Pore_Pressure_Prediction_MLP-ANN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9ebc4e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:15.102825Z",
     "start_time": "2023-02-28T20:38:11.803809Z"
    }
   },
   "outputs": [],
   "source": [
    "# plots\n",
    "# import exp358\n",
    "import os,  sys # for common import for everyone\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import pandas as pd\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "# from scipy.interpolate import interp1d\n",
    "from scipy import interpolate # interpolate needs numbers\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "from scipy import optimize # inversion\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3d64b28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:15.116299Z",
     "start_time": "2023-02-28T20:38:15.106179Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed7aeaa",
   "metadata": {},
   "source": [
    "# Reading data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db174d9a",
   "metadata": {},
   "source": [
    "## df_well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b14ca173",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:15.326293Z",
     "start_time": "2023-02-28T20:38:15.119199Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MD</th>\n",
       "      <th>AHT10_3</th>\n",
       "      <th>AHT20_1</th>\n",
       "      <th>AHT30_1</th>\n",
       "      <th>AHT60_1</th>\n",
       "      <th>AHT90_1</th>\n",
       "      <th>AHT90_3</th>\n",
       "      <th>APLC_1</th>\n",
       "      <th>APLC_2</th>\n",
       "      <th>APLC_4</th>\n",
       "      <th>...</th>\n",
       "      <th>invert_EricksonJarrard_high</th>\n",
       "      <th>Poisson_ratio</th>\n",
       "      <th>Fracture_Pressure</th>\n",
       "      <th>Excess_pore_pressure</th>\n",
       "      <th>Lithostatic_Load</th>\n",
       "      <th>Lithostatic_Load_lamda</th>\n",
       "      <th>Void_Ratio</th>\n",
       "      <th>effective_stress</th>\n",
       "      <th>VELP</th>\n",
       "      <th>VELS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51062</th>\n",
       "      <td>1975.02356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.37598</td>\n",
       "      <td>1.45421</td>\n",
       "      <td>1.69695</td>\n",
       "      <td>0.91105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.56321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363820</td>\n",
       "      <td>0.336905</td>\n",
       "      <td>1.554125e+07</td>\n",
       "      <td>-8.348533e+06</td>\n",
       "      <td>20.566016</td>\n",
       "      <td>-3462.823361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.942594e+06</td>\n",
       "      <td>2654.90968</td>\n",
       "      <td>1316.68686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51063</th>\n",
       "      <td>1975.05005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.37984</td>\n",
       "      <td>1.44298</td>\n",
       "      <td>1.68640</td>\n",
       "      <td>0.90267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.55603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363807</td>\n",
       "      <td>0.336983</td>\n",
       "      <td>1.534605e+07</td>\n",
       "      <td>-8.749534e+06</td>\n",
       "      <td>21.589061</td>\n",
       "      <td>-2617.821082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.344258e+06</td>\n",
       "      <td>2655.09844</td>\n",
       "      <td>1316.54142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51064</th>\n",
       "      <td>1975.09998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38711</td>\n",
       "      <td>1.42180</td>\n",
       "      <td>1.66650</td>\n",
       "      <td>0.88687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363784</td>\n",
       "      <td>0.337131</td>\n",
       "      <td>1.497833e+07</td>\n",
       "      <td>-9.505832e+06</td>\n",
       "      <td>23.533793</td>\n",
       "      <td>-1828.297478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.101909e+06</td>\n",
       "      <td>2655.45423</td>\n",
       "      <td>1316.26729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51065</th>\n",
       "      <td>1975.10400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38770</td>\n",
       "      <td>1.42010</td>\n",
       "      <td>1.66490</td>\n",
       "      <td>0.88560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363781</td>\n",
       "      <td>0.337143</td>\n",
       "      <td>1.495344e+07</td>\n",
       "      <td>-9.557168e+06</td>\n",
       "      <td>23.667315</td>\n",
       "      <td>-1786.614709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.153355e+06</td>\n",
       "      <td>2655.48288</td>\n",
       "      <td>1316.24522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51066</th>\n",
       "      <td>1975.10408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38771</td>\n",
       "      <td>1.42003</td>\n",
       "      <td>1.66483</td>\n",
       "      <td>0.88557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363781</td>\n",
       "      <td>0.337143</td>\n",
       "      <td>1.495297e+07</td>\n",
       "      <td>-9.558131e+06</td>\n",
       "      <td>23.669829</td>\n",
       "      <td>-1785.786761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.154320e+06</td>\n",
       "      <td>2655.48346</td>\n",
       "      <td>1316.24477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 184 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               MD  AHT10_3  AHT20_1  AHT30_1  AHT60_1  AHT90_1  AHT90_3  \\\n",
       "51062  1975.02356      NaN  0.37598  1.45421  1.69695  0.91105      NaN   \n",
       "51063  1975.05005      NaN  0.37984  1.44298  1.68640  0.90267      NaN   \n",
       "51064  1975.09998      NaN  0.38711  1.42180  1.66650  0.88687      NaN   \n",
       "51065  1975.10400      NaN  0.38770  1.42010  1.66490  0.88560      NaN   \n",
       "51066  1975.10408      NaN  0.38771  1.42003  1.66483  0.88557      NaN   \n",
       "\n",
       "       APLC_1   APLC_2  APLC_4  ...  invert_EricksonJarrard_high  \\\n",
       "51062     NaN  0.56321     NaN  ...                     0.363820   \n",
       "51063     NaN  0.55603     NaN  ...                     0.363807   \n",
       "51064     NaN  0.54249     NaN  ...                     0.363784   \n",
       "51065     NaN  0.54140     NaN  ...                     0.363781   \n",
       "51066     NaN  0.54142     NaN  ...                     0.363781   \n",
       "\n",
       "       Poisson_ratio  Fracture_Pressure  Excess_pore_pressure  \\\n",
       "51062       0.336905       1.554125e+07         -8.348533e+06   \n",
       "51063       0.336983       1.534605e+07         -8.749534e+06   \n",
       "51064       0.337131       1.497833e+07         -9.505832e+06   \n",
       "51065       0.337143       1.495344e+07         -9.557168e+06   \n",
       "51066       0.337143       1.495297e+07         -9.558131e+06   \n",
       "\n",
       "       Lithostatic_Load  Lithostatic_Load_lamda  Void_Ratio  effective_stress  \\\n",
       "51062         20.566016            -3462.823361         NaN      7.942594e+06   \n",
       "51063         21.589061            -2617.821082         NaN      8.344258e+06   \n",
       "51064         23.533793            -1828.297478         NaN      9.101909e+06   \n",
       "51065         23.667315            -1786.614709         NaN      9.153355e+06   \n",
       "51066         23.669829            -1785.786761         NaN      9.154320e+06   \n",
       "\n",
       "             VELP        VELS  \n",
       "51062  2654.90968  1316.68686  \n",
       "51063  2655.09844  1316.54142  \n",
       "51064  2655.45423  1316.26729  \n",
       "51065  2655.48288  1316.24522  \n",
       "51066  2655.48346  1316.24477  \n",
       "\n",
       "[5 rows x 184 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drilling_12b= pickle.load(open(\"0a_05_time_series.pickle\",\"rb\"))\n",
    "df_drilling_12b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "320b08a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:16.055115Z",
     "start_time": "2023-02-28T20:38:15.329572Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MD</th>\n",
       "      <th>AHT10_3</th>\n",
       "      <th>AHT20_1</th>\n",
       "      <th>AHT30_1</th>\n",
       "      <th>AHT60_1</th>\n",
