{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# mf6adj demonstration using a simple box model\n",
    "\n",
    "In this notebook, we test mf6adj on a simple box problem and compare the results with an analytical solution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:44.878076Z",
     "iopub.status.busy": "2026-06-02T18:36:44.877905Z",
     "iopub.status.idle": "2026-06-02T18:36:46.146262Z",
     "shell.execute_reply": "2026-06-02T18:36:46.145403Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pathlib as pl\n",
    "import platform\n",
    "import shutil\n",
    "import sys\n",
    "from datetime import datetime\n",
    "\n",
    "import flopy\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyemu\n",
    "\n",
    "try:\n",
    "    import mf6adj\n",
    "except ImportError:\n",
    "    sys.path.insert(0, str(pl.Path(\"../\").resolve()))\n",
    "    import mf6adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.148758Z",
     "iopub.status.busy": "2026-06-02T18:36:46.148443Z",
     "iopub.status.idle": "2026-06-02T18:36:46.152402Z",
     "shell.execute_reply": "2026-06-02T18:36:46.151599Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MF6 binary: /home/runner/work/mf6adj/mf6adj/.pixi/envs/default/bin/mf6\n",
      "Using MF6 library: /home/runner/work/mf6adj/mf6adj/.pixi/envs/default/bin/libmf6.so\n"
     ]
    }
   ],
   "source": [
    "mf6_bin, lib_name = mf6adj.get_conda_mf6_paths()\n",
    "print(f\"Using MF6 binary: {mf6_bin}\")\n",
    "print(f\"Using MF6 library: {lib_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## Copy the MODFLOW Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.154075Z",
     "iopub.status.busy": "2026-06-02T18:36:46.153916Z",
     "iopub.status.idle": "2026-06-02T18:36:46.156590Z",
     "shell.execute_reply": "2026-06-02T18:36:46.155822Z"
    }
   },
   "outputs": [],
   "source": [
    "org_ws = pl.Path(\"xd_box_chd_ana\")\n",
    "assert pl.Path(org_ws).exists()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "Set up a local copy of the model files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.161889Z",
     "iopub.status.busy": "2026-06-02T18:36:46.161683Z",
     "iopub.status.idle": "2026-06-02T18:36:46.176894Z",
     "shell.execute_reply": "2026-06-02T18:36:46.176124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('xd_box_chd_ana_working')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = pl.Path(\"xd_box_chd_ana_working\")\n",
    "if pl.Path(ws).exists():\n",
    "    shutil.rmtree(ws)\n",
    "shutil.copytree(org_ws, ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.178583Z",
     "iopub.status.busy": "2026-06-02T18:36:46.178427Z",
     "iopub.status.idle": "2026-06-02T18:36:46.335940Z",
     "shell.execute_reply": "2026-06-02T18:36:46.335159Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading simulation...\n",
      "  loading simulation name file...\n",
      "  loading tdis package...\n",
      "  loading model gwf6...\n",
      "    loading package dis...\n",
      "    loading package ic...\n",
      "    loading package sto...\n",
      "    loading package chd...\n",
      "    loading package wel...\n",
      "    loading package oc...\n",
      "    loading package npf...\n",
      "  loading solution package xdbox...\n"
     ]
    }
   ],
   "source": [
    "sim = flopy.mf6.MFSimulation.load(sim_ws=ws)\n",
    "m = sim.get_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.338048Z",
     "iopub.status.busy": "2026-06-02T18:36:46.337822Z",
     "iopub.status.idle": "2026-06-02T18:36:46.342378Z",
     "shell.execute_reply": "2026-06-02T18:36:46.341666Z"
    }
   },
   "outputs": [],
   "source": [
    "ib = m.dis.idomain.array[0, :, :].astype(float)\n",
    "ib[ib > 0] = np.nan\n",
    "ib_cmap = plt.get_cmap(\"Greys_r\")\n",
    "ib_cmap.set_bad(alpha=0.0)\n",
    "\n",
    "\n",
    "def plot_model(arr, units=None):\n",
    "    arr[~np.isnan(ib)] = np.nan\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
    "    cb = ax.imshow(arr, cmap=\"plasma\")\n",
    "    plt.colorbar(cb, ax=ax, label=units)\n",
