{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# 2. Confined Aquifer Test - Grindley\n",
    "**This test is taken from AQTESOLV examples.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Conceptual Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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. 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 conducted in 1953 in Grindley, Illinois, US. It was reported by Walton (1962). The aquifer is an 18 ft thickness sand and gravel layer under confined conditions. The pumping well fully penetrates the formation, and pumping was conducted for 8 hours at a rate of 220 gallons per minute. The effect of pumping was observed at observation well 1, located 824 ft away from the well.\n",
    "\n",
    "The time-drawdown data for the observation well was obtained from AQTESOLV documentation (Duffield, 2007), while data from the pumping well was obtained from the original paper from Walton (1962). Following AQTESOLV documentation (Duffield, 2007), we have assumed that both well and observation well radii are 0.5 ft.\n",
    "\n",
    "A simplified cross-section of the model area can be seen 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((-200, 0), width=1400, height=3, fc=\"b\", zorder=0, alpha=0.1)\n",
    "ax.add_patch(sky)\n",
    "\n",
    "# Aquifer:\n",
    "ground = plt.Rectangle(\n",
    "    (-200, -20),\n",
    "    width=1400,\n",
    "    height=18,\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",
    "    (-200, -2),\n",
    "    width=1400,\n",
    "    height=2,\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",
    "    (-8, -20), width=16, height=20, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "ax.add_patch(well)\n",
    "\n",
    "# Wellhead\n",
    "wellhead = plt.Rectangle(\n",
    "    (-10, 0), width=20, 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",
    "    (-8, -20),\n",
    "    width=16,\n",
    "    height=18,\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=10, y=0.75, dx=16, dy=0, color=\"#00035b\")\n",
    "ax.add_patch(pumping_arrow)\n",
    "ax.text(x=29, y=0.75, s=r\"$ Q = 220 \\frac{gal}{min}$\", fontsize=\"large\")\n",
    "# Piezometers\n",
    "piez1 = plt.Rectangle(\n",
    "    (820, -20), width=8, height=20, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "screen_piez_1 = plt.Rectangle(\n",
    "    (820, -20),\n",
    "    width=8,\n",
    "    height=18,\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",
    "\n",
    "ax.add_patch(piez1)\n",
    "\n",
    "ax.add_patch(screen_piez_1)\n",
    "\n",
    "# last line\n",
    "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n",
    "ax.add_line(line)\n",
    "ax.text(x=780, y=0.75, s=\"Obs Well 1\", fontsize=\"large\")\n",
    "\n",
    "ax.set_xlim([-200, 1050])\n",
    "ax.set_ylim([-20, 3])\n",
    "ax.set_xlabel(\"Distance [ft]\")\n",
    "ax.set_ylabel(\"Relative height [ft]\")\n",
    "ax.set_title(\"Conceptual Model - Grindley 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": [
    "b = -5.4846  # aquifer thickness in m (Converted from 18 ft)\n",
    "Q = 1199.218  # constant discharge in m^3/d (Converted from 220 gallons/minute)\n",
    "r = 251.1552  # distance from observation well to test well in m (converted from 824 ft)\n",
    "rw = 0.1524  # screen radius of test well in m (Converted from 0.5 ft)"
   ]
  },
  {
   "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",
    "The observation well is referred to as ***Well 1*** and the pumping well as ***Well 3***.\n",
    "\n",
    "For each piezometer, we will load the data as a numpy array and split time and drawdown into two different 1d arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Observation well (Well 1)\n",
    "data1 = np.loadtxt(\"data/gridley_well_1.txt\")\n",
    "t1 = data1[:, 0]\n",
    "h1 = data1[:, 1]\n",
    "\n",
    "# Loading Pumping Well data (Well 3)\n",
    "data2 = np.loadtxt(\"data/gridley_well_3.txt\")\n",
    "t2 = data2[:, 0]\n",
    "h2 = data2[:, 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 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n",
    "w = ttim.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n",
    "ml.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": [
    "<a id='step_5_1'></a>\n",
    "### Step 5.1. Calibration Using the Observation Well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin the calibration using the data from observation well (well 1) as our data set."
