Malaria_Detection_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Malaria Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import copy\n",
    "import csv\n",
    "import random\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from scipy.stats import randint\n",
    "from itertools import cycle\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from scipy.sparse import csr_matrix\n",
    "from scipy import stats\n",
    "from my_ml_lib import DataManipulationTools, MetricTools, PlotTools\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import different classifiers\n",
    "from sklearn import svm\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.decomposition import PCA\n",
    "from skimage.feature import hog, local_binary_pattern, greycomatrix, greycoprops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_images(path):\n",
    "    images = []\n",
    "    labels = []\n",
    "    num1 = 32\n",
    "    num2 = 32\n",
    "    for file_name in os.listdir(path):\n",
    "        file_path = path + '/' + file_name\n",
    "        for img_name in os.listdir(file_path):\n",
    "            if not img_name.startswith('.'):\n",
    "                if img_name.endswith('.png'):\n",
    "                    img = cv2.imread(file_path + '/' + img_name)\n",
    "                    new_img = cv2.resize(img, (num2, num1))\n",
    "                    images.append(new_img)\n",
    "                    if file_name == 'Parasitized':\n",
    "                        label = 0\n",
    "                    else:\n",
    "                        label = 1\n",
    "                    labels.append(label)\n",
    "    \n",
    "    return np.array(images), np.array(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save / Load Extracted Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_feature(feature, name):\n",
    "    # saving all our feature vectors in pickled file\n",
    "    with open('cache/' + name + '.pkl', 'wb') as fp:\n",
    "        pickle.dump(csr_matrix(feature), fp)\n",
    "    \n",
    "    print(f'Feature saved with name cache/{name}.pkl')\n",
    "\n",
    "def load_feature(feature_name):\n",
    "    return pickle.load(open(feature_name, 'rb')).A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save / Load Trained Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_model(model):\n",
    "    filename = input('Enter model file name:')\n",
    "    pickle.dump(model, open('models/'+filename + '.pkl', 'wb'))\n",
    "    print(f'Successfully saved model in models/{filename}.pkl')\n",
    "\n",
    "def load_model(model_name):\n",
    "    return pickle.load(open(model_name, 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of oriented gradients (HOG) based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_hog(images, name='hog', save=False):\n",
    "#     result = np.array([hog(img, block_norm='L2') for img in images])\n",
    "    \n",
    "#     if save:\n",
    "#         save_feature(result, name)\n",
    "        \n",
    "#     return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hog(images, name='hog', save=False):\n",
    "    # HOG Feature Extraction\n",
    "    cell_size = (4, 4)    # h x w in pixels\n",
    "    block_size = (2, 2)     # h x w in cells\n",
    "    nbins = 9\n",
    "    \n",
    "    # winSize is the size of the image cropped to an multiple of the cell size\n",
    "    # cell_size is the size of the cells of the img patch over which to calculate the histograms\n",
    "    # block_size is the number of cells which fit in the patch\n",
    "    hog_desc = cv2.HOGDescriptor(_winSize=(images[0].shape[1] // cell_size[1] * cell_size[1],\n",
    "                                              images[0].shape[0] // cell_size[0] * cell_size[0]),\n",
    "                                    _blockSize=(block_size[1] * cell_size[1],\n",
    "                                                block_size[0] * cell_size[0]),\n",
    "                                    _blockStride=(cell_size[1], cell_size[0]),\n",
    "                                    _cellSize=(cell_size[1], cell_size[0]),\n",
    "                                    _nbins=nbins)\n",
    "    \n",
    "    def get_image_hog(image):\n",
    "        # HOG feature\n",
    "        f = hog_desc.compute(image)\n",
    "        \n",
    "        res = np.array(f)\n",
    "        return res.flatten()\n",
    "    \n",
    "    # HOG for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        f = get_image_hog(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY))\n",
    "        features.append(f)\n",
    "    \n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gabor based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_gabor(images, name=\"gabor\", save=False):\n",
    "    \n",
    "    # Gabor filter banks with different orientations and at different scales\n",
    "    filters = []\n",
    "    ksize = 9\n",
    "    sigma = 0.1\n",
    "    gamma = 0.5\n",
    "    phi = 0\n",
    "    \n",
    "    # define the range for theta and nu\n",
    "    for theta in np.arange(0, np.pi, np.pi / 8):\n",
    "        for nu in np.arange(0, 6*np.pi/4, np.pi / 4):\n",
    "            kern = cv2.getGaborKernel((ksize, ksize), sigma, theta, nu, gamma, phi, ktype=cv2.CV_32F)\n",
    "            kern /= 1.5*kern.sum()\n",
    "            filters.append(kern)\n",
    "    \n",
    "    # function to convolve the image with the filters\n",
    "    def process(img, filters):\n",
    "        accum = np.zeros_like(img)\n",
    "        for kern in filters:\n",
    "            fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)\n",
    "            np.maximum(accum, fimg, accum)\n",
    "            return accum\n",
    "        \n",
    "    \n",
    "    def get_image_gabor(image):\n",
    "        # Local Binary Pattern\n",
    "        f = []\n",
    "\n",
    "        # calculating the local energy for each convolved image\n",
    "        for j in range(40):\n",
    "            res = process(cv2.cvtColor(image, cv2.COLOR_RGB2GRAY), filters[j])\n",
    "            res = np.array(res)\n",
    "            f.append(np.sum(np.multiply(res, res)))\n",
    "\n",
    "        # calculating the mean amplitude for each convolved image\n",
    "        for j in range(40):\n",
    "            res = process(cv2.cvtColor(image, cv2.COLOR_RGB2GRAY), filters[j])\n",
    "            res = np.array(res)\n",
    "            f.append(np.sum(abs(res)))\n",
    "        return f\n",
    "    \n",
    "    # Gabor descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        f = get_image_gabor(img)\n",
    "        features.append(f)\n",
    "    \n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KAZE based features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_kaze(images, name='kaze', save=False):\n",
    "    \n",
    "    # KAZE descriptor for 1 image\n",
    "    def get_image_kaze(image, vector_size=32):\n",
    "        algo = cv2.KAZE_create()\n",
    "        kps = algo.detect(image)\n",
    "        kps = sorted(kps, key=lambda x: -x.response)[:vector_size]\n",
    "        \n",
    "        # Making descriptor of same size\n",
    "        # Descriptor vector size is 64\n",
    "        needed_size = (vector_size * 64)\n",
    "        if len(kps) == 0:\n",
    "            return np.zeros(needed_size)\n",
    "        \n",
    "        kps, dsc = algo.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        \n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "        return dsc\n",
    "    \n",
    "    # KAZE descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        dsc = get_image_kaze(img)\n",
    "        features.append(dsc)\n",
    "    \n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale-invariant feature transform (SIFT) based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sift(images, name='sift', save=False):\n",
    "    \n",
    "    # SIFT descriptor for 1 image\n",
    "    def get_image_sift(image, vector_size=15):\n",
    "        alg = cv2.xfeatures2d.SIFT_create()\n",
    "        kps = alg.detect(image, None)\n",
    "        kps = sorted(kps, key=lambda x: -x.response)[:vector_size]\n",
    "        \n",
    "        # Making descriptor of same size\n",
    "        # Descriptor vector size is 128\n",
    "        needed_size = (vector_size * 128)\n",
    "        if len(kps) == 0:\n",
    "            return np.zeros(needed_size)\n",
    "        \n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "            \n",
    "        return dsc\n",
    "    \n",
    "    # SIFT descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        dsc = get_image_sift(img)\n",
    "        features.append(dsc)\n",
    "\n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_sift_kp(image):\n",
    "    alg = cv2.xfeatures2d.SIFT_create()\n",
    "    kps = alg.detect(image, None)\n",
    "    kps = sorted(kps, key=lambda x: -x.response)[:15]\n",
    "\n",
    "    # Making descriptor of same size\n",
    "    # Descriptor vector size is 128\n",
    "    needed_size = (15 * 128)\n",
    "    if len(kps) == 0:\n",
    "        dsc = np.zeros(needed_size)\n",
    "    else:\n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "    return kps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speeded-up robust features (SURF) based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_surf(images, name='surf', save=False):\n",
    "    # SURF descriptor for 1 image\n",
    "    def get_image_surf(image, vector_size=4):\n",
    "        alg = cv2.xfeatures2d.SURF_create()\n",
    "        kps = alg.detect(image, None)\n",
    "        kps = sorted(kps, key=lambda x: -x.response)[:vector_size]\n",
    "        \n",
    "        # Making descriptor of same size\n",
    "        # Descriptor vector size is 64\n",
    "        needed_size = (vector_size * 64)\n",
    "        if len(kps) == 0:\n",
    "            return np.zeros(needed_size)\n",
    "        \n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "            \n",
    "        return dsc\n",
    "    \n",
    "    # SURF descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        dsc = get_image_surf(img)\n",
    "        features.append(dsc)\n",
    "    \n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_surf_kp(image):\n",
    "    alg = cv2.xfeatures2d.SURF_create()\n",
    "    kps = alg.detect(image, None)\n",
    "    kps = sorted(kps, key=lambda x: -x.response)[:4]\n",
    "\n",
    "    # Making descriptor of same size\n",
    "    # Descriptor vector size is 64\n",
    "    needed_size = (15 * 64)\n",
    "    if len(kps) == 0:\n",
    "        dsc = np.zeros(needed_size)\n",
    "    else:\n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "    return kps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local-Binary-Pattern (LBP) based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lbp(images, name='lbp', save=False):\n",
    "    result = np.array([local_binary_pattern(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY), 10, 3).flatten() for img in images])\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gray Level Co-occurrence Matrix (GLCM) based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_glcm(images, name='glcm', save=False):\n",
    "    \n",
    "    # GLCM feature for 1 image\n",
    "    def get_image_glcm(img):\n",
    "        image = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "        \n",
    "        # GLCM Feature Extraction\n",
    "        Grauwertmatrix = greycomatrix(image, [1, 2, 3], [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4],\n",
    "                                                  symmetric=False, normed=True)\n",
    "        \n",
    "        contrast = greycoprops(Grauwertmatrix, 'contrast')\n",
    "        dissimilarity = greycoprops(Grauwertmatrix, 'dissimilarity')\n",
    "        homogeneity = greycoprops(Grauwertmatrix, 'homogeneity')\n",
    "        energy = greycoprops(Grauwertmatrix, 'energy')\n",
    "        correlation = greycoprops(Grauwertmatrix, 'correlation')\n",
    "        ASM = greycoprops(Grauwertmatrix, 'ASM')\n",
    "        \n",
    "        # Merge all the features\n",
    "        f = np.array([contrast, dissimilarity, homogeneity, energy, correlation, ASM])\n",
    "        return f.flatten()\n",
    "    \n",
    "    \n",
    "    # GLCM descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        dsc = get_image_glcm(img)\n",
    "        features.append(dsc)\n",
    "    \n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flattened Image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_flattened(images, color=cv2.COLOR_RGB2GRAY, name='flattened', save=False):\n",
    "    \"\"\"\n",
    "    color: default RGB2GRAY, if None is passed then color is used as it is.\n",
    "    \"\"\"\n",
    "    color_images = []\n",
    "    if color is not None:\n",
    "        for img in images:\n",
    "            color_images.append(cv2.cvtColor(img, color))\n",
    "    else:\n",
    "        color_images = images\n",
    "    \n",
    "    count = len(color_images)\n",
    "    \n",
    "    result = np.array(color_images).reshape(count, -1)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color Histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_color_hist(images, name='color_hist', save=False):\n",
    "    histograms = []\n",
    "    for img in images:\n",
    "        histograms.append(cv2.calcHist([img], [0, 1, 2],None, [8, 8, 8], [0, 256, 0, 256, 0, 256]).flatten())\n",
    "    \n",
    "    result = np.array(histograms)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_features(features, horizontal=True):\n",
    "    \"\"\"\n",
    "    Array of features [f1, f2, f3] where each fi is a feature set \n",
    "    eg. f1=rgb_flat, f2=SIFT, etc.\n",
    "    \"\"\"\n",
    "    if horizontal:\n",
    "        return np.hstack(features)\n",
    "    else:\n",
    "        return np.vstack(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_features_minmax(train, test):\n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    norm_train = min_max_scaler.fit_transform(train)\n",
    "    norm_test = min_max_scaler.transform(test)\n",
    "    \n",
    "    return norm_train, norm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_features_zscore(train, test):\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    norm_train = min_max_scaler.fit_transform(train)\n",
    "    norm_test = min_max_scaler.transform(test)\n",
    "    \n",
    "    return norm_train, norm_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(train_x, train_y, model_name='NB', validation=None):\n",
    "    \"\"\"\n",
    "    Possible model names: ['NB', 'SVM', 'XGB', 'MLP', 'ADA', 'BAG', 'RF']\n",
    "    default = 'NB'\n",
    "    \n",
    "    validation: (val_x, val_y) tupple for validation accuracy score.\n",
    "    \n",
    "    return: trained model\n",
    "    \"\"\"\n",
    "    model = None\n",
    "    if model_name == 'SVM':\n",
    "        model = svm.SVC(gamma='scale', probability=True)\n",
    "    elif model_name == 'XGB':\n",
    "        model = XGBClassifier(n_estimators=200, max_depth=5, n_jobs=2)\n",
    "#         model = XGBClassifier()\n",
    "    elif model_name == 'MLP':\n",
    "        model = MLPClassifier(hidden_layer_sizes=(100,100,100), max_iter=800, alpha=0.0001,\n",
    "                     solver='sgd', verbose=10, tol=0.000000001)\n",
    "    elif model_name == 'ADA':\n",
    "        model = AdaBoostClassifier(n_estimators=50)\n",
    "    elif model_name == 'BAG':\n",
    "        model = BaggingClassifier(n_jobs=2, n_estimators=50)\n",
    "    elif model_name == 'RF':\n",
    "        model = RandomForestClassifier(n_estimators=200, max_depth=10)\n",
    "    elif model_name == 'KNN':\n",
    "        model = KNeighborsClassifier(n_neighbors=5, weights='distance', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)\n",
    "    else:\n",
    "        model = GaussianNB()\n",
    "    \n",
    "    model.fit(train_x, train_y)\n",
    "    \n",
    "    if validation is not None:\n",
    "        y_hat = model.predict(validation[0])\n",
    "        acc = metrics.accuracy_score(validation[1], y_hat)\n",
    "        print(f\"Validation Accuracy in '{model_name}' = {acc}\")\n",
    "        cm = metrics.confusion_matrix(validation[1], y_hat)\n",
    "        print(cm)\n",
    "        recall = cm[0][0] / (cm[0][0] + cm[0][1])\n",
    "        precision = cm[0][0] / (cm[0][0] + cm[1][0])\n",
    "        f1 = 2*(precision*recall)/(precision+recall)\n",
    "        print(f\"Recall in '{model_name}' = {recall}\")\n",
    "        print(f\"Precision in '{model_name}' = {precision}\")\n",
    "        print(f\"F1 Score in '{model_name}' = {f1}\")\n",
    "               \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(fpr, tpr, class_name, area):\n",
    "    figure(num=None, figsize=(12, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "    \n",
    "    plt.plot(fpr,tpr)\n",
    "\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_combine_roc(test_y, prob):\n",
    "    n_classes = 7\n",
    "    name_arr = ['RF', 'BAG', 'ADA', 'NB', 'SVM', 'XGB', 'KNN']\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    for i in range(n_classes):\n",
    "        fpr[i], tpr[i], _ = metrics.roc_curve(test_y, prob[:, i])\n",
    "        roc_auc[i] = metrics.roc_auc_score(test_y,  prob[:, i])\n",
    "\n",
    "    figure(num=None, figsize=(12, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "    colors = cycle(['darkorange', 'cornflowerblue', 'red', 'purple', 'pink', 'violet', 'green'])\n",
    "    for i, color in zip(range(n_classes), colors):\n",
    "        plt.plot(fpr[i], tpr[i], color=color,\n",
    "                 label='ROC curve for ' + name_arr[i] + ' class (area = {1:0.2f})'\n",
    "                 ''.format(i, roc_auc[i]))\n",
    "\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend()\n",
    "#     plt.savefig('output/roc_lda_on_pca_uninfected.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_mat(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues, figsize=(7,7), path=None, filename=None):\n",
    "        \"\"\"\n",
    "        cm: confusion matrix to be plotted.\n",
    "        classes: array of labels or class names.\n",
    "        title: title of the confusion matrix.\n",
    "        cmap: color of the plot matrix.\n",
    "        figsize: tupple (width, height) representiong size of the plot.\n",
    "        path: destination where the plot image will be saved.\n",
    "        filename: name to save the file with on the specified path. (if None, title is used)\n",
    "        \n",
    "        # Source: https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "        \"\"\"\n",
    "        cm = cm.astype(np.int64)\n",
    "        plt.figure(figsize=figsize)\n",
    "        plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "        plt.title(title)\n",
    "        plt.colorbar()\n",
    "        tick_marks = np.arange(len(classes))\n",
    "        plt.xticks(tick_marks, classes, rotation=45)\n",
    "        plt.yticks(tick_marks, classes)\n",
    "\n",
    "        fmt = 'd'\n",
    "        thresh = cm.max() / 2.\n",
    "        for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "            plt.text(j, i, format(cm[i, j], fmt),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        plt.grid(False)\n",
    "        plt.ylabel('True label')\n",
    "        plt.xlabel('Predicted label')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        if path:\n",
    "            if filename is None:\n",
    "                plt.savefig(path + title + '.png')\n",
    "            else:\n",
    "                plt.savefig(path + filename + '.png')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA projected data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pca_projection(mat, name_arr):\n",
    "    plt.figure(figsize=(15,30))\n",
    "    for i, img in enumerate(mat, start=1):\n",
    "        plt.subplot(5, 2, i)\n",
    "        y = np.var(mat[i-1], axis=0)\n",
    "        x = list(range(1, len(y)+1))\n",
    "        plt.plot(x, y, '--o')\n",
    "        plt.ylabel('Variance')\n",
    "        plt.xlabel('Data Projected on Eigen Vector Number')\n",
    "        plt.xticks(x)\n",
    "        plt.title(name_arr[i-1])\n",
    "    plt.savefig('output/pca/subplot.jpg')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_key_points(image, kp):\n",
    "    img = cv2.drawKeypoints(image, kp, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and split data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read and Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data_x, full_data_y = read_images('cell_images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset size :  (27558, 32, 32, 3) (27558,)\n"
     ]
    }
   ],
   "source": [
    "print(\"dataset size : \", full_data_x.shape, full_data_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One Image size:  (32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"One Image size: \", full_data_x[2].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_x, test_x, data_y, test_y = train_test_split(full_data_x, full_data_y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imgs, val_imgs, train_y, val_y = train_test_split(data_x, data_y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data : (17636, 32, 32, 3)        Label:  (17636,)\n",
      "Validation data : (4410, 32, 32, 3)    Label:  (4410,)\n",
      "Test data : (5512, 32, 32, 3)          Label:  (5512,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train data :\", train_imgs.shape, \"       Label: \", train_y.shape) \n",
    "print(\"Validation data :\", val_imgs.shape, \"   Label: \", val_y.shape) \n",
    "print(\"Test data :\", test_x.shape, \"         Label: \", test_y.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save train, validation, test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('data/train_imgs.npy', train_imgs)\n",
    "np.save('data/train_y.npy', train_y)\n",
    "np.save('data/val_imgs.npy', val_imgs)\n",
    "np.save('data/val_y.npy', val_y)\n",
    "\n",
    "np.save('data/data_x.npy', data_x)\n",
    "np.save('data/data_y.npy', data_y)\n",
    "np.save('data/test_x.npy', test_x)\n",
    "np.save('data/test_y.npy', test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imgs = np.load('data/train_imgs.npy')\n",
    "train_y = np.load('data/train_y.npy')\n",
    "val_imgs = np.load('data/val_imgs.npy')\n",
    "val_y = np.load('data/val_y.npy')\n",
    "\n",
    "data_x = np.load('data/data_x.npy')\n",
    "data_y = np.load('data/data_y.npy')\n",
    "test_x = np.load('data/test_x.npy')\n",
    "test_y = np.load('data/test_y.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22046, 32, 32, 3), (5512, 32, 32, 3), (22046,), (5512,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.shape, test_x.shape, data_y.shape, test_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 32, 32, 3), (4410, 32, 32, 3), (17636,), (4410,))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_imgs.shape, val_imgs.shape, train_y.shape, val_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training & Validation set features:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "infected_img = train_imgs[train_y == 0][0]\n",
    "uninfected_img = train_imgs[train_y == 1][0]\n",
    "classes = []\n",
    "classes.append(infected_img)\n",
    "classes.append(uninfected_img)\n",
    "class_label= []\n",
    "class_label.append('infected image')\n",
    "class_label.append('uninfected image')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hog_img = np.zeros((2, 32, 32))\n",
    "lbp_img = np.zeros((2, 32, 32))\n",
    "sift = np.zeros((2, 32, 32, 3), dtype='uint8')\n",
    "surf = np.zeros((2, 32, 32, 3), dtype='uint8')\n",
    "\n",
    "for i in range(2):\n",
    "    temp1, hog_img[i] = hog(classes[i], block_norm='L2', visualize=True)\n",
    "    lbp_img[i] = local_binary_pattern(cv2.cvtColor(classes[i], cv2.COLOR_RGB2GRAY), 10, 3)\n",
    "    sift[i] = draw_key_points(cv2.cvtColor(classes[i], cv2.COLOR_RGB2GRAY), return_sift_kp(classes[i]))\n",
    "    surf[i] = draw_key_points(cv2.cvtColor(classes[i], cv2.COLOR_RGB2GRAY), return_surf_kp(classes[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vis = []\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(classes[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(hog_img[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(lbp_img[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(sift[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(surf[i])\n",
    "\n",
    "img_name_arr = []\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append(class_label[i])\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('HOG')\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('LBP')\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('SIFT')\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('SURF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,10))\n",
    "for i, img in enumerate(data_vis, start=1):\n",
    "    plt.subplot(5, 2, i)\n",
    "    plt.title(img_name_arr[i-1])\n",
    "    frame1 = plt.gca()\n",
    "    for tick in frame1.axes.get_xticklines():\n",
    "        tick.set_visible(False)\n",
    "    for tick in frame1.axes.get_yticklines():\n",
    "        tick.set_visible(False)\n",
    "    for xlabel_i in frame1.axes.get_xticklabels():\n",
    "        xlabel_i.set_visible(False)\n",
    "    for xlabel_i in frame1.axes.get_yticklabels():\n",
    "        xlabel_i.set_visible(False)\n",
    "    plt.imshow(data_vis[i-1], cmap='gray')\n",
    "plt.savefig('output/feature_visualization.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HOG features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/hog_train.pkl\n",
      "Feature saved with name cache/hog_val.pkl\n"
     ]
    }
   ],
   "source": [
    "hog_train = get_hog(train_imgs, name='hog_train', save=True)\n",
    "hog_val = get_hog(val_imgs, name='hog_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "hog_train = load_feature('cache/hog_train.pkl')\n",
    "hog_val = load_feature('cache/hog_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1764), (4410, 1764))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hog_train.shape, hog_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LBP features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/lbp_train.pkl\n",
      "Feature saved with name cache/lbp_val.pkl\n"
     ]
    }
   ],
   "source": [
    "lbp_train = get_lbp(train_imgs, name='lbp_train', save=True)\n",
    "lbp_val = get_lbp(val_imgs, name='lbp_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbp_train = load_feature('cache/lbp_train.pkl')\n",
    "lbp_val = load_feature('cache/lbp_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1024), (4410, 1024))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbp_train.shape, lbp_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KAZE Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/kaze_train.pkl\n",
      "Feature saved with name cache/kaze_val.pkl\n"
     ]
    }
   ],
   "source": [
    "kaze_train = get_kaze(train_imgs, name='kaze_train', save=True)\n",
    "kaze_val = get_kaze(val_imgs, name='kaze_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaze_train = load_feature('cache/kaze_train.pkl')\n",
    "kaze_val = load_feature('cache/kaze_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 2048), (4410, 2048))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaze_train.shape, kaze_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SIFT Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/sift_train.pkl\n",
      "Feature saved with name cache/sift_val.pkl\n"
     ]
    }
   ],
   "source": [
    "sift_train = get_sift(train_imgs, name='sift_train', save=True)\n",
    "sift_val = get_sift(val_imgs, name='sift_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sift_train = load_feature('cache/sift_train.pkl')\n",
    "sift_val = load_feature('cache/sift_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1920), (4410, 1920))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sift_train.shape, sift_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SURF Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/surf_train.pkl\n",
      "Feature saved with name cache/surf_val.pkl\n"
     ]
    }
   ],
   "source": [
    "surf_train = get_surf(train_imgs, name='surf_train', save=True)\n",
    "surf_val = get_surf(val_imgs, name='surf_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "surf_train = load_feature('cache/surf_train.pkl')\n",
    "surf_val = load_feature('cache/surf_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 256), (4410, 256))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_train.shape, surf_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gabor Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/gabor_train.pkl\n",
      "Feature saved with name cache/gabor_val.pkl\n"
     ]
    }
   ],
   "source": [
    "gabor_train = get_gabor(train_imgs, name='gabor_train', save=True)\n",
    "gabor_val = get_gabor(val_imgs, name='gabor_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "gabor_train = load_feature('cache/gabor_train.pkl')\n",
    "gabor_val = load_feature('cache/gabor_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 80), (4410, 80))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gabor_train.shape, gabor_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GLCM Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/glcm_train.pkl\n",
      "Feature saved with name cache/glcm_val.pkl\n"
     ]
    }
   ],
   "source": [
    "glcm_train = get_glcm(train_imgs, name='glcm_train', save=True)\n",
    "glcm_val = get_glcm(val_imgs, name='glcm_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "glcm_train = load_feature('cache/glcm_train.pkl')\n",
    "glcm_val = load_feature('cache/glcm_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 72), (4410, 72))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glcm_train.shape, glcm_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flattened Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RGB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/flat_rgb_train.pkl\n",
      "Feature saved with name cache/flat_rgb_val.pkl\n"
     ]
    }
   ],
   "source": [
    "flat_rgb_train = get_flattened(train_imgs, None, name='flat_rgb_train', save=True)\n",
    "flat_rgb_val = get_flattened(val_imgs, None, name='flat_rgb_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_rgb_train = load_feature('cache/flat_rgb_train.pkl')\n",
    "flat_rgb_val = load_feature('cache/flat_rgb_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 3072), (4410, 3072))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_rgb_train.shape, flat_rgb_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GRAY:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/flat_gray_train.pkl\n",
      "Feature saved with name cache/flat_gray_val.pkl\n"
     ]
    }
   ],
   "source": [
    "flat_gray_train = get_flattened(train_imgs, name='flat_gray_train', save=True)\n",
    "flat_gray_val = get_flattened(val_imgs, name='flat_gray_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_gray_train = load_feature('cache/flat_gray_train.pkl')\n",
    "flat_gray_val = load_feature('cache/flat_gray_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1024), (4410, 1024))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_gray_train.shape, flat_gray_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Color Histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/hist_train.pkl\n",
      "Feature saved with name cache/hist_val.pkl\n"
     ]
    }
   ],
   "source": [
    "hist_train = get_color_hist(train_imgs, name='hist_train', save=True)\n",
    "hist_val = get_color_hist(val_imgs, name='hist_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_train = load_feature('cache/hist_train.pkl')\n",
    "hist_val = load_feature('cache/hist_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 512), (4410, 512))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_train.shape, hist_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduce Features by PCA:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HOG Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_hog_train, norm_hog_val = norm_features_minmax(hog_train, hog_val)\n",
    "norm_hog_train, norm_hog_val = norm_features_zscore(hog_train, hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_hog_train = pca.fit_transform(norm_hog_train)\n",
    "pca_hog_val = pca.transform(norm_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_hog_train.npy', pca_hog_train)\n",
    "np.save('cache/pca_hog_val.npy', pca_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_hog_train = np.load('cache/pca_hog_train.npy')\n",
    "pca_hog_val = np.load('cache/pca_hog_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_hog_train.shape, pca_hog_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LBP Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_lbp_train, norm_lbp_val = norm_features_minmax(lbp_train, lbp_val)\n",
    "norm_lbp_train, norm_lbp_val = norm_features_zscore(lbp_train, lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_lbp_train = pca.fit_transform(norm_lbp_train)\n",
    "pca_lbp_val = pca.transform(norm_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_lbp_train.npy', pca_lbp_train)\n",
    "np.save('cache/pca_lbp_val.npy', pca_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_lbp_train = np.load('cache/pca_lbp_train.npy')\n",
    "pca_lbp_val = np.load('cache/pca_lbp_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_lbp_train.shape, pca_lbp_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KAZE Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_kaze_train, norm_kaze_val = norm_features_minmax(kaze_train, kaze_val)\n",
    "norm_kaze_train, norm_kaze_val = norm_features_zscore(kaze_train, kaze_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_kaze_train = pca.fit_transform(norm_kaze_train)\n",
    "pca_kaze_val = pca.transform(norm_kaze_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_kaze_train.npy', pca_kaze_train)\n",
    "np.save('cache/pca_kaze_val.npy', pca_kaze_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_kaze_train = np.load('cache/pca_kaze_train.npy')\n",
    "pca_kaze_val = np.load('cache/pca_kaze_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_kaze_train.shape, pca_kaze_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SIFT Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_sift_train, norm_sift_val = norm_features_minmax(sift_train, sift_val)\n",
    "norm_sift_train, norm_sift_val = norm_features_zscore(sift_train, sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=5)\n",