       "      <th>AHT90_1</th>\n",
       "      <th>AHT90_3</th>\n",
       "      <th>APLC_1</th>\n",
       "      <th>APLC_2</th>\n",
       "      <th>APLC_4</th>\n",
       "      <th>...</th>\n",
       "      <th>invert_EricksonJarrard_high</th>\n",
       "      <th>Poisson_ratio</th>\n",
       "      <th>Fracture_Pressure</th>\n",
       "      <th>Excess_pore_pressure</th>\n",
       "      <th>Lithostatic_Load</th>\n",
       "      <th>Lithostatic_Load_lamda</th>\n",
       "      <th>Void_Ratio</th>\n",
       "      <th>effective_stress</th>\n",
       "      <th>VELP</th>\n",
       "      <th>VELS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126487.000000</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126487.000000</td>\n",
       "      <td>125682.000000</td>\n",
       "      <td>126313.000000</td>\n",
       "      <td>125715.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>1.268010e+05</td>\n",
       "      <td>1.268010e+05</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126793.000000</td>\n",
       "      <td>1.268010e+05</td>\n",
       "      <td>126801.000000</td>\n",
       "      <td>126801.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3181.054461</td>\n",
       "      <td>7.294273</td>\n",
       "      <td>8.134969</td>\n",
       "      <td>8.866878</td>\n",
       "      <td>9.584755</td>\n",
       "      <td>9.351129</td>\n",
       "      <td>9.369472</td>\n",
       "      <td>0.252433</td>\n",
       "      <td>0.216393</td>\n",
       "      <td>0.252652</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270764</td>\n",
       "      <td>0.298917</td>\n",
       "      <td>3.617778e+07</td>\n",
       "      <td>2.207132e+06</td>\n",
       "      <td>0.142584</td>\n",
       "      <td>0.155903</td>\n",
       "      <td>0.212498</td>\n",
       "      <td>8.922642e+06</td>\n",
       "      <td>3331.592522</td>\n",
       "      <td>1783.297222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>677.039351</td>\n",
       "      <td>13.266289</td>\n",
       "      <td>18.788358</td>\n",
       "      <td>25.508295</td>\n",
       "      <td>34.901241</td>\n",
       "      <td>30.751502</td>\n",
       "      <td>30.786774</td>\n",
       "      <td>0.068731</td>\n",
       "      <td>2.393556</td>\n",
       "      <td>0.069169</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064610</td>\n",
       "      <td>0.047777</td>\n",
       "      <td>1.012633e+07</td>\n",
       "      <td>4.046281e+06</td>\n",
       "      <td>3.770971</td>\n",
       "      <td>18.969206</td>\n",
       "      <td>0.109187</td>\n",
       "      <td>6.494308e+06</td>\n",
       "      <td>402.485096</td>\n",
       "      <td>353.997851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1975.023560</td>\n",
       "      <td>0.774400</td>\n",
       "      <td>0.263100</td>\n",
       "      <td>0.692100</td>\n",
       "      <td>0.413900</td>\n",
       "      <td>0.298330</td>\n",
       "      <td>0.646000</td>\n",
       "      <td>0.039820</td>\n",
       "      <td>-333.010500</td>\n",
       "      <td>0.026580</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087147</td>\n",
       "      <td>0.169230</td>\n",
       "      <td>1.420352e+07</td>\n",
       "      <td>-1.166515e+07</td>\n",
       "      <td>-787.907426</td>\n",
       "      <td>-3462.823361</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>8.485464e+02</td>\n",
       "      <td>2642.450010</td>\n",
       "      <td>1209.063960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2593.500000</td>\n",
       "      <td>2.139880</td>\n",
       "      <td>2.068460</td>\n",
       "      <td>2.041400</td>\n",
       "      <td>2.032130</td>\n",
       "      <td>2.036650</td>\n",
       "      <td>2.040420</td>\n",
       "      <td>0.206950</td>\n",
       "      <td>0.206860</td>\n",
       "      <td>0.206920</td>\n",
       "      <td>...</td>\n",
       "      <td>0.223758</td>\n",
       "      <td>0.263145</td>\n",
       "      <td>2.720689e+07</td>\n",
       "      <td>-1.803230e+05</td>\n",
       "      <td>-0.041793</td>\n",
       "      <td>0.514981</td>\n",
       "      <td>0.128452</td>\n",
       "      <td>4.191816e+06</td>\n",
       "      <td>3035.100240</td>\n",
       "      <td>1504.247280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3183.800050</td>\n",
       "      <td>3.323170</td>\n",
       "      <td>3.162780</td>\n",
       "      <td>3.104390</td>\n",
       "      <td>3.029380</td>\n",
       "      <td>3.031600</td>\n",
       "      <td>3.039110</td>\n",
       "      <td>0.260500</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.260730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279873</td>\n",
       "      <td>0.315574</td>\n",
       "      <td>3.580936e+07</td>\n",
       "      <td>1.017324e+06</td>\n",
       "      <td>0.118049</td>\n",
       "      <td>0.589377</td>\n",
       "      <td>0.171262</td>\n",
       "      <td>7.480483e+06</td>\n",
       "      <td>3288.553600</td>\n",
       "      <td>1688.438760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3772.444580</td>\n",
       "      <td>7.114880</td>\n",
       "      <td>7.109780</td>\n",
       "      <td>7.104360</td>\n",
       "      <td>7.080310</td>\n",
       "      <td>7.110300</td>\n",
       "      <td>7.124130</td>\n",
       "      <td>0.303047</td>\n",
       "      <td>0.303940</td>\n",
       "      <td>0.303640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327122</td>\n",
       "      <td>0.337212</td>\n",
       "      <td>4.345021e+07</td>\n",
       "      <td>3.339621e+06</td>\n",
       "      <td>0.335591</td>\n",
       "      <td>0.697339</td>\n",
       "      <td>0.284423</td>\n",
       "      <td>1.219720e+07</td>\n",
       "      <td>3571.346190</td>\n",
       "      <td>2036.840760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4349.800780</td>\n",
       "      <td>175.288800</td>\n",
       "      <td>305.695890</td>\n",
       "      <td>462.426910</td>\n",
       "      <td>881.688480</td>\n",
       "      <td>810.287720</td>\n",
       "      <td>810.287720</td>\n",
       "      <td>0.750740</td>\n",
       "      <td>3.296900</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.364722</td>\n",
       "      <td>0.379826</td>\n",
       "      <td>6.150835e+07</td>\n",
       "      <td>1.705721e+07</td>\n",
       "      <td>393.695676</td>\n",
       "      <td>0.998808</td>\n",
       "      <td>0.612513</td>\n",
       "      <td>3.144209e+07</td>\n",
       "      <td>4414.149900</td>\n",
       "      <td>2736.801510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 184 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  MD        AHT10_3        AHT20_1        AHT30_1  \\\n",
       "count  126801.000000  126487.000000  126801.000000  126801.000000   \n",
       "mean     3181.054461       7.294273       8.134969       8.866878   \n",
       "std       677.039351      13.266289      18.788358      25.508295   \n",
       "min      1975.023560       0.774400       0.263100       0.692100   \n",
       "25%      2593.500000       2.139880       2.068460       2.041400   \n",
       "50%      3183.800050       3.323170       3.162780       3.104390   \n",
       "75%      3772.444580       7.114880       7.109780       7.104360   \n",
       "max      4349.800780     175.288800     305.695890     462.426910   \n",
       "\n",
       "             AHT60_1        AHT90_1        AHT90_3         APLC_1  \\\n",
       "count  126801.000000  126801.000000  126487.000000  125682.000000   \n",
       "mean        9.584755       9.351129       9.369472       0.252433   \n",
       "std        34.901241      30.751502      30.786774       0.068731   \n",
       "min         0.413900       0.298330       0.646000       0.039820   \n",
       "25%         2.032130       2.036650       2.040420       0.206950   \n",
       "50%         3.029380       3.031600       3.039110       0.260500   \n",
       "75%         7.080310       7.110300       7.124130       0.303047   \n",
       "max       881.688480     810.287720     810.287720       0.750740   \n",
       "\n",
       "              APLC_2         APLC_4  ...  invert_EricksonJarrard_high  \\\n",
       "count  126313.000000  125715.000000  ...                126801.000000   \n",
       "mean        0.216393       0.252652  ...                     0.270764   \n",