   ]
  },
  {
   "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   = 28\n",
      "    # data points      = 22\n",
      "    # variables        = 2\n",
      "    chi-square         = 0.01702166\n",
      "    reduced chi-square = 8.5108e-04\n",
      "    Akaike info crit   = -153.614838\n",
      "    Bayesian info crit = -151.432753\n",
      "[[Variables]]\n",
      "    kaq0:  22.4340184 +/- 0.22268669 (0.99%) (init = 10)\n",
      "    Saq0:  3.8208e-06 +/- 7.4239e-08 (1.94%) (init = 0.0001)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq0, Saq0) = -0.8829\n"
     ]
    }
   ],
   "source": [
    "# unknown parameters: kaq, Saq\n",
    "ca_0 = ttim.Calibrate(ml)  # Create the Calibrate object, calling the model to the object\n",
    "ca_0.set_parameter(name=\"kaq0\", initial=10)  # Setting the parameters for calibration\n",
    "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n",
    "ca_0.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)  # Setting the observation data\n",
    "ca_0.fit(\n",
    "    report=True\n",
    ")  # Fitting the model. We can hide the message below setting report = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results from calibration are stored in the ```.parameters``` attribute of the calibration object.\n",
    "We can also call the ```.rmse``` method to check the fitting error (RMSE)"
   ]
  },
  {
   "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>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>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>22.434018</td>\n",
       "      <td>2.226867e-01</td>\n",
       "      <td>0.992630</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[22.434018360199843]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>7.423936e-08</td>\n",
       "      <td>1.943043</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[3.820778056138699e-06]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        optimal           std  perc_std  pmin  pmax  initial  \\\n",
       "kaq0  22.434018  2.226867e-01  0.992630  -inf   inf  10.0000   \n",
       "Saq0   0.000004  7.423936e-08  1.943043  -inf   inf   0.0001   \n",
       "\n",
       "                       parray  \n",
       "kaq0     [22.434018360199843]  \n",
       "Saq0  [3.820778056138699e-06]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 0.02781567953669162\n"
     ]
    }
   ],
   "source": [
    "display(ca_0.parameters)\n",
    "print(\"rmse:\", ca_0.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we plot the model with our observation data:\n",
    "\n",
    "* First, we have to compute the model calculated heads at the observation location. For this, we use the ```.head```method in the model object (```ml```). This method takes the following arguments\n",
    "    * the positions ```x``` and ```y``` of the piezometric well (or any other point of interest). In our case, our well is located at position ```x= r1``` and ```y = 0```.\n",
    "    * the time intervals, defined by the numpy array ```t```, for the computation of the heads. In our case, this is defined by the variable ```t1```.\n",
    "\n",
    "    * Another optional input is ```layers```, which can be a list, integer or an array defining the model layers. When we do not assign anything, the head is computed for all layers.\n",
    "\n",
    "The output is a numpy array with dimensions ```(nl,nt)```, where ```nl``` is the number of layers and ```nt``` is the number of time intervals.\n",
    "\n",
    "* Now, we can compare both observations and predictions in a plot:"
   ]
  },
  {
   "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": [
    "hm_0 = ml.head(x=r, y=0, t=t1)  # Compute heads at observation well location\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.semilogx(t1, hm_0[0], label=\"ttim results\")  # Plotting TTim model Results\n",
    "plt.semilogx(t1, h1, \".\", label=\"obs well 1\")  # Plotting Observed points\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Prediction vs Observations - Calibrated Model 1\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check how it performs with the Pumping Well data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm_0_2 = ml.head(x=0, y=0, t=t2)  # Compute heads at observation well location\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.semilogx(t2, hm_0_2[0], label=\"ttim results\")  # Plotting TTim model Results\n",
    "plt.semilogx(t2, h2, \".\", label=\"well 3\")  # Plotting Observed points\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\n",
