Transient_Sim1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopy is installed in /Users/ralf/.virtualenvs/flopy/lib/python3.7/site-packages/flopy\n",
      "3.7.7 (default, Mar 10 2020, 15:43:33) \n",
      "[Clang 11.0.0 (clang-1100.0.33.17)]\n",
      "numpy version: 1.18.3\n",
      "matplotlib version: 3.2.1\n",
      "pandas version: 1.0.3\n",
      "flopy version: 3.3.0\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import clear_output, display\n",
    "\n",
    "import time\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "\n",
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('pandas version: {}'.format(pd.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Creating new model with name: UMONS_SmallModel_Transient_Sim1\n",
      "--------------------------------------------------\n",
      "\n",
      "Parsing the namefile --> ./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.mfn\n",
      "\n",
      "--------------------------------------------------\n",
      "External unit dictionary:\n",
      "{7: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.obs, filetype:OBS, 8: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.chob, filetype:CHOB, 9: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.dis, filetype:DIS, 10: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.ba6, filetype:BAS6, 11: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.lpf, filetype:LPF, 12: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.oc, filetype:OC, 13: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.rch, filetype:RCH, 16: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.wel, filetype:WEL, 14: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.chd, filetype:CHD, 15: filename:./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.pcg, filetype:PCG}\n",
      "--------------------------------------------------\n",
      "\n",
      "ModflowBas6 free format:True\n",
      "\n",
      "loading dis package file...\n",
      "   Loading dis package with:\n",
      "      1 layers, 221 rows, 227 columns, and 52 stress periods\n",
      "   loading laycbd...\n",
      "   loading delr...\n",
      "   loading delc...\n",
      "   loading top...\n",
      "   loading botm...\n",
      "      for 1 layers and 0 confining beds\n",
      "   loading stress period data...\n",
      "       for 52 stress periods\n",
      "adding Package:  DIS\n",
      "   DIS  package load...success\n",
      "   OBS  package load...skipped\n",
      "loading flwob package file...\n",
      "adding Package:  CHOB\n",
      "   CHOB package load...success\n",
      "loading bas6 package file...\n",
      "adding Package:  BAS6\n",
      "   BAS6 package load...success\n",
      "loading lpf package file...\n",
      "   loading IBCFCB, HDRY, NPLPF...\n",
      "   loading LAYTYP...\n",
      "   loading LAYAVG...\n",
      "   loading CHANI...\n",
      "   loading LAYVKA...\n",
      "   loading LAYWET...\n",
      "   loading hk layer   1...\n",
      "   loading hani layer   1...\n",
      "   loading vka layer   1...\n",
      "   loading ss layer   1...\n",
      "   loading sy layer   1...\n",
      "Adding UMONS_SmallModel_Transient_Sim1.cbc (unit=40) to the output list.\n",
      "adding Package:  LPF\n",
      "   LPF  package load...success\n",
      "loading oc package file...\n",
      "head file name will be generated by flopy\n",
      "Adding UMONS_SmallModel_Transient_Sim1.hds (unit=3) to the output list.\n",
      "adding Package:  OC\n",
      "   OC   package load...success\n",
      "loading rch package file...\n",
      "   loading rech stress period   1...\n",
      "   loading rech stress period   2...\n",
      "   loading rech stress period   3...\n",
      "   loading rech stress period   4...\n",
      "   loading rech stress period   5...\n",
      "   loading rech stress period   6...\n",
      "   loading rech stress period   7...\n",
      "   loading rech stress period   8...\n",
      "   loading rech stress period   9...\n",
