{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59c61afc-6244-4003-809f-19be36210389",
   "metadata": {},
   "source": [
    "# History Matching\n",
    "\n",
    "The following tutorial demonstrates how one may calibrate a simulation model via history matching using `calisim`. We will first import our required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6de47f05-d1ac-4e93-bece-c2e61e198271",
   "metadata": {},
   "outputs": [],
   "source": [
    "from calisim.data_model import (\n",
    "\tDistributionModel,\n",
    "\tParameterDataType,\n",
    "\tParameterSpecification,\n",
    ")\n",
    "from calisim.example_models import SirOdesModel\n",
    "from calisim.history_matching import (\n",
    "\tHistoryMatchingMethod,\n",
    "\tHistoryMatchingMethodModel,\n",
    ")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.integrate import solve_ivp\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcc3a3c-1801-4031-b81c-bcb031a30ee0",
   "metadata": {},
   "source": [
    "## SIR Model Parameters and Initial Conditions\n",
    "\n",
    "We next define our forward model. We will use an SIR (susceptible, infected, and recovered) compartmental model, combined with SciPy's solver for ordinary differential equations. The SIR model is expressed as a system of ordinary differential equations where:\n",
    "\n",
    "| Parameter | Value | Description |\n",
    "|-----------|-------|-------------|\n",
    "| β (beta)  | 0.4   | Infection rate: probability of transmission per contact per time unit |\n",
    "| γ (gamma) | 0.1   | Recovery rate: fraction of infected recovering per time unit |\n",
    "|           |       | **Average infectious period = 1 / γ = 10 time units** |\n",
    "\n",
    "With the following compartments:\n",
    "\n",
    "| Compartment | Symbol | Initial Value | Description |\n",
    "|------------|--------|---------------|-------------|\n",
    "| Susceptible | S0     | 999           | Individuals who can catch the disease (N - I0 - R0) |\n",
    "| Infected    | I0     | 1.0           | Individuals currently infected and can spread the disease |\n",
    "| Recovered   | R0     | 0             | Individuals recovered or removed; no longer infectious |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0de1d6a9-c2d0-48fa-ba60-7bc029c3434b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sir_simulate(parameters: dict) -> np.ndarray | pd.DataFrame:\n",
    "    def dX_dt(_: np.ndarray, X: np.ndarray) -> np.ndarray:\n",
    "        S, I, _ = X \n",
    "        dotS = -parameters[\"beta\"] * S * I / parameters[\"N\"]\n",
    "        dotI = (\n",
    "            parameters[\"beta\"] * S * I / parameters[\"N\"] - parameters[\"gamma\"] * I\n",
    "        )\n",
    "        dotR = parameters[\"gamma\"] * I\n",
    "        return np.array([dotS, dotI, dotR])\n",
    "\n",
    "    X0 = [parameters[\"S0\"], parameters[\"I0\"], parameters[\"R0\"]]\n",
    "    t = (parameters[\"t\"].min(), parameters[\"t\"].max())\n",
    "    x_y = solve_ivp(\n",
    "        fun=dX_dt, y0=X0, t_span=t, t_eval=parameters[\"t\"].values.flatten()\n",
    "    ).y\n",
    "\n",
    "    df = pd.DataFrame(dict(dotS=x_y[0, :], dotI=x_y[1, :], dotR=x_y[2, :]))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505e399b-e7d4-4f2e-9e0a-0d81ff8a3488",
   "metadata": {},
   "source": [
    "We will perform a simulation study with the following ground-truth parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "137169dd-d35e-499a-9917-a6fe6c253215",
   "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>beta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>N</th>\n",
       "      <th>I0</th>\n",
       "      <th>R0</th>\n",
       "      <th>S0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beta  gamma     N   I0  R0     S0\n",
       "0   0.4    0.1  1000  1.0   0  999.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SirOdesModel()\n",
    "pd.DataFrame(model.GROUND_TRUTH, index=[0]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78777eb-fff5-4d7a-86c9-ad796c442319",
   "metadata": {},
   "source": [
    "When supplied to our forward model, these ground-truth parameters will generate the observed data below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5967ef99-3a70-4da2-9d41-4cf49af165d1",
   "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>dotS</th>\n",
       "      <th>dotI</th>\n",
       "      <th>dotR</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>998.534208</td>\n",
       "      <td>1.349201</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>997.906105</td>\n",
       "      <td>1.819995</td>\n",
       "      <td>0.273899</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>997.059813</td>\n",
       "      <td>2.454180</td>\n",
       "      <td>0.486007</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>995.919926</td>\n",
       "      <td>3.308098</td>\n",
       "      <td>0.771976</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>994.385263</td>\n",
       "      <td>4.457212</td>\n",
       "      <td>1.157524</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dotS      dotI      dotR  day\n",
       "0  999.000000  1.000000  0.000000    0\n",
       "1  998.534208  1.349201  0.116592    1\n",
       "2  997.906105  1.819995  0.273899    2\n",
       "3  997.059813  2.454180  0.486007    3\n",
       "4  995.919926  3.308098  0.771976    4\n",
       "5  994.385263  4.457212  1.157524    5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observed_data = model.get_observed_data()\n",
    "observed_data.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8151d66c-e478-40c3-858e-a48b542e41b9",
   "metadata": {},
   "source": [
    "Let's view the trajectory of infected individuals over time in days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e6e0791-4e30-44a6-9deb-4b7312c860d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='day', ylabel='dotI'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "observed_data.plot.scatter(\"day\", \"dotI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07d848d7-ef0e-483f-ba31-10d713116864",
   "metadata": {},
   "source": [
