRaoqSmp4ADkJ667r2FQKuh2iC9xXawCAIEGgq0HLAN1XljWuVa/sS2lfK0rae7ESDQV+Bu1sm1rC89Mk3OZPaqCBDYT+BuZr2SkGmvhPx7bwEHIL2F3X95gVZDtUF6+a1gAQQINBZoOWAbqvPmtMq1/IntK2Vqe1N3JECgj8DdrJNrWT96ZJqMyexVESCwp8Dd3PqThkzbc6+stCoHICt1y7uWCLQcqg3TJS30UAIEJhVoOVwbqvMmt8y1/KltK+VpW093I0Cgn8DdrJNrWU96ZJqMyexVESCwp8Dd3HIAsud+2GVVDkB26aR1dBMwVHejdWMCBA4WaD1Y+6Eo30w9ci1/ertKP1C1s3QnAgT6CDzJOrmW9aRHpsmXzF4VAQJ7CjzJrp9EZNqee2WlVTkAWalb3rVEwFBdwu6hBAhsLtB6sDZU5xumR67lT29b6Ueqtp7uRoBAW4EnWSfXsl70yDTZktmrIkBgT4En2eUAZM89scOqHIDs0EVr6CrQY6h+f2GDdde2uTkBAhMLGKprm9Mr16pWJU+r5D2XAIE/CTzNOgcg2f7qkWlyJbNXRYDAngJP8+s7FZm2515ZaVUOQFbqlnctEegxVDsAKWmlhxIgMImAobq2Eb1yrWpVfqiqkvdcAgQcgNTvgR6ZJlfq++oNCBCoE/CtVmfvyf0EHID0s3XnTQR6DNUfNIbrTTaJZRAgcEnAUH2Jq3lxz1xr/rLBDWVpgKSEAIHhAk+zzv+1bNayHpkmVzJ7VQQI7CnwNL++U5Fpe+6VlVblAGSlbnnXEoEeQ7UDkJJWeigBApMIGKprG9Ez12pX5v95yWp/zydA4JdAi5zzY1G2m3pkmgOQzF4VAQJ7CrTIsK8yMm3PvbLSqhyArNQt71oi0GOodgBS0koPJUBgEgFDdW0jeuZa7cocgFT7ez4BAg5ARu+BHpnmAGR0Fz2PAIGZBHyrzdQN79JKwAFIK0n32Vagx1D9GcuAve3WsTACBH4QMFTXbo3euVa7Oocg1f6eT+B0gVYZ5/9aNttJrTPNt1nmrooAgX0FWuXYZyGZtu9+WWVlDkBW6ZT3LBNoPVR/XYghu6y1HkyAQJGAoboI/j8f2zvXalf36+mydYYueAcCZwq0yjg/FmX7p3WmyY/MXRUBAvsKtMoxByD77pEVV+YAZMWueeehAq2H6u9e3qA9tKUeRoBAsYChurYBI3KtdoUOQKr9PZ/AyQKtMs4BSLaLWmea77LMXRUBAvsKtMoxByD77pEVV+YAZMWueeehAq2HagcgQ9vnYQQITChgqK5tyohcq13hr6f7EWuGLngHAucJtMo4ByDZ3mmdabIjc1dFgMC+Aq1yzAHIvntkxZU5AFmxa955qEDrofqnlzdsD22rhxEgUCTQY6B+X4ofivKGjsq1/I36VcrWfrbuTIDA9wKtck6uZTusdabJjcxdFQEC+wq0yjEHIPvukRVX5gBkxa5556ECrYdqByBD2+dhBAhMJtBjoHYAcq3Jo3Lt2lv1q/ZjVj9bdyZA4HeBlhnnACTbXS0zTV5k5qoIENhboGWWfUjJtL33zAqrcwCyQpe8Y6lAy6H6TwsxcJe22cMJEBgk0GOgdgByrXmjcu3aW/Wrlq/9bN2ZAIG/BVrnmx+Lst3VMtPkRWauigCBvQVa55lvtb33yyqrcwCySqe8Z5lAy6HaAUhZGz2YAIFJBHoM1Ibqa80dlWvX3qpftR+0+tm6MwECDkCq90DLTJMX1d30fAIEZhDo8b3mUH+Gzp79Dg5Azu6/1QcCLYfqV48zdL8S8u8ECKws0GOY/vAwVOc7Y2Su5W/Vt1K+9vV1dwKnC/TIN7mW7aqWmSYrMnNVBAjsLSDT9u7vqatzAHJq5607Fmg5VCcPNXgnSmoIEFhRoMcw7QDk+k4YnWvX37DPFfK1j6u7EiDw9tYj3xyAZDurZabJicxcFQECewvItL37e+rqHICc2nnrjgVaDtXJQw3eiZIaAgRWFOgxTDsAub4TRufa9Tfsc4V87ePqrgQIOACp3AOtMk1GVHbRswkQmE2g9XebQ/3ZOnze+zgAOa/nVnxRoNVQnT7W8J1KqSNAYDWB1oP05/UbqvPdMDrX8jfrXylj+xt7AoHTBHplm1zLdlKrTJMPmbcqAgTOEGidbTLtjH0z8yodgMzcHe82hUCroTpdjOE7lVJHgMBqAq0HaQcg93bA6Fy795b9rpKz/WzdmcBpAnKtvuOtMk021PfSGxAgMI9A63xzADJPb099Ewcgp3beumOBVkN1/MC3tzcD+BUttQQIrCLQepB2AHKv8xW5du9N+10lZ/vZujOBkwTkWn23W2WaXKjvpTcgQGAegdb55gBknt6e+iYOQE7tvHXHAq2G6viBDkCuUKklQGAhgdaDtAOQe82vyLV7b9rvKj909bN1ZwKnCPTMtHdDPxZlO6lVpsmFzFsVAQJnCLTOOJl2xr6ZeZUOQGbujnebQqDVUH1lMQbwK1pqCRBYQaD1EP11zYbqfBdU5Fr+dmMq5ewYZ08hsKtA70xzAJLvnFaZJhdyc5UECOwv0DrnfKvtv2dmX6EDkNk75P3KBVoN1VcXYgi/KqaeAIGZBVoP0Q5A7ne7Ktfuv3H7K2Vse1N3JHCSQO9McwCS76ZWmSYXcnOVBAjsL9A65xyA7L9nZl+hA5DZO+T9ygVaDdVXF2IIvyqmngCBmQVaD9EOQO53uyrX7r9xvytlbT9bdyawq0DvPPtw82NRtoNaZJosyKxVESBwjkDrrJNp5+ydWVfqAGTWznivaQRaDNV3FmMQv6PmGgIEZhVoPUQ7ALnf6apcu//G/a6Utf1s3ZnArgK988wByLWd0yLTZME1c9UECOwv0DrrHIDsv2dmX6EDkNk75P3KBVoM1XcWYRC/o+YaAgRmFGg9QH+3RkN13vmqXMvfcFylrB1n7UkEdhEYkWnvVnIt2zEtMk0WZNaqCBA4R6B11sm0c/bOrCt1ADJrZ7zXNAIthuo7izGI31FzDQECMwq0HqAdgDzrclWuPXvrflfL23627kxgJ4ERWfbZy49F2e5pkWlyILNWRYDAOQKtM0+mnbN3Zl2pA5BZO+O9phFoMVTfWYxB/I6aawgQmFGg9QDtAORZl6ty7dlb97ta3vazdWcCOwmMyDIHINd3TItMkwPX3V1BgMDeAq0zzwHI3vtlhdU5AFmhS96xVKDFUH13AYbxu3KuI0BgJoHWA7QDkGfdrcy1Z2/e72p528/WnQnsIDAix746+bEo2zktMk0GZNaqCBA4R6B17sm0c/bOrCt1ADJrZ7zXNAIthuq7izGM35VzHQECMwm0HqAdgDzrbmWuPXvzflfL23627kxgB4EROeYA5N5OaZFpMuCevasIENhXoHXuOQDZd6+ssjIHIKt0ynuWCbQYqp+8vIH8iZ5rCRCYQaD1AO0A5FlXq3Pt2dv3u1re9rN1ZwIrC4zIMLl2f4e0yDR//+/7u5IAgT0FWmefA5A998lKq3IAslK3vGuJQIuh+umLG8qfCrqeAIFKgdYDtB+KnnVzhlx7toK+V8vcvr7uTmAlgRH59ZOHH4uyndIi0/zdz6xVESBwjkDr/JNp5+ydWVfqAGTWznivaQRaDNVPF2MofyroegIEKgVaD9AOQJ51c4Zce7aCvlfL3L6+7k5gJYER+eUA5NmOaJFp/u4/64GrCRDYT6B1/jkA2W+PrLYiByCrdcz7DhdoMVQ/fWlD+VNB1xMgUCnQeoB2APKsmzPk2rMV9L1a5vb1dXcCKwiMyK1XDn4seiX0699bZJq/+5m1KgIEzhFonYMy7Zy9M+tKHYDM2hnvNY1Ai6H66WIM5U8FXU+AQJVA6+H5p3UYqvMOz5Br+dvWVMrdGndPJTCLwKjs+tN65Vq2G1pkmr/5mbUqAgTOEOiRgTLtjL0z8yodgMzcHe82hUCLofrpQgzlTwVdT4BAlUCPAfq7tRiq8w7PkGv529ZVyt46e08mUCkwKrderVGuvRL69e8tMs3f+8xaFQECZwj0yEGZdsbemXmVDkBm7o53m0KgxVD9dCGG8qeCridAoEqgxwDtAORZN2fItWcrGHO17B3j7CkEZhMYlVuv1u3HoldCDkAyIVUECBC4JtAjB2XatR6obi/gAKS9qTtuJjDDD0V+hNlsU1kOgYMEegzQDkCebaAZcu3ZCsZdLX/HWXsSgRkERmVWslY/FiVK/hcgmZIqAgQI5AI9slCm5f4q+wg4AOnj6q4bCczwQ5EfYDbaUJZC4CCBHsPzT3yG6nxjzZBr+dvWV8rg+h54AwKjBEbm1qs1ybVXQr/+vUWm+TufWasiQOAMgR5ZKNPO2Dszr9IByMzd8W5TCLQYqp8uxFD+VND1BAhUCPQYnh2APO/kDLn2fBXj7iCDx1l7EoEqgZF5la7Rj0WZVItM83c+s1ZFgMAZAj0yUaadsXdmXqUDkJm7492mEGgxVD9diKH8qaDrCRCoEOgxPDsAed7JGXLt+SrG3UEGj7P2JAIVAiOz6sr6/FiUabXINH/nM2tVBAjsL9ArE2Xa/ntn9hU6AJm9Q96vXKDFUP10EYbyp4KuJ0CgQqDXAP3dWgzVeYdnyLX8besrZXB9D7wBgZ4CI7PqyjrkWqbVItP8nc+sVREgsL9Ar0yUafvvndlX6ABk9g55v3KBFkN1i0UYzFsougcBAiMFeg3QDkCedXGWXHu2irFXy+Cx3p5GYKTAyKy6si4/FmVaLTLN3/jMWhUBAnsL9MxDmbb33llhdQ5AVuiSdywVaDFUt1iAwbyFonsQIDBKoOcA7QDkWRdnybVnqxh7tQwe6+1pBEYIjM6pq2vyY1Em1irT/J3PvFURILCvQM9clGn77ptVVuYAZJVOec8ygVZD9dMFGMqfCrqeAIGRAj0HaAcgzzo5S649W8X4q+XweHNPJNBTYHROXV2LH4sysVaZ5m985q2KAIF9BXrmokzbd9+ssjIHIKt0ynuWCbQaqlsswGDeQtE9CBAYIdBzgHYA8qyDM+Xas5WMv1oOjzf3RAI9BEZn1J01+LEoU2uVaf6+Z96qCBDYV6BnNsq0fffNKitzALJKp7xnmUCrobrFAgzmLRTdgwCBEQI9B2gHIM86OFOuPVvJ+Kvl8HhzTyTQUmB0Nj15dz8WZXqtMs3f98xbFQEC+wr0zEiZtu++WWVlDkBW6ZT3LBNoNVS3WIDBvIWiexAgMEKg5wDtAORZB2fKtWcrGX+1HB5v7okEWgqMzqYn7+7HokyvZab5G5+ZqyJAYD+B3vko0/bbM6utyAHIah3zvsMFWg7VLV7eYN5C0T0IEOgt0HuI/vr+huq8o7PlWv7mc1TK4Tn64C0IXBEYnUlX3u2nWrmWKbbMNH/fM3NVBAjsJ9A7J2XafntmtRU5AFmtY953uEDLobrFyxvMWyi6BwECPQV6D9DfvbuhOu/obLmWv/kclXJ4jj54CwJXBCpy6cr7ybX7Wi0zzd/3+31wJQEC6wqMyEjfauvuj13e3AHILp20jm4CLYfqVi9pOG8l6T4ECPQQGDFEf31vQ3XeyRlzLX/7OSrl8Bx98BYEEoGKTEre61WNXHsl9OvfW2eav++ZuyoCBPYRGJGTMm2f/bLqShyArNo57z1MoPVQ3eLFDeYtFN2DAIFeAiOGaAcg97s3Y67dX03NlXK4xt1TCVwVqMijq+/4U70fizLJ1pnm73vmrooAgT0ERuWkTNtjv6y8CgcgK3fPuw8RaD1Ut3hpg3kLRfcgQKCXwKhB+vP7G6rzbs6Ya/nbz1Mpi+fphTch8J1ARRa17IRcyzR7ZJq/75m9KgIE1hcYlZUybf29svoKHICs3kHv312gx1Dd4qUN5i0U3YMAgdYCo4bor+9tqM47OWuu5SuYo1IOz9EHb0Fgx8OP9zXJtWxv98g0f98ze1UECKwvMOrbTaatv1dWX4EDkNU76P27C/QYqlu8tMG8haJ7ECDQWmDUEO0A5H7nZs21+yuqu1IW19l7MoE/CVRlUcuu+LEo0+yRaf62Z/aqCBBYX2BUXsq09ffK6itwALJ6B71/d4EeQ3Wrlzact