Simple_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a920652",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.append('scripts') # Adds higher directory to python modules path.\n",
    "\n",
    "\n",
    "from TH_post import *\n",
    "from TH_proc import *\n",
    "from MeshMaker import *\n",
    "from Aux_Functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc40b5b1",
   "metadata": {},
   "source": [
    "# Auxiliar Functions\n",
    "\n",
    "Claculate a hydrate stability curve for different sailinities as in TOUGH+H "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "df1f75d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'pressure [Pa]')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "plt.style.use('seaborn-white')\n",
    "rcParams['figure.dpi'] = 300\n",
    "rcParams['font.size'] = 8\n",
    "rcParams['figure.figsize'] = [5,4]\n",
    "\n",
    "\n",
    "T = np.linspace(0,25, 1001)\n",
    "P = v_Equil_P(T)\n",
    "\n",
    "NaCl_1 = 1/100\n",
    "NaCl_2 = 3.5/100\n",
    "\n",
    "Tshift_1 = Tshift_NaCl(NaCl_1)\n",
    "Tshift_2 = Tshift_NaCl(NaCl_2)\n",
    "\n",
    "PT_fig, PT_ax = plt.subplots()\n",
    "PT_ax.plot(T,P, label = '$CH_4$(h) in fresh water')\n",
    "PT_ax.plot(T-Tshift_1,P, label = '$CH_4$(h) in {:.1f}wt.% NaCl brine'.format(NaCl_1*100))\n",
    "PT_ax.plot(T-Tshift_2,P, label = '$CH_4$(h) in {:.1f}wt.% NaCl brine'.format(NaCl_2*100))\n",
    "PT_ax.legend()\n",
    "\n",
    "PT_ax.set_xlabel('temperature [$\\degree$ C]')\n",
    "PT_ax.set_ylabel('pressure [Pa]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec76b5e6",
   "metadata": {},
   "source": [
    "# Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d33792",
   "metadata": {},
   "source": [
    "#### Read and inspect input file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7be1b5",
   "metadata": {},
   "source": [
    "Read predefined Input file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "00aec06d",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_in = r'simulation_files/sample_problem_Test1P/Test_1P.in'\n",
    "ip_data = read_TH_data(file_in)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56746d4d",
   "metadata": {},
   "source": [
    "Inspect the ELEME section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dba09f58",
   "metadata": {},
   "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>raw</th>\n",
       "      <th>ElName</th>\n",
       "      <th>NSEQ</th>\n",
       "      <th>NADD</th>\n",
       "      <th>MA12</th>\n",
       "      <th>elem_vol</th>\n",
       "      <th>elem_aht</th>\n",
       "      <th>elem_pm</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>elem_activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A00 1              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A00 2              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A00 3              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A00 4              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A00 5              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A00 6              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A00 7              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 7</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A00 8              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 8</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A00 9              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A00 9</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A0010              10.1000E+000.4000E+00      ...</td>\n",
       "      <td>A0010</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9510</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A00 0              10.1000E-020.4000E-02      ...</td>\n",
       "      <td>A00 0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  raw ElName   NSEQ   NADD  \\\n",
       "0   A00 1              10.1000E+000.4000E+00      ...  A00 1                 \n",
       "1   A00 2              10.1000E+000.4000E+00      ...  A00 2                 \n",
       "2   A00 3              10.1000E+000.4000E+00      ...  A00 3                 \n",
       "3   A00 4              10.1000E+000.4000E+00      ...  A00 4                 \n",
       "4   A00 5              10.1000E+000.4000E+00      ...  A00 5                 \n",
       "5   A00 6              10.1000E+000.4000E+00      ...  A00 6                 \n",
       "6   A00 7              10.1000E+000.4000E+00      ...  A00 7                 \n",
       "7   A00 8              10.1000E+000.4000E+00      ...  A00 8                 \n",
       "8   A00 9              10.1000E+000.4000E+00      ...  A00 9                 \n",
       "9   A0010              10.1000E+000.4000E+00      ...  A0010                 \n",
       "10  A00 0              10.1000E-020.4000E-02      ...  A00 0                 \n",
       "\n",
       "     MA12  elem_vol  elem_aht  elem_pm       X    Y    Z elem_activity  \n",
       "0       1     0.100     0.400      NaN  0.0510 -0.5 -0.5                \n",
       "1       1     0.100     0.400      NaN  0.1510 -0.5 -0.5                \n",
       "2       1     0.100     0.400      NaN  0.2510 -0.5 -0.5                \n",
       "3       1     0.100     0.400      NaN  0.3510 -0.5 -0.5                \n",
       "4       1     0.100     0.400      NaN  0.4510 -0.5 -0.5                \n",
       "5       1     0.100     0.400      NaN  0.5510 -0.5 -0.5                \n",
       "6       1     0.100     0.400      NaN  0.6510 -0.5 -0.5                \n",
       "7       1     0.100     0.400      NaN  0.7510 -0.5 -0.5                \n",
       "8       1     0.100     0.400      NaN  0.8510 -0.5 -0.5                \n",
       "9       1     0.100     0.400      NaN  0.9510 -0.5 -0.5                \n",
       "10      1     0.001     0.004      NaN  0.0005 -0.5 -0.5             I  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ip_data.ELEME.processed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13491b34",
   "metadata": {},
   "source": [
    "Inspect the ROCKS section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f4a4d38",
   "metadata": {},
   "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>Name</th>\n",
       "      <th>NAD</th>\n",
       "      <th>DensG</th>\n",
       "      <th>Poros</th>\n",
       "      <th>Perm1</th>\n",
       "      <th>Perm2</th>\n",
       "      <th>Perm3</th>\n",
       "      <th>KThrW</th>\n",
