{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28db8d67",
   "metadata": {},
   "source": [
    "# MOSE 3×5 Optimization\n",
    "\n",
    "This notebook optimizes a three-variable MOSE design against five stakeholder objectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5916d027",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.interpolate import pchip_interpolate\n",
    "\n",
    "from genetic_algorithm_pfm.genetic_algorithm_pfm.algorithm import GeneticAlgorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c639b4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stakeholder configuration (edit weights to match stakeholder input)\n",
    "TOTAL_LENGTH = 1600.0\n",
    "\n",
    "OBJECTIVE_KEYS = ['initial_cost', 'maintenance_cost', 'sight', 'accessibility', 'water_quality', 'overtopping_risk']\n",
    "\n",
    "STAKEHOLDER_DATA = [\n",
    "    {\n",
    "        'name': 'Municipality',\n",
    "        'weight': 0.5,\n",
    "        'objective_weights': {\n",
    "            'initial_cost': 0.40,\n",
    "            'maintenance_cost': 0.25,\n",
    "            'sight': 0.05,\n",
    "            'accessibility': 0.10,\n",
    "            'water_quality': 0.10,\n",
    "            'overtopping_risk': 0.10,\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        'name': 'Residents',\n",
    "        'weight': 0.2,\n",
    "        'objective_weights': {\n",
    "            'initial_cost': 0.10,\n",
    "            'maintenance_cost': 0.10,\n",
    "            'sight': 0.25,\n",
    "            'accessibility': 0.20,\n",
    "            'water_quality': 0.25,\n",
    "            'overtopping_risk': 0.10,\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        'name': 'Environmental agency',\n",
    "        'weight': 0.1,\n",
    "        'objective_weights': {\n",
    "            'initial_cost': 0.05,\n",
    "            'maintenance_cost': 0.05,\n",
    "            'sight': 0.10,\n",
    "            'accessibility': 0.10,\n",
    "            'water_quality': 0.55,\n",
    "            'overtopping_risk': 0.15,\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        'name': 'Shippig companies',\n",
    "        'weight': 0.15,\n",
    "        'objective_weights': {\n",
    "            'initial_cost': 0.10,\n",
    "            'maintenance_cost': 0.10,\n",
    "            'sight': 0.30,\n",
    "            'accessibility': 0.20,\n",
    "            'water_quality': 0.20,\n",
    "            'overtopping_risk': 0.10,\n",
    "        },\n",
    "    },\n",
    "]\n",
    "\n",
    "\n",
    "def _normalize(values):\n",
    "    values = np.asarray(values, dtype=float)\n",
    "    total = values.sum()\n",
    "    if total <= 0:\n",
    "        raise ValueError('Weights must sum to a positive value.')\n",
    "    return values / total\n",
    "\n",
    "\n",
    "STAKEHOLDER_NAMES = [item['name'] for item in STAKEHOLDER_DATA]\n",
    "STAKEHOLDER_WEIGHTS = _normalize([item['weight'] for item in STAKEHOLDER_DATA])\n",
    "STAKEHOLDER_OBJECTIVE_WEIGHTS = np.vstack([\n",
    "    _normalize([item['objective_weights'].get(key, 0.0) for key in OBJECTIVE_KEYS])\n",
    "    for item in STAKEHOLDER_DATA\n",
    "])\n",
    "\n",
    "# Preference knot points (edit to match stakeholder data)\n",
    "initial_cost_knots = [5.0e8, 1.0e9, 2.0e9, 3.5e9, 8.0e9]\n",
    "initial_cost_pref = [100, 95, 90, 30, 0]\n",
    "\n",
    "maintenance_cost_knots = [1.0e10, 10.0e11, 1.5e12]\n",
    "maintenance_cost_pref = [100, 40, 0]\n",
    "\n",
    "sight_knots = [0, 5, 10]\n",
    "sight_pref = [0, 50, 100]\n",
    "\n",
    "access_knots = [0, 5, 10]\n",
    "access_pref = [0, 50, 100]\n",
    "\n",
    "water_knots = [0, 5, 10]\n",
    "water_pref = [0, 50, 100]\n",
    "\n",
    "overtopping_knots = [0.0, 0.15, 0.30, 0.45, 0.60]\n",
    "overtopping_pref = [100, 85, 60, 25, 0]\n",
    "\n",
    "# Design variable bounds: x1 (m), x2 (-), x3 (-) — adjust if needed\n",
    "bounds = [\n",
    "    [0.0, TOTAL_LENGTH],  # x1\n",
    "    [1.0, 10.0],          # x2\n",
    "    [0.5, 5.0],           # x3\n",
    "]\n",
    "\n",
    "# Optional constraints placeholder: add callables if domain limits are required\n",
    "constraints = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ff13671e",
   "metadata": {},
   "outputs": [],
   "source": [
    "MIN_CLOSING_TIME = 1e-3\n",
    "\n",
    "\n",
    "def compute_initial_cost(x1, x2, x3):\n",
    "    x1 = np.asarray(x1, dtype=float)\n",
    "    x2 = np.asarray(x2, dtype=float)\n",
    "    x3 = np.asarray(x3, dtype=float)\n",
    "\n",
    "    movable_cost_per_meter = (\n",
    "        5.2e5\n",
    "        + 4.8e4 * np.power(x2, 1.5)\n",
    "        + 2.2e5 / np.maximum(x3, MIN_CLOSING_TIME)\n",
    "    )\n",
    "    permanent_cost_per_meter = (\n",
    "        1.8e4\n",
    "        + 5.5e3 * np.power(x2, 1.1)\n",
    "    )\n",
    "    return x1 * movable_cost_per_meter + (TOTAL_LENGTH - x1) * permanent_cost_per_meter\n",
    "\n",
    "\n",
    "def compute_maintenance_cost(x1, x2, x3):\n",
    "    x1 = np.asarray(x1, dtype=float)\n",
    "    x2 = np.asarray(x2, dtype=float)\n",
    "    x3 = np.asarray(x3, dtype=float)\n",
    "\n",
    "    movable_per_meter = (\n",
    "        1.25e6\n",
    "        + 4.8e5 * x2\n",
    "        + 1.5e6 / np.power(np.maximum(x3, MIN_CLOSING_TIME), 1.2)\n",
    "    )\n",
    "    fixed_per_meter = (\n",
    "        8.5e4\n",
    "        + 2.2e4 * x2\n",
    "    )\n",
    "\n",
    "    movable_cost = movable_per_meter * x1 * 180.0\n",
    "    fixed_cost = fixed_per_meter * (TOTAL_LENGTH - x1) * 55.0\n",
    "    return movable_cost + fixed_cost\n",
    "\n",
    "\n",
    "def compute_sight(x1, x2, x3):\n",
    "    x1 = np.asarray(x1, dtype=float)\n",
    "    x2 = np.asarray(x2, dtype=float)\n",
    "\n",
    "    movable_view = (x1 / TOTAL_LENGTH) * 10.0\n",
    "    fixed_view = ((TOTAL_LENGTH - x1) / TOTAL_LENGTH) * 10.0 / np.maximum(x2, 1e-6)\n",
    "    raw = movable_view + fixed_view\n",
    "    return np.clip(raw, sight_knots[0], sight_knots[-1])\n",
    "\n",
    "\n",
    "def compute_accessibility(x1, x2, x3):\n",
    "    x1 = np.asarray(x1, dtype=float)\n",
    "    x3 = np.asarray(x3, dtype=float)\n",
    "    raw = (x1 / TOTAL_LENGTH) * 10.0 - (10.0 / 7.0) * x3\n",
    "    return np.clip(raw, access_knots[0], access_knots[-1])\n",
    "\n",
    "\n",
    "def compute_water_quality(x1, x2, x3):\n",
    "    x1 = np.asarray(x1, dtype=float)\n",
    "    x3 = np.asarray(x3, dtype=float)\n",
    "    raw = (x1 / TOTAL_LENGTH) * 10.0 - (10.0 / 24.0) * x3\n",
    "    return np.clip(raw, water_knots[0], water_knots[-1])\n",
    "\n",
    "\n",
    "def compute_overtopping_risk(x1, x2, x3):\n",
    "    x2 = np.asarray(x2, dtype=float)\n",
    "    # Exponential decay model: higher gates reduce annual overtopping risk\n",
    "    raw = 0.65 * np.exp(-0.35 * (x2 - 1.0))\n",
    "    return np.clip(raw, overtopping_knots[0], overtopping_knots[-1])\n",
    "\n",
    "\n",
    "def preference_initial_cost(x1, x2, x3):\n",
    "    costs = compute_initial_cost(x1, x2, x3)\n",
    "    costs = np.clip(costs, initial_cost_knots[0], initial_cost_knots[-1])\n",
    "    return pchip_interpolate(initial_cost_knots, initial_cost_pref, costs)\n",
    "\n",
    "\n",
    "def preference_maintenance_cost(x1, x2, x3):\n",
    "    costs = compute_maintenance_cost(x1, x2, x3)\n",
    "    costs = np.clip(costs, maintenance_cost_knots[0], maintenance_cost_knots[-1])\n",
    "    return pchip_interpolate(maintenance_cost_knots, maintenance_cost_pref, costs)\n",
    "\n",
    "\n",
    "def preference_sight(x1, x2, x3):\n",
    "    scores = compute_sight(x1, x2, x3)\n",
    "    return pchip_interpolate(sight_knots, sight_pref, scores)\n",
    "\n",
    "\n",
    "def preference_accessibility(x1, x2, x3):\n",
    "    scores = compute_accessibility(x1, x2, x3)\n",
    "    return pchip_interpolate(access_knots, access_pref, scores)\n",
    "\n",
    "\n",
    "def preference_water_quality(x1, x2, x3):\n",
    "    scores = compute_water_quality(x1, x2, x3)\n",
    "    return pchip_interpolate(water_knots, water_pref, scores)\n",
    "\n",
    "\n",
    "def preference_overtopping_risk(x1, x2, x3):\n",
    "    risk = compute_overtopping_risk(x1, x2, x3)\n",
    "    return pchip_interpolate(overtopping_knots, overtopping_pref, risk)\n",
    "\n",
    "\n",
    "def objective(values):\n",
    "    x1 = values[:, 0]\n",
    "    x2 = values[:, 1]\n",
    "    x3 = values[:, 2]\n",
    "\n",
    "    preferences = {\n",
    "        'initial_cost': preference_initial_cost(x1, x2, x3),\n",
    "        'maintenance_cost': preference_maintenance_cost(x1, x2, x3),\n",
