Kaggle/Digit Recognizer/Simple Keras and deep learning digit recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ab687b49ad4821c20d0cf58580ddbee262a5826c"
   },
   "source": [
    "### Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "trusted": true,
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from seaborn import countplot\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import optimizers\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, Activation, BatchNormalization\n",
    "from keras.models import Sequential\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e8dae2362b2cec6977a2a2fae792955d3d8428cb"
   },
   "source": [
    "### Auxiliar functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "trusted": true,
    "_uuid": "a9ea3b0f994f8d2b2fedd091f8cc3e1fe9d0b5dc",
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "def plot_metrics_primary(acc, val_acc, loss, val_loss):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, sharex='col', figsize=(20,7))\n",
    "\n",
    "    ax1.plot(acc, label='Train Accuracy')\n",
    "    ax1.plot(val_acc, label='Validation accuracy')\n",
    "    ax1.legend(loc='best')\n",
    "    ax1.set_title('Accuracy')\n",
    "\n",
    "    ax2.plot(loss, label='Train loss')\n",
    "    ax2.plot(val_loss, label='Validation loss')\n",
    "    ax2.legend(loc='best')\n",
    "    ax2.set_title('Loss')\n",
    "\n",
    "    plt.xlabel('Epochs')\n",
    "    \n",
    "    \n",
    "def plot_confusion_matrix(cnf_matrix, labels): \n",
    "    cnf_matrix_norm = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "    df_cm = pd.DataFrame(cnf_matrix_norm, index=labels, columns=labels)\n",
    "    plt.figure(figsize=(20,7))\n",
    "    sns.heatmap(df_cm, annot=True, fmt='.2f', cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "78cb4f7b332e076828efe07c336cc976c5861f6f"
   },
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "TRAIN_PATH = '../input/train.csv'\n",
    "TEST_PATH = '../input/test.csv'\n",
    "SUBMISSION_NAME = 'submission.csv'\n",
    "\n",
    "# Model parameters\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 45\n",
    "LEARNING_RATE = 0.001\n",
    "HEIGHT = 28\n",
    "WIDTH = 28\n",
    "CANAL = 1\n",
    "N_CLASSES = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f91c93b6141d160b3b2f63a2b12ecfe186e44277"
   },
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "trusted": true,
    "_uuid": "9c5c17d8ad649fe9f2f54753b8de96ee6378cbc9"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(TRAIN_PATH)\n",
    "test = pd.read_csv(TEST_PATH)\n",
    "labels = train['label']\n",
    "train = train.drop(['label'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5bd936f36c010701e87111c2df40f4f0c52aea85"
   },
   "source": [
    "### Let's take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "trusted": true,
    "_uuid": "f072a8d5218ebf5f865bd66224e6c462b44c358d",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pixel0</th>\n      <th>pixel1</th>\n      <th>pixel2</th>\n      <th>pixel3</th>\n      <th>pixel4</th>\n      <th>pixel5</th>\n      <th>pixel6</th>\n      <th>pixel7</th>\n      <th>pixel8</th>\n      <th>pixel9</th>\n      <th>pixel10</th>\n      <th>pixel11</th>\n      <th>pixel12</th>\n      <th>pixel13</th>\n      <th>pixel14</th>\n      <th>pixel15</th>\n      <th>pixel16</th>\n      <th>pixel17</th>\n      <th>pixel18</th>\n      <th>pixel19</th>\n      <th>pixel20</th>\n      <th>pixel21</th>\n      <th>pixel22</th>\n      <th>pixel23</th>\n      <th>pixel24</th>\n      <th>pixel25</th>\n      <th>pixel26</th>\n      <th>pixel27</th>\n      <th>pixel28</th>\n      <th>pixel29</th>\n      <th>pixel30</th>\n      <th>pixel31</th>\n      <th>pixel32</th>\n      <th>pixel33</th>\n      <th>pixel34</th>\n      <th>pixel35</th>\n      <th>pixel36</th>\n      <th>pixel37</th>\n      <th>pixel38</th>\n      <th>pixel39</th>\n      <th>...</th>\n      <th>pixel744</th>\n      <th>pixel745</th>\n      <th>pixel746</th>\n      <th>pixel747</th>\n      <th>pixel748</th>\n      <th>pixel749</th>\n      <th>pixel750</th>\n      <th>pixel751</th>\n      <th>pixel752</th>\n      <th>pixel753</th>\n      <th>pixel754</th>\n      <th>pixel755</th>\n      <th>pixel756</th>\n      <th>pixel757</th>\n      <th>pixel758</th>\n      <th>pixel759</th>\n      <th>pixel760</th>\n      <th>pixel761</th>\n      <th>pixel762</th>\n      <th>pixel763</th>\n      <th>pixel764</th>\n      <th>pixel765</th>\n      <th>pixel766</th>\n      <th>pixel767</th>\n      <th>pixel768</th>\n      <th>pixel769</th>\n      <th>pixel770</th>\n      <th>pixel771</th>\n      <th>pixel772</th>\n      <th>pixel773</th>\n      <th>pixel774</th>\n      <th>pixel775</th>\n      <th>pixel776</th>\n      <th>pixel777</th>\n      <th>pixel778</th>\n      <th>pixel779</th>\n      <th>pixel780</th>\n      <th>pixel781</th>\n      <th>pixel782</th>\n      <th>pixel783</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      ],
      "text/plain": [
       "   pixel0  pixel1  pixel2    ...     pixel781  pixel782  pixel783\n0       0       0       0    ...            0         0         0\n1       0       0       0    ...            0         0         0\n2       0       0       0    ...            0         0         0\n3       0       0       0    ...            0         0         0\n4       0       0       0    ...            0         0         0\n\n[5 rows x 784 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "trusted": true,
    "_uuid": "a1a89d69bf199447df484bd83e87ad58937ff84e",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 784)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6539d92aba554059f35648692f6413290bc37afc"
   },
   "source": [
    "As we can see we have 42000 records on the train data, and our data is just a bunch of pixels (784 per image to be more exactly), each image comes as a 28x28 matrix of pixels.\n\nLet's take a look at our train data by label category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "trusted": true,
    "_uuid": "ae43eb726cf17e0cfff5d9cb8142e03501e008f1",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faca4d84208>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "trusted": true,
    "_uuid": "caa30bdb2f9e50210529dbceddbe37abfc342f4f",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4684\n7    4401\n3    4351\n9    4188\n2    4177\n6    4137\n0    4132\n4    4072\n8    4063\n5    3795\nName: label, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8211e517f110d9fc5a14a501dd451418a77a38b9"
   },
   "source": [
    "We have less labels 5 but luckily we won't have to deal with highly imbalanced data, this will make our work easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a06c428f44eecbfd8bd1525a74ebf2581602eb2a"
   },
   "source": [
    "### Reshape data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "trusted": true,
    "_uuid": "010de6bd3f415b5f05cf901828af9b953ec5b146"
   },
   "outputs": [],
   "source": [
    "# Reshape image in 3 dimensions (height, width, canal)\n",
    "train = train.values.reshape(-1,HEIGHT,WIDTH,CANAL)\n",
    "test = test.values.reshape(-1,HEIGHT,WIDTH,CANAL)\n",
    "# Turn labels into np arrays\n",
    "labels = labels.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "trusted": true,
    "_uuid": "95923655ee18e97c9c8b4e1dcf8f0b55f6db9126"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 28, 28, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bae59f1b06f210e5d86e13b6fe6ab66b558a4e27"
   },
   "source": [
    "Would be better to visualize some of the records as a images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "trusted": true,
    "_uuid": "fbf587357c47f70db3681672096897bc20fcb5dc",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAEYCAYAAAAZNO4sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3X28lHP+x/HXx1HSjdyUJJGHjZSb3MX+ZLGKrLWydiMkdimriLXu1vLLj9217nY93G4Ustlo3eQmhVjEgy02SimtlUpKblJuSp3P74+Za+ac0zmdOTNzzXVdM+/n49HjnLlm5pqP8Tmf+c73+t6YuyMiIsW3SdQBiIiUKxVYEZGQqMCKiIREBVZEJCQqsCIiIVGBFREJiQqsiEhIyqLAmtlwM5thZmvM7N6o45HSM7OtzexRM/vKzBaa2clRxySlFcc6sGnUARTJR8A1wFHA5hHHItG4DVgLdAB6Ak+Z2Vvu/k60YUkJxa4OlEUL1t0fcffHgE+jjkVKz8xaAScAV7j7anefBjwODIo2MimlONaBsiiwUvF2Bda5+/wax94CekQUjwigAivloTXwZZ1jK4E2EcQikqECK+VgNbBFnWNbAKsiiEUkQwVWysF8YFMz61rj2N6ALnBJpMqiwJrZpmbWAqgCqsyshZmVywgJaYS7fwU8AvyfmbUys4OB44D7o41MSimOdaAsCizwO+Ab4FLg1PTvv4s0Iim1c0gNzVkO/B34lYZoVZzY1QHTgtsiIuEolxasiEjsqMCKiISkoAJrZv3MbJ6ZLTCzS4sVlCSL8kCUA/XLuw/WzKpIDY/pCywGpgMD3X1O8cKTuFMeiHKgYYUMYegFLHD39wHMbDypoTENvqlmlvgrau5uUccQM03Kg3LIAWCFu7ePOogYUQ40oJAugk7Aohq3F6eP1WJmQ9JLiM0o4LUkvhrNgzLMgYVRBxAzyoEGhD4I191HAaOgbD65pImUA1KpOVBIC3YJ0LnG7R3Sx6SyKA9EOdCAQgrsdKCrme1sZs2Bk0itwSmVRXkgyoEG5N1F4O7rzGw4MIXU3N8xmppYeZQHohxoWEmnypZD34tGERSmHHIAeMPd9486iKSqpByo2BWnnnvuOQCOOOIIBg8eDMDYsWOjDElytPXWWwPQunVrAIYNG5a578ADDwTg9ttvB+DLL1PrcE+ZMgWtu1G+qqqqALjuuuuorq4G4NJLU/Md1q9fH1lcmiorIhKSimvBvvDCCwAcfPDBAFRXV6tlkwBt2qR2fzn66KP529/+BsCmmzacvh07dgSgc+fUxe377ruPP/3pTwB88MEHIUYqUWjevDkAF1xwQebYFVdcAUTbgq2YAnv55ZcD8P3vfx/IfqV46KGHePjhhyOLSzZuyy23BOD++1NrZx9zzDE5Pa9r1661bg8dOpT+/fsDcNxxxwEwb948AFauXFmUWEXqUheBiEhIKmIUQf/+/fn73/8OZL9KzJo1C4BDDjmEVaty3xtPowgK09Qc6NevHwCTJk0qeiznnHMOAHfeeWdTn6pRBAUIow5svvnmAHz11VeZY8HFzzvuuKPYLwc55oBasCIiISnrPtjgAsf//u//Zlqun332GZDtAG9K61VKp3fv3gBccsklOT9nxIgRfPTRRwD85je/AbLDtupz/fXXA/Dpp58yYcKEfEOVmAr62kNqweZELVgRkZCUZQu2V69eANx1110A7LHHHpn7zj33XACeeOKJ0gcmOTv//PMBOPTQQze4b8aM1Ip3r7/+eq3jL7zwArNnzwZg8uTJQHZSwoQJEzJ5EWjVqhUAAwYMUAtWQlFWBXbQoEFAaswjkBnfunLlyszMrSlTpkQTnOTMzNhkk/q/XJ1yyiksX74cgKlTpzZ4juBiR/Bz8uTJ7L9/6ppE3XN369aNH//4xwA8+eSThQUvUoO6CEREQlI2LdgOHTpw0UUX1XvfxIkTOeOMM0ockeRrr732ykwKqGvatGksWrSo3vs2ZuTIkZmheXW7A3r06MGxxx4LqAWbVMFsrWeffZa+fftGHE2WWrAiIiFJfAs2mEr5zDPP0KNHj1r3BUOwHn9ca/8myc4777zBsWBVrO+++y7v87766qu1zrXFFlvkfS6Jl7Vr1wJw7733qgUrIlIJEt+CDYba1ByKFQgmGmgyQbJ88cUXGxz717/+BcDnn3+e93mXLl0KZKfdnnTSSZn7jjrqKCC7xuzq1avzfh0pvWBltWAxp7hIbIFt164dkB3PapZdIuC1114Dsl8bJBmCr+zjx4/f4L4+ffoAsO222+Z1kaumcePGAbUL7I477ghAs2bNCjq3RCP4/zZ8+PCII6lNXQQiIiFJbAv21ltvBWDvvfcGUpMKgosYQWtnzZo10QQneQm+5m277bahvs6SJdpRWkpDLVgRkZAkrgUb9L3usssutY5/9913mS1B1HJNpuDi1rhx4zjllFMijkakcGrBioiEJFEt2G233ZYHHngAgH333ReAb7/9FoCzzz5b0xwTLthu+dlnn22wBTthwoRMH3tTh1IFk1KCxYBqCnY1qG+ImEi+ElVgjz/+eA4//PBax4LxkcGmeJJ8EydOZObMmQD07Nmz1n29evXi+eefB7KLcQc7BW9M+/btueGGGwDYc889a933zTffZLqXtMOwFJO6CEREQtJoC9bMOgNjgQ6AA6Pc/WYz2xp4EOgCfAAMcPf8p9lsxMCBAwEyrQzIzis/+eSTw3hJqaHUObBy5UrOO+88ILvdR811JoJ1Xa+66iqg9uyuYJ2BYIugFi1aAKlugbot18CkSZNYuHBhoWGXvTjUgobccsstpXy5nOXSgl0HXOju3YGDgGFm1h24FJjq7l2BqenbUp6UAwLKgyZrtAXr7kuBpenfV5nZXKATcBxwWPph9wH/BHLfoS4Hbdu2BeDqq68GoE2bNpn7brzxRiA7v1zCE0UOTJs2Dcj+vx89ejSQXXsCshsjvvnmm5ljn3zyCQAtW7bc4PEN0XYxuYmyFjQmWHek5pT5OGjSRS4z6wLsA7wOdEi/4QAfk/raUN9zhgBD8g9R4kQ5IND0PKjUHMi5wJpZa+Bh4Hx3/7LmJ4W7u5nVe/nV3UcBo9LnaNIl2mDb3frWB9VanqUXRQ489NBDAHTq1AnIfnNpSPv27Rs958qVKwEYOnQoAE899VRTQqp4+eRBITnQFHEbBZJTgTWzZqTe0HHu/kj68DIz6+juS82sI7C82MEFiysH4yM32WSTzNYQXbt2LfbLyUZElQOBu+++G4C+ffvSr1+/vM4RbIB44oknAqlF2qVpos6DpGn0IpelPp5GA3Pd/aYadz0ODE7/PhiYWPzwJA6UAwLKg3xYY01qM+sNvAzMAqrTh39Lqu/lIWBHYCGpoRmfNXKuvNrvc+bMAVKrLf3+978H6p+NUwruHq9e9BKIQw4EWrRokZnJdeSRRwLZNUDNLPMVMfjaGgzfueqqq1i3bh2Q7SIowBvuvn+hJ0maYuVBGF0Ehx56KFB70slhhx0GwEsvvVTsl4MccyCXUQTTgIaKyhFNjUqSRzkgoDzIR6Mt2KK+WIid26VSiS3YYiqHHKBCW7DFUkk5oKmyIiIhUYEVEQmJCqyISEhUYEVEQqICKyISEhVYEZGQlHpHgxXAV+mfSdCO2rHuFFUgZSTpOQDKg0IlLQcgz1pQ0nGwAGY2IyljCJMUa5Ik6X1NUqxJkrT3Nd941UUgIhISFVgRkZBEUWBHRfCa+UpSrEmSpPc1SbEmSdLe17ziLXkfrIhIpVAXgYhISFRgRURCUrICa2b9zGyemS0ws1ht62tmnc3sBTObY2bvmNmI9PGRZrbEzGam//0o6liTTnkglZQDJemDNbMqYD7QF1gMTAcGuvuc0F88B+l9hDq6+5tm1gZ4A+gPDABWu/sNkQZYJpQHUmk5UKoWbC9ggbu/7+5rgfGk9lKPBXdf6u5vpn9fBQT7vUtxKQ+konKgVAW2E7Coxu3FxDRx6+z3DjDczN42szFmtlVkgZUH5YFUVA7oIlcNdfd7B+4AdgF6AkuBGyMMT0pEeSDFyoFSFdglQOcat3dIH4uN+vZ7d/dl7r7e3auBu0h9vZH8KQ+konKgVAV2OtDVzHY2s+bASaT2Uo+FhvZ7T3d4B44HZpc6tjKjPJCKyoGSLFfo7uvMbDgwBagCxrj7O6V47RwdDAwCZpnZzPSx3wIDzawn4MAHwNBowisPygOptBzQVFkRkZDoIpeISEhUYEVEQqICKyISEhVYEZGQqMCKiIREBVZEJCQqsCIiIVGBFREJiQqsiEhIVGBFREKiAisiEhIVWBGRkKjAioiEpKwKrJl1NbNvzexvUccipWVmW5vZo2b2lZktNLOTo45JSi9ueVCS9WBL6DZSC/pK5bkNWAt0ILWtx1Nm9lbM1hqV8MUqD8pmPVgzOwn4KTAH+J67nxpxSFIiZtYK+BzYw93np4/dDyxx90sjDU5KJo55UBZdBGa2BfB/wK+jjkUisSuwLvijSnsL6BFRPBKN2OVBWRRY4GpgtLsvjjoQiURr4Ms6x1YCbSKIRaITuzxIfB9sep+cPqT2L5fKtBrYos6xLYBVEcQi0YldHiS+wAKHAV2AD1MbQtIaqDKz7u6+b4RxSenMBzY1s67u/l762N6ALnBVltjlQeIvcplZS2p/av2GVMH9lbt/EklQUnJmNp7Ujp9nkrp6PAn4H40iqCxxy4PEt2Dd/Wvg6+C2ma0GvlVxrTjnAGOA5cCnpD5gVVwrT6zyIPEtWBGRuCqXUQQiIrFTUIE1s35mNs/MFpiZBnRXKOWBKAfql3cXgZlVkbpq1xdYTGqK6kB3n1O88CTulAeiHGhYIS3YXsACd3/f3dcC44HjihOWJIjyQJQDDShkFEEnYFGN24uBAzf2BDNL/BU1d7eoY4iZJuVBOeQAsMLd20cdRIwoBxoQ+jAtMxsCDAn7dSS+yjAHFkYdQNJUag4UUmCXAJ1r3N4hfawWdx8FjIKy+eSS2hrNA+VA2VMONKCQPtjpQFcz29nMmgMnAY8XJyxJEOWBKAcakHcL1t3XmdlwYApQBYzRzJnKozwQ5UDDSjqTqxy+GugiV2HKIQeAN9x9/6iDSKpKyoHEr0UglcXM2G677QA455xzAOjYsSMAv/zlLzd4/D333APAyJEjWbw4tVxwdXV1KUKVEqqqqgLguuuu45BDDgFg//1T9e/ll18GYNiwYcyePbukcWmqrIhISGLZRbBgwQIA5s6dC8AJJ5wAwNq1a/N63c0335w+ffoA8MQTT+R1joC6CAqT79fDFi1aADB48GDuuOOOvF77wgsvBODmm28GCmrJqougAMXsImjWrBkA9957LwADBw7kqaeeAuCLL74AYMCAAUCqfvz85z8HYPLkyYW+dE45EMsCu8MOOwDw3nupNXO33357AD7//PO8XrdTp048+uijAPTq1SuvcwRUYAvT1D+uVq1aAfDqq68CsOeeexYcw7nnngvAbbfdlu8pVGALUMwC+8c//hGASy65BIA777wz03UUmDp1KgCHH344X331FQB77LEHAAsX5j2kOaccUBeBiEhIYnmRK7gY8d133wGpjmuAs846K+9zBh3ehx56KAAvvvhiISFKibRr1w4oTss1ELRggy6nMWPGsH79+qKdX8J3/PHHA3DBBRcAMGvWLABGjBixwWM/+ugjAD777DO23nprAH72s58BcOONN4Yap1qwIiIhiWUfbCDouN57770BOPDAA/O60NWpUycWLUqtRXHEEUcA8MILLzT5PKA+2ELlmgMdOnQA4LnnngOgR48Nt7YPvuE8+OCDAJnhOUBmKNdmm23W6GvtvvvuzJs3L5ewAuqDLUChfbAtWrRg+vTpQDYvevfuDWT76uvTpUuXzP2ffvopAPvttx+Q1wV09cGKiEQpln2wgf/+978AnHbaaQC0bduWTz5p+l6Ga9asYeXKlUWNTcL161//Gqi/5frxxx8DMHToUKD+oXdHHnkkkB0psMsuuzT4WhMnTuTqq68GYNy4cQVELaUwYsSITF6MGTMGgNdff73R53355ZeZ34PnByOUPvjggyJHmRLrAvvmm28W5TwrVqwo+QwOyV+zZs34yU9+0uD9//nPf4CNj2l+5plngOxFjMsuu4zOnTvX+9hdd92VK664AoCXXnoJINOlJPHRsmVLAE499dTMsWCYVi4XKbfYYotM11GpqItARCQksW7BrlmzpujnPPbYY4H8L3JJ+EaMGMFuu+1W731r167l2muvzflcd955JwCPP/54ZrLJAQccsMHjdt11V2DDi2rr1q3LPXAJVTCBoEePHtx9991AeF/ti0UtWBGRkMS6BRt0ShdzEHgwFzm4iCLxc/3119PQ8MHp06dn5po3xUcffZQZnL6xlmzXrl2B1KpdEi/BehRAZlhdU2rDyJEjM78HF72/+eab4gTXALVgRURCEusW7GuvvQZkr+hec801DB8+HMgOMs9V0Oq59NJLAWjTpg0Aq1atKkqsUhrB5JN8BFMm+/fvD8C///1vALbddtsNHrvTTjsB2ZXdJHrHHZfdCfyxxx5r8vODbyeQXSN22bJlhQe2EbEusIFgDYLJkyfz5z//GYB33323SecI/rjatm0LwEEHHQTAs88+W6wwJSGWLl0KwLffftvgY4Kx11deeWVJYpKGBbP6vve97wGp8fHBWOimMLNM108u42aLQV0EIiIhSUQLNljP8fPPP+cvf/kLAP369WvSOYIugq+//rq4wUliBd0NaqUmQ3Dh85133sms65qLYIJC+/btM+dYsmTJxp5SNGrBioiEJBEt2JryXVMg2D7i7bffBrLrSL7yyitq1Vao1q1bN3hfsF2RRC9YES3Y3SJYPyBXwXWXLbfcMnPs/fffL1J0G6cWrIhISBLVgn3ssccy6zduumkq9JpTGYNPtr322gvIjhQ45phjMpujBfcFLrvsssxCH1IZgoVkgp0N6vOPf/yjVOFII4K/8Xw3Pf3hD38IwDbbbJM5RzCqKGyJKrBjx47lzDPPBMgUxeCr/9FHH83BBx8MQPPmzYHsykgjR47MLLAbjIG8+OKLgY0v0Cvxc/HFF2fWkWjq17wuXboAqQ9cyO5IWlNQdLUGQXwEf89BF0GugsX1b7/99syxYHW1Uo1vVheBiEhIGm3BmllnYCzQAXBglLvfbGZbAw8CXYAPgAHunt++2jmaNWsW8+fPB+Dss8+udd+kSZMy+97PmDGj1s+aPvvsMyDbgpXGlToHZs6cmdkmqK6uXbsybNgwgMz/743ZcccdATjvvPMYPHgwkPqqWNfo0aMBuOOOOwAaXAuhksWlFrRs2TJz4au+Fff23XdfILvmRHAxc9q0adxyyy1hhVWvXFqw64AL3b07cBAwzMy6A5cCU929KzA1fVvKk3JAQHnQZI22YN19KbA0/fsqM5sLdAKOAw5LP+w+4J/AJaFEmbZy5Uq6detW0DlWrFhRpGgqR6lz4PDDD+f5558HoGfPnhvcf9555wHQp08fILvma02nn346kJ1/XnOITl2zZ8/m8ssvB6C6ujr/wMtcVLUgmBQQrB9wyCGHcNRRRwGpdX5r2mabbTIXMYOW6yuvvALAL37xi7ym2BaiSRe5zKwLsA/wOtAh/YYDfEzqa0N9zxkCDMk/RIkT5YBA0/OgUnMg5wJrZq2Bh4Hz3f3Lmutlurs3tBWvu48CRqXPoY6tBCtVDnzxxReZTQgffvjhDe6vqqoCYM899wSyGxs2VbBPW58+fVi+fHle56hE+eRBIXUgWDnvgQceAFIt2GDKfHBfsMnlqaeemuljD1q+wWOjWBktpwJrZs1IvaHj3P2R9OFlZtbR3ZeaWUcgERkaLE84c+ZMIDt0Rzau1DkQLEc3aNAgAO6///6CzxmswBYU70ceSf1nhLE1UbmKshY8/fTTAKxevTrzd1vf4utBN08wW7O+D+lSafQil6U+nkYDc939php3PQ4MTv8+GJhY/PAkDpQDAsqDfFhjw1HMrDfwMjALCK4A/JZU38tDwI7AQlJDMz5r5Fyx6SIItnVesmQJZ5xxRs7Pc/eK20skyhwIvn5utdVWAJx//vmZhZeDLoL6jB07FoAPP/wQSK0tMGHCBKAokwjecPf9Cz1J0hQrDwqtAx06dGD33XcHsuv2du/eHUjN0LrpplTtnzZtWiEv05icciCXUQTTgIaKyhFNjUqSRzkgoDzIR6Mt2KK+WAxasMG0u+nTpwNw6623ctddd+X8/EpswRZTHHKgCCqyBVsslZQDmiorIhKSRC32UgzBajoNTcUUESkWtWBFREKiAisiEhIVWBGRkKjAioiEpNQXuVYAX6V/JkE7ase6U1SBlJGk5wAoDwqVtByAPGtBScfBApjZjKSMIUxSrEmSpPc1SbEmSdLe13zjVReBiEhIVGBFREISRYEdFcFr5itJsSZJkt7XJMWaJEl7X/OKt+R9sCIilUJdBCIiIVGBFREJSckKrJn1M7N5ZrbAzGK1ra+ZdTazF8xsjpm9Y2Yj0sdHmtkSM5uZ/vejqGNNOuWBVFIOlKQP1syqgPlAX2AxMB0Y6O5zQn/xHKT3Eero7m+aWRvgDaA/MABY7e43RBpgmVAeSKXlQKlasL2ABe7+vruvBcaT2ks9Ftx9qbu/mf59FRDs9y7FpTyQisqBUhXYTsCiGrcXE9PErbPfO8BwM3vbzMaY2VaRBVYelAdSUTmgi1w11N3vHbgD2AXoCSwFbowwPCkR5YEUKwdKVWCXAJ1r3N4hfSw26tvv3d2Xuft6d68G7iL19UbypzyQisqBUhXY6UBXM9vZzJoDJ5HaSz0WGtrvPd3hHTgemF3q2MqM8kAqKgdKslyhu68zs+HAFKAKGOPu75TitXN0MDAImGVmM9PHfgsMNLOegAMfAEOjCa88KA+k0nJAU2VFREKii1wiIiFRgRURCYkKrIhISFRgRURCogIrIhISFVgRkZCowIqIhEQFVkQkJCqwIiIhUYEVEQmJCqyISEhUYEVEQqICKyISksQXWDNbXeffejO7Jeq4RKT0zOyfZvZtjXowL8p4El9g3b118A/YDvgGmBBxWCISneE16sJuUQaS+AJbxwnAcuDlqAMRESm3AjsYGOtaRVykkv3RzFaY2StmdliUgZTNjgZmthPwPvA9d/9v1PGISOmZ2YHAHGAtqf2+bgV6uvt/ooinnFqwg4BpKq4ilcvdX3f3Ve6+xt3vA14BfhRVPOVUYE8D7os6CBGJFQcsqhcviwJrZv8DdEKjB0QqlpltaWZHmVkLM9vUzE4BfgBMjiqmkmzbXQKDgUfcfVXUgYhIZJoB1wDdgPXAu0B/d58fVUBlc5FLRCRuyqKLQEQkjlRgRURCUlCBNbN+ZjbPzBaY2aXFCkpEkkW1oH5598GaWRUwH+gLLAamAwPdfU7xwhORuFMtaFghowh6AQvc/X0AMxsPHEdqFkW9zCzxV9TcPbIxdSIx1aRaUA51AFjh7u0be1AhXQSdgEU1bi9OH6vFzIaY2Qwzm1HAa4lIfDVaC8qwDizM5UGhj4N191HAKCibTy4RaaJKrQOFtGCXAJ1r3N4hfUxEKotqQQMKKbDTga5mtrOZNSe1cs3jxQlLRBJEtaABeXcRuPs6MxsOTAGqgDHu/k7RIhORRFAtaFhJp8qWQ9+LRhGIFKYc6gDwhrvv39iDNJNLRCQkKrAiIiEpl+UKRUTo1q0b5557LgCbbbYZAB06dADgmGOOyTxu+vTpADzyyCMAPP3007z99ttFj0d9sE2kPliRwhSzDrRp0waAP/zhDwCcdtpptG7duu7rAbCxWvftt98yYUJqvf7TTz89l5dWH6yISJRi2UVw/PHHA3DUUUcB8OijjwKwYsWKzGM+/PBDALbZZhsAWrVq1eD5fvCDH9C/f38A5s6dC2Q/8YLziEhy7LTTTgC8+OKLAHTunJ3nMGnSJAC+++47ILcW7D777MOJJ54IwMqVKwG46KKLAFi7dm3ecaoFKyISkli2YLt16wbAWWedBcCZZ54JpD6Jgk+hRYtSa0u0a9cOgJYtW2buq/uJVfN5wbmDFqyIJMtmm23GAw88AMCOO+4IZP/Wx48fz6BBgwCorq7O+ZytW7fm5JNPBuCnP/0pkKopoBasiEgsxbIFu8kmqbr/q1/9CoCXXnoJSPWlNkXv3r0BOPXUUzPHxo0bB6jvVSSpbr75Zg466KBax4K/6/PPP79JLdfA6tWrGTVqFEDmZzHEssAGF6TuuusuAN59991aP3MVXCxzd+bMSa39q64BkWQ74YQTMt2A9957LwAXXHABkL1AFRfqIhARCUksW7CB4IJUUwVDtoIOcDPj2muvBWoP9RKR5Dj66KMBaNu2beai1sZarltuuSUAm26aKnPuzqefflqKUDPUghURCUnsWrDdunXLtFyDPth8zgGw2267Aan5xsFkBRFJlmBNgSuvvBKAqqqqzH11W64dO3bMXBwPfgaTkdasWZO5gFWMSQS5UAtWRCQksWvBQuH9pPfffz+QnXDwzDPP8PXXXxccl4iUXrCgS69evTLHnnzySSA7CemSSy4BoH379pnH19W8eXOGDx8OZGvM1VdfHU7QabFcTSuYnRVoasFdv349kJ3dcc455xRtbJtW0xIpTFNX02rWrBkAzz33HJAd354+F1B7nYFgKcJZs2bVOs8JJ5xA27ZtAfj4448B2HfffQFYtmxZU0ICraYlIhKtsuoiCGZ6BZ9qgWAmmIgkT7Aq1uWXXw7A1KlTM63aVatWAdmZXNdee22DszR79+6dGbrVsWNHAHbZZRcgrxZsTtSCFREJSSxbsPkKhmcF/THBdhBNnWIrIvEzbdo0AHr06JEZqvXNN98Aua0t4u6Z2hBMOFiyZEkYoWaoBSsiEpKyasEecsghQLYP9rHHHosyHBEJwYIFC5r0+OCbbadOnTLH3njjDQAWLlxYvMDqUVYFtm4XQbA9jIhUrvvuuw+g1maIpZrZqS4CEZGQNNqCNbPOwFigA+DAKHe/2cy2Bh4EugAfAAPc/fPwQt24/fbbLzNouO4wLREpXFJqQSBYaeuAAw4AUt+Rfd7bAAAD+UlEQVRsR48eDcA999xTkhhyacGuAy509+7AQcAwM+sOXApMdfeuwNT0bREpX6oFTdRoC9bdlwJL07+vMrO5QCfgOOCw9MPuA/4JXBJKlDkq5bRfkUqTlFoQTDi68cYbgew32lWrVnHNNdcA2ckLYWvSRS4z6wLsA7wOdEi/4QAfk/raUN9zhgBD8g9RROKmqbWgUutAzgXWzFoDDwPnu/uXNfs53d0bWsDB3UcBo9LnCLWJGcSkPliR8ORTC8KuA8EW28OHD8+s9Rp8ow1aqxdffHHJNzvNqcCaWTNSb+g4d38kfXiZmXV096Vm1hFYHlaQuQre0Hw3SRSRjYuyFhx44IEAbL/99plhVkOGpBrF5557LgDdu3ff4Hk33XQTAH/961/DCGujGr3IZamPp9HAXHe/qcZdjwOD078PBiYWPzwRiQvVgqbLpQV7MDAImGVmM9PHfgtcCzxkZr8EFgIDwgkxN2eddVama+B3v/sdgBbZFimuSGvBdtttB8DYsWMzaxAEa0fXvMD93nvvAWSGZF1//fVhhJOTXEYRTAMa6tQ8orjhiEhcqRY0XSx3NMjHsmXLMpubBdv0hkE7GogUJt860LNnTwBeeeUVWrRoEZwLgLfeegtITYENWq4hr5SlHQ1ERKKU+BZs+/btAVi+fDnV1dVA7W19i00tWJHChD1cs0RyasEmfjWt4AOiurqaOXPmRByNiEiWughEREKS+BZssEFimN0CIiL5UAtWRCQkpW7BrgC+Sv9MgnbUjnWnqAIRKSNJqwOQZy0o6SgCADObkcvVtzhIUqwiSZK0v61841UXgYhISFRgRURCEkWBHRXBa+YrSbGKJEnS/rbyirfkfbAiIpVCXQQiIiFRgRURCUnJCqyZ9TOzeWa2wMxita2vmXU2sxfMbI6ZvWNmI9LHR5rZEjObmf73o6hjFUm6SqoFJemDNbMqYD7QF1gMTAcGunssVmdJ7yPU0d3fNLM2wBtAf1Irs6929xsiDVCkTFRaLShVC7YXsMDd33f3tcB4Unupx4K7L3X3N9O/rwKC/d5FpLgqqhaUqsB2AhbVuL2YmBawOvu9Aww3s7fNbIyZbRVZYCLloaJqgS5y1VB3v3fgDmAXoCewFLgxwvBEpESKVQtKVWCXAJ1r3N4hfSw26tvv3d2Xuft6d68G7iL19UZE8ldRtaBUBXY60NXMdjaz5sBJpPZSj4WG9ntPd3gHjgdmlzo2kTJTUbWgJMsVuvs6MxsOTAGqgDHu/k4pXjtHDe33PtDMegIOfAAMjSY8kfJQabVAU2VFREKii1wiIiFRgRURCYkKrIhISFRgRURCogIrIhISFVgRkZCowIqIhOT/AW8qRYeRUkzsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(9):\n",
    "    plt.subplot(330 + (i+1))\n",
    "    plt.imshow(train[i][:,:,0], cmap=plt.get_cmap('gray'))\n",
    "    plt.tight_layout()\n",
    "    plt.title(labels[i]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a8b5649dd5db29f6650e50509fde60afc977b730"
   },
   "source": [
    "For a human label these would be a easy task, but can our model perform well enought?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "15c49c2f60ead9b6cb9a377646bc63b81d70ff1c"
   },
   "source": [
    "### One-hot encode the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "trusted": true,
    "_uuid": "163b94e9fb8b086199ee663c8fa4990f839c20a0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "labels = pd.get_dummies(labels).as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f48f98e0aea228972e5f191d747f053045e8ce15"
   },
   "source": [
    "### Normalize data\n* Normalizing data should improve our model convergence time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "trusted": true,
    "_uuid": "1d3bc2797a39d800af702edb51574a13cc2e75b9"
   },
   "outputs": [],
