{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Confined Aquifer Test - Schroth\n",
    "**This test is taken from examples presented in MLU tutorial.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import ttim\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [5, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This test example was a pumping test conducted near San Francisco, US, and reported by Brian et al. (1997).\n",
    "The system consists of a confined system of two aquifers separated by an aquitard layer. The upper aquifer layer is located from 46 to 49 m depth, followed by an aquitard layer from 49 to 52 m depth and the second aquifer at 52 to 55 m depth.\n",
    "\n",
    "The lower aquifer is pumped by a well, named EW-712 that fully penetrates the formation. An observation well is placed 46 m away from the well, in the lower aquifer formation, and it is named MW-616. The last observation well is placed in the upper aquifer right on top of the pumping well. However, data was not available for this well. The radius of all wells was 0.05 m.\n",
    "\n",
    "All wells before pumping had water levels around 20 m depth, which means that the system can be characterized as confined.\n",
    "\n",
    "The time-drawdown data for the observation well MW-616 and pumping well EW-712 was obtained from MLU documentation (Duffield, 2007).\n",
    "\n",
    "In this example, we are reproducing the results obtained by Yang (2020). We will use TTim to test two hypotheses: the first is that the lower aquifer is, by confinement, disconnected to the upper aquifer, and the second is there is enough leakage from the upper aquifer to consider a leaky aquifer relation between them.\n",
    "\n",
    "A simplified cross-section of the model area can be seen below:"
   ]
  },
  {
   "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 1400x900 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(figsize=(14, 9))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "# sky\n",
    "sky = plt.Rectangle((-20, 0), width=100, height=3, fc=\"b\", zorder=0, alpha=0.1)\n",
    "ax.add_patch(sky)\n",
    "\n",
    "# Aquifer 1:\n",
    "ground = plt.Rectangle(\n",
    "    (-20, -55),\n",
    "    width=100,\n",
    "    height=3,\n",
    "    fc=np.array([209, 179, 127]) / 255,\n",
    "    zorder=0,\n",
    "    alpha=0.9,\n",
    ")\n",
    "ax.add_patch(ground)\n",
    "\n",
    "# Aquifer 2:\n",
    "\n",
    "ground = plt.Rectangle(\n",
    "    (-20, -49),\n",
    "    width=100,\n",
    "    height=3,\n",
    "    fc=np.array([209, 179, 127]) / 255,\n",
    "    zorder=0,\n",
    "    alpha=0.9,\n",
    ")\n",
    "ax.add_patch(ground)\n",
    "\n",
    "# Confining bed:\n",
    "confining_unit = plt.Rectangle(\n",
    "    (-20, -52),\n",
    "    width=100,\n",
    "    height=3,\n",
    "    fc=np.array([100, 100, 100]) / 255,\n",
    "    zorder=0,\n",
    "    alpha=0.7,\n",
    ")\n",
    "ax.add_patch(confining_unit)\n",
    "\n",
    "well = plt.Rectangle(\n",
    "    (-1.5, -55), width=3, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "ax.add_patch(well)\n",
    "\n",
    "# Wellhead\n",
    "wellhead = plt.Rectangle(\n",
    "    (-2.5, 0), width=5, height=1.5, fc=np.array([200, 200, 200]) / 255, zorder=2, ec=\"k\"\n",
    ")\n",
    "ax.add_patch(wellhead)\n",
    "\n",
    "# Screen for the well:\n",
    "screen = plt.Rectangle(\n",
    "    (-1.5, -55),\n",
    "    width=3,\n",
    "    height=3,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen.set_linewidth(2)\n",
    "ax.add_patch(screen)\n",
    "pumping_arrow = plt.Arrow(x=5, y=0.75, dx=3, dy=0, color=\"#00035b\")\n",
    "ax.add_patch(pumping_arrow)\n",
    "ax.text(x=9, y=0.75, s=r\"$ Q = 220 \\frac{gal}{min}$\", fontsize=\"large\")\n",
    "# Piezometers\n",
    "piez1 = plt.Rectangle(\n",
    "    (45, -55), width=2, height=55, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "screen_piez_1 = plt.Rectangle(\n",
    "    (45, -55),\n",
    "    width=2,\n",
    "    height=3,\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, 100], ydata=[0, 0], color=\"k\")\n",
    "ax.add_line(line)\n",
    "ax.text(x=45, y=0.75, s=\"Obs Well 1\", fontsize=\"large\")\n",
    "\n",
    "ax.text(\n",
    "    x=-17,\n",
    "    y=-40,\n",
    "    s=\"Upper Formations\",\n",
    "    fontsize=\"large\",\n",
    "    bbox={\"facecolor\": \"w\", \"alpha\": 0.9},\n",
    ")\n",
    "ax.text(x=-17, y=-48, s=\"Upper Aquifer\", fontsize=\"large\")\n",
    "ax.text(x=-17, y=-51, s=\"Aquitard\", fontsize=\"large\")\n",
    "ax.text(x=-17, y=-54, s=\"Lower Aquifer\", fontsize=\"large\")\n",
    "# Complete the figure:\n",
    "\n",
    "upper_formations = plt.Rectangle(\n",
    "    (-20, -46), width=100, height=46, fc=\"white\", hatch=\"-/\", zorder=0, alpha=0.9\n",
    ")\n",
    "ax.add_patch(upper_formations)\n",
    "\n",
    "ax.set_xlim([-20, 80])\n",
    "ax.set_ylim([-55, 3])\n",
    "ax.set_xlabel(\"Distance [m]\")\n",
    "ax.set_ylabel(\"Relative height [m]\")\n",
    "ax.set_title(\"Conceptual Model - Schroth Example\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. Set basic parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = 82.08  # constant discharge in m^3/d\n",
    "zt0 = -46  # top boundary of upper aquifer in m\n",
    "zb0 = -49  # bottom boundary of upper aquifer in m\n",
    "zt1 = -52  # top boundary of lower aquifer in m\n",
    "zb1 = -55  # bottom boundary of lower aquifer in m\n",
    "rw = 0.05  # well radius in m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Load data of the pumping and observation well\n",
    "\n",
    "The preferred method of loading data into TTim is to use numpy arrays.\n",
    "\n",
    "The data is in a text file where the first column is the time data in ***days*** and the second column is the drawdown in ***meters***\n",
    "\n",
    "The observation well is referred to as ***Well 1*** and the pumping well as ***Well 3***.\n",
    "\n",
    "For each piezometer, we will load the data as a numpy array. We further split the data into two different 1d arrays, one for time and another for drawdown.  Finally, we convert the time data from minutes to days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading data for the pumping well\n",
    "data1 = np.loadtxt(\"data/schroth_obs1.txt\", skiprows=1)\n",
    "t1 = data1[:, 0]\n",
    "h1 = data1[:, 1]\n",
    "r1 = 0  # Pumping well is at distance 0 to pumping\n",
    "\n",
    "# Loading data for the observation well\n",
    "data2 = np.loadtxt(\"data/schroth_obs2.txt\", skiprows=1)\n",
    "t2 = data2[:, 0]\n",
    "h2 = data2[:, 1]\n",
    "r2 = 46  # distance between observation well2 and pumping well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step_4'></a>\n",
    "## Step 4. Create TTim model\n",
    "\n",
    "We begin by considering the underlying aquifer as a single confined aquifer (overlying aquifer and aquitard are excluded).\n",
    "\n",
    "In this example, we are using the ModelMaq model to conceptualize our aquifer. ModelMaq defines the aquifer system as a stacked vertical sequence of aquifers and leaky layers (aquifer-leaky layer, aquifer-leaky layer, etc). A thorough explanation of the ModelMaq and TTim one-layer modelling conceptualization is given in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_0 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n",
    "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)])\n",
    "ml_0.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calibration workflow has been described in detail in the notebook: [Confined 1 - Oude Korendijk](confined1_oude_korendijk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".....................................................................................................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 114\n",
      "    # data points      = 40\n",
      "    # variables        = 2\n",
      "    chi-square         = 111.249388\n",
      "    reduced chi-square = 2.92761548\n",
      "    Akaike info crit   = 44.9157987\n",
      "    Bayesian info crit = 48.2935577\n",
      "[[Variables]]\n",
      "    kaq0:  1.03205045 +/- 0.10475776 (10.15%) (init = 10)\n",
      "    Saq0:  0.04013559 +/- 0.02029442 (50.56%) (init = 0.0001)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq0, Saq0) = -0.9309\n"
     ]
    }
   ],
   "source": [
    "ca_0 = ttim.Calibrate(ml_0)\n",
    "ca_0.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n",
    "ca_0.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=0)\n",
    "ca_0.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=0)\n",
    "ca_0.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimal</th>\n",
       "      <th>std</th>\n",
       "      <th>perc_std</th>\n",
       "      <th>pmin</th>\n",
       "      <th>pmax</th>\n",
       "      <th>initial</th>\n",
       "      <th>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>1.032050</td>\n",
       "      <td>0.104758</td>\n",
       "      <td>10.15045</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[1.032050454746927]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.040136</td>\n",
       "      <td>0.020294</td>\n",
       "      <td>50.56464</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[0.04013558949623235]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       optimal       std  perc_std  pmin  pmax  initial                 parray\n",
       "kaq0  1.032050  0.104758  10.15045  -inf   inf  10.0000    [1.032050454746927]\n",
       "Saq0  0.040136  0.020294  50.56464  -inf   inf   0.0001  [0.04013558949623235]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.6677034233071018\n"
     ]
    }
   ],
   "source": [
    "display(ca_0.parameters)\n",
    "print(\"RMSE:\", ca_0.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the model with our observation data:"
   ]
  },
  {
   "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_0 = ml_0.head(r1, 0, t1)\n",
    "hm2_0 = ml_0.head(r2, 0, t2)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.semilogx(t1, h1, \".\", label=\"well\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n",
    "plt.semilogx(t1, hm1_0[-1], label=\"ttim model - well\")\n",
    "plt.semilogx(t2, hm2_0[-1], label=\"ttim model - obs_well\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure shows a poor fit specially for the well. Probably well effects might be relevant in this case, so we will try to calibrate them next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5.2. Model Calibration with skin resistance and wellbore storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve the fit, we will try to calibrate the model adding wellbore storage and skin resistance to the pumping well.\n",
    "\n",
    "For this, we create a new model and add two extra parameters to the ```Well``` object:\n",
    "\n",
    "* The radius of the caisson ```rc```, which we use to simulate wellbore storage. In this case, we use a value in meters (float);\n",
    "* The skin resistance ```res```, a float value with the unit in days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_1 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n",
    "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=5, tsandQ=[(0, Q), (1e08, 0)])\n",
    "ml_1.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the method ```.set_parameter_by_reference``` to calibrate the ```rc``` and ```res``` parameters in our well.\n",
    "\n",
    "```.set_parameter_by_reference``` takes the following arguments:\n",
    "* ```name```: a string of the parameter name\n",
    "* ```parameter```: numpy-array with the parameter to be optimized. It should be specified as a reference, for example, in our case: ```w1.rc[0:]``` referencing to the parameter ```rc``` in object ```w1```.\n",
    "* ```initial```: float with the initial guess for the parameter value.\n",
    "* ```pmin``` and ```pmax```: floats with the minimum and maximum values allowed. If not specified, these will be ```-np.inf``` and ```np.inf```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 456\n",
      "    # data points      = 40\n",
      "    # variables        = 4\n",
      "    chi-square         = 13.6484660\n",
      "    reduced chi-square = 0.37912406\n",
      "    Akaike info crit   = -35.0100928\n",
      "    Bayesian info crit = -28.2545750\n",
      "[[Variables]]\n",
      "    kaq0:  1.95216341 +/- 0.05266715 (2.70%) (init = 10)\n",
      "    Saq0:  1.1460e-04 +/- 3.3108e-05 (28.89%) (init = 0.0001)\n",
      "    rc:    0.00259274 +/- 0.02350862 (906.71%) (init = 0.2)\n",
      "    res:   39.6497351 +/- 721.164682 (1818.84%) (init = 3)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(rc, res)    = -1.0000\n",
      "    C(kaq0, Saq0) = -0.8649\n",
      "    C(Saq0, rc)   = -0.1048\n",
      "    C(Saq0, res)  = +0.1035\n"
     ]
    }
   ],
   "source": [
    "ca_1 = ttim.Calibrate(ml_1)\n",
    "ca_1.set_parameter(name=\"kaq0\", initial=10)\n",
    "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n",
    "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc[:], initial=0.2)\n",
    "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_1.res[:], initial=3)\n",
    "ca_1.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=0)\n",
    "ca_1.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=0)\n",
    "ca_1.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimal</th>\n",
       "      <th>std</th>\n",
       "      <th>perc_std</th>\n",
       "      <th>pmin</th>\n",
       "      <th>pmax</th>\n",
       "      <th>initial</th>\n",
       "      <th>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>1.952163</td>\n",
       "      <td>0.052667</td>\n",
       "      <td>2.697886</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>[1.952163412833443]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>28.890824</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>[0.00011459526678264751]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rc</th>\n",
       "      <td>0.002593</td>\n",
       "      <td>0.023509</td>\n",
       "      <td>906.710108</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>[0.002592737824702783]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res</th>\n",
       "      <td>39.649735</td>\n",
       "      <td>721.164682</td>\n",
       "      <td>1818.838589</td>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>[39.649735078890686]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        optimal         std     perc_std  pmin  pmax  initial  \\\n",
       "kaq0   1.952163    0.052667     2.697886  -inf   inf  10.0000   \n",
       "Saq0   0.000115    0.000033    28.890824  -inf   inf   0.0001   \n",
       "rc     0.002593    0.023509   906.710108  -inf   inf   0.2000   \n",
       "res   39.649735  721.164682  1818.838589  -inf   inf   3.0000   \n",
       "\n",
       "                        parray  \n",
       "kaq0       [1.952163412833443]  \n",
       "Saq0  [0.00011459526678264751]  \n",
       "rc      [0.002592737824702783]  \n",
       "res       [39.649735078890686]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.5841332469428127\n"
     ]
    }
   ],
   "source": [
    "display(ca_1.parameters)\n",
    "print(\"RMSE:\", ca_1.rmse())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_1 = ml_1.head(r1, 0, t1)\n",
    "hm2_1 = ml_1.head(r2, 0, t2)\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.semilogx(t1, h1, \".\", label=\"well\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n",
    "plt.semilogx(t1, hm1_1[-1], label=\"ttim model - well\")\n",
    "plt.semilogx(t2, hm2_1[-1], label=\"ttim model - obs_well\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.legend()\n",
    "plt.title(\"Model Results - One Aquifer + Well Parameters\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has improved significantly. However, we can visually see that the fit is not very good for late time data. Let us investigate if we can do better with a three-layer model such as the one defined in our conceptual model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step_6'></a>\n",
    "## Step 6. Create a two-aquifer conceptual model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Until so far, we have only considered horizontal flow in our TTim models. The assumption is sufficient in fully-penetrated wells in confined aquifers. However, it might not represent the situation so well in a more complex scenario, where the vertical flow is a relevant component of the groundwater flow. In TTim, this is done by assigning more layers to the model.\n",
    "\n",
    "An advantage of TTim is the ability to create a model with more than one aquifer layer. We will explore this feature under the ```ModelMaq``` model as in [Step4](#step_4).\n",
    "\n",
    "When we use ModelMaq, the leaky layers are located in between the aquifers. These leaky layers only have vertical flow and are characterized by the parameters resistance to vertical flow (```c```) and storage (```Sll```). The specific flux is computed as (Bakker, 2013):\n",
    "\n",
    "$$q_n = \\frac{h_n-h_{n-1}}{c_n}$$\n",
    "\n",
    "where $q_n$ is the vertical flux from layer $n$ to layer $n-1$, $h_n$ is the head in layer $n$ and $c_n$ is the vertical resistance to flow. $c_n$ is computed as: $H_n/k_n$ where, $H_n$ is the leaky-layer thickness and $k_n$ the vertical hydraulic conductance. $c_n$ is the inverse of the parameter Leakance ($L_n = 1/c_n$), that is used in MODFLOW (Harbaugh, 2005) or analytical solutions of leaky-layers, such as in Hantush (1955).\n",
    "\n",
    "Alternatively, we can also model the interface of two aquifers that are not separated by a leaky layer. In that case, the leaky layer is set to 0 m thickness, and the model calculates the resistance to vertical flow by a finite differences scheme (Bakker, 2013).\n",
    "\n",
    "In the model construction, we have to set the parameters for each layer (which consists of an aquifer layer and an aquitard layer).\n",
    "\n",
    "For our two-layer model, we have to set:\n",
    "\n",
    "- The hydraulic conductivity: ```kaq```. It is a list/array with a float element for every aquifer, for example: ```[kaq0,kaq1]```.\n",
    "- The top and bottom of each aquifer: ```z``` defined by a list/array ```[zt0,zb0,zt1,zb1,...]```, where the inputs are a sequence of top and bottoms of the aquifer layers. The aquitard layers are defined by the space between the aquifer layers. The top of the first aquitard is the bottom of the first aquifer, the bottom of the first aquitard is the top of the second aquifer, and so on. But they can also have 0 thickness. In case the parameter ```topboundary``` is set to ```semi```, where we assume we have semi-confined conditions, we have to add one more element to the beginning of the list, which is the top of the aquitard layer that is above of the first aquifer.\n",
    "- The specific storage: ```Saq```. It is a list/array with a float element for every aquifer, for example: ```[Saq0, Saq1]```.\n",
    "- The minimum time for which TTim solve the groundwater flow: ```tmin```, a float.\n",
    "- And the maximum time: ```tmax```, float.\n",
    "- ```topboundary``` is the parameter that sets whether the upper layer is a confined unit or a semi-confined unit. We can assign this parameter as:\n",
    "    * ```'conf'```: This means that the upper layer is sealed from above in a confined condition;\n",
    "    *```'semi'```: This means that the upper layer receives leakage from phreatic storage (```phreatictop = True```) or from a fixed head above the upper leaky-layer (```phreatictop = False```).\n",
    "- TTim automatically assumes the ```topboundary``` is confined. In this case, we assume the ```topboundary``` is confined, so we do not need to set this parameter.\n",
    "- The resistance to vertical flow: ```c``` of the aquitard layers, which is a list with length n-1 where n is the number of aquifer layers, setting the resistance of each aquitard layer. In the case of semi-confined conditions (```topboundary = \" semi\"```), we also add a first element representing the resistance of the aquitard above the first aquifer.\n",
    "- The storage ```Sll``` of the aquitard layers: float/list/array with the specific storage values for each aquitard layer. In case a float is defined, the same storage is assumed for every layer. In case ```topboundary = semi``` and ```phreatictop = True```, the first element of ```Sll``` is the specific yield (see example [Unconfined - 1 - Vennebulten](unconfined1_vennebulten.ipynb)). \n",
    "- ```phreatictop```: Is a boolean (True/False). If ```True```, the first element in ```Saq``` is considered phreatic storage (Specific Yield), and it is not multiplied by the layer thickness. The default value is ```False``` in ```ModelMaq```. This parameter is normally ```True``` only in unconfined aquifers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_2 = ttim.ModelMaq(\n",
    "    kaq=[17.28, 2],\n",
    "    z=[zt0, zb0, zt1, zb1],\n",
    "    c=200,\n",
    "    Saq=[1.2e-4, 1e-5],\n",
    "    Sll=3e-5,\n",
    "    topboundary=\"conf\",\n",
    "    tmin=1e-4,\n",
    "    tmax=0.5,\n",
    ")\n",
    "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)], layers=1)\n",
    "ml_2.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7. Calibrate Multi-Aquifer Model\n",
    "\n",
    "Now we follow the procedures in step 5 again, but calibrating the parameters of both aquifers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.1. Calibrate model without wellstorage and skin resistance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Complementing the prior calibration, we add the hydraulic parameters of both layers 0 and 1 and the parameters of the aquitard layer in between. The procedure is the same as explained in Step 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................."