       "std         2.393556       0.069169  ...                     0.064610   \n",
       "min      -333.010500       0.026580  ...                     0.087147   \n",
       "25%         0.206860       0.206920  ...                     0.223758   \n",
       "50%         0.260870       0.260730  ...                     0.279873   \n",
       "75%         0.303940       0.303640  ...                     0.327122   \n",
       "max         3.296900       0.761500  ...                     0.364722   \n",
       "\n",
       "       Poisson_ratio  Fracture_Pressure  Excess_pore_pressure  \\\n",
       "count  126801.000000       1.268010e+05          1.268010e+05   \n",
       "mean        0.298917       3.617778e+07          2.207132e+06   \n",
       "std         0.047777       1.012633e+07          4.046281e+06   \n",
       "min         0.169230       1.420352e+07         -1.166515e+07   \n",
       "25%         0.263145       2.720689e+07         -1.803230e+05   \n",
       "50%         0.315574       3.580936e+07          1.017324e+06   \n",
       "75%         0.337212       4.345021e+07          3.339621e+06   \n",
       "max         0.379826       6.150835e+07          1.705721e+07   \n",
       "\n",
       "       Lithostatic_Load  Lithostatic_Load_lamda     Void_Ratio  \\\n",
       "count     126801.000000           126801.000000  126793.000000   \n",
       "mean           0.142584                0.155903       0.212498   \n",
       "std            3.770971               18.969206       0.109187   \n",
       "min         -787.907426            -3462.823361       0.000100   \n",
       "25%           -0.041793                0.514981       0.128452   \n",
       "50%            0.118049                0.589377       0.171262   \n",
       "75%            0.335591                0.697339       0.284423   \n",
       "max          393.695676                0.998808       0.612513   \n",
       "\n",
       "       effective_stress           VELP           VELS  \n",
       "count      1.268010e+05  126801.000000  126801.000000  \n",
       "mean       8.922642e+06    3331.592522    1783.297222  \n",
       "std        6.494308e+06     402.485096     353.997851  \n",
       "min        8.485464e+02    2642.450010    1209.063960  \n",
       "25%        4.191816e+06    3035.100240    1504.247280  \n",
       "50%        7.480483e+06    3288.553600    1688.438760  \n",
       "75%        1.219720e+07    3571.346190    2036.840760  \n",
       "max        3.144209e+07    4414.149900    2736.801510  \n",
       "\n",
       "[8 rows x 184 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drilling_12b.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6046e3a0",
   "metadata": {},
   "source": [
    "# NEURAL NETWORKS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90aaef1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:29.144085Z",
     "start_time": "2023-02-28T20:38:16.057615Z"
    }
   },
   "outputs": [],
   "source": [
    "#----------------------- Artificial Neural Network for classification --------------------#\n",
    "#importing required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import *\n",
    "from sklearn.preprocessing import *\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "adf474d1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:29.450658Z",
     "start_time": "2023-02-28T20:38:29.146195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126411, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_ML= df_drilling_12b[['MD_mbsf','AHT90_3','DTCO_2','DTSM_1',\n",
    "                          'ECGR_2','VCL_1','VPVS_1','PHIT_1','PEFZ_4','RHOZ_4','Pore_Pressure_Estimated_DT']]\n",
    "df_new_ML = df_new_ML.dropna()\n",
    "df_new_ML.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82abc5a",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "157e572c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:29.480348Z",
     "start_time": "2023-02-28T20:38:29.454430Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tk/dcwsd9m50476mx54d6mxfp_00000gn/T/ipykernel_12179/1871822838.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  X = df_new_ML.drop(\"Pore_Pressure_Estimated_DT\", 1)       # feature matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MD_mbsf</th>\n",
       "      <th>AHT90_3</th>\n",
       "      <th>DTCO_2</th>\n",
       "      <th>DTSM_1</th>\n",
       "      <th>ECGR_2</th>\n",
       "      <th>VCL_1</th>\n",
       "      <th>VPVS_1</th>\n",
       "      <th>PHIT_1</th>\n",
       "      <th>PEFZ_4</th>\n",
       "      <th>RHOZ_4</th>\n",
       "      <th>Pore_Pressure_Estimated_DT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52979</th>\n",
       "      <td>83.09524</td>\n",
       "      <td>32.32600</td>\n",
       "      <td>113.94030</td>\n",
       "      <td>228.00533</td>\n",
       "      <td>39.47200</td>\n",
       "      <td>0.02050</td>\n",
       "      <td>1.99881</td>\n",
       "      <td>0.30080</td>\n",
       "      <td>1.87300</td>\n",
       "      <td>2.15550</td>\n",
       "      <td>2.015733e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52980</th>\n",
       "      <td>83.10000</td>\n",
       "      <td>32.56354</td>\n",
       "      <td>113.94000</td>\n",
       "      <td>228.16000</td>\n",
       "      <td>39.58338</td>\n",
       "      <td>0.02087</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.30081</td>\n",
       "      <td>1.87460</td>\n",
       "      <td>2.15511</td>\n",
       "      <td>2.015719e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52981</th>\n",
       "      <td>83.11481</td>\n",
       "      <td>33.30276</td>\n",
       "      <td>113.99233</td>\n",
       "      <td>228.57863</td>\n",
       "      <td>39.93000</td>\n",
       "      <td>0.02200</td>\n",
       "      <td>2.00296</td>\n",
       "      <td>0.30084</td>\n",
       "      <td>1.87959</td>\n",
       "      <td>2.15388</td>\n",
       "      <td>2.015674e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52982</th>\n",
       "      <td>83.11481</td>\n",
       "      <td>33.30276</td>\n",
       "      <td>113.99233</td>\n",
       "      <td>228.57863</td>\n",
       "      <td>39.93000</td>\n",
       "      <td>0.02200</td>\n",
       "      <td>2.00296</td>\n",
       "      <td>0.30084</td>\n",
       "      <td>1.87959</td>\n",
       "      <td>2.15388</td>\n",
       "      <td>2.015674e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52983</th>\n",
       "      <td>83.11482</td>\n",
       "      <td>33.30312</td>\n",
       "      <td>113.99236</td>\n",
       "      <td>228.57883</td>\n",
       "      <td>39.93016</td>\n",
       "      <td>0.02200</td>\n",
       "      <td>2.00296</td>\n",
       "      <td>0.30084</td>\n",
       "      <td>1.87959</td>\n",
       "      <td>2.15388</td>\n",
       "      <td>2.015674e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MD_mbsf   AHT90_3     DTCO_2     DTSM_1    ECGR_2    VCL_1   VPVS_1  \\\n",
       "52979  83.09524  32.32600  113.94030  228.00533  39.47200  0.02050  1.99881   \n",
       "52980  83.10000  32.56354  113.94000  228.16000  39.58338  0.02087  2.00000   \n",
       "52981  83.11481  33.30276  113.99233  228.57863  39.93000  0.02200  2.00296   \n",
       "52982  83.11481  33.30276  113.99233  228.57863  39.93000  0.02200  2.00296   \n",
       "52983  83.11482  33.30312  113.99236  228.57883  39.93016  0.02200  2.00296   \n",
       "\n",
       "        PHIT_1   PEFZ_4   RHOZ_4  Pore_Pressure_Estimated_DT  \n",
       "52979  0.30080  1.87300  2.15550                2.015733e+07  \n",
       "52980  0.30081  1.87460  2.15511                2.015719e+07  \n",
       "52981  0.30084  1.87959  2.15388                2.015674e+07  \n",
       "52982  0.30084  1.87959  2.15388                2.015674e+07  \n",
       "52983  0.30084  1.87959  2.15388                2.015674e+07  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_new_ML.drop(\"Pore_Pressure_Estimated_DT\", 1)       # feature matrix\n",
    "y = df_new_ML['Pore_Pressure_Estimated_DT']               # target feature\n",
    "df_new_ML.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f471b34b",
   "metadata": {},
   "source": [
    "#### Forward Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4db3fb7a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:30.369743Z",