       "      <th>SpcHt</th>\n",
       "      <th>PoMedRGrain</th>\n",
       "      <th>...</th>\n",
       "      <th>PhiCoeff2</th>\n",
       "      <th>PhiCoeff3</th>\n",
       "      <th>PhiCoeff4</th>\n",
       "      <th>PhiCoeff5</th>\n",
       "      <th>PhiCoeff6</th>\n",
       "      <th>LoComp</th>\n",
       "      <th>SatAtLoComp</th>\n",
       "      <th>HiComp</th>\n",
       "      <th>SatAtHiComp</th>\n",
       "      <th>DeltaSat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DIRT1</td>\n",
       "      <td>1</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.960000e-13</td>\n",
       "      <td>2.960000e-13</td>\n",
       "      <td>2.960000e-13</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BOUND</td>\n",
       "      <td>0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name NAD   DensG  Poros         Perm1         Perm2         Perm3  KThrW  \\\n",
       "0  DIRT1   1  2600.0    0.3  2.960000e-13  2.960000e-13  2.960000e-13    3.1   \n",
       "1  BOUND   0  2600.0    0.0  0.000000e+00  0.000000e+00  0.000000e+00  100.0   \n",
       "\n",
       "    SpcHt  PoMedRGrain  ...  PhiCoeff2  PhiCoeff3  PhiCoeff4  PhiCoeff5  \\\n",
       "0  1000.0          NaN  ...        NaN        NaN        NaN        NaN   \n",
       "1  1000.0          NaN  ...        NaN        NaN        NaN        NaN   \n",
       "\n",
       "   PhiCoeff6  LoComp  SatAtLoComp  HiComp  SatAtHiComp  DeltaSat  \n",
       "0        NaN     NaN          NaN     NaN          NaN       NaN  \n",
       "1        NaN     NaN          NaN     NaN          NaN       NaN  \n",
       "\n",
       "[2 rows x 50 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ip_data.ROCKS.processed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dfa6cfc",
   "metadata": {},
   "source": [
    "#### Edit the input file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4dc35d",
   "metadata": {},
   "source": [
    "Change porosity of the first rock type. And porosity model MOP(8).\n",
    "\n",
    "Use `write_TH_data` function to write updated dataframe into file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d391046",
   "metadata": {},
   "outputs": [],
   "source": [
    "ip_data.ROCKS.processed.loc[0,'Poros'] = 0.15\n",
    "ip_data.ROCKS.modified = True\n",
    "\n",
    "ip_data.PARAM.processed.P_1.MOP_items[8] = str(2)\n",
    "ip_data.PARAM.modified = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ded458a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating with function MEMORY\n",
      "Updating with function ROCKS\n",
      "Updating with function PARAM\n",
      "\n",
      "ŇTest_1PÓ ... Hydrate dissociation - depressurization\n",
      "MEMORY\n",
      "'HYDRATE-EQUILIBRIUM'\n",
      "    2    3    4  .FALSE.                        ! NumCom, NumEqu, NumPhases, binary_diffusion\n",
      "'Cartesian'     15    15     5 .FALSE. .FALSE.  ! coordinate_system, Max_NumElem, Max_NumConx, ElemNameLength, active_conx_only, boundaries_in_matrix\n",
      "   2                                            ! MaxNum_SS\n",
      "   2                                            ! MaxNum_Media\n",
      ".FALSE. .FALSE. .FALSE. 'Saturation'            ! element_by_element_properties, porosity_perm_dependence, scaled_capillary_pressure, Option_tortuosity_CompuMethod\n",
      ".FALSE. 'Continuous'                            ! coupled_geochemistry, property_update [= 'Continuous', 'Iteration', 'Timestep']\n",
      ".FALSE. ' ' 'Continuous'    0                   ! coupled_geomechanics, geomechanical_code_name, property_update, num_geomech_param\n",
      "ROCKS----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8\n",
      "DIRT1    1 2.600E+03 1.500E-01 2.960E-13 2.960E-13 2.960E-13 3.100E+00 1.000E+03          \n",
      " 1.000E-08           5.000E-01                               5.000E-02 3.000E+00                              \n",
      "BOUND    0 2.600E+03 0.000E+00 0.000E+00 0.000E+00 0.000E+00 1.000E+02 1.000E+03          \n",
      "\n",
      "RPCAP----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8\n",
      "    9           .120       .02        3.\n",
      "    7        0.45000   1.10e-1   8.0E-05      1.E6     1.0e0\n",
      "\n",
      "HYDRATE--1----*----2----*----3----*----4----*----5----*----6----*----7----*----8\n",
      "     1                       ! HCom%NCom\n",
      "'CH4'  6.0d0 1.00d00         ! Name, hydration number, mole fraction in composite hydrate\n",
      "     1                       ! Number of coefficients in thermal conductivity polynomial\n",
      "  4.5e-1                     ! Coefficients in the thermal conductivity polynomial\n",
      "     1                       ! Number of coefficients in the specific heat polynomial\n",
      "  2.1e03                     ! Coefficients in the specific heat polynomial\n",
      "     0                       ! Number of coefficients in density polynomial\n",
      ".FALSE.                      ! inhibitor_present,T_MaxOff,C_MaxOff,MW_Inhib,D_Inhib,H_InhSol,DifCo_Inh  \n",
      "2                            ! F_EqOption\n",
      "'EQUILIBRIUM'                ! Type of dissociation\n",
      "START----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8\n",
      "----*----1 MOP: 123456789*123456789*1234 ---*----5----*----6----*----7----*----8\n",
      "PARAM----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8----*----9----*---10\n",
      "   3 080     002100030020020000400803000   0.00E-5                              \n",
      "            8.640E+5    1.0e00   8.64E+6              9.8060                    \n",
      "     1.E-5     1.E00                                  1.0e-8            AqH                                   \n",
      "             6.000e6              5.0e-1                7.20                                                            \n",
      "\n",
      "\n",
      "ELEME\n",
      "A00 1              10.1000E+000.4000E+00          0.5100E-01-.5000E+00-.5000E+00\n",
      "A00 2              10.1000E+000.4000E+00          0.1510E+00-.5000E+00-.5000E+00\n",
      "A00 3              10.1000E+000.4000E+00          0.2510E+00-.5000E+00-.5000E+00\n",
      "A00 4              10.1000E+000.4000E+00          0.3510E+00-.5000E+00-.5000E+00\n",
      "A00 5              10.1000E+000.4000E+00          0.4510E+00-.5000E+00-.5000E+00\n",
      "A00 6              10.1000E+000.4000E+00          0.5510E+00-.5000E+00-.5000E+00\n",
