oversampling repeticao.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IF702 Redes Neurais\n",
    "Projeto de redes neurais utilizando Base de Dados do Tipo 2, Detecção de Células de Câncer em Mamografias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras import optimizers\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, roc_auc_score, confusion_matrix, accuracy_score\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import matplotlib\n",
    "matplotlib.use('nbagg')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leitura e Limpeza dos Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para a leitura da base de dados foi feita utilizando a biblioteca pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set = pd.read_csv('mammography.csv')\n",
    "\n",
    "data_set.columns = ['X1','X2','X3','X4','X5','X6','CLASS'] # renomeando as colunas para ficar CLASS em vez de class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removendo exemplos repetidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7845\n"
     ]
    }
   ],
   "source": [
    "columns = data_set.columns.tolist()[:-1] # remove a coluna da classe da lista de colunas\n",
    "# print (columns)\n",
    "data_set.drop_duplicates(subset=columns, # seleciona apenas as 6 primeiras colunas para verificar duplicatas\n",
    "                         keep=False, # remove todos os exemplos repetidos\n",
    "                         inplace=True)  # Remove exemplos repetidos\n",
    "print (len(data_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renomeando a classe -1 para 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set['CLASS'] = data_set['CLASS'].map(lambda x : 0 if (x == -1) else 1)\n",
    "# print (data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estatisticas da base de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7845.000000</td>\n",
       "      <td>7845.000000</td>\n",
       "      <td>7845.000000</td>\n",
       "      <td>7845.000000</td>\n",
       "      <td>7845.000000</td>\n",
       "      <td>7845.000000</td>\n",
       "      <td>7845.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.333764</td>\n",
       "      <td>0.200042</td>\n",
       "      <td>0.251736</td>\n",
       "      <td>0.365734</td>\n",
       "      <td>0.160780</td>\n",
       "      <td>0.402400</td>\n",
       "      <td>0.032250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.025813</td>\n",
       "      <td>1.136427</td>\n",
       "      <td>1.101461</td>\n",
       "      <td>0.988616</td>\n",
       "      <td>1.157123</td>\n",
       "      <td>0.939678</td>\n",
       "      <td>0.176674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.784415</td>\n",
       "      <td>-0.452501</td>\n",
       "      <td>-0.591631</td>\n",
       "      <td>-0.859553</td>\n",
       "      <td>-0.377866</td>\n",
       "      <td>-0.945723</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.145333</td>\n",
       "      <td>-0.408265</td>\n",
       "      <td>-0.276061</td>\n",
       "      <td>-0.859553</td>\n",
       "      <td>-0.377866</td>\n",
       "      <td>-0.945723</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.111790</td>\n",
       "      <td>-0.271133</td>\n",
       "      <td>-0.005571</td>\n",
       "      <td>0.550163</td>\n",
       "      <td>-0.377866</td>\n",
       "      <td>0.845975</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.508993</td>\n",
       "      <td>0.219887</td>\n",
       "      <td>0.400163</td>\n",
       "      <td>1.027382</td>\n",
       "      <td>0.387549</td>\n",
       "      <td>1.132403</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.508443</td>\n",
       "      <td>5.085849</td>\n",
       "      <td>29.477769</td>\n",
       "      <td>9.591164</td>\n",
       "      <td>23.617122</td>\n",
       "      <td>1.949027</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X1           X2           X3           X4           X5  \\\n",
       "count  7845.000000  7845.000000  7845.000000  7845.000000  7845.000000   \n",
       "mean      0.333764     0.200042     0.251736     0.365734     0.160780   \n",
       "std       1.025813     1.136427     1.101461     0.988616     1.157123   \n",
       "min      -0.784415    -0.452501    -0.591631    -0.859553    -0.377866   \n",
       "25%      -0.145333    -0.408265    -0.276061    -0.859553    -0.377866   \n",
       "50%       0.111790    -0.271133    -0.005571     0.550163    -0.377866   \n",
       "75%       0.508993     0.219887     0.400163     1.027382     0.387549   \n",
       "max      31.508443     5.085849    29.477769     9.591164    23.617122   \n",
       "\n",
       "                X6        CLASS  \n",
       "count  7845.000000  7845.000000  \n",
       "mean      0.402400     0.032250  \n",
       "std       0.939678     0.176674  \n",
       "min      -0.945723     0.000000  \n",
       "25%      -0.945723     0.000000  \n",
       "50%       0.845975     0.000000  \n",
       "75%       1.132403     0.000000  \n",
       "max       1.949027     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estatísticas sobre as variáveis\n",
    "data_set.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separando as classes da base de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criando função para separando a base de dados pelas classes, para assim poder garantir que vai ter exemplos de cada classe em todos os conjuntos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def separar_classes(data):\n",
    "    zero = data[data.CLASS == 0]\n",
    "    um = data[data.CLASS == 1]\n",
    "    \n",
    "    return [zero, um]\n",
    "\n",
    "# print (len(separar_grupos(data_set)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Divisão dos Dados em Treino, Validação, e Teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def separar_grupos_tvt(data): \n",
    "    \"\"\"\n",
    "    Divisão da base de dados\n",
    "    Treinamento = 50%\n",
    "    Validação = 25%\n",
    "    Teste = 25%\n",
    "    \"\"\"\n",
    "    \n",
    "    # classe zero\n",
    "    zero_train, zero_validation = train_test_split(data[0], # base de dados que vai ser dividida\n",
    "                                                   test_size=1/2, # proporção da divisão dos dados\n",
    "                                                   random_state=42)\n",
    "    zero_validation, zero_teste = train_test_split(zero_validation, # base de dados que vai ser dividida\n",
    "                                                   test_size=1/2, # proporção da divisão dos dados\n",
    "                                                   random_state=42)\n",
    "    \n",
    "    #classe um\n",
    "    um_train, um_validation = train_test_split(data[1], # base de dados que vai ser dividida\n",
    "                                                   test_size=1/2, # proporção da divisão dos dados\n",
    "                                                   random_state=42)\n",
    "    um_validation, um_teste = train_test_split(um_validation, # base de dados que vai ser dividida\n",
    "                                                   test_size=1/2, # proporção da divisão dos dados\n",
    "                                                   random_state=42)\n",
    "    \n",
    "    return [(zero_train, zero_validation, zero_teste),(um_train, um_validation, um_teste)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampling dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replicando os dados da classe minoritaria para ter a mesma quantidade de exemplos das duas classes na MLP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oversampling_replacement(data):\n",
    "    um_train = data[1][0]\n",
    "    um_validation = data[1][1]\n",
    "    um_train = np.resize(um_train, data[0][0].shape)\n",
    "    um_validation = np.resize(um_validation, data[0][1].shape)\n",
    "    \n",
    "    return [data[0],(um_train, um_validation, data[1][2])]\n",
    "\n",
    "def oversampling_SMOTE(data):\n",
    "    '''Faz o oversampling usando o algoritmo SMOTE\n",
    "    \n",
    "    Parametros:\n",
    "        data (array-like): Array das amostras, com as amostras de treinamento no 1o indice, de validacao no 2o e teste no 3o\n",
    "    \n",
    "    Returns:\n",
    "        array-like: Array das amostras, apos o oversampling\n",
    "    '''\n",
    "    sm = SMOTE(random_state=42)\n",
    "    \n",
    "    train_features = data[0][:, :-1]\n",
    "    train_labels = data[0][:, -1]\n",
    "    features, labels = sm.fit_sample(train_features, train_labels)\n",
    "    train = np.zeros((len(labels), 7))\n",
    "    for i in range(len(train)):\n",
    "        train[i] = np.concatenate((features[i], np.array([labels[i]])), axis=0)\n",
    "    # Sem isso, os 0s tenderiam a ficar acima dos 1s\n",
    "    np.random.shuffle(train)\n",
    "    \n",
    "    validation_features = data[1][:, :-1]\n",
    "    validation_labels = data[1][:, -1]\n",
    "    features, labels = sm.fit_sample(validation_features, validation_labels)\n",
    "    validation = np.zeros((len(labels), 7))\n",
    "    for i in range(len(validation)):\n",
    "        validation[i] = np.concatenate((features[i], np.array([labels[i]])), axis=0)\n",
    "    np.random.shuffle(validation)\n",
    "    \n",
    "    return [train, validation, data[2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Juntando as classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Juntando as classes zero e um dos conjuntos de treinamento, validação e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def join_class(data):\n",
    "    train = np.concatenate((data[0][0], data[1][0]), axis=0)\n",
    "    validation = np.concatenate((data[0][1], data[1][1]), axis=0)\n",
    "    test = np.concatenate((data[0][2], data[1][2]), axis=0)\n",
    "\n",
    "    np.random.shuffle(train)\n",
    "    np.random.shuffle(validation)\n",
    "    np.random.shuffle(test)\n",
    "    \n",
    "    return [train, validation, test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592 253\n",
      "3796 1898 1898 126 63 64\n",
      "3796 1898 1898 3796 1898 64\n",
      "7592 3796 1962\n"
     ]
    }
   ],
   "source": [
    "# data_set_oversampling = oversampling(separar_grupos_tvt(separar_classes(data_set)))\n",
    "# print (data_set_oversampling)\n",
    "sep = separar_classes(data_set)\n",
    "print (len(sep[0]), len(sep[1]))\n",
    "grupos = separar_grupos_tvt(sep)\n",
    "print (len(grupos[0][0]), len(grupos[0][1]), len(grupos[0][2]), \n",
    "       len(grupos[1][0]), len(grupos[1][1]), len(grupos[1][2]))\n",
    "over = oversampling_replacement(grupos)\n",
    "print (len(over[0][0]), len(over[0][1]), len(over[0][2]), \n",
    "       len(over[1][0]), len(over[1][1]), len(over[1][2]))\n",
    "\n",
    "join_c = join_class(over)\n",
    "print (len(join_c[0]), len(join_c[1]), len(join_c[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separando entrada de saida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1.0: 3796, 0.0: 3796})\n",
      "Counter({1.0: 1898, 0.0: 1898})\n",
      "Counter({0.0: 1898, 1.0: 64})\n"
     ]
    }
   ],
   "source": [
    "X_train = join_c[0][:,:-1]\n",
    "y_train = join_c[0][:,-1]\n",
    "\n",
    "X_validation = join_c[1][:,:-1]\n",
    "y_validation = join_c[1][:,-1]\n",
    "\n",
    "X_test = join_c[2][:,:-1]\n",
    "y_test = join_c[2][:,-1]\n",
    "# print (y_validation)\n",
    "\n",
    "\n",
    "# utilizado para verificar a quantidade de exemplos de cada classe que tem nos conjuntos de validação, teste e treinamento\n",
    "import collections\n",
    "print (collections.Counter(y_train))\n",
    "print (collections.Counter(y_validation))\n",
    "print (collections.Counter(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalização dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_validation = scaler.transform(X_validation)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definição e Treino da Rede"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algumas funções auxiliares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_final_losses(history):\n",
    "    train_loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    idx_min_val_loss = np.argmin(val_loss)\n",
    "    \n",
    "    return {'train_loss': train_loss[idx_min_val_loss], 'val_loss': val_loss[idx_min_val_loss]}\n",
    "\n",
    "def plot_training_error_curves(history, name):\n",
    "    train_loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(train_loss, label='Train')\n",
    "    ax.plot(val_loss, label='Validation')\n",
    "    ax.set(title='Error Curves: ' + str(name), xlabel='Epochs', ylabel='Loss (MSE)')\n",
    "    ax.legend()\n",
    "    plt.show()\n",
    "    \n",
    "def plot_training_acc_curves(history, name):\n",
    "    train_loss = history.history['acc']\n",
    "    val_loss = history.history['val_acc']\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(train_loss, label='Train')\n",
    "    ax.plot(val_loss, label='Validation')\n",
    "    ax.set(title='Accuracy Curves: '+ str(name), xlabel='Epochs', ylabel='Accuracy')\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "learnRate = 0.001\n",
    "graficoName = \"oversampling repetição, lr=0.001, rede=\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNA 1 (10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7592 samples, validate on 3796 samples\n",
      "Epoch 1/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.3512 - acc: 0.5904 - val_loss: 0.4399 - val_acc: 0.5738\n",
      "Epoch 2/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.3032 - acc: 0.6592 - val_loss: 0.3898 - val_acc: 0.6014\n",
      "Epoch 3/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.2491 - acc: 0.6985 - val_loss: 0.3161 - val_acc: 0.6765\n",
      "Epoch 4/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1925 - acc: 0.7383 - val_loss: 0.2386 - val_acc: 0.7421\n",
      "Epoch 5/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1534 - acc: 0.7836 - val_loss: 0.1898 - val_acc: 0.7524\n",
      "Epoch 6/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1315 - acc: 0.8350 - val_loss: 0.1613 - val_acc: 0.8153\n",
      "Epoch 7/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1209 - acc: 0.8558 - val_loss: 0.1480 - val_acc: 0.8498\n",
      "Epoch 8/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1154 - acc: 0.8638 - val_loss: 0.1388 - val_acc: 0.8604\n",
      "Epoch 9/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1120 - acc: 0.8670 - val_loss: 0.1338 - val_acc: 0.8580\n",
      "Epoch 10/100000\n",
      "7424/7592 [============================>.] - ETA: 0s - loss: 0.1098 - acc: 0.8684"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-e29965751553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m                          \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# utilizado para misturar as amostras a cada epoca\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m                          \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_validation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                          callbacks=[early_stopping])\n\u001b[0m",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    865\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    866\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 867\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1596\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1598\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1599\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m     def evaluate(self, x, y,\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1195\u001b[0m                             val_outs = self._test_loop(val_f, val_ins,\n\u001b[1;32m   1196\u001b[0m                                                        \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1197\u001b[0;31m                                                        verbose=0)\n\u001b[0m\u001b[1;32m   1198\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1199\u001b[0m                                 \u001b[0mval_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_test_loop\u001b[0;34m(self, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1337\u001b[0m                     \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1339\u001b[0;31m                 \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1340\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1341\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2271\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2272\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2273\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2274\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m           if (not is_tensor_handle_feed and\n\u001b[0;32m-> 1096\u001b[0;31m               not subfeed_t.get_shape().is_compatible_with(np_val.shape)):\n\u001b[0m\u001b[1;32m   1097\u001b[0m             raise ValueError(\n\u001b[1;32m   1098\u001b[0m                 \u001b[0;34m'Cannot feed value of shape %r for Tensor %r, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mis_compatible_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    718\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    719\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mx_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_dim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 720\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mx_dim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    721\u001b[0m           \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/env/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mis_compatible_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     93\u001b[0m       \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mDimension\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mother\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mare\u001b[0m \u001b[0mcompatible\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \"\"\"\n\u001b[0;32m---> 95\u001b[0;31m     \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_dimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m     return (self._value is None or other.value is None or\n\u001b[1;32m     97\u001b[0m             self._value == other.value)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Aqui criamos o esboço da rede.\n",
    "classifier = Sequential()\n",
    "\n",
    "classifier.add(Dense(10, activation='relu', input_dim=6)) # camada escondida\n",
    "classifier.add(Dense(1, activation='relu')) #\n",
    "\n",
    "sgd = optimizers.SGD(lr=learnRate)\n",
    "classifier.compile(optimizer=sgd, \n",
    "                   loss='mean_squared_error', # metrica de erro\n",
    "                   metrics=['accuracy']) # metrica de sucesso\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=50)\n",
