LAB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import shap\n",
    "\n",
    "\n",
    "import datetime\n",
    "import time\n",
    "import multiprocessing  as mp\n",
    "from itertools import product\n",
    "\n",
    "from keras.models import Sequential,Model\n",
    "from keras.layers import *#Dense, Input, Concatenate, Lambda,LSTM,Dropout,Layer,Flatten\n",
    "from keras.layers import ConvLSTM2D,BatchNormalization,TimeDistributed,MaxPooling3D\n",
    "from keras.optimizers import RMSprop, Adam\n",
    "from keras import optimizers\n",
    "from keras.utils.vis_utils import plot_model\n",
    "import keras.losses as kl\n",
    "import pydot\n",
    "from keras import backend as K\n",
    "from keras import optimizers\n",
    "import keras\n",
    "import pickle\n",
    "import tensorflow as tf\n",
    "from keras.utils import multi_gpu_model\n",
    "import sys\n",
    "import warnings\n",
    "import os\n",
    "#from tqdm import tqdm\n",
    "os.chdir('/gneven/Desktop/ETH/Sem2/Projeckt/Pyhton/')\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "from functions import * \n",
    "\n",
    "idx = pd.IndexSlice\n",
    "\n",
    "import keras.backend.tensorflow_backend as tfback\n",
    "\n",
    "tf.config.threading.set_intra_op_parallelism_threads(7)\n",
    "tf.config.threading.set_inter_op_parallelism_threads(7)\n",
    "\n",
    "def _get_available_gpus():\n",
    "    \"\"\"Get a list of available gpu devices (formatted as strings).\n",
    "\n",
    "    # Returns\n",
    "        A list of available GPU devices.\n",
    "    \"\"\"\n",
    "    #global _LOCAL_DEVICES\n",
    "    if tfback._LOCAL_DEVICES is None:\n",
    "        devices = tf.config.list_logical_devices()\n",
    "        tfback._LOCAL_DEVICES = [x.name for x in devices]\n",
    "    return [x for x in tfback._LOCAL_DEVICES if 'device:gpu' in x.lower()]\n",
    "\n",
    "tfback._get_available_gpus = _get_available_gpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.00000e+00, 1.00000e+00, 1.10000e+01, 2.80000e+01, 5.32101e+05,\n",
       "        1.17208e+05, 5.65300e+03, 1.80000e+02, 1.40000e+01, 2.00000e+00]),\n",
       " array([-1677. , -1326.9,  -976.8,  -626.7,  -276.6,    73.5,   423.6,\n",
       "          773.7,  1123.8,  1473.9,  1824. ], dtype=float32),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FrameT = pd.read_pickle(os.getcwd()+'/DataPickles/BusFrame32.pickle')\n",
    "FrameT[np.isnan(FrameT)] = 0\n",
    "# FrameT[FrameT>400] = 0\n",
    "# FrameT[FrameT<-400] = 0\n",
    "np.max(FrameT)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.hist(FrameT.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "239\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n",
      "in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n"
     ]
    }
   ],
   "source": [
    "FrameT = pd.read_pickle(os.getcwd()+'/DataPickles/BusFrameT.pickle')\n",
    "\n",
    "config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=6, \n",
    "                        inter_op_parallelism_threads=6, \n",
    "                        allow_soft_placement=True,\n",
    "                        device_count = {'CPU': 6})\n",
    "\n",
    "session = tf.compat.v1.Session(config=config)\n",
    "tf.compat.v1.keras.backend.set_session(session)\n",
    "\n",
    "\n",
    "FrameT[np.isnan(FrameT)] = 0\n",
    "# FrameT[FrameT>400] = 0\n",
    "# FrameT[FrameT<-400] = 0\n",
    "FrameT = FrameT[:2,:,:,:,:]\n",
    "meanF = np.mean(FrameT)\n",
    "FrameT -= meanF\n",
    "std = np.std(abs(FrameT))\n",
    "FrameT /= std \n",
    "\n",
    "out_dt = 1\n",
    "in_dt = 30\n",
    "dt = 30\n",
    "\n",
    "starttime = 7*3600\n",
    "endtime = 9*3600\n",
    "\n",
    "# 6.5 -- 10.5\n",
    "in_data = [6.5*3600,10.5*3600]\n",
    "\n",
    "diff = int((starttime- in_data[0])/dt)\n",
    "\n",
    "\n",
    "input_bus = np.ndarray((FrameT.shape[0],int((endtime-out_dt-starttime)/dt),in_dt,FrameT.shape[2],FrameT.shape[3],FrameT.shape[4]))#FrameT.shape[2]\n",
    "output_bus = np.ndarray((FrameT.shape[0],int((endtime-out_dt-starttime)/dt),out_dt,FrameT.shape[3],FrameT.shape[4]))\n",
    "for i in range(int((endtime-out_dt-starttime)/dt)):\n",
    "    input_bus[:,i,:,:,:,:] = FrameT[:,(diff+i-in_dt):(diff+i),:,:,:]\n",
    "    output_bus[:,i,:,:,:] = FrameT[:,(diff+i):(diff+i+out_dt),2,:,:]\n",
    "    \n",
    "vali = input_bus#[[0,6,12,18,24,29,34],:,:,:,:]\n",
    "valo = output_bus#[[0,6,12,18,24,29,34],:,:,:,:]\n",
    "input_bus = np.delete(input_bus,[0,6,12,18,24,29,34], axis = 0)\n",
    "output_bus = np.delete(output_bus,[0,6,12,18,24,29,34], axis = 0)\n",
    "\n",
    "batch_size =input_bus.shape[1]\n",
    "print(batch_size)\n",
    "\n",
    "input_bus = input_bus.reshape(input_bus.shape[0]*input_bus.shape[1],input_bus.shape[2],input_bus.shape[3],input_bus.shape[4],input_bus.shape[5])\n",
    "output_bus = output_bus.reshape(output_bus.shape[0]*output_bus.shape[1],output_bus.shape[2],output_bus.shape[3],output_bus.shape[4])\n",
    "\n",
    "vali = vali.reshape(vali.shape[0]*vali.shape[1],vali.shape[2],vali.shape[3],vali.shape[4],vali.shape[5])\n",
    "valo = valo.reshape(valo.shape[0]*valo.shape[1],valo.shape[2],valo.shape[3],valo.shape[4])\n",
    "\n",
    "output_bus = np.expand_dims(output_bus,axis = 4)\n",
    "valo = np.expand_dims(valo,axis = 4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(478, 375)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valo.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_norm_0 (BatchNormaliza (None, 30, 3, 13, 1)      4         \n",
      "_________________________________________________________________\n",
      "conv_lstm_1 (ConvLSTM2D)     (None, 30, 64, 13, 1)     86016     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 30, 64, 13, 1)     0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 30, 64, 13, 1)     4         \n",
      "_________________________________________________________________\n",
      "conv_lstm_2 (ConvLSTM2D)     (None, 32, 13, 1)         61568     \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 32, 13, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 32, 13, 1)         4         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 416)               0         \n",
      "_________________________________________________________________\n",
      "repeat_vector_2 (RepeatVecto (None, 10, 416)           0         \n",
      "_________________________________________________________________\n",
      "reshape_2 (Reshape)          (None, 10, 13, 1, 32)     0         \n",
      "_________________________________________________________________\n",
      "conv_lstm_3 (ConvLSTM2D)     (None, 10, 13, 1, 32)     41088     \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 10, 13, 1, 32)     0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 10, 13, 1, 32)     128       \n",
      "_________________________________________________________________\n",
      "conv_lstm_4 (ConvLSTM2D)     (None, 10, 13, 1, 64)     123136    \n",
      "_________________________________________________________________\n",
      "test (TimeDistributed)       (None, 10, 13, 1, 1)      65        \n",
      "=================================================================\n",
      "Total params: 312,013\n",
      "Trainable params: 311,943\n",
      "Non-trainable params: 70\n",
      "_________________________________________________________________\n",
      "Epoch 1/30\n",
      "239/239 [==============================] - 16s 66ms/step - loss: 2.7264 - accuracy: 0.0000e+00\n",
      "Epoch 2/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 2.2424 - accuracy: 0.0000e+00\n",
      "Epoch 3/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 1.9075 - accuracy: 0.0000e+00\n",
      "Epoch 4/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 1.6690 - accuracy: 0.0000e+00\n",
      "Epoch 5/30\n",
      "239/239 [==============================] - 6s 26ms/step - loss: 1.4944 - accuracy: 0.0000e+00\n",
      "Epoch 6/30\n",
      "239/239 [==============================] - 6s 26ms/step - loss: 1.3324 - accuracy: 0.0000e+00\n",
      "Epoch 7/30\n",
      "239/239 [==============================] - 6s 24ms/step - loss: 1.2032 - accuracy: 0.0000e+00\n",
      "Epoch 8/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 1.0905 - accuracy: 0.0000e+00\n",
      "Epoch 9/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 1.0363 - accuracy: 0.0000e+00\n",
      "Epoch 10/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.9763 - accuracy: 0.0000e+00\n",
      "Epoch 11/30\n",
      "239/239 [==============================] - 7s 31ms/step - loss: 0.9324 - accuracy: 0.0000e+00\n",
      "Epoch 12/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 0.8909 - accuracy: 0.0000e+00\n",
      "Epoch 13/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.8584 - accuracy: 0.0000e+00\n",
      "Epoch 14/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.8098 - accuracy: 0.0000e+00\n",
      "Epoch 15/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 0.7832 - accuracy: 0.0000e+00\n",
      "Epoch 16/30\n",
      "239/239 [==============================] - 6s 25ms/step - loss: 0.7512 - accuracy: 0.0000e+00\n",
      "Epoch 17/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.7224 - accuracy: 0.0000e+00\n",
      "Epoch 18/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.6905 - accuracy: 0.0000e+00\n",
      "Epoch 19/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 0.6598 - accuracy: 0.0000e+00\n",
      "Epoch 20/30\n",
      "239/239 [==============================] - 7s 31ms/step - loss: 0.6432 - accuracy: 0.0000e+00\n",
      "Epoch 21/30\n",
      "239/239 [==============================] - 7s 31ms/step - loss: 0.6252 - accuracy: 0.0000e+00\n",
      "Epoch 22/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.6020 - accuracy: 0.0000e+00\n",
      "Epoch 23/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.5910 - accuracy: 0.0000e+00\n",
      "Epoch 24/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.5739 - accuracy: 0.0000e+00\n",
      "Epoch 25/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.5593 - accuracy: 0.0000e+00\n",
      "Epoch 26/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.5365 - accuracy: 0.0000e+00\n",
      "Epoch 27/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 0.5270 - accuracy: 0.0000e+00\n",
      "Epoch 28/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.5194 - accuracy: 0.0000e+00\n",
      "Epoch 29/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.5026 - accuracy: 0.0000e+00\n",
      "Epoch 30/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 0.4972 - accuracy: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "filter1 = 64\n",
    "filter2 = 32\n",
    "kernel1 = 5\n",
    "kernel2 = 5\n",
    "\n",
    "epochs = 10\n",
    "model = Sequential([\n",
    "    BatchNormalization(name = 'batch_norm_0', input_shape = (in_dt,input_bus.shape[2],input_bus.shape[3],input_bus.shape[4])), \n",
    "\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_1',filters=filter1, kernel_size=(kernel1, 1),stateful=False\n",
    "               , padding='same', return_sequences=True,data_format  =\"channels_first\"),\n",
    "    Dropout(0.2),\n",
    "\n",
    "    BatchNormalization(),\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_2',filters=filter2, kernel_size=(kernel2, 1),stateful=False\n",
    "               ,data_format  =\"channels_first\"\n",
    "               ,padding='same', return_sequences=False),\n",
    "\n",
    "    Dropout(0.2),\n",
    "\n",
    "    BatchNormalization(),\n",
    "\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    RepeatVector(out_dt),\n",
    "\n",
    "    Reshape((out_dt,output_bus.shape[2],1,filter2)),\n",
    "\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_3',filters=filter2,  kernel_size=(kernel2, 1),stateful=False\n",
    "               , padding='same', return_sequences=True),\n",
    "    Dropout(0.1),\n",
    "    BatchNormalization(),\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_4',\n",
    "               filters = filter1, kernel_size = (kernel1, 1),stateful=False, \n",
    "               padding='same',\n",
    "               return_sequences = True),\n",
    "    #    Reshape((10,202472)) ,           \n",
    "    #    \n",
    "    #    TimeDistributed(Dense(out.shape[2]*20)),\n",
    "    ##    \n",
    "    #    TimeDistributed(Dense(32,)),\n",
    "    #    \n",
    "    TimeDistributed(Dense(1), name='test'),\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "])\n",
    "# optimizer = optimizers.Adam(clipvalue=0.5)\n",
    "#optimizer = optimizers.Adam(clipnorm=1.)\n",
    "#     model = multi_gpu_model(model, gpus=6)\n",
    "model.compile(loss='mean_squared_error', optimizer = 'adam',metrics=['accuracy'])\n",
    "model.summary()\n",
    "\n",
    "autoencoder_train = model.fit(input_bus, output_bus, epochs=30,\n",
    "                              batch_size=batch_size, verbose=1, shuffle=False)\n",
    "\n",
    "\n",
    "model1 = model\n",
    "error = np.array([])\n",
    "\n",
    "\n",
    "# for e in range(epochs):\n",
    "#     errorp = 0\n",
    "#     print('epoque :',e+1)\n",
    "#     for day in range(FrameT.shape[0] -2):\n",
    "#         model.fit(input_bus[day,:,:,:,:,:], output_bus[day,:,:,:,:,:], epochs=1,\n",
    "#                   batch_size=batch_size, verbose=1, shuffle=False)\n",
    "#         model.reset_states()\n",
    "#         print('Day :',day+1,', epoch :', e+1)\n",
    "\n",
    "#     for p in range(1,2):\n",
    "#         predicty = model.predict(input_bus[-p,:,:,:,:,:])\n",
    "#         errorp += np.sum(abs(predicty-output_bus[-p,:,:,:,:,:]))\n",
    "            \n",
    "#     print('Epoch',e,'Error:',errorp)\n",
    "        \n",
    "#     error = np.append(error,errorp)\n",
    "            \n",
    "      #,validation_data = (vali,valo)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_norm_0 (BatchNormaliza (None, 30, 1, 13, 1)      4         \n",
      "_________________________________________________________________\n",
      "conv_lstm_1 (ConvLSTM2D)     (None, 30, 64, 13, 1)     83456     \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 30, 64, 13, 1)     0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 30, 64, 13, 1)     4         \n",
      "_________________________________________________________________\n",
      "conv_lstm_2 (ConvLSTM2D)     (None, 32, 13, 1)         61568     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 32, 13, 1)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 32, 13, 1)         4         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 416)               0         \n",
      "_________________________________________________________________\n",
      "repeat_vector_3 (RepeatVecto (None, 10, 416)           0         \n",
      "_________________________________________________________________\n",
      "reshape_3 (Reshape)          (None, 10, 13, 1, 32)     0         \n",
      "_________________________________________________________________\n",
      "conv_lstm_3 (ConvLSTM2D)     (None, 10, 13, 1, 32)     41088     \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 10, 13, 1, 32)     0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 10, 13, 1, 32)     128       \n",
      "_________________________________________________________________\n",
      "conv_lstm_4 (ConvLSTM2D)     (None, 10, 13, 1, 64)     123136    \n",
      "_________________________________________________________________\n",
      "test (TimeDistributed)       (None, 10, 13, 1, 1)      65        \n",
      "=================================================================\n",
      "Total params: 309,453\n",
      "Trainable params: 309,383\n",
      "Non-trainable params: 70\n",
      "_________________________________________________________________\n",
      "Epoch 1/30\n",
      "239/239 [==============================] - 16s 68ms/step - loss: 2.5838 - accuracy: 0.0000e+00\n",
      "Epoch 2/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 2.1446 - accuracy: 0.0000e+00\n",
      "Epoch 3/30\n",
      "239/239 [==============================] - 6s 26ms/step - loss: 1.8658 - accuracy: 0.0000e+00\n",
      "Epoch 4/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 1.6363 - accuracy: 0.0000e+00\n",
      "Epoch 5/30\n",
      "239/239 [==============================] - 6s 26ms/step - loss: 1.4258 - accuracy: 0.0000e+00\n",
      "Epoch 6/30\n",
      "239/239 [==============================] - 6s 25ms/step - loss: 1.2361 - accuracy: 0.0000e+00\n",
      "Epoch 7/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 1.1065 - accuracy: 0.0000e+00\n",
      "Epoch 8/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 1.0046 - accuracy: 0.0000e+00\n",
      "Epoch 9/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.9489 - accuracy: 0.0000e+00\n",
      "Epoch 10/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.9103 - accuracy: 0.0000e+00\n",
      "Epoch 11/30\n",
      "239/239 [==============================] - 6s 25ms/step - loss: 0.8817 - accuracy: 0.0000e+00\n",
      "Epoch 12/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 0.8321 - accuracy: 0.0000e+00\n",
      "Epoch 13/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.7878 - accuracy: 0.0000e+00\n",
      "Epoch 14/30\n",
      "239/239 [==============================] - 6s 25ms/step - loss: 0.7570 - accuracy: 0.0000e+00\n",
      "Epoch 15/30\n",
      "239/239 [==============================] - 6s 25ms/step - loss: 0.7265 - accuracy: 0.0000e+00\n",
      "Epoch 16/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 0.6933 - accuracy: 0.0000e+00\n",
      "Epoch 17/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.6695 - accuracy: 0.0000e+00\n",
      "Epoch 18/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.6408 - accuracy: 0.0000e+00\n",
      "Epoch 19/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 0.6360 - accuracy: 0.0000e+00\n",
      "Epoch 20/30\n",
      "239/239 [==============================] - 6s 26ms/step - loss: 0.6111 - accuracy: 0.0000e+00\n",
      "Epoch 21/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 0.5894 - accuracy: 0.0000e+00\n",
      "Epoch 22/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 0.5760 - accuracy: 0.0000e+00\n",
      "Epoch 23/30\n",
      "239/239 [==============================] - 8s 32ms/step - loss: 0.5652 - accuracy: 0.0000e+00\n",
      "Epoch 24/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 0.5454 - accuracy: 0.0000e+00\n",
      "Epoch 25/30\n",
      "239/239 [==============================] - 7s 27ms/step - loss: 0.5326 - accuracy: 0.0000e+00\n",
      "Epoch 26/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.5187 - accuracy: 0.0000e+00\n",
      "Epoch 27/30\n",
      "239/239 [==============================] - 7s 30ms/step - loss: 0.5034 - accuracy: 0.0000e+00\n",
      "Epoch 28/30\n",
      "239/239 [==============================] - 6s 27ms/step - loss: 0.4952 - accuracy: 0.0000e+00\n",
      "Epoch 29/30\n",
      "239/239 [==============================] - 7s 29ms/step - loss: 0.4824 - accuracy: 0.0000e+00\n",
      "Epoch 30/30\n",
      "239/239 [==============================] - 7s 28ms/step - loss: 0.4746 - accuracy: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "filter1 = 64\n",
    "filter2 = 32\n",
    "kernel1 = 5\n",
    "kernel2 = 5\n",
    "input_bus = input_bus[:,:,2:,:,:]\n",
    "\n",
    "epochs = 10\n",
    "model_1 = Sequential([\n",
    "    BatchNormalization(name = 'batch_norm_0', input_shape = (in_dt,input_bus.shape[2],input_bus.shape[3],input_bus.shape[4])), \n",
    "\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_1',filters=filter1, kernel_size=(kernel1, 1),stateful=False\n",
    "               , padding='same', return_sequences=True,data_format  =\"channels_first\"),\n",
    "    Dropout(0.2),\n",
    "\n",
    "    BatchNormalization(),\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_2',filters=filter2, kernel_size=(kernel2, 1),stateful=False\n",
    "               ,data_format  =\"channels_first\"\n",
    "               ,padding='same', return_sequences=False),\n",
    "\n",
    "    Dropout(0.2),\n",
    "\n",
    "    BatchNormalization(),\n",
    "\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    RepeatVector(out_dt),\n",
    "\n",
    "    Reshape((out_dt,output_bus.shape[2],1,filter2)),\n",
    "\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_3',filters=filter2,  kernel_size=(kernel2, 1),stateful=False\n",
    "               , padding='same', return_sequences=True),\n",
    "    Dropout(0.1),\n",
    "    BatchNormalization(),\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_4',\n",
    "               filters = filter1, kernel_size = (kernel1, 1),stateful=False, \n",
    "               padding='same',\n",
    "               return_sequences = True),\n",
    "    #    Reshape((10,202472)) ,           \n",
    "    #    \n",
    "    #    TimeDistributed(Dense(out.shape[2]*20)),\n",
    "    ##    \n",
    "    #    TimeDistributed(Dense(32,)),\n",
    "    #    \n",
    "    TimeDistributed(Dense(1), name='test'),\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "])\n",
    "# optimizer = optimizers.Adam(clipvalue=0.5)\n",
    "#optimizer = optimizers.Adam(clipnorm=1.)\n",
    "#     model = multi_gpu_model(model, gpus=6)\n",
    "model_1.compile(loss='mean_squared_error', optimizer = 'adam',metrics=['accuracy'])\n",
    "model_1.summary()\n",
    "\n",
    "hist_1 = model_1.fit(input_bus, output_bus, epochs=30,\n",
    "                              batch_size=batch_size, verbose=1, shuffle=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# for e in range(epochs):\n",
    "#     errorp = 0\n",
    "#     print('epoque :',e+1)\n",
    "#     for day in range(FrameT.shape[0] -2):\n",
    "#         model.fit(input_bus[day,:,:,:,:,:], output_bus[day,:,:,:,:,:], epochs=1,\n",
    "#                   batch_size=batch_size, verbose=1, shuffle=False)\n",
    "#         model.reset_states()\n",
    "#         print('Day :',day+1,', epoch :', e+1)\n",
    "\n",
    "#     for p in range(1,2):\n",
    "#         predicty = model.predict(input_bus[-p,:,:,:,:,:])\n",
    "#         errorp += np.sum(abs(predicty-output_bus[-p,:,:,:,:,:]))\n",
    "            \n",
    "#     print('Epoch',e,'Error:',errorp)\n",
    "        \n",
    "#     error = np.append(error,errorp)\n",
    "            \n",
    "      #,validation_data = (vali,valo)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "6692/6692 [==============================] - 232s 35ms/step - loss: 2.5456 - accuracy: 0.0000e+00\n",
      "epoch : 0  over\n",
      "Epoch 1/1\n",
      "2390/6692 [=========>....................] - ETA: 2:18 - loss: 1.6973 - accuracy: 0.0000e+00"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-4943afb76908>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\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[1;32m      5\u001b[0m     hist.append(model.fit(input_bus, output_bus, epochs=1,\n\u001b[0;32m----> 6\u001b[0;31m                   batch_size=batch_size, verbose=1, shuffle=False)  )\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#     predicty.append(model.predict(vali[:239,:,:,:,:]))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\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[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\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    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\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[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3738\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3739\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3740\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\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   3741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3742\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1079\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m     \"\"\"\n\u001b[0;32m-> 1081\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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   1082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1119\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1120\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1121\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\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   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1222\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[0;32m-> 1224\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1225\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\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[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\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[0;32m--> 511\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    512\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "predicty = list()\n",
    "hist = list()\n",
    "# for e in range(30):\n",
    "#     hist.append(model.fit(input_bus, output_bus, epochs=1,\n",
    "#                   batch_size=batch_size, verbose=1, shuffle=False)  )\n",
    "    \n",
    "# #     predicty.append(model.predict(vali[:239,:,:,:,:]))\n",
    "\n",
    "\n",
    "    \n",
    "#     print('epoch :' ,e, ' over' ) \n",
    "\n",
    "\n",
    "#     #\n",
    "# print(time.time()-t1)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.plt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "You must compile a model before training/testing. Use `model.compile(optimizer, loss)`.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-199be8aa1af4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m autoencoder_train = model.fit(input_bus, output_bus, epochs=30,\n\u001b[0;32m----> 4\u001b[0;31m                   batch_size=batch_size, verbose=1, shuffle=False)\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1152\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1153\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1154\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m   1155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1156\u001b[0m         \u001b[0;31m# Prepare validation data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m    506\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m                 raise RuntimeError('You must compile a model before '\n\u001b[0m\u001b[1;32m    509\u001b[0m                                    \u001b[0;34m'training/testing. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m                                    'Use `model.compile(optimizer, loss)`.')\n",
      "\u001b[0;31mRuntimeError\u001b[0m: You must compile a model before training/testing. Use `model.compile(optimizer, loss)`."