    "    plt.imshow(ib, cmap=ib_cmap)\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "Run the existing model in our local workspace.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.344198Z",
     "iopub.status.busy": "2026-06-02T18:36:46.344030Z",
     "iopub.status.idle": "2026-06-02T18:36:46.494741Z",
     "shell.execute_reply": "2026-06-02T18:36:46.493927Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/runner/work/mf6adj/mf6adj/.pixi/envs/default/bin/mf6\n",
      "xd_box_chd_ana_working\n",
      "['xdbox.tdis', 'xdbox.dis', 'xdbox.chd', 'xdbox.sto', 'xdbox.cbb', 'xdbox.oc', 'mfsim.nam', 'xdbox.nam', 'test.adj', 'xdbox.ims', 'xdbox.npf', 'xdbox.ic', 'xdbox.wel']\n",
      "mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                        VERSION 6.8.0.dev0+a6a4984.dirty\n",
      "                               ***DEVELOP MODE***\n",
      "\n",
      "   MODFLOW 6 compiled Jun 01 2026 18:46:14 with Intel(R) Fortran Intel(R) 64\n",
      "   Compiler Classic for applications running on Intel(R) 64, Version 2021.6.0\n",
      "                             Build 20220226_000000\n",
      "\n",
      "This software is preliminary or provisional and is subject to \n",
      "revision. It is being provided to meet the need for timely best \n",
      "science. The software has not received final approval by the U.S. \n",
      "Geological Survey (USGS). No warranty, expressed or implied, is made \n",
      "by the USGS or the U.S. Government as to the functionality of the \n",
      "software and related material nor shall the fact of release \n",
      "constitute any such warranty. The software is provided on the \n",
      "condition that neither the USGS nor the U.S. Government shall be held \n",
      "liable for any damages resulting from the authorized or unauthorized \n",
      "use of the software.\n",
      "\n",
      " \n",
      " MODFLOW runs in SEQUENTIAL mode\n",
      " \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2026/06/02 18:36:46\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n",
      "    Solving:  Stress period:     1    Time step:     1\n",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2026/06/02 18:36:46\n",
      " Elapsed run time:  0.143 Seconds\n",
      " \n",
      " Normal termination of simulation.\n"
     ]
    }
   ],
   "source": [
    "print(mf6_bin)\n",
    "print(ws)\n",
    "print(os.listdir(ws))\n",
    "pyemu.os_utils.run(mf6_bin.name, cwd=ws)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "Now plot some heads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.496773Z",
     "iopub.status.busy": "2026-06-02T18:36:46.496596Z",
     "iopub.status.idle": "2026-06-02T18:36:46.659854Z",
     "shell.execute_reply": "2026-06-02T18:36:46.658875Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'layer 1 final heads')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hds = flopy.utils.HeadFile(pl.Path(ws) / \"xdbox.hds\")\n",
    "final_arr = hds.get_data()\n",
    "fig, ax = plot_model(final_arr[0, :, :], units=\"meters\")\n",
    "ax.set_title(\"layer 1 final heads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "The main requirement for using mf6adj is an input file that describes the performance measures. Fortunately, this file follows a modern format similar to other MF6 input files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.661977Z",
     "iopub.status.busy": "2026-06-02T18:36:46.661757Z",
     "iopub.status.idle": "2026-06-02T18:36:46.664749Z",
     "shell.execute_reply": "2026-06-02T18:36:46.664131Z"
    }
   },
   "outputs": [],
   "source": [
    "pm_fname = \"xdbox.dat\"\n",
    "fpm = open(pl.Path(ws) / pm_fname, \"w\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "16",
   "metadata": {},
   "source": [
    "Here we use a performance measure defined as a single simulated head at one location and one simulation time. For this type of performance measure, an analytical solution exists, so it can be used for comparison. Let's make one of those:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "## Run mf6adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.666815Z",
     "iopub.status.busy": "2026-06-02T18:36:46.666641Z",
     "iopub.status.idle": "2026-06-02T18:36:46.670331Z",
