examples/inverse_problem_matvec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "acceptable-sacramento",
   "metadata": {},
   "source": [
    "# Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acoustic-feelings",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "from neal import SimulatedAnnealingSampler\n",
    "from dwave.system import EmbeddingComposite, DWaveSampler\n",
    "\n",
    "import dwaveutils.inverse as dwinv\n",
    "from dwaveutils.utils import Binary2Float\n",
    "\n",
    "# suppress all warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# create random generator\n",
    "rng = np.random.default_rng(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respective-lawsuit",
   "metadata": {},
   "source": [
    "# Set up forward modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becoming-voice",
   "metadata": {},
   "source": [
    "## Create `fwd_model` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "greenhouse-seattle",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_params = 20  # number of model parameters\n",
    "\n",
    "A = (100 - 0.01) * rng.random((num_params, num_params)) + 0.01\n",
    "def fwd_model(model_params):\n",
    "    b = A @ model_params\n",
    "    return b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crazy-royalty",
   "metadata": {},
   "source": [
    "# Set up inverse problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proof-garlic",
   "metadata": {},
   "source": [
    "## Create synthetic observed response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "elegant-publicity",
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_params = 0.1  # difference between low_param and high_param\n",
    "low_param_value = 1.0\n",
    "high_param_value = low_param_value + diff_params\n",
    "low_high = (low_param_value, high_param_value)\n",
    "\n",
    "num_obs = 10  # number of observations\n",
    "bin_model_params = rng.integers(0, 2, size=num_params)\n",
    "model_params = Binary2Float.to_two_value(bin_model_params, low_param_value, high_param_value)\n",
    "measure_index = np.sort(rng.choice(num_params, size=num_obs, replace=False))\n",
    "\n",
    "def resp_all2meas_func(measure_index):\n",
    "    def _resp_all2meas_func(resp):\n",
    "        resp = resp[measure_index]\n",
    "        # resp = resp.ravel() if resp.shape[0] == 1 else resp\n",
    "        return resp\n",
    "    return _resp_all2meas_func\n",
    "\n",
    "obs_resp = dwinv.utils.fwd_modeling(fwd_model, model_params, resp_all2meas_func=resp_all2meas_func(measure_index))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "consistent-begin",
   "metadata": {},
   "source": [
    "# Solve inverse problem with sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "collected-positive",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\n",
      "  - bin_model_params = [0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0]\n",
      "  - qubo_obj = 0.00000000e+00\n",
      "  - obj = 1.65137008e+04\n",
      "Iteration: 1\n",
      "  - bin_model_params = [0 1 0 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.64240946e+04\n",
      "  - obj = 8.96062178e+01\n",
      "Iteration: 2\n",
      "  - bin_model_params = [0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = 0.00000000e+00\n",
      "  - obj = 3.12487713e+03\n",
      "Iteration: 3\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1]\n",
      "  - qubo_obj = -3.09739295e+03\n",
      "  - obj = 7.34024259e+02\n",
      "Iteration: 4\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -7.34024259e+02\n",
      "  - obj = 1.22879440e+03\n",
      "Iteration: 5\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 0]\n",
      "  - qubo_obj = -1.22879440e+03\n",
      "  - obj = 1.84506760e+03\n",
      "Iteration: 6\n",
      "  - bin_model_params = [0 0 0 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 1]\n",
      "  - qubo_obj = -1.84506760e+03\n",
      "  - obj = 1.74871110e+03\n",
      "Iteration: 7\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.74871110e+03\n",
      "  - obj = 1.24750429e+03\n",
      "Iteration: 8\n",
      "  - bin_model_params = [0 0 0 0 1 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.24750429e+03\n",
      "  - obj = 1.73660515e+03\n",
      "Iteration: 9\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.73660515e+03\n",
      "  - obj = 1.12973297e+03\n",
      "Iteration: 10\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1]\n",
      "  - qubo_obj = -1.12973297e+03\n",
      "  - obj = 1.27118541e+03\n",
      "Iteration: 11\n",
      "  - bin_model_params = [0 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 0 1 0 0]\n",
      "  - qubo_obj = -1.27118541e+03\n",
      "  - obj = 2.23797147e+03\n",
      "Iteration: 12\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -2.23797147e+03\n",
      "  - obj = 3.51003278e+02\n",
      "Iteration: 13\n",
      "  - bin_model_params = [0 0 0 0 1 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1]\n",
      "  - qubo_obj = -3.51003278e+02\n",
      "  - obj = 2.81295236e+03\n",
      "Iteration: 14\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1]\n",
      "  - qubo_obj = -2.81295236e+03\n",
      "  - obj = 6.23092740e+02\n",
      "Iteration: 15\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 0]\n",
      "  - qubo_obj = -6.23092740e+02\n",
      "  - obj = 1.69053295e+03\n",
      "Iteration: 16\n",
      "  - bin_model_params = [0 0 0 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.69053295e+03\n",
      "  - obj = 9.96007187e+02\n",
      "Iteration: 17\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1]\n",
      "  - qubo_obj = -9.96007187e+02\n",
      "  - obj = 1.42793229e+03\n",
      "Iteration: 18\n",
      "  - bin_model_params = [0 0 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.42793229e+03\n",
      "  - obj = 1.70465661e+03\n",
      "Iteration: 19\n",
      "  - bin_model_params = [0 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1]\n",
      "  - qubo_obj = -1.70465661e+03\n",
      "  - obj = 2.34892902e+03\n",
      "Iteration: 20\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0]\n",
      "  - qubo_obj = -2.34892902e+03\n",
      "  - obj = 8.32506443e+02\n",
      "Iteration: 21\n",
      "  - bin_model_params = [0 0 0 0 1 0 1 1 1 0 1 1 1 0 1 0 1 1 0 1]\n",
      "  - qubo_obj = -8.32506443e+02\n",
      "  - obj = 2.47482111e+03\n",
      "Iteration: 22\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1]\n",
      "  - qubo_obj = -2.47482111e+03\n",
      "  - obj = 1.34159590e+02\n",
      "Iteration: 23\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.34159590e+02\n",
      "  - obj = 2.11225685e+03\n",
      "Iteration: 24\n",
      "  - bin_model_params = [0 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -2.08477266e+03\n",
      "  - obj = 7.30076355e+02\n",
      "Iteration: 25\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 0 1 0 0]\n",
      "  - qubo_obj = -7.02592167e+02\n",
      "  - obj = 2.23313337e+03\n",
      "Iteration: 26\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -2.20564918e+03\n",
      "  - obj = 6.83219085e+01\n",
      "Iteration: 27\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0]\n",
      "  - qubo_obj = -4.08377207e+01\n",
      "  - obj = 3.14911347e+03\n",
      "Iteration: 28\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 1]\n",
      "  - qubo_obj = -3.12162928e+03\n",
      "  - obj = 6.46681993e+02\n",
      "Iteration: 29\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 0 1 1 0 1 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -6.19197805e+02\n",
      "  - obj = 1.79835890e+03\n",
      "Iteration: 30\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.77087471e+03\n",
      "  - obj = 1.25350648e+03\n",
      "Iteration: 31\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1 1 0 1]\n",
      "  - qubo_obj = -1.22602229e+03\n",
      "  - obj = 1.54464344e+03\n",
      "Iteration: 32\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0]\n",
      "  - qubo_obj = -1.51715925e+03\n",
      "  - obj = 4.64041890e+02\n",
      "Iteration: 33\n",
      "  - bin_model_params = [0 0 0 0 0 1 0 1 0 1 1 1 0 1 1 1 1 0 0 1]\n",
      "  - qubo_obj = -4.36557702e+02\n",
      "  - obj = 3.33479505e+03\n",
      "Iteration: 34\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 0]\n",
      "  - qubo_obj = -3.30731086e+03\n",
      "  - obj = 1.88679560e+03\n",
      "Iteration: 35\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.85931141e+03\n",
      "  - obj = 1.04971136e+03\n",
      "Iteration: 36\n",
      "  - bin_model_params = [0 0 0 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.04971136e+03\n",
      "  - obj = 9.62988526e+02\n",
      "Iteration: 37\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1]\n",
      "  - qubo_obj = -9.62988526e+02\n",
      "  - obj = 2.02005089e+03\n",
      "Iteration: 38\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -2.02005089e+03\n",
      "  - obj = 7.92421453e+02\n",
      "Iteration: 39\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0]\n",
      "  - qubo_obj = -7.92421453e+02\n",
      "  - obj = 1.89977945e+03\n",
      "Iteration: 40\n",
      "  - bin_model_params = [0 0 0 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 1]\n",
      "  - qubo_obj = -1.89977945e+03\n",
      "  - obj = 1.74871110e+03\n",
      "Iteration: 41\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.74871110e+03\n",
