{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30568338",
   "metadata": {},
   "source": [
    "# AutoFitter Tutorial\n",
    "\n",
    "This notebook demonstrates how to use the `AutoFitter` class for automatic distribution selection and comparison.\n",
    "\n",
    "## Overview\n",
    "\n",
    "`AutoFitter` is designed to:\n",
    "- Automatically test multiple probability distributions\n",
    "- Select the best-fitting distribution based on various criteria (RMSE, AIC, BIC, etc.)\n",
    "- Support all 113 SciPy continuous distributions\n",
    "- Use lazy initialization for memory efficiency\n",
    "- Provide comprehensive comparison tables and rankings\n",
    "\n",
    "## ⭐ Best Practice: Use RMSE for Distribution Selection\n",
    "\n",
    "**For real-world data, we strongly recommend using RMSE (Root Mean Square Error) as the primary criterion** for selecting the best distribution. Here's why:\n",
    "\n",
    "- **RMSE is robust**: Directly measures fit quality without being affected by sample size\n",
    "- **Avoids p-value inflation**: P-values from goodness-of-fit tests (KS, Chi-square) can become unreliable with large datasets due to the \"large sample size effect\" (see MagicAdjuster tutorial for details)\n",
    "- **Practical significance**: RMSE reflects actual fit quality, not just statistical significance\n",
    "\n",
    "In this tutorial, we use **synthetic data** where p-values are more reliable. However, keep in mind that real-world data often exhibits the large sample size effect, making RMSE the safer choice."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e23d530c",
   "metadata": {},
   "source": [
    "## Setup and Data Generation\n",
    "\n",
    "Let's create some sample data that follows a known distribution to test AutoFitter's capability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44d33e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 1000 wind speed measurements\n",
      "Min: 0.69 m/s\n",
      "Max: 27.57 m/s\n",
      "Mean: 7.04 m/s\n",
      "Std: 3.70 m/s\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import magica as ma\n",
    "from magica.core.auto_fitter import AutoFitter\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(123)\n",
    "\n",
    "# Generate mixed wind speed data (complex scenario)\n",
    "# Simulate different weather conditions\n",
    "low_wind = np.random.weibull(2, 400) * 6 + 1    # Calm periods\n",
    "normal_wind = np.random.lognormal(2, 0.5, 400)  # Variable periods  \n",
    "high_wind = np.random.gamma(3, 2, 200)          # Storm periods\n",
    "\n",
    "# Combine all periods\n",
    "wind_data = np.concatenate([low_wind, normal_wind, high_wind])\n",
    "\n",
    "print(f\"Generated {len(wind_data)} wind speed measurements\")\n",
    "print(f\"Min: {wind_data.min():.2f} m/s\")\n",
    "print(f\"Max: {wind_data.max():.2f} m/s\")\n",
    "print(f\"Mean: {wind_data.mean():.2f} m/s\")\n",
    "print(f\"Std: {wind_data.std():.2f} m/s\")\n",
    "\n",
    "# Visualize the data\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(wind_data, bins=50, density=True, alpha=0.7, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Wind Speed (m/s)')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Wind Speed Distribution')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(wind_data[:200], 'b-', alpha=0.7, linewidth=0.8)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Wind Speed (m/s)')\n",
    "plt.title('Wind Speed Time Series (first 200 points)')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf8505e",
   "metadata": {},
   "source": [
    "## Basic AutoFitter Usage\n",
    "\n",
    "Let's start with the default configuration that tests a curated set of stable distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce69804c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded data: DataProcessor(length=1000, dtype=float64)\n",
      "\n",
      "Created AutoFitter: AutoFitter(candidates=16, criterion=rmse, not fitted)\n",
      "Default candidates (16): ['weibull_min', 'lognorm', 'gamma', 'norm', 'expon', 'rayleigh', 'chi2', 'beta', 'uniform', 'logistic', 'gumbel_r', 'pareto', 'invgamma', 'maxwell', 'triang', 'laplace']\n"
     ]
    }
   ],
   "source": [
    "# Load data into MagicA\n",
    "processor = ma.read_data(wind_data)\n",
    "print(f\"Loaded data: {processor}\")\n",
    "\n",
    "# Create AutoFitter with default settings\n",
    "auto_fitter = processor.get_auto_fitter(criterion='rmse')\n",
    "print(f\"\\nCreated AutoFitter: {auto_fitter}\")\n",
    "print(f\"Default candidates ({len(auto_fitter.candidates)}): {auto_fitter.candidates}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95618ecb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding best distribution from default candidates...\n",
      "Testing 16 distributions...\n",
      "  [1/16] Fitting weibull_min...\n",
      "  [2/16] Fitting lognorm...\n",
      "  [3/16] Fitting gamma...\n",
      "  [4/16] Fitting norm...\n",
      "  [5/16] Fitting expon...\n",
      "  [6/16] Fitting rayleigh...\n",
      "  [7/16] Fitting chi2...\n",
      "  [8/16] Fitting beta...\n",
      "  [9/16] Fitting uniform...\n",
      "  [10/16] Fitting logistic...\n",
      "  [11/16] Fitting gumbel_r...\n",
      "  [12/16] Fitting pareto...\n",
      "  [13/16] Fitting invgamma...\n",
      "  [14/16] Fitting maxwell...\n",
      "  [15/16] Fitting triang...\n",
      "  [16/16] Fitting laplace...\n",
      "✓ All distributions fitted successfully\n",
      "\n",
      "🏆 Best Distribution: invgamma\n",
      "📊 RMSE: 0.005408\n",
      "📈 AIC: 5223.26\n",
      "📉 BIC: 5237.99\n",
      "🔍 KS p-value: 0.987903\n",
      "⚙️ Parameters: (np.float64(11.904082560566334), np.float64(-4.656117427894967), np.float64(127.54022278354164))\n",
      "  [16/16] Fitting laplace...\n",
      "✓ All distributions fitted successfully\n",
      "\n",
      "🏆 Best Distribution: invgamma\n",
      "📊 RMSE: 0.005408\n",
      "📈 AIC: 5223.26\n",
      "📉 BIC: 5237.99\n",
      "🔍 KS p-value: 0.987903\n",
      "⚙️ Parameters: (np.float64(11.904082560566334), np.float64(-4.656117427894967), np.float64(127.54022278354164))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_continuous_distns.py:796: RuntimeWarning: invalid value encountered in sqrt\n",
      "  sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_continuous_distns.py:801: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
      " improvement from the last ten iterations.\n",
      "  a, b = optimize.fsolve(func, (1.0, 1.0))\n"
     ]
    }
   ],
   "source": [
    "# Find the best distribution automatically\n",
    "print(\"Finding best distribution from default candidates...\")\n",
    "best_result = auto_fitter.fit_best_distribution()\n",
    "\n",
    "print(f\"\\n🏆 Best Distribution: {best_result['distribution']}\")\n",
    "print(f\"📊 RMSE: {best_result['rmse']:.6f}\")\n",
    "print(f\"📈 AIC: {best_result['aic']:.2f}\")\n",
    "print(f\"📉 BIC: {best_result['bic']:.2f}\")\n",
    "print(f\"🔍 KS p-value: {best_result['ks_pvalue']:.6f}\")\n",
    "print(f\"⚙️ Parameters: {best_result['parameters']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e6ddb4",
   "metadata": {},
   "source": [
    "## Comprehensive Distribution Comparison\n",
    "\n",
    "Let's look at how all tested distributions performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "700d01b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Distribution Ranking (by RMSE):\n",
      "==========================================================================================\n",
      "Rank Distribution    RMSE         AIC        KS p-value   Status\n",
      "==========================================================================================\n",
      "🎯 Top 10 Distributions (with KS p-value > 0.05):\n",
      "------------------------------------------------------------------------------------------\n",
      "1    invgamma        0.005408     5223.3     0.987903     ✅ Good\n",
      "2    gumbel_r        0.005897     5224.6     0.980487     ✅ Good\n",
      "3    lognorm         0.007012     5223.3     0.938914     ✅ Good\n",
      "4    gamma           0.012079     5227.6     0.672461     ✅ Good\n",
      "5    weibull_min     0.024757     5255.5     0.063202     ✅ Good\n",
      "\n",
      "⚠️  Showing remaining distributions (KS p-value < 0.05):\n",
      "------------------------------------------------------------------------------------------\n",
      "6    beta            0.022798     5263.6     0.042823     ⚠️ Fair\n",
      "7    logistic        0.027776     5379.4     0.005291     ⚠️ Fair\n",
      "8    rayleigh        0.031457     5267.5     0.002930     ⚠️ Fair\n",
      "9    laplace         0.032053     5397.8     0.000141     ⚠️ Fair\n",
      "10   maxwell         0.039045     5305.1     0.000121     ⚠️ Fair\n",
      "\n",
      "📊 Summary:\n",
      "✅ Successful fits: 16\n",
      "📈 Good statistical fit (p > 0.05): 5\n",
      "⚠️  Fair fit (p ≤ 0.05): 11\n",
      "❌ Failed fits: 0\n",
      "\n",
      "💡 Note: In this synthetic data example, we can filter by p-value > 0.05.\n",
      "   However, for real-world data, p-values often become unreliable due to\n",
      "   the 'large sample size effect'. In such cases, use RMSE alone!\n"
     ]