    "    \"Model Prediction vs Observations - Calibrated Model 1, Results in the Pumping Well\"\n",
    ")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, although the model presented an initial good fit, when we challenged it with an outside sample, it performed poorly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.2. Calibration using the Pumping Well data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We proceed to calibrate using only the data from the pumping well (Well 3).\n",
    "The initial inputs can be checked in [***step 5.1***](#step_5_1)"
   ]
  },
  {
   "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   = 26\n",
      "    # data points      = 14\n",
      "    # variables        = 2\n",
      "    chi-square         = 0.04383783\n",
      "    reduced chi-square = 0.00365315\n",
      "    Akaike info crit   = -76.7284159\n",
      "    Bayesian info crit = -75.4503013\n",
      "[[Variables]]\n",
      "    kaq0:  27.8994053 +/- 0.73167958 (2.62%) (init = 10)\n",
      "    Saq0:  1.7017e-04 +/- 5.8271e-05 (34.24%) (init = 0.0001)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq0, Saq0) = -0.9971\n"
     ]
    }
   ],
   "source": [
    "# unknown parameters: kaq, Saq\n",
    "ca_1 = ttim.Calibrate(ml)\n",
    "ca_1.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n",
    "ca_1.series(name=\"well3\", x=0, y=0, t=t2, h=h2, layer=0)\n",
    "ca_1.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 0.055957785012068\n"
     ]
    }
   ],
   "source": [
    "# ca_1.parameters\n",
    "print(\"rmse:\", ca_1.rmse())"
   ]
  },
  {
   "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": [
    "hm_1 = ml.head(0, 0, t2)\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.semilogx(t2, hm_1[0], label=\"ttim results\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"well 3 observations\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Prediction vs Observations - Calibration 2\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.3. Model Calibration with both datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now will proceed to calibrate the model using both datasets at the same time. We begin by creating a new model so we can compare the different results later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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=1, topboundary=\"conf\")\n",
    "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n",
    "ml_1.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create a new ```Calibrate``` object. The difference from the previous calibration objects is that now we add a second observation series to the object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 30\n",
      "    # data points      = 36\n",
      "    # variables        = 2\n",
      "    chi-square         = 2.65993955\n",
      "    reduced chi-square = 0.07823352\n",
      "    Akaike info crit   = -89.7877595\n",
      "    Bayesian info crit = -86.6207216\n",
      "[[Variables]]\n",
      "    kaq0:  38.0498874 +/- 0.52461689 (1.38%) (init = 10)\n",
      "    Saq0:  1.2466e-06 +/- 2.0173e-07 (16.18%) (init = 0.0001)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq0, Saq0) = -0.7693\n"
     ]
    }
   ],
   "source": [
    "ca_2 = ttim.Calibrate(ml_1)\n",
    "ca_2.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n",
    "ca_2.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)  # Adding observation Well 1\n",
    "ca_2.series(name=\"well3\", x=0, y=0, t=t2, h=h2, layer=0)  # Adding Pumping Well (Well 3)\n",
    "ca_2.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>38.049887</td>\n",
       "      <td>5.246169e-01</td>\n",
       "      <td>1.378761</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[38.04988743506795]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>2.017319e-07</td>\n",
       "      <td>16.182479</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[1.2466067726979446e-06]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        optimal           std   perc_std  pmin  pmax  initial  \\\n",
       "kaq0  38.049887  5.246169e-01   1.378761  -inf   inf  10.0000   \n",
       "Saq0   0.000001  2.017319e-07  16.182479   0.0   inf   0.0001   \n",
       "\n",
       "                        parray  \n",
       "kaq0       [38.04988743506795]  \n",
       "Saq0  [1.2466067726979446e-06]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 0.2718220184324906\n"
     ]
    }