    "        'sight': preference_sight(x1, x2, x3),\n",
    "        'accessibility': preference_accessibility(x1, x2, x3),\n",
    "        'water_quality': preference_water_quality(x1, x2, x3),\n",
    "        'overtopping_risk': preference_overtopping_risk(x1, x2, x3),\n",
    "    }\n",
    "\n",
    "    stacked_preferences = np.vstack([np.asarray(preferences[key], dtype=float) for key in OBJECTIVE_KEYS])\n",
    "    stakeholder_scores = STAKEHOLDER_OBJECTIVE_WEIGHTS @ stacked_preferences\n",
    "    return STAKEHOLDER_WEIGHTS.tolist(), stakeholder_scores.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "817be327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running GA with minmax\n",
      "\u001b[93mThe type of aggregation is set to minmax\u001b[0m\n",
      "Generation   Best score   Mean             Max stall    Diversity    Number of non-feasible results\n",
      "0            13.4024      16.2116          0            0.003        0           \n",
      "1            13.3512      15.0277          0            0.074        0           \n",
      "2            13.3263      14.5562          0            0.202        0           \n",
      "3            13.318       14.1547          0            0.293        0           \n",
      "4            13.3143      13.7             0            0.302        0           \n",
      "5            13.3139      13.6565          0            0.302        0           \n",
      "6            13.3138      13.7751          0            0.302        0           \n",
      "7            13.3137      13.6885          0            0.309        0           \n",
      "8            13.3137      13.6803          1            0.306        0           \n",
      "9            13.3137      13.6171          2            0.313        0           \n",
      "10           13.3137      13.6123          3            0.307        0           \n",
      "11           13.3137      13.5756          4            0.312        0           \n",
      "12           13.3137      13.5582          5            0.31         0           \n",
      "13           13.3137      13.5712          6            0.308        0           \n",
      "14           13.3137      13.5745          7            0.314        0           \n",
      "15           13.3137      13.5802          8            0.308        0           \n",
      "16           13.3137      13.5647          9            0.313        0           \n",
      "17           13.3137      13.5719          10           0.311        0           \n",
      "Stopped at gen 17\n",
      "Execution time was 0.4455 seconds\n",
      "Optimal design for minmax:\n",
      "  x1 = 1598.44\n",
      "  x2 = 2.97\n",
      "  x3 = 2.12\n",
      "  Metrics:\n",
      "    initial_cost: 1389801762.827\n",
      "    maintenance_cost: 945108499610.543\n",
      "    sight: 9.994\n",
      "    accessibility: 6.966\n",
      "    water_quality: 9.108\n",
      "    overtopping_risk: 0.326\n",
      "  Preference scores:\n",
      "    initial_cost: 93.09\n",
      "    maintenance_cost: 43.83\n",
      "    sight: 99.94\n",
      "    accessibility: 69.66\n",
      "    water_quality: 91.08\n",
      "    overtopping_risk: 54.40\n",
      "  Stakeholder aggregates:\n",
      "    Municipality: 74.70\n",
      "    Residents: 80.82\n",
      "    Environmental agency: 82.06\n",
      "    Tourism Business: 81.26\n",
      "------------------------------------------------------------\n",
      "Running GA with tetra\n",
      "\u001b[93mThe type of aggregation is set to tetra\u001b[0m\n",
      "Generation   Best score   Mean             Max stall    Diversity    Number of non-feasible results\n",
      "\u001b[93mNo initial starting point for the optimization with tetra is given. A random population is generated.\u001b[0m\n",
      "0            -100.0       -51.4204         1            0.003        0           \n",
      "1            -100.0       -81.6565         1            0.093        0           \n",
      "2            -100.0       -90.1092         1            0.279        0           \n",
      "3            -100.0       -95.003          1            0.3          0           \n",
      "4            -100.0       -95.3815         2            0.227        0           \n",
      "5            -100.0       -94.9287         3            0.306        0           \n",
      "6            -100.0       -95.6988         4            0.303        0           \n",
      "7            -100.0       -96.0846         5            0.303        0           \n",
      "8            -100.0       -96.9431         6            0.307        0           \n",
      "9            -100.0       -96.1977         7            0.306        0           \n",
      "10           -100.0       -96.1558         8            0.312        0           \n",
      "11           -100.0       -95.7922         9            0.308        0           \n",
      "12           -100.0       -96.2284         10           0.307        0           \n",
      "Stopped at gen 12\n",
      "Execution time was 80.6890 seconds\n",
      "Optimal design for tetra:\n",
      "  x1 = 1598.44\n",
      "  x2 = 3.23\n",
      "  x3 = 1.62\n",
      "  Metrics:\n",
      "    initial_cost: 1494718779.117\n",
      "    maintenance_cost: 1048659264948.342\n",
      "    sight: 9.993\n",
      "    accessibility: 7.681\n",
      "    water_quality: 9.317\n",
      "    overtopping_risk: 0.298\n",
      "  Preference scores:\n",
      "    initial_cost: 92.71\n",
      "    maintenance_cost: 36.53\n",
      "    sight: 99.93\n",
      "    accessibility: 76.81\n",
      "    water_quality: 93.17\n",
      "    overtopping_risk: 60.47\n",
      "  Stakeholder aggregates:\n",
      "    Municipality: 74.26\n",
      "    Residents: 82.61\n",
      "    Environmental agency: 84.45\n",
      "    Tourism Business: 82.95\n",
      "------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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w7r//fo0YMcLabubMmSpZsqR+//133XXXXenek9qwYcPUqNHlxU4LFiyo++67z/r5gw8+0NKlS/X111+re/fu6d6fG3jtPd0AAAAAAO+2fft2rV27Vnnz5k332v79+3XXXXdd8/3Vq1dP83NMTIyGDh2qb775RsePH5fD4VBcXBwz3QAAAACAfx+Xy6UWLVpYi12nVqxYseu+P0+ePGl+7tevn1auXKmPPvpI5cqVU1BQkNq0aZPmMWS5DUU3AAAAACBT/P390zwW7f7779fixYtVpkyZq67mfeV7rmXDhg3q1KmTnn76aUnJi28fOnTopuP2JI8upAYAAAAAyD3KlCmjn376SYcOHdKZM2f0xhtv6Ny5c2rXrp22bNmiAwcOKCIiQl26dLEK7Svfc+UzuFMrV66clixZoqioKP38889q3779NbfPDSi6AQAAAACZ8tZbb8nHx0cVK1ZU4cKFlZiYqB9++EFOp1NNmjRRpUqV9OabbypfvnzWs7avfM+17s/++OOPVaBAAdWqVUstWrRQkyZNdP/99+dU99zCZq61fPi/xIULF5QvXz5FR0crNDTU0+EAwK1lvs3TEaRj2+fpCDJmhvzrd8kA8K8RHx+vgwcPqmzZsgoMDPR0OLiKa31Pma0jmekGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADeh6AYAAAAAwE0ougEAAADAQ3iYlHfLju+HohsAAAAAcpiPj48kKTEx0cOR4FpiY2MlSX5+fjf8Gb7ZFQwAAAAAIHN8fX0VHBys06dPy8/PT3Y786HexBij2NhYnTp1Svnz57dOktwIim4AAAAAyGE2m03FihXTwYMHdfjwYU+Hg6vInz+/ihYtelOfQdENAAAAAB7g7++v8uXLc4m5l/Lz87upGe4UFN25yXybpyPIWHsWfwAAAHA321DvOxY0QzgOvFl2u12BgYGeDgNuxI0DAAAAAAC4CTPduGneeNZV4swrAAAAAM9jphsAAAAAADeh6AYAAAAAwE0ougEAAAAAcBOKbgAAAAAA3ISF1AAAAIDUvPUxrQByJWa6AQAAAABwE4puAAAAAADchKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADeh6AYAAAAAwE0ougEAAAAAcBOKbgAAAAAA3ISiGwAAAAAAN6HoBgAAAADATSi6AQAAAABwE4puAAAAAADchKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADeh6AYAAAAAwE0ougEAAAAAcBOKbgAAAAAA3ISiGwAAAAAAN6HoBgAAAADATSi6AQAAAABwE4puAAAAAADchKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADfx6qLb4XBo0KBBKlu2rIKCgnTHHXdo2LBhcrlc1jbGGIWHh6t48eIKCgpSvXr19Msvv3gwagAAAAAAknl10T1q1ChNnTpVkyZN0m+//abRo0frww8/1MSJE61tRo8erbFjx2rSpEnaunWrihYtqkaNGunixYsejBwAAAAAAC8vujdv3qyWLVuqefPmKlOmjNq0aaPGjRtr27ZtkpJnuceNG6d3331XrVq1UqVKlTR79mzFxsZq/vz5Ho4eAAAAAPBv5+vpAK6lTp06mjp1qn7//Xfddddd+vnnn7Vx40aNGzdOknTw4EGdPHlSjRs3tt4TEBCgRx99VJs2bdKrr76a4ecmJCQoISHB+vnChQuSpKSkJCUlJUmS7Ha7fHx85HQ601zOntLucDhkjLHafXx8ZLfbr9qe8rkpfH2T//QOhyNT7X5+fnLJLqcCrDabjHwVL5d85ZRfqnaXfJUgp3zlStVul1M+SpRT/nLJJ1V7knzkkEMBMqnOw/goSXY55FCgjGyp2hNkl0tJCpIkBf3zlgRXglxyKcgelCb2OFec7LIrwB5w3XYjo3hXvHxtvvKzXY7dJZcSXAnp2p3GqUSTKH+bv3xsl/uUZJKsv2OOf08ul5xOp9Vms9nk6+t71far5ZhX5R59ok831afkMcFXCZJccijtGOGrOEl2OZR2jPBTnNvGPV9bkhzGoQB7gOypxr0kk9weaA+ULdW4d7XxLbvHvdR5QO7RJ/rkwT7J96aOjazYs3ncC7Lf3LGRO8a9lO+R3KNP/9Y+ZYZXF91vv/22oqOjVaFCBauzw4cPV7t27SRJJ0+elCSFhYWleV9YWJgOHz581c8dOXKkhg4dmq49IiJCwcHBkqRSpUqpWrVq2rVrl44cOWJtc/fdd6tChQrasmWLTp8+bbVXrVpVpUuXVmRkZJpL22vWrKkiRYooIiIizZdSv359BQUFafny5WliaNasmeLi4rR27VqrzdfXV82bN9cZnyraHBhutYe4jqhBXE8d9a2nqIDuVnthx07VShiqfX5ttNe/rdVeKmmVqiVO1i7/rjri1+hynxIXqkLSQm0JGKDTvtUu9ylhkko7VisyaLQu2ktd7lN8uIo4oxQRPEMOW7AWVElu77Gnh84kntGCKgvS9KndrnYq5F9IEytcvi0g1hmr9v9rryohVRR+5+U+HYk7op57e6pegXrqXupyn3Ze2KmhB4aqTVgbtS16uU+rzq7S5KOT1fX2rmpU8HKfFp5cKEme+Z7OnNHmzZut9pCQEDVo0EBHjx5VVFSU1V64cGHVqlVL+/bt0969e612r8w9