    "\n",
    "history = classifier.fit(X_train, y_train,\n",
    "                         epochs=100000, # quantidade de epocas que a rede neural vai executar\n",
    "                         verbose=1,\n",
    "                         shuffle=True, # utilizado para misturar as amostras a cada epoca\n",
    "                         validation_data=(X_validation, y_validation),\n",
    "                         callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1920/1962 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.053948230156627516, 0.93323139653414888]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.evaluate(X_test, y_test)\n",
    "# print (test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusão\n",
      "[[1803   95]\n",
      " [   9   55]]\n",
      "\n",
      "Train Loss:       0.0589\n",
      "Validation Loss:  0.0834\n",
      "Accuracy:         0.9470\n",
      "MSE:              0.0530\n",
      "AUROC:            0.9269\n"
     ]
    }
   ],
   "source": [
    "plot_training_error_curves(history, graficoName + \"(10)\")\n",
    "plot_training_acc_curves(history, graficoName + \"(10)\")\n",
    "\n",
    "## Fazer predições no conjunto de teste\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred_class = classifier.predict_classes(X_test, verbose=0)\n",
    "\n",
    "## Matriz de confusão\n",
    "print('Matriz de confusão')\n",
    "print(confusion_matrix(y_test, y_pred_class))\n",
    "\n",
    "## Computar métricas de desempenho\n",
    "losses = extract_final_losses(history)\n",
    "print()\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Train Loss:\", value=losses['train_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Validation Loss:\", value=losses['val_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Accuracy:\", value=accuracy_score(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"MSE:\", value=mean_squared_error(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"AUROC:\", value=roc_auc_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNA 2 (20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7592 samples, validate on 3796 samples\n",
      "Epoch 1/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.4148 - acc: 0.5308 - val_loss: 0.3987 - val_acc: 0.5551\n",
      "Epoch 2/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.3234 - acc: 0.5902 - val_loss: 0.3586 - val_acc: 0.5761\n",
      "Epoch 3/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.2664 - acc: 0.6677 - val_loss: 0.3255 - val_acc: 0.6420\n",
      "Epoch 4/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.2291 - acc: 0.7117 - val_loss: 0.2905 - val_acc: 0.7065\n",
      "Epoch 5/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1928 - acc: 0.7480 - val_loss: 0.2477 - val_acc: 0.7221\n",
      "Epoch 6/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1645 - acc: 0.7931 - val_loss: 0.2203 - val_acc: 0.7885\n",
      "Epoch 7/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1381 - acc: 0.8143 - val_loss: 0.2046 - val_acc: 0.7919\n",
      "Epoch 8/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1251 - acc: 0.8322 - val_loss: 0.1908 - val_acc: 0.7972\n",
      "Epoch 9/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1179 - acc: 0.8460 - val_loss: 0.1810 - val_acc: 0.7956\n",
      "Epoch 10/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1134 - acc: 0.8631 - val_loss: 0.1731 - val_acc: 0.8093\n",
      "Epoch 11/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1103 - acc: 0.8650 - val_loss: 0.1672 - val_acc: 0.8322\n",
      "Epoch 12/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1079 - acc: 0.8676 - val_loss: 0.1619 - val_acc: 0.8393\n",
      "Epoch 13/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1061 - acc: 0.8738 - val_loss: 0.1580 - val_acc: 0.8467\n",
      "Epoch 14/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1046 - acc: 0.8776 - val_loss: 0.1545 - val_acc: 0.8456\n",
      "Epoch 15/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1033 - acc: 0.8818 - val_loss: 0.1515 - val_acc: 0.8462\n",
      "Epoch 16/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1022 - acc: 0.8820 - val_loss: 0.1490 - val_acc: 0.8469\n",
      "Epoch 17/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1011 - acc: 0.8824 - val_loss: 0.1467 - val_acc: 0.8469\n",
      "Epoch 18/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1001 - acc: 0.8833 - val_loss: 0.1449 - val_acc: 0.8467\n",
      "Epoch 19/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0992 - acc: 0.8833 - val_loss: 0.1432 - val_acc: 0.8469\n",
      "Epoch 20/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0985 - acc: 0.8833 - val_loss: 0.1414 - val_acc: 0.8472\n",
      "Epoch 21/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0978 - acc: 0.8832 - val_loss: 0.1396 - val_acc: 0.8475\n",
      "Epoch 22/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0972 - acc: 0.8834 - val_loss: 0.1385 - val_acc: 0.8475\n",
      "Epoch 23/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0966 - acc: 0.8846 - val_loss: 0.1376 - val_acc: 0.8475\n",
      "Epoch 24/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0961 - acc: 0.8850 - val_loss: 0.1364 - val_acc: 0.8475\n",
      "Epoch 25/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0956 - acc: 0.8913 - val_loss: 0.1351 - val_acc: 0.8475\n",
      "Epoch 26/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0951 - acc: 0.8927 - val_loss: 0.1339 - val_acc: 0.8475\n",
      "Epoch 27/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0947 - acc: 0.8929 - val_loss: 0.1329 - val_acc: 0.8475\n",
      "Epoch 28/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0943 - acc: 0.8927 - val_loss: 0.1320 - val_acc: 0.8556\n",
      "Epoch 29/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0940 - acc: 0.8928 - val_loss: 0.1313 - val_acc: 0.8556\n",
      "Epoch 30/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0936 - acc: 0.8929 - val_loss: 0.1305 - val_acc: 0.8556\n",
      "Epoch 31/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0933 - acc: 0.8930 - val_loss: 0.1295 - val_acc: 0.8559\n",
      "Epoch 32/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0930 - acc: 0.8928 - val_loss: 0.1288 - val_acc: 0.8559\n",
      "Epoch 33/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0927 - acc: 0.8928 - val_loss: 0.1280 - val_acc: 0.8559\n",
      "Epoch 34/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0924 - acc: 0.8929 - val_loss: 0.1275 - val_acc: 0.8556\n",
      "Epoch 35/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0922 - acc: 0.8933 - val_loss: 0.1271 - val_acc: 0.8559\n",
      "Epoch 36/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0919 - acc: 0.8929 - val_loss: 0.1265 - val_acc: 0.8562\n",
      "Epoch 37/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0917 - acc: 0.8932 - val_loss: 0.1259 - val_acc: 0.8562\n",
      "Epoch 38/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0915 - acc: 0.8936 - val_loss: 0.1254 - val_acc: 0.8564\n",
      "Epoch 39/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0912 - acc: 0.8937 - val_loss: 0.1248 - val_acc: 0.8564\n",
      "Epoch 40/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0910 - acc: 0.8933 - val_loss: 0.1240 - val_acc: 0.8564\n",
      "Epoch 41/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0908 - acc: 0.8937 - val_loss: 0.1235 - val_acc: 0.8564\n",
      "Epoch 42/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0906 - acc: 0.8944 - val_loss: 0.1233 - val_acc: 0.8641\n",
      "Epoch 43/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0904 - acc: 0.8936 - val_loss: 0.1228 - val_acc: 0.8643\n",
      "Epoch 44/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0902 - acc: 0.8940 - val_loss: 0.1224 - val_acc: 0.8649\n",
      "Epoch 45/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0900 - acc: 0.8934 - val_loss: 0.1220 - val_acc: 0.8649\n",
      "Epoch 46/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0898 - acc: 0.8940 - val_loss: 0.1216 - val_acc: 0.8654\n",
      "Epoch 47/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0896 - acc: 0.8915 - val_loss: 0.1211 - val_acc: 0.8654\n",
      "Epoch 48/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0894 - acc: 0.8916 - val_loss: 0.1208 - val_acc: 0.8654\n",
      "Epoch 49/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0892 - acc: 0.8932 - val_loss: 0.1204 - val_acc: 0.8651\n",
      "Epoch 50/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0890 - acc: 0.8924 - val_loss: 0.1198 - val_acc: 0.8651\n",
      "Epoch 51/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0889 - acc: 0.8929 - val_loss: 0.1195 - val_acc: 0.8651\n",
      "Epoch 52/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0887 - acc: 0.8937 - val_loss: 0.1190 - val_acc: 0.8659\n",
      "Epoch 53/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0885 - acc: 0.8907 - val_loss: 0.1187 - val_acc: 0.8656\n",
      "Epoch 54/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0883 - acc: 0.8911 - val_loss: 0.1183 - val_acc: 0.8662\n",
      "Epoch 55/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0882 - acc: 0.8905 - val_loss: 0.1181 - val_acc: 0.8656\n",
      "Epoch 56/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0880 - acc: 0.8920 - val_loss: 0.1178 - val_acc: 0.8656\n",
      "Epoch 57/100000\n",
      "7592/7592 [==============================] - ETA: 0s - loss: 0.0876 - acc: 0.892 - 0s - loss: 0.0878 - acc: 0.8921 - val_loss: 0.1174 - val_acc: 0.8662\n",
      "Epoch 58/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0877 - acc: 0.8913 - val_loss: 0.1170 - val_acc: 0.8662\n",
      "Epoch 59/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0875 - acc: 0.8929 - val_loss: 0.1167 - val_acc: 0.8664\n",
      "Epoch 60/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0874 - acc: 0.8919 - val_loss: 0.1163 - val_acc: 0.8662\n",
      "Epoch 61/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0873 - acc: 0.8921 - val_loss: 0.1160 - val_acc: 0.8662\n",
      "Epoch 62/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0871 - acc: 0.8929 - val_loss: 0.1158 - val_acc: 0.8662\n",
      "Epoch 63/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0870 - acc: 0.8961 - val_loss: 0.1156 - val_acc: 0.8656\n",
      "Epoch 64/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0868 - acc: 0.8957 - val_loss: 0.1154 - val_acc: 0.8656\n",
      "Epoch 65/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0867 - acc: 0.8961 - val_loss: 0.1149 - val_acc: 0.8662\n",
      "Epoch 66/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0866 - acc: 0.8946 - val_loss: 0.1148 - val_acc: 0.8662\n",
      "Epoch 67/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0864 - acc: 0.8957 - val_loss: 0.1144 - val_acc: 0.8662\n",
      "Epoch 68/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0863 - acc: 0.8992 - val_loss: 0.1142 - val_acc: 0.8662\n",
      "Epoch 69/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0862 - acc: 0.8979 - val_loss: 0.1140 - val_acc: 0.8662\n",
      "Epoch 70/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0860 - acc: 0.8983 - val_loss: 0.1138 - val_acc: 0.8664\n",
      "Epoch 71/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0859 - acc: 0.8992 - val_loss: 0.1134 - val_acc: 0.8664\n",
      "Epoch 72/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0858 - acc: 0.8996 - val_loss: 0.1132 - val_acc: 0.8664\n",
      "Epoch 73/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0856 - acc: 0.8971 - val_loss: 0.1131 - val_acc: 0.8664\n",
      "Epoch 74/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0855 - acc: 0.9000 - val_loss: 0.1128 - val_acc: 0.8664\n",
      "Epoch 75/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0854 - acc: 0.8999 - val_loss: 0.1125 - val_acc: 0.8667\n",
      "Epoch 76/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0853 - acc: 0.8998 - val_loss: 0.1125 - val_acc: 0.8667\n",
      "Epoch 77/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0852 - acc: 0.8998 - val_loss: 0.1121 - val_acc: 0.8667\n",
      "Epoch 78/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0851 - acc: 0.8998 - val_loss: 0.1119 - val_acc: 0.8667\n",
      "Epoch 79/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0849 - acc: 0.8995 - val_loss: 0.1118 - val_acc: 0.8751\n",
      "Epoch 80/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0848 - acc: 0.9000 - val_loss: 0.1116 - val_acc: 0.8751\n",
      "Epoch 81/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0847 - acc: 0.9003 - val_loss: 0.1115 - val_acc: 0.8751\n",
      "Epoch 82/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0846 - acc: 0.9002 - val_loss: 0.1112 - val_acc: 0.8751\n",
      "Epoch 83/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0845 - acc: 0.9000 - val_loss: 0.1109 - val_acc: 0.8751\n",
      "Epoch 84/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0844 - acc: 0.9003 - val_loss: 0.1107 - val_acc: 0.8754\n",
      "Epoch 85/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0843 - acc: 0.9006 - val_loss: 0.1106 - val_acc: 0.8754\n",
      "Epoch 86/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0842 - acc: 0.9007 - val_loss: 0.1105 - val_acc: 0.8754\n",
      "Epoch 87/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0841 - acc: 0.9017 - val_loss: 0.1103 - val_acc: 0.8754\n",
      "Epoch 88/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0840 - acc: 0.9013 - val_loss: 0.1102 - val_acc: 0.8754\n",
      "Epoch 89/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0839 - acc: 0.9023 - val_loss: 0.1101 - val_acc: 0.8754\n",
      "Epoch 90/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0838 - acc: 0.9037 - val_loss: 0.1099 - val_acc: 0.8754\n",
      "Epoch 91/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0837 - acc: 0.9046 - val_loss: 0.1097 - val_acc: 0.8757\n",
      "Epoch 92/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0836 - acc: 0.9049 - val_loss: 0.1095 - val_acc: 0.8757\n",
      "Epoch 93/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0835 - acc: 0.9049 - val_loss: 0.1094 - val_acc: 0.8754\n",
      "Epoch 94/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0834 - acc: 0.9042 - val_loss: 0.1092 - val_acc: 0.8754\n",
      "Epoch 95/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0833 - acc: 0.9049 - val_loss: 0.1091 - val_acc: 0.8751\n",
      "Epoch 96/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0832 - acc: 0.9050 - val_loss: 0.1088 - val_acc: 0.8754\n",
      "Epoch 97/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0831 - acc: 0.9044 - val_loss: 0.1088 - val_acc: 0.8751\n",
      "Epoch 98/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0831 - acc: 0.9054 - val_loss: 0.1086 - val_acc: 0.8751\n",
      "Epoch 99/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0830 - acc: 0.9053 - val_loss: 0.1084 - val_acc: 0.8751\n",
      "Epoch 100/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0829 - acc: 0.9053 - val_loss: 0.1083 - val_acc: 0.8754\n",
      "Epoch 101/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0828 - acc: 0.9054 - val_loss: 0.1082 - val_acc: 0.8754\n",
      "Epoch 102/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0827 - acc: 0.9057 - val_loss: 0.1080 - val_acc: 0.8754\n",
      "Epoch 103/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0826 - acc: 0.9057 - val_loss: 0.1078 - val_acc: 0.8754\n",
      "Epoch 104/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0825 - acc: 0.9050 - val_loss: 0.1077 - val_acc: 0.8757\n",
      "Epoch 105/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0825 - acc: 0.9057 - val_loss: 0.1076 - val_acc: 0.8757\n",
      "Epoch 106/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0824 - acc: 0.9056 - val_loss: 0.1074 - val_acc: 0.8757\n",
      "Epoch 107/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0823 - acc: 0.9056 - val_loss: 0.1073 - val_acc: 0.8757\n",
      "Epoch 108/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0822 - acc: 0.9058 - val_loss: 0.1073 - val_acc: 0.8751\n",
      "Epoch 109/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0822 - acc: 0.9053 - val_loss: 0.1070 - val_acc: 0.8754\n",
      "Epoch 110/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0821 - acc: 0.9054 - val_loss: 0.1068 - val_acc: 0.8754\n",
      "Epoch 111/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0820 - acc: 0.9062 - val_loss: 0.1067 - val_acc: 0.8754\n",
      "Epoch 112/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0819 - acc: 0.9053 - val_loss: 0.1066 - val_acc: 0.8754\n",
      "Epoch 113/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0818 - acc: 0.9060 - val_loss: 0.1065 - val_acc: 0.8754\n",
      "Epoch 114/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0818 - acc: 0.9056 - val_loss: 0.1063 - val_acc: 0.8754\n",
      "Epoch 115/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0817 - acc: 0.9054 - val_loss: 0.1062 - val_acc: 0.8754\n",
      "Epoch 116/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0816 - acc: 0.9053 - val_loss: 0.1060 - val_acc: 0.8754\n",
      "Epoch 117/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0816 - acc: 0.9053 - val_loss: 0.1059 - val_acc: 0.8754\n",
      "Epoch 118/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0815 - acc: 0.9060 - val_loss: 0.1059 - val_acc: 0.8751\n",
      "Epoch 119/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0814 - acc: 0.9056 - val_loss: 0.1059 - val_acc: 0.8754\n",
      "Epoch 120/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0813 - acc: 0.9062 - val_loss: 0.1058 - val_acc: 0.8754\n",
      "Epoch 121/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0813 - acc: 0.9057 - val_loss: 0.1055 - val_acc: 0.8754\n",
      "Epoch 122/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0812 - acc: 0.9057 - val_loss: 0.1054 - val_acc: 0.8754\n",
      "Epoch 123/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0811 - acc: 0.9057 - val_loss: 0.1053 - val_acc: 0.8757\n",
      "Epoch 124/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0811 - acc: 0.9057 - val_loss: 0.1051 - val_acc: 0.8757\n",
      "Epoch 125/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0810 - acc: 0.9070 - val_loss: 0.1050 - val_acc: 0.8759\n",
      "Epoch 126/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0810 - acc: 0.9063 - val_loss: 0.1050 - val_acc: 0.8759\n",
      "Epoch 127/100000\n",
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      "Epoch 249/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0750 - acc: 0.9103 - val_loss: 0.0973 - val_acc: 0.8862\n",
      "Epoch 250/100000\n",
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      "Epoch 255/100000\n",
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      "Epoch 256/100000\n",
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      "Epoch 257/100000\n",
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      "Epoch 258/100000\n",
