{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Confined - Double Porosity Test, Nevada\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Conceptual Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this following benchmark example, we demonstrate by reproducing Yang (2020) how we can apply TTim in fractured media. We further compare the values obtained in TTim with the ones simulated in AQTESOLV and MLU by Yang (2020)\n",
    "\n",
    "The example is taken from Kruseman and de Ridder (1990). It is based on a pumping test conducted in a fractured tertiary volcanic aquifer near Yucca Mountains, Nevada, US. Although conventional Darcy's flow is not applied to fracture flow, in this case, we can assume that fracturing is pervasive enough to flow occur as Darcy's flow at the aquifer scale. Flow to the well comes from fractures, while the matrix exchanges water with the fractures.\n",
    "\n",
    "The borehole has 1219 m depth and penetrated a 400 m thick sequence of fractured volcanic rocks with water entry points. At every entry point, the head is more or less the same, so they have good hydraulic connection. The water table is about 470 m below depth, which indicates confined conditions.\n",
    "\n",
    "Drawdown data was sampled at the well, named ***UE-25b#1*** and at an observation well, named ***UE-25a#1***, 110 m away. Three pumping tests were conducted at the site and will be the object of our investigation.\n"
   ]
  },
  {
   "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",
    "\n",
    "def conc_confined5():\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((-20, 0), width=170, height=150, fc=\"b\", zorder=0, alpha=0.1)\n",
    "    ax.add_patch(sky)\n",
    "\n",
    "    # Aquifer:\n",
    "    ground = plt.Rectangle(\n",
    "        (-20, -1219),\n",
    "        width=170,\n",
    "        height=400,\n",
    "        fc=np.array([209, 179, 127]) / 255,\n",
    "        zorder=0,\n",
    "        alpha=0.9,\n",
    "        hatch=\"xx\",\n",
    "    )\n",
    "    ax.add_patch(ground)\n",
    "\n",
    "    # Confining bed:\n",
    "    confining_unit = plt.Rectangle(\n",
    "        (-20, -1219 + 400),\n",
    "        width=170,\n",
    "        height=1219 - 400,\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",
    "        (-2, -1219), width=4, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    "    )\n",
    "    ax.add_patch(well)\n",
    "\n",
    "    # Wellhead\n",
    "    wellhead = plt.Rectangle(\n",
    "        (-4, 0),\n",
    "        width=8,\n",
    "        height=55,\n",
    "        fc=np.array([200, 200, 200]) / 255,\n",
    "        zorder=2,\n",
    "        ec=\"k\",\n",
    "    )\n",
    "    ax.add_patch(wellhead)\n",
    "\n",
    "    # Screen for the well:\n",
    "    screen = plt.Rectangle(\n",
    "        (-2, -1219),\n",
    "        width=4,\n",
    "        height=400,\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=4, y=25, dx=20, dy=0, color=\"#00035b\", zorder=3)\n",
    "    ax.add_patch(pumping_arrow)\n",
    "    ax.text(x=28, y=55, s=r\"$ Q = 3093 \\frac{m^3}{d}$\", fontsize=\"large\")\n",
    "    # Piezometers\n",
    "    piez1 = plt.Rectangle(\n",
    "        (99, -1219), width=2, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    "    )\n",
    "    screen_piez_1 = plt.Rectangle(\n",
    "        (99, -1219),\n",
    "        width=2,\n",
    "        height=400,\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=[-20, 150], ydata=[0, 0], color=\"k\")\n",
    "    ax.add_line(line)\n",
    "    ax.text(x=100, y=55, s=\"Obs Well 1\", fontsize=\"large\")\n",
    "\n",
    "    ax.set_xlim([-20, 150])\n",
    "    ax.set_ylim([-1219, 150])\n",
    "    ax.set_xlabel(\"Distance [m]\")\n",
    "    ax.set_ylabel(\"Relative height [m]\")\n",
    "    ax.set_title(\"Conceptual Model - Nevada Example\")\n",
    "\n",
    "\n",
    "conc_confined5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conc_confined5():\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((-20, 0), width=170, height=150, fc=\"b\", zorder=0, alpha=0.1)\n",
    "    ax.add_patch(sky)\n",
    "\n",
    "    # Aquifer:\n",
    "    ground = plt.Rectangle(\n",
    "        (-20, -1219),\n",
    "        width=170,\n",
    "        height=400,\n",