    }
   ],
   "source": [
    "# Get comprehensive comparison table\n",
    "comparison = auto_fitter.get_comparison_table(sort_by='rmse')\n",
    "\n",
    "print(\"📊 Distribution Ranking (by RMSE):\")\n",
    "print(\"=\" * 90)\n",
    "print(f\"{'Rank':<4} {'Distribution':<15} {'RMSE':<12} {'AIC':<10} {'KS p-value':<12} {'Status'}\")\n",
    "print(\"=\" * 90)\n",
    "\n",
    "successful_results = [(dist, result) for dist, result in comparison.items() if result['success']]\n",
    "failed_results = [(dist, result) for dist, result in comparison.items() if not result['success']]\n",
    "\n",
    "# For synthetic data, we can also filter by p-value > 0.05 (good statistical fit)\n",
    "# Note: For real-world data, this filtering may be too strict due to large sample size effect\n",
    "good_fit_results = [(dist, result) for dist, result in successful_results if result['ks_pvalue'] > 0.05]\n",
    "\n",
    "print(\"🎯 Top 10 Distributions (with KS p-value > 0.05):\")\n",
    "print(\"-\" * 90)\n",
    "for i, (dist, result) in enumerate(good_fit_results[:10], 1):\n",
    "    print(f\"{i:<4} {dist:<15} {result['rmse']:<12.6f} {result['aic']:<10.1f} {result['ks_pvalue']:<12.6f} ✅ Good\")\n",
    "\n",
    "if len(good_fit_results) < 10:\n",
    "    print(\"\\n⚠️  Showing remaining distributions (KS p-value < 0.05):\")\n",
    "    print(\"-\" * 90)\n",
    "    fair_fit_results = [(dist, result) for dist, result in successful_results if result['ks_pvalue'] <= 0.05]\n",
    "    for i, (dist, result) in enumerate(fair_fit_results[:10-len(good_fit_results)], len(good_fit_results)+1):\n",
    "        print(f\"{i:<4} {dist:<15} {result['rmse']:<12.6f} {result['aic']:<10.1f} {result['ks_pvalue']:<12.6f} ⚠️ Fair\")\n",
    "\n",
    "print(f\"\\n📊 Summary:\")\n",
    "print(f\"✅ Successful fits: {len(successful_results)}\")\n",
    "print(f\"📈 Good statistical fit (p > 0.05): {len(good_fit_results)}\")\n",
    "print(f\"⚠️  Fair fit (p ≤ 0.05): {len(successful_results) - len(good_fit_results)}\")\n",
    "print(f\"❌ Failed fits: {len(failed_results)}\")\n",
    "\n",
    "print(f\"\\n💡 Note: In this synthetic data example, we can filter by p-value > 0.05.\")\n",
    "print(f\"   However, for real-world data, p-values often become unreliable due to\")\n",
    "print(f\"   the 'large sample size effect'. In such cases, use RMSE alone!\")\n",
    "if failed_results:\n",
    "    print(f\"\\nFailed distributions: {[dist for dist, _ in failed_results]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7df6354e",
   "metadata": {},
   "source": [
    "## Using the Best-Fitted Distribution\n",
    "\n",
    "Once we have the best distribution, we can use it just like a regular MagicAdjuster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77a2a59e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best adjuster: MagicAdjuster(data_size=1000, distribution='invgamma')\n",
      "\n",
      "📊 Wind Speed Statistics (from best-fitted invgamma):\n",
      "Mean: 7.04 m/s\n",
      "Median (50th percentile): 6.36 m/s\n",
      "95th percentile: 13.96 m/s\n",
      "99th percentile: 19.12 m/s\n",
      "\n",
      "⚡ Risk Assessment:\n",
      "Probability of exceeding 15 m/s: 0.0359 (3.59%)\n",
      "Probability of calm conditions (≤ 5 m/s): 0.3224 (32.24%)\n"
     ]
    }
   ],
   "source": [
    "# Get the adjuster for the best distribution\n",
    "best_adjuster = auto_fitter.get_best_adjuster()\n",
    "print(f\"Best adjuster: {best_adjuster}\")\n",
    "\n",
    "# Use it like any MagicAdjuster - calculate key statistics\n",
    "mean_wind = best_adjuster.stats(moments='m')\n",
    "percentile_50 = best_adjuster.ppf(0.5)   # Median\n",
    "percentile_95 = best_adjuster.ppf(0.95)  # 95th percentile\n",
    "percentile_99 = best_adjuster.ppf(0.99)  # 99th percentile\n",
    "\n",
    "print(f\"\\n📊 Wind Speed Statistics (from best-fitted {best_result['distribution']}):\")\n",
    "print(f\"Mean: {mean_wind:.2f} m/s\")\n",
    "print(f\"Median (50th percentile): {percentile_50:.2f} m/s\")\n",
    "print(f\"95th percentile: {percentile_95:.2f} m/s\")\n",
    "print(f\"99th percentile: {percentile_99:.2f} m/s\")\n",
    "\n",
    "# Calculate probabilities for specific thresholds\n",
    "prob_exceed_15 = 1 - best_adjuster.cdf(15)  # P(wind > 15 m/s)\n",
    "prob_below_5 = best_adjuster.cdf(5)          # P(wind ≤ 5 m/s)\n",
    "\n",
    "print(f\"\\n⚡ Risk Assessment:\")\n",
    "print(f\"Probability of exceeding 15 m/s: {prob_exceed_15:.4f} ({prob_exceed_15*100:.2f}%)\")\n",
    "print(f\"Probability of calm conditions (≤ 5 m/s): {prob_below_5:.4f} ({prob_below_5*100:.2f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5949cd4d",
   "metadata": {},
   "source": [
    "## Testing All Available Distributions\n",
    "\n",
    "For the most comprehensive analysis, let's test ALL 113 available distributions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be1de314",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌍 Total available distributions: 111\n",
      "First 20: ['alpha', 'anglit', 'arcsine', 'argus', 'beta', 'betaprime', 'bradford', 'burr', 'burr12', 'cauchy', 'chi', 'chi2', 'cosine', 'crystalball', 'dgamma', 'dpareto_lognorm', 'dweibull', 'erlang', 'expon', 'exponnorm']\n",
      "Last 20: ['skewcauchy', 'skewnorm', 'students_t', 't', 'trapezoid', 'trapz', 'triang', 'truncexpon', 'truncnorm', 'truncpareto', 'truncweibull_min', 'tukeylambda', 'uniform', 'vonmises', 'vonmises_line', 'wald', 'weibull', 'weibull_max', 'weibull_min', 'wrapcauchy']\n",
      "\n",
      "⚠️  Warning: Testing all distributions takes longer but is more comprehensive\n",
      "Created comprehensive AutoFitter: AutoFitter(candidates=111, criterion=rmse, not fitted)\n"
     ]
    }
   ],
   "source": [
    "# Get all available distributions\n",
    "all_distributions = AutoFitter.get_all_available_distributions()\n",
    "print(f\"🌍 Total available distributions: {len(all_distributions)}\")\n",
    "print(f\"First 20: {all_distributions[:20]}\")\n",
    "print(f\"Last 20: {all_distributions[-20:]}\")\n",
    "\n",
    "# Create AutoFitter with ALL distributions\n",
    "print(\"\\n⚠️  Warning: Testing all distributions takes longer but is more comprehensive\")\n",
    "auto_fitter_comprehensive = processor.get_auto_fitter(\n",
    "    candidates=all_distributions,  # Use ALL 113 distributions!\n",
    "    criterion='rmse'  # ⭐ Use RMSE (most reliable criterion)\n",
    ")\n",
    "\n",
    "print(f\"Created comprehensive AutoFitter: {auto_fitter_comprehensive}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff6d985b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔍 Testing ALL 113 distributions... (this may take 30-60 seconds)\n",
      "Testing 111 distributions...\n",
      "  [1/111] Fitting alpha...\n",
      "  [2/111] Fitting anglit...\n",
      "  [3/111] Fitting arcsine...\n",
      "  [4/111] Fitting argus...\n",
      "  [5/111] Fitting beta...\n",
      "  [5/111] Fitting beta...\n",
      "  [6/111] Fitting betaprime...\n",
      "  [6/111] Fitting betaprime...\n",
      "  [7/111] Fitting bradford...\n",
      "  [8/111] Fitting burr...\n",
      "  [7/111] Fitting bradford...\n",
      "  [8/111] Fitting burr...\n",
      "  [9/111] Fitting burr12...\n",
      "  [9/111] Fitting burr12...\n",
      "  [10/111] Fitting cauchy...\n",
      "  [11/111] Fitting chi...\n",
      "  [12/111] Fitting chi2...\n",
      "  [10/111] Fitting cauchy...\n",
      "  [11/111] Fitting chi...\n",
      "  [12/111] Fitting chi2...\n",
      "  [13/111] Fitting cosine...\n",
      "  [14/111] Fitting crystalball...\n",
      "  [15/111] Fitting dgamma...\n",
      "  [16/111] Fitting dpareto_lognorm...\n",
      "  [13/111] Fitting cosine...\n",
      "  [14/111] Fitting crystalball...\n",
      "  [15/111] Fitting dgamma...\n",
      "  [16/111] Fitting dpareto_lognorm...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_continuous_distns.py:1956: RuntimeWarning: divide by zero encountered in divide\n",
      "  out = (a * b) / ((a - k) * (b + k)) * np.exp(k * m + k ** 2 * s ** 2 / 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [17/111] Fitting dweibull...\n",
      "  [18/111] Fitting erlang...\n",
      "  [19/111] Fitting expon...\n",
      "  [20/111] Fitting exponnorm...\n",
      "  [21/111] Fitting exponpow...\n",