      "   loading rech stress period  10...\n",
      "   loading rech stress period  11...\n",
      "   loading rech stress period  12...\n",
      "   loading rech stress period  13...\n",
      "   loading rech stress period  14...\n",
      "   loading rech stress period  15...\n",
      "   loading rech stress period  16...\n",
      "   loading rech stress period  17...\n",
      "   loading rech stress period  18...\n",
      "   loading rech stress period  19...\n",
      "   loading rech stress period  20...\n",
      "   loading rech stress period  21...\n",
      "   loading rech stress period  22...\n",
      "   loading rech stress period  23...\n",
      "   loading rech stress period  24...\n",
      "   loading rech stress period  25...\n",
      "   loading rech stress period  26...\n",
      "   loading rech stress period  27...\n",
      "   loading rech stress period  28...\n",
      "   loading rech stress period  29...\n",
      "   loading rech stress period  30...\n",
      "   loading rech stress period  31...\n",
      "   loading rech stress period  32...\n",
      "   loading rech stress period  33...\n",
      "   loading rech stress period  34...\n",
      "   loading rech stress period  35...\n",
      "   loading rech stress period  36...\n",
      "   loading rech stress period  37...\n",
      "   loading rech stress period  38...\n",
      "   loading rech stress period  39...\n",
      "   loading rech stress period  40...\n",
      "   loading rech stress period  41...\n",
      "   loading rech stress period  42...\n",
      "   loading rech stress period  43...\n",
      "   loading rech stress period  44...\n",
      "   loading rech stress period  45...\n",
      "   loading rech stress period  46...\n",
      "   loading rech stress period  47...\n",
      "   loading rech stress period  48...\n",
      "   loading rech stress period  49...\n",
      "   loading rech stress period  50...\n",
      "   loading rech stress period  51...\n",
      "   loading rech stress period  52...\n",
      "adding Package:  RCH\n",
      "   RCH  package load...success\n",
      "loading wel package file...\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     1\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     2\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     3\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     4\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     5\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     6\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     7\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     8\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper     9\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    10\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    11\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    12\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    13\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    14\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    15\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    16\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    17\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    18\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    19\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    20\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    21\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    22\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    23\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    24\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    25\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    26\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    27\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    28\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    29\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    30\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    