     "shell.execute_reply": "2026-06-02T18:36:46.669423Z"
    }
   },
   "outputs": [],
   "source": [
    "l, r, c = 1, 41, 21  # the layer row and column\n",
    "sp, ts = 1, 1  # stress period and time step\n",
    "pm_name = \"pm_single\"\n",
    "fpm.write(f\"begin performance_measure {pm_name}\\n\")\n",
    "fpm.write(f\"{sp} {ts} {l} {r} {c} head direct 1.0 -1e30\\n\")\n",
    "fpm.write(\"end performance_measure\\n\\n\")\n",
    "fpm.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19",
   "metadata": {},
   "source": [
    "Now we should be ready to go. The adjoint solution process requires one forward model run and then a solve for the adjoint state, which uses the forward solution components (for example, the conductance matrix, RHS, heads, and saturation). The adjoint solve has two important characteristics: it is linear, regardless of the forward model's linearity, and it proceeds backward in time, starting with the last stress period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:46.672019Z",
     "iopub.status.busy": "2026-06-02T18:36:46.671860Z",
     "iopub.status.idle": "2026-06-02T18:36:47.027225Z",
     "shell.execute_reply": "2026-06-02T18:36:47.026432Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-06-02 18:36:46,673 - Logger instance 'Mf6Adj-xdbox-71a8a691' created.\n",
      "2026-06-02 18:36:46,674 - Running from /home/runner/work/mf6adj/mf6adj/examples/xd_box_chd_ana_working\n",
      "2026-06-02 18:36:46,675 - Structured grid found\n",
      "2026-06-02 18:36:46,696 - MODFLOW 6 version: 6.8.0.dev0\n",
      "2026-06-02 18:36:46,697 - Processing adjoint file: xdbox.dat\n",
      "2026-06-02 18:36:46,698 - Starting flow solution\n",
      "2026-06-02 18:36:46,791 - Flow (stress period,time step) (1,1) converged in 2 iters, took   0.001519 mins\n",
      "2026-06-02 18:36:46,819 - Flow solution finished and took  0.0020078 minutes\n",
      "2026-06-02 18:36:46,828 - Starting solve_adjoint at 2026-06-02 18:36:46.828952\n",
      "2026-06-02 18:36:46,831 - Structured grid found, shape: (1, 80, 100)\n",
      "2026-06-02 18:36:46,834 - Starting adjoint solve for PerfMeas: pm_single (kper, kstp) (1, 1)\n",
      "2026-06-02 18:36:46,836 - Solving for lambda\n",
      "2026-06-02 18:36:46,837 - Solving with direct with solver options: {'use_umfpack': True}\n",
      "2026-06-02 18:36:46,858 - Solving for lambda took: 0.021637 seconds\n",
      "2026-06-02 18:36:46,963 - Adjoint solve took: 0.128761 seconds to solve adjoint solution for PerfMeas: pm_single (kper, kstp) (1, 1)\n",
      "2026-06-02 18:36:46,963 - Write group to hdf file\n",
      "2026-06-02 18:36:46,983 - Formulate composite sensitivities\n",
      "2026-06-02 18:36:46,983 - Writing composite sensitivities\n",
      "2026-06-02 18:36:46,996 - Adjoint solve took:    0.16794 seconds for pm 'pm_single' at (kper,kstp) (1, 1)\n",
      "2026-06-02 18:36:46,997 - Finalizing Mf6Adj\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "took: 0.351643\n"
     ]
    }
   ],
   "source": [
    "forward_hdf5_name = \"forward.hdf5\"\n",
    "start = datetime.now()\n",
    "import mf6adj\n",
    "\n",
    "adj = mf6adj.Mf6Adj(pm_fname, lib_name, logging_level=\"INFO\", working_directory=ws)\n",
    "adj.solve_forward_model(\n",
    "    hdf5_name=forward_hdf5_name\n",
    ")  # solve the standard forward solution\n",
    "dfsum = adj.solve_adjoint()  # solve the adjoint state for each performance measure\n",
    "adj.finalize()  # release components\n",
    "duration = (datetime.now() - start).total_seconds()\n",
    "print(\"took:\", duration)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "Let's see what happened."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22",
   "metadata": {},
   "source": [
    "## Plot the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.029230Z",
     "iopub.status.busy": "2026-06-02T18:36:47.029056Z",
     "iopub.status.idle": "2026-06-02T18:36:47.033101Z",
     "shell.execute_reply": "2026-06-02T18:36:47.032455Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['forward.hdf5', 'adjoint_solution_pm_single.hdf5']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[f for f in os.listdir(ws) if f.endswith(\"hdf5\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24",
