{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exchange-current density from resistance\n",
    "\n",
    "In this example we show how to fit the exchange-current density to resistance data calculated from synthetic pulse data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybamm\n",
    "import ionworkspipeline as iwp\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by generating synthetic pulse data for a half-cell. In this example we use the standard symmetric exchange-current density function and will fit the reference value. We set up the model and parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pybamm.lithium_ion.SPMe(\n",
    "    {\"working electrode\": \"positive\", \"particle\": \"uniform profile\"}\n",
    ")\n",
    "model.events = []\n",
    "parameter_values = pybamm.ParameterValues(\"Xu2019\")\n",
    "\n",
    "\n",
    "def j0(c_e, c_s_surf, c_s_max, T):\n",
    "    j0_ref = pybamm.Parameter(\n",
    "        \"Positive electrode reference exchange-current density [A.m-2]\"\n",
    "    )\n",
    "    c_e_init = pybamm.Parameter(\"Initial concentration in electrolyte [mol.m-3]\")\n",
    "\n",
    "    return (\n",
    "        j0_ref\n",
    "        * (c_e / c_e_init) ** 0.5\n",
    "        * (c_s_surf / c_s_max) ** 0.5\n",
    "        * (1 - c_s_surf / c_s_max) ** 0.5\n",
    "    )\n",
    "\n",
    "\n",
    "parameter_values.update(\n",
    "    {\n",
    "        \"Positive electrode reference exchange-current density [A.m-2]\": 5,\n",
    "        \"Positive electrode exchange-current density [A.m-2]\": j0,\n",
    "    },\n",
    "    check_already_exists=False,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then simulate a GITT experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_rate = 1 / 15\n",
    "pause_s = 10  # 10s\n",
    "step_s = 5 * 60  # 5m\n",
    "step_h = step_s / 3600\n",
    "rest_s = 1 * 3600  # 1h\n",
    "max_cycles = int(1 / C_rate / step_h * 2)\n",
    "V_min = parameter_values[\"Lower voltage cut-off [V]\"]\n",
    "\n",
    "gitt_experiment = pybamm.Experiment(\n",
    "    [\n",
    "        (\n",
    "            pybamm.step.rest(pause_s, period=1),\n",
    "            pybamm.step.c_rate(\n",
    "                C_rate,\n",
    "                duration=step_s,\n",
    "                period=step_s / 100,\n",
    "                termination=f\"{V_min} V\",\n",
    "            ),\n",
    "            pybamm.step.rest(rest_s, period=rest_s / 100),\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "sim = pybamm.Simulation(\n",
    "    model, parameter_values=parameter_values, experiment=gitt_experiment\n",
    ")\n",
    "sol = None\n",
    "i = 0\n",
    "\n",
    "# Run until the voltage drops below the lower cut-off\n",
    "# during step 1 (the discharge step)\n",
    "while sol is None or sol.cycles[-1].steps[1].termination == \"final time\":\n",
    "    sol = sim.solve(starting_solution=sol)\n",
    "    i += 1\n",
    "    if i > max_cycles:\n",
    "        raise ValueError(\"Reached maximum number of cycles\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We export the solution data to a dataframe. Note: for synthetic data we can directly store the stoichiometry, whereas in practice we would need to compute this from the half-cell capacity and the charge passed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = [\n",
    "    \"Time [s]\",\n",
    "    \"Current [A]\",\n",
    "    \"Voltage [V]\",\n",
    "    \"Positive electrode stoichiometry\",\n",
    "]\n",
    "df = pd.DataFrame(sim.solution.get_data_dict(variables))\n",
    "df = df.rename(columns={\"Cycle\": \"Cycle number\", \"Step\": \"Step number\"})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we calculate the 1s resistance from the solution data using the function `iwp.objectives.pulse.calculate_pulse_resistance`. We pass in the step number \"1\" which is the \"on\" step of our pulse data (steps 0 and 2 are the \"off\" steps). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = [1]\n",
    "dts = [1]\n",
    "data = iwp.objectives.pulse.calculate_pulse_resistance(\"positive\", df, steps, dts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the calculated resistance as a function of stoichiometry "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SOC'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x=\"SOC\", y=\"Resistance [Ohm]\", style=\"o-\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we set up the objective that will be used to perform the fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = iwp.objectives.HalfCellResistanceModel(\"positive\")\n",
    "objective = iwp.objectives.Resistance(data, {\"model\": model})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we fit the exchange-current density and an extra scalar \"contact resistance\" parameter that accounts for any other resistances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    \"Positive electrode reference exchange-current density [A.m-2]\": iwp.Parameter(\n",
    "        \"j0_ref\", initial_value=1, bounds=(0.1, 10)\n",
    "    ),\n",
    "    \"Contact resistance [Ohm]\": iwp.Parameter(\"R_contact\"),\n",
    "}\n",
    "data_fit = iwp.DataFit(objective, parameters=parameters)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We pass in the known parameter values and run the fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Positive electrode reference exchange-current density [A.m-2]': 5.0471362704469644,\n",
       " 'Contact resistance [Ohm]': 2.6525385564927095}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make sure we're not accidentally initializing with the correct values by passing\n",
    "# them in\n",
    "params_for_pipeline = {k: v for k, v in parameter_values.items() if k not in parameters}\n",
    "data_fit.run(params_for_pipeline)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Resistance': [(<Figure size 1000x600 with 1 Axes>,\n",
       "   <Axes: xlabel='SOC', ylabel='Resistance [mOhm]'>)]}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RgfJV2eCLBtUesp6liHa7/SvHxMfHB0PApk2bmDx5cjCMASxYsIDm5mZ27NgRHDN//vxez7NgwQI2bdoEdLelLyws7DUmLCyM+fPnB8d8kc/no7m5uddjoBiGQVFREW63m5ycHGJjYwkPDyc2NpacnBzcbjdFRUUDtnzRbDaTnp7OqFGjyM/P5yc/+QkvvvgiBQUFwaWfv//975k8eTIxMTGMHj2a//zP/6S1tRXoXi565ZVX0tTUFJxtu/feewF48sknmTFjBnFxcaSnp/O9731vUM1KioiIiMjgN5S39QyaQBYIBLj55ps56aSTmDRp0iHH1NfX84tf/IJrr702eK26urpXGAOCX1dXV3/lmObmZjweD/X19fj9/kOO6XmOL1q2bBkJCQnBx+jRo/v2hvvA5XJRXl6Ow+E46EBpk8mEw+GgvLwcl8s1YDV80be+9S2mTp3Kc889B3QH2D/84Q/s2LGDJ554gvXr13P77bcDcOKJJ/LQQw8RHx+P0+nE6XTy4x//GIDOzk5+8Ytf8NFHH/HCCy9QWlrKFVdccdTeh4iIiIgMbaHY1tOfBs3B0EuXLmX79u28/fbbh7zf3NzMkiVLyMvLC86uhNJdd93FrbfeGvy6ubl5wEKZ1+vF5/NhtVoPed9qtVJTU4PX6x2Q1/8yEydOZNu2bQDcfPPNwetjx47ll7/8Jddddx1//OMfiYqKIiEhAZPJRHp6eq/nuOqqq4K/HjduHH/4wx844YQTaG1tJTY29qi8DxEREREZmr5sW09xcTE1NTUDtq2nPw2KGbIbbriBV155hTfeeIPMzMyD7re0tLBw4ULi4uJ4/vnniYyMDN5LT0+npqam1/ier3v+8f9lY+Lj47FarSQnJxMeHn7IMV8MED3MZjPx8fG9HgPFYrFgNpvxeDyHvO/xeDCbzUd9P5VhGMEZu9dee43TTz+dUaNGERcXx2WXXUZDQwPt7e1f+RyFhYWcffbZZGVlERcXx6mnngp0d84UEREREfkyod7W019CGsgMw+CGG27g+eefZ/369WRnZx80prm5mTPPPJOoqCheeumlg0LHnDlz+Pjjj3vtO1q3bh3x8fHk5eUFx7z++uu9vm/dunXMmTMHgKioKKZPn95rTCAQ4PXXXw+OCSW73U5WVhZOp/OgP1CGYeB0OsnKyvrKvXcD4ZNPPiE7O5vS0lLOOusspkyZwrPPPkthYSGPPfYY0L0/78u0tbWxYMEC4uPj+b//+z8+/PBDnn/++a/9PhERERGRwbit50iEdMni0qVLeeqpp3jxxReJi4sL7tdKSEjAarUGw1h7ezv/+Mc/ejXPSElJITw8nDPPPJO8vDwuu+wyHnjgAaqrq/nZz37G0qVLMZvNAFx33XU8+uij3H777Vx11VWsX7+ep59+mlWrVgVrufXWW7n88suZMWMGM2fO5KGHHqKtrY0rr7zy