+kSfbqZPeZLHhPqxPRRkzmh5nrRjRLOYdoqzFdLa4MtjhK+JVfPY9m4b99qELdTCkws1oMwAVQu9PO5NOjJJq8+t1ujyo1Uq6PK4F74/XFEXozTj3hkK9gm22rN73Ev9fZN79Ik+ebBPfm1u6tjI6lM2j3sLqtzcsZE7xr2U74vco0//xj5t375dmWEzqU8reJmFCxeqX79++vDDD3XvvfcqKipKvXr10tixY9WxY0dt2rRJtWvX1vHjx1WsWDHrfV27dtXRo0e1YsWKDD83o5nukiVL6syZMwoNDZXkpWdq5vt45Ux3vv3//F29bKY7aXDSLXtGjT7Rp1zVpy/yJcfuRTPdwX9450x37MDYy30i9+gTffJcn74I9sqZ7rz7vW+mO3pAdHKfyD369C/s07lz51SwYEFFR0dbdWRGvLroLlmypAYMGKA33njDavvggw80b9487dmzRwcOHNCdd96pHTt2qFq1y2fsWrZsqfz582v27NmZ+j0XLlxQvnz5rvvH8rj5tutv4wG2fZ6OIGNmiNemNvDv4oVjF+MWgGvywnFL8s6xi3EL/2aZrSO9eiG12NhY2e1pQ/Tx8bHOMpQtW1ZFixbVqlWrrNcTExO1fv161apVK0djBQAAAADgSl59T3eLFi00fPhwlSpVSvfee6927typsWPHqkuXLpKSLwPo1auXRowYofLly6t8+fIaMWKEgoOD1b59ew9HDwAAAAD4t/PqonvixIl677331K1bN506dUrFixfXq6++qsGDB1vb9O/fX3FxcerWrZvOnz+vGjVqKCIiQiEhIR6MHAAAAAAAL7+nO6dwT/fN8cb7iyTuMQK8hheOXYxbAK7JC8ctyTvHLsYt/JvdEvd0AwAAAACQm1F0AwAAAADgJhTdAAAAAAC4CUU3AAAAAABu4tWrlwMAAOQmtqHeuQAXi10BgOcw0w0AAAAAgJtQdAMAAAAA4CYU3QAAAAAAuAlFNwAAAAAAbkLRDQAAAACAm1B0AwAAAADgJhTdAAAAAAC4CUU3AAAAAABuQtENAAAAAICbUHQDAAAAAOAmFN0AAAAAALgJRTcAAAAAAG5C0Q0AAAAAgJvcUNH9999/a/r06Ro4cKDOnTsnSdqxY4eOHTuWrcEBAAAAAJCb+Wb1Dbt27VLDhg2VL18+HTp0SF27dtVtt92mpUuX6vDhw5ozZ4474gQAAAAAINfJ8kx3nz591KlTJ+3bt0+BgYFWe9OmTRUZGZmtwQEAAAAAkJtluejeunWrXn311XTtJUqU0MmTJ7MlKAAAAAAAbgVZLroDAwN14cKFdO179+5V4cKFsyUoAAAAAABuBVkuulu2bKlhw4YpKSlJkmSz2XTkyBENGDBArVu3zvYAAQAAAADIrbJcdH/00Uc6ffq0ihQpori4OD366KMqV66cQkJCNHz4cHfECAAAAABArpTl1ctDQ0O1ceNGrVmzRjt27JDL5dL999+vhg0buiM+AAAAAAByrSwV3Q6HQ4GBgYqKilKDBg3UoEEDd8UFAAAAAECul6Wi29fXV6VLl5bT6XRXPAAAAJkz3+bpCAAAuK4s39M9aNAgDRw4UOfOnXNHPAAAAAAA3DKyfE/3hAkT9Mcff6h48eIqXbq08uTJk+b1HTt2ZFtwAAAAAADkZlkuup966ik3hAEAAAAAwK0ny0X3kCFD3BEHAAAAAAC3nCwX3Sm2b9+u3377TTabTRUrVlS1atWyMy4AAAAAAHK9LBfdp06dUtu2bbVu3Trlz59fxhhFR0erfv36WrhwoQoXLuyOOAEAAAAAyHWyvHp5jx49dOHCBf3yyy86d+6czp8/r927d+vChQvq2bOnO2IEAAAAACBXyvJM94oVK7R69Wrdc889VlvFihU1efJkNW7cOFuDAwAAAAAgN8vyTLfL5ZKfn1+6dj8/P7lcrmwJCgAAAACAW0GWi+4GDRrozTff1PHjx622Y8eOqXfv3nrssceyNTgAAAAAAHKzLBfdkyZN0sWLF1WmTBndeeedKleunMqWLauLFy9q4sSJ7ogRAAAAAIBcKcv3dJcsWVI7duzQqlWrtGfPHhljVLFiRTVs2NAd8QEAAAAAkGvd8HO6GzVqpEaNGmVnLAAAAAAA3FKyfHl5z549NWHChHTtkyZNUq9evbIjJgAAAAAAbglZLroXL16s2rVrp2uvVauWFi1alC1BAQAAAABwK8hy0X327Fnly5cvXXtoaKjOnDmTLUEBAAAAAHAryHLRXa5cOa1YsSJd+3fffac77rgjW4ICAAAAAOBWkOWF1Pr06aPu3bvr9OnTatCggSTp+++/15gxYzRu3Ljsjg8AAAAAgFwry0V3ly5dlJCQoOHDh+v999+XJJUpU0affPKJXnzxxWwPEAAAAACA3OqGHhn2+uuv6/XXX9fp06cVFBSkvHnzZndcAAAAAADkelm+pzsuLk6xsbGSpMKFC+vs2bMaN26cIiIisj04AAAAAABysywX3S1bttScOXMkSX///bceeughjRkzRi1bttQnn3yS7QECAAAAAJBbZbno3rFjh+rWrStJWrRokYoWLarDhw9rzpw5mjBhQrYHCAAAAABAbpXlojs2NlYhISGSpIiICLVq1Up2u10PP/ywDh8+nO0BAgAAAACQW93Qc7q//PJLHT16VCtXrlTjxo0lSadOnVJoaGi2BwgAAAAAQG6V5aJ78ODBeuutt1SmTBnVqFFDNWvWlJQ8612tWrVsDxAAAAAAgNwqy0V3mzZtdOTIEW3btk0rVqyw2h977DF9/PHH2RqcJB07dkwvvPCCChYsqODgYFWtWlXbt2+3XjfGKDw8XMWLF1dQUJDq1aunX375JdvjAAAAAAAgq7JcdEtS0aJFVa1aNdntl9/+0EMPqUKFCtkWmCSdP39etWvXlp+fn7777jv9+uuvGjNmjPLnz29tM3r0aI0dO1aTJk3S1q1bVbRoUTVq1EgXL17M1lgAAAAAAMgqX08HcC2jRo1SyZIlNWvWLKutTJky1v8bYzRu3Di9++67atWqlSRp9uzZCgsL0/z58/Xqq6/mdMgAAAAAAFi8uuj++uuv1aRJEz3zzDNav369SpQooW7duqlr166SpIMHD+rkyZPWYm6SFBAQoEcffVSbNm2i6AYAAACA67ANtXk6hHTMEOPpELKNVxfdBw4c0CeffKI+ffronXfe0ZYtW9SzZ08FBAToxRdf1MmTJyVJYWFhad4XFhZ2zceXJSQkKCEhwfr5woULkqSkpCQlJSVJkux2u3x8fOR0OuVyuaxtU9odDoeMuZwIPj4+stvtV21P+dwUvr7Jf3qHw5Gpdj8/P7lkl1MBVptNRr6Kl0u+csovVbtLvkqQU75ypWq3yykfJcopf7nkk6o9ST5yyKEAmVR3HPgoSXY55FCgjGyp2hNkl0tJCpIkBf3zlgRXglxyKcgelCb2OFec7LIrwB5w3XYjo3hXvHxtvvKzXY7dJZcSXAnp2p3GqUSTKH+bv3xsl/uUZJKsv2OOf08ul5xOp9Vms9nk6+t71far5ZhX5R59ok831afkMcFXCZJccijtGOGrOEl2OZR2jPBTnNvGPV9bkhzGoQB7gOypxr0kk9weaA+ULdW4d7XxLbvHvdR5QO5lok/X2T9Zsedg7qXsE6+1f/JE7kny4jEimVflnnxv6tjIij2bcy/IfnPHRu7IvZTv0SvHiNyYex7qkyS3HpffSO7llu8pM7y66Ha5XKpevbpGjBghSapWrZp++eUXffLJJ3rxxRet7Wy2tGdmjDHp2lIbOXKkhg4dmq49IiJCwcHBkqRSpUqpWrVq2rVrl44cOWJtc/fdd6tChQrasmWLTp8+bbVXrVpVpUuXVmRkZJr7yWvWrKkiRYooIiIizZdSv359BQUFafny5WliaNasmeLi4rR27VqrzdfXV82bN9cZnyraHBhutYe4jqhBXE8d9a2nqIDuVnthx07VShiqfX5ttNe/rdVeKmmVqiVO1i7/rjri1+hynxIXqkLSQm0JGKDTvpdXoK+aMEmlHasVGTRaF+2lLvcpPlxFnFGKCJ4hhy1YC6okt/fY00NnEs9oQZUFafrUblc7FfIvpIkVJlptsc5Ytf9fe1UJqaLwOy/36UjcEfXc21P1CtRT91KX+7Tzwk4NPTBUbcLaqG3Ry31adXaVJh+drK63d1Wjgpf7tPDkQknyzPd05ow2b95stYeEhKhBgwY6evSooqKirPbChQurVq1a2rdvn/bu3Wu1e2Xu0Sf6dDN9ypM8JtSP7aEgc0bL86QdI5rFtFOcrZDWBl8eI3xNrJrHtnfbuNcmbKEWnlyoAWUGqFro5XFv0pFJWn1utUaXH61SQZfHvfD94Yq6GKUZ985QsE+w1Z7d417q75vcy0SfrrN/svqUg7mXsk+81v7JE7knyXvHiH94Ve75tbmpYyOrT9mcewuq3NyxkTtyL+X78soxIjfmnof6JMmtx+U3knu54XtKvcD3tdhM6lMlmTR37lxNnTpVBw8e1ObNm1W6dGmNGzdOZcuWVcuWLbP6cVdVunRpNWrUSNOnT7faPvnkE33wwQc6duyYDhw4oDvvvFM7duxI87iyli1bKn/+/Jo9e3aGn5vRTHfJkiV15swZ61nj3nb2yc/PT675Pl45051v/z9/Vy+b6U4anMSZT/pEn7yhT1/kS47di2a6g//wzpnu2IGxl/tE7l2/T/ODvG6mO2Wf6G0z3bHvxXrvGPEPr8q9L4K9cqY7737vm+mOHhCd3CdvHCNyY+55qE/+I/y9bqY78Z3Em+pTTnxP586dU8GCBRUdHW3VkRnJ8kz3J598osGDB6tXr14aPny4FWz+/Pk1bty4bC26a9euneZMgyT9/vvvKl26tCSpbNmyKlq0qFatWmUV3YmJiVq/fr1GjRp11c8NCAhQQED6y678/Pzk5+eXps3Hx0c+Pj7ptk35cjPbfuXn3ki7XS7ZFZdBu0N2pb+0wUcO+WTYnqj0PUrZMWTUHp9xjP/EEudK2x7nSh+jS64stTuMQw6TPvartSeaRCmD00ce+Z7s9jQr+1+v/Wo55lW5R5/ok26mT2n/jftlMI5Jrgzb3TXuOf4ZLxJcGY978a6Mx72Mxqurtd/IuJfR90ruXaNP19k/Za49e3Pvyn3i1fZPnsg97x0jbrzdfX1K/o5v9Ngoc+1Zz704180fG2V37l35vXjVGJErcy+ZJ/rk7uPyrOZebv6e0v2+TG2VysSJE/Xpp5/q3XffTfOLq1evrv/9739Z/bhr6t27t3788UeNGDFCf/zxh+bPn69p06bpjTfekJR8RqJXr14aMWKEli5dqt27d6tTp04KDg5W+/btszUWAAAAAACyKssz3QcPHkxzKXeKgIAAxcTEZEtQKR588EEtXbpUAwcO1LBhw1S2bFmNGzdOzz//vLVN//79FRcXp27duun8+fOqUaOGIiIiFBISkq2xAAAAAACQVVkuusuWLauoqCjrEu8U3333nSpWrJhtgaV44okn9MQTT1z1dZvNpvDwcIWHh2f77wYAAAAA4GZkueju16+f3njjDcXHx8sYoy1btmjBggUaOXJkmgXPAAAAAAD4t8ty0d25c2c5HA71799fsbGxat++vUqUKKHx48erbdu21/8AAAAAAAD+JW7oOd1du3ZV165ddebMGblcLhUpUiS74wIAAAAAINe7oYXUHA6Hypcvr0KFClnt+/btk5+fn8qUKZOd8QEAAAAAkGtl+ZFhnTp10qZNm9K1//TTT+rUqVN2xAQAAAAAwC0hy0X3zp07Vbt27XTtDz/8sKKiorIjJgAAAAAAbglZLrptNpsuXryYrj06OlpOpzNbggIAAAAA4FaQ5aK7bt26GjlyZJoC2+l0auTIkapTp062BgcAAAAAQG6W5YXURo8erUceeUR333236tatK0nasGGDLly4oDVr1mR7gAAAAAAA5FZZnumuWLGidu3apWeffVanTp3SxYsX9eKLL2rPnj2qVKmSO2IEAAAAACBXuqHndBcvXlwjRozI7lgAAAAAALil3FDR/ffff2vLli06deqUXC5XmtdefPHFbAkMAAAAAIDcLstF97Jly/T8888rJiZGISEhstls1ms2m42iGwAAAAAyMt92/W1wy8nyPd19+/ZVly5ddPHiRf399986f/689d+5c+fcESMAAAAAALlSlovuY8eOqWfPngoODnZHPAAAAAAA3DKyXHQ3adJE27Ztc0csAAAAAADcUrJ8T3fz5s3Vr18//frrr6pcubL8/PzSvP7kk09mW3AAAAAAAORmWS66u3btKkkaNmxYutdsNpucTufNRwUAAAAAwC0gy0X3lY8IAwAAAAAAGcvyPd2pxcfHZ1ccAAAAAADccrJcdDudTr3//vsqUaKE8ubNqwMHDkiS3nvvPc2YMSPbAwQAAAAAILfKctE9fPhwffbZZxo9erT8/f2t9sqVK2v69OnZGhwAAAAAALlZlovuOXPmaNq0aXr++efl4+NjtVepUkV79uzJ1uAAAAAAAMjNslx0Hzt2TOXKlUvX7nK5lJSUlC1BAQAAAABwK8hy0X3vvfdqw4YN6dq/+OILVatWLVuCAgAAAADgVpDlR4YNGTJEHTp00LFjx+RyubRkyRLt3btXc+bM0TfffOOOGAEAAAAAyJWyPNPdokULff7551q+fLlsNpsGDx6s3377TcuWLVOjRo3cESMAAAAAALlSlma6HQ6Hhg8fri5dumj9+vXuigkAAAAAgFtClma6fX199eGHH8rpdLorHgAAAAAAbhlZvry8YcOGWrdunRtCAQAAAADg1pLlhdSaNm2qgQMHavfu3XrggQeUJ0+eNK8/+eST2RYcAAAAAAC5WZaL7tdff12SNHbs2HSv2Ww2Lj0HAAAAAOAfWS66XS6XO+IAAAAAAOCWk+V7ulOLj4/PrjgAAAAAALjlZLnodjqdev/991WiRAnlzZtXBw4ckCS99957mjFjRrYHCAAAAABAbpXlonv48OH67LPPNHr0aPn7+1vtlStX1vTp07M1OAAAAAAAcrMsF91z5szRtGnT9Pzzz8vHx8dqr1Klivbs2ZOtwQEAAAAAkJtlueg+duyYypUrl67d5XIpKSkpW4ICAAAAAOBWkOWi+95779WGDRvStX/xxReqVq1atgQFAAAAAMCtIMuPDBsyZIg6dOigY8eOyeVyacmSJdq7d6/mzJmjb775xh0xAgAAAACQK2V5prtFixb6/PPPtXz5ctlsNg0ePFi//fabli1bpkaNGrkjRgAAAAAAcqVMzXRPmDBBr7zyigIDA3XkyBE1btxYTZo0cXdsAAAAAADkapma6e7Tp48uXLggSSpbtqxOnz7t1qAAAAAAALgVZGqmu3jx4lq8eLGaNWsmY4z+/PNPxcfHZ7htqVKlsjVAAAAAAAByq0wV3YMGDVKPHj3UvXt32Ww2Pfjgg+m2McbIZrPJ6XRme5AAAAAAAORGmSq6X3nlFbVr106HDx9WlSpVtHr1ahUsWNDdsQEAAAAAkKtl+pFhISEhqlSpkmbNmqXatWsrICDAnXEBAAAAAJDrZfk53R07dtTff/+tuXPnav/+/erXr59uu+027dixQ2FhYSpRooQ74gTgbvNtno4gvfbG0xEAAAAANyXLRfeuXbvUsGFD5cuXT4cOHVLXrl112223aenSpTp8+LDmzJnjjjgBAAAAAMh1MvXIsNR69+6tTp06ad++fQoMDLTamzZtqsjIyGwNDgAAAACA3CzLM93btm3TtGnT0rWXKFFCJ0+ezJagAAAAAAC4FWR5pjswMFAXLlxI1753714VLlw4W4ICAAAAAOBWkOWiu2XLlho2bJiSkpIkSTabTUeOHNGAAQPUunXrbA8QAAAAAIDcKstF90cffaTTp0+rSJEiiouL06OPPqpy5copJCREw4cPd0eMAAAAAADkSlm+pzs0NFQbN27UmjVrtGPHDrlcLt1///1q2LChO+IDAAAAACDXytJMt8PhkK+vr3bv3q0GDRrorbfeUv/+/XOs4B45cqRsNpt69epltRljFB4eruLFiysoKEj16tXTL7/8kiPxAAAAAABwLVkqun19fVW6dGk5nU53xXNVW7du1bRp01SlSpU07aNHj9bYsWM1adIkbd26VUWLFlWjRo108eLFHI8RAAAAAIDUsnxP96BBgzRw4ECdO3fOHfFk6NKlS3r++ef16aefqkCBAla7MUbjxo3Tu+++q1atWqlSpUqaPXu2YmNjNX/+/ByLDwAAAACAjGS56J4wYYI2bNig4sWL6+6779b999+f5j93eOONN9S8efN0l7EfPHhQJ0+eVOPGja22gIAAPfroo9q0aZNbYgEAAAAAILOyvJBay5YtZbPZ3BFLhhYuXKgdO3Zo69at6V47efKkJCksLCxNe1hYmA4fPnzVz0xISFBCQoL1c8pzx5OSkqxHodntdvn4+MjpdMrlclnbprQ7HA4ZY6x2Hx8f2e32q7anfG4KX9/kP73D4chUu5+fn1yyy6kAq80mI1/FyyVfOeWXqt0lXyXIKV+5UrXb5ZSPEuWUv1zySdWeJB855FCATKrzMD5Kkl0OORQoI1uq9gTZ5VKSgiRJQf+8JcGVIJdcCrIHpYk9zhUnu+wKsAdct93IKN4VL1+br/xsl2N3yaUEV0K6dqdxKtEkyt/mLx/b5T4lmSTr75jj35PLleYWDJvNJl9f36u2Xy3Hcjz3kn+LHEr7PfkpznO5x/d0i/QpeUzwVYIklxxKO0b4Kk45nXu+tiQ5jEMB9gDZU