      "A00 7              10.1000E+000.4000E+00          0.6510E+00-.5000E+00-.5000E+00\n",
      "A00 8              10.1000E+000.4000E+00          0.7510E+00-.5000E+00-.5000E+00\n",
      "A00 9              10.1000E+000.4000E+00          0.8510E+00-.5000E+00-.5000E+00\n",
      "A0010              10.1000E+000.4000E+00          0.9510E+00-.5000E+00-.5000E+00\n",
      "A00 0              10.1000E-020.4000E-02          0.5000E-03-.5000E+00-.5000E+00 I    \n",
      "     \n",
      "CONNE\n",
      "A00 0A00 1                   10.5000E-030.5000E-010.1000E+01\n",
      "A00 1A00 2                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 2A00 3                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 3A00 4                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 4A00 5                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 5A00 6                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 6A00 7                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 7A00 8                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 8A00 9                   10.5000E-010.5000E-010.1000E+01\n",
      "A00 9A0010                   10.5000E-010.5000E-010.1000E+01\n",
      "A0010A0011                   10.5000E-010.5000E-030.1000E+01\n",
      "\n",
      "GENER \n",
      "\n",
      "\n",
      "INCON----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8\n",
      "A00 0           0.30000000E+00  Aqu\n",
      "             2.700e6              00.0e0                1.00                 \n",
      "\n",
      "\n",
      "INTERFACES----*----2----*----3----*----4----*----5----*----6----*----7----*----8 \n",
      "&Interface_General_Info  number_of_interfaces = 1 /\n",
      "   &Individual_Interface_Specifics  interface_name         = 'Int_1',\n",
      "                                    number_of_surfaces     = 1,\n",
      "                                    sign_of_flow_direction = 'DIR'\n",
      "                                    /\n",
      "      &Surface_Specifics  definition_mode       = 'NameList',   \n",
      "                            number_of_connections = 1,             ! Range (min and max) along the first coordinate axis\n",
      "                            format_to_read_data = '*',\n",
      "                            /\n",
      "'A00 0A00 1'\n",
      "\n",
      "\n",
      "ENDCY----1----*----2----*----3----*----4----*----5----*----6----*----7----*----8\n"
     ]
    }
   ],
   "source": [
    "new_ip_data = update_TH_data(ip_data)\n",
    "print()\n",
    "\n",
    "list_data=[]\n",
    "\n",
    "for key in new_ip_data:\n",
    "    if new_ip_data[key]['active']:\n",
    "        list_data+=new_ip_data[key]['raw']\n",
    "\n",
    "str_data='\\n'.join(list_data)\n",
    "\n",
    "print(str_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7aa67a8",
   "metadata": {},
   "source": [
    "# Post-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a234695",
   "metadata": {},
   "source": [
    "Read output and store it into a pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e972393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read pickle data\n"
     ]
    },
    {
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       "      <td>-0.5</td>\n",
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       "      <td>26.999529</td>\n",
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       "      <th>864000.0</th>\n",
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       "      <td>0.9510</td>\n",
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       "      <td>-0.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2.999220e+02</td>\n",
       "      <td>0.29028</td>\n",
       "      <td>-0.065388</td>\n",
       "      <td>26.999513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ElName       x    y    z   I  J  K     dx   dy   dz  ...     S_gas  \\\n",
       "time                                                          ...             \n",
       "0.0       A00 0  0.0005 -0.5 -0.5   0  0  0  0.001 -1.0 -1.0  ...  0.000000   \n",
       "0.0       A00 1  0.0510 -0.5 -0.5   1  0  0  0.100 -1.0 -1.0  ...  0.000000   \n",
       "0.0       A00 2  0.1510 -0.5 -0.5   2  0  0  0.100 -1.0 -1.0  ...  0.000000   \n",
       "0.0       A00 3  0.2510 -0.5 -0.5   3  0  0  0.100 -1.0 -1.0  ...  0.000000   \n",
       "0.0       A00 4  0.3510 -0.5 -0.5   4  0  0  0.100 -1.0 -1.0  ...  0.000000   \n",
       "...         ...     ...  ...  ...  .. .. ..    ...  ...  ...  ...       ...   \n",
       "864000.0  A00 6  0.5510 -0.5 -0.5   6  0  0  0.100 -1.0 -1.0  ...  0.063242   \n",
       "864000.0  A00 7  0.6510 -0.5 -0.5   7  0  0  0.100 -1.0 -1.0  ...  0.063734   \n",
       "864000.0  A00 8  0.7510 -0.5 -0.5   8  0  0  0.100 -1.0 -1.0  ...  0.064106   \n",
       "864000.0  A00 9  0.8510 -0.5 -0.5   9  0  0  0.100 -1.0 -1.0  ...  0.064358   \n",
       "864000.0  A0010  0.9510 -0.5 -0.5  10  0  0  0.100 -1.0 -1.0  ...  0.064494   \n",
       "\n",
       "          S_icd  X_inh      k_rg      k_rw   k_adj_F      perm_abs  porosity  \\\n",
       "time                                                                           \n",
       "0.0         0.0    NaN       NaN       NaN       NaN  3.039750e+14   0.30000   \n",
       "0.0         0.0    NaN       NaN       NaN       NaN  3.039750e+14   0.30000   \n",
       "0.0         0.0    NaN       NaN       NaN       NaN  3.039750e+14   0.30000   \n",
       "0.0         0.0    NaN       NaN       NaN       NaN  3.039750e+14   0.30000   \n",
       "0.0         0.0    NaN       NaN       NaN       NaN  3.039750e+14   0.30000   \n",
       "...         ...    ...       ...       ...       ...           ...       ...   \n",
       "864000.0    0.0    0.0  0.000719  0.699450  0.239276  2.999220e+02   0.29028   \n",
       "864000.0    0.0    0.0  0.000746  0.696869  0.237780  2.999220e+02   0.29028   \n",
       "864000.0    0.0    0.0  0.000766  0.694941  0.236723  2.999220e+02   0.29028   \n",
       "864000.0    0.0    0.0  0.000780  0.693659  0.236080  2.999220e+02   0.29028   \n",
       "864000.0    0.0    0.0  0.000787  0.693017  0.235866  2.999220e+02   0.29028   \n",
       "\n",
       "             P_cap      P_aqu  \n",
       "time                           \n",
       "0.0            NaN        NaN  \n",
       "0.0            NaN        NaN  \n",
       "0.0            NaN        NaN  \n",
       "0.0            NaN        NaN  \n",