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      "Epoch 259/100000\n",
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      "Epoch 260/100000\n",
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      "Epoch 261/100000\n",
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      "Epoch 262/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0745 - acc: 0.9100 - val_loss: 0.0969 - val_acc: 0.8862\n",
      "Epoch 263/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0745 - acc: 0.9096 - val_loss: 0.0968 - val_acc: 0.8862\n",
      "Epoch 264/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0745 - acc: 0.9096 - val_loss: 0.0968 - val_acc: 0.8862\n",
      "Epoch 265/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0744 - acc: 0.9094 - val_loss: 0.0967 - val_acc: 0.8862\n",
      "Epoch 266/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0744 - acc: 0.9094 - val_loss: 0.0966 - val_acc: 0.8862\n",
      "Epoch 267/100000\n",
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      "Epoch 268/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0743 - acc: 0.9094 - val_loss: 0.0966 - val_acc: 0.8865\n",
      "Epoch 269/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0743 - acc: 0.9099 - val_loss: 0.0966 - val_acc: 0.8865\n",
      "Epoch 270/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0743 - acc: 0.9096 - val_loss: 0.0965 - val_acc: 0.8865\n",
      "Epoch 271/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0742 - acc: 0.9096 - val_loss: 0.0965 - val_acc: 0.8862\n",
      "Epoch 272/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0742 - acc: 0.9094 - val_loss: 0.0964 - val_acc: 0.8865\n",
      "Epoch 273/100000\n",
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      "Epoch 274/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0741 - acc: 0.9095 - val_loss: 0.0964 - val_acc: 0.8865\n",
      "Epoch 275/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0741 - acc: 0.9094 - val_loss: 0.0963 - val_acc: 0.8865\n",
      "Epoch 276/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0741 - acc: 0.9095 - val_loss: 0.0963 - val_acc: 0.8865\n",
      "Epoch 277/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0740 - acc: 0.9094 - val_loss: 0.0963 - val_acc: 0.8865\n",
      "Epoch 278/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0740 - acc: 0.9094 - val_loss: 0.0963 - val_acc: 0.8865\n",
      "Epoch 279/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0740 - acc: 0.9096 - val_loss: 0.0963 - val_acc: 0.8865\n",
      "Epoch 280/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0740 - acc: 0.9096 - val_loss: 0.0963 - val_acc: 0.8865\n",
      "Epoch 281/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0739 - acc: 0.9095 - val_loss: 0.0962 - val_acc: 0.8865\n",
      "Epoch 282/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0739 - acc: 0.9095 - val_loss: 0.0962 - val_acc: 0.8865\n",
      "Epoch 283/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0739 - acc: 0.9096 - val_loss: 0.0961 - val_acc: 0.8865\n",
      "Epoch 284/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0738 - acc: 0.9096 - val_loss: 0.0961 - val_acc: 0.8865\n",
      "Epoch 285/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0738 - acc: 0.9096 - val_loss: 0.0961 - val_acc: 0.8865\n",
      "Epoch 286/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0738 - acc: 0.9096 - val_loss: 0.0961 - val_acc: 0.8865\n",
      "Epoch 287/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0737 - acc: 0.9096 - val_loss: 0.0960 - val_acc: 0.8865\n",
      "Epoch 288/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0737 - acc: 0.9096 - val_loss: 0.0960 - val_acc: 0.8865\n",
      "Epoch 289/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0737 - acc: 0.9094 - val_loss: 0.0960 - val_acc: 0.8865\n",
      "Epoch 290/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0736 - acc: 0.9096 - val_loss: 0.0960 - val_acc: 0.8865\n",
      "Epoch 291/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0736 - acc: 0.9095 - val_loss: 0.0959 - val_acc: 0.8865\n",
      "Epoch 292/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0736 - acc: 0.9096 - val_loss: 0.0959 - val_acc: 0.8865\n",
      "Epoch 293/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0736 - acc: 0.9096 - val_loss: 0.0959 - val_acc: 0.8865\n",
      "Epoch 294/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0735 - acc: 0.9096 - val_loss: 0.0958 - val_acc: 0.8865\n",
      "Epoch 295/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0735 - acc: 0.9096 - val_loss: 0.0958 - val_acc: 0.8865\n",
      "Epoch 296/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0735 - acc: 0.9096 - val_loss: 0.0958 - val_acc: 0.8865\n",
      "Epoch 297/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0734 - acc: 0.9096 - val_loss: 0.0958 - val_acc: 0.8865\n",
      "Epoch 298/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0734 - acc: 0.9098 - val_loss: 0.0958 - val_acc: 0.8862\n",
      "Epoch 299/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0734 - acc: 0.9096 - val_loss: 0.0957 - val_acc: 0.8865\n",
      "Epoch 300/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0733 - acc: 0.9096 - val_loss: 0.0957 - val_acc: 0.8865\n",
      "Epoch 301/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0733 - acc: 0.9095 - val_loss: 0.0956 - val_acc: 0.8867\n",
      "Epoch 302/100000\n",
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      "Epoch 303/100000\n",
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      "Epoch 304/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0732 - acc: 0.9098 - val_loss: 0.0956 - val_acc: 0.8867\n",
      "Epoch 305/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0732 - acc: 0.9098 - val_loss: 0.0956 - val_acc: 0.8865\n",
      "Epoch 306/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0732 - acc: 0.9096 - val_loss: 0.0956 - val_acc: 0.8865\n",
      "Epoch 307/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0731 - acc: 0.9095 - val_loss: 0.0955 - val_acc: 0.8867\n",
      "Epoch 308/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0731 - acc: 0.9095 - val_loss: 0.0955 - val_acc: 0.8870\n",
      "Epoch 309/100000\n",
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      "Epoch 310/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0731 - acc: 0.9098 - val_loss: 0.0955 - val_acc: 0.8870\n",
      "Epoch 311/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0730 - acc: 0.9096 - val_loss: 0.0955 - val_acc: 0.8870\n",
      "Epoch 312/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0730 - acc: 0.9098 - val_loss: 0.0955 - val_acc: 0.8870\n",
      "Epoch 313/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0730 - acc: 0.9099 - val_loss: 0.0954 - val_acc: 0.8867\n",
      "Epoch 314/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0729 - acc: 0.9098 - val_loss: 0.0954 - val_acc: 0.8867\n",
      "Epoch 315/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0729 - acc: 0.9096 - val_loss: 0.0954 - val_acc: 0.8867\n",
      "Epoch 316/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0729 - acc: 0.9096 - val_loss: 0.0953 - val_acc: 0.8870\n",
      "Epoch 317/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0729 - acc: 0.9099 - val_loss: 0.0953 - val_acc: 0.8867\n",
      "Epoch 318/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0728 - acc: 0.9096 - val_loss: 0.0953 - val_acc: 0.8867\n",
      "Epoch 319/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0728 - acc: 0.9099 - val_loss: 0.0952 - val_acc: 0.8867\n",
      "Epoch 320/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0728 - acc: 0.9089 - val_loss: 0.0952 - val_acc: 0.8867\n",
      "Epoch 321/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0727 - acc: 0.9094 - val_loss: 0.0952 - val_acc: 0.8867\n",
      "Epoch 322/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0727 - acc: 0.9085 - val_loss: 0.0952 - val_acc: 0.8870\n",
      "Epoch 323/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0727 - acc: 0.9074 - val_loss: 0.0952 - val_acc: 0.8867\n",
      "Epoch 324/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0727 - acc: 0.9078 - val_loss: 0.0952 - val_acc: 0.8870\n",
      "Epoch 325/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0726 - acc: 0.9062 - val_loss: 0.0952 - val_acc: 0.8865\n",
      "Epoch 326/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0726 - acc: 0.9079 - val_loss: 0.0951 - val_acc: 0.8872\n",
      "Epoch 327/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0726 - acc: 0.9066 - val_loss: 0.0951 - val_acc: 0.8875\n",
      "Epoch 328/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0725 - acc: 0.9061 - val_loss: 0.0951 - val_acc: 0.8872\n",
      "Epoch 329/100000\n",
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      "Epoch 330/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0725 - acc: 0.9066 - val_loss: 0.0950 - val_acc: 0.8870\n",
      "Epoch 331/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0725 - acc: 0.9063 - val_loss: 0.0950 - val_acc: 0.8862\n",
      "Epoch 332/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0724 - acc: 0.9057 - val_loss: 0.0950 - val_acc: 0.8862\n",
      "Epoch 333/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0724 - acc: 0.9079 - val_loss: 0.0950 - val_acc: 0.8870\n",
      "Epoch 334/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0724 - acc: 0.9090 - val_loss: 0.0950 - val_acc: 0.8870\n",
      "Epoch 335/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0723 - acc: 0.9092 - val_loss: 0.0949 - val_acc: 0.8870\n",
      "Epoch 336/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0723 - acc: 0.9081 - val_loss: 0.0949 - val_acc: 0.8870\n",
      "Epoch 337/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0723 - acc: 0.9077 - val_loss: 0.0949 - val_acc: 0.8870\n",
      "Epoch 338/100000\n",
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      "Epoch 339/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0722 - acc: 0.9099 - val_loss: 0.0949 - val_acc: 0.8867\n",
      "Epoch 340/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0722 - acc: 0.9096 - val_loss: 0.0949 - val_acc: 0.8867\n",
      "Epoch 341/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0722 - acc: 0.9106 - val_loss: 0.0949 - val_acc: 0.8867\n",
      "Epoch 342/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0721 - acc: 0.9103 - val_loss: 0.0948 - val_acc: 0.8867\n",
      "Epoch 343/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0721 - acc: 0.9121 - val_loss: 0.0948 - val_acc: 0.8867\n",
      "Epoch 344/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0721 - acc: 0.9128 - val_loss: 0.0948 - val_acc: 0.8867\n",
      "Epoch 345/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0721 - acc: 0.9129 - val_loss: 0.0948 - val_acc: 0.8870\n",
      "Epoch 346/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0720 - acc: 0.9137 - val_loss: 0.0948 - val_acc: 0.8867\n",
      "Epoch 347/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0720 - acc: 0.9135 - val_loss: 0.0947 - val_acc: 0.8870\n",
      "Epoch 348/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0720 - acc: 0.9136 - val_loss: 0.0947 - val_acc: 0.8870\n",
      "Epoch 349/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0720 - acc: 0.9136 - val_loss: 0.0947 - val_acc: 0.8870\n",
      "Epoch 350/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0719 - acc: 0.9136 - val_loss: 0.0947 - val_acc: 0.8870\n",
      "Epoch 351/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0719 - acc: 0.9135 - val_loss: 0.0947 - val_acc: 0.8870\n",
      "Epoch 352/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0719 - acc: 0.9136 - val_loss: 0.0947 - val_acc: 0.8867\n",
      "Epoch 353/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0718 - acc: 0.9132 - val_loss: 0.0947 - val_acc: 0.8870\n",
      "Epoch 354/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0718 - acc: 0.9137 - val_loss: 0.0946 - val_acc: 0.8870\n",
      "Epoch 355/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0718 - acc: 0.9136 - val_loss: 0.0946 - val_acc: 0.8867\n",
      "Epoch 356/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0718 - acc: 0.9136 - val_loss: 0.0946 - val_acc: 0.8867\n",
      "Epoch 357/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0717 - acc: 0.9133 - val_loss: 0.0946 - val_acc: 0.88670.\n",
      "Epoch 358/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0717 - acc: 0.9137 - val_loss: 0.0946 - val_acc: 0.8867\n",
      "Epoch 359/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0717 - acc: 0.9135 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 360/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0717 - acc: 0.9135 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 361/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0716 - acc: 0.9136 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 362/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0716 - acc: 0.9133 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 363/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0716 - acc: 0.9136 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 364/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0716 - acc: 0.9139 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 365/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0715 - acc: 0.9133 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 366/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0715 - acc: 0.9136 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 367/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0715 - acc: 0.9140 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 368/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0715 - acc: 0.9135 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 369/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0714 - acc: 0.9139 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 370/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0714 - acc: 0.9135 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 371/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0714 - acc: 0.9136 - val_loss: 0.0945 - val_acc: 0.8867\n",
      "Epoch 372/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0714 - acc: 0.9137 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 373/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0713 - acc: 0.9133 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 374/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0713 - acc: 0.9137 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 375/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0713 - acc: 0.9140 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 376/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0713 - acc: 0.9140 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 377/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9141 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 378/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9135 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 379/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9139 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 380/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9136 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 381/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9143 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 382/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0711 - acc: 0.9140 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 383/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0711 - acc: 0.9141 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 384/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0711 - acc: 0.9137 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 385/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0711 - acc: 0.9141 - val_loss: 0.0944 - val_acc: 0.8867\n",
      "Epoch 386/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0711 - acc: 0.9140 - val_loss: 0.0944 - val_acc: 0.8870\n",
      "Epoch 387/100000\n",
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      "Epoch 487/100000\n",
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      "Epoch 494/100000\n",
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      "Epoch 495/100000\n",
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      "Epoch 496/100000\n",
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      "Epoch 497/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0691 - acc: 0.9195 - val_loss: 0.0939 - val_acc: 0.8886\n",
      "Epoch 498/100000\n",
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      "Epoch 500/100000\n",
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      "Epoch 501/100000\n",
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      "Epoch 503/100000\n",
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      "Epoch 512/100000\n",
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      "7592/7592 [==============================] - 0s - loss: 0.0688 - acc: 0.9198 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 517/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0687 - acc: 0.9198 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 518/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0687 - acc: 0.9199 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 519/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0687 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 520/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0687 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 521/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0687 - acc: 0.9199 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 522/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0687 - acc: 0.9202 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 523/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9199 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 524/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9199 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 525/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 526/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9199 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 527/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 528/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 529/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9197 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 530/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0685 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 531/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0685 - acc: 0.9203 - val_loss: 0.0939 - val_acc: 0.8886\n",