   ],
   "source": [
    "display(ca_2.parameters)\n",
    "print(\"rmse:\", ca_2.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters are quite different from the first two models. We can also see that the errors have increased significantly. Let's plot the model results with the observations and check why we have large errors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_2 = ml.head(r, 0, t1)\n",
    "hm2_2 = ml.head(0, 0, t2)\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.semilogx(t1, hm1_2[0], label=\"ttim model results - obs 1\")\n",
    "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n",
    "plt.semilogx(t2, hm2_2[0], label=\"ttim model results - well 3\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"well3\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Prediction vs Observations - Calibration 3\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen in the Figure above, TTim could not adjust both curves simultaneously and ended up fitting only Well 3.\n",
    "\n",
    "Fortunately, in TTim, we can improve this fit by simulating skin resistance and wellbore storage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.4. Model Calibration with skin resistance and wellbore storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this, we create a new model and add two extra parameters to the ```Well``` object:\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). The function of the radius of the caisson to account for wellbore storage is explained in the previous notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk);\n",
    "* The skin resistance ```res```, a float value unit of time (in our case days). The effect of the skin resistance is explained in [Confined 1 - Oude Korendijk](confined1_oude_korendijk)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_2 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n",
    "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, rc=0.2, res=0.2, tsandQ=[(0, Q)], layers=0)\n",
    "ml_2.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters 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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "................................................................................................................................................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 157\n",
      "    # data points      = 36\n",
      "    # variables        = 4\n",
      "    chi-square         = 1.29522162\n",
      "    reduced chi-square = 0.04047568\n",
      "    Akaike info crit   = -111.694136\n",
      "    Bayesian info crit = -105.360061\n",
      "[[Variables]]\n",
      "    kaq0:  38.2995480 +/- 0.40009772 (1.04%) (init = 10)\n",
      "    Saq0:  8.9386e-07 +/- 1.1957e-07 (13.38%) (init = 0.0001)\n",
      "    res:   6.4345e-07 +/- 0.00281642 (437703.99%) (init = 0.2)\n",
      "    rc:    0.42229993 +/- 0.05321807 (12.60%) (init = 0.2)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(res, rc)    = -0.8481\n",
      "    C(kaq0, Saq0) = -0.7001\n",
      "    C(Saq0, rc)   = -0.3154\n",
      "    C(Saq0, res)  = +0.2090\n",
      "    C(kaq0, res)  = +0.1196\n",
      "    C(kaq0, rc)   = -0.1170\n"
     ]
    }
   ],
   "source": [
    "ca_3 = ttim.Calibrate(ml_2)\n",
    "ca_3.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n",
    "ca_3.set_parameter_by_reference(\n",
    "    name=\"res\", parameter=w_2.res, initial=0.2, pmin=0\n",
    ")  # Here we add pmin = 0 to avoid unrealistic values\n",
    "ca_3.set_parameter_by_reference(name=\"rc\", parameter=w_2.rc, initial=0.2)\n",
    "ca_3.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n",
    "ca_3.series(name=\"obs3\", x=0, y=0, t=t2, h=h2, layer=0)\n",
    "ca_3.fit(report=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='adjusted_pars_ca_3'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "        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>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>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>3.829955e+01</td>\n",
       "      <td>4.000977e-01</td>\n",
       "      <td>1.044654</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[38.29954799927271]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>8.938626e-07</td>\n",
       "      <td>1.195721e-07</td>\n",
       "      <td>13.377015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[8.938626090415625e-07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res</th>\n",
