code/ML-old-baseline-model-final-all-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "import keras\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(1248)\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../data/juliet.csv.zip')\n",
    "#x = pd.read_csv(\"../data/graph_all.csv.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66347"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x['type'] = x['type'] - 1 \n",
    "y = data.iloc[:,6:]\n",
    "y_label = y.iloc[x['type']]\n",
    "x = x.drop(columns=\"type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60316, 128)\n",
      "(66347, 122)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.774966</td>\n",
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       "      <td>-0.600595</td>\n",
       "      <td>-1.495095</td>\n",
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       "      <td>1.390430</td>\n",
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       "      <td>-0.218646</td>\n",
       "      <td>-0.918204</td>\n",
       "      <td>0.479776</td>\n",
       "      <td>-0.845197</td>\n",
       "      <td>1.502448</td>\n",
       "      <td>-0.824413</td>\n",
       "      <td>0.782784</td>\n",
       "      <td>-0.315855</td>\n",
       "      <td>-0.180076</td>\n",
       "      <td>-0.119244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047266</td>\n",
       "      <td>-0.236197</td>\n",
       "      <td>0.419226</td>\n",
       "      <td>0.228021</td>\n",
       "      <td>-0.307598</td>\n",
       "      <td>0.849559</td>\n",
       "      <td>0.442569</td>\n",
       "      <td>-0.098779</td>\n",
       "      <td>2.077631</td>\n",
       "      <td>0.355881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.280127</td>\n",
       "      <td>-0.561671</td>\n",
       "      <td>0.912020</td>\n",
       "      <td>-0.510684</td>\n",
       "      <td>1.587624</td>\n",
       "      <td>-0.373373</td>\n",
       "      <td>0.028488</td>\n",
       "      <td>-0.575941</td>\n",
       "      <td>-0.335440</td>\n",
       "      <td>-0.979650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360942</td>\n",
       "      <td>-0.506887</td>\n",
       "      <td>0.554335</td>\n",
       "      <td>0.365092</td>\n",
       "      <td>0.261050</td>\n",
       "      <td>1.110404</td>\n",
       "      <td>0.269509</td>\n",
       "      <td>0.170101</td>\n",
       "      <td>0.610466</td>\n",
       "      <td>0.605643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.374337</td>\n",
       "      <td>-0.087385</td>\n",
       "      <td>0.365638</td>\n",
       "      <td>-0.644513</td>\n",
       "      <td>1.130477</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>0.216007</td>\n",
       "      <td>-0.879599</td>\n",
       "      <td>0.488447</td>\n",
       "      <td>-0.274179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.986118</td>\n",
       "      <td>-0.640120</td>\n",
       "      <td>0.190380</td>\n",
       "      <td>0.071200</td>\n",
       "      <td>-1.019643</td>\n",
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       "      <td>0.837845</td>\n",
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       "        x_0       x_1       x_2       x_3       x_4       x_5       x_6  \\\n",
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       "2 -0.218646 -0.918204  0.479776 -0.845197  1.502448 -0.824413  0.782784   \n",
       "3 -0.280127 -0.561671  0.912020 -0.510684  1.587624 -0.373373  0.028488   \n",
       "4  0.374337 -0.087385  0.365638 -0.644513  1.130477 -1.249283  0.216007   \n",
       "\n",
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       "4 -0.879599  0.488447 -0.274179  ...  0.986118 -0.640120  0.190380  0.071200   \n",
       "\n",
       "      x_122     x_123     x_124     x_125     x_126     x_127  \n",
       "0  0.442113  0.071850  0.773860 -0.819406  1.079028 -0.114064  \n",
       "1 -0.038601  0.884941  0.585291 -0.678294  1.390430  0.233143  \n",
       "2 -0.307598  0.849559  0.442569 -0.098779  2.077631  0.355881  \n",
       "3  0.261050  1.110404  0.269509  0.170101  0.610466  0.605643  \n",
       "4 -1.019643  0.338337  0.925309 -0.115115  0.837845 -0.313931  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CWE-015</th>\n",
       "      <th>CWE-023</th>\n",
       "      <th>CWE-036</th>\n",
       "      <th>CWE-078</th>\n",
       "      <th>CWE-090</th>\n",
       "      <th>CWE-114</th>\n",
       "      <th>CWE-121</th>\n",
       "      <th>CWE-122</th>\n",
       "      <th>CWE-123</th>\n",
       "      <th>CWE-124</th>\n",
       "      <th>...</th>\n",
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       "      <th>CWE-780</th>\n",
       "      <th>CWE-785</th>\n",
       "      <th>CWE-789</th>\n",
       "      <th>CWE-832</th>\n",
       "      <th>CWE-835</th>\n",
       "      <th>CWE-843</th>\n",
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       "      <th>CWE-OTHERS</th>\n",
       "    </tr>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CWE-015  CWE-023  CWE-036  CWE-078  CWE-090  CWE-114  CWE-121  CWE-122  \\\n",
       "0    False    False    False    False    False     True    False    False   \n",
       "1    False    False    False    False    False     True    False    False   \n",
       "2    False    False    False    False    False     True    False    False   \n",
       "3    False    False    False    False    False     True    False    False   \n",
       "4    False    False    False    False    False     True    False    False   \n",
       "\n",
       "   CWE-123  CWE-124  ...  CWE-775  CWE-780  CWE-785  CWE-789  CWE-832  \\\n",
       "0    False    False  ...    False    False    False    False    False   \n",