      "Epoch 532/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0685 - acc: 0.9198 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 533/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0685 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 534/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0685 - acc: 0.9202 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 535/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0685 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 536/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0684 - acc: 0.9202 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 537/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0684 - acc: 0.9199 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 538/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0684 - acc: 0.9202 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 539/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0684 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 540/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0684 - acc: 0.9199 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 541/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0683 - acc: 0.9202 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 542/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0683 - acc: 0.9197 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 543/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0683 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 544/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0683 - acc: 0.9200 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 545/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0683 - acc: 0.9202 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 546/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0683 - acc: 0.9200 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 547/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0682 - acc: 0.9198 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 548/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0682 - acc: 0.9202 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 549/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0682 - acc: 0.9200 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 550/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0682 - acc: 0.9200 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 551/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0682 - acc: 0.9202 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 552/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0682 - acc: 0.9203 - val_loss: 0.0939 - val_acc: 0.8888\n",
      "Epoch 553/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0681 - acc: 0.9199 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 554/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0681 - acc: 0.9202 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 555/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0681 - acc: 0.9200 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 556/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0681 - acc: 0.9200 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 557/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0681 - acc: 0.9200 - val_loss: 0.0940 - val_acc: 0.8888\n"
     ]
    }
   ],
   "source": [
    "# Aqui criamos o esboço da rede.\n",
    "classifier = Sequential()\n",
    "\n",
    "classifier.add(Dense(20, activation='relu', input_dim=6)) # camada escondida\n",
    "classifier.add(Dense(1, activation='relu')) # \n",
    "\n",
    "sgd = optimizers.SGD(lr=learnRate)\n",
    "classifier.compile(optimizer=sgd, \n",
    "                   loss='mean_squared_error', # metrica de erro\n",
    "                   metrics=['accuracy']) # metrica de sucesso\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=50)\n",
    "\n",
    "history = classifier.fit(X_train, y_train,\n",
    "                         epochs=100000, # quantidade de epocas que a rede neural vai executar\n",
    "                         verbose=1,\n",
    "                         shuffle=True, # utilizado para misturar as amostras a cada epoca\n",
    "                         validation_data=(X_validation, y_validation),\n",
    "                         callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1728/1962 [=========================>....] - ETA: 0s[0.045346897361262739, 0.93832823637185603]\n"
     ]
    }
   ],
   "source": [
    "test_loss = classifier.evaluate(X_test, y_test)\n",
    "print (test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusão\n",
      "[[1784  114]\n",
      " [   7   57]]\n",
      "\n",
      "Train Loss:       0.0689\n",
      "Validation Loss:  0.0938\n",
      "Accuracy:         0.9383\n",
      "MSE:              0.0617\n",
      "AUROC:            0.9479\n"
     ]
    }
   ],
   "source": [
    "plot_training_error_curves(history, graficoName + \"(20)\")\n",
    "plot_training_acc_curves(history, graficoName + \"(20)\")\n",
    "\n",
    "## Fazer predições no conjunto de teste\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred_class = classifier.predict_classes(X_test, verbose=0)\n",
    "\n",
    "## Matriz de confusão\n",
    "print('Matriz de confusão')\n",
    "print(confusion_matrix(y_test, y_pred_class))\n",
    "\n",
    "## Computar métricas de desempenho\n",
    "losses = extract_final_losses(history)\n",
    "print()\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Train Loss:\", value=losses['train_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Validation Loss:\", value=losses['val_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Accuracy:\", value=accuracy_score(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"MSE:\", value=mean_squared_error(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"AUROC:\", value=roc_auc_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNA 3 (10, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7592 samples, validate on 3796 samples\n",
      "Epoch 1/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.2915 - acc: 0.6067 - val_loss: 0.2657 - val_acc: 0.6304\n",
      "Epoch 2/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.2166 - acc: 0.6974 - val_loss: 0.2288 - val_acc: 0.6812\n",
      "Epoch 3/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1865 - acc: 0.7346 - val_loss: 0.2054 - val_acc: 0.7047\n",
      "Epoch 4/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1680 - acc: 0.7640 - val_loss: 0.1898 - val_acc: 0.7487\n",
      "Epoch 5/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1553 - acc: 0.7916 - val_loss: 0.1790 - val_acc: 0.7779\n",
      "Epoch 6/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1463 - acc: 0.8114 - val_loss: 0.1709 - val_acc: 0.7853\n",
      "Epoch 7/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1394 - acc: 0.8211 - val_loss: 0.1650 - val_acc: 0.7908\n",
      "Epoch 8/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1340 - acc: 0.8331 - val_loss: 0.1604 - val_acc: 0.8058\n",
      "Epoch 9/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1295 - acc: 0.8385 - val_loss: 0.1572 - val_acc: 0.8359\n",
      "Epoch 10/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1258 - acc: 0.8494 - val_loss: 0.1538 - val_acc: 0.8438\n",
      "Epoch 11/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1224 - acc: 0.8526 - val_loss: 0.1517 - val_acc: 0.8514\n",
      "Epoch 12/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1195 - acc: 0.8575 - val_loss: 0.1499 - val_acc: 0.8506\n",
      "Epoch 13/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1170 - acc: 0.8630 - val_loss: 0.1481 - val_acc: 0.8414\n",
      "Epoch 14/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1149 - acc: 0.8679 - val_loss: 0.1468 - val_acc: 0.8411\n",
      "Epoch 15/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1130 - acc: 0.8718 - val_loss: 0.1459 - val_acc: 0.8480\n",
      "Epoch 16/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1114 - acc: 0.8780 - val_loss: 0.1446 - val_acc: 0.8469\n",
      "Epoch 17/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1100 - acc: 0.8797 - val_loss: 0.1435 - val_acc: 0.8464\n",
      "Epoch 18/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1087 - acc: 0.8832 - val_loss: 0.1428 - val_acc: 0.8459\n",
      "Epoch 19/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1075 - acc: 0.8866 - val_loss: 0.1419 - val_acc: 0.8617\n",
      "Epoch 20/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1064 - acc: 0.8857 - val_loss: 0.1416 - val_acc: 0.8606\n",
      "Epoch 21/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1054 - acc: 0.8849 - val_loss: 0.1408 - val_acc: 0.8604\n",
      "Epoch 22/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1045 - acc: 0.8829 - val_loss: 0.1399 - val_acc: 0.8609\n",
      "Epoch 23/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1037 - acc: 0.8828 - val_loss: 0.1396 - val_acc: 0.8606\n",
      "Epoch 24/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1029 - acc: 0.8825 - val_loss: 0.1387 - val_acc: 0.8614\n",
      "Epoch 25/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1022 - acc: 0.8830 - val_loss: 0.1380 - val_acc: 0.8617\n",
      "Epoch 26/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1016 - acc: 0.8845 - val_loss: 0.1375 - val_acc: 0.8620\n",
      "Epoch 27/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1010 - acc: 0.8855 - val_loss: 0.1371 - val_acc: 0.8614\n",
      "Epoch 28/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1005 - acc: 0.8892 - val_loss: 0.1362 - val_acc: 0.8617\n",
      "Epoch 29/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1000 - acc: 0.8892 - val_loss: 0.1355 - val_acc: 0.8530\n",
      "Epoch 30/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0995 - acc: 0.8907 - val_loss: 0.1351 - val_acc: 0.8609\n",
      "Epoch 31/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0990 - acc: 0.8955 - val_loss: 0.1340 - val_acc: 0.8530\n",
      "Epoch 32/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0986 - acc: 0.8944 - val_loss: 0.1340 - val_acc: 0.8609\n",
      "Epoch 33/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0982 - acc: 0.8971 - val_loss: 0.1329 - val_acc: 0.8609\n",
      "Epoch 34/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0978 - acc: 0.8973 - val_loss: 0.1327 - val_acc: 0.8609\n",
      "Epoch 35/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0974 - acc: 0.8974 - val_loss: 0.1316 - val_acc: 0.8609\n",
      "Epoch 36/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0970 - acc: 0.8967 - val_loss: 0.1314 - val_acc: 0.8606\n",
      "Epoch 37/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0966 - acc: 0.8971 - val_loss: 0.1305 - val_acc: 0.8609\n",
      "Epoch 38/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0962 - acc: 0.8974 - val_loss: 0.1303 - val_acc: 0.8604\n",
      "Epoch 39/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0959 - acc: 0.8981 - val_loss: 0.1294 - val_acc: 0.8606\n",
      "Epoch 40/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0955 - acc: 0.8998 - val_loss: 0.1287 - val_acc: 0.8606\n",
      "Epoch 41/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0952 - acc: 0.9013 - val_loss: 0.1283 - val_acc: 0.8606\n",
      "Epoch 42/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0949 - acc: 0.9015 - val_loss: 0.1276 - val_acc: 0.8604\n",
      "Epoch 43/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0945 - acc: 0.9020 - val_loss: 0.1272 - val_acc: 0.8601\n",
      "Epoch 44/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0942 - acc: 0.9017 - val_loss: 0.1261 - val_acc: 0.8609\n",
      "Epoch 45/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0938 - acc: 0.9019 - val_loss: 0.1247 - val_acc: 0.8612\n",
      "Epoch 46/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0936 - acc: 0.9020 - val_loss: 0.1245 - val_acc: 0.8601\n",
      "Epoch 47/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0933 - acc: 0.9017 - val_loss: 0.1242 - val_acc: 0.8599\n",
      "Epoch 48/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0930 - acc: 0.9019 - val_loss: 0.1234 - val_acc: 0.8680\n",
      "Epoch 49/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0927 - acc: 0.9048 - val_loss: 0.1221 - val_acc: 0.8599\n",
      "Epoch 50/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0924 - acc: 0.9021 - val_loss: 0.1212 - val_acc: 0.8599\n",
      "Epoch 51/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0921 - acc: 0.9035 - val_loss: 0.1205 - val_acc: 0.8596\n",
      "Epoch 52/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0919 - acc: 0.9040 - val_loss: 0.1195 - val_acc: 0.8604\n",
      "Epoch 53/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0916 - acc: 0.9027 - val_loss: 0.1195 - val_acc: 0.8591\n",
      "Epoch 54/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0913 - acc: 0.9050 - val_loss: 0.1182 - val_acc: 0.8606\n",
      "Epoch 55/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0910 - acc: 0.9046 - val_loss: 0.1181 - val_acc: 0.8591\n",
      "Epoch 56/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0908 - acc: 0.9050 - val_loss: 0.1170 - val_acc: 0.8601\n",
      "Epoch 57/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0905 - acc: 0.9056 - val_loss: 0.1166 - val_acc: 0.8599\n",
      "Epoch 58/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0903 - acc: 0.9057 - val_loss: 0.1162 - val_acc: 0.8591\n",
      "Epoch 59/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0900 - acc: 0.9053 - val_loss: 0.1152 - val_acc: 0.8604\n",
      "Epoch 60/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0898 - acc: 0.9060 - val_loss: 0.1149 - val_acc: 0.8599\n",
      "Epoch 61/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0895 - acc: 0.9056 - val_loss: 0.1143 - val_acc: 0.8599\n",
      "Epoch 62/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0893 - acc: 0.9053 - val_loss: 0.1139 - val_acc: 0.8599\n",
      "Epoch 63/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0891 - acc: 0.9056 - val_loss: 0.1134 - val_acc: 0.8604\n",
      "Epoch 64/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0888 - acc: 0.9052 - val_loss: 0.1128 - val_acc: 0.8599\n",
      "Epoch 65/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0886 - acc: 0.9052 - val_loss: 0.1122 - val_acc: 0.8601\n",
      "Epoch 66/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0884 - acc: 0.9053 - val_loss: 0.1121 - val_acc: 0.8593\n",
      "Epoch 67/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0882 - acc: 0.9057 - val_loss: 0.1117 - val_acc: 0.8588\n",
      "Epoch 68/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0879 - acc: 0.9089 - val_loss: 0.1109 - val_acc: 0.8604\n",
      "Epoch 69/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0877 - acc: 0.9070 - val_loss: 0.1108 - val_acc: 0.8593\n",
      "Epoch 70/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0875 - acc: 0.9083 - val_loss: 0.1102 - val_acc: 0.8601\n",
      "Epoch 71/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0873 - acc: 0.9085 - val_loss: 0.1097 - val_acc: 0.8606\n",
      "Epoch 72/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0871 - acc: 0.9089 - val_loss: 0.1094 - val_acc: 0.8604\n",
      "Epoch 73/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0869 - acc: 0.9095 - val_loss: 0.1087 - val_acc: 0.8606\n",
      "Epoch 74/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0867 - acc: 0.9096 - val_loss: 0.1085 - val_acc: 0.8604\n",
      "Epoch 75/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0864 - acc: 0.9092 - val_loss: 0.1080 - val_acc: 0.8596\n",
      "Epoch 76/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0862 - acc: 0.9095 - val_loss: 0.1079 - val_acc: 0.8591\n",
      "Epoch 77/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0860 - acc: 0.9091 - val_loss: 0.1072 - val_acc: 0.8596\n",
      "Epoch 78/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0857 - acc: 0.9092 - val_loss: 0.1068 - val_acc: 0.8596\n",
      "Epoch 79/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0855 - acc: 0.9090 - val_loss: 0.1062 - val_acc: 0.8606\n",
      "Epoch 80/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0853 - acc: 0.9092 - val_loss: 0.1062 - val_acc: 0.8599\n",
      "Epoch 81/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0851 - acc: 0.9092 - val_loss: 0.1057 - val_acc: 0.8614\n",
      "Epoch 82/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0849 - acc: 0.9095 - val_loss: 0.1056 - val_acc: 0.8604\n",
      "Epoch 83/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0847 - acc: 0.9092 - val_loss: 0.1051 - val_acc: 0.8609\n",
      "Epoch 84/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0845 - acc: 0.9095 - val_loss: 0.1049 - val_acc: 0.8609\n",
      "Epoch 85/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0843 - acc: 0.9095 - val_loss: 0.1047 - val_acc: 0.8609\n",
      "Epoch 86/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0841 - acc: 0.9094 - val_loss: 0.1045 - val_acc: 0.8612\n",
      "Epoch 87/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0839 - acc: 0.9096 - val_loss: 0.1043 - val_acc: 0.8612\n",
      "Epoch 88/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0837 - acc: 0.9098 - val_loss: 0.1040 - val_acc: 0.8612\n",
      "Epoch 89/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0835 - acc: 0.9098 - val_loss: 0.1037 - val_acc: 0.8614\n",
      "Epoch 90/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0834 - acc: 0.9096 - val_loss: 0.1036 - val_acc: 0.8617\n",
      "Epoch 91/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0832 - acc: 0.9104 - val_loss: 0.1035 - val_acc: 0.8609\n",
      "Epoch 92/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0830 - acc: 0.9100 - val_loss: 0.1033 - val_acc: 0.8609\n",
      "Epoch 93/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0828 - acc: 0.9106 - val_loss: 0.1032 - val_acc: 0.8609\n",
      "Epoch 94/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0827 - acc: 0.9102 - val_loss: 0.1028 - val_acc: 0.8612\n",
      "Epoch 95/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0825 - acc: 0.9098 - val_loss: 0.1023 - val_acc: 0.8622\n",
      "Epoch 96/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0824 - acc: 0.9100 - val_loss: 0.1024 - val_acc: 0.8617\n",
      "Epoch 97/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0822 - acc: 0.9102 - val_loss: 0.1022 - val_acc: 0.8614\n",
      "Epoch 98/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0821 - acc: 0.9100 - val_loss: 0.1022 - val_acc: 0.8612\n",
      "Epoch 99/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0819 - acc: 0.9100 - val_loss: 0.1020 - val_acc: 0.8612\n",
      "Epoch 100/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0818 - acc: 0.9100 - val_loss: 0.1018 - val_acc: 0.8612\n",
      "Epoch 101/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0816 - acc: 0.9102 - val_loss: 0.1017 - val_acc: 0.8612\n",
      "Epoch 102/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0815 - acc: 0.9100 - val_loss: 0.1015 - val_acc: 0.8612\n",
      "Epoch 103/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0813 - acc: 0.9099 - val_loss: 0.1012 - val_acc: 0.8617\n",