5J0HwIEWgmMGqIdgNzv2My5dn9VdVfK4jp7TybwnUBVDrXuhh+LMtEemebvemavigCBtQVG5qVMW3uv7PD2DkB26KI1dBXoMVS3emHDeStJ9yFAoJXAyEH68zsbqvMOzpxr+SrmqZTF8/TCmxCoyqAe8nItU+2Raf6uZ/aqCBBYW2BkZsq0tffKDm/vAGSHLlpDV4EeQ3XLFzagt9R0LwIEngiMHKK/vqehOu/c7LmWr2SeSlk8Ty+8ybkClRnUQ12uZaq9Ms3f9cxfFQECawqMzkyZtuY+2emtHYDs1E1r6SLQa6hu9bKG81aS7kOAwFOB0YP05/c1VOfdmz3X8pXMVSmP5+qHtzlPoDKDemjLtUy1V6b5m575qyJAYE2B0Zkp09bcJzu9tQOQnbppLV0Eeg3VrV7WcN5K0n0IEHgqMHqQdgByr2Oz59q9VdVfJY/re+ANzhWozJ9e6n4symR7ZZq/6Zm/KgIE1hQYnZsybc19stNbOwDZqZvW0kWg11Dd6mUN560k3YcAgScCo4for+9qqM67N3uu5SuZs1Iuz9kXb7WnQHX29FSVa5luz0zz9zzrgSoCBNYSqMhOmbbWHtnxbR2A7NhVa2oq0HOobvGiBvMWiu5BgMBTgYpB+vM7G6rzDs6ea/lK5qyUy3P2xVvtKVCdPT1V5Vqm2zPT/D3PeqCKAIG1BCqyU6attUd2fFsHIDt21ZqaCvQcqlu9qOG8laT7ECBwR6BiiP76nobqvHMr5Fq+mjkr5fKcffFW+wjMkDu9NeVaJtwz0/wtz3qgigCBdQSq8lOmrbNHdn1TByC7dta6mgn0HKqbveTb25sBvaWmexEgcEWgapD+/I6G6rxjq+RavqI5K+XynH3xVnsIzJA7vSXlWibcO9P8Lc/6oIoAgfkFKrNTps2/P3Z/Qwcgu3fY+h4L9B6qH7/gf97AcN5K0n0IELgqUDlMf7yroTrv2iq5lq9o3krZPG9vvNnaAjPkTm9BuZYJ9840f8ezPqgiQGB+gcrslGnz74/d39AByO4dtr7HAr2H6scv+OkGBvSWmu5FgEAiUDlIf34/Q3XSrV81K+Vavqp5K2XzvL3xZmsJzJI3o9TkWiY9ItP8Hc96oYoAgXkFqjNUps27N055Mwcgp3TaOm8LjBiqb7/clwsN560k3YcAgVSgepj+eE9DddoxByC5VLtK+dzO0p3OFZglb0Z1QK5l0iO+1fwNz3qhigCBOQVmyE+ZNufeOOmtHICc1G1rvSUwYqi+9WLfXGQ4byXpPgQIpAIzDNTv72qoTjvmACSXalspo9t6uttZArNkzUh1uZZpj/pW8zc864cqAgTmE5ghQ2XafPvitDdyAHJax633ssCoofryi/1wgeG8laT7ECDwSmCGYfrjHQ3Vr7r197+vlmv5ytaolNNr9MlbziMwU9aMVJFrmfaoTPO3O+uHKgIE5hKYJUNl2lz74sS3cQByYtet+ZLAqKH60kv9odhw3krSfQgQeCUwy0D9/p6G6lfdcgCSC/WtlNN9fd19L4GZcjtYh/YAACAASURBVGa0rFzLxEd9q/nbnfVDFQECcwnMkqMyba59ceLbOAA5sevWfElg1FB96aVeFBvQW2q6FwECPwnMMlA7ALm2R1fMtWsrXKdaXq/TK286VmCmfBm78r+f5seiTH5Upvl7nfVDFQEC8wjMlKUybZ59ceqbOAA5tfPWHQuMGqrjFwoKDegBkhICBB4LGKofE5bcYMVcK4Ea8FB5PQDZI5YUmClfqgD9WJTJj8o0f6+zfqgiQGAegZmyVKbNsy9OfRMHIKd23rpjgVFDdfxCQaEBPUBSQoDAI4GZBur3hRiq83aumGv56taslNtr9s1btxeYLVvarzC/o1zLrEZmmr/VWU9UESAwh8BMmSrT5tgTJ7+FA5CTu2/tkcDIoTp6obDIgB5CKSNA4JbATAO1A5BrLVw1166tct1q+b1u77z5fYHZMuX+Stpd6ceizHJkpvn7nPVEFQEC9QKz5apMq98Tp7+BA5DTd4D1vxQYOVS/fJmLBYb0i2DKCRB4KTDbMP3xwobql637r4KVcy1f5fqVMnz9HlrBa4FZM+X1m/evkGuZ8ehM87c564sqAgTqBGbMVplWtx88+ZeAAxA7gcALgdFDdcuGGNBbaroXAQLvAjMO1O/vZajO9+fKuZavcv1KGb5+D63gzwKz5sksfZNrWSdGZ5q/zVlfVBEgUCcwY77KtLr94MkOQOwBApHA6KE6eqkLRYb0C1hKCRB4KTDjQO0A5GXbfitYPdeurXbtahm+dv+8vQOQJ3vAj0WZ3uhM83c564sqAgTqBGb8XpNpdfvBkx2A2AMEIoHRQ3X0UheKDOkXsJQSIPBSYMaB2gHIy7Y5ALlGNG21TJ+2NV4sEJg1P4JXLynxY1HGXvGt5m9x1htVBAiMF5g1a2Xa+L3gib8L+H8Cy44g8EKgYqhu2RQDektN9yJwtsCsA7UDkGv7cvVcu7baPatl+5593XlVM+fHrO5+LMo6U5Fp/gZnvVFFgMB4gVnzVqaN3wue6ADEHiBwSaBiqL70gkGxIT1AUkKAwEuBWQdqByAvW/dbwQ65dm3Fe1fL+L37u/LqZs6MFVz9WJR1qSrT/O3N+qOKAIFxAjPnrkwbtw886XsB/wsQO4PAC4Gqobp1YwzprUXdj8BZAjMP1A5Aru3FXXLt2qr3r5bz+/d4lRXOnherOPqxKOtUZab5u5v1SBUBAv0FZs9emdZ/D3jCnwUcgNghBByA2AMECBB4KWCofkm0TEHlj0XLIC3+on6UW7yBi77+7DmxGqsfi7KOVWaav7VZj1QRINBfYPYMlmn994AnOACxBwg8Eqgcqh+9+DcXG9Jbi7ofgXMEDNX79HqnXNunK31WIvf7uLrrL4HZc2H1PvmxKOtgdab5O5v1SRUBAn0FZs9kmda3/+7+WsD/AuS1kYrDBaqH6tb8hvTWou5H4AwBQ/U+fd4t1/bpTJ+VyP0+riffdfY82KU3fizKOlmdaf7GZn1SRYBAX4HZs1mm9e2/u78WcADy2kjF4QLVQ3VrfkN6a1H3I7C3wOzD9Ie+oTrfh7vlWr5yle8C5gD74IrAKhlwZU2r1Mq1rFMzZJq/q1mvVBEg0F5glZyWae17747XBByAXPNSfaDADEN1S3YDektN9yKwv4Cher8e75Zr+3VozIrMA2OcV37KKn//Vzb+07v7sSjr7AyZ5u9p1itVBAi0F1glq2Va+9674zUBByDXvFQfKDDDUN2S3YDeUtO9COwtsMpA/d4FQ3W+F3fLtXzlKr8TMBfYF58FVvq7v3vn5FrW4Vkyzd/SrF+qCBBoJ7BSZsu0dn13p3sCDkDuubnqIIFZhuqW5Ab0lpruRWBfAUP1nr3dMdf27NT4VZkPxpvP8sSV/t7PYtb7PfxYlAnPkmn+fmb9UkWAQBuB1XJbprXpu7vcF3AAct/OlYcIzDJUt+Y2pLcWdT8C+wmsNFgbqvP9t2uu5QIqEwFzQqK0R81Kf+v3EM9WIdcyp5kyzd/NrGeqCBB4LrBadsu05z13h2cCDkCe+bn6AIGZhurW3Ib01qLuR2APgdUG6nd1Q3W+93bOtVxB5VMBM8RTwZrrV/z7XiNV/1S5lvVgpkzzdzHrmSoCBJ4JrJjlMu1Zz139XMAByHNDd9hcYKahujW1Ib21qPsR2EPAUL1HH39axc65tnfn5l+duWLeHq34d31ezTFv5seizHmmTPM3MOuZKgIEngmsmOky7VnPXf1cwAHIc0N32FxgpqG6NbUhvbWo+xHYQ8BQvUcfHYDs3cdZV2e2mKszK/49n0uw7m38WJTZz/at5m9g1jdVBAjcF1gx22Xa/X67so2AA5A2ju6yscBsQ3UPaoN6D1X3JLCmwIoD9bu0oTrfbyfkWq6hsqeA+aKn7j/vverf77FK6zxNrmW9mjHT/O3LeqeKAIFrAivnvEy71mvV7QUcgLQ3dcfNBGYcqlsTG9Jbi7ofgTUFDNVr9u3qW5+Qa1dN1PcTMGP0s/2488p/u/vrrPsEPxZlvZs10/zty/qnigCBXGDlvJdpeZ9V9hFwANLH1V03Eph1qG5NbEhvLep+BNYSWHmgfpc2VOf77ZRcy0VUjhAwZ7RVXv1vdluNPe8m17K+zppp/uZl/VNFgEAmsHruy7Ssz6r6CTgA6WfrzpsIzDpU9+A1qPdQdU8CawgYqtfoU4u3PCnXWni5RzsBc8Zzy9X/Vj8XOOcOfizKej1rpvl7l/VPFQECmcDq+S/Tsj6r6ifgAKSfrTtvIjDrUN2L17DeS9Z9CcwrsPpA/S5rqM7312m5lsuoHClg3nitvcPf5terVPGTgFzL9sbMmebvXNZDVQQI/Flgh3lAptnl1QIOQKo74PnTC8w8VPfAM6j3UHVPAvMK7DBQOwC5tr9Oy7VrOqpHC5g7fhff5W/y6H204/P8WJR1dfZM8zcu66MqAgR+FthhNpBpdni1gAOQ6g54/vQCsw/VPQAN6j1U3ZPAnAI7DNQOQK7trRNz7ZqQ6gqBU2ePXf4GV+yZ3Z/px6Ksw7Nn2ql/27LuqSJA4JXALnOCTHvVaf/eW8ABSG9h919eYPahuhewYb2XrPsSmENgl2H6Q9NQne+rU3MtF1JZLbDDDLLb39jqPXHi8+Va1vUVMm2Hv2lZN1QRINBaYJd5Qqa13hnud1XAAchVMfXHCawwVPdoikG9h6p7EphHYJdh2gHI9T11aq5dl3LFTAIj55Ld/j7O1Efvkgv4sSizWiHTRv79ytRUESCwgsBO84hMW2HH7f2ODkD27q/VNRBYYahusMwfb2Fg76nr3gRqBHYaph2AXN9Dp+fadTFXECBAYLyAH4sy85UyzXdV1lNVBAi8ve32vSbT7OpqAQcg1R3w/OkFVhqqe2Ea1nvJui+B8QK7DdMOQK7vIbl23cwVBAgQGC3gx6JMfLVM812V9VUVgdMFdvtmk2mn7+j69TsAqe+BN5hcYLWhugenQb2HqnsSqBHYbZh2AHJ9H8m162auIECAwGgBPxZl4qtlmu+qrK+qCJwssOP3mkw7eUfPsXYHIHP0wVtMLLDaUN2L0rDeS9Z9CYwT2HGYdgByff/ItetmriBAgMBoAT8WZeIrZprvqqy3qgicKLDr95pMO3E3z7VmByBz9cPbTCiw4lDdi9Gw3kvWfQmMEdh1oH7XM1Tne0iu5VYqCRAgUCUg1zL5VTPNd1XW3x2rvs7j9sKOXb63Jt9q99xcRSARcACSKKk5WmDVobpX0wxovWTdl0A/gZ2H6Q81PxTl+0eu5VYqCRAgUCUg1zL5VTPNN1XW392qvpvJk73w+bqkfje3E9az+/eaTDthF8+9Rgcgc/fH200gsOpQ3YvOwNVL1n0J9BPYfaB+lzNU5/tHruVWKgkQIFAlINcy+ZUzzXdV1uOdqlocgLx72Ds77Yq3N99qe/XTauYUcAAyZ1+81UQCKw/VvRgNXL1k3ZdAe4ETBmoHINf2jVy75qWaAAECFQIOQDL11TPNd1XW5x2qfprJkz1w9+BkB7cT1nDC95pMO2Enz71GByBz98fbTSCw+lDdkzAZ1no+370JEPhZ4IRB+vPqDdX5fw1yLbdSSYAAgSoBuZbJ75BpvqmyXq9c9ae5/FX/n1y7stkJ737S95pMO2FHz71GByBz98fbTSCww1Ddk/HVwNbz2e5NgIADkA8BQ3X+X4Ncy61UEiBAoEpArmXyO2Sa76ms16tWvfqR+1X/n16/qtvu7/2qr7utX6bt1tH11uMAZL2eeePBAjsM1T3JXg1sPZ/t3gQIfC9w2kD9rmCozv9rkGu5lUoCBAhUCci1TH6XTPNNlfV7tap0Jv+p/+n17y720Dq740pf11nVn99Upu3SyXXX4QBk3d5580ECuwzVPbkMWz113ZvANYETB2oHINf2iFy75qWaAAECFQJ+LMrUd8o031RZz1epujKTtzgAcQiyxs64si/WWFH2ljItc1LVT8ABSD9bd95EYKehundLDO29hd2fwM8Cpw7THyKG6vy/DrmWW6kkQIBAlYBcy+R3yzTfU1nfZ6+6Ope3OgBxCDL3zri6L+ZezbW3k2nXvFS3F3AA0t7UHTcT2G2o7t0eQ3tvYfcn8E+Bk4dpByDX/4uQa9fNXEGAAIHRAn4sysR3yzTfUlnfZ666M5e3PABxCDLn7rizL+Zcyb23kmn33FzVTsABSDtLd9pUYLehekSbDO4jlD2DwC+B04dpByDX/0uQa9fNXEGAAIHRAn4sysR3zDTfUlnvZ6y6O5e3PgBxCDLX7ri7L+ZaxbO3kWnP/Fz9XMAByHNDd9hcYMehelTLDO+jpD3nVAHD9N+dN1Tn/xXItdxKJQECBKoE5Fomv3Om+ZbK9sAMVU9n8h4HIB8u9lHtDnm6N2rfvt3TZVo7S3e6J+AA5J6bqw4S2Hmo7t1Gw1ZvYfc/WcAw/Xv3DdX5fw1yLbdSSYAAgSoBuZbJ755pvqeyfVBd9XQu73kA8m5jH9XskKf7ouat+zxVpvVxdddcwAFIbqXyUIHdh+oRbTVwjVD2jJMEDNP/7LahOv8vQK7lVioJECBQJSDXMvkTMs23VLYXqqpazOW9D0AcgozfHS32xfi37vdEmdbP1p0zAQcgmZOqgwVOGKpHtNfgPkLZM04QMEx/32VDdb775VpupZIAAQJVAnItkz8h03xHZXuhoqrVXD7iAMQhyLgd0mpfjHvj/k+Saf2NPeHPAg5A7BACLwROGKpHbQLD+yhpz9lRwCD9564aqvNdL9dyK5UECBCoEpBrmfwJmeYbKtsLFVWt5vNRByAOQcbsklb7YszbjnmKTBvj7Ck/CzgAsTsIOAAp2QMG+RJ2D11QwACdNc1QnTm9V53wY1GuoZIAAQJzCsi1rC8nZZrvp2xPjKhqPZ+PPAD58LGf2u+U1vui/RvW3VGm1dl78i8BByB2AgEHICV7wMBVwu6hiwkYovOGGapzq5N+LMpVVBIgQGAuAbmW9eO0TPMNle2LnlW95vPvetvrWQ5B2u+Q3r1q/8Zj7yjTxnp72j8FHIDYFQQcgJTvAYN8eQu8wGQCBujrDTFU52an/ViUy6gkQIDAPAJyLevFiZnm2ynbG62res/nFQcgDkLa7JLee6PNW9beRabV+nu6/wWIPUDgpcCJQ/VLlA4FBvkOqG65rIAh+nrrDNW5mVzLrVQSIECgSkCuZfKnZppvp2x/tKzqPZ9XHoC8O9lT93ZL731x763mu0qmzdeT097I/wLktI5b72WBU4fqy1ANLjB0NUB0i6UFDND322eozu3kWm6lkgABAlUCci2TPznTfDtle6RF1YgZvfoA5MPJvsp3zIh9kb/N3JUybe7+nPB2DkBO6LI1PhI4eah+BPfwYoPXQ0CXLyVgeH7eLkN1bijXciuVBAgQqBKQa5m8TPN/uZ/tlHtVI2f0WQ5AHIS83isj98Xrt1mjQqat0aed39IByM7dtbYmAobqJoy3b+Ig5DadCxcRMEC3aZShOneUa7mVSgIECFQJyLVMXqY5AMl2yr2qkXP6bAcgDkK+3zMj98S9XTvnVTJtzr6c9FYOQE7qtrXeEjBU32JrfpGDkOakblgoYHBuj2+ozk3lWm6lkgABAlUCci2Tl2l/O/leyvbMq6qqOX3WA5DPXqfusao98WqvrvTvMm2lbu35rg5A9uyrVTUUMFQ3xGxwq1OHrgZ0bjGJgAG6TyMM1bmrXMutVBIgQKBKQK5l8jLtdyffStm++amqck5f4QDk3e20PVa5J57t5rmulmlz9ePEt3EAcmLXrfmSgKH6Etew4tMGr2GwHtRcwNDcnPTbGxqqc2e5llupJECAQJWAXMvkZdo/nXwnZXvna1X1zL7KAciH2wn7rHpP3NvJc14l0+bsy0lv5QDkpG5b6y0BQ/UttmEXnTB4DcP0oOYChubmpD/e0FCdW8u13EolAQIEqgTkWiYv07538o2U7Z+Pqhlm9tUOQHY/CJlhT1zbxXNXy7S5+3PC2zkAOaHL1vhIwFD9iG/YxYb8YdQe9ELAsFyzRQzVubtcy61UEiBAoEpArmXyMu1nJ99H2R6aZXZf9QDks/Iue26WPZHt4DWqZNoafdr5LR2A7Nxda2siYKhuwlhyk10GsBI8D40FDMgxVddCQ3XOK9dyK5UECBCoEpBrmbxMe+3km+h7o9lm+K99mu39Xu+07ytW2X+7eN/tU+/rZFpvYfd/JeAA5JWQfz9ewFC9/hZYZehaX/qsFRiS5+q3oTrvh1zLrVQSIECgSkCuZfIyLXPyPfTLaeb5/XOPZn7PbMf9XDXTXtzZ+WmfWl8v01qLut9VAQcgV8XUHydgqN6v5TMNXfvp7r0iQ/K8/TVU572Ra7mVSgIECFQJyLVMXqZlTp+rTvsWMr9f3yNVV/Tcm/ZBVVd/PVem1fp7+tubAxC7gMALAUP1/luk56C1v97+KzQsr9FjQ3XeJ7mWW6kkQIBAlYBcy+RlWub0teqE7x8z/L29MdtVd/aq3s/WRQcg83XkvDdyAHJez634ooCh+iLYBuV3hqwNlm0JXwQMzmttCT8U5f2Sa7mVSgIECFQJyLVMXqZlTj9V7frdY45/ti9cTaC1gExrLep+VwUcgFwVU3+cgKH6uJaXLnjXj5BKVB9Alfrjnm2ozq3lWm6lkgABAlUCci2Tl2mZU1K18neIeT/psBoCdQIyrc7ek38JOACxEwi8EDBU2yIzCKz8QdLazwdOa9E97meozvso13IrlQQIEKgSkGuZvEzLnK5UrfDd4XvgSkfVEqgXkGn1PTj9DRyAnL4DrP+lgKH6JZGCYoEVPlI+E/lgKd4wmz7eUJ03Vq7lVioJECBQJSDXMnmZljm1qhr53eGboVXX3IdAvYBMq+/B6W/gAOT0HWD9LwUM1S+JFEwkMPKj5MqyfcBc0VJ7R8BQnavJtdxKJQECBKoE5FomL9Myp15Vrb49fCv06pD7EphDQKbN0YeT38IByMndt/ZIwFAdMSmaSKDVh0iLJfmYaaHoHomAoTpR+lUj13IrlQQIEKgSkGuZvEzLnHpVtfru8M3Qq0PuS2AOAZk2Rx9OfgsHICd339ojAUN1xKRoYoFWHybpEn3ApFLqWgoYqnNNuZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLfs98FHIDYBwReCBiqbZEdBEYcgjj42GGnrLsGQ3XeO7mWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37MdgNgDBAIBQ3WApGQpgdaHIQ4+lmr/ti9rqM5bK9dyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqe7YDEHuAQCBgqA6QlCwn0OIQxMHHcm3f+oUN1Xl75VpupZIAAQJVAnItk5dpmZMqAgQIVArItEp9z3YAYg8QCAQM1QGSkiUFnh6COABZsu3bvrShOm+tXMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2Q5A7AECgYChOkBSsrTAlYMQhx5Lt3rrlzdU5+2Va7mVSgIECFQJyLVMXqZlTqoIECBQKSDTKvU92wGIPUAgEDBUB0hKlhdID0EcgCzf6m0XYKjOWyvXciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6nu2AxB7gEAgYKgOkJRsIfCnQxAHH1u0eOtFGKrz9sq13EolAQIEqgTkWiYv0zInVQQIEKgUkGmV+p7tAMQeIBAIGKoDJCVbCDgA2aKNxy7CUJ23Xq7lVioJECBQJSDXMnmZljmpIkCAQKWATKvU92wHIPYAgUDAUB0gKdlG4LtDEP/rj23au/VCDNV5e+VabqWSAAECVQJyLZOXaZmTKgIECFQKyLRKfc92AGIPEAgEDNUBkpJtBByAbNPK4xZiqM5bLtdyK5UECBCoEpBrmbxMy5xUESBAoFJAplXqe7YDEHuAQCBgqA6QlGwj4ABkm1YetxBDdd5yuZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLfsx2A2AMEAgFDdYCkZBsBByDbtPK4hRiq85bLtdxKJQECBKoE5FomL9MyJ1UECBCoFJBplfqe7QDEHiAQCBiqAyQl2wg4ANmmlcctxFCdt1yu5VYqCRAgUCUg1zJ5mZY5qSJAgEClgEyr1PdsByD2AIFAwFAdICnZRsAByDatPG4hhuq85XItt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvmc7ALEHCAQChuoASck2Ag5AtmnlcQsxVOctl2u5lUoCBAhUCci1TF6mZU6qCBAgUCkg0yr1PdsBiD1AIBAwVAdISrYRcACyTSuPW4ihOm+5XMutVBIgQKBKQK5l8jItc1JFgACBSgGZVqnv2Q5A7AECgYChOkBSso2AA5BtWnncQgzVecvlWm6lkgABAlUCci2Tl2mZkyoCBAhUCsi0Sn3PdgBiDxAIBAzVAZKSbQQcgGzTyuMWYqjOWy7XciuVBAgQqBKQa5m8TMucVBEgQKBSQKZV6nu2AxB7gEAgYKgOkJRsI+AAZJtWHrcQQ3XecrmWW6kkQIBAlYBcy+RlWuakigABApUCMq1S37MdgNgDBAIBQ3WApGQbAQcg27TyuIUYqvOWy7XcSiUBAgSqBORaJi/TMidVBAgQqBSQaZX6nu0AxB4gEAgYqgMkJdsIOADZppXHLcRQnbdcruVWKgkQIFAlINcyeZmWOakiQIBApYBMq9T3bAcg9gCBQMBQHSAp2Urg4xDk3//137Zal8XsLWCozvsr13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVep7tgMQe4BAIGCoDpCUECBAoFjAUJ03QK7lVioJECBQJSDXMnmZljmpIkCAQKWATKvU92wHIPYAgUDAUB0gKSFAgECxgKE6b4Bcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFJCgACBYgFDdd4AuZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLfsx2A2AMEAgFDdYCkhAABAsUChuq8AXItt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvmc7ALEHCAQChuoASQkBAgSKBQzVeQPkWm6lkgABAlUCci2Tl2mZkyoCBAhUCsi0Sn3PdgBiDxAIBAzVAZISAgQIFAsYqvMGyLXcSiUBAgSqBORaJi/TMidVBAgQqBSQaZX6nu0AxB4gEAgYqgMkJQQIECgWMFTnDZBruZVKAgQIVAnItUxepmVOqggQIFApINMq9T3bAYg9QCAQMFQHSEoIECBQLGCozhsg13IrlQQIEKgSkGuZvEzLnFQRIECgUkCmVep7tgMQe4BAIGCoDpCUECBAoFjAUJ03QK7lVioJECBQJSDXMnmZljmpIkCAQKWATKvU92wHIPYAgUDAUB0gKSFAgECxgKE6b4Bcy61UEiBAoEpArmXyMi1zUkWAAIFKAZlWqe/ZDkDsAQKBgKE6QFJCgACBYgFDdd4AuZZbqSRAgECVgFzL5GVa5qSKAAEClQIyrVLfsx2A2AMEAgFDdYCkhAABAsUChuq8AXItt1JJgACBKgG5lsnLtMxJFQECBCoFZFqlvmc7ALEHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgS0F/uWvv/76a8uVWRQBAgQIECBAgAABAgQIECBAgAABAgQIECBwrIADkGNbb+EECBAgQIAAAQIECBAgQIAAAQIECBAgQGBfAQcg+/bWyggQIECAAAECBAgQIECAAAECBAgQIECAwLECDkCObb2FEyBAgAABAgQIECBAgAABAgQIECBAgACBfQUcgOzbWysjQIAAAQIECBAgQIAAAQIECBAgQIAAAQLHCjgAObb1Fk6AAAECBAgQIECAAAECBAgQIECAAAECBPYVcACyb2+tjAABAgQIECBAgAABAgQIECBAgAABAgQIHCvgAOTY1ls4AQIECBAgQIAAAQIECBAgQIAAAQIECBDYV8AByL69tTICBAgQIECAAAECBAgQIECAAAECBAgQIHCsgAOQY1tv4QQIECBAgAABAgQIECBAgAABAgQIECBAYF8BByD79tbKCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAsQIOQI5tvYUTIECAAAECBAgQIECAAAECBAgQIECAAIF9BRyA7NtbKyNAgAABAgQIECBAgAABAgQIECBAgAABAscKOAA5tvUWToAAAQIECBAgQIAAAQIECBAgQIAAAQIE9hVwALJvb62MAAECBAgQIECAAAECBAgQIECAAAECBAgcK+AA5NjWWzgBAgQIECBAgAABAgQIECBAgAABAgQIENhXwAHIvr21MgIECBAgQIAAAQIECBAgQIAAAQIECBAgcKyAA5BjW2/hBAgQIECAAAECBAgQIECAAAECBAgQIEBgXwEHIPv21soIECBAgAABAgQIECBAgAABAgQIECBAgMCxAg5Ajm29hRMgQIAAAQIECBAg8P/buinVRwAAEsFJREFUs2MCAAAABGH9W9sDF8F5QoAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwICyO31hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAga6AANL91jICBAgQIECAAAECBAgQIECAAAECBAgQIHArIIDcXm84AQIECBAgQIAAAQIECBAgQIAAAQIECBDoCggg3W8tI0CAAAECBAgQIECAAAECBAgQIECAAAECtwIDkTO18Eb+8y0AAAAASUVORK5CYII=\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im, y = images_training_generator.__getitem__(2)\n",
    "print(im.shape)\n",
    "plt.figure(figsize=(8,8))\n",
    "for i in range(9):\n",
    "    ax = plt.subplot(3,3,i+1)\n",
    "    ax.axis('off')\n",
    "    plt.imshow(im[i,:,:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we define the images model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def images_model():\n",
    "    #image = Input(shape=(image_max_size, image_max_size, 1), name='image')\n",
    "    image = keras.engine.input_layer.Input(shape=(image_max_size, image_max_size, 1), name='image')\n",
    "    # Pass it through the first convolutional layer\n",
    "    x = Conv2D(12, (4, 4), activation = 'relu')(image)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))(x)\n",
    "\n",
    "    # Now through the second convolutional layer\n",
    "    x = (Conv2D(32, (4, 4),  activation = 'relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    # Now through the second convolutional layer\n",
    "    x = (Conv2D(32, (4, 4),  activation = 'relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    x = (Conv2D(16, (4, 4),  activation = 'relu'))(x)\n",
    "    x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    #x = (Conv2D(16, (5, 5),  activation = 'relu'))(x)\n",
    "    #x = (MaxPooling2D(pool_size=(2, 2)))(x)\n",
    "\n",
    "    # Flatten our array\n",
    "    x = Flatten()(x)\n",
    "    # Define the pre-extracted feature input\n",
    "    x = Dropout(.5)(x)\n",
    "\n",
    "    # Add a fully connected layer just like in a normal MLP\n",
    "    x = Dense(100, activation='relu',name='dense_for_ens')(x)\n",
    "\n",
    "    # Get the final output\n",
    "    out = Dense(n_cats, activation='softmax')(x)\n",
    "    # How we create models with the Functional API\n",
    "    model = Model(inputs=image, outputs=out)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "image (InputLayer)           (None, 512, 512, 1)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 509, 509, 12)      204       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 254, 254, 12)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 251, 251, 32)      6176      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 125, 125, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 122, 122, 32)      16416     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 61, 61, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 58, 58, 16)        8208      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling (None, 29, 29, 16)        0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 13456)             0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 13456)             0         \n",
      "_________________________________________________________________\n",
      "dense_for_ens (Dense)        (None, 100)               1345700   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 99)                9999      \n",
      "=================================================================\n",
      "Total params: 1,386,703\n",
      "Trainable params: 1,386,703\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "49/49 [==============================] - 3s 64ms/step - loss: 4.2770 - acc: 0.0804 - val_loss: 2.9543 - val_acc: 0.3229\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 2s 44ms/step - loss: 1.9245 - acc: 0.4860 - val_loss: 1.9913 - val_acc: 0.4688\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 1.0099 - acc: 0.7092 - val_loss: 1.9908 - val_acc: 0.5312\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.6272 - acc: 0.8138 - val_loss: 1.9339 - val_acc: 0.5312\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 2s 46ms/step - loss: 0.4735 - acc: 0.8406 - val_loss: 1.8308 - val_acc: 0.6146\n",
      "Epoch 6/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.2754 - acc: 0.9043 - val_loss: 1.8685 - val_acc: 0.5625\n",
      "Epoch 7/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.2690 - acc: 0.9158 - val_loss: 1.9166 - val_acc: 0.5729\n",
      "Epoch 8/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.1548 - acc: 0.9554 - val_loss: 2.1891 - val_acc: 0.5625\n",
      "Epoch 9/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.1034 - acc: 0.9707 - val_loss: 1.9981 - val_acc: 0.6354\n",
      "Epoch 10/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0834 - acc: 0.9770 - val_loss: 2.2450 - val_acc: 0.5729\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 11/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0389 - acc: 0.9834 - val_loss: 2.3042 - val_acc: 0.6146\n",
      "Epoch 12/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0307 - acc: 0.9936 - val_loss: 2.4771 - val_acc: 0.5729\n",
      "Epoch 13/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0147 - acc: 0.9974 - val_loss: 2.5127 - val_acc: 0.6042\n",
      "Epoch 14/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0114 - acc: 0.9962 - val_loss: 2.6556 - val_acc: 0.6250\n",
      "Epoch 15/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.0127 - acc: 0.9962 - val_loss: 2.6443 - val_acc: 0.6146\n",