      "  [22/111] Fitting exponweib...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.1).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.0666666666666664).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.0999999999999996).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.133333333333333).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.1999999999999993).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.099999999999999).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.0999999999999988).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.266666666666665).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.399999999999997).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.3666666666666636).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.4999999999999956).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.4666666666666615).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.5999999999999925).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.8999999999999915).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.2999999999999883).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.933333333333323).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.199999999999986).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.1222222222222094).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.655555555555549).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.966666666666656).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.266666666666654).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.7666666666666577).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.8777777777777658).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.894444444444435).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.096296296296285).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.8490740740740645).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9382716049382607).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.960185185185173).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.0431069958847616).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.897582304526739).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9945987654320874).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9218364197530757).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9703446502057504).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.983521947873789).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9458804869684396).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9363311614083107).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9211634087791385).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.940639288980327).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9445701874714114).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9524472260326053).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.951400002540251).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9129957746955792).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.884321336940493).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9045922313107067).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9404834773521307).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.898361771344315).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.913397273482212).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.909514205833748).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.8760197356626067).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9243675419297497).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.935478622060712).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.935833183619537).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9472518880815155).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9135867106269533).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9300056442022724).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9467403063521607).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.95068521418623).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.96384405031447).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.930942388319867).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.942790826844087).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.956200505564297).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.96929793624531).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9639511807768724).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9780101793555396).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9711004401741787).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.985255246839225).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(2.9862527412673665).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.0007722736913456).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.0230581577548694).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.0324262497974344).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.066663784307716).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.0637320513338406).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.1170474154250583).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.1829434997179753).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.1858347324848184).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.2672230198497925).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.2265626263398333).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.307977913842829).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.3845069797226994).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.402602917648923).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.5442238083248156).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.7234183462448147).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.452029446821679).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.435148830046934).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.4556307838274636).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.482144716916734).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.648691034289979).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.663881587581238).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.77824796634839).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.460466418865699).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.5090953851914506).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.633331477264795).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.72218182398346).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.7437392144926243).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.53128461777243).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.709903069887524).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.8067928053832754).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.769469980775826).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6902786858798846).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.7103655757977796).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.5491910205783643).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.613591466779592).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.614580327348226).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.67135409624697).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.568485784396459).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6748956279474494).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6732293167178787).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6433059371280656).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.7127789741020654).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6826329298714167).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6882723466836405).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.7368990841525553).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.666704223884188).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.636709547033304).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.693761617334875).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.687170219446205).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6841015715715155).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.706959855301017).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.727087671009432).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6887012871610914).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.692496534791429).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.7039579750710603).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.689065672446402).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.7097151118211484).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6894034753588496).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.67368393309677).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6986408747499553).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.697961584153754).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.69128965037324).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.693726132196601).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.692803934142722).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6990861641517627).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691824147557078).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.685304279965405).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.688638428661543).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.690888023200989).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.690687209614863).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.689729443113091).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6913004714460813).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.694689857198318).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6901512857957366).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.688755919485816).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6917919304784954).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6905870215373993).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691122108968911).