      "  - obj = 2.40223386e+03\n",
      "Iteration: 42\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1]\n",
      "  - qubo_obj = -2.37474967e+03\n",
      "  - obj = 1.05834406e+02\n",
      "Iteration: 43\n",
      "  - bin_model_params = [0 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -7.83502178e+01\n",
      "  - obj = 2.86387681e+03\n",
      "Iteration: 44\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 1]\n",
      "  - qubo_obj = -2.83639263e+03\n",
      "  - obj = 1.21127858e+03\n",
      "Iteration: 45\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.18379439e+03\n",
      "  - obj = 8.97061807e+02\n",
      "Iteration: 46\n",
      "  - bin_model_params = [0 0 0 0 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 0]\n",
      "  - qubo_obj = -8.69577619e+02\n",
      "  - obj = 1.67154562e+03\n",
      "Iteration: 47\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 1]\n",
      "  - qubo_obj = -1.64406143e+03\n",
      "  - obj = 2.11225685e+03\n",
      "Iteration: 48\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0]\n",
      "  - qubo_obj = -2.11225685e+03\n",
      "  - obj = 1.15028186e+03\n",
      "Iteration: 49\n",
      "  - bin_model_params = [0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 0 1 1 0 1]\n",
      "  - qubo_obj = -1.15028186e+03\n",
      "  - obj = 2.64052617e+03\n",
      "Iteration: 50\n",
      "  - bin_model_params = [0 0 0 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1]\n",
      "  - qubo_obj = -2.64052617e+03\n",
      "  - obj = 2.76286768e+02\n"
     ]
    }
   ],
   "source": [
    "initial_bin_model_params = rng.integers(0, 2, size=num_params)  # initial guess\n",
    "problem_params = {\n",
    "    \"fwd_model_func\": fwd_model,\n",
    "    \"obs_resp\": obs_resp,\n",
    "    \"low_high\": (low_param_value, high_param_value),\n",
    "    \"resp_all2meas_func\": resp_all2meas_func(measure_index)\n",
    "}\n",
    "problem = dwinv.problem.BinaryInverseProblem(problem_params)\n",
    "\n",
    "sampler = SimulatedAnnealingSampler()\n",
    "# sampler = EmbeddingComposite(\n",
    "#     DWaveSampler(solver={'qpu': True}, postprocess=\"sampling\")\n",
    "# )  # use postprocess\n",
    "sampling_params = {\"num_reads\": 100}\n",
    "iter_params = {\"num_iter\": 50, \"verbose\": True}\n",
    "solver = dwinv.solver.BinaryInverseIterativeSolver(problem, sampler, sampling_params, iter_params)\n",
    "\n",
    "result = solver.solve(initial_bin_model_params, iter_params=iter_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "static-fleece",
   "metadata": {},
   "source": [
    "# Show result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjacent-thumbnail",
   "metadata": {},
   "source": [
    "## Compare predicted and synthetic model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "technical-greenhouse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted bin_model_params = [0 0 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1]\n",
      "Synthetic bin_model_params = [0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1]\n",
      "  - # wrong = 3\n",
      "  - % correct = 85.0 %\n"
     ]
    }
   ],
   "source": [
    "print(f\"Predicted bin_model_params = {result['bin_model_params']}\")\n",
    "print(f\"Synthetic bin_model_params = {bin_model_params}\")\n",
    "\n",
    "num_wrong = sum(abs(bin_model_params - result[\"bin_model_params\"]))\n",
    "percentc = (100 * (num_params - num_wrong) / num_params)\n",
    "print(f\"  - # wrong = {num_wrong}\")\n",
    "print(f\"  - % correct = {percentc:.1f} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "existing-regard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAJOCAYAAABcC3QrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAA9hAAAPYQGoP6dpAACuVUlEQVR4nOzdeXxU1d0/8M/sk8ky2RcgIYsrLoBQkKpULYqtorZaEa0gj+JTKv2pqa3yyCZacWkprRstimitW61LWxS1KC4VSwWpVVFLEkhYsu+TZNb7+2Nyh7lkkkwyy90+79crL2Vy79wzmXPP/d5zz/kegyAIAoiIiIiINMIodwGIiIiIiOKJAS4RERERaQoDXCIiIiLSFAa4RERERKQpDHCJiIiISFMY4BIRERGRpjDAJSIiIiJNYYBLRERERJrCAJeIiIiINIUBLhGp0qpVq2AwGHDttdfG7T23bdsGg8GA0tLSuL0nERElHwNcIgIAXHvttTAYDDAYDLBYLGhsbBxy+1dffTW0vcFgwKZNm5JTUCIAu3fvxqpVq1jviCgiBrhENIDP58Mzzzwz5DZPPvlkkkpDNNDu3btx5513MsAloogY4BKRRElJCQDgqaeeGnSb1tZWbN68GWlpacjOzk5W0YiIiKLCAJeIJGbMmIGKigp88skn+PzzzyNu89xzz8Hj8eCyyy5DSkpKkktIREQ0NAa4RDTANddcA2DwXlzx9fnz5w/7Xm63G2vXrsX06dPhdDqRkpKC448/HpWVlaivrx9y36+++grz5s1Dfn4+UlJScMIJJ+DOO++E2+2O6nP89a9/xSWXXILCwkJYrVbk5+djzpw5eOONN6LaP1r79u0LjUUGgH/84x+46KKLkJeXB4fDgUmTJuGhhx5CIBCIuH9zczMeeeQRXHLJJTjhhBOQnp6O1NRUTJgwAZWVlTh06FBUx/3oo49w+eWXo6ioCCaTCTfffHNo2127duH222/HmWeeiZKSEthsNuTk5ODss8/GY489Br/fH/EY4ZP5BEHAww8/jMmTJyMtLQ1FRUVYsGABDhw4ENr+v//9LxYsWIBx48bBbrfj5JNPxoYNG4b8+wUCAfzhD3/Aeeedh7y8PFitVowZMwZz587FP//5zwHbGwwGLFy4EADw7rvvSsaCGwwGbNu2bcA+H3zwAa688kqMGzcu9NlnzZqFZ599FoIgDNj+6AmHr7/+Or7zne8gPz8fRqMR69atC23773//G/Pnz0dpaSlsNhvS09NRXl6OCy64AOvWrUNPT8+Qn5+IEkAgIhIEYcGCBQIAYe7cuUJVVZUAQBg7dqzg9/sl23311VcCAKG4uFjw+/3C2LFjBQDCE088MeA9GxsbhcmTJwsABACCzWYT0tPTQ//OysoStm/fHrE87777ruBwOELbZmRkCFarVQAgzJgxQ1i6dKkAQFiwYMGAfT0ej3D11VeH9hX3D//3z3/+8wH7vfPOOwIAYfz48SP629XU1ITe98UXXxTMZrMAQMjMzAz9PwDh0ksvFbxe74D9f/rTn4a2MZvNQnZ2tmAymUKv5eXlCf/+97+HPO5zzz0XOpbT6RQsFotw0003hbbNyckJbetwOITMzEzJ3+O73/1uxLKtXLky9HeeO3euAECwWq1CampqaN+ysjKhsbFR2L59e+h9nU6nYDAYQtvcf//9Ef92nZ2dwqxZs0LbGQwGyXdlNBqFBx98ULJPQUFBaBuLxSIUFBRIfv7xj39Itv/5z38+oC6El+3KK68cUM/D68Ivf/nLUNkyMzMFk8kk/PrXvxYEQRA2b94sWCwWSR0/uq7t2bMn4mcnosRhgEtEgiBIA1xBEIQzzjhDACC8+eabku3uuOMOAYBw++23C4IgDBngXnDBBaFA9oUXXhB8Pp8gCILwr3/9SzjllFMEAEJBQYHQ1NQk2a+1tVXIz88XAAinnXaasHv3bkEQgoHrk08+KTgcDsHpdA4a4N58880CAOGYY44RXnjhBaG7u1sQhGAw9cgjj4SC7GeeeUayXzwCXKfTKVxwwQVCdXW1IAiC0N3dLdx///2C0WgUAAi/+MUvBuz/m9/8RrjnnnuETz/9NBRk+nw+4eOPPxZmz54tABBOOukkIRAIDHrctLQ04bLLLhNqamoEQRAEr9cb+n9BEIR58+YJzz77rHD48OHQa93d3cIf/vAHobCwcNAgVAxwnU6nkJaWJjz99NOC2+0WAoGA8N5774X2/d///V9h/PjxwkUXXSRUVVUJgiAIHR0dwo9+9CMBgGC324Xm5uYB73/ppZeGvuc33nhD6O3tFQQhWAfuvvtuwWKxCEajUfjggw8k+z3xxBMCAOFb3/rWkN/NunXrQvXs97//vdDe3i4IgiD09PQIzz33XKj899xzj2Q/sS7Y7XbBZDIJP/7xj4X6+npBEASht7dXqKurEwRBEMrKygQAwkUXXSR89dVXof07OjqE9957T1i0aJHkeyCi5GCAS0SCIAwMcH/3u98JAIRrrrkmtE0gEBDGjx8vABC++OILQRAGD3Dfe++9UPC1ZcuWAcerr68XsrKyBADC8uXLJb9bvXq1AEDIyckZEPwKgiD84Q9/CL330QHu119/LRgMBiEvL0+ora2N+FmfffbZUNAYLh4B7kknnST09fUN2EYMFDMyMgSXyxX1e/f19QkTJkwQAAjbtm0b9LhnnHHGgF7IaInfVWlp6aDlBiBs2rRpwO+feuqp0O+PO+64Ab3Afr9fOOaYYwQAwpNPPin53VtvvSUAEI4//vhQ4Hm0NWvWCACECy+8UPJ6NAFuW1ubkJaWJtjt9tBN0tE+/PBDwWAwCFlZWYLb7Q69LtYFAMK8efMi7tvQ0BDaRgx+iUgZOAaXiCK64oorYLfb8dJLL8HlcgEIjnfcv38/pk6dihNPPHHI/V988UUAwNSpUzF79uwBvy8oKMCPfvQjAMALL7wQcd9FixYhNzd3wL5XX301xo8fH/G4Tz31FARBwNy5c1FcXBxxm8svvxw2mw2ff/45Dh8+POTnGKmf/vSnsNlsA16vrKyE3W5HZ2cn3nzzzajfz2az4bzzzgMQHNs71HGNxtE16WeddRYyMzOxb9++Qcf7jhs3LjQ2O9ysWbNC/3/rrbfCbDZLfm80GnHOOecAAD777DPJ78RUc4sWLYLT6Yx43KuvvhoA8M477ww6Tngwf/7zn9Hd3Y1Zs2Zh4sSJEbeZMWMGysrK0NbWhp07d0bc5mc/+1nE19PS0kJ/83jXIyKKDQNcIoooMzMTc+bMgcvlwp///GcAI5tctmvXLgAIBTeRnHvuuQCAr7/+OhREezyeUPaGb33rWxH3MxgMmDlzZsTfffjhhwCCwVNhYWHEn3HjxsHr9QIA6urqhv0sI3H22WdHfD0jIwOTJ08GcORvE+7LL7/EkiVLcOqppyIjIwNGozE0aeo3v/kNAAwafALBQG04f/rTn3DppZeipKQEKSkpkolZ7e3tQx5jwoQJEQPo/Pz80P+ffPLJEfctKCgAALS1tUleF7+ru+++e9Dv6hvf+AYAoKenBy0tLcN+xkjv//bbbw/6/oWFhaE6EKkupKSkDBocOxyOUB2dPXs27r77buzevXvEgTgRxZ95+E2ISK/mz5+PP/3pT/jDH/6AH/zgB3jxxRdhsVgwb968YfdtamoCAIwdO3bQbcaNGwcAEAQBzc3NSE1NRWtrayhAGDNmzKD7Dva+Yk9aV1cXurq6hi1nvGe4D/V5xd+JfxvRc889h/nz54eCbqPRCKfTGeoJ7u7uhsvlCt0ERJKXlzfo73w+H6644gq8/PLLoddsNhtyc3NhMplCZQoEAoMeo6ioKOLr4v7RbCN+PpH4XYnB9XBG+l2J79/T0xPVvpG2ycnJGbJn/LHHHsNFF12EPXv2YPny5Vi+fDnS0tIwc+ZMzJs3D1deeeWAXm0iSjz24BLRoC644ALk5eXh7bffxkMPPYSuri585zvfiThsYDB9fX0JLOFAYiquX//61xCC8wyG/BmsxzVZmpqasGjRIni9XsydOxcff/wx+vr60NbWhvr6etTX1+OWW24BgIjprEThgebRNmzYgJdffhkOhwO//e1vUVdXh76+PjQ1NYWOId5MDHWMeBO/q5dffjmq70pM2TXS97/pppuiev9rr712wHsM9XcFgPLycnz66ad4+eWXccMNN+DEE09Ed3c3XnvtNVxzzTWYPn06uru7R1RuIoodA1wiGpTZbMa8efMQCARwxx13AEDEcZiRiD2KtbW1g24j5k81GAyhoDk7OzsUVAz1SH6w34mPw4c6biJFU+bw3tbXX38d3d3dmDBhAp555hlMmTIFFotFsl9DQ0NMZfrTn/4EAFi+fDl+8pOfhHrORX6/H83NzTEdYzQS/V0lqy6YzWZceuml+N3vfocvvvgChw8fxgMPPAC73Y5du3bhzjvvTOjxiWggBrhENCRxvK3X60VWVhbmzJkT1X6nnXYagODEtMF6Bd9++20AwHHHHYfU1FQAgNVqxUknnQQAeO+99yLuJwjCoL8Tx6Ju2bIlqnLG27vvvhvx9a6urtDYW/FvAxwJ8k899dSIj8IFQQj9nUZLPIY4Bvho//jHP5Le0w4c+a5ef/31Ee8r/q2G6nEW33/btm3o7e0dRQlHp7CwELfeemtooY3B6gQRJQ4DXCIa0pQpU7Bq1Sr89Kc/xbp16yJmCIjk8ssvBwB8/vnnePXVVwf8vqGhAevXrwcQzNgQ7gc/+AGA4KP11tbWAfs+99xz2LdvX8Tjzp8/HwaDAXv27MHvfve7Ict49KSnePjVr34Fj8cz4PV169ahr68PGRkZOP/880Ovi9kDPvvss4jB2oYNG1BVVRVTmcRj/Oc//xnwO5/Ph2XLlsX0/qMlDgl44403hr0hOfq7ysjIADD0+N0f/OAHSE1NRVtbG1avXj2i94+G1+sdMsAWl7GOduU9IoofBrhENKyVK1fil7/8ZVTZE0RnnXUWLrjgAgDA//zP/+DFF18MTR7buXMnzj//fLS1taGgoAA33XSTZN8bb7wR+fn5aG5uxuzZs/Hpp58CCAYUTz/99JBppSZMmBAas/rjH/8YS5culSwl29XVhTfffBM//OEPQ4F0PNXW1uJ73/teKADv6enBr371K6xatQoAcNttt8HhcIS2nzVrFgwGAz777DP8v//3/0IBW2dnJx544AHceOONyMnJialMYpqxu+66C6+++mroe/jyyy8xZ84c7NixI9SDnkwXXHABvv/970MQBHzve9/DAw88IJmA19raildeeQUXX3wxKisrJfuKvfxffPFFxOV8geAEsTVr1gAA7r33XixatAhff/116Pe9vb14//33sXjxYnzzm98ccfk///xznHzyyVi3bh2+/vrrULDr9Xrx5z//GWvXrgWAiGnyiCjBEphjl4hU5OiFHqI13FK9kyZNCiXDt9vtA5bq/fDDDyO+77Zt24SUlBTJCmE2m01A/1K9t99++6Armfl8PmHx4sUDlmc9evnYs88+W7JfopfqveSSSyIuh3vLLbdIypqZmRla+Wz27Nmh1eOO/qzhxx1KS0uLUFFREdrWYrGElpM1mUzCE088EVrA45133pHsG75U72DE9x1sxa6h3qO7uzu0mhnClsMNrycAhGuvvXbAvjNnzgz9Pjs7Wxg/frwwfvz4Acs/33XXXZLvPTU1VcjKygr9jRFhkYto6sInn3wiKaPNZhOys7Ml7zt16lSho6Nj0PcgosRgDy4RJUxeXh62b9+OX/7yl5g6dSosFgs8Hg+OPfZY3Hzzzfj8888Hzd/6rW99C5988gnmzp2LvLw8uN1ulJaWYtWqVXj77beHHCphMpnwyCOP4IMPPsAPf/hDjB8/Hm63G319fSgpKcHFF1+Mhx56KLSgRDxddtlleOedd3DhhRfCZDLBbDZj4sSJePDBB/HSSy9FTBm1du1a/P73v8fkyZNhs9ng9/sxefJkrFu3Dps3b445zVR2djY++ugjLF68ODTBLCUlBZdeeinefffdiNkDkiU1NRUvv/wy/va3v+H73/8+xowZg56eHni9XhxzzDG44oor8MQTT+DBBx8csO9LL72EH//4xygrK0N3dzf279+P/fv3DxhPvGzZMvz73//GDTfcgGOPPTaUDq2oqAizZ8/G/fffj/fff3/EZT/xxBPx4osv4kc/+hEmT56MzMxMdHZ2wul04swzz8SDDz6If/zjH6HhFESUPAZBSGJOGCIiDdq3bx/KysoAJDfNFhERRcYeXCIiIiLSFAa4RERERKQpDHCJiIiISFMY4BIRERGRpnCSGRERERFpCntwiYiIiEhTGOASERERkaYwwCUiIiIiTWGAS0RERESawgCXiIiIiDSFAS4RERERaQoDXCIiIiLSFAa4RERERKQpDHCJiIiISFMY4BIRERGRpjDAJSIiIiJNYYBLRERERJrCAJeIiIiINIUBLhERERFpCgNcIiIiItIUBrhEREREpCkMcImIiIhIUxjgEhEREZGmMMAlIiIiIk1hgEtEREREmsIAl4iIiIg0hQEuEREREWkKA1wiIiIi0hQGuERERESkKQxwiYiIiEhTGOASERERkaYwwCUiIiIiTWGAS0RERESawgCXiIiIiDSFAS4RERERaQoDXCIiIiLSFAa4RERERKQpDHCJiIiISFMY4BIRERGRppjlLoASBAIBHDp0COnp6TAYDHIXh4iIiIiOIggCurq6MGbMGBiNQ/fRMsAFcOjQIRQXF8tdDCIiIiIaRl1dHcaNGzfkNgxwAaSnpwMI/sEyMjJkLg0RERERHa2zsxPFxcWhuG0oDHCB0LCEjIwMBrhEREREChbNcFJOMiMiIiIiTWEPLlGc+AMCdtS0orGrD/npdkwry4bJmPhJi3IcV0+fVc7jykFPf2M9fVa5jqunzyrnceWg9M+quAD3vffewwMPPICdO3fi8OHDePnll3HppZcOuc+2bdtQWVmJzz//HMXFxVi2bBmuvfbapJSXCAC2fHYYd/71Cxzu6Au9VuS0Y+WcCbjg5CJNHVdPn1XO48pBT39jPX1WuY6rp88q53HloIbPqrghCi6XCxMnTsTDDz8c1fY1NTW48MILcc4552D37t24+eabcf311+ONN95IcEmJgrZ8dhiLn94lOdEBoL6jD4uf3oUtnx3WzHH19FnlPK4c9PQ31tNnleu4evqsch5XDmr5rAZBEAS5CzEYg8EwbA/ubbfdhs2bN+Ozzz4LvXbllVeivb0dW7Zsieo4nZ2dcDqd6Ojo4CQzGhF/QMCZ97094EQPl59uw59+NCOuj278AQGXr9+Opi530o4rxzGVfFwDgEKnHR/cdq6iHsuNButxYo+pt+Pq6bNGc1w9tRWJ/qwjiddUH+DOnDkTp512GtatWxd67YknnsDNN9+Mjo6OiPu43W643Ucqoph2ggEujdT2qhbM2/CR3MUgGT276HTMqMiRuxgxYT0mSjw9tRWJ+qwjCXAVN0RhpOrr61FQUCB5raCgAJ2dnejt7Y24z5o1a+B0OkM/XOSBRquxa/AeL9IHLdQBLXwGIqXTwnkW7WdQwmdV3CSzZFi6dCkqKytD/xZ7cIlGKj/dHtV2p5dnIzfNFrfjNne78VF1a1KPK8cx1XDcaOuAkrEeJ/aYejuunj7rSI6rp7ZCCZ9V9QFuYWEhGhoaJK81NDQgIyMDKSkpEfex2Wyw2eJXuUm/ppVlo8hpR31HHyKN9RHHI/3x+tPjPubrzPveTupx5TimGo47rSw7bseUC+txYo+pt+Pq6bOO5Lh6aiuU8FlVP0RhxowZ2Lp1q+S1t956CzNmzJCpRKQnJqMBK+dMiPg7sflcOWdC3Afbhx/36HdO1HHlOKZSjnu0RB5XDqzHiT2m3o6rp8969HGPxrZCPooLcLu7u7F7927s3r0bQDAN2O7du1FbWwsgOLxg/vz5oe1/9KMfobq6Gj//+c/x5Zdf4pFHHsELL7yAW265RY7ikw5dcHIRHrn6tAENaqHTjkd/eFrCcgJecHIRHv3haSh0Sh8FJfK4chxTEcfNSO5x5cB6rM3PKtdx9fRZJcfVSVvx8FWnDXhdaZ9VcVkUtm3bhnPOOWfA6wsWLMCmTZtw7bXXYt++fdi2bZtkn1tuuQVffPEFxo0bh+XLl49ooQemCaNYNXT2Yfo9wScJJxalY8VFJ2l65Ry9rRLkDwg4+4F3UNfWC6MB+GL1BbBbTAk/brId7ujFjDVvAwAmjMnA8gsnaPq71WM95t848cedef/bONjeB7PRgC9WXwCrWXF9iTGra+3BWfe/AwA4ZWwG/u+7yWkrRhKvKW4M7tlnn42hYu5NmzZF3OeTTz5JYKmIhlbV2B36/29W5CY1FYzJaEh66hk5jin3cU8tzkRdWy8CAnCwvRcVeWlJL0eiVTW6Qv9/RkUO6zGPq8pjyn3cU8Zm4mB7PXwBAYc7ejE+JzXp5Ui0qqYj17wzjslTZPoz7d1WEMkg/GTXYuBD0u81/IZGS1iPiWJXkX8koA0/p7SkuunIzXBFnjIDeAa4RHFQpYKTnWIT/r2Gf99aIglw8xngEo2G9GaYbYVcGOASxUH4yV7Oni9NCr9oVeugV6Y8lzdqRKMRfg2obtZmWyEJcHOVec1jgEsUB2JgkGE3IzfNKnNpKBHKcrX/2FH8XJkOC7JTWY+JRqM8/GmPZntwg58rN80Kp8Mic2kiY4BLFKMejw8H24PLQlfkp8FgkD//H8Vfqs2MMf2ph6qaXENOhlWjbrcPhzuCy2tW5LEeE41Wht2C/PTgYlJavBnu7POiqcsNQNlPLBngEsVIOtheuSc7xU4ca9bR60WLyyNzaeKrhuPIieJGvBa0uDxo79FWW6GWax4DXKIYVTeHjVtkYKBp0nG42nr0GD5WUMkXLSI1kGZS0FZbEZ5FRsk3wwxwiWIkPdkZGGiZZGydxh49htdjJT92JFKD8rCJV5prK1SQQQFggEsUM+YO1Q8t58Jlqjui+AkP/DQd4Co0gwLAAJcoZmJgYDYaMD7HIXNpKJEkAa5GL1oWkwHF2azHRLGo0HAmBXF4ltVsxNisFJlLMzgGuEQxCAQE1PSPXSzJccBi4imlZQUZNqRaTQC0Na7OHxBCY8nH56SyHhPFaIwzBXZL8DzSUt5snz+AfS3BtqI8NxUmo3KzrbAVI4rBoY5e9HkDAKRjrkibDAZDaHzqgbYe9Hn9MpcoPg6198LjE+sxhycQxcpoNKCs/5pQ29oDrz8gc4nio66tF15/MEWi0ofkMcAlioFk3GI+AwM9EB89BgRgf0uPzKWJj70qmTRCpCZiW+ELCJppK6STUZV9zWOASxQDZlDQHy2Ow2U9Joo/TbYVKppUzQCXKAZqOtkpPiSzozWSSYEZFIjiT4uZFNSyyAPAAJcoJtUMDHRHsthDszYmmoVPgmEOXKL4CL8maGVhmKomDlEg0gXxZM9JtSLTYZW5NJQM43McMPRPHNZKr4zYg5ubZoMzxSJzaYi0oSxXewvDiJ+jyGlHqs0sc2mGxgCXaJQ6+7xo7HIDUP6jGoofu8WE4qxgntiqxm4IgiBziWLT0eNFc7dYj5XdI0OkJg6rGWMzg3litdBWtLo8aOvxAlB+7y3AAJdo1KqZQUG3xEDQ5fGjodMtc2liU9XMDApEiSIGgp19PjR3e2QuTWyqVTbnhAEu0Shx5rl+aWl2NOsxUeJoqq1ggEukD9XN6hlsT/EV3tOp9lWKwifKcYgCUXxJ2wp1TzSrUlEGBYABLtGoha8vroaTneKnXDJ5ROUXLfbgEiVMhYYmmqlpkQeAAS7RqImNldVkxLj+SUekD1rKbymW32Y2Ykz/hBgiig8tthUOqwmFGXaZSzM8BrhEo+DzB7CvJdhzV5abCpPRIHOJKJlyUq2hdFpqXuzB6w+ElhBlPSaKv/x0G9L602mpOcB1+/yoa+sFEOy9NaqgrWCASzQKB9p64fUHU76o4VENxZfBYAiNVz3U0Ycej0/mEo1OXWsPfIFgPWYGBaL4C28rDrT1os/rl7lEo1Pb0gO/2FaoZCgTA1yiUVDbbFKKv/AVv9Q6eUQyaSSXN2pEiSC2FYKA0JM/tVHjNY8BLtEoSE525sDVJS2k/5HWY3VctIjUJjw7SfjkZDUJvxlWy1NLRQa4Dz/8MEpLS2G32zF9+nTs2LFjyO3XrVuH448/HikpKSguLsYtt9yCvr6+JJWW9IgZFEhy0VJrDy4zKBAlnOZuhlXSViguwH3++edRWVmJlStXYteuXZg4cSJmz56NxsbGiNs/88wzuP3227Fy5Urs2bMHjz/+OJ5//nn83//9X5JLTnoSfrKXq+Rkp/jSwuzo8HKXcYgCUUJoo60I3sQbDOppKxQX4K5duxaLFi3CwoULMWHCBKxfvx4OhwMbN26MuP2HH36IM844A1dddRVKS0tx/vnnY968ecP2+hLFQkyOX5BxZIYs6UtJtgPm/pnEahyDKwhC6KI1xmlHKusxUUKMz3FATDqg1raiuv9pz7isFNgtJplLFB1FBbgejwc7d+7ErFmzQq8ZjUbMmjUL27dvj7jPN7/5TezcuTMU0FZXV+O1117Dd7/73UGP43a70dnZKfkhilary4NWV3BNcbU8qqH4s5iMKMkJ5j+ubupGoH+GsVq0ujzo6PUC4FMIokSymU0ozg62FVVN3RAEdbUVTV1udLmDmWLKc9XTVigqwG1ubobf70dBQYHk9YKCAtTX10fc56qrrsLq1atx5plnwmKxoKKiAmefffaQQxTWrFkDp9MZ+ikuLo7r5yBtq1bhWCRKDPH7d/sCONjeK3NpRka67KY6HjkSqZXYVvR4/KjvVNccob0qveYpKsAdjW3btuGee+7BI488gl27duGll17C5s2bcddddw26z9KlS9HR0RH6qaurS2KJSe2kg+0ZGOiZmiePMIMCUfKoOZNC+LAKNWUNUtSgq9zcXJhMJjQ0NEheb2hoQGFhYcR9li9fjmuuuQbXX389AOCUU06By+XCDTfcgDvuuANG48AY3mazwWazxf8DkC5US9KlMDDQs6MzKZx9vIyFGSFmUCBKnqNvhs88NlfG0oyMGjMoAArrwbVarZgyZQq2bt0aei0QCGDr1q2YMWNGxH16enoGBLEmU3AAtNrGuZA6sOeLRNLFHtTVgytOlATUk9eSSK3U3FaoMQcuoLAeXACorKzEggULMHXqVEybNg3r1q2Dy+XCwoULAQDz58/H2LFjsWbNGgDAnDlzsHbtWkyePBnTp0/H3r17sXz5csyZMycU6BLFk3iyp1hMKMqwy1wakpO0B1dtF61geR1WEwpZj4kSSs15s8WnPel2M/LS1PP0W3EB7ty5c9HU1IQVK1agvr4ekyZNwpYtW0ITz2prayU9tsuWLYPBYMCyZctw8OBB5OXlYc6cOfjFL34h10cgDfP4Aqht7QEQvJM1irlfSJcyHVbkplnR3O1R1UWrz+tHXX89rshLg8HAekyUSNmpVmQ6LGjv8arqZrjX4w9NoFVbW6G4ABcAlixZgiVLlkT83bZt2yT/NpvNWLlyJVauXJmEkpHe1ba64O9PB6WmsUiUOOV5aWjubkVTlxudfV5k2C1yF2lY+1t6IGY140RJosQzGAyoyEvDzv1tONzRB5fbp4rc0zXN6l21U1FjcImUbm+jOsciUeJUSMbWqaMXl6nuiJIv/GYyPHBUMumcE3Vd8xjgEo2AWmeTUuJI0/+o49Ejl5omSr5yFaYVlLQVKlrkAWCASzQiDHDpaGrMhVul0ryWRGomaStUczN8pK04RmVtBQNcohEQT3aDASjLVdfJTomhzgA3WE6DASjNYT0mSgY1ZlIQhzOZjAaUZKurrWCASxQlQRBCJ/sYZwpSrExDR8DYrBRYzcGmVA0XLUEQQr1HxVkO2C2sx0TJUJztgMUUzEKghpvhQEAIzSsYn+0ItXNqoa7SEsmoqduNrj4fAC7wQEeYjAaU9feC7m9xwecPyFyioTV2ueHy+AFwoiRRMllMRpRkOwAEJ5mJGXmU6nBnH3q96m0rGOASRSl8/XCmVqJw4jhWr19AXVuvzKUZGpfoJZKPeM65fQEcamdbkUijDnB37tyJ1atXo6GhIeLv6+vrsXr1auzevXu0hyBSFE4wo8GoafII6zGRfMKf/u1V+DAFtbcVow5wf/WrX+Gxxx5Dfn5+xN8XFBTg8ccfx9q1a0ddOCIlCc9xqsaTnRJHTRPNJBkU+CSCKKnUdDNcrfJsK6MOcLdv345zzjln0GXbDAYDzj33XPzjH/8YdeGIlER6N6u+k50SR02LPUgTt/NGjSiZwq8d1Qpf7EHNOXCBGALc+vp6jBs3bshtxowZg8OHD4/2EESKIp7s6TYz8tJtMpeGlKRMkv5HHb0yGXYzclKtMpeGSF/KVdSDK7Zl2alWZKmwrRh1gOtwONDU1DTkNk1NTbDZGAiQ+vV6/DjYPyGgPD9t0CcXpE9pNjMKM+wAlB3g9nh8oXpcwXpMlHTOFAty04JxkZLTCnb1edHQ6Qag3ieWow5wJ02ahFdffRXd3ZEb887OTrz66quYNGnSaA9BpBg1zS4I/Rld1HqyU2KJY9TaerxodXlkLk1kHEdOJD/xGtLc7UZHj1fm0kRW06z+tmLUAe4NN9yApqYmnHfeefj0008lv/v3v/+N888/H83NzbjhhhtiLiSR3Kqb1T2blBJPDRPN1D4rmkgLwse+VzWzrUgU82h3nDt3Ll5//XU89dRTmDx5MgoKCjB27FgcPHgQDQ0NEAQB8+fPx7x58+JZXiJZMAcuDUc60awb3yjNlrE0kVUzgwKR7I6elHpaSZaMpYks/JqnxkUegBgXeti0aRPWr1+PCRMmoL6+Hjt37kR9fT1OOukk/P73v8emTZviVEwieWnhbpYSq1wF68xLZkWzHhPJolwFk1K1cM0bdQ+u6IYbbsANN9yAnp4etLe3IzMzEw6HIx5lI1IM8WQ3GQ0oyWH9poHUkN9SDLzNRgPGsx4TyeIYVbQVwXJZTUaMy0qRuTSjE3OAK3I4HAxsSZMCASH0aLck2wGb2SRziUiJCjPscFhN6PH4FdkrEwgIqOkf71eS44DFxJXaieQwJjMFNrMRbl9AkW2FPyBgX3MPAKA01wGzStsKdZaaKInqO/vQ6/UDAMpz1TkWiRLPaDSEHj3WtfXC7fPLXCKpQx296PMGAKj3kSORFpiMBpT1X0tqW3vg9QdkLpHUgbYeePzqbyui7sEtLy+HwWDA3//+d5SVlaG8vDyq/QwGA6qqqkZdQCK5ceUnilZ5bho+O9gJf0BAbUsPji1Il7tIIeHjgtU6aYRIKyry0vBlfRe8fgF1rT2KGhMvHauv3rYi6h7cQCCAQCAg+bcgCMP+hO9DpEbhY6Q485yGouRUYdJ6rJyLKZEeVSh4Uqo0a5B624qoe3D37ds35L+JtKqKyfEpSuJiD4ACL1oamBVNpBWSXLhN3TgPBTKWRkored85BpdoGOEnu5IeI5HyKDmTgjTA5ZMIIjkpuq3QQA5cIIYA99xzz8VTTz015DZPP/00zj333NEegkgRxJM9y2FBdqpV5tKQkpXlpsJgCP5/VbOyenDFTCC5aVZkOliPieRUFjZhuVphbYV4M5yfbkO63SJzaUZv1AHutm3bhh2msH//frz77rujPQSR7LrdPtR39gFQ96MaSg67xYSxmcGckdWN3RAEQeYSBXX2edHY5QYQnAhHRPJKtZlR5LQDAPYqqK1oc3nQ4vIAUP81L6FDFHp7e2E2xy3VLlHSVXPcIo2QWE+63D409QeVcpMs0Zuv3keORFoithUdvV609geVcpOMv1V5WxFTgGsQn8UdRRAE7N+/H6+99hqKi4tjOQSRrBgY0EiF3wjtVUgmBWZQIFIeJWZS0NKk6hEFuEajESaTCSZTcCWnVatWhf4d/mM2m1FeXo7du3fjyiuvHHGhHn74YZSWlsJut2P69OnYsWPHkNu3t7fjxhtvRFFREWw2G4477ji89tprIz4u0dEk+QD5aJeiEH4jVK2Qi5ZWZkUTaUl4JoVqpdwMa+ip5YjGD8ycOTPUa/vee++hpKQEpaWlA7YzmUzIycnBt7/9bVx//fUjKtDzzz+PyspKrF+/HtOnT8e6deswe/ZsfPXVV8jPzx+wvcfjwXnnnYf8/Hy8+OKLGDt2LPbv34/MzMwRHZcoEi7yQCMVfiOklFy4WpkVTaQlbCsSa0QB7rZt20L/bzQasXDhQqxYsSKuBVq7di0WLVqEhQsXAgDWr1+PzZs3Y+PGjbj99tsHbL9x40a0trbiww8/hMUSnO0XKegmGg3xZLeYDCjOSpG5NKQGSsyFK148rSYjxmU5ZC4NEQHKbCvEnmS7xYgxTnVf80Y9BrempgY33XRTPMsCj8eDnTt3YtasWaHXjEYjZs2ahe3bt0fc5y9/+QtmzJiBG2+8EQUFBTj55JNxzz33wO9X1jrwpD7+gICalmCjU5qTCrOJaaNpeHlpNqTbg30HSshv6fMHsK+/HpflpsJkjDx3goiSqzDDDoc1OORTCT24Hl8A+1t7AAR7l40qbytGfcUeP348nE4nAMDlcuGTTz7B+++/H1Nhmpub4ff7UVAgXdGjoKAA9fX1Efeprq7Giy++CL/fj9deew3Lly/Hr371K9x9992DHsftdqOzs1PyQ3S0g2298PiCS02r/VENJY/BYAiNXTvY3otej7w323VtvfD6gymIOFGSSDnC24q61h64ffK2FbWtPfAHxLZC/UPyYuqSOnDgAC677DJkZWVh6tSpOOecc0K/++CDDzBhwgTJsIZECAQCyM/Px+9//3tMmTIFc+fOxR133IH169cPus+aNWvgdDpDP8z0QJFoabA9JVd4famROYk7U90RKZeYSSEgAPtbemQti3RStfpvhkcd4B4+fBjTp0/Hq6++iosuuggzZsyQJCqePn06Ghsb8fzzz0f9nrm5uTCZTGhoaJC83tDQgMLCwoj7FBUV4bjjjgtldgCAE088EfX19fB4IueVW7p0KTo6OkI/dXV1UZeR9IMBLo1WuST9j7yPHiUXLT6JIFKUcgUt2au1SdWjDnDvvPNONDY24q233sJLL72E8847T/J7i8WCs846C//4xz+ifk+r1YopU6Zg69atodcCgQC2bt2KGTNmRNznjDPOwN69exEIBEKvff311ygqKoLVGnk5SpvNhoyMDMkP0dG0drJT8kjWmZc7wG3UTl5LIq1Rbluh/pvhUQe4r732Gi6++GLJsISjlZSU4NChQyN638rKSmzYsAFPPvkk9uzZg8WLF8PlcoWyKsyfPx9Lly4Nbb948WK0trbipptuwtdff43NmzfjnnvuwY033ji6D0bUL3xWK3u+aCSOUdDsaGkPLgNcIiVRUiaF8HzZWsj7Pup1dBsaGnDssccOuY3FYoHLNbIvbO7cuWhqasKKFStQX1+PSZMmYcuWLaGJZ7W1tTAaj8TlxcXFeOONN3DLLbfg1FNPxdixY3HTTTfhtttuG/mHIgojjl3MS7chw26RuTSkJiXZwWwF/oAgewL36v4xwIUZdqTZuHQ6kZKU5qTCYAAEQd7FHgRBCA2RGJuZghSraZg9lG/UrV12dvawY1e//vrrQcfODmXJkiVYsmRJxN9FmrQ2Y8YMfPTRRyM+DtFg2ns8aO4OjuHWwqMaSi6r2Yjx2Q5UN7tQ3eRCICDIknKn1eUJrXHPDApEymO3mFCc5UBtaw+qmlwQBCG0oFYyNXd70NnnA6CdJ5ajHqJwxhln4C9/+cug6bv++9//YsuWLUMOYSBSKi2tx03yEC8SvV4/Dnf2yVKGai41TaR4YlvR7fahscstSxm0OKl61AHuz372M/T19eFb3/oWXn/9dfT0BNNbuFwuvP7665gzZw6MRiN++tOfxq2wRMmixZOdkqtCAbOjpfVYG70yRFqjuLZCI5OqRz1EYfr06fjd736HxYsX46KLLgq9LmYkMJvN2LhxI0466aTYS0mUZNWcYEYxOnp29Mzj8pJeBsmTCI1ctIi05ui24pvH5Ca9DNVN2sqgAMQQ4ALA//zP/+Css87CI488go8++ggtLS1wOp04/fTTsWTJEhx//PHxKidRUrEHl2IVPua1WqbZ0VzkgUj5KvLkz6SgxWtezFNqjz32WPz617+OR1mIFEM82W1mI8ZmpshcGlKj8DGvcuW3FC+WKRYTCjPsspSBiIZWroBcuOJx02xm5KfbZClDvMW0VC+RFnn9AdT2L5lYnpcmy+x3Ur+sVCuyU4OLzchx0XL7/KhtFetxKusxkULlplmRYQ/2N8rxtKfP68eBtl4Awd5kObI4JELMPbh+vx9fffUV2tra4Pf7I24zc+bMWA9DlDS1rT3wBYLLTmtlLBLJoyIvFa0uDxo63ejq8yI9ifmUa1t64A/VY208ciTSIoPBgIr8NHxS246D7b3o8fjgsCYvZ/W+FheEYFOhqbYipr/gXXfdhV//+tfo6OgYcrvBAl8iJQqfxcqVnygWFXlp+Ne+NgBATbMLp47LTNqxmeqOSD0q8oIBLhBsK04a40zascOX6NXSpOpRB7j3338/Vq5cCafTiWuuuQbFxcUwm7lKDqlflQZnk5I8jp4dndwANzztD+sxkZJJ24okB7ganGAGxBDgbtiwAWPHjsWuXbuQl5f89DdEiaLVk52SL7w3JLyXJBmquMgDkWpI24rkjtnXYg5cIIZJZnV1dbj00ksZ3JLmSAID9uBSDI7uwU0m8UmEwQCU5bIeEymZvG1F8HhGAzA+x5HUYyfSqAPcgoIC+Hy+eJaFSHaCIIRmsY5x2pM60J+0Z1xWCqymYDObzIuWIAio7u8FGpuZghSrKWnHJqKRG5/jgLk/00kyc+GGX/NKsh2wmbXTVow6wL3iiivw1ltvwe2WZ91kokRocXnQ0esFoK1HNSQPs8mI0txgj8i+5iNZDRKtqduNLnewA4LDbIiUz2IyoqS/97SmuRuBJLUV9Z196PEEEwFobVL1qAPcO++8E0VFRbj88stRU1MTzzIRySZ87BMDA4oHcfyrxx/AgbaepBxTq7OiibRMbCv6vAEc6uhNyjHD2wqtTaqO+vlreXn5gNe8Xi8OHTqE1157DU6nE5mZmQO2MRgMqKqqiqmQRMnCDAoUbxX5qcDnwf+vaurG+JzE1ytOlCRSn4r8VPx9T/D/q5pcGJeV+PGwWm4rou7BDQQCEARB8mM2m1FSUoKSkhI4nc4BvxcEAYFAIJHlJ4qrag2f7CQPyeSRJGVS0PJFi0irpG1FcsbsazWDAjCCHtx9+/YlsBhEyiDNoKCtk53kIcfsaMmTCObAJVIFOdqK8KWByzWWbWXUY3CJtEgMDFKtJhRk2GQuDWlB+BjYZK0zLz6JSLebkZfGekykBhUytBViIJ3psCA71ZqUYybLqAPcc889F0899dSQ2zz99NM499xzR3sIoqTq8/pR1z8JqCI/DQaDQeYSkRak2y3ITw8Gmcnolen1+HGwPThBpTyP9ZhILTIdVuT0B5nJaCu63T4c7ugDEOw91lpbMeoAd9u2bcMOW9i/fz/efffd0R6CKKn2tbgg9Gdm4bhFiiexPrW4PGhzeRJ6rJrm8HqsrUeORFonthWNXW509nkTeqwajU+qTugQhd7eXpjNTJRP6qDlsUgkr/BxsNXNie2Z4QQzIvWStBUJHqYQ3hZpsa2IKcAdrDtbEATs378fr732GoqLi2M5BFHSSHLgamw2KclLOnkkwRctSa8M6zGRmoSfs9UJHqYQfs3T4qTqEQW4RqMRJpMJJlNwKbdVq1aF/h3+YzabUV5ejt27d+PKK69MSMGJ4o09X5Qo5UmcHS2tx3wSQaQm4ZNSE99WaHuIwojGD8ycOTPUa/vee++hpKQEpaWlA7YzmUzIycnBt7/9bVx//fVxKShRooknu9EQXBecKF7CLx6JzoUrXhRNRkNo6U8iUodk5s0W2wqLyYDibO21FSMKcLdt2xb6f6PRiIULF2LFihXxLhNR0gmCEHocNC7LAbvFJHOJSEvGOFNgtxjR5w0k9LFjICCEhiiUZDtgM7MeE6nJuCwHrCYjPP5AQntw/QEB1c3BtmJ8TiosJu1ljR31DLCampqIS/MSqVFDpxsujx+ANh/VkLyMRgPKc9PwxeFO7G/tgccXgNUc/wvK4c4+9HpZj4nUymQ0oCw3FV81dGFfiws+fwDmBASfh9p74fEFV5rV6qTqUf/Vxo8fD6fTGc+yEMmG428p0cSJi/6AgNrWnoQcg0tNE6mfmEnB6xdwoK03IcfYq+ElekUx5fDyer149dVXsWPHDrS1tcHv9w/YxmAw4PHHH4/lMEQJp+X1uEkZwntJqpq6cUwC6pl0VrQ2e2WItK48VzoptTQBPaySrEEavRkedYB76NAhnHfeefjyyy8hiFnFIxhtgPvwww/jgQceQH19PSZOnIgHH3wQ06ZNG3a/5557DvPmzcMll1yCV155ZcTHJX3Sw8lO8gq/cUrU2LoqpggjUr3wXLhVTd349okFcT+G1jMoADEEuD/96U+xZ88ezJs3D4sWLUJxcXHcFnV4/vnnUVlZifXr12P69OlYt24dZs+eja+++gr5+fmD7rdv3z7ceuutOOuss+JSDtIPcbA9wJ4vSoxkZFLgUBsi9UtGJoXw4UxazIELxBDgvvnmm5g5cyb++Mc/xrM8AIC1a9di0aJFWLhwIQBg/fr12Lx5MzZu3Ijbb7894j5+vx9XX3017rzzTrz//vtob2+Pe7lIu8QeXGeKJbQWOFE8hT92TNRqZmIGhexUK7JYj4lUKTzgTFRbIfbg5qbZ4EyxJOQYchv1JLO+vj5Mnz49nmUBAHg8HuzcuROzZs0KvWY0GjFr1ixs37590P1Wr16N/Px8XHfddcMew+12o7OzU/JD+uVy+3Coow9AsJdtsBX6iGKRYjVhbGYKgOAN1VBDu0aj2+1DfeeRekxE6pRmM6Mwww4gMSsfdvR40dztBqDttmLUAe7JJ5+M/fv3x7MsAIDm5mb4/X4UFEjHnBQUFKC+vj7iPh988AEef/xxbNiwIapjrFmzBk6nM/TD5YT1raaZ4xYpOcThL519PjR3e+L63pJHjrmsx0RqJrYVrS4PWl3xbSuqmvUxqXrUAe7PfvYz/OUvf8EXX3wRz/KMWFdXF6655hps2LABubm5Ue2zdOlSdHR0hH7q6uoSXEpSsiodjEUiZahI4JK90kwg2u2VIdKD8LYi3ovD6GVS9ajH4Obn52POnDn45je/iZtuuglTpkwZdOGHmTNnRv2+ubm5MJlMaGhokLze0NCAwsLCAdtXVVVh3759mDNnTui1QCCYvNhsNuOrr75CRUWFZB+bzQabzRZ1mUjb9DCblJTh6EwKp5fnxO29wyejaPmiRaQHkkmpTd2YWpodt/fWy6TqUQe4Z599NgwGAwRBwF133TXkuMVI+XEHY7VaMWXKFGzduhWXXnopgGDAunXrVixZsmTA9ieccAL+85//SF5btmwZurq68Jvf/IbDD2hYzIFLyRJ+0aqO89i68MkoDHCJ1C38WhTvtiK8B/cYDbcVow5wV6xYkbDJOJWVlViwYAGmTp2KadOmYd26dXC5XKGsCvPnz8fYsWOxZs0a2O12nHzyyZL9xZ7ko18nikQ82c1GA0qyHTKXhrQsoUMU+ntwLSYDxmWlxPW9iSi5ypMwnMlmNmJMpnbbilEHuKtWrYpjMaTmzp2LpqYmrFixAvX19Zg0aRK2bNkSmnhWW1sLozH+azOT/gQCQmiS2fgcBywJWPObSJSfbkOazYxuty+uFy1/WD0uzUlNyNr1RJQ8RRl2pFhM6PX645pJwesPYH9LcKnwstxUmIzazRoUn5UZEmDJkiURhyQAwLZt24bcd9OmTfEvEGnSwfZeuH3BMducYEaJZjAYUJGXin8f6MCBtl70ef2wW0wxv++Bth54/MF6zOEJROpnNBpQnpeKzw91ora1Bx5fAFZz7Deuta098AWCKQq13laM+q81ceJEPProo+jq6opneYiSiis/UbKJ9UwQgH0t8emZCR+jxwwKRNogthX+gIDa1gS0FRqeYAbEEOB+8cUXWLJkCcaMGYNFixbh448/jme5iJKCGRQo2SSZFOK0DCdv1Ii0J/xc3puItkLjk6pHHeAeOHAAd911F/Ly8vD4449j+vTpmDp1KjZs2ACXKzFrJxPFm55OdlKG8lxp+p94YC5nIu0pz0tAW6GTHLhADAFuQUEB/u///g/V1dV4/fXXcemll+LTTz/Fj370I4wZMwY//vGPsXv37jgWlSj+whNoV3D1J0qCo3PhxkN4T7CW81oS6Ukisq6Ev09ZrrbbirhMtZ09ezb+/Oc/o66uDnfddRdyc3Pxu9/9DlOmTMHpp5+OTZs2oa+vLx6HIoorcYhCbpoVTodF5tKQHozPcUCcuBzvi1Z+ug0ZdtZjIi0oy02FIdRWxP5kXBCE0PsUOe1ItSk2z0BcxDWXTEFBAZYuXYq1a9dizJgxEAQBO3bswHXXXYfi4mKsW7cunocjiklHrxdNXW4AfKxLyWMzm0L5lqubXBAEIab3a+/xoKV/rXqtP3Ik0pMUqwlj+/PUVjd1x9xWtLo86Oj1AtBHWxG3APfgwYO48847MX78eHz/+99HfX09Lr74YrzyyitYvnw5TCYTfvrTn2L58uXxOiRRTKo5MYdkIt5Q9Xj8qO+M7elWeM8OhycQaYvYVnT1+dDU7Y7pvfQ2qTqmAFcQBLz22mu45JJLUFZWhjvvvBNerzc0NveVV17BxRdfjFWrVuG///0vpkyZgscffzxeZSeKid5OdlIOyTrzMc6OZgYFIu1KWFuhg0nVow5w77rrLpSVlWHOnDn461//im9+85t47rnnQuNwi4uLJdunp6djzpw5aGhoiLnQRPHAHlySSzwnj+jtokWkJ3FtK8IyKJTrYFL1qEcYr1y5EhkZGfjxj3+MxYsXY8KECcPuM2XKFMyfP3+0hySKK/Z8kVzCA9HqGC9aekrcTqQ34dem6hgnmlU362tBmFEHuOvXr8fVV1+N1NTo/0jf/e538d3vfne0hySKK3GIgtVsxNisFJlLQ3oi7ZWJz2NHu8WIMU7WYyItCQ9E4/W0x2E1oTDDHtN7qcGoA9wbbrhhwGt/+ctf8Pbbb0MQBMycOROXXXZZTIUjShSfP4D9/cukluemwiTmbSJKguxUKzIdFrT3eGO6aHn9AdS29AAAynLTYGQ9JtKUvDQb0m1mdLl9MbUVfV4/6lqDbUVFXhoMBu23FSMag/vXv/4VM2fOxLvvvjvgdwsXLsT3vvc9/Pa3v8WDDz6IK664ggEuKVZdWy+8/mDKFc48JzmIvbiHO/rQ7faN6j32t/TAFxD634/1mEhrDAYDyvuHNB1s70Wvxz+q99nf0oP+pkI3bcWIAty//OUv2LVrF6ZPny55/W9/+xuefPJJOBwOLFu2DPfddx/Ky8vxyiuv4Nlnn41rgYniQU/LFZIyhV9kakY5TIHjyIm0T2wrBAGoaR5dW1Gtw+W8RxTg7tixA2eddRbsdunYjY0bN8JgMOCJJ57A6tWr8bOf/Qzvv/8+7HY7/vjHP8a1wETxwMCA5CaZPNI8ukePkglmzKBApEnxaCv0eM0bUYBbX1+Pk046acDr7733HjIzMyVDEgoLC3HhhRfik08+ib2URHGmx5OdlEUy0awxHhctfTx2JNIbaVsx2qc9+sqgAIwwwG1ra4PVapW8Vltbi9bWVpx55pkDBi2XlZWhpaUl9lISxRlXfyK5hde70WZSCA9wy3JZj4m0SLLYwygnmon7GQxAaY4+2ooRBbjp6ek4cOCA5LWdO3cCACZPnhxxn6OHMxApgTgeqTDDjlTbqJOJEI1acbYDFlOwU2A0Fy1BEEI9v2MzU+Cwsh4TaVFJjiOU6SfWtmJcVgrsFlNcy6dUIwpwTznlFGzevBnd3Uf+wC+//DIMBgPOPPPMAdvX1NSgqKgo9lISxVGry4O2Hi8A/TyqIeWxmIwY39+TUtPsgl+c4hylFpcHnX3B7At8CkGkXTazCSXZDgDBcfeBEbYVjV1uuPqzL+hpSN6IAtyrr74abW1t+Na3voXf/va3WLJkCf74xz+isLAQ55xzjmRbQRDwwQcfRLXCGVEycfwtKYX46NHtC+BQe++I9mUmECL9ENuKXq8f9Z19I9pXr23FiJ5pXXfddXjppZfwxhtvYPfu3RAEARaLBb/5zW9gMkm7vLdu3Yr6+nrMmjUrrgUmipVeT3ZSnmC6ngYAwN6mbhT399JEo4pL9BLpRnleGrCnEUCwk2ZMZvSrFuq1U2dEAa7RaMTmzZvx7LPP4sMPP0ROTg6+//3vY9KkSQO2bW5uxk033YSLL744XmUliovw9bj5aJfkdHQmhXOOz496X71etIj0SDLRrLEbZx2bF/W+er0ZHvGsBKPRiKuvvhpXX331kNtdeeWVuPLKK0ddMKJEYQ8uKUVFDJkUJAEuc+ASaZrkZjiGtkIvizwAIxyDS6QF4snusJpQmMEsHySf8ItN9QhnR4uLPKTZzMhPt8W1XESkLLEs9iC2FRl2M3LTrMNsrR0McElX3D4/alt7AASHJxiNhmH2IEocZ4oFef3B6Uh6Zfq8ftS1BetxRV7qgBzkRKQtWalWZKcGg9ORLPbQ4/HhYP8E1or8NF21FQxwSVdqW3ogZljh8ARSgvL+BRqau93o6E9fN5x9LS4I/fVYT48cifRMbCvqO/vQ7fZFtY9kOW+dtRUMcElXJGORcvV1spMyhY+frYry0WN4D46eJo0Q6VnFKIY0Scff6qutUGyA+/DDD6O0tBR2ux3Tp0/Hjh07Bt12w4YNOOuss5CVlYWsrCzMmjVryO1Jv/S4Hjcpm/SiFd2jx2pmUCDSnfBrVvRtBXtwFeX5559HZWUlVq5ciV27dmHixImYPXs2GhsbI26/bds2zJs3D++88w62b9+O4uJinH/++Th48GCSS05KxwwKpDSjWWeeGRSI9EeaSWEUbYXOrnmKDHDXrl2LRYsWYeHChZgwYQLWr18Ph8OBjRs3Rtz+j3/8I3784x9j0qRJOOGEE/DYY48hEAhg69atSS45KZ14shsMQFkue3BJfkfnwo2G+CTCaADG50S/OAQRqVf5qALcYFthNhp011YoLsD1eDzYuXOnZAU0o9GIWbNmYfv27VG9R09PD7xeL7KzsyP+3u12o7OzU/JD2icIQuhxzdjMFNgtpmH2IEq8MZkpsJmDTXE0Fy1BEELbFWc7YDOzHhPpQXFWCiymYBaEaDIpBAJCaDhTSY4DFpPiQr6EUtynbW5uht/vR0FBgeT1goIC1NfXR/Uet912G8aMGTPoMsFr1qyB0+kM/RQXF8dcblK+pi43uvpnnurtUQ0pl8loCD1N2N/SA68/MOT29Z196PH4AbAeE+mJ2WREaU6wrahpccEvpgQaxMH2Xrh9wfZEj5OqFRfgxuree+/Fc889h5dffhl2e+Qk/kuXLkVHR0fop66uLsmlJDns1fFYJFI2cRytLyCgrj9P82CqdbrsJhEduXZ5fAEcbOsdctvwZen1OKlacQFubm4uTCYTGhoaJK83NDSgsLBwyH1/+ctf4t5778Wbb76JU089ddDtbDYbMjIyJD+kfcygQEo1kmU49TxphEjvwq9dww1p0vukasUFuFarFVOmTJFMEBMnjM2YMWPQ/e6//37cdddd2LJlC6ZOnZqMopLKMLUSKdVIMimEX7S4yAORvoQPNRi2rdD5Nc8sdwEiqaysxIIFCzB16lRMmzYN69atg8vlwsKFCwEA8+fPx9ixY7FmzRoAwH333YcVK1bgmWeeQWlpaWisblpaGtLS9PelUmThPWN6S3hNyjaSTApVHKJApFuShWFGFODqr61QZIA7d+5cNDU1YcWKFaivr8ekSZOwZcuW0MSz2tpaGI1HOp8fffRReDweXH755ZL3WblyJVatWpXMopOCiYFDut2MvDSbzKUhOiI8ZV20F61MhyW0Nj0R6UN458xwmRTE8fo5qVZkOvTXVigywAWAJUuWYMmSJRF/t23bNsm/9+3bl/gCkar1evw42B4ckF+RlwaDwSBziYiOSLWZMcZpx6GOPlQ1uSAIQsQ66nL7cLijDwDrMZEeZdgtyE+3obHLjeohlvbu7POiscsNQJ/DEwAFjsElSoTwhkCvJzspm/josaPXi1aXJ+I2Nc0cnkCkd+I1rLnbg/aeyG1FNSdVM8Alfajm+FtSuHLJMIXIjx7Dhy9wghmRPkmGKQzWVoRPRtVhDlyAAS7phN5nk5LyRTN5RO9pf4jo6LSCg7QV4dc89uASaVf4Xe4xOj3ZSdnCL1rVg120OESBSPfCb4arB+nBlS4Io8+bYQa4pAtiz5fJaEBJNgMDUp5oFnsQ67HFZEBxtiMp5SIiZYkmb7b4utVkxLgsfbYVDHBJ8wIBITQ5Z3y2A1Yzqz0pT0GGDalWE4DIFy1/WD0uyXbAYmI9JtKjMc4U2C3B8z9SW+HzB7CvJdhWlOWmwmTUZ7YVtpCkeYc7+9Dr9QPgBDNSLoPBEJo4Vtfag77+Ois61N4Lty8AQL+PHIkIMBoNKOufOFbb0gOvPyD5fV1bL7x+AYC+r3kMcEnzODGH1EJ89BgQgP0tPZLf7ZVMGmE9JtIzsa3wBYQBbQWXpQ9igEuaxwwKpBZDTTTjpBEiEg3VVjCDQhADXNI8nuykFkOlCtP7uvJEdIS0rZBOSg1fwlfPN8MMcEnzJIs86DThNanDUAncJYnbdXzRIqKjF4YZ/Ga4LFe/N8MMcEnzxJM9O9WKrFSrzKUhGlxpTioM/ROeB160ggFvbpoNzhRLsotGRApSPkSqMPHfBRk2pNv121YwwCVN6+rzoqHTDYCPdUn57BYTivtzVlY3uSAIwZnQHb1eNHezHhNRkMNqxtjMFADStqLV5UFbjxeAvocnAAxwSeM4MYfURgxgu90+NHYFg9pqZlAgoqOIvbgdvV60uDwAmEEhHANc0rTqZp7spC7h42vFcbdVknHk7MEloqNWPwy1FZyMKmKAS5oWPptUzwmvST2kS/ZGuGixB5eIcPSSvS7JfwFORmWAS5rGHLikNhEvWmEZFI5hPSYiDHIz3MibYREDXNI08aS3mowYl5Uic2mIhhcpF251czDQtZmNGJPJekxE0rai+qi2IsViQlGGXZZyKQUDXNIsf0DAvubgEoaluQ6YTazupHw5qdZQGrDqJhe8/gD2twQvWmW5qTAZDXIWj4gUIj/dhjSbGUDwaY/b50dta/CaV56XCqPO2wpe8UmzDrT1wOMPAOACD6QeBoMhNF78YHsvvqrvgtcfTAHEYTZEJApvK+raevDfhm74A8G2Qu/jbwEGuKRhXKKX1Co8kN26pzHsddZjIjpCbCsEgW3F0RjgkmZxPW5Sq/D6+tae+iOv63zSCBFJhQeykraC1zwGuKRdzKBAahV+0frsYGfY66zHRHREeJvAtkKKAS5pVnUTc+CSOg02fq6MizwQUZhIbYXBwLYCYIBLGib24Oan25But8hcGqLojc9xwHzUDOgipx2p/TOmiYiAYFtxdLKEMc4UpFhN8hRIQRjgkia1uTyhtbn5qIbUxmIyoiTHIXmN9ZiIjma3mFCcfVRbwbH6AAB2BySZPyBgR00rGrv6kJ9ux7Sy7KTktdTTcf0BAX/598HQv8vyHENsTaRM5bmpkmE2Zbmsx0Q0UHluKva39IT+zbYiSLEB7sMPP4wHHngA9fX1mDhxIh588EFMmzZt0O3/9Kc/Yfny5di3bx+OPfZY3Hffffjud7+bxBIPb8tnh3HnX7/A4Y6+0GtFTjtWzpmAC04u4nETdMy//fswZh6bl9DPShRPWz47jO1VLZLXXt19CGcck8t6TEQhWz47jB01rZLXXt51EDPKc3TfVihyiMLzzz+PyspKrFy5Ert27cLEiRMxe/ZsNDY2Rtz+ww8/xLx583Ddddfhk08+waWXXopLL70Un332WZJLPrgtnx3G4qd3SQIvAKjv6MPip3dhy2eHedwEHbOzz5fQz0oUT2I9dnn8ktdZj4koHNuKoRkEQRDkLsTRpk+fjm984xt46KGHAACBQADFxcX4yU9+gttvv33A9nPnzoXL5cLf/va30Gunn346Jk2ahPXr1w97vM7OTjidTnR0dCAjIyN+H6SfPyDgzPveHhB4hctNs+J310yN6+N7f0DADU99HBqLquXjDndMA4BCpx0f3HYulzolxRqurWA9JiJAv23FSOI1xQ1R8Hg82LlzJ5YuXRp6zWg0YtasWdi+fXvEfbZv347KykrJa7Nnz8Yrr7wScXu32w232x36d2dnZ8Tt4mVHTeuQwS0ANHd7cNmjHya0HHo+rgDgcEcfdtS0YkZFTtKOSzQSw7UVrMdEBLCtiIbihig0NzfD7/ejoKBA8npBQQHq6+sj7lNfXz+i7desWQOn0xn6KS4ujk/hB9HYNXRwS8nD74KULNr6yXpMpG9sK4anuB7cZFi6dKmkx7ezszOhQW5+uj2q7WafVIAiZ0rcjnu4oxdvfN6gi+NGe8xovwsiOURbP1mPifSNbcXwFBfg5ubmwmQyoaFBGqw0NDSgsLAw4j6FhYUj2t5ms8Fms8WnwFGYVpaNIqcd9R19iDTgWRwr88jVU+I+FvbM+97WxXGjPea0suy4HI8oEaJtK1iPifSNbcXwFDdEwWq1YsqUKdi6dWvotUAggK1bt2LGjBkR95kxY4ZkewB46623Bt0+2UxGA1bOmQAgWOnCif9eOWdC3AeC6+m4cn1WonhiPSaiaLCtGJ7iAlwAqKysxIYNG/Dkk09iz549WLx4MVwuFxYuXAgAmD9/vmQS2k033YQtW7bgV7/6Fb788kusWrUKH3/8MZYsWSLXRxjggpOL8OgPT0OhU/q4oNBpx6M/PC1h+er0dFy5PitRPLEeE1E02FYMTZFpwgDgoYceCi30MGnSJPz2t7/F9OnTAQBnn302SktLsWnTptD2f/rTn7Bs2bLQQg/3339/1As9JDpNWDg9rSgm13Hl+qxE8cR6TETR0FNbMZJ4TbEBbjIlM8AlIiIiopEbSbymyCEKRERERESjxQCXiIiIiDRFcWnC5CCO0kj0imZERERENDpinBbN6FoGuAC6uroAIOErmhERERFRbLq6uuB0OofchpPMEMyze+jQIaSnp8NgSPzMQ3HltLq6Ok5qGwb/VtHh3yk6/DtFh3+n6PDvFB3+naLHv9XQBEFAV1cXxowZA6Nx6FG27MEFYDQaMW7cuKQfNyMjgxU4SvxbRYd/p+jw7xQd/p2iw79TdPh3ih7/VoMbrudWxElmRERERKQpDHCJiIiISFMY4MrAZrNh5cqVsNlschdF8fi3ig7/TtHh3yk6/DtFh3+n6PDvFD3+reKHk8yIiIiISFPYg0tEREREmsIAl4iIiIg0hQEuEREREWkKA1wiIiIi0hQGuERERESkKQxwiYiIiEhTGOASERERkaYwwCUiIiIiTWGAS0RERESawgCXiIiIiDSFAS4RERERaQoDXCIiIiLSFAa4RERERKQpDHCJiIiISFMY4BIRERGRpjDAJSIiIiJNYYBLRERERJrCAJeIiIiINIUBLhERERFpCgNcIiIiItIUBrhEREREpCkMcImIiIhIUxjgEhEREZGmMMAlIiIiIk1hgEtEREREmsIAl4iIiIg0hQEuEREREWkKA1wiIiIi0hQGuERERESkKQxwiYiIiEhTGOASERERkaYwwCUiIiIiTWGAS0RERESawgCXiIiIiDTFLHcBlCAQCODQoUNIT0+HwWCQuzhEREREdBRBENDV1YUxY8bAaBy6j5YBLoBDhw6huLhY7mIQERER0TDq6uowbty4IbdhgAsgPT0dQPAPlpGRIXNpiIiIiOhonZ2dKC4uDsVtQ2GAC4SGJWRkZDDAJSIiIlKwaIaTcpIZEREREWkKe3CJ4sTv9eHLFzajt/YAUkrG4YQrLoTJkvhTzB8QsKOmFY1dfchPt2NaWTZMxsROlpTjmHIeVw6y/Y11VI/lItvfWIbjsh5ruB4r/LMaBEEQ5C5EuPfeew8PPPAAdu7cicOHD+Pll1/GpZdeOuQ+27ZtQ2VlJT7//HMUFxdj2bJluPbaa6M+ZmdnJ5xOJzo6OjhEgUblk3WPY8zK21HQ2Rx6rSEjF4fuvBeTb74uYcfd8tlh3PnXL3C4oy/0WpHTjpVzJuCCk4s0c0w5jysHuT6rnuqxXOT6G8txXNZj7dZjuT7rSOI1xQ1RcLlcmDhxIh5++OGotq+pqcGFF16Ic845B7t378bNN9+M66+/Hm+88UaCS0oU9Mm6xzHxluuRF9aYAkBeZzMm3nI9Pln3eEKOu+Wzw1j89C5JAwMA9R19WPz0Lmz57LAmjinnceUg12fVUz2Wi1x/YzmOy3ocpMV6rJbPqrge3HAGg2HYHtzbbrsNmzdvxmeffRZ67corr0R7ezu2bNkS1XHYg0uj5ff60JxbhLzO5oh3iwEAjc48uL/eG9fHY/6AgMvXb0dTl3vQbfLTbfjTj2bE7ZGRHMeM5rgGAIVOOz647VxFPR4bDX9AwJn3vT3gwhEuIX9jrw/2YysUV4819d3K1VbI8N3K1lawHifccG1Uoj/rSOI11Y/B3b59O2bNmiV5bfbs2bj55psH3cftdsPtPlIROzs7E1U80rgvX9iMk47qKQhnBFDY0YQrb34YH5WcmryCAWjscuNbD2zT/DEFAIc7+rCjphUzKnKSeux421HTOmRwCyTmb3x67ad4ToH1WEvfrVxthVK/W9ZjdRqujVLSZ1XcEIWRqq+vR0FBgeS1goICdHZ2ore3N+I+a9asgdPpDP1wkQcard7aA1Ftl9/dluCSUGPX0IGhGsj1GaKtn3LVYy18t3K1FUr/buNJ6Z9VC/U42s+ghM+q+h7c0Vi6dCkqKytD/xYTBxONVErJ0CupiHKPHY+LTo3fwPvmbjc+qm4ddrvTy7ORm2ZT7TFHctz8dHvcjimXaD9DvP/Gud7x0W0nUz3WwncrV1shx3crV1vBepx40X4GJXxW1Y/BnTlzJk477TSsW7cu9NoTTzyBm2++GR0dHVEdh2NwabSiHVeX13Qo7mO+zrzvbdR39CHSCZyIcVByHFPO48pBtr+xjuqxXGT7G8twXNZjKU3VY5k/q6qzKIzUjBkzsHXrVslrb731FmbMmCFTiUhPTBYzDt15L4Bg4xlO/PfhVWvinn/RZDRg5ZwJAIINSjjx3yvnTIhrAyPHMeU8rhzCP+vREvo3VkA9Pprmvlu5/sYyHFe2toL1OOHU9FkVF+B2d3dj9+7d2L17N4BgGrDdu3ejtrYWQHB4wfz580Pb/+hHP0J1dTV+/vOf48svv8QjjzyCF154AbfccoscxScdmnzzddi99jHUp+dKXm905uHfv34sYXkXLzi5CI/+8DQUOqWPggqddjz6w9MSkotQjmMOddzcNFtCjysH8bOmWEyS1xP9N55883X4968fQ1OGTPU4I7l1Sg6Tb74On/wq+W2FHN+tXG0F63HiXXByER6+6rQBryvtsypuiMK2bdtwzjnnDHh9wYIF2LRpE6699lrs27cP27Ztk+xzyy234IsvvsC4ceOwfPlyLvRASdXQ2YcZd7+JaQc+xyRzL+bMnqLplXPkXMls6Uuf4oWPgxN2HrxyMuZMGpPw48rhew9/gE/qgsOsNi38Bs46Nk/zK0Cd/cA7qGvrhckIfH7nBbAfFeRrwaH2Xpx5z1uYduBznGbpw4Xnn8aVzBJxXBnr8cz738bB9j6YjQZ8sfoCWM2K60uMWV1rD866/x0AwCljM/B/352QlO9W1WnCzj77bAwVc2/atCniPp988kkCS0U0tKrGbgSMJnxUcipOOrMMJ10U+RFOIpiMhqSnY5HjmOJxzzk+PxTg1rS4kl6GZBAEAdXNPQCAMU47zj4+P2nHNlnMOOnqS5J2vNBxjQacOi4TdW298AeCgWB5XlrSy5FoVU1H2opTZ5bjpO+emLRjy/HdytZWyFiPTxmbiYPt9fAFBBzu6MX4nNSklyPRqpq6Q/9/xjF5sqcEi0R7txVEMgg/2Ss0eFFWkor8I3/f8L+7lrS4POjo9QKQfl6tq8g7EghUNWnz5qWqMbyt0F7gQ0BFfng91mYbVR12fiq1HjPAJYqDKhWc7FoxPscB8SmYVi8e0iBIRwGuDm5epG2Ffr5bPQn/XqsaNXqjFt6po9CbcAa4RHEQfrJr8bGqktjMJhRnOwAEexEUNo0gLqqbj1wUy3V0w1See+TcqdZogFvdzLZC68K/1/DvW0skAW6uMusxA1yiOBAf12TYzchNs8pcGu0Te0h6PH7Ud8q/Yk686bUHt1wXQxSCnyvLYUF2KtsKLZLUY8324AY/V26aFU6HRebSRMYAlyhGPR4fDrYHl4WuyE+DwSB//j+tq9D4BUSvY7pTbWYU9aeV2tvYrbne+W63L3RDpqfvVW8y7BbkpwdXaNPiUJvOPi+autwAlP0UggEuUYyqOaYu6SRj3DR4ARF7R1KtJhRkxG8pUzUQv9uOXi9aXR6ZSxNf1Tq9cdEj8fttcXnQ3qO1eqyOax4DXKIY6XW8pJwkY9w0FuD2ef040BZMEabHJwLh51D4uaUFksAgn22FlkkzKWirHqslEwgDXKIY6XW8pJy0nE5qf0sPAv1P5stzlXvxSBTpDHRt3bxIJqMqdGIOxUf496u1p0xqyKAAMMAliplex0vKKTvVisz+iQ2avnjosD5pefiJWgIDip2WU96pIYMCwACXKGZiD6LZaMD4HIfMpdEHg8EQCoQOd/Sh2+2TuUTxI3kioMMgSNuPdoOfx2IyoDgrRebSUCJpeSKsONTGajZirILrMQNcohgEAgJq+vMcluQ4YDHxlEqW8AtIjYYCIb334BZm2OGwmgBoq+fLHxBCS0uX5qTCzLZC08Y4U2C3BL9jLc0T8PkD2Ndfj8tzU2EyKneOAM8wohgc6uhFnzcAgGPqkk2rydTFiVUGA3T5RMBgMIQmmtW19sDt88tcovg42NYLj6+/rVDwxByKD6PRgLL+a0Jtaw+8/oDMJYqPurZeeP3BSQJKvwFngEsUgyrOipaNFicjCYIQ+izFWQ7YLSaZSyQP8bsNCMFJd1qg9555PRKfMvkCgnbqcWP4SnzKvuYxwCWKATMoyEeLmRQaOt1weYI9lkpOv5NoWrx5YYCrP1qcMKmmeswAlygGajrZtaY42wGLKTj+ixcPbdF8YKDDyYN6pMVMCmpZ5AFggEsUE+nJrt8eNzlYTEaUZAfHqNY0u+APqH9Z12oGQQCOWuxBI73z4U8ZlP5ol+KjQpP1mEMUiHRBPNlzUq3IdFhlLo3+iD0Ibl8Ah9p7ZS5N7CRBkA4XeRCV5aZCXMBNOz1fwc+Rl25Dht0ic2koGcpyw4dRaaMei5+jyGlHqs0sc2mGxgCXaJQ6+7xo7HIDUP6jGq0K7+Xcq4ELCB9jB9ktJozrz69Z1eSCIKi7d769x4Pmbg8APunRE4fVjLGZ/fW4sVv19bjV5UFbjxeA8ntvAQa4RKPGdeXlp7XJSOJncKZYkJOq7ycC4nfb7faFbiTVqkpF4xYpvsRAsLPPF7rJUatqlc0RYIBLNErMoCA/LWVS6PH4cKijD0DwcxkMyk2gngxaunnh5EH90tKESbXVYwa4RKMUvriAGh7XaJFksQeVXzzUNDs5GcLPqapmdd+8SJ/28LvVk/DvW+0TzdT2JIIBLtEoha8vroaTXYucKRbkptkAqL8HVzo7mfVJqz24ep48qEcVGppopqZFHgAGuESjJjZWVpMR47L0t6SqUojDFJq73ejonwChRlVMOSehxUe7NrMxNOmI9EFLuXDF8jusJhRm2GUuzfAY4BKNgs8fwL6WYEBSlpsKk1Hf4yXlJLmANKv3AsIMClK5aVZk2INpiNT8aNfrD6C2f5nW8rw0GNlW6Ep+ug1p/em01Bzgun1+1LUFUzGW56Wqoh4zwCUahQNtvfD6gylf1PCoRsvCH/mqORASy242GkILWOiZwWAIDdU42N6L3v4ljNWmtrUHvv5FSNgzrz8GgyH0vR9o60WfV6X1uKUntJiOWobkMcAlGgW1zSbVMi08AgwEhNAkuZIcBywmNs2A9NyqVmnvvHTcItsKPRK/d0FA6Mmf2qjxmsdWlGgUpI+T2Ssjp2M0MBnpYHsv3L4AAPVcPJIh/NxS6yRCjq0mSTrDRvXXY7U8tVRkgPvwww+jtLQUdrsd06dPx44dO4bcft26dTj++OORkpKC4uJi3HLLLejr60tSaUmPmEFBOcZkpsBmDjZlau3BVWPvSDJoIZMCv1vSwoRJNdZjxQW4zz//PCorK7Fy5Urs2rULEydOxOzZs9HY2Bhx+2eeeQa33347Vq5ciT179uDxxx/H888/j//7v/9LcslJT5jSSTlMRkNozff9LT3w+gMyl2jk2MsXmdYCA7X0fFF8aWEYldhGGQwItbdKp7gAd+3atVi0aBEWLlyICRMmYP369XA4HNi4cWPE7T/88EOcccYZuOqqq1BaWorzzz8f8+bNG7bXlygW1f2J5wsyjsyQJfmIgZAvIKCutUfm0oxcNTMoRFSS7QhlKFHjBEJBEELlHpuZAoeVbYUejc9xQEw6oNp63P8EZVxWCuwWk8wlio6iAlyPx4OdO3di1qxZodeMRiNmzZqF7du3R9znm9/8Jnbu3BkKaKurq/Haa6/hu9/97qDHcbvd6OzslPwQRavV5UGrK7imuFoe1Wid2pfslTz+y2WdElnNRozvzyhR3dyNQP8sbrVocXnQ0RvMzczeW/2ymU0o7q/HVU3dEAR11eOmLje63D4AQLmK2idFBbjNzc3w+/0oKCiQvF5QUID6+vqI+1x11VVYvXo1zjzzTFgsFlRUVODss88ecojCmjVr4HQ6Qz/FxcVx/RykbdUqHIukdWp/BCgG5blpVjgdFplLoyziEKA+bwCHOnplLs3IhI8bZluhb+L33+Pxo75TXXOE9qr0mqeoAHc0tm3bhnvuuQePPPIIdu3ahZdeegmbN2/GXXfdNeg+S5cuRUdHR+inrq4uiSUmtZMOtmevjBKoeTJSR68XTV1uABzPHYmaMylwbDWJ1JxJIXxYhZqyBilqQFBubi5MJhMaGhokrzc0NKCwsDDiPsuXL8c111yD66+/HgBwyimnwOVy4YYbbsAdd9wBo3FgDG+z2WCz2eL/AUgXqiXpUhiQKEGZitd75xOBoYUP2ahq7Ma3jsuTsTQjo8aZ55QYR0+YPPPYXBlLMzJqrceK6sG1Wq2YMmUKtm7dGnotEAhg69atmDFjRsR9enp6BgSxJlNwALTaxrmQOnBJVeVJtZlR5AyujV7V5FLVuV/NXr4hhfcYqW2xh2pmW6F+4d9/tcpuwtWYAxdQWA8uAFRWVmLBggWYOnUqpk2bhnXr1sHlcmHhwoUAgPnz52Ps2LFYs2YNAGDOnDlYu3YtJk+ejOnTp2Pv3r1Yvnw55syZEwp0ieJJPNlTLCYUZdhlLg2JKvLScLijDx29XrS6PMhJU8dTGrX2jiRLuaQHV12PdsW2ItVqQkGGOuojJYaaJ8KKw77S7WbkqaRdBRQY4M6dOxdNTU1YsWIF6uvrMWnSJGzZsiU08ay2tlbSY7ts2TIYDAYsW7YMBw8eRF5eHubMmYNf/OIXcn0E0jCPL4Da/jRU5XmpMIq5X0h2FXmp+GBvM4DgBYQBrjZkpVqRk2pFi8ujquEnfV4/6tqCbUVFfhoMBrYVepadakWmw4L2Hq+q6nGvx4+D7cHJnRV56qrHigtwAWDJkiVYsmRJxN9t27ZN8m+z2YyVK1di5cqVSSgZ6V1tqwv+/lRFDEaU5ehMCtPKsmUsTfTE3hyr2YixWSkyl0aZKvLS0OJqRWOXG119XqTblZ9pYl+LC+JIGbYVZDAYUJGXhp3723C4ow8utw+pKsihXtOs3lU7FTUGl0jp9jaqcyySHoQ/ylbLGDefP4D9LcE6VZ6bGlrUgKTCzzW1JMrn2Go6Wng9CA8clUw650Rd9ZgBLtEI8HGycqkxnVRdWy+8/mA3H2+YBqfGJXvD09VxghkB0nqgmnocPlFSRYs8AAxwiUaEAa5yFWbY4bAGJ5aq5uLBhQCiIr15Ucl3y7aCjqLGfN3hnQXHsAeXSLvEk91gkOZeJfmJY9wAoK61B31ev8wlGh6DoOhIAwN19M6LbYXRAIzPcchcGlICNWZSEId7mYwGlGSr65rHAJcoSoIghE72Mc4UpFiZhk5pxMf8AQHY39Ijc2mGxwA3OuOyHLCagpcrNfTghrcVxdkO2C1sKyhYFyym4Dh7NdTjQEAIjSUfn+2A1ayukFFdpSWSUVO3G119PgBc4EGpKlSWTD18IlIZx+AOymQ0oDQ32Au6v6UHPn9A5hINraHTDZcn+AShnE96qJ/FZERJdrAe1zQfycijVIc7+9Db/yRMjXMEGOASRSn80ShnRSuT2iYjiWUszLAjTQUpg+QkfrcefwAH2nplLs3Q2DNPgxHrg9sXwKF2hddjlc8RYIBLFCVetJRPTZkUWl0etPV4Aagv/Y4c1HTzwuW8aTDh9WGvmuqxCq95DHCJoiTNa6m+k10PSnNSIS60o6ogiPVpWGrKpKD2ni9KHDVlUpBc81R4E84AlyhK0oBEfSe7HtgtJozrXw2suskFQVDuGLdqBrgjIl3IQ9m989XNHM5EkYXXh2qFL/ag5hy4AANcoqiJJ3u6zYy8dJvMpaHBiMFit9uHxi63zKUZXPgQCjVO4Ei28jz19eA6UyzITrXKXBpSknIV9eCK51l2qhVZKqzHDHCJotDr8eNg/4SA8vw0GAxcUlWp1PIIkI+xRybdbkFBRvDGUsnjq11uHw519AEI9taxraBwzhQLctOUX4+7+rxo6Ax2EKj1KQQDXKIo1DS7ID7tVuvJrhdqmYwkls1hNaEwwy5zadRB/G5bXR60ujwylyaymmaO1aehideQ5m43OvonmiqNFuoxA1yiKFQ3s7dNLcpVsFqQ2+dHbWtwIYryvFQYjezli0b4d6vUPMfMoEDDCa8XVc0qqMcqveYxwCWKAnPgqocaenBrW3oQCD0RUOfFQw7ShTyUefNSxWwrNAxV1ONG9c8RYIBLFAUt3M3qRW6aFRn24KIJir14qHx2slzUcPMi+W5VGhhQYqlhwqQWrnkMcImiIJ7sJqMBJTkOmUtDQzEYDKFHgAfbe9Hj8clcooGqVJ5fUi6SR7tKDQz6Jw+ajYbQsqxE4Y5RwURY8fyymoyh1ItqwwCXaBiBgBDqCSzJdsBmNslcIhqO0h8BMoPC6BRl2JFiCZ5/ShxfHQgIock543McsJh4iaWBxmSmwGYO1g0l3qj5AwL2NQfnCJTmOmBWaT1WZ6mJkqi+sw+9Xj8AoDyXvW1qUK7wZOpV/WUyGIAy1qmoGY2G0N+rtrUHHl9A5hJJHWzvhbu/TLxxocGYjqrHXr+y6vGBth54/OqvxzEFuKtXr8Z777035Dbvv/8+Vq9eHcthiGTFWdHqo+RcuIIgoLq/TGMzU2C38InASIjnoD8goLZVWTcv0vG3bCtocGIb5fULqOvPqKIUWhlHHlOAu2rVKmzbtm3Ibd577z3ceeedsRyGSFbSx8nqPdn1RMmTkZq63OhyB8cFq7l3RC7h5+DeRqUFuMy2QtGpUHA6Q2nWIPW2UQkfouD1emE0ciQEqRfT/qjP+BwHzP25ZZV28dirgdnJclLyzQuf9lC0lDxhUit53xMeee7atQu5ubmJPgxRwoSf7HzsqA4WkzE0g72muRsBMemsAjCDQmyUnGJJ8rSH6d9oCEoeRqWFHLgAYB7pDueee67k35s2bYo4TMHv96Ourg779+/HvHnzRl1AIrmJJ3uWw4LsVKvMpaFoleelobrZhT5vAIc6ejEuSxkpm6rZgxuT8LzBSsuQIU5ozE2zwemwyFwaUrLwyaVKmwgr3jjmp9uQbldvPR5xgBsezBoMBuzbtw/79u0bsJ3RaEROTg7mzp2LX//617GUkUg23W4f6jv7ADAYUZuK/FT8fU/w/6uaXIoJcMN7cNXcOyKXFKsJYzNTcLC9F1VN3RAEAQaD/Esdd/R60dTlBsDvlYaXajOjyGnH4Y4+7G1UTj1uc3nQ4vIAUP81b8RDFAKBQOhHEASsWrVK8pr44/P50NDQgGeeeQZ5eXmJKDtRwrG3Tb2U+ghQLEu63Yy8NJvMpVEncfxiV58PTd1umUsTxLaCRkqsJx29XrT2B5Vyk4y/VfkQqpjG4D7xxBO45JJL4lUWIsWp5nhJ1VLiZKRejx8H23sBBMunhB4bNZLMQFdIJgVmUKCRUmImBS1Nqo4pwF2wYAEmTpwYr7KEPPzwwygtLYXdbsf06dOxY8eOIbdvb2/HjTfeiKKiIthsNhx33HF47bXX4l4u0h9JPkBOGlGV8IuHUsZq1jRr5+Ihp/DJnuE9TnKqZgYFGqHwelKtkJvwKg09iYgpwF2/fj0qKipw6NChiL8/ePAgKioq8Pjjj0f9ns8//zwqKyuxcuVK7Nq1CxMnTsTs2bPR2NgYcXuPx4PzzjsP+/btw4svvoivvvoKGzZswNixY0f1mYjCMe2PemU6rMjpnxSolB5crSRQl5sye3CZQYFGJrzTRDFtlEYyKAAxBrjPPPMMioqKMGbMmIi/Hzt2LMaNG4enn3466vdcu3YtFi1ahIULF2LChAlYv349HA4HNm7cGHH7jRs3orW1Fa+88grOOOMMlJaW4lvf+lZCepZJf8ST3WIyoDgrRebS0EiJPRCNXW509nllLo22ekfkdIwCh5+Ij3atZiPGsq2gKIQPe1PKEAWxJ9luMWKMU931OKYA96uvvho2kDz11FPx5ZdfRvV+Ho8HO3fuxKxZs44U0GjErFmzsH379oj7/OUvf8GMGTNw4403oqCgACeffDLuuece+P3+6D8IUQT+gICalmCjU5qTCrOJC5aoTfgFRAnDFMIvYsdwTPeo5aXbkG4LJgFSQoDr9Qewv7+tKM9NhcnIsdU0vMIMOxzW4FLdSqjHHl8A+/uXDS7PTYNR5fU4pit2R0cHMjMzh9wmIyMDbW1tUb1fc3Mz/H4/CgoKJK8XFBSgvr4+4j7V1dV48cUX4ff78dprr2H58uX41a9+hbvvvnvQ47jdbnR2dkp+iI52sK0XHl8AgPof1eiV5BGgAjIpiGUwGQ0oyWadGi2DwRA6Jw+296LPK2+HRl1rD7z+4GIi7JmnaBkMhlB9qWvtgdsnbz2ube2Bv39RHC0MyYspwC0qKsKnn3465DaffvppQtOEBQIB5Ofn4/e//z2mTJmCuXPn4o477sD69esH3WfNmjVwOp2hn+Li4oSVj9SLj5PVT9KDK/NkpEBACE0yG5/tgNXMJwKxEM9JQZBO3pNDNTMo0CiJ9SUgAPtbemQti3RStfrrcUwt7DnnnIMtW7bggw8+iPj7999/H6+//jq+/e1vR/V+ubm5MJlMaGhokLze0NCAwsLCiPsUFRXhuOOOg8lkCr124oknor6+Hh5P5LxyS5cuRUdHR+inrq4uqvKRvjDAVT9pLlx5g6DDnX3o7e9p5BOB2IX3MMn9eFc6eZBtBUWvXEH5urU2qTqmAPe2226D1WrFrFmzUFlZiTfffBOff/453nzzTdxyyy0477zzYLPZcNttt0X1flarFVOmTMHWrVtDrwUCAWzduhUzZsyIuM8ZZ5yBvXv3IhAIhF77+uuvUVRUBKs18rKqNpsNGRkZkh+io2ntZNejcVkOWPvHTsseBDXyhimelJRJgTfDNFpKytcdfh5p4UlETAHu8ccfjxdeeAE2mw3r1q3Dd77zHZx66qn4zne+g9/85jew2+3405/+hBNPPDHq96ysrMSGDRvw5JNPYs+ePVi8eDFcLhcWLlwIAJg/fz6WLl0a2n7x4sVobW3FTTfdhK+//hqbN2/GPffcgxtvvDGWj0bEJVU1wGQ0hNZ839figs8fGGaPxGEQFF+KCgzYVtAoKSmTQvgwLi3kfTfH+gYXXnghqqursWnTJvzzn/9Ee3s7MjMzcfrpp2PBggXIyckZ0fvNnTsXTU1NWLFiBerr6zFp0iRs2bIlNPGstrYWRuORuLy4uBhvvPEGbrnlFpx66qkYO3Ysbrrppqh7jYkGI6ZLyUu3IcNukbk0NFrlean4qqELXr+AA229KJVpbBlXxYuvkhwHjIbg2EW5x1eLbUWR045UW8yXVdKR0pxUGAzBseRyLvYgCELoKdPYzBSkWE3D7KF8cTkTc3Jy8NOf/jQebwUAWLJkCZYsWRLxd9u2bRvw2owZM/DRRx/F7fhE7T0eNHcHx3Br4VGNnh3d0ydXgMtV8eLLZjahJNuBfS09qGp0IRAQZElr1OryoK0nmGOZPfNSgUBg0LkwdMSUsWk43NGLnt4+9Pb2yrKEd6vLg3SLgHSLCVPGpaKvry/pZRBZrVZJR+Zo8VaTKAItrcetd9JHgN349okFQ2ydOGKAm51qRVZq5PkBNDIVeWnY19KDXq8f9Z19GJOZ/MT0XJ0uMo/Hg5qaGsn8GIrspulO9HnTAQBV1TWy5FF2+/xYdU4+ACDNZkZNTU3SyyAyGo0oKysbdB5VtEYU4L733nsAgGnTpsFut4f+HY2ZM2eOrGREMuJ4Se1QQiaFrj4vGjrd/eVhEBQvFflp2PplcBn3qqZueQJcTh4cQBAEHD58GCaTCcXFxXHpjdOy1M4+tPUEe7qLslKQakv+kLg2lweGrmCvbX66Xbab8EAggEOHDuHw4cMoKSmJqTd7RAHu2WefDYPBgD179uC4444L/TsaXFmM1KSak0Y0oyxX2oMrh2o+EUiI8FydVY3dOOvYxOVcHwxvhgfy+Xzo6enBmDFj4HA45C6O4qX6DGgXR3KYrLDbbUkvg9AnwGAO9ranp6bALuO8k7y8PBw6dAg+nw8Wy+jLMaIAd8WKFTAYDMjNzZX8m0hreNHSjnS7BQUZNjR0ulEt04IA4ZOgWJ/iJzx9n2zfLScPDiB2aMX6iFkvbJYjE7rcPnmGdIQfN7w8chDrjd/vT16Au2rVqiH/TaQVYoBrMxsxVobHnhRfFXlpaOh0o9XlQavLg+wkP34LHxrBJwLxo4RUYeJxHVYTCjPsspRBqdgBFh1b2KqGci07LS4TbDIYYJZhDHC4eNUbDowhOorXH0Bt/5KJ5XlpsszMpvgKD4TkSMXDJwKJkZ1qRZYj2MMjx/hqt8+P2laxrUhlQEejYjYaQhPLPDL04AYCQui4NotRM/WYAS7RUWpbe+ALCAA4IUgrJKteyRjgWk1GjMviE4F4Em8Y6jv70O32JfXY+1t60N9U8MaFRs1gMMBmDg4L8PgD8IuVKkncYQvg7ProHzAYDGhvbx90e4PBgFdeeWXQ3+/btw8GgwG7d++OXyFHYURDFM4999xRHcRgMEiW3yVSsvBZ0VxXXhvKJT24ye3p8wcE7GsO9vKV5jpgNrFfIZ7K81Lx8f42AEBNkwunjHMm7djV7JmnOLGZjehPpACPz48Ua/KyuLrDhkVYomifDh8+jKysrEQWKS5G9BeMtMgCEAxgBWHgHYf4ula6u0kfpDlw2YOrBeGTkZLdg3ugrQee/h4SBkHxd/Q43GQGuMyXnWB+P/D++8Dhw0BREXDWWYBJ/StsRWKzHAks3b4AUmKYJiAIAvx+P8zm6EK88AlmFtPw8VphYeGoy5ZMI+pKCAQCkp++vj5cfPHFKCsrwxNPPIGamhr09vaipqYGGzduRHl5OS655BJZV8QgGimOl9Seogw7UvpnBid7vXcuBJBYck40kz7t4XcbVy+9BJSWAuecA1x1VfC/paXB1xPk7LPPxk9+8hPcfPPNyMrKQkFBATZs2ACXy4WFCxciPT0dxxxzDF5//XXJfp999hm+853vIC0tDQUFBbjmmmvQ3Nwc+v2WLVtw5plnIjMzEzk5ObjoootQVVUV+r3H48Gyn1Xi21NOwDeOKcTJJxyDNWvWAIj8uL+9vR0GgyHU6bht2zYYDAa8/vrrmDJlCmw2Gz744AMEAgGsWbMGZWVlSElJwcSJE/Hiiy9Kyv7aa69hxmmnYNoxRbjuijk4fLBu2L/T0UMUduzYgcmTJ8Nut2Pq1Kn45JNPJNuvXr0aY8aMQUtLS+i1Cy+8EOecc05CFwKJ6VnZXXfdhY8//hgff/wxFixYgPHjx8Nms2H8+PG49tpr8c9//hM7duzAXXfdFa/yEiUcAxLtMRoNoe+ytrUnNGM4GcInP/GGKf7k7J0Xj2cwSPMtU4xeegm4/HLgwAHp6wcPBl9PYJD75JNPIjc3Fzt27MBPfvITLF68GD/4wQ/wzW9+E7t27cL555+Pa665Bj09wWFH7e3tOPfcczF58mR8/PHH2LJlCxoaGnDFFVeE3tPlcqGyshIff/wxtm7dCqPRiO9973uh4O63v/0tXn/tb3jgkY14ddsO/ObRx1FaWjrist9+++249957sWfPHpx66qlYs2YNnnrqKaxfvx6ff/45brnlFvzwhz/Eu+++CwCoq6vD97//fZw96wK88MZ7uGzeNVh+x/+N6Jjd3d246KKLMGHCBOzcuROrVq3CrbfeKtnmjjvuQGlpKa6//noAwMMPP4wPP/wQTz75ZEIXAYlpkMcf//hHXHbZZcjMzIz4++zsbFx++eV4+umnsXr16lgORZQUgiCExmiOcdrhSOI4KEqs8rw0fH6oE/6AgNqWHhxbkJ6U4/KJQGIVZ6XAYjLA6xeSmklBEITQ04BxWSmwy5w7VDP8fuCmm4AIwx4hCMG7iZtvBi65JCHDFSZOnIhly5YBAJYuXYp7770Xubm5WLRoEYBg/v9HH30Un376KU4//XQ89NBDmDx5Mu65557Qe2zcuBHFxcX4+uuvcdxxx+Gyyy6THGPjxo3Iy8vDF198gZNPPhm1tbU47thjcdq0bwIGwF5WhuNG0T6tXr0a5513HgDA7Xbjnnvuwd///nfMmDEDAFBeXo4PPvgAv/vd7/Ctb30Ljz76KCoqKlC5/G4EBAHHH3882g9W47777ov6mM888wwCgQAef/xx2O12nHTSSThw4AAWL14c2sZkMuHpp5/GpEmTcPvtt+O3v/0tHnvsMZSUlIz4M45ETKHzoUOHhk3kbLFYcPjw4VgOQ5Q0LS4POnq9AKQ9Q6R+0kwKyQuEuCpeYplNRozPCf5da1pcSZuB3tTlDmVt4I1LHL3//sCe23CCANTVBbdLgFNPPTX0/yaTCTk5OTjllFNCrxUUFAAAGhuDS0T/+9//xjvvvIO0tLTQzwknnAAAoWEI//3vfzFv3jyUl5cjIyMj1DtbW1sLALj22muxe/duXHz2VNy74jZs2/r3iPOahjN16tTQ/+/duxc9PT0477zzJGV76qmnQuXas2cPvvGNaQj0H8tmNoWC4WiJvcV2+5Ec0JHeo7y8HL/85S9x33334eKLL8ZVV1014s83UjF1T40bNw6vvvoqfvGLX0QMdN1uN1599VWMHTs2lsMQJQ3XldcuucZqisfKT7chXcblL7WsIi8Vexu74fEFcLCtFyU5iV8edm/4UKZcthVxE22HWII6zo5eOctgMEheEyfNi8MLuru7MWfOnIi9nkVFRQCAOXPmYPz48diwYQPGjBmDQCCAk08+GR5PMG3CaaedhpqaGjz1/Mt45523cevia7Hlz7Pw0p//HHqEHx7wer3eiGVPTT1yA93dHayfmzdvHhCD2WxHlgL2h71v+IITifDee+/BZDJh37598Pl8UU+CG62YPs2CBQuwd+9enHvuuXjvvfdCy/P5/X68++67+Pa3v43q6mpce+218SgrUcIxg4J2yRHgtrk8aHF5Bhyf4kuO77aKS/QmRn9QGLftEuy0007D559/jtLSUhxzzDGSn9TUVLS0tOCrr77CsmXL8O1vfxsnnngi2traBrxPRkYGLrviCqy8/ze47+GNePmll9Da2oq8vDwAkDwJjya/7IQJE2Cz2VBbWzugXMXFxQCAE088ER//61+hfWwWIz766KMRff4TTzwRn376qSSZQKT3eP755/HSSy9h27ZtqK2tTcrcrJgC3Ntvvx0XX3wxPvzwQ5xzzjmw2+0oKCiA3W7Hueeeiw8//BBz5szB7bffHq/yEiUU81pqV1luKsSMhckaolDdHFafGAQljCwBLp/2JMZZZwHjxgGDpRc1GIDi4uB2CnDjjTeitbUV8+bNw7/+9S9UVVXhjTfewMKFC+H3+5GVlYWcnBz8/ve/x969e/H222+jsrJS8h5r167Fs88+i/1V/8W+6r14a/OryC8oRGZmJlJSUnD66aeHJo+9++67oTHCQ0lPT8ett96KW265BU8++SSqqqqwa9cuPPjgg3jyyScBAD/60Y9QXbUXa+9ejn1V/8WrL76ATZs2jejzX3XVVTAYDFi0aBG++OILvPbaa/jlL38p2UYck3vffffhzDPPxBNPPIF77rlnxMH0SMUU4FosFrzyyit4+umnce6558LpdKK1tRVOpxPf/va38cc//hGvvPJKwruhieJFmkGBFy0tSbGaMMYZXEWsurF7VGPcRooZFJKjXIaV6jh5MEFMJuA3vwn+/9FBrvjvdesUkw93zJgx+Mc//gG/34/zzz8fp5xyCm6++WZkZmbCaDTCaDTiueeew86dO3HyySfjlltuwQMPPCB5j/T0dNx///2YNfObuPqic3HoQC2eev7I8ISNGzfC5/NhypQpuPnmm3H33XdHVba77roLy5cvx5o1a3DiiSfiggsuwObNm1FWVgYAKCkpwcMbn8Y7b7yGH8w+Cxsf2yCZLBeNtLQ0/PWvf8V//vMfTJ48GXfccYdkuIYgCLj22msxbdo0LFmyBAAwe/ZsLF68GD/84Q9DQykSwSAko5VXuM7OTjidTnR0dCAjI0Pu4pCMZt7/Dmpbe5BqNeGzO2dzkRKNmb9xB977ugkAsOOObyM/3T7MHrFZ8/oe/O7dagDAU/8zDTOPy0vo8fSqo9eLiXe+CQCYVpaNF/53ZBNlRuOMe9/GwfZeZNjN+PfK89lWhOnr60NNTQ3Kysokk49G5KWXgtkUwiecFRcHg9vvfz8u5VQanz+ALw53AgDSbOakdLLsOdwJrz8Ak9GACUUZiqjHQ9WfkcRr7Fol6tfn9aOuLZjbsCI/TREnOsVXRV5qKMCtanQlPMAN78FlBoXEcaZYkJduQ1OXWzLMKFF6PX4cbO8FEHzSw7YiAb7//WAqMJ2sZAYEM4KYjUb4AgHJ6mKJ4g8I8PavsmgzmzRXj+MS4H766ad45plnsGfPHrhcLvz9738HEFyBY8eOHTjvvPNUsW4x6du+Flco9SIfOWrT0WM1Z1TkJPR4YrBltxhDwyMoMSryUtHU5UZztwftPR5kOmJY63QYkrHVbCsSx2QCzj5b7lIklc1shM8TgNcfgD8QgCmBCyGEL3hjT3AGBTnE/IlWrFiB0047Dffffz/++te/4p133gn9LhAIYN68eXj66adjPQxRwknylXJVIk1K5lhNjy+A/a3BJwLluWkwGrXVO6I05ZKbl8ROImQGBUoUm+VIWJboXlxP2PuHH1crYvpEzz33HO6++26cd9552L17N5YuXSr5fXl5OaZOnYq//OUvMRWSKBkks6K5yIMmHRMWBFUnOAiqbe0JLTrA+pR4FZLvNrE3L8y2QoliMx8ZguH2JjbA7Qt7//DjakVMAe5vf/tbHHPMMXj11Vdx6qmnRlzs4cQTT8R///vfWA5DlBScFa19eek2pNuCI7MS3YMrycjBJwIJl8yV6pgvOzqcwz5y4YsthA8hSITw90/0Ig8jEa96E9Mn+s9//oPZs2cPuVzvmDFj0NDQEMthiJJCvGgZDcD4JKyERMlnMBhQ3t+berC9F72exF1AJDdM7MFNuGTmwhWf9piMBpRkM8A9mql/Ipi4UhdFL5lDFMT3NxgMsCgowBXrjSnGCYUxTTITBCGUp20wDQ0No08TQpQkgiCEHjuOy3LAbtHe4xoKqshNxb/r2iEIQE2zCxPGJCY1oDQHLoOgRBubmQKb2Qi3L5DQADcQEEKTzMZnO2BVUGCgFGazGQ6HA01NTbBYLMPGCXSEIAiA3wtBENDT40dfamKuRYIgoK+vD4IgwGIyweN2J+Q4IxUIBNDU1ASHwxHzGgox7X3sscfiww8/HPT3gUAAH3zwAU466aRYDkOUcA2dbrj6e/MYjGhbeG9qVVN34gJcyRAF9uAmmtFoQFluKr6s70JtSw+8/gAspvgHVoc6ekNjF7kYTGQGgwFFRUWoqanB/v375S6O6rR29sHrF2AwAEKXPSHpu3yBABo6gkFtisUIdNvifozRMhqNKCkpiflzxxTgXnHFFVi2bBl+9atf4ac//emA399zzz3Yu3cvbrrpplgOQ5RwHH+rH+E3MImaaBb+RGBsZgpSrHwikAwV+Wn4sr4LvoCA2taehJzL1cygEBWr1Ypjjz2WwxRG4am/fh7K1/3U/0zD2Kz4D5n7Z00LVr3zHwDAldNKsOiUsrgfY7SsVmtcev1jCnBvvvlm/OlPf8LPf/5zvPDCC6Fo+9Zbb8X777+Pjz/+GKeffjpuuOGGmAtKlEgcL6kfyRir2dztQWefDwAXeEgmyXfb2J2QAFfSVrBnfkhGo5FDFEchJyMNB7vqAQD72r2oKIr/3/C/zW4c7Ao+tSzKztDk9xRTiJySkoJ33nkH11xzDXbt2oUdO3ZAEASsXbsWO3fuxA9/+ENs2bJlVOMoHn74YZSWlsJut2P69OnYsWNHVPs999xzMBgMuPTSS0d8TNIvSYow9uBqWkmOA6b+nLSJCnD5REAeycikIL0Z5s0LxV94vUpcG6X9OQIxr2TmdDqxadMmrF27Fv/617/Q0tICp9OJadOmIS9vdOuuP//886isrMT69esxffp0rFu3DrNnz8ZXX32F/Pz8Qffbt28fbr31Vpx11lmj/TikU9XNXFJVL2xmE4qzUrCvpQfVTS4EAkLcF2HgEwF5JKN3XrL8MntwKQGkTyISc6MWnstZq2PJY+rBLS8vx4033ggAyM7OxuzZs3HVVVfhwgsvHHVwCwBr167FokWLsHDhQkyYMAHr16+Hw+HAxo0bB93H7/fj6quvxp133ony8vJRH5v0SezBdaZYkJOauCU+SRnEC0iv14/6zr64v3+1DnpHlKgsN3x8dWICXDGDQk6qFVlsKygBwgPO8GWh40nswc1Ns8GZYknIMeQWU4Db1NQEp9MZr7IACOY/27lzJ2bNmhV6zWg0YtasWdi+ffug+61evRr5+fm47rrrhj2G2+1GZ2en5If0y+X24VBHMMipyEtNyIxVUpajMynEW/h7HqPR3hElSrWZMcYZHEtY1eSK+0IDXX1eNHQGZ55z6AklSprNjMKMI/U43jp6vGjuFuuxdm/AYwpwTzrpJFRVVcWrLACA5uZm+P1+FBQUSF4vKChAfX19xH0++OADPP7449iwYUNUx1izZg2cTmfop7i4OOZyk3rVNIf3tvGipQeSsZqNiQtw02xm5KUrJ/2OHog3Lx29XrS44juDP7xnnkOZKJHE+tXq8qA1zvW4qlkfQ6hiCnD/3//7f/jrX/+KTz/9NF7lGbGuri5cc8012LBhA3Jzc6PaZ+nSpejo6Aj91NXVJbiUpGRVOhiLRFLlkrGa8e0h6fP6caCtFwCfCMghfFnkeN+8cPIgJUt4/Yr3cBu9TKqOaZLZuHHjMGvWLJxxxhn43//9X3zjG99AYWFhxAZ95syZUb1nbm4uTCbTgOV9GxoaUFhYOGD7qqoq7Nu3D3PmzAm9FggEk3CbzWZ89dVXqKiokOxjs9lgs7FXhYL0MJuUpBI5Gamm2QXxybiWLx5KJR1+4sL08py4vTczKFCySDOCdGNqaXbc3lsvk6pjCnDPPvtsGAyGUGqwoXoq/P7o1ny3Wq2YMmUKtm7dGkr1FQgEsHXrVixZsmTA9ieccAL+85//SF5btmwZurq68Jvf/IbDD2hYnPGuP9mpVmQ5LGjr8cZ9sQfpQgCsT8mWyJ4v6eRBfreUOOFtR7zbqPAeXC3PEYgpwF2xYkVCHr9VVlZiwYIFmDp1KqZNm4Z169bB5XJh4cKFAID58+dj7NixWLNmDex2O04++WTJ/pmZmQAw4HWiSMST3Ww0oCQ7/ivGkDJV5KXh4/1tqO/sQ7fbhzRbzFkTARy9RK92e0eUKpG98+L7WU1GjEvA6lJEovIk1GOb2YgxmSlxfW8lialFX7VqVZyKITV37lw0NTVhxYoVqK+vx6RJk7Bly5bQxLPa2tq4LONGFAgIoUlm43McCVm7npRJDHCBYE/fqeMy4/K+fCIgr4IMG1KtJrg8/riOr/b5A9jX3AMAKM09slgIUSIUZdiRYjGh1xvfeuz1B7C/JViPy3JTNV2PRx3g1tbW4l//+hcMBgO+8Y1vxH0owJIlSyIOSQCAbdu2Dbnvpk2b4loW0q6D7b1w+4JjtjnBTF/KjxrjFu8A12gI3jRRchkMBpTnpeE/BztQ19aDPq8fdosp5vc90NYLjz/YVnB4AiWa0WhAeV4qPj/UidrWHnh8AVjNsXfA1Lb2wBcIThLQej0e1V/r1ltvRXl5Oa644gr84Ac/QFlZGX72s5/Fu2xECcdZ0folHasZnx4SQRBC71WS7YDNHHtgRSMnTtARBIR6q2IVnnCfbQUlg1jP/AEBta3xaaP0tAjNiAPcZ599FmvXroUgCDjhhBNw/PHHhyaZPfvss4koI1HCMIOCfiVisYf6zj70eIITahkEyScR43DDl0xlBgVKhvB6vDdOS/bqaQjViAPcxx57DGazGX//+9/x+eef44svvsAbb7wBo9GIxx9/PBFlJEoYPZ3sJFWclQKLKTj+LF7rvYe/j5bT7yid5OYlTrlwpZMH2VZQ4h09jCoe9JIDFxhFgPvpp5/ikksuwTnnnBN6bdasWbjkkkuwe/fueJaNKOHC0whV8KKlK2aTEeNzgheQmmYX/IHYl3XlkBdlSEhgIFkQhjcvlHgJeRIR9j5lGs/yMuIAt62tDSeccMKA10844QS0t7fHo0xESSMOUchNs8LpsMhcGko2cViKxx/AgbbYx2ryiYAylOakQsxgGa8Z6OL7FGTYkG5nW0GJV5Yb33osCELofYqcdqTGKTWiUo04wA0EArBYBp7cFosFghB7DwhRsnT0etHU5QbADAp6Fe+JZlwIQBnsFhOK+/PUVjd1x3xtanN50OryAOD3SsmTYjVhbH+e2njU41aXBx29XgD6qMejyqLAtdVJC6r5OFn34v0IUHyPTIcF2anWmN+PRk/snXd5/GjodMf0XuEZFDg8gZJJ7Hzp6vOhqTu2eqy3SdWjCnBXrVoFk8kk+Vm9ejUADHjdZDLBbNZ2Nzipk95OdhoonpkUut0+HO7oC74vb5hkF8+bF0kGBX63lETh16ZYJ8PqbQjVqAJcQRBG9BMIBOJdbqKYsQeXyuN48ajhDZOixHOpU04eJLnE90ZNX5lARty1ymCVtIIXLcqwW5CXbkNTl1vyGHo0uBCAsoTfZMQ6vlrytEcHPV+kHPGcJ1DdrK9czrGv+0akUuJFy2o2YmxWisylIbmIgVBztwftPZ5Rv4+e8kuqQTyHn4hPe1IsJhRl2GN6L6KRCA9E4/UkwmE1oVAH9ZgBLumSzx/A/pZggFuemwqTkRMn9Ur6CHD0PSTh+3IikvxyUq1wpgQz/sSy2IPHF8D+1mAKubLcVBjZVlAS5aXZkN6fziuWALfP60ddfz2uyEvTRbIABrikS3VtvfD6gylXGIzoW7zGaor7WkwGFGc7Yi4XxcZgMITO7UMdfXC5faN6n9rWI4uAcHgCJZvBYEB5f7072N6L3v6lwEdqf0sPxLVs9DJHgAEu6RIfJ5OoIg6rXvkDQmh82/icVFhMbFqVIPzcrmkeXe/83kZOHiR5ifVOEEZfj6slK/Hp45rHVph0iRPMSBSPSRyH2nvh8QX6349BkFLEYwY6Jw+S3CRt1Cgnw+rxmscAl3RJjyc7RTY2MwU2c7ApHG0QtJf1SZGkvfOju3lhDlySm+RGbZTpDKWZQPRxE84Al3SJE4JIZDQaQo/salt64PWPPBWiJL8kgyDFiEcmhfD9ynLZVlDyxWMYlbifwQCU5uijHjPAJV0SxyMVZtiRauNKe3on3uT4AgL2t/SMeH+uiqdMJdkOmPuzHowmk4IgCKHAYGxmClKspriWjygaJTmOUKaf0QS4giCE6v+4rBTYLfqoxwxwSXdaXR609XgB6OdRDQ1NOg535BcQPU7gUAOLyYiSnGBGi5pmFwLiNPIoNXd70NUXzL7ADAokF5vZhJL+zCzVTSOvx41dbrj6sy/oaZgNA1zSHY6/paPFOlZT3Ccv3RbKvUrKIJ7jbl8AB9t7R7SvtK3gzTDJR6x/vV4/6jv7RrSvXrMGMcAl3dHryU6Di2W2fUePF83dbgDBRUNIWWL5bqvYM08KEUu+br126jDAJd0JX4+bE8wIkNaDEV88wtNI8TG24pTH0DtfxRy4pBCSp0wjHE+u1zkCDHBJd9iDS0dzWM0Y4wyuzV7V2A1BiH6MG+uTssWrB/cYfrcko1iWFNfrkwgGuKQ74snusJpQmGGXuTSkFGLva2efDy0uT9T7hT8R0FPviFqEfycjnUAoJtVPt5mRl26La7mIRiKWxR7EBWwy7GbkplnjWi4lY4BLuuL2+VHbGkwDVZ6XCmN/6hUiaTL16C8g7MFVtkyHNXRRH0nPV5/XjwNtwUlp5flpMBjYVpB8slKtyE7tr8cjWOyhx+MLTa6s0Fk9ZoBLulLb0gMxwwqDEQo32kwK4hMBm9mIsZkpcS8XxU58LNvU5UZHrzeqfWqaXRBHqlRw8iApgDiJtb6zD91uX1T7VDfpdyU+BrikK5KxSLn6OtlpaKOZpez1B0ILQ5Tl8omAUo1mmIJk5jknD5ICjCZft3T8rb5u1BQb4D788MMoLS2F3W7H9OnTsWPHjkG33bBhA8466yxkZWUhKysLs2bNGnJ70i89rsdN0RnNxaOutQe+/kcCDIKUS/rdRtc7X63TmeekXOHXrNHVY321UYoMcJ9//nlUVlZi5cqV2LVrFyZOnIjZs2ejsbEx4vbbtm3DvHnz8M4772D79u0oLi7G+eefj4MHDya55KR0HC9JgynIsCG1fynWaIcoVOn44qEmo8mkoNfcoaRcrMcjo8gAd+3atVi0aBEWLlyICRMmYP369XA4HNi4cWPE7f/4xz/ixz/+MSZNmoQTTjgBjz32GAKBALZu3ZrkkpPSiSe7wRB8pEwkMhgMoV7YurYe9Hn9w+7Dla7UIZbAwGhAaLlfIjmNZhiVeBNuNhowXmf1WHEBrsfjwc6dOzFr1qzQa0ajEbNmzcL27dujeo+enh54vV5kZ2dH/L3b7UZnZ6fkh7RPEITQ45qxmSmwW0wyl4iURpzEIQjAvpbhe3H5REAdxmalwGoOXu6i6Z0PBITQTPWSbAdsZrYVJL/irBRYTMFx/tFkUggEhNBwq5IcBywmxYV8CaW4T9vc3Ay/34+CggLJ6wUFBaivr4/qPW677TaMGTNGEiSHW7NmDZxOZ+inuLg45nKT8jV1udHVP/OUwQhFIk0VFkWAG9aLwicCymUyGlCWE/x+9re44PMHhty+vrMPvf09+GwrSCnMJiNK++txTYsL/sDQC9IcbO+F2xes63qcVK24ADdW9957L5577jm8/PLLsNsjJ/FfunQpOjo6Qj91dXVJLiXJYa+OxyJRdMInig030UwQhFBv4BinHak2c0LLRrERJ+h4/QLq+vPbDkYyMYeTB0lBxGuXxxfAweHqcbO+J1UrLsDNzc2FyWRCQ0OD5PWGhgYUFhYOue8vf/lL3HvvvXjzzTdx6qmnDrqdzWZDRkaG5Ie0jxkUaDgjGavZ6vKEcqoyCFK+kSzkwbHVpFTh167h2ii9D6FSXIBrtVoxZcoUyQQxccLYjBkzBt3v/vvvx1133YUtW7Zg6tSpySgqqUw1e3BpGONzHBBT2Q43VjP89+UcnqB4I7l5keYOZVtByhE+1GAk9ViP1zxFPlOrrKzEggULMHXqVEybNg3r1q2Dy+XCwoULAQDz58/H2LFjsWbNGgDAfffdhxUrVuCZZ55BaWlpaKxuWloa0tL096VSZJKAhL0yFIHdYsK4LAdqW3tQ1dQNQRAGXdqSCwGoS3neCHq+dB4YkHKFtzUjq8f6u+YpMsCdO3cumpqasGLFCtTX12PSpEnYsmVLaOJZbW0tjMYjnc+PPvooPB4PLr/8csn7rFy5EqtWrUpm0UnBxMc16XYz8tJsMpeGlKoiLxW1rT3o8fhR39mHImfk5Xf1/vhPbaQplobpne+fYJjlsCA71ZrQchGNhORGbZiJsOJY8pxUKzId+qvHigxwAWDJkiVYsmRJxN9t27ZN8u99+/YlvkCkar0ePw62BwfkV+SlDdorR1SRl4Z3vmoCELxADBbgSiZwMMBVvDSbGYUZdtR39g05gbDb7UN9Zx8Afq+kPBl2C/LTbWjscqO6efB63NnnRWOXG4B+67HixuASJUJ4Q6DXk52iE+0jQPF3qVYTCjL4REANxAk6bT1etLo8Ebep4ep0pHBivWzu9qC9J3I9ruakaga4pA/VHH9LUQqfMDbYbPs+rx91rT3B7flEQDWimaAjnWDGtoKURzqePPIwhfC2S485cAEGuKQTnDRC0ZL24Ea+eOxv6YGYY12PkzfUqiJv+JsXthWkdNFkBJFOgtVnG8UAl3QhPFA5RqcnO0UnJ9UKZ4oFwOCLPTDlnDpJFvJojnzzwkUeSOmkC9JEUY912kYxwCVdEHtrTEYDSrIZ4NLgDAZDqKfvUEcfXP3LO4djijB1imaxB/G7tZgMKM6KPMGQSE4VUaS8E1+3mowYl+VISrmUhgEuaV4gIKCmv7dmfLYDVjOrPQ0tPBCqidDTx5zK6lSYYYfDagIQOTDwB4RQz+74nFSYTWwrSHnGOFNgtwTrZqR67PMHsK8lWI/LclNhMupzjgDPXtK8w5196PX6ATAYoeiUDzPGTXzNYABKc1in1MJoNKCsfxJhbWsP3D6/5PcH23rh8QUAcGw1KVewHgfbqNqWHnj9Acnv69p64fUHJwno+ZrHAJc0jwn5aaSGmowkCELoteIsB+wWU1LLRrER24CAEJwsGI4TzEgtxDbKFxAG1GPOEQhigEuax4sWjZQkk8JRQxQau9xweYI9f+zlU5/wNuDoSYRsK0gtoq7HOp5UzQCXNI8nO41USbYD5v5xa0f34PKJgLqFtwFHp4GrYgYFUomh0hmGL+Gr5zaKAS5pnmSRB50mvKaRsZiMKMkJzjyuaXbBLya9xdELAbA+qY1ksYejb164yAOphGRBmiF6cMty9VuPGeCS5okne3aqFVmpVplLQ2oh9ny4fQEcau8NvS7p5WMQpDpluakQF547OjAQH/XmpduQYbcku2hEUSsfIlWY+O+CDBvSdVyPGeCSpnX1edHQ6QbAYIRGZrDVgpgDV91SrCaMzQzmt61uckEQgr3zHT1eNHd7ALCtIOVzWM0R63Gry4O2Hi8AfQ9PABjgksZxNRcarYpB1nsX65QzxYIcPhFQJbEt6HL70NQVvAGuaubYalIXsRe3o9eLFlfw5owZFI5ggEuaVs2LFo2SdBJHsB71eHw42D9coTwvFQaDPhOoq114W7C3/7sNH4/LsdWkBpFW5pNmAtH3kwgGuKRp4bNJOWmERqIiwmQkPhHQhvIIvfMcW01qE+kpk3SVRX23UQxwSdOY15JGy+mwIDctOAThyMWD9UkLhu/54ndLyhdpnoAkjaHO5wgwwCVNE096q8mIcVkpMpeG1EbsAWnudqOj13tUDy57+dQqPBdudf9CHuLYRZvZGJq8Q6Rk4QGsWH/F+pxiMaEowy5LuZSCAS5plj8gYF9zcAnD0lwHzCZWdxqZo1cLYgYFbchLsyHdbgYQ7PHy+gOh5U7L89JgNHJsNSlffroNabb+etzkgtvnR22rWI9TdV+PecUnzTrQ1gOPPwCACzzQ6Bw9xk0cqmA2GlCS7ZCrWBQjg8EQ6p0/2N6Lr+q74OtfzINj9UktgvU4WF/r2nrw34bu0KI0eh9/CzDAJQ3jEr0Uq/Ae3P82dqGmPytHSY4DFj4RULXwm5etexrDXmdgQOoh1ldBOLoe85rHFpo0i+txU6zC680/9jajzxsY8DqpU/h3+Nae+rDXGRiQeoTXV2k9ZhvFAJc0i7OiKVZjs1JgNQebyc8OdoZeZ31Sv/DvkN8tqRXr8eAY4JJmVTcxBy7FxmQ0oDx3YN1hfVK/YwYZtsTvltQk0lhbgwEoi9Bu6Q0DXNIssQc3P92GdLtF5tKQWkUKeNg7on4l2akwHTXLfIzTDofVLFOJiEZufI4DRydLGONMQYrVJE+BFIQBLmlSm8sTWpubwQjFIlL94ThN9bOajQMyYTD1G6mN3WJCMetxRLxVTTJ/QMCOmlY0dvUhP92OaWXZA3oREnJcrw9fvrAZvbUHkFIyDidccSFMlsR//XIc1+/14Z9P/BkXf/EJGtOyUD71goQej7Tt6AA3w27mEwGNqMi2oWDXduR3twXbimnflbtIRCNWkW1H0ScfHanH01mPAQCCQj300EPC+PHjBZvNJkybNk345z//OeT2L7zwgnD88ccLNptNOPnkk4XNmzdHfayOjg4BgNDR0RFrsYf0+n8OCaff83dh/G1/C/2cfs/fhdf/cyihx93168eE+oxcQQhmEhEEQKjPyBV2/foxzR030jEPJ+Gzknat37ZXcs4m67ylxNr168eEw+lsK0jd9FaPRxKvKXKIwvPPP4/KykqsXLkSu3btwsSJEzF79mw0NjZG3P7DDz/EvHnzcN111+GTTz7BpZdeiksvvRSfffZZkks+uC2fHcbip3fhcEef5PX6jj4sfnoXtnx2OCHH/WTd45h4y/XI62yWvJ7X2YyJt1yPT9Y9rpnjDnbM/AR/VtKuLZ8dxr2vfzng9USft5RYYluR38W2gtSL9XhoBkEQBLkLcbTp06fjG9/4Bh566CEAQCAQQHFxMX7yk5/g9ttvH7D93Llz4XK58Le//S302umnn45JkyZh/fr1wx6vs7MTTqcTHR0dyMjIiN8H6ecPCDjzvrcHBLfhctOs+N01U+M6XMHv9WHc5BOR19kccbB1AECjMw8Hd30R12EDchw32mPmNR1KytAMUr/hzlsDgEKnHR/cdm5ShhlRfPi9PjTnFrGtIFXTaz0eSbymuE/t8Xiwc+dOLF26NPSa0WjErFmzsH379oj7bN++HZWVlZLXZs+ejVdeeSXi9m63G263O/Tvzs7OiNvFy46a1iGDWwBo7vbgskc/jOtxT6/9FM8d1ZsZzgigsKMJNy/bgI9KTlX1caM95ucvbMZJV18Sl2OStg133goADnf0YUdNK2ZU5CSvYBSTL1/YjJPYVpDKsR4PT3FDFJqbm+H3+1FQUCB5vaCgAPX19RH3qa+vH9H2a9asgdPpDP0UFxfHp/CDaOwaOrhNlPzutrhup+TjRvtevbUH4nZM0rZoz1u5zm8anWjbALYVpGSsx8NTXA9uMixdulTS49vZ2ZnQIDc/3R7VdrNPKkCRMyVux003HgD+Ovx240+uwLWnl6r6uNEeM6VkXFyOR9oX7Xkb7XakDNG2AWwrSMlYj