{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# 3. Confined Aquifer Test - Sioux Example\n",
    "**This test is taken from AQTESOLV examples.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Conceptual Model\n",
    "\n",
    "In this example, we reproduce the work of Yang (2020) to use the pumping test data to demonstrate how TTim can be used to model and analyze pumping tests in a single layer, confined setting, in multiple piezometers. Furthermore, we compare the performance of TTim with other transient well hydraulics software AQTESOLV (Duffield, 2007) and MLU (Carlson and Randall, 2012).\n",
    "\n",
    "This example is a pumping test done in Sioux Flats, South Dakota, USA. The data comes from the AQTESOLV documentation (Duffield, 2007).\n",
    "The aquifer is 50 ft thick and is bounded by impermeable layers. The test was conducted for 2045 minutes (~34 hours), with a constant pumping rate of 2.7 $ft^3/s$. Drawdown data has been collected at three piezometers located 100, 200 and 400 ft away, respectively. The well radius is 0.5 ft.\n",
    "\n",
    "We can resume the conceptual model in the picture below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "##Now printing the conceptual model figure:\n",
    "\n",
    "fig = plt.figure(figsize=(14, 9))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "# sky\n",
    "sky = plt.Rectangle((-50, 0), width=500, height=3, fc=\"b\", zorder=0, alpha=0.1)\n",
    "ax.add_patch(sky)\n",
    "\n",
    "# Aquifer:\n",
    "ground = plt.Rectangle(\n",
    "    (-50, -55),\n",
    "    width=500,\n",
    "    height=50,\n",
    "    fc=np.array([209, 179, 127]) / 255,\n",
    "    zorder=0,\n",
    "    alpha=0.9,\n",
    ")\n",
    "ax.add_patch(ground)\n",
    "\n",
    "# Confining bed:\n",
    "confining_unit = plt.Rectangle(\n",
    "    (-50, -5),\n",
    "    width=500,\n",
    "    height=5,\n",
    "    fc=np.array([100, 100, 100]) / 255,\n",
    "    zorder=0,\n",
    "    alpha=0.7,\n",
    ")\n",
    "ax.add_patch(confining_unit)\n",
    "well = plt.Rectangle(\n",
    "    (-4, -55), width=8, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "ax.add_patch(well)\n",
    "\n",
    "# Wellhead\n",
    "wellhead = plt.Rectangle(\n",
    "    (-5, 0), width=10, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n",
    ")\n",
    "ax.add_patch(wellhead)\n",
    "\n",
    "# Screen for the well:\n",
    "screen = plt.Rectangle(\n",
    "    (-4, -55),\n",
    "    width=8,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen.set_linewidth(2)\n",
    "ax.add_patch(screen)\n",
    "pumping_arrow = plt.Arrow(x=5, y=0.75, dx=8, dy=0, color=\"#00035b\")\n",
    "ax.add_patch(pumping_arrow)\n",
    "ax.text(x=13, y=0.75, s=r\"$ Q = 2.7 \\frac{ft^3}{s}$\", fontsize=\"x-large\")\n",
    "# Piezometers\n",
    "piez1 = plt.Rectangle(\n",
    "    (98, -55), width=4, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "piez2 = plt.Rectangle(\n",
    "    (198, -55), width=4, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "piez3 = plt.Rectangle(\n",
    "    (398, -55), width=4, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "\n",
    "screen_piez_1 = plt.Rectangle(\n",
    "    (98, -55),\n",
    "    width=4,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_1.set_linewidth(2)\n",
    "screen_piez_2 = plt.Rectangle(\n",
    "    (198, -55),\n",
    "    width=4,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_2.set_linewidth(2)\n",
    "screen_piez_3 = plt.Rectangle(\n",
    "    (398, -55),\n",
    "    width=4,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_3.set_linewidth(2)\n",
    "ax.add_patch(piez1)\n",
    "ax.add_patch(piez2)\n",
    "ax.add_patch(piez3)\n",
    "ax.add_patch(screen_piez_1)\n",
    "ax.add_patch(screen_piez_2)\n",
    "ax.add_patch(screen_piez_3)\n",
    "# last line\n",
    "line = plt.Line2D(xdata=[-50, 500], ydata=[0, 0], color=\"k\")\n",
    "ax.add_line(line)\n",
    "ax.text(x=100, y=0.75, s=\"P100\", fontsize=\"large\")\n",
    "ax.text(x=200, y=0.75, s=\"P200\", fontsize=\"large\")\n",
    "ax.text(x=400, y=0.75, s=\"P400\", fontsize=\"large\")\n",
    "ax.set_xlim([-50, 450])\n",
    "ax.set_ylim([-55, 3])\n",
    "ax.set_title(\"Conceptual Model - Sioux Example\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. Load Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import ttim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. Set basic parameters\n",