      "\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 00015: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f6947583898>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The intention of this notebook is not to generate the **best** model, however here worths to mention that this model overfits and is not generalizing well.\n",
    "Note the difference between the accuracy of the training and the validation set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc', 'lr'])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = model.history.history\n",
    "history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6946eb2f98>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.subplot(3,1,2, sharex=ax1)\n",
    "plt.plot(history['acc'])\n",
    "plt.plot(history['val_acc'])\n",
    "plt.subplot(3,1,3, sharex=ax1)\n",
    "plt.plot(history['lr'], color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we use the keras image data generator for the augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_augmentator= ImageDataGenerator(\n",
    "        #featurewise_center=True,\n",
    "        #featurewise_std_normalization=True,\n",
    "        rotation_range=30,\n",
    "        zoom_range=0.1,\n",
    "        width_shift_range=0.02,\n",
    "        height_shift_range=0.02,\n",
    "        horizontal_flip=True,\n",
    "        vertical_flip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims, ys = test_augmentator.flow(X_img_tr, y_tr, batch_size=9).next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "for i in range(9):\n",
    "    ax = plt.subplot(3,3,i+1)\n",
    "    ax.axis('off')\n",
    "    plt.imshow(ims[i,:,:,0])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data generator with augmentation\n",
    "\n",
    "we make a generator with augmentation.\n",
    "\n",
    "The key point here compared with the generator from above is that in the method \\_\\_data_generation we implement the ImageDataGenerator from keras.\n",
    "\n",
    "**Note**: the performance of this generator is very poor compared with the other. Try to find out why!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "class ImageGenerator(keras.utils.Sequence):\n",
    "    'Generates data for Keras'\n",
    "    def __init__(self, features, targets, \n",
    "                 n_classes =99, \n",
    "                 batch_size=32,  \n",
    "                 shuffle=True, \n",
    "                 repeats = 1):\n",
    "        \n",
    "        \n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        # since we are using data agumentation we repeat the number of times we show each image.\n",
    "        # we show the same original image but it can be rotated or flipper each time, so it is not the \"same\" image\n",
    "        self.list_IDs = np.repeat(np.arange(self.n_vals),repeats) #OJO con esto, deberian ser las imagenes validas si queremos hacer bien las cosas\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        \n",
    "        self.agumentator = ImageDataGenerator(\n",
    "        #featurewise_center=True,\n",
    "        #featurewise_std_normalization=True,\n",
    "        rotation_range=30,\n",
    "        zoom_range=0.1,\n",
    "        width_shift_range=0.01,\n",
    "        height_shift_range=0.01,\n",
    "        horizontal_flip=True,\n",
    "        vertical_flip=True)\n",
    "          \n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        X, y = self.__data_generation(list_IDs_temp)\n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __data_generation(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "\n",
    "        X, y = self.agumentator.flow(X, y, batch_size=self.batch_size).next()\n",
    "        \n",
    "#        # Generate data\n",
    "#        for i, ID in enumerate(list_IDs_temp):\n",
    "#            # Store sample\n",
    "#            X[i,] = np.load('data/' + ID + '.npy')\n",
    "#\n",
    "#            # Store class\n",
    "#            y[i] = self.labels[ID]\n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### This is an attempt to speed up the augmentation process. \n",
    "\n",
    "I [opened an issue in keras](https://github.com/keras-team/keras/issues/12120) and I took this idea from there. For the moment it is not working fast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing.pool import ThreadPool\n",
    "\n",
    "import keras\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import OrderedEnqueuer\n",
    "\n",
    "\n",
    "class ImageGeneratorParallel(keras.utils.Sequence):\n",
    "    'Generates data for Keras'\n",
    "\n",
    "    def __init__(self, features, targets,\n",
    "                 n_classes=99,\n",
    "                 batch_size=32,\n",
    "                 shuffle=True,\n",
    "                 repeats=1,\n",
    "                 parallel = True):\n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        # since we are using data agumentation we repeat the number of times we show each image.\n",
    "        # we show the same original image but it can be rotated or flipper each time, so it is not the \"same\" image\n",
    "        self.list_IDs = np.repeat(np.arange(self.n_vals),\n",
    "                                  repeats)  # OJO con esto, deberian ser las imagenes validas si queremos hacer bien las cosas\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        self.pool = None\n",
    "        self.parallel = parallel\n",
    "\n",
    "        self.agumentator = ImageDataGenerator(\n",
    "            # featurewise_center=True,\n",
    "            # featurewise_std_normalization=True,\n",
    "            rotation_range=30,\n",
    "            zoom_range=0.1,\n",
    "            width_shift_range=0.01,\n",
    "            height_shift_range=0.01,\n",
    "            horizontal_flip=True,\n",
    "            vertical_flip=True)\n",
    "\n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        if self.parallel:\n",
    "            if self.pool is None:\n",
    "                self.pool = ThreadPool(4)\n",
