examples/Notebooks/flopy3_drain_return.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 18:42:56) \n",
      "[Clang 10.0.1 ]\n",
      "numpy version: 1.18.5\n",
      "matplotlib version: 3.2.2\n",
      "flopy version: 3.3.3\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\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": {},
   "outputs": [],
   "source": [
    "# creat the model package\n",
    "m = flopy.modflow.Modflow(\"drt_test\", model_ws=\"temp\", exe_name=\"mfnwt\", version=\"mfnwt\")\n",
    "d = flopy.modflow.ModflowDis(m, nlay=1, nrow=10, ncol=10, nper=1, perlen=1, \n",
    "                             top=10, botm=0, steady=True)\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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.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 1.2.0 03/01/2020                        \n",
      "                    BASED ON MODFLOW-2005 Version 1.12.0 02/03/2017                       \n",
      "\n",
      "                    SWR1 Version 1.04.0 09/15/2016                       \n",
      "\n",
      " Using NAME file: drt_test.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:30:53\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Groundwater-Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:30:54\n",
      " Elapsed run time:  0.036 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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104614160>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASUAAAEICAYAAAAZVeKfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUeklEQVR4nO3dfYxldX3H8fdnZnZZdgGB4gNPupoalJoCulWUiFakRSVArFpIIWht1qQ+oKVRtElNk9rSxhpoNLYbfEBBFBGrIS0PBYnVWhTWp8XVYhBhdWVZVmBBd3cePv3jnrX3zszOnbnnnjm/2fm8kpO599x7f+ebYfbD+Z17zvnKNhERpRhpu4CIiG4JpYgoSkIpIoqSUIqIoiSUIqIoCaWIKEpCqVCSPinpb9uuI2KxJZT2A5Jul/RnNT5fZABK2iDpR5KmJL2x7XpicSSUonWSxvbx0neBPwc2LmI50bKEUiEknSRpo6Sdkj4HrOp67TBJN0h6SNIvq8fHVK99AHgp8GFJj0v6cLX+ckkPSHpM0l2SXjpgXbOOI+lpkn4l6be63vuCqsYV1fM/lbS5qvkmSc/oeq8lvVXSPcA9s23b9kds3wrsGqT2WJoSSgWQtBL4N+DTwOHA54E/6nrLCPAJ4BnA04FfAx8GsP1XwH8Bb7N9kO23VZ/5FnBiNd5ngM9LWsXCzTqO7V8AtwNv6Hrv+cBnbY9LOgd4H/Ba4MlVjddMG/sc4EXA8QPUFfuphFIZTgZWAJfZHrd9HZ0wAMD2w7a/YPtXtncCHwBeNteAtq+qPjdh+5+AA4DjFlpYn3GupBNESBoFzqMTrABvAf7e9mbbE8DfASd27y1Vr++w/euF1hX7r4RSGY4Cfubeq6N/uveBpNWS/lXSTyU9BnwVOLQKgllJuriaOj0q6RHgScARCy2szzhfAo6X9CzgdOBR29+sXnsGcLmkR6rP7QAEHN01/AMLrSf2fwmlMmwFjpakrnVP73p8MZ29kxfZPgQ4tVq/9/09t3qojvu8h87U6jDbhwKPdr1/XvqNY3sXcC3wJ8AF/P9eEnQC5y22D+1aDrT9313vyS0qYoaEUhm+AUwA75A0Jum1wAu7Xj+YznGkRyQdDrx/2ucfBJ417f0TwEPAmKS/Bg7pU8OopFVdy8p5jvMp4I3AWcBVXev/BXivpN8BkPQkSa/vU0MPSSur42ACVlR15W92P5f/wAWwvYfOAeE3Ar8E/hi4vustlwEHAtuB/wFunDbE5cDrqm+5/hm4CfgP4H/pTAN30X+qdAmd4Nu73DafcWx/HZgCNtq+r2v9F4F/AD5bTTk3Aa/qU8N0N1e1vATYUD0+dc5PxJKn3OQt6pJ0G/AZ21e0XUssfQmlqEXS7wG3AMdW3wxG1NJ3+ibp45K2SdrUte5wSbdIuqf6eVizZUaJJF0J/CfwzgRSDEvfPSVJpwKPA5+y/bxq3T8CO2xfKukSOt/MvKfxaiNivzev6ZuktcANXaH0I+DltrdKOhK43faCT8yLiJhuXxdC9vNU21sBqmB6yr7eKGk9sB5gzWq94Dm/vXLATc7u3j0HD3W8vZ7YdUAj42rPgk4VmpeRiaEP2ei4mmjmOObIxNTwB52YHP6YABPN/HIfm9qx3faTGxl8kQwaSvNmewOdr3NZd8Iqf/Omp/f5xMKc95NXDHW8ve64Z20j467cMvywO3Db0IfsjLu9mfBYtX28kXEP2D78q1VGtj869DEBph56uJFxb/7Vp3/a/11lG/Q8pQeraRvVz4b+WUTEcjNoKH0ZuLB6fCGda6AiImqbzykB19C5DOI4SVskvRm4FDi9uhfO6dXziIja+h5Tsn3ePl46bci1RETk2reIKEtCKSKKklCKiKIklCKiKAmliChKQikiipJQioiiJJQioigJpYgoSkIpIoqSUIqIoiSUIqIoCaWIKEpCKSKKklCKiKIklCKiKI03Duj2kz0Hc8FPXzbUMTdte9pQx/uNXzXzqxlp4J75aqjhhqaaaRygBpqOALCUuj039LvdH2RPKSKKklCKiKIklCKiKAmliChKQikiipJQioiiJJQioigJpYgoSkIpIoqSUIqIoiSUIqIoCaWIKEpCKSKKklCKiKIklCKiKAmliChKQikiipJQioii1AolSe+SdLekTZKukbRqWIVFxPI0cChJOhp4B7DO9vOAUeDcYRUWEctT3bvjjwEHShoHVgM/n+vNj+9eyTfufWbNTfaaenzFUMfba+yx0UbGHf318Mcc2TP8MaGZJgcAmmyoIUET40421eUg9mXgPSXbPwM+CNwPbAUetX3z9PdJWi/pTkl3Tu58YvBKI2JZqDN9Oww4G3gmcBSwRtL5099ne4PtdbbXjR68ZvBKI2JZqHOg+5XAT2w/ZHscuB54yXDKiojlqk4o3Q+cLGm1JAGnAZuHU1ZELFd1jindAVwHbAS+X421YUh1RcQyVevbN9vvB94/pFoiInJGd0SUJaEUEUVJKEVEURJKEVGUhFJEFKXutW8RsZ/5w99f44d3TM5Yf9f3dt9k+4ymt59QiogeD+2Y4Os3HjVj/eqj7jtiMbafUIqIHsaMe+ae0mJJKEVEDwPjtHfLloRSRPQwsNsJpYgohG32uJkb8c1HQikiehgxjlrbfkIpInpMAbvc3imMCaWI6GFgPKEUEaUwYtzNNM6Yj8UNpfER+MVwW8Ot2NXM3HdFQz0OxhroZjK2u5mDkiMTTXUdaeibnakGxm3qgG+L3271M4XY5Wa6BM1H9pQiooct9iybPaWIKF7n5MmEUkQUwohdUwufvkm6D9gJTAITttdJOhz4HLAWuA94g+1fzjVObl0SET06B7rHZizz9Pu2T7S9rnp+CXCr7WcDt1bP55RQiogeU3SOKU1fBnQ2cGX1+ErgnH4fSChFRA9b7PaKGct8PgrcLOkuSeurdU+1vbUzrrcCT+k3SI4pRUQPA3tmn64dIenOrucbbHf3ejzF9s8lPQW4RdIPB9l+Qikiekwhds9+oHt717GiGWz/vPq5TdIXgRcCD0o60vZWSUcC2/ptP9O3iOix94zu6ctcJK2RdPDex8AfAJuALwMXVm+7EPhSv+1nTykietgDXWbyVOCLkqCTK5+xfaOkbwHXSnozcD/w+n4DJZQioscg5ynZvhc4YZb1DwOnLWSshFJE9OjcJSBndEdEIYyYSChFRCmmLHZPthcNCaWI6JGbvEVEYTJ9i4iCZPoWEUUxMNHi9K3WliUdKuk6ST+UtFnSi4dVWES0xGJianTGsljq7ildDtxo+3WSVgKrh1BTRLRoCtiziCE03cChJOkQ4FTgjQC29wB7hlNWRLTFiImppfnt27OAh4BPSDoBuAu4yHZPH5DqvirrAcaedBgH/HK43UdGxoc63G+M7mpm3Ca6mYzuaaibyZ5mOm6MjDfUyWOygd/D5OTwxwQ81V5b7H7sdveU6sThGPB84KO2TwKeYJZbXdreYHud7XVjq9fU2FxELIa9e0rTl8VSZ0tbgC2276ieX0cnpCJiiZv0yIxlsQy8Jdu/AB6QdFy16jTgB0OpKiJaY4vxydEZy2Kp++3b24Grq2/e7gXeVL+kiGiTgckBp2uSRoE7gZ/ZPnPRWyzZ/k51vOh3bZ/Tb2MRsQQYJq0ZyzxdBGzuep4WSxFRjxETkyMzln4kHQO8Briia/WCWyzlMpOI6GHD1OzTt37dTC4D3g0c3LWup8VS1elkTgmliJhhcmrW6do+u5lIOhPYZvsuSS+vs+2EUkT0MBrkQPcpwFmSXg2sAg6RdBVpsRQRtRk8pRnLnB+x32v7GNtrgXOB22yfT1osRcQwTPUJoQW4lLRYiog6bJiax7dt+/68bwdurx6nxVJE1NV/utakhFJE9KqOKbUloRQRMyWUIqIYJqEUEWXxZEIpIkphUEM3B52PhFJETKNM3yKiIAYyfYuIkiyb6ZsmYcXO4Y/ZhNHdzYw7tmv4XSxGdzfUzWS8mXE12cxfvKYaGNcNdR1xi//q50EtNlvJnlJE9Mr0LSJKs2ymbxFRPuWUgIgojVqcvuUmbxHRq9pTmr7MRdIqSd+U9F1Jd0v6m2r94ZJukXRP9fOwfptPKEXETFOzLHPbDbzC9gnAicAZkk4mLZYiYhgWuqfkjserpyuqxQzQYimhFBG99j19O0LSnV3L+u6PSRqV9B06zQFusX0H01osAWmxFBELI/Z5UvI+WywB2J4ETpR0KPBFSc8bZPvZU4qIXgMc6O75uP0InXt0n0HVYgkgLZYiYmADfPv25GoPCUkHAq8EfkhaLEVEbR7omtIjgSsljdLZ2bnW9g2SvkFaLEVEXQs9o9v294CTZlmfFksRUZOZz3lJjUkoRUQPASMJpYgoSkIpIoqRuwRERGlGGrqj67y2XXeA6tTyb0u6YRgFRUTL9h7oXtgFuUMzjJMnLwI2D2GciCiAqHdGd121pm+SjgFeA3wA+Iu+H5iCsV/X2eIsNTTVOGBPM3dOb+Im/03d4H9kvKEb/E809BfeREOChpoceKrFO/P3YxiZbK++untKlwHvZo6dO0nr915VPLHriZqbi4jF0Oae0sChJOlMYJvtu+Z6n+0NttfZXje2as2gm4uIxVLzgty66kzfTgHOkvRqYBVwiKSrbJ8/nNIiog2dW5cswemb7ffaPsb2WuBc4LYEUsR+YAnvKUXEfmpkosVtD2MQ27fbPnMYY0VEywya8oxlLpKOlfQVSZurbiYXVevTzSQi6hnwPKUJ4GLbzwVOBt4q6XjSzSQiarPR5Mxl7o94q+2N1eOddE6oPpoBupnkmFJE9HK9b98kraVzw7cZ3UwkpZtJRCzcPqZrR0i6s+v5Btsbej4nHQR8AXin7cekhbf/TihFRK997ynN2WJJ0go6gXS17eur1Q9KOrLaS0o3k4hYuL0nTy7kmJI6u0QfAzbb/lDXS+lmEhE1VacELNApwAXA96suuQDvAy4l3Uwioh6jBd4dwfbX6OxkzSbdTCKihprfvtWVUIqIGQaYvg1NQikieshu7kZ885BQioiZphJKEVEKN3jL4nlIKEVELzt7ShFRlmXz7Zs8/G4eTd0Rb7SxDiFLp5vJQs9Vmbem/uAb+L+73dDvoKlxh8GGifa6UWZPKSJmyvQtIophw2T2lCKiFCbTt4goSb59i4iSmEzfIqIgNp5or8dSbvIWETNNTs1c+pD0cUnbJG3qWpcWSxFR095v36Yv/X0SOGPaurRYioiabDw+MWPp/zF/FdgxbXVaLEVEPQY8+55R324ms0iLpYioZ6d33HTL+DVHzPLSdtvTp2dDl1CKiB5DDp60WIqIoiy4xVJCKSKGQtI1wDeA4yRtqdoqXQqcLuke4PTq+ZwyfYuIobB93j5eWlCLpewpRURREkoRUZSEUkQUZeBQknSspK9I2izpbkkXDbOwiFie6hzongAutr1R0sHAXZJusf2DIdUWEcvQwHtKtrfa3lg93glsBo4eVmERsTwN5ZQASWuBk4A7ZnltPbAeYOWawxgZ8m1amup5PjLeyLCMNNDJo6l2OI2N29RdDZv4W2jo7yv2rfaBbkkHAV8A3mn7semv295ge53tdWMHrKm7uYjYz9UKJUkr6ATS1bavH05JEbGc1fn2TcDHgM22PzS8kiJiOauzp3QKcAHwCknfqZZXD6muiFimBj7QbftrgIZYS0REzuiOiLIklCKiKAmliChKQikiipJQioiiJJQioigJpYgoSkIpIoqSUIqIoiSUIqIoCaWIKEpCKSKKklCKiKIklCKiKAmliChKQikiijKUbibzJYbffUQNNcZoqksKDQwrL51aG+WG/hhiUWVPKSKKklCKiKIklCKiKAmliChKQikiipJQioiiJJQioigJpYgoSkIpIoqSUIqIoiSUIqIoCaWIKEpCKSKKklCKiKIklCKiKAmliChKQikiipJQioii1AolSWdI+pGkH0u6ZFhFRcTyNXAoSRoFPgK8CjgeOE/S8cMqLCKWpzp7Si8Efmz7Xtt7gM8CZw+nrIhYrup0MzkaeKDr+RbgRdPfJGk9sL56uvuOz/zlphrbXExHANvbLmIBllK9S6lWWFr1Htd2AXXVCSXNsm5GUx7bG4ANAJLutL2uxjYXzVKqFZZWvUupVlha9Uq6s+0a6qozfdsCHNv1/Bjg5/XKiYjlrk4ofQt4tqRnSloJnAt8eThlRcRyNfD0zfaEpLcBNwGjwMdt393nYxsG3V4LllKtsLTqXUq1wtKqdynVOiu5qZbPEREDyBndEVGUhFJEFGVRQmkpXY4i6VhJX5G0WdLdki5qu6Z+JI1K+rakG9qupR9Jh0q6TtIPq9/xi9uuaV8kvav6G9gk6RpJq9quqZukj0vaJmlT17rDJd0i6Z7q52Ft1jiIxkNpCV6OMgFcbPu5wMnAWwuvF+AiYHPbRczT5cCNtp8DnEChdUs6GngHsM728+h8mXNuu1XN8EngjGnrLgFutf1s4Nbq+ZKyGHtKS+pyFNtbbW+sHu+k84/m6Har2jdJxwCvAa5ou5Z+JB0CnAp8DMD2HtuPtFvVnMaAAyWNAasp7Dw8218FdkxbfTZwZfX4SuCcRS1qCBYjlGa7HKXYf+TdJK0FTgLuaLeSOV0GvBuYaruQeXgW8BDwiWq6eYWkNW0XNRvbPwM+CNwPbAUetX1zu1XNy1Ntb4XO/2CBp7Rcz4ItRijN63KU0kg6CPgC8E7bj7Vdz2wknQlss31X27XM0xjwfOCjtk8CnqDQ6UV1LOZs4JnAUcAaSee3W9XysBihtOQuR5G0gk4gXW37+rbrmcMpwFmS7qMzLX6FpKvaLWlOW4AttvfueV5HJ6RK9ErgJ7Yfsj0OXA+8pOWa5uNBSUcCVD+3tVzPgi1GKC2py1Ekic4xj822P9R2PXOx/V7bx9heS+f3epvtYv9vbvsXwAOS9l7JfhrwgxZLmsv9wMmSVld/E6dR6EH5ab4MXFg9vhD4Uou1DKTOXQLmZcDLUdp0CnAB8H1J36nWvc/2v7dY0/7k7cDV1f+g7gXe1HI9s7J9h6TrgI10vpH9NoVdwiHpGuDlwBGStgDvBy4FrpX0ZjrB+vr2KhxMLjOJiKLkjO6IKEpCKSKKklCKiKIklCKiKAmliChKQikiipJQioii/B88g65smi3bTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following  executable to run the model: /Users/jdhughes/.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 1.2.0 03/01/2020                        \n",
      "                    BASED ON MODFLOW-2005 Version 1.12.0 02/03/2017                       \n",
      "\n",
      "                    SWR1 Version 1.04.0 09/15/2016                       \n",
      "\n",
      " Using NAME file: drt_test.nam \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:30:54\n",
      "\n",
      " Solving:  Stress period:     1    Time step:     1    Groundwater-Flow Eqn.\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/02/18 11:30:54\n",
      " Elapsed run time:  0.004 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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c79b310>"
      ]
     },
     "execution_count": 8,
     "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"
    }
   ],
   "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": 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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}