      "Epoch 104/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0812 - acc: 0.9098 - val_loss: 0.1011 - val_acc: 0.8620\n",
      "Epoch 105/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0810 - acc: 0.9100 - val_loss: 0.1010 - val_acc: 0.8622\n",
      "Epoch 106/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0809 - acc: 0.9098 - val_loss: 0.1009 - val_acc: 0.8617\n",
      "Epoch 107/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0808 - acc: 0.9100 - val_loss: 0.1008 - val_acc: 0.8622\n",
      "Epoch 108/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0807 - acc: 0.9100 - val_loss: 0.1004 - val_acc: 0.8625\n",
      "Epoch 109/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0805 - acc: 0.9100 - val_loss: 0.1003 - val_acc: 0.8625\n",
      "Epoch 110/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0804 - acc: 0.9106 - val_loss: 0.1003 - val_acc: 0.8622\n",
      "Epoch 111/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0803 - acc: 0.9102 - val_loss: 0.0999 - val_acc: 0.8638\n",
      "Epoch 112/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0802 - acc: 0.9107 - val_loss: 0.1000 - val_acc: 0.8633\n",
      "Epoch 113/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0801 - acc: 0.9106 - val_loss: 0.0999 - val_acc: 0.8630\n",
      "Epoch 114/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0799 - acc: 0.9106 - val_loss: 0.0996 - val_acc: 0.8630\n",
      "Epoch 115/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0798 - acc: 0.9106 - val_loss: 0.0998 - val_acc: 0.8630\n",
      "Epoch 116/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0797 - acc: 0.9111 - val_loss: 0.0997 - val_acc: 0.8628\n",
      "Epoch 117/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0796 - acc: 0.9104 - val_loss: 0.0995 - val_acc: 0.8628\n",
      "Epoch 118/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0795 - acc: 0.9106 - val_loss: 0.0992 - val_acc: 0.8628\n",
      "Epoch 119/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0794 - acc: 0.9107 - val_loss: 0.0988 - val_acc: 0.8630\n",
      "Epoch 120/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0793 - acc: 0.9107 - val_loss: 0.0989 - val_acc: 0.8630\n",
      "Epoch 121/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0792 - acc: 0.9114 - val_loss: 0.0989 - val_acc: 0.8628\n",
      "Epoch 122/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0791 - acc: 0.9114 - val_loss: 0.0988 - val_acc: 0.8628\n",
      "Epoch 123/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0790 - acc: 0.9111 - val_loss: 0.0986 - val_acc: 0.8628\n",
      "Epoch 124/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0789 - acc: 0.9116 - val_loss: 0.0986 - val_acc: 0.8628\n",
      "Epoch 125/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0788 - acc: 0.9110 - val_loss: 0.0981 - val_acc: 0.8714\n",
      "Epoch 126/100000\n",
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      "Epoch 127/100000\n",
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      "Epoch 128/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0785 - acc: 0.9098 - val_loss: 0.0982 - val_acc: 0.8707\n",
      "Epoch 129/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0784 - acc: 0.9085 - val_loss: 0.0976 - val_acc: 0.8714\n",
      "Epoch 130/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0783 - acc: 0.9078 - val_loss: 0.0975 - val_acc: 0.8714\n",
      "Epoch 131/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0782 - acc: 0.9075 - val_loss: 0.0973 - val_acc: 0.8728\n",
      "Epoch 132/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0782 - acc: 0.9077 - val_loss: 0.0974 - val_acc: 0.8722\n",
      "Epoch 133/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0781 - acc: 0.9077 - val_loss: 0.0969 - val_acc: 0.8728\n",
      "Epoch 134/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0780 - acc: 0.9081 - val_loss: 0.0972 - val_acc: 0.8722\n",
      "Epoch 135/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0779 - acc: 0.9077 - val_loss: 0.0972 - val_acc: 0.8801\n",
      "Epoch 136/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0778 - acc: 0.9077 - val_loss: 0.0969 - val_acc: 0.8801\n",
      "Epoch 137/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0777 - acc: 0.9074 - val_loss: 0.0967 - val_acc: 0.8722\n",
      "Epoch 138/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0776 - acc: 0.9074 - val_loss: 0.0966 - val_acc: 0.8722\n",
      "Epoch 139/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0775 - acc: 0.9073 - val_loss: 0.0967 - val_acc: 0.8799\n",
      "Epoch 140/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0775 - acc: 0.9073 - val_loss: 0.0964 - val_acc: 0.8720\n",
      "Epoch 141/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0774 - acc: 0.9073 - val_loss: 0.0963 - val_acc: 0.8722\n",
      "Epoch 142/100000\n",
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      "Epoch 143/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0772 - acc: 0.9074 - val_loss: 0.0962 - val_acc: 0.8801\n",
      "Epoch 144/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0771 - acc: 0.9078 - val_loss: 0.0963 - val_acc: 0.8801\n",
      "Epoch 145/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0770 - acc: 0.9075 - val_loss: 0.0961 - val_acc: 0.8801\n",
      "Epoch 146/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0770 - acc: 0.9078 - val_loss: 0.0958 - val_acc: 0.8809\n",
      "Epoch 147/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0769 - acc: 0.9079 - val_loss: 0.0960 - val_acc: 0.8801\n",
      "Epoch 148/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0768 - acc: 0.9081 - val_loss: 0.0961 - val_acc: 0.8799\n",
      "Epoch 149/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0767 - acc: 0.9079 - val_loss: 0.0959 - val_acc: 0.8880\n",
      "Epoch 150/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0766 - acc: 0.9078 - val_loss: 0.0956 - val_acc: 0.8804\n",
      "Epoch 151/100000\n",
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      "Epoch 152/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0765 - acc: 0.9081 - val_loss: 0.0955 - val_acc: 0.8880\n",
      "Epoch 153/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0764 - acc: 0.9079 - val_loss: 0.0956 - val_acc: 0.8880\n",
      "Epoch 154/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0763 - acc: 0.9078 - val_loss: 0.0954 - val_acc: 0.8883\n",
      "Epoch 155/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0763 - acc: 0.9078 - val_loss: 0.0954 - val_acc: 0.8883\n",
      "Epoch 156/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0762 - acc: 0.9081 - val_loss: 0.0955 - val_acc: 0.8880\n",
      "Epoch 157/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0761 - acc: 0.9075 - val_loss: 0.0952 - val_acc: 0.8880\n",
      "Epoch 158/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0760 - acc: 0.9079 - val_loss: 0.0950 - val_acc: 0.8880\n",
      "Epoch 159/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0760 - acc: 0.9078 - val_loss: 0.0949 - val_acc: 0.8880\n",
      "Epoch 160/100000\n",
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      "Epoch 161/100000\n",
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      "Epoch 162/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0757 - acc: 0.9079 - val_loss: 0.0947 - val_acc: 0.8886\n",
      "Epoch 163/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0757 - acc: 0.9081 - val_loss: 0.0949 - val_acc: 0.8880\n",
      "Epoch 164/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0756 - acc: 0.9085 - val_loss: 0.0948 - val_acc: 0.8880\n",
      "Epoch 165/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0755 - acc: 0.9081 - val_loss: 0.0948 - val_acc: 0.8880\n",
      "Epoch 166/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0755 - acc: 0.9079 - val_loss: 0.0944 - val_acc: 0.8886\n",
      "Epoch 167/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0754 - acc: 0.9078 - val_loss: 0.0944 - val_acc: 0.8888\n",
      "Epoch 168/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0753 - acc: 0.9083 - val_loss: 0.0945 - val_acc: 0.8886\n",
      "Epoch 169/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0752 - acc: 0.9071 - val_loss: 0.0944 - val_acc: 0.8880\n",
      "Epoch 170/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0752 - acc: 0.9083 - val_loss: 0.0945 - val_acc: 0.8886\n",
      "Epoch 171/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0751 - acc: 0.9082 - val_loss: 0.0943 - val_acc: 0.8886\n",
      "Epoch 172/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0750 - acc: 0.9081 - val_loss: 0.0941 - val_acc: 0.8888\n",
      "Epoch 173/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0750 - acc: 0.9069 - val_loss: 0.0943 - val_acc: 0.8888\n",
      "Epoch 174/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0749 - acc: 0.9069 - val_loss: 0.0942 - val_acc: 0.8888\n",
      "Epoch 175/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0748 - acc: 0.9062 - val_loss: 0.0943 - val_acc: 0.8886\n",
      "Epoch 176/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0748 - acc: 0.9082 - val_loss: 0.0940 - val_acc: 0.8888\n",
      "Epoch 177/100000\n",
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      "Epoch 178/100000\n",
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      "Epoch 179/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0746 - acc: 0.9049 - val_loss: 0.0938 - val_acc: 0.8888\n",
      "Epoch 180/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0745 - acc: 0.9049 - val_loss: 0.0936 - val_acc: 0.8888\n",
      "Epoch 181/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0745 - acc: 0.9049 - val_loss: 0.0937 - val_acc: 0.8888\n",
      "Epoch 182/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0744 - acc: 0.9052 - val_loss: 0.0938 - val_acc: 0.8888\n",
      "Epoch 183/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0743 - acc: 0.9050 - val_loss: 0.0936 - val_acc: 0.8888\n",
      "Epoch 184/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0743 - acc: 0.9050 - val_loss: 0.0937 - val_acc: 0.8888\n",
      "Epoch 185/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0742 - acc: 0.9049 - val_loss: 0.0934 - val_acc: 0.8812\n",
      "Epoch 186/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0742 - acc: 0.9052 - val_loss: 0.0936 - val_acc: 0.8809\n",
      "Epoch 187/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0741 - acc: 0.9050 - val_loss: 0.0935 - val_acc: 0.8809\n",
      "Epoch 188/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0741 - acc: 0.9052 - val_loss: 0.0935 - val_acc: 0.8809\n",
      "Epoch 189/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0740 - acc: 0.9052 - val_loss: 0.0935 - val_acc: 0.8809\n",
      "Epoch 190/100000\n",
      "7592/7592 [==============================] - ETA: 0s - loss: 0.0739 - acc: 0.904 - 0s - loss: 0.0739 - acc: 0.9050 - val_loss: 0.0933 - val_acc: 0.8817\n",
      "Epoch 191/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0739 - acc: 0.9053 - val_loss: 0.0934 - val_acc: 0.8812\n",
      "Epoch 192/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0738 - acc: 0.9049 - val_loss: 0.0932 - val_acc: 0.8817\n",
      "Epoch 193/100000\n",
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      "Epoch 194/100000\n",
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      "Epoch 195/100000\n",
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      "Epoch 196/100000\n",
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      "Epoch 197/100000\n",
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      "Epoch 198/100000\n",
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      "Epoch 199/100000\n",
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      "Epoch 200/100000\n",
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      "Epoch 201/100000\n",
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      "Epoch 202/100000\n",
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      "Epoch 203/100000\n",
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      "Epoch 204/100000\n",
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      "Epoch 205/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0732 - acc: 0.9052 - val_loss: 0.0929 - val_acc: 0.8825\n",
      "Epoch 206/100000\n",
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      "Epoch 207/100000\n",
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      "Epoch 208/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0730 - acc: 0.9056 - val_loss: 0.0929 - val_acc: 0.8822\n",
      "Epoch 209/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0730 - acc: 0.9056 - val_loss: 0.0929 - val_acc: 0.8820\n",
      "Epoch 210/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0729 - acc: 0.9056 - val_loss: 0.0928 - val_acc: 0.8822\n",
      "Epoch 211/100000\n",
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      "Epoch 212/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0728 - acc: 0.9057 - val_loss: 0.0927 - val_acc: 0.8822\n",
      "Epoch 213/100000\n",
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      "Epoch 214/100000\n",
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      "Epoch 215/100000\n",
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      "Epoch 216/100000\n",
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      "Epoch 217/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0726 - acc: 0.9057 - val_loss: 0.0925 - val_acc: 0.8822\n",
      "Epoch 218/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0726 - acc: 0.9056 - val_loss: 0.0926 - val_acc: 0.8820\n",
      "Epoch 219/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0725 - acc: 0.9056 - val_loss: 0.0923 - val_acc: 0.8822\n",
      "Epoch 220/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0725 - acc: 0.9056 - val_loss: 0.0923 - val_acc: 0.8822\n",
      "Epoch 221/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0724 - acc: 0.9056 - val_loss: 0.0923 - val_acc: 0.8822\n",
      "Epoch 222/100000\n",
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      "Epoch 223/100000\n",
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      "Epoch 224/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0723 - acc: 0.9056 - val_loss: 0.0922 - val_acc: 0.8822\n",
      "Epoch 225/100000\n",
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      "Epoch 226/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0722 - acc: 0.9056 - val_loss: 0.0922 - val_acc: 0.8825\n",
      "Epoch 227/100000\n",
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      "Epoch 228/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0721 - acc: 0.9061 - val_loss: 0.0923 - val_acc: 0.8822\n",
      "Epoch 229/100000\n",
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      "Epoch 230/100000\n",
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      "Epoch 231/100000\n",
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      "Epoch 232/100000\n",
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      "Epoch 233/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0719 - acc: 0.9060 - val_loss: 0.0921 - val_acc: 0.8817\n",
      "Epoch 234/100000\n",
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      "Epoch 235/100000\n",
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      "Epoch 236/100000\n",
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      "Epoch 237/100000\n",
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      "Epoch 238/100000\n",
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      "Epoch 239/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0717 - acc: 0.9056 - val_loss: 0.0918 - val_acc: 0.8822\n",
      "Epoch 240/100000\n",
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      "Epoch 241/100000\n",
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      "Epoch 242/100000\n",
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      "Epoch 243/100000\n",
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      "Epoch 244/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0715 - acc: 0.9067 - val_loss: 0.0917 - val_acc: 0.8820\n",
      "Epoch 245/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0715 - acc: 0.9065 - val_loss: 0.0917 - val_acc: 0.8822\n",
      "Epoch 246/100000\n",
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      "Epoch 247/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0714 - acc: 0.9062 - val_loss: 0.0916 - val_acc: 0.8833\n",
      "Epoch 248/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0714 - acc: 0.9063 - val_loss: 0.0915 - val_acc: 0.8836\n",
      "Epoch 249/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0713 - acc: 0.9070 - val_loss: 0.0915 - val_acc: 0.8830\n",
      "Epoch 250/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0713 - acc: 0.9069 - val_loss: 0.0915 - val_acc: 0.8822\n",
      "Epoch 251/100000\n",
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      "Epoch 252/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9067 - val_loss: 0.0914 - val_acc: 0.8836\n",
      "Epoch 253/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9073 - val_loss: 0.0915 - val_acc: 0.8833\n",
      "Epoch 254/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0712 - acc: 0.9073 - val_loss: 0.0914 - val_acc: 0.8833\n",
      "Epoch 255/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0711 - acc: 0.9074 - val_loss: 0.0914 - val_acc: 0.8825\n",
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      "Epoch 364/100000\n",
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      "Epoch 365/100000\n",
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      "Epoch 367/100000\n",
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      "Epoch 368/100000\n",
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      "Epoch 371/100000\n",
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      "Epoch 372/100000\n",
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      "Epoch 373/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0677 - acc: 0.9185 - val_loss: 0.0896 - val_acc: 0.8775\n",
      "Epoch 374/100000\n",
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      "Epoch 376/100000\n",
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      "Epoch 379/100000\n",
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      "Epoch 381/100000\n",
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      "Epoch 384/100000\n",
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      "Epoch 385/100000\n",
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      "Epoch 386/100000\n",
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      "Epoch 497/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0648 - acc: 0.9206 - val_loss: 0.0891 - val_acc: 0.8796\n",
      "Epoch 498/100000\n",
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      "Epoch 500/100000\n",