    "## History Matching via the Sequential Iterative Ensemble Smoother\n",
    "\n",
    "Next, let's use `calisim` to perform calibration via history matching using the simulated and observed number of infections. \n",
    "\n",
    "To start with, we'll need to define our `ParameterSpecification` parameter specification using Normally distributed priors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea51de89-24d7-415f-a7ef-1a8c9c1493ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_spec = ParameterSpecification(\n",
    "\tparameters=[\n",
    "\t\tDistributionModel(\n",
    "\t\t\tname=\"beta\",\n",
    "\t\t\tdistribution_name=\"normal\",\n",
    "\t\t\tdistribution_args=[0.35, 0.05],\n",
    "\t\t\tdata_type=ParameterDataType.CONTINUOUS,\n",
    "\t\t),\n",
    "\t\tDistributionModel(\n",
    "\t\t\tname=\"gamma\",\n",
    "\t\t\tdistribution_name=\"normal\",\n",
    "\t\t\tdistribution_args=[0.08, 0.01],\n",
    "\t\t\tdata_type=ParameterDataType.CONTINUOUS,\n",
    "\t\t),\n",
    "\t]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c97665b1-9667-46b5-851d-785b202fd7ab",
   "metadata": {},
   "source": [
    "This contains information concerning the various parameter names, probability distributions, ranges, distribution parameters, and data types.\n",
    "\n",
    "We next need to define a wrapper function around our forward model to ensure there's compatibility with the `calisim` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb19efc9-4b8e-4305-91da-24a49f402931",
   "metadata": {},
   "outputs": [],
   "source": [
    "def history_matching_func(\n",
    "\tparameters: dict, simulation_id: str, observed_data: np.ndarray | None, t: pd.Series\n",
    ") -> float | list[float]:\n",
    "    simulation_parameters = model.GROUND_TRUTH.copy()\n",
    "    simulation_parameters[\"t\"] = t\n",
    "    \n",
    "    for k in [\"beta\", \"gamma\"]:\n",
    "        simulation_parameters[k] = parameters[k]\n",
    "    \n",
    "    simulated_data = sir_simulate(simulation_parameters).dotI.values\n",
    "    return simulated_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd242206-8cfd-4fb9-a2fd-1d22fb4b9b55",
   "metadata": {},
   "source": [
    "The last step is to create a `HistoryMatchingMethodModel` specification for the calibration procedure itself, which we then supply to a `HistoryMatchingMethod` calibrator. We'll use the `sequential iterative ensemble smoother` (SIES) method via the [iterative_ensemble_smoother engine](https://iterative-ensemble-smoother.readthedocs.io/en/latest/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "87556257-9750-489c-a758-52b9707a846f",
   "metadata": {},
   "outputs": [],
   "source": [
    "specification = HistoryMatchingMethodModel(\n",
    "\texperiment_name=\"ies_history_matching\",\n",
    "\tparameter_spec=parameter_spec,\n",
    "\tobserved_data=observed_data.dotI.values,\n",
    "\tmethod=\"sies\",\n",
    "\tn_samples=50,\n",
    "\tn_iterations=8,\n",
    "\toutput_labels=[\"Number of Infected\"],\n",
    "\tcovariance=np.eye(observed_data.dotI.values.shape[0]),\n",
    "\tcalibration_func_kwargs=dict(t=observed_data.day),\n",
    ")\n",
    "\n",
    "calibrator = HistoryMatchingMethod(\n",
    "\tcalibration_func=history_matching_func, specification=specification, engine=\"ies\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75479309-257a-4ab1-8e3e-74d3032593c9",
   "metadata": {},
   "source": [
    "Finally, we'll run the calibration procedure. This is composed of 3 steps:\n",
    "\n",
    "1. **Specify**: Define your calibration problem: Parameter distributions, observed data, objective/discrepancy function, and calibration settings (like algorithm, directions, iterations)\n",
    "2. **Execute**: Run the actual calibration process (simulation + optimization/inference)\n",
    "3. **Analyze**: Process, summarize, and optionally save plots/metrics of the calibration results\n",
    "\n",
    "Or **SEA**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "58cbdf66-9aee-4e95-af09-3b1e4699daed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<calisim.history_matching.implementation.HistoryMatchingMethod at 0x7f8823db3190>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calibrator.specify().execute().analyze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb68c1d3-7ded-4e9f-aafe-26995f536a4f",
   "metadata": {},
   "source": [
    "Let's view the parameter estimates produced by the calibrator, with some measure of uncertainty. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7677a4e5-684a-4137-b17b-380b7979617d",
   "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>parameter</th>\n",
       "      <th>estimate</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>ground truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beta</td>\n",
       "      <td>0.400196</td>\n",
       "      <td>0.001135</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gamma</td>\n",
       "      <td>0.100281</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parameter  estimate  uncertainty  ground truth\n",
       "0      beta  0.400196     0.001135           0.4\n",
       "1     gamma  0.100281     0.000669           0.1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([\n",
    "    { \"parameter\": estimate.name, \"estimate\": estimate.estimate, \"uncertainty\": estimate.uncertainty, \"ground truth\": model.GROUND_TRUTH[estimate.name] }\n",
    "    for estimate in calibrator.get_parameter_estimates().estimates\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e84822-792a-47d6-99b5-b9ed02846d70",
   "metadata": {},
   "source": [
    "The history matching calibrator is able to retrieve the ground-truth parameter values from our simulation study."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9549ea-e92d-441a-9d47-9bd384a8e4c5",
   "metadata": {},
   "source": [
    "## History Matching via the Ensemble Smoother with Multiple Data Assimilation\n",
    "\n",