    "        fc=np.array([209, 179, 127]) / 255,\n",
    "        zorder=0,\n",
    "        alpha=0.9,\n",
    "        hatch=\"xx\",\n",
    "    )\n",
    "    ax.add_patch(ground)\n",
    "\n",
    "    # Confining bed:\n",
    "    confining_unit = plt.Rectangle(\n",
    "        (-20, -1219 + 400),\n",
    "        width=170,\n",
    "        height=1219 - 400,\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",
    "        (-2, -1219), width=4, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    "    )\n",
    "    ax.add_patch(well)\n",
    "\n",
    "    # Wellhead\n",
    "    wellhead = plt.Rectangle(\n",
    "        (-4, 0),\n",
    "        width=8,\n",
    "        height=55,\n",
    "        fc=np.array([200, 200, 200]) / 255,\n",
    "        zorder=2,\n",
    "        ec=\"k\",\n",
    "    )\n",
    "    ax.add_patch(wellhead)\n",
    "\n",
    "    # Screen for the well:\n",
    "    screen = plt.Rectangle(\n",
    "        (-2, -1219),\n",
    "        width=4,\n",
    "        height=400,\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=4, y=25, dx=20, dy=0, color=\"#00035b\", zorder=3)\n",
    "    ax.add_patch(pumping_arrow)\n",
    "    ax.text(x=28, y=55, s=r\"$ Q = 3093 \\frac{m^3}{d}$\", fontsize=\"large\")\n",
    "    # Piezometers\n",
    "    piez1 = plt.Rectangle(\n",
    "        (99, -1219), width=2, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    "    )\n",
    "    screen_piez_1 = plt.Rectangle(\n",
    "        (99, -1219),\n",
    "        width=2,\n",
    "        height=400,\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=[-20, 150], ydata=[0, 0], color=\"k\")\n",
    "    ax.add_line(line)\n",
    "    ax.text(x=100, y=55, s=\"Obs Well 1\", fontsize=\"large\")\n",
    "\n",
    "    ax.set_xlim([-20, 150])\n",
    "    ax.set_ylim([-1219, 150])\n",
    "    ax.set_xlabel(\"Distance [m]\")\n",
    "    ax.set_ylabel(\"Relative height [m]\")\n",
    "    ax.set_title(\"Conceptual Model - Nevada Example\")\n",
    "\n",
    "\n",
    "conc_confined5()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Step 1. Import the Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import ttim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. Set the Basic Parameters for the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = 400  # aquifer thickness [m]\n",
    "Q = 3093.12  # constant pumping rate [m^3/d]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Load Data\n",
    "\n",
    "Dataset is stored in a text-file for each well, where the first column is the time in days and the second is the drawdown in meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pumped well UE-25b#1\n",
    "data1 = np.loadtxt(\"data/double-porosity-pumpingwell.txt\", skiprows=1)\n",
    "t1 = data1[:, 0]\n",
    "h1 = data1[:, 1]\n",
    "\n",
    "# Observation well UE-25a#1\n",
    "data2 = np.loadtxt(\"data/double-porosity-110m.txt\", skiprows=1)\n",
    "t2 = data2[:, 0]\n",
    "h2 = data2[:, 1]\n",
    "r = 110  # distance from obs to pumped well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. Create conceptual model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conceptualize the model in TTim, we have to approximate the fractured system to a possible ModelMaq configuration. We can do this by creating a first layer that represents the matrix of the aquifer, followed by a 1 m thick aquitard and a second layer that represents the fractures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def conc_mmaq_confined5():\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((-20, 0), width=170, height=150, fc=\"b\", zorder=0, alpha=0.1)\n",
    "    ax.add_patch(sky)\n",
    "\n",
    "    # Aquifer:\n",
    "    ground_2 = plt.Rectangle(\n",
    "        (-20, -1219 + 401),\n",
    "        width=170,\n",
    "        height=400,\n",
    "        fc=np.array([209, 179, 127]) / 255,\n",
    "        zorder=0,\n",
    "        alpha=0.9,\n",
    "        hatch=\"oo\",\n",
    "    )\n",
    "    ax.add_patch(ground_2)\n",
    "    ground = plt.Rectangle(\n",
    "        (-20, -1219),\n",
    "        width=170,\n",
    "        height=400,\n",
    "        fc=np.array([209, 179, 127]) / 255,\n",
    "        zorder=0,\n",
    "        alpha=0.9,\n",
    "        hatch=\"xx\",\n",
    "    )\n",