+VekkluD7QHypZq3Lva+Jbd417qPCD3MtGn6+yfrNhzMPdS9onX2j95IvckefEYkcyrck++N3VsZMWezbkXZL+5YyN35F7K9+iVY4RX5l7y381dx+VW7FnMPcm9x+U3kntePUakyr3MyHLRHR4entW33LCjR4/qzTffVEREhAIDA6+63ZUnAYwx1zwxMHLkSA0dOjRde0REhIKDgyVJpUqVUrVq1bRr1y4dOXLE2ubuu+9WhQoVtGXLFp0+fdpqr1q1qkqXLq3IyMg095PXrFlTRYoUUURERJovpX79+goKCtLy5cvTxNCsWTPFxcVp7dq1Vpuvr6+aN2+uMz5VtDkw3GoPcR1Rg7ieOupbT1EB3a32wo6dqpUwVPv82mivf1urvVTSKlVLnKxd/l11xK/R5T4lLlSFpIXaEjBAp32rXe5TwiSVdqxWZNBoXbSXutyn+HAVcUYpIniGHLZgLfjnNvsee3roTOIZLaiyIE2f2u1qp0L+hTSxwkSrLdYZq/b/a68qIVUUfuflPh2JO6Kee3uqXoF66l7qcp92XtipoQeGqk1YG7UterlPq86u0uSjk9X19q5qVPBynxaeXChJnvmezpzR5s2brfaQkBA1aNBAR48eVVRUlNVeuHBh1apVS/v27dPevXutdo/lnoIUZyuktcGXvydfE6vmse09l3t8T7dGn/Ikjwn1Y3soyJzR8jxpx4hmMe1yPPfahC3UwpMLNaDMAFULvZx7k45M0upzqzW6/GiVCro87oXvD1fUxSjNuHeGgn2CrfbsHvdSf9/kXib6dJ39k9WnHMy9lH3itfZPnsg9Sd47RvzDq3LPr81NHRtZfcrm3FtQ5eaOjdyReynfl1eOEd6Ye//kgruOy60+ZTH3JPcel99I7nn1GPFP7m3fvl2ZYTOpT/9cQ2xsrPr166cvv/xSSUlJatiwoSZMmKBChQpl6hfdiC+//FJPP/20fHwunylxOp2y2Wyy2+3au3evypUrpx07dqhatctfYMuWLZU/f37Nnj07w8/NaKa7ZMmSOnPmjEJDQyV56Rm1+T5eOdOdb/8/f1cvm+lOGpyUy858JvNY7n3hL6+b6X42ie/pVujTF/mSY/eime7gP7xzpjt2YOzlPpF71+/T/CCvm+lO2Sd620x37Hux3jtG/MOrcu+LYK+c6c673/tmuqMHRCf3yRvHCG/MvX/2id420+2/z/tmuhPfSUwbuzeNEf+0nzt3TgULFlR0dLRVR2Yk00V3v379NGXKFD3//PMKDAzUggULVK9ePX3xxReZefsNuXjxYrrLxDt37qwKFSro7bff1r333qvixYurd+/e6t+/vyQpMTFRRYoU0ahRo/Tqq69m6vdcuHBB+fLlu+4fy+Pm59xl/Vlh2+fpCDJmhmQqtZHCG/OrPd/hLcELc4tx6xZBbmUauZVFXphbknfmF7mVReRWpuWG3MpsHZnpy8uXLFmiGTNmqG3b5EsIXnjhBdWuXVtOpzPNTHR2CgkJUaVKldK05cmTRwULFrTae/XqpREjRqh8+fIqX768RowYoeDgYLVv394tMQEAAAAAkFmZLrqPHj2qunXrWj8/9NBD8vX11fHjx1WyZEm3BJcZ/fv3V1xcnLp166bz58+rRo0aioiIUEhIiMdiAgAAAABAykLR7XQ65e/vn/bNvr6ZXrEtu6xbty7NzzabTeHh4Tm6wBsAAAAAAJmR6aLbGKNOnTopIODyzfXx8fF67bXXlCdPHqttyZIl2RshAAAAAAC5VKaL7o4dO6Zre+GFF7I1GAAAAAAAbiWZLrpnzZrlzjgAAAAAALjl2K+/CQAAAAAAuBEU3QAAAAAAuEmmLy8HgJxmG2rzdAgZMkOMp0MAAABALsFMNwAAAAAAbkLRDQAAAACAm1B0AwAAAADgJhTdAAAAAAC4CUU3AAAAAABuQtENAAAAAICbUHQDAAAAAOAmFN0AAAAAALgJRTcAAAAAAG5C0Q0AAAAAgJtQdAMAAAAA4CYU3QAAAAAAuAlFNwAAAAAAbkLRDQAAAACAm1B0AwAAAADgJhTdAAAAAAC4CUU3AAAAAABuQtENAAAAAICbUHQDAAAAAOAmFN0AAAAAALgJRTcAAAAAAG5C0Q0AAAAAgJtQdAMAAAAA4CYU3QAAAAAAuAlFNwAAAAAAbkLRDQAAAACAm1B0AwAAAADgJhTdAAAAAAC4CUU3AAAAAABuQtENAAAAAICbUHQDAAAAAOAmFN0AAAAAALgJRTcAAAAAAG5C0Q0AAAAAgJtQdAMAAAAA4CYU3QAAAAAAuAlFNwAAAAAAbkLRDQAAAACAm1B0AwAAAADgJhTdAAAAAAC4CUU3AAAAAABuQtENAAAAAICbUHQDAAAAAOAmFN0AAAAAALgJRTcAAAAAAG5C0Q0AAAAAgJtQdAMAAAAA4CYU3QAAAAAAuAlFNwAAAAAAbuLVRffIkSP14IMPKiQkREWKFNFTTz2lvXv3ptnGGKPw8HAVL15cQUFBqlevnn755RcPRQwAAAAAwGVeXXSvX79eb7zxhn788UetWrVKDodDjRs3VkxMjLXN6NGjNXbsWE2aNElbt25V0aJF1ahRI128eNGDkQMAAAAAIPl6OoBrWbFiRZqfZ82apSJFimj79u165JFHZIzRuHHj9O6776pVq1aSpNmzZyssLEzz58/Xq6++6omwAQAAAACQ5OVF95Wio6MlSbfddpsk6eDBgzp58qQaN25sbRMQEKBHH31UmzZtumrRnZCQoISEBOvnCxcuSJKSkpKUlJQkSbLb7fLx8ZHT6ZTL5bK2TWl3OBwyxljtPj4+stvtV21P+dwUvr7Jf3qHw5Gpdj8/P7lkl1MBVptNRr6Kl0u+csovVbtLvkqQU75ypWq3yykfJcopf7nkk6o9ST5yyKEAmVQXP/goSXY55FCgjGyp2hNkl0tJCpIkBf3zlgRXglxyKcgelCb2OFec7LIrwB5w3XYjo3hXvHxtvvKzXY7dJZcSXAnp2p3GqUSTKH+bv3xsl/uUZJKsv2OOf08ul5xOp9Vms9nk6+t71far5ViO517yb5FDab8nP8V5LPcC7YGypcq9q+VYTudeUlKSd44RXpt7yd+LrxIkueRQ2u/JV3HK6dzztSXJYRwKsAfInir3kkxyu6dyL3UeeN0Y4Y25d539kxV7DuZeyj7xWvsnT+SeJC8eI5J5Ve7J96aOjazYszn3guw3d2zkjtxL+R69cozwytxL/ru567jcij2LuSe597j8RnLPq8eIVLmXGbmm6DbGqE+fPqpTp44qVaokSTp58qQkKSwsLM22YWFhOnz48FU/a+TIkRo6dGi69oiICAUHB0uSSpUqpWrVqmnXrl06cuSItc3dd9+tChUqaMuWLTp9+rTVXrVqVZUuXVqRkZFpLm2vWbOmihQpooiIiDRfSv369RUUFKTly5eniaFZs2aKi4vT2rVrrTZfX181b95cZ3yqaHNguNUe4jqiBnE9ddS3nqICulvthR07VSthqPb5tdFe/7ZWe6mkVaqWOFm7/LvqiF+jy31KXKgKSQu1JWCATvtWu9ynhEkq7VityKDRumgvdblP8eEq4oxSRPAMOWzBWlAlub3Hnh46k3hGC6osSNOndrvaqZB/IU2sMNFqi3XGqv3/2qtKSBWF33m5T0fijqjn3p6qV6Ceupe63KedF3Zq6IGhahPWRm2LXu7TqrOrNPnoZHW9vasaFbzcp4UnF0qSZ76nM2e0efNmqz0kJEQNGjTQ0aNHFRUVZbUXLlxYtWrV0r59+9KsVeCx3FOQ4myFtDb48vfka2LVPLa9x3JvdPnRKhV0OffC94cr6mKUZtw7Q8E+wVZ7Tufe8uXLvXOM8Nbcy5P8vdSP7aEgc0bL86T9nprFtMvx3GsTtlALTy7UgDIDVC30cu5NOjJJq895LvdSf99eN0Z4Y+5dZ/9k9SkHcy9ln3it/ZMnck+S944R//Cq3PNrc1PHRlafsjn3FlS5uWMjd+ReyvfllWOEN+beP7ngruNyq09ZzD3JvcflN5J7Xj1G/JN727dvV2bYTOrTP17sjTfe0LfffquNGzfq9ttvlyRt2rRJtWvX1vHjx1WsWDFr265du+ro0aPpLk9PkdFMd8mSJXXmzBmFhoZK8tIzavN9vHKmO9/+f/6uXjbTnTQ4KZed+Uzmsdz7wl/eNtMdtN87Z7qjB0R75xjhrbn3Rb7k2L1opjv4D++c6Y4dGHu5T942Rnhj7s0P8rqZ7pR9orfNdMe+F+u9Y8Q/vCr3vgj2ypnuvPu9b6Y7ekDylaheOUZ4Y+79s0/0tplu/33eN9Od+E5i2ti9aYz4p/3cuXMqWLCgoqOjrToyI7liprtHjx76+uuvFRkZaRXcklS0aFFJyTPeqYvuU6dOpZv9Ti0gIEABAekvu/Lz85Ofn1+aNh8fH/n4+KTbNuXLzWz7lZ97I+12uWRXXAbtDtmV/tIGHznkk2F7otL3KOUfZ0bt8RnH+E8sca607XGu9DG65MpSu8M45DDpY79ae6JJlDI4feSR78lul92efo3Cq7VfLcc8k3su63tNzVO5F+/KOPcyypmrtbsj91L/7bxqjPDa3Ev7d84ox3I69xz/jBcJLu/KvYy+V+8aI7ws966zf8pce/bm3pX7xKvtnzyRe947Rtx4u/v6lPwd3+ixUebas557ca6bPzbK7ty78nvxqjHCK3Mv7d8zu4/LM9eece65+7g8q7nn3WNEsqvlWLrfl6mtPMQYo+7du2vJkiVas2aNypYtm+b1smXLqmjRolq1apXVlpiYqPXr16tWrVo5HS4AAAAAAGl49Uz3G2+8ofnz5+urr75SSEiIdQ93vnz5FBQUJJvNpl69emnEiBEqX768ypcvrxEjRig4OFjt27f3cPQAAAAAgH87ry66P/nkE0lSvXr10rTPmjVLnTp1kiT1799fcXFx6tatm86fP68aNWooIiJCISEhORwtAAAAAABpeXXRnZk13mw2m8LDwxUeHu7+gAAAAAAAyAKvvqcbAAAAAIDcjKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADeh6AYAAAAAwE0ougEAAAAAcBOKbgAAAAAA3ISiGwAAAAAAN6HoBgAAAADATSi6AQAAAABwE4puAAAAAADchKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADeh6AYAAAAAwE0ougEAAAAAcBOKbgAAAAAA3ISiGwAAAAAAN6HoBgAAAADATSi6AQAAAABwE4puAAAAAADchKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADeh6AYAAAAAwE0ougEAAAAAcBOKbgAAAAAA3ISiGwAAAAAAN6HoBgAAAADATSi6AQAAAABwE4puAAAAAADchKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADeh6AYAAAAAwE0ougEAAAAAcBOKbgAAAAAA3ISiGwAAAAAAN6HoBgAAAADATSi6AQAAAABwE4puAAAAAADchKIbAAAAAAA3oegGAAAAAMBNKLoBAAAAAHATim4AAAAAANyEohsAAAAAADe5ZYruKVOmqGzZsgoMDNQDDzygDRs2eDokAAAAAMC/3C1RdH/++efq1auX3n33Xe3cuVN169ZV06ZNdeTIEU+HBgAAAAD4F7sliu6xY8fqpZde0ssvv6x77rlH48aNU8mSJfXJJ594OjQAAAAAwL9Yri+6ExMTtX37djVu3DhNe+PGjbVp0yYPRQUAAAAAgOTr6QBu1pkzZ+R0OhUWFpamPSwsTCdPnszwPQkJCUpISLB+jo6OliSdO3dOSUlJkiS73S4fHx85nU65XC5r25R2h8MhY4zV7uPjI7vdftX2lM9N4eub/Kd3OByZavfz85Mr1ianAqw2m4x8lSCXfOSUX6p2l3yVKKd85ErVbpdTPkqSU35yySdVe5J85JRD/jKpzsP4KEl2OeVQgIxsqdoTZJdRkgIlSYGJ//xdXQkyMgq0B6aJPd4VL5tsCrAHXLfdyCjBlSAfm4/8bJdjd8mlRFdiunancSrJJMnP5icf2+U+JZkkXbhwwTPfk8slp9NptdlsNvn6+l61/Wo5luO5FytJNjmU9nvyU7xc8kzuBSQGyJYq966WYzmde2fPnvXOMcJbcy82+XvxVYIkI4fSfk++ildO555PQpKcxil/u7/sqXIvySS3B9g9k3tnz5693CdvGyO8Mfdir71/smLPwdxL2Sdea//kidy7cOGC944R//Cq3Iv1ualjIyv2bM69wMSbOzZyR+6ljFteOUZ4Y+79s09013G5FXsWc0/x7j0uv5HcS71PlLxsjPin/dy5c8l/q1R5lhGbud4WXu748eMqUaKENm3apJo1a1rtw4cP19y5c7Vnz5507wkPD9fQoUNzMkwAAAAAwC3o6NGjuv3226/6eq6f6S5UqJB8fHzSzWqfOnUq3ex3ioEDB6pPnz7Wzy6XS+fOnVPBggVls9kyfA/c78KFCypZsqSOHj2q0NBQT4eDWwi5BXcht+Au5BbchdyCO/3b8ssYo4sXL6p48eLX3C7XF93+/v564IEHtGrVKj399NNW+6pVq9SyZcsM3xMQEKCAgLSXVOTPn9+dYSILQkND/xX/SJHzyC24C7kFdyG34C7kFtzp35Rf+fLlu+42ub7olqQ+ffqoQ4cOql69umrWrKlp06bpyJEjeu211zwdGgAAAADgX+yWKLqfe+45nT17VsOGDdOJEydUqVIlLV++XKVLl/Z0aAAAAACAf7FbouiWpG7duqlbt26eDgM3ISAgQEOGDEl36T9ws8gtuAu5BXcht+Au5BbcifzKWK5fvRwAAAAAAG9lv/4mAAAAAADgRlB0AwAAAADgJhTdAAAAAAC4CUU3AAAAAABuQtENAIAbuFyuND+zbim8zZU5CrhLSq4ZYxgLkWO8aYyj6MYtLfUgD3gjY4ycTqenw0A2O3nypOz25F3sJ598on379slms3k4KiCtlBw9ceKEJPaVcJ+UXIuOjpbNZiPXkCNS8i4qKkoJCQmejcWjvx1wI5fLZf1jO3jwoIejATIWExMjHx8fSdLmzZv1999/ezYg3LQff/xRZcqU0c6dO9W7d28NGTJEvr6+ng4LyNCiRYt0zz33KCYmhhNDcKvPP/9cderU0YULF8g1uE1MTIykyycRZ86cqS5dulg1gadQdOOWtGjRIs2dO1eS1LdvX7300kvWP0LAW6xdu1ZNmjRRYmKievfura5du3rVpVC4MQ8//LDatWun+vXra8aMGVqzZo3Kli3r6bCADNWoUUPly5fX0qVLJTHbDffx8/OTr6+vTp48Kcm7Lv3FraFfv34aMmSIzp8/b53YMcaoQoUK8vPz8+j4RtGNW9L27dvVuXNnNW/eXJ9++qkmTJigPHnyeDosII3z58/L399f99xzj2bPnq1vvvlGt912m6fDQjaoUKGCNZvDCT94i4wOOAsXLqwSJUpo8eLFksQMJLJFRrnWqlUrSVJ4eLgkeXzmEbee6OhorV+/XpMmTdKZM2ckSefOnZO/v78kz45vZDtuSSNHjlSFChUUERGhAQMGqHLlyp4OCbCkHIy0atVKZcuW1cGDB1W+fHnlz59fkrjHOxe6csbm9ddf1y+//KJWrVrp8ccf15o1aySlPxBlVhE5KeWA8/jx41ZbYGCg3n//fa1bt06LFi3yVGi4xaTk2pX7sxEjRuj333/X1q1bPREWblEp+9Jp06apQYMG+uqrrzR58mTFxsbq4sWLHr+fW6Loxi3kyoPeBx54QO3bt9eQIUM0e/ZsD0UFpOVyuayDkYSEBDVs2FCTJk1Svnz59PTTT+vPP/+Uj4+PkpKSPBwpsiJlxmb//v367bffFBoaqnvuuUezZs1S06ZN1bp1a61fv9767t977z0dPHiQWUXkiNQnd/773/+qadOm6tq1q06cOKGYmBhVrlxZTZo00ebNmyVx2S9uXOpcmzhxoh588EFNmjRJ0dHRkqT77rtPly5d0rp169JtD9yMlHFr1KhRevTRR/Xll19q8uTJOnv2rEJDQ7V+/XqtX79eGzdu1ObNm7Vo0SKdPXs2x+KzGbIdt4DUi6YtXrxYt99+u2rUqCFJevfddzV69GhNnz5dHTt2tN7zyy+/6N577/VIvPh3Sp2nEyZMUP78+dWmTRsFBwdr8eLFmjx5sowxmjdvnkqUKCFJWrFihWrVqqXQ0FBPho4MjB8/XjVr1tRDDz0kSerfv7+WLVumI0eOqF27durSpYtq1aolSXr++ef11Vdf6d1339WKFSt05swZ7dq1y1pED3CXQ4cOqUyZMpKkr7/+WuXLl9e3336rr776SkeOHFGTJk302muv6bffftPrr7+unTt36s477/Rs0MiV/v77b+uKra+++kqVKlXS0KFDdfDgQe3fv1/9+/dXmzZttGnTJvXu3VuRkZHkGm5a6mOr/fv3WznVp08fRUZG6s8//9SpU6dUu3Zt7du3Ty6XS6GhoSpZsqS+//77nLvNwQC5nMvlsv6/f//+pnTp0mbGjBnmxIkTVvvAgQONv7+/mTZtmjl8+LB5+umnzdNPP+2JcAHTr18/U7RoUTNu3Lg0ebpkyRJTv359U7NmTbNx40bTpEkT88gjj6TJcXiH//3vfyYwMNB06NDB7N692yxatMiULVvWLFq0yMyZM8dUqFDBtGjRwqxcudJ6T8+ePU