       "0.0            NaN        NaN  \n",
       "...            ...        ...  \n",
       "864000.0 -0.064352  26.999672  \n",
       "864000.0 -0.064767  26.999610  \n",
       "864000.0 -0.065078  26.999562  \n",
       "864000.0 -0.065285  26.999529  \n",
       "864000.0 -0.065388  26.999513  \n",
       "\n",
       "[198 rows x 25 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_in = r'simulation_files/sample_problem_Test1P/Test_1P.in'\n",
    "\n",
    "data = get_output(file_in, write=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a33742b7",
   "metadata": {},
   "source": [
    "Read Time_Series and Hydrate_Status files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c567f5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 subdomain(s)\n",
      "0 interface(s)\n",
      "1 Hydrate_Status file\n",
      "1 source/sink group(s)\n"
     ]
    }
   ],
   "source": [
    "file_in = r'simulation_files/sample_problem_Test2D/Test_2D.in'\n",
    "\n",
    "sds, itf, hyd_st, ss_gr = read_Time_Series(file_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1150c488",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000044</td>\n",
       "      <td>0.074437</td>\n",
       "      <td>0.744372</td>\n",
       "      <td>0.074251</td>\n",
       "      <td>0.742508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21073.7</td>\n",
       "      <td>0.025558</td>\n",
       "      <td>0.255584</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.001864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.47222E-04</td>\n",
       "      <td>0.025502</td>\n",
       "      <td>0.765662</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.074498</td>\n",
       "      <td>2.234340</td>\n",
       "      <td>0.074313</td>\n",
       "      <td>2.228760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21099.9</td>\n",
       "      <td>0.025497</td>\n",
       "      <td>0.765531</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.005579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.10185E-04</td>\n",
       "      <td>0.025402</td>\n",
       "      <td>1.781750</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.074598</td>\n",
       "      <td>5.218250</td>\n",
       "      <td>0.074413</td>\n",
       "      <td>5.205290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21130.0</td>\n",
       "      <td>0.025398</td>\n",
       "      <td>1.781450</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.012959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.73611E-03</td>\n",
       "      <td>0.025262</td>\n",
       "      <td>3.802740</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.074738</td>\n",
       "      <td>11.197300</td>\n",
       "      <td>0.074555</td>\n",
       "      <td>11.169700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21139.1</td>\n",
       "      <td>0.025258</td>\n",
       "      <td>3.802090</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.027556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.58796E-03</td>\n",
       "      <td>0.025109</td>\n",
       "      <td>7.820140</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.074891</td>\n",
       "      <td>23.179900</td>\n",
       "      <td>0.074712</td>\n",
       "      <td>23.123600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21065.6</td>\n",
       "      <td>0.025104</td>\n",
       "      <td>7.818780</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.056268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.29167E-03</td>\n",
       "      <td>0.024993</td>\n",
       "      <td>15.818000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.002785</td>\n",
       "      <td>0.075007</td>\n",
       "      <td>47.182000</td>\n",
       "      <td>0.074831</td>\n",
       "      <td>47.069600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20832.9</td>\n",
       "      <td>0.024989</td>\n",
       "      <td>15.815200</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.112419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.46991E-02</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>31.785400</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.005665</td>\n",
       "      <td>0.075051</td>\n",
       "      <td>95.214600</td>\n",
       "      <td>0.074880</td>\n",
       "      <td>94.992900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20412.5</td>\n",
       "      <td>0.024945</td>\n",
       "      <td>31.779700</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.221723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.95139E-02</td>\n",
       "      <td>0.024957</td>\n",
       "      <td>63.730300</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.011514</td>\n",
       "      <td>0.075043</td>\n",
       "      <td>191.270000</td>\n",
       "      <td>0.074877</td>\n",
       "      <td>190.836000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19852.7</td>\n",
       "      <td>0.024952</td>\n",
       "      <td>63.718800</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.433850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.91435E-02</td>\n",
       "      <td>0.024977</td>\n",
       "      <td>127.672000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.023408</td>\n",
       "      <td>0.075023</td>\n",
       "      <td>383.328000</td>\n",
       "      <td>0.074862</td>\n",
       "      <td>382.483000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19221.5</td>\n",
       "      <td>0.024972</td>\n",
       "      <td>127.648000</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.844918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.18403E-01</td>\n",
       "      <td>0.024993</td>\n",
       "      <td>255.635000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.047592</td>\n",
       "      <td>0.075007</td>\n",
       "      <td>767.365000</td>\n",
       "      <td>0.074852</td>\n",
       "      <td>765.724000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18548.2</td>\n",
       "      <td>0.024988</td>\n",
       "      <td>255.587000</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>1.641010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.36921E-01</td>\n",
       "      <td>0.025006</td>\n",
       "      <td>511.701000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.096755</td>\n",
       "      <td>0.074994</td>\n",
       "      <td>1535.300000</td>\n",
       "      <td>0.074843</td>\n",