    "    ax.add_patch(ground)\n",
    "\n",
    "    # Confining bed:\n",
    "    confining_unit_1 = plt.Rectangle(\n",
    "        (-20, -1219 + 400),\n",
    "        width=170,\n",
    "        height=1,\n",
    "        fc=np.array([100, 100, 100]) / 255,\n",
    "        zorder=0,\n",
    "        alpha=0.7,\n",
    "    )\n",
    "    ax.add_patch(confining_unit_1)\n",
    "    confining_unit = plt.Rectangle(\n",
    "        (-20, -1219 + 801),\n",
    "        width=170,\n",
    "        height=1219 - 801,\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",
    "        (-2, -1219), width=4, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    "    )\n",
    "    ax.add_patch(well)\n",
    "\n",
    "    # Wellhead\n",
    "    wellhead = plt.Rectangle(\n",
    "        (-4, 0),\n",
    "        width=8,\n",
    "        height=55,\n",
    "        fc=np.array([200, 200, 200]) / 255,\n",
    "        zorder=2,\n",
    "        ec=\"k\",\n",
    "    )\n",
    "    ax.add_patch(wellhead)\n",
    "\n",
    "    # Screen for the well:\n",
    "    screen = plt.Rectangle(\n",
    "        (-2, -1219),\n",
    "        width=4,\n",
    "        height=400,\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=4, y=25, dx=22, dy=0, color=\"#00035b\")\n",
    "    ax.add_patch(pumping_arrow)\n",
    "    ax.text(x=29, y=55, s=r\"$ Q = 3093 \\frac{m^3}{d}$\", fontsize=\"large\")\n",
    "    # Piezometers\n",
    "    piez1 = plt.Rectangle(\n",
    "        (99, -1219), width=2, height=1219, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    "    )\n",
    "    screen_piez_1 = plt.Rectangle(\n",
    "        (99, -1219),\n",
    "        width=2,\n",
    "        height=400,\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=[-20, 150], ydata=[0, 0], color=\"k\")\n",
    "    ax.add_line(line)\n",
    "    ax.text(x=100, y=55, s=\"Obs Well 1\", fontsize=\"large\")\n",
    "\n",
    "    ax.set_xlim([-20, 150])\n",
    "    ax.set_ylim([-1219, 150])\n",
    "    ax.set_xlabel(\"Distance [m]\")\n",
    "    ax.set_ylabel(\"Relative height [m]\")\n",
    "    ax.set_title(\"Conceptual ModelMaq Model - Nevada Example\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "conc_mmaq_confined5()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, for the TTim model, we will adopt the parameters for the first layer from the results obtained from Kruseman and de Ridder (1970):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = 0.1 / H  # hydraulic conductivity of matrix calculated by K&dR\n",
    "Sm = 3.85e-4  # specific storage of matrix calculated by K&dR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can construct the two-layer model:\n",
    "Instructions on how to construct this model are in:\n",
    "* [Confined 4 - Schroth](confined4_schroty.ipynb)\n",
    "* [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml = ttim.ModelMaq(\n",
    "    kaq=[km, 1],\n",
    "    z=[0, -400, -401, -801],\n",
    "    c=5,\n",
    "    Saq=[Sm, 1e-3],\n",
    "    Sll=0,\n",
    "    topboundary=\"conf\",\n",
    "    tmin=1e-5,\n",
    "    tmax=3,\n",
    ")\n",
    "w = ttim.Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n",
    "ml.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Calibrate the model using both Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this calibration procedure, we only calibrate the parameters of the fractured system and keep the matrix values fixed. We also calibrate the wellbore storage parameter ```rc``` that is the radius of the caisson considered for storage.\n",
    "Instructions on how to set this calibration are in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".........................................................................................................................................................................................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 198\n",
      "    # data points      = 138\n",
      "    # variables        = 4\n",
      "    chi-square         = 5.47351190\n",
      "    reduced chi-square = 0.04084710\n",
      "    Akaike info crit   = -437.371988\n",
      "    Bayesian info crit = -425.662973\n",
      "[[Variables]]\n",
      "    kaq1:  0.87696320 +/- 0.00699050 (0.80%) (init = 10)\n",