3dunVNq1atTGJiojHGGKfT6aku4F9g/fr1pnbt2mbZsmWmV69exmazmTNnzlivT5s2zXTu3NkEBASYl19+2dhsNtOnTx+TlJTkwaiRG3311Vfm4YcfNqdOnTK9evUyefLkMRcvXjTGGHPq1Cnz/vvvm/r165sSJUqYZs2amdKlS5v58+cbY4xxOByeDB25WOp96Pvvv29q165tNm7caLUNHDjQVKtWzXTr1s2cPXvWGGPMmTNnjMPhsN6bU8dYzHTjljF69GiNHTtWS5YsUc2aNdNdtvnee+9p+PDhqlixomw2m3bs2CE/Pz8PRYt/q6lTp2rw4MFavXq1qlSpIin5MvOAgABJySuajxgxQr/++qvuuusuRUREWCtucimyd1m7dq1efvllNWnSRAUKFFDp0qX1yiuvSEp+Juirr76qwoULq2fPnmrcuLEk6fTp0ypUqJBsNpscDgePEoNbnDlzRoUKFdLBgwf1xhtv6JdfftGFCxe0YcMGVapUSYmJidbCQpK0ceNGLVy4UCtXrpQxRr/++qv8/f0Zd3BdFy5cUGhoqE6fPq0KFSooX758Onv2rCIjI3XfffelyaFjx47pl19+0YgRI7Rz506VL19e27Zt83APkFulzq23335bc+bM0eTJk1W1alXdcccd1nZvvfWW1qxZo9atW6tr164qUqSI9VrqWfKcCBjI9S5cuGAaNGhgxo0bZ4wx5tChQ+a7774zHTp0MN26dbPO2kdGRprvvvvOOqvK2XzktL59+5qePXsaY4z5/fffzcyZM03VqlVNixYtzJw5c4wxxsTGxppff/3VOgtLnnoXp9NpnRlftWqVKVOmjPHx8TGDBg1Ks93PP/9sHnroIfPkk0+aL7/8Ms1rXL0Ad3nttdfMu+++a+3nPvjgAxMYGGgefPBBs3TpUmu7lHElJRcTExPNX3/9ZUqXLm2GDh2a43Ej92nVqpWZN2+edeVOz549jc1mM9WrVzeHDx+29mFXzmRfvHjRrFq1ytx3333WbDeQWTt37kzz8/r1603ZsmXN5s2bjTHJY9mZM2fMypUrrfHt7bffNiVLljQzZ87M6XAtLKSGW4Kfn5+Cg4P1888/a/bs2erZs6dGjRql06dPa9WqVWrbtq0kqW7dunr88cfl4+Mjp9PJLBPcyqS6kCjl/8+dO6cFCxbo448/1gsvvKAlS5aofv36crlcmjZtmi5evKigoCDdc889stvtcrlc5KkXMcbIbrfLZrMpMTFRDRs21Ny5c3X77bdr06ZNaWZtqlSpok8//VS7du3SDz/8kOZzmD2Eu9StW1dDhgyRj4+PEhIS1KJFC3377bcqVqyYJkyYoAULFkiSNa6k5KKPj4+KFCmiJ598Un/++afH4kfuUaNGDT3zzDPWVYNPPvmkvv/+e504cUIdOnTQnj17JCnd2hV58+ZVzZo1VaBAAf3vf//L8biRe73yyiuaN29emrZTp07J6XTq4Ycf1v/+9z8NHTpUDz/8sJo1a6ZGjRrJ5XLpP//5j/r3768XX3zRQ5GzejlyoYxWVQ0MDFTDhg116NAhdevWTVWqVNHw4cP13Xff6emnn1ZISEi697CAEdwp9Srl0uWie+bMmapbt67mzp2rZ555RsOHD9fYsWPVrVs3JSUlpXusBc8x9R4m1aVs8+fP18iRIxUTE6M6depo1qxZOnDggMaPH6+dO3da76lSpYpWr16tkSNHeips/EukPJauffv28vPz06xZs9S+fXvddtttatCggT788EMFBQXp008/1f/93/9Z7xs/frzi4+OtsebYsWPat2+fEhMTPdIPeL8dO3ZISl480t/fXxMmTNCIESNUtWpV1a9fXz/99JP27dun119/XXv37rXe99///ldS8liaJ08eFSpUSPv375fD4WAVc2RKly5drP3p4cOHJSWf/DHGqHLlymrYsKFOnjypIUOG6LffftOaNWu0bNkySVL37t2tSTeP8NgcO3ADUi+YsHDhQvPRRx+ZIUOGmL179xpjjLl06ZI5cOBAmvc0bNjQdOvWLUfjxL9b6jydNGmSee6550yrVq3M+++/b7WnLDBjTPKlUI8//rhp3bo1lx17qdTf6e7du02tWrXMPffcY8aPH29iYmKMMZcvNX/hhRfSXf5mDIsFwX3++9//mvz585u5c+dabaNGjTI1atQwHTt2NIcPHzbGJN/S0rx5c/PII4+YAQMGmCeeeMIUKlTIys3ff//dPPLII2b79u0e6Qe837vvvmtq165tIiIirLaXX37ZlChRwowfP95aHPT48eOmePHipk6dOmbOnDmmWbNm5s4777TG0h9++MHcfffd5ueff/ZIP5D7pN6Hzpgxwzz22GNm3bp1xpjkS84HDhxovvzyS3Pu3DljTPKtpzVr1jRr1671RLjpsJAacqW33npLn332mR588EHt3LlThQoV0rPPPqu33npLwcHBio6O1p49ezRkyBAdP35cO3bs4BJd5LgBAwZo5syZevbZZxUfH68FCxaoTp06+vTTT1WqVCldvHhRX3zxhRYtWqQ///xT27dvl5+fX84u7IEs6dOnj3799VcZY7Rnzx45nU717t1br732mvLkyaPvv/9er776qu6++26NGzdO5cuX93TI+BfYvn27Zs6cqbVr1+rtt9+2Ho85adIkLVy4UHfccYc++OADlSpVSvv379eHH36offv2KTg4WEuWLLEuD05KSlJcXByPKMRVrVmzRkOHDtVtt92m1157TU2aNJEk9e3bV0uWLNGbb76ptm3bqmjRojp58qSefPJJ+fj4KDAw0FoYVJIcDofOnj2rsLAwT3YHuYRJdaXZoUOHdP78eb3yyisqW7as3nzzTdWuXdvaNjExUdHR0erSpYtOnTqlTZs2ecfVrZ6t+YGs++qrr0zx4sXTnIl/6623TJ06dcyHH35onE6nWbZsmXnqqadMy5YtrQU+mGVCTtq5c6cpWbKk+f777622ffv2mRIlSphWrVoZY5Ifo9KvXz/z/PPPW4sasWia91qwYIEpUKCA2blzp4mJiTFOp9O0a9fOVKtWzYwdO9aa8f7mm29M69ateRwY3K53797W///666/m9ddfN3fffbeZNWuW1T5hwgRTu3Zt06FDB2vG++LFi+bSpUvWlTWMO8iKjRs3mjp16pgnn3zSLF++3Grv1auXKVOmjPn444+tGe+YmBhz5MiRNLnG2IisSJ0vPXv2NAEBAcaY5KvLHn74YdO6dWtrxjspKcnMnj3b1K1b1zz00ENeVQNQdCPXmTp1qrn33ntNdHS0NYhfunTJvPTSS6Z69erWdjt37mT1Z3jMxo0bTYkSJczRo0eNMcYa+Hfs2GHy5MljrWYdExNj5bE37BSQ7O233za///57mrYxY8aY++67L813FhMTY5544gkTFhZmxo4day5dumSMuXyQwMEl3GXDhg3miSeeMAkJCVbbzp07r1p416lTx3Ts2NEcOnQozedwSwtuxLUK77Jly6a51DwF4yFuxu+//25efvllq8A2JrnwrlmzpmndurWJjIw0xhjz/fffm7Fjx3rdZAbXL8KrZbRomo+PjxITE5WYmCibzaakpCTlyZNHgwYN0o4dO7R27VpJUtWqVVn9GTkidZ7Gx8dLkm6//XadO3dO69atkyTrsvGSJUuqRIkSOnfunCQpODhYNptNxhjvuPwJ+v7773X27FmVLVs2Tbufn5/i4+MVHx9vrV4eHBysUaNGKS4uTosXL9a8efPkdDqt2wO4TQDuUqNGDX399dfy9/fX/PnzJSXv91599VU1aNBA//nPf/TZZ59Jknr06KHnnntOP/74o+bOnZvmc1hJH9eT0bFY7dq1NXz4cJ07d05Tp07Vd999J0n6+OOP9fTTT+vtt99WZGRkmvcwHuJGLVy4UE2bNtWOHTt0zz33yOFwSJIaNmyoYcOG6cSJE5owYYLWr1+vBg0aqHfv3vL19fWqJxWR/fBaqe9rnTt3rlW8tGjRQqdOndI777wjSdb9QRcvXtQ999yjQoUKpfkcBnm4U+o8/eSTTzRq1Cj9+eefKlWqlLp06aJJkyZZK2fa7XblyZNHAQEB6QpsDny9x2OPPab//ve/8vX11f/93/9p+/btkqRnnnlGf/31l/r37y9J8vf3lyRFR0eradOmKlKkiKZNm6ZLly55LHb8e/j5+clms2nfvn3q0aOHGjZsKEm67777Miy8u3fvrg8++EADBw70YNTIbVLv4/bu3auffvpJZ8+elcPh0COPPKIPPvggXeE9ZswYjRo1Sq1bt/Zk6LiFOJ1OlShRQn/88YcSEhLk6+urpKQkSZcL76ioKEVERKR5n1dNZnh6qh24nn79+pkSJUqY4cOHmzNnzhhjjPnuu+9MSEiIadeunVm5cqX58ccfTbNmzUzNmjW5fAke0a9fPxMWFmamT59uDh48aIwxZsuWLaZdu3bmrrvuMu+8846ZOnWqadiwoalSpQqXknup1Jeh/f7776Zq1armiSeeMDt27DDGJI89efPmNW3btjVr1641O3fuNE2bNjVvvfWWOXPmjLHZbGbhwoWeCh//AldeDh4XF2eWLl1qKlSoYJo0aWK1R0VFmW7dupl7773XTJo0Kc17GH+QGalz7Z133jH33nuvyZMnj2nQoIEZPny4iYuLM8YYs27dOlO3bl3z9NNPmyVLlqT5DHIN2eXrr782VapUMQ8//LD5888/jTGXb90zxpitW7d6db5RdMOrTZkyxRQqVMhs3749zX1rxlx+3ESpUqVM+fLlTb169ax/fBTeyElLliwxt99+u9m0aVO616KioszIkSNNqVKlzCOPPGJat27tVQt74LLU48ZXX31lzpw5Y7744gvTqFEj07JlS7Nr1y5jTPK9jOXLlzelSpUyJUqUMA899JCJjY01p0+fNhUqVDAbNmzwVBdwC3O5XOnGjJT9YmJiolm2bJm58847TePGja3Xf/75Z9O+fXvTrl0743K5uH8bN+T99983YWFhZuXKlebvv/82Tz/9tClZsqTp27eviY2NNcYYs379elOhQgXz9ttvezha3GpSj1tLliwxjz76qHnsscfMsWPHjDFpC29jvPfYikeGwau99tprCg4O1tixY+V0OuXj45PmUqdLly7pxIkTSkpKUoUKFWS32+VwOLzm/g38OwwfPlxr1qxRRESEdSlTSr6miI2NlY+PjwICAiSJPPUyJtXjSN555x3NmjVLgwcP1uuvv66FCxdq2rRpCg0NVXh4uKpWraoLFy7o0KFDMsaocuXKstvtGjRokD7//HOtW7dOJUqU8HCPcCvZt29fmsfPjR49Wj/99JNOnjypjh07qmnTpipZsqS+/fZbvfnmmypXrpxWrFghSfrjjz90xx13yG63p8lzIDN27dqlrl27atiwYWrSpInWrFmjFi1aqF69etq/f7+eeuophYeHKzAwUFFRUapcubJ3XdKLW0LqsWvx4sWaPHmyfH19NX36dJUqVcrD0WUON7vCazkcDu3cuVOnTp2SlHxfhjFGdrtdCQkJ+uWXX5Q3b16VL19eFStWZNE05LiUc5YxMTFKTExM85qPj48cDoeWLl2qo0ePKjg42Cq4jTHkqZdJ2Zm///77+vTTT/X111+rffv2kqS2bdvqzTffVGxsrIYOHaodO3YoNDRUVapU0X333afffvtNHTp00NSpU/XFF19QcCNbffzxx7r77ru1ZcsWSdLgwYM1atQolShRQnfddZcGDhyogQMHKioqSs2bN9e4ceN0+PBhVa9eXZJUrlw5a/9IwY3ruXLRtPLly+uNN97Qww8/rMjISLVv317jx4/Xt99+q2LFiumzzz7TG2+8ofj4eFWtWlU+Pj5yOp0eih63qpQFZyWpdevW6tGjh06cOKExY8Z4OLLMo+iG1/L19VXTpk21f/9+/fTTT5IuHxgfOnRI77zzjn755Zc072HRNOSklHx86KGH9MMPP1gLpqWIiYnRvHnz9MMPP2T4PniXc+fOKTIyUuPGjdODDz6oS5cuae3ateratavi4+PVoEEDJSYmqlevXtq/f78kWavOV6hQQZGRkapatapnO4FbTrt27dSuXTs1atRImzZtUnx8vBYtWqQJEyZo1qxZmjdvnn777TdNnDhRcXFxatiwoYYOHapy5cqlKaDYPyIzUvJk165dkqSgoCA9++yzypcvn+bOnau2bduqU6dOkqSKFSuqZMmSypcvn7WwpORli1fB66WMUylFdUar5UtpC++nn35akyZN0tixY3MmyGzA5eXwaps2bVLnzp1Vq1Ytvfjii6pfv77+/PNPvfHGG/r777+1Zs0aBnd4hR49emjGjBmaOHGiHnjgAfn5+alv3746ffq0tmzZQp7mAufPn1elSpXUuXNnNW7cWFOmTNHBgwflcrn0559/atiwYQoICNCWLVs0YcKENEXMlbcTADdr1KhR6tOnj/z8/HT69Gn17NlTS5cuVYECBbRgwQLVq1fP2vabb75R69attWrVKj3yyCNpbl9JfUsWkBkbN27UI488omnTpunll1+22p944gmFhIRowYIFkpKvAnryySfVrl072Ww2cg03ZcOGDapbt66ka49bufU2GYpueExm/9FEREQoPDxcf/31lxwOh/Lnzy8fHx/99NNP1rOPGeThabGxsfrwww/18ccfy9/fX0WKFFGBAgW0Zs0a+fn5UZTlEjNmzFC/fv3kdDr12muvqVGjRmrYsKGef/55BQUFafr06da2jD1wly1btqhHjx7auHGj9VjMv/76S++//76mTJmi2bNnq0OHDkpKSrJer1ixol566SX17dvXk6HjFuB0OvX+++9r5MiRmjp1qjp37iyHw6Hw8HCtXr1aRYsW1dmzZ3X+/Hn9/PPP6dbbATIjdc78/PPPqlatmsaOHatevXqlez211PXDvn37VKxYMeXNmzfH4r5R3FQIjzh8+LBKly6dqUG6cePGuuOOO3T8+HH99NNPuuOOO/TUU09Z98xybyy8QXBwsIYMGaI2bdpYz2l+8MEHWdwvl3nppZfUqFEjJSQkWAtXuVwu/fXXX3rooYfSbMsBJtzloYce0o8//iibzaZvvvlGDRs2VFhYmAYNGqTo6Gi9/vrrKlWqlB599FFJyc+KT0hIUEhIiIcjR25z5QRIyi0zgwcPlt1u18svvyyXy6WXXnpJvXv3ls1m0/79+1WwYEFNnTrVuoebk8rIipQ1miRp8uTJ2rNnj4KDg9WnTx8lJSWpX79+1loUqfe1qfN14sSJ+uyzz/Tll1/miqKbmW7kuNGjR2vAgAGKiopSlSpVbvjsKIM8ctL1rsy42uvkae516dIlRUVFadSoUTp8+LB27NjByRPkqP3796t8+fLq3LmzpkyZooCAAJ0+fVpvvvmmvvzyS3Xr1k1FixbVunXrdPjwYe3cuZMcxQ0ZNWqUKlWqpObNm1v7M5fLpeHDhys8PFzTp09X586d0+3TOKmMmzFo0CBNmzZNEyZMUHx8vNatW6fFixdr4MCBeueddyRdnvFOfZz13//+VwMGDNAnn3yitm3berILmca/EuS4559/Xj/99JMaNGig77//Xvfdd1+mCu8rixpmmeBOV+ZkSu5drbi+2usU3LmTMUbbtm3TmDFjlJSUpO3bt8vX15eTKHCr1ONOUlKS7rzzTn377bd67rnn5OPjo4kTJ6pw4cIaN26c9TjNli1bqm3btmrbti05ikxLva9KTExUVFSUBg4cqIiICDVs2NCaiezbt682b96sbt26KT4+Xq+//nqaz6Dgxo06deqUIiIiNGrUKKtwTrm69YMPPlBQUJB69+4tu92e5laa//73v+rfv79mzZqlVq1aebILWULVghxXokQJTZ48WY888ojq16+vn3/+2bqE5GpS7xyOHj0qiRWg4T6pD3wXLFig//znP+rTp4/27NmT6dnu3377LUdihXvYbDbVrFlTw4YN0/Lly+Xn5yeHw0ExA7dJPe589tlnmjdvns6fP6+mTZtq0aJFmjdvnnr06KGEhAQVKVJE77//vp5++mnFxMTohRdekK+vLzmKTEvZV509e1b+/v7673//q1dffVXNmzfXqlWrrNeDg4N1xx136O6779b8+fOV+gJZjsNwM3x9fXX48GHr0cCSVLx4cXXp0kX333+/+vbtaz0S7MqCe+bMmbmq4JYouuEhRYsW1eTJk1WvXr3rFt6pC5lPPvlEb775pv7666+cDhn/IikHvv369dOAAQO0bds2HT58WBUrVtT8+fPlcDjSvSd1nk6aNEkPPPCADh06lJNhI5sFBASoWrVq1tjEjA7cJfX9jf3799fAgQNls9kUGxsrKXn2Z8mSJZo7d6569uyphIQEFStWTNOnT9eKFSuszyBHcT2pj7MmTpyoZs2aaffu3QoNDdWoUaPUsWNHtWjRQhEREXI6nXI4HDp9+rTGjh2ryMjINI9tAjIro+P70NBQPfnkk9q6dav27t1rtd9+++26//771bBhQ40fP17z5s2TJP3f//2fevXqpVmzZql169Y5Fnt2oehGjsjoH1uxYsU0ceLEaxbeqQuZadOmqW/fvmrfvr3CwsJyLHb8Oy1evFjz58/XV199pUWLFunNN9+UJPn7+1sHtikHHlfeZzRkyBDNmjVLZcqU8UjsyH7czgJ3Sn3Cbt68eVq2bJk6deqkEiVKSJIuXryoxx9/3BqXUlYuL1CggLXfZNYR15P6aorIyEg5HA5t3bpVgwcP1p49exQaGqqPPvpIL7/8sh5//HE1btxYVatW1d69