   "metadata": {},
   "source": [
    "mf6adj uses the widely available HDF5 format to store information. These files hold low-level details about the adjoint solution. However, mf6adj.solve_adjoint() also returns a higher-level summary of the results. Let's look at that first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.035093Z",
     "iopub.status.busy": "2026-06-02T18:36:47.034914Z",
     "iopub.status.idle": "2026-06-02T18:36:47.038289Z",
     "shell.execute_reply": "2026-06-02T18:36:47.037671Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dfsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.040124Z",
     "iopub.status.busy": "2026-06-02T18:36:47.039959Z",
     "iopub.status.idle": "2026-06-02T18:36:47.043416Z",
     "shell.execute_reply": "2026-06-02T18:36:47.042720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pm_single']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dfsum.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.045085Z",
     "iopub.status.busy": "2026-06-02T18:36:47.044913Z",
     "iopub.status.idle": "2026-06-02T18:36:47.053603Z",
     "shell.execute_reply": "2026-06-02T18:36:47.052959Z"
    }
   },
   "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>k11</th>\n",
       "      <th>k33</th>\n",
       "      <th>wel6_q</th>\n",
       "      <th>rch6_recharge</th>\n",
       "      <th>ss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>node</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>-0.000066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>-0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>-0.000190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002156</td>\n",
       "      <td>0.002156</td>\n",
       "      <td>-0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7996</th>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7997</th>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7998</th>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7999</th>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8000</th>\n",
       "      <td>-0.000001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           k11  k33    wel6_q  rch6_recharge        ss\n",
       "node                                                  \n",
       "1     0.000003  0.0  0.000000       0.000000  0.000000\n",
       "2     0.000005  0.0  0.000543       0.000543 -0.000066\n",
       "3     0.000005  0.0  0.001084       0.001084 -0.000130\n",
       "4     0.000005  0.0  0.001622       0.001622 -0.000190\n",
       "5     0.000005  0.0  0.002156       0.002156 -0.000247\n",
       "...        ...  ...       ...            ...       ...\n",
       "7996 -0.000002  0.0  0.000959       0.000959  0.000110\n",
       "7997 -0.000002  0.0  0.000719       0.000719  0.000084\n",
       "7998 -0.000002  0.0  0.000480       0.000480  0.000057\n",
       "7999 -0.000002  0.0  0.000240       0.000240  0.000029\n",
       "8000 -0.000001  0.0  0.000000       0.000000  0.000000\n",
       "\n",
       "[8000 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfhw = dfsum[\"pm_single\"]\n",
    "dfhw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28",
   "metadata": {},
   "source": [
    "Those are the node-scale sensitivities for the selected performance measure. Plotting them is easiest using the HDF5 file itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.055552Z",
     "iopub.status.busy": "2026-06-02T18:36:47.055379Z",
     "iopub.status.idle": "2026-06-02T18:36:47.059253Z",
     "shell.execute_reply": "2026-06-02T18:36:47.058582Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['composite', 'solution_kper:00000_kstp:00000']\n"
     ]
    }
   ],
   "source": [
    "result_hdf = \"adjoint_solution_pm_single.hdf5\"\n",
    "hdf = h5py.File(pl.Path(ws) / result_hdf, \"r\")\n",
    "keys = list(hdf.keys())\n",
    "keys.sort()\n",
    "print(keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30",
   "metadata": {},
   "source": [