     "start_time": "2023-02-28T20:38:29.486024Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('MD_mbsf',\n",
       " 'AHT90_3',\n",
       " 'DTCO_2',\n",
       " 'DTSM_1',\n",
       " 'ECGR_2',\n",
       " 'VCL_1',\n",
       " 'VPVS_1',\n",
       " 'PHIT_1',\n",
       " 'PEFZ_4',\n",
       " 'RHOZ_4')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#importing the necessary libraries\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "# Sequential Forward Selection(sfs)\n",
    "ForwardSelection = SFS(LinearRegression(),\n",
    "          k_features=10,\n",
    "          forward=True,\n",
    "          floating=False,\n",
    "          scoring = 'r2',\n",
    "          cv = 0)\n",
    "ForwardSelection.fit(X, y)\n",
    "ForwardSelection.k_feature_names_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3cc4d1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:30.386636Z",
     "start_time": "2023-02-28T20:38:30.371516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0,)</td>\n",
       "      <td>[0.830726480211508]</td>\n",
       "      <td>0.830726</td>\n",
       "      <td>(MD_mbsf,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 3)</td>\n",
       "      <td>[0.9365646164353747]</td>\n",
       "      <td>0.936565</td>\n",
       "      <td>(MD_mbsf, DTSM_1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 3, 8)</td>\n",
       "      <td>[0.9405616348718537]</td>\n",
       "      <td>0.940562</td>\n",
       "      <td>(MD_mbsf, DTSM_1, PEFZ_4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 3, 7, 8)</td>\n",
       "      <td>[0.9472131038662643]</td>\n",
       "      <td>0.947213</td>\n",
       "      <td>(MD_mbsf, DTSM_1, PHIT_1, PEFZ_4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 3, 7, 8, 9)</td>\n",
       "      <td>[0.954305280651626]</td>\n",
       "      <td>0.954305</td>\n",
       "      <td>(MD_mbsf, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 2, 3, 7, 8, 9)</td>\n",
       "      <td>[0.9579491732147868]</td>\n",
       "      <td>0.957949</td>\n",
       "      <td>(MD_mbsf, DTCO_2, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 2, 3, 6, 7, 8, 9)</td>\n",
       "      <td>[0.979000547728706]</td>\n",
       "      <td>0.979001</td>\n",
       "      <td>(MD_mbsf, DTCO_2, DTSM_1, VPVS_1, PHIT_1, PEFZ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(0, 2, 3, 5, 6, 7, 8, 9)</td>\n",
       "      <td>[0.9792291978056238]</td>\n",
       "      <td>0.979229</td>\n",
       "      <td>(MD_mbsf, DTCO_2, DTSM_1, VCL_1, VPVS_1, PHIT_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0, 1, 2, 3, 5, 6, 7, 8, 9)</td>\n",
       "      <td>[0.9792968625642025]</td>\n",
       "      <td>0.979297</td>\n",
       "      <td>(MD_mbsf, AHT90_3, DTCO_2, DTSM_1, VCL_1, VPVS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</td>\n",
       "      <td>[0.9793094082243815]</td>\n",
       "      <td>0.979309</td>\n",
       "      <td>(MD_mbsf, AHT90_3, DTCO_2, DTSM_1, ECGR_2, VCL...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       feature_idx             cv_scores avg_score  \\\n",
       "1                             (0,)   [0.830726480211508]  0.830726   \n",
       "2                           (0, 3)  [0.9365646164353747]  0.936565   \n",
       "3                        (0, 3, 8)  [0.9405616348718537]  0.940562   \n",
       "4                     (0, 3, 7, 8)  [0.9472131038662643]  0.947213   \n",
       "5                  (0, 3, 7, 8, 9)   [0.954305280651626]  0.954305   \n",
       "6               (0, 2, 3, 7, 8, 9)  [0.9579491732147868]  0.957949   \n",
       "7            (0, 2, 3, 6, 7, 8, 9)   [0.979000547728706]  0.979001   \n",
       "8         (0, 2, 3, 5, 6, 7, 8, 9)  [0.9792291978056238]  0.979229   \n",
       "9      (0, 1, 2, 3, 5, 6, 7, 8, 9)  [0.9792968625642025]  0.979297   \n",
       "10  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)  [0.9793094082243815]  0.979309   \n",
       "\n",
       "                                        feature_names  \n",
       "1                                          (MD_mbsf,)  \n",
       "2                                   (MD_mbsf, DTSM_1)  \n",
       "3                           (MD_mbsf, DTSM_1, PEFZ_4)  \n",
       "4                   (MD_mbsf, DTSM_1, PHIT_1, PEFZ_4)  \n",
       "5           (MD_mbsf, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)  \n",
       "6   (MD_mbsf, DTCO_2, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)  \n",
       "7   (MD_mbsf, DTCO_2, DTSM_1, VPVS_1, PHIT_1, PEFZ...  \n",
       "8   (MD_mbsf, DTCO_2, DTSM_1, VCL_1, VPVS_1, PHIT_...  \n",
       "9   (MD_mbsf, AHT90_3, DTCO_2, DTSM_1, VCL_1, VPVS...  \n",
       "10  (MD_mbsf, AHT90_3, DTCO_2, DTSM_1, ECGR_2, VCL...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a dataframe for the SFS results \n",
    "df_SFS_results = pd.DataFrame(ForwardSelection.subsets_).transpose()\n",
    "df_SFS_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a5c1a6a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:30.438058Z",
     "start_time": "2023-02-28T20:38:30.391133Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pwavodij/opt/anaconda3/lib/python3.9/site-packages/numpy/core/_methods.py:262: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/Users/pwavodij/opt/anaconda3/lib/python3.9/site-packages/numpy/core/_methods.py:254: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "    fig.rubberband_canvas.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       " * https://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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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    }
   ],
   "source": [
    "fig1 = plot_sfs(ForwardSelection.get_metric_dict(), kind='std_dev')\n",
    "plt.title('Sequential Forward Selection (w. StdErr)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14418a12",
   "metadata": {},
   "source": [
    "#### Stepwise Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "71f571e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:32.469143Z",
     "start_time": "2023-02-28T20:38:31.367882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('MD_mbsf',\n",
       " 'AHT90_3',\n",
       " 'DTCO_2',\n",
       " 'DTSM_1',\n",
       " 'ECGR_2',\n",
       " 'VCL_1',\n",
       " 'VPVS_1',\n",
       " 'PHIT_1',\n",
       " 'PEFZ_4',\n",
       " 'RHOZ_4')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StepwiseSelection = SFS(LinearRegression(),\n",
    "          k_features=(3,10),\n",
    "          forward=True,\n",
    "          floating=True,\n",
    "          cv = 0)\n",
    "StepwiseSelection.fit(X, y)\n",
    "StepwiseSelection.k_feature_names_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36c28d21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:32.489077Z",
     "start_time": "2023-02-28T20:38:32.471807Z"
    }
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0,)</td>\n",
       "      <td>[0.830726480211508]</td>\n",
       "      <td>0.830726</td>\n",
       "      <td>(MD_mbsf,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 3)</td>\n",
       "      <td>[0.9365646164353747]</td>\n",
       "      <td>0.936565</td>\n",
       "      <td>(MD_mbsf, DTSM_1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 3, 8)</td>\n",
       "      <td>[0.9405616348718537]</td>\n",
       "      <td>0.940562</td>\n",
       "      <td>(MD_mbsf, DTSM_1, PEFZ_4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 3, 7, 8)</td>\n",
       "      <td>[0.9472131038662643]</td>\n",
       "      <td>0.947213</td>\n",
       "      <td>(MD_mbsf, DTSM_1, PHIT_1, PEFZ_4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 3, 7, 8, 9)</td>\n",
       "      <td>[0.954305280651626]</td>\n",
       "      <td>0.954305</td>\n",
       "      <td>(MD_mbsf, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 2, 3, 7, 8, 9)</td>\n",
       "      <td>[0.9579491732147868]</td>\n",
       "      <td>0.957949</td>\n",
       "      <td>(MD_mbsf, DTCO_2, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 2, 3, 6, 7, 8, 9)</td>\n",
       "      <td>[0.979000547728706]</td>\n",
       "      <td>0.979001</td>\n",
       "      <td>(MD_mbsf, DTCO_2, DTSM_1, VPVS_1, PHIT_1, PEFZ...</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(0, 2, 3, 5, 6, 7, 8, 9)</td>\n",
       "      <td>[0.9792291978056238]</td>\n",
       "      <td>0.979229</td>\n",
