{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Leaky Aquifer Test - Texas Hill Example\n",
    "**This example is taken from AQTESOLV examples.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "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 as ttm\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [5, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Conceptual Model\n",
    "\n",
    "This pumping test, taken from the AQTESOLV examples, was done in the location of 'Texas Hill'. A pumping well was screened at an aquifer located between 20 ft and 70 ft depths. The aquifer is overlain by an aquitard. The formation at the base of the aquifer is considered an aquiclude.\n",
    "\n",
    "Three observation wells are located at 40, 80 160 ft distance. They are denominated OW1, OW2 and OW3, respectively. Pumping lasted for 420 minutes at a rate of 4488 gallons per minute. The hydrogeological conceptual model can be seen in the figure below.\n",
    "\n",
    "In this example, we will reproduce the work of Xinzhu (2020). We compare the aquifer test results using Hantush's solution (Hantush 1955) in the software AQTESOLV (Duffield, 2007) and TTim. We also explore the capabilities of TTim to account for aquitard storage and wellbore effects such as wellbore storage and skin effect, which cannot be simulated with Hantush's solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Conceptual Model 1\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "##Now printing the conceptual model figure:\n",
    "\n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "# sky\n",
    "sky = plt.Rectangle((-20, 0), width=220, height=8, fc=\"b\", zorder=0, alpha=0.1)\n",
    "ax.add_patch(sky)\n",
    "\n",
    "# Aquifer:\n",
    "ground = plt.Rectangle(\n",
    "    (-20, -70),\n",
    "    width=220,\n",
    "    height=50,\n",
    "    fc=np.array([209, 179, 127]) / 255,\n",
    "    zorder=0,\n",
    "    alpha=0.9,\n",
    ")\n",
    "ax.add_patch(ground)\n",
    "\n",
    "# Confining bed:\n",
    "confining_unit = plt.Rectangle(\n",
    "    (-20, -20),\n",
    "    width=220,\n",
    "    height=20,\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",
    "    (-2, -70), width=4, height=70, 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",
    "    (-2, -70),\n",
    "    width=4,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen.set_linewidth(2)\n",
    "ax.add_patch(screen)\n",
    "pumping_arrow = plt.Arrow(x=2.5, y=0.75, dx=5, dy=0, color=\"#00035b\")\n",
    "ax.add_patch(pumping_arrow)\n",
    "ax.text(x=7.5, y=2, s=r\"$ Q = 4488$\", fontsize=\"large\")\n",
    "# Piezometers\n",
    "piez1 = plt.Rectangle(\n",
    "    (39, -70), width=2, height=70, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "screen_piez_1 = plt.Rectangle(\n",
    "    (39, -70),\n",
    "    width=2,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_1.set_linewidth(2)\n",
    "\n",
    "piez2 = plt.Rectangle(\n",
    "    (79, -70), width=2, height=70, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "screen_piez_2 = plt.Rectangle(\n",
    "    (79, -70),\n",
    "    width=2,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_2.set_linewidth(2)\n",
    "\n",
    "piez3 = plt.Rectangle(\n",
    "    (159, -70), width=2, height=70, fc=np.array([200, 200, 200]) / 255, zorder=1\n",
    ")\n",
    "screen_piez_3 = plt.Rectangle(\n",
    "    (159, -70),\n",
    "    width=2,\n",
    "    height=50,\n",
    "    fc=np.array([200, 200, 200]) / 255,\n",
    "    alpha=1,\n",
    "    zorder=2,\n",
    "    ec=\"k\",\n",
    "    ls=\"--\",\n",
    ")\n",
    "screen_piez_3.set_linewidth(2)\n",
    "\n",
    "\n",
    "ax.add_patch(piez1)\n",
    "ax.add_patch(screen_piez_1)\n",
    "ax.add_patch(piez2)\n",
    "ax.add_patch(screen_piez_2)\n",
    "ax.add_patch(piez3)\n",
    "ax.add_patch(screen_piez_3)\n",
    "\n",
    "# last line\n",
    "line = plt.Line2D(xdata=[-200, 1200], ydata=[0, 0], color=\"k\")\n",
    "ax.add_line(line)\n",
    "\n",
    "ax.text(x=39, y=2, s=\"P40\", fontsize=\"large\")\n",
    "ax.text(x=79, y=2, s=\"P80\", fontsize=\"large\")\n",
    "ax.text(x=159, y=2, s=\"P160\", fontsize=\"large\")\n",
    "\n",
    "\n",
    "ax.set_xlim([-20, 200])\n",
    "ax.set_ylim([-70, 8])\n",
    "ax.set_xlabel(\"Distance [ft]\")\n",
    "ax.set_ylabel(\"Relative height [ft]\")\n",
    "ax.set_title(\"Conceptual Model - Texas Hill Example\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set basic parameters\n",
    "\n",
    "Here we have previously converted the values to ***meters*** and ***days***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = 24464.06  # constant discharge, m^3/d\n",
    "b1 = 6.096  # overlying aquitard thickness, m\n",
    "b2 = 15.24  # aquifer thickness, m\n",
    "zt = -b1  # top boundary of aquifer, m\n",
    "zb = -b1 - b2  # bottom boundary of aquifer, m\n",
    "rw = 0.1524  # well radius, m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset of observation wells\n",
    "\n",
    "Each observation well is located in a text file, where the first column is time data in days and the second column is drawdown in meters.\n",
    "We also declare the distance of each observation to the wells: (```r1```:```r3```)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = np.loadtxt(\"data/texas40.txt\")\n",
    "t1 = data1[:, 0]\n",
    "h1 = data1[:, 1]\n",
    "r1 = 12.191  # distance between obs1 to pumping well in m\n",
    "\n",
    "data2 = np.loadtxt(\"data/texas80.txt\")\n",
    "t2 = data2[:, 0]\n",
    "h2 = data2[:, 1]\n",
    "r2 = 24.383  # distance between obs2 to pumping well in m\n",
    "\n",
    "data3 = np.loadtxt(\"data/texas160.txt\")\n",
    "t3 = data3[:, 0]\n",
    "h3 = data3[:, 1]\n",
    "r3 = 48.766  # distance between obs3 to pumping well in m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a conceptual model\n",
    "\n",
    "We define a `ModelMaq` model for the semi-confined aquifer using the methods described in previous notebooks.\n",
    "In this initial model, we will model the overlying layer as an aquitard with storage (```Sll```, the storage parameter, is defined in ModelMaq)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_0 = ttm.ModelMaq(\n",
    "    kaq=10,\n",
    "    z=[0, zt, zb],\n",
    "    Saq=0.001,\n",
    "    Sll=0,\n",
    "    c=10,\n",
    "    tmin=0.001,\n",
    "    tmax=1,\n",
    "    topboundary=\"semi\",\n",