e/XII49YBTe5hqxInXd79uzR7t27JSXPdDdt2lS7d+/Wp59+al0hePHiRf35559q06aNateureXLl0uSKleurGXLluW6Ge4UHEXA7VL/Y/vpp5/0448/asuWLZIuX2r+6KOPqn79+tq1a5d1AHFlIdOvXz/NmzdPbdq08Vhf8O/x119/6fHHH1fVqlX1+eef64knntCUKVPUpk0bXbhwQRcuXJDNZpPD4UiTp/3799e0adP03HPPebgHAHKbrVu3qk2bNqpevbr++OMPLViwQPXr19dTTz2lzZs3q1mzZpo7d67Onj2b5jJyTgohM1LyZMCAAXruuecUExOjjh07at26dXr99de1d+9ehYaGatKkSfrss89UuXJltWzZUtu2bbPWC6DgRlalzrsGDRqoYcOGeuihh7R37161atVKb7/9tlauXKkXXnhBLVq0UL169bR//3698sorKlOmjPbt26eEhATdc889atiwoYd7c+NYvRxulbpwHjRokBYsWCC73a6//vpLAwYMUP/+/eXr66uTJ0+qW7du2rBhg7777jtVr17d+oypU6dqwIABmjFjRq68nATeL6OF/AYMGKBdu3apR48eeu655zRq1ChrAZkpU6Zo165dmjBhgvz9/SVdXklz+vTp5CmA60o97iQmJsrf31/dunXT77//rkceeURr1qxRaGiobrvtNl24cEH79u3Thg0blD9//gw/A8iMqKgoNWrUSPPnz1ejRo0kJc8+1qtXT/fee68mTJigihUrppvVZpVyZFXqRR2/+uor9enTRx9//LECAgI0fPhwHTp0SIsXL9aDDz6oTZs2afv27dq8ebPuvPNODRo0SAEBAerYsaOk5KtdAwICPNmdm2cAN3G5XNb/v//++yYsLMxERkaauLg407dvX2Oz2Uy/fv1MYmKiMcaYEydOmEceecQ8/vjj1vvmzZtnQkNDzaJFi3I8fvw7OJ1O6/9XrVplfvzxR2OMMRs3bjT333+/8fPzM+PHj7e2iYmJMU8++aR5/fXXrRyPiIgwNpuNPAWQKanHndmzZ5vFixcbh8Nh1qxZY9q3b2/uvPNOM2rUKLNjxw5jjDHTpk0zTZs2tfaXwI3asWOHKVasmNm9e7cxxlg5tX37dhMYGGieffZZs2vXLmv71MdyQGbExsam+fn//b//ZyZPnmzGjRtntTkcDlO/fn1TsmRJs2XLlnSfcfjwYTNw4ECTP39+K1dzO06PItvNmTNHMTEx1tnR33//XVu3btX06dNVt25drVy5UjNmzNBLL72kcePGadCgQYqPj1fRokW1dOlSffvtt9ZnJSYmauHChcwcwi1MqsWL3n77bfXo0UNRUVG6ePGiqlatqpo1a6pcuXL666+/dOTIEW3cuFFt2rTRkSNHNGHCBCvHa9Sooc2bN5OnAK7rynGnf//+unDhgs6cOaP69etr2rRp2rJli/r3769q1arJGKMvv/xS+fLlY6YRN6148eK6dOmSVq9eLSl5VWhjjEqVKqUyZcpo6dKlevPNN637a7mcHFlRp04dLVmyxPo5JiZGAwYMUPfu3a2nDxlj5OPjo9WrV6t8+fJ67rnntGHDBmudnNjYWH344Yf65ptvtHbtWt17770e6Uu282TFj1vPxx9/bNq2bZvmLP7p06fNtGnTzKVLl8zGjRtNyZIlzaRJk4wxxrzyyivGZrOZV1991TgcDus9SUlJOR47/r3GjBljChcubDZu3Gji4+Ot9ujoaNOnTx9TtWpV4+fnZx544AHTpEkTa2aAPAVwoz7++GNTtGhRs23btjTtKbNE58+fN998841p0qSJqVy5sjXuMPOIG5VybPbBBx+YkiVLmrlz51qvXbp0ybz22mtmy5YtpmjRoqZhw4bmf//7n6dCRS41ffp06zgqISHBGGPMsWPHTJ06dUyFChXMH3/8YYy5PI45nU5TuXJl07p16zSfc+7cOXPixIkcjNz9uKcb2S7lvp8ffvhBFStWVIECBRQTE6M8efKof//+On78uD799FMFBQVp0KBB2rZtmxISEvT9999zbxpylDFGcXFxatWqlRo2bKi33nrLei0pKUl+fn5KSkpSYmKiduzYodKlS+v222+X3W7n/jYAN8wYow4dOqhYsWL68MMPdfDgQe3YsUNTp05VkSJF9Nprr6lcuXIaMGCAXC6XZs2aZT2Hm3EHN2v//v2aMGGC5s6dq+eff16lSpXSd999p7Nnz+rnn3/WX3/9pVKlSqlJkyZatGiRtXYJcDXmilXtP/jgA9ntdnXv3l2hoaE6ceKEGjVqpMDAQC1evFilS5e23mOMkcvlsu7/vlXXqmDkRrZJWQjG19dXa9euVefOnfXSSy/pzTffVGhoqBITE/Xzzz/rtttuU1BQkBITE7V79269/vrratmypaRb9x8avJPNZpPNZtPRo0etA9mUhT/8/PwUFxenffv2qUqVKqpbt671Pp7ZDCArrjwgTUxMVGJion7//Xd9+OGHWrVqlfz8/JQ/f35dunRJQ4cO1erVq/XBBx/o9ttvt56UwLiDrLgy71Lceeed6t+/v6pWraqxY8eqQIECuu2227Ry5UpJUlhYmA4fPqwLFy5QcCNTrsyz+Ph4jRgxQnny5FHnzp1VrFgxrVq1So0aNVKbNm20ePFilSpVynqvj4+Pdfx1q9YBzHQj223dulUPPvig3nzzTf30009q0aKFunfvrnz58mnu3Lnq2LGjGjdurOPHj0uSduzYIV9fX579CLfL6KSOMUZ16tRRgQIF9M0336R5bc+ePZoxY4a6deumsmXL5mSoAG4RqccdY4ySkpLk7++vDRs2aOjQodq3b59ee+01NWzYUA8++KDGjBmjNWvW6JtvvrH2iewfkRk3MnGRmJhoFTqpH4OZ+pF0wLVcLe9GjBihQYMGaezYserSpYs14/3444/r/Pnz2rp1q8LCwjwQsWdwyhQ3bcmSJVq0aJHmz5+v3r17KzIyUps3b9b48ePVq1cvLV26VMYY9ejRQx06dJCfn59WrFih++67T8OHD7ee/cgAD3dKvVP47bfflD9/fvn7+6tgwYIaPny4mjdvrm7dumnKlClKTExUUlKSevfuLZvNptKlS3s4egC5UepxZ8yYMfr555+1fft2vfrqq2rZsqVWr16t06dPq3DhwpKSi+s1a9aoYMGCaT6HghuZkZJr7733nooUKaIePXpcc3tjjHx9fa33OZ1OrqZAlqQe43755RclJiaqRIkSKlKkiN555x05HA716dNHktSlSxcVK1ZM3377rQYOHKhChQp5MvQcx0w3booxRsuXL1erVq1UsWJFHThwQJs2bUqz0mCvXr20ceNGtWrVSt26dVP+/Pmt+2Ulnv2InDVw4EB98cUXunTpkpo2baqXX35ZtWvX1pw5c9S9e3eVK1dOISEhSkxMVGxsrLZt22at7sqBL4AbMXDgQM2YMUODBw/WpUuX9Omnn+qee+7RnDlzdNtttyk6OlqRkZGaOnWqjhw5oh07djDuINNSFz5Lly5V7969NX/+fNWqVeua7yO/kF369eunRYsW6cSJE3rggQd03333acqUKZKkYcOGadiwYRozZow6deqkfPnyWe/7N0263ZoXzcPtmjVrpp07d8pms6l58+Zq3Lixfv75Z9WrV88quJOSkiRJ48aNU926dfXVV1/pP//5jy5dumQV3JIouOFWqc8rfvfdd/p//+//afLkyerXr5/OnDmj/v37KzIyUi+++KJ+/vlnNWrUSLVq1VLr1q21fft2+fn5WZfbAUBWbd26VV999ZWWLVum7t27q06dOjp69Kiee+453XbbbZKks2fPasaMGQoJCdHOnTsZd5AlKQX3mjVrtGrVKvXo0UO1atXStebVUhfcn376qSZMmJAjseLW4HK5rP//4osvtGTJEk2dOlWrVq1Ss2bNFBkZqVatWkmSBg8erA8++EC9e/fWihUr0nzOv6XglsQjw5B1DofD9O3bN82jlT799FMzfvx4kz9/ftOhQwerPS4uzvr/V155xXTs2JHHncAjvvrqK9OzZ08zfvx4q23t2rWmVatWpmbNmiYiIiLD96V+lB0AZNWmTZtM1apVjTHGfP755yZv3rzmk08+McYkP6Zp+fLlxpjkx+qkPNKJxxEiq3799VdTvnx5kydPHjNo0CCrPaNjrtRtU6dONSEhIWbJkiU5EiduLStWrDC9evUyH3zwgdUWExNjFi5caO69914zatQoq33u3Ln/6rGNohs35aOPPjJr1qyxfv76669NaGhomsLbGGM2bNhgjLk80FN4Iyf98ssvpkaNGiZ//vxmxIgRaV5bu3atad26talTp45ZtmyZhyIEcCtIKZpT+/bbb82dd95pFi5caPLly2cmTZpkvbZq1Srz/PPPm717917zM4ArZXQ8tXjxYlOpUiVTrVo1s2nTpmu+z5jkgjtfvnxm0aJF7g0Wt6QTJ06YQoUKGZvNZl5++eU0ryUkJJjnnnvOtGnTJt0x/7+18Obyctwwl8ullStX6umnn9YPP/wgSXriiSc0f/58LVu2TG3bttWvv/6qpk2batiwYWmex8clc8hJFStWVL9+/VShQgXNnz9f27dvt16rV6+eevbsKT8/v3SrlwNAZqW+r3bq1KmaOnWqpOTbsUqXLq127dpp+PDheuONNyQlP1Jn3Lhxio+PV7ly5azPuVUfl4Ps43K5rOOo6OhonT17VpLUqlUrDR8+XDabTVOmTEmzrzP/XGqe8r5p06apf//+mjFjhlq3bp3DPUBuZK64XaFo0aL6/vvvValSJW3atElr1qyxXvP391fVqlV19OhRxcTEpHnfv/W2UhZSQ6Zl9EiApKQkPf/881qzZo2+/PJL1alTR8YYrV27Vu3bt1f+/PkVGhqqH374Ic193IC7XOuRKYsXL9aUKVOUN29ehYeHq1q1atZrUVFRqlKlCge8AG5Kv3799Pnnn+vFF1/Ua6+9pttvv10bN25Unz59dO7cOYWHh+v8+fP65ptvdOzYMUVFRcnX1/eGHveEf5/UExf/+c9/9O233yo6OloFChTQhAkTdN9992nJkiUaOXKk7rnnHr355pt64IEH0nzGlClT9NZbb2nevHnWfbfAtaQen86dO6d8+fIpKSlJgYGB2r59u9q3b6+77rpLr776qpo1a6bz58/rqaeeUvHixfX55597OHrvQNGNTEn9j+3gwYOSZD232Bij1q1bKzIy0iq8Jenvv//Wnj179NBDD8lut7NKOdwudZ7OmzdPO3fuVEhIiKpXr64nnnhCkrRw4UJNnz5defLk0dChQ1W1atWrfgYAZMXcuXPVt29ffffdd2kKHafTqd9//11Dhw7Vzz//rCJFiqh8+fL65JNPrEXT2D8iKwYPHqz//ve/+vDDD1W9enU1bdpUBQsW1PLly1W0aFEtWrRIH330kQoVKqQxY8bo7rvvliSdOXNGAwYMUJMmTfTMM894uBfIDVKf6Hn//fe1YsUKORwOVa5cWf369dPdd9+tLVu2qEOHDjp9+rTuuecehYWF6fTp01q9erUCAgK4ylUU3ciiAQMGaNmyZTp8+LBefPFFdezYUTVq1JCUfFnTxo0btXTpUtWuXTvN+yhkkJPefvttzZkzR4899pjOnj2rv/76Sy+++KJ69eolKbnwnjVrlmJiYjRr1iyVL1/eswEDuCUMHDhQx48f1+zZs61H4VxZUP/1118qWLCg1UbBjaw6duyYnn76aQ0ZMkTNmzdXRESEnnnmGY0ePVqvvvqqtd1nn32mjRs3atq0aWmOwaKjo9M8tgm4mtTF8uTJk/Xuu+9q2LBhOnDggHbv3q0dO3Zo3bp1qlKliqKiotS+fXv5+PioX79+evHFFyVJiYmJ8vf392Q3vEPO30aO3CT1ys0LFiwwpUuXNv/3f/9npk2bZsqXL2+efvpp8/3331vbtGnTxthsNvPzzz97IlzATJ061dxxxx3mxx9/NMYYM2PGDOPv729Kly6dZnXNmTNnmp49e7JoEYAbktHY0aFDB1O3bl3r55QFhOLi4szq1avTbc+iosiMK/Pkl19+MSVLljTGGLN8+XKTN29eM3XqVGOMMRcuXDATJ05M9xns63AzfvzxR9OpUyfz+eefW2379+83rVq1MiVKlDCHDx82xhizc+dOqz7Ytm2bp8L1Skw9IkP/+c9/dODAAev5eevXr9fOnTv13nvv6ZlnnlHXrl01b948HTlyRBMmTNDatWslJT+rb8CAAdazuoGc5HQ6dejQIXXq1Ek1atTQV199pb59+2rIkCFq2rSpxo0bp48//liS1LlzZ40fP152uz3N8yYB4HpSX721fft2HTt2TJJUvXp1nTp1SmvXrlViYqI1Q3ThwgWFh4fru+++S/M5//bLLXF9x44ds/IkZbHP0qVLq2TJknr11Vf13HPPaezYsdYM9/Hjx7VgwQKtXr06zedwtSFu1LfffqtOnTpp5cqVCg0NtdrLli2roUOHqmjRolqxYoWMMapataoWLlyovXv36u2339a2bds8GLl34V8g0vn9998VFRWl0qVLS5KOHj2qZs2a6cMPP9SJEyes7R566CFNmTJFf/75pyZNmmQ98H7EiBHWJXWAO5kr7o7x8fHRu+++q+eff16HDx/WgAED9N577+mdd95R27ZtlZCQoEGDBunTTz9N8z4ORgBkljHGGjPeeecdde7cWZs2bZLL5dIrr7yiwMBAvf3221q2bJlOnjypP/74Q126dJHL5VLjxo09HD1yk7Vr16pz58764Ycf1Lt3bz355JP6888/Zbfbdf/992vhwoVq06aNunbtKil5Rfy+ffsqX758atCggYejx62iUaNGatCggaKjo7Vw4UJdunRJUvJJw3vuuUfGGO3bt082m00ul0v333+/Zs2apejoaBUtWtTD0XsPbiJCOnfddZcWLFggm82mb775RjVr1tT69evVunVrbdiwQVFRUdbiUw899JA++eQTtWrVSnfddZcef/xx63O4Rw3ulHqmKT4+Xv7+/rLb7cqbN6/y5s2rr776Sn5+furQoYOk5IK8SZMmevLJJ9W+fXtPhg4gF0uZdRw+fLhmzJih+fPn64EHHpDdbldgYKA2bdqkli1basiQIfrjjz9UsWJF+fn5aePGjfLx8bHu9QauJyQkRPHx8erYsaPOnTunX3/9VbfffrskqVevXjp48KCioqL0wgsvqFSpUvrhhx90/vx5bd++3bqKi5PKyIqMcsbf39+6SnDDhg2aMGGCBg4cKJvNJofDIZvNppCQEEmyCu+HHnpIGzduVEBAQI73wVuxkBqu6uTJk6pRo4bq16+vsWPHau/evXruuef02GOPqU+fPqpcubK17W+//aa77rqLAwnkCHPFI1O2bt2q8+fPa9iwYbr//vsVHBysVatWqWPHjho+fLhatmypF198UaVLl9akSZNks9k48AVwQ4wxOnfunJo1a6ZXXnlFL730kvVayoJBSUlJ+v3337V7924VLVpUderUyXBRNeBqUvZzAwcO1JgxY1SjRg0NGzZM9evXt7Y5ePCgli1bpiVLligsLEylSpXSyJEj5evrS64hy1IX3PPnz9fu3bsVHBysBx54QE2bNlViYqJ69uyplStXqlSpUnrwwQd14MAB/frrr9q9e3e6fDOsWJ4GRTeuaceOHXrllVd033336aOPPtKvv/6qdu3a6bHHHlPfvn1VqVKlNNtTyMDdUu8UPvzwQ40cOVJdu3bVTz/9pKioKI0aNUrt27dXXFyc3n77bX399dfKmzevChQooK1bt8rPz48dAYCbcvToUT3wwANauHChGjRokGZciouL0/nz51W8ePE072H/iKxIyaklS5bIbrdr0qRJCggIULdu3dS8efNrvpdcw83o16+f5s6dq/vuu08xMTHatGmTBg8erPDwcCUmJuqtt97S9OnTVbduXbVo0ULdu3eXxJMYrodrTnBN999/vz799FPt2LFDb731lipWrKgFCxZo3bp1GjRokA4cOJBmewZ5uFvKge2hQ4d05MgRLVmyRKNGjdK6devUpUsXvfvuu5o3b56KFCmiUaNGadmyZRo7dqy2b99uPQ+XghtAZmW00GKxYsWs21ik5HHJ6XRKknbu3KnFixfrwoULad7D/hHXkzrXUv6/VatWeuqpp/TBBx8oNjZWU6ZMSbMg38yZM9PlKLmGGxUREaE5c+boyy+/1MqVK7Vy5UpNnz5dI0aM0Icffih/f3+NGTNGHTt2lMPhUHx8vLW+Dnl3bcx0I1N27typLl266IEHHtBHH32knTt3auLEiVq0aBH3CyHHLV68WM8884zKlCmjOXPmqE6dOtZrffv21ezZs/XBBx+oXbt2aZ5Fytl/AFmRegZ79erVunTpkowxevrppzVy5Eh98cUXat++vd566y1JyTM9TzzxhEJCQvR///d/nOBDpqXOtalTpyoqKkrR0dF65pln1LhxY+XNm1c//fSTBg4cKF9fXzVq1EiRkZHaunWrjh8/zrEYbsiVx0Vz587VmDFjtH379jTtH3/8sYYPH661a9eqcuXKSkxMVI8ePbRr1y41bdpU/fr1U1BQkCe6kGvwLxSZUq1aNc2cOVNRUVF66aWX9OCDD1qXPPG4JeS01q1bq1OnTjp06JB2796thIQE67UxY8aoc+fO6tatm/UouxQU3AAy68pVyjt16qRhw4apffv26t69u6pXr666detq+vTpatKkibp27apHHnlEx44d0/z582Wz2dI9YQG4mpRcS3nqRr58