6H8wX2AymcjPz6empoaSkpKDuiwmJiaSn59/0B/EgbR+/Xo+/vhjbrnlFgoLCwkEAvzud78jLKw74z/99NO9xkdFReH3+3td27VrFw0NDfzmN78JLvfcvHnz0XkDIiIiIjKkDdZtPX0V0kD2pz/9CYB58+b1uv74449zxRVXUFRUxPvvvw9ATk5OrzH79u1j7NixhIeH88orr3D99dczZ84cYmJiuPzyy7n//vuDY7Ozs1m1ahW33HILDz/8MJmZmfztb39jwYIFwTEXX3wxdXV13HPPPVRXV3P88cezZs2agxp9hIrD4WDRokUUFRVRXl5OTU0NZrOZ3Nxc8vPzB3RtrM/no7q6+qC292eddRY/+MEP2L59O52dnTzyyCOcffbZvPPOOyxfvrzXc4wdO5bW1lZef/11pk6dSnR0NFlZWURFRfHII49w3XXXsX37dn7xi18M2PsQERERkeHjwG09h+o9EKptPX01qM4hG8oG+hyyHoZh4HK58Hq9WCwW7Hb7gM6MXXHFFTzxxBMAREREkJiYyNSpU/ne977H5ZdfHpwRe/DBB/mf//kf3G43c+fO5dJLL+UHP/gBjY2NwQOhr7/+elauXElDQwM///nPuffee/nnP//JT37yE5xOJ/n5+dx1112cc845bNmyheOPP/6QNekcMhERERExDIPVq1dTXFxMTk5Or38TG4ZBSUkJubm5LF68+KiuJIO+nUOmQNZPjlYgE32eIiIiItLty7os9mzrWbhwYUi6LPYlkA2atvciIiIiIiJ9EcptPf1FgUxERERERIYsh8PB4sWLj+q2nv6kQCYiIiIiIkOayWQiKSkp1GUckUFxMLSIiIiIiMhIpEAmIiIiIiISIgpkIiIiIiIiIaI9ZCIiIiIiMuQ9/WEFlqhwTp+YSox56MScoVOpiIiIiIjIIRiGwV3Pf4w/YPDeXacPqUCmJYtyRObNm8fNN98c6jJERERERGjr8OMPGAAkWCNDXE3fKJDJV7riiiswmUwHPR544AF+8YtfBMeNHTuWhx56KHSFioiIiMiI1eTpBCAqPAxL5NCKOENnLk9CZuHChTz++OO9rqWkpBAeHh6iikREREREPudu7wAg3ho5ZA6E7jG04qOEhNlsJj09vdfj9NNPDy5ZnDdvHmVlZdxyyy3BGTQRERERkaOlZ4bMFj20liuCZshCxjAMPJ3+kLy2NTK8X0PTc889x9SpU7n22mv54Q9/2G/PKyIiIiJyOJo/C2RDbf8YKJCFjKfTT949r4bktXfev4DoqMP/rX/llVeIjY0Nfr1o0aJe9+12O+Hh4cTFxZGent5vdYqIiIiIHI4mBTIZzk477TT+9Kc/Bb+OiYnhu9/9bggrEhERERH5nLtdgUz6yBoZzs77F4TstfsiJiaGnJycAapGREREROSb0QyZ9JnJZOrTssHBLioqCr8/NHviRERERGRkG8qBTF0WpV+MHTuWN998k8rKSurr60NdjoiIiIiMIApkMuLdf//9lJaWMn78eFJSUkJdjoiIiIiMIEM5kA2fNXMyIFasWHHI6xs2bOj19ezZs/noo48GviARERERkS8YyoFMM2QiIiIiIjKkDeWDoRXIRERERERkSNMMmYiIiIiISAgEAgbNCmQiIiIiIiJHX4uvi4DR/et4BTIREREREZGjp2d2zBIZhiUyPMTV9J0C2VFkGEaoSxgW9DmKiIiISI+hvH8MFMiOisjI7j8c7e3tIa5keOj5HHs+VxEREREZuYZ6INM5ZEdBeHg4NpuN2tpaAKKjozGZTCGuaugxDIP29nZqa2ux2WyEhw+9KWkRERER6V/udgUyOQzp6ekAwVAmR85mswU/TxEREREZ2TRDJofFZDLhcDhITU2ls7Mz1OUMWZGRkZoZExEREZGgzwNZVIgrOTIKZEdZeHi4AoWIiIiISD8wDINqVzMAZlMXhmEMua1BauohIiIiIiJDjtPpZPXq1Wzd+SkA5SW7WL16NU6nM8SV9Y0CmYiIiIiIDClOp5OCggKKi4vxh5sBSIw1U1xcTEFBwZAKZQpkIiIiIiIyZBiGQVFREW63m5ycHHxG9y6spBgzOTk5uN1uioqKhszZtQpkIiIiIiIyZLhcLsrLy3E4HJhMJlq7uq/HRH7eSK+8vByXyxXaQg+TApmIiIiIiAwZXq8Xn8+H1WoFoPWzBuaxn7UrtFqt+Hw+vF5viCrsGwUyEREREREZMiwWC2azGY/Hg2FAW08g++wYMo/Hg9lsxmKxhK7IPlAgExERERGRIcNut5OVlYXT6aS9yyBAd5v7mMju/WVOp5OsrCzsdnuIKz08CmQiIiIiIjJkmEwm8vPzsdls7CwpA8AcZuBrb6WkpITExETy8/OHzHlkCmQiIiIiIjKkOBwOFi1aROrocUD3odBut5vc3FwWLlyIw+EIcYWHLyLUBYiIiIiIiPSVw+Fg4tTp8FEhGUnxXHTRKdjt9iEzM9ZDgUxERERERIakxvbujh5ptliSkpJCXM2R0ZJFEREREREZkhrbOwCwx0SFuJIjp0AmIiIiIiJDkuuznveJ0QpkIiIiIiIiR1VjW88MWWSIKzlyCmQiIiIiIjIkuT5bspioJYsiIiIiIiJHV3CGTEsWRUREREREji7NkImIiIiIiITI53vIFMhERERERESOGn/AwO1Rl0UREREREZGjrsnTiWF0/zoxWl0WRUREREREjhrXZ8sVE6yRRIQP3VgzdCsXEREREZERq7F96O8fAwUyEREREREZghpaP+uwOISXK4ICmYiIiIiIDEGaIesHy5Yt44QTTiAuLo7U1FTOPfdciouLe43xer0sXbqUpKQkYmNjOf/886mpqek1pry8nCVLlhAdHU1qaiq33XYbXV1dvcZs2LCB/Px8zGYzOTk5rFix4qB6HnvsMcaOHYvFYmHWrFl88MEH/f6eRURERETkm+vZQzaUOyxCiAPZxo0bWbp0Ke+99x7r1q2js7OTM888k7a2tuCYW265hZdffpmVK1eyceNGqqqqOO+884L3/X4/S5YsoaOjg3fffZcnnniCFStWcM899wTH7Nu3jyVLlnDaaaexdetWbr75Zq655hpeffXV4Jh///vf3Hrrrfz85z+nqKiIqVOnsmDBAmpra4/OhyEiIiIiIodtOJxBBmAyjJ5mkaFXV1dHamoqGzduZO7cuTQ1NZGSksJTTz3FBRdcAMCuXbs49thj2bRpE7Nnz6agoICzzjqLqqoq0tLSAFi+fDl33HEHdXV1REVFcccdd7Bq1Sq2b98efK1LLrkEt9vNmjVrAJg1axYnnHACjz76KACBQIDRo0dz4403cuedd35t7c3NzSQkJNDU1ER8fHx/fzQiIiIiInKAW5/eynNFldy5aCLXnTo+1OX00pdsMKj2kDU1NQFgt9sBKCwspLOzk/nz5wfHTJw4kaysLDZt2gTApk2bmDx5cjCMASxYsIDm5mZ27NgRHHPgc/SM6XmOjo4OCgsLe40JCwtj/vz5wTFf5PP5aG5u7vUQEREREZGjIzhDpiWL/SMQCHDzzTdz0kknMWnSJACqq6uJiorCZrP1GpuWlkZ1dXVwzIFhrOd+z72vGtPc3IzH46G+vh6/33/IMT3P8UXLli0jISEh+Bg9evSRvXEREREREekzV3snAIlDfMnioAlkS5cuZfv27fzrX/8KdSmH5a677qKpqSn4qKioCHVJIiIiIiIjxud7yIZ22/uIUBcAcMMNN/DKK6/w5ptvkpmZGbyenp5OR0cHbre71yxZTU0N6enpwTFf7IbY04XxwDFf7MxYU1NDfHw8VquV8PBwwsPDDzmm5zm+yGw2Yzabj+wNi4iIiIjIN9KoLovfnGEY3HDDDTz//POsX7+e7OzsXvenT59OZGQkr7/+evBacXEx5eXlzJkzB4A5c+bw8ccf9+qGuG7dOuLj48nLywuOOfA5esb0PEdUVBTTp0/vNSYQCPD6668Hx4iIiIiIyODg6/TT4vvsmCtfG4OoT2GfhXSGbOnSpTz11FO8+OKLxMXFBfdrJSQkYLVaSUhI4Oqrr+bWW2/FbrcTHx/PjTfeyJw5c5g9ezYAZ555Jnl5eVx22WU88MADVFdX87Of/YylS5cGZ7Cuu+46Hn30UW6//Xauuuoq1q9fz9NPP82qVauCtdx6661cfvnlzJgxg5kzZ/LQQw/R1tbGlVdeefQ/GBEREREROSSn08kb7xUBYMJgzUvPMWZMFvn5+TgcjhBX13chbXtvMpkOef3xxx/niiuuALoPhv7Rj37EP//5T3w+HwsWLOCPf/xjr6WEZWVlXH/99WzYsIGYmBguv/xyfvOb3xAR8Xne3LBhA7fccgs7d+4kMzOTu+++O/gaPR599FH+53/+h+rqao4//nj+8Ic/MGvWrMN6L2p7LyIiIiIysJxOJwUFBeyu8/DPxrHERxrcldeC0+nEZrOxaNGiQRHK+pINBtU5ZEOZApmIiIiIyMAxDIPVq1dTXFxMR1IOyz8JJzPG4I7jDQzDoKSkhNzcXBYvXvylEz9Hy5A9h0xERERERORQXC4X5eXlOBwOWjq7A1fcZw0WTSYTDoeD8vJyXC5XCKvsOwUyEREREREZ9LxeLz6fD6vVSnP3EWTEHdBg0Wq14vP58Hq9oSnwCCmQiYiIiIjIoGexWDCbzXg8Hlo6umfI4g84gszj8WA2m7FYLCGq8MgokImIiIiIyKBnt9vJysrC6XTS3NndBiMusvt/DcPA6XSSlZWF3W4PZZl9pkAmIiIiIiKDnslkIj8/H5vNRk1T97LE2IgAra2tlJSUkJiYSH5+fsgbevSVApmIiIiIiAwJDoeDRYsW4Y+IBqC9oRq3201ubi4LFy4cFC3v+yqkB0OLiIiIiIj0hcPhwEMk0Ml3Fs/n2Awbdrt9yM2M9VAgExERERGRIcPX5afJ091mcdL40diio77mOwY3LVkUEREREZEho6G1A4DIcBMJ1sivGT34KZCJiIiIiMiQUdfiAyA51jxklykeSIFMRERERESGjJ5AlhJnDnEl/UOBTEREREREhoy61s8CWawCmYiIiIiIyFFVf8CSxeFAgUxERERERIaM4AyZliyKiIiIiIgcXdpDJiIiIiIiEiL1rVqyKCIiIiIiEhKaIRMREREREQkRBTIREREREZEQaO/ooq3DD0BybFSIq+kfCmQiIiIiIjIk1Ld0AGCJDCPWHBHiavqHApmIiIiIiAwJda1eoHu5oslkCnE1/UOBTEREREREhoS6z2bIhkuHRVAgExERERGRIcAwDEqrGwBIiDJhGEaIK+ofw2PhpYiIiIiIDFtOp5OioiLe3NYEJOCuLmf16kby8/NxOByhLu8b0QyZiIiIiIgMWk6nk4KCAoqLi+kMtwKQFBNFcXExBQUFOJ3OEFf4zSiQiYiIiIjIoGQYBkVFRbjdbnJycmgzIgFIjY0kJycHt9tNUVHRkF6+qEAmIiIiIiKDksvlory8HIfDgclkwt19JjQ2M5hMJhwOB+Xl5bhcrtAW+g0okImIiIiIyKDk9Xrx+XxYrd1LFZu6myxi++xMaKvVis/nw+v1hqjCb06BTEREREREBiWLxYLZbMbj8dDhh7au7rPHbJ91vfd4PJjNZiwWSwir/GYUyEREREREZFCy2+1kZWXhdDpxd3TvE4sMM7CGd+8vczqdZGVlYbfbQ1zpkVMgExERERGRQclkMpGfn4/NZmPH3kqge7liW1srJSUlJCYmkp+fj8lkCnGlR06BTEREREREBi2Hw8GiRYuIT8sCwBzw4Ha7yc3NZeHChUP+HDIdDC0iIiIiIoOaw+EgLTsXios5LnsUF53zLex2+5CeGeuhQCYiIiIiIoNeTXN3z/vsdDtJSUkhrqb/aMmiiIiIiIgMetVN3a3tHQlDt6PioSiQiYiIiIjIoOds7g5k6QpkIiIiIiIiR1fNZzNk6fEKZCIiIiIiIkdNlz9AbYuWLIqIiIiIiBx19a0dBAwIDzORFGsOdTn9SoFMREREREQGNWeTB4C0ODPhYUO/1f2BFMhERERERGRQq/msoUfaMFuuCApkIiIiIiIyyDmHact7UCATEREREZFBrrpnhmyYdVgEBTIRERERERnkhuuh0KBAJiIiIiIig1zPksX0BGuIK+l/CmQiIiIiIjJoGYZBVWMbANH4MAwjxBX1r4hQFyAiIiIiInIoTqeTwsIinG4DMPHhxnX49o8iPz8fh8MR6vL6hWbIRERERERk0HE6nRQUFLB1Vwl+us8ey0yKo7i4mIKCApxOZ4gr7B8KZCIiIiIiMqgYhkFRURFutxtbRjYA8ZEGtvhYcnJycLvdFBUVDYvliwpkIiIiIiIyqLhcLsrLy3E4HLh83bNjSZ81WDSZTDgcDsrLy3G5XCGssn8okImIiIiIyKDi9Xrx+XxYrVZcvu5riebP71utVnw+H16vNzQF9iMFMhERERERGVQsFgtmsxmPx/P5DNkBgczj8WA2m7FYhv65ZApkIiIiIiIyqNjtdrKysnA6nTR8Nglmt3TvFzMMA6fTSVZWFna7PYRV9g8FMhERERERGVRMJhP5+fnYbDaqmzsAsEUGaG1tpaSkhMTERPLz8zGZTCGu9JtTIBMRERERkUHH4XCwcOFC2uheq+ip34/b7SY3N5eFCxfqHLL+8Oabb3L22WeTkZGByWTihRde6HW/tbWVG264gczMTKxWK3l5eSxfvrzXGK/Xy9KlS0lKSiI2Npbzzz+fmpqaXmPKy8tZsmQJ0dHRpKamctttt9HV1dVrzIYNG8jPz8dsNpOTk8OKFSsG4i2LiIiIiMhhirGl4PV3//ryC87hoosuYvHixcMmjEGIA1lbWxtTp07lscceO+T9W2+9lTVr1vCPf/yDTz75hJtvvpkbbriBl156KTjmlltu4eWXX2blypVs3LiRqqoqzjvvvOB9v9/PkiVL6Ojo4N133+WJJ55gxYoV3HPPPcEx+/btY8mSJZx22mls3bqVm2++mWuuuYZXX3114N68iIiIiIh8pYrGdgCSY6MYP3Y0SUlJw2KZ4oFMxiA5Tc1kMvH8889z7rnnBq9NmjSJiy++mLvvvjt4bfr06SxatIhf/vKXNDU1kZKSwlNPPcUFF1wAwK5duzj22GPZtGkTs2fPpqCggLPOOouqqirS0tIAWL58OXfccQd1dXVERUVxxx13sGrVKrZv3x58nUsuuQS3282aNWsOq/7m5mYSEhJoamoiPj6+Hz4REREREZGR7dUd1fzHk4VMHW3jxaUnhbqcw9aXbDCo95CdeOKJvPTSS1RWVmIYBm+88QaffvopZ555JgCFhYV0dnYyf/784PdMnDiRrKwsNm3aBMCmTZuYPHlyMIwBLFiwgObmZnbs2BEcc+Bz9IzpeQ4RERERETn69jd6AMi0WUNcycCJCHUBX+WRRx7h2muvJTMzk4iICMLCwvjrX//K3LlzAaiuriYqKgqbzdbr+9LS0qiurg6OOTCM9dzvufdVY5qbm/F4PFitB/8B8Pl8+Hy