    "\n",
    "- We will work with time in days and length in meters from this step onwards\n",
    "- The parameters below have already been converted to m and days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = 6605.754  # constant discharge in m^3/d\n",
    "b = -15.24  # aquifer thickness in m\n",
    "rw = 0.1524  # well radius in m\n",
    "r1 = 30.48  # distance between obs1 to pumping well\n",
    "r2 = 60.96  # distance between obs2 to pumping well\n",
    "r3 = 121.92  # distance between obs3 to pumping well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Load data of observation and pumping well\n",
    "\n",
    "The preferred method of loading data into TTim is to use numpy arrays.\n",
    "\n",
    "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n",
    "\n",
    "For each piezometer, we will load the data as a numpy array. We further split the data into two different 1d arrays, one for time and another for drawdown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = np.loadtxt(\"data/sioux100.txt\")\n",
    "t1 = data1[:, 0]\n",
    "h1 = data1[:, 1]\n",
    "\n",
    "\n",
    "data2 = np.loadtxt(\"data/sioux200.txt\")\n",
    "t2 = data2[:, 0]\n",
    "h2 = data2[:, 1]\n",
    "\n",
    "data3 = np.loadtxt(\"data/sioux400.txt\")\n",
    "t3 = data3[:, 0]\n",
    "h3 = data3[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step_4'></a>\n",
    "## Step 4. Creating a TTim conceptual model\n",
    "\n",
    "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_0 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\")\n",
    "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n",
    "ml_0.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.1. Model Calibration with all observation wells\n",
    "\n",
    "We calibrate the model with all observation wells.\n",
    "We begin by assuming no wellbore storage or skin resistance, and we only calibrate the hydraulic conductivity and specific storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".............................................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 58\n",
      "    # data points      = 77\n",
      "    # variables        = 2\n",
      "    chi-square         = 0.00121634\n",
      "    reduced chi-square = 1.6218e-05\n",
      "    Akaike info crit   = -847.289973\n",
      "    Bayesian info crit = -842.602363\n",
      "[[Variables]]\n",
      "    kaq0:  282.798193 +/- 1.12249494 (0.40%) (init = 10)\n",
      "    Saq0:  0.00420840 +/- 3.3245e-05 (0.79%) (init = 0.0001)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq0, Saq0) = -0.8100\n"
     ]
    }
   ],
   "source": [
    "# unknown parameters: k, Saq\n",
    "ca_0 = ttim.Calibrate(ml_0)\n",
    "ca_0.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n",
    "ca_0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n",
    "ca_0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n",
    "ca_0.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n",
    "ca_0.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>layers</th>\n",
       "      <th>optimal</th>\n",
       "      <th>std</th>\n",
       "      <th>perc_std</th>\n",
       "      <th>pmin</th>\n",
       "      <th>pmax</th>\n",
       "      <th>initial</th>\n",
       "      <th>inhoms</th>\n",
       "      <th>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>None</td>\n",
       "      <td>282.798193</td>\n",
       "      <td>1.122495</td>\n",
       "      <td>0.396924</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>None</td>\n",
       "      <td>[[282.7981927108409]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>None</td>\n",
       "      <td>0.004208</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.789965</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>None</td>\n",
       "      <td>[[0.0042084036125535065]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     layers     optimal       std  perc_std  pmin  pmax  initial inhoms  \\\n",
       "kaq0   None  282.798193  1.122495  0.396924  -inf   inf  10.0000   None   \n",
       "Saq0   None    0.004208  0.000033  0.789965  -inf   inf   0.0001   None   \n",
       "\n",
       "                         parray  \n",
       "kaq0      [[282.7981927108409]]  \n",
       "Saq0  [[0.0042084036125535065]]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.003974497435435633\n"
     ]
    }
   ],
   "source": [
