classifier_3class_boundary_ndim_boundary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from tensorflow import set_random_seed\n",
    "# for reproducibility \n",
    "np.random.seed(1)\n",
    "set_random_seed(1)\n",
    "\n",
    "from keras.layers import Dense, Input, concatenate\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Lambda, Dropout, Activation, Lambda\n",
    "from keras.layers import Reshape, Conv2DTranspose\n",
    "from keras.models import Model, Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.datasets import mnist\n",
    "from keras.datasets import fashion_mnist\n",
    "from keras.losses import mse, binary_crossentropy\n",
    "from keras.utils import plot_model\n",
    "from keras import backend as K\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a classifier with outlier samples only from the boundary.\n",
    "\n",
    "class Classifier:\n",
    "\n",
    "    # 3 Class classifier - class 2 represents outliers and class 0,1 represent inliers.\n",
    "    # Class 0 sampled from a circle centered at (x, y) = (-2, 0) with radius 0.5\n",
    "    # Class 1 sampled from a circle centered at (x, y) = (+2, 0) with radius 0.5\n",
    "    # Class 2 sampled from rectagle with origin (x, y) = (-3, -1) with h=2, w=6 excluding regions covered by inliers.\n",
    "\n",
    "    # Generate n points uniformly within a circle of radius r, centered at (x, y)\n",
    "    # return as a numpy array n x 2 of dtype = float32\n",
    "    def __init__(self):\n",
    "        self.number_of_classes = 3\n",
    "        self.input_dim = 2\n",
    "        self.epochs = 200\n",
    "        self.batch_size = 32\n",
    "        # There are 2 inlier circles\n",
    "        # Inlier circle centers on the 1st axis; for all other dimensions, the centers are at 0.\n",
    "        self.x1 = -10\n",
    "        self.x2 = +10\n",
    "        self.r = 2.0\n",
    "        # Number of inlier samples for each class\n",
    "        self.n = 1000\n",
    "        # width of the outlier outer square side\n",
    "        self.s = 100\n",
    "        self.data, self.labels = self.get_data()\n",
    "        self.data_outlier, self.labels_outlier = self.get_outlier_data()\n",
    "        print(self.data.shape, self.labels.shape)\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.data, self.labels, test_size=0.5, shuffle=True, stratify=self.labels,\n",
    "                                                            random_state=1)\n",
    "        self.X_test = np.concatenate((self.X_test, self.data_outlier), axis=0)\n",
    "        self.y_test = np.concatenate((self.y_test, self.labels_outlier), axis=0)\n",
    "        self.model = self.get_model()\n",
    "\n",
    "        self.mu = np.mean(self.X_train, axis=0)\n",
    "        self.sigma = np.std(self.X_train, axis=0)\n",
    "\n",
    "        self.data = self.preprocess_data(self.data)\n",
    "        self.data_outlier = self.preprocess_data(self.data_outlier)\n",
    "        self.X_train = self.preprocess_data(self.X_train)\n",
    "        self.X_test = self.preprocess_data(self.X_test)\n",
    "\n",
    "        self.Y_train = np_utils.to_categorical(self.y_train, self.number_of_classes)\n",
    "        self.Y_test = np_utils.to_categorical(self.y_test, self.number_of_classes)\n",
    "\n",
    "    # return True if point p (a list) lies inside a hypersphere centered at the origin with radius r\n",
    "    def is_in_hypersphere(self, r, p):\n",
    "        p = np.array(p)\n",
    "        return np.sum(p*p) <= r**2\n",
    "\n",
    "\n",
    "    # return True if point p (a list) lies inside a hypersphere centered at the (x, 0, 0, ...) with radius r\n",
    "    def is_in_hypersphere_centered_at(self, x, r, p):\n",
    "        p = np.array(p)\n",
    "        p[0] -= x\n",
    "        return np.sum(p*p) <= r**2\n",
    "\n",
    "\n",
    "    # uniformly sample a point from a square centered at the origin with side = s\n",
    "    def sample_point_from_square(self, s):\n",
    "        p = []\n",
    "        # first point\n",
    "        for i in range(0, self.input_dim):\n",
    "            p.append(np.random.uniform(-1*s/2, s/2, 1))\n",
    "        return p\n",
    "\n",
    "\n",
    "    def preprocess_data(self, x):\n",
    "        #return (x-self.mu)/self.sigma\n",
    "        return x\n",
    "\n",
    "\n",
    "    # sample points from 2D gaussian centered at origin\n",
    "    def sample_point_from_gaussian(self, sigma):\n",
    "        mean = np.zeros(self.input_dim)\n",
    "        cov = np.zeros((self.input_dim, self.input_dim), np.float32)\n",
    "        np.fill_diagonal(cov, sigma*sigma)\n",
    "        p = list(np.random.multivariate_normal(mean, cov, 1).T.squeeze())\n",
    "        return p\n",
    "\n",
    "        \n",
    "    # sample from a hypersphre with rejection sampling\n",
    "    def get_gaussian_data(self, x, r, n):\n",
    "        data = np.zeros((n, self.input_dim))\n",
    "        count = 0\n",
    "        while count<n:\n",
    "            data[count, :] = self.sample_point_from_gaussian(r)\n",
    "            # update the point with offset for the 1st dimension\n",
    "            data[count, 0] += x\n",
    "            count += 1\n",
    "\n",
    "        data = data.astype(np.float32)\n",
    "        return data\n",
    "\n",
    "\n",
    "    # generate n outlier points uniformly sampled within square centered at the origin\n",
    "    # with side=s, excuding the inlier circles\n",
    "    def get_outlier_data_sub(self, s, x1, x2, r, n):\n",
    "        count = 0\n",
    "        data = np.zeros((n, self.input_dim))\n",
    "        while count < n:\n",
    "            p = self.sample_point_from_square(s)\n",
    "            if (not self.is_in_hypersphere_centered_at(x1, r, p)) and (not self.is_in_hypersphere_centered_at(x2, r, p)):\n",
    "                data[count, :] = p\n",
    "                count += 1\n",
    "        return data\n",
    "\n",
    "    def get_outlier_data(self):\n",
    "        data = self.get_outlier_data_sub(self.s, self.x1, self.x2, self.r*3, self.n/2)\n",
    "        labels = np.ones((self.n/2, 1))*2\n",
    "        return data.astype(np.float32), labels.astype(np.float32)\n",
    "\n",
    "\n",
    "    # gets a point sampled from region within concentric cercles centered at the origin with radii r1 and r2. \n",
    "    def get_random_sample_from_strip(self, r1, r2):\n",
    "        p = self.sample_point_from_square(2*r2)\n",
    "        while (not self.is_in_hypersphere(r2, p)) or (self.is_in_hypersphere(r1, p)):\n",
    "            p = self.sample_point_from_square(2*r2)\n",
    "        return p\n",
    "\n",
    "\n",
    "    def get_boundary_outlier_data(self, x1, x2, r, n):\n",
    "        count = 0\n",
    "        data = np.zeros((n, self.input_dim))\n",
    "        # boundary of first circle\n",
    "        while count < n/2:\n",
    "            data[count, :] = self.get_random_sample_from_strip(self.r*3, self.r*3+1)\n",
    "            # update the point with offset for the 1st dimension\n",
    "            data[count, 0] += x1 \n",
    "            count += 1\n",
    "        # boundary of second circle\n",
    "        while count < n:\n",
    "            data[count, :] = self.get_random_sample_from_strip(self.r*3, self.r*3+1)\n",
    "            # update the point with offset for the 1st dimension\n",
    "            data[count, 0] += x2 \n",
    "            count += 1\n",
    "        return data\n",
    "\n",
    "\n",
    "    def get_data(self):\n",
    "        # class 0 data\n",
    "        data = self.get_gaussian_data(self.x1, self.r, self.n)\n",
    "        labels = np.ones((self.n, 1))*0\n",
    "        print(labels.shape)\n",
    "        # class 1 data\n",
    "        data = np.concatenate((data, self.get_gaussian_data(self.x2, self.r, self.n)), axis=0)\n",
    "        labels = np.concatenate((labels, np.ones((self.n, 1))*1), axis=0)\n",
    "        print(labels.shape)\n",
    "        # boundary outlier data\n",
    "        data = np.concatenate((data, self.get_boundary_outlier_data(self.x1, self.x2, self.r, self.n)), axis=0)\n",
    "        labels = np.concatenate((labels, np.ones((self.n,1))*2), axis=0)\n",
    "        print(labels.shape)\n",
    "        return data.astype(np.float32), labels.astype(np.float32)\n",
    "    \n",
    "    \n",
    "    def get_model(self):\n",
    "        model = Sequential()\n",
    "        model.add(Dense(500, input_shape=(self.input_dim,)))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(500))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(self.number_of_classes))\n",
    "        # model.add(Convolution2D(10,3,3, border_mode='same'))\n",
    "        # model.add(GlobalAveragePooling2D())\n",
    "        model.add(Activation('softmax'))\n",
    "        model.compile(loss='binary_crossentropy', optimizer=Adam(0.001), metrics=['accuracy'])\n",
    "        return model\n",
    "\n",
    "\n",
    "    def train(self):\n",
    "        # fit the model\n",
    "        self.model.fit(self.X_train, self.Y_train, epochs=self.epochs, validation_data=(self.X_test, self.Y_test), batch_size=self.batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1)\n",
      "(2000, 1)\n",
      "(3000, 1)\n",
      "((3000, 2), (3000, 1))\n",
      "Train on 1500 samples, validate on 2000 samples\n",
      "Epoch 1/200\n",
      "1500/1500 [==============================] - 0s 218us/step - loss: 0.3738 - acc: 0.8347 - val_loss: 0.3801 - val_acc: 0.8732\n",
      "Epoch 2/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.3364 - acc: 0.8711 - val_loss: 0.3914 - val_acc: 0.8747\n",
      "Epoch 3/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.3338 - acc: 0.8789 - val_loss: 0.3720 - val_acc: 0.8808\n",
      "Epoch 4/200\n",
      "1500/1500 [==============================] - 0s 98us/step - loss: 0.3218 - acc: 0.8764 - val_loss: 0.2992 - val_acc: 0.8912\n",
      "Epoch 5/200\n",
      "1500/1500 [==============================] - 0s 91us/step - loss: 0.3186 - acc: 0.8836 - val_loss: 0.3219 - val_acc: 0.8838\n",
      "Epoch 6/200\n",
      "1500/1500 [==============================] - 0s 88us/step - loss: 0.2840 - acc: 0.8909 - val_loss: 0.2765 - val_acc: 0.8972\n",
      "Epoch 7/200\n",
      "1500/1500 [==============================] - 0s 91us/step - loss: 0.2875 - acc: 0.8840 - val_loss: 0.2265 - val_acc: 0.9103\n",
      "Epoch 8/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.2627 - acc: 0.8940 - val_loss: 0.2247 - val_acc: 0.9022\n",
      "Epoch 9/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.2553 - acc: 0.8989 - val_loss: 0.1941 - val_acc: 0.9198\n",
      "Epoch 10/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.2372 - acc: 0.9007 - val_loss: 0.1744 - val_acc: 0.9183\n",
      "Epoch 11/200\n",
      "1500/1500 [==============================] - 0s 95us/step - loss: 0.2049 - acc: 0.9184 - val_loss: 0.1553 - val_acc: 0.9243\n",
      "Epoch 12/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.1693 - acc: 0.9344 - val_loss: 0.1184 - val_acc: 0.9690\n",
      "Epoch 13/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.1412 - acc: 0.9482 - val_loss: 0.1143 - val_acc: 0.9600\n",
      "Epoch 14/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.1107 - acc: 0.9656 - val_loss: 0.0932 - val_acc: 0.9643\n",
      "Epoch 15/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.1054 - acc: 0.9653 - val_loss: 0.0860 - val_acc: 0.9633\n",
      "Epoch 16/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0849 - acc: 0.9769 - val_loss: 0.0577 - val_acc: 0.9867\n",
      "Epoch 17/200\n",
      "1500/1500 [==============================] - 0s 101us/step - loss: 0.0803 - acc: 0.9676 - val_loss: 0.1013 - val_acc: 0.9497\n",
      "Epoch 18/200\n",
      "1500/1500 [==============================] - 0s 95us/step - loss: 0.0723 - acc: 0.9747 - val_loss: 0.0844 - val_acc: 0.9587\n",
      "Epoch 19/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0633 - acc: 0.9782 - val_loss: 0.0655 - val_acc: 0.9803\n",
      "Epoch 20/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0885 - acc: 0.9658 - val_loss: 0.0756 - val_acc: 0.9737\n",
      "Epoch 21/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0906 - acc: 0.9667 - val_loss: 0.0691 - val_acc: 0.9713\n",
      "Epoch 22/200\n",
      "1500/1500 [==============================] - 0s 94us/step - loss: 0.0661 - acc: 0.9782 - val_loss: 0.0518 - val_acc: 0.9863\n",
      "Epoch 23/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0540 - acc: 0.9853 - val_loss: 0.0536 - val_acc: 0.9863\n",
      "Epoch 24/200\n",
      "1500/1500 [==============================] - 0s 89us/step - loss: 0.0463 - acc: 0.9893 - val_loss: 0.0573 - val_acc: 0.9800\n",
      "Epoch 25/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0594 - acc: 0.9800 - val_loss: 0.0590 - val_acc: 0.9853\n",
      "Epoch 26/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0543 - acc: 0.9858 - val_loss: 0.0621 - val_acc: 0.9833\n",
      "Epoch 27/200\n",
      "1500/1500 [==============================] - 0s 89us/step - loss: 0.0483 - acc: 0.9867 - val_loss: 0.0483 - val_acc: 0.9870\n",
      "Epoch 28/200\n",
      "1500/1500 [==============================] - 0s 96us/step - loss: 0.0504 - acc: 0.9853 - val_loss: 0.0581 - val_acc: 0.9777\n",
      "Epoch 29/200\n",
      "1500/1500 [==============================] - 0s 88us/step - loss: 0.0512 - acc: 0.9822 - val_loss: 0.0657 - val_acc: 0.9847\n",
      "Epoch 30/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0613 - acc: 0.9813 - val_loss: 0.0605 - val_acc: 0.9860\n",
      "Epoch 31/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.0447 - acc: 0.9840 - val_loss: 0.0480 - val_acc: 0.9880\n",
      "Epoch 32/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0508 - acc: 0.9822 - val_loss: 0.0523 - val_acc: 0.9853\n",
      "Epoch 33/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0615 - acc: 0.9773 - val_loss: 0.0555 - val_acc: 0.9867\n",
      "Epoch 34/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0456 - acc: 0.9858 - val_loss: 0.0993 - val_acc: 0.9710\n",
      "Epoch 35/200\n",
      "1500/1500 [==============================] - 0s 95us/step - loss: 0.0515 - acc: 0.9862 - val_loss: 0.0456 - val_acc: 0.9873\n",
      "Epoch 36/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0570 - acc: 0.9769 - val_loss: 0.0994 - val_acc: 0.9683\n",
      "Epoch 37/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0523 - acc: 0.9796 - val_loss: 0.0561 - val_acc: 0.9870\n",
      "Epoch 38/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0446 - acc: 0.9862 - val_loss: 0.0824 - val_acc: 0.9693\n",
      "Epoch 39/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0365 - acc: 0.9884 - val_loss: 0.0488 - val_acc: 0.9853\n",
      "Epoch 40/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0393 - acc: 0.9876 - val_loss: 0.0773 - val_acc: 0.9810\n",
      "Epoch 41/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0427 - acc: 0.9858 - val_loss: 0.0645 - val_acc: 0.9827\n",
      "Epoch 42/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0725 - acc: 0.9738 - val_loss: 0.0805 - val_acc: 0.9707\n",
      "Epoch 43/200\n",
      "1500/1500 [==============================] - 0s 96us/step - loss: 0.0500 - acc: 0.9858 - val_loss: 0.0448 - val_acc: 0.9870\n",
      "Epoch 44/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0463 - acc: 0.9840 - val_loss: 0.0430 - val_acc: 0.9893\n",
      "Epoch 45/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0376 - acc: 0.9884 - val_loss: 0.0589 - val_acc: 0.9847\n",
      "Epoch 46/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0376 - acc: 0.9898 - val_loss: 0.0670 - val_acc: 0.9753\n",
      "Epoch 47/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0501 - acc: 0.9862 - val_loss: 0.0681 - val_acc: 0.9790\n",
      "Epoch 48/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0437 - acc: 0.9867 - val_loss: 0.0511 - val_acc: 0.9883\n",
      "Epoch 49/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0369 - acc: 0.9884 - val_loss: 0.0453 - val_acc: 0.9880\n",
      "Epoch 50/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0412 - acc: 0.9858 - val_loss: 0.0568 - val_acc: 0.9817\n",
      "Epoch 51/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0505 - acc: 0.9856 - val_loss: 0.0509 - val_acc: 0.9863\n",
      "Epoch 52/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0396 - acc: 0.9867 - val_loss: 0.0757 - val_acc: 0.9763\n",
      "Epoch 53/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0422 - acc: 0.9840 - val_loss: 0.0688 - val_acc: 0.9870\n",
      "Epoch 54/200\n",
      "1500/1500 [==============================] - 0s 96us/step - loss: 0.0415 - acc: 0.9867 - val_loss: 0.0786 - val_acc: 0.9767\n",
      "Epoch 55/200\n",
      "1500/1500 [==============================] - 0s 89us/step - loss: 0.0472 - acc: 0.9831 - val_loss: 0.0761 - val_acc: 0.9692\n",
      "Epoch 56/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0434 - acc: 0.9862 - val_loss: 0.0643 - val_acc: 0.9813\n",
      "Epoch 57/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0518 - acc: 0.9836 - val_loss: 0.0549 - val_acc: 0.9833\n",
      "Epoch 58/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0504 - acc: 0.9853 - val_loss: 0.0537 - val_acc: 0.9820\n",
      "Epoch 59/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0442 - acc: 0.9853 - val_loss: 0.0520 - val_acc: 0.9853\n",
      "Epoch 60/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0300 - acc: 0.9907 - val_loss: 0.0564 - val_acc: 0.9867\n",
      "Epoch 61/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0345 - acc: 0.9880 - val_loss: 0.0511 - val_acc: 0.9880\n",
      "Epoch 62/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0288 - acc: 0.9911 - val_loss: 0.0593 - val_acc: 0.9830\n",
      "Epoch 63/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0317 - acc: 0.9880 - val_loss: 0.0490 - val_acc: 0.9863\n",
      "Epoch 64/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0372 - acc: 0.9902 - val_loss: 0.0594 - val_acc: 0.9793\n",
      "Epoch 65/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0324 - acc: 0.9916 - val_loss: 0.0554 - val_acc: 0.9850\n",
      "Epoch 66/200\n",
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      "Epoch 67/200\n",
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      "Epoch 68/200\n",
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      "Epoch 69/200\n",
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      "Epoch 70/200\n",
      "1500/1500 [==============================] - 0s 88us/step - loss: 0.0344 - acc: 0.9898 - val_loss: 0.0505 - val_acc: 0.9883\n",
      "Epoch 71/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0262 - acc: 0.9911 - val_loss: 0.0583 - val_acc: 0.9863\n",
      "Epoch 72/200\n",
      "1500/1500 [==============================] - 0s 80us/step - loss: 0.0516 - acc: 0.9840 - val_loss: 0.0762 - val_acc: 0.9810\n",
      "Epoch 73/200\n",
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      "Epoch 74/200\n",
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      "Epoch 75/200\n",
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      "Epoch 76/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0320 - acc: 0.9916 - val_loss: 0.0544 - val_acc: 0.9880\n",
      "Epoch 77/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0342 - acc: 0.9876 - val_loss: 0.0588 - val_acc: 0.9883\n",
      "Epoch 78/200\n",
      "1500/1500 [==============================] - 0s 89us/step - loss: 0.0616 - acc: 0.9813 - val_loss: 0.0651 - val_acc: 0.9837\n",
      "Epoch 79/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0372 - acc: 0.9867 - val_loss: 0.0518 - val_acc: 0.9883\n",
      "Epoch 80/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0284 - acc: 0.9911 - val_loss: 0.0545 - val_acc: 0.9847\n",
      "Epoch 81/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0336 - acc: 0.9893 - val_loss: 0.0626 - val_acc: 0.9867\n",
      "Epoch 82/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0419 - acc: 0.9849 - val_loss: 0.0588 - val_acc: 0.9790\n",
      "Epoch 83/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0448 - acc: 0.9853 - val_loss: 0.0682 - val_acc: 0.9840\n",
      "Epoch 84/200\n",
      "1500/1500 [==============================] - 0s 80us/step - loss: 0.0425 - acc: 0.9884 - val_loss: 0.0573 - val_acc: 0.9880\n",
      "Epoch 85/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0323 - acc: 0.9884 - val_loss: 0.0495 - val_acc: 0.9887\n",
      "Epoch 86/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0331 - acc: 0.9893 - val_loss: 0.0546 - val_acc: 0.9803\n",
      "Epoch 87/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0399 - acc: 0.9853 - val_loss: 0.0595 - val_acc: 0.9863\n",
      "Epoch 88/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0417 - acc: 0.9840 - val_loss: 0.0531 - val_acc: 0.9880\n",
      "Epoch 89/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0373 - acc: 0.9880 - val_loss: 0.0571 - val_acc: 0.9880\n",
      "Epoch 90/200\n",
      "1500/1500 [==============================] - 0s 89us/step - loss: 0.0322 - acc: 0.9907 - val_loss: 0.0543 - val_acc: 0.9870\n",
      "Epoch 91/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0342 - acc: 0.9893 - val_loss: 0.0517 - val_acc: 0.9883\n",
      "Epoch 92/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0448 - acc: 0.9862 - val_loss: 0.0537 - val_acc: 0.9863\n",
      "Epoch 93/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0306 - acc: 0.9889 - val_loss: 0.0547 - val_acc: 0.9870\n",
      "Epoch 94/200\n",
      "1500/1500 [==============================] - 0s 97us/step - loss: 0.0362 - acc: 0.9871 - val_loss: 0.0627 - val_acc: 0.9820\n",
      "Epoch 95/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0323 - acc: 0.9884 - val_loss: 0.0536 - val_acc: 0.9887\n",
      "Epoch 96/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0289 - acc: 0.9920 - val_loss: 0.0487 - val_acc: 0.9893\n",
      "Epoch 97/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0317 - acc: 0.9907 - val_loss: 0.0513 - val_acc: 0.9867\n",
      "Epoch 98/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.0483 - acc: 0.9858 - val_loss: 0.0470 - val_acc: 0.9880\n",
      "Epoch 99/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0235 - acc: 0.9942 - val_loss: 0.1036 - val_acc: 0.9627\n",
      "Epoch 100/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0425 - acc: 0.9849 - val_loss: 0.0757 - val_acc: 0.9820\n",
      "Epoch 101/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0442 - acc: 0.9836 - val_loss: 0.0755 - val_acc: 0.9757\n",
      "Epoch 102/200\n",
      "1500/1500 [==============================] - 0s 89us/step - loss: 0.0339 - acc: 0.9876 - val_loss: 0.0738 - val_acc: 0.9867\n",
      "Epoch 103/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0355 - acc: 0.9889 - val_loss: 0.0524 - val_acc: 0.9853\n",
      "Epoch 104/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0345 - acc: 0.9889 - val_loss: 0.0687 - val_acc: 0.9843\n",
      "Epoch 105/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0398 - acc: 0.9840 - val_loss: 0.0496 - val_acc: 0.9867\n",
      "Epoch 106/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0281 - acc: 0.9920 - val_loss: 0.0920 - val_acc: 0.9637\n",
      "Epoch 107/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0405 - acc: 0.9849 - val_loss: 0.0693 - val_acc: 0.9860\n",
      "Epoch 108/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0455 - acc: 0.9840 - val_loss: 0.0683 - val_acc: 0.9853\n",
      "Epoch 109/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0321 - acc: 0.9902 - val_loss: 0.0499 - val_acc: 0.9877\n",
      "Epoch 110/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0303 - acc: 0.9920 - val_loss: 0.0545 - val_acc: 0.9890\n",