      "    Saq1:  5.0877e-06 +/- 5.0675e-07 (9.96%) (init = 0.0001)\n",
      "    c1:    13.0035244 +/- 1.59723924 (12.28%) (init = 10)\n",
      "    rc:    0.10560361 +/- 0.00320824 (3.04%) (init = 0)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq1, c1)   = +0.8580\n",
      "    C(kaq1, Saq1) = -0.7312\n",
      "    C(Saq1, c1)   = -0.5463\n",
      "    C(Saq1, rc)   = -0.4011\n",
      "    C(kaq1, rc)   = +0.1007\n"
     ]
    },
    {
     "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>kaq1</th>\n",
       "      <td>0.876963</td>\n",
       "      <td>6.990504e-03</td>\n",
       "      <td>0.797126</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[0.8769632032421197]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq1</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>5.067510e-07</td>\n",
       "      <td>9.960305</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[5.0877062902632275e-06]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>13.003524</td>\n",
       "      <td>1.597239e+00</td>\n",
       "      <td>12.283126</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[13.003524396048254]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rc</th>\n",
       "      <td>0.105604</td>\n",
       "      <td>3.208241e-03</td>\n",
       "      <td>3.038003</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[0.1056036071201357]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        optimal           std   perc_std  pmin  pmax  initial  \\\n",
       "kaq1   0.876963  6.990504e-03   0.797126  -inf   inf  10.0000   \n",
       "Saq1   0.000005  5.067510e-07   9.960305   0.0   inf   0.0001   \n",
       "c1    13.003524  1.597239e+00  12.283126  -inf   inf  10.0000   \n",
       "rc     0.105604  3.208241e-03   3.038003  -inf   inf   0.0000   \n",
       "\n",
       "                        parray  \n",
       "kaq1      [0.8769632032421197]  \n",
       "Saq1  [5.0877062902632275e-06]  \n",
       "c1        [13.003524396048254]  \n",
       "rc        [0.1056036071201357]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.19915604354014974\n"
     ]
    }
   ],
   "source": [
    "ca = ttim.Calibrate(ml)\n",
    "ca.set_parameter(name=\"kaq1\", initial=10)\n",
    "ca.set_parameter(name=\"Saq1\", initial=1e-4, pmin=0)\n",
    "ca.set_parameter(name=\"c1\", initial=10)\n",
    "ca.set_parameter_by_reference(name=\"rc\", parameter=w.rc, initial=0)\n",
    "ca.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n",
    "ca.series(name=\"UE-25a#1\", x=r, y=0, t=t2, h=h2, layer=1)\n",
    "ca.fit(report=True)\n",
    "display(ca.parameters)\n",
    "print(\"RMSE:\", ca.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, a good fit has been obtained. Reasonable standard deviations of the estimates and a low value for AIC and BIC gives us confidence in the adopted parameters. Now we can check how the results plot with the observations:"
   ]
  },
  {
   "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": [
    "hm1 = ml.head(0, 0, t1)\n",
    "hm2 = ml.head(110, 0, t2)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n",
    "plt.semilogx(t1, hm1[-1], label=\"TTim UE-25b#1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n",
    "plt.semilogx(t2, hm2[-1], label=\"TTim UE-25a#1\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Results - Simulation 1\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7. Simulate parameters of both fracture and matrix\n",
    "\n",
    "Now we will repeat the procedures of steps 5 and 6, but this time we will let TTim find the parameters for both the matrix and the fractures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml1 = ttim.ModelMaq(\n",
    "    kaq=[1, 1],\n",
    "    z=[0, -400, -401, -801],\n",
    "    c=5,\n",
    "    Saq=[1e-3, 1e-3],\n",
    "    Sll=0,\n",
    "    topboundary=\"conf\",\n",
    "    tmin=1e-5,\n",
    "    tmax=3,\n",
    ")\n",
    "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n",
    "ml1.solve()"
   ]
  },
  {
   "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   = 354\n",
      "    # data points      = 138\n",
      "    # variables        = 6\n",
      "    chi-square         = 3.50592808\n",
      "    reduced chi-square = 0.02656006\n",
      "    Akaike info crit   = -494.846181\n",