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      "7592/7592 [==============================] - 0s - loss: 0.0644 - acc: 0.9206 - val_loss: 0.0890 - val_acc: 0.8799\n",
      "Epoch 516/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0644 - acc: 0.9214 - val_loss: 0.0891 - val_acc: 0.8793\n",
      "Epoch 517/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0644 - acc: 0.9215 - val_loss: 0.0891 - val_acc: 0.8791\n",
      "Epoch 518/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0644 - acc: 0.9212 - val_loss: 0.0891 - val_acc: 0.8791\n",
      "Epoch 519/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0643 - acc: 0.9210 - val_loss: 0.0891 - val_acc: 0.8791\n",
      "Epoch 520/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0643 - acc: 0.9212 - val_loss: 0.0890 - val_acc: 0.8867\n",
      "Epoch 521/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0643 - acc: 0.9214 - val_loss: 0.0890 - val_acc: 0.8865\n",
      "Epoch 522/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0643 - acc: 0.9203 - val_loss: 0.0890 - val_acc: 0.8793\n",
      "Epoch 523/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0643 - acc: 0.9206 - val_loss: 0.0889 - val_acc: 0.8807\n",
      "Epoch 524/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0642 - acc: 0.9212 - val_loss: 0.0889 - val_acc: 0.8793\n",
      "Epoch 525/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0642 - acc: 0.9212 - val_loss: 0.0890 - val_acc: 0.8791\n",
      "Epoch 526/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0642 - acc: 0.9214 - val_loss: 0.0891 - val_acc: 0.8791\n",
      "Epoch 527/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0641 - acc: 0.9212 - val_loss: 0.0890 - val_acc: 0.8793\n",
      "Epoch 528/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0641 - acc: 0.9216 - val_loss: 0.0889 - val_acc: 0.8801\n",
      "Epoch 529/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0641 - acc: 0.9215 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 530/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0641 - acc: 0.9215 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 531/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0641 - acc: 0.9210 - val_loss: 0.0889 - val_acc: 0.8796\n",
      "Epoch 532/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0640 - acc: 0.9216 - val_loss: 0.0889 - val_acc: 0.8796\n",
      "Epoch 533/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0640 - acc: 0.9212 - val_loss: 0.0890 - val_acc: 0.8875\n",
      "Epoch 534/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0640 - acc: 0.9215 - val_loss: 0.0889 - val_acc: 0.8796\n",
      "Epoch 535/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0639 - acc: 0.9215 - val_loss: 0.0889 - val_acc: 0.8875\n",
      "Epoch 536/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0639 - acc: 0.9214 - val_loss: 0.0889 - val_acc: 0.8796\n",
      "Epoch 537/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0639 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8799\n",
      "Epoch 538/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0639 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8875\n",
      "Epoch 539/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0638 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8872\n",
      "Epoch 540/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0638 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8865\n",
      "Epoch 541/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0638 - acc: 0.9216 - val_loss: 0.0889 - val_acc: 0.8870\n",
      "Epoch 542/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0637 - acc: 0.9218 - val_loss: 0.0889 - val_acc: 0.8801\n",
      "Epoch 543/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0637 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8796\n",
      "Epoch 544/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0637 - acc: 0.9219 - val_loss: 0.0891 - val_acc: 0.8870\n",
      "Epoch 545/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0637 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 546/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0636 - acc: 0.9216 - val_loss: 0.0890 - val_acc: 0.8875\n",
      "Epoch 547/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0636 - acc: 0.9220 - val_loss: 0.0889 - val_acc: 0.8875\n",
      "Epoch 548/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0636 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8870\n",
      "Epoch 549/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0636 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8870\n",
      "Epoch 550/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0636 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8872\n",
      "Epoch 551/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0635 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8867\n",
      "Epoch 552/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0635 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8870\n",
      "Epoch 553/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0635 - acc: 0.9222 - val_loss: 0.0891 - val_acc: 0.8862\n",
      "Epoch 554/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0635 - acc: 0.9216 - val_loss: 0.0889 - val_acc: 0.8867\n",
      "Epoch 555/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0634 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8796\n",
      "Epoch 556/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0634 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8862\n",
      "Epoch 557/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0634 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8870\n",
      "Epoch 558/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0634 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 559/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0633 - acc: 0.9220 - val_loss: 0.0889 - val_acc: 0.8878\n",
      "Epoch 560/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0633 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8801\n",
      "Epoch 561/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0633 - acc: 0.9218 - val_loss: 0.0890 - val_acc: 0.8875\n",
      "Epoch 562/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0633 - acc: 0.9218 - val_loss: 0.0890 - val_acc: 0.8865\n",
      "Epoch 563/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0632 - acc: 0.9218 - val_loss: 0.0890 - val_acc: 0.8804\n",
      "Epoch 564/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0633 - acc: 0.9222 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 565/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0632 - acc: 0.9219 - val_loss: 0.0889 - val_acc: 0.8878\n",
      "Epoch 566/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0632 - acc: 0.9219 - val_loss: 0.0891 - val_acc: 0.8862\n",
      "Epoch 567/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0632 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8875\n",
      "Epoch 568/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0632 - acc: 0.9220 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 569/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0631 - acc: 0.9220 - val_loss: 0.0891 - val_acc: 0.8867\n",
      "Epoch 570/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0631 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8870\n",
      "Epoch 571/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0631 - acc: 0.9219 - val_loss: 0.0891 - val_acc: 0.8867\n",
      "Epoch 572/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0631 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8867\n",
      "Epoch 573/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0631 - acc: 0.9218 - val_loss: 0.0890 - val_acc: 0.8870\n",
      "Epoch 574/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0630 - acc: 0.9218 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 575/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0630 - acc: 0.9220 - val_loss: 0.0892 - val_acc: 0.8862\n",
      "Epoch 576/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0630 - acc: 0.9222 - val_loss: 0.0891 - val_acc: 0.8862\n",
      "Epoch 577/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0630 - acc: 0.9222 - val_loss: 0.0891 - val_acc: 0.8862\n",
      "Epoch 578/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0629 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8875\n",
      "Epoch 579/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0629 - acc: 0.9222 - val_loss: 0.0891 - val_acc: 0.8865\n",
      "Epoch 580/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0629 - acc: 0.9220 - val_loss: 0.0890 - val_acc: 0.8878\n",
      "Epoch 581/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0629 - acc: 0.9220 - val_loss: 0.0891 - val_acc: 0.8862\n",
      "Epoch 582/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0628 - acc: 0.9222 - val_loss: 0.0891 - val_acc: 0.8870\n",
      "Epoch 583/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0628 - acc: 0.9220 - val_loss: 0.0890 - val_acc: 0.8875\n",
      "Epoch 584/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0628 - acc: 0.9222 - val_loss: 0.0891 - val_acc: 0.8870\n",
      "Epoch 585/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0628 - acc: 0.9220 - val_loss: 0.0890 - val_acc: 0.8870\n",
      "Epoch 586/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0627 - acc: 0.9219 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 587/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0627 - acc: 0.9222 - val_loss: 0.0890 - val_acc: 0.8875\n",
      "Epoch 588/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0627 - acc: 0.9223 - val_loss: 0.0892 - val_acc: 0.8865\n",
      "Epoch 589/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0627 - acc: 0.9220 - val_loss: 0.0890 - val_acc: 0.8865\n",
      "Epoch 590/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0626 - acc: 0.9218 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 591/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0626 - acc: 0.9227 - val_loss: 0.0892 - val_acc: 0.8867\n",
      "Epoch 592/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0626 - acc: 0.9222 - val_loss: 0.0890 - val_acc: 0.8872\n",
      "Epoch 593/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0626 - acc: 0.9224 - val_loss: 0.0891 - val_acc: 0.8875\n",
      "Epoch 594/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0625 - acc: 0.9224 - val_loss: 0.0891 - val_acc: 0.8878\n",
      "Epoch 595/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0625 - acc: 0.9227 - val_loss: 0.0891 - val_acc: 0.8865\n",
      "Epoch 596/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0625 - acc: 0.9224 - val_loss: 0.0892 - val_acc: 0.8872\n",
      "Epoch 597/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0624 - acc: 0.9223 - val_loss: 0.0892 - val_acc: 0.8875\n",
      "Epoch 598/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0624 - acc: 0.9222 - val_loss: 0.0892 - val_acc: 0.8872\n",
      "Epoch 599/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0624 - acc: 0.9223 - val_loss: 0.0893 - val_acc: 0.8872\n",
      "Epoch 600/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0624 - acc: 0.9228 - val_loss: 0.0891 - val_acc: 0.8875\n",
      "Epoch 601/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0624 - acc: 0.9228 - val_loss: 0.0891 - val_acc: 0.8872\n",
      "Epoch 602/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0623 - acc: 0.9229 - val_loss: 0.0892 - val_acc: 0.8875\n",
      "Epoch 603/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0623 - acc: 0.9223 - val_loss: 0.0892 - val_acc: 0.8875\n",
      "Epoch 604/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0623 - acc: 0.9228 - val_loss: 0.0893 - val_acc: 0.8872\n",
      "Epoch 605/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0622 - acc: 0.9235 - val_loss: 0.0892 - val_acc: 0.8872\n"
     ]
    }
   ],
   "source": [
    "# Aqui criamos o esboço da rede.\n",
    "classifier = Sequential()\n",
    "\n",
    "classifier.add(Dense(10, activation='relu', input_dim=6)) # camada escondida\n",
    "classifier.add(Dense(15, activation='relu')) # camada escondida\n",
    "classifier.add(Dense(1, activation='relu')) # \n",
    "\n",
    "sgd = optimizers.SGD(lr=learnRate)\n",
    "classifier.compile(optimizer=sgd, \n",
    "                   loss='mean_squared_error', # metrica de erro\n",
    "                   metrics=['accuracy']) # metrica de sucesso\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=50)\n",
    "\n",
    "history = classifier.fit(X_train, y_train,\n",
    "                         epochs=100000, # quantidade de epocas que a rede neural vai executar\n",
    "                         verbose=1,\n",
    "                         shuffle=True, # utilizado para misturar as amostras a cada epoca\n",
    "                         validation_data=(X_validation, y_validation),\n",
    "                         callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1632/1962 [=======================>......] - ETA: 0s[0.052386035601571429, 0.93068297655453613]\n"
     ]
    }
   ],
   "source": [
    "test_loss = classifier.evaluate(X_test, y_test)\n",
    "print (test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Ht4DYB60Jcfsfxbr8OUiEJePoH8cc6CtPr5exbcY+r7/ZvHcOai/8/aH/vo2d6K/GQ1Hvm+DpsPMfOjBug80RHmeho411vGoxWvPh/5xAdYDv7pOBZmHRPB1GBhXXzjzH/NPjLpmouG+0Vrvow28bzOr99gPut1drR2+7lPt0ToB0NtFIOb+hq51mmX5WJ+2qP9F4LoIxPMLxLV949E4V72ttu8rxnNvWjz3Y9Qft8715Xabad5voAd68wUqk6D7g+dFQkD9v59rjPVZ9SeLR/MvAvmvsUy06bnzoN1lzAHQdRHIYtQ/lvzHWK61LwKxCoAt6QMIuH+MmfeZq6EBpo/HstegYRheDN0e5pbC1411mruPuIbJMu8zP4QGmU0W78fF7gAYBT3Nmwjr47x5//L1IpAHjXlW4yh+H/WPay3pAwjoGYJDaFxjARDw/bNrqcYCoOfFYzDqsR86KoDn2UPXMDB/b4V6kclz0A37lJfnz4JeOj/fNM811MUi6DAaH0IvSTcPn+A6jbMeOrzCe9DTPeZfUK7z/d9CrwIaBb1C1HMoivbwHgAnQw9WQ6BfOpOgp5A9T9edC/2l5hoG5i3oqbCNaHiLGdcXpMD69Ig3F0IPEk5oP4/3oX9QfeAee9DlWmjoSIFeLdgN2gq7E80PgC8ZyxSh/ufk8v/gvjXPOOi2/gTuQOL6QvkOOgZdrFHv7nCPNWYOa1nw7aDsGgamwFjXh3BvH6sg3lrvow28b7Ot0FaqjtD3OAAaxM3l+bpPtUfrBEDzMDBdoAE0F/ql7rlOsywf69MWDb8IBhjz5kND/hjoF0oOGoZfT656W23fs6Db1wntU/we9Eq/UdD3Za6bwHoYmHK4L1gBdD/KgrYKuj6TDtC+SqdNy/0Aur/uhX62z8N9mjseDffZpo5jV0P/ZndC/0ZjoF9Q21E/AALuz2eJseyXsB4Gpi0aBjtvmhMAW8r1pXsS+iPnQ+h+cBQNv6StWqJug/79mO8mUQ7dDp5mwd3q9Cb0u6EaDS9quQnav60z9LPMNz32PFWcBd+ORYnwfvX/i3AP79LJqFtP6LFquGm5x1B/GJge8D4MjOvMySLoNn0Xuu8ehQYzf/3DWL/V7UVd22q6scwE0zyz5nx2Vp+9lWhTWZuM17kem0dQiIP+HfWADrXVFboNnbC+t/En0L//xrokUAvMh37ojY2KPgl6exdX68nZ0IP/HugB8hR0R7/N43WvQneuCuiBOhH1f4GeBQ1HrgFrE6AtS1nwPQCeDz1dlwNtuk6AHrg91wHo6Z5h0FNUrgFq42HdKrQQ1k3RTfkxdGdNNupTCe278CUatpq9BG3xqoQebB5B4wNBe/NTuC+Q8HZj8HOhv0gzjPJy4A7nri+of0D/+F2D7WZDD2Set7/Kgu+/yl0DQefDff/PJ7ws21rvow28b7M3oSE3F7pf7od+IZlPTfm6T7VH6wTATwF8DP3irYB2A7jJ47WudZr5Wp+2aBgAz4aG8uPQbb4C2sKTi/rD11gx19vKD6BhLhXuv/0t0IO8eTsL3ANBp8P9ZWSup8tFxrKuWyoeh16c8YbHck9Cf+i5fsC1N+bHo+E+az6OuVojPY9jf4VefV4Od0h/FQ0DIKBf8nuM+p2A/nj0bMX+i/Fabxc9mAUiAAI60P1saEtYMXTMSqsuH95CwP3Qv8Fy6HfBcFh/Uf8IeubjOPSzToZ1a357eD8VGu+xbA70R3xTEtH48E9toX/nBdD3sR/6vefZ8vZ3aF/GCuh+5m0gaED3zS3Qv68i6A+Jvqj/w66lzoG+d6sLQbxtO6vWOF8+u3ON1073oV5tGyk70bTcw9BuBsehfy/50O+eP3pZ7ybUH52DyFbfoemR8In80QZNh/tAOh9an6aGl2gtrgB4JnB9EdqtH/SHp+ddUaj5XFcQe/tBGe66QM+Y+Tr0WEs9Dm2Zu9Hmcry5xSi/qWsKiFrFL6G/SmKDXREKa20QvABo1UE9Dlofz2Fx7HImBUBAWyqtruRtTZth3T+Smu9dtOzitnBxLrTVztvZktbSH75dhGWXGWh4NyqiVncpdDiAROgpac9Tn0StqQ2CFwDbQ/fzz6B9AKfB3YctUM6EAHghtG9fDLR/l+epdyIiigDtoV9K2WidjrpEjWmD4AXA26B96HKhrd2u2z6eG8A6nAkB8BfQjvllaPl4l0RERERERERERERERERERERERERERERh73vQ219FceLEiRMnTpxCaroEvt3zm6iBS9D4iOWcOHHixIkTpzN3Mt/DnMhnUQDk8OHDUlhYyIkTJ06cOHEKgenw4cOuABgV5BxBISoKgBQWFgoRERGFhsLCQgZA8gsDIBERUYhhACR/MQASERGFGAZA8hcDIBERUYhhACR/MQASERGFGAZA8hcDIBERUYhhACR/MQASERGFGAZA8hcDIBERUYhhACR/MQASERGFGAZA8hcDIBERUYhhACR/MQASERGFGAZA8hcDIBERUYhhACR/MQASERHZpLbWact6GQDJXwyAREREraim1inlVTWy60iBPDp4tWTmlLR6GQyA5C8GQCIiChlOp1NKK6vrHtfUOiXtaKFUVNfUzauorpGSCvcyJwrLJbe4QkRECsur5GRheb2WuYrqGtl5uEA2Z+ZJVm6J7D1eJFm5JbLjcL7sOJwvh/JK5VBeqTh2HJPUowUSOy9VXo9Plp6ONHn7qy3yztdb5etNWfL1pix5sN9KubLjIomOcdRNb3+1pdW3AwMg+YsBkIiImq20slrW78+R3OKKeoFMRGT7oXwZsixd5m8/KnuOF8rcbUfkaH6ZnCwql1V7T8qM5Gzp6UiTmG92SL+EPfK/2dvl8zk7pdfC3fLJrO1y1xfL5apOi+SqToukbf9V8kDflXJVp0VydWd3sHI9f/nnCxvMu7SDPr6vzwpp239V3fNXdnK//oqOC+WqTovk/r4r5PquCfUCW2tOz47eIEXlVa2+/RkAyV8MgEREfnI6G+/n5XQ660LAqaIK2XWkoN7z+aWVUlld26zyVu49KV8uT5eMk8VSWV0rB3NK5PDpUtl0IFeSM/Mk42SxHMkvk6qaWtl0IFd6LEiTgUv2Sk9Hmjw7eoN0+Han9Fq0Wzp9t1PaT0yS/83eLn0X75EpGzKlf8JeiZ2XKrHzUuXlCUl1YealcZtkxKoMeWHsxrqQ5ZqeH7NRnhy21rYg1ZqTZ92jYxxyS7clclv3pRId45BrOi+Wqzotklu7L5Vbui2RKzsukjYdHPLwoMR6rXvvTUuRfgl7pNfC3fLyhCR5ctha6ZewR9JPFEn/hL0ybEV6sz7X5mAAJH8xABJRRCssr5Lyqpp685xOp+QWV8iszYfkaH6ZFJRVSV5JpZRX1Uh5VY0UlVdJdq6eFvxs9g65vcdSGbYiXaYnZcuszYdkdOJ+iZ2XKt3mp8nwlRnyYL+VckXHhfLMqA11LVa/67lMru68SG4wWp9u7rZE7ui5TK7rouHjhq4JOsUmyNMj1snDgxLln6PWy1+HrZVrOi8Oeohqanp08Gq5p9dyaeMRtm6KWyLPjt4gL09Iku4L0uSRQaslOsYh9/ZeIZ/M2i4dvt0hY1bvl+zcUsnKLRHHjmOyPiNHDuWVyp7jhbJ89wk5XlAuR/LL6qbjBeWyYMdROZhTIkfyy+RUUYWcLqmU5Mw8WZ+RI5k5JZJbXCFH8suksLxKqmtq5Wh+mRzKK5Wkg3my/VC+1NY6pbbWKWWVNQ32kcrq2roAn19aKQdOFQdk32wMAyD5iwGQiILuWEFZXZ+sk4Xl4nQ6paqmVjYeyJXyqhqpqK6RgzklsnLvSVmTfkqOFZTJ/O1HZcCSvfLO1K0yIzlb3pm6VcatOSDHCjQQFFdUy9jVB6Tv4j0yctX+ulOOM5KzZVnaCXly+Dp5cvi6uhadRwevlmdHb5CnR6yTm+KWNNpadKZMd/RcJpcZgdIVLG/utkRu77G0Xr2v6rRI3pqyRZ4avk7+OWq9dF+QJo8OXi1/H7leOny7U0au2i+vx2+Wd6duleu7JsjDgxKlz+I90j9hrwxcuk86ztkp701LkY9mbJN3p26VXgt3y+Jdx+RQXql8OGObxHyzQ2LnpcrQ5emyaOcxmbopWyqra6W21imV1bWSkn1a2o3X1sOqmvotYk6ns17/PfINAyD5iwGQiJqlvKpGnE6nOJ1Oqa6pleqaWtl9rFAmrD0oKdmnZfnuEzJ5Q6bEzkuV6UnZMnxlhoxYlSFzUg7L3G1HZObmQzJ+7UEZuHSffD5nZ91pw+u6uFu1Hhuypl7frTMxhF32+UK5ouNCuafXcnlsyBq5ITahLmy9MXmz9Fq0W3osSJP3p6dIu/Gb5IPpKfLxzO2yaOcxWbnnpEzekCkp2aflu5QjsmjnMdmSlSer9p6U1KMFkpJ9WlbuOVk3zd12RFbsOSHzth+V/gl7ZdyaA1JsXORwsqhc1u/PqQvKrtPRFdU1Ul1TK6dLKqW6xvfTkFU1tU2e0qbgYwAkfzEAEpFXeSWVMnDJXvlgeoo8O3qD3PnFsnoh6MqOixpc8WjX1KaDQ+7vu6KuxevuXsvl6RHrmnzd3b2Wy+vxmyVufqr0WbxHnhq+ri5Qdv5ul8xIzpb/TkuR4SszxLHjmCzaqVd6FpVXSU2tUzZn5smuIwVSXlUjxRXVUlxRLaWV1VJb66wXrGpqNRTX2DTuG5EZAyD5iwGQiCylHi2Q23ss8ymcXdtlcV1frz8OWCV/GphYd4rywxnb5P3pKfLSuE3yzKgN8vjQNfLqpGT5ZJZedPD2V1tk5Kr94thxTP43e7sMXLpPxq89KOPWHJAdh/Nl04FcOVFYXnfqsLK6VpIO5tX12zuSXyZLUo9LrRHACsurpLC8Sg7llTY43ehSUFpl2deLKFQwAJK/GACJyNLr8ZslOkaH0ui+IE0mrjsor01Klo5zdsrcbUdk3JoDkmlceeoKXznGWGtEZC8GQPIXAyARNVBVU1s3Ntq2Q/nBrg4ReWAAJH8xABJFuJ2HC+RYQZnklVTKij0npNfC3XJ7j6V1V5TadS9TImo5BkDyFwNgmGjOVX5ELuszchrt29fh253BriIRWWAAJH8xAIYA1xhZWbklMmVDphSWV0l+aaX0S9gjTw5fJ7f3WCrXdF4sUzdlB7mmFGp6OtLqBb7beyyTPw5YJQ8NTJRei3Y3GCCZiM4MDIDkLwZAmzidTjlZWC6pRwvk1UnJcmNsgjzYb6UMXZ4uk9YdlGEr0mVaUrZMNP6/JPW4rM/IkUU7j8mclMMyYe1BeXzomrovZl9G/m/TwSFzUg7LqSJ2xCfvnE6nrNp7Uk4VVcgTX+o+9t60FBm5aj8DH1GIYAAkfzEANsE1VITT6ZTxaw/K3b2Wyz29lsszo/RemoOX7ZNei3bL6/HJ8o+R6+WxIWvk0cGr5eZuS5oMbP5Ot3ZfKgOW7JX+CXvljp7u4Tqu7rxI1qbnBHnL0ZmkpKJasnNLpaCsSkYl7m+wLx3KKw12FYmoGRgAyV8MgI1YvvuERMc4pN34TfL8mI3NDmjmuxc8M3qD/HdairTtv0qeGbVBPpieIq/Hb5a3v9oiH0xPkceGrDFu4bRMHhm0Wl6PT5b/fL1FejrSJDkzT7JzS+VofpnkFlfItkP5UmLcBcDFsy/XQwMT68Jr6tECOVlY3uh7ra6plX0nimT9/hw5VVQhJwrLZeOBXDlVVCHHC8plx+F8KSitkuKKaqmqqZXs3FIZnbhfPp65XTYeyJVlaSdk7OoDMnjZPhm7+oDsPqb