4enuAA3NzcXJpMJDQ0NktcbGhpQWFgYcZ/CwsIRbW+z2WCz2eJT4ChMK8tGkdOO+o4+RBrwLI7le+TqKfEdg/ud49Hw8LJhx+jcfOd18R2DK8Nxoz3mCVdcGJfjkfZFe95OK8tOdtEoBidccSEafpzLtoJUjfV4eIobomC1WjFlyhRs3bo19FogEMDWrVsxY8aMiPvMmDFDsj0AvPXWW4Nun2wmowEr50wAELwohhP/vXLOhLhPVDFZzDh0570AgpU9nPjvw6vWxH0AuhzHleuzknbJdd5SYrGtIC1gPR6e4gJcAKisrMSG/9/evcc0db9/AH8XFeqQy2blJoh4xTgFccJwczplAhrFuExwfBUvc85AhrcFt6hgzHReJpmXoPt+FVycTo1TE9004IA5h4jCNnXKEAubk4uSgBW8kPbz+2OhPyttaRlt4fT9SprYz3nOh8958pyTZ2c97X//i/379+PGjRtYsmQJGhsbMX/+fADA3LlzdR5CS05OxpkzZ/D555/j5s2bSEtLw+XLl5GUlGSrQ2gl6mVvZPwnBF5uuv8708tNjoz/hCDqZW+L/N1RSxfi1/T/4Z6rQme81q0Pfk3/H0YtXSiZv2urYyXpstV5S5bFawVJAevYuE75NWEAsHPnTmzZsgXV1dUIDg7G9u3bERYWBgCYMGEC+vfvj6ysLG380aNHsXr1alRUVGDw4MHYvHkzpkwx7dc8LP01Yc/iL5lZ55fMbHGsJF22Om/JsnitICmwpzo2p1/rtA2uNVmzwSUiIiIi85nTr3XKjygQEREREbUXG1wiIiIikhRpfkjDTC2f0rD0L5oRERERUfu09GmmfLqWDS4AlUoFABb/RTMiIiIi+ndUKhXc3NyMxvAhM/zzPbt3796Fi4sLZDLLPxnd8stpf/31Fx9qawNzZRrmyTTMk2mYJ9MwT6ZhnkzHXBknhIBKpYKPjw8cHIx/ypZ3cAE4ODjA19f6P2fn6urKAjYRc2Ua5sk0zJNpmCfTME+mYZ5Mx1wZ1tad2xZ8yIyIiIiIJIUNLhERERFJChtcG3ByckJqaiqcnJxsvZROj7kyDfNkGubJNMyTaZgn0zBPpmOuOg4fMiMiIiIiSeEdXCIiIiKSFDa4RERERCQpbHCJiIiISFLY4BIRERGRpLDBtZBdu3ahf//+kMvlCAsLw6VLl4zGHz16FIGBgZDL5RgxYgS+++47K63UdjZu3IgxY8bAxcUFHh4emDFjBkpLS43uk5WVBZlMpvOSy+VWWrFtpKWltTrmwMBAo/vYYz3179+/VZ5kMhkSExP1xttLLf3444+YNm0afHx8IJPJcOLECZ3tQgisXbsW3t7e6NmzJyIiIlBWVtbmvOZe4zo7Y3lqbm5GSkoKRowYAWdnZ/j4+GDu3Lm4e/eu0Tnbc+52BW3V1Lx581odd1RUVJvz2lNNAdB7vZLJZNiyZYvBOaVaU5bABtcCDh8+jOXLlyM1NRXFxcUICgpCZGQkamtr9cb//PPPmD17NhYuXIiSkhLMmDEDM2bMwLVr16y8cuvKz89HYmIiLl68iOzsbDQ3N2Py5MlobGw0up+rqyuqqqq0r8rKSiut2HaGDx+uc8w//fSTwVh7raeioiKdHGVnZwMA3nnnHYP72EMtNTY2IigoCLt27dK7ffPmzdi+fTt2796NwsJCODs7IzIyEo8fPzY4p7nXuK7AWJ6amppQXFyMNWvWoLi4GN9++y1KS0sxffr0Nuc159ztKtqqKQCIiorSOe5Dhw4ZndPeagqATn6qqqqwb98+yGQyvP3220bnlWJNWYSgDhcaGioSExO179VqtfDx8REbN27UGz9r1iwxdepUnbGwsDCxePFii66zs6mtrRUARH5+vsGYzMxM4ebmZr1FdQKpqakiKCjI5HjW0z+Sk5PFwIEDhUaj0bvdHmsJgDh+/Lj2vUajEV5eXmLLli3asfr6euHk5CQOHTpkcB5zr3FdzfN50ufSpUsCgKisrDQYY+652xXpy1VCQoKIiYkxax7WlBAxMTFi4sSJRmPsoaY6Cu/gdrCnT5/iypUriIiI0I45ODggIiICBQUFevcpKCjQiQeAyMhIg/FS1dDQAAB46aWXjMY9fPgQ/v7+8PPzQ0xMDK5fv26N5dlUWVkZfHx8MGDAAMTHx+PPP/80GMt6+uc8PHDgABYsWACZTGYwzh5r6VlKpRLV1dU69eLm5oawsDCD9dKea5wUNTQ0QCaTwd3d3WicOeeulOTl5cHDwwNDhw7FkiVLUFdXZzCWNQXU1NTg9OnTWLhwYZux9lpT5mKD28Hu378PtVoNT09PnXFPT09UV1fr3ae6utqseCnSaDRYunQpXnvtNbz88ssG44YOHYp9+/bh5MmTOHDgADQaDcaOHYs7d+5YcbXWFRYWhqysLJw5cwYZGRlQKpUYN24cVCqV3njWE3DixAnU19dj3rx5BmPssZae11IT5tRLe65xUvP48WOkpKRg9uzZcHV1NRhn7rkrFVFRUfjqq69w7tw5bNq0Cfn5+YiOjoZardYbz5oC9u/fDxcXF8ycOdNonL3WVHt0t/UCiAAgMTER165da/OzROHh4QgPD9e+Hzt2LIYNG4Y9e/Zg/fr1ll6mTURHR2v/PXLkSISFhcHf3x9Hjhwx6b/27dHevXsRHR0NHx8fgzH2WEv07zU3N2PWrFkQQiAjI8NorL2eu3Fxcdp/jxgxAiNHjsTAgQORl5eHSZMm2XBlnde+ffsQHx/f5oOu9lpT7cE7uB1MoVCgW7duqKmp0RmvqamBl5eX3n28vLzMipeapKQknDp1Crm5ufD19TVr3x49emDUqFG4deuWhVbX+bi7u2PIkCEGj9ne66myshI5OTl47733zNrPHmuppSbMqZf2XOOkoqW5raysRHZ2ttG7t/q0de5K1YABA6BQKAwetz3XFACcP38epaWlZl+zAPutKVOwwe1gjo6OGD16NM6dO6cd02g0OHfunM7domeFh4frxANAdna2wXipEEIgKSkJx48fxw8//ICAgACz51Cr1bh69Sq8vb0tsMLO6eHDhygvLzd4zPZaTy0yMzPh4eGBqVOnmrWfPdZSQEAAvLy8dOrlwYMHKCwsNFgv7bnGSUFLc1tWVoacnBz07t3b7DnaOnel6s6dO6irqzN43PZaUy327t2L0aNHIygoyOx97bWmTGLrp9yk6JtvvhFOTk4iKytL/P777+L9998X7u7uorq6WgghxJw5c8SqVau08RcuXBDdu3cXW7duFTdu3BCpqamiR48e4urVq7Y6BKtYsmSJcHNzE3l5eaKqqkr7ampq0sY8n6t169aJs2fPivLycnHlyhURFxcn5HK5uH79ui0OwSpWrFgh8vLyhFKpFBcuXBARERFCoVCI2tpaIQTr6VlqtVr069dPpKSktNpmr7WkUqlESUmJKCkpEQDEtm3bRElJifbp/88++0y4u7uLkydPit9++03ExMSIgIAA8ejRI+0cEydOFDt27NC+b+sa1xUZy9PTp0/F9OnTha+vr/jll190rldPnjzRzvF8nto6d7sqY7lSqVRi5cqVoqCgQCiVSpGTkyNCQkLE4MGDxePHj7Vz2HtNtWhoaBAvvPCCyMjI0DuHvdSUJbDBtZAdO3aIfv36CUdHRxEaGiouXryo3TZ+/HiRkJCgE3/kyBExZMgQ4ejoKIYPHy5Onz5t5RVbHwC9r8zMTG3M87launSpNq+enp5iypQpori42PqLt6LY2Fjh7e0tHB0dRd++fUVsbKy4deuWdjvr6f+dPXtWABClpaWtttlrLeXm5uo9z1pyodFoxJo1a4Snp6dwcnISkyZNapU/f39/kZqaqjNm7BrXFRnLk1KpNHi9ys3N1c7xfJ7aOne7KmO5ampqEpMnTxZ9+vQRPXr0EP7+/mLRokWtGlV7r6kWe/bsET179hT19fV657CXmrIEmRBCWPQWMRERERGRFfEzuEREREQkKWxwiYiIiEhS2OASERERkaSwwSUiIiIiSWGDS0RERESSwgaXiIiIiCSFDS4RERERSQobXCIiiaqoqIBMJsO8efNsvRQiIqtig0tEZAMymQwymczWyyAikiQ2uEREREQkKWxwiYiIiEhS2OASEXUSz35mtqKiAnFxcVAoFJDL5XjllVdw6tQpvfupVCosX74cvr6+kMvlCAwMxLZt26DRaAz+raamJmzcuBHBwcFwdnZGr169EB4ejkOHDunEFRYWwtHREQMGDEBDQ4POtqqqKnh6eqJXr164efPmv08AEVEHYYNLRNTJVFZWIjQ0FBUVFZgzZw5iY2Nx7do1xMTEIDc3Vyf2yZMnmDRpEtLT06FQKJCcnIzx48dj/fr1WLZsmd756+vr8frrr+OTTz5Bt27dsGDBAiQkJODevXt49913sXr1am1sWFgYNmzYAKVSiUWLFmnHNRoN4uPjUVtbi127diEwMNAyySAiag9BRERWB0A8fwlWKpXa8bS0NJ1tZ86cEQBEdHS0zvinn34qAIiZM2cKtVqtHb99+7Z48cUXBQCRkJCgs09CQoIAIDZt2qQz/ujRIxEZGSlkMpkoKSnRjms0GjFlyhQBQOzevVsIIURaWpoAIObOndveFBARWYxMCCFs1VwTEdmrlm9QePYSXFFRgYCAAPj7+6O8vBzdunXT2cff3x+NjY24f/++dmzw4MG4ffs2/vjjDwwcOFAnPi0tDevWrUNCQgKysrIAAHV1dfD09MSoUaNQVFTUal2//vorgoOD8dFHH2Hz5s3a8fv37yM4OBh1dXXYunUrkpOTMWjQIFy5cgXOzs7/Oh9ERB2pu60XQEREuoKDg1s1twDg5+eHgoIC7XuVSoVbt27Bz8+vVXMLABMmTMC6det0xoqKiqBWqyGTyZCWltZqn+bmZgDAjRs3dMYVCgUOHjyIiRMnIikpCXK5HIcPH2ZzS0SdEhtcIqJOxt3dXe949+7ddR4ca3noy9PTU2+8l5dXq7G6ujoA/zS6+u7gtnj48GGrsdDQUPTr1w9KpRJvvvkmgoKCDO5PRGRLfMiMiKiLcnNzAwDU1NTo3V5dXW1wn2XLlkEIYfD1/MNsAJCcnAylUgmFQoHvv/8eX3/9dQceDRFRx2GDS0TURbm4uGDQoEH4+++/UV5e3mp7Xl5eq7HQ0FA4ODjg/PnzZv2tI0eO4Msvv8Qbb7yB4uJi9OnTBx988AHKysrau3wiIothg0tE1IXNnz8fGo0GKSkpOh9fUCqV2L59e6t4Dw8PxMfH4/Lly1i/fj3UanWrmPLyciiVSu3727dvY9GiRejduzcOHjwIPz8/7N+/H42NjYiNjcWTJ08sc3BERO3EBpeIqAtbsWIFxowZg2PHjiEkJAQpKSlYvHgxQkJCMG7cOL377Ny5E6+++irWrl2LYcOGYcGCBfj444+RkJCA0NBQDBo0CIWFhQD+eegsLi4ODx48QGZmJvr27QsAiI6OxooVK1BSUoKVK1da7XiJiEzBBpeIqAtzcnJCTk4Oli1bhnv37uGLL75Afn4+Vq9ejfT0dL37uLq6Ij8/Hzt27IBCocCxY8ewbds25ObmwsXFBenp6XjrrbcAAKtWrUJRURE+/PBDTJs2TWeeDRs2IDQ0FDt37sTx48ctfqxERKbi9+ASERERkaTwDi4RERERSQobXCIiIiKSFDa4RERERCQpbHCJiIiISFLY4BIRERGRpLDBJSIiIiJJYYNLRERERJLCBpeIiIiIJIUNLhERERFJChtcIiIiIpIUNrhEREREJClscImIiIhIUtjgEhEREZGk/B+udlW5LDkfXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_params = {\n",
    "    'axes.titlesize': 'x-large',\n",
    "    'axes.labelsize': 'large',\n",
    "    'font.size': 12,\n",
    "    'figure.figsize': [8, 6],\n",
    "    'figure.titlesize': 'x-large',\n",
    "    'lines.linewidth': 2,\n",
    "    'lines.markersize': 6,\n",
    "    'legend.fontsize': 'small',\n",
    "    'xtick.labelsize': 'small',\n",
    "    'ytick.labelsize': 'small',\n",
    "}\n",
    "mpl.rcdefaults()  # add this line for the update to take effect\n",
    "mpl.rcParams.update(plot_params)\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
    "\n",
    "ax[0].plot(bin_model_params, marker=\"o\")\n",
    "ax[0].set_ylabel(\"Synthetic\")\n",
    "\n",
    "ax[1].plot(result[\"bin_model_params\"], marker=\"o\")\n",
    "ax[1].scatter(\n",
    "    measure_index,\n",
    "    result[\"bin_model_params\"][measure_index],\n",
    "    marker=\"o\", color=\"red\", zorder=np.inf, label=\"measured idx\"\n",
    ")\n",
    "ax[1].legend()\n",
    "ax[1].set_xlabel(\"Index\")\n",
    "ax[1].set_ylabel(\"Predict\")\n",
    "\n",
    "plt.suptitle(\"Model parameters\")\n",
    "\n",
    "plt.show()\n",
    "mpl.rcdefaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resistant-spare",
   "metadata": {},
   "source": [
    "## Compare predicted and observed responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "radical-essence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted response =\n",
      "[1077.6206 1241.5619  945.2396  960.5228 1095.2267 1115.091   937.8155\n",
      "  910.8324  857.0288 1276.4103]\n",
      "\n",
      "Observed response  =\n",
      "[1082.4979 1244.7067  946.4625  961.9921 1098.0668 1117.683   936.9696\n",
      "  913.8756  859.5181 1276.5863]\n",
      "\n",
      "Residual sum of squares = 6.83219085e+01\n"
     ]
    }
   ],
   "source": [
    "pred_resp = dwinv.utils.fwd_modeling(fwd_model, result[\"bin_model_params\"], (low_param_value, high_param_value), resp_all2meas_func=resp_all2meas_func(measure_index))\n",
    "\n",
    "with np.printoptions(precision=4, suppress=True):\n",
    "    print(f\"Predicted response =\\n{pred_resp}\\n\")\n",
    "    print(f\"Observed response  =\\n{obs_resp}\\n\")\n",
    "    print(f\"Residual sum of squares = {result['obj']:.8e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "intensive-arrow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams.update(plot_params)\n",
    "\n",
    "fig, ax = plt.subplots(3, 1, sharex=True)\n",
    "\n",
    "ax[0].plot(obs_resp)\n",
    "ax[0].set_ylabel(\"Observed\")\n",
    "\n",
    "ax[1].plot(pred_resp)\n",
    "ax[1].set_ylabel(\"Predict\")\n",
    "\n",
    "ax[2].plot(pred_resp - obs_resp, color=\"red\")\n",
    "ax[2].set_ylim(-1.1 * max(abs(pred_resp - obs_resp)), 1.1 * max(abs(pred_resp - obs_resp)))\n",
    "ax[2].axhline(color=\"black\", linestyle=\"--\", linewidth=1)\n",
    "ax[2].set_xlabel(\"Index\")\n",
    "ax[2].set_ylabel(\"Residual\\n(pred - obs)\")\n",
    "\n",
    "plt.suptitle(\"Response\")\n",
    "\n",
    "plt.show()\n",
    "mpl.rcdefaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fourth-answer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams.update(plot_params)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, sharex=True, figsize=(8, 6))\n",
    "ax.scatter(obs_resp, pred_resp)\n",
    "ax.plot([np.min(obs_resp), np.max(obs_resp)], [np.min(obs_resp), np.max(obs_resp)], color=\"black\", linestyle=\"--\", linewidth=1)\n",
    "ax.set_xlabel(\"Observed response\")\n",
    "ax.set_ylabel(\"Predicted response\")\n",
    "ax.set_title(\"Crossplot\")\n",
    "plt.show()\n",
    "\n",
    "mpl.rcdefaults()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}