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "\n",
    "autoencoder_train = model.fit(input_bus, output_bus, epochs=30,\n",
    "                  batch_size=batch_size, verbose=1, shuffle=False)\n",
    "print(time.time()-t1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb0c43c5f50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = autoencoder_train.history['loss']\n",
    "\n",
    "fig = plt.figure(2)\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.plot(hist_1.history['loss'],'v',label = '1')\n",
    "            \n",
    "ax.plot(loss,'*',label = '3')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict1 = model1.predict(vali[:239,:,:,:,:])\n",
    "predict2 = model.predict(vali[:239,:,:,:,:])\n",
    "which = 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb0c3688f10>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.sum(abs(predict1-predict2)))\n",
    "fig = plt.figure(3)\n",
    "ax = fig.gca()\n",
    "ax.plot(predict1[:,1,which,0,0],'v')\n",
    "ax.plot(predict2[:,1,which,0,0],'*')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAj0AAAGtCAYAAAD9H8XfAAAD0ElEQVR4nO3BAQ0AAADCoPdPbQ8HFAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAiQEDcgABMViuJQAAAABJRU5ErkJggg==\" width=\"639.85\">"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'predicty' 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-22-5a01be53a092>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Time from start (7am)[min]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Lateness'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\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     12\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicty\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwhich\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'*'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Epoch %i'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'v'\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 'predicty' is not defined"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "which = 1\n",
    "\n",
    "fig = plt.figure(3)\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "plt.title('Prediction')\n",
    "ax2 = plt.subplot(3,1,2)\n",
    "ax3 = plt.subplot(3,1,3)\n",
    "plt.xlabel('Time from start (7am)[min]')\n",
    "plt.ylabel('Lateness')\n",
    "for i in range(0,len(predicty),10):\n",
    "    ax1.plot(predicty[i][:,1,which,0,0],'*',label = 'Epoch %i' %i)\n",
    "    ax2.plot(i,hist[i].history['loss'],'v')\n",
    "    \n",
    "ax3.plot(predicty[i][:,1,which,0,0]*10,'*',label = 'Epoch %i' %i)\n",
    "\n",
    "ax3.plot(valo[:239,1,which,0,0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax3.plot(predicty[i][:,1,which,0,0]*10,'*',label = 'Epoch %i' %i)\n",
    "\n",
    "ax3.plot(valo[:239,1,which,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' 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-1-b93d91a7724c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mFrameT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'BusFrame32.pickels'\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      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstartTime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mendTime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m)\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[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "startTime = 7*3600\n",
    "endTime = 10*3600\n",
    "dt = 60\n",
    "\n",
    "FrameT = np.load('BusFrame32.pickels')\n",
    "\n",
    "x = np.asarray([*range(startTime,endTime,dt)])\n",
    "y = FrameT[:,0,0,496]\n",
    "w = np.isnan(y)\n",
    "y[w] = 0.\n",
    "spl = UnivariateSpline(x, y, w=~w)\n",
    "%matplotlib notebook\n",
    "for i in 50:\n",
    "    spl.set_smoothing_factor(i)\n",
    "    plt.plot(x, spl(x), lw=1,label = i)\n",
    "    \n",
    "plt.plot(x[~w], y[~w], 'r', ms=3)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" width=\"639.85\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3a4f8b2090>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "stops = get_stops_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "allfile = os.listdir('data/data2/Times/')\n",
    "\n",
    "Data = pd.read_csv('data/data2/Times/'+allfile[0])\n",
    "Data['datum_nach'] = pd.to_datetime(Data['datum_nach'],format=\"%d.%m.%y\")\n",
    "Data = Data.loc[Data['datum_nach'].dt.dayofweek < 5,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1102001, 34)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['kurs'] = 100*Data['linie'] + Data['kurs']\n",
    "Data = Data[~Data['fw_lang'].str.contains('DEP|Einfahrt|Ausfahrt|GAR')]\n",
    "\n",
    "\n",
    "# 281423\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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jp9Fv34P4/VVvzdpeHScPn0agiCgqqoKe3fFw9W1t1g+/se/RDNyMT9PEmvY7PkICpws2wCO8vEVdSdNnGKVATQXs1wDqDMe7bVHjBiBLVu2ADC82N/SVuP82YsQBAFHU9KN9p8pAzhlyhREREQAkJq29v2nUqmw568J4mdvXKswMN15X12WtO/ixYsxdepUAG0GcOTIkQbtPGzoY/hddIxVBlCXRzrT1T5enW77eHUms31s7ftEZwCzsrIk57zmtd9jxGM+Zg2gpVgAQKVSISkpyWR+/OvTT9vuoH91xcDE68qa134PnxEjJO2iT/t+1IcG0Dxd0gA2NTWhqKgIX3/9NdasWYOHH34YFy5ckJUAHU3G9l8MU8TExBidwWavBDqXfgx7XkjBvvkJSFy+BvvmJ2DPCyk4l37M6s/+bcEBxId/jncio7Fv3t+we9HHHdI1pb0z/HP8bf57OBCz0aYx20tX1xbvzNvlcDE7ev8d+l0ccjK/QuV3/8bdulpZMd+tq4W2pAba4hvQllTg1FflRg3gl+cu369TI0u7ve6NsnJoi2+iuqQW2uIbuFlZqbevTTdiSTi8vLxQXFws0Wo/QtFe93E/P6xa/lqbbmk5tMVaZB7PxsP9+uPll6IM4rU0QlFVUY6zJ8/h7Bc5yDmVibLCcuScahuO/OLzzyUxh0wJwfP/PdugLYwZwLt1tfjmy29F3W+z8iUxV5fcwqznZuHXzz5rdczt2+Ovf9oGdzd3VJfcgrZYK7bz9q3b4e7ubqBtaghYF/OJ1AwIgoBzX54x2n9HDqRAEARcyS2S6JobObpbV4sr54shCAI+TDosxnTjWvl9/e/xVcY5CIKAE6kZkvY1NwSs03Z3c8fbb74t5pWuLW5VV4t5VF5aKsn50b6PY9WKaNytqzU60lVe2jYEnHLwQ9woK8fT//dphC9c0jZp5f6yKqaGgNt/F8IXhuOZcT9HdcktfHDwY4NYbpSVi7Foi7VY/eqrHRp1O3TgffTq1QuVl6+j4nKxOASsLb4plvCFS/DLX/4CADgEbAe6pAHUZ/LkyViyZMkDHwLu7DuAAFCjrcKP92P6sbkZNVrTBtXcZ/ct3IidESewb/57bcNwC2I7rGtKe2dE29Ifto7ZXrq6tnDEmB25/xoaGnDhwgXcaTdTUg4/Njej9d493CiuxKmvK+H9eiqG3jd+Q9e0/cyvqEXrvXtiDNboAoC2+KdJADeKKyX77rW0YOnSpfD09MSVK1cMdHTPKB8+fFjUrayoaHtg/t2DqC6tg7b4Bk4dP4uH+z2M1157DQAM4o2Ojoafn59EOzIyUvKMsn7MugkJG17/nbivqakJHh4eiI83vHtrzACaagtt8Y37PyuxYP58zJw5U1HMf/3jNri7eYi6una+UFDQNmEiO1vUPnv2rMEkkIceekiyqsPmuDh4enqitbXVaP/dqqlpe3Y8KUnUvX79utFnx9uPAB08cEB8dlx3vjfatUlbmw/AlnY3E3Rtrj8JJCcnR6yjO6eLhYUA2vpf224plPZ5pGuz/LMaMY9MxZucnAy1Wo3a+8vmrF69Gn6+vpJlVvT7ZNy4cVi6dKmkj/z8/LAiMgrVpXX47vxFg1h0bafL6aT975uMpf1z98by4+c//7n4nX5yzP/BwrD57fKuEn6+vlizZg0Aw++XsX7UhwbQPA5hACdNmoQFCxbISgBzXwxLydiVJoHYAt3SHAfXx2D3oo8lw3K7X/wEBzbEyFo6xJT2wd/H4N1Va3/SjjiB3Ys+wbur1uLA+pgup+uIbeGIMVvU/fyo1W8C0S1TcrOiEtUlt5BXoMXYjZ8j5K0M7D56Eb/+8wmMjf0cRdf+bXXMd2pviUt8VJfc+mkIquSWuOzHndpbWLp0KTw8PJCRkYGqqiqxtH+ncWRkJLy8vJCeno7Tp04hcMJ4+I8ejevf3ZQM+z4387e4oMnFpfx8fHf5ErRaraihW6XglVdeQWFhIRISEgxWKTAW8+9f3wh3N3e8+7e/4cv0E/jt/zxnsCRJWVkZcnNzERsbiz59+iA3Nxe5ubm4c+eO0XbWtUVmWg5OHM3E1OBgjP/lL3AmMxO5ubmyY26v+/af4vUme9wSl2wJnjIZAQEByM7ORnZ2Np544gnJMjC1tbUYOHAg5syZg/z8fKSkpMDd3Q2x69eLbfGvj77AiMd8cD77gth/Ly56QewXjUaDSZMmGV09YvLkydBoNEhPT4eXlxeioqLE2PO/PocRj/mIzwC2tXkM3N3d8X5iInKyzmDOnDlGl4Exdk463Qvfnhd1dUuvvLBgATw9PfHRB//EqRPpmPCLQMlyQjerrmP0aD+j8era+/zXX8HV1QURLy7Fl+lfYfubfxWXXtEtq6JbBiYhIQFf55zF0ogl6O3qim/uT6jRltSIsXz68Uc/5fTjj6Pq/iz+qu9uwM/PFxMnTMDpU6cMYjGXH+8lJLQNL5fcwt4d+6FSqfCXN3e0LQOzaCl6u7ri22/OSZaBMdeP+tAAmqfLGcC1a9ciMzMTJSUlyMvLw7p169CjRw8cP34cgOUEMPdF1iHnD6wcunoCGXsg39QSKDbRdjRdR2wLR4xZr7wTfdJqA2jsWbzyq7fEpT/+XVKL8qtthsVajGkbK6YWMU5MTBS1GhoaEBUVhb59+8LF2QXBk0Ogybogary2co1RDW9vb0lMGRkZePLJJ+Hk5IShQ4carFNqLL5/l9TitZVrMKD/QKid1Hhm3C/FGco6FixYYPT4J0+eNNsWjz4yxOjn5MbcXsvUMjDVpXW4dL4EYWFh4jqwYWFhRheCnjBhAtRqNQYNGoTVL6+VLAGjPwu4urQOZZeqf+oXFxeEhobi2rVrEt2ysjJMmzYNLi4u6Nu3L6KioiTLzejPAjbW5oGBgQZt/v333xs9J3O6ZZeqsWjBEvzsoZ8ZzaPq0jp8c6bAaLzt2/vD5KN44vEAODk54VGvIfjjpm3iPh3x8fHw9vaGk5MTAvzH4KPDxyTHkRvLlElT4eJsGIup/Ggfi65sfeMtPPrIEKOxGHy/TPRje2gAzdPlDOCiRYvEZOzfvz8mT54smj9AXgKY+iK3x9IfWDl09QQyuzTH/SJn6RBT2jvDjS8ZortT1dV0HbEtHDFmS7rJGzdbbQDNLlMi3kmyvKRMR7XtpdtdYnbEtrB37JaW3tEWV5rdr2SJI2OflfOZjugqalcFeQHQAFqiyxlAR8IREsjU0hy6InfmqLXajqbLmDtP19wfZXOYmpWrK3JnFFurbS/d7hazI7aFvWM3N9Nczn4l2h39jJKY5B7PVv1HA2geGkAFOEICGS7NkS753RENhGJdR2wLR4yZBpAxd4KuJW0aQBpAGkDj0AAqwBES6KelOd7F7hdTsWvxMex6MRX75r9n1dIhprR3v/Ah4sM/x96F/8DehYexM/xz7Fn0UZfVdcS2cMSYzel21AC2X0qkuuQWqktq7y+fIX/ZF3Pa1SU14hIf2uLv2/5dckuxLmO2r65O2x5tYe/YDZchkupZ2q9Eu6OfURKTueNpS8vvt2stbpQq09VBA2geGkAFOEoC6ZbfqNFWoeGHHyS/20JbW3kNPzY348fmZmgrr3V5XUdsC0eM2ZRuRw0g8NNSIj82N+NeS4vkd6X82NyM5qYmtN67h9Z799Dc1GQzXcZsX12dtj3aor2+PdsEMFwSSM5+Jdod/YySmMxpNTc1ofn+Ej+2aFsaQPPQACqACUSI9SgxgIQQIhcaQPPQACqACUSI9dAAEkI6AxpA89AAKoAJRIj10AASQjoDGkDz0AAqgAlEiPXQABJCOgMaQPPQACqACUSI9dAAEkI6AxpA89AAKoAJRIj10AASQjoDGkDz0AAqgAlEiPU4qgHcvHkznnrqKfTp0wf9+/fHzJkzcenSJUmdxsZGREVFoV+/fnB1dcX06dNRXl4u7j9//jxmz54NLy8vODs7w9fXF9u3bzc4VkZGBsaOHQu1Wo1hw4bJelVla2srYmJiMHjwYDg7O2PixIkoKCiQ1Nm0aROeeeYZuLi4wMPDQ/a55+XlITAwEM7OzvD09ERsbCxaW1vF/c3NzYiNjcVjjz0GtVqNgIAA/Otf/xL3m3qPsq4sWLAAAFBTU4O5c+fC3d0d7u7umDt3rtF3AZuLxRiW+gVoe4VoaGgoXF1d0a9fPyxfvhxN95ckMYWcNpdzTvaKV04excfHY+jQoVCr1Rg7diwyMzMdOpb20ACahwZQAUwgQqzHpgaw4hsgcVrbTzszdepUJCYmoqCgAOfPn8e0adMwZMgQ3L17V6wTGRmJRx55BGlpadBoNAgKCsKYMWPQ0tICAEhISMDy5cuRkZGBq1ev4v3334eLiwt27NghahQXF8PV1RUrV65EYWEh9u3bB5VKhQ8++MBsfFu3boWbmxuOHDmC/Px8zJo1C4MHD8bt27fFOhs2bMC2bdvw6quvyjaAdXV1GDhwIGbPno38/HwcOXIEbm5ueOutt8Q60dHR8PT0xNGjR3H16lXs2rULzs7O0Gg0AICqqiqxbN++He7u7pJttbVti/2GhITA398fWVlZyMrKgr+/P0JDQ62KxRiW+qWlpQX+/v4ICgqCRqNBWloaPD09ERUVpbjNLZ2TveKVk0fJyclQqVTYt28fCgsLsXLlSvTu3RtlZWUOGYs+NIDmoQFUABOIEOuxqQE8uhqIcQeORSvXshKtVgtBEHDq1CkAQG1tLVQqFZKTk8U6lZWV6NGjBz777DOTOsuWLUNQUJD4e3R0NHx9fSV1IiIi8PTTT5vUaG1txaBBg7B161ZxW2NjIzw8PLBnzx6D+omJibIN4K5du+Dh4YHGxkZx25YtW+Dp6SneeRs8eDB27twp+dzMmTMRFhYm+9iFhYUQBAFnz54Vt2VnZ0MQBPFOq5xY9JHTL8eOHUOPHj1QWVkp1klKSoJarTb5911Om8s5J3vFKyePxo0bh8jISEkdX19frFmzxiFj0YcG0Dw0gApgAhFiPYoN4K0yoFIDVOYCfxzeZgD/OLzt90pN2/5OoKioCIIgID8/HwBw4sQJCIKAmpoaSb2AgABs2LDBpE5YWBiee+458fcJEyZgxYoVkjopKSno1asXmk28GeHq1asQBEG846ZjxowZmD9/vkF9awzgvHnzMGPGDMk2jUYDQRBQXFwMAOjbty/eeecdSZ3Zs2fD29tb9rETEhKMbvfw8MD+/ftlx3Ly5EkIgoCSkhIA8vpl/fr1CAgIkOyvqamBIAj44osvAAAlJSUQBAEnT54EIK/N5ZyTPraK11IeNTU1oWfPnkhJSZHUWbFiBQIDAx0yFn1oAM1DA6gAJhAh1qPYAMa4tyseej/vFzvT2tqK6dOnY/z48eK2gwcPwsnJyaBucHAwlixZYlQnKysLKpUKx48fF7f5+PggLi5OUu/MmTMQBAHXr183qqPb3/4uDACEh4fj2WefNahvjQEMDg5GeHi4ZFtlZSUEQUBWVhYAYM6cORg9ejSuXLmCe/fu4fjx43BxcTHaHqaOHRcXBx8fH4PtPj4+2Lx5s+xYcnJyMGrUKFRUVACQ1y/h4eEIDg42qOPk5IRDhw4BACoqKjBq1Cjk5OQAkNfmcs5JH1vFaymPdO125swZSZ24uDiMHDnSIWPRhwbQPDSACmACEWI9ig3gt4eB2L56RvB+ie3btt/OLFu2DN7e3pIH0E1doKZMmYKIiAiD7QUFBejfvz/eeOMNyXZj5uD06dMQBAFVVVU4cOAAevfuLZbMzEyTBnHx4sWYOnWqwbFNmbDRo0eLuiEhIQCMX2ArKiogCAKys7MBtA2Hz5w5Ez169EDPnj0xcuRILFu2DC4uLrKP3f5i354RI0Zgy5YtsmPRR06/mDLKKpUKSUlJRnXltLmcc7JXvJbySN8469i0aRNGjRrlkLHoQwNoHhpABTCBCLEemzwDWJlr3ABW5touUBNERUXBy8tLHHLUYc0Q1YULFzBgwACsW7fOQN/ScNnt27dRVOdif/IAACAASURBVFQklvr6epsNAZeWloq6ujtocoZddTQ0NKCiogKtra2Ijo7G6NGjZR/bVkPA+thqGFMfDgF3vVj0oQE0Dw2gAphAhFiPbQ3gQ9KfdjSAra2teOmll+Dp6YkrV64Y7Nc9pH748E93IK9fv27wkHpBQQEGDBiA1atXGz1OdHQ0/Pz8JNsiIyNlTQJ58803xW1NTU02mwTy0EMPSZb12Lp1q9mJF83NzRg+fDjWrl0r+9i6CRO6IVYAOHv2rMEkEGtjkdMvuokM7e/mJScny5oEYq7N5ZyTveKVk0fjxo3D0qVLJXX8/PwMJl44Siz60ACahwZQAUwgQqzHJgawtgL40whg70Tg64S2n38a0bbdTixduhQeHh7IyMiQLGFSX18v1omMjISXlxfS09Oh0WgwadIkyTIVumHfsLAwiYZWqxU1dEtmvPLKKygsLERCQoLsZWA8PDyQkpKC/Px8zJkzx2BJkrKyMuTm5iI2NhZ9+vRBbm4ucnNzcefOHZO6tbW1GDhwIObMmYP8/HykpKTA3d1dsvTK2bNnceTIEVy9ehWZmZmYNGkShg0bZnS9O3PmMyQkBAEBAcjOzkZ2djaeeOIJyZIpcmLRfwYQsNwvuqVMJk+eDI1Gg/T0dHh5eUmWMtF/BlBum1s6J2O6tohXTh7pll5JSEhAYWEhXn75ZfTu3RulpaUOGYs+NIDmoQFUABOIEOux2TIwPzYCurs+ra1tv9sRU4sYJyYminUaGhoQFRWFvn37wsXFBaGhobh27Zq4PyYmxqiG/mzZjIwMPPnkk3BycsLQoUOtWgh60KBBUKvVCAwMFGco61iwYIHR4+tmtpoiLy8PEyZMgFqtxqBBg7Bx40bJHbeMjAz4+flBrVajX79+mDdvnsHkCB3mDOD333+PsLAwuLm5wc3NDWFhYUYXgjYXi/4sYMByvwBt5njatGlwcXFB3759ERUVJVluRn8WMCCvzS2dkzFdW8QLyMuj+Ph4eHt7w8nJCWPHjhWXNXLUWPTr0wCahgZQAUwgQqzHUd8EQghxLGgAzUMDqAAmECHWQwNICOkMaADNQwOoACYQIdZDA0gI6QxoAM1DA6gAJhAh1kMDSAjpDGgAzUMDqAAmECHWQwNICOkMaADNQwOoACYQIdZDA0gI6QxoAM1DA6gAJhAh1kMDSAjpDGgAzUMDqAAmECHWQwNICOkMaADNQwOoACYQIdZDA0gI6QxoAM1DA6gAJhAh1kMDSAjpDGgAzUMDqAAmECHWQwNICOkMaADNQwOoACYQIdbjqAZw8+bNeOqpp9CnTx/0798fM2fOxKVLlyR1GhsbERUVhX79+sHV1RXTp09HeXm5uP/8+fOYPXs2vLy84OzsDF9fX2zfvt3gWBkZGRg7dizUajWGDRtm1buABw8eDGdnZ0ycOBEFBQWSOps2bcIzzzwDFxcXk+/jNUZeXh4CAwPh7OwMT09PxMbGSt6/CwC3bt3CsmXLxPfi+vr64ujRowBMv0dZVxYsWAAAqKmpwdy5c+Hu7g53d3fMnTvX6LuALcWij6V+AdreZxsaGgpXV1f069cPy5cvR1NTk1ldOW0u55zsFa+cPIqPj8fQoUOhVqsxduxYZGZmOnQs7aEBNA8NoAKYQIRYjy0NYMGNAiz6bBEKbhRYrqyQqVOnIjExEQUFBTh//jymTZuGIUOG4O7du2KdyMhIPPLII0hLS4NGo0FQUBDGjBmDlpYWAEBCQgKWL1+OjIwMXL16Fe+//z5cXFywY8cOUaO4uBiurq5YuXIlCgsLsW/fPqhUKnzwwQdm49u6dSvc3Nxw5MgR5OfnY9asWRg8eDBu374t1tmwYQO2bduGV199VbYBrKurw8CBAzF79mzk5+fjyJEjcHNzw1tvvSXWaWpqwlNPPYXf/OY3OH36NEpLS/Hll1/i/PnzAICqqiqxbN++He7u7pJttbW1AICQkBD4+/sjKysLWVlZ8Pf3R2hoqFWxGMNSv7S0tMDf3x9BQUHQaDRIS0uDp6cnoqKiFLe5pXOyV7xy8ig5ORkqlQr79u1DYWEhVq5cid69e6OsrMwhY9GHBtA8NIAKYAIRYj22NICbz26G/7v+2JKzxQaRWYdWq4UgCDh16hQAoLa2FiqVCsnJyWKdyspK9OjRA5999plJnWXLliEoKEj8PTo6Gr6+vpI6ERERePrpp01qtLa2YtCgQdi6dau4rbGxER4eHtizZ49B/cTERNkGcNeuXfDw8EBjY6O4bcuWLfD09BTvvO3evRuPPfYYmpubLeqZOnZhYSEEQcDZs2fFbdnZ2RAEQbzTKicWfeT0y7Fjx9CjRw9UVlaKdZKSkqBWq03+fZfT5nLOyV7xysmjcePGITIyUlLH19cXa9ascchY9KEBNA8NoAKYQIRYj1IDWHmnEgU3C3Dh5gUEJgfC/11/BCYH4sLNCyi4WYDKO5WWRWxAUVERBEFAfn4+AODEiRMQBAE1NTWSegEBAdiwYYNJnbCwMDz33HPi7xMmTMCKFSskdVJSUtCrVy+TBuvq1asQBAEajUayfcaMGZg/f75BfWsM4Lx58zBjxgzJNo1GA0EQUFxcDAD49a9/jbCwMISHh2PAgAF4/PHHERcXZ/TOjKljJyQkGN3u4eGB/fv3y47l5MmTEAQBJSUlAOT1y/r16xEQECDZX1NTA0EQ8MUXXwAASkpKIAgCTp48CUBem8s5J31sFa+lPGpqakLPnj2RkpIiqbNixQoEBgY6ZCz60ACahwZQAUwgQqxHqQH0f9ffYrE3ra2tmD59OsaPHy9uO3jwIJycnAzqBgcHY8mSJUZ1srKyoFKpcPz4cXGbj48P4uLiJPXOnDkDQRBw/fp1ozq6/e3vwgBAeHg4nn32WYP61hjA4OBghIeHS7ZVVlZCEARkZWUBAEaNGgW1Wo1Fixbh3LlzSEpKQt++fREbGyv72HFxcfDx8THY7uPjg82bN8uOJScnB6NGjUJFRQUAef0SHh6O4OBggzpOTk44dOgQAKCiogKjRo1CTk4OAHltLuec9LFVvJbySNduZ86ckdSJi4vDyJEjHTIWfWgAzUMDqAAmECHWo9QAfnr1U4x5b4xR4zfmvTH49OqnNo7YkGXLlsHb21vyALqpC