       "1    False    False  ...    False    False    False    False    False   \n",
       "2    False    False  ...    False    False    False    False    False   \n",
       "3    False    False  ...    False    False    False    False    False   \n",
       "4    False    False  ...    False    False    False    False    False   \n",
       "\n",
       "   CWE-835  CWE-843  CWE-119  CWE-120  CWE-OTHERS  \n",
       "0    False    False    False    False       False  \n",
       "1    False    False    False    False       False  \n",
       "2    False    False    False    False       False  \n",
       "3    False    False    False    False       False  \n",
       "4    False    False    False    False       False  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y_label, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Neural tutorial](https://towardsdatascience.com/neural-networks-from-scratch-easy-vs-hard-b26ddc2e89c7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import RMSprop, Adadelta, Adam\n",
    "base_model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "base_model.add(Dense(128, input_dim=x_train.shape[1], activation='sigmoid'))\n",
    "base_model.add(Dense(128, activation='sigmoid'))\n",
    "base_model.add(Dense(units=y_train.shape[1], activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 122)               15738     \n",
      "=================================================================\n",
      "Total params: 48,762\n",
      "Trainable params: 48,762\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/20\n",
      "60316/60316 [==============================] - 4s 74us/step - loss: 2.6740 - categorical_accuracy: 0.3178\n",
      "Epoch 2/20\n",
      "60316/60316 [==============================] - 4s 70us/step - loss: 1.6996 - categorical_accuracy: 0.5116\n",
      "Epoch 3/20\n",
      "60316/60316 [==============================] - 6s 92us/step - loss: 1.4182 - categorical_accuracy: 0.5834\n",
      "Epoch 4/20\n",
      "60316/60316 [==============================] - 4s 71us/step - loss: 1.2675 - categorical_accuracy: 0.6251\n",
      "Epoch 5/20\n",
      "60316/60316 [==============================] - 5s 77us/step - loss: 1.1677 - categorical_accuracy: 0.6493\n",
      "Epoch 6/20\n",
      "60316/60316 [==============================] - 4s 74us/step - loss: 1.0957 - categorical_accuracy: 0.6696\n",
      "Epoch 7/20\n",
      "60316/60316 [==============================] - 4s 73us/step - loss: 1.0420 - categorical_accuracy: 0.6832\n",
      "Epoch 8/20\n",
      "60316/60316 [==============================] - 4s 72us/step - loss: 1.0003 - categorical_accuracy: 0.6940\n",
      "Epoch 9/20\n",
      "60316/60316 [==============================] - 4s 68us/step - loss: 0.9660 - categorical_accuracy: 0.7053\n",
      "Epoch 10/20\n",
      "60316/60316 [==============================] - 5s 85us/step - loss: 0.9372 - categorical_accuracy: 0.7124\n",
      "Epoch 11/20\n",
      "60316/60316 [==============================] - 5s 78us/step - loss: 0.9134 - categorical_accuracy: 0.7191\n",
      "Epoch 12/20\n",
      "60316/60316 [==============================] - 5s 76us/step - loss: 0.8927 - categorical_accuracy: 0.7249\n",
      "Epoch 13/20\n",
      "60316/60316 [==============================] - 5s 77us/step - loss: 0.8740 - categorical_accuracy: 0.7289\n",
      "Epoch 14/20\n",
      "60316/60316 [==============================] - 4s 75us/step - loss: 0.8577 - categorical_accuracy: 0.7342\n",
      "Epoch 15/20\n",
      "60316/60316 [==============================] - 4s 74us/step - loss: 0.8437 - categorical_accuracy: 0.7385\n",
      "Epoch 16/20\n",
      "60316/60316 [==============================] - 5s 79us/step - loss: 0.8303 - categorical_accuracy: 0.7427\n",
      "Epoch 17/20\n",
      "60316/60316 [==============================] - 5s 77us/step - loss: 0.8191 - categorical_accuracy: 0.7457\n",
      "Epoch 18/20\n",
      "60316/60316 [==============================] - 5s 76us/step - loss: 0.8074 - categorical_accuracy: 0.7481\n",
      "Epoch 19/20\n",
      "60316/60316 [==============================] - 4s 72us/step - loss: 0.7986 - categorical_accuracy: 0.7505\n",
      "Epoch 20/20\n",
      "60316/60316 [==============================] - 4s 71us/step - loss: 0.7898 - categorical_accuracy: 0.7535\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8d7c744a58>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model.compile(optimizer=Adadelta(), loss='categorical_crossentropy', metrics=['categorical_accuracy'])\n",
    "base_model.fit(x, y_label, epochs=20, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('../data/baseline-model-multilabel','wb') as f:\n",
    "    pickle.dump(base_model,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12064/12064 [==============================] - 1s 52us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.7897102067973316, 0.7490881962864722]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisations and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cve_predict_table(df, model, script, bug_label):\n",
    "\n",
    "\t'''Function takes in a 'vectorised' script and returns a dataframe containing:\n",
    "\t- The true CVE label\n",
    "\t- The predicted CVE label in descending order of certainty\n",
    "\t- The models certainty of the prediction\n",
    "\t'''\n",
    "\n",
    "\tnew_df = df\n",
    "\n",
    "\tnew_df['flaw_id'] = new_df.groupby(['flaw']).ngroup()\n",
    "\tnew_df = new_df.filter(['flaw','flaw_id'])\n",
    "\tnew_df = new_df.drop_duplicates()\n",
    "\n",
    "\tdf2 = pd.DataFrame({\"flaw\":['na', 'na', 'na'], \n",
    "\t\t\"flaw_id\":[119, 120, 121]}) # to get complete list of bugs thus far\n",