     ]
    }
   ],
   "source": [
    "ca_2 = ttim.Calibrate(ml_2)\n",
    "ca_2.set_parameter(name=\"kaq0\", initial=20, pmin=0, pmax=200)\n",
    "ca_2.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n",
    "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n",
    "ca_2.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n",
    "ca_2.set_parameter_by_reference(\n",
    "    name=\"Sll\", parameter=ml_2.aq.Sll[:], initial=1e-4, pmin=0\n",
    ")\n",
    "ca_2.set_parameter(name=\"c1\", initial=100, pmin=0)\n",
    "ca_2.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=1)\n",
    "ca_2.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=1)\n",
    "ca_2.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimal</th>\n",
       "      <th>std</th>\n",
       "      <th>perc_std</th>\n",
       "      <th>pmin</th>\n",
       "      <th>pmax</th>\n",
       "      <th>initial</th>\n",
       "      <th>parray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>199.161550</td>\n",
       "      <td>13053.628430</td>\n",
       "      <td>6554.291438</td>\n",
       "      <td>0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>[199.1615501616947]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kaq1</th>\n",
       "      <td>0.097464</td>\n",
       "      <td>0.323649</td>\n",
       "      <td>332.068998</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>[0.09746438855592698]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>5.610347</td>\n",
       "      <td>2477.773317</td>\n",
       "      <td>44164.354256</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00010</td>\n",
       "      <td>[5.610346530366302]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq1</th>\n",
       "      <td>1.737358</td>\n",
       "      <td>5.461490</td>\n",
       "      <td>314.355977</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>[1.737358478821435]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sll</th>\n",
       "      <td>0.063543</td>\n",
       "      <td>15.739979</td>\n",
       "      <td>24770.624116</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00010</td>\n",
       "      <td>[0.06354292456986621, 0.06354292456986621]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>0.001425</td>\n",
       "      <td>0.006811</td>\n",
       "      <td>477.970244</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>[0.0014250704284297644]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         optimal           std      perc_std  pmin   pmax    initial  \\\n",
       "kaq0  199.161550  13053.628430   6554.291438     0  200.0   20.00000   \n",
       "kaq1    0.097464      0.323649    332.068998     0    inf    1.00000   \n",
       "Saq0    5.610347   2477.773317  44164.354256     0    inf    0.00010   \n",
       "Saq1    1.737358      5.461490    314.355977     0    inf    0.00001   \n",
       "Sll     0.063543     15.739979  24770.624116     0    inf    0.00010   \n",
       "c1      0.001425      0.006811    477.970244     0    inf  100.00000   \n",
       "\n",
       "                                          parray  \n",
       "kaq0                         [199.1615501616947]  \n",
       "kaq1                       [0.09746438855592698]  \n",
       "Saq0                         [5.610346530366302]  \n",
       "Saq1                         [1.737358478821435]  \n",
       "Sll   [0.06354292456986621, 0.06354292456986621]  \n",
       "c1                       [0.0014250704284297644]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.744305083413874\n"
     ]
    }
   ],
   "source": [
    "display(ca_2.parameters)\n",
    "print(\"RMSE:\", ca_2.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The more complex model has improved the fit significantly from the first model. However, it showed worse RMSE, AIC and BIC values than the second model, with wellbore storage and skin resistance. We also have to note the unrealistic values found for the hydraulic conductivity of the upper layer and the storage parameters.\n",
    "\n",
    "Next, we plot this model results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Model Results - Two Aquifer Model')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAH2CAYAAACfjy+TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACAp0lEQVR4nO3dd1xTVx8G8Ocm7A0GBJXl1rqtWqQy3FtLXdWKqLXaat2tew/c1dZtraPaV9u6qtat4Fbce4M4cCAKyCa57x+W1IgyE24Iz7fNR3Jz7rm/xBt9PDk5VxBFUQQRERERkYGRSV0AEREREZEuMOgSERERkUFi0CUiIiIig8SgS0REREQGiUGXiIiIiAwSgy4RERERGSQGXSIiIiIySAy6RERERGSQGHSJiIiIyCAx6BLpkdWrV0MQBAiCgJCQkEyPi6KIsmXLQhAE+Pn5afXYgiBg4sSJud4vIiICgiBg9erVOWqXcZPJZLC3t0ejRo2wd+/evBWtZX5+fhqva2JiIiZOnPje3wtdCwoK0ni9PnQLCgoq8NoyDB06FIIgoHXr1gV2zIz3SEREhMb2sWPHws3NDUZGRrCzsyuQGgzxfUpkaIykLoCIMrO2tsbKlSsz/SUZGhqKu3fvwtraWprCtOC7775D165doVQqcePGDUyaNAktW7bEwYMH4ePjI3V5GhITEzFp0iQA0Hpgyc64cePQr18/9f1z586hf//+mD59Ovz9/dXbHR0dC7SuDGlpaVi3bh0AYPfu3Xj06BFKliyp8+O2atUKJ06cgIuLi3rbtm3bMG3aNIwZMwYtWrSAqampzusADPt9SmQoGHSJ9FDnzp2xfv16LFq0CDY2NurtK1euhJeXF+Li4iSsLn/c3NzwySefAAC8vb1Rrlw5+Pr6YuXKlXoXdKVUpkwZlClTRn0/OTkZAFCuXDn16yelbdu24fnz52jVqhV27tyJNWvWYPTo0To/rqOjY6Zwf+XKFQDAwIED4eTkpJXjJCYmwsLCIss2hvw+JTIUnLpApIe++OILAMD//vc/9bbY2Fhs2rQJvXr1eu8+MTEx+Pbbb1GyZEmYmJigdOnSGDNmDFJSUjTaxcXFoU+fPihWrBisrKzQvHlz3Lp167193r59G127doWTkxNMTU1RqVIlLFq0SEvP8o2PP/4YAPD06VON7U+ePEHfvn1RqlQpmJiYwNPTE5MmTUJ6erpGuyVLlqB69eqwsrKCtbU1KlasqBG4Jk6cCEEQMh33Qx+BZ4iIiFAHqkmTJmWaKvD8+XN8/fXXcHV1hampKRwdHeHt7Y39+/fn9aXIlZ07d0IQBISFham3bdq0CYIgoFWrVhptq1Wrhs8//1x9Pzk5GaNGjYKnpydMTExQsmRJ9O/fH69evcrx8VeuXAkTExOsWrUKrq6uWLVqFURRfG+dNWrUgKmpKTw9PTFnzpxMvydZfaz+7kf17/6+eXh4YOzYsQCA4sWLZ2q/ceNGeHl5wdLSElZWVmjWrBnOnz+vcYygoCBYWVnh8uXLaNq0KaytrdGoUaNsX4Oi9D4lKqwYdIn0kI2NDTp06IBff/1Vve1///sfZDIZOnfunKl9cnIy/P39sXbtWgwdOhQ7d+7El19+iVmzZiEgIEDdThRFtG/fHr/99huGDRuGLVu24JNPPkGLFi0y9Xnt2jXUqVMHV65cwdy5c7Fjxw60atUKAwcOVH+crw3h4eEAgPLly6u3PXnyBHXr1sWePXswfvx47Nq1C71790ZwcDD69OmjbrdhwwZ8++238PX1xZYtW7B161YMGTIECQkJ+a7LxcUFu3fvBgD07t0bJ06cwIkTJzBu3DgAQPfu3bF161aMHz8ee/fuxS+//ILGjRvjxYsX+T52Tvj6+sLY2FgjWO/fvx/m5uYIDQ1FWloaAODZs2e4cuUKGjduDOC/c2DOnDno3r07du7ciaFDh2LNmjVo2LBhpsD1Pg8fPsTevXvRrl07ODo6okePHrhz5w4OHz6s0e7AgQNo164drK2tsWHDBsyePRt//PEHVq1apbXXYcuWLejduzeAN1MoTpw4ga+++goAMH36dHzxxReoXLky/vjjD/z222+Ij49HgwYNcO3aNY1+UlNT0bZtWzRs2BDbtm3L0TlelN6nRIWWSER6Y9WqVSIAMSwsTDx06JAIQLxy5YooiqJYp04dMSgoSBRFUfzoo49EX19f9X5Lly4VAYh//PGHRn8zZ84UAYh79+4VRVEUd+3aJQIQFyxYoNFu2rRpIgBxwoQJ6m3NmjUTS5UqJcbGxmq0HTBggGhmZibGxMSIoiiK4eHhIgBx1apVWT63jHYzZ84U09LSxOTkZPHChQuil5eX6OLiIoaHh6vb9u3bV7SyshLv37+v0cecOXNEAOLVq1fVtdjZ2WV53AkTJojv+6Mu47V++7i+vr4ar+vz588zvS4ZrKysxMGDB2d5bG3KOB/+/PNP9bZPP/1UbNiwofp+2bJlxe+//16UyWRiaGioKIqiuH79ehGAeOvWLVEURXH37t0iAHHWrFka/W/cuFEEIC5fvjzbWiZPniwCEHfv3i2Koijeu3dPFARB7N69u0a7evXqiSVKlBCTkpLU2+Li4kQHBweN35OszqF3X//3/b5l/B4/f/5cvS0yMlI0MjISv/vuO43+4uPjRWdnZ7FTp07qbT169BABiL/++mu2z/3tGgzxfUpkaDiiS6SnfH19UaZMGfz666+4fPkywsLCPvhx6MGDB2FpaYkOHTpobM/4mP3AgQMAgEOHDgEAunXrptGua9euGveTk5Nx4MABfPbZZ7CwsEB6err61rJlSyQnJ+PkyZN5el4jRoyAsbExzMzMUKNGDVy5cgXbt2+Hh4eHus2OHTvg7++PEiVKaBw7Y0QrNDQUAFC3bl28evUKX3zxBbZt24bo6Og81ZQXdevWxerVqzF16lScPHlSPYKanbefT3p6+ns/7s+pRo0a4dixY0hKSsL9+/dx584ddOnSBTVq1MC+ffsAvBnldXNzQ7ly5QC8OVcAZFqtoWPHjrC0tFSfKx8iiqJ6ukKTJk0AAJ6envDz88OmTZvU81ITEhIQFhaGgIAAmJmZqfe3trZGmzZt8vycc2rPnj1IT09HYGCgxuttZmYGX1/f966W8Pb0jpwy1PcpkaFg0CXSU4IgoGfPnli3bh2WLl2K8uXLo0GDBu9t++LFCzg7O2eai+rk5AQjIyP1x+kvXryAkZERihUrptHO2dk5U3/p6en4+eefYWxsrHFr2bIlAOQ5VA4aNAhhYWE4evQo5syZg7S0NLRr107jI/+nT59i+/btmY790UcfaRy7e/fu+PXXX3H//n18/vnncHJyQr169dQhT5c2btyIHj164JdffoGXlxccHBwQGBiIJ0+efHCfiIiITM8pI7TnRePGjZGSkoKjR49i3759UCgUqFmzJho3bqye0nDgwAH1tAXgv3Pg3S90CYIAZ2fnbKdeHDx4EOHh4ejYsSPi4uLw6tUrvHr1Cp06dUJiYqJ6vurLly+hUqkynVtA5vNNFzLmfNepUyfTa75x48ZM56+FhYXGF8pyylDfp0SGgqsuEOmxoKAgjB8/HkuXLsW0adM+2K5YsWI4deoURFHU+Ev02bNnSE9Ph0KhULdLT0/HixcvNP4SfTec2dvbQy6Xo3v37ujfv/97j+np6Zmn51SqVCn1F9C8vb3h7OyML7/8EhMmTMDChQsBAAqFAtWqVfvgcy5RooT65549e6Jnz55ISEjA4cOHMWHCBLRu3Rq3bt2Cu7u7ejQxJSVFY9mp/AYAhUKB+fPnY/78+YiMjMTff/+NkSNH4tmzZ+q5ve+r++0vjwFAhQoV8lxDvXr1YGVlhf379yMiIgKNGjWCIAho1KgR5s6di7CwMERGRmoE3Yxz4Pnz5xphVxRFPHnyBHXq1MnymCtXrgQAzJs3D/PmzXvv43379oW9vT0EQXhv8H9329u/R2/Lz3znjHP+r7/+gru7e7bt3/eFxZwyxPcpkaFg0CXSYyVLlsT333+PGzduoEePHh9s16hRI/zxxx/YunUrPvvsM/X2tWvXqh8HAH9/f8yaNQvr16/HwIED1e1+//13jf4sLCzg7++P8+fPo1q1ajAxMdHm09LQrVs3/PLLL1ixYgW+//57uLu7o3Xr1vjnn39QpkwZ2Nvb56gfS0tLtGjRAqmpqWjfvj2uXr0Kd3d39ZSIS5cuaYS47du3Z9tnRjBOSkrKsp2bmxsGDBiAAwcO4NixYx9sZ2Jiog752mBsbAwfHx/s27cPDx48wIwZMwAADRo0gJGREcaOHasOvhkaNWqEWbNmYd26dRgyZIh6+6ZNm5CQkJDlagMvX77Eli1b4O3tjalTp2Z6/JdffsH69etx5coVVKlSBXXr1sXmzZsxe/ZsdZiNj4/P9NoXL14cZmZmuHTpksb2bdu25f5F+VezZs1gZGSEu3fv5mlKQm4UhfcpUWHFoEuk5zLCS1YCAwOxaNEi9OjRAxEREahatSqOHj2K6dOno2XLluoRvaZNm8LHxwc//PADEhIS8PHHH+PYsWP47bffMvW5YMECfPrpp2jQoAG++eYbeHh4ID4+Hnfu3MH27dvVcz21YebMmahXrx6mTJmCX375BZMnT8a+fftQv359DBw4EBUqVEBycjIiIiLwzz//YOnSpShVqhT69OkDc3NzeHt7w8XFBU+ePEFwcDBsbW3VobZly5ZwcHBA7969MXnyZBgZGWH16tV48OBBtnVZW1vD3d0d27ZtQ6NGjeDg4ACFQgF7e3v4+/uja9euqFixIqytrREWFobdu3drfHu+IDRq1AjDhg0DAPXvs7m5OerXr4+9e/eiWrVqGmvLNmnSBM2aNcOIESMQFxcHb29vXLp0CRMmTEDNmjXRvXv3Dx5r/fr1SE5OxsCBA997AY1ixYph/fr1WLlyJX788UdMmTIFzZs3R5MmTTBs2DAolUrMnDkTlpaWiImJUe8nCAK+/PJL/PrrryhTpgyqV6+O06dPZwp2ueHh4YHJkydjzJgxuHfvHpo3bw57e3s8ffoUp0+fhqWlpVZXJSgK71OiQknSr8IRkYa3v82dlXe/zS2KovjixQuxX79+oouLi2hkZCS6u7uLo0aNEpOTkzXavXr1SuzVq5doZ2cnWlhYiE2aNBFv3Ljx3tUFwsPDxV69eoklS5YUjY2NRUdHR7F+/fri1KlTNdogF6suzJ49+72Pd+zYUTQyMhLv3LkjiuKbFQ8GDhwoenp6isbGxqKDg4NYu3ZtccyYMeLr169FURTFNWvWiP7+/mLx4sVFExMTsUSJEmKnTp3ES5cuafR9+vRpsX79+qKlpaVYsmRJccKECeIvv/yS7aoLoiiK+/fvF2vWrCmampqKAMQePXqIycnJYr9+/cRq1aqJNjY2orm5uVihQgVxwoQJYkJCQpavQ169b9UFURTFixcvigDEcuXKaWzP+Ib+0KFDM/WVlJQkjhgxQnR3dxeNjY1FFxcX8ZtvvhFfvnyZZQ01atQQnZycxJSUlA+2+eSTT0SFQqFu8/fff4vVqlUTTUxMRDc3N3HGjBnvXQkjNjZW/Oqrr8TixYuLlpaWYps2bcSIiIg8r7qQYevWraK/v79oY2Mjmpqaiu7u7mKHDh3E/fv3q9v06NFDtLS0zPK5v82Q36dEhkYQxXx85ZeIiCiXJk6ciEmTJuVrxQkiopzgqgtEREREZJAYdImIiIjIIHHqAhEREREZJI7oEhEREZFBYtAlIiIiIoPEoEtEREREBolBl4iIiIgMEoMuERERERkkBl0iIiIiMkgMukRERERkkBh0iYiIiMggMegSERERkUFi0CUiIiIig8SgS0REREQGiUGXiIiIiAwSgy4RERERGSQGXSIiIiIySAy6RERERGSQGHSJiIiIyCAx6BIRERGRQWLQJSIiIiKDxKBLRERERAaJQZeIiIiIDBKDLhEREREZJAZdIiIiIjJIDLpEREREZJAYdImIiIjIIDHoEhEREZFBYtAlIiIiIoPEoEtEREREBolBl4iIiIgMEoMuERERERkkBl0iIiIiMkgMukRERERkkBh0iYiIiMggGUldgL5RqVR4/PgxrK2tIQiC1OUQERER0TtEUUR8fDxKlCgBmezD47YMuu94/PgxXF1dpS6DiIiIiLLx4MEDlCpV6oOPM+i+w9raGsCbF87GxkbiaoiIiIjoXXFxcXB1dVXntg9h0H1HxnQFGxsbBl0iIiIiPZbdNFN+GY2IiIiIDBKDLhEREREZJAZdIiIiIjJIDLpEREREZJAYdImIiIjIIDHoEhEREZFBYtAlIiIiIoPEoEtEREREBolBl4iIiIgMEoMuERERERkkBl0iIiIiMkgGGXQXL14MT09PmJmZoXbt2jhy5IjUJRERERFRATO4oLtx40YMHjwYY8aMwfnz59GgQQO0aNECkZGRUpdGRAUl9hEQfvjNr0REVGQJoiiKUhehTfXq1UOtWrWwZMkS9bZKlSqhffv2CA4Oznb/uLg42NraIjY2FjY2NroslYh0QDy7BuLWIYCoAgQZ0GIWUKNr/juOewy8DAfsPQGbEgW3LxFRISGYm0MQhAI5Vk7zmlGBVFNAUlNTcfbsWYwcOVJje9OmTXH8+PH37pOSkoKUlBT1/bi4OJ3WSEQ6FPsI4tYhuPln8f+2/TkXwFzJSiIiKioqnDsLwcJC6jI0GNTUhejoaCiVShQvXlxje/HixfHkyZP37hMcHAxbW1v1zdXVtSBKJSJdiLn7ZiSXiIgIBjaim+HdYXNRFD84lD5q1CgMHTpUfT8uLo5hl6iwcigDwUhAhQ5R/20TZED/03mfMnD/GLC+Y+bt3f4C3Ovrbt+cuvA7sOsH7U/VICLKJcHcXOoSMjGooKtQKCCXyzON3j579izTKG8GU1NTmJqaFkR5RKRrtiUhtF0AYftgQFQCghxoMx9wLpv3PktUBowFzZFiQQ6UqARk9xFdfvbNidhHwL7vAXlG/0pg3w/ARy0A25L57/99x4u5CziU0U3/RERaZlBTF0xMTFC7dm3s27dPY/u+fftQv76WRk+ISL/VCgQGXwZ67Hjza63A/PVnWxJos+BNQAX+C885CXr52Tcn3jdVQ1QCMfe00//bzq0F5lcB1rR58+u5tdo/BhGRlhnUiC4ADB06FN27d8fHH38MLy8vLF++HJGRkejXr5/UpRFRQbEtqd0Rx1qBQJlGbwKkQ+nc9Z2ffbPjUObNdIV3R4wdSmvvGMCbkdztg/47jqgCtg9+87x0PbLLUWQiygeDC7qdO3fGixcvMHnyZERFRaFKlSr4559/4O7uLnVpRFSY5Sc8azt4v91vmwVvQufbUzW0faysRo51GT7Prf0vYAuyN881vyP0RFSkGNw6uvnFdXSJqNCJfaSbEeO3+59fJfPI8eDLugu6ujomR4iJDEJO85pBzdElIiqSbEsCng10F9x0Pdf4fXQx/5jzjImKHIObukBERDqgy7nG76Pt+cecZ0xUJHFEl4iIckbXI8fvHkubo8gFuULF2ziKTCQpjugSEZF+0uYockGtUPE2XY4ic5SYKEc4oktERPpLW6PIhjLPGOAoMVEucESXiIiKhsI+zxjgXGOiXOKILhERFR2FeZ4xwLnGRLnEEV0iIiJd0fYoMucaE+UKR3QlFhWbhON3oxEVmyR1KUREpAvaHEXmXOP/xD4Cwg+/+ZXoAziiK6GNYZEYtfkyVCIgE4DggKroXMdN6rKIiEifca6xdJeH5gh0ocMRXYlExSapQy4AqERg9OYrHNklIqLsFeW5xh8Kyboe2dXVPGWOTOsUR3QlEh6doA65GZSiiIjoRLjYmktTFBER0fvo01zjrEKyroK/ruYpG8rItB6PdHNEVyKeCkvIBM1tckGAh8JCmoKIiIiyoi9zjTNC8tt0/YU8XcxTNpSRaT1fkYNBVyIutuYIDqgKufAm7coFAdMDqnA0l4iIioZagcDgy0CPHW9+zelIphRfyNNFuJZiqThth2upwnoucOqChDrXcYNPeUdERCfCQ2HBkEtEREWLbcm8BdSC/kJeRrjePvhNGNVGuJZiqThtT/uQYhpJLjHoSszF1pwBl4iIKLfyGpLzStvhWhfhOTvaDtdShPVcYtAlIiIiyglth+vCPjItRVjPJUEURTH7ZkVHXFwcbG1tERsbCxsbG6nLyZOo2CSERyfAU2HJ0WIiIiLSFPtIu+Fa2/3lQE7zGkd0DQwvQkFERERZ0vbIdEFPI8kFrrpgQHgRCiIiIqL/MOgakKwuQkFERERU1DDoGhBehIKIiIjoPwy6BoQXoSAiIiL6D7+MZmB4EQoiIiKiNxh0DRAvQkFERETEqQtEREREZKAYdImIiIjIIDHoEhEREZFBYtAlIiIiIoPEoEtEREREBolBl/ItKjYJx+9G81LDREREpFe4vBjly8awSIzafBkqEZAJQHBAVXSu4yZ1WUREREQc0aW8i4pNUodcAFCJwOjNVziyS0RERHqBQZfyLDw6QR1yMyhFERHRidIURERERPQWgwm6ERER6N27Nzw9PWFubo4yZcpgwoQJSE1Nlbo0g+WpsIRM0NwmFwR4KCykKYiIiIjoLQYTdG/cuAGVSoVly5bh6tWr+PHHH7F06VKMHj1a6tIMloutOYIDqkIuvEm7ckHA9IAqvPwwERER6QVBFEUx+2aF0+zZs7FkyRLcu3cvx/vExcXB1tYWsbGxsLGx0WF1hiMqNgkR0YnwUFgw5BIREZHO5TSvGfSqC7GxsXBwcMiyTUpKClJSUtT34+LidF2WwXGxNWfAJSIiIr1jMFMX3nX37l38/PPP6NevX5btgoODYWtrq765uroWUIVEREREpEt6H3QnTpwIQRCyvJ05c0Zjn8ePH6N58+bo2LEjvvrqqyz7HzVqFGJjY9W3Bw8e6PLpEBEREVEB0fs5utHR0YiOjs6yjYeHB8zMzAC8Cbn+/v6oV68eVq9eDZksd1mec3SJiIiI9JvBzNFVKBRQKBQ5avvo0SP4+/ujdu3aWLVqVa5DLhEREREZDr0Pujn1+PFj+Pn5wc3NDXPmzMHz58/Vjzk7O0tYGRERERFJwWCC7t69e3Hnzh3cuXMHpUqV0nhMz2dnEBEREZEOGMxn+0FBQRBF8b03fXbu6TmkqdKkLkPvRMUm4fjdaETFJkldChERERVSBjOiWxg9ev0IPXb3QDGzYmhbpi0+K/cZPG09pS5LchvDIjFq82WoREAmAMEBVdG5jpvUZREREVEhYzAjuoXR/bj7cDBzwIvkF1h1dRXabm2LwF2B2HJ7CxLTEqUuTxJRsUnqkAsAKhEYvfkKR3aJiIgo1xh0JVS/RH3s77gf8/3nw7eUL2SCDOefncf44+PR8M+GmHh8Ii49v6T30y+0KTw6QR1yMyhFERHRRTP4ExERUd5x6oLEjGXGaOTWCI3cGuFpwlP8ffdvbLmzBQ/iH2DT7U3YdHsTytqVxWdlP0ObMm1gb2Yvdck65amwhEyARtiVCwI8FBbSFUVERESFkt5fMKKg6cMFI1SiCmefnsXm25ux7/4+pChTAABGMiP4u/ojoFwAvFy8IJfJJalP1zaGRWL05itQiiLkgoDpAVU4R5eIiIjUcprXGHTfoQ9BV6Oe1DjsurcLm+9sxrUX19TbnS2d0a5MO7Qv2x6lrEtl0UPhFBWbhIjoRHgoLOBiay51OURERKRHGHTzSN+C7ttuxtzE5tubsePeDsSlxqm313Oph4CyAWjk3gimclMJKyQiIiLSPQbdPNLnoJshRZmCg5EHsfn2ZpyMOqnebmNig1alWyGgXAAqOlSUsEIiIiIi3WHQzaPCEHTf9uj1I2y9sxVb72zFk4Qn6u21nGqhx0c94OfqB5nAxTWIiIjIcDDo5lFhC7oZlColTkadxObbm3HwwUGkq9IBAB42Hgj8KBBtSreBmZGZxFUSERER5R+Dbh4V1qD7tmeJz/D79d/xx80/EJ8WDwBwMHNAl4pd0KVCF4NfooyIiIgMG4NuHhlC0M2QkJaAzbc347drvyEqIQoAYCY3Q7uy7RBYORBuNlyyi4iIiAofBt08MqSgmyFdlY599/dh1ZVVuB5zHQAgQEAjt0bo8VEP1HCqIW2BOhAVm4Tw6AR4Kiy5PBkREZGBYdDNI0MMuhlEUUTYkzCsvroaRx4dUW+v4VgDQVWC4FfKzyAuQrExLBKjNl+GSgRkAhAcUJUXnCAiIjIgDLp5ZMhB9213Xt7B2mtrsePeDqSp0gAA7jbuCKwciLZl2hbaL65FxSbBe8bBTJcQPjrSnyO7REREBiKneY3rThVRZe3LYrL3ZOz5fA++qvoVrE2scT/uPqacnIKmfzXF4guLEZMcI3WZuRYenaARcgFAKYqIiE6UpiAiIiKSDINuEedo4YhBtQZhf4f9GFl3JEpalcTLlJdYcnEJmv7VFFNOTEFEbITUZeaYp8ISMkFzm1wQ4KGwkKYgIiIikgynLryjqExd+JB0VTr2R+7H6iurcfXFVQBvvrjm7+qPoCpBqOlUU+IKs7cxLBKjN1+BUhQhFwRMD6jCObpEREQGhHN086ioB90MoijizNMzWHN1DUIfhqq3V3esjqCPguDv6q/XX1yLik1CRHQiPBQWnJtLRERkYBh084hBN7N7r+5h7bW1+Pvu3+ovrrlauyKwciDalW0HcyMGSSIiIio4DLp5xKD7YdFJ0fj9+u/YeHMj4lLjAAB2pnboXKEzvqj4BYqZF5O4QiIiIioKGHTziEE3e4lpidh6ZyvWXluLR68fAQBM5aboUqELvqr6FezM7KQtkIiIiAwag24eMejmnFKlxIHIA1h9dTUuR18GAFgZW6HHRz0QWDkQFsZc6YCIiIi0j0E3jxh0c08URRx9dBQ/nf8JN2JuAAAczBzwdbWv0bF8R5jITSSukIiIiAwJg24eMejmnUpUYU/EHiw8vxCR8ZEAgBKWJfBtjW/RunRrvV6lgYiIiAoPBt08YtDNvzRVGrbe2YqlF5biWdIzAEAZ2zL4rtZ3aOjaEIIgZNMDERER0Ycx6OYRg672JKUn4X83/oeVl1eqV2mopqiGQbUGoa5LXYmrIyIiosKKQTePGHS1Ly41DquvrMa66+uQlJ4EAPBy8cKgWoPwkeIjiasjIiKiwoZBN48YdHUnOikayy4uw1+3/0K6Kh0A0MS9Cb6r+R08bT0lro6IiIgKCwbdPGLQ1b0H8Q+w+MJi7Ly3EyJEyAU52pVth2+qfwNnS2epyyMiIiI9x6CbRwy6BefWy1v4+dzPCHkYAgAwkZmgS8U3F52wN7OXtjgiIiLSWwy6ecSgW/AuPLuA+efm4+zTswAAS2NL9UUnLI0tJa6OiIiI9A2Dbh4x6EpDFEUce3wMC84t0LjoxOBag9GubDvIBJnEFRIREZG+YNDNIwZdaalEFfZG7MXP539WX3SimqIaRtcbzRUaiIiICEDO85pBDpOlpKSgRo0aEAQBFy5ckLocygWZIENzz+bY2m4rhtYeCgsjC1yKvoQvdn6BiccnIiY5RuoSiYiIqJAwyKD7ww8/oESJElKXQflgLDdGzyo9sf2z7WhdujVEiNh0exNab2mN36//rl6eTCpRsUk4fjcaUbFJktZBREREH2ZwQXfXrl3Yu3cv5syZI3UppAVOFk4IbhCMNc3XoKJDRcSnxiP4dDA67eiEM0/OSFLTxrBIeM84iK4rTsF7xkFsDIuUpA4iIiLKmkEF3adPn6JPnz747bffYGFhkaN9UlJSEBcXp3Ej/VOreC1saLUBY+uNha2pLW6/vI2ee3rih8M/4GnC0wKrIyo2CaM2X4bq35ntKhEYvfkKR3aJiIj0kMEEXVEUERQUhH79+uHjjz/O8X7BwcGwtbVV31xdXXVYJeWHXCZH54qdsaP9DnQq3wkCBOwK34U2W9vgl8u/IFWZqvMawqMT1CE3g1IUERGdqPNjExERUe7ofdCdOHEiBEHI8nbmzBn8/PPPiIuLw6hRo3LV/6hRoxAbG6u+PXjwQEfPhLTFzswO47zGYUPrDajhWANJ6UlYcG4BAv4OwOGHh3V6bE+FJWSC5ja5IMBDkbNPEIiIiKjg6P3yYtHR0YiOjs6yjYeHB7p06YLt27dDEP5LIUqlEnK5HN26dcOaNWtydDwuL1a4iKKIHfd2YN7ZeYhOenOe+JXyww91foCrjW5G5zeGRWL05itQiiLkgoDpAVXQuY6bTo5FREREmRW5dXQjIyM15tc+fvwYzZo1w19//YV69eqhVKlSOeqHQbdwep36GssuLcO6a+uQLqbDRGaCXlV7oU/VPjCRm2j9eFGxSYiIToSHwgIutuZa75+IiIg+rMgF3XdFRETA09MT58+fR40aNXK8H4Nu4Xbv1T3MOD0DJ6JOAADK2pXFVO+pvNgEERGRASnSF4ygoqu0XWksa7IMc3znwMHMAXde3UG3f7rhp3M/FciX1YiIiEh/GOyIbl5xRNdwxCTHYMapGdgVsQvAm9HdKd5TUEVRReLKiIiIKD84oktFnoOZA2b5zsKPfj9qjO7OPzsfKcoUqcsjIiIiHWPQJYPX2L0xtrXbhpaeLaESVVh5ZSU6be+ES88vSV0aERER6RCDLhUJdmZ2mOkzE/P956OYWTHci72H7ru6Y97ZeRzdJSIiMlAMulSkNHJrhG3tt6F16dZQiSqsurIKHbd3xIVnF6QujYiIiLSMQZeKHFtTWwQ3CMbPDX+Go7kjwmPDEbgrEHPC5iA5PVnq8oiIiEhLGHSpyPJz9cOWdlvQtkxbiBCx5toaju4SEREZEAZdKtJsTW0x7dNpWNRoEZzMnRARF4HAXYGYFTYLSelJUpdHRERE+cCgSwTAp5QPtrTfgvZl20OEiN+u/YYOf3fAuafnpC6NiIiI8ohBl+hfNiY2mOI9BYsbLYaThRMi4yMRtDsIM0/PRGJaotTlERERUS4x6BK9o0GpBtjabis+L/c5RIhYd30dOmzvgDNPzkhdGhEREeUCgy7Re1ibWGNi/YlY2ngpnC2d8SD+AXru6Ynpp6ZzdJeIiKiQYNAlyoJ3SW9sabsFHcp3AAD878b/EPB3AMKehElcGREREWWHQZcoG1YmVpjgNQHLmiyDi6ULHr1+hF57emH6qem8qhoREZEeY9AlyqH6JepjS7st6FS+E4A3o7vd/+mOB/EPdHK8qNgkHL8bjahYLnNGRESUF4IoiqLUReiTuLg42NraIjY2FjY2NlKXQ3rq2KNjGHVkFF6mvIS1sTWmfDoFjdwaaa3/jWGRGLX5MlQiIBOA4ICq6FzHTWv9ExERFWY5zWsc0SXKA++S3vijzR+o4VgD8WnxGHxoMOaEzUGaKi3ffUfFJqlDLgCoRGD05isc2SUiIsolBl2iPHK2dMavzX9Fj8o9AABrrq1Br9298CThSb76DY9OUIfcDEpRREQ0V3sgIiLKDQZdonwwlhljeJ3hmO8/H9bG1rjw/AI6be+E44+O57lPT4UlZILmNrkgwENhkc9qiYiIihYGXSItaOTWCBvbbEQlh0p4mfIS/fb3w8LzC6FUKXPdl4utOYIDqkIuvEm7ckHA9IAqcLE113bZREREBo1fRnsHv4xG+ZGiTMGs07Pwx60/AAD1nOthhs8MKMwVue4rKjYJEdGJ8FBYMOQSERG9Jad5jUH3HQy6pA077+3EpBOTkJSeBEdzR8zymYWPnT+WuiwiIiKDwFUXiCTUqnQrbGi1AWVsy+B50nN8tfcrrLy8EipRJXVpRERERQaDLpGOlLYrjd9b/Y42pdtAKSox/9x8DDw4ELEpsVKXRkREVCQw6BLpkIWxBaZ9Og0TvSbCRGaC0Ieh6Li9Iy4/vyx1aURERAaPQZdIxwRBwOflP8f6VuvhZu2GqIQoBO4OxPrr68Ep8kRERLrDoEtUQCo6VMSG1hvQxL0J0lXpmHF6BoaHDsfr1NdSl0ZERGSQGHSJCpC1iTXm+s7FiDojYCQYYe/9veiyswtuxtyUujQiIiKDw6BLVMAEQcCXlb/E6har4WzpjPtx99Htn27YfHszpzIQERFpEYMukUSqO1bHn63/xKclP0WKMgUTjk/A2GNjkZiWKHVpREREBoFBl0hCdmZ2WNRoEQbVGgSZIMPfd/9Gt3+64V7sPalLIyIiKvRydGW0WrVq5a5TQcDff/+NkiVL5rkwqfDKaCSVsCdh+OHwD4hOioaFkQUm1p+IFp4tpC6LiIhI72j1EsAymQzDhg2DlZVVtgcWRREzZszAtWvXULp06dxVrQcYdElK0UnR+OHwDwh7EgYA6FyhM36o8wNM5CYSV0ZERKQ/tB50nzx5Aicnpxwd3NraGhcvXmTQJcqDdFU6Fl9YjBWXVwAAahevjQX+C2BraitxZURERPohp3ktR3N0w8PD4ejomOODX7t2De7u7jluT0T/MZIZYWCtgVjUaBEsjS1x9ulZdN/VHQ/jH0pdGhERUaGSo6Dr7u4OQRBy3Kmrqyvkcnmei8qPnTt3ol69ejA3N4dCoUBAQIAkdRDll08pH6xtsRbFLYojPDYc3f7phivRV6Qui4iIqNAwystOycnJuHTpEp49ewaVSqXxWNu2bbVSWF5s2rQJffr0wfTp09GwYUOIoojLly9LVg9RfpW3L4/fW/2O/gf640bMDfTc3RMzfWaioVtDqUsjIiLSezmao/u23bt3IzAwENHR0Zk7EwQolUqtFZcb6enp8PDwwKRJk9C7d+8898M5uqSPEtISMCx0GI49OgYBAkbUHYFulbpJXRYREZEktDpH920DBgxAx44dERUVBZVKpXGTKuQCwLlz5/Do0SPIZDLUrFkTLi4uaNGiBa5evZrlfikpKYiLi9O4EekbS2NLLGy4EB3Kd4AIETNOz8DM0zOhVEn3niMiItJ3uQ66z549w9ChQ1G8eHFd1JNn9+69WWB/4sSJGDt2LHbs2AF7e3v4+voiJibmg/sFBwfD1tZWfXN1dS2okolyxUhmhPGfjMegWoMAAOuur8Ow0GFISk+SuDIiIiL9lOug26FDB4SEhOiglPebOHEiBEHI8nbmzBn1XOExY8bg888/R+3atbFq1SoIgoA///zzg/2PGjUKsbGx6tuDBw8K6qkR5ZogCPiq6leY5TMLxjJjHIg8gK/2fIUXSS+kLo2IiEjv5PrLaAsXLkTHjh1x5MgRVK1aFcbGxhqPDxw4UGvFAW+mSnTp0iXLNh4eHoiPjwcAVK5cWb3d1NQUpUuXRmRk5Af3NTU1hampqXaKJSogLTxbwMnCCQMPDsSl6Ev48p8vsaTxEnjYekhdGhERkd7IddD9/fffsWfPHpibmyMkJERj2TFBELQedBUKBRQKRbbtateuDVNTU9y8eROffvopACAtLQ0RERFc05cMUu3itbGu5Tp8s/8bPHz9EF/u+hI/+f+EWsVzd8luIiIiQ5XrVRecnZ0xcOBAjBw5EjJZrmc+6NTgwYPx119/4ddff4W7uztmz56N7du348aNG7C3t89RH1x1gQqbF0kv8N3B73A5+jJMZCaY9uk0NPdsLnVZREREOqOzVRdSU1PRuXNnvQu5ADB79mx06dIF3bt3R506dXD//n0cPHgwxyGXqDAqZl4MK5utREPXhkhVpeL7w99j5eWVyOW/YYmIiAxOrkd0hwwZAkdHR4wePVpXNUmKI7pUWClVSsw5Mwfrrq8DAHQq3wmj6o2CkSxP14UhIiLSWznNa7n+G1CpVGLWrFnYs2cPqlWrlunLaPPmzct9tUSUb3KZHCPqjkBJq5KYFTYLf9z6A48THmOO7xxYGltKXR4REVGBy/WIrr+//4c7EwQcPHgw30VJiSO6ZAgORB7AyMMjkaxMRiWHSljYaCGcLJyy3S8qNgnh0QnwVFjCxda8AColIiLKvZzmtVwHXUPHoEuG4vLzyxhwcABikmPgbOmMxY0Wo5x9uQ+23xgWiVGbL0MlAjIBCA6ois513AqwYiIiopzR2ZfRiKhwqOpYFetaroOHjQeeJDxB4K5AnIw6+d62UbFJ6pALACoRGL35CqJiedU1IiIqvHIUdAMCAhAXF5fjTrt164Znz57luSgi0g5Xa1esa7kOtYvXxuu01/hm3zfYdmdbpnbh0QnqkJtBKYqIiE4soEqJiIi0L0dBd9u2bXj+/Dni4uKyvcXGxmL79u14/fq1rmsnohywNbXF8ibL0cKzBdLFdIw9NhaLLyzWWH7MU2EJmaC5n1wQ4KGwKOBqiYiItCdHqy6Ioojy5cvruhYi0hETuQlmNJiBUlalsOLyCiy5uASPXj/CRK+JMJYbw8XWHMEBVTF68xUoRRFyQcD0gCr8QhoRERVqOfoyWmhoaK47/uSTT2BqapqnoqTEL6ORofvr1l+YenIqlKIS9ZzrYZ7/PNiYvDnXo2KTEBGdCA+FBUMuERHpLa66kEcMulQUHH10FMNChiExPRFl7cpiUaNFKGFVQuqyiIiIcoSrLhDRB31a8lOsabEGTuZOuPPqDrr90w3XXlyTuiwiIiKtYtAlKqIqOlTE+lbrUc6+HKKTohG0OwiHHx6WuiwiIiKtYdAlKsKcLZ2xpvkaeLl4ISk9Cd8d/A4bb2yUuiwiIiKtYNAlKuKsTayxqPEifFb2M6hEFaaemopFFxaB0/eJiKiwY9AlIhjLjDGp/iT0r9EfALD04lL8eO5Hhl0iIirUch10nz59iu7du6NEiRIwMjKCXC7XuBFR4SQIAvpV74eRdUcCAFZdWYWZYTMZdomIqNDK0QUj3hYUFITIyEiMGzcOLi4uEAQh+52IqNDoVqkbTOQmmHJiCtZfX48UZQrGfTIOMoEfABERUeGS66B79OhRHDlyBDVq1NBBOUSkDzqW7wgTmQnGHx+Pv279hVRlKibXnwy5jJ/aEBFR4ZHrIRpXV1d+lElUBLQr2w4zGsyAXJDj77t/Y+SRkUhTpUldFhERUY7lOujOnz8fI0eOREREhA7KISJ90sKzBeb6zoWRzAi7I3ZjeMhwpCpTpS6LiIgoR3J9CWB7e3skJiYiPT0dFhYWMDY21ng8JiZGqwUWNF4CmCizww8PY8ihIUhVpeLTkp/iR78fYWZkJnVZRERUROU0r+V6ju6PP/7IL6ARFTE+pXywsNFCDDw4EEcfHcWAgwPwk/9PsDC2kLo0IiKiD8r1iK6h44gu0YedeXIG/Q/0R2J6Imo51cLixothaWwpdVlERFTE5DSv5XqObrdu3bBixQrcunUrXwUSUeHzsfPHWN50OayNrXHu2Tl8ve9rxKXGSV0WERHRe+U66FpZWWHu3LmoWLEiSpQogS+++AJLly7FjRs3dFEfEemZ6o7VsaLZCtia2uLS80v4as9XeJX8SuqyiIiIMsnz1IUnT54gJCQEISEhCA0Nxa1bt+Dk5ISoqCht11igOHWBKGduxtzE1/u+RkxyDMrZl8PyJsuhMFdIXRYRERUBOpu6kMHa2hr29vawt7eHnZ0djIyM4OzsnNfuiKiQqeBQAauarYKjuSNuv7yNXnt64WnCU6nLIiIiUst10B0xYgQ++eQTKBQKjB07FqmpqRg1ahSePn2K8+fP66JGItJTpe1KY3Xz1XC2dEZ4bDh67umJx68fS10WERERgDxMXZDJZHB0dMSQIUPQrl07VKpUSVe1SYJTF4hy79HrR+i9pzcevX4EF0sXrGy6Eq42rlKXRUREBkpnUxfOnz+PMWPG4PTp0/Dx8YGzszM6d+6MJUuW4Pr16/kqmogKp5JWJbG6+Wp42HggKiEKQbuDEB4bLnVZRERUxOV7Hd2LFy9i/vz5WLduHVQqFZRKpbZqkwRHdInyLjopGl/t+Qp3Y++imFkxrGi6AuXsy0ldFhERGRidXRkNeDOqm7HiwpEjRxAXF4caNWrA398/zwUTUeGnMFfg1+a/ou++vrgRcwO99vTC8ibLUamYYU1xIiKiwiHXI7r29vZ4/fo1qlevDj8/P/j5+cHHx8dgRj85okuUf7Epsei3rx+uvLgCaxNrLG28FNUcq0ldFhERGYic5rVcB90dO3YYVLB9F4MukXbEp8bj2/3f4sLzC7A0tsTKpivxkeIjqcsiIiIDoLMvo7Vu3Vrd4cOHD/Ho0aO8V0lEBsvaxBrLmixD7eK1kZCWgL77++L2y9tSl0VEREVIroOuSqXC5MmTYWtrC3d3d7i5ucHOzg5TpkyBSqXSRY1EVEhZGFtgUaNFqKaohtiUWPTZ2wf34+5LXRYRERURuQ66Y8aMwcKFCzFjxgycP38e586dw/Tp0/Hzzz9j3Lhxuqgxx27duoV27dpBoVDAxsYG3t7eOHTokKQ1ERV1cYkyBJWditI2ZfEi+QW+2vsVLypBREQFItdzdEuUKIGlS5eibdu2Gtu3bduGb7/9VtKpDOXKlUP58uURHBwMc3NzzJ8/H6tXr8bdu3dzfHliztEl0p6NYZEYtfkyVCIgM4qH20dr8CL1Idys3bC6+Wo4WjhKXSIRERVCOpujGxMTg4oVK2baXrFiRcTExOS2O62Jjo7GnTt3MHLkSFSrVg3lypXDjBkzkJiYiKtXr35wv5SUFMTFxWnciCj/omKT1CEXAFTp1nh4rTuKW7ggMj4SX+/7Gi+TX0pbJBERGbRcB93q1atj4cKFmbYvXLgQ1atX10pReVGsWDFUqlQJa9euRUJCAtLT07Fs2TIUL14ctWvX/uB+wcHBsLW1Vd9cXXnZUiJtCI9OUIfcDOlpthhYeS6czJ1w59Ud9N3XF/Gp8dIUSEREBi/XUxdCQ0PRqlUruLm5wcvLC4Ig4Pjx43jw4AH++ecfNGjQQFe1ZuvRo0do164dzp07B5lMhuLFi2Pnzp2oUaPGB/dJSUlBSkqK+n5cXBxcXV05dYEon6Jik+A946BG2JULAo6O9EcSotBzd0/EJMeghmMNLGuyDBbGFtIVS0REhYrOpi74+vri1q1b+Oyzz/Dq1SvExMQgICAAN2/e1EnInThxIgRByPJ25swZiKKIb7/9Fk5OTjhy5AhOnz6Ndu3aoXXr1oiKivpg/6amprCxsdG4EVH+udiaIzigKuSCAOBNyJ0eUAUutuYobVsay5osg7WJNS48v4CBhwYiRZmSTY9ERES5k+sR3YIWHR2N6OjoLNt4eHjg2LFjaNq0KV6+fKkRVsuVK4fevXtj5MiROToev4xGpF1RsUmIiE6Eh8ICLrbmGo9dfH4Rffb2QVJ6EvxK+WGe/zwYy4wlqpSIiAqLnOY1o5x0dunSpRwfuFo17V7mU6FQQKFQZNsuMTERACCTaQ5Sy2Qyru9LJCEXW/NMATdDdcfqWNRoEb7Z/w1CHoZg9JHRmNFgBuQyeQFXSUREhihHQbdGjRoQBAGiKEL492NIAMgYDH57m1Kp1HKJOePl5QV7e3v06NED48ePh7m5OVasWIHw8HC0atVKkpqIKHt1nOtgnt88DDo0CLsjdsPMyAyT6k+CTMj1zCoiIiINOfqbJDw8HPfu3UN4eDg2bdoET09PLF68GBcuXMCFCxewePFilClTBps2bdJ1vR+kUCiwe/duvH79Gg0bNsTHH3+Mo0ePYtu2bZKuBkFE2fMp5YNZPrMgE2TYemcrZp6eCT2fVUVERIVArufo1q1bFxMnTkTLli01tv/zzz8YN24czp49q9UCCxrn6BJJ5++7f2PM0TEAgD5V+2BgrYESV0RERPpIZ6suXL58GZ6enpm2e3p64tq1a7ntjohIrW2ZthhbbywAYMXlFVhxaYXEFRERUWGW66BbqVIlTJ06FcnJyeptKSkpmDp1KipVqqTV4oio6OlcsTOG1R4GAPjp/E9Yf329xBUREVFhlaMvo71t6dKlaNOmDVxdXdVzXy9evAhBELBjxw6tF0hERU9QlSAkpidiycUlmHF6BiyMLPBZuc+kLouIiAqZPK2jm5iYiHXr1uHGjRsQRRGVK1dG165dYWlpqYsaCxTn6BLpB1EUMefMHKy9thYCBMz0mYkWni2kLouIiPRATvOa3l8woqAx6BLpD1EUMeXkFPx5608YCUaY5zcP/m7+UpdFREQS09mX0UqUKIGuXbti+fLluHXrVr6KJCLKiiAIGPvJWLQu3RrpYjqGhQ7DiccnpC6LiIgKiVwH3blz58LGxgbz5s1DxYoV4eLigi5dumDp0qW4fv26LmokoiJMJsgwxXsKGrk1QpoqDYMODcK5p+ekLouIiAqBfE1dePr0KQ4dOoQdO3Zg48aNUKlUkl0ZTVs4dYFIP6UqUzHw0EAce3QMVsZW+KXZL/io2EdSl0VERBLIaV7L9aoLAPD69WscPXoUoaGhCAkJwfnz51G1alX4+vrmuWAioqyYyE3wo9+P+Gb/Nzj79Cz67uuLVc1WoZx9OalLIyIiPZXrEd169erh0qVLqFKlCvz8/ODj44MGDRrAzs5ORyUWLI7oEum3hLQE9NnbB5ejL0NhrsDq5qvhbuMudVlERFSAdPZltNu3b8PCwgKlS5dG6dKlUbZsWYMJuUSk/yyNLbGk8RKUty+P6KRofLX3Kzx+/VjqsoiISA/lOujGxMTg0KFD8Pb2xv79++Hr6wtnZ2d07twZS5cu1UWNREQabE1tsazJMnjYeOBJwhP02dsHzxOfS10WERHpmXyvo3v27FksXLgQ69at45fRiKhAPUl4gqDdQXj0+hHK2pXFqmarYGdmJ3VZRESkYzqbunD+/Hn8+OOPaNeuHRwcHPDJJ5/g8uXLGDRoEP7+++98FU1ElBvOls5Y0WQFnMydcOfVHfTd3xfxqfFSl0VERHoi1yO6RkZGqFmzJnx9fdVfRjOkkU+O6BIVPvde3UPQ7iC8THmJmk41sbTxUlgYW0hdFhER6YjOLgEcFxdn0AGQQZeocLoRcwO99vRCfGo8PnH5BAsbLYSp3FTqsoiISAd0NnWB4Y+I9FFFh4pY0ngJzI3McTLqJIaHDke6Kl3qsoiISEK5DrpKpRJz5sxB3bp14ezsDAcHB40bEZFUqjtWx6JGi2AqN0XIgxBMPjEZ+fy+LRERFWK5DrqTJk3CvHnz0KlTJ8TGxmLo0KEICAiATCbDxIkTdVAiEVHO1XGug1k+syATZNhyZwt+Pv+z1CUREZFEch10169fjxUrVmD48OEwMjLCF198gV9++QXjx4/HyZMndVEjEVGuNHRriPGfjAcArLi8Auuvr5e4IiIikkKug+6TJ09QtWpVAICVlRViY2MBAK1bt8bOnTu1Wx0RUR59Xv5z9K/RHwAw8/RM7I7YLXFFRERU0HIddEuVKoWoqCgAQNmyZbF3714AQFhYGExN+Q1nItIffav1RecKnSFCxOgjo3Eq6pTUJRERUQHKddD97LPPcODAAQDAoEGDMG7cOJQrVw6BgYHo1auX1gskIsorQRAwqu4oNHFvgjRVGgYdGoTrL65LXRYRERWQfF8C+NSpUzh27BjKli2Ltm3baqsuyXAdXaLCLyo2CeHRCfBUWMLF1hwpyhT029cPZ56eQTGzYvit5W9wtXaVukwiIsojnVwwIi0tDV9//TXGjRuH0qVLa6VQfcOgS1S4bQyLxKjNl6ESAZkABAdURec6bohPjUfP3T1x8+VNuFm7YW2LtShmXkzqcomIKA90csEIY2NjbNmyJd/FERHpQlRskjrkAoBKBEZvvoKo2CRYm1hjSeMlKGlVEpHxkfj2wLdISEuQtmAiItKpPM3R3bp1qw5KISLKn/DoBHXIzaAURUREJwIAHC0csbTxUtib2uPai2sYcmgI0pRpElRKREQFwSi3O5QtWxZTpkzB8ePHUbt2bVhaWmo8PnDgQK0VR0SUG54KS8gEaIRduSDAQ2Ghvu9h64FFjRah997eOBF1AmOPjUVwg2DIhFz/u5+IiPRcrr+M5unp+eHOBAH37t3Ld1FS4hxdosJtY1gkRm++AqUoQi4ImB5QBZ3ruGVqd+zRMQw4MADpYjq6V+6O7z/+HoIgSFAxERHllk6+jFYUMOgSFX5RsUmIiE6Eh8ICLrbmH2y3/e52jD46GgAwtPZQ9KzSs6BKJCKifMhpXsv11AUiIn3nYmueZcDN0KZMG8Qkx2DOmTmYd3YeFOYKtCnTpgAqJCKigpCjoDt06NAcdzhv3rw8F0NEVNB6fNQDzxOfY821NRh/bDzsTO3QoFQDqcsiIiItyFHQPX/+vMb9s2fPQqlUokKFCgCAW7duQS6Xo3bt2tqvkIhIx4Z+PBTRydHYeW8nhoUOwy9Nf0E1x2pSl0VERPmUo6B76NAh9c/z5s2DtbU11qxZA3t7ewDAy5cv0bNnTzRowFEQIip8ZIIMU+pPwavkVzj2+Bj6H+iPtS3WwtP2w1++JSIi/Zfr9XTmzp2L4OBgdcgFAHt7e0ydOhVz587VanFvmzZtGurXrw8LCwvY2dm9t01kZCTatGkDS0tLKBQKDBw4EKmpqTqriYgMh7HcGPP85qFKsSp4lfIK/fb1w7PEZ1KXRURE+ZDroBsXF4enT59m2v7s2TPEx8drpaj3SU1NRceOHfHNN9+893GlUolWrVohISEBR48exYYNG7Bp0yYMGzZMZzURkWGxMLbAosaL4G7jjscJj9Fvfz/EpcZJXRYREeVRrpcXCwwMRGhoKObOnYtPPvkEAHDy5El8//338PHxwZo1a3RSaIbVq1dj8ODBePXqlcb2Xbt2oXXr1njw4AFKlCgBANiwYQOCgoLw7NmzDy49kZKSgpSUFPX9uLg4uLq6cnkxoiLsYfxDdN/VHdFJ0ahdvDaWNVkGU7mp1GUREdG/crq8WK5HdJcuXYpWrVrhyy+/hLu7O9zd3dGtWze0aNECixcvzlfR+XHixAlUqVJFHXIBoFmzZkhJScHZs2c/uF9wcDBsbW3VN1dX14Iol4j0WCnrUljaeCmsjK1w9ulZjDw8EkqVUuqyiIgol3IddC0sLLB48WK8ePEC58+fx7lz5xATE4PFixdnuhxwQXry5AmKFy+usc3e3h4mJiZ48uTJB/cbNWoUYmNj1bcHDx7oulQiKgQqOFTATw1/grHMGPsj92P6qeng9XWIiAqXPF/c3dLSEtWqVUP16tXzHHAnTpwIQRCyvJ05cybH/b3v8p2iKGZ5WU9TU1PY2Nho3IiIAKCOcx3MaDADAgT8cesPLL20VOqSiIgoFyS9MtqAAQPQpUuXLNt4eHjkqC9nZ2ecOnVKY9vLly+RlpaWaaSXiCinmno0xejk0Zh2ahoWX1iM4hbFEVAuQOqyiIgoByQNugqFAgqFQit9eXl5Ydq0aYiKioKLiwsAYO/evTA1NeWFLIgoX7pU7IJnic+w4vIKTD4xGQpzBXxK+UhdFhERZSPPUxcKWmRkJC5cuIDIyEgolUpcuHABFy5cwOvXrwEATZs2ReXKldG9e3ecP38eBw4cwPDhw9GnTx9ORyCifPuu5ndoW6YtlKISw0OH4/Lzy1KXRERE2cj18mJSCQoKeu/SZYcOHYKfnx+AN2H422+/xcGDB2Fubo6uXbtizpw5MDXN+bJAOV2ugoiKnjRVGr47+B2OPToGe1N7/NbyN7jbuEtdFhFRkZPTvFZogm5BYdAloqwkpiWi556euPbiGkpalcS6luugMNfOFCwiIsoZna2jS0RUlFkYW2BRo0VwtXbFo9eP8O3+b5GQliB1WURE9B4MukREuaQwV2Bp46VwMHPA9ZjrGBoyFGmqNKnLIiKidzDoEhHlgZuNGxY1WgRzI3Mcf3wcE49P5AUliIj0DIMuEVEeVVFUwVzfuZALcvx992/8dP4nqUsiIqK3MOgSEeVDg1INMMFrAgDgl8u/4Pfrv0tcERERZWDQJSLKp8/KfYYBNQYAAGacnoH99/dLXBEREQEMukREWvF1ta/RqXwniBAx4vAInH16VuqSiIiKPAZdIiItEAQBo+uNRkPXhkhVpeK7g9/h7qu7UpdFRFSkMegSEWmJXCbHTJ+ZqOFYA/Gp8ei3vx+eJDyRuiwioiKLQZeISIvMjMzwc8Of4WnriScJT/DN/m8QlxondVlEREUSgy4RkZbZmdlhaeOlcDR3xJ1XdzD40GCkKlOlLouIqMhh0CUi0oESViWwpPESWBlbIexJGEYfHQ2VqJK6LCKiIoVBl4goB6Jik3D8bjSiYpNyvE8FhwqY7z8fRjIj7InYg5mnZ/LqaUREBYhBl4goGxvDIuE94yC6rjgF7xkHsTEsMsf71nOph+mfTgcA/H7jd6y4vEJXZRIR0TsYdImIshAVm4RRmy9D9e9ArEoERm++kquR3RaeLTCy7kgAwM/nf8aft/7URalERPQOBl0ioiyERyeoQ24GpSgiIjoxV/10q9QNX1f7GgAw9eRU7Lu/T1slEhHRBzDoEhFlwVNhCZmguU0uCPBQWOS6rwE1BqBD+Q5QiSqMODwCp6NOa6lKIiJ6HwZdIqIsuNiaIzigKuTCm7QrFwRMD6gCF1vzXPclCALG1huLxm6NkaZKw8BDA3H9xXVtl0xERP8SRH4FWENcXBxsbW0RGxsLGxsbqcshIj0RFZuEiOhEeCgs8hRy35aiTMG3+7/F6Sen4WDmgN9a/AY3GzctVUpEZPhymtc4oktElAMutubwKlMs3yEXAEzlpljgvwCVHCohJjkGX+/7Gs8Tn2uhSiIiehuDLhGRBKxMrLC48WK4Wbvh0etH6Le/Hy8VTESkZQy6REQSUZgrsKzJMijMFbj18ha+O/AdktOTpS6LiMhgMOgSEUmolHUpLG28FNbG1jj37By+P/w90lXpUpdFRGQQGHSJiCRWwaECfm70M0zlpgh5EIJJJybxUsFERFrAoEtEpAdqF6+N2T6zIRfk2HpnK+afmy91SUREhR6DLhGRnvB388cErwkAgF+v/Io1V9dIXBERUeHGoEtEpEc+K/cZhtQeAgCYc2YO/r77t8QVEREVXgy6RER6pudHPdGjcg8AwPhj43H44WGJKyIiKpwYdImI9IwgCBj68VC0LdMWSlGJYSHDcP7ZeanLIiIqdBh0iYj0kEyQYWL9ifAp5YNkZTL6H+iP2y9vS10WEVGhwqBLRKSnjGXGmOM7BzUcayA+NR799vXDo9ePpC6LiKjQYNAlItJj5kbmWNhoIcralcWzpGfou68vXiS9kLosIqJCgUGXiEjP2ZraYmnjpShhWQL34+7j2wPfIiEtQeqyiIj0HoMuEVEhUNyyOJY1WQZ7U3tce3ENgw4NQqoyVeqyiIj0WqEJutOmTUP9+vVhYWEBOzu7TI9fvHgRX3zxBVxdXWFubo5KlSphwYIFBV8oEZGOeNh6YEnjJbAwssCpqFMYdWQUlCql1GUREemtQhN0U1NT0bFjR3zzzTfvffzs2bNwdHTEunXrcPXqVYwZMwajRo3CwoULC7hSIiJNUbFJOH43GlGxSfnu6yPFR5jvPx9GMiPsvb8XwaeDIYqiFqokIjI8gljI/oRcvXo1Bg8ejFevXmXbtn///rh+/ToOHjyY4/7j4uJga2uL2NhY2NjY5KNSIiJgY1gkRm2+DJUIyAQgOKAqOtdxy3e/uyN244fQHyBCxLfVv8U3Nd4/CEBEZIhymtcKzYhuXsTGxsLBwSHLNikpKYiLi9O4ERFpQ1RskjrkAoBKBEZvvqKVkd3mHs0xpt4YAMDii4ux4caGfPdJRGRojKQuQFdOnDiBP/74Azt37syyXXBwMCZNmpSrvlUqFVJT+SUQIgAwMTGBTGbQ/2bOs/DoBHXIzaAURUREJ8LF1jzf/Xeu2BkxyTFYfHExpp+aDisTK7Qu3Trf/RIRGQpJg+7EiROzDZlhYWH4+OOPc9Xv1atX0a5dO4wfPx5NmjTJsu2oUaMwdOhQ9f24uDi4urp+sH1qairCw8OhUqlyVRORoZLJZPD09ISJiYnUpegdT4UlZAI0wq5cEOChsNDaMfpV74eY5BhsuLkBY46OgQABrUq30lr/RESFmaRBd8CAAejSpUuWbTw8PHLV57Vr19CwYUP06dMHY8eOzba9qakpTE1Nc9S3KIqIioqCXC6Hq6srR7GoyFOpVHj8+DGioqLg5uYGQRCkLkmvuNiaIzigKkZvvgKlKEIuCJgeUEUro7kZBEHAqHqjkKZKw6bbmzD66GiIEDmyS0QEiYOuQqGAQqHQWn9Xr15Fw4YN0aNHD0ybNk1r/WZIT09HYmIiSpQoAQsL7Y3IEBVmjo6OePz4MdLT02FsbCx1OXqncx03+JR3RER0IjwUFloNuRlkggzjvcYDADbd3oQxR9/M3WXYJaKirtDM0Y2MjERMTAwiIyOhVCpx4cIFAEDZsmVhZWWFq1evwt/fH02bNsXQoUPx5MkTAIBcLoejo6NWalAq36xXyY9oif6T8X5QKpUMuh/gYmuuk4D7tveFXVEU0aZMG50el4hInxWaoDt+/HisWbNGfb9mzZoAgEOHDsHPzw9//vknnj9/jvXr12P9+vXqdu7u7oiIiNBqLfx4lug/fD/oj4ywKwgC/rr1F8YeezN9i2GXiIqqQreOrq5ltS5bcnIywsPD4enpCTMzM4kqJNIvfF/oH5WowpSTU/DXrb8gQMC0T6cx7BKRQeE6uqQzHh4emD9/vvq+IAjYunWrZPUQkSaZIMO4T8ahY/mOECFizNEx2H53u9RlEREVuEIzdYGIiHJOJsgw9pM3Uxf+vPWn+gtqHNkloqKEI7pERAYqI+x2Kt9JPbL7992/pS6LiKjAMOhKJCo2CcfvRmvlUqDZ2b59O+zs7NQXubhw4QIEQcD333+vbtO3b1988cUXAIDjx4/Dx8cH5ubmcHV1xcCBA5GQkKDzOolI+2SCDGM+GaMOu2OPjmXYJaIig0FXAhvDIuE94yC6rjgF7xkHsTEsUqfH8/HxQXx8PM6fPw8ACA0NhUKhQGhoqLpNSEgIfH19cfnyZTRr1gwBAQG4dOkSNm7ciKNHj2LAgAE6rZGIdCcj7Hau0Fkddrfd2SZ1WUREOsegW8CiYpMwavNl9SVBVSIwevMVnY7s2traokaNGggJCQHwJtQOGTIEFy9eRHx8PJ48eYJbt27Bz88Ps2fPRteuXTF48GCUK1cO9evXx08//YS1a9ciOTlZZzUSkW7JBBlG1xutDrvjjo1j2CUig8egW8DCoxM0rnsPAEpRRER0ok6P6+fnh5CQEIiiiCNHjqBdu3aoUqUKjh49ikOHDqF48eKoWLEizp49i9WrV8PKykp9a9asGVQqFcLDw3VaIxHplkyQYUy9MRphd+udrVKXRUSkM1x1oYB5KiwhE6ARduWCAA+Fbi8p7Ofnh5UrV+LixYuQyWSoXLkyfH19ERoaipcvX8LX1xcAoFKp0LdvXwwcODBTH25ubjqtkYh0TxAEjKn3ZgWGjTc3Yvyx8RBFEZ+V+0ziyoiItI9Bt4C52JojOKAqRm++AqUoQi4ImB5QReeXB82Ypzt//nz4+vpCEAT4+voiODgYL1++xKBBgwAAtWrVwtWrV1G2bFmd1kNE0nk37E44PgEAGHaJyOAw6Eqgcx03+JR3RER0IjwUFjoPucB/83TXrVuHBQsWAHgTfjt27Ii0tDT4+fkBAEaMGIFPPvkE/fv3R58+fWBpaYnr169j3759+Pnnn3VeJxEVDIZdIioKOEdXIi625vAqU6xAQm4Gf39/KJVKdai1t7dH5cqV4ejoiEqVKgEAqlWrhtDQUNy+fRsNGjRAzZo1MW7cOLi4uBRYnURUMDLCbpcKXSBCxITjE7Dl9hapyyIi0hpBFEUx+2ZFR1bXTk5OTkZ4eDg8PT1hZmYmUYVE+oXvi8JPFEVMPzUdG25ugAABk+pP4sguEem1rPLa2ziiS0RUyOX3AjSCIGB0vdH4ouIXHNklIoPCObpERIXYxrBI9drcMgEIDqiKznVyv0KKIAgYVXcUAOB/N/6H8cfHQ4SIgHIB2i6ZiKjAcESXiKiQ0vYFaDLCbteKXQEAE45PwObbm7VVLhFRgWPQJSIqpHRxARpBEDCy7kiNsLvp1qb8lElEJBkGXSKiQirjAjRv08YFaDLCbrdK3QAAE09MxF+3/spXn0REUmDQJSIqpDIuQCMX3qRdbV6ARhAEjKgzQh12J52YxLBLRIUOv4xGRFSI6fICNBlhV4CAddfXYdKJSVCJKnSq0ElrxyAi0iUGXSKiQs7F1lxnF58RBAE/1PkBALDu+jpMOTkF4bHhGPbxMBjJ+FcIEek3Tl0gIqIsZYTdvtX6AngTePvt64eXyS8lroyIKGsMuoSQkBAIgoBXr15JXUq2Vq9eDTs7O/X9iRMnokaNGpLVQ1RUCIKAATUHYJ7fPJgbmePUk1PosqMLbsTckLo0IqIPYtAlIqIca+LeBOtbrkcpq1J4nPAY3f/pjl3hu6Qui4jovRh0iYgoV8rZl8OG1htQv0R9JCuT8cPhHzDv7DwoVUqpSyMi0sCgK5XYR0D44Te/FoCUlBQMHDgQTk5OMDMzw6effoqwsDCNNseOHUP16tVhZmaGevXq4fLly+rH7t+/jzZt2sDe3h6Wlpb46KOP8M8//2R73Nq1a2Pu3Lnq++3bt4eRkRHi4uIAAE+ePIEgCLh58yYAIDU1FT/88ANKliwJS0tL1KtXDyEhIVp4BYhIm2xNbbG40WL0qtILALDqyir0P9AfsSmxEldGRPQfBl0pnFsLzK8CrGnz5tdza3V+yB9++AGbNm3CmjVrcO7cOZQtWxbNmjVDTEyMus3333+POXPmICwsDE5OTmjbti3S0tIAAP3790dKSgoOHz6My5cvY+bMmbCyssr2uH5+fuqgKooijhw5Ant7exw9ehQAcOjQITg7O6NChQoAgJ49e+LYsWPYsGEDLl26hI4dO6J58+a4ffu2ll8RIsovuUyOIbWHYJbPLJjJzXDs8TF8sfML3H7J9ysR6QcG3YIW+wjYPggQVW/uiypg+2CdjuwmJCRgyZIlmD17Nlq0aIHKlStjxYoVMDc3x8qVK9XtJkyYgCZNmqBq1apYs2YNnj59ii1btgAAIiMj4e3tjapVq6J06dJo3bo1fHx8sj22n58fjhw5ApVKhUuXLkEul6N79+7q8BsSEgJfX18AwN27d/G///0Pf/75Jxo0aIAyZcpg+PDh+PTTT7Fq1SrtvzBEpBUtPFvgt5a/oYRlCTyIf4Bu/3TD/vv7pS6LiIhBt8DF3P0v5GYQlUDMPZ0d8u7du0hLS4O3t7d6m7GxMerWrYvr16+rt3l5eal/dnBwQIUKFdSPDxw4EFOnToW3tzcmTJiAS5cu5ejYPj4+iI+Px/nz5xEaGgpfX1/4+/sjNDQUgGbQPXfuHERRRPny5WFlZaW+hYaG4u7du/l+HYhIdyo6VMSG1htQz7kektKTMCRkCH4+/zNU7/55R0RUgBh0C5pDGUB452UX5IBDaZ0dUhTFN4f59zKhb29/d9u7Mh7/6quvcO/ePXTv3h2XL1/Gxx9/jJ9//jnbY9va2qJGjRoICQlBaGgo/Pz80KBBA1y4cAG3b9/GrVu34OfnBwBQqVSQy+U4e/YsLly4oL5dv34dCxYsyMMzJ6KCZG9mj6VNlqJ75e4AgOWXlmPgwYGIT42XuDIiKqoYdAuabUmgzYI34RZ482ub+W+260jZsmVhYmKinhcLAGlpaThz5gwqVaqk3nby5En1zy9fvsStW7dQsWJF9TZXV1f069cPmzdvxrBhw7BixYocHd/Pzw+HDh3C4cOH4efnBzs7O1SuXBlTp06Fk5OTuoaaNWtCqVTi2bNnKFu2rMbN2dk5vy8DERUAI5kRfqjzA6Z/Oh0mMhOEPgxF151dcS9Wd59aERF9CIOuFGoFAoMvAz12vPm1VqBOD2dpaYlvvvkG33//PXbv3o1r166hT58+SExMRO/evdXtJk+ejAMHDuDKlSsICgqCQqFA+/btAQCDBw/Gnj17EB4ejnPnzuHgwYMaITkrfn5+2L17NwRBQOXKldXb1q9fr562AADly5dHt27dEBgYiM2bNyM8PBxhYWGYOXNmjlZ4ICL90aZMG6xtsRbFLYojIi4CXXd2RciDEKnLIqIihkFXKrYlAc8GOh3JfduMGTPw+eefo3v37qhVqxbu3LmDPXv2wN7eXqPNoEGDULt2bURFReHvv/+GiYkJAECpVKJ///6oVKkSmjdvjgoVKmDx4sU5OnbGl9Z8fX3VUyF8fX2hVCo1gi4ArFq1CoGBgRg2bBgqVKiAtm3b4tSpU3B1ddXGy0BEBegjxUfY0HoDajnVQkJaAr47+B2WXlzKebtEVGAEMWMCJwEA4uLiYGtri9jYWNjY2Gg8lpycjPDwcHh6esLMzEyiCon0C98XlJ00ZRpmhc3ChpsbAACN3Bph2qfTYGlsKXFlRFRYZZXX3sYRXSIi0iljuTHGfDIGk+pPgrHMGAciD6Dbzm6IjIuUujQiMnCFJuhOmzYN9evXh4WFBezs7LJs++LFC5QqVQqCIODVq1cFUl9R1a9fP42lwN6+9evXT+ryiEiPBJQLwKrmq+Bo7oi7sXfRZWcXHH10NPsdiYjyqNBMXZgwYQLs7Ozw8OFDrFy5MssA2759e6SmpmLXrl14+fJltsH4bZy6kDvPnj1TX873XTY2NnBycirgiqig8X1RdETFJiE8OgGeCku42JrnuZ/nic8xJGQILj6/CAECBtYaiN5Veme73CERUYacTl0wKsCa8mXSpEkAgNWrV2fZbsmSJXj16hXGjx+PXbt2FUBlRZuTkxPDLFERsDEsEqM2X4ZKBGQCEBxQFZ3ruOWpL0cLR/za7FdMPzUdm25vwoJzC3Aj5gYm158MC2MLLVdOREVZoZm6kBPXrl3D5MmTsXbtWshkOXtqKSkpiIuL07gREdF/omKT1CEXAFQiMHrzFUTFJuW5TxO5CSZ4TcC4T8bBSDDCnog96L6rOx7GP9RS1UREBhR0U1JS8MUXX2D27Nlwc8v5KENwcDBsbW3VNy5jRUSkKTw6QR1yMyhFERHRifnqVxAEdKrQCSubrUQxs2K49fIWuuzsgpNRJ7PfmYgoByQNuhMnToQgCFnezpw5k6O+Ro0ahUqVKuHLL7/MVQ2jRo1CbGys+vbgwYO8PBUiIoPlqbCE7J3ps3JBgIdCO9MMahWvhQ2tN+CjYh8hNiUWfff1xdqra1FIvkJCRHpM0qA7YMAAXL9+PctblSpVctTXwYMH8eeff8LIyAhGRkZo1KgRAEChUGDChAkf3M/U1BQ2NjYaNyIi+o+LrTmCA6pC/u+XxeSCgOkBVfL1hbR3OVs6Y02LNWhbpi1Uogqzz8zGqKOjkJyerLVjEFHRI+mX0RQKBRQKhVb62rRpE5KS/psvFhYWhl69euHIkSMoU6aMVo5BRFRUda7jBp/yjoiIToSHwkKrITeDqdwUU72nonKxypgdNhs77+3EvVf3sMB/AVysXLR+PCIyfIVmjm5kZCQuXLiAyMhIKJVKXLhwARcuXMDr168BAGXKlEGVKlXUN09PTwBApUqVuCpALvj5+WHw4MFSl5EvgiBg69atOW4fFBSE9u3b66yevAgJCdFYB3r16tW5WiaPSBdcbM3hVaaYTkJuBkEQ0K1SNyxvshz2pva4HnMdAX8HYPWV1UhTpunsuERkmApN0B0/fjxq1qyJCRMm4PXr16hZsyZq1qyZ4zm8Rdn7wuu7QSrD5s2bMWXKlIIrjojoPeq61MWG1htQVVEVr9NeY+7ZuWi/rT0ORR7i3F0iyrFCE3RXr14NURQz3fz8/N7b3s/PD6IochQslxwcHGBtbS11GUREKGFVAutarsMU7ylQmCsQGR+JgYcG4ut9X+P2y9tSl0dEhUChCbqUN0FBQQgNDcWCBQvUK1lERETA398fAGBvbw9BEBAUFAQg8+ivh4cHpk6disDAQFhZWcHd3R3btm3D8+fP0a5dO1hZWaFq1arZjqwLgoBly5ahdevWsLCwQKVKlXDixAncuXMHfn5+sLS0hJeXF+7evaux35IlS1CmTBmYmJigQoUK+O233zQev337Nnx8fGBmZobKlStj3759mY796NEjdO7cGfb29ihWrBjatWuHiIiI3L+YWfj888/x3Xffqe8PHjwYgiDg6tWrAID09HRYW1tjz549AABRFDFr1iyULl0a5ubmqF69Ov766y+t1kRkCGSCDO3LtseOz3bgq6pfwVhmjJNRJ9FhewdMPTkVL5NfSl0iEekxBt18EEURiWmJktxy+tHdggUL4OXlhT59+iAqKgpRUVFwdXXFpk2bAAA3b95EVFQUFixY8ME+fvzxR3h7e+P8+fNo1aoVunfvjsDAQHz55Zc4d+4cypYti8DAwGxrmjJlCgIDA3HhwgVUrFgRXbt2Rd++fTFq1Ch1UB4wYIC6/ZYtWzBo0CAMGzYMV65cQd++fdGzZ08cOnQIAKBSqRAQEAC5XI6TJ09i6dKlGDFihMYxExMT4e/vDysrKxw+fBhHjx6FlZUVmjdvjtTU1By9hjnh5+eHkJAQ9f3Q0FAoFAqEhoYCePPlyOTkZHh7ewMAxo4di1WrVmHJkiW4evUqhgwZgi+//FLdnojeiIpNwvG70YhLlGFQrUHY1n4bGrs1hkpUYePNjWi1pRV+u/Yb0lQ5m7+b0V9+LnZBRIVHobkEsD5KSk9Cvd/rSXLsU11P5ehSmba2tjAxMYGFhQWcnZ3V2x0cHAC8uYRvdtM7WrZsib59+wJ4M1d6yZIlqFOnDjp27AgAGDFiBLy8vPD06VONY7yrZ8+e6NSpk8Y+48aNQ7NmzQAAgwYNQs+ePdXt58yZg6CgIHz77bcAgKFDh+LkyZOYM2cO/P39sX//fly/fh0REREoVaoUAGD69Olo0aKFuo8NGzZAJpPhl19+gfDv0kirVq2CnZ0dQkJC0LRp02xfw5zw8/PDoEGDEB0dDblcjqtXr2LChAkICQnBt99+i5CQENSuXRtWVlZISEjAvHnzcPDgQXh5eQEASpcujaNHj2LZsmXw9fXVSk1Ehd2HLjv8o/+PCHsShpmnZ+Lmy5uYFTYLf9z8A9/X+R4+pXxy3R8RGS6O6FK2qlWrpv65ePHiAICqVatm2vbs2bN895OcnKy+DPP169fVI6AZvL29cf36dfXjbm5u6pALQB0cM5w9exZ37tyBtbU1rKysYGVlBQcHByQnJ2eaJvEhGftZWVmhX79+721TpUoVFCtWDKGhoThy5AiqV6+Otm3bqkdoQ0JC1AH22rVrSE5ORpMmTTT6Xrt2bY5rIjJ02V12uI5zHWxsvRHjvcbDwcwBEXER6H+gP/rt74d7r+7luj8iMkwc0c0HcyNznOp6SrJjFxRjY2P1zxmjou/bplKptN5PxrYMoiiqt71vqsS77VUqFWrXro3169dnauvo6JhlvRkuXLig/vlDFxQRBAE+Pj4ICQmBiYkJ/Pz8UKVKFSiVSly+fBnHjx9Xz33OeH47d+5EyZIlNfoxNTXNUU1Ehi6ryw5nLG8ml8nRsXxHNPdojuWXlmPd9XU49ugYPn/8ObpU7IJ+1fvB1tQ2x/0RkeFh0M0HQRByNH1AaiYmJlAqlZm2Aci0XZ9UqlQJR48eRWBgoHrb8ePHUalSJQBA5cqVERkZicePH6NEiRIAgBMnTmj0UatWLWzcuBFOTk55vupd2bJlc9TOz88Py5cvh4mJCSZPngxBENCgQQPMmTMHSUlJ6tHpypUrw9TUFJGRkZymQPQBGZcdfjucfuiyw9Ym1hj28TB0KN8Bc8LmIORhCNZdX4ft97ajf43+6Fi+Y676IyLDwakLRYCHhwdOnTqFiIgIREdHQ6VSwd3dHYIgYMeOHXj+/Ln6whv65Pvvv8fq1auxdOlS3L59G/PmzcPmzZsxfPhwAEDjxo1RoUIFBAYG4uLFizhy5AjGjBmj0Ue3bt2gUCjQrl07HDlyBOHh4QgNDcWgQYPw8OFDrdbr5+eHq1ev4vLly2jQoIF62/r161GrVi110La2tsbw4cMxZMgQrFmzBnfv3sX58+exaNEirFmzRqs1ERVWebnssLuNO35u9DOWNVmGsnZlEZsSi+mnpqPj9o4ITziv88sYE5H+4YhuETB8+HD06NEDlStXRlJSEsLDw+Hh4YFJkyZh5MiR6NmzJwIDA7F69WqpS9XQvn17LFiwALNnz8bAgQPh6emJVatWqddOlslk2LJlC3r37o26devCw8MDP/30E5o3b67uw8LCAocPH8aIESMQEBCA+Ph4lCxZEo0aNcrzCO+HVKlSBQqFAu7u7uq+fX19oVQqM43cTpkyBU5OTggODsa9e/dgZ2eHWrVqYfTo0Vqtiagwy+tlh+uXqI8/2/yJP2/9iUUXFuHOqzvou68v/Er5YeOAAUhLVujsMsZEpF8EkZeY0RAXFwdbW1vExsZmCkLJyckIDw+Hp6cnzMzMJKqQSL/wfUH6LDYlFksuLsGGGxugFJUwkhmhW8Vu6FapG1ysXKQuj4jyKKu89jZOXSAiIoNla2qLkXVHYnPbzfAu6Y10VTrWXFuDppua4st/vsS6a+vwLDHrFWOIqPDiiO47OKJLlDt8X1BhcvjhYfx65Vece3oOIt789SdAQO3itdHcozkauzdGMfNiEldJRNnJ6Ygug+47GHSJcofvCyqMniU+w96IvdgdsRsXn19Ub5cJMtR1rqsOvRnLkxGRfmHQzSMGXaLc4fuCCruo11HYE7EHuyN24+qLq+rtRoIRPinxCZp7NEdDt4awNrHOW/+xSQiPToCnwlJrX4DTRZ/aVBD16ftrQLrFoJtHDLpEucP3BRmSB3EPsOf+HuwO342bL2+qtxvLjOFd0hvNPZrD39U/x2uo6+Kyw/p+KeOCqE/fXwPSPQbdPGLQJcodvi/IUN2LvfdmpDd8N+7F/ndZYVO5KXxK+aCZRzP4lPL54JUqo2KT4D3jYKaLVBwd6Z/nEUhd9KlNBVGfvr8GVDByGnS5ji4REdF7lLYtjW+qf4N+1frh9qvb2B2+G3si9iAyPhL77u/Dvvv7YG5kDj9XPzT3aI5PS34KE7mJen9dXHZY3y9lXBD16ftrQPqFQZeIiCgLgiCgvH15lLcvj+9qfofrMdexO2I39kbsxaPXj7ArfBd2he+ClbEVGro1RDOPZvBy8dLJZYf1/VLGBVGfvr8GpF+4ji4REVEOCYKAysUqY2jtodgVsAvrW65H98rd4WThhNdpr/H33b/R/0B/+P3hh6VXZ6BP03TIBRUA7Vx2OC+XRi5IBVGfvr8GpF84R/cdRX2Orp+fH2rUqIH58+dLXUqeCYKALVu2oH379jlqHxQUhFevXmHr1q06raugjqMN754HHh4eGDx4MAYPHpypbVF4XxBlRyWqcOHZBfVI74vkF+rHbE3sUc3hU9RyroLqxcvB09YTxcyKQfg3qOVFVGxSri+NXJAKoj59fw1ItzhHl9TeF15DQkLg7++Ply9fws7OTr198+bNMDY2LvgiiYgKMZkgQ63itVCreC2MqDMCZ5+exe6I3dh/fz9eprzEkSfbceTJdnV7axNreNp4wsPWA562nvC08YSnrSdcrV1hLM/+z2AXW3O9DncFUZ++vwakHxh0SYODg4PUJRARFWpymRx1XeqirktdjK43GqejTuPY42MIjw1HeGw4Hr1+hPjUeFyKvoRL0Zc09xXkKGVdSjME23rCw8YD9mb2Ej0josKLc3QNXFBQEEJDQ7FgwQIIggBBEBAREQF/f38AgL29PQRBQFBQEIA3o79vfzzt4eGBqVOnIjAwEFZWVnB3d8e2bdvw/PlztGvXDlZWVqhatSrOnDmTZR2CIGDZsmVo3bo1LCwsUKlSJZw4cQJ37tyBn58fLC0t4eXlhbt372rst2TJEpQpUwYmJiaoUKECfvvtN43Hb9++DR8fH5iZmaFy5crYt29fpmM/evQInTt3hr29PYoVK4Z27dohIiIi9y9mNi5fvoyGDRvC3NwcxYoVw9dff43Xr19najdp0iQ4OTnBxsYGffv2RWpqqvqxv/76C1WrVlX30bhxYyQkJGR7XJlMhujoaADAy5cvIZPJ0LFjR3Wb4OBgeHl5qe9fu3YNLVu2hJWVFYoXL47u3bur9yci7TGSGaF+yfr4vs73WNx4MXZ9vgthX4Zhc9vNmOs7FwNqDEDr0q3xUbGPYGFkAaWoxP24+wh5GILVV1djwvEJCNwVCJ+NPmiwoQECdwVi/LHxWHVlFf659w8ORR7CyaiTuPj8Im6/vI2H8Q/xIukFEtMSwZmJRBzRzRdRFCEmJUlybMHcPEfzuxYsWIBbt26hSpUqmDx5MgDA0dERmzZtwueff46bN2/CxsYG5uYf/vjnxx9/xPTp0zFu3Dj8+OOP6N69O7y9vdGrVy/Mnj0bI0aMQGBgIK5evZplTVOmTMG8efMwb948jBgxAl27dkXp0qUxatQouLm5oVevXhgwYAB27doFANiyZQsGDRqE+fPno3HjxtixYwd69uyJUqVKwd/fHyqVCgEBAVAoFDh58iTi4uIyzSFNTEyEv78/GjRogMOHD8PIyAhTp05F8+bNcenSJZiYmLyn0txLTExE8+bN8cknnyAsLAzPnj3DV199hQEDBmD16tXqdgcOHICZmRkOHTqEiIgI9OzZEwqFAtOmTUNUVBS++OILzJo1C5999hni4+Nx5MiRbP+yqlKlCooVK4bQ0FB8/vnnOHz4MIoVK4bDhw+r24SEhMDX1xcAEBUVBV9fX/Tp0wfz5s1DUlISRowYgU6dOuHgwYNaeT2I6MNM5aYoZ18O5ezLaWwXRRHPk56rR34j4iLUP0clROFVyiucf3Ye55+dz/GxzI3MNW4WRhYwN37nfsbPxhaZ2769v/F/20zlpvmaY0xUUBh080FMSsLNWrUlOXaFc2chWGS/lIqtrS1MTExgYWEBZ2dn9faMKQpOTk4ac3Tfp2XLlujbty8AYPz48ViyZAnq1KmjHjEcMWIEvLy88PTpU41jvKtnz57o1KmTxj7jxo1Ds2bNAACDBg1Cz5491e3nzJmDoKAgfPvttwCAoUOH4uTJk5gzZw78/f2xf/9+XL9+HREREShVqhQAYPr06WjRooW6jw0bNkAmk+GXX35R/6G8atUq2NnZISQkBE2bNs32NcyJ9evXIykpCWvXroWlpSUAYOHChWjTpg1mzpyJ4sWLAwBMTEzw66+/wsLCAh999BEmT56M77//HlOmTEFUVBTS09MREBAAd3d3AEDVqlWzPbYgCPDx8UFISAg+//xzhISEoEePHlizZg2uXbuG8uXL4/jx4xgyZAiAN6PktWrVwvTp09V9/Prrr3B1dcWtW7dQvnx5rbwmRJQ7giDAycIJThZOqOdST+OxxLRERMZHIiL2v/AbkxyDpPQkJKYnIik9SeOW4d372iITZDCTm703IGdsM5GZQCbIIAgCZPj3V0H2Zhve+vnfx9U/C7Ks2//7syAIECCof5UJMvXrqP5PeP+vb7fL6PPN/++0/0AfADK1U+//zr6Z2r5VQ8ax3972vjo+9Lw+VHNOa8no933PR739fcf917vPSS7IIZfJtX6+5QeDLmWrWrVq6p8zAtvbASxj27Nnz7IMujnpJzk5GXFxcbCxscH169fx9ddfa/Th7e2NBQsWAACuX78ONzc3dcgFoPHxPACcPXsWd+7cgbW15jXqk5OTM02T+BArKyv1z19++SWWLl2aqc3169dRvXp1dcjNqFWlUuHmzZvq51u9enVYvPUPFC8vL7x+/RoPHjxA9erV0ahRI1StWhXNmjVD06ZN0aFDB9jbZz8vz8/PD8uXLwcAhIaGYsqUKQgPD0doaChiY2ORlJQEb29v9Wty6NAhjeeV4e7duwy6RHrIwtgCFR0qoqJDxWzbqkQVktOTMwXgxLTMgfi92zL2S8u8LUWZoj5GYnoiEtMTNVaYoKKta/leGOU1ROoyNDDo5oNgbo4K585KduyC8vYqDBn/wnvfNpVKpfV+3v1oTBRF9bb3faT/bnuVSoXatWtj/fr1mdo6OjpmWW+GCxcuqH/+0BImb9eVXU0faiOXy7Fv3z4cP34ce/fuxc8//4wxY8bg1KlT8PT0zHJ/Pz8/DBo0CHfu3MGVK1fQoEED3L17F6GhoXj16hVq166tDvsqlUo90vwuFxeXbGslIv0mE2SwMLaAhbH2L6CgVCmRrEzOFJIzgvDb29JUaRBFESpRBRVU//0sqiDizc/ZPZ7pZ1GECiqNfUW8+bvg7fsiRLz5/9///t1PvU0UNdplHD/jMeCddqKI6NfJePAy8d9XQkRJezPYW5hk6k+9//u2vdNnfEoaXiamqF9fO3NjmJvI3lv/288z0/bs2r61j0olQimKwL9tBEF7c7lXH49AaaNIdK7jprU+84tBNx8EQcjR9AGpmZiYQKlUZtoGINN2fVKpUiUcPXoUgYGB6m3Hjx9HpUqVAACVK1dGZGQkHj9+jBIlSgAATpw4odFHrVq1sHHjRvWXv/KibNmy2bapXLky1qxZg4SEBPWo7rFjxyCTyTRGSC9evIikpCT1nOiTJ0/CyspKPSotCAK8vb3h7e2N8ePHw93dHVu2bMHQoUOzPH7GPN2pU6eievXqsLGxga+vL4KDg/Hy5Uv1/FzgzWuyadMmeHh4wMiIfwQQUc7JZXJYyixhaWwJFKGVvaJik+A946DG1dju3RdwdKR/npc4e1+fyUL++szLMeVvHfN9wfh9ITojuEfFJqLJj6H/9icCohFGb74Cn/KOerP0G1ddKAI8PDxw6tQpREREIDo6GiqVCu7u7hAEATt27MDz58/fuzqA1L7//nusXr0aS5cuxe3btzFv3jxs3rwZw4cPBwA0btwYFSpUQGBgIC5evIgjR45gzJgxGn1069YNCoUC7dq1w5EjR9Qf5w8aNAgPHz7UWq3dunWDmZkZevTogStXruDQoUP47rvv0L17d/W0BQBITU1F7969ce3aNezatQsTJkzAgAEDIJPJcOrUKUyfPh1nzpxBZGQkNm/ejOfPn6uDfVYy5umuW7cOfn5+AN5MFUlNTcWBAwfU2wCgf//+iImJwRdffIHTp0/j3r172Lt3L3r16qXX//AhIpJKeHSCRjgEAKUoIiI68f07SNRnfo+ZMS9aLpPDSGYEY5kxjOXGMJGbwFRuCjOj/+ZlWxhb4GksoFKaASozQGUOiMY6fw65xaBbBAwfPhxyuRyVK1eGo6MjIiMjUbJkSUyaNAkjR45E8eLFMWDAAKnLzKR9+/ZYsGABZs+ejY8++gjLli3DqlWr1KFNJpNhy5YtSElJQd26dfHVV19h2rRpGn1YWFjg8OHDcHNzQ0BAACpVqoRevXohKSkpzyO872NhYYE9e/YgJiYGderUQYcOHdCoUSMsXLhQo12jRo1Qrlw5+Pj4oFOnTmjTpg0mTpwI4M20iMOHD6Nly5YoX748xo4di7lz52p8uS4r/v7+UCqV6tdHEAQ0aNAAAPDpp5+q25UoUQLHjh2DUqlEs2bNUKVKFQwaNAi2traQyfhHAhHRuzwVlpC9MwtNLgjwUOT9U11d9FnQx5TiOeQWLwH8jqJ+CWCi3OL7goiKgo1hkRi9+QqUogi5IGB6QJV8z0XVRZ8FfUwpngOQ80sAM+i+g0GXKHf4viCioiIqNgkR0YnwUFhobQ6qLvos6GNK8RxyGnT5TRSiQuB9S4Fl2LVrl3qKAhER6Y6LrbnWg5wu+izoY0rxHHKKQZeoEHh7ibN3lSxZsuAKISIiKkQYdIkKgZwscUZERESaCs1XrKdNm4b69evDwsIiy0vWrl69GtWqVYOZmRmcnZ11spoApzUT/YfvByIi0leFZkQ3NTUVHTt2hJeXF1auXPneNvPmzcPcuXMxe/Zs1KtXD8nJybh3757WapDL5epazAvwymRE+iw1NRXAf+8PIiIifVFogu6kSZMAvBmxfZ+XL19i7Nix2L59Oxo1aqTe/tFHH2mtBiMjI1hYWOD58+cwNjbmmqNU5KlUKjx//hwWFha8yhoREekdg/mbad++fVCpVHj06BEqVaqE+Ph41K9fH3PnzoWrq+sH90tJSUFKyn/XmY6Li/tgW0EQ4OLigvDwcNy/f1+r9RMVVjKZDG5ubhAEIfvGREREBchggu69e/egUqkwffp0LFiwALa2thg7diyaNGmCS5cuwcTE5L37BQcHq0eLc8LExATlypVTf1xLVNSZmJjw0w0iItJLkgbdiRMnZhsyw8LC8PHHH2fbl0qlQlpaGn766Sc0bdoUAPC///0Pzs7OOHToEJo1a/be/UaNGoWhQ4eq78fFxWU5Agy8GcHiwvhERERE+k3SoDtgwAB06dIlyzYeHh456svFxQUAULlyZfU2R0dHKBQKREZGfnA/U1NTmJqa5ugYRERERFR4SBp0FQoFFAqFVvry9vYGANy8eROlSpUCAMTExCA6Ohru7u5aOQYRERERFR6FZo5uZGQkYmJiEBkZCaVSqb5SVNmyZWFlZYXy5cujXbt2GDRoEJYvXw4bGxuMGjUKFStWhL+/v7TFExEREVGBKzRBd/z48VizZo36fs2aNQEAhw4dgp+fHwBg7dq1GDJkCFq1agWZTAZfX1/s3r0bxsbGOT5OxuL3Wa2+QERERETSychp2V20SBB5WSMNDx8+zPbLaEREREQkvQcPHqinrL4Pg+47VCoVHj9+DGtr6yzXBa1Tpw7CwsKy7OtDbTJWdnjw4AFsbGzyXbOUcvI66Psx89tfXvbPzT45bZtdu6weN5Rz0hDOx/z2mdd9tX1O8nx8o6DPSX07H/O6vz79GcnzUf+OWadOHZw+fRrx8fEoUaJElktcFpqpCwVFJpNl+S+DDHK5PNsTPrs2NjY2hf5Nk5PXQd+Pmd/+8rJ/bvbJadvs2uWkn8J+ThrC+ZjfPvO6r7bPSZ6PbxT0Oalv52Ne99fHPyN5PurPMeVyOWxtbWFra5ttW67ynkf9+/fXSpvCTornqO1j5re/vOyfm31y2ja7djwfC88x89NnXvfV9jnJ8/GNgn6e+nY+5nV//hmpG0Xxz0hOXZBAXFwcbG1tERsbW+j/dUiGgeck6ROej6RPeD4WbhzRlYCpqSkmTJjAC1WQ3uA5SfqE5yPpE56PhRtHdImIiIjIIHFEl4iIiIgMEoMuERERERkkBl0iIiIiMkgMukRERERkkBh0iYiIiMggMegWAomJiXB3d8fw4cOlLoWKuPj4eNSpUwc1atRA1apVsWLFCqlLoiLswYMH8PPzQ+XKlVGtWjX8+eefUpdEhM8++wz29vbo0KGD1KUQuLxYoTBmzBjcvn0bbm5umDNnjtTlUBGmVCqRkpICCwsLJCYmokqVKggLC0OxYsWkLo2KoKioKDx9+hQ1atTAs2fPUKtWLdy8eROWlpZSl0ZF2KFDh/D69WusWbMGf/31l9TlFHkc0dVzt2/fxo0bN9CyZUupSyGCXC6HhYUFACA5ORlKpRL8tzJJxcXFBTVq1AAAODk5wcHBATExMdIWRUWev78/rK