{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Unconfined Aquifer Test - Moench Example\n",
    "\n",
    "Test for an anisotropic water-table aquifer.\n",
    "\n",
    "**This test is taken from examples presented in MLU tutorial.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import ttim as ttm\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [5, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Introduction and Conceptual Model\n",
    "\n",
    "This test is based on a synthetic example reported by Barlow and Moench (1999), utilizing an analytical solution developed by Moench and Allen (1997) for the transient flow of partially-penetrating wells in unconfined aquifers. The data reported has been used in MLU (Carlson and Randall, 2012) to check the model performance.\n",
    "\n",
    "We will reproduce the work of Yang (2020) to explore the performance of anisotropic water table aquifer modelling with TTim and compare the results with the published values and the MLU solution.\n",
    "\n",
    "The conceptual model of the test is of an aquifer, partially saturated with water (10 m water table). A pumping well is screened from 5 to 10 m depth. The well and the well-casing radius is 0.1 m. Drawdown is recorded at the pumping well and four piezometers located at two different distances and two different depths. Two piezometers, PS1 and PS2, are located at one-meter depth below the water table and 3.16 and 31.6 m distance, respectively. Another two (PD1 and PD2) piezometers are at 7.5 m depth below the water table and the same distances,  directly below the previous piezometers. The figure below shows the location of the well and the piezometers\n",
    "\n",
    "Pumping starts at time t = 0 at a constant rate of 172.8 m3/d. Drawdown is recorded until t = 3 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "##Now printing the conceptual model figure:\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "# sky\n",
    "sky = plt.Rectangle((-20, 2), width=70, height=5, fc=\"b\", zorder=0, alpha=0.1)\n",
    "ax.add_patch(sky)\n",
    "\n",
    "# Aquifer:\n",
    "ground = plt.Rectangle(\n",
    "    (-20, -10),\n",
    "    width=70,\n",
    "    height=12,\n",
    "    fc=np.array([209, 179, 127]) / 255,\n",
    "    zorder=0,\n",
    "    alpha=0.9,\n",
    ")\n",
    "ax.add_patch(ground)\n",
    "\n",
    "\n",
    "well = plt.Rectangle(\n",
    "    (-0.5, -10), width=1, height=12, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "ax.add_patch(well)\n",
    "\n",
    "# Wellhead\n",
    "wellhead = plt.Rectangle(\n",
    "    (-0.75, 2),\n",
    "    width=1.5,\n",
    "    height=1.5,\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",
    "    (-0.5, -10),\n",
    "    width=1,\n",
    "    height=5,\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=1.1, y=3, dx=3, dy=0, color=\"#00035b\")\n",
    "ax.add_patch(pumping_arrow)\n",
    "ax.text(x=4.2, y=3, s=r\"$ Q = 172.8$ m$^3$/d\", fontsize=\"large\")\n",
    "\n",
    "# Piezometers\n",
    "piez1 = plt.Rectangle(\n",
    "    (31.4, -10), width=0.5, height=12, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "screen_piez_1 = plt.Rectangle(\n",
    "    (31.4, -8),\n",
    "    width=0.5,\n",
    "    height=1,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_1.set_linewidth(2)\n",
    "screen_piez_2 = plt.Rectangle(\n",
    "    (31.4, -1.5),\n",
    "    width=0.5,\n",
    "    height=1,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_2.set_linewidth(2)\n",
    "\n",
    "ax.add_patch(piez1)\n",
    "ax.add_patch(screen_piez_1)\n",
    "ax.add_patch(screen_piez_2)\n",
    "\n",
    "piez2 = plt.Rectangle(\n",
    "    (2.75, -10), width=0.5, height=12, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "screen_piez_3 = plt.Rectangle(\n",
    "    (2.75, -8),\n",
    "    width=0.5,\n",
    "    height=1,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_3.set_linewidth(2)\n",
    "screen_piez_4 = plt.Rectangle(\n",
    "    (2.75, -1.5),\n",
    "    width=0.5,\n",
    "    height=1,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_4.set_linewidth(2)\n",
    "\n",
    "ax.add_patch(piez2)\n",
    "ax.add_patch(screen_piez_3)\n",
    "ax.add_patch(screen_piez_4)\n",
    "\n",
    "# last line\n",
    "line = plt.Line2D(xdata=[-200, 1200], ydata=[2, 2], color=\"k\")\n",
    "ax.add_line(line)\n",
    "\n",
    "# Water table\n",
    "line2 = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"b\")\n",
    "ax.add_line(line2)\n",
    "\n",
    "ax.text(-9, 0.5, s=\"Water Table\", fontsize=\"large\", color=\"b\", bbox={\"fc\": \"w\"})\n",
    "ax.text(4.5, -1, s=\"PS1\", bbox={\"fc\": \"w\"})\n",
    "ax.text(34, -1, s=\"PS2\", bbox={\"fc\": \"w\"})\n",
    "ax.text(4.5, -7.5, s=\"PD1\", bbox={\"fc\": \"w\"})\n",
    "ax.text(34, -7.5, s=\"PD2\", bbox={\"fc\": \"w\"})\n",
    "\n",
    "ax.set_xlim([-10, 50])\n",
    "ax.set_ylim([-10, 4])\n",
    "ax.set_xlabel(\"Distance [m]\")\n",
    "ax.set_ylabel(\"Relative height [m]\")\n",
    "ax.set_title(\"Conceptual Model - Vennebulten Example\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. Set basic parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = 10  # aquifer thickness, m\n",
    "Q = 172.8  # constant discharge rate, m^3/d\n",
    "rw = 0.1  # well radius, m\n",
    "rc = 0.1  # casing radius, m\n",
    "r1 = 3.16  # distance of closer wells, m\n",
    "r2 = 31.6  # distance of wells more far away, m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Load datasets of observation wells\n",
    "\n",
    "The dataset for each well consists of a column with the time data in seconds and drawdown in meters. We are loading it and converting it to days and meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data0 = np.loadtxt(\"data/moench_pumped.txt\", skiprows=1)\n",
    "t0 = data0[:, 0] / 60 / 60 / 24  # convert time from seconds to days\n",
    "h0 = -data0[:, 1]  # converting drawdown to heads\n",
    "data1 = np.loadtxt(\"data/moench_ps1.txt\", skiprows=1)\n",
    "t1 = data1[:, 0] / 60 / 60 / 24  # convert time from seconds to days\n",
    "h1 = -data1[:, 1]\n",
    "data2 = np.loadtxt(\"data/moench_pd1.txt\", skiprows=1)\n",