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691155526961106).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.690954899141018).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.692428006596547).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6907204659959394).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.69007305225875).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6913622109235593).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6909029417381007).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6909577335458037).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6914627630776478).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6909060402663663).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691195757349467).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6910151136931306).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6912311763762347).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6913678977914497).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6913178380176426).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691090794774259).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.69078979668768).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6912191073645895).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691454678743689).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691043199885697).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6910042249734616).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691060962824155).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.691204043422972).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6910834107700157).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6912012457817536).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6910311935194287).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/optimize/_optimize.py:560: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6911721289032995).\n",
      "  fx = function(np.copy(x), *(wrapper_args + args))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_distn_infrastructure.py:2760: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6911721289032995).\n",
      "  obj = func(vals, data)\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_continuous_distns.py:3662: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value np.float64(3.6911721289032995).\n",
      "  return super().fit(data, *args, **kwds)\n",
      "/Users/danilocoutodesouza/Documents/Programs_and_scripts/MagicA/magica/core/magic_adjuster.py:495: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value array(3.69117213).\n",
      "  theoretical_cdf = self.fitted_distribution.cdf(sorted_data, *self.fitted_params)\n",
      "/Users/danilocoutodesouza/Documents/Programs_and_scripts/MagicA/magica/core/magic_adjuster.py:502: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value array(3.69117213).\n",
      "  log_likelihood = np.sum(self.fitted_distribution.logpdf(self.data, *self.fitted_params))\n",
      "/Users/danilocoutodesouza/Documents/Programs_and_scripts/MagicA/magica/core/magic_adjuster.py:510: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value array(3.69117213).\n",
      "  log_likelihood = np.sum(self.fitted_distribution.logpdf(self.data, *self.fitted_params))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_stats_py.py:7780: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value array(3.69117213).\n",
      "  cdfvals = cdf(x, *args)\n",
      "/Users/danilocoutodesouza/Documents/Programs_and_scripts/MagicA/magica/core/magic_adjuster.py:457: RuntimeWarning: The shape parameter of the erlang distribution has been given a non-integer value array(3.69117213).\n",
      "  bin_probs = np.diff(self.fitted_distribution.cdf(bin_edges, *params))\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_continuous_distns.py:2306: RuntimeWarning: invalid value encountered in add\n",
      "  logp = (np.log(a) + np.log(c) + sc.xlogy(a - 1.0, exm1c) +\n",
      "/Users/danilocoutodesouza/miniconda3/envs/magica/lib/python3.11/site-packages/scipy/stats/_continuous_distns.py:2306: RuntimeWarning: invalid value encountered in add\n",
      "  logp = (np.log(a) + np.log(c) + sc.xlogy(a - 1.0, exm1c) +\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [23/111] Fitting f...\n",
      "  [24/111] Fitting fatiguelife...\n",
      "  [25/111] Fitting fisk...\n",
      "  [26/111] Fitting foldcauchy...\n",
      "  [27/111] Fitting foldnorm...\n",
      "  [28/111] Fitting gamma...\n",
      "  [29/111] Fitting gausshyper...\n",
      "  [30/111] Fitting genexpon...\n",
      "  [31/111] Fitting genextreme...\n",
      "  [32/111] Fitting gengamma...\n",
      "  [30/111] Fitting genexpon...\n",
      "  [31/111] Fitting genextreme...\n",
      "  [32/111] Fitting gengamma...\n",
      "  [33/111] Fitting genhalflogistic...\n",
      "  [34/111] Fitting genhyperbolic...\n",
      "  [33/111] Fitting genhalflogistic...\n",
      "  [34/111] Fitting genhyperbolic...\n",
      "  [35/111] Fitting geninvgauss...\n",
      "  [35/111] Fitting geninvgauss...\n",
      "  [36/111] Fitting genlogistic...\n",
      "  [37/111] Fitting gennorm...\n",
      "  [38/111] Fitting genpareto...\n",
      "  [39/111] Fitting gibrat...\n",
      "  [40/111] Fitting gompertz...\n",
      "  [41/111] Fitting gumbel_l...\n",
      "  [42/111] Fitting gumbel_r...\n",
      "  [43/111] Fitting halfcauchy...\n",
      "  [44/111] Fitting halfgennorm...\n",
      "  [36/111] Fitting genlogistic...\n",
      "  [37/111] Fitting gennorm...\n",
      "  [38/111] Fitting genpareto...\n",
      "  [39/111] Fitting gibrat...\n",
      "  [40/111] Fitting gompertz...\n",
      "  [41/111] Fitting gumbel_l...\n",
      "  [42/111] Fitting gumbel_r...\n",
      "  [43/111] Fitting halfcauchy...\n",
      "  [44/111] Fitting halfgennorm...\n",
      "  [45/111] Fitting halflogistic...\n",
      "  [46/111] Fitting halfnorm...\n",
      "  [47/111] Fitting hypsecant...\n",
      "  [48/111] Fitting invgamma...\n",
      "  [49/111] Fitting invgauss...\n",
      "  [50/111] Fitting invweibull...\n",
      "  [51/111] Fitting irwinhall...\n",
      "  [52/111] Fitting jf_skew_t...\n",
      "  [53/111] Fitting johnsonsb...\n",
      "  [45/111] Fitting halflogistic...\n",
      "  [46/111] Fitting halfnorm...\n",
      "  [47/111] Fitting hypsecant...\n",
      "  [48/111] Fitting invgamma...\n",
      "  [49/111] Fitting invgauss...\n",
      "  [50/111] Fitting invweibull...\n",
      "  [51/111] Fitting irwinhall...\n",
      "  [52/111] Fitting jf_skew_t...\n",
      "  [53/111] Fitting johnsonsb...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danilocoutodesouza/Documents/Programs_and_scripts/MagicA/magica/core/auto_fitter.py:208: UserWarning: Failed to fit irwinhall: Failed to fit irwinhall distribution: The generic `fit` implementation is unreliable for this distribution, and no custom implementation is available. Consider using `scipy.stats.fit`.\n",
      "  warnings.warn(f\"Failed to fit {distribution}: {e}\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [54/111] Fitting johnsonsu...\n",
      "  [55/111] Fitting kappa3...\n",
      "  [56/111] Fitting kappa4...\n",
      "  [57/111] Fitting ksone...\n",
      "  [58/111] Fitting kstwo...\n",
      "  [59/111] Fitting kstwobign...\n",
      "  [60/111] Fitting landau...\n",
      "  [61/111] Fitting laplace...\n",
      "  [62/111] Fitting laplace_asymmetric...\n",
      "  [63/111] Fitting levy...\n",
      "  [64/111] Fitting levy_l...\n",
      "  [65/111] Fitting loggamma...\n",
      "  [66/111] Fitting logistic...\n",
      "  [67/111] Fitting loglaplace...\n",
      "  [68/111] Fitting lognorm...\n",
      "  [69/111] Fitting loguniform...\n",
      "  [70/111] Fitting lomax...\n",
      "  [57/111] Fitting ksone...\n",
      "  [58/111] Fitting kstwo...\n",
      "  [59/111] Fitting kstwobign...\n",
      "  [60/111] Fitting landau...\n",
      "  [61/111] Fitting laplace...\n",
      "  [62/111] Fitting laplace_asymmetric...\n",
      "  [63/111] Fitting levy...\n",
      "  [64/111] Fitting levy_l...\n",
      "  [65/111] Fitting loggamma...\n",
      "  [66/111] Fitting logistic...\n",
      "  [67/111] Fitting loglaplace...\n",
      "  [68/111] Fitting lognorm...\n",
      "  [69/111] Fitting loguniform...\n",
      "  [70/111] Fitting lomax...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danilocoutodesouza/Documents/Programs_and_scripts/MagicA/magica/core/auto_fitter.py:208: UserWarning: Failed to fit kstwo: Failed to fit kstwo distribution: Iteration of zero-sized operands is not enabled\n",
      "  warnings.warn(f\"Failed to fit {distribution}: {e}\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [71/111] Fitting maxwell...\n",
      "  [72/111] Fitting mielke...\n",
      "  [73/111] Fitting moyal...\n",
      "  [74/111] Fitting nakagami...\n",
      "  [75/111] Fitting ncf...\n",
      "  [76/111] Fitting nct...\n",
      "  [76/111] Fitting nct...\n",
      "  [77/111] Fitting ncx2...\n",
      "  [77/111] Fitting ncx2...\n",
      "  [78/111] Fitting norm...\n",
      "  [79/111] Fitting norminvgauss...\n",
      "  [78/111] Fitting norm...\n",
      "  [79/111] Fitting norminvgauss...\n",
      "  [80/111] Fitting pareto...\n",
      "  [81/111] Fitting pearson3...\n",