      "Epoch 111/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0279 - acc: 0.9916 - val_loss: 0.0669 - val_acc: 0.9867\n",
      "Epoch 112/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0366 - acc: 0.9867 - val_loss: 0.0724 - val_acc: 0.9843\n",
      "Epoch 113/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0319 - acc: 0.9911 - val_loss: 0.0495 - val_acc: 0.9887\n",
      "Epoch 114/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0380 - acc: 0.9853 - val_loss: 0.0661 - val_acc: 0.9850\n",
      "Epoch 115/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0440 - acc: 0.9858 - val_loss: 0.0582 - val_acc: 0.9820\n",
      "Epoch 116/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0332 - acc: 0.9893 - val_loss: 0.0474 - val_acc: 0.9893\n",
      "Epoch 117/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0417 - acc: 0.9836 - val_loss: 0.0563 - val_acc: 0.9880\n",
      "Epoch 118/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0349 - acc: 0.9876 - val_loss: 0.0570 - val_acc: 0.9883\n",
      "Epoch 119/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0264 - acc: 0.9920 - val_loss: 0.0540 - val_acc: 0.9890\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0352 - acc: 0.9858 - val_loss: 0.0634 - val_acc: 0.9857\n",
      "Epoch 121/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0492 - acc: 0.9849 - val_loss: 0.0626 - val_acc: 0.9837\n",
      "Epoch 122/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.0400 - acc: 0.9876 - val_loss: 0.0482 - val_acc: 0.9887\n",
      "Epoch 123/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0451 - acc: 0.9849 - val_loss: 0.0886 - val_acc: 0.9823\n",
      "Epoch 124/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0447 - acc: 0.9809 - val_loss: 0.0776 - val_acc: 0.9843\n",
      "Epoch 125/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0290 - acc: 0.9893 - val_loss: 0.0603 - val_acc: 0.9880\n",
      "Epoch 126/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0359 - acc: 0.9876 - val_loss: 0.0505 - val_acc: 0.9867\n",
      "Epoch 127/200\n",
      "1500/1500 [==============================] - 0s 111us/step - loss: 0.0224 - acc: 0.9938 - val_loss: 0.0600 - val_acc: 0.9880\n",
      "Epoch 128/200\n",
      "1500/1500 [==============================] - 0s 103us/step - loss: 0.0271 - acc: 0.9916 - val_loss: 0.0548 - val_acc: 0.9873\n",
      "Epoch 129/200\n",
      "1500/1500 [==============================] - 0s 86us/step - loss: 0.0248 - acc: 0.9916 - val_loss: 0.0566 - val_acc: 0.9863\n",
      "Epoch 130/200\n",
      "1500/1500 [==============================] - 0s 102us/step - loss: 0.0428 - acc: 0.9862 - val_loss: 0.0732 - val_acc: 0.9843\n",
      "Epoch 131/200\n",
      "1500/1500 [==============================] - 0s 102us/step - loss: 0.0384 - acc: 0.9858 - val_loss: 0.0543 - val_acc: 0.9837\n",
      "Epoch 132/200\n",
      "1500/1500 [==============================] - 0s 104us/step - loss: 0.0277 - acc: 0.9902 - val_loss: 0.0621 - val_acc: 0.9853\n",
      "Epoch 133/200\n",
      "1500/1500 [==============================] - 0s 105us/step - loss: 0.0309 - acc: 0.9920 - val_loss: 0.0483 - val_acc: 0.9897\n",
      "Epoch 134/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0421 - acc: 0.9862 - val_loss: 0.0561 - val_acc: 0.9883\n",
      "Epoch 135/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0407 - acc: 0.9858 - val_loss: 0.0550 - val_acc: 0.9887\n",
      "Epoch 136/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0344 - acc: 0.9898 - val_loss: 0.0469 - val_acc: 0.9890\n",
      "Epoch 137/200\n",
      "1500/1500 [==============================] - 0s 108us/step - loss: 0.0329 - acc: 0.9902 - val_loss: 0.0488 - val_acc: 0.9877\n",
      "Epoch 138/200\n",
      "1500/1500 [==============================] - 0s 97us/step - loss: 0.0251 - acc: 0.9929 - val_loss: 0.0491 - val_acc: 0.9880\n",
      "Epoch 139/200\n",
      "1500/1500 [==============================] - 0s 94us/step - loss: 0.0311 - acc: 0.9880 - val_loss: 0.0667 - val_acc: 0.9883\n",
      "Epoch 140/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0332 - acc: 0.9920 - val_loss: 0.0544 - val_acc: 0.9867\n",
      "Epoch 141/200\n",
      "1500/1500 [==============================] - 0s 98us/step - loss: 0.0330 - acc: 0.9893 - val_loss: 0.0558 - val_acc: 0.9853\n",
      "Epoch 142/200\n",
      "1500/1500 [==============================] - 0s 99us/step - loss: 0.0277 - acc: 0.9911 - val_loss: 0.0621 - val_acc: 0.9810\n",
      "Epoch 143/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0333 - acc: 0.9902 - val_loss: 0.0589 - val_acc: 0.9880\n",
      "Epoch 144/200\n",
      "1500/1500 [==============================] - 0s 88us/step - loss: 0.0325 - acc: 0.9889 - val_loss: 0.0562 - val_acc: 0.9877\n",
      "Epoch 145/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0250 - acc: 0.9920 - val_loss: 0.0524 - val_acc: 0.9863\n",
      "Epoch 146/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0213 - acc: 0.9942 - val_loss: 0.0679 - val_acc: 0.9847\n",
      "Epoch 147/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0362 - acc: 0.9884 - val_loss: 0.0536 - val_acc: 0.9877\n",
      "Epoch 148/200\n",
      "1500/1500 [==============================] - 0s 88us/step - loss: 0.0441 - acc: 0.9840 - val_loss: 0.0716 - val_acc: 0.9847\n",
      "Epoch 149/200\n",
      "1500/1500 [==============================] - 0s 83us/step - loss: 0.0292 - acc: 0.9916 - val_loss: 0.0599 - val_acc: 0.9803\n",
      "Epoch 150/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0243 - acc: 0.9933 - val_loss: 0.0555 - val_acc: 0.9883\n",
      "Epoch 151/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0247 - acc: 0.9924 - val_loss: 0.0553 - val_acc: 0.9880\n",
      "Epoch 152/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0315 - acc: 0.9876 - val_loss: 0.0529 - val_acc: 0.9880\n",
      "Epoch 153/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0275 - acc: 0.9911 - val_loss: 0.0640 - val_acc: 0.9877\n",
      "Epoch 154/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.0255 - acc: 0.9907 - val_loss: 0.0507 - val_acc: 0.9903\n",
      "Epoch 155/200\n",
      "1500/1500 [==============================] - 0s 102us/step - loss: 0.0263 - acc: 0.9916 - val_loss: 0.0482 - val_acc: 0.9887\n",
      "Epoch 156/200\n",
      "1500/1500 [==============================] - 0s 109us/step - loss: 0.0230 - acc: 0.9929 - val_loss: 0.0633 - val_acc: 0.9863\n",
      "Epoch 157/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.0271 - acc: 0.9924 - val_loss: 0.0501 - val_acc: 0.9890\n",
      "Epoch 158/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0253 - acc: 0.9924 - val_loss: 0.0598 - val_acc: 0.9883\n",
      "Epoch 159/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0335 - acc: 0.9889 - val_loss: 0.0739 - val_acc: 0.9860\n",
      "Epoch 160/200\n",
      "1500/1500 [==============================] - 0s 106us/step - loss: 0.0391 - acc: 0.9876 - val_loss: 0.0837 - val_acc: 0.9823\n",
      "Epoch 161/200\n",
      "1500/1500 [==============================] - 0s 100us/step - loss: 0.0375 - acc: 0.9853 - val_loss: 0.0821 - val_acc: 0.9837\n",
      "Epoch 162/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0416 - acc: 0.9876 - val_loss: 0.0572 - val_acc: 0.9873\n",
      "Epoch 163/200\n",
      "1500/1500 [==============================] - 0s 88us/step - loss: 0.0252 - acc: 0.9916 - val_loss: 0.0545 - val_acc: 0.9870\n",
      "Epoch 164/200\n",
      "1500/1500 [==============================] - 0s 95us/step - loss: 0.0267 - acc: 0.9902 - val_loss: 0.0546 - val_acc: 0.9877\n",
      "Epoch 165/200\n",
      "1500/1500 [==============================] - 0s 99us/step - loss: 0.0221 - acc: 0.9920 - val_loss: 0.0518 - val_acc: 0.9873\n",
      "Epoch 166/200\n",
      "1500/1500 [==============================] - 0s 102us/step - loss: 0.0212 - acc: 0.9929 - val_loss: 0.0456 - val_acc: 0.9900\n",
      "Epoch 167/200\n",
      "1500/1500 [==============================] - 0s 97us/step - loss: 0.0235 - acc: 0.9924 - val_loss: 0.0740 - val_acc: 0.9833\n",
      "Epoch 168/200\n",
      "1500/1500 [==============================] - 0s 102us/step - loss: 0.0353 - acc: 0.9867 - val_loss: 0.0643 - val_acc: 0.9820\n",
      "Epoch 169/200\n",
      "1500/1500 [==============================] - 0s 105us/step - loss: 0.0306 - acc: 0.9898 - val_loss: 0.0589 - val_acc: 0.9883\n",
      "Epoch 170/200\n",
      "1500/1500 [==============================] - 0s 98us/step - loss: 0.0249 - acc: 0.9916 - val_loss: 0.0549 - val_acc: 0.9863\n",
      "Epoch 171/200\n",
      "1500/1500 [==============================] - 0s 87us/step - loss: 0.0240 - acc: 0.9924 - val_loss: 0.0467 - val_acc: 0.9893\n",
      "Epoch 172/200\n",
      "1500/1500 [==============================] - 0s 102us/step - loss: 0.0305 - acc: 0.9902 - val_loss: 0.0572 - val_acc: 0.9893\n",
      "Epoch 173/200\n",
      "1500/1500 [==============================] - 0s 101us/step - loss: 0.0289 - acc: 0.9889 - val_loss: 0.0530 - val_acc: 0.9893\n",
      "Epoch 174/200\n",
      "1500/1500 [==============================] - 0s 94us/step - loss: 0.0209 - acc: 0.9933 - val_loss: 0.0555 - val_acc: 0.9880\n",
      "Epoch 175/200\n",
      "1500/1500 [==============================] - 0s 89us/step - loss: 0.0475 - acc: 0.9831 - val_loss: 0.1286 - val_acc: 0.9540\n",
      "Epoch 176/200\n",
      "1500/1500 [==============================] - 0s 100us/step - loss: 0.0372 - acc: 0.9867 - val_loss: 0.0522 - val_acc: 0.9883\n",
      "Epoch 177/200\n",
      "1500/1500 [==============================] - 0s 111us/step - loss: 0.0250 - acc: 0.9924 - val_loss: 0.0447 - val_acc: 0.9890\n",
      "Epoch 178/200\n",
      "1500/1500 [==============================] - 0s 91us/step - loss: 0.0254 - acc: 0.9907 - val_loss: 0.0584 - val_acc: 0.9877\n",
      "Epoch 179/200\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0262 - acc: 0.9907 - val_loss: 0.0574 - val_acc: 0.9893\n",
      "Epoch 180/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0234 - acc: 0.9924 - val_loss: 0.0479 - val_acc: 0.9880\n",
      "Epoch 181/200\n",
      "1500/1500 [==============================] - 0s 94us/step - loss: 0.0272 - acc: 0.9898 - val_loss: 0.0854 - val_acc: 0.9800\n",
      "Epoch 182/200\n",
      "1500/1500 [==============================] - 0s 91us/step - loss: 0.0485 - acc: 0.9831 - val_loss: 0.0524 - val_acc: 0.9877\n",
      "Epoch 183/200\n",
      "1500/1500 [==============================] - 0s 84us/step - loss: 0.0262 - acc: 0.9911 - val_loss: 0.0517 - val_acc: 0.9880\n",
      "Epoch 184/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0433 - acc: 0.9871 - val_loss: 0.0559 - val_acc: 0.9883\n",
      "Epoch 185/200\n",
      "1500/1500 [==============================] - 0s 111us/step - loss: 0.0264 - acc: 0.9907 - val_loss: 0.0717 - val_acc: 0.9860\n",
      "Epoch 186/200\n",
      "1500/1500 [==============================] - 0s 101us/step - loss: 0.0336 - acc: 0.9871 - val_loss: 0.0487 - val_acc: 0.9870\n",
      "Epoch 187/200\n",
      "1500/1500 [==============================] - 0s 108us/step - loss: 0.0267 - acc: 0.9902 - val_loss: 0.0629 - val_acc: 0.9800\n",
      "Epoch 188/200\n",
      "1500/1500 [==============================] - 0s 97us/step - loss: 0.0344 - acc: 0.9876 - val_loss: 0.0623 - val_acc: 0.9863\n",
      "Epoch 189/200\n",
      "1500/1500 [==============================] - 0s 93us/step - loss: 0.0214 - acc: 0.9938 - val_loss: 0.0476 - val_acc: 0.9900\n",
      "Epoch 190/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0200 - acc: 0.9938 - val_loss: 0.0539 - val_acc: 0.9890\n",
      "Epoch 191/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0196 - acc: 0.9933 - val_loss: 0.0507 - val_acc: 0.9897\n",
      "Epoch 192/200\n",
      "1500/1500 [==============================] - 0s 94us/step - loss: 0.0257 - acc: 0.9916 - val_loss: 0.0537 - val_acc: 0.9893\n",
      "Epoch 193/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0373 - acc: 0.9853 - val_loss: 0.0484 - val_acc: 0.9883\n",
      "Epoch 194/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0292 - acc: 0.9902 - val_loss: 0.0464 - val_acc: 0.9893\n",
      "Epoch 195/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0219 - acc: 0.9942 - val_loss: 0.0539 - val_acc: 0.9880\n",
      "Epoch 196/200\n",
      "1500/1500 [==============================] - 0s 90us/step - loss: 0.0255 - acc: 0.9924 - val_loss: 0.0596 - val_acc: 0.9873\n",
      "Epoch 197/200\n",
      "1500/1500 [==============================] - 0s 86us/step - loss: 0.0215 - acc: 0.9920 - val_loss: 0.0598 - val_acc: 0.9860\n",
      "Epoch 198/200\n",
      "1500/1500 [==============================] - 0s 92us/step - loss: 0.0245 - acc: 0.9938 - val_loss: 0.0464 - val_acc: 0.9900\n",
      "Epoch 199/200\n",
      "1500/1500 [==============================] - 0s 96us/step - loss: 0.0294 - acc: 0.9916 - val_loss: 0.0550 - val_acc: 0.9883\n",
      "Epoch 200/200\n",
      "1500/1500 [==============================] - 0s 91us/step - loss: 0.0195 - acc: 0.9947 - val_loss: 0.0483 - val_acc: 0.9880\n"
     ]
    }
   ],
   "source": [
    "classifier = Classifier()\n",
    "\n",
    "classifier.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": {
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LJGbkRFiBguZDBY0yunwBT+JSJRMl+5780VggUPAKflaVIzRu/lNgJijKRRHFSyOShQiVxsOZFGCtb2FAbRL5MnGS9cGhH8I5O0FMt6WxlDEbDDrVeWFU52nEKgyBITXJ2Wq3GiIZGaARa8BMzcveE4zl4Kn0sbE2CxEfAhYOoFTuTrPEP+NlEeM54HIB+cLQ4enPOkgGJWhM9o4WMDq/Cy8OoUb15PB098X4XN5HPXzIWC+ADyuT8EmKnQ6tMDBEB6npImAqAqjDZafKJ5g1suxP+4sRn4xF0t6folmISnI09rDL2axlMbI+NBI2JZEieI60evAzM2qDPOadCFf+O4pz08ZK9IXFoGrDyXxbyB7FWC+0JFPI0JZlIiMbCBwApD5QdHuiBQtfGYRD46e9qbxMioURhK7S3pR2j1GDAglssJPgPyDsk+/a4DAIfLfzK3BQh526rbmejp4bJeiRph2nqgwkTwjFJ6jkFemQYZYeBx0IZOQxUB+QUm+fGFywTEkqtH0JCm3UdjQ8+sKLXKBq3Cq4xQC1YaATuGp3J/lmqZcPXpG/Ygfq6ARMSlPuEmGxcwXCFkJUZ1pT2o2XwD49wey1wP+fcGCJohcIGkwOWnRW6GZi8tfFOQYkSdHGlFENio8txR+8+sAzTDKdDKssigZ2+DxivkBmikGPGcLchPuA+d50BiHWeOgqLUWORfwu9IgV8wAp3dM3m7h4VIblZKXny9895Txo4wfl17AlBgpmFdFwnI+WNhMsIDrxbnkhgYlNNOLJZXKzWk3aQVCp0ELHFisf065BAnkTs8VhpQ0msjIIde01O8BCwIiFwIJtxQ5X+7ynBMc2g/kepJh/Nj/xQyEPFUQXjO8RtDCwVMel0YXCwT8rjLYagsbQ4VCeiJR+3awkMfAz7YvFAYjw0j+poU+5RKe6iCFQHUioJNXhRKdha3fEFrM906HQ6Ep2E4KzyuxpBfh6mEh0Or+VuQ83ZYOxBUQG5JnxrGhIe9r5OJi4WEpXDoDyP4G8O8HFvxACVWjVB02QBZXiOYPVpdISS3Q464Dtx2RWxQuA2L0AdeEd6fAm1US5mQ0OYw6f2KfLmCEr877VF3XVsZPdSHvRdf1hUVQ4gshay147o9glMxobgYe3884tKi+lhQefQbIWirLUcPfA6OEZCOZkOdsFcnNFP9nxOchOEHOAPnHxa4OGXOla9oejhKu7HcFCyyPpbAahZoMm4henkZ1SWnLRE+fB6S/LXa1CH8HTPMDj7dXf1HI7ElhrNALj+cfBgu4SRhAnAjU0l4pMGxCnpfeIcoForyHCNpJdpXGlahCoZAaecKuE5pjjFG5rmoKAe9GgGdvkJWRtKbDXhO6XSU1Pec3IPkekTuHwMGGbl6eITNze5HTeM4P4EnkCTaatZ8hUkp/GxQXWihYwGAwS0u3QOIZn4CnvVzwngh9EVrgraIPnfiFcncIoydTrweL9XJYg68Hs17t0jX0lKeArC/ksZTcHU7vAN9tvvDdU56fMta3LywCZxBwIiBMHGbExzkQ+ZWs7MjbKRITWdQSMEtrcSrP/6eIYYGgu6CFPCR/y1wBnkreEJPcPVJewTm6PHraDCBjTsFLLXoTNHMD+VLLjwVSn5bGB70wg8a7VKIq1aHtpbc03k+BvN/BMxYAlkvAwqYWCZnp6bOBdKLsp9AbheBI7iJE5AVQMjMld4KZRbiPjDqe9wd42ruC94Q8QeeWvZP3SbjwSyFY9N1Xq5q5NyAgvDrCQ+Jf6nD0s1cAnLT5xNFAzHaAZ4kKSFFiHjjGwbbOyQBJtBtEDAiZAqRRnp290fPFABYAFvNjqZI2hQfFM+aBZywU1Zf2RGeZgN0MPGcbeMoUmXtIVVxhzzhYpV3FWY/rKT1c1LR60OpsdfXUWnmcL3z3fMb4WblyJZ555hkcOnQIYWFh4r/vvpsE+0pvvrAInBo/RSpBmCAUE4YBVW2QAaBFOk7TM5cAIs5PISsTWOgzYIFkOBHx2SSDeNA4nPh3AslDIvV6RMVY1krAdlokJxNbLFWGVLRx2ynwuGuMbigkVTobrH72EpnESY3yiYLvlppjlE9A/EEi9yERMLWQJe6l6JAVVMExsLBXwQIKqddXdGLqfIWACwgIIWHbSXDhgQ10acNQUrf62c5Fva8xW2UlFlFGUBO8WJLoUGw6KLmYQt/WniKEhZyNIH0/8fyIPCOjajJ6A0Ae37w9YIEjBaOz03dRzi/gSSMKfiKOHsovorygpAcAG5EkGuSpfldDi6Tk5pIbzz9GtfyAuaUDFz1tFpAh5wD/IWCWZuC0SSIKAFMdFxCvXYf4wnfPJ4yf9evXY9y4cVi0aBGuvvpqpKam4uzZs2jVqlWZK9YXFoHTFw4lDSfeJpONWYhhGIgMQmh1tjtOKeD6IWNGB/x6AGGvQjMZ4S2hCl1YrV0TbnZYrwSnxOcskr2gRomO80osPRV5RZT8aIoCs1C+TdEmynIp9EY7yrBXANN5RsiMKtFIHXoamOEidzZfWQ5LZe3k2BpUhGOISBx56vRCVS80zuJVXZLlmkmjS5A3KhbaMh8wdYDHEeBEIUE6WnY9LlNzsMhFbnFiFR6UTl7P5P/J9W/tDy1iJvTYroAeb6zxFtBiDMmJUo2Ow+Ck80fhYuv1YnOB9JcKnivDk1Ps2S5WUi9L8vXEcUDuj4Uqy0yAtRuY/0DwnO/ArN2LbDx43gFwksih8Dw1ax8waw+pFM8CwPP2gug0QISmohlGGovAzpy38MHuU2gVHYOHLu8Ki0lWyNXW5gvfPZ8wfi677DLcddddGD+e1Ifda76wCEpChHR3qKqJE919nvGSYRHQ6v7iOIVnzAEnQkNH1ZMG0Ms2epVDWkLP/h6gEJRoxI8zUYaRhAK0nTvHZOQeELFZ8aYn3gXkkiuag4W+DBZYNBFaj+1huMQZ4NcZWuQnMhyX9QWYuYWQ0yhNPFVUtmQuAKNE66BRRUIBRKomQ4CSRZbFfFPMrS5zKKjsl3KAmgH5h4wXbA8HB4l7K08drRAoHwIyjPxRoWfLbHBSle3ppisKI57y8PL/AQu6Q4S3ObcJOQq7mLDcEFA4yz3vpgi16ckAiaJmLiySw0Mh5jRbA2Tm5aFecEFJvGCHp/wkYo8mg0ZUj1ogNcgoUZveISaANkVB4wDS6rO34Ifk5i3jA4N76FxMzUKkVbwv7InXoiy+cKEDsOqf8/HQL9fCrGmY3PVqjOvYqXw3p4ac5QvfvVpv/GRkZCAkJASvvvoq5syZg+TkZHTv3h1vvfUW6tUrXnZ57tr0hUVQeM4iXJSxQCYmB40D13Ok0ZFOOTkZYKHPObwzwrggzg2RDGxUaRmdsZitBoW8SYoXJt8H5HwLaHXAopaBx11blDTQ7wrB7+EsYZhevPzsRQXD9OsOLZJe7gVNp8Ro4W0h1elO0KIWiRe2KLulFyQpQIc86bb7n8ptifmZ62lGiWwvMEubYq8wncjaRD4C2T/NgADiKDGBBY0txm1SQ95/apg1FAGe/S04JSg7GoWipxfbMJQ0PbmhmSGfaQqZ1dnu/Lmk0BqjvLaCELg7kHGeBZ48WfJrBY7AtsS+GLfyS+TqNky49DI8dqXzqk499WWZdG1qDGgRogKVhT4F5tdRhNd4MmmJ2d9HBkXFOQOjijAj8i6eU63ePujE5J7+WrEpkBm09EhLPP3bNcL4GXvJpSWOzZ35e/OxvvDdq/XGz8mTJ9GoUSO0b98elPcTFRUlcn0o7PXNN98UW3/PPfccnn++KE+FkDnwkabH9QNs/0gjwtweyN8lZ86iwGLWgtHLxmhFd172klby/DSVniBKIPQfBFBlFREO5m4WlVIs4gPJvEzl7eQpCXsLWkABs6wzqPWE24E8GguFsCin6JwqE0q0pIoNCnuFvyGqzXj2N+DCXW8nTysaVhPGEY0rZxtgvUZUeIiEzYxFgpSRW64AkigPIdegw/+0WLK2AwsKMxDhIeHm1wVa5HwfWTFqmt6IAPHecJKPIHZlYl0OuqvEtXvu+HWSgsik0JDhlSWGdluc1P86h6TQk3Mfu/ILbDp+TO4fGMPB+x4S/1+46bl/AImywku0gNughRUkVAvW6MQ7DTHU4qOz6UBKrhW5ugn1Aom2g3J8boAW/gb0hMGiKEI2C+A/AMjZAlha4Z39g/HObwfQNCwcC28ejPohRckaPYmDN/SljB9vuAsVHAN5eiIiIoTXZ+xYWYJ55MgRXHDBBUhLS0NQUFCpV/CFRVDk5XKGvBrOGJcBSX9/neNwPfkhSXAmdoihQMQCMP0keM4vhvChs90XhbfeEJ4Y2I4ALLiY9IWzGyLc5cQDokUVo8kv6QYWpd8vIE+zH69nLCpaiRL0sMHWLBXqhRtdvAzlPFj0N2DmJk4vJ1TnKYeJ9Mn8B8ryeC0CLOTRgvJ+Mqyy14BZOoEFOA/vVXC5q9MVAlKTjpiQuQ0sd4swWijBl6e/JbwlLGy6Y00WhkswttMGgvi2SLIFWYD/LXLTQZxALBws+mswkoSphPbKT1sxd7fkDWoUGobvRo4pdhU9aw2QIitJRbNeCy2CiEsLmky6nmN4cSgMfYEMtZkaY9qOMDzUZjMCzQZrNYWoI1eA8bMgVnjp+dEAaz9HErfjfcF5MWOsEmDwii594btX6z0/tJKaNGkiqrvONX4o8Tk4uHRlbl9YBILgi/JvTA3BqXJLiI2SJ4eS+rIKHsbwWdD8e0GWyFqlJhZpA+mJYCFPgFmJVZmKtj4Cd+I+lh0VN0Iq62kX4TbiBSHhReIYCiZG6IKdpMyNKMRIzeoC3EhULjIokzRkYr4vsSxY5PyIMvq20kjTzzjU6oVHiT5Gcb2MXALiC/qoTLLGysJF9Vt7EZAUD5QfQ0Sj8nlzVEIZsjAIHAktlMg9izaePksKDxfRsKMyeFkSLyVlnhR5QJXRcm02fPL7bsRnZWJk+w44L0SKrBZugr2dQuacqCRMQNTX0MhLq6eCyuHFjIOo9D5UeHGhU3J1Nwdp4R+n/0ZbdlOhLoMAzR/QEwBTKyB0MhhVglFu0TlK8VxPAc+QivIynC2LOmpj84Xvnk8YP9OnT8fy5cuxZs0aREZGirDXqVOnnIa9zl3ItX0RkOHDE4ZJKQeK70etEBVdZNxQ9YMM+xgvUevNAJJlDo2Jkoh7gWlRUsmdkoFzNgDEBE3iiIkjgPy/CsFJLMqhYMH3lVjSWtUvEV0nNtqeRhmvXYPIyShMRO0/p8QdL7fFSo0z4SGSIpCOD4i5PbToFTKc5uA/sRi6ZBUv6a9qzNT1vBsBuRZL05eTunXC80ibHr/OBeXeCRQuKqjklDOl54IabYTywSKXFpPBqGpEyABC3h9ScsMUIy6vJ00Acn6QQ/G7qsRydz13H5A4qIBTzHxRQZUnGTURCx2bOPu8RKVp1kq5uSFvtQhtl3yNqsajMq5X27974ovGfSChxWazYfLkyfj4YxLPA3r06IF33nlHJTzTSyN3r/EyMF50QRMdSunCMCIhUbtwacAgQWxW0AzCMvp3ygUSFSbkMr4aLPw9wZgqE4ZPyFyckAfKnRxZGQ849Skq2sgrQ4nRoprM3sjzZYTtaJcX/LDIUSJyQ1AOBSVvk5J7yBMypBBPXh1qRItPu21Do8x0AbSYNVKJOnGUJInU6gsj0/7irqy5qX59DwFOazH2KoN9/BxpFrtHx0q5LKslOMIL9LQ0IGKvBfQThUAzDHmRL+QvqqKYX2evBLUISaGpAbSYTU7HqZOsjj2UrcUIWR7pRRKfQ7CYLcXC8DpVu2avLOoRK0USxCsBcnNQyvhxE7DaeHhtXwR65mcA8ePYW8gT0IJGO/7keYdAZa3M1ADceg2QQLkqThLAKedHyD3QOyQQWt09NWo5iHJ2ouUnosPAu4GAW4GshWBatJCtAIW1Uiado1ZNOFhFgrMob6eQobklwFMM/SEyBLtBi5gtsBBhOFKoz1wMEMV/yCOKAbpGrZKaMVief0hVd48AACAASURBVESyIVN+DFIA1AVgD+eaABYG8ETjWY2EVpeIBwE95Vljc0MVk1cClvPBzE0ls/o5rOylIRGfmYmlf+1FdGAgbr2oDUya3XtUefjxzM/BU0lNHkDoCzJRmWR1rL3Bwl9zhLD0hOFG4QQXYX5BdqjHGgUe7YSX9tymx3YH9NOF/pm4yl4ECyhKt1F5s6v6nmv7d098pnzB81ORpVPbFwHx4HBSSrYn9UYuB/O7uETIRAUVMTLrmUCe4WY+9+iAYdDCSlB2rsjNqORzpUBrnoPLpPDlePpscNIKKyEZHNYB0CJmQOQkZH0lE8G1SFmCa6KPj2x6XF+jmo4SMdvK8nhirCX9MXPDSp6h6r6mI8CJJTnra8mTQ0UDxLeT9Slg6SzXWiFBTrGeyauZ9pr0VNqbuYMMc5OH0r8/tPDXxS+C3yd7tfSGBNxYRP7FHdz6LV6AY0lJ0DnH/V2uwP86X+HO6eU+VkrKcCD3F0O3jKrVKMdwNpj1GkFgKCgA8g8YG7ggIGwqQHlOLMioEiUme9koT0+Eu/IOAzmr5D8GTgQLHl3rNy21/btHt1IZP2U8arV9EYhwDBk/RDsfcAtY8EMuc+Hosb0Bncri7Y0BgeNkhdM5JarlfqOVcCJFa39dtxvJsSnoNvgKBASVrk9U0esLjbF4UrFPpbIv6d0pbAiZLwYjbiGH6rQJLHJJMUNSP3tpQRix8KBIR4yOt1xY0aGq82spAiKZmdagUDW3iWcNmXYpBzNY6AtOWcx1Cl3rcYVQCQcLnypzfig/j0JlJOZrOyNz8krZ/JQFrU3XceG7Mx2+4Z5Nm2POjZQrWHWN5/xoiKtKD7V8Di+FnvQ/IIfoTexVqES4+iBY8F3FBifCh4KpnbzZ+YD/UCDwZmh+l1TdRKrxSrX9u6eMHxcWly8sAhdgKHaInvYGkCHDOQWNxAq/BzNFladLt8754q01+OCRj6FpGlpfcSFmbCksnuhWVy4dXDRhmbw2nYD8XwvODZ8lWJ+FBphopHw/wZE/ZT9QetpI6NXObF3QBTd3Qn7QfFgDlDK8SzfFxw4SHDZnC0m7WLoCedsc642FPg0mig/IefOvTAr2uwwUEnLoVlFOnuViaFGGxAOZAiTdQqFYWpNEPVHnVwc7e3kgfvK7jfjsr70wMYbZA25Cz2bNy9NNuc+Rpe4fiSpP5t/fUWAhk6KL5gKxiLlOld95/gnweCJiLdRMTcGivnJZjLXcE/CCE33hu6c8P2UsNF9YBO4+a2JXJF7C9twfP5nzEv4eNHNjd7sr1/FPXDcNOzcYhGQM2JC3VBhCldV47q/gifYSX8rl6W2EEsiICQSr+6sIF/D4PlLAETawiE+KVY7Q+PSstQZXSUHuFOca/tgWjMeGNse9b43BwHv7VdZUVL81GAHpvdhgkIPOAvL3AJS353epIfvgD55/FDx+oJF0HwhEr5Hs5xSOpYpOSuA3RTtQ0EmWhYRIhUdEA6v7R7lDXsLw4hyHEhMQZvVH3TKoRKryVugkokpcR/YW/DA0EjB20vTcv4HUxw0JnoIDWMT8EvUHq3IulX0tX/juKeNHGT9uP0cicTfuauMjz5x6ONzu1M0TvvlkC14d9a5IW7jihk5od9VFyM7IwcD7+iE0yvPsq2I3SbkTVPVh7S5Zq9PfFyEI8THxu1jKaaQ8bmgQ9ZCK7k7Cf6LAMnOe5CGxdASy1+Hon7GYOjYKp45ZYQ20YnX6IjcRUYf7AgKiOpEoKIjss4QcMZ75maxedHgXAwBzY6GE7oxck+cfBieRUD0OLOQxr6Gi8PT95Dmbwcn7IzZtGpgQUi1OWsqz1oKnPCgvr7UAdCpvpypOzdD1K1sWydNjr+r+lPFT1Yh74fV8YREI4UAKxVCFBCkch0wRH235wX9JVoBYLhHl60wLFHdJ7C4zPpZlofTxZ1QaXrXt8J5jSIlLxebl2/DNx1tEgXnLzufjzR+mVe1AjKtJcdMHCslpfAxmvdylsbw+bha+XbgVXOeo37wuFhyg5GrVFALOEeC2M+DcDGS8C2R9KcvQI96V6uTC89PfSWhVA6uzrUS6CXreKztXz9lsUnNyEGA2V4lSOs+UnFuM3nO0iXHS9PhbgPw/C36JmAdGBqf12iI5eWITQ6FFZnaq91eT164vfPeU56eMFVqTF4EwakinJ+dHI5n5f849EdnrwZNpp2PXwCLXblfwvD/BE2Q5J+cmsEDSr/pN8tSEvVwluT2uvEDu7fw4Du6k3RkQGBqAr5NJM6zqm565Ekilcngj0dIJYVpJo8pIzcSiF5YjNTEdw5+8BQ3OrxwJgapHRV3R0wjoJDKc/moh9ma6AiU8PwsWOFRcTs/8Ekh97JxjABbzQ5HqQ0+Pzd3+XtiyCQt+34UIf38sGTQUF0YVhOPc7ctTx8uKTKkxRo3VPeD0vamnvl4o4dwk+L9YxKwSWeA9Nb6q6Kcmf/dcxUcZP7XZ+MlaBZ4y2bEDZJHLnLKzShXo+wqVuy8G87sM/x3YhvphowRCtnxAM5nBGGnimIGAQdCoTNQL2pZl2zB9+JvCazJ62m0Y/iQxuFZ90wVDLnGmcMDcThIZlqPqrbp24FWPmLqiuwiI6syzHQoYih0dkGbea46wFoVgkbsTPG8HkL1ZEI2y4AlC+sFbWlJWFi79aJYYDgmY3ta2Pab1sJOFVt8o9dT3gMyC3CApRVPcS6THXiFlMRyNDNCpYIHV8/7xJGLK+PEkmjW0r5q8CIiPh6fQ7k9WFpVo/AgNLOIC2SSUjFnwveKjvWX5duz8egr63xGHvb8G4+ZxSdA0Q/TTfyC08Je95q4mnU1GbnYu6japU21j0s9eIkkSqZnbQov+wq2xCEZtUqGn++B3OVjEh2BMVX65BWItP5gLL+39BbM0NQa0cOl1CCbBzzzwpPFA7nbA72owu+gnlbqTx9YNssLKhjInPx+d57yPjNxcUazw8OVX4u5O1c8greefAeJJrkaYZUDAkCLK8XZcdNJQy15bhPhU8HX5965s6Cq9/5r83XMVHOX5KQOpmrwIZNjrGSDnJyDgZrDgB9zyRKQmpuGeSx/D2X/iENMwCh/t7I0AkDJ0XUnKR/k+XtI+emwRVsxYhYYtz8MrG6cg+jzSJ6vapqe+LBKZhaEZ+qJTzpXSRkSq0jyZqk9kQiYCR4H59/RaSYGqRVddjRDgOdvAk6Q3VjYrWMx6wcAufhfJuo8YGx4NCCUSvzclzw8RIUYuKCbYWZ3I/hV7Fp/8sRuNwyIwvmOnKsn7KWu+onCBcqZsxGFmk+864kM6p+k5PwPkMScySVMjQRjJgie69Y4tayzV9XtN/u65ipkyfmqx8VPqh5bc4lS9UEpYhtvikZ/0NHIzzsAU8Tj8Q11L3nV18XnqODLO7mh2j+jObDFh8KM3Ysx0yXdSlU0kQObvkzpmVPrvZuO5v4MnDi50llTklkrahT94bnasDq81CIgiBJJZEVpT1MjAmQ7m10l+gElAN2lEwXwD7gCyiNMnT/xbSd7fygSIPDvJ2dk4LySkxhgGpBIvWLFNTQRBIiWYI/8oYOngKPrQyUDKPyyhMzWFFkMUBLWjKeOndtzHCs2iNi4CnrlMlsISpTvFs0tgdNWTHzXcukR+Fg5W52eXX14Uhvr4uWWiYmzks0MQVT+iQvehtJNTE9Iw5Ly7YMuzwWQx4a6X78Cgh0iDrOY02tETF4tQrM5cBtiIgt9olo7Qoj6rOZNRI600BLieCZ440pCnsF+GKi1zjVDpfFGdybO/B/x7Y9WhOPSvQ/krGjRmMkhIC+RWKm2gRsd/x8Vi6IqlyMjLxY0XtsKb/agKrWY1nncAPOFWADmAqRlY9NciqVlPGCxpB6iZL4IW/WXNmlgpo62N371zp6s8P2Us19q4CPQzRFCYLStBLF2hRc13ioKeNNFgRKUqsACwuntcNn4e6/MCft/8t/BctLmyFd7YVLlaXzvW7waxPjdv3wSjpw2D2UKK1NXTBDts8r2ALRYs9HGwADvrs/Px8MylMjxJu3hTE5GcWlg6Q3KvjK2eyairehUCeuqLQObHzsWF6YmOWglmaSXG/N2xI5iw+mv0b3QQHaJicXmLu3HReX2qdD7PbPoWS/78AzbyigL4ZezdiAkKqtIxVPRiPP198PR3Cz2TIYApBgh9EsigMDcHC30GzNyiopfymvNr43dPGT9uLq/asgi47SxgOwlY2oHHEQuxXaWY1I3PB4tcWKx0XZCfJd0H6EmyjDaAtK1ca6Nb3Y+TB+U16jWrg4VH3nPtxFpwlJ48GcgmIUQKLZrB6v5eRHDy3CnqSfcamkMlTN6vO1jEe9XCpVQLbketmYLg9iG9KYc2VeGpmQD4gdXZ4hDdXH1wPx7asFYYHhRAXTBwEK5u0rRK8fjk992YunUTyYsi2OqH7WMmwGquvo1JeSbPc34BTxopq1wden4MMLcCC7hRqt5roeXp2mvPqS3fvdIAVp6fMpZfbVgEPG8veMIw6Ro3twdCpwGUnJtv1wUygQXfL5