31\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    32\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    33\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    34\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    35\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    36\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    37\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    38\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    39\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    40\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    41\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    42\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    43\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    44\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    45\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    46\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    47\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    48\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    49\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    50\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    51\n",
      "   loading <class 'flopy.modflow.mfwel.ModflowWel'> for kper    52\n",
      "adding Package:  WEL\n",
      "   WEL  package load...success\n",
      "loading chd package file...\n",
      "   implicit ipakcb in ./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.chd\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     1\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     2\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     3\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     4\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     5\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     6\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     7\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     8\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper     9\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    10\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    11\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    12\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    13\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    14\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    15\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    16\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    17\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    18\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    19\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    20\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    21\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    22\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    23\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    24\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    25\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    26\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    27\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    28\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    29\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    30\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    31\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    32\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    33\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    34\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    35\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    36\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    37\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    38\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    39\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    40\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    41\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    42\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    43\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    44\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    45\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    46\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    47\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    48\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    49\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    50\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    51\n",
      "   loading <class 'flopy.modflow.mfchd.ModflowChd'> for kper    52\n",
      "adding Package:  CHD\n",
      "\n",
      "CHD PACKAGE DATA VALIDATION:\n",
      "  No errors or warnings encountered.\n",
      "\n",
      "   CHD  package load...success\n",
      "loading pcg package file...\n",
      "adding Package:  PCG\n",
      "   PCG  package load...success\n",
      "Warning: external file unit 140 does not exist in ext_unit_dict.\n",
      "Warning: external file unit 40 does not exist in ext_unit_dict.\n",
      "Warning: external file unit 3 does not exist in ext_unit_dict.\n",
      "Warning: external file unit 0 does not exist in ext_unit_dict.\n",
      "\n",
      "   The following 9 packages were successfully loaded.\n",
      "      UMONS_SmallModel_Transient_Sim1.dis\n",
      "      UMONS_SmallModel_Transient_Sim1.chob\n",
      "      UMONS_SmallModel_Transient_Sim1.ba6\n",
      "      UMONS_SmallModel_Transient_Sim1.lpf\n",
      "      UMONS_SmallModel_Transient_Sim1.oc\n",
      "      UMONS_SmallModel_Transient_Sim1.rch\n",
      "      UMONS_SmallModel_Transient_Sim1.wel\n",
      "      UMONS_SmallModel_Transient_Sim1.chd\n",
      "      UMONS_SmallModel_Transient_Sim1.pcg\n",
      "   The following 1 packages were not loaded.\n",
      "      UMONS_SmallModel_Transient_Sim1.obs\n"
     ]
    }
   ],
   "source": [
    "# load the model\n",
    "project_name = \"UMONS_SmallModel_Transient_Sim1\"\n",
    "model_ws = os.path.join('.', \"Transient_Sim1\")\n",
    "ml = flopy.modflow.Modflow.load(\n",
    "    project_name+\".mfn\", \n",
    "    model_ws=model_ws, \n",
    "    verbose=True,\n",
    "    check=False, \n",
    "    exe_name=\"mf2000\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xul:0; yul:11017.70536; rotation:0; proj4_str:None; units:meters; lenuni:2; length_multiplier:1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.dis.sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting LPF package Util3d instance: hk\n",
      "plotting LPF package Util3d instance: hani\n",
      "plotting LPF package Util3d instance: vka\n",
      "plotting LPF package Util3d instance: ss\n",
      "plotting LPF package Util3d instance: sy\n",
      "plotting LPF package Util3d instance: vkcb\n",
      "plotting LPF package Util3d instance: wetdry\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x11ff05b50>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1200c76d0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x12352c190>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x123802c90>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1201d3ed0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x123db1a10>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1239674d0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2Mvz9AjE6NmPcVDXt1vhK4LsR8c+BfwU8BFwMbI2IRcDW9BzgTLJzShcBq4GrASTNBdYAp5Jds29NLXCtnMrwBbQKC4gxjZuqpnB4SjqW7JzRawAi4oWIeJrDL0i6Hnh3erwCuDYydwKzJc0HlgFbImJfROwHtgDLi9bLzMpvbEzjpqppp+U5BDwBfDVdZPQrko4B5kXEnrTMY8C89Hiii5k2c5FTACStrl0otY16m1kPRcDY6IxxU9W0U+NZwMnA1RHxRuCXvNhFByBdRy/aeI/DRMTaiFgyTRdMtRa461495fmbje+yD1S3nayFuCsitqXnG8nC9PHUHSf93JvmT3Qx02Yucmpm/WLQ93lGxGPAo5Jen4pOBx7k8AuSrgJuTo83AeelUffTgGdS934zsFTSnDRQtDSVmVm/GtP4qWLaPc7zPwPXpSs7PwJ8gCyQb5R0PvBT4L1p2VuAs4Bh4FdpWSJin6RPk10pGuDSTlyo1MxeVJ4uO9mOvAqGZb22wjMi7gUa7X88vcGyAVwwweusA9a1UxfrPt8nyYqK0QEPTzOzlgVorNeVaF/1jg8ws4prsL+ziW68pOWStqezFC9uMP8oSTek+dsknZibd0kq3y5pWSd+C4enWZ8r1f5OSBf01PhpEunul18kO1NxMXCOpMV1i50P7I+I1wJXAJ9L6y4muxncSWQn4PxV7W6a7XB4mlnXaWz8NIVTgOGIeCQiXgA2kJ21mJc/u3EjcLokpfINEXEgInaSDVqf0u7v4PA0s65TjJ+A42pnEKZpdW6VZs5EPLRMRIwAzwCvaHLdlnnAyMy6q9ZtH+/JKp096JanmXVdgW57M2ciHlpG0izgWOCpJtdtmcPTCvMxnlaEolB43gUskjSUTspZSXbWYl7+7MazgdvS8eWbgJVpNH6I7LKY32/393C33cy6Ti0eJB8RI5I+RHbq9kxgXUQ8IOlS4O6I2ER2ecyvSRoG9pEFLGm5G8lOHx8BLoiI0XZ/B4enFeJWpxVW8CD5iLiF7DTvfNknco+fB94zwbqXAZe1/q4Tc3ia9bHSHeNZ0wdnGDk8zazr+uH0TIentcxddmtLn5zb7vA061Nl7bILUNvDNb3n8DSz7nLL08ysGIenmVmrwt12Myupsu7vrOmHlqdPz7SWlf2LaSUXZMd51k8V45anmXWVgBkVDMt6bnlaIW59Wlvc8jQza1GfHKrklqcV5tanFTVjdPxUNW55mll31QaMKs4tT2uLW5/WKlHoYsil4/C0tjlArSUBM0Zj3FQ17rabWddVsaVZzy1P6wi3Pq1pxe5hVDoOT+sYB6g1I7skXYyb2npNaa6kLZJ2pJ9zJlhuVVpmh6RVufI7JG2XdG+afmOq93R4Wkc5QG1K09PyvBjYGhGLgK3p+WEkzQXWAKcCpwBr6kL23Ih4Q5r2TvWGDk/rOAdob1Vh+88YGT+1aQWwPj1eD7y7wTLLgC0RsS8i9gNbgOVF37Dt8JQ0U9IPJX07PR+StE3SsKQb0j2WSfdMviGVb5N0Yu41Lknl2yUta7dO1ns7L7zo0GR2mACNxbgJOE7S3blpdQuvOi8i9qTHjwHzGiyzAHg093xXKqv5auqy/5mkKe+N3InR9guBh4CXp+efA66IiA2SvgScD1ydfu6PiNdKWpmW+z1Ji8nur3wScDxwq6TXdeK+ylYO9QHa6B5I7Yas76tUHbXjPBt4MiKWTLiedCvwygazPp5/EhEhqdWdqOdGxG5JLwNuAv4AuHayFdoKT0kLgd8lux/yR1Javx14X1pkPfBJsvBckR4DbAS+kJZfAWyIiAPAznTD+lOA/9tO3ay8pqM12kxAW0lEsQGiiDhjonmSHpc0PyL2SJoPNNpnuRt4W+75QuCO9Nq7089fSLqeLIMmDc92u+1/CfwJL55s9Qrg6Yio7cHIN4sPNZnT/GfS8lM1pQ+RtLrWpG+z3tbnvMugxKLzo+3AJqA2er4KuLnBMpuBpZLmpIGipcBmSbMkHQcg6QjgncD9U71h4fCU9E5gb0TcU/Q1WhURayNiyWRNezMrv2kYbf8s8A5JO4Az0nMkLZH0FYCI2Ad8GrgrTZemsqPIQvQ+4F6yFupfT/WG7XTb3wK8S9JZwNFk+zyvBGZLmpValwtTRUg/TwB2SZoFHAs8lSuvya9jZi2oRGs7tTw7+pIRTwGnNyi/G/hg7vk6YF3dMr8E3tTqexZueUbEJRGxMCJOJBvwuS0izgVuB85Oi+Wbz/lm9dlp+UjlK9No/BCwCPh+0XqZ5VUiTAbMdBwk3wvTcW77x4ANkj4D/BC4JpVfA3wtDQjtIwtcIuIBSTcCDwIjwAUeaTfrY+lQparrSHhGxB28OGr1CNlIVf0yzwPvmWD9y8hG7M2s7wUareDJ7HV8VSUz665p2OfZCw5PM+u6fui2+9x263seNCoXRaCRsXFT1bjlaWbdN1a9sKznlqcNBLc+SyToi5anw9MGRr8HaGV+v4is5Vk/VYzD0wZKZQKmz/XDQfIOTxs4DtAei4CR0fFTxTg8bSA5QHusD7rtHm03s+6KgNHqtTTrOTxtYNVan75wcpcFleym13O33Qaeu/Dd5tF2s77hAO2iIOu2108V4/A0SxygXRJBjIyMm6rG4WmW4wDtktGx8VPFODzN6jhAp1lttL2D3XZJcyVtkbQj/ZwzwXLflfS0pG/XlQ9J2iZpWNINko6c6j0dnmbWXRHEwZFxU5suBrZGxCJga3reyF+Q3ZO93ueAKyLitcB+4Pyp3tDhaWZdFUCMjo6b2rQCWJ8erwfe3fC9I7YCv8iXSRLwdmDjVOvn+ThPM+uqX8S+zVsOfv24BrOOlnR37vnaiFjb5MvOi4g96fFjwLwWqvQK4Ol0x1+AXcCCqVZyeJo1sPPCi3zw/DSJiOVF1pN0K/DKBrM+Xvf6IWnarzTi8DSzSoiIMyaaJ+lxSfMjYo+k+cDeFl76KWC2pFmp9bkQ2D3VSt7naTaBKo26V6mu02QTsCo9XgXc3OyKERHA7cDZrazv8DSbhEOpMj4LvEPSDuCM9BxJSyR9pbaQpP8NfAM4XdIuScvSrI8BH5E0TLYP9Jqp3lBZ6FZPN/ZpmNWUff9nl0P+nohY0s03LCO3PM3MCnB4mpkV4PA0a4L3fVo9h6dZxTnYe8PhadYkh5TlOTzNWrDzwoscoga0EZ6STpB0u6QHJT0g6cJU3vDSUMpclS75dJ+kk3OvtSotv0PSqone08ysLNppeY4A/zUiFgOnARdIWszEl4Y6E1iUptXA1ZCFLbAGOBU4BVgz0bX4zMrCrU8rHJ4RsScifpAe/wJ4iOxKJBNdGmoFcG1k7iQ7l3Q+sAzYEhH7ImI/sAUodOEAs25ygA62juzzlHQi8EZgGxNfGmoB8GhutdplnyYqb/Q+qyXdXXfZKrOecYAOrrbDU9KvATcBH46IZ/Pz0gn3HTuNMiLWRsQSnxpmlnF4905b4SnpCLLgvC4ivpmKH0/dceouDbUbOCG3eu2yTxOVm1WCA2wwtTPaLrIrjzwUEZ/PzZro0lCbgPPSqPtpwDOpe78ZWCppThooWprKzMxKq52LIb+F7EZKP5J0byr7U7JLQd0o6Xzgp8B707xbgLOAYeBXwAcAImKfpE8Dd6XlLo2IfW3Uy8xs2hUOz4j4P4AmmH16g+UDuGCC11oHrCtaFzOzbvMZRmZmBTg8zTrAg0aDx+FpZlaAw9PMrACHp5lZAQ5PM7MCHJ5mHeJBo8Hi8DSrKId1bzk8zcwKcHiadZBbg4PD4WnWYQ7QweDwNJsGDtD+5/A0myYO0P7m8DSbRg7Q/uXwNJtmDtD+5PA06wIHaP9xeJqZFeDwNOsStz77i8PTrIscoP3D4WlmVoDD08ysAGU3taweSdWsuBkwdOXlTS9bwq7+PRGxpNeV6DWHp5m1yuGJu+1mZoU4PM3MCnB4mpkV4PA0MyvA4WlmVoDD08ysAIenmVkBDk8zswIcnmZmBTg8zcwKcHiamRUwq9cVaMM/Adt7XYkJHAc82etKTKLM9XPdiutW/V7dhfcovSqH5/ayXpxA0t1lrRuUu36uW3Flr1+/cbfdzKwAh6eZWQFVDs+1va7AJMpcNyh3/Vy34spev75S2Yshm5n1UpVbnmZmPePwNDMroHLhKWm5pO2ShiVd3KX3PEHS7ZIelPSApAtT+VxJWyTtSD/npHJJuirV8T5JJ+dea1VafoekVR2s40xJP5T07fR8SNK2VIcbJB2Zyo9Kz4fT/BNzr3FJKt8uaVkH6zZb0kZJD0t6SNKbS7bt/jj9Xe+X9HVJR/dq+0laJ2mvpPtzZR3bVpLeJOlHaZ2rJKlIPQ2IiMpMwEzgx8BrgCOB/wcs7sL7zgdOTo9fBvwjsBj478DFqfxi4HPp8VnAdwABpwHbUvlc4JH0c056PKdDdfwIcD3w7fT8RmBlevwl4D+lx38EfCk9XgnckB4vTtvzKGAobeeZHarbeuCD6fGRwOyybDtgAbATeEluu72/V9sPeCtwMnB/rqxj2wr4flpWad0zp/v7069TzyvQ4gfrzcDm3PNLgEt6UI+bgXeQneE0P5XNJztwH+DLwDm55ben+ecAX86VH7ZcG/VZCGwF3g58O30xngRm1W83YDPw5vR4VlpO9dsyv1ybdTs2hZPqysuy7RYAj6agmZW237Jebj/gxLrw7Mi2SvMezpUftpyn1qaqddtrH/SaXamsa1I37Y3ANmBeROxJsx4D5qXHE9Vzuur/l8CfAGPp+SuApyNipMH7HKpDmv9MWn666jYEPAF8Ne1W+IqkYyjJtouI3cDlwM+APWTb4x7Ks/2gc9tqQXo8HXUcOFULz56S9GvATcCHI+LZ/LzI/pV3/bgvSe8E9kbEPd1+7ybNIuuGXh0RbwR+Sdb1PKRX2w4g7T9cQRbyxwPHAMt7UZdm9HJb2eGqFp67gRNyzxemsmkn6Qiy4LwuIr6Zih+XND/Nnw/snaKe01H/twDvkvQTYANZ1/1KYLak2rUL8u9zqA5p/rHAU9NUN8haN7siYlt6vpEsTMuw7QDOAHZGxBMRcRD4Jtk2Lcv2g85tq93p8XTUceBULTzvAhalkdAjyXbYb5ruN00jktcAD0XE53OzNgG1kcxVZPtCa+XnpdHQ04BnUrdrM7BU0pzU4lmaygqLiEsiYmFEnEi2PW6LiHOB24GzJ6hbrc5np+Ujla9Mo8lDwCKywYW2RMRjwKOSXp+KTgcepATbLvkZcJqkl6a/c61+pdh+Dd6z8LZK856VdFr6Xc/LvZa1qtc7XVudyEYY/5FsNPPjXXrPf0PWVboPuDdNZ5Ht69oK7ABuBeam5QV8MdXxR8CS3Gv9B2A4TR/ocD3fxouj7a8h+/IOA98AjkrlR6fnw2n+a3LrfzzVeTsdHIUF3gDcnbbft8hGgEuz7YBPAQ8D9wNfIxsx78n2A75Otu/1IFmr/fxObitgSfo9fwx8gbqBPE/NTz4908ysgKp1283MSsHhaWZWgMPTzKwAh6eZWQEOTzOzAhyeZmYFODzNzAr4/2wE7Ne1clZiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.lpf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting DIS package Util2d instance: top\n",
      "plotting DIS package Util3d instance: botm\n",
      "plotting DIS package Util3d instance: _ModflowDis__thickness\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x125399c50>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1260ea0d0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1263beb90>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.dis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote ./Transient_Sim1/UMONS_SmallModel_Transient_Sim1.shp\n"
     ]
    }
   ],
   "source": [
    "ml.dis.export(os.path.join(model_ws, project_name+\".shp\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Writing packages:\n",
      "   Package:  DIS\n",
      "   Package:  CHOB\n",
      "   Package:  BAS6\n",
      "   Package:  LPF\n",
      "   Package:  OC\n",
      "   Package:  RCH\n",
      "   Package:  WEL\n",
      "   Package:  CHD\n",
      "   Package:  PCG\n",
      " \n"
     ]
    }
   ],
   "source": [
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following  executable to run the model: /usr/local/bin/mf2000\n",
      "\n",
      "                                  MODFLOW-2000\n",
      "    U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUND-WATER FLOW MODEL\n",
      "                             Version 1.19.01 03/25/2010                      \n",
      "\n",
      " Using NAME file: UMONS_SmallModel_Transient_Sim1.nam                                                                                                                                                                     \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2020/04/30 15:45:45\n",
      "\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2020/04/30 15:45:51\n",
      " Elapsed run time:  5.786 Seconds\n",
      "\n",
      " Normal termination of MODFLOW-2000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.run_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output inspection\n",
    "\n",
    "First, lets look at the list file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>STORAGE_IN</th>\n",
       "      <th>CONSTANT_HEAD_IN</th>\n",
       "      <th>WELLS_IN</th>\n",
       "      <th>RECHARGE_IN</th>\n",
       "      <th>TOTAL_IN</th>\n",
       "      <th>STORAGE_OUT</th>\n",
       "      <th>CONSTANT_HEAD_OUT</th>\n",
       "      <th>WELLS_OUT</th>\n",
       "      <th>RECHARGE_OUT</th>\n",
       "      <th>TOTAL_OUT</th>\n",
       "      <th>IN-OUT</th>\n",
       "      <th>PERCENT_DISCREPANCY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-01-01 16:47:59.998970</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.410400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4104</td>\n",
       "      <td>-0.000206</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-02 09:35:59.997940</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.410400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4104</td>\n",