7VHZuKcOoD5xOp6zYc0IyTuo9RqtramXXkQIpNO4/6lJUXiVztx2RZWknZNiK9LrbmX08c7v8ddha6bVwt3yxcLe8NG6T/GHAKrm+a4Jc9vnCBvdjNU/tJybxzg9EIYYBkPzFANiIPwxYVe+L8vLPF0rfxXvkq41Z0mNBmnw8c7u8PCFJPpq5TYYsS5eRq/bL3G1HJCH1uKzfnyNVNbVSWim2nnIAACAASURBVFkdsPtczt5yWBJSj8stRuvjbd2Xyu+MlsHLPl8oDw9KlE9nbZdBS/fJ0OXp8sLYjXLnF8vkxtiEVr+bw6UdHLJ894m6uzbsOBx+Q4kcKyiTovIqKa+qkZnJh2TQUg2/u44UyNRN2bJ63ymZt/2o9F60R3osSJNPZm2XN6dsltGJ+6XXwt0yZvV+Gb4yQx4fukYe7Leybtvd33eF3NNred0+d2/vFfLYkDVyo3GrsZZOnp9xu/GbZF0GwzlRKGIAJH8xABqsbt90t/ElHB3jkJvilkhmTkkQatZ8mzPzWnQK+vquCfJA35V1rUW391jmteXoyo6L5NHBq+Wvw9bWO/0cHeOQRwevbrD88JUZwd4sDRRXVEt+aaWUVFRLYXlV3b1VV+87JZM3ZMrXm7KksLxKjuaXyaG8Umk3fpM8OXydvB6/Wd6bllLvh4Gdp/o9p6s6Lar7fG/vsVRenpAknb7bKe9PT5EPpqdI17m7ZHpStkxPypaFO49Jdm6p7D9VLE6nU4rKq6SiuoZDuxCFOAZA8ldEBkDX6a51GTnyxuTNcoMx4G278Zskbn6qTN6QKd9uPVz3hfuHAatCJvy5FFdUy4o9J2RLVp6cLCyX9BNFMm/7URmyLF1ivtkhH8/cLt0XpMmUDZmycOcx2Zp9ui4UuO556vp/SUW17D1eJGlHC6WovEoKSqsa3GKrqqZWejrSZODSfVJQVlWvRSs6xiFvf7Wl7m4RGSeL5FhBmZRWVkvGySLJOFksW7JOy/ZD+bI+I0fWpJ+SGcnZ0i9hj7wyMUk+m71D+ifslT6L98jsLYdl8oZMGbfmgPRYkCYvjdtUF3xem5QsD/RdKc+O3iCvTUqWp4avkzenbJYPpqfIe9NS5B8j18uzozfIC2M3Srvxm+Tqzg1bPW/rvrRFoezG2AR5a8qWuvHz/jQwUW6KWyL39l4hr8cny7tTt8qXy9Nl4NJ98uqkZPlgeoq8MHajPDlsrQxfmSFLUo/LgVPFMmVjlkzekCnfbDksheVVciS/TFKyT0tC6nHZdaRADuaUSGV1rdTW8p6zRJGMAZD8FREBcMP+XPn35M1ywugH135iks9f7P8ctT7ItQ9NtbVOWbn3ZN3dJFwtV1ahK1Sm+/qskF4Ld0u3+Wny5pTNMmvzIckrqZS0o4V1/S1ra528kpaIbMcASP6KiADY2Jf6f6elyKeztkvsvFTpNj9NXpmYJI8MWi0PD0qUDt/ubPLiCWpcQWlVg75rbTo46k4tX9pBg+F9fVbUDXVzZadF8sLYjfLC2I3y12Fr5YPpKfLWlC3y1pQt0m78Jnl1kl5x/eaUzTJp3UEZt+aAjFtzQKZszJKVe0/KlI1ZMmVjliSkHq/3/PztR+XbrYdlRnK2TNmYVXeRS2F5lVRW18qxgrK619XWOuXAqWL5LuWI5JdWysmicl4oQURnDAbA0PIugCwAFQCSANzZyLI/ANAVwAFj+R0AHvNznVbCPgCWV9V4DX+7jhQEu3oR4XhBuWTmlMihvFLJzCmpay3LL61s0FpWWV0rRR5XvhIRUX0MgKHjOQCVAF4FcB2AsQDyAVzkZfm+AI4CeBzAZQD+A6AcwK1+rNNKWAVAp9MpSQfzpN34TfLs6A3y95HrvXbQn7juYLCrS0RE1CIMgKEjCcBw0+OzoAGvg5flj0Fb98y+BfC1H+u0ElYBsPeiPY2e7r239wrZcThfJm/IbHARAxERUahgAAwN5wCoAfA3j/mTAczz8po8AK97zPsaerq3pesEgB9CdxbXdAnCJAAWlVfVXXDw6ODV0j9hr8xJOSxfb8qSaUnZUlhexaEviIgoLDAAhoZfQT+kezzm94O24lmZBiANwJXQlr2HAZRBT/m2dJ0AEGe8rt4UDgFw0rqDEh2jd8BgZ30iIgpnDIChoSVh7UIAcwHUQlv69gEYAe0H2NJ1AmHaAlhRXSO/N8adm7IhM9jVISIishUDYGho6elaAPgRNKR9D3phSForrNMsJPsA7jpSIO3Gb5Lbui+Vyz9fKHd9sbxuMF7Pe+QSERGFGwbA0JEEYJjp8VkAjsD3CzZ+AGA/gF6tuE4gRAPgm1M2N7jA47oui2VtOu9rSkRE4Y8BMHQ8Bx2r7xUA1wIYAx2y5WLj+SkAepuWvwvA36FDwDwAYAWAgwDOb8Y6fRFSAbCqplb+OmxtXegbvGyfvDIxSYYuT5fTJZXBrh4REVFAMACGlvcAZEMv5EiChjyXRADxpscPAtgNDXi50ID4q2au0xchFQDTjhbWa/Wr5lAuRIFTXihSlm96XKCPM9eJbInXx0QUEAyA5K+QCoALdx6rC3/vTUsJdnWIwseqPiJ9okV6/9b7FHe+SGyUSK/fiPRpo/83T2PaBvtdEEUMBkDyV0gFwOErMyQ6xiF/GpjI24URtaaB1zYMdM2duv2fCIdgIgoIBkDyV0gFwE9nbZfoGIcMWZYe7KoQhQ+nU6T7BRriDq4WObXPehpyc8PQN/BakdOZ7sfmU8REZBsGQPJXSAXAf45aL9ExDpm77Uiwq0IUPsry3QGuqtz7ct+83jAATn9Rn/viEn2ckxGYOhNFOAZA8ldIBECn0ylzUg7X9f/LOFkU7CoRhY+cdKNv368bX27x5w0D4IKP9Lmht+jjrA3215eIGADJbyERANdl5NSFv39NSAp2dcKb0ylSa1xdXZYvkrFcJG2eSOFRvcqzvEDkdJbI8u4iexx6+m/+ByLD7xKZ/ZrIgg9FpvxNpP9VIgOuERl+p/7fapr4eOMtThQYmes0vA29tfHl1g5uGAAT++pz4x/Wx2nz7K8vETEAkt9CIgAOXLKXrX+B4HSKTHhUZOS9Iks6+X9RgC9T5rpgv2tK/U4/i/GPNL7ctmkNP7/NE/W56S/q4+Tx9teXwsfpTJGTu0VqqnUood3zRbbPECnJ1X3r1F73sk6ntjAn9hVJ+VqkskT7pnpeeFRZIrIsVuTbN0Q2TxApOiGStV5k5ssi64aKpHxVv69qZYn+qBURyd0vsn6YyOxXRbZOEdmXoD9qKoq0XulLRWqqRAoOixzbLrLwU1324GqRjaNEMpaJJI0V2ThSZFVvkW1TRbI36fKtjAGQ/HVGBcAVe07Iij0nGsx/bVKyRMc4ZNK6g0GoVQQx9wXr9nP3/4f9rvEQN/JePeB9ebu2BK4dLHIoWQ+6W+L1/8d31p9GP6CvTZ0T7HdNSWP1s5jxUuPL7Uto+Nlvm6rPzX9fH6/qY399KbSkfK1nDAqP1p9/NMW9H439Q/39yvOYkzRWZMyD3o9BfS/V7gi7vtXhjHz58Tn4BpEpT4t88St9vHWyyJe32fNDd1XvVt+sDIDkrzMmAOYWV8hlny+U6BiHLNp5rN5zrnv9JmfmBal2ESJ3f8MD1/Fd+lxNlUh1pXtyOvVXu+v/Ivqvr8OAzGin69802p73EioKDotUFOu/eQd0XvFJkf0rG58yloms/1Jk52z9gl3SSVvo6pZZodt2SSeR1f30X/P/Ezq6p/GP1O/P583+lQ33j4xl+tyKHu4v4oSOIku7aB2cTv2i37/S3QpSmqf/37NQZElnfQ+7F4gkj9OWn1V99L3V8L7eIa+80L2vxP+l/nObJ/geoLpf6P5h+vUzIj1/GZgzFOap128azov/q/v/Q2/VK+VH3a9h9ds3REbdJzLkJm0dbGUMgOSvMyYArtx7su407zWdF8ve43qqd0vWaYmOcUibDg4pruAXgq2yNzY8wBWfsqcsxye6/uXd7Vn/mSonXYPZptG6DVyDK58J09pBjde9ulIHe57ztoa2pV3cgX/rFOt1znnL/f+ev9BWZnPrsuc09o/u/+9dZP/nQfZyXWAUGyUy6Ib6z63s1fx9dPhd+tqKYpG8g3oa1tX67JoGXC0y4THr1w++QY9zO2bqKd1NY0Qy14pMfkr3y56/1LMTEx4T6X+ldmkoyxfJz9bTwPPe077OmyfoepxOkTUDRVb3D/gYmAyA5K8zJgAOWZZe7zZvH0xPkdpapzw6eLVExzjk45nbg13F8Ld7fv2DZdzPRGpr7CkrsZ+WMfdde9ZvJ6dT5Og2/QJyOkWO7dC+SjXV+v8lnfRLafzD2iIw6xWR796xHkevsRaPkfc2Pk14VL9Uu1+g/ZvGPFj/+Yl/1gt0Jj0h4vhYW/gmPaHBc0nn+tPqfiJlp1u+TarKRKY+p1+is9rX34fM7ytrve/bYNOYVvrAKGgy15r26QvqhyTP4GY1pc6p/3jyk9blmC9QmvhnbX3zXNd37wTmPQcIAyD564wJgK5+fv+dllIXAtuN3yTRMQ65oWuC5JdWBruK4c/zlEz/q+wra8skLWPqsy17fWmeyMk97iuWm6O2Vjue5x0UyT8kUl2h851OPVWZ8rWeDq827XM56SJpc/W1s15xf6G1pKVtwDUakma112C28H/6RXlwtbZQxEZpGPf1vVSWNn8b2MV1VffSrtbvPXmc79tpRY/gvhfy365v6n+mpaZuPFOfa3of8OyW8u0b1uVsn+FeZvZr1hexhdnZBgZA8tcZEQCdTqfc0XOZRMc4ZHNmnrTtv6pea+DAJXubXgn5L7Fv/QPmqPvsK2vvIi1jzIP6uLZWQ9Cx7Xp6xmraOkVkcQe98q7HRe56fnmbhqq+lzWcvrxNpN/l+v8BV+t4da5O3+ap72XWX0Aj7jGucD3P9+Aypq12+k4ep1cNru6n/do2jtLO7JUl3rdLTbX7isRQtmGE9bZZ8KHv23H++8F+F+SvjaPqf6Yn0tzPjf590/tAdUX9+04v6WRdzoFVpmU6ax9Sqx8fYYQBkPx1RgTAYwVlEh3jkMs+XyhllTWy70SR9FiQJo8OXi09HWlSWd2CVh7yXW2NXkDg2W/mq7/bV+bhLaZf5t1Exv2pZa1pLZ16XNzw9GRzpg0jtD9Q3PnaT6iqTAdK/vqfeiFDpPNs+XF1oHeNF+ia+l/lfRtPeyHY74L8tSyu/me6dbL7uQFXN/431vu3utyIe9zz1g+zLufkHvcyG0fqUC+e69u9wP73G0AMgOSvMyIAJqQel+gYhzw6eHVQ6xG2ik6IzH1H+4OV5GrrW3mhjm81+zXvQx/Medu+OpXmeT+FOvRW7etjNc38lw40vaSznpbdPEFbBtOXaeuCeTqyRU/nHkrSxwcS9VTR8Z16qrK8UMch2xIvsnex9hFKnaODXefu19PUWyZpa8LWyXrV6h6Hnjp24b1vrZn7fsVGuX9cuG4Z55qmPO09AIx7KNjvIrxVlmoXiPxDeoFDSY7uz5Ul7vn5h7S/65Z4Hc8u9Tsdn2/nbJ3KTutYnsWn9EdQwWH9m9q7WH9Yzn2n4eeauU6fa+oH2LA7tJ5f/d09b8cs6/dSdtq9zPYZOjyR5/rSlwVs0wYCAyD564wIgD0daRId45D/zeaFHrZYO6hhi8ug65tu5Ur5yt56nUjV1r9Fn+m0Z6HIka0t69dHZ5acjPr7kuNj631scQfv+9/gG4P9Ls48TqcGr72Lm162LF/7lw67o/404m49Ndv7t/63pDc2jf2jDoHiOb9PtF516/pB4K0ermFjEjq65x300kjgdLqXSZunfXxdjyf+WaTfFXrlcBhhACR/BT0AVlbXyu09lkp0jEMW7zrW9Auo+Rb+r/EDdfJ4vTpz3RDtY1Oap2PREbWUefy32CjvY765rgb3NlWV6xd3hcUdgCqKrOdbqSqvfwGCP4pP6SlHX6ejKdoitXmi3jVi2gvaF/S7/2hr9sx/afeBaS/o4ylP6wVCU542Hpum4Xe6t83oB/Qq+vSlOqzQhuHaur/H4fuAxnE/894S3+Minb74lQaoumD2Vw3nzQmDrnE/603niWyfLrKipw4R1OMivaq99291LL0Dq3R7l+bpIOUj7238817wkfYprirTxwfXaEt+bU1Y3nKSAZD8FfQAmLjvlETHOOR3PZdJVQ1bfmzhumrVapr+YrBrR+HI3CITG6X3lLba/zwvEoiNqj/Ib9zPdGiZuJ+JDLxWZOB1xnStzu/2c4/5XqYeF+v6Blzd9LKNTQOukWZdEBTsafid2g0ic617MofDPQv188o7oME2fZmGVc9WeFc/4aMp7nmVJcZdPrrp1fw7Z+uV88d26IVM64ZoH8CksXpxU94BPaW8dpCOAXh0W+D2xzDEAEj+CnoAnLIhU6JjHPLvyZuDVoewN+kJ718QCz4Mdu0oXG2J14A26QmRouPuuzm4plW9tUVn7B+1JfC7d/Rq7DUDdADpYNztwdep92/16lRfpr6X6tXnw36npzPnvK1Xpk98XFsFl8XpbRS/eV1DVvJ4bbXfPEFbyDyn7E16KnTzBB0Lsu9l2jo46xVtQRt4rXapyM+2Hpz4aIq2Pib2ZXeLEMYASP4KegDss3iPRMc4pOvcXUGrQ9hr7F6+NtyjkqhOSY779FvxKQ0f+Yd8e21trV68k7tfL7w5mlJ/yjuoz3nOt5pO7tZ70fqybFOT5z1tiYKAAZD8FfQA+MF0Hfh5VOL+oNUh7DV2c/Tk8cGuHRERNRMDIPkr6AHwmdEbJDrGIXO3HQlaHcJaTVXjp7LCbGwsIqJIwABI/gp6ALy/7wqJjnFIcmYrXaFH6uQekfGPNN2X6VBysGtKRETNxABI/gpqAKytdcoVHRdKdIxDDp8+g+5nGoqObdfhH5xOHejYs8O9+XZK5ikcbjtGRBRhGADJX0ENgMcLyutuAcchYPw06AYNdP0urz9MRd/L9ArD5PEiawaKjP2DyJCbddytOW9bXyVIRERnNAZA8ldQA+D6/TkSHeOQB/utDEr5YaOmumHL3tBb2L+PiChMMQCSv4IaAKduypboGIe0n5gUlPLDRvEpd/Cb9oKOoxaGI98TEZFiACR/BTUAuu4BHDc/1b5C8g/pIKnpS+0rI9hc973sEx3smhARUQAwAJK/ghoAX4/fLNExDpmyIdO+QqY+624dC1cH1+j7+/L2YNeEiIgCgAGQ/BXUAPjHAaskOsYha9JP2VeI+ebp4Sr1O31/Ex4Ndk2IiCgAGADJX0ELgDWBGgImEgJg8jh9f9NfDHZNiIgoABgAyV9BC4DZuaUSHeOQKzstktpaG4ciMQfAUBzy5FCySNnpxpdZ1Uff37z/BqZOREQUVAyA5K+gBcDEfackOsYhDw9KtLcgcwCsKLa3rNa2bZoxtt8VIgkdRVb01MGeDyTqlb6r+4scWCUy81+63PJuwa4xEREFAAMg+StoAXDSuoMSHeOQNyZvds+srRGprnQ/djpFjmwV2Zeg97RtCdcAybFRInkH/at0S1RX6CQiUl4osqSTSNrchsudzhJJ7KvDuEx6QuSLS5q+jZvntGFEYN8bEREFBQMg+StoATB2XqpExzik16LdOqO2RmTY70SG3SFyOlNk1zciI++tH3Acn4jkpOttz9KX6XR8p8jhzSK1pjuJOJ067MuaAYG57+3WySJf/V3LTF+qQWxxB5HZr+odNwZeK7JtqshX/3DXZdT9Ios+0+VmvSISd751qOt+ocj6YbrsnLd0G315m8jU50SmPa////I2kfEP87ZuREQRggGQ/BW0APjyhCSJjnHI9KRsnVF8svktXlZTj4u8P7e8u8i3b4oMuFpk+F0iSWNFjqaILOkssnawSPZGkfnv67RjpkjeAZFv/i2S2E9kwDUivX+jt1ZbFKN338jJEOl/VevUOzZKZPQDIoOuF4n/i4bV9KUip/YG/LMhIqIzGwMg+StoAfCBvislOsYhmw7k6oy8gw0D0bA7RAqPuYc5MU+9fysy4u7WC1+tNQ37nZ7CXdJZZGUvrfv0F7XFL/6v2oevNE/vzbsszj3tXRTwz4CIiEITAyD5KygBsLK6Vi7t4JDoGIecLDRuWXZ8lztE7V2kF2zU1uhzeQcaBi3HJ/pcbY2Gx4zl2op4dJvIsli9gGLXNyJjHhQZcrNI/yubDm992nh/bvJTIrvn6+lYz9O1PX8psnGU3pGDiIjIZgyA5K+gBMCMk8USHeOQ67osFqdraJZDSRqmBt/Y8AXlBQ0DWWLf5hdcdFxkz0JtkZv2goa2xH4aJmuq6y+7xyEy8Drtt7dzdv2LUw4kisx4SSTlK5GS3ObXg4iIyA8MgOSvoATApWknJDrGIY8PXeOeuX+lBrsR9zR8gdPZMABunhCYyobi2IFERBTWGADJX0EJgGNXH5DoGIe8O3Wre+buBRrsxj1k/SLPALh7QWAqS0REdIZhACR/BSUAdvh2p0THOGTAEtMVrjtmGn3tnrR+kWcAPJQUmMoSERGdYRgAyV9BCYDPj9ko0TEO+XbrYffMzRM02E17wfpFngEwGIM6ExERnQEYAEPLuwCyAFQASAJwZxPLfwhgH4ByAIcBDAbwI9PzcdAP3zztbWadghIA7/piuUTHOGRr9mn3zPXDNNh987r1izwDYKjd1o2IiKiVMACGjucAVAJ4FcB1AMYCyAdwkZflX4QGxRcBtAHwCIBjAAaZlokDkArgF6bpgmbWK+ABsKi8Sm6ImSU3xsyQ/FLTlbWJfTXYzX/f+oVrBrrD38DrAlNZIiKiMxADYOhIAjDc9PgsAEcBdPCy/HAAKzzmDQSwzvQ4DsB2P+sV8AC4Zu8xOdL1UimO/YXexs1laRcNd4s/t36h06nj/JUXiFSVBaayREREZyAGwNBwDoAaAH/zmD8ZwDwvr3kRQAHcp4kvA7AHQEfTMnEASqEtgwcBTAXw2ybq8kPozuKaLkGAA+A4x3p3S96kJ9xPOD7ReSt6BqwuREREoYgBMDT8Cvoh3eMxvx+0ZdCb9wFUAag2Xj/K4/k/A3gGwE0AHgWwAUA2gJ82ss44NOw3GNAA2HX0VHcANA/6POdtnbd2UMDqQkREFIoYAENDSwJgWwAnAPwbwI0AngZwCECXRso5H0AhgNcbWSboLYAd+w1yB8Bev9Z75e6cLTLzZZ23aUzA6kJERBSKGABDQ0tOAa8F0N9jXjsAZdD+g95sBtC7GXULeB/A7j06NX4/3pSvA1YXIiKiUMQAGDqSAAwzPT4LwBF4vwhkK4C+HvNegAbAs7285lwAp6Gnjn0V8AA4sMvbjQfA1DkBqwsREVEoYgAMHc9Bh3V5BcC1AMZAh4G52Hh+Cuq33MUBKALwPIBLATwMYD+AmaZlBgB4EDpMzL0AlgHIAXBhM+oV0ABYXVMr4zo913gAzFgekLoQERGFKgbA0PIe9CKNSmiL4F2m5xIBxJsefx9ALDT0lUP7/42A9vNzmQG9ArgS2po4A8DlzaxTQANgXkmlfNf5sYahb/KTIsnjReb9V6SqPCB1ISIiClUMgOSvgAbAzJwSWdv5noYBcM7bASmfiIgoHDAAkr8CGgC3H8qXPV2u19DX42J3AFwWF5DyiYiIwgEDIPkroAFwTfopye16iYa+MQ+6A2DS2ICUT0REFA4YAMlfAQ2Ajm3ZUtv1POOevx+4A+DuBQEpn4iIKBwwAJK/AhoAv03cIhIbJbWx54uU5oks6SQy7z2R6oqAlE9ERBQOGADJXwENgDPnLxSJjZLC7m0CUh4REVE4YgAkfwU0AE6fOlEkNkqO97ktIOURERGFIwZA8ldAA2D8yF4isVFyZOgjASmPiIgoHDEAkr8CGgAn9dULP45OaBeQ8oiIiMIRAyD5K6ABcELsyyKxUZI3+6OAlEdERBSOGADJXwELgEXlVXX3Aa5c3Nn28oiIiMIVAyD5K2ABMO1oocR3+oeO+7eip+3lERERhSsGQPJXwALgyj0nZWqnpzQAJvazvTwiIqJwxQBI/gpYAJy77YjM7vwXDYBrB9teHhERUbhiACR/BSwATtmYJXM7P6YBcMMI28sjIiIKVwyA5K+ABcARqzJkYec/aQBMGmt7eUREROGKAZD8FbAA2GfxHlnaua0GwC3xtpdHREQUrhgAyV8BC4CdvtspiZ3v1wC4bZrt5REREYUrBkDyV8AC4PvTU2R957s1AO6cbXt5RERE4YoBkPwVsAD46qRkSe5yhwbAtHm2l0dERBSuGADJXwELgP8ctV62dblFA+DexbaXR0REFK4YAMlfAQuAjw5eLWldbtQAmLHc9vKIiIjCFQMg+StgAfDe3iskvcu1GgAPrrG9PCIionDFAEj+ClgAvCE2QTK7XKkBMHuT7eURERGFKwZA8ldAAmBtrVPadHDIka6XagA8stXW8oiIiMIZAyD5KyABsKCsSqJjHHKq6280AB7fZWt5RERE4YwBkPwVkACYlVsi0TEOye/6Sw2Ap/bZWh4REVE4YwAkfwUkAG4/lC/RMQ4pjb1IA2DeQVvLIyIiCmcMgOSvgATAxH2nJDrGIVWxP9cAWHDE1vKIiIjCGQMg+SsgAXDutiMSHbNAw19slEjxKVvLIyIiCmcMgOSvgATA+PWZckXMXHcALMu3tTwiIqJwxgBI/gpIABy8bJ9cG/ONOwBWltpaHhERUThjACR/BSQAxs5LlZtiZrgDYE21reURERGFMwZA8ldAAuAH01PkjpivjQB4nojTaWt5RERE4YwBkPwVkAD4ysQkuScmXgNg9wttLYuIiCjcMQCSvwISAJ8esU5+32G8BsAvLrG1LCIionDHAEj+CkgA/POQNfJQh9EaAPu0sbUsIiKicMcASP4KSAD8Q/9V8ucOwzUA9r/K1rKIiIjCHQMg+SsgAfCeXsvlyQ5DNAAOusHWsoiIiMIdAyD5KyAB8NbuS+UfHQZoABx6q61lERERhTsGQPJXQALgtV0Wy/Of99UAOPwuW8siIiIKdwyA5C/bA6DT6ZRLOzjklc97aAAc/YBtZREREUUCBkDyl+0BsLK6VqJjHPLO5501AE583LayiIiIIgEDYGh5F0AWgAoASQDubGL5DwHsA1AO4DCAwQB+5Oc6PdkeAAvLqyQ6xiGfdvxUA+DXz9hWFhERUSRgL08VDAAAIABJREFUAAwdzwGoBPAqgOsAjAWQD+AiL8u/CA11LwJoA+ARAMcADPJjnVZsD4Ani8olOsYhcZ3e0wA4q71tZREREUUCBsDQkQRguOnxWQCOAujgZfnhAFZ4zBsIYJ0f67RiewA8lFcq0TEOGdTlTQ2Ac9+1rSwiIqJIwAAYGs4BUAPgbx7zJwOY5+U1LwIogPuU7mUA9gDo6Mc6AeCH0J3FNV0CmwNg+okiiY5xyPjYVzQALoqxrSwiIqJIwAAYGn4F/ZDu8ZjfD9qK5837AKoAVBuvH9UK64wzXldvsjMA7jicL9ExDpnZ/XkNgMu721YWERFRJGAADA0tCWttAZwA8G8ANwJ4GsAhAF38WCcQhBbApIN5Eh3jkEU9ntYAuGagbWURERFFAgbA0NCS07VrAfT3mNcOQBm0r19LTwF7sr0P4Op9pyQ6xiGre/5ZA+CmMbaVRUREFAkYAENHEoBhpsdnATgC7xdsbAXQ12PeC9AAeHYL12nF9gC4JPW4RMc4ZMsXf9QAmPK1bWURERFFAgbA0PEcdFiXVwBcC2AMdMiWi43npwDobVo+DkARgOcBXArgYQD7Acxsxjp9YXsAnLf9qETHOGT3F/dqAEz9zrayiIiIIgEDYGh5D0A2dOy+JAB3mZ5LBBBvevx9ALHQ0FcO7f83AsD5zVinL2wPgDM3H5LoGIdkfXGbBsD0ZbaVRUREFAkYAMlftgfAKRsyJTrGISd6XqcBMGuDbWURERFFAgZAe10W7AoEgO0BcOzqAxId45CCHpdqADy2w7ayiIiIIgEDoL2cAFZBr771vAdvuLA9AH65PF2iYxxS0e1iDYC5+20ri4iIKBIwANrrFgBDAZyC3pVjDNx35ggXtgfA/gl7JTpmgThjz9MAWHTCtrKIiIgiAQNgYHwfwN8BzIfemSMVwMcALgxmpVqJ7QHwi4W75daYaRr+YqNEqitsK4uIiCgSMAAG1g8BfAQdesVp/DsFwC+DWSk/2R4AY+elypMdhmj4G3C1beUQERFFCgbAwLgDwEgApwEcBtATOjbfAwCWA0gOXtX8ZnsA7Dhnp7zzeWcNgOMfsa0cIiKiSMEAaK+PAeyCnvadC+Av0LttmP0aeku2UGV7APx01nbp3fFNDYDf/Nu2coiIiCIFA6C9MgB8jsZP8Z4DvRNHqLI9AH4wPUW+6vS0BsAVPWwrh4iIKFIwAJK/bA+A73y9VVZ1fkAD4NYptpVDREQUKRgA7fUqgGcs5j+D0G71M7M9AL4ev1n2dLleA2DGctvKISIiihQMgPZKB/AHi/kPAtgX4LrYxfYA+K8JSZLb9RINgCdSbSuHiIgoUjAA2qsCQBuL+W0AlAe0JvaxPQC2G7PGPQZgSa5t5RAREUUKBkB7HQLwpMX8pwAcCXBd7GJ7AHxj2FyR2Cipjfu5SG2tbeUQERFFCgZAe/UFkAU9DXy2Mf3RmDcgaLVqXbYHwI8HTRSJjZLyPlfZVgYREVEkYQC01zkAZkLv+lFlTDUAJhrPhQP77wTSr79IbJQUDb3PtjKIiIgiCQNgYFwFvfL3LwCig1yX1mZ7ABzQ63OR2CjJG/e0bWUQERFFEgZA8pftAXBcz7dEYqPk1NQ3bSuDiIgokjAA2u/XAN4B0AfAII8pHNgeACd3by8SGyU5sz+2rQwiIqJIwgBor4cAlELvB1wNYBuAfAAFAFYGsV6tyfYA+FXciyKxUZI793PbyiAiIookDID2SgbQzfh/MYDLAJwLYB6A/wSrUq3M9gA4LfYZkdgoOb0g1rYyiIiIIgkDoL2KAVxu/D8fwPXG/2+GDgUTDmwPgDO7/E0kNkoKEr6wrQwiIqJIwgBorxMArjX+vxvuQaFvBlASlBq1PtsD4OzOfxGJjZLi5QNsK4OIiCiSMADaay6AN4z/DwCQAaATgK0AlgerUq3M1gBYU+uUuZ0fE4mNktLVX9pSBhERUaRhALTXZQBuMv7/EwCjAewE8C3CZzxAWwNgeVWNLOr8kEhslFRsGGNLGURERJGGAdA+ZwP4PYDzg10Rm9kaAAvKqmRZ5wdFYqOkOjneljKIiIgiDQOgvSoAXBrsStjM1gCYU1whqzvfJxIbJc7t020pg4iIKNIwANprC3QswHBmawA8VlAmGzrfLRIbJbLrW1vKICIiijQMgPZ6DDr4818A/BK6kc1TOLA1AGbllsjmLndoANy9wJYyiIiIIg0DoL2cpqnWNLkehwNbA2DGySLZ3uUWDYD7lthSBhERUaRhALTXg01M4cDWAJh2tFB2d7leA+D+lbaUQUREFGkYAMlftgbAbYfyZX+XqzUAZq6zpQwiIqJIwwBor983MYUDWwNgcmaeZHe5XAPgoWRbyiAiIoo0DID2clpM5r6A4cDWALg+I0eOdW2jAfDoNlvKICIiijQMgPY6z2O6AMDDADYhfIaHsTUArtx7UnK6/loD4MndtpRBREQUaRgAg+NB6P2Aw4GtAXBJ6nEp7HqxBsDc/baUQUREFGkYAIPjGgAlwa5EK7E1ADp2HJPyrv+nATA/25YyiIiIIg0DoL1u8phuhg4OnQhgXfCq1apsDYBzUg5LTdfzNAAWHbelDCIiokjDAGgv10UfnheCbIC2AoYDWwPgrKRMDX+xUSKlebaUQUREFGkYAO0V7TH9BsCPglqj1mdrAJy2bq87AFYU2VIGERFRpGEAJH/ZGgCnrNrhDoDVFbaUQUREFGkYAO31JYD3Lea/B2BIgOtiF1sDYPzSZHcAdDptKYOIiCjSMADa6yiA2y3m3wbgSIDrYhdbA+DERWtFYqOkOu7ntqyfiIgoEjEA2qsCwBUW868wnmuJdwFkGa9PAnBnI8smQj9cz2mhaZl4i+cTmlEfWwPg+HkrRGKjpKLbxbasn4iIKBIxANorFXq619N/AexuwfqeA1AJ4FUA1wEYCyAfwEVelv85gF+YpusB1ABob1omHsBij+V+1ow62RoAx3y7WCQ2Ssq6X2LL+omIiCIRA6C9XgNQBqAb9O4fDwLoDqAUwBstWF8SgOGmx2dBTzN38PH1HwIoAvAT07x4AHNbUBcXWwPg6JnzRGKjpLhnG1vWT0REFIkYAO33H2h/P9cYgAcB/KsF6zkH2nr3N4/5kwHM83Edu6CthmbxAAoAnAKwD8AoAP/XyDp+CN1ZXNMlsDEAjpo6SyQ2Sgq/uNKW9RMREUUiBsDAuRDAuX68/lfQD+oej/n9oC2DTbnTeL1nn8HnATwJ4EZouNwNIBnA2V7WEweLfoV2BcCRk78SiY2S/N7X2bJ+IiKiSMQAaK9LAVxpMf9KAG2auS5/A+AYADt9WO4yo5yHvDwf0BbAERMniMRGyem+N9uyfiIiokjEAGiv1QBesZjfDnqFbnP4cwr4JwAKAXzgY1k5AN7ycVlb+wAOGztaJDZK8vrfYcv6iYiIIhEDoL2K4H0YmIIWrC8JwDDT47Og/QubugikPXTYmMb69rn8GtpX8Ukf62RrAPxy1HCR2CjJHXiXLesnIiKKRAyA9ioEcKvF/NsBFLdgfc9Bg9wrAK6FntbNB3Cx8fwUAL0tXrcWwAyL+ecC6A/gbugp6YcAbAWQDj3V6wtbA+DQEUM0AA6+z5b1ExERRSIGQHstADAL9S+oOBvAN9Cx91riPQDZ0PEAkwDcZXouEXpVr9nV0A/4YYt1/RjAEugVwFXQAabHwh0ofWFrABz85QANgEPb2rJ+IiKiSMQAaK/rAOQC2A9gkjHth/axuyGI9WpNtgbAQYP7aAAc9pAt6yciIopEDID2+xWAXtDbr30DoCv0ThsMgD4YOLCHBsARj9qyfiIiokjEABhYUQDehJ66rQ1yXVqLrQFwQL9YvQp41BO2rJ+IiCgSMQAGxu+hw7WUQC+w6APgd0GtUeuxNwD26aQBcOxTtqyfiIgoEjEA2ucX0OFZMgCchA7fUg3tFxhO7A2AvTroQNDj/2HL+omIiCIRA6A9FkCHgJkG4Am4rwJmAGym/j0+1VvBTXrOlvUTERFFIgZAe9QAGISGt4FjAGym/t0+FImNkoLJL9myfiIiokjEAGiPuwGMg94JJAk6dt8FYABstgFx74nERknh1Pa2rJ+IiCgSMQDa6ycAXgOwDjrQci30frw/DWalWpm9AbDL2yKxUVI84w1b1k9ERBSJGAAD52oA/QAcB1AOYH5wq9Nq7B0HsPO/RWKjpHT2f2xZPxERUSRiAAy8swH8DQyATXI6nTKo46sisVFSNue/rb5+IiKiSMUASP6yLQBWVtfK0E7/EomNkop5H7f6+omIiCIVAyD5y7YAWFpZLSM6tROJjZIqx2etvn4iIqJIxQBI/rItABaWV8noTi+IxEZJ9eKOrb5+IiKiSMUASP6yLQDmlVTK+E7PicRGiXNpbKuvn4iIKFIxAJK/bAuAJwvLZVKnf4rERoms6NHq6yciIopUDIDkL9sC4JH8Mvmq09MaAFf1afX1ExERRSoGQPKXbQEwK7dEpv3/9u48PKr67vv4R7RABAeQKm41WhCr1hUtone1ti6NaJe79aGbVcpj1du21lqfKEIGwaUFcaVFwSWoiOJuEUS4WbQCgQKyiGLYl0RZZE8IhHyfP36TzGSywjk/cHLer+s6l5k5J18OJxjenDNzcvePXQBOHRT6fAAAoooARFDeArDwi232Sp+rXAB+8HDo8wEAiCoCEEF5C8BPi7fa631yXAB++Hjo8wEAiCoCEEF5C8AFazbb230udwE4fWjo8wEAiCoCEEF5C8C5qzbZO30udQFYMCz0+QAARBUBiKC8BeCs5RttfJ9LXADOeib0+QAARBUBiKC8BeC0JRtsYp+LXQDOfi70+QAARBUBiKC8BeD7n62zKX3+ywXg3BdDnw8AQFQRgAjKWwBO+uQL+6BPNxeA818JfT4AAFFFACIobwH43sef2/S+XV0ALnw99PkAAEQVAYigvAXg2PlFNrPvuS4AF70d+nwAAKKKAERQ3gLwrY/W2ty+Z7sA/HRs6PMBAIgqAhBBeQvA12avtvl9z3AB+Nl7oc8HACCqCEAE5S0AX565yhb1/bYLwCX/G/p8AACiigBEUN4C8IUZK2xx31NcAC6bGvp8AACiigBEUN4CcMS05bak78kuAFd8GPp8AACiigBEUN4C8KkPltmKvp1cAK4qCH0+AABRRQAiKG8B+MSUJbY675suANfMDn0+AABRRQAiKG8BOGRSoRXnZbsALJoX+nwAAKKKAERQ3gLw4QmLbV3ecS4AP/849PkAAEQVAYigvAXgoHc/tS/zjnEBuG5x6PMBAIgqAhBBeQvA+8cusi15HVwAblgS+nwAAKKKAERQ3gJwwL8+tpK8r7sA/HJF6PMBAIgqAhBBeQvA+FsLrSyvnQvAzWtCnw8AQFQRgAjKWwD2fn2+7clr4wJw6+ehzwcAIKoIQATlLQBzR89x8RePmW3fEPp8AACiigDMPLdIWiFpp6QCSd+pZ9spcl/c9OWdlG0OktRfUrGkUkkTJZ20F/vjLQD/36jpyQDcuS30+QAARBUBmFl6SCqT1FPSqZKGSdok6cg6tj9c0lEpy2mSyiVdn7JNrqTNkn4s6QxJb0laJqllI/fJ3xnA56ckA7B8d+jzAQCIKgIwsxRIGpLyuJmktZLubOTn/1nSVkmtEo8Pkjvz99eUbdrInV38RSNn+gvA/PFm8Zjt6dcu9NkAAEQZAZg5msudvftJ2vMj5M7aNcYCubOGlb4p98U/K227qZIerWNGC7k/LJXLsfIUgHc+9bZZPGa7+x8Z+mwAAKKMAMwcx8h9obqlPT9Q7sxgQ76T+PzU1wxekHju6LRtR0t6uY45/VTL6wq9vAv4yVfM4jHbee83Qp8NAECUEYCZI2gAPilpftpz+xKA++0MYO9/jjSLx6zkgU6hzwYAIMoIwMwR5BJwK0lbJN2a9vy+XAJO5+01gH0ef8YsHrPtfz819NkAAEQZAZhZCiQ9nvK4maQ1avhNINfLvbGjfdrzlW8CuT3luZi+Im8CiT861Cwes60Pnh36bAAAoowAzCw95OLsOkmnyF3W3SSpQ2L9c5IeqOXzPpD0Uh0zcxMzfiTpdElv6ityG5gBjzxmFo/Z5ofPD302AABRRgBmnj9IWil3P8ACSV1T1k2RlJ+2/clyX+DL6phXeSPoz+XicqKkznuxP94C8IGHHjSLx2zToxeFPhsAgCgjABGUtwAcNPg+s3jMNg65LPTZAABEGQGIoLwF4MMP3mMWj9n6od1Dnw0AQJQRgAjKWwAOGdTbLB6zdcP+O/TZAABEGQGIoLwF4BN//6tZPGZfPP3L0GcDABBlBCCC8haAT/3tVrN4zIrzrw99NgAAUUYAIihvATjigZvN4jErev73oc8GACDKCEAE5S0AX7y/l1k8Zmtf/GPoswEAiDICEEF5C8DR9/3WLB6zNaP/GvpsAACijABEUN4C8I17f2EWj9nq1+4OfTYAAFFGACIobwE4ZsB/uwB8q3/oswEAiDICEEF5C8Dx/a82i8ds1Zi/hz4bAIAoIwARlLcAnNT/h2bxmK0c90joswEAiDICEEF5C8B/3/N9F4AThoY+GwCAKCMAEZS3AJx5z3ddAE5+JvTZAABEGQGIoLwF4Jx+57sAfP+F0GcDABBlBCCC8haAH/U7zwXgv18KfTYAAFFGACIobwG4oF8X9y7gaa+GPhsAgCgjABGUtwD8OH6Wuw/gjDdCnw0AQJQRgAjKWwB+Gj/D/Si4Wf8KfTYAAFFGACIobwH4Wfw0F4Czx4Y+GwCAKCMAEZS3AFwaP8UsHrO1c98LfTYAAFFGACIobwG4PO9ks3jMiuZPCn02AABRRgAiKG8BuCqvk1k8ZsUL3w99NgAAUUYAIihvAbg275suABd9GPpsAACijABEUN4C8PO8bLN4zL5YXBD6bAAAoowARFDeAnBd3jfM4jFbt2R26LMBAIgyAhBBeQvAjXnHmMVjtn7ZR6HPBgAgyghABOUtALfkHWUWj9mGFQtDnw0AQJQRgAjKWwBuyzvSLB6zjas/CX02AABRRgAiKC8BWFFRYaV57c3iMdu0dkmoswEAiDoCEEF5CcDyPRVWltfOLB6zzZ8vD3U2AABRRwAiKC8BuKt8j+3Ja2MWj9mWdatDnQ0AQNQRgAjKSwDu3LXLLB4zi8ds28aiUGcDABB1BCCC8hKApaWlVQG4fdP6UGcDABB1BCCC8hKAO3ZsqwrAkq1fhjobAICoIwARlJcA3LZ1U1UAlu7YGupsAACijgBEUF4CcMumDVUBWFZaEupsAACijgBEUH4CcMPnVQG4e9euUGcDABB1BCCC8hKAm75YXRWAe8r3hDobAICoIwARlJcA3Fi8wp39y2trFRUVoc4GACDqCEAE5SUAN6xZYhaP2c689qHOBQAABCCC8xKA61d9ahaP2Y68I0KdCwAACEAE5yUA1y3/2Cwes615HUKdCwAACMBMc4ukFZJ2SiqQ9J0Gtm8r6R+SiiWVSfpM0pUp6/vJffFTl0/3cp+8BOAXy+aZxWO2Ke/oUOcCAAACMJP0kIu4npJOlTRM0iZJR9axfXNJsyS9I+lCSSdIuljSmSnb9JO0UNJRKcvX93K/vATg54WzzeIx25B3XKhzAQAAAZhJCiQNSXncTNJaSXfWsf1NkpZK+lo9M/tJ+ijgfnkJwOJPZ5rFY7Yu7/hQ5wIAAAIwUzSXVC7pJ2nPj5D0Vh2fM1bSC3JnCr+QO9PXW9LBKdv0k7RDUpGkZZJGSjq+gX1pIfeHpXI5Vh4CsGjRNLN4zIryTgx1LgAAIAAzxTFyX6Ruac8PlDszWJtP5V4r+LSkLnKXkDdKiqdskyPpGklnSLpC0jRJKyUdVs++9FPN1w2GHoBrF35gFo/ZmryOoc4FAAAEYKbYlwD8TNIqVT/