      "  [82/111] Fitting powerlaw...\n",
      "  [83/111] Fitting powerlognorm...\n",
      "  [84/111] Fitting powernorm...\n",
      "  [80/111] Fitting pareto...\n",
      "  [81/111] Fitting pearson3...\n",
      "  [82/111] Fitting powerlaw...\n",
      "  [83/111] Fitting powerlognorm...\n",
      "  [84/111] Fitting powernorm...\n",
      "  [85/111] Fitting rayleigh...\n",
      "  [86/111] Fitting rdist...\n",
      "  [87/111] Fitting recipinvgauss...\n",
      "  [85/111] Fitting rayleigh...\n",
      "  [86/111] Fitting rdist...\n",
      "  [87/111] Fitting recipinvgauss...\n",
      "  [88/111] Fitting reciprocal...\n",
      "  [89/111] Fitting rel_breitwigner...\n",
      "  [90/111] Fitting rice...\n",
      "  [91/111] Fitting semicircular...\n",
      "  [92/111] Fitting skewcauchy...\n",
      "  [93/111] Fitting skewnorm...\n",
      "  [94/111] Fitting students_t...\n",
      "  [88/111] Fitting reciprocal...\n",
      "  [89/111] Fitting rel_breitwigner...\n",
      "  [90/111] Fitting rice...\n",
      "  [91/111] Fitting semicircular...\n",
      "  [92/111] Fitting skewcauchy...\n",
      "  [93/111] Fitting skewnorm...\n",
      "  [94/111] Fitting students_t...\n",
      "  [95/111] Fitting t...\n",
      "  [96/111] Fitting trapezoid...\n",
      "  [97/111] Fitting trapz...\n",
      "  [98/111] Fitting triang...\n",
      "  [99/111] Fitting truncexpon...\n",
      "  [95/111] Fitting t...\n",
      "  [96/111] Fitting trapezoid...\n",
      "  [97/111] Fitting trapz...\n",
      "  [98/111] Fitting triang...\n",
      "  [99/111] Fitting truncexpon...\n",
      "  [100/111] Fitting truncnorm...\n",
      "  [101/111] Fitting truncpareto...\n",
      "  [102/111] Fitting truncweibull_min...\n",
      "  [100/111] Fitting truncnorm...\n",
      "  [101/111] Fitting truncpareto...\n",
      "  [102/111] Fitting truncweibull_min...\n",
      "  [103/111] Fitting tukeylambda...\n",
      "  [103/111] Fitting tukeylambda...\n",
      "  [104/111] Fitting uniform...\n",
      "  [105/111] Fitting vonmises...\n",
      "  [106/111] Fitting vonmises_line...\n",
      "  [104/111] Fitting uniform...\n",
      "  [105/111] Fitting vonmises...\n",
      "  [106/111] Fitting vonmises_line...\n",
      "  [107/111] Fitting wald...\n",
      "  [108/111] Fitting weibull...\n",
      "  [109/111] Fitting weibull_max...\n",
      "  [110/111] Fitting weibull_min...\n",
      "  [111/111] Fitting wrapcauchy...\n",
      "✓ All distributions fitted successfully\n",
      "\n",
      "🏆 Best Distribution (from ALL 113): alpha\n",
      "📊 RMSE: 0.003774 ⭐ (Primary criterion)\n",
      "📈 AIC: 5224.72\n",
      "📉 BIC: 5239.44\n",
      "🔍 KS p-value: 0.999941\n",
      "\n",
      "🔬 Comparison:\n",
      "Default selection (16 dists): invgamma (RMSE: 0.005408)\n",
      "Comprehensive (111 dists): alpha (RMSE: 0.003774)\n",
      "Improvement: 30.22% reduction in RMSE\n",
      "  [107/111] Fitting wald...\n",
      "  [108/111] Fitting weibull...\n",
      "  [109/111] Fitting weibull_max...\n",
      "  [110/111] Fitting weibull_min...\n",
      "  [111/111] Fitting wrapcauchy...\n",
      "✓ All distributions fitted successfully\n",
      "\n",
      "🏆 Best Distribution (from ALL 113): alpha\n",
      "📊 RMSE: 0.003774 ⭐ (Primary criterion)\n",
      "📈 AIC: 5224.72\n",
      "📉 BIC: 5239.44\n",
      "🔍 KS p-value: 0.999941\n",
      "\n",
      "🔬 Comparison:\n",
      "Default selection (16 dists): invgamma (RMSE: 0.005408)\n",
      "Comprehensive (111 dists): alpha (RMSE: 0.003774)\n",
      "Improvement: 30.22% reduction in RMSE\n"
     ]
    }
   ],
   "source": [
    "# Find best from ALL distributions (this takes a moment...)\n",
    "print(\"🔍 Testing ALL 113 distributions... (this may take 30-60 seconds)\")\n",
    "best_comprehensive = auto_fitter_comprehensive.fit_best_distribution()\n",
    "\n",
    "print(f\"\\n🏆 Best Distribution (from ALL 113): {best_comprehensive['distribution']}\")\n",
    "print(f\"📊 RMSE: {best_comprehensive['rmse']:.6f} ⭐ (Primary criterion)\")\n",
    "print(f\"📈 AIC: {best_comprehensive['aic']:.2f}\")\n",
    "print(f\"📉 BIC: {best_comprehensive['bic']:.2f}\")\n",
    "print(f\"🔍 KS p-value: {best_comprehensive['ks_pvalue']:.6f}\")\n",
    "\n",
    "# Compare with default selection\n",
    "print(f\"\\n🔬 Comparison:\")\n",
    "print(f\"Default selection ({len(auto_fitter.candidates)} dists): {best_result['distribution']} (RMSE: {best_result['rmse']:.6f})\")\n",
    "print(f\"Comprehensive ({len(all_distributions)} dists): {best_comprehensive['distribution']} (RMSE: {best_comprehensive['rmse']:.6f})\")\n",
    "\n",
    "improvement = ((best_result['rmse'] - best_comprehensive['rmse']) / best_result['rmse']) * 100\n",
    "print(f\"Improvement: {improvement:.2f}% reduction in RMSE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa9f502",
   "metadata": {},
   "source": [
    "## Top Performers Analysis\n",
    "\n",
    "Let's analyze the top-performing distributions from the comprehensive test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e165718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Comprehensive Results Summary:\n",
      "✅ Successfully fitted: 109/113 distributions\n",
      "📈 Good statistical fit (p > 0.05): 38\n",
      "⚠️  Fair fit (p ≤ 0.05): 71\n",
      "❌ Failed to fit: 2 distributions\n",
      "Success rate: 96.5%\n",
      "\n",
      "🏆 Top 15 Distributions (by RMSE, filtered by p > 0.05):\n",
      "===============================================================================================\n",
      "Rank Distribution         RMSE         AIC        KS p-val   Fit Quality\n",
      "===============================================================================================\n",
      "🥇1  alpha                0.003774     5224.7     0.9999     Excellent\n",
      "🥈2  jf_skew_t            0.003931     5230.2     0.9990     Excellent\n",
      "🥉3  nct                  0.004464     5225.8     0.9981     Excellent\n",
      "  4  genextreme           0.004785     5223.5     0.9972     Excellent\n",
      "  5  invweibull           0.004786     5223.5     0.9972     Excellent\n",
      "  6  burr                 0.004959     5224.1     0.9552     Excellent\n",
      "  7  mielke               0.004960     5224.1     0.9552     Excellent\n",
      "  8  burr12               0.005249     5222.1     0.9877     Excellent\n",
      "  9  invgamma             0.005408     5223.3     0.9879     Excellent\n",
      "  10 exponnorm            0.005820     5228.7     0.9339     Excellent\n",
      "  11 gumbel_r             0.005897     5224.6     0.9805     Excellent\n",
      "  12 genlogistic          0.005911     5226.8     0.9799     Excellent\n",
      "  13 fisk                 0.006003     5232.6     0.9799     Excellent\n",
      "  14 f                    0.006604     5225.0     0.9529     Excellent\n",
      "  15 johnsonsu            0.006873     5225.3     0.9377     Excellent\n",
      "\n",
      "💡 Important Notes:\n",
      "   • Ranking by RMSE (most reliable for distribution selection)\n",
      "   • Filtered by p-value > 0.05 (valid for this synthetic data)\n",
      "   • For real-world large datasets, use RMSE alone without p-value filtering\n",
      "\n",
      "❌ Failed distributions (first 10): ['irwinhall', 'kstwo']\n"
     ]
    }
   ],
   "source": [
    "# Get comprehensive comparison\n",
    "comprehensive_comparison = auto_fitter_comprehensive.get_comparison_table(sort_by='rmse')  # ⭐ Sort by RMSE\n",
    "successful_comprehensive = [(dist, result) for dist, result in comprehensive_comparison.items() if result['success']]\n",
    "failed_comprehensive = [dist for dist, result in comprehensive_comparison.items() if not result['success']]\n",
    "\n",
    "# Filter by p-value > 0.05 for synthetic data (optional for real data)\n",
    "good_fit_comprehensive = [(dist, result) for dist, result in successful_comprehensive if result['ks_pvalue'] > 0.05]\n",
    "\n",
    "print(f\"📊 Comprehensive Results Summary:\")\n",
    "print(f\"✅ Successfully fitted: {len(successful_comprehensive)}/113 distributions\")\n",
    "print(f\"📈 Good statistical fit (p > 0.05): {len(good_fit_comprehensive)}\")\n",
    "print(f\"⚠️  Fair fit (p ≤ 0.05): {len(successful_comprehensive) - len(good_fit_comprehensive)}\")\n",
    "print(f\"❌ Failed to fit: {len(failed_comprehensive)} distributions\")\n",
    "print(f\"Success rate: {len(successful_comprehensive)/113*100:.1f}%\")\n",
    "\n",
    "print(f\"\\n🏆 Top 15 Distributions (by RMSE, filtered by p > 0.05):\")\n",
    "print(\"=\" * 95)\n",
    "print(f\"{'Rank':<4} {'Distribution':<20} {'RMSE':<12} {'AIC':<10} {'KS p-val':<10} {'Fit Quality'}\")\n",
    "print(\"=\" * 95)\n",
    "\n",
    "for i, (dist, result) in enumerate(good_fit_comprehensive[:15], 1):\n",
    "    quality = \"Excellent\" if result['ks_pvalue'] > 0.1 else \"Good\"\n",
    "    emoji = \"🥇\" if i == 1 else \"🥈\" if i == 2 else \"🥉\" if i == 3 else \"  \"\n",
    "    print(f\"{emoji}{i:<2} {dist:<20} {result['rmse']:<12.6f} {result['aic']:<10.1f} {result['ks_pvalue']:<10.4f} {quality}\")\n",
    "\n",
    "print(f\"\\n💡 Important Notes:\")\n",
    "print(f\"   • Ranking by RMSE (most reliable for distribution selection)\")\n",