    "\n",
    "\tnew_df = new_df.append(df2)\n",
    "\tnew_df = new_df.sort_values(by = ['flaw_id'], ascending = True)\n",
    "\tnew_df = new_df.reset_index()\n",
    "\tnew_df = new_df.filter(['flaw','flaw_id'])\n",
    "\n",
    "\tbug_label = np.array(bug_label)\n",
    "\tbug_label = np.asscalar(np.where(bug_label == True)[0])\n",
    "\n",
    "\tscript = script.values.reshape(1,128)\n",
    "\n",
    "\toriginal_label_one_hot = keras.utils.to_categorical(bug_label, num_classes = 122)\n",
    "\toriginal_labels = np.tile(original_label_one_hot, reps = (122,1)) # repeat encoding for each type of bug\n",
    "\n",
    "\toriginal_script = np.tile(script, reps = (122,1))\n",
    "\n",
    "\tprediction = model.predict(script)\n",
    "\n",
    "\t# Format results into a summary dataframe:\n",
    "\n",
    "\toriginal_label = pd.Series(\n",
    "\t\tnp.apply_along_axis(np.argmax, axis = 1, arr = original_labels),\n",
    "\t\tname = 'True CVE',\n",
    "\t\t)\n",
    "\n",
    "\toriginal_label = original_label.transform(lambda x: new_df.iloc[x,0])\n",
    "\n",
    "\tpredicted_label = pd.Series(\n",
    "\t\trange(122,),\n",
    "\t\t#np.apply_along_axis(np.argmax, axis = 1, arr = prediction),\n",
    "\t\tname = 'Model Prediction',\n",
    "\t\t)\n",
    "\n",
    "\tpredicted_label = predicted_label.transform(lambda x: new_df.iloc[x,0])\n",
    "\n",
    "\tprediction_certainty = pd.Series(prediction.reshape(122,), name = 'Prediction Certainty',)\n",
    "\n",
    "\tsummary = pd.concat(\n",
    "\t\t[\n",
    "\t\toriginal_label,\n",
    "\t\tpredicted_label,\n",
    "\t\tprediction_certainty,\n",
    "\t\t],\n",
    "\t\taxis='columns',\n",
    "\t\t)\n",
    "\n",
    "\tsummary = summary.sort_values(by = ['Prediction Certainty', 'Model Prediction'], ascending = False)\n",
    "\tsummary = summary.filter(['Model Prediction', 'Prediction Certainty'])\n",
    "\n",
    "\tprint('Neural Network predictions for ' + original_label[0] + ':')\n",
    "\n",
    "\treturn summary.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_script = x_test.iloc[0,]\n",
    "example_label = y_test.iloc[0,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network predictions for CWE-762:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Prediction</th>\n",
       "      <th>Prediction Certainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>CWE-762</td>\n",
       "      <td>0.998271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>CWE-476</td>\n",
       "      <td>0.000739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>CWE-675</td>\n",
       "      <td>0.000311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CWE-122</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>CWE-775</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model Prediction  Prediction Certainty\n",
       "110          CWE-762              0.998271\n",
       "66           CWE-476              0.000739\n",
       "101          CWE-675              0.000311\n",
       "7            CWE-122              0.000180\n",
       "112          CWE-775              0.000100"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cve_predict_table(data, base_model, example_script, example_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "import matplotlib\n",
    "from matplotlib.colors import LogNorm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict= base_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f22701d60b8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = pd.DataFrame(\n",
    "    data=metrics.confusion_matrix((y_test.values+0).argmax(axis=1), y_predict.argmax(axis=1)), \n",
    ")\n",
    "confusion_figure, confusion_axes = matplotlib.pyplot.subplots()\n",
    "confusion_figure.set_size_inches(15, 12)\n",
    "confusion_axes.set_title(\n",
    "    'Confusion matrix showing the log frequency of \\n'\n",
    "    'correct and incorrect bug classification predictions.'\n",
    "    '\\n\\n'  # hack to avoid overlap with x-axis labels below\n",
    ")\n",
    "sns.heatmap(confusion_matrix, norm=LogNorm(confusion_matrix.min().min(),confusion_matrix.max().max()),\n",
    "            vmin = 0.001, vmax=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = pd.DataFrame(\n",
    "    data=metrics.confusion_matrix((y_test.values+0).argmax(axis=1), y_predict.argmax(axis=1)), \n",
    "    #index=['True Normal', 'True Bad'], \n",
    "    #columns=['Predicted Normal', 'Predicted Bad'],\n",
    ")\n",
    "\n",
    "confusion_figure, confusion_axes = matplotlib.pyplot.subplots()\n",
    "confusion_figure.set_size_inches(15, 12)\n",
    "confusion_axes.set_title(\n",
    "    'Confusion matrix showing the frequency of \\n'\n",
    "    'correct and incorrect bug classification predictions.'\n",
    "    '\\n\\n'  # hack to avoid overlap with x-axis labels below\n",
    ")\n",
    "\n",
    "confusion_axes.xaxis.tick_top()  # move x-axis labels to top of matrix\n",
    "_ = sns.heatmap(\n",
    "    confusion_matrix,\n",
    "    annot=False,\n",
    "    fmt=\"d\",\n",
    "    cmap=sns.color_palette(\"Blues\"),\n",
    "    vmin=0,\n",
    "    ax=confusion_axes,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrices = metrics.multilabel_confusion_matrix(\n",
    "    (y_test+0).values,\n",