    "pca_sift_train = pca.fit_transform(norm_sift_train)\n",
    "pca_sift_val = pca.transform(norm_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_sift_train.npy', pca_sift_train)\n",
    "np.save('cache/pca_sift_val.npy', pca_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_sift_train = np.load('cache/pca_sift_train.npy')\n",
    "pca_sift_val = np.load('cache/pca_sift_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 5), (4410, 5))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_sift_train.shape, pca_sift_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SURF Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_surf_train, norm_surf_val = norm_features_minmax(surf_train, surf_val)\n",
    "norm_surf_train, norm_surf_val = norm_features_zscore(surf_train, surf_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=5)\n",
    "pca_surf_train = pca.fit_transform(norm_surf_train)\n",
    "pca_surf_val = pca.transform(norm_surf_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_surf_train.npy', pca_surf_train)\n",
    "np.save('cache/pca_surf_val.npy', pca_surf_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_surf_train = np.load('cache/pca_surf_train.npy')\n",
    "pca_surf_val = np.load('cache/pca_surf_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 5), (4410, 5))"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_surf_train.shape, pca_surf_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gabor Fearures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_gabor_train, norm_gabor_val = norm_features_minmax(gabor_train, gabor_val)\n",
    "norm_gabor_train, norm_gabor_val = norm_features_zscore(gabor_train, gabor_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arijitiiest/Desktop/Workspace/Project/Malaria Detection/env/lib/python3.6/site-packages/sklearn/decomposition/_pca.py:545: RuntimeWarning: invalid value encountered in true_divide\n",
      "  self.explained_variance_ / total_var.sum()\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=5)\n",
    "pca_gabor_train = pca.fit_transform(norm_gabor_train)\n",
    "pca_gabor_val = pca.transform(norm_gabor_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_gabor_train.npy', pca_gabor_train)\n",
    "np.save('cache/pca_gabor_val.npy', pca_gabor_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_gabor_train = np.load('cache/pca_gabor_train.npy')\n",
    "pca_gabor_val = np.load('cache/pca_gabor_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 5), (4410, 5))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_gabor_train.shape, pca_gabor_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GLCM Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_glcm_train, norm_glcm_val = norm_features_minmax(glcm_train, glcm_val)\n",
    "norm_glcm_train, norm_glcm_val = norm_features_zscore(glcm_train, glcm_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=5)\n",
    "pca_glcm_train = pca.fit_transform(norm_glcm_train)\n",
    "pca_glcm_val = pca.transform(norm_glcm_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_glcm_train.npy', pca_glcm_train)\n",
    "np.save('cache/pca_glcm_val.npy', pca_glcm_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_glcm_train = np.load('cache/pca_glcm_train.npy')\n",
    "pca_glcm_val = np.load('cache/pca_glcm_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 5), (4410, 5))"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_glcm_train.shape, pca_glcm_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flattened Image:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RGB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_flat_rgb_train, norm_flat_rgb_val = norm_features_minmax(flat_rgb_train, flat_rgb_val)\n",
    "norm_flat_rgb_train, norm_flat_rgb_val = norm_features_zscore(flat_rgb_train, flat_rgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_flat_rgb_train = pca.fit_transform(norm_flat_rgb_train)\n",
    "pca_flat_rgb_val = pca.transform(norm_flat_rgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_flat_rgb_train.npy', pca_flat_rgb_train)\n",
    "np.save('cache/pca_flat_rgb_val.npy', pca_flat_rgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_flat_rgb_train = np.load('cache/pca_flat_rgb_train.npy')\n",
    "pca_flat_rgb_val = np.load('cache/pca_flat_rgb_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_flat_rgb_train.shape, pca_flat_rgb_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GRAY:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_flat_gray_train, norm_flat_gray_val = norm_features_minmax(flat_gray_train, flat_gray_val)\n",
    "norm_flat_gray_train, norm_flat_gray_val = norm_features_zscore(flat_gray_train, flat_gray_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=6)\n",
    "pca_flat_gray_train = pca.fit_transform(norm_flat_gray_train)\n",
    "pca_flat_gray_val = pca.transform(norm_flat_gray_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_flat_gray_train.npy', pca_flat_gray_train)\n",
    "np.save('cache/pca_flat_gray_val.npy', pca_flat_gray_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_flat_gray_train = np.load('cache/pca_flat_gray_train.npy')\n",
    "pca_flat_gray_val = np.load('cache/pca_flat_gray_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 6), (4410, 6))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_flat_gray_train.shape, pca_flat_gray_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Color Histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm_hist_train, norm_hist_val = norm_features_minmax(hist_train, hist_val)\n",
    "norm_hist_train, norm_hist_val = norm_features_zscore(hist_train, hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_hist_train = pca.fit_transform(norm_hist_train)\n",
    "pca_hist_val = pca.transform(norm_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/pca_hist_train.npy', pca_hist_train)\n",
    "np.save('cache/pca_hist_val.npy', pca_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_hist_train = np.load('cache/pca_hist_train.npy')\n",
    "pca_hist_val = np.load('cache/pca_hist_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_hist_train.shape, pca_hist_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature PCA variance comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_array = []\n",
    "pca_array.append(pca_hog_train)\n",
    "pca_array.append(pca_lbp_train)\n",
    "pca_array.append(pca_sift_train)\n",
    "pca_array.append(pca_surf_train)\n",
    "pca_array.append(pca_kaze_train)\n",
    "pca_array.append(pca_gabor_train)\n",
    "pca_array.append(pca_glcm_train)\n",
    "pca_array.append(pca_hist_train)\n",
    "pca_array.append(pca_flat_gray_train)\n",
    "pca_array.append(pca_flat_rgb_train)\n",
    "len(pca_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7fUcREREJrEc+2MDNTy2horrOdxQRkcBQsRdwWalJvHvzqVx5Um/fUURERAJr0rBCahsivLKszHcUEZHAULEXA7IzkgFoiGjdnoiISHOGds2mW4d0deUUEdmPir0YccszS7n8zx/4jiEiIhJIZsY5Qwp59+PtbNu913ccEZFAULEXIzplpfL26m2s377HdxQREZFAmjSskOSwsWxjpe8oIiKBoGIvRnxtZFfM4Ml5G3xHERERCaQB+e2Y/18TGNs/z3cUEZFAULEXIwpz0jmxXx5PzivR2j0REZFmmBkZKUkARDRWioio2IslF4zsysaKGt79eJvvKCIiIoFUtbees+97iz+/s9Z3FBER71TsxZAJA/P54ekDGJDfzncUERGRQMpMTcIwdeUUEUHFXkxJSw5zzcl96dw+zXcUERGRwDpnaAGLSypYt63KdxQREa+8FHtmNsDMFu73U2lmN5hZBzN71cw+ij7m+sgXZM45Xli8iVkrtviOIiIiEkhnDykE4PnFursnIonNS7HnnFvpnBvmnBsGjAT2AE8DNwMznXP9gJnRYznAb2at5u7XVvmOISIiEkiFOekc0zNXUzlFJOEFYRrnqcDHzrlPgHOBh6LnHwIme0sVUGbGBSO7srikghVl2kdIRESkOVeP7cN3TuitrpwiktCCUOxdDDwSfZ7vnNsUfV4G5PuJFGyThxeRHDaemFviO4qIiEggnfqVfC48phuhkPmOIiLijddiz8xSgEnAEwe+5pxzwGe+jjOzq8xsrpnN3bp1axukDJ4OmSmM/0o+Ty8opbY+4juOiIhIIG3dtZdHP1hP458UIiKJx/edvTOA+c65zdHjzWZWABB9/EwXEufcA865YudccV5eXhtGDZYLi7uRk55MaXm17ygiIiKBNGvFFm6etoTFJRW+o4iIeOG72LuEf03hBHgWuDz6/HLgmTZPFCPG9s9j5g/G0qtTpu8oIiIigTTx6C4kh43n1KhFRBKUt2LPzDKBCcC0/U7fDkwws4+A8dFjaUYoZJgZNXUNVO2t9x1HREQkcLLTkxnbvzPPL96kRi0ikpC8FXvOuSrnXEfnXMV+57Y75051zvVzzo13zu3wlS8WlO+pZdTPXuNv733iO4qIiEggTRpWSFllDXPW6U8KEUk8vqdxSgvkZKTQP78dT8zboMXnIiIizRj/lc5kpoRZVFLuO4qISJtTsRfjLizuxpqtVcxfv9N3FBERkcDJSEni3amnctVJfXxHERFpcyr2YtyZQwrISAlrzz0REZGDyE5PBtAsGBFJOCr2YlxWahJnDi7guUUb2VOrRi0iIiLN+Y9HFjB12hLfMURE2pSKvThw9dg+/OPK40hPDvuOIiIiEkgpSSFeWLyJmroG31FERNqMir040LdzFsO65WBmvqOIiIgE0qShhezaW88bK7f6jiIi0mZU7MWJ0vJqpk5bzCfbq3xHERERCZzRfTrSMTOF5xZrg3URSRwq9uJEyOCxORt4cp4atYiIiBwoKRzizMEFzFy+maq9WuMuIolBxV6cKMhO58R+eTw5r4SGiLqNiYiIHOjC4m5cN64fDerKKSIJQsVeHLmwuBubKmp4Z/U231FEREQCZ3DXbK49pS/t05J9RxERaRMq9uLI+IGdyclI5vG5G3xHERERCaSaugZeWLyJij11vqOIiBxxKvbiSGpSmG8e14OC7DTfUURERAJp1eZdXPvwfF5etsl3FBGRIy7JdwBpXTeeNsB3BBERkcAaXJRNj44ZPLdoExcd0913HBGRI0p39uKQc47FJeW+Y4iIiASOmXHOkELe/XgbW3ft9R1HROSIUrEXh56YW8Kk37zD8k2VvqOIiIgEzqRhhUQcvLhEUzlFJL6p2ItDEwbmkxIO8cRc7bknIiJyoP757RiQ3465n+z0HUVE5IhSsReHcjNTmDAwn+kLS6mtj/iOIyIiEjgPX3ksv754mO8YIiJHlIq9OHVBcVd2VNXy+orNvqOIiIgETsesVMzMdwwRkSNKxV6cOrFfHl3ap/H8Yq1HEBERac79b6zmsj+97zuGiMgRo60X4lQ4ZPz9ilH06JjpO4qIiEggpYRDvLN6O2u27qZ3XpbvOCIirU539uJYv/x2pCTpP7GIiEhzzh5SiBk8t0izYEQkPqkSiHPT5pfwzT9/gHPOdxQREZFA6ZKdxqieHXh2UanGSRGJSyr24lx9xPHmqq3MX6/20iIiIgc6Z2ghH2+tYvmmXb6jiIi0OhV7ce6swQVkpIR5fI723BMRETnQmYML+NbonmSlqo2BiMQfFXtxLjM1ibMGF/D84o3sqa33HUdERA6BmYXNbIGZPR897mVms81stZk9ZmYpvjPGiw6ZKdw6aRDdO2b4jiIi0upU7CWAC4q7UVXbwItLynxHERGRQ3M9sHy/4zuAu51zfYGdwBVeUsWpSMTxwdodrNtW5TuKiEirUrGXAI7pmculx3anh761FBEJPDPrCpwF/Cl6bMA44MnoJQ8Bk/2ki0+7a+v5+oOzeei9db6jiIi0KhV7CcDM+PlXB3NMzw6+o4iIyBe7B/ghEIkedwTKnXNNc/FLgKLm3mhmV5nZXDObu3Xr1iOfNE60T0vmlAF5vLB4Ew0RdeUUkfihYi+BfLK9irc/2uY7hoiIHISZnQ1scc7NO5z3O+cecM4VO+eK8/LyWjldfDtnaCFbdu1l9trtvqOIiLQaL8WemeWY2ZNmtsLMlpvZ8WbWwcxeNbOPoo+5PrLFs1ueXcaUJxfpW0sRkeAaA0wys3XAozRO37wXyDGzpnaRXYFSP/Hi16lH5ZOREtYG6yISV3zd2bsXeNk5dxQwlMZF6DcDM51z/YCZ0WNpRReM7MamihreXq27eyIiQeScm+qc6+qc6wlcDLzunLsMmAWcH73scuAZTxHjVnpKmAkD83l/zXZtsC4icaPNiz0zywZOAh4EcM7VOufKgXNpXHQOWnx+RIwf2JmcjGSemLvBdxQREflybgJuNLPVNK7he9Bznrj032cPZMYNJ9HYE0dEJPb52EG0F7AV+IuZDQXm0dhiOt851zR3ogzI95AtrqUmhZk8rIiHZ6+nfE8tORnapklEJKicc28Ab0SfrwFG+cyTCDpmpfqOICLSqnxM40wCRgC/c84NB6o4YMqma5w/0ewcCnUaa5kLirvicCxYX+47ioiISOC8vLSMM+99i5q6Bt9RRERazEexVwKUOOdmR4+fpLH422xmBQDRxy3NvVmdxlpmUGE2c348nlOO6uw7ioiISOBkpSbx4aZKZq1o9s8QEZGY0ubFnnOuDNhgZgOip04FPgSepXHROWjx+RHVNH1TXTlFREQ+7bjeHeiUlcJzizf6jiIi0mI+1uwBXAf8w8xSgDXAt2ksPB83syuAT4ALPWWLe5GI4xt/nk3//Hbccs4g33FEREQCIykc4qzBBTw6ZwO7aupol5bsO5KIyGHzsvWCc25hdCrmEOfcZOfcTufcdufcqc65fs658c65HT6yJYJQyMhJT2H6glL21mtNgoiIyP7OGVrI3voIry3f7DuKiEiL+NpnTzy7oLgrO/fUMXO51iSIiIjsb0T3XC49tjvdcjN8RxERaREVewnqxH55dGmfpj33REREDhAKGT//6mCKe3bwHUVEpEVU7CWocMj42sgi/rlqK2UVNb7jiIiIBM7abVUsLa3wHUNE5LD5atAiAXBRcXfy26eRmRr2HUVERCRQnHNc8dc5dMlO4+Erj/MdR0TksOjOXgLr3jGDbx7fU53GREREDmBmnD20kPfWbGdLpWbAiEhsUrGX4GrqGnh49npNUxERETnApKEFOAcvLNnkO4qIyGFRsZfgGiKOn73wIX97b53vKCIiIoHSt3M7vlLQnucWaYN1EYlNKvYSXGZqEmcNKeD5xZuo2lvvO46IiEignDO0gKWllWzfvdd3FBGRL61FxZ6Z5ZvZg2b2UvR4oJld0TrRpK1cWNyNPbUNvKhpKiIirULjY/y47NgezPnxeDpmpfqOIiLypbX0zt5fgRlAYfR4FXBDCz9T2tjIHrn07pTJE3NLfEcREYkXf0XjY1zITk8mO0ONzEQkNrW02OvknHsciAA45+qBhhankjZlZpxf3JW6SISaOv3nExFpBRof48iHGyv52u/eZfWWXb6jiIh8KS0t9qrMrCPgAMzsOEBtHWPQ1Sf14elrxpCWrD33RERagcbHONIxK4X563fy3CItdxCR2NLSYu9G4Fmgj5m9A/wNuK7FqaTNhUIGQMWeOhoiznMaEZGYp/ExjuS3T+O4Xh15btFGnNMYKSKxo0XFnnNuPjAWGA18FxjknFvcGsGk7S1Yv5Njfv4ab6/e5juKiEhM0/gYf84ZWsiabVUs21jpO4qIyCFraTfOa4Es59wy59xSIMvMrmmdaNLWBhVmk5kS5vG5G3xHERGJaRof488ZR3chKWTac09EYkpLp3Fe6Zwrbzpwzu0ErmzhZ4onKUkhJg8v4tVlmynfU+s7johILNP4GGdyM1O48qTeDCxs7zuKiMgha2mxFzYzazowszCQ0sLPFI8uGNmN2oYIzyzUN5ciIi2g8TEO3XT6UZw7rMh3DBGRQ9bSYu9l4DEzO9XMTgUeiZ6TGDWwsD1HF7XXVE4RkZbR+BindlbVMu+THb5jiIgckqQWvv8mGhee/3v0+FXgTy38TPHsp5OOJlcbyIqItITGxzj14+lL+GDtTt6fOo6kcEu/MxcRObJaVOw55yLA76I/EidG9sj1HUFEJKZpfIxfZw8p5MUlZcxeu4MxfTv5jiMi8rla2o1zjJm9amarzGyNma01szWtFU78WVJSwY2PLWRvfYPvKCIiMUfjY/wad1RnMlPC6sopIjGhpfMPHgR+BZwAHAMURx8lxu3YU8u0BaXMXL7FdxQRkVik8TFOpSWHOW1QF15aWkZtfcR3HBGRz9XSYq/COfeSc26Lc25700+rJBOvTujbiYLsNDVqERE5PBof49g5QwuoqK5j3ic7fUcREflcLW3QMsvM7gKmAXubTjrn5rfwc8WzcMj42oiu3P/GasoqauiSneY7kohILNH4GMdO7JfHG/95Mj07ZfqOIiLyuVpa7B0bfSze75wDxrXwcyUAzh/Zld/MWs1T80u49pS+vuOIiMQSjY9xLDkcUqEnIjGhpd04T2mtIBI8PTtlcu6wQtqntfQ7ARGRxKLxMf7trKpl6rQlfHVEERMHdfEdR0SkWS3+K97MzgIGAfvm+Tnn/l9LP1eC4d6Lh/uOICISkzQ+xrf26cnMW9+4Zk/FnogEVUu3Xvg9cBFwHWDABUCPVsglAVLXEGHZxgrfMUREYobGx/gXDhlnDS7g9ZVbqKyp8x1HRKRZLe3GOdo5901gp3Pup8DxQP+Wx5Ig+dkLy7ng9+9RtbfedxQRkVih8TEBTBpWSG19hFeXbfYdRUSkWS0t9qqjj3vMrBCoAwoO5Y1mts7MlpjZQjObGz3XIboJ7UfRx9wW5pNWcPaQAvbUNvDCkk2+o4iIxIrDHh8ldgzvlkPX3HSe1QbrIhJQLS32njezHOAuYD6wDnjkS7z/FOfcMOdcU7eym4GZzrl+wMzosXg2skcuvTtl8uTcEt9RRERiRUvHR4kBZsYVJ/RiRPdcnHO+44iIfEZLu3H+T/TpU2b2PJDmnGvJ4q5zgZOjzx8C3gBuasHnSSswM84v7sqdL69k7bYqeqndtIjI5zoC46ME1LfH9PIdQUTkoA7rzp6ZjYs+ntf0A5wFnBp9figc8IqZzTOzq6Ln8p1zTXMFy4D8Zv7ZV5nZXDObu3Xr1sOJL4fhayO6EjJ4UVM5RUQOqpXGR4kxtfUR5q7b4TuGiMhnHO6dvbHA68A5zbzmgGmH8BknOOdKzawz8KqZrfjUhzjnzOwzcyKccw8ADwAUFxdrzkQbyW+fxvPXnchRXdr5jiIiEmStMT5KjHngzY/55aureO/mU+mSnfbFbxARaSOHVew5524xsxDwknPu8cP8jNLo4xYzexoYBWw2swLn3CYzKwC2HM5ny5GxavMurvzbXDaWV1OYk86UiQOYPLzIdywRkcBojfFRYs8Zgwv4xSureGHJJq44QdM6RSQ4DrtBi3MuAipvOlMAACAASURBVPzwcN5rZplm1q7pOXAasBR4Frg8etnlwDOHm09a1/QFpUydtoTS8mocUFpezdRpS5i+oNR3NBGRQGnJ+CixqU9eFkU5adz+0nJ63fwCY25/XeOjiARCS7txvmZm/2lm3aLbJnQwsw6H8L584G0zWwR8ALzgnHsZuB2YYGYfAeOjxxIAd81YSXVdw6fOVdc1cNeMlZ4SiYgE2uGOjxKDpi8oZXPlXuoanL4QFZFAaVE3TuCi6OO1+51zQO/Pe5Nzbg0wtJnz24FTW5hJjoCN5dVf6ryISII7rPFRYtNdM1ZSH/l0G4GmL0S13EFEfGrp1guamJ4gCnPSKW2msCvMSfeQRkQk2DQ+JhZ9ISoiQdXSO3uY2dHAQGBf+ynn3N9a+rkSLFMmDmDqtCWfmsppwI0T+vkLJSISYBofE4e+EBWRoGrRmj0zuwW4L/pzCnAnMKkVcknATB5exG3nDaYoJx0DOrdL5YenD+BrI7v5jiYiEjgaHxPLlIkDSE8Of+pcctiYMnGAp0QiIo1aemfvfBrX3i1wzn3bzPKB/2t5LAmiycOLml17sG33XjplpXpIJCISWBofE0jT2HjXjJVsLK8mOSmEc46h3XI8JxORRNfSbpw10RbT9WbWnsZ98XSrJ4Hc89oqTr/nLXZW1fqOIiISJBofE8zk4UW8c/M41t5+Fv+ccjIZKUnc8OgC6hoivqOJSAI7rGLPzH5rZicAH5hZDvBHYB4wH3ivFfNJwJ02sAvle2q59bllvqOIiHin8VEACrLTuf28wSwqqeCe11b5jiMiCexwp3GuAu4CCoEq4BFgAtDeObe4lbJJDBhY2J7rxvXj7tdWcebgAiYO6uI7koiITxofBYAzBhdw8THdGvfecw4z8x1JRBLQYd3Zc87d65w7HjgJ2A78GXgZ+KqZqT1jgrnmlD4MKmzPj59equmcIpLQND7K/m47bzA/OvMrKvRExJsWrdlzzn3inLvDOTccuASYDKxolWQSM5LDIX5xwVAAVm3e5TmNiIh/Gh8F2FfkzV23g/95/kOcc1/wDhGR1tXSrReSzOwcM/sH8BKwEjivVZJJTPlKQXvevukUju3d0XcUERHvDnd8NLM0M/vAzBaZ2TIz+2n0fC8zm21mq83sMTNLOcL/CtKKZq/dwYNvr+XJeSW+o4hIgjncBi0TzOzPQAlwJfAC0Mc5d7Fz7pnWDCixIy05TCTi+MfsT9ih6ZwikoBaYXzcC4xzzg0FhgGnm9lxwB3A3c65vsBO4Ioj828gR8LVY/twbK8O3PLsMtZtq/IdR0QSyOHe2ZsKvAt8xTk3yTn3sHNOv72EdduruPXZZfz3M0t9RxER8aFF46NrtDt6mBz9ccA44Mno+YdonBYqMSIcMu6+aBhJIeP6xxZqOwYRaTOH26BlnHPuT865na0dSGJb77ws/mNcP55fvImXlmzyHUdEpE21xvhoZmEzW0jj3nyvAh8D5c65+uglJUBRy9NKWyrMSee284awaEM5T2k6p4i0kcPdekHkoK4+uQ8zPizjJ9OXMqpXBzpmpfqOJCISM5xzDcCw6D59TwNHHep7zewq4CqA7t27H5mActjOGlJARsoxjO2f5zuKiCSIFjVoEWlOU3fOypo6bnlWm62LiBwO51w5MAs4Hsgxs6YvaLsCpQd5zwPOuWLnXHFengqKIDrlqM6EQsbmyhoq9tT5jiMicU7FnhwRR3Vpz62TBnHpKH2zLCJyqMwsL3pHDzNLp3FD9uU0Fn3nRy+7HFAztBi2p7aes+97mx9NX6LtGETkiFKxJ0fMZcf2YHTfTgAazEREDk0BMMvMFgNzgFedc88DNwE3mtlqoCPwoMeM0kIZKUl8a3RPXli8iafmN3uTVkSkVWjNnhxxv3plJWu37+G+S4b7jiIiEmjOucXAZ35ZOufWAKPaPpEcKVeP7cM/V23llmeWckzPXHp0zPQdSUTikO7syRGXmhzmuUUbeWGxunOKiIjAv7ZjCIeM6x/VdgwicmSo2JMj7rsn9WZI12z+65mlbNu913ccERGRQCjKSefn5w2mKDed2noVeyLS+lTsyRGXFO3Oubumnv+avlTr90RERKLOHlLIby8dQWaqVtaISOtTsSdton9+O64f34+Zy7ewZluV7zgiIiKBsmbrbq7621wqqrUdg4i0HhV70ma+e1JvXrrhRPrkZfmOIiIiEigV1XXMXLGFn2gGjIi0IhV70maSwqF9hd789Ts1mImIiEQN757L98f347lFG5mm7RhEpJWo2JM29+qHmznv/nd5Xt05RURE9vn3k/syqmcH/vuZpXyyXUseRKTlVOxJmztlQB5Du+Xw388sZesudecUERGB6HYMFw8jFDJ+PXO17zgiEgdU7EmbSwqH+OUFQ6iqbVB3ThERkf0U5aTzj+8cy8++erTvKCISB1TsiRd9O7fjxgn9eXlZGc9pOqeIiMg+Q7rmkJYcZvfeej7avMt3HBGJYd6KPTMLm9kCM3s+etzLzGab2Woze8zMUnxlk7Zx5Ym9OXlAHmEz31FEREQC55p/zOdbf5mj7RhE5LD5vLN3PbB8v+M7gLudc32BncAVXlJJmwmHjL9+exRnDSnwHUVERCRwvj++H2WVNVryICKHzUuxZ2ZdgbOAP0WPDRgHPBm95CFgso9s0vYiEcdf31nLC5rOKSIiss/w7rnccGo/nl20kacXaDsGEfnyfN3Zuwf4IRCJHncEyp1z9dHjEqDIRzBpew6YvnAjP56+hC27anzHERERCYxrTmnajmEZ67fv8R1HRGJMmxd7ZnY2sMU5N+8w33+Vmc01s7lbt25t5XTiQzhk/OKCoeypbeAnT2uqioiISJNwyPjVRUM5tlcHwmGtcReRL8fHnb0xwCQzWwc8SuP0zXuBHDNLil7TFWh2voJz7gHnXLFzrjgvL68t8kob6Ns5ix9M6M8rH27m2UUbfccREREJjK65GTz4rWMoykn3HUVEYkybF3vOuanOua7OuZ7AxcDrzrnLgFnA+dHLLgeeaets4td3TuzN8O453PrsMqr21n/xG0RERBLIjqpavvPQHOas2+E7iojEiCDts3cTcKOZraZxDd+DnvNIG2uazvnby0aQmZr0xW8QERFJIMlhY9Xm3dzw6EIqa7Qdg4h8Ma/FnnPuDefc2dHna5xzo5xzfZ1zFzjn9vrMJn70yctidJ9OABrIRERE9tMuLZl7Lh62bzsGEZEvEqQ7eyL7PPLBek6+6w22VKo7p4iISJMR3XO5/tR+PLNwI9O1HYOIfAEVexJIx/bqQNXeen6k7pwiIiKfcs3JfSjukct9r39EQ0RjpIgcnIo9CaTeeVlMmTiA15ZvZvpCfXMpIiLSJCkc4r5Lh/Pk1aMJh7Qdg4gcnIo9Caxvj+lFcY9cbn32Q03nFBER2U9Bdjq5mSnUNUR49+NtvuOISECp2JPACoeMO88fgnOO+et3+o4jIiISOPfP+piv/2k28z7Rdgwi8lkq9iTQeudl8c7N4zj96ALfUURERALn307oSVFuOtdrOwYRaYaKPQm8dmnJAMxYVkZZhaZzioiINGmXlsw9Fw1nU0UN/63tGETkACr2JCZs2VXD9Y8u4EdPL1F3ThERkf2M7JHLf4zrx3RtxyAiB1CxJzGhc7s0pkw8itdXbOGp+RrIRERE9nftKX04f2RX+nbO8h1FRAJExZ7EjG+P7skxPXP56XPLNJ1TRERkP0nhEL+4YChHF2UDaBaMiAAq9iSGhELGXecPpa4hwtRpizWQiYiIHKA+Okb+euZq31FEJACSfAcQ+TJ6dsrk1nMGkZYc9h1FREQkcJLCIWrqIvz69Y84oV8nRvbI9R1JRDzSnT2JOReP6s7k4UWYme7uiYiIHOD/nTuIwpw0bnhsAbu0HYNIQlOxJzHrH7M/4dqH56vgExER2U/Tdgwby2u45ZllvuOIiEeaxikxq7Y+wotLynhiXgkXFnfzHUdERCQwRvbI5bpxfbl/1mre+XgbWyr3UpiTzpSJA5g8vMh3PBFpI7qzJzHr8uN7MqpXB/7n+Q/ZVFHtO46IiEigdM9Nx8zYXLkXB5SWVzN12hLtxSeSQFTsScxq7M45hPoGx9Rp2mxdRERkf7989SP21kc+da66roG7Zqz0lEhE2pqmcUpM69Exk5tOH8Ctz33IMT+byfbdmqYiIiICsLG8+VkvBzsvIvFHd/Yk5mWnJZOaFGLbbk1TERERaVKYk97s+bTkMNt3723jNCLig4o9iXm/eHVVs9NUpk5bwj2vreLZRRs9JRMREfFnysQBpB+wL21SyNhb38DFD7xPJKLlDyLxTtM4JeYdbDpKdV0D9878iB4dMpg0tBCAf/+/eazdVkWPjhn06JhJ9w4ZDOjSjmN6dmjLyCIiIkdc03KGu2asZGN59b5lDoMK27O5ci+hkFHfEKFkZzU9O2V6TisiR4KKPYl5hTnplDZT8BXlpDPzB2PZuad237mji7KprY/w8dYqZq3cSm19hON6d+DRq44H4Ft/+YCGiGssBjtk0q1DBkd1aadBUEREYtLk4UXNrmHvl98OgL++u447X17Jtaf05eqTe5OaFP7MtSISu1TsScybMnEAU6ctobquYd+59OQwUyYOIC05TEH2v9YsXHtK333PIxHH5l01VNf+632dslJZtXkXzy3aREV1HQDnjSjiVxcOwznH1x+cTX67NLp3zKBHxwy6d8ikb14W2RnJzWabvqD0M9+oqnGMiIgExaRhhSzcUM7dr63iucUb+flXBzOql2a7iMQLFXsS8w42TeWLiqpQyD5VCAL84oKh+55X7Knjkx1VpEXXO1TXNdAQcby/ZjtPLyylaaeHa0/pw5SJR1FZU8eUJxbtmx5asnMPf3ln3b71hE2NY/bPLCIi4lPndmn85tIRfG3kFn7y9FIu/MN7TJk44FNfjopI7FKxJ3HhYNNUWiI7I5khGTn7jjNSkvZN96ypa6BkZzXrd1TRLTcDgJ1VtZ+aHtqcpv2NVOyJiEiQnDKgM6/eeBL3vPYRx0bv7NXWR0gOG2bmOZ2IHC4VeyKHIS05TN/OWfTtnLXvXI+Ombx249h900NH3/Y6zfU5Ky2vpr4hQlJYzXBFRCQ4MlKS+NGZX9l3/LMXPmTd9j387+Sj6dYhw2MyETlc+mtTpJU1TQ892P5GABPufpOnF5TQoLbXIiISUL3zspi7bgcT7v4nv//nx9Q1ND9rRUSCS8WeyBHS3P5G6ckhvj2mJ6lJIb7/2CKu/cd8T+lEREQ+3+Wje/LqjWM5sV8et7+0gnPue5sVZZW+Y4nIl6BpnCJHyOc1jolEHDOWldEurbGLZ2VNHW+u2sqZRxcQCmlthIiIBENhTjp//GYxLy8t446XV5CRrD8dRWKJl/9jzSwNeBNIjWZ40jl3i5n1Ah4FOgLzgG8452oP/kkiwXawxjGhkHHG4IJ9x0/NK+Gnz33IgPzVXD++H6cP6qKiT0REAuP0o7swYWA+4ZDhnONHTy9lbP88Tj+6i+9oIvI5fE3j3AuMc84NBYYBp5vZccAdwN3Oub7ATuAKT/lE2tQ3j+/Jry8ZTn0kwjX/mM+Zv36Ll5eW+Y4lIiKyTzj6JWRldT0LN5Rz9f/N48q/zWVTRbXnZCJyMF6KPddod/QwOfrjgHHAk9HzDwGTPcQTaXPhkDFpaCGvfH8s9148jNr6CI98sN53LBERkc/Izkjm2e+N4eYzjuKtj7Yy/pf/5C/vrFXTMZEA8tagxczCZrYQ2AK8CnwMlDvn6qOXlADajEwSSjhknDusiFe+fxK/urBxg/cNO/Yw+bfv8NqHm3FOA6mIiPiXHA5x9dg+vPr9sYzs2YHfzlrN7pr6L36jiLQpb8Wec67BOTcM6AqMAo46lPeZ2VVmNtfM5m7duvWIZhTxJSkcomNWKgBllTXsqKrlO3+by7m/fYdZK7ao6BMRkUDo1iGDh759DM9+7wSyM5Kpb4jwxzfXsKdWhZ9IEHjfesE5Vw7MAo4HcsysqWlMV6C0mesfcM4VO+eK8/Ly2jCpiB/H9OzAzB+M5c6vDWFHVS3f/uscLvzDe5ouIxKHzKybmc0ysw/NbJmZXR8938HMXjWzj6KPub6zijQxs317y77z8XZ+9uJyTrv7TWat3OI5mYh4KfbMLM/McqLP04EJwHIai77zo5ddDjzjI59I0CSHQ1x4TDde/8HJ3HbeYI7v02nfQvklJRW60ycSP+qBHzjnBgLHAdea2UDgZmCmc64fMDN6LBI4Y/vn8dhVx5GaFOLbf5nD9x6ez5ZdNb5jiSQsX5ulFAAPmVmYxoLzcefc82b2IfComf0vsAB40FM+kUBKSQpxyaju+44XrN/JV+9/l+IeuXx/Qn9G9+mImbZsEIlVzrlNwKbo811mtpzG9evnAidHL3sIeAO4yUNEkS90bO+OvHj9ifz+jTX8dtZqNlfW8MTVo33HEklIXoo959xiYHgz59fQuH5PRA7BwML2/M/ko/nt66u57E+zGdWrA98f35/j+3T0HU1EWsjMetI4Vs4G8qOFIEAZkO8plsghSU0Kc/34fpw9tICaugYAdtXUUVZRQ7/8dp7TiSQO72v2ROTwpSaF+cZxPXhjysn8dNIg1m2r4rt/n8vuvVoYLxLLzCwLeAq4wTlXuf9rrnHedrNzt9XETIKmT14WgwqzAbjv9dWc+eu3+OUrK/cVgCJyZPmaxikirSgtOczlo3ty0THdWFG2i6zUJCIRx4+nL+W8EUUc07OD74gicojMLJnGQu8fzrlp0dObzazAObfJzApo3LboM5xzDwAPABQXF2sxrwTKd0/qzdZde7nv9dU8v3gTZwzuwjMLNrKxvJrCnHSmTBzA5OHadUukNenOnkgcSUsOM6xbDgDrd+zh1Q/LuOD37/GNB2cz75OdntOJyBexxkW3DwLLnXO/2u+lZ2lsXAZqYCYxqmNWKndfNIy/XzGKyupa7p/1MaXl1TigtLyaqdOWMH3BZxqxi0gLqNgTiVM9O2Xy5g9P4UdnHsWHGyv52u/e5Zt//oCtu/YCMH1BKWNuf51eN7/AmNtf1wArEgxjgG8A48xsYfTnTOB2YIKZfQSMjx6LxKQT++WRmhT+zPnqugbunLHCQyKR+KVpnCJxLCMliatO6sNlx/bg7+9/wstLy8jJSGb6glJunraYmroI8K9vVAFNoRHxyDn3NnCwlrqntmUWkSNpU0Xz2zFsLK/h/jdWc/7IrnRul9bGqUTij+7siSSAzNQkrh7bh6evGU1yOMSdL6/YV+g1qa5r4I6X9Y2qiIgceU2bsB8oJSnEnS+vZPRtr/Pjp5e0cSqR+KM7eyIJpGkPvoN9o9p0fsOOPfzg8UXkZ6dRkJ1Gfvs0urRPo7hnLvnt9U2riIi0zJSJA5g6bQnV+3XlTE8Oc9t5gxncNZvH5mwgNyMFgIaI48G31zBpaBFdsjUGiXwZKvZEElBhTjql5dWfOd+5XSpAY0tsg8Ul5cxYVkNtfeNdwPsvG8GZgwt49+NtXP/oQrq0bywEC7LT6JKdxrnDCumam0FNXQMNEUdm6pf/FTN9QSl3zVip7mwiInGs6ff6wX7f/+jMr+y7dlFJOT9/cQW3v7SCcUflc+mx3RjbvzPh0MFmPItIExV7IgnoYN+oNg2u/fLb8fh3jwfAOUf5njrKKmsozG6cdpOTnsK4AZ0pq6yhZOce5qzbQUV1HaP7dKRrbgbPL97Efz6xiHZpSXRp31gIdmmfxg9OG0CX7DQ2VVSzfXctXbLT6JCRQig6YE9fUPqpXFpLKCISvyYPLzqk3+0juufyzykn8+icDTwxt4TXlm+mIDuNR686jh4dM9sgqUjsUrEnkoC+6BvV/ZkZuZkp5Gam7Ds3sLA9d5w/5FPXVdc2kBxuLNoGF2Vz0+lHsbmyhk0V1ZRV7mXV5q384LQBADw1r4RfvLIKgOSw7ZsmWlpe/akCFBrXEt41Y6WKPRGRBNajYyY3nX4UN07oz8zlm5mxbDNdczMAmDa/hKzUJMYd1ZmksNpRiOxPxZ5IgjrUb1QPVXrKv9poD+jSjgFd2h302klDi+jbOYuyihrKKvdSVlHN5sq9lB20O9tnp5yKiEjiSQ6HOP3oAk4/ugBonH3y53fWsrS0kvz2qVxY3I0Li7vRrUOG56QiwaBiT0TaXPeOGXTv+NmBeMztrze7ltABNz25+DN3E0VEJLGZGdOvGcOslVt55IP1/HbWan4zazVTJg7gmpP7+o4n4p3udYtIYEyZOID05E9vtJuaFGLiwHwGFrYHoK4hwj2vreKT7VU+IoqISMAkhUNMGJjPn791DG/fNI7rT+3Hsb06ALB6yy7ueHmFxgxJWLqzJyKBcShrCReXlPPrmR9xz2sfMaZvRy4Z1Z0JA/NJTQof7GNFRCRBFOakc8P4/vuO56zbyQNvruF3b3ysMUMSkjnnfGc4bMXFxW7u3Lm+Y4hIGyurqOGJuRt4dM4GSsur6ZCZwvRrxjQ7NVTih5nNc84V+84RKzRGijQ6cMwoyknnn1NOVjMXiRufNz7qzp6IxJwu2Wlcd2o/rjmlL2+v3sarH5bRNbdxW4i/v/8JWalhzji6gLRkfXMrIpLoDhwz1u/YQ1I4hHOOHz29lON6d2DioC4aMyQuqdgTkZgVDhlj++cxtn8e0NiV7cl5JSzaUM6tz37IV4cXcemx3emff/DOoCIikhiaxowmO/fU8c7qbTzywXpyMpI5b3hXLhnVjWUbKw9payKRWKBpnCISVyIRx/trtvPInA3MWFpGbUOEH0zoz3Wn9vMdTVpI0zi/HI2RIl8sEnG8+/F2HpmznleWlVHX4EgJh6htiOy7Jj05zG3nDVbBJ4GlaZwikjBCIWN0306M7tuJHVW1TJtfwnG9OwKwbGMFj3ywnktGdWdQYbbnpCIi4lsoZJzQrxMn9OvEtt17GfeLN6isqf/UNdV1DUydtoSVm3dRmJ3Gpcf2IBwyauoaSE0KYWae0ot8MRV7IhK3OmSm8J0Te+87XraxksfnlvB/769nSNdsLj6mO5OGFZKVql+FIiKJrlNWKrsOKPSaVNc18Mc315CaFOLrx/UA4OanFvPS0jIKstMoyE6nIDuN3nmZfG9c40yS0vJqMpLD5GQkqyAUb/QXjogkjAuLuzFxYBeeXlDCIx9s4EdPL+Hemat456Zx6somIiIU5qRTWl79mfNFOem89cNT2Lmndl/hNnFQFzq3T2NjeTWbKmqYvXYHH26q3Ffs3fjYQmav3UFacmhfMTi8ew5TJh4FwLxPdpKREqYgO43s9EMrCKcvKNV6QvlSVOyJSELJzkjmW2N6cfnonizYUM4n26v2dWX73sMLOK53B84dXkT7tGTfUUVEpI1NmTiAqdOWUF3XsO9cenKYKRMHEAoZHbNS950/Y3ABZwwu+NT79++Fcc0pfTlt0G42lVezqbKGTeXVbCyv2ff69x9byPode/b9Mwpy0jhtYBduPqOxGHxmYSnt05IpyEmjoH06r6/YzI+eXrovW2l5NVOnLQFQwScHpWJPRBKSmTGiey4juucCsKOqlrXbqnhhySZ+9uJyzh5SyCWjujGie66m34iIJIimoulw757tP17s3y26OfddMpySndVsqqimrKKGTRU1tEtr/NM8EnFMeWLxpxrFGHBgW8XqugbumrFSxZ4clLpxiohEOedYUlrBIx9s4NmFpVTVNvDAN0Zy2qAugKbP+KZunF+OxkiR2OWco6yyho3lNdFCsJr/fWH5Qa//r7MHcmK/TvTrnKUvKP8/e3ceXkV99n/8fWcjGyGEBAgJEHZQRJBFUVFUFLcqrtValdrWpdZqtdpa+/xqn8dWW6qP+lhrrWurdV9q64KK4lY3Ntk32fc9bAGy3L8/ziQNNIEk5GROTj6v6zpX5syZM+eTALm5Z+b7nRZIs3GKiNSBmTGgMJsBhdn84ox+vD59Ncf3iRyVveHZqfzjq9WUBwfIdPmMiIhEi5kF4/zSqtY9/smSGscTJiUY//PP2QDcf/Egzjq8E8U7S9lTXkFe61b/sb20LGr2RERqkNEqiQuHdq56/t7cdVWNXiVdPiMiIk2ltvGEd557GEOK2vLxgg0c0yNyq6FXpq7g9n/Mpl9+FiN65TKiVy5Di3JITU4MK76ERM2eiEgd1DYd96rgKKu769IZERGJmgONJ7xoWJeqbUf0zuPm0X34eMEGHv9kMQ9/uIj0lEQm/WIU6SlJbNqxh+y0ZBISVLfinZo9EZE6qG067k7ZaazYvJNLHvmc848o5IIhnenYJjWEhCIiEu/GDCqo09UkPfIyufaEnlx7Qk927inj88Wb+HrddtJTIv/1v+6ZKcxdvY1jekZuKD+iV+5el4xK/NCNpURE6uDm0X1I2+fyl8rpuLfvLqMgO42735nP0XdN4HtPfsm7s9dSVm0WNRERkTCkpyRxQp/2fG9E96p13xzaheN65/Gvrzdyy4vTGX7ne9z0/FdVr++qdqmoNG9NfmbPzDoDfwE6EJlB9mF3v8/McoDngCJgCXChu29u6nwiIjU50OUzf/v+USzZsIPnJi3nhUkrmDhvMp/eehJ5rVtRWl5Bsm7aLiIiMeKswztx1uGdcHfmrtnGxws2kJ8duSpl665Shv36XQYUZnNcr1yO7ZXHYQVtSNQln81Sk996wczygXx3n2JmrYHJwBhgLLDJ3e8ys58Bbd39p/vbl6aVFpFYVFpewYyVxVX38PvWnz8jMcG4eFgXRvXrQEqSGr+G0K0X6kc1UkQaYuP23Tz80SI+XrCBWau2AtAmLZm7LzicUYd0qHGMum5NFK6YuvWCu68GVgfL28xsDlAAnA2MDDZ7EpgI7LfZExGJRcmJCVWNnrszrFsOz325nB88PYXczBTOO6KQi4d1oSg3I+SkIiIie2uX2YpblggWXAAAIABJREFUT+sHp0Uav0++3shH89dX1ax/TF/N3W/PY0SvXI7tmcemnbv5n3/MqZolVLcmii2h3lTdzIqAD4H+wDJ3zw7WG7C58vk+77kSuBKgS5cug5cuXdpkeUVEGqq8wvlw/nqe+WIZE+au47bT+3HFsd3YXVaOO5oOuw50Zq9+dGZPRKLhk4UbeOzjxXy6aCM799Q+tq8gO5VPfnZSEyZrufZXH0Nr9swsE/gA+LW7v2xmW6o3d2a22d3b7m8fKmQi0hyt27qLtJREWqcm8/yXy/n1G3M4Z1ABFw/rQp+OrcOOF7PU7NWPaqSIRNOesgqmLtvMNx/+rNZtuuVm0CGrFX06tOZXZ/cHYPLSTQB0yEqlfetUDW1oBDF1GSeAmSUDLwFPu/vLweq1Zpbv7quDcX3rwsgmIhJt7bP+fWuG3h1bM6JXLn/7fBlP/GsJg7pkc/HQLpw/uFD3PxIRkZiVkpTAkd3bUVDLrYkyWiVySKcs1m3dxcotu6rW//c/ZvPViuKq5+0yUji2Vy73XTQIgKc/X0qCGR2yWtEhK5UOWankpKc0uCa29PGEYczGacCjwBx3v6faS68BlwN3BV//3tTZRESa2sDO2TzwrSPYtGMPL09ZwTNfLOOpz5dy4dDOAFXFSUREJBbdPLoPt748o2rMHkRuTfTrMYfV2FTdfeHhrNhcwtqtu1i7dTdrtu4iv9pB0PsnLGDt1t17vee0/h3547cHA3DLi1+RnpJExzapVQ1h99zMGu9x++rUlXtla4njCcM4s3cMcCkww8ymBet+TqTJe97MvgssBS4MIZuISChyMlL43ojufPfYbmzasQeA4pJSTrx7Ij3yMrloWBfOHtiJrNTkkJOKiIj824FuTbSvnu1b07N97UMWPv7piazfFmkC1xbvYu3WXVUHPSsqnKnLtrC6eBfbd5dVvWfs0UXcftah7C4r5/T7PqJDViods1J5e/bavZpQgJLScsaNn9dimr1QJ2g5WBqPICLxbOeeMl6cvIJnvljOnNVbSUtO5IwB+fzwhJ4tciZPjdmrH9VIEYln23eXRc4OFu+iXWYr+nRsTXFJKT97aXrVWcOaLi+tdMohHeick07ntmkc36c93ZpxXY25MXsiInJg6SlJXDa8iEuP6sr0FcU8++UyXpu2iquP7w7Ais07yUhJ4oP561v0eAQREWl5MlslkZmXSY+8zKp1bdKSqy73BDj6rgmsqjZesFKrpAQWb9jBhwvWs6u0gvsyUuiWm8GXSzZxzVOTKWybXtUIds5J56S+7fcab9+cqNkTEYlxZsbhnbM5vHM2v/zGoVW3abjzzbm8NWM1YJQHV2nE0niElj4oXkREwnXL6L41jie889zIeEJ3Z8P2PaSnROpq69QkRvXrwPLNO/lq+RbenLGasgrnxauH0z4rlX98tYo735hDYU46ndum0zknjc5t0zn50A71HmbRVDVSzZ6ISDNS/X58PzyhJxPnrmPHnprHI7wydSW7SstJT0kkPSWJtJREhhXlVE3+8tjHi0lOSiAtOZH0lETSkhPp0i6dHnmZuDurindVvdYqKYHI/Fp1o0HxIiIStgONJzQz8lq3qtq+b8cs7jpvQNXzsvIK1mzdRW5mZJv2rVtxVPd2LN+8k08WbmDN1shZw4+7n0BWajKPfLSIpz9fRmHbtODsYKQZPOXQDrRK+nf9bsoaqWZPRKSZ6pefVesNbVdtKWFAYRtK9pSzYfsedu7ZScmeclqnRn7tuzt3vD6bin2Gbf97kHsFx9z1XtX6BIscDb1mZA9+eGIviktKufyxL6qaxLSUSFN4xoBOHN87j9++NbfFD4oXEZHwjRlU0OC6k5SYQGHb9KrnR3Zvx5Hd21U931VazsotJeS3iUwgU9g2nUPys1i+eSczV65m885SEgzm3XEaAHe+OYeJc9ezdOMOdpVV7PVZ0aqRavZERJqxTrXc36hTdtpe4xZqMuP20ezcU07JnnJ2lpaxc0857TJSAEgw47fnHcbOPeXs3FPOrtLI10M7tQGgvMJpnZrErtJyiktKI/vYU85hhdkArCn+zzESEGlCRURE4kFqcuJeYwZP7d+RU/t3rHq+fXcZa4pLSE6M3Di+a04GnXO2M2/tthr3F40aqWZPRKQZq+3+RjeP7rPf95kZGa2SyGhVcxlISUrgm0O71Pr+nIwU/vrdI2t9PT87tcZB8bpnoIiItBSZrZL2us3Et47swreO7MIxd71X64HaxpbQ6HsUEZEmM2ZQAXeeexgF2WkYUJCdVjXwPEy3jO5LWrXxhVC3JlRERCTe3Ty6T5PVSJ3ZExFp5g5mPEK01PcmuyIiIi1FU9ZINXsiIhIVsdiEioiIxIKmqpG6jFNERERERCQOqdkTERERERGJQ2r2RERERERE4pCaPRERERERkTikZk9ERERERCQOqdkTERERERGJQ2r2RERERERE4pCaPRERERERkThk7h52hgYzs/XA0kbYVS6woRH209iUq/5iNVus5oLYzRaruSB2s8V7rq7untcI+2kRGqlGxurfKYjdbMpVf7GaLVZzQexmi9VcELvZGiNXrfWxWTd7jcXMJrn7kLBz7Eu56i9Ws8VqLojdbLGaC2I3m3JJY4vlP7tYzaZc9Rer2WI1F8RutljNBbGbLdq5dBmniIiIiIhIHFKzJyIiIiIiEofU7EU8HHaAWihX/cVqtljNBbGbLVZzQexmUy5pbLH8Zxer2ZSr/mI1W6zmgtjNFqu5IHazRTWXxuyJiIiIiIjEIZ3ZExERERERiUMtutkzs8fMbJ2ZzQw7S3Vm1tnM3jez2WY2y8yuDzsTgJmlmtkXZvZVkOtXYWeqzswSzWyqmf0z7CzVmdkSM5thZtPMbFLYeSqZWbaZvWhmc81sjpkNDzsTgJn1CX5WlY+tZnZD2LkAzOzHwd/9mWb2jJmlhp0JwMyuDzLNCvtnVdPvVTPLMbN3zGxB8LVtmBnlwFQf6081smFUI+uVKWbrI6hG1jFLk9fIFt3sAU8Ap4YdogZlwE3ufghwFHCtmR0SciaA3cCJ7n44MBA41cyOCjlTddcDc8IOUYsT3H1gjE35ex/wlrv3BQ4nRn527j4v+FkNBAYDO4FXQo6FmRUAPwKGuHt/IBG4KNxUYGb9ge8Dw4j8OZ5pZj1DjPQE//l79WfABHfvBUwInktsewLVx/pSjWw41cg6iNX6CKqR9fAETVwjW3Sz5+4fApvCzrEvd1/t7lOC5W1EfsEUhJsKPGJ78DQ5eMTEoE8zKwTOAB4JO0tzYGZtgOOARwHcfY+7bwk3VY1OAr5294O9MXRjSQLSzCwJSAdWhZwHoB/wubvvdPcy4APg3LDC1PJ79WzgyWD5SWBMk4aSelN9rD/VyPjRTGpkrNVHUI08oDBqZItu9poDMysCBgGfh5skIrgMZBqwDnjH3WMiF3AvcAtQEXaQGjjwtplNNrMrww4T6AasBx4PLut5xMwywg5Vg4uAZ8IOAeDuK4HfA8uA1UCxu78dbioAZgIjzKydmaUDpwOdQ860rw7uvjpYXgN0CDOMxIdYq4+gGtlAqpENEzP1EVQjD1JUa6SavRhmZpnAS8AN7r417DwA7l4eXD5QCAwLTo+HyszOBNa5++Sws9TiWHc/AjiNyCVHx4UdiMjRtyOAP7r7IGAHMXZpnZmlAGcBL4SdBSC4hv5sIv8J6ARkmNm3w00F7j4H+C3wNvAWMA0oDzXUfnhkCuiYONshzVcs1kdQjWwg1ch6irX6CKqRjSUaNVLNXowys2Qihexpd3857Dz7Ci5neJ/YGNNxDHCWmS0BngVONLOnwo30b8HRLtx9HZFr64eFmwiAFcCKakedXyRS2GLJacAUd18bdpDAKGCxu69391LgZeDokDMB4O6Puvtgdz8O2AzMDzvTPtaaWT5A8HVdyHmkGYv1+giqkfWhGtkgsVYfQTXyYES1RqrZi0FmZkSuE5/j7veEnaeSmeWZWXawnAacDMwNNxW4+63uXujuRUQua3jP3UM/mgRgZhlm1rpyGTiFyCUFoXL3NcByM+sTrDoJmB1ipJpcTAxdokLk0pSjzCw9+Dd6EjEwYB/AzNoHX7sQGYvwt3AT/YfXgMuD5cuBv4eYRZqxWK2PoBrZEKqRDRZr9RFUIw9GVGtkUmPurLkxs2eAkUCuma0Afunuj4abCogchbsUmBFc+w/wc3d/I8RMAPnAk2aWSORAwfPuHlNTOMegDsArkd97JAF/c/e3wo1U5Trg6eBykEXAd0LOUyUo+icDV4WdpZK7f25mLwJTiMwIOBV4ONxUVV4ys3ZAKXBtmBMJ1PR7FbgLeN7MvgssBS4MK5/Ujepjg6hG1p9qZD3FYn0E1ci6CqNGWuTSUBEREREREYknuoxTREREREQkDqnZExERERERiUNq9kREREREROKQmj0REREREZE4pGZPREREREQkDqnZkzozs3Izm2Zms8zsKzO7ycz2+3fIzIrM7FsH8VkzzewFM0uvx3uHmNn99f3M4L031OezgveMNLNGn17bzCaa2bzg5zAtmNIYM7vazC5r7M+r9rmXB1MDV1+Xa2brzaxVPff180bIM9HMJlV7PsTMJh7sfoN9jTWzBxpjXyLSsqlG1voe1cja96UaKVGnZk/qo8TdB7r7oUTu8XIakfuD7E8RUO9CVu2z+gN7gKurv2hmtd4j0t0nufuPGvCZADcA9SpkUXZJ8HMY6O7nA7j7Q+7+lyh+5ivAyfsU9POBf7j77nruq16FzCJq+r3U3sxOq+dnR11wPy0REVCNDINqZIRqpNRKzZ40iLuvA64Efhj88ikys4/MbErwODrY9C5gRHDU7cf72W5/PgJ6BkcHPzKz14DZZpZqZo+b2Qwzm2pmJ8DeRxHNLMPMHjOzL4Jtzg7WJ5rZ74OjotPN7Doz+xHQCXjfzN4PtjvFzD4Nsr5gZpnB+lPNbK6ZTQHOrSn0fvKNNbOXzewtM1tgZr+rz8/ezG43s58Ey0OD/NPMbJyZzaz2/Y0zsy+D16+q9rOZaGYvBvmfNovczbaSu28FPgC+UW31RcAzZpZnZi8F+/3SzI4J9ptZ7XudbmbnmdldQFqQ7elguxuDn/lMM7shWFdkkaOzfwFmAp1r+LbHAbfV8LPY66ijmf3TzEYGy9uDn8EsM3vXzIYF3/siMzur2m46B+sXmNkvq+3r28Hfm2lm9qfKohXs924z+woYfuA/MRFpaVQjVSNVI1UjY4a766FHnR7A9hrWbQE6EDnSlxqs6wVMCpZHAv+stn2N29X2WUAS8HfgmmBfO4BuwWs3AY8Fy32BZUBq9c8EfgN8O1jOBuYDGcH+XgSSgtdygq9LgNxgORf4EMgInv8U+H/BZywP8hvwfPXvsdr3UFu+scAioE3wfCnQuYb3TwTmAdOCx7hg/e3AT4LlmcDwYPkuYGawfCXwi2C5FTAJ6Bb8bIqBQiIHez4Fjq3hs88HXgmWOwGrgETgb5XbA12AOcHyb4F7q72/7b5/Z4DBwIzg558JzAIGETmyXQEcVcvfhYnAEOA94IRgeWLw2ljggWrb/hMYGSw7cFqw/ArwNpAMHA5Mq/b+1UA7IC34eQ4B+gH/AJKD7R4ELqu23wvD/veohx56xNYD1UjVSNXIyv2qRsbQo9bT/CL1lAw8YGYDgXKg90Ful2Zm04Llj4BHgaOBL9x9cbD+WOD/ANx9rpktrWF/pwBnVR7lI1I4ugCjgIfcvSx4/6YaMhwFHAJ8EhzYSyHyi78vsNjdFwCY2VNECse+9pdvgrsXB++fDXQlUhz3dYm7T6phPWaWDbR290+DVX8Dzqz2fQ8ws/OD522IFN49RH6GK4J9TCNSSD7eZ/evAw+aWRZwIfCSu5eb2SjgkGoHOrOCI7mjiBzZJPh+N9fy83jF3XcEn/0yMAJ4DVjq7p/V9H1WcwfwCyL/oaiLPcBbwfIMYLe7l5rZDCLfc6V33H1jtUzHAmVECu+XwfeaBqwLti8HXqpjBhERUI1UjVSNlJCo2ZMGM7PuRP5RryMyLmEtkSNCCcCuWt724zpuV+LuA/f5PIgctaxXTOA8d59Xw77q8t533P3ifd47sJbt66P6df3lNP6/RQOuc/fxe62MXL5xwM929xIzews4h0iBujF4KYHI0cW9/tzq+PPcnwP+ubr7e2Z2B5H/YFQqY+/L0VOrLZe6Rw4zEjkqujvYT4XtPZ7F2ZsT+fk96e631hBll7uXHyiviLRsqpEHRTVyb6qR0mAasycNYmZ5wENELg9wIkfFVrt7BXApkcsZALYBrau9tbbtGuIj4JIgT28iRyPn7bPNeOC6ymvuzWxQsP4d4KrKX2hmllND3s+AY8ysZ7BNRvA5c4EiM+sRbLdXoatnvgZz9y3ANjM7Mlh1UbWXxwPXmFly5eebWUY9P+IZIgWsA5GjtRC5zOO6yg2qFfV3gGurrW8bLJZWZiDy8xhjZulBlnOCdfVxB3BLtedLgIFmlmBmnYFh9dwfRAba55hZGjAG+ASYAJxvZu2D7yfHzLo2YN8i0gKpRqpGBvtVjZTQqdmT+qgcSDwLeJfIL7VfBa89CFweDMjty7+PQk0Hyi0yDfWP97NdQzwIJASXHDwHjPV/z4RVeSTqf4hcFjM9yP0/wfpHiIwPmB5kqZwN7WHgLTN7393XE7le/Rkzm05weUpwxO5K4HWLDD6vvHShPvnq6mn797TS79bw+neBPweXmmQQGWtQ+f3NBqZYZED6n6j/kdF3iIxFeK7a0b8fAUMsMsB8Nv+eAe4OoK1FBpV/RWTcAER+ntPN7Gl3nwI8AXwBfA484u5T6xPI3d8A1ldb9QmwmMj3ej8wpZ7fI0Gel4j8XX3JIzPVzSZyOczbwZ/9O0B+A/YtIi2HaqRqpGqkxBz7999PkfhgZucBZ7n75WFniTYzy3T37cHyz4B8d78+5FgiIhKjVCNVI6Vl0Zg9iSsWmS7418AVYWdpImeY2a1E/i0vJXKUVURE5D+oRqpGSsujM3siIiIiIiJxSGP2RERERERE4pCaPRERERERkTikZk9ERERERCQOqdkTERERERGJQ2r2RERERERE4pCaPRERERERkTikZk9ERERERCQOqdkTERERERGJQ2r2RERERERE4pCaPRERERERkTikZk8kRpjZsWb2LzMrNrNNZvaJmQ01s7Fm9nG17ZaYWYmZba/2+Hm15V1mVl7t+awwvy8REZH62k9NvN3MnqphezeznsHyxKAWbjezDWb2spnlV9v2djMr3aeO3tKU359IU1GzJxIDzCwL+Cfwf0AOUAD8Cthdy1u+4e6Z1R6/qVwGrgY+rfbaoU3yTYiIiDSCBtTEmvwwqIk9gUzg9/u8/tw+dfR3jRBdJOao2ROJDb0B3P0Zdy939xJ3f9vdp4cdTEREpIk1Wk109y3Aq8DAxg4p0hyo2ROJDfOBcjN70sxOM7O2YQcSEREJSaPVRDNrB5wLLGy0dCLNiJo9kRjg7luBYwEH/gysN7PXzKxDLW951cy2BI9XmyyoiIhIlDWgJtbkfjMrBjYAucB1+7x+YbU6usXMOjVKeJEYo2ZPJEa4+xx3H+vuhUB/oBNwby2bj3H37OAxpulSioiIRN9+amIZkFx9WzOrfF5abfWP3L0NMABoCxTu8xHPV6uj2e6+KirfiEjI1OyJxCB3nws8QaTAiYiItFj71MRlQNE+m3Qj0gSurOG9M4A7gD+YmUU1qEgMUrMnEgPMrK+Z3WRmhcHzzsDFwGfhJhMREWlaB6iJbwF9zexSM0s2sxzgN8BL7l5Wyy6fBDoAZzVBfJGYomZPJDZsA44EPjezHUQK2kzgplBTiYiINL1aa6K7rwNOA64C1gXrtwDX1LYzd98D3Af8V5Rzi8Qcc/ewM4iIiIiIiEgj05k9ERERERGROKRmT0REREREJA6p2RMREREREYlDavZERERERETiUFLYAQ5Gbm6uFxUVhR1DRESawOTJkze4e17YOZoL1UgRkZZhf/WxWTd7RUVFTJo0KewYIiLSBMxsadgZmhPVSBGRlmF/9VGXcYqIiIiIiMQhNXsiIiIiIiJxSM2eiIiIiIhIHFKzJyIiIiIiEofU7ImIiIiIiMQhNXsiIiIiIiJxSM2eiIiIiIhIHFKzJyIiIiIiEoea9U3VD9arU1cybvw8Vm0poVN2GjeP7sOYQQVhxxIREQmV6qOISHxosc3eq1NXcuvLMygpLQdg5ZYSbn15BoAKmoiItFiqjyIi8aPFXsY5bvy8qkJWqaS0nHHj54WUSEREJHyqjyIi8aPFNnurtpTUa72IiEhLoPooIhI/Wmyz1yk7rV7rRUREWgLVRxGR+NFim72bR/chLTlxr3VpyYncPLpPSIlERETCV1N9BPjWkV1CSCMiIgcjas2emXU2s/fNbLaZzTKz64P1OWb2jpktCL62Ddabmd1vZgvNbLqZHRGtbBAZZH7nuYdRUO1I5feP66bB5yIi0qJVr48GdMxqRVZqIk9/tpT123aHHU9EROohmrNxlgE3ufsUM2sNTDazd4CxwAR3v8vMfgb8DPgpcBrQK3gcCfwx+Bo1YwYVMGZQATv3lHH8uInMW7Mtmh8nIiLSLFTWx0ozVxbz9OdLyUprsZN4i4g0S1H7re3uq4HVwfI2M5sDFABnAyODzZ4EJhJp9s4G/uLuDnxmZtlmlh/sJ6rSU5J4fOxQuudlRPujREREmp3+BW2489wBAGzdVUpmShIJCRZyKhEROZAmGbNnZkXAIOBzoEO1Bm4N0CFYLgCWV3vbimBdk+hf0Ib0lCRKyyuoqPCm+lgREZFmo7iklDEPfMLvdBsGEZFmIerNnpllAi8BN7j71uqvBWfx6tVZmdmVZjbJzCatX7++EZNGppUedc8H/GP6qkbdr4iISF0FV7a8aGZzzWyOmQ2vbbx7U8tKTeLonu146IOvefaLZWFEEBGReohqs2dmyUQavafd/eVg9Vozyw9ezwfWBetXAp2rvb0wWLcXd3/Y3Ye4+5C8vLxGzdsxK5WMlCTufns+e8oqGnXfIiIidXQf8Ja79wUOB+YQGd8+wd17AROC503OzLj9G4dyXO88fvHqTD5ZuCGMGCIiUkfRnI3TgEeBOe5+T7WXXgMuD5YvB/5ebf1lwaycRwHFTTFer7qEBOOWU/uwbNNOnv1SRyxFRKRpmVkb4Dgi9RN33+PuW4iMa38y2OxJYEw4CSEpMYE/fGsQPfIyufqpySxcp8nNRERiVTTP7B0DXAqcaGbTgsfpwF3AyWa2ABgVPAd4A1gELAT+DPwgitlqdXzvPI7slsP9ExayY3dZGBFERKTl6gasBx43s6lm9oiZZVD7ePe9RHOoQ3WtU5N5dOwQjuyWQ1ZactQ+R0REDo5Fhs01T0OGDPFJkyY1+n4nL93MeX/8F/8zpj+XHtW10fcvIiL1Z2aT3X1I2DmiycyGAJ8Bx7j752Z2H7AVuM7ds6ttt9nd9ztuL1o1siZl5RWUu9Mq6T9vxi4iItG1v/rYJLNxNjeDu7blme8fxSXDuoQdRUREWpYVwAp3/zx4/iJwBLWPdw9deYXz3ScncfML02nOB5BFROKRmr1aDO/RjoQEo7RcE7WIiEjTcPc1wHIz6xOsOgmYTe3j3UOXmGAM65bDa1+t4n/fXRB2HBERqUbN3n68P3cdR9/1Hqu2lIQdRUREWo7rgKfNbDowEPgNtY93jwk/GNmD8wcXcv+EBbwydUXYcUREJJAUdoBY1qtDJsU7S7nv3QX89vwBYccREZEWwN2nATWNvTipqbPUlZnxm3MOY8Xmnfz0xRkUZKczrFtO2LFERFo8ndnbj8K26Xz7qK68MHk5C9dtDzuOiIhIzEpJSuChbw9mcNe2pKdoohYRkVigZu8Arj2hB2nJidz99rywo4iIiMS07PQUnrnyKPoXtAFgd1l5yIlERFo2NXsH0C6zFd8/rjtvzlzDovU6uyciIlIXv31rLpc/9gV7yjTRmYhIWNTs1cH3RnTnpWuG0z0vM+woIiIizUKfDq35bNEmbntlhm7JICISEk3QUgeZrZIY3DUy0LysvIKkRPXIIiIi+zNmUAGLN+zgvgkLKMrN4NoTeoYdSUSkxVHXUg/3vbuAC/70qY5QioiI1MENo3px9sBOjBs/j9enrw47johIi6Nmrx4K2qYxddkW3py5JuwoIiIiMc/M+O15Azi2Zy6ODpSKiDQ1NXv1cM6gAnp3yOT34+dRVq4B5yIiIgeSmpzIX787jDMHdAKgvEJNn4hIU1GzVw+JCcbNo/uyaMMOXpi8Iuw4IiIizYKZAfD3aSs564GP2bqrNOREIiItg5q9ehrVrz1HdMnmwYkLdXRSRESkHvIyWzFvzTaufXoKpbpCRkQk6tTs1ZOZcee5A/jb944iMcHCjiMiItJsHN0zl9+cexgfLdjAL1+bpQnPRESiTLdeaIA+HVtXLZdXuJo+ERGROrpwSGcWb9jBHyd+TffcDL43onvYkURE4pbO7DVQWXkF33n8C+55Z17YUURERJqVm0/pwxmH5VNcorF7IiLRpDN7DZSUmEBWWjKPfryYy4cX0T4rNexIIiIizUJCgnH/xYOqroxx96pJXEREpPHozN5BuPHk3pSVO/e/tyDsKCIiIs1KZaM3bfkWzvy/j1ldXBJyIhGR+KNm7yB0bZfBxcO68OwXy1myYUfYcURERJqd1OQElm7cyRVPTGLH7rKw44iIxBU1ewfpuhN7kpyYwB8nfh12FBERkWanb8csHvjWIOat2cqPnpmq2xqJiDQiNXsHqX1WKn++bAi3ndkv7CgiIiLN0sg+7fnVWYcyYe467nh9dthxRETihiZoaQTH9soFoKLCSdBtGEREROrt0uFFLNqwg+WbdlJaXkFyoo5Hi4gcLP0mbSSLN+zg1Ps+5LO6ZOEAAAAgAElEQVRFG8OOIiIi0iz94oxD+NOlQ0hOTNAN10VEGoGavUaS3yaV4pJSfvfWXBUoERGRBkhMMBITjDXFu/jmw58xd83WsCOJiDRravYaSWpyIjeM6s2UZVt4Z/basOOIiIg0W46zdOMOrnj8S9Zt2xV2HBGRZkvNXiO6YHAh3XMzGDd+nmYTExERaaD8Nmk8evlQNu8s5ftPTqJkT3nYkUREmiU1e40oKTGBm07pw4J123ljxuqw44iIiDRb/QvacP/Fg5i+spgbn59GhQ6iiojUW9SaPTN7zMzWmdnMauueM7NpwWOJmU0L1heZWUm11x6KVq5oO/2wjjx4yRGc1r9j2FFERESatZMP6cBtp/dj4brtbCkpDTuOiEizE81bLzwBPAD8pXKFu3+zctnM7gaKq23/tbsPjGKeJmFmnH5YPgDujpluxSAiItJQ3z22G5cc2ZW0lMSwo4iINDtRO7Pn7h8Cm2p6zSId0IXAM9H6/LC9PWsNo+/9kO27y8KOIiIi0myZGWkpiewqLeeGZ6fyycINYUcSEWk2whqzNwJY6+4Lqq3rZmZTzewDMxsRUq5G0yErlflrt/PIR4vCjiIiItLs7SmvYPbqrVzz1GQWrtsedhwRkWYhrGbvYvY+q7ca6OLug4Abgb+ZWVZNbzSzK81skplNWr9+fRNEbZjDO2dzWv+O/PnDRWzcvjvsOCIiIs1aVmoyj14+lJSkBK544kvVVhGROmjyZs/MkoBzgecq17n7bnffGCxPBr4Getf0fnd/2N2HuPuQvLy8pojcYD8Z3YddZRU88P7CsKOIiIg0e51z0vnzZUNYu3UXV/51MrtKdUsGEZH9CePM3ihgrruvqFxhZnlmlhgsdwd6Ac3++sceeZlcMLiQpz9bxrqtuimsiIjIwRrUpS33XDiQReu3s2TjjrDjiIjEtKjNxmlmzwAjgVwzWwH80t0fBS7iPydmOQ74bzMrBSqAq929xsldmpvrR/XizAGdaJ+VGnYUERGRuHDGgHxG9M4lKzU57CgiIjEtas2eu19cy/qxNax7CXgpWlnClN8mjfw2aYBuxSAiItJYslKTcXf+8P5CCtqmcc6gwrAjiYjEnGjeZ0+quffd+SxYt50/fOuIsKOIiIjEhbIK55OFG5m8dDMF2ekM65YTdiQRkZgS1mycLU5SgvH69NVMXhoXV6eKiIiELjkxgYe+PZjCnDSu/OsklmzQGD4RkerU7DWR7xzTjdzMVvz2zXm4e9hxRERE4kKb9GQeHzsUA6544ku27NwTdiQRkZihZq+JZLRK4kcn9eSLJZuYOD927w8oIiLS3HRtl8HDlw1hdfEuvlisK2hERCqp2WtCFw3tQpecdO5+W2f3RESkZma2xMxmmNk0M5sUrMsxs3fMbEHwtW3YOWPN0KIcPvrpCZxyaMewo4iIxAw1e00oJSmBcecP4N5vDtSsnCIisj8nuPtAdx8SPP8ZMMHdewETgueyj9zMVgC8PWsNj3zU7G/XKyJy0DQbZxM7snu7qmXdikFEROrobCL3rgV4EpgI/DSsMLHuzZlreGXqSvLbpHHGgPyw44iIhEZn9kKwc08Z1zw1mac+XxZ2FBERiT0OvG1mk83symBdB3dfHSyvATqEE615uPPcwxjctS03Pj+NKcs2hx1HRCQ0avZCkJacyMbte7h/wgJ27ikLO46IiMSWY939COA04FozO676ix4Z9F3jwG8zu9LMJpnZpPXrW+5kYKnJiTx86WA6ZKVy5V8msXzTzrAjiYiEQs1eCMyMW07tw/ptu3n8kyVhxxERkRji7iuDr+uAV4BhwFozywcIvq6r5b0Pu/sQdx+Sl5fXVJFjUrvMVjw2dii7yyp47atVYccREQmFmr2QDCnKYVS/9jz0wde6J5CIiABgZhlm1rpyGTgFmAm8BlwebHY58PdwEjYvPdtn8tYNx/GDkT3CjiIiEgo1eyH6yeg+bN9dxsMfasYwEREBImPxPjazr4AvgNfd/S3gLuBkM1sAjAqeSx0UZKdhZsxbs4173pmvWx+JSIui2ThD1LdjFvd+cyAjerXsS21ERCTC3RcBh9ewfiNwUtMnih+vz1jN/RMWkJWaxPdGdA87johIk1CzF7KzBxaEHUFERCTu3XBSLxas3cav35hDl5x03XxdRFoEXcYZAxas3cY5D37C1+u3hx1FREQkLiUkGPdcOJABhdlc/+w0Zq4sDjuSiEjUqdmLAW0zUpi3Zht3vz0v7CgiIiJxKy0lkT9fNpicjBT+pPHyItIC6DLOGJCb2YrvjejO/RMW8NXyLRzeOTvsSCIiInGpfetUnr3yKDpkpYYdRUQk6nRmL0Z8f0Q3cjJSGDdeZ/dERESiqXNOOilJCWzesYe7355HeYVm6BSR+KRmL0a0Tk3m2hN68vHCDfzr6w1hxxEREYl7781dx/+9t5A7Xp8ddhQRkajQZZwx5JIju5CanMDgrm3DjiIiIhL3zhtcyKxVW3nsk8V0y83gsuFFYUcSEWlUavZiSGpyIpcc2TXsGCIiIi3GbWf0Y+nGHdz+2iy65KQzsk/7sCOJiDQaXcYZg8bPWsO3H/mcsvKKsKOIiIjEtcQE4/6LB9G3YxZ3vTmXCo3fE5E4ojN7MerjhRt4acoKvjm0S9hRRERE4lpGqyQeGzuUxAQjIcHCjiMi0mh0Zi8GnXJIBwZ2zubedxewq7Q87DgiIiJxr2ObVPJat6K0vIIHJy6kZI/qr4g0f2r2YpCZ8dNT+7K6eBd//XRp2HFERERajKnLtjBu/Dx+/Nw0XdIpIs2emr0YNbxHO47rnccfJi5k267SsOOIiIi0CMO65XDb6f14a9Yafqd734pIM6cxezHs56f3ZfmmEjJb6Y9JRESkqXz32G4s3rCDhz74mqJ26Vw0TOPnRaR50pm9GNa3YxYnH9IBMw0WFxERaSpmxu1nHcqIXrn85o05FJfoChsRaZ6i1uyZ2WNmts7MZlZbd7uZrTSzacHj9Gqv3WpmC81snpmNjlau5uj+CQu48805YccQERFpMZITE/jDJUfw3FXDaZOWHHYcEZEGieaZvSeAU2tY/7/uPjB4vAFgZocAFwGHBu950MwSo5itWVm7dRePfrSYZRt3hh1FRESkxchKTaZffhYAL05ewcbtu0NOJCJSP1Fr9tz9Q2BTHTc/G3jW3Xe7+2JgITAsWtmamx+d1IukROOedzRQXEREpKmt2LyT216ZwZV/naxbIolIsxLGmL0fmtn04DLPtsG6AmB5tW1WBOsE6JCVyneO6cbfv1rF7FVbw44jIiLSohS2TeeeCwcyeelmbnlxOu66JYOINA9N3ez9EegBDARWA3fXdwdmdqWZTTKzSevXr2/sfDHr6uN7kJWazO/f1tk9ERGRpnbGgHxuHt2H175axb3vLgg7johInTTpnP7uvrZy2cz+DPwzeLoS6Fxt08JgXU37eBh4GGDIkCEt5tBam7Rk7jr3MDrnpIcdRUREpEX6wcgeLN6wg/vfW8CZA/Lp1aF12JFERParSZs9M8t399XB03OAypk6XwP+Zmb3AJ2AXsAXTZmtOTjtsPywI4iIiLRYZsZvzjmMMQML1OiJSLMQtWbPzJ4BRgK5ZrYC+CUw0swGAg4sAa4CcPdZZvY8MBsoA651d42ArkFxSSm/em0WZwzI56R+HcKOIyIi0qKkJCVwbK9cAD79eiP5bVIpys0IOZWISM2i1uy5+8U1rH50P9v/Gvh1tPLEi/SURKYu38KsVVsZ2ac9iQm64bqIiEhT21VazvXPTiWzVRIv/+BostNTwo4kIvIfwpiNUw5CcmICN53Sm3lrt/H3aTUOaxQREZEoS01O5A+XHMGKzSVc/dRk9pRVhB1JROQ/qNlrhk7vn0//gizueWc+u8t0tauIiEgYhhbl8LvzB/DZok3c9soM3ZJBRGKOmr1mKCHBuGV0X1ZsLuGZz5eFHUdERKTFGjOogOtP6sULk1cwftbaA79BRKQJNelsnNJ4RvTK5Rdn9NMMnSIiIiG7YVQvendozSmHaOI0EYktavaaKTPjeyO6hx1DRESkxTMzzhgQOfi6fNNONu/cw4DC7JBTiYjoMs5mb96abXzn8S/YuH132FFERERaNHfn+mencsUTX7J8086w44iIqNlr7hIT4IP563lw4tdhRxEREWnRzIzfnX84u8squOKJL9m6qzTsSCLSwqnZa+Z6tm/N+YML+eunS1m5pSTsOCIiIi1az/aZ/Onbg1m8YQfXPj2F0nLdkkFEwqNmLw5cP6o3GNz7zvywo4iIiLR4R/fM5dfn9OejBRt4+MNFYccRkRZME7TEgYLsNC47qiuPfbKYK4/rTq8OrcOOJCIi0qJ9c2gXUpISGH1ox7CjiEgLpmYvTvzghJ60y2xFQdu0sKOIiIgIcM6gQgC27y5j7uqtDCnKCTmRiLQ0uowzTuRkpHDNyB6kp6h/FxERiSW/em0Wlz76BTNXFocdRURaGDV7cebtWWu4+YWvcPewo4iIiAhw86l9yMlI4btPfsnqYk2mJiJNR81enFldvIsXJq/gowUbwo4iIiINZGaJZjbVzP4ZPO9mZp+b2UIze87MUsLOKHXXvnUqj44dwo7d5Zz34L8YfucEuv3sdY656z1enboy7HgiEsfU7MWZi4d1obBtGr8bP5eKCp3dExFppq4H5lR7/lvgf929J7AZ+G4oqaTB+nbM4ltHdmZV8S5WF+/CgZVbSrj15Rlq+EQkatTsxZmUpARuOqU3M1du5Y2Zq8OOIyIi9WRmhcAZwCPBcwNOBF4MNnkSGBNOOjkYr09f8x/rSkrLGTd+XghpRKQlULMXh846vIC+HVtz99vzdTNXEZHm517gFqDyF3g7YIu7lwXPVwAFYQSTg7NqS83j9VZuKeHpz5eydVdpEycSkXinZi8OJSYY/+/MQ7hhVC8SzcKOIyIidWRmZwLr3H1yA99/pZlNMrNJ69evb+R0crA6Zdd8e6SkBOO2V2Yy9I53ufONOTVuIyLSEGr24tTRPXM5e2ABCQlq9kREmpFjgLPMbAnwLJHLN+8Dss2s8t46hUCNg7zc/WF3H+LuQ/Ly8poir9TDzaP7kJacuNe6tORExp0/gNd+eAzfHNqZ9lmpAOwpq+C+dxewZMOOMKKKSJzQTdnimLvzxw++JiMlicuPLgo7joiIHIC73wrcCmBmI4GfuPslZvYCcD6RBvBy4O+hhZQGGzMocvXtuPHzWLWlhE7Zadw8uk/V+gGF2VXbTlu+hfsmzOd/353P0KK2XDC4M6cPyCezlf7rJiJ1p98YcczMmLxkM18u2cSYgQW0SU8OO5KIiDTMT4FnzewOYCrwaMh5pIHGDCqoau72Z1i3HP71s5N4eeoKXpy0gltems4vX5vFG9ePoFtuRhMkFZF4oMs449xPRvdh2+4y/vjB12FHERGRenD3ie5+ZrC8yN2HuXtPd7/A3XeHnU+ir2ObVH4wsicTbjqel645mrHHFFHULh2AB95bwL3vzmf5pp0hpxSRWKYze3GuX34WYwYW8Pgnixl7dBEd26SGHUlERETqwcwY3LUtg7u2rVo3Z8023pixmnvfXcDw7u24YEghp/XPJy0lcT97EpGW5oBn9sysg5k9amZvBs8PMTPdzLUZ+fGo3lS4c/97C8KOIiLSIqh2SrT94VtH8NEtJ3Djyb1ZuaWEG5//it8EM3m6O+4eckIRiQV1uYzzCWA80Cl4Ph+4IVqBpPF1aZfOf515COcdodsyiYg0kSdQ7ZQoK2ybzo9O6sUHN4/k+auGM/aYIiAyucuJd3/AH95fWOu9/USkZahLs5fr7s8T3Nw1uKlreVRTSaO7bHgRg7vmhB1DRKSlUO2UJmNmDOuWQ4+8TAAqHNq3bsW48fM45rfvcemjn/P3aSvZU1YRclIRaWp1afZ2mFk7wAHM7CigOKqpJCo279jDz1+ZwcyV+uMTEYky1U4JzeCubXnuquF8cPNIrjuhJ4vW7+C/Xp1JRXBp54btu3WZp0gLUZcJWm4EXgN6mNknQB6Re/1IM5OYaLwxYzUrNpfwlyuGhR1HRCSeqXZK6Lq2y+DGU/pww6jeLNm4g9TkRNydCx/6lIQE4/zBhZw7qKDqRu4iEn8OeGbP3acAxwNHA1cBh7r79AO9z8weM7N1Zjaz2rpxZjbXzKab2Stmlh2sLzKzEjObFjweavi3JLXJSk3m2pE9+XD+ev719Yaw44iIxK2G1k6RaEhIMLoHl3iWVzhXHtedNmnJ3PXmXIbf9R5XPPElXy7ZFHJKEYmGuszGeS2Q6e6z3H0mkGlmP6jDvp8ATt1n3TtAf3cfQGSw+q3VXvva3QcGj6vrFl/q69LhXclvk8rv3pqnSzhERKLkIGqnSFQlJSZw0bAuvHTN0bx30/FcdVx3Zq/ayrqtkVs3rt+2m5kri/V/BJE4UZcxe9939y2VT9x9M/D9A73J3T8ENu2z7u1gkDrAZ0BhPbJKI0hNTuSGUb2YtnwL42etDTuOiEi8alDtFGlK3fMyueXUvnzysxMZfWgHAJ79Yhln/t/HnHbfRzzy0SI2bN8dckoRORh1afYSzcwqn5hZIpDSCJ99BfBmtefdzGyqmX1gZiMaYf9Si/OOKOSq47pzaKessKOIiMSraNVOkUaXmGAkJUb+S3jZ8CL+Z0x/WiUlcMfrczjqNxO45qnJVFToTJ9Ic1SXCVreAp4zsz8Fz68K1jWYmd0GlAFPB6tWA13cfaOZDQZeNbND3X1rDe+9ErgSoEuXLgcTo8VKSkzg1tP7hR1DRCSeNXrtFGkKbdKTufSorlx6VFfmr93Gi5NXULyzlISEyLGLv3y6hKFFOfTL1wFjkeagLs3eT4kUqWuC5+8AjzT0A81sLHAmcJIHF4S7+25gd7A82cy+BnoDk/Z9v7s/DDwMMGTIEB1mOgjz127joQ++5jfnHEZqcmLYcURE4kmj1k6RMPTu0JqfVzs4vGnHHu745xz2lFfQvyCLCwZ35uyBnchO10lrkVhVl9k4K9z9j+5+fvD4k7s36MawZnYqcAtwlrvvrLY+L7jEBTPrDvQCFjXkM6TuNmzfzctTVvLUZ0vDjiIiElcas3aKxIqcjBQ++/lJ/PIbh1BRAb98bRbDfj2BCXM0B4BIrDrgmT0zOwa4HegabG+Au3v3A7zvGWAkkGtmK4BfEpl9sxXwTjCU4bNg5s3jgP82s1KgArja3TUHcJQd3SOXEb1y+cP7C7lwaGeyUpPDjiQiEhcaWjtFYl1ORgrfOaYb3zmmG7NWFfPi5BUM7JwNwJszVjNtxRYuGNyZnu0zQ04qIlC3yzgfBX4MTAbqfFTS3S+uZV81bfsS8FJd9y2N55bRffnGAx/zyIeLuPGUPmHHERGJFw2qnSLNyaGd2nBopzZVz2et2sojHy3mTx8sYlCXbM4fXMg3Du+kg8kiIarLbJzF7v6mu69z942Vj6gnkyZxWGEbzhiQzyMfL2b9Nk2vLCLSSFQ7pcX5yeg+fHbrSdx2ej927C7jtldmMvaxL6pe1737RJpeXc7svW9m44CXCSZRAXD3KVFLJU3qppN70z03g7QUTdIiItJIVDulRcpr3YrvH9ed743oxoyVxezYHTmxvW1XKWf/4RNO75/PeYML6ZabEXJSkZahLs3ekcHXIdXWOXBi48eRMHTPy+QmXcIpItKYVDulRTMzBhRmVz3fvKOUzm3TeXDiQh54fyFDi9pyweDOnDEgn4xWdfnvqIg0xAH/dbn7CU0RRML33ty1fLW8mB+f3DvsKCIizZpqp8jeurRL58krhrGmeBevTF3JC5OXc8tL0xnYJZveHVqzdVcpmSlJVffzE5HGUadDKWZ2BnAokFq5zt3/O1qhJByfL97Ewx8u4rTDOtK3o26WKiJyMFQ7Rf5TxzapXDOyB1cf353Zq7fSu0NrAP7r1ZlMWbaZ844o5LwjCumckx5yUpH4cMAJWszsIeCbwHVEpo6+gMhU0hJnrjm+B5mtkvj9+HlhRxERadZUO0X2z8z2mslz9KEd6ZqTwX0TFjDid+9z8cOfMX7WmhATisSHuszGebS7XwZsdvdfAcMBXecXh7LTU7j6+B68O2cdk5boNociIgdBtVOkHk4/LJ+nvnckH//0RG46uTerikuYumwLAOUVzqQlmzSbp0gD1KXZKwm+7jSzTkApkB+9SBKm7xxTRF7rVvz2rbn6pSoi0nCqnSINUJCdxnUn9WLiT0Zyw6heAHy0YD3nP/QpJ/x+Ig+8t4BVW0oOsBcRqVSXMXv/NLNsYBwwhchsYo9ENZWEJj0liV+c0Y9dpeW4g2mctIhIQ6h2ihwEMyM1OXJLqKFFOfz+gsN5YdJyfv/2fO5+Zz7H9szlngsHkte6FQCvTl3JuPHzWLWlhE7Zadw8ug9jBhWE+S2IxASrz9kbM2sFpLp7cfQi1d2QIUN80qRJYccQEZEmYGaT3X3IgbeMLWHVTtVIiUfLNu7kxSkr+NfCDTx31XASE4xf/WMWf/t8GbvLKqq2S0tO5M5zD1PDJy3C/upjrWf2zOxEd3/PzM6t4TXc/eXGDCmxpay8gqc+W0rHNmmc2r9j2HFERJoF1U6R6OrSLp0bT+7NjcFtotydv3y6lPKKvU9elJSWM278PDV70uLt7zLO44H3gG/U8JoDKlhxLMGM5yetYNvuUk7s256UpLoM7xQRafFUO0WakJlRUVHzVWoa2yeyn2bP3X9pZgnAm+7+fBNmkhiQkGDccmofxj7+Jc9+uYzLhheFHUlEJOapdoo0vU7ZaaysobHrlJ3G7FVb+fUbsznr8E6cemg+bdKTQ0goEp79nq5x9wrglibKIjHm+N55HNkth//P3p3HR1VffRz/nCwkgUACJGxh3wUUghRQkGIBERdEq9a1WrVoa11q69o+VZ+2T21ptda6Vq37VsW1KiKKgiL7DiI7yL6FNYEs5/ljbjDGhDWTm0y+79drXrnzm9+9c+YScubc5ff7x7jF7N5bEHY4IiLVgnKnSOW6eWgnUoLBXIqlJMZz89BObNm9lzXbcrn1tbn0+uNYrnp6Km/OWsPegsKQohWpXIdybd6HZvZrM2thZg2KH1GPTEJnZtxyamc279rHkxOXhx2OiEh1otwpUklGZGfxp3OOJSs9BSMyfUPx4Cwndcjk418P5K1f9OPyE1szb80Obntt7v57/FZs3q3CT2LaQUfjNLOyvuW7u7eNTkiHTiONVY6/vP8l/TtkcGK7jLBDEZEarDqNxlkVcqdypMh3FRU5yzbvpn2jVACG3T+Br7ft4dSuTRjeoxkntG1IQrzGKZDq5YhG4yzm7m0qPiSpTm45tXPYIYiIVCvKnSJVU1yc7S/03J1bT+3EW7PX8t689fxn+tdkpNbixsEduaRvq5AjFakYhzKpOmbWDegCJBe3ufsz0QpKqp6cPft48OMl/KRfG5qlp4QdjohIlafcKVK1mRkDOzViYKdG5OUXMn7RRt6evY66yZGvxxt25PHkxOWc2b0ZXZvVw8xCjljk8B202DOzO4GBRBLWu8AwYCKghFWD7NpbwNOfr2RHbgF/Pve4sMMREanSlDtFqpfkxHhO7daUU7s13d82a3UOT0xczqOfLqNtRh3O7N6M4T2a0S4zNcRIRQ7PoVyUfC4wCFjv7j8BugNpUY1Kqpzm9WtzSd9W/Gf6apZs3BV2OCIiVZ1yp0g1N7RrE6b+ZjD/d/axNK6XzD8+Wszgez9h8669AOwrKAo5QpGDO5TLOPPcvcjMCsysHrARaBHluKQKuvbkdrw8dRV/+2ARD19yfNjhiIhUZcqdIjGgfp1aXNSnJRf1acmGHXlMXbGVjNQkAEY+O42deQUM796M045tSmbdpJCjFfmucs/smdmDZtYfmGJm6cC/gOnADGBSJcUnVUjD1CR+OqAt781bz+zVOWGHIyJS5Sh3isSuxvWSOeO4Zvufn9C2Ibv3FnDnW/Pp838fcsnjk/lwwYYQIxT5rgOd2fsKGAU0A3YDLwJDgHruPqcSYpMq6KqT2rJmWy7ptRPDDkVEpCo6qtxpZsnAp0ASkRz9qrvfaWZtgJeAhkSKx0vdfV90PoKIHIqrv9+Oq7/fjq827OTt2Wt5a/ZaFm/cxeAujdmzr4APF25k8DGNqF3rkMZDFImKQ5lnrxVwQfBIIZK4XnD3xdEP78A0h5CISM1RzebZO6LcaZHh/uq4+y4zSyQyqMsNwE3AaHd/ycweAWa7+8MH2pZypEjlcnfyC51aCXG8O3cdP39+BimJ8Qzu0pjh3ZsxoGMGSQnxYYcpMehA+fGgA7S4+0p3/7O7ZwMXAiOALys4Rqlmlm7axZ/eXUhR0YEPFoiI1ERHmjs9ongUrMTg4cAPgFeD9qeD7YlIFWJm1EqIfLU+tWsTXhrZl7N7ZjFx8SZ++sw0vveHD1m3PTfkKKWmOZSpFxKIDBl9AZGRxcYDd0U1KqnyZq3K4dFPl3Fc83ROP67pwVcQEalBjiZ3mlk8kUs12wMPAkuBHHcvCLp8DWRVbMQiUpHi4oy+bRvSt21D7h7elYlLNjNp6Raa1ItMu/mndxeSl1/I8B7NyG5Rn7g4zeEn0VFusWdmQ4gcjTwNmELkXoGR7r67kmKTKmxEdhaPfrqUv36wiFO6NiYx/lBm8RARiW0VkTvdvRDoEQzw8jrQ+TDefyQwEqBly5aHEbmIREtifBwnd2rEyZ0a7W/L2ZPPG7PW8PSklWSlp3BG96acnZ1F5yb1QoxUYtGBvqHfDnwOHOPuw939BRV6Uiw+zrh5aGeWb97Nq9O/DjscEZGqosJyp7vnAB8DJwDpwdlCgObAmnLWeczde7l7r8zMzCN5WxGpBH8+9zim/XYw9/2oOx0bp/LEhOW8PHU1AIVFzrJNmtNYKka5Z/bc/QdHu3EzexI4A9jo7kab8ioAACAASURBVN2CtgbAy0BrYAVwvrtvC25Kv5/I0dA9wOXuPuNoY5DoGXxMI45vVZ+/f/gVZ2dnkZyom45FpGY72txpZplAvrvnmFkKkZE8/0yk6DuXyJnCy4A3jzZWEQlX3eREzs5uztnZzdm2ex/5RZFJ2qeu2MoFj31B12b1GN69GWd0b0ZWekrI0Up1Fe1r754CTi3Vdhswzt07AOOC5xC5t6FD8BgJHHCUMQmfmXHrqZ0Z3r0Z+YVFYYcjIhILmgIfm9kcYCow1t3fAW4FbjKzJUSmX3gixBhFpILVr1OLRnUj9/N1aJTK/5zRhcT4OP703pf0u+cjfvjw56zN0eAucviiOvGHu39qZq1LNZ8FDAyWnyZy0/qtQfszHpkL4gszSzezpu6+LpoxytHp3aYBvds0CDsMEZGYEMzFl11G+zKgd+VHJCKVrWFqElf2b8OV/duwasse3p6zlk+/2kRm3SQA/jNtNUXunNq1KWma91gOIoxZHhuXKODWA42D5SxgdYl+xaONqdirBiYu3syqrXu4qI8GBBARERGpCC0b1ubak9tz7cnt97e9MWsNny3Zwm/fmMf3OzZieI9mmrxdyhXqb4W7u5kd1kRtGmmsanpl2mrGLtjA4GMa0SgYVlhEREREKtZzV/ZhztfbeWv2Wt6Zs5YPF25gRI9m/P2CyEUB+YVFGiVd9gvjN2GDmTUFCH5uDNrXAC1K9CtztDGNNFY13TSkI/mFRTzw0ZKwQxERERGJWWZG9xbp/M8ZXZh02yBeGtmXq05qC8DSTbs4/vdjueXV2UxcvJkCjalQ44VR7L1FZCQx+PaIYm8BP7aIvsB23a9XfbTOqMOFvVvy4pRVrNisGTpEREREoq148vZuWWkAGDC4S2PenbueS56YTN8/jePON+excWdeuIFKaKJa7JnZi8AkoJOZfW1mVwL3AEPMbDEwOHgO8C6wDFgC/Av4eTRjk4p33Q/akxgfx71jvwo7FBEREZEap21mKvee34Npvx3Mwxf3pHebBoyeuYakhMj0WFNXbGXemu1ExkOUmiDao3FeWM5Lg8ro68C10YxHoqtRvWR+OaSD5tsTERERCVFyYjzDjm3KsGObsregcH+x9+f3vmTaym20zazD8O7NOLN7M9plpoYcrUST7t6UCjVyQDvqJSfS756PaHPbf+l3z0e8MfM7t16KiIiISCUoLvQAHvtxL/54djca1U3i/nGLGfS3T7h99JwQo5No0xitUqHemLmG20bPIS8/ckPwmpxcbh89F4AR2VlhhiYiIiJSozWoU4uL+7Ti4j6tWL89j//OXUfz+ikAbN29j2uem84ZxzVlWLem++f1e2PmGkaNWcTanFyapadw89BO+k5XjajYkwo1asyi/YVesdz8QkaNWaQ/DCIiIiJVRJO0ZK7s32b/87U5uWzfk8/v3pzPXW/Np1/7DLLSU3hj1hodxK/GVOxJhVqbk3tY7SIiIiISvm5ZaYz55QAWrd/JW7PX8PbsdUxYvPk7/XQQv3rRPXtSoZqlp5TZnpSoXzURERGRqq5Tk7rcPLQzn9w8ECunjw7iVx/6Bi4V6uahnUgpNRpnYrxxXq8WABrqV0RERKQaMLNyD+KX1y5Vj4o9qVAjsrP40znHkpWeggFZ6SmMOrc7vz+rGwDPfbGS616cSc6efeEGKiIiIiIHVNZBfLPI3MpSPeiePalwI7Kzyr2OOy+/iPfmrmPq8q2MOu84TuqQWcnRiYiIiMihKP4+VzwaZ/06tdi2ex9vzlrLiOwsza1cDajYk0r10wFtOaFdQ258eRaXPjGFy09szW3DOuuPhYiIiEgVVPog/uszv+Y3r8/jy/U76dEiPcTI5FDoMk6pdN2y0njnuv78pF9rnp60glmrc8IOSUREREQOwdnZzfn0lpNV6FUTKvYkFMmJ8dx5Zlc++tVA+rZtCMAXy7ZQUFh0kDVFREREJEwZqZEJ11+YvIrfvD5XA/BVYSr2JFRtMuoAsHTTLi761xec/+gkVm7ZHXJUIiIiInIwa3NyeX7yKv7v3YUq+KooFXtSJbTLTOW+H/VgycZdDLt/Ai9OWaU/GiIiIiJV2K9O6chlJ7TiXxOW88BHS8IOR8qgYk+qjLN6ZPH+jQPIbpnO7aPn8vPnZ6jgExEREamizIw7z+zKD3s2596xX/HkxOVhhySlaDROqVKapafw7BV9eOrzFRS5Y2ZhhyQiIiIi5YiLM/78w2PZs6+AfRp7ocpRsSdVTlyccUX/NvuffzB/PeMWbuR/zuxCapJ+ZUVERESqkoT4OB68qCdxcZGD9Lv3FlBH39mqBF3GKVXess27+c/01Qy7/1OmrdgadjgiIiIiUkpxoTdvzXYG/OVjPv5yY8gRCajYk2rgmu+34+WrTwDg/Ecn8Zf3v2RfgS4TEBEREalqWjasTdP0ZK55bjpfLNsSdjg1noo9qRa+17oB790wgPOOb8FD45fy0Zcbwg5JREREREqpl5zI0z/pTYsGtbnq6WnMXp0Tdkg1moo9qTZSkxL487nHMfrnJzK0axMAvly/g6IijdgpIiIiUlU0TE3iuSv7UL9OIpf9eworNmsO5bCo2JNqp2fL+pgZ67bncvaDn3Ppk5NZm5MbdlgiIiIiEmiSlszzV/blzOOa0Sw9JexwaiwVe1JtNamXzJ1ndmHmqhyG/v1T3py1JuyQRERERCTQsmFtfj+iG7US4ti6ex/rt+eFHVKNo2JPqi0z44LeLXn3+pPo0CiVG16axfUvzqRQl3WKiIiIVBnuzpVPT+Xix79gy669YYdTo6jYk2qvdUYdXrn6BH59Skcy6yYRH6eJ2EVERESqCjPj9mHHsCYnlx8/OYXtuflhh1RjqNiTmJAQH8cvftCB/zmjCwAzVm3j7rfnk5dfGHJkIiIiItK7TQMeueR4vtqwkyuemsqefQVhh1QjqNiTmPTFsi38+7MVnPHAROat2R52OCIiIiI13sBOjbj/gmxmrtrG799ZEHY4NYKKPYlJPx/Ynmeu6M3OvHxGPPgZD368hIJCTcQuIiIiEqbTjm3KQxf35JdDOoYdSo2gYk9i1oCOmYy5cQBDuzVh1JhFjJ6h0TpFREREwnZqt6Y0qptMQWERL0xepTmToyihst/QzDoBL5doagv8DkgHfgpsCtrvcPd3Kzk8iTHptWvxzwuzObdncwZ0zARgTU4uzdKSMdNALiIiIiJhGTN/A3e8PpeF63bwv2d11XezKKj0M3vuvsjde7h7D+B4YA/wevDyfcWvqdCTimJmnNy5EfFxxrbd+zjrnxP56TPT2ayhf0VERERCc9qxTbh6QFue/WIlo8YsCjucmBT2ZZyDgKXuvjLkOKSGSEtJ5GcD2/Pp4k0Mve9Txi7YEHZIIiIiIjWSmXHbsM5c1KclD41fykPjl4QdUswJu9i7AHixxPNfmNkcM3vSzOqHFZTErrg448r+bXjnuv40rpfMT5+Zxm2vzWFfgQZvEZHwmVkLM/vYzBaY2XwzuyFob2BmY81scfBTOVJEYoKZ8fuzunFWj2Y89PFSNu7ICzukmBJasWdmtYDhwH+CpoeBdkAPYB3wt3LWG2lm08xs2qZNm8rqInJQHRvX5Y1r+/Hzge3YkZdPYryuEReRKqEA+JW7dwH6AteaWRfgNmCcu3cAxgXPRURiQnyc8dfzuvP6z0+kUb3ksMOJKWGe2RsGzHD3DQDuvsHdC929CPgX0Lusldz9MXfv5e69MjMzKzFciTW1EuK45dTO/PPCnpgZKzbv5t4PFuksn4iExt3XufuMYHknsBDIAs4Cng66PQ2MCCdCEZHoSIyPo0PjugD8+7PlvD9vfcgRxYYwi70LKXEJp5k1LfHa2cC8So9IaqS4uMhZvTHz1/OPj5Zw9kOfsXjDzpCjEpGazsxaA9nAZKCxu68LXloPNA4pLBGRqMovLOKt2Wu5/sWZTFisq/iOVijFnpnVAYYAo0s0/8XM5prZHOBk4JdhxCY119Xfb8ejlx7Puu15nPHARP792XLN+yIioTCzVOA14EZ331HyNXd3oMw/TrrVQUSqu8T4OJ66vDdtM+sw8pnpTFuxNeyQqrVQij133+3uDd19e4m2S939WHc/zt2HlziCKVJphnZtwpgbB9C/fQZ3v72Apz5fEXZIIlLDmFkikULveXcvPii6ofgKmODnxrLW1a0OIhIL0mon8uyVfWialsxPnprKvDXbD76SlCns0ThFqpzMukk8flkv7vtRdy7o3QKA7XvyQ45KRGoCi8wo/ASw0N3vLfHSW8BlwfJlwJuVHZuISGXKrJvEs1f1oV5yInO+VrF3pBLCDkCkKjIzzs5uDkDuvkLOfugzjm2exv8O70Za7cSQoxORGNYPuBSYa2azgrY7gHuAV8zsSmAlcH5I8YmIVJqs9BTG3jSA2rUiJUtRke8fa0EOjc7siRxEYrxxdnYW/52zjlPv/5TPlmwOOyQRiVHuPtHdLbiloUfweNfdt7j7IHfv4O6D3V03sYhIjVBc6E1auoXTH5jIBs3Dd1hU7IkcREJ8HNcN6sDon59ISq14Ln58Mne/PZ+9BYVhhyYiIiJSIyQnxrFyy24ufWIy23bvCzucakPFnsghOq55Ov+97iQuP7E1s1fnEG+6jEBERESkMmS3rM/jl/VixZY9XPbvKezM03gKh0LFnshhSKkVz13Du/LiyL4kxMexZddeHp+wjEJN0SAiIiISVSe2y+Dhi3uyYO0Ornx6Gnn5usrqYFTsiRyBpIR4AN6YtZY//HchP3p0Equ27Ak5KhEREZHYNuiYxtz7ox60qF+bBA3WclAq9kSOwhX9WvP3H/Vg0YadDLv/U16euorIfMciIiIiEg3Duzfjb+d3JyE+jq279+kKqwNQsSdyFMyMEdlZvH/jAI5rns6tr83lkU+WhR2WiIiISMzbtbeAcx76jNtHz6FIBV+ZVOyJVICs9BSev6oPd53ZhfN6Rebn03XkIiIiItGTmpTA8B5ZvDLta37/3wW6uqoMmlRdpILExRmX92sDQGGRc8njk+nQOJXfnt6FOkn6ryYiIiJS0X45uAM78/L592crqJucyE1DOoYdUpWiM3siUVDkTq/WDXhp6mqG3T+B6Ss1/7GIiIhIRTMz/uf0Lpzfqzn/GLeYV6auDjukKkXFnkgUJMbHcduwzrw88gSK3DnvkUn8dcwi9hUUhR2aiIiISEyJizP+dM5xXPeD9gzu0jjscKoUFXsiUdS7TQPeu+EkftizOe/MWUt+oYo9ERERkYoWH2f86pRONKhTi30FRUxetiXskKoEFXsiUVY3OZFR53Xnrev6Uycpgdx9hbwydbVGjRIRERGJgvvHfcXFj09m3MINYYcSOhV7IpWkXnIiAK/N+JpbXpvDj5+cwrrtuSFHJSIiIhJbrvl+O7o0q8fPnp/B50s3hx1OqFTsiVSyi/u05P/OPpbpK7cx9L5PeXv22rBDEhEREYkZdZMTefonvWndsDZXPT2Nmau2hR1SaFTsiVQyM+OiPi1594aTaJuZynUvzuQf4xaHHZaIiIhIzKhfpxbPXdmHjNQkrn9pZo0dN0GTf4mEpE1GHV695gQeHr+U049rCkTm54uPs5AjExEREan+GtVL5vmr+rAjL5/E+Jp5jqtmfmqRKiIhPo7rBnWgbWYq7s51L87g9+8sIC+/MOzQRERERKq9Fg1q07VZGgDPTFrBmpyaNV6Cij2RKqKwyMlMTeKJics584GJzFuzPeyQRERERGLChh15jBqziEsfn8ymnXvDDqfSqNgTqSIS4uO4+6xuPH1Fb7bn5nP2Q5/x0PglFGqKBhEREZGj0rheMv++/Hus257HpU9MZvue/LBDqhQq9kSqmO93zGTMjQM4pUsTHp+wnG179oUdkoiIiEi116t1Ax778fEs27Sby5+awu69BWGHFHUaoEWkCqpfpxb/vCib9TvyyEhNorDIGbdwA7v3FvDXD75ibU4uzdJTuHloJ0ZkZ4UdroiIiEi1cFKHTP5xYTbXvTiDSUu3MLhL47BDiioVeyJVlJnRNC0FgLdmr+GXL88mzqD4qs41ObncPnougAo+ERERkUN0arcmjL/5ZLLSU8IOJep0GadINXBW9yzSUhIofftebn4ho8YsCicoERERkWqquNAbv2gjt746J2bHSFCxJ1INxMUZO3LLvq58bU4u01du1XQNIiIiIodp4bqdvDxtNb99Yx7usVfw6TJOkWqiWXpKmXPDNE5L5rxHJpGSGM/3O2VySpcmnNypEWm1E0OIUkRERKT6+NnAduzIy+fh8Uupl5zAbcM6Y2Zhh1VhQiv2zGwFsBMoBArcvZeZNQBeBloDK4Dz3X1bWDGKVCU3D+3E7aPnklviDF5KYjy/HtKRjLpJfLBgA2MXbODduetJiDP+cWE2px3bFHePqT9aIiIiIhXplqGd2JVXwKOfLqNucgK/+EGHsEOqMGGf2TvZ3TeXeH4bMM7d7zGz24Lnt4YTmkjVUjwIy6gxi8ocjXNgp0b84axuzP46hw8WbOC45mkAvD5zDU9MXM4pXZowpEtjjmlaV8WfiIiISMDMuHt4V3bvLWBNTm5MHSgPu9gr7SxgYLD8NDAeFXsi+43IzjrgyJtxcUZ2y/pkt6y/vy01KYHkxHj+Pu4r7vvwK5rXT2FIl8bccdoxJMbrtl0RERGRuDjjL+ceR3ycYWbsLSgkKSE+7LCOWpjFngMfmJkDj7r7Y0Bjd18XvL4eiO2JL0QqwSldm3BK1yZs2rmXcQs38MGCDUxbsW1/offspBVk1k1mQMcMateqasd/RERERCpHQvDdaPXWPVz8+GRuH9aZYcc2DTmqoxPmN7v+7r7GzBoBY83sy5IvursHheC3mNlIYCRAy5YtKydSkRiQWTeJC3q35ILeLSkKhhcuKnIe+WQZa3JySUqI46QOGQzp0phBxzQmIzUp5IhFREREKl/D1Fo0qpvE9S/N5F+14hnYqVHYIR2x0K7hcvc1wc+NwOtAb2CDmTUFCH5uLGO9x9y9l7v3yszMrMyQRWJGXJzt/zn+5oG8cFUfLuzdkoXrdnLra3N5fMJyAPILi1i+eXeYoYqIiIhUqtq1Enji8u/RoVFdrnluOlOWbw07pCMWSrFnZnXMrG7xMnAKMA94C7gs6HYZ8GYY8YnUJInxcZzYPoO7hndl4q0n8851/bm4T+Ss+eRlWzn5r+MZcu8njBrzJbNW5+w/KygiIiISq9JSEnn2yt5kpadwxVNTmb92e9ghHZGwzuw1Biaa2WxgCvBfd38fuAcYYmaLgcHBcxGpJGZGt6w0WjSoDUDHJqnceWYXMlKTeOSTZYx48DNOuGccq7bsCTlSERERkehqmJrEc1f14eTOjWhev3bY4RyRUO7Zc/dlQPcy2rcAgyo/IhEpS6O6yfykXxt+0q8NOXv28dGXG5m4eDNZ9VMA+NsHi1i+eTendG3CwE6Z1EvWRO4iIiISO5qmpfDAhdkA5OUXsnX3Ppqlp4Qc1aHT0HsickjSa9finJ7NOadn82+1f7FsC+/MWUdivNG3bUPOzs76Th8RERGR6u6mV2Yx5+vtvHrNiTRJSw47nEOiSbZE5Ij96pROTL5jMK/97ASu6NeGr7flMnHJZgDcnX9/tpyvNuzEXff5iYiISPV29YB25OzJ55InJrNl196wwzkkOrMnIkclPs44vlUDjm/VgNuGdWZvQREAq7bu4e63FwDQqmFtTunSmCFdmnB8q/rEB6OBioiIiFQX3Vuk8/hlvbjsySn8+MkpvDiyb5W/hUVn9kSkwpgZyYnxALRqWIfJdwzij2d3o3XDOjz1+QrOf3QSY+avB2BnXj55+YVhhitS5ZjZk2a20czmlWhrYGZjzWxx8LN+mDGKiNRkfds25JFLjmfR+p386pXZYYdzUDqzJyJR07heMhf3acXFfVqxMy+fT77axICOkfkxn/1iJQ+MW8KAjhkM6dKEQZ0bUb9OrZAjFgndU8A/gWdKtN0GjHP3e8zstuD5rSHEJiIiwMmdG/HgxT1pm1En7FAOSsWeiFSKusmJnHFcs/3PT2yXwfrteXwwfwNj5m8gzqBf+wye/knv/ZO+i9Q07v6pmbUu1XwWMDBYfhoYj4o9EZFQDe3aBIiMUfD2nHWc1q0JCfFV76JJFXsiEooeLdLp0SKdu4d3Ze6a7YxdsIFte/btL/RufXUOjeslcUrXJnRtVg8zFYBSYzV293XB8noic9WKiEgVMHn5Vq5/cSaf9GzOqHOPq3IHrFXsiUiozIzjmqdzXPP0/W0FhUWs3Lqb/0xfzT8+WkKztGSGdGnMuce34NjmaSFGKxIud3czK3d4WzMbCYwEaNmyZaXFJSJSU/Vt25CbhnTk3rFfkZoUz13Du1apA9Qq9kSkykmIj+OlkSewZddePvpyIx8s2MDL01bTOqMOxzZPI2fPPj5fuoUBHTNJTdKfMYl5G8ysqbuvM7OmwMbyOrr7Y8BjAL169dKcJyIileC6H7RnZ14+/5qwnLrJifx6aKewQ9pP35JEpMpqmJrEeb1acF6vFuTuK6QomK/voy83ctMrs6kVH0e/9g0Z0qUJg7s0olHd6jHBqchhegu4DLgn+PlmuOGIiEhJZsYdpx3Drr0FPPzJUob3aEbHxnXDDgsAq86THffq1cunTZsWdhgiUskKCouYvnIbYxds4IMFG1i1dQ9mMOm2QTRJSyYvv3D/FBASO8xsurv3CjuOaDKzF4kMxpIBbADuBN4AXgFaAiuB891968G2pRwpIlK5CoucOV/nkN2ycmfIOVB+1Jk9Eal2EuLj6NO2IX3aNuQ3px/DVxt2MWX5FpqkRc7s3fTKLL5cv5MhXRpzSpcmZLdIr3I3TIuUxd0vLOelQZUaiIiIHLb4ONtf6H0wfz179hUyIjsr1JhU7IlItWZmdGpSl05NvrlcYmCnRuzMK+CJCct59JNlZKQm8eMTWnH9oA4hRioiIiI1gbvz7Bcr+XzpFlJqxe+fpiEMKvZEJOac36sF5/dqwfbcfMYvigzwUnxib19BEbe8OpuBnRpxcqdGpNVO5I2Zaxg1ZhFrc3Jplp7CzUM7hX4kTkRERKonM+PhS47n4scnc90LM3ny8u/Rv0NGKLGo2BORmJWWkshZPbI4q8c3hduqrbv5fOkW3pi1loQ4o01GbVZs2UN+YeT+5TU5udw+ei6ACj4RERE5IqlJCTz9k+9xwWNf8NNnpvHcVb05vlWDSo9DxZ6I1CjtG9Xli9sHMfvrHMYu2MBjny6joOjbA1Xl5hdy2+g5fL50M+m1a5GWksilJ7SiXnIia3Jy2bprH+m1E0mrnUjdpIQqNZ+OiIiIVA3ptWvxzJW9Of+RSYxbuFHFnohIZYgLbqDOblmfh8cvLbNPXn4Rn3y1iZw9+ewtKOJH32sBwEtTVvHAR0u+2ZZFziBOvPUH1ElK4JWpq/ls6WbSUxJJS0kkLSgWf9gzCzNj0869OE5aSiJJCRoxVEREJJY1qpvMm9f2p15KpOxy90o9SKxiT0RqtGbpKazJyf1Oe1Z6Cp/d9gMA8vILSUqIA+Ccns05NiuNnNx8duTmk7Mnn+25+dSuFSnc1u/IY+aqHLbn5rMjLx93qF0rnnOPbw7AH/+7gDdmrQUi7WkpibRsUJuXrz4BgGe/WMnXW/dQLyUxcvYwJZHG9ZL5XusG34pFZxNFRESqh7TaiQAs3rCTm1+dwz8vyqZ5/dqV8t4q9kSkRrt5aCduHz2X3PzC/W0pifHcPLTT/ucl5+xrk1GHNhl1yt3e9YM67B/1s6jI2ZlXwI68/P2vX9C7Jce3qs/2EoViraCQBJi0dDPjFm5kb0HR/rbOTery/o0DALjoX18w++vt1EtOIL12LeqlJNKzZTp3ntkViBSL+wqK9p9ZTK8dKRZbNKicpCIiIiJlKyhylm3axSWPT+aVa06gUd3kqL+nij0RqdGKB2GJxmiccXFGWnBvX7G+bRvSt23Dctd56OLjgcgZvOKCsMi/uafwwt4tObHdHnJy97E9t4CcPfso8TKPfbqU1Vu/faZy8DGNefyyyFyr3x/1MfsKiiKXmAbF4ICOmVzcpxUAr0xdTZ2khP1nFdNSEmmYWovatQ4/XWiUUxERkW8c07QeT13Rm0sen8zwByZiZqzfnhfVHKliT0RqvBHZWVWuCElOjCc5MZ7G9b591O+8Xi0OuN74X5/MrrwCcnL3kbMnn5zcfOolf/Onfli3pmzetZftufls35PP8s27aZuZCkBhkXPLa3O+s80r+rXhd2d2IS+/kGH3T9hfJKalJJKeksjgLo05qUMmefmFfLZkM2kpiUxfuZX7xi4mLzhDqVFORUREoGfL+lx+YmseKjFmQDRzpIo9EZEYEl/ibGKrMk4g3jasc7nrxhlMvmNQpEjcsy9yZjE3n3ZBMbivsIhuWWnk7NnH1t37WLZpN9tz88mqn8JJHTLZsCOPK5+eVu72c/MLGTVmkYo9ERGp0d4M7t0vKVo5UsWeiIgAkUlgG9dL/s7ZxGL1khN54MLsctdvXC+ZN6/tx/bcfH785JQy+6wtYzAcERGRmqS8XBiNHKliT0REKkRyYjzdW6QDkdFMyxrltFl6SmWHJSIiUqWUNxJ4NHJk3MG7iIiIHJ6bh3YiJfHb8wiWHuVURESkJqrMHKkzeyIiUuGiOcqpiIhIdVaZOVLFnoiIREVVHOVURESkKqisHKnLOEVERERERGJQpRd7ZtbCzD42swVmNt/Mbgja7zKzNWY2K3icVtmxiYiIiIiIxIowLuMsAH7l7jPMrC4w3czGBq/d5+5/DSEmERERERGRmFLpxZ67rwPWBcs7zWwhoJs6REREREREKlCo9+yZWWsgG5gcNP3CzOaY2ZNmVr+cdUaa2TQzm7Zp06ZKilRERERERKR6Ca3YM7NU4DXgp0xxewAAIABJREFURnffATwMtAN6EDnz97ey1nP3x9y9l7v3yszMrLR4RUREREREqpNQij0zSyRS6D3v7qMB3H2Duxe6exHwL6B3GLGJiIiIiIjEgjBG4zTgCWChu99bor1piW5nA/MqOzYREREREZFYYe5euW9o1h+YAMwFioLmO4ALiVzC6cAK4OpgMJcDbWsTsLICwsoANlfAduTbtF+jR/s2OrRfo6Oi9msrd9f1+4eognKk/k9Ej/ZtdGi/Ro/2bXRUxH4tNz9WerFXFZnZNHfvFXYcsUb7NXq0b6ND+zU6tF+rL/3bRY/2bXRov0aP9m10RHu/hjoap4iIiIiIiESHij0REREREZEYpGIv4rGwA4hR2q/Ro30bHdqv0aH9Wn3p3y56tG+jQ/s1erRvoyOq+1X37ImIiIiIiMQgndkTERERERGJQTW62DOzJ81so5lpTr8KZGYtzOxjM1tgZvPN7IawY4oFZpZsZlPMbHawX+8OO6ZYYmbxZjbTzN4JO5ZYYmYrzGyumc0ys2lhxyOHRvkxOpQfo0c5MrqUI6OjMnJkjb6M08wGALuAZ9y9W9jxxAozawo0dfcZZlYXmA6McPcFIYdWrZmZAXXcfZeZJQITgRvc/YuQQ4sJZnYT0Auo5+5nhB1PrDCzFUAvd9fcTNWI8mN0KD9Gj3JkdClHRkdl5MgafWbP3T8FtoYdR6xx93XuPiNY3gksBLLCjar684hdwdPE4FFzj9ZUIDNrDpwOPB52LCJVgfJjdCg/Ro9yZPQoR1ZvNbrYk+gzs9ZANjA53EhiQ3AZxSxgIzDW3bVfK8bfgVuAorADiUEOfGBm081sZNjBiFQVyo8VTzkyapQjoyfqOVLFnkSNmaUCrwE3uvuOsOOJBe5e6O49gOZAbzPT5VVHyczOADa6+/SwY4lR/d29JzAMuDa4PFCkRlN+jA7lyIqnHBl1Uc+RKvYkKoLr5V8Dnnf30WHHE2vcPQf4GDg17FhiQD9geHDd/EvAD8zsuXBDih3uvib4uRF4HegdbkQi4VJ+jD7lyAqlHBlFlZEjVexJhQtukn4CWOju94YdT6wws0wzSw+WU4AhwJfhRlX9ufvt7t7c3VsDFwAfufslIYcVE8ysTjAIBWZWBzgF0OiOUmMpP0aPcmR0KEdGT2XlyBpd7JnZi8AkoJOZfW1mV4YdU4zoB1xK5OjPrOBxWthBxYCmwMdmNgeYSuR+BA2BLFVZY2Cimc0GpgD/dff3Q45JDoHyY9QoP0aPcqRUN5WSI2v01AsiIiIiIiKxqkaf2RMREREREYlVKvZERERERERikIo9ERERERGRGKRiT0REREREJAap2BMREREREYlBKvbkkJlZYTBM9Hwzm21mvzKzA/4OmVlrM7voKN5rnpn9x8xqH8a6vczsH4f7nsG6Nx7OewXrDDSzCh/e2czGm9miEsNzvxq0X2NmP67o9yvxvpcFw66XbMsws01mlnSY27qjAuIZb2bTSjzvZWbjj3a7wbYuN7N/VsS2RKRmU44sdx3lyPK3pRwpUadiTw5Hrrv3cPeuRCYrHQbceZB1WgOHnchKvFc3YB9wTckXzSyhvBXdfZq7X38E7wlwI3BYiSzKLg72Qw93PxfA3R9x92ei+J6vA0NKJfRzgbfdfe9hbuuwEplFlPV3qZGZDTvM9446M4sPOwYRqTKUIyufcmSEcqSUS8WeHBF33wiMBH4R/PFpbWYTzGxG8Dgx6HoPcFJw1O2XB+h3IBOA9sHRwQlm9hawwMySzezfZjbXzGaa2cnw7aOIZlbHzJ40sylBn7OC9ngz+2twVHSOmV1nZtcDzYhMyvpx0O8UM5sUxPofM0sN2k81sy/NbAZwTllBHyC+y81stJm9b2aLzewvh7PvzewuM/t1sPy9IP5ZZjbKzOaV+HyjzGxq8PrVJfbNeDN7NYj/eTOzktt39x3AJ8CZJZovAF40s0wzey3Y7lQz6xdsN7XEZ51jZj80s3uAlCC254N+NwX7fJ6Z3Ri0tbbI0dlngHlAizI+9ijgN2Xsi28ddTSzd8xsYLC8K9gH883sQzPrHXz2ZWY2vMRmWgTti83szhLbuiT4vZllZo8WJ61gu3+zyCSoJxz8X0xEahrlSOVI5UjlyCrD3fXQ45AewK4y2nKAxkSO9CUHbR2AacHyQOCdEv3L7FfeewEJwJvAz4Jt7QbaBK/9CngyWO4MrAKSS74n8H/AJcFyOvAVUCfY3qtAQvBag+DnCiAjWM4APgXqBM9vBX4XvMfqIH4DXin5GUt8hvLiuxxYBqQFz1cCLcpYfzywCJgVPEYF7XcBvw6W5wEnBMv3APOC5ZHAb4PlJGAa0CbYN9uB5kQO9kwC+pfx3ucCrwfLzYC1QDzwQnF/oCWwMFj+M/D3EuvXL/07AxwPzA32fyowH8gmcmS7COhbzu/CeKAX8BFwcrA8PnjtcuCfJfq+AwwMlh0YFiy/DnwAJALdgVkl1l8HNARSgv3ZCzgGeBtIDPo9BPy4xHbPD/v/ox566FG1HihHKkcqRxZvVzmyCj3KPc0vcpgSgX+aWQ+gEOh4lP1SzGxWsDwBeAI4EZji7suD9v7AAwDu/qWZrSxje6cAw4uP8hFJHC2BwcAj7l4QrL+1jBj6Al2Az4IDe7WI/OHvDCx398UAZvYckcRR2oHiG+fu24P1FwCtiCTH0i5292lltGNm6UBdd58UNL0AnFHicx9nZucGz9OIJN59RPbh18E2ZhFJJBNLbf6/wENmVg84H3jN3QvNbDDQpcSBznrBkdzBRI5sEnzebeXsj9fdfXfw3qOBk4C3gJXu/kVZn7OEPwC/JfKF4lDsA94PlucCe90938zmEvnMxca6+5YSMfUHCogk3qnBZ00BNgb9C4HXDjEGERFQjlSOVI6UkKjYkyNmZm2J/KfeSOS+hA1EjgjFAXnlrPbLQ+yX6+49Sr0fRI5aHlaYwA/dfVEZ2zqUdce6+4Wl1u1RTv/DUfK6/kIq/v+iAde5+5hvNUYu3zjoe7t7rpm9D5xNJEHdFLwUR+To4rf+3Q5xfx7IQf9d3f0jM/sDkS8YxQr49uXoySWW890jhxmJHBXdG2ynyL59P4vzbU5k/z3t7reXEUqeuxceLF4RqdmUI4+KcuS3KUfKEdM9e3JEzCwTeITI5QFO5KjYOncvAi4lcjkDwE6gbolVy+t3JCYAFwfxdCRyNHJRqT5jgOuKr7k3s+ygfSxwdfEfNDNrUEa8XwD9zKx90KdO8D5fAq3NrF3Q71uJ7jDjO2LungPsNLM+QdMFJV4eA/zMzBKL39/M6hzmW7xIJIE1JnK0FiKXeVxX3KFEUh8LXFuivX6wmF8cA5H9McLMagexnB20HY4/ALeUeL4C6GFmcWbWAuh9mNuDyI32DcwsBRgBfAaMA841s0bB52lgZq2OYNsiUgMpRypHBttVjpTQqdiTw1F8I/F84EMif9TuDl57CLgsuCG3M98chZoDFFpkGOpfHqDfkXgIiAsuOXgZuNy/GQmr+EjU74lcFjMniPv3QfvjRO4PmBPEUjwa2mPA+2b2sbtvInK9+otmNofg8pTgiN1I4L8Wufm8+NKFw4nvUD1v3wwr/WEZr18J/Cu41KQOkXsNij/fAmCGRW5If5TDPzI6lsi9CC+XOPp3PdDLIjeYL+CbEeD+ANS3yE3ls4ncNwCR/TnHzJ539xnAU8AUYDLwuLvPPJyA3P1dYFOJps+A5UQ+6z+AGYf5GQnieY3I7+prHhmpbgGRy2E+CP7txwJNj2DbIlJzKEcqRypHSpVj3/x+isQGM/shMNzdLws7lmgzs1R33xUs3wY0dfcbQg5LRESqKOVI5UipWXTPnsQUiwwX/EfgirBjqSSnm9ntRP4vryRylFVEROQ7lCOVI6Xm0Zk9ERERERGRGKR79kRERERERGKQij0REREREZEYpGJPREREREQkBqnYExERERERiUEq9kRERERERGKQij0REREREZEYpGJPREREREQkBqnYExERERERiUEq9kRERERERGKQij2RkJjZCjMbXOL5BWa2zcy+HzwfaGZuZreWWu8RM9tV6rEn6Dsg6ONmtrtUn1sq9xOKiIgcXJD/Jgd5a2Ow/HMzsxJ97gpyW59S615uZoUlct0yM/tZqT7Nzex5M9sSvMcUMzujVJ+SeXONmd1rZvHR/eQi0adiT6QKMLPLgAeB0939k6D5MmAr8OOSfd39GndPLfkARgMfA5+V6Nq9VL+/VMJHEREROWRm9ivgfmAU0ARoDFwD9ANqBX2MSC78Tk4MTCqRD38I/MXMsoN1GwATgX1AVyADuA94wczOLbWd7sE2vg/8CLiiAj+qSChU7ImEzMyuBv4GDHX3z4O2OsC5wLVABzPrdYD1fwacDFzo7oWVELKIiMhRM7M04H+Bn7v7q+6+0yNmuvvF7r436HoS0BS4HrjAzGqVt013nwksBI4Jmn4J7AKudPf17p7r7i8CfwT+VvLsYYltLCFy8LRHBX1UkdCo2BMJ18+IJLpB7j6tRPs5RJLTf4AxRM7yfYeZfY/I0dAfufuGKMcqIiJSkU4AkoA3D9LvMuBt4JXg+ZnldQzyYkegOKcOAV5z96JSXV8BWgZ9S2+jM5ECc8lB4hKp8lTsiYRrCPAFMLdU+2XAy8GZuheIHMlMLNkhuDTlP8Dv3H1iGdueYWY5JR5DoxC/iIjIkcoANrt7QXGDmX0e5KxcMxtgZrWB84AX3D0feJXvXsrZN1hnJzAFeBZYXOI91pXx3utKvF5shpntJnJmcDzw0NF9PJHwqdgTCdfPiBxVfLz4UhIza0Hksszngz5vAsnA6cUrBX2fA6a5+73lbLunu6eXeIyJ1ocQERE5AluADDNLKG5w9xPdPT14LQ44GygA3g26PA8MM7PMEtv5IshzdYnc99cV+L/gtc1ELgEtrWmJ14v1BFKJ3K/XB6hzFJ9NpEpQsScSrg3AICKXixQfQbyUyP/Nt81sPbCMSLFX8lLO3wLt0c3jIiJSfU0C9gJnHaDPZUQKsFVBTvwPkAhcVFbn4JaG1/jmUs8PgXPMrPR33vOB1cBXpdZ3d38liO13h/VpRKogFXsiIXP3tUQKvlPN7D4iie1uIjeGFz9+CJxmZg2D6RpuAX7o7jtCCltEROSouHsOkXz3kJmda2Z1zSzOzHoQOauWRSQ/nsE3+bA78GfKHpUTM2tI5Gzg/KDpPiANeMLMmphZspldCPwGuNndvZzw7gF+amZNKuKzioTFyv8dF5FoMrMVwFXu/mHwvA2RI4wJQCN331Sq/3zgYSKDtwwA8srY7NXu/ryZObAHKPkf/HF3v7HCP4iIiMhRMLOLgRuAbsBuIle0PEHkksyz3P34Uv2bASuBbKBX0Dc3eHkPMA64wd03Bv1bEikQhxIZEGYB8Ad3f7PENh3oEIzEWdz2HrDA3X9V0Z9ZpLKo2BMREREREYlBuoxTREREREQkBqnYExERERERiUEq9kRERERERGKQij0REREREZEYpGJPREREREQkBiWEHcDRyMjI8NatW4cdhoiIVILp06dvdvfMsOOoLpQjRURqhgPlx2pd7LVu3Zpp06aFHYaIiFQCM1sZdgzViXKkiEjNcKD8qMs4RUREREREYpCKPRERERERkRikYk9ERERERCQGqdgTERERERGJQSr2REREREREYpCKPRERERERkRikYk9ERERERCQGqdgTERERERGJQdV6UvWj9cbMNYwas4i1Obk0S0/h5qGdGJGdFXZYIiIiIiIiR63GFntvzFzD7aPnkptfCMCanFxuHz0XQAWfiIiIiIhUezX2Ms5RYxbtL/SK5eYXMmrMopAiEhERERERqTg1tthbm5N7WO0iIiIiIiLVSY0t9pqlpxxWu4iIiIiISHVSY4u9m4d2IiUx/lttKYnx3Dy0U0gRiYiIiIiIVJwaO0BL8SAs97z3Jet35FEvOYH/PaubBmcREREREZGYUGPP7EGk4PvijkF0aJTKacc2VaEnIiIiIiIxo8ae2SvpjWv7USdJu0JERERERGJHjT6zV0yFnoiIiIiIxBoVe4C784sXZvCPcYvDDkVERERERKRCqNgDzIxNO/fy/rz1YYciIiIiIiJSIVTsBQZ0zGTBuh1s3rU37FBERERERESOmoq9QP/2GQB8tmRzyJGIiIiIiIgcPRV7gW5ZaaTXTmTCYhV7IiIiIiJS/WkYykB8nHFxn5bUS04MOxQREREREZGjpmKvhJuHdg47BBERERERkQqhyzhL2VtQyPrteWGHISIiIiIiclRU7JUy4sHPuX30nLDDEBEREREROSoq9kr5Xuv6fLFsK3sLCsMORURERERE5Iip2Culf/sMcvMLmbEyJ+xQREREREREjpiKvVJOaNeQ+Dhj4pJNYYciIiIiIiJyxFTslVI3OZHsFulM1Hx7IiIiIiJSjWnqhTL8emgnaiWoDhYRERERkepLFU0Z+rZtSM+W9cMOQ0REYoiZnWpmi8xsiZndVsbrSWb2cvD6ZDNrXer1lma2y8x+XVkxi4hI9aZirxwTF2/m7dlrww5DRERigJnFAw8Cw4AuwIVm1qVUtyuBbe7eHrgP+HOp1+8F3ot2rCIiEjtU7JXjmUkruOe9L3H3sEMREZHqrzewxN2Xufs+4CXgrFJ9zgKeDpZfBQaZmQGY2QhgOTC/kuIVEZEYoGKvHCd1zGRNTi4rtuwJOxQREan+soDVJZ5/HbSV2cfdC4DtQEMzSwVuBe6uhDhFRCSGqNgrx0ntMwCYsFhTMIiISKjuAu5z910H62hmI81smplN27RJ+UtEpKZTsVeOVg1r07x+ChM0BYOIiBy9NUCLEs+bB21l9jGzBCAN2AL0Af5iZiuAG4E7zOwXZb2Juz/m7r3cvVdmZmbFfgIREal2NPVCOcyMkzpk8vnSzbg7wW0TIiIiR2Iq0MHM2hAp6i4ALirV5y3gMmAScC7wkUduHD+puIOZ3QXscvd/VkbQIiJSvanYO4DfnH4MtRPjVeiJiMhRcfeC4GzcGCAeeNLd55vZ/wLT3P0t4AngWTNbAmwlUhCKiIgcMRV7B5CapN0jIiIVw93fBd4t1fa7Est5wHkH2cZdUQlORERiku7ZO4hHP1nKL16YEXYYIiIiIiIih0XF3kHs2lvAe/PWsyMvP+xQREREREREDpmKvYPo3z6DwiJn0tItYYciIiIiIiJyyFTsHUR2y/rUqRWv+fZERERERKRaUbF3ELUS4ujbtiETNd+eiIiIiIhUIyr2DsHpxzWlT5uG7C0oDDsUERERERGRQ6K5BQ7BOT2bc07P5mGHISIiIiIicshCObNnZr80s/lmNs/MXjSzZDNrY2aTzWyJmb1sZrXCiK087s7anNywwxARERERETkklV7smVkWcD3Qy927AfHABcCfgfvcvT2wDbiysmM7kLvfXsCw+ydQWORhhyIiIiIiInJQYd2zlwCkmFkCUBtYB/wAeDV4/WlgREixlalnq/psz81n7prtYYciIiIiIiJyUJVe7Ln7GuCvwCoiRd52YDqQ4+4FQbevgazKju1A+rVrCMCErzQFg4iIiIiIVH1hXMZZHzgLaAM0A+oApx7G+iPNbJqZTdu0qfIKr4apSf/P3p2H11WWex//3knTJB3TIS0dGUuhgFCoCBSQeVIGERUHREVxnsUD6hEOoqCc4xFx5ACCrwgioCAqM8ggAm1BoIVSpkIHaEvnIW2TPO8fe6WkbVqaZlg7O9/Pde0re82/vZrm2fcansVuw/vxwPM+gkGSJElS8cvjMs4jgJdSSvNTSmuBm4CJQE12WSfASGB2SwunlC5LKU1IKU2ora3tnMSZg8bUMmXmIpavrn/rmSVJkiQpR3k8euEVYL+I6AWsAg4HJgH3AqcA1wGnAzfnkG2zTtlnBHuPrqGiPPKOIkmSJEmb1enFXkrpkYi4AZgC1AOPA5cBfwWui4gLsnFXdHa2t7LTkL7sNKRv3jEkSZIk6S3l8lD1lNK5wLkbjH4R2DeHOK3y/Lxl/POFN/jo/tvlHUWSJEmSNimvRy90WfdNn893b57K3CU+YF2SJElS8bLYa6UDxwwG4IEZ9sopSZIkqXhZ7LXS2KF9qe1byYMWe5IkSZKKmMVeK0UEB+40mAefX0BjY8o7jiRJkiS1yGJvKxw0ZjDLV9fzysKVeUeRJEmSpBbl0htnV3fcHsM4bo9hVFWU5x1FkiRJklpksbcVLPIkSZIkFTsv49xK902fxym//Cer1jTkHUWSJEmSNmKx1waTZi7i0ZcX5h1DkiRJkjZisbeV3rH9IHqWl/HAc/PzjiJJkiRJG7HY20rVPcuZsN0AHnze5+1JkiRJKj4We21w0Jhann1tGfOW1uUdRZIkSZLWY2+cbfDOnWuZPHMhy1bXMyTvMJIkSZLUjMVeG4wb3o/LT3973jEkSZIkaSNextkO5i2rI6WUdwxJkiRJWsdir43+/tRc9v3+3Ux/fVneUSRJkiRpHYu9NtprdA0AD86wV05JkiRJxcNir42G9a9mx9re3G+xJ0mSJKmIWOy1g4PG1PLoS29Qt7Yh7yiSJEmSBFjstYuDxgymbm0jU2YuyjuKJEmSJAEWe+1ivx0GcdHJe7DzNn3zjiJJkiRJgM/Zaxe9K3tw6r6j844hSZIkSet4Zq+dLFyxhmsffYVFK9bkHUWSJEmSLPbay8w3VnDOTU/xwPP2yilJkiQpfxZ77eRtI2voV9WDB2fMzzuKJEmSJFnstZfysmDiToN5YMYCUkp5x5EkSZLUzVnstaODxtQyd0kdL8xfkXcUSVKRiYhjImJ6RDwfEWe3ML0yIv6QTX8kIrbLxh8ZEZMj4qns52GdnV2S1DVZ7LWjg8YMBuDJWYtzTiJJKiYRUQ78HDgWGAd8MCLGbTDbGcCilNJOwP8CP8zGLwCOTyntAZwO/L/OSS1J6up89EI7GjWwF5O+cwSD+1TmHUWSVFz2BZ5PKb0IEBHXAScC05rNcyJwXvb+BuBnEREppcebzTMVqI6IypTS6o6PLUnqyjyz184s9CRJLRgBvNpseFY2rsV5Ukr1wBJg0AbzvBeYYqEnSdoSFnvt7NWFKznjqseYPHNR3lEkSSUkInajcGnnpzczz5kRMSkiJs2fb+/QktTdWey1s/69KrjvufncN31e3lEkScVjNjCq2fDIbFyL80RED6A/8EY2PBL4E/DRlNILm9pISumylNKElNKE2tradowvSeqKLPbaWb+qCvYc2Z8HZvhwdUnSOo8BYyJi+4joCZwK3LLBPLdQ6IAF4BTgnpRSioga4K/A2SmlhzotsSSpy7PY6wAHjqnlyVmLWbJybd5RJElFILsH7wvA7cAzwPUppakRcX5EnJDNdgUwKCKeB74GND2e4QvATsB3I+KJ7DWkkz+CJKkLsjfODnDwmMH89O4Z/POFBRy7x7C840iSikBK6W/A3zYY991m7+uA97Ww3AXABR0eUJJUcjyz1wH2HFXD/jsMoke5u1eSJElSPjyz1wEqysu49sz98o4hSZIkqRvz1FMHWrWmgZVr6vOOIUmSJKkbstjrIK8tqWPP8+/gxikb9qwtSZIkSR3PYq+DDO1XSW2fSh54zofaSpIkSep8FnsdJCI4aMxgHn7hDeobGvOOI0mSJKmbsdjrQAeNqWXZ6nr+PWtJ3lEkSZIkdTMWex3ogB0HEQEPzliQdxRJkiRJ3YyPXuhAA3r35KKT92D86AF5R5EkSZLUzVjsdbAPvH103hEkSZIkdUNextnB1tQ3cvvU15g2Z2neUSRJkiR1IxZ7HSyR+Mp1T3DdY6/kHUWSJElSN2Kx18Eqe5Sz3w4D7aRFkiRJUqey2OsEB46p5cUFK5i1aGXeUSRJkiR1ExZ7neDgMYMBH8EgSZIkqfNY7HWCnYb0YWi/Sh5/ZXHeUSRJkiR1Ez56oRNEBH/5woHU9q3MO4okSZKkbsJir5MM6VeVdwRJkiRJ3Ugul3FGRE1E3BARz0bEMxGxf0QMjIg7I2JG9nNAHtk6SkNj4ps3/Jvf/Wtm3lEkSZIkdQN53bN3CXBbSmkXYE/gGeBs4O6U0hjg7my4ZJSXBU/PXsqtT87JO4okSZKkbqDTi72I6A8cDFwBkFJak1JaDJwIXJ3NdjVwUmdn62gHjRnM5JmLWLG6Pu8okiRJkkpcHmf2tgfmA7+JiMcj4vKI6A0MTSnNzeZ5DRiaQ7YOddCYWtY2JB59aWHeUSRJkiSVuDyKvR7A3sAvU0rjgRVscMlmSikBqaWFI+LMiJgUEZPmz5/f4WHb04TtBlDZo4wHfN6eJEmSpA6WR7E3C5iVUnokG76BQvH3ekQMA8h+zmtp4ZTSZSmlCSmlCbW1tZ0SuL1UVZRz4l7DGdi7Iu8okiRJkkpcpz96IaX0WkS8GhFjU0rTgcOBadnrdOCi7OfNnZ2tM/zolD3zjiBJkiSpG8jrOXtfBK6JiJ7Ai8DHKZxlvD4izgBmAu/PKVuHa2xMLF9TT78qz/BJkiRJ6hi5FHsppSeACS1MOryzs+Th2EseYJdhfbnk1PF5R5EkSZJUotp0z15EDI2IKyLi79nwuOzMnDZj12F9eej5BTQ2ttgHjSQpJ7ZrkqRS0tYOWq4CbgeGZ8PPAV9p4zpL3oFjalmwfA3PvLY07yiSpPVdhe2aJKlEtLXYG5xSuh5oBEgp1QMNbU5V4g7caTAAD/oIBkkqNrZrkqSS0dZib0VEDCJ7Jl5E7AcsaXOqErdN/yp2HtrH5+1JUvGxXZMklYy2dtDyNeAWYMeIeAioBU5pc6pu4OtHjaW6ojzvGJKk9dmuSZJKRpuKvZTSlIh4JzAWCGB6SmltuyQrcUfvtk3eESRJG7BdkySVkrb2xvl5oE9KaWpK6WmgT0R8rn2ilb7HX1nEAzPm5x1DkpSxXZMklZK23rMkOXMvAAAgAElEQVT3qZTS4qaBlNIi4FNtXGe38aPbpvODvz2bdwxJ0pts1yRJJaOtxV55RETTQESUAz3buM5u48Axg3lm7lLmL1uddxRJUoHtmiSpZLS12LsN+ENEHB4RhwPXZuO0BQ4eUwvAQ8/bK6ckFQnbNUlSyWhrb5z/AXwa+Gw2fCdweRvX2W3sNrwfA3pV8MCMBZw0fkTecSRJtmuSpBLS1t44G4FfZi+1UllZMHGnwUyeuTDvKJIkbNckSaWlTcVeREwEzgO2zdYVQEop7dD2aN3DucfvRr/qtp5glSS1B9s1SVIpaWuVcQXwVWAy0ND2ON1Pbd/KvCNIkt5kuyZJKhltLfaWpJT+3i5JurErH3yJWYtW8d3jx+UdRZK6O9s1SVLJaGtvnPdGxMURsX9E7N30apdk3cjMN1Zw7aOvsLreg8iSlDPbNUlSyWjrmb13ZD8nNBuXgMPauN5u5cAxtVz98Ewmz1zEATsOzjuOJHVnHdauRcQxwCVAOXB5SumiDaZXAr8F9gHeAD6QUno5m3YOcAaFS0u/lFK6va15NufPj8/m4tunM2fxKobXVHPW0WPtNbqduG87hvu147hvO0Zn7de29sZ5aHsF6c7222Eg5WXBgzMWWOxJUo46ql3LHs7+c+BIYBbwWETcklKa1my2M4BFKaWdIuJU4IfAByJiHHAqsBswHLgrInZOKXXI5SB/fnw259z0FKvWFlY/e/EqzrnpKQC/4LWR+7ZjuF87jvu2Y3Tmfo2UUttWEPEuCg1QVdO4lNL5bcy1RSZMmJAmTZrUGZvqcO/71T+pW9vIX754YN5RJKkoRcTklNKEt56zzdtp93YtIvYHzkspHZ0Nn5Ot98Jm89yezfNwRPQAXgNqgbObz9t8vs1tc2vbyIkX3cPsxas2Gt+zvIzxo2t499uGcdr+27FqTQMf+82jG813yj4jed+EUSxcsYbP/m7yRtM/st+2HL/ncOYsXsVX//DERtM/ddAOHDFuKC/MX863si8/zX3xsDEcOGYwU+cs4fy/TNto+jePGcs+2w5k8syF/Oi26RtN/+7x49hteH8enLGAS++ZsdH0H5y8BzvW9uGuaa/zfw+8uNH0//3AXgyvqeYv/57D7/41c6Ppv/zIPgzs3ZM/TnqVGybPWm/a468sZk1D40bLNO1bgD98en8ALrv/Be5+Zt5681VVlHP1J/YF4Kd3z+Ch5xesN31Ar5786rR9APjhbc8yZeai9aYP61/FT04dD8B//WUq0+YsXW/6DrW9ufDktwFwzk1P8uL8FetNHze8H+cevxsAX7nuceYuqVtv+t7bDuA/jtkFgM/8v8ksWrlmvekTdxrMlw4fA8DpVz5K3dr1j1ccvusQzjx4RwA+8OuNf7039bu3JfsVuvfvHsBVH9+X6p7l/L+HX+bWJ+duNL2l370t3bfd9XevSWv/7m1qv46oqeahs1t/Icnm2sc23bMXEb8CPgB8kUL31O+j0F21Wuno3bZh5IBqGhrbVnxLkrZeB7ZrI4BXmw3Pysa1OE9KqR5YAgzawmUBiIgzI2JSREyaP3/+VgWd00KhB7T4xUSts6l96L5tG/drx3HfdoxN7b9N/f1tizad2YuIJ1NKb2v2sw/w95TSQe0XcdNK6cyeJGnzOuPMXke1axFxCnBMSumT2fBpwDtSSl9oNs/T2TyzsuEXKNxDeB7wr5TS77LxV2SZbtjcNtv7zN7WHnHWm9y3HcP92nHctx2jvfdrh53ZA5pSroyI4cBaYFgb19mtLV9dn3cESerOOqpdmw2MajY8MhvX4jzZZZz9KXTUsiXLtpuzjh5LdUX5euOqK8o56+ixHbXJbsN92zHcrx3HfdsxOnO/trXYuzUiaoCLgSnAy8C1bQ3VXZ13y1SO/t/7aet9lJKkrdZR7dpjwJiI2D4ielLocOWWDea5BTg9e38KcE8qNAi3AKdGRGVEbA+MATa+aaSdnDR+BBeevAcjaqoJCkeaLzx5DztjaAfu247hfu047tuO0Zn7tc0dtKxbUaHL6KqU0pJ2WeEWKLXLOH/3r5l8589Pc8/X38kOtX3yjiNJRaWzOmhptr12bdci4jjgJxQevXBlSun7EXE+MCmldEtEVAH/DxgPLAROTSm9mC37beATQD3wlS158HuptZGSpJZtrn3cqkcvRMRhKaV7IuLkFqaRUrppa9bb3R08phaAB59fYLEnSZ2oM9q1lNLfgL9tMO67zd7XUegQpqVlvw98v60ZJEndy9Y+Z++dwD3A8S1MS4DF3lYYPagXowf24v7nFvDR/bfLO44kdSe2a5KkkrNVxV5K6dyIKKPQG9j17ZypWztozGBufmIOaxsaqShv6y2VkqQtYbsmSSpFW11NpJQagW+2YxYB75swinOPH+fz9iSpk9muSZJKzdZextnkroj4BvAHYN2j7lNKC9u43m5rr1E17DWqJu8YktRd2a5JkkpGW4u9D2Q/P99sXAJ2aON6u7VXF67kyVlLeNfbfGShJHUy2zVJUsloU7GXUtq+vYLoTd+7dSp3TJvHF34Pw2uqOevosT7PRJI6ge2aJKmUtPXMHhGxOzAOqGoal1L6bVvX2139+fHZ3PfcAqBwKHn24lWcc9NTABZ8ktQJbNckSaWiTd09RsS5wKXZ61DgR8AJ7ZCr27r49