    "display(ca_0.parameters)\n",
    "print(\"RMSE:\", ca_0.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model has achieved a good fit and parameters with a low confidence interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_0 = ml_0.head(x=r1, y=0, t=t1)\n",
    "hm2_0 = ml_0.head(x=r2, y=0, t=t2)\n",
    "hm3_0 = ml_0.head(x=r3, y=0, t=t3)\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n",
    "plt.semilogx(t1, hm1_0[0], label=\"ttim result 1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n",
    "plt.semilogx(t2, hm2_0[0], label=\"ttim result 2\")\n",
    "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n",
    "plt.semilogx(t3, hm3_0[0], label=\"ttim result 3\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.legend()\n",
    "plt.title(\"Calibration Results vs Observations - Model 1\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually, the model seems to have a good fit with the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.2. Model Calibration with wellbore storage\n",
    "\n",
    "In this new model, we investigate whether the well parameters are relevant in the fit.\n",
    "\n",
    "We begin by reloading the model and creating a ```Well``` object with one extra parameters:\n",
    "\n",
    "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_1 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\")\n",
    "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, tsandQ=[(0, Q)], layers=0)\n",
    "ml_1.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` parameter in our well.\n",
    "\n",
    "```.set_parameter_by_reference``` takes the following arguments:\n",
    "* ```name```: a string of the parameter name\n",
    "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n",
    "* ```initial```: float with the initial guess for the parameter value.\n",
    "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...........................................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 56\n",
      "    # data points      = 77\n",
      "    # variables        = 3\n",
      "    chi-square         = 0.00116245\n",
      "    reduced chi-square = 1.5709e-05\n",
      "    Akaike info crit   = -848.779204\n",
      "    Bayesian info crit = -841.747788\n",
      "[[Variables]]\n",
      "    kaq0:  283.919199 +/- 1.31248212 (0.46%) (init = 10)\n",
      "    Saq0:  0.00415514 +/- 4.3806e-05 (1.05%) (init = 0.0001)\n",
      "    rc:    0.78951363 +/- 0.20812990 (26.36%) (init = 0.2)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq0, Saq0) = -0.8656\n",
      "    C(Saq0, rc)   = -0.6688\n",
      "    C(kaq0, rc)   = +0.5302\n"
     ]
    }
   ],
   "source": [
    "# unknown parameters: k, Saq, res, rc\n",
    "ca_1 = ttim.Calibrate(ml_1)\n",
    "ca_1.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n",
    "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc, pmin=0.1, initial=0.2)\n",
    "ca_1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n",
    "ca_1.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n",
    "ca_1.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n",
    "ca_1.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>layers</th>\n",
       "      <th>optimal</th>\n",
       "      <th>std</th>\n",
       "      <th>perc_std</th>\n",
       "      <th>pmin</th>\n",
       "      <th>pmax</th>\n",
       "      <th>initial</th>\n",
       "      <th>inhoms</th>\n",
       "      <th>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>None</td>\n",
       "      <td>283.919199</td>\n",
       "      <td>1.312482</td>\n",
       "      <td>0.462273</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>None</td>\n",
       "      <td>[[283.91919931261856]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>None</td>\n",
       "      <td>0.004155</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>1.054250</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>None</td>\n",
       "      <td>[[0.004155143996825365]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rc</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.789514</td>\n",
       "      <td>0.208130</td>\n",
       "      <td>26.361787</td>\n",
       "      <td>0.1</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[[0.7895136254264451]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     layers     optimal       std   perc_std  pmin  pmax  initial inhoms  \\\n",