    "The `composite` group contains sensitivities of the performance measure to the model inputs, summed across all adjoint solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.061223Z",
     "iopub.status.busy": "2026-06-02T18:36:47.061055Z",
     "iopub.status.idle": "2026-06-02T18:36:47.066773Z",
     "shell.execute_reply": "2026-06-02T18:36:47.066064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.        , 0.00054254, 0.0010837 , ..., 0.00047285,\n",
       "         0.00023644, 0.        ],\n",
       "        [0.        , 0.00054393, 0.00108647, ..., 0.00047291,\n",
       "         0.00023647, 0.        ],\n",
       "        [0.        , 0.00054671, 0.00109203, ..., 0.00047304,\n",
       "         0.00023653, 0.        ],\n",
       "        ...,\n",
       "        [0.        , 0.00057099, 0.00114049, ..., 0.0004798 ,\n",
       "         0.00023991, 0.        ],\n",
       "        [0.        , 0.00056802, 0.00113457, ..., 0.0004797 ,\n",
       "         0.00023986, 0.        ],\n",
       "        [0.        , 0.00056654, 0.00113162, ..., 0.00047964,\n",
       "         0.00023983, 0.        ]]], shape=(1, 80, 100))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp = hdf[\"composite\"]\n",
    "plot_keys = [i for i in grp.keys() if len(grp[i].shape) == 3]\n",
    "plot_keys\n",
    "grp[\"wel6_q\"][:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32",
   "metadata": {},
   "source": [
    "Here is a simple routine to plot these sensitivities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.068455Z",
     "iopub.status.busy": "2026-06-02T18:36:47.068290Z",
     "iopub.status.idle": "2026-06-02T18:36:47.917564Z",
     "shell.execute_reply": "2026-06-02T18:36:47.916728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for pkey in plot_keys:\n",
    "    arr = grp[pkey][:]\n",
    "    for k, karr in enumerate(arr):\n",
    "        karr[karr == 0.0] = np.nan\n",
    "        fig, ax = plot_model(karr)\n",
    "        ax.set_title(pkey + f\", layer:{k + 1}\", loc=\"left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34",
   "metadata": {},
   "source": [
    "Define the analytical solution (Lu and Vesselinov, WRR, 2015; doi:10.1002/2014WR016819)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.919635Z",
     "iopub.status.busy": "2026-06-02T18:36:47.919452Z",
     "iopub.status.idle": "2026-06-02T18:36:47.927657Z",
     "shell.execute_reply": "2026-06-02T18:36:47.927004Z"
    }
   },
   "outputs": [],
   "source": [
    "H1, H2 = 1001.0, 1000.0  # Heads at both sides\n",
    "L1, L2 = 500.0, 400.0  # Domain size\n",
    "nlay, nrow, ncol = m.dis.nlay.data, m.dis.nrow.data, m.dis.ncol.data\n",
    "delrow, delcol = m.dis.delr.data[0], m.dis.delc.data[0]\n",
    "L1 = delrow * ncol\n",
    "L2 = delcol * nrow\n",
    "H = 1.0\n",
    "k = 10.0  # Hydraulic conductivity\n",
    "T = k * H  # Transmissivity\n",
    "D = L1 * L2\n",
    "\n",
    "\n",
    "def alpha(m):\n",
    "    return m * np.pi / L1\n",
    "\n",
    "\n",
    "def beta(n):\n",
    "    return n * np.pi / L2\n",
    "\n",
    "\n",
    "def omega_square(m, n):\n",
    "    return (alpha(m)) ** 2 + (beta(n)) ** 2\n",
    "\n",
    "\n",
    "def phi_s(x, y, xs, ys, M=5, N=5):\n",
    "    listofmindices = [item + 1 for item in range(M)]\n",
    "    Sum = 0.0\n",
    "    a = [0.5]\n",
    "    for i in [item + 1 for item in range(N)]:\n",
    "        a.append(1.0)\n",
    "    for n in range(N + 1):\n",
    "        for m in listofmindices:\n",
    "            Sum = Sum + a[n] * np.sin(alpha(m) * x) * np.cos(beta(n) * y) * np.sin(\n",
    "                alpha(m) * xs\n",
    "            ) * np.cos(beta(n) * ys) / omega_square(m, n)\n",
    "    Sum = (4 / (T * D)) * Sum\n",
    "    return Sum\n",
    "\n",
    "\n",
    "def get_analytical_adj_state(xs, ys, M, N):\n",
    "    list_adj_state = []\n",
    "    for j in range(nrow):\n",
    "        for i in range(ncol):\n",
    "            x = m.modelgrid.xcellcenters[j][i]\n",
    "            y = m.modelgrid.ycellcenters[j][i]\n",
    "            list_adj_state.append(phi_s(x, y, xs, ys, M=M, N=N))\n",
    "    array_adj_state = np.array(list_adj_state)\n",
    "    print(\"array_adj_state = \", array_adj_state)\n",