    "        indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        if self.parallel:\n",
    "            X, y = self.__data_generation_threads(list_IDs_temp)\n",
    "        else:\n",
    "            X, y = self.__data_generation(list_IDs_temp)        \n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __data_generation(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples'  # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "        X = np.array(self.agumentator.flow(X, batch_size=self.batch_size, shuffle=self.shuffle).next())\n",
    "\n",
    "        return X, y\n",
    "\n",
    "    def __data_generation_threads(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples'  # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = self.features[list_IDs_temp]\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "        X = np.array(self.pool.map(lambda xi: self.agumentator.random_transform(self.agumentator.standardize(xi)), X))\n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "generate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are using different generators for the training and validation set.\n",
    "\n",
    "In the training set we use the same image `augmentation_image_repetition`\n",
    " times but the repetitions are different samples from the augmentator, so probably the differ each other and can be thought as different images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "augmentation_image_repetition = 3\n",
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,}\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "\n",
    "\n",
    "\n",
    "params['repeats'] = augmentation_image_repetition\n",
    "images_training_generator = ImageGenerator(X_img_tr,y_tr, **params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "148/148 [==============================] - 24s 162ms/step - loss: 4.4576 - acc: 0.0203 - val_loss: 3.5000 - val_acc: 0.0938\n",
      "Epoch 2/100\n",
      "148/148 [==============================] - 21s 139ms/step - loss: 2.9845 - acc: 0.1917 - val_loss: 2.5237 - val_acc: 0.3021\n",
      "Epoch 3/100\n",
      "148/148 [==============================] - 21s 141ms/step - loss: 2.3898 - acc: 0.3024 - val_loss: 2.1413 - val_acc: 0.3333\n",
      "Epoch 4/100\n",
      "148/148 [==============================] - 21s 143ms/step - loss: 2.1632 - acc: 0.3522 - val_loss: 1.9069 - val_acc: 0.4062\n",
      "Epoch 5/100\n",
      "148/148 [==============================] - 22s 148ms/step - loss: 1.8686 - acc: 0.4177 - val_loss: 1.7976 - val_acc: 0.4271\n",
      "Epoch 6/100\n",
      "148/148 [==============================] - 22s 148ms/step - loss: 1.7273 - acc: 0.4485 - val_loss: 1.5806 - val_acc: 0.5521\n",
      "Epoch 7/100\n",
      "148/148 [==============================] - 20s 138ms/step - loss: 1.5671 - acc: 0.4932 - val_loss: 1.7310 - val_acc: 0.4583\n",
      "Epoch 8/100\n",
      "148/148 [==============================] - 21s 145ms/step - loss: 1.4177 - acc: 0.5422 - val_loss: 1.3968 - val_acc: 0.5729\n",
      "Epoch 9/100\n",
      "148/148 [==============================] - 22s 149ms/step - loss: 1.3289 - acc: 0.5659 - val_loss: 1.4276 - val_acc: 0.4583\n",
      "Epoch 10/100\n",
      " 89/148 [=================>............] - ETA: 8s - loss: 1.1936 - acc: 0.6025"
     ]
    }
   ],
   "source": [
    "#model.fit_generator(images_training_generator, \n",
    "#                    validation_data=images_validation_generator,\n",
    "#                    epochs=100, \n",
    "#                    callbacks = callbacks\n",
    "#                   )\n",
    "\n",
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks,\n",
    "                    use_multiprocessing=True,\n",
    "                    max_queue_size=12,\n",
    "                    workers=4\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we improved the performance in the validation set. Although the model it still overfitting, the performance is much better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "history = model.history.history\n",
    "plt.figure()\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.subplot(3,1,2, sharex=ax1)\n",
    "plt.plot(history['acc'])\n",
    "plt.plot(history['val_acc'])\n",
    "plt.subplot(3,1,3, sharex=ax1)\n",
    "plt.plot(history['lr'], color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "one last try with more data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "augmentation_image_repetition = 5\n",
    "batch_size = 16\n",
    "\n",
    "model = images_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()\n",
    "\n",
    "\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,}\n",
    "images_validation_generator = DataGenerator(X_img_val,y_val, **params) # <-- no augmentation\n",
    "params['repeats'] = augmentation_image_repetition\n",
    "images_training_generator = ImageGenerator(X_img_tr,y_tr, **params)\n",
    "\n",
    "\n",
    "model.fit_generator(images_training_generator, \n",
    "                    validation_data=images_validation_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks,\n",
    "                    use_multiprocessing=True,\n",
    "                    max_queue_size=10,\n",
    "                    workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that using data augmentation with 5 repetitions makes the network to generalize better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "history = model.history.history\n",
    "plt.figure()\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.subplot(3,1,2, sharex=ax1)\n",
    "plt.plot(history['acc'])\n",
    "plt.plot(history['val_acc'])\n",
    "plt.subplot(3,1,3, sharex=ax1)\n",
    "plt.plot(history['lr'], color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('./model_image_5_repts.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features and Image generator\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we are going to build a generator that returns both, images and features.\n",
    "\n",