    "t2 = data2[:, 0] / 60 / 60 / 24  # convert time from seconds to days\n",
    "h2 = -data2[:, 1]\n",
    "data3 = np.loadtxt(\"data/moench_ps2.txt\", skiprows=1)\n",
    "t3 = data3[:, 0] / 60 / 60 / 24  # convert time from seconds to days\n",
    "h3 = -data3[:, 1]\n",
    "data4 = np.loadtxt(\"data/moench_pd2.txt\", skiprows=1)\n",
    "t4 = data4[:, 0] / 60 / 60 / 24  # convert time from seconds to days\n",
    "h4 = -data4[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. Creating a TTim model\n",
    "\n",
    "We will create an initial model divided in the same way as in the MLU documentation. A first layer with 0.1 m thick and phreatic storage, followed by a 2 m thick layer where the shallow piezometers are located, a 3 m layer and finally, a 5 m layer where the pump is placed and the last piezometers are screened. Additionally, we will set the model parameters with the given ones in Barlow and Moench (1999) and compare the results with the heads given in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set kaq, Saq, Sy and kzoverkh as given in Moench (1997)\n",
    "kaq = 1e-4 * 60 * 60 * 24  # convert from m/s to m/d\n",
    "Sy = 0.2\n",
    "Saq = 2e-5\n",
    "kzoverkh = 0.5  # kzoverkh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml1 = ttm.Model3D(\n",
    "    kaq=kaq,\n",
    "    z=[0, -0.1, -2.1, -5.1, -10.1],\n",
    "    Saq=[Sy, Saq, Saq, Saq],\n",
    "    kzoverkh=kzoverkh,\n",
    "    tmin=1e-5,\n",
    "    tmax=3,\n",
    "    phreatictop=True,\n",
    ")\n",
    "w1 = ttm.Well(ml1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=[3])\n",
    "ml1.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the TTim model with values obtained by Moench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm0 = w1.headinside(t0)[0]\n",
    "hm1 = ml1.head(r1, 0, t1, layers=1)[0]\n",
    "hm2 = ml1.head(r1, 0, t2, layers=3)[0]\n",
    "hm3 = ml1.head(r2, 0, t3, layers=1)[0]\n",
    "hm4 = ml1.head(r2, 0, t4, layers=3)[0]\n",
    "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n",
    "plt.semilogx(t0, hm0, label=\"ttim pumped\")\n",
    "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n",
    "plt.semilogx(t1, hm1, label=\"ttim PS1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n",
    "plt.semilogx(t2, hm2, label=\"ttim PD1\")\n",
    "plt.semilogx(t3, h3, \".\", label=\"PS2\")\n",
    "plt.semilogx(t3, hm3, label=\"ttim PS2\")\n",
    "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n",
    "plt.semilogx(t4, hm4, label=\"ttim PD2\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.title(\"Model Results - Simulation 1\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually, TTim's solution is close to Moench's solution, but not quite the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate RMSE of TTim model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.06131793579847181\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(\n",
    "    np.sum(\n",
    "        np.sum((h1 - hm1) ** 2)\n",
    "        + np.sum((h2 - hm2) ** 2)\n",
    "        + np.sum((h3 - hm3) ** 2)\n",
    "        + np.sum((h4 - hm4) ** 2)\n",
    "        + np.sum((h0 - hm0) ** 2)\n",
    "    )\n",
    "    / (len(h1) + len(h2) + len(h3) + len(h4) + len(h0))\n",
    ")\n",
    "\n",
    "print(\"RMSE:\", rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has obtained a very close result with a good approximation to the analytical solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6. Model Calibration\n",
    "\n",
    "Now, instead of using the given values, we will try to find them through the TTim optimization framework. One can learn more about the calibration procedure and how to set the parameters in the following notebook: [Unconfined Test - Vennebulten](unconfined1_vennebulten.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml2 = ttm.Model3D(\n",
    "    kaq=1,\n",
    "    z=[0, -0.1, -2.1, -5.1, -10.1],\n",
    "    Saq=[0.1, 1e-4, 1e-4, 1e-4],\n",
    "    kzoverkh=1,\n",
    "    tmin=1e-5,\n",
    "    tmax=3,\n",
    "    phreatictop=True,\n",
    ")\n",
    "w2 = ttm.Well(ml2, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n",
    "ml2.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..................................................................\n",
      "Fit succeeded.\n"
     ]
    }
   ],
   "source": [
    "ca2 = ttm.Calibrate(ml2)\n",
    "ca2.set_parameter(name=\"kaq0_3\", initial=1)\n",
    "ca2.set_parameter(name=\"Saq0\", initial=0.2)\n",
    "ca2.set_parameter(name=\"Saq1_3\", initial=1e-4, pmin=0)\n",
    "ca2.set_parameter(name=\"kzoverkh0_3\", initial=0.1, pmin=0)\n",
    "ca2.seriesinwell(name=\"pumped\", element=w2, t=t0, h=h0)\n",
    "ca2.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n",
    "ca2.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n",
    "ca2.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n",
    "ca2.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n",
    "ca2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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_3</th>\n",
       "      <td>9.067633</td>\n",
       "      <td>0.022352</td>\n",
       "      <td>0.246505</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>[9.06763268224383, 9.06763268224383, 9.0676326...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.172888</td>\n",
       "      <td>0.004374</td>\n",
       "      <td>2.529859</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>[0.17288811536695828]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq1_3</th>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>9.175881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[3.8713577102722496e-05, 3.8713577102722496e-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kzoverkh0_3</th>\n",
       "      <td>0.535053</td>\n",
       "      <td>0.010142</td>\n",
       "      <td>1.895517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>[0.5350534715216693, 0.5350534715216693, 0.535...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              optimal       std  perc_std  pmin  pmax  initial  \\\n",
       "kaq0_3       9.067633  0.022352  0.246505  -inf   inf   1.0000   \n",
