{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f70e1295-a64c-41e1-a832-b97af218b6bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext jbmagics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524f4623-d74e-4d46-813c-c27883e89708",
   "metadata": {},
   "source": [
    "# Problem adaption\n",
    "\n",
    "## Create a sinusoidal groundwater level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7401c05-ffbd-42ca-a4c4-c2d8fdb193b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from flopy.mbase import run_model\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from pymf6tools.custom_print import CustomPrint\n",
    "\n",
    "from helpers import make_model_input, get_flux, plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d2a473-7a52-4e6f-ae6b-d632cd65ffac",
   "metadata": {},
   "source": [
    "The water level with oscillate between 320 and 325 meters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c00e2f6-3f9f-4072-b9cf-d66f333b4769",
   "metadata": {},
   "outputs": [],
   "source": [
    "h_mean, h_min, h_max = 320.0, 315.0, 325.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c04b10d1-2476-4a62-a590-cee7439ff1db",
   "metadata": {},
   "outputs": [],
   "source": [
    "amplitude = (h_min - h_max) / 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cc2c97-6cfb-4979-a465-0d30a4107265",
   "metadata": {},
   "source": [
    "Using 401 discrete time steps, we get this sinus curve of the groundwater level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba2f1b4e-4af8-476f-9cd4-61cdc656e829",
   "metadata": {},
   "outputs": [],
   "source": [
    "ntimesteps = 401\n",
    "ihalf = int(ntimesteps / 2) + 1\n",
    "x = np.linspace(-np.pi, np.pi, ntimesteps)\n",
    "chd_head = amplitude * np.sin(x) + h_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "082cce12-4629-45eb-9a52-1dd448be0ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axhline(h_mean, color=\"black\", ls=\":\")\n",
    "ax = plt.gca()\n",
    "ax.axvline(ihalf, color=\"black\", ls=\":\")\n",
    "ax.plot(chd_head, color=\"blue\")\n",
    "ax.set_xlabel(\"Simulation Time, days\")\n",
    "ax.set_ylabel(\"Groundwater Head, m\")\n",
    "ax.set_xlim(0, 400);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5220861d-5f8c-4036-b63d-ea6a98fb8055",
   "metadata": {},
   "source": [
    "## Creating a model\n",
    "\n",
    "Using `flopy`, we create a simple MODFLOW model.\n",
    "It is has 2 layers, 1 row, and 1 column. \n",
    "There are two boundary conditions, \n",
    "(a) constant head will in the lower layer (layer 2) and \n",
    "(b) the river boundary cell in the upper layer model (layer 1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93fcd678-44cf-475d-bc97-ef604890bef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```python\n",
       "\"\"\"Helpers to create a MF6 model.\"\"\"\n",
       "\n",
       "from pathlib import Path\n",
       "\n",
       "import flopy.mf6 as fp\n",
       "import matplotlib.pyplot as plt\n",
       "```\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%include helpers.py\n",
    "end_at = \"import matplotlib\"\n",
    "import_module = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5fc6788-bc2b-4294-af91-219d8f15030d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```python\n",
       "def make_model_input(name, chd_head, h_mean):\n",
       "    \"\"\"Create model input.\"\"\"\n",
       "    ws = Path(name)\n",
       "    sim = fp.MFSimulation(sim_name=name, sim_ws=ws, memory_print_option='all')\n",
       "    pd = [(1, 1, 1.0)] * chd_head.shape[0]\n",
       "    tdis = fp.ModflowTdis(sim, nper=len(pd), perioddata=pd)\n",
       "    ims = fp.ModflowIms(\n",
       "        sim, complexity='simple', outer_dvclose=1e-6, inner_dvclose=1e-6\n",
       "    )\n",
       "    gwf = fp.ModflowGwf(\n",
       "        sim,\n",
       "        modelname=name,\n",
       "        print_input=True,\n",
       "        save_flows=True,\n",
       "    )\n",
       "    dis = fp.ModflowGwfdis(\n",
       "        gwf,\n",
       "        nlay=2,\n",
       "        nrow=1,\n",
       "        ncol=1,\n",
       "        delr=1.0,\n",
       "        delc=1.0,\n",
       "        top=360,\n",
       "        botm=[220, 200],\n",
       "    )\n",
       "    npf = fp.ModflowGwfnpf(\n",
       "        gwf,\n",
       "        k=50.0,\n",
       "        k33=10.0,\n",
       "    )\n",
       "    ic = fp.ModflowGwfic(gwf, strt=chd_head[0])\n",
       "    condref = 1.0\n",
       "    spd = [((0, 0, 0), h_mean, condref, 319.0)]\n",
       "    riv = fp.ModflowGwfriv(\n",
       "        gwf, stress_period_data=spd, pname='RIVER', print_flows=True\n",
       "    )\n",
       "    spd = {idx: [((1, 0, 0), h)] for idx, h in enumerate(chd_head)}\n",
       "    chd = fp.ModflowGwfchd(gwf, stress_period_data=spd, print_flows=True)\n",
       "    oc = fp.ModflowGwfoc(\n",
       "        gwf,\n",
       "        head_filerecord=f'{name}.hds',\n",
       "        budget_filerecord=f'{name}.cbc',\n",
       "        printrecord=[('budget', 'all')],\n",
       "        saverecord=[('head', 'all'), ('budget', 'all')],\n",
       "    )\n",
       "    sim.write_simulation()\n",
       "    return sim\n",
       "```\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%include helpers.py\n",
    "start_at = \"def make_model_input\"\n",
    "end_at = \"return sim\"\n",
    "import_module = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8726714-83dd-4a64-9530-ae87ec9624fb",
   "metadata": {},