umsqW9cb9yqtQ1cfPv0nBJJUvdhuyZJKiVt7dv/FOBw4LWU0seBPYH+bU7Vjc1ZvKpV4yVJ7cp2TZJUMtpa7NVlXVXXR0Q/YB4wqu2xuq/hNdWtGi9Jale2a5KkkrFVxV5E/DwiDgQejYga4P+AycAU4OF2zNftnHX0WKorytcbV11RzllHj80pkSSVPts1SVIp2toOWp4DLgaGU3gO0bXAkUC/lNKT7ZStW2rqhOXi26czZ/EqhtdU8663beND1iWpY9muSZJKzlYVeymlS4BLImJb4FTgSqAauDYiVqWUZrRjxm7npPEj1hV9KSU+ftVjXPXQTHYe2pc9RnrriCS1N9s1SVIpatM9eymlmSmlH6aUxgMfBE4Cnm2XZAIgIvjx+/dicJ+efPaaySxZuTbvSJJUsmzXJEmlpK2PXugREcdHxDXA34HpwMntkkzrDOzdk599eG9eX1rH165/gkYv6ZSkDmG7JkkqJVvbQcuREXElMAv4FPBXYMeU0qkppZvbM6AK9h49gG8ftyt3PzuPO6a9nnccSSoptmuSpFK0tR20nAP8Hvh6SmlRO+bRZpx+wHZsO7g3h+xcm3cUSSo1tmuSpJKztR20HNbeQfTWIoJDxw4B4MX5y+ld2YOh/apyTiVJXZ/tmiSpFLX1oerKQd3aBk697F984fdTWNvQmHccSZIkSUXIYq8Lqqoo59vv2pXHXl7ExbdPzzuOJEmSpCJksddFnbjXCD6y32guu/9Fbnv6tbzjSJIkSSoyFntd2H++exxvG9mfs/74b155Y2XecSRJkiQVEYu9LqyyRzk//9DenDJhJEP6VeYdR5IkSVIRsdjr4kYN7MW5x+9GVUU5dWsb8o4jSZIkqUhY7JWI15bUcewlD3D9Y6/mHUWSJElSEbDYKxG1fSsZXlPFf978NFPnLMk7jiRJkqScWeyViPKy4JJTxzOgV08+d80UltatzTuSJEmSpBxZ7JWQwX0q+dmHxjN70Sq+cf2/SSnlHUmSJElSTnIr9iKiPCIej4hbs+HtI+KRiHg+Iv4QET3zytaVTdhuIGcfuwtvrFjDstX1eceRJEmSlJM8z+x9GXim2fAPgf9NKe0ELALOyCVVCTjjwO257sz96FdVkXcUSZIkSTnJpdiLiJHAu4DLs+EADgNuyGa5Gjgpj2ylICKoKC9j0Yo1fO36J5i3rC7vSJIkSZI6WV5n9n4CfBNozIYHAYtTSk3XHc4CRuQRrJS8vqyOvz01ly9f+wT1DY1vvYAkSZKkktHpxV5EvBuYl1KavJXLnxkRkyJi0vz589s5XWnZZZt+XHDSHjz84hv8+M7n8o4jSZIkqRPlcWZvInBCRLwMXEfh8s1LgJqI6JHNMxKY3dLCKaXLUkoTUkoTamtrOyNvl3bKPiM59e2j+MV9L3D3M6/nHUeSJElSJ+n0Yi+ldE5KaWRKaTvgVOCelNKHgXuBU7LZTgdu7uxspeq8E3Zjt+H9+NFt02ls9HEMkiRJUnfQ461n6TT/AVwXERcAjwNX5JynZFRVlPPr0/ahqqKcsrLIO44kSZKkTpBrsZdSug+4L3v/IrBvnnlK2cgBvQCob2jkrmde55jdh+WcSJIkSVJHyvM5e8rBdY+9ymd+N4WbpszKO4okSZKkDmSx182c+vZRvGP7gXzrT08x/bVleceRJEmS1EEs9rqZHuVlXPqh8fStquCzv5vM8tX1b72QJEmSpC7HYq8bGtK3ip99cDwzF67kP//8dN5xJEmSJHWAYuqNU53oHTsM4vsn7c7uI/rnHUWSJElSB/DMXjd26r6j1xV7i1asyTmNJJWmiBgYEXdGxIzs54BNzHd6Ns+MiDg9G9crIv4aEc9GxNSIuKhz00uSujKLPXHp3TM49pIHeGP56ryjSFIpOhu4O6U0Brg7G15PRAwEzgXeQeExROc2Kwr/O6W0CzAemBgRx3ZObElSV2exJw7dZQgLV67hy9c9QUNjyjuOJJWaE4Grs/dXAye1MM/RwJ0ppYUppUXAncAxKaWVKaV7AVJKa4ApwMhOyCxJKgEWe2L3Ef05/4TdePD5BVxy94y840hSqRmaUpqbvX8NGNrCPCOAV5sNz8rGrRMRNcDxFM4OSpL0luygRQB84O2jmDRzEZfeM4O9R9dwyNgheUeSpC4jIu4Ctmlh0rebD6SUUkS0+hKKiOgBXAv8NKX04mbmOxM4E2D06NGt3YwkqcRY7AmAiOB7J+7OSwtWsHJNQ95xJKlLSSkdsalpEfF6RAxLKc2NiGHAvBZmmw0c0mx4JHBfs+HLgBkppZ+8RY7LsnmZMGGC1+VLUjfnZZxap7pnOTd8Zn+O22NY3lEkqZTcApyevT8duLmFeW4HjoqIAVnHLEdl44iIC4D+wFc6IaskqYRY7Gk9EQHA7x95he//dVrOaSSpJFwEHBkRM4AjsmEiYkJEXA6QUloIfA94LHudn1JaGBEjKVwKOg6YEhFPRMQn8/gQkqSux8s41aIX5y/n8gdfYo+RNZyw5/C840hSl5VSegM4vIXxk4BPNhu+Erhyg3lmAdHRGSVJpckze2rRfxy7CxO2HcDZNz7J8/OW5R1HkiRJUitZ7KlFFeVl/OxDe9OrZzmf+d0UVqyuzzuSJEmSpFaw2NMmbdO/ip+eOp6XFqzg3uktdR4nSZIkqVh5z54264CdBnPP19/JtoN65x1FkiRJUitY7OktNRV6P75zOtc++ioLlq1meE01Zx09lpPGj8g5nSRJkqSWWOxpi9w0+VUuvft5mp7QO3vxKs656SkACz5JkiSpCHnPnrbI/9w5Y12h12TV2gYuvn16LnkkSZIkbZ7FnrbInMWrWjVekiRJUr4s9rRFhtdUt2q8JEmSpHxZ7GmLnHX0WKorytcbV11RxllHj6W+oTGnVJIkSZI2xWJPW+Sk8SO48OQ9GFFTTQAjaqq58OS3sc+2Azjix//wOXySJElSkbE3Tm2xk8aP2KjnzdeW1NGrZw/OuOox/uuE3Tht/+3yCSdJkiRpPZ7ZU5ts07+KP35mfw4dO4T/vHkq5/9lGg2NG/bbKUmSJKmzWeypzXpX9uCyj07gYwdsx5UPvcSv738h70iSJElSt+dlnGoX5WXBeSfsxl6jajhy3NC840iSJEndnmf21K5OGj+C3pU9WLG6nk9ePYlpc5bmHUmSJEnqliz21CFeX1rH07OX8L5f/ZN7n7WnTkmSJKmzWeypQ+xQ24c/f34i2w3uzRlXP8ZvH34570iSJElSt2Kxpw6zTf8qrv/0/hy2yxC+e/NUfv0PO26RJEmSOovFnjpU78oe/Pq0CXzpsJ04bo9heceRJEmSug2LPXW48rLga0eNZdTAXjQ2Jv7njum8tqQu71iSJElSSbPYU6d6ccEKrnzwJU76+UNMnbMk7ziSJElSybLYU6faaUgf/viZA4iA9/3qYe5+5vW8I0mSJEklyWJPnW7c8H78+fMT2aG2N5/67SR+/8greUeSJEmSSo7FnnIxtF+hp85jdx/GDrW9844jSZIklRyLPeWmV88e/PzDe7PfDoMA+OuTc1m+uj7nVJIkSVJpsNhTUXh14Uq+fN3jvO9XDzN3yaq840iSJEldnsWeisKogb24/PQJvLpwJSf9/CGenm1PnZIkSVJbWOypaBwydgg3fHZ/yiN4368e5q5p9tQpSZIkbS2LPRWVXbYp9NS505A+zF3qg9clSZKkrdUj7wDShob0q+LGzx5Azx6FYxHT5ixl56F96FHusQlJkiRpS/ntWUWpqdB7bUkd7/3lP/nUbyfZU6ckSZLUChZ7Kmrb9K/iO+/elftnLLCnTkmSJKkVLPZU9D78jm258mNv59WFKznxZ/bUKUmSJG0Jiz11Ce/cuZYbP3sAFeVl3DhlVt5xJEmSpKJnBy3qMsZu05dbvjCR/tUVACxasYaaXhVERM7JJEmSpOLjmT11KYP6VNKjvIxFK9bw7ksf5D9vfpr6hsa8Y0mSJElFxzN76pL6V1fw7rcN49f3v8irC1dx7O5DufSeF5izeBXDa6o56+ixnDR+RN4xJUmSpNxY7KlLKisLzjluV7Yb3Jtv3fQU9z83n5RNm714Fefc9BSABZ8kSZK6rU6/jDMiRkXEvRExLSKmRsSXs/EDI+LOiJiR/RzQ2dnU9Xxw39EM7N1zXaHXZNXaBi6+fXoumSRJkqRikMc9e/XA11NK44D9gM9HxDjgbODulNIY4O5sWHpLC1esaXH8nMU+k0+SJEndV6cXeymluSmlKdn7ZcAzwAjgRODqbLargZM6O5u6puE11S2Oj4Af3/kc85bVdXIiSZIkKX+59sYZEdsB44FHgKEppbnZpNeAoTnFUhdz1tFjqa4oX29cz/IydtmmLz+9ewYTL7qH7906Lad0krTltypExOnZPDMi4vQWpt8SEU93fGJJUinIrdiLiD7AjcBXUkpLm09LKSXY6DaspuXOjIhJETFp/vz5nZBUxe6k8SO48OQ9GFFTTQAjaqr50Slv429fPph7v3EIH37HttT2rQSgoTFxx9TXfFyDpM72lrcqRMRA4FzgHcC+wLnNi8KIOBlY3jlxJUmlIAp1VSdvNKICuBW4PaX042zcdOCQlNLciBgG3JdSGru59UyYMCFNmjSp4wOrZNz9zOuccfUkRtRUc9r+23Lq20dR06tn3rEkbYGImJxSmpB3jq2xJW1cRHwwm+fT2fCvs/muzQ6Q3gacCVyfUtr9rbZpGylJ3cPm2sc8euMM4ArgmaZCL3ML0HTJyunAzZ2dTaXvkLFDuOy0fRg9sBcX/f1Z9r/wHr79p6dYvro+72iSStuW3KowAni12fCsbBzA94D/AVZ2WEJJUsnJ4zl7E4HTgKci4ols3LeAi4DrI+IMYCbw/hyyqcSVlwVH7bYNR+22Dc/MXcpvHnqJSS8vold2z9/LC1YwemAvysoi56SSupqIuAvYpoVJ324+kFJKEbHFl9VExF7Ajimlr2b3um9u3jMpnP1j9OjRW7oJSVKJ6vRiL6X0ILCpb9KHd2YWdW+7DuvHj07Zk/qGRsrKgrq1DbznFw8xoFdPPjZxO96790h6V+ZxPERSV5RSOmJT0yLi9YgY1uwyznktzDYbOKTZ8EjgPmB/YEJEvEyh3R4SEfellA7ZYHlSSpcBl0HhMs6t+ySSpFKRa2+cUjHoUV74b1BeFpx3wm70ra7guzdPZb8L7+aCW6cxd4nP65PUZltyq8LtwFERMSDrmOUoCve2/zKlNDyltB1wIPBcS4WeJEkbstiTMhXlZZy41whu/vxEbvrcARwydghX/fNlZi0qFHt1axvIo0MjSSXhIuDIiJgBHJENExETIuJygJTSQgr35j2Wvc7PxkmStFW8Rk1qwd6jB7D36AHMe/eu1PYpPLbhe7dOY8ori/nExO04fs/hVG3wbD9J2pSU0hu0cKtCSmkS8Mlmw1cCV25mPS8Db9kTpyRJ4Jk9abOG9K2i0IEs7LPtABoaGznrhieZeNE9/PiO6cxbWpdzQkmSJKllntmTttDJe4/kPeNH8M8X3uA3D73Epfc+z4IVa/jBe/bIO5okSZK0EYs9qRUigok7DWbiToN5ecEKKnoUTo5PnrmQ7//1GT4+cXuO2X0bKso9aS5JkqR8WexJW2m7wb3XvV9aV88bK9bwxWsfZ1j/Kk7bf1s++PbR/OO5+Vx8+3TmLF7F8Jpqzjp6LCeNH7GZtUqSJEntw2JPageHjh3CwV+v5d5n5/Gbf77Ej26bzq//8QJr6htZtbYRgNmLV3HOTU8BWPBJkiSpw1nsSe2kvCw4YtxQjhg3lGdfW8qH/u8RVq2tX2+eVWsbOO8vUxnSt5KdhvZhSN+qnNJKkiSp1FnsSR1gl236sWjFmhanLV65lg9d/gjfOGpnvnDYGOYtq+OTV09ieP9qhtdUM7ymiuE11YwfXcOw/tWdnFySJEmlwmJP6iDDa6qZvXjVRuOH9qvkfz+wFyNqCoXc6rWN1PTqyfPzl3P/jPmsXNMAwH+/b09O2Wck/351MV+89vF1RWBTUXjoLrUM619NSmnd4yFa48+Pz/Z+QkmSpBJmsSd1kLOOHss5Nz3FqrUN68ZVV5RzzrG7csCOg9eNGzWwF7/9xL4ApJRYsmotcxbXsU3/wiWelRVl7DWqhrlLVvHIiwt5bWkdDY2J33/yHQzrX83fn36Ns298kuE11YyoqWZYVhS+b59R1PatpG5tA+VlsV4PoX9+fPZ62byfUJIkqfRY7EkdpKloas3Zs4igpldPanr1XDdul2368dMPjl83XN/QyLxlqxnYuzDPiJpqTho/gjmL65izeBWTX1nE4pVrOXb3YdT2reR3/5rJD/72DEP7VTGsf6EQvG/6/PWKUCjcT3jx7dMt9iRJkkpEpJTyzrDVJkyYkCZNmpR3DKnorFxTT2WPcsrLgimvLOK+Z+cxZ0mhGJyzeBUvv7Fyk8vuOqwfQ/tVcuXpb6esLHj0pYW8sXw1Q/pVMqRvFbV9K6mqKO/ETyMVRMTklNKEvHN0FbaRktQ9bK599MyeVIJ69Xzzv/beowew9+gB602feNHdzF5c18Jy5QzvX8XKNQ2UlRXuA7zqny/xt6deW2++HQb35p5vHALAbx56ideW1FHbt5Ih/aoY0reS4f2rGT2o11Zl915CSZKk9mGxJ3VDZx29S4v3E/7gPXtsVFhdcNIefP7QnZi3bDXzl65m3rK69TqE+deLb3Dvs/NZ09C4btxuw/vx1y8dBMDnrpnMguVrGNK3cGZwSL9KdtmmL4eMHQLA8tX19O5ZTkR4L6EkSVI7stiTuqHW3E84sHdPBvbuyW6bWNevT5uwrmOZectWM2/papp3Djq4TyULlq3h6dlLmLdsHivXNHDErkPXFXuH/fd9LF61lto+lcxbVsfahvUvLV+1toHzb53GkL6V9K2qoG9VDwb16Unfqop22Ret4VlHSZLUlXjPnqROtXx1PavXNjCoTyXw5mWg85at5k+Pz96idXxi4vZ89/hxrFrTwEE/upd+VT3oW9VjXTF44l7DOWb3YaxcU8/vH3mFftn4pumjBvZiYO+erXpsxYZnHaFwNvTCkzc+G6qO4T17rWMbKUndg/fsSSoafSp70KfyzT89H5+4/br3j760sMVnE9b2reSnp45nWd1altXVs0NtbwAaU+LIcUPXjV9Wt5bXl9Yxf3nhgfbzl63mgr8+s9H6zjt+HB+buD3Pvb6c4y99MCsEC8Vgn8oefO7QHTloTC2zFq3k+sdepW9VBT+/9/kWezC98O/PcOCYwVRVlFPVo4wezR5x0Vk84yhJklpisSepaGzq2YTfPm5X9t9x0Ebz967swYUn77HJ9Y0a0It/f/coljYrBpfV1bPz0L4A9K+u4BMHbr9esbisrp6mCx5eWbiSS+99ns1dAPH60tVMuOCudcMV5cE1n9yPfbcfyF3TXudHtz9bKASzV3VFGd8+bhyjB/Vi8syF3DHtdap6lFPds1AsVvcs57g9htG3qoJZi1Yye9GqwrSKcqqzdQzq3XNdBzre5yhJkjbFYk9S0diaZxNuTllZ0L9XBf17tXx/3zb9qzj72F02ufwBOw7mhe8fx/I19Rz543/w+tLVG81TU13B147ambq1Daxa00hdfQPD+lcB0KeqBzvW9mHV2gbq1jYU7mtc2kBDVj0+M3cZv3noZdbUN2603b5VFdz8xBwuvn36Rtuc/J0jGNSnkh/fMZ1L73meDWvRpmcmVvYo48nZS5pdxtqDftUVHJrdL7m0bi3lEfTKOshpb55xlCQpXxZ7korKSeNHFFVBUFYW9Kuq4Jxjd23xrON5J+y2ybz77TCI/XbY+Ixkk4/sty0f2W9bGhsTq+sb1xWFQ/oW7mc8afwI9hpVw6o1DdTVN1C3tjBPU+c0e42u2ajQazJn8SoeeWkh1zwyc71Ob3r3LGfq+ccA8J9/fpqbn5hDeVmsKwZHDejF7z+1HwBXPfQSL7+xkn7VFevuixzSr2pdsfjakrp1y2747EXPOEqSlD+LPUnaAu191rG5srKgumfhUs7mRtRUM6KmepPLHbbLUEbUVLd4n+PwmmrOO2E3zj1+HHVrG1lWt5aldfWsWvNmsfqe8SMYN6xfs8tc66mqePOew8mvLOa+6fNYvvrNS1t32abvumLvs9dM5vFXFgPQs0cZ/ap6sO/2A/nFh/fh4tunt3iP48W3T7fYkySpk1jsSdIWKrazjrDp+xzPOnosABFvFpJD+q2/7CFjh5PbnpIAACAASURBVKx7BEZLLv3geAAaGxPL1xSKwfpmz1P8wqE7MXvxKpbV1bN0VaGYHFFTuIR1TgsF6ObGS5Kk9mexJ0ldWEeecWzSdClrvw2ebXj4rkM3uczwzZxxlCRJncNiT5K6uK54xlGSJHU8iz1JUrvrjDOOkiRp8yz2JEkdohjPOEqS1J2UvfUskiRJkqSuxmJPkiRJkkqQxZ4kSZIklSCLPUmSJEkqQRZ7kiRJklSCLPYkSZIkqQRZ7EmSJElSCbLYkyRJkqQSFCmlvDNstYiYD8xsh1UNBha0w3ram7lar1izFWsuKN5sxZoLijdbqefaNqVU2w7r6RbaqY0s1t+pUuC+7Rju147jvu0Y7bFfN9k+dulir71ExKSU0oS8c2zIXK1XrNmKNRcUb7ZizQXFm81cam/+23Uc923HcL92HPdtx+jo/eplnJIkSZJUgiz2JEmSJKkEWewVXJZ3gE0wV+sVa7ZizQXFm61Yc0HxZjOX2pv/dh3Hfdsx3K8dx33bMTp0v3rPniRJkiSVIM/sSZIkSVIJ6tbFXkRcGRHzIuLpvLM0FxGjIuLeiJgWEVMj4st5ZwKIiKqIeDQi/p3l+q+8MzUXEeUR8XhE3Jp3luYi4uWIeCoinoiISXnnaRIRNRFxQ0Q8GxHPRMT+eWcCiIix2b5qei2NiK/knQsgIr6a/e4/HRHXRkRV3pkAIuLLWaapee+rlv6uRsTAiLgzImZkPwfkmVFvrVjbx66uWNv3UlDs31G6umL9jtXVdcZ3xG5d7AFXAcfkHaIF9cDXU0rjgP2Az0fEuJwzAawGDksp7QnsBRwTEfvlnKm5LwPP5B1iEw5NKe1VZF0WXwLcllLaBdiTItl3KaXp2b7aC9gHWAn8KedYRMQI4EvAhJTS7kA5cGq+qSAidgc+BexL4d/x3RGxU46RrmLjv6tnA3enlMYAd2fDKm5XUZztY1dXrO17KSj27yhdXTF/x+rqOvQ7Yrcu9lJK9wML886xoZTS3JTSlOz9Mgr/uUbkmwpSwfJssCJ7FcVNnxExEngXcHneWbqCiOgPHAxcAZBSWpNSWpxvqhYdDryQUmrrg6HbSw+gOiJ6AL2AOTnnAdgVeCSltDKlVA/8Azg5rzCb+Lt6InB19v5q4KRODaVWK9b2sasr1va9FBTzd5Suzu9YXVu3Lva6gojYDhgPPJJvkoLsNP4TwDzgzpRSUeQCfgJ8E2jMO0gLEnBHREyOiDPzDpPZHpgP/Ca7LOPyiOidd6gWnApcm3cIgJTSbOC/gVeAucCSlNId+aYC4GngoIgYFBG9gOOAUTln2tDQlNLc7P1rwNA8w0jFoNja91JQxN9Rurpi/o7V1XX4d0SLvSIWEX2AG4GvpJSW5p0HIKXUkF1eNxLYN7uELFcR8W5gXkppct5ZNuHAlNLewLEULtk5OO9AFM5Q7Q38MqU0HlhBkV1aFxE9gROAP+adBSC7z+xECoXycKB3RHwk31SQUnoG+CFwB3Ab8ATQkGuozUiFLqA92q5urRjb91JQjN9Rurou8B2rq+vw74gWe0UqIiooNATXpJRuyjvPhrJL/u6lOO7pmAicEBEvA9cBh0XE7/KN9KbsjBAppXkU7j3bN99EAMwCZjU76nkDheKvmBwLTEkpvZ53kMwRwEsppfkppbXATcABOWcCIKV0RUppn5TSwcAi4Lm8M23g9YgYBpD9nJdzHik3xd6+l4Ii+47S1RX1d6yurjO+I1rsFaGICAr3Uj2TUvpx3nmaRERtRNRk76uBI4Fn800FKaVzUkojU0rbUbjs756UUu5nXAAiondE9G16DxxF4bK7XKWUXgNejYix2ajDgWk5RmrJBymSSzgzrwD7RUSv7P/o4RTJzeoRMST7OZrC/Xq/zzfRRm4BTs/enw7cnGMWKTfF2r6XgmL9jtLVFfN3rK6us74j9mjvFXYlEXEtcAgwOCJmAeemlK7INxVQOIpyGvBUdu05wLdSSn/LMRPAMODqiCincKDg+pSSXfBu3lDgT4X2nR7A71NKt+UbaZ0vAtdkl0u+CHw85zzrZH/0jgQ+nXeWJimlRyLiBmAKhR71HgcuyzfVOjdGxCBgLfD5PDvbaenvKnARcH1EnAHMBN6fVz5tmSJuH7u6Ym3fS4HfUdTVdMp3xCjcPiFJkiRJKiVexilJkiRJJchiT5IkSZJKkMWeJEmSJJUgiz1JkiRJKkEWe5IkSZJUgiz2tMUioiEinoiIqRHx74j4ekRs9ncoIraLiA+1YVtPR8QfI6JXK5adEBE/be02s2W/0pptZcscEhHt3r1zRNwXEdOz/fBE1u0/EfGZiPhoe2+v2XZPz7pdbz5ucETMj4jKVq7rW+2Q576ImNRseEJE3NfW9Wbr+lhE/Kw91iWpe7ON3OQytpGbXpdtpDqcxZ5aY1VKaa+U0m4UnoF2LIVnaG3OdkCrG7Jm29odWAN8pvnEiNjkMyJTSpNSSl/aim0CfAVoVUPWwT6c7Ye9UkqnAKSUfpVS+m0HbvNPwJEbNOinAH9JKa1u5bpa1ZBFQUt/l4ZExLGt3HaHy57nJElgG5kH28gC20htksWetkpKaR5wJvCF7I/PdhHxQERMyV4HZLNeBByUHXX76mbm25wHgJ2yo4MPRMQtwLSIqIqI30TEUxHxeEQcCusfRYyI3hFxZUQ8ms1zYja+PCL+Ozsq+mREfDEivgQMB+6NiHuz+Y6KiIezrH+MiD7Z+GMi4tmImAKc3FLozeT7WETcFBG3RcSMiPhRa/Z9RJwXEd/I3r89y/9ERFwcEU83+3wXR8Rj2fRPN9s390XEDVn+ayIKT/NsklJaCvwDOL7Z6FOBayOiNiJuzNb7WERMzNbbp9lnfTIi3hsRFwHVWbZrsvm+lu3zpyPiK9m47aJwdPa3wNPAqBY+9sXAt1vYF+sddYyIWyPikOz98mwfTI2IuyJi3+yzvxgRJzRbzahs/IyIOLfZuj6S/d48ERG/bmq0svX+T0T8G9j/rf/FJHU3tpG2kbaRtpFFI6Xky9cWvYDlLYxbDAylcKSvKhs3BpiUvT8EuLXZ/C3Ot6ltAT2Am4HPZutaAWyfTfs6cGX2fhfgFaCq+TaBHwAfyd7XAM8BvbP13QD0yKYNzH6+DAzO3g8G7gd6Z8P/AXw328arWf4Arm/+GZt9hk3l+xjwItA/G54JjGph+fuA6cAT2evibPx5wDey908D+2fvLwKezt6fCXwne18JTAK2z/bNEmAkhYM9DwMHtrDtU4A/Ze+HA3OAcuD3TfMDo4Fnsvc/BH7SbPkBG/7OAPsAT2X7vw8wFRhP4ch2I7DfJn4X7gMmAPcAh2bv78umfQz4WbN5bwUOyd4n4Njs/Z+AO4AKYE/giWbLzwUGAdXZ/pwA7Ar8BajI5vsF8NFm631/3v8fffnyVVwvbCNtI20jm9ZrG1lEr02e5pdaqQL4WUTsBTQAO7dxvuqIeCJ7/wBwBXAA8GhK6aVs/IHApQAppWcjYmYL6zsKOKHpKB+FhmM0cATwq5RSfbb8whYy7AeMAx7KDuz1pPCHfxfgpZTSDICI+B2FhmNDm8t3d0ppSbb8NGBbCo3jhj6cUprUwngiogbom1J6OBv1e+DdzT732yLilGy4P4WGdw2FfTgrW8cTFBqSBzdY/V+BX0REP+D9wI0ppYaIOAIY1+xAZ7/sSO4RFI5skn3eRZvYH39KKa3Itn0TcBBwCzAzpfSvlj5nMxcA36HwhWJLrAFuy94/BaxOKa2NiKcofOYmd6aU3miW6UCgnkLD+1j2WauBedn8DcCNW5hBksA20jbSNlI5sdjTVouIHSj8p55H4b6E1ykcESoD6jax2Fe3cL5VKaW9NtgeFI5atiom8N6U0vQW1rUly96ZUvrgBsvutYn5W6P5df0NtP//xQC+mFK6fb2Rhcs33nLbKaVVEXEb8B4KDdTXskllFI4urvfvtoX7c3Pe8t81pXRPRFxA4QtGk3rWvxy9qtn7tSkVDjNSOCq6OltPY6x/P0tifYnC/rs6pXROC1HqUkoNb5VXUvdmG9kmtpHrs43UVvOePW2ViKgFfkXh8oBE4ajY3JRSI3AahcsZAJYBfZstuqn5tsYDwIezPDtTOBo5fYN5bge+2HTNfUSMz8bfCXy66Q9aRAxsIe+/gIkRsVM2T+9sO88C20XEjtl86zV0rcy31VJKi4FlEfGObNSpzSbfDnw2Iiqath8RvVu5iWspNGBDKRythcJlHl9smqFZo34n8Plm4wdkb9c2ZaCwP06KiF5Zlvdk41rjAuCbzYZfBvaKiLKIGAXs28r1QeFG+4ERUQ2cBDwE3A2cEhFDss8zMCK23Yp1S+qGbCNtI7P12kYqdxZ7ao2mG4mnAndR+KP2X9m0XwCnZzfk7sKbR6GeBBqi0A31Vzcz39b4BVCWXXLwB+Bj6c2esJqORH2PwmUxT2a5v5eNv5zC/QFPZlmaekO7DLgtIu5NKc2ncL36tRHxJNnlKdkRuzOBv0bh5vOmSxdak29LXRNvdit9VwvTzwD+L7vUpDeFew2aPt80YEoUbkj/Na0/MnonhXsR/tDs6N+XgAlRuMF8Gm/2AHcBMCAKN5X/m8J9A1DYn09GxDUppSnAVcCjwCPA5Smlx1sTKKX0N2B+s1EPAS9R+Kw/Baa08jOS5bmRwu/qjanQU900CpfD3JH9298JDNuKdUvqPmwjbSNtI1V04s3fT6k0RMR7gRNSSqfnnaWjRUSflNLy7P3ZwLCU0pdzjiVJKlK2kbaR6l68Z08lJQrdBX8f+ETeWTrJuyLiHAr/l2dSOMoqSdJGbCNtI9X9eGZPkiRJkkqQ9+xJkiRJUgmy2JMkSZKkEmSxJ0mSJEklyGJPkiRJkkqQxZ4kSZIklSCLPUmSJEkqQRZ7kiRJklSCLPYkSZIkqQRZ7EmSJKnNIuKQiJjVydv8e0Sc3pnblLoSiz0pJxFxakQ8EhErImJe9v5zUXBVRFywieUiIr4UEU9ny86KiD9GxB7Z9KsiIkXEiRss97/Z+I91wseTJHVREfGhiJgUEcsjYm5WUB2YQ477IuKTG4xbr6BMKR2bUrp6C9aVImKnjsgpFTOLPSkHEfF14BLgYmAbYCjwGWAi0PMtFr8E+DLwJWAgsDPwZ+BdzeZ5Dvhos+31AN4PvNA+n0CSVIoi4mvAT4AfUGibRgO/AE7c3HLtsN3yjlx/R8raWKkoWexJnSwi+gPnA59LKd2QUlqWCh5PKX04pbR6M8uOAT4PfDCldE9KaXVKaWVK6ZqU0kXNZv0LcGBEDMiGjwGeBF7roI8lSerimrVPn08p3ZRSWpFSWptS+ktK6axsnsqI+ElEzMleP4mIyk2sb9fs7NziiJgaESc0m3ZVRPwyIv4WESuAQ7cy87qzfxGxU0T8IyKWRMSCiPhDNv7+bPZ/Z2crP5CN/1REPB8RCyPilogY3my9R0XE9Gxdv8jW27Sdj0XEQ9kVM28A50XEjhFxT0S8kW37moioaba+lyPirIh4Mrsq54qIGJqdNV0WEXc1a7OldmOxJ3W+/YFK4OatWPZwYFZK6dG3mK8uW/+p2fBHgd9uxfYkSd3H/kAV8KfNzPNtYD9gL2BPYF/gOxvOFBEVFA483gEMAb4IXBMRY5vN9iHg+0Bf4MF2yP+9bHsDgJHApQAppYOz6XumlPqklP4QEYcBF1K46mUYMBO4Lss+GLgBOAcYBEwHDthgW+8AXqRw9vP7QGTrGw7sCowCzttgmfcCR1K4Iud44O/At4BaCt/Jv9TGzy9txGJP6nyDgQUppfqmERHxz+zI56qIOHgzyw4C5m7hdn4LfDQ7svhOCpd6SpK0KYPYoH1qwYeB81NK81JK84H/Ak5rYb79gD7ARSmlNSmle4BbgQ82m+fmlNJDKaXGlFLdJrb306x9XBwRi7N1bMpaYFtgeEqpLqW0uQLyw8CVKaUp2RU15wD7R8R2wHHA1OzsZj3wUza+MmZOSunSlFJ9SmlVSun5lNKd2RU384EfU2h7m7s0pfR6Smk28ADwSHZVTx2FAnv8ZvJKW8ViT+p8bwCDm1/jn1I6IKVUk03b3P/LNygcgXxLWSNXS+Eo7K0ppVVbH1mS1A1s1D61YDiFs2BNZmbjWprv1ZRS4wbzjmg2/OoWZPpSSqmm6QW8ezPzfpPCGbZHs8tGP7GZedf7HCml5RQ+/4im7M2mJWDDXkb/P3v3HV51efdx/P3NyQQSwggjYW9BRiQiCg5w4Ba1Wm1d1Yqtq45SoU+f7qHirtZt1dZZxT0QEYuAoEGmMgUEEkYYIRCycz9/5OATY1g5Se5zTj6v6zpXzrl/v3PyoVfl5nvu9Z3swSmZL5pZjpkVAP+m6svd6jZXe15Uy+sW+8krUicq9kQa36dACXVb7D4N6GRmWQd5/7+BW9EUThERObC9/dPY/dyTS9Xo2V5dgm213dfZzGJq3JtT7bWrY85aOec2Oeeuds6lA9cA/9jPDpzf+XOYWXOqRjZzqJpB06naNav+eh/Z/xpsG+icSwEuoarwFPFKxZ5II3PO5VM17eUfZvYDM0s2sxgzGwI0r3ZrwMwSqz3inXMrqdoV7YXg9tPxwWsXmdmEWn7dA1StD5hRyzUREZFvOed2Ar8FHjKzsWbWzMzizOw0M7szeNsLwG/MLC24tu23VH2xWNNcYA/wq+BnnEDVOrUXGyq/mV1gZnuLsh1UFV97RxY3Az2q3f4C8BMzGxLcYOavVE2rXAu8AwwM/m8QS9XGaB0O8OuTgd3ATjPLAMbXx59JJFQq9kQ8cM7dCdxC1ZSTzcHHo8BtwOzgbROomtax9/FRsP1G4EHgISCfquMUzqVqIXzN37PdOTctOAVFRERkv5xzd1PVP/0GyKNquuL1/P+67z8D2VTt8LwY+CLYVvNzSqkq7k4DtlL1ReVlzrllDRj/SGCume0G3gR+4ZxbHbz2e+CZ4Nq/C51zHwL/C7xK1UheT4KbmjnntgIXAHdSNbWzP1V/5n3ulk3Vl7hHADupKhYn1+8fTaRuTP8GFBERERGpXXAq6gbgx8656b7ziBwKjeyJiIiIiFRjZmPMLDU4xfPXVK2/m+M5lsghU7EnIiIiIvJdR1O1TGIrVdNRx2pXa4lEmsYpIiIiIiIShTSyJyIiIiIiEoVU7ImIiIiIiEShWN8BQtG2bVvXrVs33zFERKQRzJs3b6tzLs13jkihPlJEpGnYX/8Y0cVet27dyM7O9h1DREQagZl94ztDJFEfKSLSNOyvf9Q0ThERERERkSikYk9ERERERCQKqdgTERERERGJQir2REREREREopCKPRERERERkSikYk9ERERERCQKqdgTERERERGJQir2REREPDOzVDN7xcyWmdlSMzu6xnUzswfMbJWZLTKzI3xlFRGRyBHRh6qH6vX5OUyaspzc/CLSU5MYP6YvYzMzfMcSEZGm537gfefcD8wsHmhW4/ppQO/g4yjg4eDPBqH+UUQkOjTZYu/1+TlMnLyYorIKAHLyi5g4eTGAOjQREWk0ZtYSOA64AsA5VwqU1rjtHOBZ55wD5gRHAjs65zbWdx71jyIi0aPJTuOcNGX5tx3ZXkVlFUyastxTIhERaaK6A3nAP81svpk9YWbNa9yTAayv9npDsK3eqX8UEYkeTbbYy80vOqR2ERGRBhILHAE87JzLBAqBCXX5IDMbZ2bZZpadl5dXpzDqH0VEokeTLfbSU5MOqV1ERKSBbAA2OOfmBl+/QlXxV10O0Lna607Btu9wzj3mnMtyzmWlpaXVKYz6RxGR6NFki73xY/qSFBf4TltSXIDxY/p6SiQiIk2Rc24TsN7M9nZAJwJf1bjtTeCy4K6cw4GdDbFeD2rvH+MCpv5RRCQCNdkNWvYuMp80ZTk5wakpPz22uxafi4iIDzcAzwV34lwN/MTMfgbgnHsEeBc4HVgF7AF+0lBBqvePuflFxAaMFgmxnDawQ0P9ShERaSBNttiDqg5tbGYGRaUVHDdpOoUlFQd+k4iISD1zzi0Asmo0P1LtugOua6w8e/tHgE9W5nHNv+axJKeAoV1bNVYEERGpB0262NsrKT7AW9ePpH1Kgu8oIiIiYeXY3mnMum00rZrH+44iIiKHqMmu2aupQ8tEzIzthaVUfYEqIiIiAK2ax+OcY3Xebt9RRETkEKjYq2beN9s55vZpzFq1zXcUERGRsHLvhys544GZbNlV7DuKiIgcJBV71Rye0ZLWzeK5e+pyje6JiIhUc15mBmUVlTwwbaXvKCIicpBU7FWTEBvg+tG9mb8un49X1O0wWhERkWjUrW1zLh7WhRc+W6/pnCIiEULFXg0/GNqJTq2SuHfqCo3uiYiIVHPDib1IiI3h7g9W+I4iIiIHodGLPTNLNLPPzGyhmX1pZn8Itj9tZmvMbEHwMaSxswHEx8Zw4+jeLM7ZyZKcAh8RREREwlK75ER+emwPvli3g51FZb7jiIjIAfg4eqEEGO2c221mccBMM3sveG28c+4VD5m+47wjMjiiayq92iX7jiIiIhJWfn58T649oSeJcQHfUURE5AAafWTPVdk72T8u+Air+ZKxgZhvC73iMh20LiIisldSfIDEuAAl5RWs377HdxwREdkPL2v2zCxgZguALcBU59zc4KW/mNkiM7vXzLyfcP63d5dy7j9mU1kZVrWoiIiId1c+/Tnj/jVPfaSISBjzUuw55yqcc0OATsAwMzscmAj0A44EWgO31fZeMxtnZtlmlp2X17A7ZvZPT2HpxgLeW7KpQX+PiIhIpLkwqzNLNxbw5sJc31FERGQfvO7G6ZzLB6YDpzrnNganeJYA/wSG7eM9jznnspxzWWlpaQ2a78xB6fRq14L7PlxBhb65FBER+dZZg9Lp3zGFuz5YTkm5ljyIiIQjH7txpplZavB5EnAysMzMOgbbDBgLLGnsbDUFYoybTurNyi27eXuRvrkUERHZKybGmHBaPzbsKOL5uet8xxERkVr4GNnrCEw3s0XA51St2XsbeM7MFgOLgbbAnz1k+57TD+9I3/bJPP7Jap27JyIiUs2xvdsyolcb/ruiYZdViIhI3TT60QvOuUVAZi3toxs7y8GIiTHu/eEQ2qUkUDXoKCIiIgBmxkM/OoKUxDjfUUREpBY+ztmLOP3TUwBwzuFcVQEoIiIikNosHoDthaUY0Kp5vN9AIiLyLa8btESSHYWlnPfwbF75YoPvKCIiImGlsKScE+/+mHumrvAdRUREqlGxd5BSm8VRUel4YNpKSssrfccREREJG80TYjljUEde+Gwda7cW+o4jIiJBKvYOkplx88l92LCjiFfmaXRPRESkuhtP7E1cIIa7PljuO4qIiASp2DsEJ/RJI7NLKg9+tFJnComIiFTTLjmRq4/tztuLNrJoQ77vOCIigoq9Q2Jm3HJyH3J3FvPqvBzfcURERMLK1cf1oE3zeGboKAYRkbCg3TgP0chebXng4kxO6d/edxQREZGwkpwYx4e3HK8dOUVEwoRG9g6RmXH24HQS4wK+o4iIiISdvYXemq2FVFY6z2lERJo2FXt19PHyLZz3j1kUlWrtnoiISHVfrNvBiXd/zFuLcn1HERFp0lTs1VGz+Fi+WJfPc3O/8R1FREQkrAzplErfDinc9cFyHVckIuKRir06Gta9NSN7teXhj79mT2m57zgiIiJhIybGmHBaP9ZvL+J5fSkqIuKNir0Q3Hxyb7YVlvLsp+rIREREqjuud1uO6dmGBz5axa7iMt9xRESaJBV7IRjatTXH90nj0f9+ze4Sje6JiEjdmNlaM1tsZgvMLLuW6yeY2c7g9QVm9lsfOQ+FmXHbqf0oLCnni3U6d09ExAcdvRCiX53al/Xbi2ger905RUQkJKOcc1v3c/0T59yZjZamHgzunMqciSfqKAYREU80sheiAektOfXwDpiZ7ygiIiJhZ2+htzpvt+ckIiJNj4q9euCc474PV/DEJ6t9RxERkcjkgA/MbJ6ZjdvHPUeb2UIze8/MBjRmuFA9NXMNp9w7g7VbC31HERFpUlTs1QMz46vcAu6ftpKde7QIXUREDtlI59wRwGnAdWZ2XI3rXwBdnXODgb8Dr9f2IWY2zsyyzSw7Ly+vYRMfgjMHdyQuEMPdU1f4jiIi0qSo2KsnN53Uh13F5Tw5U6N7IiJyaJxzOcGfW4DXgGE1rhc453YHn78LxJlZ21o+5zHnXJZzListLa0Rkh+cdsmJ/PTY7ry1MJfFG3b6jiMi0mSo2Ksn/dNTOH1gB56atZYdhaW+44iISIQws+Zmlrz3OXAKsKTGPR0suDjczIZR1X9va+ysoRh3XA9aNYvjjveX+Y4iItJkqNirRzed1IfC0nIe19o9ERE5eO2BmWa2EPgMeMc5976Z/czMfha85wfAkuA9DwAXOeecp7x1kpwYx/Wje7N0YwGbC4p9xxERaRJ09EI96tM+mV+fdhhH9WjtO4qIiEQI59xqYHAt7Y9Ue/4g8GBj5moIlwzvwg+P7EyLBP3zQ0SkMehv23p29XE9fEcQEREJSwmxARJiobyiktz8Yrq0aeY7kohIVNM0zgawaWcxEycvYssuTVMRERGp6cYX53PpU3MpLa/0HUVEJKqp2GsARWUVvJy9gUc+1to9ERGRmi7I6sw32/bw4ufrfEcREYlqKvYaQPe2zTkvM4N/z/1Gi9BFRERqOKFPGsN7tOaBaSvZXVLuO46ISNRSsddAbhjdm8pKxz+mr/IdRUREJKyYGbed2o+tu0t5QjtYi4g0GBV7DaRLm2ZckNWJFz5bT25+ke84IiIiYSWzSytOO7wDM1bkEWGnSIiIRAztxtmArhvVCzMjNmC+o4iIiISd288bRIvEWILnxYuISD1TsdeAOrVqxl/PHeg7hoiISFhq2SwOgILiMkrKKklLTvCcSEQkumgaZyOY980OiofIHwAAIABJREFUnpq5xncMERGRsFNaXslp933Cn9/5yncUEZGo46XYM7NEM/vMzBaa2Zdm9odge3czm2tmq8zsJTOL95Gvvr21MJe/vLuUb7YV+o4iIiISVuJjYxibmc4bC3JZkrPTdxwRkajia2SvBBjtnBsMDAFONbPhwB3Avc65XsAO4CpP+erVtSf0JDbGeGCaduYUERGp6Zrje5LaLI473l/mO4qISFTxUuy5KruDL+OCDweMBl4Jtj8DjPUQr961S0nk0uFdeW3+Blbn7T7wG0RERJqQlMQ4rh/Vi09WbmXmyq2+44iIRA1va/bMLGBmC4AtwFTgayDfObf3dNUNQEYt7xtnZtlmlp2Xl9d4gUN0zfE9SYgN8MC0lb6jiIiIhJ1Lj+5KRmoSHy/f4juKiEjU8LYbp3OuAhhiZqnAa0C/g3zfY8BjAFlZWRFzME9acgLXj+4FgHNO20yLiIhUkxAb4K0bRtK6eVQs1xcRCQvej15wzuWb2XTgaCDVzGKDo3udgBy/6erXdaN6+Y4gIiIStvYWeuu376F9SiLxsdo0XEQkFL5240wLjuhhZknAycBSYDrwg+BtlwNv+MjXkCorHW8vymXVll2+o4iIiISdVVt2M/ruj3np83W+o4iIRDxfX5l1BKab2SLgc2Cqc+5t4DbgFjNbBbQBnvSUr8HsKi5nwquLuWfqCt9RREREwk7PtOYc0aUV909bSWFJ+YHfICIi++RrN85FzrlM59wg59zhzrk/BttXO+eGOed6OecucM6V+MjXkFo2i+PKkd15d/EmvszVeUIiIiLVmRkTTuvH1t2lPPHJGt9xREQimibDe3DVyO4kJ8Zy34famVNERKSmzC6tOHVABx6b8TVbd0fd974iIo1GxZ4HLZPiuPrYHkz9ajOLN2h0T0REpKbxp/alvNIxd/V231FERCKW9904m6qfjOjGtGVbKCgu8x1FREQk7PRMa8GnE0/UUQwiIiFQsedJcmIcb1w3wncMERGRsLW30Fudt5seaS08pxERiTyaxulZUWkFby3M9R1DREQkLL06bwMn3vNfluRo2YOIyKFSsefZC5+t44YX5pO9VmsSREREajqpf3taJsVx55TlvqOIiEQcFXueXTysC21bJHDvhzp3T0REpKaWSXFcP6oXM1bkMXvVVt9xREQiioo9z5LiA/z8hJ7MWrWNOau3+Y4jIiISdi4Z3pX0lonc/v4ynHO+44iIRAwVe2Hgx0d1oV1yAvdMXaFOTEREpIbEuAC3nNKXtVsLWbO10HccEZGIod04w0BiXIBrT+jJu4s3sauknJTEON+RRESkEZnZWmAXUAGUO+eyalw34H7gdGAPcIVz7ovGzunTuZkZnHxYe6Yv38KlT35Gbn4R6alJjB/Tl7GZGb7jiYiEJRV7YeKyo7tx+THdqOrPRUSkCRrlnNvXorTTgN7Bx1HAw8GfTUYgxpi+fAsTJy+iqKwSgJz8IiZOXgyggk9EpBaaxhkmYmIMM2NLQTHLN+3yHUdERMLLOcCzrsocINXMOvoO1dgmTVn+baG3V1FZBZO0U6eISK1U7IUR5xwXPz6HX726SGv3RESaFgd8YGbzzGxcLdczgPXVXm8Itn2HmY0zs2wzy87Ly2ugqP7k5hcdUruISFOnYi+MmBlXH9uDhevzmb58i+84IiLSeEY6546garrmdWZ2XF0+xDn3mHMuyzmXlZaWVr8Jw0B6atIhtYuINHUq9sLM+UM70bl1knbmFBFpQpxzOcGfW4DXgGE1bskBOld73SnY1qSMH9OXpLjAd9rM4Jen9PGUSEQkvKnYCzNxgRhuHN2bJTkFTP1qs+84IiLSwMysuZkl730OnAIsqXHbm8BlVmU4sNM5t7GRo3o3NjODv503kIzUJAxol5zAraf04dwjOvmOJiISlrQbZxg6NzODh//7NV/mFnDKgA6+44iISMNqD7wW3I05FnjeOfe+mf0MwDn3CPAuVccurKLq6IWfeMrq3djMjFp33pw0ZRl92idzzhDtyikispeKvTAUG4jh3RuPJbHGVBUREYk+zrnVwOBa2h+p9twB1zVmrkhSUl7B52t38ND0r9lcUMzVx/bQUUYiImgaZ9jaW+it2LyLykqt3RMREdmXhNgAz145jDMGdeSv7y7jD299RYX6ThERFXvh7PO12znl3hm8s7jJLcsQERE5JIlxAf5+USZXjezO07PXcuvLC3xHEhHxTtM4w9jQLq3o074F9324gtMHdiQQoykpIiIi+xITY/zvmf3p2DKRDi0TfccREfFOI3thLCbGuPmkPnydV8ibC5vcDtsiIiJ18tNje3DmoHQAPvhyEzk6dF1EmigVe2FuzIAOHNYxhfs/XEl5RaXvOCIiIhGjsKSciZMXc+5Ds/gqt8B3HBGRRqdiL8xVje71ZnNBCUs37vIdR0REJGI0T4jl+auHE4gxLnz0U2at2uo7kohIo1KxFwFO7t+eWRNGM7BTS99RREREIkrfDslMvvYYOrVK4op/fsbr87UsQkSaDhV7EcDMaN08HuccmwuKfccRERGJKB1bJvHyz44mq2trVmzWLBkRaTq0G2cE+fVrS5ixIo/pvzyB+FjV6SIiIgcrJTGOZ64cRlygamfrddv2kNEqSTtdi0hUU8UQQcYMaE9OfhEvZ6/3HUVERCTixMfGYGbsKCzl3H/M4trn5lFcVuE7lohIg1GxF0GO75PG0K6teGj6KnVOIiIiddSqeTzXjerFB19t5sdPzGVHYanvSCIiDaLRiz0z62xm083sKzP70sx+EWz/vZnlmNmC4OP0xs4W7syMW07uw8adxbz0uUb3RERE6urKkd156EdHsDhnJ+c/Mpv12/f4jiQiUu98jOyVA7c65/oDw4HrzKx/8Nq9zrkhwce7HrKFvWN6tmFY99ZM/mIDzjnfcURERCLW6QM78u+rjmLrrhIemLbSdxwRkXrX6Bu0OOc2AhuDz3eZ2VIgo7FzRCoz474fDqF183jMtKhcREQkFMO6t+a160bQsWUiABWVTpu2iEjU8Lpmz8y6AZnA3GDT9Wa2yMyeMrNW3oKFufTUJBLjApSWV2rtnoiISIh6prWgWXwsu0vKOe/h2bw6b4PvSCIi9cJbsWdmLYBXgZuccwXAw0BPYAhVI3937+N948ws28yy8/LyGi1vuNlRWMqouz7m2U/X+o4iIiISFZxztEgIcOt/FvLQ9FVaLiEiEc9LsWdmcVQVes855yYDOOc2O+cqnHOVwOPAsNre65x7zDmX5ZzLSktLa7zQYaZV83h6pDXnkf+uprCk3HccERGRiJecGMc/rxjG2CHpTJqynN+8voSKShV8IhK5fOzGacCTwFLn3D3V2jtWu+1cYEljZ4s0N5/ch+2FpTzz6VrfUURERKJCfGwM91w4hJ8d35Pn5q7jL+8s9R1JRKTOGn2DFmAEcCmw2MwWBNt+DVxsZkMAB6wFrvGQLaIc0aUVo/qm8diM1Vw6vCvJiXG+I4mIiES8mBhjwmn96NamGSN7t/UdR0Skzhp9ZM85N9M5Z865QdWPWXDOXeqcGxhsPzu4a6ccwM0n9yF/TxmvL8j1HUVERCSqXDSsC51aNaOy0vHXd5eybpvO4hORyOJ1N04J3aBOqbz686O55KguvqOIiIhEpfU79vBy9nrOe3gWizfs9B1HROSgqdiLAkO7tsbMKK+o9B1FREQk6nRt05xXfnYMCbEBfvjYp0xfvsV3JBGRg6JiL0q8s2gjx905nfw9pb6jiIiIRJ1e7Vrw2rXH0L1tc376TDaTv9BZfCIS/lTsRYme7ZqzsaCYxz9Z7TuKiIhIVGqXkshL1xzNqL7tyEhN8h1HROSAVOxFiX4dUjhjYEf+OWst2ws1uiciItIQWiTE8sTlWRzVow0A05dv0TIKEQlbKvaiyE0n9aaorIJHZ3ztO4qIiEjU+zJ3Jz/55+eM+9c89pSW+44jIvI9KvaiSK92yZwzOJ1/ffoNBcVlvuOIiMhBMrOAmc03s7druXaFmeWZ2YLg46c+Msr3DUhvyZ/HHs7Hy7cw5t4ZDP/bNLpPeIcRt3/E6/NzfMcTEfFyqLo0oFtP6cuVI7uTogPWRUQiyS+ApUDKPq6/5Jy7vhHzyEG6ZHhX1uTt5slZa79ty8kvYuLkxQCMzczwlExERCN7Uadz62YM6pQKgHPOcxoRETkQM+sEnAE84TuL1M37X27+XltRWQWTpiz3kEZE5P+p2ItCzjkmTl7EX95Z6juKiIgc2H3Ar4D97fJxvpktMrNXzKxzI+WSg5SbX3RI7SIijUXFXhQyMyor4dk537BxpzoaEZFwZWZnAlucc/P2c9tbQDfn3CBgKvDMfj5vnJllm1l2Xl5ePaeVfUnfxzEM+2oXEWksKvai1PWje1FZ6fjHdO3MKSISxkYAZ5vZWuBFYLSZ/bv6Dc65bc65kuDLJ4Ch+/ow59xjzrks51xWWlpaQ2WWGsaP6UtSXOB77ZcO7+IhjYjI/wup2DOz9mb2pJm9F3zd38yuqp9oEorOrZtx4ZGdefHzdeRoGomISIOrS5/onJvonOvknOsGXAR85Jy7pMbndqz28myqNnKRMDI2M4O/nTeQjNQkDOiQkkiLhACvzc+luKzCdzwRacJCHdl7GpgCpAdfrwBuCvEzpZ5cP6oXhvHQ9FW+o4iINAVPU099opn90czODr680cy+NLOFwI3AFSHmlAYwNjODWRNGs+b2M5jz6xN56MdDWb55F39+5yvf0USkCQu12GvrnHuZ4KJy51w5oK+wwkR6ahJ3XziY60b18h1FRKQpCKlPdM597Jw7M/j8t865N4PPJzrnBjjnBjvnRjnnljVEeKlfx/dJY9xxPfj3nHV8tma77zgi0kSFes5eoZm1ARyAmQ0HdoacSurNWYPTD3yTiIjUB/WJ8h2/PKUv/TumcGS3Vr6jiEgTFerI3i3Am0BPM5sFPAvcEHIqqVdrthZy6ZNzWbu10HcUEZFopj5RviM+NoaxmRmYGbn5RZRX7O90DRGR+hdSseec+wI4HjgGuAYY4JxbVB/BpP40Twjw+drtPDBtpe8oIiJRS32i7MvGnUWMuW8G96sfFpFGFupunNcBLZxzXzrnlgAtzOza+okm9aVdciKXHd2N1xfksGrLbt9xRESikvpE2ZeOLZM4dUAHHpy+itlfb/UdR0SakFCncV7tnMvf+8I5twO4OsTPlAZwzXE9SIwLaHRPRKThqE+UffrDOQPo3rY5N7+0gO2Fpb7jiEgTEWqxFzAz2/vCzAJAfIifKQ2gTYsELj+mG28tymXF5l2+44iIRCP1ibJPzeJj+fvFmewoLGP8fxbinPMdSUSagFB343wfeMnMHg2+vibYJmFo3LE9aJEQS3pqku8oIiLRSH2i7NeA9Jb8+vR+LMktoLSikoTYgO9IIhLlQi32bqOqM/t58PVU4IkQP1MaSKvm8TpzT0Sk4ahPlAO6/JhuVBsAFhFpUCEVe865SuDh4EMixJQvN5G9djv/c0Z/31FERKKG+kQ5GHsLveWbdnHXB8u574dDaJ4Q6nfvIiK1C3U3zhFmNtXMVpjZajNbY2ar6yucNIylGwt4/JM1LMnRWb8iIvVFfaIcih17Spm2dDO/feNL31FEJIqFukHLk8A9wEjgSCAr+FPC2JUju5OSGMt9H67wHUVEJJqoT5SDNrxHG64f3ZtXv9jA6/NzfMcRkSgVarG30zn3nnNui3Nu295HvSSTBpOSGMe443rw4dItLFyff+A3iIjIwVCfKIfkxtG9GNatNf/z2mLWbi30HUdEolCoxd50M5tkZkeb2RF7H/WSTBrUFSO6k9osjns1uiciUl/UJ8ohiQ3EcN9FQ4gNxPCPj1f5jiMiUSjUFcFHBX9mVWtzwOgQP1caWIuEWH592mHEBgznnHYGExEJnfpEOWTpqUm8OG44PdNa+I4iIlEo1N04Rx3qe8ysM/As0J6qTvAx59z9ZtYaeAnoBqwFLnTO7Qgln+zfhUd29h1BRCRq1KVPFAE4rGMKADv3lLF2WyGDO6d6TiQi0SLkvX7N7AxgAJC4t80598f9vKUcuNU594WZJQPzzGwqcAUwzTl3u5lNACZQdWaRNKDisgqemb2WrG6tGNq1te84IiIRrQ59osi3bnl5AQvW5/PeL46lXUrigd8gInIAoR698AjwQ+AGwIALgK77e49zbqNz7ovg813AUiADOAd4JnjbM8DYULLJwXEOnpi5hklTlvuOIiIS0erSJ4pUN/H0fhSWlnPzywuorHS+44hIFAh1g5ZjnHOXATucc38Ajgb6HOybzawbkAnMBdo75zYGL22iapqnNLCk+ADXntCTOau3M/vrrb7jiIhEspD6RJFe7ZL5/VkDmLVqGw//92vfcUQkCoRa7BUFf+4xs3SgDOh4MG80sxbAq8BNzrmC6tecc46q9Xy1vW+cmWWbWXZeXl7dk8u3Lh7WhfYpCdw7dQVV/9OLiEgd1LlPFNnrh0d25sxBHbln6grmfaOtC0QkNKEWe2+bWSowCfiCqo1VXjjQm8wsjqpC7znn3ORg82Yz6xi83hHYUtt7nXOPOeeynHNZaWlpIcYXgMS4ANeP6sXna3cwc5VG90RE6qhOfaJIdWbGX88byLmZGXRuleQ7johEuFB34/xT8OmrZvY2kOic27m/91jVHv9PAkudc/dUu/QmcDlwe/DnG6Fkk0Nz4ZGdmbNmOymJcb6jiIhEpLr0iSK1SUmM464LBgNQUemIMXREkojUSZ2KPTMb7Zz7yMzOq+Ua1UbrajMCuBRYbGYLgm2/pqrIe9nMrgK+AS6sSzapm4TYAA/9SGf/iogcqhD7RJF9KiguY9yz2ZwzJIOLh3XxHUdEIlBdR/aOBz4CzqrlmgP22bE552ZStUtZbU6sYx6pJ1sKinn1ixx+dnwPfYsoInJw6twniuxPi/hY4gIx/P7NLxnatRV92if7jiQiEaZOxZ5z7ndmFgO855x7uZ4ziUcfr8jjjveX0btdC07qrw1RRUQORH2iNJSYGOPuCwdz+v2fcP3zX/Dm9SNJjAv4jiUiEaTOG7Q45yqBX9VjFgkD52Vm0LVNM+7RzpwiIgdNfaI0lHbJidx94RBWbN7Nn97+ynccEYkwoe7G+aGZ/dLMOptZ672PekkmXsQGYvjFib35amMBU77c7DuOiEgkUZ8oDeL4Pmlcc1wP/rsij51FZb7jiEgEsVBGb8xsTS3NzjnXo+6RDl5WVpbLzs5ujF/VpJRXVHLKfTOID8Tw7o3HEhOjtXsi4p+ZzXPOZfnOsS+++8Sa1EdGl9LySorKKmiZpF2zReS79tc/hnr0QvdQ3i/hKTYQw80n9eGjZVsoLC0nWccxiIgcUKh9opkFgGwgxzl3Zo1rCcCzwFBgG/BD59zaUH6fRJb42BjiY2MoKa/g33PWcfnRXYkNhDpBS0SiXUjFHoCZHQ70BxL3tjnnng31c8Wvswanc9bgdN8xREQiSoh94i+ApUBKLdeuAnY453qZ2UXAHcAPQ4wrEeiTFVv509tfkb+nlFtP6es7joiEuZC+EjKz3wF/Dz5GAXcCZ9dDLgkTX+UWMGf1Nt8xRETCXih9opl1As4AntjHLecAzwSfvwKcaDofp0k6qX97LhjaiQenr2L2qq2+44hImAt1/P8HVJ2Nt8k59xNgMNAy5FQSFpxz3PLyAn49eTHlFZW+44iIhLtQ+sT7qNrNc19/2WYA6wGcc+XATqBNSGklYv3hnAF0b9ucm15awLbdJb7jiEgYC7XYKw5uN11uZinAFqBz6LEkHJgZN53Um9VbC3ljQa7vOCIi4a5OfaKZnQlscc7NCzWAmY0zs2wzy87Lywv14yRMNYuP5e8XZ5K/p4zfvL7EdxwRCWN1KvbM7CEzGwl8ZmapwOPAPOAL4NN6zCeejRnQgQHpKTzw0UqN7omI1KIe+sQRwNlmthZ4ERhtZv+ucU8OwcLRzGKpGjH83hx759xjzrks51xWWlpaXf9IEgEGpLfkrgsHc8vJfXxHEZEwVtcNWlYAk4B0oBB4ATgZSHHOLaqnbBIGzIybT+rDT5/NZvL8HC7M0sCtiEgNIfWJzrmJwEQAMzsB+KVz7pIat70JXE5V8fgD4CMXytlJEhXOrraRWv6eUlKbxXtMIyLhqE4je865+51zRwPHUfXN4lPA+8C5Zta7HvNJGDjxsHYc3aMNu4rLfUcREQk7DdUnmtkfzWzvBi9PAm3MbBVwCzAhxNgSRf767lLO/cdsdpeonxaR7wrpUPXvfJBZJlUd3CDnXKBePvQAdGBs43HOoY3fRMSncD9UvToffWJN6iObjjmrt/Gjx+cwNjODey4c4juOiDSy/fWPoR69EGtmZ5nZc8B7wHLgvFA+U8KTmeGc4+PlWygpr/AdR0Qk7KhPFF+G92jD9aN7M/mLHF6bv8F3HBEJI3XdoOVkM3sK2ABcDbwD9HTOXeSce6M+A0r4mPfNDq745+e8/Pl631FERMKG+kQJBzeO7sWwbq35zWtLWLO10HccEQkTdR3ZmwjMBg5zzp3tnHveOae/WaLc0K6tOLJbKx6cvoriMo3uiYgEqU8U72IDMdx30RDaJieQs6PIdxwRCRN13aBltHPuCefcjvoOJOHLzLj55D5sLijhhc/W+Y4jIhIW1CdKuEhPTWLaLcczsndb31FEJEyEeqi6NDHH9GzL8B6t+cfHX1NUqtE9ERGRcBIbiKGy0vHEJ6uZvmyL7zgi4pmKPTlkt5zcl4CZ1gSIiIiEobLKSl6Zt4Fb/7OQzQXFvuOIiEcq9uSQDevemhm/GkX/9BTfUURERKSGhNgAD/4ok6LSCm5+aQEVlfVzzJaIRB4Ve1In8bExlJRX8GXuTt9RREREpIZe7ZL5/dn9mf31NjL/+AHdJ7zDiNs/4vX5Ob6jiUgjUrEndfbryUu45Im57C4p9x1FREREaogPxBAwKCguxwE5+UVMnLxYBZ9IE6JiT+rssqO7smNPGc/MXus7ioiIiNRw1wcrqKgxg7OorIJJU5b7CSQijU7FntTZ4M6pnHRYOx6bsZqC4jLfcURERKSa3Pzaz9vbV7uIRB8VexKSm07qw86iMv45c63vKCIiIlJNempSre0dWiY2chIR8UXFnoTk8IyWjBnQnjmrt+GcdvsSEREJF+PH9CUpLvC99qS4gM7KFWkiVOxJyCZdMJjnrz4KM/MdRURERILGZmbwt/MGkpGahAEZqUlcenRX1m4rZNy/sikuU8EnEu1ifQeQyJeSGAdA/p5SYmLs29ciIiLi19jMDMZmZnynbVBGS8a/soh3Fm3k/KGdPCUTkcagYk/qxc49ZRx353QuGd6VX53az3ccERER2YcLsjrTt0MyAzNa+o4iIg1M0zilXrRsFscJfdvx9Oy1bNtd4juOiIiI7MegTqmYGUs3FvDHt76islLr7kWikZdiz8yeMrMtZrakWtvvzSzHzBYEH6f7yCZ1d+OJvSkuq+DRGat9RxEREZGDMGNFHk/NWsNv3liijdZEopCvkb2ngVNrab/XOTck+Hi3kTNJiHq1a8HYIRk8++latuwq9h1HREREDmDccT34+Qk9eX7uOv709lIVfCJRxsuaPefcDDPr5uN3S8O68cTevDY/h1PumcHOojLSU5MYP6bv9xaHi4iIiH9mxq/G9KWotIKnZq2hWXyAX47p6zuWiNSTcNug5XozuwzIBm51zu3wHUgOzYL1+cTHxpBfVAZATn4REycvBlDBJyIiEobMjN+d1Z+S8goWbsinrKKSuIC2dRCJBuH0X/LDQE9gCLARuLu2m8xsnJllm1l2Xl5eY+aTgzBpynJKyiu/01ZUVsGkKcs9JRIREZEDMTP+MnYgT1yeRVwghvKKygO/SUTCXtgUe865zc65CudcJfA4MGwf9z3mnMtyzmWlpaU1bkg5oNz8okNqFxERkfAQE2MkxAbYWVTG+Y98ynNzv/EdSURCFDbFnpl1rPbyXGDJvu6V8JWemlRre7uUhEZOIiIiInWRFBegbfN4/ue1Jbwyb4PvOCISAl9HL7wAfAr0NbMNZnYVcKeZLTazRcAo4GYf2SQ048f0JSku8L32/MJSPvxqs4dEIiIicijiY2N46MdHMLJXW371ykLeXpTrO5KI1JGv3TgvrqX5yUYPIvVu7yYsk6YsJze/iPTUJK4a2Y3J83PI0VROERGRiJAYF+Cxy4ZyxVOfc9OLC2geH8uofu18xxKRQxRuu3FKFBibmfG9nTcvGd6NuIABMHf1Nvp2SCa1WbyPeCIiInIQmsXH8uQVWUyYvJhe7Vr4jiMidRA2a/YkusXHxmBm7Ckt52f/nsdZD85kSc5O37FERLwzs0Qz+8zMFprZl2b2h1ruucLM8sxsQfDxUx9ZpelJTozjoR8dQefWzaisdHydt9t3JBE5BCr2pFE1i4/lqSuOpKzccf7Ds3lVC79FREqA0c65wVQdP3SqmQ2v5b6XnHNDgo8nGjeiCNw/bSVn/30mX6zTMcgikULFnjS6zC6tePvGkWR2SeXW/yzkf19fQkWl8x1LRMQLV2XvcElc8KG/FCXs/PioLqQlJ3D5U59pdo5IhFCxJ160bZHAv686iquP7U55ZSWBGPMdSUTEGzMLmNkCYAsw1Tk3t5bbzjezRWb2ipl1buSIIrRLSeS5q4eTkhjHpU/OZcXmXb4jicgBqNgTb2IDMfzPGf35y9iBACzbVMDc1ds8pxIRaXzOuQrn3BCgEzDMzA6vcctbQDfn3CBgKvBMbZ9jZuPMLNvMsvPy8ho2tDRJGalJPPfTo4gLxHDl059TWl7pO5KI7IeKPfEuJjiqd8d7y/jRE3N5cuYanNMMJhFpepxz+cB04NQa7duccyXBl08AQ/fx/secc1nOuay0tLSGDStNVre2zXn+6qO44/xBxMf9a6wLAAAgAElEQVTqn5Ii4Uz/hUrYuP/iTEb3a8ef3v6KX7y4gD2l5b4jiYg0ODNLM7PU4PMk4GRgWY17OlZ7eTawtPESinxfr3bJjOjVFoB3Fm1k085iz4lEpDYq9iRspCTG8eglQxk/pi9vLcrl3Idms7lAnYeIRL2OwHQzWwR8TtWavbfN7I9mdnbwnhuDxzIsBG4ErvCUVeQ7dhSWMmHyIn70xBzydpUc+A0i0qhU7ElYiYkxrhvVi2d+MozOrZvRSgevi0iUc84tcs5lOucGOecOd879Mdj+W+fcm8HnE51zA5xzg51zo5xzy/b/qSKNo1XzeJ664kg25hdz6ZNz2VFY6juSiFSjYk/C0nF90nji8iziY2PYXljKYzO+plLHM4iIiISdI7u15vHLsli9tZDLnvqMguIy35FEJEjFnoS91+bn8Nd3l3HlM5+Tv0ffGIqIiISbkb3b8sglR7BsUwHvL9nkO46IBKnYk7B35Yhu/Hns4cxatZWzHpzJV7kFviOJiIhIDaP7teeDm4/nwiwdAykSLlTsSdgzMy4Z3pWXrjma0vJKznt4FjNW6PwoERGRcNO9bXMAFm3I5xcvzqekvMJzIpGmTcWeRIwjurTi7RuO5ZT+HRiQnuI7joiIiOzDsk27eGNBLje+MJ+yCh28LuKLij2JKGnJCTxwcSZtWiRQVlHJn97+ii06nkFERCSsXJjVmd+d1Z8pX27m1pcXUqFN1kS8ULEnEWvZxl08P3cdZ/x9Jp+v3e47joiIiFTzkxHdue3Ufry5MJeJkxdpV20RD1TsScQa2Kklr183ghYJsVz82ByenrUG59SRiIiIhIufn9CTG0/szfbCUspV7Ik0uljfAURC0bdDMm9cP4JbXlrI79/6ik0FJUw4rZ/vWCIiIhJ080m9qah0xAZiKCqtIDEuBjPzHUukSdDInkS8lMQ4Hrt0KOPH9OWswR19xxEREZFqzIzYQAwFxWWc9/Bs7vtwpe9IIk2Gij2JCjExxnWjejEgvSUAt7+3jOnLtnhOJSIiInu1iI/l8PQU7p+2koc//tp3HJEmQdM4JersKS1nxoo8Hp3xNb84sTc3ju5NTIymi4iIiPgUE2Pcfv4gissrueP9ZazcXMDcNTvIzS8iPTWJ8WP6MjYzw3dMkaiikT2JOs3iY3n158dwbmYG9324kp8+m83OPWW+Y4mIiDR5gRjjngsHc3h6CpPn55KTX4QDcvKLmDh5Ma/Pz/EdUSSqqNiTqJQUH+DuCwbzp3MGMGNFHhc9PkdbPouIiISBuEAM2wtLv9deVFbBpCnLPSQSiV6axilRy8y49Ohu9E9PYdvuUk3lFBERCRMbdxbX2p6bX9TISUSim0b2JOoN7dqaUwZ0AOC5ud/w+ze/pKyi0nMqERGRpis9NanWdjN49L9fU1hS3siJRKKTij1pUtZt38PTs9fy48fnsmVX7d8qioiISMMaP6YvSXGB77TFx8bQu30yf3tvGSPv+IgVm3d5SicSPVTsSZMy8bTDuP+iISzO2cmZD8xk3jfbfUcSERFpcsZmZvC38waSkZqEARmpSdx5/iCm3HQcr117DGMGdKBH2+YAzPtmOzuLtNGaSF2Yc5G7aUVWVpbLzs72HUMi0LJNBVzzr3lszC9m+vgTyNjHdBIRCR9mNs85l+U7R6RQHynRoLS8kmNun0ZJeSU/GdGdq0Z0p2WzON+xRMLK/vpHLyN7ZvaUmW0xsyXV2lqb2VQzWxn82cpHNmka+nVI4c3rRzLpgkHfFnrarVNERCS8xMfG8PRPhnFMzzY8MG0lI+74iLumLGdHLbt5isj3+ZrG+TRwao22CcA051xvYFrwtUiDaZkUxzlDqg5vnbt6G2P/MYt12/Z4TiUiIiLVHZ7RkkcvzeK9XxzL8X3SeOjjVSzdVOA7lkhE8FLsOedmADUXS50DPBN8/gwwtlFDSZNWUl7J2q2FnPXgTKYv3+I7joiIiNRwWMcUHvrxEXz8yxM4ukcbAO58fxl/eecrbbomsg/htEFLe+fcxuDzTUB7n2GkaTmuTxpv3TCS9NQkrnz6cx6YtlLTOkVERMJQ1zbNMas6O3fHnlKenLmGY++Yzh/e+pLNBSr6RKoLp2LvW65q15ha/6VtZuPMLNvMsvPy8ho5mUSzrm2aM/nnxzB2SAb3TF3B24s3HvhNIiIi4s3fzhvEtFtP4KzB6Tz76Tcce+d0Jn+xwXcskbARTsXeZjPrCBD8WetcOufcY865LOdcVlpaWqMGlOiXFB/gngsH8/hlWZw5sCNQtROYiIiIhKfubZtz1wWDmX7rCZw7JINBnVIBWLdtDzn5RZ7TifgVTsXem8DlweeXA294zCJNmJlxcv/2xMQYG3bsYdRdH/PmwlzfsURERGQ/urRpxh0/GESvdi0AuOP9ZZwwaToTJy9i/XZtwCZNk6+jF14APgX6mtkGM7sKuB042cxWAicFX4t4FR8bQ3pqIje+MJ8/vf0VZRUa5ROR+mVmiWb2mZktNLMvzewPtdyTYGYvmdkqM5trZt0aP6lIZPmfMw7j4mFdeHVeDifc9THj/7OQtVsLfccSaVSxPn6pc+7ifVw6sVGDiBxAu+REnr96OH95ZylPzlzD4pydPPSjI0hLTvAdTUSiRwkw2jm328zigJlm9p5zbk61e64CdjjnepnZRcAdwA99hBWJFOmpSfzxnMO59oRePDrja56fu442LRKYcFo/39FEGk04TeMUCUtxgRh+f/YA7vvhEBZtyOeBaSt9RxKRKOKq7A6+jAs+am5SVv14oleAE23vdoQisl8dWibyu7MG8Mlto/jZ8T0AmL58Cze+MJ+Vm3d5TifSsLyM7IlEorGZGRzWMYVOrZIAKCguIzkhFv17S0RCZWYBYB7QC3jIOTe3xi0ZwHoA51y5me0E2gBbGzWoSARrl5z47fPc/CI+XLqZtxblcvrAjtwwuhf9OqR4TCfSMDSyJ3II+nZIpnlCLEWlFVz4yKfc+p+FFJdV+I4lIhHOOVfhnBsCdAKGmdnhdfkcHU8kcnB+fFRXZt42mmtP6Ml/l+dx6n2f8Ls3lgDw+vwcRtz+Ed0nvMOI2z/i9fk5ntOK1J2KPZE6SIiN4bTDO/La/BzOf3i2dvkSkXrhnMsHpgOn1riUA3QGMLNYoCWwrZb363gikYPUunk848f0Y+Zto7jxxN4MyGjJ6/NzmDB5ETn5RTggJ7+IiZMXq+CTiKViT6QOYmKMX5zUm6cuP5L12/dw5t9n8t8V+hZdRA6dmaWZWWrweRJwMrCsxm3Vjyf6AfCRc67muj4RqYPUZvHccnIfLszqzKQpyyku++7O20VlFdw5peZ/kiKRQcWeSAhG9WvHWzeMpGPLRCZNWUZlpf7tJSKHrCMw3cwWAZ8DU51zb5vZH83s7OA9TwJtzGwVcAswwVNWkaiWu49D2HPzi9lVXAbAjsJSKtTfS4TQBi0iIerapjmvXTuCnUVlxMQYu0vKqXSOlMQ439FEJAI45xYBmbW0/7ba82LggsbMJdIUpacmkVNLwdciIZbkYL9+26uL+HT1NoZ1a83wHm04qkdr+ndMITagMRQJP/p/pUg9SIoP0KFl1S5fE15dxDkPzmL5Jm3nLCIiEknGj+lLUlzgO21JcQH+PPb/90w6f2gnzhyUzpqthfzl3aWc/eAsLnny/zfQXbVlN+UV350KKuKLRvZE6tnlx3Tj2ue+YOxDs7jzB4M4a3C670giIiJyEMZmZgAwacpycvOLSE9NYvyYvt+2A4wZ0IExAzoAsKWgmDlrthMfqDqGqaS8gjMe+ITYGCOrW2uO6lE1+jcwoyVxGvkTDyyS13dnZWW57Oxs3zFEvmdLQTHXPvcF2d/s4KqR3ZlwWj/9JS8SIjOb55zL8p0jUqiPFGl8JeUVTP1qM3NWb2PO6u2s2rIbqBoxvG5ULwpLylm2qYCBGanEx+rfBVI/9tc/amRPpAG0S0nk+auH89d3l/L6/BzGHdeD9imJB36jiIiIRKyE2ABnDkrnzEFVs3q27i7hszXbOaxj1YHtc1Zv46pnskmKC5DVrRVHda8a+RvUScWfNAyN7Ik0sK27S2jbIoHKSseqvN30aZ/sO5JIRNLI3qFRHykSfnbuKWP211uZu2Y7c1ZvY1lwff/7Nx1Lvw4pLNtUQEFROYM7tyQhNnCATxOpopE9EY/atkgA4KlZa7jj/WX89qwBXHJUF8zMczIRERFpTC2bxXHawI6cNrAjUHWMw2drt9OnXdUXwc/MXssLn60nITaGI7q0+na3z6O6t8bMeH1+zn7XE4rUpPFikUZywdDOjOjVlv99fQnjX1lEcVmF70giIiLiUavm8YwZ0IGYmKovgG87tR+PXTqUS4Z3paC4jPumreCX/1n4baE3/pWF5OQX4YCc/CImTl7M6/Nz/P4hJKxpZE+kkbRsFsdTlx/JfdNW8sC0lSzbVMDDPx5K59bNfEcTERGRMJDaLJ5TBnTglOBunzv3lLEhfw8Ak6Yso6ziu8uvisoquOP9ZRrdk33SyJ5II4qJMW45uQ9PXp7FxvxiNhcU+44kIiIiYaplszgGpLcEIDe/9n8zbNxZ1V5Z6dhZVNZo2SQyqNgT8eDEw9rzyW2jyOrWGoBPv95GZWXkbpYkIiIiDSs9NanW9nbJVXsDzF+/g6w/T+Xypz7jhc/WsXV3SWPGkzClYk/Ek2bxVbOo56/bwcWPz+Gaf8+joFjfyImIiMj3jR/Tl6S47+7QmRQX4NenHwZAWotErhzZnbXbCpk4eTHD/vIhFz76KTn5RT7iSphQsSfi2ZDOqfz2zP58tGwLYx+cxcrNu3xHEhERkTAzNjODv503kIzUJAzISE3ib+cN/Ha9Xpc2zZh42mF8/MsTeP+mY7lhdG8qKh1pwV3Bn5+7joemr+LrvN0e/xTS2HTOnkiYmLt6G9c9P589peXcdcFgTg9uyywiVXTO3qFRHyki1d304nxeX5ALQO92