L1PNV++upXTB/2JhEE4fFF96PbrVWj7Oyp8VekHz3lGSBrhSxHZoFAzHahleSMCFKqwK8G0sjzU1JjYOEfgPn3qMiw1Lk1HAGetw88gWQrjKY1Aoih2NKBMp3BAm4As7R2/Jxrs+HBDWvwwz/Hcd35F+LlXn1FSXlVNlJ2J8/P8eQk3NamHVpEVr7uX2XMj+fuAr1HkfYOgLRClzADlrZFtNIKX59nfgGevVLqhwWOcdlzXhlzcKfP2vDdK2u+yvjxAeNHT3sVyFjg4AZh0evB0143tKkMAIKfgBY8uqz14tbvebkyydLiR9TwvtO4ngSeMgWwnQasVwIZc2RyefjbonrL3gqqw4ycjcIQafUB/UwBYWLk8gqpbfsO+rV3pjLZ+XEg+0vA1AIscpHXEI26gzolQP+TkowWEZE1zwtERhC9Tyn5mScb0/aDVq8QI7Txrzzv70JCxzVrA6OMH3dWdC09tiYsAq5nALYjMnxlyE8U2X1kb5T8MeS21UJF0iOP7V7o4QUQPh+a/5Uu38XF0z8XpeUtOjTFc59PQnC4d8Xxk+NSMHPCbCScSsK4l25Hhx5tXZ6bJw/UY7sZRgzBfwE0Epkkk0ZPBY/t5PxStEMMeRDI+hw8ezOYf79aQZzmSVxrY1+iwgi6I2xV0hyJ6JC0pspDoFnduJ1NT8cNSxYiPitTGD9f33YHAi01b3NE3lpJKcCBwDuhhT5VDFpJS0AbSsp3IgLEaWCBkjHf21tN+O5VFEPl+SkDQW9fBFxPBI+/EdBjJYkZVSIQ6dk5jef8AOQfBLdcJnaLPH02kGVUEJHSM9Hea64Jgp4+ehYjzydGaFlaPuLZIUKSwZvaG+PeB4mf2mw2BIYE4MvEBZWq+l7S3PWE24A8Up9ngN9V0CI/lMYPMfXGdgU4ffAKNw0sZHIxJl6ed9AgP+wM5neJN0GtxuIBBPSM+QDp6FErRW3cA5cSXWw/8S+e3/o9IvwD8Hrv69AgtGpyVhbv/R3Pb/ke+ToVUcg8pG5VnIdUUQx59nfgdL+IWiDwdqHr5cwQ1bO3A8l3yXQD2pgS47uTzWlFx1MZ53v7d88Tc1bGT003frK+lq5wkVxLoZWZYP7XOZ0Vz/rSOJYDQZMAcwPAdhwIuB2aKczl9RT/XwKGN54oytiptHz8KyNwy4Mll7Bu/Hgz/vppP7oP7Yq4EwnITM1C39E9hFFSWe3F29/ElmXbodt0WPzMWJWxCCYTJYVWbaNEc1kpYgGC7wbL3SZI0ZJSuyHpv91o0mQxtPxfCwZlagBGIS5TgcYRt52WSeoi0dUGFrUczNKuaieirlapCOhn6H7mGNegPJKLAdt/YCGTRC6Pp9ulH85CUnYWTIyhV/MWeL9/oVyiUi52NCkRC/bsEsbSmA6Xuk1KuOv0KQxevgRmTRNX+W7kGDQMdf3d42kc3O1PbDZjyUNuFDPQ/Qlyni6gx/UDbEcdl2ARC4RmYk1oyvipCXepksfo7YuAPAI84UZi2ZIu8+g1YObmTlGRpFyH5G8sAiz6K/DUF2SyJBHpWS50Gc1vF20VYa+Wl7XAvW+PhZ/Vuet6+6qdePZmEmE0LssgPDBtr7oIr333rMvXc/fAU0fO4LlbXgPxDU2cMQo9h1/tbhceP15PnQpkfko54Ph5YwieG90ErUiFfjMlQiYA5uZgWpRIji7ceM4W8CTaQVIj9/kUsEBKYFetJiPAORmzsgScn+0IIMuYjv3+26sFd3tc5uSS2e8iJSdHGD89mjXHhwNKp2Oggdl0HZfPnY3k7Czh6bi/8xW4r7P7xKc//vsPdpw6iWubtUD7up5jid8bexaL/tiNZuGRGNexk8PA8uQa4bb/wOPshQdmIGgCtJAHnL9v424CbET3YTS/rmARH9QI4VNv/+554p4qz08ZKNaERcBzdwPkUfC7qtSkWD35MaMEm0Iwl0mDic6jF7BgKF3viTVVpI/PZ67GnMcXIT9P6ovZm3+QFavSFnn8etThf4dP4+dVv6FVlwvQpmvLSrlGeTotbHymJZtwa2uZhzTnzxlofCGXITBzcRe6qAgjA5eoClgYWPRKMJNSfS/PPfCWc7hgYh4P8Ez5/FkuB/L2yOH5dQFyfzIKFCxgdcn4oaR4z7Utx49h0rfroetcaGoNb39xqZ3vPPUfNh8/ilk7pZeSStdvatkKb/R1nf7Cc6Mv3lNWXh46z/kAWXm50JiGyVdeLQygymh62kwgYy5gPh8sYm6JSed63hEggYxKytGSRi6084CgOwX3F9Mqj/i1ovOuCd+9Cs+RC05+1UpCwJsXgfgoUsgr/wBY0ETA3BicKov0bCCgv3Cds7w/wemjmrUcMLUCLOeDiSS9O8CTxhn5KBzQ6kCr86PHFwJ5Xv53xZM4ezwOjVs3xL9/nxTXoDAZeWRKark5edgwf5NQau83pgf8/F17+afEp4p8JBI5teXpmLHleeFl8obGMz4BT5smhmKzAU/fcQEO/1kXnx68AZYcCl1yQ0RRHlO4EbOvkM6gF65Wc8IE3oC7N45B5oLtKjI0FvWVKFUXoZWkR+TvzCpzwAKJYdjJmmD+5RIr/TP2LG78bJEwYjg4vhx6Oy4uwQuzLy4WN3y2SHiJKFeHPhhBFj8svmWwRz03FblPcRkZ6DL3A9EFjfOO9h3wbPeCysqK9F2Rc7ktFpyY8vN2OASmReGJqan00lcx9YCrc/Hm756rcyjrOOX5KQMhb14ElEvC02cZO0Ti76DQE+0yqNHfdk4PmVwoQibBD4EFyxCK0JKiKjCeAxb2cqXxyNjybdj/yyEhkFqvRV34+VlElVhpD/7LI9/G5qWSh+jqW7rgqSWkWF122/vDPjzcXXLmmP3MGDNtmND68obGeTb4WWLXFvRHOPlPe1jrzUKd0EeB3J+NIZqg1dvnDcNVY6gkBETJevJ4gIoQHISFGhA+G0ibDvA8yd2TQ1pR+dLPUudXh9FbtOSdcsSWuC0yvPbQQdy/fjWIh4eMhZl9r8eACyUz9Lnt831/4fFvNwiyROIJeqlnH9zQsiX8zd5VpTVl07eghOrIgAAsHTQUzSMiQVxHlUmqKAoRbMcAvyuFXNCOU/8hwGJBuzoFEiI850fwJDtDe4GvgdXZ47UJ0N783fPUY6mMnzKQ9OZFoKe9DWS8X2hHce5kpDBmQSPj517xv6psh3YdFd4f8uKExYRi/r43ERRWemn8iBb34syxWDHM6IZRWPKv3NWV1bIzc3D3JZPw36HToNDarJ2voFFLqXhd3U18tOKvBYhtm/Kzgh8EC56AgvuoAZbWYJGfAdnr5H31v75UdujqnpO6vnsISMPnISBnrUE90RAw1RHCtTzjIyP0Rc9sMAAKiRkbl5CnwMxNZJWmFgPk2EPU9ExPBAsmKgvXG3HtDFnxGfbFx6FVdDSW3ToMwX7OvavxmZkYsOQTxGZkoGFoKFYPG4FQqz+2/nMcr/y0FfWCg/FKr36IDgx0fQCFjqSQ1eGkRFH6XtGy99ScbARa/ECeoNs+X4oTqSkY2qYdXuzZ2+NeFp6zHTyJvNdMeHJe+PNhfLJXan1N6dYDoztQHpdsgiU6+2tBXyHeydZroUXQu9s7mzd/9zyFmDJ+ykDSmxeB4Iqh5DtuZxwl+nXaKdob7czob3+A+QOWiyTRXhVTsS999WssmLLEkfczc+sLZYaiKJl69qOfiImMfWk4bnvsZpfXfFZGNg7uOILGFzVARN3iZf8ud0SfHl3Hh5MW4scvf8HVt1yO8a+NqNBLVEgUZC4FM9WVtPjMBJH4mr0KpAmG/OPSRa6TgcQA//7Qwl93Z8jqWC9GQFbudTdGSPf3FmjhssRdJ8HSXAqP6IDW0FgDhidXawLopwx5BXrOySii/5nAQl8ACxzs9qzJ6xOfmYGogECYjOqrkjohY+lochLOj4gUng1Kfm7/wTvIys8XicWDLmqDl64t0A2jpGiLZkJQCQaV/Tp0XP9PF+J0ehrqBwdj9bCRCPf3x7w9u/Dbqf9EXlzPZs4LOEqb8Myff8IHO39FnlFST1VlzcI9m2MjddYWOjafN3wzAvuSpAHYOjoGq4eTJAbAszeAihaYtQdgbgnocVId/pzCBrdvYCWe4M3fPU9NWxk/Ndj4ES9MqtbKJL4e0pppA4Q8KZXYiVnY1BzI/wvM2hlMi/TUmnG7n/J4fugi/+w7KbxFTds0cvuanjrhl7W7MOXGl8U4mMYwfc2TuKxvB0917+iHU25WwtAigqbiRy0aWh27DInHL6s6rGIEOM8CP9u5UFk7OYDagUXOA/QU8NSnZBg69FnwtLeMxGfyFPQEcjYaozUD1l4ASaZY2hiyCbJ0vKoa5f60nfU2cnVZyBATGIjPbr1NGBjzdv+G6T9sFuG0uzpehnZ166F38xZODSzSH3tww1oRfqOQ2ow+14nNBWmS2f9ty6hxaBDiHg/Rp3t/x7ObvxOhOoumYfvYCYgMKJ9nqiRMiTtNhrNMgKkB/vfrRGw8+q/IoRrfsRMmX9kNPO8PQxHe8MIH3i3yt5i5cVXdqnJdRxk/5YKtdp3k7YtAkOWlzwGyFgoBUrkbJI8Puc6tACNa9TfBrN0qdGOI22flrA3Ck3LjPX1hMrvHmXPiwH84tOsYOvZqh/AY1xN2KYz17n1zcOzPExj2xM246uYuFZqHuyf/suY3TBn4ijR+GMO01U+g83WeJxmUZfBU/XZO/UHgXdBCJ7k7bHW8FyOgJ44Fcinfx97Mks/nHL4YKmhA5mKAmcH9+wPkGbL9A2j1wKKWSe9hNTYpnLoONq4Lw6VjvfOwbPBtwiOUnmvPPYT47fZ2F+P5a64tNtrDiQm4bvHH4hgyVNYMHym0yF7b9oPDa/PV0NvdTqwmz9Ts33bg77hYDG93Mbo2qhxjgzYtQurC2h25ehBWHzqAALMJ/ZpaZEVmziaDCdqed0kQ+IFFrwIzN6vGu1f6pb39u+cJ4JTnpwwUa8Ii0GmHmDH7nJBXoYmZmkCL+abc64XyFEa3egBnj8eKRF0yQu58nrwUld8WTV2BxdNWiJAZGVzLz85BSATlQ1RNo7DXrAfn46evdojE67tn3FkpTNE8c4Xc9duNH/8BYIG3A5aOFQqzVQ1K6iruIMBzfpKVloIoj5oJLOwlUf5cuPH8w3JDY+kInj4DIFZhEcbWjARo97wh7ozR1WOv+XgO/k1JEaUVbWLqYOWwERiwZCEOJsQ7WJypL/Lc/DDazlVVtHciPvzh3+O4qnETXFq/ARIyMzHs86UiD6j/BS3xVr/+VS7I6ur8zz2O83zwxDtl6JqFAZGLgKQxMtTlaBSqfA4ssGreoeWZS0347pVnXoXPUcZPLTB+ZAn1y+cYP/a8AAZY2peoOuzKAqJqreuswwSjM7UrbuyEF756zJVTK3wMcQStmLkaNoMn6N63x+CGiX2qha25wpMppQOBrcj7OQr43wTN0rTUy5HEyPef/oimbRvhypsojKJaTUKA2+LBs1YCuVsBSyew4HuKlKzzrLXgKQ/KKfl1BywtgYx5hYyfHS7L0VQmLj+fPIFHN64TeT9kpFxcrz5Iv+ujXTux58wp7D5zWpjzEy/tjElXuk40Ss9DZVdqVQYuPO8v8AR7fqJF3FdOlWCJZOjYvbrK81MZ2LvbpzJ+aoPxw/PA094E8v8GAm4FM8WAs3BAyCpA8oSYG7q7Nooc//5D8/HFW2tF+fj01U+gYy9Zsl3ZLSk2Bc8MfBkHdxbQxJPnadQLt1X2pUvtP/ZEPN65dw6yM3IwceYoNG/fpMrGQ6HA5wfejvFPH0RONkNq7lPofMOQKru+upDnERDixPppwNRMJMLqiROA3E0FFwp5BchdL0IsLPj+SpG88PSsyIAh44i8CBfXrYv/0iipOQQzf94mwlFjL7kU1zZv4fZlqV8qv6c+brmoDdoWKit3uzMPnyDlL64xjFQbWPi7YP69QV48EjoVgrTWK0XOj30z6Y1cP8rz4+GFURO784VF4Op9OXM8FoGhAQiNdE0A1dV+6TgSIP3py19Fbs1Vt3QpllPUz3qbw/tz8TVt8Pr3z7nTvcePfeL66dj97R9ivPVb1MOCA297/BpCF4xYgEl/Leg+aAY/08lDp6El9UbdRjKvIjfHCv/G28G0qgsHenyyPtwhz/8XPGEQwFMAyyVgkYvAMxYB6YbQKWHjdyW0SAp71bwWl5mBgZ8twpn0dMQEBglNMUqYNjMNP48rORH5v7RULPtrrwiZUdUX5QVRWyVyjdaKUJvFZMK2MeMR7l95OoHuIs7z/gbPXg1mbgsW4JwBm+dslpQH1MwtAD0dLOSBEnUZ3R1DRY/3he+eT3l+srKy0K5dO8THxyM5Odml9eELi8AlINw4iKq0ti7bjgs7NUeX/pe6dOa798/FV9X56gAAIABJREFUqvc3it1Qk9aN0KxtIwyZPBDnd5BJgW9N/BCrZ38jitqeXPwgetxG4oIVawd/O4Id6/bgkmvbovUVLYUBlpqQjvCY0DLzbB686mn8vf2gGG9U/Qh89p9Ua/dk01NfBjKp3F/SF7A6P4uqPcpDSt53CcKjpBYURcy00MfAguxEap4cheqrshHg6e8b4rd58j5HrRAVYDzuWqO8nSiV74YWYoTBKntAgPDYkJFC+lt+JqJj4CDtLOLhOT8yyq0RLPnzD1F5ZVdypyowSm6mdvell4kS+mFt2oukZLpuTFAQLoiMQrcFH4E4hqjq64mrumPMJfJdMmP7j6KMPd/oY93td6JlVIEQsFuDq6aD9bjeMnm9SDOD1aFwZukcaFUxZF/47vmU8TNp0iTs2LEDe/bsqdXGD7leSTmcmTwnGujqA5eamIY7mt2DvJx84amZuupxdLm+gOyrpH4KkxqKD4DGRGLzsjMfifweevke2XMcQWGBqN+8/FUuFEbbunw7AoL9MWM8iQwCuo3j1W+eEX+fOnwG7bu1xssbn4bFr2QG2wM7j+CFwa8jJzMXk+bf69IcXcXQfhxPfw/0P2n8WMDq/OLw7tgy1gOp94vxS64XEjsd7u4l1PHVjABxPPHUZ4GspYY4Md3nzcLIFc9x5lKANKAMTqiqGO4nv+/Gc1u+F56W7k2aokfT5iKHhwgDqRFh4G1tXQ97/3b6Pwxe/pnICyItsWYRESJJ+urGTbDln2MOQ8jfbEZ2vjT0X+3VD5O/lUSOtMRvbtUar/e5DkSIOGjZp9ifEC9+u6pRYyy46VavSogWeXu200I/sSRNNilv8rtRnWvPBaJE9p1e4cFVxk9VPGlVdI1du3ZhxIgRmDFjBoYOHVprjR9BqJVMO0QbEPwQtOCJVYSwvAwZBfd1Jp0qKS9xx9O34vanBznGQN4VW75eTAX+42eXgiq7Cjd6AFdnLHJZ16usiebl5mHUhfcj8XSSJFw0qDdMZg09hl2FzZ/95CBifPXbZ3BJz3ZldVkpv3M9BcjeCE7Ga/ZGIP+wYIJmVsolKGg860tQsrsgTAt9UjFBV8rdqNxOZbEChbfoA2gGIhdD8ytdZLRyRwQM/2KZ8MBQIwNIsFIXuihJN3x92x1uDWPz8WNYe/gAujZsjJtatRbnvrfjF7z9yzZHSXvhDvu1uEB4nFYe3C/+/5ObbkXnBg1RWJaDxjazz/W4oaVzWQ63Buihg4nMUISqhVhtZ7DIhU69yEIdPu01sUmFniCMJRbycLGKPw8Ny+1ulPHjNmTeeUJ+fj66dOmCN954QwzwpptuqrXGj54w2NhR0JYpCFrd3VV6U8jAePCqKTi484jID3rui8kIiQgSWl70b4/3nYaM1ExMeH0kBj04wDE2esH++eN+nDx0CnMeX4y0hHSMmjoUw58sMJwqOpHYf+Nwe9N7RDfkWfIPtCIrPRsR9cJxz8xReHH4m3KfyYD5+99Cg/M9q5xOht+h344isn4E6jRy7qYXpbLx10sFd+SDhb1ZYt5AEUMo7w9ATwb8uoIxqvRTzdsREEYFkeQJBXdpXrCYn0TBQnW2hX/sEWEqMi5aRUXjQEKC4PKxG0OUqExhKHfaO79uF4nO1J66+hqR7Ey5QMNWLBVhr8KN9iTTyLvUph2OJiUiIiDAQVD4x9kzuGnpYsfhHw24CbvOnEJ0YBDuaHexyAGqzqaT0HTWlwX30whVV+eYynNtZfyUBzUvPOeVV17B/v37MX/+fGzevLlU4+e5557D888/X2QW3iB8L6pBcr4HTPXB/DqViLKe8gyQZXhQLG2gRS2v8juSn5ePY3v/xT9/n8Sro94VScHXje2J5LhU/LzqN7GTNFlMWJv1qVPOHMppIc+Mn7V42ImSff2sZtRpXPCB2P/rIRzYcQSXD7gUdZuU/OGgfskw2/fzQWgmTYTkQiOD0ahVAwSFBuLbRVvxx5a/0W3wFejUx7O7b5ozMUX/smaXSOZ+cd1T6Hhtcc+SSHKOK1wSHAj4tQcLex3MVMfpveSZyw2OICaYf7UICpWp5u0IFON2sg6EFvGaVwz71/9OgqQnujRohLErv8CuM6dFHs64jp1wS6vWZcphnDuJy+d+ILTBqDUJC8emO2V+2qZjR7H87z/xV5zU8evT4nx0a9wUVzcpmeph3eGD+P7YUfRo2gyvb/sRJ9NSxbnjO16GR7teVa348ayvwFOIBoRYnxuDRa8VHD887XUg7zfJuk8hai2iymWG3AFGGT/uoOWlxx45cgQ9evTA7t27ERUVVabxc+40qnsR6PmnABI8zPnOIMqygYW9WqJ7lPMcgFzpPAMsaGS1ylo8ef107Fi/R0BKOBI/z5qPvgW36QiKCMLY6cOFoeEqaeEnzy/DwueXC8/MpHn3os+d1+CvbQfwULcpMJk0+Af54+PD75RajZabk4e/ftqPes3qoH6z8ucOubvcKRdqUPQYAwug98hrRK7QuU3kgAjPT0FpP5HaEYWBFjbN6WV1IlXL3W78poHV3Vdmwra741fHex4BPcWe6yO9Kizmh2pnbS5plnk2W4W8KvetXYWNRw+L7il/55VefQURIrE70wNNgbXPBg0VoS1XWmxGOsau/NJhNNE5ZAzNvfEWV06v1GOIxBK2E4B/PzAtHHr8ICBfCp7KWDt5p/KBgCElPtOVOkAXOq/u754LQ6zwIbU+4XnBggWYOHEiwsKkpEJubq4IedWpUwcrV65E586lE8RV5yLg+SfB43sXV233HwAtfIaMxafPlArg1r5gIY941Udv0bQVwljRNIYmbRqBBE0/eW4Z/tp+UHhfyAPS4Px6+GjvDJdYk2+OHIX0ZLl7pDDaB7tew2evfAXKF8rPlYmSM3+YirZXejYHYNHU5Vg393t06NkWD82eALPF/bAShbxGt7wfcScTRM7T/e/dhQET6N4Wb3r6LCD9nUL3nYyfQdDCpjs9nmcsMPJGNMCvM7RI+qCo5i0I8NzfwLO/BWyxAE8WzN3Mvyd47m7wxBH0VhJEhiziQ696fj2JX05+Plbs+0vofRE3D5kAL/+0FfP37BKXoWTo6T17Y3Drti5d9tlN32Lx3j+gF8pGevnaPhjSpnry9EoaNCU/83giPZSVmec2FrO1WgpTygK5Or97ZY3NU7/XeuOHyttTUmSVArVt27Zh9OjROHDggPAEWSwlV/TQ8dW5CHjGPIO5uejttueBSJp88iZQvoBUiBa06n5dJH06K31unlpEJfVDYabvFv2A5NgU9B3dA6FRkh/otdHvYeMnmx2Ep5/HzXP8VtqYJvV6Hn/+sE/Mtt+Ynnjw/fE4/tcJ3Nv5ceRm5YoqsNm/v46AIH+3pnb62FnEnUhA6ysuLGbYFE7gpsTohz68G31H9XCrf/vBSWeT8e3CrcLrRFxGJZGb8fzj4Am3ADxd6rRRQnP4zJLDXlTym/ujlELw7wPG3Jt/uSajTnIJAXEv468znlG7vpMJLGaLuJ/cliDV280tvVrlmyZLmy1Scady94q2J77dIIwhe8l7i4hIfD5kGEKtztcuVYxtOnYMVzZqjCsaNcbUrZvw8e+7RRk8NT9Nwy/jJiLM33vWPs87IJ9jUa1pTxmn8RE/F60FKxD9PTRz9eZ4OTXKjET3it5nbz6/1hs/54JfVs5PMcu8GhcBz/kZPGlkwZD8h4IF3SaVnOlxKlxZYC9dEr+YwUKngQVWvwvY2eIXYqE3viJeplRW/vomMtQkgVlpjRKlV3/wDayBfug/vpejFJ1EV4//dVIYL4EhxcnOdn23F7Mf+RjhdcIwaf49iG5QwFOy+/u9Iglbt+kg8sTXvnu2yFg8afyUNb/Cv3NKXrb9C5hbAXo8ePIDgC1eVnX5O/cYudO/OrZqEODZ34MnU8VlUcFaFr0OzNwC4hnPWg5maQsE3llE4qJqRujaVRKzMnHbCqm31af5+Xjv+hvcyvshAdNDiQnCeCEDx64JRlePDgzE9jETSuyPkp77LlognksSLF152x1oEBqKyd9swB+xZ9AqKgYPX3Gl2+Knrs28/EcVkx0K/wDM2l1uaDMXSgPI1AQgT23yJBHqZsH/8wrKiurc9JcfcffO9Dnjxz14qtfzIw2cH8CzNwHWntD8iybzSY6QF4CcDUL8EDl28VIyfp4HCxzs7nQr7fisjGzBr0Oq8Jf164ATB04h9t94XHxN61L5dFwdEOXyfDjpExzedQyDHr5BiJAK/DgHhcsyUjJBnhtiYyZPVPvurfHUpw9i1gPzsXbudyIpm9rSUx8isl5Ekct6Iuzl6jycHacnPwJkrzXCYFYg+AEwv0sAUyMg/5hMiGbew3BbkbnWtnOpUIEn3ArYjshSdvrgBQwDC31GlDjzOKqaIs8AM8RN7bpQ3oPE8eQkjFn5Jf5JTnKYcJ8PHoZL6p+Hebt/E0rmfZq3EOGsd379WQz8/i5XCDZnauS1IcOJnjBia954xyjM3/MbXt32ozy28xV48PKuJU54w5FDuGfNKpEXRGEzyheia3l7E5IW8SRWS+SVfoC5KVjQaFHWzim0Lf4dgP9AIHtNwToQFWJF30FVPVdl/FQ14l54vepcBFT5Qy9LZiq75Frm/7wDZC0TeR8s7OUSCbaqA2bBiPzzQWFkjHh2MEY+61ktqhUzVmHOE4sFsSJVchHjckSdMGH83Bg6QmhwUe4RkaxRo3yjBz8YL6rOXhv1njCMohtG4aEPJ4j8nhYXN8WQSTe6lItU2XjqyZOF6GmBCrhmuM0p/JAHmM8Hi/oSjFkreyiqfzcREM8llbLTs+x/vbhHjNH9A3jeQfAEO92DFMFkwcWT4N28pMcPH7LiM+w6dcqRX0Ml8JtGjsWZjDQMXUHkjDKN96KYGByMTxB/d27YEJO6Xg0qTT+amIjFf/7uYHheM2wELoqpg79iz4rgD/EGldZSc3KEPMY/KcmoHxyMVcNGOErfPT5ZD3copEsyZgNZXxQ8vywK4IQToRYIBAwFsuxM7iaDzDTUwyNxr7vq/O65N9LyH608P2VgV12LgGd+Dp76pHCXs5AnwYJGlf8uV+DMw7uPITsjG22ubOVSaColPhXTh72JkwdO4c4Xhor8GMr96WsmVWPZyPAgbp2Ovdq5nEBMCcNzHluMPZv/RP+7ehdLFqZE6iUvfeEgKVx0bJaj7H3byh147/55ohrs333EnwNhID32yf/Qc9hV+HXdbsHsfGnv9pjQ4VHk5eWL3+9/dxz6j6/+EJMof6fy2fx/AV2Ov1gLmSJ4nUDJ8JpfBe64OtWTCOhpbwAZhvRJ4FhooZMd3QvDKIW8eqsBUwOwyCVemfx645KFoqqKtg2BZgveuX6AYH3edPwoxq38yuGRIT4ekqOgVjcoWPD4kKFEJIXEzEz/XScoCN+OGIMAN/OGKGH6WHKSKJN391xP3s/y9KUnPwZkE/ePs6YB1n7yB9thsKB7XOL1Ks843Dmnur577oyxoscq46cMBKtrEehx/QrKnbVoaHUkQVhVtq/fW493/zdXXJLK1KlCqaz20eSF+PytNcIDQ0SCX8TPR3B4EIbUH4ekswWJ59QPeVsenXcvet3RzdEtha+c8fsQB89rY96Dni+TRqlCrGmbRo7zkuNS8PSAl0Tuz5BHb8DI5wqMrcJj/vLttVg/73tc0qsd7nrlDiGdYW/EIUQVWdTMFhOGTr4Jo6ZWr3p84bFzPRM84SYpdOpoQt7RSKKkfww15BGUyGlZa7UqftdJ4Vs/JS+lxQD+fcFMjYHAEY4EZ66nAyzQa/J90nNzsfn4UTQOCxd5NHvOnMb961cLz82MPtfj8obyuaO/H9m4Dt8cPSy4efpf2BKPblwnfrv+ggsFG3OeLp/XV67tI0rluzVp6vDa/Jeaike/WYfk7Gw8170nuhj9VsV9qcpr6Ge7A/x0yZdkodDq7qzKIZV5rer67pU5MA8eoIyfMsCsrkWgJz8MZEttG/hdAS1SGiFV2e7p9BgO7ZJ8MwEh/liZQkl6pbe5TyzG8hmrHOGnL+LnISgsCDs37sHTA16GLd/m6EAza5g8/z5ce/vVIGboZwa+gp0bfsdFl1+AVzZOQUBwQR7Lqg824r0H5jmU3d/5+UW06nxBWcNx63faib8x7n1smL9JeI1mbH2hRCZmtzr24MHCAEocApB+EO3Fza0LcYjIC7GwmWAB/T14VdVVeRHQU54HspYYp1POD61/kj7oAjCLUfZevurB8o6ptPMooXjAkoWgBGWqxJrdfyB6tzgfS//ai1k7fsFF0TFCY0vnOradOIGW0dFoFl6Qn0IeGmqk3TVw6SKh1dUoNAxrh49EkF9Rj+SE1V/j+2NHRMVWZEAAdtwl2dddaT/++w8e/3YD/C1mvNNvgAijeWvT4wcD+aTj5awxg8biRcePnCQvslYCFMYWodKqZ62uru9eVd5DZfyUgXZ1LQLOs4AMMjby5S5Rk2XiPHu9VIDWIgFbGqAFgYW9CGZu7PF1M/fJxVj22koR7rrsug6Y+vXjIPZmUl+n8NaN9/QtlhyclpQuStlP7P8PI54dIsJK9kYcPZlpWcLzsvHjzYLh+IH3x4swGIWeyHMj2J/NJjwyZyJ6jyyg0M/OzMHUwW/gz5/2o9/oHrh7xiiXwnDugLJ56U/49MUv0KjVeXj4o7sRFFr96srOxq9nbwMy3gbMzQAeAGQvKnIYi/oKzCL1k1SrXgQ4twmCUuHdSZWadzLXQzzNUpRWcL1UbrlzQmamCENdGBVdqgjo2fR0XDFvthgdhamGtmmHB7t0BTE0C/UxTcO4Sy7Fl/v3gYgGTZqGFYOHOa20OpOehoMJCbikXn2EWIvnoxFJIQmbkvET7u+PXeNdz3fqOnc2zmSkizFeWv88LL3Vezy05644bjsFHncDgDTjJwp19RYhaqb5A35XFTFw9KT7JJs/IR54B7TQp6p8EVfXd68qJ6qMHy81fpwNi16gPPay4qSHLFgmvJqbeHTtUK7OD5//IvSveg67UgiMUlLxijdWCTXxBhfWx5y9Mz1yzcN7jmFiR5kPQX0//9VjgriwwQX10by9nBcZRq6UxJdnQMTAPLjuOFHyTiEv0hSjxGxva9wWBx5HAqfkQdABv26GNhT9rQGhb0EL7Ottw1bjobuVMsVQbycPCIWDpJeERa8HMzevNIx2nT4lxEpzbTb0bt4CH/QfKAwZMhzObWSI3LBkIQ4kxAuj5MMBA3Fx3foO44eqrSyahmyb9ODS36Tw3rVRY+yPj8PAlheheUSkS3OhHJ4H1q1GSk4OpvboJUJixCT94o9bRKhtdIeOuLHlRcX6ysjNxXWffgwKm9EcKAy38Gbve1YLD5xThVfSvYK2AqHPQPPvVSJG+tnOggxTNPMF0KKpEqxqmzJ+qhZvr7yaNy0Crqcaxk9RzhABnN+V0CLnVzqGj/ebht82ShcuJQWvz/3MYwbJ90t+xNYV20Xi8dfvbcCJfSdBHGZTlj+CFW+sxP5fD6PXHVfjkbn3OKqwcrNzhXeKCAQHPTQA57WoVy4MKGdocL1xMpJkMaH3ndfgurHXolXn853OjwzDtR99h9NHzwrOoXOvSx6yjQs2gzxWpGtWOIRXrgEaJ/G8P2TptKMxIHCUeKmywJFg1awIXpG5+cK53EaJw3lA0gQg/zAQOBwsZIrHniFnGD71/TcibGUnBGwVHY398fHo2/x8vOuEr4eMiy3/HEfjsDC0NSqxPt37O2bt/AXBFj8cSUp0kBPar0dmFHmF/M0W/DRmPILPCXG5em8/+X03pv2wWeQTUZ/Lbr0NW/89LhKdSRZjzaEDeHjjOjGXhqGhaBgSipeu7YtGBoM/bZAW7/0d+xPiMaR1W6/h/uG5v4MnDpfVmZaLwSLpvek8nKWnvgRkynd5dRW7eNN3z9W14+5xyvNTBmLetgh0CoWlTT1n1CRr0KVKZA0oPEW5OZS7M/iRGzD+tUIkjGVgGX8qEdYAvzK1vM4cj8WI5tIFTgYWyVX8vf2Ao5LrrW3T0fryC8XvxO3zxdtryS0kyAsXHn2v3B+Sr95dh0VTVyA4Igj/HZQJik3bNETsiQS069YaU5Y+BGuAdN9/PnM1Ppy8UCR1E3P1kn8/EOE6e6P8JAoP0uaayBNf3jDF3WfT6fFS9X1AEe0vFjEHzFqQNO6RC6lOKowAzz8BniZzOVjIE0VC03r6B0D6IsDyf/auAjyK6/ue2bhgCUECFHd3d3ctUNxdW2gpFAoV3KFQ3N0diru7u3sSEoiH7Py/cye72Y0QT/n3l/t9/Up237x58+btvDv3nntORuFt0tmUjfP5IuuAjsuoIwclSmNtYQn/z0FG56V61mxY0PDL3EKTTh0XGYpcTs7oWbwk+u3ZKY6Jga867HnJ45PDKZRINCYXNvfCWUw/c8oIlHa2swNL3ensjKpcTcZB3iFahmTJcLxzD7Pu19+8Djp7fD20sbDA6a49I2WNjsm44tpW/5HEhoZydkb7/oFiGbF4qwhpB10FFOt/LX39te17cZ3/iI5Pcn7+nzg/GqEhw+Y7Q8ixQh49SjKtTDblFGGMTQwj3ofYHYMwKJmX9y46BIcU9oLTMXUCDONZ+PNKrJuwTf6s2rochq8eHOlQrx27hSFVR0uai9bqx8bYMGWHpKRoS+7MQMZcrvLvUU0m4MyOCxIhoqO023+1WQVXZCehM0WpiVwlc6B2pypmDtNAchKdumt2KK9p4NzuEg2iTek6B/uWHTGSI25yW2wmqNq7+I8gTQDNPrkdtnnywRc/pqckAqu+RBYhLxTndUmSFvEztfHai969FRB0TevTqiB0zuvln2rQHajujczO9SWx4rgOir+j7ffuiNOQ0sYWY44dNuuSERtqavkFBcInKEj4eQzOC4+ptnyxsT1JCn8qXxFX377B2RfPJYIUrOol6sPKrtIZMmJl029jxP5sOhgvf39037kVt96/Q6t8BbHkqqb9xfQWIznE+RDsTCvlmgErm5nzhdFRW3DpgpFT6GCHLmaA7LjOZWyPV/33QfVkJakloEsOxeXwV/2bTXJ+Ynun/0PHfS2LQNPx6mwys1aAQzchRVOU2PG63D3/ADN6zxcsz5DFfYwORUxv3/dVRomzoA9WkSm3K4atHICcxcwxDHWsW5tVepny8IQ9n6lDw8jKLr/VWD9xG64euSmaXqYg6hsnbmNYnT8R4BuAdqNaoGMkJe6m52CKrG2WPjIebgwj1gxG5W9D37yXjFyLteO3CC7VUFpPx2rYigGo2rq8dHX77H0MrT4aAb6B4vD9uLSf2WUQ1M3KMVrrYU3QdWzbmE7rF9tLRUjwa83xVWIutBqvg0nqzGwG5N6Qvdmd6UjNAYZFVuhc9kXq/MC2IXQppyT4TDKC0mXbZkklMXpjmkBnZIh/f5M8BQ517CpjIQC63OJ5xnY8Zl6DxqiRLYdw9+x5cA/k9ymWLj1ee3sjp5NzhI4P024sn6+TI5fggqJj/G323b0Dex/elyjOqmYtkS1VKqk6I1dp7xKl4GxvL7/hex7ukmojZqjFhjXw8PND3Ry5MLtug1hHgqMzxpi0IVs/Pt/XKrgsYpeej8n54tL2a9n34nINUR2bFPmJYoa+lkWgKUCbc9coqRZDsTGXvIjqhpt+3zFXf7x++FZSNwUq5MGUw2NicrixbQOHtgjwo1ifZowAzb00Cemzhpafts7YA+6vtHA1bf2bhcLAHNZO77iAmX0XSFvOvbNrKqx6MveLD7AAvwA5f3InrSIuKmNEhpEZmqW1JdqNbIG2I5qLM0QMT+qMTji17QJ8PH1A3iFyDBWrXghdx7UxY3z28fLBR3dvESqNCIj98sFrEVzNUuCbr+YBHNXcJH0ftxlQg19Cdf9WA7ZaFgA+PxcEvwjT2miOM03/aR7gM8vIz/QgeBTOexRE7ew5ResqIY3l7GtvXsc/Dx/g1POn4fA7KWxscblnaOXVzLOnMf1sKM/YjtbtkD8KVmbT8Z958VwA1wbb1rpdlKzOhrZ0bB54eMic0MmKyEYfOYjl166IMzerbkPUyJYdjCDxmIQqkIjL/aFun8qKrqCbgEUaKXVXHLp/VWP9Wva9uMxzVMcmOT9RzNDXsAgMVU6hQDidRoqWej8Ui9jl1nnZ7bL2wdtn7+VHRwzN9BN/RLVeIvx+7uAl2DyD2lPmRqfg78uT4JDcHm+evseY5pPw0e0Tuo1vZ4ygmB7BdFrL9N0lvUWHLG/pnPhxWT9kyBG5vMe9iw/xZ+tpCPQPwtAlfVGsRqEor4Fs0SMbjsf5vVfglC4lyBlE0dOB5X+RVBUdrjkXJoQr44+y43+hgfD+UDgz8DJg10RKaJXgu8Iaq1hm/BdG9L99Smo2qd5/GXWbvkQ7oKoBQMAJnHkdiLY7bknqiHpYhzp0gY1lwkfzhLTw9AlcefsGFTNlxqIrF+EbFCTaWQ1z5TG7kVtu35Ky9FrZc6BeztyR3mQ6Vr8c2i/RGrJAT6xZR0DKJD8kbxDTV+QOqpY1G/Y8uI+Xn7zQJHc+uDjEnlYi16xp+KxqKXFWfq1u1lIiVj8d2AcPfz/8UrEKSmX4en4L+k8zAJ+/Tap2FcCmOpRkP0aKA0rsX9XXsO8l9DUnOT9fsfMjujBMdTG94TgIOsceogeEoCuATXkoFhnitD6IrWFqxtrWCj+vHGgsKY9pp3TOTm45h6k9/sYnD2+zwzuMaYn2I6NXhvru2XtJR9Giy7A8oNwI3Dl3X7A3LpmcsfopHypRG8fs/voDUqROJsKqlw5cw0+1NCA5z00eocZ9Q2jno+7uX2uh+q7VxG1Dyqa1gViJ1IXicgiKLonpOTFvjuq/B6qnAc9mCcXlaJQvKL8dO4wVVy8bIzDUzcqcMmViDlvOZcDYxSZaQvHR+RfPI4WNjVSKGUrpZ9dtiMqZs6D9lg249OY1ymUX/q+1AAAgAElEQVT8BosaNcXG2zfxawgIO52Do2B3KH1hcPoIyl5x7Qouv34tDgwZpJnmimhsTdauwm23dwKK7lm8FIaUq4ABe3ZKSo4OFzmELnbv89VEVlTvuVC9Z4ahLOELLdnZj38VGn1Jzk+i//y+vhP+G4tAiNGgQP04GvDbEPIjUaCkufhVb2Ys/6aG1vY5GraB1mVsG3w3rCmYmvJ6/xEumVJ/8SG0ZOQabJi8A9/kzYD+f3XDkTUnxalpNqg+LK3Cvw3/WPM3wQLxwZ0hRzosucOHSsyNFWadKG2hAowMTT44Wqq0YmscDx0qArFZuh+bDSU651b9dkL1GmryIGXFmcbBojjvgGIV+Vt6dPpPahOzGdDrAwC3uiEabMmguGyP8iXl3MsXaLt5vWzUBVzSYEurtrEGDMdstPHT2sPPF6UX/m2M7BhK6pmGWtSoGapkySonIvszJS4Y/WEkaMudW2aYIzo/W1q2RfpkySRqs/n2TaNDyL5GVa6KtgWLYNDeXSKpUTlzVinV9w0KlFL+lLZ2aJE3v8wd2aMPhrBH21tZ4Xqv/gn2G4zpLJLAVvUcFUJkaCA+1HrRCC//fTzQv7HvxXQe49o+KfITxQwm5iLQB5wGvIZoeAFYAzY1gAA6EtzMbKGkPfdVvBV8acrIbzOu3Uxc2n8N+cvnxoi1g+H+0gOM0DAqxFQYnZmWQxqjTIPikXZFB6RNpl7CJE1HkLgc/mcwskV7vv8InYWCGb0WiPhqs4H1UbZxyQi1wSI7Efl99i87CucMTkjzTWqJYBWslBflGpFMMvZGjTNWqNGaDaqHXlMSRphWqgC9pwOBZwHrcoDPQm4zQo4Gp43Q6SLGScT+ypKODDsDos7+8XeAvC12LQGvQSFNFE3GIvnv0Fl9mYD0mZenqJaXcs2YKCmv+LyLrz99RPklC6RLS0VBsfQZ4BXgL4SKA0uXw5Enj7H4ykVcfvNa5C54jT2Kl0DPndulUsxgJE/8vmx5id7UX70ct93emw3TztJSpDX67d5pFFOdXru+aIqFNRIoEiz9wc8PY6pUQ63sMZPC4f149OGDpMvoPCWEqYEXoXp8F9q1ZX4ozpu/CictMfe9hJjb6PSZ5Px8Jc6PACXfs4zahD3DIgdgXQYIfgrFsRcU67htyNFZEHQqmAZjBVh8GfW+Nk7dYeTpYb8sHV//ZoEZSNntpTsmdJwNz3deEjEa1WiCcQgZc7ui74wuKFGrsFRaDak2WsDEVb+rgB4T2wmA2fPdR2TOlxGzz42Hrb3Gx8OKtlV/bhLHpuu4trBzsDX2yehM1/yD8frhG+j1qgidtvieNPThjW23zd6Lc3suoWrrCmbSGxG1b5etD94+0R7eqTM6Cw9QYpg+6C7g0QFQPwAU0CR1vlV+wK7FV/FQTYw5SOxz6Mm7RMJCQm516QD9yzBDUKCkWg7FpnRiDy3W59t65zZGHTkgpfHzGzZBntTm8hus9Jp9/gwuvHopbMxUbmfkKrdzaixs2FSqsGg/H/xHojKmpoOCiTVro2zGb3DH/T167dwmKSv+Z6gk4/m//yc8jnBmnfoYsFdjPGY0aGnj5qiYOWK+HOJ+Tj5/ioJp0iGnc/SxkbyelhvXSrl8jlRO2Nmmg1xfTI2O15mXz0UPLUuI/pnwdH2aDgRdAqwrAj4GhnwdkGIKFNUTqu9qwKoklOS/SCWnqvpD/dAHCDwH2NaHkmJcgovgJjk/Mb3b/8H2ibUIVJ9lUD/9aT6DicDaTFmH4fXGCtCX5el3zt6HrYMNxu39RcgF42qsiDqw8jjmDFoCVa+XNJDB1jz/W4gJDTa2zXRheA7+rJeKsVodK2PLTE0lmqXmxPXMuTgB2//ah71LDhk5dgigZrqMSvK08ft+QfGahSV91dylC3y9/EARVUaGaneuiquHb6JItQJIl8UF9e1DS9ArNi+NURuGRHjJ5/ddwYh6Y424iLkXJyJHUS2cH5ERBL5lljb2hr1qof/sbnGdyiiPV32WQ/000UThXWZO2yio/2bXLMo+khrEfAb076sBwS9CtuOI6P/Iwk2Npvghuoz5CL98BNNRjMqQ0dk1WXJpnH/ODPh9/iwpqqpZsoYjQmSF1eob14x8OmxHeYrFjZrhvru7cApR8qLY/L+MhIWmo2Dp+pRa9UT9nfIbO+7dERmNJnlCy+CpHUa+nz67toucRtmMmTCvQRP03b1dpDSa58mPoeUrRujU0/GosWKxECTSTdraqg3cfH3hH/wZ1bNmF3B5ZBaWK4iirGGdv6jugXdgIGquWCJ6aqQQYCozn0saqL4boH4cZYQywL6blv5iJaDdt4C74eXLEkry36DYt4DqtxmqF/W9QtLZ4kiXiWoIcfo+sfa9OA0yjgcnRX6imMDEWgRq4IUQ+vOQAVlVgJJygggeqp+fAp/vCIuzootfIOS6iduwdNRa0dEyGFmJSzcoLkKmcbHjm8/iz9ZTxZmp0ro8zu2+BN+PftJluqwuWPFwjln3v7ecghNbzkm1l52jLbZ5LcfuBQcws+9CYwXY8FUD8eGtF/7+YRl0OgXJnJNhxJpBQopoYamDotNh5umxSOmSHH8NXISTW84bz8FoFvtWoUrJ+sIbU0FOnyNrT0p1WcOetUCAdorU2gZgavuXHxVwOMvhuSb+3D0cJWsXiXR6iH86t/sy+H+m93i+hDQ12A3q+3KRnMJS3hgJaFKYDiM/1L+gFJ2Q1/9v9q0GnIbqReoEYtJIDvUqHP+xknIWFNuvT3ON1VnN1q/G9XdvRbNrbfNWKJreFaUWzgXFUImfaZgrtzgqptZl+2YcffLYiNnhBt8kTz6RoOiwdaM0JVHiQ3e3kC07/B2ikvuFSJTcjz99ggWXL0gkqVvREiCuiKKsZG/efOcWOO7kNjbY3KpthCSGp54/Q7stxEtCrqtMhkw48fyp/HYb5cqDqbXNr8d0dMefPUGnrZvk2jnGQx26xjj1df7VC7TauM54fkM6T/VZBPXTVJNqwFBcnhp0CyrJS8Xo/PwCxb4NVP/9UD37G9eU6DgympuAllj7XgJeQpRdJzk/UUxRYi4CeYgGngb03lAs0oqaO4KfaLwhrObRpYWSeg8UXezLQsNe7u6FBzGzzwIjASEjLDRqWg1d2g8Zc0ZeZh7Z1J3ceg4z+izAJ/dPxlQX+XRSpU2J98/djGXsM06aR7peP36LP1tPB3E4THGVbVgCjBy1y9YX3h985HQtfmiA7hPaY+/iw8LJU69bdexZfBBrxm6BhZWFMfoT5coHUL5pKQxfPVCwOUt/WStVXgRkk0E6LEu1n4+/RH6uH7+Nso1KYNSGHyIEYEfnvAnRRtV/gPqORI0maVMlpSaQqKQAVDqdQRIJ0vSC2ifEMP7n+5T0NaUryLxuVVJLh1llgcJSZiVhHeDYTD6xMdVDGJyJ1+lUpBiGV6wiDM6TTh6Dk509fq1cTdJY5M4ZsHcnHni4o1GuvFh5/YqUxmdLmQrFXDPg5wqVMOvcGVCfiykwWgbHZHjpbQ7qNYzT2c4e57v3DjfsjwH+KLlgrkSMGKEZUrYCehQviWVXL2HMUXN2aidbOyFNvOX2Dl2KFEfN7DmkP/bByIu7n5/04eqYDE+8NLFQVqRd7mlOShp2EIxGMbpUM1uOWJXh8/zVly+Bp7/2srexZRsUTpsOos/4oTsQdCNE1224MXLF1LrqPRXwXQdYF4OScrqwQGufzwUCj0GxawLFPuEV7BNz34vNuo2PY5Kcn6/I+eFQ9BQ8JBMozaaGaLuo3iRD48YFKE7roFgXjY97L30wkrH013W4feYeKjQthTM7LuHi/qsSRaEY56qnc2GfLGag2cYpOxgjPKYDbdK/Lo6uPyUpLQKhcxQxTxv5efvB/bUnXLOnNYuUtHTtjg9vtAcXAdMrHpJHRTNKazRJ2TFa80FwNCNDhvQYDyIO6M3jd9i98IAxjbb25Xw4p08VYZ9MpVnEIv8frQHGsZHqtwuq9wxxnimeiORjoChBEMZhN0PUwRKw7whd8p/ieLakw6OaAcFqeLTXdJosc0NxWhOtas1jT59g0L5dsFB0+KtewwTlqGFJeeWli2STJsZlbv1GkYKDp5w+IeXsdEpo9XLkQvtCRVA6YyZNe+vwAangYrqMkSBKYTz76GWcJltL6oqFRpjZ5k7fQeEq24jVKbt4nhxHx6VL0eIYVr4SCs6dKfIbYc1S0QnPD/s73bUXHKysMPzQflx/+wYVvsksx2+/ewdTz5wUnFB2J2cZO3XK7BIIzMwxvvfxAaNIJIRkBOv/kyU5P/+f7lYCjTWxF4H+XSVA/0a7GotMUFJMDWF2JpgypSaIp4sek3FspmTxiNVYP3m70UFgaihzvkzwePMBswcsho+nL3pMai+g5D2LDiJ3yZxoPri+mbNCdXSv917CbEucDlNK8h8UweH8unEIKjQ1B38+v/sSA8qOAAHXxWsVxtjdw419Tus1T7TD+AbUuF8d4Q2i+ChTSawu+zZtN3GCeK5wnP1803NJhm/yZEQyJ0c06F0Tw+toYpO0b/JkEFmM8e1nSUosf/k8mHbst/8EOFhI9JhO1WUEfBcBfmsBXRooTqvNRDZjs06Sjol6BlT/A1A9GWGgs2ABJcV4KHaNozyw8tKFeB7iNORxTo3dbaPn3EfZcSQN6GyQE4eg4PKZIq9Km3bmpEhLGKI6dCToPJzr1ltAz4Y0kzy6FAVrmrfC5FMncOnNK0lT/VyhMsgHdPE104JAMmtrXOnZz+y39vLTR3Gwbr5/h6tvXgtQeEad+lImT/A0FeUNfEScVc4PPzM4ZEc7dsPO+3cw7cwpozL8ma69hO2ZTNND9u8Vp4TPiVb5C+L3qjViO23xfpx2XUGxliuKzwEl9r4Xn2OPbl9JkZ8oZiqxF4H+0zTAR9OEgi49FJf9wOfHwOebUh1ADFBC2uMbzzCg7HD4+wSIIzDlyGiJdIxuPglndlwUDEvqDE7weO0pUSOmyagLVrN9ZeOwKBo69/tlonZeql5RuL3w0LSyQoDLDXrWlLJ1AqsZXaLR6dowebsxTbbw5jRkzquxstJhotyEPjgYW2ftwY0Td5A5fyYMW8E8OPD26XusGL0BabO4SFUX+XWe3X4pD1UCnH9YGBpaZ191rVubAa+Z4hq8oCdcMqZGwYp5hPQwvmz95G3YMmM38pbJJWzVhiq0+Oo/sn7IFaW6twQ+s9LGAorTMsAyH6DYJeF9EnryQ/o3x3AwarsGinXk9A6GYTVcs0LKvOlclHDNIE7E12D3PdxRb9WycHIYp7v0BJ0WamoZjM5P7xKl0atEKey+f1fSZwRO55szAwHBGnCXaSACgU2tzsql4szwt9skd150LloczdevlogRoyfF07uKSnv9HLmw99F9wfssvnJJQNbdihVHszz5MOfCOey4exufQ1JvJzv3EO4gWt6/phvPTwA19cJMjaBogrGT2WjVoollkrb2aBei/dUQSopJ/2qaNLH3vcSaZ9PzJDk/Ucx6YiwCNeAsVP+tUKwKgcBVzfnRwsPC+2BVIFHXBivA3j11Q6Y8rvj0wUdSQENrjMG1I7fkrYsq5QbgMrE8bYY3k6gK00n1etQwKyfnwOlw9Cs1DA8uP5E3rioty+HIulOwtrPGuD0jUKhSPhxacwLj28+UaA5L7VkJ5pDCHNt0fu9ljKjPiitSqihQgzVcgZW1pQAvCdrmeBgNqt+jJso2KI4SdYqES1Otm7gVi35ebXyDZB8smSeYOqzRwds0bafgixr1rYOsBb6J9r14ce8VOufR+iSeqPvE9lJxlhimfn4O1U1ToKfzA/u20CX/JTFOnXQOkxkQsKr/QdHgU+waRGtuHn3wwB/Hj4DpHOJtMiQPD8CPVkexaERhUKavzpB4sWBhdCtWwtjL3gf3pdLKVAyVbQzRk5lnT2HmuTPSnmmwHsVKigK8KcFn1WWL8OKjl3xGQsKx1WuZjdLUOaFqe6kMmTD3Qmi0aVebDiKeWmbR3/D095ffMCU0mufNj8NPHqHb9i0yvuTWNhJ9Ysrpg58vHnt5ooBLWpTNlAkzzp4WB2dBw6aSFjOYQcPMWmeBBY2aoOI3EZfQx2JaozwkPBA68Z/7poNMjH0vyklJ4AZJzk8UE5zQi0ANfgv1fdXQ0keHboAPCcMsAcUGisthKLrwAqAJvC5A5fP+ZYZLVIXaWtQAY1rIxt4GQxb1xslt53Fo1XG45kiHnMWySpUW31RL1i2K37aa40n4gBpW+3dcOnBdIkWMHskTSoGAmkdvHoqnt17g5sk7ePXgjXDoZC2oPZR4LEHGNnbWEinqVvB77dJNn8CGyTCRqeY4d3qvjHSafL390KvIELx/4SHX1WtKRzQdEL4ChNVwS35ZY3T61r9eEO3I0Iv7r9GZrNF0PyiZMbkjiHuKi10+dF2IGItWL4jyTUpF2hVxPur7WoD+nTjSrDaCVVGtrNYyLxTrwnEZRtKx/7EZ4O9s6dXLwqp8+/17sCaSxjLvzClSYtyJo7jj5ibAYkZhCI5uV6gIOhcpbuacEWfz7YY1xqjLlFp1zVTcn3p6ijOT0s4O/UqWESV2g7E8nuBrw097aNkKklaj/IfB/mnbCRfevMTwg/uNn9HxmVSzDobu3yvjNxzfrmBhrLlxzRipYtUXSRfbFCwEawvLcBVcuWdPM6bPKMPBsbPai6zUCW2q344QpnYm83RQXA5GyQyekGNK6H0vIcce3b6TnJ9/2/kJugnVvWnIKKygOA4AWMYYdB2wrfuvCd1tnrELC35cKVEUo2+hU1ChWWmMWv+DfOTvGyBOSZe8A/Hi3mv5LFW6lFj/agGCAoNw9/xDpM+WViIz5NuhsYyejklQgNYvq7eYojq9/YJEgqYcHo08pULZWIfX+1MESGndJrQTjM6JzWfB6JShlJz9sz86VuyfRodt0a3pX7y7xDH9s+yopMqqti4fDudDJ21Ms8mg0rwBZ7DJbXG01eN58i0zd8t/ecvklPRbXMgjGUkiKSONY5t65DcUrBjKixL2YtVgd40h3CI7YFUAqltNQE/geDCUVEuh2LA6LMkSYwb0PssB/92agKVQDYQs1MQ4uck5CGreeOuGAIdb5itgTO8Qi0NGZIM0heGQnd+1F5zO3AvnBENjo7NAu0KFsejKJSkhd7S2wfHO3eXFhyKp/NxgAlYuUgzDKoSmxL90uabl6YzaMOpF/qBRRw4ax0UCxcuvX2HOhbNGJ4dCrN/mKwCKrw7Zv0cA1FlSpETWVKmw/9FDs/EMr1BZKtoiMlaHkWWb5mJvj9fe3kjvmAybW7ZBWseE1ciT54vvIqiBl6CQkNS2WiKvDPPTJTk//+r0fx0nT+hFIPIEnn2BgIOAzhWK8zqtzP1ftmObTuOPllPNsDGciwa9amLAX92No2NaaOGwldg4dad81nVcG4mg/FB1NO6eeyAprClHxmBc2xl498wNwcF6dP7jO3i+9RJHiU4H1eVpdF7qd6+BAXO0/ungNE+tOU20lGmSY8ObRfLvVw/foFMuRlVUGSOFSMs3KSm8QN6evqjXvTpeP3qHHEWzIH3W2M3n2LYzcHjNCeP5G/Wtjf6zEp6sMLJbf27PZYxoMFaiXsQpcZ5Y6h8dM8efWAEOPaBLFj7NF52+ktrEbAb0ftsA0V8LsWS/QudgjnWJWY+xb/3HscNYfu2KOCvFXTOIAjqN5elMtdHBkd+ajS06FikqkRISGq66ftUYQamaOSuOPntidEjI1Hz6xTPjoAwBWFZ39S1ZBi3zFxC1+oiMDg9L56kWT+dl4eWL+ODvJ9EmOh0spa+3ejk+BQYgnaMj9rbtiHc+Pmi6bpU4cOT6IQ6IKTDipCiASiFVKs8zfdhh6yb4BAUirYOjlK3/UqlKpGzNrz99wpIQZXsSONLo4FEotXuxhGfXj+yuShTXb4cGh9C/Bxwoct059osgGkcm9L4XjSEkeJOkyE8UU5wYi0C8fuFjSR4hGFXV+0L1JL35ZcCuGZTkoxL0zZFOR8/CQ+D20sNsdkhTMuXQaBSspBFscdwjG43H2V2XpJpr0N89xMFZ9ccm43F0aFp83wAtfmiEw6tPIF22NJLqMrz5MkLUNnNv0f2ixMSAv7oJXofGyEwr1x5mYyC+qMPolrh56i5+qPKrfEc8TY9JHYxpK1aMdc03SMgQiQGac2ECHl19ImSOrtnT4edVA82IDOnA8T/TqAxxSnWsQvk0itUshAn7/l2GXorDDq44EvcvPZaS/9lnx0VIyGg6YernZ1B9FgO65IDfbkDPTcoKivNaKFYFE/wBk3QCQO/1G+BnkoK1bQxdykmxnprA4GABETOyUjdHrhiJoDZZuxLX3r2Vc1Oz6kZvLS1LXprvNq0XB6JO9pyYVbeB9Eum4m47toiMBaNCZCoyYZKCrYWFsC+bGsdVP2du7Lx3Rz5OZWsnFWusuDI1RpTI3sz2TG95BwQY6uKwuvm3pOREsfSuwi90190NBdKkFWdHG2+ApNdMq78M6S4SNbJd9lROEh1ipVhMAMx0giovWyjXy0fz3/UbGfmDYn3Tonmg7AXBj7W9wEIrj9d7dATI/2Y0ilyfh8LfdAJZYux7CTT0aHeb5PxEMVVfwyKg1ov68Y9QELTThgTFbJzadh6jm03SUj0mOBpOFVmVZ54Zh9wlsofj2KnQvDRObDobbkazFPwGpesWRZ2u1SMkTSS54T9LjyBDzvSo3jYUILlj7j78NWhJOOJCpnrG7fsFw+v+iWtHb0mV16zTY4VEkbZlxi7MGbxU/k2+ok6/t8aSX9YKtofYmyZ960gKjcKoxDSRvND3kx96Tu6A5oNCQal9Sv6Ex9eeilPWdWwbtBwadZlytH950WzIezC91zzsX3EM+cvlxq+bh8Lbw1sq7iJSuQ/brSa9wJSkooluWuUFLLNDZx0KZI3mUJKaxXIG9EH3AfdGIbg+HeC0ATrr2Dueg/ftNjoWxN0wPRRdIyaG5d607sVKSPm5qW26dQMXXr8SHhxqZi28dAETTx03RoTCnseC9BUmADxWcBFETEAxCQ8NJeg8jt+taPqtEefzx7EjEnEiRw+NOCJDhZbhPFUyZxFl+IjShKaUAKbjIo6IThudqhm166FuzvDCp3Qg+duysSQrd3g79/IFtt+7g6Lp0ksFWWKlKfVeozRKClZoppoP1aoY8C6U102DSlpDSXseihIz/rXorhG2+xr2vZiMNzZtk5yfKGYtoRaBqveC6v0XoAZAcewNxSJdpCNR/bZB9aLURIi2S+rdUCw1JtOEsDdP3qFL3kFgSo7SFMKfYzAF6DauHVr92FhwJ90KfC/CoExn9ZnWCYtHrJEyeQNGxnR8dERGbvgB03vOFxV2Oho75+2XtmRaph6XwRjlaJS8gzgsEdmC6+Qfygj31x+QKk0KM0bmnkV+wKNroWH4acd/ww9VRhudnzqdq4lA6fvn7kjm7IhP7t5yCjpGe/zXGB90jCAdWHEMqdKmQKVvyybaA5Cg5vsXHwm+inxKA8trVVqMcPWd2RWkCoiOCTPsW0bpiK9SAIvMwhjOf4s4YpLWV3SmMV7a6IO9gKCLgFUR6Cycouxz0+2bOPz4kaSDGuU2x3WR/djdz1f6ILvygQ6hqeEoOwZA0LHf5yApHTfd1E88e4qOIk2hCHZuf/vO4Ge/Hzts5vyYvg9lSp7CyEnEc9/rOwiWFhbY//A+eu0yrwwjfmdstZpomV9z/BhNarN5vfRNvh43Xx+4+WmMyKb2U7mKqJI1WziiQMpr9NuzE8H6YORxccGrT5+k9J2Oi+GJRSX4m33M07uMOA3cs0tkbqbVpr5YeOcoOvMY321UNSjk9xrSsyXnyRJq0GXjqZ56p0DGDJNhbR89HFVsx5hQ+15sx5MQxyU5P1HMakItAv2H/hrOhz9Ty/zQpdb0cCIy4Wwh/0/QWSh2LaHYU+4ifu3WmXsSYSlQIY88EMn3c2HfVRSqnA8LflyBq0duGk84Yf8oFKuuPcA+un/CwVXHkTazi8g+UHF945TteP/CHfcuPArnvDilSynpKG7MjCIxqkKzsbfGopvTpR8agdZNnTqJI0VRUtdsaQVUTQfK1sFWmKc5XkZuWJl26/R9WNtaIk/pXMIpdPXIDfCF0jVHWiy9O0tK6ZeOXCvRpXzlcmH1n5uNemYGSQ9n11RY9WRuojk5Ed3BC/9clYgWHTFyAv25azgGlqeooYaJ+nnlAFRpVT7aN19PWnxvAr+pZs9tS9s0YZkHutTbo91PUsMvz4D6+YXGoWQRffXwyHrk5t16k6YLRdvaqi0KpQ19OWLl1YJLF+Q7VkT1LvlltXiWlo85ekgwOyMrVY1QC4t9rb5+FaOPHjI6OsubtBB2aWJ+KHzaoXBR5HByEvwMHRYqu1O4s8eObRK9aVewCH6rqmHQCB6mdEZYAHWJ9K5yLK+HDMv+n4PxxvuTVJodevwwHIcQ+yL4WafosLddR7Oxc56+27ROvmfEiJEeA2bJMHdMf13u0RdkzX7n6yMRrUZrV8rYaHTejnb693B8pmtAXlbc6oSI5PKFRXs2SgA+BB9fdEsnHO40SHiTEtISat9LyDHHtO//vPMTEBCAfv364cCBA3Bzc0OGDBnw448/okuX6L0tJdQi0Ls1Bj7f1u6Xzhm6NKdBbhapCLHKDcWmSkzvZazbrx63GUtGaARlZGvuNaWTWV8rxqzHit83SgSIFVmb3RbDxu7LJGCMCh3feAYPrj7B2nEawSHnMkPOdHj54I3G/EwGaBOZd5bJj9013HhuOlzLx6xHuixp0GdGZynx3jnvHymvJ3nhz7X/kMiUmSlA0WoFcOXQTSmNZ8qL2CWmiJzSp0TtTlUkhTS730I5P+Uu2Nf147fw/M4rwdAEBQXBNVs6/LbtJ7hkjPtmFpMbs+K3DVg9NtQxm94TyJsAACAASURBVH1uPB5ff4a9iw+iaLWCaP/rtzEWSVX1nwDFGiqlUwLPacOxawFdit9iMrSktpHMgP7TdMCHIr0WUFLOhGIbvchcZBO6/e5t/PDPHnEEGC2ZW88cc8LfDFmSCcYtnC5q7b1WG9eKcjq30vwuabCtdTuzU7N66p6HO0qkz4DuO7fgiacn6KQwRRVZWsi0AxIDEjPEqIshkkQ1d4KQIzND9Ih8PMMqVJLoEmUxDEZskb21NXwCA2XcnIepteqaRcEWXb6IiSePmaXW6Ag1zJUHF1+/hJXOAn9WqynXRgeOGKYCLmmkQu30i+dG0Pea5uZEh//mQleD3wF+66H6Hw4hKA0dzbanOXDNf0iM0pyxvZaE2vdiO56EOO4/7/z4+PhgwoQJ6NixI7Jly4azZ8+ibt26WLduHWrVMifYimiCE2oRcHFrSr2foST/E7Cto/H9qBTwJC/L7Dg/RKO7YDqzVP2uRjlvWlFlOH5wpZG4dequvIGUa1xS2Jl/+3aKlLoPWdQHpepGrDVG0PCacVuwZ+FBAULT6vesgV3zDsi/JeKiqsboD0vOx+4ZYWR2Djv+UU0mgBVPmuNEXbKIU2KG4xglkjYhT1pWSGXK7YonN59LE4KGhy7pK6Xu7bP1NT+dAqk8G/R3z+hOY7y049j6lf4ZAb4ByFYoM2afGxdtXqGoBqBS80vwBDaAfauvgkY/qjH/f/he/4ZR0ABtqFYloHNeHadhM6JCqQhGWxh5Wda4ebSckMhOWnfVMtxzdxMn4psUKXCkY2ikg6mjzts3S9SEFVn723WSKiqCk+OCc9l65xaGHfhHOHJcHBzwzMszXBTIdLyUuvgUGCgfkcSQESU6MTvu3oFngL+Aplnpxb4M9tzLCw3XrhDwMx1BA76I2l6bW7YVuQ5ajx1bceBxaMk7GZ8NpfIDS5WNlXBpnG7wFw6Wqkw6QJ+fAt6hws/B9iPhZ90CyW0TDudjOqyE2vcSat5i0+9/3vmJaFKaNWuGAgUK4Lffon7zTchFINpL0AtwTf38AKqbgWTPEnDoAl2yIVD1HoD/HuiVb6CzrRDrBxLTPn8NWIyULskxZuuPyJjL1Tg1cwcvwdbZe8WhqNyqPH5eoVWAGKxJqo7w8dLSJTmLZYOto41ITNAJMfD6RDTP5Aqa+/1SM0JCO0dbIyaIqR2Wp5P8kKrtdE6Y/lr9dK4ZuzOrz8jj81PN34QMMSoTIsVgvThXHKNpdMnwnaGPvYFrBbjd2rUHggJD3zz5PYHVU49GvUaiGo/h+6e3nuPOuQcoVqPQFyNKTOMxOpareLYY8wJJ6NxnHuC3FbCpAiXZj+Fo8onlEh4pnRMUy0zRHX5Su0hmQO/WFPh8T0tTCJP2CDBVrSixJ8fjfSSZYHwIb5598VywMUxBTa9Tz8hcfOv9Oww7uA+3SGoYEoE90L4zsqXSMElkTL746hXypnYRzpx8LmkiXQOsqNp69zZsLCzRMFduibJQz4sRGz5DN9y6gWEH9kXITWrodHTlavDw88Nf588Y0191s+eUKBer2kZVrhru+cdKsMcfPPDq00f027tLuqIj17VocfxUvpL8vePeHQzcu0vegapkyYZFjZpKZOj627eokS07MiZPfBLZiCZS770Q8J4Y+pVlHsCyOGBbGTpbLROgBt3ViEqtS0JJwKKFhNz3vpYHyf+c8+Pv748cOXJg+vTpaNGiRZT3IaEXgRp0E9C7Q7UqBXwIUYCGjfD9wDInVLfaCA58BUXRY86o/Gg+bI4Q+MXEmIJqmKw9Av0CBWeTo1g2jFz/vaSTaIzQnNh8DsFBnwXYG7aKaNWfmwQvw3L2YSsG4MDKY7i0/6pEbFhpteJBqMo6+/N87yWgZoKKDWSGhvHmLZtLIirU8vrwRsP+lKhdGFcO3TDqek07/jsOrjwGP29/qXCioCqdmRrtK+Hw2pPhqr/kgWdtgZEbhiB91jQCZN44dYfgezgGSnUYrEDFvMIkTScoR5EsmH12vHx16eB1rJ+0DRf/uSp/876TgLHHxA4xmepI2z669hSsHhN5kGR2WHp3ZpRl6rE5MUnSVA9Dib4OSsoZUGwNiu5aj/oPA4GAPRqwNeUcKLbR4wqKzXj+68doNBUfoPqskNJj1a61xukT8A9gVRhKqiWAYqUVNsShNJnOydIrl3Dj3VsR5KSSelyMaapyi+eLg2VwfBhxIakhozWnnz+T6BN/B4bv+5cqg8FlIsab9dq5DYeePJL1TTV3prA6Fi6KPiZ4pKeeH1DNhMHZMH46K4zWVMiUGY88P0iUyiCeamhDx2VJ4+aolDm85MSfx4+AKTD53YYcQOxR24JFjFPEEn4CqstkyCQpQAKt6aDxvIyEpbAlJk4z8g7RmaOzF5foV0zvj56M7FKQEGpK6oPGFxQ1+LXG2i6EAJ+hOK1PsKrfhN73Yjo3CdH+f8r54Q+zffv2ePnyJQ4ePBghdmL06NEYM2aM2VxHVLkUHzdD9dsC1YtSEKQ9riKpLgTdAiwyCnBSDX4D9b329kIqjb2rU+Phw07CpxMT4/ibpe4siumGVy/iYRbdnIY030RPKJUVYFY2VqLz9e7Ze0zvvQD+3v7oM70zchTNKg89w4NiUpe/cHDlceHOMbVv8mXE1CNjZNPvX+ZniYLQGO2hM+L3yR+5SmQXHa1PH7zl/pC9OcBPC4c7pLDH2pfzsXbCVmyevgup0iTHq4caZ0nrn5ui659tEBgQJMcYxhLoHyht6ABS+4tjpUCrxxtPVP2uPBySmwMH14zfIoBpVpIR85PSJeK3QgKyWQkWFBCEmh2rRCpYSnZoVsAxvUflekOqbsI/IyUCFN+mBpyB+sHgsFFJfJKZppQQpr010YqzqQldKnPnNb7H9F/sTyJsH8nfswbQuQCpFkNnlROi08eXGDFLwL4z4LcKUH0BhwHQJaPKe8yNrMxUNacjQmfhZJee4XhzYtKrEAuu1Ogg2B+ryibWqGOUfFh8+SImhMHTMBW2vkVrUOOLgOVymUJ17orO+wteAeExPvvadjKmnz74+aH4AmKjNCM+h7giOjsEO594/kyuzzSFZWjLCNKChk1QNUs24/HvfLyx5MolAX+bAqvZL/FH+9p1itB5+ev8WVCHzJAmI5likRDs1MprV4RRmta7eCkMLV8xJtMap7Z6r19D0tIh2j9cV6m5T2n4Sr3/McDTkLK0hJJ8BBT7hCHLTHJ+4nQrv66D+bDq3bs3Ll68KODnFCmiF+pMyEWg9+gOBB41TpSS9rZZqFxKzd1aQxd8RZyfX9rlRP7KHYXkL6Z248RtTO46Fy/vazIUjACR7O9LlUN0GJjeckzpIPiTiIwOzp/fTcfJreekMuyPHcMwqdNfOL75rFmll10yO/Sd0Vkqx5KnTo6MOdPhzO5LRnFSMkEPXzMI6yZsxe0z942nosOlhrDO5iiWFbNOjzMbxrvnboKPyZQ7A/7+YRk2Td8pKSWySseW2Tk6czur30Lsmn9AUoUUT/19G6kIzI2OV5NUncRBIv5IggSqKqlHSm8kSxX/lPmyKX/6A/DbCdhU1EraGXkIMfnevQHwmW+YwVpazCF64P/ozMv/Shs16D5UdxORWsURSpozgtUI/VwBLAsBn8kWLLBdKGlvQFEi5pb50txRdJPpIMOGTfxLLmeNBC82RmeB5IVHnjxGSltbbGrZxqySimkkVkUxDUWjg0TmZFZYMVpkqNgiNocaXSQbNI2+GErNTdNo7IfXMP3MKWNkh4SKM+s2QOG/ZxkBz2GoxSR19l2BQhhTpbr822D1Vy/HHbf3kabSbvcZGCFWioSJTdeugn/wZ7nmXW3aw9ZS+40QH8XvaSlsbHG5ZxgsYGwmO5rHqOpnwH8H1MCbUqAA/52A3g1wHATFKi/UD4QjEH7A8q8UUFJvg2IRCl+I5mmi1Swh971oDSARGv1PRH74wO/bty/OnDkjEZ9UqVJFe2oTchGoPiuhfvpdHooUnYwIKMk39SeXt2LrnKuwS5kLXf74LsY4EMPFMmJDYVCWjxN7s/DGtHDYE87VmnGbxXnhbi1K7AB+WNQHdTpTgNXcDKXZPI4bPNsxbTau3QwzfiASEPp+8kWAb6BEefKWzYmnN18I1sdgLhmd4O8bKGzPBhu8oBc+vPkgZfNMmZHrJu03LsKBYxqSdnvlge8yauBkfl6/e3X0mdlFUmkEc0/rMQ/kDuoxqT1K1ysuEaIja09KNKvSt2XCKb9HtUC6F/oeT25owGkq2m92WxLuEJ6vcYqOEgHj3BBPVaFpaRSqlDdBUl6RjVn9/BgIfgZYl9LwZfoPACUXKKNiUydRQ/tRzev/l++1FIQ51wpFiBHsBtVzIKB+Auy/A/SfAb/l2oalc4HicjRW803W4ZYb1+IlnZJceYSfJq4pGf5m2Z+znT1YYTbr/Bnh0plWu74QERJ4/cTLE9fevBFOHKbFWoWU4BucEDpRdEfOdO0lfRF83GXbZqMw6tz6jVA7e6hW38uPH/HTgX04ZSKH8U+7jqi1ctkXb/3F7n2Qys4c7Jtz1lSz9FijnHmw4/4dcYaa5M6LqbVDhYo/BQSgz+7tuPb2LToVKYo2BQrjwQd3FE3naiZw+uuRg1h9/Zq81FAqY2Ejg+5i4q5MvddIwI/0J4yek6Mrk/YbpunSQUm9M05p1KiuJiH3vajOnVjf/084P3R8Tpw4gUOHDsE5pAIguhOc0ItADTihefe2tROMsZMRnLmDl+Lo+lMoXCU/yjUphQLl80QIur24/yp+rvNnOJLCQpXySTQlrN27+BB9S2pRD2KC/tjxMzZN24nLB68ZdcGY1hq6pB9m9J5vTGexvVHd3aRTcvrYO9rCMZWjqMcXrlIAU7rNxd4lh7QXHp0iD9ueUzqi2UDtzfvx9ac4t/eKaIwZXgOdMzjB96OfpOaSOTngYwiRIdszLZY6kzPePqHiuSJK672nauX93BAog0HG6eK1CmPwvJ5mBIqGNr82nShirDRDyi2iNUV8FMdF/iISPKZ2jZrgLrprMzrttDRMRw0nQDV3580SXVRVfyDgNGCZKUEJM6Mzxv+vbTQOpWna8C2LQXFeLRg9bZNS5IVGcVoK+MyHGuwGxaFznISK6Wiw9DsqqQauYaaC9jy4J0BhMjl/yVFy9/VFqYVz5afDCA+1skhA6JosmaSliI1hBIgcP83WrZbICFNLPA/RJ/w9nuvWG8729oJJYsRI9mhFEabnMhkzoXvREjj0+BEWX71kTFExvZUlZSrBGTGKQ+6dsFgf9lPxm8xY2rh5uGuotHQBXnz8aLZ8Utna4s+qNVEjew6Q3yi1nT2mnjmJ7awa8/czSnPMrtMAdXLmMosksSNGtLbcuSXRrRZ580cbcH7lzWv03rVNeIsm1ayNGtniRkKrJ6+bzwLN+SGTs2VuIEjDI8IiB3QumpZiQllC73sJNe6Y9Pufd36ePn2KLFmywMbGBpYmVObt2rXD33//HeVc/RuLQO89D2DFjmV2KKnmQdHFbcM0dWgYdfll3feo2CxiYrQTW87i95ZTjSkrQzl6x99aoc3w5hHO155FBwWIzJL35oMbYGqPv7F/+VGJ/LCMvP/srtg6aw+Sp06GV/ffQGelw80TdyPsi/OdMberpNnajmgmoOpexYaaVYzxwArNSuHXjUMFR9Oj0BB5EJtijBgh4t8mNEKR3uvM+TNi4XVtEyPfz/eVNc0wXvuPS/uJ5IapndoeIv8RQtA45+IE5CwaikWIclElYgPtDXK9kTBNSX1AMGWqewvgs0ZcqaRaCMUm8bANiXj5CXoqLcU4Dgg4DNg2hy5ZL+jfVw8hqaPzUxg6Fi7EgxGky5QRK5NYxURdrrDGCiYqs/sGBsE7SMPJ0TFZ2fRblA3B5yy7egnjTxyT8u4exUqi/DeZQa4dg/NDh8WAn6GDw1TXkaeaiClLz8tlzISfD+0Xp0GuH0D5jN/g7waN4WBtLc5Dhy0bcealFhWV35FJn6ZjHly6HCjPwYgOIzNHnz4GK9DouPE54GhljQa5cmPjrZtSor+ocTMBUxts293boNSHqfFcBIQ//vBBxmBvaSnpNBOOemNzOjcTa9aJh7sDtNiwRtTmeR4qwp/t1jtO/aqqH9RPk4HPz6A49gQs0kN1+xZQtZSckvw3KPah2oNxOlkEB/8b+158X0NU/f3nnZ+oJiCq7xNrEagBp6GS4NAyJ0DMhhhL3rtDl2xwVMP84vdXDt/A0BpjjJGT0ZuHolyjiFWK6TCM7zALp7efRwqXFFJuTWBwxWZloh1mZ+n4ijEbhP25Ue/aGFRppFRo0ZkgySHJBL9kOgs+WCGyFfV71hShVFOZC96T0VuGSin6nEFLpPos7NONlV4EadMBI1CaY9JHwgtEZfRH158Jx9H147fh46mV9TMK9dPSfqjWxtwx2Lf0sKTRDM7W7zuGoUz94nG6RzyYIOqj609Liqxii5in4sIOgJgxAczrGeGiWQNpTkNRfYxAekm52rWGLsXoOI//f60DNeCIRhwZsvgUp1WSwla9hgtmQ0kxGYpV3KUTgoKDBSjMqA+jMKZl3KZzXmflUiH0MzU6A3PqNUSt7Dnh/zkIBebOMgMHWyo6rP+2NQhw3nn/rpmUH6NAWVKkxIMPoQLHtbLlwD+PtEKFsEZnioSFdJRYXt92y4YvLonczs7Y1aajWfSFx264eV3SbQ1y5kZDQxQJQINceYTUsZRrBuRPk1b6Pv/yBchVxIhSANPLioI2BQuJcj0tLH4orBDr4Q5dkDll9GEQkV1Ql+2bcfzpE1kJVKQ/GEPJEdN+1cDzAKuAbWtCscggX2lSNXmMaw3W5aFzCp9qj6/fYGLte/E13tj0k+T8RDFribEIyOysurGEkT+dEKS/BGg150ch4M0E6BfTG80fzrJf1wnGpWyjkug+sd0XWYIp8tkyfTcE+gWJw9Jrakc06Vc3pqeV9nRADASCRtLBaM45eX9+3/EThtcdq1V+2Vhi1KahApZOny2tKM8/vf0inIQGx0ypjSD/QNw++0AwRHSAWg9rgmd3XuLuuQd4fveVOEbE+5zeflFAyUypaaXL2gCrt6+IoYv6hkt7kdxxWJ0/cPOEplrNFNv8K5OR3DlZpFdmWg0XWaPxHWbKPeIQGvauhX4zu8Zqzg0HkdlZfWfulBHgDPtOGqdUMDmTSKg5K1w5fJxO/D9ysOq/B6rn96G4jOR/QJcA0jPE3hSYO9PIdEyhzYgiFvnmzJB0DY1OAEvWWR01o059SWfRiSo2fw58gzTWZBpTT71KlMI/D+/jrnuo48RUUy6n1JLKmnz6hPTHTZ2l69//Q5qEiO1Kz35G5XUKly6+ctHMAQnrjJzt2itSksFLr1+ixQaScmrGMRiex9u/ay/8QyxJZ8qMJfK05nnzY2i5Cqi6bJGAw03lLnjugaXLYvrZUIV0slrT+YurUaKDEiK+QUEYUbFKrMHoasBJqB9YgGAJKPZQXA5B0WnPFb1HFyDwVEipu6Klw+xaQUk2LE77Q0TXnhj7XlznPK7HJzk/UcxgYiwCevqqh6Fk0QqwawwE3dZYPuENWJWC4rQE7q8+4u8fliPQPwg9JrYzIyqM60IwPZ7A6LZZ+mgPHCsLNO1fTxTPY2vzhrAKaxdcs6cVDh5y/0RGH5A+Wxp4uX2E70etbJZg4hxFs+DyoRtkpUHTgfUEn7Nj3j7M7L3wi0Oiv2hIezEN1m7Ut2g7ojlGNBiLc7s1sUDT+yvOj4mIa+O+tdFvVsS6P4w2Ter8lzhePG7EmsGo/G3ZcOMhh9K4NjNwbOMZ0U37c/dw2DmEcoqYHkCHk7pntIy50mPJnZmxnXI5Tt4WP3QCAg0PewspvVaSfa8xifsfBCwzQ7GOe9QqTgP9f3qwCFF6DgYC9oe8tFgIP5diFf8UBgsunsek0yeQxsEBk2vWQdaUTkjrqFULkkyQ67j4fJaba0zT+VK7YGeb8L/Z869eSLUV2aMDmRaGir/rN0bPXduMd4EVUIbIBdcQSQLf+nijWZ784gwR39JsfXgW62TWNrjQvbc4XbR5F89h6umTxgq1sOmvXE7O2N3WPPJjGMTNd28x4eRxAUaH1QejI/dblepoXaAQrr59g6brGHELNWqPjaxURb6jthejWgRi0ymhPlmZhX/DLUQYloKqHMPXYqr3LKjehGMEac8nYvSsCkDWWsBJwHt2iOxFaCKPEUfFOuJIfmyvKzH2vdiOLb6OS3J+opjJxFgEsrA9OgFB5wGLDFCcNgqrs/ppbIgiN3EZyzG8yUEjkJg8NPOvTonzOnhw+TF2LziAb/JmRKO+tSXCwgfe9N7zsXv+AVCIlKSDrtkjV52PziAIbmbfdy88xNrxm4VU0WCGiJBdMlsBKIfF6VjZWiHIX3sY0Bki2eGWGWFy/QKUttO4jCKx735uinUTtxkjRTwvdcKa9KuHxb+sRrrMafDy4RsZA40EjisfhfKSmHZLGYqeRYZoLNKqioXXp0bojFKh/ccaGks0I1nkaKrTpVqEI5w/dDk2TNkh33X+o3WkGKvozLehjT7oHuDRUYj4AFbL+ISssQ1QLGJfKh2TMfxX20pakZgfX0OlkhUUxz5QHBOmPJpOwPKrl/HbscMSTaFCOnWqfti/RyI7nYoUw5LLF6Vse37DxiieXkuZRGTU4yKDM6MnZHSm4xRAPg1oVU5LmzQXHM4d9/fI7exijOYwekTnJmylFY9jCm12vQaSYiNvz8jDB/HW21sqv1hO7x0YaBaJcbK1E+xSnRw58XOFyuLAUXF+9Y1rWHLlory5ULCU10qniy8/LK2nk7W7bQfB/5Cssfj8OWZAaTpZNbNlx9z6jeV6BA8Ywo/Ev089fyZYIbabVbcBSrhGPk+JvXbVoDtQ3SlcHSAQCMV5C6D3hOr5ExB0MmQ45vEzjewwlNAxPsacGPtefIwzLn0kOT9RzF5iLQKJhOjfa5IDiiXUgGNQP3Q3LnYl9T/oV24OWF3FmHXqDE5Y83xeXO49/Lz90Mq1h5FEsN+srmjYK1TvzMfLB7aOtjEuAw87qJcPXmNkw/Fwf/0BPSd3hO9HXywavgqfA7WHLckPn31BtsLU+YnogtNldUH97jVRuVU57JizD3uXHBY5DjOckE4RZmWDTAf74b2t2706Bs7pbkwDUupjy6w9MscEb0cW8WJEh/igO2fuS1UYZT8iMjI700mS8+kUjNnyI8o2LBFhW64Bsk9bWFkib+nQ8uC43GS9exsg6FJIqNzQkxWUZEOk+ijJYj8Dqu8aqB9ZARmqMac4rUnQSFrZRfMkCkNjpRRTWO98NIef0Z4d32kEi19Kk9OJMuXLYftRhw9g5fWQaqIQJXnyANFJorOxtkUr/HJov0SMymbICFVRcOZFKKjZMIuMGjXNk08qrGQcAGxDQMeRzTTbFHd1RV7nNNh695akjgxVXxQ4JQHh2Oo1kc4xOa6/e4M8zi7iDBlszfWrGHFY0wukWSi6EKcuFToXLY7CaeP24hb7FRK7I9XgtxoPl3VhqJS88AkTAVZcAIN0iv130DnGDVwd0SgTa9+L3QzFz1FJzk8U8/hvLgLVbyuYElNs60GxKS+b7ejmk4Sp+Kdl/UVkNC5mlt6y1KFJ/3roNSX+Q8Cjmk7Eme3nJaJDB4DM0gPKjpAoDR2f57dfRpoGS5E6mZSpf4lle8Wj2UiXRQNAur10FwfH1sEWrEKjNIdQrFjokCptCri/0rABpsbP63StjjbDm8HGzhrkLuJ9/yavK2b3X4y3T9+jw68tUb5JKXHgRjWeAEbMDJIbrAj70mbDyreDq46heK0iaPF9g3jPz39pDejdW4WUyBo2aI0kLknWIi6/HAhXkvpxMuBvAuq1aQBdqqlx6ziKo7tu34wTz54KZof8OYyU3HZ7J2uKOJ1FjZpF2kNgcDB67dyKo0+fiL7XvAaNjSSAYRXS2xUsgpXXNdAwjbw/jNwYbEDJ0vj70gWwT4PR6SiTMaMopodNVRnaMEJlisOJzmTxGAqbHunYNcLSczJE11mlRd8kSmRnb0xrEdN0rVf/OAnDRmeMCdVG/4Zs7KHzLudJYLCzzGNINV9CXdfX0G+S8xPFXfgvLwJy50zpOldmgM7B/GtTkC6LC/YsPCRPEVZBWdtax2mdMt3VKHl7ITc0WKl6xZAmkzPKNy0N99cemNw5fGrJPoU9cpXIhisHb0R+/pAxDp7XS9qQZXpMi8mC22ncrw6+/aEhuhf8QTTC0udIi7G7RmB0s4l4++S9ED2aGiu7qDk24C8t2nZ29yWMajTeqDhvaW2Jze5LhKF657z9ZtigJXdmJBj+KraTL/iA4OdQLXIAn1jpFyCimxTgVKyKAXbNEtUJi+11fI3Hqf77oHoOCgE6m4zQqiR0zub4k/geP1NRrGQi+Jf4FQ8/X0w5fVL4dIaUqyiSFw893NFp2ya8+vQJrsmSY23zVsiQPDn2PbwvpfCGyA9TPuQBorES7KcD/+Dq29foWrSEAKBPPg8h1YvgIuholc6QScDQjCKVzZAJmVOlAjmDeB6DMepjAGHzs7ypU+O2W6jWnp2lFfw+ayltU2NVVsXMWXD4yWOjs2RgtWbVm42lpaT6aGSjrrR0oVwX54Xq9G9ComP8/lD7LsgSTWJbcgGNOHQAr70/YUjZCmYSHvF9L6PTn/59fSCYivShLy9SRWjXMDqHx7rNf3nfM0xKkvMTxfKIz0Wg+u+H6rsWsCoGxbF3OLXtWK/UWBzISMofrafi2IYzxqO3f1qBKV3m4MRWDY9TqUVZDF81MMa90+GhUCnB0rlL5kBDx3bh+iD+pUDFPBJpMYCPv3Qi++R2gpkxZX82tJ98aLSUso9vPwt+nzQ6fjpzrO6iXIZBT2vx7ekig0Hm5Z5FhhqlPgz9mKq4k1vo4RUTkUEFErH6pcF4Xek6tQAAIABJREFU0R4zGJ2ida/mI7lT5JVeMZ7AOB6gacaReNICsMgMJfUuIPCkpkWlSwUlxVQolhkhdPqs9lIiBmDHcRj/ycOp1q66NQaCqeIexiyLQJeanEr/rpFkkGSDBqPA6K+Vq4Fg51YbQ3mH6BSVypAxwsESqDz7fOizwcXOXlJRH/z5+1IwomJldClaXBwPKrkb0lCsSmPaikaHjBpg51+9NJ6DKu17QpwjRmma5MknhIrPP2pAfxIrUsoitZ0D0jo6oMfObeL8UMpje+t2AqCef+k8SGa4unkrYaSmUW9s5bXLKJreFZ5+flh1g5IimpXJkBF5UrsIDUDzPPnRNG++SG8Q039rb14XELmdlRWu9uwn9AL/luk9ugKBx7XTW5WFwmpC2/oJ/uISn/vevzV3UZ03yfmJYobiaxGowa+gvifQlR48BSfHQrH7MnW6qvcAPj8CrPLHK/szQbhM3VB7Sh+sSiqKGz+diLaZe+P9C63klXw79snsMXRJX7MU25aZu4W0kO2opv7Dwt6o/G0540xO7DQL+5cfk78b9KolDg5Zn8MaCRAp97B55q5wPD2GtnRiWLWVr2xu3Dpzz8gXZIrnqd6uogipGkyIEvOkh+fbj0ZniTIWdH6e33mJ9NnTonMeShCYj4gYH6b9Hl59gt7FfzSL7nCsJF48t/ey8AVxXKze6vx7axSokDeq31mifq8nONJ/ayj/TJpzUEm+R8kFOkQ21aE49oDq0RlQvQHHodA5RlzVlqgD/8pPpuq9oXq0luiZZhaA4qDNIfRQko2E4mAQNU24i2FUhlpajXPnlchOWKu3ahnuhOhT8btBpcthQOmyIj+x8+4dbLpzU5yGCTXq4LM+GKeeP0cuZ2cBPtOo6N55+2ZJaREQ3TR3XjTJm18qwzbduikRF5Z2s9qsTvZcyJg8uXEz7r9nB3bdj8AxDBlk9lROEpkhAaEOCnoWLymiqj5BgXji6YnJp07AM0QglWDoRQ2byjiI22E6rffu7dqzSVGEgXl8jdrGy6dY6byLFyT6Q5V2L39/+H4OMpIxGhruadvR6DSFnbsh/+wByRMNAOkbvQfAOqR6jWX15DgiBorkj3GhH4nu6tC/4bMlJLVoVQo6Z409O6Etvva9hB5nXPpPcn6imL34WgRq0C2o7k1CzkbA6WAoDpFvOKoIJDbVHqwWWTQRO/I6xIO1z94Hbx6/l560Mu1Bwv9jbWOFDVO2Y/7QFWZnSZUuJda/ItU6BHBtkLMwNGKEZ29AKB9HXZvvhLCPxoqqvYHr8PjGM8zqu1BwSwb7cVk/AfYOqTZa8D+tfmwipIp/tJqGAP9AcZpYGfVNngx4cusFds7dZ0xDmQ6wdINiOLuToF7NyCRduHJ+nNxyTpPQUCCyG7P7L8Kbx+9gbWctGmHkMzJ1gKYe/U2cQFZcLSYgO0h76HCOKO9BUkMKvdJsHGyw6d0i2Nhpistfk6n+h6F6MhWohtAkrID6riSgUgqAzk8NrYowQFOvlglKcxM6XcwFN7+m607osaj+e0PSXSGiDjbVgORjoFB2QJcy3suNw14PVdFnnD2N5dcuCz9PSls7HOvULRyehSXePXdulc2fPD9Ta9fFrHNnMPfCOcHE8JlG54HMzA89PSQ9xkqqDd+2FnBxjx1bceAxUy2aJbO2xoKGTSVKxIhI+SXzJb1lACVT3Z3yE3SKwiq3h72G/qXKoG/JMiKQOvLwAZGgYD+LGjUVZuoBe3caf5Ic6+gq1dG+UBF0274Fh548MqxWicYMKFUW/UqVkc/Irtx8wxqz09EJikguI6KIF9OHW+/cFkdn1fWroGL88IpVhDfIYN1DhGCZXmNKrHfJiFny43Md6j16hER+KEI8AopD/GMyIxpvfO178TkX8d1XkvMTxYzG1yIQzpWPv2hidZZ5RPNH0Zkzi6p6L0D9DMXCGarPYqifWMoewvfgtBKKdak433/qYPUorFUfGeyv8+ORq3h249+UjJjecz5unroj0Y8MuVyx+NZ0+f7asVv4oYom/2Bq+z6vM1ZMtUjbFV7vQzV3/tw1XKQvJneZg33LDhsdjo3vFoki/OWD16UrEhcufzBbQMW3Tt9D3tI5kDqDs3z39NZzdCtAQrnwZmNvI8ruBvtpRX/cv/AI2+bslSiNfQo7/LpxiLHknM4M9bw+EUitV+X7YjUKSRSKRjBzvzI/axw+iiIO2rCVA+Dx5gMGVRgpxzDyQ0wRZT/Gt5uJSweuo1qbCmDFXFRvhH4+/lLGa+cYP85sRHOifn4IBL8BrEtCUayFI0T9+LuW9ko5GarXr0CgFp0TS7USOpu4r68Ib9B/5EPDC4xKVmzooQvhYEmMyyPYuNbKJVI6bhqwPN6pu+B5orJcs6eFAxrTqfkUAmKmM9W7RGkMKlNOoi9MLZkCkxl5Wd+iteB3Cs+bHe50W1q1legMXzZyzp4WqdL6kLLlhZ/HL+gzKi7VXqjopLQtWBidixRHteWLzI5d0+xbFHfNCI7fYLYWlhIxovNBZ4WRqjkXzn4Ro2Q4lhEbgsJNq934bK61cimeeXmKszSqUlXBU4W1vH9NN9IBFE2XHptatolq2uP8PYWtEUAMZgooNuF5xOJ8gkg6iK99L6HGFx/9Jjk/UcxifC8CcoMoinkOWdX7QPUaCgRo5Zr08EFiKw/+uMj0aQVqMkWHl+XBlccivcDNmemoLPkzmV3hz3X+kGomgyV3dsTKJ3PDEe+xaorSEVRZ7zmpPTLn0/rhg2JWv4XYt+SwkC3Smgyoi77TyUoKkbSY2Hm2WSQmd8nsmH12PI5vOoPfWk4RJyl7kSyYfXYcGjq2l/QbjaDjfYHmWkg836HVJ/D83kus+n1TlGueAqJTj42BY0pHkDeHpIptR7bAqW3nBf9jMDpAJWoVxpitP8LK2lwn6fzeyzi87iTO7bkMr3cfZS6ZEsxTOgfq27WVOeDxFZuXkbL1yV3+MuKKJh8eLVGnyIzisuPazZQ+ItINi/IC46mB3m8r4PWTmTRDfBOlxdNQv6pu1l6chQDfQzj1LjM6lvo+0QCx1O361oTtmJNSNmMm0e2KytlmNKbJulUC4mXUwlCJ1at4SWy5cxtuvj7icCxt3AwOVtaio8XPGSkiAJhRlpxOTrjv4SFl64z0/PNQk7hgdMZSZ4HjnbtJ6TujOQaHynDjeAw1+gL1WiSVWKAjHbuhw9aNkr4TnqxGTZHG3sEoZ2E4luzU1BQjMeE731AOr+OduwvPzwMPd6n04jjCRnmYViPHkMHoZBFP9FtVRj5DjQDqgn/PMn7A8/G8YW3Q3l1C+Mgef61cFR0LF/uq1mZ8Dia+9734HFt89ZXk/EQxkwmxCIgdgGIDRdE2Xb3XCMDPpGRWSQld2nNS5i5KvjbVoFhGTzize6Hv8fTWC3kY5CxOp2Oc8QpfP36LDtn7Gf8mXmfRrelwzRY7HozAgCCRhXBIHsq58Web6eLkUMuLxpQYnYQRq1kdAyE5pJp6ybpFhdtnQPkRRs0t4mpWPdGqzwz29/dkh465gjHJBOv3qGnsZ2zbGTi85kS4uz1sxQDYOtiAYqWl6hSVsD4ZmU2N19BsQD3R+BpQbrgwVBNDNOPkH5JG+6PVVGM6btaZschTKnKOnk65BxiB1nTUVj6OmEQxvn7gkfUjwF1y1AQcBWwbaLw/cZBQSejxfg39B/usxtG7i7DnRTZsepIH1bNmk3RQQhpJBQfu24VDjx4yXyXrk0aCw6Z58xsrniIbA3E+lH8gwzHL1RkxqZE1u/RF3A/TVxQUpXTFkP178dTLU8DE5Asi6eHkU8fF4dn34IHgZ2glXTNgeu16OPLkMW67vUfjPHmRzjEZKi1ZEC7iY6PT4fdqNXD25Utsuq0J6dJ+KFtBxrPg0nkUTpMO0+rUl/56mTBNs90uyli4pMHc82eF4ZpGp6Zq1qz4vmwFvPzohZ47txnPm97BEdu+a4tzL1+i/56dRlFVQ6Tnl0pVInRamCbc/+ihtF/QsImkC8Ma5540A8lsbFAsvWtC3nazvvlbhc9CMPKo2H8HxUZL9SWkJcS+l5DjjU3fSc5PFLMW34tA/3Ei4LtQC2MylWWVG/p3VQC9idinVTHonEMxNDG5sV3yDcKLuy/l4Za9cBbMvTjRePiu+fsxvdd8+ZuRi3YjWwh/TVyMUaTbZ+6JllaOIlnxQ9Vfcf3YLeH0YXVW5ZZlRRssVZqU4U7TrcBgcdRMrdv4dtIXcT605i6dhecnpmZgZ2YYXiJHd15iw+TtCArUsEgGo87Zgp9CQYQENT+6SlmREAshUy1UOZ+k3j66hXIObfVcBjtHWyz6eTXO77uMmu2rSCrsSzai/lhcPHBNUn8FK+XFpAPhU4gxvdbYthflaI9uGrM4HWxqfIU45LHt8796nBpwBuqHULmIloeaoUyWBhhSrgLkZQbUhou7QGbY+SO4uU9IeTqXYqfCxdCqQMFoa0eREZqaU4b4x8KGTVCNzk8YO/b0iZTH04jdGVmpKqpmySp4oIuvXhodD36f1sEBp7tq9BIGoxNEJysyq/JNZhx5Fvq7mlC9Fn46+I/xfEw11c+ZWwRcDcZIzb1+g/HL4QNYY1K9xe85F1SmP9i+C2quXII33tozgp9TrqNm9hwirvr7scN48emj/N6C9MGSXjOwSZuOlY4NsVKUD8mYPMVXtYxZIayRafKF0gpKmhMJstZMLzq+972vakINj3b1S+xxX+OIE3lM8bkIzIUmqabdHIpDL6hu1UOvSpcRSuqNUHRa5UVMjRVRkzrNllTNT8v7G7E8FADtlj9UHb5EnSLCNkyQc0zsw1tP/FznTxEUZUqN+BgaU1ZL78yUyq2tMzXhQ5aBk5AxZ7Gs6DC6JRb/sgapXZ3Qf3ZX/LPsKHbM3Qcv908RVnqVqF0EjfrUFkFWQ8m5jb218Ok4pLQXp+3F3VdYO36riKeSiTq5kyPePdM4RBjNadCzFiiZQVV4nU5BpnwZ8e7pe6Nqe66S2fHdsKb47dspguMhODt99nTSr8HylsmBlGlSSgrMEM1iFZxjKkcBgdPBi8zoGC4fvU6wTAP+6gaHFA6SFlw9drOc77vhTZHS5d970Op9NwMffw5NfaVaAMWmckyWw/9MW9Vvp5aaRjD0qoKDHt+jWp5usAw6BtWTchYEpA4XQKrqfwhq0FUotnWhWFGJO/ZGPEu7LRuMzsvqZi1RJqN5KvtLvc86dxrTzlAMU7N59RujYubMkrZKbe9gTNuxZL368sWSEiPWh6XxdByYSgorRsp+CDamyOnLjx/RcdtGePj6SoXV6xAnJKIxGfppnicfehQvhdqrlmrPCZ0OP5ariG7FSoh8x/gTx6TCc1z1WijumgEVl2jYoLBmAQX3+g+WEv7Wm0IpBnZ+1x75XNJIX38cPxIO62TAJ8X+riTukfpPMwAfsvlrL25k+1cssyToIOJz30vQgcah86TITxSTF5+LgOA19V35kJJjHRTHfoB9a+0z8eotoCT7GYpDB/CtHAFHAJ0rFOvCcbjF2qFXDt/A0Op8e4A4Ar2mdkLTAfXM+mWUZOmotbi0/xpqd65mJnVhaEhnZO34LcZKKNMOOoxpiRVjNhhLxE2FQlldxeopOgtpM6cWJ4UPWmtrK/l/cNBnI25G+4VrPVMwdNtfe+D3yR8/LOqNnEXDh6NZtUXuHqrHB/oFGrXBeP6sBb8xRnJ4L/mZQYyUHEaMMg2pOhp3zj1AtsKZBcNDZymsGa6FfdTpUhXNBjUIh6cyPYYpwaZOnWQ8hrRZj0mxF4eN8wII6UANug/VZwlgkRb4/H/sXQV4FGfXPbPxECO4u7u7W4Hi7u5SKFYoFG9pgaLFobhDcS1e3N3dgwQIIUTY+Z9zZ2ezGyHCAv375f7P/3wlO/POO+/Ozpy599xzHgKBoTwoeK+AwTE80dNWx/7/PI6qvtfsZoKOAo6loMSfLkRyI32YWJqWa9YNitdEky0NQbE9lER7pIEhtsF3U6ovb71xTQQJCRCiE8/8/YUATCuMsYcOmHk+axs2lX+z24q/OxKcWf4qlDylCAPSk4v/zXbvFRfPf1SpuXuholh6gZ1RkfvphZ0rgU67fAUwoERpTDhyUAAKtYCmVauJeI6aoCq71OjITsFGijoWmTMD7z9YZ2zlPgYF5P4kc3fHpmtXsP/eHSnp0VuMwWzR0L27woGfzU1bSgv/14zbr3wx6cgh8UxjCZDAMbJQPzyB+rIJ8OEh4NIAiseoz16ituVz72uu88eOHQd+ovhmbH0RqMGXofovFJE5xOugdeKwNZnmiPbZpQWeN031RX0ghDVyIxTPiVBcrIFKTC8otmm3yNBNCMCMAhVzY8yOIVbDkIxLboyuoTPj9FgpnVnG2ombMWvAInMWxPKz0vWLStcT7SV0kMEMVFiPLTeveGb9HRqVzr86WQjUm+f8jcc3n4YajzrYoceUdlbcncjOm0TvLvn7W31MkFemUQkz14cmpuToiBmpURVFa5bX+IBhRoZz4fc9uvEEcWG3TIraO9jD2c0JHX5tIcrXlkGPtIPrjiNRqgRmsjM7ump5ttQ6w+wNqNauAr6b3jHKr41AjhpMV4/fkPOmt5gtuDjC8Xm/HRDxQ3bG2YlxIkLYuq8VRZTEx6EYvl4mKsrF+RdsIPwLyk8EHQHss0F9Ow14r2nPSBencxWob+nFpHdprrLJy0tMTp3gocKieQIieO1USJseF58/E12cboWKSDeWHqZ3DCkh7Wjexmxg+tflS+hrMkylXQRNSx+8De3g1HV2Vl++aAWQOB6tKF6KGGJoONLWwuRYzOxVZOKKVIfuvmWjZJwGlSwjYI9k7yXnziKtlxeWXziHJyawxWONr1wNtbNmE4Xq2adOCBBrkze/aBZRH4jg5/jDB3J+JH43yZX7X0FUZlfbvdevhWP0baYs+L3Kx+/v2r0oULqBEXQIsE8PxT5jTC6LGG1r6+dejA7+hTaOAz9fGPxEdjg15LbW0uiQB7DPCNXHou3YuTYMXqHcndheG+1y9jYbiHol9sSqJ3Oshto2b7e4uevlHbq55yxhnbYPDgrG3EFLcePUbZRuUAwz+yw0d2sxqzNwSU8RHEycJiHylc+JJaPW4vopTZ9DD5aodOf0rpPaoE4P7YdPcUG20evmoyRAsw1fLw3tX30Yi0asEpXmPnM6SxlJD4K7vhWG4cKBK1JuIwk7U8H0ArKuHtM6UxiDln4nGkd5y+fA3uUHcWbvRTFFZYnNcqxvnBqHluMUiMhjiyENMLTub5KF6j2zkxC5eVPqXmQgbp65LZkrCkJWblUWXKc+ZYfhytHr4ilGInTi1FG/bRJczh6wyJxZiwiAxub7D0eq5yCOpbWshfoacG0DgwdLYHHxsRWQzsznVQEjFZTtgfiLNB0W9R2UeO0AZohe1NPW1D4XlATL5AXnSwa7rpqu1cpAzI60zJMXP5WhwKoW9AcjuZiPU0stnCV1Gogn1sC/d8DV0QGdCxSWh3O5dOnNJTG9dEWAs7huA9RZvhhBJhK2HE9RkMk7Aa5aiCzy7w2z5xI+Tc0sWZHR2zoTxt8QgQCd31utW41zJnVqjvV3izZi4KrHhadPUXfVUgFczCLR8oJZlE4b1wm44j4UUTzUtqNNXhr04+67cxuHH9xDxfQZxQWepcGVF8/DLyhQOsg8nGKmkp5r+hQRduR6Egguq9foo5eIGnIP6uthQPAJ08uLAsV70WfTlooDP1/yF/svPdaXuAjUDy+gPq8IUNOBdV2v+cDbcTbN/HB5t8zZhQkdZ2g3wF+aovEA604Vlo5G1B8vWj4Vm5dGz2nMTOnvhhF/QTP7LcDq8Vo3FjMcfWZ3Qan6RfFbq6mi38MsD41BIwsSsjPmSycf//HdPOEB6XYUS+5OR+JUmnw97SvqJWwrQoMsIzXoUxPtfrbW2Xh675mIKBb6Jh/+XrgPM/sulPIZsy96FP22AEZu+AE7F+3DuHbTzJ1mzAKly5navB3XievF86d+T48/2mN4vXFSPuR4bvHj4a8X80UokRkePco2Ki6lOpqqTuoyW+bLmHlmnChERxUCQDvPMu83/9pkpMiYLKrdovzc+LQIoFqauhqgJKDmVBZ5YCuGf489R5Qn8xU3UINOaSrPEg5Q3HpCcetkNSMhQLNEYZ8BivL5hSPPPnmMXtu3yAN5fOWqyJowoXhdscOLwazNP207IHE8N/k3MyIH7t3Bm/fv0f9vjXRMzZ9dLdui3MK55v3YSs4sDctP+Wb+Ia3qBBwUHcybJClmnjqBS898zOeezssLo8tXxgfViNbr1li1nhNkEbDQoiJHYs2EWI+Bu3ZIiY2aPUVTpJLylR55kyTDivqN4GBSWebfCXZIZiZoYIaHXCX+TQ/esS5362VWZv7Uy+XU40eov2qZnDuB2s4WbbD0/FnMP3NauEks261q0CRGh+H5UhbAxd5e2vxZaowsVOMbk0K7ZgGihT0Qrw0M7uSh2T6+xHPP9rOO2YhxmZ8o1utzXwRqwBaob4YB6ivTTEzvVoZ0gFt7KPaZbZo2JxCh+nJsHqjU47l85DpSZEoq4oPv3gagXsI2CAnSHvBs/+bDmiWsRcNXmrk3CZJ74cUj/fysF/zPq5ORMpP2cF825i8hOBtDPogK86qnc836QxQYbJRcKxsRZNXtWV1KQgx6be1ZflCOSV5RjuJZ4OBkjzN7QltrZT87BcVrFkL1TpUwodMsIT/rMX7fcOQulR0+959jWN2xeHbvOWr3rIYqbcoJkZpZLZbD2BJPzZJEKRNIWz5vhlSoJugiKBq8vDfKNCyOLbP/xuRuc8wgJroZHIKluQOXyHg1u36DSi1tQ0A2vqbApoXvlPsQGL6AFUPMbkf//q2laeF5ZcDI61mF4r3Cpr/P2KxAzWWLcNEEQtJ4eWF3y3aYcOQfTDt+zAxA1jRoIr5XDHpp3fJ9KbYST/39QfBEl3dyZ8rMnyOqy3xd4J3I28UV/7TpgH/u3xWOCoHNs3fv8Pzdu3BTtdS+OXz/Lpr9tdpqGwIggrOaWULtYAKCg5FjOsuE2vGqpM+IPXfvINCC40Ou0x/Vagh42nPnlqhRl02bzixUWH3pAiuzVOoIEfzYKlhq+8mCO0Q16j+OHcWpJ1pjBKUALnWNuQciZQyooWQpuBjRnI1+EwB/awkQWS33IZK9VT5clcwjSLC3UZbxcz/3bPXdfMo4ceDnC4Mfqd2+Www1+CLgUhsgiVI4GGGDb5XfiQfT145Tu84LUKDezp0L9+Ho7ICJ/4zCvMHLcWLbae3GZVBQpXU5EVZk6/jKcevNZSNmSd76hpIiyQ2iLxi7sVgiev38DfpXGoHb5++J+KGdnQHs9mIrPrk2jEHVRos4oygye7iIfhHLX3cu3kfXggOkzGQpMlKtQ0UBIJbhFt8VCZInwN2L981/5pubczxncX4nKCI/afv8PWb/rg6/Nses/otkmwELu2PXov3we+WPTmNbmjNWty/ew875+1Cgch4UqJRbxibheXy7aTi3/zK+7VQJzX6s91W/RuH8UNPnw13AoQgMjpGbO37Vif4/OLgx5AEQdAyKQw7hTalvJwCB+yXTI75f9jmgeI6AonwZ6xNmJc48eSwrx5ISS0Hs3qqzYokAlcLJU2BRnQaSPSGnpsvmDZLFYGaHWQzdHZ37s1xFKwnqA+mxtE5DrL1yUTy3/qbeUCSxtkET3PB9iXNPn0gJiyKJlsKD5OzMq1kXzPSwhb5LwcJomCMXSv45S7g6vDd+V6Q4qJ7cZv1as0Ahnd8vdu2JYXt3YfF5jVzeKnc+DClTTnR3WCqzVL1O6OKCYx262uxKItCruXyRtNOzpMdusV23b4Kihzxuh/wFpX3+c4UGfihRYvL4cm4OBB8CPljSCWhbUxaG+GFBUuxmFQd+Yrdu/6m9bH0RqAHroZqVdWmM6KShdkrm26UDPuj8FAXwGAaDa8zSqZEtPrM9G6fvENuJGl2rIEGy6GmSsPQzsdMMKIZQ4jI7t6p2qIiN07abD8fy1vRTvyFp2sTw9XktnWUUMWR7esb86XHBwtNr49vFcHYNfTAs++UvaQnXvbR0M9NyTUpiwIIecoyqTo1DO8wUoEi1/OLXtXbSZszuH8qRYVaIc6FXGK0togryg5ii0o9duXVZ4SwxA0OQxxsyRQ0JkmiBMWa7NUmcnmUEXwRNBHl/Xp4EjwT/7hKS6Pv4dgGCTgIOeYGQZ4B6Xwj4BnfbvTFHtfb/Hz83kncRsBRQkkJJsFRK0+orvvVbXmv2UNz7aBygWASBzK8H9wtxmGrELDt9LJjF+XH3Til7jSxXURzMGTTiJKig/YWeXaBrObugdFBCe4wNVy9jy41r+CZDJmlfJ+em8erlZisHZmz4lKexqX6WzNIwa6FbYFTLlBlVM2QWQUZ+xiyNJSBheYfKyZJ1sihR1cqcFYce3Mczk3ozVZ4HliqD3tu3mE+5YLIUWFavIfLOnAp/k2N8Mjc3HGzbCeMP/4NZJ49Ld5gePFcCwLDcolh8FeZdSKhmW39qTy9zCY7/poN9Rm/vKOkBsT22GnRW85MzPqf0v4iRwqko1Gdlww+puMKQ5ExsD2W1n62fezaZlI0Hicv8RLGgtr4IjH5TAX9KqZtuDe4jgHfzTSietw2SI5kJMgAO+WBIYG3WF9vvf87AJVg9fqM8xFNkToY550M7Pj42JjVwqNisB4EPOTk/re6D31pPlVZuloGY8WHmxzJYYovn6SqZFCo1M8jX+WXbj0JMpnAgVY4H1xhjlY3Rx/BO6oUVJkNVKkeToGwZVduVR63uVdGt8A8CUmiESi6QnZ0dFo5YadWRRg0fbkMX+7DBchgzSizbzbk4AbP7L8b9q4+QIU8anNhxRkQWCciqtC6L3jOtxd3ocD+z30LzsXrN7Cg6Qnmk5Dj3AAAgAElEQVTL57TyS9OPSWBIsEauUNvRTc1ijrH9XmOzn9F/MeA3wmLXUCUXJdF+KHaxU/yOzVz+P+2jfnhk/dBxKArFtS7U1ySKm97K5YSYte0Oxa1LrE6v0OzpoNEmwQWF/yZE0QkUk4OQ60M+DiNHosToX6K0OXOidU9VRe2s2bH1+lV02xpeWZ2t2Z5OzmKASmsJihsyPJ2c0DZfAUw9dsQKiEQ1NwIV3W5D35a2HXRw12NZvQY49fgxxh4KVWhP6e6B/W06iDBhw1XLwh2TZO3+JUpFdfh//efG5zVNHZnaVBWvyZLhUZ/SWiNM+79LAxg8R9vknGz93LPJpGw8SBz4iWJBbXkRiLmpbzcgyKIc4zFO42IEnwxzA+WVbjskP7DqKJzYrqWM+SDf4LcIlw9fA5WQk6WzJiBaLgnb30c1niBvNtlLZEbpesVElfnCP5eRuWAGeHi7I23OVMhTLgf2rzyMdLnTSFZmas+5WD91mwCKBn1rYtkvmrAfg/wZlsnYVZapQHoRSqTGELNBRb4tgH0rNFG2hv1roemguvi11RTpLmPorfr6HEkkZvaGreH5KuSSz2ljsXHGDsneMBvTqH8tXDlyA34v/XD+nytWrfdci8YDaoPWHzU6VxFXd86lZcbuePHopQA9tsCTrDxsbT8rKw/O4frp2+haILTFnkCKa0WQRfXour2qCxjTY3j9cTi84YQAsRQZk2LeZWsrDRv/viMczui/DPCLSFma6rGH4trdI/kSNJHSQhZZHgXwXge86Q+E3APsMwMhLCtmADxGwuAQu1bknNMmi5UEgUHZNOmEEPupwettwdnTIHmX4IIWDRuvXsHF5z547OdnlaWh2nKVDJlQXkQPjZLdIRmZwZZskowXnzuD8z5PhfBM0EQw9Hvlaqi7Uiu1RSec7ezw/oMlaNT2Su7mjkdv/bR7laKIoSpVqnk8PTLGj48dLTQ/wfuvX2P+mVP48+wp+bdlC3x05kFNI2ofEVBNqVpD2uK/doge15tBQPA1ABayAfa5YEi4BsagC4DfKMDgAbhQDsMecCwczjcytudhy+debOfwufeLAz9RrLAtLwJx1vZtY31EJYGJ7MybgAIoHlqbLCNeJxjc+9jkGqBC8U+1fxXAQZG+fSsPC8+FWjhjdw9D7tKRc0DYgv7ikS/yVciJG6fvoGexQdqcFKDnHx2Qq1RWKf2wdESA8+OyXuLWrm/DchE7v0Rf54Mq4ILBLBAVnyl4yJtzwhTe8E4WX1rjOc7AJd/h8MYT4TI++oJwPHpjkXzMoP0EHevpG0vw0WlcK+Qtmx29y/yEwHea2GDR6gXw4cMH3L1wX7rQCJhGbRpodqTnONTtqelhLUjIY/Wd1xWVWljX9h9cf4w2WXpG+B0R4NXq9g26TdJu0oxepYbg0qErQgZneWzNs3k2+X5jMogx6Bzwsn7oLrwG8U4E1Aweg2My1P/ctkbfHkCg1iEF0E2dHZrU9FGhJPhLuEAqH0qk5XqMgOL68RbmiBaQgn1D9uwS3szMb2tF28qCY11+5oMXAQGiAm3J5eGY7AhjloVlLFpkrLx4QYCN3s+p50STu7vjnzYdhf+z69YNER98HxwiflZ5kiZDkTnTQZd5IqaSqdMgd9KkYrvxOvC9iCOybf2vK5civTaKpkiJ6dVrYtapE8ILChu0feYdgvNnBxkNVcMGP9vRvLVVGzzLhSzhZfJOiIY5ckrmLKpgOSv3jKmyDhyzSY5cGB7G+DSqMT7H50bqvAVfCK0QyEHoClDXZtmdj83bls+9z7E+thgzDvxEsYq2vAjU9zuhvqKxqOk241QNCNRr2wrgkB9K/D8AlWJW76HYay3gtgoSi1lumdR5Fk7uPGcelg/o7lOix09gGzy1ePQgWHF1d8aDaxrhkndSCvrtX3NEjsXOjIz504nWDtfSM5EHXvlo4I5gZOjqPvij558ICgyCZ0IPIVTrUaF5KRzdfMqKLM3PSHim/hAJyuze8n/tj9/a/IFLh67ilU8oUZNZJ2rsMBtEcKW34nsmdMeQWr8KwCJAGbl+AIpUL2C1zGNaThbujx4s9zUdVA8thjaw2o7ZpWquTSPlF6XMnAx/XtG6WRgXD10VEcPAd4HoM7cryjWmujdA2xACRPKjdF8zW33vauAhqG+GAko8KF6/Ax/uQfW1JNLrjxtK5+/WBDjjIsIVEJ2ft1zDZ4BjQe3tW8IeisdPUN/+YdIA4gWeBoZEO7/YSlKYsM/OrVrXVIZMmFa9pvnYc06dkOyGzo0h/4aq0XzoM7tCZeTd0kkF8f4iOGGwHXuJiWTMf48pXxk/7NbBn5ZlSenhIX5aTdauBLu3eIzGOXIJr+jgvbvmLI4+mewJE+HKi+dI5uZuRay2XCiOS9AlICuSIJF7eX1ddgByLh03rRMNo4zxvbGyQWN4ObuY9yZx+e5rX+RMlARO9lojBXlRuWdMkTkT/DTPlcdKE+mLfXlhDmR8XgcIMQFIJQlgcKZmhyaf4FQCcG3x2XhGnIotn3tfaw2jOm4c+IlihWx5EahqCNRXfYGgvYBTBSiev0J91Q8I3KzxBLz/hOJoIW4Y1bcXy88H1xyDo5tYZtOCyse0eegwpvlHvar4oKfIIA1Cg95rCrYsYdE/y9zNZeqeCn4fJCWjnCWz4sI/VBGOOH7dMUS8tXRhQ8utWK4iX8gS0Oifbwtebi4n0TNsOctq4ek82hwd7KB+MEr7PPV8CNRoMKqDn9GbB6FQlbxWEyRYYicZTUuPbj4pWSm6uEckVMhW97N7TW31rIQkjY+XjzXdkTajm6DpwLrhxtZvMPxfkrPbZO0p3CKCsalHfjF3ksXyK7bazehTXCNM8s3RsTiU+DOgvmgYenO12PpL+AbZ4pz+DWOoxtdQn1cHjD6atUWCDVrWh51ffMFxqgRD/FDgG905M3uz585tyd7ExD2cXB5dI4fg4XqP7wXYXPR5iolHD+H4o4ei4VM1YyYMLVMBPbZuxJ1Xr/B9sRKomTmrgBzSlJvlygu2YbOjqf/f2604OZXSZxArhtWXQmUkeAyOufk6SzSAAzMoOXOjaMrUyJk4MW68fIk1ly/I54TZdAvjcSLyDIvuGnE7VwcHXOgSmnWlZUfjNStkCI7du2gJdC+sOaBfef4M9VYuRUBIiHCdaPOhawexA27coX+QytMT4yp9I+39XzvYDSzPCnLJaHXEa8xcnDRA8RwPxaX6Z5umLZ97n22SnzhwHPiJYgFtfRFIq3vgTqjBN0WiHE6loFAQzeD9SR5AMbkOyI/5veMMyYhQAVkAgr1BSlhsEY8s1k3Zihl9Fpi1a7hdw341pUx1etcFyazQwHT+4Og50js6OyJ/5Vw4siEUiFkeO7K1ZzaHROjj286Io/yG6dvx5rnGEYgo8pTLiRqdKiFb0UwCXsQvadAS/LPmqHCMmM1xs1CLDjsGrS9I3I7MxJTgpUmqTnjvHyilvQKVcwvgsXOwR7Yims/Qx+LsvoviL8ZgholEaPKkbBVGn5KAkcRUgp+SUOJPhur7HRC0L/SGqngC8drB4GZN6LbVHP6r41CADsHnoCoeQhRXaT3w2sQBcywNg7e1inpU60CNnQoL/5TvhZmT9Y2ahRMFjGyMeadPYvSBvVLuIQeHXBmWuQrNnobXgYECCChQ+GOpsgICbvq+xP67t1EiVRorkMVsCJ3S2c0U0ftEPAcHc9eVPpcM8b0lm0IOkJPBThzUWboiQFlYux7qr4r6nsBt2T0VWVC/J3vCxDj9VMsyf5s5CyZ/8615c3a9VVzEtdOC5bWlJuXknw/sxZzTofcZmp+S28M1Y8aH3KpcSZLaTBgxqu85Jp8bnxYE1NCMtrwou38PuLYFQi5owNvGVQJbP/dicr5fats48BPFStv6ItC6vSzeBu2zAm79xB9IccgNuDb/rOlMCgnOG7RUSMJdJ7bG5K7azVkeuj83Q4M+NSJdkT8HL8PKsevNbeFZi2QS+wbL+HnrIBmTHlrRCTqeU6RQj7Dr7eDsgGBTlknf5o9jY8SIdGqPuVb+W/rn7CIjifrdmwC4erpi1pmxSJImsdhn0CG+0Dd5ET+JF3YtOSAda4yI+Dz8+/t3gaLXc/3UbTTsWzNScHh693kRNXTzdMUPi3vGSESSZbs22Xrhtc9rGOzsMOXoz8iY1zYlT8k2Bp3SursUd8BjDPCmn8mM0/Ros88ifJUvoUYcnWvi/8s2orxLna5glpBNnD37nEDI+dDrOcmFGAnP7bp1U0o3/Gb4UB5ZtgIa5dS0o6IKgnpmjNgpVi1TFgEeBDIUESQIYoamTJp0OP/0idhYMJilIcgiGNBb5JklqbZ0YVSHC/c5/buKpUoDChxaWl60zZsf885oZGRGaJHVeoj8SZPhlEmvKLKD6/ykpG7uGF62Aiqmz2DelOeYfdokUbDmMWpkyYoJVbTsCFvnyUfSY2Llaph35qSIQ+pt/yzHrWnY1FwSi/ECfKYd1ICNUF+TZ0k4GQzYpYXivUzzkAvQuoEVz7FQXGrZbAa2fu7ZbGI2HCgO/ESxmLa+CIw+FQHjvTBH1ZLBQpL8zOnM6vGaSXs6gyWpRCm8cWjDCWQpnBEj1g8I181kOdHnD1+Ifs/D609Qsm5hHFhz1Po8FIi31brJW8P5eUWZ41YgPB4SiJmNEn+xsExM09HWvvgTY9tMw+ENx62Oz/JbowG10bh/bfn7q2dvJLuzefZOkHtD81G2+pNbRGXpLvn74dFNDXhRn2jRrT/CXQ0EewR9ug4QW9jLNymJqu2szU31HTl/8nnYHRaVNYjlwV49e40zuy9IuStlZk2JN6LgdiwnRqXTJJ2FLMG8WwzYpdZ8gJiZ4APbx8IZ3KEIFO9ZUJRQboQN7y//6aFU/7lQ/X43m5jKyRqSA0ZmJmgcmwWGhH/FaA1Ylqq+dKFwYajzs6VpSyRx02wpYhLsgKIxKTu5HOwMUt5iOadEqtTYfP1qOOuJXytWQd1sOeQQBEyVF8+3UnruVKAQFp47Ey4zwy4wgo2PxYRKVTHnzAlcfBa5zQ33T+XhgftvLDMcUZ/xqgaNUSBZCvOGBI8jD+wRf69J31RHcneS0oEFZ09h+L495u2yJkiIB35vwnGKVjdoYpUFYyaKHlwc72uG2WRZfQeVcihvJwHvZodmbh0KwkDdKRuFrZ97NpqWTYeJAz9RLKetLwIjBavehwp4QfEGVL2b4fOrOnfM3Qf3rj6UjAnFAFkuIheGfBZmG1jiuXfloTyELYUI6Zo+tcc8Ef7r+Ud7WbW22XqZMy8cQwdVXkk88drnjfkz53hOUhL6WLCkpPtgaQ+RMLpxpp0bD6yN1iMa48fqv+DkDq11P2ykz5MGkw+NluxT+5zfax8rkI4u3WGeCtELh63Eyb814nfecjkxZlv4TqflJssNHfzox6LCNW00LMPSO61hv1qgOrQtY++Kg/il+WQ5h47jWqLB95Fn6dSQ+1Cf6wCNJ58AcG0FuHYAXpLvw04SIxTP36G4hJYObDnf//pY6rtVUN/8ZCFRYQc414fiUgH48BRwrg7FEHPgwgcusy8Uz4upYaa+5lRp3nv3thVfZ1ndhtIuTgFFXVuHr1w0HN3UpKWVmOLsk8fxy8H98v6RJ0lSrG3UDNeeP0fVpQusSmEEBbpAYUTfN4nUFFlcdO5MOD0fy+3jOzujZe58mHTscLQvG2bGRpStgMbRyIy9CghA8T9n4X2Ipo1DP65qGTNjzEHyszSDVwK5fa07mNeBbvIt/lot+7TPVwCDSkUgLBjt2dpuQ/XdUqhvRlpcdwrg2gkGD9O9zgaHsvVzzwZTsvkQceAniiW19UWgGt9B9e0E0N7CsQTgOQxgJw7bZ+1SQfFeDsUuavfv2F4JzN6s/n0TggODscGk0MySFx/W37Qtjy4F+gv5OEWmZKLY7BJPcysmIffhjSeSzWBb/NhdQ4UITE+tzAUyYEaf+WbCMXkvaXOkEjDTfUpb9KtgKagXfubcXgclUZ0XCcF1elYDxQUtDUvD7kffLwI5qkfrwX0ZbK8nt8kjkTue3NJ0SpgxYomN587P9WDH2s/NJuHUzrOi9KzHwMU9Ub6ptYga147lNoarhyvWv9KEHW0VnfP3k7IdwyOBG9Y8C+U3hD2GavSFSq6PtGGbVwBK/AUAy6uBewG75FAcrYnetprrf3kclcAmcDdUuyzyvwg+BhiSQnHMB7g2i1GZ63OtU9v1a7D/7h2zIjPNTX+uUEkEDGeePI5bL1+ItYSzgwPI13FztHae77hxHf6+HWplcaVbLymRZTOVlfR5dy1YBNNOhMkAs1PLwVEyJgQVLKfp3mNhzzdXoqRoljs3fj6wD2+CAlEgWXKxkFh35ZJoAIUlRbvaO8DT2QmP376VbjEapSawUMD2DwrCyP17JNNFsjNb8RmdN63H7tu3EKIaxcS1UY5cyJYwMUqkTi2q09devEDVTJmRJYFmpMzotmUjtt24ZvY5o3+X3iX2ub63j40rprrvlgLGl0AQ11z/bRP8dITBwzayKJyDrZ97X2O9ojrm/wT4CQ4ORu/evbFkyRL5Ups1a4YJEybA3tTu+LFF+lIXAQXUpBWZIjVfIPhQb5G+G969eSdllJEbBkg5a/aAUKsIAhxmRBhN03TBs/uUWIeIIlKDRyfzPrzxGK0zh3ZdkJC88nEo0XP69/OxdiI72iIOlqRK1y+GpT+vjdaZk68UEhRG3TSCPR2c7RH8PnS7iQdHiV4ROU/U+CHgojM8gd/VEzfRr/wws8dXr+nWnmp0nF83datenRR9nvdv3+PPwculm6zNqMYY/O0YDfwoWiZp1MYfhFSeJF1iKeNZZtKidaJhNiI/iQKOjGxFM+P3fR8HlaIrRV8g4aDwHd8AxWsaFOfysTl83D5cRba6P2fpmnINHwBPCoDaaTYhwZegvhks4qSK1xQoDlm/2pqR/Ntz22ZRYWZZioCCzuNUaA4b3HbUgb0gofin0uXF4JT8GPJkCD5yJk6CCukyoGmuPDh4/654c30wGlE3aw78UrEyhu3ZhYXnI7dVKJQsBU4+eWTO/JAbpHOCiqdIhROPHiDIolWzSIoU6FWkBCYdPYQrz5+Lp5geBFNNc+ZBq7z5xJssLBj57eABzDl9QtreuW3z3HnRs3Ax1FqxGA9MJTVmjHhe5Pqk9/LCk7f+IiqZLWEiLKhdz9ztNeHIQU2HSAW8XV3A0h/VrWtlyRYtDSFbfvmawGZxCx9IWuhYNHk4loLBe67NDvmlnns2m3AsBvqfAD9Dhw7F+vXrsXXrVlmiqlWrom7duvjpJ6asPx5f4yKQh9ab0VoHmNdYKHaa67mtgwBg/+ojYiaav0Iu0aDpXWqIdH6xU2nhjSnStk19IPJeuC1BEzMuFCoc/ld/pMqSHCd3nMOMvvPxIVgTLyzfrCQGLrJ2OWbGicalIxv9LgCDNxR373jwf62pl1J7x/epSdyRf9DLVBREDNNywgyOV2JP+D6J2Ck+onXKUzaHZKuCg0JQ3aWpeZOSdYug428txLz04Lpj5mzSXy/nS1lQD2aRxrSYjEc3nqDNqCbi3N6j2CBcP8m3YwWZCqTDlaOaLxszWU1/rIvNs/6WORIcEfzU/a4aukwII3IZgy+V5GuCSJYXqRwdHQ8xITyz+yhwj0leYYwVsZmGp9SVUgxfv703Bkvx1TY1vh4OBCwxHZ+qx/z/YHlxodYPVF6T7KorDIN3zEnDtjwxAp8t168iXfz4qJ4pa6Tu4VWXLMD1ly8EEBRLmRoL62gCmOzconLy6staWzszP8y23HsT+jud8s23wi0iwArbGSaeYAD6FS8lre87bl5HsFHFkN07rMxAwp6zvaKI7UbmBAnRZv2acOPWyJwFkyy6vCz3py7RsvNnQzNeBgNKpkqDCukziGbRx4Kg6LsixdCtkNYez3b/P8+cwhP/tzj16BEuPfeRUn7HAoXknL5kGP2XA35hnlfOtYH36+Qa1F5qrO2FPmV+X+O59ynzjc2+/xPgJ1WqVJLpqV9f+1GvWrUKffv2xd27d6Ncsy99ERiD7wIvvjHVcw2AcxUYvL6cBcL5A5dx6fA10f2h2B7d0hskbS9+VZbBBzyJvxQzpGmq2URUAco2LI4fl/UOt7a0xOhbYbgAAQIYcoEC/LS3urBdXUVq5EeeMjkwq9+icOBHAxiahYRl0ESV+kNhy2FdJrTGt50rw9HJQTbXBQx5DjW6VBYbDssgr2n964Vi48FOL76U9pnbJVwHVrO0XUSdmkGtJAIjBsFj6QbFcWD14VAzVtMBlj+cFSVZOcqLMpob0MAU/gugEtzEawnFYG1mq4bcgPqyBWB8Abi2gcGDHlVxEdkKGENuA8+rhPlYL8wY2HITCn6cSsMQf+ZXW0zfgACUnj9bQAmzIHRTL5UmLdZfuQyfd29RP1tOc7mo+LyZ4ljOM8mROImUkvQov2Au7ryO/CWjSvqMUh7TO6ZoTOrzzh+lUqfBc39/+Aa+F6DA0tq4QwdEW4cdaR8LgpAfSpTC+5APGHc41M9L38fbxQUH23SUrA9FELtsXo9zT59KG3/6+PHRd6f17zmdV3zsatkWT976yW954tGDWHXpopyvmLCaBiZYG1a2AprlyhNuetn+mGg2emVpjlm0zxV77twS65HCKVJKeQ7Bp6C+bG2R9TEdOf5iKHZJtFZ3O03l3lbxpZ97tpp3TMb5z4MfX19feHt74/r168iYUfPa4X9nzpwZr169gqen50fX63NcBMLcf78ZoL6PSx0odonNczC+aAMEWxh4OlWGIb7Wjv01gp5XLTNQlVoLPthpZMrXscYD64CEYAEbdOZQFHgl8hDnc3Y7hY12OXuL07seliRp8o7YisVyFru2dFDEbV3cnLVsUZhwdLZHkEVZiyaps8+Px8YZO8U8VM9g9ZrRUdrbvRJ5ClDbMX8vHt16KjYfFDt8dCO01V4/xLQTv2J8++m4dU4DyOlypcbM0+OsZnBg7VGMaT5JHO9/WtUHT+74YMusnaCuUMFv8uLHqj/LuuhgzMnVCauezIaL25fprDLScDNgvTZnhzzhTHKNr4dqvnKm93Al8dFwAOlrXHP/xmOqH3yg0mTS3JzAWertiCxVO0gJDO/maOVrz5Gxytjy3jDjxDFQeI8clI75C8Woa1BfOxp+1lmhZaiYselZpBjsFIO4oLPlPa1XfGlvn33qBI48uIfTTx7D2d5BlJ0LJg/tnvrh7+1YeYnkeC1YGgu06O5iGzuzI/priLezM+bXro/+O7eJPYbWwwrEd3EBARnvEVytkEgUSdmRViFdOgSGfDB3pRGU6OBKX3W61zObxBZ6Erh1d/kk8eKJk70+Hx57StVvpfVfD64xs1okOI87fFDKblSCLps2nawNu926FCwi5T89ftqzC4tNpb3xlaqiTrbI7YA+5fq988oXFRbOM6/bkpp5UNiZ999Q13ptfEVkKwyun+77FtF8P8dz71PW5XPs+58HP/fv30fq1Knx7NkzJEyokdn434kTJwY/S5nSWsp/2LBhGD58uNVam9sMbfQNqP6LTf4/doBdYigJ/9Z4A7zE+SYeRL8bUfqAknA7FPtUNjpyzIehDxZ9rvROrtTZUqBwtfyo1LIM4if2RPtc30vHGEtEJEgTgETU4r1n5SGMbTUllDQchsnILIyc/4ewP3Ltd54yU7JQCw3+yaA5sYeNso1L4MelvUSMce2kzeZtPBK6Y+7FCVg0fJWUo2jYSFL30zvPwukIceyFN6ZiaJ3fpFTHIIF71tnx4Y5HPtmd8/fx7MELjGk+WXSB6OVFy5BD64/j3IFLAuRYLqzToyqyF7PuEIv5NxL9PYzPa1tI5HvAkOSE9XX9dgbUt/Rg45orQIINMDh8uflF/0y+/pbquxVQ3/C+EJZr5gLFcyjgUBCKfepPnug/9+6aXdY52NK6DUXpOaZx8tFDNFqzQng25NhsadYKY/7Zb0Vi7lusJCYeOSS/BVpJHGvfJRyH5n1wMIrMnQm/IK1bs3/xUgKgGARTzIIcfhhqScO/k1/EkpvlrzMsgOF25B+RiE3+UNaECdEuf0F4ODrhr6uXsfHalY8KHnL/7IkSo27W7Pjln31mcBSRS/yYCpWF3B1V9Ny2CdtuXJfSFsdeb5EB498uPPMRYjgzSZ8rLFWqCVrnVDKghOeM8KbXlEWJPx+KU7HPMpU48PNZlvXLDqpnfm7cuIEMGTRBLP53pkyZvnjmh10iqt84IOgEYHxoXggl8QkodOflYyj4CvCqB6AGQ/Ecrfm4fOV4cvspZvVfDCoR+7/yl9Rx+zHNRRCRfKCrx28iVdYUSJo2kbSXXz56HRQa1Lumjm09Ja3peogKcpXcOL4lcpJk2FMuWbcojm05KZmhii3LSPYmomD5qeuE1mbvLsttUmVNjpdPXoUr4VlukzpbSlTvVAFXjt7Eh+AQ3L54X1rk+8/vJl1tlsEb4uAav+DYltNWYIzlvI1+i6XbbfmYdbh7+QFqE/gUzRyrb5IiiPQiu37yFmp2qYL2v0ZPCFMN2AL1Ndtf6QmUF1B9AZf6MLh10q41oz/gk9+sMQXnGjB4WWe3YjXh/+BOKlWcX9DXLQzgjvcdDO7dbHbGzPiwy0gXJZxZvZbwVWIaEXUqHbh3Bx03mTKBABK5uooJqt72fqJDlwitHUrOm4lHbzUleGZ+CFZSe3qhROo0IFi7/UqzcmGwVHPH11dKX3pEZmMR9u8Ns+fC2isX8UFeamiA8fFgeYwO9Bd9fLDikiYsSZDFzNXRh6EZ5tpZsokbfVRRa/lis3O8i709jrTrDPcICOJRjfMpnzOD1WHjX9h3946UClfVrQwP/8aaX5whBeDWVexTFDoDuDb8lEN9dN848PPZlvbLDkzOz8SJE1GvXj058OrVq/H999/j3r2wYvP/etoAACAASURBVIPh52XLi8D4ooXWFmv5sxb/H01cT8ipdH1nG6MhAZQEa6DYRS549yVXkdyfas6hROFiNQthxLr+8pZE7Rl2ISVOlRC+Pq+ke8zV3QXzLk8UJeURDcaFE0RMmi5xOBVoNrrRuy864ejioPmLhblDRtUJ9rG2en6mE6/J1TTY26FC01LSrr909Fr4+b5Fo/61QVVqxvNHL9EkpQYk9LIfr5dkGZJg/pXJWDV+I+YOXCLZLJb4Vj2dY5YOiM456tvQS23ej0vN/KHZ53+XTFR0QjW+hBqwGfD7ObS8xevKIRdIdlZ9CgEqH1R2gGvTOFf3jyyqGngUzAAhkI0TRsDtezOQjM53EZ1tSLL9fsdWkPfBDqvxlataubNHZwxuM/bgAcw+fUJ+nywlHWnXSXg9Jf6cFeEQrfLkw9AyEXcBZpr8O8LTmcNLcbGrqluhIth56yb67tgqFheW5arozp3bMetRJUNGHLh7B68jMTcleJpdow7Kp0svxGgCBj2YodFNURO6uArXaHCpslJKjCxoiNp+w1rJgxJEFUpOe4zPBzAimwe/M2ba3BydpAzHDkN8uK2ZmiouUIOvA8HHAcdiNre10Odky+deTL73L7ntf77sxcVkV9emTZuwZYsmLlitWjXUrl37i3d7hRpMmr5ir9kagje1t2tvlxopW/xb3HpA+Rd5LY1uOhF7lx+UstaQld/jzN4LOPjXMWkfN4fF61yGfOngc/cZMhfMgJM7z0ZIXFbsFGkbLVAljzjNh1ho6Xzsh8D91DCE5+j8cOg/ljpLcty5GPpmaCmoaFlOIwBiyzqzSdv+3CMlNB3Y8FgEhM3SdoXfCz8BOPkq5EKCFN5o9mM9JM+QFNN6/YkN07YJGGTEluy8edZOTOk+VxOBVIBFN/8QRWrL2L/6ME7tPIfitQujcNV8Vp+pAWuhvv4xNHWueAB2SaF40YH8uSaTT80f90FQDKE8h+is5//iNqrKbkWS9r+s6u+Gq5fFnPTbTFlQJIpSGHkrM04eE8BDYJM+vqZdVWb+HPj4v7V6Z9jdoi3SeHlFWK5mt1iv7VvMnJqPfd/L6zWSzA+D2aRjD++jzfq1whGqlC4DHvv74ebLl+IrFlVQPHF942YC3KafOIo/jh81z+GHEqVx+skj6UwjyZkRVn+If4so43SgdQcRXYwsLLM/YY1To5rzl/hcDbmpcc90WkSirVDsQjlatppDHPix1Up+5XHIy+jVqxeWLtXkv5s3b/5VdH6MrwYB71eHrob7SMCxIAwOWlpb/fAM6jO+ffHmYJSHk+IcudHol15WtrezxEVSM/VsRjWeECFHhwDCwclBykb6g7/ViEa4duImDm+w5p0YDIpGoDa5r1MVmZkOK0AVwYmGzeCQ00MQovMow3KC2OrOOSdOnRAjN/wgbe10m89bISemdpsr5GiOSSVnnXdEM1OSujdO227u6mJX2hYKjZmCcgE7F+4T+wy2v1vynah/1KfMULx47IvaPaui28S2sfrKSNKe88NiXDx0DbW7V0WFZtZtthcOXkHv0kO0Y6vAzLPjkC6nxj8xvp0FvJ1mAj60IeD/a5o/EDJ9zF3HY3US//GdKF6KoP2AXQrJqtk69t25jbYb1sp3zP/b06otUnp8vFmDc6CwX7ctGwSMMIvEktC3Sxfh5XtNYsLb2QUFkifHrtu3UDxVasz+trbwfqgEfcf3Jfr9vd3KvsLF3gHpvLxw9/UrMTfVAQZ5PgQrJE1bhl9gIAJCgpE4nqZ0/ffNG+hjao23JE5b7sMxJ1apLt5cjDeB70HHes6pQfacGF2+kvl39up9ABqvWYlrL7Suy6iC+9LE1JLMbLnP2ssX0W/nNvmFdMhfEANLlolqyC/6ufpuLdQ3us+XAsVrMhTnsB2Inz6lOPDz6Wv4/34EW14EIpD2agAQfEHjX0C7AcHjZxhctYyPGnQW6vtNYnKquERuXxDbhWWpZvufe4SYzIdoTPynAvzfY3i9ceKknrt0NhzdcjpC0nHXSW2E4EtysXh0AVh4c6rwdBaPtAB/EZwENYd0FePIzpGlrWTpEssN/eG1xwJcuk5sg/lDV8DvxdtwZOgEyeNj6rExVsrNlmNT34hO98yY7Fi4Dw+vPUKt7lVFE2hglVEyng6ICJDGbB+MfOWj94AjYGR57lMFDj/2fe9YsBe/d5ghmSHOlfpLxWoUNIHpj3DGHEtDcW0A1e83wJAMitd4rXU2LmK0Aqpq1DK2IZdMLy1TbP5Aot4Oib3B1L1iFtEiy6JPlhmdLps34P6b16ifPYe8VNDH66GfJoaX1M0Ne1q2Q7kFc0W7Jmyw1ENuDPkultwgfTsvJyfsbd1eLDcIaugKT2uMZ+/egV5ZYQUH2WYfEBwknzPzRPXluiuXyn4REaD143BM2laM3L9Xyl/U+6EQY8s8+US00DLo2UV1aH1dPvbFUdnZLyhIEr31suWQtn62koed973XryQ7ldk7QYzujzz2tONHseDsaeRLmgwTuJYO1mAwRhdWBBsb/RdrJsUS7lAS7bB5mztHtuVz71PP+XPt/z9R9vqUxfscF4Ex8Bjga+H7ZJcRhkQWfl+xmDAVin+q9SsC3gagz+wukoUIG3wQt8zYXbIqfJh3Ht9KrCIiiqD3QZg7aKlo3TTqX0se9lQ4ntlnQTjtGsv9qZFD64iCVfLi0IbjuHLkuhyDQn9j2/zxUUsKAovS9YuKwSdNSRnU7mHn1Ln9l0SFmW+9NEDlfzMjomsG2TvYY8yOwVL6WTJqjXlKTIY07F8b7X9pFuNVpYXGrH4Lw51v8VqFBGBEFD73nkkGKWGKqHU3WNuf3ns+ts3bjezFMmPo2n6x4gS9feWPnsV/xP0rD6XE+Pu+4XBycYJqfAXVh90gkRhPOtfRJBdAo1s7wKUuDJ6jY7xO/2s7qCH3gJDLgGMhKAZvCK/KRxPGk2KLSz0YPMmxsl3QP6vuiqVieMrszeI6DaRV2zKG79uNJefPhitR6WKD9LNiV9eh+/ci9NlixmVB7frYeuMall3QPO/Cxo0e3+OXA3ux5cY1VM6QCUNKl4tQPJGdZbNOHTdnhsqlTY/yadNhyN5d5iE5L77AWNL2fq1QGVUyZjabu4Y9PvV69G4rS3JwRHMlcOI2vIdT/+fGS91DUduahGmCoF8qVJZ5rLx4Ho/f+qFxjtyRZoY+9o2yw42GsPrYg0qWQeu8bCiwXRh9SgBGk0GsfWYYEm6y3eAWI32O595nmegnDBoHfqJYvM9xEYhmyDOWLkw/e7uMUBJuNLe7x+b77Ft+mAAE8lLc4sfDXy+0H6FlsGuodnyKZWn36IrNS2PAgh4RHm7J6DVYNELL3BDQrPaZi32rjmBiJ2YYtLdPy5IVszGsObFsJFpAH1TMOD3WrPdjKQjIzz98MAqQsXeiBYXmUeOVxAPTjv8G1WjE9O8XSAYoW7FMos+z4Y9tZhDi6uGCd29MWTPT7MkBcnVzNitGW55UlkIZQSPTsEFBx3mDlyJBcm9kL5IZj289RZU25cTUlcEsWZd8/cxAjGtmZ2dAk0F10WpYo3DjrZmwSVrsuR0BKL3SPhYEcN0L/yCbUOeI6s81u8YuhU1Jglc+b0BrEXan6aG+3wH17QzNaZwWKvI4ItihxPZQwI/rooMfPrRHxeby+5/ZRw2+CvVFXQ1Qsikh4TbR9ZG/hVyVv2ulCAqV2jZIhn7+7p1kcC4/fyZaNWXTpjcbcf58YC/mnz0dDvwUSZESaT298F3R4ij55+wIgQ/1cToWKIwWufLgh107sPYKs1ihwauGxyqeMjVG/xPaaUmF5+qZreURCNSKzGF7tnVkT5gIl56HOrs3zp5LQAY5f9QaqpU1u4ARBrVu6M+l3yN1gMQM0+K6DXDy0SMhhtM/TA/OnZpEejmNnJ38yZLD3dEJ/YqXRMPVy2X9LINZpW3NWmHmyWMYd+gfAUpJ4rlhX+v2kSpiR/atMjtVcZHmt0dgRWCoc5JieiUIr4y8T5MEir6/8Tmvs8va71iENMOvc0yPFdH2n+O5Z4t52XKMOPATxWra8iIgS190VQyegH0WwE9/y7aD4vkLFJfasf5uRzQYL7oyBA6J0ybCohtaB1nYGNXod+xbdRgEK79s/dHs3RV2O2Yk1v9Bsq6WNWgyqA4OrD4C53jO0kHCzM7aCZukNJSvQk78vPVH7F1+SLI7zCox+/Hjsl7i2cXoW2GYcGwYVG5++cQXPvefI+hdsGRwzMEXZzdneCf1wuNbPnLvY8YqbBDgvfX1N7Maw7nCW+zQc1oH1Ohc2WoIjlnLq5X4c+nBOTu5OGLZ/RmI5xkPFDFkpuvpvWdmnMosFk1TeTwGQQd5TOQaTek2B75PNTVctv7Pu0QNnciDLfDtc2hK2CRi953TVfSTPlfwe1N9igCqyZ7AiQ7k5bSyFwnQnuOsBDc/1zz+P4+r+s+F6ke9J9M16zUdBucKWkdO4F75beDDNSgO2T8LAOLaHX/0AE3WrBQYS1PPXS3aSkbn9fv34ru149YNK4BzvH0Xs5oz1ZB33LwpjeS5EifB3VevxD1+WvWawstheW30gX2i/aMDjtKp0yCxmzt+KF5K+DqWXVVdChZG5wKFrVrC6UpfcPY0s4N6ZN+3Xvoi12hujTpW5aeLPk8lS+RAHSDViFNPHsswzOZ0LlgYC8+exutATXtID+ry8JxyTZ8sfCQGFadJwmZWhrykjdcJUEODHCCKJfbcuklKhPo5X+zSM1YlK85r/tlTon80qlylWBmhGsnRezsJUDygeC+Qa0kP9cNDqH78zAlwbQUl5Bxgnw2KQzab/qxs+dyz6cRsOFgc+IliMW15ERiflQM+8Ees6ViEhj0Uj5+guDaO9VfLh+603vPFlbzDmGZIlyu8wjIH50P/zoX78ErsIb5dYYNghw92lm8GVB4pAIQPZJZmGAQIg5f3Rql6RQXA0LiTmRLuw/JLj6IDRYyQYoiTD/+MeB6aZ9SrZ6+x7Oe/RASQAI08FT2DFHYOXHOWyT5mXsrsz9C1fXH/8iP4vXyLhcNXWpXUyH3hOJx737ldIzzPb5waR9iB9ueVSeD4TVJ1NnN9hBulAOWblpRyIbNRjCk95mDTjJ3y3+QW6X5jpeoXw6Alof5mJD2v+X2juL3X71PDzAHaMmcX2M2Vp0x2tBvTDHZhShmxviAi2NHovwTwsxbwpO6P4jE6xtwGW87r/9NYavBFU+bH9Pu1zw1DQo3HphpfQ31WhsQ9iwxQ7DJ5H1sTChOyA0rnuegPfX0fGoJOOnpY/lk8ZSosrhvars0yEPV+4ju7IG/S8J6Bow/sFc6KrphsOQ9yZoaUKov+u3bIn3Wys5ezs1hiWBKw6ZQ+5dhh0QGyvNOxVEcukGWwnXtG9ZqomF5T4NeDGaTVly7gbWAQpp+kRIh2zP4lSmH68WPiAs8gIGqXr4DYaPB3Wnb+HCv/MX08gi1mZCyJ1gOKlcSi82dkP3bGsTWfpOpfK1YBuT87bt4QQBVVd52trmFVDYL6lHxCU1OCc1UYvCaEG14z2K2iWdNAheK9GIpjQVtNI47zY7OV/H88kE3Bz1MKzVmnXekADceiULwmQlGco7VSzJSwJEStHM+EkbdtRmsw00bUsOlfcYR0RJVrUgI/LOop5RO+ybKc1rfcMNmS4GfAwh7i6xVRcG4EROyqIol4YudZkk3pv6A73vsH4rviP0q2KKKQThaCFoOCvOVz4syu8zDY2SFnyay4dvKmtTihAuQqmQ0XD16RrAu9vsKCqWxFM2HU5kHwiK91moQNurif2auZNup38hK1C2Pomr44s+ci+lfUgALPmaU/8qAoS1Cgch6M3qT5YDVN01nOl+Hs5ozS9YoiTfaUqNntGyuSc+f8/XDnoqaEW6V1WfSe2TkmX88nbysaUk9zR6BQDCgJd0Kxjxgsf/KB/4MDGJ9VAz5oJrb0VTIkOSX/qQZfhvqilumMHYB4HWFwtzb4le1UFddevkB8Z2dzF1REy3T9xQt02PQXXr1/j2FlKqB2Vu3t/uyTx6i/arlkd1J6eEjZJiyx9tzTJ3gTGIhiKVNZOZC/DwnG5KOH8cjPD50KFhYnc8ugYGHj1cuFpBxRbGnaUkpH++/eEfd0BsEHgUf7/KEPX54jbSfmnT5pZWVBtWn+zgncdHFFjrGuUTOQk6QHHdWnHjsSodDh/Jp10XrDWvO25BLRt6x5rrySbaqxbFGEfmQEPq729ngTiW4QB2yZOy+YzeqwcT0uPntqBgE0ey2R6vP/RoQ8T56eZGcNJs+9fuZzfeG7Ffb+w+FkCIajQXd1pyxKZ5FGsVXY8rlnqznZepy4zE8UK2rLi0BrUxxs9QBSEmyG4pAp2t8rwUWvkoOl5ZxKwlOO/BJtwbuPHWTdlK2Y2TeUzFyhaUlUalUWBSrlkZv17x2mY8eCfZI5oo5N6xFRZ6map+sqJSOCqFylssn/0wuMvKCwUahqPlGMplgiwUaqLCmwesImPH/wQlzn249phoHfWJNxLQUNHcgdCqMRxOxRg+9roF0EZOc7F+9hxW/rcXr3ebx46IvEqRLgpzX9kLlAeiwavhKLRmhv8zxGhjxppdVdL2lRYJBCg8yOtcnWy2z9Ie31qoqJB0YhR3FrHkQN9xZ476+V2PjZxH9G4dLhqyIXwPX4YWEP5K9IcPJ5QrupsuRFIrmlAoojlMT/QGEpNi6itQJWytkidNgZRr9pQACvmUCNkKq4Q0mwCop9+nBjUgCQnBpaO8ytWUce3BFFp03rsev2TQEJznb2uNi1pzlDR5Cy6tIF3Hv1Sjyp6mfPGa25//rPfsw9c1JUmu0NdphVoxbKpNE4bnpIdujubbTbSMfw0GDm5HznHnB2cBCtoDLz50p5jNuTgF0slSavcObJY4zcv0f8wiIKghDuo7fZV82YGbWzhpZ2Hrx5jdLz51jtKiak9A50dsbIshXRY9smK/BEltuAkqWRwt0TPbZuCqcP7cyXqMRJxJ6j5fo14t8Vz8ERby04QzxgniRJhSfE8pUu0EiX+Z5FiqN7YZ3UHvVSr7l0AcP27RZeIyUGKmWwzmp9bAQ1+ApU/zlaKdqtu/mlWLrnHuWFm31ouY8JaXF1914OxTG8IWvUM414C1s+92I7h8+9Xxz4iWKFbX0RGI0fgLfTgeDDUFzqQXEleTL6cfPsHXTOp70JkCTbaEDtaAERbs+y1ISOM/DwxhO0Gt4IhavlM5dZ9q08hJ+bTQpt6bY3iIjglKO/IEvBDNKVZMkBWvlkjnh7RRZvXvqhVcYeckwSo3OWyiag6Ycqo4QQ7eLhgnzlcgpPKVfp7Bi5YYB0KF0/dQs9ig6SeZg91RRg5PoBWDtpC05LNsiAglXyiK2EHiJxE0btmX9Lmj4JqrQuh8YDapt5OizBNUrWwawvJGvpaC/zaz6kPmrHbyXlQwYJxJQFuHJMe9PndsPW9EWR6gWw+veNmD1gsZXWEQFX98nt8G2nSlZLs2LseswZsNhq/64F++P66duS4WYWj+KFnzNERuHtFMCQWEQN8eEW4NIICFgFvN+mKcbGnwqFfIK4+OgKqB+Y7QsS81JrcVKDKD8rrk2hGMJnHPnQpSCfHtUyZsbUahFLWhAkbbh2RYAKjUHJ3dGlKZgV+mbJfLMBJru0SqaOOjPRa9tm8c3SfyoENIfadQJFBcMGNW+Y4SFQIe+mRuas4sie3N0dXQsWwa1Xvth24xoKJEsh/mMEZE/8/NDGIisT0SJa+m9tbtIC2RJZC3Y+8/dHkbnWRF5mjNgx1rtYCSlbdd+yUVr6LYOk7af+obYaYY9NnEDD0r7Ftax149UrcOLxQysQNaxMeTz1fyuGr3rpj8BpXePm0nIfnWBmrPCc6aH3JgAnO3YV89RPiadv38L+RTF4OQbSA1rud4pDVihek2yu9Gzr596nnPfn2jcO/ESxsv+2i4AdW1QVDgwIkk6s4es0TZfoBNu2107eorWHUzYdqmRVxv49VIxJV4/fiL0rD0mJiXdHgpTvZ3dB5VZlYdn9RQFDdn/RwsIy3rzww9l9l5AhTxr8VPs33DWVeZihYrdVmuypQEE+luzYLp4opdYOTh4QS2OMPuWG4vx+djNowXIWMxU0JR3TYoqU5fjDT5UthTjEmzvP7NhhZhRAGM/LFa+f6SlhmqDyrdEArySe+HX7EDDrM7qxNRmZhOPvpnVEtfYVwM45ltM4tqWpLTM7bMUfvJx+WZBOLXZsWUaCZPEx7eSvEfKpCLq4djoPiueqk8BZKpt5Zhy2ztkla1SuSUkUqmKtaRKd7zim26iBB6H6UnyRj0MS78dAcdFLNzEd7X9zezXoDNSXOq9GAz8Gt44RLgavpwqL5uHhmzfy0OWDuFOBwhFu+zLgHYbu3S1lpv7FSyJfslCrG9o+tFqvSTowkzKybAU0ymmdOQwIDoazvb381h/7+UkZ6nXgexx5cN+K97K9WWvpIGMmiV5WdbJkw5TjR7Dl+jVUyZAJfYqVkCwIH+gspRG8sDTUu6imIcXj1F6xBNdfvgA7rGiGGrZFgeKILLl5OTnDN1DLgNKCYl+r9kLW/vvWTem4YkfY0LLlkXfGVASFaXTI4p0Qc2vVEZXqj9lmcH7s2GLbethI7emJva3ay58pG/DLAZqiGvFNxszSQs/SGzWMBu/5GzdfvhA3eHKAEsWLvpo3xRipFG0Zqxo0FpD4KSGlxD1j0Tb9EiR0piebHey8Z4lLgK3j3/bcs/X5cbw48BPFqv4bL4IH1x+DmRoSjYtUi76OxNQec7Fp5k5zB5fcOB3s0OHXFqjbq7qsBMUJmVli2zfBCZ3aySvSdX/uXX6Ahv1qCaeHTu9pc6aWm6ufrx9aZtAyPRHFkrvTxfuLZbsN07aLE3yNrpUxs+8iKXXRYb3ntPYYUGmkeXf6dxF4Jc+YDMc2nxJQoJOg0+ZMhVc+r6W9m51nbvFd8fD6EwEncweGKjCHnUv+Crnw44reaJi0vRk4EQCyK63RgFqSCSPA3Dh9B9ZP24bnD0K1QZjVadi3ppiUzh+yHFvnaiRwRrYimdB7dmfx/YquoOHj20+lQ4xlr26T2khGbljdsULcZnZr/rXJSJbu84oOhs1aKF7ToTiX+xz3mv/smNJFxy5Of7Y5aw92xfN3KC7fRnjOzGysvHReHtB1s+WIcUs1B2UJhH5Whx/cR5YECbGifmNxSWcwu0Q1aH6WN0kyaQ1vu34tTj7WzJRzJEosPKJ7FEPMlkPIveweO/XkkZYFUVUzeOGrx9yadaVjKud0TQ2cf2OZiuUcBrNDrU1AjJkkDhHaK6YtQcvc+dCzSFEUnB2aEUkaz03AGM9F1/thKZDlJQKY348cDLd+o8pWwGALraCIFphgkOUtjvHzP+xc0/Jc/Her3PkwpEzk1zezbD/s2o7N16+hZKo0mFL12xh3bDFj9O3ShcLrYiR0jYd9rdrFqnss7Plxfhef+SC5awASxEtgNsS29Y/r3/jcs/k5qpavtrYe/T8w3n/pIqBuDRWaH15/LCadfNkPS2Am6OmYt6/wUwh6mHHh/x7ZdBIsuZVrXALn9l3C+PbaTcw9fjwE+AciVZbkuH0+YqNYgobFt6aBFhTzhyyT8hVvoIlSJRSQxbB3tBPrhtW/h4p2EZC1GdVEylC6UrR+SbFbjRkvdqOxVEXj0CtHryNLoQz4ruTgCLu4uG/+irnw646f8Pi2DzZO24bcZXOgaPUC5iv19vm7WDNxM5KkToQ0OVPhl2aTBHBRhJDeXU0H1cWQmr/i7N6L0uau1w96zeiI6h0rCbDk+fG8MuRNIzye6AKYsKKKEw6MFEHHzx3qu6VQAzYATmWgxOsc1/kViwUXAPSUhGRTzsOxBAzemubL5wrNADMI7MLSy2E81q5bN9Fp83p56BOMjKtcVUjOuvs6NX8oFkjQoasbZ582KcLWdAKG6dVqolCKFCi/YB5embI2eZMkFYuMOlmzo06W7Ki4+E9N50tVMa1aTcw/cxLHHmlgi/FPmw4CAorNnQHf96HyEhGtDZ3jtzVrLRmZFn+twmOTozy3nVC5KnrvoLGsdfA8aV6a2sMTCVxcwawZs1gskTETY4CCXkWLo2uhIpGCTVplLDp7BhOOHpLBOebYSlXNRPOYfI9c+9OPHwnIZKcYs1wxCfUD7TpCoNiFksBVNQAIOgnYpYVir3moMdghBjDDF6rvFZNjRbTtf+m5F9laxGV+orhK/qsXATVstv+5GzlLZhMFZ/3maWWiCUhZjeWkEfXHSxmMHU3M4Nw6dzfavy+WcPauOChZJpZ9wgoUciCCsD5zOmNs22lmQFGkWj64J3DH34v2hzsWy2HykmpUNTsLVYW9vZ1wahr0q4lFw1aZ93FydULgu0Aph806O17mH1EQuDRI2h5vfWmRYUDzwfVRv28NGEM+iO6PHm2zfYf7Vx/JP+lzRmVmHaTQvoPikLodBucz6eAoZC6g+bd9LFgW61VyiIDTQt/kFQ8yXU8oqn1j8jnJzwjcDlCbxuXbaHcZxuQY/4vbGl801KxrxPH9OxjcunyVZQhbdllWtyHehQQLEZjBLAy7wUjs/a5IMekGG7Fvtwgk6sGXEwIj8nzoLt+VYCqSs+lTtAQmHj0kpSg6sU+vXktKde02rBVVZZbImM1hRmrEvj1Ycel8hEKLlsP3LlocF3188LeJ8K1/trJ+Y9z2fYmpx48gnqMTWufOK/cuZll23LwuQEnn6ljS+vnfPJeJ32gZ7rBBjSSqM7O9Xg8Cv5nVa6FC+qh/u7b8otWAdVBfD9Ba2N1/hBKvlQAcTUjzmgZ0vJcJwdlIAdO3E5hqNGkC2Ubv57/63LP8nuLATxRX7X/pIvB/8w4OjvZwdI78LeTupfvomLuPmQxcqWVpJEieAKvGbzBnX6h1s2/lYdHreDsKdgAAIABJREFU0dLkGj9I5984uznh/VtrAbKobg4thjVE88H10DR1Z/g+fS2AhiUkZlDCZn2iGoufC+AJIDFQwe97h8PR1QkBfgHSGcdM1uWj13Fi2xnkr5Rbuq+oY3Rixxk5Lz0yFUyPacd+DXe4wxtPgGKRBFwDF38nmkd6/NxsIvYsC03XE/A1/bEeWvzUIDrTlm46kq3JwbJ8m4/WztHYSA08DNW3FwB6y9kBjiVh8J4djT3jNolqBVQqaAesAwzegHO1z/L9RTUHPviPP3yACz5PcebpE+kEI2eFwev1wZs3KLtgjsbwUhQROEzu7onuhYqg+V+rJFPC33TRFCkxq0YdMRUtv3BeOG2eyOZBwHCte29zdoVco3VXL4m31+VnPph24pgQuHl8AhK2yYfl9nDs4WXKY87pk2ZSM8ftlL+QcKQi+l2Qi5R35tRw0+L4DJ4TVZ7dHJ1w9OED1CSoswA1NI7Vido8VjI3d1m3HoWLfvHv0UpKwZAAhsSHoQZfg/pCL6PaA/HaSWt7dDSBorpmIvr8v/Tci+z848DP/wj4kW6jHxZLR9XPWwYhd+nQ1tKwS9AifVc8uaPJ0DMjM3rzQAyrO06yJyz/dJ/aDkNqjBGics7iWREUFIxM+dIhQTJvEDAwC3Lx0BX0LvVTpKtrVmg2bTF4xfcoWbcwHt14IqUvKjyf3HkOl49eC1fCEjFBn9cwmmw2PvYVEnwQeOxctE/EFyleyGMN/vYXjdtgVIW/xFb2iOKXbYPFnZ5dXW9fv0O7n5uiUosyUt6SbJODvdVubF/vV3GE8KEI4HiXH7t7qKhaf+mQDA+l8GnDYEqfG5+y3Z3AxxSKKwxJznzpqcUd7zOtQLsNf2HPnVsyOstP32S0ltGgLUb1pQvlc+mSNnFhCE5IWNaDoIHqx7d9fTHj5LEI9XYsMyv6fuTbnOnUHddfPBcAUXPFYvgGBCDEqKJQ8uQ48djEKwIwqUo1fLc9Yk/DASVKC4dp8O6dcuyR5SoKP4lZKh6DQaI1s1MEK/wtknd04J51RpqAidvQRiOhiys6blpnBl6bm7YUN/sX7/xRK0s2tFi3Wkp/fJ/b0KR5OA0ky6+MYIo+agSZ5G6VSpMGhZKHlqI+5es1vuoDvDeV9hwLweC9ACx5qc8qAkb+dkMA98FA4C4giFpL1E2zA1xbw+ARsedgTOcTB35iumL/we3/KxdBVecmGllYAQpWzoNftlJvKOJgW/vaSTS8ZJcUUL1TZbQe0UgAETu5fqz+C87sPi/AwT2+GwLfB5m1bqhe3GlsS8lgkDhNgjQzQknSJsLTu6E2EUnSJITP/RdWqswkKw9Z2cc8KYor0qxVbzuXG7WdAfHix4Pfc62Tg/8mwKFzetjgZwQgPf9ojwkdZ8rH3LZsoxL4e8l+M6iSN0niFGOYXnkA9AWjWKMeHHOd73y4uIXpdHvpJ11sJKHzGCQvXzt+ExnypROpgK8Rxuf1gJDzmlia1zwYnIvD+LQwoGoWHBIujWHw1F2iv8Ys445pqxVgN1X2aRopmfFNhkwYXq4C3BwczWRbgoSf9u7C8gvnBDToKtHsgiJIIYfIfK0riqgb8wHPklZYsOPt4oKXAdYee9w3abx4eOLvL+Nbko3Z1k8SNvk4JBNToZn+ZBE5srM77GSHLqizYokIFtKY9Mrz53Ie3xUuJvyl5Rc10jgd7tN4eYHeZxyfXW0EeRnje2N1wybiQs9YfO4MRuzfYy6LfZspM7bdvCHAiUBtSd0GOHj/nnTVkUT+saCb/Mh9e6xKgQtq1YtUsykm37FqfAe8I0ANBlxbmjW46AmJwL/FGkl91RMw8oXNABiSaJlG9x5QlE9rp9fn+V957n1s3eMyP1FclV/qIqCwFYKOAo7FAfs0UP0mSH1XidcWipPWUvop0TJTdzy980zAQPUOFdFjqtbuGVGwXFTbq5XJpNROFInZBUb+CcUGJ3WZjePbTguvxSmes5U/Fstfyx7MEg0gltmo47N51t+4euy6lbihu7ebdJjtmL/XTHrmXDYHLIWjk4PckNZO3IxTu8/DwcEeB9cdE4BSr1d1yQjdu/xQ0tF5yubQyMcmDzL9fOp9/63wbDLlTwe3+G5olbG7KEuzhDZq0w+Y0GmWCCgyeF7MRL1+9ga5y2XHnfP3hffDA6bOlhJ3LoQSudkSv/7VQquOLtp8dMzTV/anLtDMs+PDaSDxfNhpR4D0TdtyyFo4+sKWsfnejUGngZcW5quG1FASbQGCjkN9MwxQHIF4XaF8pfJMbM7p37qP9IwIyHQxC5aqQSegvupB2WconmO/SAcd50F1Y/JfWFoqlCIljj18IO3nA0qUwvwzp4UcTd5LWq/4aLpmBY48fCDLmsLdQ/7ebM0KqzIUVag75C8knWAkUlu+HkSU+fnYd1QoeQrMq1lXvMnabvjLvOk3GTLi6MOH8AsKlHnzd53Kw1Nc6y2zURGNzSwQOUyDS5c1f0zAddP3BZLFc4ebqQOOH5LXU3zeTASEaArziV1d4WNSsiZQu9qtl5Ua9sfO5ac9f0vmR18PzqNTgULoUyxi5XtbX7tGs1uAAXAsZfPS9Zd67tl6XWIyXhz4iWK1bHURyA0yYAXUkKuauKFDqCKrGnIT6nNd6MwAuLQCAuZpztF09k18BIrBPSbfa7htyZ1ZOXa98F0aD6wTZTs2wc2CYSuRPEMSKRfdOntHxqQgIb2yxrWbJhkZgg96VlkKDNJW4tcdQ8zZkRYZuuHJbR+rOXFdi9UqhOxFMmHuoCVywyPQmHVuvPw3wc7w+uPM2Zh+87tJ633ecjnx6OYTzP1hCewc7VGyTmGMahTqfUNwopfDWo9sLN1WFw5cxpM7PvBI4C7/ptI0O9+WjFojKsvpc6VGdxMYPLH9rPB5GPGTeoGcp5W/bZB/Ozg7YOCinlYcH/5915ID+K31VM3Q1aBg4JLvJLtkGTsX7pM140LZOzmIeaqH96d9px+7IIyBhwHfVhabKCapfM1FPi5stwLGN6OBdwtkQMVjhHj0GZ/XAULojM6WyqQwJA5P2rfdDEJHIveFBp0ejk6igszgg50PZ3Z3kedDRWmCEAIltk3r8VfDpkjt6QV6gy08d0YyPc1z58XwshXEGJSEYMuIDPw4MKMUVnHUVGaL5+AAezs76YLS5zamQhUM2bNTMjckVRMk/ViqrGgHfSx4fJbBSKgulTqtZKkWnjstekUkWhM80QWeAowEJewGyzh5fITEbXKeqOLMMtveu7dRO0s2sPQWGe+O/CW6xOsGqiwTrmrQxMqi43N8v/qYasAmqG9+0sxP488QsUNbhq2ee7ack63HigM/UayorS4CNWAt1NeaHxTZ+ki4HQZTu6LG7udnBDtMYyYDjKFtokqiA1DsPq/eS2TLcOGfyxhU7WcEmNzP0+RIiTnnQ8EG272pebN8TKgUPktD9Xt/iw6/tZBht/25B7+bWuMty0tsV2dZ6djW0wIKyjctJZ5iDGZJ/ug515wtGr93uJmn9PDGY/QuNUSI0VR6JmCJKthunzFvWkw9Oga3L9yVrip2nZFYPPPsOHMHGO0mqKFkGQlTeOP5w5fIWiQTxu4aGg443r/6UEjiEoqCORd+R4qM1qaR839ajhW/rjOfz5yLE5Amm204AhGdu6p+gOrbGQjaF/qxRfu1GvIACNwNOOSE4hh9raio1vl/7XNVDYbKUiJMXUJ26WFItA3Gl621TC7Bj30mGBJu/KJLQ+JzsbkzwfZtZjDZPh4QEixAiHYWc2rWwZxTx/HzPxooIyj6vlgJUUBmUBMoKOSDAAf9HkjLCh0UUXG5fo5cWHQuPF8sJhkheov9XL4S6qzUtLloJVE6TTrh+2y4ejlCoCJEaTs7lEqdRtrnaSXBFnu28FP1OawAIoHfDyVKo22+Aui9fQvWXw0VUdW/lOvde2PrjWvyub4/y2nUN4os6F5P1WXacZAzlcLDNj6LH7tQhFQffFZKX4qdtS+bLS8wWz33bDknW48VB36iWFFbXQRGv/GA/6xQN3e7/2PvKsCjuLr2OxsPIYK7FnfXQqFA8UJxd7dC8ZZihQKFQinuXqC4U9zd3d0JxInu/M97Znezm2yIkIR+/XO+p8/XZmfu3Lkzu3PmnFdyQpdaA7WpYa+0yg+xGEoKAyDVUFC1yQxd6n3xfd1jNB7bOVSTNgoLMoEYvWkIytQqFml/MqCIz2Ew+anWuhIGLe5l2o40bkavUkNN4GL3NG4iimgcn7RweonRGqJQpXzoV+FHPLr2VEQM2aoyOp7P+WEp6EUWsdUV3Uk5ONvj75cL0CJzN/h7h2MVStYsil93/AgmMeNaTJPWlLXgeQ1f9T0qNykX6WNqIF3cf1V0hLIXimwzQEFDmroyYavQsDRGrhuYKCwSvc8UIIB4JzvAuZ2GGVCSA2G01eDbd5jBEbpUdMuX9HmEFVBDrkN919bglxb+IX2WQNsLVoTUICiuw6x6fCX0gt5//06MRdO6uKB4ugwYffiAJBW/16glFR6/4GBUX74Ir/39pYKyoWlL8b+KKphQlV0wR/R9GNT6IQ2egGhjBSS25zSzdj3UyPEFOmzegKNPHoGVIYKOIyYw1hIqtvCYgJhva44zMs6FyU+dXLnx0MtLMEKN8hXAI28v/Hr0kLStyPxiy2/HnVvot2u7abw1jZvFG4g5tutibXsmPurbOoD+JWiIraTcnGCGxPH13IuP806oMZKSn0RKftTQR1DfUhHV6GiuQEl7HYpioyVA+ncAXaFZ/Xlvhsdx7gKd6yDBwBB3Q0uJhKBAW1uGuxcfoEdxS/YA1ZD/PDked87dx4JhK+Ge2k2Umfn/S0eukVYSlZnHbB6C4l9HNuo8vO4ExjWfKmDp7/rXwYapGrA6YnT9rY2m1KxA6PmLb/6BVBk1OwwmPkyACKq2BlLmNm6pk1tYXPBv33T4CgXK58XvXSx9g/jZBs/FUg16evu5Rt2300UySuV21OzJX87StDSmX86Q4BD4vvODR1r3BL2GrPpA9dW0Pwg6DX0OvO8JhLENwzB/lNARuo+4QidF7FZA7zUQCGRryVwBh+vZG8pn0vgxPwOyvrpv2ywA358qVUGHopErfAQ5n3j6WPy1iLMhWPjU0ydoWagImhcsLL87N9++EQsKD0cnFIlAJ8+bKrV8zraPNeCycT5MSlgtCqQwqFkw4SqaLj3efwjA6EOaGSorKRGpB6xcsW0XmZJg/Zrl9PBACidnnH/+HNk9PAQ0zXXgPGioWihtWnQvWVrMZYukSy9/J96IgOiDD+/Lvox2RYrHSeQwdndSzLbWrGg6GDa2hZJ8GJRkWnU9viMp+YnvFf0fHC8+bwL9h+2AN32hVMCxPnTukyOtiOo3F6ofjQ8NSZLHKoSiCIbXGifAXgJ4Jx8YHclXy9rSEjC8dvJmYSCxYhHRi8vaPkbBb3lo6vUY2fA3nNxKOmV4UFF5y+zdoJcXtytRvbCwxwZ+PUrwNWz35yqeQ/y8IoZUiBpMNKlL00XdaBpqvi1xQ3SuF7FABfhly1AxE2Ww1bb+923CJHty+wWuH79l2pXVGSZOZ3dftGiHZSuUWapIIopooMYadyL9/e9XC9Eqaw+xzODxcpfMKf/u+fy9YIio1tx4QH183Sr+fXTi82tBZViVQOewJ4BDFSjus6DStNS7v+VhFGdApXmrI5SU601A3ficy399LL0vLS1YzeUD3ZhQ2kJJuRaKXeJLG0Rc70ZrV5mc1akwfLl7n49ekvU3rmHo3t2mygfbUbc832LppQtydlO/qS14GjKijMHkIZSSClaCqsrmNhe5UqS0ADDn9EiBHS3bSgur/24art6KVvwwh7uHML3k9ykKXNHxjt2kssPkr0LmLLj6+jUmHT9iMUMbRYfqOXOKHADj2utXghfK4uYBF3s7jDtySJIlnvfhDl0EEJ5QQY8xWpVQg2lA2QroXNy6V6Ma9gbq2xpSTWTCrX1vE+Y+i8/nXkKt26eOm1T5iWYF4/smUEMfa1oNdoWsypGT9aV6NhbHaOgyQkm1Dad33RJdHSYmZCYNmN9dPK8+FqSVt87eUzYh9brtqGZoMayh/DcrEAQg00nczt7ONMzFA1fF/oL+W0OW9UHFhmXkmHMHLsV6swoNRQ6PrD+FkKBwejlxLjQzpU4PIyqX8lXjN2DxT3+ZjpmndE6EBoWJdUZU4ZrSBQuvT5NzJ7XePKgnxIqNMXKVyI5ZZybBRO03fMCkyKi6bFyTuj1qwNfTT1hnZIbNH7Ica3/TwM0EVl88eFWSNCZyKdK7S0uOvl6JVXmLy5db9V8I1ZeAbe3aKCn+hkrVYd/RZsMlB1xHAz6UOwiE4vqzuJAnRexWQFR3qbAb9hywzQIEXwQUFyhONaE41ojdYAmw9eA9u7DJgG8hdXtri49XCZZdugDieszbSGwZGRWTS2bIKLTycUcOikJ0To+U4lEWVRBwTBAzqzVU5jFPkZhUtC1SDCMrV5XdW6xfI+KDxrBTdAixklSldnZGVjcPwS+Zg7VTGmj3xC51KlYC8y+cM1Vz6ubOi+1MrMzqRqz0EDO0+NtGojxN01a27vh3JkzHnzw2VbL2tG6PnCm0qnNCxKiD+7DyymWx8+C6XOjaES427wHFFopNeihK+G+0PD+CDkG1K6b9DikOUGy/iPdpxfdzL94nGA8DJiU/0Szi57gJ1LBnQMhtwL6ksLxoJdGt6EDTTAm65cP5Y/HmqSdaZu2u4S3tbMQji4akxNj0KjUEz++9QsZc6TDz9ASTdUOPEoPBVhf3Icj3ryeaNo64Cbf9U1hNrJLw+LsWHcDW2bu1KShAr+kdQX0gI9Oq3+wuqNst8gOgU4H+UrExBpOSLxuVQfuxLRD4IQjH1p/CynHrYP67x2SLTu88p8rNyqPfrC5CU8+SP5PggBqn7SQ0c86j3ahm4vU1suEkodlHFaTkb/NfaSFS2KnA90Khjyp4Lwxd3keA2RGDZqindlxAptzpY2RlEZPvrueL99g25x9pk9XpWi1GVhdq4G6oXv0M2DIdlNT75QdSfU32maHloLhL8o3go4bt7KFLR1uGpIjLCqj+S6D6jrfYVfFYDsVBAw/HJphoPPb2Et0Zatp8ShAPQ6d0Ynu6Fi8l2J+PBangtVYtxUuDlxYTicxubuJAz5YQgciZXF0FM8NWEX23zKtAEcdukCcvHnl7S/WJVR5WY4yJFLdtX6SYsLrYaqOrO1tThp8T5E2ZGjc830gyUCZjZjz0eo+X/pSf0CKmoGqew6DypPmfl/2rZc+Ji69eILm9A2bXqY/cKVOJvlGJ+bNkXCY/NJt97OWFq29eoV2RYrK/eVD3iPpG5TNnMQkufsp1+v3EMcFNcW1yuvphV81tUKBhJMHvaqrt0EUAN+t9JgEBC7RNXEdDcW7xKVOItO/neO7F6wnEYLCk5CeaRUqsm0DVe2utCqL4zTJ94/SOrD8J/lOiRhF80z5mrts7FuzD35M3I1eJHBgwv4ewlPavOoIJbf6UhEZ+gMY2R6sfG8m/D6s1Dhf2XZHPshfKgjnnf7NYHSZONBClDo/PO1/0LDlEtIPoyN5yWEMxOyWWh1pCQ5b2sdoiqm7TxEKxmetbtm4JwQiR2n50AxkylkFFZ7afjEHgclBAsFRjuL/xM3qIMTnREX8QHCIJm7ldBfcnfT0sNBQ9p3a0mB8ByT/WHi/O8FBU6MOsows6jmspKteez9+hfIPSsqYEXncp/IN4crFVaN6ii8F3MMpNOuTrh5f3X0kbscXwhpLYRRcmSYXgi1Cc6kNxKC+76N/1AIIParvb5haWFz5Qa0VvoGIbPovuAEmfR1oBvWd7IMScIWgLxfWnWFfTWIFotn6NVFWoRry5eWtRJo5trLpyCfsf3EftXLnlQR7TGHlwH7ivsfKTzd1d6N78m6ONLfYZlKPtdDYioLj1tnU2Fo/H5MTNwQFeQUGCJSILy1pM+LoGhu/fY2phpXZyxpsPbMcClbJkxcRqNSVpu/fOE9UjUO2jOi9HW1ukcHTEcz8/wSIt+baRsNYIpN5y+ybueHpK68zB1gbDKlSWBI+ChYsvnYe7oyOWNmiEQmnCDUXNj8PW4KA9u+T8ymbKhGzuKZDVzV2YZEbl6Ziut3E7JqmsplFNe3yZu8hiaykpAPvKkXR89K9KhgPtbfNCl0qrWMdXJNZzL77mG5dxkpKfaFYtMW4CNfS+1upS/fDqWVpsXtkOLX9qliA6MBFBzHRBJ/2bQe2bBUNWiFoyxQwz5Az/AaDw4b6VR+Hk4ojKTctJghEcGCzu6PTgouZPtyI/CJuJ7u3zL09BinQeFqtLxeYfvhpp8bf0OdNi3LZhcqya9s3jcg+b9qHAIM1AD/99AgUq5BXfrr7lhpvc5u0cbJE6SyoULJ8H/eZ0kySOwapN6xy9hM5PEcRStYshZYYU2LXAkmXnmMwRbUc3xbyBy8TLjDT9P46NE+VqY4uRlax63Wug95+dPulciGuqZd/ClKSWqVtCkqq4hqr3gur7B6B+ECVYgqFFSFN9B8WlV4KUzuM61/+1/fTvulhKCsAZSuq9UGw+rhIc8TwpSNh8/Rr5Mx/arDhEhf+Iao1OPHksHl3G1H1js1Yoktb6g9x8DCoiU+CQyQojmZ09Vn3XRLR2jAwqMsLMKzcfu07ch2ENl2O+X9vCRYU+b4yI7bFzXXrCw0lTLV5w/iwWXTwnlRp6kDHJqZwlG3bfD1dgT+2cDNO+qY3fTx6TihNfBkhD39OmA+acPY0pJ47KnIytOFaw1jfVWr50Ye+8daNoEPUqVQYDrAgWtt+0Hocfh7foeZ7838DyFeBgYysGr9ndPTCvbgOkThZuhhzTe1oN/AeqV2/LzW2yQ5faUGU3fKJ/1zW8cuvcCjrXqBX7Y3ps8+0S47kXl3nF5z5JyU80q5kYN4HqNwOq32wTTqNvnbxIle1LjFo/KD6vtWmsZhm7gjR2JjCVmpTDj6todAnB+lBfhzgY4mGo0sx4cPWx0NhfPdbsKegC32l8q0hz833vJzTxHEWyWk3cJnecid1LwisMFRuVkTZTzfZV0GRgffQqPVTEFMPC9Gg+pAE++AZi27w9Jip8dIvBhOf2mbtSfSK+Z8Lun1C0akFhhxE8zaoXg5T97pPbCX6Hcef8faliMcSIdHgjtBnZBNdP3hYNIlLxrx2/hRsnb0ulx9xug4rUTIS6FR2EJzefSeVn3LbhKB1BDuDBlUe4dvw2StQojPTZY6bZNKPvQmyesUvmy6Su1DdFo1uCpM8/wwrovX8BPmh+WRJ2FaFLSZHS2MVLP198tXShPJzZZlrSoJGI98UmqI3zwz87pXojruR1vxU6+seCLCpiXiiQaIyuxUuKOjTNUY1Barm5/YXx71+4e+CuV3hllmmPEZD8MUA09y+cJh3uvHtrUl2OmPzMqFVXQNYES1OokD5dZ58/w88H9oH2GkwSDxmSER6XiU+9PPnEFoMVNEYmVzccat8ZvXZsFS0f82DV5kA77UWFYO91N66ZEjYCxAkUNw+2z4iLIpjbyD3jOtMbbNPN65JUsQLElt7wLz+Oy4zqmug9mwEhF8w+doaS5igUXXjbkl5fWuXWEWCFV7H0GIzNPWNt28R47n3qHD91/6TkJ5oVTIybQA06CPV9N4SEACGBCtqWyYc02b6I1Hb61Itt3J/O7YtHrBaBvy4TW4vqM2P+0BXCouJLW9b8mTHnwm9iUUFdHCYixiATilih2Mbmmbsw6/vFooLs5OoEf68AEwh5xukJyJQrHQ6sPi6mpuXql8S7l16i9cPWmlsaVwE7s8VFd/aIwXYgkxkCwxlMYjpPaI1G/TUnZFLYO+QlFkb7rNOvrdB4gKaqzRYZ9YceXHkMByd7ofITT9SnzDDcvfBAhAuNlHojcJqtvaJVCmDSHq2SRRkCMszYAsxZxPKBRcyWJFcKYO9ohyW3pkeqillbS761Us06mZuzSAkkxb9vBVS9H9Q3FaSipoUOisciU7sxtjO+9Ooldt65heLpM6BGzthboNDskwafZ54/Q6Us2TCvXgNROv5YEOtTbN5M0yZMIkiNn3TssFRYGEwyjnToIklZteWLTclKLo8USOeSHMeePBJAs6u9PWrlyoMCqdMIbolWFsQFmYOTjQdikvBNzlxSZRlz6ID87tCbyxhF0qTFDc+3QnHntmRCkZ5ebfki0ezhPImLIqbJGPwbz5l6Pt/v2o5X/v4ICAkGKfm9S5VFv93bpXrF83G0tcP0mnVQOVt22X3ayeOYdfaUJD9Mes507iFMNPPgd5L4pAde7/DIywt/Xbsi2kkL6zVEiw1rZWzOlarT/cvGzZZIgPSeTYDQm+G6cLZfaLo+ViARsb3HYrJ9Yjz3YjKPhNwmKfmJZnUT6yZgAnT3zDZM6HwHni+dMG1PQWTNnw5wagFFF/vyaVxumsHVxwjmh0Fm1a7g1SL81zGfVhkyRt9ZXaS1w2C1iJUaB0ftDenm6TtiD+H73l+wST2mtjexo+SHY/lhPL75TBKQFWPXmcYctXEQKnxLpVzLYCVlWM1fcOngdRPtPaK2DxOcXYv2S4XELWVyPLn1XDy2Zp6ZKJYYxvhrwkZsnL4DbPVVbFgWswcsloRqzKbBkrSwOkQQONt1rx+/EYFHBhMd4zFZ5fmuf11cPnRdKkbEK41YMyCSu7v5WWyZtRuz+i+Wlhpj/I7hKFUzslBkXK6ZxQ+z3geq90hA/xSKy/fx4gn3qXP6r+6v+XndhKr3Ad4bWVQ28n3Vuf382U/b+BCO6USIYyGehcHqy5CKlaRt9M+9u5IM/FC2AnqVLgtWp8ovIr1fw/VQD6hL8ZIYuvcf+IUEY3TlqmIMyiTs7Itn0gJKk8wFPbdvwX4DZoj70oqiRPoM6FemPNwcNeNRYp4qLp4Pn6BAqVzCZkS0AAAgAElEQVQNq1hJKOcM82pKkTl/mipQTLaY/BiZZExqepUqi75lygm255uVGn6GytEtONdiJREYFioA7IisTR5/5plTAjhnu9Ga4COB19+tXSWtsXKZMkulicaprEjtuXcXM8+eFBbcmK++Fm2kuAZ131TPNkAYW3qGJqZTa8GSGV3cjRpxcT3Gx/ZLrOdeQsw9pmMmJT/RrFRi3wTE1th8GA5dsKb+DIcvofPQWFcJHVO7zgFB0ozGA+qi2+R20sb5qd4EnNl5AY4ujuJtVf5bTQ34rwkbsGi4RlsvXDm/WFP0KD4I3gbHdf599KbBUglZN2Wr/FrSOoK4oIhu6T+t6Y/KTcrj8a1nGNVwkoxRolohaRWx2mOu5ixO7va28EjnhmSuzqIY7fdesxdIlSmFeHc16FUL+cuHixHyYUW22qNrT1C93VfoXXqoYHxYhaLCc4th32HVuPVIly0NuvzWRvBAHfN/jxf3CDimxIAOOYpkEwXnXMVzYnzLaSYvr5HrB5nWxNo1oh0HHe4DA4KkqrXg6tRIlP2YXFtW4U5tOyeJGtcvYmgeU/RDokaUI5S0Z6HQwDQp4n0FzNk2sCumtSmUFFBS/gXFVqsk/NuD93VQWKhUQJjg0PSTas8MPtRrfZFbLCbcHJ2Q3d1daPNUhv7l8EH4h2jVluEVaRuh6dIweaDQ4bxzpyWJ4CObCRIrT8YKknFNTnTsJkBm0usXXDgrRqszatXDjru3MOrgfhFEpAXHkcePBKvD4y5v2Fhac7VXhbcYG+bJi4DQMKGm0xiVrTninIjzYaLGZMocd8SKTsuChdG7dLlILa2YXC9Wh2afPWWiwW9v2Ra06EiIUIMOQzUXvOVqOjYAAjdrCs8eCxLMmiaxn3sJsX7RjZmU/ESzQp/jJtC/+UazH2DoUkGXxtJrKrqLGpfPb529JwkBg5UOVjO+bFRW/tvYfuGD28lFAx8y6ji3FHC0MejVFRgQbOHyTpPPFWP/Fv0fYnFIXTJqk1G5mcmVnYMdFl6bijRZUqN5pm7CpPpYFKiYF31ndBKcjbVgcsT21epn80zCjmIs2nEmFBsdnJI5SuXJ662PUOWJPTq9/TwC/QPl8+JfF0L1tl8hV8kc6JhHa5VxTVjJ+mFBDxGbHFh1lOnQ5r5jUc377TNPwUPlK5c7TkB2AqC7FRmIJ7efS9I1euNglK9vaUmh9/4R+LDB5BGnpL0IRdHeqpMifldA/6q4EBQkbPNDSbFY0/hR7KCGeWpeffqXUJIPguIQtTDm+RfPpeJSOE1aNC1A7S8NKJzQ8TYgAM3Xr8b99+9RN3cejPiyCsosDFc+p7Fn/tRpMP7oIamasLLBBIaJxLe582KjQT+I897QrJUkH123bpKqyseCVaXvy1ZA79JlpcJCjJMx2CZTVT1e+vtL0sTW2ZYWbeT3x7gu1C4iLscYxNtMqlYT9XLnwbB9/8g8quf8QvSD+Nne+3dBKjnFGo0gcNLfa+XKjek1tZZ4bGLHndvos3MbbHSKtM6OdewKd8fw38TYjBWTbfU+vwIBiw2bipWr4eWGPfTy0PG+S4D4HM+9BDiNjw6ZlPxEs+KJcRPIj6VXHyDskbQrqMcijr0Ml8HQuZjZXcTDHcIqysE1xyXxqNK8grRszB/obHnxIV++QSkRM0yTJZUkBBHDpK9j9kH339th7g9LTQkOWU9zBy6zEERkIsGE5/dDYwRnk71gZpzcdg7Ors5ikREUEA68jOp0M3yRziSoaLGNmQBIlnwZ0fW3tihTu7jMgS0vYwVJEwgDClbMKx5kbXNaMiyYQDUaUAd7lx8RJWueUOeJrZEqQwoBaXPtyF6r1KicCTgdD5cmyiFeP3krCtQMVqvqdK0Oth/Ng/pQ6vseQNgL7aHr3DQhp/T/emz9uw7hxqUGto3qvwyq72QRp9MwQGxzOkFJe95kY2O+aExAKi6eZ2JQsdpCQb7ECOr/MCkwWlJQwZiVEiPNvUyGTKJzE9Gzi4lDzhQppKVkTCZOdeqOPju34vTzqDWyzM+JLK3qOXJKktJ3p6W9jbOtLQJCQ8VotVymLFjWkIKv4UHvra23iYUJD37lZ9f5Fj22b9ZYXIqC2bXry/iM669fo/6aFRYVIIo+7mzVLtZLzUSMxyeGiSBnJojxGWJzFHoXsC0gcIdITEJdBs3bi81Jp4bQuY2Lz8ObxkqM516CTDwWg/6nk5/t27dj4sSJuHLlCuzs7FCpUiVMmzYNmTLF3E07MW4CLbtfbsjodVDSnNE0HNQwKFSOjeeY2n2uYGT4o0HBvsFLesvD/I+e83Hgr6MoVrUQhv/1vdDEaQmhDwvDDwt7mvSFSO1+dP0pqLw8tukUvH7sKTOkNtD0E+NQzyVcSZYU7UIV82LB0JUC9q3ZsSqCPgRLEnL77H1UaV4eJ7edx72LD+SHi9sS38MfGcdkDgj0NyRCCh/64SrN5jgc+Xe2pmx0YqoaEhRqUkFjkrXJayme3X6OfhV/EuA2kztjEkQbjSJVCmL1r2ROWAb1kX5a3R/b5+0VxWq2wqZ0mS3HcXZzxl+P58DegHXinu9fe2P/yiNIkzU1KjYsHa9v8az89CwxBA+vPpYf8V+2DpOkzjxUKoez7UXrCufWkVpeNNAVRVidezzfUf//hlP1/sCH9YDiJA8hEKT6mjguY7PHmBrYQUl7ySob58ab16jzF7/3GqC4T+lyUhFJjKCz+UADK8za8SpmzoL3gYFaxURVhbZN8UNnOzt0LlYC00+flN3SJXMRIDSZUkY1aW5Lb6zrb15/9FR4zjQ13X73tvwWGSn1XDkanP7TukMknSP6iNG240OoZYVpTu166LFja6Tkh4yvtpvWwTcoSPBH973eS2I1qdo3YqdBgDk9z6iJNP/8WaR0chYA9qcKTMblGsrLy9v6mi+fqPtvheq/CPAnE1gH2GSF4kFm8CxA5wHFpZ8FAywux4xqn8R47sXnfOMy1n86+Vm1ahXc3NxQuXJleRD16dMHN2/exPHjMW8jJcZNoPf9DfBn+ZJfaFsoaU4n2E3Nm6TtF73w4r72w0QBwdVPNQCjeVD7poFHe9OfqLI8bEU/Afn2Lf+j0L+pZjz34mR4vfERgT+K/zk4OWBYrV9wbs9lAQkPXKQlTd6ePnj79B0y5EwryQ39vYwgYiZFxvZZ3tJfoMO4FlJtyfhFeszos1Ao58bXTF6P5Cld4ENckQLRHSJ2h59/UTw7ftk2DAsGr8D+v46IUCGToc3eyzSKuk+AKEPTuV1UphUFBSvkFQVtGo5GjIZ9a6PnNKORIISptmXWLoSFavDKFQ9mIW1Wrd/P5LF97r5iG8KHBRWy6adG/FF8xQe/Dzj7z2VZw4iMMpmDZ3MghIB1PWBXEopLF8D+S4127DsD8J+u3V/uM6E4xkwoM77m/l8fRxg6r0sbKj46aYFBpbCfIgq8L8JqY/XVK0jn4oJmBQqJizqT2C5bN4kHVZpkyURvJiE9pMyvAe/RhRfOYcONa+K3Zaz4sMXl4uCARfW/Q4bkySUhIE6mSf6CmHXmlICTvQODsOYa7RhUASJvatoSHbZsxJsAfwEy0y8ro6srJhw9hHnnzwqAulPxklh55ZIAoc3lQ8m2ImCYSQ0TMv4/P6eNxanOWqWT3lev/f3Eo4vKy0sunZfvmBHkzMSJ7vAEaLMt9XX2HPjZ0Paiseue+/dAZR8mPfT9orjh2mtX8dvxI6bKF4/n+eGDJGCtChUx2W4k5n2r91sE+E00AZwVj4XS2sKHv/nlBpyaxVo7Kq7zT4znXlznFl/7/aeTn4iLdPnyZRQrVgxBQUGwtY2ZLkJi3ARkjKjewwXno7j0heL4DcSZO2AlVPoGOTWCEvYUoM+L3aeXxVf8sg5Lf9bE1FoO/06sLyIGf1wGfT0alw9dkxbWyPUDxetr2ai1+OvXjcLyYsw6O1G8r8yD1PGzuy8J44otM6pCj2kyBRf2XhHadttRTaUNRewK17dqqy+xb8VhwdW4pUoOr9c+puHouXX73D2L8Sft/RnTe86XJIQWEMEfNPAlKzWzzkwE8TWjvpss4oOk8tMHjYnPwCqjhLrOhChr/kzCBDuziwJrGg7JvJrE8Wafn4QvioaDVzmPAZV+lspVyW+KYPyOH03VHapfN0wRnixyf573hreLJCFMjLDAocgBFSjJf4SSrC30L2mHYqAE25WCLiWB0UkRnyugBp8GNbtgkxkIuQeEUquFN1YyVN7RE6/8/eShPaBcBfQoqVlf8L+ZNBA3Eh0lPT7nahyLTKm+O7fhyutX6Fi0OLqWKCUJQETsEa0sqPvDxCRj8uR45utronWz/bPx5nVTW+lSt95I7qDd816BHzD64H5sjtCqMh6fVRf6br39ECCq1qS10yh1cvWaQvX/9eghScAYTF6MAoUR14LU9jl1v40k6Djm0H6suHJJ9mNV52SnbnJuBFr/cuSgqeXIsZnMMZH6KlsOLKyv+SAmZui9hgCBZhXoVHugs81qMQV5LgSSDBMMONZNMEJDYjz3EnNtrR3r/1Xy8+eff2Lu3Lm4ejXmPkaf6yZQ/eZD9aNBpfYQM7q8a2/t1eWvwUEh8PfyF++n2AYp6QQg5yuTK8r2DJOYi/uvSsJCFWceh+ang6uNEfaTi0cyLL0zI0q3+PlDVmDtb5sF32Nugsq5skVGFteXjcui78zOUpEhs4ssKvPInC8jnhj8tpicpM+ZTtSn02ZOiVwlckoVh7YfrCJRfbn1T5YYAeNYW+f8IwmTeXzVtDwOrz9p0hoihoagZ1atqraoaNU8lhWpt8/eIVvBzNKGMwYfYu1y9cGL+68sjrHBU6PTJ0bofWcB/pbrB/tKcr+oPkbzV2IFWv4r6NiJsSaJdQzNVmQD1NA7UJwaQaXbeyDxLCr0SmbkXvON6ZtcJ1ceTK8Vc7AtxybDav3NayibMTP+qFlHAMjE6PCwcbHAiMm6MNlhclQpazZUXDQPrwM0JhiFAZkAPfHxwaDyFXH3naewu5hgkN59tktPi0Quz4yppgqL+XGZcKR0djYxzPhZTncPbGzeWqpNHC/3jKkWWB1zTy8mM54GKwzu+0WKlPinteULCK0jpp08hjcBAaK9Qy8vBplpNBQ9//KFtL1IXWclyNnOHksafIcS6TPKdjRmJYia87RGfY/JOsZ0G73PGCBglckCVklzPlIHQO8z3gCRIBO4OnQerObGf3yu5178n0nUI/6/SX7Onz+PqlWr4u+//0b16lryYC1GjRqF0aPNHbC1t7PEDk2+3IrfkmND6NwnilZO/y9HSHuoWptKgtsxf1s7uOYYDq49jpI1iqJut6jPN7rzOrPrgri1s83VbkwzSS5unLojFZRy9UogVUbrbsesDNV2bBm+dkYSi9lSrnu90CSwyHkQ1EshQtpmGGvjNFIN8NEE5EhtJ2bHiAOq0aEK+s/pihNbzgoNv2SNIggJDoXXa2/ZhrikYlULipjj4fUnMLaJMZnUzprO9ZM7zRL9ncx5MyJlRg8U/jI/Wv3USJIYVqYy59F+BGMS7994iybRg8uPxJj1Y8nYx8bb/9dR7F58AEW+yi8U/NgwgAQ34D0CCD4mD17FbSJU34k099IOqUstZqeKkjjVqJis239hGzVgHVQfarCw5eUIpNoJ+M0Fgg4A+ud46J8d9Xd/hRC95h1Fr6mYBhlhjf/WJCXYZvqlSjWpUvy0f498TUZVriqtNAoE0niUScCnBlti9JuidQOZVEXTpccYKhsrCiZXr4UGecPbueOPHMSCC+fkkLSrGPXV16bD87ez0Jw/wSTEPJj4ZEjuKu7sBH6bhyay+BU6FC2BassWiSFqRKf5biVKScJlDO7D894dIfmJzTr4BweLqKF5Ba7Tlg04+FDDI/5eg+edPzZDxmpbNeyFiN0ijDpd/aEkC8dOGgfSv62tAaIZijt0aU/H6hgx3Tgp+YnpSn2G7erWrQsCmqOKBw8eIFs2TWmXgOdq1aphypQpaN26daxm+7luAv27TkCwZsegBSmOYVDcJoth5ZwBS7Bpxk4T/mTlo9lIk1l7qzF3gSep6dddP6FE9SKxOm/jxgO/HoVLBzRqKSs4Oz7wzSRyUMOGCREZVq4pSFlV0SZHL9HgkeRRgVSQmLwQH0Mq+rrXiwSLYx4Prz3B0Y2npK1WqGI+LBq+Cvcu0UtHQZb8GfHwymMTk4z7UYiRAGYGkyeqMlMZ2hhso614OFuOR/NR6vww2Aojy4v7XD9xG+OaT5W/syX2dasvQWo8o9OvLdF8SOxL4DxH88pQxBUj4PrYptNyPOomGbc1V6LmZzRq5bVLnoI0ai2DvHXmLh5cfYKydYtbVX5W1VAg+ARU6s7Y5YP6jnL5BnqwvC3+Ead7IWmnqFdA7zMBCKD+jNYOVlLtA8KeQzUJINrC03YIbJM1NXlVxXQ9CRyuawBGM2mYUqOWMLUeG8xCSREnlujiyxcyJPE6RtVi4zHYTnrh6ytYnJgYcNIegmBgBpMBJ1tbeAcFSSL0RwSKeKn5s00VmBzuHtjbtqPp1Ebs34OVVy9HOlVjBYcAancHmpD6WmzDOd7s9b1gfZZfvigVGGOCxQSM2CK26owaPsRKzapTX/4eX0GhSFaejFE1Ww7RHvqcQQC06mtQ13duD53r8ASZzud67iXIyUQx6H++8mNMfCZMmIAOHcLBqzFd5M91ExDRr/r9acAMuCPYth8WjjiCXct9UL/HN0iTLTVmf79YEgG6nK95Pl/0axhndl8Uh3JNnM8G/ed1i7ETfMR1IeCY/lqMzHkyYP5ly+oJ/04gbvfig4V6zioLsTIUC6RTep8yw+H9VsPwMPkhRZuq0U1+qB8jMDCVlpeOWitMK7bIhtQYa6oK0fKi1fBGqNmxiugPrRq/AUt+Xm0CUhvPpcukNmg6sL4wvEjpd0vtKoBhrzfeWDtpM3ze+UmlhUH9n2TuzvB+o/0Y0wV+7XPLdplxXHqZzRu0TLZlVcwaCDmq+2xK59n4Z+lBodB/27uWCVhNcHe/8j/KbmwtpkjvgTdPPEFLEeoJXTt2E8NqjpOkia72i2/+YRVTpAafgfq+u7CQkHyYphul2ENJ1h2KLvZO4TH9vvx/3U4NuaMlmdT+sSsJuM+Bon8B1VOzUBHbC2lZh1dFYrpWZCqNPrQfrABVy5FTnNb77NqGfQLkhSgNUwyQwUpNswIFMf5rTYGdQSNQGpTSWZ2CfOuatIiWzTT7zClMPnFUEm5Wk575+JiqL0aBQuP4xAztundHfm+o+DzarPJTYNYfFswststSOifDtdevZDyKEl7q3gc5pk+xWI4Ujk4421VTWGcweSu3cI60p4zF45ROTgJSLpYuPVY0bGL1nGjHweSJiWLrQkVE/DA20eTvv3Dp5UuEqXqpaLUp/Pn99dSQq9r32q5YrKrCsTnvz/Xci80cP3Xb/3Tyc+3aNWl1/fLLL+jSxVITJaYLl9A3gRpyDWrgXij2JaA4VDRNS1VD5E2SbQzFuQ3WT7+KhcNWItRgkTDn4m+4fPC6mGnW7lrNApjLqsmIbyfi7K6LYuXw276RFuKEMT13+dEJDMa637fB770fvvu+jtU21/m9l7WkxJA8UBnaaBq6a/EBTOk0Sz5jO6n9mNg7t9MUdMMfOyShKlAxj5h9kl3GthY1b0hVpy7RmkmbsXmGQRnb7CRb/9wE7UZF1rz5/ssRkkwwyDhjyyx3iRyCbzqy4ZQgrUrXLi6CgleO3MCvrbWKCVlvZHGxZbZ3xWGoYXpJkqyx5qJa62YZupgqVNQsWnqbia7GGpvcURuXrC6y8th+Y7JD2j2rPuumbjNZZSy4+rv4sBmDqrCs8qgERYo3EMMDcGkPBF8Q7R/FsVpsboGkbWO4AvrQlwA9mVRKCqSAkmqzVoH7sBmwrwAlWadYP6xYfai1ciloq8BkgUDcKtlygC2aZZcvgLqhfKizLcb2ELcng8pcL2jttSv4cf8eU/KyoF5DVM1uSVKIeIpMZI4+fiQ2EsQWEXhspKCf7NTdItFgYkIlaFudjVSGyGQzBmnmxx8/kn2/y5sfv9WohSfe3uKx9e5DAEZV/hpfZcuOcgvnCiDcGK72DjjasatJhZn+YzRfNeoSyW+NouBi9z5SlYqqNTz//BlMOsYKuiIeXAQ8R/Tr+tjlZbtu9907SJXMOdYmszG8bf6VmyX0c+/fcNL/6eSHlZ6lS5fC2dnZYq2vX7+OLFlipp+TkDeB9HjfGB9EIVBSrIJir0nFR4wd8/dieq8Fmj6NAiy7OyNad3AmLuY6NAl1w719/g7tc/WR5IEPauJ0UmdOiSc3n0uS8P2cruL/lTXfx/WV6B5P09J8ZXObfsyYyDVN30WA3VRfJuao9YjG6F58kCgmM3g8XidS3pk8SEnd8Hroljo5Zp6eaKKkm69B3WSthLllDLKz1r6Yr7X35u+FPlQviSVbc50K9sfj609lU7b2Fl6bhh/r/gpiogi2ZvK0XcCKMYs5PyzB+mnbpYrF82k3upnFjnz43Dx9V7SWjEHzVwLHf/hqpBwzU54MmHvxN9jZ28kmRoNc0tm18pjhHyU1oL4NV34m5scmQ8wmmrTVR1eAisQIWAq+xMAmpwFwLlJ7UFzHQnFu8kkr+NTHG5WWLJAx2O5h5YHqxRGDycHue3cEjFwmAp5o0YWz+MXgkcX99rRuL8Dk8UcOSTIxtEIlZHWPmjTB1tLSSxdw7/07tCpYGDlTpERgaKgkE6xKkenl7ugoCRfnaB40Ne28ZSNC9GHid8XKkLVge6vp36ulOmWs7DQvUAhjqlSTNl1ENWgep3SGTFjVKPylhmsw8dhhqQ59X7a8gJup+vz3tSsmWvz5rj0TVJH5Uy62GvoYCD4K2BWPF1bvp8wlIZ97nzKv+Nz3P538xMdCJeRNoAYdh/reyE6whZJ8mFWQG8+DSc+8wctx7fgtfNurJqq3qRwfpxdvY9y//BA9SgwWbZ2I0W92F9TtFl6Gt3ZQJhtTu2keZgRo95vdVf6dFPUG7poSK9le1dtWxqBFvcSDjF5kDOJ5jEmMzlaHqi2+FLFE+o1RcJAtM2vRtcgPojBtHlR8pip1xBZW7zLDROOI8UWx7OJqz//+sc6vIM29/Zhmkoxwf2KeogsmN2zBsaLDBDGqN1d6oZ3eeQFl6hRHpcblZFjiggh4L1qloAXTjm1S1Y9rYgCX2lcBdI4imMYHtPHvSspNUOwSDrgZ3bn/lz7X+/8N+JJJx/ue2DOqzxgUaIRVp9mgqPoAAUIrSnhVJCbrwEpOnVXLJPHgPWMNzxPdOIP37sK66+GWEGe79MD3u3YIi0kPVRIWVmUmVvsmyvuQ7SNWj/65f1fAy6z2sGXF+5ZVKAbNRCnUaB6kyPM4XB1WaOi7ldzeQSjw3+/eIePwuKUzZpJWGE1DjdUdHRQBPrcvWlzOffj+PVhz7YokWl2Kl0K7IsVEdNEYNGelVhCTNWKgjnboiquvX6HF+rXiRUZQ+K9m7cDo1i0xP1fDXkN9W0NrZ5GokHKjRQIkuEnVX2QTYkOAiOs5JORzL65ziu/9kpKfaFY0IW8CVf0A1bMxEHrHUCbfBMUmXXxf40QZj3o7zTNqCUvE6Di+JVoM/ThQsG/54bhx8o7sSgzTNr+VINV+38qjIlhIjSBS6yfvH2VKTM7tvYyhhnab8ZhMJtgKiglLiwBtOtmbBA7peKEoUiVafm+mxWkQp8TKG3/FS1QvLO3Hmp2qImV6DxFM7FlqqLQI+d/0KUvmlixR1t38IGroXe1+UgMA24JQUq7RvKb076G+/Q7QPwN06YGUm6Cz8Uj0+f3XDqiGPoTq2UBbb9MNmArQs8rGoApnU4At7MANgC4DlJSrYl11I9388KMHYv4ZFzuFddevYsje3dKOIn5nT+sO+Hb1Clx/+8bikkRsl5l/uPOu5mllbhIa8XoSe7TyO8v2snlCwkSF58JkK5ubOx56e0lSQxD24fYaLOHc82dosm61/DsrPh2LlkDLQoWRziW5AK8pkkiaf8QKE7dvv2k9jjx+KImWg42NWF4UT59e2nF0iucY/9bQ+80D/CYbpmcLUJ/LuZkog+upzM7vtf4VYFsISsoVUKgsnoCRkM+9BJx2rIZOSn6iWa6EvgmoDCvURZssCarqHKu7Ig4b80eM1ZHbZy0FCV1TJZdEwjm5kyQz9BGz9uay6KdVWDNxs1R3in5VABN2j8BvHWZg7woqNWtv0nW7V0e/WeEJ1vN7L0VbJ2LMufCbReWGgOtjm84gS15WZiwVl0UpVq8XQcerR29KO4nMKpqi+nsHwCONm8Xw637fCuoXMcmiivPSO39i0/SdmD9kuQmPRRFGWoR8jhBvIApi2uY1CaCpaiDUVyUMlR9bwLkNdK7DPsf0/lPH1PuMAwJWGNqJfOLWkDdzcd220B82VkNtgWRdoEveP9HXgSrSxNrQxJTWEyeePEa7zetNIn+c0OjKVdGmCC06IseiC+dEFPBjQW+y+nksv190Ye+3axtuvn1rgelxY8ssOFiSH7bc9rftZBqa4G6KEJLB5Whjg7sGS4ruJUvjh3LhuMiIcyE7rePmDfAN0kDRxEiRHbepWSsUiEcWWHxfPE0mISJrixWtMLG0kOSaiY8hFPc/oDjWiu9pWIyX0M+9BJ18DAdPSn4+c/ITw+v0P7EZkxtSxk9sOyd4Gca8y1OQvWAWzB24VIC6abOkxuQDowS8bB5MQA6tPYEAnwB83bqS4GxaZ+8pKs3msdVvhQU9nmalK39ZB28xH9UqN8vuzZDx+cO6b+VhzOy3GAG+HzQ39A2DhVrOIF5n/uAVSJHBA43618GkdjMQFBCM9mObY8XYdVIRqtGuMgYtDjc9pQjjgdXU0NFii88yPL39QhI/Hpt2G0tuT7fQL/rcF08f+gp4a3QWtwGcWwWRUusAACAASURBVELnahQ9/Nyz+989vuq/GKrvJEOi4wglzQkxNVVJe//ApMgYxnYYcUAjoTjHHvQf21W6//6dtIDypkqNWl/ktrr7XU9P1Fu9XNzas7i64Z82HaJUmR6+7x+svkbrlPBgJaZsxkzoXLyUWGFYY1JxHtWWhzuPs+nH70nLgoWl8sRjU7eocFrLijfbYUcfP0TnrZssjrm9RRvk+4iZKL/zCy6cxa9HD5v2q5QlK5Y0CBc/ZRXIPzgk1uKQtN4gvb5SlmxI6xJ/wqX6972AII1RG5NQUvwNxT5u0iUxGZ/bJCU/MV2p//B2/x9ugrhcPv7IUFn52Z2XIrJIqwgGrSX6V/pZPK7IxCJVe+qRsaL5wyD1nkyw7lOid1ReOW49lozQSuAMW3tbbA9YGUk/h6afy0b9jevHb6Fu9xqo3ETDHWybuwfTe823oL7z2L3+6CjVnm9d24L6RJxTpjzpxcSVQY+yW2fumcxPVz2eYzq/C/uvYFitccK2oi3HsOV9ZR8y0ghQLlGjiElvKbbryjX9a8JGnNp2DlVaVESD3p/2dqeGPhCHdzVgNRD0j+Eh7Qwl9R4oNponWVLEfQVET8l/PtTQe8LIND6QeB3Vtw2BsOuGwd0Ahy9FcwnJOlp1eI/7LCLvydYS3eLZItLYX3VRJ3ceq4cglocmoVRU/ljQBX7y8WPQq0QJhcfaxs1RMkPUQqBnnj9Fs3WalY4xaGi6q1U7ZI+Gds7EqfryxRbHiy754THOPHuKZuvDj9m6cFEBWzOOPXmEjps1AHbX4qVQOWs2EXNkNWxS9W+ibI3RIJWGqhprzF4qVfQ5i49QP2yB6j3QMJRRAclcy9rsKDZ5oKTakuC4n/8Pz72kyk80d+//h5sgLl9gusL/3nWOOJy7p3HD8vszpaXFmNlvEbbO2i0MLwarP71LDxU7DhsbG3T6tRWa/GDUP4l8dCYmW2buxqPrT3Dj9B3cu6Cxuhg7AlfhxYPXUmHye++PPjM6o2xdtnQix+9d5mDnwn0WH4zf+SNKfVMUTJjqubQWB3gSVBQzx3h6klE5mxhDOsuzBWbUUOJg7195wfutr/iDxSf48NSO8xhRf4IpWfvj+DjkL2v9rd38pAQ75v0zEHoDSrKuIoJJyrv6njgKPqrIdjTgUhQP6NKGK+PG5don7WN9Baj1I2/wdkWhUvCQ6s6y/rZQ0l6NNdg5tutMj6yxRw4IoJgAaQZxM52LlcTgCsbKH4RivvX2TWRK7iaU95jcw0yi2Po6+uQRjhmo63w872vbUbBIUQWTq767tmHnXQ3Px2AralzV6mhaIPrWMKs/ow4dEIB0p6Il0KOU5okWVRB8TS+wGadPmBqPW5u3QYE0WqWZis0HHj6QfyduyMXOTtpvOkWHWl/kitJ2ZN65M5hy4qgJjL26UTMBacdXqCFXoIbQwNlbIygEHdREbtnGJoswjKrOYXI4JdUOKLZfxNehrY7z/+G5l5T8RHMLJcZNIGZ1oMmndVZSbO9yOrLbOdpHyXKKOB41ZTbP3IUCFfKgy4TWJsVk43ZMRtjaSZne3QTkndZ9HnYs2Gt6UDNBINiXcfXoDQz8erRUR0hbn3Z0rOBpNk7fLpo0pHYbEyVr50YfLooryg+lnQ1CAkMEC0SgcasfG2Fg1dGmqgyFEze9J5MpcnAepIXTw4xh52CLLT7L5dhkzQ2tORaBfkHajgawM693ka8KIGOu9MKXr9OtuoWGUmyvRWy2p63FpHZ/imo35zFux3BJ1KILvc8kIIDrxfNUoKQ+pvnCfVhvcogGNAwBHcYJpEyK+F0BNeyNQbaCCs+hgOtowHcKoPoCyYdCl8zScyp+jw4BIlNQkG0kJhdkRFEAkCBjihqy/cXgdtWWLxLRwlC9ivFVq6FZwcIxng71fEYd2i+Jw7CKldEoX4EY7fvMxxu1Vi2TahQTsh2s