    "print(f\"   • Filtered by p-value > 0.05 (valid for this synthetic data)\")\n",
    "print(f\"   • For real-world large datasets, use RMSE alone without p-value filtering\")\n",
    "\n",
    "if failed_comprehensive:\n",
    "    print(f\"\\n❌ Failed distributions (first 10): {failed_comprehensive[:10]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8466ab",
   "metadata": {},
   "source": [
    "## Custom Distribution Selection\n",
    "\n",
    "For specific applications, you might want to test only relevant distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d409964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌪️  Testing wind-specific distributions: ['weibull_min', 'weibull_max', 'rayleigh', 'lognorm', 'gamma', 'chi2', 'maxwell', 'rice', 'gumbel_r', 'genextreme']\n",
      "Testing 10 distributions...\n",
      "  [1/10] Fitting weibull_min...\n",
      "  [2/10] Fitting weibull_max...\n",
      "  [3/10] Fitting rayleigh...\n",
      "  [4/10] Fitting lognorm...\n",
      "  [5/10] Fitting gamma...\n",
      "  [6/10] Fitting chi2...\n",
      "  [7/10] Fitting maxwell...\n",
      "  [8/10] Fitting rice...\n",
      "  [9/10] Fitting gumbel_r...\n",
      "  [10/10] Fitting genextreme...\n",
      "  [7/10] Fitting maxwell...\n",
      "  [8/10] Fitting rice...\n",
      "  [9/10] Fitting gumbel_r...\n",
      "  [10/10] Fitting genextreme...\n",
      "✓ All distributions fitted successfully\n",
      "\n",
      "🏆 Best wind-specific distribution: genextreme\n",
      "📊 RMSE: 0.004785 ⭐ (Primary criterion)\n",
      "🔍 KS p-value: 0.997229\n",
      "📈 AIC: 5223.54\n",
      "\n",
      "🌪️  Wind-Specific Ranking (by RMSE, p > 0.05):\n",
      "----------------------------------------------------------------------\n",
      " 1. ✅ genextreme      RMSE: 0.004785, p: 0.997229\n",
      " 2. ✅ gumbel_r        RMSE: 0.005897, p: 0.980487\n",
      " 3. ✅ lognorm         RMSE: 0.007012, p: 0.938914\n",
      " 4. ✅ gamma           RMSE: 0.012079, p: 0.672461\n",
      " 5. ✅ weibull_min     RMSE: 0.024757, p: 0.063202\n",
      "\n",
      "⚠️  Distributions with p ≤ 0.05 (fair fit):\n",
      " 6. ⚠️  rice            RMSE: 0.031457, p: 0.002930\n",
      " 7. ⚠️  rayleigh        RMSE: 0.031457, p: 0.002930\n",
      " 8. ⚠️  maxwell         RMSE: 0.039045, p: 0.000121\n",
      " 9. ⚠️  chi2            RMSE: 0.290847, p: 0.000000\n",
      "10. ⚠️  weibull_max     RMSE: 0.464287, p: 0.000000\n",
      "✓ All distributions fitted successfully\n",
      "\n",
      "🏆 Best wind-specific distribution: genextreme\n",
      "📊 RMSE: 0.004785 ⭐ (Primary criterion)\n",
      "🔍 KS p-value: 0.997229\n",
      "📈 AIC: 5223.54\n",
      "\n",
      "🌪️  Wind-Specific Ranking (by RMSE, p > 0.05):\n",
      "----------------------------------------------------------------------\n",
      " 1. ✅ genextreme      RMSE: 0.004785, p: 0.997229\n",
      " 2. ✅ gumbel_r        RMSE: 0.005897, p: 0.980487\n",
      " 3. ✅ lognorm         RMSE: 0.007012, p: 0.938914\n",
      " 4. ✅ gamma           RMSE: 0.012079, p: 0.672461\n",
      " 5. ✅ weibull_min     RMSE: 0.024757, p: 0.063202\n",
      "\n",
      "⚠️  Distributions with p ≤ 0.05 (fair fit):\n",
      " 6. ⚠️  rice            RMSE: 0.031457, p: 0.002930\n",
      " 7. ⚠️  rayleigh        RMSE: 0.031457, p: 0.002930\n",
      " 8. ⚠️  maxwell         RMSE: 0.039045, p: 0.000121\n",
      " 9. ⚠️  chi2            RMSE: 0.290847, p: 0.000000\n",
      "10. ⚠️  weibull_max     RMSE: 0.464287, p: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Define wind-specific distributions\n",
    "wind_specific_distributions = [\n",
    "    'weibull_min',      # Most common for wind\n",
    "    'weibull_max',      # Alternative Weibull\n",
    "    'rayleigh',         # Theoretical wind model\n",
    "    'lognorm',          # Common for environmental data\n",
    "    'gamma',            # Flexible shape\n",
    "    'chi2',             # Similar to gamma\n",
    "    'maxwell',          # Physical distribution\n",
    "    'rice',             # For wind with persistent component\n",
    "    'gumbel_r',         # For extreme values\n",
    "    'genextreme'        # Generalized extreme value\n",
    "]\n",
    "\n",
    "# Test wind-specific distributions\n",
    "auto_fitter_wind = processor.get_auto_fitter(\n",
    "    candidates=wind_specific_distributions,\n",
    "    criterion='rmse'  # ⭐ Use RMSE (most reliable)\n",
    ")\n",
    "\n",
    "print(f\"🌪️  Testing wind-specific distributions: {wind_specific_distributions}\")\n",
    "best_wind = auto_fitter_wind.fit_best_distribution()\n",
    "\n",
    "print(f\"\\n🏆 Best wind-specific distribution: {best_wind['distribution']}\")\n",
    "print(f\"📊 RMSE: {best_wind['rmse']:.6f} ⭐ (Primary criterion)\")\n",
    "print(f\"🔍 KS p-value: {best_wind['ks_pvalue']:.6f}\")\n",
    "print(f\"📈 AIC: {best_wind['aic']:.2f}\")\n",
    "\n",
    "# Show wind-specific ranking\n",
    "wind_comparison = auto_fitter_wind.get_comparison_table(sort_by='rmse')  # ⭐ Sort by RMSE\n",
    "\n",
    "# Filter by p-value > 0.05 for synthetic data\n",
    "successful_wind = [(d, r) for d, r in wind_comparison.items() if r['success']]\n",
    "good_fit_wind = [(d, r) for d, r in successful_wind if r['ks_pvalue'] > 0.05]\n",
    "\n",
    "# Store for later comparison\n",
    "good_fit_custom = {d: r for d, r in good_fit_wind}\n",
    "\n",
    "print(f\"\\n🌪️  Wind-Specific Ranking (by RMSE, p > 0.05):\")\n",
    "print(\"-\" * 70)\n",
    "for i, (dist, result) in enumerate(good_fit_wind, 1):\n",
    "    status = \"✅\"\n",
    "    print(f\"{i:2}. {status} {dist:<15} RMSE: {result['rmse']:.6f}, p: {result['ks_pvalue']:.6f}\")\n",
    "\n",
    "if len(good_fit_wind) < len(successful_wind):\n",
    "    print(f\"\\n⚠️  Distributions with p ≤ 0.05 (fair fit):\")\n",
    "    fair_fit_wind = [(d, r) for d, r in successful_wind if r['ks_pvalue'] <= 0.05]\n",
    "    for i, (dist, result) in enumerate(fair_fit_wind, len(good_fit_wind)+1):\n",
    "        print(f\"{i:2}. ⚠️  {dist:<15} RMSE: {result['rmse']:.6f}, p: {result['ks_pvalue']:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb30ff0f",
   "metadata": {},
   "source": [
    "## Different Selection Criteria Comparison\n",
    "\n",
    "Different criteria can lead to different \"best\" distributions. Let's compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "601bc7c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔍 Comparing Different Selection Criteria\n",
      "\n",
      "==========================================================================================\n",
      "\n",
      "🏆 Top 3 by RMSE (filtered by p > 0.05):\n",
      "  🥇 alpha:\n",
      "     RMSE=0.003774, AIC=5224.7, BIC=5239.4, p=0.9999\n",
      "  🥈 jf_skew_t:\n",
      "     RMSE=0.003931, AIC=5230.2, BIC=5249.8, p=0.9990\n",
      "  🥉 nct:\n",
      "     RMSE=0.004464, AIC=5225.8, BIC=5245.4, p=0.9981\n",
      "\n",
      "🏆 Top 3 by AIC (filtered by p > 0.05):\n",
      "  🥇 burr12:\n",
      "     RMSE=0.005249, AIC=5222.1, BIC=5241.8, p=0.9877\n",
      "  🥈 invgamma:\n",
      "     RMSE=0.005408, AIC=5223.3, BIC=5238.0, p=0.9879\n",
      "  🥉 lognorm:\n",
      "     RMSE=0.007012, AIC=5223.3, BIC=5238.0, p=0.9389\n",
      "\n",
      "🏆 Top 3 by BIC (filtered by p > 0.05):\n",
      "  🥇 gumbel_r:\n",
      "     RMSE=0.005897, AIC=5224.6, BIC=5234.4, p=0.9805\n",
      "  🥈 invgamma:\n",
      "     RMSE=0.005408, AIC=5223.3, BIC=5238.0, p=0.9879\n",
      "  🥉 lognorm:\n",
      "     RMSE=0.007012, AIC=5223.3, BIC=5238.0, p=0.9389\n",
      "\n",
      "==========================================================================================\n",
      "💡 Important Notes:\n",
      "   ⭐ RMSE is the most robust criterion (works for all data types)\n",
      "   • All three criteria generally agree for this synthetic data\n",
      "   • AIC/BIC may prefer more complex models (more parameters)\n",
      "   • For large datasets, RMSE is preferred over p-value-based selection\n",
      "   • P-value filtering works here because this is synthetic data with moderate sample size\n"
     ]
    }
   ],
   "source": [
    "# Compare different criteria\n",
    "print(\"🔍 Comparing Different Selection Criteria\\n\")\n",
    "print(\"=\" * 90)\n",
    "\n",
    "criteria = ['rmse', 'aic', 'bic']\n",
    "for criterion in criteria:\n",
    "    # Get top 3 distributions by this criterion (with p > 0.05)\n",
    "    sorted_by_criterion = sorted(\n",
    "        good_fit_comprehensive, \n",
    "        key=lambda x: x[1][criterion]\n",
    "    )\n",
    "    \n",
    "    print(f\"\\n🏆 Top 3 by {criterion.upper()} (filtered by p > 0.05):\")\n",
    "    for i, (dist, result) in enumerate(sorted_by_criterion[:3], 1):\n",
    "        emoji = \"🥇\" if i == 1 else \"🥈\" if i == 2 else \"🥉\"\n",
    "        print(f\"  {emoji} {dist}:\")\n",
    "        print(f\"     RMSE={result['rmse']:.6f}, AIC={result['aic']:.1f}, BIC={result['bic']:.1f}, p={result['ks_pvalue']:.4f}\")\n",
    "\n",
    "print(\"\\n\" + \"=\" * 90)\n",
    "print(\"💡 Important Notes:\")\n",
    "print(\"   ⭐ RMSE is the most robust criterion (works for all data types)\")\n",
    "print(\"   • All three criteria generally agree for this synthetic data\")\n",
    "print(\"   • AIC/BIC may prefer more complex models (more parameters)\")\n",