       "      <td>1532.120000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17836.4</td>\n",
       "      <td>0.025002</td>\n",
       "      <td>511.604000</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>3.182440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.73958E-01</td>\n",
       "      <td>0.025020</td>\n",
       "      <td>1024.120000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.196661</td>\n",
       "      <td>0.074980</td>\n",
       "      <td>3070.880000</td>\n",
       "      <td>0.074834</td>\n",
       "      <td>3064.720000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17092.0</td>\n",
       "      <td>0.025015</td>\n",
       "      <td>1023.920000</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>6.167690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.48032E-01</td>\n",
       "      <td>0.025035</td>\n",
       "      <td>2049.540000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.399550</td>\n",
       "      <td>0.074965</td>\n",
       "      <td>6141.460000</td>\n",
       "      <td>0.074824</td>\n",
       "      <td>6129.510000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16331.9</td>\n",
       "      <td>0.025030</td>\n",
       "      <td>2049.140000</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>11.954400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.89618E+00</td>\n",
       "      <td>0.025048</td>\n",
       "      <td>4101.490000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.811114</td>\n",
       "      <td>0.074952</td>\n",
       "      <td>12281.500000</td>\n",
       "      <td>0.074815</td>\n",
       "      <td>12258.300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15584.7</td>\n",
       "      <td>0.025043</td>\n",
       "      <td>4100.680000</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>23.192600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.79248E+00</td>\n",
       "      <td>0.025053</td>\n",
       "      <td>8206.110000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1.644790</td>\n",
       "      <td>0.074947</td>\n",
       "      <td>24560.900000</td>\n",
       "      <td>0.074814</td>\n",
       "      <td>24515.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14864.6</td>\n",
       "      <td>0.025048</td>\n",
       "      <td>8204.470000</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>45.061300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.58507E+00</td>\n",
       "      <td>0.025018</td>\n",
       "      <td>16404.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>3.333400</td>\n",
       "      <td>0.074982</td>\n",
       "      <td>49131.000000</td>\n",
       "      <td>0.074853</td>\n",
       "      <td>49043.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14049.0</td>\n",
       "      <td>0.025013</td>\n",
       "      <td>16400.700000</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>87.478500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.00000E+01</td>\n",
       "      <td>0.024999</td>\n",
       "      <td>21620.100000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4.416170</td>\n",
       "      <td>0.075001</td>\n",
       "      <td>64779.900000</td>\n",
       "      <td>0.074873</td>\n",
       "      <td>64665.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13575.7</td>\n",
       "      <td>0.024994</td>\n",
       "      <td>21615.700000</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>114.029000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>/ 1.000E+00/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time:days  Q_G:kg/s       M_G(kg)  Q_Gw:kg/s   M_Gw:kg  Q_A:kg/s  \\\n",
       "0    1.15741E-04  0.025563      0.255628   0.000004  0.000044  0.074437   \n",
       "1    3.47222E-04  0.025502      0.765662   0.000004  0.000132  0.074498   \n",
       "2    8.10185E-04  0.025402      1.781750   0.000004  0.000307  0.074598   \n",
       "3    1.73611E-03  0.025262      3.802740   0.000004  0.000658  0.074738   \n",
       "4    3.58796E-03  0.025109      7.820140   0.000004  0.001364  0.074891   \n",
       "5    7.29167E-03  0.024993     15.818000   0.000004  0.002785  0.075007   \n",
       "6    1.46991E-02  0.024949     31.785400   0.000004  0.005665  0.075051   \n",
       "7    2.95139E-02  0.024957     63.730300   0.000005  0.011514  0.075043   \n",
       "8    5.91435E-02  0.024977    127.672000   0.000005  0.023408  0.075023   \n",
       "9    1.18403E-01  0.024993    255.635000   0.000005  0.047592  0.075007   \n",
       "10   2.36921E-01  0.025006    511.701000   0.000005  0.096755  0.074994   \n",
       "11   4.73958E-01  0.025020   1024.120000   0.000005  0.196661  0.074980   \n",
       "12   9.48032E-01  0.025035   2049.540000   0.000005  0.399550  0.074965   \n",
       "13   1.89618E+00  0.025048   4101.490000   0.000005  0.811114  0.074952   \n",
       "14   3.79248E+00  0.025053   8206.110000   0.000005  1.644790  0.074947   \n",
       "15   7.58507E+00  0.025018  16404.000000   0.000005  3.333400  0.074982   \n",
       "16   1.00000E+01  0.024999  21620.100000   0.000005  4.416170  0.075001   \n",
       "17  / 1.000E+00/       NaN           NaN        NaN       NaN       NaN   \n",
       "\n",
       "          M_A:kg  Q_Aw:kg/s       M_Aw:kg  X_s:kg/kg  M_s:kg    Q_H:W  \\\n",
       "0       0.744372   0.074251      0.742508        0.0     0.0  21073.7   \n",
       "1       2.234340   0.074313      2.228760        0.0     0.0  21099.9   \n",
       "2       5.218250   0.074413      5.205290        0.0     0.0  21130.0   \n",
       "3      11.197300   0.074555     11.169700        0.0     0.0  21139.1   \n",
       "4      23.179900   0.074712     23.123600        0.0     0.0  21065.6   \n",
       "5      47.182000   0.074831     47.069600        0.0     0.0  20832.9   \n",
       "6      95.214600   0.074880     94.992900        0.0     0.0  20412.5   \n",
       "7     191.270000   0.074877    190.836000        0.0     0.0  19852.7   \n",
       "8     383.328000   0.074862    382.483000        0.0     0.0  19221.5   \n",
       "9     767.365000   0.074852    765.724000        0.0     0.0  18548.2   \n",
       "10   1535.300000   0.074843   1532.120000        0.0     0.0  17836.4   \n",
       "11   3070.880000   0.074834   3064.720000        0.0     0.0  17092.0   \n",
       "12   6141.460000   0.074824   6129.510000        0.0     0.0  16331.9   \n",
       "13  12281.500000   0.074815  12258.300000        0.0     0.0  15584.7   \n",