   "source": [
    "train = train / 255.0\n",
    "test = test / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "87d72ef70d1c448811c55b64e322c2dbe5640d02"
   },
   "source": [
    "### Split data in train and validation (90% ~ 10%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "trusted": true,
    "_uuid": "3853224b767c94c194ddb82e3534b2df37528163"
   },
   "outputs": [],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(train, labels, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ad7167a00430f3603259628fc635affe647c338a"
   },
   "source": [
    "### Data augmentation\n* Data augmentation is used to create more data from the current set we have, here, using the Keras API for data augmentation (ImageDataGenerator), we can generate more samples while feeding the model, the new data is created adding some noise to the real data.\n* In this case data augmentation seems to be very useful, as we saw most of the data seems to be the original one but with a bit of distortion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "trusted": true,
    "_uuid": "9774228bea9af8e20f571c4dffe66dc256f7dfca"
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        horizontal_flip=False,  # randomly flip images\n",
    "        vertical_flip=False,  # randomly flip images\n",
    "        rotation_range=15,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        zoom_range = 0.15, # Randomly zoom image \n",
    "        width_shift_range=0.15,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.15)  # randomly shift images vertically (fraction of total height)\n",
    "\n",
    "datagen.fit(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "654d260d26398abafd353d7ce8f01032fc595d86"
   },
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "trusted": true,
    "_uuid": "7ee3da8b687719b3a3de90473db89ee7357033d2"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(5,5),padding='Same', input_shape=(HEIGHT, WIDTH, CANAL)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(filters=32, kernel_size=(5,5),padding='Same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3),padding='Same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3),padding='Same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(256))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(N_CLASSES, activation = \"softmax\"))\n",
    "\n",
    "optimizer = optimizers.adam(lr=LEARNING_RATE)\n",
    "model.compile(optimizer=optimizer , loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "51a2becc651915d509e0cb3c1802f1f41c805754"
   },
   "source": [
    "Let's take a look at our model parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "trusted": true,
    "_uuid": "89c221a6aa89cca6dfea87290c15c2eb6a9a13ca",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size: 42000\nEpochs: 45\nLearning rate: 0.001\nBatch size: 64\nInput dimension: (28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Dataset size: %s' % train.shape[0])\n",
    "print('Epochs: %s' % EPOCHS)\n",
    "print('Learning rate: %s' % LEARNING_RATE)\n",
    "print('Batch size: %s' % BATCH_SIZE)\n",
    "print('Input dimension: (%s, %s, %s)' % (HEIGHT, WIDTH, CANAL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "trusted": true,
    "_uuid": "872713ee93dba73c3e6be5461748ca44c732e8b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "trusted": true,
    "_uuid": "c8d680342129fee2737123609fcfc9460c916f98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/45\n - 22s - loss: 0.4241 - acc: 0.8675 - val_loss: 0.0662 - val_acc: 0.9793\nEpoch 2/45\n - 17s - loss: 0.1469 - acc: 0.9562 - val_loss: 0.0379 - val_acc: 0.9876\nEpoch 3/45\n - 17s - loss: 0.1139 - acc: 0.9668 - val_loss: 0.0704 - val_acc: 0.9776\nEpoch 4/45\n - 17s - loss: 0.0932 - acc: 0.9714 - val_loss: 0.0372 - val_acc: 0.9881\nEpoch 5/45\n - 17s - loss: 0.0900 - acc: 0.9730 - val_loss: 0.0305 - val_acc: 0.9893\nEpoch 6/45\n - 17s - loss: 0.0794 - acc: 0.9754 - val_loss: 0.0292 - val_acc: 0.9905\nEpoch 7/45\n - 17s - loss: 0.0725 - acc: 0.9779 - val_loss: 0.0440 - val_acc: 0.9860\nEpoch 8/45\n - 17s - loss: 0.0687 - acc: 0.9793 - val_loss: 0.0253 - val_acc: 0.9929\nEpoch 9/45\n - 18s - loss: 0.0648 - acc: 0.9802 - val_loss: 0.0236 - val_acc: 0.9921\nEpoch 10/45\n - 17s - loss: 0.0616 - acc: 0.9813 - val_loss: 0.0204 - val_acc: 0.9936\nEpoch 11/45\n - 17s - loss: 0.0597 - acc: 0.9821 - val_loss: 0.0242 - val_acc: 0.9933\nEpoch 12/45\n - 17s - loss: 0.0546 - acc: 0.9830 - val_loss: 0.0230 - val_acc: 0.9924\nEpoch 13/45\n - 17s - loss: 0.0576 - acc: 0.9827 - val_loss: 0.0344 - val_acc: 0.9881\nEpoch 14/45\n - 17s - loss: 0.0518 - acc: 0.9842 - val_loss: 0.0216 - val_acc: 0.9938\nEpoch 15/45\n - 17s - loss: 0.0488 - acc: 0.9851 - val_loss: 0.0224 - val_acc: 0.9938\nEpoch 16/45\n - 17s - loss: 0.0485 - acc: 0.9848 - val_loss: 0.0251 - val_acc: 0.9933\nEpoch 17/45\n - 17s - loss: 0.0458 - acc: 0.9856 - val_loss: 0.0208 - val_acc: 0.9943\nEpoch 18/45\n - 17s - loss: 0.0467 - acc: 0.9854 - val_loss: 0.0247 - val_acc: 0.9924\nEpoch 19/45\n - 17s - loss: 0.0426 - acc: 0.9863 - val_loss: 0.0248 - val_acc: 0.9914\nEpoch 20/45\n - 17s - loss: 0.0447 - acc: 0.9860 - val_loss: 0.0198 - val_acc: 0.9940\nEpoch 21/45\n - 17s - loss: 0.0392 - acc: 0.9877 - val_loss: 0.0180 - val_acc: 0.9936\nEpoch 22/45\n - 17s - loss: 0.0403 - acc: 0.9880 - val_loss: 0.0172 - val_acc: 0.9938\nEpoch 23/45\n - 17s - loss: 0.0449 - acc: 0.9868 - val_loss: 0.0203 - val_acc: 0.9940\nEpoch 24/45\n - 17s - loss: 0.0367 - acc: 0.9895 - val_loss: 0.0206 - val_acc: 0.9940\nEpoch 25/45\n - 17s - loss: 0.0394 - acc: 0.9881 - val_loss: 0.0186 - val_acc: 0.9940\nEpoch 26/45\n - 17s - loss: 0.0376 - acc: 0.9891 - val_loss: 0.0185 - val_acc: 0.9936\nEpoch 27/45\n - 17s - loss: 0.0360 - acc: 0.9883 - val_loss: 0.0177 - val_acc: 0.9945\nEpoch 28/45\n - 17s - loss: 0.0376 - acc: 0.9885 - val_loss: 0.0232 - val_acc: 0.9936\nEpoch 29/45\n - 17s - loss: 0.0358 - acc: 0.9888 - val_loss: 0.0216 - val_acc: 0.9924\nEpoch 30/45\n - 17s - loss: 0.0327 - acc: 0.9895 - val_loss: 0.0212 - val_acc: 0.9938\nEpoch 31/45\n - 17s - loss: 0.0345 - acc: 0.9899 - val_loss: 0.0266 - val_acc: 0.9938\nEpoch 32/45\n - 17s - loss: 0.0361 - acc: 0.9889 - val_loss: 0.0269 - val_acc: 0.9933\nEpoch 33/45\n - 18s - loss: 0.0322 - acc: 0.9904 - val_loss: 0.0219 - val_acc: 0.9940\nEpoch 34/45\n - 17s - loss: 0.0325 - acc: 0.9902 - val_loss: 0.0215 - val_acc: 0.9938\nEpoch 35/45\n - 17s - loss: 0.0331 - acc: 0.9897 - val_loss: 0.0162 - val_acc: 0.9952\nEpoch 36/45\n - 17s - loss: 0.0329 - acc: 0.9901 - val_loss: 0.0211 - val_acc: 0.9936\nEpoch 37/45\n - 17s - loss: 0.0307 - acc: 0.9907 - val_loss: 0.0192 - val_acc: 0.9950\nEpoch 38/45\n - 17s - loss: 0.0301 - acc: 0.9907 - val_loss: 0.0201 - val_acc: 0.9950\nEpoch 39/45\n - 17s - loss: 0.0320 - acc: 0.9903 - val_loss: 0.0188 - val_acc: 0.9955\nEpoch 40/45\n - 17s - loss: 0.0298 - acc: 0.9904 - val_loss: 0.0176 - val_acc: 0.9933\nEpoch 41/45\n - 17s - loss: 0.0290 - acc: 0.9911 - val_loss: 0.0175 - val_acc: 0.9945\nEpoch 42/45\n - 17s - loss: 0.0303 - acc: 0.9914 - val_loss: 0.0224 - val_acc: 0.9931\nEpoch 43/45\n - 17s - loss: 0.0292 - acc: 0.9910 - val_loss: 0.0298 - val_acc: 0.9921\nEpoch 44/45\n - 18s - loss: 0.0270 - acc: 0.9916 - val_loss: 0.0204 - val_acc: 0.9945\nEpoch 45/45\n - 17s - loss: 0.0296 - acc: 0.9907 - val_loss: 0.0196 - val_acc: 0.9955\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(datagen.flow(x_train,y_train, batch_size=BATCH_SIZE),\n",
    "                              epochs=EPOCHS, validation_data=(x_val, y_val),\n",
    "                              verbose=2, steps_per_epoch=x_train.shape[0] // BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "trusted": true,
    "_uuid": "afd37bd2ba37387d2f393d5e9dacd8d4d9d27f24"
   },
   "source": [
    "Let's take a look at our model loss and accuracy training graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "trusted": true,
    "_uuid": "781f97c05ce86622fc1dfc2e9481795ac616fbc0",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIsAAAG5CAYAAAAOBbRvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xl81NW9//HXyUwmyWRf2EIS9n3fQUBE64IbaC0Vwa211l57tVp7L7e3i1urba0/aq+111q8rlDr0rpWrVXBBWSRHZGdJOwJ2ffM+f1xJiEggeyTwPv5eOQxM9/vd75zZoZl5p3P+RxjrUVERERERERERAQgLNQDEBERERERERGR9kNhkYiIiIiIiIiI1FJYJCIiIiIiIiIitRQWiYiIiIiIiIhILYVFIiIiIiIiIiJSS2GRiIiIiIiIiIjUUlgkIiIiIiIiIiK1FBaJyCkZYz4wxhwxxkSEeiwiIiIicmLGmF3GmK+Fehwi0vEpLBKRkzLG9ASmAha4vA0f19tWjyUiIiIiIiJHKSwSkVO5DlgG/B9wfc1GY0yUMea3xpjdxph8Y8xHxpio4L4pxphPjDF5xphMY8wNwe0fGGNuqnOOG4wxH9W5bY0xtxpjtgJbg9t+FzxHgTFmlTFmap3jPcaYHxtjthtjCoP7040xjxpjflv3SRhjXjXG3NEaL5CIiIhIe2aM+Y4xZpsxJjf4mSg1uN0YY/6fMeZg8LPWemPM0OC+i40xm4KfsbKNMXeF9lmISFtSWCQip3Id8Fzw50JjTJfg9oeAMcBZQBLwH0DAGNMDeAv4PdAJGAmsacTjzQImAIODt1cEz5EEPA/81RgTGdx3JzAHuBiIA74FlABPAXOMMWEAxpgU4GvB+4uIiIicMYwx5wIPALOBbsBuYHFw9wXA2UB/ID54TE5w35+B71prY4GhwL/acNgiEmIKi0SkXsaYKUAP4AVr7SpgO3BNMIT5FnC7tTbbWlttrf3EWlsOXAP801q7yFpbaa3NsdY2Jix6wFqba60tBbDWPhs8R5W19rdABDAgeOxNwE+stVusszZ47GdAPnBe8LirgQ+stQea+ZKIiIiIdDRzgYXW2tXBz2r/BUwKthqoBGKBgYCx1m621u4L3q8SGGyMibPWHrHWrg7B2EUkRBQWicjJXA+8Y609HLz9fHBbChCJC4+Ol17P9obKrHvDGHOXMWZzcKpbHu63XikNeKyngHnB6/OAZ5oxJhEREZGOKhVXTQSAtbYIVz3U3Vr7L+B/gEeBg8aYx40xccFDv46r3t5tjPnQGDOpjcctIiGksEhETijYf2g2MM0Ys98Ysx+4AxiBK2EuA/qc4K6Z9WwHKAb8dW53PcExts4YpuKmt80GEq21CbiKIdOAx3oWmGmMGQEMAv5Wz3EiIiIip7O9uEpxAIwx0UAykA1grX3EWjsG1wKgP/Cj4PYV1tqZQGfc56gX2njcIhJCCotEpD6zgGrcB4eRwZ9BwFJcH6OFwMPGmNRgo+lJxpgIXG+jrxljZhtjvMaYZGPMyOA51wBXGmP8xpi+wLdPMYZYoAo4BHiNMT/D9Saq8QRwnzGmX7BB43BjTDKAtTYL1+/oGeClmmltIiIiIqe5cGNMZM0PsAi40RgzMvhZ7ZfAcmvtLmPMOGPMBGNMOO6XemW4HpQ+Y8xcY0y8tbYSKAACIXtGItLmFBaJSH2uB5601u6x1u6v+cGVKs8F5gPrcYFMLvArIMxauwdXsvzD4PY1uGokgP8HVAAHcNPEnjvFGN4G/gF8iSufLuPYaWoP437L9Q7uQ8yfgag6+58ChqEpaCIiInLmeBMorfNzDvBT4CVgH64q++rgsXHAn4AjuM9aOcBvgvuuBXYZYwqAW3Cf/0TkDGGstac+SkSkAzLGnI2bjtbD6h87ERERERGRBlFlkYicloLl1LcDTygoEhERERERaTiFRSJy2jHGDALycI24F4R4OCIiIiIiIh2KpqGJiIiIiIiIiEgtVRaJiIiIiIiIiEgtb6gHcLyUlBTbs2fPUA9DREREWtGqVasOW2s7hXoccpQ+g4mIiJzeGvP5q92FRT179mTlypWhHoaIiIi0ImPM7lCPQY6lz2AiIiKnt8Z8/jrlNDRjzEJjzEFjzIZ69htjzCPGmG3GmHXGmNF19l1vjNka/Lm+oYMSEREREREREZHQaEjPov8DLjrJ/hlAv+DPzcBjAMaYJODnwARgPPBzY0xicwYrIiIiIiIiIiKt65RhkbV2CZB7kkNmAk9bZxmQYIzpBlwIvGutzbXWHgHe5eShk4iIiIiIiIiIhFhL9CzqDmTWuZ0V3Fbf9q8wxtyMq0oiIyOjBYYkIiIiIiIiIi2lsrKSrKwsysrKQj0UOYXIyEjS0tIIDw9v8jnaRYNra+3jwOMAY8eOtSEejoiIiIiIiIjUkZWVRWxsLD179sQYE+rhSD2steTk5JCVlUWvXr2afJ6G9Cw6lWwgvc7ttOC2+raLiIiIiIiISAdSVlZGcnKygqJ2zhhDcnJysyvAWiIsehW4Lrgq2kQg31q7D3gbuMAYkxhsbH1BcJuIiIiIiIiIdDAKijqGlnifTjkNzRizCDgHSDHGZOFWOAsHsNb+EXgTuBjYBpQANwb35Rpj7gNWBE91r7X2ZI2yRUREREREREQkxE4ZFllr55xivwVurWffQmBh04YmIiIiIiIiIme6nJwczjvvPAD279+Px+OhU6dOAHz22Wf4fL5TnuPGG29k/vz5DBgwoEGP+cQTT7BhwwYWLFjQ9IF3YO2iwbWIiIiIiIiIyIkkJyezZs0aAO6++25iYmK46667jjnGWou1lrCwE3fbefLJJ1t9nKeTluhZJCIiIiIiIiLSprZt28bgwYOZO3cuQ4YMYd++fdx8882MHTuWIUOGcO+999YeO2XKFNasWUNVVRUJCQnMnz+fESNGMGnSJA4ePHjSx9m5cyfTp09n+PDhnH/++WRlZQGwePFihg4dyogRI5g+fToA69evZ9y4cYwcOZLhw4ezY8eO1nsBWpEqi0RERERERESkwe55bSOb9ha06DkHp8bx88uGNPp+X3zxBU8//TRjx44F4MEHHyQpKYmqqiqmT5/OVVddxeDBg4+5T35+PtOmTePBBx/kzjvvZOHChcyfP7/ex/i3f/s3brrpJubOncvjjz/OD37wA1588UXuuecePvjgA7p06UJeXh4Af/jDH7jrrrv45je/SXl5Oa5zT8ejyiIRERERERER6ZD69OlTGxQBLFq0iNGjRzN69Gg2b97Mpk2bvnKfqKgoZsyYAcCYMWPYtWvXSR9j+fLlXH311QBcd911LF26FIDJkydz3XXX8cQTTxAIBAA466yzuP/++/n1r39NZmYmkZGRLfE025wqi0RERERERESkwZpSAdRaoqOja69v3bqV3/3ud3z22WckJCQwb948ysrKvnKfug2xPR4PVVVVTXrsP/3pTyxfvpzXX3+d0aNH8/nnn3PttdcyadIk3njjDS666CIWLlzI2Wef3aTzh5Iqi0RERE6ksgxyd0J1ZahH0vashUNboPhwqEcip4HDReVs3teyUxVEREROpKCggNjYWOLi4ti3bx9vv/12i5x34sSJvPDCCwA8++yzteHPjh07mDhxIvfddx+JiYlkZ2ezY8cO+vbty+23386ll17KunXrWmQMbU2VRSIi0rYK98Pm1yC2K/SfAZ4Q/VdUUQx5mZC3B/L3uMva25lQdMAdl9gLvvZzGDwLjAnNWFtbIAAHN8Huj4M/n0DxIbj4IRj/nVCPTjq4Jz/eyf9+uINtv7w41EMREZHT3OjRoxk8eDADBw6kR48eTJ48uUXO++ijj/Ktb32LBx54gC5dutSurHbHHXewc+dOrLVccMEFDB06lPvvv59FixYRHh5Oamoqd999d4uMoa2Z9tZsaezYsXblypWhHoaIiLSkylL44g1Yuwi2/wusm9NNfDqMuwlGXwf+pKadu3A/rHsBNrzY8EqYylIozT12W1g4xKdBQgYkpENCD4hKhJULXZCSNh4uuB8yJjRtnO1JoBr2r3fB0K6PYc8nUHrE7YtPhx6Toedk6HMexHdvlSEYY1ZZa8ee+khpK631GezR97fxm7e38OX9M/B5VdQuItJRbd68mUGDBoV6GNJAJ3q/GvP5S5VFIiInU1kKWStcpYW10OMsSBsHPn+oR9b+BQKQuQzWPA+b/g7lBRCXBlPugGGzIWcbLP8j/PPn8MGDMHw2TPgudGnAHPiKEtjy5rHhU/ex0Gd6w8bm8QWDoR4uHEnIgJguEHaCL7JjvwVrnoN//QIWXgCDLoev3Q3JfRrzarjpbLs/gR0fQEo/GHQZRMQ27hzNYa17zVY9BXuWQXm+257YEwZc4sKhHpMhsUfbjUnOCFHhHgBKKqrweX2nOFpERETaA4VFIqFSnON+m7/rY9j9keuPMvF7MGoeeCPabhzWQuZnEOZxX5ijO7XeVBtroaoMyvKhrCB4me+2tbSIGBcCxKc17vUsL4LM5Uen4mSvguoKMMEQwQZcBUr3MS446jkZ0ie6xzsdleTCgQ1QVQGR8XV+4sAbeeI/K7k7YO1i95O3G8KjYfBMGDkHekw5Gsh0HgiDLoUDG2H5/7rqoNVPQc+pMOEWGDDD/bmsEQjAnk9dQLTxb1BR6N7jKXfCiKtdANMawjyu8mno1+HTR+GjBS50GXcTnP0fEJ1c/33LCmDbP93xW99xf94xgIU3fugCoxFzoNfZxz7XlnZkN7z1H/DlP9zf8yGzoOcUFw61UuWQSI3oiJqwqJoE5ewiIiIdgsIiaX2BgPtC2Z57fQQC8MEv4fCXDTveE/HV6SrxaRAeVf99ig7Cro+OhhAHg0s4eqMgfRx4SuCNO2HJQ67yYvR1EN7KyywW7ofXbndfIGt4I4OVFulHKy4SMo5ej4h1FSLHBz5l+a5Soe7tr+wvcMFLmzKuN07tc6l5Xj3c9agk2Pu5C+x2fQz71kCgCowHUke50KLnFMiY6E63Z/nRYz/+HXz0cPDYkcGpO8FjI+NbZvjlRa6/z7Z/QqCBjZYj4o4+v5r3Li715GFEoNqFPPvXu3Bo/wZ3WZBd/308vmMDpIg4KC+E7JWAgd7nwPT/doGQL7r+83QZApc/4qp1Vj8NK56Av8x1Yx/3HXeeL153IVHeHvDFuPBpxNXHhk+tzRcN0/4DRl8PHzwAnz3uqqam/tD9Oan5+5qf7cKhLW/CzqXuffMnw8BLXQDWe7p7bdcugg2vwLq/QGyqq6waeQ10GtByY66qgE9/Dx/+xgWeF9zvxuoJb7nHEDmFKJ/7uFlS0bSVZkRERKTtqWeRtA5rXUXG2kWw4SUXGkTGHf1CWfsFM+Ho9sh4V9WSPsF9yW1Ln/4B3v4vSOrTsC9RFSVQuNeFCnVFd/7qF/RDX7hgIWerO8YX455jzZSP1NHg9bnXbMcH8OGvXcVRTFeYfBuMubHlpzxZ696XN37oqnrO/Qkk93NNffN2H9vkt/hQ487tjXLvaUQcRCWc4D0PVqVEJhy97Y3AVVu0oLL84PM5roFxfvaJQ5eaaqGa9yV9wqmrhepWIe362P2ZD1S68ChjkgsGBl4MSb0bN/ZANexc4ipzNr8KlSUQ261hAZS1rvdM8cHjnp/X/XmMzzgamkUmwOEtLhg6uMk9DrjxdxoAXYZC16HuMiK2TvCXd+IwsCw4rWnQpW6aWVMrVqqrXNCy/H9dMOcG5UKjEXNOHT61lYNfuCl0X/7D/X0fMsu9b/vWuv1Jfdz7P+ASSB9/4rCusgy+fAvWLHKBoK12IeWIOTD0qpNXLZ3Kro/g9TvdezzwUpjxKxdqtxPqWdT+tNZnsPc2H+DbT63k1e9PZnhaQoufX0RE2oZ6FnUsze1ZpLBIjlVV7n773NTfOudlwrrg9JOcbS44GHiJ64lRt7rk+OqTisJjz5OQ4SoGanto9Gy9yqTDW+GPU9wX0TmLG/44gWoo3HfcCkp1r2dBdbkLSjImBZ/LFOg24tSrP+36CD78lfviGd0Jzvp3GPvtlpnqVHTIVTBtftX13pn12Mmn71SUuOdS8/zKi44LfYKhX0RccGpSG06ha4pAtauoqgmSig+6MKQl+hDV9Dfa8SFseQsObnTbOw0KBkeXuHCwvkqYQ1tcwLruBVfRExEPQ69wwUH6hMb9HagsPfq+1QR/df+sFu4DrHvvug47Ggx1HQadBraf93H/ehfC9T2//U6X2rkE3vkJ7FvnQqEBM1xAlNKvce9Z0UFY/6L7M7B/nQv4+pzrpqj1mAxdhzds5biiQ/DuT915EjLcimb9L2z682slCovan9b6DPbJ9sNc86flLL55IhN7NyMAFRGRkFJY1LEoLJKWU1kGj53lvkh2Hghdhh2tKug6rP6VisoLYdOr7ovJrqVuW48pborI4JkuQDiV6ioXIuVnuilauz5ylzWrFcV1d1+Wepzlpvkk922Z8Ki6ChZe6IKtW5e76UotJRCAkhz3ujW1F8nuT2HJr10D36gkOOv7blpOQ17TE9n0Krx+h3utp/8YzrqtdfuknOlyd7qqky/eCDbIrnZNlPtf5IKjXme7MG7DS7D2eTcdznig79fc358BM04+tbE5qipcWBud0r6niHYU1rrKrJaqeDqw0U1x2/Kmmx4I4It1Uxxr/h1MHXVssB8IuJ5P/7wbKopdZeLUu9ptM3aFRe1Pa30GW5OZx6xHP2bhDWM5d2CXFj+/iIi0DYVFHYtWQ5OWs/wxyN3ueuXkZ8P299wX2BqxqS40qgmQfNHuS+7m19yXpKTerj/J8NmuEqgxPF4XqviTXOXNxO+5Lz6Htxzt87PjA1j/gjs+pguc93MYNbd5z/mTR1x/la//uWWDInDVIzGdmneOHpPg2lcgc4ULjd67Fz5+xIUNNVVXSb1P/WW/JNc1t13/V/f6znoNugxu3tjk1JJ6uT/LE7/npoZtfdcFRxtedl/qw/1uhaxApfu7deEv3dSj2Db4MuX1Nf/PpxxlTMtOjesyBC78hfsp2Bec6hgM0be9644J97tKph5ToPMg+HiBq2zrORUu+W3L9j4SaYZo39EG1yIiIk01ffp05s+fz4UXHq2YXrBgAVu2bOGxxx6r934xMTEUFRWxd+9ebrvtNl588cWvHHPOOefw0EMPMXZs/TnKggULuPnmm/H73S/iLr74Yp5//nkSEpo3xfruu+8mJiaGu+66q1nnaWkKi8QpOgRLH3YhxOW/r7P94Fcb3m5/72ivnsh4VwExYo6bxtOSFQphYe4LUOdBMP477jf3OdvcF6a1i+Hvt7ppWYNnNu38BzbC+7+EwbPcKkftWfo4mPtXyF7tVmPa9k833Q9cL5sek49Oczt+6suXb8Ort0HJYTjnxzD1TjW3DYWoRBekDp/tpnvuWureG28EDL/ahbAiJxLXDYZd5X7A/Xu9++OjvbLev99t96fAFf8Lw7+pajFpV6JqwqJyhUUiItJ0c+bMYfHixceERYsXL+bXv/51g+6fmpp6wqCooRYsWMC8efNqw6I333yzyefqCBQWifPBA27awvn3Hbs9pjP0Pc/91Kgqd02biw+7kKK1V+yqYYwLQlL6uS9DT8+El25yqwz1nNK4c1VVwCvfdQ2YL3m443yx6j4arvqzC84ObanzhfEj2BD8hy+6s5um0mOya7S75lnoPATmvuCqiiT0vBFuqlnfr4V6JNJOVVUHKCiroqo6QGK0j3BPnT5XMZ1cM+0hs9ztklz3dz11pAsl67DWkltcQeaRUjJzS8g8UkJllWVYWhwj0hJIjmknvanktBat1dBERKQFXHXVVfzkJz+hoqICn8/Hrl272Lt3L1OnTqWoqIiZM2dy5MgRKisruf/++5k589iigl27dnHppZeyYcMGSktLufHGG1m7di0DBw6ktLS09rjvfe97rFixgtLSUq666iruueceHnnkEfbu3cv06dNJSUnh/fffp2fPnqxcuZKUlBQefvhhFi5cCMBNN93ED37wA3bt2sWMGTOYMmUKn3zyCd27d+fvf/87UVH1t5lYs2YNt9xyCyUlJfTp04eFCxeSmJjII488wh//+Ee8Xi+DBw9m8eLFfPjhh9x+++0AGGNYsmQJsbGxLfZ6KywSt6LPqidh3E3Qqf+pj/dGhD508Pnhmr/Awotg0Ry48U03jaehlj7kKqa++VzzVhsKFWNcX6nOA2Hct114lLvj6JS9XR/Dpr+5ZuVTfwjT/rP9NCwWOUNVVgfYeqCIzfsKOFRUTn5pJXklleSXVtS5Xkl+SSWF5cd+qU7wh5MSE0FKjI/kmAg6Ba+nxESQHBNBUvQIcndWkJm7k8wjJWTmHg2Hjp/6Y4z7JwMgPSmKEWkJjEx3P0NS42urQERaSm1lUaUqi0REThtvzXffp1pS12Ew48F6dyclJTF+/HjeeustZs6cyeLFi5k9ezbGGCIjI3nllVeIi4vj8OHDTJw4kcsvvxxTT1HAY489ht/vZ/Pmzaxbt47Ro0fX7vvFL35BUlIS1dXVnHfeeaxbt47bbruNhx9+mPfff5+UlJRjzrVq1SqefPJJli9fjrWWCRMmMG3aNBITE9m6dSuLFi3iT3/6E7Nnz+all15i3rx59T7H6667jt///vdMmzaNn/3sZ9xzzz0sWLCABx98kJ07dxIREUFeXh4ADz30EI8++iiTJ0+mqKiIyMiWLeJQWCRu1RxfLEybH+qRNI4/Ca59Gf58ATz7dfj2Ow3rlZS9GpY8dHQJ7tOBMZDcx/2Mud59E8zb7fY1tn+USAcTCFiy80rZdqiI8spqvGFheD0GnycMr6fudYM3LAyfJ4xIXxjJ0RF4wlqnqrC4vIrN+wrYuLeATXsL2Lgvny/3F1FRHag9JtxjiI/yER/lJcHvo0tcJP27xBIfFU58VDgJ/nC8njByiso5XFTO4cIKcorL2bS3gMOF5V8JlGr4fR7SE/2kJ0VxVt/k4HV3Oz3RlU2vz85nbWYea7Py+HxPHq+v2weAJ8wwoEssI9ITGJkez+S+KaQlts8G2dJxRHjDCDOahiYiIs1XMxWtJiz685//DLhq6h//+McsWbKEsLAwsrOzOXDgAF27nrgv7ZIlS7jtttsAGD58OMOHD6/d98ILL/D4449TVVXFvn372LRp0zH7j/fRRx9xxRVXEB3t+ldeeeWVLF26lMsvv5xevXoxcuRIAMaMGcOuXbvqPU9+fj55eXlMmzYNgOuvv55vfOMbtWOcO3cus2bNYtYsV10+efJk7rzzTubOncuVV15JWlpaQ17CBlNY1BFVV7rqkS/fcc1Na6YiNMW292DrO276WUessIlPg3kvuQqjZ650gVF0Sv3HV5bBK7e4BtkX1Z9ad3jGKCSSFlFZHeCjrYfJKa4gJsJLbKSXmAgvMZFeYoOXUeGeen9r05KqA5Y9uSVsPVDI1oNFbDtYxNaDhWw/WExpEyoWwgx0io2gS1wknWMj6RIXQde4SHc7rma7C5TKKgOUVVZTXuUuyyqrKasKUB68LKusJqeogo1789m0t4CdOcW11TtJ0T6GpMZx45SeDO4Wx5DUeFITIpv9upVVVpNTXMHhwnJyiytI8IeTkeQnKdp3yvNO7J18zBLmBwvKWJt1NEB6fd1eFn22h/tmDuHaST2bPEYRcKXx0T6vGlyLiJxOTlIB1JpmzpzJHXfcwerVqykpKWHMmDEAPPfccxw6dIhVq1YRHh5Oz549KSsra/T5d+7cyUMPPcSKFStITEzkhhtuaNJ5akREHJ3d4fF4jpnu1hhvvPEGS5Ys4bXXXuMXv/gF69evZ/78+VxyySW8+eabTJ48mbfffpuBAwc2eazHU1jUUZTlu6bGX7zpVlQqz3fbl//RLaPe59zGnzNQDe/8BBJ6wITvtux421LnQXDNC/D05fDcN+D611zj6xN5/xduhbV5L7l+RSKnmeqAJczQ7PBm28FC/royi5dWZ3O4qPykx4YZgkFSOHFR4XRPiKR7QhRpiX66J0YFr0edMsQor6rmUGE5BwrKOFBw9DI7r5StBwrZcbiYiqqjlTnd4iPp2zmGOeOT6dclhr6dY4j2eakKBKisDlBZbamqtlQGAu6y2m2vqraUVFZzqKCM/cHHyDpSwuo9R8gtrmjW69Y9IYohqXHMHNmdIalxDOkeR9e4yFYJ0yLDPXRPcK9vc3WOi+T8wZGcP9itxBcIWHblFJPg9zX73CLgpqKpZ5GIiDRXTEwM06dP51vf+hZz5syp3Z6fn0/nzp0JDw/n/fffZ/fu3Sc9z9lnn83zzz/Pueeey4YNG1i3bh0ABQUFREdHEx8fz4EDB3jrrbc455xzAIiNjaWwsPAr09CmTp3KDTfcwPz587HW8sorr/DMM880+rnFx8eTmJjI0qVLmTp1Ks888wzTpk0jEAiQmZnJ9OnTmTJlCosXL6aoqIicnByGDRvGsGHDWLFiBV988YXCojNGXiZseQu2vOl60QQq3Wo3gy6DgRe71ceengUvXO8qajoPatz5P38GDm6CbzzV8fvZZEyAb/wfLJ4LL1wLc/7ilgava88y+OT3MOZGNRaWDsday5GSymCAUvaVQKVm2+GichL8Pib2TmJSsHqkb+eYBoUVhWWVvLFuHy+szGT1njw8YYZzB3Zm9th0+neJoai8iqKyKndZXkVhzfWyo7fzSirIOlLK8h25X5kmFRXuqQ2PuidGUV1tg2FNGQeD1THHC/cYusZH0rdTDGf370TfzjH06+yCodjIll/Vr7yqmoMF5RwsPPq6WuuCmcjwMCK87jIy3EOEN+yY7XHB6WOng7AwQ+9O9YTuIk0QHaHKIhERaRlz5szhiiuuYPHixbXb5s6dy2WXXcawYcMYO3bsKUOT733ve9x4440MGjSIQYMG1VYojRgxglGjRjFw4EDS09OZPHly7X1uvvlmLrroIlJTU3n//fdrt48ePZobbriB8ePHA67B9ahRo0465aw+Tz31VG2D6969e/Pkk09SXV3NvHnzyM/Px1rLbbfdRkJCAj/96U95//33CQsLY8iQIcyYMaPRj3cyxtbUybcTY8eOtStXrgz1MELDWti/zlVmPKkfAAAgAElEQVQPbXnTXQdI7gcDZsDAS1xAFFan+Wh+FvzpPPD44DvvudXLGqK8EB4ZDUm94Vv/6DirgZ3K6mfg1e/DsNluCemw4ApCFcXw2GSw1fC9TyCi5brES9t4c/0+HvtgO4O7xTFzVCoTeyUT1kr9ZkKppv/Olv2FbDlQyJcHCvnyQBHbDxUdU1VTI9EfTpfg1KkucRF0jo1kb34py7bnsDfflcymxES48KiPC496p0TXhkfWWj7bmcsLK7N4c/0+Siur6ds5hm+OTWfWqO50im16kJxfWknWkRKyj5SSdaSU7LxSdzuvlOwjpXg9YcFpXxF0joukS2wkXePrXo8kISr8tHyfBYwxq6y1Y0M9DjmqNT+DXfy7paQmRPLE9eNa5fwiItL6Nm/ezKBBjSxQkJA50fvVmM9fqiwKtaoK2P1RMCB6CwqyAAPpE+D8e2HAxW6p+PrEp8E1i+HJi2HR1XD9626lsFP5aAEUH4Q5i0+foAhg9LVQdAD+dZ8Lzi78hdv+z7vhyE644Q0FRR3MkeIKfvbqRl5bu5feKdG8vm4vf1mZSde4SC4fmcrMkakM7hbXqGk+OUXlLNuRy7IdOWzYm096ot9NGUqNZ0hqHInRrTv1pmZZ9PzSytoePFv2u2Bo68GiY3773j0hin5dYpjSN5lu8VF0iQsGKrGur06E98QrV1nr+vss25HDp9tz+HRHTm0T4y5xEUzsnUxaYhRvrNvHrpwSYiK8zBrVndlj0xiZntAi06Zco+Z4hqTGN/tcItKx+X0eVRaJiIh0IAqLQqE0L9h/6A13WV4A3ijXd2j6f0G/CyGmU8PPlzoKvv6Em4L1ynfdtLKaipoTyc+CT/8Hhn0D0sY0//m0N1N/CEUH3XOM6QLdhsNnj8PEf4OeU0I9OmmE9zYfYP7L68krqeCH5/fne+f0obLa8s/NB/j7mmwWfrSTx5fsoF/nGGaN6s7lI1JJT/pqWHqkuILlO3NYtiOXT7fnsOVAIQDRPg9DUuNZuSuXV9furT0+NT6SwcHgyPWdiSc1/ti+M2WV1RSWVVFQVklBaWWd6+6yZin0gtJK8o5bGr2w7Kt9O1JiIhjQNYZvjkunf5dY+neJpV+XGOKaONXKGEOP5Gh6JEfzzXEZWGvZebjYvQY7cvh4Ww6Hi8qZ2DuJ287rx0VDu+L36b8EkfbGGHMR8DvAAzxhrT1hR1FjzNeBF4Fx1tqVwW3/BXwbqAZus9a+3Taj/ip/hJeC0spQPbyIiIg0kr4ZtJXiw7DhJRcQ7f4YAlUQ3dmtZDbgYuh9DoQ3o0npwEtcFc3bP4b37oHz76n/2PfudZfn/bzpj9eeGeNWOis+CO/+FCIT3FS+834W6pGdUQIB2+TpQwVlldz72iZeXJXFwK6xPHXjeAanxgHg9cBlI1K5bEQqR4oreGP9Pv6+JpvfvL2F37y9hbE9Epk5qjtdYiP4dIcLiL7YX4C1rmfO2J6JzByVyqTeyQztHk+4xwWrR4or2Li3gI1782sv3/viQO2KVgn+cJL8PgqCodCJpoTVdfyy6J1jI+nfOZa44JLoCVHhxPvD6RoXxYCusSS1cjWTMa4HTe9OMVwzwYVHReVVrdL3R0RahjHGAzwKnA9kASuMMa9aazcdd1wscDuwvM62wcDVwBAgFfinMaa/tTYk5T3+cA/785u2AoyIiLQf1to2WQVXmqcl2g0pLGoL1sLTM+HABkgZAGf9uwuIuo89eQVQY038N8jZDh8vgKReMOaGrx6TvQrW/cVV3ySkt9xjtzdhYa5nUUku7FrqVj9rThgngFtpK/tIKYeKyskpKudwUQWH61w/VFQevF1BQVklw7vHc/7gLpw/uCv9uzSsyfKSLw/xny+t42BhOf9+bl/+/dx++Lwn/nuSGO1j3sQezJvYg8zcEl5du5e/r8nmp3/bAECEN4yxPRP54fn9mdQnmWHdE056rin9UpjS7+jqBiUVVWzeV8imYIBUWF5FXGQ4cVFedxnpJS4qvHZbbKS7Hhvpxe9rm+Xkm8oYo6BIpP0bD2yz1u4AMMYsBmYCm4477j7gV8CP6mybCSy21pYDO40x24Ln+7TVR30C/ghNQxMR6egiIyPJyckhOTm5XX/OPdNZa8nJySEyMrJZ51FYdCKv3wHeSLjogZY5X/ZqFxRd/BCM/07LnPNEjIEZv4a83fD6nZCQ4aa21bAW3v5viO4EU+5ovXG0F