      "    Bayesian info crit = -477.282659\n",
      "[[Variables]]\n",
      "    kaq0:  8.3798e-08 +/- 2.7038e-05 (32265.92%) (init = 1)\n",
      "    Saq0:  1.4424e-04 +/- 1.4640e-05 (10.15%) (init = 0.0001)\n",
      "    kaq1:  0.90902373 +/- 0.00603961 (0.66%) (init = 1)\n",
      "    Saq1:  3.3892e-06 +/- 3.0267e-07 (8.93%) (init = 0.0001)\n",
      "    c1:    15.5844403 +/- 1.43538189 (9.21%) (init = 100)\n",
      "    rc:    0.10855545 +/- 0.00253751 (2.34%) (init = 0)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq1, Saq1) = -0.7423\n",
      "    C(kaq1, c1)   = +0.7105\n",
      "    C(Saq0, kaq1) = -0.6759\n",
      "    C(Saq0, Saq1) = +0.5464\n",
      "    C(Saq1, rc)   = -0.4120\n",
      "    C(Saq1, c1)   = -0.3831\n",
      "    C(Saq0, c1)   = -0.2784\n",
      "    C(kaq0, c1)   = +0.1418\n",
      "    C(kaq0, Saq1) = +0.1270\n",
      "    C(kaq1, rc)   = +0.1182\n",
      "    C(kaq0, Saq0) = +0.1102\n",
      "    C(Saq0, rc)   = -0.1095\n"
     ]
    },
    {
     "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>8.379800e-08</td>\n",
       "      <td>2.703820e-05</td>\n",
       "      <td>32265.923602</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>[8.379799754099082e-08]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>1.442428e-04</td>\n",
       "      <td>1.463980e-05</td>\n",
       "      <td>10.149414</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[0.00014424278513025524]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kaq1</th>\n",
       "      <td>9.090237e-01</td>\n",
       "      <td>6.039612e-03</td>\n",
       "      <td>0.664406</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>[0.9090237295301099]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq1</th>\n",
       "      <td>3.389227e-06</td>\n",
       "      <td>3.026748e-07</td>\n",
       "      <td>8.930495</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[3.3892272003344104e-06]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>1.558444e+01</td>\n",
       "      <td>1.435382e+00</td>\n",
       "      <td>9.210353</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>inf</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>[15.584440297512415]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rc</th>\n",
       "      <td>1.085555e-01</td>\n",
       "      <td>2.537508e-03</td>\n",
       "      <td>2.337523</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>[0.10855545326039162]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           optimal           std      perc_std          pmin  pmax   initial  \\\n",
       "kaq0  8.379800e-08  2.703820e-05  32265.923602  1.000000e-08   inf    1.0000   \n",
       "Saq0  1.442428e-04  1.463980e-05     10.149414  1.000000e-08   inf    0.0001   \n",
       "kaq1  9.090237e-01  6.039612e-03      0.664406  1.000000e-08   inf    1.0000   \n",
       "Saq1  3.389227e-06  3.026748e-07      8.930495  1.000000e-08   inf    0.0001   \n",
       "c1    1.558444e+01  1.435382e+00      9.210353  1.000000e-08   inf  100.0000   \n",
       "rc    1.085555e-01  2.537508e-03      2.337523  0.000000e+00   inf    0.0000   \n",
       "\n",
       "                        parray  \n",
       "kaq0   [8.379799754099082e-08]  \n",
       "Saq0  [0.00014424278513025524]  \n",
       "kaq1      [0.9090237295301099]  \n",
       "Saq1  [3.3892272003344104e-06]  \n",
       "c1        [15.584440297512415]  \n",
       "rc       [0.10855545326039162]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.1593903257365926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: divide by zero encountered in divide\n",
      "  laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: invalid value encountered in divide\n",
      "  laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:38: RuntimeWarning: invalid value encountered in multiply\n",
      "  self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef\n"
     ]
    }
   ],
   "source": [
    "ca1 = ttim.Calibrate(ml1)\n",
    "ca1.set_parameter(name=\"kaq0\", initial=1, pmin=1e-8)\n",
    "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-8)\n",
    "ca1.set_parameter(name=\"kaq1\", initial=1, pmin=1e-8)\n",
    "ca1.set_parameter(name=\"Saq1\", initial=1e-4, pmin=1e-8)\n",