9SUKVMQERFhsL2goAD9+/fHG2+8IdluzBycPn0agiCgqqoKBw4cQO/evcWSmZlp0iAuXrwYU6dONTi2KRM2evRoUTckJASA8QtsRUUFBEFAdna2GPOjjz4queP35z//GYMGDZJ97PYX+/aMGDECW7ZskR2LPnL6xZRRVqlUSEpKMqorp83lnJO94rWUR/rGWcemTZswatQoh4xFHxpA89AAKoAJRIj12OIZwAs3Lxg1gBduXrBhpMaJioqCl5eXOOSow5ohqgsXLmDAgAFYt26dgb6l4bLbt2+jqKhILPX19TYbAi4tLRV1dXfQ5Ay7BgYGYvLkyZI6x44dgyAIBrNB7T0ErI+thjH14RBw14tFHxpA89AAKoAJRIj12NIAPvHuE5Kf9jSAra2teOmll+Dp6YkrV64Y7Nc9pH748GFx2/Xr1w0eUi8oKMCAAQOwevVqo8eJjo6Gn5+fZFtkZKSsSSBvvvmmuK2pqclmk0AeeughiZHbunWrZOLF2rVr4e3tjXv37ol1tm/fjsGDB8s+tm7ChG6IFQDOnj1rMAnEUiz6yOkX3USG9nfzkpOTZU0CMdfmcs7JXvHKyaNx48Zh6dKlkjp+fn4GEy8cJRZ9aADNQwOoACYQIdZjCwNYdbcKE5MnYtans3D40mHM+nQWJiZPRNXdKhtGKmXp0qXw8PBARkaGZAmT+vp6sU5kZCS8vLyQnp4OjUaDSZMmSZap0A37hoWFSTS0Wq2ooVsy45VXXkFhYSESEhJkLwPj4eGBlJQU5OfnY86cOQZLkpSVlSE3NxexsbHo06cPcnNzkZubizt37pjUra2txcCBAzFnzhzk5+cjJSUF7u7ukqVXrl27hj59+iAqKgqXL19GamoqBgwYgE2bNhnomTOfISEhCAgIQHZ2NrKzs/HEE09IlkyRE4v+M4CA5X7RLWUyefJkaDQapKenw8vLS7KUif4zgHLb3NI5GdO1Rbxy8ki39EpCQgIKCwvx8ssvo3fv3igtLXXIWPShATQPDaACmECEWI+tloFpamkS7/q0traiqcX8or1KMbWIcWJiolinoaEBUVFR6Nu3L1xcXBAaGopr166J+2NiYoxqeHt7S46VkZGBJ598Ek5OThg6dKhVC0HrFmIODAwUZyjrWLBggdHj62a2miIvLw8TJkyAWq3GoEGDsHHjRoM7bllZWfj5z38OtVqNxx57zOpZwADw/fffIywsDG5ubnBzc0NYWJjRhaDNxaI/Cxiw3C9AmzmeNm0aXFxc0LdvX0RFRUmWm9GfBQzIa3NL52RM1xbxAvLyKD4+Ht7e3nBycsLYsWPFZY0cNRb9+jSApqEBVAATiBDrcdQ3gRBCHAsaQPPQACqACUSI9dAAEkI6AxpA89AAKoAJRIj10AASQjoDGkDz0AAqgAlEiPXQABJCOgMaQPPQACqgMxPo09078M68Xfh09w7LlbuINmO2v649te2l29DQgG9yclBZVIFb1dU21b5VXQ1tcbXNde2p7Wi69tRmzPbXtae2PWPuCDSA5qEBVEBnJlDCwljsjDiBhIWGr1bqqtqM2f669tS2l25DQwO+ysxCxXc3cKPYtu/tvVFcierSOpvr2lPb0XTtqc2Y7a9rT217xtwRaADNQwOoAHsnUPaxj5G6ZydS98Zjz6IPsTPiBPYs+hCpe+ORumcnso993OW0GbP9dR095n+9m4CcL8+h4rsb0JbU4Ja2Gre01bhTe8uyiBHu1N4SNbQlNagurbOJrj21HU2XMTu2rqPGrBQaQPPQACrA3gm0M+LET2VJuvTn/dLVtBmz/XUdPeZ3ok8i50sNKr67gerSOknpCPoaxkpHsZe2o+kyZsfWddSYlUIDaB4aQAXYO4H+vmod4sOPSy/K90t8+HH8fZXhe0QftDZjtr+uo8ds1ACW1OHmtQrLIka4ea0C1SUmLj4KdO2p7Wi6jNmxdR01ZqXQAJqHBlABnZFAqXvjjV6MU/fGd1ltxmx/XUeO2ZgBvKVV9tD4LW210QuQUl17ajuaLmN2bF1HjVkJNIDmoQFUQKcawCVpkp82NRA21mbM9td15JhpAB1TlzE7tq6jxqwEGkDz0AAqoDMS6Fz6Mex5IQX75icgcfka7JufgD0vpOBc+rEuq82Y7a/ryDG/+8onyMn8CtevlkBb3DYR5G5drSLdu3W10JbUQFt8AzfKym2m2177d6/9Dv8VEIDevXvj4X4PI3Tab3Dp0iVJ3cbGRkRFRaFfv35wdXXF9OnTUV5eLu4/f/48Zs+eDS8vLzg7O8NnuA82rd9kEHNGRgbGjh0LtVqNYcOGWf0uYGe1M37x81/gy7R0ie6mTZvwzDPPwMXFxeT7eI2Rl5eHwMBAODs7Y9DAwYh++XVoS69JtP/yl79g5MiRcHZ2hpeXF15++WXxwmvqPcq6Eva/c6AtqcGV3CL89v/7b7j1aXtv7uxZzxt9F7AuFk9PT8TGxhq8l1i//8ovVeHF+S+i789+BlcXV0ydHILLhRck9crKyhAaGgpXV1f069cPy5cvR1OT6fdL362rRXXx93htxWoMHDDgfpv/El+dzZbUq6mpwdy5c+Hu7g53d3fMnTvX4JzsFa9+Hm3fts3ge7L1D3+C95AhUKvVGDt2LDIzMyUalnIaAC4W5OPZSVPh6uKKvj/7GRYvWIyKy/+WfP/k5HR8fDyGDh2qKJb20ACahwZQAZ2VQDXaKvzY3AwA+LG5GTXaqi6vzZjtr2tPbXvG/O/Ka7hw4QIaGhrQeu+eeJyOUJ+Xj9L5C1Cfl48fm5vReu8eACjW1efH5mZMffZZJCYmIj8vD+e+/hrTpk3DkCFDcPfuXbFeZGQkHnnkEaSlpUGj0SAoKAhjxoxBS0sLACAhIQHLly9HRkYGrl69incTE+Hi4oIdO3aIMRcXF8PV1RUrV65EYWEh9u3bB5VKhQ8++MBsjFu3boWbmxuOHDmCXI0Gs55/HoMHD0Zdba3YFhs2bMC2bdvw6quvyjaAdXV1GDhwIGbPno38/Hz84/BhuLm54a233hJjPnDgANRqNQ4ePIiSkhJ8/vnnGDx4MF5++WUAQFVVlVi2b98Od3d3ybba+zGGTJ0Kf39/nDl9GpmZmfD390doaKjJWI4cOSLGYo4lS5aI/fLNuXP41a9+JemXlpYW+Pv7IygoCBqNBmlpafD09ERUVJRZ3c1xcWKb5337LZ7/7W8xePBg3L59W6wTEhICf39/ZGVlISsry+Cc7BWvqTw6nJwsfk+SDh2CSqXCvn37UFhYiJUrV6J3794oKysTdSzltC6WX/3qV/jm3DkxlmXLllmMpX1OJycnK45FHxpA89AAKoAJRIj12PJNIFVvbELhKF9UbYqzQWTWodVqIQgCTp06BQCora2FSqVCcnKyWKeyshI9evTAZ599ZlJn2bJlCAoKEn+Pjo6Gr6+vpE5ERASefvppkxqtra0YNGgQtm7dKm5rbGyEh4cH9uzZY1A/MTFRtgHctWsXPDw80NjYKG7bsmULPD09xTtvL730EiZNmiT53Kuvvorx48fLPnZhYSEEQcDZs2fFbdnZ2RAEQbzTKicWfeT0y7Fjx9CjRw9UVv60fl1SUhLUarXJv+9y2lzOOdkrXjl5NG7cOERGRkrq+Pr6Ys2aNQ4Ziz40gOahAVQAE4gQ61FqAJsrKlCfX4D6ggJcfuYXKBzli8vP/AL1BQWozy9Ac0XnzDosKiqCIAjIz88HAJw4cQKCIKCmpkZSLyAgABs2bDCpExYWhueee078fcKECVixYoWkTkpKCnr16oVmE3c1r169CkEQoNFoJNtnzJiB+fPnG9S3xgDOmzcPM2bMkGzTaDQQBAHFxcUA2i74Hh4eyMnJEePx9fXFli1bZB87ISHB6HYPDw/s379fdiwnT56EIAgoKSkBIK9f1q9fj4CAAMn+mpoaCIKAL774AgBQUlICQRBw8uRJ8Rwttbmcc9LHVvFayqOmpib07NkTKSkpkjorVqxAYGCgQ8aiDw2geWgAFcAEIsR6lBrAwlG+Fou9aW1txfTp0yV3uA4ePAgnJyeDusHBwViyZIlRnaysLKhUKhw/flzc5uPjg7g46R3NM2fOQBAEXL9+3aiObn/7uzAAEB4ejmeffdagvjUGMDg4GOHh4ZJtlZWVEAQBWVlZ4ra//vWvUKlU6NWrFwRBwNKlS43qmTp2XFwcfHx8DLb7+Phg8+bNsmPJycnBqFGjUHH/PwJy+iU8PBzBwcEGdZycnHDo0CEAQEVFBUaNGiWaXDltLuec9LFVvJbySNduZ86ckdSJi4vDyJEjHTIWfWgAzdPlDODmzZvx1FNPoU+fPujfvz9mzpxpcKt84sSJBg8Rz5o1S1JHzoO31j5MrA8TiBDrUWoAaz/5BIWjHzdu/kY/jtpPPrFxxIYsW7YM3t7ekgfQTV2gpkyZgoiICIPtBQUF6N+/P9544w3JdmPm4PTp0xAEAVVVVThw4AB69+4tlszMTJMGcfHixZg6darBsU2ZsNGjR4u6ISEhAIxfYCsqKiAIArKz2yY8nDx5EgMHDsS+ffuQl5eHlJQUPProo/jDH/4g+9jtL/btGTFihHgnUU4s+sjpF1NGWaVSISkpyaiunDaXc072itdSHhkz8QCwadMmjBo1yiFj0YcG0DxdzgBOnToViYmJKCgowPnz540+aD1x4kSEh4cbPETcHksP3nb0YeL2MIEIsR5bPANYX1Bg1ADWFxTYMFLjREVFwcvLSxxy1GHNENWFCxcwYMAArFtnuLC2peGy27dvo6ioSCz19fU2GwIuLS0VdXV30OQMu44fPx6vvfaapM77778PFxcX3Ls/4cDSsW01BKyPrYYx9eEQcNeLRR8aQPN0OQOoj/6D1kCbAVy5cqXJz9jrYWJ9mECEWI9NDaCvn+SnPQ1ga2srXnrpJXh6euLKlSsG+3UPqR8+fFjcdv36dYOH1AsKCjBgwACsXr3a6HGio6Ph5+cn2RYZGSlrEsibb74pbmtqarLZJJCHHnpIssTI1q1bJX8rx44di+joaMnnDh06BGdnZ4MZmpYmgeiGWAHg7NmzBn+3LcWij5x+0U1kaH83Lzk5WdYkEHNtLuec7BWvnDwaN26cwVC9n5+fwcQLR4lFHxpA83R5A6j/oDXQZgAffvhh9OvXD6NHj8aqVask0+7t9T/JxsZG1NXViaW8vLzbJxAh1mILA9hcVYXLvxyP4uf+BzVJySh+7n9w+Zfj0Vxlu+Vq9Fm6dCk8PDyQkZEhGX2or68X60RGRsLLywvp6enQaDSYNGmSZJkK3bBvWFiYREOr1YoauiUzXnnlFRQWFiIhIUH2MjAeHh5ISUlBfn4+5syZY7AkSVlZGXJzcxEbG4s+ffogNzcXubm5uHPnjknd2tpaDBw4EHPmzEF+fj5SUlLg7u4uGS2JiYmBm5sbkpKSUFxcjOPHj2P48OF4/vnnDfTMmc+QkBAEBAQgOzsb2dnZeOKJJyQjN3Ji0X8GELDcL7qlTCZPngyNRoP09HR4eXlJllXRfwZQbptbOidjuraIV04e6ZZeSUhIQGFhIV5++WX07t0bpaWlDhmLPjSA5unSBtDYg9YA8Le//Q1paWnIz89HUlIShg4diilTpoj7bfUwsT4xMTFGFzHtzglEiLXYahmYe01N4l2f1tZW3DOzaK8tMLWIcWJiolinoaEBUVFR6Nu3L1xcXBAaGopr166J+039DfH29pYcKyMjA08++SScnJwwdOhQqxeCVqvVCAwMlPzHGQAWLFhg9Pi6ma2myMvLw4QJE6BWqzFo0CBs3LhRcsftxx9/xMaNGzF8+HA4Ozvj0UcfxbJly4wueGzOAH7//fcICwuDm1vbQtBhYWFGn902F4v+LGDAcr8AbeZ42rRpcHFxQd++fREVFSUZIdKfBQzIa3NL52RM1xbxAvLyKD4+Ht7e3nBycsLYsWMlo22OGIt+fRpA03RpA2jsQWtjnDt3DoIg4JtvvgFgv4eJeQeQEOXYch1AQggxBQ2gebqsATT1oLUxWltbJQtE2msIWB8mECHWQwNICOkMaADN0+UMoKUHrY2Rn58vmShir4eJ9WECEWI9NICEkM6ABtA8Xc4AWnrQ+rvvvkNsbCy+/vprlJSU4OjRo/D19cWTTz4peRDUFg8TW4IJRIj10AASQjoDGkDzdDkDaOlB62vXriEwMBB9+/aFk5MThg8fjhUrVuD777+X6NjiYWJLMIEIsR4aQEJIZ0ADaJ4uZwAdCSYQIdZDA0gI6QxoAM1DA6gAJhAh1kMDSAjpDGgAzUMDqAAmECHWQwNICOkMaADNQwOoACYQIdZDA0gI6QxoAM1DA6gAJhAh1kMDSAjpDGgAzUMDqAAmECHWQwNICOkMaADNQwOoACYQIdbjqAZw8+bNeOqpp9CnTx/0798fM2fOFBeW19HY2IioqCj069cPrq6umD59uuRVlufPn8fs2bPh5eUFZ2dn+Pr6Yvv27QbHysjIwNixY6FWqzFs2DCr3gU8ePBgODs7Y+LEiSgoKJDU2bRpE5555hm4uLiYfB+vMfLy8hAYGAhnZ2d4enoiNjZWsmTWxIkTjS7f9Zvf/EZ81625EhMTA6DtnbKhoaFwdXVFv379sHz5csli/R1tm5qaGsydOxfu7u5wd3fH3LlzjS4LZu4cjWGpv+Wek73i/eCDD+Dn5wcnJyf4+fkhJSVFsl9OzjhaLO2hATQPDaACmECEWI8tDWB1aR0+/PM3qC61/3dw6tSpSExMREFBAc6fP49p06ZhyJAhuHv3rlgnMjISjzzyCNLS0qDRaBAUFIQxY8aIi9QnJCRg+fLlyMjIwNWrV/H+++/DxcUFO3bsEDWKi4vh6uqKlStXorCwEPv27YNKpcIHH3xgNr6tW7fCzc0NR44cQX5+PmbNmoXBgwfj9u3bYp0NGzZg27ZtePXVV2UbwLq6OgwcOBCzZ89Gfn4+jhw5Ajc3N8mi+d9//71k4f6CggL07NkTiYmJaGlpkexbtWoVHn/8ccm2O3fuoKWlBf7+/ggKCoJGo0FaWho8PT0RFRWluG1CQkLg7++PrKwsZGVlwd/fX/JiADnnaAxL/S3nnOwVb1ZWFnr27InNmzfj4sWL2Lx5M3r16oWzZ8+KdeTkjCPFog8NoHloABXABCLEemxpAE8lX8bOiBPITL5sg8isQ6vVSl5BWVtbK3knOQBUVlaiR48e+Oyzz0zqLFu2DEFBQeLv0dHR8PX1ldSJiIjA008/bVKjtbUVgwYNwtatW8VtjY2N8PDwwJ49ewzqJyYmyjaAu3btgoeHBxobG8VtW7ZsMfvazL/85S9wc3OTmGMdMTExGDNmjMH2Y8eOoUePHqisrBS3JSUlQa1Wi39jO9I2uleDtjcb2dnZBq8GtfYc5fS3nHOyV7zPP/88QkJCJNpTp07F7NmzAcjLGUeLRR8aQPPQACqACUSI9Sg1gHU361FdWgdt2W0kvJaJnREnkPBaJrRlt1FdWoe6m/U2jtg4RUVFEAQB+fn5AIATJ05AEATU1NRI6gUEBGDDhg0mdcLCwvDcc8+Jv0+YMAErVqyQ1ElJSUGvXr3Q3NxsVOPq1asQBAEajUayfcaMGZg/f75BfWsM4Lx58zBjxgzJNo1GA0EQUFxcbPQz/v7+CA8PN7rPlAFcv349AgICJNtqamogCAK++OILAPLaJjExEYLw06UtISHB6Ll6eHhg//79ss/x5MmTEAQBJSUlAOT1t5xz0sdW8T766KPYtm2bpM62bdswZMgQAPJyxtFi0YcG0Dw0gApgrp1fmwAAIABJREFUAhFiPUoN4M6IExaLvWltbcX06dMxfvx4cdvBgwfh5ORkUDc4OBhLliwxqpOVlQWVSoXjx4+L23x8fBAXFyepd+bMGQiCgOvXrxvV0e1vf6cJAMLDw/Hss88a1LfGAAYHBxuYucrKSgiCgKysLIP6OTk5EAQBOTk5RvVMGcDw8HAEBwcbbHdycsKhQ4cAyGublJQUjBo1StwfFxcHHx8fA10fHx9s3rxZ9jnm5ORg1KhRqKioACCvv+Wckz62ilelUuHgwYOSOu1jlpMzjhaLPjSA5qEBVAATiBDrUWoAL52tQvzSL4wav/ilX+DS2SobR2zIsmXL4O3tLXng35QhmDJlCiIiIgy2FxQUoH///njjjTck241d0E6fPg1BEFBVVYUDBw6gd+/eYsnMzDRpEBcvXoypU6caHNuUARw9erSoqxuyM2ZgKyoqIAgCsrOzDTSWLFkCf39/g+06zBlAY2ZVpVIhKSkJgOW2MUZcXBxGjhxpsH3EiBHYsmULAOvPEZDX33LOyV7xqlQqA5N54MABqNVqAKb/U9E+ZxwtFn1oAM1DA6gAJhAh1mOLZwC1ZbeNGkBt2W3LH1ZIVFQUvLy8DIY/rRkCvnDhAgYMGIB169YZ6Fsa5rx9+zaKiorEUl9fb7Mh4NLSUlFXd6fLmiHgH374Ae7u7kZnNuuw9xCwPrYaxtSHQ8BdLxZ9aADNQwOoACYQIdZjUwMYeULy054GsLW1FS+99BI8PT1x5coVg/26SQGHDx8Wt12/ft1gEkhBQQEGDBiA1atXGz1OdHQ0/Pz8JNsiIyNlTQJ58803xW1NTU02mwTy0EMPSZYu2bp1q9EJEomJiVCr1bh586ZJPUuTQNrfBUpOTjaYBGJt2+gmD7Qfkj579qzBRAa556hDTn/LOSd7xfv888/j17/+tUQ7JCTEYOKFuZxxtFj0oQE0Dw2gAphAhFiPLQzgnZoG7F/9Jf6x+Svkn6rAPzZ/hf2rv8SdGvutLbh06VJ4eHggIyNDsoRJff1Pk04iIyPh5eWF9PR0aDQaTJo0SbIsiG7YNywsTKKh1WpFDd1SJ6+88goKCwuRkJAgexkYDw8PpKSkID8/H3PmzDFYRqOsrAy5ubmIjY1Fnz59kJubi9zcXNy5c8ekbm1tLQYOHIg5c+YgPz8fKSkpcHd3N7pEyvjx4zFr1iyzcZoygLolUyZPngyNRoP09HR4eXkZXQbGXNvoPwMItJmNgIAAZGdnIzs7G0888YRk+RA556j/DCBgub/lnJMxXVvEe+bMGfTs2RNbt27FxYsXsXXrVqNLr1jKGUeKRR8aQPPQACqACUSI9dhqGZiW5nviHYbW1la0NN+zRXgmMbWIcWJiolinoaEBUVFR6Nu3L1xcXBAaGopr166J+2NiYoxqeHt7S46VkZGBJ598Ek5OThg6dKhVC0EPGjQIarUagYGB4gxlHQsWLDB6/JMnT5rVzsvLw4QJE6BWqzFo0CBs3LjR4M7Y5cuXIQiCZEKLMUwZQKDNoE6bNg0uLi7o27cvoqKiJMuLAJbbRn8WMNC2TmFYWBjc3Nzg5uaGsLAwo4sZmztH/VnAgOX+lnNOxnRtES8A/POf/8SoUaOgUqng6+uLI0eOSPbLyRlHi6U9NIDmoQFUABOIEOtx1DeBEEIcCxpA89AAKoAJRIj10AASQjoDGkDz0AAqgAlEiPXQABJCOgMaQPPQACqACUSI9dAAEkI6AxpA89AAKoAJRIj10AASQjoDGkDz0AAqgAlEiPXQABJCOgMaQPPQACqACUSI9ej+KLdfP48QQmzNDz/8QANoBhpABTCBCLGelpYWXLx4EWVlZaivr0dDQwMLCwuLzUp9fT1qa2tRVFSES5cu4d49wzVCef2mAVQEE4iQjnHnzh1cvHgRhYWFLCwsLHYppaWlklfUtYfXbxpARTCBCOk4LS0tD/xOAQsLy39maW5uNvkeZ4DXb4AGUBFMIEIIIcTx4PWbBlARTCBCCCHE8eD1mwZQEUwgQgghxPHg9ZsGUBFMIEIIIcTx4PWbBlARTCBCCCHE8eD1mwZQEUwgQgghxPHg9ZsGUBFMIEIIIcTx4PWbBlARTCBCCCHE8eD1mwZQEUwgQgghxPHg9ZsGUBFMIEIIIcTx4PWbBlARTCBCCCHE8eD1mwZQEUwgQgghxPHg9ZsGUBFMIEIIIcTx4PWbBlARTCBCCCHE8eD1mwZQEQ8ygT7dvQPvzNuFT3fvUFSno5+xl3ZXivlBt4Ujxsy2cOyYu1JbOGLMD1q3I9rWflaJvj21rIUGkAZQEQ8ygRIWxmJnxAkkLIxVVKejn7GXdleK+UG3hSPGzLZw7Ji7Uls4YswPWrcj2tZ+Vom+PbWshQaQBlARnZ1A2cc+RuqenUjdG489iz7EzogT2LPoQ6TujUfqnp3IPvaxrDod0bWmniPH/KDbwhFjZls4dsxdqS0cMeYHrdvRdrZ3eys5XmdAA0gDqAh7JpDu1njiK2vEW+Q7I078VJakS38aKybqdEQXgN20H3TM++bvx775CV2iLeRqs/+Maz/otmD/dY+Y981PwL75+7tELsvV/nT3DqNDrrZq7/bXLZ1+R4/XGdAA0gAqwp4JpLs1vm/+e9gZ0XaL/O+r1iE+/LjRL2V8+HH8fdU6i3XembezQ7oA7KbdFWLuKm1hSXvnkjT2nxntffN2sP8cuP8cKeaukstycy5hYazRIVdbtLcuFn39jh6vM6ABpAFUhK0TSHdr/OCGGOxe9LHky7v7xU9wcH0M3l211ugXJ3VvvKiTujfeaJ13lr6O3S9+ItVd9LFsXYvajhbz4rXYvegT8ffdiz7Gu6vW4p2INcrbwk4x734xlf1nQfudpa93z/5TEHNX6r8uHfPitT99TyJOYPeiT+7/zbBPzsnNZfM596kYqy723Ys+kgy5mvqsnPY+uD7GYEh394uf4N1V68Tj6Q/xyjmevaEBpAFUhK0TyNgXwuSt+Pt/JHQ/jX5RdXVspGtP7QcecxdqC1na7D952uw/x+6/rhxzF8plRTHf17O6vY0dx2R7p1t9PHtDA0gDqAhbJ5DZ2/i6En4cu1/8FPvmJyBx+Rrsm5+APS+k4Fz6MVHnXPox7HkhRayzd0Gy+T8qMnXtqd2lYg5Pe6Btoa/9zvzN7D9rtZekY+/Cw+w/R+0/R4t5iR3/ZsjI5Q7lXMRPQ676cclpb12d/cvWWL5udeB49oYGkAZQEfZIIFO3xnUldW88arRV+LG5GQDwY3MzarRVBjr6dT56+8820bWndleJuSu0hb52qv7D5l0w5q7SfzsjTuCjt7ex/xy4/xwt5q6Qy/ralnJOF7epuOS0t66OpetWR49nT2gAaQAVYVcDKN4aT5f83tFb5PbSdfiYLfyR6ioxs/8cO2a2xX9QzI76N6N9sfGQq8nh4Qc4xGsJGkAaQEXYI4F+ujX+Lna/mIpdi49h14up2Df/PUW3yH/SfQ+77uvufjEV++a/q/jWu8PGvOgj7Az/HHsXHsbehf9AfPjn2P3ChzZqC9vGzP7rHG1H02XMnRvz7hc+RHz459i78B/Yu/AwdoZ/jj2LPuribZGAhCVrsTP8c8SHf453ItbafMjVcEj3PexckoZ98999YEO8lqABpAFUhL0SSHdrvEZbhYYffpD83hV1HTlmbeU1/NjcjB+bm6GtvNat24IxO6YuY+7cmB3xbwYAaCuvQVt5DYB9hlz1h3SLL+Q/0CFeS9AA0gAqgglECCGEOB68ftMAKoIJRAghhDgevH7TACqCCUQIIYQ4Hrx+0wAqorMSyNj7FG31OXtpM2bluo4YM/vPsWP+T+w/R425o/r2ylclutYczx7HMQYNIA2gIjorgYy9T9FWn7OXNmNWruuIMbP/HDvm/8T+c9SYO6pvr3xVomvN8exxHGPQAHZBA7h582Y89dRT6NOnD/r374+ZM2fi0qVLkjqNjY2IiopCv3794OrqiunTp6O8vFxSp6ysDKGhoXB1dUW/fv2wfPlyNDU1SepkZGRg7NixUKvVGDZsGHbv3m1VrPZMIN17gdu/X1H/fYod/Zy9tI/8+U+MWaGuI8bM/nPsmP8T+89RY7amXke0rY3r4O9jcGB9TId0rTne7kUf4d1Va/HuqrUm3x9sa2gAu6ABnDp1KhITE1FQUIDz589j2rRpGDJkCO7evSvWiYyMxCOPPIK0tDRoNBoEBQVhzJgxaGlpAQC0tLTA398fQUFB0Gg0SEtLg6enJ6KiokSN4uJiuLq6YuX/3975R0dV3vn/iiYIbkgXtQhSdbtidYtYPduq51Q4iiB7RLvbPVvrCgNMmATbKGrOqSQlBNoVqKu0lkkmYRIIPyI/lPwipoYk/tqSwCkbeojSVhELNGSbb01BRUKwvL9/hBnmx7137r3PvTP3ybxf59wDmbn3/byfz/OZ+3wyufe5ixfj0KFDCAaDyMjIwKuvvmrYq5MJFL1oZ1v0vxc2q8clRZueLenK6JnjJ7fnYT9+Enk2s58VbaH+mtQ11Z5ebC22kwgWgC4sAGPp6+uDoih4++23AQAnT55ERkYGtm3bFt6np6cHI0aMwOuvvw4AaG5uxogRI9DT0xPeZ+vWrRg5cmR4sH/0ox/h5ptvjmorLy8Pd911l2FvTiaQ3nOBQ89TtHqcU9qV835Cz4K6Mnrm+MnteTiOn6yezexnRdusL7+vVf1JIgZ0LbVnYzuJYAEoQQH4wQcfQFEUdHd3AwDa29uhKAr6+/uj9psyZQqWLVsGACguLsaUKVOi3u/v74eiKHjjjTcAAPfccw+efPLJqH1qa2tx2WWXYfDC4pWxDAwM4NSpU+Ht+PHjjiaQ1qOHEj1Ox8hxTmnTs7iujJ45fnJ7Ho7jJ6tnq/pO5auIrtn27G5HDxaALi8Az58/j4ceegjf/va3w6/V1NQgMzMzbt8ZM2YgNzcXAODz+TBjxoy4fTIzM/Hyyy8DACZNmoTnnnsu6v09e/ZAURScOHFC1U9JSQkURYnbHC8ATT5P0chxTmnTs7iujJ45fnJ7Ho7jJ6tnq/pO5auIrtn27G5HDxaALi8Af/CDH+D666+PusFDqwC8//77kZeXB2CoAJw5c2bcPhkZGdi6dSuAoQJw5cqVUe//+te/hqIo6O1Vf2RNsr8BjH++orHnKRo5ziltehbXldEzx09uz8Nx/GT1bFXfqXwV0TXTXmBBHcq99ba2owcLQBcXgPn5+Zg4cSKOHDkS9Xoq/wQcSzISKPb5ikafp2jkOKe06VlcV0bPHD+5PQ/H8ZPVs1V9p/JVRNdMe060owULQBcWgOfPn8cPf/hDTJgwAe+//37c+6GbQLZv3x5+7cSJE6o3gUT+KXfbtm1xN4HccsstUdqLFi1yzU0ghBBCCHEGzt8uLAAff/xxZGdn46233kJvb294+/zzz8P7LFq0CBMnTkRbWxu6urpw3333qS4DM336dHR1daGtrQ0TJ05UXQbm6aefxqFDh1BVVeWqZWAIIYQQ4gycv11YAKrdZKEoCjZs2BDe58yZM8jPz8fYsWMxatQozJ49G8eOHYvSOXr0KB588EGMGjUKY8eORX5+PgYGBqL2eeutt3D77bcjMzMTN9xwg6sWgiaEEEKIM3D+dmEBKBNMIEIIIUQ+OH+zABSCCUQIIYTIB+dvFoBCMIEIIYQQ+eD8zQJQCCYQIYQQIh+cv1kACsEEIoQQQuSD8zcLQCGYQIQQQoh8cP5mASgEE4gQQgiRD87fLACFYAIRQggh8sH5mwWgEEwgQgghRD44f7MAFIIJRAghhMgH528WgEIwgQghhBD54PzNAlAIJhAhhBAiH5y/WQAKwQQihBBC5IPzNwtAIZhAhBBCiHxw/mYBKAQTiBBCCJEPzt8sAIVgAhFCCCHywfmbBaAQTCBCCCFEPjh/swAUgglECCGEyAfnbxaAQjCBCCGEEPng/M0CUAgmECGEECIfnL9ZAArBBCKEEELkg/M3C0AhmECEEEKIfHD+ZgEoBBOIEEIIkQ/O3ywAhWACEUIIIfLB+ZsFoBCpSKBdgbWonFuGXYG1th7jlC49y61Lz+7VldHzcB2/dPIscrxoe3bCApAFoBCpSKCq+Svgz2tH1fwVth7jlC49y61Lz+7VldHzcB2/dPIscrxoe3bCApAFoBDJSqDO5gY0lfvRVFGKcm8d/HntKPfWoamiFE3lfnQ2N1g6xildek6OZ8ZCbs9WdGX0PFzHL508W2nH6v7JggUgC0AhkpVA/rz2i1tuW/S/FzYrxzilS8/J8cxYyO3Ziq6Mnofr+KWTZyvtWN0/WbAAZAEoRLISaFNBEUp9u1VPDqW+3dhUUGTpGKd06Tk5nhkLuT1b0ZXR83Adv3TybKUdq/snCxaALACFSGYCNVWUqn6AmipKw/vEXmBr5Bgj+8RqGz2Gns15ZizSz7MVXTs9uykWw9lzbBtBT5Vtno14CXqqom68sKJttM+h/gU9602NUbJhAcgCUIiUFIC5rZofoNgLbOOOufCv6klGRzdW24hutHb01/70LNv4yehZjpyzomunZzfFQsbxM+pZrQ27PKt7aY07NvLGC7OejbbTVFEa1T8zY5RsWACyABQimQlU/8sXUe6txzrPZpTlvBb+EG146llUFyxBTXFJ3AW2NSXLUe6tR9BThQ1PLEHQU4XyBbXY39acUHfr8uXYUlyCmqUlaKqIvni3ZvlyBLwNCHo2aup2Njfg5eVD7fsvfP1f6tuNcm89Nix+VlU3HT27dfxk9CxjzlnRHa45J+P46Xl+efly3Rsv/LmtWDdvMyq9xQh6NpryrHXzxP62ZpQvqEXQsxGV3mKsm7c5XGyF47W0BBsWP5vQsx4X2wnFfCPKvI2ofPxZBLwN4QIw4G1EdUEhNix+FoEFdbpjlGxYALIAFCKZCRT5G6DmhcMar58bHAQAnBscRH9fr3ldnfec0k1Lzy6KhYyeUz5+MnpmLBz3bHQ/q9qx9Pf12tLfRPT39YZjHqersemNUbJhAcgCUIhkJpDehbRad5CJXtDrz22F39dqSdsp3eHp2X3jJ6Nn5hzHz02ek3GziKX++lrj/qxsJpZa7WnFMBTjVN3soQULQBaAQiQ7gbQupK0uKFJ93Y4LekUu3nVKd7h5duv4yeiZOWdMm+OXHM9W9hM9xun+mm1PVNcpWACyABQiZQVgzIW04ZO5xQts9S7QFbl41ynd4ebZreMno2fmnDFtjl9yPFvZT/QYp/ubsD0WgNLAAlCAZCdQ/IW3QxfSNvp/ofq60QtstXT3tzXrvpcq3eHm2a3jJ6Nn5pwxbY5fcjxb2U/0GKf7q9deYEEdSn0tqJi/AxXzt8Pva0G5tz6lN3towQKQBaAQqUigyAtvIy+k1XpdVFdU2ynd4ebZrbGQ0TNzzpg2Y5Ecz1b2Ez3GyLGisdRqr6/nGM4NDuLc4CD6eo6l/GYPLVgAsgAUIlkJFLvwqZ1aTmnTM2Ohpqv2s13asumq/WyXNj3L63nD00ts04zUjdVzMt5W90kmLABZAAqRrASKXfjUTi2ntOmZsVDTVfvZLm3ZdNV+tkubnuX1HPRstE0zUjdWz8l4W90nmbAAZAEohJMJpLZwaGghT61FQM1oBbwNqC4oQiCn0TbtgLc+rB3wNob/n46eGQu5PTMW9JxMzzXFJRcXUL5wQ0YgpxFblpWY1tSKRbm3DjXFJaguWIIty+IXAbcj3rFads5jdsMCkAWgEE4mUNRdVAKLdWpqxW52aBvY0sYzYyG3Z8aCnpPtOYF3M6jqGVwE3NY2bGzPblgAsgAUwskEElkE1IxWsrTpmbEYLp4ZC3pOpq6VxZmdWgTcaBshLTvnMbthAcgCUAinE8jOxTr11mhKhjY9MxbDxTNjQc/J1LW6hp6Wrugi4EbaiNSycx6zExaALACFSFoBGLNYZ9BTZfhOqtCdV9XPqC/8GrdA7DOFhv1pakduLvIceRdaXGyd8uzSWNAzY0HPxnWTes4I/6n0olezd8/uCqxF0FOl3nfBRcAjMbKgdPUzhba1ZycsAFkACuF0Aqkt1lm2sBn+PON3UoXuvKr0rIzRqoY/txVBz0ZseGIJyhfUXdjvOcP+1LQr8wpR6muB39eCqtxCV3mOvAstMrZOeXZzLOiZsaBn47rJOWdUI5DThLKFzSjLaULQsxHlC2pR6VlpSjdSu2xhs+Yi4KFYlC+os7wIdCgmeloh/+UL6mxbdNoOWACyABQiGQnU39eLPY21aCr3Y1fZWgQM3EmldedV/UsvYlfZL9HZ3IBzg4P41YZ12FX6ywt3thm7Q8uIdl/PMbRv3eQKz3p3odX/4gV0NNYBgGOe3RQLeub40bO7zxlv7KjBmdOn8eaOGuwq/SXqX1pj+O5Zde16NAXWoqncj47GOry5owZN5X7Uv7Qm7DfgrUP9S2ss3wVc/9KLcX2pWVqCLcUlMXdkD/nfVTrUz1TDApAFoBDJSqDoPzUkvpPK6P5mdZ3UTrWujJ7TYfxk9MxYyO3ZzP5u8Wz0GCu6Whhuz2V3/4ZgAcgCUIhkJZDZO6mM7m/lDi2ntFOtK6PndBg/GT0zFnJ7lvGcYfSYpK0u4WtVv1bSQjtOwQKQBaAQyUwgI3dShS78DXrWX7ywWGd/M7qRjyiyW9spz2Z1ZfScDuMXdQG+JJ7t1JXRs+w5J+M5w+jnxEzOJXqcnp6W2bt/nXx8nxosAFkACpGSAlDnTqrQhb/+vHZUzn0+4f5mdUOPKHJC2w26MnpOh/Grmr/C0DFu8mynroyeZc85Gc8ZRj8nZnNO73F6elpG2zHTnp2wAGQBKEQyE0jtjuDyBbWou3DBbXVB4cVHCeW1I5CzCwFvIyrm1aDSV6h555WervYjinYh4G3AOs9my9pOeRbRldFzWoyftxFVjy+54Lla905C13gW1N3f1ozO5gapPA+rnJPxnGHwc5Io58w8Tq/+ly9qaum1E8LJx/clggUgC0Ahkp1A/X29ODc4CAA4NziI/r7e8Ic/0RbaX0hX44JiW7Rdoiuj53Qbv8hjZPFsVhcw/vgxt3gezjkn4zkj8hgjuoC1x+lpaem1E8JKe3bBApAFoBBuSKBNBUVDF9xqnQhyWy1dcOvUI4qc9uyEroyeOX70nApd5pz8np16nJ4b2ovEDfN3qmEBKIBbEkjrYlt/nthq63q6Tmq7UVdGzxw/enaTroye0238Emnb2U6q2gvhlvk7lbAAFMAtCeT4SUbjEUVuPIHxZK6iy/Gj52TrMufk95zgcXq2F4BJai+EW+bvVOLKAvDtt9/G7NmzMX78eCiKgrq6uqj3582bB0VRorY777wzap+BgQHk5+fjyiuvxOjRo/HQQw/h+PHjUfscPXoUs2fPxujRo3HllVfiiSeewNmzZw37dEsC7W9rRmBBHUp9LaiYvwMV87fD72tBubde6HE7Fy/iVX9Ekai2U56d0JXRM8ePnlOhy5yT37Pe4/TsfpRbstsL4Zb5O5W4sgBsbm7Gj3/8Y+zcuVOzAJw1axZ6e3vD28cffxy1z6JFi3DttdeitbUVXV1duPfee3Hbbbfhiy++AAB88cUXmDx5Mu699150dXWhtbUVEyZMQH5+vmGfbkqg/r5e9PUcw7nBQZwbHERfzzHdi5bN6IYu3j1z+nTUz2727ISuk9ocP+d1ndSm52hd5pzz2k57jrx548h73bo3c8jWHuCu+TtVuLIAjESrAPzOd76jeczJkyeRkZGBbdu2hV/r6enBiBEj8PrrrwMYKjJHjBiBnp6e8D5bt27FyJEjDScEE4gQQgiRD87fEheA2dnZuPrqqzFp0iQsXLgQf/7zn8Pvt7e3Q1EU9Pf3Rx03ZcoULFu2DABQXFyMKVOmRL3f398PRVHwxhtvGPLGBCKEEELkg/O3pAXgtm3b0NTUhO7ubjQ2NuK2227D17/+dQwMDAAAampqkJmZGac1Y8YM5ObmAgB8Ph9mzJgRt09mZiZefvllVS8DAwM4depUeDt+/HjaJxAhhBAiGywAJS0AYzlx4gQyMjKwc+dOANoF4P3334+8vDwAQwXgzJkz4/bJyMjA1q1bVdspKSmJu/kk3ROIEEIIkQ0WgMOkAASAG2+8EatXrwbg3J+A+Q0gIYQQIj8sAIdJAfiXv/wFI0eOxMaNGwFcvAlk+/bt4X1OnDihehPIiRMnwvts27aNN4EQQgghwxzO3y4tAD/99FMcOHAABw4cgKIoWLNmDQ4cOICjR4/i008/RUFBATo6OvDRRx/hzTffxN13341rr70Wn3zySVhj0aJFmDhxItra2tDV1YX77rtPdRmY6dOno6urC21tbZg4caK0y8AQQgghxBicv11aAL755puq19rNmzcPn3/+OWbOnImrr74aGRkZuO666zBv3jwcO3YsSuPMmTPIz8/H2LFjMWrUKMyePTtun6NHj+LBBx/EqFGjMHbsWOTn54dvJDECE4gQQgiRD87fLi0AZYEJRAghhMgH528WgEIwgQghhBD54PzNAlAIJhAhhBAiH5y/WQAKwQQihBBC5IPzNwtAIZhAhBBCiHxw/mYBKAQTiBBCCJEPzt8sAIVIZQLtCqxF5dwy7AqsNfVeKrVl06Xn1OvK6Dndxk9Gz8mOhaimGQ0rbTn5+RXVdwoWgCwAhUhlAlXNXwF/Xjuq5q8w9V4qtWXTpefU68roOd3GT0bPyY6FqKYZDSttOfn5FdV3ChaALACFSHYCdTY3oKncj6aKUpR76+DPa0e5tw5NFaWoWVqCLcUlqu81lfvR2dyQEm3ZdOk59bpN5X7sfPG/dd93m+d0Gz8ZPSc7FgFvPaoLClFdUIiAt8G0ZiLPkRpG97OibdWXiH4yYAHIAlCIZCeQP6/94pbbFv2vgfdSoS2bLj27TFdGz2kwfjJ6Tnos9HLZgKZRz2b2s6ItFEuBmDppyWjMAAAgAElEQVQNC0AWgEIkO4E2FRSh1Ldb+wOW26r6XqlvNzYVFAlqq3+4E2mnRNfXalmXns3qWss5/VjsRmBBPXPOxeOXWs+SjZ9FzUS6pb7dCHp+gkBOI4Ken+rup9aWfpx3Y51nc9w1e6Fr+aoW6vvaVFCU0HvVwqKUXjvIApAFoBCpSKCmilLNk0qF52XV15sqSoW1tTYj2snWrZz730K69GxcVyTnrMSCOeee8UuV5+EwfkY19XSbKkoR9GyEP68dQU+17n7m4/E8/Hnx1+xFXstnpD29fVJ97SALQBaAQqS0AAz95h75G3z4tbaon02fwNS0w22Y1062biCnSUW3zdWxkNGzaM4ZigVzzrXjlzLPLh2/muISba8x8d6yrMSQpp7nyoWFUa9VLixU3c9QARgbj4hrFmuKS1BdsARblpVEXctXXVCUsD0t/eqCopRfO8gCkAWgEKlIoP1tzShfUKtxclH/E8b+tmZxbZWtfEGtIW2ndYOeKsPabomFjJ7tzjkrsWDOuWf8UubZpeNnVC+0GSXS84YnlqiPX8w4bnhiCYKeqoRx0YyH1nV9Jtuz4t3IPnbAApAFoBCpSqD+vl5UP2PgmhPfbmxYvMSStu5vsb5WbHi6EP19va7QPTc4iE0FBrRdFAsZPTuRc6ZiwZxz3fglw7Ms42f4OkCD1/+peQaA4LyfaBdNuW1Y5/kJAODc4KChuETFQ+/aS618ebpQt71I7xueLtRsw+i1g2ZjpwULQBaAQqQ6gRJdc2LmOhMz2m7UpefU68romeMnt2e3jZ+R6wBFPQO4+OfXmK1asDjS8q/Vnp0xMnrtoF2kev52AywABUh1AsVfX2HtOh5d7TQ+mdOzji5zTkrPHD9ntePiG7nZEOMQF6+/i/4TqW0FoMo1e2qv2xIjE9cOsgC0FxaAAiQrgbRuhb94fcVGlOU0oWxhMwI5TQh6qsPXYlh9zNb+tmaUe+vh97WgYv52VMzfgVJfCwIL6sLXeFh5nJKoroi222Iho+d0zDmOnws8u3T8YtsJzl2HgLcBQU8VqnIL4fe1oNTXgsq8wrhr5ESWOGn0/2KoKPK1Ijj3lyhb2Ax/bisa/b8Q0o29Zi/kudH/CwS8DShb2Izyeduxbt4Ww9ddGmkj4G1AcO66sGctH1ba04IFIAtAIZKVQHq3woeur+jv68WZ06ejfk50rBHtvp5jODc4iHODg+jrORZ1jYfVxymJ6IpouzEWMnpOt5zj+LnDsxvHT62dyvk/CV/z1tdzDH09xwDEXyMnusRJpeen4ePPnD6NI+9126Ibec1epOfKeT8Jf5tZOX+5qesuE7UR0o70rOXDLlgAsgAUwskEcvIxPbI9ZkvEM2NBz27SlTEWMnp2MhaRx5o9T4sucaJ1/JZlQ0u11BSXWNLVa29LccmFx9k1hgvAgLcB1QWFqFlaIqSdykfFsQBkASiEkwkUfe0IH7NlVZuxoGfX6tKztLpW9rWyv6U+27h0iur4q2zC2g4u96IFC0AWgEI4mUCJHodUtVD/kUd6t9pXzvuJ7iOA9G6zr1xYpLMkQKv2sgoJdJ3y7GQsdJdN0I0Fx8/tnt06ftY9c/yMjp+e39hlSMwuWWJliZPIa/oSPiYvgW6klpFrBY0slRPr2WgbyVruRQsWgCwAhXA6gfTuUEt0fYfWo41Cd1FpaVfOfV5XN3R9iZa2VV0nPTulm0ib4ye3ZzeOn1XPHD/j45fIbyxa7WjdsWp2/9hr+rSON7JUS6SW0WsFzd4pbaYNs7GwExaALACFSFoBqPI4JLVrJbYsXYagpwrVz0Rcr6GxzMNF7eiv3gPeBl3d0LUaar6iTroGdEN3zVU/U4imilLHPNupa8azlu5wGT8ZPcs4fnZ45vjpj5/a9WhafkN0Njeg+plCrPNsji5gEjzuTeuRcdUFRah+phDBuetQlffshfjFPzKtukD9kW9aS7XUFJeE+xbI2TUU25ym8OPe/L6WoXhqXHdnpAAMxSLoqULZhTEJeBvC4xM5lluWLgt/K6g1x7EATA4sAAVwOoFUH4ekc62Ekf1Ct9Hvb2u2VTd0i74Z3bjftJPs2YquGc/Dffxk9Czj+Jn1zPEzP35m/JqJnRp2+VFbqkVt6RRNXY1NdR5KsAyP2Tb8eUPfCiZjuRctWACyABQiGQnU39eLDU8Xal/34duNKl8RmipKUXbhtzu1rdS3G9VPLYn6Tbcs8gHoIrrPFKG/rzesHczRu+bnom74N22da1cc8/zU0COenPZc/9IaWz2nevxKfbuxftGSCA/u9yz7+Jn1zPEzPn5GHj/mz2tHac4u1L+0JuqO6Eqfvme965ITXsenUxCWXnhcn9oSKWpLp6xftAT+XAOPqMsdirPat4BaS+WE4qwbi5g2QrkR+kaz7ucvYk9jbVxfnIYFIAtAIZxOoMgLaPW+ho87eahsoa/Uje5vVteSdoKTHT27LxbVzxSZ1k6152EzfjZ45vhpe9E9x2pp6MRZj4Tnc4OejWBovGI2S9qC+ZxsWACyABTC6QSKvIBW61qJSl+CB5Bf+JCFThpGH1huVjeh9oX9g3ODBn77baVnq+NnxLNALCrnPm/YA8fPofGz4pnjp7qpFoAJvn3TvyP6Ypz1iD+fGyye8swXgLp3S6v0z8zdt0bjnCimTt/xqwYLQBaAQjiRQFqLY25dvhzl3nqs81THXSuh9dtkpbdY9ZoKrf3LFjaHT0pbly+/eLGxQV097dAJqNzboHm32rp5mzWvA6HnxLqVc//LkGfRWERdiM6cS/r4WfHM8dP2HLr5IfJ6tEpvseqxcTfEJIiz1o0Vsde+rZu3WV0vgWej1Cxfrqsf2qoLCk0vwKwZiwtFX9nC17DOsxkBr/plAcm44UMNFoAsAIVwIoHUPkBqX5VHXiuhdyeV2jUVcfsnaM+oriXtiH13la2N6xs9m9A16NnWWDDnkj5+rvI8jMYPuHgNXaK7U61oxxJ5vd6usrUx7cXGU+zPpnb4tUs7FXf8qsECkAWgEE4kkJXFMc3eSRX1m65npebX86W+3Qjk7DJ1h5ZRbX9uGyrmbbNdNx08x453xbxtUnnm+HH83DZ+sefVROdUEW19r9UI5DShbGEzShf+SljXjljYqW02N5yEBSALQCGcSiCtr9T1flMy++DsyP2bAms127PyQG4j2vUvveiIbrp4jt2//qUXpfLM8eP4mdV2MhaJjlVrT0Rbr73+vl6cOX0a/X29tug65deKtpXccAoWgCwAhXC8AEzSV+VOtueUNj3L7ZmxkNszYyG3ZxljYTcsAFkACuFUAiV7cUwn23NKm57l9sxYyO2ZsZDbs4yxsBsWgCwAhXAygZL9VbmT7TmlTc/J0ZZN10ltenZe10ltenZe12ltu2AByAJQCCYQIYQQIh+cv1kACpGKBIp8Ooidx8imS8/u1ZXRM8dPbs+yjZ+Z/a2OYaJjRXSNtOeEvp2wAGQBKEQqEijy6SB2HiObLj27V1dGzxw/uT3LNn5m9rc6homOFdE10p4T+nbCApAFoBBOJlDkb09aTwfRW2le65hgThHKcppQmfesFLpNFaUI5hQhkLMLG55eQs8uHT8ZPQ+n8Yv0XJX3bFqPX/UzhVg3bwvWeTbafv6sfqYQQU8Vtixdhs7mhvDPAW9DQt1EPrYsXZbwnF/9TNFQ+8XLVMdQq51SXwuqnynEluIS1CwtMRwLI98gblm6LNxeKA4Bb2PE/+sT5p6R9uyGBSALQCGcTKDI3578eRHrKRlcud3IMbLpBj3V9OzS8ZPRM8dPbs9GjnHT+dOoplXPuu1Y9GzkG0Szmx7J/NaQBSALQCHsTiCt3/qqfEXw55p7OoiRB75Xen4ih274RNVKzy4dPxk9D7vxi5zQ0338LmwBb4Mj2qU5TRd0jT9RQ3/sdqMsZ+hZuYnO+f68dgRymjS/VdtUUBR+JnF8O60X1+fT8Kz3TaXaN4hlObt0vSaKtdW/OIjCApAFoBB2J5DmyVznQ6W3uKbWE0Wk05UxFjJ6FtSV0fOwGj8DntNm/AxuTmlr6RqKQ4J4qO0Ti4hnw57M+NWJiRFNJ2AByAJQCLsTSPc3xNDrJlZXv7giu5E/A7hJV+OB6PTsal0ZPcs9fgk8q+gP+/HT2nytznjW6EPCAtDI2MWe81U2rW/VKn1FxnVjYqH/TWXrxVjqjWHsMTox0WvPyrOJjcICkAWgEE4kkNZviDXLl5teXT36Id3PqepWPv6sK3U3PLEEFfO20bNE4yejZ9nHT89zwLsrrcav0rtUszARPn96i7WLnohtnWezrq7aUzJCN0voedbqm1ZRtb+t2ZCuViy05qGmilLN9wLeBgQ9VajKLYTf14JSXwsq8woNxVqvPadgAcgCUAhHC0CV385EHrJeXXDhN8KYr9irC4pcqQsA1U8toWfJxk9GzzKPn67nxUvSavz0/rwqev7UOi/HnacDaxPqxvqof2lNwnO+3ryghRFdrVjotaf1Xv1La8KafT3H0NdzzHCsrfRPFBaALACFcCKBnHqOYqP/F/DntqJsYTMqPT9D2cJm+HNb0ej/hSt16VluXXqm52Tr7m9rRrm3Hn5fCyrmb0fF/B0o9bUgsKDOgWfnVsOf24qgZ6PwedrIOd/KvCAyl+gd68QclYrnB7MAZAEohFMJ5NRzFI+8140zp08DAM6cPo0j73W7WtdJbXp2XtdJbXpOjrZsuv19vejrOYZzg4M4NziIvp5jjj0798h73badp42c80W/KTbrUe9YJ+aoZD8/mAUgC0AhmECEEEKIfHD+ZgEoBBOIEEIIkQ/O3ywAhWACEUIIIfLB+ZsFoBBMIEIIIUQ+OH+zABSCCUQIIYTIB+dvFoBCMIEIIYQQ+eD8zQJQCCYQIYQQIh+cv1kACsEEIoQQQuSD8zcLQCGYQIQQQoh8cP5mASiEkwm0K7AWlXPLsCuw1la9DU8vsVXXSW16jtcOetYj6KliLCT1zPFzVttp3V2BtbafmyPbCHqqEPSsdyTWapqifTF7vFOxswILQJcWgG+//TZmz56N8ePHQ1EU1NXVRb1//vx5lJSUYPz48bj88ssxbdo0vPvuu1H79Pf3Y86cORgzZgzGjBmDOXPm4K9//WvUPgcPHsTUqVNx+eWXY8KECVixYgXOnz9v2KeTCVQ1fwX8ee2omr9Ccx8zJ7uQXtCz0VZdM9pmT6Ju8my3roh2qmPB8RPTTnUsZBy/VHuOPB8bOTeb1Y9sw4y2kaJOz2/se2YLtNDxFfNf1T0mpFs593lD/UsGLABdWgA2Nzfjxz/+MXbu3KlaAK5evRpZWVnYuXMnuru78cgjj2D8+PH45JNPwvvMmjULkydPRkdHBzo6OjB58mTMnj07/P6pU6cwbtw4fP/730d3dzd27tyJrKwsvPDCC4Z92p1Anc0NaCr3o6miFOXeOvjz2lHurUNTRSmayv3obG6I2j/RyS6kt2VZCQI5jUMnl9xW+PPaEfA2oKa4xJKuVW0jJ1G3erZb14z2luISVBcUIuBtDE8QAW8DqgsKUbO0hOPH8Rv245cKz5Hn44C34YJeY8T/6zXPzWZiUrM0lB8NEfnRiOqCQmwpjs+P2LhovVc593nVuaRmaQm2FJeovlc5978TFmhq81S4PZVYdDY3hHXLFjYnnNeSBQtAlxaAkcQWgOfPn8c111yD1atXh18bGBhAdnY2ysvLAQCHDh2CoijYu3dveJ/Ozk4oioLf//73AICysjJkZ2djYGAgvM+qVaswYcIEw98C2p1AoQ/S0AmsLfrfC1vow1dTXHLxhBE62eU0Ysuyiye7KL1Y3ZjNjG6c1wTaF0+i9eGTcmgyDHgbok4EbvLshK4tnjl+HL80GL9UezY6dqENMF+MWtFWK9y2FJegZmlJXFEWFQu1mGi8p1egGY1zyK+R9lIBC0AJC8APP/wQiqKgq6srar+HH34YHo8HAFBVVYXs7Ow4rezsbKxfvx4AMHfuXDz88MNR73d1dUFRFBw5ckTVy8DAAE6dOhXejh8/bmsCbSooQqlvt+oHqtS3G5sKikyd7PT0RHQTeY3UNnsSda1nG3Steg5NIKqbr5Xjx/Eb/uOXAs9GdCO1AXNFbjg/fDr5kduqrm2hqIvMOc2cNFCgacZRb9PwEhm7ZMMCUMICcM+ePVAUBT09PVH7+Xw+zJw5EwDw3HPPYdKkSXFakyZNwsqVKwEAM2bMgM/ni3q/p6cHiqKgo6ND1UtJSQkURYnb7EygpopS1Q9KU0UpAHOTlZ6eqK5RbSsnURk8W9F1wrMbYsHx4/gNV8+JdCO1ncyPhNq5rfqFZIymkX6peQ1R6dMuAkPHGIlFZP+SDQtAiQvAEydORO23cOFCPPDAAwCGCsCbbropTuvGG2/EqlWrAAwVgLm5uVHv/+lPf4KiKOjs7FT14vQ3gEDEySD0G9qFfyM/KEZPdup6bbbomtE2exKVwbNVXSFto3Hj+HH8hvn4JctznG7kpqLtZH7o7atb1KnMJVrzTKL2bctpjdglExaAEhaAqfwTcCxOJND+tmaUL6hF0FOFDU8sQdBThfIFtdjf1hzex8zJ7qLeRpTlNKFsYTMCOU0IeqqFdM1omz2JutKzTbqWtb318PtaUDF/Oyrm70CprwWBBXUcP45feoxfijxHno+rcgvh97Wg1NeCyrxC1XOz1ZgEFtSh1NeCivk7UDF/O/y+FpR76w1oJy7q1s3bHDeXxM4z6zybNXXV2N/WHHcNZaifRgrzdZ7NqrFLJiwAJSwAQzeB/OxnPwu/dvbsWdWbQPbt2xfeZ+/evXE3gXzpS1/C2bNnw/usXr06pTeBhOjv68W5wUEAwLnBQfT39Ua9f/HDW43AhZNdWU4Tgp6Nqh+qkF5/Xy/OnD4d9bOIrlHtyJNNZV4hSn0t8PtaUJWrfRJ1j2d7dUW0+3qO4dzgIM4NDqKv5xjHj+OXBuOXes+R5+O+nmPo6zkGQP3cHK2fuBiNbCNRfsTGJVFRF/RUIbCgFvt+tUvVb2S/9r3ehPIFdbpfPMTyTt12BC60t/4Hz2KdyjGRntb/4KLuvtebVPuXTFgAurQA/PTTT3HgwAEcOHAAiqJgzZo1OHDgAI4ePQpgqFDLzs5GbW0turu78eijj6ouAzNlyhR0dnais7MTt956a9QyMCdPnsS4cePw6KOPoru7G7W1tRgzZkxKl4Exg9GTndt0AWMnUTd5tltXRs8cP7k9yzx+MnlOhr7eFwSJvjywqityjIgnJ2EB6NIC8M0331S92WLevHkALi4Efc0112DkyJGYOnUquru7ozQ+/vhjPPbYY8jKykJWVhYee+wx1YWg77nnHowcORLXXHMNli9f7pqFoAkhhBDiDJy/XVoAygITiBBCCJEPzt8sAIVgAhFCCCHywfmbBaAQTCBCCCFEPjh/swAUIlUJZOZB4HbpOqlNz3J7Zizk9sxY2Ketp2OXrlZbQU8Vgp71tuqnagySAQtAFoBCpCqBIh8EHvogbXh6CSrnlqFy7vPh9+zU3RVYq/sAcqPalXOfD+uF2qBn5zzbrcuc4/gZ1ZVl/GILJxHPIXYF1qJi/qtROiHPwbnPW9YNaccWTyHPsb7NFFp6umqxUHtPq73Y1+2IsSgsAFkACpHMBNJ6EHjopFIxfzv8ee0oW9gcfq+pQv1h3mZ0183bGj4Jxz6A3Ip22cJfDenN+Tkq5/6Cnh32XDFvm626zLnhNH7bpIqFk+MX8hv7vhFttXYidQM5jaguKELphX6ULWw2rRtJqHgKelaiZmkJqgsKLy7KnNeOgLcR1QWF2FJcgkrPSsOFVqSuWqyaKkpRs7QEW4pLNOOk1V5kMS8aY7tgAcgCUIhkJlDowx25mrqZB4EL6zqpTc9ye2Ys5PbMWAhph+hsblDX09I3qBvSji3KzGxahVZCXQtjEGpvy7ISVBcsQU1xSby2xRjbCQtAFoBCJDOBEj5YW+NkpvUwb1FdW7Sd0qXn5MSCOcfxGy7jJ6Adwoq2P89Y0aMaX504J9rXsq6vVf3xfIk0EmgbjbGdsABkAShEshPIyAPSYzcjD9u2ouukNj3L7ZmxkNszY2FeG7BQZOYaL3pECtjILbbQMqsb9cxhQ300UKSaiLGdsABkAShEygpAvd/Awh88/Yd5W9Z1Upue5fbMWMjtmbGIft+EdlwbBrZqk994WS1gIze1vujqqsQiLo468awuKLI9xnbBApAFoBDJTqCoB6R7iy98gNpU/13n2ZzwYd5WdCu9Sw09KDxWe928zdF6UScDenbMs026zDmOX8pj4cT4qWzrPJux4YklpjyHuFgchWLQGuc59O/W5csN60ZrRxdPZd5GlPpaUDF/x9CNWb4WBLyNhgstLd2AtwFBT1VcLCLjGH7PW6+qES4AY14XibFdsABkAShEKhIo9GDt/W3NCCyoRYWnGoGcJpQtbEZZThPWeTaifEEt9r3eZOlB4Il097c1W3rI+L5f7Ro6qXuqUOkrQqmvBX5fCyoXFoVPAvRsn2e7dZlzyfXM8dMbv0Jbxq9sQR38MYVTubce+15vAgDTnoGI4tVThfU/eBZBTxXKFtQh4K2PKabqTBc9qoXXglq8U7cDfT3HcG5wEOcGB9HXcwzv1O1Q3VetTT3dc4ODqrEIxTH0nlZ7jf5fqLxeJxRju2AByAJQiFQnUOhD2N/XizOnT0f97GZdAOjrOYa+nmMA7DkJ0LPzujJ65vjJ7dnp8YstnOyMcaRPtdfs0k72vmY17Oq73aR6/nYDLAAFYAIRQggh8sH5mwWgEEwgQgghRD44f7MAFIIJRAghhMgH528WgEIwgQghhBD54PzNAlAIJhAhhBAiH5y/WQAKwQQihBBC5IPzNwtAIZhAhBBCiHxw/mYBKAQTiBBCCJEPzt8sAIVgAhFCCCHywfmbBaAQyUqgXYG1qJxbhl2BtbYfY1Y71boyek6H8ZPRM2Mht2c3nDPMHmNF16iWndpq7Wx4eomt+k75NQoLQBaAQiQrgarmr4A/rx1V81fYfoxZ7VTryug5HcZPRs+Mhdye3XDOMHuMFV2jWnZqq7UT9Gy0Vd8pv0ZhAcgCUAgnE6izuQFN5X40VZSi3FsHf147yr11aKooRVO5H53NDZaPMaudal0ZPafD+MnombGQ27Mbzhlmj7Giq4WaVsDbgOqCIgRyGoW01drZsqwkrOvPbQ23V1NcYknfzliIwgKQBaAQTiaQP6/