    ")\n",
    "w_0 = ttm.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n",
    "ml_0.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Calibration\n",
    "\n",
    "Using all three observation wells, we calibrate for the hydraulic parameters of the aquifer (```kaq``` and ```Saq```) and for the aquitard (```c``` and ```Sll```)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".................................................................................................................................................................................................\n",
      "Fit succeeded.\n"
     ]
    }
   ],
   "source": [
    "# unknown parameters: kaq, Saq, c, Sll\n",
    "ca_0 = ttm.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.set_parameter(name=\"Sll0\", initial=1e-4, pmin=0)\n",
    "ca_0.set_parameter(name=\"c0\", initial=100)\n",
    "ca_0.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n",
    "ca_0.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n",
    "ca_0.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n",
    "ca_0.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>2.244897e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>2.136000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sll0</th>\n",
       "      <td>1.165379e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c0</th>\n",
       "      <td>4.372017e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           optimal\n",
       "kaq0  2.244897e+02\n",
       "Saq0  2.136000e-04\n",
       "Sll0  1.165379e-07\n",
       "c0    4.372017e+01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0602417265169352\n"
     ]
    }
   ],
   "source": [
    "display(ca_0.parameters.loc[:, [\"optimal\"]])\n",
    "print(\"RMSE:\", ca_0.rmse())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a0qtXLzQaDQ899BAajYZ+/fphaWlJeHg4Go2Gdu3aodFo6NOnDzY2Njf9HtwqyVyJelGp1PgH92TE9NeZ9vkCwsdOxM7FnfKSQipKDlGau5CS3J/Y9O1v5GTkm7q5QgghbqOuXbsa/t+iRQsAunTpUm1ZWlraDfdTU2ZLp9OhKIrh66ioKEPmSqPREBkZCUBERESV5XfffXedzqO+JHMlbpmdswt9R42lZeBAVnywkoqSo2jLzqAtv0Bp/gW+f2U7wYPupevAwdi7uJm6uUIIYTrmNvoMkqmOfbsOZW5u+H9lEHS9ZVqt9rrbt2/fHoCTJ09etxsvLi6OoKAgw9dRUVHMmjWLixcvsmXLFkN3YkREBJ9++ilJSUmcO3fOMN7K2CS4qqPUglSScpPwdfDF07ZhB8A1dc6etphZ+KE290OnzaO85CgVJccozstm97Il7FmxlLa9+hI8aBg+nbpW+dQhhBB3BEWpVdecqVlYWFQpd1C5DKi23BgGDRqEi4sLc+bMqRZc/fbbbyQkJFQp/xAWFoalpSWff/45RUVF9OzZE4CQkBBycnL48ssvsbKyom/fvkZvO0hwVSfLE5Yzc9dMtDotKkXFjNAZjGo3ytTNajTsnK2InNARzeI4wB4L23D6T5mEmVkiR9av5sLJ4yTs2UnCnp24eLei26BhdIq4G0ubxv+HRggh7iR+fn7s2bOHxMRE7OzscHFxoXXr1iiKwqpVqxg6dCjW1tbY2dkZ5fi2trZ8+eWXjBs3jmnTpvHMM8/g4ODApk2bePHFFxk9ejQPPvigYX1ra2v69OnDp59+Snh4OGq1GtBny0JDQ/n0008NAdi1YmNjKS0tJTMzk7y8PA4fPgxAcHDwLbW/yQRXs2bN4o8//uDw4cNYWFhUK2JmbKkFqYbACkCr0zJz10zCvMMkg3WNoHBvfINcyEkrwtHDGjtnK8CXjmH9SU/SB1mx22LIvHSBmAVfsn3JQgLviiR40DDcW/ubuvlCCCGA6dOnM2nSJIKCgigqKuLcuXP4+fkxc+ZMXnnlFR555BGio6NZsGCB0dowevRoYmJimD17Nv3796eoqIi2bdvy+uuv8/zzz1fr/YiKimLr1q2G8VaVIiIi2Lhx43W7BIcOHcr583+WE+revTtQ81iv2lJ0t7qH22TGjBk4OTlx4cIFvvnmm3oFV7m5uTg6OpKTk4ODg0Odtt2bspcp66dUW/7t4G/p5dmrzm25k5UUFhK7bTNH1q+uUvHdu0MQwYOH0a53GGbX9M03RmVlZaxevZqhQ4dWGUcgREOQ68u4buVeUFfFxcWcO3cOf39/rKysjHosYXy1/Xk2mczVzJkzAYwaJd+Ir4MvKkVlyFwBqBQVPvYysXFdWdrY0H3wfQQPGsaFk8c5vO4PTu/bZSjnYOPoRJe7B9F14BAc3DxM3VwhhBCiTppMcFUfJSUllJT8OUVLbm4uoP9UWFZWVqd9uVq48kbvN5i9+x3K0aJSqXmj9xu4WrjWeV/iT57tOjKkXUfyszI5odnA8c3rKcjKZM+Kn9j76y/49wih68B79QPgVY2nckjlz1x+9sIY5PoyLvm+CmNr1sHVe++9Z8h4XWv9+vX1KiJmgQWzY6Nw3b2PjH59KbdRWH1qdUM0VQBY2uM5+G8UXDhPTkIsRZcvcfbAXs4e2IuZnSNOHYJwaNMelbmFqVtqsGHDBlM3QTRjcn0ZR2FhoambIJo5kwZXb7311nWDn2vt27ePkJCQeu3/1Vdf5R//+Ifh69zcXHx8fBg0aFC9+9mTFyykJC0L++VrUGt24/DggziOHYeZm2u99idqtv+PQ+xZ/hsVJbGU5+eQcWAXOScO0bFfJF3vGYprS9N1yZaVlbFhwwbuueceGRMjGpxcX8ZV2YshhLGYNLh65plnGDdu3A3X8fPzq/f+LS0tqz12CfpHM+v7B6v1wgVk/7KMzO+/o/xSCllffEn2N9/icP/9uEyahFWH9vVur/hTflYxh9blY25zN2bW/agoiaWi9DBlxZkc27iWYxvX4tu5K8GD7yOgZx9UVx+7vd1u5VoS4mbk+jIO+Z4KYzNpcOXm5oabW9Oq2K12cMD10UdwiZ5I3oYNXFmwgOIjR8lZvpyc5cuxDQvFZdIkbO+6q1GNEWpqstOKDHOMKooFZlbBqC27ETbSkuQTWzizfw9Jx4+SdPwo9q7udLvnXroMGIyNg6NpGy6EEOKO12TGXCUlJZGZmUlSUhIVFRWGQl9t27Y1WhGzG1HMzHC4914c7r2XwkOHyFy4iLz16ynYuYuCnbuwaNMGl+hoHEcMR2Vtfdvb19Q5eVijKFUncVepFdr3DaHHvf3IzUjjyIY1HNu0jrwr6Wz/cRG7li2hY1h/ggffh2dAO9M1XgghxB2tydS5mjx5MgsXLqy2PCYmplrBsJoYu7ZJ6YWLZH3/Pdm//II2Xz9ZsdrREadx43B++GHMW0hZgbqI3XEJzeI4dFpQVBA5viNB4VVnky8vLeXUrm0cWruKy2cTDMs9/Nvh32MAXSIjcPSwb/C2SR0iYUxyfRmX1LkS9VXbn2eTCa4awu36harIzydn+XIyF31H2YUL+oXm5jgOvVc/LuuaySbFjeVnFf+l2nvNUk6f4tDaVcTt2IZOW65fqNgQ0CuKgY88iJ1Lwz10IDc/YUxyfRmXBFeivppdEdGmRG1nh0t0NM7jx5O3aROZCxZSdPAgOSt/I2flb9j07o3L5EnYRUbKuKybsHO2umlQVcmrbQfsx7fm3LH2lBcfp7zkCOjyObP3D87tX0u7PmEED7mPlh2CZNJoIYQQRiN3diNS1GocBg3C74fF+P38Ew7DhoFaTeHevVx46mnO3juUzMWL0UrNlQaTnVYEii1m1n2wdJyCue19KGYt0WorOLVrG0tnvMx3rzzHsZj1lJWW3HyHQghxB0tOTmbKlCl4e3tjYWFB69atee6557hy5QoAr7zyCoGBgVW2OXnyJIqiMHHixCrLv/vuO8zNzcnPzycxMZEpU6bg7++PtbU1AQEBzJgxg9LS0tt2bsYkwdVtYt2lCy3nfEDbjRtwfWwKKgcHSs+f5/I775IQGUXanDmUpaYa7fipBansTdlLaoHxjtEYVA6EB1AUNWqL9lg5juWB1/9D56hBmJlbkJ54lvVffMJXTz3C1h8WkJuRZtpGCyFEI3T27FlCQkKIj49nyZIlnD59mi+++IJNmzYRGhpKZmYmUVFRxMXFkXrN/Uuj0eDj40NMTEyV/Wk0Gnr37o2dnR1xcXFotVq+/PJLTpw4wdy5c/niiy947bXXbvdpGoWMuTIRbUEB2St+JfO7RZSdvzp5sZkZDoMH4zJ5MtZdOjfYsZYnLGfmrplodVpUiooZoTMY1W5Ug+2/sbnRQPiivFyObV7PkQ2ryU3XB1WKoiIgpA/dh9yPT6cuteoylDExwpjk+jKupjrmKrUglaTcJHwdfPG09WygFtbs3nvv5fjx48THx2N9zVPvqampBAQEEB0dzQcffICzszOLFi0y1K0cO3YsPXr0YPbs2Rw4cIC2bdsCEBAQwEMPPcS777573eP95z//Yd68eZw9e9bo51Zftf15SubKRFS2trhMGE/A6tW0+u9n2PTqBeXl5P7xB4ljxpA4fgK5Gzagq6i4peOkFqQaAisArU7LzF0zm3UGKyjcm+hZYYx8oTvRs8KqPGFobe9A7xGjmfLJ14yY/ga+nbuh02k5vW8XP7/zGotefIYjG9ZQVlxswjMQQoiqlicsZ/CywUxZP4XBywazPGG5UY+XmZnJunXreOqpp6oEVgCenp6MHz+epUuXYmNjQ69evapkqbZs2cKAAQMIDw83LE9OTubs2bNERUXVeMycnBxcXFyMc0K3mQRXJqao1dgPGEDr7xbht+wXHIbfD2ZmFB04wMW/P8uZIfeSuWgRFfkF9dp/Um6SIbCqpNVpSc5LbojmN1p2zla07OBc42B4lUpN2159GfN/s5g853O63TMUc0srMpLPs/F//+XLJyehWfQ12akpt7nlQghRlSk+JCckJKDT6aqNp6oUGBhIVlYW6enpREZGotFoAIiNjaWoqIju3bsTERFhWB4TE4OlpSVhYWHX3d+ZM2f49NNPeeKJJ4xxOredBFeNiHWnTrT8979pu2kTro8/jtrRkbLkZC7Pfo/TkZFcfv/flF28WKd9+jr4olKq/phVigofe9PNy9fYuLbyZeBjTzFt3gIio6fi1MKLksICDvyxkm+en8aK92eSePgAOq325jsTQogG1hg/JFeOKFIUhaioKOLj47l06RIajYZ+/fqhVqurBFcajYa+fftWy4IBXLp0iSFDhjBmzBgee+yx23kaRiPBVSNk3sIDjxeep60mBs+3ZmDh7482P5/M+fM5PWgwF55/gaKrFepvxtPWkxmhMwwBVuWYq9vRX9/UWNna0XPYCB796Ev+9soM/IJ7gk7H2YP7WPbeDOb/40kOrvmdEnm6UwhxG5niQ3Lbtm1RFIXY2Njrvh8XF4ezszNubm6Eh4djYWGBRqMhJiaGiIgIAEJCQsjJySE+Pp6YmJjrdgleunSJqKgoQkND+eqrr4x2PrebDGhvAnRaLflbt5K5cCGFu3Ybllt364bLI5OxHzgQxezGJctSC1JJzkvGx95HAqs6yLx0kcPrV3FCs5HSoiIAzK2sCbwrimxLW0aOe1gGHIsGJwPajaspDmg3xYNJgwcP5sSJEyQkJNQ4oH3evHkA9O/fn44dO7Jy5Up+//13evfuDcCgQYPo3bs3s2bNYsuWLfTv39+wn4sXLxIVFUXPnj35/vvvUavVRj2fhiAV2q+jqQZX1yqOiyNz4SJyV61CV1YGgLm3N84TJuA0ZjRq+4af6kVAaVEhsVtjOLRuFZkX/0zF+3TuRs+hw/HvHoJK1fj/MIimQYIr42qKwRXc/g/JCQkJhIWFERgYyLvvvou/vz8nTpzgxRdfpKSkhN27dxsGoM+YMYO5c+cC+sHwZlc/8M+aNYv333+f8vJysrKysLS0BPQZq4iICHx9fVm0aFGVwMrTs/EmACS4uo7mEFxVKk9PJ2vJErKW/EhFVhYAKhsbHEc/gMvEiVj4yJgqY9DpdCQdO8KBNSs5d3CfYbljC0+C7xlKp6h7sLaTAFfcGgmujKupBlemcP78ed566y3Wrl3LlStX8PT0ZOTIkcyYMQNX1z+nFNNoNERFRTFkyBDWrFljWL59+3buuusuBgwYwMaNGw3LFyxYwCOPPHLdYzbmsESCq+toTsFVJW1xMTm//07mwoWUnj6jX6hSYT9gAC6TJ2Hdo4dM9WIEZWVlrPzpR1zLi4ndsoniAv1E3WYWlgT2iyB48H14+LUxcStFUyXBlXFJcCXqS+YWvEOorKxwHjMGp9GjKdi+g8yFCynYvp28DRvI27ABqy5dcJk0CYfBg1Dkj3SDMrdzoN/QcQQPeZCjmzZzdv9GMi+e59jm9RzbvJ6WHTvRfch9tO0VivomY+KEEEI0H/IXv5lQFAW7u/phd1c/ShISyFy0iJyVv1F87BiXpk8n7QNPXCaMx+nBB1E3k6xdYxC3K5VtSxLQ6RyBUfR5wIysS7tJ2LODi3EnuBh3AjtnF7recy9dBwzB1snZ1E0WQghhZFKKoRmybNcOr3feoW3MZtz+/gxqV1fKU1NJ+2AOpyOjuPzee3WulyWqKy9SrgZWlUsUjmoqiJr0HFP/+y19HxiHjaMT+VmZ7PxpMV899QirP/2AS/FxjXpMgRBCiFsjwVUzZubqivvTT9M2ZjNes2Zh2a4d2sJCMhcu4vSgwVz8xz8pOnbc1M1sssoLVfw1RtJpISetCHsXN8IfnMDU/85n6DP/xKtdB7QV5ZzcrmHJ/01n8Wv/4MSWTZQ3kxnghRBC/EmCqzuAysICpwdG4f/bSny+/grbsFCoqCB39WoSx4zhfPQk8mJipAJ5HZnZaPnrswKKChw9/qwHY2ZuTuBdUTz87hzGz55Lp4gBqM3NuXw2gbWfz+WrpyazbclCcjPSbnPrhRBCGIsEV3cQ/bisu/D99lv8Vyw3zGNYuHcvF558irP33U/Wzz+jLSkxdVObBDNrHXc91I7KwsmKCiLHd6xxPkPPgHYMeeoFpn2+gH7jorF3dacoL5e9v/7M/555jN/mzCbp+FHpMhRCiCZOSjHc4cpSU8n87juyl/6ENl9fTkDt6orz+IdxfughzJxlAPb1XPuofEl+BTlpRTh6WNcYWF2PtqKCM/v3cGjdKpJPHDUsd23lS/CgYQT1j8LC2sYYzReNnJRiMC4pxSDqS+pcXYcEVzWryM8n++dfyFy0iPKUFAAUKyucRv0Nl0mTsGjd2sQtbFwa+uaXkXyew+tWcWLrZsqvZg4trK0J6n83wYOG4drK95aPIZoOCa6MS4IrUV+1/XlKt6AAQG1nh+sjk2m7fh3e//kPlkGB6IqLyfphCWeG3MuFvz9L4cFDtdpXakEqe1P2klqQauRWNx9uPq0Z+NjTPPHFIqImT8PZuxWlRUUcXvcHC/75FD/NfJX43dupKC83dVOFEELchNS5ElUo5uY43n8fDvcNo3DPXq7M/5aCLVsNRUmtg4NxefQR7AcMQLnOJJummFy0OSkrUePR5i7G/N89ZF44xeH1qzizfy/JscdIjj2mr5k18F66DBiMnbOLqZsrhBBERkYSHBzMRx99ZOqmNBqSuRLXpSgKtn374Pvll7T5/TccHxiFYm5O0eHDXHz2Oc7cO5TMH35AW1Rk2Ca1INUQWAFodVpm7popGaxait1xiUWv7WTl3EN89/ouCvI8GDH9DR777Bv6/G3snzWzfl7M108/wqqP3ufCyeMyAF4I0eAiIyN5/vnnqyzTaDQoikJ2dnaV5cuXL+edd94xSjt27tzJ0KFDcXZ2xsrKii5dujBnzhwqKioM6/Tt25cnn3yyynbz5s1DURS++eabKsunTJlCWFiY4XxGjBiBl5cXtra2BAcHs3jx4gZptwRX4qYs27XDe9Ys2m7ehOsTj6NydKQsKYnLb7/D6cgo0j7+mPKMDJJykwyBVSWtTktyXrKJWt505GcVo/k+zlA3S6cDzeI48rOKcXBzp9+4ifqaWX+fjnf7QLQVFZzatY2lb73Cdy/9nSMb1lBaXHTjgwghhBG4uLhgb9/wE9avWLGCiIgIWrVqRUxMDHFxcTz33HPMmjWLcePGGT5YRkVFERMTU2VbjUaDj4/PdZdHRUUB+sCta9euLFu2jKNHj/Loo48SHR3N77//fsttl+BK1JqZuzsezz9Pu5jNtHjjDcx9fKjIyeHKvC84ffcAXD/+iVZXqhZ+UikqfOx96n3MO2X8VnZaUY0FSSuZmZsT2C+Sh975DxPf/4Qudw/CzMKS9KRENv7vv3z5xCQ2z/+SKxeSbnPrhRDNyeTJk9myZQsff/wxiqKgKAqJiYmGoMTZ2RlFUZg8eTJQPcvl5+fHu+++S3R0NHZ2drRu3ZqVK1eSnp7OiBEjsLOzo0uXLuzfv7/GNhQUFDB16lSGDx/OV199RXBwMH5+fjz22GMsXLiQX375hZ9++gnQB1enTp0i5erDWABbtmzh1VdfRaPRGJYlJydz9uxZw3m89tprvPPOO4SFhREQEMCzzz7LkCFDWLFixS1/DyW4EnWmsrHBZcJ4AtauoeXHH2PVrSu60lJKf/2DD78q45WfKwg6r0WFwozQGXjaetbrOMsTljN42WCmrJ/C4GWDWZ6wvIHPpPFw8rC+aUHSa3n4tWHQ48/y+BcLiYyeirOXN6VFhRxa+zsL/vkUS2e+QtzOrVSUl92G1gshakun06EtLDTJq7ZDCD7++GNCQ0OZOnUqKSkppKSk4OPjw7JlywAMgczHH39c4z7mzp1LeHg4hw4dYtiwYUycOJHo6GgmTJjAwYMHadu2LdHR0TW2af369Vy5coXp06dXe+/++++nffv2LFmyBIDw8HDMzc0NgVRsbCxFRUU8+uij5ObmkpCQAEBMTAwWFhaGbsHrycnJwcXl1sezyoB2UW+KWo3D4EHYD7qHokOHuPLtt+Rv2kyP0zp6nNahDmqHp7sVOv9yFLO6XWo1jd8K8w6rd7DWmNk5WxE5oSOaxXHotDcvSFrJytaOnsNG0OPe+zl/7DBHNqzmzP69XIg9zoXY49g4OtHl7kF0HTAEB3eP23Q2Qoia6IqKONWjp0mO3eHgARSbm9fOc3R0xMLCAhsbGzw9//x7Wxl0eHh44OTkdMN9DB06lMcffxyAN998k3nz5tGrVy/GjBkDwMsvv0xoaCiXL1+ucoxK8fHxAAQGBl53/x07djSsY2trS69evdBoNDz00ENoNBr69euHpaUl4eHhaDQa2rVrh0ajoU+fPtjU8D345Zdf2LdvH19++eUNz602JLgSt0xRFGx69MCmRw9KExO5smABOSt+pSL2FBf/8U/Mvb1xmTwJx1EPoLazrdU+bzR+qzkGVwBB4d74BrnUqyCpolLh5htEz/v96TPqEc7s13Bs8zoKsjLZs+In9v76C/7de9Jt0FD8uvVApar+pKcQQjSUrl27Gv7fokULALp06VJtWVpa2nWDq0o1ZbZ0Oh3KNen+qKgofv75Z0A/rioyMhKAiIgINBoNU6dORaPREB0dfd39aTQaJk+ezNdff02nTp1qcYY3JsGVaFAWfn54vfUW7s8+S9aSJWQt/oGyS5e4PPs90j/7L85jx+I8YQLmLW6cRfF18EWlqKoEWLc6fqspsHO2qlNQVSl2xyXDgHhFgcgJUUz9bCxnDuzhyPrVJB0/wtmD+zh7cB8O7i3oOnAIXaLuwcbRqeFPQghRI8Xamg4HD5js2LfLtcVvK4Og6y3T1jCnbfv27QE4efLkdbvx4uLiCAoKMnwdFRXFrFmzuHjxIlu2bDF0J0ZERPDpp5+SlJTEuXPnDOOtrrVlyxbuv/9+PvzwwxqDr7qS4EoYhZmLC+5PP43rlCnkrPyNzPnz9Vmtr7/myoIFON53Hy6PTMbq6i/QX3naejIjdEa1mlnNNWt1K2p60tA3yIX2fcJp3yeczEsXObpxNSc0m8hNv8z2JQvZ+dNi2vUOpevAIfgEdUFRyRBMIYxNUZRadc2ZmoWFRZVyB5XLgGrLjWHQoEG4uLgwZ86casHVb7/9RkJCQpXyD2FhYVhaWvL5559TVFREz576rteQkBBycnL48ssvsbKyom/fvlX2pdFouO+++3j//feZNm1ag7Vfgqs6Sskp4lxGAf5utng53r5PAU2VysoK57EP4jRmNPkxMVz5dj5FBw6Qs2IFOStWYHvXXbhOeRSbPn2qpHgBRrUbRZh3GMl5yfjY+0hgVYMbPWlYmQVz8W5JZPRUwsdFc2rnNo5sWE3q6XhO7drGqV3bcPL0osvdg+kcOVCyWUII/Pz82LNnD4mJidjZ2eHi4kLr1q1RFIVVq1YxdOhQrK2tsbOzM8rxbW1t+fLLLxk3bhzTpk3jmWeewcHBgU2bNvHiiy8yevRoHnzwQcP61tbW9OnTh08//ZTw8HDUV4tcm5ubExoayqeffmoIwCppNBqGDRvGc889xwMPPEBqqv6pdAsLi1se1C4fVetg6b4kwv+1mYe/3kP4vzazdJ888l5bikqF/YAB+C3+Hr8fl2A/eDCoVBRs20bS5Ec498AD5Py+Cl1Z1afbPG096eXZSwKrG6jLk4bmFpZ0jhzI+FkfMuFfH9PtnqFYWFuTnZrCth8W8OWTk/l97r84f/QwuhrS9UKI5m/69Omo1WqCgoJwd3cnKSmJli1bMnPmTF555RVatGjBM888Y9Q2jB49mpiYGJKTk+nfvz8dOnTgww8/5PXXX+fHH3+s9oE8KiqKvLw8w3irShEREeTl5VXrElywYAGFhYW89957eHl5GV6jRt36rCIycXMtpeQUEf6vzWiv+W6pFYXtr0RJBqueSpOSyFy4iOzly9FdrfRu5uWFS3Q0TmNGozbSJ6KG0Ngm1o3dcanak4ZB4d612ra0uIhTu7ZxbOM6Uk6fMix3bOFpyGbZOjkbq+niOhrb9dXcyMTNor5q+/OUbsFaOpdRUCWwAqjQ6UjMKJTgqp4sfH3x/L83cHvmabKXLiXz+8WUp6SQ9v77ZPz3vziNfRCX6GjMrz5VImp2K08aWlhZ0yVqEF2iBpGWeJZjm9cRuzWGnMupV8dmfU9ASB+6DhhC6y7BMjZLCCFuQoKrWvJ3s0WlgJ2uADMqyMQBtaLg59b4ByY2dmbOzrg98QQujzxC7u+/c+Xb+ZSePUvmN9+SuXARjsOG4vLII1h17GjqpjZqdXnSMD+rmOy0Ipz+Eoh5+LVhwKNP0v/hRzi1eztHN60lJT6OhD07SdizU/+k4YDBdIocKBNHCyFEDSS4qiUvR2veG9WF1JVvMU39Oz9rI3EZ+IJkrRqQytISp9GjcRw1ivytW8n8dj6Fe/eSs/I3clb+hm1YGC6PPIJtv/Bqfe2i9qqXbajehWhuZUXnyIF0jhxIelIixzatI3brZv2Thj8uYsdP3xPQsw9dBw6hdddgqZslhBDXqFdwderUKZYsWcK2bdtITEyksLAQd3d3unfvzuDBg3nggQeqjMhvLsb28qX4RCZWiaVEq9eDZhNkPADhz4FnZ1M3r9lQVCrsIyOxj4yk6NhxMufPJ3fdOgp27qRg504s27fH5ZFHcBw2FOXqo8F/lVqQSlJuEr4OvjIY/ho3KttQU9bL3dePux95nLsenkT87h0c3bSOS6diOb1vF6f37cLB3YMuUYPoFDkQe1e323g2QgjRONVpQPuhQ4d46aWX2LZtG2FhYfTu3ZuWLVtibW1NZmYmx48fZ9u2beTm5vLSSy/x/PPPN6ogq0EGMep0cG4r7PgIzmz+c3m7QdDvBfANpdqjW+KWlV64SNZ3i8j++Re0hYUAmHl44DxhAs5jH0Tt6GhYd3nC8mr1sUa1u/WnP67VVAccXziVxcq5h6otH/lCd1p2qP2g9Yzk8xzbtI4TWzdRUlAAgKKoaN2tO50jBxIQ0hezJvR9aWya6vXVVMiAdlFftf151im4at26NS+++CIPP/zwDWtA7Nq1i7lz5xIcHMxrr71Wt5YbUYP/Ql06rA+yYlfqCwsB+PSB8Oeh/RCQgb8NriI3l+yffiJz0XeUp6UBoNjY4PTAA7hMiibT2YzBywZXq+y+7oF1DZrBaqo3v/ysYha9trNKXSxFBdGzwupVGb6stISEPTs5unEtF+NOGJZb2dkT2C+STpEDaeEf0BBNv6M01eurqZDgStSXUYKr0tJSQ4VWY6xvbEb7hbpyBnZ+CocXQ0Wpfpl7R32Q1WU0qOWPY0PTlZaSu2YNV76dT8mpq+UDVCrK+ofwZusDnPGumj38dvC39PLs1WDHb8o3v1sp23AjWamXOKHZxIktG8nPvGJY7u7Xhs6RAwnsF4m1vXFvZM1FU76+mgIJrkR9GSW4auqM/guVlwq758H+b6EkV7/MoRWEPQM9osGidpMWi9rT6XQU7NxJ5vwFFGzfblge6wOreqs40FZBUaslc/UX+VnF9SrbUBtabQVJRw9zTLORM/t2UVFeDoDazIyAnn3oHHUPrbt1l0HwN9DUr6/GToIrUV+3pc7V3r170Wg0pKWlVZt88cMPP7yVXTdN9p5wz0y46x/6AGvX55B7Ada+Alvehz5PQO9pYCOPsDcURVGwCw/HLjyc4lOnyJy/gOxVvxOUXEFQspYUZ1DGDsVDkYzJteo7QXRtqFRq/IJ74hfck6L8POK2azges5G0xDPE79lB/J4d2Lm4EtT/bjpHDsTZq6VR2iGEEKZS78zV7NmzeeONN+jQoQMtWrSo8mi8oihs3rz5Blubxu38tAJAWTEc+QF2fAJZ5/TLzG2gxyQIfRqcfIzfhjtQ2eU0Lsz/kqJffkXJ1w9+Vzs64jRuHM7jH8bcw+PWjyGZhSpqqpt1rbTEsxzXbODk9i0U5+UalrfsGERQ/7tp37cfVraNtyr/7STXl3FJ5krUl9G7BVu0aMH777/P5MmT69vG2+62B1eVtBX6Qe/b50LqUf0ylRl0eVBfxsFDimMag7aggOwVv5K5cCFlycn6hebmOA4bhssjk7Hq0KHe+5ab359qUzfrWuVlZZw9sIfjmo0kHj6I7urDB2pzcwJ69Caw/934B/dAbXbnfl/l+jIuCa5qLzk5mbfeeos1a9aQkZGBl5cXI0eO5M0338TV1ZVXXnmFlStXcvLkScM2J0+eJCgoiAkTJvDdd98Zln/33Xc8+uijZGVlYWdnx/Dhwzl8+DBpaWk4OzszcOBA3n//fby9b30MqLHU9udZ78fZVCoV4eHh9d38zqJSQ+dR8PhWmLAc/PuDtlyf1fq8Dyx5CJL3mrqVzY7K1haXCeMJWLuGlp9+gnWPHlBWRs6vv3JuxEiSHn2U/G3buIOGHTa4mupm5WcV17iNmbk57fv2Y9QrbzH182+56+HJuPm0pqKsjPg9O1j5n3f44olJbPr2C1JOn5KfjxAmcvbsWUJCQoiPj2fJkiWcPn2aL774gk2bNhEaGkpmZiZRUVHExcWRmppq2E6j0eDj40NMTEyV/Wk0Gnr37o3d1Xljo6Ki+Omnnzh16hTLli3jzJkzjB49+raeo7HUO7h64YUX+O9//9uQbalRYmIiU6ZMwd/fH2trawICApgxYwalpaW35fgNRlGg7QCY9Ds8thkC7wcUOLUavrkHvr0X4teD3EwalKJW43DPPfj9sBi/pT9if6++TEbBzl0kT53G2fvvJ/uXX9CWlJi6qU1OdlpRtctVp4WctKJabW/v4kbvEaOJ/s9nTHz/E3oOG4GNoxPFebkcXreKH17/J/NfeILdy5eSm55mhDMQounIzyrmwqmsG354aUhPP/00FhYWrF+/noiICHx9fbn33nvZuHEjFy9e5PXXX6dfv36Ym5uj0WgM22k0Gp5++mny8vI4ffp0leVRUVGGr1944QX69u1L69atCQsL45VXXmH37t2UlZXdlvMzpnoPaJ8+fTrDhg0jICCAoKCgaqnr5cuX33LjKsXFxaHVavnyyy9p27Ytx48fZ+rUqRQUFPDBBx802HFuq1Y9Yez3kJEAOz6GIz9C0k74YSd4dNIXJO30N1DLDEUNybpbN1rNnXu1KOl3ZP/yC6Wnz5Dyxv+RNvcjnB9+COeHHsLsBnXcxJ+cPKxRFKrVzXL0qNu0UIqi4OHXBg+/NvQf/yjnjx0mdutmTu/bTVbKRXYs/Y4dS7+jVWBnAu+Kon2fcKzsZHyWuHPUtfv9VmVmZrJu3TpmzZqFtXXV32dPT0/Gjx/P0qVL+fzzz+nVqxcxMTGMGzcOgC1btvDiiy+yZcsWYmJiaNu2LcnJyZw9e7ZKcPXX4y1evJiwsLBm0RVe78zV3//+d2JiYmjfvj2urq44OjpWeTWkIUOGMH/+fAYNGkSbNm0YPnw406dPb9AAzmTc2sGIz+D5oxD6DFjYQdoJWP4YfNod9n4NpYWmbmWzY9GqJS1efYW2mhg8XnoJMy8vKq5cIePTzzgddTcpb86g5OxZUzez0bNztiJyQkeUq39JKutm1WUC6b9+Elep1fgH92TYsy/y5FffMfjJ5/Ht3BUUhQsnj7Phq0+ZN20Cv/7nHeJ2bKGs+PZ8ihfCVOrT/X6rEhIS0Ol0BAYGXvf9wMBAsrKySE9PJzIy0pC5io2NpaioiO7duxMREWFYHhMTg6WlJWFhYVX28/LLL2Nra4urqytJSUmsXLnSaOd0O9U7LbJo0SKWLVvGsGHDGrI9tZaTk3PDKvEAJSUllFzT1ZObq39CqaysrPGlHa3d4e63IPR5VAe+RbXvK5TsJFg9HZ3mX2h7TUXbcwpYO5m4oc2MlRUOEydgP24s+Rs3kr1wESUnTpD9009k//QTNv374zQpGutevao8EVt5/ZSVlXG58DJJeUn42vvSwqaFqc7EZNr1dsernQO5GUU4uFlj52xZq9+vuF2pbFuSYPgkftdD7egYWrUWmWJmTofwCDqER5B3JYNTO7dyaudWriSf58z+PZzZvwczS0va9OhN+9C7aN01uFkMhL/2+hINr6l9X2/U/W6skio3UzkWUlEUoqKimD17NpcuXUKj0dCvXz/UajURERF88skngL5LsG/fvtWyYC+++CJTpkzh/PnzzJw5k+joaFatWlXl721TVO/gysXFhYAA00xrcebMGT799FPmzJlzw/Xee+89Zs6cWW35+vXrsbGxMVbzGkBH1O3ew/fKNgLSVmNbmIF6y3vots0l0S2KM+6DKbaQbiujmDgB63PncN62HduTJyncupXCrVsp9vYm6667yOvaBcz+/LV5b9V7rCxaiQ4dCgojrEcQYhliwhMwsYTarVZepJCqsQX0f0B1Otj6Qzzxlw5hZn2DMYdqK5zvGoRNdib558+Qd/4M5fl5xO/aRvyubagsLLHz8cOudQDWHl4oTXwKqg0bNpi6Cc1SYWHT6g1oqO73umjbti2KohAbG8vIkSOrvR8XF4ezszNubm6Eh4djYWGBRqMhJiaGiIgIAEJCQsjJySE+Pp6YmJjrVhdwc3PDzc2N9u3bExgYiI+PD7t37yY0NNRo53Y71LsUw/z581m7di3z58+vd6Dy1ltvXTf4uda+ffsICfnzZnXp0iUiIiKIiIjgf//73w23vV7mysfHh4yMjNtbiuFWaMtRYn9FvesTlLRYAHQqc3RdHqSi7zP6bkVhFKWJieR8v5jclSvRXe16Unt44PTww1iPHMGKXeuZkzsHLVXnMfxjxB93ZAarLi7FZ7Pq02PVlt/3bBe82znVuF1+Vgm56UU4uOszZDqdjstnE4jftZ2E3dspyM4yrGvr5ExAr1Da9QnDq33HJlURvqysjA0bNnDPPfc0i/EnjU1ubi5ubm5NqhSDsaatupHBgwdz4sQJEhISqmScUlNTCQgIIDo6mnnz5gHQv39/OnbsyMqVK/n999/p3bs3AIMGDaJ3797MmjWLLVu20L9//xqPl5ycjK+vLzExMURGRhr13OrL6HWuunfvzpkzZ9DpdPj5+VX7A3Dw4MGb7iMjI4OMjIwbruPn52c4gUuXLhEVFUWfPn1YsGABqjp+KjVZnauGoNNBwgZ9rayknVcXKhB4H4S/oB8gL4yiPCuL7KU/kbn4eyrS9derYm1NYrc2/Cc4jjRn485j2BzVZwLpmw3o1WoruBB7gridW0jYvYPignzDe7ZOzrTtHUaHvuG0DOzU6AMtqXNlXE21zpUxp626noSEBMLCwggMDOTdd9/F39+fEydO8OKLL1JSUsLu3bsNw3NmzJjB3LlzAf3gdLOrGf5Zs2bx/vvvU15eTlZWFpaWloB+hpe9e/fSr18/nJ2dOXv2LG+++SYpKSmcOHHCsF5jY/Tg6mYZpxkzZtRntzW6ePEiUVFR9OzZk++//x61uu5/HJt0cHWtpD2w4yN9CYdKfnfpnzAMuFt/5/mLlJwizmUU4O9mi5ej8VLJzZm2tJTcVX+QuWABJfHx+mUK7GuvsKq3ilMt9VO/NPQ8hs1VXT6J1zUYy0nP49Su/Vw+c4Dzx/ZRUlBgeM/G0Yl2vcNo37cfrYIaZ6AlwZVxNdXgyhTOnz/PW2+9xdq1a7ly5Qqenp6MHDmSGTNm4OrqalivsszCkCFDWLNmjWH59u3bueuuuxgwYAAbN240LD927BjPPfccR44coaCgAC8vL4YMGcIbb7xBy5aNd0qsZjVxc2VXoK+vL4sWLaoSWHl61v4m1myCq0ppJ/VlHI79rC9KCuDZFfo9D0Ej9cVLgaX7knh1+TG0OlAp8N6oLozt5WuyZjd1Op2O3K3bSJgzB9urQRbAGS8Fq4fHMGDS6ygWFiZsYdNR20/iF05lsXLuoWrLR77QnZYdnKss+2uGq/9DbbGxv0z8rh2c3r+rSqBl7eBI25A+tO0dim/nYMwaSSAjwZVxSXAl6qtRBFc6na5BRvwvWLCARx55pMZj1FazC64qZSfDrv/CwYVQdnWgprM/hD9Liv/fCP9gJ9prvk1qRWH7K1GSwboFlTe/Ae3bc3nB/yhZvQHl6hNIZh4eOD/8ME5jH8TM2fkmexK1UdvM1c3WqygvI+nYEeL37OD03l1Vug4trK3xDw6hbe9Q2nQPwcLadA+9SHBlXBJcifoyyvQ3gYGB/PDDDzetjJ6QkMCTTz7J+++/X5fd12jy5MnodLrrvgT6CaDv/Re8cAIiXwVrZ/1E0atewPXrEB5X/YY9fz4dU6HTkZjRtJ6Waaws27Yl4P05tN+iwe3Zv6N2d6M8LY30jz7idGSUvl7WNRWKRf3Utp7WzSrGq83M8e8ewuAnnuOJr77ngdffods9Q7F1dqG0qIhTu7bxx8f/5vPHHmb5v97i6Ka15Gdl3o5TFEI0I3XKXG3evJmXX36Z06dPM2jQIEJCQvD29sbKyoqsrCxiY2PZvn07sbGxPPPMM7z22muNKkPUbDNXf1VaAAcXwc7PIPcCALk6a36oGMg35UPIVFwkc3WLasosaEtLyVuzhisLF1IS++dEprbh4bhMnoRtePgNywOkFqSSlJuEr4OvjNu6jpt1I9ZlbFZ+VjHZaUU4eVhj62hByul4Tu/bxel9u8hKuVRlXc+AdrTp2ZuAnn1wb+1v9Bo8krkyLslcifoyarfgzp07Wbp0KVu3biUxMZGioiLc3Nzo3r07gwcPZsKECTg5Od1K+43ijgmuKpWXwvFfyNn4AY75ZwAo0ZlxofVIAka8Bq6mqVPWHNzs5qfT6Sjav5/MRYvI27jJUKDGok0bXKIn4jh8OKq/lDBZnrCcmbtmotVpUSkqZoTOYFS7UbflfJqT2gyUv9GThzqdjisXkji9dxdnDuwh9UzV4l32ru606dGLgJ69adWpC+YWDf9UkwRXxiXBlaivRjHmqrG544KrSlotmYd/w3zXx9inV5bIUCBoOIQ/Dy17mLJ1TVJdbn6lyclkff892b8sQ3t1MLXK0RHnB8fgPH485p6epBakMnjZYLS6qjWz5MnD+rlRhquuTx7mZ2Vy9uA+zh7cy/mjhykv/bN2npm5BT6duuDfPQT/4BCcPL0apP0SXBmXBFeivmr785RZge8EKhUuPUZC9xGQtAu2fwQJ6yB2pf7lH6Ev49Am8rplHMStsfDxocWrr+L297+Ts3w5mYu+o+zCBa58/T+ufDsfh8GDSRkWUiWwAtDqtCTnJUtwVQ92zlY1Pn1Y26lE/uw2tKHrgMF0HTCYstISko8f5cyBPZw7dIC8K+mcO3yAc4cPAF/i7NUS/+Ce+HXrQavAzpjLzVSIO1KdgqvKOYJq49lnn61zY4SRKQq0DtO/Lp+4WsbhFzi3Rf/yCtaXcQgcbijjIBqO2s4Ol+honMePJz8mhsyFiyjct4/c1auxXb2ad1sq/NFLYU8HBa1KQaWo8LH3MXWzm53aTCVSU7ehuYUlbXr0ok2PXvruw+Tz+uDq0H4unoolK+UiWSkXObjmN9RmZrTsGETrrj1o3bU7Hq39m/x0PEKI2qlTt6C/v3+Vr9PT0yksLDSMr8rOzsbGxgYPDw/Onj3boA1tCHdst+CNZJ2/WsZhEZTrn6jCpQ2EPQvdHgJz+eR9PQ3VbVMcG0vmwkXkrF4NV0s5ZNjD+p5qQh57mREhExuqyeIaNxqXVZtuw2sHw1cuKyksJOnYYRKPHCTx6EFy09OqHNPK3gGfwM60CuqCT6cuuLXyrTHYkm5B45JuQVFfRukWPHfunOH/P/zwA59//jnffPMNHTp0AODUqVNMnTqVxx9/vJ7NFredc2sY+m+IeAn2fgV7voTMs7DqedC8B32fhJBHwcrR1C1tlqyCgvB+/194TP8nWUt+5MqSH3DLyuZhTQXKrjlcui8OlwkTsAoMNHVTm5WgcG98g1yuOy7rZt2GNWW1LG1s8GrfA2unQPqOnkJZcSbnjx4k8eghkk8cozgvl4S9O0nYq5++qi7BlhCiaan3gPaAgAB++eUXunfvXmX5gQMHGD16dJVArLGQzFUtlOTrs1i7PoPci/pllg76AKvvk2Av43/AeJkFbUkJuavXkPndoiqlHGxCQnCeOBH7AXejmF3/M5GUcWgYN8pcATW+lxSbWeMTiDkZ+Zw7FEtexmkunz3JxVOxlF8zqTyAlZ09rQI749OpC17tA9lz7ATDhg2TzJURSOaqYUVGRhIcHMxHH31k6qYYnVGKiF4rJSWFsqvdGNeqqKjg8uXL9d2tMDVLOwh9Cp49DCPngVsHKMnVz2X4URf4/Tm4csbUrWy2VJaWOP1tJP7LltH6h8XY3zsE1GoK9+/n4nPPcfqeQWR8/TXlWVlVtluesJzBywYzZf0UBi8bzPKE5SY6g6bvRgVLa8pqpZ7JMQRWoB/PpVkcR35WMbE7LrH4//ayY1k+x7Z6EhT5JM98u5SH3vmAfg9Nwq9bD8wtrSjOz+P0vl3ELPiKH157gXPLvuOPj/7FrmXLOBpzkJyMfIS4XSIjI3n++eerLNNoNCiKQnZ2dpXly5cv55133jFKO3bu3MnQoUNxdnbGysqKLl26MGfOHCoqKgzr9O3blyeffLLKdvPmzUNRFL755psqy6dMmUJYmP6D0qlTp4iKiqJFixZYWVnRpk0b3njjjevGNnVV76cFBwwYwNSpU/nmm2/o2bMniqKwf/9+Hn/8cQYOHHjLDRMmZmYBwQ9D13EQvxa2z4ULe+HAAjiwEIJG6Ae/e3evtqlMEn3rFEXBpkcPbHr0oCw1lawffyR76U+Up6SQPudDMj77Lw7334fLxIlkt3I01McC/VOGM3fNJMw7TDJY9VRTt2FNg+F1UKegyzfIBe/2HfFu35E+I8dQUV7O5bOnSY49xoXYY1yMi6WspJgz+/dwZv+eq3tU4+TlR0CPzni164hX2w7Yu7kbvaCpEDfj4uJilP2uWLGCBx98kEceeYSYmBicnJzYuHEjL730Ert37+ann35CURSioqJYsWJFlW01Gg0+Pj7ExMQwZcqUKsvHjRsHgLm5OdHR0fTo0QMnJyeOHDnC1KlT0Wq1zJ49+5baXu9uwfT0dCZNmsTatWsNaevy8nIGDx7MggUL8PDwuKWGGYN0C94Cna5qGYdKbSL1tbKulnG4UyaJNsWAY21JCbl/rCbz+++qdBmWd+vIRwHx7G+nf8qw0reDv6WXZ6/b0rY7yfUGw/sGuVy3u/CeRzux/n8nqu3jehNOX6u4qIjl8xeTvjcDbdkFtBUpoCuptp6NoxNe7Trg1bYDXu060KJNOyxtTDcnYlNhym5BnU5XrUv4djGztKxVMD558mQWLlxYZVnlOVxr0qRJLFiwoFq3oJ+fH4899hjx8fEsX74cV1dXPvnkE8LCwnjsscfYtGkT/v7+zJ8/n5CQkOu2oaCggNatWxMREcGyZcuqvPf7778zfPhwfvzxR8aOHcv69esZPHgwly5dwstLX2vO09OTGTNmMGvWLC5c0M9UkpycjK+vLxs2bKgxCfSPf/yDffv2sW3btuu+b/Q6V+7u7qxevZr4+Hji4uLQ6XQEBgbSvn37+u5SNGY1lXE4q9G/vILJ6vEUry+3QavT96dodfDa8uP0b+8uGawGoLK0xGnU33D820iKDh0i87vvyFu/AbMjcUw/AukOsL6His3dFAps1VLGwUhqympFTuhYLejyCnC8admH61GbmaG28sTMqg1Y9dbPparNRleegl/ncnIzEkk/f47CnOyq2S1FwbWlDy3atMXDrw3urdvg7uePtZ29sb4doo7KS0r4ZNJokxz72YW/1Kr22scff0x8fDydO3fm7bffBvT3/GXLlvHAAw9w6tQpHBwcsLau+TqeO3cus2fP5v/+7/+YO3cuEydOJDw8nEcffZT//Oc/vPzyy0RHR3PixInrBnzr16/nypUrTJ8+vdp7999/P+3bt2fJkiWMHTuW8PBwzM3N0Wg0PPTQQ8TGxlJUVMSjjz7Kyy+/TEJCAu3atSMmJgYLCwtDt+BfnT59mrVr1zJq1K3PjHHLRUTbt28vAdWdpkUnGPUVRL3+ZxmHlMM4/zGNDeYt+KriPpZX3EUJFoZJoiW4ajjVugyX/MjlJd/hnlvIeI2WMdugMKIrjt3ToYt0CxrD9YqU1iXoqqnA6bXMbLSGwExRFBS1M4q5M3c/qi8JUVZaQtq5s6SePsWlhFOknj5FbnoaVy4kceVCErFbNxv2Ze/qjrufPx6t/XH3a4NH6zY4erSQpxPFdTk6OmJhYYGNjQ2enn/+Dans/vPw8LjpFHdDhw41VA548803mTdvHr169WLMmDEAvPzyy4SGhnL58uUqx6gUHx8PQGANT0p37NjRsI6trS29evUyBFcajYZ+/fphaWlJeHg4Go2Gdu3aodFo6NOnDzZ/ye6GhYVx8OBBSkpKmDZtmiGgvBW3FFxduHCB3377jaSkJEpLS6u89+GHH95Sw0QT8JcyDtrdX+Jfcpn3VN/wgtky5pcPYYl2IH5u0k1hLOaenni88DxuTz1J8vIl5C1ZikV8IhabD5G4+UGsunTBefzDONx7LyrLhp8DT1RVl6DrZsysddz1UDu2/Zhw3cDM3MKSlh0CadkhkJ5Xtzm0/iTbl25HW56GtiIdS+tsinIzyLuSTt6VdM4e2GvYv7mlFQ7urXDz9aFFG39cW/rg0tIHR3eP2x50Xa9uWHNlZmnJswt/Mdmxb5euXbsa/t+iRQsAunTpUm1ZWlradYOrSjWNXNLpdFUyXlFRUfz888+AflxVZGQkABEREWg0GqZOnYpGoyE6OrravpYuXUpeXh5HjhzhxRdf5IMPPuCll16q5ZleX72Dq02bNjF8+HD8/f05deoUnTt3JjExEZ1OR48eMlfdHcXWDaJeQxX2LIdWfoznif/hpWTysvmP/MPsd8z3HIG+T4GD98339RcyOL52VJaWtH5oMrpxkyg+epSsH34gd/Uaio8dI+WVV0l7/984jR6N87ixmLdsKWUbbrMbTcdzIx1DPfHv4l6rwCw/q5hdK1JQmQegMr86KbsKJs/tRlFOCunnz5GWeI7082dJP59IWUkxVy6c5sqF05zaGWPYj5m5Bc7eLXFp6YO9qxfmVm54tfXBq50fVrZ21Y55vaCoLsHSjSbRruk8m3IgpijKHTEt0rVjUSuDoOst02qrTvtVqbJH7OTJk9ftxouLiyMoKMjwdVRUFLNmzeLixYts2bLF0J0YERHBp59+SlJSEufOnSMqKqravnx89EMogoKCqKioYNq0afzzn/9Era7/TCX1Dq5effVV/vnPf/L2229jb2/PsmXL8PDwYPz48QwZMqTeDRJNmKUd3R98nZTMv5Owfyl+p77G/Mop2Pkp7P4Cuo6F8GfBvUOtdnenDI5vSIqiYN2tG9bduuHx0ktk/7KMrB9/pDwlhStff82Vb74hr1d7PmlzmqOtdSgqNTNCZzCq3a2PMRDGUdvArKYyEcV5KloFdqZVYGdAH5wsfHU72opMdBVX0FVkotNm4uheRE5aCuVlpaSfP0f6+eq1Cq3sHXBq4YlTCy/Ky+w5f6IcRXFAUdsTOTGEznf51ClYys8qrvFpyuudc10DMVF/FhYWVcodVC4Dqi03hkGDBuHi4sKcOXOqBVe//fYbCQkJVco/hIWFYWlpyeeff05RURE9e+rzuSEhIeTk5PDll19iZWVF3759b3hcnU5HWVlZjRmz2qp3cHXy5EmWLFmi34mZGUVFRdjZ2fH2228zYsSIajUnxJ3Dy8UBBk2FgVPg9Ab9E4ZJO+Hw9/pXh2H6Mg4+vWvcR0pOkSGwAhkcXx9mrq64PT4N1ymPkq/RkPXDDxTs3IX9njhe3wOXXGBDdx0fFL0lZRuagdrMmQj6IAxUqNRuoHYzLB/0eHe82jmQk3aZi6fOsml+ZQCWjU6bDbpCivNySc3LJfV0fLXjr/vsW7Z950JxgRWo7FFUDiiKLZu+jcfCoi9uvp7YOjtjbmFZpS21mUQb6h6IiVvj5+fHnj17SExMxM7ODhcXF1q3bo2iKKxatYqhQ4dibW2NnZ3dzXdWD7a2tnz55ZeMGzeOadOm8cwzz+Dg4MCmTZt48cUXGT16NA8++KBhfWtra/r06cOnn35KeHi4Ietkbm5OaGgon376qSEAq7R48WLMzc3p0qULlpaWHDhwgFdffZWxY8diVkOx5tqq99a2traUXH2c1NvbmzNnztCpUycAMjIybqlRoplQqaD9YP0rea/+CcO4VXDqD/3LN1RfxqHdIP261ziXUWAIrCrJ4Pj6UczMsB84EPuBA9m/51e2fvIaEcd0eGfCpE1aHtqiJS32DRwmP411cLDUTWqiKouf3mzw/I2CMJVKjbOnNwU51phZVR1Hq9OVEvmwJ5ZWhSQeO8OJLbH6Jxi1uei0+UAFhTlXrtu2lR+sMvzfwtoGW2cX7JycsbR1pLyoCBQbFMUWVNaoVFagtKE43xxLGxvD+K+6BGLi1k2fPp1JkyYRFBREUVER586dw8/Pj5kzZ/LKK6/wyCOPEB0dzYIFC4zWhtGjRxMTE8Ps2bPp378/RUVFtG3bltdff53nn3++2t+qqKgotm7dahhvVSkiIoKNGzdW6xI0MzPj/fffJz4+Hp1OR+vWrXn66ad54YUXbrnt9a5zNXLkSIYNG8bUqVN56aWXWLFiBZMnT2b58uU4OzuzcePGW25cQ5M6V41Aejzs/BiOLAXt1Sq47oH67sLOo/XFS9FnrsL/tblKgKVWFLa/EtUogqumOrFuakEqg5cNxqK4grtO6LjnkBa/a+YXtmzfHqdxY3EcPhy1kT6Ripu7lesrP6v4pmO0bjRxdeU+bjR59V/f1w8uLiDiIW82L9x3NeDKQ6ctAF0Btk7lFOVkU15WSl0oigpLOzus7ewxt7Il40IZKNao1B6YWfWoNqF2bcn0N6K+avvzrHdwdfbsWfLz8+natSuFhYVMnz6d7du307ZtW+bOnUvr1q3r3XhjkeCqEclNgd2fw/75UJqnX+bQUj/wvecksLRn6b4kXlt+nAqdDrWiMHtU50Yz5qqpBlegnyqnsqK7CoX3XKcQvDON3NWr0RUXA6DY2OA4bBhOY8di3bmTiVt857kd19fNgrCbBWA1vV/Tcp1OR2lRIflZmRRkZVGQnUlBVib52VnkXE4nNyOTivJCSgsLKM7Po6ykuMa2q8z9sXT4W7U21ZYEV6K+jB5cNUUSXDVCRdlwYD7sngf5V+ektHKEXlOhzxOkVNiRmFGIn5tNo8hYVWrKwRXoM1jJecn42PsYxlpV5OSQs/I3spYupfTMn/NHWnXujPO4sTgMHYqqhurf8vRhw2os19fNArCa3q9N9uxmyktLKS7Ipzgvl+L8fIry9f/mpGWiNnOky4Coeu9bgitRX7cluMrOzuaXX37hzJkzvPjii7i4uHDw4EFatGhBy5Yt67tbo5HgqhErK4ajP8KOTyDz6o3dzEo/v2HY38GljWnb9xeN5eZnDDqdjqL9+8n6cSl569ejuzqJqcrODscRI3Aa+yBW1xQOrpIJU1Ty9GEDaM7XV2MgwZWoL6NPf3P06FEGDhyIo6MjiYmJTJ06FRcXF1asWMH58+dZtGhRfXct7kTmVtBzMnSfCHF/wI6P4OIB2P+tfrLowOH6cVkte95kR+JWKYqCTa9e2PTqRXlmJjkrVpC19CfKkpLIWryYrMWLse7RA+dxYynsFyyTRgshxF/UuwzvP/7xDyZPnkxCQkKV6O3ee+9l69atDdI4cQdSqSFoODy2CSb/AW3v0T8SFPsrfH03LLgPEjZQ7bEhYRRmLi64TplCwNo1+HzzP+wHDQK1mqKDB7n00stkDRnF+I1leF358+eh1WlJzks2YauFaHzuoBE4zVptf471zlzt27ePL7/8stryli1bkpqaWt/dCqGnKODXT/+6fEJfiPTYz5C4Tf/yCIKwZ6HzA4YnDIXxKCoVduHh2IWHU3Y5jZzly8j66WfKU1K4fy/cv7eCE76wuauKfYEyabQQlSrrLZWWlt5womPRNBQWFgLctLu+3sGVlZUVubm51ZafOnUKd3f3+u5WiOpadIK/fQF3v6Ef+H5gAaTFwq9PwOZ3qjxhKIzPvIUHbk8+ieu0aeRv3crJbz/Gdt8pOiVBpyQtFZvN0SV/QdEDo7Hq3OmGdbNkILxo7szMzLCxsSE9PR1zc3NUMll2k6TT6SgsLCQtLQ0nJ6ebTo1T7wHt06ZNIz09nZ9++gkXFxeOHj2KWq1m5MiR9O/fn48++qg+uzUqGdDeTBRl68di7fnizycMLR2h16PQ5wmw9zT6nIQy4LiqS2eOkrZsKdZrd6K99Gfm2rJDB5weeACH++/DzNm5yjYyEL5mcn0Z1+2+F5SWlnLu3Lka59ETTYeTkxOenp43LbZc7+AqNzeXoUOHcuLECfLy8vD29iY1NZW+ffuyZs0abG1t69VwY5LgqpkpL4GjS/VPGF5J0C9TW3DG+36mnQnljNbbaHMSys3v+nRaLYV79pD9yzLyNmxAV6ovGqmYm2M3cABOD4zGNiyUy0VpDF422DAQHkClqFj3wDrJYCHXl7GZ4l6g1WopLa1bEVXRuJibm9d6Mud6dws6ODiwfft2YmJiOHDgAFqtlh49ejBw4MD67lKIujGzhB7REDwB4tfop9dJ3kNA8jI2WSxjQ0VPviofxmvLkTkJbxNFpcI2NBTb0FB93axVq8hetoyS2JPkrVlL3pq1mHl7UXBPH1xsK8hw/PPTX+VAeAmuRHOkUqmkFMMd5JZmJty0aRObNm0iLS0NrVZLXFwcP/zwAwDffvttgzRQiJtSqaDjMOg4jGO71pKy+t8MUh/gnquvw9oAcg9k4hXxEKhvbTJOUXtqR0dcxo/HZfx4imNjyf5lGTmrVlF+KQXLhb/yGXDMTyGmm8K+dgoVFjIQXgjRPNT7TjNz5kzefvttQkJC8PLyksleRaPgFhTBiN8q8C+/yBT1Gh5QbyNYdQa2PgNH/wOhT0PweLCUefNuJ6ugIDzfDMLjpRfJ27CR7GXLKNy9m26JOrol6iiwhNLI7jh0uoiuR4sa/57IAHghRFNQ7zFXXl5e/Pvf/2bixIkN3SajkTFXd4Zr5yT0UHL5tvNhOl/4CYoy9StYOUGvKdB7GtjX7wYtY2JuXemFC1z8cRGFv69GdfmKYbm5jw+OI0bgOGI4Fj5/ZrLupAHwcn0Zl9wLhLHVO7hydXVl7969BAQENHSbjEZ+oe4cKTlFVeckLC2EIz/Arv9C5ln9SmoL6PoghP4dPDrWaf9y82s4Oq2Wwn37yVm5kry1a9FerSMDYN2zJ44jR1B8V3eGrH/gjhkAL9eXccm9QBhbvQtuPPbYY4bxVUI0Nl6O1oQGuP45iN3CBno9Bs/sh7Hfg08fqCiFQ9/D531g8Rg4t1Uqv5uAolJh26c33rNn0W77Nrz/829sw8JAUSg6cIDU/3uTrEGj+PuKMoLPaFFp9T8jqQQvhGis6jTm6h//+Ifh/1qtlq+++oqNGzfStWvXap+uPvzww4ZpoRANSaWGwPv1r6Q9sOtTOLkKEtbrX17d9JXfg0aAWjIGt5vKxgbH++/H8f77Kbt8mdzffyf7118pPX2G8JMQflJHti3sCFTY1UlNK7tWpm6yEEJUU6duwaioqNrtVFHYvHlzvRtlLJIKFtd15Qzs/hwOLYbyIv0yRx/o+6S+1MN1Kr9Lt83to9PpKD4Ry8GFczDbuAuHoj/fM/fxweG+YTjedx+W1wxRaOoD3+X6Mi65Fwhjq/eYq6ZIfqHEDRVcgf3fwJ4voTBDv8zSEUIm6yu/O3gbVpWbn2mkZCeTErMWxy1HKNuyE13Rn5GWZWAgjvcNY0egwv+d/rhJD3yX68u45F4gjE2CKyH+qqwYjv4IOz/7s/K7ygy6jIHQZ8Czs9z8GgFtYSF5m2PIXbWK/O3bobzc8F6sD+wIUrG7o0KBrfq6A98bc3ZLri/jknuBMDapqCjEX5lbQc/J0D0aEtbBzk/h/A44skT/CrgbpfdTMvjdxFQ2NjjeNwzH+4ZRnpVF3voNXFi+BLMjcQQlQ1Cylinr4XhrLSkli3B74DHMXFyAO6usgxDi9pPMlRC1cfGAPpMV+ytcLQeQY+WD7aBXSfe7j3NZZUabJFrUXmpBKg/PH0Tf2ArCY7W0Sb3mTbUa2z690UWFMb7gE3Ks//zTd72yDqbMbEnmyrjkXiCMTYIrIeoiKxF2f4Hu4CKUsgIAUnQuLCgfzFJtFK+OCm3wSaJF3VyblfLKVni98G589yVTHBtrWKdCgeOtFXYH6qfeybVV+Hbwt/Ty7FVtH6bIbElwZVxyLxDGJsGVEPVQlpvO0e9fpdXlDbRQsgEo0FmyTBvBkClv4eHXybQNvMOlFqSSnJeMj72PIetUmpRE7tp1ZP7xOxWnEgzrahWIa6XQ88GnaDlsFFccVQxeNviGBUuNndWS4Mq45F4gjE2CKyHqoaysjI+XrOGrWC3D1TuZol5DoCoJAB0KSod7oe9T4NcPZN7NRuf3LV+zb8nH9I6rICC16nsV7f34yTOJvR0ULrr9+bOrzGzdKKvVUEGXBFfGJfcCYWwSXAlRD2VlZfywYjUzD5mhLxiuI1QVy1T1au5WH/pzRc+u+smiO40CMwtTNVdcR2V2yzvfHOsdR8jbsJHCAweqPKhwyQX2t1M42E7NR39fi2KmrjGrtfPSzgbrSpTgyrjkXiCMTYIrIeqh8uZX0KIr/7fyJBU6HWpFYfaozoz1L4Hd8+DwD38WJbXzhN6PQcgUsHExbeNFjcqvXCFv82ZO//o9VofiMfszhkLt5ERxryA+sdvNEX+FYss/s1ofRHzAS1tfqrErsa4ZLQmujEvuBcLYmkwphuHDh3P48GHS0tJwdnZm4MCBvP/++3h7e998YyGMZEzPVkQFeladJBrgvg/h7jfgwHzY+zXkpcDmd2HrHOg2Tt9l6N7etI0X1Zi5uuI8Zgy9xowhJfU0qTFrcdh7ivKd+6jIzsZ8w07+CZSp4YSvwqEAhSNt1eh0uiqBFfw592FDZrSEEE1Dk8lczZ07l9DQULy8vLh48SLTp08HYOfOnbXeh3xaEQ2lTpmF8lJ9CYddn0HKkT+Xt71H32XYJlLGZTVyuvJyCg8eJD9GQ8q637C8dKXK+yqflvzRIoWDARDrq1BmpqBSVHx/7/dMWDPhhoPjr0cyV8Yl9wJhbE0mc/XCCy8Y/t+6dWteeeUVRo4cSVlZmfzxEY2bmQV0fVBf4f38Ttj1Xzi1Gk5v0L88OunnMewyRl/AVDQ6ipkZtr17Y9u7Ny1efomLx/eSvnkttvtPUX7oGNrki9ybDPfuhxIzOOmr4BU1iJLTp9FqK6oEz5UZrcZWFV4I0XCaTHB1rczMTBYvXkxYWNgNA6uSkhJKSkoMX+fm5gL6T4VlZWVGb6doviqvnzpfRy17w+jekHkW1b6vUR35ASXtBPz2DLpNM8npNJG4lmPwbumDl6MEWo2VR4fueHToDoC2oIDC3Xso3LaNvG1bsUxLJ/isDs6uhm9WM88ejvgrHPVXiPVVyLVX42XtdcNr59rr63LhZZLykvC196WFTYvbcn7Nnfz9F8bWZLoFAV5++WU+++wzCgsL6du3L6tWrcLV1bXG9d966y1mzpxZbfkPP/yAjY2NMZsqRK2YlxfQ+ooG//QN2JRlAlCiM+N3bSiXPAfRulVrE7dQ1IlOh8Xly9jGx2MTn4D1uXOorpnzECDHzR5dQBCFAW0oatOGCnv7Gne3v2Q/K4tWotMX+GCE9QhCLEOMfRbNXmFhIQ8//LB0CwqjMWlwVVPwc619+/YREqL/Y5KRkUFmZibnz59n5syZODo6smrVKpQaxqtcL3Pl4+NDRkaG/EKJW1JWVsaGDRu45557GqRbOiUzj/988iFT1KsJVp0xLC/26o1Z6BPoOgzVTx4tmhRtcTHFBw+SvmUDRXv3YXYmudqclOb+/lj3CsG6Vy+sQ0Iwc3OjrKyMX9b9wpzcOWipOl7rjxF/3DCDJZmum8vNzcXNzU2CK2E0Jg2uMjIyyMjIuOE6fn5+WFlV7x65cOECPj4+7Ny5k9DQ0FodTwYxiobS0AOOd57J4OGv9wAQrJzmEbO1DFXtwVyp0K/g0EpfyqHHJCnl0IRV5ORQuH8/hXv3UrB3HyVxcdWCLYs2bbAK6clWJZP/ttCQY1v1w+O10/T8lamn7Wkq5F4gjK1JdQteKzk5GV9fX2JiYoiMjKzVNvILJRpKQwdXKTlFhP9r89WCpHreShbr+p/G/tgiKLz6dJqZtX5wfJ8noEXQLR9XmFZFdjaFBw7cMNi64AqnWikkeCucbqnif9PW4uXQstq+UgtSbzptz1/XN9XE1KYm9wJhbE2in2Hv3r3s3buXfv364ezszNmzZ3nzzTcJCAioddZKiMbMy9Ga90Z14bXlxw0FSZ8b1R/7XhNg4CtwfBnsmQepx+DgQv3Lvz/0eRLaDwaV2tSnIOpB7eSE/YAB2A8YAFwNtvbvJ2/3bi5v2oxVSgqtrkCrKzoGHNEBWnKXjKC0Sxesu3bFOrgb1l27YubmRlJuUo21tv4aPNU2w3UnB2BC3Iomkbk6duwYzz33HEeOHKGgoAAvLy+GDBnCG2+8QcuW1T/B1UQ+rYiGYqw6RCk5RdULklbS6fSlHPZ8AXGroPJG6uwHvaeRGjCas3lq/N1sq28rmpTK62twWBhpe7aQdWA3NvEXqTgZj66wsNr65i1bQqf2fKPdyikvON8CSs2V62auapvhqk0A1lDB1+0O4uReIIytSWSuunTpwubNm03dDCGMzsvRuubASFHAL1z/yk6Cff+DAwshKxHWvYa9bianK/rzlvYepvztXsb28r2tbRcNT+3khO99o/G9bzSgL2ZacuYMRYePUHT0CMVHj1Jy+gxlFy/CxYtEX91Oq+jnRbTv1BXz0lXkdwzEKrAjZq6utcpwpRakGgKryvdn7ppJmHeYYZ2GmsBaxomJ5qhJBFdCiL9w8oV73oaIl8nZ8z2XN3xMe9VFos02EM0Gdv6+kEzVdFyCh4Nafs2bC8XMDKsOHbDq0AHnsQ8CUJGfT/GxYxQdOUrRkSMUHDmMKjOLVleArUdJ23rUsL2ZuzvO7dvwsE7LOXdI8lBIdQadmRofex/DejcLwG4UfNVlup/aBHHX20a6KkVjJ391hWjKLGw54f0AD5e2JFx1nGj1BgaqDhCmOgG/PwJbfSDkUegRDbZupm6tMAK1nR22oaHYXh1/qtPpKE9PpyQujuKTcRTHnaTkZByl589Tnp4O6emMvGb7chVUeLtQvm8WaQEBWAYE4O3liFW5QrHZn6NGVIrKEIDVFHwdST9Sp2CpLuPEQLJcoumQ4EqIJs7fzRaVorBD24Ud2i60JJ0JZpt43G47qpxk2DQTNP+Czg9A76nQsoepmyyMSFEUzD08MPfwwK5/f8NybWEhJfHxFF8NuvJOHKX8zDnMiooxu5BO3oWNsGGjYf1FQJojXHJRSHNW6Nb9Hmz3xFLik4ePSwtUiqrauK0bTWB9vWDJ18H3uvu5NotWqT5ZLiFMRYIrIZq4vz5pmKp44DJiFqpgDzixHPZ8CSmH4cgP+lfLEOg9DTqNBDNLUzdf3CYqGxusg4OxDg4GwIurWa7Llyk5c4bSM2coOXNW/+/Zs1RkZuKRAx45Ojing4NruPDNGsP+vnNzJMEmlzRHuOKgENrjftqfq8A7SyHdXkuZmb4+V03BEoCnrSczQmdUy0Y1RJZLCFNqEk8LNhR5QkQ0FGM9LXgranzSUKeDiwdg71dwYgVUlAJQZulCSdfx2IU9pn/iUDQajeH6Ks/KovTMGUrPJ1GalERp0nnKkpIpPX8ebX7+TbfPtoUsOwX3Vu1o6dcZM3f3v7w8MPNwR2VhQWpBKsl5yfjY+9xwrFVd6njdiNwLhLFJ5kqIZqLGJw0VBVqF6F+D3uXob5/gdmox3iWZmO/7FN2+z1DaDoCQKdBukAyAFwCYOTtjFhKCTUjVuQx1Oh0V2dmUnT9PaXIyZRcvUXap6ktXXIxTATgV6OByPDkH4ms8jsrRETN3Nzzc3Sl3ciLFyQm1kxNqR8cq/zo7OvF20HRmHP8PFYruhlkuIUxN/ooKcQdJqbBn5LFQFF1vBqoOMl69kf7qY3B6o/7l0FI/xU6PaHDwMnVzRSOkKIo+8HJ2NnQxXssQfF26RHl6epVXRUYG5Wl/fq0rK0Obk0NpTg6lp89UP9hftAeWAOV9u+H+348ksBKNlgRXQtxBzmUUXJ1iR806bS/WaXvhW36ZJT1O0vLcMsi9CJrZsOV96DhU/6ShfySoVKZtuGgyrg2+bkSn06HNyfkz+MrIoCI7h4rsbP0rJ6fqv9nZhu5IZ3sPCaxEoybBlRB3EP2ThVSZw/AinqgGPQS270Lsb7D/W0jaCSd/17+c/SHkEQieALaupmu8aFYURdF3+zk5YdmuXa220ZWVUZGbC1rtzVcWwoTk46gQd5DKJwvViv5JLrWiMHtUZ/1YLTNL6DoGHl0DT+3WP1Fo6QBZ52DDm/BhR1g2Fc7vqja5sBC3g2JujpmrK2bu7qZuihA3JJkrIe4wY3v50r+9e81zGAJ4BMLQ/8DAt/STRu/7Rl/O4dhP+pd7oL7LsNtYsHK83acghBCNmmSuhLgDeTlaExrgevMJni1s9YPbH98CU2Og+0Qws4b0k7DmRZjTEX77O1w6dHsaLoQQTYBkroQQtdOyh/416F04+hPs/wbS4+DgIji4iHyXTlQET8Cx18Ng7WTq1gohhMlI5koIUTfWTtBnmn5c1iNrOO89lBKdGXaZJ3Dc/Crl/2mvH5t1bpuMzRJC3JEkcyWEqB9FIcWpO1HnJuCoG87f1Dt4UK2hI8l/js1y9ofuEyB4vNTNEkLcMSRzJYSot8q6WVk48G3FvQwp/RcjSt7mcruHwMJe/6Th5ndgbhD8MBZOroKKMlM3WwghjEoyV0KIeqteN0vhOO3Q3jcNrOfAiV/h0HeQtAvi1+pfth4Q/BB0jwa3tiZsvRBCGIdkroQQ9XbDulkWttB9PDy6Fp7eB2HPgq07FKTBjo/hs56UfjWIhPVfkJqeYeIzEUKIhiOZKyHELalV3Sz39jDoHRjwpj57dfA7tAkbsLi0h3aX9lC4400SWw3Cb8Bj4HcXqNS3/0SEEKKBSHAlhLhlXo7WN6+ZBaA2h8D7SfEeyKh//cIo1VYeUG+ljSoVv4u/w6Lf9ZNHdx0L3R7SB2VCCNHESLegEOK2O5dRQIrOhf9WjOTu0jmMKnmLxeUDKLdw0E8evf1D+G8v+Ppu2Ps1FGYCkJJTxM4zGaTkFJn4DIQQomaSuRJC3HZVB8IrHNS150hFB+5+/Bu8UjVw5EdI2AAXD+hfa18l2SOCd5K7ElMRTIVixnujujC2l6+Jz0QIIaqTzJUQ4rarcSC8qzN0+hs8vBT+GQeD3wPPLqAtwyd1I1+Zf8huy6d5Sz2fZSuWkZJdYOIzEUKI6iRzJYQwiZsOhLfzgNCnIPQpDu/fzt5fP+dv6u24KzlEm20gmg0Uf/01dH8QuoyBFp1McyJCCPEXElwJIUymtgPhW7Tryb8qxvN++TjCVCcYqd7BYNU+7Aouwva5+pdHEHQZDZ1Hk6Ly4FxGAf5utrUbaC+EEA1IgishRKNX2Y342vLjbNN2ZaeuG7oRbRltfwKO/QIJ6yEtFja9DZve5qK2PWsqwlin7cM/R/WTsVlCiNtKgishRJNw/W7E9voxWkVZcPJ3Sg4txTxpByGqeEJU8bylW8je3wPJKZqEY48HwM7d1KchhLgDSHAlhGgyauxGtHaGHtEccBzK81+v5X71Lu5X7yRYdZZQJRY2vwwxr4JfP30wFjiclHJb6ToUQhiFBFdCiGbD382WDMWZbyqG8k3FUFopadyn3ss/WsZicfkwnNsK57ai/WM6ZyoC+aOiDxu1IUyXrkMhRAOSUgxCiGbjryUeUmiB/4hXsXhyCzx3BAbOpLRFN1S6CvqpjvOe+TfstngK/9/HkBPzMWQnmfgMhBDNgWSuhBDNSo0lHpz9oN/z7PeawMv/+41hqj0MUe8lWHWW3kocbHlT//LqBoH3Q+BwcO9g0nMRQjRNElwJIZqdG5V48Hez5SIt+KJiOF9UDMebDIaYHeBlvwQsL+6GlCP61+Z3KXLwp6LdEOy6DodWvUEtfzKFEDcnfymEEHeUa8s6VOh0XFbc6TBiOpa9fKEgA06t5tKun3BN24V17jk4ME//snaBdoOgw72keoRzNleRwfBCiOuS4EoIccepsevQ1o2UgDH0+9kVG90k+quOMlB9kLtVh3AqyoSjP8LRH3HWmVGuDeRrXTA9B45lWORdgH5iaXkCUQghwZUQ4o5UU9fhuYwCtDrIx4bV2r6s1vZFTQW/DTejdYaGjP2/4qe6TH/1MfpzDDTfUX7Yn7NOYcyKb8VubSBlikWViaUl6BLiziLBlRBCXMPfzRaVAlrdNQsVM1w6RXE0ozcP7+xPgHKJKNVholSH6aWKwyL7HO2zz7HQAop0FuzWBrJ9ZTfSXR9jc4YTr644jlYHKoUqQZcQonmS4EoIIa7x1zFZakVh9qjOhoyTSlE4o2vJmYqW/K9iGI5KEd/0LyBhxwqi1IfxVLKIUh8hiiOwaBF36VyZpe7Kdm0X9mgDeW35cfq3d5cMlhDNmARXQgjxFzWNybpe4PXaqN60bO/Og1vd0Jbr6KAk0191lP7qY4SZncJbe4WHzGJ4iBgA4rUtUf6Igm4DoXU/mZJHiGZIgishhLiOmsZk1RR4VQZdp3S+nNa2pu3IV0nzt+WVD7/kLtVRQlWxBCpJtFddhPjv9S8A9476aXn8+l032JLxWkI0PRJcCSFEHV0v8Kop6Br6twn6TFe5DhelgE/CCuhndgoSt8PlY5Aep3/t+59+R+6BqHzD8Mqy5rddrXhxTaqM1xKiiZHgSgghGkhdgi4ACjPh/A59oJW4HS4fh/STqNNP0hvonfgZnc1bsV/bgcO6AL5ZcYn+7R7Gy8n29p6YEKJOFJ1Op7v5as1Dbm4ujo6O5OTk4ODgYOrmiCasrKyM1atXM3ToUMzNzU3dHNFcXA22Ks5sIfPoWtxLk6utUm5uj5lPCLQKgVa9oGUI2Loa3pduxJuTe4EwNslcCSFEY2HjAoH3o207hNWFd/HJoUJClDi6q07TXXWarspZrMvy4GyM/lXJ2Q9a9eJgRQBvH7bhpNa3Wq0tIcTtI8GVEEI0Qk6W8M8Rffm/lY6sK++NWlF4b2RHHvTNhwv74OIB/b8Z8ZCVCFmJ9AB+tYBynYrTupac/K01uXkDcPDrAS26VMlwCSGMR4IrIYRopMb0bEVUoGf18VpeXaHXFP3/i7Lh4gGSjm3l9EENwarTuCj5dFSS6UgybN0OW6/u0KEltOgMnl30rxadwNlfJqQWooE1ud+okpIS+vTpw5EjRzh06BDBwcGmbpIQQhhNTSUhDKydoO0AzN3DeGxvb7Q6HZ5kEqQ6T2fVeZ7sWIj1lVjIOge5F/WvhHWGzXVqCxSXAHDvoH+5tdeXh3BtC+ZWxj9BIZqhJhdcvfTSS3h7e3PkyBFTN0UIIRqNawucpupcSde5MXjEJKwrx1wV58LlE5B6jLPHd1Fw/hBtlYtYV5RC+kn961qKinLH1uTZtsbCPQBbz/bg0gZc/MGpNZhZ3P6TFKKJaFLB1Zo1a1i/fj3Lli1jzZo1pm6OEEI0Kjcs+2DlAK1DSXEKZuCvLdHqRqOgpaVyhfaqS3w0wAqHvLP6MVzpcVCcg1n2OZyzz8FFTdUDKSpwbKXvUnTy1b8cW4GjDzj56Lsf1fIUrbhzNZng6vLly0ydOpVff/0VGxubWm1TUlJCSUmJ4evc3FxA/xh9WVmZUdop7gyV149cR8IYbuX6crMxw83XocbtT6fmGial1qHigs6dCxXuHG0VQh9/FwBSsosY/eHvBCgXaa1cxk+5jJ+Syt0tCjDPOY9SVgDZSfrXdegUFdh5orNrAXYtqv2r/7+nvhq96vbfhuT3VhhbkwiudDodkydP5oknniAkJITExMRabffee+8xc+bMasvXr19f6wBNiBvZsGGDqZsgmjFjXF/ZJaCgRodiWKag48zh3Vy52jOYkKOQpnMiTefELjoZ1nvGoYJ2rbRYludwLiWN0ynptFSu0FJJp4dNBl5KBtalV1DryiHvEkrepRu2RYdCqZkdxWZOFJs7UWpmR5mZHaVqO0rNbClVV/+6XG0DinLD/d5MYWHhLW0vxM2YtIjoW2+9dd3g51r79u1j586dLF26lK1bt6JWq0lMTMTf3/+mA9qvl7ny8fEhIyNDCseJW1JWVsaGDRu45557pIioaHDGvr5+PnCBN1bGGqbVeXdEEGN6tjK8n5JTTOScrYYMF+jX0/yzP16OVjd8f3tCGh/9thsvMmihyuaxbtb0dClByb8MBWmQf1n///w0FF1FnduuU9To2t5DxYPf1/v8c3NzcXNzkyKiwmhMmrl65plnGDdu3A3X8fPz491332X37t1YWlpWeS8kJITx48ezcOHC625raWlZbRsAc3NzuSGKBiHXkjAmY11fD/f1v36Jh6t83cwNg+MrdDrUisLsUZ3xdbMH4EJOTpXACkCrg6MX83jjtzi0OifScIIK2HRIYfsrUdWfeNRqofAK6/YcZsmmfbgp2Tgr+fytgw1BTuVQlAVFmfp/C6/+v6wQRVdBZomO0sLyelegl99ZYWwmDa7c3Nxwc3O76XqffPIJ7777ruHrS5cuMXjwYJYuXUqfPn2M2UQhhGiWblbi4UaD4/3dbFEpVAmw1IoCf1kGUKHTkZhRWP1YKhUpFXY8ubEUra6bYfG3sTUEY8DPuxOYs3IPungd6f/aLBXoRaPVJMZc+fpW/eWxs7MDICAggFatWl1vEyGEELeopgDs2rIP12a2erZ2vm7Q5ed2/TGu5zIKah2MpeQU8fLKeLQ6Z/0CHby2/Dj927vLHIqi0WkSwZUQQojGpabM1vWCrpqCn5oyYNcLxuoSiAlhak0yuPLz88OE4/CFEEJw/czWDWttXWf72gZjdQnEhDC1JhlcCSGEaLxuOmXPNWobjNUlEBPC1CS4EkIIYVK1DcbqkhUTwpQkuBJCCNFk1CUrJoSpqEzdACGEEEKI5kSCKyGEEEKIBiTBlRBCCCFEA5LgSgghhBCiAd1RA9ora2Pl5uaauCWiqSsrK6OwsJDc3FyZp0w0OLm+jKvyHiD1EoWx3FHBVV5eHgA+Pj4mbokQQghTy8vLw9HR0dTNEM2QoruDQnetVsulS5ewt7dHURRTN6dGvXr1Yt++fU3uWPXdV123q8v6N1u3vu/n5ubi4+NDcnIyDg4OtWpLYyHXV8OsX5v15PpqnMfS6XTk5eXh7e2NSiWjY0TDu6MyVyqVqklM9KxWq2/bH9SGPFZ991XX7eqy/s3WvdX3HRwcmtzNT66vhlm/NuvJ9dV4jyUZK2FMErI3Qk8//XSTPFZ991XX7eqy/s3WvdX3myK5vhpm/dqsJ9dX8zmWEHVxR3ULCtFQcnNzcXR0JCcnp8llFkTjJ9eXEE2bZK6EqAdLS0tmzJiBpaWlqZsimiG5voRo2iRzJYQQQgjRgCRzJYQQQgjRgCS4EkIIIYRoQBJcCSGEEEI0IAmuhBBCCCEakARXQgghhBANSIIrIYwsOTmZyMhIgoKC6Nq1Kz///LOpmySakb/97W84OzszevRoUzdFCHGVlGIQwshSUlK4fPkywcHBpKWl0aNHD06dOoWtra2pmyaagZiYGPLz81m4cCG//PKLqZsjhEAyV0IYnZeXF8HBwQB4eHjg4uJCZmamaRslmo2oqCjs7e1N3QwhxDUkuBJ3vK1bt3L//ffj7e2Noij8+uuv1db5/PPP8ff3x8rKip49e7Jt27Z6HWv//v1otVp8fHxusdWiKbid15YQovGQ4Erc8QoKCujWrRufffbZdd9funQpzz//PK+//jqHDh3irrvu4t577yUpKcmwTs+ePencuXO116VLlwzrXLlyhejoaL766iujn5NoHG7XtSWEaFxkzJUQ11AUhRUrVjBy5EjDsj59+tCjRw/mzZtnWBYYGMjIkSN57733arXfkpIS7rnnHqZOncrEiRMbutmiCTDWtQWg0Wj47LPPZMyVEI2EZK6EuIHS0lIOHDjAoEGDqiwfNGgQO3furNU+dDodkydP5u6775bAShg0xLUlhGicJLgS4gYyMjKoqKigRYsWVZa3aNGC1NTUWu1jx44dLF26lF9//ZXg4GCCg4M5duyYMZormpCGuLYABg8ezJgxY1i9ejWtWrVi3759Dd1UIUQdmZm6AUI0BYqiVPlap9NVW1aTfv36odVqjdEs0QzcyrUFsG7duoZukhDiFknmSogbcHNzQ61WV8skpKWlVcs4CFEXcm0J0XxJcCXEDVhYWNCzZ082bNhQZfmGDRsICwszUatEcyDXlhDNl3QLijtefn4+p0+fNnx97tw5Dh8+jIuLC76+vvzjH/9g4sSJhISEEBoayldffUVSUhJPPPGECVstmgK5toS4M0kpBnHH02g0REVFVVs+adIkFixYAOgLPf773/8mJSWFzp07M3fuXPr373+bWyqaGrm2hLgzSXAlhBBCCNGAZMyVEEIIIUQDkuBKCCGEEKIBSXAlhBBCCNGAJLgSQgghhGhAElwJIYQQQjQgCa6EEEIIIRqQBFdCCCGEEA1IgishhBBCiAYkwZUQQgghRAOS4EqIRk6j0aAoCtnZ2bfleBMnTmT27Nk3XMfPz4+PPvoIgJKSEnx9fTlw4MBtaJ0QQjR+ElwJ0chERkby/PPPG74OCwsjJSUFR0dHox/76NGj/PHHH/z973+v9TaWlpZMnz6dl19+2YgtE0KIpkOCKyEaOQsLCzw9PVEUxejH+uyzzxgzZgz29vZ12m78+PFs27aNkydPGqllQgjRdEhwJUQjMnnyZLZs2cLHH3+MoigoisKCBQuqdAsuWLAAJycnVq1aRYcOHbCxsWH06NEUFBSwcOFC/Pz8cHZ25u9//zsVFRWGfZeWlvLSSy/RsmVLbG1t6dOnDxqNxvC+Vqvl559/Zvjw4VXalJaWxv3334+1tTX+/v4sXry4WrtdXV0JCwtjyZIlRvm+CCFEU2Jm6gYIIf708ccfEx8fT+fOnXn77bcBOHHiRLX1CgsL+eSTT/jxxx/Jy8tj1KhRjBo1CicnJ1avXs3Zs2d54IEH6NevH2PHjgXgkUceITExkR9//BFvb29WrFjBkCFDOHbsGO3atePo0aNkZ2cTEhJS5ViTJ08mOTmZzZs3Y2FhwbPPPktaWlq1NvXu3Ztt27YZ4bsihBBNiwRXQjQijo6OWFhYYGNjg6enJwBxcXHV1isrK2PevHkEBAQAMHr0aL777jsuX76MnZ0dQUFBREVFERMTw9ixYzlz5gxLlizhwoULeHt7AzB9+nTWrl3L/PnzmT17NomJiajVajw8PAzHiY+PZ82aNezevZs+ffoA8M033xAYGFitTS1btiQxMbGhvyVCCNHkSHAlRBNkY2NjCKwAWrRogZ+fH3Z2dlWWVWaYDh48iE6no3379lX2U1JSgqurKwBFRUVYWlpWGdt18uRJzMzMqmSzOnbsiJOTU7U2WVtbU1hY2CDnJ4QQTZkEV0I0Qebm5lW+VhTlusu0Wi2gH0+lVqs5cOAAarW6ynqVAZmbmxuFhYWUlpZiYWEBgE6nM+zrZjIzM3F3d6/fCQkhRDMiwZUQjYyFhUWVgegNoXv37lRUVJCWlsZdd9113XWCg4MBiI2NNfw/MDCQ8vJy9u/fT+/evQE4derUdWtuHT9+nO7duzdou4UQoimSpwWFaGT8/PzYs2cPiYmJZGRkGLJPt6J9+/aMHz+e6Oholi9fzrlz59i3bx/vv/8+q1evBsDd3Z0ePXqwfft2w3YdOnRgyJAhTJ06lT179nDgwAEee+wxrK2tqx1j27ZtDBo06JbbKoQQTZ0EV0I0MtOnT0etVhMUFIS7uztJSUkNst/58+cTHR3NP//5Tzp06MDw4cPZs2cPPj4+hnWmTZtWrdTC/Pnz8fHxISIiglGjRjFt2rQqg94Bdu3aRU5ODqNHj26QtgohRFOm6CoHVQgh7njFxcV06NCBH3/8kdDQ0FpvN2bMGLp3785rr71mxNYJIUTTIJkrIYSBlZUVixYtIiMjo9bblJSU0K1bN1544QUjtkwIIZoOyVwJIYQQQjQgyVwJIYQQQjQgCa6EEEIIIRqQBFdCCCGEEA1IgishhBBCiAYkwZUQQgghRAOS4EoIIYQQogFJcCWEEEII0YAkuBJCCCGEaEASXAkhhBBCNKD/B+YNAuMI0EI9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x300 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",
    "hm3_0 = ml_0.head(r3, 0, t3)\n",
    "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n",
    "plt.semilogx(t1, hm1_0[0], label=\"ttim OW1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n",
    "plt.semilogx(t2, hm2_0[0], label=\"ttim OW2\")\n",
    "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n",
    "plt.semilogx(t3, hm3_0[0], label=\"ttim OW3\")\n",
    "plt.xlabel(\"time(d)\")\n",
    "plt.ylabel(\"head(m)\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the specific storage of the aquitard (```Sll```) is very close to the minimum limit (zero), we will test to set Sll to 0 and remove it from the calibration:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model calibration without aquitard storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  1\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_1 = ttm.ModelMaq(\n",
    "    kaq=10,\n",
    "    z=[0, zt, zb],\n",
    "    Saq=0.001,\n",
    "    Sll=0,\n",
    "    c=10,\n",
    "    tmin=0.001,\n",
    "    tmax=1,\n",
    "    topboundary=\"semi\",\n",
    ")\n",
    "w_1 = ttm.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n",
    "ml_1.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "............................................\n",
      "Fit succeeded.\n"
     ]
    }
   ],
   "source": [
    "# unknown parameters: kaq, Saq, c\n",
    "ca_1 = ttm.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(name=\"c0\", initial=100)\n",
    "ca_1.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n",
    "ca_1.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n",
    "ca_1.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n",
    "ca_1.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kaq0</th>\n",
       "      <td>224.633797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saq0</th>\n",
       "      <td>0.000213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c0</th>\n",
       "      <td>43.882402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         optimal\n",
       "kaq0  224.633797\n",
       "Saq0    0.000213\n",
       "c0     43.882402"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.06024025968001043\n"
     ]
    }
   ],
   "source": [
    "display(ca_1.parameters.loc[:, [\"optimal\"]])\n",
    "print(\"RMSE:\", ca_1.rmse())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hm1_1 = ml_1.head(r1, 0, t1)\n",
    "hm2_1 = ml_1.head(r2, 0, t2)\n",
    "hm3_1 = ml_1.head(r3, 0, t3)\n",
    "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n",
    "plt.semilogx(t1, hm1_1[0], label=\"ttim OW1\")\n",
    "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n",
    "plt.semilogx(t2, hm2_1[0], label=\"ttim OW2\")\n",
    "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n",
    "plt.semilogx(t3, hm3_1[0], label=\"ttim OW3\")\n",
    "plt.xlabel(\"time(d)\")\n",
    "plt.ylabel(\"head(m)\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model with fixed Sll has a similar performance to the former model. The second model has an AIC value of -432.269, two units lower than the former model (-430.268). Thus, Sll should be set to zero (default value) and keep removed from the calibration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we compare the simulations done with TTim with the one done in AQTESOLV. The values are almost identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k [m/d]</th>\n",
       "      <th>Ss [1/m]</th>\n",
       "      <th>c [d]</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AQTESOLV</th>\n",
       "      <td>224.726</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>43.964</td>\n",
       "      <td>0.059627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttim</th>\n",
       "      <td>224.633797</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>43.882402</td>\n",
       "      <td>0.060240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             k [m/d]  Ss [1/m]      c [d]      RMSE\n",
       "AQTESOLV     224.726  0.000212     43.964  0.059627\n",
       "ttim      224.633797  0.000213  43.882402  0.060240"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\"],\n",
    "    index=[\"AQTESOLV\", \"ttim\"],\n",
    ")\n",
    "t.loc[\"AQTESOLV\"] = [224.726, 2.125e-4, 43.964]\n",
    "t.loc[\"ttim\"] = ca_1.parameters[\"optimal\"].values\n",
    "t[\"RMSE\"] = [0.059627, ca_1.rmse()]\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "* Carlson F, Randall J (2012) MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems. Ground Water 50(4):504–510\n",
    "* Duffield, G.M., 2007. AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n",
    "* 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"
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