       "      <td>-0.000201</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-03 02:23:59.991760</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.410400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4104</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-03 19:11:59.995880</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.410400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4104</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-04 12:00:00.000000</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.410400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4104</td>\n",
       "      <td>-0.000206</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-12-28 04:48:01.054688</th>\n",
       "      <td>0.017050</td>\n",
       "      <td>0.4431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.8704</td>\n",
       "      <td>8.512900e-08</td>\n",
       "      <td>0.016918</td>\n",
       "      <td>0.8559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>-0.002407</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-12-28 21:35:59.472656</th>\n",
       "      <td>0.016533</td>\n",
       "      <td>0.4432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>7.625600e-08</td>\n",
       "      <td>0.016907</td>\n",
       "      <td>0.8559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>-0.002854</td>\n",
       "      <td>-0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-12-29 14:24:00.527344</th>\n",
       "      <td>0.017362</td>\n",
       "      <td>0.4433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.8708</td>\n",
       "      <td>2.561100e-09</td>\n",
       "      <td>0.016897</td>\n",
       "      <td>0.8559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>-0.001953</td>\n",
       "      <td>-0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-12-30 07:11:58.945312</th>\n",
       "      <td>0.017623</td>\n",
       "      <td>0.4433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.8712</td>\n",
       "      <td>8.021000e-08</td>\n",
       "      <td>0.016886</td>\n",
       "      <td>0.8559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>-0.001618</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-12-31 00:00:00.000000</th>\n",
       "      <td>0.016973</td>\n",
       "      <td>0.4434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4102</td>\n",
       "      <td>0.8706</td>\n",
       "      <td>2.120900e-08</td>\n",
       "      <td>0.016875</td>\n",
       "      <td>0.8559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8728</td>\n",
       "      <td>-0.002197</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>520 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            STORAGE_IN  CONSTANT_HEAD_IN  WELLS_IN  \\\n",
       "1979-01-01 16:47:59.998970    0.000000            0.0000       0.0   \n",
       "1979-01-02 09:35:59.997940    0.000000            0.0000       0.0   \n",
       "1979-01-03 02:23:59.991760    0.000000            0.0000       0.0   \n",
       "1979-01-03 19:11:59.995880    0.000000            0.0000       0.0   \n",
       "1979-01-04 12:00:00.000000    0.000000            0.0000       0.0   \n",
       "...                                ...               ...       ...   \n",
       "1979-12-28 04:48:01.054688    0.017050            0.4431       0.0   \n",
       "1979-12-28 21:35:59.472656    0.016533            0.4432       0.0   \n",
       "1979-12-29 14:24:00.527344    0.017362            0.4433       0.0   \n",
       "1979-12-30 07:11:58.945312    0.017623            0.4433       0.0   \n",
       "1979-12-31 00:00:00.000000    0.016973            0.4434       0.0   \n",
       "\n",
       "                            RECHARGE_IN  TOTAL_IN   STORAGE_OUT  \\\n",
       "1979-01-01 16:47:59.998970       0.4102    0.4102  0.000000e+00   \n",
       "1979-01-02 09:35:59.997940       0.4102    0.4102  0.000000e+00   \n",
       "1979-01-03 02:23:59.991760       0.4102    0.4102  0.000000e+00   \n",
       "1979-01-03 19:11:59.995880       0.4102    0.4102  0.000000e+00   \n",
       "1979-01-04 12:00:00.000000       0.4102    0.4102  0.000000e+00   \n",
       "...                                 ...       ...           ...   \n",