       "      <td>6.434531e-07</td>\n",
       "      <td>2.816420e-03</td>\n",
       "      <td>437703.989463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>[6.434531347743189e-07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rc</th>\n",
       "      <td>4.222999e-01</td>\n",
       "      <td>5.321807e-02</td>\n",
       "      <td>12.601959</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>[0.42229992958966134]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           optimal           std       perc_std  pmin  pmax  initial  \\\n",
       "kaq0  3.829955e+01  4.000977e-01       1.044654  -inf   inf  10.0000   \n",
       "Saq0  8.938626e-07  1.195721e-07      13.377015   0.0   inf   0.0001   \n",
       "res   6.434531e-07  2.816420e-03  437703.989463   0.0   inf   0.2000   \n",
       "rc    4.222999e-01  5.321807e-02      12.601959  -inf   inf   0.2000   \n",
       "\n",
       "                       parray  \n",
       "kaq0      [38.29954799927271]  \n",
       "Saq0  [8.938626090415625e-07]  \n",
       "res   [6.434531347743189e-07]  \n",
       "rc      [0.42229992958966134]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 0.18967967322374477\n"
     ]
    }
   ],
   "source": [
    "display(ca_3.parameters)\n",
    "print(\"rmse:\", ca_3.rmse())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_3 = ml_2.head(r, 0, t1)\n",
    "hm2_3 = ml_2.head(0, 0, t2)\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.semilogx(t1, hm1_3[0], label=\"ttim model results - obs 1\")\n",
    "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n",
    "plt.semilogx(t2, hm2_3[0], label=\"ttim model results - well 3\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"well3\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Prediction vs Observations - Calibration 4\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we see in the picture that we have significantly improved both models by adding the resistance and the wellbore storage. We can make a critique of our current model that the adjusted resistance value is too low and with a very high standard deviation ([adjusted parameters](#adjusted_pars_ca_3)). Let's now disregard the skin resistance and check our model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5.5. Model Calibration with wellbore storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by creating a model and adding a well with no resistance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_3 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n",
    "w_3 = ttim.Well(ml_3, xw=0, yw=0, rw=rw, rc=0.2, res=0, tsandQ=[(0, Q)], layers=0)\n",
    "ml_3.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we calibrate without changing the resistance parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..........................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 39\n",
      "    # data points      = 36\n",
      "    # variables        = 3\n",
      "    chi-square         = 1.29522443\n",
      "    reduced chi-square = 0.03924923\n",
      "    Akaike info crit   = -113.694058\n",
      "    Bayesian info crit = -108.943502\n",
      "[[Variables]]\n",
      "    kaq0:  38.2995600 +/- 0.39115473 (1.02%) (init = 10)\n",
      "    Saq0:  8.9355e-07 +/- 1.1511e-07 (12.88%) (init = 0.0001)\n",
      "    rc:    0.42225006 +/- 0.02777563 (6.58%) (init = 0.2)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq0, Saq0) = -0.7468\n",
      "    C(Saq0, rc)   = -0.2669\n"
     ]
    }
   ],
   "source": [
    "ca_4 = ttim.Calibrate(ml_3)\n",
    "ca_4.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_4.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n",
    "ca_4.set_parameter_by_reference(name=\"rc\", parameter=w_3.rc, initial=0.2)\n",
    "ca_4.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n",
    "ca_4.series(name=\"obs3\", x=0, y=0, t=t2, h=h2, layer=0)\n",
    "ca_4.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>3.829956e+01</td>\n",
       "      <td>3.911547e-01</td>\n",
       "      <td>1.021303</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[38.29956001601816]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>8.935489e-07</td>\n",
       "      <td>1.151142e-07</td>\n",
       "      <td>12.882810</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[8.935488717831674e-07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rc</th>\n",
       "      <td>4.222501e-01</td>\n",
       "      <td>2.777563e-02</td>\n",