       "Saq0         0.172888  0.004374  2.529859  -inf   inf   0.2000   \n",
       "Saq1_3       0.000039  0.000004  9.175881   0.0   inf   0.0001   \n",
       "kzoverkh0_3  0.535053  0.010142  1.895517   0.0   inf   0.1000   \n",
       "\n",
       "                                                        parray  \n",
       "kaq0_3       [9.06763268224383, 9.06763268224383, 9.0676326...  \n",
       "Saq0                                     [0.17288811536695828]  \n",
       "Saq1_3       [3.8713577102722496e-05, 3.8713577102722496e-0...  \n",
       "kzoverkh0_3  [0.5350534715216693, 0.5350534715216693, 0.535...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.010384176554876806\n"
     ]
    }
   ],
   "source": [
    "display(ca2.parameters)\n",
    "print(\"RMSE:\", ca2.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values are close to the values in Moench."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm0_2 = ml2.head(0, 0, t0, layers=3)[0]\n",
    "hm1_2 = ml2.head(r1, 0, t1, layers=1)[0]\n",
    "hm2_2 = ml2.head(r1, 0, t2, layers=3)[0]\n",
    "hm3_2 = ml2.head(r2, 0, t3, layers=1)[0]\n",
    "hm4_2 = ml2.head(r2, 0, t4, layers=3)[0]\n",
    "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n",
    "plt.semilogx(t0, hm0_2, label=\"ttim pumped\")\n",
    "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n",
    "plt.semilogx(t1, hm1_2, label=\"ttim PS1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n",
    "plt.semilogx(t2, hm2_2, label=\"ttim PD1\")\n",
    "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n",
    "plt.semilogx(t3, hm3_2, label=\"ttim PS2\")\n",
    "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n",
    "plt.semilogx(t4, hm4_2, label=\"ttim PD2\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.title(\"Model Results - Calibration 1\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calibration resulted in a lower error than the simulated model, which could mean that we could improve the conceptualization of the model in TTim. In this next step, we will increase the number of layers to better account for the vertical flow component. We will create an 18-layers model with 0.5 m thick layers, except at the layers in the piezometers where a 1 m thickness has been established to make sure the piezometers lie at the centre of the layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.   -0.5  -1.5  -2.   -2.5  -3.   -3.5  -4.   -4.5  -5.   -5.5  -6.\n",
      "  -6.5  -7.   -8.   -8.5  -9.   -9.5 -10. ]\n",
      "2e-05\n"
     ]
    }
   ],
   "source": [
    "## Model Configuration:\n",
    "\n",
    "nlay = 18\n",
    "zlayers = np.concatenate(\n",
    "    (\n",
    "        np.array([0, -0.5, -1.5]),\n",
    "        np.linspace(-2, -7, 11),\n",
    "        np.array([-8, -8.5, -9, -9.5, -10]),\n",
    "    )\n",
    ")  # elevation of each layer\n",
    "Saq_n = 1e-4 * np.ones(nlay)\n",
    "Saq_n[0] = 0.1  # Setting the first storage as specific yield\n",
    "print(zlayers)\n",
    "print(Saq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1   , 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001,\n",
       "       0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001,\n",
       "       0.0001, 0.0001])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Saq_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  9\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml3 = ttm.Model3D(\n",
    "    kaq=1, z=zlayers, Saq=Saq_n, kzoverkh=1, tmin=1e-5, tmax=3, phreatictop=True\n",
    ")\n",
    "w3 = ttm.Well(ml3, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=range(9, 18))\n",
    "ml3.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".........................................................\n",
      "Fit succeeded.\n"
     ]
    }
   ],
   "source": [
    "ca3 = ttm.Calibrate(ml3)\n",
    "ca3.set_parameter(name=\"kaq0_19\", initial=1, pmin=0)\n",
    "ca3.set_parameter(name=\"Saq0\", initial=0.2, pmin=0)\n",
    "ca3.set_parameter(name=\"Saq1_19\", initial=1e-4, pmin=0)\n",
    "ca3.set_parameter(name=\"kzoverkh0_19\", initial=0.1, pmin=0)\n",
    "ca3.seriesinwell(name=\"well\", element=w3, t=t0, h=h0)\n",
    "ca3.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n",
    "ca3.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=13)\n",
    "ca3.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n",
    "ca3.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=13)\n",
    "ca3.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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_19</th>\n",
       "      <td>8.695434</td>\n",
       "      <td>0.020723</td>\n",
       "      <td>0.238320</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>[8.695433717306143, 8.695433717306143, 8.69543...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.196104</td>\n",
       "      <td>0.004616</td>\n",
       "      <td>2.354037</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>[0.1961037602402571]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq1_19</th>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>8.513175</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[3.8988993330990596e-05, 3.8988993330990596e-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kzoverkh0_19</th>\n",
       "      <td>0.488059</td>\n",
       "      <td>0.008575</td>\n",
       "      <td>1.756888</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>[0.48805919609868, 0.48805919609868, 0.4880591...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               optimal       std  perc_std  pmin  pmax  initial  \\\n",
       "kaq0_19       8.695434  0.020723  0.238320     0   inf   1.0000   \n",
       "Saq0          0.196104  0.004616  2.354037     0   inf   0.2000   \n",
       "Saq1_19       0.000039  0.000003  8.513175     0   inf   0.0001   \n",
       "kzoverkh0_19  0.488059  0.008575  1.756888     0   inf   0.1000   \n",
       "\n",
       "                                                         parray  \n",
       "kaq0_19       [8.695433717306143, 8.695433717306143, 8.69543...  \n",
       "Saq0                                       [0.1961037602402571]  \n",
       "Saq1_19       [3.8988993330990596e-05, 3.8988993330990596e-0...  \n",