       "      <td>(MD_mbsf, DTCO_2, DTSM_1, VCL_1, VPVS_1, PHIT_...</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0, 1, 2, 3, 5, 6, 7, 8, 9)</td>\n",
       "      <td>[0.9792968625642025]</td>\n",
       "      <td>0.979297</td>\n",
       "      <td>(MD_mbsf, AHT90_3, DTCO_2, DTSM_1, VCL_1, VPVS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</td>\n",
       "      <td>[0.9793094082243815]</td>\n",
       "      <td>0.979309</td>\n",
       "      <td>(MD_mbsf, AHT90_3, DTCO_2, DTSM_1, ECGR_2, VCL...</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                       feature_idx             cv_scores avg_score  \\\n",
       "1                             (0,)   [0.830726480211508]  0.830726   \n",
       "2                           (0, 3)  [0.9365646164353747]  0.936565   \n",
       "3                        (0, 3, 8)  [0.9405616348718537]  0.940562   \n",
       "4                     (0, 3, 7, 8)  [0.9472131038662643]  0.947213   \n",
       "5                  (0, 3, 7, 8, 9)   [0.954305280651626]  0.954305   \n",
       "6               (0, 2, 3, 7, 8, 9)  [0.9579491732147868]  0.957949   \n",
       "7            (0, 2, 3, 6, 7, 8, 9)   [0.979000547728706]  0.979001   \n",
       "8         (0, 2, 3, 5, 6, 7, 8, 9)  [0.9792291978056238]  0.979229   \n",
       "9      (0, 1, 2, 3, 5, 6, 7, 8, 9)  [0.9792968625642025]  0.979297   \n",
       "10  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)  [0.9793094082243815]  0.979309   \n",
       "\n",
       "                                        feature_names  \n",
       "1                                          (MD_mbsf,)  \n",
       "2                                   (MD_mbsf, DTSM_1)  \n",
       "3                           (MD_mbsf, DTSM_1, PEFZ_4)  \n",
       "4                   (MD_mbsf, DTSM_1, PHIT_1, PEFZ_4)  \n",
       "5           (MD_mbsf, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)  \n",
       "6   (MD_mbsf, DTCO_2, DTSM_1, PHIT_1, PEFZ_4, RHOZ_4)  \n",
       "7   (MD_mbsf, DTCO_2, DTSM_1, VPVS_1, PHIT_1, PEFZ...  \n",
       "8   (MD_mbsf, DTCO_2, DTSM_1, VCL_1, VPVS_1, PHIT_...  \n",
       "9   (MD_mbsf, AHT90_3, DTCO_2, DTSM_1, VCL_1, VPVS...  \n",
       "10  (MD_mbsf, AHT90_3, DTCO_2, DTSM_1, ECGR_2, VCL...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a dataframe for the SFS results \n",
    "df_SFS_results = pd.DataFrame(StepwiseSelection.subsets_).transpose()\n",
    "df_SFS_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f78deed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:32.532669Z",
     "start_time": "2023-02-28T20:38:32.491131Z"
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     "name": "stderr",
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      "/Users/pwavodij/opt/anaconda3/lib/python3.9/site-packages/numpy/core/_methods.py:254: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
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       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "    fig.rubberband_canvas.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       " * https://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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plot_sfs(StepwiseSelection.get_metric_dict(), kind='std_dev')\n",
    "plt.title('Stepwise Selection (w. StdErr)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7c6a25",
   "metadata": {},
   "source": [
    "## independent and dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca22d87d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:32.542274Z",
     "start_time": "2023-02-28T20:38:32.534736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([20157331.05714931, 20157185.39068233, 20156742.63557394, ...,\n",
       "        36970612.00488539, 36650970.63591424, 36626360.93236218]),\n",
       " array([[8.30952400e+01, 3.23260000e+01, 1.13940300e+02, ...,\n",
       "         3.00800000e-01, 1.87300000e+00, 2.15550000e+00],\n",
       "        [8.31000000e+01, 3.25635400e+01, 1.13940000e+02, ...,\n",
       "         3.00810000e-01, 1.87460000e+00, 2.15511000e+00],\n",
       "        [8.31148100e+01, 3.33027600e+01, 1.13992330e+02, ...,\n",
       "         3.00840000e-01, 1.87959000e+00, 2.15388000e+00],\n",
       "        ...,\n",
       "        [2.41977491e+03, 9.17256000e+00, 6.87300000e+01, ...,\n",
       "         1.50390000e-01, 1.96587000e+00, 2.39962000e+00],\n",
       "        [2.41984012e+03, 9.18600000e+00, 6.87300000e+01, ...,\n",
       "         1.51280000e-01, 1.93479000e+00, 2.39979000e+00],\n",
       "        [2.41984451e+03, 9.18690000e+00, 6.87300000e+01, ...,\n",
       "         1.51340000e-01, 1.93270000e+00, 2.39980000e+00]]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify and columns rows to independent and dependent.\n",
    "# Here we consider the top part with depths assuming the hydrostatic pressure is equal to the pore pressure\n",
    "X_independent_variable = df_new_ML.iloc[:,:-1].values\n",
    "X_independent_variable\n",
    "\n",
    "\n",
    "Y_dependent_variable = df_new_ML.iloc[:,-1].values\n",
    "Y_dependent_variable, X_independent_variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e300c6",
   "metadata": {},
   "source": [
    "## Feature Scaling and Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c426d339",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:32.566169Z",
     "start_time": "2023-02-28T20:38:32.544523Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "### Sandardization of data ###\n",
    "\n",
    "PredictorScaler=StandardScaler()\n",
    "TargetVarScaler=StandardScaler()\n",
    " \n",
    "# Storing the fit object for later reference\n",
    "PredictorScalerFit=PredictorScaler.fit(X_independent_variable)\n",
    "Y_dependent_variable = Y_dependent_variable.reshape(1,-1)\n",
    "Y_dependent_variable = Y_dependent_variable.transpose()\n",
    "TargetVarScalerFit=TargetVarScaler.fit(Y_dependent_variable)\n",
    " \n",
    "# Generating the standardized values of X and y\n",
    "X=PredictorScalerFit.transform(X_independent_variable)\n",
    "y=TargetVarScalerFit.transform(Y_dependent_variable)\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acd69023",
   "metadata": {},
   "source": [
    "## Split Train and Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e384eb5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:38:32.588134Z",
     "start_time": "2023-02-28T20:38:32.568121Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101128, 10)\n",
      "(101128, 1)\n",
      "(25283, 10)\n",
      "(25283, 1)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    " \n",
    "# Quick sanity check with the shapes of Training and testing datasets\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb13bf1a",
   "metadata": {},
   "source": [
    "## MLP Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1c781d6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T20:41:54.417214Z",
     "start_time": "2023-02-28T20:38:32.590493Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-28 21:38:32.596702: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5057/5057 [==============================] - 5s 842us/step - loss: 0.0769\n",
      "Epoch 2/50\n",
      "5057/5057 [==============================] - 4s 854us/step - loss: 0.0083\n",
      "Epoch 3/50\n",
      "5057/5057 [==============================] - 4s 818us/step - loss: 0.0076\n",
      "Epoch 4/50\n",
      "5057/5057 [==============================] - 4s 770us/step - loss: 0.0073\n",
      "Epoch 5/50\n",
      "5057/5057 [==============================] - 4s 779us/step - loss: 0.0071\n",
      "Epoch 6/50\n",
      "5057/5057 [==============================] - 4s 713us/step - loss: 0.0070\n",
      "Epoch 7/50\n",
      "5057/5057 [==============================] - 4s 719us/step - loss: 0.0068\n",
      "Epoch 8/50\n",
      "5057/5057 [==============================] - 4s 703us/step - loss: 0.0068\n",
      "Epoch 9/50\n",
      "5057/5057 [==============================] - 4s 810us/step - loss: 0.0067\n",
      "Epoch 10/50\n",
      "5057/5057 [==============================] - 5s 965us/step - loss: 0.0066\n",
      "Epoch 11/50\n",
      "5057/5057 [==============================] - 4s 746us/step - loss: 0.0065\n",
      "Epoch 12/50\n",
      "5057/5057 [==============================] - 4s 825us/step - loss: 0.0065\n",
      "Epoch 13/50\n",
      "5057/5057 [==============================] - 4s 792us/step - loss: 0.0064\n",