       "14  24560.900000   0.074814  24515.800000        0.0     0.0  14864.6   \n",
       "15  49131.000000   0.074853  49043.500000        0.0     0.0  14049.0   \n",
       "16  64779.900000   0.074873  64665.800000        0.0     0.0  13575.7   \n",
       "17           NaN        NaN           NaN        NaN     NaN      NaN   \n",
       "\n",
       "    Q_CH4G:kg/s     M_CH4G:kg  Q_CH4A:kg/s   M_CH4A:kg  \n",
       "0      0.025558      0.255584     0.000186    0.001864  \n",
       "1      0.025497      0.765531     0.000186    0.005579  \n",
       "2      0.025398      1.781450     0.000184    0.012959  \n",
       "3      0.025258      3.802090     0.000182    0.027556  \n",
       "4      0.025104      7.818780     0.000179    0.056268  \n",
       "5      0.024989     15.815200     0.000175    0.112419  \n",
       "6      0.024945     31.779700     0.000171    0.221723  \n",
       "7      0.024952     63.718800     0.000166    0.433850  \n",
       "8      0.024972    127.648000     0.000161    0.844918  \n",
       "9      0.024988    255.587000     0.000155    1.641010  \n",
       "10     0.025002    511.604000     0.000151    3.182440  \n",
       "11     0.025015   1023.920000     0.000146    6.167690  \n",
       "12     0.025030   2049.140000     0.000141   11.954400  \n",
       "13     0.025043   4100.680000     0.000137   23.192600  \n",
       "14     0.025048   8204.470000     0.000133   45.061300  \n",
       "15     0.025013  16400.700000     0.000129   87.478500  \n",
       "16     0.024994  21615.700000     0.000127  114.029000  \n",
       "17          NaN           NaN          NaN         NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss_gr['Wells']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9f4b48c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 subdomain(s)\n",
      "1 interface(s)\n",
      "1 Hydrate_Status file\n",
      "0 source/sink group(s)\n"
     ]
    }
   ],
   "source": [
    "file_in = r'simulation_files/sample_problem_Test1P/Test_1P.in'\n",
    "\n",
    "sds, itf, hyd_st, ss_gr = read_Time_Series(file_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90eec826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = [10,4]\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3)\n",
    "\n",
    "hyd_st.plot('Time(d)', 'CH4VolRate(m3/s)', ax=ax1)\n",
    "ax1.set_xscale('log')\n",
    "ax1.set_yscale('log')\n",
    "\n",
    "hyd_st.plot('Time(d)', 'RemainHydr(kg)', ax=ax2)\n",
    "ax2.set_xscale('log')\n",
    "ax2.set_yscale('log')\n",
    "\n",
    "\n",
    "itf['Int_1']['negQ_G:kg/s'] = itf['Int_1']['Q_G:kg/s']*-1\n",
    "itf['Int_1'].plot('Time:days','negQ_G:kg/s', ax=ax3)\n",
    "ax3.set_xscale('log')\n",
    "ax3.set_yscale('log')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f66fc5b",
   "metadata": {},
   "source": [
    "# Interactive display using Bokeh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f8cc38",
   "metadata": {},
   "source": [
    "These scripts are not 100% proof and would need some tweaking depending on the simulation files.\n",
    "\n",
    "They cover only 2D and 1D simulations using cartesian grids\n",
    "\n",
    "Adjust the `set_env BOKEH_ALLOW_WS_ORIGIN=localhost:8888` line accroding to your local specs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4221109e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: BOKEH_ALLOW_WS_ORIGIN=localhost:8888\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\": \"T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\": \"98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\": \"89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%set_env BOKEH_ALLOW_WS_ORIGIN=localhost:8888\n",
    "\n",
    "from TH_post_Bokeh import *\n",
    "from bokeh.layouts import column, layout, row\n",
    "from bokeh.models import ColumnDataSource, Slider, Select, LinearColorMapper, Button, Div, HoverTool, Span\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.themes import Theme\n",
    "from bokeh.io import show, output_notebook\n",
    "from bokeh.palettes import Viridis256\n",
    "from bokeh.transform import  factor_cmap\n",
    "from bokeh.embed import server_document\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1fcbbe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tough_app(doc):\n",
    "\n",
    "    #Define plotting dimensions\n",
    "    grid_dim = pd.DataFrame(index = range(3))\n",
    "    grid_dim['dim'] = ['x','y','z']\n",
    "    grid_dim['idx'] = ['I', 'J', 'K']\n",
    "    grid_dim['size'] = [data.I.max()+1, data.J.max()+1, data.K.max()+1]\n",
    "\n",
    "    grid_dim = grid_dim.sort_values(by=['size', 'dim'], ascending=[False, True])\n",
    "\n",
    "\n",
    "    if (grid_dim['size']>1).all():\n",
    "        print('3D grid')\n",
    "        dim = ''.join(grid_dim['dim'][:2].to_list())\n",
    "        button_idx = grid_dim.idx[0]\n",
    "\n",
    "    elif len(grid_dim[grid_dim['size']>1]) == 2:\n",
    "        print('2D grid')\n",
    "        dim = ''.join(grid_dim['dim'][:2].sort_values().to_list())\n",
    "        button_idx = grid_dim.idx[0]\n",
    "\n",
    "\n",
    "    else:\n",
    "        print('1D grid')\n",
    "        dim = ''.join(grid_dim['dim'][:2].to_list()[::-1])\n",
    "        button_idx = grid_dim.idx[1]\n",
    "\n",
    "    #Retrieve time steps\n",
    "    tsteps=data.index.get_level_values(0).drop_duplicates()\n",
    "    tsteps=np.sort(tsteps)\n",
    "    n_time_steps=len(tsteps)\n",
    "\n",
    "\n",
    "    #Selector\n",
    "    i = 0\n",
    "    param ='MA12'\n",
    "    param_select = list(data)[11:]\n",
    "\n",
    "    i_idx_0 = data[button_idx].max()//2\n",
    "\n",
    "    #widgets\n",
    "    select_pmt=Select(value=param, title='Parameter',options=param_select)\n",
    "\n",
    "    slider_time= Slider(start=0, end=n_time_steps-1, value=i, step=1, title=\"Time step\")\n",
    "\n",
    "    slider_1D = Slider(start=data[button_idx].min(), end=data[button_idx].max()+1, value=i_idx_0, step=1, title=\"{:s}-index\".format(button_idx))\n",
    "\n",
    "    i_idx = slider_1D.value\n",
    "\n",
    "    #     slider_X_axis = Slider(start=data.I.min(), end=data.I.max(), value=data.I.max()//2, step=1, title=\"I-index\")\n",
    "    #     slider_Y_axis = Slider(start=data.J.min(), end=data.J.max(), value=data.J.max()//2, step=1, title=\"J-index\")\n",
    "\n",
    "    button_time_next = Button(label=\"Next\")\n",
    "    button_time_previous = Button(label=\"Previous\")\n",