94pbbFv3vhc3qMWa1U60ro+d0GD8ZPTMWcns2s7+T2maOsaKrhaqWVhsmtTXbSdCeZV3BWIjCApAFoBBOJtCmgiKU+narfuBKfbuxqaDI8jFmtVOtK6PndBg/GT0zFnJ7dsM5w+wxVnS10NMS1TbbjhV9O2MhCgtAFoBCOJ1ATRWlqh+UpopS4WPMaqdaV0bP6TB+MnpmLOT27IZzhtljrOiabc8ObTPtWNW3MxYisABkAShE0grAC9dehP41dDJKcIxZ7VTryug5HcZPRs+Mhdye3XDOMHuMFV2z7dmhrd9Omy36dsZCBBaALACFcDqB9rc1o3xBLYKeKmx4YgmCniqUL6jF/rZm4WPMaqdaV0bP6TB+MnpmLOT27IZzhtljrOgab68a/txWBD0bhbXV29mIspwmlC1sRiCnCUFPtZC+nbEQgQUgC0AhkpFA/X29ODc4CAA4NziI/r5e244xq51qXRk9p8P4yeiZsZDbsxvOGWaPsaJrtL0j73Xbpq3WTn9fL86cPh31sx26gL1+zcACkAWgEEwgQgghRD44f7MAFIIJRAghhMgH528WgEIwgQghhBD54PzNAlAIJhAhhBAiH5y/WQAKwQQihBBC5IPzNwtAIZhAhBBCiHxw/mYBKAQTiBBCCJEPzt8sAIVgAhFCCCHywfmbBaAQTCBCCCFEPjh/swAUgglECCGEyAfnbxaAQjCBCCGEEPng/M0CUAgmECGEECIfnL9ZAArBBCKEEELkg/M3C0AhmECEEEKIfHD+ZgEoBBOIEEIIkQ/O3ywAhWACEUIIIfLB+ZsFoBBMIEIIIUQ+OH+zABSCCUQIIYTIB+dvFoBCuCWBdgXWonJuGTY8vQSVc8uwK7DWVu2gpwpBz3ppdGWMBT07qxvSlikWIW16dl5XtlgkQzs2FmqvOdGOk+1F4pb5O5WwABTALQlUNX8F/HntCHo2wp/Xjqr5K2zXlk1XxljQM2OhpU3PjEUqtCN11V5zoh0n24vELfN3KmEBKEAqE6izuQFN5X7UFJcg4G0YOhHktsKf145ATiO2LCtBU7kfnc0NlrRrlpaguqDwonZeOwLeRlQXFGJLcYnrdGWMBT07qxvSlikW9Jw8XdlikQztpnI/mipKUe6tu6DbgOqCwgvt1cOf145ybx2aKkqF4hPbTkhzS3EJapaWqL5ntT0tWACyABQilQkU+uBHbbltqq/boi2broyxSFPPjAU9u0LX5bFIqrZGLNTes6UdtfY03rMLFoAsAIVIZQJtKihCqW+37kmg1LcbmwqKLGn7fa06J4BW1+nKGAt6dlY3pC1TLOg5ebqyxSIZ2oliYld8NNvJbdXsn9X2tGAByAJQiFQnUFNFqe4HtKmi1BFt2XTp2f2eGQt6dpOuWz2nUjsZ8WmqKNV9z05SPX+7ARaAAqQ6gcIflAvXrlz8yrzVtScwx3VljAU9Mxb0nDpdiWKRNO1wTCK+jYt5zd7YX9TUe89OUj1/uwEWgAKkOoH2tzWjfEEtgp5qBHKaULawGWU5TQh6NqJ8QS32tzULaQcW1KHU14KK+TtQMX87/L4WlHvrXasrYyzo2VndkLZMsaDn5OnKFotkaA/FpAobnliCoKcK5d56BBbURb9mQ3zi2rmgqfeenaR6/nYDUhaAJSUlUBQlahs3blz4/fPnz6OkpATjx4/H5ZdfjmnTpuHdd9+N0pNwIJIAABNsSURBVOjv78ecOXMwZswYjBkzBnPmzMFf//pXUz7ckED9fb04NziI/r5enDl9OurnEFbXdOrv60VfzzGcGxzEucFB9PUcS4quVe1UxkJNx6iujOPnhGe3jZ+Mnodzzg3H8YvVM7oWoRltNc96/kMxABCOhdprZmKhto+ept57duGG+TvVSFsAfv3rX0dvb2946+vrC7+/evVqZGVlYefOneju7sYjjzyC8ePH45NPPgnvM2vWLEyePBkdHR3o6OjA5MmTMXv2bFM+ZEkgp9Z0cnKtqOHgmbGg52R7Zizk8hyr58RahLFtVM1fYat/I1pOr+lnBVnmbyeRtgC87bbbVN87f/48rrnmGqxevTr82sDAALKzs1FeXg4AOHToEBRFwd69e8P7dHZ2QlEU/P73vzfsw80JpL6mU33Emk5Da0iZXWPJKd3h5bkB1QVFCOQ0Mhb0zJzj+Om24cRahGr9CLUR8DZG/L/ekn+9tfxCWkb2SSVunr+ThbQF4OjRozF+/HjccMMNeOSRR/Dhhx8CAD788EMoioKurq6oYx5++GF4PB4AQFVVFbKzs+N0s7OzsX79es12BwYGcOrUqfB2/Phx1yZQ1MXBNq7p5JTusPbMWNAzc47jp9dGgrasoulbY7Okq7eWX4J9UgkLQEkLwObmZrz66qs4ePAgWltbMW3aNIwbNw5/+ctfsGfPHiiKgp6enqhjfD4fZs6cCQB47rnnMGnSpDjdSZMmYeXKlZrtql176NYEcmpNJyfXihrunhkLembO0bOZNkTXvjPaD7Pt6OmGtIzsk0pYAEpaAMby2WefYdy4cXjxxRfDBeCJEyei9lm4cCEeeOABAEMF4E033RSnc+ONN2LVqlWa7cj0DSDg3JpOTq4VNZw9Mxb0zJyjZzNt2LH0iZF+WGnHyHp9yVrTzwosAIdJAQgA999/PxYtWuTon4BjcXsCObWmk5NrRQ0rz4wFPTPnUhcLF3vWbsO+tQg124jcBNoxsl5fstb0s4Lb5+9kMCwKwIGBAVx77bVYsWJF+CaQn/3sZ+H3z549q3oTyL59+8L77N27d1jdBAKor7UUWFCHcm+90BpLTukOH8/V8Oe2IujZyFjQM3Mu6bFwv+f4Nuxfi1CtH1W5hfD7WlDqa0FlXqGQfyPr9SVrTT8ruH3+TgZSFoAFBQV46623cOTIEezduxezZ89GVlYW/vjHPwIYWgYmOzsbtbW16O7uxqOPPqq6DMyUKVPQ2dmJzs5O3HrrrcNyGRizazqlWne4eD7yXjdjQc/MuSToyuo5tg29tQhFifTc13MMfT3HAIj7NxKLZKzpZwUZ5m+nkbIADK3rl5GRgQkTJuC73/0u3nvvvfD7oYWgr7nmGowcORJTp05Fd3d3lMbHH3+Mxx57DFlZWcjKysJjjz0m5ULQhBBCCDEH529JC0C3wAQihBBC5IPzNwtAIZhAhBBCiHxw/mYBKAQTiBBCCJEPzt8sAIVgAhFCCCHywfmbBaAQTCBCCCFEPjh/swAUgglECCGEyAfnbxaAQjCBCCGEEPng/M0CUAgmECGEECIfnL9ZAArBBCKEEELkg/M3C0AhmECEEEKIfHD+ZgEoxMmTJ6EoCo4fP45Tp05x48aNGzdu3CTYjh8/DkVRcPLkyVSXEimDBaAAoQTixo0bN27cuMm3HT9+PNWlRMpgASjA3/72Nxw/fhwnT55U/c0i3b4ZTNd+p3Pf2W/2O122dO37cO33yZMncfz4cfztb39LdSmRMlgAOsCpU+l5bUG69htI376z3+x3upCufU/XfqcDLAAdIF0/MOnabyB9+85+s9/pQrr2PV37nQ6wAHSAdP3ApGu/gfTtO/vNfqcL6dr3dO13OsAC0AEGBgZQUlKCgYGBVFtJKunabyB9+85+s9/pQrr2PV37nQ6wACSEEEIISTNYABJCCCGEpBksAAkhhBBC0gwWgIQQQgghaQYLQEIIIYSQNIMFIICVK1fin//5n/F3f/d3uPrqq/Gd73wHv//976P2mTZtWtwjZB555JGoffr7+zFnzhyMGTMGY8aMwZw5c/DXv/41ap+DBw9i6tSpuPzyyzFhwgSsWLEC58+fj9rn1VdfxS233ILMzEzccsstqK2tTVm/AaCjowP33nsvRo8ejezsbEybNg2ff/65tP0GEvf9o48+0nx00I4dO8L7HT16FLNnz8bo0aNx5ZVX4oknnsDZs2ej2nrrrbdwxx13YOTIkfiHf/gHBAKBOD+lpaW44YYbMHLkSNxxxx145513UtJvAOjt7cWcOXMwbtw4jB49GrfffjteeeWVqH1kG3Mj/T58+DD+9V//FVdddRWysrLwH//xH/i///s/qftdVlaGW2+9FVlZWcjKysJdd92F5ubm8PsDAwPIz8/HlVdeidGjR+Ohhx6KezSWbDkeIlHfKyoqMG3aNGRlZUFRlLhxBOQbb0C/3x9//DHy8/Nx0003YdSoUfjKV76CJ554Iu55uLKOOTEHC0AADzzwADZs2IB3330Xv/3tb/Hggw/iuuuuw2effRbeZ9q0afD5fOjt7Q1vsR+aWbNmYfLkyejo6EBHRwcmT56M2bNnh98/deoUxo0bh+9///vo7u7Gzp07kZWVhRdeeCG8T0dHBy699FKsXLkSv/vd77By5Upcdtll2Lt3b0r63dHRgTFjxmDVqlV499138f777+OVV16JWhJAtn4b6fsXX3wRNda9vb1YsWIFrrjiCnz66afhfSZPnox7770XXV1daG1txYQJE5Cfnx9u58iRIxg9ejQWL16MQ4cOIRgMIiMjA6+++mp4n23btiEjIwPBYBCHDh3C4sWLccUVV+Do0aNJ7zcA3H///fjmN7+Jffv24cMPP8RPf/pTjBgxAl1dXeF9ZBvzRP3+7LPP8NWvfhX/9m//hoMHD+LgwYP4zne+g29+85tRj4qSrd+NjY147bXX8Ic//AF/+MMfUFRUhIyMDLz77rsAgEWLFuHaa69Fa2srurq6cO+99+K2227DF198AUDOHDfa95///OdYtWoVVq1apVkAyjbeifrd3d2N7373u2hsbMThw4fR3t6OSZMm4d///d/Dx8s85sQcLABV6Ovrg6IoePvtt8OvTZs2DYsXL9Y85tChQ1AUJepD3dnZCUVRwt80lJWVITs7O6p4WrVqFSZMmBD+jfF73/seZs2aFaX9wAMP4Pvf/74tfdNDrd933nknli5dqnnMcOg3oN73WL7xjW/A6/WGf25ubsaIESPQ09MTfm3r1q0YOXJkeNHUH/3oR7j55pujdPLy8nDXXXeFf/7Wt76FRYsWRe1z8803Y8mSJUJ9MoJav6+44gps2rQpar+xY8eisrISwPAY89h+t7S0YMSIEVGL3fb390NRFLS2tgIYHv0GgL//+79HZWUlTp48iYyMDGzbti38Xk9PD0aMGIHXX38dwPDI8UhCfY/kzTffVC0Ah8t4A+r9DrFjxw5kZmbi3LlzAIbfmBNtWACq8MEHH0BRFHR3d4dfmzZtGq666ipceeWV+Kd/+icUFBTgk08+Cb9fVVWF7OzsOK3s7GysX78eADB37lw8/PDDUe93dXVBURQcOXIEAPCVr3wFa9asidpnzZo1uO6662zrnxax/f7zn/8MRVHwy1/+EnfffTe+/OUvY+rUqfif//mf8DHDod+A+phHsn//fiiKgj179oRfKy4uxpQpU6L2CxUNb7zxBgDgnnvuwZNPPhm1T21tLS677DIMDg7i7NmzuPTSS+P+JPTkk09i6tSpdnRNF7V+P/DAA3jwwQfx8ccf429/+xu2bt2KK664AocPHwYwPMY8tt+NjY249NJLoybyzz//HCNGjEBJSQkA+fv9xRdfYOvWrcjMzMR7772H9vZ2KIqC/v7+qP2mTJmCZcuWARgeOQ7E9z0SrQJQ9vEG9PsdIhgM4qqrrgr/PFzGnCSGBWAM58+fx0MPPYRvf/vbUa+vW7cOra2t6O7uxtatW3HDDTfg/vvvD7//3HPPYdKkSXF6kyZNwsqVKwEAM2bMgM/ni3q/p6cHiqKgo6MDAJCRkYGampqofWpqapCZmWlL/7RQ63fot92xY8di/fr16OrqwlNPPYXMzEy8//77AOTvN6A95pE8/vjjuOWWW6Je8/l8mDFjRty+mZmZePnllwEMxeG5556Len/Pnj1QFAUnTpwIxyGysASG4nrTTTdZ7ZIhtPp98uRJPPDAA1AUBZdddhnGjBmD3bt3R3mTeczV+t3X14cxY8Zg8eLFOH36ND777DP88Ic/hKIoyM3NBSBvvw8ePIgrrrgCl156KbKzs/Haa6/ptjljxoxwn2XPca2+R6JVAMo63oCxfgPAX/7yF1x33XX48Y9/HH5N9jEnxmEBGMMPfvADXH/99XEXQscS+kbof//3fwFoJ/aNN96IVatWAYg+sYb405/+BEVR0NnZCWDoZBH6kIXYsmULRo4cablPRlDrd+gDXVhYGLXvrbfeGv4aX/Z+A4nH/PPPP0d2dnbUdT3A0Ily5syZcftnZGRg69atAKInixC//vWvoSgKent74yaLEP/1X/+Fr33tayLdSohWv/Pz8/Gtb30LbW1t+O1vf4vly5cjOzsbBw8eBCD/mGv1u6WlBV/96ldxySWX4NJLL8WcOXNwxx134PHHHwcgb7/Pnj2LDz74AL/5zW+wZMkSXHXVVXjvvfc0i5D7778feXl5AOTPca2+R6JXAMo43oCxfp86dQp33nknZs2ahcHBwfDrso85MQ4LwAjy8/MxceLE8Ff3epw/fz7q+hmZ/1yg1e8jR45AURRs3rw56vXvfe97+M///E8AcvcbMDbmmzZtQkZGBvr6+qJel/lPJVr9Pnz4MBRFCV8oH2L69OnhokDmMTcy3v/v//2/cDEwbtw4PP/88wDk7nck06dPR25ublr9CThEqO+RDOc/AYeI7fcnn3yCu+++G9OnT8eZM2ei9h1uY060YQGIoWLuhz/8ISZMmBD+02Yiuru7oy4iD10wvG/fvvA+e/fujbtg+Etf+lLU7fSrV6+Ou2D4X/7lX6LamjVrliMXDCfq9/nz5zFhwoS4m0C+8Y1vhL8VlLHfob4ZHfNp06ZF3SUXInSx9IkTJ8Kvbdu2Le5i6dg/HS9atCjuYunQt0whbrnlFkculk7U74MHD0JRFBw6dCjq9ZkzZ4b/1CXjmFv5jLe3t+OSSy4J90nGfqtx3333Yd68eeGbQLZv3x5+78SJE6o3gciU43qE+h5JoptAZB9vILrfp06dwl133YVp06bh9OnTcfsOtzEn2rAAxND1XdnZ2Xjrrbeilv0IrXV3+PBhrFixAr/5zW/w0Ucf4bXXXsPNN9+M22+/PbxcAjD0oZ4yZQo6OzvR2dmJW2+9NWrJgJMnT2LcuHF49NFH0d3djdraWowZMybqT4t79uzBpZdeitWrV+N3v/sdVq9e7diSAYn6DQwtlTBmzBi88sor+OCDD7B06VJcfvnl4RsCZOy30b4DQzcLXHLJJfjVr34VpxFaLmH69Ono6upCW1sbJk6cqLpcwtNPP41Dhw6hqqpKc7mEqqoqHDp0CE899RSuuOIK/PGPf0x6vwcHB3HjjTfinnvuwb59+3D48GG88MILuOSSS6KuI5JtzI2M9/r169HZ2YnDhw9j8+bNGDt2LJ555pkoHdn6XVhYiHfeeQcfffQRDh48iKKiIowYMSJ8TeeiRYswceJEtLW1oaurC/fdd5/qMjAy5bjRvvf29uLAgQMIBoNQFAXvvPMODhw4gI8//jisIdt4J+r3J598gjvvvBO33norDh8+HPVZGA5jTszBAhDQXPB3w4YNAIBjx45h6tSpGDt2LDIzM/GP//iPePLJJ6NOFMDQIpuPPfZYeAHOxx57THXR0HvuuQcjR47ENddcg+XLl8ctGvrKK6/ga1/7GjIyMnDzzTdj586dKel3iFWrVmHixIkYPXo07r777qi7gGXsN2C874WFhZg4cWLUWnCRHD16FA8++CBGjRqFsWPHIj8/P+pOUmBowdTbb78dmZmZuOGGGzQXTL3++uuRmZmJO+64Q3c5GhGM9Pv999/Hd7/7XXz5y1/G6NGjMWXKlLhlYWQbcyP9fvbZZzFu3DhkZGRg0qRJePHFF+P8ytZvr9cbzqurr74a06dP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     "text": [
      "(980,)\n",
      "\n",
      "(980,)\n",
      "\n",
      "(980,)\n",
      "\n",
      "(980,)\n",
      "\n",
      "(980,)\n",
      "\n"
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       "<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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>linie</th>\n",
       "      <th>richtung</th>\n",
       "      <th>betriebsdatum</th>\n",
       "      <th>fahrzeug</th>\n",
       "      <th>kurs</th>\n",
       "      <th>seq_von</th>\n",
       "      <th>halt_diva_von</th>\n",
       "      <th>halt_punkt_diva_von</th>\n",
       "      <th>halt_kurz_von1</th>\n",
       "      <th>datum_von</th>\n",
       "      <th>...</th>\n",
       "      <th>fahrweg_id</th>\n",
       "      <th>fw_no</th>\n",
       "      <th>fw_typ</th>\n",
       "      <th>fw_kurz</th>\n",
       "      <th>fw_lang</th>\n",
       "      <th>umlauf_von</th>\n",
       "      <th>halt_id_von</th>\n",
       "      <th>halt_id_nach</th>\n",
       "      <th>halt_punkt_id_von</th>\n",
       "      <th>halt_punkt_id_nach</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>386145</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>06.01.20</td>\n",
       "      <td>10452</td>\n",
       "      <td>3201</td>\n",
       "      <td>24</td>\n",
       "      <td>1307</td>\n",
       "      <td>10</td>\n",
       "      <td>IHAG</td>\n",
       "      <td>06.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>109988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HOLZ - STRV</td>\n",
       "      <td>215519</td>\n",
       "      <td>2807</td>\n",
       "      <td>2836</td>\n",
       "      <td>48161</td>\n",
       "      <td>46028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>386663</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>06.01.20</td>\n",
       "      <td>10452</td>\n",
       "      <td>3201</td>\n",
       "      <td>24</td>\n",
       "      <td>1307</td>\n",
       "      <td>10</td>\n",
       "      <td>IHAG</td>\n",
       "      <td>06.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>109988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HOLZ - STRV</td>\n",
       "      <td>215519</td>\n",
       "      <td>2807</td>\n",
       "      <td>2836</td>\n",
       "      <td>48161</td>\n",
       "      <td>46028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>389199</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>07.01.20</td>\n",
       "      <td>10075</td>\n",
       "      <td>3201</td>\n",
       "      <td>24</td>\n",
       "      <td>1307</td>\n",
       "      <td>10</td>\n",
       "      <td>IHAG</td>\n",
       "      <td>07.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>109988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HOLZ - STRV</td>\n",
       "      <td>215519</td>\n",
       "      <td>2807</td>\n",
       "      <td>2836</td>\n",
       "      <td>48161</td>\n",
       "      <td>46028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>389832</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>07.01.20</td>\n",
       "      <td>10075</td>\n",
       "      <td>3201</td>\n",
       "      <td>24</td>\n",
       "      <td>1307</td>\n",
       "      <td>10</td>\n",
       "      <td>IHAG</td>\n",
       "      <td>07.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>109988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HOLZ - STRV</td>\n",
       "      <td>215519</td>\n",
       "      <td>2807</td>\n",
       "      <td>2836</td>\n",
       "      <td>48161</td>\n",
       "      <td>46028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>392126</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>10.01.20</td>\n",
       "      <td>10093</td>\n",
       "      <td>3201</td>\n",
       "      <td>24</td>\n",
       "      <td>1307</td>\n",
       "      <td>10</td>\n",
       "      <td>IHAG</td>\n",
       "      <td>10.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>109988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HOLZ - STRV</td>\n",
       "      <td>214896</td>\n",
       "      <td>2807</td>\n",
       "      <td>2836</td>\n",
       "      <td>48161</td>\n",
       "      <td>46028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1295732</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>09.01.20</td>\n",
       "      <td>10075</td>\n",
       "      <td>3201</td>\n",
       "      <td>22</td>\n",
       "      <td>1000</td>\n",
       "      <td>11</td>\n",
       "      <td>GLAU</td>\n",
       "      <td>09.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>115243</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>STRV - HOLZ</td>\n",
       "      <td>215519</td>\n",
       "      <td>2454</td>\n",
       "      <td>2457</td>\n",
       "      <td>44729</td>\n",
       "      <td>44335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1295872</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>10082</td>\n",
       "      <td>3201</td>\n",
       "      <td>22</td>\n",
       "      <td>1000</td>\n",
       "      <td>11</td>\n",
       "      <td>GLAU</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>115243</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>STRV - HOLZ</td>\n",
       "      <td>215519</td>\n",
       "      <td>2454</td>\n",
       "      <td>2457</td>\n",
       "      <td>44729</td>\n",
       "      <td>44335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1296234</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>10082</td>\n",
       "      <td>3201</td>\n",
       "      <td>22</td>\n",
       "      <td>1000</td>\n",
       "      <td>11</td>\n",
       "      <td>GLAU</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>115243</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>STRV - HOLZ</td>\n",
       "      <td>215519</td>\n",
       "      <td>2454</td>\n",
       "      <td>2457</td>\n",
       "      <td>44729</td>\n",