       "1979-12-28 04:48:01.054688       0.4102    0.8704  8.512900e-08   \n",
       "1979-12-28 21:35:59.472656       0.4102    0.8700  7.625600e-08   \n",
       "1979-12-29 14:24:00.527344       0.4102    0.8708  2.561100e-09   \n",
       "1979-12-30 07:11:58.945312       0.4102    0.8712  8.021000e-08   \n",
       "1979-12-31 00:00:00.000000       0.4102    0.8706  2.120900e-08   \n",
       "\n",
       "                            CONSTANT_HEAD_OUT  WELLS_OUT  RECHARGE_OUT  \\\n",
       "1979-01-01 16:47:59.998970           0.410400     0.0000           0.0   \n",
       "1979-01-02 09:35:59.997940           0.410400     0.0000           0.0   \n",
       "1979-01-03 02:23:59.991760           0.410400     0.0000           0.0   \n",
       "1979-01-03 19:11:59.995880           0.410400     0.0000           0.0   \n",
       "1979-01-04 12:00:00.000000           0.410400     0.0000           0.0   \n",
       "...                                       ...        ...           ...   \n",
       "1979-12-28 04:48:01.054688           0.016918     0.8559           0.0   \n",
       "1979-12-28 21:35:59.472656           0.016907     0.8559           0.0   \n",
       "1979-12-29 14:24:00.527344           0.016897     0.8559           0.0   \n",
       "1979-12-30 07:11:58.945312           0.016886     0.8559           0.0   \n",
       "1979-12-31 00:00:00.000000           0.016875     0.8559           0.0   \n",
       "\n",
       "                            TOTAL_OUT    IN-OUT  PERCENT_DISCREPANCY  \n",
       "1979-01-01 16:47:59.998970     0.4104 -0.000206                -0.05  \n",
       "1979-01-02 09:35:59.997940     0.4104 -0.000201                -0.05  \n",
       "1979-01-03 02:23:59.991760     0.4104 -0.000208                -0.05  \n",
       "1979-01-03 19:11:59.995880     0.4104 -0.000207                -0.05  \n",
       "1979-01-04 12:00:00.000000     0.4104 -0.000206                -0.05  \n",
       "...                               ...       ...                  ...  \n",
       "1979-12-28 04:48:01.054688     0.8728 -0.002407                -0.28  \n",
       "1979-12-28 21:35:59.472656     0.8728 -0.002854                -0.33  \n",
       "1979-12-29 14:24:00.527344     0.8728 -0.001953                -0.22  \n",
       "1979-12-30 07:11:58.945312     0.8728 -0.001618                -0.19  \n",
       "1979-12-31 00:00:00.000000     0.8728 -0.002197                -0.25  \n",
       "\n",
       "[520 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfl = flopy.utils.MfListBudget(os.path.join(model_ws, project_name+\".list\"))\n",
    "df_flux, df_vol = mfl.get_dataframes(start_datetime=\"01-01-1979\")\n",
    "df_flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = df_flux.groupby(lambda x:x.split('_')[-1],axis=1).groups\n",
    "df_flux_in = df_flux.loc[:,groups[\"IN\"]]\n",
    "df_flux_in.columns = df_flux_in.columns.map(lambda x:x.split('_')[0])\n",
    "\n",
    "df_flux_out = df_flux.loc[:,groups[\"OUT\"]]\n",
    "df_flux_out.columns = df_flux_out.columns.map(lambda x:x.split('_')[0])\n",
    "\n",
    "\n",
    "df_flux_delta = df_flux_in - df_flux_out\n",
    "df_flux_delta.iloc[-1,:].plot(kind=\"bar\",figsize=(10,10),grid=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's look at the simulated head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you pass the model instance, then the plots will be offset and rotated\n",
    "h = flopy.utils.HeadFile(os.path.join(model_ws, project_name+\".hds\"), model=ml)\n",
    "h.times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h.plot(totim=h.times[-1], contour=True, grid=False, colorbar=True, figsize=(10,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write heads to shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote ./Transient_Sim1/UMONS_SmallModel_Transient_Sim1_head.shp\n"
     ]
    }
   ],
   "source": [
    "#let's write these heads to shapefile\n",
    "h.to_shapefile(os.path.join(model_ws, project_name+\"_head.shp\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}