2tpS6D/sWgmw+HDx9GmzZtUKJECQiCgK1bt2Zqs3jxYnh6esLMzAy1a9fGkSNHcnWM4cOHIzg4WEsVk6EriHPy1atXqF69OkqVKoUffvgBCoVCS9WToSmI8zHDmTNnoFKp4Orqms+qyZAV5DlJ+oFBNx8SEhJQvXp1LFy48L2Pb9y4EYMHD8aYMWNw/vx5NGjQAC1atEBkZKS6Te3atVGlSpVMt8ePH2Pbtm0oX748ypcvX1BPiQo5XZ+TAGBnZ4eLFy8iPDwcv//+O54+fVogz40Kn4I4HwHgxYsXCAwMxPLly3X+nKhwK6hzkvSISFoBQNyyZYvGtrp164r9+vXT2FaxYkVx5MiROepz5MiRYqlSpUR3d3exWLFioo2NjThp0iRtlUwGThfn5Lv69esn/vHHH3ktkYoQXZ2PycnJYoMGDcS1a9dqo0wqQnT5Z+ShQ4fEzz//PL8lkhZwRFdHUlNTcfbsWTRt2lRje9OmTXH8+PEc9REcHIwHDx4gIiICc+bMQZ8+fTB+/HhdlEtFgDbOyadPnyIuLg4AEBcXh8OHD6NChQpar5UMnzbOR1EUERQUhIYNG6J79+66KJOKEG2ck6R/jKQuwFBFR0dDqVSiePHiGtuLFy+OJ0+eSFQVFWXaOCcfPnyI3r17QxRFiKKIAQMGoFq1aroolwycNs7HY8eOYePGjahWrZp6ruVvv/2GqlWrartcKgK09fd2s2bNcO7cOSQkJKBUqVLYsmUL6tSpo+1yKYcYdHVMEASN+6IoZtqWE0FBQVqqiIq6/JyTtWvXxoULF3RQFRVV+TkfP/30U6hUKl2URUVYfv/e3rNnj7ZLonzg1AUdUSgUkMvlmf4V+OzZs0z/WiQqCDwnSZ/wfCR9w3PSMDHo6oiJiQlq166Nffv2aWzft28f6tevL1FVVJTxnCR9wvOR9A3PScPEqQv58Pr1a9y5c0d9Pzw8HBcuXICDgwPc3NwwdOhQdO/eHR9//DG8vLywfPlyREZGol+/fhJWTYaM5yTpE56PpG94ThZBEq74UOgdOnRIBJDp1qNHD3WbRYsWie7u7qKJiYlYq1YtMTQ0VLqCyeDxnCR9wvOR9A3PyaJHEEVev5OIiIiIDA/n6BIRERGRQWLQJSIiIiKDxKBLRERERAaJQZeIiIiIDBKDLhEREREZJAZdIiIiIjJIDLpEREREZJAYdImIiIjIIDHoEhEREZFBYtAlItIjISEhEAQBr169KvBjC4IAQRBgZ2eXZbuJEyeiRo0aGvcz9p0/f75OayQiyg0GXSIiifj5+WHw4MEa2+rXr4+oqCjY2tpKUtOqVatw69atXO0zfPhwREVFoVSpUjqqiogob4ykLoCIiP5jYmICZ2dnyY5vZ2cHJyenXO1jZWUFKysryOVyHVVFRJQ3HNElIpJAUFAQQkNDsWDBAvXH/hEREZmmLqxevRp2dnbYsWMHKlSoAAsLC3To0AEJCQlYs2YNPDw8YG9vj++++w5KpVLdf2pqKn744QeULFkSlpaWqFevHkJCQvJU64wZM1C8eHFYW1ujd+/eSE5O1sIrQESkewy6REQSWLBgAby8vNCnTx9ERUUhKioKrq6u722bmJiIn376CRs2bMDu3bsREhKCgIAA/PPPP/jnn3/w22+/Yfny5fjrr7/U+/Ts2RPHjh3Dhg0bcOnSJXTs2BHNmzfH7du3c1XnH3/8gQkTJmDatGk4c+YMXFxcsHjx4nw9dyKigsKpC0REErC1tYWJiQksLCyynaqQlpaGJUuWoEyZMgCADh064LfffsPTp09hZWWFypUrw9/fH4cOHULnzp1x9+5d/O9//8PDhw9RokQJAG/m0e7evRurVq3C9OnTc1zn/Pnz0atXL3z11VcAgKlTp2L//v0c1SWiQoEjukREes7CwkIdcgGgePHi8PDwgJWVlca2Z8+eAQDOnTsHURRRvnx59fxZKysrhIaG4u7du7k69vXr1+Hl5aWx7d37RET6iiO6RER6ztjYWOO+IAjv3aZSqQAAKpUKcrkcZ8+ezfQFsbfDMRGRoWPQJSKSiImJicYXyLSlZs2aUCqVePbsGRo0aJCvvipVqoSTJ08iMDBQve3kyZP5LZGIqEAw6BIRScTDwwOnTp1CREQErKys4ODgoJV+y5cvj27duiEwMBBz585FzZo1ER0djYMHD6Jq1apo2bJljvsaNGgQevTogY8//hiffvop1q9fj6tXr6J06dJaqZWISJc4R5eISCLDhw+HXC5H5cqV4ejoiMjISK31vWrVKgQGBmLYsGGoUKEC2rZti1OnTn1wZYcP6dy5M8aPH48RI0agdu3auH//Pr755hut1UlEpEuCKIqi1EUQEZH0BEHAli1b0L59+zzt7+HhgcGDB2e62hsRkVQ4oktERGpffPFFri/lO336dFhZWWl1RJqISBs4oktERACAO3fuAADkcjk8PT1zvF9MTAxiYmIAAI6OjrC1tdVJfUREucWgS0REREQGiVMXiIiIiMggMegSERERkUFi0CUiIiIig8SgS0REREQGiUGXiIiIiAwSgy4RERERGSQGXSIiIiIySAy6RERERGSQ/g/A8zfq3YoDtQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_2 = ml_2.head(r1, 0, t1)\n",
    "hm2_2 = ml_2.head(r2, 0, t2)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.semilogx(t1, h1, \".\", label=\"well\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n",
    "plt.semilogx(t1, hm1_2[-1], label=\"ttim model - well\")\n",
    "plt.semilogx(t2, hm2_2[-1], label=\"ttim model - obs_well\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.legend()\n",
    "plt.title(\"Model Results - Two Aquifer Model\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.2. Calibrate the two-layer model with wellparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will add the skinresistance and wellbore storage parameters to our well and resume calibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_3 = ttim.ModelMaq(\n",
    "    kaq=[19, 2],\n",
    "    z=[zt0, zb0, zt1, zb1],\n",
    "    c=200,\n",
    "    Saq=[4e-4, 1e-5],\n",
    "    Sll=1e-4,\n",
    "    topboundary=\"conf\",\n",
    "    tmin=1e-4,\n",
    "    tmax=0.5,\n",
    ")\n",
    "w_3 = ttim.Well(\n",
    "    ml_3, xw=0, yw=0, rw=rw, rc=0.2, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n",
    ")\n",
    "ml_3.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ca_3 = ttim.Calibrate(ml_3)\n",
    "ca_3.set_parameter(name=\"kaq0\", initial=20, pmin=0)\n",
    "ca_3.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n",
    "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-7)\n",
    "ca_3.set_parameter(name=\"Saq1\", initial=1e-5, pmin=1e-7)\n",
    "ca_3.set_parameter_by_reference(\n",
    "    name=\"Sll\", parameter=ml_3.aq.Sll[:], initial=1e-4, pmin=1e-7\n",
    ")\n",
    "ca_3.set_parameter(name=\"c1\", initial=100, pmin=0)\n",
    "ca_3.set_parameter_by_reference(name=\"res\", parameter=w_3.res[:], initial=0, pmin=0)\n",
    "ca_3.set_parameter_by_reference(name=\"rc\", parameter=w_3.rc[:], initial=0.2, pmin=0)\n",
    "ca_3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n",
    "ca_3.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n",
    "ca_3.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(ca_3.parameters)\n",
    "print(\"RMSE:\", ca_3.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see a much better fit and AIC BIC values from the previous simulation. One thing to consider is the tiny storage values of the first aquifer and the aquitard. It might mean that it is troubling to fit all these parameters together. The small result of the skin resistance and its very high standard deviation might also mean it is irrelevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm1_3 = ml_3.head(r1, 0, t1)\n",
    "hm2_3 = ml_3.head(r2, 0, t2)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.semilogx(t1, h1, \".\", label=\"well\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n",
    "plt.semilogx(t1, hm1_3[-1], label=\"ttim model - well\")\n",
    "plt.semilogx(t2, hm2_3[-1], label=\"ttim model - observation well\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Results - Two Aquifers + Well Parameters\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot showed the significant fit improvement with the new model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.3. Calibrate Model with some fixed parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this new model, we will fix some of the parameters. Thus, we expect to have better confidence in estimating the others.\n",
    "\n",
    "We will fix the hydraulic parameters of the first aquifer layer, the storage coefficient of the leaky layer and the skin resistance of the well. The fixed parameters were defined from the estimates of Brian et al. (1997)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_4 = ttim.ModelMaq(\n",
    "    kaq=[17.28, 2],\n",
    "    z=[zt0, zb0, zt1, zb1],\n",
    "    c=200,\n",
    "    Saq=[1.2e-4, 1e-5],\n",
    "    Sll=3e-5,\n",
    "    topboundary=\"conf\",\n",
    "    tmin=1e-4,\n",
    "    tmax=0.5,\n",
    ")\n",
    "w_4 = ttim.Well(\n",
    "    ml_4, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n",
    ")\n",
    "ml_4.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...............................................................\n",
      "Fit succeeded.\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 60\n",
      "    # data points      = 40\n",
      "    # variables        = 4\n",
      "    chi-square         = 0.56419452\n",
      "    reduced chi-square = 0.01567207\n",
      "    Akaike info crit   = -162.449426\n",
      "    Bayesian info crit = -155.693908\n",
      "[[Variables]]\n",
      "    kaq1:  1.93430644 +/- 0.01232844 (0.64%) (init = 1)\n",
      "    Saq1:  1.3182e-05 +/- 2.3131e-06 (17.55%) (init = 1e-05)\n",
      "    c1:    239.727821 +/- 20.5021300 (8.55%) (init = 100)\n",
      "    rc:    0.05268527 +/- 4.0799e-04 (0.77%) (init = 0.2)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(kaq1, c1)   = +0.8394\n",
      "    C(Saq1, rc)   = -0.5818\n",
      "    C(kaq1, Saq1) = -0.5034\n",
      "    C(Saq1, c1)   = -0.2139\n",
      "    C(c1, rc)     = -0.1116\n"
     ]
    }
   ],
   "source": [
    "ca_4 = ttim.Calibrate(ml_4)\n",
    "ca_4.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n",
    "ca_4.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n",
    "ca_4.set_parameter(name=\"c1\", initial=100, pmin=0)\n",
    "ca_4.set_parameter_by_reference(name=\"rc\", parameter=w_4.rc[:], initial=0.2, pmin=0)\n",
    "ca_4.series(name=\"well\", x=r1, y=0, t=t1, h=h1, layer=1)\n",
    "ca_4.series(name=\"obs_well\", x=r2, y=0, t=t2, h=h2, layer=1)\n",
    "ca_4.fit(report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>kaq1</th>\n",
       "      <td>1.934306</td>\n",
       "      <td>0.012328</td>\n",
       "      <td>0.637357</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>[1.93430643512287]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq1</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>17.547945</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>[1.3181571984599572e-05]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>239.727821</td>\n",
       "      <td>20.502130</td>\n",
       "      <td>8.552253</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>[239.72782149888832]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rc</th>\n",
       "      <td>0.052685</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.774393</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>[0.05268527152690705]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         optimal        std   perc_std  pmin  pmax    initial  \\\n",