    "    button_play = Button(label='► Play', width=80)\n",
    "\n",
    "    #Define Data Sources\n",
    "    data_source_2D = ColumnDataSource(data=data.loc[tsteps[i]])\n",
    "    data_source_1D = ColumnDataSource(data=data[data[button_idx] == slider_1D.value].loc[tsteps[i]])\n",
    "\n",
    "\n",
    "    #Create 2D figures\n",
    "    top_2D_figure = create_figure1(data,select_pmt.value,tsteps,data_source_2D, plane=dim)\n",
    "    bottom_2D_figure = create_figure2(data,tsteps,data_source_2D, plane=dim)\n",
    "\n",
    "    top_2D_figure.add_tools(HoverTool(tooltips = set_str_tooltips(data, select_pmt.value)))\n",
    "    bottom_2D_figure.add_tools(HoverTool(tooltips = set_str_tooltips(data, 'S_hyd','S_aqu','S_gas')))\n",
    "\n",
    "\n",
    "    #Create 1D figures\n",
    "\n",
    "    top_1D_figure = create_figure1_1D(data,select_pmt.value,tsteps,data_source_1D, axis = dim[1])\n",
    "    top_1D_figure.xaxis.formatter.power_limit_low = -1\n",
    "    top_1D_figure.xaxis.formatter.precision = 1\n",
    "    top_1D_figure.xaxis.ticker.desired_num_ticks = 3    \n",
    "\n",
    "\n",
    "    bottom_1D_figure = create_figure2_1D(data,tsteps,data_source_1D, axis = dim[1])\n",
    "\n",
    "    Xloc=data[data[button_idx]== i_idx].x.max()\n",
    "    vline = Span(location=Xloc, dimension='height', line_color='red', line_width=1)\n",
    "    top_2D_figure.renderers.extend([vline])\n",
    "    bottom_2D_figure.renderers.extend([vline])\n",
    "\n",
    "    #PT-diagram\n",
    "    if 'P_EqHydr' in param_select:\n",
    "\n",
    "        X_range = (0,25)\n",
    "        Y_range = (data['P'].min(),data['P'].max())\n",
    "\n",
    "        plot_options = dict(toolbar_location=None,\n",
    "                            x_range=X_range,\n",
    "                            y_range=Y_range)  \n",
    "\n",
    "        PT_fig = figure(**plot_options, plot_height=700)\n",
    "\n",
    "        label_X = 'Temperature [degC]'\n",
    "        label_Y = 'Pressure [MPa]'\n",
    "\n",
    "\n",
    "\n",
    "        PT_fig.xaxis.axis_label = label_X\n",
    "\n",
    "        PT_fig.yaxis.axis_label = label_Y\n",
    "\n",
    "        PT_fig.line(x='T', y='P', \n",
    "                    color = '#ffa600', \n",
    "                    line_width=2, \n",
    "                    legend_label = 'Pressure',\n",
    "                    source = data_source_1D)\n",
    "\n",
    "        PT_fig.line(x='T', y='P_EqHydr', \n",
    "                    color = '#bc5090', \n",
    "                    line_width=2, \n",
    "                    legend_label = 'Stability curve',\n",
    "                    source = data_source_1D)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    VAR=select_pmt.value\n",
    "\n",
    "\n",
    "    def cb_slider_1D(attrname, old, new):\n",
    "        global i_idx\n",
    "        i_idx=new\n",
    "        time_step=slider_time.value\n",
    "\n",
    "        data_source_1D.data=data[data[button_idx] == i_idx].loc[tsteps[time_step]].to_dict('list')\n",
    "\n",
    "        vline.location=data[data[button_idx] == new].x.max()\n",
    "\n",
    "    def update(attr,old,new):\n",
    "        #Update colors in 2d Xsections\n",
    "        top_2D_figure.renderers[0].glyph.update(fill_color=set_color(data,select_pmt.value))\n",
    "        top_1D_figure.renderers[0].glyph.update(x=select_pmt.value)\n",
    "\n",
    "        #Update data in 1D plots\n",
    "        top_1D_figure.xaxis.axis_label = select_pmt.value\n",
    "        top_1D_figure.x_range.reset_start\n",
    "        top_1D_figure.x_range.reset_end\n",
    "\n",
    "        try:\n",
    "\n",
    "            minv = data[select_pmt.value].min()\n",
    "            maxv = data[select_pmt.value].max()\n",
    "\n",
    "            if minv == 0 and maxv == 0:\n",
    "                minv -= 0.5\n",
    "                maxv += 0.5\n",
    "\n",
    "\n",
    "\n",
    "            elif minv/maxv > 0.97:\n",
    "                delta = (10**np.floor(np.log10(np.abs(minv))))/2\n",
    "                minv -= delta\n",
    "                maxv += delta\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "            top_1D_figure.x_range.start = minv\n",
    "            top_1D_figure.x_range.end = maxv\n",
    "\n",
    "        except:\n",
    "            top_1D_figure.x_range.start = 0\n",
    "            top_1D_figure.x_range.end = 1\n",
    "\n",
    "\n",
    "        #Update Hovertool\n",
    "\n",
    "        #Modification due to bug updating value only on tooltips\n",
    "        top_2D_figure.tools.pop(-1)\n",
    "        parameter_name = select_pmt.value\n",
    "        tooltips_str = set_str_tooltips(data, parameter_name)\n",
    "        top_2D_figure.add_tools(HoverTool(tooltips = tooltips_str))\n",
    "    #         top_2D_figure.hover.tooltips = tooltips_str\n",
    "\n",
    "\n",
    "    def cb_next():\n",
    "        i = slider_time.value\n",
    "        i = i + 1\n",
    "        ev = \"button next\"\n",
    "        time_step = i % (n_time_steps)\n",
    "        slider_time.value = time_step\n",
    "\n",
    "    #         print_info(ev,time_step)\n",
    "\n",
    "    def cb_previous():\n",
    "        i = slider_time.value\n",
    "        i = i - 1\n",
    "        ev = \"button previous\"\n",
    "\n",
    "        time_step= i % (n_time_steps)\n",
    "        slider_time.value = time_step\n",
    "\n",
    "    #         print_info(ev,time_step)\n",
    "\n",
    "\n",
    "    def cb_slider(attrname, old, new):\n",
    "        global i\n",
    "        time_step = slider_time.value\n",
    "        i = slider_time.value\n",
    "        title.text = '<h3>'+ time_string(tsteps[i]) +'</h3>'\n",
    "        data_source_2D.data=data.loc[tsteps[time_step]].to_dict('list')\n",
    "    #         data_source_wcol.data = data.loc[(data.MA12=='WATER') & (data.index == tsteps[i])].to_dict('list')\n",
    "\n",
    "        data_source_1D.data=data[data[button_idx] == slider_1D.value].loc[tsteps[time_step]].to_dict('list')\n",
    "\n",
    "\n",
    "\n",
    "    def animate_update():\n",
    "        time_step = slider_time.value + 1\n",
    "        if time_step > n_time_steps-1:\n",
    "            time_step = 0\n",
    "        slider_time.value = time_step\n",
    "\n",
    "\n",
    "    callback_id = None\n",
    "\n",
    "    def animate():\n",
    "        global callback_id\n",
    "        if button_play.label == '► Play':\n",