    "Let's reuse the [iterative_ensemble_smoother engine](https://iterative-ensemble-smoother.readthedocs.io/en/latest/). But we'll demonstrate how to alter the calibration algorithm to the Ensemble Smoother with Multiple Data Assimilation (ESMDA) with minimal code changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d890c798-e02f-448c-974f-88e0106ecfcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<calisim.history_matching.implementation.HistoryMatchingMethod at 0x7f8824174250>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gQapWrZpGjx6t6OhoFSpUSLNmzVLHjh0lSfv27VOFChW0du1a1a5dWwsXLlSrVq106tQpx+ipyZMn67333tO5c+fk6up6y9dnpBQAAAAAAP8uavdObRo+RFdOn9JlN1cdK1FKF4OKK7agvy7m99FZD1+ddcuvRFu+mz5HPvOKgnVSQeYpBaRcUMCVeAXEJCswKlmeJ1Llfiha9vhkJXimKsY3ny7n91JqkLcKlb9PVWvXU5ng8nJzdrvtzMlpyboQe0HHzp/SocOHdf70cSWePy8zJkFOcUlyTUyS25UUuSUmK19islyvJMstScqXYsrVyZT8PGT6ecj0dZHpZVOat11pHilK9LQrzt1QnJuheFcnxTs7K87ZVYdtpbRLobpgFEqXw8WeoqKXTiv42GGVOLJPZY4ekrvNUIH7Sqv2y71UqFSZO/59yQvu6fS9rl27yt/fX6NGjVL9+vUdpVR4eLgaNmyoS5cuyc/Pz3F88eLF9eabb6pnz57q06ePfv75Z23bts2x//DhwypVqpS2bNmi+++//7rXS0pKUlJSUro3FxISQikFAAAAAMDfpKWmaMvUKdq74Edteaie9lasqOOeBXXR2e+mjzHMNBXSORXWKQWmnVOhxBgFxF1RwLlk+Z9IkfufcXI+l6AktxTF+Ri6nN9LiYW85FMmROVr1VW50tXkl+/mz3+vmaapmIQYHbhwWvtOHtO540eVcO6s7NGxcopPlGtCstwS/xqNdeVqmeV2JVkeiWnyKuKnpKoeOl3qivb4+WqPUyXtVmXFGOnfT760RBU9f1pFj/2pEof3qMTJY8rn4qTAKhUV9tJb8gsuYs2bz6bu2fS92bNna8uWLdq4ceN1+6KiouTq6pqukJKkwMBARUVFOY75+/pS1/Zf23cjgwcPVv/+/TMaFQAAAACAPOHCkT+1blBf7TLt2lunnta/8bEu/aOI8jRjrxZP9jMKSL6kQvEJKnQ5WQEnrsjjWJI8j8Yq1UjRFc9kXfbzVFxBD6lEIYW0aaz7qtRWsE+wbP8ync8qhmHI19NXNTx9VaNYeSns9h4XnZCgqUvnK2HjJgX+kqJHjh9V89LxSq60SUdLJGuvTwHtNqporyopwclTBwNL6mBgSalmQ3mmxKvo2VMqeuyQfu/zgULOnpKbm4uK1qimsJfflqef/71907lEhkqp48eP64033tDSpUuVL9/Nh/dltt69e6tXr16O+9dGSgEAAAAAkFfZ09K0a8532vntdG0Nq6Ndrdpoh08pxzfbeZkxqpOyRhXOXFRg1BX5HkmU9+FYGYnJSna/omgfV0UX8FBsEV95dqipwjXqqVShMnJzuv3pdjmZr4eH3mrbRWrbRZJ07NIlTfv1e7ns3Kug9efU7OxBtakUr6Tyq3UwJFV7PYO0x6ii/aqgeBdP7S9SVvuLlJXCmsk3KUZFok4q5NhB/d6zu4IvnJObRz4VrxOmWt3eVD4PT4vfbfaUoVJq8+bNOnv2rB544AHHtrS0NK1cuVLjx4/X4sWLlZycrMuXL6cbLXXmzBkFBQVJkoKCgrRhw4Z0z3vmzBnHvhtxc3OTm1ve+EMBAAAAAMC/iY46pfWfDtTumAva/fCjWvfmBzrv/P8jc8qZexR2cbfuX3VRvvtO6GRRV6UV8ZT5cDnlf7u+ygRXsnS6XXZVLH9+9X36Fcf9TSePad6CufLef0SFI86qQ8wf6lAtRlfKLtUfwdIe92LarVAdVFlFu/kouriP9hSvID3SWv5XLqnIyeMqfvygVr/UVQEx0XLzcVeZRxvqwWdelfNtrKedF2RoTanY2FgdPXo03bbnn39e5cuX13vvvaeQkBAVKlRI3333nTp06CBJ2r9/v8qXL3/dQuenT59WQECAJGnKlCl65513dPbs2dsqn1joHAAAAACQl5h2u/YvWqAdkydoa/Ua2hVaRVt9SyvVcJEkeZjxqp2yTrUOndJ9v51WnGeM7K1qqnXnd+Th4mFx+pzPbrfrl727tG7Jz/L/87SCj51RcXuazGr+ii0Tr/1BTtrjUka7VVmHVUrmX6PVrgmIO3e1pDp2QCX+3KcCV+Lknt9b5Zu11P0du8rJOcOrK2Vr93Sh87/7+0LnkvTqq6/qt99+0/Tp0+Xj46PXXntNkrRmzRpJV0dWVatWTcHBwRo6dKiioqL0zDPP6IUXXtCnn36aqW8OAAAAAICcLP7SRW0YOli7jh/Wnrr1ta7EfTrtEuDYX8o8qLDLO/TA6osquOuojpT30ENv9lKVUrUsTJ37JaemavqGNTq8cpkKHT2nkCOnVNTLTSnVfBRdMkZ7CubTHudy2q3KOmEUT/dYm2lXYPRZFT15TCWO7VexIwfkl5Qoj0K+qtbpKVVq0s6aN5WJ7tlC57cyatQo2Ww2dejQQUlJSWratKkmTpzo2O/k5KQFCxbo1VdfVVhYmDw9PdW1a1cNGDAgs6MAAAAAAJDjmKapI6sjtXn0cG2vXFm7a1TTpsaPK9m4OuXLzUzUg6nrVevIKVVYcEJJbpeU0up+PfzZbDV1Ye2irODq7KyXHqorPVRXknTxSqImRy5R3Mb1Cgy3qcLRU6obvEeplQ/pfPEY7fL31h5bRe1RZZ0xCuu0X5BO+wVpY6UH5WRPVfClMyp68og2/bpMo3JBKXW77nqklBUYKQUAAAAAyG0S4+O1cfQI7dy1Tfvq1de6kmV1zLWwY39R85geit6qB9ZdVPCWwzp6n4tqvtlTVcvUkWEYFibHPx26GK0vl/8k2849Cjp2TuVOnJZnmUJKrSidKhar3d4FtMeorN2qosvG/68H9vCFTfqh4wsWJs8clo2UAgAAAAAAt+/E1k3aOORTbb+vtPaGVtH6sMZKNNwlSS5msmqkblLNY8dU+bcomcYZJbQMVdjAmWri6m1xctxMaX9fffr4s9LjV++vPnlKPy6ZL48/jih4vaGGZ0+peZU0pdy3UceKxGm3Z7B2q4pqxJ2wNngWo5QCAAAAACCLpSQlauvUydqyaqX+qPuI1j//vA65hTj2B5mnFBazRdU3XlCxDX/qeFlDwZ++rurlHmVUVA70cJFgPfz8fyVJaXa7/rd/v9ZELJT/wZMqstyuNvEn1Lpako4lu1ucNGtRSgEAAAAAkEXO/rFP6z4dqB3BgdpXLVRrX3xL8bar60A5mam6P22LHjxxRKG/npaz/bQuN6+kWn2+VhM3X4uTI7M42WzqVKGCOlWoIEmKS0nRl1s36/DvK3Rf4QIWp8tarCkFAAAAAMA9lJqSop2zvtam337VgUce0obSpbUvX0nH/oLmWdWO26zqm8+p1NojOl0yRRXeeE0PVmjMqCjkSKwpBQAAAACAhS4dP6p1gwdpu6+n/qhaRatfelMxtqs/oBtmmkLTdqjmqT9VbVGUPBOP6XyT8qrx0xcq4J63Rssg76KUAgAAAAAgk9jtadr70zxt+P47HarzoDa0aqqd+UrJNGySJD/zomrHb9YD287qvtWHda5YvII/6K7alVvI9tcxQF5BKQUAAAAAQCY4uWWTZn/9uf54sKZWvdhDF235Hfsqpu1SzdMH9MDis/KNPaSzjcuq+rxJKuRRyMLEgLUopQAAAAAAuAtpqan6rffbWlsmRHPaPKs4w1uS5GXGqFbCJlXfeUYVIg/rYvAlFX7rv3q4WltGRQGilAIAAAAA4I6d2r5Fs76erBWPNtAmz/KSpKL2o6obtV0PLD+rgHN/6FSjkrr/h7EK8gqyOC2QvVBKAQAAAACQQfa0NP36wTtaW6qI5rR8RnGGt5zMVDWOWaEW3+2V3f2wAnq8rLoPjJazjR+9gRvhTwYAAAAAABlweuc2zZwxWZH162mjZwVJUoj9qB7bulFV125RsaEfqUrphyxOCWR/lFIAAAAAANwGe1qaFn70vtaUDNKcFk8p9q/RUY1iV6jFrD1KrJaiJnN+kouTi9VRgRyBUgoAAAAAgFuI2rNLM7+aoBX16zpGRxW1H9NjW9er2rotKjG0jyqVCrM4JZCzUEoBAAAAAHAT9rQ0LerzkdYUL6jvW3RxjI5qGBOpFrN3K+X+FDX5/mdGRwF3gFIKAAAAAIAbOLNvz9XRUXXraINnRUn/Pzrq/nWbVWJYf1UsWcvilEDORSkFAAAAAMDf2O1pWtyvr9YUza/ZzZ5QrOEjm5mmhjGRavn9biXdn6LGc36Ri43RUcDdoJQCAAAAAOAvZ/fv07dfjVfkIw9p/V+jo4rYj6vdlg2qvmGjSg4fpArFa1icEsgdKKUAAAAAAHme3Z6mJQMGaG0RH81q2ulvo6NWqsXsnUqtaVfj7xkdBWQmSikAAAAAQJ527o/9f42Oqq11HpUkXR0d1XbLBlXfuFGlRnyiCiHVLU4J5D6UUgAAAACAPMluT9OSQQO1Nthbs5o87hgd1SBmlVrM3qW0WmlqMpvRUcC9QikFAAAAAMhzzh88oG+mjlfkIzW1zqOyJCnYfkJtt65XjQ3rVWbUUJUrUs3akEAuRykFAAAAAMgzTLtdiwcP0vogD81s0l4xhq9sZpoejVmllt/vlvmQXU1n/yZnGz8uA/caf8oAAAAAAHnChT8P6dupY7WiTk2t/dvoqDZbNqjGpnUqO3KYyhWpanFKIO+glAIAAAAA5Gqm3a5lQ4doTSE3zWx0dXSUYabp0ejVajlnp8yHDTX7jtFRQFbjTxwAAAAAINe6cPSwZn4xRhFh1bXWo4okqbD9pNps2aCam9eq3MgRKhtcxeKUQN5EKQUAAAAAyHVMu13Lho/QuoKGvm3QTtGG31+jo35Xyzk7ZNS1qdksRkcBVuJPHwAAAAAgV7l07Ii+mTJOK8Kqao1HqKSro6Nab96gB7euUfnRo1UmsJLFKQFQSgEAAAAAcgXTbteyESO0oYChrxu0doyOqhezRq3m7JBzPSc1n7lITjYnq6MCEKUUAAAAACAXuHT8qL6dOk4RD4b+bXTUKbXasl4Pbv1dFUaNVZnAihanBPB3lFIAAAAAgBzLNE2Fjx6tdX5p+rpeq/8fHRW9Ri3n7pDro85q8e1iRkcB2RClFAAAAAAgR7Lb0zTp47cV8VA1rfaoKkkKsp9S683rVXPbalUaNV6lAytYnBLAzVBKAQAAAABynJSkJE0Y/JHm1K+nP51LyDDTVDd6rVrO3aF8DZ3V8tsljI4CsjlKKQAAAABAjnIlNkaTJg3RjEca6owtSJ5mrDptC9dDm1aq8piJKlmonNURAdwGSikAAAAAQI4RfeqkvvzhC02p0ViXjfzyt5/XMyuWq0ShKLVgdBSQo1BKAQAAAAByhDP79ujrlT9pUuXGSjA8FZx2Qs/8EqFKDQqqSdvBVscDkEGUUgAAAACAbO/Plas088/1mlKmoVIMV5VOOahn50ao2kv1VKtmJ6vjAbgDlFIAAAAAgGxt45y5+jHtlKYXe1Sm4aTKibv07Mxw1Rz4kiqUrG11PAB3iFIKAAAAAJBtLR4/Tj8H2/S/QvUkSbViN6rLrHA9PGGwihQoYW04AHeFUgoAAAAAkC3NHNBXvz0QouWeNSRJj15YpY4/LleDr75Sfg9/i9MBuFuUUgAAAACAbMU0TX3e5z39WqeKNrpVkWHa1fJkuJoti1DzGXPl4eJhdUQAmYBSCgAAAACQbaSlpmriJx/of3Vrap9zWTmbKXrs4DLV2bpCbactkIvNxeqIADIJpRQAAAAAIFtITryiSWMG6tuH6+q4rajczQQ9vm2Zqp7foCcmLpJhGFZHBJCJKKUAAAAAAJaLu3xJX84YrS9qPqrzRiH5mpf15Orlquh3Sp0+XWB1PAD3AKUUAAAAAMBSl08c0/Rfv9H4Kg0VZ/gowH5GTy9cptCHvdSs3ZdWxwNwj1BKAQAAAAAsc2LbNs3aulgTyjZUkpFPxVKP6ul5EXqwa3XVrvWM1fEA3EOUUgAAAAAAS+xatFQ/XNihL4o3VJrhrHJJ+/X09+Gq80EXVSxbz+p4AO4xSikAAAAAQJaLnPG1fva8pJnBDSVJ98dv01Ozlqve2L4KKVjG4nQAsgKlFAAAAAAgS/1vxHAtLOupBd5XR0PVubROnX5cpkZfTFIBz0IWpwOQVSilAAAAAABZwjRNfTWgrxY+WFKr890vSWpyeoVaLV6uVtO+l4eLh8UJAWQlW0YOnjRpkkJDQ+Xj4yMfHx+FhYVp4cKFjv3169eXYRjpbq+88kq65zh27JhatmwpDw8PBQQE6J133lFqamrmvBsAAAAAQLZk2u2aNPBDfR9WXqvz3S+bmaZ2fy5Ri5UL9di0HymkgDwoQyOlihYtqiFDhqhs2bIyTVMzZsxQ27ZttXXrVlWqVEmS9OKLL2rAgAGOx3h4/P+FJS0tTS1btlRQUJDWrFmj06dP69lnn5WLi4s+/fTTTHpLAAAAAIDsJC01RZOH9dWsh2rqkFNJuZpJar9ruWqcXq0nJi2SYRhWRwRgAcM0TfNunsDf31/Dhg1Tt27dVL9+fVWrVk2jR4++4bELFy5Uq1atdOrUKQUGBkqSJk+erPfee0/nzp2Tq6vrDR+XlJSkpKQkx/2YmBiFhIQoOjpaPj4+dxMfAAAAAHAPJcbH6csvhumLKrUVZSssTzNOT6xbrqpuf+iJt2ZaHQ/APRATEyNfX99b9jYZmr73d2lpaZo9e7bi4+MVFhbm2D5z5kwVLFhQlStXVu/evZWQkODYt3btWlWpUsVRSElS06ZNFRMTo927d9/0tQYPHixfX1/HLSQk5E5jAwAAAACySOyF8/pyxiiNC62vKFth5bdf0HPLf1WdMkkUUgAyvtD5zp07FRYWpsTERHl5eWnevHmqWLGiJKlLly4qXry4goODtWPHDr333nvav3+/fvzxR0lSVFRUukJKkuN+VFTUTV+zd+/e6tWrl+P+tZFSAAAAAIDs6dyhQ5oVMVdjyjdQguGpwmmn9NTPy1Xn8fIKe+RFq+MByAYyXEqVK1dO27ZtU3R0tH744Qd17dpVkZGRqlixol566SXHcVWqVFHhwoXVsGFDHTp0SKVLl77jkG5ubnJzc7vjxwMAAAAAss7BdRv0w8FITSjVQCmGq0ol/6mnf1iuem+3V6VyDa2OByCbyHAp5erqqjJlykiSqlevro0bN2rMmDH6/PPPrzu2Vq1akqSDBw+qdOnSCgoK0oYNG9Idc+bMGUlSUFBQhsMDAAAAALKXDT/O00+pf+qr4AYyDSdVurJbT89ergbD3lHxwApWxwOQjdzxmlLX2O32dIuQ/922bdskSYULF5YkhYWFaefOnTp79qzjmKVLl8rHx8cxBRAAAAAAkDMtnDRF39iO68tCDWUaTqoZvVn/+e4XtZ44hEIKwHUyNFKqd+/eat68uYoVK6bY2FjNmjVLK1as0OLFi3Xo0CHNmjVLLVq0UIECBbRjxw717NlTdevWVWhoqCSpSZMmqlixop555hkNHTpUUVFR+uijj9S9e3em5wEAAABADjZzyGAtrOKvZR51JUn1zq7RYwuXqM1Xs+Th4mFxOgDZUYZKqbNnz+rZZ5/V6dOn5evrq9DQUC1evFiNGzfW8ePHtWzZMo0ePVrx8fEKCQlRhw4d9NFHHzke7+TkpAULFujVV19VWFiYPD091bVrVw0YMCDT3xgAAAAA4N4zTVNTBvbVglqltdG1qgzTrmbHI9V47WJ1+PIXuTi5WB0RQDZlmKZpWh0io2JiYuTr66vo6Gj5+PhYHQcAAAAA8iS7PU2ff9Zfc2pW0l6ncnI2U9R2f7jq/BmpzkMXyGbc9YoxAHKg2+1tMrzQOQAAAAAAKUlJ+nLCYE17sIaO2YrJ3UxQ+y3L9WDadj0x7Der4wHIASilAAAAAAAZciUuVl/PGK0J1R7SOSNAPvbL6rRquWqXiVerp3+wOh6AHIJSCgAAAABw2xLj4/TVd2M1ukI9xRo+KpR2Vk8uWqZ6rYqpTv0eVscDkINQSgEAAAAAbktqSrK++maURtz3qBIML4WkHFOX+cvVqHtjVanUyup4AHIYSikAAAAAwC2Zdrs+n/SJxleupwTDSyWT/1TX7xer2Sc9VKJwFavjAciBKKUAAAAAAP/KNE1NGtFXXzzwkC4Z/iqcekpd/rdMHcb0UyHvwlbHA5BD8f2cAAAAAIB/NXFwX317//2KshWWv/2CnvplqTp88haFFIC7QikFAAAAALipqYM/0fya5fWnUyl5mbHqvGyp2r7RXsGFSlsdDUAORykFAAAAALihuWPG67f7g7TTuaJczUQ9vmaZWrevorKlH7E6GoBcgFIKAAAAAHCdFXPm6rcyhta4VZeTmar225ercRVX3V/zKaujAcglWOgcAAAAAJDO7sjV+sn1uBZ6NJAktToQoUdcj6pB2ykWJwOQmzBSCgAAAADgEPXHH/r+zBp953u1kGp8PFL1Tm9Qh+4UUgAyFyOlAAAAAACSpJiz5/TNhh81NbixJKnO+fVqsnW5uoz62eJkAHIjRkoBAAAAAJSUkKAZC6ZobPCjshtOeiBmu9qGL9bTI3+yOhqAXIpSCgAAAADyuNSUFE37eoRGl6inFMNNFa7sU8dfFqvLhDkyDMPqeAByKUopAAAAAMjDTNPUF5M+0Zj7HlK84aUSyUfU6X9L9dTkr+RsY8UXAPcOpRQAAAAA5GGTR/bTF5Vr6pJRQEGpp9X5x6XqMn6Y3JzzWR0NQC5HKQUAAAAAedSkIX31bbVQnTKKKL/9ojr/ulRPDn5Hvu7+VkcDkAdQSgEAAABAHvTlsMGaX72sDtlKy9OM0xPLluqJXp0V6F/C6mgA8ghKKQAAAADIY74fN0kLqxTSdufKcjWT1GHNMj32+P0qWexBq6MByEMopQAAAAAgDwn/fp6WlErVarcasplpard9uVpW91DVBzpZHQ1AHsNXKQAAAABAHrF71Vr96nZIv3o0kiS1PBCh+p4nVK/5RIuTAciLGCkFAAAAAHnAif0H9L/TKzTT92oh1fD4KtU/t1HtX6KQAmANRkoBAAAAQC4Xe/6CZm+cq8nBTSVJYec3qun2ZXpqxE8WJwOQl1FKAQAAAEAulpyQqK9/nqSxJRrLbjipWsxOtYlYqGcmzLc6GoA8jul7AAAAAJBLpaWlavo3QzWqRD0lG24qd2W/Oi5YrGfHz5VhGFbHA5DHUUoBAAAAQC5kmqa+nPipRpd9SHGGt4olH1WneUv17KQv5WRzsjoeAFBKAQAAAEBu9MXI/vq84gO6aBRUYGqUOs9fqufGj5Srs5vV0QBAEqUUAAAAAOQ6k4cO0DdVK+mkraj87JfU+beleubTd+Xp5mN1NABwoJQCAAAAgFxk6rChmn9/CR1wKisPM16dli/VU28/rUL5i1kdDQDSoZQCAAAAgFxi9vgpWhLqp23OoXIxk9Vh3VJ1ery6ihW53+poAHAdSikAAAAAyAUivp+n5SWvaKXrg7KZaWq7Y7keq+mryvd3sDoaANwQpRQAAAAA5HC7Vq3Xb24H9ItHPUlS84Mr1NjntB5q3NPiZABwc5RSAAAAAJCDnfjjkOZFLdc3vk0kSY+e+F0Nzm9S2/+MtTgZAPw7Z6sDAAAAAADuTPzFy5q78TtNLtxMklTr/CY13rFUTw2bb20wALgNlFIAAAAAkAOlJiXp6/njNLpEY6UZzgqN2aXWKxbq+fHzrI4GALeF6XsAAAAAkMPY09I0bfpQjShRT0lGPpW9clDtFy7Sf8bPlWEYVscDgNtCKQUAAAAAOYhpmvpy0iCNLltbcYaPQpKP6fGfF6vbhK9kM/gRD0DOwRULAAAAAHKQL0YN0OcVHtAFo5ACUs+o089L9cKYkXJxcrU6GgBkCKUUAAAAAOQQk4cN0rdVK+iELUS+9svqtHCZ/vPp+/Jw87Y6GgBkGKUUAAAAAOQAXwwbpp+rhegP231yNxPUIWKpnn/nGRXwLWJ1NAC4I5RSAAAAAJDNfT95qpaHemuLc1W5mMlqv26pnu70oIoUDrU6GgDcMUopAAAAAMjG/ti8S2uKXNYK19oyTLta7whXhwf9VDG0rdXRAOCuUEoBAAAAQDaVGJegn/fP1xzPBpKkpodWqrnfOT3U6A2LkwHA3aOUAgAAAIBs6uuZwzUh6FGZhk01L2xVg9Pr1Pq5EVbHAoBM4Wx1AAAAAADA9SaP7KfPq9XUFcNTJZOOqEXEcj07YbbVsQAg0zBSCgAAAACymeljJuinKiV12igiP/sltf01XC+OnWZ1LADIVJRSAAAAAJCN7Fi1UetLJ2mrc1U5m8lqtypcLw18R85OLlZHA4BMRSkFAAAAANlEwuVY/XZyieb9tbB5s/2r9HiTsvL3C7E4GQBkvgyVUpMmTVJoaKh8fHzk4+OjsLAwLVy40LE/MTFR3bt3V4ECBeTl5aUOHTrozJkz6Z7j2LFjatmypTw8PBQQEKB33nlHqampmfNuAAAAACCHstvt+uaHUZoUcLWQqn1ukxrE71D1h561OBkA3BsZKqWKFi2qIUOGaPPmzdq0aZMaNGigtm3bavfu3ZKknj176pdfftHcuXMVGRmpU6dOqX379o7Hp6WlqWXLlkpOTtaaNWs0Y8YMTZ8+XX369MncdwUAAAAAOcwXYwdqQqkHlWS4q/SVQ2q6cpm6vD3D6lgAcM8Ypmmad/ME/v7+GjZsmDp27KhChQpp1qxZ6tixoyRp3759qlChgtauXavatWtr4cKFatWqlU6dOqXAwEBJ0uTJk/Xee+/p3LlzcnV1veFrJCUlKSkpyXE/JiZGISEhio6Olo+Pz93EBwAAAADLfTFytL4PLaRdTpXkn3ZBz82fr7fHjpbN5mR1NADIsJiYGPn6+t6yt7njNaXS0tI0e/ZsxcfHKywsTJs3b1ZKSooaNWrkOKZ8+fIqVqyY1q5dK0lau3atqlSp4iikJKlp06aKiYlxjLa6kcGDB8vX19dxCwlhPjUAAACA3GHNkkitLy/tcqokVzNJ7VaE6+XBH1NIAcj1MlxK7dy5U15eXnJzc9Mrr7yiefPmqWLFioqKipKrq6v8/PzSHR8YGKioqChJUlRUVLpC6tr+a/tupnfv3oqOjnbcjh8/ntHYAAAAAJDtxJ6/qPDLq7XAvb4kqfnulXqqQw35egdYGwwAsoBzRh9Qrlw5bdu2TdHR0frhhx/UtWtXRUZG3otsDm5ubnJzc7unrwEAAAAAWclut+vbn8fr8xJNJUkPRW1QQ9ufqnT/OxYnA4CskeFSytXVVWXKlJEkVa9eXRs3btSYMWP0xBNPKDk5WZcvX043WurMmTMKCgqSJAUFBWnDhg3pnu/at/NdOwYAAAAA8oKp4wZofJU6SjHcdF/CATVet0ydxsyxOhYAZJk7XlPqGrvdrqSkJFWvXl0uLi5avny5Y9/+/ft17NgxhYWFSZLCwsK0c+dOnT171nHM0qVL5ePjo4oVK95tFAAAAADIET4fPkzfVSqnC0YhFUo9qza/Ltcro7+3OhYAZKkMjZTq3bu3mjdvrmLFiik2NlazZs3SihUrtHjxYvn6+qpbt27q1auX/P395ePjo9dee01hYWGqXbu2JKlJkyaqWLGinnnmGQ0dOlRRUVH66KOP1L17d6bnAQAAAMgTIn5apDWV8mmvUwW5mYlqFx6hV4YPlmEYVkcDgCyVoVLq7NmzevbZZ3X69Gn5+voqNDRUixcvVuPGjSVJo0aNks1mU4cOHZSUlKSmTZtq4sSJjsc7OTlpwYIFevXVVxUWFiZPT0917dpVAwYMyNx3BQAAAADZ0IVTZxWZukWLfVpIklruWKHnnm8sL4/8FicDgKxnmKZpWh0io2JiYuTr66vo6Gj5+PhYHQcAAAAAbsmeZtfn3wzSJ8WaK9VwUd2T6/R0ygG1eX6E1dEAIFPdbm9z12tKAQAAAABuberEARpb7GGlGi6qELdfDbYuo5ACkKdl+Nv3AAAAAAAZM2nYEM16oJIuGf4KTI1Sq4XhennCXKtjAYClGCkFAAAAAPfQr9/N06oq3jpgKyt3M0Ftl0botTGjWNgcQJ5HKQUAAAAA98ipwye11m2vwt3qyDDtarklQi/26CRXFw+rowGA5SilAAAAAOAesKekad6qqZrm10SSVP/EOrUunqKQYtUtTgYA2QOlFAAAAADcA19+MUhjitZTmuGsyjF79OjuSDV9vJ/VsQAg22ChcwAAAADIZJOGfqKvq1dRjOGnwimn1GpJuF6a8L3VsQAgW2GkFAAAAABkornTZiqiagEdtpWSpz1O7ZaEq/vYCVbHAoBsh1IKAAAAADLJkX2HtTH/ca10rS3DtKvVphV65d0X5OLkZnU0AMh2KKUAAAAAIBPYU1L1y+av9a1PY0lSg6O/67FQDwUGlLc4GQBkT5RSAAAAAJAJvpr6icYUflR2w0lVL+/So4d/V/3mvayOBQDZFgudAwAAAMBdmjxykL6qVlVxho+KJp9Qi+XL9cL42VbHAoBsjZFSAAAAAHAXZk7+SosqB+qYUULe9hi1XRShHmO+sDoWAGR7lFIAAAAAcIf2bN6tzUXOaZ1LTTmZqWq1LkLd+7wuJycmpQDArVBKAQAAAMAdSIq/osX7/6fvPK8ubN7w0Gp1rhMs//zFLU4GADkDpRQAAAAAZJBpmvr226EaF9RApmHTAxe3q8G5LapV/0WrowFAjsGYUgAAAADIoCljBmlK6ANKMLxUPOmomq4I13PjvrY6FgDkKIyUAgAAAIAMmDZusn6pVFQnjRD52C+rzaIIvTbmK6tjAUCOQykFAAAAALdp4+rN2lIyVpuc75ezmaI2v0fotQG9ZbM5WR0NAHIcSikAAAAAuA1XYuK04sSvmvvXwuaN/1itri2ryMcn0OJkAJAzUUoBAAAAwC2Ypqlv54zQhIBGkqSa57eqSeI+Vane0eJkAJBzsdA5AAAAANzC1LEDNaFKTSUaHiqZeERNfl+qJ0fPsjoWAORojJQCAAAAgH/xxegx+l+l4ooygpU/7aJaLwpXj5HfWh0LAHI8SikAAAAAuIlVS1drc9lUbXOqKhczWW1WRujNwf1l2PhRCgDuFldSAAAAALiBmPOX9fulcM33aChJarJ3pZ7rVEceHvktTgYAuQOlFAAAAAD8g2ma+v6XMZpUsIkkqfaZzWrhfEwVKjezOBkA5B6UUgAAAADwD1+MG6hxxWspycinMgmH1GjDMnV4eazVsQAgV+Hb9wAAAADgb6ZN+Fw/VQjRWSNI/mkX1HJRuHqM+87qWACQ6zBSCgAAAAD+cnDPYe0KuaTNzvfL2UxR65Ur1GvYZ1bHAoBciVIKAAAAACTZ0+xavHmGvvNqLElqePB3PduxttzyeVmcDAByJ0opAAAAAJA0ddJAjStSV3bDSaHRu/Xo5e2qFNrS6lgAkGuxphQAAACAPG/yqJGaU6WMLhv+Ckw5o6bLI/TcuGlWxwKAXI2RUgAAAADytE3rtmn7fXbtcaokNzNRrSIi1HPMZKtjAUCuRykFAAAAIM9KTUrRyj9/0jyPRpKkJntW69X/PiGbjUklAHCvUUoBAAAAyLOmT/tEE4MaSJJqnN+mJi7HVTTkfotTAUDeQP0PAAAAIE+aMGKwZlWrrDjDW0WTT6jR7xF6fPQMq2MBQJ7BSCkAAAAAec7SX8K1oZKHDtnKyMOMV8ulkXpjJAubA0BWopQCAAAAkKckRMdp85XVWuxWT5LUbOtKvdm7uwwbPx4BQFbiqgsAAAAgzzBNU7PmDNfkgo0lSQ9FbVS74nblL1DC2mAAkAdRSgEAAADIMyaP+kSTSz+gRMNdJROP6NEtEWrS8UOrYwFAnsRC5wAAAADyhDnfzFF45QI6YRSTtz1aLZZE6LVRs6yOBQB5FiOlAAAAAOR6Z06e1S7PP7TKJUyGaVeL9ZF665O+VscCgDyNUgoAAABArmaapn5aMlHT/JpIkuodX6enHykmDw9/i5MBQN5GKQUAAAAgV5sydoDGF6+tFMNV98UfUN0Dq1XzkeesjgUAeR5rSgEAAADItaZNmqpfKoborBEk/7QLarYkQv8dyzpSAJAdMFIKAAAAQK50cN9R7SlyVpucH5CTmaqWqyP1zogRVscCAPyFUgoAAABArmPaTS3ZME2zvK6uI9Xg0O/q1vERubi4W5wMAHANpRQAAACAXGfqhP4aV/QRpRnOqhK9Rw0v71T5yo2tjgUA+BvWlAIAAACQq0waNVr/q1JGl4wCCkg9q0bhEXpu7FSrYwEA/iFDI6UGDx6smjVrytvbWwEBAWrXrp3279+f7pj69evLMIx0t1deeSXdMceOHVPLli3l4eGhgIAAvfPOO0pNTb37dwMAAAAgT9u0cad23ZeiXU6V5WomqVV4hN4ZPcnqWACAG8jQSKnIyEh1795dNWvWVGpqqj744AM1adJEe/bskaenp+O4F198UQMGDHDc9/DwcPw6LS1NLVu2VFBQkNasWaPTp0/r2WeflYuLiz799NNMeEsAAAAA8qLUpBStPvA//RjURpLUeO8qvdaji2w2JogAQHaUoavzokWL0t2fPn26AgICtHnzZtWtW9ex3cPDQ0FBQTd8jiVLlmjPnj1atmyZAgMDVa1aNQ0cOFDvvfee+vXrJ1dX1+sek5SUpKSkJMf9mJiYjMQGAAAAkAdM/2qQJpRrINOw6YEL29XcNUqFi1SxOhYA4CbuaqHz6OhoSZK/v3+67TNnzlTBggVVuXJl9e7dWwkJCY59a9euVZUqVRQYGOjY1rRpU8XExGj37t03fJ3BgwfL19fXcQsJCbmb2AAAAABymQnDh2jGfZUUa/iqSPJJNVwdoY4vDrc6FgDgX9zxOFa73a4333xTderUUeXKlR3bu3TpouLFiys4OFg7duzQe++9p/379+vHH3+UJEVFRaUrpCQ57kdFRd3wtXr37q1evXo57sfExFBMAQAAAJAkLf51hTZVyqcDtvvkbiaoxfIV6jXyK6tjAQBu4Y5Lqe7du2vXrl1avXp1uu0vvfSS49dVqlRR4cKF1bBhQx06dEilS5e+o9dyc3OTm5vbnUYFAAAAkEtdiY7XtvhILSzUVpLUbNtKvdX7DRm2u5oUAgDIAnd0pe7Ro4cWLFigiIgIFS1a9F+PrVWrliTp4MGDkqSgoCCdOXMm3THX7t9sHSoAAAAAuJGZc4ZqcsFGkqTaZzapXQnJL/+//4wCAMgeMlRKmaapHj16aN68eQoPD1fJkiVv+Zht27ZJkgoXLixJCgsL086dO3X27FnHMUuXLpWPj48qVqyYkTgAAAAA8rBJIwdpSukHdMXwVInEo6q/eYWatn/f6lgAgNuUoel73bt316xZs/TTTz/J29vbsQaUr6+v3N3ddejQIc2aNUstWrRQgQIFtGPHDvXs2VN169ZVaGioJKlJkyaqWLGinnnmGQ0dOlRRUVH66KOP1L17d6boAQAAALgtc2b+qMhK+XXMKC5ve4yaLw3XmyO/tToWACADDNM0zds+2DBuuH3atGl67rnndPz4cT399NPatWuX4uPjFRISoscee0wfffSRfHx8HMcfPXpUr776qlasWCFPT0917dpVQ4YMkbPz7XVkMTEx8vX1VXR0dLrnBQAAAJD7nTl9XpPWTtLk/K1lmHY9vn6Bhr7xhvJ5+FodDQCg2+9tMjRS6lb9VUhIiCIjI2/5PMWLF9dvv/2WkZcGAAAAAJmmqZ8XjdOXxVtIkh45sV5dHylDIQUAORBfSQEAAAAgx5gybqAmFK+lFMNNZeMP6tGDa1T94S5WxwIA3IEMjZQCAAAAAKtM+/wr/VohWFFGsPKnXVSzZSv06uhvrI4FALhDjJQCAAAAkO0dOnhC+4JOa4NzDTmZqWrxe6TeGzbc6lgAgLtAKQUAAAAgWzPtppasmapvvZtKkh79c61e7FhXzi75LE4GALgblFIAAAAAsrUvJvbXuKIPK81wVqWYvWp4eafKV2podSwAwF1iTSkAAAAA2dbE0WM0r3JpXTQKqlDqOTUOj9DzY6ZYHQsAkAkYKQUAAAAgW9q0aY/2lk3UTqcqcjWT1HJFhN4bNcnqWACATEIpBQAAACDbSU1O0e9/fK8f3BtLkhrtW603//uUDJuTxckAAJmFUgoAAABAtjP9y0GaENRApmHT/Rd3qGW+MwoqUsnqWACATMSaUgAAAACylQkjP9P3VSsqxvBV4eRTarA6Qh1GTbM6FgAgkzFSCgAAAEC2sXjRam2p4KI/bOWUz7yiFstX6J2RX1kdCwBwD1BKAQAAAMgW4qPjtSN6mX7N10CS1HT7Sr3b+w3JMCxOBgC4FyilAAAAAGQLs78fqkmFri5sXuvMZnUq5Srf/EUsTgUAuFcopQAAAABYbtLoQfq8zP1KMDxVPPGo6m9eoYZte1odCwBwD7HQOQAAAABLfTN9tiIq+uuYUUJe9lg1WxahniO+sToWAOAeY6QUAAAAAMscPRKlP3wPaKXLQzJMu1qsj9AHgwZYHQsAkAUopQAAAABYwrSbWhQ5UdN8m0mS6h1bp26NqsjN3cfiZACArEApBQAAAMASUyf217iQh5VquKh87H41OLlZVR98zOpYAIAswppSAAAAALLchFFjNL9KaZ03AlQw9byaRoTrpVFfWh0LAJCFGCkFAAAAIEtFrtyqPfclaadTFbmYSWq5Ilzvj5hodSwAQBajlAIAAACQZZISErXl5Hz96N5IktRk7yq93aOrDJuTxckAAFmNUgoAAABAlvnmm081PrCRTMOm6ue3q51vnAoVLmd1LACABVhTCgAAAECWmDBqkL4NraZ4w1shScfVYF24Wo+YYXUsAIBFGCkFAAAA4J6bPetHrarop8O2UvK0x6n50gi9RSEFAHkapRQAAACAe+rEiSjtzbdLK1wflmHa1XxThD4c1M/qWAAAi1FKAQAAALhnTNPUb8sm6iu/ppKkR06s13/qV5Sbu4/FyQAAVqOUAgAAAHDPTBnfT+OK1VGK4aZycQfU4OhGPVCrg9WxAADZAAudAwAAALgnJowZp18qldQ5I1AF0i6o6fJwvTL6K6tjAQCyCUZKAQAAAMh069fu1L7SCdrmVE3OZopaRIar98jxVscCAGQjlFIAAAAAMlVKUrLWHv5BP3g0liQ13r9K77z6jAybk8XJAADZCaUUAAAAgEz19bRBGh/UUKZh0/0XdqidZ7QCgstbHQsAkM2wphQAAACATDN+5KeaVbWq4gwfFUk+qYZrw9V2xHSrYwEAsiFGSgEAAADIFHPm/Ky1lbz1p620POzxarE0Qm9TSAEAboJSCgAAAMBdOxd1UXudtmm56yMyTLtabF6hDwd8bHUsAEA2RikFAAAA4K6Ypqn5C0fry/xNJUkPn9qobnXLKZ+Hr8XJAADZGaUUAAAAgLvy+bgBGlf8ISUbbiobf1AN/tyg+2t3tDoWACCbY6FzAAAAAHds0oSJWlAhRGeNIPmnXVTT5RF6ddSXVscCAOQAjJQCAAAAcEe2bt2n/cVitNn5ATmbKWqxKlwfDB9jdSwAQA5BKQUAAAAgw1KTU7Vqz3f63rOxJKnhgdV69+WnZXNiMgYA4PZQSgEAAADIsK+mDtT4wg1kGk6qdnGn2uS7pIDg8lbHAgDkIPxvDAAAAAAZMm7Up/o+tLJiDF8FJ59Sw7UR6jD8K6tjAQByGEZKAQAAALht839apI0VPHXQVlbuZoJahEfoHQopAMAdoJQCAAAAcFsuX4jR7pT1WuJWT5LUfMsKfdjnQ4tTAQByKkopAAAAALdkmqbmzB+uKf5NJUl1Tm3Qi2Gl5e7pZ20wAECORSkFAAAA4JYmje2viSVrK8nIpzIJf6rBwbW6v84TVscCAORgLHQOAAAA4F99PulzLawYoigjWH5pl9R02XJ1HzXN6lgAgByOkVIAAAAAbmr3niP6o8hFbXSuLiczVS1/j9CHw8dYHQsAkAtQSgEAAAC4odSUVEVsnqbvvJpIkhoe/F3vdntSNicmXAAA7l6GSqnBgwerZs2a8vb2VkBAgNq1a6f9+/enOyYxMVHdu3dXgQIF5OXlpQ4dOujMmTPpjjl27JhatmwpDw8PBQQE6J133lFqaurdvxsAAAAAmWbaFwM1rkh92Q0nhV7erTbO5xRYtILVsQAAuUSGSqnIyEh1795d69at09KlS5WSkqImTZooPj7ecUzPnj31yy+/aO7cuYqMjNSpU6fUvn17x/60tDS1bNlSycnJWrNmjWbMmKHp06erT58+mfeuAAAAANyVsSOH6JtyFRVt5FdQymk1XB2ujv8ZZHUsAEAuYpimad7pg8+dO6eAgABFRkaqbt26io6OVqFChTRr1ix17NhRkrRv3z5VqFBBa9euVe3atbVw4UK1atVKp06dUmBgoCRp8uTJeu+993Tu3Dm5urre8nVjYmLk6+ur6Oho+fj43Gl8AAAAADfw66II/WBu08J8jyqfmaCnFs/TJ58NszoWACCHuN3e5q7WlIqOjpYk+fv7S5I2b96slJQUNWrUyHFM+fLlVaxYMa1du1aStHbtWlWpUsVRSElS06ZNFRMTo927d9/wdZKSkhQTE5PuBgAAACDzRV+K0/bYlVqY71FJUrNtK/XRxx9anAoAkBvdcSllt9v15ptvqk6dOqpcubIkKSoqSq6urvLz80t3bGBgoKKiohzH/L2Qurb/2r4bGTx4sHx9fR23kJCQO40NAAAA4F98/79h+rzA1YXNw05vUreaxeTu5WdtKABArnTHpVT37t21a9cuzZ49OzPz3FDv3r0VHR3tuB0/fvyevyYAAACQ10wcM0CTSz2oJMNdpa4cVsP9a1TzkS5WxwIA5FJ39F2uPXr00IIFC7Ry5UoVLVrUsT0oKEjJycm6fPlyutFSZ86cUVBQkOOYDRs2pHu+a9/Od+2Yf3Jzc5Obm9udRAUAAABwG6Z+8ZUWVyysU0YR+dovq9nScPUY9ZXVsQAAuViGRkqZpqkePXpo3rx5Cg8PV8mSJdPtr169ulxcXLR8+XLHtv379+vYsWMKCwuTJIWFhWnnzp06e/as45ilS5fKx8dHFStWvJv3AgAAAOAO/PHHcf0RcFrrnWvKyUxVi9/D9dHwUVbHAgDkchkaKdW9e3fNmjVLP/30k7y9vR1rQPn6+srd3V2+vr7q1q2bevXqJX9/f/n4+Oi1115TWFiYateuLUlq0qSJKlasqGeeeUZDhw5VVFSUPvroI3Xv3p3RUAAAAEAWs6fZtXTdVM0s2lqS9Oifa/T+fzrL5nRHkyoAALhtGfqbZtKkSZKk+vXrp9s+bdo0Pffcc5KkUaNGyWazqUOHDkpKSlLTpk01ceJEx7FOTk5asGCBXn31VYWFhcnT01Ndu3bVgAED7u6dAAAAAMiwqZP7a1yFekoznFU5eo9a2aMUWJQZDACAe88wTdO0OkRGxcTEyNfXV9HR0fLx8bE6DgAAAJAjjRnxqeZVK6l9tgoKTDmjLovm6r2RU62OBQDI4W63t7njb98DAAAAkHNNnz5L6yt7a5+tgtzMRLWICKeQAgBkKUopAAAAII/ZtHmf9vgfVrjrIzJMu1psCVefD3tbHQsAkMdQSgEAAAB5SMzlOK36Y5a+8WomSXr0yBq93vhBuXv5WRsMAJDnUEoBAAAAeYRpmvr+f0M1JrCZTMNJ1S7uVDszShWqNrI6GgAgD6KUAgAAAPKIL8b309iSDyvR8FDJK0fUdNNydeo2yOpYAIA8ytnqAAAAAADuvXEjP9HcqhV1zghQgdTzar5siXqOnG51LABAHsZIKQAAACCXmzJ1ulZW9tcftnJyNxPUOnyp+oyYYHUsAEAeRykFAAAA5GKr12zX3sDTWuUSJpuZplYblqt/n48kw7A6GgAgj6OUAgAAAHKpSxdjteb4PM32bCpJanjod/Xq2ERu7t4WJwMAgFIKAAAAyJXsaXbN+WmYJhRqJtOwqca5berik6iSZWpZHQ0AAEmUUgAAAECu9OXk/hpTvK6SjHwqk/CnmuxereZPvGt1LAAAHPj2PQAAACCXGTdqoGaHVtFFo6ACUs+qefhivT5iqtWxAABIh5FSAAAAQC4yaeJkLa8UqEO2MvKwx6v10qX6cMTnVscCAOA6lFIAAABALrE8YqN2F4/WOpcH5WSmqtX6ZRrwySCrYwEAcEOUUgAAAEAuEBV1UevP/6ofPK5+016TP1bqw+efkJOLm8XJAAC4MUopAAAAIIdLS03T/EWjNalAc0lSrTOb1TXEQ4GFy1ucDACAm6OUAgAAAHK4L6f01+hi9ZViuOm+uANqdnC96rd8xepYAAD8K759DwAAAMjBxo8ZoG+q3K/Lhr+CUk6r6YolenXEF1bHAgDglhgpBQAAAORQE8aO1eKKRXTUKCkve6xaL12mDymkAAA5BKUUAAAAkAP9tjhS28ska6NzdTmbKWq9Zpn6D/nM6lgAANw2SikAAAAghzl58pw2xa3Qz+6NJElN90aqz6svyObE6hwAgJyDUgoAAADIQVJTUjV/+XhNyd9CkvTQqY16uWoR5S8YYnEyAAAyhlIKAAAAyEGmTR2oMUUbKNVwUcXY/WoZtUMPPvyk1bEAAMgwxvcCAAAAOcS40f00I7S6YgxfFUk+qSYrF6vb8K+sjgUAwB1hpBQAAACQA4wbM1y/Vi6hE0Yx+dij1WrZUr1PIQUAyMEopQAAAIBs7sefFmrLfU7a5lRNLmayWq9aqn6fjbQ6FgAAd4VSCgAAAMjG/jxyWlvtm7Qw36OSpOY7I9T/zTdk2PinPAAgZ+NvMgAAACCbSklK0YLVk/WVb3NJUt0T69SjThV5+RWyOBkAAHePUgoAAADIpqbNGKSxwY2UZjirSvQetYs/rNAaLayOBQBApuDb9wAAAIBsaMLYvppa+UHFGd4KSTquJmuWq8vQL62OBQBApqGUAgAAALKZMaOG6pcqZXXaKCK/tEtqsXyJ3hlGIQUAyF2YvgcAAABkI9/Pma9NFfJpl1NluZpJarNyifoPHWt1LAAAMh2lFAAAAJBN7N13VFtdd2upW10Zpl0tty3XgPd7S4ZhdTQAADIdpRQAAACQDSQlJmnR5q/0tU8zSVL9Y2vVq3k95fPwsTgZAAD3BqUUAAAAkA1M/+YTjSvcRHbDSdUu7VQnp0sqW6GO1bEAALhnKKUAAAAAi00Y20cTSj+kBMNTJRKPqPGmSD32zEdWxwIA4J7i2/cAAAAAC40e9Ynmh1bQWSNI/mkX1GLpUr01cqrVsQAAuOcYKQUAAABY5OtvZ2ttRT/ts1VQPvOK2oQvUZ+RE6yOBQBAlqCUAgAAACywdccBbff5U5GudWSYdrXatFyD+vW3OhYAAFmGUgoAAADIYglxCVq2+1vN8rr6TXsND6/Wh10ek7NrPouTAQCQdSilAAAAgCxkmqa+/n6IJgQ2k2nYVP3Cdj3tb1PhopWsjgYAQJailAIAAACy0OTxfTW+5MNKNNxV6sqfarr7dzV7rIfVsQAAyHJ8+x4AAACQRSaM7qtvQu/XeSNAhVLPqtmyxXp95BSrYwEAYAlKKQAAACALjB01QD+EVtQRo6S87LFqvXyJ+oz83OpYAABYhlIKAAAAuMdGj/xEv4WW0B+2cnI3E9Q+cqE++fQzq2MBAGAp1pQCAAAA7qExo4YrvEqgdjiFytVMUrvfF+nTj/rKsDlZHQ0AAEtRSgEAAAD3yPiJk7Smooc2ONeQs5mithuXaPBbb8vZ1c3qaAAAWI5SCgAAALgHpn89S5tKJinS9SEZpl2tti/ToJe6KZ+Hj9XRAADIFiilAAAAgEz2v58XaaP/CS3KV1+S1GJvuPp1aiff/MHWBgMAIBvJcCm1cuVKtW7dWsHBwTIMQ/Pnz0+3/7nnnpNhGOluzZo1S3fMxYsX9dRTT8nHx0d+fn7q1q2b4uLi7uqNAAAAANlBxKpNWqfN+p9nE0lS40ORerdeLQUVqWBxMgAAspcMl1Lx8fGqWrWqJkyYcNNjmjVrptOnTztu3333Xbr9Tz31lHbv3q2lS5dqwYIFWrlypV566aWMpwcAAACyka07Dyri3G/6xrulJKne8TV6o2IRlav8iMXJAADIfpwz+oDmzZurefPm/3qMm5ubgoKCbrhv7969WrRokTZu3KgaNWpIksaNG6cWLVpo+PDhCg5mSDMAAAByniPHz2rh3m/1RcHWkqTaUZvUzddUjYc6WpwMAIDs6Z6sKbVixQoFBASoXLlyevXVV3XhwgXHvrVr18rPz89RSElSo0aNZLPZtH79+hs+X1JSkmJiYtLdAAAAgOzi0uU4/bhqvCYUbCnTcNIDF7brqSsn1KRtd6ujAQCQbWV6KdWsWTN9/fXXWr58uT777DNFRkaqefPmSktLkyRFRUUpICAg3WOcnZ3l7++vqKioGz7n4MGD5evr67iFhIRkdmwAAADgjiReSdK3Pw/V6KCWSjNcVDl6j9of2arH/9PP6mgAAGRrGZ6+dyudO3d2/LpKlSoKDQ1V6dKltWLFCjVs2PCOnrN3797q1auX435MTAzFFAAAACyXmpKqad99opElmirZcFO5+ANqs22FXug3xepoAABke/dk+t7flSpVSgULFtTBgwclSUFBQTp79my6Y1JTU3Xx4sWbrkPl5uYmHx+fdDcAAADASqZpaur0ARpZoqGuGJ4qdeWwWq1arNcppAAAuC33vJQ6ceKELly4oMKFC0uSwsLCdPnyZW3evNlxTHh4uOx2u2rVqnWv4wAAAACZ4vPJ/TSmdD3FGr4qmnxCrcJ/0zufTbU6FgAAOUaGp+/FxcU5Rj1J0uHDh7Vt2zb5+/vL399f/fv3V4cOHRQUFKRDhw7p3XffVZkyZdS0aVNJUoUKFdSsWTO9+OKLmjx5slJSUtSjRw917tyZb94DAABAjjB5fB9NrPiQLhkFFJgSpTZLftUHI76wOhYAADmKYZqmmZEHrFixQo8++uh127t27apJkyapXbt22rp1qy5fvqzg4GA1adJEAwcOVGBgoOPYixcvqkePHvrll19ks9nUoUMHjR07Vl5eXreVISYmRr6+voqOjmYqHwAAALLU+DEfa1qVB3XSCFGBtPPquOhn9R8+1upYAABkG7fb22S4lMoOKKUAAABghTGj+uj70Kr601ZavvbLenzJzxr02UirYwEAkK3cbm+T6d++BwAAAORGo0YO1M+h5fWnrbQ87XF6LPw3DRoywupYAADkWPd8oXMAAAAgpxs18hMtDg3RXqeKymdeUfuVv2nwwCGSYVgdDQCAHItSCgAAAPgXo8eNUWTlQtrmVE0uZrLarVmkIR/3l2Hjn9IAANwN/iYFAAAAbmLql9O0vqyT1rk8KCczVW03L9Fnvd6Rk7OL1dEAAMjxKKUAAACAG/huznytLXxJEW4PyzDtar1zmQa/+JLcPG7vG6MBAMC/o5QCAAAA/mHx8lVa675fv7o3kCQ1/yNCA5/oKG+/AIuTAQCQe1BKAQAAAH+zcftehcdGao5XU0lSo8Or9H69WioUVMbiZAAA5C6UUgAAAMBfDhw5rV8PzNUM31aSpEdOrlPP8sV0X4WHLU4GAEDuQykFAAAASDp7Llr/W/e5PvdvKUmqdXaLXvSWqj/U1uJkAADkTpRSAAAAyPPi4xL07eKRGh/QUqbhpPsv7dSTcSfUpO0rVkcDACDXopQCAABAnpaSnKIvfxiiMcHNlWq4qFLMXrX/c7M6d+tjdTQAAHI1SikAAADkWXa7XVO/HqjRxZoqycinsgkH1XrbSr349mirowEAkOs5Wx0AAAAAsMrnU/pp1H0NlGB4qkTiEbVauUhvfjbV6lgAAOQJlFIAAADIkyZO/FgTytdTjOGnIikn1Xr5b3pvOIUUAABZhel7AAAAyHMmjPtYU8qH6YJRUAGpZ9RmyW/6cPgUq2MBAJCnUEoBAAAgTxk3+mNNr1hDUUaw/NMu6LElv6nv8AlWxwIAIM9h+h4AAADyjFEj++h/VUN13FZcPvZotVu2UP2HjbE6FgAAeRKlFAAAAPKEkSMH6tfQcjpoKysPM16PrfhNnw4ZbnUsAADyLKbvAQAAINcbPXqIllcpqt1OleRmJqr9qoUa0n+w1bEAAMjTKKUAAACQq40dPUyrKuTXZuf75Wwm67H1CzX0o34ybPxTGAAAKzF9DwAAALnWqFEDtbxyEW1yfkA2M03tti7VZ2++K5sT/wwGAMBq/G0MAACAXGnsqD76KbSC9tkqyNlMUavtyzTkhZfl5u5pdTQAACBKKQAAAORCE8d9rG+rVtcxo4TymVfUdv1iDenRU+7evlZHAwAAf6GUAgAAQK6RlpamKVP7a1Klh3XOCJSPPVptVy7SZ30GMmUPAIBshr+ZAQAAkCskJSXry28HanTZhoox/FQg7bzaLl2oTz8bYXU0AABwA5RSAAAAyPHOnY/RzN9GaHTJ5ko0PFQk5aRaL1mofsPHWx0NAADcBKUUAAAAcrQd+45owfbpmli0lVINF5W6clitIhbqg+FTrI4GAAD+BaUUAAAAcqwl4asVER2p6YXayDRsqhS7T602rVDPYRRSAABkd5RSAAAAyJFmfDtb6/2O60e/lpKkGue36fGofeo6YLLFyQAAwO2glAIAAECOM3bsKK2/z0XL3RpLkuqeXKeXfGxq9NoQi5MBAIDbRSkFAACAHGXEyIFaUSVYG52ryzDtanpopd6uFarKoQ2sjgYAADKAUgoAAAA5xuiRH2tBaEXtdaogZzNFrXcs0ydPd5F/weJWRwMAABlEKQUAAIAcYfzYj/Rd1eo6aispNzNRj21YqM9ef0duHl5WRwMAAHeAUgoAAADZmmmamjS5j6ZUflhnjSB522P02KqF+qzPJzJsNqvjAQCAO0QpBQAAgGwrJTlFU74eqLHlGijayK8CaefVbvlCfTJ4hNXRAADAXaKUAgAAQLYUExOvL+cN1ZhSzZRoeCg45ZRaLf1NA4aNtzoaAADIBJRSAAAAyHYOHDmluWu/0MSQVko1XFQq8bBahS/UB8OmWB0NAABkEkopAAAAZCsr12zSwqjFmh7YSqbhpEqx+9Rq0wr1pJACACBXoZQCAABAtvHdnP9ptfuf+l/+lpKkGhe36fFTf6jrgMkWJwMAAJmNUgoAAADZwvjx47SutLQsX2NJUt1T6/Syr4savvapxckAAMC9QCkFAAAAyw0fOUgrKwdpg0sNGaZdTf+M1HthNVSh0iNWRwMAAPcIpRQAAAAsNXLER/qtagXtcaokJzNVbXYt0+CnnpFfgSJWRwMAAPcQpRQAAAAsM3b0h5pTrYaO2ErKzbyixzYu1mevvys3dw+rowEAgHuMUgoAAACWGD/xY30Z+rDOGIXlbY9Ru1WLNLTPIBk2m9XRAABAFqCUAgAAQJZKS0vTxC8HaHz5Boo28qtA2nm1C1+kTz4dbnU0AACQhSilAAAAkGUSEq7o8++HaGyZJrpieKpwyim1WbZQ/YeOszoaAADIYpRSAAAAyBLHT57Tt5ETNbF4S6UYriqVeFjNly/Sx8M/tzoaAACwAKUUAAAA7rlN2/dq3oEf9VVQK5mGkyrG7VPLjZF6i0IKAIA8i1IKAAAA99SPP/+mcO3WDwVaSpKqX9quTif+VNcBkyxOBgAArJThrzZZuXKlWrdureDgYBmGofnz56fbb5qm+vTpo8KFC8vd3V2NGjXSgQMH0h1z8eJFPfXUU/Lx8ZGfn5+6deumuLi4u3ojAAAAyH4mT5mkec6H9IN3Y0nSI6fX6618hrq+3t/iZAAAwGoZLqXi4+NVtWpVTZgw4Yb7hw4dqrFjx2ry5Mlav369PD091bRpUyUmJjqOeeqpp7R7924tXbpUCxYs0MqVK/XSSy/d+bsAAABAtjNi5CAtKuGspe6PSJKa/blCn9xfRQ1aPGtxMgAAkB0Ypmmad/xgw9C8efPUrl07SVdHSQUHB+utt97S22+/LUmKjo5WYGCgpk+frs6dO2vv3r2qWLGiNm7cqBo1akiSFi1apBYtWujEiRMKDg6+5evGxMTI19dX0dHR8vHxudP4AAAAuEdGDP9YC6uV1y6nSnIyU9V6zzINe/p5efsFWh0NAADcY7fb22R4pNS/OXz4sKKiotSoUSPHNl9fX9WqVUtr166VJK1du1Z+fn6OQkqSGjVqJJvNpvXr19/weZOSkhQTE5PuBgAAgOwnMTFJEz7/WLMeqKVdTpXkZiaq46ZfNabb6xRSAAAgnUxd6DwqKkqSFBiY/h8cgYGBjn1RUVEKCAhIH8LZWf7+/o5j/mnw4MHq3591BwAAALKzX35dqo3x6zWjbEslGfnkY49W29VLNPTjgTJsmfr/QgEAQC6QI/510Lt3b0VHRztux48ftzoSAAAA/mbk8D762vmQphRqpSQjn8peOagui+doWN9PKKQAAMANZepIqaCgIEnSmTNnVLhwYcf2M2fOqFq1ao5jzp49m+5xqampunjxouPx/+Tm5iY3N7fMjAoAAIBMkJSUrKnTB2rmA7V00igqw7Tr0RNr1TY1Wk8MvfEX4wAAAEiZXEqVLFlSQUFBWr58uaOEiomJ0fr16/Xqq69KksLCwnT58mVt3rxZ1atXlySFh4fLbrerVq1amRkHAAAA99Bvi8O1LnqtY7qer/2yWm4I14Dub8jLp4DV8QAAQDaX4VIqLi5OBw8edNw/fPiwtm3bJn9/fxUrVkxvvvmmBg0apLJly6pkyZL6+OOPFRwc7PiGvgoVKqhZs2Z68cUXNXnyZKWkpKhHjx7q3LnzbX3zHgAAAKw3YngfrascrFWFWkqSyl45qEYR4eo7bKLFyQAAQE6R4VJq06ZNevTRRx33e/XqJUnq2rWrpk+frnfffVfx8fF66aWXdPnyZT388MNatGiR8uXL53jMzJkz1aNHDzVs2FA2m00dOnTQ2LFjM+HtAAAA4F5KSkrWF18N1Kz7a+mk7f+n6z1mxuhxCikAAJABhmmaptUhMiomJka+vr6Kjo6Wj4+P1XEAAADyhIVLIrTm8hp9XbBhuul6g3r0koe3n9XxAABANnG7vU2mrikFAACA3Gn4iI+1rnJRrf7bdL2GEeHqx+goAABwhyilAAAAcFPJScn6/KuB+q7a36frrVF7xasjhRQAALgLlFIAAAC4oUXLIvT7xTWaUa6Vkg23v6brRWhQj55M1wMAAHeNUgoAAADXGT7sY60LLcJ0PQAAcM9QSgEAAMDh6nS9AZpVvbZOGVen6zU4sVYddEXtKaQAAEAmopQCAACAJGnp8pWKvLBKX5dr7Ziu12pDuAa99pbcvXytjgcAAHIZSikAAABo+LCPtK5KiGO63n1/Tdfry+goAABwj1BKAQAA5GEpKSmaNLX/P6brrVFHW7Ieo5ACAAD3EKUUAABAHrUsfKVWnPvHdL2N4RrU4225e/lYHQ8AAORylFIAAAB5kGO6XsC16XoH1Cg8XH2GT7I4GQAAyCsopQAAAPKQlJQUTfzndL2Ta/S4c5raUUgBAIAsRCkFAACQRywPX6Xwc6v0zd+m67XeEK6Br70jdy9vq+MBAIA8hlIKAAAgDxg+9COtDQ3R7wEtJF2drtcwPEJ9h7OYOQAAsAalFAAAQC6WlpamcZ/306wa/z9dr+HJNerkKrWhkAIAABailAIAAMilVqz6XUtPReqb8q3STdcb9Pq7yufpZXU8AACQx1FKAQAA5ELDh32ktVX+MV0vIkJ9hzE6CgAAZA+UUgAAALmIY7pe9TCdMor8NV3vd3Vys6kNhRQAAMhGKKUAAAByiZW/r9XiExF/m653SW02RGjQm+/Lzd3D6ngAAADpUEoBAADkAiNHfKDVlUpozV/T9cpdOaCGkRHq8xmjowAAQPZEKQUAAJCDTf1yqg55n9Pc+5spzvBxTNfr7O6kVhRSAAAgG6OUAgAAyIFOn72o2fNG6YcyVXXIVkOSFJR6Wo03rtGgXh/ILZ+7xQkBAAD+HaUUAABADjNy+IfaXDFY4WUfk2nY5GZe0aPHNuiRy2fU7YNPrY4HAABwWyilAAAAcogZ38zQH64nNeeBpoo1fCRJ1WJ36sHV6zRg6ASL0wEAAGQMpRQAAEA2d+FSrL6eM1T/Kx2qg05XFzIPSo1Sw81r9d5zXRXQ5hmLEwIAAGQcpRQAAEA2NnLYB9pSKVjLy7aTaTjJzUxU/ePrVfdilLq9P9jqeAAAAHeMUgoAACAbmjnrO+2xHdbc6k0UY/hJkqrG7lLt39er/2fjrA0HAACQCSilAAAAspHo6Dh9NWuIfiwbqgN/TdULTI1Swy1r9d6zzyqwzdMWJwQAAMgclFIAAADZxLBhH2hHxWAtK/eYTMNJrmaSHj2xXnXOntBL7w21Oh4AAECmopQCAACw2Ny5P2h76n7Nrd5E0X9N1QuN26Ww3zeq/5Ax1oYDAAC4RyilAAAALJKQcEVTpn+iH++roj+cWkqSAtLOqPHWdXr36acV2JqpegAAIPeilAIAALDA8GG9taNCsJaVf0z2v6bq1T+5QXVOH9fL7zJVDwAA5H6UUgAAAFlo3k8/aUvcLs2p3kTRRn5JUmj8boWtXq/+Q8ZanA4A/q+9ew+Pqj7wP/45c89tciE3Akm4B+SqICFVaStUwMqqoMXLdml19VmL7EqkalwVaK0odJVqqexuXfn1t0u9bFfd1raKKGgVsKKoAUEMILckQIDc53rO/jEhGLmIkMxJwvv1POeZmXPOzHwmfp8v+OF7JgAQP5RSAAAAcdDcHNCypx/Ui4OGa6v/2KV6Ez9crx9fd716XnGjzQkBAADii1IKAACgg/18UZk+Pi9PK794qd6+d1Wyb49uu+sRu+MBAADYglIKAACgg7z8xz9q/aEP9NyY7+iIkSFJGt64Wd94e70WLOS36gEAgHMbpRQAAEA7CwVDWvrrn+ilQcO0pVfsUr2s6H5N/HCd7rz2e+p9xQ02JwQAALAfpRQAAEA7WvzIvdp0Xq5WDrlKUcMltxXStyrXq2T3Lv3onsV2xwMAAOg0KKUAAADawcqVq/RW9Vo9f+FEHW65VG9Y02Zd9Pa7WvDQEnvDAQAAdEKUUgAAAGchFArpl//2E/2+aKg+6XWFpNilet/5aJ3mXPM95X+XS/UAAABOhFIKAADgDP38kXu1aUiuXj2v7aV6Y3ft1Oyyf7E7HgAAQKdGKQUAAPA1LV5cpooBWXrtwslqMPySpGFNn+iid97Vgp89ZnM6AACAroFSCgAA4DREo1E9vuQ+fTywp14fPVUBI1GSlB2t1oTydzXn6mtV8N3rbU4JAADQdVBKAQAAnEIoFNLSX83TewN7683zr1LY8EqS8iJ7ddGnH+mbyV5dc8f9NqcEAADoeiilAAAATqC2tkFP/Wah3hlQoLUjpitqxP7a1Cf0uUo++UhXDy/S+FllNqcEAADouiilAAAAvmD33v167sXHtXpAX703dLoswyFJGhTYpuLyct00aYqGTGJlFAAAwNmilAIAAJC08ePNWvnOM3qt7wB9eN41rfuHNW3WmA/LVXrTrcqecq2NCQEAALoXSikAAHBOe/31N7Rux5t6taBIWwZNkyQZlqlRjeW64P1y3XfnfUr47g02pwQAAOh+KKUAAMA56dnnntWWpk/15/yh2tHvSkmS04poTN1HGvHeJs2b97BcU902pwQAAOi+KKUAAMA55V+XPa5dSQ36c94I7c2aKknyWEEVH/5QQzeUa/7PfiFdZdicEgAAoPtztPcLzp8/X4ZhtNkGDx7cejwQCGjWrFnq0aOHkpOTNX36dFVXV7d3DAAAgDaWLPqpfvzMI1o6qEhP9b5cex29lWA16dL97+jv//wbPT/9Vs1/6HHJoJACAACIhw5ZKTV06FC99tprx97Edext5syZo5dfflnPP/+8UlNTdfvtt2vatGl6++23OyIKAAA4xy16+D7t6ZeqlWMu0mEjQ5KUbNXr4soPNOjjzbr34WXSDJtDAgAAnIM6pJRyuVzKzc09bn9tba2eeuoprVixQpdeeqkk6emnn9aQIUO0bt06jRs37oSvFwwGFQwGWx/X1dV1RGwAANCNLH6kTBUDs/Xa2MvUYPglSWnmIV28Z6OGVOzUnT953OaEAAAA57YOKaW2bdumvLw8+Xw+lZSUaOHChSooKNCGDRsUDoc1ceLE1nMHDx6sgoICrV279qSl1MKFC7VgwYKOiAoAALqZny+6R5uL8vT6hVMVMBIlSVnR/bp454caXV+nv5/zU5sTAgAAQOqAUqq4uFjLly9XUVGRKisrtWDBAl1yySUqLy9XVVWVPB6P0tLS2jwnJydHVVVVJ33NsrIylZaWtj6uq6tTfn5+e0cHAABdVDQa1ZIl/6z3iwr05pirFTa8kqS8yD5d9NmHGp+UpGv//sc2pwQAAMAXtXspNWXKlNb7I0aMUHFxsQoLC/Xcc88pISHhjF7T6/XK6/W2V0QAANBNBINB/fLJBVo7qFBrz5+uqOGWJBWGP9dFWz7StJEjdPFtZTanBAAAwIl0yOV7X5SWlqZBgwbps88+03e+8x2FQiEdOXKkzWqp6urqE34HFQAAwIkcOHBIy595VG8N7Ke/jpguy3BKkgYFt2lceblunnKlii670uaUAAAAOJUOL6UaGhpUUVGh73//+xo9erTcbrdWrVql6dOnS5K2bt2qXbt2qaSkpKOjAACALu7ttev01sY/6o3+A/XhsGta9w9r3qyxH5Zr7q2zlTH5WhsTAgAA4HS1eyk1d+5cTZ06VYWFhdq3b5/mzZsnp9Op66+