      "Epoch 14/50\n",
      "5057/5057 [==============================] - 4s 771us/step - loss: 0.0063\n",
      "Epoch 15/50\n",
      "5057/5057 [==============================] - 4s 727us/step - loss: 0.0063\n",
      "Epoch 16/50\n",
      "5057/5057 [==============================] - 5s 991us/step - loss: 0.0062\n",
      "Epoch 17/50\n",
      "5057/5057 [==============================] - 4s 808us/step - loss: 0.0062\n",
      "Epoch 18/50\n",
      "5057/5057 [==============================] - 4s 831us/step - loss: 0.0061\n",
      "Epoch 19/50\n",
      "5057/5057 [==============================] - 5s 896us/step - loss: 0.0061\n",
      "Epoch 20/50\n",
      "5057/5057 [==============================] - 4s 824us/step - loss: 0.0061\n",
      "Epoch 21/50\n",
      "5057/5057 [==============================] - 5s 1ms/step - loss: 0.0060\n",
      "Epoch 22/50\n",
      "5057/5057 [==============================] - 5s 908us/step - loss: 0.0060\n",
      "Epoch 23/50\n",
      "5057/5057 [==============================] - 4s 717us/step - loss: 0.0060\n",
      "Epoch 24/50\n",
      "5057/5057 [==============================] - 4s 710us/step - loss: 0.0059\n",
      "Epoch 25/50\n",
      "5057/5057 [==============================] - 4s 710us/step - loss: 0.0059\n",
      "Epoch 26/50\n",
      "5057/5057 [==============================] - 4s 739us/step - loss: 0.0059\n",
      "Epoch 27/50\n",
      "5057/5057 [==============================] - 4s 733us/step - loss: 0.0059\n",
      "Epoch 28/50\n",
      "5057/5057 [==============================] - 4s 770us/step - loss: 0.0059\n",
      "Epoch 29/50\n",
      "5057/5057 [==============================] - 4s 747us/step - loss: 0.0059\n",
      "Epoch 30/50\n",
      "5057/5057 [==============================] - 4s 745us/step - loss: 0.0058\n",
      "Epoch 31/50\n",
      "5057/5057 [==============================] - 4s 822us/step - loss: 0.0058\n",
      "Epoch 32/50\n",
      "5057/5057 [==============================] - 4s 744us/step - loss: 0.0058\n",
      "Epoch 33/50\n",
      "5057/5057 [==============================] - 4s 731us/step - loss: 0.0058\n",
      "Epoch 34/50\n",
      "5057/5057 [==============================] - 4s 735us/step - loss: 0.0058\n",
      "Epoch 35/50\n",
      "5057/5057 [==============================] - 4s 803us/step - loss: 0.0058\n",
      "Epoch 36/50\n",
      "5057/5057 [==============================] - 4s 796us/step - loss: 0.0058\n",
      "Epoch 37/50\n",
      "5057/5057 [==============================] - 4s 786us/step - loss: 0.0058\n",
      "Epoch 38/50\n",
      "5057/5057 [==============================] - 4s 850us/step - loss: 0.0057\n",
      "Epoch 39/50\n",
      "5057/5057 [==============================] - 4s 878us/step - loss: 0.0057\n",
      "Epoch 40/50\n",
      "5057/5057 [==============================] - 4s 806us/step - loss: 0.0057\n",
      "Epoch 41/50\n",
      "5057/5057 [==============================] - 4s 887us/step - loss: 0.0057\n",
      "Epoch 42/50\n",
      "5057/5057 [==============================] - 4s 801us/step - loss: 0.0057\n",
      "Epoch 43/50\n",
      "5057/5057 [==============================] - 4s 762us/step - loss: 0.0057\n",
      "Epoch 44/50\n",
      "5057/5057 [==============================] - 4s 795us/step - loss: 0.0057\n",
      "Epoch 45/50\n",
      "5057/5057 [==============================] - 4s 831us/step - loss: 0.0057\n",
      "Epoch 46/50\n",
      "5057/5057 [==============================] - 4s 788us/step - loss: 0.0056\n",
      "Epoch 47/50\n",
      "5057/5057 [==============================] - 4s 746us/step - loss: 0.0056\n",
      "Epoch 48/50\n",
      "5057/5057 [==============================] - 4s 745us/step - loss: 0.0056\n",
      "Epoch 49/50\n",
      "5057/5057 [==============================] - 4s 777us/step - loss: 0.0056\n",
      "Epoch 50/50\n",
      "5057/5057 [==============================] - 4s 729us/step - loss: 0.0056\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7f49e09f10>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    " \n",
    "# create ANN model\n",
    "model = Sequential()\n",
    " \n",
    "# Defining the Input layer and FIRST hidden layer, both are same!\n",
    "model.add(Dense(units=20, input_dim=10, kernel_initializer='normal', activation='relu'))\n",
    " \n",
    "# Defining the Second layer of the model\n",
    "# after the first layer we don't have to specify input_dim as keras configure it automatically\n",
    "model.add(Dense(units=10, kernel_initializer='normal', activation='tanh'))\n",
    "# Defining the Second layer of the model\n",
    "# after the first layer we don't have to specify input_dim as keras configure it automatically\n",
    "model.add(Dense(units=5, kernel_initializer='normal', activation='sigmoid'))\n",
    " \n",
    "# The output neuron is a single fully connected node \n",
    "# Since we will be predicting a single number\n",
    "model.add(Dense(1, kernel_initializer='normal'))\n",
    " \n",
    "# Compiling the model\n",
    "# Optimising the model using the \"Adam\"\n",
    "model.compile(loss='mean_squared_error', optimizer='adam',)\n",
    " \n",
    "# Fitting the ANN to the Training set\n",
    "# model.fit(X_train, y_train, validation_data=[X_test, y_test], \n",
    "#                   batch_size = 20, epochs = 50, verbose=1)\n",
    "# Fitting the ANN to the Training set\n",
    "model.fit(X_train, y_train, \n",
    "                  batch_size = 20, epochs = 50, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e1362b16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:49.796010Z",
     "start_time": "2023-02-28T20:41:54.418913Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 0s 525us/step\n",
      "1 Parameters: batch_size: 5 - epochs: 5 Accuracy: 101.9922568448381\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tk/dcwsd9m50476mx54d6mxfp_00000gn/T/ipykernel_12179/2004902786.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  SearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 0s 531us/step\n",
      "2 Parameters: batch_size: 5 - epochs: 10 Accuracy: 103.36522339807074\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tk/dcwsd9m50476mx54d6mxfp_00000gn/T/ipykernel_12179/2004902786.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  SearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 0s 544us/step\n",
      "3 Parameters: batch_size: 5 - epochs: 50 Accuracy: 101.37573995215129\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tk/dcwsd9m50476mx54d6mxfp_00000gn/T/ipykernel_12179/2004902786.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  SearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 0s 513us/step\n",
      "4 Parameters: batch_size: 10 - epochs: 5 Accuracy: 102.74078104927956\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tk/dcwsd9m50476mx54d6mxfp_00000gn/T/ipykernel_12179/2004902786.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  SearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 0s 512us/step\n",
      "5 Parameters: batch_size: 10 - epochs: 10 Accuracy: 102.97252269005324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tk/dcwsd9m50476mx54d6mxfp_00000gn/T/ipykernel_12179/2004902786.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  SearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 1s 614us/step\n",
      "6 Parameters: batch_size: 10 - epochs: 50 Accuracy: 100.59439014231604\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tk/dcwsd9m50476mx54d6mxfp_00000gn/T/ipykernel_12179/2004902786.py:43: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  SearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\n"
     ]
    }
   ],
   "source": [
    "# Defining a function to find the best parameters for MLP\n",
    "def FunctionFindBestParams(X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # Defining the list of hyper parameters to try\n",
    "    batch_size_list=[5, 10]\n",
    "    epoch_list  =   [5, 10, 50]\n",
    "    \n",
    "    import pandas as pd\n",
    "    SearchResultsData=pd.DataFrame(columns=['TrialNumber', 'Parameters', 'Accuracy'])\n",
    "    \n",
    "    # initializing the trials\n",
    "    TrialNumber=0\n",
    "    for batch_size_trial in batch_size_list:\n",
    "        for epochs_trial in epoch_list:\n",
    "            TrialNumber+=1\n",
    "            # create MLP model\n",