    "            button_play.label = '❚❚ Pause'\n",
    "            callback_id = doc.add_periodic_callback(animate_update, 200)\n",
    "        else:\n",
    "            button_play.label = '► Play'\n",
    "            doc.remove_periodic_callback(callback_id)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    select_pmt.on_change('value',update)\n",
    "\n",
    "    slider_time.on_change('value',cb_slider)\n",
    "    slider_1D.on_change('value',cb_slider_1D)\n",
    "    button_time_next.on_click(cb_next)\n",
    "    button_time_previous.on_click(cb_previous)\n",
    "    button_play.on_click(animate)\n",
    "\n",
    "\n",
    "    buttons_row = row(button_play,button_time_previous, slider_time, button_time_next, slider_1D,width=800)\n",
    "    top_row = row(top_2D_figure,top_1D_figure )\n",
    "    bottom_row = row(bottom_2D_figure, bottom_1D_figure)\n",
    "    title = Div(text='<h3>'+ time_string(tsteps[i]) +'</h3>')\n",
    "\n",
    "    if 'P_EqHydr' in param_select:\n",
    "        display_layout = layout([title],\n",
    "                                [select_pmt],\n",
    "                                [buttons_row],\n",
    "                                [[[top_row],\n",
    "                                [bottom_row]],\n",
    "                                PT_fig])\n",
    "\n",
    "    else:    \n",
    "        display_layout = layout([title],\n",
    "                                [select_pmt],\n",
    "                                [buttons_row],\n",
    "                                [top_2D_figure,top_1D_figure],\n",
    "                                [bottom_2D_figure,bottom_1D_figure])\n",
    "\n",
    "\n",
    "    doc.add_root(display_layout)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d738d9e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read pickle data\n"
     ]
    }
   ],
   "source": [
    "file_in = r'simulation_files/sample_problem_Test1P/Test_1P.in'\n",
    "\n",
    "data = get_output(file_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fba34ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.bokehjs_exec.v0+json": "",
      "text/html": [
       "\n",
       "<script id=\"1002\">\n",
       "  var xhr = new XMLHttpRequest()\n",
       "  xhr.responseType = 'blob';\n",
       "  xhr.open('GET', \"http://localhost:63070/autoload.js?bokeh-autoload-element=1002&bokeh-absolute-url=http://localhost:63070&resources=none\", true);\n",
       "  \n",
       "  xhr.onload = function (event) {\n",
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       "    src = URL.createObjectURL(event.target.response);\n",
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       "xhr.send();\n",
       "</script>"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D grid\n"
     ]
    }
   ],
   "source": [
    "show(tough_app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bedf0a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No pickle data available. Read and process\n",
      "Processing single file\n",
      "No INCON data processed\n",
      "No INCON data processed\n",
      "2500  cells.  2500  cells are active.\n",
      "No explictit INCON file\n",
      "Read default initial contidions from Input file\n",
      "AGH: 3 phases Aqueous+Hydrate+Gas\n",
      "No INCON data processed\n",
      "1 steps\n",
      "Rocks referenced in ELEME as indexes\n"
     ]
    }
   ],
   "source": [
    "file_in = r'simulation_files/sample_problem_Test2D/Test_2D.in'\n",
    "data = get_output(file_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d75935a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.bokehjs_exec.v0+json": "",
      "text/html": [
       "\n",
       "<script id=\"1294\">\n",
       "  var xhr = new XMLHttpRequest()\n",
       "  xhr.responseType = 'blob';\n",
       "  xhr.open('GET', \"http://localhost:63073/autoload.js?bokeh-autoload-element=1294&bokeh-absolute-url=http://localhost:63073&resources=none\", true);\n",
       "  \n",
       "  xhr.onload = function (event) {\n",
       "    var script = document.createElement('script'),\n",
       "    src = URL.createObjectURL(event.target.response);\n",
       "    script.src = src;\n",
       "    document.body.appendChild(script);\n",
       "  };\n",
       "xhr.send();\n",
       "</script>"
      ]
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     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "server_id": "377cd326e9fa4536b1b1a9e0932d4d01"
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     },
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2D grid\n"
     ]
    }
   ],
   "source": [
    "show(tough_app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3256314b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No pickle data available. Read and process\n",
      "Processing single file\n",
      "1521  cells.  1521  cells are active.\n",
      "INCON element-specific initial conditions\n",
      "Processing element specific initial conditions\n",
      "Aqu: 1 phase Aqueous\n",
      "15 steps\n",
      "Rocks referenced as string names\n"
     ]
    }
   ],
   "source": [
    "file_in = r'simulation_files/2D_HydrForm_sediments/BSR.in'\n",
    "data = get_output(file_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9eecdb40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.bokehjs_exec.v0+json": "",
      "text/html": [
       "\n",
       "<script id=\"1586\">\n",
       "  var xhr = new XMLHttpRequest()\n",
       "  xhr.responseType = 'blob';\n",
       "  xhr.open('GET', \"http://localhost:63076/autoload.js?bokeh-autoload-element=1586&bokeh-absolute-url=http://localhost:63076&resources=none\", true);\n",
       "  \n",
       "  xhr.onload = function (event) {\n",
       "    var script = document.createElement('script'),\n",
       "    src = URL.createObjectURL(event.target.response);\n",
       "    script.src = src;\n",
       "    document.body.appendChild(script);\n",
       "  };\n",
       "xhr.send();\n",
       "</script>"
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     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "server_id": "89dae69660694b47b2fd835ab8cab337"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2D grid\n"
     ]
    }
   ],
   "source": [
    "show(tough_app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e12cb4c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "068aa4cc",
   "metadata": {},
   "outputs": [],
   "source": []
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