    "print(\"   • For large datasets, RMSE is preferred over p-value-based selection\")\n",
    "print(\"   • P-value filtering works here because this is synthetic data with moderate sample size\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b028c524",
   "metadata": {},
   "source": [
    "## Visualization: Comparing Top Distributions\n",
    "\n",
    "Let's visually compare the top 3 distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38aaabdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing top 3 distributions (by RMSE, p > 0.05): ['alpha', 'jf_skew_t', 'nct']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdEAAAJOCAYAAABYwk4SAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XV4FFcXwOHfxj0QI4QQEtwteGkTNBSnuLsVd2mLFYpT3K3FC8UKxSnuQYrT4hLDSSC68/2x326z2Si2CZz3efYhe+fOnTMzu8vs3TvnqhRFURBCCCGEEEIIIYQQQgghhAETYwcghBBCCCGEEEIIIYQQQqRX0okuhBBCCCGEEEIIIYQQQiRBOtGFEEIIIYQQQgghhBBCiCRIJ7oQQgghhBBCCCGEEEIIkQTpRBdCCCGEEEIIIYQQQgghkiCd6EIIIYQQQgghhBBCCCFEEqQTXQghhBBCCCGEEEIIIYRIgnSiCyGEEEIIIYQQQgghhBBJkE50IYQQQgghhBBCCCGEECIJ0okuRDqzfPlyVCoVd+7c+eDbateuHd7e3rrnd+7cQaVSMWXKlA++bYBRo0ahUqk+yrbE50ulUjFq1Chjh5EktVpN4cKFGTdunK5M+954/PixESMTn4OhQ4dStmxZY4chhBBCpFp6v7ZLzoEDB1CpVBw4cOCt192wYcP7DywRv/32G05OToSHh3+U7QmIiYkhe/bszJ0719ihCCESIZ3oIl1SqVSperzNxUdavHnzho4dO1K4cGEcHR2xs7OjWLFizJgxg5iYmBTX117oaB+WlpZkyZIFf39/fvrpJ8LCwt5LnK9fv2bUqFEf/Hi8jfQc2/ui/fFB+zA1NcXLy4sGDRpw/vx5vbrx65mZmeHk5ISvry99+vThypUrKbYd/1GuXLlUxXfz5k26du1Kzpw5sbKywsHBgS+++IIZM2bw5s2b93EIxDtYs2YN9+/fp2fPnh992/7+/nqvKWtra4oWLcr06dNRq9V6deO/FseOHZtoey1btkSlUmFnZ6dXrlar+fXXXylbtixOTk7Y29uTN29e2rRpw4kTJ3T1En5mJnysXbv2/R+Ej0ytVjNp0iR8fHywsrKiaNGirFmzJtXrP3/+nC5duuDq6oqtrS2VKlXi7NmzBvW8vb0TPYbdunXTq9e3b18uXLjA1q1b33nfhBBCZByXL1+mVatWZMuWDUtLSzw8PGjVqlWi16Mfo720XE+/rStXrjBq1KiPMlgpo4uLi2PkyJH06tVL77ou4fWFlZUVefLkYdCgQTx9+vSDxfPnn3+m6YeTjHqNa25uTv/+/Rk3bhyRkZGp3l8hxMdhZuwAhEjMihUr9J7/+uuv7Nmzx6C8QIECHzSON2/ecPnyZWrWrIm3tzcmJiYcO3aMfv36cfLkSVavXp2qdnr37k3p0qWJi4sjLCyMY8eOMXLkSKZNm8Zvv/1G5cqVdXVbt25Ns2bNsLS0THWcr1+/ZvTo0YDmgiG1Fi1aZHAR8b4lF9v333/P0KFDP+j2P6bmzZtTs2ZN4uLiuHr1KvPmzWPHjh2cOHGC4sWL6+pVq1aNNm3aoCgKL1684MKFC/zyyy/MnTuXiRMn0r9//yTbjs/V1TXFmLZv307jxo2xtLSkTZs2FC5cmOjoaI4cOcKgQYO4fPkyCxcufOd9T8/evHmDmVn6/e9u8uTJNGvWDEdHR6Ns39PTk/HjxwPw+PFjVq9eTb9+/QgLC9MbHa9lZWXFmjVr+P777/XKIyIi2LJlC1ZWVgbr9O7dmzlz5lCvXj1atmyJmZkZ169fZ8eOHeTMmdPgByHtZ2ZC5cuXf5ddTRe+++47JkyYQOfOnSldujRbtmyhRYsWqFQqmjVrluy6arWaWrVqceHCBQYNGoSLiwtz587F39+fwMBA8uTJo1e/ePHiDBgwQK8sb968es/d3d2pV68eU6ZMoW7duu9nJ4UQQqRrGzdupHnz5jg5OdGxY0d8fHy4c+cOS5YsYcOGDaxbt4569eoZpb3UXk+/jStXrjB69Gj8/f317sYVhv744w+uX79Oly5dDJbFv76IjIwkMDCQ6dOnc/DgQU6dOvVB4vnzzz+ZM2dOmjrSM+o1bvv27Rk6dCirV6+mQ4cOqd5fIcRHoAiRAfTo0UNJTy/Xnj17KoASFBSUbL2//vpLAZT169cbLDt//rzi5uamZMqUSXn06NE7xRMWFqYAysiRI1NVPzw8PNHy27dvK4AyefLkd4rnXWLLiJI6blu3blUApUuXLroyQOnRo4dBG48fP1bKly+vAMr27dtTbDs1bt26pdjZ2Sn58+dP9DX2zz//KNOnT09zuxlBXFyc8ubNG2OHkaKzZ88qgLJ371698pEjRyqAEhYW9kG37+fnpxQqVEiv7M2bN0qOHDkUe3t7JTY2VleufS1+8803CqCcP39eb71Vq1Yp5ubmSp06dRRbW1tdeXBwsKJSqZTOnTsbbF+tVishISG658l9ZhrL/fv3ladPn76Xth48eKCYm5vrfQao1Wrlyy+/VDw9PfWOd2LWrVtncHxCQ0OVTJkyKc2bN9ermyNHDqVWrVqpimvDhg2KSqVSbt68mYa9EUIIkRH9+++/io2NjZI/f34lNDRUb1lYWJiSP39+xc7OTrl169ZHbS+t19Nv891i/fr1CqD89ddfaV73fdFe67xNDB/zOqlu3bpKxYoVDcqTur4YOHCgAig3btz4IPGktT8go1/j1q5dW/nyyy9TVVcI8fFIOheRYUVERDBgwACyZ8+OpaUl+fLlY8qUKSiKoldPpVLRs2dPVq1aRb58+bCyssLX15dDhw699ba1IxeeP3/+1m0UK1aM6dOn8/z5c2bPnq0rTywn+pkzZwgICMDFxQVra2t8fHx0v0rfuXNHNyJ59OjRulvCtL/St2vXDjs7O27evEnNmjWxt7enZcuWumVJjcL4+eefyZEjB9bW1vj5+XHp0iW95f7+/omOeo/fZkqxJZYTPTY2lh9//JFcuXJhaWmJt7c3w4cPJyoqSq+et7c3tWvX5siRI5QpUwYrKyty5szJr7/+qlcvJiaG0aNHkydPHqysrHB2dqZixYrs2bMn0f1+n7R3GNy+fTvFus7OzqxduxYzM7NER0a8jUmTJhEeHs6SJUvImjWrwfLcuXPTp08f3fO0HvsDBw5QqlQprK2tKVKkiC5lz8aNGylSpIjuvXbu3Dm99bWvyVu3bhEQEICtrS0eHh6MGTPG4P07ZcoUKlSogLOzM9bW1vj6+iaaBzL++7xQoUJYWlqyc+dO3bL4o1ZevXpF37598fb2xtLSEjc3N6pVq2aQEmP9+vX4+vpibW2Ni4sLrVq14uHDh4nuy8OHD6lfvz52dna4uroycOBA4uLikjgz/9m8eTMWFhZ89dVXiS5//PgxTZo0wcHBAWdnZ/r06aN3a6efnx/FihVLdN18+fIREBCQYgwJWVlZUbp0aV69ekVoaKjB8vLly+Pj42NwJ86qVauoUaMGTk5OeuW3b99GURS++OILg7ZUKhVubm5pjjEttJ+phw4domvXrjg7O+Pg4ECbNm149uxZiuvv3bsXDw8PWrZsyV9//WXwGk2LLVu2EBMTw7fffqsrU6lUdO/enQcPHnD8+PFk19+wYQNZsmThm2++0ZW5urrSpEkTtmzZYvBeBYiOjiYiIiLZdqtWraqLTwghxKdt8uTJvH79moULFxrc1eji4sKCBQsIDw9n8uTJRmkvodReT587d46vv/4aBwcH7OzsqFKlil46jeXLl9O4cWMAKlWq9N7Tg969e5dvv/2WfPnyYW1tjbOzM40bN05V6hh/f38KFy5MYGAgFSpU0H3fmz9/fqL11Wo148aNw9PTEysrK6pUqcK///6rV+fw4cM0btwYLy8vLC0tyZ49O/369UtVKsfIyEh27typuz5IDXd3dwCDuz+vXbtGo0aNcHJywsrKilKlShmkkEvp+1q7du2YM2cOoJ8aM60y0jVutWrVOHLkyAdNkSOESDvpRBcZkqIo1K1bl59//pkaNWowbdo08uXLx6BBgxJNhXHw4EH69u1Lq1atGDNmDE+ePKFGjRoGHcNJiY6O5vHjx9y/f59NmzYxZcoUcuTIQe7cud9pPxo1aoS1tTW7d+9Osk5oaCjVq1fnzp07DB06lFmzZtGyZUvdRaGrqyvz5s0DoEGDBqxYsYIVK1bodbLExsYSEBCAm5sbU6ZMoWHDhsnG9euvvzJz5kx69OjBsGHDuHTpEpUrVyYkJCRN+5ea2BLq1KkTI0aMoGTJkvz888/4+fkxfvz4RNMc/PvvvzRq1Ihq1aoxdepUMmfOTLt27bh8+bKuzqhRoxg9ejSVKlVi9uzZfPfdd3h5eSWaQ/h9u3nzJqDpIE8NLy8v/Pz8OHHiBC9fvtRb9vr1ax4/fqz3SCkv/x9//EHOnDmpUKFCqraf1mPfokUL6tSpw/jx43n27Bl16tRh1apV9OvXj1atWjF69Ghu3rxJkyZNDNIGxcXFUaNGDbJkycKkSZPw9fVl5MiRjBw5Uq/ejBkzKFGiBGPGjOGnn37CzMyMxo0bs337doOY9u/fT79+/WjatCkzZsxI8geibt26MW/ePBo2bMjcuXMZOHAg1tbWXL16VVdn+fLlNGnSBFNTU8aPH0/nzp3ZuHEjFStWNPjxLC4ujoCAAJydnZkyZQp+fn5MnTo1VWlyjh07RuHChTE3N090eZMmTYiMjGT8+PHUrFmTmTNn6t1W27p1a/7++2+Dz7LTp09z48YNWrVqlWIMidHmhsyUKVOiy5s3b87atWt1HcqPHz9m9+7dtGjRwqBujhw5AM2PEq9fv07V9l+9emXwen/8+PE7dWD37NmTq1evMmrUKNq0acOqVauoX79+im1Wr16dXr16sX//fipXrkzu3LkZN26cwQ8qqXHu3DlsbW0NUpGVKVNGtzyl9UuWLImJif7lW5kyZXj9+jU3btzQK9+/fz82NjbY2dnh7e3NjBkzEm3X0dGRXLlycfTo0bTukhBCiAzmjz/+wNvbmy+//DLR5V999RXe3t788ccfRmkvodRcT1++fJkvv/ySCxcuMHjwYH744Qdu376Nv78/J0+e1MXRu3dvAIYPH677XvK+0oOePn2aY8eO0axZM2bOnEm3bt3Yt28f/v7+qbr+efbsGTVr1sTX15dJkybh6elJ9+7dWbp0qUHdCRMmsGnTJgYOHMiwYcM4ceKEbpCUlva6q3v37syaNYuAgABmzZpFmzZtUowlMDCQ6OhoSpYsmejymJgY3bXZgwcP+OOPP5g2bRpfffUVPj4+unqXL1+mXLlyXL16laFDhzJ16lRsbW2pX78+mzZt0tVL6fta165dqVatGoDuvCVM85paGeUa19fXF0VROHbsWBr2TgjxwRlnALwQaZPw9q3NmzcrgDJ27Fi9eo0aNVJUKpXy77//6soABVDOnDmjK7t7965iZWWlNGjQIFXbX7Nmja4dQClVqpTy999/p7heam7bKlasmJI5c2bd82XLlimAcvv2bUVRFGXTpk0KoJw+fTrJNpJLmdK2bVsFUIYOHZroshw5cuiea29ls7a2Vh48eKArP3nypAIo/fr105X5+fkpfn5+KbaZXGzalBVa58+fVwClU6dOevW0twfu379fV5YjRw4FUA4dOqQrCw0NVSwtLZUBAwboyooVK5bqlAZvS3vcRo8erYSFhSnBwcHKgQMHlBIlSiiA8vvvv+vqkkQ6F60+ffoogHLhwgW9thN7JHcb6IsXLxRAqVevXqr24W2O/bFjx3Rlu3bt0r127t69qytfsGCBQaza12SvXr10ZWq1WqlVq5ZiYWGhl8Lk9evXevFER0crhQsXVipXrqxXDigmJibK5cuXDfYt4evP0dEx2XMQHR2tuLm5KYULF9ZLCbNt2zYFUEaMGGGwL2PGjNFro0SJEoqvr2+S29Dy9PRUGjZsaFCufW/UrVtXr/zbb7/Ve308f/5csbKyUoYMGaJXr3fv3oqtrW2SqZu0/Pz8lPz58ythYWFKWFiYcu3aNWXQoEEKYPC+iX+b9aVLlxRAOXz4sKIoijJnzhzFzs5OiYiIUNq2bat3q6uiKEqbNm0UQMmcObPSoEEDZcqUKcrVq1cN4tF+Zib1SCmFVmK0n6m+vr5KdHS0rnzSpEkKoGzZsiVV7cTExChbtmxR6tevr5ibmyumpqZKzZo1lY0bN+q1m5xatWopOXPmNCiPiIhI8nM6PltbW6VDhw4G5du3b1cAZefOnbqyOnXqKBMnTlQ2b96sLFmyRPnyyy8VQBk8eHCibVevXl0pUKBAqvZDCCFExvT8+fNUXR/WrVtXAZSXL19+tPbSej0d/9qufv36ioWFhV5askePHin29vbKV199pSv7kOlcEl6zKoqiHD9+XAGUX3/9VVeWWDoXPz8/BVCmTp2qK4uKilKKFy+uuLm56a4ztOsWKFBAiYqK0tWdMWOGAigXL15MNp7x48crKpVK71o9MYsXLzZoT0v7PSDh44svvlAeP36sV7dKlSpKkSJFlMjISF2ZWq1WKlSooOTJk0dXlprva2+TziUjX+M+evRIAZSJEyemep+FEB+ejEQXGdKff/6JqampbjSB1oABA1AUhR07duiVly9fHl9fX91zLy8v6tWrx65du1KVcqFSpUrs2bOH9evX061bN8zNzVO8PT617OzsePXqVZLLtb+Sb9u2LcWRx8np3r17quvWr1+fbNmy6Z6XKVOGsmXL8ueff7719lND237Cuwm0E9ckHH1csGBBvVEvrq6u5MuXj1u3bunKMmXKxOXLl/nnn38+VNg6I0eOxNXVFXd3d/z9/bl58yYTJ05MduR9QtoZ3xO+Jrp06cKePXv0Hkml8QB0I9nt7e1Ttd23OfbxJ8ApW7YsoLnl1svLy6A8/jnR6tmzp+5vbTqW6Oho9u7dqyu3trbW/f3s2TNevHjBl19+meidBH5+fhQsWDCFPdW8Jk6ePMmjR48SXX7mzBlCQ0P59ttv9SYQqlWrFvnz5090FHy3bt30nn/55ZeJ7nNCT548IXPmzEku79Gjh97zXr16Af+dL0dHR+rVq8eaNWt0I1ji4uJYt24d9evXx9bWNsUYrl27hqurK66uruTPn5/JkydTt25dli9fnuQ6hQoVomjRoqxZswaA1atXU69ePWxsbBKtv2zZMmbPno2Pj49u5FSBAgWoUqVKoiO6R4wYYfB637Nnj8FttGnRpUsXvRH/3bt3x8zMLNWfa2ZmZtStW5dNmzbx4MEDJk6cyN27d/nmm2/w9PRk0KBBKX5Gv3nzJtFJo7Wvs5RusU7L+lu3bmXw4MHUq1ePDh06cPDgQQICApg2bRoPHjwwaCNz5sw8fvw42e0LIYTI2LTXlyldH2qXJ/cd5UO0B2m/no6Li2P37t3Ur1+fnDlz6sqzZs1KixYtOHLkiMEdnh9C/GvWmJgYnjx5Qu7cucmUKVOq7oA1MzOja9euuucWFhZ07dqV0NBQAgMD9eq2b98eCwsL3XPt96H4157x44mIiODx48dUqFABRVFSvPPtyZMnAEleo5YtW1Z3bbZt2zbGjRvH5cuXqVu3ru5a5OnTp+zfv58mTZrojb5+8uQJAQEB/PPPP7prwA/1fS0jX+Nqj71cmwmRvkgnusiQ7t69i4eHh8EFm/Z2vLt37+qV58mTx6CNvHnz8vr1a8LCwlLcXpYsWahatSqNGjVi3rx51K5dm2rVqhEcHPwOe6ERHh6e7IWnn58fDRs2ZPTo0bi4uFCvXj2WLVuWaO7bpJiZmeHp6Znq+kkdr9Tk9HsXd+/excTExCBNjru7O5kyZTI4r/E7a7UyZ86sl+d4zJgxPH/+nLx581KkSBEGDRrE33//nWwccXFxBAcH6z2io6NTjF/b0b1v3z4CAwMJDQ1l8ODBKa4XX3h4OGD4ZSRPnjxUrVpV75Fc56uDgwOQui8r8O7H3tHREYDs2bMnWp4w97SJiYneFx3QvMYAvdfZtm3bKFeuHFZWVjg5OelSBL148cJgH+LfPpqcSZMmcenSJbJnz06ZMmUYNWqU3pcO7b7my5fPYN38+fMbHAsrKyuDHKAJX4fJ0XZ+JybhezFXrlyYmJjoHaM2bdpw7949Dh8+DGhyeIeEhNC6detUbd/b25s9e/awa9cu5s6dS7Zs2QgLC9P7ASExLVq0YP369fz7778cO3Ys0dtctUxMTOjRoweBgYE8fvyYLVu28PXXX7N///5E0wUVKVLE4PVetWpVvS+MaZXwWNrZ2ZE1a9a3+lxzc3NjwIABHDt2jE6dOhEaGsqUKVNS/HHV2to60c9ubZ77+F943/f6KpWKfv36ERsbm2j+V0VR3iq/qBBCiIwjLZ3jKpUKFxcXQNMhGv+6WHsd9rbtJSet19NhYWG8fv060eu2AgUKoFaruX//forbTejNmzcG3wdSqj9ixAjdfF0uLi64urry/PnzRK9bE/Lw8DAY/JDYtTEYXodrvxPEv/a8d+8e7dq1w8nJSTdnj5+fH0Cq4oGkr1FdXFx012a1atVi+PDhLF68mGPHjrF48WJAk/pRURR++OEHXUe29qFN36jNS/4239dSIyNf42qPvVybCZG+SCe6EG+hUaNGhIeHv/MkbDExMdy4cSPZ3OoqlYoNGzZw/PhxevbsycOHD+nQoQO+vr66DteUWFpaGuTQfVdJ/YeempH9b9t2QqampomWx7/g++qrr7h58yZLly6lcOHCLF68mJIlS+ou8BJz//59smbNqvdITT46bUd35cqVKVmyZKIjRlNy6dIlTE1NU90hnBQHBwc