    "            model = Sequential()\n",
    "            # Defining the Input layer and FIRST hidden layer, both are same!\n",
    "            model.add(Dense(units=20, input_dim=10, kernel_initializer='normal', activation='relu'))\n",
    "\n",
    "            # Defining the Second layer of the model\n",
    "            # after the first layer we don't have to specify input_dim as keras configure it automatically\n",
    "            model.add(Dense(units=10, kernel_initializer='normal', activation='tanh'))\n",
    "            # Defining the Second layer of the model\n",
    "            # after the first layer we don't have to specify input_dim as keras configure it automatically\n",
    "            model.add(Dense(units=5, kernel_initializer='normal', activation='sigmoid'))\n",
    " \n",
    "            # The output neuron is a single fully connected node \n",
    "            # Since we will be predicting a single number\n",
    "            model.add(Dense(1, kernel_initializer='normal'))\n",
    " \n",
    "            # Compiling the model\n",
    "            model.compile(loss='mean_squared_error', optimizer='adam')\n",
    " \n",
    "            # Fitting the ANN to the Training set\n",
    "            model.fit(X_train, y_train ,batch_size = batch_size_trial, epochs = epochs_trial, verbose=0)\n",
    " \n",
    "            MAPE = np.mean(100 * (np.abs(y_test-model.predict(X_test))/y_test))\n",
    "            \n",
    "            # printing the results of the current iteration\n",
    "            print(TrialNumber, 'Parameters:','batch_size:', batch_size_trial,'-', 'epochs:',epochs_trial, 'Accuracy:', 100-MAPE)\n",
    "            \n",
    "            SearchResultsData=SearchResultsData.append(pd.DataFrame(data=[[TrialNumber, str(batch_size_trial)+'-'+str(epochs_trial), 100-MAPE]],\n",
    "                                                                    columns=['TrialNumber', 'Parameters', 'Accuracy'] ))\n",
    "    return(SearchResultsData)\n",
    " \n",
    " \n",
    "######################################################\n",
    "# Calling the function\n",
    "ResultsData=FunctionFindBestParams(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2af1b5",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "10d3aebd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:50.455066Z",
     "start_time": "2023-02-28T21:04:49.797964Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 1s 593us/step\n"
     ]
    }
   ],
   "source": [
    " \n",
    "# Generating Predictions on testing data\n",
    "Predictions=model.predict(X_test)\n",
    " \n",
    "# Scaling the predicted Price data back to original price scale\n",
    "Predictions=TargetVarScalerFit.inverse_transform(Predictions)\n",
    " \n",
    "# Scaling the y_test Price data back to original price scale\n",
    "y_test_orig=TargetVarScalerFit.inverse_transform(y_test)\n",
    " \n",
    "# Scaling the test data back to original scale\n",
    "Test_Data=PredictorScalerFit.inverse_transform(X_test)\n",
    "# Test_Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7519d597",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:51.082418Z",
     "start_time": "2023-02-28T21:04:50.457218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "791/791 [==============================] - 0s 587us/step\n"
     ]
    }
   ],
   "source": [
    "r2_no=r2_score(y_test_orig,Predictions)\n",
    "MAPE = np.mean(100 * (np.abs(y_test-model.predict(X_test))/y_test))\n",
    "Accuracy = 1 - MAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dc4e6573",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:51.089397Z",
     "start_time": "2023-02-28T21:04:51.084690Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score                : 0.994524562429022\n",
      "Mean Absolute Error     : -2.5933716852936115\n",
      "Mean Absolute Error     : 3.5933716852936115\n"
     ]
    }
   ],
   "source": [
    "print('R2 Score                :',r2_no)\n",
    "print('Mean Absolute Error     :',MAPE)\n",
    "print('Mean Absolute Error     :',Accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "78bd7fcd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:51.116336Z",
     "start_time": "2023-02-28T21:04:51.102413Z"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MD_mbsf</th>\n",
       "      <th>AHT90_3</th>\n",
       "      <th>DTCO_2</th>\n",
       "      <th>DTSM_1</th>\n",
       "      <th>ECGR_2</th>\n",
       "      <th>VCL_1</th>\n",
       "      <th>VPVS_1</th>\n",
       "      <th>PHIE_1</th>\n",
       "      <th>PEFZ_4</th>\n",
       "      <th>RHOZ_4</th>\n",
       "      <th>Pore_Pressure_Estimated_DT</th>\n",
       "      <th>Predicted_Pore_Pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1663.52005</td>\n",
       "      <td>1.85314</td>\n",
       "      <td>86.72252</td>\n",
       "      <td>144.59477</td>\n",
       "      <td>53.74742</td>\n",
       "      <td>0.08578</td>\n",
       "      <td>1.66750</td>\n",
       "      <td>0.24888</td>\n",
       "      <td>1.86336</td>\n",
       "      <td>2.23004</td>\n",
       "      <td>3.596584e+07</td>\n",
       "      <td>36078124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2088.32050</td>\n",
       "      <td>10.28945</td>\n",
       "      <td>75.43761</td>\n",
       "      <td>125.58761</td>\n",
       "      <td>52.63114</td>\n",
       "      <td>0.06706</td>\n",
       "      <td>1.66330</td>\n",
       "      <td>0.16060</td>\n",
       "      <td>2.02651</td>\n",
       "      <td>2.37019</td>\n",
       "      <td>3.753977e+07</td>\n",
       "      <td>37556204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.31998</td>\n",
       "      <td>31.72083</td>\n",
       "      <td>113.58000</td>\n",
       "      <td>234.28999</td>\n",
       "      <td>56.58872</td>\n",
       "      <td>0.09229</td>\n",
       "      <td>2.06000</td>\n",
       "      <td>0.32821</td>\n",
       "      <td>1.78964</td>\n",
       "      <td>2.09962</td>\n",
       "      <td>2.030027e+07</td>\n",
       "      <td>20473692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260.32010</td>\n",
       "      <td>6.20683</td>\n",
       "      <td>109.17750</td>\n",
       "      <td>225.24250</td>\n",
       "      <td>132.31265</td>\n",
       "      <td>0.35294</td>\n",
       "      <td>2.06625</td>\n",
       "      <td>0.23587</td>\n",
       "      <td>3.17544</td>\n",
       "      <td>2.25920</td>\n",
       "      <td>2.155073e+07</td>\n",
       "      <td>21310526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303.86990</td>\n",
       "      <td>12.10861</td>\n",
       "      <td>107.99688</td>\n",
       "      <td>214.57779</td>\n",
       "      <td>61.53796</td>\n",
       "      <td>0.12018</td>\n",
       "      <td>1.98334</td>\n",
       "      <td>0.30301</td>\n",
       "      <td>2.24791</td>\n",
       "      <td>2.17181</td>\n",
       "      <td>2.215362e+07</td>\n",
       "      <td>21832356.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MD_mbsf   AHT90_3     DTCO_2     DTSM_1     ECGR_2    VCL_1   VPVS_1  \\\n",
       "0  1663.52005   1.85314   86.72252  144.59477   53.74742  0.08578  1.66750   \n",
       "1  2088.32050  10.28945   75.43761  125.58761   52.63114  0.06706  1.66330   \n",
       "2   100.31998  31.72083  113.58000  234.28999   56.58872  0.09229  2.06000   \n",
       "3   260.32010   6.20683  109.17750  225.24250  132.31265  0.35294  2.06625   \n",
       "4   303.86990  12.10861  107.99688  214.57779   61.53796  0.12018  1.98334   \n",
       "\n",
       "    PHIE_1   PEFZ_4   RHOZ_4  Pore_Pressure_Estimated_DT  \\\n",
       "0  0.24888  1.86336  2.23004                3.596584e+07   \n",
       "1  0.16060  2.02651  2.37019                3.753977e+07   \n",
       "2  0.32821  1.78964  2.09962                2.030027e+07   \n",
       "3  0.23587  3.17544  2.25920                2.155073e+07   \n",
       "4  0.30301  2.24791  2.17181                2.215362e+07   \n",
       "\n",
       "   Predicted_Pore_Pressure  \n",
       "0               36078124.0  \n",
       "1               37556204.0  \n",
       "2               20473692.0  \n",
       "3               21310526.0  \n",
       "4               21832356.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_independent_columns = ['MD_mbsf','AHT90_3','DTCO_2','DTSM_1',\n",
    "                          'ECGR_2','VCL_1','VPVS_1','PHIE_1','PEFZ_4','RHOZ_4']\n",