    "    filepath = pl.Path(ws) / \"lamb_Analytical.txt\"\n",
    "    with open(filepath, \"w\") as file:\n",
    "        # with open('lamb_Analytical.txt', 'w') as file:\n",
    "        for value in array_adj_state:\n",
    "            file.write(f\"{value}\\n\")\n",
    "    array_adj_state_2D = np.reshape(array_adj_state, (nrow, ncol))\n",
    "    return array_adj_state_2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.929418Z",
     "iopub.status.busy": "2026-06-02T18:36:47.929245Z",
     "iopub.status.idle": "2026-06-02T18:36:47.935306Z",
     "shell.execute_reply": "2026-06-02T18:36:47.934679Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_colorbar_contour(x, y, l_anal, l_num, vmin, vmax):\n",
    "    _, axes = plt.subplots(1, 2, figsize=(14, 5), constrained_layout=True)\n",
    "    ax = axes[0]\n",
    "    ax.set_title(\"Analytical\", fontsize=16)\n",
    "    cs = ax.contourf(x, y, l_anal, zorder=1, vmin=vmin, vmax=vmax)\n",
    "    ax.contour(cs, colors=\"k\", linewidths=1.0, linestyles=\"-\")\n",
    "    plt.colorbar(cs)\n",
    "    ax.set_xlabel(\"x (m)\", fontsize=14)\n",
    "    ax.set_ylabel(\"y (m)\", fontsize=14)\n",
    "    ax.text(-60.0, 420.0, \"a\", weight=\"bold\", fontsize=18)\n",
    "    plt.sca(ax)\n",
    "    plt.xticks(\n",
    "        [0, 100, 200, 300, 400, 500], [\"0\", \"1000\", \"2000\", \"3000\", \"4000\", \"5000\"]\n",
    "    )\n",
    "    plt.yticks(\n",
    "        [0, 50, 100, 150, 200, 250, 300, 350, 400],\n",
    "        [\"0\", \"500\", \"1000\", \"1500\", \"2000\", \"2500\", \"3000\", \"3500\", \"4000\"],\n",
    "    )\n",
    "    ax = axes[1]\n",
    "    ax.set_title(\"MF6-ADJ\", fontsize=16)\n",
    "    cs = ax.contourf(x, y, l_num, zorder=1, vmin=vmin, vmax=vmax, levels=cs.levels)\n",
    "    ax.contour(cs, colors=\"k\", linewidths=1.0, linestyles=\"-\")\n",
    "    plt.colorbar(cs)\n",
    "    ax.set_xlabel(\"x (m)\", fontsize=14)\n",
    "    ax.set_ylabel(\"y (m)\", fontsize=14)\n",
    "    ax.text(-60.0, 420.0, \"b\", weight=\"bold\", fontsize=18)\n",
    "    plt.sca(ax)\n",
    "    plt.xticks(\n",
    "        [0, 100, 200, 300, 400, 500], [\"0\", \"1000\", \"2000\", \"3000\", \"4000\", \"5000\"]\n",
    "    )\n",
    "    plt.yticks(\n",
    "        [0, 50, 100, 150, 200, 250, 300, 350, 400],\n",
    "        [\"0\", \"500\", \"1000\", \"1500\", \"2000\", \"2500\", \"3000\", \"3500\", \"4000\"],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:36:47.937022Z",
     "iopub.status.busy": "2026-06-02T18:36:47.936825Z",
     "iopub.status.idle": "2026-06-02T18:42:41.311086Z",
     "shell.execute_reply": "2026-06-02T18:42:41.310165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array_adj_state =  [0.00028554 0.00085347 0.00142167 ... 0.00061386 0.00036824 0.00012284]\n"
     ]
    }
   ],
   "source": [
    "# M & N should be large enough to get convergence\n",
    "# of the analytical series solution (e.g., M=N=100)\n",
    "lam_anal = get_analytical_adj_state(100.0, 200.0, M=100, N=100)\n",
    "file_path = pl.Path(ws) / \"lamb_Analytical.txt\"\n",
    "with open(file_path, \"r\") as file:\n",
    "    lamb_ana = [float(line.strip()) for line in file]\n",
    "    lamb_ana = np.array(lamb_ana)\n",
    "    lamb_ana_arr = lamb_ana\n",
    "    lamb_ana = np.reshape(lamb_ana, (nrow, ncol))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-02T18:42:41.312918Z",
     "iopub.status.busy": "2026-06-02T18:42:41.312706Z",
     "iopub.status.idle": "2026-06-02T18:42:41.722160Z",
     "shell.execute_reply": "2026-06-02T18:42:41.721285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lam_adj = grp[\"wel6_q\"][:]\n",
    "x = np.linspace(0, L1, ncol)\n",
    "y = np.linspace(0, L2, nrow)\n",
    "x_center = [x[i] + delcol / 2.0 for i in range(len(x))]\n",
    "y_center = [y[i] + delrow / 2.0 for i in range(len(y))]\n",
    "y = y[::-1]\n",
    "y_center = y_center[::-1]\n",
    "vmin, vmax = -lamb_ana.max(), -lamb_ana.min()\n",
    "contour_intervals = np.arange(vmin, vmax, 0.003)\n",
    "plot_colorbar_contour(x, y, -lamb_ana, -lam_adj[0], vmin, vmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}