       "      <td>44335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1297238</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>10082</td>\n",
       "      <td>3201</td>\n",
       "      <td>9</td>\n",
       "      <td>1357</td>\n",
       "      <td>11</td>\n",
       "      <td>KALK</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>115243</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>STRV - HOLZ</td>\n",
       "      <td>215519</td>\n",
       "      <td>1906</td>\n",
       "      <td>2247</td>\n",
       "      <td>49953</td>\n",
       "      <td>42936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1297448</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>10082</td>\n",
       "      <td>3201</td>\n",
       "      <td>9</td>\n",
       "      <td>1357</td>\n",
       "      <td>11</td>\n",
       "      <td>KALK</td>\n",
       "      <td>08.01.20</td>\n",
       "      <td>...</td>\n",
       "      <td>115243</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>STRV - HOLZ</td>\n",
       "      <td>215519</td>\n",
       "      <td>1906</td>\n",
       "      <td>2247</td>\n",
       "      <td>49953</td>\n",
       "      <td>42936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>389 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         linie  richtung betriebsdatum  fahrzeug  kurs  seq_von  \\\n",
       "386145      32         1      06.01.20     10452  3201       24   \n",
       "386663      32         1      06.01.20     10452  3201       24   \n",
       "389199      32         1      07.01.20     10075  3201       24   \n",
       "389832      32         1      07.01.20     10075  3201       24   \n",
       "392126      32         1      10.01.20     10093  3201       24   \n",
       "...        ...       ...           ...       ...   ...      ...   \n",
       "1295732     32         2      09.01.20     10075  3201       22   \n",
       "1295872     32         2      08.01.20     10082  3201       22   \n",
       "1296234     32         2      08.01.20     10082  3201       22   \n",
       "1297238     32         2      08.01.20     10082  3201        9   \n",
       "1297448     32         2      08.01.20     10082  3201        9   \n",
       "\n",
       "         halt_diva_von  halt_punkt_diva_von halt_kurz_von1 datum_von  ...  \\\n",
       "386145            1307                   10           IHAG  06.01.20  ...   \n",
       "386663            1307                   10           IHAG  06.01.20  ...   \n",
       "389199            1307                   10           IHAG  07.01.20  ...   \n",
       "389832            1307                   10           IHAG  07.01.20  ...   \n",
       "392126            1307                   10           IHAG  10.01.20  ...   \n",
       "...                ...                  ...            ...       ...  ...   \n",
       "1295732           1000                   11           GLAU  09.01.20  ...   \n",
       "1295872           1000                   11           GLAU  08.01.20  ...   \n",
       "1296234           1000                   11           GLAU  08.01.20  ...   \n",
       "1297238           1357                   11           KALK  08.01.20  ...   \n",
       "1297448           1357                   11           KALK  08.01.20  ...   \n",
       "\n",
       "         fahrweg_id  fw_no  fw_typ  fw_kurz      fw_lang  umlauf_von  \\\n",
       "386145       109988      1       1        1  HOLZ - STRV      215519   \n",
       "386663       109988      1       1        1  HOLZ - STRV      215519   \n",
       "389199       109988      1       1        1  HOLZ - STRV      215519   \n",
       "389832       109988      1       1        1  HOLZ - STRV      215519   \n",
       "392126       109988      1       1        1  HOLZ - STRV      214896   \n",
       "...             ...    ...     ...      ...          ...         ...   \n",
       "1295732      115243      2       1        2  STRV - HOLZ      215519   \n",
       "1295872      115243      2       1        2  STRV - HOLZ      215519   \n",
       "1296234      115243      2       1        2  STRV - HOLZ      215519   \n",
       "1297238      115243      2       1        2  STRV - HOLZ      215519   \n",
       "1297448      115243      2       1        2  STRV - HOLZ      215519   \n",
       "\n",
       "         halt_id_von halt_id_nach halt_punkt_id_von  halt_punkt_id_nach  \n",
       "386145          2807         2836             48161               46028  \n",
       "386663          2807         2836             48161               46028  \n",
       "389199          2807         2836             48161               46028  \n",
       "389832          2807         2836             48161               46028  \n",
       "392126          2807         2836             48161               46028  \n",
       "...              ...          ...               ...                 ...  \n",
       "1295732         2454         2457             44729               44335  \n",
       "1295872         2454         2457             44729               44335  \n",
       "1296234         2454         2457             44729               44335  \n",
       "1297238         1906         2247             49953               42936  \n",
       "1297448         1906         2247             49953               42936  \n",
       "\n",
       "[389 rows x 34 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "startTime = 7*3600\n",
    "endTime = 9*3600\n",
    "Data = Data[Data['linie'] == 32]\n",
    "for day in Data['datum_nach'].unique():\n",
    "    DataP = Data[Data['datum_nach'] == day]\n",
    "    DataP = DataP[(DataP['soll_ab_von'] < endTime)&(DataP['soll_ab_von'] > startTime)]\n",
    "    plt.plot(DataP['soll_ab_nach'],DataP['halt_diva_nach'],'*',label = day)\n",
    "    print(DataP['soll_ab_nach'].shape)\n",
    "    print()\n",
    "    \n",
    "    \n",
    "plt.legend()  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Data[(Data['kurs'] == 3201 )& (Data['ist_ab_von'] < endTime)&(Data['ist_ab_von'] > startTime)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_kurs = set([])\n",
    "DataO = Data[Data['datum_nach'] == Data['datum_nach'].unique()[-1]]\n",
    "minO = np.inf\n",
    "for  day in Data['datum_nach'].unique():\n",
    "    if Data.loc[Data['datum_nach'] == day,'fw_lang'].unique().shape[0] < minO:\n",
    "        DataO=Data[Data['datum_nach'] == day]   \n",
    "    \n",
    "    \n",
    "for  day in Data['datum_nach'].unique():\n",
    "    DataP = Data[(Data['datum_nach'] == day)]\n",
    "    for kurs in DataP['kurs'].unique():\n",
    "        Datapi = DataP[DataP['kurs'] == kurs]\n",
    "        transVAr = Datapi.loc[~Datapi['soll_ab_nach'].isin(DataO.loc[DataO['kurs'] == kurs,'soll_ab_nach']),'fw_lang'].values\n",
    "        for i in range(transVAr.shape[0]) :\n",
    "            service_kurs.add(transVAr[i])\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "allfile = os.listdir('data/data2/Times/')\n",
    "\n",
    "\n",
    "\n",
    "Data = get_time_data('data/data2/Times/'+allfile[0])\n",
    "\n",
    "for str in allfile[1:]:\n",
    "       Data = pd.concat([Data,get_time_data('data/data2/Times/'+str)])\n",
    "    \n",
    "Data = Data.loc[Data['datum_nach'].dt.dayofweek < 5,:]\n",
    "#delete depot run\n",
    "Data = Data[~Data['fw_lang'].str.contains('DEP|Einfahrt|Ausfahrt')]\n",
    "\n",
    "Data['Lateness'] = (Data['ist_ab_nach']-Data['soll_ab_nach'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is  0.05199615380411645 % of lateness higher than 20min\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAc3ElEQVR4nO3dfbQcVZnv8e/PhICIBJAokIAJNwgT9ApyCKIDk+tFSIAQx3GQiHNBkAgzOPgyI0FciooXUO5cBHIXxCGEUSYQEYFAnIw4YmCJkAMOLyEGYoDhEJTDOwjI23P/qH2aTtt9us+h9+mu5PdZq9ep2lW16+nqTj/Ztat2KSIwMzMDeFOnAzAzs+7hpGBmZhVOCmZmVuGkYGZmFU4KZmZW4aRgZmYVTgpmbSBpoqSQNDrN/0TSUW2qez9Jq6vmH5B0QDvqTvWtlDStXfVZuTkp2Iga7g+apBskfTpHTDlExIyIuKTZeimRTG5S140RsWs74pK0UNLpNfXvHhE3tKN+Kz8nBbMuNtDyMBspTgrWFSRtLelaSf2SnkzTE9KybwH7AedLek7S+ak8JB0v6b60zTxJqqrzGEmr0rJlkt6ZyiXp/0p6VNLTku6U9O607GBJ90h6VtLDkv6hQbyjJJ0t6TFJa4FDapZXWjaSJkv6RdrXY5IuT+XL0+p3pPf1cUnTJPVJOlnS74CLB8pqQtg7xfmkpIslbZbqPFrSTTWxRIphDnAk8KW0vyVpeaX1JmlTSedIWpde50jaNC0biO2L6dg9IulTrX/KVgZOCtYt3gRcDLwT2Al4ATgfICJOBW4EToyILSLixKrtDgX2Bt4LHA4cBCDpI8CXgY8C49L2i9I2BwL7A+8CtgI+Djyell0EfCYi3gq8G/iPBvEel/a9J9ADfGyQ9/ZN4N+BrYEJwHnpfe2flr83va/L0/x2wDbpWMxpUOeR6b3+t/Q+vjLI/kn7mw9cCnw77W9mndVOBd4P7EFxTKfW1L0dMBYYDxwLzJO0dbN9W3k4KVhXiIjHI+JHEfF8RDwLfAv4ixY2PTMinoqI/wJ+TvFjBvAZ4IyIWBURrwD/G9gjtRZeBt4K7AYorfNI2u5lYIqkLSPiyYi4vcF+DwfOiYiHIuIJ4IxBYnyZ4gd+h4h4MSJuGmRdgNeAr0XEHyPihQbrnF+1728Bs5vU2aojgW9ExKMR0Q98HfibquUvp+UvR8RS4DmgLf0d1h2cFKwrSNpc0oWSHpT0DLAc2ErSqCab/q5q+nlgizT9TuC7kp6S9BTwBCBgfET8B0UrZB7we0nzJW2Ztvsr4GDgwXTKZ98G+90BeKhq/sFBYvxS2vet6UqfY5q8p/6IeLHJOrX73qHJ+q3agfXfS23dj6ckO6D6mNsGwEnBusUXKf7HuU9EbElxegeKH1OAoQ7n+xDFaaCtql5vjohfAkTEuRGxF7A7xemXf0zlKyJiFvB24CpgcYP6HwF2rJrfqVEgEfG7iDguInagaMH8vyZXHLXyXmv3vS5N/wHYfGCBpO2GWPc6ioRar27bCDgpWCdsImmzqtdoitM5LwBPSdoG+FrNNr8Hdh7CPi4ATpG0O4CksZL+Ok3vLWkfSZtQ/Ii+CLwqaYykIyWNjYiXgWeAVxvUvxj4e0kT0jn1uY0CkfTXA53mwJMUP8wD9Q71fQ34u7TvbSj6Tgb6I+4Adpe0R+p8Pq1mu2b7WwR8RdI4SdsCXwV+MIz4rKScFKwTllIkgIHXacA5wJuBx4BfAf9Ws813gY+lq23ObbaDiPgxcBZwWToddTcwIy3eEvgexQ/0gxSdzGenZX8DPJC2OR74ZINdfA9YRvEjfDtw5SDh7A3cIuk54BrgpIi4Py07DbgkneY6vNn7qvKvFJ3Xa9PrdICIuBf4BnA9cB9Q239xEUWfyVOSrqpT7+lAL3AncFd6b6fXWc82UPJDdszMbIBbCmZmVuGkYGZmFU4KZmZW4aRgZmYVpR5sa9ttt42JEyd2Ogwzs1K57bbbHouIcfWWlTopTJw4kd7e3k6HYWZWKpIa3oHv00dmZlbRNS0FSftRDMY1GpgSER/ocEhmZhudrC0FSQvSuOt315RPl7Ra0hpJc6HydKnjgWuBpk+sMjOz9st9+mghML26II16OY9iyIEpwGxJU6pW+QSvj3tvZmYjKGtSiIjlFEMWV5sKrImItRHxEnAZMAtA0k7A0xHxTKM6Jc2R1Cupt7+/P1foZmYbpU50NI9n/bHg+1IZFE9yuniwjSNifkT0RETPuHF1r6gyM7Nh6kRHs+qUBUBE1A6XXL8CaSYwc/LkwYakNzOzoepES6GP9R8QMgE/xMPMrCt0oqWwAthF0iTgYeAIis7llkXEEmBJT0/PcRnisw3IxLnXdWS/D5x5SEf2a/ZG5b4kdRFwM7CrpD5Jx6bnu55I8YCSVcDiiFg5xHpnSpr/9NNPtz9oM7ONWNaWQkTMblC+lOLpW8Ot1y0FM7MMSjnMhVsKZmZ5lDIpRMSSiJgzduzYTodiZrZBKWVSMDOzPEqZFHz6yMwsj1ImBZ8+MjPLo5RJwczM8ihlUvDpIzOzPEqZFHz6yMwsj1ImBTMzy8NJwczMKkqZFNynYGaWRymTgvsUzMzyKGVSMDOzPJwUzMyswknBzMwqnBTMzKyilEnBVx+ZmeVRyqTgq4/MzPIoZVIwM7M8nBTMzKzCScHMzCqcFMzMrMJJwczMKkZ3OoABkt4EfBPYEuiNiEs6HJKZ2UYna0tB0gJJj0q6u6Z8uqTVktZImpuKZwHjgZeBvpxxmZlZfblPHy0EplcXSBoFzANmAFOA2ZKmALsCN0fEF4ATMsdlZmZ1ZE0KEbEceKKmeCqwJiLWRsRLwGUUrYQ+4Mm0zquN6pQ0R1KvpN7+/v4cYZuZbbQ60dE8Hnioar4vlV0JHCTpPGB5o40jYn5E9EREz7hx4/JGama2kelER7PqlEVEPA8c21IF0kxg5uTJk9samJnZxq4TLYU+YMeq+QnAug7EYWZmNTqRFFYAu0iaJGkMcARwzVAq8IB4ZmZ55L4kdRFwM7CrpD5Jx0bEK8CJwDJgFbA4IlYOsV4PnW1mlkHWPoWImN2gfCmw9A3UuwRY0tPTc9xw6zAzsz9VymEu3FIwM8ujlEnBfQpmZnmUMimYmVkepUwKPn1kZpZHKZOCTx+ZmeVRyqRgZmZ5lDIp+PSRmVkepUwKPn1kZpZHKZOCmZnl4aRgZmYVpUwK7lMwM8ujlEnBfQpmZnmUMimYmVkeTgpmZlbhpGBmZhVOCmZmVlHKpOCrj8zM8ihlUvDVR2ZmeZQyKZiZWR5OCmZmVuGkYGZmFU4KZmZW4aRgZmYVXZMUJE2TdKOkCyRN63Q8ZmYbo6xJQdICSY9KurumfLqk1ZLWSJqbigN4DtgM6MsZl5mZ1Ze7pbAQmF5dIGkUMA+YAUwBZkuaAtwYETOAk4GvZ47LzMzqyJoUImI58ERN8VRgTUSsjYiXgMuAWRHxWlr+JLBpozolzZHUK6m3v78/S9xmZhurTvQpjAceqprvA8ZL+qikC4HvA+c32jgi5kdET0T0jBs3LnOoZmYbl9Ed2KfqlEVEXAlc2VIF0kxg5uTJk9samJnZxq4TLYU+YMeq+QnAug7EYWZmNTqRFFYAu0iaJGkMcARwzVAq8IB4ZmZ55L4kdRFwM7CrpD5Jx0bEK8CJwDJgFbA4IlYOsV4PnW1mlkHWPoWImN2gfCmw9A3UuwRY0tPTc9xw6zAzsz/VNXc0m5lZ55UyKfj0kZlZHqVMCu5oNjPLo5RJwS0FM7M8WupoljQOOA6YWL1NRByTJ6zBuaPZzCyPVq8+uhq4EbgeeDVfOGZm1kmtJoXNI+LkrJEMgYe5KJ+Jc6/rdAhm1oJW+xSulXRw1kiGwB3NZmZ5tJoUTqJIDC9Keja9nskZmJmZjbyWTh9FxFtzB2JmZp3X8jAXkg4D9k+zN0TEtXlCMjOzTmnp9JGkMylOId2TXielso7wfQpmZnm02qdwMPDhiFgQEQsonrvcsY5ndzSbmeUxlDuat6qa9q+xmdkGqNU+hTOAX0v6OcXjNPcHTskWlZmZdUSrVx8tknQDsDdFUjg5In6XMzAzMxt5g54+krRb+vs+YHuK5ys/BOyQyjrCHc1mZnk0ayl8AZgD/J86ywL4UNsjaoEHxDMzy2PQpBARc9LkjIh4sXqZpM2yRWVmZh3R6tVHv2yxzMzMSmzQloKk7YDxwJsl7UnRyQywJbB55tjMzGyENetTOAg4GpgA/FNV+bPAlzPFZGZmHdKsT+ES4BJJfxURPxqhmMzMrENavXnt3ZJ2ry2MiG+0MxhJbwGWA1/zgHtmZiOv1Y7m54A/pNerwAyK5zUPStICSY9KurumfLqk1ZLWSJpbtehkYHGLMZmZWZu1ekfzevcpSDobuKaFTRcC5wP/UrXtKGAe8GGKm+FWSLoG2IFiBFZf6mpm1iEtP0+hxubAzs1WiojlkibWFE8F1kTEWgBJlwGzgC2AtwBTgBckLY2I12rrlDSH4oY6dtppp2GGb2Zm9bSUFCTdRXEHM8AoYBww3P6E8RRDZQzoA/aJiBPTvo4GHquXEAAiYj4wH6CnpyfqrWNmZsPTakvh0KrpV4DfR8Qrw9yn6pRVftwjYmHTCqSZwMzJkycPMwQzM6unpY7miHgQeBvFaZ6PAu95A/vsA3asmp8ArBtKBX7IjplZHq0+jvOrwCUUiWFbYKGkrwxznyuAXSRNkjQGOILWOq2r4/EoqWZmGbR6+mg2sOfAoHjp+cy3A6cPtpGkRcA0YFtJfRT3H1wk6URgGUX/xIKIWDnM+M3MrI1aTQoPUFwqOjBS6qbAb5ttFBGzG5QvBZa2uO9623vobDOzDJoNiHceRSfwH4GVkn6a5j8M3JQ/PDMzG0nNWgq96e9twI+rym/IEk2LfPWRmVkerQyI13V8+sjMLI9mp48WR8ThNTevVUTEf88W2SDcUjAzy6PZ6aOT0t9DB11rhLmlYGaWR7PTR4+kAewuiogDRigmMzPrkKY3r0XEq8Dzknz7sJnZBq7V+xReBO5Kl6T+YaAwIv4+S1RNuE/BzCyPVpPCdelVrWMjlLpPwcwsj1aTwlYR8d3qAkknNVrZzMzKqdXHcR5Vp+zoNsZhZmZdoNl9CrOBTwCT0iMzB2wJPJ4zsMG4T8HMLI9mp49+CTxCMVx29XOanwXuzBVUM+5TMDPLo9l9Cg8CD0o6AHghIl6T9C5gN+CukQjQzMxGTqt9CsuBzSSNB34GfApYmCsoMzPrjFaTgiLieYpHcZ4XEX8JTMkXlpmZdULLSUHSvsCRvH6/QquXs5qZWUm0mhQ+B5wC/DgiVkraGfh5vrAG52c0m5nloYiO3Zj8hvX09ERvb2/zFa3jJs6tvSHecnngzEM6HYJ1OUm3RURPvWXN7lM4JyI+J2kJ9Z+ncFibYjQzsy7QrF/g++nv2bkDMTOzzmt2n8Jt6e8vJI1L0/0jEZiZmY28QTuaVThN0mPAb4B7JfVL+urIhGdmZiOp2dVHnwM+COwdEW+LiK2BfYAPSvp8OwOR9GeSLpB0haQT2lm3mZm1pllS+F/A7Ii4f6AgItYCn0zLBiVpgaRHJd1dUz5d0mpJayTNTfWuiojjgcOBur3iZmaWV7OksElEPFZbmPoVNmmh/oXA9OqC9MznecAMiruiZ0uakpYdBtxEMZSGmZmNsGZJ4aVhLgMgIpYDT9QUTwXWRMTaiHgJuAyYlda/JiI+QHHndF2S5kjqldTb3+8+bzOzdmp2Sep7JT1Tp1zAZsPc53jgoar5PmAfSdMoxlbaFFjaaOOImA/Mh+LmtWHGYGZmdTS7JHVUhn2q/q7iBuCGlirwQ3bMzLJodeyjduoDdqyanwCsG0oFEbEkIuaMHTu2rYGZmW3sOpEUVgC7SJokaQxwBHBNk23W4wHxzMzyyJoUJC0CbgZ2ldQn6diIeAU4EVgGrAIWR8TKodTrloKZWR5Zn4kQEbMblC9lkM7kZtynYGaWRydOH71hbimYmeVRyqTgPgUzszxKmRTcUjAzy6OUScEtBTOzPEqZFNxSMDPLo5RJwczM8nBSMDOzilImBfcpmJnlUcqk4D4FM7M8SpkUzMwsDycFMzOrKGVScJ+CmVkepUwK7lMwM8ujlEnBzMzycFIwM7MKJwUzM6twUjAzs4pSJgVffWRmlkcpk4KvPjIzy6OUScHMzPIY3ekAzKy9Js69riP7feDMQzqyX2svtxTMzKzCScHMzCq6KilI+oik70m6WtKBnY7HzGxjkz0pSFog6VFJd9eUT5e0WtIaSXMBIuKqiDgOOBr4eO7YzMxsfSPRUlgITK8ukDQKmAfMAKYAsyVNqVrlK2m5mZmNoOxJISKWA0/UFE8F1kTE2oh4CbgMmKXCWcBPIuL23LGZmdn6OtWnMB54qGq+L5V9FjgA+Jik4+ttKGmOpF5Jvf39/fkjNTPbiHTqPgXVKYuIOBc4d7ANI2K+pEeAmWPGjNkrS3RmZhupTrUU+oAdq+YnAOta3djDXJiZ5dGppLAC2EXSJEljgCOAa1rd2APimZnlMRKXpC4CbgZ2ldQn6diIeAU4EVgGrAIWR8TKVut0S8HMLI/sfQoRMbtB+VJg6XDqlDQTmDl58uQ3ElrHeGwaM+tWXXVHc6vcUjAzy6OUo6SWvaXQKZ1qoZhZeZQyKUTEEmBJT0/PccOtwz+QZmZ/qpSnj8zMLI9SJgVfkmpmlkcpk4I7ms3M8ihlUjAzszxKmRR8+sjMLI9SJgWfPjIzy6OUScHMzPJwUjAzs4pSJgX3KZiZ5VHKpOA+BTOzPEqZFMzMLA8nBTMzq3BSMDOzCicFMzOrKOXQ2X6egpl1g04OwZ/rSYqlbCn46iMzszxKmRTMzCwPJwUzM6twUjAzswonBTMzq+iapCBpZ0kXSbqi07GYmW2ssiYFSQskPSrp7pry6ZJWS1ojaS5ARKyNiGNzxmNmZoPL3VJYCEyvLpA0CpgHzACmALMlTckch5mZtSBrUoiI5cATNcVTgTWpZfAScBkwK2ccZmbWmk70KYwHHqqa7wPGS3qbpAuAPSWd0mhjSXMk9Urq7e/vzx2rmdlGpRPDXKhOWUTE48DxzTaOiPmSHgFmjhkzZq+2R2dmpdPJ4SY2NJ1oKfQBO1bNTwDWDaUCD3NhZpZHJ1oKK4BdJE0CHgaOAD4xlAo8IJ5Z9/H/1jcMuS9JXQTcDOwqqU/SsRHxCnAisAxYBSyOiJVDqdctBTOzPLK2FCJidoPypcDS4dbrloKZWR5dc0fzULilYGaWRymTgqSZkuY//fTTnQ7FzGyDUsqk4JaCmVkepUwKZmaWRymTgk8fmZnlUcqk4NNHZmZ5lDIpmJlZHoqITscwbJL6gQcHWWVb4LERCueNKlOs4HhzKlOs4HhzyhXrOyNiXL0FpU4KzUjqjYieTsfRijLFCo43pzLFCo43p07E6tNHZmZW4aRgZmYVG3pSmN/pAIagTLGC482pTLGC481pxGPdoPsUzMxsaDb0loKZmQ2Bk4KZmVWUOilI+qyk1ZJWSvp2VfkpktakZQdVlU9PZWskza0qnyTpFkn3Sbpc0piMMf+DpJC0bZqXpHNTTHdKel/VukelmO6TdFRV+V6S7krbnCup3nOv30iM35H0mxTPjyVtVbWsa49tg/dSN64RjmFHST+XtCp9V09K5dtI+mk6Nj+VtHUqH/J3IlPcoyT9WtK1ab7uZylp0zS/Ji2fWFVH3e9Lhli3knRF+t6ukrRvtx5fSZ9P34O7JS2StFlXHduIKOUL+B/A9cCmaf7t6e8U4A5gU2AS8FtgVHr9FtgZGJPWmZK2WQwckaYvAE7IFPOOFE+cexDYNpUdDPwEEPB+4JZUvg2wNv3dOk1vnZbdCuybtvkJMKPNcR4IjE7TZwFndfuxbfA+GsY1wt/V7YH3pem3AvemY/ltYG4qn1t1nIf8ncgU9xeAfwWuHeyzBP4WuCBNHwFcPtj3JVOslwCfTtNjgK268fgC44H7gTdXHdOju+nYjug/jjYf3MXAAXXKTwFOqZpfRvEDui+wrHa99MV4jNd/BNdbr80xXwG8F3iA15PChcDsqnVWpx+R2cCFVeUXprLtgd9Ula+3XoaY/xK4tNuPbYPY68bVBd/dq4EPD3zWqWx7YPVwvhOZYpwA/Az4EHDtYJ/lwPcgTY9O66nR9yVDrFtS/NCqprzrji9FUniIIvGMTsf2oG46tmU+ffQuYL/UpPqFpL1T+cBBH9CXyhqVvw14KopnR1eXt5Wkw4CHI+KOmkVDjXd8mq4tz+UYiv9V0SSmjh3bQTSKq2NS839P4BbgHRHxCED6+/a02lCPcw7nAF8CXkvzg32WlbjS8qfT+iMV785AP3BxOt31z5LeQhce34h4GDgb+C/gEYpjdRtddGyzPqP5jZJ0PbBdnUWnUsS+NUXzb29gsaSdKbJoraB+/0kMsn674/0yxWmZP9lsiHG1Jd7BYo2Iq9M6pwKvAJc2iTX7sR2mTu9/PZK2AH4EfC4inhmkKyjrZ9+MpEOBRyPiNknTmsQ02LKROv6jgfcBn42IWyR9l+J0USMdizf1a8yiOOXzFPBDYMYg+x3xWLs6KUTEAY2WSToBuDKKttOtkl6jGDyqj+Lc/YAJwLo0Xa/8MWArSaNTJq5evy3xSnoPxZfgjvRDMAG4XdLUQeLtA6bVlN+QyifUWb8tsVbFfBRwKPA/0zFmkFhpUN62YztMg8U7oiRtQpEQLo2IK1Px7yVtHxGPSNoeeDSVD/U70W4fBA6TdDCwGcXpmXNo/FkOxNsnaTQwFnhikPfRbn1AX0TckuavoEgK3Xh8DwDuj4h+AElXAh+gm45tu8/vjdQLOB74Rpp+F0VTSsDurN8Bs5aiw3F0mp7E652Ou6ftf8j6nTx/mzn2B3i9T+EQ1u/0ujWVb0NxnnTr9Lof2CYtW5HWHehoPrjN8U0H7gHG1ZR3/bGtibdhXCP8XRXwL8A5NeXfYf2O0G8P9zuRMfZpvN7RXPezBP6O9TtDFw/2fckU543Armn6tHRsu+74AvsAK4HN0/4vAT7bTcd2RP9xtPngjgF+ANwN3A58qGrZqRS98aupujKH4qqDe9OyU6vKd6a4omdN+nA2zRz7A7yeFATMSzHdBfRUrXdMimkN8Kmq8p70vn8LnE9NB1sb4ltDkWT/M70uKMuxrfNe6sY1wjH8OUXT/s6qY3owxbnhnwH3pb8DSX/I34mMsU/j9aRQ97OkaE38MJXfCuzc7PuSIc49gN50jK+i+FHvyuMLfB34Tfo3/H2KH/auObYe5sLMzCrKfPWRmZm1mZOCmZlVOCmYmVmFk4KZmVU4KZiZWYWTglkVSc8NYd1pkj6QMx6zkeakYDZ80yjuRjXbYDgpmDUhaWYaePHXkq6X9I40sN3xwOcl/aek/SQtTOP0/1LSWkkfq6rjHyWtSOP3fz2VvUXSdZLuSGPrfzyVnynpnrTu2Z14z7bx6uqxj8y6xE3A+yMiJH0a+FJEfFHSBcBzEXE2gKRjKYZg/nNgN+Aa4ApJBwK7AFMp7qa9RtL+wDhgXUQckrYfK2kbiuHKd0v72wqzEeSkYNbcBODyNKjaGIoxcRq5KiJeA+6R9I5UdmB6/TrNb0GRJG4EzpZ0FsVQEjemQc9eBP5Z0nUU4+2bjRifPjJr7jzg/Ih4D/AZivFoGvlj1bSq/p4REXuk1+SIuCgi7gX2ohh/5wxJX41ilMypFCOqfgT4t3a/GbPBuKVg1txY4OE0fVRV+bMUw0o3swz4pqRLI+I5SeOBlyn+/T0RET9IVz0dnZ65sHlELJX0K4qB0MxGjJOC2fo2l1T9ZLt/ohiK+YeSHgZ+RTFUMcASij6DWRTDH9cVEf8u6c+Am9PzNJ4DPglMBr6TngXyMnACxTOcr5a0GUUL4/NtfG9mTXmUVDMzq3CfgpmZVTgpmJlZhZOCmZlVOCmYmVmFk4KZmVU4KZiZWYWTgpmZVfx/Hv3zMPROBDgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(10)\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.hist(Data['Lateness'])\n",
    "ax.set(xlabel = 'Latness',ylabel = 'Distribution',title = 'Latness distribution')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "test = Data[Data['Lateness']> 1200]\n",
    "\n",
    "print('there is ',Data[Data['Lateness']> 1200].shape[0]*100/Data.shape[0],'% of lateness higher than 20min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29 janvier neige et 4 grosse pluie 17\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(11,figsize = (12,6))\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.bar(range(len(test['linie'].unique())),test.groupby(['linie']).count()['richtung'])\n",
    "ax.set(xlabel = 'Line',ylabel = 'Distribution',title = 'Latness distribution')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "plt.xticks(range(len(test['linie'].unique())),rotation=90)\n",
    "ax.set_xticklabels(test.groupby(['linie']).count().index)\n",
    "\n",
    "fig = plt.figure(12)\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.hist(test['Lateness']/60)\n",
    "ax.set(xlabel = 'Latness [min]',ylabel = 'Distribution',title = 'Latness distribution')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "fig = plt.figure(13,figsize = (12,6))\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.bar(range(len(test['datum_nach'].unique())),test.groupby(['datum_nach']).count()['richtung'])\n",
    "ax.set(xlabel = 'Day',ylabel = 'Distribution',title = 'Latness distribution')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "plt.xticks(range(len(test['datum_nach'].unique())),rotation=90)\n",
    "ax.set_xticklabels(test.groupby(['datum_nach']).count().index)\n",
    "\n",
    "print('29 janvier neige et 4 grosse pluie 17')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '2020-01-06 00:00:00'),\n",
       " Text(0, 0, '2020-01-07 00:00:00'),\n",
       " Text(0, 0, '2020-01-08 00:00:00'),\n",
       " Text(0, 0, '2020-01-09 00:00:00'),\n",
       " Text(0, 0, '2020-01-10 00:00:00'),\n",
       " Text(0, 0, '2020-01-13 00:00:00'),\n",
       " Text(0, 0, '2020-01-14 00:00:00'),\n",
       " Text(0, 0, '2020-01-15 00:00:00'),\n",
       " Text(0, 0, '2020-01-16 00:00:00'),\n",
       " Text(0, 0, '2020-01-17 00:00:00'),\n",
       " Text(0, 0, '2020-01-20 00:00:00'),\n",
       " Text(0, 0, '2020-01-21 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-23 00:00:00'),\n",
       " Text(0, 0, '2020-01-24 00:00:00'),\n",
       " Text(0, 0, '2020-01-27 00:00:00'),\n",
       " Text(0, 0, '2020-01-28 00:00:00'),\n",
       " Text(0, 0, '2020-01-30 00:00:00'),\n",
       " Text(0, 0, '2020-01-31 00:00:00'),\n",
       " Text(0, 0, '2020-02-03 00:00:00'),\n",
       " Text(0, 0, '2020-02-04 00:00:00'),\n",
       " Text(0, 0, '2020-02-05 00:00:00'),\n",
       " Text(0, 0, '2020-02-06 00:00:00'),\n",
       " Text(0, 0, '2020-02-07 00:00:00'),\n",
       " Text(0, 0, '2020-02-10 00:00:00'),\n",
       " Text(0, 0, '2020-02-11 00:00:00'),\n",
       " Text(0, 0, '2020-02-12 00:00:00'),\n",
       " Text(0, 0, '2020-02-13 00:00:00'),\n",
       " Text(0, 0, '2020-02-14 00:00:00'),\n",
       " Text(0, 0, '2020-02-17 00:00:00'),\n",
       " Text(0, 0, '2020-02-18 00:00:00'),\n",
       " Text(0, 0, '2020-02-19 00:00:00'),\n",
       " Text(0, 0, '2020-02-20 00:00:00'),\n",
       " Text(0, 0, '2020-02-21 00:00:00')]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(14,figsize = (12,6))\n",
    "\n",
    "test = test[~(test['datum_nach'] == test['datum_nach'].unique()[15])]\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.bar(range(len(test['linie'].unique())),test.groupby(['linie']).count()['richtung'])\n",
    "ax.set(xlabel = 'Line',ylabel = 'Distribution',title = 'Latness distribution')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "plt.xticks(range(len(test['linie'].unique())),rotation=90)\n",
    "ax.set_xticklabels(test.groupby(['linie']).count().index)\n",
    "\n",
    "fig = plt.figure(15)\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.hist(test['Lateness']/60)\n",
    "ax.set(xlabel = 'Latness [min]',ylabel = 'Distribution',title = 'Latness distribution')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "fig = plt.figure(16,figsize = (12,6))\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.bar(range(len(test['datum_nach'].unique())),test.groupby(['datum_nach']).count()['richtung'])\n",
    "ax.set(xlabel = 'Day',ylabel = 'Distribution',title = 'Latness distribution')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "plt.xticks(range(len(test['datum_nach'].unique())),rotation=90)\n",
    "ax.set_xticklabels(test.groupby(['datum_nach']).count().index)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/gneven/ETH/Sem2/Projeckt/Pyhton/DataPickles/coor.pickle','rb') as pelo:\n",
    "    Results = pickle.load(pelo)\n",
    "    \n",
    "with open('/gneven/ETH/Sem2/Projeckt/Pyhton/DataPickles/Data_line.pickle','rb') as f:\n",
    "    line = pickle.load(f)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       " Text(0, 0, '91203'),\n",
       " Text(0, 0, '91204'),\n",
       " Text(0, 0, '91205'),\n",
       " Text(0, 0, '91206'),\n",
       " Text(0, 0, '91601'),\n",
       " Text(0, 0, '91602'),\n",
       " Text(0, 0, '91611'),\n",
       " Text(0, 0, '91612')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results_t0 = pd.DataFrame(Results[:,0,:],index = line[170:183])\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "fig = plt.figure(1,figsize = (12,4))\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "ax.plot(Results[3,:,:].T)\n",
    "# ax.plot(range(line.shape[0]),Results[,1,:])\n",
    "ax.plot(range(170,183),Results[3,:,170:183].T,'*')\n",
    "ax.set(xlabel = 'Buses',ylabel = 'MI',title = 'Mutual info for 1 bus, for every time')\n",
    "plt.xticks(range(line.shape[0]),rotation=90)\n",
    "ax.set_xticklabels(line)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Bus   Occ\n",
       "20  704  1265\n",
       "11   61   602\n",
       "10   46   469\n",
       "9    32   300\n",
       "12   62   273\n",
       "22  912   250\n",
       "16  185   186\n",
       "6    12   158\n",
       "17  304   151\n",
       "23  916    64\n",
       "19  701    41\n",
       "4     9    23\n",
       "8    31    21\n",
       "14   80    20\n",
       "18  308    20\n",
       "0     4    20\n",
       "7    15    12\n",
       "1     6     6\n",
       "15   89     6\n",
       "21  910     4\n",
       "3     8     3\n",
       "2     7     3\n",
       "5    10     2\n",
       "13   72     1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.idxmax(Results,axis = 0)\n",
    "# numpy.where(Results == numpy.amax(arr))\n",
    "N_max = 10\n",
    "max_n = np.empty((13,30,N_max))\n",
    "for b in range(Results.shape[0]):\n",
    "    for t in range(Results.shape[1]):\n",
    "        Results_i = Results[b,t,:]\n",
    "        max_n[b,t,:] = np.argpartition(Results_i, -N_max)[-N_max:]\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "fig = plt.figure(2,figsize = (12,4))\n",
    "\n",
    "ax = fig.gca()\n",
    "\n",
    "# ax.plot(range(line.shape[0]),Results[,1,:])\n",
    "ax.plot(max_n[2,:,:],'*')\n",
    "ax.set(xlabel = 'Time',ylabel = 'Buses',title = '10 best Mutual info ')\n",
    "# plt.xticks(range(line.shape[0]),rotation=90)\n",
    "# ax.set_xticklabels(line)es\n",
    "\n",
    "\n",
    "line_a = line[max_n.astype('int')].ravel()\n",
    "line_a = np.floor(line_a/100).astype('int')\n",
    "bins = np.bincount(line_a)\n",
    "bin_o = []\n",
    "for i in range(len(bins)):\n",
    "    if bins[i] != 0:\n",
    "#         print('Bus :',i,' ; ',bins[i])\n",
    "        bin_o.append([i,bins[i]])\n",
    "        \n",
    "bin_o = pd.DataFrame(bin_o,columns = ['Bus','Occ'])\n",
    "bin_o.sort_values('Occ',ascending = False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "EOFError",
     "evalue": "Ran out of input",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mEOFError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-0e54164fc4f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#     model = pickle.load(f)\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[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'caseof.pickles'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mmodel_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\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      7\u001b[0m     \u001b[0mhist_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mEOFError\u001b[0m: Ran out of input"
     ]
    }
   ],
   "source": [
    "# with open('/gneven/ETH/Sem2/Projeckt/Pyhton/DataPickles/step108_all.pickle','rb') as f:\n",
    "#     predicty = pickle.load(f)\n",
    "#     hist = pickle.load(f)\n",
    "#     model = pickle.load(f)\n",
    "with open('caseof.pickles','rb') as p:\n",
    "    model_1 = pickle.load(p)\n",
    "    hist_1 = pickle.load(p)\n",
    "    \n",
    "   \n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'input_bus' 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-28-2e8d7a071e33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mkernel1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mkernel2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0minput_bus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_bus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\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      6\u001b[0m \u001b[0mout_dt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'input_bus' is not defined"
     ]
    }
   ],
   "source": [
    "filter1 = 64\n",
    "filter2 = 32\n",
    "kernel1 = 5\n",
    "kernel2 = 5\n",
    "input_bus = input_bus[:,:,2:,:,:]\n",
    "out_dt = 1\n",
    "epochs = 10\n",
    "\n",
    "valo = np.squeeze(valo)\n",
    "output_bus = np.squeeze(output_bus)\n",
    "model_1 = Sequential([\n",
    "    BatchNormalization(name = 'batch_norm_0', input_shape = (in_dt,input_bus.shape[2],input_bus.shape[3],input_bus.shape[4])), \n",
    "\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_1',filters=filter1, kernel_size=(kernel1, 1),stateful=False\n",
    "               , padding='same', return_sequences=True,data_format  =\"channels_first\"),\n",
    "    Dropout(0.2),\n",
    "\n",
    "    BatchNormalization(),\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_2',filters=filter2, kernel_size=(kernel2, 1),stateful=False\n",