       "      <td>6.578006</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>[0.42225006040826946]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           optimal           std   perc_std  pmin  pmax  initial  \\\n",
       "kaq0  3.829956e+01  3.911547e-01   1.021303  -inf   inf  10.0000   \n",
       "Saq0  8.935489e-07  1.151142e-07  12.882810   0.0   inf   0.0001   \n",
       "rc    4.222501e-01  2.777563e-02   6.578006  -inf   inf   0.2000   \n",
       "\n",
       "                       parray  \n",
       "kaq0      [38.29956001601816]  \n",
       "Saq0  [8.935488717831674e-07]  \n",
       "rc      [0.42225006040826946]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 0.18967987836835382\n"
     ]
    }
   ],
   "source": [
    "display(ca_4.parameters)\n",
    "print(\"rmse:\", ca_4.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that we got very similar results from the previous models. The standard deviations are also in a reasonable range. As pointed out by Yang (2020), without skin resistance, we have a lower AIC (-113 versus -111). Thus, the skin resistance does not add information to the model, and the current model is preferred."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6. Comparison of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.1. Error comparison and model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_945f2\">\n",
       "  <caption>Fit statistics for the tested models</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_945f2_level0_col0\" class=\"col_heading level0 col0\" >RMSE</th>\n",
       "      <th id=\"T_945f2_level0_col1\" class=\"col_heading level0 col1\" >AIC</th>\n",
       "      <th id=\"T_945f2_level0_col2\" class=\"col_heading level0 col2\" >BIC</th>\n",
       "      <th id=\"T_945f2_level0_col3\" class=\"col_heading level0 col3\" >Calibration scheme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_945f2_level0_row0\" class=\"row_heading level0 row0\" >Model 1</th>\n",
       "      <td id=\"T_945f2_row0_col0\" class=\"data row0 col0\" >0.027816</td>\n",
       "      <td id=\"T_945f2_row0_col1\" class=\"data row0 col1\" >-153.614838</td>\n",
       "      <td id=\"T_945f2_row0_col2\" class=\"data row0 col2\" >-151.432753</td>\n",
       "      <td id=\"T_945f2_row0_col3\" class=\"data row0 col3\" >Obs 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_945f2_level0_row1\" class=\"row_heading level0 row1\" >Model 2</th>\n",
       "      <td id=\"T_945f2_row1_col0\" class=\"data row1 col0\" >0.055958</td>\n",
       "      <td id=\"T_945f2_row1_col1\" class=\"data row1 col1\" >-76.728416</td>\n",
       "      <td id=\"T_945f2_row1_col2\" class=\"data row1 col2\" >-75.450301</td>\n",
       "      <td id=\"T_945f2_row1_col3\" class=\"data row1 col3\" >Well 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_945f2_level0_row2\" class=\"row_heading level0 row2\" >Model 3</th>\n",
       "      <td id=\"T_945f2_row2_col0\" class=\"data row2 col0\" >0.271822</td>\n",
       "      <td id=\"T_945f2_row2_col1\" class=\"data row2 col1\" >-89.787760</td>\n",
       "      <td id=\"T_945f2_row2_col2\" class=\"data row2 col2\" >-86.620722</td>\n",
       "      <td id=\"T_945f2_row2_col3\" class=\"data row2 col3\" >Obs 1 + Well 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_945f2_level0_row3\" class=\"row_heading level0 row3\" >Model 4</th>\n",
       "      <td id=\"T_945f2_row3_col0\" class=\"data row3 col0\" >0.189680</td>\n",
       "      <td id=\"T_945f2_row3_col1\" class=\"data row3 col1\" >-111.694136</td>\n",
       "      <td id=\"T_945f2_row3_col2\" class=\"data row3 col2\" >-105.360061</td>\n",
       "      <td id=\"T_945f2_row3_col3\" class=\"data row3 col3\" >Obs 1 + Well 3, res + rc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_945f2_level0_row4\" class=\"row_heading level0 row4\" >Model 5</th>\n",
       "      <td id=\"T_945f2_row4_col0\" class=\"data row4 col0\" >0.189680</td>\n",
       "      <td id=\"T_945f2_row4_col1\" class=\"data row4 col1\" >-113.694058</td>\n",
       "      <td id=\"T_945f2_row4_col2\" class=\"data row4 col2\" >-108.943502</td>\n",
       "      <td id=\"T_945f2_row4_col3\" class=\"data row4 col3\" >Obs 1 + Well 3, rc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16040f910>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    columns=[\"RMSE\", \"AIC\", \"BIC\", \"Calibration scheme\"],\n",
    "    index=[\"Model 1\", \"Model 2\", \"Model 3\", \"Model 4\", \"Model 5\"],\n",
    ")\n",
    "\n",
    "t[\"RMSE\"] = [ca_0.rmse(), ca_1.rmse(), ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n",
    "\n",
    "t[\"AIC\"] = [\n",
    "    ca_0.fitresult.aic,\n",
    "    ca_1.fitresult.aic,\n",
    "    ca_2.fitresult.aic,\n",
    "    ca_3.fitresult.aic,\n",
    "    ca_4.fitresult.aic,\n",
    "]\n",
    "\n",
    "t[\"BIC\"] = [\n",
    "    ca_0.fitresult.bic,\n",
    "    ca_1.fitresult.bic,\n",
    "    ca_2.fitresult.bic,\n",
    "    ca_3.fitresult.bic,\n",
    "    ca_4.fitresult.bic,\n",
    "]\n",
    "\n",
    "t[\"Calibration scheme\"] = [\n",
    "    \"Obs 1\",\n",
    "    \"Well 3\",\n",
    "    \"Obs 1 + Well 3\",\n",
    "    \"Obs 1 + Well 3, res + rc\",\n",
    "    \"Obs 1 + Well 3, rc\",\n",
    "]\n",
    "t.style.set_caption(\"Fit statistics for the tested models\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first model had overall better statistics with lower RMSE and AIC, BIC. However, it does not fit with the data in the pumping well. Therefore if we were to update the statistics with the residuals from the pumping well, this result would be worse.\n",
    "Comparing the models estimated with both drawdowns, the last model performed best. It has a larger RMSE than the one-well models, however less bias as it fits well both datasets. Another highlight is the lower AIC and BIC values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.2. Comparison of TTim model performance with values simulated by AQTESOLV and MLU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results simulated by different methods with two datasets simultaneously are presented 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 both the pumping well and observation well data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>MLU</th>\n",
       "      <td>38.094</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQTESOLV</th>\n",
       "      <td>37.803</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim</th>\n",
       "      <td>38.04988743506795</td>\n",
       "      <td>1.2466067726979446e-06</td>\n",
       "      <td>-</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim-rc</th>\n",
       "      <td>38.29956</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.42225</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    k [m/d]                Ss [1/m]       rc  RMSE\n",
       "MLU                  38.094                0.000001        -  0.26\n",
       "AQTESOLV             37.803                0.000001        -  0.27\n",
       "ttim      38.04988743506795  1.2466067726979446e-06        -  0.27\n",
       "ttim-rc            38.29956                0.000001  0.42225  0.19"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    columns=[\"k [m/d]\", \"Ss [1/m]\", \"rc\"], index=[\"MLU\", \"AQTESOLV\", \"ttim\", \"ttim-rc\"]\n",
    ")\n",
    "t.loc[\"MLU\"] = [38.094, 1.193e-06, \"-\"]\n",
    "t.loc[\"AQTESOLV\"] = [37.803, 1.356e-06, \"-\"]\n",
    "t.loc[\"ttim\"] = np.append(ca_2.parameters[\"optimal\"].values, \"-\")\n",
    "t.loc[\"ttim-rc\"] = ca_4.parameters[\"optimal\"].values\n",
    "t[\"RMSE\"] = [0.259, 0.270, ca_2.rmse(), ca_4.rmse()]\n",
    "t.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see good agreement between model results in both hydraulic conductivity and specific storage values. TTim was able to calculate a better fit with wellbore storage added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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\"] = [38.094, 2.622 * 1e-2 * 38.094]\n",
    "t1.loc[\"AQTESOLV\"] = [37.803, 2.745 * 1e-2 * 37.803]\n",
    "t1.loc[\"TTim\"] = [\n",
    "    ca_2.parameters.loc[\"kaq0\", \"optimal\"],\n",
    "    2 * ca_2.parameters.loc[\"kaq0\", \"std\"],\n",
    "]\n",
    "t1.loc[\"TTim - rc\"] = [\n",
    "    ca_4.parameters.loc[\"kaq0\", \"optimal\"],\n",
    "    2 * ca_4.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([36, 40])\n",
    "plt.xlabel(\"Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error bar plot shows that TTim has similar confidence intervals to the other models. The model with wellbore storage has a slightly smaller error range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "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",
    "* Walton, W.C., 1962. Selected analytical methods for well and aquifer evaluation. Illinois.department of Registration & Education.bulletin 49.\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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