   "source": [
    "Create the input files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb50cc7f-2be4-40ad-862a-ea07b42bb697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing solution package ims_-1...\n",
      "  writing model rivercond...\n",
      "    writing model name file...\n",
      "    writing package dis...\n",
      "    writing package npf...\n",
      "    writing package ic...\n",
      "    writing package river...\n",
      "INFORMATION: maxbound in ('gwf6', 'riv', 'dimensions') changed to 1 based on size of stress_period_data\n",
      "    writing package chd_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'chd', 'dimensions') changed to 1 based on size of stress_period_data\n",
      "    writing package oc...\n"
     ]
    }
   ],
   "source": [
    "name = 'rivercond'\n",
    "sim = make_model_input(name, chd_head, h_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c1aa520-d33f-4f85-b663-3939e43d3a22",
   "metadata": {},
   "source": [
    "and run the model:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009fbc08-25c3-4653-b8cd-5bf3b9051845",
   "metadata": {},
   "source": [
    "## Running the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "123df720-ada6-4f63-90be-12b812f29a53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.6.0 12/19/2024\n",
      "\n",
      "        MODFLOW 6 compiled Dec 19 2024 21:26:44 with GCC version 13.3.0\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      "\n",
      " MODFLOW runs in SEQUENTIAL mode\n",
      "\n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2025/01/05  8:01:10\n",
      "\n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      "\n",
      "    Solving:  Stress period:   401    Time step:     1\n",
      "\n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2025/01/05  8:01:10\n",
      " Elapsed run time:  0.082 Seconds\n",
      "\n",
      " Normal termination of simulation.\n"
     ]
    }
   ],
   "source": [
    "sim.run_simulation(custom_print=CustomPrint(show_each_stress_period=False));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bc5812-d2ed-4abb-b43d-11e3cfcde212",
   "metadata": {},
   "source": [
    "## Looking at the results\n",
    "\n",
    "We retrieve the flux from the river boundary cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e51886a-bd5a-41a8-b0ca-b17648c2b5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```python\n",
       "def get_flux(sim, name):\n",
       "    \"\"\"Get flux data model output.\"\"\"\n",
       "    gwf = sim.get_model(name)\n",
       "    bud = gwf.output.budget()\n",
       "    riv = bud.get_data(text='RIV')\n",
       "    flux = [float(entry['q'][0]) for entry in riv]\n",
       "    return flux\n",
       "```\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%include helpers.py\n",
    "start_at = \"def get_flux\"\n",
    "end_at = \"return\"\n",
    "import_module = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9486d662-567b-4f36-9b8b-75c0c7d62701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```python\n",
       "def get_flux(sim, name):\n",
       "    \"\"\"Get flux data model output.\"\"\"\n",
       "    gwf = sim.get_model(name)\n",
       "    bud = gwf.output.budget()\n",
       "    riv = bud.get_data(text='RIV')\n",
       "    flux = [float(entry['q'][0]) for entry in riv]\n",
       "    return flux\n",
       "```\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%include helpers.py\n",
    "start_at = \"def get_flux\"\n",
    "end_at = \"return\"\n",
    "import_module = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "998735a5-5490-490b-9ff1-aae9f8f5761c",
   "metadata": {},
   "outputs": [],
   "source": [
    "flux = get_flux(sim, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d18a37-b490-404a-824f-46661f5fef27",
   "metadata": {},
   "source": [
    "and plot its values over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3858dfcb-7c45-4242-86d0-41ea1f0f09ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```python\n",
       "def plot(flux, vmin=-0.6, vmax=0.6, cell_name='river'):\n",
       "    \"\"\"Plot the river flux.\"\"\"\n",
       "    fig, ax = plt.subplots(\n",
       "        nrows=1,\n",
       "        ncols=1,\n",
       "        layout='constrained',\n",
       "        figsize=(4.5, 5),\n",
       "    )\n",
       "    ax.set_title(f'Flux in {cell_name} cell')\n",
       "    ax.set_xlim(0, 400.0)\n",
       "    ax.set_ylim(vmin, vmax)\n",
       "    ax.set_xlabel('Simulation time, days')\n",
       "    ax.set_ylabel('Flow tate, m$^3$/d')\n",
       "    ax.axhline(0, lw=0.5, ls='-.', color='black')\n",
       "    ax.plot(\n",
       "        flux,\n",
       "        color='black',\n",
       "        lw=1.0,\n",
       "        label='River flux',\n",
       "    )\n",
       "    ax.legend()\n",
       "    return ax\n",
       "```\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%include helpers.py\n",
    "start_at = \"def plot\"\n",
    "end_at = \"return\"\n",
    "import_module = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "650d16b9-f1cd-4e48-8a7a-36809dadfd0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(flux);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cb940a3-b0c6-42c4-8136-53b51981fcdc",
   "metadata": {},
   "source": [
    "The flux shows the same pattern as the groundwater level.\n",
    "The river conductance is constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc1ef535-8602-4536-b887-64f3906654e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}