rabbl/tutorial-1.ipynb

## tutorial-1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import flopy\n",
    "\n",
    "# Assign name and create modflow model object\n",
    "modelname = 'tutorial1'\n",
    "mf = flopy.modflow.Modflow(modelname, exe_name='mf2005')\n",
    "\n",
    "# Model domain and grid definition\n",
    "Lx = 1000.\n",
    "Ly = 1000.\n",
    "ztop = 0.\n",
    "zbot = -50.\n",
    "nlay = 1\n",
    "nrow = 10\n",
    "ncol = 10\n",
    "delr = Lx/ncol\n",
    "delc = Ly/nrow\n",
    "delv = (ztop - zbot) / nlay\n",
    "botm = np.linspace(ztop, zbot, nlay + 1)\n",
    "\n",
    "# Create the discretization object\n",
    "dis = flopy.modflow.ModflowDis(mf, nlay, nrow, ncol, delr=delr, delc=delc,\n",
    "                               top=ztop, botm=botm[1:])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables for the BAS package\n",
    "ibound = np.ones((nlay, nrow, ncol), dtype=np.int32)\n",
    "ibound[:, :, 0] = -1\n",
    "ibound[:, :, -1] = -1\n",
    "strt = np.ones((nlay, nrow, ncol), dtype=np.float32)\n",
    "strt[:, :, 0] = 10.\n",
    "strt[:, :, -1] = 0.\n",
    "bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=strt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Add LPF package to the MODFLOW model\n",
    "lpf = flopy.modflow.ModflowLpf(mf, hk=10., vka=10., ipakcb=53)\n",
    "\n",
    "# Add OC package to the MODFLOW model\n",
    "spd = {(0, 0): ['print head', 'print budget', 'save head', 'save budget']}\n",
    "oc = flopy.modflow.ModflowOc(mf, stress_period_data=spd, compact=True)\n",
    "\n",
    "# Add PCG package to the MODFLOW model\n",
    "pcg = flopy.modflow.ModflowPcg(mf)\n",
    "\n",
    "# Write the MODFLOW model input files\n",
    "mf.write_input()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following  executable to run the model: /usr/local/bin/mf2005\n",
      "\n",
      "                                  MODFLOW-2005     \n",
      "    U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUND-WATER FLOW MODEL\n",
      "                             Version 1.12.00 2/3/2017                        \n",
      "\n",
      " Using NAME file: tutorial1.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2019/10/29 11:51:37\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Ground-Water Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2019/10/29 11:51:37\n",
      " Elapsed run time:  0.002 Seconds\n",
      "\n",
      "  Normal termination of simulation\n"
     ]
    }
   ],
   "source": [
    "# Run the MODFLOW model\n",
    "success, buff = mf.run_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0GpgBLIiIM4F32XPoD3T2+w9QrldcRhVoxwNH89FKXV0hbQhIOpwqAO6LiEdLc7dMpX4OcKmkV4EHqE4Jbqeq+DQwb2TrGAfHX7YfC2w/lANusxHYGBHLy/oiqlDolvcf4AvA+ojYFhEfAI9SfS7d8hkMShkCkgQsBNZGxLdbNnXFVOoRcVNETImIPqqLUU9GxJeBZcDs0q19/AOva3bpP2K/ZSNiC/C6pFNK0yxgDV3y/hevATMlHVX+Pg28hq74DPYy0hclRuiizmepDjWfBVaVx8VU52hLgZeAfwXGlf4C/gF4GXiO6orwiL+OMrbPAYvL8knAz6mmfH8YOLK0jynr68r2kzpg3NOBFeUz+GdgbLe9/8AtwAvAauAHwJHd9BkMPHzbsFlyKU8HzGwPh4BZcg4Bs+QcAmbJOQTMknMImCXnEDBL7v8BLI1Fv1k8rW8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Post process the results\n",
    "import matplotlib.pyplot as plt\n",
    "import flopy.utils.binaryfile as bf\n",
    "\n",
    "plt.subplot(1, 1, 1, aspect='equal')\n",
    "hds = bf.HeadFile(modelname + '.hds')\n",
    "head = hds.get_data(totim=1.0)\n",
    "levels = np.arange(1, 10, 1)\n",
    "extent = (delr / 2., Lx - delr / 2., Ly - delc / 2., delc / 2.)\n",
    "plt.contour(head[0, :, :], levels=levels, extent=extent)\n",
    "plt.savefig('tutorial1a.png')\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(1, 1, 1, aspect='equal')\n",
    "\n",
    "hds = bf.HeadFile(modelname+'.hds')\n",
    "times = hds.get_times()\n",
    "head = hds.get_data(totim=times[-1])\n",
    "levels = np.linspace(0, 10, 11)\n",
    "\n",
    "cbb = bf.CellBudgetFile(modelname+'.cbc')\n",
    "kstpkper_list = cbb.get_kstpkper()\n",
    "frf = cbb.get_data(text='FLOW RIGHT FACE', totim=times[-1])[0]\n",
    "fff = cbb.get_data(text='FLOW FRONT FACE', totim=times[-1])[0]\n",
    "\n",
    "modelmap = flopy.plot.ModelMap(model=mf, layer=0)\n",
    "qm = modelmap.plot_ibound()\n",
    "lc = modelmap.plot_grid()\n",
    "cs = modelmap.contour_array(head, levels=levels)\n",
    "quiver = modelmap.plot_discharge(frf, fff, head=head)\n",
    "plt.savefig('tutorial1b.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}