    "    np.rint(y_predict)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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b46RwcTqJxcXpJBYXpwPkbj48l7g4HXI5H55LXJwORHPipWa22MwqiV6MNjjPPjX+OGchDmHsKMTPfR3Tl7WF8zZoPjyX+CB8M6GeocGJwpt1B3IYzptLXJwO5HA14lzizbqDmW3K1Xx4Lmn0uXXHaSjerDuJxcXpJBYXp5NYXJxOYnFxOonFxekkFhenk1j+HwCIA32ZLvxWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JF6eHbM6HZxMvTg8Ec+LrzOxjM9tP8KNow/LsU+7HOeM4hFFdCN/7cqYvk82Hn5t7D9PjB+FrCBUMDY4Uvln3QBbDebOJF6cHspiNOJv4Zt2DmR3M1nx4Nsn53LrHU1l8s+6JLF6cnsjixemJLF6cnsjixemJLF6cnsjixemJLP8PjEW5O3q9ezsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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o7HvVqw0ZN8/pqTmdQ2khq0PB0tAjwIqwMCW1DT2VVDX6craCXK3t2BN9KVz0JcGa+nDgR3Xx34vTE6MfcBnwvqTY17hbgRHpir703foBQKJuPfzZJ6oTZaI5Qejx4MXpiTBenJ7I4sXpAVK3Hp5KvDg9sUzGUwjCfXsSjNB7ptcrL05PQF/ceriZ7SL4YbQhafap/uc5M3EK40Ah/NlXs7YeL5y3TuvhqcRPwh8k1HRtPUr4bt0DKQznTSVenB5IYTbiVOK7dQ9mtjtV6+GppN7X1j2euuK7dU9k8eL0RBYvTk9k8eL0RBYvTk9k8eL0RBYvTk9k+X9C0ba+bCT1CQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4vM/zDB/yEs6P1mHbls1cVawYCR5YtODUqZPM+mgij/bqEzDPtyuTqH3zLeGs0i9CVZeragezv1NVG6pqTVXtrKqnjf2UOa5p0ne6zh+qqjVUNVFVc1ysKztyWsjrh0BJXJg6dQnuh+/8vLEsI+Msw155gVZ33k3TFs4gRVx8BW5v0RoRIbH2zYhEcSwtlTKmek9aujCsVaSbfSnJ7N+bQt+ezojfoYMH6NerKyMn/INycc6yOElLF9KiTfj9FZEqOOsfVcBZL2mCqo42k4dnAgnAb8CDqppquo5G4wxhpgM9VHWDKas7kPng/aaqTiEP5NQgqoAzeTQ162cBVuXlgrlFVRk9/HWqJFTjvi6P+exNmrXihw3rqFvvNlJ27yIj4yylzZqX58+f55tli3l7/KSCdC3XJNSoxbS5y3zHPTu3Z9T703xrdJ44/jubN63nxVeHhctFNxnAC6q6QURKAetFZAnQA2cm/HARGYhThb/MxTPhG+HMhG/kmgnfAEfk60Vkjpl4HBQ5iXMeUNKMuV6EiCwP9mLB8NPmTXy1aB4J1Wv57jzdez/Lnffcy6i3htCn2/3ExBRhwOA3ENPS+PH79ZS/5loqVqqcXdEFxtt/Gcjmjd9x7Gga3f7Ulkcef5q7OtwXMP+qpK+od1sTihUvHjBPYWFmFO01+7+LyFacPspOXFiteArOSsUv45oJD6wx8UcVTd4lmZM9jMDbAdOD9Uncz2sFQUG8iNRyMYFezLpk6yHfd9+2dvyfyX4mvA8zVp6Es57mblWNNXbBGbaMNePvw1X1G5O2FEe0LYFiqvqmsb8KnFTVEcF+Ls/2c1ryT7W4q337uX1hgYiUBD4BnlfVY+Lq/7Iz4S1hQ0SK4AjzY1X91JjtTHhLeDFV9kRgq6qOdCXZmfCWsNMUeAzYLCKZDeDBhPalBUFhG0SXAYEaRO7vPlAeL2OrdYtnseK0ACAi7UxoxQ7T2R52rDgtmFCK8TijPrWBribkIqxYcVrACaXYYSZ6nMF591CnMPtU8K31SHwQv1xwf/c5rJXkL7yiUcF7mD22K+kKIbcjRF7CVusWCHF4Raiw4rSA02FeyyyiUBQnVGNOmH2y1brF9xa2vjjDjNHAhybkIqwU+AiRxZJXbLVu8SxWnBbPYsVp8SxWnBbPYsVp8SxWnBbPYsVp8Sz/Dxaw9DJbZY0HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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86huY/Pdx7N6dz+9vvh4IOkU33lo929OonDsbS6oLvAoMNbOtkp4m2NbQ3OdfgaurweXUHOf07E2ixjklZRJUSNPM7JFS0tsAb5lZR7cVDWb2Z5c2DbjPZb3PzM539r3yVQQ/Q3QA07Zx+TfKVfAC/BywNCxMSS1Cq5JKRl++pGCv1pb8HH0pXPQlwZz6QOCyyvjvxekp4nTgSuBzSUWvcXcR9LaTEn3pm/UDgFjNevjZx8oTZVJuhshTc/Di9EQWL05PZPHi9ACJmw9PJF6cHhI5H55IvDg9EMyJ55rZCjPLJ/hhtH5J9qnqxzlTcQjjQCH87MuYviwtnLdS8+GJxA/C1xDKGRocKXyz7oEEhvMmEi9ODyRwN+JE4pt1D2a2J1Hz4YmkyufWPZ7K4pt1T2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2T5f6CaLRcsbMLDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F6YksXpyeyOLF6QFyNx6eS7w4PYmVjB8mCPftSdBC75lfr7w4PQHH48bDzayE4IfRhufZp5rv54xjF8b+QvjeZxi+TDUefkLNe1g5vhO+jlDNH2bNK75a90AOw3lziRenB3K4GnEu8dW6BzMrzdV4eC6p8bF1j6e6+GrdE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1n+H+KUCQQrx+KxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cBVwNvCtplbNNJOhtpyX60jfrtYB4zXr43serE2UybobIU3fw4vREFi9OT2Tx4vQAyZsPTyZenB6SOR+eTLw4PRDMieeb2Xoz20/ww2gj0uxT6sc5M3EIo7YQvvdK/MOsscJ5qzUfnkz8IHwdoZKhwZHCN+seSGI4bzLx4vRAErMRJxPfrHsws4PJmg9PJimfW/d4qotv1j2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2R5R9GNB3koL2RjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BD6MNyLFP2e/njGMXxrFC+NqXMXxZ2nh41+x7mBrfCV9FKGdocKTwj3UPZDCcN5N4cXogg9mIM4l/rHsws8OZGg/PJFkfW/d4Kop/rHsiixenJ7J4cXoiixenJ7J4cXoiixenJ7J4cXoiy/8DvJWnpNqC4J8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JMNrOSfSl79b3A5J16+Frn6xNlIndCpGn7uDF6YksXpyeyOLF6QEytx6eSbw4PWRyPTyTeHF6IFgTLzSzz8xsN8GPog3MsU/Zn+eM4xTG/kL42lexfFnZevip2fcwNX4Svo5QzdDgSOG7dQ9kMJw3k3hxeiCD2Ygzie/WPZhZSabWwzNJ1tfWPZ6a4rt1T2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2T5BwV+aU5iaS+ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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enB4gc/PhmcSL00Mm58MziRenB4I58QIz22BmpQQ/jDYqxz5lf5wzjkMYRwvha1/N9GWy+fDzsu9hevwgfD2hhqHBkcI36x7IYDhvJvHi9EAGsxFnEt+sezCzskzNh2eSrM+tezx1xTfrnsjixemJLF6cnsjixemJLF6cnsjixemJLF6cnsjy/9SwxuQndlBJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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t2BV9KVz0JYEohwF1epB5cXoSnAZcCXwoKfEaN4qgtZ2T6MtYDl96difZ8GX43ifLE2Wi20Ho2e/x4vREFi9OT2Tx4vQAmRsPzyRenB4yOR6eSbw4PRCMiReZ2QozKyP4YbTBOfYp+/2ccezC2FcI3/s0w5fVhfPWaTw8k/hO+P2EGoYGRwpfrXsgg+G8mcSL0wMZXI04k/hq3YOZ7czUeHgmyfrYusdTV3y17oksXpyeyOLF6YksXpyeyOLF6YksXpyeyOLF6Yks/w9W96tHmKF9YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EaQQWE6cRWEycBpC8+fBkYuI0SOZ8eDIxcRrgzYnnqeoWVS3B+2G0oSn2qebHOcM4hHGy4L/2caYvKwvnrdZ8eDKxQfg6QhVDgwOFPdYNSGI4bzIxcRqQxGzEycQe6waqejRZ8+HJpMbn1g2juthj3QgsJk4jsJg4jcBi4jQCi4nTCCwmTiOwmDiNwPL/MQhqk7pBk6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5sni5WhvybfSl4KIv8ebUhwC3lMZ/E6cR4hrgdmC9iIR+xk0AhqYq+tKa9fOAaM26/95HqxNkAjfYZhghTJxGYDFxGoHFxGkAiZsPTyQmTiOUyXgGXrhvG7weepvUemXiNDw64+bDVfUk3j9G659in5I/zpmOQxjnC/57X8z0ZaT58KuS72FsbBD+AqGEocGBwpp1AxIYzptITJwGJDAbcSKxZt1AVU8naj48kSR9bt0wSos160ZgMXEagcXEaQQWE6cRWEycRmAxcRqBxcRpBJb/B6I9u5al8T4hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sJk4jsJg4DSB18+GpxMRpkMr58FRi4jTAmxMvUtUtqlqO98NowzLsU/rfc4bxFcapgv/ex5m+rCmct1bz4anEXsLXExIMDQ4U1q0bkMJw3lRi4jQghdmIU4l16waqejRV8+GpJO1z64ZRW6xbNwKLidMILCZOI7CYOI3AYuI0AouJ0wgsJk4jsPw/4djZ8NhfkBgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nfHg+cXE6EM2Jl5rZB2ZWTvTDaP0L7FPdj3MW4xBGfSH+2Sv7D7NWFc5bq/nwfOKD8A2EaoYGJwpv1h3IYzhvPnFxOpDHbMT5xJt1BzPbmq/58HxS53PrjlNbvFl3EouL00ksLk4nsbg4ncTi4nQSi4vTSSwuTiex/BPT/B+QugvTNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AaFd1tJn1dfY96lUEP0NUjWkXShdZFgqmhp4EVoSFKalF6K2k5OjLZxTkam3J7uhL4aIvCebUBwEXVsZ/L05PgpOBS4BlkhK3cSMJRts5ib703Xo1IFW3Hr72qepEmVjOEHlqBl6cnsjixemJLF6cHiBz8+GZxIvTQybnwzOJF6cHgjnxVWb2iZkVEvww2oAc+5T955xxfIRRXQhf+zKmL0sL563UfHgm8Q/hawjlDA2OFL5b90AGw3kziRenBzKYjTiT+G7dg5ntytR8eCbJ+ty6x1NZfLfuiSxenJ7I4sXpiSxenJ7I4sXpiSxenJ7I4sXpiSz/D031uuZn/jVGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4cTqA1I2HpxInTkdsJePJeOG+LfFa6C0z65UTp8OjPf54uJnl4b0UrXuGfUp/P2cUuzBOFoL3XslfzBpvPPyi9HuYHNcJX04o7th6mHDVugNSGM6bSpw4HZDC1YhTiavWHZjZkVSNh6eStI+tOxylxVXrjtDixOkILU6cjtDixOkILU6cjtDixOkILU6cjtDyL9lwwoNMCjy5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NQ4Mjhe/WPZDFcN5s4sXpgSxmI84mvlv3YGaHsrUenk1yvrbu8dQW3617IosXpyeyeHF6IosXpyeyeHF6IosXpyeyeHF6Isv/A6vFU+i6iyd5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cTmJxcTqJxcXpJBYXp5NYXJxOYnFxOonl/wGpN96HmgiDlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JLF6cnsjixemJLF6cnsjixemJLF6cnsjy/6IPRok9Ed7SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5dY+npvhm3RNZvDg9kcWL0xNZvDg9kcWL0xNZvDg9kcWL0xNZ/h+SYV7JNiJcfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/Dx8w5bFwYBrzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PwtcTqhgaHCl8s+6BDIbzZhIvTg9kMBtxJvHNugczO5ap+fBMUuNz6x5PdfHNuieyeHF6IosXpyeyeHF6IosXpyeyeHF6IosXpyey/D+V31tvDbbTgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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havwgfD2hmqHBkcI/1j2QwXDeTOLF6YEMZiPOJP6x7sHMjmRqPjyT1PrcusdTU/xj3RNZvDg9kcWL0xNZvDg9kcWL0xNZvDg9kcWL0xNZ/h9YNtkIq1QPogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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L0xNZvDg9kcWL0wNkbj48k3hxesjkfHgm8eL0QDAnXmxmq82snOCH0Ybn2Kfsj3PGcQjjYCF87uuYvqxtPvzM7HuYGj8I30ioZ2hwpPC3dQ9kMJw3k3hxeiCD2Ygzib+tezCzikzNh2eSrM+tezwNxd/WPZHFi9MTWbw4PZHFi9MTWbw4PZHFi9MTWbw4PZHl/wG/jjpi6BBhywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UMBtxIfHHuoOZbSvUeHghafCxdcepL/5YdxKLi9NJLC5OJ7G4OJ3E4uJ0EouL00ksLk4nsfw/dYDlEP0wc80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cTmJxcTqJxcXpJBYXp5NY/h9gu21MDgx5MwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NjhT+tu6BDIbzZhIvTg9kMBtxJvG3dQ9mdiRT8+GZpMbn1j2e6uJv657I4sXpiSxenJ7I4sXpiSxenJ7I4sXpiSxenJ7I8v/88Gcyxogl9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NwuttpyT60pr1k4BYzbr/2seqE2RCN0NkVB9MnEZgMXEagcXEaQDJmw9PJiZOI5LJOCnz4cnExGmANye+QVU3qmoB3g+j9U2xT5U/zhnGIYyTBf+1l/g/zBptPvySyvcwPjYIX00o79x6kLBm3YAkhvMmExOnAUnMRpxMrFk3UNXCZM2HJ5NKn1s3jIpizboRWEycRmAxcRqBxcRpBBYTpxFYTJxGYDFxGoHl/wHMHPCyk4m4PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4LnFxOqlMxjlZD88lLk4HojXxcjNbaGYVRC9FG5xnnxp+nrMQpzB2FeK/vbK/mDXdenifhvcwOz4J30ioZWhwovBu3YEchvPmEhenAznMRpxLvFt3MLMtuVoPzyUNvrbuOPXFu3Unsbg4ncTi4nQSi4vTSSwuTiexuDidxOLidBLL/wMzYA3aC9lm5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1CAqkWWY2Lk76McAMM+vklqLBzO5zabOAu1zWu8ysr7OXy5cKWdfP6ak6bUPLRVaGgqGhJ4CVYWFKahGalVQx+vIZBWu1tmRf9KVw0ZcEY+qDgZ9Ux38vTk+Ms4ArgH9KWupsowla2xmJvvTV+kFAomo9/OwT5YkyWTdC5Kk9eHF6IosXpyeyeHF6gPSNh6cTL05PbCXjtIyHpxMvTg8EY+IFZrbGzPYS/DDawAz7VPP9nNnYhXGwEH72Sv7DrPHGw8+oeQ+T4zvhawlVDA2OFL5a90Aaw3nTiRenB9K4GnE68dW6BzMrTtd4eDqp8bF1j6e6+GrdE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1n+DcMwt/Kcn2mqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ahh3Rl8JFXxKsqQ8Gzq2J/16cngQnABcAn0hKPMaNIBht5yX60nfruwGpuvXwvU9VJ8rEboXIU3fw4vREFi9OT2Tx4vQA2VsPzyZenB6yuR6eTbw4PRCsiReb2Rdmto3gR9EG5Nmn3M9zxnEKY3chfO+rWL5Mth5+XO49TI+fhK8jVDM0OFL4bt0DWQznzSZenB7IYjbibOK7dQ9mtj1b6+HZJOdr6x5PTfHduieyeHF6IosXpyeyeHF6IosXpyeyeHF6IosXpyey/AsjPNQd4n1mFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rwsKUS7vtdstHXz6vIFdrC/ZFXwoXfUmwpj4QuKwq/ntxemKcBlwJLJUUu40bQfC0nZXoSz+sHwQkGtbD1z5RmyiTEytEnpqJF6cnsnhxeiKLF6cHSN96eDrx4vTEMhmnZT08nXhxeiCN6+HpJOPznLk4hXGwEL72Sv7DrPHWw0/OvIfJ8ZPwNYQq/jBrVvHDugfSGM6bTrw4PZDGbMTpxA/rHsxsT7rWw9NJxtfWPZ6q4od1T2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2T5f6Bd23dopL76AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ibKQyZG3fRjXXvM+eMY2OnZM+ozUqGzPX89OGTO7pE4z+bdm8if59ezJi3P9RM71WIUcXP5JOI1hxuA5gBCl1o1zy8CtAA2ANcL2ZbXOho1EEQ5h7gFvNbKE7V28g+2H7UTObQBwU1iGqQ5A8ui3vdwGOXVqjmGl78SV8tXAe55z/KzLX/sChQwep6nq7R44cYc7MDxk25vmSdqtINGjclIlvz8z53KdHV0Y+O7FYe+sxcgj4TzNbKOkkYIGk6cCtBJnwT0gaRNCEDyR3Jnwbgkz4NhGZ8K0IRL5A0lsu8TgmChPnO0AVN+aaC0mzYr1YLAx7eBBfL5pP1o7t9Lq2CzfddheXdbuakX8cQr9e/065cuW5f/AjyPUsli5ZQK2TT6FuvfqFnLlkiOb/5W59zjDiMoo2uP2dkpYTxCi7c3RB2AkEi8EOJCITHpjr5h/VdXWnZyd7OIFfAUyK1SdFPrMVB8XxIlJPbvJ7Mev05VtyfvvLzqxV5PiAGyufTbBk4Vozq+7sIhi2rO7G358wszmubAaBaDsBFc3sUWf/H2CvmQ2P9XuFNs7pOX4apv8iZ78I0zSy61UBXgMGmFmWImJeJZ0J78VZSihohCgbSeUJhPmymb3uzIlaFz5mQhuE95QsrskeDyw3sxERRT4T3pN02gO3AF9Lyu4ADyax68LHhO8QnQDk1yGK/O3zqxNmfLPuCS1enB4AJF3hplZkuGB70vHi9OCmUowhGPVpAfR0Uy6SihenB4KpFBku0eMAwUvRuifZp+Lvrafig/iJQuRvX0gQPtr0ijbF72HB+FBSKaEoQfiw4Zt1DyR4ekWi8OL0QBAwb+oWUUgjmKpR8i+lz4Nv1j3ZL7q6h2CYsSzwnJtykVSKfYTI44kX36x7QosXpye0eHF6QosXpye0eHF6QosXpye0eHF6Qss/AWQCGggobEiyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z2RVuPjpmttcdt3LpKyPOH2xmx5tZGzMrcT2k4ihpIa/vomyL+S10qqgfmB9kUB4fhqrfoCGpDY7Of/N073UuacuXlnBWeIhcgiasSGoqaaakHyQtcfPYy3Vd+LJS0puzIcFydZcUsW2L9aZl5aj6qTQ4uhHrfl4NwLeL5tOsecvKun1MhH0JmiLIBv7LzNoB3YA7Xad63CLhS6pzfgDUcWOuBZD0aSw3jJXbBjzAs08MInv/fhodcywDBj3B57M+4dXnh5K5PYPH7r+Llq3a8OTwVyrTrahEW4Jm6uQ3mPzGODLSt9Hvhivp3O0M+g8sPK+s8nERRRvc/k5JSwn6KHsDvVy28cCnBHXR/Eh4YJ6bf9TY5Z2RF+whKS8S/s2y+lThy9FUxIdIPQWJthzNjKVb85/9ub9LLfVqtW6sfBbBkoU/m1k9ZxfBsGU9N/4+1MzmuLSPCUTbC0g2s6ec/WFgj5kNK+vvSph+Tk/ZaVG/dv5+KSLh8/LVAd4BBpjZDkV8LqiyI+G9OKsIJXTCAyCpGoEwJ5jZu868SVJjM9tQhkj4XoXsn8bic0J0wnsqHldkjwaWmtnwiCQfCe+JO92B64DFkvIawIMIllaPSyS8bxAdAkRrEEU++2h5wowv1j2hxYvTA4Ck893UijTF4TM+ReHF6cFNpXiJYNSnHXCNGx2KK16cHgiGGdNcoEcWweddesfZp4pvrSdiRfxQIfLZl9AJX9T0iq4V72Hx+K6kKkJpOuHDhi/WPVDO0yvKCy9ODwQd5q3dIgrVCSLbp8bZJ1+se4LpvZL6EQwzJgFjzGxJnN2q+BEijydWfLHuCS1enJ7Q4sXpCS1enJ7Q4sXpCS1enJ7Q4sXpCS3/D4ldVkUbn+oKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TpxFY/g/Sd8L4/8GGkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BK2vjvxenJ8kZwNXAx5KSj3F3E7S2CxJ96av1fYB01Xr43qfLE2ViN0LkqT94cXoiixenJ7J4cXqA3I2H5xIvTg+5HA/PJV6cHgjGxIvNbIWZ7SZ4KdrAAvuU/37OOHZh7CuE7301w5epxsNPzb+HmfGd8PWEGoYGRwpfrXsgh+G8ucSL0wM5XI04l/hq3YOZledqPDyX5H1s3eOpLb5a90QWL05PZPHi9EQWL05PZPHi9EQWL05