+RQXF6eRI0dq6NChio6OVo0aNTR69GgVLlxYixcvVlJSko4ePSq73U6uIctiYmKs46JDhw5Jkm677Tb9/vvv2rNnj6TLT4l55JFH5Ovrq5iYGEnJi6tNmjRJd9xxhyIjIxUXF5fzHchluPAemVatWjVNmTJFU6ZMUXBwsNXO2VW405Uraabcjz1z5kzFxMRowIABKl68uJo2bSo/Pz9Jyfd6lypVSi1atPBU2AByuZRZ6tGjR+uzzz7Tl19+qYceekgTJ07Um2++qbi4OHXu3FkNGjTQ7NmzFRMTozp16mjEiBEsZIVMS73GyMyZM7Vo0SKtXLlS999/v5YtW6annnpKcXFxio+P1/Dhw1W9enVNmjRJxhgVKFDAWkGaXENWREREaMWKFRo9erTefPNNff/999q2bZsqVaqkhx9+WB999JHefvttVahQQZJUpEgRFShQQPHx8ZKSr+rx8/PTZ599prNnz+q2227zZHdyBS4vR5al7CB4FAXcLXWOzZw5U1u3bpUxRhUqVFCvXr0kSc8++6xWrVql2bNnpym8U3AwAuBGHT9+XO+8846aNm2q5557TkuWLNHLL7+sN954QxMmTFCzZs00evRolSxZMs37uJUFWXXx4kWtWLFCu3fv1tChQ/Xll1+qS5cuCg8P159//qkZM2aoU6dOGjx4cJrbpjgWw40YNmyYFi1apODgYP3xxx/64YcfdPfdd0tKPt6aOXOmChQooC5duui2227TyJEjdf78eW3evNnKN3Ivayi6cUNY/Rk5qX///po3b56effZZ5cmTRyNHjlT37t01YcIESVLbtm31/fffa9KkSWrdujVFNoBsER8fr++++07169fXH3/8oWeeeUa9e/dWz549NWbMGPXr10+PPvqo5syZk67wBjJr4cKFWrNmjYYOHWo9Wq558+Z64YUX1LdvXx07dkwPPvigfH191bNnT2sNASCrUp8QfOaZZ7R48WJ17NhR48ePV2hoqLXdggUL9NVXX+mLL75QlSpVVKBAAa1cuVJ+fn6cVLxBnJ7ADaHgRk6JjIzU4sWLtWjRIo0bN07Vq1eXv7+/7r33XmubhQsXqkqVKpo1axYFN4BsExgYqCeeeEL58+fX999/r4oVK6pjx46SpICAAL3wwgsKDAxUiRIlPBwpcpMr57v++OMP/f/27j2oqqr/4/ibm5QiaqZ4qUBMKUTLgUKxcUoaRzIvQTVGqaNiIoqS4igqXhLl4gXvOdzUFESwkw6a95HQGUdHFBOV0jTvQCN5JEcl4Tx/NOcE+vx+jyEI6uf1j+Pe+5xZM5zZe332Wuu7cnNz+eOPP2jZsiW//PILRqMRPz8/AIqLi3nnnXeIjIxkwoQJddFkeUqYw/LMmTNp3rw54eHh5OfnM2fOHK5cuWK57rPPPiM9PZ0zZ86wdetW9uzZY9kBRoG7ehS6RaReMRfmM/976dIl2rRpg4+PD99//z1Dhgxh6dKljBo1ips3b7Jz504A9u7dyw8//FBn7RaRp5P5Rd7Zs2e5efMmVlZW3Llzh507d9K3b1+2b9+uoqLy0CrPFCwpKQFg+vTp2NnZERoaCkCTJk2wt7cnKyuLgoICZsyYQaNGjQgKCqqyPZ3Iw6p8f0pKSiI6Opphw4YRFxfHgAED2Lt3L4sXL+bq1auW606dOoWrqytt27a13OM0sFF9Ct0iUm/cvXvXsj7o/PnzADg6OtKsWTNSUlIYMmQICxYs4MsvvwTg0KFDbNy40XKtOr4iUtPMASkoKIhDhw7Ro0cPunTpwoULFwgICLBcp7WN8jDMv6e5c+cSGBhIVlYWAN9++y2XL18mPj6eLl260KtXLxISEujVqxfFxcUkJCRYKuJrpFH+LfP9adeuXRQVFZGWloa3tzfw90ufgIAAsrOziYmJITc3l969e1v6Wvd/h1SP1nSLSL2QmZnJlStXCAsLIywsjN27d5Obm0t+fj4BAQFcunSJ+fPnM3HiRABu375NQEAATk5OpKSkaMmDiNS6o0ePYjAYcHR0ZMKECapSLtVSXl5OYGAgmZmZNGzYkHHjxvHJJ5+wadMmzp49y4IFC3jxxRc5c+YMRqMRHx8fy1pv/dakug4ePEhgYCDXr19nw4YN9O3bl7t372Jvbw/8vfNLZmYmRUVFtG3bluzsbBo0aFDHrX56KHSLSL0we/ZsZs+ezXvvvcexY8fIycnBw8MDgNTUVAYPHkxoaCg9evTA0dGRhQsXUlxcTG5uLra2tiruJyKPnUKQVNe+fftYs2YN3bp147vvvqNDhw6UlJRw+PBhS7G+ylS8Sh7VtWvXSElJIT4+Hj8/P9atWwdAWVmZJVwXFBRgNBrx8vLSi54aptAtIvWGj48Phw8fJjw8nJiYmCrn1q5dS0pKCnl5eXTu3JkWLVqQkZGhSpoiIvJEiI+PB+Crr76ioqKCoKAgTCYTq1atIiMjg5ycHJKTkwE4fvw4nTt3rsvmyhPs/u28zCPaRqORVatWsWrVKgICAliwYAFQNXibqW9Vs/TqQkTqjHl0uqysDDs7O9zc3HjjjTeYP38+rVu3ZsSIETg4OGAymRg6dCj9+/fHaDTi4OBA8+bNsbKy0ltYERGp98rKyrh16xazZs3i8OHDBAUFkZiYiJeXF/Hx8UyZMoXAwEAcHR05ceIE7u7udd1keUJVDtxLlizhp59+Ii8vj5CQEN5//33GjRuHyWRi/fr1WFtbExcXR4MGDR4I6grcNUsj3SJSJyrf3G/fvs3zzz9vOTdr1izmzJnDokWLCAoKolGjRgCcPHmyylZhmlIuIiJPkpMnTxIZGcm1a9dwd3enV69eGAwGIiIi8PLyAv55tmmkUR7FlClTWLNmDePHj8dkMhEXF0f//v1JTEyktLSU5ORkUlNT6datGwkJCXXd3KeehodE5LEzmUyWwL1s2TL2799P69at6d+/P76+vsyaNQsrKyvCw8MpKyvDz8+PqVOncuPGDXJyciwdEgVuERF5knTq1ImEhAQOHDjAvHnzSE1NxcHBgS5dulhCt6qUy6M6ePAgBoOBrKws3nrrLY4cOUJkZCS9e/fG3t4ee3t7Ro0aRWlpKb/99psGMR4DjXSLyGNV+cYeExNDdHQ0X3zxBdu3b+eVV17h448/ZuzYsQBERUWxaNEiWrVqxXPPPcehQ4ews7Ory+aLiIjUmMjISBYvXoyXlxf79u2r6+bIE+r+qeHZ2dlERERw8OBB0tPTGTlyJHFxcYwePZo///yTo0eP0rNnT4xGI46OjpYXPQretUcj3SLy2FR+KBw9epRff/2VLVu28O6771JYWMikSZPYuHEjJpOJ0NBQpk+fjq+vL3/99Rc9evRQJU0REXkqmAPOnDlzGDBgAF27dq1yXOTfMPetSktLady4MSUlJVy9epWMjAyCg4OJjY1l9OjRAOTk5LBu3Tpefvll2rVrB+h39zhol3MRqXUxMTHcuHHD8lDYsGEDwcHBHD58GBcXFwBatWpFdHQ0Li4uZGZmsmLFCgC6d+9Oz549sbGxoby8XIFbRESeeOaRRcCyPVN5ebmCj1RbYmIi3t7eAPj7+9OxY0cGDRrEtGnTCAkJAeDOnTt88803VFRU4OzsbPmsfne1T6FbRGpVeno6p0+fpnHjxpZjr732Go6Ojpw/f549e/ZYjr/00kvExMTg6urKsmXLMBgMVb5L69tERORpcX/Q0TNOHoV5toS57xQSEkL37t1Zv349WVlZJCYmMnDgQM6fP09qairW1tZUVFTUZZOfKVrTLSK1zjytfOvWrXh6etK6dWsKCgoYP3489+7dY8yYMfj7+1uuv3jxImvXrmXq1KnqhIiIiIhU8t+mg5eUlPDpp5/i4uJCUlIS5eXl5OTksHLlSvbv38+rr76Kq6srycnJ2NnZqTr+Y6bQLSK1pvJD4dChQwwbNoy3336b2NhYnJycOHHiBBMnTgQgODi4SvA200NBRERE5EFGo5EmTZpY/r9nzx769u3L5s2b8fPzsxy/du0aLVq0wMbGBisrK9XHqQOaXi4itabyW1hvb2+GDx/OuXPnmDp1KoWFhXTu3JlFixZhZWVFYmIi69evf+A7FLhFREREqlq4cCGDBg0iPj4e+Hugw9fXF39/f7Zt28bt27e5d+8eAE5OTtja2lpqCShwP34K3SJS4/6vNULh4eEMGDCAgoICpk2bRmFhIR4eHixcuJDi4mJyc3Mfc0tFRERE6r/7+1Y+Pj44OzuzcuVKPD09SUxMxGg0EhAQQEZGBiUlJdja2mIymapsJ6aiaXVD08tFpEZV3hZsy5YtFBQU0KpVKzw8PPD09AT+fjtrMBh4/fXXmTt3Lk5OTpw7dw4XF5cqDwYRERGRZ13lvtXmzZu5evUqzZs3p0uXLjRt2pSoqCiOHDlCcXExsbGxhIaG0qdPH1JSUjRjsJ7Q3AIRqTGV36ZOnjyZtLQ02rdvT3l5ORUVFUyZMoV+/fpZ1nFv2bKF0aNHk5SUhKurK1D1wSIiIiLyrDP3i8LDw1m7di1ubm7k5eXh7u5OcHAwK1as4OrVqyxfvpyZM2fy+++/YzQa1Z+qR/SXEJEaY56ytGzZMtLT08nIyCA7Oxt/f3+OHDlCeHg4mZmZAEycOBFfX19atmxJ06ZNLd+hB4SIiIhIVZs2bSItLY0dO3awf/9+Ll++zJtvvklycjIpKSm0adOGefPmkZmZSVpaGps2baqyH7zULU0vF5EaVVpaytixY/Hy8iI0NJSsrCwGDx5MSEgIJ0+eJD8/nyVLlvDhhx8C/1Q41wi3iIiIyH8XFxeHwWBg//792NjYYG1tTVFRESEhIdy8eZPdu3c/8BlVKa8/1MMVkUdyf2GPxo0bM336dD744ANOnz5NWFgYX3/9NfPmzeOjjz7i4sWLDB48mF27dgFY3sIqcIuIiIhUZR4ftbW15c6dO5SVlWFtbc29e/dwcnIiIiKCvXv3kpeX98BnFbjrD/VyRaTaKo9O79ixg40bN3Lq1Ck6dOhA+/btOXjwIG3btmXEiBEANGvWjH79+hEVFYWvr6/le1RJU0RERORB5j5Snz59yM/PZ8GCBcA/gbq8vBwPDw8cHR3rrI3yv+n1h4hUmzlwR0REsHTpUtq2bcv58+eJj49n1KhR2NracvbsWY4ePYq3tzfJycl4eHgQEhKClZUV5eXlqqopIiIi8j+4u7uTnJzMyJEjKS0txd/fn2bNmjF79myaNm2Ki4tLXTdR/h8K3SLyr5nXYZtMJi5cuMCBAwfYs2cPbm5urF69mnHjxnHr1i28vb3p1q0bAwcO5IUXXqBBgwYYDAbLZxW4RURERB7O0KFDcXBwIDQ0lA0bNtCwYUNatmxJdnY21tbWqo9Tj6mQmoj8K5Vv6CUlJVy/fp2UlBSioqIsIXrJkiVMmDCBxYsX4+HhwY0bNygqKiIoKAhbW1uNcIuIiIhUU2FhIUVFRZSVleHp6WlZ46013PWXQreIVMu0adPYvXs3P//8My4uLmRkZODm5mY5v3jxYiZPnsykSZOIioqyHFfgFhEREak5GuGu//TXEZGHUrlKeXp6OqtXr2bw4MEMHz6cs2fPkpSUxIULFyzXhIWFMWPGDPbt21dlj0gFbhEREZGao8Bd/2mkW0T+lR9//JGMjAy8vb0ZMmQIACtXriQ6OprPP/+c0aNH4+zsbLm+8vpvVSkXERERkWeNJv6LyEMrLCxkxIgRFBUV0bFjR8vxkJAQTCYTMTEx2NjYMGLECFxdXQEUuEVERETkmaa5CCLy0Fq1aoXBYKBNmzZs27aNEydOWM6NGTOGqVOnEhsby65du6p8ToFbRERERJ5Vml4uIv/a8ePHGTZsGF5eXowfP55OnTpZzhkMBgYMGKC12yIiIiIiKHSLSDUdO3aMoKAgPD09CQsLw93dvcp5VSkXEREREVHoFpFHcOzYMUaNGoWzszNxcXG0a9eurpskIiIiIlKvaE23iFRb165dWb58OY0bN65SsVxERERERP6mkW4ReWTm6uQVFRXaK1JEREREpBKFbhGpEdoWTERERETkQRqSEpEaocAtIiIiIvIghW4RERERERGRWqLQLSIiIiIiIlJLFLpFREREREREaolCt4iIiIiIiEgtUegWERERERERqSUK3SIiIiIiIiK1RKFbREREREREpJYodIuIiIiIiIjUEoVuERERERERkVryH2LBlQ5B7EZyAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paradigms = ['minmax', 'tetra']\n",
    "markers = ['o', '*', 's']\n",
    "colours = ['orange', 'green', 'blue']\n",
    "\n",
    "history = []\n",
    "\n",
    "for idx, paradigm in enumerate(paradigms):\n",
    "    options = {\n",
    "        'n_bits': 10,\n",
    "        'n_iter': 500,\n",
    "        'n_pop': 600,\n",
    "        'r_cross': 0.8,\n",
    "        'max_stall': 10,\n",
    "        'aggregation': paradigm,\n",
    "        'var_type': 'real'\n",
    "    }\n",
    "\n",
    "    print(f'Running GA with {paradigm}')\n",
    "    ga = GeneticAlgorithm(objective=objective, constraints=constraints, bounds=bounds, options=options)\n",
    "    score, design_variables, _ = ga.run()\n",
    "\n",
    "    x1_opt, x2_opt, x3_opt = design_variables\n",
    "\n",
    "    metrics = {\n",
    "        'initial_cost': compute_initial_cost(x1_opt, x2_opt, x3_opt),\n",
    "        'maintenance_cost': compute_maintenance_cost(x1_opt, x2_opt, x3_opt),\n",
    "        'sight': compute_sight(x1_opt, x2_opt, x3_opt),\n",
    "        'accessibility': compute_accessibility(x1_opt, x2_opt, x3_opt),\n",
    "        'water_quality': compute_water_quality(x1_opt, x2_opt, x3_opt),\n",
    "        'overtopping_risk': compute_overtopping_risk(x1_opt, x2_opt, x3_opt),\n",
    "    }\n",
    "\n",
    "    preferences = {\n",
    "        'initial_cost': float(preference_initial_cost(np.array([x1_opt]), np.array([x2_opt]), np.array([x3_opt]))[0]),\n",
    "        'maintenance_cost': float(preference_maintenance_cost(np.array([x1_opt]), np.array([x2_opt]), np.array([x3_opt]))[0]),\n",
    "        'sight': float(preference_sight(np.array([x1_opt]), np.array([x2_opt]), np.array([x3_opt]))[0]),\n",
    "        'accessibility': float(preference_accessibility(np.array([x1_opt]), np.array([x2_opt]), np.array([x3_opt]))[0]),\n",
    "        'water_quality': float(preference_water_quality(np.array([x1_opt]), np.array([x2_opt]), np.array([x3_opt]))[0]),\n",
    "        'overtopping_risk': float(preference_overtopping_risk(np.array([x1_opt]), np.array([x2_opt]), np.array([x3_opt]))[0]),\n",
    "    }\n",
    "\n",
    "    _, stakeholder_pref_arrays = objective(np.array([[x1_opt, x2_opt, x3_opt]]))\n",
    "    stakeholder_preferences = {\n",
    "        name: float(stakeholder_pref_arrays[i][0])\n",
    "        for i, name in enumerate(STAKEHOLDER_NAMES)\n",
    "    }\n",
    "\n",
    "    history.append({\n",
    "        'paradigm': paradigm,\n",
    "        'design': (x1_opt, x2_opt, x3_opt),\n",