    "TestingData=pd.DataFrame(data=Test_Data, columns=X_independent_columns)\n",
    "TestingData['Pore_Pressure_Estimated_DT']=y_test_orig\n",
    "TestingData['Predicted_Pore_Pressure']=Predictions\n",
    "TestingData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "91cf3aaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:51.277945Z",
     "start_time": "2023-02-28T21:04:51.118417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "    fig.rubberband_canvas.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       " * https://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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f4b65dc40>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(sharey=True,figsize=(5,5))\n",
    "\n",
    "\n",
    "# ax.scatter(df_new_ML['Pore_Pressure_Estimated_DT']/1e6, \n",
    "#            df_new_ML['MD_mbsf'], c='b', s=1,label='Orig')\n",
    "ax.scatter(TestingData['Pore_Pressure_Estimated_DT']/1e6,\n",
    "           TestingData['Predicted_Pore_Pressure']/1e6, \n",
    "           c='r', s=1,label='Pred')\n",
    "ax.set_ylabel('Predicted Pore Pressure [MPa]')\n",
    "ax.set_xlabel('Pore Pressure from Eaton [MPa]')\n",
    "\n",
    "# ax.set_y\n",
    "# ax.legend()\n",
    "a, b = np.polyfit(TestingData['Pore_Pressure_Estimated_DT']/1e6,\n",
    "           TestingData['Predicted_Pore_Pressure']/1e6, 1)\n",
    "\n",
    "#add line of best fit to plot\n",
    "plt.plot(TestingData['Pore_Pressure_Estimated_DT']/1e6, a*TestingData['Pore_Pressure_Estimated_DT']/1e6+b) \n",
    "# ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e8cc7b",
   "metadata": {},
   "source": [
    "# combine results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2315d889",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:51.299370Z",
     "start_time": "2023-02-28T21:04:51.280476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MD_mbsf</th>\n",
       "      <th>AHT90_3</th>\n",
       "      <th>DTCO_2</th>\n",
       "      <th>DTSM_1</th>\n",
       "      <th>ECGR_2</th>\n",
       "      <th>VCL_1</th>\n",
       "      <th>VPVS_1</th>\n",
       "      <th>PHIE_1</th>\n",
       "      <th>PEFZ_4</th>\n",
       "      <th>RHOZ_4</th>\n",
       "      <th>Pore_Pressure_Estimated_DT</th>\n",
       "      <th>Predicted_Pore_Pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1663.52005</td>\n",
       "      <td>1.85314</td>\n",
       "      <td>86.72252</td>\n",
       "      <td>144.59477</td>\n",
       "      <td>53.74742</td>\n",
       "      <td>0.08578</td>\n",
       "      <td>1.66750</td>\n",
       "      <td>0.22930</td>\n",
       "      <td>1.86336</td>\n",
       "      <td>2.23004</td>\n",
       "      <td>3.596584e+07</td>\n",
       "      <td>3.612361e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2088.32050</td>\n",
       "      <td>10.28945</td>\n",
       "      <td>75.43761</td>\n",
       "      <td>125.58761</td>\n",
       "      <td>52.63114</td>\n",
       "      <td>0.06706</td>\n",
       "      <td>1.66330</td>\n",
       "      <td>0.14402</td>\n",
       "      <td>2.02651</td>\n",
       "      <td>2.37019</td>\n",
       "      <td>3.753977e+07</td>\n",
       "      <td>3.664451e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.31998</td>\n",
       "      <td>31.72083</td>\n",
       "      <td>113.58000</td>\n",
       "      <td>234.28999</td>\n",
       "      <td>56.58872</td>\n",
       "      <td>0.09229</td>\n",
       "      <td>2.06000</td>\n",
       "      <td>0.31351</td>\n",
       "      <td>1.78964</td>\n",
       "      <td>2.09962</td>\n",
       "      <td>2.030027e+07</td>\n",
       "      <td>2.060617e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260.32010</td>\n",
       "      <td>6.20683</td>\n",
       "      <td>109.17750</td>\n",
       "      <td>225.24250</td>\n",
       "      <td>132.31265</td>\n",
       "      <td>0.35294</td>\n",
       "      <td>2.06625</td>\n",
       "      <td>0.17721</td>\n",
       "      <td>3.17544</td>\n",
       "      <td>2.25920</td>\n",
       "      <td>2.155073e+07</td>\n",
       "      <td>2.188703e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>303.86990</td>\n",
       "      <td>12.10861</td>\n",
       "      <td>107.99688</td>\n",
       "      <td>214.57779</td>\n",
       "      <td>61.53796</td>\n",
       "      <td>0.12018</td>\n",
       "      <td>1.98334</td>\n",
       "      <td>0.28281</td>\n",
       "      <td>2.24791</td>\n",
       "      <td>2.17181</td>\n",
       "      <td>2.215362e+07</td>\n",
       "      <td>2.188703e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MD_mbsf   AHT90_3     DTCO_2     DTSM_1     ECGR_2    VCL_1   VPVS_1  \\\n",
       "0  1663.52005   1.85314   86.72252  144.59477   53.74742  0.08578  1.66750   \n",
       "1  2088.32050  10.28945   75.43761  125.58761   52.63114  0.06706  1.66330   \n",
       "2   100.31998  31.72083  113.58000  234.28999   56.58872  0.09229  2.06000   \n",
       "3   260.32010   6.20683  109.17750  225.24250  132.31265  0.35294  2.06625   \n",
       "4   303.86990  12.10861  107.99688  214.57779   61.53796  0.12018  1.98334   \n",
       "\n",
       "    PHIE_1   PEFZ_4   RHOZ_4  Pore_Pressure_Estimated_DT  \\\n",
       "0  0.22930  1.86336  2.23004                3.596584e+07   \n",
       "1  0.14402  2.02651  2.37019                3.753977e+07   \n",
       "2  0.31351  1.78964  2.09962                2.030027e+07   \n",
       "3  0.17721  3.17544  2.25920                2.155073e+07   \n",
       "4  0.28281  2.24791  2.17181                2.215362e+07   \n",
       "\n",
       "   Predicted_Pore_Pressure  \n",
       "0             3.612361e+07  \n",
       "1             3.664451e+07  \n",
       "2             2.060617e+07  \n",
       "3             2.188703e+07  \n",
       "4             2.188703e+07  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR_Testing_Data= pickle.load(open(\"0a_05_RFR_Testing.pickle\",\"rb\"))\n",
    "RFR_Testing_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60d7d789",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:51.372954Z",
     "start_time": "2023-02-28T21:04:51.301883Z"
    }
   },
   "outputs": [
    {
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       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "    fig.rubberband_canvas.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       " * https://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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
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   "source": [
    "fig,ax=plt.subplots(sharey=True,figsize=(6,9.5))\n",
    "\n",
    "\n",
    "# ax.scatter(df_new_ML['Pore_Pressure_Estimated_DT']/1e6, \n",
    "#            df_new_ML['MD_mbsf'], c='b', s=1,label='Orig')\n",
    "ax.scatter(TestingData['Pore_Pressure_Estimated_DT']/1e6,\n",
    "           TestingData['MD_mbsf'], \n",
    "           c='gray', s=5,label='Eaton Pore Pressure')\n",
    "\n",
    "ax.scatter(TestingData['Predicted_Pore_Pressure']/1e6,\n",
    "           TestingData['MD_mbsf'], \n",
    "           c='r', s=1,label='MLP Pore Pressure')\n",
    "ax.scatter(RFR_Testing_Data['Predicted_Pore_Pressure']/1e6,\n",
    "           RFR_Testing_Data['MD_mbsf'], \n",
    "           c='b', s=1,label='RFR Pore Pressure')\n",
    "\n",
    "ax.scatter(df_drilling_12b['Hydrostatic_Pressure_at_bit']/1e6,\n",
    "           df_drilling_12b['MD_mbsf'], \n",
    "           c='g', s=1,label='Hydrostatic Pressure')\n",
    "\n",
    "ax.scatter(df_drilling_12b['Lithostatic_Pressure_at_bit']/1e6,\n",
    "           df_drilling_12b['MD_mbsf'], \n",
    "           c='brown', s=1,label='Lithostatic Pressure')\n",
    "\n",
    "\n",
    "ax.set_ylabel('Measured Depth [mbsf]',fontsize='large')\n",
    "ax.set_xlabel('Pressure [MPa]',fontsize='large')\n",
    "\n",
    "ax.legend(fontsize='large',loc='upper right',  markerscale=2, scatterpoints=3)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30d6845",
   "metadata": {},
   "source": [
    "# EXPORT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cd80e112",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-02-28T21:04:51.378600Z",
     "start_time": "2023-02-28T21:04:51.374988Z"
    }
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "883675ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}