LTj18A6cdnhH+qeneE4modpf/6iRPZEwcVSPNrxz40j6dUimQAusRUREpB7dd1Ems/+PvTuPr6K8+///+mQPJCQsSUgCCLLKjiIuWMV9Q8XWu7W1rbb9Vdt6d7lrrdq73y733d3e3VdbW+1m64oWF6TigloX9h3ZZU9YTSBk/fz+OBM8hAQSyMlMTt7PxyOPzJmZM+d9DnAuPjPXXNedF/CNq0bSOyeDXz6/hp/PXn1o+9It+zR+QBLS1AsiEVLUI4sHbzmLtNTYeZg5q8s5pbjHoYnZRURERI5XSX42N00exE2TB7GrsprK6joA3t51gKk/f5nC3EwuGVXEZaOKOePkXqSn6rpQZ6c/QZGIaSz09lfX8bkHFnDVz19mwdt7Qk4lIiIiyaR3TiYn9e4OQJ/cDH56/XhOO6knj8zbwofvfZ3Tv/0v3li/O+SUcqJ0ZU8korpnpvHnT5zBp/4yjw/89jW+cfUostNT+OGzb7F1bxUl+dncfulwTaQqIiIiJ6RbRhrXjC/lmvGlVNXU89LqcmYu3c6QwhwA/vHm27y0eieXjerL+SMKyclUCdFZ6E9KJMJGl+bxz/88h8//YyFfeWwJqSlGfdCffsveKu56dAmACj4RERFpF9kZqVw6qi+Xjup7aN3+6npeX7eLJxdvIyMthXOH9uHy0cW877R+AExfsIW7Z67SyegIUjdOkYjr2T2DP950OrmZaYcKvUZVtfX8YObKkJKJiIhIV/Dxcwbx+lcu4qFPncVHzjyJFdsqeOCNt4FYofflhxezZW8Vzrsno6cv2BJuaAF0ZU+kU0hNsUM3UTe1de9BrvjpHIYU5nDr+UMY3jeXg7X1mEFmWmqzzxERERFpi9QU4/SBvTh9YC++euUp7D0QGzn8+8+spKa+4bB9q2rr+c5TK3R1LwJU7Il0EiX52WzZW3XE+u6ZqRTkZjJv4x7qGmJftk8s2spdjy5hQK9uDC7ozuDCHIYUxObUyc1K7+joIiIikkTMjJ7dMwDYvu9gs/uUVVQDsSt9//m3+QwrzGVY31yGFeUwvCiXgtxMzKzDMndVKvZEOonbLx3OXY8uoaq2/tC67PRUvj1tzBFnzkYW9+DWKYNZU17J2rL9vPTWTmrqG7hgRCEA972ynpnLdjCkMIfBBd0ZUpjLkMIcinroi1dERERar6WT0cV5WUBsdPHMtBRmrdjBP+ZuOrT9VzecyhVjilm/cz9zVpcztDCX4X1z6RUUkdI+VOyJdBKNBV1rboAeXZrH6NK8Q4/r6hvYvKeK3sF8fRlpqRysq2f6wi1UHIx1D81MS2H5/1xGqsFDczdRVlEdFIM5nNS7m+baERERkSO0dDL6jstGADCsKJe/33wWADsrq3lrewVv7ahgwoB8AF5ft4uvPb7s0HP75GQwrCiX779vLP17dWPP/hpSUoy8bPVMOh6RK/bMbANQAdQDde4+MdxEItExbULpcfV/T0tNYWCf7ocef+iMAXzojAG4O+WV1awpq2RnZQ2pKbGres+tKOOZZdsP7Z+eapw9uA/3f3wSAK+u2UlOVhqDC3LoruGXRUREuqy2nIzuk5NJnyGZnD2kz6F1Hzi9P1OGF7JqRwWrd1SwansFb5VV0iO47eSPr6znZ7PX0LdHVqwbaGEOw/rmcu2E0ladiO7qI4VG9X9p57v7zrBDiCQ7M6MwN4vC3KzD1v/mI6dRWV3H2rJK1pRVsra8km4Z7w72cseji9m0O9ZlozgviyGFOVx0ShE3nj0QgL0HasjLTleXUBERkS7geE9GQ+z/In3zsuibl8V5wwqO2H7RyCKyM9J4a0fsiuCf1u0iPcW47tTYtA93z1zJym0VDC3KZXjfHIYGt6ZkpacyfcGWw646dsVpq6Ja7IlIyHIy0xjXP59x/fOP2PbHm05nTVAIxorB/by9+wAA9Q3OpO88R3Z6anA/YKwr6OQhfQ7rWioiIiJyLGP75TO237v/F6lvcHa8c5CUoDdSqhmb91Tx0upyautjU1SdXNCd2bdN4e6Zqw7rXgqxkULvnrlKxV6IHHjWzBz4rbvfE7/RzG4GbgYYMGBACPFEJDagS26z22rrG7jzshGsLY8VgrNXlvHg3M3cdvEwRpfmsbOymg///vVDI4Q2/j65oDtZ6ZoqQkRERFqWmmKU5GcfevzFS4bzxUuGU1vfwIad+3lrRyX1Hiv6tjYzcMzR1iejKBZ757j7FjMrBGaZ2Up3f6lxY1D83QMwceJEb+kgIhKOrPRUPn7OoMPW7T1Qc2i5qqaekvxslm7Zx9NLttE4T/zd143lPyb2Z/3O/fz1tY0MKcw5dFWwp0bmkiRmZv2BPwFFxE543uPuP22yzxTgcWB9sOpRd/+fjswpIhJl6akpDC3KZWjRuyejWxoptFtm7ORybX0DP5+9hrMH92bCgPyknJ84csWeu28JfpeZ2WPAJOCloz9LRKIsv9u7xVr/Xt34w02nA3Cwtp71O/eztrySCQN6ArB+ZyV/fm0j1XXvTtDau3sGf7jpdMb1z2fjrv2s37mfIYU5lORlH+rGIdKJ1QG3uft8M8sF5pnZLHdf3mS/Oe4+NYR8IiKdUnMjhWalp/DfV5wCwOodlfxi9mp+9txqstJTmDSoN+cM6c1V40oozstu6bCdSqSKPTPrDqS4e0WwfAmgM5ciSSorPZVTintwSnGPQ+suGFHE8v+5jK17q+LuCaykOD82iMzTS7fzvadXArGhnU8O7gv85tWjyO+Wwb6qWrLSU5Ly7JwkJ3ffBmwLlivMbAVQCjQt9kREpA2ONVLoyJIeLPz6Jby+bjevrNnJy2t28p2nVjKmNJ/ivGyWb32HRZv3cs6QPvTv1S3Mt3LcIlXsEevC8lgwgl8a8Dd3fybcSCLS0VJTjP69utG/VzfODyaCb/TB0wcwoX/+oQnj15RXsuDtvYemgPjxrLf482sbGdCrG4MLcg51B33fqaUtjg7a1Ydllugws4HABOD1ZjafZWaLgK3Al9x9WTP7iIhInGONFNojK52LRxZx8cgiALbvO3hoYvdnl2/nJ/9aDcCAXt2YPKQ3k4f04dJRfTvN/MORKvbcfR0wLuwcIhJded3SOePk3pxxcu9mt18ysoiczLRDA8S8+FYZvbtnct1psSGa73p0CRt37T9UBG7fd5A/vLyeg0G30a44LLNEg5nlAI8AX3D3d5psng+c5O6VZnYFMB0Y2swxNIiZiMgJ6Jv37nRUn79wKFPHlhy66jdj0TaeXLyNy0cXAzBz2Xay0lOZNLAX2RnR7FFk7p13jJOJEyf63Llzw44hIhFWW9/AzsrqQ33vfzhzFXPW7GRtWSWV1XUtPi8zLYWLRhbRLT2Vcf3z+fCZJwHw19c34g7dMlLplpFKdkYapfnZDCnMAaCs4iBZ6al0S08lrZOc9esszGyeu08MO0cimFk6MAOY6e4/asX+G4CJR5uTVm2kiEj7qqtv4O3dBzi5INbmX/aTl1i5vYKM1BROPSmfc4b0Ycrwwg6faupo7WOkruyJiLS39NSUw26y/tKlw/nSpcNxd8oqqjnjO881+7zqugZWbHuHqpp6Yr0/Y8Xe955eScXBw4vE607rxw//I9YpYfL3Zh+a5ycjNYXsjFQ+fOYAbr90BDV1DXz496+TfahQjP2+YEQhF4wo4mBtPQ/O3UR2eirdMtIO7TOoT3eKemRR3+BUHKwlOyOVjNQUTVqfJCz2B3kvsKKlQs/M+gI73N3NbBKQAuzqwJgiIl1eWmrKoUIP4NHPnM2bG/bwanDl74fPvsXa8v38+APjcXf+8eYmJg7syeCCnNDabBV7ItIlmRlFPbIobWFY5tL8bGbfNuWI9a/ceQFVNfUcqKnnQE0dVTX15HdLB8Dd+cbVo+K211NVU8fI4tgZvrqGBlJSYlNRbN0bbK+NTUVxwYgi9h6o5WuPH3kb1n9fcQqfPPdkNu7azwX/9yIQu68xOz1WDP73FacwbUIp68or+drjyw4Vkd0yUslOT+N9p5UyqiSPHe8c5IVVZWRnpNEt/d2Cc3BhDj2y0qmpa6CuoYGstNR2GeVU90K22mTgI8ASM1sYrPsKMADA3X8DXAd82szqgCrgeu/MXXNERJJAt4w0zhtWwHnDCgDYVVnNgZrYyJ9v7z7AncFtIUU9Mpk8pA+TB/dhyvACeudkdlgbqWJPRLq05oZlzk5P5fZLhze7f4+sdHpkpTe7zcy44YyTWnytbhlp/P3ms1rcXpCbyZv/fVGsWKytC4rFegYEI4D17JbB16aOpKo2Vmg2bm+cXLa23jlQU8fOyupgn9j2SYN6Maokj5XbK7jjkSVHvO59HzudKcMLmb1yB5/6y/xDn0FjMfjrG05jTL885qwu575XNsQVk2lkZ6TysckDKczNYk1ZBUu3vEN2RioL3t7DH1/ZcGgKDd0L2TJ3fxk4anXt7r8AftExiURE5Hj0zsmkcUSBk3p356Xbz+eVtbGrfs+vLOPR+Vv45YdOpba+gTseWdwhbaSKPRHp0o41LHNHSk0xCnIzW9zes3vGERPWxxveN5dHPzO5xe1nDOrFy3ecf/iVx9o6xgT3FgwtyuXOy0ccuiLZWCzmZceK26qaera/c/DwK5u19Vx3Wj8Kc+H5leV8+6kVLb5+VW09d89cpWJPRES6hAG9uzGg9wA+OGkADQ3Oiu3vMKBXNy77yZzD5hOGxLWRKvZEpMs71rDMySIrPZV+PVueJ2hwQQ6Dz8tpcfslo/pyyai+R6xv7E34gUn9ufCUQg7U1HPVz1+muT6GW5vpMisiIpLsUlKMUSWxk6sttYWJaCNV7ImIyAlpvOk8votrSQv3QjZ2ORUREemqOrKN1LjgIiLS7m6/dDjZ6YfPOXS0eyFFRES6io5sI3VlT0RE2l2U7oUUERGJko5sI1XsiYhIQnSVeyFFRETaqqPaSHXjFBERERERSUIq9kRERERERJKQij0REREREZEkpGJPREREREQkCanYExERERERSUIq9kRERERERJKQij0REREREZEkZO4edobjZmblwMZ2OFQfYGc7HEcOp881cfTZJoY+18Ror8/1JHcvaIfjdAnt1EZG+d9EVLMpV9tFNVtUc0F0s0U1F0Q3W3vkarF97NTFXnsxs7nuPjHsHMlGn2vi6LNNDH2uiaHPtfOK8p9dVLMpV9tFNVtUc0F0s0U1F0Q3W6JzqRuniIiIiIhIElKxJyIiIiIikoRU7MXcE3aAJKXPNXH02SaGPtfE0OfaeUX5zy6q2ZSr7aKaLaq5ILrZopoLopstobl0z56IiIiIiEgS0pU9ERERERGRJKRiT0REREREJAl16WLPzP5gZmVmtjTsLMnEzPqb2fNmttzMlpnZ58POlAzMLMvM3jCzRcHn+s2wMyUTM0s1swVmNiPsLMnEzDaY2RIzW2hmc8POI60T1fYxyu1L1L+jo/odF9XvCDPLN7OHzWylma0ws7MikGl48Dk1/rxjZl8IO1cjM/uv4O/+UjN7wMyyws4EYGafDzItC/vzau671cx6mdksM1sd/O7Znq/ZpYs94D7gsrBDJKE64DZ3HwmcCdxqZiNDzpQMqoEL3H0cMB64zMzODDlTMvk8sCLsEEnqfHcfH8X5jaRF9xHN9jHK7UvUv6Oj/B0Xxe+InwLPuPsIYBwR+OzcfVXwOY0HTgMOAI+FHAsAMysFPgdMdPfRQCpwfbipwMxGA58EJhH7c5xqZkNCjHQfR3633gk85+5DgeeCx+2mSxd77v4SsDvsHMnG3be5+/xguYLYF2RpuKk6P4+pDB6mBz8aYakdmFk/4Erg92FnEYmCqLaPUW5fovwdre+4tjGzPOBc4F4Ad69x973hpjrChcBad98YdpA4aUC2maUB3YCtIecBOAV43d0PuHsd8CLw3rDCtPDdeg1wf7B8PzCtPV+zSxd7knhmNrOZPMAAACAASURBVBCYALwebpLkEHTDWQiUAbPcXZ9r+/gJ8GWgIewgSciBZ81snpndHHYYSR5RbF8i/B0d5e+4KH5HDALKgT8GXV9/b2bdww7VxPXAA2GHaOTuW4AfAm8D24B97v5suKkAWAq8x8x6m1k34Aqgf8iZmipy923B8nagqD0PrmJPEsbMcoBHgC+4+zth50kG7l4fdN/oB0wKuifICTCzqUCZu88LO0uSOsfdTwUuJ9bl7tywA0nnF9X2JYrf0Z3gOy6K3xFpwKnAr919ArCfdu5adyLMLAO4Gngo7CyNgvvMriFWKJcA3c3sw+GmAndfAXwfeBZ4BlgI1Ica6ig8Nideu/YIULEnCWFm6cQa4r+6+6Nh50k2QXeS54nmPTWdzWTgajPbAPwduMDM/hJupOQRnO3F3cuI3VsyKdxE0tl1hvYlYt/Rkf6Oi+h3xGZgc9yV2YeJFX9RcTkw3913hB0kzkXAencvd/da4FHg7JAzAeDu97r7ae5+LrAHeCvsTE3sMLNigOB3WXseXMWetDszM2L93Fe4+4/CzpMszKzAzPKD5WzgYmBluKk6P3e/y937uftAYt1iZrt76Gcjk4GZdTez3MZl4BJiXWpEjkuU25eofkdH+Tsuqt8R7r4d2GRmw4NVFwLLQ4zU1AeJUBfOwNvAmWbWLfh3eiERGNQGwMwKg98DiN2v97dwEx3hCeDGYPlG4PH2PHhaex6sszGzB4ApQB8z2wx83d3vDTdVUpgMfARYEty7APAVd38qxEzJoBi438xSiZ2oedDdIzWEtkgTRcBjsXafNOBv7v5MuJGkNSLcPka5fdF3dNtF+Tvis8Bfgy6T64CPhZwHOFQUXwzcEnaWeO7+upk9DMwnNmruAuCecFMd8oiZ9QZqgVvDHGynue9W4HvAg2b2CWAj8P52fc1Y11ARERERERFJJurGKSIiIiIikoRU7ImIiIiIiCQhFXsiIiIiIiJJSMWeiIiIiIhIElKxJyIiIiIikoRU7EmrmVm9mS00s2VmtsjMbjOzo/4dMrOBZvahE3itpWb2kJl1a8NzJ5rZz9r6msFzv9CW1wqeM8XM2n14bTN7wcxWBZ/DwmBIY8zsU2b20fZ+vbjXvTEYGjh+XR8zKzezzDYe6yvtkOcFM5sb93iimb1woscNjnWTmf2iPY4lIl2b2sgWn6M2suVjqY2UhFOxJ21R5e7j3X0UsTleLic2P8jRDATa3JDFvdZooAb4VPxGM2txjkh3n+vunzuO1wT4AtCmhizBbgg+h/Hufh2Au//G3f+UwNd8DLi4SYN+HfBPd69u47Ha1JBZTHPfS4VmdnkbXzvhgvm0RERAbWQY1EbGqI2UFqnYk+Pi7mXAzcB/Bl8+A81sjpnND37ODnb9HvCe4Kzbfx1lv6OZAwwJzg7OMbMngOVmlmVmfzSzJWa2wMzOh8PPIppZdzP7g5m9EexzTbA+1cx+GJwVXWxmnzWzzwElwPNm9nyw3yVm9u8g60NmlhOsv8zMVprZfOC9zYU+Sr6bzOxRM3vGzFab2Q/a8tmb2TfM7EvB8ulB/oVmdreZLY17f3eb2ZvB9lviPpsXzOzhIP9fzWKz2TZy93eAF4Gr4lZfDzxgZgVm9khw3DfNbHJw3Jy497rYzN5nZt8DsoNsfw32+2LwmS81sy8E6wZa7Ozsn4ClQP9m3vbdwH8381kcdtbRzGaY2ZRguTL4DJaZ2b/MbFLw3teZ2dVxh+kfrF9tZl+PO9aHg783C83st42NVnDc/zOzRcBZx/4TE5GuRm2k2ki1kWojI8Pd9aOfVv0Alc2s2wsUETvTlxWsGwrMDZanADPi9m92v5ZeC0gDHgc+HRxrPzAo2HYb8IdgeQTwNpAV/5rAd4APB8v5wFtA9+B4DwNpwbZewe8NQJ9guQ/wEtA9eHwH8LXgNTYF+Q14MP49xr2HlvLdBKwD8oLHG4H+zTz/BWAVsDD4uTtY/w3gS8HyUuCsYPl7wNJg+Wbgq8FyJjAXGBR8NvuAfsRO9vwbOKeZ174OeCxYLgG2AqnA3xr3BwYAK4Ll7wM/iXt+z6Z/Z4DTgCXB558DLAMmEDuz3QCc2cLfhReAicBs4Pxg+YVg203AL+L2nQFMCZYduDxYfgx4FkgHxgEL456/DegNZAef50TgFOCfQHqw36+Aj8Yd9/1h/3vUj370E60f1EaqjVQb2XhctZER+mnxMr9IG6UDvzCz8UA9MOwE98s2s4XB8hzgXuBs4A13Xx+sPwf4OYC7rzSzjc0c7xLg6sazfMQajgHARcBv3L0ueP7uZjKcCYwEXglO7GUQ++IfAax399UAZvYXYg1HU0fL95y77wuevxw4iVjj2NQN7j63mfWYWT6Q6+7/Dlb9DZga977Hmtl1weM8Yg1vDbHPcHNwjIXEGpKXmxz+SeBXZtYDeD/wiLvXm9lFwMi4E509gjO5FxE7s0nwfve08Hk85u77g9d+FHgP8ASw0d1fa+59xvkW8FVi/6FojRrgmWB5CVDt7rVmtoTYe240y913xWU6B6gj1vC+GbzXbKAs2L8eeKSVGUREQG2k2ki1kRISFXty3MzsZGL/qMuI3Zewg9gZoRTgYAtP+69W7lfl7uObvB7Ezlq2KSbwPndf1cyxWvPcWe7+wSbPHd/C/m0R36+/nvb/t2jAZ9195mErY903jvna7l5lZs8A1xJroL4YbEohdnbxsD+3Vn6eR3PMP1d3n21m3yL2H4xGdRzeHT0rbrnWPXaakdhZ0ergOA12+P0szuGc2Od3v7vf1UyUg+5ef6y8ItK1qY08IWojD6c2Uo6b7tmT42JmBcBviHUPcGJnxba5ewPwEWLdGQAqgNy4p7a03/GYA9wQ5BlG7Gzkqib7zAQ+29jn3swmBOtnAbc0fqGZWa9m8r4GTDazIcE+3YPXWQkMNLPBwX6HNXRtzHfc3H0vUGFmZwSrro/bPBP4tJmlN76+mXVv40s8QKwBKyJ2thZi3Tw+27hDXKM+C7g1bn3PYLG2MQOxz2OamXULslwbrGuLbwFfjnu8ARhvZilm1h+Y1MbjQexG+15mlg1MA14BngOuM7PC4P30MrOTjuPYItIFqY1UGxkcV22khE7FnrRF443Ey4B/EftS+2aw7VfAjcENuSN49yzUYqDeYsNQ/9dR9jsevwJSgi4H/wBu8ndHwmo8E/W/xLrFLA5y/2+w/vfE7g9YHGRpHA3tHuAZM3ve3cuJ9Vd/wMwWE3RPCc7Y3Qw8abGbzxu7LrQlX2v91d4dVvpfzWz/BPC7oKtJd2L3GjS+v+XAfIvdkP5b2n5mdBaxexH+EXf273PARIvdYL6cd0eA+xbQ02I3lS8idt8AxD7PxWb2V3efD9wHvAG8Dvze3Re0JZC7PwWUx616BVhP7L3+DJjfxvdIkOcRYn9XH/HYSHXLiXWHeTb4s58FFB/HsUWk61AbqTZSbaREjr3791MkOZjZ+4Cr3f3GsLMkmpnluHtlsHwnUOzunw85loiIRJTaSLWR0rXonj1JKhYbLvjbwMfDztJBrjSzu4j9W95I7CyriIjIEdRGqo2UrkdX9kRERERERJKQ7tkTERERERFJQir2REREREREkpCKPRERERERkSSkYk9ERERERCQJqdgTERERERFJQir2REREREREkpCKPRERERERkSSkYk9ERERERCQJqdgTOQ5mNtDM3MzSws4iIiISRWorRcKnYk/kKMxsg5lVmVll3E9JG48xxcw2N1n3DTP7S/umPeJ1rzez181sv5mVBcufMTMLtt9nZjXBe9ptZrPMbEQzx3nBzPaYWWbw+OLgeH3i9sk0sxVm9qlEvicREYmeztpWNsm9PWgXc5rsM9HMZgTt4F4zW25m3zaznsH2m8ysPu59rzOzTycqs0hbqdgTObar3D0n7mdr2IGOxcxuA34K3A30BYqATwGTgYy4XX/g7jlAKbAFuLfJcQYC7wEcuBrA3WcB/wyO3+irwDbgt+3+ZkREpDPodG1l4KqgHRwPTADuatxgZmcDLwCvACPcPR+4DKgDxsUd49+N7xt4H/ADM5vQQflFjkrFnkg7MLOPBVe2KoKzercE67sDTwMlcWf9PgR8BfhA8HhRsG+emd1rZtvMbIuZfcvMUoNtN5nZy2b2w+Ds4nozu7yFLHnA/wCfcfeH3b3CYxa4+w3uXt30Oe5eBTxIrLGL91HgNeA+4Ma49V8EppjZlWY2GvhP4P9zdz/Oj1BERJJclNrKptx9OzCTw9vBHwB/dPfvuvuOYL+33f3r7v5CC8dZAKwATjmOj0ik3akPtUj7KAOmAuuAc4GnzexNd58fNDR/cfd+jTub2TBgiLt/OO4Y9wXHGQJ0B2YAm3j3atkZwP1AH+Bm4F4zK22mwDoLyAQeb234oKH9ILCmyaaPAj8CXgdeM7Mid9/h7vuCLpu/IXZF75vuvq61ryciIl1SlNrKw5hZP+ByYHbwuDux9vSrbXmDZnY6MAyY25bniSSKruyJHNv0oJ/+XjOb3twO7v6ku68NrqC9CDxLrPtjq5hZEXAF8AV33+/uZcCPgevjdtvo7r9z93piDVkxse6ZTfUBdrp7XdzxXw3yV5nZuXH7fsnM9gIVwDnAR+Kecw5wEvCgu88D1gIfinvP/yR21S8F+Flr36uIiCSlztZWxueuIFYwlgFfD9b3JNa+bY97/R8E72+/mcUXgWcG6yuAN4A/A6tb+75EEknFnsixTXP3/OBnWnM7mNnlZvaaxQY62UusMerT3L4tOAlIB7Y1NpbEzlIWxu1zqMFx9wPB4mE3kgd2AX0sbvQzdz87uNdgF4f/u/9hsH4gUAUMj9t2I/Csu+8MHv+Nw7tyAiwDVrp7Q6vepYiIJKvO1lbG584FpgAj4vLsARqIFYuNx/ty0GY+xuG9414L3ncusfvkRwHfacP7EkkYFXsiJ8hio1Q+AvwQKAoagqcAC3ZprutI03WbgGqgT1xj2cPdRx1HpH8Hx7qmtU9w97eBzwM/NbNsM8sG3g+cF4xQth34L2CcmY072rFERESaimBbefgLxa403hfkw933E7uF4b1tPM4OYu/zqhPNJNIeVOyJnLgMYvfIlQN1wX0Hl8Rt3wH0DgZOiV830MxSANx9G7HuLP9nZj3MLMXMBpvZeW0N4+57gW8CvzKz68wsNzjeeGL3N7T0vFnAVmL3OEwD6oGRxG5WH0/sZvM5xO7jExERaYtItZUt+AlwcdxJzS8DHzezO82sEA7d2zeopQOYWW/gWmI9X0RCp2JP5AS5ewXwOWKjWe4hdl/bE3HbVwIPAOuCbiclwEPB5l1mNj9Y/iixxnB5cJyHies+0sZMPyA2YuaXiTWWO4h1dbkDePUoT707eM7NxEYge9vdtzf+AL8AbjBNkCsiIm0QxbaymYzlwJ+ArwWPXwYuIDaYzFtBt9FniE3H8PO4p57VOIoosZE4y4HPtkcmkRNlGildREREREQk+ejKnoiIiIiISBJSsSciIiIiIpKEVOyJiIiIiIgkIRV7IiIiIiIiSUjFnoiIiIiISBLq1MOn9+nTxwcOHBh2DBER6QDz5s3b6e4FYefoLNRGioh0DUdrHzt1sTdw4EDmzp0bdgwREekAZrYx7AydidpIEZGu4Wjto7pxioiIiIiIJCEVeyIiIiIiIklIxZ6IiIiIiEgSUrEnIiIiIiKShFTsiYiIiIiIJCEVeyIiIiIiIklIxZ6IiIiIiEgS6tTz7J2o6Qu2cPfMVWzdW0VJfja3XzqcaRNKw44lIiISKrWPIiLJocsWe9MXbOGuR5dQVVsPwJa9Vdz16BIANWgiItJlqX0UEUkeXbYb590zVx1qyBpV1dZz98xVISUSEREJn9pHEZHk0WWLva17q9q0XkREpD2Z2QYzW2JmC81sbrCul5nNMrPVwe+ewXozs5+Z2RozW2xmpyYql9pHEZHk0WWLvZL87DatFxERSYDz3X28u08MHt8JPOfuQ4HngscAlwNDg5+bgV8nKpDaRxGR5NFli73bLx1OdnrqYeuy01O5/dLhISUSERHhGuD+YPl+YFrc+j95zGtAvpkVJyKA2kcRkeTRZYu9aRNK+e57x1DUIxOAHllpfPe9Y3TzuYiIdBQHnjWzeWZ2c7CuyN23BcvbgaJguRTYFPfczcG6dtfYPpbGXcn7ypUj1D6KiHRCXXY0Tog1aNMmlHL1L17GXaOMiYhIhzrH3beYWSEwy8xWxm90dzczb8sBg6LxZoABAwYcd7DG9nHl9ne47CdzaGg47kOJiEiIuuyVvXjvn9ifoUU51NarNRMRkY7h7luC32XAY8AkYEdj98zgd1mw+xagf9zT+wXrmh7zHnef6O4TCwoKTjjjiL49uHJsMXnZ6Sd8LBER6Xgq9oAPn3kSP3r/eNJT9XGIiEjimVl3M8ttXAYuAZYCTwA3BrvdCDweLD8BfDQYlfNMYF9cd8+E+uWHTlXPFxGRTqpLd+OM5+6sLd/PkMKcsKOIiEjyKwIeMzOItcV/c/dnzOxN4EEz+wSwEXh/sP9TwBXAGuAA8LGODFtVU8+mPQcYVpTbkS8rIiInSMVe4KF5m/nyw4t57rbzGFyggk9ERBLH3dcB45pZvwu4sJn1DtzaAdGa9em/zmPjrgPMvu08ggJVREQ6AfVbDJw7tAAzeHJxh/SKERER6TQuH92X9Tv3s2jzvrCjiIhIG6jYC/TNy+L0k3oxY/HWsKOIiIhEymWji8lITWH6giPGhBERkQhTsRdn6rhi3tpRyVs7KsKOIiIiEhl52elceEohMxZvpU4jV4uIdBoq9uJcPrqYFIMZ6sopIiJymGvGl7KzsoY31u8OO4qIiLSSBmiJU5CbyZ8+fgYTBuSHHUVERCRSzh9RwIzPnsOokh5hRxERkVZSsdfEOUP7hB1BREQkcjLTUhldmhd2DBERaQN142zG7+es4y+vbQw7hoiISKRUHKzl9ocW8cxS3e4gItIZqNhrxotvlXPPS+uITWskIiIiAN0z0nh17S7+8eamsKOIiEgrqNhrxlVjS3h79wGWbNF8QiIiIo1SUoyrx5fw0uqd7KysDjuOiIgcg4q9Zlwyqoi0FNOonCIiIk1MG19KfYMzY5HmpRURiToVe83I75bBe4b24cnF29SVU0REJM7wvrmcUtyD6QtV7ImIRJ2KvRZMm1DKyQXd2XugNuwoIiIikfLRs05iwoB8TbAuIhJxmnqhBdeML+Wa8aVhxxAREYmcD04aEHYEERFpBV3ZO4ayioM0NKgrp4iISLyGBmfuht263UFEJMJU7B3FnNXlnPGd55i7cU/YUURERCLl8UVbuO43/2bRZo1cLSISVSr2jmLCgJ5kpKbw5GLdhC4iIhLvwlOKyEhLYfqCLWFHERGRFqjYO4qczDTOH17IU0u3U6+unCIiIof0yErnolMKmbF4qwZqERGJKBV7xzB1XDHlFdW8vn5X2FFEREQi5ZrxpeysrOHlNTvDjiIiIs1QsXcMF4woJDs9lSc1wbqIiMhhpgwvIC87nZnLtocdRUREmqGpF46hW0Yav7xhAqcU9wg7ioiISKRkpqXy4C1ncXJB97CjiIhIM1TstcIFI4rCjiAiIhJJw/vmhh1BRERaoG6crfTk4m3c/+qGsGOIiIhEzh9eXs9djy4OO4aIiDShYq+VZi3fzo//9Ra1GnFMRETkMDsrq3lw7mZ2VlaHHUVEROKo2GulqWNL2HugViOOiYiINDFtQin1Dc6MRZqXVkQkSlTstdJ7hvUhNyuNGYs0KqeIiEi8YUW5jCzuwWMLVeyJiESJir1WykxL5ZKRfXl2+Xaq6+rDjiMiIhIp0yaUsGjTXtbv3B92FBERCSSs2DOz/mb2vJktN7NlZvb5YH0vM5tlZquD3z2D9WZmPzOzNWa22MxOTVS24zV1XDGl+dls23sw7CgiIiKRcvW4Uq4cU0x9g+5tFxGJikROvVAH3Obu880sF5hnZrOAm4Dn3P17ZnYncCdwB3A5MDT4OQP4dfA7MqYMK+D84YVhxxAREYmcvnlZ/PKGyJ2nFRHp0hJ2Zc/dt7n7/GC5AlgBlALXAPcHu90PTAuWrwH+5DGvAflmVpyofMfDzACoqqmnpk5nLkVERJpaW17Jpt0Hwo4hIiJ00D17ZjYQmAC8DhS5e+MoJ9uBxhnLS4FNcU/bHKyLlFXbKzjtW7OYvbIs7CgiIiKRsr+6jit+Ood7X14fdhQREaEDij0zywEeAb7g7u/Eb3N3B7yNx7vZzOaa2dzy8vJ2TNo6gwu6k52eyozFGnFMREQkXvfMNC48pZB/LtqqeWlFRCIgocWemaUTK/T+6u6PBqt3NHbPDH43XiLbAvSPe3q/YN1h3P0ed5/o7hMLCgoSF74FaakpXDa6L8+tKONATV2Hv76IiEiUTRtfyq79NZqXVkQkAhI5GqcB9wIr3P1HcZueAG4Mlm8EHo9b/9FgVM4zgX1x3T0jZerYEqpq69WVU0REpIkpwwvJy05n+oIjzteKiEgHS+SVvcnAR4ALzGxh8HMF8D3gYjNbDVwUPAZ4ClgHrAF+B3wmgdlOyKRBvSjIzeTJxZGsRUVEREKTkZbClWOLefGtcg1mJiISsoRNveDuLwPWwuYLm9nfgVsTlac9paYY37l2DCX5WWFHERERiZwvXDiUOy4bQUZah4wDJyIiLUjkPHtJ7eKRRcfeSUREpAsq7KGToSIiUaBTbifgjfW7uf/VDWHHEBERiZx5G/fwvl+/SnlFddhRRES6LBV7J2Dmsu18+8kVvHOwNuwoIiIikdIjK415G/doqiIRkRCp2DsBV44tpqa+gVnLdoQdRUREJFKGFuUysriHRuUUEQmRir0TMKF/PqX52TprKSIi0oxpE0pYtHkf68orw44iItIlqdg7AWbG1LHFzFm9k30H1JVTREQk3tXjSjGD6Qt1UlREJAwq9k7Q1LEl9OyewdqdOmspIiISr29eFp+YPIjhRblhRxER6ZI09cIJGl3ag9fuupDUlJamFBQREem6vjp1ZNgRRES6LF3ZO0FmRmqK0dDg1NQ1hB1HREQkcvbsr+GN9bvDjiEi0uWo2GsH5RXVTP7+bB6atynsKCIiIpHz/x5fyqf+Mo/aep0UFRHpSCr22kGfnAyy01OZsWhb2FFEREQi5+pxJezeX8PLq3eGHUVEpEtRsdcOGkflfH39LsoqDoYdR0REJFKmDC8kv1s6j2nOPRGRDqVir51MHVdCg8PTS7aHHUVERDoJM0s1swVmNiN4PMjMXjezNWb2DzPLCNZnBo/XBNsHhpm7rTLSUrhiTDHPLt9OZXVd2HFERLoMFXvtZFhRLsOKcnhysbpyiohIq30eWBH3+PvAj919CLAH+ESw/hPAnmD9j4P9OpVrJ5RysLaBN9bvCjuKiEiXoWKvHd1+6Qg+ff7gsGOIiEgnYGb9gCuB3wePDbgAeDjY5X5gWrB8TfCYYPuFwf6dxmkDevLS7edzwYiisKOIiHQZmmevHV08Ug2YiIi02k+ALwONM473Bva6e2M/x81AabBcCmwCcPc6M9sX7N9pRjxJSTEG9O4WdgwRkS5FV/ba2ZqySv787w1hxxARkQgzs6lAmbvPa+fj3mxmc81sbnl5eXseul0crK3nlj/P5c+vbQw7iohIl6Bir53NXLad//f4MrbsrQo7ioiIRNdk4Goz2wD8nVj3zZ8C+WbW2OumH9A4fOUWoD9AsD0POOLmN3e/x90nuvvEgoKCxL6D45CVnsrmPVU8NFfz0oqIdAQVe+1s6thiAJ5cvDXkJCIiElXufpe793P3gcD1wGx3vwF4Hrgu2O1G4PFg+YngMcH22e7uHRi53UwbX8rizftYW14ZdhQRkaSnYq+dndS7O2NK85ihUTlFRKTt7gC+aGZriN2Td2+w/l6gd7D+i8CdIeU7YVePL8EMHteceyIiCadiLwGmji1m8eZ9vL3rQNhRREQk4tz9BXefGiyvc/dJ7j7E3f/D3auD9QeDx0OC7evCTX38inpkcfbg3kxfuJVOenFSRKTTULGXAFeOLSY7PZXl2/aFHUVERCRybjxrINdOKKW6riHsKCIiSU1TLyRAv57dWPC1i8lKTw07ioiISORcMqovl4zqG3YMEZGkpyt7CdJY6DU0qIuKiIhIUzV1DcxavoPael3dExFJFBV7CXKwtp6rfv4yv3x+TdhRREREIuelt8r55J/mMmd19OYDFBFJFir2EiQrPZXMtBSNyikiItKMc4cVkN8tnccWaKoiEZFEUbGXQFPHFrNqRwWrd1SEHUVERCRSMtJSuHJMMbOWb6eyui7sOCIiSUnFXgJdMaYYM3R1T0REpBnXTijlYG0DM5duDzuKiEhSUrGXQIU9sjhjUC9mLNZcQiIiIk2ddlJP+vXM5pU1O8OOIiKSlDT1QoLdcu5gyiuraXBItbDTiIiIRIeZ8dCnzqJvj6ywo4iIJCUVewl2/ojCsCOIiIhEVnFedtgRRESSlrpxdoDyimoefHOTunKKiIg043cvrePGP7wRdgwRkaSjYq8DzF65gy8/sphlW98JO4qIiEjkmMGLb5Wztrwy7CgiIklFxV4HuHRUX9JSjH8u1lxCIiIiTV09roQUg8cXbAk7iohIUlGx1wHyu2VwztA+PLl4m7pyioiINFHYI4uzB/dh+kKNXi0i0p5U7HWQqWNL2LynikWb94UdRUREJHKmTSjl7d0HmP/23rCjiIgkDRV7HeTikUVkpKWw4O09YUcRERGJnEtHFXHDGQPIy04PO4qISNLQ1AsdJC87nTe/chF53dSIiYiINJWblc63rx0TdgwRkaSiK3sdqLHQ0/0IIiIiR3J3Fm7ay8rtGr1aRKQ9qNjrQO7Op/48j+8+vTLsKCIiIpFT1+B87I9v8Mvn14YdRUQkKajY60BmhuNMX7CF+gZdQaALowAAIABJREFU3RMREYmXnprClWOLmbV8O5XVdWHHERHp9FTsdbCpY0soq6jmzQ27w44iIiISOddOKOVgbQMzl24PO4qISKenYq+DXXhKIVnpKTy5eFvYUURERCLn1AE96d8rm+kLNcG6iMiJUrHXwbplpHHhiCKeXrqNuvqGsOOIiIhEipkxbXwpS7bs40CNunKKiJwITb0QghvOGMDIkh7U1jtpqWGnERERiZZPnnsyt54/hKx0NZIiIidCxV4Izh7Sh7OH9Ak7hoiISCT1yHp3qiIzCzmNiEjnpW6cIamqqeepJduoVVdOERGRIyzctJeLfvQia8oqw44iItJpqdgLyctrdvKZv87nlTU7w44iIiISOSX5WazfuZ/HNVCLiMhxU7EXknOH9SE3M40ZGpVTRETkCIW5WUwe0ofpC7fgrrlpRUSOh4q9kGSmpXLxqCJmLttOTZ26coqIiDQ1bXwpm3ZXMf/tPWFHERHplFTsheiqsSVUHKxjzurysKOIiIhEzqWj+5KVnsL0BVvDjiIi0imp2AvR5CF9yMtO59W1u8KOIiIiEjk5mWncdvFwzhtWEHYUEZFOKWFTL5jZH4CpQJm7jw7WfQP4JNB4Kesr7v5UsO0u4BNAPfA5d5+ZqGxRkZGWwjNfeA99e2SFHUVERCSSPnnuyWFHEBHptBJ5Ze8+4LJm1v/Y3ccHP42F3kjgemBU8JxfmVmXmEm1OC9bcwiJiIgcxeY9B3hm6fawY4iIdDoJK/bc/SVgdyt3vwb4u7tXu/t6YA0wKVHZouY7T63gq9OXhB1DREQkkn774jo+//cFVBysDTuKiEinEsY9e/9pZovN7A9m1jNYVwpsittnc7DuCGZ2s5nNNbO55eXJMbBJxcE6Hp2/haqa+rCjiIiIRM60CSVU1zUwc9mOsKOIiHQqHV3s/RoYDIwHtgH/19YDuPs97j7R3ScWFCTHDdtXjS3mQE09z68qCzuKiIhI5Jw6oCf9e2VrgnURkTbq0GLP3Xe4e727NwC/492umluA/nG79gvWdQlnnNybPjkZzFisoaVFRESaMjOuHV/KK2t2UvbOwbDjiIh0Gh1a7JlZcdzDa4GlwfITwPVmlmlmg4ChwBsdmS1MqSnG5aOLmb2yjP3VdWHHERERiZxrJpRiZszbqAnWRURaK5FTLzwATAH6mNlm4OvAFDMbDziwAbgFwN2XmdmDwHKgDrjV3bvUDWzvPbWU1BSjqrae7pkJ+2MRERHplAYX5DDvqxeR3y0j7CgiIp1GwqoKd/9gM6vvPcr+3wa+nag8UTdhQE8mDOh57B1FRES6qMZCz901bZGISCvoElKENDQ4czfuYWRJD3J0dU9EROQwdfUNXP7Tl9i+r5rK6jpK8rO5/dLhTJvQ7ADeIiJdXhhTL0gLFm7ey/t/+2+eXaaJY0VERJqasXgba8v3U1FdhwNb9lZx16NLmL6gy4zpJiLSJir2ImR8v3xK8rKYsXhb2FFEREQi5+6Zq2jww9dV1dZz98xV4QQSEYk4FXsRkpJiXDm2mDmry9l3oDbsOCIiIpGydW9Vm9aLiHR1xyz2zKzIzO41s6eDxyPN7BOJj9Y1TR1bQm29M3O5unKKiESd2siOVZKf3ab1IiJdXWuu7N0HzARKgsdvAV9IVKCubmy/PPr3ymb2irKwo4iIyLHdh9rIDnP7pcPJTk89bF12eiq3Xzo8pEQiItHWmmKvj7s/CDQAuHsd0KXmwOtIZsafPn4GP//QhLCjiIjIsamN7EDTJpTy3feOoTQ/GwNK87P57nvHaDROEZEWtGZ8//1m1pvYROiY2ZnAvoSm6uIG9ekedgQREWmd42ojzSwLeAnIJNYWP+zuXzezQcDfgd7APOAj7l5jZpnAn4DTgF3AB9x9QwLeT+RNm1B6qLh7YVUZ9768nivGFJORpmEIRESaas034xeBJ4DBZvYKscbmswlNJdz/6gbueHhx2DFEROTojreNrAYucPdxwHjgsqBQ/D7wY3cfAuwBGu//+wSwJ1j/42C/Lq/BnTmrd/KPN98OO4qISCQds9hz9/nAecDZwC3AKHdXFZJgOyureWjeJsorqsOOIiIiLTjeNtJjKoOH6cGPAxcADwfr7wemBcvXBI8Jtl9oZtYub6ITO394IZMG9uKnz61hf3Vd2HFERCKnNaNx3grkuPsyd18K5JjZZxIfrWubOraEBodnNMG6iEhknUgbaWapZrYQKANmAWuBvcF9fwCbgcab0UqBTXDovsB9xLp6dmlmxh2Xj2BnZTV/eHl92HFERCKnNd04P+nuexsfuPse4JOJiyQAw/vmMrQwhxmLtoYdRUREWnbcbaS717v7eKAfMAkYcaJhzOxmM5trZnPLy8tP9HCdwmkn9eTikUX89qV17NlfE3YcEZFIaU2xlxrfVcTMUoGMxEWSRleOLeaNDbvZ8c7BsKOIiEjzTriNDIrF54GzgHwzaxw8rR+wJVjeAvQPXiMNyCM2UEvTY93j7hPdfWJBQUFb30undcdlw/nOe8eQl50edhQRkUhpTbH3DPAPM7vQzC4EHgjWSYJdNa6EK0YXc6BGo3iLiETUcbWRZlZgZvnBcjZwMbCCWNF3XbDbjcDjwfITwWOC7bPd3dvtXXRyQwpzuXpcCSkpXf42RhGRw7Rm6oU7iN10/ung8Szg9wlLJIcMLsjhlzecGnYMERFp2fG2kcXA/cGVwBTgQXefYWbLgb+b2beABcC9wf73An82szXAbuD6dnwPSeP3c9axZW8VX79qVNhRREQi4ZjFnrs3AL8OfiQE63fuJy87nV7d1XtWRCRKjreNDEbsnNDM+nXE7t9ruv4g8B/HGbPL2PHOQe5/dQMfnDSAYUW5YccREQlda0bjnGxms8zsLTNbZ2brzWxdR4QT2LK3ivN/+AKPzNscdhQREWlCbWS0fGbKELpnpPGDZ1aFHUVEJBJac8/evcCPgHOA04GJwW/pAKX52Ywu7cGMJdvCjiIiIkdSGxkhPbtncMt5J/OvFTuYu2F32HFERELXmmJvn7s/7e5l7r6r8SfhyeSQqWNLWLRpL5t2Hwg7ioiIHE5tZMR8/JxBFORm8v1nVqIxbESkq2tNsfe8md1tZmeZ2amNPwlPJodcOaYYgBmLdXVPRCRi1EZGTLeMNL5z7RjuvHwEcbNiiIh0Sa0ZjfOM4PfEuHUOXND+caQ5/Xt1Y1z/fJ5Ztp1PTxkcdhwREXmX2sgIunhkUdgRREQioTWjcZ7fEUHk6O6+bixFuVlhxxARkThqI6Orqqae/31yOZMG9mLahNKw44iIhKI1V/YwsyuBUcChasPd/ydRoeRIGkJaRCSa1EZGU2ZaCos37+XFVeVcPqYvmWmpYUcSEelwrZl64TfAB4DPAkZsnp+TEpxLmvHM0m186aFFYccQEZGA2sjoSkkx7rhsBFv2VvGX194OO46ISChaM0DL2e7+UWCPu38TOAsYlthY0pxt+w7y8LzNrCmrCDuKiIjEqI2MsPcMLWDykN788vk1VBysDTuOiEiHa02xVxX8PmBmJUAtUJy4SNKSK8YUY6ZROUVEIkRtZMTdcdkIdu+v4Xcvaa57Eel6WlPszTCzfOBuYD6wAXggkaGkeUU9sjh9YC9mLN6muYNERKJBbWTEje2Xz/9eM4r/mNg/7CgiIh2uNaNx/m+w+IiZzQCy3H1fYmNJS64aW8z/e3wZq3ZUMKJvj7DjiIh0aWojO4ePnDUw7AgiIqFosdgzswvcfbaZvbeZbbj7o4mNJs25bHQx/1y8jQM19WFHERHpstRGdj4bd+3nm/9czjeuGsWA3t3CjiMi0iGOdmXvPGA2cFUz2xxQQxaCgtxMHrzlrLBjiIh0dWojO5ms9FReXbuT/5u1ip9ePyHsOCIiHaLFYs/dv25mKcDT7v5gB2aSVth7oIa6BqdPTmbYUUREuhy1kZ1PUY8sPj55EL96YS03n3syo0rywo4kIpJwRx2gxd0bgC93UBZppf3VdZz53ef4/Zz1YUcREemy1EZ2PrecN5i87HR+8MyqsKOIiHSI1ozG+S8z+5KZ9TezXo0/CU8mLeqemcakQb15cslWjcopIhIutZGdSF52OreeP5gX3yrn32t3hR1HRCThjjkaJ/CB4PetcescOLn940hrTR1TzJcfWczizfsY1z8/7DgiIl2V2shO5qNnDaSuwRldqhGtRST5tWbqhUEdEUTa5tJRffnv6UuYsXirij0RkZCojex8stJT+f/bu/PwqOqz/+PvezLZE5IASSBhky3IjoKigoIboCgurdUuajdtpVZrq9W2z2O3p9ra1drWuv1c6lL3XRBRXEFlk30TAQkQwhICIevM9/fHnMQEEiSY5Ewmn9d1zZUzZ86cuecknJv7nO9y1YT+fochItImDufOHmY2FBgMJNWuc8492FpByefLSIln/IBsXlqylZ+ddTRm5ndIIiIdknJk+/Teuh08MHcD//jqMQTjDqdXi4hI+/O5xZ6Z3QxMIJLIXgamAO8ASmQ+u2FyAcnxcSr0RER8ohzZfpVW1DBzeRFPLdzMV8b08jscEZFWcTiXsr4EnAZsc859ExgBaLziKDCoWyd6d0n1OwwRkY5MObKdmjQkl1G9MvnLrLVUVIf8DkdEpFUcTrFX4Q0vXWNmnYDtQM/WDUsO1/wNu7jhyY8IhzUqp4iID5Qj2ykz46eTB7GttIIH3tvgdzgiIq2iyWLPzP5hZuOAD8wsE7gbWAAsBOa2UXzyOQpLynl8/mbmb9ztdygiIh2GcmRsGNu3CxMKsvnnnI/ZU17tdzgiIi3uUH321gC3AXlAGfAocAbQyTm3pA1ik8Nw+tG5JMUHeHHJFo47SlM7iYi0EeXIGHHjlEF89GkJqQlxfociItLimryz55z7m3PuBOBkYCdwHzADON/MBrRRfPI5UhODnDooh5eXbiOkppwiIm1COTJ2DOrWia+M6aUROUUkJn3umc05t9E593vn3CjgEuA8YFWrRyaHberwPHbsq+T99Tv9DkVEpENRjowdD83byB9m6FcnIrHlc4s9Mwua2Tlm9jDwCrAauKDVI5PDNrEghyF5nSir0mhiIiJtSTkydny8fR//fms9Hxfv8zsUEZEWc6gBWs4ws/uAzcB3gZeAfs65i51zz7VVgPL5khPieOmH4zljcK7foYiIdAjKkbHnB6f2JykY4E+vrvY7FBGRFnOoO3s3Ae8BRzvnznXOPeKcK2ujuOQIVNWE2VVW5XcYIiIdgXJkjOmalsh3xvfl5aXb+OjTEr/DERFpEYcaoOVU59w9zjmN6d8OhMOOiX+cw62vrPQ7FBGRmKccGZu+e3JfuqQm8Hv13RORGKGhp2JEIGAcf1RnZi4voqom7Hc4IiIi7U5aYpDfnDeU6RP7+x2KiEiLULEXQ84e3p095dW8u26H36GIiIi0S2cN685J/bv6HYaISItQsRdDxg/IplNSkBeWbPE7FBERkXarvCrEb15cwStLt/odiojIF6JiL4YkBANMGtKNWcuLqKzRNAwiIiJHIiEY4N11O/j9jFVUh9Q1QkTar1Yt9szsPjPbbmbL6q3rbGazzGyt9zPLW29mdruZrTOzJWZ2TGvGFqu+e3Jf7v/WGOIDquNFRESORFzAuGFyARt27uexDz/1OxwRkSPW2hXB/cDkA9bdCMx2zg0AZnvPAaYAA7zHFcC/Wjm2mDQwN51je3cmEDC/QxEREWm3JhbkMKZPFrfPXsv+qhq/wxEROSKtWuw5594Cdh2wehrwgLf8AHBevfUPuoh5QKaZdW/N+GLVuu37+OXzy6moVlNOERGRI2Fm3DhlEMV7K7nvnU/8DkdE5Ij40dYv1zlX2+N5G5DrLecD9dtKbPbWSTMVlVZw/3sbeGPVdr9DERERabeO7d2Zm6YMYsowXXsWkfbJ145dzjkHuOa8x8yuMLP5Zja/uLi4lSJr344/qjNd0xJ4cYlGERMREfkirjylH/2y0/wOQ0TkiPhR7BXVNs/0ftbefioEetbbroe3rgHn3F3OudHOudHZ2dmtHmx7FIwLMHloN2avKqKsUv0MREREvojNu/cz/ZGFFJaU+x2KiEiz+FHsPQ9c5i1fBjxXb/2l3qicY4E99Zp7SjNNHZ5HRXWY19WUU0RE5AsxM2atKOIvs9b4HYqISLO09tQLjwJzgQIz22xm3wZuBc4ws7XA6d5zgJeB9cA64G7gqtaMLdaN6dOZgblplJRX+x2KiIhIu5afmcylY3vz9MLNrCna63c4IiKHLdiaO3fOXdLES6c1sq0DprdmPB1JXMCYee3JmGkKBhERkS9q+sT+/PfDT/nDjNXcc9lov8MRETksmnk7hpkZzjlKK3R3T0RE5IvISk3gylP68trKIuZvOHBWKRGR6NSqd/bEf5fcPY+0xCD3XDbG71BERETatW+NO4rKmrBG5xSRdkN39mLc0LwM3lxTzB713RMREflCUhKC/PjMArJSE/wORUTksKjYi3FnD+9Odcjx6vJtfociIiISEz74ZBfXPraIULhZUwWLiLQ5FXsxbmTPTHpkJWuCdRGRKGJmPc3sDTNbYWbLzewab31nM5tlZmu9n1neejOz281snZktMbNj/P0GHVtRaQXPLt7Cs4sOmg5YRCSqqNiLcWbG2cO78+66Hewuq/I7HBERiagBfuycGwyMBaab2WDgRmC2c24AMNt7DjAFGOA9rgD+1fYhS62zh3VnaH4n/jxrDZU1Ib/DERFpkoq9DuDiMb2446ujSEmM8zsUEREBnHNbnXMLveW9wEogH5gGPOBt9gBwnrc8DXjQRcwDMs2sexuHLZ5AwPjp5EEUlpTz8LxNfocjItIkFXsdwFFdU5k8tDuJQRV7IiLRxsz6AKOA94Fc51xtu/ttQK63nA98Wu9tm711B+7rCjObb2bzi4uLWy1mgfEDsjmpfxfueGMdezXFkYhEKRV7HcT20gr+PnstO/dV+h2KiIh4zCwNeAq41jlXWv8155wDmjUCiHPuLufcaOfc6Ozs7BaMVBpz05Sjuea0AbqYKiJRS/PsdRA7y6r406w1ZKUm8PWxvf0OR0SkwzOzeCKF3sPOuae91UVm1t05t9VrprndW18I9Kz39h7eOvHR0PwMhuZn+B2GiEiTdGevgxjULZ2+2am8uGSL36GIiHR4ZmbAvcBK59yf6730PHCZt3wZ8Fy99Zd6o3KOBfbUa+4pPnti/qfcPnut32GIiBxExV4HYWZMHZ7H+5/sYntphd/hiIh0dCcB3wBONbPF3uMs4FbgDDNbC5zuPQd4GVgPrAPuBq7yIWZpwsJNJfz99bVs2rnf71BERBpQM84O5Jzh3bl99lpeWbaNy07s43c4IiIdlnPuHcCaePm0RrZ3wPRWDUqO2LWnD+CJ+ZuY9Ne3qKgOkZeZzPWTCjhv1EFj6IiItCkVex3IgNx0huZ3okh39kRERFrM3I93AkZ5dWTOvcKScm56eimACj4R8ZWKvQ7muenjiAs0dTFZREREmuu2maupCTccOLW8OsRtM1er2BMRX6nPXgdTW+hV1oR8jkRERCQ2bCkpb9Z6EZG2omKvA7rxqSVcctc8v8MQERGJCXmZyY2u75KW0MaRiIg0pGKvA+rVJYWFm0rYvFujhomIiHxR108qIDm+4cTqBuwuq+KFjzTlkYj4R8VeBzR1WB4ALy3RFE0iIiJf1Hmj8rnlgmHkZyZjQH5mMr89byjH9M7i6kcX8Y831hEZUFVEpG1pgJYOqFeXFEb0yODFJVu58pR+focjIiLS7p03Kv+gwVguPLYHNzy5hNtmrmbTzv3ceuEwzDRImoi0Hd3Z66CmDs9jaeEeNu4s8zsUERGRmJQUH8ffLh7JD0/tT35Wsgo9EWlzurPXQZ0zIo+k+ACZKeo8LiIi0lrMjOvOLKh7vmDjbnLSE+nZOcXHqESko1Cx10F1y0jiGyf08TsMERGRDqMmFOa6xxdTVlnDPZeNYWTPTL9DEpEYp2acHdi+yhoe+2ATn+7SqJwiIiKtLRgX4N7LxpCcEMfFd81lxrJtfockIjFOxV4Htq+ihpueWcoziwr9DkVERKRD6J+TxjNXncTR3Tvx/YcXcPdb6zVSp4i0GhV7HVi3jCTG9O7Mi0s0B5CIiEhb6ZqWyKPfHcuUod1Y/GkJqvVEpLWo2Ovgpo7ozpqifawp2ut3KCIiIh1GUnwcd1xyDH/+yggCAWPrnnL2VlT7HZaIxBgVex3clKHdCRi8qAnWRURE2lQgYCQG4wiFHd+6fz5fvnMuW0rK/Q5LRGKIir0OLjs9kbF9u7C+eJ/foYiIiHRIcQHjpimD2Ly7nPP/+S7LCvf4HZKIxAgVe8J9l4/hjq8e43cYIiIiHdbJA7N56vsnEmfGRf+ey+yVRX6HJCIxQMWekBQfB0A4rB7iIiIifinols6z00+iX3Yaf5u9lpDysoh8QZpUXQC44/W1vPDRVmZcOx4z8zscERGRDimnUxL/vXIs+ypriAsYFdUh4uMCxAWUm0Wk+XRnT4BI373VRXtZqn4CIiIivkpJCJKTnkQ47PjBI4u48qEF7K+q8TssEWmHVOwJAJOGdCMYMI3KKSIiEiUCAWNc/y68vqqIi/49l+2lFX6HJCLtjIo9ASAzJYHxA7ry0pKtOM3uKiIiEhUuP+ko7r50NOuLyzjvH++yalup3yGJSDuiYk/qTB2eR2FJOYs+LfE7FBEREfGcdnQuj195AiHn+N5DC6gJhf0OSUTaCQ3QInXOGJLLD3f2p1unJL9DERERkXqG5mfw7PST2LG3imBcAOecBlQTkc+lYk/qdEqKp292Gl++cy5bSsrJy0zm+kkFnDcq3+/QREREOrzuGcl0z0gG4E+vrqEm7LhhUgEBjdQpIk1QsSd1nl1UyI1PL6GiOtI8pLCknJueXgqggk9ERCRKOOcoKa/iP/M2sWlXGX++aGTdnLkiIvWpz57UuW3m6rpCr1Z5dYj/e3mlJlwXERGJEmbGb6YN5RdnH80ry7Zxyd3z2LGv0u+wRCQKqdiTOltKyhtdX7y3kmcWFQKwt6Kakv1VbRmWiIiIHMDM+M74vvzra8ewcmspF/17LlU1GrhFRBpSM06pk5eZTGEjBV9WSjynFGQDkaaeNz+/nJE9M5lQkMOEgmyG5mWov4CIiIgPJg/tzmMZyWzatZ+EoK7hi0hDOitInesnFZB8QJv/5Pg4bj5nCF3TEgEY27cLPzh1AKGw4y+vreHcO97luN/NprwqBEBIzT1FRETa1MiemZw7Ig+Al5Zs5ckFm32OSESihe7sSZ3aQVhum7m6ydE4B+Smc90Z6Vx3xkB27qvk7bU7+Lh4H8kJkSLxOw98yO791UwoyGZCQQ7D8jOI010/ERGRVuec4+mFm5m9ajubdpbxozMGanoGkQ7OnGu/d2JGjx7t5s+f73cYUs+/5nzMzOXb+GhzCc5B59QELj+xDz88bYDfoYlIO2dmC5xzo/2Oo71QjuyYqmrC/OLZpTw+fzPTRubxhy8NJzGokTpFYtmh8qPu7EmL+v6Efnx/Qj92lVXx9tpi5qwuJj0p8mdWVlnDN+59n3EDsplQkM2IHpm66yciItKCEoIBfn/hcHp3SeW2matZunkPFdUhtu6p0Py5Ih2Qij1pFZ1TE5g2Mp9pIz9LKEWlFQDc8fpabp+9lqyUeMYPyGb6xP4UdEv3K1QREZGYYmZMn9ifotIKHnl/EzVef3rNnyvS8WiAFmkzfbPTePqqk1jwizO4/ZJRTByUw3sf76gb1OW9j3fwp1dXs2DjLg30IiIi8gXNXrm9rtCrVV4d4pZXVvoUkYi0Nd3ZkzaXlZrAuSPyOHdEHuGwo7bv+KJNJfzjjXX8/fV1ZCTHM35AVyYU5HD+qHw19xQREWmmpubPLSqtZPwfXufEvl353QXDlGNFYpiKPfFV/fn5pk/sz9eP783b6yJ9/d5cU8yiTSVceEykqcnLS7eSnZ7IqJ6ZBON0U1pERORQmpo/NyM5yNHdOvHJzrK6Qu9/nl1GTTjM2L5dOKFvF3I6JbV1uCLSClTsSVTJSIln6vA8pg6P3PXbvrcSM8M5x69fWMG20go6JQUZPyCbUwqymTAwWwlJRESkEddPKuCmp5dSXh2qW5ccH8evzh16UJ+9fZU1vLayiEc/+BSAftmpXHJcL74zvm+bxiwiLcu3Ys/MNgB7gRBQ45wbbWadgf8CfYANwEXOud1+xSj+CgSMbhmRQs7MmPmjk3l33Q7eWLWdN9cU89LSrVx6Qm9+PW0oobBjwcbdjOqVSbzu+omIiBzW/Lm1/vKVkYTCjuVb9jBv/U7mfryTypowABXVIS7813sc2zuLE/p24fi+XeicmtCm30VEjoxv8+x5xd5o59yOeuv+AOxyzt1qZjcCWc65nza1D80h1HE551i5dS/JCXEc1TWVRZt2c/4/3yM9Kci4/l3rJnXP1V0/kZihefaaRzlSWsqWknJufHop8zfsYn9V5C7hoG7p/OLswYwb0NXn6ESkPc2zNw2Y4C0/AMwBmiz2pOMyMwbndap7PiA3nX997Zi6vn6vLNsGwBPfO4ExfTqzt6KapPg43fUTERFpprzMZB781nFUh8Is2VzCvPW7mPvxTjolR/4b+dqKIv7y2pq6/n7H9e1Mp6R4n6MWEfC32HPAq2bmgH875+4Ccp1zW73XtwG5vkUn7UpaYpApw7ozZVh3nHOsLtrLnNXFDMvPAODONz/mwfc2cpJ31++Ugmy6ZyT7HLWIiEj7ER8X4NjenTm2d2emT+z/2fpggPSkIA/N28i973xCwGBIXgYPfOs4Oqcm4JzDTCN+ivjBz2JvnHOu0MxygFlmtqr+i8455xWCDZjZFcAVAL169WqbSKVdMTMGdevEoG6f3fkb1z+bXWVVzFldzIzlkbt+x/TK5Knvn1gAQpwBAAAgAElEQVQ3AIwSkYiISPOdMjCbUwZmU1EdYtGmEuau38mKLaVkpUTu7v3smWWs3FoaufPXrwuje2eRmhjk2UWFh9WfUESOnG/FnnOu0Pu53cyeAY4Disysu3Nuq5l1B7Y38r67gLsg0h+hLWOW9uuEfpEE45xj7fZ9zFm9nbLKUF2B96U759I5NaGur19+5md3/ZSMREREPl9SfFxdvq1vYG4aa4r2cs/b67nzzY8JBozh+Rms3La3bqTQwpJybnx6CYDvOVZ5X2KJLwO0mFkqEHDO7fWWZwG/Bk4DdtYboKWzc+6GpvajzufSEmpCYf73+eXMWbWdLXsqABiQk8ZVE/thWKPDVt9ywTCd+EXamAZoaR7lSIk2+6tqmL9hN3PX7+TheRsprahpdLuM5Hi+MqYnPzvraJxzfPXu90lJiCMlMUhqQhwpCUHGDejCqYNyqQ6FeW7xlsjrCXGkJgZJSYije0ZyXRNS5xrO63sozy4qVN6XdicaB2jJBZ7x7qoEgUecczPM7EPgcTP7NrARuMin+KQDCcYF+N35w3DOsW77PuasLmbOmshN5dtmrm5wwgcorw5x6yurOGdEXt1ktCIiInJoKQlBTh6YzckDs7lzzsdNbnf+qHyGeIOwVYccobBjW2kF+6tClFXWsL8qRGpiHKcOymVvRQ0/eeKjg/bxkzMH8oNTB1BYUs64379BcnwcqYmRQjElIY7pE/tzzog8tpSU88eZq0lJjCM1IcgjH2xqNO/fNnO1ij1pl3wp9pxz64ERjazfSeTunkibMzMG5KYzIDed754cmUT2uv8enEAAtpVWMPfjnYwb0JUPN+zisQ8+pUdWMvlZyfTIjPzMz0wmqNE/RUREDpKXmUxhSflB6/Mzk/nluUPqnicEAzz+vROa3E9Gcjxv3zCRsqoayipDlFeFKKuqoV92KgCpCUGuOW0A+6tqKKsKsd8rFtMSI/8F3ltRwwfelBJllTV1cwseaEsjsYq0B9E29YJIVGkqGWUkxzOoezoAW/dU8O66HRTtraB+q+jZPz6FftlpvLx0K68s20Z+ZjL5mUleIZhC/5w03RkU6aDM7D5gKrDdOTfUW9cZ+C/QB9gAXOSc222RZjB/A84C9gOXO+cW+hG3SEu5flJBo80lr59U0Kz9xAWMnp1Tmnw9KzWBH50xsMnXC7ql885PT617fuKts9lSUnHQdppEXtorFXsih9BUMvrVuUPompYIwLkj8jh3RB5VNWG27algc8l+CneX1w3ysrOsiiWbS5ixbCvVoc+qwRW/nkRKQpD73vmEuet3kp+ZHLk76N0ZHJafoRFCRWLX/cAdwIP11t0IzK7Xb/1GInPNTgEGeI/jgX95P0XardomkdE2EMoNkwYdlPeNSC7/1QvL+enkQSTFx/kXoEgzqdgTOYTmJKOEYIBeXVLo1aXhFcZvjO3NN8b2Jhx2FO+rZPPucopKK0hJiPzzq6gJsXFnGe+t20FZVSS5ZCTH89HNZwLwfy+tYHXRvgbFYO8uKYzqldWaX11EWpFz7i0z63PA6mnABG/5AWAOkWJvGvCgi4yoNs/MMmtHrm6baEVax3mj8n0v7g7UWN6/9vQBLCvcw/97dwPvrdvJnd84lqO6pvocqcjhUbEn8jlaKhkFAkZupyRyOyU1WH/VhP5cNaE/zjn2lFezeXc5peXVda/HxwXYXVbFssI97CqrAmBQt3RmXHsyANMfXsiOfZUN+gsOzE1XMSjS/uTWK+C2ERnMDCAf+LTedpu9dSr2RFpBY3n/y6N7MmFQDn96dTWZyfE+RSbSfCr2RKKEmZGZkkBmSsN+ATdMHsQNkyPL+6tq2FJSQUW95iU5nRLZvreCeR/vZFtpBWEHpw3K4d7LxwAw7Y53MLMGxeCw/AwVgyJRzDnnzKzZcyOZ2RXAFQC9evVq8bhEOrKJBTlMGJiNmVEdCvOHGav47vi+5BxwEVckmqjYE2lHUhKC9M9Ja7Du5nM+G7WsOhTpN1gTjvwf0TnH4LwMPt21nxVbSpm1ooiqmjBfO74Xo3plURMKc9zvZpPbKalBM9GxfbswrEcGtfNwHth3UBPOirSKotrmmWbWHdjurS8Eetbbroe37iDOubuAuyAyz15rBivSEdXmwxVbSnlo3kaeWljIrRcM48wh3XyOTKRxKvZEYkh8XKDBqGRmxi0XDKt7Hg47dpRVgvdfwMqaMFOHd6dwdzmbd+/n/fU72VtZw/WTChjWI4NtpRVM/OMc8jK9gWMykyktr2b2qu11w1MXlpRz41NL2FtZzbnD84kPGonBuA430qgKYGkBzwOXAbd6P5+rt/4HZvYYkYFZ9qi/noi/RvTM5MWrx3PNY4u44qEFXHJcL/5n6tF1/fFFooU5134v/I0ePdrNnz/f7zBEYsoer79gRnI82/dWcPdb6yksKadwdzmFJeXs2Ff1ufu48+vHMnloN95cU8wVD84nIS5AMM6IjwsQHxfgrxePZEyfzry9tpjbZq4mGDCCcYG67f5n6mD6Zacxb/1Onpi/mXjvvcE4IyEuwHfG9yU7PZGPPi1h7vqd3n6NYCDy8+zh3UlJCLK+eB8bdpZF3hsIkBCMbDMkrxNBry9kWVVNXVzBOCM+ECApPtCskVCfXVTY6Kitt1wwTAVfCzKzBc650X7H0RLM7FEig7F0BYqAm4FngceBXsBGIlMv7PKmXrgDmExk6oVvOuc+N/kpR4q0vqqaMH+atZq73lrPaYNyuOeyMX6HJB3QofKjLj+ISAMZ9Tqe56Qn8fOzBzd4/agbX6KpS0T/O3Uw1aEwBd0icxDmZyZz+Yl9qAqFqQk5asJhqmpcXef2+LgAXVITqA45qkNh9lfVUBN2hL1mqEWlFcxbv5PqUJhqbx/V4TAXjelJdnoiH3yyi1tfWXVQHCcPzCYlIcjzH23hr6+tPej1pb88k/S4AP+cs4673/7koNfX/+4szODm55bxxILNBAOfFZvpSfG8dt0pANw2cxXvrN3Byq17qQo1nIi3vDrEbTNXq9iTRjnnLmnipdMa2dYB01s3IhE5EgnBADdNOZpTBmbTKSmS2yqqQ8THBTpcCxeJTir2RKRZmppoPj8zmW+NO6rBuv45adx01tFN7mts3y6M7dulydenjcxn2simi6VvntSHr43tVVcs1ng/u3iT315yXC8mFOQ0LBZDYZK9OZKmDs9jQE461eHPXqsOOQJegh7btwsJwUCD/cfFfZa805PiyUxJOKjQq1VYUs5Dczdw5pBuB43CKiIisePEfl3rln/1wgrWF+/jz18ZWTfnrohf1IxTRJpFTRYPdtKtrzdaAAcDVjdYzqhemUwa0o2zh3Vv0K9SDl8sNeNsC8qRIv54csFmbn5uGYGA8bvzh3HOiDy/Q5IYd6j8GGjrYESkfTtvVD63XDCM/MxkjMgdvY5c6AFcP6mg7m5hreT4OP745RG8dt3J/OTMgdSEHLe+sopnFkUGUayoDrF8yx7a8wU3ERE52JeO7cHL14ynf04aVz+6iOv+u5i9FdWf/0aRVqA7eyIiLeBwRuMsLCknIS5AdnoiM5dv48qHFtAjK5lJQ7oxeWg3jumVpT4eh6A7e82jHCnir5pQmDveWMd973zCC1ePo3eXVL9Dkhh1qPyoYk9ExAe7y6p4dcU2Zi4v4p21O6gKhemalsDzPxhHnvp4NErFXvMoR4pEhz37q8lIicc5x3OLtzB1eHeCcWpcJy1Ho3GKiESZrNQEvjKmF18Z04u9FdXMWV3MvPU76Z4RGcjlty+uYPveSiYP7cYpA7NJTdTpWkSkPcpIiYzSOXf9Tq7972Luf28Df7t4pO70SZvQZQUREZ+lJ8Vzzog8/u/8YXXz+8UFjHfW7eCqhxcy6jez+M4DH/LKUs2jLSLSXp3Yryt3fHUU64v3cdbf3uaJ+Z+q37a0OhV7IiJR6KazjuaDn53GY1eM5avH9WLFllLe/XgHAOGw4+H3N7KlkRFARUQkek0dnseMa09mWI8Mrn9yCf/z3DK/Q5IYp3ZBIiJRKhgXqJuL8OZzBtdNd7Fiayk/fybyH4QRPTI40xvgpV92mp/hiojIYcjLTObh74zl7rfXU9At3e9wJMbpzp6ISDtgZqQkRK7PDc3PYPaPT+GGyQUA3DZzNaf96U3eWxe581dRHVLTIBGRKBYXML53Sj8mFuQAcMfra/ndyyuprAl9zjtFmkd39kRE2qF+2WlcNaE/V03oz9Y95cxaUcSxfbIA+Mcb63h6YSFnDM5l0pBujOmTpZHfRESilHOOHfuquP+9Dbyzdge3XzKS/jm64yctQ9lfRKSd656RzKUn9CExGJnYfUSPTI7u3olHPtjEJXfP47jfzeaXzy/3OUoREWmMmfHLc4dwz6Wj2VZawdm3v8NDczeohYa0CN3ZExGJMacPzuX0wbmUVdbw5ppiZi7fxr7KmrrXb3llJUPyMphYkE16UryPkYqISK3TB+cyo+d4rn9iCb98YQUn9OuiO3zyhanYExGJUamJQc4a1p2zhnWvW7envJqnFhTy7zfXkxAX4KT+XZg0pBtnDM6lS1qij9GKiEhOehL3f3MMSwv31BV6a4r2MjBXRZ8cGTXjFBHpQDKS43n/Z6fxxPdO4NITerOueB83Pr2U11YWAbBnfzWbd+/3OUoRkY7LzBjeIxOAd9ft4My/vMX/PLuM8ioN3iLNpzt7IiIdTFzAGNOnM2P6dObnZx/Niq2l9MhKAeC5jwr53+eWMzS/E5MGd2PS0G4MyEmrm+xdRETazrG9s/j2uKO4951PmLt+J+eNyuPR9z9lS0k5eZnJXD+pgPNG5fsdpkQxa8+dP0ePHu3mz5/vdxgiIjFj8+79vLRkKzOXb2PhphIA+man8vIPx5MUH+drbGa2wDk32tcg2hHlSJHY8fbaYq76zwL2Vja8u5cUH+CW84dx/jE9fIos4tlFhdw2c7WKUJ8cKj/qzp6IiNTpkZXClaf048pT+lFUWsGrK4r4pLisrtC74cmPSIqPY9KQbhx3VGfiNaWDiEirGz8gm7Sk+IOKvYrqMD96/CNufHopicEAifFx/P7CYZw6KJdFm3bzqxdW1K1PDAZIDAaYPrE/R3fvxKptpTy7aIv3eoDEYGSbSUO6kZ2eyJaSclYX7fXeF3ktKT5Az84pJAbjqKwJ4Ry8smQrP3t2GeXVkdgKS8q56emlACr4ooCKPRERaVRupyS+MbZ33XPnHOXVYZ7/aAsPzt1IZko8pw3K5aLRPTi+bxcfIxURiX3b9lQ0+drlJ/WhsjpMZU2YnPQkAAJmpCcFqawJs6e8msrqEFU1YfZXRUZnXl9cxn3vfkJVTbjBvoblZ5Cdnshba4q50Sva6nvtupPpn5POf+Zt4jcvrmg0nvLqEL9+cQXvf7KLzqnxZKUkkJmSQOfUeMb1zyYhGKCyJkR8IEAgoG4CrUnFnoiIHBYz4++XjKK8KsSba4p5dfk2Zq3YxlFdUzi+bxf2V9Uwa0UREwfl0CkpXs16RERaUF5mMoUl5Qetz89M5qYpRx+0fkTPTB769vFN7q92tOZw2FEVihSKlTUhMpMTADhjcC4Du6V7RWSIypowVTVhumUkAzC6dxY/nTyI389Y1ej+d5VVMWvFNnbvryYU/qzb2KrfTAbglpdX8eDcDV4hGE/n1AQ6pyZw59ePxcx4a00xW/eUk5WSQFZqAlkpkaKxuSNHR2suaqu4VOyJiEizJCfEMXloNyYP7UZ1KFx3VfjttTu45rHFxMcZfbPTWF+8j+pQJMGrWY+IyBdz/aQCbnp6aV1zSYDk+Diun1TwhfYbCBhJgTivuf5nc692SUs8ZGE1omcmI3pm8p95G5ssQt+98VTCYcfeyhpK9lexe391XbeACQXZpCUG2b2/KvIoq2bnvqq6AcEe+3ATLy/d1mCfOemJfPDz0wH4+TNLWbG19LNiMSWB3l1T61qkrNxaypzV2/nba2up8PJUtOSiZxcVNvhdtmZcGqBFRERaRDjsWPTpbmYuL+Ledz5pcCW3Vm3yPxIaoKV5lCNFYk803qU6sHCBSBF6ywXDvlBs+6tq2FVWRcn+anaVRQpCgGkjI/v8y6w1LNi4m937P9umf04aL1w9DoCpf3+bZYWlje47PzOZlIQ4Nu7aT8DAMAIGEwbl8I+vHgPAmX95k11lkeKzdptJQ3L51bShAEz+61tU1oQxizSZNeDs4d259vSBOOeY+vd3CNS+1/s5bWQ+l53YhxNvmc2WRprlHmmO1AAtIiLS6gIB49jenTm2d2fufmt9o9tsaeTqr4iIHJ7zRuX7XtwdqDaeli5CUxKCpCQE6ZHV+Os/OmPgQeuqQ5/1P/zVuUO58F/vNfreLSXl3HTWIHaVVeOcI+wczkH/nLS6bU4dlMveimrCjrptCrp1qnt9WH4GlTXhuveGnSM7PXIn1Dno1ikJR2R97T5qBzXb2kT/y9bIkSr2RESkxTXVtyQvM9mHaEREpDVFSxFaf4ToY3tnkX+IXHTFyf0Oua8bpww65Ou3fXlEk68FAsa9l49p8vW2zJEaM1tERFrc9ZMKSD5gXr6W6FsiIiJyuKI1F7VlXLqzJyIiLa61mvWIiIgcrmjNRW0ZlwZoERGRdkEDtDSPcqSISMdwqPyoZpwiIiIiIiIxSMWeiIiIiIhIDFKxJyIiIiIiEoNU7ImIiIiIiMQgFXsiIiIiIiIxSMWeiIiIiIhIDFKxJyIiIiIiEoNU7ImIiIiIiMSgdj2pupkVAxtbYFddgR0tsJ9YomNyMB2Tg+mYHEzH5GAtdUx6O+eyW2A/HUIL5cho/nuO1tgUV/NFa2zRGhdEb2zRGhdEb2wtEVeT+bFdF3stxczmNzXrfEelY3IwHZOD6ZgcTMfkYDom7Vc0/+6iNTbF1XzRGlu0xgXRG1u0xgXRG1trx6VmnCIiIiIiIjFIxZ6IiIiIiEgMUrEXcZffAUQhHZOD6ZgcTMfkYDomB9Mxab+i+XcXrbEpruaL1tiiNS6I3tiiNS6I3thaNS712RMREREREYlBurMnIiIiIiISgzp0sWdm95nZdjNb5ncs0cDMeprZG2a2wsyWm9k1fscUDcwsycw+MLOPvOPyK79jigZmFmdmi8zsRb9jiRZmtsHMlprZYjOb73c80cDMMs3sSTNbZWYrzewEv2OSzxet+TGa81S054poPWdH63kzGs9dZlbgHafaR6mZXet3XLXM7Efe3/4yM3vUzJL8jgnAzK7xYlru9/Fq7NxqZp3NbJaZrfV+ZrXkZ3boYg+4H5jsdxBRpAb4sXNuMDAWmG5mg32OKRpUAqc650YAI4HJZjbW55iiwTXASr+DiEITnXMjo3F4Z5/8DZjhnBsEjEB/M+3F/URnfozmPBXtuSKaz9nReN6MunOXc261d5xGAscC+4FnfA4LADPLB34IjHbODQXigIv9jQrMbCjwXeA4Ir/HqWbW38eQ7ufgc+uNwGzn3ABgtve8xXToYs859xawy+84ooVzbqtzbqG3vJfIiS3f36j85yL2eU/jvUeH7uxqZj2As4F7/I5FopeZZQAnA/cCOOeqnHMl/kYlhyNa82M056lozhU6ZzdPOzl3nQZ87Jzb6Hcg9QSBZDMLAinAFp/jATgaeN85t985VwO8CVzgVzBNnFunAQ94yw8A57XkZ3boYk+aZmZ9gFHA+/5GEh285i+Lge3ALOdcRz8ufwVuAMJ+BxJlHPCqmS0wsyv8DiYKHAUUA//Paz52j5ml+h2UxIZozFNRnCui+ZwdjefN9nDuuhh41O8gajnnCoE/ApuArcAe59yr/kYFwDJgvJl1MbMU4Cygp88xHSjXObfVW94G5LbkzlXsyUHMLA14CrjWOVfqdzzRwDkX8ppN9ACO85oFdEhmNhXY7pxb4HcsUWicc+4YYAqR5mUn+x2Qz4LAMcC/nHOjgDJauHmKdEzRmqeiMVe0g3N2NJ43o/rcZWYJwLnAE37HUsvrZzaNSKGcB6Sa2df9jQqccyuB3wOvAjOAxUDI16AOwUWmSWjRFgEq9qQBM4snkkAfds497Xc80cZrxvEG0dmXpa2cBJxrZhuAx4BTzew//oYUHbwrmzjnthPpR3GcvxH5bjOwud7djSeJ/AdK5Ii1hzwVZbkiqs/ZUXrejPZz1xRgoXOuyO9A6jkd+MQ5V+ycqwaeBk70OSYAnHP3OueOdc6dDOwG1vgd0wGKzKw7gPdze0vuXMWe1DEzI9I+faVz7s9+xxMtzCzbzDK95WTgDGCVv1H5xzl3k3Ouh3OuD5FmJK8753y/euc3M0s1s/TaZeBMIs1HOizn3DbgUzMr8FadBqzwMSRp56I5T0Vrrojmc3a0njfbwbnrEqKoCadnEzDWzFK8f6enEQWD2gCYWY73sxeR/nqP+BvRQZ4HLvOWLwOea8mdB1tyZ+2NmT0KTAC6mtlm4Gbn3L3+RuWrk4BvAEu9PgcAP3POvexjTNGgO/CAmcURuUDyuHMuqoaulqiQCzwTyXEEgUecczP8DSkqXA087DU7Wg980+d45DBEcX6M5jylXNF80XzejMpzl1cUnwFc6Xcs9Tnn3jezJ4GFREbNXQTc5W9UdZ4ysy5ANTDdz8F2Gju3ArcCj5vZt4GNwEUt+pmRpqEiIiIiIiISS9SMU0REREREJAap2BMREREREYlBKvZERERERERikIo9ERERERGRGKRiT0REREREJAap2JPDZmYhM1tsZsvN7CMz+7GZHfJvyMz6mNlXv8BnLTOzJ8wspRnvHW1mtzf3M733Xtucz/LeM8HMWnx4bTObY2arveOw2BvSGDP7npld2tKfV+9zL/OGBq6/rquZFZtZYjP39bMWiGeOmc2v93y0mc35ovv19nW5md3REvsSkY5NObLJ9yhHNr0v5UhpdSr2pDnKnXMjnXNDiMzxMoXI/CCH0gdodiKr91lDgSrge/VfNLMm54h0zs13zv3wCD4T4FqgWYmslX3NOw4jnXNfAnDO3emce7AVP/MZ4IwDEvqXgBecc5XN3FezEplFNHZeyjGzKc387FbnzaclIgLKkX5QjoxQjpQmqdiTI+Kc2w5cAfzAO/n0MbO3zWyh9zjR2/RWYLx31e1Hh9juUN4G+ntXB982s+eBFWaWZGb/z8yWmtkiM5sIDa8imlmqmd1nZh9420zz1seZ2R+9q6JLzOxqM/shkAe8YWZveNudaWZzvVifMLM0b/1kM1tlZguBCxoL+hDxXW5mT5vZDDNba2Z/aM6xN7NfmtlPvOUxXvyLzew2M1tW7/vdZmYfeq9fWe/YzDGzJ734HzaLzGZbyzlXCrwJnFNv9cXAo2aWbWZPefv90MxO8vabVu+7LjGzC83sViDZi+1hb7vrvGO+zMyu9db1scjV2QeBZUDPRr72bcDPGzkWDa46mtmLZjbBW97nHYPlZvaamR3nfff1ZnZuvd309NavNbOb6+3r697fzWIz+3dt0vL2+ycz+wg44fN/YyLS0ShHKkcqRypHRg3nnB56HNYD2NfIuhIgl8iVviRv3QBgvrc8AXix3vaNbtfUZwFB4Dng+96+yoCjvNd+DNznLQ8CNgFJ9T8T+B3wdW85E1gDpHr7exIIeq919n5uALp6y12Bt4BU7/lPgf/1PuNTL34DHq//Het9h6biuxxYD2R4zzcCPRt5/xxgNbDYe9zmrf8l8BNveRlwgrd8K7DMW74C+IW3nAjMB47yjs0eoAeRiz1zgXGNfPaXgGe85TxgCxAHPFK7PdALWOkt/x74a733Zx34NwMcCyz1jn8asBwYReTKdhgY28TfwhxgNPA6MNFbnuO9djlwR71tXwQmeMsOmOItPwO8CsQDI4DF9d6/FegCJHvHczRwNPACEO9t90/g0nr7vcjvf4966KFHdD1QjlSOVI6s3a9yZBQ9mrzNL9JM8cAdZjYSCAEDv+B2yWa22Ft+G7gXOBH4wDn3ibd+HPB3AOfcKjPb2Mj+zgTOrb3KRyRx9AJOB+50ztV479/VSAxjgcHAu96FvQQiJ/5BwCfOubUAZvYfIonjQIeKb7Zzbo/3/hVAbyLJ8UBfc87Nb2Q9ZpYJpDvn5nqrHgGm1vvew83sS97zDCKJt4rIMdzs7WMxkUTyzgG7fwn4p5l1Ai4CnnLOhczsdGBwvQudnbwruacTubKJ9313N3E8nnHOlXmf/TQwHnge2Oicm9fY96znt8AviPyH4nBUATO85aVApXOu2syWEvnOtWY553bWi2kcUEMk8X7ofddkYLu3fQh46jBjEBEB5UjlSOVI8YmKPTliZtaXyD/q7UT6JRQRuSIUACqaeNuPDnO7cufcyAM+DyJXLZsVJnChc251I/s6nPfOcs5dcsB7RzaxfXPUb9cfouX/LRpwtXNuZoOVkeYbn/vZzrlyM5sBnE8kQV3nvRQgcnWxwe/tMI/noXzu79U597qZ/ZbIfzBq1dCwOXpSveVq5yKXGYlcFa309hO2hv1ZHA05IsfvAefcTY2EUuGcC31evCLSsSlHfiHKkQ0pR8oRU589OSJmlg3cSaR5gCNyVWyrcy4MfINIcwaAvUB6vbc2td2ReBv4mhfPQCJXI1cfsM1M4OraNvdmNspbPwu4svaEZmadG4l3HnCSmfX3tkn1PmcV0MfM+nnbNUh0zYzviDnnSoC9Zna8t+riei/PBL5vZvG1n29mqc38iEeJJLBcIldrIdLM4+raDeol9VnA9Hrrs7zF6toYiByP88wsxYvlfG9dc/wWuKHe8w3ASDMLmFlP4Lhm7g8iHe07m1kycB7wLjAb+JKZ5Xjfp7OZ9T6CfYtIB6QcqRzp7Vc5UnynYk+ao7Yj8XLgNSIntV95r/0TuMzrkDuIz65CLQFCFhmG+keH2O5I/BMIeE0O/gtc7j4bCav2StRviDSLWeLF/Rtv/T1E+gcs8WKpHdDBqWQAAAFVSURBVA3tLmCGmb3hnCsm0l79UTNbgtc8xbtidwXwkkU6n9c2XWhOfIfrYftsWOnXGnn928DdXlOTVCJ9DWq/3wpgoUU6pP+b5l8ZnUWkL8J/6139+yEw2iIdzFfw2QhwvwWyLNKp/CMi/QYgcjyXmNnDzrmFwP3AB8D7wD3OuUXNCcg59zJQXG/Vu8AnRL7r7cDCZn5HvHieIvK3+pSLjFS3gkhzmFe93/0soPsR7FtEOg7lSOVI5UiJOvbZ36dIbDCzC4FznXOX+R1LazOzNOfcPm/5RqC7c+4an8MSEZEopRypHCkdi/rsSUyxyHDB/wd8y+9Y2sjZZnYTkX/LG4lcZRURETmIcqRypHQ8urMnIiIiIiISg9RnT0REREREJAap2BMREREREYlBKvZERERERERikIo9ERERERGRGKRiT0REREREJAap2BMREREREYlB/x+oOfinonRgawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x2160 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_projection(pca_array, ['HOG', 'LBP', 'SIFT', 'SURF', 'KAZE', 'GABOR', 'GLCM', 'Color Histogram', 'Flatten GRAY', 'Flatten RGB'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduce Features by LDA:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HOG Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_hog_train = lda.fit_transform(norm_hog_train, train_y)\n",