       "kaq1    1.934306   0.012328   0.637357     0   inf    1.00000   \n",
       "Saq1    0.000013   0.000002  17.547945     0   inf    0.00001   \n",
       "c1    239.727821  20.502130   8.552253     0   inf  100.00000   \n",
       "rc      0.052685   0.000408   0.774393     0   inf    0.20000   \n",
       "\n",
       "                        parray  \n",
       "kaq1        [1.93430643512287]  \n",
       "Saq1  [1.3181571984599572e-05]  \n",
       "c1        [239.72782149888832]  \n",
       "rc       [0.05268527152690705]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.11876389590424886\n"
     ]
    }
   ],
   "source": [
    "display(ca_4.parameters)\n",
    "print(\"RMSE:\", ca_4.rmse())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new estimates had much better confidence intervals when we used part of the parameters fixed. The RMSE, BIC and AIC indicators also showed better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_4 = ml_4.head(r1, 0, t1)\n",
    "hm2_4 = ml_4.head(r2, 0, t2)\n",
    "plt.figure()\n",
    "plt.semilogx(t1, h1, \".\", label=\"well\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"obs_well\")\n",
    "plt.semilogx(t1, hm1_4[-1], label=\"ttim model - well\")\n",
    "plt.semilogx(t2, hm2_4[-1], label=\"ttim model - observation well\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.ylabel(\"drawdown [m]\")\n",
    "plt.title(\"Model Results - Two Aquifer Model + Well Parameters\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8. Comparison of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results simulated with the two aquifer models are present below. Furthermore, Yang (2020) compared TTim results with the results obtained from MLU (Carlson & Randall, 2012) and the published results obtained by Brian et al. (1997). The authors of the original paper applied a finite-difference model of radial flow with a trial and error approach to find the optimal parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>k0[m/d]</th>\n",
       "      <th>k1[m/d]</th>\n",
       "      <th>Ss0[1/m]</th>\n",
       "      <th>Ss1[1/m]</th>\n",
       "      <th>Sll[1/m]</th>\n",
       "      <th>c[d]</th>\n",
       "      <th>res</th>\n",
       "      <th>rc</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MLU</th>\n",
       "      <td>17.424</td>\n",
       "      <td>0.00006</td>\n",
       "      <td>1.747</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brian et al.</th>\n",
       "      <td>17.28</td>\n",
       "      <td>3.456</td>\n",
       "      <td>0.00012</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim</th>\n",
       "      <td>199.16155</td>\n",
       "      <td>0.097464</td>\n",
       "      <td>5.610347</td>\n",
       "      <td>1.737358</td>\n",
       "      <td>0.063543</td>\n",
       "      <td>0.001425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.744305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim-rc</th>\n",
       "      <td>5.492296</td>\n",
       "      <td>1.251603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.745506</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.055127</td>\n",
       "      <td>0.178735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim-fixed upper</th>\n",
       "      <td>17.28</td>\n",
       "      <td>1.934315</td>\n",
       "      <td>0.00012</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>239.719879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.052688</td>\n",
       "      <td>0.118764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    k0[m/d]   k1[m/d]  Ss0[1/m]  Ss1[1/m]  Sll[1/m]  \\\n",
       "MLU                  17.424   0.00006     1.747  0.000006   0.00004   \n",
       "Brian et al.          17.28     3.456   0.00012  0.000015   0.00003   \n",
       "ttim              199.16155  0.097464  5.610347  1.737358  0.063543   \n",
       "ttim-rc            5.492296  1.251603       0.0  0.000045  0.000003   \n",
       "ttim-fixed upper      17.28  1.934315   0.00012  0.000013   0.00003   \n",
       "\n",
       "                        c[d]       res        rc      RMSE  \n",
       "MLU                      216       NaN       NaN  0.023452  \n",
       "Brian et al.             NaN       NaN       NaN       NaN  \n",
       "ttim                0.001425       NaN       NaN  0.744305  \n",
       "ttim-rc             0.745506  0.000018  0.055127  0.178735  \n",
       "ttim-fixed upper  239.719879       NaN  0.052688  0.118764  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    columns=[\n",
    "        \"k0[m/d]\",\n",
    "        \"k1[m/d]\",\n",
    "        \"Ss0[1/m]\",\n",
    "        \"Ss1[1/m]\",\n",
    "        \"Sll[1/m]\",\n",
    "        \"c[d]\",\n",
    "        \"res\",\n",
    "        \"rc\",\n",
    "    ],\n",
    "    index=[\"MLU\", \"Brian et al.\", \"ttim\", \"ttim-rc\", \"ttim-fixed upper\"],\n",
    ")\n",
    "t.loc[\"ttim-rc\"] = ca_3.parameters[\"optimal\"].values\n",
    "t.iloc[2, 0:6] = ca_2.parameters[\"optimal\"].values\n",
    "t.iloc[4, 5] = ca_4.parameters[\"optimal\"].values[2]\n",
    "t.iloc[4, 7] = ca_4.parameters[\"optimal\"].values[3]\n",
    "t.iloc[4, 0] = 17.28\n",
    "t.iloc[4, 1] = ca_4.parameters[\"optimal\"].values[0]\n",
    "t.iloc[4, 2] = 1.2e-4\n",
    "t.iloc[4, 3] = ca_4.parameters[\"optimal\"].values[1]\n",
    "t.iloc[4, 4] = 3e-5\n",
    "t.iloc[0, 0:6] = [17.424, 6.027e-05, 1.747, 6.473e-06, 3.997e-05, 216]\n",
    "t.iloc[1, 0:6] = [17.28, 3.456, 1.2e-04, 1.5e-5, 3e-05, np.nan]\n",
    "t[\"RMSE\"] = [0.023452, np.nan, ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first TTim model showed unrealistic results. The second model, although improving the fit, had wide standard deviations (and confidence intervals). That indicates that this solution, with more parameters, in multi-aquifer configuration, is non-unique. \n",
    "\n",
    " When we kept some of the data fixed with the values from Brian et al., we improved the fit and the confidence interval of the estimates of the model.\n",
    "\n",
    "MLU results have very low RMSE. However, the specific storage of the upper aquifer (Ss0) is probably too high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.1. Comparison of estimates and model error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preparing the DataFrame:\n",
    "t1 = pd.DataFrame(\n",
    "    columns=[\"kaq - opt -l1\", \"kaq - 95% -l1\", \"kaq - opt -l2\", \"kaq - 95% -l2\"],\n",
    "    index=[\"MLU\", \"Brian\", \"TTim - rc\", \"TTim - fixed upper\"],\n",
    ")\n",
    "simulation = [\"MLU\", \"Brian\", \"TTim - rc\", \"TTim - fixed upper\"]\n",
    "t1.loc[\"MLU\"] = [17.424, 124.160 * 1e-2 * 17.424, 1.747, 4.521 * 1e-2 * 1.747]\n",
    "t1.loc[\"Brian\"] = [17.28, np.nan, 3.456, np.nan]\n",
    "t1.loc[\"TTim - fixed upper\"] = [\n",
    "    17.28,\n",
    "    np.nan,\n",
    "    ca_4.parameters.loc[\"kaq1\", \"optimal\"],\n",
    "    2 * ca_4.parameters.loc[\"kaq1\", \"std\"],\n",
    "]\n",
    "t1.loc[\"TTim - rc\"] = [\n",
    "    ca_3.parameters.loc[\"kaq0\", \"optimal\"],\n",
    "    2 * ca_3.parameters.loc[\"kaq0\", \"std\"],\n",
    "    ca_3.parameters.loc[\"kaq1\", \"optimal\"],\n",
    "    2 * ca_3.parameters.loc[\"kaq1\", \"std\"],\n",
    "]\n",
    "\n",
    "# Plotting\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "plt.errorbar(\n",
    "    x=t1.index,\n",
    "    y=t1[\"kaq - opt -l1\"],\n",
    "    yerr=[t1[\"kaq - 95% -l1\"], t1[\"kaq - 95% -l1\"]],\n",
    "    marker=\"o\",\n",
    "    linestyle=\"\",\n",
    "    markersize=12,\n",
    "    label=\"upper aquifer\",\n",
    ")\n",
    "plt.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=\"lower aquifer\",\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": [
    "The MLU model has a larger confidence interval for the first aquifer layer, while TTim got lower values for the generic model (TTim - rc) and a relatively small range. The model with some fixed values has a small confidence interval. Confidence intervals were not reported in Brian et al. (1997)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "* Bakker, M. Semi-analytic modeling of transient multi-layer flow with TTim. Hydrogeol J 21, 935–943 (2013). https://doi.org/10.1007/s10040-013-0975-2\n",
    "* Brian, Schroth, T., N., Narasimhan, 1997. Application of a numerical model in the interpretation of a leaky aquifer test. Ground Water .\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",
    "* Newville, M.,Stensitzki, T., Allen, D.B., Ingargiola, A. (2014) LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python.https://dx.doi.org/10.5281/zenodo.11813. https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021).\n",
    "* Yang, Xinzhu (2020) Application and comparison of different methodsfor aquifer test analysis using TTim. Master Thesis, Delft University of Technology (TUDelft), Delft, The Netherlands."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}