    "               ,data_format  =\"channels_first\"\n",
    "               ,padding='same', return_sequences=False),\n",
    "\n",
    "    Dropout(0.2),\n",
    "\n",
    "    BatchNormalization(),\n",
    "\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    RepeatVector(out_dt),\n",
    "\n",
    "    Reshape((out_dt,375,1,filter2)),\n",
    "\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_3',filters=filter2,  kernel_size=(kernel2, 1),stateful=False\n",
    "               , padding='same', return_sequences=True),\n",
    "    Dropout(0.1),\n",
    "    BatchNormalization(),\n",
    "\n",
    "    ConvLSTM2D(name ='conv_lstm_4',\n",
    "               filters = filter1, kernel_size = (kernel1, 1),stateful=False, \n",
    "               padding='same',\n",
    "               return_sequences = True),\n",
    "    #    Reshape((10,202472)) ,           \n",
    "    #    \n",
    "    #    TimeDistributed(Dense(out.shape[2]*20)),\n",
    "    ##    \n",
    "    #    TimeDistributed(Dense(32,)),\n",
    "    #    \n",
    "    TimeDistributed(Dense(1), name='test'),\n",
    "\n",
    "    Reshape((375,)),\n",
    "\n",
    "\n",
    "])\n",
    "# optimizer = optimizers.Adam(clipvalue=0.5)\n",
    "#optimizer = optimizers.Adam(clipnorm=1.)\n",
    "#     model = multi_gpu_model(model, gpus=6)\n",
    "model_1.compile(loss=kl.mean_absolute_error, optimizer = 'adam',metrics=['accuracy'])\n",
    "model_1.summary()\n",
    "\n",
    "hist_1 = model_1.fit(input_bus, output_bus, epochs=30,\n",
    "                              batch_size=batch_size, verbose=1, shuffle=False)\n",
    "\n",
    "with open('caseof.pickles','rb') as p:\n",
    "    model = pickle.load(model_1,p)\n",
    "    hist = pickle.load(hist_1,p)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('caseof.pickles','rb') as p:\n",
    "    model = pickle.load(p)\n",
    "    hist = pickle.load(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_norm_0 (BatchNormaliza (None, 30, 1, 375, 1)     4         \n",
      "_________________________________________________________________\n",
      "conv_lstm_1 (ConvLSTM2D)     (None, 30, 64, 375, 1)    83456     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 30, 64, 375, 1)    0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 30, 64, 375, 1)    4         \n",
      "_________________________________________________________________\n",
      "conv_lstm_2 (ConvLSTM2D)     (None, 32, 375, 1)        61568     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 32, 375, 1)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 32, 375, 1)        4         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12000)             0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 1, 375, 1, 32)     0         \n",
      "_________________________________________________________________\n",
      "conv_lstm_3 (ConvLSTM2D)     (None, 1, 375, 1, 32)     41088     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 1, 375, 1, 32)     0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1, 375, 1, 32)     128       \n",
      "_________________________________________________________________\n",
      "conv_lstm_4 (ConvLSTM2D)     (None, 1, 375, 1, 64)     123136    \n",
      "_________________________________________________________________\n",
      "test (TimeDistributed)       (None, 1, 375, 1, 1)      65        \n",
      "_________________________________________________________________\n",
      "reshape_2 (Reshape)          (None, 375)               0         \n",
      "=================================================================\n",
      "Total params: 309,453\n",
      "Trainable params: 309,383\n",
      "Non-trainable params: 70\n",
      "_________________________________________________________________\n",
      "(478, 30, 3, 375, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "keras is no longer supported, please use tf.keras instead.\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n",
    "print(vali.shape)\n",
    "explainer = shap.DeepExplainer(model, tf.convert_to_tensor(vali[:239,:,2:3,:,:],dtype = 'float32'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# explainer\n",
    "# shap_values = explainer.shap_values(tf.convert_to_tensor(vali[:10,:,2:3,:,0:],dtype = 'float32'))\n",
    "\n",
    "# shap_values_0 = explainer.shap_values(tf.convert_to_tensor(valo[:239,:],dtype = 'float32'))\n",
    "# hidden_layers1 = keras.backend.function([model.layers[0].input],[model.layers[1].output]) \n",
    "# hidden_layers2 = keras.backend.function([model.layers[0].input],[model.layers[4].output]) \n",
    "# hidden_layers3 = keras.backend.function([model.layers[0].input],[model.layers[9].output]) \n",
    "# hidden_layers4 = keras.backend.function([model.layers[0].input],[model.layers[12].output]) \n",
    "\n",
    "\n",
    "# LSTM1 = np.asarray(hidden_layers1([vali[:239,:,2:3,:,:]]))\n",
    "# LSTM2 = np.asarray(hidden_layers2([vali[:239,:,2:3,:,:]]))\n",
    "# LSTM3 = np.asarray(hidden_layers3([vali[:239,:,2:3,:,:]]))\n",
    "# LSTM4 = np.asarray(hidden_layers4([vali[:239,:,2:3,:,:]]))\n",
    "\n",
    "\n",
    "hidden_layers1_in = keras.backend.function([model.layers[0].input],[model.layers[0].output]) \n",
    "hidden_layers2_in = keras.backend.function([model.layers[0].input],[model.layers[3].output]) \n",
    "hidden_layers3_in = keras.backend.function([model.layers[0].input],[model.layers[8].output]) \n",
    "hidden_layers4_in = keras.backend.function([model.layers[0].input],[model.layers[11].output]) \n",
    "\n",
    "\n",
    "LSTM1_in = np.asarray(hidden_layers1_in([vali[:239,:,2:3,:,:]]))\n",
    "LSTM2_in = np.asarray(hidden_layers2_in([vali[:239,:,2:3,:,:]]))\n",
    "LSTM3_in = np.asarray(hidden_layers3_in([vali[:239,:,2:3,:,:]]))\n",
    "LSTM4_in = np.asarray(hidden_layers4_in([vali[:239,:,2:3,:,:]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57600,)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LSTM1_in.shape\n",
    "LSTM1[0,0,:,:,:30,0].ravel().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def ANN(shapeIN,shapeOut):\n",
    "ANN = Sequential([Flatten(input_shape = LSTM1_in.shape[2:]),\n",
    "            Dense(57600),\n",
    "#         Reshape(shapeOut),\n",
    "\n",
    "    ])\n",
    "\n",
    "ANN.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "ResourceExhaustedError",
     "evalue": "OOM when allocating tensor with shape[11250,720000] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator mklcpu [Op:RandomUniform]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-62-3c6d77d11260>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mANN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLSTM1_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mLSTM1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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[0mmodel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\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[0;32m<ipython-input-61-5245a1a197f3>\u001b[0m in \u001b[0;36mANN\u001b[0;34m(shapeIN, shapeOut)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mANN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshapeIN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshapeOut\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[1;32m      2\u001b[0m     ANN = Sequential([Flatten(input_shape = shapeIN[2:]),\n\u001b[0;32m----> 3\u001b[0;31m         \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m720000\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      4\u001b[0m \u001b[0;31m#         Reshape(shapeOut),\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[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/sequential.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, layers, name)\u001b[0m\n\u001b[1;32m     92\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\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     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/sequential.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m    180\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_source_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[1;32m    181\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m             \u001b[0moutput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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    183\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m                 raise TypeError('All layers in a Sequential model '\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36msymbolic_fn_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_SYMBOLIC_SCOPE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mget_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\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[0;32m---> 75\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     76\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, **kwargs)\u001b[0m\n\u001b[1;32m    461\u001b[0m                                          \u001b[0;34m'You can build it manually via: '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m                                          '`layer.build(batch_input_shape)`')\n\u001b[0;32m--> 463\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\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    464\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/layers/core.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m    893\u001b[0m                                       \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'kernel'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m                                       \u001b[0mregularizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel_regularizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                                       constraint=self.kernel_constraint)\n\u001b[0m\u001b[1;32m    896\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m             self.bias = self.add_weight(shape=(self.units,),\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36madd_weight\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, trainable, constraint)\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m         weight = K.variable(initializer(shape, dtype=dtype),\n\u001b[0m\u001b[1;32m    280\u001b[0m                             \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m                             \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/initializers.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, shape, dtype)\u001b[0m\n\u001b[1;32m    225\u001b[0m             \u001b[0mlimit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m             x = K.random_uniform(shape, -limit, limit,\n\u001b[0;32m--> 227\u001b[0;31m                                  dtype=dtype, seed=self.seed)\n\u001b[0m\u001b[1;32m    228\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(shape, minval, maxval, dtype, seed)\u001b[0m\n\u001b[1;32m   4355\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtf_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_scope\u001b[0m\u001b[0;34m(\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[1;32m   4356\u001b[0m         return tf_keras_backend.random_uniform(\n\u001b[0;32m-> 4357\u001b[0;31m             shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed)\n\u001b[0m\u001b[1;32m   4358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(shape, minval, maxval, dtype, seed)\u001b[0m\n\u001b[1;32m   5624\u001b[0m     \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5625\u001b[0m   return random_ops.random_uniform(\n\u001b[0;32m-> 5626\u001b[0;31m       shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed)\n\u001b[0m\u001b[1;32m   5627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/random_ops.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(shape, minval, maxval, dtype, seed, name)\u001b[0m\n\u001b[1;32m    270\u001b[0m           shape, minval, maxval, seed=seed1, seed2=seed2, name=name)\n\u001b[1;32m    271\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m       \u001b[0mrnd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_random_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_uniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed2\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    273\u001b[0m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrnd\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmaxval\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mminval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m     \u001b[0;31m# TODO(b/132092188): C++ shape inference inside functional ops does not\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_random_ops.py\u001b[0m in \u001b[0;36mrandom_uniform\u001b[0;34m(shape, dtype, seed, seed2, name)\u001b[0m\n\u001b[1;32m    722\u001b[0m         \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 724\u001b[0;31m       \u001b[0m_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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    725\u001b[0m   \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m   \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   6604\u001b[0m   \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\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[1;32m   6605\u001b[0m   \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6606\u001b[0;31m   \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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   6607\u001b[0m   \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[11250,720000] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator mklcpu [Op:RandomUniform]"
     ]
    }
   ],
   "source": [
    "model1 = ANN(LSTM1_in.shape,LSTM1.shape)\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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