    "lda_hog_val = lda.transform(norm_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_hog_train.shape, lda_hog_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/lda_hog_train.npy', lda_hog_train)\n",
    "np.save('cache/lda_hog_val.npy', lda_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_hog_train = np.load('cache/lda_hog_train.npy')\n",
    "lda_hog_val = np.load('cache/lda_hog_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_hog_train.shape, lda_hog_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LBP Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_lbp_train = lda.fit_transform(norm_lbp_train, train_y)\n",
    "lda_lbp_val = lda.transform(norm_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/lda_lbp_train.npy', lda_lbp_train)\n",
    "np.save('cache/lda_lbp_val.npy', lda_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_lbp_train = np.load('cache/lda_lbp_train.npy')\n",
    "lda_lbp_val = np.load('cache/lda_lbp_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_lbp_train.shape, lda_lbp_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SIFT:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_sift_train = lda.fit_transform(norm_sift_train, train_y)\n",
    "lda_sift_val = lda.transform(norm_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/lda_sift_train.npy', lda_sift_train)\n",
    "np.save('cache/lda_sift_val.npy', lda_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_sift_train = np.load('cache/lda_sift_train.npy')\n",
    "lda_sift_val = np.load('cache/lda_sift_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_sift_train.shape, lda_sift_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Color Histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_hist_train = lda.fit_transform(norm_hist_train, train_y)\n",
    "lda_hist_val = lda.transform(norm_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/lda_hist_train.npy', lda_hist_train)\n",
    "np.save('cache/lda_hist_val.npy', lda_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_hist_train = np.load('cache/lda_hist_train.npy')\n",
    "lda_hist_val = np.load('cache/lda_hist_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_hist_train.shape, lda_hist_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Features normal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train = None\n",
    "features_val = None\n",
    "evs = []\n",
    "for ft, fv in zip([hog_train, hist_train, surf_train, flat_rgb_train, lbp_train, kaze_train, sift_train, gabor_train, glcm_train], \n",
    "                  [hog_val, hist_val, surf_val, flat_rgb_val, lbp_val, kaze_val, sift_val, gabor_val, glcm_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train is None:\n",
    "        features_train = ft\n",
    "        features_val = fv\n",
    "    else:\n",
    "        features_train = combine_features([features_train, ft])\n",
    "        features_val = combine_features([features_val, fv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10596), (4410, 10596))"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train.shape, features_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Features PCA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train = None\n",
    "features_val = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_hog_train, pca_lbp_train, pca_sift_train, pca_flat_rgb_train, pca_hist_train, pca_glcm_train], \n",
    "                  [pca_hog_val, pca_lbp_val, pca_sift_val, pca_flat_rgb_val, pca_hist_val, pca_glcm_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train is None:\n",
    "        features_train = ft\n",
    "        features_val = fv\n",
    "    else:\n",
    "        features_train = combine_features([features_train, ft])\n",
    "        features_val = combine_features([features_val, fv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 50), (4410, 50))"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train.shape, features_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Features LDA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train_lda = None\n",
    "features_val_lda = None\n",
    "evs = []\n",
    "for ft, fv in zip([lda_hog_train, lda_hist_train, lda_lbp_train, lda_sift_train], \n",
    "                  [lda_hog_val, lda_hist_val, lda_lbp_val, lda_sift_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train_lda is None:\n",
    "        features_train_lda = ft\n",
    "        features_val_lda = fv\n",
    "    else:\n",
    "        features_train_lda = combine_features([features_train_lda, ft])\n",
    "        features_val_lda = combine_features([features_val_lda, fv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 4), (4410, 4))"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train_lda.shape, features_val_lda.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA on PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_on_pca_train = lda.fit_transform(features_train, train_y)\n",
    "lda_on_pca_val = lda.transform(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('cache/lda_on_pca_train.npy', lda_on_pca_train)\n",
    "np.save('cache/lda_on_pca_val.npy', lda_on_pca_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_on_pca_train = np.load('cache/lda_on_pca_train.npy')\n",
    "lda_on_pca_val = np.load('cache/lda_on_pca_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_on_pca_train.shape, lda_on_pca_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_t = pca_hist_train\n",
    "features_v = pca_hist_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_t.shape, features_v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'RF' = 0.8689342403628117\n",
      "[[1872  340]\n",
      " [ 238 1960]]\n",
      "Recall in 'RF' = 0.8462929475587704\n",
      "Precision in 'RF' = 0.8872037914691943\n",
      "F1 Score in 'RF' = 0.866265617769551\n"
     ]
    }
   ],
   "source": [
    "model1 = train_model(features_train, train_y, model_name='RF', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob1 = model1.predict_proba(features_val)\n",
    "prob10 = prob1[:,0].reshape(prob1.shape[0], 1)\n",
    "prob11 = prob1[:,1].reshape(prob1.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'BAG' = 0.8773242630385487\n",
      "[[1910  302]\n",
      " [ 239 1959]]\n",
      "Recall in 'BAG' = 0.8634719710669078\n",
      "Precision in 'BAG' = 0.8887854816193579\n",
      "F1 Score in 'BAG' = 0.8759458839715663\n"
     ]
    }
   ],
   "source": [
    "model2 = train_model(features_train, train_y, model_name='BAG', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob2 = model2.predict_proba(features_val)\n",
    "prob20 = prob2[:,0].reshape(prob2.shape[0], 1)\n",
    "prob21 = prob2[:,1].reshape(prob2.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'ADA' = 0.8650793650793651\n",
      "[[1861  351]\n",
      " [ 244 1954]]\n",
      "Recall in 'ADA' = 0.8413200723327305\n",
      "Precision in 'ADA' = 0.8840855106888361\n",
      "F1 Score in 'ADA' = 0.8621728051887885\n"
     ]
    }
   ],
   "source": [
    "model3 = train_model(features_train, train_y, model_name='ADA', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob3 = model3.predict_proba(features_val)\n",
    "prob30 = prob3[:,0].reshape(prob3.shape[0], 1)\n",
    "prob31 = prob3[:,1].reshape(prob3.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'NB' = 0.6616780045351474\n",
      "[[ 913 1299]\n",
      " [ 193 2005]]\n",
      "Recall in 'NB' = 0.412748643761302\n",
      "Precision in 'NB' = 0.825497287522604\n",
      "F1 Score in 'NB' = 0.5503315250150693\n"
     ]
    }
   ],
   "source": [
    "model4 = train_model(features_train, train_y, model_name='NB', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob4 = model4.predict_proba(features_val)\n",
    "prob40 = prob4[:,0].reshape(prob4.shape[0], 1)\n",
    "prob41 = prob4[:,1].reshape(prob4.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'SVM' = 0.861904761904762\n",
      "[[1892  320]\n",
      " [ 289 1909]]\n",
      "Recall in 'SVM' = 0.8553345388788427\n",
      "Precision in 'SVM' = 0.8674919761577258\n",
      "F1 Score in 'SVM' = 0.8613703619394492\n"
     ]
    }
   ],
   "source": [
    "model5 = train_model(features_train, train_y, model_name='SVM', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob5 = model5.predict_proba(features_val)\n",
    "prob50 = prob5[:,0].reshape(prob5.shape[0], 1)\n",
    "prob51 = prob5[:,1].reshape(prob5.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.9129251700680272\n",
      "[[1974  238]\n",
      " [ 146 2052]]\n",
      "Recall in 'XGB' = 0.8924050632911392\n",
      "Precision in 'XGB' = 0.9311320754716981\n",
      "F1 Score in 'XGB' = 0.9113573407202217\n"
     ]
    }
   ],
   "source": [
    "model6 = train_model(np.array(features_train), train_y, model_name='XGB', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob6 = model6.predict_proba(features_val)\n",
    "prob60 = prob6[:,0].reshape(prob6.shape[0], 1)\n",
    "prob61 = prob6[:,1].reshape(prob6.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'KNN' = 0.780045351473923\n",
      "[[1646  566]\n",
      " [ 404 1794]]\n",
      "Recall in 'KNN' = 0.744122965641953\n",
      "Precision in 'KNN' = 0.8029268292682927\n",
      "F1 Score in 'KNN' = 0.7724073205068044\n"
     ]
    }
   ],
   "source": [
    "model7 = train_model(np.array(features_train), train_y, model_name='KNN', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob7 = model7.predict_proba(features_val)\n",
    "prob70 = prob7[:,0].reshape(prob7.shape[0], 1)\n",
    "prob71 = prob7[:,1].reshape(prob7.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob10, prob20, prob30, prob40, prob50, prob60, prob70), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FI5WmaYS8IVrKWqjaVoWmaahBlYpNFdgT7DTsb2DHih2Y7CZC3p4Hm/uafCRMTCB5WjItZS2MmzmOmMwYxl8wvqOLU9fXi8mMwRpjxR5/av/tEydPwoc45bSHjuMZi3GqBYxQWKPFq9Hs1lsjmtoeWzzdQ4U3oO8LHud6TA6rQqxSR364mFhDHfmHNpJUuYP4ugpi6jzYD9dj8AVRWk5gNpZrrtFnXVq4UJ95admyvm/ew6revUlV9YHSYVUPFGYT1Dbq1zrRbk9Hy0jRuyjJomJilNlWtY3tVdtZ9o9lfZaLscaQG5fL2dlnd+yrdlezdNJSzs45O6JsWlQaNpOsBNIfmqZRt7eOPa/vwWQ1sfWPW4kaF0Xp6lLUkHpc1wh5Q+Sdl4er2kX6memMmzWuY//kyyeTODFxMN+CEBEkfIgx51gtGl1Dx6kULNr5gxqVjWHK6sOU1YeobuoMGS5v33OtWE1gtyo4rAaSYgw4LErbtoLdouCo+ghH3WdEuevIfX01BjtEbypFMWkYFA3CAfADx1pU22qFq67SZ3CaP18fj2A06gOs09P17WPN0uT1gcsLza2dMzN5j2MOelXtHI8RDEGsPniSUFgfXO20t7V+qBATFTnN60muUyHEUCppKqG0uZSPDn7Em/veZEf1jh7LXTfjOuo8dUxNnkqcLY4fzvkhTsvxrTEjjo+mabiqXNQX1dOwX289ba1oxVXton5fPaVrSgn7u3/CU7e3rqPLU0xWDBmzM3CkOEjITyAuL45wIExSQRIGswFbnG1MLEwnxg4JH2LU6i1kHKtF41QJHaqmUd+qUl7fGTTK6sPUNKt0HellNkKc00BqrJFJ6QqxDgNxTgOxjrYvp77PblEwGbt8it91lqmgCirwxV74v14qlGEFoxWiYyE5GjweuOUWSEqC6dMhKwuys0+spUDT9O5OYRWKj+hdm/qa+tVh089JitefK4reBcpuBRR9DQlpqRBjyM6anawpXcMzW55hS+UWbCZbj7M2WYwWzsk5h3h7POfnnc+y05dhMco0yP2lqRq+Zh87X9zJZ498RtATxFPnwRJlQTEo+FuOb1E+xagw47oZTLx0IrHZsYybNQ6DTPYgRikJH2JU6Clo9BYyTpVwAXorRrOns2tUk1vtaNUorw/h69JzSAFS4gzMHG8mM9FEZqKRjAQjiTEGDMdzw901bDQGoWo//BXorQt4fj689BIkJsK4cXprRn9omt5lqrZRb8Hw+bsHDY9PbyEJh/UWC6tFb6mIjdIHcUvLhBjlwmqY33/+e8JamJKmEpr9zaw9spbihmLSotK6DZiuaK2I2PaFfJyXdx5JjiQWZC1gctJkLhh/waiYhWk0CLgCrFu+jv3v7qd8Q8+/HM1OMzFZMR2tEc1HmkmZlkLmvEzMDjPx4+NRjAoJ+Qk4kh1YnBICxdgi4UOMWMeaynYshwyPX6XBpdLs7gwX7QGjfSxGi0eNCBddOa0KOSntAUN/TE8wYjUf1XKxqofF91pCUO7vHAzdFAJvGHZUwWGgsZdKL12qj7uwWOD3vwdbP/p5N7VCdZ2+5oTNoq903dDce/lJOXroSIqTgCFGtfcPvM/h5sNsLN9InC0OT9DD+vL17Kjewfj48RTVF/V6bqWrknmZ8yL2ZcdmkxWTxVVTr2JJwRIZf3ECfE0+Qj59DFrAHaC5tLnH9SRaK1rxN/sp+aSEXS/t6nY8YUICyVOTmXzFZAqvKcRkk1svcWqTfwFixDi6daNr4BjLQQNAVTUO1YTYWRpkZ2mQwzXhXsdeRNkUYhwG8lJNXbpGGYhxKMQ5DKTGGYl1KJ2fgK57ED7vIWTU79YfoyfDtla929Rfq8B1jBHkqan6uIgvf1kPGzffDJMnn8ibhZ5m+D5cCY0t+qxSwS6DztsXzGuXkaKvju206YFDiFEqrIbZVbuLg40H+ePWP/J20dt9li+qL2Ja8jR21e7i7a+/TYI9AYfZQVZsFvG2+DE9TexA0TSNoLvnT20aDjTwxrVv4Kpy4ak9yWmy23zr/W+RfXY2ZrvM0CXE0SR8iBHB+6kX3yq9C01768ZYDxxNbpXdR/SwsftIELdfvyF3WBVm5ptJjzcS22XsRYxDDx1m4zFuMI5e8bs9ZCROhb1uKPZCVQA2GyBsgMDe7tfIyYE77+zcDgSgsFDvPtV1le0TEQ7Dmq3HXz42CibnyYxRYlSpbK1ke/V2QqoeoA80HCCs6qs23/XhXSQ7kjEajGiaRqWr5ymjl0xawvdmf4+M6AxirDFYTVbSotKG8m2MWq2VrQRcAVorWgm0BqjaVoXZYeaDH3+AI9lxwqGi8OuFOJL0BfG89V4y52X2WC7kD5E8NZnkqcnE5cT1+30IMZZJ+BDDrmvwsJ1jG7Nhwx/U2F8ZZNeRILuPhKho6GxhyEk2sjjbTGG2mbxUE8Yemvb71DVwdA0b7Y8Jl8Jlj/ZwoqoP9m5pgdtu0wd9z5unD/w+UeEwVNeDLwAtbjh6Yan6Lt2m4qK7L4AXCkN8DKQnS9gQo0Ktu5b3DrzHg58+iDfoPa41MKrd1czPmg/oXaIONBzg1jNvJcYaw7LTl5HsTB7sao9aFZsrCLgix/6VrSuj4UADpWtKj7lInqfWQ8ZZGbiqXOSdl9ftuBpWUYMqS/+0VFoshBhEEj7EsOhpPMdYCx6hsMbB6hD7ykPsLQ9ysCpE+5TssQ6FeQUWpmaZmZppJsZxAuMUjm7ZgM7AETUZnJMhPBee3Q+7dkFTE7C7s+xPfwoLFughIzb2xLsuhcP6DFPtKwV6vNDs6nldjJ5CxOxCfaYpIUaBQDjAO0XvUNJUwiu7X6G4sZismCwqWiu6tVxkxWRR6arkW9O/RV5cHjmxOQAYFAMTEiagoTE9dTpRlqjheCsjVkt5C64qFwAhX4iit4s6xkWUri7l0EeHTuh6Z9xwBs5kJyF/iJRpKdgT7ThTnKSeloolSgZvCzHcJHyIIdXTAn9jpXtVWNU4XBtmX3mQvWVBDlSFCLQNXbCaoCDDzNQsE9OyzKQnGE+sf3bXwHFkN1QA1ix9u9wP+6Nguwto70LVQ1eqpUvh2Wch+Tg/WVVVfeB3OAwVtfp0tJV1xz5vUg5EOfTZpYQYhfwhP4+tf4x7Prqnx+M17hpyYnNIciQRb4vne7O/x1VTryIjJmOIazo6aKrG1ue2svY3a7HF2TA7zJSsKsGZ4sRd7T7u65idZmbdPIv48fER17Y4LeSdl0d0RjRGs4wDE2Kkk/AhhkRPoWO0Bw5N06huVtlzJMjusiD7ykN4A/q4DZMRJqSZKMgwMznDRG6KKXKNjL50DRqaBi1hqC+CN4EvuhY80vP53/mOfp6mwVNPnfgUt6qqT2e796hPG5taO5+nJupdpKL0vtCYjN27UQkxjPwhPw3ehoh9n5R8wrsH3iXVmdqxr6K1gnf3v0uKM4X9Dft7vFZ+fD5fL/w6p6edztk5Z5PiTBnUuo9Wmqqx5Y9baJ8tQ9M03rnlnW7lDGYD9gQ77mo3WfOzaChuIPecXNLO0Me1hINhJlw8AbNT7/oUnxeP2SHdoIQYKyR8iEHT21S5ozl0BEIa2w8F2HUkyJ6yEA0u/X0pCuSlGJmSaWZyppn8VBNm00mEjeYQfF4EpYAvGja19nzOZZfBxImdg7+tVrjwQn1F8JMZL6Fp+mrg+w7ps0t1NTEH0CCxbRpbWYhPjBCbKzaz6vAqql3V2M12NlZsJMYaw4s7XzzhazX7m5meOp3ylnLOzTuXnTU7uXvB3Xx7+rcxGuTT9KOpYZVP7vsEg8mAp97Dntf24Kp09Vo+fXY6+Rfls+DuBVij+7nmjxBiVJPwIQbF0bNXjfbQUdEQ5tNdPtYVBfC0zUqVFmfg3EIrU7PMTEo34bCe4PoS7aGjfjfUAr8+ukCX4HH77RAdDVddBTNmnPwb0TR9OttQWB+rYTBAXRO0HtX1wWLWB35nj5OgIYZNZWslLf4Wntr8FK3+VtaWrWV37W7SotKoclUd8/w5GXOYkzGnYzsQDjAleQpfmvglzMbOT9LtJvspPdC7ckslLWUtoMCRtUdoLG7EFmejtaKV/e/sx5nq7La+RW9Bw2AycOH/XEjyVP37qYU1ss/OloXyhBAdJHyIAXV096rRPIg8ENLYXBzg091+DlTqgzfGxRtYcqadM8abSYw+wU9D28NGUNW7Ur26HxqAWhM0dlnXIjdXn3lq0SL9ecoAdPHQNNi4U18ZvC/ZaZCRqocPIQZZjbuGnTU7MSgG6jx1VLZW8sSmJyhpKsEf9qNqao/n1XnqWJC1gFpPLYn2RH4878dMTtLXmkmPTifaGo3JcGr+efO3+DtmhNI0jaZDTaihzu9jS3kL/mY/Vdur2PL0luO6prvaTdaCrIh98ePjqdpaxRV/u4KECQmY7eaIsRhCCNGbU/O3sxgwfS0MOFpbOqoaw6za5WPtPr2Vw2SEuZMsLJpmZUKa6fgGivc0I1XdbjgAPHV04bbgMX48bNwICQkD8C7QB4qrGmzdExk6rBa9VcNkAqddb9mQBfvEIFM1lfcOvMdb+96iwdfAy7tePuY5ObE5nJl+JnlxeczNnMt5eeed0sECIBwI03CggaptestP0+EmDrx7gJjMGHa/ths12HNg64tiUJhx3QxyF+cCYHaYyTtfn4rWZDXJeAshxIA6dX+Di34bSwsDhsIa2w4F+WSXj33lehhIi9NbOeYVWHDa+uhS1dfUt4lTwROGHxZ1ZIwOV18N3/iGPn7jWHpaEbwrX0Afq9HYok+DGzr6xdoUTtDHbggxQDRNY0P5BmrdtWys2Miu2l0k2hM7jm+s2IjZYGZjxcYez5+bOZdvT/82AJ6gh9NSTmN2xmwS7AMUwkepun11bH5qM64qF5YoC00lTRz88OBxnZtzTg6pM/RB9YHWALHZsThTOmefCwfCpBSmYE+wkzojFYPxBLuMCiFEP0j4ECetvcVjNHetavWq/HuHj8/2+Gn2aBgNMHuChcWFViaO66OVo69F/QCsBZD9ZaifBrd9t8t+K/zoR3DvveBw9F6xhmYoPqKvpWE09LyGxvFIS4RACApypSuVOGnNvmaKG4tZeWhlxxoVz29/nvVl64/7GjHWGFr8Ldw1/y6+Nf1bJDmSSI9OH6wqjzpH1h2hekc1DfsbWPfouj7LxmbHMmnpJBLyE4jNicVgNJB/UT4GkwHFoHQbnyGEECOJhA9xwjrGdTSqGJIMozJ4BMMaH+/w8c5mH96ARmK0gSvm2lgw2dr7gn+9BY7EqZB9ORgv1LtN3XVX2wn7Is/fvl1fTbwrTdMDRlkVoIDLA26PPiC8XTgMdhso6N2keqKhh5T4GH02KmndECfIF/IRCOsfKDT5mvj40Me8sP0FPin55Jjnnp93PrlxuczNnEtObA7TUqbhMHeGa6fZGTHA+1QXDoQp21DGP7/7TyzRFtzVblorus9sN+nLkzjjhjPIPScXAKPVKCtvCyFGPQkfok9Hj+mA7uM6RhNN09h6MMir6zzUtqgkxRi4drGTmflmDD21cvQWOBpy4V/A1t36gPCah4GHI89dsgRSUyErS2/tiI4Grw9KKvSuUUYDhPvon50UB1PzZbYpMWg0TeP1Pa/z1Ve+esyyl064FLvZzs2zbsaoGAlrYQpTCqX14ii+Jl/Hat3Npc2EfHoXyP3v7ufIZ0eo319P2B+OOCcuLw57op3MOZnMvWMuligLmXMyh7zuQggxFCR8iF71NKaj/floHNdxuDbEy595KKoIYTPDlfPsnH+arff1ONY9CGvv058nTgXLJCidCD//N/i7DOCuqYF58+DQIVi2DBYvhvPOA7NZ7y4VCOpjMQ5XQ31T53lhVW/J0DQwm/Rpbe02sLUFOgkdYgD9aeuf+O3a37K3bi/58fkAFDcWR5Q5L+88cmNz0dBwB92cm3su3zztm0Rbo4ejyiNawBXA1+yj6VAT65avIy43ji/++gXumuNbsTs6I5qJ/zGRC359Afb40fW7VAgh+kPCh4jQ08KAo3lMR1jV2F4SZNVOH7vLQigKLJpq5bKz7JHdq/oaNH7wQnj2COwtAoo6j//kJ/DAA2CzdXnBsB4mtu4Fj6/nSquf3AAAACAASURBVFnMMC0fYqIG5D0K0Zsvqr9g6YtLKWkqidhf3FjMjNQZFKYUUu+p58opV/L4pY8f30xup5BwIMympzax+5XdRKdH46n1ULunts/F9ECfhrbwG4Voqj5RRNLkJNAgdUYqKdNSMJhkgLcQ4tQl4UNE6BjLET+6FwZscqus2eNn1S4fTW59IPmsfAtfmmUjK+moH/ujWzhAn6K2Ogd+exj4UN+XkKAHjT/+UV9ZPCNDb72oa4RmF5RVd6+Ixax3nwoEYVyyPs1tb+M2hDhJdZ46fvLhTwhrYcpayihpKuFgY/eZkX59wa+5a/5dEjKOomkaZevLKH6/mObDzRR/WIy72h2xPkZP0menkzk3E3+zn4U/W4jFacGeYJepaYUQog8SPkQH76de1Dp9EHnsrbHDXZ2TUtsc5o0NXrYcDBBWId5p4LKzbCycYiXOedSnjV1XGAcovAeUc+GFF+Bvf4ss+/DD8LOf6c/DYdi2D+pc0NR9kCgpCfp4jnHJEO3sflyIkxQIB6hx17D68GqafE088tkjlDaX9lo+xhrD6Wmn8/bX35auU4CnzsPOF3ey5r/XEHAFcCQ5QIHG4sZez4nNjmXBPQuYecNMfSYpRWaTEkKI/pDwITq0d7cabYPIAXwBjXc3e/lwu4+QClMyTZxbaGN6rhljTzcK6x6EVffBDsCRDn+sAB5p++riV7+Cq78JIRXWbAG7FVze7tfLz9LHaiTJCr+i/zRN44PiDyhvLWdTxSaSHEnUeep4YtMTPZZ3mB0szl3M16Z9jaUFS7GarNhMth7LnmrCgTCtFa28deNbHPx3ZGuQYlSIy4kjdXoq7ho3iZMSueA3F+BIdJAw4dReZ0QIIQaLhA8BRLZ6jKZuVqqmsX5fgNfXe2j2aGQmGvnaQgcFGT10e2hv6dA0eHQP7Go/UNFZZtl34Vs36qt/m00QDEFlfedxlxdsVvAHYO50WTtD9MuhxkN8Xv45O6p3EGvTWxsf3/A45a3lfZ532+zbMBvMLClYwqxxszrOFbpDKw/x5/P+jNlpJuiOXCOnYGkBWQuzOOv7Z8m0tUIIMQwkfJziOtbsaBtcPppaPcobQjz/sZuSmjBRNoVvn+Ng4RQrhqNbOrp2r9oGrDjqQo8/Djn5kD8R6po79wdDEBulrx6en6WP3ZC+8mIANPuaifv1sddiefi8h5mUOImCxALMRjNWo5W8+LwhqOHIoobUjulrAUL+EI3FjbSUtRD0BlEMCpv+sImGAw0dU9sCBN1B0menoxgU4vPiOf27p5N/Yf5wvAUhhBBtJHycwo6eSne0DC7XNI1Pdvl55TMPYRUumGFlyZl2HNYexnTsfRH+ew/sB4xA1+n1/7ICzr8Iig7r212Dx6JZEjREv7kCLvbU7kFDn/Xo1nduZUvllogy159xPRMTJrIweyEWowUNjSlJU075MRoBV4CG4gaemf0MarDvgd9HM5gNnPaN07jsuctkcL0QQowwEj5OYe1jPEbTVLqtXpUXVrrZXhIkMdrAjRc6yU9r6zoRsSBgEJ7dD3u7nGyywkXnwdeXwfiJ+iri7cEDIDddb+mIiZLgIU7aewfe4+a3b6bWXYs31MP4IPQxGhMTJvLyVS8zKXHSENdwZGsobuB3E37XbX/ChAQmLen8Xrmr3WSfnY0aVkmekoymaqTOSMWZLJM8CCHESCbh4xTU0dWqcfSM8XD7VHYeCfLKZ/rYjrMmWvim/fc43vuLXiCkwZY9+vANQzK8URt5gfXroXC6XkZV9eAB+viNtERIT9YXBRTiJPhCPu784E5W7FhBs7854thpKafx9cKvYzKYCKkhrp52NfkJp27Xn+YjzdTtraN6ezVmh5manTU0HWoi6A3q62js7vy3W3BZAfHj45l540ySpyQPY62FEEIMFAkfp5ieulqNRB6/yv7KEHvLg+wrD1FWF0YDrHi43vIoc8s/0MdwVALPm/WWjg61YLHCwnNg+XKw2KGmATbt6iwyIUufCtcgi32J49fobeT94vf5+mtfZ0bqDACa/c3dFvG74Ywb+PWFvybBLjMmaZpG5ZZKNv7fRrY9t+24z7u78W5scTJjlxBCjDUSPk4RRw8sH2ldrTRNo6gixBeHg+wtD1JaF0bTu8kTTSOzjFso8L7LjPD7xDvaprNNnAp37ga6BI9HH4VJp0F0W5kmL9Cl60tSPEzMllmqRJ/cATcrS1YSUvXBy5+Xf87fd/49ImRsr97OhIQJGBUj6dHpNPmaWHH5Cs4ffz4x1phhqvnIoYZV1j+2ng/v/LDbsbzz8yhYWkBMZgy2eBvWGCuJExOxxliHoaZCCCGGkoSPU0TXblYjbWB5g0vl76vdbDukhwinVeGMPDMFgX9TcOCnpGv7UBKnghUouAXyboK5c+Fwl/EaoZDeirFlD7g8nfvHZ4Ldpq8qbpcbG6EH3Y0VG1mxYwUGxUBpcynV7mqK6ouwmWyomkpFa0Wv52dEZ/CjuT/itrNuk7U02hR/WEzRW0WUri7FXeumtbz74pszrp3BWbefRer0VIxm4zDUUgghxEgg4eMUYogfWSuXq5rGqp1+Xl/vwReE2RMsXDLTRmaiEcP6X8Ha+/SC8x+Aeb/Un2/dCuPGRV7oueeg1QM7iuhoLhmXBJNyh+y9iJGn3lPP63tex2rSQ6c/5OeOD+7AFXD1eV5hSiHxtnhq3DU8etGjGA1GAuEAObE5nJ52OvF2WUgSwFXl4tOHPmXj/23sdixhYgJBd5D48fFkzsvkwt9cOAw1FEIIMRJJ+BDDorw+xF8+8VBcHSIhysBNFzk4Ladt/Mm6B7sHj8ZG+N3v4L77Oi9SWQktfqhvgu37OvcXToDEY6+hIMaGsBpmdelqtlZuZUP5Bpr9zeyq2cWRliN9nnf3gru5bfZtGA1GnGanLNTXh5byFnxNPjx1HorfL2bNf6/pVmbenfOY96N5WGOtWJwjcyyZEEKI4Sfh4xTQdfXykWDNHj8rVrlRNX2NjsvOcmAzt01te3TwmHk3PPAA3H9/5EVqG2BXced2tBMKcvXuVWJM0zSN57Y9x/X/vP6YZW0mG09+6cmOgd9Wk5XFuYuxGOXm+Fj8LX5W/nIlG/53Q5/lrv3oWrLPzpauVEIIIY6LhI8xruvsVsM9s5Wmaby10ctbm3wkRhu4+aIo8lJNR63PsVt/nP8AzPkFGNtuaEwmeODXsGCRvt01eBTkQlrSkL0PMfSK6ouY9fQskhxJ3WaWAjg391ymJE3hkgmXsDh3MWajWcZjHEUNq/hb/ATdQQ6tPETAFeC9298jJjMGa4wVNaxSu6sWg8mAYlQI+8MR50/96lSSpyUTDoYpvKaQlNNSZAE/IYQQJ0zCxxg3UhYSDIU1Vqxy89neADnJRn6Q+gyx/3pOP9geOBKn6l/a2XDRb4Hf6ov9XXEN/ODHnRczmSDaoc9YVZArCwKOQRWtFTR6GznQcICvvPSVjv2ugIt4WzyugIvfXvhbbp9zu9wAH4dNT23inVve6fFYU0kTtjgbjmQH8ePj8bf6yZyTCYC30cvCny5k0pdkIUQhhBADQ8LHGNa1u9VwBg9fQOPJ913sOhKk0LCOm133YSvdrB9sDxwTr4JfrIJPPgF2w4RJ8JvHISEx8mIzCiAueqjfghhA9Z56/vrFXzEqeqtWWAvzfxv/j8yYTMJqmFWHV/V43uz02axatgq7WbrW9UVTNZqPNOOqclG+oZzNT22OWLhv5k0z8Tf5Kfx6IYpBYeJ/TMRgGhldMoUQQox9Ej7GqJHS3aqkJsRzH7upaAhzdujPfDN4J0ZbgR44Cq7RB5N7POB0dp508Zfgp/d3bhsMkJ0G2eOklWOUCqkhbv/X7Tyx6YleyxTVF2ExWjpWAr9iyhVMTpyM1WTlljNvIcWZMoQ1Hj00TcPb4GXvG3t568a3ei03+SuTufr1q6WlSAghxLCS8DFGDXd3q0BI458bvXywzYdBgSvMT3KJ9+coXafNBVi9GhYt6tze9gU06aEJRYGFZ8gq5KNUk6+J5euW89CnD6GhRRxLcabw4pUvYlD0/7caGmemn0mUJWo4qjoqNB5q5J1b3yHkDeGudVO3pw57gh1vg7db2XGzxpG7OBc1pDL58snEZsUSP16mCBZCCDH8JHyMQcPd3Wp/ZZAXVrqpblLJcVSzTP0JmS0f6K0dXYPHP/4BX2nrz//Du+DyqzuDh9UMc2cMed3FwLA+ZCUQDnRsx9nicJqdPLv0WS7Kv6gjdIhOalilpaylY7u5tJl3v/cu0enRFH9Q3OM53gYvuYtzaS5tJnlqMvkX53PmLWdKNyohhBAjloSPMai91WOou1v5AhpvbPCw8gs/JiN8dZ6dC7Zdg7FlH8RN0LtZtSsv7wwet35fDx7tEuNg6vghrbvoH1fAxaaKTTyw6gE+KfmkY/+F4y/kq1O/yk2zbhq+yo1galil6K0iXrr8pV7L1Oyswew0E3QHSZ2eypV/v5L4/HhMVvn1LYQQYvSRv15jzHC0emiaxoaiAK+u89Ds0ZgwzsR15zpJ2/MwNHyht3gs29V5wgcfwMUX692qzGa45jp9f2qizF41ypQ2l5LzWE6Pxz5d9iln55w9xDUauTRVw1Xlomp7Ff/6wb9oLG7ssdy8H8/reG6ym1h8/2IMRmnJEEIIMTZI+BhjhrrVo7QuxN8/9XCgKkSUTeHb5zhYONWKQVE61+4ouAZqamDVKvh//w/WrYO7fwmXLom8WFaaBI9RwhfyYX84MtwuLVhKiiOFH8//MZOTJg9TzYafq8pF9Y7qjm1N1Xjp8pcI+UI9lk+clMi0a6ax6N5FslCfEEKIMU/CxxgylK0ebp/Kmxu8rNrtB+DcQiuXnWXHaWv7hHbdg/r6HYlTwbMAUlNh0mS48+fw3493XshoALtNVicfwSpbK/mg+AOOtBzhsfWP4Qq48If9HccXZi/kL5f/hdy43OGr5BBTwyq+9vFJbcrWlfH3JX/v87zY7FiyFmSRPDWZBT9ZgNEiYUMIIcSpRcLHGDFUU+uqqsbqPX7e3ODF5dO7WH3jbAdZSV1+lNY9CGvv058XXAPzL4DEJHj6L5EXy02HnPRBq6s4edWuam5951aa/c18fOjjbsfjbHG0+FvYccsOpqVMG4YaDj5N1Tj474P4W/1Ubqlk0xObSJiQQO3uWoLuYJ/nTloyiUlLOhfmC3lDFH6tEGeKs4+zhBBCiLFPwscY0DV4DObUusVVQf622kNpbZhYh8L1FziZM9ESuW5A1+Ax9z6491M4/2K496HOMvNPB7P86I0kzb5m/vP9/2TtkbXkxObw4cEPI47HWGP42xV/w2Qwcf748zEZxu7/v4rNFWx4bAM7VuzofmxjBVFpUQTdQWKyYpj4HxM7jvmb/URnRrP4vsVYooZvbR0hhBBiJBu7dxCniKEKHp/t9fPCx24MBrj4dBtfPtOObfOvYO1Rs/TU79YffVfDwgfgk42RxxeeAUbpajKSHG46TO7/5nZsF9UXEWWJwmK0sPPWnSTYE7CarMNXwUEU8odY+cuVlHxcQt3eOgKuQLcyFz92MVGpUVhjrORflC/T2AohhBD9IOFjlBuKxQS3Hgzwwko3CdEGfvilaMYlGCNbOBKndhZOnAoxt0HiWfDJXZ37J+dBUrw+xkOMCKc9cRo7a3ZG7Nv3/X1MSpzUyxljQ9ATZMUlK6jcXEnQ0737lC3eRv6F+Sy6dxEphbKquhBCCDGQJHyMAYM5wHxveZCnP3ARZVP40ZJoUuOOCh5dVywPh+HlNyGxy9SrDhvkZ0FC7KDUT5ycq1+5uiN45MXlUZhSyItffRGH2THMNeu/+qJ6dr2yC1+jD2+jF7PdDOgDwiu3VHYrrxgULnvuMqZdPQ2TTX4lCiGEEINJ/tKKXpXUhPj9u62YTQr/+eVjBI/HH4cnn4Ynnte3X38JHvuNTJ07Aq0pXcMru18B4PnLnue6068b5hqdvIArQNW2Kt7/0ft46jw0lTQd97m3bL+FlNNSIscsCSGEEGJQSfgYxbpOrTvQKhvC/O/bragq3L4kiuzkth+V9rU72oPHm2/CfQ/CXT/vDB7uVgkeI9Sbe9/k8pcuB2BB1oJRGTxaK1tZnr4cg9mAGlQjjsXlxgGQOj2Vs24/C2uMldjszlY3a7QVs8M8pPUVQgghRKdBDx/79+/nuuuuo66ujtjYWJ5//nmmTYucmlNVVe68807ee+89TCYTiYmJPPPMM0yYMGGwqzcqeT/1EtgVQK3Tb7wGempdX0Djd++24g1ofO+SKCalt92sta/dYS2Abclw02nw++fg8acjL3DpYgkeI8xb+95i6YtLO7YT7AmsvG7lMNboxATcAd664S12vtg5RkUNqmQtyMJd42b2bbMpWFpAfF78MNZSCCGEEMcy6OHj5ptv5qabbmLZsmW8+uqrLFu2jI0bI2dA+uc//8lnn33G9u3bMZvNPPTQQ/zsZz/j5ZdfHuzqjUqBXQHURr3FwzLNMuDjPV5Z66G2ReWaBQ6m57YFm/buVh8D7+6Dmc/pwaPduCQYnwUmmclqpAiEA7x34D1e2/Maf97+5479s9Nns+GGDSO+u1E4EO6Y9nbXy7sijuWem8vX/vE1rNFjcxYuIYQQYqwa1PBRU1PDpk2b+OCDDwC48sor+f73v8+BAwciWjUURcHv9+Pz+TCZTLS0tJCZmTmYVRu1una1ir114Adx7ywN8OluPwUZJs6b3uXGbt9LUAe8Czz6B5g1u/PY6ZMhNmrA6yJO3MbyjXz5718mNy6Xz8s/jzg20kPHkXVH2PzUZra/sL3XMt/81zeZcIm0iAohhBCj1aCGjyNHjjBu3DhMJv1lFEUhOzub0tLSiPCxZMkSVq5cSVpaGtHR0WRkZLBq1arBrNqoM9hdrQDcPpUXVrqxmWHZuU4M7TepqgpJ3wUlGV5JguS26UczUyEvAwwyfe5wq2itIGN5Rsd2jbuGcVHjUDWVyydfzremf4sF2QuGsYadwsEwzaXN1BfVs/2F7ex6aRcxmTG0lLVElHOmOonNiiVzXiZzfzRXulQJIYQQY8CIGHC+adMmdu7cSXl5OTExMdxzzz3ccsstrFixolvZ5cuXs3z58o5tl8s1lFUdNoPd1QrgxTUemtwa1y52khTT1n2q2QXb9kL0ORDdpXBGqj6FrhhWnx7+lAMNB7j+n9d37JuXOY81312DQRk5oTDoCbJjxQ7evvntHo+3lLWQfmY6tngbS55e0jFwXAghhBBjy6CGj6ysLCorKwmFQphMJjRNo7S0lOzs7Ihyf/7znznvvPOIi9NvOK677jouuuiiHq95xx13cMcdd3Rsn0rdswzxg9PVCmDLwQDriwKclm1m4RQLNLbAjqLIQtdeBdML4a23BqUO4sRsLN/IOc+fE7Gv+s5qUpzDvzBeOBAmHAhTtqGMFRetQFO1iOPRGdFMWjKJuNw4suZnkXN2Ti9XEkIIIcRYMqjhIyUlhZkzZ7JixQqWLVvGa6+9RmZmZrdZrMaPH8+7777LnXfeicVi4e2336awsHAwqya6qG0J8+eVbhxWhWsXO1DWbYdgqLPAP1+Hd16Az7dAaurwVVQAoGkaV796Na/ufhWAwpRCfn3Br5mSNGXYg4e/xc8jsY/0eCx+fDwLf7aQwmsKsUQNfLdBIYQQQox8g97t6qmnnmLZsmX813/9FzExMTz3nD5D0g033MDSpUtZunQpt912G3v27GHGjBmYzWbS0tJ48sknB7tqAvAHNf7wLxcTLS5uy66GzV0OfvQ+/OoX8KurYG/5sNVR6B7f8Dg/fO+H3fZvvHEjNpNtGGrUydfso+STEl76yksd+/IvykcNq2SclcGCnyzAFje8dRRCCCHE8FM0TdOOXWzkyszMpKysbLirMeian2gGGNBuV5qm8fQHbloqm7krr7LzQLARProZHjnUXnDAXlMcv0A4wCNrHuFI8xFWl65mX/2+iONrv7uWeVnzhql2nf7xnX+w7fltEfvuKL+D6PToXs4QQgghxFjW1/35iBhwLvo2WCuZv7fVx6biAA9PadR3uHbA3ushcSr8vi2M/PznA/qa4tha/C1M+t0kqt3V3Y7dOe9OfnPhb4Z9utxwIEzJqhI+uucjKrfoPyunffM0EiYksPj+xcNaNyGEEEKMXBI+RoHArgAwsNPr7iwN8MZ6L8vSy0gx6Ndn7w0w/wGY90t4ZDLs2wc//emAvaY4thp3Dan/0zmuZnLSZO4/537mZM4hPTodi3H4xkr8+55/s275OuJy4mg40BBxLO30NK5YccUw1UwIIYQQo4WEj1HCkGQYsOl1yxtCPP2BmyXJtSyIbwse/kMw/349eNx5px48pk0Dp3NAXlP0LRgOYnkoMlgcvP0gefF5w1IfV5WLsvVlbH56MwFXgNLVpR3HGg40kDgpkebSZs7/7/OZ/JXJMjWuEEIIIY6LhI9TTLNH5XfvuLCG/SxJadV3GvfARd/Wx3asXg2PPqrvnz279wuJAdU1eMzNnMurV71KRkxGH2cMnKAnSGtFKwc/OoirysWq+3tf4HPJs0uYef3MIamXEEIIIcYeCR+nEH9Q4/fvtnKBs4YLsrusJj3vG/pjQQHs39/5vG1mMjF4NE3D8GDnWJ7i24sZHz9+0F7P1+xj5S9XYrKaqNtbR9FbRb2WHTdzHJMvn8xZ3z8LS5QFg2nkLFoohBBCiNFJwscIN1CDzVVV4/l/t/Dz5P2dO7UQzDkDjEZwuzuDxy9/Cffc06/XE317/8D7vL7ndZ7e8nTHvlXLVg1q8Hh27rOUb+h5yuTshdlkzMkgdUYqKYUppJ2eNuyD2oUQQggx9kj4GMG8n3rxrfIB/R9svvGzSm52VHTuqPszpCeBfa6+/fvf649nnAEPPNCv1xJ921Wzi0v+eknEvmeXPMuinEUD+joBV4Ctz23lvdvf63bs+vXXE5UahTXGij1hYMYSCSGEEEIci4SPEax9livbObZ+DTYv23CQOao+O5Gm+lHK7oZvr+kscOgQrGnbfuyxk34d0buwGubGt27kuW2RXdn2/2A/+fH5A97K8NlvPuPfd/87Yl/8+HgufuxiCpYUDOhrCSGEEEIcLwkfI1TX7lb9CR77i11M9OnBI9D0EZYD9+jT6bZTVfjBD+Cdd/TtCRP6U23Rgz21e5j6h6kR+74y+Su8eOWLWE3WAXkNTdUIeoOs/e1aVj0QOWD8qleuYvLlkzEYZcyGEEIIIYaXhI8RqN/drVrcUFWH1tTKRK9+nbDm04NH4lR9Ol2Am2+Gp9vGHERH660f6ekD8RZEmz9t/RPX//P6ju3Xrn6NK6YM7HoYb9/6Npuf3Nxt/5wfzuGSxy7p4QwhhBBCiOEh4WME6nd3q32HwOOjvSNPk2IjruQmfWP8lfCPf8Ddd+treQA4HLB8OUyf3v/KCx769CHuXXlvt/3N9zQTY40Z0Nf61w//1RE84sfHkzkvk6lXTaVgaYEMGBdCCCHEiCPhY4TqV3crj97a8XB5Hg0++NXEZ6Buq97qcdVTUFPTWfZXv4Jf/GIAaiw+L/+cS/96KQ3eztW/L51wKfMy5/GLRb8YkDDgb/Wz5/U9fLHiC2p21uCqcgEw88aZLHl6Sb+vL4QQQggxmCR8jDD9nlpX0wBwG62UNCl85Sw7jp1/0Y/FXQI1y/XnDz+sd7tKTByAWp/a/CE/todtEftunnUzT375yQF7jTW/XsNH93zU4zEJHkIIIYQYLSR8jDDtXa5OaqyHpsH6HQA0eiDKpnC+7zGo3623ejy4Ui83fTr87GcDVeVTgj/kp8HbQJOviWX/WEaCPYHPyz+PaOUAuKzgMl656hXMRnO/Xq9qWxX1RfV46jwc/PAge9/cqx9QICo1igv/50Ky5mcRlxOHYpDuVUIIIYQYHSR8jEAn3eVqXwkEggA8V5bExQlrsG1oCxkF10DDn/Tnq1b1fL7oRtVUzL8yo2pqt2NK26iaRTmLiLHG8If/+ANZsVn9er3a3bX8YdofejyWNT+L73723X5dXwghhBBiOEn4GCv8AaiuB+CN+iQaFRvnNt6vH5v/AGR+Bw7fB7m5EBc3bNUcTapcVYx7dFzH9sxxM1mQtQBXwMV959xHTlzOgL1W0BPkv5z/FbFv1i2zSMhPIHVGKs5kJ2mnpw3Y6wkhhBBCDAcJH2PF7oMAaMC7VTFclbEa64EtnVPrzpmjl4uPH746jhLBcJBvvv5NXtn9Sse+LTdt4YxxZwzo6wRcAUrXlPLRzz6iamtVx/6cRTlc+/G1si6HEEIIIcYcCR8jyEkPNm/1QIs+69FvqnKxG/0sOvAN/VjBNdDYCJ9/rm9/8snAVXiM0DQNV0D//nmCHib8bkLHNkDrT1uJskQN2Ou1VrTy9i1vU/RWUbdjd9XehSPJMWCvJYQQQggxkkj4GEFOerD54Qr9fKOZA/UGLjK9jg233t1q3i8hKUkvZzRCzMCuMzHabancwqynZ/V47JPrPuGc3HMG7LWaS5t54bwXaCxu7NhnMBtY+NOFzP/xfKwxA7PauRBCCCHESCXhY4Q5ocHmoRAUl0F9EwDPNuWiEOQ81yOd3a2qq6FeHwtCefkg1Xp0WntkLQv+tKBj+zunfweARl8jyy9aTl58Xr9fI+QLsfu13bzxrTci9kdnRHPN69eQcVZGv19DCCGEEGK0kPAxWmkafLYtYtfW0hCzwm+RqJVBwQPg90Na2yDl88+H1NRhqOjI0ORrYvHziwHYXr0du8mON+TtOK7+Uh3wFcGPrD3Cnxb8KWJf5rxMlj67lOSpyQP6WkIIIYQQo4GEjxHihMd7hEKdz0+byF92mAA/F4Se0Ltb7c+F+V0WvnvjjaOvcMpYd2Qd8/80v2M7JzaHspYyLsq/iClJU7hz/p0DHjzcte6I4HH5Xy5n2jXTMJqNogpV3wAAIABJREFUA/o6QgghhBCjiYSPEeKEx3u0ePTHcUm4HNGs31NLrvoF+XFu+MwBd13XWXbbNoiOHuAajw6lzaURwWPf9/cxKXHSoL3eC+e9QMnKkoh9v/D/AqNFQocQQgghhISPEaBrq8dxj/doW0wQq4VP//UvAto8Lgg9gTL5GrjsN/qxK6+EV18dnEqPEu8UvdPxvO6uOhIdiYPyOjU7a3jitCci9mXOy+Rr//iaBA8hhBBCiDYSPkaAk5rlqq3bVbj0n6ysOJs4Kph11hkw4w6ovU8v8/LLA13VUUPTNJ7f9jzfe/d7AHx87ccDHjxC/hBrf7uWlfeujNh/w4YbZCC5EEIIIUQPJHyMECfU6gFQ2wDA3r37aFKu5opxazHN/wXMm6cfX7wYDKfeInWaprHgTwtYV7YuYv/C7IUD9hruGjc7Vuzggx9/ELF/zn/O4fyHz8fsMA/YawkhhBBCjCUSPobZCQ801zQ4VK4vLAis8F+L3eTn7Ev/A84+G9av18s99tgg1Xjkuv+T+3lg1QMd26nOVB4890G+c/p3MBv7HwgOrz7M84ue77b/G+9+g9xzciV0CCGEEEIcg4SPYXZCXa5a3bBlT8emL+SnzjCFa+Y5iKr4/+zdeVhU9f7A8fcMmyCgiIKo4Q6pyOa+JZq55JZZVy1NNLNsvXnd0vbyppWa5b2/7JZb5pKZabnvWmmpgLiCqAgqiIIgyDoz5/fHgYMTCqPOoNDn9Tw8Z/ue7/nMwZ7mw3eLh19/VS+88QYEBdkg2vtLfHo8h5MP88nvn/Bb4m9m16y5QODBLw+yftx6s3N129UlaGQQrV9obfWZsoQQQgghKitJPu4DFne5Ss/Udq894MvkTUbq6k7TrWVr+HKTemHoUPj3v20U6f1j3p/zeGXjKyXOf9HnC15u+7JVnmHMNzKrzixyUovXAwkeFcyArweg00vCIYQQQghxuyT5uIduq8tVfgGcOa/uhzzIkl8VDBQwzPEz7PTfqSuZA4webbuA7xOHkw9riccD7g8wvft0ujXsRj33elZ9zodOH2r73T7sRpepXaSVQwghhBDiLkjycQ/dVperfYe13SNpDhyOv04bu23420WB0Qjvv69edHGxRaj3jTxDHsHzgwGo41aHhNcTrP4MQ56BxWGLteNxR8bhFeBl9ecIIYQQQvzd/P2mQ7rPWNTlKq74C3ZB20BW/JaDkz6fJ7P+CQUm6N69uGy7djaK9P5QZXrxqu3nXz9vk2dEfxvN+f1q3YOXD5bEQwghhBDCSiT5uN8ZjHAhRd1/sCFbjhtJyTDRz+4bPEiC7R6wZ496/V//AvvK25hVZ1YdbT/q+SibdIHKScvh5+d+BqD3570JGBpg9WcIIYQQQvxdVd5vqpXFb5Hq1tEBvD3ZvSkdTzc9PQzfQ41mcCpPvb5xI/Tufe/itJGfTv7ED8d/4PClwyRlJQGwefhmgmpbfzavyycu89/m/9WO27zYxurPEEIIIYT4O5Pk434Wf7F4v1VzrmaZuHrdRBfPY9inRMN6D4i4ql7v1OnexGhD07ZP49+/ms/cNSp4FD0b97RK/RmJGez9916unr7KtcRrXDl5Rbs2IWUCejtpGBRCCCGEsCZJPu5nGYVT6zZvDI4OnE1UB6g3zFS7BXHZFbgKGzaAm9u9idEGCowF9Frai53xOwEYFjCM6d2nU8etDk72TlZ5xuphqzm64miJ89UbVOeVU6+gt5fEQwghhBDC2iT5uF8pirquh14HtTwAOJNiAKCR/jhcbwRHz4CPD/Tpcy8jtarsgmyCvwzmVNopAJrUaMKywcvuul5FUci8kMnOt3YStSjK7NrYQ2Px9PPE0dWCWceEEEIIIcQdk+TjfpV5Xd3eMKj67CUDTg5QZ+5uiM5STxat71EJKIpC1X9X1Y6/f+J7nmzx5F3Vd+38NTb/czMnfjxhds2llgsDvhmAf3//O65fCCGEEELcHkk+7leZ2erWpxYAJpPCuRQDrXNi0BUlHnXqQHz8vYnPBl7d+Kq2H/1CNC29W95RPdlXslnzzBriNsaVuBY8Ophmg5rh18/vjuMUQgghhBB3RpKPe6TM1c2L1vaoro7luJhmJM8AA+arK3sT5ApRF8oh0vKhKArzDswDYPng5XeceJgMJn4K/8ks8QgYFkDQyCCa9GpilViFEEIIIcSdkeTjHil1dfPcvOL9GtUAOFs43sMlr3AQ+ti6No2vPCmKoq1aDjA0YOht15F9JZvZdWdjzDdq55478Bx1Wtcp5S4hhBBCCFGeJPm4h265unlahrqt6aGN+YhLVpMPx8uJUBNwsSunKG3HpJj4/tj3DFs9TDv37aBvb7ue7NRsPqn1idm5J1Y+gU8rn7uOUQghhBBCWI8kH/eb9Ew4VdjlqmZ1AHILFCJO59P68h70mTlgB/gPuXcxWkF6bjoeMz3Mzm0ZvoVHGj9i0f2KovBFky+4euaq2fmp2VNxcHawWpxCCCGEEMJ6JPm435w8o271em2K3T9j88gtgFGznlavNWsKHd6+RwHevQJjgVni8X7Y+7zV9S2L7k2PT2f7G9vN1uhwre2Kk7sTYw+NlcRDCCGEEOI+JsnH/SQxGfIK1P3OIaDToSgKu47l0TFiFY5FM2DtPHLvYrSCrWe2avvnXz9PXXfLxq/8OPxHjnxn/tnHHR2HVwsvq8YnhBBCCCFsQ5KP+4XJBGfOq/sN6mhjPc6mGEm8bOC53+eq155oB07WWeW7vCmKQvcl3dkVvwuAr/p9VWbikZeZR1JEEuvHrefKiSsA1Ghag8HLB1OnlQwmF0IIIYSoSCxKPjIyMpg2bRrx8fH88ssvHD9+nMOHDzNs2LCybxaWuZpZvF+/+Ev1rqO5vPLlU/icKFwkb8Vv5RyY9by04SUt8QB4OvDpUssb8gzMcJ9hdq7XZ71o/1p7W4QnhBBCCCFs7BaLTJh7/vnnqV27NvGFC9o1bNiQmTNn2jKuv59jhetS+DXQTl3PNXEwLp8GFyLBBXi5J9hVzFmujCYj/3fw/wBYM2QNyjsKLg4uty5fYGR6lenacbvX2vF85POSeAghhBBCVGAWtXzExsayYsUKVq9eDYCzszOKotg0sMqsxAKD6deg6H16uGnlfo/Jh5wc3K9ehkbO8MXmexDt3buef50O33QAQK/T89iDj92yrGJSuHz8Mgs6L9DO/fPcP6nmW83mcQohhBBCCNuyKPlwdDRfCC8nJ0eSj7tgtsDg9Rw4HKte8KoBVdTxHIqisPtYLi3O7lKvKW43qen+l5mXifsMd+140cBFtywb83MMKwasMDs3fMtwSTyEEEIIISoJi5KPbt26MX36dHJzc9m2bRtz5sxh0KBBto6tUtPX1OPcpQrsOVR88sGG2u7JCwYupZt4OnaxemLUy+UcoXU8trK4lePYi8doXqv5Tculx6ebJR5tX21Ly2Etqde+ns1jFEIIIYQQ5cOiMR8ffPABer0ed3d3pk6dSqdOnXjnnXdsHVvlZzAW7z/USpvhCmBTZA46TPia/lRPDBxYzsHdvbi0OHac3QFAxNiIWyYeAIeXHNb231Heoc/cPpJ4CCGEEEJUMha1fBw/fpw33niDN954QzsXHR1NYGCgzQL7W/g9St16e5olHgmXDRxPNNDGbidVlQz1ZAUcaD5+83gAfFx9CPEJMbtmMpqI2xRHytEUtk/Zrp0P3x1eniEKIYQQQohyZFHLR3h4uEXnxG0w3tDqUc/b7NKmyFwAel//AFxqlmdUVpOWk8bPsT8D8Odzf5a4HrMuhuX9lpslHiHPhlCvg7R2CCGEEEJUVqW2fKSkpJCcnExOTg5HjhzRBplnZGRw/fr1cgmw0ipayby+D7gWTzl7OcPIwbhcmht346scAUMIcOXexHiHsguy8fzYUzuu526eUORl5vH9498D0LhnY7pM60LtkNo4uVXMxROFEEIIIYRlSk0+li9fzmeffcbFixcZMGCAdr5atWpMmjTJ5sFVWjfOFNbAfIXvLZv3ohBIb8Pn0PE9mFA4tsbVtRwDvDuvbXxN2896I6vE9UNfFQ+y7/+//jKblRBCCCHE30Sp3a5ee+01zp49y5tvvsnZs2e1n6ioKJ599tnyirHyKco96niZnb6WbeK3K/7UN0XxYLuHocaQ4ov165dffHdh8tbJfB35NQBHxh2hqmNVs+uRCyPZOmErAE98/4QkHkIIIYQQfyMWjfl46623AMjLy+PatWvajyVOnTpFx44d8fPzo02bNhw7duym5Y4cOUJYWBjNmjWjWbNm/PjjjxZ+hIolZ08OprTC7MPJwezajiO5FOBEnyqr0XV8G159Vb3w5JPlHOWd2R2/m49//xiA+tXqE+AVYHY971oe60avA8Ctrhv+A/zLPUYhhBBCCHHvWDTb1R9//EF4eDixsbFm5403Dpq+heeff56xY8cSHh7ODz/8QHh4OAcOHDArk52dzcCBA1myZAmdO3fGaDSSlpZ2Gx+j4tAWGKyVBY7Ff/XPLVDYGZWOl+k8IXZ71JNZhV2WPv+8vMO8bXvP7SVscRgA1ZyqEf/PeO1a4r5E1o9bz6XDl7Rz48+PL+cIhRBCCCHEvWZRy8err77KokWLCAwMJD09nffff5+PP/64zPtSUlI4ePAgw4cPB2Dw4MEkJiYSFxdnVm7ZsmW0b9+ezp07A2BnZ0etWrVu97Pc93L25GC6YkJfNR/n+lfBvXgcx6G4fLKNVXjE8H/oH3xSnQ3r99/B0RFq176HUZfOpJgY/uNwHlr0kHYubXJx4mgymljUdZFZ4jE5fXK5xiiEEEIIIe4PFiUfBQUFtGvXDoPBgJubG9OmTWPFihVl3peYmIiPjw/29moDi06nw9fXl4SEBLNyx48fx8nJiX79+hEcHMwzzzzD5cuX7+Dj3N+0Vo+ahS0azsWzO0VFRKNTjLSudhI6vA256nS7eHiUd5gWu5pzlRoza/Ddke8AqOFcg9xpueh16j8rQ66BD+w/wFRgAmDKtSm8o7xDlWpV7lnMQgghhBDi3rEo+XBwUMcmeHp6EhERweXLl62aHBgMBrZt28b8+fOJjIykbt26jBs37qZlZ8+eTb169bSfrKySsyndz/SuBWqrRxVHbWHBfIPC8Yw6NDH9geuDfSAurnh2q/bt72G0t6YoCjU+rkFGnroI4tJBS0mdlIqTvZN2fbrzdK38M9ufkal0hRBCCCH+5iwa8zF06FBSU1OZOnUqXbt2paCggA8++KDM+x544AGSkpIwGAzY29ujKAoJCQn4+vqalfP19aVbt27UratOOzt8+HB69ep10zrHjx/P+PHF4wXq1atgi9KZCgebBzTVTp08X0A+zgRViVRbPW6cVnfevHIO0DIN5jbQ9mNfjqWpZ/HnMRYY+dDxQ+349cTXca/nXp7hCSGEEEKI+5BFLR+vv/46np6e9OzZk7S0NC5fvswrr7xS5n1eXl6EhoaydOlSAFavXk29evVo0qSJWbl//OMfHDhwQJtBa8OGDQQFBd3uZ7n/FQ3Qd3KEqs7a6cN/7AcgyO439YRX4RS8+flwnyVXxy8fR/eejoQMtevcobGHzBIPgLkN52r7w34eJomHEEIIIYQALEg+FEUhNTXV7HjBggU0atTIogfMnz+f+fPn4+fnx4wZM1i4cCEAY8aMYd06ddpVX19fpk6dSseOHQkMDGTHjh18+eWXd/J5KobGxQmF6ff3ib7iSW3TKWo366ie1OkgJAQcHG5Rwb3T4r8ttP1FAxcR6hOqHeem57LqH6vIvJAJwGtnX8Ovn1+5xyiEEEIIIe5PpXa72rBhA8OGDSMrK4vu3bszffp0hgwZQq1atSwacA7g7+/Pvn37Spz/+uuvzY5HjBjBiBEjbiP0ikwd68G+90nYv4b0Ki/TyztC7XJ1n8rKz6L1V621Y+PbRm1gOcCxVcf44R8/aMfBo4Kp3qB6ucYohBBCCCHub6UmH9OmTWPZsmU8/PDDrFq1iu7duzN58mRt0UFxm5S/HMes5LDdAACCOnUt/3gsdP7aeR6Y84B2vGLwCrPEY1HYIs7tPqcdv3D4BbwDvcs1RiGEEEIIcf8rtduVoij07duXKlWqMGLECDw9PSXxuEM5e3IwpRe2eNgVv/Yox8dwraKjsXdhHpifD2fO3IMIb+7E5RNmicepV04xJGAIiqKw96O9vKd7T0s8Qp4N4W3j25J4CCGEEEKImyq15UOvN89NPD09bRpMZWa2srmHOqA81eTNeaUpHeo7oNcXJiYHD6rb+2CFd4PJQPP/NteOC94qwF5vz5WTV/hPs/+YlX1s8WMEPVMJJwkQQgghhBBWU2ryER0dTY0aNbTja9euUaNGDRRFQafTkXYffEGuSPQu+TgHG7X1PY6b1DEUgfUdiwt16qRuX3ihvMMr4a0dxa1chrcM2OntAPhx+I/a+W4fdKPLtC7oCj+TEEIIIYQQt1Jq8nH69OnyiqPyM6mrfGMwaqcumBoDUN9L/VLP1avF5ceOLa/IburZtc+yIGoBADue2aElHtHfRZN0KAmASamTcK7hfMs6hBBCCCGEuFGpyUf9+vXLK46/jxqFa17se5/zeUE42WXj6eahnisoULcjRsANLU7lbegPQ1l5bCUAzWs1J6xBGADrxqwj8ptIAHw7+0riIYQQQgghbotFiwwKK3IsXLsjZiUX9Q9Sx+Ua+qIuS7Gx6tbN7d7EhrqIYFHiMb79eI69eAydTse1C9e0xOOhtx5i1N5R9yxGIYQQQghRMZXa8iGsyGgyO7ymeJCpq0VQfafikydOqFvne9ei8Nqm1wAI8ApgVq9ZAMTvimdxt8UAVPWqSrf3u92z+IQQQgghRMUlLR/lpWjMh6M6uPyCSV0hvm4Nu+IyRS0gjzxSnpGZ2RW/C4DfRv8GQHp8upZ4ALwQfe8HwgshhBBCiIrJouTDYDAwa9Ysxo0bB6gD0Xfs2GHTwCqTnD05mDILG5mquwI3JB+edre6rdw9tfopDCYDAO5O6tiUdc+u066/kfUGrt6u9yQ2IYQQQghR8VnU7erll1/GaDTy66+/Aup6H0OGDOFg0ZoUolTaGh/e18FeXYDvpi0f99jyo8sB+M+jxWt4OLioY1QmX52MY1XHm94nhBBCCCGEJSxq+di/fz//+9//qFKlCgDVq1enoGhmJmERvUs+zs1yteMLSiPcSMPd5d73fDucfBjde2qXrwdrPsiLbV4EIDs1m9hfYtHb66lSvcq9DFEIIYQQQlQCFn3zLUo6ihiNRkwm0y1KixKKBpsXJmwmReGiqQF19WfvYVDFnv7xaW3/4x4fa/tbJ20FwGSQ37UQQgghhLh7FnW7CgwMZOnSpZhMJuLi4pg5cyZhYWE2Dq1yyNmTg+kq6F0AL08AUq+ZyMOFuvrTwA2Dy597rtzjS8xI5NjlYwAY3zaCSZ3datuUbVz44wIAL514qdzjEkIIIYQQlY9FLR+zZ89m7969JCcn06lTJ/R6PTNnzrR1bJWCNt6jVhZ4qQsHXtj3HYB5y8eVK8X7rVqVS2y1P62N72e+ANRwroFep2d23dks7rZYSzzc6rjh6e9ZLvEIIYQQQojKzaKWD1dXV+bPn8/8+fNtHU+lpHc14Fz/KuAD+97nQkw6OPSjToNGxYV++UXd9u8PNWvaNJ58Yz71ZtfjcvZlANrVbcf6p9YT8U0E1y9dB6DbB93oML6DNuBcCCGEEEKIu2VRy0doaCj//e9/SU9Pt3U8lVPR+BgnB4hZyWVdQwBqd76hm9VLhV2bnnnG5uGk5aRpiceOZ3awf8x+Nj2xiZ/H/AxAyJgQHnrzIUk8hBBCCCGEVVmUfMyZM4c///yTJk2aMGTIEDZv3oyiKLaOrXIwGtWtowM4qF/mLzu2wMVJR9Uqha8/MhKys9X9/v1tHtLbO98G4KU2L9HGpQ3fPfodcRvjAAh7P4x+/9fP5jEIIYQQQoi/H4u6XXXt2pWuXbty/fp1Vq1axUcffcRzzz1HQkKCreOr+IpmuqpfRzt1WfGhlvsNeV9oqLoNCwMnJ5uFklOQQ+PPG5OUlQRAG6c2zKo9S7ve6oVWdH2rq82eL4QQQggh/t4sSj6KZGVlcfnyZVJSUqhWrZqtYqpcilqI3KsCUKA4kq7UorF74eKCJ04Ul922zaahvLf7PS3xGBcwjqvPXtWuDV03FL9+fjZ9vhBCCCGE+HuzqNvVjz/+SP/+/WnZsiXx8fEsXryYI0eO2Dq2yqGod5qrCwBXlNoo6ItbPtatU7eDB4Od7VY7NykmZv6mzlC2KngV3k94k3EuA4BhvwzDv78/Op3OZs8XQgghhBDCopaPL7/8klGjRrFq1aoSCw6KUmReLx5sXuiKona/qlWtMPmwL/wVvP66TUMZtHKQtn/0saPoUBONV069Qo0mNWz6bCGEEEIIIcDC5GPLli22jqNyylPX+MCuMNHY9z4pmXbgCDXd/9LKYaNWj3xjPo98+wh7zu0B4KnvnkKHjpZPtWTQ0kHS2iGEEEIIIcpNqcnHv/71L2bNmsWgQTf/kvrjjz/aLLBKxb4wsYhZyRX9UwB4FbV8bNxo00fvObdHSzyCU4PxO+XHI588QscJHW36XCGEEEIIIf6q1OQjLCwMgMcee6w8YvlbuOzUEjsFPKoWJh+Jieq2UaNb33QXkjLVAeY9fu9B5y2dAXB/wN0mzxJCCCGEEKI0pSYf/QvXnPD29qZ3795m1zZt2mS7qCqJnINgynZE71S8JsplUx083fXo9TrIy4PYWHBxAS8vm8TwzE/qooXuSWrC8eQPT9J8cHObPEsIIYQQQojSWDTb1dSpUy06J8zlx6qDzR0bqcmHoqgDzmsVjffIzVW3DRva5PkbTm3Q9psfb06vOb0k8RBCCCGEEPdMqS0fsbGxnDx5koyMDNYVTQkLZGRkkF20Ire4ubx8KDCgdwHnduoMYRl4kk+V4pmufvtN3XboYJMQnlz1JACdfu2EvdGeum3r2uQ5QgghhBBCWKLU5GPfvn0sWrSIlJQU5syZo513d3dn1qxZpdwpSFPX0EAH1FC7PF0xFU6zW7TGR9++6tYGXa7GzRhHdoGaIPbY1oMeH/egXod6Vn+OEEIIIYQQlio1+Rg5ciQjR47km2++4dlnny2vmCqHlDSgOjg4QOFMYUmKLwBe1Qq7Xen16jogH3xg1UdfSr3El3lfAtBrUy+6TO1Cp4mdrPoMIYQQQgghblepycepU6do2rQpbdq0ITo6usT1wMBAmwVW4WUXjuewL16/I970IAC+tezh2jU18Rg4UE1CrGjEtyMA0Ck6Nv62EZ1e1vIQQgghhBD3XqnJx+uvv84vv/zCwIEDS1zT6XScOXPGZoFVeHYlE4pzpgepRioeVT1g8+/qycxMqz425koMWzO2AvC1x9eSeAghhBBCiPtGqcnHL7/8AsDZs2fLJZhKxWSCG774FxgVzpsa00L/JzpdU3XqK4Cnn7bqY3su7QlAwzMNeXjMw1atWwghhBBCiLthUX+fAwcOaLNbff/990yYMIGLFy/aNLAK7dp1yCuA4uU9OL9zPkYcaGB30maPfeGXF0jISADgiXVPUL9LfZs9SwghhBBCiNtlUfIxZswYnJycOHXqFNOmTcPBwYFRo0bZOraKK/2aur2h5SP+7HkAGjxQyyaPPBB1gPmH5gPQbn87woaH2eQ5QgghhBBC3CmLkg87Ozvs7OzYuHEj48aN46OPPiIlJcXWsVVcOh055zwwZRb3ajtragZAg67PWP1xeZl5vDv2XQDqnq/Lp10/5dEvHrX6c4QQQgghhLgbFiUfeXl5XLp0iZ9//pmwsDAAjEajLeOq8PIvuwLg2MIRgHMmfzx1Sbg5W3dmK4BVH6xiQ191NfMF4xfQeUpnqz9DCCGEEEKIu2XRN+HXX38df39/qlWrRmhoKKdPn8bDw8PWsVVcl1IB0NcA54ecyS1QSFLq00B/w3iPxESrPCorOYtPT32qHXdoZJvV0oUQQgghhLhbFo/5SE9P54cffgCgYcOGbN261aaBVWh2NywiCCRcNqBgZ558/PabunV2vqtH7f5gN0cDjgJw6pVTuDm53VV9QgghhBBC2IpFyYfBYOCTTz6hZ8+e9OzZk9mzZ6O38sJ4lc4Ny2vE/7kRgPpFyUdWFixZou4/8sgdPyLtdBrbvtuG0V7tAtfIo9Ed1yWEEEIIIYStlbrOR5Hx48dz+vRpXnzxRXQ6HV9//TUJCQl8/vnnto6vUoi/lAdAff8W6omYmOKLnp53VGfkwkjWjV7H/EnqDFd9GvZBr5OEUAghhBBC3L8sSj527dpFVFSU1trRt29fQkNDbRpYRZYTWxXTdUf0hT2q4k3+1Nadw6XzFPOCM2aA7vZXIL9w4ALrRq+jwL6AHJccAJY8seRuwxZCCCGEEMKmLPpTuaIomEwms2NFUUq54+8t/2IVQJ3p6nquictKPfPxHncpKzkLgKOPq2M9arrUpKZLTavVL4QQQgghhC1Y1PLRu3dvevbsSXh4OABLliyhT58+toyrwtNXzcf5IQ+OJRQAN4z3uEuKorBiwAoAfDr6QDqsGLzCKnULIYQQQghhSxYlHzNnzuSrr75i3bp1ADzxxBOMHTvWpoFVFsnp6mDwuvozVqnv1PpTAGS6ZvJl+pcAeFX1skrdQgghhBBC2FKpyYfRaOTLL78kJiaGVq1aaVPtijLc0CUtK1ftruauSyu+fuHCHVf9/RPfA/Dff/1XO9fCq8Ud1yeEEEIIIUR5KXXMx0svvcS3336Ls7Mzs2fPZvr06eUVV8V1LUtNPgrzj8wcdcdNl15cZt48dXub0xVv+ucmjHlGcp1yydGpA80vjL8gs1wJIYQQQogKodRvrXv27GHPnj3MnDmTXbt2sWbNmvKKq+LKzlW3duqrzco1ocOEK9fU8yYTFC3nlgBhAAAgAElEQVTQ+Mwzt1V14m/qqugr3lLHeLSv1546bnXuPmYhhBBCCCHKQanJR5UqVXB0dATAw8PDbMYrcXM5ETpM2Y5gr/Zoy8xRqMo19LrCd7doUXFhb2+L6zUWGLl48CKngk8Rb4hXqxq4qNR7hBBCCCGEuJ+UOuYjJSXFbCHBvx6/+uqrtousgsqPNQF6HJuox5k5Cq66jOIC2dnq9sYkxAIrBqqtHRcfuAjAsIBh+Nf0v8tohRBCCCGEKD+lJh+PPPIIkZGRNz3W3cHieH8Xepd8nMOqA5CVmYm34WLJQg0b3ladpzefBsCvnx87k3byXth7dx2nEEIIIYQQ5anU5GPhwoXlFUflYSzsXlXFCZOicN3giBup4D/kjqs05BlQTAqNezUmuU4yJEFVx6pWClgIIYQQQojyIdMk2VB2noIJe9ycjNDh7TuuJ2FvAgC56blEJkdSy6UWPq4+1gpTCCGEEEKIciHJhy0UznRVNM2uKxmllS5TxP8iAAgID+DwpcOE+IRItzchhBBCCFHhSPJhRTl7ctSZrgoVLTBotsaHwXBbdSZFJnHs+2MAmNqZyDXkElo79O6DFUIIIYQQopxZnHwUFBRw+vRpW8ZS4eUfywfAsZ661Vo+ipIPRYHXX1f3HRzKrM9kNPFV6FfacZw+DoBQH0k+hBBCCCFExWNR8rFr1y7q169Pt27dADhw4ADDhw+3aWAVld4lH+dm6urjWTlqy4c21e6+fcUF27Qps671L67X9t/Me5OIJLX7lSQfQgghhBCiIrIo+ZgyZQp79+7F09MTgDZt2phNwVuaU6dO0bFjR/z8/GjTpg3Hjh27ZVlFUejevTvVq1e3qO77XVHLh9btKlFdoZxJk7RFCG8lNyOXiK/UZKPPvD7YOdoRkRRBNadqNPJoZLOYhRBCCCGEsBWLkg+j0Ujjxo3NzhWtfF6W559/nrFjxxIbG8vkyZMJDw+/Zdk5c+aUeE5FlnVWbelwozD5WLNG3bZtW+a9W/61BQDnGs60faktJsVEZHKkDDYXQgghhBAVlkXJR5UqVcjKytK+9B45cgRnZ+cy70tJSeHgwYNaF63BgweTmJhIXFxcibLHjh3jp59+YsqUKbcT/30tMy0FAFf/HuoJfeHrLiX5uHr2Kt/1+Y7Ib9SWpX+s/gcAcWlxZOVnyWBzIYQQQghRYZXe96fQW2+9Rc+ePblw4QLDhw9n27ZtLFu2rMz7EhMT8fHxwb6wi5FOp8PX15eEhASaNGmilSsoKOC5557jm2++wc7O7g4/yv0nU6mOE9k4dppmfqF27Vve83mjz7X95k82p0FYAwAZ7yGEEEIIISo8i5KPnj170rRpUzZt2oSiKLz33ntW7R713nvv8fjjj9OsWTPi4+NLLTt79mxmz56tHWdlZVktDmvLUqoVznRVz6LymUmZ2v6ktEk4exS3LknyIYQQQgghKjqLkg+Ahg0bMm7cuNuq/IEHHiApKQmDwYC9vT2KopCQkICvr69Zud27d5OQkMC8efMwGAxcu3aNBg0acODAAWrVqmVWdvz48YwfP147rlfPsi/25W7f+2QZH8PdPr3ssoVm11GTqsa9GpslHqAmHy4OLvh5+lk1TCGEEEIIIcqLRclHw4YNbzrI+cyZM6Xe5+XlRWhoKEuXLiU8PJzVq1dTr149sy5XAHv37tX24+PjCQ4OLrMF5L5kUqfWxWDEdHIVGbqxPOBaYNGtu9/fre0/vvRxs2uKohCRFEFw7WDs9JWnW5oQQgghhPh7sSj5+OWXX7T93Nxcvv32W23a3bLMnz+f8PBw/v3vf+Pu7s7ChQsBGDNmDAMGDGDAgAF3EPZ9SincVncjXamJUedIzfoPWnTrrnd2ATBgwQBcarqYXTuXcY6ruVdlsLkQQgghhKjQLEo+WrRoYXbcqlUrOnbsyFtvvVXmvf7+/uy7cXG9Ql9//fVNyzdo0ID0dMu7Kt2XnBy5oqiDyj3db2ipyM4u89aQUSElzsl4DyGEEEIIURlYNNXuX6WmppKcnGztWCqVK4oPADXdCl+xyQRr16r7f+nCtm+2mpx5B3rftC5JPoQQQgghRGVgUctHSEjxwnZGo5Fz584xadIkmwZW0aUWJh+e7oXJx59/Fl/8y+rmyVFqItfm5TY3rSsiKQJHO0ea12pu/UCFEEIIIYQoJxYlH5999lnxDfb2NGrUCB8fH5sFVeHFreVKjjPY39DyUdTlavp0s6Ln9p4j+ttoAJr0Nh+ID+pg80NJhwj0DsTBzsGmYQshhBBCCGFLZSYfRqORr7/+mm+//bY84qnYjEZ1m3yAVF0fXOxycXEqTD6K1ibx8DC7JfaXWABqNa9FtQeqlagyKSuJlOspPOb/mM3CFkIIIYQQojyUOebDzs6O2NjY8oil8sg7wxX7xnh6VC0+d/CguvX3104ZC4z8/vHvADz+nfn0ukVkvIcQQgghhKgsLOp21a1bN8aOHUt4eDiurq7a+cDAQJsFVpEZDZlcVbzwLepypShw6RL4+UH37lq5za9v1va9g2SwuRBCCCGEqNxKTT6GDRvG8uXLWblyJQBbt27Vrul0ujIXGfy7Slc8MWFfPNg8Lk7dXrtmVi4rKQuAZ7Y/c9NFHAEikyOx09nR0rulzeIVQgghhBCiPJSafJw8eRKAs2fPlkswlUWqorZi1HQrXOMjN1fdPv20WTlFUVclbNi94S3rikiKoHmt5lSxr2L9QIUQQgghhChHpY75uNVf40XprmapSUVN97+8Xnd3bTcrOYuTa06WWs+V7CskZCRIlyshhBBCCFEplNryER0dTY0aNUqcVxQFnU5HWlqazQKrkEwmAFJ1dQHwLBrz0a6dui1s6Tiy7Ag/Pv1jmdVFJkUCMt5DCCGEEEJUDqUmH/7+/mzYsKG8YqnYjEYwqclFimNzMBR2u/rxR8jJUcu0bw/Aptc2AVDVqyovHH7hllXKYHMhhBBCCFGZlJp8ODk5Ub9+/fKKpWK7XjiuQzGRYvLGtYqOKo46+PRT9fzbb0OvXiiKQvYVdcHBCZcmlFplRHIEOnQEeQfZMnIhhBBCCCHKRaljPooGRIvbYMrkiuJT3OWqqNvau+8CkHkhEwA7R7syq4pIisDP0w83JzdbRCqEEEIIIUS5KjX5iIyMLK84KrycP4yYsh0BSFdqmQ82d3aGwsH76efSAWj9YutS68vIzSAuLU66XAkhhBBCiEqjzBXOhWXyY4zqjvsZFPTF0+z+xe53dwOgty/91UclRwEy3kMIIYQQQlQeknxYi8mI3iWf3FoxAMULDN7AWGDkzDZ1Ycbg8OBSq5PB5kIIIYQQorKR5MNq1G5ViVnqmI6abiVfbV5GHqDOcuXVwqvU2iKS1eQjpHaINYMUQgghhBDinpHkw8qummoB3LTbVfyueADqtq1bZj0RSRE0rN4QD2cPq8YnhBBCCCHEvSLJh7UULjB4lZoA1Chq+UhMVNcAAWLWqV2yAkcEllrV9fzrnLxyUrpcCSGEEEKISqXUdT7EbShcYPCa0QVHcnFy0KkJSXQ0ANcvXyf6W3Xfp5VPqVVFX4rGpJgk+RBCCCGEEJWKtHxYizrkg3SDA44ULjgYFweAsWETPvX6VCtao3GNUquSweZCCCGEEKIykpYPa9LpyKcKDjp1YDnHjlGAPf8+O1wrMvX61DKrKUo+ZLC5EEIIIYSoTKTlw5oUI/kGBUcKk48XXuAoLbXLL8e+jIOLQ5nVRCRHUNetLt6u3raKVAghhBBCiHInyYc1KUbyccahirN6nJKCqfAVD1o6CM+mnmVWkWfI42jKUelyJYQQQgghKh3pdmVlBXZuuLv7wKlT6olGjeCMuraHJY6mHMVgMkjyIYQQosIxmUwoinKvwxBC2JhOp0Ovv7M2DEk+rCxfqYKjA3DpEgAn8xre1v2RyZGADDYXQghRceTn55OQkEBBQcG9DkUIUU4cHBzw9fXF0dHxtu6T5MPK8nHCwU4Hu3ejAHEXXADwCih9RfMiMtOVEEKIiiYhIQE3Nzc8PT3R6XT3OhwhhI0pikJqaioJCQk0adLktu6V5MPKDDjiaK+DDz/kOq4A2Dvb4+bjZtH9EUkR1HSpSV23sldBF0IIIe41k8lEQUEBnp6e2NvL1woh/i48PT1JS0vDZDLdVhcsGXBuVepfexzsAV9fEqkHQJNelmWEBpOBw5cOE+oTKn85EkIIUSEUjfGQ/28J8fdS9N/87Y7zkuTDBpzsdaDTgW99AJo/2dyi+05eOUmuIZfQ2tLlSgghhBBCVD6SfNiAo72aCRoUu9u6T8Z7CCGEEEKIykySDxtwKOzyeixD7Xald7DsNUvyIYQQQty9Bg0a4O/vT3BwMP7+/syYMcPs+oULFxg6dCiNGjWiadOmdO3alf3795uVOXToEL1796ZRo0a0bt2aTp068dNPP5Xnx7gjzz77LM2bN2fQoEF3Vc+iRYuoVq0awcHBBAQE0K1bN2JjY7Xr4eHh1K1bl+DgYO3ndoSFhZXL+5w3b16J3//97tSpU3Ts2BE/Pz/atGnDsWPHblrOZDIxYcIEAgICePDBB3n22WfJz88HID4+Hjs7O7Pfz+nTpwG4dOkSbdu2xWAwlNtnupEkHzbgXJANMTE469V/AI0ebmTRfRFJEVRzqkYjD8vKCyGEEOLmVq5cSVRUFDt27OCjjz7izz//BOD69euEhYUREhLCmTNnOHXqFG+//Tb9+/fn6NGjABw7doxevXrx0ksvcebMGQ4ePMiqVavIyMiwSazW+hJ46dIlVqxYwZEjR1izZs1dP79bt25ERUVx9OhRWrVqxT//+U+z6xMnTiQqKkr7ud/k5OQwe/ZsXnnlldu6z2QyYTKZbBRV2Z5//nnGjh1LbGwskydPJjw8/KblvvnmGyIiIoiIiODEiRPo9Xrmzp2rXXdzczP7/TRu3BgAb29vOnbsyJIlS8rj45QgyYe1mAwUDbdpuOZrFHREpTcAwM6p7O5XJsVEZHIkIT4hMmhPCCFExbVmACxqYbufNQNuK5y6devy4IMPcu7cOQCWL1+Oh4cHkydP1so8/PDDjBo1io8//hiAGTNmMHr0aPr376+VqVOnDiNHjrzpMxYuXEhwcDBBQUG0bt2a+Ph44uPjqV69ulYmKyvL7P/vOp2Od955hzZt2vDGG2/g5+fHwYMHteuLFi3SWi+Sk5P5xz/+Qdu2bWnZsiVvvvlmiRjS09Pp1q0bubm5tGrVihkzZmA0Gpk4cSIBAQEEBATwyiuvaH8ZDw8PZ/To0Tz00EMEBASU+R4ffvhh7R3ejhMnTtCrVy8CAwMJDAzkyy+/LFFm2bJltGvXjpCQEIKCgvj555+1ax9++CHNmjXT/np/7tw5cnJyGDJkCM2bNycoKIiePXve9Nk//PADnTp1ompVdaHnI0eO0LlzZ0JDQ2nevDkffvihVvbdd99l8ODB9OrVi4CAAJKSkti8eTOdO3emVatWtG3blp07dwLq76Nbt260atWKFi1a8PLLL1stWUlJSeHgwYMMHz4cgMGDB5OYmEhcXFyJsocPH6ZHjx44Ojqi0+no06cP3377rUXPGTZsGPPnz7dKzLdL5sSzFsVE0WxX9pg4TJB2ycHZoczb49LiyMrPksHmQgghhBWdPHmS1NRUwsLCAIiIiKBDhw4lynXo0EH7Un/o0CGmT59uUf27du3i/fff5/fff8fHx4fs7GxA/RJZFjs7Ow4cOACAh4cHixYtonXr1oCa0EyYMAGAkSNHMnXqVLp27YrBYKBfv36sWrWKJ598UqurevXqbNiwgeDgYK0V4v/+7/84cOAAhw4dws7OjgEDBjBnzhwt8Tp06BC//vorbm6lLwdgMplYs2YNQ4cONTv/ySefsGjRIgD69u1b4p0ZDAYGDhzIe++9x7BhwwC4cuVKifp79erFsGHD0Ol0xMfH0759e86dO0d2djaffvopSUlJODs7k52djV6vZ+PGjaSnp3P8+HEA0tLSbhr3rl27aNeunXbcoEEDtm/fjpOTEzk5OXTs2JEePXrQvn17APbt20dkZCTe3t6cOXOGd999l82bN+Pu7k5cXBxdunTRksqff/4ZV1dXjEYjAwcO5Pvvvy/xfgBef/11LWn5q/nz55vFB5CYmIiPj482bbVOp8PX1/em62m0atWK+fPn8/LLL+Ps7Mz3339PfHy8dv369eu0adMGo9HIY489xrRp07Czs9PujY6O5tq1a7i7u980PluR5MOqdKAoNJo9jc2oWXifL/qg05fdkiHjPYQQQlQKg9bd6wgAGDJkCHq9npiYGObMmUOtWrVs8pz169czYsQIfHx8AHBxcbH43tGjR2v7zzzzDCEhIcyaNYsLFy4QGxtLnz59uH79Otu3b+fSpUta2aysLGJiYsqsf9u2bYSHh+Pk5ATAc889x3/+8x8t+XjyySdLTTx27txJcHAwCQkJ1KhRgz/++MPs+sSJE0t0xbpRTEwMubm5WuIBULNmzRLlzp49y9NPP8358+ext7cnLS2Ns2fP0rRpU5o2bcrw4cPp2bMnffv2pV69egQFBXHixAlefPFFunbtyqOPPnrT558/f57evXtrxzk5Obz44otERUWh1+tJTEwkKipKSz4effRRvL29Adi0aRNxcXE89NBD2v16vZ6EhATq1q3L5MmT+fXXX1EUhZSUFAICAm6afMyZM+eW7+duhYeHc+7cObp27YqzszM9evRgy5YtAPj4+HDhwgW8vLxIS0tjyJAhzJo1i0mTJgFgb2+Ph4cHFy9eLPfkQ7pdWYtObd1wyVH7g+oLO2E17dvUotsl+RBCCCGsZ+XKlZw4cYItW7YwZcoUjhw5AkBoaCj79u0rUX7fvn2Ehqr/D27VqtVNy9wOe3t7jEajdpybm1uijKurq7Zfr149Wrduzdq1a1m8eDHDhw/H3t5eW0Nh//79Wt/9uLi4m3a9Kstfu3Xf+PybKRrzcf78efz8/HjxxRdv+5mWGDp0KGPGjOHo0aNERUXh6upKbm4udnZ27N+/n3/+85+kpKTQvn179u7dS6NGjTh+/Di9e/fmt99+IyAggKtXr5ao18XFxey9T506lZo1axIZGcnhw4cJCwszu37j+1AUhUceecRszMSFCxdo2rQps2fPJiUlhT/++IPo6Gieeuqpm/5+QW35uHHQ940/f03mAB544AGSkpK0cTiKopCQkICvr2+JsjqdjnfffZfIyEh+//13mjdvTosWLQBwcnLCy8sLgBo1ajB69Gj27t1rdn9ubi7Ozs63/L3YiiQf1pCn9p/U3fA6U+vfXhIRkRSBi4MLfp5+Vg1NCCGE+Dvr0aMH48aN076sDxs2jNTUVGbOnKmV2bFjBwsWLGDixIkATJo0iQULFrB+/XqtTHJyMosXLy5Rf//+/Vm6dClJSUkAZGdnk52dTe3atVEUResaZMng3lGjRrFgwQKWLFmitYq4urrSrVs3sxmbLl68yPnz5y367EuWLCE/Px+DwcDXX399y/ERpXFxceHrr79mw4YNREZGWnyfv78/Li4uLF++XDt3s25XV69epWHDhgAsXbpUSyQyMzO5dOkSXbp04a233qJz585ERkZy/vx5dDodAwYM4NNPP0VRFBITE0vUGxgYaNZCdPXqVerVq4e9vT0xMTFs3br1lrH36tWLbdu2ER0drZ0rmrTg6tWr1K5dmypVqpCcnMyqVatuWc+cOXPMEpgbf/7a5QrAy8uL0NBQli5dCsDq1aupV69eiS5XoCYPRe/qypUrzJgxQ2vZSElJoaCgAIC8vDx+/PFHQkJCtHsvXbqETqfjgQceuGXstiLJhzVcSQfAoBTPFhFzTs0kHV0dy7xdURQikiIIrh2Mnf721gYRQgghROneeustfv31Vw4dOkTVqlXZtWsXhw4domHDhjRt2pR3332XdevWERgYCEDLli3ZuHEjc+fOpVGjRrRs2ZLHH38cDw+PEnU/9NBDvPPOO/Tq1YugoCC6du3K5cuXsbe354svvqBfv360adNG+yJYmoEDB3LgwAG8vb1p1qyZdv67774jLi6OgIAALZbU1NQy6xs7diyhoaGEhoYSHBxMgwYNSu0mVZo6deowYcIE3n77bYvvsbe3Z+3atSxcuJCWLVsSFBTE6tWrS5SbO3cuTzzxBCEhIURGRmp/5c/IyODxxx+nZcuWBAYGUlBQwMiRIzly5AidOnUiKCiIkJAQRowYof3ubvTEE0+wefNm7fjNN99k4cKFBAYGMmXKFLp3737L2Js0acKyZct4/vnnCQoKolmzZnz22WcAvPbaa/zxxx+0aNGCESNG0KNHD4vfiSXmz5/P/Pnz8fPzY8aMGSxcuFC7NmbMGNatU7s2ZmRk0LFjR1q0aEGXLl144YUXtEkSfv31V20Af2hoKLVr12batGlaPZs2bWLQoEHo9eWfCuiU210T/T5Tr149i7J/mzoSS8aWauQacvm3nZGpEzvyJeMAeEd5p8zb49PjaTi3IS+3eZkvHv3C1tEKIYQQVmM0GomNjcXPz08bzCrE/aJv3768++67tGnT5l6Hcl/p0qULX331lVmSe7tK+2+/tO/n0vJhDUY1fzOYTHilnCYWtetUq+dbWXS7jPcQQgghhLC+zz//3GywvlC7XI0bN+6uEo+7IbNdWYMOUEyYFKiTdBI96gCzwBElmwBvRpIPIYQQQgjra9y4sba4nlB5e3vz1FNP3bPnS8uHFSnoaHdwNTmo4z3sHCxrfo5IisDRzpHmtZrbMjwhhBBCCCHuKWn5sCIFPblOrpygEQAOLmUvLqgoCoeSDhHoHYiDXdnlhRBCCCGEqKik5cOq1Pmzq3IdgFotyl7QKCkriZTrKbKyuRBCCCGEqPQk+bAiheLFe6p6Vy2xmM/NRCap82XLeA8hhBBCCFHZSfJhBTkxLphyqqCgQ68DE3Zg4QTGRYPNQ3xCyigphBBCCEs0aNAAf39/goOD8ff3N1ugD+DChQsMHTqURo0a0bRpU7p27cr+/fvNyhw6dIjevXvTqFEjWrduTadOnfjpp5/K82PckWeffZbmzZszaNCgu6pn0aJFVKtWjeDgYIKCgggMDGTt2rVmZRRFoWHDhjz88MMl7j948CB9+vShYcOGtGrVipCQED788MPbimHXrl0EBwff1eewRHZ2Nq1btyYzM9Pmz7KmDz/8UBtQf+MaHn914MABbV2U4OBgduzYoV0LDw+nbt262qrrRQttAkyYMIFly5ZZPW4Z82EF+RerAHDG6So1FD0XqIuzwWTRvRHJEdjp7Gjp1dKWIQohhBB/KytXriQ4OJgLFy7QvHlzunfvTtu2bbl+/TphYWGMGTOGFStWALB9+3b69+/Pzp07CQgI4NixY/Tq1YuFCxdqi7ZdvHix1BWx74bBYMDe/u6/kl26dIkVK1Zw7dq121pz5VbP79atm5Zw7d+/n/79+zNw4EDt+vbt26levTrR0dGcPXtWW6X8yJEj9O7dm0WLFtGvXz8A0tLSSiSB94t58+YxcOBA3Nzcbus+a/3e7sSePXtYvnw50dHR2Nvb06lTJzp27Ejfvn3NyimKwqBBg1i0aBE9evQgNjaWHj16EBMTg7OzOkHSxIkTb7r45KRJk+jcuTNDhgyx6ho+knxYid45lwNVU3lEURuTHKpaNng8IimC5rWa4+zgbMvwhBBCiHIxb0MmKRmW/QHuTnhV0/Pyo5Z/Saxbty4PPvgg586do23btixfvhwPDw8mT56slXn44YcZNWoUH3/8MUuWLGHGjBmMHj1aSzxAXeF75MiRN33GwoULmTt3Loqi4ODgwA8//ABAcHAw6enpAGRlZeHm5kbR2s46nY63336bDRs2EBYWxtq1a1m2bBmtW7cG1JaHtWvXsmbNGpKTk3n11VeJj48nJyeHgQMHlmhFSE9Pp1u3buTm5tKqVSuGDh3KxIkTmTJlChs3bgTUZGLWrFk4OjoSHh6OXq8nLi6OlJQUTp48Wep7TE9PL7HC+zfffMNzzz1HTEwMCxYs4IMPPgBg5syZjBkzRks8AGrUqMHHH398y/pnzpzJ0qVL0ev1ODs7m/11HtQv+n379iU1NZWcnByCgoL43//+R9WqVTl16hTh4eFkZWVhMpm09/Pzzz8zbdo09Ho9BoOB6dOnmyVPRebPn8+WLVu04wkTJrB7924KCgpwd3fnf//7H/7+/jf9vb399tuMHz+ew4cPk5ubS/v27Zk3bx6Ojo7Mnj2b5cuXU1BQgIODA59//jkdOnQo9T1bauXKlYwYMYKqVasCMHr0aJYvX14i+UhNTeXy5cvaKux+fn5Ur16djRs38vjjj5f6DC8vLxo3bsyWLVvo06ePVeIG6XZlVQWKI7rCN9qkT5Myy1/JvkJCRoKM9xBCCCFs5OTJk6SmphIWFgZARETETb8AdujQgUOHDgFqlytLvyTu2rWL999/n40bN3L48GH27NmDl5eXRffa2dlx4MABPvnkE8LDw1m0aJF2beHChYwePRqAkSNH8tJLL/Hnn38SGRnJwYMHWbVqlVld1atXZ8OGDbi5uREVFcWUKVP46quvOHDgAIcOHSIqKorTp08zZ84c7Z5Dhw6xfv36WyYeO3fuJDg4GD8/PwYPHszs2bO1a2lpaWzatImnnnqKZ599lkWLFmEyqUlnREQE7dq1s+gdACxevJjVq1fz66+/cvjwYTZu3IiTk1OJd7Vs2TIOHjzI0aNHqVatGl988QWgtlz069ePw4cPc+TIEcaPHw/Am2++yfz584mKiiI6OpquXbuWeHZiYiIZGRlma4FMnjyZAwcOEBUVxYsvvshrr71WIpai39u//vUvunTpwp9//snhw4cxmUzMnTsXgBEjRmj1fPHFF4waNeqmnz8mJkbr9vTXn1vdk5CQQP369bXjBg0akJCQUKJczZo18SB2zhMAACAASURBVPHx4fvvvwfULlgxMTHEx8drZebOnUtgYCD9+vUjKirK7P4OHTqwffv2m8Zwp6Tlw4oKcEJf9hhzjQw2F0IIUdncTquELQ0ZMgS9Xk9MTAxz5syhVq2yZ6C8E+vXr2fEiBH4+PgA4OLiYvG9RckFwDPPPENISAizZs3iwoULxMbG0qdPH65fv8727dvNVunOysoiJiamzPq3bdtGeHi49kX+ueee4z//+Y/W6vPkk0+W2tXoxm5XR48epUePHkRERFCnTh2+++47+vTpQ/Xq1alevTre3t5s3rz5pn8hnzhxIlu3buXy5cts2bKFFi1amF3/5ZdfeOGFF6hWrRpAiRYWULsPzZkzh/Xr12MwGMjIyKBjx44APPTQQ0ycOJGsrCy6du2q/ZX/4Ycf5rXXXuOJJ56gZ8+eNx0/cv78eby9vc3Obd26lS+++ILMzExMJhNpaWlm12/8vf3000/s27dPS8xycnK0LkqRkZFMnz6d1NRU7O3tiYmJIScnR+vuVMTf37/El35rWrt2LZMnT+ajjz6iRYsWdO7cWesuNn36dHx8fNDr9axZs4Y+ffpw6tQpXF1dAahduzbHjx+3ajySfFhRAY5k5hT+g7JgwLmsbC6EEELYRtGYj23bttG/f3+6d+9Oy5YtCQ0N5auvvipRft++fYSGqv8/btWqFfv27burQdv29vYYjUbtODc3t0SZoi94APXq1aN169asXbuWY8eOMXz4cOzt7bX79u/fT5UqVe44HqDELJw3Pr8sAQEB+Pr68ttvv/Hkk0/yzTffkJycTIMGDQDIzMzkm2++oU+fPoSEhPDnn39q7++TTz4B1L/OFxQU3FHsy5YtY8eOHezevRt3d3c+//xzrWvW4MGD6dixI1u3bmXevHl89tlnbNiwgdmzZ3Ps2DF27tzJyJEjefrpp5k0aZJZvS4uLma/m4SEBF5++WUOHDhA48aNiY6O5qGHHjK758b3pigKq1evxs/Pz6xMfn4+jz/+ODt37qRNmzZcu3aNatWqkZeXVyL5iImJYciQITf93CEhISxcuLDEeV9fX86dO6cdx8fH4+vre9M6goKC2LRpk3bcrFkzLQGsW7eudn7QoEFMmTKFmJgYWrVqBaj/bv8a792SbldWVIAjWXnqXxec3J3KKK0ONtehI8g7yNahCSGEEH9LPXr0YNy4cbz55psADBs2jNTUVGbOnKmV2bFjBwsWLNBm+pk0aRILFixg/fr1Wpnk5GQWL15cov7+/fuzdOlSkpKSAHXmpOzsbGrXro2iKNpfjZcsWVJmrKNGjWLBggUsWbJE++u6q6sr3br9P3v3HRbFuT58/LsUOyrRiChRQClK2aUZMSqgRrCAHssRxYI9aqo/eyy8icYuMWpiiUCUWI4mlthihBAbKi6igAWIoqKABpFQpc37B4eJK4sV1Jw8n+va62R3nnnmnpnlOPc+zUNjsPbt27dJSUl5qnPftGkThYWFFBcX8+2339K9e/cn7qdNSkoKiYmJWFpaolaruXv3Lrdv3yY5OZnk5GR+//13fv75Z+7evcu0adPYsGEDBw4ckPcvj0EbHx8f1q5dS1ZWFlA2vuThxA0gMzOTxo0bU79+fbKzszW6qCUmJmJkZMTw4cNZsmSJPHPZ5cuXsbGx4f3332fChAkVZjSDslaHO3fukJ+fD0BWVhb6+voYGxsjSRKrV69+7HXp27cvixcvls8tMzOTpKQkCgoKKCwslBOC8i5i2pS3fGh7aUs8oKzVavPmzeTm5vLgwQOCgoLw9fXVWrb8uwnI42S6dOkCoPE9OnXqFBkZGbRu/dfQgUuXLqFUVu1zqkg+qlCRVINGqUkAmHqYPrF8dGo0lo0sMaj5ejRRC4IgCML/ojlz5nD8+HHUajV169YlIiICtVqNmZkZFhYWBAQEsHfvXuzt7QGws7Pj4MGDrFy5EnNzc+zs7OjXr5/W7kCdO3dm3rx5eHp6olQqcXNz4+7du+jp6bFq1Sp69+6Ni4vLU/3i36dPH6KiojAyMqJNmzby599//z1JSUnY2trKsWRkZDyxvnHjxuHo6IijoyMqlQpTU1OtsxpVpnzMh0qlwtPTky+++AKlUsnGjRvx9fVFR+evx8iGDRvy7rvvsnnzZpRKJQcOHGDlypWYmZnRrl07PDw8mDBhQoUWAigbG1HeeqFUKunZsycPHjzQKDN8+HDy8vKwsrKiR48edOrUSd62c+dO7OzscHBwYNCgQaxduxaAWbNmYWNjg4ODA5s3byYgIKDCsWvVqkX37t3lVhQ7Ozt8fX2xsbHBxcWl0taEcoGBgdSuXRuVSoW9vT1du3YlOTmZ+vXrM3/+fNq1a4eTkxM1atR46uv+NNzd3Rk0aBB2dna0adOGd999Vx7gf/bsWXr27CmXXb9+PZaWllhYWPDTTz+xa9cuuRXM398fOzs7VCoVn3zyCTt27JC7v0mSRFhY2AtP2/wohVQ+7cLflImJyVNl/9Up68u7UFzMzDowblJP9tCXwfsGY9mr4h+YvE9BFg0XN2Sw7WC29K/6OZQFQRAE4WUoKSkhISEBS0vLKp2OUxBeljNnzvDZZ5+xb9++Vx3Ka+XQoUOEhoYSGhqqdfvj/vYf93wuWj6qQkkhIFHEk7talYtJKxtYJMZ7CIIgCIIgvDrt2rWjX79+f7tFBqtbVlbWY6dHfl5iwHlVKC0GRdm6HpJCIQabC4IgCIIg/I08PIOVUKayQfAvqtpbPhITE+nQoQOWlpa4uLgQHx9foUx4eDjt2rWjbdu22NjYMG3aNHmu6L+PZ5hjl7LB5gAOTR2qIxhBEARBEARBeO1Ue/Ixfvx4xo0bR0JCAtOnT8ff379CGUNDQ7Zt28bFixdRq9WcPHnyqWaF+DuLTo3GrKEZhrUrDl4TBEEQBEEQhP9F1Zp83Llzh7NnzzJ06FCgbB7mmzdvkpSUpFHOwcEBc3NzoGzWAZVKpbHy4t/JHzR+Ypncwlwu/3FZdLkSBEEQBEEQ/lGqNfm4efMmxsbG8iqKCoWCFi1aaF3+vVxaWho7d+6Upwt71IoVKzAxMZFfOTk51RL780qXylbJrNukbqVlLqRfoFQqFcmHIAiCIAiC8I/yWs129eeff+Lt7c20adNwdnbWWmby5MmkpKTIr2dZnfNl0KNskZnmLs0rLSMGmwuCIAiCIAj/RNWafLz11lukpqbKqz5KksSNGze0LtiSnZ2Nl5cXffr0YfLkydUZVrVTPGG6q/LkQww2FwRBEISqZ2pqipWVFSqVCisrK43VwQFu3bqFr68v5ubmWFhY4ObmVmH1a7VajZeXF+bm5jg7O/POO++we/ful3kaz2X06NG0bdu2yhaGmzdvHrq6uly/fl3jc3d3d8zMzFCpVLRu3Zp3331XY0X4ciNGjKB+/frk5uY+87FDQkLo27fvc8f+tFJTU2nfvv3farKj0tJSPvjgA1q1akXr1q0fuxL7oUOHcHZ2xt7envbt23P+/PkKZcLDw9HV1eXLL7+UP5syZQpbtlT9WnTVmnw0adIER0dHeXGSH374ARMTE41l2wFycnLw8vLCy8uL2bNnV2dI1Uq3pOiJiQeUzXTV3KA5RvWMXkJUgiAIgvDPs337dmJiYggPD2fhwoWcOXMGgNzcXNzd3XFwcODq1askJiYyd+5cvL29iYuLAyA+Ph5PT08mTZrE1atXOXv2LDt27CArK6taYi3/kfZFpaens23bNmJjY9m1a9cLH7+0tJSQkBDc3d0JDg6usD0wMJCYmBiSkpL49NNPGT16ND/++KO8/c8//+Snn35CqVSyY8eOZz+hl+Tzzz9n0qRJGiu2P42qum/PIzQ0lIsXL5KQkMCZM2dYunSp1hllMzMz8fPz47vvvuPChQssXboUPz8/jTJZWVnMmDFDY1V0gGnTphEQEEBJSUmVxl7t3a7WrVvHunXrsLS0ZNGiRfKXd8yYMezduxeAlStXcubMGX788UdUKhUqlYoFCxZUd2hVrnXiSS7TBukx0+4+KH5A/J140eVKEARB+N/k4wM2NtX38vF5pnCaN2+OtbW1/Mv91q1bMTQ0ZPr06XKZrl27MnLkSHlBtUWLFjFq1Ci8vb3lMs2aNWPEiBFajxEcHIxKpUKpVOLs7ExycjLJyck0bNhQLpOTk4NC8dfzgUKhYN68ebi4uDBz5kwsLS05e/asvD0kJERuvUhLS+Pf//437dq1w87OTusPtffv38fDw4OCggKcnJxYtGgRJSUlTJ06FVtbW2xtbfnggw8oLCwEwN/fn1GjRtG5c2dsbW21ntcvv/yCkZERy5YtIzg4+LEtA+7u7gQEBLBw4UL5s61bt9KtWzcmT57Mxo0bK923sLBQjlOpVOLl5VWhTFpaGh4eHjg5OWFjY8P7778vx3Pq1CmcnJxQqVTY2tryzTffAPDtt9/Stm1bVCoVdnZ2nD59ukK9BQUFbN++nf79+8uf+fn5yS0FvXr1Ii0tDUC+p9OnT8fR0ZHVq1c/9t5MmTIFFxcXVCoVnTt35sqVK5Veg2e1fft2xo4di66uLm+88QaDBg1i69atFcr9/vvvNGrUCBsbGwA6derEjRs3iI6Olsu8//77zJ49m0aNGmns26RJE1q1asXhw4erLG54CcmHlZUVkZGRJCQkcPbsWezs7ICyL4TPf/8P5NNPP6WoqIiYmBj59emnn1Z3aFVOcevJv4jE342nqLRIJB+CIAiC8BJcvnyZjIwM3N3dAYiOjsbV1bVCOVdXV9RqNVDW5UpbGW0iIiL47LPPOHjwIOfPn+fo0aM0adLkqfbV1dUlKiqKpUuX4u/vT0hIiLwtODhYXvhuxIgRTJo0iTNnznDu3Dm5JeZhDRs25MCBAxgYGBATE8OMGTNYv349UVFRqNVqYmJi+P333wkMDJT3UavV7N+/n8uXL2uNb+PGjYwaNQoHBwcaNWrEkSNHHns+b7/9tsav7+X79+7dm8TExEofvhcuXEhCQgJqtZrz58+zefPmCmUaNmzITz/9hFqt5sKFCyQnJ/Of//xH3n/KlCnExMQQFxeHr68vAP/3f/9HWFgYMTExREdHyw/gD4uKisLMzIw6derIn3355ZecPXuWCxcu0KlTJwICAuRtWVlZ2NjYEB0dzccff/zYezN9+nSioqKIiYlh4sSJfPTRR1rP/9dff5V/fH/0Vdnz8I0bN2jZsqX83tTUVOuEThYWFmRkZHDy5EkA9u7dS3Z2tjyr7M6dO9HR0ZGfyR/l6upKWFiY1m3PS6xwXoX0cvIA6OzToNIyYryHIAiC8D/tv70aXrVBgwaho6PDlStXCAwM5M0336yW4+zfv59hw4ZhbGwMoPEQ+yQPr6o9fPhwHBwcWL58Obdu3SIhIYEePXqQm5tLWFgY6enpctmcnJyn+hX9yJEj+Pv7U7NmTQDGjh3LmjVr5FafgQMHYmBgoHXfjIwMDh8+zIYNG+RYN27cSPfu3Ss9niT91fU8NjaW1NRUunfvjo6ODkOHDiUoKIjFixdX2G/fvn0sXrxYjlPbvSotLWX69OkcP34cSZK4c+cOtra2+Pr64uHhweeff05iYiJdunShY8eOQFmL1rBhw/D29qZHjx5YWlpWqDclJQUjI81u8Fu2bGHz5s0UFBRQUFBA48Z/LaOgr68vLyHxpHvzyy+/sGrVKrKzsyktLeXevXtar5uHhwcxMTHaL+oLatCgATt37mTmzJnk5OTg6upK27Zt0dPTIy0tjfnz5xMREVHp/k2bNuXixYtVGpNIPqpQz8NfsgU/DI1qVFpGzHQlCIIgCNVv+/btqFQqjhw5gre3N126dMHOzg5HR0fWr19foXxkZCSOjmX/Njs5OREZGflCg7b19PQ0+soXFBRUKPPwjJ0mJiY4OzuzZ88e4uPjGTp0KHp6evJ+p06dolatWs8dD6DR7evR4z9q8+bNFBcXo1QqASgpKSEjI4OMjIwK3XPKRUVFyV24Nm7cSHZ2tryOW1FREaWlpSxYsEBeguFZrFixgjt37nD69Glq1arF5MmT5Wvz8ccf06dPH44cOcKsWbOwtbXl66+/5ocffkCtVhMREUHPnj2ZP3++3CpSrk6dOhr35vjx43z11VdERkbSpEkT9u7dy9y5czXKl48NKU+2tN2bGzdu8P777xMVFUWrVq24cOECnTt31npuv/76K5988onWbb169dI6FKFFixZcv35dbqFLTk7WOqETlCU3Hh4eADx48ICmTZvStm1b1Go1qampqFQqAP744w/27t3L3bt35WMWFBRQu3ZtrfU+r9dqqt2/NwW5tf/b4uHiUmmp6NRoGtdpjEl9k5cUlyAIgiD8c3Xr1o0JEybIffEHDx5MRkaGxi/w4eHhBAUFMXXqVKBsoG1QUJDG7E1paWl89913Fer39vYmNDSU1NRUAPLy8sjLy6Np06ZIkiT/arxp06Ynxjpy5EiCgoLYtGmT3CpSr149PDw8NGbsun37NikpKU917ps2baKwsJDi4mK+/fbbx7ZcPGzjxo3s3LlTHr9y8+ZN+Vy1OXbsGAEBAUyfPp3CwkJCQ0M5deqUvP+tW7do0aKF1hmxfHx8WLlyJQ8ePADg7t27FcpkZmbStGlTatWqRVpamka3sytXrmBmZsbYsWOZNWsWp06dori4mN9//x1nZ2emTJnCgAED5EkHHmZvb6/RipSZmYmBgQGNGjWisLCQdevWVXqNHndvsrKy0NfXx9jYGEmSHjsbVXnLh7ZXZWOgBw4cyIYNGygpKeHevXts376dQYMGaS1b/t2EssH1Xbp0oXXr1vTq1Yv09HT5Hg0YMIC5c+dqHPPSpUtyAlpVRPJRheSB5vr6WrcXlxZzPv08jsaOFX59EARBEAShesyZM4fjx4+jVqupW7cuERERqNVqzMzMsLCwICAggL1792Jvbw+AnZ0dBw8eZOXKlZibm2NnZ0e/fv0wNDSsUHfnzp2ZN28enp6eKJVK3NzcuHv3Lnp6eqxatYrevXvj4uJCUVHRE+Ps06cPUVFRGBkZ0aZNG/nz77//nqSkJGxtbeVYMjIynljfuHHjcHR0xNHREZVKhampKR9//PET9ztz5gx37tyhW7duGp/7+flpDBz/5JNPUKlUWFhY8P/+3/9jw4YN9O/fn927d9OyZUusra0fu3+56dOnY2lpKcepbWD/Rx99xOnTp7GxsWHYsGEasa1evRobGxscHByYPXs2y5cvp6SkhFGjRmFra4tKpUKtVmtdysHMzAwjIyN5rIqXlxdWVlZYWVnRqVMnuVWgMpXdGzs7O3x9fbGxscHFxaXSVonnNWzYMKytrbGwsMDFxYXJkyfL46r37t3LmDFj5LJz587F2tqa1q1bc/369ccO/n+YJEmEhYVV2bTN5RTSwx30/oZMTEyeKvuvTllLryMpanJtrg9783vRJ7gPKv+KX9a4O3HYfWPHjHdmsLDbQi01CYIgCMLfS0lJCQkJCVhaWqKrq/uqwxGEZ7Zjxw4iIiJYs2bNqw7ltXLo0CFCQ0Mrbe163N/+457PxZiPl0iM9xAEQRAEQXi9DBw4kPT0dEpLS595rY//ZVlZWfL001VJJB9V6HrJW4DmbA8PE8mHIAiCIAjC6+f9999/1SG8diobQ/KiRHpXhUqksianJrba5/eOTo2mQc0GmBuav8ywBEEQBEEQBOG1IJKPavBGqzcqfFYqlXIu7RwOxg5isLkgCIIgCILwjySSj5ck6V4SOYU5ODYVXa4EQRAEQRCEfyaRfLwkYryHIAiCIAiC8E8nko8qVKMov9JtIvkQBEEQhJfD1NQUKysrVCoVVlZWGovAAdy6dQtfX1/Mzc2xsLDAzc2NU6dOaZRRq9V4eXlhbm6Os7Mz77zzDrt3736Zp/FcRo8eTdu2bV94bYaQkBAUCgWbN2+WP9u3bx/u7u7ye4VCgZ2dHSqVCmtraz744AONVd2fhqmpKTExMS8U69OYMmUK27Ztq/bjVKXTp0+jVCqxtLSkS5cu3Lp1S2u5/Px8hg8fjq2tLba2tvj4+MgLNUZGRqJSqVCpVNjY2DB+/Hh5MccLFy7Qo0ePl3Y+5UTy8ZJEp0ZTR78Olo0sX3UogiAIgvA/b/v27cTExBAeHs7ChQvl1a1zc3Nxd3fHwcGBq1evkpiYyNy5c/H29iYuLg6A+Ph4PD09mTRpElevXuXs2bPs2LGDrKysaom1uLi4SupJT09n27ZtxMbGsmvXrhc+fsuWLZk7dy6FhYWV7nvs2DFiYmK4cOECR48e5dChQ88cd3W7desWBw4ceObZm6rqvjyP0tJS/Pz8+PLLL0lISKBnz56VLhC5bt068vLyiI2NJS4uDiMjI5YuXQqAUqkkKiqKmJgYYmNjuXPnDl9//TVQtrp7zZo1CQ8Pf2nnBWKq3SqVSz2tn0uSRHRqNKqmKnR1xAJMgiAIwv+urT5byfw9s9rqN2xlyOC9g5+6fPPmzbG2tub69eu0a9eOrVu3YmhoyPTp0+UyXbt2ZeTIkSxZsoRNmzaxaNEiRo0ahbe3t1ymWbNmWlfeBggODmblypVIkoS+vj47d+4EQKVScf/+fQBycnIwMDCQp+NXKBTMnTuXAwcO4O7uzp49e9iyZQvOzs5AWcvDnj172LVrF2lpaXz44YckJyeTn59Pnz59mD9/vkYM9+/fx8PDg4KCApycnPD19WXq1KnMmDGDgwcPAuDh4cHy5cupUaMG/v7+6OjokJSUxJ07d7h8+XKF81KpVOjq6rJmzRo++eSTx17n/Px8Hjx4oHUVeCj7BX7q1KlkZ2cjSRKff/45ffr00SizYsUKtm7dSlFREfr6+nz11Ve4urpSWlrKhx9+SFhYGDVq1EBPT48TJ06QnZ2Nn58fqampKBQKnJycCA4OrnDsoKAg+vfvL0/4ExYWxuzZsykoKKCwsJDJkyczevRoAK3XZfPmzaxevZqioiLq1avHqlWrUCqVxMbGMmHCBPLy8igoKGDIkCHMnj37sdfpaanVavT09PDw8ABg/Pjxcsy1atXSKKtQKMjLy6OoqAgdHR1ycnLk1c7r1KkjlyssLCQ/P19j4qPBgwezbt06unTpUiVxPw2RfFShK1gDoKOv2aB0Pes6mQWZYrC5IAiCILxkly9fJiMjQ+4uFB0djaura4Vyrq6u8oOjWq1mwYIFT1V/REQEn332GSdPnsTY2Ji8vDwA7ty588R9dXV1iYqKAsDQ0JCQkBA5+QgODmbKlCkAjBgxglmzZuHm5kZxcTG9e/dmx44dDBw4UK6rYcOGHDhwAJVKJXdj+uabb4iKikKtVqOrq4uPjw+BgYFy4qVWqzl+/DgGBgaVxvjFF1/g5uYmP5w/qlOnTvLDev/+/enQoUOFMvfu3aNv377s3LmTTp06UVpaKidlDxs2bBiTJ08G4NSpU/j7+3P58mXOnz9PWFgY8fHx6OjokJWVRY0aNQgNDcXMzIzDhw/Lx9EmIiJCI3lydHTk+PHj6Orqcu/ePRwcHPD09MTExKTCdTlx4gRbt27l6NGj1KxZk2PHjjFkyBDi4+MxNTUlLCyMmjVrkp+fT4cOHejWrRvt27evEMOgQYO4cuWK1vh++ukn3nrrLY3Pbty4QcuWLeX3BgYG1K9fn9u3b2Nurrlkw/jx4zl58iRNmjRBV1eXt99+W2PdkuTkZPr06cPvv/9Or169mDhxorzN1dWVSZMmaY2ruojkoxrUNKip8V6M9xAEQRD+KZ6lVaI6DRo0CB0dHa5cuUJgYCBvvvlmtRxn//79DBs2DGNjY0Dzl+YnGTVqlPzfw4cPx8HBgeXLl3Pr1i0SEhLo0aMHubm5hIWFkZ6eLpfNycmp9EH2YUeOHMHf35+aNcueS8aOHcuaNWvk5GPgwIGPTTwArKys8PHxYfHixVqTtmPHjtGwYUPy8vLo378/q1at4oMPPtAoExkZiZWVFZ06dQJAR0eHN96ouCzBuXPnWLBgARkZGejp6XHlyhXy8/MxNzenuLiYUaNG4eHhQa9evdDR0aF9+/YEBgbyf//3f3Tu3BkvLy+t55CSkoKRkZH8PiMjg9GjR5OQkICenh4ZGRnExcXJycfD12XPnj2cP3+et99+W97/3r175Ofnk5+fz8SJE4mJiUFHR4ebN28SExOjNfnYvn37Y6/zizh8+DClpaWkpaWho6ODv78/c+fOlVvHTE1NOX/+PDk5OQwdOpQff/wRX19fAJo2bUpGRobWFpXqIpKPqvLfRc3d3tWvsEkkH4IgCILwcm3fvh2VSsWRI0fw9vamS5cu2NnZ4ejoyPr16yuUj4yMxNGx7N9pJycnIiMjX2jQtp6ensbg64KCggpl6tX7q7u2iYkJzs7O7Nmzh/j4eIYOHYqenp6836lTp1744fDRdcYePv7jBAQEoFQqMTU1rbRMnTp18Pb2Zv/+/RWSj6dRWFhIv379+PXXX3FxceHPP/+kQYMGPHjwgIYNGxIXF8dvv/3Gr7/+ysyZMzl69Ciurq7ExMRw5MgRfvzxR+bMmcO5c+fQ1dXs4l6nTh2N6//ee+/Rs2dPfvjhBxQKBY6OjhrbH74ukiQxYsQIvvjiiwoxf/DBBzRu3Jhz586hp6dHv379tN5nePaWjxYtWnD9+nX5fXZ2NllZWTRr1qzC/uvXr2fIkCHy98PPz09rvPXq1cPX15fvv/9eTj4KCgrQ1dWlRo0aWmOrDmLAeRWzalNxTEd0ajQ1dGvQ9s22ryAiQRAEQfjn6tatGxMmTJC7VA0ePJiMjAwWL14slwkPDycoKIipU6cCMG3aNIKCgti/f79cJi0tje+++65C/d7e3oSGhpKamgpAXl4eeXl5NG3aFEmSuHjxIgCbNm16YqwjR44kKCiITZs2ya0i9erVw8PDQ2PGrtu3b5OSkvJU575p0yYKCwspLi7m22+/pXv37k/cvwsu/QAAIABJREFU71HNmjVjzJgxWh9oy5WUlBAREYGVlVWFbR06dCAxMZFjx44BZYOpH+0iVT7+okWLFgCsWrVK3nb37l1yc3Pp3r07X3zxBaamply8eJFr165Rr149/v3vf7Nq1SoSEhLIycmpcHx7e3uNB//MzExatmyJQqHg6NGjnD9/vtLz8vHxITQ0lBs3bsixnz17Vq7HxMREbqX55ZdfKq2nfAIEba9HEw8oS4CLior49ddfgbJB5d7e3loTUHNzcw4fPowkSUiSxP79+7G1tQUgKSmJoqIioCzB27VrF/b29vK+ly5dwtbWFh2dl5cSiOSjiulrSRzPpZ3D3sgefd2KrSKCIAiCIFSvOXPmcPz4cdRqNXXr1iUiIgK1Wo2ZmRkWFhYEBASwd+9e+aHMzs6OgwcPsnLlSszNzbGzs6Nfv35aB1N37tyZefPm4enpiVKpxM3Njbt376Knp8eqVavo3bs3Li4u8gPg4/Tp04eoqCiMjIxo06aN/Pn3339PUlIStra2ciwZGRlPrG/cuHE4Ojri6OiISqXC1NS00hmTnmTGjBn8+eefFT7v1KkTKpUKOzs7dHV1mTdvXoUyhoaG7Nq1ixkzZmBvb4+joyMnTpzQKFO/fn3mz59Pu3btcHJy0vgl/ubNm7z77rvY29vL08n26NGDiIgInJycUKlUdOjQgaVLl9KgQYMKxx8wYAA///yz/H7RokXMmDEDlUpFUFCQRpcqbee3ZMkS/vWvf6FUKrGxsZGn7J09ezbBwcHY29szY8aMKh20raOjQ2hoKB999BGWlpbs27ePwMBAeXvPnj3lJCggIICcnBz52qSnp8tjlsLDw3FwcECpVOLg4ICRkRFz5syR6zl06BADBgyosrifhkIqn3bhb8rExOSpsv/qlLX0OhI1WTltHZOm1KLx0r9m0EjNTqXZimaMcxzHOu91rzBKQRAEQah6JSUlJCQkYGlpWaG7iyC8DkpLS2nXrh27d++Wx3UIZS0hzs7OhIeH07hx42fe/3F/+497PhctH9WsfLyHg7HDK45EEARBEAThn0dHR4d169aRnJz8qkN5rVy7do1FixY9V+LxIsSA8yqiKB9x/sjgLTHYXBAEQRAE4dVycnJ61SG8dqysrLSO0aluouWjqvXTnBkjOi0aXYUudk3sXlFAgiAIgiAIgvB6EMlHVXh41MwjU5VFp0bT9s221Nav/XJjEgRBEARBEITXjEg+qoRC66d/5P3BjawbosuVIAiCIAiCICCSj2p1LvUcIMZ7CIIgCIIgCAKI5KNaicHmgiAIgiAIgvAXkXxUo+i0aBQoUBopX3UogiAIgvCPYWpqipWVFSqVCisrK43VwQFu3bqFr68v5ubmWFhY4ObmxqlTpzTKqNVqvLy8MDc3x9nZmXfeeYfdu3e/zNN4LqNHj6Zt27b861//enLhx7h//z5Dhw7F1tZWXtxvy5Yt3L59m7p161ZY5PDcuXM0btyYwsJC/P39USgUnDt3Tt6enZ1NvXr1UKlUzxRHQEDAcy+M+CzOnz9Pr169qv04VSkvL4/BgwfTunVrLC0t2blzZ6VlN2/ejFKpxNbWlq5du8ortj8sODgYhUKh8T0fOHAgJ0+erNK4RfJRBSpbpTE6NRrLRpYY1DR4qfEIgiAIwj/d9u3biYmJITw8nIULF3LmzBkAcnNzcXd3x8HBgatXr5KYmMjcuXPx9vYmLi4OgPj4eDw9PZk0aRJXr17l7Nmz7Nixg6ysrGqJtbi4uErqSU9PZ9u2bcTGxrJr164XOv7s2bN58803iY2N5cKFC0RGRuLi4kKzZs3o0qULW7Zs0Si/ceNGhg0bJq9M7uTkRFBQkLx9+/btGqu2v25mzpzJjBkznnm/qrp3z2PZsmXUrFmTpKQkfv75ZyZOnFghKQS4fPkyU6dO5dChQ8TFxTFy5EgmTJigUSY5OZkNGzbQvn17jc8//fTT57oujyOSjypRccB5VkEWSfeSRJcrQRAE4Z8lLhGi4qrvFZf4TOE0b94ca2trrl+/DsDWrVsxNDRk+vTpcpmuXbsycuRIlixZAsCiRYsYNWoU3t7ecplmzZoxYsQIrccIDg5GpVKhVCpxdnYmOTmZ5ORkGjZsKJfJyclBofjreUGhUDBv3jxcXFyYOXMmlpaWnD17Vt4eEhIit16kpaXx73//m3bt2mFnZ8fs2bMrxHD//n08PDwoKCjAycmJRYsWUVJSwtSpU7G1tcXW1pYPPviAwsJCAPz9/Rk1ahSdO3fG1ta2Qn0pKSkYGxvLMRsYGGBhYQGUta48nFg8ePCALVu2MHr0aPmzfv36sW/fPh48eCBfo1GjRmm9fgBZWVmMGTMGW1tblEql1rKxsbF07NgRR0dH2rZty/z58+VtP/30E/b29qhUKmxtbdmzZw8A8+fPp02bNqhUKlQqlfw9eNiNGzeIj4+nU6dOQFlC4enpibOzMzY2NgwZMoTc3FwAIiIisLGxYfTo0ahUKnbt2kViYiK9evXCxcUFe3t7Vq9eLdft5+eHs7Mz9vb29OrVi7S0tEqvwbPavn077733HgBmZma4u7trTTrj4uKwt7fH2NgYgJ49e3Lw4EE5USktLWXMmDGsWrWKmjVrauyrUqm4e/culy5dqrK4xSKD1SQmLQYQ4z0EQRAE4VW6fPkyGRkZuLu7AxAdHY2rq2uFcq6urvJDvVqtZsGCBU9Vf0REBJ999hknT57E2NiYvLw8AO7cufPEfXV1dYmKigLA0NCQkJAQnJ2dgbKH9SlTpgAwYsQIZs2ahZubG8XFxfTu3ZsdO3YwcOBAua6GDRty4MABVCoVMTFlzyDffPMNUVFRqNVqdHV18fHxITAwUE681Go1x48fx8CgYg+Njz76iAEDBrB9+3ZcXV3x8vKid+/eAPTu3Zv33nuPmJgY+QHcwsJCI4mpU6cO7777Lrt370apVCJJ0mNbPj7++GNq167NhQsX0NHR4e7duxXKmJqaEhYWRs2aNcnPz6dDhw5069aN9u3bM3v2bNatW4erqyulpaX8+eefZGZmsmzZMlJTU6lduzZ5eXno6FT83f23337DxcVF475s2bKFRo0aIUkSEydOZNWqVXILwKVLl/j666/ZuHEjJSUlvP3224SGhmJtbU1eXh7t27fn7bffxsXFhS+//JI333wTKEtqAwICWLt2bYUYvv/+e5YuXar12owdO5ZJkyZV+PzGjRu0bNlS4/po606lVCqJjo4mISEBS0tLQkNDkSSJ69ev06hRI1asWME777xT6UKMrq6uhIWFVVnLlUg+qokYbC4IgiD8I9lavOoIABg0aBA6OjpcuXKFwMBA+QGwqu3fv59hw4bJvyrXqVPnqfd9+Nf94cOH4+DgwPLly7l16xYJCQn06NGD3NxcwsLCSE9Pl8vm5ORw5cqVJ9Z/5MgR/P395V+zx44dy5o1a+TkY+DAgVoTDwAPDw9u3LjBb7/9xsmTJxk/fjx9+/ZlzZo16OnpMXz4cIKCgvjqq68ICgrSaPV4+Pzmzp2LUqlk5MiRj4113759nD59Wk4OtN2v/Px8Jk6cSExMDDo6Oty8eZOYmBjat29P165d5YSpe/fuqFQqSkpKsLCwYOjQoXTv3p1evXphYmJSod6UlBSMjIzk95IkERgYyP79+ykuLiYrK4sOHTrI283NzXFzcwPgypUrxMfH4+vrK2/Pzs7m4sWLuLi4sGXLFjZv3kxBQQEFBQU0btxY6/n7+fnh5+f32Gv0vCwsLFi7di3Dhw+nuLiYXr160bBhQ/T09IiLi+OHH37g6NGjle7ftGlTUlJSqiwekXxUk+i0suTDoanDK45EEARBEP55tm/fjkql4siRI3h7e9OlSxfs7OxwdHRk/fr1FcpHRkbi6Fj2g6GTkxORkZEvNGhbT0+PkpIS+X1BQUGFMvXq1ZP/28TEBGdnZ/bs2UN8fDxDhw5FT09P3u/UqVPUqlXrueMBNLp9PXp8berWrUvPnj3p2bMnvXv3pnv37qxZswYoSyw6duzIhx9+SGRkpNbBzu3bt+f27dtcunSJixcvolarXyj+WbNm0bhxY86dO4eenh79+vWTr8+KFSuIj4/n119/ZcSIEfj5+TFt2jROnTrFyZMniYiIoH379mzdulXuXlWuTp06Gvdny5YthIeH89tvv1G/fn2++uorwsPD5e0PXzdJknjjjTfk1qaHHT9+nK+++orIyEiaNGnC3r17mTt3rtZze56WjxYtWnD9+nU58U1OTqZ79+5a6xgwYAADBgwAyrrxLV68mNatW/Pdd9+RnJwsd6lLS0tj3LhxpKamyuNCCgoKaNCggdZ6n4cY81FFHh10Hp0ajVlDMwxrG76SeARBEARBgG7dujFhwgS5S9XgwYPJyMhg8eLFcpnw8HCCgoKYOnUqANOmTSMoKIj9+/fLZdLS0vjuu+8q1O/t7U1oaCipqalA2QxEeXl5NG3aFEmSuHjxIgCbNm16YqwjR44kKCiITZs2ya0i9erVw8PDQ2PGrtu3bz/VL9HdunVj06ZNFBYWUlxczLffflvpw+mjDh8+TGZmpvxerVbTqlUr+b21tTXW1tYMHjyY/v37U79+fa31rFy5kmXLllXawlLOx8eHZcuWUVpaCqC121VmZiYmJibo6elx5coVfvnlF3nb5cuXsbGx4f3332fChAmcOnWK7Oxs0tPT6dSpE3PmzKFjx44aM3CVs7e312hJyszMpHHjxtSvX5/s7GxCQkIqjdvKyor69esTHBwsf5aUlMS9e/fIzMzEwMCARo0aUVhYyLp16yqtx8/Pj5iYGK0vbYkHlLVclXfhunbtGhEREfTt21dr2fLvZ0lJCdOnT2fSpEnUqVOHCRMmkJqaKo9Tat++PevXr9cYkH7p0iWUyqqbuVUkH1VAQkH5oHNDc0NyC3O5/Mdl0eVKEARBEF4Dc+bM4fjx46jVaurWrUtERARqtRozMzMsLCwICAhg79692NvbA2BnZ8fBgwdZuXIl5ubm2NnZ0a9fPwwNK/6g2LlzZ+bNm4enpydKpRI3Nzfu3r2Lnp4eq1atonfv3ri4uFBUVPTEOPv06UNUVBRGRkYa/eu///57kpKSsLW1lWPRNqvRo8aNG4ejoyOOjo6oVCpMTU2fetra2NhYeTC6vb09e/fuJTQ0VKPM6NGjOXv2rNYuV+W6du3KoEGDnni8wMBAHjx4gJ2dHSqVilmzZlUoM3v2bIKDg7G3t2fGjBl06dJF3jZr1ixsbGxwcHBg8+bNBAQEkJWVRb9+/bCzs8Pe3p6ioiKtkwZ07NiRlJQU7t27B5R1gcvLy8PKyooePXpUaCl5mJ6eHvv27ePHH3/E3t5eHoyen5+Pl5cXVlZWWFlZ0alTp2eeZvhJpk6dSn5+Pq1atcLT05PVq1fL3brWrl2r0coyatQo2rZti6WlJQ0aNOCLL754qmPk5uYSGxtLt27dqixuhSRJlc0U+7dgYmJSpf3QnkfmkhQUCj1Wz1zL7OIAIm9G0iGoAwu6LGBWp4p/PIIgCILwv6KkpEQeyKqrq/uqwxGE51Le5am89Usos3btWlJSUjRmFiv3uL/9xz2fi5aPKlCevtm0KZu+Tgw2FwRBEARB+Pv46KOPnjgG5p9IR0eHmTNnVmmdYsB5FZCksi5XUmnZ/55LK+tPKAabC4IgCIIgvP5q1KhRYeE9oazrXlUTLR9VyKJ1WX/O6NRomhs0x6ie0RP2EARBEARBEIR/DpF8VCEdHXhQ/IC4O3Giy5UgCIIgCIIgPEIkH1VIVwHxd+MpKi0SXa4EQRAEQRAE4REi+agK/x1wrlCIweaCIAiCIAiCUBmRfFQBxX+zD4Werkg+BEEQBOEVMzU1xcrKCpVKhZWVlcYCfQC3bt3C19cXc3NzLCwscHNz49SpUxpl1Go1Xl5emJub4+zszDvvvMPu3btf5mk8l9GjR9O2bdsXWp0dyqae7dWrl/w+OzubVq1aySt9S5LE6tWrsbOzw9raGkdHR7p3786vv/4KlK22rauri0qlQqVSYW1trXW61idRKBTcv3//hc7laQwcOJDIyMhqP05V2rdvH9bW1lhYWNCvXz/+/PNPreXS09Pp168f9vb2tGnThi+//FLetmDBAvkeqVQq6tevz+TJkwG4cOECPXr0qPrApb+55s2bv+oQpD++uCllLkmVLgXukd7e8LbUeEljqbS09FWHJQiCIAjVrri4WLp48aJUXFz8qkORtWzZUjp37pwkSZKUkpIi1a9fXzp9+rQkSZKUk5MjtW7dWlq0aJFc/siRI1Ljxo2l2NhYSZIkKS4uTmrUqJG0d+9eucytW7ekkJCQaom3qKioSupJS0uT6tSp88z3QtvxS0pKpA4dOkgbNmyQJEmSxowZI02aNEne/umnn0qurq7SzZs35c/OnDkjffPNN5IkSdK1a9ekBg0ayNvu378vGRkZSXFxcc8UGyBlZmY+0z7P6vTp01KXLl2eeb+qum/PIzs7W2rSpIl06dIlSZIkadKkSdKUKVO0lh0yZIj06aefSpJU9v1XKpXSmTNnKpQrKCiQ3njjDens2bPyZ3369JHCwsK01vu4v/3HPZ+LqXarwn+7XUlSCefTz9O5ZWcUCsWrjUkQBEEQXoGcbTmUZJZUW/26hrrU83369RiaN2+OtbU1169fp127dmzduhVDQ0OmT58ul+natSsjR45kyZIlbNq0iUWLFjFq1Ci8vb3lMs2aNdO6OjZAcHAwK1euRJIk9PX12blzJwAqlUr+1T4nJwcDAwOk/y4OplAomDt3LgcOHMDd3Z09e/awZcsWnJ2dAQgJCWHPnj3s2rWLtLQ0PvzwQ5KTk8nPz6dPnz4VWhHu37+Ph4cHBQUFODk54evry9SpU5kxYwYHDx4EwMPDg+XLl1OjRg38/f3R0dEhKSmJO3fucPnyZY36dHR0CAkJoXPnzhQVFREREUFMTIx8LsuWLSMmJgYTExN5HxcXF1xcXLReo9zcXCRJon79+lq379+/n4CAAAoLC1EoFKxbt463335bo8yUKVP47bffKCoqon79+mzYsAErKyvy8/Px9/cnNjYWfX19jIyMOHz4MImJifj7+5OTk0NpaanW6wawbt06hgwZIr/fsmULK1eupLCwkNLSUubPny9/F9zd3bG3tycqKoratWsTHh7OsmXL+M9//kNxcTFNmjRh3bp1tGzZkrCwMGbPnk1BQQGFhYVMnjz5savBP4uDBw/i4OCAtbU1ABMnTqR79+7yYokPO3/+PJMmTQKgbt26dO7cmc2bN1e4V7t37+att97CyclJ/mzw4MGsW7dOYzX5FyWSjypQvs7HrdppFPxZgGNT0eVKEARBEF4Hly9fJiMjA3d3dwCio6NxdXWtUM7V1ZXZs2cDZV2uFixY8FT1R0RE8Nlnn3Hy5EmMjY3Jy8sD4M6dO0/cV1dXl6ioKAAMDQ0JCQmRk4/g4GCmTJkCwIgRI5g1axZubm4UFxfTu3dvduzYwcCBA+W6GjZsyIEDB1CpVHKS8M033xAVFYVarUZXVxcfHx8CAwPlxEutVnP8+HEMDAy0xmdhYcGHH37IxIkTiYiIoG7dugBcvHiRmjVryg++lcnOzkalUskrYU+bNo233nqrQrmEhARGjhzJ0aNHsba2pqioSL6OD5s+fTrLli0DYNu2bXz00UccOnSIQ4cOcf/+fS5evAjAvXv3AFi9ejW9e/eWF8kr//xRERERfPLJJ/J7T09PBg8ejEKhIDk5mfbt23P9+nVq1qwpx3v06FH09fXZsmULV65cITIyEl1dXTZv3szEiRPZv38/jo6OHD9+HF1dXe7du4eDgwOenp4aCVu5Tp06kZ2drTW+8vv3sBs3btCyZUv5vampKampqRQXF6Onp/l47+TkxJYtW2jfvj0ZGRn8/PPPWFlZVTjOxo0bKyRHrq6ucuJSVUTyUYVSDG9CuhjvIQiCIPxzPUurRHUaNGgQOjo6XLlyhcDAQN58881qOc7+/fsZNmwYxsbGANSpU+ep9x01apT838OHD8fBwYHly5dz69YtEhIS6NGjB7m5uYSFhZGeni6XzcnJ4cqVK0+s/8iRI/j7+8sPzWPHjmXNmjVy8jFw4MBKEw+AkpISfvrpJ0xMTDh37hxubm7ytod7eOTn5+Pq6kphYSHNmjXjyJEjABgYGMiJ0L179+jatSsuLi74+PhoHOeXX37By8tLTmb09fVp0KBBhXh++eUXVq1aRXZ2NqWlpXIyoVQquXTpEhMnTsTNzY2ePXsC0LlzZ6ZOnUpOTg5ubm5069ZN63mmpKRgZPTX2mzXrl3Dz8+PlJQU9PT0uHfvHteuXZPjGzp0KPr6+kBZa0FUVJTcWlBS8lerX0ZGBqNHjyYhIQE9PT0yMjKIi4vTmnwcO3ZMa2xVYfny5UyZMgUHBweaNGmCu7s7d+/e1Shz/fp1jh8/zrZt2zQ+b9q0KRkZGRQUFFCrVq0qiUcMOK9CMblJgEg+BEEQBOFV2759O5cuXeLw4cPMmDGD2NhYABwdHbUOLI6MjMTRsezfbycnpxcefKynp6fxIFpQUFChTL16fyVqJiYmODs7s2fPHr777juGDh2Knp6e3E3r1KlTxMTEEBMTQ1JSktxK8ywe7RL+8PG1Wbp0KY0bN+bo0aMsXLiQxMREANq2bUtBQYGcANWuXZuYmBi+/vpr/vjjD611vfHGG7z77rv8/PPPzxw3lP3S//777xMaGkpcXBzbtm2Tr6m5uTkXL17Ey8uLEydOYGtrS2ZmJv379+fEiRNYWVnJrSDa1KlTR+P++Pr6MmbMGOLi4oiJiaFevXoa2x++bpIkMXPmTPnexMbGyt+19957j44dOxIbG0tMTAyWlpZavwdQ1vLx8MDvh18Pf4/KtWjRguvXr8vvk5OTMTY2rtDqAdC4cWNCQkI4f/48v/zyCwqFAhsbG40ywcHB9OnThzfeeEPj84KCAnR1dalRo4bWuJ+HSD6qQPlsV9E5iTSo2QBzQ/NXHJEgCIIgCADdunVjwoQJ8sP64MGDycjIYPHixXKZ8PBwgoKCmDp1KgDTpk0jKCiI/fv3y2XS0tL47rvvKtTv7e1NaGgoqampAOTl5ZGXl0fTpk2RJEnuCrRp06Ynxjpy5EiCgoLYtGmT3CpSr149PDw8NGbsun37NikpKU917ps2baKwsJDi4mK+/fZbunfv/sT9AGJjY/nqq69Yt24dZmZmBAQEMGrUKEpLS6lXrx6TJ09mzJgx3Lp1S94nNze30voePHggJwKP8vT05Oeff5bHnRQVFZGVlaVRJisrC319fYyNjeWZtsqlpKSgUCjw8fFh2bJlSJLEzZs3SUxMxMjIiOHDh7NkyZIKM5qVs7e312hJyszMxMzMDIDQ0FAyMzMrPa++ffuydu1auRWmqKiIc+fOyfW0bNkShULB0aNHOX/+fKX1HDt2TE5gHn092uUKwMvLi+joaPmaff311/j6+mqtOyMjg6KiIgDOnTvH7t27mThxory9tLSU4OBgreNRLl26hK2tLTo6VZcyiOTjRRUVgQSlUinncpNwMHYQg80FQRAE4TUyZ84cjh8/jlqtpm7dukRERKBWqzEzM8PCwoKAgAD27t2Lvb09AHZ2dhw8eJCVK1dibm6OnZ0d/fr1w9DQsELdnTt3Zt68eXh6eqJUKnFzc+Pu3bvo6emxatUqevfujYuLi/zw9zh9+vQhKioKIyMj2rRpI3/+/fffk5SUhK2trRxLRkbGE+sbN24cjo6OODo6olKpMDU15eOPP37ifkVFRQwfPpxly5bJ3cnee+89atasKU/TumDBAgYMGCB3l+rYsSOrVq1i4cKFcj3lYz7KX0qlkgkTJlQ4XuvWrQkODmbo0KEolUrefvvtCt3K7Ozs8PX1xcbGBhcXF1q0aCFvi42N5Z133kGpVOLg4MCwYcOwt7dn586d2NnZ4eDgwKBBg1i7dq3W8x0wYIBGi8zKlSsZMGAADg4OnDt3TuNYj/Lz88Pf3x8PDw+USiUqlUqejnjRokXMmDEDlUpFUFBQhQH0L8LAwIBvv/2Wvn370rp1a1JSUpgzZ468XaVScfv2bQDOnDlD27ZtadOmDePHj+c///mPfF+hrHuejo4OXbt2rXCcQ4cOMWDAgCqLG0Ahlbfn/U2ZmJg8VfZfbXJzyQjM5Jr+dVwKOjK5/WSWey5/dfEIgiAIwktUPpjY0tJS6y+0gvC6y8nJoUOHDkRGRsqD6gUoLCzE2dmZ8PBwGjduXGH74/72H/d8Llo+qsj50v/2JRXjPQRBEARBEP426tWrR2BgINeuXXvVobxWrl27xqJFi7QmHi9CzHZVBfRq63O+QCQfgiAIgiAIf0fauhz901lZWWkdo/OiRMvHiyotBeCCFEsd/TpYNrJ8xQEJgiAIgiAIwutJJB8vSiqbZu18yQVUTVXo6oj+roIgCIIgCIKgjUg+qsAN6Sb3yRIrmwuCIAiCIAjCY4jkowqIweaCIAiCIAiC8GTVnnwkJibSoUMHLC0tcXFxIT4+Xmu5jRs3YmFhQatWrRg7duxTzYf9urhQEgeI5EMQBEEQBEEQHqfak4/x48czbtw4EhISmD59Ov7+/hXKXLt2jTlz5nDs2DGSkpJIT09n/fr11R1alZAkiQulF6hBDdq+2fZVhyMIgiAI/3impqZYWVmhUqmwsrLSWB0c4NatW/j6+mJubo6FhQVubm4VVr9Wq9V4eXlhbm6Os7Mz77zzDrt3736Zp/FcRo8eTdu2bfnXv/71QvWEhITQt29f+f2RI0do1qwZv/32G8nJySgUCo0VsXNycjQWWXZ3d6dRo0YaK5UPGDCAkJCQZ4rD1NSUmJiY5z+RpzRlyhS2bdtW7cepSqdPn0apVGJpaUmXLl00Vpt/WH5+PsOHD8fW1hZbW1t8fHzbFPrCAAAabklEQVS4e/euvD02NhZ3d3fatGlDmzZt+PHHHwG4cOECPXr0qPK4qzX5uHPnDmfPnmXo0KEA9O/fn5s3b5KUlKRRbufOnfj4+NC0aVMUCgXvvfceW7durc7QqkxxicT50ljaYo2+rv6rDkcQBEEQBGD79u3ExMQQHh7OwoULOXPmDAC5ubm4u7vj4ODA1atXSUxMZO7cuXh7exMXV9aTIT4+Hk9PTyZNmsTVq1c5e/YsO3bs0HiQrkrFxcVVUk96ejrbtm0jNjaWXbt2Vdnxf/jhB0aOHMm+fftwc3MDoE6dOhw8eJCLFy9Wul/9+vUrJH6vo1u3bnHgwAEGDRr0TPtV1X17HqWlpfj5+fHll1+SkJBAz549K129ft26deTl5REbG0tcXBxGRkYsXboUgLy8PPr06cP8+fO5dOkScXFxdOrUCQB7e3tq1qwpr9heVap1nY+bN29ibGyMnl7ZYRQKBS1atODGjRu0bt1aLnfjxg1atmwpvzc1NeXGjRta61yxYgUrVqyQ3+fk5FRT9E8nLi6BdOkO7+L2SuMQBEEQhNeBz1Yffs/8vdrqb2XYir2D9z51+ebNm2Ntbc3169dp164dW7duxdDQkOnTp8tlunbtysiRI1myZAmbNm1i0aJFjBo1Cm9vb7lMs2bNGDFihNZjBAcHs3LlSiRJQl9fn507dwKgUqm4f/8+UPa8YmBggCRJQNkz0dy5czlw4ADu7u7s2bOHLVu24OzsDJS1POzZs4ddu3aRlpbGhx9+SHJyMvn5+fLD4sPu37+Ph4cHBQUFODk54evry9SpU5kxYwYHDx4EwMPDg+XLl1OjRg38/f3R0dEhKSmJO3fucPnyZa3nFhQUxPz58zly5IjGmg/6+vrMnDmTmTNnsmfPHq37Tp8+nblz5/LBBx/QrFmzym8SEBkZydSpU8nOzkaSJD7//HP69OmjUWbFihVs3bqVoqIi9PX1+eqrr3B1daW0tJQPP/yQsLAwatSogZ6eHidOnCA7Oxs/Pz9SU1NRKBQ4OTkRHBys9Rz79+8vt9yEhYUxe/ZsCgoKKCwsZPLkyXIrj7brtnnzZlavXk1RURH16tVj1apVKJVKYmNjmTBhAnl5eRQUFDBkyBBmz5792OvwtNRqNXp6enh4eABlPY3KY65Vq5ZGWYVCQV5eHkVFRejo6JCTk4OdnR0AW7ZsoX379nTs2BEAXV1d3nzzTXnfwYMHs27dOrp06VIlccPfcJHByZMnM3nyZPm9iYnJK4wGlB0cOH33IHpN6r7SOARBEARBqOjy5ctkZGTg7u4OQHR0NK6urhXKubq6yg+GarWaBQsWPFX9ERERfPbZZ5w8eRJjY2Py8vKAst4fT6Krq0tUVBQAhoaGhISEyMlHcHAwU6ZMAWDEiBHMmjULNzc3iouL6d27Nzt27GDgwIFyXQ0bNuTAgQOoVCq5m9I333xDVFQUarUaXV1dfHx8CAwMlBMvtVrN8ePHMTAw0BrfsWPHiIyM5OzZs7z11lsVtr/33nt8+eWXnDhxAqVSWWF706ZNGT9+PPPmzWPDhg2VXod79+7Rt29fdu7cSadOnSgtLZWTtocNGzZMfgY8deoU/v7+XL58mfPnzxMWFkZ8fDw6OjpkZWVRo0YNQkNDMTMz4/Dhw/JxtImIiOCTTz6R3zs6OnL8+HF0dXW5d+8eDg4OeHp6ys+cD1+3EydOsHXrVo4ePUrNmjU5duwYQ4YMIT7+/7d370FR3ef/wN+7rJK0FowVgYALooIiLMtylRjjoomgotXgJRUvRTBiMjVNqyGMiTeS0hiGZrQppAqRoBnbqEjUWotI1FYboCwVISAWBAQjXWwUCuLC5/cHP89XAujiZRfx/Zphht3z2XOePfuI5zmfy56Hs7Mzjh8/DktLS7S0tCAoKAjTpk1DYGBgtxgWLlyIsrKyHuP78ssvu53/79+4/9GPfgQrKyvU1dXBxcWlS9tXX30Vf//73zFixAhYWFggICAAr7/+OgCgpKQElpaWmDVrFmpra6FSqZCYmCgVIBMnTsRrr73WY1z365EWHyNHjkR9fT0MBgMUCgWEEKiuroZSqezSTqlU4uLF/7tLUlVV1a1NfyVXKOD/coi5wyAiIuoX+tIr8SgtXLgQcrkcZWVlSEpK6nI392E6fPgwlixZAnt7ewCdw5GMFRkZKf2+dOlSeHt7IzExEZcvX0Z5eTlCQ0PR3NyM48eP49tvv5XaNjU19Xqheqfs7GwsX74clpaWAIDo6Gj87ne/k4qP+fPn91p4AICrqysaGxuRkZGBt99+u9v2QYMGYcuWLXjrrbdw9OjRHvexdu1auLm59dqzAnT2eri5uUnDfeRyOYYNG9atXWFhId577z3o9XooFAqUlZWhpaUFLi4uMBgMiIyMhFarxcyZMyGXyxEYGIikpCT88pe/xOTJkxES0vP1Wm1tLWxtbaXHer0eK1asQHl5ORQKBfR6PYqLi6Xi487zdvDgQRQVFSEgIEB6fWNjI1paWtDS0oLVq1dDp9NBLpejpqYGOp2ux+Jj7969vZ6fB3Xs2DF0dHTgypUrkMvlWL58Od59913Ex8fDYDAgOzsbZ8+exbPPPou4uDjExMRIvXd2dnbQ6/U99qjcr0c652PEiBHQaDTIyMgA0Dlm0NHRscuQK6BzLkhWVhauXLkCIQSSk5OxaNGiRxkaERERDWB79+5FaWkpjh07htjYWJw79/+XxddocObMmW7tz5w5A42mc9VKHx+fHtv0hUKhQHt7u/S4tbW1W5shQ4ZIvzs6OsLX1xcHDx7Erl27EBERId24BTrv9Ot0Ouh0OlRUVNzX8J07J4R///g9sbW1RW5uLnbt2tVtmNdtr7zyCpqbm3sdemVlZYW33nqrx+KlL9ra2jBv3jx8+OGHKC4uxsmTJwEAN2/ehLW1NYqLi/HTn/4U33zzDVQqFSoqKjBx4kTodDoEBARg//798PPz6/KZ3PaDH/ygy+ezatUqTJo0CefOnYNOp4Orq2uX7XeeNyEEli1bJn02Op0O9fX1ePrppxEXF4fhw4ejsLAQRUVFmDJlSo95AHQWy2q1usefmpqabu2VSiUuXbokPb5x4wa+++67Hoe3ffLJJ5g7dy6eeuopDB48GIsXL8aJEyek/Wi1Wjg4OEAmkyEiIqLL4gutra2wsLDA4MGDe/1s+uqRr3aVkpKClJQUuLq6IiEhQRprFxUVhayszrsjLi4u2LRpE5577jmMGTMGNjY2ePXVVx91aERERDTATZs2DTExMdLF+iuvvAK9Xo/f/OY3UpucnBykpqZi7dq1AIB169YhNTUVhw8fltpcuXIFu3bt6rb/sLAwZGRkoL6+HkDnBN7//e9/sLOzgxBCmpCdnp5+z1h/9rOfITU1Fenp6VKvyJAhQ6DVartM3K6rq0Ntba1R7z09PR1tbW0wGAzYsWMHXnrppXu+7k729vbIzc3F7t27sWnTpm7bZTIZEhIS7loMxcTEQKfToaCgoMftQUFBuHDhAk6dOgWgczL194dI3Z5/cXtkzLZt26RtDQ0NaG5uxksvvYT3338fzs7OKCkpQWVlJYYMGYIFCxZg27ZtKC8v73GusEql6tKTdO3aNTg5OUEmk+HkyZMoKirq9b3Nnj0bGRkZ0lzljo4O5OfnS/txdHSUemn++te/9rqf2wsk9PTT05A3Hx8f3Lp1SyoiUlJSEBYW1mPvhIuLC44dOwYhBIQQOHz4MDw8PAAACxYsQF5eHq5fvw4AOHLkSJchdKWlpfDw8IBc/vBKhkdefLi5ueHMmTMoLy9Hfn6+NMFlx44dmD17ttQuOjoaFy9exMWLF7Fz504MGsSVo4iIiOjBvfPOOzh9+jQKCgrwwx/+ELm5uSgoKMCoUaMwduxYbNy4EVlZWVCpVAAAT09P/PnPf8ZHH30EFxcXeHp6Yt68eXjmmWe67Xvy5MnYsGEDpk+fDi8vL7zwwgtoaGiAQqHAtm3bMGvWLPj5+Rn1/WVz5sxBXl4ebG1tMX78eOn53bt3o6KiAh4eHlIser3+nvtbuXIlNBoNNBoN1Go1nJ2de10R6W7s7OyQm5uLP/7xj9iwYUO37dOnT+82z+BOlpaW2Lx5M6qqqnrc/swzz+DAgQOIjY2FSqWCRqPB3/72ty5trKysEB8fD39/f/j4+HS5E19TU4MXX3wRKpVKWk42NDQUubm58PHxgVqtRlBQELZu3Qpra+tuxw8PD8df/vIX6XFCQgJiY2OhVquRmpraZUjV9z3//PP44IMPMHfuXHh5eWHChAnSkr3r169HWloaVCoVYmNjH+qkbblcjoyMDKxZswaurq44dOgQkpKSpO0zZsyQiqCNGzeiqalJOjfffvutNKdJqVQiLi4OQUFBUKlUyMnJQXJysrSfo0ePIjw8/KHFDQAycbs/7zHl6OhoVPVPRERED197ezvKy8vh6uoKCwsLc4dD1GcdHR3w9/dHZmam2Rcy6k/a2trg6+uLnJwcDB8+vNv2u/3bv9v1+SPv+SAiIiIi6q/kcjlSUlJ67Zl5UlVWViIhIaHHwuNBPHZL7RIRERERPUw+Pj7mDqHfcXNz6/LdLg8Lez6IiIjovt1eQekxH8VNRH105xdm9gV7PoiIiOi+yeVyDBo0CHq9Hj/+8Y/7fCFCRI8fIQT0ej0GDRrU55WwWHwQERHRA1Eqlaiuru71G6SJaOAZNGjQfX0pOIsPIiIieiCDBw/GmDFj0NHRweFXRE8AmUx239/9weKDiIiIHoqH+UVkRDQw8a8EERERERGZBIsPIiIiIiIyCRYfRERERERkEjLxmM8Ms7S0hI2NjbnDQFNTE4YMGWLuMKgfYm5Qb5gbdDfMD+oNc4N6019yo6GhATdv3uxx22NffPQXjo6OqK2tNXcY1A8xN6g3zA26G+YH9Ya5Qb15HHKDw66IiIiIiMgkWHwQEREREZFJWGzcuHGjuYMYKCZOnGjuEKifYm5Qb5gbdDfMD+oNc4N6099zg3M+iIiIiIjIJDjsioiIiIiITILFBxERERERmQSLjz64cOECgoKC4OrqCj8/P5w/f77Hdjt37sTYsWMxevRoREdH49atWyaOlEzNmNzIycmBv78/3N3dMWHCBKxbtw4dHR1miJZMydi/GwAghEBwcDCGDh1qwgjJnIzNj3PnzmHKlCkYP348xo8fj/3795s4UjI1Y3Kjo6MDb775Jtzd3aFSqaDValFRUWGGaMmUfv7zn8PZ2RkymQw6na7Xdv32elSQ0bRarUhLSxNCCPGnP/1J+Pr6dmvz73//W9jb24v6+nrR0dEhwsLCxPbt200cKZmaMbnxz3/+U1y8eFEIIURLS4t47rnnpNfQwGVMbtyWmJgooqKihLW1tYmiI3MzJj+am5vFqFGjxKlTp4QQQhgMBnH16lVThklmYExuHDhwQPj7+4u2tjYhhBBbtmwR8+fPN2WYZAZfffWVqKmpEU5OTqKwsLDHNv35epQ9H0a6evUq8vPzERERAQB4+eWXUVNT0+0OwxdffIHZs2fDzs4OMpkMq1atwueff26OkMlEjM0Nb29vuLi4AACeeuopqNVqVFVVmTpcMiFjcwMAzp8/j8zMTMTGxpo6TDITY/Njz549CAwMxKRJkwAAFhYWsLGxMXm8ZDrG5oZMJsPNmzfR2toKIQSuX78OR0dHc4RMJjR58uR7fs79+XqUxYeRampqYG9vD4VCAaDzH7xSqUR1dXWXdtXV1XBycpIeOzs7d2tDA4uxuXGnK1eu4IsvvsCsWbNMFSaZgbG5cevWLURHRyMlJQUWFhbmCJXMwNj8KCkpgaWlJWbNmgW1Wo2lS5eioaHBHCGTiRibG2FhYZgyZQrs7Oxgb2+P48ePY/PmzeYImfqZ/nw9yuKDyMSuX7+OsLAwrFu3Dr6+vuYOh/qBTZs2Yd68eRg/fry5Q6F+yGAwIDs7GykpKSgsLISDgwNiYmLMHRb1A/n5+SguLsbly5dRV1eHqVOnYtWqVeYOi+iuWHwYaeTIkaivr4fBYADQOTG0uroaSqWySzulUolLly5Jj6uqqrq1oYHF2NwAgBs3biAkJARz5szBm2++aepQycSMzY2vvvoK27Ztg7OzMyZNmoTr16/D2dmZd7cHuL78v6LVauHg4ACZTIaIiAicPXvWHCGTiRibG+np6dIiFXK5HMuWLcOJEyfMETL1M/35epTFh5FGjBgBjUaDjIwMAMC+ffvg6OiIMWPGdGn38ssvIysrC1euXIEQAsnJyVi0aJE5QiYTMTY3mpqaEBISgpCQEKxfv94coZKJGZsbp06dwqVLl1BVVYXTp0/DysoKVVVVHNc/wBmbHwsWLEBeXh6uX78OADhy5Ai8vLxMHi+ZjrG54eLigpycHLS1tQEADh06BA8PD5PHS/1Pv74eNd9c98fPN998IwIDA8XYsWOFj4+P+Ne//iWEEGLFihXi4MGDUrtPPvlEuLi4CBcXFxEZGSmtQkEDlzG5ER8fLxQKhfDy8pJ+4uPjzRk2mYCxfzduq6ys5GpXTxBj8yM9PV1MmDBBeHp6ipCQEFFdXW2ukMlEjMmN1tZWERUVJcaNGyc8PT3Fiy++KK2qSAPXypUrhYODg7CwsBAjRowQo0ePFkI8PtejMiGEMHcBREREREREAx+HXRERERERkUmw+CAiIiIiIpNg8UFERERERCbB4oOIiIiIiEyCxQcREREREZkEiw8iIiIiIjIJFh9ERI8xZ2dnuLm5Qa1WQ61WIyoq6q7tp0yZgszMzIdy7E8//RTW1tZQq9WYMGECQkNDUV1dfV/7evfdd7F7924AQG5uLo4ePSptq6urw/PPP/9QYr7tzvPm5uaGhIQEo16XmZnJbxcnInoACnMHQERED2bv3r1Qq9VmObZWq5WKmTVr1uAXv/gF9u3b1+f9bN68Wfo9NzcX//3vfxESEgIAePbZZ3Hq1KmHE/Adbp+3y5cvw93dHcHBwfD397/razIzM6FWqxEYGPjQ4yEiehKw54OIaIDZs2cPAgIC4O3tDS8vL3z55Zc9ttuxYwfc3d2hVqvh6emJf/zjHwCACxcuYObMmfDz84NKpcL27duNOu706dNRVlYGAPjss8+gUqmgUqkwc+ZMXL58GQBw9uxZ+Pj4QK1Ww8PDA7///e8BAMuXL8dvf/tb6HQ6JCcnY/fu3VCr1di8eTOqqqowdOhQAMB7772H119/XTpmU1MThg0bhoaGBgDAhx9+CH9/f2g0GoSEhODSpUv3jNvBwQHjxo2T2h4/fhwTJ06Et7c3JkyYgJ07dwIAjhw5gqysLGzduhVqtRo7duyQ3mtAQAA0Gg0mT56MoqIio84XEdETydxfsU5ERPfPyclJuLq6Ci8vL+Hl5SX2798v/vOf/4iOjg4hhBCVlZXC1tZWtLa2CiGEeOGFF8SBAweEEEJYWVmJuro6IYQQbW1t4saNG8JgMAgfHx9RWloqhBCiublZeHp6iq+//rrbsdPS0sScOXOEEEIYDAaxfPlysXTpUnHu3Dlha2sramtrhRBCxMfHi5CQECGEELNnzxZ79uyR9tHY2CiEEGLZsmUiKSlJCCHEhg0bxJo1a6Q2lZWVwtraWgghRHV1tbCxsZHeT2pqqpg3b54QQojdu3eLqKgoYTAYhBBCpKenixkzZvR63goLC4UQQpSWlorRo0eLq1evSjHd3oderxdKpVLU1NR0i1MIIU6fPi1CQ0OleE6ePCnc3d17PCYREQnBYVdERI+57w+7ys/Px+LFi1FbWwuFQoHGxkZUVlZi3LhxXV43depULFmyBGFhYQgNDYWrqytKSkpw/vx5LFq0SGp348YNlJSUwM/Pr9uxT5w4IR1bo9EgMTERn3/+OUJCQuDg4AAAWL16NTZv3oz29nZotVps2bIFFy5cQHBwMCZNmtSn9zpy5Eh4e3sjKysL8+fPx6effoq1a9cC6BwSlZeXBx8fHwBAe3v7Xfe1cOFCyOVylJWVISkpCTY2NgAAvV6PFStWoLy8HAqFAnq9HsXFxXB0dOy2j4MHD6KoqAgBAQHSc42NjWhpacHTTz/dp/dGRPQkYPFBRDTALFq0CAkJCQgPDwcADBs2DK2trd3a7du3DwUFBcjNzcWMGTMQHx8PT09PDBs2DDqdzqhj3TnnozcymUz6/Y033sCcOXOQnZ2NuLg4eHh44OOPP+7DuwMiIyORlpYGHx8fVFRUSHNDhBB4++23sXLlSqP2c7toy87ORlhYGIKDg+Hp6YlVq1ZhxowZ2LdvH2QyGTQaTY/n7/Yxly1bhvfff79P74GI6EnFOR9ERAPMtWvXMGrUKABARkYGrl271q2NwWDAxYsX4evri1/96lcIDw/H119/DTc3N1hZWSEtLU1qW1FRgcbGRqOPr9VqcfToUdTV1QEAkpOTMXXqVFhYWKCsrAyjRo1CdHQ04uLielw5ysrKCt99912v+//JT36CvLw8/PrXv0ZERAQUCoX0fHJyshTrrVu3UFhYeM94p02bhpiYGKxfvx5A5/lzcnKCTCbDyZMnu8zh+H5ss2fPRkZGhrTKV0dHB/Lz8+95TCKiJxV7PoiIBpiPPvoI4eHhGDp0KIKDg6FUKru1aW9vR2RkJBobG6FQKGBjY4O0tDQoFAocOnQIb7zxBpKSktDe3o7hw4djz549Rh/fw8MDW7dulXokRo4ciT/84Q8AgO3btyMnJweDBw+GhYUFEhMTu71+7ty5+Oyzz6BWqzFv3jwsXbq0y3ZLS0ssWLAAH3/8MUpLS6XnFy9eDL1eD61WC6CzwIqMjIS3t/c9Y37nnXcwZswYFBQUICEhAatXr8aWLVugVqu7DKlasmQJli9fjszMTLz22muIiorCBx98gLlz58JgMKCtrQ0zZ86Er6+v0eeLiOhJIhNCCHMHQUREREREAx+HXRERERERkUmw+CAiIiIiIpNg8UFERERERCbB4oOIiIiIiEyCxQcREREREZkEiw8iIiIiIjIJFh9ERERERGQSLD6IiIiIiMgk/h8sqzUhEoghuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob11, prob21, prob31, prob41, prob51, prob61, prob71), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'RF' = 0.8428571428571429\n",
      "[[1839  373]\n",
      " [ 320 1878]]\n",
      "Recall in 'RF' = 0.831374321880651\n",
      "Precision in 'RF' = 0.8517832329782307\n",
      "F1 Score in 'RF' = 0.841455044612217\n"
     ]
    }
   ],
   "source": [
    "model8 = train_model(features_train_lda, train_y, model_name='RF', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob8 = model8.predict_proba(features_val_lda)\n",
    "prob80 = prob8[:,0].reshape(prob8.shape[0], 1)\n",
    "prob81 = prob8[:,1].reshape(prob8.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'BAG' = 0.8405895691609977\n",
      "[[1834  378]\n",
      " [ 325 1873]]\n",
      "Recall in 'BAG' = 0.8291139240506329\n",
      "Precision in 'BAG' = 0.8494673459935155\n",
      "F1 Score in 'BAG' = 0.8391672386181652\n"
     ]
    }
   ],
   "source": [
    "model9 = train_model(features_train_lda, train_y, model_name='BAG', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob9 = model9.predict_proba(features_val_lda)\n",
    "prob90 = prob9[:,0].reshape(prob9.shape[0], 1)\n",
    "prob91 = prob9[:,1].reshape(prob9.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'ADA' = 0.8399092970521542\n",
      "[[1811  401]\n",
      " [ 305 1893]]\n",
      "Recall in 'ADA' = 0.8187160940325497\n",
      "Precision in 'ADA' = 0.8558601134215501\n",
      "F1 Score in 'ADA' = 0.8368761552680222\n"
     ]
    }
   ],
   "source": [
    "model10 = train_model(features_train_lda, train_y, model_name='ADA', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob10 = model10.predict_proba(features_val_lda)\n",
    "prob100 = prob10[:,0].reshape(prob10.shape[0], 1)\n",
    "prob101 = prob10[:,1].reshape(prob10.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'NB' = 0.845578231292517\n",
      "[[1843  369]\n",
      " [ 312 1886]]\n",
      "Recall in 'NB' = 0.8331826401446655\n",
      "Precision in 'NB' = 0.8552204176334107\n",
      "F1 Score in 'NB' = 0.8440577055186628\n"
     ]
    }
   ],
   "source": [
    "model11 = train_model(features_train_lda, train_y, model_name='NB', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob11 = model11.predict_proba(features_val_lda)\n",
    "prob110 = prob11[:,0].reshape(prob11.shape[0], 1)\n",
    "prob111 = prob11[:,1].reshape(prob11.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'SVM' = 0.8555555555555555\n",
      "[[1890  322]\n",
      " [ 315 1883]]\n",
      "Recall in 'SVM' = 0.8544303797468354\n",
      "Precision in 'SVM' = 0.8571428571428571\n",
      "F1 Score in 'SVM' = 0.8557844690966719\n"
     ]
    }
   ],
   "source": [
    "model12 = train_model(features_train_lda, train_y, model_name='SVM', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob12 = model12.predict_proba(features_val_lda)\n",
    "prob120 = prob12[:,0].reshape(prob12.shape[0], 1)\n",
    "prob121 = prob12[:,1].reshape(prob12.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.8360544217687075\n",
      "[[1819  393]\n",
      " [ 330 1868]]\n",
      "Recall in 'XGB' = 0.8223327305605787\n",
      "Precision in 'XGB' = 0.8464402047463937\n",
      "F1 Score in 'XGB' = 0.8342123366200412\n"
     ]
    }
   ],
   "source": [
    "model13 = train_model(features_train_lda, train_y, model_name='XGB', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob13 = model13.predict_proba(features_val_lda)\n",
    "prob130 = prob13[:,0].reshape(prob13.shape[0], 1)\n",
    "prob131 = prob13[:,1].reshape(prob13.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'KNN' = 0.8317460317460318\n",
      "[[1827  385]\n",
      " [ 357 1841]]\n",
      "Recall in 'KNN' = 0.8259493670886076\n",
      "Precision in 'KNN' = 0.8365384615384616\n",
      "F1 Score in 'KNN' = 0.8312101910828025\n"
     ]
    }
   ],
   "source": [
    "model14 = train_model(features_train_lda, train_y, model_name='KNN', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob14 = model14.predict_proba(features_val_lda)\n",
    "prob140 = prob14[:,0].reshape(prob14.shape[0], 1)\n",
    "prob141 = prob14[:,1].reshape(prob14.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob80, prob90, prob100, prob110, prob120, prob130, prob140), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob81, prob91, prob101, prob111, prob121, prob131, prob141), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA on PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'RF' = 0.8333333333333334\n",
      "[[1878  334]\n",
      " [ 401 1797]]\n",
      "Recall in 'RF' = 0.849005424954792\n",
      "Precision in 'RF' = 0.824045634050022\n",
      "F1 Score in 'RF' = 0.8363393453573814\n"
     ]
    }
   ],
   "source": [
    "model15 = train_model(lda_on_pca_train, train_y, model_name='RF', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob15 = model15.predict_proba(lda_on_pca_val)\n",
    "prob150 = prob15[:,0].reshape(prob15.shape[0], 1)\n",
    "prob151 = prob15[:,1].reshape(prob15.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'BAG' = 0.7623582766439909\n",
      "[[1660  552]\n",
      " [ 496 1702]]\n",
      "Recall in 'BAG' = 0.7504520795660036\n",
      "Precision in 'BAG' = 0.7699443413729128\n",
      "F1 Score in 'BAG' = 0.76007326007326\n"
     ]
    }
   ],
   "source": [
    "model16 = train_model(lda_on_pca_train, train_y, model_name='BAG', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob16 = model16.predict_proba(lda_on_pca_val)\n",
    "prob160 = prob16[:,0].reshape(prob16.shape[0], 1)\n",
    "prob161 = prob16[:,1].reshape(prob16.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'ADA' = 0.8333333333333334\n",
      "[[1866  346]\n",
      " [ 389 1809]]\n",
      "Recall in 'ADA' = 0.8435804701627486\n",
      "Precision in 'ADA' = 0.8274944567627495\n",
      "F1 Score in 'ADA' = 0.8354600402955004\n"
     ]
    }
   ],
   "source": [
    "model17 = train_model(lda_on_pca_train, train_y, model_name='ADA', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob17 = model17.predict_proba(lda_on_pca_val)\n",
    "prob170 = prob17[:,0].reshape(prob17.shape[0], 1)\n",
    "prob171 = prob17[:,1].reshape(prob17.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'NB' = 0.8206349206349206\n",
      "[[1694  518]\n",
      " [ 273 1925]]\n",
      "Recall in 'NB' = 0.7658227848101266\n",
      "Precision in 'NB' = 0.8612099644128114\n",
      "F1 Score in 'NB' = 0.8107202680067002\n"
     ]
    }
   ],
   "source": [
    "model18 = train_model(lda_on_pca_train, train_y, model_name='NB', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob18 = model18.predict_proba(lda_on_pca_val)\n",
    "prob180 = prob18[:,0].reshape(prob18.shape[0], 1)\n",
    "prob181 = prob18[:,1].reshape(prob18.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'SVM' = 0.8319727891156462\n",
      "[[1854  358]\n",
      " [ 383 1815]]\n",
      "Recall in 'SVM' = 0.8381555153707052\n",
      "Precision in 'SVM' = 0.8287885561019223\n",
      "F1 Score in 'SVM' = 0.8334457181389077\n"
     ]
    }
   ],
   "source": [
    "model19 = train_model(lda_on_pca_train, train_y, model_name='SVM', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob19 = model19.predict_proba(lda_on_pca_val)\n",
    "prob190 = prob19[:,0].reshape(prob19.shape[0], 1)\n",
    "prob191 = prob19[:,1].reshape(prob19.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.8331065759637188\n",
      "[[1870  342]\n",
      " [ 394 1804]]\n",
      "Recall in 'XGB' = 0.8453887884267631\n",
      "Precision in 'XGB' = 0.8259717314487632\n",
      "F1 Score in 'XGB' = 0.8355674709562109\n"
     ]
    }
   ],
   "source": [
    "model20 = train_model(lda_on_pca_train, train_y, model_name='XGB', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob20 = model20.predict_proba(lda_on_pca_val)\n",
    "prob200 = prob20[:,0].reshape(prob20.shape[0], 1)\n",
    "prob201 = prob20[:,1].reshape(prob20.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'KNN' = 0.7882086167800454\n",
      "[[1727  485]\n",
      " [ 449 1749]]\n",
      "Recall in 'KNN' = 0.780741410488246\n",
      "Precision in 'KNN' = 0.7936580882352942\n",
      "F1 Score in 'KNN' = 0.7871467639015498\n"
     ]
    }
   ],
   "source": [
    "model21 = train_model(lda_on_pca_train, train_y, model_name='KNN', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob21 = model21.predict_proba(lda_on_pca_val)\n",
    "prob210 = prob21[:,0].reshape(prob21.shape[0], 1)\n",
    "prob211 = prob21[:,1].reshape(prob21.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob150, prob160, prob170, prob180, prob190, prob200, prob210), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob151, prob161, prob171, prob181, prob191, prob201, prob211), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "hog_comp_train = combine_features([hog_train, hog_val], horizontal=False)\n",
    "lbp_comp_train = combine_features([lbp_train, lbp_val], horizontal=False)\n",
    "kaze_comp_train = combine_features([kaze_train, kaze_val], horizontal=False)\n",
    "sift_comp_train = combine_features([sift_train, sift_val], horizontal=False)\n",
    "surf_comp_train = combine_features([surf_train, surf_val], horizontal=False)\n",
    "gabor_comp_train = combine_features([gabor_train, gabor_val], horizontal=False)\n",
    "glcm_comp_train = combine_features([glcm_train, glcm_val], horizontal=False)\n",
    "flat_rgb_comp_train = combine_features([flat_rgb_train, flat_rgb_val], horizontal=False)\n",
    "flat_gray_comp_train = combine_features([flat_gray_train, flat_gray_val], horizontal=False)\n",
    "hist_comp_train = combine_features([hist_train, hist_val], horizontal=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y = np.hstack([train_y, val_y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arijitiiest/Desktop/Workspace/Project/Malaria Detection/env/lib/python3.6/site-packages/sklearn/decomposition/_pca.py:545: RuntimeWarning: invalid value encountered in true_divide\n",
      "  self.explained_variance_ / total_var.sum()\n"
     ]
    }
   ],
   "source": [
    "pcas_array = []\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca_hog_comp_train = pca.fit_transform(hog_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=30)\n",
    "pca_lbp_comp_train = pca.fit_transform(lbp_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=20)\n",
    "pca_kaze_comp_train = pca.fit_transform(kaze_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=15)\n",
    "pca_sift_comp_train = pca.fit_transform(sift_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "pca_surf_comp_train = pca.fit_transform(surf_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "pca_gabor_comp_train = pca.fit_transform(gabor_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "pca_glcm_comp_train = pca.fit_transform(glcm_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=20)\n",
    "pca_flat_rgb_comp_train = pca.fit_transform(flat_rgb_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=20)\n",
    "pca_flat_gray_comp_train = pca.fit_transform(flat_gray_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca_hist_comp_train = pca.fit_transform(hist_comp_train)\n",
    "pcas_array.append(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_train_features=[pca_hog_comp_train,\n",
    "                        pca_lbp_comp_train,\n",
    "                        pca_kaze_comp_train,\n",
    "                        pca_sift_comp_train,\n",
    "                        pca_surf_comp_train,\n",
    "                        pca_gabor_comp_train,\n",
    "                        pca_glcm_comp_train,\n",
    "                        pca_flat_rgb_comp_train,\n",
    "                        pca_flat_gray_comp_train,\n",
    "                        pca_hist_comp_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/hog_test.pkl\n",
      "Feature saved with name cache/lbp_test.pkl\n",
      "Feature saved with name cache/kaze_test.pkl\n",
      "Feature saved with name cache/sift_test.pkl\n",
      "Feature saved with name cache/surf_test.pkl\n",
      "Feature saved with name cache/gabor_test.pkl\n",
      "Feature saved with name cache/glcm_test.pkl\n",
      "Feature saved with name cache/flat_rgb_test.pkl\n",
      "Feature saved with name cache/flat_gray_test.pkl\n",
      "Feature saved with name cache/hist_test.pkl\n"
     ]
    }
   ],
   "source": [
    "hog_test = get_hog(test_x, name='hog_test', save=True)\n",
    "lbp_test = get_lbp(test_x, name='lbp_test', save=True)\n",
    "kaze_test = get_kaze(test_x, name='kaze_test', save=True)\n",
    "sift_test = get_sift(test_x, name='sift_test', save=True)\n",
    "surf_test = get_surf(test_x, name='surf_test', save=True)\n",
    "gabor_test = get_gabor(test_x, name='gabor_test', save=True)\n",
    "glcm_test = get_glcm(test_x, name='glcm_test', save=True)\n",
    "flat_rgb_test = get_flattened(test_x, None, name='flat_rgb_test', save=True)\n",
    "flat_gray_test = get_flattened(test_x, name='flat_gray_test', save=True)\n",
    "hist_test = get_color_hist(test_x, name='hist_test', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_features= [hog_test,\n",
    "                lbp_test,\n",
    "                kaze_test,\n",
    "                sift_test,\n",
    "                surf_test,\n",
    "                gabor_test,\n",
    "                glcm_test,\n",
    "                flat_rgb_test,\n",
    "                flat_gray_test,\n",
    "                hist_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_comp_train = None\n",
    "features_test = None\n",
    "comp_pcas = []\n",
    "\n",
    "for i, (f_train, f_test) in enumerate(zip(reduced_train_features, \n",
    "                                          test_features)):\n",
    "    if i == 6:\n",
    "        continue\n",
    "    f_test = pcas_array[i].transform(f_test)\n",
    "    \n",
    "    if features_comp_train is None:\n",
    "        features_comp_train = f_train\n",
    "        features_test = f_test\n",
    "    else:\n",
    "        features_comp_train = combine_features([features_comp_train, f_train])\n",
    "        features_test = combine_features([features_test, f_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22046, 135), (5512, 135))"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_comp_train.shape, features_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_model = train_model(features_comp_train, data_y, model_name='XGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = comp_model.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8450653120464441\n",
      "[[2306  417]\n",
      " [ 437 2352]]\n",
      "0.8406853809697412 0.8468600807932427 0.8437614343212587\n"
     ]
    }
   ],
   "source": [
    "acc = metrics.accuracy_score(test_y, y_hat)\n",
    "print(acc)\n",
    "cm = metrics.confusion_matrix(test_y, y_hat)\n",
    "print(cm)\n",
    "precision = cm[0][0] / (cm[0][0] + cm[1][0])\n",
    "recall = cm[0][0] / (cm[0][0] + cm[0][1])\n",
    "f1 = 2*(recall * precision) / (recall + precision)\n",
    "print(precision, recall, f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat1 = model1.predict(features_val)\n",
    "y_hat2 = model2.predict(features_val)\n",
    "y_hat3 = model3.predict(features_val)\n",
    "y_hat4 = model4.predict(features_val)\n",
    "y_hat5 = model5.predict(features_val)\n",
    "y_hat6 = model6.predict(features_val)\n",
    "y_hat7 = model7.predict(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm1 = MetricTools.confusion_matrix(val_y, y_hat1, 2)\n",
    "cm2 = MetricTools.confusion_matrix(val_y, y_hat2, 2)\n",
    "cm3 = MetricTools.confusion_matrix(val_y, y_hat3, 2)\n",
    "cm4 = MetricTools.confusion_matrix(val_y, y_hat4, 2)\n",
    "cm5 = MetricTools.confusion_matrix(val_y, y_hat5, 2)\n",
    "cm6 = MetricTools.confusion_matrix(val_y, y_hat6, 2)\n",
    "cm7 = MetricTools.confusion_matrix(val_y, y_hat7, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1876.  336.]\n",
      " [ 248. 1950.]]\n",
      "[[1902.  310.]\n",
      " [ 242. 1956.]]\n",
      "[[1861.  351.]\n",
      " [ 244. 1954.]]\n",
      "[[ 913. 1299.]\n",
      " [ 193. 2005.]]\n",
      "[[1892.  320.]\n",
      " [ 289. 1909.]]\n",
      "[[1974.  238.]\n",
      " [ 146. 2052.]]\n",
      "[[1646.  566.]\n",
      " [ 404. 1794.]]\n"
     ]
    }
   ],
   "source": [
    "print(cm1)\n",
    "print(cm2)\n",
    "print(cm3)\n",
    "print(cm4)\n",
    "print(cm5)\n",
    "print(cm6)\n",
    "print(cm7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotTools.confusion_matrix(cm1, list(range(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Stacking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 30), (4410, 30))"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train1 = None\n",
    "features_val1 = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_hist_train, pca_hog_train, pca_lbp_train], \n",
    "                  [pca_hist_val, pca_hog_val, pca_lbp_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train1 is None:\n",
    "        features_train1 = ft\n",
    "        features_val1 = fv\n",
    "    else:\n",
    "        features_train1 = combine_features([features_train1, ft])\n",
    "        features_val1 = combine_features([features_val1, fv])\n",
    "features_train1.shape, features_val1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.8990929705215419\n",
      "[[1958  254]\n",
      " [ 191 2007]]\n",
      "Recall in 'XGB' = 0.8851717902350814\n",
      "Precision in 'XGB' = 0.9111214518380643\n",
      "F1 Score in 'XGB' = 0.8979591836734695\n"
     ]
    }
   ],
   "source": [
    "model1 = train_model(features_train1, train_y, model_name='XGB', validation=(features_val1, val_y))\n",
    "prob1 = model1.predict_proba(features_val1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 26), (4410, 26))"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train2 = None\n",
    "features_val2 = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_hist_train, pca_flat_gray_train, pca_flat_rgb_train], \n",
    "                  [pca_hist_val, pca_flat_gray_val, pca_flat_rgb_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train2 is None:\n",
    "        features_train2 = ft\n",
    "        features_val2 = fv\n",
    "    else:\n",
    "        features_train2 = combine_features([features_train2, ft])\n",
    "        features_val2 = combine_features([features_val2, fv])\n",
    "features_train2.shape, features_val2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.8893424036281179\n",
      "[[1894  318]\n",
      " [ 170 2028]]\n",
      "Recall in 'XGB' = 0.85623869801085\n",
      "Precision in 'XGB' = 0.9176356589147286\n",
      "F1 Score in 'XGB' = 0.8858746492048644\n"
     ]
    }
   ],
   "source": [
    "model2 = train_model(features_train2, train_y, model_name='XGB', validation=(features_val2, val_y))\n",
    "prob2 = model2.predict_proba(features_val2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 20), (4410, 20))"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train3 = None\n",
    "features_val3 = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_kaze_train, pca_sift_train, pca_surf_train], \n",
    "                  [pca_kaze_val, pca_sift_val, pca_surf_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train3 is None:\n",
    "        features_train3 = ft\n",
    "        features_val3 = fv\n",
    "    else:\n",
    "        features_train3 = combine_features([features_train3, ft])\n",
    "        features_val3 = combine_features([features_val3, fv])\n",
    "features_train3.shape, features_val3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.754421768707483\n",
      "[[1508  704]\n",
      " [ 379 1819]]\n",
      "Recall in 'XGB' = 0.6817359855334539\n",
      "Precision in 'XGB' = 0.7991520932697403\n",
      "F1 Score in 'XGB' = 0.7357892168821665\n"
     ]
    }
   ],
   "source": [
    "model3 = train_model(features_train3, train_y, model_name='XGB', validation=(features_val3, val_y))\n",
    "prob3 = model3.predict_proba(features_val3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob1 = model1.predict_proba(features_val)\n",
    "prob2 = model2.predict_proba(features_val)\n",
    "prob3 = model3.predict_proba(features_val)\n",
    "prob4 = model4.predict_proba(features_val)\n",
    "#prob5 = model5.score(features_val, )\n",
    "prob6 = model6.predict_proba(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob = (prob1 + prob2 + prob3 + prob4 + prob6) / 5\n",
    "y_hat = np.argmax(prob, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8843537414965986"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(val_y, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_hat = model.predict(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = MetricTools.confusion_matrix(val_y, y_hat, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotTools.confusion_matrix(cm, list(range(2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotTools.confusion_matrix(cm, list(range(2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}