+4NfNzc3f7M2KiIiIiEgv+z9bspiZOHwD2aCeIXvkkUd47733eOmllygsLOR3v/sdS5cu5bXXXgt1aSxbtoyEhITgY/To0aEuSURERERkWKn8bIZslALZ0efxePjJT37C73//e84++2ymTJnCDTfcwMUXX8xvf/tbANLT0+no6MDtdvf63pqaGtLT04Njvth1sefrrxsTHx9/yNkxgLvuuoumpqbgo6Ki4hu/ZxERERER+VxwyaIC2dHX2dlJZ2cnYWG9SwwPDycQCADdDT4iIyN5/fXXg/eLi4spLy9nzpw5AMyZM4ePP/6Y2tra4Jh169YRHx9PXl5ecMyBz9Ezpuc5DsVsNhMfH9/rISIiIiIi/afS3RPIokNcycAJ6R6ynpO2e+zbt4+tW7dit9vJysri1FNP5bbbbsNqtTJmzBg2btzI//7v//L73/8egISEBK6++mpuvfVW7HY78fHx3HjjjcyZM4fZs2cDcOaZZ5KXl8dll13GAw88QHV1NT/72c9YunQpZnP3IXPXXXcdjz76KLfffjtXXXUV69ev5+mnn2bVqlVH/0MRERERERGavZ00eToBGKWmHgNj8+bNnHbaacGvb731VgAuv/xyVqxYwb/+9S/uuusuLr30UlwuF2PGjOFXv/oV1113XfB7HnzwQcLCwjj//PPx+XwsWLCAP/7xj8H74eHhvPLKK1x//fXMmTOHmJgYLr/8cu6///7gmOzsbFatWsUtt9zCww8/TGZmJn/7299YsGDBUfgURERERETki3r2jyVGRxJjHtS9CL+RQXMO2VCnc8hERERERPrPup3V/PB/C8lNsfLUFVOx2+1D5lDoYXMOmYiIiIiIjDxOp5O1bxcC4G+u4+mnn2b16tU4nc4QV9b/FMhERERERGTQcDqdFBQUsNvpAiDTbsVms1FcXExBQcGwC2UKZCIiIiIiMigYhkFRURFut5suSyIAqVYTsbGx5OTk4Ha7KSoqYjjtulIgExERERGRQcHlclFeXo7D4aDe230t5bMGiyaTCYfDQXl5OS6XK3RF9jMFMhERERERGRS8Xi8+nw+zxRoMZMmWz+9brVZ8Ph9erzc0BQ4ABTIRERERERkULBYLZrOZmmYvXYaJMJNBovnz+x6PB7PZjMVi+fInGWIUyEREREREZFCw2+1kZWXxqbMRgCQzhH/W6d4wDJxOJ1lZWdjt9hBW2b8UyEREREREZFAwmUzk5+fTGWUDIDGyC7/fT2trKyUlJSQmJpKfnz9kziM7HMP3yGsRERERERlyHA4HiaMnQG0NcXgpLa3GbDaTm5tLfn4+Docj1CX2KwUyEREREREZVOp93TNgp50wie8cdwoWiwW73T6sZsZ6KJCJiIiIiMigUtrQDkBeVgqjRqWFuJqBpT1kIiIiIiIyaBiGQVlDGwBjkmJCXM3AUyATEREREZFBo761g/YOP2EmyEy0hrqcAXdYSxb/8Ic/9PmJr7zySuLi4vr8fSIiIiIiMnL1zI5l2KyYI8JDXM3AO6xAdvPNN5OZmUl4+OF9IBUVFZx11lkKZCIiIiIi0ic9+8fGjoDlitCHph6bN28mNTX1sMYqiImIiIiIyJH4fP9YdIgrOToOaw/Zz3/+c2JjYw/7SX/yk58Mq9OzRURERETk6OiZIRspgeywZsh+/vOf9+lJ77rrriMqRkRERERERi7DMNhb0wSAPSqAYRjD8uyxA+kcMhERERERCTmn00lhYRF7av1AODvf34i14VPy8/NxOByhLm/A9LntfUNDA0uXLiUvL4/k5GTsdnuvh4iIiIiISF84nU4KCgrYuqsEbyAcEwZjk2MoLi6moKAAp9MZ6hIHTJ9nyC677DJKSkq4+uqrSUtLG/ZTiCIiIiIiMnAMw6CoqAi32010Wg7Ug90MifGx2OJyKCkpoaioiMWLFw/L7NHnQPbWW2/x9ttvM3Xq1IGoR0RERERERhCXy0V5eTkOh4OPWrsDV9pn/TxMJhMOh4Py8nJcLhdJSUkhrHRg9HnJ4sSJE/F4PANRi4iIiIiIjDBerxefz4fVaqXG81kgs35+32q14vP58Hq9IapwYPU5kP3xj3/kpz/9KRs3bqShoYHm5uZeDxERERERkcNlsVgwm814PB5qPpv3SbMawfsejwez2YzFYglRhQOrz0sWbTYbzc3NfOtb3+p1vaclpd/v77fiRERERERkeLPb7WRlZVFcXEx1exzw+QyZYRg4nU5yc3OHbQPBPgeySy+9lMjISJ566ik19RARERERkW/EZDKRn5/PfmcNrtruaylmP62tHpxOJ4mJieTn5w/b3NHnQLZ9+3a2bNlCbm7uQNQjIiIiIiIjjMPh4NiZp0LJLixhfur2l2KxmMnNzR3255D1OZDNmDGDiooKBTIREREREek3TUb3OsVch42LzrsQi8WC3W4ftjNjPfocyG688Ub+67/+i9tuu43JkycTGRnZ6/6UKVP6rTgRERERERkZSmpbAZjosDFq1KgQV3P09DmQXXzxxQBcddVVwWsmk0lNPURERERE5IjtqesOZONTY0JcydHV50C2b9++gahDRERERERGsD11bQCMT4kNcSVHV58D2ZgxYwaiDhERERERGaECAYO9n82Q5aQqkH2t3bt388Ybb1BbW0sgEOh175577umXwkREREREZGTY39iOrytAZJgJa8CDYUQP+2YePfocyP76179y/fXXk5ycTHp6eq8PymQyKZCJiIiIiMhhczqd/Ou1IgDiw7w8+8xKsrKyhn27+x59DmS//OUv+dWvfsUdd9wxEPWIiIiIiMgI4XQ6KSgoYFuVCUglMz4Sm81GcXExNTU1LFq0aNiHsrC+fkNjYyMXXnjhQNQiIiIiIiIjhGEYFBUV4Xa76bQmA5ARA7GxseTk5OB2uykqKsIwjBBXOrD6HMguvPBC1q5dOxC1iIiIiIjICOFyuSgvL8fhcFDV3r0NKuOzjvcmkwmHw0F5eTkulyuEVQ68w1qy+Ic//CH465ycHO6++27ee++9Qx4MfdNNN/VvhSIiIiIiMux4vV58Ph9mi5VqT/e1jOjP71utVmpqavB6vaEp8Cg5rED24IMP9vo6NjaWjRs3snHjxl7XTSaTApmIiIiIiHwti8WC2WymsslHZyCSyDCDZMvn9z0eD2azGYvF8uVPMgwcViDTYdAiIiIiItKf7HY7WVlZrPqoEogl3QphnzVwNwwDp9NJbm4udrs9pHUOtD7vIetRX19PfX19f9YiIiIiIiIjhMlkIj8/n7bIBABSozrx+/20trZSUlJCYmIi+fn5w/48sj4FMrfbzdKlS0lOTiYtLY20tDSSk5O54YYbcLvdA1SiiIiIiIgMRw6Hg7DETADiaaO0tBS3201ubi4LFy4c9i3voQ/nkLlcLubMmUNlZSWXXnopxx57LAA7d+5kxYoVvP7667z77rskJiYOWLEiIiIiIjK8lLk7ATj/9DlMc1iwWCzY7fZhPzPW47AD2f33309UVBR79uwhLS3toHtnnnkm999//0ENQERERERERA7F2+mntKENgJnHjCI1fng38DiUw16y+MILL/Db3/72oDAGkJ6ezgMPPMDzzz/fr8WJiIiIiMjwtbumlYABidGRpMSZQ11OSBx2IHM6nRx33HFfen/SpElUV1f3S1EiIiIiIjL87apuBiA3PW7ELFH8osMOZMnJyZSWln7p/X379g37lpQiIiIiItI/DMPgo9I6AMbYojAMI8QVhcZhB7IFCxbw05/+lI6OjoPu+Xw+7r77bhYuXNivxYmIiIiIyPDjdDpZvXo1b2/fC4C7dAerV6/G6XSGuLKjr09NPWbMmMGECRNYunQpEydOxDAMPvnkE/74xz/i8/l48sknB7JWEREREREZ4pxOJwUFBbjdbhq6JgCQbbdQXFxMTU0NixYtGhHt7nscdiDLzMxk06ZN/Od//id33XVXcErRZDJxxhln8OijjzJ69OgBK1RERERERIY2wzAoKirC7XaTmpVDS20YJgxykq1EpeZQUlJCUVERixcvHjF7yg47kAFkZ2dTUFBAY2Mju3fvBiAnJ0d7x0RERERE5Gu5XC7Ky8txOByUtXcHrlQrmMMBTDgcDsrLy3G5XCQlJYW01qOlT4GsR2JiIjNnzuzvWkREREREZBjzer34fD6sViv7G7uvjY75/L7VaqWmpgav1xuaAkOgz4HM6/XyyCOP8MYbb1BbW0sgEOh1v6ioqN+KExERERGR4cNisWA2m/F4POxviwMgM/bz7ooejwez2YzFMnIOiO5zILv66qtZu3YtF1xwATNnzhwxaztFREREROSbsdvtZGVlUVxcTEXrZ4HssxkywzBwOp3k5uaOqC1RfQ5kr7zyCqtXr+akk04aiHpERERERGSYMplM5OfnU1pVS0Nt98ROhtVPa6sHp9NJYmIi+fn5I2rSp8+BbNSoUcTFxQ1ELSIiIiIiMsw5HA7GTD0J9uwmIaKL2v2lmM1mcnNzyc/PH1Et76EPB0P3+N3vfscdd9xBWVnZN37xN998k7PPPpuMjAxMJhMvvPDCQWM++eQTzjnnHBISEoiJieGEE06gvLw8eN/r9bJ06VKSkpKIjY3l/PPPp6amptdzlJeXs2TJEqKjo0lNTeW2226jq6ur15gNGzaQn5+P2WwmJyeHFStWfOP3JyIiIiIiB3N6u+eFpmencuGFF3LRRRexePHiERfG4AgC2YwZM/B6vYwbN464uDjsdnuvR1+0tbUxdepUHnvssUPe37NnDyeffDITJ05kw4YNbNu2jbvvvrvXJr9bbrmFl19+mZUrV7Jx40aqqqo477zzgvf9fj9Lliyho6ODd999lyeeeIIVK1Zwzz33BMfs27ePJUuWcNppp7F161ZuvvlmrrnmGl599dU+fjoiIiIiIvJ1dlQ1AZCfncyoUaNISkoaUcsUD2Qyek54Pkzz58+nvLycq6++mrS0tIM+uMsvv/zICjGZeP755zn33HOD1y655BIiIyN58sknD/k9TU1NpKSk8NRTT3HBBRcAsGvXLo499lg2bdrE7NmzKSgo4KyzzqKqqoq0tDQAli9fzh133EFdXR1RUVHccccdrFq1iu3bt/d6bbfbzZo1aw6r/ubmZhISEmhqaiI+Pv6IPgMRERERkZFg/u83UlLbyuNXnsBpuamhLqff9SUb9HkP2bvvvsumTZuYOnXqERd4OAKBAKtWreL2229nwYIFbNmyhezsbO66665gaCssLKSzs5P58+cHv2/ixIlkZWUFA9mmTZuYPHlyMIwBLFiwgOuvv54dO3Ywbdo0Nm3a1Os5esbcfPPNX1qfz+fD5/MFv25ubu6fNy4iIiIiMoy1+TrZW9cKQIbFj2EYI3Z2DI5gyeLEiRPxeDwDUUsvtbW1tLa28pvf/IaFCxeydu1avvOd73DeeeexceNGAKqrq4mKisJms/X63rS0NKqrq4NjDgxjPfd77n3VmObm5i99r8uWLSMhISH4GD169Dd+zyIiIiIiw5nT6eTvz60lYEBMWBfrV7/A6tWrcTqdoS4tZPocyH7zm9/wox/9iA0bNtDQ0EBzc3OvR3/pOXD629/+NrfccgvHH388d955J2eddRbLly/vt9c5UnfddRdNTU3BR0VFRahLEhEREREZtJxOJwUFBWze092Ab0x8GDabjeLiYgoKCkZsKOvzksWFCxcCcPrpp/e63jPV6Pf7+6Ww5ORkIiIiyMvL63X92GOP5e233wYgPT2djo4O3G53r1mympoa0tPTg2M++OCDXs/R04XxwDFf7MxYU1NDfHw8Vqv1kPWZzWbMZvORv0ERERERkRHCMAyKiopwu920WSdAK4yNg9jYWHJycigpKaGoqIjFixePuOWLfQ5kb7zxxkDUcZCoqChOOOEEiouLe13/9NNPGTNmDADTp08nMjKS119/nfPPPx+A4uJiysvLmTNnDgBz5szhV7/6FbW1taSmdm8YXLduHfHx8cGwN2fOHFavXt3rddatWxd8DhEREREROXIul4vy8nIcDgflxd2Ba8xnRxubTKbu6+XluFwukpKSQljp0dfnQHbqqaf224u3trZSUlIS/Hrfvn1s3boVu91OVlYWt912GxdffDFz587ltNNOY82aNbz88sts2LABgISEBK6++mpuvfVW7HY78fHx3HjjjcyZM4fZs2cDcOaZZ5KXl8dll13GAw88QHV1NT/72c9YunRpcIbruuuu49FHH+X222/nqquuYv369Tz99NOsWrWq396riIiIiMhI5fV68fl8GJFWar2fBbLYz+9brVZqamrwer0hqjB0DmsP2bZt24J7ug7Hjh07Djp4+VA2b97MtGnTmDZtGgC33nor06ZNC54R9p3vfIfly5fzwAMPMHnyZP72t7/x7LPPcvLJJwef48EHH+Sss87i/PPPZ+7cuaSnp/Pcc88F74eHh/PKK68QHh7OnDlz+P73v88PfvAD7r///uCY7OxsVq1axbp165g6dSq/+93v+Nvf/saCBQsO+z2LiIiIiMihWSwWzGYzu10dAKRYDGIiP7/v8Xgwm829zhseKQ7rHLLw8HCqq6tJSUk5rCeNj49n69atjBs37hsXOFToHDIRERERkUMzDIPVq1fzZFE977UlMyPF4PJjjOC9kpIScnNzh80esn4/h8wwDO6++26io6MPq4COjo7DGiciIiIiIsOfyWQiPz+fh4o2AZAe6cPvD8fj8eB0OklMTCQ/P39YhLG+OqxANnfu3IOaa3yVOXPmfGl3QhERERERGXnS09OpD8QAXSQG3JSWtmA2m8nNzSU/Px+HwxHqEkPisAJZTxMNERERERGRI7G/0UOjp4vIcBPXf/ccjK4OLBYLdrt9RM6M9ehzl0UREREREZG+MAyDtz+pAOCYlGgcqckjOoQd6LC6LIqIiIiIiBwJp9PJ6tWreXZDEQDmtmpWr16N0+kMcWWDgwKZiIiIiIgMCKfTSUFBAcXFxdT5uxsEjrdFUFxcTEFBgUIZCmQiIiIiIjIADMOgqKgIt9vNmHE5VHrCAZiYYiYnJwe3201RURGHcQrXsKZAJiIiIiIi/c7lclFeXo7D4WB/m4kuw0RcpEGKpbsNvsPhoLy8HJfLFepSQ+qIAtmTTz7JSSedREZGBmVlZQA89NBDvPjii/1anIiIiIiIDE1erxefz4fVamVvc/e17Djo6eVhtVrx+Xx4vd7QFTkI9DmQ/elPf+LWW29l8eLFuN1u/H4/ADabjYceeqi/6xMRERERkSHIYrFgNpvxeDzsae5OYePjP1+e6PF4MJvNWCyWUJU4KPQ5kD3yyCP89a9/5ac//Snh4eHB6zNmzODjjz/u1+JERERERGRostvtZGVlUVXlZG9L97Vx8d3/axgGTqeTrKws7HZ76IocBPocyPbt28e0adMOum42m2lra+uXokREREREZGgzmUzk5+fTFZ1Ee5eJSJNBhsVPa2srJSUlJCYmkp+fP+LPI+tzIMvOzmbr1q0HXV+zZg3HHntsf9QkIiIiIiLDgMPhICEnv/vXUT4qyktxu93k5uaycOFCHA5HiCsMvYi+fsOtt97K0qVL8Xq9GIbBBx98wD//+U+WLVvG3/72t4GoUUREREREhqhPG7t7TpyRP54LZ8zGYrFgt9tH/MxYjz4HsmuuuQar1crPfvYz2tvb+d73vkdGRgYPP/wwl1xyyUDUKCIiIiIiQ5BhGLy/px6A40fFkZGRoSD2BSbjG5zE1t7eTmtrK6mpqf1Z05DU3NxMQkICTU1NxMfHh7ocEREREZGQcjqdvPFeET95H8Dg5tH7mTB2NPn5+cN+qWJfssERNfXYvXs3ANHR0cEwtnv3bkpLS/terYiIiIiIDCtOp5OCggLe/KQKgIxoSLMnUFxcTEFBAU6nM8QVDh59DmRXXHEF77777kHX33//fa644or+qElERERERIYowzAoKirC7XbTYk0HYEICxMbGkpOTg9vtpqioiG+wUG9Y6XMg27JlCyeddNJB12fPnn3I7osiIiIiIjJyuFwuysvLcTgc7P7sQOgJCd3hy2Qy4XA4KC8vx+VyhbLMQaPPgcxkMtHS0nLQ9aamJvx+f78UJSIiIiIiQ5PX68Xn89ERbqXWY8KEwYSEz+9brVZ8Ph9erzd0RQ4ifQ5kc+fOZdmyZb3Cl9/vZ9myZZx88sn9WpyIiIiIiAwtFosFs9nMzvpOAEbFQPQBvd09Hg9msxmLxRKiCgeXPre9/+///m/mzp1Lbm4up5xyCgBvvfUWzc3NrF+/vt8LFBERERGRocNut5OVlcUrH7qB6F6zY4Zh4HQ6yc3NxW63h6rEQaXPM2R5eXls27aNiy66iNraWlpaWvjBD37Arl27mDRp0kDUKCIiIiIiQ4TJZCI/P58qfywAo80e/H4/ra2tlJSUkJiYSH5+vs4j+0yfZ8gAMjIy+PWvf93ftYiIiIiIyBBnGAYur0FjZwQmDBI76ykt9WE2m8nNzR0R55D1xREFMrfbzQcffEBtbS2BQKDXvR/84Af9UpiIiIiIiAwtTqeToqIiVu9sAJJIi+ogPclGbm4uY8aMwW63a2bsC/ocyF5++WUuvfRSWltbiY+P7/WBmkwmBTIRERERkRGo5zBot9tNdSAbgGMSuq97PB4cDofC2CH0eQ/Zj370I6666ipaW1txu900NjYGHzpLQERERERk5DnwMOjx43PY2xYJwHEpkToM+mv0OZBVVlZy0003ER0dPRD1iIiIiIjIEHPgYdC1XhONHSYiTAY58ToM+uv0OZAtWLCAzZs3D0QtIiIiIiIyBPUcBm21Wtnl7r42Ph6iwrt/rcOgv1yf95AtWbKE2267jZ07dzJ58mQiIyN73T/nnHP6rTgRERERERn8eg6D9ng8fOKOA2Ci7fPliToM+sv1OZD98Ic/BOD+++8/6J7JZMLv93/zqkREREREZMjoOQx6x65PKWnqDmTHJnbf02HQX63PgeyLbe5FRERERGRk6zkM+r19LjoCJuIiAqSZ/bS2enA6nToM+isc0TlkIiIiIiIiB0pPT6c9YSzsbyYjvJnS0losFosOg/4aRxTI2tra2LhxI+Xl5XR0dPS6d9NNN/VLYSIiIiIiMjT0HAj91qcdQBSjI1ux2+3MmDGDvLw8zYx9hT4Hsi1btrB48WLa29tpa2vDbrdTX19PdHQ0qampCmQiIiIiIiNIz4HQVa4WajtzAJiVlUBDfRXvv/8+drtds2Nfoc9t72+55RbOPvtsGhsbsVqtvPfee5SVlTF9+nR++9vfDkSNIiIiIiIyCB14ILTHNg6AzBiD9MQYHQh9mPocyLZu3cqPfvQjwsLCCA8Px+fzMXr0aB544AF+8pOfDESNIiIiIiIyCB14IPSOxu5liZM+666oA6EPT58DWWRkJGFh3d+WmppKeXk5AAkJCVRUVPRvdSIiIiIiMmj1HAgdaf78QOjj7J/PhulA6K/X5z1k06ZN48MPP2TChAmceuqp3HPPPdTX1/Pkk08yadKkgahRREREREQGoZ4DoT+p78DrjyQu0iAr9vP7OhD66/V5huzXv/51cFPer371KxITE7n++uupq6vjz3/+c78XKCIiIiIig1PPgdCFTh8AxyVC2GcNFXsOhM7KytKB0F+hzzNkM2bMCP46NTWVNWvW9GtBIiIiIiIydIwZM4Z973YfhTXO2o7fH4XHowOhD1efZ8i+9a1v4Xa7D7re3NzMt771rf6oSUREREREBjmn08nq1at5Yf0mmvxRmIwA9R+/xUcffYTb7SY3N5eFCxeq5f3X6PMM2YYNGw46DBq6N/S99dZb/VKUiIiIiIgMXj1nj7ndbqrCxwAwPs7P6NhUoqOjmTt3rg6EPkyHHci2bdsW/PXOnTuprq4Ofu33+1mzZg2jRo3q3+pERERERGRQOfDssZycHFZv7150NzUlnKmOqZSUlFBaWkpeXl6IKx0aDjuQHX/88ZhMJkwm0yGXJlqtVh555JF+LU5ERERERAaXA88ea+00sae5+/oU+8FnjyUlJYW22CHgsAPZvn37MAyDcePG8cEHH5CSkhK8FxUVRWpqKuHh4QNSpIiIiIiIDA49Z49ZrVberwMDE6NjDOyfdba3Wq3U1NTo7LHDdNiBbMyY7rWhgUBgwIoREREREZHBrefsMY/Hw0euOACmJn1+GLTOHuubPndZfOKJJ1i1alXw69tvvx2bzcaJJ55IWVlZvxYnIiIiIiKDS8/ZY6WVNRS7u69N/Wxlos4e67sjOhjaarUCsGnTJh599FEeeOABkpOTueWWW/q9QBERERERGTxMJhPTpk2jvCsev2EiKbKT5KguWltbKSkp0dljfdTntvcVFRXk5OQA8MILL3DBBRdw7bXXctJJJzFv3rz+rk9ERERERAYRp9PJli1bKG7rXpJortvF6tV7GDduHFOmTCE/P19nj/VBn2fIYmNjaWhoAGDt2rWcccYZQPdaUo/H07/ViYiIiIjIoNFz/tiOXZ9S4Y8HYMFxaWRkZBATE8O0adMUxvqozzNkZ5xxBtdccw3Tpk3j008/ZfHixQDs2LGDsWPH9nd9IiIiIiIyCBx4/pjPnkNnnQm72WDamGQYk0RJSQlbtmzB4XBouWIf9HmG7LHHHmPOnDnU1dXx7LPPBs8WKCws5Lvf/W6/FygiIiIiIqF34PljRfXdMWJaMphMB58/JoevzzNkNpuNRx999KDr9913X78UJCIiIiIig0/P+WOmKCvbG7uvTU/+vN29zh87MocVyLZt28akSZMICwtj27ZtXzl2ypQp/VKYiIiIiIgMHj3nj22p6aQzEEmKxSAz5vP7On/syBxWIDv++OOprq4mNTWV448/HpPJhGF8noZ7vjaZTPj9/gErVkREREREQiMxMRGbzcY7H/uAaPKTDXq2ivWcP5abm6vzx/rosPaQ7du3j5SUlOCv9+7dy759+4KPnq/37t3bpxd/8803Ofvss8nIyMBkMvHCCy986djrrrsOk8nEQw891Ou6y+Xi0ksvJT4+HpvNxtVXX01ra2uvMdu2beOUU07BYrEwevRoHnjggYOef+XKlUycOBGLxcLkyZNZvXp1n96LiIiIiMhw1dNd8dN9FVR+1l3RKNtMXV2dzh/7hg5rhmzMmDGH/PU31dbWxtSpU7nqqqs477zzvnTc888/z3vvvUdGRsZB9y699FKcTifr1q2js7OTK6+8kmuvvZannnoKgObmZs4880zmz5/P8uXL+fjjj7nqqquw2Wxce+21ALz77rt897vfZdmyZZx11lk89dRTnHvuuRQVFTFp0qR+e78iIiIiIkNNTxhzu900J4zDaA4nwWilYe921lYUM3XqVJ0/9g2YjAPXHh6GJ554guTkZJYsWQLA7bffzl/+8hfy8vL45z//ecSBzWQy8fzzz3Puuef2ul5ZWcmsWbN49dVXWbJkCTfffDM333wzAJ988gl5eXl8+OGHzJgxA4A1a9awePFi9u/fT0ZGBn/605/46U9/SnV1NVFRUQDceeedvPDCC+zatQuAiy++mLa2Nl555ZXg686ePZvjjz+e5cuXH1b9zc3NJCQk0NTURHx8/BF9BiIiIiIig4lhGKxevZri4mJycnJ4dEcYnzaZWDI6wJyEJnbv3s3EiRO5+OKLCQvrcwP3Yasv2aDPn9qvf/1rrFYrAJs2beLRRx/lgQceIDk5mVtuueXIKv4SgUCAyy67jNtuu43jjjvuoPubNm3CZrMFwxjA/PnzCQsL4/333w+OmTt3bjCMASxYsIDi4mIaGxuDY+bPn9/ruRcsWMCmTZu+tDafz0dzc3Ovh4iIiIjIcHJgq3t3h4ndTd3XZ6RCQkICEydOxO12B/9dLX3X50BWUVFBTk4OAC+88AIXXHAB1157LcuWLeOtt97q1+L++7//m4iICG666aZD3u9pNHKgiIgI7HY71dXVwTFpaWm9xvR8/XVjeu4fyrJly0hISAg+Ro8e3bc3JyIiIiIyyPW0urdarWyuAwMTOfEGyZ81UrRarfh8PrW6/wb6HMhiY2NpaGgAYO3atZxxxhlAdxtMj8fTb4UVFhby8MMPs2LFikG5MfCuu+6iqakp+KioqAh1SSIiIiIi/cpisRAVFUVNTS2bnAEAZqR8vuNJre6/uT4HsjPOOINrrrmGa665hk8//ZTFixcDsGPHDsaOHdtvhb311lvU1taSlZVFREQEERERlJWV8aMf/Sj4Ounp6dTW1vb6vq6uLlwuF+np6cExNTU1vcb0fP11Y3ruH4rZbCY+Pr7XQ0RERERkOPH5fNTU1PD8xs3UdUQQZvihvAiXyxVsdZ+VlaVW999AnwPZY489xpw5c6irq+PZZ58lKSkJ6J7R+u53v9tvhV122WVs27aNrVu3Bh8ZGRncdtttvPrqqwDMmTMHt9tNYWFh8PvWr19PIBBg1qxZwTFvvvkmnZ2dwTHr1q0jNzeXxMTE4JjXX3+91+uvW7eOOXPm9Nv7EREREREZSpxOJ2vWrMFkMtGe2t15PL2rhpr9pbzzzjsUFhaq1X0/OKy29wey2Ww8+uijB12/7777+vziPWcW9Ni3bx9bt27FbreTlZUVDHs9IiMjSU9PJzc3F4Bjjz2WhQsX8sMf/pDly5fT2dnJDTfcwCWXXBJskf+9732P++67j6uvvpo77riD7du38/DDD/Pggw8Gn/e//uu/OPXUU/nd737HkiVL+Ne//sXmzZv5y1/+0uf3JCIiIiIy1BmGQVFREW63m6nH57PyQxP4wdGxn/DwcBoaGnA4HCxYsECt7r+hI+pN+dZbb/H973+fE088kcrKSgCefPJJ3n777T49z+bNm5k2bRrTpk0D4NZbb2XatGncc889h/0c//d//8fEiRM5/fTTWbx4MSeffHKvIJWQkMDatWvZt28f06dP50c/+hH33HNP8AwygBNPPJGnnnqKv/zlL0ydOpVnnnmGF154QWeQiYiIiMiIdGB3xR1uE23+MOIiDb496xhOPvlkFixYQFpaGmazOdSlDnl9niF79tlnueyyy7j00kspKirC5/MB0NTUxK9//WtWr1592M81b948+nIMWmlp6UHX7HZ78BDoLzNlypSv7QB54YUXcuGFFx52LSIiIiIiw9WB3RU3lXcvR5yVCokJ3X0T/H4/paWl6q7YD/o8Q/bLX/6S5cuX89e//pXIyMjg9ZNOOomioqJ+LU5ERERERI4+i8WC2WymusnLzs+OGJuTpu6KA6HPgay4uJi5c+cedD0hIQG3290fNYmIiIiISAglJiZis9lY82lT8OyxVGv3PXVX7F99DmTp6em9GnH0ePvttxk3bly/FCUiIiIiIqHhdDopKCigtLSMT7w2AOzuXdTV1QWb8qm7Yv/p8x6yH/7wh/zXf/0Xf//73zGZTFRVVbFp0yZ+/OMfc/fddw9EjSIiIiIichT0hDG3201X0jh8TTFEGl107v2AteVhTJ06lSlTppCfn6/uiv2kz4HszjvvJBAIcPrpp9Pe3s7cuXMxm838+Mc/5sYbbxyIGkVEREREZIAd2Oo+JyeHvxd3L6ab7QhjweRz2L17N2PHjmXRokWEhR1Rs3Y5hD4HMpPJxE9/+lNuu+02SkpKaG1tJS8vj9jYWDweD1ardSDqFBERERGRAXRgq/umDhPbGrqvn5wOCTEJTJw4EbfbTWNj40HnBcuRO+JoGxUVRV5eHjNnziQyMpLf//73ZGdn92dtIiIiIiJylHi9XrxeL52dnby2z0MAE+PjDTJiuu9brVZ8Pp9a3fezww5kPp+Pu+66ixkzZnDiiSfywgsvAPD444+TnZ3Ngw8+yC233DJQdYqIiIiIyAByu93s3r2b9Rs2sqkuHICMthJcLhegVvcD5bCXLN5zzz38+c9/Zv78+bz77rtceOGFXHnllbz33nv8/ve/58ILLyQ8PHwgaxURERERkQHgdDp577336OjowBmeSkeYBbPhI6J6O4Ut0eTn5+NyucjNzVWr+3522DNkK1eu5H//93955plnWLt2LX6/n66uLj766CMuueQShTERERERkSGop5lHU1MTJ598MvW2PADGGU4caam4XC7efvttbDabWt0PgMOeIdu/fz/Tp08HYNKkSZjNZm655Rb9hoiIiIiIDGEHNvNwm2JpCA/DhEFaawm1HS1ERUVhNpuZNWuWWt0PgMMOZH6/n6ioqM+/MSKC2NjYASlKRERERESODq/Xi8/nw2q18sLe7smW45MMzpg8g87OTsLCwmhoaMBms4W20GHqsAOZYRhcccUVmM1moPs37rrrriMmJqbXuOeee65/KxQRERERkQFjsViIiopib1U9m+vSAZiXYRAfHw9Aa2srFotFzTwGyGEHsssvv7zX19///vf7vRgRERERETm6fD4fNTU1rK2KxJ/kwNbVSEPxp9jGjycxMRGn06lmHgPosAPZ448/PpB1iIiIiIjIUeZ0OlmzZg1+wqhP/KyZR1cpFRUVVFdX43A4GDt2rJp5DKAjPhhaRERERESGrp7uim63m67MfDrDzMTgI8VXRXh4OA0NDQAsWLBAzTwG0GHPkImIiIiIyPDR010xLd3Bvz7tnv06Y0wkJ8SfRGdnJx0dHXR1dQV7SMjAUCATERERERmBPB4PDQ0N7PHFUes1YQ03OCkdLBHdzTz8fj+lpaV4vd4QVzq8acmiiIiIiMgI43Q6eeutt/h4+3bWlAcAyDGqaG92Bcd4PB7MZrO6Kw4wBTIRERERkRHE6XRSUFBAVVUVlqwptEYlEW74sdVuobCwEJfLhWEYOJ1OsrKy1F1xgGnJooiIiIjICHFgI48JEyawut0PbeDwlpKaEI3L5WL79u2kp6djt9vVXfEo0AyZiIiIiMgI0dPIw+FwUNZqYk9bFGEYzExoob29na6uLioqKsjIyGDhwoXqrngUaIZMRERERGSE8Hq9eL1eOjs7eamsEzBzQorBvAmTaWkZi9frpbq6mlNOOUVh7CjRDJmIiIiIyAjhdrvZvXs3L729ld1tZjAMHO5tNDY2Eh8fT3R0