    "ca1.set_parameter(name=\"c1\", initial=100, pmin=1e-8)\n",
    "ca1.set_parameter_by_reference(name=\"rc\", parameter=w1.rc, initial=0, pmin=0)\n",
    "ca1.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n",
    "ca1.series(name=\"UE-25a#1\", x=110, y=0, t=t2, h=h2, layer=1)\n",
    "ca1.fit(report=True)\n",
    "display(ca1.parameters)\n",
    "print(\"RMSE:\", ca1.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the fit has slightly improved, as the AIC and BIC values. However, from the table above, one can see that we have low confidence in the hydraulic conductivity of the matrix. Nevertheless, we can see it is a  small value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ht1 = ml1.head(0, 0, t1)\n",
    "ht2 = ml1.head(110, 0, t2)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n",
    "plt.semilogx(t1, ht1[-1], label=\"TTim UE-25b#1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n",
    "plt.semilogx(t2, ht2[-1], label=\"TTim UE-25a#1\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Results - Simulation 1\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall the curves follow the trends of the drawdown and the fit is good in general."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8. Analysis and Comparison of the Results under different methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final important step is to compare the data obtained from this model with the data from other Aquifer Analysis software. Yang (2020) compared TTim results with the published results in Kruseman and de Ridder (1990), here abbreviated to K&dR, and with the results obtained from the software AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012).\n",
    "\n",
    "Kruseman et al. (1970) solved the problem using a graphical method, where the transmissivity was calculated as one aquifer and the storativity was separated between matrix and fractures. The MLU (Carlson & Randall, 2012) solution used a similar approach to our TTim model by simulating the matrix as a very-low transmissivity aquifer on top of the fractured aquifer and separated by a zero-storage aquitard. Yang (2020) solved the problem in AQTESOLV using a double-porosity analytical solution proposed by Moench (1984).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: divide by zero encountered in divide\n",
      "  laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:35: RuntimeWarning: invalid value encountered in divide\n",
      "  laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab))\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/ttim-env/lib/python3.10/site-packages/ttim/well.py:38: RuntimeWarning: invalid value encountered in multiply\n",
      "  self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        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>km [m/d]</th>\n",
       "      <th>Sm [1/m]</th>\n",
       "      <th>kf [m/d]</th>\n",
       "      <th>Sf [1/m]</th>\n",
       "      <th>c</th>\n",
       "      <th>rc</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K&amp;dR</th>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQTESOLV</th>\n",
       "      <td>0.149</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>-</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.031736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLU</th>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>12.38</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.434638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTim1</th>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.876963</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>13.003524</td>\n",
       "      <td>0.105604</td>\n",
       "      <td>0.199156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTim2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.909024</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>15.58444</td>\n",
       "      <td>0.108555</td>\n",
       "      <td>0.15939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         km [m/d]  Sm [1/m]  kf [m/d]  Sf [1/m]          c        rc      RMSE\n",
       "K&dR       0.8325  0.000375    0.8325  0.000004          -         -         -\n",
       "AQTESOLV    0.149  0.000551     0.937  0.000006          -      0.11  0.031736\n",
       "MLU       0.00025  0.000385     0.874  0.000008      12.38       0.1  0.434638\n",