       "kaq0   None  283.919199  1.312482   0.462273  -inf   inf  10.0000   None   \n",
       "Saq0   None    0.004155  0.000044   1.054250  -inf   inf   0.0001   None   \n",
       "rc      NaN    0.789514  0.208130  26.361787   0.1   inf   0.2000    NaN   \n",
       "\n",
       "                        parray  \n",
       "kaq0    [[283.91919931261856]]  \n",
       "Saq0  [[0.004155143996825365]]  \n",
       "rc      [[0.7895136254264451]]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0038854583861349557\n"
     ]
    }
   ],
   "source": [
    "display(ca_1.parameters)\n",
    "print(\"RMSE:\", ca_1.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new model has better statistics: lower AIC and BIC, lower RMSE and lower standard deviations for hydraulic conductivities and specific storage. We proceed with plotting the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_1 = ml_1.head(r1, 0, t1)\n",
    "hm2_1 = ml_1.head(r2, 0, t2)\n",
    "hm3_1 = ml_1.head(r3, 0, t3)\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n",
    "plt.semilogx(t1, hm1_1[0], label=\"ttim1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n",
    "plt.semilogx(t2, hm2_1[0], label=\"ttim2\")\n",
    "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n",
    "plt.semilogx(t3, hm3_1[0], label=\"ttim3\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.legend()\n",
    "plt.title(\"Model Calibration Results - Model 2\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, we can see that there is good agreement between the model calculated heads and the observed ones for all observation wells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6. Analysis of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.1. Analysis of Fit Statistics\n",
    "\n",
    "Some of the fit statistics are stored in the ```fitresult``` attribute of the calibration object. This object is a lmfit ```MinimizerResult``` object. ```lmfit``` is the python library doing the calibration for TTim behind the scenes. We accessed below the AIC and BIC values of this object.\n",
    "\n",
    "Check the lmfit documentation (Newville et al. 2014) to learn more about this object and lmfit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_ae025\">\n",
       "  <caption>Fit statistics for the tested models</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ae025_level0_col0\" class=\"col_heading level0 col0\" >RMSE</th>\n",
       "      <th id=\"T_ae025_level0_col1\" class=\"col_heading level0 col1\" >AIC</th>\n",
       "      <th id=\"T_ae025_level0_col2\" class=\"col_heading level0 col2\" >BIC</th>\n",
       "      <th id=\"T_ae025_level0_col3\" class=\"col_heading level0 col3\" >Calibration scheme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ae025_level0_row0\" class=\"row_heading level0 row0\" >Model 1</th>\n",
       "      <td id=\"T_ae025_row0_col0\" class=\"data row0 col0\" >0.003974</td>\n",
       "      <td id=\"T_ae025_row0_col1\" class=\"data row0 col1\" >-847.289973</td>\n",
       "      <td id=\"T_ae025_row0_col2\" class=\"data row0 col2\" >-842.602363</td>\n",
       "      <td id=\"T_ae025_row0_col3\" class=\"data row0 col3\" >All Obs Wells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ae025_level0_row1\" class=\"row_heading level0 row1\" >Model 2</th>\n",
       "      <td id=\"T_ae025_row1_col0\" class=\"data row1 col0\" >0.003885</td>\n",
       "      <td id=\"T_ae025_row1_col1\" class=\"data row1 col1\" >-848.779204</td>\n",
       "      <td id=\"T_ae025_row1_col2\" class=\"data row1 col2\" >-841.747788</td>\n",
       "      <td id=\"T_ae025_row1_col3\" class=\"data row1 col3\" >All Obs Wells + wellbore storage</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x164f85e10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    columns=[\"RMSE\", \"AIC\", \"BIC\", \"Calibration scheme\"], index=[\"Model 1\", \"Model 2\"]\n",
    ")\n",
    "\n",
    "t[\"RMSE\"] = [ca_0.rmse(), ca_1.rmse()]\n",
    "\n",
    "t[\"AIC\"] = [ca_0.fitresult.aic, ca_1.fitresult.aic]\n",
    "\n",
    "t[\"BIC\"] = [ca_0.fitresult.bic, ca_1.fitresult.bic]\n",
    "\n",
    "t[\"Calibration scheme\"] = [\"All Obs Wells\", \"All Obs Wells + wellbore storage\"]\n",