/Xqmpqbr55ptVWlqqjIwM+f1+zZ49WyUlJSf9knMAAHBuCwaD+uUT87SjT5bWphdp7+BpkiTDMnV+U7ku+KBc982dL9/lN9icFAAAAF9Hu5dSe/bs0fXXX6+amhplZWXp4osv1rp165SVlSVJeuyxx+RwODR9+nQFg0FNmjRJv/rVr9o7BgAA6OIeXbRAh3JcerdnoT6+4NrWS/TcVkgX1Jfr/A3luv+BRXJe0eELvwEAANABDMuyLLtDfF11dXVKTU1VbW2t/H6/3XEAAEA7eeuNN/XOJyv1QWGe3k0YriYjufVY3/AOjdpdoYJt21X2MP+gBQAA0Fmdbm/DPy0CAABbHb08r6JPttamD1blkOmtx9LNQ7rg4Cfqv2mHyu56QAnJV9uYFAAAAO2JUgoAANji54/M06GeXq3P7aPNF3xPluGQJHmsoEY0fqIhn+7UdwYO0GUzZtmcFAAAAB2BUgoAAMTNyldf1XsVb+uDgjz99cJJajYSW4/1D1XELs/b/rnufmipjSkBAAAQD5RSAACgQwWaA1r6q/n6tE+O1qUNVvXgY5fg9TAP6oIDW9Rv03aV3fugfAmJp3glAAAAdCeUUgAAoEMsevifdahXstbn9tGW849dnue1AhrZsFlDPv1cU0eP1sXX3W5zUgAAANiBUgoAALSbP/7pZX2wc73eL+ytDWOvUMBIaD02KLhNI3dvV+GuPZr70ydsTAkAAIDOgFIKAACclYbGRj257Kf6tG+u1qWdpwODp7Uey4we0JiDW9Tn4+26b/4iudxuG5MCAACgM6GUAgAAZ2TRwjIdzE/V+py+2nrBda37fVazRtVv1nmf7tS1F39L50+cLV13ihcCAADAOYlSCgAAnLYXX/qdyqs+1ob83nq/+EoFDZ8kybBMDQp9ppGfb1ffqgOaM+9fbE4KAACAzo5SCgAAnFJ9fb2W/ftD2tK3p9alDlGN/6rWYznRKl2w/1P13VSh+37yqBxOp31BAQAA0KVQSgEAgOOs+M/l2nlkuyp69dCmlALtPH9G67EEq1Hn132iwVt36u8uv1KDJ/6jjUkBAADQVVFKAQAABYNB/eKx+3UoL12fZOVoU94ANfQa1XrcsEwNCX4auzzvcJ3+sexh+8ICAACgW6CUAgDgHPW73/1Wn+7brIpemdrsz9eOsdfJMhytxxOsRg1urtDAyiplbt+tBx56wsa0AAAA6G4opQAAOEeEw2E9+i/36UjPVH2SnaNN6QNVnzGkzTn5kd0qOrJLBTurNCY3S9O+z6V5AAAA6BiUUgAAdGN/ePklfVzxnnb06qFNqfnaPvZ7soxjX0aeYDWpqLlCA6sqlVmxR3ff/4h8CQk2JgYAAMC5glIKAIBuxLIsLVp4j2rzUvVJdrY2JQxQ3fDpbc7pFdmrwbWfq+DzSo1Iy9D1N8+xKS0AAADOZZRSAAB0ca+98oo2fPKmtvfO0qbU3qoY13Y1lM9qVlEgthoqe/te3fXPC1kNBQAAANtRSgEA0AUt+tldOtIzVVtzslWeMEC1I69tczwvGlsN1efzKhV5fZo5616bkgIAAAAnRikFAEAX8M5f3tGav76sz3tnalNab1WUzJD5hdVQXiugQYEKDdq/Tznb9+qusofkS0i0MTEAAABwapRSAAB0UosW3q3abH9sNVRifx0Z1XY1VM/oPhXVfq6+u6vUz3Doln+aZ1NSAAAA4OujlAIAoBM4VHNYv/63n6k+M1V7MtNUkZyjiuJrFTWO/VHtsQIaFNyuov37lFOxR3PvflCJSck2pgYAAADOHKUUAAA2WPqLR1RrNKgyK1Wfp2Zoh6+3Doz72+POy4lWaUj9TvXdVaX8cFg/mvszG9ICAAAA7Y9SCgCADrZ18yd66Q9P63Bmmnb1SNPOxBztGv5thQ1Pm/MMy1RPs1KFTZXqfeiwsnZWqrR0npL9k21KDgAAAHQcSikAANrZzx+6V02pbu3LStVOf6Z2ePJVe+ENx52XbNWrT3C3CmoPKrfqkPwHanTPg49LhmFDagAAACC+KKUAADgLr732it7b+IYOZqdpV3q6dibkas+46W1+M54kOa2Iekf3qrCxSnkHDit930HNmHaDBg8/vqwCAAAAzgWUUgAAnKZQKKQli8vU2MOvPVnp2pmcqR3uAjWNvu64c9PNQ+oT3KP8wzXKrqpRTjik2fcssiE1AAAA0DlRSgEAcBL//+ll2n1kt6qz07QrLUM7fD1V9Y3vH3eexwqqILJbhfX7lbf/kPx7D+if7nxA/vRLbUgNAAAAdA2UUgAASDp48KB+/e8LVZeVrj090rQzOVs7C0cp1Gfccedmm1Xq01yp/EM1ytxbo6HZefrezbNtSA0AAAB0XZRSAIBzyv7qA1r+1MMK+JN0KC1Z1f4UVfoyVOnKVu2441dBJViN6hParcL6A8qtPix/1UHdPf9ROV38EQoAAACcDf5GDQDolnZt/1zP/PYJNaclqyY9WdXJyaryZWifK0f1JceXT5JkWKZ6mftU2FSp/IOHlF55SJd949sqmXz8d0YBAAAAODuUUgCALm1zebl+/79PqznNr5q0JFWn+FXpS1elM0cN3/jbkz4v0zyg3PAB5TQeUVZtvVJrapXa2KzS+Y/FMT0AAABw7qKUAgB0CR/89a96ZeWzak5P0cG0ZFWnpKjS20OVzhw1nWLlU6Z1QLmhg8ppOqKsI/VKramTvzms0nmL4/wJAAAAAHwRpRQAoFNZ+8ZqrVn3sprSUnQwPVnVyUfLp1w1l5x45ZNhmcq29sfKp8ZaZR6pk/9grXrI0ux7HonzJwAAAABwOiilAAC2WPGbf9fnlZ+qMfVo+ZSqSm+Gqhy5Coy78YTPcVhRZZtHy6fYyqeUmjplu7267c4Fcf4EAAAAAM4GpRQAoEP859NPanf1dgWTk1SXkqAjSQk6nJCkGneKalzpOtz7Apn5F57wuU4rohyzWrmhGmU31irzcOw7n3r5U3XTrHvj/EkAAAAAdARKKQDAGVn+68e178BuhVISVetPVG1Sgg75knTI41eNM12HCsfK6lNyytdwWeFY+RSsiV12d7heyQfr1L9nnm686Z/i9EkAAAAA2IFSCgBwQk89uUQHaivVnJKg+pQEHU5K1GFfompaSqfD/S6S1d95ytdwWyFlWIfUI3JE6cF6pTc1Kq2+WYn1jfLU1uubl1ymSyZeEadPBAAAAKAzoZQCgHPUvz6+WIea9iuQktRaOh3yJanG49chZ7oOF42XZThO+RpuK6QeVo0yIkeUEaxXelOT0uqblFjXJG9tvS6d+F2NGz85Tp8IAAAAQFdCKQUA3cwLz/9GW7duVMTnVTDRp6Yknxp8HtV7far3+HTYnaIaZ4aODJtweqWTWXNspVNzbKVTUm2T3HX1mjTlbzSmZEqcPhkAAACA7oRSCgC6gPfXr9XKV55XxOdWOKGlaErwqN4XK5rqXImqdyap1uFXfY+hsi4acVqv67GC6mEeXenUELu8rqFZibWN8tQ36Iop12rEOEonAAAAAO2PUgoAbHLk8GE99eQjCslUONGr5iSfGhK8avB5Ve/1qc6doDpnkuqcKapTqiKX/N1pv7ZhmUpRnfxmvfzRBvnDTUoJBpQcCCilKaikuga56hp19bSZOm/k5R34KQEAAADgxCilAKCd7Kqo0O+ef1rBSLOiHo9CPo9CPreafR41+Lxq8HpV5zlaNCWrzkhVoOS6r/UeiVaj/Gad/GaD/OFGpYRjRVNyU1CJTQF5GwNyNjfpglFjNeWqGzvokwIAAADA2aOUAoAvWfv6Sr311p8UcTkU9fkU8rkU9HnU7HEr4PWoyeNWk8ujJpdPjQ6fmhwJanIkqlFJChdf87Xfz2MFlWrVKsVskD/SqJRQc+uqpqTGoLxNQbkbm5ThT9dtd9zfAZ8YAAAAAOKPUgpAt/X/lj2mvdV7ZCW4FPF4W1YtuRXwuNXs8ajJ3bI5vWpyJKjRkahGI1FNypA1/vQvlfsyhxVVkhqUZDUp0WxWUrRZyeFmpYQCSmkOKKkpqISmoFyNzfKEo/rBrXOVk1fcjp8cAAAAADo/SikAncrq117Wu395QxGnJcvrUtTlUtTjUdDjVMjjUtjlUsDtUtDlUsjlUsDlUcDhUtDhUcDhjW2GV81KVKDo21LRmWfxWAElWY1KtJqVZDYrMRpQYiSoxHBICaGQEkJh+QIheQNhuQMhOQJBeSxLl191g4aN/Gb7/VAAAAAAoBuilAJwVrZ+/LHWvvWKqvbvUdTtlNweRT0uhdxOhT1uhdyx+0G3W0FXrEwKON0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      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "specification.method = \"esmda\"\n",
    "calibrator = HistoryMatchingMethod(\n",
    "\tcalibration_func=history_matching_func, specification=specification, engine=\"ies\"\n",
    ")\n",
    "calibrator.specify().execute().analyze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1d8280-8439-4f51-812c-04f536cdd195",
   "metadata": {},
   "source": [
    "Again, let's view the parameter estimates produced by the calibrator, with some measure of uncertainty. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ef00f9a-75dd-4485-b369-b32ef0a03bd1",
   "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>parameter</th>\n",
       "      <th>estimate</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>ground truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beta</td>\n",
       "      <td>0.400021</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gamma</td>\n",
       "      <td>0.100035</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parameter  estimate  uncertainty  ground truth\n",
       "0      beta  0.400021     0.000296           0.4\n",
       "1     gamma  0.100035     0.000213           0.1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([\n",
    "    { \"parameter\": estimate.name, \"estimate\": estimate.estimate, \"uncertainty\": estimate.uncertainty, \"ground truth\": model.GROUND_TRUTH[estimate.name] }\n",
    "    for estimate in calibrator.get_parameter_estimates().estimates\n",
    "])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "89a48b90-7fb6-43f2-a4ac-5edc5bbf3fca",
   "metadata": {},
   "source": [
    "The history matching calibrator is able to retrieve the ground-truth parameter values from our simulation study using both SIES and ESMDA."
   ]
  }
 ],
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