       "TTim1     0.00025  0.000385  0.876963  0.000005  13.003524  0.105604  0.199156\n",
       "TTim2         0.0  0.000144  0.909024  0.000003   15.58444  0.108555   0.15939"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    columns=[\"km [m/d]\", \"Sm [1/m]\", \"kf [m/d]\", \"Sf [1/m]\", \"c\", \"rc\"],\n",
    "    index=[\n",
    "        \"K&dR\",  #'Moench',\n",
    "        \"AQTESOLV\",\n",
    "        \"MLU\",\n",
    "        \"TTim1\",\n",
    "        \"TTim2\",\n",
    "    ],\n",
    ")\n",
    "t.loc[\"TTim1\"] = np.concatenate(\n",
    "    (np.array([0.00025, 3.850e-04]), ca.parameters[\"optimal\"].values)\n",
    ")\n",
    "t.loc[\"TTim2\"] = ca1.parameters[\"optimal\"].values\n",
    "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\"]\n",
    "# I don't know where these values for Moench come from:\n",
    "# t.loc['Moench'] = [0.1728, 3.000e-4, 0.864, 1.500e-6, '-', '-']\n",
    "t.loc[\"AQTESOLV\"] = [0.149, 5.512e-4, 0.937, 5.533e-6, \"-\", 0.11]\n",
    "t.loc[\"MLU\"] = [0.00025, 3.850e-04, 0.874, 8.053e-6, 12.380, 0.1]\n",
    "t[\"RMSE\"] = [\"-\", 0.031736, 0.434638, ca.rmse(), ca1.rmse()]\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, TTim model 1 showed similar results to MLU but with a slightly better fit. AQTESOLV obtained the best fit using Moench's analytical solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.1. Comparison of estimates and model error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preparing the DataFrame:\n",
    "t1 = pd.DataFrame(\n",
    "    columns=[\"kaq - opt -l1\", \"kaq - 95% -l1\", \"kaq - opt -l2\", \"kaq - 95% -l2\"],\n",
    "    index=[\"AQTESOLV\", \"MLU\", \"K&dR\", \"TTim - all params\", \"TTim - fixed upper\"],\n",
    ")\n",
    "simulation = [\"AQTESOLV\", \"MLU\", \"K&dR\", \"TTim - rc\", \"TTim - fixed upper\"]\n",
    "t1.loc[\"MLU\"] = [0.00025, np.nan, 0.874, 1.221 * 1e-2 * 0.874]\n",
    "t1.loc[\"KdR\"] = [0.8325, np.nan, 0.8325, np.nan]\n",
    "t1.loc[\"AQTESOLV\"] = [0.149, 291.236 * 1e-2 * 0.149, 0.937, 1.946 * 1e-2 * 0.937]\n",
    "t1.loc[\"TTim - fixed upper\"] = [\n",
    "    0.00025,\n",
    "    np.nan,\n",
    "    ca.parameters.loc[\"kaq1\", \"optimal\"],\n",
    "    2 * ca.parameters.loc[\"kaq1\", \"std\"],\n",
    "]\n",
    "t1.loc[\"TTim - all params\"] = [\n",
    "    ca1.parameters.loc[\"kaq0\", \"optimal\"],\n",
    "    2 * ca1.parameters.loc[\"kaq0\", \"std\"],\n",
    "    ca1.parameters.loc[\"kaq1\", \"optimal\"],\n",
    "    2 * ca1.parameters.loc[\"kaq1\", \"std\"],\n",
    "]\n",
    "\n",
    "# Plotting\n",
    "\n",
    "# plt.figure(figsize = (10,7))\n",
    "fig, ax = plt.subplots(2, 1, figsize=(10, 7), sharex=True)\n",
    "ax[0].errorbar(\n",
    "    x=t1.index,\n",
    "    y=t1[\"kaq - opt -l1\"],\n",
    "    yerr=[t1[\"kaq - 95% -l1\"], t1[\"kaq - 95% -l1\"]],\n",
    "    marker=\"o\",\n",
    "    linestyle=\"\",\n",
    "    markersize=12,\n",
    "    label=\"aquifer matrix\",\n",
    "    color=\"red\",\n",
    ")\n",
    "ax[0].legend()\n",
    "ax[1].errorbar(\n",
    "    x=t1.index,\n",
    "    y=t1[\"kaq - opt -l2\"],\n",
    "    yerr=[t1[\"kaq - 95% -l2\"], t1[\"kaq - 95% -l2\"]],\n",
    "    marker=\"o\",\n",
    "    linestyle=\"\",\n",
    "    markersize=12,\n",
    "    label=\"fractures\",\n",
    ")\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": [
    "Hydraulic conductivities varied between simulations. When estimated, the matrix conductivity had higher uncertainty. Uncertainty ranges are similar for the fractured portion. However, the solutions do not always overlap each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n",
    "* 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",
    "* Kruseman GP, De Ridder NA (1990) Analysis and evaluation of pumping test data, 2nd edn. ILRI Publ. 47, ILRI, Wageningen, The Netherlands\n",
    "* Moench, A. F. (1984), Double-Porosity Models for a Fissured Groundwater Reservoir With Fracture Skin, Water Resour. Res., 20( 7), 831– 846, doi:10.1029/WR020i007p00831.\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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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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