PZPHi9ESW/wcr9uQzq8MEIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RJi9niDy1Ay9OT2Tx4vREFi9OD5C5+fBM4sXpIZPz4ZnEi9MDwZx4sZmtMLNSgh9GG5Rjn7L/nDMfH2EcKITvfZrpy0ThvNWaD88k/iF8LaGSocGRwnfrHshgOG8m8eL0QAZ3I84kvlv3YGZ7MzUfnkmyPrfu8VQX3617IosXpyeyeHF6IosXpyeyeHF6IosXpyeyeHF6Isv/Azk3qUg8bdxYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VmDgN8MbEc1V1q6oW4b0YbUSafar855xhfIRxtuA/91L+i1mjhfNWaDw8ldhD+GpCgqHBgcIu6wakMJw3lZg4DUjhasSpxC7rBqpanKrx8FRS6WPrhlFR7LJuBBYTpxFYTJxGYDFxGoHFxGkEFhOnEVhMnEZg+X8xxBbY89yuLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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05QsKcrV24JvoS+GiLwlEOQKoVUPmxelJMAC4DlgrKXEZdy/BaDsr0Zexnb70fEOy6cvwuU9WJ8rEcobIUz/w4vREFi9OT2Tx4vQA6ZsPTydenB7SOR+eTrw4PRDMieeb2cdmVkLwx2hDs+xT5u9zxvEWxpFC+NxXM31ZWThvrebD04m/CV9PSDE0OFL4bt0DaQznTSdenB5IYzbidOK7dQ9mdiBd8+HpJONz6x5PbfHduieyeHF6IosXpyeyeHF6IosXpyeyeHF6IosXpyey/D+vtkgs83BTiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vraSS0Zd/Ei9Xa1NORF8KLvoSb019EHBjefw3cRoRugNDgE9EJPIYNwZvtJ2W6Evr1k8DYnXr/msfq02QCd0KkVF5MHEagcXEaQQWE6cBJG89PJmYOA2SuR6eTEycBnhr4tmq+rmqHsP7UbR+afYp9fOcYZzCOF3wX/tSli+jrYdflnoP42OT8JWEMoYGBwrr1g1IYjhvMjFxGpDEbMTJxLp1A1U9nqz18GSS8rV1wygv1q0bgcXEaQQWE6cRWEycRmAxcRqBxcRpBBYTpxFY/h+Jz1IpXufE4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N+m5Adc16+N5XVyfKxGKGyNMw8eL0RBYvTk9k8eL0AJmbD88kXpweMjkfnkm8OD0QzIkvM7PlZraN4IfRBubYp+yPc8ZxCGN3IXzv00xfJpsP75V9D1PjB+EbCHX8Ydac4pt1D2QwnDeTeHF6IIPZiDOJb9Y9mNmOTM2HZ5Ksz617PHXFN+ueyOLF6YksXpyeyOLF6YksXpyeyOLF6YksXpyeyPL/E+dGtYllvq8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Bi9MTWbw4PZHFi9MDpG8+PJ14cXpI53x4OvHi9EAwJ15oZuvMrJTgj9FGZNmnzN/njOMtjMOF8LGvYfqyuvnwMzPvYWr8TfgGQi1DgyOFP617II3hvOnEi9MDacxGnE78ad2DmZWlaz48nWR8bt3jqS/+tO6JLF6cnsjixemJLF6cnsjixemJLF6cnsjixemJLP8PvtzSMT1V3YkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jtJg4DSBx8+GJxMRpRDIZv4oX7tsKr4feKlivTJyGR3vcfLiqHsP7YbSeAftU+uOcyTiEcabgv/dxpi8Lmw+/svQ9jI0NwpcTihgaHCqsWTcggeG8icTEaUACsxEnEmvWDVT1RKLmwxNJqc+tG0ZJsWbdCC0mTiO0mDiN0GLiNEKLidMILSZOI7SYOI3Q8v/TBC47De1J7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tS6oTZfwIkSeyeHF6IosXpyeyeHF6gPSNh6cTL05PIpPxCwThvh0IeugdsuuVF6cnoCtuPNzMDhD8MNrALPuU+feccXyFcawQvvalDF8WNx7eLfMeJse/hK8klDE0OFL4x7oH0hjOm068OD2QxmzE6cQ/1j2Y2aF0jYenk4yPrXs85cU/1j2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2R5f8Bd+i0jtgZqTIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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enJ7I4sXpiSxenB4gdevhqcSL00Mq18NTiRenB4I18Vwz22RmxQQ/jDYswz6lf54zjlMYxwrh776a5cvKwnlrtR6eSvwkfD0hydDgSOG7dQ+kMJw3lXhxeiCF2YhTie/WPZjZoVSth6eStK+tezy1xXfrnsjixemJLF6cnsjixemJLF6cnsjixemJLF6cnsjy/5+nPKDGxorBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UTA09ChSGhSmpXWhVUuXoy6cU5Gptzw/Rl8JFXxLMqQ8HzqmJ/16cngR9gfOBDyUlXuPGEPS28xJ96Zv1XYBkzXr43ierE2ViN0PkqTt4cXoiixenJ7J4cXqA7M2HZxMvTg/ZnA/PJl6cHgjmxIvM7Asz207wo2hD8uxT7sc54ziEsasQvvfVTF9WNR9+TO49TI0fhK8jpBkaHCl8s+6BLIbzZhMvTg9kMRtxNvHNugczK83WfHg2yfncusdTU3yz7oksXpyeyOLF6YksXpyeyOLF6YksXpyeyOLF6Yks/wI41uKyf9vHWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S4O+Sko9xdxD0tgsSfemb9X2AdM16+NqnqxNlYjdD5Gk6eHF6IosXpyeyeHF6gNzNh+cSL04PuZwPzyVenB4I5sTLzGyVmVUR/DDa6AL7lP9xzjgOYewrhK99PdOXqebDT86/h5nxg/BNhCxDgyOFb9Y9kMNw3lzixemBHGYjziW+WfdgZtW5mg/PJXmfW/d4Gotv1j2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2R5f8BdrBOe2z3mB0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EbobIU3/w4vREFi9OT2Tx4vQA2ZsPzyZenB6yOR+eTbw4PRDMiReb2WozKyP4YbRRefYp9+OccRzCOFgIX/sapi+rmw8/M/cepscPwtcTMgwNjhS+WfdAFsN5s4kXpweymI04m/hm3YOZlWdrPjyb5Hxu3eOpK75Z90QWL05PZPHi9EQWL05PZPHi9EQWL05PZPHi9ESW/wf32rqsdMbMAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4PZHFi9MTWbw4PZHFi9MTWbw4PZHFi9MTWf4f+HjcUmnLRVIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T1YkysZsh8tQevDg9kcWL0xNZvDg9QPbmw7OJF6eHbM6HZxMvTg8Ec+KFZrbGzHYT/DDawBz7VP3jnHEcwjhYCN/7DNOXycJ5qzQfnk38IHwtoYKhwZHCN+seyGI4bzbx4vRAFrMRZxPfrHswsz3Zmg/PJtU+t+7xVBXfrHsiixenJ7J4cXoiixenJ7J4cXoiixenJ7J4cXoiy78AYZv9VL6EmlAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1Ld9HXwoXfUkwpz4U+HlV/Pfi9MQ4GbgEWCIp9hh3M0FvOyPRl75ZrwMkatbD1z5RnSiTdTNEnr0HL05PZPHi9EQWL04PkL758HTixemJZTJOy3x4OvHi9EAwJ55vZsvNrIjgh9EGZdinmh/nzMYhjLpC+NqnmL6MNx9+Qs17mBw/CL+XUMHQ4Ejhm3UPpDGcN514cXogjdmI04lv1j2YWXG65sPTSY3PrXs8VcU3657I4sXpiSxenJ7I4sXpiSxenJ7I4sXpiSxenJ7I8v9zSiFjdr0KdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3HqbHT8I3EGoYGhwp/G3dA1kM580mXpweyGI24mzib+sezGx/ttbDs0nO19Y9ntrib+ueyOLF6YksXpyeyOLF6YksXpyeyOLF6YksXpyeyPL/mPjDz2NL8B0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SmYwfIwj37UTQQ++UXa+8OD0B3XDz4WZ2gOBH0fpn2afKH+eM4xBGdSF870uZvkw2H3525XuYHj8IX0MoY2hwpPDNugcyGM6bSbw4PZDBbMSZxDfrHsysIFPz4Zmk0ufWPZ6K4pt1T2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2T5f8fmEpD5Qg1ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7SPDDaP0z7FP6xznjOIRxrBC+9mVMX5Y0H94t/R4mxw/CVxPKGRocKXyz7oEUhvOmEi9OD6QwG3Eq8c26BzPLT9V8eCpJ+9y6x1NZfLPuiSxenJ7I4sXpiSxenJ7I4sXpiSxenJ7I4sXpiSz/D/ztO3DDVn34AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7eHmCTChGiIzqiYnTCCwmTiOwmDgNIHXj4anExGmQyvHwVGLiNMAbE89T1W2qWoT3YrSBGfYp/f2cYezCOFXwX3tJ/GLWWOG8lRoPTyXWCV9NKGdocKCwat2AFIbzphITpwEpXI04lVi1bqCqxakaD08laR9bN4zKYtW6EVhMnEZgMXEagcXEaQQWE6cRWEycRmAxcRqB5f8Bc3KkMYwSy1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4qovv1j2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2R5f8BjOY6kDLDmQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/mDXZfPgFufcwPX4Qvp5QzdDgSOGbdQ9kMZw3m3hxeiCL2YiziW/WPZjZ4WzNh2eTnM+tezw1xTfrnsjixemJLF6cnsjixemJLF6cnsjixemJLF6cnsjy/2WH3RhN2r8CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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enB4ge/Ph2cSL00M258OziRenB4I58SIzW2dmZQQvRhuaZ59yf58zjrcwDhfC576K6ctU4by1mg/PJv4mfD2hmqHBkcJ36x7IYjhvNvHi9EAWsxFnE9+tezCzfdmaD88mOZ9b93hqi+/WPZHFi9MTWbw4PZHFi9MTWbw4PZHFi9MTWbw4PZHl/wGjeEwNIlJ1GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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onLda8+HpxA/C1xIqGRocKXyz7oE0hvOmEy9OD6QxG3E68c26BzM7nK758HSS8bl1j6e6+GbdE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1n+HwHnPqibzQPoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eyOLF6QEyNx+eSbw4PWRyPjyTeHF6IJgTLzSzNWZWSvDDaMNz7FP2xznjOIRRXwjf+2qmL5PNh5+VfQ/T4wfhGwg1DA2OFL5Z90AGw3kziRenBzKYjTiT+Gbdg5mVZWo+PJNkfW7d46krvln3RBYvTk9k8eL0RBYvTk9k8eL0RBYvTk9k8eL0RJb/B7mLX9YJiSUmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3mdkgZz8sX0VIuXFOT/lpG9ousiwUTA09D6wOC1NSWmhVUvHoy9cU7NXakkPRl8JFXxLMqY8AflIZ/704PTH6ANcAn0ta7mx3E/S2kxJ96Zv1GkBpzXr42ZeWJ8qk9AyRp2bjxemJLF6cnsjixekBEjcfnki8OD0kcj48kXhxeiCYE88ys3Vmlkfwo2hJX2xa5eOcqTiEUVMIP3vF/2HWksJ5KzUfnkj8IPxRQjlDgyOFb9Y9kMBw3kTixemBBO5GnEh8s+7BzPITNR+eSKp8bt3jqSy+WfdEFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vRElv8HBci9RtREKGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8FsPfncLoyTNofFMLJo4dHhR/DY2B20VkM04SjWY4M5SiTBgG5BymwXkZplH36mu5qEyZLLaq1eOIqVr9jLoRESUIC3euYXp6OpJ3RuciYV/KHpb9uIhb29yZaStZqjTghAqnHz+e6dtV9RoQEeFkH6lV50r2peScFyoQqOrzJgVNdZwGzbeq2p5QDdMQkQfcuXCCzbrVSYwY2IeUPbvo+eKATLEGkvEjB/NAl+6kHTuaxT7stZdZvuR7qlSP46Enepyx39dzPqV+oz8Hys2C8BxBDNM4mzvnK74K3C/f0xIn+KrmV2rVuYKx789k2PgpzPhgAunHjwfkvBksXbyQyLJlqVGz9hllT/fuR+Kn86hSLZZF32QdZv527hw2rFvD3+8t9IRxv6KqC1S1jfm8UVWvM0m52ppkCajq7+Z7vCnf6Np/gEnWVVNV88wklxt5JfJa5WNL4vS8vpx+4HhVra+q9QO9MFTV6nFElCjJlk3JAT3vmqSV/LT4Ox5o25pBfXux6pdlDO7XO7M8LCyMps1bsfi701nlVixfwkfvv8vLA0dQrHhg8ob6QkSqiMh8EVkjTs6kp4w9aGEaeT37onFmNh/M/luAHwp7Un+ze+cOKlSMJiw8nJTdO9m+ZbPfW7550alzNzp17gbAqhXLmDk1kWdeGsDO7Vu5NKYqqsqSxd8RYxpLv/13HaMGv0q/IaOJKlsuoL764ATQU1V/EZGLgJ9FZB7QCSdMY6A4mQV74Tzu3WEaDXHCNBq6wjTqA2qOM8vMii8QeYlzNlDaTAjIgogsKOjJCsKgvr1IWrGcw4dS6XBnS9o/+DgXlYlk3PCBHEo9SN9/PklcfE36Dx3LmlUrmDFlImHh4VwgF9Clx/NERhUqbMWvqCpDB7zEsWNHQZXY+Mvp2vMFACaMGcbvacd4/eVnAagQXYk+A0cE09ddOPmRUNX/ichanD7KBE7Pz5yMMzfzOVxhGsAScYLjKpm68zJmIhmBtwKmFtQnKeqUgEWxEKklK74WZp23dl/mtW9Zu8Jj+MiVlB0zVr4QqAtsVdUoYxecYcsoM/4+UFW/N2Xf4Ij2JiBCVV819peANFUdUtDf5dlOeMvZE2sS6EL+V9MQkdLAJ0B3VT3s7pYLdJiGZ/s5LYFHRIrhCHOKqs40Zn+tqFFgrDgtQOYjewKwVlWHuopsmIYl6DQG7geSRCSjAdwbZwWNoIRp2AbROYCvBpH72vuq42XsY93iWaw4LQCISCsTWpEsQVjGJyesOC2YUIrROKM+tYF7TchFULHitIATSpFsJnqk48zpDMxKCrlQ5K31UHwRP1dwX3vJZTUNcg6vaFj0HuaO7Uo6T8jvCJGXsI91C/g5vMJfWHFawOkwr2GSKBTHmdk+K8g+2ce6JXMVtidwhhnDgImqujrIbhX9CJHFUljsY93iWaw4LZ7FitPiWaw4LZ7FitPiWaw4LZ7FitPiWf4PXbXuT72TtekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5qreZWT9n36deZfAzRDWYtqF0keWhYGrocWBZWJiSmoXeSiobffmMglytzfk++lK46EuCOfVhwK+q4r8XpyfGScCFwMeSYo9xNxL0tjMSfemb9RpAomY9fO0T1YkykRts83hieHF6IosXpyeyeHF6gNTNh6cSL04PqZwPTyVenB4I5sQLzWylme0h+GG0gRn2Kf3jnNk4hFFTCF/7cqYv482H/zT9HibHD8LXEioYGhwpfLPugRSG86YSL04PpDAbcSrxzboHM9ubqvnwVJL2uXWPp6r4Zt0TWbw4PZHFi9MTWbw4PZHFi9MTWbw4PZHFi9MTWf4f+/Y0C1GKjj8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cHiB14+GpxIvTk8hkPIEg3LcDwRN6h8x65cXpCeiGGw83s4MEP4w2IMM+pf89ZxxfYRwrhK99McOXhY2Hd0+/h8nxL+ErCCUMDY4Uvln3QArDeVOJF6cHUpiNOJX4Zt2DmeWkajw8laR9bN3jKSu+WfdEFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vRElv8HvwY3Npb51kgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PGccujMOF8LWvYviyovHwvun3MDm+E76eUM3Q4Ejhm3UPpDCcN5V4cXoghdmIU4lv1j2YWVGqxsNTSdrH1j2e2uKbdU9k8eL0RBYvTk9k8eL0RBYvTk9k8eL0RBYvTk9k+X9a/yLR5uGSVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ohL97pf9h1mTr4efl3sP0+En4RkItQ4Mjhe/WPZDFcN5s4sXpgSxmI84mvlv3YGYHs7Uenk1yvrbu8dQV3617IosXpyeyeHF6IosXpyeyeHF6IosXpyeyeHF6Isv/AyjewtC2QEWCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OEHkaB16cnsjixemJLF6cHiBz8+GZxIvTQybnwzOJF6cHgjnxIjNbZ2blBD+MNjzHPmX/OWccH2EcLISvfYrpy5rCees1H55J/EP4RkIdQ4Mjhe/WPZDBcN5M4sXpgQxmI84kvlv3YGZ7MjUfnkmyPrfu8dQX3617IosXpyeyeHF6IosXpyeyeHF6IosXpyeyeHF6Isv/A33EX63ClGWtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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adz1Bbzsn0Ze+Wd8DSNWsh+99qjpRJhYzRJ7aiRenJ7J4cXoiixenB8jcfHgm8eL0kMn58EzixemBYE58hZl9ambbCH4YbWiOfcr+OGcchzD2FML3voLpy2Tz4T2y72F6/CB8LaGaP8yaU3yz7oEMhvNmEi9OD2QwG3Em8c26BzPbnqn58EyS9bl1j6e6+GbdE1m8OD2RxYvTE1m8OD2RxYvTE1m8OD2RxYvTE1n+BT8R1x5LtIMyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3pFd5zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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16wakMJw3lZg4DUhhNuJUYt26gaqeTtV6eCpJ+9q6YVQX69aNwGLiNAKLidMILCZOI7CYOI3AYuI0AouJ0wgs/w+fZ2EzNqynrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yeHF6gMzNh2cSL05PYiXjjMyHZxIvTg8Ec+KrzWyNme0n+FG0QTn2KfvjnHEcwqgqhO+90v8wa7L58O7Z9zA9fhC+mlDG0OBI4Zt1D2QwnDeTeHF6IIOrEWcS36x7MLODmZoPzyRZn1v3eCqKb9Y9kcWL0xNZvDg9kcWL0xNZvDg9kcWL0xNZvDg9keX/AbnAsUewjU4sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z4yF7h9BbSdWjL19SkKu1I/uiL4WLviQQ5UigTh2ZF6enkjOB64Hlkipv4yYTjLYzEn2ZtdOXnn3Emr4Mn/tYbaJMVs4QeRoGXpyeyOLF6YksXpweIHXz4anEi9NDKufDU4kXpweCOfFCM1ttZmUEP4w2LMM+pf85ZzY+wjhUCJ/7BNOXNYXz1mk+PJX4h/ANhFqGBkcKf1n3QArDeVOJF6cHUpiNOJX4y7oHM9ubqvnwVJL2uXWPp674y7onsnhxeiKLF6cnsnhxeiKLF6cnsnhxeiKLF6cnsvw/TLbPZFGGnAoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EVz2f18Y9z9ZfN9G4aQseHTSM2AoVvGUWfvkZna89I29LafMMYYyEP5Mn52h/GeIzF+SsmVP9FSsRUVFRvPXebGZ8soBNKevYtiUVgCEjxzBz7kLq1m/IskVBTy4RcvLz8kjdtIGbbrubN6d9RGyFWD7+4KRfetbMd4mKiuKa7jeF0crTUdUlqnqzWd+iqu1UtbGq3mXGo6OqmWa7scnf4rP/WFW9SFWbqWrA+ZCKItBEXj/7WdZyMnSqsAv0BhmE+sNQlSpX4bIr2vJd8gpvWlRUFFdfdwMrli4K6bnOhGrVa5JQvYb3Cd+pazdSNzouxIVfzWP1N8sY+vw417yKiEhdEVksIr+IyHoRGWTSXRsJXxNnurpbClnSgz1pSck4dJCjR5wpEbOyMvlxzUoS6zVg105n4gVVZeWKpSTWb1hWJgXkgmoJVK9Ri507tgHw03fJ1GvQiDXJK/jkXzN4/uXXiY2tUPRBypZc4GlVbQF0AAYYp3rYIuEDvXN+AVQyfa6nICJLgjlhMBxMP8DEcc+Rn5ePaj5XXdOdth07M2xAP44fPwaqNGzclAFPP1tWJhWLRwc/w9/GjCQ3J4daF9Zh8MgxDHn4fnJysnn2qccAp1E0cOhfw2wpmIii