    "        'metrics': metrics,\n",
    "        'preferences': preferences,\n",
    "        'stakeholder_preferences': stakeholder_preferences,\n",
    "    })\n",
    "\n",
    "    print(f\"Optimal design for {paradigm}:\")\n",
    "    print(f\"  x1 = {x1_opt:.2f}\")\n",
    "    print(f\"  x2 = {x2_opt:.2f}\")\n",
    "    print(f\"  x3 = {x3_opt:.2f}\")\n",
    "    print('  Metrics:')\n",
    "    for key, value in metrics.items():\n",
    "        print(f\"    {key}: {value:.3f}\")\n",
    "    print('  Preference scores:')\n",
    "    for key, value in preferences.items():\n",
    "        print(f\"    {key}: {value:.2f}\")\n",
    "    print('  Stakeholder aggregates:')\n",
    "    for name, value in stakeholder_preferences.items():\n",
    "        print(f\"    {name}: {value:.2f}\")\n",
    "    print('-' * 60)\n",
    "\n",
    "# Optional: bar plot comparison of preference scores\n",
    "labels = OBJECTIVE_KEYS if history else []\n",
    "x = np.arange(len(labels))\n",
    "width = 0.25\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "for i, record in enumerate(history):\n",
    "    scores = [record['preferences'][label] for label in labels]\n",
    "    ax.bar(x + i * width, scores, width, label=record['paradigm'], color=colours[i % len(colours)])\n",
    "\n",
    "ax.set_xticks(x + width)\n",
    "ax.set_xticklabels(labels, rotation=45)\n",
    "ax.set_ylabel('Preference score')\n",
    "ax.set_ylim(0, 110)\n",
    "ax.legend()\n",
    "ax.grid(linestyle='--', axis='y')\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7e09defb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paradigm | x1 [m] | x2 [-] | x3 [-] | Initial cost | Maintenance cost | Sight | Accessibility | Water quality | Overtopping risk\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "  minmax | 1598.44 |   2.97 |   2.12 |    1.390e+09 |      9.451e+11 |  9.99 |         6.97 |          9.11 |           0.326\n",
      "   tetra | 1598.44 |   3.23 |   1.62 |    1.495e+09 |      1.049e+12 |  9.99 |         7.68 |          9.32 |           0.298\n",
      "\n",
      "Stakeholder weights (normalized):\n",
      "  Municipality: 0.526\n",
      "  Residents: 0.211\n",
      "  Environmental agency: 0.105\n",
      "  Tourism Business: 0.158\n",
      "\n",
      "Stakeholder aggregate preference scores:\n",
      "Paradigm    |       Municipality |          Residents | Environmental agency |   Tourism Business \n",
      "--------------------------------------------------------------------------------------------------\n",
      "      minmax|              74.70 |              80.82 |              82.06 |              81.26 \n",
      "       tetra|              74.26 |              82.61 |              84.45 |              82.95 \n"
     ]
    }
   ],
   "source": [
    "# Summary of optimal design variables and key metrics\n",
    "if not history:\n",
    "    raise RuntimeError('Run the GA cell first to populate history.')\n",
    "\n",
    "print('Paradigm | x1 [m] | x2 [-] | x3 [-] | Initial cost | Maintenance cost | Sight | Accessibility | Water quality | Overtopping risk')\n",
    "print('-' * 150)\n",
    "for record in history:\n",
    "    x1_opt, x2_opt, x3_opt = record['design']\n",
    "    metrics = record['metrics']\n",
    "    print(f\"{record['paradigm']:>8} | {x1_opt:7.2f} | {x2_opt:6.2f} | {x3_opt:6.2f} | \"\n",
    "          f\"{metrics['initial_cost']:12.3e} | {metrics['maintenance_cost']:14.3e} | \"\n",
    "          f\"{metrics['sight']:5.2f} | {metrics['accessibility']:12.2f} | {metrics['water_quality']:13.2f} | \"\n",
    "          f\"{metrics['overtopping_risk']:15.3f}\")\n",
    "\n",
    "print()\n",
    "print('Stakeholder weights (normalized):')\n",
    "for name, weight in zip(STAKEHOLDER_NAMES, STAKEHOLDER_WEIGHTS):\n",
    "    print(f\"  {name}: {weight:.3f}\")\n",
    "\n",
    "print()\n",
    "print('Stakeholder aggregate preference scores:')\n",
    "row_header = 'Paradigm'.ljust(12)\n",
    "for name in STAKEHOLDER_NAMES:\n",
    "    row_header += f\"| {name:>18} \"\n",
    "print(row_header)\n",
    "print('-' * len(row_header))\n",
    "for record in history:\n",
    "    row = f\"{record['paradigm']:>12}\"\n",
    "    for name in STAKEHOLDER_NAMES:\n",
    "        row += f\"| {record['stakeholder_preferences'][name]:18.2f} \"\n",
    "    print(row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2083713b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preference curves with GA solutions overlaid\n",
    "if not history:\n",
    "    raise RuntimeError('Run the GA cell first to populate history.')\n",
    "\n",
    "objectives = [\n",
    "    {\n",
    "        'key': 'initial_cost',\n",
    "        'title': 'Initial Costs',\n",
    "        'knots_x': initial_cost_knots,\n",
    "        'knots_y': initial_cost_pref,\n",
    "        'compute': compute_initial_cost,\n",
    "        'xlabel': 'Initial cost [€]'\n",
    "    },\n",
    "    {\n",
    "        'key': 'maintenance_cost',\n",
    "        'title': 'Maintenance Costs',\n",
    "        'knots_x': maintenance_cost_knots,\n",
    "        'knots_y': maintenance_cost_pref,\n",
    "        'compute': compute_maintenance_cost,\n",
    "        'xlabel': 'Maintenance cost [€]'\n",
    "    },\n",
    "    {\n",
    "        'key': 'sight',\n",
    "        'title': 'Sightline',\n",
    "        'knots_x': sight_knots,\n",
    "        'knots_y': sight_pref,\n",
    "        'compute': compute_sight,\n",
    "        'xlabel': 'Sight score [-]'\n",
    "    },\n",
    "    {\n",
    "        'key': 'accessibility',\n",
    "        'title': 'Accessibility',\n",
    "        'knots_x': access_knots,\n",
    "        'knots_y': access_pref,\n",
    "        'compute': compute_accessibility,\n",
    "        'xlabel': 'Accessibility score [-]'\n",
    "    },\n",
    "    {\n",
    "        'key': 'water_quality',\n",
    "        'title': 'Water Quality',\n",
    "        'knots_x': water_knots,\n",
    "        'knots_y': water_pref,\n",
    "        'compute': compute_water_quality,\n",
    "        'xlabel': 'Water quality score [-]'\n",
    "    },\n",
    "    {\n",
    "        'key': 'overtopping_risk',\n",
    "        'title': 'Overtopping Risk',\n",
    "        'knots_x': overtopping_knots,\n",
    "        'knots_y': overtopping_pref,\n",
    "        'compute': compute_overtopping_risk,\n",
    "        'xlabel': 'Overtopping risk [-]'\n",
    "    },\n",
    "]\n",
    "\n",
    "n_cols = 3\n",
    "n_rows = 2\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for idx_obj, obj in enumerate(objectives):\n",
    "    ax = axes[idx_obj]\n",
    "    knots_x = np.array(obj['knots_x'], dtype=float)\n",
    "    knots_y = np.array(obj['knots_y'], dtype=float)\n",
    "\n",
    "    # gather GA results for this objective\n",
    "    samples = [knots_x.min(), knots_x.max()]\n",
    "    for record in history:\n",
    "        x1_opt, x2_opt, x3_opt = record['design']\n",
    "        samples.append(obj['compute'](x1_opt, x2_opt, x3_opt))\n",
    "\n",
    "    x_min = min(samples)\n",
    "    x_max = max(samples)\n",
    "    if np.isclose(x_min, x_max):\n",
    "        x_max = x_min + 1.0\n",
    "\n",
    "    x_range = np.linspace(x_min, x_max, 300)\n",
    "\n",
    "    # Clip into knot domain to avoid extrapolation beyond preference definition\n",
    "    x_range_clipped = np.clip(x_range, knots_x.min(), knots_x.max())\n",
    "    pref_curve = pchip_interpolate(knots_x, knots_y, x_range_clipped)\n",
    "\n",
    "    ax.plot(x_range, pref_curve, color='black', label='Preference curve')\n",
    "\n",
    "    for i, record in enumerate(history):\n",
    "        x1_opt, x2_opt, x3_opt = record['design']\n",
    "        metric_value = obj['compute'](x1_opt, x2_opt, x3_opt)\n",
    "        pref_value = record['preferences'][obj['key']]\n",
    "        ax.scatter(metric_value, pref_value, color=colours[i % len(colours)],\n",
    "                   marker=markers[i % len(markers)], s=120, edgecolor='black',\n",
    "                   label=f\"Optimal {record['paradigm']}\")\n",
    "\n",
    "    ax.set_title(obj['title'])\n",
    "    ax.set_xlabel(obj['xlabel'])\n",
    "    ax.set_ylabel('Preference score')\n",
    "    ax.set_ylim(-5, 105)\n",
    "    ax.grid(linestyle='--')\n",
    "\n",
    "    if idx_obj == 0:\n",
    "        ax.legend()\n",
    "\n",
    "# Hide the unused subplot if objectives < rows*cols\n",
    "for j in range(len(objectives), len(axes)):\n",
    "    axes[j].axis('off')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bb15a95",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mude-base",
   "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}