    "This generator is a mix of the two previous generators.\n",
    "The most complicated part is the image generation with augmentation, but above you can see an example of just this.\n",
    "\n",
    "#### Generator of multiple inputs!\n",
    "\n",
    "There is not much documentation online on how to make a generator that produces two inputs. The **key point** is in the `__getitem__` method: We have to return a tuple with a list of the inputs and the output value: `return ([Xf,Xi], y)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EnsembleGenerator(keras.utils.Sequence):\n",
    "\n",
    "    def __init__(self, x_tuple, targets, \n",
    "                 n_classes =99, \n",
    "                 batch_size=32,  \n",
    "                 shuffle=True,\n",
    "                 repeats = 1,\n",
    "                 validate = False):\n",
    "\n",
    "        # in validation mode we want the data as it comes, no agumentation or changes\n",
    "        self.validate = validate\n",
    "        if self.validate:\n",
    "            repeats = 1\n",
    "            \n",
    "        features, images = x_tuple[0], x_tuple[1]\n",
    "        self.n_classes = n_classes\n",
    "        self.n_vals = len(targets)\n",
    "        # since we are using data agumentation we repeat the number of times we show each image.\n",
    "        # we show the same original image but it can be rotated or flipper each time, so it is not the \"same\" image\n",
    "        self.list_IDs = np.repeat(np.arange(self.n_vals),repeats) #NOTE: this is not the best implementation for cases where you load the images from disk\n",
    "        self.batch_size = batch_size\n",
    "        self.features = features\n",
    "        self.images = images        \n",
    "        print('feature: {}'.format(self.features.shape))\n",
    "        print('images: {}'.format(self.images.shape))\n",
    "        self.shuffle = shuffle\n",
    "        self.targets = targets\n",
    "        self.targets_mc = keras.utils.to_categorical(targets, num_classes=self.n_classes)\n",
    "        self.indexes = np.arange(len(self.list_IDs)) #this is ok\n",
    "        \n",
    "        self.agumentator = ImageDataGenerator(\n",
    "                                    #featurewise_center=True,\n",
    "                                    #featurewise_std_normalization=True,\n",
    "                                    rotation_range=30,\n",
    "                                    zoom_range=0.1,\n",
    "                                    width_shift_range=0.01,\n",
    "                                    height_shift_range=0.01,\n",
    "                                    horizontal_flip=True,\n",
    "                                    vertical_flip=True)\n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "\n",
    "        # Generate data\n",
    "        Xi, yi = self.__image_generation(list_IDs_temp)        \n",
    "        Xf, yf = self.__feature_generation(list_IDs_temp)\n",
    "        if np.all(yf==yi):\n",
    "            y = yf\n",
    "        else:\n",
    "            print(np.argmax(self.targets_mc[list_IDs_temp],axis=1))\n",
    "            print(np.argmax(yf, axis=1))\n",
    "            print(np.argmax(yi, axis=1))\n",
    "            raise ValueError('not equal')\n",
    "                \n",
    "            \n",
    "        return ([Xf,Xi], y)\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = self.list_IDs\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "\n",
    "    def __feature_generation(self, list_IDs_temp):\n",
    "        y = self.targets_mc[list_IDs_temp]\n",
    "        X = self.features[list_IDs_temp]\n",
    "        return X, y\n",
    "        \n",
    "    def __image_generation(self, list_IDs_temp):\n",
    "        # Initialization\n",
    "        X = self.images[list_IDs_temp]\n",
    "        yr = self.targets_mc[list_IDs_temp]\n",
    "        if not self.validate:\n",
    "#            X, y = self.agumentator.flow(X, y, batch_size=self.batch_size).next()\n",
    "            X, y = self.agumentator.flow(X, yr, batch_size=len(list_IDs_temp), shuffle=False).next()            \n",
    "            if not np.all(y==yr):\n",
    "#                print('not equal after agumentator')\n",
    "                raise Exception('not equal after agumentator')\n",
    "        else:\n",
    "            y = yr\n",
    "        \n",
    "\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we initialize the generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "params = {'batch_size': batch_size,\n",
    "          'n_classes': 99,\n",
    "          'shuffle': True,\n",
    "         }\n",
    "params['repeats'] = 5\n",
    "ensemble_training_generator = EnsembleGenerator((X_num_tr,X_img_tr),y_tr, **params)\n",
    "params['validate']=True\n",
    "ensemble_validate_generator = EnsembleGenerator((X_num_val,X_img_val),y_val, **params)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ensemble_training_generator.features.shape)\n",
    "print(ensemble_training_generator.images.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition of the architecture of the ensemble\n",
    "\n",
    "Here we implement the ensemble based on the two previous networks. \n",
    "\n",
    "1. We use the defined architecture of the features, and we keep some of its layers\n",
    "2. We use some of the layers of the CNN used to analyse the images\n",
    "3. We ensemble this two architectures and add some more layers to generate the output.\n",
    "\n",
    "#### Implementation of the previous architectures\n",
    "Some comments:\n",
    "\n",
    "- To implement previous defined models/architectures there is a little workaround needed to use the input layer. \n",
    "> When loading an existing model, the `model.layers[0]` is not a tensor, and in order to build the model layer, we need a tensor. Thats why we implement the input layer in a different way using `model.input`. `model.input` returns a tensor and then everything works. \n",
    "\n",
    "- Also note that in the two other models images_model and features_model we added the name 'dense_for_ens'. We used this as a flag to determine were to break and stop adding layers for that model to the ensemble one. We do this for the two architectures.\n",
    "\n",
    "- Note that we ensemble the two models using a [Concatenate layer](https://keras.io/layers/merge/). In this point is important that the two input tensors have the same shape. \n",
    "> For example: We can not concatenate the output of a 2D convolutional with a Dense. We will be able to concatenate any layer after a Flatten (2D -> 1D) .\n",
    "\n",
    "- The last thing is that `Model` now has to inputs. When there are more than one input we pass the multiple inputs a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ensemble_generator():\n",
    "\n",
    "    # features architeture\n",
    "    f_model = features_model()    \n",
    "    feat_layers= []\n",
    "    ## this method does not works\n",
    "    #x = f_model.layers[0]\n",
    "    #feat_layers.append(x)\n",
    "    ## this method works\n",
    "    #input_layer = f_model.layers[0]\n",
    "    #x = Input(shape=input_layer.get_input_shape_at(0)[1:])\n",
    "    # this is the neatest way I've found so far to\n",
    "    input_layer = f_model.input\n",
    "    x = input_layer\n",
    "    feat_layers.append(x)\n",
    "    for lay in f_model.layers[1:]:\n",
    "        lay.name = 'feat_' + lay.name\n",
    "        x = lay(x)\n",
    "        feat_layers.append(x)  \n",
    "        if 'dense_for_ens' in lay.name:\n",
    "            break    \n",
    "    \n",
    "    # images architecture\n",
    "    i_model = images_model()\n",
    "    im_layers = []\n",
    "    x = i_model.input\n",
    "    im_layers.append(x)\n",
    "    for lay in i_model.layers[1:]:\n",
    "        lay.name = 'im_' + lay.name\n",
    "        x = lay(x)\n",
    "        im_layers.append(x)\n",
    "        if 'dense_for_ens' in lay.name:\n",
    "            break\n",
    "            \n",
    "    #ensemble architecture\n",
    "    xe = Concatenate(axis = -1)([feat_layers[-1], im_layers[-1]])\n",
    "    xe = Dropout(0.1)(xe)\n",
    "    xe = Dense(100, activation='relu', name='Dense_conc')(xe)\n",
    "    xe = Dense(50, activation='relu')(xe)\n",
    "\n",
    "    # Get the final output\n",
    "    out = Dense(n_cats, activation='softmax')(xe)\n",
    "\n",
    "    # generate model\n",
    "    model = Model(inputs=[feat_layers[0],im_layers[0]], outputs=out)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ens_model = ensemble_generator()\n",
    "ens_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "ens_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#feature = Input(shape=(192,), name = 'feature_input')\n",
    "#xf = Dense(100, activation = 'relu')(feature)\n",
    "#xf = Dropout(0.3)(xf)\n",
    "#xf = Dense(100, activation='relu')(xf)\n",
    "#\n",
    "#image = Input(shape=(image_max_size, image_max_size, 1), name='image_input')\n",
    "## Pass it through the first convolutional layer\n",
    "#xi = Conv2D(12, (6, 6), activation = 'relu')(image)\n",
    "#xi = (MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))(xi)\n",
    "#xi = (Conv2D(32, (4, 4),  activation = 'relu'))(xi)\n",
    "#xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "#xi = (Conv2D(32, (4, 4),  activation = 'relu'))(xi)\n",
    "#xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "#xi = (Conv2D(16, (4, 4),  activation = 'relu'))(xi)\n",
    "#xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "#if image_max_size>256:\n",
    "#    xi = (Conv2D(16, (4, 4),  activation = 'relu'))(xi)\n",
    "#    xi = (MaxPooling2D(pool_size=(2, 2)))(xi)\n",
    "#xi = Flatten()(xi) # Flatten our array\n",
    "#xi = Dropout(.5)(xi)\n",
    "#xi = Dense(100, activation='relu')(xi)\n",
    "#\n",
    "#xe = Concatenate(axis = -1)([xf, xi])\n",
    "#xe = Dropout(0.1)(xe)\n",
    "#xe = Dense(100, activation='relu', name='Dense_conc')(xe)\n",
    "#xe = Dense(50, activation='relu')(xe)\n",
    "#\n",
    "## Get the final output\n",
    "#out = Dense(n_cats, activation='softmax')(xe)\n",
    "## How we create models with the Functional API\n",
    "#model = Model(inputs=[feature,image], outputs=out)\n",
    "#model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "#model.summary()\n",
    "#\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ens_model.fit_generator(ensemble_training_generator, \n",
    "                    validation_data=ensemble_validate_generator,\n",
    "                    epochs=100, \n",
    "                    callbacks = callbacks,\n",
    "                    use_multiprocessing = True,\n",
    "                    #max_queue_size = 10,\n",
    "                    #workers=4\n",
    "                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the intention of this notebook was to discuss some points on the implementation of the ensemble and the fit generation, it is worth to notice that after ensembling, the performance of the model increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = ens_model.history.history\n",
    "plt.figure()\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.subplot(3,1,2, sharex=ax1)\n",
    "plt.plot(history['acc'])\n",
    "plt.plot(history['val_acc'])\n",
    "plt.subplot(3,1,3, sharex=ax1)\n",
    "plt.plot(history['lr'], color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last comments about performance\n",
    "\n",
    "to increase the performance (accuracy for example) of the model we could try to do some _transfer learning_: we can implement a resnet as the backbone of the images_model with it first layers frozen and just train the last fully connected layers and use this ones to feed the ensemble.\n",
    "\n",
    "Additionally we can try to generate a more complex architecture for the features model to increase the performance of this model and then feed the ensemble with one of these layers."
   ]
  }
 ],
 "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
}