examples/Notebooks/flopy3_drain_return.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:47.627135Z",
     "iopub.status.busy": "2022-12-15T13:09:47.626571Z",
     "iopub.status.idle": "2022-12-15T13:09:48.964792Z",
     "shell.execute_reply": "2022-12-15T13:09:48.960412Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.15 (default, Oct 18 2022, 09:36:49) \n",
      "[GCC 11.2.0]\n",
      "numpy version: 1.21.6\n",
      "matplotlib version: 3.5.3\n",
      "flopy version: 3.3.6\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "from tempfile import TemporaryDirectory\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\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(\"flopy version: {}\".format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.018714Z",
     "iopub.status.busy": "2022-12-15T13:09:49.018081Z",
     "iopub.status.idle": "2022-12-15T13:09:49.030797Z",
     "shell.execute_reply": "2022-12-15T13:09:49.029685Z"
    }
   },
   "outputs": [],
   "source": [
    "# temporary directory\n",
    "temp_dir = TemporaryDirectory()\n",
    "modelpth = temp_dir.name\n",
    "\n",
    "# creat the model package\n",
    "m = flopy.modflow.Modflow(\n",
    "    \"drt_test\",\n",
    "    model_ws=modelpth,\n",
    "    exe_name=\"mfnwt\",\n",
    "    version=\"mfnwt\",\n",
    ")\n",
    "d = flopy.modflow.ModflowDis(\n",
    "    m,\n",
    "    nlay=1,\n",
    "    nrow=10,\n",
    "    ncol=10,\n",
    "    nper=1,\n",
    "    perlen=1,\n",
    "    top=10,\n",
    "    botm=0,\n",
    "    steady=True,\n",
    ")\n",
    "b = flopy.modflow.ModflowBas(m, strt=10, ibound=1)\n",
    "u = flopy.modflow.ModflowUpw(m, hk=10)\n",
    "n = flopy.modflow.ModflowNwt(m)\n",
    "o = flopy.modflow.ModflowOc(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.035619Z",
     "iopub.status.busy": "2022-12-15T13:09:49.034812Z",
     "iopub.status.idle": "2022-12-15T13:09:49.041589Z",
     "shell.execute_reply": "2022-12-15T13:09:49.040411Z"
    }
   },
   "outputs": [],
   "source": [
    "# create the drt package\n",
    "spd = []\n",
    "for i in range(m.nrow):\n",
    "    spd.append([0, i, m.ncol - 1, 5.0, 50.0, 1, 1, 1, 1.0])\n",
    "d = flopy.modflow.ModflowDrt(m, stress_period_data={0: spd})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.045961Z",
     "iopub.status.busy": "2022-12-15T13:09:49.045078Z",
     "iopub.status.idle": "2022-12-15T13:09:49.075946Z",
     "shell.execute_reply": "2022-12-15T13:09:49.074734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /home/runner/.local/bin/mfnwt\n",
      "\n",
      "                                  MODFLOW-NWT-SWR1 \n",
      "    U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUNDWATER-FLOW MODEL\n",
      "                             WITH NEWTON FORMULATION\n",
      "                             Version 1.3.0 07/01/2022                        \n",
      "                    BASED ON MODFLOW-2005 Version 1.12.0 02/03/2017                       \n",
      "\n",
      "                    SWR1 Version 1.05.0 03/10/2022                       \n",
      "\n",
      " Using NAME file: drt_test.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2022/12/15 13:09:49\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Groundwater-Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2022/12/15 13:09:49\n",
      " Elapsed run time:  0.001 Seconds\n",
      "\n",
      "  Normal termination of simulation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the drt model\n",
    "m.write_input()\n",
    "m.run_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.081295Z",
     "iopub.status.busy": "2022-12-15T13:09:49.080601Z",
     "iopub.status.idle": "2022-12-15T13:09:49.372681Z",
     "shell.execute_reply": "2022-12-15T13:09:49.371569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'data Layer 1'}>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heads for the drt model\n",
    "hds = flopy.utils.HeadFile(os.path.join(m.model_ws, m.name + \".hds\"))\n",
    "hds.plot(colorbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.376714Z",
     "iopub.status.busy": "2022-12-15T13:09:49.376346Z",
     "iopub.status.idle": "2022-12-15T13:09:49.384807Z",
     "shell.execute_reply": "2022-12-15T13:09:49.383566Z"
    }
   },
   "outputs": [],
   "source": [
    "# remove the drt package and create a standard drain file\n",
    "spd = []\n",
    "for i in range(m.nrow):\n",
    "    spd.append([0, i, m.ncol - 1, 5.0, 1.0])\n",
    "m.remove_package(\"DRT\")\n",
    "d = flopy.modflow.ModflowDrn(m, stress_period_data={0: spd})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.388607Z",
     "iopub.status.busy": "2022-12-15T13:09:49.388306Z",
     "iopub.status.idle": "2022-12-15T13:09:49.411910Z",
     "shell.execute_reply": "2022-12-15T13:09:49.410834Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: /home/runner/.local/bin/mfnwt\n",
      "\n",
      "                                  MODFLOW-NWT-SWR1 \n",
      "    U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUNDWATER-FLOW MODEL\n",
      "                             WITH NEWTON FORMULATION\n",
      "                             Version 1.3.0 07/01/2022                        \n",
      "                    BASED ON MODFLOW-2005 Version 1.12.0 02/03/2017                       \n",
      "\n",
      "                    SWR1 Version 1.05.0 03/10/2022                       \n",
      "\n",
      " Using NAME file: drt_test.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2022/12/15 13:09:49\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Groundwater-Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2022/12/15 13:09:49\n",
      " Elapsed run time:  0.001 Seconds\n",
      "\n",
      "  Normal termination of simulation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the drain model\n",
    "m.write_input()\n",
    "m.run_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.416601Z",
     "iopub.status.busy": "2022-12-15T13:09:49.416238Z",
     "iopub.status.idle": "2022-12-15T13:09:49.789947Z",
     "shell.execute_reply": "2022-12-15T13:09:49.788736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'data Layer 1'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the heads for the model with the drain\n",
    "hds = flopy.utils.HeadFile(os.path.join(m.model_ws, m.name + \".hds\"))\n",
    "hds.plot(colorbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:09:49.795242Z",
     "iopub.status.busy": "2022-12-15T13:09:49.794098Z",
     "iopub.status.idle": "2022-12-15T13:09:49.799767Z",
     "shell.execute_reply": "2022-12-15T13:09:49.798711Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    # ignore PermissionError on Windows\n",
    "    temp_dir.cleanup()\n",
    "except:\n",
    "    pass"
   ]
  }
 ],
 "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": "3.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}