       "kzoverkh0_19  [0.48805919609868, 0.48805919609868, 0.4880591...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.009766722717519069\n"
     ]
    }
   ],
   "source": [
    "display(ca3.parameters)\n",
    "print(\"RMSE:\", ca3.rmse())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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_19</th>\n",
       "      <td>8.695434</td>\n",
       "      <td>0.020723</td>\n",
       "      <td>0.238320</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>[8.695433717306143, 8.695433717306143, 8.69543...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.196104</td>\n",
       "      <td>0.004616</td>\n",
       "      <td>2.354037</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>[0.1961037602402571]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq1_19</th>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>8.513175</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[3.8988993330990596e-05, 3.8988993330990596e-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kzoverkh0_19</th>\n",
       "      <td>0.488059</td>\n",
       "      <td>0.008575</td>\n",
       "      <td>1.756888</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>[0.48805919609868, 0.48805919609868, 0.4880591...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               optimal       std  perc_std  pmin  pmax  initial  \\\n",
       "kaq0_19       8.695434  0.020723  0.238320     0   inf   1.0000   \n",
       "Saq0          0.196104  0.004616  2.354037     0   inf   0.2000   \n",
       "Saq1_19       0.000039  0.000003  8.513175     0   inf   0.0001   \n",
       "kzoverkh0_19  0.488059  0.008575  1.756888     0   inf   0.1000   \n",
       "\n",
       "                                                         parray  \n",
       "kaq0_19       [8.695433717306143, 8.695433717306143, 8.69543...  \n",
       "Saq0                                       [0.1961037602402571]  \n",
       "Saq1_19       [3.8988993330990596e-05, 3.8988993330990596e-0...  \n",
       "kzoverkh0_19  [0.48805919609868, 0.48805919609868, 0.4880591...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.009766722717519069\n"
     ]
    }
   ],
   "source": [
    "display(ca3.parameters)\n",
    "print(\"RMSE:\", ca3.rmse())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm0_3 = ml3.head(0, 0, t0, layers=15)[0]\n",
    "hm1_3 = ml3.head(r1, 0, t1, layers=1)[0]\n",
    "hm2_3 = ml3.head(r1, 0, t2, layers=13)[0]\n",
    "hm3_3 = ml3.head(r2, 0, t3, layers=1)[0]\n",
    "hm4_3 = ml3.head(r2, 0, t4, layers=13)[0]\n",
    "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n",
    "plt.semilogx(t0, hm0_3, label=\"ttim pumped\")\n",
    "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n",
    "plt.semilogx(t1, hm1_3, label=\"ttim PS1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n",
    "plt.semilogx(t2, hm2_3, label=\"ttim PD1\")\n",
    "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n",
    "plt.semilogx(t3, hm3_3, label=\"ttim PS2\")\n",
    "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n",
    "plt.semilogx(t4, hm4_3, label=\"ttim PD2\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Increasing the number of layers has not improved the model calibration performance. Let's see how the model behaves with the same parameters from the analytical solution:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.1. Simulating multi-layered model with Moench's parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  9\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml4 = ttm.Model3D(\n",
    "    kaq=kaq,\n",
    "    z=zlayers,\n",
    "    Saq=[Sy] + list(np.repeat(Saq, nlay - 1)),\n",
    "    kzoverkh=kzoverkh,\n",
    "    tmin=1e-5,\n",
    "    tmax=3,\n",
    ")\n",
    "w4 = ttm.Well(ml4, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=range(9, 18))\n",
    "ml4.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u8HmzZ73OiBHzQ/YzBoMEYR/vERa8fgxNBcUAi780iZxpURQf2EdR4V6KD+7H0RjYOGRkVg45s+aQOzOP9AmTMQaxibS4vP4K3f4+XqhCV3U628KzoYGaqmKe/9cqols0kho1RtXDSBvkNEdBc/NlP880cmS7gByN2ReYltGjMaWnY7C0Nc/cVbGLBzY/0OEzfnXDr5ibOrfPP9twZ3N7ONbs4Gizg0+b7Bz1TU1q53/qLW6XPxiTmhtIcdmZHBNJrq/2mJRk4OSpz9OfNcJg3Y+UIOxDEIJeM7RV24kfFUnMiMAe7qrqper0KYoK91BcuI+K0ycCOqlZIiPJmjaT3Fl55M6cQ2yS1BZF74U7dL02G+6yMlxlZbjPleEu803nzuEqK0Nrabn0BysKplGj/AHpGpnA/1b8jsp4jbJkhfoYJeQ1wqF6P7K7NE3jnNPtC0UHR5vtfNrYwmm7q8tLrLH2Zn9AjjGfZG7qFtKUcoxAZsaTjB//7z1qBd1dwbwfKUHYxyDsiZYGG2cP7tdriwf2YW+wBaxPzswmZ2YeuTPzGD1pCkZT/3QFEaKnehq6mqbhra8PDMgyPSDdvuDUHI5LfkZtDJgnTyT3yuuImDaViKlTMY/qZBSkXpL7kV1zqiqnWpwcbbLzaZODo00tHGlsodrT+b1nk+ompbGWlPp6cl0tzEuMY3J2FtnZ2SQnJ/e5tWqw70dKEPZjELanqSpVRacpKtxDUeFeKk+eQGt3L8QcEUnWtBnk+oIxbuQomuoc1FfbSeik9inEYKZpGt7aWn9Ius6d882XYy8pxlt6DqWTS3amkSOJmDaNiKlTiZg2lcipUzGNHNnj48v9yN6pc3vaao5Ndg7bmjlhd2LvMHSSpl9OtV0g29HElXFRzMwcTXZ2NqmpqT0OxmDfj5QgDFMQXsze1MjZg/spLtxLUeFeWmz1AeujR6ThtKejmMZgNGeQ/5XJIe2zKMRAora04Dh2DMfhIziOHMF+5DCuM0WdtoY1jRrlC8cpREydSuS0aZiSL33bQe5HBo+qaZxqcbLb1szOukZ21DZwrpOaY7SjhbSGWjKabcyKtjIvLYWxuTmkpaVddng5qREGyUALwvY0VaW6+IzvEupeyk8cQ2v3C68YkzFF5HH//ywjflRsGEsqRPi0heNhXzgewXX6TOBgsT6mlJSAWmPE1KkB4Sg1wtA673KzyxeM22tsHHO68V40rq3Z4yaloZbRjfVcEWHiMylJTMzJJiMjo9O+jXKPMAgGchBe7EzhOd75v3dQ3UV4XScAvdlzRGwCc265jSuuv4nIGAlEIdTmZn842o8cwXHkU1xnugjH1FRfjVEPxn9GFvPM0RdkoIB+0OJV2d/QzCf1TXxYXceBFictFw9Qr6kkN9pIa6xlqgkWJicwKyebzMxM/7jRwRoJSIJwEARha59FTQNNdeB1HcLj3A9qEwAmq5Xp+UuYffPtJKQMr18oIS7H29SM89hRvdbou7TqKirqNByVlFG4JucQ/5mrSFt8I5Y+PKHmUqRhTiCvpnGs2cEndY18eL6OPQ0tnO/kMSZx9iZSG2qZhJf5I2KZnzWanOzsbnX4vxQJwkEQhNCxz+LVXxiHgVPseWcT588WAaAoBsZdOZ85n7uT9AmTwlxiIQYub1MzzqOf+muNjsOHcRUXdwhHc2Ym0QsWEL1wAVHz5mEa0fcHPcpl2O4553Cxq76JD6tr+aSukSKv/si49qxuF6kNtXwpJYFHF/W+074E4SAJQui8z6KmaZQcOsCedzdRXLjXv236hMnMuXUpY+fMw2AYfEO8CdHfvE3NOD49Qsuu3TTv2IH9wIHAQdsVhYjJk/VQXLCAqLw8DBE9b70tDXN6p8HjZa+tmQ+qLrD9go2jbhWX71mr34o3883ZU3v/2RKEgycIL6empJg9777JsY8K8Pp+gRNS0ph9y+1Mu+Y6zL34pRViuPI2NdOyZzctO3bQvH0HzpMnA9YrFguRs2f7a4wRU6boj/G6DKkRBodb1TjcZOfD6lpuTRlBbmz05XfqggThEArCVk11tRT+/R0ObH4PR7N+HzEiJpYZ19/MrBs/R3RC3y/vCDHcuKurafnkE5q376B5+3Y8VYHjChvi4oieN4/ohQuIXrAAc3Z2lyOqyD3CgUWCcAgGYSu3w8Hhgi3sfe+v2KoqATCaTExelE/eLXeQnJl9mU8QQnRG0zRcRcU079hO844dtOz8BLWpKWAbU3qaXltcsJDoBfMxJSUFrO/vcW9F1yQIh3AQtlJVL6d3f8Lud96g4sQx//LcmXnkfW4pWdNmhGQsQCGGC83jwXHkCM3bt9O8fQcthYXgDny6g3XixLaGN3PmYOhjS8eeGM5dNbpDgnAYBGF75SeOsuedTZzctcPfQm5kzhjmfG4pExcs8j8NQ4ZzE6L31JYWWvbu1S+j7tiB89ixwA3MZqJmziT6MwuJWbwY68SJIfsyKpdhL0+CcJgFYav6ygr2vvdXDhdswePUHywck5jErBtvxRo7k4//VIKmgaLA4i9PkuHchOgDz4ULNO/cSfMO3/3F8oqA9abUVGIWX0PM4sVEz5/fq9aonZGGOd0jQThMg7CVvamRg1veZ9/7b7Ub39SM0Todo3UWBmM8igHue3ah1AyFCAJN03CXlNC8fTtNH3xI844dAU/eUCIiiJ4/n5jFi4lZfA3m1N4HlnTV6B4JwmEehK08bjfHPipgxxt/pqG6zLdUwWCegCliDnd+60ZGT5TWpkIEm+pw0PLJJzQWFNBUsA1PRWBt0Tp5MjGLryF28WIipk9H6cGTGqRG2D0ShBKEARpr7bz6rd/hcexF9ZT4l6dPnMb8pZ8nZ2aeNKwRIkQ0TcN54gRNWwtoKijQO/W3+9NrTEoi5uqr9Uuon1mIMSbmsp8p9wgvT4JQgrCD1uHcvK5qvM49qO62ZyUmZWQx59Y7mfSZazB1MiK8ECJ4PLW1NH3wAU0F22j+6KPALhpmM9Fz5/guoS7GkpXV5edIV41LkyCUIOxU++HcVG8D+957i4P//Dtuhx2A6BGJzL7pNq647kYioi//rVQI0Teay0XLvn3+2qLr7NmA9ZYxY/z3FaNmzUKRL6rdNiCCsK6ujkcffZS33noLgNtuu40f/ehHJCQkdLnPsmXLeO211wKWzZs3j507d3brmBKEPedobuLgP/7G/vffoqmuFgBzRCRXfHYJs2+6nbiRo8JcQiGGD2dREU3bttFUsI2WPXsCxkU1xMURc9VVxOQvJvqqq4IyWHh3DcY+iwMiCG+66SbOnTvHhg0bAPjqV79KTk4Ob7/9dpf7LFu2jKqqKl555RX/MovFQmJiYreOGawgrLDZKappJjc5mrT4yF5/zmDi9bg59vEH7Hn7DWpK9W+lisHAxAWLmHPrnaTkjg1zCYUYXryNjTR//LFeW/zgA7x1dW0rDQYiZ83yN7ixjBsnfRYvEvYgPHr0KFOmTGHnzp3MmzcPgJ07d7JgwQKOHTvGxIkTO91v2bJl1NfX8+abb/bquMEIwo27S3jyjUOoGhgUWHfndO6Z2/V1+qFG0zSKD+xjz9tvUHL4gH951rQZzLn1TnJmzJaGNUL0M83rxX7wIE0F22gqKMB5/HjAenNmJjH5i4nNzycqLw/FYgnKcQdzC9Xu5oEpVAXYsWMH8fHx/hAEmD9/PvHx8Wzfvr3LIAQoKChg1KhRJCQkcM011/Dss88yalTnl+ecTidOX8dx0H/wvqiw2X0hqBGNg2YtkqfeOMzVE0YOm5qhoijkzswjd2YeVWf0ZyMe3/EhJYcPUHL4AMmZ2b6GNVdjNMn9CiH6g2I0EjVrFlGzZjHq6ytwl5f7umYU0LLzE9ylpdT9+jfU/fo3GGJiiF50FbH5+UQvWtSnS6glDSUBIQigaiqljaUDPgi7K2RBWFlZ2Wl4jRo1isrKyi73u+mmm7j77rvJzs6mqKiI73znO1x77bXs3bsXq9XaYft169axZs2aoJW7qKYZo+bhB+afM0ap4Iuup3FoVoprWoZNELaXMmYctzz6LRZ98X72vf9XDv5zMzWlZ/nbT17ko9+/xqybbmPG9Tdhjer9o1KEED1nTk8n8d57Sbz3XtTmZpp37KBx61aaCrbhvXCBxvf/RuP7f9Mvoc6eRWx+PjH5+Vhyc3t0RScrLguDYuhQI8yMzQzFjxUWPb40unr16ssGz+7du9m8eTOvvfYaxy+qvo8fP54HHniAJ554olvHq6ioIDs7mz/84Q/ceWfHa9Kd1QgzMzN7fWm0wmbnnv/5A3+1fJsRShN/987hEc/X+eCJzw7LILxYa8Oafe+/RbOvYY0lMpLp197A7JtvJy55pH9bGddUiP6nqSqOgwdp3FpA09atOE+cCFhvzs4idnE+MddeS9Ts7rVClXuEF6mpqaGmpuaS2+Tk5PC73/2Ob3zjG9TX1wesS0hI4MUXX+Tf//3fu33M8ePH8+CDD7Jy5crLbhuse4R/2fRnfmNei1VxcyLnXibc/xN9gE4B+Eas+Xgbe95+gwvn9A76BqPR37CmpiyCgt8ek3FNhQgz17kymgr0UGzetSvg6RmGuDhiFi0iJj+fmEVXYYyP7/JzBmOfxQHTWOaTTz7hyiuvBOCTTz5h/vz5l2wsc7ELFy4wevRoNmzYwH333XfZ7YPZarRh75+Z+MEj+oIb1sKC/+r15w1VmqZRXLiX3W+/QemRg/7lBlMWxog5GEz6Q0xlXFMhws/b1OxrhbqVpm3bAluhGo1E5eURk59PbP5iLDk54Spm0IQ9CEG/31deXs7LL78M6N0nsrOzA7pPTJo0iXXr1rF06VKamppYvXo1d911F2lpaRQXF/PUU09RUlLC0aNHiY2Nvewxg96PcPuPYPO3AQXufhWm3tH3zxyiqs6cYvfbb3Bi50doqn4/QTEkYTBnYTCmcN0Di5m0cDIGgzHMJRVCaF4v9gMHaNq6lcatW3GdOh2w3pKb6w/FyFmzUEwha1LSQbD6LA6IIKytre3Qof6ll14K6FCvKAqvvPIKy5Ytw263c8cdd7B//37q6+tJS0sjPz+f733ve2Rmdu/GbNCDUNPg/cdh1wYwWuH+tyBrft8/dwirOFnCxu/9HK/zMBD4EFNzRCQpuWNJGTOOlLHjSR0zjoTUdOmOIUSYuUpL/aHYsjuwI78xPp7oq68mNn8x0YsWYexGpaS3gnk/ckAEYTiEZGQZ1QsbvwzH34PIEfDAPyB5XHA+e4j69ONytv6mEK+zCFWtIi6xgcYLpf5nJLZnjYoOCMaUMeOJGzlKwlGIMPE2NtL80Uc0bt1K87YP8NpsbStNJqLmzNE78ufnY8nODtpxg91nUYIw2EOsuVrgtc9B2V4YkaOHYczIy+42nLUf1zRmRASq6qW27BxVZ05RefokVWdOcr64CI/b1WHfiNg4fyimjB1H6pjxxCQmdRmO0kJViNDQPB7shYV614ytBbjOnAlY33oJNWbxNUTNnt2nS6jBfs6iBGEoxhptOg+/vA7qimF0Htz/DliignuMYcbr8XDhXAlVZ05RdeYkladPcf5sEarX02HbqPgEUseO12uPY8aTOnY80Qkj9KdqSAtVIfqF6+xZmgoKaNxa0PlYqN1shdoZqREGScgH3a45pYehvQ4m3gL3/Aak8UdQedxuakqK/cFYdeYkNaVn/Q1w2otOSMTePAKDMQXFOApFsaAYjNz6aB4xidGYzGYMJhNGkxmT2YzRbMZgNPX5smt/1kCH6rHE4Nc2FupWmrZ9gLd9dzmjkajZs/UnZ+TnY8nN6dbvndwjDIJ+efpEyU547TbwOuHKr8JNz0kfwxBzu5ycLy6i6sxJ/6XV2rJz/ucp9pTRbMZoMmM0mfT51vdmMybfq8Fk0sPT1H69Cdt5F2XHbWgYUVDImZFCam4CisGAwWjUJ4P+qhgN/vnW5YrRiKH98s6WGY0oBiNnCmvY+WYRYERRTFx97xSmXZ0Vkvun/V2zltAdWtpaoRbQVLAV58lTAev9HfnzF+tjoV6iI3+w+ixKEIb6MUxH3oQ/LQM0WPJ9WPjfoTuW6JTLYefsoWO8/5N/4vVUonkvgOYGvETEGFG9brweD163u9Pa5GBmMlswWSwYLRZfTdeCyWzBaDHr61qXWXzb+Zbp8/o2+np93uNS2Pb703q4KxZQrBiMVr783cXEjwp+C0G5nD30uc6d8z9jsUNH/iCOhXopEoT98TzC7S/B5qf1+btfhalLQ3s80alPPy6n4PVjaCooBlj8pY5/VFXVi9ftxuv24PW49cntmzwePG43qm+Zxxeebdu1va+raODojnOgeQENUEFTyZ6eSGSMCVX1onq9aF6vf15V1Y7LvCqq6lvmXx64zOP24Ha49WPgDcOZ1ZnMFqzR0VijovXX6BiskVFt81HRRPjXx/i3i/DNmy56CkJTnYNfP7Wd9n95Qj3ggtQ+w8vb1Ezzdt/jpLZtw1tb27YyhI+TkiDsjyDUNPjbE/DJz/Q+hvf9FbIXhPaYolMXt1AN5XH66494+2Ppv6ZeFMXL3U/OIiLKgMftwuNy6eHtcuJxu/G4XXhdLjxuN163C4/Lt8y3rb5eX9a2jQuX3UnlmQugedBwg+bUpyAwmkwBAYlmpbrEiaJYwRCFYohFMcTy2fvnMjZvDNao6KBe+pXa58DiHwu1oICmrZ08Tiojw3dfcTFRc+di6MPjpCQI++sJ9aoX/ngfHHvH18dwCySPD/1xRdh0pwY6FI51zRcnMHZ2As7mZpwtvql1vrkJR3P7ZU04m5txtH/f0gK9+PNitkYQm5RMbPJIYhKTiE0aSWxS62sysUnJ3X7aSThqn6JnLn6clOZq606V9PDDjPr6il5/tgRhfwUh+PoY3gpleyAhGx78B8R0/vxEMTT0Vw10MB9LU1VcDoc/JNuCsomzh8s5ubsEzduMpjUSHe/G7bDhaGrs1mdbIiOJSUz2BWO7oExMIjZZD0xLZBTnjtfx1xf3d9j/jq/PYvTE0NyXksuwvac2N9O8c6feZ3HbNjL+93+Jmj27158nQdifQQjQXAO/uA7qiiB9Nix7ByzyjD4hutJZ6LqdDhovXKDxwnmaai/QWHOextoaGi/U0HRBf3U0N3Xr8y2RUUSPSMJ23oCixKIYE1CMyRhMydy/7gZiE4P/WDW5DBs8beMVG3r9GRKE/R2EABdO62For4UJN8EXXpc+hkIEmdvh0MOxpsYXkudp8oVnY63+6mxuvuRnWCKjSM7MJjkr2/eaQ3JWDpExvW8hK5dhB57u5kH/DSc+HCSNhXs36pdJT7yvD9Z98/PSx1CIIDJHRJCYnkFiekaX27gcdhovtNUkL5RVUl1cQlNtGfWV5bjsLZSfOEr5iaMB+8WMSCTJF4wjs3JIzswmMSMTs8V62XLVV9s73BLVVLBV2yUIBzgJwmDLvBLu3AB/vB92/wISsuAz/y/cpRJiWLFERJI0OpOk0R2fWuP1uKkrL+N86VkulJ7lfEkxNSVnaThfRVNdLU11tZw92HZfUVEMJKSm+WqPOf7XhNTUgEeKJYyKRFHoUCOMHxX8S7AiuOTSaKjs+An8/Ul9/vO/gml3ha8sQojLctlbqCktoaZUD8aakmLOl57F0djQ6fYmi5WkjEw9HDOzSM7KobYqgh2bKkBTQt7KF6RhzuXIpdFwW/A1qC+BT34Km5ZDbBpkLwx3qYQQXbBERpE+YRLpEyb5l2maRoutnvMlxQG1xwvnSvC4nL7B4gOHEouIjiU+JYvRk6cQFZOAsyUBa1TwB+eXhjnBIzXCUGrfxzAiQe9jOHJCeMskhOgzVfViq66ipqSt9lhTepa6ivIO498qioHk7BxGT5xM+sQpjJ44mbjkvnWvkoY53SOtRgdCEAK47XrjmXO79fuFD/5T+hgKMUR5XC4ulJVSeeoE5cc/pez4p9iqqzpsF5s0kvSJk/3hODI7J+B+4+WEo3/kYCRBOFCCEPQ+hr+8HmrPQPosWPau9DEUYphoqqv1heJRyo59SnXx6Q6DwFsiI0kbP4n0CZMZPXEKaRMmYonoupGN1Ai7R4JwIAUh6H0Mf3k9tFyACTfCPa+DUW7RCjHcuB0OKtrVGMtPHMNlbwnYRjEYGJmdy+iJU/Sa46QpxCYmB2zTn8PvDVYShAMtCAFKd+mXST0OmPMA3PKC9DEUYphTVS8XSkt8NcYjlJ84SsP56g7bxY0cpdcYJ01l9MTJJGVm0WJz9+vwe4OthaoE4UAMQoCjb8PGrwAaXLcGrloR7hIJIQaYxtoayn2XUsuOf8r54qIOjXBaW7mOnjSV7OkzSRkzDoMxNCNZDdYWqgMiCJ999lneffddCgsLsVgs1NfXX3YfTdNYs2YNGzZsoK6ujnnz5vHjH/+YqVOnduuYAz4IAXb+DP62Up+/65dUZN1CUU0zucnRpMVL51shRCCXvYWKkyd8l1KPUn7iGG6