    "t.style.set_caption(\"Fit statistics for the tested models\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit statistics show that the models have very similar performance as all indicators are closely related. By RMSE and AIC criteria, we should pick Model 2, while by BIC, we should pick Model 1. The result means that we cannot exclude one model in favour of the other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.2. Summary of Calibrated Parameters and comparison with different Software solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We present the results simulated in TTim under different configurations below. Furthermore, Yang (2020) compared TTim results with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012). In both software, the model was calibrated with observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>k [m/d]</th>\n",
       "      <th>Ss [1/m]</th>\n",
       "      <th>rc</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AQTESOLV</th>\n",
       "      <td>282.659</td>\n",
       "      <td>0.004211</td>\n",
       "      <td>-</td>\n",
       "      <td>0.003925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLU</th>\n",
       "      <td>282.684</td>\n",
       "      <td>0.004209</td>\n",
       "      <td>-</td>\n",
       "      <td>0.003897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim</th>\n",
       "      <td>282.7981927108409</td>\n",
       "      <td>0.0042084036125535065</td>\n",
       "      <td>-</td>\n",
       "      <td>0.003974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim-rc</th>\n",
       "      <td>283.919199</td>\n",
       "      <td>0.004155</td>\n",
       "      <td>0.789514</td>\n",
       "      <td>0.003885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    k [m/d]               Ss [1/m]        rc      RMSE\n",
       "AQTESOLV            282.659               0.004211         -  0.003925\n",
       "MLU                 282.684               0.004209         -  0.003897\n",
       "ttim      282.7981927108409  0.0042084036125535065         -  0.003974\n",
       "ttim-rc          283.919199               0.004155  0.789514  0.003885"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    columns=[\"k [m/d]\", \"Ss [1/m]\", \"rc\"], index=[\"AQTESOLV\", \"MLU\", \"ttim\", \"ttim-rc\"]\n",
    ")\n",
    "t.loc[\"AQTESOLV\"] = [282.659, 4.211e-03, \"-\"]\n",
    "t.loc[\"ttim\"] = np.append(ca_0.parameters[\"optimal\"].values, \"-\")\n",
    "t.loc[\"ttim-rc\"] = ca_1.parameters[\"optimal\"].values\n",
    "t.loc[\"MLU\"] = [282.684, 4.209e-03, \"-\"]\n",
    "t[\"RMSE\"] = [0.003925, 0.003897, ca_0.rmse(), ca_1.rmse()]\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TTim achieved similar results with the other software. The TTim model with wellbore storage had a slightly better RMSE error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preparing the DataFrame:\n",
    "t1 = pd.DataFrame(\n",
    "    columns=[\"kaq - opt\", \"kaq - 95%\"], index=[\"MLU\", \"AQTESOLV\", \"TTim\", \"TTim - rc\"]\n",
    ")\n",
    "simulation = [\"MLU\", \"AQTESOLV\", \"TTim\", \"TTim - rc\"]\n",
    "t1.loc[\"MLU\"] = [282.684, 0.783 * 1e-2 * 282.6842]\n",
    "t1.loc[\"AQTESOLV\"] = [282.659, 0.394 * 1e-2 * 282.659]\n",
    "t1.loc[\"TTim\"] = [\n",
    "    ca_0.parameters.loc[\"kaq0\", \"optimal\"],\n",
    "    2 * ca_0.parameters.loc[\"kaq0\", \"std\"],\n",
    "]\n",
    "t1.loc[\"TTim - rc\"] = [\n",
    "    ca_1.parameters.loc[\"kaq0\", \"optimal\"],\n",
    "    2 * ca_0.parameters.loc[\"kaq0\", \"std\"],\n",
    "]\n",
    "\n",
    "# Plotting\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "plt.errorbar(\n",
    "    x=t1.index,\n",
    "    y=t1[\"kaq - opt\"],\n",
    "    yerr=[t1[\"kaq - 95%\"], t1[\"kaq - 95%\"]],\n",
    "    marker=\"o\",\n",
    "    linestyle=\"\",\n",
    "    markersize=12,\n",
    ")\n",
    "# plt.legend()\n",
    "plt.title(\"Error bar plot of calibrated hydraulic conductivities\")\n",
    "plt.ylabel(\"K [m/d]\")\n",
    "plt.ylim([278, 289])\n",
    "plt.xlabel(\"Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error bar plot shows AQTESOLV with the lowest confidence interval. The models in TTim have larger confidence intervals. However, they are still small. All models seem to agree, and there is a wide overlap in the confidence intervals for hydraulic conductivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n",
    "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n",
    "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n",
    "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands."
   ]
  }
 ],
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