NHa7HavVGupSRwwFMhERERGREcDpdPLee+/R0dHBXmsuAFlGDS1VeyksLKShoUGNPEJAgUxEREREZJjraebR1NTE+BnzcFkzwTA4NlBKWloaLpeLt99+G5vNpkYeR5kCmYiIiIjIMHdgM4+3m2wAjAtvIKytntraWqKiojCbzcyaNUt7x44yNfUQERERERnmPB4PDQ0N1Phj2NFoIgyD701NxNp1Mp2dnYSFhdHQ0IDNZgt1qSOOAtkwZBgGLpcLr9eLxWLBbrdr2llERERkhHI6nbz11lt8vH07uxyjwAzjwuqI9IYR/9lesdbWViwWCxaLJcTVjjwKZMOM0+mkqKiI8vJyfD4fZrOZrKws8vPzNf0sIiIiMsI4nU4KCgpobGzEnDWVprBUwowAaXWFFLaGMX36dBITE3E6neTm5qqZRwgokA0jPf+Hc7vdOBwOrFYrHo+H4uJiampqWLRokUKZiIiIyAjR08jD7XYzPmcCz7UEwAujPHtIjzfjcrnYvn076enp2O12NfMIETX1GCYO/D9cTk4OsbGxhIeHExsbS05ODm63m6KiIgzDCHWpIiIiInIUuFwuysrKiI2N5a2yNiq9kUSFBZhja6G9vZ2uri4qKirIyMhg4cKF+sF9iGiGbJg4sHPOF3+yYTKZcDgclJeX43K5SEpKClGVIiIiInK0lJWVsWXLFgiL4K340yAc8kyV5I0bTWTuOLxeL9XV1ZxyyikKYyGkGbJhwuv14vP5ep2qbhgGzc3NNDQ00NnZidfrxev1hrBKERERETkanE4nb7/9Ni6Xi8ro8XjDY7AYPmzVRRQVFdHV1UV0dDR2u73Xvx/l6NMM2TBhsVgwm814PB5iY2NxuVzs3buX2tpaOjs7CQQCWK1W3G43o0aNCnW5IiIiIjJAeraydHZ2kpM3hWd94wE4PmI/mY5UnE4nJSUl2Gw2Jk6cqEYeIaYZsmHCbreTlZWF0+mkoaGBwsJC9u/fT0xMDKmpqXR0dODz+Xj//fdxOp2hLldEREREBkjPVpaMjAxKY4+jKyyKmK4mRvnKMQwDq9XKzp07iYqKUiOPQUCBbJgwmUzk5+eTkJDAW2+/zaeeaJJT0wGoqakhKSmJk08+Wc09RERERIa5nkOg9zV28EFj97liJ1qdtLe1UVNTQ2dnJ0lJSZx00knaOzYIaMniMOJwOJg9ezZ/39FFZVQmHa2fkNu1h4yMDNLTu8NZbGwsZWVlau4hIiIiMgwdeAj0J45RGGYTWSYXJ41PInJiOp2dnXR0dNDV1cWYMWNCXa6gQDbs2Gw2pqSEUdkEZbETOS0tlk5XFdu3b6ezs5Pw8HAMw2D27NkKZCIiIiLDyIGHQJuyptMclkq44Sez4X2K2roPgbbb7ZSUlOgQ6EFESxaHGYvFwuSELnJiu/AbJl6qtLK/spKYmBjS0tKIjIykoaGBt99+W3vJRERERIaJA8+kzRo3gR1RuQCMad+FI95MS0sL27dvZ/fu3SQmJmrv2CCiQDbM2O12xozJYnZkGWEEqAtPpivtOKxWKyaTCY/HQ15eHp2dndpLJiIiIjJMHHgI9Au7fbR0hZMY6efExHYdAj3IacniMNPT3GPPnj2klXyEM2kaHwbGkthRQ2tjPTExMcH9ZDt37mTWrFkkJyeHuGoRERER+SZ6DoH2RMTxbvxpYIJpRgk548dybO4EHQI9iGmGbBhyOBycfPLJHEsFVn8bHsxs848iPj6e8PBwtm/fTmFhIe+//z6rV6/W0kURERGRIaznEOgGl4vimCkYpjDSAw2YnDt0CPQQoEA2TI0ZM4YZ06Zy9igPAKXmcbj8Fpqbm4mJicFmsxEfH09ZWRkFBQUKZSIiIiJDUCAQYMOGDdTX1xM1biZ1ESmEEWBWVAUOh4O2tjZKSkqoqqoiKytLjTwGIQWyYap7L9kY0gMNTLEHMDCxNXIi6ekOLBYLbrcbu93OqFGjqKyspLCwUPvJRERERIYQp9PJv//9b1588UU+LaukkBwAHO7tRHobdQj0EKE9ZMNUz16ympoajq3YwXbjWJoik9nVUUfU/kLa2tro6urC7XYTCASorq4mOzub4447LtSli4iIiMjX6GlxX1ZWRmRkJBWOk+nEgrXDja3qPWptCURFRREeHq5DoAc5BbJhzOFwsGjRIsLXriXjrS3sTz6BrabxTPBuJjEmhtTUVMxmMx6Phz179vDqq69it9v1f1YRERGRQezAFvcTJkxguwtKSQUM5lor6ExOwm63c9xxx9HZ2alDoAc5LVkc5hwOB0uWLOHsY2JIj/TiD4uiduwZjBs3HqvVSlhYGGFhYcE1xmqFLyIiIjK4HdjivtXbxc7oKQBMNDlJDWvDbrfT1tZGZGQkra2tjBkzRnvHBjEFshEgKSmJScflcVLEXsIMPy5zOntIBbp/wuJyuYiNjSUxMZGdO3fS0NAQ4opFRERE5Mv0tLgvLCzkyY9b8JgsRHU0EV+2EY/HEwxiOgR6aNCSxRGgZz/Zrl27SCv9EGfabAr9Y7D7amis3BvcT+ZyuWhubiYtLY0zzjhDSxdFREREBpmeFvcul4vIzOOotI4DIMf1AZ6WJmprwzAMg87OTiZOnMi8efP0b7pBTjNkI4TD4WDhwoUcb20gvrOBTiJ4t2scLa2txHy2n0yt8EVEREQGr569Y52dneTmTeajqO5mbBPCasjPjCUlJYWUlBSys7P59re/zcUXX6wwNgQokI0geXl5LFm8mDPiq4kwGbgt6XSOPoHx48erFb6IiIjIINfQ0MDOnTuxWCzsiMrFEx6L2d9OXkcxhmEQHR1NaWkpKSkpzJs3j7Aw/VN/KNCSxRHEZDIxffp0amtrqSit5IOOTPbETWWc90PclXvUCl9ERERkkHI6naxdu5b3338fX/JEPraNBcNghv8TOjzN1Lg71eJ+iFIgG2F6WuGbXl3Lji01tFnTeCcwgazWbcTFqhW+iIiIyGDTc+ZYZWUlZlsq2xKmA5Dl2Y3dqGPipEnExMTQ0dGhFvdDkOYxRyCHw8HZZy3hO45mokwBmqOSaRk7l/Hj1QpfREREZDAxDIPCwkIqKyvJyBjFbvscOkxR2GnlpDgX7e3t1NTUkJiYqBb3Q5QC2QiVlJTEnMkTOCmqDICy6IlUGwmAWuGLiIiIDBY7d+5k9erV7Nu3j39traU2zI4p0ElW5Xo6fR4SEhLYv38/27ZtU4v7ISqkgezNN9/k7LPPJiMjA5PJxAsvvBC819nZyR133MHkyZOJiYkhIyODH/zgB1RVVfV6DpfLxaWXXkp8fDw2m42rr76a1tbWXmO2bdvGKaecgsViYfTo0TzwwAMH1bJy5UomTpyIxWJh8uTJrF69ekDe82DR0wp/UrwPe+MnYDLxjj8Ht6eLPXv2UF9fT21tLUVFRbz//vusXr1aXRdFREREjiKn08maNWuorKyExEz2xHTv689uLMTvrqK2tha3201zczNjxoxh4cKF2mYyBIU0kLW1tTF16lQee+yxg+61t7dTVFTE3XffTVFREc899xzFxcWcc845vcZdeuml7Nixg3Xr1vHKK6/w5ptvcu211wbvNzc3c+aZZzJmzBgKCwv5n//5H+69917+8pe/BMe8++67fPe73+Xqq69my5YtnHvuuZx77rls37594N78INDTCv+E8FKiu5rxEMU7aoUvIiIiEnKBQIANGzZQXV1NnD2ZDyMmESCM0SYXc1INUlJSSE1NJT8/n1mzZrF48WKFsSHKZAySzUEmk4nnn3+ec88990vHfPjhh8ycOZOysjKysrL45JNPyMvL48MPP2TGjBkArFmzhsWLF7N//34yMjL405/+xE9/+lOqq6uJiooC4M477+SFF15g165dAFx88cW0tbXxyiuvBF9r9uzZHH/88Sxfvvyw6m9ubiYhIYGmpibi4+OP8FM4+gzDYNWqVbxe+AkveY/Fb5jIafmI2YntAFRVVWG328nLy6OyspJp06axZMkSTYWLiIiIDBCn08mGDRt48cUXCY+I4OPY6TTbjsFieDkncjtmUxcej4fW1lZGjRpFfn4+ixcv1r/PBpG+ZIMhtYesqakJk8mEzWYDYNOmTdhstmAYA5g/fz5hYWG8//77wTFz584NhjGABQsWUFxcTGNjY3DM/Pnze73WggUL2LRp05fW4vP5aG5u7vUYinpa4U8encSsyAoA9sROpsob2Wvp4rvvvsu+ffsoKChg586dIa5aREREZHjq6ai4a9cuIiMj8WTOpNl2DAT8xG17muZ6J4FAgEAggNPpJCYmRvvGhrghE8i8Xi933HEH3/3ud4Mps7q6mtTU1F7jIiIisNvtVFdXB8ekpaX1GtPz9deN6bl/KMuWLSMhISH4GD169Dd7gyHU0wr/zHHRJLaWYpjCeNuUR6PHH1y6mJaWhs1mY//+/bz66qtauigiIiLSzw7sqJicnEyrNY0tRjYAk7p2k+hvpLy8nJqaGtxuN5mZmSxYsEBLFYe4IRHIOjs7ueiiizAMgz/96U+hLgeAu+66i6ampuCjoqIi1CV9Iw6Hg7POWsIFo73YIzroCI/GOW4x2eNygq3wTSYTiYmJwWn0QCAQ6rJFREREho0DOyq+/9EnfBw3E8MUxqguJ8dHu5k8eTJpaWlMmTKF7OxsFi1aRF5eXqjLlm9o0B8M3RPGysrKWL9+fa81mOnp6dTW1vYa39XVhcvlIj09PTimpqam15ier79uTM/9QzGbzZjN5iN/Y4NQUlISx0+aiGvzDl7oPIamqBQKjS5mUkZzczOffPIJERER+P1+XnzxRQDmzZunn8qIiIiIfEMHdlTMHp/Du1HT6SKGiPY6jK3/R31eLomJiXR1ddHQ0MDYsWOZPn26lioOA4N6hqwnjO3evZvXXnuNpKSkXvfnzJmD2+2msLAweG39+vUEAgFmzZoVHPPmm2/S2dkZHLNu3Tpyc7v/UPeMef3113s997p165gzZ85AvbVBqacVfkZsOKP3rwegOOBgS5OZoqIiPB4PWVlZZGZmEhkZya5du9R5UUREROQbOrCjYmJiIjvCx1NLApF0MTesmFhLJOXl5ezfv5/Ozk4mTpyoFvfDSEgDWWtrK1u3bmXr1q0A7Nu3j61bt1JeXk5nZycXXHABmzdv5v/+7//w+/1UV1dTXV1NR0cHAMceeywLFy7khz/8IR988AHvvPMON9xwA5dccgkZGRkAfO973yMqKoqrr76aHTt28O9//5uHH36YW2+9NVjHf/3Xf7FmzRp+97vfsWvXLu699142b97MDTfccNQ/k1DraYWfl9BFdmt3844dlslgzyI/P5+UlBQ6OjqIiIggKSmJyspKCgsLGSTNOkVERESGFKfTyb///W9efPFFnE4nn3Yksjt8DABzwvcyKi6CyZMnk5qaSnp6Ot/+9re5+OKLFcaGkZC2vd+wYQOnnXbaQdcvv/xy7r33XrKzsw/5fW+88Qbz5s0Dug+GvuGGG3j55ZcJCwvj/PPP5w9/+AOxsbHB8du2bWPp0qV8+OGHJCcnc+ONN3LHHXf0es6VK1fys5/9jNLSUiZMmMADDzzA4sWLD/u9DNW294fS0wq/qGgLGwK57PXGYDW8LIncQWdLQ3DpYkZGBoFAAKvVyn/+539y3HHHhbp0ERERkSGjp6NiWVkZe/fuJWpUHuuNSRimMOIq3uWkuAaSkpLweDzs2bOHU089le9+97sKY0NAX7LBoDmHbKgbToEMPv8LYnfpfl5om4AnMp6ErkZiP/x/hAW6OO6440hKSqK9vZ1du3Yxc+ZMLrroIvLy8rSWWURERORr9PwAfMuWLdhsNt75qJiipNPpMEWR4a8m5qN/E6kfgA9ZfckGg76ph4RGTyt864YN7Fy1nh2ORTRFJNI18VxOt+wlIT6elpYWysvLaW1t5YMPPqCuro7Fixczffp0/eRGRERE5Cv0dFT0er10hUVRFHsSHaYobIEmTjWX0zF5Mi6XiylTptDU1MS0adPUUXGYGtRNPSS0HA4HF198MZcs+RansAOT4actMYfdMZNpaWlhz5497N+/n5SUFI499li8Xi9btmxRow8RERGRr3BgR8V4WyKfJp9Ch8VOuK8Z64dP0FhfQ1RUVLCjYmZmpjoqDmMKZPKVwsLCmDdvHpPSoxld+QYAnwQcbG6OoaamhtTUVLKysrBYLHR0dBATE6NGHyIiIiJfoufw5/r6euLi4ykih2psRODnFGMHcZGGOiqOMFqyKF+rp/Pinj2PEdH6MXtjJ7M/ZTbjc1vJSer+Sc2uXbuoq6sDICoqiurqarKzs7XOWUREROQAPUsVPR4Pn4SNpSEqCwyD6Z3bGB1nImnyZBoaGkhJSSE/P5+LL76YsDDNoQxn+t2Vw5KXl8fixYuZl9rJcRY3mMIoHXU6LlNCr6WLWVlZ2Gw29u/fz6uvvqqliyIiIiKfOXCpoitpEg2O2QDEflqAe8dbNDU1YRgGjY2NOBwO5s2bpzA2Auh3WA6LyWRi+vTpZGaOYmbYXuy+avyE807EVCpbjeDSxfDwcEwmE4mJiTidTjZs2EAgEAh1+SIiIiIhdeDhz52OyXwUngtAbtceJpkbaGlpYdeuXbjdbjIzM1mwYIGWKY4QWrIoh62n82JhYSFVBWt5s8vAE+Ogefrl5PuLiIuNoLm5OXhOmd/v58UXXwRg3rx5+ktFRERERqSeH1K/+OKLtMRksjNpJphMjO0q5wRLHeTkkJSURHNzM2lpaZx00knqqDiCaIZM+sThcLBkyRJu+s//4NuJVZg99XRFRLPJPIOK+haKiorweDxkZWUxatQoAoEAH3zwAStXrqSqqirU5YuIiIgcVVVVVaxcuZIPPviA5shEilPmQlg41vpP6HzvKerr6zAMA7PZTHNzMykpKeqoOMJohkz6zGQycdxxx3HZxVD3x7/ybtcc2iJi2WSezqiYPUzOHYfJZOLTTz+lrq6OqKgoysvLaWho4D/+4z/IyMgI9VsQERERGXBVVVX8+c9/Zvv27fjj0tmVejoBwknxNzArZj+fRlspLy+nq6uLQCCgpYojlGbI5Ijl5eXxnUXzOTuulNiwLnwWO41TvkenKfKgRh8Oh4Pt27fzzDPPqNGHiIiIDHtOp5NnnnmG7du3EzNqAp+kn0Eg0kpE036Si58nwgSTJ08mLS2NKVOmkJ2dzaJFi7RUcQRSIJMj1tPoY+LoFE4P30mE30tjWDwbmISzoSnY6CMsLIzIyEgiIyOpqKjQGWUiIiIyrPU08Ni3bx+BuDTej56Fj0hsgSaOqVpLQ3Ul5eXlREZG6vBn0ZJF+WZ6Gn1YN2xg7+p1fJJ+Bu2WFJh5FVlsB7ooKSmhvr6e9vZ2wsPDKSgo0BllIiIiMiwd2MCjLTyOHWlnECCKhEAzZ0Z9Skf2aCzhBnV1dURERBAWFsbEiRPVAG0EUyCTb8zhcHDxxRcDsO7DLbzJNNotKbwTmE7Gp8/S2eqmq6uLzMxMUlJSKC4u5umnn+aiiy4iLy9PPwkSERGRYaGqqopnnnmGffv20RYey+7MRQRMZiJanDj2F9CRPZq4uDgmTpyI1WolNTWVmTNn6vDnEU6/89IvwsLCmDdvHsePTeWY/auJCvhoCotnz+jF+MMtxMXFkZCQQHl5Oa2trXzwwQc89thjrFq1SnvKREREZMjraeCxceNG9jX62JF2Bl6TmdiuJiZUrsHlrKC8vBy/309zczNer5dx48bp8GfRDJn0H4fDwQUXXEBDQwMd259n75iz6IhJo+a4ixnd+j7VzjJqamrIzMxkzJgx1NTUsHHjRvbu3csFF1yg7osiIiIy5BiGwc6dO3nuuecoKioiPnsKH0afQIBIIlqqydxfQGZqItawAHV1dURHR+PxeJg0aRLnn3++limKApn0r4yMDP7jP/6D8L/9ja7CF6jKvYD2KDvvx55IbFMxjtRUkpKSqKqqoqKiAsMw2Lt3r1rii4iIyJDjdDopLCykoKCA3bt30xCexA5LdxhLDLhx7F9NvbOC6PAAEyZMICIigpSUFMaNG8f555+vf/cIoEAmAyAjI4Pzzz+f6urHSG/cyJaEk/BFJRCY+UOOaX2P6srdtLS0EB0dTXp6Oh0dHRQWFvK3v/2N888/X/vKREREZNDr2S9WUVGB2+0m9pg57I7OxwiLIKZ1PydFfErEIRp4zJw5Uw08pBcFMhkQeXl5LF68mC1btpBnqeZf1cm0R9r4IO5k0sPqsEZ4SE5OJhAIUF9fj8vl4p133qG6uprFixczffp0/UUlIiIig9KBBz5HRkayyxNHU+YMDFMYMY0lpO1dTX1SIjk5OWrgIV9LfxpkQPScUTZq1ChM3mamu9/E1tWIP9xM1THn0ZU8gbi4OPbu3UtNTQ3R0dFkZ2fjdrvZuHEjK1eupKqqKtRvQ0RERKSXAw98Tnc4aMmcjfvYczFMYcQ1fIJj72oCnT7q6+tpa2tTAw/5WpohkwHTc0ZZYWEh1dXVJG9/Ck/WQnz28ZSNWUxYw3uEefYRERFBTEwMLpeL/fv3a1+ZiIiIDEoHHvhssUbzaezx7KZ7RU9CTRHJFRuJiYnBbE5g//79VFdX09HRoQYe8pUUyGRAORwOlixZQnZ2Ns899xzvffg67jgLlZGj2Jt8Igm+MFKq36O9vZ2mpibtKxMREZFB6cADnzuNMHYkzMSLAwyDSV2fYqr/gJauLtra2nA4HERERDBq1CiysrLUwEO+kgKZDDiTycRxxx1HYmIiXV1dfLz9VUiZQaVtCk2jZmOKTcFesoqIiAjtKxMREZFB58ADn31EUTZ2EV5TPPg7GV25npzUMBg/HqfTSUVFBfv27WP8+PGceuqp+veLfC2TYRhGqIsYDpqbm0lISKCpqYn4+PhQlzNoHdiRaKvbTIl9FoYpDHNLJeMq15GZYqO6upqWlhYiIiLIycmhubmZUaNGMXr0aJ1XJiIiIkfVgQ08/HHpfGw7Cb8lgciAj9Glq2kt3UZmZiYTJ06kqamJsrIyZs2axXnnnacVPiNYX7KBZsjkqMrIyODCCy+ksLCQ9oICPLufpzJ7Mb64UZQdcxERztfxeDzaVyYiIiIh9cUDn6PGzeRj61T8hBPe3sDY/a8yNimahszMXgc+T58+nauvvlr/VpHDphmyfqIZsr458C+5N7d+SuX4c2gPi8bk7yR5z2psLfsA6OzsJCIigsmTJ9PR0UFZWRkzZ87UvjIREREZMAce+Pzp7t1U26fRMnYuACn+BlI+fZF6ZwWZmZlMmDCBffv2MWrUKB34LEGaIZNBr9e+sj//ma07n6c041s0WR3UHfNtTPVbiCvdqH1lIiIiclQduL2ioamF5kkX0hLVHbCSGrYxJ6aWMB34LP1IM2T9RDNkR67nL76yiv1sakulKv5YAKyNe8iu3sioVLv2lYmIiMiAO3C/WJc1iR2Jc+iKScVk+EkpfZ34hh0kJyeTk5NDIBCgrKxMBz7LIWmGTIaUA/eVeQsKCOwrozrrdDyJ4ylLSCe8+g3tKxMREZEB88X9YpHjZrHDMpkuUwRhHW1k7H0Fa5uTzq4u6uvrcTgcdHZ26sBn6ReaIesnmiH75g78y3Djtr1UZi/GExaNKdCFvXQ9iY07MaF9ZSIiItI/ev7tsXnzZjZu3EhFZRVVKbNoy5wJQJy3hqTiF4nobPvswGcz+/fvZ8yYMcEDn/VDYTkUzZDJkHTwvrLnKHecSmP0aBrGnYm/aRz2PWuIiDAO2lf29ttvU1payqmnnsqMGTMUzEREROQrOZ1OXnvtNdauXUt9fT0NngAtky/EE9O9Byy5fgtTTeXURgRo8ejAZxk4miHrJ5oh6189+8rKKyrY3BJPWcLxGKYwIjwuxla+RnZiVHBfmd/vJy4ujsbGRmw2G8nJyZx55pnMnz9fG2tFRESkl55ZsTVr1rBlyxYMw8CXcixFERMJRFgxdXpJKlmFra2c5ORk0tLSqK6upqKigri4OMaPH6/GYvK1+pINFMj6iQJZ/zuw5ey2qlYqxy4kYI4nzAgwurGIiL1v0eHz4fP5iIuLIxAIMGnSJFwuFyaTiWnTprFw4ULNlomIiAjQ+98We/fupbndh+/YJdTbJgJg9dRh2/kc/qZqoqOjD9oioQOf5XBpyaIMCw6HgyVLlpCdnc1zzz3Hu4XP0ZCzmNqIVMrsM7CEJRGz/TmizRAXF4fP56OjowOPx0NdXR0NDQ3s2bNHP8USERGRXu3s3W43CWOOo8Q8hU5rEhgGKQ0fkVD+JrExVpq6YoiPj6euro7q6mo6Ojp04LMMGAUyGdR67Svr+jMfb3+FaPtxlNny8dqy6Zh1PXE179BWs4Po6GiqqqqCHRkzMzNxu91s3LiRvXv3qj2+iIjICGQYBjt27ODvf/87n376KTGxcXwSSKclfi5GWDgRnW2k7VtDsr+BgDmK1tZWAFJTU4mJidF+MRlwWrLYT7RkceAd+JOtktpWSlJOpiXCBoCt8RMya9/H1+omJiYm+PtQV1fHmDFj8Pl8jB8/nksuuYSxY8dit9u11EBERGSY62nc8fLLL7Nr1y5iHdm4c8+hPTodgKi6YpL3riE1IRqfz0dWVhZ1dXXU1dURGxtLdna2VtrIEdGSRRmWDjyvrL2gAM/Op6i0T6Nl1GzcicfSGjOarJq3cFVsw+v1YhgG0dHRxMXF0dLSwmuvvcaePXs44YQTmDJlCvn5+frLVUREZBj6YuOOjs5Ooo47g+rRpxAIi8TU5cNW+joxNdvo8PloCfMTCASIiorCarUyZswY7UWXo0YzZP1EM2RHzxf/km2KTGZvyom0hcUAYKnaQlL5BqwREBMTQyAQwOPx0NXVRUpKCmlpadjtdpKTk7WMUUREZJj5YuMOd2c4zRPPpi02E4B4j5PYnS/Q1VRLdHS0ujXLgFCXxRBQIDv6Djw/pLbBzX77NNpGzQSTifCOFkY53yKxfT/t7e3BZYzR0dE0NzeTlZVFU1MTEyZM4Oqrr+a4447TT79ERESGuAO3N1RWOamzT6bEkosRHgn+DmylG5hgctLV2Ynb7Q5ub4iNjSUzM1PnmUq/USALAQWy0OiZLdu8eTMbN25kd2OAqtHfwh+dBECcu4TR9e/TWrsfr9dLXFwcHo+HpKQkmpubaWxsZOLEiZx99tn6SZiIiMgQZBgGLpeL0tJS/v3vf1NSUkJnbBq74qbTGdf93/XYtiriil/G764mPj6e2NhYWltbyczMpL6+Xu3spd8pkIWAAllo9QSz5557jvc+LMKb8y32Ro7FMIVh8vuIKXkdW/3HxMZEs3//fpKTk7HZbLS0tBAfH09MTIzWiouIiAwxTqeToqIiPv74Y95//332lVcScfzZNCQfj2EKI6zLS8zutSS4dpKclERTU1Pwv//t7e0kJSWRn5/Pf/zHf2gLg/QrNfWQEad3e/wutm/fwNS4dHbGHE9HXAatuYsJZM3AvWUlkZGRpKSk0NXVRSAQoKOjg/r6eurr69mxY4eWK4iIiAwBPcsT6+vrcTU20mLLoWXsRfjN3f/4TWjeQ+Le1/C3NeLz+WhpaQFg1KhR1NTUkJKSwpw5c7SfXEJOM2T9RDNkg8cX/4L+2GOj3DYNI8IMRoCEhp2Ma91OQ1VZr2WM8fHxwZ+caUOviIjI4HOo5YlGXBo7rZPw2cYCENnZQnLZelI7awgEArS0tKhxhxx1WrIYAgpkg8sXlzDsdTbgn/xtWpMmAhDW5SV67wZs9duIjbayf/9+7HY74eHhTJo0CZfLhclk0jJGERGRQaBna0JhYSG7d+9m+/bt7KtwEn782biSpnRvUQh0YS17m8TqzaQkJgTPFXO73VRUVKhxhxxVCmQhoEA2+Hzxp2g7duyg2h9LyzEL6YhOBcDscxGx7QWiXHuDB0gfeChkTEwMGRkZ+stbREQkRA7sqtzU1ES7x0ND3Hiaxp5GICoWgGjXbsY0FtJW8/nql0AgwOTJk/H7/ZSVlalxhxxVCmQhoEA2uFVVVbFy5Uref/99Ojq78I2azo7wcQQiowGIde8huWIjCRFdwXPL2tvbey1jTEpK4sQTT2T27NmMGTMGu92uv9BFREQGyBfPHTUMA1PqBD7szMQX273MMNLbSHL5G0TW7yYqKorY2Nhex9yMGTMGj8fDpEmT1LhDjioFshBQIBv8DvwJW319Pa5WLx0TTqcxZSqYwjAF/CS5dxJX8S5mo6PXMsbs7Gz27duHz+dj9OjRHHPMMeTk5GjWTEREpJ998Ugbp9NJo9+M55gzaYodA0BYoJOo3a9jKdvE2KxMmpqaiIqKwu/309XVRUpKCmlpadjtdlJSUjj//PMVxuSoUiALAQWyoeHQf8lbaMtdREvMKADC/B1E7X0LS9k7jM10BP+S9/l8dHV1ERYWRlRUFDExMcTHx2tTsIiIyDfUs82grKyM9957j02bNlFfX09Dux/fhPk0JR0LpjAwAsTVbmNMyw78bY1UVFQEf3g6btw4ampqqKysJDs7mxNOOIEpU6aQn5+v/0bLUadAFgIKZEPLoZZB/P/27jw47ru+//jzu/ct7UpaXdYVXzmcy0mbJoamAROXQICWI6FMSkmZTCFhgHAkJaSm5QophcIMJBCYcTodCAM0+QVIQ4JDoGncHHbShMTxEUu2LGm1ulZ7n9/v7w9ZW8uWZMnX2vLrMeM//N3vfvez8sfafX3fn6PSuJJtlQ6KgRYA7KUs4cH/wTu4FbfTgcfjobe3t7pv2Zo1axgZGdHiHyIiIsfg4IW4XnjhBfr7+3F4AxjnXM0+7wosuxOYml4Q6f8vSA3jcrkIh8PE43F8Ph/pdJquri6y2SzLly/n+uuvp7u7W9MLpGa0D5nIEUzvWxaJRIhGozz22GOM73yGSOLXGJ2XEG+5jIo3wmjXm3A0r6V9fBvpXVuoVCo0NTWRz+fJZDLkcjlGRka0h5mIiMhROHirmomJCSy7i8JZf0qs+ZLqPG9fNobtpf+Hc7KfSFcXk3Y7drudkZERHA4HdXV1LF++XMMT5bSlCtlxogrZ6Wu2YYypTBZj+TqGmy6haPMAYCRj1O3fQrd9nMnERHWsuhb/EBERWbjZ9hJz+YJsLzeRXvYnVBxeAJy5cer2Pkldei9Oh2POud2dnZ1ceOGFGp4opxQNWawBBbLT32zDGMNNzTw95mEiuhbL+X8fEKH+/6Y++TpOh12Lf4iIiCzA7HuJDeJe8xbGo2sp2dwAGJlRWka30Zzv1w1QOW0pkNWAAtnSceh+J7lcjnwFks0Xk2r7Y8wDwcxVnMS+43HcQy/R3dkx7+IfwWBQHxoiInLGmW2xjsnJSTKFMqP155BqvwzT5QfAkZ+gdfxFki8/gQ2L5cuXV5euHxsb0/6gclpRIKsBBbKl5dC7eDt37pyaZOzx4zjnzfR5llfHtjsLk9QPPYdn6EXcTvthi3/09PSwe/duVc1EROSMMdfnqN1fj/2cN7PP1Y3pmJoS4CwkCA8+g3vof3G7nDgODE+sq6vD6/VqES05LSmQ1YAC2dI01zK848kM5lnrGGu6uHpnz1bKEE1sx9r5OyZHBlm1alW1alYqlVQ1ExGRJW86iG3evJmnnnqKfD5PLpcjY/Mx2XIpmeYLwDa1ppy7OIn9tcdxxWaONKlUKmQyGez2qZuc2mZGTkcKZDWgQLb0zbb4RzJbwFi+jtGGC8jbpypmlIv4hv+XrvzrxPe8SjAYJBAIqGomIiJL0qE3L59++ml27dqFZVmEutcwEDqXXMPqqX3EAFdqENee37HcnaFYyM+5l1h3dzdr1qzR56Oclk6bQPb73/+ef/7nf2br1q0MDQ3x4IMP8q53vav6uGVZbNy4kfvuu49EIsG6deu45557WLlyZfWc8fFxPvaxj/GLX/wCm83Gu9/9br71rW8RCASq57z00kvcfPPNPPfcczQ1NfGxj32Mz372szPa8tOf/pQ777yTvr4+Vq5cyde+9jWuueaaBb8XBbIzx2yLfzQ0RXlmsMhY08WUgwfu3FkmrvirdGZ3kdn3ChMTE/NWzXw+Hx6Ph3Xr1rF+/Xp98IiIyCltrmGJGDZK0XPItP8R2UB79Xxb7FXqY8/T4Smwv7+fYDBIU1OT9hKTJem02Ycsk8lw4YUXcuONN/KXf/mXhz1+99138+1vf5v777+fnp4e7rzzTjZs2MCrr76KxzM17vgDH/gAQ0NDPP7445RKJT70oQ9x00038aMf/QiY+mFMl7jvvfdeXn75ZW688Ubq6+u56aabAHj66ad5//vfz1e/+lXe/va386Mf/Yh3vetdbNu2jTVr1py8H4icFmbbw6xvz+uEcjncw38gF+wgvewycvVnUWxew27WYNStJRR/EYwx0un0YVUzy7Lw+/309fWxb98+nnvuOQ1nFBGRU85ci3Tkcjlypo1M5xWkW9ZS8dQdeIKJrX8b4eGtuLIjU3uNrVpFIBDA6XRqLzERTqEhi4ZhzKiQWZZFW1sbn/rUp/j0pz8NwOTkJM3NzWzatInrr7+e7du3c+655/Lcc89x6aWXAvDoo49yzTXXsH//ftra2rjnnnu44447iMViuFwuAG6//XYeeughXnvtNQCuu+46MpkMv/zlL6vt+ZM/+RMuuugi7r333gW1XxWyM9NcdwddLhctq9fySqWFVGR1dby8rZTFvf95usv9JAZ7mZiYoLOzk4GBASKRCKFQiHQ6TaVSwTRNDWcUEZFTwnyfd82r17K9HCUZORvszqknFDL4h19kuTnAyN6dMz7vZhu+r73EZKk5bSpk8+nt7SUWi7F+/frqsbq6Oi677DK2bNnC9ddfz5YtW6ivr6+GMYD169djs9l45pln+Iu/+Au2bNnCn/7pn1bDGMCGDRv42te+xsTEBOFwmC1btnDrrbfOeP0NGzbw0EMPnfD3Kae36WrZueeee/gdw6HdtORepq73t6Si55NuuZiKO0Su50/ZbpnY/K9QF3+R5OReKpUKkUgE0zQZHR0lFApht9sZGhpibGyMl156iccff1xVMxEROWnmq4YVShXyzWsYa11LX6ij+hwjsZ9gbBu+kVdJjI1grF5NY2MjyWSSVCpFOBzGZpuaSxaPx2lvb9dnm5zxTtlAFovFAGhubp5xvLm5ufpYLBYjGo3OeNzhcBCJRGac09PTc9g1ph8Lh8PEYrF5X2c2hUKBQqFQ/XsymVzM25MlxjAMGhoaaGho4OKLL+bKK6+ceRdx71PUDz1H3blvZBet5EKdmG3nM9F2PkZ6hED8JQzbKKN7d1GpVAgGg9W7iC6Xi2g0yu7du/n3f/93fve736lqJiIiJ8z8oz8uZm8xQjJyNtaB7V+wTFzDr9CZ30N678skJiZo6ewklbAzPj5OKBSisbGxOvqjvb1dn2MiBzllA9mp7qtf/Sr/+I//WOtmyClo3qrZ/hdZlttC2h4k1XwRmaY1WIEmUoE386JlYnO9Qmj0DyQn9lCpVGhqamJycpK+vj4sy8Jms6lqJiIix9181bB82STfciFjLRfRF1r2f0/KJgjE/5fO8gDDva9RDgZpbGggdaAaFolEiEQiDA0NYbPZWLFihT6zRGZxygaylpYWAIaHh2eMIx4eHuaiiy6qnhOPx2c8r1wuMz4+Xn1+S0sLw8PDM86Z/vuRzpl+fDZ///d/P2OYYzKZpKOjY87z5cxzxKrZrkcJ7nmCYssasi0Xk/O3YLadT6LtfMhNEoi/TKm8n3R64LBFQFQ1ExGR42GuapjT5SJ69h/RVwyTiqzGOrCJM5aJc2QHy/J95Hq3kZgYx3HQIh3pdLo6LNHlcmFZFt3d3VpBWGQep2wg6+npoaWlhc2bN1cDWDKZ5JlnnuEjH/kIAJdffjmJRIKtW7dyySWXAPDEE09gmiaXXXZZ9Zw77riDUqmE0zk10fTxxx9n9erVhMPh6jmbN2/mE5/4RPX1H3/8cS6//PI52+d2u3G73cf7bcsSNVfVbGqvlucI7n2Wjq5zGPR0k4muwfLWke56A9sBR2Qvnvw+RvY8o6qZiIgcs/mqYVnDQ7rrjWSja9jrjVSfY2TG8MdfoqsySKx3B1YwSGNDhFRykpGREfx+P93d3ezevVvDEkUWqaarLKbTaXbv3g3AxRdfzDe+8Q2uuuoqIpEInZ2dfO1rX+Ouu+6asez9Sy+9NGPZ+7e+9a0MDw9z7733Vpe9v/TSS6vL3k9OTrJ69WquvvpqbrvtNv7whz9w44038s1vfnPGsvdXXnkld911F29729t44IEH+MpXvrKoZe+1yqIs1vRdyc2bN/PUU0+Rz+erE6Uz9WeRabmIfF1XdSNNKmXc47tpLfSTfO1pQgHfgjecvuSSS2hpaaFQKODxeBTSRETOQHNVwxzeAP6z30gvTeSDBy3QUSniHH6VZeUBsr0vkDjCfpp+v59gMKibgiKcRhtDP/nkk1x11VWHHf/gBz/Ipk2bqhtDf//73yeRSPCGN7yB7373u6xatap67vj4OLfccsuMjaG//e1vz7kxdGNjIx/72Me47bbbZrzmT3/6Uz7/+c9XN4a+++67tTG0nBTzTZ7uWLWGVzM+JsOrKQf+bwitUcoSSfdh7d1Kpu9/Wb1q5ZwfkHa7nWKxSGdnJz09Pfj9fqLRqO5YiogsYdNVsFwuRy6Xq44ymq6GZfIlJgMdZCJnk4+chWVzTj8R28guguPb6TTGGdzXe9iw+fluAOqzRWTKaRPIlhIFMjlW8w0hKRaL5D0N5JrXkGk8l4orWH2erZgmktlLcff/EK5MEAz4qx+aDocDj8fD2NhYtTIWCATI5XK6iykissQc/DmyY8cO+vr66O3tZXh4eCqcFcsYbedh676UuKMFa3rPMMCZHcU58AJdxKv7ZKoaJnL0FMhqQIFMjqf5qmbLV6xg+1iFidBycg2rweWrPs9RyuAafoVy77OsijgY2L+fQCCA3+9n//79uFwuuru7aW9v5w9/+IOGNoqILAGHfmbs2rWLZDKJaZoE6sLEbQ3EHC0Uo2eD01t9nrOYxOx7jnDyddoDBvv7+1UNEzlOFMhqQIFMToQjVc0qFhTDPeSaziFTvwJzehUswFHKYg2+TGtlBCu2nZHYID09PRSLRVwuF4VCQUMbRUROY3PNRS4ZLgqNKxn3LKPYsGJGJcxeSOIb24GtfytdIRsj8biqYSIngAJZDSiQyYk2X9VsxYoV9O7dx7grSqp+JWbb+Zj2g1YBLRdxjO6ipRInteNpQm47dXV1RxzamM1m8Xg8rFu3jje/+c2qnomI1Mhcc8KmVuvdRdkVxH3WHzHm7aBQ1/F/C0IBtlyCcLaf/M7/xp7oZ1l7G8PDw6qGiZxACmQ1oEAmJ8uRqmamaWJzOKk0nMVksJtceAXlg+acYZn4MkPYY69Q2PMcq9sb2D/L0MaWlhb8fj979+6lXC5TV1en6pmIyEk035ywsfFx0s4wlejZFBpXUwzM3DvVSAwQSPbSVBhkaPtzdHd1kUqliMfjrFy5knQ6rWqYyAmkQFYDCmRSC/NVzdasWcPg4CC9fX3kvU34Vv4JyWA3JX90xjVc+QnMgZdpNRKYsR2MxAZpa2tjdHSUxsZG7HY7sVgMu91OIBCYdWGQyy67jFAohNfrxev16sNbRGSRpsNXPp/H7XYTi8XYtm3bjDlhJcOFGV3FmKuFSV8blnvmzTZ/bpjC688STPZiJuMAdHV10d/fP+OmW11dHV6vV9UwkRNIgawGFMiklmarmiWTSbxeL+l0mrGxMQqFAg0NDaQtN5n6s0jXL8dsOGvGsBYqJezjvXgTvRT6trGqtZ7JRIJ4PF4NaYcuDJJKpfD5fITDYaLRKD09PfT09LB69WrdWRURWYChoSG2bdvGvn37iMfj9Pb2snfvXhxOJ3lvlHSoi3z9WeR80Zm/s0t53Ik+Kv3/SyQ/QFdzhP379zM+Pk5dXR2JRKI6d9jpdJJIJGhoaKBUKgGoGiZyAimQ1YACmZwqDq6aDQ8Pk81mqx/uTqcTy7KqQxtx+ag0LCcZ6CBf3zNzaCPgLKYwYtuxj+wkkB1kcmRoxsIgyWSSRCKBx+Ohvb0dn89HMpmsVtO0eqOIyPyGhob4z//8TxKJBC63h2d39PPKcI6Up5ly4wpMp2/G+c5MHFt8B21MMLbjOTDLuN1uUqkUq1evxjRN9uzZg8fjqc4Dnv79nEqlsNlsBINBVq5cqWqYyAmkQFYDCmRyqplv+MtcQxsLrnrc3WtJeFuxmlaAzXHQBU1sySEi5VFye1+irhDHVikSj8fnvQM71+qNCmkicqZL50t872eP8WzvKJOOCK9PmpRwzDyplCeYHSD7+nM0lUdp8Nro7e2l66A5YR0dHcRiMSKRCKFQiHQ6TaVSIZ/PawSDSI0okNWAApmcDhYytDGTyWCaJtG2ZaS9LUx4WqHlHErehkMuZmKfHMAxtoc2R5qJXc8T9LpnzFGw2Wyzrt44MTGhkCYiZ5zBRI7n906wtW+crfsm2D6YpHLItzC7VcadGqS+PI5ropfhV/+H9tYWRkZGgLnnhNlsNiKRCENDQ9hsNlasWKE5viI1pEBWAwpkcrqZa2hjX18fyWQSh8NBZ2cn2WyWWCxG3nBTv/JSJj0t5EIdmL5DAppZwZuLY5/oI9//CmeFDEb29866eqNhGAwNDSmkiciSVa6YvBZL8XzfOM/vnWDb3gkGJ/OHnRe0lVgZttPsyDD22nO0+ix2vrYdj8eDw+Fg9+7d+P1+gCOOSPD5fNWtStavX6+hiCI1pEBWAwpkcrqabWjjE088wVNPPUWhUJh1YZB8Pk/R4WfS3Yy7Yw1pfzsVT91h1zZSw4SKo9jGekn3vUR3o5/xsTGCwSAej+eoQlpTUxMrV66kq6tLd3tF5JSRzJd4YV+CrXsn2Lp3nBf3JcgUKzPOsdsMzm0NcUlXmEu6wiyvg9/95/+jvr4e0zR56qmn8Pl8DAwMMDY2ht/vr27WPNucXc0JEzl1KZDVgAKZLCULWRjENE1cLhdjY2PU1deTs/koh7uJW0Fs0RWUfY2HXddeymKNvk6DOQljfSR6X6ano42xBYa06SX4Lcti5cqVnHfeefT09LBq1SoNyRGRY3LwzakjVeMty2L/RI7n946zde8Ez/dNsGM4xaHfqIIeB2s7p8LXpV1hLuyox+92zLjOI488wo4dO1i+fDnbtm2r7gu5Z88ehoeHWbZsmVa1FTkNKZDVgAKZLEUL2RfHNE1CoRDZbJaBgQEKhQJ1dXX4wlHS7kZK9Z1M2CNYkc6Zi4QAWCbu/ATl+Os0GmmssT4m+l6hp6vzsJBmGAZOp5NwOEw+n8dms+H3+ymVSpRKpcO+oCikichc5vrdNjw8jMPhwOPx0NnZydq1a2ltbaVUMXllMFmtfj3fN0E8VTjsup0RH5d2hVnbFebS7jCrokFstvl/7xy8yqLH42H79u1MTEyQy+UoFAra91HkNKVAVgMKZHKmOHhhkB07dtDX10dvby/Dw8PVLzh+v7+6emNfXx/FYpG2jk4mbXWk3E3YostJuxrAFz78BSolvPkxisO7aLLnsMb2Mdb3KqGAn2w2y+rVqykWi/T392MYBg6HA4fDQXNzc3XZfYU0EZk2X/g6uPrvcrlob2+nra2NSMsyelMGE/Z6Mt4Wtsez5EvmjOs67QbntdVVq1+XdIWJhjxH1cZD9yGLx+NYlkU0Gq3Op9VQRJHTiwJZDSiQyZlo+otOLpcjl8uRTCZ55pln5tyYuqWlBYBYLEaxWKSlexWTjjBpVwNGYzdZTxO4fIe/UKWELTWMPTlIk6NAaaSXid4/EPK5yWaznHXWWeRyOZxOJ/F4vBrUFhvSPJ6pL1NaQETk9LSY8BUOh0mn04xMZsm6w/jaVuFo7CJhqyPjOPxzvN7n5JLOA9WvA8MPPU77CWs76HeRyOlMgawGFMhEpsw3/+zgL0GzhbRCsUjzWeeRdIZJuxoxGjrJuRvAOctdZ8vElhnFNjlIxJ4nM7iLYCWNlRllMpFYdEgLBAI4HI7q3ehDV3nM5/PkcjlV10Rq7NAbQdM3U44UvlLpDPGsRdYdwdOynIK3kYwrjDXLgkQAIXKsDNtpMlJ8+F1vYu2K9iMOPxQRmaZAVgMKZCIzHc2d6tlDWolIx0piBQeu5uUYkU7Sjjrwzv4likoRW2qYkJWhEO/FV05iS8ZIDu9j+Rwhra6ujlwuR6VSIRKJYLPZZqzy2NjYiNPpxDRNgsHgYdU1j8dDPp9XWBM5To4Uunbt2kUsFiOVSlU3nx8dHcXlclEfjjBetDNatJN3h3E2dlL0RMi76sDumvX17LkJAuVJnJlhVjZ6aSRJOZPg8ssvZ3x8nPe+9720t7ef5J+CiJzOFMhqQIFM5MiONqTl83kcDgdOpxOn08n4+Djh1i6G8nY8LSsoB5opeiKYgSiWbY4hRKU83lKCyvh+vMUEtvQImeFezorWMRIfJhAIYJomhcLURP3GxkYMw2Dfvn2USiUaGxtZtWoVmUyGycnJanXN6/UC4PF4NBRSZIFmG543XYVOJpPs3LmzOj81mUzOCF2WZVEul3F7/fhbeuhPlkniw6hrwR5eRtFdP/fvgXIBV3YUX3Gc9P4dtPkh0fcKRrlAd3c3uVyOc889F7/fz/DwMBdffDE2m433ve99NDQ0zH5NEZFZKJDVgAKZyNFZaEirfglzu7HZbExMTGCaJpZl0dTURKlUwuP18Xo8ia91BWYgSs4VxhZup+Cqg7m+oFkVjPQYPjMNyWEKo/tpCzpIDe4m6LQoFgpMTEzQ3NxMOBymUqlUV300zalJ/i6Xi2AwOGPVRw2FlDPRfEFrrqGF8XicdDpNqVQik8kwMTFRvfkSqqvHFmxkV2ySyYoLIxSFYBTT10DFGwbDNms7jEoJkjG8pQT+corEvtfoCDkY3bsDA4u2tjb6+vro7Owkk8lUN1y22WzVhTPS6TTt7e2sXbuWa665Rv8vRWRRFMhqQIFM5PiZb7n9WCxGPB6f+aXtwLL7sVjssJDm9Xp5vXcvwbblmMFmss46XI2dpA0fpr9hziFMAEaliC0zipEeIWQrUhgfxE8eMzlCMtZHKOAnnU6zatUqEonE1IbZxeJxGwp5cHVNwU1OFQudw3Vw0DJN87ChheFwmEwmQ9m0GMlUsPwN2OpaSOGl7A1j+howfWEw5lk4o5TDkRnFSMVwZEepM/KM971CT7SO2NAQwKzhq7u7e9b9D7u7u1m9ejX9/f14PB6uuOIK3vrWt9La2nqSfroislQokNWAApnIiTXbio4HD2saHh5eVEjbs2cP9eEw9kADKbx4ol1MlJ0QiGIFo5TdoTnvvk81yMTIJbDlJgjZSpQSMfJjA/itHPmxAXpaGxiJx496KOTB1TWg+qV2ocFN89pkoeYKWHB4fzrScMKDg1alUmF8fBy7001j1yr2T2SZLDuwBRrxNLZTcgUp2P2Y7hDM1zcrJVzFJOZkDOPADRJ3KUmTB+J7d2I78FzLsmhtbaW/v39B4Wu2zeen9/wKhUJcffXVrF+/XmFMRI6KAlkNKJCJnHzHI6S1tbXhcDhIJBIMDw8TDodxuVykMjnCHSvYN1HA09SJPRQlWXFir2um5AqC3Tlv2wyzDLlJPGYeKztBaTJOo89OdnQAv61EJTXGZHyAluboYUMhD66u5XI5AHw+34KDWyaTIZ/PL2he26HDyQ49pjB3elhIqJrt2FwBa7b+BFQXxXE6ndTV1eHx+Xmtb5DxvIk90EBdSydlZ4C06aTircf01mO6gkd+A5US7uIkpYkh6mwFsvF9uAoJIq4ysd6d9HR3EY/HyeVy2O12DMOgvb2deDxOsVjE7XaTyWTo6ekhHo8vOHxNV6s7Ozvp6enRnl8ictwokNWAApnIqWGxIa2urg6fz8fOnTurd/g7OjoAGBkZwTCM6ry1dDpNc3Mzr+/pxR9pJo0Hb1MH7oY2xgs2LG8Y0xfG8tbPX12bVinjLGewshO4KlnITVKcHCUa8pAaGcRvL5Mdi2GUcjQ2TH2BPFJwczgcuFwuCoXCEee1ATOGkx1tNU4B79gc2mcPrm4er1A127/xofO1pv8v7Nq1i0w2h+XyY/OGsPnryRtuSg4/picE3npMdwjDV0/Z4Zu/ujWtXMRZTGKmR3HkJ/EbBVJDfbQEnYzv343LKtLS3ExfXx+tra0kEglKpdKMilcymWR8fJxgMFgdKjwyMjK1yE84XJ3TOV2FXmj4mp7PqUV3ROR4UiCrAQUykVPXkULagoZd2e34fL7qYiKmaeJyuXC73YTDYXbu3EkgECCdTlMfacDuD5OqOPE3tTOeN8EXxhFsIosLW6CBitO3sNAGYFZwVHJYuUlclTzkkxSTozQG3GTG43jtJrnECEYhg1HKYLMqrFyxguHh4TnntR1ceVu2bBn79+8nm81Wjy1mGOVCvvybpkkgEKC1tZUVK1bMuqjJsQS9oz222PBzvI/NdsNgurq5kJ/1fKHq4H/P9mXL6B+KkymB5fThDIZxBCKkSgYVpw/LFcAZilBx+CnaPZgOz8L7p1XByCWxF1O4zRz58Rhht0E6vg9XKUWT10b/nh10d3UxOjo6I2gdHL7a2toYHR0lEAhQKBRIJBLVitfBQ3+LxSI2m41wOEwymSSTyRAKhWadp6nwJSK1okBWAwpkIqeXo1mY4NAvvy6Xi3Q6Xd1k2rIsHA7HvEMhq1W23j5CzZ14G1oZSuSoa+lismhheOuw+8PkcWHz1U0Ft8WqlHCaBax8mkpuEqdZpJKdJOxzkZ0cw2O3yCcnMMp56n0urEIGl2EyMTKEYZk0NDQsqBo3V5ib69jo6Gh1CwPDMGYsarLQUHc8jx08tPNYguaxvP5s/SmVSuFyuSgWi1QqlRk/w/6BQTJFE8vhxuWvw+WvI5kvUbF7sJxefJEolsNLAQd5y4Hl9GG4/VQc3rlXGp2LZWIUsxjFNB6rSHEyjpGfhNwk7kqOepfFyL5ddLc2MhKPzxm0mpqaiMVidHV1kUgkmJiYmBG0CvOsZDrX4jh2u51YLIZlWSxbtqxaDZ5tJVOFLxGpBQWyGlAgE1k6FrNH0nRFAw6fX7PYoZB79uw5LLjtH4xhuf2Eoh3EkznCrV0kChaGN4TTX0+2YsMRCFOyubCcfrA7ju3Nlws4zCJWIYvDKkEpSymbIuR1k89M4rJBMZvCqBQJeFxYpTwOwyQ9MQaVIvUBH2Yxh8tmMTEyjGFVCAQCFItFQqEQmUyGUqlEoVCoLmqymFB3vI4dPLRzrvBzrK+VyebA7sQTCNHc3slQfJRcsYJld+Ly+nF6A6TzRUzsmHYnNrcfm9uHO1BHMlucqlI53FhOD4bTh2l3YR1h7uIRVUoYpSzOSoFyehy/wySfGMFRzhF0WkwM7aM17CcR66ecnQSzUp2DdegcrkOHE84VtKLRKIZhVH/eC1ngJplMUiwW590+oqmpiZUrV9LV1aW9/kTklKNAVgMKZCJnjrnm/ByPoZAHV9emN8YGqmFmruA2MDBAqVzGcLixeYNEWjqJJ7OYTh+m04vdV48rUEe6YOIJhSmYNiyHB8Pto2JzgcN9Yn5YZuXAnxIOA6xyCbNcwIaFWSritBsYVgU7JqV8DqwKbqcdq1zCbkA+l8XAxON2Y5oV7IZBLpcFy8LnOXDMZpDNZKaOeT1YlQp2u41MNouFgc/vxzQtbHY72QPHAAybnUgkwvj4BBZMzYUyDDxePyYGht1JvljCstlxu71UMDDsDoplE2x2HC43JjawOSibFtjsGE43luHAsjuxbMcYjudTLmA3S5iFDG7DpJSdxF7O47VVyIzHCfvdZCaGMUo5Ij4Xw/17WBYNMxaPYQDRaPSwStbB1a2xsTHK5TKGYWC32+nu7iYej8+YwzXbcEKYPWhNh9RyuXzMW0AodInI6WAx2eAEflqIiCxNhmHQ0NAw62Nr165d8FDI6WX4M5kMhUKhWl0LBAL4fD7Gxsaw2abm8UwP14rH4/h8PvL5PJFIhPr6etLpNIZh4HQ4gAq2QpI6c5LhwZ0ED8xrmw5wzoMCHEx9MR8YGKC1fRnxiRSW00N9YwvDE2kaW9uZSBcwPH48gTqSmTyB+gaypQrYnTi9AXIlE5c3QMmyYTlcGA43ps0B02HEZj8wVM5FGeBAgady4OdVnOVnWJrlWGGBx/ILPDZtEOCQf8rZrjtbm8rzXPfwk4vYrDJWqYBhlnAaJsVcBq/TTjGbhkoRSnkcVpmAx05mYhSzkIVSDidlwgEvw/v7aI82MB6fGlo6W6hqPhCqgq2tFA+EeU80iqOYwmYGwbKwmKoQ+nw+KpWpClg2m63eHJgOYtPDK20221TfOVD5LRaLeL1eRkdHCYfD1eGEjY2NM4JWY2PjjKDV1dWlTdJFRGahQCYichzNFdYaGxs577zzFjwUMplMVr/AAgsObtND8QYHB3G73RSLxTkDHFD9Io1ZwV7OQTmHbbJCKJchnKkw0Tu1X5vL5cKWTtOcPzzMtbW1MTIyMuNYa/syRsYnKVpTFSS310d9QxMjY+NYhh3TsOF0ewnWRxgZmyDS2EQqmwebHV+wjkQyRag+QjaXB8PA6/OTSqUJhELk8nnAhsfrJZ3J4g8EKRQKWIYNt8dDNpvD5/dTyOcBC4/bTSaTxu/zUcjnMc0KlmVhtxlT1c1cDrNSAcvEZrfhc7tJTSYIBf3k0imwKgS8HhLjo4Tr68gkE2BWCAX8jI4M09QQIZkYxzBN6kN+hgf6aWtuZGIkhlEp0RipZ39/Px0dHYyOjlZ/Tv39/bQcCFMHDwVsbG+nNDQ0o0LlcURxFJLYKwEwKwsOVQf/G1uWVT1WKBRYtmwZlUqFycnJwwLWof2pr6+PUChEXV0d5XK5OpzQ7/fT3Ny84KClKpeIyOEUyERETpL5Kmswd3UNFh/cZpvXNluAKxaL1eFki6nGTZ936Bf9gwOerVLAUS5TKpXwOSPUWx6GR/ZUV6MMhsNEnCkmh3oJGW0UDoS6SDRKbmCAhrY2zAPHmqJRigMDNB0S/soDAzTPEghbZjk2fd7BQae9vZ2BgYEZx6Zfq+Gga4SjUbIDA9SV2ygeOBaMRkmODOB3tpEdmzrmdUZx5sZwFt0Yxal5ZbYD14Wp4a5weJjKZDL4/X7S6TROp3NGhcowjGMKVbP9GxcKBbq6ushkMrS2ts4asA7tTwvZ105BS0Rk8RTIREROEUcKbNMWEtzmm9d2aIA7eDjZ9LGFVONm+6J/6LHp4WqVSoVUKoXf76dcLhMKhXA4HOTz+TlD3Yk6dvBQPJ/Pt6Dwc7yPHRqmfD4fpVIJt9tNLBarLooxvdDIsYaq2f6Ns9ksoVCICy+88Ih7zR28PYCGE4qIHF8KZCIip5mFBrdpCwlwR1uNm+2L/qHHkskkAwMDZLNZ2tvbsSyruqjJ9LLl84W6E3Fseije3r17FxR+jvexucKU1+sFmHMp/mMJVdqsW0Tk1KRVFo8TrbIoIkvRXPu1weI2Qd67dy+7d+8mHo8ftr/bmboP2VwrCh7NZtUKVSIipxatsigiIsfFYqtxc1m2bBlXXHHFnIuaHG3QO9ZjRxN+jvcxhSkRkTObApmIiJwUxyvciYiILCW2WjdARERERETkTKVAJiIiIiIiUiMKZCIiIiIiIjWiQCYiIiIiIlIjCmQiIiIiIiI1okAmIiIiIiJSIwpkIiIiIiIiNaJAJiIiIiIiUiMKZCIiIiIiIjWiQCYiIiIiIlIjCmQiIiIiIiI1okAmIiIiIiJSIwpkIiIiIiIiNaJAJiIiIiIiUiOOWjdgqbAsC4BkMlnjloiIiIiISC1NZ4LpjDAfBbLjJJVKAdDR0VHjloiIiIiIyKkglUpRV1c37zmGtZDYJkdkmiaDg4MEg0EMw6h1c+Q4SSaTdHR00N/fTygUqnVz5Ayj/ie1pP4ntaT+J7V0PPqfZVmkUina2tqw2eafJaYK2XFis9lYtmxZrZshJ0goFNIHgtSM+p/Ukvqf1JL6n9TSsfa/I1XGpmlRDxERERERkRpRIBMREREREakRBTKRebjdbjZu3Ijb7a51U+QMpP4ntaT+J7Wk/ie1dLL7nxb1EBERERERqRFVyERERERERGpEgUxERERERKRGFMhERERERERqRIFMRERERESkRhTI5Iz3ne98h+7ubjweD5dddhnPPvvsnOfed999vPGNbyQcDhMOh1m/fv2854scyWL638EeeOABDMPgXe9614ltoCxpi+1/iUSCm2++mdbWVtxuN6tWreKRRx45Sa2VpWax/e9f//VfWb16NV6vl46ODj75yU+Sz+dPUmtlKfn973/PtddeS1tbG4Zh8NBDDx3xOU8++SRr167F7XazYsUKNm3adNzao0AmZ7Sf/OQn3HrrrWzcuJFt27Zx4YUXsmHDBuLx+KznP/nkk7z//e/nt7/9LVu2bKGjo4Orr76agYGBk9xyWQoW2/+m9fX18elPf5o3vvGNJ6mlshQttv8Vi0Xe8pa30NfXx89+9jN27NjBfffdR3t7+0luuSwFi+1/P/rRj7j99tvZuHEj27dv54c//CE/+clP+NznPneSWy5LQSaT4cILL+Q73/nOgs7v7e3lbW97G1dddRUvvvgin/jEJ/jwhz/Mr3/96+PTIEvkDPbHf/zH1s0331z9e6VSsdra2qyvfvWrC3p+uVy2gsGgdf/995+oJsoSdjT9r1wuW1dccYX1gx/8wPrgBz9ovfOd7zwJLZWlaLH975577rHOOussq1gsnqwmyhK22P538803W29605tmHLv11lutdevWndB2ytIHWA8++OC853z2s5+1zjvvvBnHrrvuOmvDhg3HpQ2qkMkZq1gssnXrVtavX189ZrPZWL9+PVu2bFnQNbLZLKVSiUgkcqKaKUvU0fa/f/qnfyIajfK3f/u3J6OZskQdTf97+OGHufzyy7n55ptpbm5mzZo1fOUrX6FSqZysZssScTT974orrmDr1q3VYY179uzhkUce4ZprrjkpbZYz25YtW2b0V4ANGzYs+PvikTiOy1VETkOjo6NUKhWam5tnHG9ubua1115b0DVuu+022traDvtPKnIkR9P/nnrqKX74wx/y4osvnoQWylJ2NP1vz549PPHEE3zgAx/gkUceYffu3Xz0ox+lVCqxcePGk9FsWSKOpv/91V/9FaOjo7zhDW/AsizK5TJ/93d/pyGLclLEYrFZ+2symSSXy+H1eo/p+qqQiRylu+66iwceeIAHH3wQj8dT6+bIEpdKpbjhhhu47777aGxsrHVz5AxkmibRaJTvf//7XHLJJVx33XXccccd3HvvvbVumpwBnnzySb7yla/w3e9+l23btvEf//Ef/OpXv+KLX/xirZsmcsxUIZMzVmNjI3a7neHh4RnHh4eHaWlpmfe5X//617nrrrv4zW9+wwUXXHAimylL1GL73+uvv05fXx/XXntt9ZhpmgA4HA527NjB8uXLT2yjZck4mt9/ra2tOJ1O7HZ79dg555xDLBajWCzicrlOaJtl6Tia/nfnnXdyww038OEPfxiA888/n0wmw0033cQdd9yBzaYag5w4LS0ts/bXUCh0zNUxUIVMzmAul4tLLrmEzZs3V4+ZpsnmzZu5/PLL53ze3XffzRe/+EUeffRRLr300pPRVFmCFtv/zj77bF5++WVefPHF6p93vOMd1RWfOjo6Tmbz5TR3NL//1q1bx+7du6s3AgB27txJa2urwpgsytH0v2w2e1jomr45MLUug8iJc/nll8/orwCPP/74vN8XF+W4LA0icpp64IEHLLfbbW3atMl69dVXrZtuusmqr6+3YrGYZVmWdcMNN1i333579fy77rrLcrlc1s9+9jNraGio+ieVStXqLchpbLH971BaZVGOxWL73759+6xgMGjdcsst1o4dO6xf/vKXVjQatb70pS/V6i3IaWyx/W/jxo1WMBi0fvzjH1t79uyxHnvsMWv58uXW+973vlq9BTmNpVIp64UXXrBeeOEFC7C+8Y1vWC+88IK1d+9ey7Is6/bbb7duuOGG6vl79uyxfD6f9ZnPfMbavn279Z3vfMey2+3Wo48+elzaoyGLcka77rrrGBkZ4R/+4R+IxWJcdNFFPProo9WJm/v27ZtxR+6ee+6hWCzynve8Z8Z1Nm7cyBe+8IWT2XRZAhbb/0SOp8X2v46ODn7961/zyU9+kgsuuID29nY+/vGPc9ttt9XqLchpbLH97/Of/zyGYfD5z3+egYEBmpqauPbaa/nyl79cq7cgp7Hnn3+eq666qvr3W2+9FYAPfvCDbNq0iaGhIfbt21d9vKenh1/96ld88pOf5Fvf+hbLli3jBz/4ARs2bDgu7TEsS3VeERERERGRWtCtVxERERERkRpRIBMREREREakRBTIREREREZEaUSATERERERGpEQUyERERERGRGlEgExERERERqREFMhERERERkRpRIBMREREREakRBTIREZF5jIyM8JGPfITOzk7cbjctLS1s2LCB//7v/66e8/TTT3PNNdcQDofxeDycf/75fOMb36BSqRx2vd/+9rdcc801NDQ04PP5OPfcc/nUpz7FwMDAyXxbIiJyilAgExERmce73/1uXnjhBe6//3527tzJww8/zJ/92Z8xNjYGwIMPPsiVV17JsmXL+O1vf8trr73Gxz/+cb70pS9x/fXXY1lW9Vrf+973WL9+PS0tLfz85z/n1Vdf5d5772VycpJ/+Zd/qdVbFBGRGjKsgz8pREREpCqRSBAOh3nyySe58sorD3s8k8nQ1dXFlVdeyc9//vMZj/3iF7/gHe94Bw888ADXXXcd+/fvZ/ny5Xz0ox/lm9/85qyvVV9ff6LeioiInKJUIRMREZlDIBAgEAjw0EMPUSgUDnv8scceY2xsjE9/+tOHPXbttdeyatUqfvzjHwPw05/+lGKxyGc/+9lZX0thTETkzKRAJiIiMgeHw8GmTZu4//77qa+vZ926dXzuc5/jpZdeAmDnzp0AnHPOObM+/+yzz66es2vXLkKhEK2trSen8SIiclpQIBMREZnHu9/9bgYHB3n44Yf58z//c5588knWrl3Lpk2bqucsZPS/ZVkYhnECWyoiIqcjBTIREZEj8Hg8vOUtb+HOO+/k6aef5m/+5m/YuHEjq1atAmD79u2zPm/79u3Vc1atWsXk5CRDQ0Mnrd0iInLqUyATERFZpHPPPZdMJsPVV19NJBKZdYXEhx9+mF27dvH+978fgPe85z24XC7uvvvuWa+ZSCROZJNFROQU5ah1A0RERE5VY2NjvPe97+XGG2/kggsuIBgM8vzzz3P33Xfzzne+E7/fz/e+9z2uv/56brrpJm655RZCoRCbN2/mM5/5DO95z3t43/veB0BHRwff/OY3ueWWW0gmk/z1X/813d3d7N+/n3/7t38jEAho6XsRkTOQlr0XERGZQ6FQ4Atf+AKPPfYYr7/+OqVSiY6ODt773vfyuc99Dq/XC8B//dd/8eUvf5ktW7aQz+dZuXIlH/rQh/jEJz6B3W6fcc3f/OY3fP3rX+fZZ58ll8vR3d3N29/+dm699VYt+CEicgZSIBMREREREakRzSETERERERGpEQUyERERERGRGlEgExERERERqREFMhERERERkRpRIBMREREREakRBTIREREREZEaUSATERERERGpEQUyERERERGRGlEgExERERERqREFMhERERERkRpRIBMREREREakRBTIREREREZEa+f8uTezLPmfWmAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_fit.plot_fit_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "iwp",
   "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": "3.11.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "6b12f1f625627d6c4a17ef696ddbbbd9bd4b12881121180de40e09e7956aa05c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}