94ImPtXKMh2jbylyQ4UlPGXFZks+mwP+4JNk+uKjwonOcZHSkwEg7rGkRLjI8rn5dMdOTz0zpc89M6XZCT5OX9wF742qAvjeibi9Rwb2hSXV/HLNzfz3PI99O0cw8vzxjAiPaHBY0xP8nPr9L782zl9+GJ/IUXlVQxPi6+3p09D+H1exvRIZEyPxCafQ0SkGboDmXVuZwET6h5gjBkNpFtr3zDG/Oi4+y477r7dj38AY8zNwM0AGRkZLTTsr1LPIhGRji8tLY2srCwOHToU6qHIKURGRpKWltascygsOt7BzbByIYSFu+qb6JRT3+dU1j7vwqfhs5t/rlPxeOGqJ2HhRfDC9fDtd6BzsAP65ldhz6dw6YIzp8mzN0JBURNZa/lkew7PLtvNu5sOUBWwTO2Xwm3n9aNrXCQpMRGkxPpIjo6ot1oHXND03uaDvLtpP88s282fP9pJgj+ccwd05vzBXTi7fyfWZ+fzoxfXknWklJvP7s2d5/cnMrxpIY8xhkHd4pr6tEVEOgxjTBjwMHBDU89hrX0ceBzcamgtM7KvivZ5Kan4ar82ERHpOMLDw+nVq1eohyFtRGHR8T5+xC1DX10BaxfDWd9v3vmqymH9izDwUohsoxWBIuPgmr/AE+fBc7PhO++5x37359B5MIy6tm3GIR1Sfkklf12VyfPL97DjcDEJ/nC+NaUX14zPoGdKdKPP1yUukmsmZHDNhAyKy6tY8uUh3t10gH9tOcjLn2fj84RRUR2gZ7Kfv353EmN7JrXCsxIR6ZCygbpzxtOC22rEAkOBD4LTAboCrxpjLm/AfdtUlM9DWWWA6oDF08R+diIiItJ2FBbVlZ8F61+AcTe53j6fPwOTbm3e0vJf/gPK8mDknJYbZ0MkpLvA6MmLYdHV0H+GWzp+3suu+khOa4GAZduhIvbmleL3eYmO8BDt8+KP8BAT4SUq/Nh+OtZa1mbl8+yy3by2di/lVQFGZyTw8OwRXDysW5OrfI4XHeFlxrBuzBjWjarqACt3H+Gfmw7g93m45Zw+Wo1LRORYK4B+xpheuKDnauCamp3W2nygtgTaGPMBcJe1dqUxphR43hjzMK7BdT/gszYc+zH8Pvf/SGllNTER+rdeRESkvdP/1nV9+gfX12fSrbD9fXjtNshaAenjm37OtYshpiv0nt5y42yo1FHw9Sdg8VwXfvU9H/qe1/bjkFZXVF7Fmj15rNp9hNV73M+JlmevYYxbmSY6wkt0hJeAtezOKcHv8/D1MWnMm9CjdvWx1uL1hDGxdzITeye36uOIiHRU1toqY8z3gbcBD7DQWrvRGHMvsNJa++pJ7rvRGPMCrhl2FXBrqFZCA2p/GVBSUaWwSEREpAPQ/9Y1SnJh1f/BsKtcY+ihV8I//gtWP930sKj4MGx9x61SFtYylRmNNvASt4z8h7+CC+4LzRjkpArKKtm0t4ADBWVEhXuI8nnw+zxEhnvw+7y126LCPfi8YVhr2ZNbwqrdR4LhUB5b9hcQsC4EGtAllkuHpzKmRyK9UvyUVgQorqiiuLyK4opqSoKXxeVVlFRUUVxeTVllNTdN6cWsUd21QpaISDtirX0TePO4bT+r59hzjrv9C+AXrTa4RqipLCopr3aT50RERKRdU1hUY8UTUFkMk293tyNiYegVsPEVF7ZExDT+nOv/CoEqGHnNqY9tTRNvcauwhSqwOs2UV1VzqLCc+KhwYiK8jVo28mBBGRv3FrAxuBT7xr0F7MktafD9vWEGr8dQVhkAIDbCy8iMBC44tx9jeiQyMiOBOIU9IiLSzhytLNKKaCIiIh2BwiKAihJY/kfodyF0GXJ0+6jr4PNnXWA0uglNodc8D91GHl2NLJQUFDVZUXkVq3cf4bOduXy2K5c1mXlUVLmwJtxjSPD7SPSH114m+n0k+H0kRYcTFxnOntyS2mDocFF57Xl7JPsZ2j2Ob45LZ3BqHOmJUZRVBiirrKakoprSympKg5clFdXB7VWUVQbo3SmaMT0S6dc5Vo1CRUSk3Tvas0groomIiHQECosA1jwHJTkw5QfHbk8fDyn93VS0xoZFBzbC/nVw0a9abpzSJnKKylmxK5fPdh5hxa5cNu7NJ2DBE2YYkhrHtRN70LdzDIVllRwpqSSvpILc4gqOlFSy83Axq0vyyCupoLLarUDsDTP07RzDtP6dGJIax5DUOAalxqkCSEREzhg1YVFxuSqLREREOgKFRdVV8MkjkDYeMiYdu88Yt8z8uz+FQ1ug04CGn3ftIgjzuh5I0q5Za1mfnc+Lq7L4eNthth8qBiDCG8bI9ARund6XcT2TGN0jscFNOa21FJVXkV9aSafYCCK8quwSEZEzl6ahiYiIdCwKizb9DfL2uL5EJ+o9M2IOvHePqy66sIE9IqurYN0LblpbdMqpj5eQyCup4G+fZ7N4RSZf7C8kMjyMSb2T+fqYNMb3TGJYWnyTQx5jDLGR4WoWLSIiQp0G1xWahiYiItIRnNlhkbXw0QJIGQD9Z5z4mJhOMGAGrF0M5/0cvL5Tn3fH+1B0AEbOadnxSrMFApZlO3L4y8pM3tqwn4qqAMO6x3P/rKFcPjJVU8NERERagT+iJixSZZGIiEhHcGaHRdvfgwPrYeajEBZW/3GjroPNr8GXb8Hgmac+79pFEJUI/S5oubEK4KZ37TxczLqsfALWkhTtIzk6gqQYH8nRPiLDT1wJtD+/jJdWZ/GXFZnsyS0hLtLL1ePSmT02naHd49v4WYiIiJxZjk5DU2WRiIhIR3Bmh0UfLYDYVBg2++TH9T3PHbf6mVOHRWX58MUbrteRN6LlxnqGyi+tZG1mHp/vyePzzCOsycwjr6Sy3uP9Pk8wQPKRFO0jKTqC3OJyPvzyEAELE3sncef5/bloaNd6gyURERFpWVHhqiwSERHpSM7csCh7FexaChfcf+qpZWEeGDUXlv4W8rMhvnv9x258BarKNAWtCaqqA3x5oIjPM4+4cGjPkdpm08ZA/86xXDSkK6MyEhiRnoDPE0ZucQU5xW41stziCnKKKsgtLienuIKDheV8sb+QMGO4ZVofZo9Np2dKdIifpYiIyJnHE2aIDA+jVGGRiIhIh3DmhkUfLYDIeBhzQ8OOHzkXlvwG1jwP035U/3FrFrkeSKmjW2SYp7NDheV8vucIn2e6YGhdVn7tbxyTon2MSk/gilHdGZWRyPC0+BM2i+7dqa1HLSIiIk3h93kp1jQ0ERGRDuHMDIsOb3U9iKbeCRGxDbtPUi/odTZ8/jRM/eGJexzl7oDMZa4R9olWVjuDlVdVs2lvQXA6mQuHso6UAuANMwxOjeMbY9IYmZHA6IxEMpL8GL2GIiIipw2/z6NpaCIiIh3EmRkWffIIeHww4ZbG3W/UdfDyTbBrCfQ+56v71y4GDAz/ZgsMsmOrqg6wNiufpVsP8dHWw6zLyqeiOgBAanwkIzMSuH5ST0ZlJDC0e7z6B4mIiJzm/D4PJeUKi0RERDqCBoVFxpiLgN8BHuAJa+2Dx+3vASwEOgG5wDxrbVZw36+BS4Aw4F3gdmutbbFn0FiF+12oM+paiOncuPsOugwiE1yj697nHLsvEHCroPU+5+Q9jU5jmbklLN16mKVbD/HxtsMUlFVhDAzvHs8Nk3syOiOBkemJdI2PDPVQRUREpI1F+byUVCosEhER6QhOGRYZYzzAo8D5QBawwhjzqrV2U53DHgKettY+ZYw5F3gAuNYYcxYwGRgePO4jYBrwQcs9hUZa9gcIVMFZ/974+4ZHwvDZsOopKMkFf9LRfXs+gbw9cO5PW26s7VxhWSXLduSydOshlm49zM7Drhl1anwkFw/rxtR+nTirTzKJ0adoIC4iIiKnvWifh1L1LBIREekQGlJZNB7YZq3dAWCMWQzMBOqGRYOBO4PX3wf+FrxugUjABxggHDjQ/GE3UVk+rHwSBs9yPYiaYtS18NnjsP6vMOG7R7evXQS+GBh4ScuMtR0qrahm5e5clu3I4dPtOazLyqcqYPH7PEzqncz1k3owtX8neqdEq9+QiIiIHMPv87A3rzLUwxAREZEGaEhY1B3IrHM7C5hw3DFrgStxU9WuAGKNMcnW2k+NMe8D+3Bh0f9Yazcf/wDGmJuBmwEyMjIa/SQabOVCKC+AKT9o+jm6DYduI9xUtPE3u0bWFSWw8e8uhPKdPkuzl1VWs3r3ERcO7chhTWYeldUWT5hheFo8N5/dm6n9OjG6RwIRXvUcEhERkfr5fV5KNQ1NRESkQ2ipBtd3Af9jjLkBWAJkA9XGmL7AICAteNy7xpip1tqlde9srX0ceBxg7NixrdPPqLIMlj0Gvae7sKc5Rl8Hb/wQ9q2B1FHwxetQUQgj57TMWEMkELB8nnmEpVsP8+n2HD7PzKOiKkCYgWFpCXxrSi8m9U5mXM8koiPOzN7oIiIi0jR+n4fick1DExER6Qga8o0/G0ivczstuK2WtXYvrrIIY0wM8HVrbZ4x5jvAMmttUXDfW8Ak4JiwqE3sWgpFB+DKx5t/rqFXwdv/7aqLUkfBmuchPgMyzmr+uduYtZZ1Wfm8vm4vb6zbx978MoyBoanxXD+pB5P6uHAoNjI81EMVERGRDszv81JaocoiERGRjqAhYdEKoJ8xphcuJLoauKbuAcaYFCDXWhsA/gu3MhrAHuA7xpgHcNPQpgELWmjsjdPvfPj+Skju2/xzRSXA4Jmub9GkW2HHB3D2jyAsrPnnbgPWWjbvK+T1dXt5fd0+9uSWEO4xnN2vE/9x0UCmD+hMvF/hkIiIiLQcv89DcUUV1lr1NhQREWnnThkWWWurjDHfB94GPMBCa+1GY8y9wEpr7avAOcADxhiLm4Z2a/DuLwLnAutxza7/Ya19reWfRgOl9Gu5c426Ftb9BV68EbAw4uqWO3cr2XawiNfW7uX1dXvZfqgYT5jhrD7JfH96Xy4c0lUBkYiIiLSaKJ+HgIXyqgCR4ep1KCIi0p41qPGMtfZN4M3jtv2szvUXccHQ8ferBr57/PbTQs8pkNQb9q2F9ImQ3CfUI6rXPzcd4LfvfsnmfQUYAxN6JXHj5F7MGNqV5JiIUA9PREREzgDRPhcQlVZUKywSERFp59SluKmMgVHz4L17221V0aHCcu5+bSNvrNtH384x/OzSwVwyvBtd4iJDPTQRERE5w/h97mNncUUVidG+EI9GRERETkZhUXOMuwmqq2D4N0M9kmNYa3lxVRb3v7GZ0opq7rqgPzef3Qeft2P0VBIREZHTjz/iaGWRiIiItG8Ki5ojMh7O+c9Qj+IYe3JK+PEr6/lo22HG9UzkgSuH07dzTKiHJSIiImc4f3AaWrHCIhERkXZPYdFpoqo6wJMf7+K3727BGxbGfbOGMnd8BmFhWm1EREREQi8q3H3sLKmoCvFIRERE5FQUFp0GNu0tYP7L61iXlc/XBnXmvllD6RYfFephiYiIiNSK1jQ0ERGRDkNhUQdWVlnN7/+1lf/9cAcJ/nD+55pRXDKsG8aomkhERETaF01DExER6TgUFnVQy3bk8OOX17PjcDFXjUnjJ5cMIsGvlUVERESkfapZDa1U09BERETaPYVFHUx+SSUPvLWZxSsyyUjy88y3xzO1X6dQD0tERETkpGori8pVWSQiItLeKSzqIKy1vLF+H3e/uokjJRV8d1pvfnBef6KCH7xERERE2rOazyyllQqLRERE2juFRR1Adl4pP/vbBt774iDDusfzfzeOY2j3+FAPS0RERKTBfJ4wvGGG4nJNQxMREWnvFBa1Y9UBy9Of7uKht7cQsPCTSwZxw1k98XrCQj00ERERkUYxxhDl81CiBtciIiLtnsKiduqL/QXMf2k9azLzmNa/E/fPGkp6kj/UwxIRERFpsmifl1KFRSIiIu2ewqJ2Jr+0kseXbOd/P9xBfFQ4v7t6JJePSMUYE+qhiYiISBszxlwE/A7wAE9Yax88bv8twK1ANVAE3Gyt3WSM6QlsBrYED11mrb2lrcZdH7/PQ7FWQxMREWn3FBa1E+uz8nl22W5eXbuX0spqrhqTxn9fPIjEaF+ohyYiIiIhYIzxAI8C5wNZwApjzKvW2k11DnveWvvH4PGXAw8DFwX3bbfWjmzLMZ9KlM+jyiIREZEOQGFRCJVWVPPaur08t2w3a7PyiQr3MHNkKvMm9lADaxERERkPbLPW7gAwxiwGZgK1YZG1tqDO8dGAbdMRNlK0z6vKIhERkQ5AYVEIbD9UxHPL9vDiqkwKyqro2zmGuy8bzBWj04iPCg/18ERERKR96A5k1rmdBUw4/iBjzK3AnYAPOLfOrl7GmM+BAuAn1tqlJ7jvzcDNABkZGS038npE+TzklVS0+uOIiIhI8ygsaiOV1QHe3XSAZ5ft5pPtOYR7DBcO6cq8iT2Y0CtJPYlERESkSay1jwKPGmOuAX4CXA/sAzKstTnGmDHA34wxQ46rRMJa+zjwOMDYsWNbvSopOsLD3jxNQxMREWnvFBa1geLyKq5+fBnrs/PpnhDFjy4cwOyx6XSKjQj10ERERKT9ygbS69xOC26rz2LgMQBrbTlQHry+yhizHegPrGydoTZMVLiXEvUsEhERafcUFrWyQMByx1/WsHFvPg/PHsHMkd3xhKmKSERERE5pBdDPGNMLFxJdDVxT9wBjTD9r7dbgzUuArcHtnYBca221MaY30A/Y0WYjr0d0hIcS9SwSERFp9xQWtbKH3tnCO5sO8PPLBnPl6LRQD0dEREQ6CGttlTHm+8DbgAdYaK3daIy5F1hprX0V+L4x5mtAJXAENwUN4GzgXmNMJRAAbrHW5rb9szhWlM9DsSqLRERE2j2FRa3o5dVZ/OGD7VwzIYMbzuoZ6uGIiIhIB2OtfRN487htP6tz/fZ67vcS8FLrjq7x/OFeKqoCVFUH8HrCQj0cERERqYf+l24lq3bnMv+l9ZzVJ5l7Lh+iBtYiIiJyxouO8ABQUqnqIhERkfZMYVEryDpSws1PryI1IZI/zB1NuH5zJiIiIkKUz4VFpZqKJiIi0q4pxWhhReVV3PTUSiqqA/z5hnEk+H2hHpKIiIhIuxDtcx0QtCKaiIhI+6aeRS2oOmC5fdHnbD1YxP/dOI4+nWJCPSQRERGRdqOmsqi4XCuiiYiItGeqLGpBv/rHF7z3xUHuvmwwU/t1CvVwRERERNoVf800NPUsEhERadcUFrWQF1Zk8viSHVw3qQfXTuoZ6uGIiIiItDv+4DQ0VRaJiIi0bwqLWsDyHTn899/WM6VvCj+7dHCohyMiIiLSLvnV4FpERKRDUFjUTHtySrjl2VWkJ/l59JrReLXymYiIiMgJqcG1iIhIx6BkoxmKyqv49lMrCFj48/XjiPeHh3pIIiIiIu1WTYPrkgpNQxMREWnPtBpaM7y4MpOtB4t45tvj6ZUSHerhiIiIiLRr/tqwSJVFIiIi7Zkqi5rhlc+zGdwtTiufiYiIiDRAVLgLi4oVFomIiLRrCouaaNvBQtZm5XPl6O6hHoqIiIhIhxAWZogK91CqaWgiIiLtmsKiJnp5dTaeMMPlI1NDPRQRERGRDiM6wqNpaCIiIu2cwqImCAQsr3yezdn9UugcGxnq4YiIiIh0GFE+hUUiIiLtncKiJli2I4d9+WVcOTot1EMRERER6VD84V6thiYiItLOKSxqgpdWZxMb4eX8wV1CPRQRERGRDsWvaWgiIiLtnsKiRiqpqOKtDfu4eFg3IoMreoiIiIhIw/g1DU1ERKTdU1jUSG9v3E9JRbVWQRMRERFpAr/PS3G5pqGJiIi0ZwqLGunl1dmkJUYxrmdSqIciIiIi0uH4fR5KK1VZJCIi0p4pLGqE/fllfLztMFeO6k5YmAn1cEREREQ6HL/Pq2loIiIi7ZzCokb4+5psAhau0CpoIiIiIk3i93ko0TQ0ERGRdk1hUQNZa3lpdRajMxLolRId6uGIiIiIdEh+n4eSymqstaEeioiIiNRDYVEDbdxbwJcHilRVJCIiItIMfp8Xa6GsMhDqoYiIiEg9FBY10Murswn3GC4b3i3UQxERERHpsPw+DwAlFZqKJiIi0l41KCwyxlxkjNlijNlmjJl/gv09jDHvGWPWGWM+MMak1dmXYYx5xxiz2RizyRjTs+WG3zYqqwO8ujab8wZ2IcHvC/VwRERERDqso2GRmlyLiIi0V6cMi4wxHuBRYAYwGJhjjPn/7N17eJx1nf//5yeTTJpJz216TM/l0APQllIoKAc5FQ+AgMthdcUTorLf/arsLq57oeL68+thd9UVd2V3QV1UhApa12LlpKKAtFAKtKXQltKmpecTzaSZZPL5/TGTEEKhKU0yk87zcV25OnPPfU/ew4U4fd3v9+cztcNp3wR+FGM8HrgJ+Gq7134EfCPGOAWYA2ztisJ70sMvbGP7vgyXzBpd6FIkSZJ6tVSyHDAskiSpmHWms2gOsDrGuDbGmAHuAC7qcM5U4MH844daX8+HSuUxxvsAYoz7YozpLqm8B9395EYGpSo485hhhS5FkiSpV3MMTZKk4teZsGg0sKHd87r8sfaWAZfkH78X6BdCGAIcDewOIdwdQlgaQvhGvlPpNUII14QQloQQlmzbtu3QP0U32tPQxG9XbOHCE0aRLHeJJ0mSpMPhGJokScWvq9KP64EzQghLgTOAjUAWKAfenn/9JGAicHXHi2OMt8QYZ8cYZ9fU1HRRSV3j3mdeJtPcwl9hctUAACAASURBVCXugiZJknTYHEOTJKn4dSYs2giMafe8Nn+sTYxxU4zxkhjjTODz+WO7yXUhPZUfYWsGfgHM6pLKe8jdT25kYk01x9cOKHQpkiRJvV6q0jE0SZKKXWfCosXAUSGECSGEJHAFsKD9CSGEoSGE1vf6HHBru2sHhhBa24XeAaw4/LJ7xvodaR5ft5NLZ9USQih0OZIkSb2eY2iSJBW/g4ZF+Y6g64BFwErgzhjj8hDCTSGEC/OnnQmsCiE8DwwHvpK/NktuBO2BEMIzQAD+s8s/RTe5Z2mugerime6CJkmS1BVSFY6hSZJU7Mo7c1KMcSGwsMOxG9s9ng/Mf4Nr7wOOP4waCyLGyN1L65g7cQijB1YVuhxJkqQjQlVrZ1GjY2iSJBUrt/d6A0+u381LO9JcMsuuIkmSpK6SLC+jIhFIN9lZJElSsTIsegN3P1lHn4oyLjhuZKFLkSRJJSqEMC+EsCqEsDqEcMMBXr82hPBMCOGpEMIfQwhT2732ufx1q0II5/ds5W8ulSy3s0iSpCJmWHQAjc1ZfrVsE/OmjaBvZacm9SRJkrpUCCEB3AxcAEwFrmwfBuX9JMZ4XIxxBvB14F/y104ltynJNGAe8L38+xWFVDLhmkWSJBUxw6IDeHDlVvbub+a9s2oLXYokSSpdc4DVMca1McYMcAdwUfsTYox72z2tBmL+8UXAHTHGxhjji8Dq/PsVhapkwjE0SZKKmG0zB7Bg2SaG9avktElDCl2KJEkqXaOBDe2e1wEndzwphPAp4DNAEnhHu2sf63Dt6xZiDCFcA1wDMHbs2C4pujOqHUOTJKmo2Vl0AHW7Gpg6qj/lCf/xSJKk4hZjvDnGOAn4e+AfD/HaW2KMs2OMs2tqarqnwAOocgxNkqSiZhpyAOlMM6lk0Yz1S5Kk0rQRGNPueW3+2Bu5A7j4LV7bo6oNiyRJKmqGRQfQkMlSVeGEniRJKqjFwFEhhAkhhCS5BasXtD8hhHBUu6fvAl7IP14AXBFCqAwhTACOAh7vgZo7JZUsJ51xDE2SpGJlInIA6aYs1ZV2FkmSpMKJMTaHEK4DFgEJ4NYY4/IQwk3AkhjjAuC6EMI5QBOwC/hg/trlIYQ7gRVAM/CpGGPRtPK4G5okScXNsOgA0o1ZqhxDkyRJBRZjXAgs7HDsxnaP/+ZNrv0K8JXuq+6tMyySJKm4OYbWQXO2hUy2hZRjaJIkSd2iKllOg2GRJElFy7Cog3RT7ouLC1xLkiR1j+pkgky2haZsS6FLkSRJB2BY1EHrXS7H0CRJkrpH6/csR9EkSSpOhkUdtH5pcYFrSZKk7lFdmRv3d0c0SZKKk2FRB/WNuS8tVa5ZJEmS1C1SdhZJklTUDIs6aHDNIkmSpG5VVZH7nuUi15IkFSfDog5a73AZFkmSJHWP1jG01o5uSZJUXAyLOmjIz867wLUkSVL3aFvgusnOIkmSipFhUQevdha5ZpEkSVJ3qM5/z0o3GhZJklSMDIs6aNsNzc4iSZKkbvHqAteOoUmSVIwMizpIO4YmSZLUrarcDU2SpKJmWNSBY2iSJEndq20MzbBIkqSiZFjUQUMmS7K8jERZKHQpkiRJR6Q+FWWE8OrGIpIkqbgYFnWQzmTb5uglSZLU9UIIpCoS1NtZJElSUTIs6iCdyba1RkuSJKl7VCXLHUOTJKlIGRZ1kM40u7i1JElSN6uuTLgbmiRJRcqwqAPH0CRJkrpfVUXCziJJkoqUYVEHDZksVRWGRZIkSd0plUzQYFgkSVJRMizqIN3UbGeRJElSN6uuLKfeMTRJkoqSYVEH6UyWVKULXEuSJHWnqgo7iyRJKlaGRR2kG7OkHEOTJEnqVnYWSZJUvAyLOkhnHEOTJEnqblWuWSRJUtEyLOqgoSlLVdIxNEmSpO6UqkhQ32hYJElSMTIsaqcp20JTNtpZJEmS1M1SleU0NGVpaYmFLkWSJHVgWNROOt8KbVgkSZLUvVq/b+1vtrtIkqRiY1jUTjq/yGLKMTRJkqRuVZ0PixxFkySp+BgWtWNnkSRJUs9oXSPSRa4lSSo+hkXttH5ZqTIskiRJ6latN+fq853dkiSpeBgWtWNnkSRJUs9o/b6VtrNIkqSiY1jUjmsWSZIk9YyUY2iSJBUtw6J27CySJEnqGY6hSZJUvAyL2jEskiRJ6hmt37fsLJIkqfgYFrXTkL+z5QLXkiRJ3at1DM3OIkmSio9hUTuvdha5ZpEkSSq8EMK8EMKqEMLqEMINB3j9MyGEFSGEp0MID4QQxrV7LRtCeCr/s6BnKz+4VKWdRZIkFStTkXZaw6KqCjuLJElSYYUQEsDNwLlAHbA4hLAgxrii3WlLgdkxxnQI4RPA14HL8681xBhn9GjRhyBV4W5okiQVq051FnXirta4/N2sp0MIvwsh1HZ4vX8IoS6E8N2uKrw7NDRl6VNRRqIsFLoUSZKkOcDqGOPaGGMGuAO4qP0JMcaHYozp/NPHgFp6ifJEGcnyMsfQJEkqQgcNi9rd1boAmApcGUKY2uG0bwI/ijEeD9wEfLXD618G/nD45Xav+sZmR9AkSVKxGA1saPe8Ln/sjXwEuLfd8z4hhCUhhMdCCBcf6IIQwjX5c5Zs27bt8Cs+RKlkwjE0SZKKUGc6iw56V4tciPRg/vFD7V8PIZwIDAd+e/jldq+GTNYRNEmS1OuEEN4PzAa+0e7wuBjjbOAq4FshhEkdr4sx3hJjnB1jnF1TU9ND1b6qOllOfaNhkSRJxaYzYVFn7motAy7JP34v0C+EMCSEUAb8M3D94RbaE9KZbNs2rpIkSQW2ERjT7nlt/thrhBDOAT4PXBhjbGw9HmPcmP9zLfA7YGZ3FvtWVCUTNDQ5hiZJUrHpqt3QrgfOCCEsBc4g90UmC3wSWBhjrHuziwvdAt0q3ZQlVekYmiRJKgqLgaNCCBNCCEngCuA1u5qFEGYC3ycXFG1td3xQCKEy/3gocBrQfmHsopBKJuwskiSpCHUmGTnoXa0Y4ybynUUhhL7ApTHG3SGEucDbQwifBPoCyRDCvhjjDR2uvwW4BWD27NnxrX6Yw9WQaW7bmUOSJKmQYozNIYTrgEVAArg1xrg8hHATsCTGuIDc2Flf4K4QAsD6GOOFwBTg+yGEFnI3B/9fh13UioJrFkmSVJw6Exa13dUiFxJdQW72vU3+jtXOGGML8DngVoAY41+2O+dqclu7vm43tWJR35hl5ICKQpchSZIEQIxxIbCww7Eb2z0+5w2uewQ4rnurO3ypZDlbX9lf6DIkSVIHBx1DizE2A613tVYCd7be1QohXJg/7UxgVQjheXKLWX+lm+rtVg1NWapcs0iSJKlHpJIJ0o6hSZJUdDq1QE8n7mrNB+Yf5D1+APzgkCvsQelMswtcS5Ik9ZBUMkHaMTRJkopOVy1wfUTI7YbmAteSJEk9IZUspz7jbmiSJBUbw6K8GCMNmaydRZIkST3EBa4lSSpOhkV5mWwLzS3RsEiSJKmHpJIJmlsimeaWQpciSZLaMSzKa72rVeUYmiRJUo9oHf9PO4omSVJRMSzKa11c0c4iSZKkntH6vctFriVJKi6GRXmGRZIkST2rqi0ssrNIkqRiYliU19AWFjmGJkmS1BOq28bQ7CySJKmYGBbltW7bameRJElSz2j93lXfaFgkSVIxMSzKe3WBa8MiSZKknpCqzHUWNTQ5hiZJUjExLMpzzSJJkqSe5QLXkiQVJ8OivNaFFatds0iSJKlHVFXkwyLH0CRJKiqGRXkNTY6hSZIk9aTqytYFrh1DkySpmBgW5bUurOgYmiRJUs9oW+DaMTRJkoqKYVFeQ/6OVp9ywyJJkqSeUFleRll4daMRSZJUHAyL8tKZLFUVCcrKQqFLkSRJKgkhBFLJche4liSpyBgW5aWbslRX2lUkSZLUk1LJhGsWSZJUZAyL8hoyWRe3liRJ6mG5sMjOIkmSiolhUV4600yqorzQZUiSJJWUqmS5nUWSJBUZw6K8tJ1FkiRJPa7aziJJkoqOYVFeOpNt275VkiRJPaMqmaDesEiSpKJiWJSXC4scQ5MkSepJ1clyGhxDkySpqBgW5TVkmu0skiRJ6mEucC1JUvExLMpzDE2SJKnnVRkWSZJUdAyL8lzgWpIkqedVV7obmiRJxcawCIgxknYMTZIkqcdVVSTY39RCtiUWuhRJkpRnWAQ0NrfQEnGBa0mSpB5WXZm7WdfQ5CiaJEnFwrAIaMjPydtZJEmS1LOq8jfrHEWTJKl4GBYB6SbDIkmSpEJIVeS+f6Ub7SySJKlYGBYB6cbcnawqx9AkSZJ6VOsYmjuiSZJUPAyLePXLSeudLUmSJPUMx9AkSSo+hkW0C4sqDYskSZJ6UnXSziJJkoqNYRHQ0JS7k+VuaJIkST2rqi0ssrNIkqRiYVhEu84iF7iWJElFJIQwL4SwKoSwOoRwwwFe/0wIYUUI4ekQwgMhhHHtXvtgCOGF/M8He7byzku1jaHZWSRJUrEwLOLV3TeqXLNIkiQViRBCArgZuACYClwZQpja4bSlwOwY4/HAfODr+WsHA18ATgbmAF8IIQzqqdoPhWNokiQVH8MiXm17trNIkiQVkTnA6hjj2hhjBrgDuKj9CTHGh2KM6fzTx4Da/OPzgftijDtjjLuA+4B5PVT3IXEMTZKk4mNYBKSbcneyqitds0iSJBWN0cCGds/r8sfeyEeAew/l2hDCNSGEJSGEJdu2bTvMct8ax9AkSSo+hkVAQyZLCFBZ7j8OSZLU+4QQ3g/MBr5xKNfFGG+JMc6OMc6uqanpnuIOIlEWqCwvMyySJKmImI6Qu5OVqkgQQih0KZIkSa02AmPaPa/NH3uNEMI5wOeBC2OMjYdybbGorix3DE2SpCJiWERuRr4q6QiaJEkqKouBo0IIE0IISeAKYEH7E0IIM4HvkwuKtrZ7aRFwXghhUH5h6/Pyx4pSVUXCziJJkoqICQn5ziIXt5YkSUUkxtgcQriOXMiTAG6NMS4PIdwELIkxLiA3dtYXuCvfIb0+xnhhjHFnCOHL5AIngJtijDsL8DE6JZVMtO1OK0mSCs+wCMMiSZJUnGKMC4GFHY7d2O7xOW9y7a3Ard1XXddJVZa3bTgiSZIKzzE0cgtcGxZJkiQVRqoiQbrRNYskSSoWhkXk1ixKuWaRJElSQVRXumaRJEnFxLCI3BhalZ1FkiRJBVGVLKfBMTRJkoqGYRGuWSRJklRIqYoE9Y6hSZJUNAyLaA2LHEOTJEkqhFRlggbH0CRJKhqdCotCCPNCCKtCCKtDCDcc4PVxIYQHQghPhxB+F0KozR+fEUJ4NISwPP/a5V39AbpCQ6bZziJJkqQCSSUT1GeaiTEWuhRJkkQnwqIQQgK4GbgAmApcGUKY2uG0bwI/ijEeD9wEfDV/PA38VYxxGjAP+FYIYWBXFd8VYoykmxxDkyRJKpRUspyWCI3NLYUuRZIk0bnOojnA6hjj2hhjBrgDuKjDOVOBB/OPH2p9Pcb4fIzxhfzjTcBWoKYrCu8qjc0txIgLXEuSJBVI6007d0STJKk4dCYsGg1saPe8Ln+svWXAJfnH7wX6hRCGtD8hhDAHSAJrOv6CEMI1IYQlIYQl27Zt62ztXaJ1McVUhWGRJElSIbwaFrnItSRJxaCrFri+HjgjhLAUOAPYCLTdGgohjAT+B/hQjPF1/cUxxltijLNjjLNranq28aj1Dlaq0gWuJUmSCqF1oxEXuZYkqTh0JiHZCIxp97w2f6xNfsTsEoAQQl/g0hjj7vzz/sCvgc/HGB/riqK7UkNTPixyDE2SJKkgWr+H1RsWSZJUFDrTWbQYOCqEMCGEkASuABa0PyGEMDSE0PpenwNuzR9PAveQW/x6fteV3XXaOosMiyRJkgqitbPIMTRJkorDQcOiGGMzcB2wCFgJ3BljXB5CuCmEcGH+tDOBVSGE54HhwFfyx/8COB24OoTwVP5nRld/iMPR+qWkqsIxNEmSpEJoW7Oo0c4iSZKKQacSkhjjQmBhh2M3tns8H3hd51CM8Xbg9sOssVu1fimxs0iSJKkw2sKiJsMiSZKKQVctcN1rtX4pqa40LJIkSSqE1o1GGhxDkySpKJR8WNT6paQq6RiaJElSIaQq8gtcO4YmSVJRKPmwqG2B6wo7iyRJkgohle/wbnAMTZKkomBYlA+LqlyzSJIkqSCSiTISZYH6RsfQJEkqBoZFmWbKAlSWl/w/CkmSpIIIIZBKJtpu4kmSpMIq+YQknclSnSwnhFDoUiRJkkpWLiyys0iSpGJQ8mFRQybrCJokSVKBpZLldhZJklQkSj4sSmeypAyLJEmSCiqVTNBgWCRJUlEwLMpkqUqWF7oMSZKkkpZKJqh3DE2SpKJgWJRptrNIkiSpwFLJcjuLJEkqEoZFjqFJkiQVXK6zyLBIkqRiUPJhUYNhkSRJUsFVuWaRJElFo+TDonRTMynXLJIkSSqo6mQ5adcskiSpKJR8WNSQyVJlZ5EkSVJBOYYmSVLxKPmwqL4xS6rCsEiSJKmQUslyMs0tNGdbCl2KJEklr6TDopaWSENTllSlY2iSJEmF1LqGZLrJ7iJJkgqtpMOi/c25LyMucC1JklRYrcsCuMi1JEmFV9JhUTpjWCRJklQMqitz38fqG13kWpKkQivpsKj1zlWVaxZJkiQVVFVFblmAtJ1FkiQVXEmHRa92FrlmkSRJKj4hhHkhhFUhhNUhhBsO8PrpIYQnQwjNIYTLOryWDSE8lf9Z0HNVvzWtnUUNrlkkSVLBlXRKUp/JtTmnKu0skiRJxSWEkABuBs4F6oDFIYQFMcYV7U5bD1wNXH+At2iIMc7o9kK7SOuyAI6hSZJUeCUdFrWOoaUcQ5MkScVnDrA6xrgWIIRwB3AR0BYWxRjX5V/r9fvNt46hucC1JEmF5xgajqFJkqSiNBrY0O55Xf5YZ/UJISwJITwWQrj4QCeEEK7Jn7Nk27Zth1PrYWtb4NqwSJKkgivxsCjX5lzlbmiSJOnIMy7GOBu4CvhWCGFSxxNijLfEGGfHGGfX1NT0fIXttH4fa8g4hiZJUqGVdFjUNoZmWCRJkorPRmBMu+e1+WOdEmPcmP9zLfA7YGZXFtfV+lbmOr131GcKXIkkSSrpsKjesEiSJBWvxcBRIYQJIYQkcAXQqV3NQgiDQgiV+cdDgdNot9ZRMUoly5kxZiD3PrOZGGOhy5EkqaSVdFjU2ubsmkWSJKnYxBibgeuARcBK4M4Y4/IQwk0hhAsBQggnhRDqgPcB3w8hLM9fPgVYEkJYBjwE/L8Ou6gVpffNrmXVlld4duPeQpciSVJJK+mUJJ3JUl4WSJaXdGYmSZKKVIxxIbCww7Eb2z1eTG48reN1jwDHdXuBXezdx4/iS79awfwnNnBc7YBClyNJUskq6ZQkncm6uLUkSVKRGFBVwfnTRvDLZZtobHZXNEmSCqWkw6KGTNb1iiRJkorIZSfWsjvdxAMrtxa6FEmSSlZJh0X1mWbXK5IkSSoib5s8lBH9+zD/ibpClyJJUskq6bDIziJJkqTikigLXDJrNL9/fhtb9+4vdDmSJJWkkg6L0oZFkiRJRefSE2vJtkR+8dTGQpciSVJJKu2wqClLlWNokiRJRWVSTV9mjR3IXUvqiDEWuhxJkkpOSYdFDZlmUhV2FkmSJBWb980ewwtb9/F03Z5ClyJJUskp6bCovtExNEmSpGL0ruNHUlle5kLXkiQVQEmHRQ1NWVKVhkWSJEnFpn+fCuZNH8GCZZvY35QtdDmSJJWUkg6L0plmUq5ZJEmSVJQuO7GWPQ1NPLBya6FLkSSppJRsWNTSEtnf1EKVaxZJkiQVpVMnDWXkgD7c9cSGQpciSVJJKdmwqCHfzuyaRZIkScUpURa4dFYtf3h+G1v27i90OZIklYySDYvqM82AYZEkSVIxu/TEWloi3LN0Y6FLkSSpZJRsWNSQae0scs0iSZKkYjVhaDWzxw1i/hN1xBgLXY4kSSWhZMOidMYxNEmSpN7gshNrWb11H09t2F3oUiRJKgklHxZVGRZJkiQVtXcdP5I+FWXMf6Ku0KVIklQSSjYscgxNkiSpd+jXp4ILpo9kwbJN7M9vUiJJkrpPp8KiEMK8EMKqEMLqEMINB3h9XAjhgRDC0yGE34UQatu99sEQwgv5nw92ZfGHwwWuJUmSeo/LTqzllf3N3LdiS6FLkSTpiHfQsCiEkABuBi4ApgJXhhCmdjjtm8CPYozHAzcBX81fOxj4AnAyMAf4QghhUNeV/9Y1uGaRJElSrzF34hBGD6xyFE2SpB7Qmc6iOcDqGOPaGGMGuAO4qMM5U4EH848favf6+cB9McadMcZdwH3AvMMv+/ClHUOTJEnqNcrKApfOGs3DL2xj8579hS5HkqQjWmfCotHAhnbP6/LH2lsGXJJ//F6gXwhhSCevJYRwTQhhSQhhybZt2zpb+2FJ58fQXOBakiSpd7j0xFpaIty91O4iSZK6U1ctcH09cEYIYSlwBrAR6PTqgzHGW2KMs2OMs2tqarqopDfnGJokSVLvMm5INXPGD2b+E3XEGAtdjiRJR6zOhEUbgTHtntfmj7WJMW6KMV4SY5wJfD5/bHdnri2UdFOWikSgIlGyG8JJkiT1OpedWMvabfUs3bC70KVIknTE6kxSshg4KoQwIYSQBK4AFrQ/IYQwNITQ+l6fA27NP14EnBdCGJRf2Pq8/LGCSzc2u16RJElSL/PO40dSVZHgriWOokmS1F0OGhbFGJuB68iFPCuBO2OMy0MIN4UQLsyfdiawKoTwPDAc+Er+2p3Al8kFTouBm/LHCi6dyTqCJkmS1Mv0rSznguNGsOCpjWx7pbHQ5UiSdETqVGtNjHEhsLDDsRvbPZ4PzH+Da2/l1U6jopFuyrq4tSRJUi903VmT+dWyTXztN8/xzfedUOhyJEk64pTsgj0NdhZJkiT1ShNr+vKRt01k/hN1PPHSrkKXI0nSEadkw6J0pplUhWsWSZIk9UZ//Y7JDO9fyRcWPEu2xZ3RJEnqSiUcFmVJVdpZJEmS1BtVV5bz+XdN5dmNe7lj8fpClyNJ0hGltMMix9AkSZJ6rfccP5KTJwzmG4tWsas+U+hyJEk6YpRsWNSQyVLlGJokSVKvFULgSxdN45X9zfzzfasKXY4kSUeMkg2L0plmO4skSZJ6uWNH9OcDp4zjx39ez7Mb9xS6HEmSjgglHBY5hiZJknQk+PS5RzM4leTGXz5Li4tdS5J02EoyLMq2RBqbW0glHUOTJEnFK4QwL4SwKoSwOoRwwwFePz2E8GQIoTmEcFmH1z4YQngh//PBnqu65w2oquDvLziWJ9fv5p6lGwtdjiRJvV5JhkXpTDOAnUWSJKlohRASwM3ABcBU4MoQwtQOp60HrgZ+0uHawcAXgJOBOcAXQgiDurvmQrpsVi0zxgzkq/c+x979TYUuR5KkXq0kw6KGTBaAKsMiSZJUvOYAq2OMa2OMGeAO4KL2J8QY18UYnwZaOlx7PnBfjHFnjHEXcB8wryeKLpSyssBNF01jR30j377/hUKXI0lSr1aSYVE6HxbZWSRJkorYaGBDu+d1+WNddm0I4ZoQwpIQwpJt27a95UKLxfG1A7nipDH84JF1PL/llUKXI0lSr2VYJEmSVKJijLfEGGfHGGfX1NQUupwu8bfnH0vfynK+uGA5MbrYtSRJb0WJhkWtaxa5wLUkSSpaG4Ex7Z7X5o9197W92uDqJNefdzSPrNnBwmc2F7ocSZJ6pRINi+wskiRJRW8xcFQIYUIIIQlcASzo5LWLgPNCCIPyC1uflz9WEq46eRxTR/bnn369ou0moSRJ6rySDotc4FqSJBWrGGMzcB25kGclcGeMcXkI4aYQwoUAIYSTQgh1wPuA74cQluev3Ql8mVzgtBi4KX+sJCTKAl+6aBov79nPzQ+tLnQ5kiT1OiU5h9XQ5BiaJEkqfjHGhcDCDsdubPd4MbkRswNdeytwa7cWWMROGj+Y984czX/+4UWOHt6Pt00eypC+lYUuS5KkXqEk0xLH0CRJko58n7vgWB5ds4O/ueMpAI4e3pe5E4dwysQhnDxxCIOrkwWuUJKk4lSaYVGjYZEkSdKRblj/Pjz892fxzMY9PLZ2B4+u2cGdS+r44aMvAXDsiH6ckg+PTpk4mIEpwyNJkqBUw6K2zqKS/PiSJEkloyJRxqyxg5g1dhCfPHMyTdkWnq7bzWNrd/Lomh3csXg9P3hkHSHAaZOG8q0rZjDUcTVJUokrybQk3dRMsryMRFkodCmSJEnqQRWJMk4cN5gTxw3mU2dNJtPcwrK63fzxhe18/w9ruOR7j3Dbh05iUk3fQpcqSVLBlORuaA2ZrCNokiRJIllexknjB/Ppc4/mpx87hfrGZi753iM8/mLJbB4nSdLrlGRYlM5kSVUYFkmSJOlVM8cO4p5PnsaQvkne/19/ZsGyTYUuSZKkgijJsKghkyVVWZITeJIkSXoTY4ekuPsTpzJz7ED+z0+X8r3frSbGWOiyJEnqUSUZFtVnmh1DkyRJ0gENTCX50UfmcNGMUXz9N6v4h3ueoSnbUuiyJEnqMSXZXpPOZKlyDE2SJElvoLI8wbcun8HYwSn+7cHVbNy9n5uvmkm/PhWFLk2SpG5Xkp1FLnAtSZKkgwkh8NnzjuFrlx7Hn1Zv533/8Sgv72kodFmSJHW7kgyL0plmUsmSbKqSJEnSIbr8pLHcdvVJ1O1q4L03P8KKTXsLXZIkSd2qJMMiO4skSZJ0KE4/uoa7rp1LCPC+/3iEn/x5Pc2uYyRJOkKVZFhUb1gkSZKkQzRlZH/u+eRpTBs1gH+45xne+Z2HeWjVVndLkyQdcUoyLGrIZKlyDE2SJEmHaMSAPvzs46fwH++fRaa5hQ/dtpgP/PfjjqZJko4oJRcWNWdbyGRb7CySJEnSH8IT7wAAIABJREFUWxJCYN70kfz202dw47un8uymPbzr3x7mb+9axuY9+wtdniRJh63kwqJ0UxbAsEiSJEmHJVlexoffNoHfX38WH3v7RH751CbO/OZD/MtvV1Hf2Fzo8iRJestKLixqyLSGRY6hSZIk6fANSFXwD++cwgOfPYNzpgznOw+u5oxv/I6fPu4i2JKk3qnkwqLWuzx2FkmSJKkrjRmc4rtXzeLuT57K+CEpPnf3M5z5zd/xpV8t50+rt5NpNjiSJPUOJddek853FlUZFkmSJPVOTfuhYRf0H1noSg5o1thB3HXtXBYt38zPFm/gx39ez21/Wke/ynJOP6aGc6YM48yjhzGoOlnoUiVJOqCSC4saXLNIkiSpd/vt5+GZ+fCZFZCsLnQ1B9S6CPa86SNJZ5r54wvbeWDlVh54biu/fvplygLMHjeYs6cM4+wpw5lUU00IodBlS5IElGBYlM4YFkmSJPVamXpY9jPIvAIr/xdOuLzQFR1UKlnOedNGcN60EbS0RJ7euIcHVm7h/pVb+eq9z/HVe59jwtBqzp06nPOnjWDmmIGUlRkcSZIKp+TCooZM65pFJffRJUmSer+Vv8oFRRUpWPaTXhEWtVdWFpgxZiAzxgzks+cdw8bdDTy4cgv3rdzKbX96kVv+sJZh/So5d+pw5k0fwSkTh1CRKLllRiVJBVZyiUl9o51FkiRJPebpu2DFL+Dy26ErxqyW3g6DJsDxfwG//zrsqYMBtYf/vgUyemAVH5g7ng/MHc+ehiZ+t2orv3l2M3c/uZEf/3k9/fuUc/aU4Zw/bTinH13jDU9JUo8ouf+3STe5wLUkSVKPaaqH5/4XXnoExp92eO+180VY9zCc9Y9w3GXw+6/Bsjvg9Ou7ptYCG1BVwUUzRnPRjNHsb8ry8AvbWbR8M/ev3MI9SzfSp6KM04+q4Zwpwzl18hBqB6UKXbIk6QhVcmGRY2iSJEk96PjL4f4vwWPfO/ywaNlPgQAzrsx1E409NXfs7Z/tmq6lItKnIsG5U4dz7tThNGdbePzFnSxavplFy7fw2xVbABgzuIq5E4dw6qShzJ00hOH9+xS4aknSkaLkEpPWBa6rKuwskiRJ6nYVVXDi1fDHf4Vd62DQ+Lf2Pi0t8NRPYNJZr46dzbgKFlwHdUtgzEldVHDxKU+UcerkoZw6eShfvHAaz2/Zx6NrtvPImh0sWr6FO5fUATCxppq5E4cwd9IQTpk4hKF9KwtcuSSptyq5sKghk6VPRRkJd5iQJEnqGSd9FB75Djz+n3D+V97ae7z4e9izAc754qvHpl4EC/82t9D1ERwWtRdC4JgR/ThmRD+uPm0C2ZbIypf38uiaHTy6dge/fGoTP/7zegCOHt6XKSP7M6mmb+5nWDXjh1TTx5umkqSDKLmwqD7T7AiaJElSTxowGqZeDE/+CM68ASr7Hfp7PPVj6DMAjn33q8f69Icp74Fnfw7nfxUqSm8MK1EWmD56ANNHD+Bjp0+kOdvCMxv38OjaHTz+4k6WrNvFL5/a1HZ+CDBmUIpJNdX5ACkXJE0d1Z++lX5HliTllNz/I6QzWUfQJEmSetopn4Bn5+dGyU7++KFd27AbVv4KZr7/9YHQjCvhmTvh+Xth2nu7rt5eqjxRxsyxg5g5dhCfPDN3rCGTZe32fazZVs+arftYsy33+JE1O2hsbgFySzRcNGMU7z9lHNNHDyjcB5AkFYVOhUUhhHnAt4EE8F8xxv/X4fWxwA+BgflzbogxLgwhVAD/BczK/64fxRi/2oX1H7KGTJaUO6FJkiT1rNrZUHsS/Pk/4KSPQVlZ56999ufQvB9m/OXrX5twBvQbBU/91LDoDVQlE0wbNYBpo14bArW0RDbubmD11n0sWr6ZXzy1kTsWb+CEMQP5wCnjePfxI9/SyFp9YzNL1+9mQFUFx9UaPElSb3TQsCiEkABuBs4F6oDFIYQFMcYV7U77R+DOGOO/hxCmAguB8cD7gMoY43EhhBSwIoTw0xjjui7+HJ2WNiySJEkqjFM+AfM/DC/8Fo6Z1/nrnvoxDJsKo2a+/rWyBJxwOfzpO/DKFug3vOvqPcKVlQXGDE4xZnCKs44dxufeOYW7n6zj9sde4vq7lvHl/13B+06s5S9PGceEodVv+D570k0sXreTx9ft5M8v7uTZjXvItkQA5k4cwifPmsTbJg8lHGE71knSkawznUVzgNUxxrUAIYQ7gIuA9mFRBPrnHw8ANrU7Xh1CKAeqgAywtwvqfstynUUlN30nSZJUeFMuzHUBPfa9zodFW1fCxifg/P8vt+DOgZxwVW63tWfuhFP/uuvqLTEDqir40GkTuPrU8Ty6dgc/fmw9P3hkHf/1xxd5+1FD+cuTx3HOlGHsTGdY/OIuHn9xB39+cSertrxCjJBMlDFjzEA+ccYkTpowmBe2vMJ/PryWD/z34xw3egCfOmsS500dQZkbzUhS0etMajIa2NDueR1wcodzvgj8NoTw10A1cE7++HxywdLLQAr4dIxxZ8dfEEK4BrgGYOzYsYdQ/qFLNzUzvF/pLX4oSZJUcIkKmPMxeOBLsGU5DJ928GuW3g5l5XD85W98Ts3RMHp2bhRt7nVvHCqpU0IInDppKKdOGsrWvfu5Y/EGfvr4eq69/Qn6Vpazr7EZgFQywYnjBvGu40YyZ8JgThgz8DVja2ccXcMH5o7jnic38u+/X8O1tz/JpJpqrj1jEhfPHE1F4hBGESVJPaqrWmyuBH4QY/znEMJc4H9CCNPJdSVlgVHAIODhEML9rV1KrWKMtwC3AMyePTt2UU0HlG7MUjXEMTRJkqSCOPFq+P3Xc2sXXfhvb35utgme/hkcPQ+qh775uTOuhF9/FjY/DSNP6LJyS92w/n34P2cfxSfPnMSDz23lgZVbmTSsmjkThjBtVP+DBj6V5QmumDOWy06sZeGzm/neQ6v52/lP8637X+Bjb5/A5SeNpcolIiSp6HQmzt8IjGn3vDZ/rL2PAHcCxBgfBfoAQ4GrgN/EGJtijFuBPwGzD7fow+GaRZIkqbcIIcwLIawKIawOIdxwgNcrQwg/y7/+5xDC+Pzx8SGEhhDCU/mf/+jp2t9QanBujaFlP4P67W9+7gv3Qf223C5oBzPtEkgkc91F6nLliTLOmzaCr112PNecPokZYwYeUmdQeaKMC08Yxb1/83Zuu/okRg7owxd/tYK3fe1BvvPAC6zYtJeWlm69ZyxJOgSd+S/8YuCoEMKEEEISuAJY0OGc9cDZACGEKeTCom354+/IH68GTgGe65rS35p0ptk1iyRJUtFrt8nIBcBU4Mr8RiLtfQTYFWOcDPwr8LV2r62JMc7I/1zbI0V31smfgGwjPHHbm5+39HaoHgaTzz34e6YGwzEXwDN35TqSVJRCCJx17DDmf+JU7vz4XI6rHcC/3Pc87/zOw8y46bd8+AeL+fffreGJl3aRaW4pdLmSVLIOmprEGJtDCNcBi4AEcGuMcXkI4SZgSYxxAfBZ4D9DCJ8mt6j11THGGEK4GbgthLAcCMBtMcanu+3TdEJDk51FkiSpV+jMJiMXkVs7EnJrRX439IYtp4YdC5PeAYv/G079GyhPvv6cfdvghUW5HdQSnbzRd8JVsOKXuY6kY9/ZtTWry82ZMJg5E+ZQtyvN4y/uZHF+N7UHn9sKQJ+KMmaOGcRJEwYzZ/xgZo0b6E1fSeohnfqvbYxxIbCww7Eb2z1eAZx2gOv2Ae87zBq7TKa5haZsNCySJEm9QWc2GWk7J3+Dbw8wJP/ahBDCUnI70f5jjPHhjr+gJzcZeZ1TPgk/viwX7hx/gK+LT/8MWpphRidG0FpNPhuqa+CpHxsW9SK1g1LUDkpxyaxaALbva2RJPjhavG4n333wBVoilJcFhvfvQ3kiUJEooyJRRjIRKE+UUdHuWEUikEqWM21Uf2aOHcS0Uf1fs/C2JOngSiqab8hkAajyjoQkSTqyvQyMjTHuCCGcCPwihDAtxri3/Uk9ucnI60w6G4ZMhse+B8dd9todzGLMjaCNnp3rQuqsREVu17Q/fx/SO3Ojaep1hvatZN70kcybPhKAV/Y38cRLu1i8bieb9zTS3NJCU7aFTHNse9zUHNnX3Nz2eO/+Ju5ZmltmNZkoY+qo/swcO5CZYwcxa+xARg+sorNNeDFG6jNZmrMtDEwdoAtOko5AJZWapJte3eZTkiSpyHVmk5HWc+pCCOXAAGBHjDECjQAxxidCCGuAo4El3V51Z5WVwcnXwsLroW4xjJnz6mubnoRtK+Hd/3ro73vClfDod+GZ+XDyNV1XrwqmX58KzjxmGGceM+yQrtu6dz9Prt/N0g27WLp+Nz99fD23/WkdADX9KpmVD48GVFWwO93E7nSGXelM/nFT7nFD7nhTNpelHl87gAumj+SC6SMYP7S6qz+qJBWN0gqL8p1FhkWSJKkXaNtkhFwodAW5nWbbWwB8EHgUuAx4ML9uZA2wM8aYDSFMBI4C1vZc6Z10wpXw4Jdz3UXtw6KlP4byPjD90kN/zxHTYcRxsOwnhkUlblj/PsybPoJ500cA0JRtYdXmV3hyfS48Wrp+F4uWb2k7P1lexqBUBYNSSQZUVTCppi+DqisYUJVkUKqCpmwL963cytd+8xxf+81zTB3Zn3ceN4J3HjeSiTV9D6m2V/Y3sWZbPXsbmjhl4hCS5Z3fWe5wxRh5duNe7n32ZY6vHdDWwSVJ7ZVUWNTQFhaV1MeWJEm9UCc3Gflv4H9CCKuBneQCJYDTgZtCCE1AC3BtjHFnz3+Kg6jsC7P+Ch79HuzeAAPHQFNDritoyoXQZ8Bbe98TroJFn4Otzx3aGFtXe+TfYNsqeM93cp1UKqiKRBnTRw9g+ugB/NXc3LGd9Rn2N2UZmKqgqiJx0NG0695xFHW70vzm2c0sfOZlvvnb5/nmb5/n2BH9uGD6SN553AiOGt4PyIUy2/Y1snrrPtZs3cfqrftYvS3355a9jW3vOaxfJR84ZRxXnjyWoX0ru+3zb31lP79YupGfP7GRVVteaTv+zuNGcNNF07v1d0vqfUKuS7l4zJ49Oy5Z0j0d0o+/uJO/+P6j/PijJ3Pa5KHd8jskSdLBhRCeiDHOLnQdelV3fgd7U7vXw7dPgFP/Gs69KRcU/fwj8Fe/hIlnvrX33LcN/uVYmPup3HsWwrKfwT35zqZ3/TOc9NHC1KFu9fKeBn7z7GbufWYzi1/aSYwweVhf+vcpZ/XWfezd39x2bnUyweRhfZk0rG/uz5q+BOD2P6/nD89vI1lexoUnjOJDp41n2qi3GJR2sL8pywMrt/LzJ+v4/fPbyLZEZo4dyGUn1nLB9JH89PH1fPv+F+jbp5wvXzSddx1vl5F0JDuU718l1WJTn8n9x7rKMTRJkqTiMHAsTHkPPPFDOOPvcwtbDxgL409/6+/ZtwYmnwtP3wlnfwHKevi734bHYcF1MP7tud993xfgqPNyn1VHlJEDqvjQaRP40GkT2Lp3P4uWb2bR8i00ZVt4zwmjmJwPhiYP68uI/n0O2Ll03rQRrN76Cj985CXmP1HH/CfqmDNhMB8+bTznTBlOeeLQutJijCyr28PPn6hjwbJN7GloYkT/Pnz89IlcemItk9qNzH3qrMmcO3U419+1jE/95El+/cwIvnzRdIbYZSSVvJIKixpcs0iSJKn4nPwJWPFL+MM3Ye3vcqHR4Y5tzbgKnr8X1j4Ek8/pkjI7Zfd6uOMq6D8a/uJHkNkH35sLC/4PfOCe1+76piPKsP59+MDc8Xxg7vhDvnbysH58+eLpXH/+Mdy5eAM/fHQd197+JKMHVvFXc8dxxUljGZCqAHJ/p9m+r5Ed9Rm2v9LIjvpGtu/LsH1f7s8Vm/awZls9leVlzJs+gktn1XLa5KEkyg78797Rw/tx9ydO5ft/WMu373+Bx9b+wS4jSaUVFrUtcF1RUh9bknpEU1MTdXV17N+/v9ClqIj06dOH2tpaKioqCl2KitnYU2DkDPjjv+Sez7jy8N/z6POhahA89ZOeC4sa98FPr4TmRrj615AanPs590vw68/C0v/JrdEkvYEBVRV87PSJfPhtE7hvxRZ+8MiLfPXe5/jW/S9Q06+SHfsaqc//naajvpXlDOmbpHZQFR99+0TedfxI+vfp3H97yxNlfOqsyZwzZTh/Oz/XZbTwmZHcdNG0N+0yashkeXbTHpbmFw1/ZuMeqioSjBtSzYShKcYPrWb8kGrGD61mZP8+lL1BYCWp+JRUatKQH0NLVdpZJEldra6ujn79+jF+/PiDLhCq0hBjZMeOHdTV1TFhwoRCl6NiFgKc8sncGj/j3w6Dxh/+e5ZXwvTLcgHN/j1vfbHszmppgbs/BltXwF/eBTXHvPraiR+G5b+ARZ+HSWfDgNHdW4t6vURZaNvJbcWmvfzk8Zd4ZX8zQ/tWMqRvkqHVlQztl2RIdf5530r6VBz+33GOGfHaLqNH1+5o6zKKMfLi9nqWrt/NUxt2s3TDLla+/ArZltwauGMGV3HCmIE0Nbewbkc9D7+wjcbmlrb3TpaXMW5wqi1IGjekmjGDU4wZVMXoQVVUlh96/TFGdtZn2LCrgQ070zRksowZnGLC0GqG96/0+4h0GEoqLEo7hiZJ3Wb//v0GRXqNEAJDhgxh27ZthS5FvcG098LKBTDnY133njOuhMX/CfM/DOf9Ewyb0nXv3dEDX4JVC+GCr7++k6msDC78Dvz7afC//xeuutNxNHXa1FH9+aeLj+ux33egLqN//31/6nY1sDvdBOS6mI6vHcC1Z0xk5phBnDBmIDX9XtuB1NIS2bx3P+t21LNue5qXdtTz4vZ6XtqRfl2QFAIM79eHMYOrGDMoRW0+RBozOMXogVW8sr+ZDbvSbNiZpm5XA3W70mzY2cCGXem2v+N11KeiLNfVlO9sGj/k1U6nQgVJMUZ2pZsoCzAwlezx3y8dipIKi1pbNvu8hdRaknRwBkXqyH8n1GnlSbjix137nqNmwXlfgd9/Df79VDjhKjjrczCgtmt/z1M/hT99C2Z/GOZcc+BzBk+Es2+E39wAy+7omlE7qRu17zL67YotzJs2ghljBjJz7CAmD+v7hmsgtSorC4waWMWogVWcOum1r7W0RLa+0siGXWnW70jng6Bc+PPY2h28/NRG3mjT7r6V5dTmg6RTJw9hzKBUrkNpcBWpinJe2lnPuu31rNuRZt32ep7f+goPPLeFpuyrb1hVkeCEMQOYO3EocycNYcaYgSTLD3OdtLx9jc1s2JkLtja0C7bq8mFXfSZLCDB73CDOnzaC86eNYMzgVJf8bqkrhfhG/ysskO7ctvUrv17B7Y+tZ+WX53XL+0tSKVu5ciVTpnTjXXv1Wgf6d+NQtm5Vz+jO72AFl94JD/8zPH4LEODka+Btn8mtJ3S41j8GP3xPbt2l998NiTdZI6alBW67ALathE89Dv1GHP7vL0Yxwp++DaEMTv1ru6h0yDLNLWzanQuPNu5qoF+firauo4GpikO+EZFtiWza3ZDvbKpnzbZ6Hn9xJys37yXGXBfS7HGDmTtpCKdMHMLxtQOoeJNd6Pbub2Ld9lynVOvPuu31rN+ZZle++6pVKpnIB1pV1OaDrb0NTSxavpnnNr8CwJSR/Tl/2nDOnzaCY0f080aLus2hfP8qqc6idCbrCJokHaF27NjB2WefDcDmzZtJJBLU1NQA8Pjjj5NMHrzd+0Mf+hA33HADxxxzzEHPbe/d7343u3fv5o9//OOhFy6p+6UGw/lfgZM/Dg99FR75LjzxI3jb/4WTr4XkW7yrv+sluOMvc51K7/vhmwdFkBtHu+hm+I/TcgteX377kRektGRzo3ZP/ij3PLMPzvqHwtakXidZXpYbGRta3SXvlygL+e6jFFDTdnx3OsNja3fy2NodPLpmB99YtAqA6mSC2eNz4dHYwSle2pHmxe37WLc9zdrt9Wzf19j2HiHAqAFVTBhazbzpI9tCrdb1mAZXJw8Y/nz63KNZvyPNouWbWbR8M99+4AW+df8LjB2caguOZo0ddMBFwbMtkUxzC5nmFhqzWRqbWmhsbmF/U5Z0JktDU5aGTHP+zxbSmea21/Y3tRAClIVc91ciBBJlgRBaH796fFB1kikj+nPU8L5dsiaWepeS6iz6zM+eYvFLO3n4797RLe8vSaWsmDqLvvjFL9K3b1+uv/761xyPMRJjpOxwt+RuZ+fOncyYMYM+ffpw//33M3bs2C577/aam5spL++d93jsLOodjujOoo62rIAHboLn74V+I+GMv4eZH4DEIfxvrPEV+O/zYM9G+NgDMPSozl/7p2/DfTfCpf8Nx1126PUXq+ZG+PlHc2tPvf162Lclt8D42V+At3+m0NVJB7VjXyOPrd3Jo2u38+iaHazZVt/22tC+lUwcWs34oSkmDO3LhKHVTBhazbghqS4JUra90sh9K7awaPlmHlmznaZsZHB1kurKxKvBUP7P5pa39nf4RFmgKl9rtiWSjZGW/J9vFgskygIThlYzZWR/pozsx5QR/Zkysn+Xrv3UkMny4vZ61m7fx5qt9by8p4Exg1McPbwfxwzvR+2gKnfT6wJ2Fr2BdCZLqqKkPrIkFcSXfrWcFZv2dul7Th3Vny+8Z9ohX7d69WouvPBCZs6cydKlS7nvvvv40pe+xJNPPklDQwOXX345N954IwBve9vb+O53v8v06dMZOnQo1157Lffeey+pVIpf/vKXDBs27HXvP3/+fC6++GIGDBjAHXfcwd/93d8Bue6mj3/847z44ouEELjllls4+eSTue222/jXf/1XQgjMmjWL2267jfe///1cdtllXHzxxQD07duXffv2cf/99/NP//RP9O3blzVr1rBy5Ure8573sGnTJvbv38+nP/1pPvrRj8L/3969h0dV3fsff6/c7zcu4U4CAgGUayCgoEQPFcSaKigiVsVb6ylqfz21RY+tWvVUrcdSf1oLrSj0KGC1eEdPxVhoFYGgoHIR0VAwIQQSciH3ZJ0/1mRIIAECCTMhn9fz7Gf27JnZs3bWEPZ88l1rA2+99Ra/+MUvqK2tJTExkXfeeYeBAweybt06EhISqK2tZcCAAWzYsIGEhFYYeiPSniUOgWuWwa6P4L37XCXMR0/BuNvc3ELRPSCmO4TGNF35U1frQpH87XDtyy0LigDGz4Utr8Hbd0HyBRDV5fiv8XeVpbB8Nnz9AVz8axj/7+7nVFPhJv8OCnPbRPxYp6hQpg3rzrRh3QHYV1zBvpJK+nSKICbsOJWDp6hLdCjXpPXhmrQ+FFdUk7ltH2t27Kemto7QoEBCggIICQog1HvbeFtoUAARIUGEBwcSHhJAeHAQ4SGBRIQEEhbsbo81tM5aS511IVKdtdR65pXallvM1txituSWsHFXIW9syvG+Jj4imJRuMQzqFk18hAu2IkODiAgJJCo0iIiQIHcbWn8/kNLKGr7OP8TO/NJGt98eLPfu1xiIjwih4FCVd1t4cCADE6NceNQt2nvbNbr1AitrLUXl1eQVV3r7JC48uMOGVB0qOTlUVUO4hqGJiHQ427ZtY8mSJaSmuj+kPPLIIyQkJFBTU0N6ejozZsxgyJAhjV5TVFTEBRdcwCOPPMJPfvITFi1axLx5847a99KlS/mv//ovYmNjmT17tjcs+tGPfsTkyZOZO3cuNTU1lJWVsWnTJh599FE+/PBDEhISKCgoOG7bN2zYwJYtW7wVS4sXLyYhIYGysjJSU1OZPn06lZWV3HbbbaxZs4a+fftSUFBAQEAAs2bN4sUXX2Tu3Lm8++67jBkzRkGRSEN9x8ON78L2lS7QeOs/Gj8eHAkxnuAouodnvQfs3QxfvgOXPA79T6JiPSAQMn4PCybCyrvgyudb5XB8pqwAXpgBOZ/C9/5wePLugEB3v6YS3r0bgkJhzE2+batIC3SNCaNrTNhpf9+YsGAyRvQkY0TP0/aebhgajSYuTw4NIrlzJFPP6e7dVlRezfa9JWz1hEhb95bwlw27vReTaonIkED6dYkiNSmemV16069LJP08VVvhIYGUVFSzY18pX+4tYXteCV/mlZC5PZ+/ZO3x7iM2PJhe8eHEhgc3WmKOuB8bHkxESCD7S6vYV1JBXnEFe4sqySupYF9xBXnFleQVVzS6Sh9AUIAhITKELtGhdI5yS5foUDpHBjOi+H367X0H0u8mof+pFUsXlVfz2Z4iNu05yKbdB/ntzBFEhvo2rulQYVG55iwSETktTqYCqC3179/fGxSBC3ieffZZampqyMnJYcuWLUeFReHh4UydOhWA0aNHs2bNmqP2m5OTw7/+9S/Gjx8PQF1dHdu2bSMlJYUPPviAZcuWARAUFERMTAzvv/8+M2fO9AY2JxLcjB8/vtHQtt/+9re8/vrrAOzZs4edO3eye/du0tPT6du3b6P93nTTTVx55ZXMnTuXRYsWeauQRKQBYyDlEhg4BQ5mQ3EulORCcc7h2+Ic2PVPd7+uxr1uzM0w9paTf9+uKW742/sPwtArYMhlrXI4p13Rt/Dny6Ew283BlHJJ48cDg9xwu5e+D2/9xFUYjZztk6aKyKmLDQ9mbHICY5Mbn8PU1NZxqKqWsqoaDlXWcKiy1t1W1d+67eHBgfTvEkW/LlHHHcYWHRbMqD7xjOoT32j7gdJKvswr5cs8FyLtLaqgqNwFS0Xl1RSVV1N1ROjTlIiQQLrFhNE1JpSRfeI862EkxoRiLewvrWR/aSX5JZXsL61if2klX+aVEF/6Fb8MfI7UgK1U20DqlvydhwJu5ItulzOoe4y38mlgYhTRTVSkVVTXsiW3mE27D7J5TxGbdh/k6/2Hhzwmd45kb3EF/btEHfcY2lKHCovKqmqJi2jb8kEREfE/kZGHJ8jcsWMHv/vd71i3bh1xcXFce+21VFRUHPWahhNiBwYGUlNTc9SqtsbbAAAdOElEQVRzli9fzv79+0lKSgJcNdLSpUt54IEHgBO/bHxQUBB1de6kpra2ttF7NWz7e++9x+rVq1m7di3h4eFMmDChybbXS0pKIj4+nszMTD755BO+853vnFB7RDqkgAA3BC2hX/PPqauDsv1QUQyd+jf/vBN13p1uONpbP4GkCa1zdbbTaf9X8OfvQflB+P5f3TE0JSjETQC+9Gp4fa6rMDqT5moSAffvoLIYaqtdqFxbDXXVbjhm/Xr9Y1FdofuIM2qC+6DAAGLDA4gNb/vv252iQhkfFcr4/p2afU5FdS3FnuCofjlUVUunyBASPYFQVGhQy4awVRRB5q+x6xZiQ2PYl/YoX3c6n94f/D/uLVhAZsEO7vr2ep6vOvwz6BkXzqBu0QxIjKK0ooZNew6yLbfEO+9U1+hQhveOY/roXgzrFcuwnnHE+klm0aHCovLqWiJCOtQhi4jIEYqLi4mOjiYmJobc3FzeffddpkyZclL7Wrp0Ke+99x5jxowBXBA1bdo0HnjgAdLT0/nDH/7A3Llzqa2t5dChQ1x44YXMnDmTO++80zsMLSEhgaSkJLKysrjiiitYsWIFtbVNl3IXFRWRkJBAeHg4X3zxBevXrwfg3HPP5c4772TXrl3eYWgNq4tmz57NnDlzWnVib5EOKSDAfcmLOnr+spMSGAzf+z0snAQrfw7T/9g6+z0dcj6F/5nu1m94E3qMOPbzg8Pg6hfdcLW/3uoCo8Hfbft2irQla2H3Olj7e9j6BtgWDMXqOhRG3wDDroLwuDZrYkcVFuzmamqVYYR1dbB5mbswwaH9mNQ5mAt/QdeIBLoCnP02rH6c9A9+zfrEHPZ+5w9sqenB9rwStu91y5od+YQFBzKsVyy3nt+PYb3iGNE7jm6xp3+Y44nqUMlJWVWNhqGJiHRwo0aNYsiQIaSkpNC3b1/OO++8k9rPzp07yc3NbTS8bcCAAYSFhZGVlcVTTz3FLbfcwoIFCwgKCmLBggWMHTuWn/3sZ5x//vkEBQUxevRonn32WX7wgx+QkZHBm2++yaWXXkpoaGiT7zlt2jQWLlzIkCFDGDRoEGlpaQAkJibyzDPPkJGRgbWWHj16sHLlSgAuv/xybrzxRm644YaTOk4RaWPdznFXDvv7I5C7CeL7QnwSxPVtvB4W03ZtqK98CD7BLy3Z/4AXr3ZfcL//KnQ+68ReFxIB1yx3w9b+MseFRwNbqeKxrhbyt8GeDfDtBijcBf3T4ezpEHeKV6msq3P73PWhqw6x1rPUuYX6dXv4frdhLgwLavr3ubRzNVWuKnDt7yFnI4TFugnyuwyCgGAXBAcEeW6D3XBM7/ZgN+9Z1vNuzrK//RLOvsIFR73GnFHVRmeE3E3uYgS7P3b9M/vlo8PxgECY9HPok4Z55Wa6v3QJ3b/7Oy6adJX3KTW1dQQY064myzb2WNfI84G2vGzrOfe9y4zUXn43l4aIyJmgqcuji++tXbuWu+++m8zMTJ+1oanPRksu3SqnR1ueg8lx1FTBP+e7LyUHd7mgo/KIK0qGJ7jwKK4vJA51V1HrOcp9+TwZVWWw833Y9hZ8uRLKC90X3ujuEJUI0d3cEtUNohMPb8/7wl0JLj4Jvr8CYk9iAt7yg7DkMti3DWa/BP0mtXwfxTmeYCjLLTmfQFWpeywszk1Evm+Lu987Dc6eAUO/d+JVYbXVkL3GVYtsextK9zb/XBMAGHdrjAuN6qohohOMvNaFAMca3iito6oMDv4LYntBaBvN9XLoAGQ9B+v/5OYw63SWC4mGz4KQyOO//kg5n0DWYvjsL+7zq2oj/1FWAO8/5Po7PAEm/8r18/GqtItz4eUb4V8fur6c8uiJB/GnQUvOvzpMWGSt5az/XMkPL+jHXRentPr+RUQ6OoVF/ufhhx9m4cKFLFu2zDsJty8oLGofFBb5EWtdeFOYfTg88q5nQ8E3gIWQaDdPUP90F7h0HnjsqoSyAncVt21vwVeroKbcBUQDp7ovvaV5LhQpyYOSvW69turo/fQY5f66Htn8fCHHVVYAz09zx3PtK9D3XFfBU1kMFQfd3CDlntuG9/dvhz1ZUOK5fHdAMHQ7G3qmQq9Ud9upv/s5FGbD56/AZ6/Avi9cmJN8gas2Gvzdo7+MVx1yP5dtb7qfU0URBEfAWf8Ggy+Dsy6C0GgaBUNN/bzr6uCbD2DDIhc02Vp31bzUm9xE6oEdanBH2yvdB+sWwvpnodxzldHILhCf7ELN+iXBcz+q2/G/8B9p31ZY+wxsXg41Fa4/x/079L+o5ftqSmUpfP6yqzbK+QSCwmHo5TDqOhc0Boe5yeEDQ1R51NbKD8KmpfD3x9zvnrG3wqS7Wxbe1da4ixf8c76rHr1qid8ExgqLmlBZU8uge9/hrosH8aP0EyyVFRGRE6awSJqjsKh9UFjUjpQVuKqXnZnw9QdQ+I3bHt3dhUb9JrlQJKa7q7TY9rYLQHZ96IKLmJ6QMs0tfc9rvjqpPrQqzXNVFCV5LmA650pPaHKKSvfBc5e4ECwo3FNNdYzvJibADSnrmQo9R7twqNuwE/ur/b6t8NnL7gt5Ybb70n3WZDhnuqvs2vbm4QAtPN4FaIO/64K44PCTP8biHNj4Z9i4GIq/dX006noXApxMVZYv1A+787d57/ZthY+egs0vuUqwQZ4+K8l1fVyYDQXZULzHMzzQIyjMfY5CoyEw1E3A3ug21H0+6m/3fgZfZ7rXDb8a0n4IXdvwfCfnU/d52fwXqCo54kHj2lEfHtUvwWGQ0N9dpbHPuFMPlOpqXeVh0R4XQncZBJGdT22f/sxaN8wsazF8scL9Huh7Hkx9zIXRJ2v7O7DiB+7zl/G0X1z1UmFREw6WVTHiV3/jvu8OYc55ya2+fxGRjk5hkTRHYVH7oLCoHSvMhq//7oKjrz84XF0R3eNwBU6XwS4cGnypf12FqTgH/jHfrYfHuUqnMM/tkfdDo0+93dbCtxtdaPT5Xw8PL4vucfjnc6wA7WTV1sCO/3XVRl+9545j4FQ3TK3b2RDT6/SEMXV1rsrqmzWw659waL+rHqutgprKI9arodazLSDIVZ91GQRdUjy3g10V1+mcl8la9xn/6Cn3cwwKhxHXuCqf5ubOqqmCot2eAOkbz+0uN+yrpsodY/2xNzzu+sciOsOYG2HUDadWTddSlaXuM1NeANUVrqKpfml4v7rc3e7Z4Cphug93gdbZ01veN8W58MmfYeMS9zNrKDzB0++DoPMg6DLQ3cb2avzvsrbahcxlB5pYClzoG9PDs/R0S1Sibyruygpg0zIXzuVvc9Wa58yA0ddDj5Gt8x6Fu+AvN7i5rXqMcle9DIs9Yok7ej2ujwswW5nCoibkHCzn3Efe59Hp5zBzzClOciciIkdRWCTNUVjUPigsOkPU1UHeZ+4Ldc4n7stJyjT3pV4aq6t11QSBoe6L4emqnCnMdhUMn/wZDuW7bYGhbphUQn9326m/W+/U3wVZJ9s2a92X4G/WQPZqyP7n4TAxrq/nC2moe//A4COqahpsq6mE/Tsgf6trf32ljgl0w2sahkhhsRweqgeNhu01XA8McV+MIxLc7bHCgpoqN6Two6fd5zuyqxselHrj6Q1w/FnVITdM7uMFrs8ju8DoOe5nFNO9+dfVVxFlPQ/bV7rqw37pbr6dHiNg/1du+Gf+Nsj/0q2XFx5+fXCk+8xWl7lAqKKo+fcKiYa6Gle505AJcFV33hCpl7uN7ua5AqXnNiy2dQLjXf90x7vldRcI9kx1xzv08raZ76qmClb/xk2UX1HUeJhtXXXTr5mbdeIXD2iBlpx/dZgBs2VV7jKG4SEd5pBFREREpKMJCHCVBd2H+7ol/i8g0M2VdLrFJ8G/3efmQdn9MRz4Cgp2urmoDux0FTO1lYefHxTm5t+J7tag4iqu+fXaavdlOHuNu3JdfSAV29vNmZQ8EZImQlzvk2t/dblrc359gLDNrdcHDScrNMYdQ3h84yUozA0NKsl1gdRlT7mhkH40abBfCIl0wdDoOS4s/niBCyj+8YQLQdJ+6IZu1ivZ6wLLrCVQ9C9XQXXu7a6qpuH8OvFJMODfDt+31lWk7d/u+n3/ly5ADIl0+4jo5ALAiE5HLAkueKwf3lqc41n2HF4v2gN5W2DH31z4dKSgME94lNh4qa869E42bxqEkw22H8p38xEd+ApCY92xjrr+1IaanYigELjwP4/ebq3791QfINXPz1ZRdOyA7zTpMMlJWVUNABHBgT5uiYiIiIiIdHhBIS64SZ7YeHtdnZvfqGCnC48Kvna3h/Ldl+mKg64qobmKhHrRPdxEzEme94jr2zrDD4PD3aS93c5pvL2myrW16hDgmefI1nnW69z9huv1w5WaW4q+dbcVRZB0nguJzrrIf4ZQ+itj3Fxb/dNdf6z7I3zyP+6Kaz1TYdhMV2W2faWr8kk+HyY/ACmXntiwJ2MgqotbkiacXPsiEtzSXEhjrev30jzPnGl5h9frlwM73Txs9ZVyJ6rPeJj4UxiSASERLW9/azLGtSEkwi/CoSN1oLDIpdwRIQqLRETOROnp6cybN4+LL77Yu23+/Pls376dZ555ptnXRUVFUVpaSk5ODnfccQcvv/zyUc+ZNGkSjz/+OKmpzVftzp8/n1tvvZWICHficckll/Diiy8SF6dL34qISAsEBLiqn7jebrLypngrEpq4cpytg95prjrkdAYrQSHQVVed9isJ/WDKryH9Hvh0KaxbACvvcpU+4/7dDb3yxyGqxniqzOLc0MZjqamC6kOeMJKmg8n69cBQF3LJCekwYdHg7jG8eEsaQ7vH+ropIiLSBmbNmsWyZcsahUXLli3jscceO6HX9+jRo8mg6ETNnz+fa6+91hsWvf322ye9L1+w1mKtJcDfrnYjIiJHa1SR0MPXrRF/FxoNabe6q6Xlb3WTlZ/OicnbUlBIm0wELR0oLIoND+bc/mfw5f5ERPzJynnuUrOtqds5MPWRZh+eMWMG9957L1VVVYSEhJCdnU1OTg4TJ06ktLSUjIwMCgsLqa6u5qGHHiIjI6PR67Ozs7n00kv5/PPPKS8vZ86cOWzatImUlBTKyw9PxHjbbbexfv16ysvLmTFjBg888ABPPvkkOTk5pKen07lzZzIzM0lKSmLDhg107tyZJ554gkWLFgFw88038+Mf/5js7GymTp3KhAkT+PDDD+nZsyevvfYa4eGNL9H8xhtv8NBDD1FVVUWnTp144YUXSExMpLS0lNtvv50NGzZgjOG+++5j+vTpvPPOO9xzzz3U1tbSuXNnVq1axf33309UVBQ//elPATj77LN58803Abj44otJS0sjKyuLt99+m0ceeeSo4wNYv349d955J4cOHSI0NJRVq1Yxbdo0nnzySUaMGAHAhAkTePrppxk+XHOliIiI+J2AAEgc6utWSDvRYcIiERE5syUkJDB27FhWrlxJRkYGy5Yt46qrrsIYQ1hYGCtWrCAmJob9+/czbtw4LrvsMkwz5fnPPPMMERERbN26lc2bNzNq1CjvYw8//DAJCQnU1tZy0UUXsXnzZu644w6eeOIJMjMz6dy58R8msrKyeO655/j444+x1pKWlsYFF1xAfHw8O3bsYOnSpfzxj3/kqquu4pVXXuHaa69t9PoJEyawdu1ajDH86U9/4rHHHuO///u/efDBB4mNjeWzz1woV1hYSH5+PrfccgurV68mOTmZgoLjj+PfsWMHixcvZty4cc0eX0pKCjNnzmT58uWMGTOG4uJiwsPDuemmm3j++eeZP38+X375JRUVFQqKRERERM4ACotERKT1HaMCqC3VD0WrD4ueffZZwA2xuueee1i9ejUBAQF8++235OXl0a1btyb3s3r1au644w4Ahg0bxrBhw7yPvfTSSyxcuJCamhpyc3PZsmVLo8eP9I9//IPLL7+cyMhIAK644grWrFnDZZddRnJysrcqZ/To0WRnZx/1+j179jBz5kxyc3OpqqoiOTkZgPfee49ly5Z5nxcfH88bb7zB+eef731OQkLCcX9mffv29QZFzR2fMYbu3bszZswYAGJiYgC48sorefDBB/nNb37DokWLuOGGG477fiIiIiLi/zQxgYiInDEyMjJYtWoVGzdupKysjNGjRwPwwgsvkJ+fT1ZWFp9++imJiYlUVFS0eP/ffPMNjz/+OKtWrWLz5s1MmzbtpPZTLzT08HwBgYGB1NTUHPWc22+/nblz5/LZZ5+xYMGCk3q/oKAg6urqvPcb7qM+xIKWH19ERASTJ0/mtdde46WXXmL27NktbpuIiIiI+B+FRSIicsaIiooiPT2dG2+8kVmzZnm3FxUV0bVrV4KDg8nMzGTXrl3H3M/555/Piy++CMDnn3/O5s2bASguLiYyMpLY2Fjy8vJYuXKl9zXR0dGUlJQcta+JEyfy6quvUlZWxqFDh1ixYgUTJ0486nnNKSoqomfPngAsXrzYu33y5Mk8/fTT3vuFhYWMGzeO1atX88033wB4h6ElJSWxceNGADZu3Oh9/EjNHd+gQYPIzc1l/fr1AJSUlHiDrZtvvpk77riDMWPGEB8ff8LHJSIiIiL+S2GRiIicUWbNmsWmTZsahUWzZ89mw4YNnHPOOSxZsoSUlGNf2ve2226jtLSUwYMH88tf/tJboTR8+HBGjhxJSkoK11xzDeedd573NbfeeitTpkwhPT290b5GjRrFDTfcwNixY0lLS+Pmm29m5MiRJ3w8999/P1deeSWjR49uNB/SvffeS2FhIWeffTbDhw8nMzOTLl26sHDhQq644gqGDx/OzJkzAZg+fToFBQUMHTqUp556ioEDBzb5Xs0dX0hICMuXL+f2229n+PDhTJ482VtxNHr0aGJiYpgzZ84JH5OIiIiI+DdjrfV1GxpJTU21GzZs8HUzRESkhbZu3crgwYN93Qw5zXJycpg0aRLbtm0jIKDpv0E19dkwxmRZa1NPRxvlxOgcTERE5MzWkvMvVRaJiIjISVmyZAlpaWk8/PDDzQZFIiIiItL+6GpoIiIiclKuu+46rrvuOl83Q0RERERamf4MKCIircbfhjaL7+kzcWqMMVOMMduNMV8ZY+Y18XioMWa55/GPjTFJDR6727N9uzHm4tPZbhEREWnfFBaJiEirCAsL48CBAwoHxMtay4EDBwgLC/N1U9olY0wg8DQwFRgCzDLGDDniaTcBhdbas4DfAo96XjsEuBoYCkwBfu/Zn4iIiMhxaRiaiIi0il69erFnzx7y8/N93RTxI2FhYfTq1cvXzWivxgJfWWu/BjDGLAMygC0NnpMB3O9Zfxl4yhhjPNuXWWsrgW+MMV959vfRaWq7iIiItGMKi0REpFUEBweTnJzs62aInEl6Arsb3N8DpDX3HGttjTGmCOjk2b72iNf2PPINjDG3ArcC9OnTp9UaLiIiIu2bhqGJiIiIdFDW2oXW2lRrbWqXLl183RwRERHxEwqLRERERPzTt0DvBvd7ebY1+RxjTBAQCxw4wdeKiIiINElhkYiIiIh/Wg8MMMYkG2NCcBNWv37Ec14HrveszwDet26W+deBqz1XS0sGBgDrTlO7RUREpJ0z/nbVGmNMPrCrDd+iM7C/Dfcvp0b94//UR/5PfeT/1EfQ11qrcU/HYYy5BJgPBAKLrLUPG2N+BWyw1r5ujAkD/gyMBAqAqxtMiP2fwI1ADfBja+3K47xXW56D6TPv/9RH/k995P/UR/5N/dOC8y+/C4vamjFmg7U21dftkKapf/yf+sj/qY/8n/pIOhp95v2f+sj/qY/8n/rIv6l/WkbD0ERERERERERExEthkYiIiIiIiIiIeHXEsGihrxsgx6T+8X/qI/+nPvJ/6iPpaPSZ93/qI/+nPvJ/6iP/pv5pgQ43Z5GIiIiIiIiIiDSvI1YWiYiIiIiIiIhIMxQWiYiIiIiIiIiIV4cJi4wxU4wx240xXxlj5vm6PQLGmEXGmH3GmM8bbEswxvzNGLPDcxvvyzZ2dMaY3saYTGPMFmPMF8aYOz3b1U9+wBgTZoxZZ4zZ5OmfBzzbk40xH3t+3y03xoT4uq0dnTEm0BjziTHmTc999ZF0GDoH8z86B/NvOv/yfzoHaz90DnbyOkRYZIwJBJ4GpgJDgFnGmCG+bZUAzwNTjtg2D1hlrR0ArPLcF9+pAf7DWjsEGAf8yPNvR/3kHyqBC621w4ERwBRjzDjgUeC31tqzgELgJh+2UZw7ga0N7quPpEPQOZjfeh6dg/kznX/5P52DtR86BztJHSIsAsYCX1lrv7bWVgHLgAwft6nDs9auBgqO2JwBLPasLwa+d1obJY1Ya3OttRs96yW4X7Q9UT/5BeuUeu4GexYLXAi87Nmu/vExY0wvYBrwJ899g/pIOg6dg/khnYP5N51/+T+dg7UPOgc7NR0lLOoJ7G5wf49nm/ifRGttrmd9L5Doy8bIYcaYJGAk8DHqJ7/hKa39FNgH/A3YCRy01tZ4nqLfd743H/gZUOe53wn1kXQcOgdrP/R/ux/S+Zf/0jlYu6BzsFPQUcIiaYestRaX0IuPGWOigFeAH1trixs+pn7yLWttrbV2BNAL9xf8FB83SRowxlwK7LPWZvm6LSIiJ0r/t/sHnX/5N52D+Tedg526IF834DT5Fujd4H4vzzbxP3nGmO7W2lxjTHdcUi8+ZIwJxp2ovGCt/atns/rJz1hrDxpjMoHxQJwxJsjzVxP9vvOt84DLjDGXAGFADPA71EfScegcrP3Q/+1+ROdf7YfOwfyWzsFOUUepLFoPDPDMfB4CXA287uM2SdNeB673rF8PvObDtnR4nnG9zwJbrbVPNHhI/eQHjDFdjDFxnvVwYDJuXoNMYIbnaeofH7LW3m2t7WWtTcL93/O+tXY26iPpOHQO1n7o/3Y/ofMv/6dzMP+nc7BTZ1wF45nPkyjOBwKBRdbah33cpA7PGLMUmAR0BvKA+4BXgZeAPsAu4Cpr7ZETMMppYoyZAKwBPuPwWN97cOPm1U8+ZowZhpuYLxAX/r9krf2VMaYfbhLZBOAT4FprbaXvWioAxphJwE+ttZeqj6Qj0TmY/9E5mH/T+Zf/0zlY+6JzsJPTYcIiERERERERERE5vo4yDE1ERERERERERE6AwiIREREREREREfFSWCQiIiIiIiIiIl4Ki0RERERERERExEthkYiIiIiIiIiIeCksEhERERERaWeMMbXGmE8bLPNacd9JxpjPW2t/ItL+BPm6ASIiIiIiItJi5dbaEb5uhIicmVRZJCIiIiIicoYwxmQbYx4zxnxmjFlnjDnLsz3JGPO+MWazMWaVMaaPZ3uiMWaFMWaTZznXs6tAY8wfjTFfGGP+1xgT7nn+HcaYLZ79LPPRYYpIG1NYJCIiIiIi0v6EHzEMbWaDx4qstecATwHzPdv+P7DYWjsMeAF40rP9SeDv1trhwCjgC8/2AcDT1tqhwEFgumf7PGCkZz8/bKuDExHfMtZaX7dBREREREREWsAYU2qtjWpiezZwobX2a2NMMLDXWtvJGLMf6G6trfZsz7XWdjbG5AO9rLWVDfaRBPzNWjvAc//nQLC19iFjzDtAKfAq8Kq1trSND1VEfECVRSIiIiIiImcW28x6S1Q2WK/l8Hy304CncVVI640xmgdX5AyksEhEREREROTMMrPB7Uee9Q+Bqz3rs4E1nvVVwG0AxphAY0xsczs1xgQAva21mcDPgVjgqOomEWn/lAKLiIiIiIi0P+HGmE8b3H/HWjvPsx5vjNmMqw6a5dl2O/CcMeYuIB+Y49l+J7DQGHMTroLoNiC3mfcMBP7HEygZ4Elr7cFWOyIR8Ruas0hEREREROQM4ZmzKNVau9/XbRGR9kvD0ERERERERERExEuVRSIiIiIiIiIi4qXKIhERERERERER8VJYJCIiIiIiIiIiXgqLRERERERERETES2GRiIiIiIiIiIh4KSwSERERERERERGv/wNUQK4gXwxC5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics_primary(history.history['acc'], history.history['val_acc'], history.history['loss'], history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b0dd5983ec488b7086c90afceccd8ce0e427756e"
   },
   "source": [
    "A good way to evaluate a classification model is to take a look at the model confusion matrix, this way we can have a better insight on what our model is getting right and what not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "trusted": true,
    "_uuid": "622fd8c7ea62ba78106a91f474123dd8e8731af6",
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix(np.argmax(y_val, axis=1), model.predict_classes(x_val))\n",
    "plot_confusion_matrix(cnf_matrix, range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "35136b972266fff51988c545ec4c6a1e7cf9be84"
   },
   "source": [
    "It seems we had pretty good results, we don't have any big confusion between labels, nice!\n\nFinally let's predict the test data and output our predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "trusted": true,
    "_uuid": "6781b1dd6eac0f908b95062e2ae4eaa4c9e540b1"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "trusted": true,
    "_uuid": "7cfd5286743b9b33a7849d9cf35c85eb26b7727f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ImageId</th>\n      <th>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      ],
      "text/plain": [
       "   ImageId  Label\n0        1      2\n1        2      0\n2        3      9\n3        4      0\n4        5      3\n5        6      7\n6        7      0\n7        8      3\n8        9      0\n9       10      3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame({\"ImageId\": list(range(1, len(predictions) + 1)), \"Label\": predictions})\n",
    "submission.to_csv(SUBMISSION_NAME, index=False)\n",
    "submission.head(10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.6.6",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}