j9xe5N4TUpa2kLZJ61bPNfjkDuGbeJLN4zFbGTwp1LgAAIAAzxb5cAp4qaWLaczlyX+zm9fxasyQ9sBf75uU1gKvnTjCLx2x5/ORQ5wIAAAIwkxRIejzlcTNJa1T3m0Dul7tlTLOU526Ve71fXVpL+lLSn/Ziv/wE4OxxZvGYLYmfGupcAABAAGaSHnKv6btO0imSnpS7DUyHxPrnVP3M3TckbZWLxs6Susu9GeTulG0elLs1zAmSLpA0QdJ6SUfsxX55CcBVM8eYxWP2Wfzboc4FAAAEYKb5g9ybNMrkzgh2TVk3RVJ+2vbdJM2QC8elqvku4JfkzgiWyZ1NfElSx73cJy8BuHLGm2bxmH0SPzPUuQAAgABEcF4CcMW0V83iMfu439mhzgUAAAQggvMTgP8ebRaP2fx+54Y6FwAAEIAIzksALn9/pFk8Zh/16xrqXAAAQAAiOD8BOOV5s3jM5tzTLdS5AIDalZeXW2lpKUsTWcrLy+v9ehOACMpLAC6b9KxZPGaz7vmvUOcCAKqrqKiwoqIiW7RoEUsTW4qKiqyioqLWrzsBiKD8BODE4WbxmBX0vyjUuQCA6irjb8OGDVZSUnLAz1yxBF9KSkpsw4YNVRFYGwIQQfkJwPeeMIvHbHr/S0KdCwBIKi8vr4o/ND2VEVjb5WACEEF5CcCl44aYxWP2Yf8fhDoXAJBUWlpqixYtspKSkgO9K/CgpKTEFi1aZKWlpTXWEYAIyk8Ajn3ULB6zDwZcHupcAEBSZQDWFgjIfPV9fQlABOUnAMcMNovHbOqAnFDnAgCSCMCmjQCET34C8O2BZvGYTb73qlDnAgCSCMDadejQwYYOHXqgdyMwAhA+eQnAJW/ebxaP2f/e96NQ5wIAkjI1AOXCpc4lHo8Hmr9u3bom8bpIAhA++QnA1weYxWM24f6fhjoXAJCUqQFYXFxctTzyyCMWi8WqPbdt27Yan1NRUWG7d+8+AHt74BCA8MnPJeBX+5nFY/be/T8LdS4AIClTAzDVs88+a23atKnx/Lhx40ySjR8/3s4880w75JBDbPr06fbJJ59Y9+7d7YgjjrDWrVtb165dbfLkydU+N/UScGlpqUmy/Px86969u2VlZVnnzp1t7Nix++O3FwgBCJ/8nAF8pa9ZPGbjHugR6lwAQFJtgVBRUWE7ynbv96Wun1jRkIYC8JxzzrFJkyZZYWGhbdq0yWbNmmXDhw+3hQsX2uLFi+2OO+6wVq1aVbthcm0BmJ2dbaNHj7bCwkK78cYbrW3btqH/3Rc2AhA++QnAl+8yi8ds7N9+GepcAEBSbYGwo2y3ZeeO2e/LjrJ9uzzbUAC+++67Dc7o2LGjDR8+vOpxbQF47733Vq3fuHGjSapx5vCrhgCET14C8P3p0+z6uwbYnx99IdS5AICkKARg+k852bx5s91666128sknW5s2baxVq1bWrFkz69u3b9U2tQXg22+/XW1O8+bN7eWXX96nfd5fCED45CUAxy0osuzcMfbzoR+GOhcAkBSFS8Dp8XPddddZ586d7Y033rD58+dbYWGhfetb37Lc3NyqbWoLwHHjxlWb06JFCxs1atQ+7fP+QgDCJy8B+M58F4DXPDEt1LkAgKQovAkk/ffWqVMnGzhwYNXjTZs2WatWrQhAYC95CcB/zVtr2bljrMeTBCAA+BLFAMzJybHzzjvP5s2bZ3PmzLGcnBw77LDDCEBgL3kJwLc+cgH4y2HTQ50LAEiKYgAWFhbaRRddZFlZWZadnW3Dhg2zrl27EoDAXvISgG/OXWPZuWPs18NnhDoXAJDUFAIQdSMA4ZOXAHx9zmrLzh1jv3mKAAQAXwjApo0AhE9eAvDV/7gA/O3TBaHOBQAkEYBNGwEIn7wE4OhZqyw7d4xd/wwBCAC+EIBNGwEIn7wE4MszXQD+7tmZoc4FACQRgE0bAQifvATgqIKVlp07xnrlzwp1LgAgiQBs2ghA+OQlAEfOcAF4wwgCEAB8IQCbNgIQPnkJwOenr7Ds3DF243P/CXUuACCJAGzaCED45CUAn5u23LJzx9jNLxCAAOALAdi0EYDwyUsA5n/oAvB/Rs4OdS4AIIkAbNoIQPjkJQCf+fcyy84dY394cU6ocwEASVEPwF//+tf2s5/9rOrxhRdeaLfffnu9n3Psscfa448/HvjXDmtOfQhA+OQlAJ/6wAXgn0YRgADgS6YG4FVXXWVXXHFFrevef/99k2Tz5s1rcE56AG7cuNG2bt1a7+fsbbgNHz7c2rdvX+P5devW2Y4dOxo9Z18QgPDJSwAOf3+pZeeOsT+/NDfUuQCApEwNwDfeeMOaNWtmq1evrrGuZ8+edu655zZqTnoANkZYAbg/EIDwyUsAPjl1iWXnjrHbXiYAAcCXTA3A3bt3W4cOHWzAgAHVnt+2bZu1bt3ahg4dart27bKePXtadna2tWzZ0jp37myPPfZYte0bugRcXFxs3bt3t5YtW9qJJ55oo0aNqhGAAwcOtNNOO82ysrLsuOOOs1tuucW2b99uZmYTJkyojKyqpXKf0+csX77crr76ajv00EMtFotZjx49bN26dVXr7777buvSpYvl5+fb8ccfb7FYzH71q1/Ztm3b6jxOBCB88hKAQ6e4ALx99EehzgUAJNUaCBUVZmXb9/9SUbFX+37HHXdYx44drSLl85555hnLysqyzZs3W2lpqcXjcZs1a5YtW7bMnnvuOcvKyrLXXnutavuGAvCyyy6zs88+2woKCmzmzJl2/vnnW8uWLauF20MPPWSTJ0+25cuX28SJE+2kk06yP/7xj2ZmVlZWZoMHD7bDDz/ciouLrbi4uCoOUwNwz549dvrpp9vFF19ss2fPtunTp9tZZ51lP/jBD6p+nbvvvttat25t11xzjS1YsMCmTJliRx55pOXl5e3d1zeBAERQXgLwH5MLLTt3jN3xCgEIAL7UGghl283isf2/lG3fq33/5JNPTJJNnjy56rnvfve79pvf/KbOz7nxxhutR48eVY9Xh5pjAAANb0lEQVTrC8CPP/7YJNmcOcnXoi9YsMAk1XsJeNSoUdahQ4eqx3VdAk4NwLFjx9ohhxxia9eurVo/b968ar9+ZQBWBqSZ2W233WYXXnhhnftCAMInLwE4ZJILwNxXG34RLwBg32RyAJqZXXDBBXbttdeamVlhYWGNIHzsscfsnHPOsfbt21urVq3sa1/7mnXr1q1qfX0B+Oqrr1qLFi2qnWE0MzvssMOqBeD48ePtkksusaOPPtpatWplLVu2NEm2c+dOM2tcAA4ePNg6depUY5vWrVvbyJEjzcwF4BlnnFFt/cCBA+2kk06q8/gQgPDJSwA+NvEzy84dY3e+Nj/UuQCApEy+BGxm9vTTT9uhhx5qW7dutd69e1e7JPz8889bVlaWDR061ObMmWOFhYXWq1cv69KlS9XnBw3AJUuWWPPmze0vf/mLzZgxwxYvXmzDhg0zSVWvzQszAFP33cxs0KBB1rFjxzqPDwEIn7wE4CMTXAD2fp0ABABfMvVNIJUq3/TxxBNP2HHHHWf33Xdf1bqbbrrJLr/88mrbX3zxxY0OwNouAS9cuLDaJeCXXnrJWrRoUe3XiMfj1QJwxIgR1rZt2xr73thLwHPnujdDEoD4qvESgA+9t9iyc8dYnzcWhDoXAJCU6QFoZtarVy9r166dHXzwwdUCavDgwdamTRt77733bPHixXbXXXdZLBZrdACamV166aXWpUsXmzlzps2aNcsuuOCCam8C+c9//lMVhEuXLrX8/Hw7+uijqwXg1KlT7aCDDrLJkyfb+vXrraSkxMxqfxPI9773PZszZ45Nnz7dzj777BpvAiEA8VXiJQBnr/zSnvpgmX1YuD7UuQCApKYQgNOmTTNJduWVV1Z7vrS01K699lpr06aNtWvXzm655Ra744479ioAi4qKLCcnx1q0aGHZ2dk2cuTIGrdvGTRokB111FGWlZVlOTk5lp+fXy0AKyoq7IYbbrD27ds3eBuYq666qsHbwKQiAHEgeQlAAIB/TSEAUTcCsOm4RdIKSTslFUj6TgPbt5X0D0nFksokfSbpyoAz0xGAAJChCMCmjQBsGnrIRVxPSadKGiZpk6Qj69i+uaRZkt6RdKGkEyRdLOnMADNrQwACQIYiAJs2ArBpKJA0JOVxM0lrJd1Zx/Y3SVoq6WshzqwNAQgAGYoAbNoIwMzXXFK5pJ+kPT9C0lt1fM5YSS/IndX7QtJCSb0lHRxgpiS1kPvDUrkcKwIQADISAdi0EYCZ7xi5L1K3tOcHyp3Fq82ncq/re1pSF7nLvRslxQPMlKR+SvvB1iIAASAjVQZC5a1J0LSUlJQQgBluX2LtM0mrlDzjJ0l/kXtDyL7OlDgDCABNRnl5uS1atMg2bNhwoHcFHmzYsMEWLVpk5eXlNdYRgJlhXy7XTpU0Me25HLkvdvN9nFkbXgMIABmsqKioKgJLSkqstLSUJcOXkpKSqvgrKiqq9etOAGaOAkmPpzxuJmmN6n7Dxv1yt3dplvLcrZKKAsysDQEIABmsoqKiKgJZmtZSVFRU42cZVyIAM0cPudf0XSfpFElPyt2ypUNi/XOSHkjZ/huStsoFXmdJ3eXeDHL3XsxsDAIQAJqA8vLyA37miiW8pbbLvqkIwMzyB0kr5e7dVyCpa8q6KZLy07bvJmmGXOQtVfV3ATdmZmMQgAAAZBgCEEERgAAAZBgCEEERgAAAZBgCEEERgAAAZBgCEEHFJNnq1atty5YtLCwsLCwsLBmwrF69mgBEIMeqlp8MwsLCwsLCwpIRy7EC9sFBcn94Yh6Wyrj0Nb+pLBwnjhXHiuOUCQvH6qt3nI6V+3sc+EqJyf1PEDvQO/IVx3FqPI5V43GsGofj1Hgcq8bhOCHy+J+gcThOjcexajyOVeNwnBqPY9U4HCdEHv8TNA7HqfE4Vo3HsWocjlPjcawah+OEyGshqV/iv6gbx6nxOFaNx7FqHI5T43GsGofjBAAAAAAAAAAAAAAAAAAAAAAAAOCr7RZJKyTtlFQg6TsHdG8OjIsk/UtSkdwtAX6Stv4gSf0lFUsqlTRR0klp2xwuaaSkrZI2S3paUmt/u3xA3CVplqRtktZJelPSyWnbtJT0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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusão\n",
      "[[1787  111]\n",
      " [   7   57]]\n",
      "\n",
      "Train Loss:       0.0635\n",
      "Validation Loss:  0.0889\n",
      "Accuracy:         0.9399\n",
      "MSE:              0.0601\n",
      "AUROC:            0.9476\n"
     ]
    }
   ],
   "source": [
    "plot_training_error_curves(history, graficoName + \"(10, 15)\")\n",
    "plot_training_acc_curves(history, graficoName + \"(10, 15)\")\n",
    "\n",
    "## Fazer predições no conjunto de teste\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred_class = classifier.predict_classes(X_test, verbose=0)\n",
    "\n",
    "## Matriz de confusão\n",
    "print('Matriz de confusão')\n",
    "print(confusion_matrix(y_test, y_pred_class))\n",
    "\n",
    "## Computar métricas de desempenho\n",
    "losses = extract_final_losses(history)\n",
    "print()\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Train Loss:\", value=losses['train_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Validation Loss:\", value=losses['val_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Accuracy:\", value=accuracy_score(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"MSE:\", value=mean_squared_error(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"AUROC:\", value=roc_auc_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNA 4 (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7592 samples, validate on 3796 samples\n",
      "Epoch 1/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1988 - acc: 0.7650 - val_loss: 0.1815 - val_acc: 0.8396\n",
      "Epoch 2/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.1072 - acc: 0.8915 - val_loss: 0.1445 - val_acc: 0.8633\n",
      "Epoch 3/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0964 - acc: 0.8971 - val_loss: 0.1303 - val_acc: 0.8767\n",
      "Epoch 4/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0908 - acc: 0.8983 - val_loss: 0.1225 - val_acc: 0.8749\n",
      "Epoch 5/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0873 - acc: 0.8981 - val_loss: 0.1166 - val_acc: 0.8754\n",
      "Epoch 6/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0846 - acc: 0.8984 - val_loss: 0.1108 - val_acc: 0.8796\n",
      "Epoch 7/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0824 - acc: 0.9016 - val_loss: 0.1085 - val_acc: 0.8783\n",
      "Epoch 8/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0808 - acc: 0.9032 - val_loss: 0.1050 - val_acc: 0.8904\n",
      "Epoch 9/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0792 - acc: 0.9038 - val_loss: 0.1038 - val_acc: 0.8896\n",
      "Epoch 10/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0778 - acc: 0.9063 - val_loss: 0.1024 - val_acc: 0.8851\n",
      "Epoch 11/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0766 - acc: 0.9082 - val_loss: 0.1016 - val_acc: 0.8836\n",
      "Epoch 12/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0754 - acc: 0.9104 - val_loss: 0.0998 - val_acc: 0.8838\n",
      "Epoch 13/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0745 - acc: 0.9120 - val_loss: 0.0989 - val_acc: 0.8841\n",
      "Epoch 14/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0736 - acc: 0.9156 - val_loss: 0.0972 - val_acc: 0.8862\n",
      "Epoch 15/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0729 - acc: 0.9156 - val_loss: 0.0968 - val_acc: 0.8988\n",
      "Epoch 16/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0722 - acc: 0.9169 - val_loss: 0.0963 - val_acc: 0.8973\n",
      "Epoch 17/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0714 - acc: 0.9182 - val_loss: 0.0955 - val_acc: 0.8930\n",
      "Epoch 18/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0709 - acc: 0.9165 - val_loss: 0.0948 - val_acc: 0.8949\n",
      "Epoch 19/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0703 - acc: 0.9183 - val_loss: 0.0943 - val_acc: 0.8988\n",
      "Epoch 20/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0697 - acc: 0.9179 - val_loss: 0.0934 - val_acc: 0.9038\n",
      "Epoch 21/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0692 - acc: 0.9202 - val_loss: 0.0931 - val_acc: 0.9002\n",
      "Epoch 22/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0686 - acc: 0.9190 - val_loss: 0.0927 - val_acc: 0.8983\n",
      "Epoch 23/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0681 - acc: 0.9208 - val_loss: 0.0923 - val_acc: 0.8991\n",
      "Epoch 24/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0675 - acc: 0.9200 - val_loss: 0.0917 - val_acc: 0.8983\n",
      "Epoch 25/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0671 - acc: 0.9206 - val_loss: 0.0920 - val_acc: 0.9020\n",
      "Epoch 26/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0667 - acc: 0.9216 - val_loss: 0.0919 - val_acc: 0.8962\n",
      "Epoch 27/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0663 - acc: 0.9215 - val_loss: 0.0913 - val_acc: 0.8991\n",
      "Epoch 28/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0660 - acc: 0.9226 - val_loss: 0.0916 - val_acc: 0.8970\n",
      "Epoch 29/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0657 - acc: 0.9229 - val_loss: 0.0915 - val_acc: 0.8973\n",
      "Epoch 30/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0653 - acc: 0.9253 - val_loss: 0.0921 - val_acc: 0.9002\n",
      "Epoch 31/100000\n",
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      "Epoch 125/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0540 - acc: 0.9468 - val_loss: 0.0972 - val_acc: 0.8857\n",
      "Epoch 126/100000\n",
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      "Epoch 249/100000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7592/7592 [==============================] - 0s - loss: 0.0476 - acc: 0.9523 - val_loss: 0.1101 - val_acc: 0.8749\n",
      "Epoch 250/100000\n",
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      "7592/7592 [==============================] - 0s - loss: 0.0461 - acc: 0.9544 - val_loss: 0.1138 - val_acc: 0.8754\n",
      "Epoch 291/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0461 - acc: 0.9539 - val_loss: 0.1163 - val_acc: 0.8688\n",
      "Epoch 292/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0461 - acc: 0.9540 - val_loss: 0.1137 - val_acc: 0.8659\n",
      "Epoch 293/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0461 - acc: 0.9544 - val_loss: 0.1147 - val_acc: 0.8683\n",
      "Epoch 294/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0460 - acc: 0.9543 - val_loss: 0.1157 - val_acc: 0.8678\n",
      "Epoch 295/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0460 - acc: 0.9543 - val_loss: 0.1143 - val_acc: 0.8757\n",
      "Epoch 296/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0460 - acc: 0.9538 - val_loss: 0.1160 - val_acc: 0.8678\n",
      "Epoch 297/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0459 - acc: 0.9538 - val_loss: 0.1149 - val_acc: 0.8670\n",
      "Epoch 298/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0459 - acc: 0.9543 - val_loss: 0.1184 - val_acc: 0.8614\n",
      "Epoch 299/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0459 - acc: 0.9544 - val_loss: 0.1162 - val_acc: 0.8764\n",
      "Epoch 300/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0458 - acc: 0.9544 - val_loss: 0.1175 - val_acc: 0.8683\n",
      "Epoch 301/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0458 - acc: 0.9540 - val_loss: 0.1146 - val_acc: 0.8762\n",
      "Epoch 302/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0458 - acc: 0.9544 - val_loss: 0.1179 - val_acc: 0.8678\n",
      "Epoch 303/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0457 - acc: 0.9542 - val_loss: 0.1147 - val_acc: 0.8678\n",
      "Epoch 304/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0457 - acc: 0.9543 - val_loss: 0.1147 - val_acc: 0.8746\n",
      "Epoch 305/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0457 - acc: 0.9540 - val_loss: 0.1169 - val_acc: 0.8606\n",
      "Epoch 306/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0457 - acc: 0.9543 - val_loss: 0.1157 - val_acc: 0.8762\n",
      "Epoch 307/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0456 - acc: 0.9544 - val_loss: 0.1171 - val_acc: 0.8593\n",
      "Epoch 308/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0456 - acc: 0.9540 - val_loss: 0.1162 - val_acc: 0.8767\n",
      "Epoch 309/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0456 - acc: 0.9548 - val_loss: 0.1160 - val_acc: 0.8675\n",
      "Epoch 310/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0455 - acc: 0.9546 - val_loss: 0.1188 - val_acc: 0.8696\n",
      "Epoch 311/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0455 - acc: 0.9544 - val_loss: 0.1164 - val_acc: 0.8685\n",
      "Epoch 312/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0455 - acc: 0.9546 - val_loss: 0.1155 - val_acc: 0.8680\n",
      "Epoch 313/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0455 - acc: 0.9546 - val_loss: 0.1172 - val_acc: 0.8678\n",
      "Epoch 314/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0455 - acc: 0.9542 - val_loss: 0.1163 - val_acc: 0.8678\n",
      "Epoch 315/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0454 - acc: 0.9547 - val_loss: 0.1173 - val_acc: 0.8688\n",
      "Epoch 316/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0454 - acc: 0.9543 - val_loss: 0.1170 - val_acc: 0.8764\n",
      "Epoch 317/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0454 - acc: 0.9547 - val_loss: 0.1185 - val_acc: 0.8617\n",
      "Epoch 318/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0453 - acc: 0.9543 - val_loss: 0.1173 - val_acc: 0.8764\n",
      "Epoch 319/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0453 - acc: 0.9547 - val_loss: 0.1176 - val_acc: 0.8683\n",
      "Epoch 320/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0453 - acc: 0.9547 - val_loss: 0.1263 - val_acc: 0.8622\n",
      "Epoch 321/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0453 - acc: 0.9551 - val_loss: 0.1165 - val_acc: 0.8754\n",
      "Epoch 322/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0452 - acc: 0.9550 - val_loss: 0.1190 - val_acc: 0.8685\n",
      "Epoch 323/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0451 - acc: 0.9546 - val_loss: 0.1188 - val_acc: 0.8696\n",
      "Epoch 324/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0452 - acc: 0.9552 - val_loss: 0.1177 - val_acc: 0.8680\n",
      "Epoch 325/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0451 - acc: 0.9547 - val_loss: 0.1173 - val_acc: 0.8672\n",
      "Epoch 326/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0451 - acc: 0.9543 - val_loss: 0.1207 - val_acc: 0.8625\n",
      "Epoch 327/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0451 - acc: 0.9548 - val_loss: 0.1188 - val_acc: 0.8604\n",
      "Epoch 328/100000\n",
      "7592/7592 [==============================] - 0s - loss: 0.0450 - acc: 0.9547 - val_loss: 0.1185 - val_acc: 0.8604\n"
     ]
    }
   ],
   "source": [
    "# Aqui criamos o esboço da rede.\n",
    "classifier = Sequential()\n",
    "\n",
    "classifier.add(Dense(20, activation='relu', input_dim=6)) # camada escondida\n",
    "classifier.add(Dense(10, activation='relu')) # camada escondida\n",
    "classifier.add(Dense(1, activation='relu')) # \n",
    "\n",
    "sgd = optimizers.SGD(lr=learnRate)\n",
    "classifier.compile(optimizer=sgd, \n",
    "                   loss='mean_squared_error', # metrica de erro\n",
    "                   metrics=['accuracy']) # metrica de sucesso\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=50)\n",
    "\n",
    "history = classifier.fit(X_train, y_train,\n",
    "                         epochs=100000, # quantidade de epocas que a rede neural vai executar\n",
    "                         verbose=1,\n",
    "                         shuffle=True, # utilizado para misturar as amostras a cada epoca\n",
    "                         validation_data=(X_validation, y_validation),\n",
    "                         callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1408/1962 [====================>.........] - ETA: 0s[0.049675309252311693, 0.93577981651376152]\n"
     ]
    }
   ],
   "source": [
    "test_loss = classifier.evaluate(X_test, y_test)\n",
    "print (test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusão\n",
      "[[1831   67]\n",
      " [  12   52]]\n",
      "\n",
      "Train Loss:       0.0663\n",
      "Validation Loss:  0.0913\n",
      "Accuracy:         0.9597\n",
      "MSE:              0.0403\n",
      "AUROC:            0.9234\n"
     ]
    }
   ],
   "source": [
    "plot_training_error_curves(history, graficoName + \"(20, 10)\")\n",
    "plot_training_acc_curves(history, graficoName + \"(20, 10)\")\n",
    "\n",
    "## Fazer predições no conjunto de teste\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred_class = classifier.predict_classes(X_test, verbose=0)\n",
    "\n",
    "## Matriz de confusão\n",
    "print('Matriz de confusão')\n",
    "print(confusion_matrix(y_test, y_pred_class))\n",
    "\n",
    "## Computar métricas de desempenho\n",
    "losses = extract_final_losses(history)\n",
    "print()\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Train Loss:\", value=losses['train_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Validation Loss:\", value=losses['val_loss']))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"Accuracy:\", value=accuracy_score(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"MSE:\", value=mean_squared_error(y_test, y_pred_class)))\n",
    "print(\"{metric:<18}{value:.4f}\".format(metric=\"AUROC:\", value=roc_auc_score(y_test, y_pred)))"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
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 },
 "nbformat": 4,
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}