/HwkaYrRoBE2osdYo7V/wTc4SJO7MHy+t00HU0uOmj9sizEyJXcVU1n/kBBZs2o5vsDsOvWtHpotLwpFUmmaLa/drdqbzFvjMteo9tH7TgX854o5rpJiqYDjWblV6fsl6uE6KKkPJjgxJjGee/G5bnEZKyn5iWbVEvomUIOOQH1PTEmo+DDBsY72g6kkj9EbWcTpLxm5GivHrodTcidM2jMCeUvn+ugZUoW57Re95ZeC7R+ajNbuEi6CxxL+j3XGi/s58SzTjv6CHIWz4uqxmxj89WjR9ilVsyjGbR9uMV9idV4+fC0tJKMOjbWJnNp+DktGrhHNogHzu4tCNB3fN/65wyTkN2bLEPmxLV69sIgpXjl8wzQUAdXrXy+K8hwf33yKmX0Xwfe9v7jXG5MJc4FDY/JToHweUVOmbhGFCwcs6ImaHarE5XsVp31YGRvTZAou7L2Mio3KYvCSXlIp+1hQG0R9S++28GaEkvqwJq7nPVSjX9tkAlJukAeCoos/rEKcTvI/upMoar9rZTg7OyjJBwDOVJRnZfHjLaX4WBImNUXmzBBKt62iw7d58qJj8ZLI4JLcoj1DLZxi8zQmI++HhnnzY3INrb1KwPArP19UzppdGFURg4lLkbkzTG7u5TJmBltTdHAfUakKymX+uHbaM18fnH76FMXTZ/iorlBc12PCscNSnWKVikEF6E7FSqB7yfBqEZMXahbRRZ4+ZLQOGXlwP1I4OWFGrXofnRcB4zfevpaKj7tj/LOttAS6guH0SUxoC53rULC9qjKRDrkEJVlbKI4147pEMd4voZ97MZ5IAm6YlPx85uRH/7Z+uBKvkgqwzQqEnANsC0BJsQKKLuaUaQKO6zi1EqAvb94vG5XBiLU/fPQMKSzYtbC2Da0dOo63bEmZM6qYHDXsWxvdJreV7dmW2r3kgFR3KjYsY0p+SAunNxarPFVbVowyiSM1vIFHexHuY4WHGBdigbj/9xU101biaoYu62MaY3yrP3D47+NSHaEY4a+7fkSRyuFvn9S/2b/yCLIXzmrC/lhbgFGNfsPJredMFSSn5I4YvWmIJHTGoMfXUAOmJwG/g580tN6b4Fqz5E+XCbo0+2VMNegoEPYEauhTIHALYF8Gitv4RHkYf9JJ/Q/uLIw6z0aabAVFJh2+0sDNNvHnNRXdshDPMvHoYaRJ5oJfq9VAaudkuPH2jWBgKmbJKtUP/jZ02LwBhx8/lBeKhfUaig8YKzrU3ZGXjPQZsKYRadaWgoXmyQ8rJeZmo5zbyoZNrCZAiy+ex8orF1EqfUaxvrC3klhFdW50X+//zw6cfPoE3+UtgB+/rBzl78nGG9cxaO8uOQdHW1sc79gtWixTdGuamJ/rw3yBN6UN7S0bzdzUpStoZ6G+a6e9FCfrDV1yDWeYkJGU/CTk6v6PjJ3QN4GeLr5BlF9XAV0mQE/jTb652EgliPiNmAZ/2Ki14/XGR9pExNV0Gm98G7U+CveZP3g52GrKVSIHxm4eYqFR88E/EC0zdwf/n9WQgYt7icAi9+tSaIDYTBDb03t6J9Tv+Y1s1zJLd1Fblu0X9RJhQmvBbSlgSBYWfaxYceo7U9P7YBWEqs4eaS2VZ33e+WLxj3/BzztALCsy5Q5XKiaji8cOCgiSOQ1f2Q9fNTO+SVnO4P1rb2GgcZ70GctVPLuoYQ+pPgYX9l+V12JzZlhMr0Fib6d//bXhnjEc2W0SdE4NTNMQ+xRPo8aSraHt9Wmqw4l9jv8rx6NshQZ01sQwYV9ajIil+mOTPsFOg8yoW55vkdXNDa4O4SDc255vUXfVcrmX2Wpi+4dO6qyMjDy4D2uuXpZEaXnDxph26ji237ltmuPVHn0tBARXXrmEXw4fRDJ7Ozja2EqFhAmJsd7IhKNnyTIYUK6C/DY89vZGKmdnPPX1EXsOY3g4OmFL89YI1ochbTIXHH70ECMP7gX/TmNSc8bYissXxVaDgGxjsIXH5MxahIaFYdOtG+B5N8pfEHlSakbP/yuhftgG1ZsvogbBzNSHoLNJA71XfyBwR3hbO+2VBH+BSejn3r/hmiRVfqK5Cgl9E1CXRX3XQ8CqsM0NhIY7INPuglLnCHsG2KSBolg6oFubOisfG6ZuQ8qMKdBsSIMY434+tgysxPyz9BCyFQiv5DBxqZ+8jWk34mQoQPjivqGNFkNmF4HT9CxLmy0NRm0YaNU7LKZfFJ57p/zfy+asJDUZUE/A1bEJVs8uH7ouGj7mnlmxGSMxt9W/Kq6ZacpJZ4cu9W6Lw6sh1zUhPm0DKK6/QHFulJhT/H91LP3rKpqYJEOXFtC/1h5aLoOhc+kc72vBykjDtatATArtJjY3a21q3TC5GXFgH0JVzfJlbt1vBddCheZCs6fLI9ao7lw2U2YM3LNLsC+F0qTD+qYtTBUWtoryz/pDKj3cvm6uPJhWs44AgGefPS04Iu5H5lfRdOmFnk5dIQKvJ1argZ47tprOm/NgAsYWGLV+iFGiVQbH/Tp7Tpkjg3T0onMthUb5dzrA09LCiGEyDkyw98A9O8W3a06db1E+mhZcvF+IeBhQ7ztdw/sYFNqVlFuh2OWB3meKAdOnB3QegumLrVJ4bKeX0M+92M4nIbZPSn6iWdWEvglIk1XfmlGabXJLq0LAbclHAIHbgJDTgC4llJTrY60OmxA3jXHM5pm6wvP5e/lPsqIIJmaI59XOCxYYIfN50LKCVPLsBTNHqux8ynwJzB5cbYzYRrCN9cexcaIx9KnB9hwp98/uvkSzwd8ifznrtOFPPU5c9lf9l0H1Hae1WtxnaDRqvRfgSKsCLVnW+80FPqzV2l4EPMcTsD4u8/2v70OTYtVrAMFVgOIRnggRcO5QGUry4VBs448ldPb5MzQ1KCKTQk4RwK4lNB0rgpprr1oqbKbUzs7Y1ao9PJycBLRbYv4ssaWg6rORDbbr7h38cuQAyNCif1hNgz4Qk5tic2fCLzhItm9VqIgwter/tVySKtIllzdoLJ5iD7ze4+tlWhuW1aYOBmuKBRfOyd/MgcicL5MeVq5sFB1q58otbTFS9QnYLjznTwuau3H/6jm+wBxDkmS8n8osmCPgZUbBNGmluvRvDfGDC9orWB7FsYaoODPU0CdQ3zUF9J6AfWUoHnMlyVHVIM22JuwVFJeOCc700i6pIhW8/3IkJT/RXN2EvgnEkuBtbbNZEODKm05rfRnpjWTrKC59obh0+9fcj8TNHN98RhhfTDY2ey2TL401dphx0kx8uhUZKOKF3GfO+d+QIWfsLT3Y/rp/6ZG0q8ytJEhhf3zjmejyEDy9f9URzP5hqTDRRq4fGK0ZrLXFXTpyjejvULiRSd66N4vipaIWXxdS1XtBDbkF+C8Agg9p9419WSguvbUHMZmE7r9DsdceikkR/ysgSu1BB6AGXwQCVgK6FIBtPsP1MLZtbAC7ItClDNeu+tSZvA0IQOUl86WVxX+WNWyMCpmzmoZlQnDjzRsUSZvOAvh88eULzDpzCpld3TCwfEVxaa+5Ygluv/OUfSlGeK1nPxPw+cKL59IaS++SHMO//Ao77tzCzwf3mcDF8+s1kMoNE5fyi+YiMCQUYape2lnU++pQpLgIBGb38MCSixdEZydMr6JfmXLY//C+gJO/y5cfA3bvks+4/YmnT3DT01LklMlTk/wFMaHaNzJPnp+TrR2arVuNW2LXoYiFx8pGTUUh+t8Yeu9xwAdDK1BhJWcvFJ1mvSH3EZMfXbo4EV7i63wT+rkXX/P8lHGSkp/PnPzw8Po31YGwR4aZGB25mfyQmUMqtoEK7z4TcKgIBF8AbLPHC92R1g9LR61FaHAo2vzcxCTmF5ObimBoCv5R96bxgHpoO9LS18faGOf3XhbGFoMA626T24noYWyCbCwmUAREp0jnjvlXfodrysi+PTSDFS2f4FCxo6jSvIIFgJlJ2uKf/hIm2zftqwiY21qQ1k+9IKPNxqb3Sz6Ld1dUa0R9ENWT7QLeMyYUhqHt8kLbzUpLLDZrnrRt1CugBh2D+p62KwZDWWP6YFsYcKigJUOql+E65IQu9c54XU5iXFi1YcupUtZssR6bb/hsPVVZthCv/bXqCeN05x6C27EWbFvVXrlMaOXE7lC00Cj6R/zRb8ePgFUpur7zjiT4+lTn7jLUjTevRX2alhP1cuc1Dd916ybsfRBuj0MjVLbaWHkiWPrEM1bEgRqGys+0k8cw/fRJUaWmYenMMyfx4P17qSZlcXMXDaJ/Y+hfVwL0L82m5gA4NdG8vOz+HVXlpOTn33jnJPKcEuMm0Pv8anDdNrhB21ONNACKC/ErOqiB26DYFQEca0D1/A4IJTDRHvBYBIVOv3Z54wyo/Kn+BJzddVEemrlK5MSfJ8ZbrDArNPZO9pE8rrjR7XP30Kv0UFFcZvWHnlqkwX8s3r18j/a5+wrDi6DkP0/+ijwlc8bqqv6z9CB+7zJb08PRKcJo+/K7yOJnrAI19GgvoGYmP4Ur5Ue5eiVRvV1lJPdwweF1JzCuxTRTUjPzzAQRPVwwdIW04/rO6gz31G7ioj6o2hh4Pn+HtiOboPWIfxdgWP2wCao3vbo0ETQJ2wJAaLiTN2zyQZeanlNJEd8roH9TDQh7bH1YxV3T/PH5WdNgcp8GxaF8fE8hzuNRPJAKzHfeeaJQmrTigs5kpXi69KiSPQfypUojQojWwjMgQIDWbDMRb2SMPffuovv2zaY0XIEiYOz97cL9u6yNN/XEMcw+p2GIzA1U2cJilYnVKgaZXNd69EWeGdMEz8Sg5QTJaYceaYKoxCDd7t3/s1ZPorooeu+xwAcNIhAxFPeZUByrQ1SffUYBOjco7nOh2EUvdhrnm8DaPJLaXvG5nP+bYyVG8qMGHTKo8dpq2B6WQa2Am9WQG4Y3fHm60UkRAN/UHKCk2hjjXvDRjafE86pI5YLYPn8PHl7V3qgI8v3rCQF3WiwbvRbLR/8ticNPawZESjD2rjiESR1mQg1TZZsf//oelRpr1hIfi6d3XuD0jvOgrk6eUpGVSinSeHjdSTEQZbWl3+yukqi8fvxWtHo4z+7FBpmUmRdenxplO4uVpoXDVyHQP1CEGlmCz1YwC2afmwTzJIrzpefY6EaT4fveT/R1SLMfvFiz5TAaoEanuxPduSfE52rYW6ie9TU3cSUF4Pqz6PwIzsf4CPJYAZ0DabRJEd8roH/7rUZYMAWrkL7h/2mTW/t+/guxVksvncf4I4dMysUrvmuM9Mlc0XDtSpM9BgHE1XPGXFF4xumT+PP0CdOYTI4mfl0DyeztcevtW5TJlMmClWZcKLbtyPA69OgBjj5+ZKLSs4Lzz707oEAhf49Z+SEzjNiiJz7esnu7wsXwVfbs6LRlo2CIepci8yxqE9T4vgdiMp6qhkBlEhx4FFDfWr6sGAdw/BaK2ySor+jdFag1IB0qQ+cR/rsck2N96jaJ8dz71Dl+6v5Jba9oVjCxbgI1+DzU4JOAPhCKQykoDuFy9MYpqno/TUWWeg+i9mlscVBUbSCUZBRViybxuP0cHfL100xAdQpaDmuIv6dslSRj0OJeqNryS+xZfggbpm3H/UsPTerIFCskgNgYrKoMqPwzrh/X6LGuKV0wZvNQocsvGLIC9688Egp5mdrFI02I4ofeb3xkW1aNzIOJUe8yQ+HvZbBjAESwMfhDsGjyMMn6deeP0uYisJnChzEBNVOo8fTOC9qhFGBX8GqpHI1rMRUX918FNX16/9kJ9ZK3kWPxODRAHbnO6LkT3cp+3s95b4jGjG1u0YbSB2wGfAaFT8pjKXQO0Semn/cs/jeProbeh+pFexFWUIOtn4QzvezCoDg1T/S3+I+tKttlvXduNVVasrq5I6OrK44/0SpZBC3TA4u4oJjGY28vUUP2CgxE6QwZseK7poLH+W7tKsEIke21u3V7Cyq9+dgEZI8/ekjaZvTlalagkLx8HH/6GL5Bwfg6ew5hdbH1tvjCeREopJghhRmJOaIeUepkMddHi+l5fep2mofXEOtJj3Fw57ZA2BsNK0Y3d77kOtaFzp0GuokXifXcS7wzinykpOQnmtVPrJtA1ftCfVMFUD9ouiDuc6A4RlYXVsNeAIF7oNpkBPhFki+IHkrKv6HYRe+Vc+XIDUla5IfNzgY9pnZA7S5fS5Jj72CH14/foFX2nuaCwVBsFNTrVkN8tIxx//IjdCsanhiwosKEgbiZDdM1dWbSzf9+uUBaTMYgQJpAabbJ2CLrOa2D2EkYg2amdFQnBknyFJ0i+7Maw30Y1ASifhATlpj4EnGfIxtOYWyTKfIjmiK9OwpXLoBef3SQtpZ50GKC1hruqV0xetNgZM6jmTYaE6+YHu9zfql5bK2aSByKwVvKdWwSxT0BL4oadNCw3hEPYhQKNCT5ijOUNKQqx48p5qeeEr8Pyy9fFL+ufQ/vSxLEhIcA5Ff+/nC2tcPqxs1w7fUr0dsh2JgVHGMsuXgeVFZOl8wFixs0MllWkE7/0s9XfL+IwSEeZ/qp8GoQRRTPPH+GV/5+aFekmFVH+E89t3/b/uqHrVC9B388+RGoOVt5OsHpCaTBdQQUAugTMRLruZeIpxTpUEnJz78l+bHQY7EFknWDLnm/SLMT+uGHdVBDLgL2FaCoPsIgEcfoGAQrNmOb/I5jm04jR5GsmHJgtLCijMHKS4c8lgqibDMtvTvDguHEZKRV1h4I+hAEfZiWlDD5KV2rGM7uvojQEA1/8tfTuUiVIfyL+3ODiTixRfPWMQYtM9gCY9DuYkT9idDZkGoJ8fNqPrQhxjadAq/XPqZ9mGy1/rmJgLRjGtQgIkbJ952fJFWVGpfFT6vJhvp4EAO09rctSJMllbTG0mXTTBITO9TAA5qAnmOdaMHuIrj3ri0Qch7QpQFSboQuyck9wS6ZZjNCBpIRd6UADrUAnavG3gmi6rbBmDL1USg2n+ceimoBaHtRfN5MLVXW6SQhIRiZzCkmLauuXJaKablMmbG0QWMZhhWWQnP+lITJqBc0qbp164VzL54Jtoj4H9LYG+crgIUXz2kvI07OONGpm4yR0MHjrbl2RYQQmcjlS51410FsKojjCT4GOH4HOH0HvK1qYU0Tfv5k9/aB4qKBxBM7kpKfxF7xf+HxEusmkH6wZzMg9CobPYZKTmSQmxq4G6oXkyJNBVRJvT/aB6G1ZaWCsp29rdXKCbE+K39ZbwICOzo7YPmDmZGqJA+vPcH2eXvxz9IDCPD5ALfUrpj4z0+Y2G4Gnt56gWZDvkW7UZYmmmt/24wFw1aaqjjWGF/Ht5zBzdN3pOpSsEJepM+RVqjxA6uOwssHFI3TIn/5PPjj6C8fvWvuXnggdPyCX+ZD8a8LoYZtU9OxI2KcrA1EgHazDKygaN5nDXrXRPffrZtUBgYE4cj6kwKWLlmDPfv4C+2t0awF51AfivsETdcnitC/6wcE/6N96lATOo+p8TehpJEirQBp7mrgP4BiC8W+tPZyQuAo2XjvWmjtascG8d7CYBKy8eZ1ONvZo17uPKLFE5egKOLUU8eR3T0FZtauK0kJo+6qZbguNHKIcOGl7r1x6OEDTDh6CHffv9NEDnU6NC9QCM0LFhYBQmuJDH20CKiukDkLhuzdjeNPNawh43rPvnC0jb2xM9tok44dxrkXz9G2SDF8m+fjL4Frr13Bj/v3yDF5PCZdLmaVrLis26fsowasM+h0OQDJfwKCDwCBWwGbrKDIrfKZXlgS67n3KWv3qfsmVX6iWcHEvAlE4yGESs+Zoyxzqv4LoNL510irTfY9YJcdikOVeC2lM6GZ3HGWVF9YJaG/FvFA1oJVoHsXH0olyTVFZMq5+T5889q99CAWDFkO7ze+SJ7CRcDHabNqrtPGGPHtRJzcelaOPXbLUMEOcd/JHWeK2jSDGJ1ve0Vt8vf6yVthlql6veB7ph4ZK/YVQR80XEb2Qlkw79KUj94BtMxokq4zKHRIsHPHcS3QdJCmQhsx+lcagRsnb0slrOcfHcSrLL5CT1fngFWGkrg2quIxH4pDZOsQNXAXaLSJD9sB9Z22sS4jdGkOxNd0ksaJ5QrQ+wt67wTx+mq/ab3gYfj96FSsJIZWrBTt7Lgt205MUkqkz/DR9jGTomGGhKFHidLoX66CCBCSHk9WFWnsBdKkkYQoVFVNVHRrk6CAIitA518+N33MVG1f205xMjtdffWySW+IdaND7Tsjk6tlK9t8HkzYFl08b9InOhDH40a7wJ+wgap+AIUQFcoj6D5+bT7hMB/dNTGfewl1DtGNm5T8RLNCn+sm4M0vzIAPW0WwDu5TodM5Qw17DdWTKqD88eCbkkFbxCYvlFSb4k32nFYRNDxl8kEw9LxLky3sHoiBuXvxIRyd7XFq+3l4pHMXDE5EAHNUy8t22YMrj8Wby7ztxu1vn7uLXqVI3daC+jxMOG6fuy/CiONbTpPCF5OtWWcnRul8fm7PJQz9RqsMscLEig3nPXvAEtja2WLU+oEoU6dEdN8RXD58HeunbkPWApnR5ufG4lLv5+UvrvW0/OBYbCfWtGtuGqt07WIYt214tGPHdAMBxItreDidXUmxMpJwoaY5Q30TPlKMYpnQlIWTWa9YxXQOSdv9O1eg4OzpYlnBKJwmLTbFQN2Ynlnzz2vtZwoN9ivzcfo9lZtppUG/LFZbeMwQvV6SH1Zb2IZef/2aiYJxvmtPq87nD73eo6pBAdq4mkzAptSoFW3Vxtrqzz9/BpOPHzUxy3a2avdRT6977zzR+O+/4B0UhG9yfoFZtevHGDcY31dfU3reoxkPqwFQ7AoKzpOCpVql0E8qtor7H4k+x8/13IvvNf7YeEnJz781+Qk+DfWduUQ7jU5/g+JUV2TR1YCtgC+ZA+GhpD4UZ70fa8tw6+w9nPvnEopXK4S8pXOZNjG3kWCbXmdjI2+d7cY0Q8th333S/Uun+O7FBiM0xKCKqwCN+9fD+mnbJLEiiJpgaKPgK2ADSQAAIABJREFU4LJ7M6KkudN/rFepIXhy87m05FhhovIz1aHZ8nNycYp2rhf2XxFrizRZU6PvzM4CvqbuT+8yw+DvHYA8pXKCmCUmQCPqT5BEkGsxeEnvKA1doz1ohA2kakC9p7B3gN8MIOQq4FTXqleU6r8cqu+vBjYgaXgTodgXg2Ibe/G72M4zafu4r0A4Wy8XFF04QSAmI446uA/LLpNpBoyrWh0tChaOdjdzO4gsbm442C52vmPbbt/E2MMHkSZZMrGa2P/gPkYf2i/JUCrnZDjcvrPV9ltgaAgqL1mA94GBpuoLNYJ2t2qPtC7Rn/cjLy842NognYtWYfYJCkLnrRulnda6UBEMqxi167txUTiHdx8+iFp1bAkMVKnuvGWjiDuOq1IdDfPlj3atpaIf+hCwzWJRndf7TDJ4dvElRcM7Ke6zoVKmImCFGUYsfn/Xo51wkr1FTJbov7/N58qA1ZCrmqCheehSQpfmhPxFH7AO8DGrLChuUNIcTxQtkRcPXqFtzt6RLn7ZuiWkRfUpsX3eHvzZe6FUaBgthjUUSwnqDRlB1Pw78TcEIFPjh//O62T8IWPCMrv/Etg52OHRjScI8g9GvR41TI7xMZ0f50DX+cAPQXKMet1roNcfHQUPteKXcEbajNMTRKiRCduFfVfgnsYNuYpbF4aL6bGN26kfNkL1/lGSH8X9T6sMQPMxNc2fRoD+hQaEZ3VICWfnxPb4Sdsn/AqoYZ4GnaY3osqtpNoCRecR4wMz2b786qVYVOSOoZP54D27BCfEaFmoiHhqxSYoZNhr51aTD1ibwkWx9fZNPPHxQaN8+U3JibUxX/j6YvudW6IMTeZYobRppXUWXfxx6jj+OHVC0oTfv6kdp0pRdMeI7vN2m9aJBhHTFWc7O1ztYUkOibi/Pswb8GwI6J9q1zblJig2KbXf8Ld1NHkKU9ho5tb8h56OPFPF6bOwAz/Xcy+69Y/Pz5MqP9Gs5ue8CVT/FQZ8D4UMFcA2F3SptsiM1aBTUN+Hu6rDbRp0TtbtGeLjhmFbZ0KbP3H47xMoVDEvHt14Cj+vABMlnUwvemeVr/9p/lFkZHUt8gMIHnZL5YqF16bizVNP9Ck7XJKLtFlSY8CC7nj92BPl65fErP6LsW/FEehsdRi7aQhK1y6Ohinbw+99uEy/8fw3eS1FMlfrcv3W1oigcNpjsMrE86vWphIGLeqFUzvOY0S9CUK5Z4K14sEsk70G14WRNV/8mFfqX1eg/4k2Pdu8puv/sWsqb5phrwEb4gXiBn6Nj3smaQzLFVD174DQewDbG0p41dFS/0UHxe13KAn4Xeas2Lraefe2VGq+yZkr1iDp+qtXSLWF4ebgiAvdNDHQhAjihCjEuPTyBZMeEdWoN8egvRff8+m/ezu237kt8yC9/2hHjQxhLYRx+ba+xtCUsIEikhMaW07vNx/w+836zk6tAMUOCm0v7MKr7vF9PlGN9zmfe4l2jup/3br1E1fyc98E/MFUfaeI/o/i0h+KbWY5I1421X8OELgTcKim0SITkCp6ft8VDKkxRmAkrIJ0+rUlQoPDkDlvBqF+U+QwTRZL0HJUS09z07FNfxeQdJOB9dB0oCWAmMnOnfP3QWFFow4Pq03ECBWpnN/kq+X1xhtN0oaX693TuGLN8/lomKK9sM8ixrAVfZEivYewxKhpFJPYtWg/5g5aJqrSYzYPMbXYTm47h1tn7uKrZuVNWKg1ZLINYbka6PpbGxF5/NTQezbR2lxMfh2qQOcx02JINWC15vhMPRC3ybFumXzq/JL2j9kKqKEPobICQDsam2xQUm02JUD6oEvAe/riaTgtJdUOKLbxUzmM2exiv9WA3Tuw7c4t2bFg6jTY0Ix4tE8PtqT8gkMsPMU23byOQXt2mRSfmbA1K1gYv1SpFumA/F2kwjMFDxMi3n0IkESMbTsKP+ZLFfVvHkkHGk7PLFwGQLHNCjjQzd0Gev9lgC/FY80d1FkByquZEyfva5EoJ8Q5WRvzcz/3EuM8kyo/0azy/9pNoKpBVq0xPvVmYjLSs6SGMSIIesymIWCbKy7x168bsWzUGlMba/n9mXHSziHL7LuUlqrW1dtWRuUm5TC22e8ICghX2+WcWS6nmGPukjkx/cS4KIHScTkn7tMqWw+x4GCQvcaK0KeGGvYSqh/HoYp3Hyg6d9OQAn5/QwaeQcjQpRcUl4R7A//Uc/n/vL/qvxCq7+8mgoI5WF1PiYuQyxqTz6k5dG6j/xVLRfHDDTeuiVnq92XKW7xc+QcHY975M6L106V4qXhRVGYlqeWGtaLSTJ2hkZWpgQOYM7r43x2LFkd2jxSYf+4M8qRKjcnVayK5gwOe+/qg+bo1eOrrg5YFC2NslWoJ+kIY3UWiIK0o8huMqWFb5P/auxI4K6c3/Jzv3jv7PtO0E21EFNq0iCxFihZLJEShkCVrUiLEXyFJixIiLZZkTVEkiiQVkSSt0+z79n3/3/ue7965d5pp7szcmbud8/v5mWbO951znu9853vPe973eUwqE2IzGQot9gnoGQ8CBR+UM37syQoWIOIGaDG1CyWoqp/K+KkJQkFwjS8YPwbRnRNHiOWkSl9mdrGm3yo1nWwdIeIXQGjuH/G48yhJ2fyrxevRqW9HXHX/8bMkyDB58Y65OLI3BTdOuZY5duyF5DRef+RtafwIYPHe2dVSk3fuK7FJv3bfG44AaDqe+rx4CShw+tH+T3P71IgBgwOm7YHSC/54Cc1aN3Zn2G7XmXrdDNYko0IaZ4+8fSxJpds3c6OiXnIYOGqnH7BCRI2FiLrDjStVlfpGwCjabHoBrPI4I+krjv1gD+5h4qaxixp3g5bwRn1375j2KLB34LvSi8kMzZdezsdjlFVGwqJ1QUhIcUjLd5ZljG0dPZaNGvIG3bryA2w5dBCDTz0NYzp1Qbf5r0mmMyFwWeu2mNCrNwa/t5hjjuzl6xEjWd3dm4WzNAs+hQg5GwYJWFMsHoPaGFryNygLerYAWiMgYTFwtHcZy3NYX2hxM+p9CL7w3avrQSvPTxUIe3sSEKuvkUEftFIgYji0mMcq7LFRsMasR4soZYZNgwi/vK7nT6X3n3nXfHz82pcsc0EkiRRvQ8dlVCjN/cXb52LX5t0Yct8A9L3pWBmP6nScJDEWTXqP023bdm6FGevLiA8pJZ3ETdMOpmPWuIUcp0OxRC9tnIr4hrGctu6pQlxAn70uuXT63nw+QsLqNtBYzxgPFJhK7bazJe9PNTOFPDV2dZ+qESADCMVbgdALIKwnOS6Qu//3+d8y3qd/1Ter4xob/9vHXhgqVqFhap+LWO/rgz924oSYWCwZco1b2VnV6easTT9gxsYNvFGJDQvDxpG3Mds0SWsMeu8dlOilrDv21pVDcf4b851U44GOjRrjl8OHHDFBNqHhh1tvqzDdvjp9qmldTlfPuA3QsyBiJvMz1Sl8IdcUKI0cDWHrKOvQcaeIAeJf56Mwo2AdkDudfycSFkLYqs4oq2k/K7vO2989T4+novsp48fHjR89bSRQtN7spQbRkNSjS6WKtKWpgwzRKN4ms3zMIhLekiyzdVD2/PYvUg+ko8P5p3GKd0XluZtfweo31zmChVfmvOV2nE11u0y7Z/K4ZB3NQp/reyEiuuIUdopb+mf7v9j+3e9Yt3QjBykT8eEJp0j9rrosXy/5Dj+v3oYeg7qwBEhti6Hnwjhiv49gyQstjo5VVPEVBIiiwMh+ESg9ABF1K/O4VFSY74WOvbRYF6PI3XF89fduzNuyGac1aIgHuvdEiKVy1m9370kBvcNXLMUP+/cx+eCMSy7DwCVv8+U2TcOdnbthbOeu7t7OrXoUhL3gl5/wX1YWKHusVYLMiiqvC7biqmHYeTTFwdRMdShzLDU/jwO56Xh7wcBB6HVimYHpVgc8WElPIy88rds6Z2xpDbeylw/FprSP7RwY2U+axKV27i56bqVA6GUQcc+yqKm3EhaU8ePByeCvt/L2JNCzZ5i7BQ2wngSR+KHk/yn+iQjaIRLfdewMOGuk4Atm/RUR7mteVefZkHzDE0P/x8dVZ/U5A89+UbEnigKUH79iGo7uT8Oo525w27tDrMxzH3iTY3NGTRt+DPNzdfpaUV26P2mSUSEv0MAx/XD79Lol/9v6zXaW5pBRR8Dc316oVTYYLaKlxcUo+O88hIVmQFgMCNsZ0himwHcfD5at7TP0l+vlTn++yblkBSJulXFbwnPBuKl5eeg6fzYHA5OX5MHuvVjh3N3yw3/7OO28UVQU+pzcykEQSKSGfUwyQjrieqJ3H0z6Zg0bF3TU9HSfizGkXcXGnLttu1vPrgumQbBHiPTFvvj7L4RqFmQXy7i+CT3Pw5p/9mBfZibGn9sD/ducwl6g3WlpHDwdH141p5e7/XGnnp5xn0xGIWNGS4KW/N0xlxmFG0xCUqLRj5KhDWYRyVsgtKrT/93pS03qePu7V5M+V/ca5fmpAjFvTwIiNCQhUxZHDL8GMDJgHLXLJpAA6k3Qosc7RkEaYZIjQpMCmB5caKmRJ695Ad+8J7mGqHyU/SbCIz2nUH3f+Y+zZ4ZK206t8OJ3lAlRVrZ+vR3P3fQKrCFWPLL4brQ5u2W15jwRH17d5FYU5hZy4Dap2h9PIoNS/CkrjcgRSbOrJoUyxmbcNoe5iygh78mPH6mR98co+QtG6g0sWbFxbVe88kAm+g5LRa8BpWjeMl3uMi3NoDVYXZNuqms8jICe8YB5LGnP5LFARD8KEelMXlq7RvdnZaHnwrl8E/LI3HZOZ9zTtbtbNyVD4YJF8x1ZVHTRpPMuYI2sLQcPYPDSdxz3fbRnb7RMSMCS37bhjIaNcHPHs+sk7qeyjtPR128pR1BUUoLHvyGRWFn6tDgJ93TrgXblBEppgzD205X49K8/EWqx4O1BV+Gsxk3cwsUTlZi7KXsKoKdBRI+HsLWHYZQSuQ8bQ3ZNPnqnQaK4xPeTQ7JFFsDSCCLpS695fWj83v7ueeIZVHWPoDF+5syZg9GjR2P69OkYN25cVbg4/u5rk4CPO1J6Mx06UCKpz8PK9K1k/IDkAkL4YGixlQt/UkwOcdZQdtSF11etB0S3/GTeV5gxejY0qwUnnX4Cy0t4MsV+5Gnj8O/O/dz9Ji0b4Y0/X3Z5VsNbjmFxUzJciFjw5Y3EZly9QmKnNHY67rrirn6VSnLQAvpQ3yfx85e/Mp8PKbq36+oqNktp+zs3/okWpzVDUlPppi9fKPj77u4TsO/3/Ti1axs8v+bx48YDGXkrYBR+CUEUBhFlR5nOR6DkQU85YEFiw1IcPRiKhs1pB0w7yEhoDbdUDxBVu04QMIr/lEHOpNHExQZEjoIWXRYITxlBzONkJe6fmnEykbwDSVW0SkhgxXXydLhTvt/3L657f6lLVZKvIIkI8ppQejmlmZOxQ/clJubKyvq9/+DfrExc2qpNtbws9I49sW4tPt71B85vcRKm9rmYPViVldmbf8S0DfYwAGDwKafhuYuP1fcjj1inea/ybchzRQru5K3yViEGb5asKPmDSQxFwrsu8Xl64c9A7isoRQwO4HY0jWt1XBzqehy+9t2ri/EGhfFz4MAB9OzZExERERg5cqRfGz80CYySf6V3x9oWIsyVmVU/0gvQD8m5wl6Asl2S8wSi+JeHLn6ChUupvLLpGbe9KKSZlbIvFT2HdK0WaWBlE5gWQLsBtenzX/Dk1S/w+fij79zDgqbO5eZ241heguqf1r0tXvj6ibp4L/iezkdklEXWf/RFuG7CYBz46xCny5MnZ9SZ97MxFhoeglc2P1vpcRZ5kLKOZrPMxvH0z6SGV5lGGOLfgRYqjzH01OuA4k2O8ZaWAhTeIcVnyW1eABHzhINErc6AUTd2GwGSggF5APIXM78P4ufKozAiOwztDuS8ZMZ5XAwR/RCM/HcgLE3Yy2v3DrjdWDUrklbXdSuWOkRG6Tjr+jM6OFLMec4ZRpUenvd37sD9X37KRInNY2LxxfU3uk2a+N2+vbjh/WWOzK1He/VGSm4uWsYn4MpT2h2zsTqal4d+b7/B8T2J4RH48vobEVfBkVZxaSl7xNLz83kME8+7gOOI6qOwt75kjyQZ1SJhFG2FQYHOhbQWU0KKK5GlUXoARspFMKCzd+jGby5DpnEWlg29ts74iqrCQRk/VSHkJ38fNGgQBgwYgIULF+KKK67wS+OHg+Xyl4ECm0X4QE6drKjIGCGTXyZyHLToilOfV7+1jhXSSe3cWTndfk8ycHZs2IXuV3bGyWecWOsnvWjye1g1ZzXa9zwVD7wx1hH8TJ6kmXfOZ3X3qZ88gpZntpCBgabrtXzDf/2yBy+PmceemHvmjEbTVu6lq9NHaOWrX7DhREbMie0kWeTxCjE8Dz/pDmSl5bChM2LSVSCOIlKFb9mhBRMZPnjRFL4FCafe/NQwDL2/dsSGet4KIMuJ1yP8Omixj0vjp2g7kEbssBQgGQ7idGJSPC0caLCRjzg9fcxZFUbq7+4hQB81zuTJmWnyNpF2HcWAOZHbacl8TMLPlAhNoypnD3av1aprkWGwNyOdJRtIamJA21Or7XF4aPXnWLrjN8dINt1yOxLd9D79uP8/XLN8CXeUPDThVisKS0vZYHnq/AuZzLB8obgjMmrIw3U8rzMdCb63YxtOjI2r0JCi+1LqPrXpKe81U46kDgNKfiWhLiB+FpA2wjR66L2Vz1wkrnAEwBtOOo5FuoZntnbFoj/b480rh6B789qvvVXPgmNrKOOnJqj52DXLly/HzJkzsXbtWvTu3dt/jZ+CT2Fk3GOia4Fo8DWEpQEMPZ2POuz6TTKjgF48IYNgKykkH/HQJVOw/bs/cE7fDnjigwccad8UoDv+gskcEEzHW4v+monExu5rDVVksNx+1gP8axImvWvmLbj01gvZyLks4joUFxazAdb1srOZRbmqsu+P/Ug/nMmeH4ubmS0fvPwpZt+3kG8dHh2OJfvnuJWKfuTfFKx+az2at22C3Vv/wZJnP3SIrj6/ZhIev3IaCvMKUVqqY8b6KWjXrW1V3T/u3/Xs6UCudNdzYVZnMz2Wls3SQ+w6ZyXonFcBMnxinoYWek6t2lUX1y0CfARGHiDy2uokC2HP8DHb1ZoCujzu5Q9k2OXQ4p6v207V4u5kgHz+159MLxFusWLkyvfZ+GkWHYOswgK0a9AQs/sPQExoxfGA9O4v+nUL6LgsxGLFH6lH0aVpM7y7fZtcJyrwQtWiu8dcSpIZd3yyEl/+/RdaxSdw6r4ngqKNoi0w0q4227MB4QNlzCYXTXKwRd7i4rHXC38E0m9gAymn2IpLPx+GIwWRWHvDSE7t90ZRxo83UPdgmxkZGejYsSM+++wztG3b1i3jZ9KkSZg82ZVh1RcUQEjCwMih+JdiuTwmvg8jb6l0p5OoacLiGmnAkF5W+XT15dM/xryH3nIwMFOsy5nnnVbjJ7Nn214+HuJFzWrBuNmj0PfmC9j4ubrJKJBMBR0rXXzDebh3rszEqqw4ss0A9kpNWi6DvelYacMHm9jo635Fp2OOll4eO4/jfOwkh+/un1Ntg+7HT7dgQv+nYbFpCIsMYwbn7LQcVnI/pXMrDtCubdFTSAtIBnxziXkSWgRJH5QVvWgbkFYWC4SI0dBi7qtt0+r6OkRAT7nUSeOJYmfI8yPfZTZ2oh8FSnYB+cStEyr5XSrx7tZhN92+9YOrP8OKnTvY4KHjJAqS3nroIO79gjKcZPD1XV26YUynitPhv9qzG6NWfuDwFn123Qi0TkjE6I8/xOo9u1k0dMngq3FackO3+1Sdij8fPIAhTgHdpAZ/YwfXI/bq3M9el4WFU4iZmp5vKRD3CpAzQz5bCnROfB/C4jomPf0OoPAr9ggZhobnds/CJS1PQcd6DNAuP1Zl/NTk6dfTNf3798eqVasqbW3Pnj2YOnUqmjZtiscfl8cGfu35IZmD1CGAfgQI6QXETAWO9jDHbwHCr4YWOwkcM5I7D7CcAIQP4viB6hLfHd6bgtvPfoA/7C1Ob46ZPzyN0PDKgx3LPwQ6mpp93xvMt3PXK7dwIDCxOn/82heszXXXrFsdBhcFHy+c+C4TDlJKfHR81HFnEHlaNnxYFvfyScFi9lhNv+01fDZ/DQfAXH7HJRj70kiX+/z9617ce95E5GbmsUDpg2/cWaOZSrFSu7fsQY/BXRw6XzW6USUXSS0vkjqgQJ5YaA3Lxmq/RCfumFwnjS/LydAafObJbqh7eRABo+RvM0PTPOKytmMSUiPVTkJq8OYFtrOBUlL/joPQoj3YA8/fqsu82UjJk+LBdKy0dsRIHM7JwbmvS+blqtLul/z2Kx77+itOnadCxIXnNj+BN0SUZp8UEXncAGu65rWffsT0jRvY20QB2dXxkvybmYELFr3uiGma2e9y9G3lGQFRo3iHZHW2nQkRRh7uIqDkH47BJNZ9o/hXGDm0Rjdn7S6DvL15i+TRmJYM0eAbjx3D1fTJK+Onpsj5yHUtWrRAbm6u42gkLS0NYWFh6Nu3L957T7KXVlV8aRJwGjsdc2kkplcE40g3M+tLQETfD0RcD+NIFxZBlYU+oBGmV6h6LKG5mbk4sPswTjytuQs5IR39rF+2Ead2a3NMMLIdS8rIIgOKXOKd+nXElA89p02z4sVVbFhZLBpatD8Br26exs1e02wUEy9SaXRSMt7c7SoASr+no76c9BwkNknw+uJS2bzjhTPjXvYKiNhnIEI6HVNVJwbYjDJBVzjFBVU1n9Xf6x8BnTS9cueUSVjEvgAtvD+Yvb3wK4iQcx0q7nycWbIdCOnkIDCt/x5X3eLU9V9j3hbiGgPu6twN47qeyz9/8PtOzNuyCe0bNMSk3n0qDdgl/a7r31+KXw8fwoUntcSsywZUK9Yos6AAHefId9wiNFzX/gxurzplzZ6/OZvtnCZN2Xvlqbif4/WBY4KOdJUCt5TWTkzPUaNh5LzGafAi8uYaEV1WZ9zu1PWl7547/a1JnYDO9kpJSeHjEHuhwOd+/fph7NixiI93L4bFlyeBUbwTRt5iqRIccSMbQsaR8rEfJI53LbSYiTWZHy7XpB1Kx/CWY6GXlHKg9DOfT8BZFx4bVzSk4UhkpmRxHA8FOP9vrXtCjRTLk5ddgDZnn3wcDTMD33+0GakH03HBtd0dCu9zxi/C0hdWsr037JFBuOnJa2s93rq6Ae1ud3y/i4/gTu9xCmCky1gQG2W3VC2JwXpQmRMkpYGtA0TCXAjhOa6luhp3sN7XyF8JI5OOZ0nGIASiwfoKPTvM43T0ShKAARACkFBt5G2Sc6V0P0Aaf7b2dZ4F5s5zojn4w/7/YNEEzmnclPtI6eUUqNwg0n1yPsrKslURt0f3XLNnN2c19jm5JQdGk6Dq2XNmoaC0hI2m26vBb+TO+Oqqjl6aBqQ4HQWGdIeWsKCumqvxfX35u1fjQZW7MKCNn/Ig+fOxl7sPXM+eCeTONLNIJF+GiHkcIsIpfdrdm5Wr99t3v+OenpLR2Rpiwc1PVpzdtOGjTXjhllcRFhXGMTmtOlZNM//lom8w7UbqNzDgjktw50wnz4Yb/aXFmAgQyeA6o9exKbJu3KLeqrw1ZSneeFx6HkdN7YLBN74JIB+g45DE99wygOqts6qhWiPAMYMFH8Io/h0i/AoI2ykV3tPIXQQjm3ir7Bs2AUSMAEK6ARkUC6cDoX2gxTsFxNe6d565AXl8KN2dDJXHzzsfI86sffyMvWeTv1kDUpinMuz0M/HE+dLDQ1pjpAd2cnw8KGYn3OY5nT53UGHSQpYlSZAbUDeKTl7dAgrXcCK+TN4IocW6cXX9VVHGT/1h7bMt+eokkFldW+Wxls2VeI94JiQX0HIIy8kwQi+B0CwA7UCJV8TSAiL+pWq71Sn1++5zHwXF6VBqOhEcNmqR7JFnN67nBM48o2ILs+GTvMUeua8v3uSG1mNxcDdl/AC3Pp6BIaMPOAWyf1ChkCHHDfCiaQXC+qmUdl98sLXoE5PgkV5f2vCy4zH7/US89A6aRST/XO04vlp0za1LL3lrIf5MS+W6lIL+4y3HT1xw66ZmpZ4L5mJ/tlRrbxwVje9urnsKAHf6p6ffDRRSgLeAiHsJIuySKi/Tj5wrWZ6dimjwHWfu+lLx1e+eJzEKKs9PTYDz1UmgZ06SmV7s2ZkCEWFPr3QdpZG3BEYWBXyTaJ49u4R0hkZAi6k6rbw8ZpQdtnfHfxxXExnjHpOsO7jPf3Qx3pv2IccJtTu3rdtHZRXdu7ioGP/9cQCNTm7oUekNd8bhTh1SvCfOIUryuWVycwy+aaU0akQ4RIM1ENqx6a16xv2m8UO5+ldDRN0OI3eB3DFSnIBwPyDdnT6qOvWHAMf+ZIwxM4SqaFdEQyRvqjEbdF2N6u7PVuHTv3bx7Ts3aYq3BrlmKNam3We+XYe5P8vAf9Ite6Rn79rcziPXUvylcdgpA1YkQSS8UWXGrYz/ml3Wh4gbocU84pE+efImvvrd8+gYDV/I4/bkiDx8L1+dBPrhDmawM9k1J0IkfX7Mgph+OAMhOechPDJfsgBLXU35oY0cCS3ad9KjKTZrzeJvORvr4hG9K1Vmr+rxUlDz2C4PY+/2fYhvFIdXf5pW7ZT2qtqo7d9prN+u+JGJE0laRCvdCBRvB8L6QlhPqPD2+pHuUgaBiuVEgIydkr/lvznTT2Y0quJ/COipVwHF8linymI7G1qi1NzypUJkga9v+QnFeilu6nAW4sI8JyRKn6gN//3La1j35ifUS2CyO9jqRwcCJURLQUdYwmTUp5R1WYyCz2DkLZPB65GjHHpZRuoggLx8VKxtoCWZckTuNFpPdXz1u+fJ4SvPTxVo+uok0NNuBopIKdh88cKvgRYrA4v52CtrCtL3fYTS4kLEJZXwi2cJ7wDo2YD1ZIjYp30+nbZLGXZ6AAAgAElEQVQmE33zF1vxcF+pZ0asy3fMuAmX3161O7ombdXnNVIh3CQ7jLoPyKH4KAqMpXPCztAS36rP7qi2PIjAscaPBdAaOZEe2hsLgYifDRFqp7jwYCfUraqNgKFnwiBqitJ/5LUiClrDn+UaXLIXxlHSEqP1meQs/gcRfhn/TT96hWn8GIDWFFry2mq3XdcX+Op3z5PjVsaPnxo/hpEP4wgJnJqxAMQPETuVNWUMyhTJJs0rGTi57uMYZGfEof+97wekweP8CA/9cwQ3tSXhSAMlJaWYvm4KTu9+bIBpYX4hfv/xLzRr08TnPEMVTUm9JAXIelSK2cZMhkHabqwCbYOIfxUi1D1hWk8uHupetUdAZnhdat5IAOEjIEJOA0LPg1H4HUAB0CSTEPscoCUC2ZOA4p0sfSEivJPR+NPB/Xh6/TrEh4fhqQsuQnLk8bm5qoMScQXN2rQRNosVd3buitgw381ipLhKI+9NgCRpUAQR+xREuJS3MYp+gZFmP/qzsm6biLwBhqEzBxCynyK6doi45yFCe1YHonqpq4yfeoHZtxvx5UmgZz0P5M2VAIb0AIq+NWNHIgEj22H8jB/cFgPGPcJHLMFQfv/xTyZCbN+rHTpdcqyYIQVuj+n0IP75bR9CwkMwc+NUnNTevWyN2uJnkOeteAu7u6E1BPQDkvm1XMyOQRpPBWsBayuIkDOhp91qPl96xKdAS3ofxCYLEeZzwa+1xSiYrmdep9QrzCFbIWImQEQMqxACI+dVk+Wd2IMB0WAdhKVRvcPVae4spOXnM78OEQO+1K+/x/ow6L3F2Hb4MB/R9znpZLx62UCP3dvdG5HgK8UvUfbYxSe3qvCYjWSFjJQLWUyY2Zzj34QW2pmFSVH8MwyRKDUWC1YC1tOYsZtkiIx08th/L1n5E5dCWFu42616refL3z1PAaE8P1Ug6cuTgMO1ijZwdohRsBrIJ4FAyZiKsMuAop9ghA6AEX43rCE2GMV/yEyDkM4Qon7TQj01YT1xnz9//ht3nCODvelobNgjgzH88aGeuPVx78EZPUf7mdpOAtCaA/q/MlU2gRZCKbZKgqVGyiUODSgRPx8GHXuV7JD315KhJZOhq4o/I2CU7IOR/6k8AqEj7JCzIeKmV8rZVN74QdJaaNam9QoBMTKfMnO6fZVBlybN8M6QqznFfeWu30Hkg6TGHh1aswD8LvNeRUpeHo/p1KQGWDWMNK/qt4z++AMQASIdWN1xTmfc2+3YY0Zm0k+T9CEGbNCi7mKyQj3tFqBonexw7MsyxMBCz4i4uR4ACr80B0Nxl6OhRZOX2veKL3/3PIWWMn6qQNIfJoFR+AOM/BWS9I6OugRR40dARN8DETFIvqD5H8PIJOZg8hL1hpZAjLPHFgrCJeXyvTv/w5V39qu1UKenJqon70Ps1defPAYFOQVM1jj100dxzsVnerKJCu9lFG6AkX5jBX+zcfaWiBorn1XJblMOgf5Fi+SNECHdYWSMpT9CxE2DCOtX5/1VDdQdAmwIp5xvJi2UQMS/BhHqmsVkz0WxMw/zNZkPAkWbACNL0lzEz4EIqT9R243/7cOwFWXs+HZpixd/2ICZP25kL8kZDRth2VD3j+S+3P0XdqUdxYA2p+KH/fvw8FdfsFfplUsvZ1LD+i6nv/oSq71TOT25IT665nqXLhh5K2BkTYJuFEETOgpKLFhyaDJGdOgOHD2/rC6tw+SBp2NL2ozy5lR67aiIuBd99j32h+9ebeeFMn6qQNDXJ4FRtNU8W7bK4Do+/lpvHnlpEMmb+VhET7/dFM8zX7yGvzGZnlxQHwJK/uKP77JZBuY//DazD9OR0NJDcxEe5bnMjdpOWE9dT7FB3674Aa3POhln9q65aGt1+mOUpsJIOY/jA8qKJKJE+BD+mCFsIJA9DSj+3qyiQSS8yTIXeuEWXkxFaHefYPmtzthVXVcEmJ091X6kc6wXQG5WHpH0BxTkHNLRcYOyTE8BhHSBlkC6UPVT/kpLxcVvLXQ0dlJcPL664WY2iMgwomIVGnbdeY9bHfp41++gNHliaY4KCcW3N90qQ4RJ2N7qHe80GV9LTHX5ib3OdxE8ZXb1w8RqX+iSQTtlSw881vsqIPOuCsZtA2ynAcWkWG8SWMZQfNAQn8lcK99pX//uuTW5qvq2q1T34yPk65PAyHsXRhYFN5s7itBLTCOH/m2RnCBaFIy8t816FskkHHEVp2LCyAGKfzOv17BgxjgsfeFrTsOmUhP1c09MzEC7h07aPXlvA5ZWgH4Q0DMB8spRzICeAxR8INNlQQs+7TrNRTJ6MrTIa2HkvWPyNQkg9FJo8RTsrIq/IsA8MZTiTjpeCJPM3k7Mz/rhzoCRIecEp7eXkX7qKRcBpWRoaEDY5dDinq1XGIh3h9LaQ60WvD5gEDo1bYYP/9iJez7/hPtxdbvT8fSF7mVYkkbYgl+2oNSQx/VkSJFB5c1CR3g//LcPESEhOLOha0wVGz8pRDuRyiKsdvqQPTkNcPJJy+RxNWdh0qaG/iNTrlQKUect5Ocmou6BiBzhzSFW2bavf/eqHIAbFZTnpyrrkHR1KLbGR4tRelgGTOqpzNyM+IUAGUPEG6IlQkTeBBExRI6BPEKlKTAsSUD6rRWMSOBw7krcc97zLBI66O5Lcfv0m3x05P7TLdeMHgpqfRQi4jrHAPSsp4A8SlV3kjUw6e85FogCnlOvBYqlkCQZSFoj+miq4s8IMGs3bTwsJ0DQO+lU9JSLgdJ/5QeUvLnEAGwUQURRjEgJDKI6oKDZ6HE+k8FJauxZhYU4I7mh2x6N344cxtCl76KwtITJERcPvpq1u3y5GMW/wch6FkbxLuhGJne10DYUUUlTJLN+0Q8cv0UbG6NgFYTtDLfYn31pzMr48aWn4aW++MMkoKMrlO6VmUEiVKpFZ9zm0I8RiVIygTJLiCiP+CnAQotORh0vpPcDoRfAKNyEYuM0hEbWTwaUlx5tlc3Oe+gtrJqzGk1aNsTTn01ATCLFUlW/lI/hKZ/RQ4soB0+SFy6kLxB6NlC4luMB7Mzd7DnKecH8GJIY4rzqd0Rd4ZMIMCdMxn0cx0M6fLRpMUgDqvSQ/IiSUVy0Ub6v1rbQkshLGDiFBFEP5GRzgDMdf/l6MUr+g5E6QL6vIgqIupe1E4Wg0IPAKP7w3ast0srzUwWC/jgJKPjZyCROGPIkCIj41wE9DUYmMTqTG70bQDsWdrnTLisUosFnHP8DjkOg66xA4kpotvoPOKztpPbE9du//wPjuk9w3KrnkK6Y+N59IHbmg38fQXLzRISESQV2kvyYfd8b2Pn9Llxx16W4aDjF9bgWI/d1lGQtwt87E7D5u6EYdO+VLtIbhp4HSp8VliaSCbZoE4yc2ZK9O4YM1TCg6BtAzwLCLlFyFp54yD5yDz19NFBIGUKUrBAPUHYQZ/aRsdNavouOTL8G0JKJ3FQVbyEgJYMmlXlq42ZBC7uQu8OxWuTtCe3h4t31Vl9r2q4/fveqO1Zl/ASi8UMEiLSgUlYIxQBRFknOi66MsZGjgOLdgK01RPhACGtL6Ol3AIWryxARDSCSV0OI6gU8f/jKZ/hq8XqceV47jJh8Naw2/9sRrX33O0wdNsOBRYcLTsdTqx5hVftdm3czMeIrm5/l/6989XO8Mm4BSovp4wW89fcsNDzxWKHCOzo9iL9/3cvm5oXDz8N986T4I8cRZD8pY4KI/yd+DnC0L62kUpMt8lZo0e4FkFZ3AVD1vY+Ann4bUPiN/JhqCYBoBJSatAaW1kD0eFPVvRQIvRhaPLF7+14p1XVMWbcWa//Zw+nud3fp5vbxl300Ww8f4vihM5Ib4YpTTq1wkP9lZbKHqFFUzTyxtUVOL9oOpF1ZdpuIUdBi7odR/Lv0CHGhTec8nyQwdGf8yvhxB6UAr+Pvk8Ao/FoaQvIzK49NiICLmaFJ8CseiLoDwnaqZB7Nc80cEfELOLvI3fLGpCV464ll8vXXBEZNG44h917u7uU+U48YoEedeT8O/HUIYZGhmPXTNKTuT8P4PlJChPiBRj8/Alfc2Q/vPfchFkx4lz1AVObv+B+at4mG0FwDNwfE3oD8bDJogFO7tsZLG6bKp+KS2m4DIm8xxQ/peVmA8KHQYimoXZVARICZgokDRqdjr4kwcueYhJYCIBkao8glqcF+jO1rWHzy5y7c9dnHzPlDZcmQq9GpSTO3u3k0Lw89F8wBcQnpBjDrsstxSUvyfJWV2Zt/xLQN63kD8dxFfTHo1PrJ1HTug6FnwThipxegtLTB0OKmMiO3kW6PkaTYPhKcHuz2+H2por9/99zBUnl+qkDJ3ycBKX8b2c+VZYNF3g4U/w4Ufe0U80OemVIgeiqQ/bATIhaIBl/xUYy7ZUSbO9lgsJerxg/Erc+68mS4ey9v1yOPTF52Pous0jw48m8Kbmh9J9uMRAXwzBeP4aw+7UG8QY9f+RyIPHHYw10x9KbFgH6Es7IkaZ0M4Fz6wkrMuX8RLDYLJi0fj679KZ6DWO4PmynwlPFiAaInymweyg6xNIKIX1ip4Km3MVLtexYBSYdgZ2LXmAJB2DrAyHrMccwiEj+GsLXxbMMeuBulrVPGV6lp/Lwz6Cp0aSaJO90p244cxsB3pUadTdNwV5duGNOpq8ulZ8+ZhfQCuYFom5iET6+r+6wpjpUkz1xIZ8B2FpC/GEb2fMD4jzgqIBLfkTGVlMGXPsZcW8M4PkvEPeezLM7Heyb+/t1zZ74p4ydAjR/KJDGypkophdKDMm3W1knSrBdvlzsUI9dp9DYg/Eog305gRgsvqYVLT4e7hWJflk//mKuHR4Vh3vbpSG7umsni7r18sd6O7//AumUbcXqPU9Djyi7HdFHPftEUIDUlCJI+5SNFe8lIyYQt1IbImAiXa42Cr2BQxpftDIAyRgpXyb/HPAUtou7Zp30R62DsE7+3xAVFSQkwIKIfASKukZ6foh9Y+kJEHkuUSVpS/D6HdHVwQHE2GZFiaq5zra5wJY/No2u+xDd792Bg21PxUPde1Tr2outHfLAM3/+3D4nhEXj/6mFoFhPr0t3h7y/Fj/v/Yx/2FW1PxbSL+tbVcPi+HNx81J62TxvE8ZKHy5EsImMqnb3j+mEirzxgJid0qlceJk+BoYwfTyHpx/fx10lg5C6EwS8pfYQFkLAMwna6YzHiRZYCnNOvZ4JD5peh4xaKO2H22EYQSe9VWztI13UmDyzML8L513T3y3if2kxXyf5KwebkxQmBSF53zPHX8e7PpJNHznKqEgLRcFu1PiK16b+61vsI8DFYPr17JwLhgyGEawaUUbRFEuaF9WHpBCN3kYwZo/c87Arm/TGKfoRBUgsohIh+lEU1/aHQkdn+rCwkR0Yi1HpsrCCl0r/56xaEWqwYfkaHCut4apyknWeQsCzpc3GxcswVCj93oqWgmLyboZFRZI/fO0IcTWS8UnLJOdAS3/ZUl+rtPv763asOQMrzUwVa/joJjJxZMHJeMQnzAJG0usKjE1KHB+nU0FYqw35eLYnVWKXYelJ15lPQ12U+pbw3YZT8DkEfLk5Vdr+QMKJxmMRYiSiNigbRcLtidHYfwoCuyVmAaXSMbGGhTNFgNYy0m4ESYg+mEg6t0VboRy6UunFcFC9UTSaFnnIZULqnLGRAJADEpp1+F6D/bd5SQCS8xQzsLFKb9awTO7sA4hdDI+oKPyv++t2rDszK+AlU40fPgEGSFsVmOrsABFOqSwVmJt5jbpE8iJgnmEbfSLvKCQ0BWJpAa7C2OvNJ1a0hAgZxusAGYUmEXrAOyCCa/EIgegK0yDJCxBreXl3mpwhwJmDOy1K3L/R8mQ1GBIfMAg6IhPc40Ba5L0tPQ+j50OJnQT9EgcCyDnkgtUbE4u69ciA7C4u2bkFCeATLRYRYLN7rjBstG4YO4zBlm5lcaNYOEAnzmVBSz1sFZNmzL60QtEaKaMn87BJKQLklvhmfVRUEyvipCqEg+Lu/TwI99XqgeJOZ2RULrSH9TLQ/IyQTKR3PUPZXBP37e6DY/B3XskGwBlgZ4yp5JoTw7YXL36alnjkFyH9TpsfG/q/MQGX6fN9mu/U3rP2tv9LTYzd+5bEWCj6U7612IkQDOpIJBQq/lqR7zAEVAp2kM4p/le99aF9o8S/WydDJOMsuKkR0SOhx52qfRa9jX5ZkQ76pw1l4uMexXFh10sFa3FSnOCtmXrdAxM2CCJOipUbOazByXiozQBNXAFoSjJRerq2F9ISIn3vMsWUtulRvl/r7d88doJTnpwqU/H0S6OljgcI1crG0NIPWQPL46OQqJ2OHY1PsOjSlZtDzCpNg7QyIxKWSdE/PhJF2oyRbC7scInaaX77U7rwU1alD5IT8MSKmV1JuprP+wrWApSXLUlRVXD9utM62gdZABoyrohAwCjfCSK8oXkeYwfBDwLEpGWOY5V1EjWO2YSLMpFggIcKAyBHy/x4uhSUlGPHhcg5AJnbmdwdfjejQ0GNaoTieNjOnO1Lge594El4fOKjWvfli958Y/+XnCLdaMbv/QHRo1LjW93S+AR9hl/7DXh1n+REpKXQ1oB9gTxsbRsICPfNhIH85gFggfgZEyLl+u3nx9++eOxNBGT8Bbvxw6iwF7ZEaeNT9EDbJm+HgFqHdYsmuMhSIVI1eeLtbPZG0aVrDyH0DRjYJKJpZTKbmlDuTLJDr6GQQFv0ojcWIm4DCz2R2HUp510exUwYTTKZCEJ+StZULHBSXJXeRpns95DxoCXMDGTI1tmogwMdeGbSB+bLcVTaIqDshom6DnvU0x5nZkxukmHFMNVqpWdWv/9mDkR+t4JlLpINTL7gIQ9qdXuHNntuwHq9u/hEhmgXzBlyJHifUXjqn09xXkZqfx5w/HRs3wbKh19ZsIDW4yjBKzMSQeFfPuJ7GGyHyvvlzUcaPPz89D/U90CYBHVuhdL/kjzFfUD1tlMlNocnjLyY6JIkL4vlZB2FpIGnbM+83PUUCIunLoOae4V1hyW8wSHAURXK2WU8FSnaaM88CRFwPFG4ASimbjhxsjaElE79SWeEgyVRKZS9moUo0+AJaOXJED01ldRs/RYDiwQxScncEwcu5JBKX8bupZ/8PyJ1vGj9WiOQfIbSoOh/tzpQjuOwdMrpkefvKoejW/IRK2z2ck4Nwmw0xFXiHatJZOkrbm5nBQqi9TmjBRlVNilG4XgrFUnZd5GgZt5NP2DYHSBg6gDS73MUn0L57FY1beX6qmA2BNAkMUhlOvUYeXWlNIZKWA0YhjBTisTCDIxNWQBR9A6N4mxTrC+3JCPEOlJhnC78HrCfINFt269rcfZ8Cqp6eOYnJzlxK9BMydoepA+gDdSKgkxfNXqxm5pZrHA9xiaDkTyCkU718tALqQQTJYPTcRQCns/PEAqKnQIuU/E9Mj5A5ASj9GyJqLETYxVWiQmsBxwRZWkBYkqus71zhz9RU3Pjhcva6kIwFHX+RJ6e+2ZZ3pR7F099+gwhbCCb2Oh8No6pv8PGaeJjYmmkDQ++lXeyZftYgou6BiBpVLXwCoXIgffcqex7K+Akm46dwHYx04v6gQvTrpCDdAEYGaUyx8xoiZhJEhHPWVxlAvFDQDpTYi1kaIxoicQWEtfYubH9bMPRD5N63e3zaQsS+II8H9VyJJ6twlyuRY6BF3+1vQ1X99QEEOIbnaH9AT2EPoUhaWW0OLvswmOMrdZB53G0F4l6EFkaeJffK3Z9+jFV/7eIYHosQ2HHH3bD5ePZWZSOTvFqUim43epxrSoZtLdZudLqHTyDUUsZPIDzFWo4hkCaBUfKPE1upAZFAQpqtzUX1iDRmeFGtWM6C0+OPXuqKaOgFksY9fIALk3EtYa/zy//asgfTR78Gi9WC++bdhhPbuU/DT53TU6+T7Nm0aEbeAhFxA1B6ALCdBuNweycSNHMo5GkjqZByhHV1PlDVQMAgQJpSTF1hawuhJYAZnEv2Atbm1QpoNojhPdX5iMgCkfQFhNW9d+CJb9bgrW1bQUKmFOD886gxfPTkr8XIfRNGzgumkDCNggwh2tzFQCS8ybqHwVYC6btX2bNTnp8qZnUgTQLe5aT05uBnKiJ+EURoF8hFdRtgPYV5Zsp2iLpMhxdhECEdpXbN0cvZvS6LfcEjwrUw1rKiX7Hr3dLIp9eLW9rfi393/sfBim3OaYmXv5cio+4WZsfOpxTXGBiWE4A0ysgpYgkRkhSQpHM6EDYACO0OEXZZUMYOuIunqlc9BCjL0EgdIuPJtGSw2KnFPRkZQ8+GkdLD6WMPiLiXIcLsMg7H70t2YSGe+W4dDmZns3L7mR7OsqoeEhXXpmP66tJE8OYu7z3W4jJCL2FOH38PXK4ploH03VPGTw1nQSBNAqOYAnTtKaYkZzEaWjSR6VVc9MzHgfx35S4oajy0qFvBiy7pf7GWUDpQRLxBMgOMWWep2E6HlrgUHFxNAcBaQw7MPF4hWYxfv9mBkPAQtOta96KNN51yF/778yAvkK06noRZm54FZ3DQztrS2CUOghZEkguBrT1E7BMQQqbzGoXfwMi4n+Om5BEY0QYASFwJUbyZvihAWD/l7anhu6cuqxwBo/BrGOn2WBQiJG0JEfcshI28jlUXnSQyiOsLBcxRI5I+Zm9SfRQyniJsNlg0V9kOT7Ut5T6eBbREiITXj8mw9FQ7gXyfQPruKeOnhjM1kCaBQcHN7LmhIFwbROIS1vuq1PihQEDS+aJibQstya5xI39lFO+CkXa1yWpK4ol58g9aA4gG38r4oqL1si1yH4c4a1a5tvrSmLlY+eoX/MtR04Zj6P0DavjE3Lts+4Y/8PzNr0CzaHjozbvQqmMLGOS9Kaa0derv2xAhHWCU7oeRIsnNOCYq+gEWltSLfgXSrnEy/OjvNkCLZcZXh4FkkEFUErQ7SPeehqpVXQSYqoIFN2l+mfEqJHeRvNltElKD0rKLf+ejWqG5CohWtz/u1p+4djUfmSWFR+DdIVfj5HjPGlwcz3T4jDL+srBLocW94Fb32CNGSR0kBht1q8MYpE0RGZvIWyrXB0r0iJse0O90IH33lPHj1vQ/tlKgTQI+rin+Se4Urc0qRIWPt4hSP4+ywVJlnchboUXfd0x9PjIj0i9SIs8cJ/ltYp8BQrrASJGZYpydUoVC/BUJNyI3Q6rMn3zGiXjtl+dr+MTcu8woTeEFUlga8gWuRo4FCB8KYW0Dg7xa2fYjMeJWGccLo36ICAzzXRuzdYeIe8oRM8XGYfoIYpQEIu+CFj3Gvc6pWgoBNxAggVMjY7yThhe9p3d7dZ79cuggbl35AfKKi/DMhZfg8janOEZyJDcHXee/xv+mQOnhZ3bkLC1PFaP4d5AsBYgUko/2iW7iOmgxj0p19syHeDMnYh5jLa7yRU8fIwlKqYSc41Bj19PvlvxdjqBoC69xInygp7ruc/cJtO9eRQCrmJ8qpl0wTILyELgqwlsAetGJ1bmKYF0OwKRIIBHCwZhGygWAni4NItIVixgsDQ0S78yaBBR+BdDZesxjeOqaGfj2/R9YYPWq8QMwcmrd6VkZectM5XWDM95ExHVmf88H9AzpzbG0AEr3yQXP1hko3QVYTwNin4NAiYyZOCZDRACxzwLZRGpIxhUdAzoZSIkfQ7PV/ZGez62kqkN1hoBetB1IcwpeDr2Qtb2qU0gbzMhfyXF9CL+q2rEyzm1du3wJMz6TL4r4fH4ZPdbx57ziYnSe9yryi0s4QPqhHr0wsqNnRD/1nLlAznOyLXpf2YMLIHoytMhroaffBhR+IxMRRCK0hsRuDxgFX8qNnuUkoHhnGTWF1hBaMnmtAZfMTv6NBhH3kluUAtV5Dr5UNxi+ewFv/GRkZOD+++/HBx98gKKiIrRp0wbr1q1DRAQd01RdgmESlEdBz5oC5L3tiGERDb6uNAPseAiyWGf+BwApw4de7FhUjYLVkrWWXfaCmZBLRDesX7aRY37OHdgJWh3FA/BiRun6pXtl17VG0JLXyYWQ+7sSsJ4MI+POsiMtW2doiW/BKNknj/n0o8yTBH2/OXwz8FtEmEGkZuxPeXBsZ0NLfKfqSadqKASqgYCeOdGMzbNCxM+BCCXDvOJC/F3E1s70FJG3MW2FkdLHNOSFGfjsftq7cyuGnoEXv12AN3YUIbvYhmYxMfh6hJ1aQ9bceugg3vz1Fz7uGnV2J2aGrk3hd7Zkr/TqON5Huqf5DrKkzxroaXQEL99zKiLhHRiWlkDKuQ5ySIT2Mg0k2hRNgYiQPErSI/SVqYOYDEQMkvw/fpzhVhXmwfDdC2jjh4Joe/Xqhfbt2+Opp55CXFwctm7ditNPPx02m3vkfMEwCVwWMCY97GNy+ZBmYsU7SXYvk+FSgwXAKFhrcgvZjZ/XIUK7H/M+Sm2dfTKOxoMxCXr6XUCh1DhDSA9oCXOk8UPHU0VbOWDbyJnOLK+8UJK3J3wgdPLo5JLb3gzwjn8LoGOzvHdlPyOuAdJHlvMIkfeH2LIlA7SWRKKUqigEPIeAfE/2SIZwitGjIx9re0A/LDcZJfs5rgdEYZH/kemNpCOh4SYD+R9mZ2wQ0eM5pq18MYp+hpFxhwzuj3kSgj2bJUD4NUzMySzUFE9oZCKjKBaTto3D2K6XoE2iexloNUGDDTk7wzqPXR6bQyQARpr8ObQntPjXYJTsgXG0r1N8VBxgkJfXXojd/gaWDOHjcKf1hj3adBxG+n1+rNdVHYyD4bsX0MbPqlWrcPvtt+Pvv/+G1WqtzrN31A2GSeAMjFHyt7lI8NeaFwQt5iEX7PSc1wAyDijAl9O5L5Vn4G6qvTNbdPY0aYCE9YWIurdCI6pMKDAUImEhRIh0kfORGnlmCr8FQi8wgw+P/3y5z3nvcCwSoh+GyCcPjM5yHnLxTpUcRpTBRoscZWyxhycawnqybL4xDkoAACAASURBVDf/A7nD5GKDSF4HUU6KwshbDCOL4pUoA8xkzUYIoCVAxJHYYeVB3zWaoOoihYCJgJH/kSlBA46x42NZFto0jW8+hnX6WWtgbnLsXpITIBKWuNBd2MHVjw426RvYoii7j7l5kEfJj5m/Jw/SKxBhF7quG+njZOwMGSdJ70Mz4+1q+gD17OeB3NfLNiNRd0EQaWtoH1PrDBCRIx06ZzolLrDuHpUKvLMJ70EL6VDT7gTUdcHw3Qto4+eBBx5gT0+zZs342Ktx48Z48MEHMXz4cLcnajBMAhfjhzIbmP31d6ntxVlaRP8uCzMYH+lYDj/BSu8o2Q0RfgUQMYx3YUKLcxtn54p6yQHpQSndbf5a3l+Lk0HQRsGnMDLudSy0iJogtcq0EPbkMNmQkeeQijCKd8JItQcnVsxibRR8DiOD0v4pWsFixii5Kk/zDjt/Ceh+InxQhartHHTJBHJOWTjUowbrHcHVNQJFXaQQqAIBSbxJ1BNUbOyVARv5dioK+j2pu1N6eyJgaSb5vWiuiiSI5PUVbmDkO9/VSVuMNhrmPbUkaMkbUDbv6ciJtP8+cyRU8GaF0uop0cJeQrpDS1hgrikmZYb1VLeJFuU6QB7k22RsHXmHk76C0MrCGSi7leNzTAke9v7QJoj6XkAeMNciktZUmgQSbJMvGL57AW383HLLLZg/fz5efvlljBo1Cps2bULfvn3x6aefokePis/FJ02ahMmTJ7vMdf7oBVExjHzJ30O6P6Tj5VRkKil5L0xpB8ffnHRxRIx0v4cPhhb7dLWR01MuLRMD5as1iOj7ISJl/AAHaKbfXHHAMcfiUIZaPhA2ECJ2GhtyVRk/evFvALvQaVHXIJI+cmGsZhd7ziscI8R9qUQ40sh7G0bWU04fHA3Q4iEafBPQqbHVfsjqAo8jII9lZ0vj33YmRPwsGJmPAEW/A8YB+b6EDYGImQAQV1XpHrmJoI0CxbiEdquwT0bRTzDS7IrpdO9zgeLvZF2T/4vfSzKkiigVvCeEU2C/qyiy2URId7lRIUFWptMgLynRb6xwubaiDvFGhTzHWmPO5hJ0vKcl89EUU3cQ23o+eaImmkbgIKBwjUzpj30eQouEkf8+jPzP5HE1bdSibmctQ1UkAsr48eGZ0L9/f9CxVmVlz549mDFjBpYvX459+yhrRxby+pAHaNq0aW6NLhgmgTtAMAEgeYMsTWBQAKDLLu5CoMiMoTGNFbtbmT/6lsbuNOGoox/uVpZiT7+NfgwiYphjV6rnrQKyKO2+ksBip9ZE0qdsxOg5s81jr04QsVNdDBGjcCMMjtUp5lgJJouztnLcheKbjCNdzEW6LH22woWZ+FdSB/AHheRCKA5IhF+rdpTVmgGqck0Q4Di8go9lxiJ5Jk0DXXo1SVNOvi8i8eMqDQzn9onOgjX9OH1c56MxEJu0UerYHFGws5FDWWYlEJG3M6kp8+NkPSmTHuwcYNyBWCB+DkDJAy6FeLQo5uimSocvN1/keSZjiSgphkBE3Q0jpVfZkVvsM3IDYmSWuw9RVdztECrVs54F8ubLOlH3QwtCAdPKgA6G715Ae34WLFiAiRMnKuOnJiup0zX88U8bbrrUw4CoMUDO/2SN0IskK7JOO0ta2KLlh99umIiGEHHPQIRSVoV7Rc9bBmQ9IiuH9IKWMM/lQp1U6CnAk4s9W6QiQ4jictZXyVyrZ9wnPxpm6rpI/tnFs3MscVo/aHEU81RxMUoPA8U7gJCONT76cw8pVUshUDUCzFBO1BLmUZUk8DyW5+Z4d+I5XbiOkwEq0rqSqeSUTUXUEB04q1HGID3oqnNnbQMt6WPoWS8AeeSlshfzuCxxGQQFZ1f2bhEHGRs/5Hm2SuOHqCpSLzevIANnDB+No+Svchp7Fojo+xweZP1w17LAaMvJ0BoQl48qhIAyfvx8HlCae+vWrTFlyhTceuut2Lx5My666CJ88sknlR57lR9yMEyCqh6zFES92KxmZckGGMUycDHsYiCDjqDsxocAIu8Acl8t+51TOnlVbdn/buiZgJ5dFjfAfEEGGzJ6+migUHJwkOQEudlh5MhAzDyKIyjmBVhE3+vWIi95jZ6VhhTpJFUgQCop85+RgcvxC1jBXRWFgD8gwJI0GaOBos0cOyeTE6pOMadYH5T8CVhbVXrMax+/y1G1yZFj5K2AkTXBVf4mcjRE5CgzbtDOTN0YiL4DwnaWW+8Vc/NkPydlaGL/x+8kH98VfgJYmnIaOwV2G1lPA4WSNZ6L5WR5rGbGBel0DRlJVCKGQYuhgG1VlPETIHPgxx9/xJgxY7Bjxw4OfJ4wYYIKeK7ms6UYIOMICaKSgUExMfaAR3sslFO8DxkQUfcBORScbP69RsZPrhRQtbYGyG3PO0ji35jMGWIycLEUIuo2F88O64lRPeF+dp88LvhQqrKHD65UlJXq0UeDBWJp8dWPSBHX4+xUqwm1qq4Q8AkEDD0dxtGBxPAnNwQUA3cc7S82SDKI4V2HiH2aEx9kVuaDQNEGwHKizAqNlOSlxpHOpofYAkTeBC16PFh7kDjGSEiZ0undVJkv2zDlACLCYdgxmSp7rDdT+psp51PGOM0p7AWfyPWMNfhMbUKfeALe7UQwbPoD+tjLE9MnGCaBOzgRwR8dDRkUzJztGhB+zPURNwN5lIJqlrg5klSt4EsZiBnaRS6ATozQzvcwSo/KAGXiEqGMFMPqxLyaDC35W3e6XKs6nFFW9CMEMVCHUqZLWdEzJwEk7spssfEQyd/XiO+oVh1UFysE6hABI/8TGJmUUWl6dEP7Qot/6bgtGkaB3PAUfAaj8FtmQK5MKZ55g3Je5feb43y0COgpF5qs6hoQ0tWRDVabYcpYxT84VtGZlsIo2gqj8Av2DIvQ3rVpIiCvDYbvnjJ+qpi6wTAJqvP26oUbgMyHAd3Ol1H+arsXiNzqoQDRwIe0l8SJbOyUcPo8xQUZ6UQoRlpgtFMs08kx8pab7nLy4gjA2qGMYySkM7SEhZV22Sg9yArsnGYfebNDYLQ6YzQKN8BIp6BLydwskr5wyXrT08eaJIn0YbBCNNymdo3VAVjV9XkEJN/XpS5JBSLxIwhbmeek/CCMgi9g5MwBSn51/EkkflhhjFBFAOhHejIxo8xWO6vO2NBZ54tFYckzTetR9WOgfP4B1rKDwfDdU8aPMn7cfk1IDNRIOc+RDo6oeyQLsl0DizhEiBOI3NwmmZpIWs0enLJUWSkOahSsNLmEpAq8lixTZ5kvhDxEuTPNfhmAtaNkqCXCQTJojsP2rKf0K5Ou4HN8ijmoujCdQeHnQOkRkLCrDOiWXCYc4+PEQM19pOwwPR0i+hGIyOurbkDVUAj4KAJMOpo1mTmsoCXxMZSIuAFGDqXOz3UrS0wKA5NMhnPiAZEdzoYIc0+8lCksaGNFx15ECGprVyvEKNYJRd8BlhMgbG0d95JUGfaMMsowU+/wMVtYIRDoFC/K+FHGj9sLjFH8xzFZFZRtYeTMoDMsiOi7gdJ/YaTdIBdBIi1LXMEByMbRQZK7R0RCJL4vA4xZaJCMm9OgJS0Du6I5/ZU8LsQeTSRl9riicJNROdbR35yMXKQeTEfztk0cWmD6IcoUMZmVQ849rpfIeeBG7uuSO4RilkjkkFJzS0kW4Ey5MxQhLjjJhYFii6oOHHUbYFVRIeAFBFxJQNncByJu4ffZyHxMxsyED4MWRXQQroXidCjl3aC4GWeBVapmPQMicbFX+K043sdB1kobmLkQpN3Fx+0FMFKvkpsvrYEMgq6EbTotPw8PffUFDmRnY3y3HjivBa0NgV+U5yfwn3GVIwyGSVAlCGYF3iFmmmnhdFaf8E6FiwZlh7FwaEgXCEGMsnLB4fRvawsOnOSAyuyX2cBhPR2ipaeUc/K+OCj4nQOpiZ+ECNBO5/v99cse3NPzMRTkFqLzpR3x5MqHOe5Gz34FyH2RcuQh4l+rUDOsovHq6beb4oXmXxM+knFLNE4Kqi5n/LiLmaqnEPB1BOQxkF3c1OxtSBdodDx9nKLTERcnNtAJ95VA0XrAOGpeYQEszSGSPq9xPJxOMUHkWQ7pDhEzkXm6WHOP3kkScLU0qrR3fPzNXmoqFEPURXL8mPIy7N2lNYqyw0R4pfeZuHY13t2+DSW6jjCrFVtHj4XNEviB0cHw3VOenypWpmCYBNVdnCnbyTmrorrXV2h8EClZHi22dre5jb0uMlODdpGnAQnvQtNIVwh49Z6F+PCVT1FaIusv2j0TjU9qCNbwyl/qWDCdMziYryj7Kc4eoxR9ET2hLDOE4hVYyd0AQvoBJZukeju3fRa0pHc9MUx1D4WATyJgUJBy1lSZ3UXyFBSrF0ZxMZUXnYgPyYCwGxjspXVlwxcNf6vRxsEo/hVG6pAyQypmEsCMzVJ+BhHXQiODiCV30gE9xyU7jAkWj17Gnmi5mZJK7yLu5SrH5Tzih1Z/juU7t6PUMFiB/rfb70KIMn58cg5Xt1PK+FHGT3XnTJ3U12mhIk4RR7EwgaKInSRjgLKeY00iZmcOH4DPF67FC7e8Cs2iITw6HIv/nY1Q6+8w0oY6FkyisgdlcpBcB8lTUIYW0dzzAnpsPIJRshfQ02BYWgMprgKk5YkP6wQEdVOFgBcRkOrwf8uj6eN4VexdlFmPS8oMFCYedPLWhg+BFju1RiOSR+Bl7zKI4iLriTJyw8iR0KLvM6VuRsmj7ogR0GIedbTHzNT5HwLZU8zfCSB8KLTYJ93u04HsLNyx6iMczMnBwz3OwxWnnOr2tf5cMRg2/cr4UcaPT7yjRu4b0ivjYvz0hhb/KnTS3Cr+WRotIhZaw00cjLdm8bfYu2MfLh7RG83aNIFRtBlG2rCyxThylNQOcuiQyd2fLEK6zkPtrnEKXdgP6gfzGZG73VFsEA1/cQgk+gRgqhMKgTpEwCg9BCN9FEBH2KE9IWIeO8Yg4jTyglUy5keLlp4ZEQGQTpglGbC0rPGRFx+x0xFX/gogpJvcxND9KWOU+hM7jYkX9TQib1xb9k433OGSecne3tShQMl20/PzmtsB2HUIr8/fWhk/Pv+I6r6DwTAJ6h5F91owineAdLaQtwjQ4iDiXmR+ID39rrLUcsuJIL0uFhkt3gQRPhQiXFLb84JJgdS02yORxuLdQOnOihu3dYNIWChp3GmhJ0V3VriWXqGyuCPSFpsITWV0ufcQVa2AQEAnL0veYieZmkSI5LWOGL6qBmnoaQC9y7Z2ENYWVVU/7t/5vT7S0SRF1IDQ83lTREXPngHkzpHXW5pBa+DE6GzelYWaCynrq9lxU/Vr1ckAuzgYvnvK81PFpA2GSeDr7y0tpJJROQci+h4OnDYyx5cZKEREGHYJRHh/l6Hoh3sQlWwlw7NANNwKFH0vWWgNKZ/hKNbTAetJECHdICLssQe+jpTqn0LAMwjo2f8zjYqyd0IkfQZhPbnKBgw9G8bRvoCeyvE5InGp22nrzC9EGxE9k9ncRdgFclPDwsIkVKoxU7QWJ7UF2fuUtxiGngoRca1bx3VVDkBVUNpeag4Eh8CbPz1nytKQWWG0wysvZhoBRN4qRVStLWBkU/AzaX0JwHIqYGsDEL8QMzNHA0lfAynENm2mxjuAoEyx+Q4man/CR/VVIeAJBFjChbSviux0FO2lPEQlsjF6/idSjJikYsIGAAXEgE7FxhsWEXlLpd1i+RzagBRv5Xgjjjuid1tEQ2v4kzRy6EibvLpaIgQfqzXwxDDVPSpBIBg2/crzozw/frUA6FnPAHlvmIaPa2aJy0BEFJCwDKJ4C2Bks/ozL6LZTwOlhyCi7gKsJ8M4ck6Zx0fEApQBFnYhhBbpV7iozioE6gIBOhLmjCkSCq6E7oHjag4TBYUkBeUi4uR7RyX2OYjQ8yt9p2S8HwkL26+n2DwD0OKhJW+si2Gpe6rvHpTxoyaBXy0Eeur1QPGPZp8pHf50gAycYwrtOO+FiDyWmM25Kqu1E0kjcZIQL5AbWS5+BZjqrEKgjhGQxs8ZTokFAOIXSpLQnNmAvk96bBI/qJgXjN7B7GfKjB/bOYBRABHzCAQxxqtS7wgoz0+9Q+57DQbDJPA91CvvEesHUUwAucWZhXY8DNIZI8LEkt+Akj1Omj1vsXChKgoBhUDdIqDnrwayxrPRgvBrJClh0bdSBoaLFSJmAkSEPRuzrD/MuJzxMFCyFSJiBETkiLrtrLp7lQgEw3dPeX6U56fKF8HXKpDGGAmjslJ8uWKU7AYKVks3vake72v9V/1RCAQDAmUCojRaEhB9x8GwHAzj9+cxKuPHn5+eh/oeDJPAQ1Cp2ygEFAIKARcEDCIXLfiKPbDO4sAKJt9GIBi+e8rzozw/vv0Wqt4pBBQCCgGFQL0ioIyfeoXbNxsLhkngm8irXikEFAIKAYWANxAIhu+e8vwoz4833i3VpkJAIaAQUAj4KALK+PHRB1Of3QqGSVCfeKq2FAIKAYWAQsC3EQiG757y/CjPj2+/hap3CgGFgEJAIVCvCCjjp17h9s3GgmES+CbyqlcKAYWAQkAh4A0EguG7pzw/yvPjjXdLtakQUAgoBBQCPoqAMn589MHUZ7eCYRLUJ56qLYWAQkAhoBDwbQSC4bunPD/K8+Pbb6HqnUJAIaAQUAjUKwLK+KlXuH2zsWCYBL6JvOqVQkAhoBBQCHgDgWD47inPj/L8eOPdUm0qBBQCCgGFgI8ioIwfH30w9dmtYJgE9YmnakshoBBQCCgEfBuBYPjuKc+P8vz49luoeqcQUAgoBBQC9YqAMn7qFW7fbCwYJoFvIq96pRBQCCgEFALeQCAYvnvK86M8P954t1SbCgGFgEJAIeCjCCjjx0cfTHW6NW/ePEybNg0HDx5E06ZN8eijj2L48OFu3yIYJoHbYKiKCgGFgEJAIRDwCATDdy+gPT9btmxBly5d8Pnnn6N3795Ys2YNLrvsMvz8889o166dWxM4GCaBW0CoSgoBhYBCQCEQFAgEw3cvoI2fFStW4KGHHsKuXbscE7Z169Z4+umnMWTIELcmcTBMAreAUJUUAgoBhUA9IFCi61i8bSuO5OZiWPsz0CQ6ph5aVU04IxAM372ANn5yc3PRq1cvPPPMM+jTpw++/PJLXHfdddi2bRsaN27s1mwPhkngFhCqkkJAIaAQqAcEXvrhe7z84/egtbdxVDS+HjGSf1al/hAIhu9eQBs/hmFg+vTpHOdTXFwMi8WCBQsWYNiwYZXOokmTJmHy5Mkuf6f7qKIQUAgoBBQCdY/A7as+xOe7/3I09PuYcQixWOq+YdWCAwFl/PjwZOjfvz9WrVpVaQ/37NmDr776ChMnTsQnn3yC9u3bs8fn8ssvx2uvvYZ+/fq5NbpgmARuAaEqKQQUAgqBekDg+33/4sYPV6BYL8X17TvgifP71EOrqglnBILhuxfQnp+xY8eioKAAlPFlLyNHjkR4eDhmzpzp1mwPhkngFhCqkkJAIaAQqCcEMgrykV1YhOaxsfXUompGGT8BNAfefvttDnj+7LPPcNppp2H79u245JJL+FiLjCB3ijJ+3EFJ1VEIKAQUAgqBQEEgGL57Ae35oYlImV1z587FkSNHkJiYiBEjRrDx424AXTBMgkB5YdU4FAIKAYWAQqD2CATDdy/gjZ/aToNgmAS1xUhdrxBQCCgEFAKBg0AwfPeU8VPFfA2GSRA4r6waiUJAIaAQUAjUFoFg+O4p40cZP7V9T9T1CgGFgEJAIRBACCjjJ4AeZk2HEgyToKbYqOsUAgoBhYBCIPAQCIbvnvL8KM9P4L25akQKAYWAQkAhUGMElPFTY+gC58JgmASB87TUSBQCCgGFgEKgtggEw3dPeX6U56e274m6XiGgEFAIKAQCCAFl/ATQw6zpUIJhEtQUG3WdQkAhoBBQCAQeAsHw3VOeH+X5Cbw3V41IIaAQUAgoBGqMgDJ+agxd4FwYDJMgcJ6WGolCQCGgEFAI1BaBYPjuKc+PG56f2k4kdb1CQCGgEFAIKAT8CQHDMPypu9XuqzJ+qg2Z714QDNa6M/pqvL47Fz3RM/V8PYGi795DPV/ffTbB0DNl/ATQU1aLSQA9zAqGop6ver6BhICaz4H0NP1vLMr48b9nVmmP1WISQA9TGT9Q81nN50BCINjms68/O2X8+PoTqkb/Jk2aBPovWIoab2A/afV81fMNJASCbT77+rNTxo+vPyHVP4WAQkAhoBBQCCgEPIqAMn48Cqe6mUJAIaAQUAgoBBQCvo6AMn58/Qmp/ikEFAIKAYWAQkAh4FEElPHjUTjVzRQCCgGFgEJAIaAQ8HUElPHj609I9U8hoBBQCCgEFAIKAY8ioIwfj8JZ/zc7ePAgRo8ejc2bN4N+3rJlCzp06ODoyNdff43zzz8fkZGRjt/deOONmDlzZv131gMtVjVeauKDDz7A+PHjsX//fpx11lmYN28eTjnlFA+07v1b/PPPPzjppJNcnic935UrV3q/cx7qQXFxMe655x68/fbbnO5+3XXXYfr06bBarR5qwXduQ+/i4sWLERIS4ujUl19+iW7duvlOJ2vRE1pnFi5ciG3btqFfv378btpLVlYWbrvtNnz88ccIDw/H2LFj8dhjj9WiNe9ferzx9u7dG99//z1sNpujo7t27UKTJk283/Eg7IEyfvz8oR8+fBjLly/HOeecgy5dulRo/FxxxRXIyMjw85HK7lc13j/++IMNniVLluDCCy/E1KlT+eft27cHxMfTbvykp6cjLi4uIJ5p+UE8/vjj+PDDD/Hpp5/yn+ijOWjQIEycODHgxkvGDz3HGTNmBNzYaEArVqyApmlYvXo1/vvvPxfjZ8SIEfw+v/vuuzhy5Ai/r08++SRuuOEGv8XieOMl44fW4nHjxvnt+AKp48r4CaCnSbvkijw/gWT8OD+uisZLO0fCgHaTVMiLkJyczIsweUj8vQSD8dO8eXP29AwZMoQf19KlS3H//fdj7969/v74jul/oBs/9gETx80vv/ziMH7y8vIQHx+P7777jjduVJ577jl+b7/55hu/f87lx0sDUsaPbz1WZfz41vOoVW8qM35oR0UGAO3AzjvvPEybNg1NmzatVVu+cHFF4x04cCDat2/PO0h76d69O6666ircfffdvtDtWvXBbvzQ8yspKUHnzp35eQbKsR55tBISEvDnn3+iVatWjBX93KZNG/ZexsbG1go/X7uYjJ+PPvqIu9W4cWPcfPPNfORH72oglfLGAG1QyENLmxP7cSYd99F7SnPA30tlxs9vv/0GXddx4okn8nP2Zy+Xvz8jZfz4+xN06n9FxsChQ4dw9OhRnHrqqUhJScG9996L33//nWOE/H2BrWi8ffr04WMS8hTYy2WXXcYxFBMmTPD7p52Tk4MdO3agY8eOyM3NxZQpU/Dee+/xsV5MTIzfj2/fvn044YQTeK4mJSXxeOhnMt7pb82aNfP7MToP4OeffwZ5usjg27RpE3/86aNI/wVSKW8MrF+/nt9Tms/2QuOn95SMen8vFRk/FO/Trl07REREYM2aNfysKR7qyiuv9Pfh+mX/lfHjw4+tf//+WLVqVaU93LNnD1q0aOH4e0XGQPmLabGh3TN9LH3NW+CJ8ZLn54wzzmCjwF569OiBoUOH+rznp7rjp/EZhsEeA1pE+/bt68Oz2b2u2T0/f/31F1q2bMkX0c+tW7cOSM9PeVRmzZqFRYsWYePGje4B5ie1KvL8nH322SgqKnJ4figuiN7TQPX8lH9UDzzwAP7991+OeVKl/hFQxk/9Y15nLbpj/JC3gDwEvmj8VBeYymJ+KLbAnv1EbvWGDRti2bJluOCCC6rbhF/UJ+NnwYIFAWH8EODkCaEA4MGDBzP+9OzIY0kfikAvs2fPZkM20I0fe8zPhg0bQEYQleeff56PANetW+f3j7kiz0/5QT300EOgY2xl/HjncSvjxzu4e7TVgoICvh+li/7www/s+aDUWTrWWrt2LXuH6L+0tDR2p5NxQGfuFovFo/2or5sdb7z2bC8KkqUjsKeffppTiemoKBBSpen5kvFKMTD5+fns4XrzzTexc+fOgImHoawuCnz95JNPeEpdeumlnCUTiNledGRJHrvo6Gj89NNPHOQ9ZswYpmoIhEJHWPQfxeD9+uuvfERL6xKtTxTvQkfy77zzjiPbi+azP8fBVDZeMvbI0KOg59DQUBAFCRn3c+fOZW+XKvWPgDJ+6h9zj7dIHpDyhYweetFeeOEFzpwhw4c+mpTx9Mwzz3Bchb+W442XxvT++++DXMqUWktBlfPnz/e5I76aYk8fCopdolguih0gegN6nqeffnpNb+lz15G3jtKByWilcv311wcsz0+vXr3YKKCPJgWxjxw5kuPV/D0ezz6pyAMyefJklzlGSRf08SeeH+Ioc+b58XcDt7Lx0maMjrVpk0KFNqM0xynAXRXvIKCMH+/grlpVCCgEFAIKAYWAQsBLCCjjx0vAq2YVAgoBhYBCQCGgEPAOAsr48Q7uqlWFgEJAIaAQUAgoBLyEgDJ+vAS8alYhoBBQCCgEFAIKAe8goIwf7+CuWlUIKAQUAgoBhYBCwEsIKOPHS8CrZhUCCgGFgEJAIaAQ8A4CyvjxDu6qVYWAQkAhoBBQCCgEvISAMn68BLxqViGgEFAIKAQUAgoB7yCgjB/v4K5aVQgoBBQCCgGFgELASwgo48dLwKtmFQIKAYWAQkAhoBDwDgLK+PEO7qpVhYBCQCGgEFAIKAS8hIAyfrwEvGpWIaAQUAgoBBQCCgHvIKCMH+/grlpVCCgEFAIKAYWAQsBLCCjjx0vAq2YVAgoBhYBCQCGgEPAOAsr48Q7uqlWFgEJAIaAQUAgoBLyEgDJ+vAS8alYhoBBQCCgEFAIKAe8goIwf7+CuWlUIKAQUAgoBhYBCwEsIKOPHS8CrZhUCCgGFvJZ0PgAAAPNJREFUgEJAIaAQ8A4CyvjxDu6qVYWAQkAhoBBQCCgEvISAMn68BLxqViGgEFAIKAQUAgoB7yCgjB/v4K5aVQgoBBQCCgGFgELASwgo48dLwKtmFQIKAYWAQkAhoBDwDgLK+PEO7qpVhYBCQCGgEFAIKAS8hIAyfrwEvGpWIaAQUAgoBBQCCgHvIKCMH+/grlpVCCgEFAIKAYWAQsBLCCjjx0vAq2YVAgoBhYBCQCGgEPAOAsr48Q7uqlWFgEJAIaAQUAgoBLyEgDJ+vAS8alYhoBBQCCgEFAIKAe8goIwf7+CuWlUIKAQUAgoBhYBCwEsI/B/qfgxDoXOpfwAAAABJRU5ErkJggg==\" width=\"638.888905813665\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot of input data\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "plt.scatter(classifier.data[:, 0], classifier.data[:, 1], s=4, c=classifier.labels.squeeze())\n",
    "plt.show()\n",
    "#plt.scatter(classifier.data_outlier[:, 0], classifier.data_outlier[:, 1], s=4, c=classifier.labels_outlier.squeeze())\n",
    "\n",
    "print(\"Plot of input data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 1)\n",
      "(400, 1)\n",
      "(600, 1)\n"
     ]
    }
   ],
   "source": [
    "# Create image for paper\n",
    "classifier.n = 200\n",
    "data, labels = classifier.get_data()\n",
    "labels = labels.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": {
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\" width=\"498.88890210493145\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "ind = np.where(labels == 0)[0]\n",
    "plt.scatter(data[ind, 0], data[ind, 1], s=10, c='#FFA500', marker='D')\n",
    "ind = np.where(labels == 1)[0]\n",
    "plt.scatter(data[ind, 0], data[ind, 1], s=10, c='b', marker='o')\n",
    "ind = np.where(labels == 2)[0]\n",
    "plt.scatter(data[ind, 0], data[ind, 1], s=10, c='#FFFF33', marker='*')\n",
    "#plt.xlim(-18, +18)\n",
    "#plt.ylim(-18, +18)\n",
    "#plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.legend(('$class\\ 0$','$class\\ 1$','$class\\ 2\\ (OOD\\ sample)$'), fontsize='x-small');\n",
    "plt.show()\n",
    "plt.savefig(\"exp1_a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": {
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\" width=\"638.888905813665\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f44bc67bcd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot classified data - for test data\n",
    "y_pred = classifier.model.predict(classifier.X_test)\n",
    "y_labels = y_pred.argmax(axis = -1)\n",
    "\n",
    "# take the max for inlier and 1-max for outlier class\n",
    "y_max = y_pred.max(axis=-1)\n",
    "ind = np.where((classifier.y_test == 2))[0]\n",
    "y_max[ind] = 1-y_max[ind]\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scatter(classifier.X_test[:, 0], classifier.X_test[:, 1], s=4, c=y_labels.squeeze())\n",
    "\n",
    "#ax = Axes3D(fig)\n",
    "\n",
    "#ax.scatter(classifier.X_test[:, 0], classifier.X_test[:, 1], y_max, c=classifier.y_test.squeeze())\n",
    "#plt.scatter(X_test[:, 0], X_test[:, 1], s=4, c=y_max.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 1)\n",
      "(800, 1)\n",
      "(1200, 1)\n",
      "(350, 2)\n",
      "(350, 1)\n"
     ]
    }
   ],
   "source": [
    "# Create image for paper\n",
    "classifier.n = 400\n",
    "data, labels = classifier.get_data()\n",
    "ind_0 = np.where(labels==0)[0][0:50]\n",
    "ind_1 = np.where(labels==1)[0][0:50]\n",
    "ind_2 = np.where(labels==2)[0]\n",
    "ind = np.random.randint(0, ind_2.shape[0], 50)\n",
    "ind_2 = ind_2[ind]\n",
    "data = np.concatenate((data[ind_0,:], data[ind_1,:], data[ind_2,:]), axis=0)\n",
    "labels = np.concatenate((labels[ind_0,:], labels[ind_1,:], labels[ind_2,:]), axis=0)\n",
    "data_outlier, labels_outlier = classifier.get_outlier_data()\n",
    "data = np.concatenate((data, data_outlier), axis=0)\n",
    "labels = np.concatenate((labels, labels_outlier))\n",
    "print(data.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": {
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\" width=\"498.88890210493145\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = classifier.model.predict(data)\n",
    "y_labels = y_pred.argmax(axis = -1)\n",
    "c_list = ['#FFA500', 'b', '#FFFF33']\n",
    "marker_list = ['D', 'o', '*']\n",
    "plt.figure(figsize=(5,5))\n",
    "for i in range(0, data.shape[0]):\n",
    "    marker = marker_list[int(labels[i])]\n",
    "    c = c_list[y_labels[i]]\n",
    "    plt.scatter(data[i, 0], data[i, 1], s=10, c=c, marker=marker)\n",
    "\n",
    "plt.legend(('$class\\ 0$','$class\\ 1$','$OOD\\ sample$'), fontsize='x-small');\n",
    "import matplotlib.lines as lines\n",
    "dimond_line = lines.Line2D([0], [0], color='w', marker='D', markerfacecolor='#000000',\n",
    "                          markersize=5, label='TC 0')\n",
    "\n",
    "circle_line = lines.Line2D([0], [0], marker='o', color='w', label='TC 1',\n",
    "                          markerfacecolor='#000000', markersize=5)\n",
    "\n",
    "star_line = lines.Line2D([0], [0], color='w', marker='*', markerfacecolor='#000000',\n",
    "                          markersize=10, label='TC 2')\n",
    "\n",
    "\n",
    "orange_line = lines.Line2D([0], [0], marker='s', color='w', label='PC 0',\n",
    "                          markerfacecolor='#FFA500', markersize=5)\n",
    "\n",
    "\n",
    "blue_line = lines.Line2D([0], [0], marker='s', color='w', label='PC 1',\n",
    "                          markerfacecolor='b', markersize=5)\n",
    "\n",
    "\n",
    "yellow_line = lines.Line2D([0], [0], marker='s', color='w', label='PC 2',\n",
    "                          markerfacecolor='#FFFF33', markersize=5)\n",
    "\n",
    "# Create a legend for the first line.\n",
    "first_legend = plt.legend(handles=[dimond_line, circle_line, star_line], loc=1)\n",
    "\n",
    "\n",
    "# Add the legend manually to the current Axes.\n",
    "ax = plt.gca().add_artist(first_legend)\n",
    "\n",
    "# Create another legend for the second line.\n",
    "plt.legend(handles=[orange_line, blue_line, yellow_line], loc=4)\n",
    "\n",
    "plt.show()\n",
    "plt.savefig(\"exp1_c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classifier.X_test.shape)\n",
    "print(classifier.model.evaluate(classifier.X_test, classifier.Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}