HPWAba1Q0mVOnkzV9JtnTZzIibXRQHmc1mO9HDoh+hC6Xi7vvvpsFCxbwy1/+slv7PPfcc/zwhz/k1VdfZcKECXz/+9/n+uuv5/jx48TGBv9J2WExf7nex3Dnj/FuWs7XHU+wU52MQYF1d07nnrlZ4S6hEGIAsURGkX3FTLKvmAmA6vVyvqSY8uOfUvrpIUoPH8TR3MSp3Ts5tXsnADFJyWRPm0n29BlkTZ9JdELvWpMOh6Hj+uXS6KuvvsqKFSsuWyPUNI309HRWrFjBypV6jcnpdJKSksL69et5+OGHL3usQVEjBFBV7L/7MpGn3sWmRXGnaw2ntdEYFYWPnsiXmqEQottU1Ut10RnOHiqk5NB+yo59itfjCdgmOTOb7CtmkjV9JhmTp12yVWp7UiPsZ0VFRVRWVrJkyRL/MqvVyjXXXMP27du7FYSDhsHAgbnPYT5xgjzDSV6zrOcW51psWgzFNS0ShEKIbjMYjKSOHU/q2PHMu+Nu3E4HZcePUnKokLOHCqkuPkNN6VlqSs+y992/YjAaSRs/iezpejCmjh2P0dR5HMSMiGDxlyd1aKE60EOwJwZUEFZWVgKQkpISsDwlJYWzZ892uo/T6cTpdPrfNzR0Pi7gQJSdmsTn3N/kz+ZV5BqqeMz0R1Z7HyAnOfjDMQkhhg+zNYKcK2aRc8UsAFoabJQeOaQH4+FCbFWVlB07QtmxI2z/0+tYIiPJmDKdbN/9xcTRmQH3F6d8Jp2sKYn91kK1v/U4CFevXs2aNWsuuc3u3buZM2dOrwt18Q1eTdO6vOm7bt26y5ZnoEqLj+TxOz/D05u+yu8s3+NLxn+SsvghqQ0KIYIqKi6eiQuuYuKCqwCor6qk5HAhZw8doOTwARyNDZzZu4sze3cB+uOoWhvdZE2bQUxiEjEjIvotAPu7q0aP7xHW1NRQU1NzyW1ycnKIiGgrfHfvEZ45c4axY8eyb98+Zs2a5V9+++23k5CQwGuvvdZhn85qhJmZmQP/HmE7FTY75k0Pklz8DmRcCf/xd+jDU5mFEKK7NFWluth3f/HwAcqOHsHjdgVsk5SRRdb0GWRPn0nm1Cu6fX+xN4LZVSNk9wiTk5NJTk6+/Ia9kJubS2pqKlu2bPEHocvlYtu2baxfv77TfaxWK1br5R+aOZClxUfC0ufgpa1wbhcc3AgzvxjuYgkhhgHFYCBlzDhSxozjyts/j8flovzEUV/Dm0Iqz5ziwrkSLpwrYf/7b2MwmsiYPIWcmXPInTGbpMzsoHTTAL0m2BqCoD/bseD1Y2RNSQxpzTCk9whLSkqora2lpKQEr9dLYWEhAOPGjSMmJgaASZMmsW7dOpYuXYqiKKxYsYK1a9cyfvx4xo8fz9q1a4mKiuLee+8NZVHDL340XPMt+Mdq2PIMTLoZIuLDXSohxDBjsljImjaDrGkz4Iv3Y29q5NyRQ5w9tJ/ig/uxVVVScvggJYcP8sFvf0VMYhI5M/LInTmbrOkziYiO6fWxw9VVI6RB+MwzzwRczmyt5W3dupXFixcDcPz4cWw2m3+bxx9/HLvdzte+9jV/h/rNmzcPnT6ElzL/a7D/t3DhFBSshxvXhrtEQohhLjImlvHzFjJ+3kI0TaO+spyiwn0UH9hL6ZFDNNVe4PDWzRzeuhnFYCB9wiRfMOYxKmcMSg9u8ySMikRR6NBVI35UaNtNyBBrA82pf8Bv7wLFCP/5MYyaHO4SCSFEp9wuJ2VHj1B8YC9FhfuoLSsNWB8Vn6C3Xp2ZR/YVs4iKu/xVrmAOJj4ghlgLh0EfhAB/+JL+MN+cRXD/2zIwtxBiULBVV1F8QK8tnj10IHAYOEUhdcw4/d7izNmkjpvQ5TMYm+ocQemqIUE4mIOwrhh+PE9/SsXnX4Fpd4a7REII0SNej5vy40cpOrCP4sK9nD9bFLA+IjqGrCtmkTszj5wZs4kZkRj0MkgQDuYgBCj4HyhYB3Gj4b92gbX3N6CFECLcmmovUHxwP0WFezl7cB/O5uaA9SOzc8mZmUfujNmkT5yM0WTu8zElCAd7ELrt8OMr9cG5r/oGXLcq3CUSQoigUL1eKk+f0BvdFO6h8sypgBYylshIsqbNYMb1N5MzY3avjyNBONiDEODYu/CHe8Fghq/thORx4S6REEIEXUuDjbMH91NcuJeiA/uwN+g9Ca5/6BGuuO7GXn+uBOFQCEJNg9fvhlNbYNx18KU/S8MZIcSQ1jrSTVHhXqZe81lik3o/gEt380DG8RrIFAVuWg9Gi96t4vj74S6REEKEVOtIN/PvvKdPIdgTEoQDXdJYWPCIPv+3J/R7h0IIIYJGgnAwuPoxvfVo/Vn4+P/CXRohhBhSJAgHA0s0LPm+Pv/RD6Gu82czCiGE6DkJwsFi6lJ9pBmPA/7+VLhLI4QQQ4YE4WChKHDzD/QxSI+9ozeeEUII0WcShIPJqMkwb7k+/97j4HFeenshhBCXJUE42CxeCdGjoPY07PxJuEsjhBCDngThYBMRD9d/V5/f9gOwlYW3PEIIMchJEA5GV9wDmfPA3QxbvhPu0gghxKAmQTgYGQx6wxkUOPwXKPow3CUSQohBS4JwsEqbAXP+Q59//3HwusNbHiGEGKQkCAeza78NkYlQ/Sns/kW4SyOEEIOSBOFgFpUIn31Gn9+6Fpqqw1seIYQYhCQIB7vZ90HaTHA2wD9Wh7s0Qggx6EgQDnYGI9z8vD5f+DqU7gpveYQQYpAJaRA+++yzLFy4kKioKBISErq1z7Jly1AUJWCaP39+KIs5+GXOhZlf1uffewxUb3jLI4QQg0hIg9DlcnH33Xfzn//5nz3a78Ybb6SiosI/vffeeyEq4RBy3SqwxkPFAdj3WrhLI4QQg4YplB++Zs0aAF599dUe7We1WklNTQ1BiYawmFGQ/xT8bSX887sw5Q69MY0QQohLGpD3CAsKChg1ahQTJkzgoYceorq669aQTqeThoaGgGnYmvsgjJoC9jr41/fCXRohhBgUBlwQ3nTTTbz++uv861//4oUXXmD37t1ce+21OJ2dP2lh3bp1xMfH+6fMzMx+LvEAYjS1NZzZ8wqUF4a1OEIIMRj0OAhXr17doTHLxdOePXt6XaB77rmHW265hWnTpnHrrbfy/vvvc+LECd59991Ot3/yySex2Wz+qbS0tNfHHhJyPgPT7wY0eO9boKrhLpEQQgxoPb5H+Mgjj/CFL3zhktvk5OT0tjwdpKWlkZ2dzcmTJztdb7VasVqtQTvekHD99+D4+3BuFxz8A8y8N9wlEkKIAavHQZicnExycnIoytKpCxcuUFpaSlpaWr8dc9CLS4NrHoctz+jTpFv0xzcJIYToIKT3CEtKSigsLKSkpASv10thYSGFhYU0NTX5t5k0aRKbNm0CoKmpiccee4wdO3ZQXFxMQUEBt956K8nJySxdujSURR165v0nJI2H5vNQ8D/hLo0QQgxYIQ3CZ555hlmzZrFq1SqampqYNWsWs2bNCriHePz4cWw2GwBGo5FDhw5x++23M2HCBO6//34mTJjAjh07iI2NDWVRhx6TBW5ar89/8jJUfRre8gghxAClaJqmhbsQwdTQ0EB8fDw2m424uLhwFyf8Nn4Zjr4N2VfBsneoaHBQVNNMbnI0afGR4S6dEEKETHfzIKQd6sUAcMNaOLkFzn7E9rc28OWdGagaGBRYd+d07pmbFe4SCiFEWA24foQiyBKyYNE3ARizbx0RmgMAVYOn3jhMhc0eztIJIUTYSRAOBwsfxRGTRapSx3+bNvkXezWN4pqWMBZMCCHCT4JwODBH0HLt9wF4wPgeY5RyAIyKQk5yVDhLJoQQYSdBOEwkzrqN8pGLsCheVptew6jA2junSYMZIcSwJ0E4XCgK6V/4XzSjhauNh9j9eYc0lBFCCCQIh5eksSgL/xuAxA9Wgas5zAUSQojwkyAcbhY9BvGZYCuFD54Pd2mEECLsJAiHG0sU3Ogbcm37j6Cm88HMhRBiuJAgHI4m3QLjl4Dqhvceg6E1uJAQQvSIBOFwpCj6OKRGK5wpgE/fDHeJhBAibCQIh6vEMXDV1/X5vz0FzsbwlkcIIcJEgnA4u2oFjMiBxnLY9ly4SyOEEGEhQTicmSPhJl8A7vwJVB8Nb3mEECIMJAiHuwk3wMRbQPXAu9JwRggx/EgQCrhxHZgi4exHcOjP4S6NEEL0KwlCASOy4Wr9UU1sfhoctvCWRwgh+pEEodAtfBQSx0JTFRT8T7hLI4QQ/UaCUOhMVrj5B/r8Jy9D5eHwlkcIIfqJBKFoM+6zMOV20Lzw7jel4YwQYliQIBSBblgL5mgo3QkHfh/u0gghRMhJEIpA8RlwzeP6/ObvgL0+rMURQohQC1kQFhcX88ADD5Cbm0tkZCRjx45l1apVuFyuS+6naRqrV68mPT2dyMhIFi9ezJEjR0JVTNGZ+V+D5InQUgP/+n64SyOEECEVsiA8duwYqqry8ssvc+TIEV588UV+9rOf8dRTT11yv+eee44f/vCHvPTSS+zevZvU1FSuv/56GhtlLMx+Y7K0NZzZ80soLwxrcYQQIpQUTeu/FhE/+MEP+OlPf8qZM2c6Xa9pGunp6axYsYKVK1cC4HQ6SUlJYf369Tz88MOXPUZDQwPx8fHYbDbi4uKCWv5h58//AYf/AqPnwANbwCBX0oUQg0d386Bf/7LZbDYSExO7XF9UVERlZSVLlizxL7NarVxzzTVs3769032cTicNDQ0BkwiSJc+CJQbK9sD+34S7NEIIERL9FoSnT5/mRz/6EcuXL+9ym8rKSgBSUlIClqekpPjXXWzdunXEx8f7p8zMzOAVeriLS4PFT+rz/1gNLbVhLY4QQoRCj4Nw9erVKIpyyWnPnj0B+5SXl3PjjTdy99138+CDD172GIqiBLzXNK3DslZPPvkkNpvNP5WWlvb0RxKXMu9hGDUF7LXwzzXhLo0QQgSdqac7PPLII3zhC1+45DY5OTn++fLycvLz81mwYAEbNmy45H6pqamAXjNMS0vzL6+uru5QS2xltVqxWq3dLL3oMaMZbnkBXrkJ9r4Gs+6DjLxwl0oIIYKmx0GYnJxMcnJyt7YtKysjPz+fvLw8XnnlFQyXaWyRm5tLamoqW7ZsYdasWQC4XC62bdvG+vXre1pUESzZC+GKL8DBP8C734CH/gUGY7hLJYQQQRGye4Tl5eUsXryYzMxMnn/+ec6fP09lZWWHe32TJk1i06ZNgH5JdMWKFaxdu5ZNmzZx+PBhli1bRlRUFPfee2+oiiq6Y8n3wBoHFYWw99Vwl0YIIYKmxzXC7tq8eTOnTp3i1KlTZGRkBKxr32Pj+PHj2Gxtj/15/PHHsdvtfO1rX6Ouro558+axefNmYmNjQ1VU0R0xo+Dab8P7j8M/v6uPSRrdvSsDQggxkPVrP8L+IP0IQ8jrgZ8vhspDMPPLcMePw10iIYTo0oDsRygGOaMJbvmhPl/4Wyj5JLzlEUKIIJAgFD2TeSXM+rI+/+439VqiEEIMYhKEoueuWwMRCVB1SB+LVAghBjEJQtFz0cnw2Wf0+X99HxqrwlseIYToAwlC0Tt5yyB9FjgbYMsz4S6NEEL0mgSh6B2DUR9xBkXvaF/8cbhLJIQQvSJBKHpvdJ5eMwR47zHwusNaHCGE6A0JQtE3n30GIhOh+lP45OVwl0YIIXpMglD0TVQiXO97KkXBOmgoD295hBCihyQIRd/N/DJkzAVXE2z+drhLI4QQPSJBKPrOYNAbzigGOPwXOFMAQIXNzvbTNVTY7OEtnxBCXELIBt0Ww0zaDJj7IOzaAO99iz/N/QMr3zyGqoFBgXV3TueeuVnhLqUQQnQgNUIRPPlPQ/RIqDnBmbefQ/UN565q8NQbh6VmKIQYkCQIRfBEJsD13wPgv42bSOOCf5VX0yiuaQlTwYQQomsShCK4ZnwBV/o8ohQn3zH/xr/YqCjkJEeFsWBCCNE5CUIRXIqC5fYXURUjNxt3cbXhAEZFYe2d00iLjwx36YQQogMJQhF8KVMxzFsOwMtJG/nosQXSUEYIMWBJEIrQWPwExKQS2VhM2qZ/g7qz4S6REEJ0SoJQhEZEHCz9GVjj4dwu+NkiOLIp3KUSQogOJAhF6IzNh+Uf6qPOOG3wp2Xw9v8Dl7QeFUIMHBKEIrRGZMO/vw9XfQNQYO+r8PN8qDoS7pIJIQQQwiAsLi7mgQceIDc3l8jISMaOHcuqVatwuVyX3G/ZsmUoihIwzZ8/P1TFFP3BaIbrVsF9b0JMCpw/Bj+/Fnb/AjQt3KUTQgxzIRti7dixY6iqyssvv8y4ceM4fPgwDz30EM3NzTz//POX3PfGG2/klVde8b+3WCyhKqboT2MWw/KP4c3/hFNb4N1v6uOS3vp/+lMshBAiDBRN67+v5D/4wQ/46U9/ypkzZ7rcZtmyZdTX1/Pmm2/26hgNDQ3Ex8djs9mIi4vrZUlFSKkq7PwJ/GM1qG6Iy4C7fgHZC8JdMiHEENLdPOjXe4Q2m43ExMt/8y8oKGDUqFFMmDCBhx56iOrq6n4oneg3BgMsfAQe3AKJY6DhHLx6M2x7DlRvuEsnhBhm+q1GePr0aWbPns0LL7zAgw8+2OV2GzduJCYmhuzsbIqKivjOd76Dx+Nh7969WK3WDts7nU6cTqf/fUNDA5mZmVIjHCycjfol0oMb9fc5i+DODRCXHt5yCSEGvZDVCFevXt2hMcvF0549ewL2KS8v58Ybb+Tuu+++ZAgC3HPPPdxyyy1MmzaNW2+9lffff58TJ07w7rvvdrr9unXriI+P90+ZmZk9/ZFEOFlj9eBb+jKYo6H4Q/jpQjj2XrhLJoQYJnpcI6ypqaGmpuaS2+Tk5BAREQHoIZifn8+8efN49dVXMRh6fjV2/PjxPPjgg6xcubLDOqkRDiEXTsOf/x0qDujvr3wYrv8umCPCWy4hxKDU3Rphj1uNJicnk5yc3K1ty8rKyM/PJy8vj1deeaVXIXjhwgVKS0tJS0vrdL3Vau30kqkYhJLGwgNb4B9rYOePYdfLcHY73P0KJI8Pd+mEEENUyBrLlJeXs3jxYjIzM3n++ec5f/48lZWVVFZWBmw3adIkNm3Sh95qamriscceY8eOHRQXF1NQUMCtt95KcnIyS5cuDVVRxUBissKNa+HeP0FUElQdgpevhv2/lT6HQoiQCFk/ws2bN3Pq1ClOnTpFRkZGwLr2V2OPHz+OzWYDwGg0cujQIX79619TX19PWloa+fn5bNy4kdjY2FAVVQxEE5bofQ43fRWKPoC//hec3gqf+yFExIe7dEKIIaRf+xH2B+lHOMSoXvjoRdi6FjQvJGTD51+BjLxwl0wIMcANyH6EQvSYwQhXP6aPVxqfBfVn4VdL4KP/T++Y71Nhs7P9dA0VNnv4yiqEGJSkRigGD3s9vP0ofPpX/f3Ya+GOn7HxmJMn3ziEqoFBgXV3TpcHAQshpEYohqDIBLj7Nbj1f8EUCaf/hfenC3lv029RfV/nVA2eeuOw1AyFEN0mQSgGF0WBvGXw1QIYNQVjSw2vWdbzhOl3mPEA4NU0imvkmYdCiO4JWatRIUJq1CR46F80v72S6IOvsdz0DgsNR/indzb1xDHpQgMoaXoXjKhk/ekWpuD1N62w2SmqaSY3OZq0+MigfW64jyXEcCT3CMWg99Fbv2L63m8TrzRfekNLrB6IUUkXTZ0tS4LIEWDs+F1x4+6Sfrsn2Z/HAgldMbR0Nw8kCMWQUHXuFK5dvyZRqyPaUwcttdBywTfV6l0veiMioS0Yo5NpMcXzmwNN1GoxuDDhwYiKiW/dPIWEmGgwmPSWrgazb96kh2nrvMHsW2/SH1hsMAVOxrb1FU0ervrBB3i1tjsYRkXhoyfyQxJSErpiqJEglCAUrVQVnLaLwvHi6aJ19rpwl9pP1RScmHFgwYmZhLg4IiKj9Uu9pkh9LFaTbzJH9mp5tUNh6cv7sGtmmojEhXlIha4YniQIJQhFX3g94KgPDMfmGhrqqvjTtkISlCbMeDDixYLKonEJRBg0/UHDqhdUD3jd+qvq9S33+Jb7Xjts6w73T+1n1yzYiCYuYSRR8Ul6zTgyoZPX+I7LzJcOzgqbnc/8z7/8LX0htDXd1mNK7XP4Cdmg20IMC0YTRCfrUztxQExCCY+/cRivpmFUFNbeOY2IYNVmVDUgNDftLWb9O4cx4SZKcfPYtZksmTACPHZwO8Djm9x28Dh7uLx13oHqtuN22rEqesvbSMVFJC6w1YGtp+fO2jE0I+L9845mM3caarBp0ZzXEijTkqjR4imuaZHapwgLqREK0QsVNjvFNS3kJEf1S6vR/jjWxt0lfPuNg0RqzSQqLTz12VSW5EboNWOHTR/QwFHf9avDBpra5edfikszYkjIwDQiC+IzLpoy9VdLdI8/Nxy1TzFwSI1QiBBKi4/stz+k/XWse+ZmcfWEkb0PXU0DZ+Plw9JeT0VVBVVVlYxU6kmlFoviBdtZfepK5IjAYLw4KGNS9IZG7RTVNAeEILT1M5XLsKKVBKEQwq9PoasoEBGnTwmXvvSYBuCr6RoSLaQp9WA755tK2837JqdNb8Bkr4PKQ51/qMEEcekBQTnVkkK+sYZzajKl2kgcWDEqCjnJUb37GS9DLsMOTnJpVAgx8DlsYCvrOigbyi7bRcarKRRraZhHTyNr0pWQMhVSp+nBqSh9LqJchh145NKoEGLoiIjXp5Qpna9XvdBYqQdih6AsRa0rxeisZ6xSDhXlULG5bV9rnB6K/mk6jJoM1pgeFTEcl2FFcEgQCiEGP4MR4kfrU+aVHVcDNFVD1WGoOgKVvtfzx8DZACU79Km9Ebm+WuP0tpBMyAFD50M05yZHY1DoUCMM1WVYkPuRwSKXRoUQw5fXDTUnfQHpC8eqI9BY0fn2lhgYNaVd7XGaXkuNiAf0e4RPXdS1ZqgMvzcYSYd6CUIhRG81XwgMxqrDUH0UvM7Ot0/I8oXiVOpix1NkHk9aziTSEkJTG5T7kd0j9wiFEKK3opNgzDX61MrrgdrTHS+vNpyD+hJ9Ov4eI4ARoHfnyJoPWQv119TpHbp39JbcjwwuCUIhhOgOowlGTtSnaXe1LbfXQdWnbZdXK32vTVXw6V/1CfSnn2TOhawF+jQ6Dyy9qzGG437kUCaXRoUQItjcDijfB2e3Q8lOKP1Eb5TTnsEM6TN9tUZfOEYldvsQ/Xk/crCSe4QShEKIgUL1QvWneiie3a63UO2sQU7yRMhe0BaMCVmX7OPYn0P9DUYDIghvu+02CgsLqa6uZsSIEVx33XWsX7+e9PT0LvfRNI01a9awYcMG6urqmDdvHj/+8Y+ZOnVqt44pQSiEGPA0Tb+n2Npt4+wOqDnecbvYdL3GmO27zzhqStDuM/bUYOyqMSCC8MUXX2TBggWkpaVRVlbGY489BsD27du73Gf9+vU8++yzvPrqq0yYMIHvf//7fPDBBxw/fpzY2NjLHlOCUAgxKDVfgNKdvnDcCeX79aeQtGeN1/tJtoZj+mz9+ZIhNli7agyIILzYW2+9xR133IHT6cRsNndYr2ka6enprFixgpUrVwLgdDpJSUlh/fr1PPzww5c9hgShEGJIcLVA2d62WmPpLnA1BW5jtOiNbsYs1qfReWDs+Le1LwZzV40B132itraW119/nYULF3YaggBFRUVUVlayZMkS/zKr1co111zD9u3bOw1Cp9OJ09nWt6ehoaHDNkIIMehYoiB3kT6B3n2j6rBeWyzZrl9Oba5uC8qCdXrL1NxFMCZfD8bk8X0eR3U4dNUIeRCuXLmSl156iZaWFubPn88777zT5baVlZUApKSkBCxPSUnh7NnOH8+ybt061qxZE7wCCyHEQGQ06a1M02fC/OX6fcbaM1D8IZzeCkXb9K4cx9/TJ4C4DD0Qx+ZD7jUQM7LHhx0OXTU6HzTvElavXo2iKJec9uzZ49/+W9/6Fvv372fz5s0YjUbuu+8+Lnc1VrnoG4ymaR2WtXryySex2Wz+qbS0tKc/khBCDD6KAkljIW8Z/Ntr8K0z8NUC+OwqPfSMVr2zf+Fv4S8PwPPj4KdXweZvw6l/6JdeuyEtPpJ1d07H6Psb3NpVY6jUBqEX9whramqoqam55DY5OTlERHS8gXvu3DkyMzPZvn07CxYs6LD+zJkzjB07ln379jFr1iz/8ttvv52EhARee+21y5ZP7hEKIQR60JXsgDNb4UxBx+c4Gi16o5sxi/VLqWkzLtkidTB21QjZPcLk5GSSk5N7VajWzG1/T6+93NxcUlNT2bJliz8IXS4X27ZtY/369b06phBCDEuWKBj3WX0CaDqvXz49vVUPx4YyKPpAn/75XYgcodckWy+ljsgJ+Lg+PbR5gAvZPcJdu3axa9currrqKkaMGMGZM2d45plnGDt2bEBtcNKkSaxbt46lS5eiKAorVqxg7dq1jB8/nvHjx7N27VqioqK49957Q1VUIYQY+mJGwvTP65OmwYVTvlAs0O8z2uvg0zf1CfTHUI31NbrJvVoPyiEqZEEYGRnJG2+8wapVq2hubiYtLY0bb7yRP/zhD1itVv92x48fx2az+d8//vjj2O12vva1r/k71G/evLlbfQiFEEJ0g6LoLUqTx8O8r+otUsv2tl1GPbcb6opgTxHs+RUoBkifpV9CHXcdZMzVG++ESH933pch1oQQQgRyNkLxR3oont7acdQbazyMXQzjrtcvvcZ1PVpYTwWz8/6A7FDfHyQIhRAiyGxlvlD8F5z+p34Ztb2Uab77kddD5jwwWXp1mGB33h9wHeqFEEIMUvGjYdaX9En16sO/ndyid8Mo29v2CKqP/1fv1D/mmrZgTMjs9mHC1XlfglAIIUT3GYyQMUef8p/Ux0g9s7UtGFtq4Ng7+gQwcpJ+X3Hcdfr4qCZrlx8drs77cmlUCCFEcKgqVB6Ak//QQ/HcLtDUtvXmKL0FamswJuZ2+IhgPmdR7hFKEAohRHjZ6/R7i63B2FQZuD5xLIy/Xg/FnKvArF/+DFbnfQlCCUIhhBg4NE2/j3jqH3owlu4MfMyUKUIPw3HX6fcWk8b2ecBwCUIJQiGEGLgcDfpIN633FhvKAtcnZMPiJ2HmF3t9CGk1KoQQYuCKiIPJt+qTpsH5Y77a4hZ9jNT6s0F/tmJXJAiFEEKEl6LAqMn6tPC/wdmkD/uWNb9fDi9BKIQQYmCxxsDEm/rtcD1+HqEQQggxlEgQCiGEGNYkCIUQQgxrEoRCCCGGNQlCIYQQw5oEoRBCiGFNglAIIcSwNuT6EbaOGNfQ0BDmkgghhAin1hy43EiiQy4IGxsbAcjM7P7DIIUQQgxdjY2NxMfHd7l+yA26raoq5eXlxMbGoigKc+fOZffu3QHbXLys/fuGhgYyMzMpLS0N2aDdnZUp2PtdatuerhtK57An+15uu67Wd3d5uM/hpcoarP1CfQ47W9b6frifw67WDaffZ03TaGxsJD09HYOh6zuBQ65GaDAYyMjI8L83Go0d/vEuXtbZNnFxcSH7R+/seMHe71Lb9nTdUDqHPdn3ctt1tb67y8N9Di9V1mDtF+pz2Nmyi98P13PY1brh9vt8qZpgqyHfWOa//uu/Lruss21CqbfH68l+l9q2p+uG0jnsyb6X266r9d1dHu5z2JdjDpRz2NmywfJ/MdTnsKt1Q+kc9nXfVkPu0mhfyfMM+07OYd/JOew7OYfBMRzO45CvEfaU1Wpl1apVWK3WcBdl0JJz2HdyDvtOzmFwDIfzKDVCIYQQw5rUCIUQQgxrEoRCCCGGNQlCIYQQw5oEoRBCiGFNgrCPWlpayM7O5rHHHgt3UQadxsZG5s6dy8yZM5k+fTo///nPw12kQam0tJTFixczZcoUrrjiCv70pz+Fu0iD0tKlSxkxYgSf//znw12UQeOdd95h4sSJjB8/nl/84hfhLk6vSavRPnr66ac5efIkWVlZPP/88+EuzqDi9XpxOp1ERUXR0tLCtGnT2L17N0lJSeEu2qBSUVFBVVUVM2fOpLq6mtmzZ3P8+HGio6PDXbRBZevWrTQ1NfHaa6/x5z//OdzFGfA8Hg9Tpkxh69atxMXFMXv2bD755BMSExPDXbQekxphH5w8eZJjx45x8803h7sog5LRaCQqKgoAh8OB1+u97CjxoqO0tDRmzpwJwKhRo0hMTKS2tja8hRqE8vPziY2NDXcxBo1du3YxdepURo8eTWxsLDfffDN///vfw12sXhmyQfjBBx9w6623kp6ejqIovPnmmx22+clPfkJubi4RERHk5eXx4Ycf9ugYjz32GOvWrQtSiQee/jiH9fX1zJgxg4yMDB5//HGSk5ODVPqBoz/OY6s9e/agquqQe/pKf57D4aKv57S8vJzRo0f732dkZFBWVtYfRQ+6IRuEzc3NzJgxg5deeqnT9Rs3bmTFihU8/fTT7N+/n0WLFnHTTTdRUlLi3yYvL49p06Z1mMrLy/nrX//KhAkTmDBhQn/9SP0u1OcQICEhgQMHDlBUVMTvfvc7qqqq+uVn60/9cR4BLly4wH333ceGDRtC/jP1t/46h8NJX89pZ1dvFEUJaZlDRhsGAG3Tpk0By6688kpt+fLlAcsmTZqkPfHEE936zCeeeELLyMjQsrOztaSkJC0uLk5bs2ZNsIo84ITiHF5s+fLl2h//+MfeFnFQCNV5dDgc2qJFi7Rf//rXwSjmgBbK/4tbt27V7rrrrr4WcdDpzTn9+OOPtTvuuMO/7tFHH9Vef/31kJc1FIZsjfBSXC4Xe/fuZcmSJQHLlyxZwvbt27v1GevWraO0tJTi4mKef/55HnroIZ555plQFHdACsY5rKqq8j9BuqGhgQ8++ICJEycGvawDWTDOo6ZpLFu2jGuvvZavfOUroSjmgBaMcygCdeecXnnllRw+fJiysjIaGxt57733uOGGG8JR3D4bcs8j7I6amhq8Xi8pKSkBy1NSUqisrAxTqQaXYJzDc+fO8cADD6BpGpqm8cgjj3DFFVeEorgDVjDO48cff8zGjRu54oor/Pd5fvOb3zB9+vRgF3dACtbv8w033MC+fftobm4mIyODTZs2MXfu3GAXd1Dozjk1mUy88MIL5Ofno6oqjz/++KBt8T0sg7DVxdezNU3r1TXuZcuWBalEg09fzmFeXh6FhYUhKNXg05fzeNVVV6GqaiiKNaj09fd5sLZ4DKXLndPbbruN2267rb+LFXTD8tJocnIyRqOxw7fF6urqDt+AROfkHAaHnMe+k3MYfMPtnA7LILRYLOTl5bFly5aA5Vu2bGHhwoVhKtXgIucwOOQ89p2cw+Abbud0yF4abWpq4tSpU/73RUVFFBYWkpiYSFZWFt/4xjf4yle+wpw5c1iwYAEbNmygpKSE5cuXh7HUA4ucw+CQ89h3cg6DT85pO+FrsBpaW7du1YAO0/333+/f5sc//rGWnZ2tWSwWbfbs2dq2bdvCV+ABSM5hcMh57Ds5h8En57SNjDUqhBBiWBuW9wiFEEKIVhKEQgghhjUJQiGEEMOaBKEQQohhTYJQCCHEsCZBKIQQYliTIBRCCDGsSRAKIYQY1iQIhRBCDGsShEIIIYY1CUIhhBDDmgShEEKIYU2CUAghxLD2/wNgcRCY0ecX4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm0_4 = w4.headinside(t0)[0]\n",
    "hm1_4 = ml4.head(r1, 0, t1, layers=1)[0]\n",
    "hm2_4 = ml4.head(r1, 0, t2, layers=13)[0]\n",
    "hm3_4 = ml4.head(r2, 0, t3, layers=1)[0]\n",
    "hm4_4 = ml4.head(r2, 0, t4, layers=13)[0]\n",
    "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n",
    "plt.semilogx(t0, hm0_4, label=\"ttim pumped\")\n",
    "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n",
    "plt.semilogx(t1, hm1_4, label=\"ttim PS1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n",
    "plt.semilogx(t2, hm2_4, label=\"ttim PD1\")\n",
    "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n",
    "plt.semilogx(t3, hm3_4, label=\"ttim PS2\")\n",
    "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n",
    "plt.semilogx(t4, hm4_4, label=\"ttim PD2\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.1.1. Checking RMSE performance improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.05070451405173345\n"
     ]
    }
   ],
   "source": [
    "rmse_n = np.sqrt(\n",
    "    np.sum(\n",
    "        np.sum((h1 - hm1_4) ** 2)\n",
    "        + np.sum((h2 - hm2_4) ** 2)\n",
    "        + np.sum((h3 - hm3_4) ** 2)\n",
    "        + np.sum((h4 - hm4_4) ** 2)\n",
    "        + np.sum((h0 - hm0_4) ** 2)\n",
    "    )\n",
    "    / (len(h1) + len(h2) + len(h3) + len(h4) + len(h0))\n",
    ")\n",
    "\n",
    "print(\"RMSE:\", rmse_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8. Summary of and analysis of calibrated values and model errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Moench</th>\n",
       "      <th>Moench - more layers</th>\n",
       "      <th>TTim</th>\n",
       "      <th>TTim - more layers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>k[m/d]</th>\n",
       "      <td>8.64</td>\n",
       "      <td>8.64</td>\n",
       "      <td>9.067633</td>\n",
       "      <td>8.695434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sy[-]</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.172888</td>\n",
       "      <td>0.196104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ss[1/m]</th>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kz/kh</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.535053</td>\n",
       "      <td>0.488059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>0.061318</td>\n",
       "      <td>0.050705</td>\n",
       "      <td>0.010384</td>\n",
       "      <td>0.009767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Moench Moench - more layers      TTim TTim - more layers\n",
       "k[m/d]       8.64                 8.64  9.067633           8.695434\n",
       "Sy[-]         0.2                  0.2  0.172888           0.196104\n",
       "Ss[1/m]   0.00002              0.00002  0.000039           0.000039\n",
       "kz/kh         0.5                  0.5  0.535053           0.488059\n",
       "RMSE     0.061318             0.050705  0.010384           0.009767"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ta = pd.DataFrame(\n",
    "    columns=[\"Moench\", \"Moench - more layers\", \"TTim\", \"TTim - more layers\"],\n",
    "    index=[\"k[m/d]\", \"Sy[-]\", \"Ss[1/m]\", \"kz/kh\"],\n",
    ")\n",
    "ta.loc[:, \"TTim - more layers\"] = ca3.parameters[\"optimal\"].values\n",
    "ta.loc[:, \"TTim\"] = ca2.parameters[\"optimal\"].values\n",
    "ta.loc[:, \"Moench\"] = [kaq, Sy, Saq, kzoverkh]\n",
    "ta.loc[:, \"Moench - more layers\"] = [kaq, Sy, Saq, kzoverkh]\n",
    "ta.loc[\"RMSE\"] = [rmse, rmse_n, ca2.rmse(), ca3.rmse()]\n",
    "ta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above shows the TTim model results, with the first two columns showing the model simulated using Moench's parameters (Barlow and Moench, 1999). The first column is the original Model 1, and the second is the last model (Model 4) with more layers. Columns 3 and 4 show the results for the calibration with Conceptual Models 1 and 4.\n",
    "\n",
    "Overall, the model accuracy improved when we added more layers, so the added complexity resulted in added accuracy. However, when we look at the calibrated data, we see that it did not improve calibration performance, and both the simple model and the more complex one had similar RMSE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "* Barlow, P.M., Moench, A.F., 1999. WTAQ, a computer program for calculating drawdowns and estimating hydraulic properties for confined and water-table aquifers. 99-4225, US Dept. of the Interior, US Geological Survey\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, G.P., De Ridder, N.A., Verweij, J.M., 1970. Analysis and evaluationof pumping test data. volume 11. International institute for land reclamation and improvement The Netherlands.\n",
    "* Moench, Allen, F., 1997. Flow to a well of finite diameter in a homogeneous, anisotropic water table aquifer. Water Resources Research 34, 2431–2432.\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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