3Wb9iIik4PgoewFdTbEZwBKcd1FvJDyw0ow/qm3KLvQEe4iIJxL+w5LaFBGuJEvRnKkrqSCmrzwJZ8rCHapa1aQLTrdlVdP/Pl5Vl5u8RTii7QrEqupLJv054ISqTijpdUWEE94SHA2rVfSuFyMS3lOuEvAJMFhVD/u+E5d1JLwV5zmCFuODBSISgyPMD1T1U5O8V0Rqq+ruEkTCdy2QviQYmyPCCW8pfUyVPRVIUdWJPlk2Et4SdjrhdGGuFRFPA3gkztTqYYmEtw2iswB/DSLfe++vjJux1brFtVhxWgAQkRvM0IpUCcNnfArDitOCGUrxNk6vTwvgPtM7FFasOC3gdDOmmkCPbJzPu/QKs02l31qPxBfxswXfex/ACV/Y8Ir2pW9h0VhX0jlCcZzwbsNW6xYI8YcGQoUVpwUch3kTM4lCOZzI9s/DbJOt1i3eD10NxOlmjAKmqer6MJtV+j1EFkuw2Grd4lqsOC2uxYrT4lqsOC2uxYrT4lqsOC2uxYrT4lr+Dz8YJsKtSJ06AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D1Ee5d/hg0tLSGf/wE0SIs04aXkhHI6XMbCwi1YD5wH2qekREZgBP4My7P4ETfZn4tMz4ZJzTEpl4jXOKSDrOBSlHVaeFKG8GvKuq7U0qGlR1qinLAR4zVR9T1T7Gflq9suCLcU5LbDR3pYuMhjhNzivAercwRaSha1VScPTl38XJ1dqIU9GXgom+xJlTHwL8PBb/rTgtAa4ChgFfikjgNu5hnN52UqIvbbNeAQjXrLt/+3B1vIwvZogslRMrTotnseK0eBYrTgsQv/nweGLFaQlkMo7LfHg8seK0gDMnnquqm1W1AOehaAOS7FPixzn9OIRRUXD/9lGmL0PNh1+ReA8jYwfhKwmlDA32FLZZt0Acw3njiRWnBeKYjTie2GbdgqoWxms+PJ4kfG7dYokV26xbPIsVp8WzWHFaPIsVp8WzWHFaPIsVp8WzWHFaPMv/A5RY+ZeSfryUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2ltQYeAn4o5ltkTQRuA1v3v02vOjL9KdlJiLjnI7kpGqcU1J9vBvSDDO7p4Ly/YFXzayrn4oGM7vTL5sB3OJXvcXMBvr2HepVh0iMczpqRsdAusjKkDc19BiwJChMSa0Dq5LKR18+Iy9Xaxu2R18KP/oSb059GFCjmREnTkecvsC5wCeS4o9x1+P1tjMSfema9d2ARM168NonqhNmIjFD5KibOHE6QosTpyO0OHE6gNTNh6cSJ04HqZwPTyVOnA7w5sTzzWy5mRXhvRRtcIZ9Sv84ZxSHMHYXgte+kunLiubDj0q/h8lxg/B1hCqGBocK16w7IIXhvKnEidMBKcxGnEpcs+7AzEpSNR+eStI+t+5w1BTXrDtCixOnI7Q4cTpCixOnI7Q4cTpCixOnI7Q4cTpCy38A3zOgnOBvbjkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PJik/DHjJzHq5VDSY2a9d2XTgDlf1DjMb7Ox71asOfoaoDtMplC6yMhRMDT0BLA4LUy7ttttMjL58WkGu1vbsib4ULvqSYE59BHBhTfz34vTE6QdcAnwkKf4YdzNBbzsn0Ze+Wa8DpGrWw9c+VZ0ok5czRJ76gRenJ7J4cXoiixenB8jcfHgm8eL0kMn58EzixemBYE58uZmtNLNSgh9GG5Zjn7I/zpmPQxh1hfC1r2T6Mtl8+HHZ9zA9fhC+nlDDH2bNKb5Z90AGw3kziRenBzKYjTiT+Gbdg5mVZWo+PJNkfW7d46kpvln3RBYvTk9k8eL0RBYvTk9k8eL0RBYvTk9k8eL0RJb/B0mp5CF6ZHHIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1T2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2T5f1luac08BYjqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x34vTk2AAcDXwT0mJx7hxBL3trERf+mb9EKCyZj383VdWJ8rEbobIU3fw4vREFi9OT2Tx4vQA6ZsPTydenB7SOR+eTrw4PRDMiReY2RozKyb4YbRhWfYp8+OccRzCOFQIf/dVTF8mmw8/PfMepsYPwtcRqhkaHCl8s+6BNIbzphMvTg+kMRtxOvHNugczK0nXfHg6yfjcusdTW3yz7oksXpyeyOLF6YksXpyeyOLF6YksXpyeyOLF6Yks/w84YkZ+sBzDQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZlRD8KNrQHPuU/XnOOE5h1BXC116pf5g12Xr42dn3MDV+Er6eUM3Q4Ejhu3UPZDCcN5N4cXogg9mIM4nv1j2Y2cFMrYdnkqyvrXs8NcV3657I4sXpiSxenJ7I4sXpiSxenJ7I4sXpiSxenJ7I8v/eWkrXaXcWuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WfelniA5i2oXSRaZCwdTQM8AyM3s8ZG8ZWpVUMfryRQW5WlvxbfSlcNGXBKIcDFTrRubF6YlxFnAV8Imk2GPcKILedlaiL3N2+tLzLYmmL8PffaIyUSZyg20eTwwvTk9k8eL0RBYvTg+QvvnwdOLF6SGd8+HpxIvTA8GceKGZrTSzEoIfRhuQZZ8yP86Zi0MYBwvh7z7F9GW8cN5qzYenEz8IX0uoZGhwpPDNugfSGM6bTrw4PZDGbMTpxDfrHsxsX7rmw9NJxufWPZ7q4pt1T2Tx4vREFi9OT2Tx4vREFi9OT2Tx4vREFi9OT2T5f8G3lHfiCHM0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label, confusion_matrix in zip(y_test.columns, confusion_matrices):\n",
    "\n",
    "    confusion_figure, confusion_axes = matplotlib.pyplot.subplots()\n",
    "    confusion_figure.set_size_inches(2, 1)\n",
    "    confusion_axes.set_title(\n",
    "        label + \n",
    "        '\\n'  # hack to avoid overlap with x-axis labels below\n",
    "    )\n",
    "    confusion_axes.xaxis.tick_top()  # move x-axis labels to top of matrix\n",
    "    _ = sns.heatmap(\n",
    "        confusion_matrix,\n",
    "        annot=True,\n",
    "        fmt=\"d\",\n",
    "        cmap=sns.color_palette(\"Blues\"),\n",
    "        vmin=0,\n",
    "        ax=confusion_axes,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "multiclass format is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-42a1dc63787d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfpr_keras\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr_keras\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthresholds_keras\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_predict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m    620\u001b[0m     \"\"\"\n\u001b[1;32m    621\u001b[0m     fps, tps, thresholds = _binary_clf_curve(\n\u001b[0;32m--> 622\u001b[0;31m         y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m     \u001b[0;31m# Attempt to drop thresholds corresponding to points in between and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m    394\u001b[0m     if not (y_type == \"binary\" or\n\u001b[1;32m    395\u001b[0m             (y_type == \"multiclass\" and pos_label is not None)):\n\u001b[0;32m--> 396\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0} format is not supported\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\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    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: multiclass format is not supported"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr_keras, tpr_keras, thresholds_keras = roc_curve((y_test.values+0).argmax(axis=1)-5, y_predict.argmax(axis=1)-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-a11d617ef7f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpr_keras\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr_keras\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Keras (area = {:.3f})'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauc_keras\u001b[0m\u001b[0;34m)\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'False positive rate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'True positive rate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ROC curve'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
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