8PDxSnfdf612PfWrOSWodPnyYunXrYmVlxdy5c/nzzz/Zs2cPLVq0SLS9lDogtZo0acKtW7eYNWsWHh4eTJ48mUKFChncwZJaSe1zajg7O6e6sx0SPz8BAQFkyZKFlStXArBy5Urc3d1TPRmUra0tVatWpXr16nTv3p0///yTU6dOMXz48GTXa968OY8fP6Zz5844OztTvXr1VG3P2dmZunXr8ueff+Ln58eRI0cMfphIzxRF4a+//qJ169a4u7uzZMkSqlSpwpo1a3Q/XiUla9asBAcHG7x+g4KCAM0X6JTW19Z9m/W1P3IlNknVs2fPUtW5IYQQIuNydHTEw8MjxQ7Kv//+G09PT13H3jfffKN3XaydmP5t20vO+7iefh/WrVtn8H0gOb169WLcuHE0adKE3377jd27d7Nnzx6cnZ0N5gZ6Vyldb8fFxVGtWjW2b9/OkCFD2Lx5M3v27NGNwE4pHm3++bRco1apUgWAQ4cO6W1j4MCBiY683rNnj+478Nt8X0uNjHyNqz32cm0mRPoinegiQ8qRIwePHj0yGPVw7do13fL4Ers17MaNG9jY2BiMIE0N7W1qqf0VPykbNmzgzZs3BAQEpFi3XLlyjBs3jjNnzrBq1SouX77M2rVrgff/C3VSxyv+RI2ZM2c2mGARDO8CSEtsOXLkQK1WG2w/JCSE58+fG5zX1HJycqJ9+/asWbOG+/fvU7RoUUaNGpVkfXd39zSlTnlf7t27x8GDBylfvnyq07Akp3bt2ty8eZPjx4+nWPdDHfukqNVqg3Qn2kkRta+z33//HSsrK3bt2kWHDh34+uuvU90xnJKsWbPy7bffsnnzZm7fvo2zszPjxo0D/vv8uH79usF6169ff6/HIn/+/Ny+fTvJ5QnPx7///otardZ7L5qamtKiRQs2bNjAs2fP2Lx5M82bN3/rzv2iRYvSqlUrFixYwL1795Ks5+XlxRdffMGBAwdo3LgxZmZmad5WqVKlABLtGH7fEh7L8PBwgoKCkpyANqE7d+4wevRocubMSeXKlTlw4AADBgzg1q1b7N27l2bNmqX4Y2Xx4sV5/fq13iS2gG7Ss+LFi6e4/tmzZw2+/J48eRIbGxvdiLWkaN9zif2/d/v27fc2uZoQQoj0q06dOty+fZsjR44kuvzw4cPcuXOHxo0b68qmTp2qd10cf2T427T3Prm6umJjY5Poddu1a9cwMTHR/Yiclu8lAQEBBt8HkrNhwwbatm3L1KlTadSoEdWqVUt0QvqkPHr0yOCOtoTXxql18eJFbty4wdSpUxkyZAj16tWjatWqKf7YrpU/f36AZK9RE4qNjQX+u6tWe8epubl5oiOvq1atqvd9J6Xva+/j+25GusbVHnu5NhMifZFOdJEh1axZk7i4OGbPnq1X/vPPP6NSqfj666/1yo8fP66Xi+7+/fts2bKF6tWrJ9vRlNhM2YDuV3Htf45v48KFC/Tt25fMmTMb5D2O79mzZwYxaDtatLf1a3O0pfYiLSWbN2/Wy+F26tQpTp48qXdcc+XKxbVr1/TS4Vy4cIGjR4/qtZWW2GrWrAnA9OnT9cqnTZsGaHJSp5U2p5+WnZ0duXPnTjYdjpWVVZpSp7wPT58+pXnz5sTFxfHdd9+9lzYHDx6Mra0tnTp1IiQkxGD5zZs3mTFjBvBhjn1K4r9/FUVh9uzZmJub60aymJqaolKp9O5uuHPnDps3b37rbcbFxRn8+OXm5oaHh4fuNVGqVCnc3NyYP3++3utkx44dXL169b0ei/Lly3Pp0qUkX49z5szRez5r1iwAg8+41q1b8+zZM7p27Up4eDitWrV6p7gGDx5MTEyM7vwnZezYsYwcOVKXqz0xwcHBXLlyxaA8Ojqaffv2JZpG6ENYuHChXs7yefPmERsba3AsE7p48SJVq1YlZ86cjBs3jhIlSrB9+3bu3LnDjz/+mKYvtvXq1cPc3Jy5c+fqyhRFYf78+WTLlo0KFSroyoOCgrh27ZpezI0aNSIkJISNGzfqyh4/fsz69eupU6eObrTe06dPDe4KiomJYcKECVhYWFCpUiW9ZS9evODmzZt62xdCCPFpGjhwIDY2NnTt2tXgOvnp06d069YNBwcHvblrfH199a6L489B8zbtvU+mpqZUr16dLVu26KU9CQkJYfXq1VSsWFF3p5g2XUpqvpdkzZrV4PtASnEk/M42a9asVN+lGxsby4IFC3TPo6OjWbBgAa6urnpze6WG9vtt/HgURdFd96fE19cXCwsLzpw5k+pt/vHHHwC6gUdubm74+/uzYMGCRDuS43+HTM33tbScu+RklGvcwMBAVCqV3hxUQgjjS/tPakKkA3Xq1KFSpUp899133Llzh2LFirF79262bNlC3759yZUrl179woULExAQQO/evbG0tNR1YIwePTrZ7axcuZL58+frJqp59eoVu3btYs+ePdSpU4fKlSunKt7Dhw8TGRlJXFwcT5484ejRo2zduhVHR0c2bdqEu7t7kuv+8ssvzJ07lwYNGpArVy5evXrFokWLcHBw0HV8WltbU7BgQdatW0fevHlxcnKicOHCFC5cOFXxJZQ7d24qVqxI9+7diYqKYvr06Tg7O+uNOunQoQPTpk0jICCAjh07Ehoayvz58ylUqJDe5D1pia1YsWK0bduWhQsX8vz5c/z8/Dh16hS//PIL9evXN+j4SY2CBQvi7++Pr68vTk5OnDlzhg0bNnywC/nUuHHjBitXrkRRFF6+fMmFCxdYv3494eHhTJs2jRo1aryX7eTKlYvVq1fTtGlTChQoQJs2bShcuDDR0dEcO3aM9evX065dO+DDHPvkWFlZsXPnTtq2bUvZsmXZsWMH27dvZ/jw4bpRsrVq1dIdjxYtWhAaGsqcOXPInTv3W+dJfPXqFZ6enjRq1IhixYphZ2fH3r17OX36NFOnTgU0I2YmTpxI+/bt8fPzo3nz5oSEhDBjxgy8vb3p16/fezsO9erV48cff+TgwYOJ3ip6+/Zt6tatS40aNTh+/DgrV66kRYsWBndGlChRgsKFC7N+/XoKFChAyZIl3ymuggULUrNmTRYvXswPP/ygu603IT8/P11+zaQ8ePCAMmXKULlyZapUqYK7uzuhoaGsWbNG92NiwltVtZ+ZCRUtWpSiRYsCMGrUKEaPHs1ff/2Fv79/ivsUHR1NlSpVaNKkCdevX2fu3LlUrFiRunXrJrteYGCgbiLRtm3b4ubmluK2kuLp6Unfvn2ZPHkyMTExlC5dms2bN3P48GFWrVql96PusGHD+OWXX7h9+7auo75Ro0aUK1eO9u3bc+XKFVxcXJg7dy5xcXF6/59t3bqVsWPH0qhRI3x8fHj69CmrV6/m0qVL/PTTTwb/5+zduxdFUahXr95b75sQQoiMIXfu3Pz66680b96cIkWK0LFjR3x8fLhz5w5Llizh2bNnrF27NtWpBd93e29j7Nix7Nmzh4oVK/Ltt99iZmbGggULiIqKYtKkSbp6xYsXx9TUlIkTJ/LixQssLS2pXLnyO/3frlW7dm1WrFiBo6MjBQsW5Pjx4+zduzfJa6iEPDw8mDhxInfu3CFv3rysW7eO8+fPs3DhQr2J0VMjf/785MqVi4EDB/Lw4UMcHBz4/fffU52excrKiurVq7N3717GjBljsPzhw4e6NILR0dFcuHCBBQsW4OLiotfpPGfOHCpWrEiRIkXo3LkzOXPmJCQkhOPHj/PgwQMuXLgApO77mvaHhN69exMQEICpqWmiOcdTkhGucQH27NnDF198kerXjxDiI1GEyAB69OihJHy5vnr1SunXr5/i4eGhmJubK3ny5FEmT56sqNVqvXqA0qNHD2XlypVKnjx5FEtLS6VEiRLKX3/9leJ2T58+rTRu3Fjx8vJSLC0tFVtbW6VkyZLKtGnTlJiYmBTX/+uvvxRA9zA3N1dcXV2Vr776Shk3bpwSGhpqsM6yZcsUQLl9+7aiKIpy9uxZpXnz5roY3NzclNq1aytnzpzRW+/YsWOKr6+vYmFhoQDKyJEjFUVRlLZt2yq2traJxte2bVslR44cuue3b99WAGXy5MnK1KlTlezZsyuWlpbKl19+qVy4cMFg/ZUrVyo5c+ZULCwslOLFiyu7du0yaDO52EaOHGlwXmNiYpTRo0crPj4+irm5uZI9e3Zl2LBhSmRkpF69HDlyKLVq1TKIyc/PT/Hz89M9Hzt2rFKmTBklU6ZMirW1tZI/f35l3LhxSnR0dKLH5G3EP24pif96MDExUTJlyqSUKFFC6dOnj3L58uV3ajspN27cUDp37qx4e3srFhYWir29vfLFF18os2bN0juu73rste+1lOLXviZv3rypVK9eXbGxsVGyZMmijBw5UomLi9Nbf8mSJbr3bf78+ZVly5Yl+rpJbNvxl2lfc1FRUcqgQYOUYsWKKfb29oqtra1SrFgxZe7cuQbrrVu3TilRooRiaWmpODk5KS1btlQePHigVyep91diMSalaNGiSseOHRNd/8qVK0qjRo0Ue3t7JXPmzErPnj2VN2/eJNrOpEmTFED56aefUrVdRdG8XwoVKpTosgMHDugdu9S+FhMek5cvXyozZsxQAgICFE9PT8Xc3Fyxt7dXypcvryxatEjvMzvhZ2bChzYWRVGUAQMGKCqVSrl69Wqy8Wg/Uw8ePKh06dJFyZw5s2JnZ6e0bNlSefLkSQpHSFHCw8NTrJMWcXFxyk8//aTkyJFDsbCwUAoVKqSsXLnSoF7btm31/i/Qevr0qdKxY0fF2dlZsbGxUfz8/JTTp0/r1Tlz5oxSp04dJVu2bIqFhYViZ2enVKxYUfntt98Sjalp06ZKxYoV39s+CiGESP8uXryotGjRQnF3d1dMTEwUQLGyskr0evRjtJfW6+n41wSKovnOFBAQoNjZ2Sk2NjZKpUqVlGPHjhmsu2jRIiVnzpyKqampAqTqO2FqPHv2TGnfvr3i4uKi2NnZKQEBAcq1a9eUHDlyKG3bttXV017rxN+u9nrszJkzSvny5RUrKyslR44cyuzZs/W2oV13/fr1euXaY7ds2TJd2ZUrV5SqVasqdnZ2iouLi9K5c2flwoULBvWSsnHjRkWlUin37t3TK8+RI4fB9xk3NzelefPmyr///mvQzs2bN5U2bdoo7u7uirm5uZItWzaldu3ayoYNG3R1UvN9LTY2VunVq5fi6uqqqFSqFK+zM/I17vPnzxULCwtl8eLFycYjhPj4VIryFrO9CZGBqFQqevToYZD6RQhhHO3atWPDhg2pnhj3U7dixQp69OjBvXv3yJQp01u3M2PGDPr168edO3fw8vJ6fwGmU2XKlCFHjhysX78+2XrLly+nffv2nD59+p1ScH2qgoOD8fHxYe3atTISXQghPmO//vor7dq1o1WrVvz666/prr1Pmb+/P48fP+bSpUvGDkUnLi6OggUL0qRJE3788Udjh/NZmT59OpMmTeLmzZtYW1sbOxwhRDySE10IIYQwopYtW+Ll5WWQ/zwtFEVhyZIl+Pn5fRYd6No0SIndYizSZvr06RQpUkQ60IUQ4jPXpk0bxo8fz4oVKxg+fHi6a098XKampowZM4Y5c+bIwJePSJuv/fvvv5cOdCHSIcmJLoQQQhiRiYnJW488ioiIYOvWrfz1119cvHiRLVu2vOfo0icHB4dkJwcWqTdhwgRjhyCEECKdGDJkCEOGDEm37YmPq2nTpjRt2tTYYXxWzM3NuXfvnrHDEEIkQTrRhRBCiAwqLCyMFi1akClTJoYPH57iJJlCCCGEEEIIIYRIO8mJLoQQQgghhBBCCCGEEEIkQXKiCyGEEEIIIYQQQgghhBBJkE50IYQQQgghhBBCCCGEECIJkhM9EWq1mkePHmFvb49KpTJ2OEIIIYQQ4jOhKAqvXr3Cw8MDE5PPZ7yLXH8LIYQQQghjSO31t3SiJ+LRo0dkz57d2GEIIYQQQojP1P379/H09DR2GB+NXH8LIYQQQghjSun6WzrRE2Fvbw9oDp6Dg0Oa1lWr1YSFheHq6vpZjR761Mh5/DTIefw0yHn8NMh5/DTIefzwXr58Sfbs2XXXo5+Ld7n+Tit5Hadfcm7SLzk36Zucn/RLzk36JecmffuY5ye119/SiZ4I7S2kDg4Ob9WJHhkZiYODg7wJMzA5j58GOY+fBjmPnwY5j58GOY8fz+eW0uRdrr/TSl7H6Zecm/RLzk36Jucn/ZJzk37JuUnfjHF+Urr+lleJEEIIIYQQQgghhBBCCJEE6UQXQgghhBBCCCGEEEIIIZIgnehCCCGEEEIIIYQQQgghRBIkJ7oQQgiRQcXFxRETE2PsMFJFrVYTExNDZGSk5BzMwOQ8vjtzc3NMTU2NHUaG9T4+9+R1nH5YWFjIORBCCCFEhiCd6EIIIUQGoygKwcHBPH/+3NihpJqiKKjVal69evXZTZj4KZHz+H5kypQJd3d3OYZp8D4/9+R1nH6YmJjg4+ODhYWFsUMRQgghhEiWdKILIYQQGYy2I8nNzQ0bG5sM0QmkKAqxsbGYmZlliHhF4uQ8vhtFUXj9+jWhoaEAZM2a1cgRZRzv83NPXsfpg1qt5tGjRwQFBeHl5SXnQgghhBDpmnSiCyGEEBlIXFycriPJ2dnZ2OGkmnRafRrkPL47a2trAEJDQ3Fzc5PULqnwvj/35HWcfri6uvLo0SNiY2MxNzc3djhCCCGEEEmSBHRCCCFEBqLNBWxjY2PkSIQQb0v7/s0ocxoYm3zufbq0aVzi4uKMHIkQQgghRPKkE10IIYTIgGT0pBAZl7x/344ct0+PnFMhhBBCZBTSiS6EEEIIIYQQQgghhBBCJMHonehz5szB29sbKysrypYty6lTp5Kse/nyZRo2bIi3tzcqlYrp06cn2/aECRNQqVT07dv3/QYthBBCiA/G29s7xf/jM5JPbX+EEMY3atQosmTJgkqlYvPmzcYOB4B27dpRv359Y4chhBBCCPFBGHVi0XXr1tG/f3/mz59P2bJlmT59OgEBAVy/fh03NzeD+q9fvyZnzpw0btyYfv36Jdv26dOnWbBgAUWLFv1Q4QshhBDpxoIFCwgPD/9o27Ozs6Nr165pWuf+/fv8+OOP7Nq1i8ePH5M1a1bq16/PiBEjMtQkqe/bqFGjGD16NACmpqZkypSJggUL8s0339C9e3csLS1T3daBAweoVKkSz549I1OmTB8oYiFEarRr145ffvkFAHNzc7y8vGjTpg3Dhw/HzOztv4ZdvXqV0aNHs2nTJsqVK0fmzJnfOdZRo0axefNmzp8/n2I97edVfHv27GHGjBkoiqIr8/f3p3jx4vIjohBCCCE+CUbtRJ82bRqdO3emffv2AMyfP5/t27ezdOlShg4dalC/dOnSlC5dGiDR5Vrh4eG0bNmSRYsWMXbs2A8TvBBCCJGOhIeH06xZs4+2vbVr16ap/q1bt6hQoQJ58+ZlzZo1+Pj4cPnyZQYNGsSOHTs4ceIETk5OHyja5MXFxaFSqTAxMd4NeoUKFWLv3r2o1WqePHnCgQMHGDt2LCtWrODAgQPY29sbLTYhxNurUaMGy5YtIyoqij///JMePXpgbm7OsGHD0tyW9rPq5s2bANSrV88oOcW1n1fxOTk56SYJFUIIIYT4FBnt22J0dDSBgYFUrVr1v2BMTKhatSrHjx9/p7Z79OhBrVq19NoWQgghhPH07NkTCwsLdu3ahZ+fH15eXnz99dfs3buXhw8f8t133+nVf/XqFc2bN8fW1pZs2bIxZ84c3TJFURg1ahReXl5YWlri4eFB7969dcujoqIYOHAg2bJlw9bWlrJly3LgwAHd8uXLl5MpUya2bt1KwYIFsbS0ZPHixVhZWfH8+XO9OPr06UPlypV1z48cOcKXX36JtbU12bNnp3fv3kREROiWh4aGUqdOHaytrfHx8WHVqlWpOj5mZma4u7vj4eFBkSJF6NWrFwcPHuTSpUtMnDhRV2/FihWUKlUKe3t73N3dadGiBaGhoQDcuXOHSpUqAZA5c2ZUKhXt2rUDYOfOnVSsWJFMmTLh7OxM7dq1dR1xQqRk3rx5FC1aFAcHBxwcHChfvjw7duzQLY+MjKRHjx44OztjZ2dHw4YNCQkJMWLE6YelpSXu7u7kyJGD7t27U7VqVbZu3Qq83WdVhw4dqFOnDqD57hS/E33x4sUUKFAAKysr8ufPz9y5c/ViefDgAc2bN8fJyQlbW1tKlSrFyZMnWb58OaNHj+bChQuoVCpUKhXLly9Pcp+0n1fxHxYWFnrpXNq1a8fBgweZMWOGrs07d+68l2MqhBBCCGEMRhuJ/vjxY+Li4siSJYteeZYsWbh27dpbt7t27VrOnj3L6dOnU71OVFQUUVFRuucvX74EQK1Wo1ar07R9tVqNoihpXk+kL3IePw1yHj8Nch71aY+H9mFMqd3+06dP2bVrF2PGjMHa2lpvvSxZstCiRQvWrVvHnDlzdB1CkydPZtiwYYwaNYpdu3bRp08f8uTJQ7Vq1diwYQM///wza9asoVChQgQHB3PhwgVduz169ODq1ausWbMGDw8PNm3aRI0aNfj777/JkycPiqLw+vVrJk6cyKJFi3B2dsbT05MRI0awYcMGOnbsCGhGfa5bt46xY8eiKAo3b96kRo0a/PjjjyxZsoSwsDB69epFz549Wbp0KaDpOHr06BH79+/H3NycPn36EBoamuz50pYnXJ4vXz6+/vprNm7cyI8//ghoBiGMGTOGfPnyERoayoABA2jXrh3bt2/H09OTDRs20KhRI65du4aDg4PueIeHh9OvXz+KFi1KeHg4I0eOpEGDBpw7dy7NI/CTileknvb1kNi1Znr8rPP09GTChAm6988vv/xCvXr1OHfuHIUKFaJfv35s376d9evX4+joSM+ePfnmm284evSosUNPd6ytrXny5Amg+XHxypUrrF27Vu+z6uLFi+TJkwdA91m1ePFinJ2dyZo1K/7+/rRv356goCBdu6tWrWLEiBHMnj2bEiVKcO7cOTp37oytrS1t27YlPDwcPz8/smXLxtatW3F3d+fs2bOo1WqaNm3KpUuX2Llzp26EuaOj4zvt54wZM7hx4waFCxdmzJgxALi6ur5Tm0IIIYQQxmTUdC7v2/379+nTpw979uzBysoq1euNHz8+0dx+YWFhREZGpikGtVrNixcvUBTFqLeFi3cj5/HTIOfx0yDnUV9MTAxqtZrY2FhiY2N15W/zw++70MaQGteuXUNRFPLkyUNMTIxB+oF8+fLx7NkzgoKCdHOiVKhQgYEDBwLQvXt3jhw5ws8//0ylSpW4c+cOWbJkwd/fH3Nzczw8PChZsiSxsbHcu3eP5cuXc/PmTTw8PADo27cvO3fuZMmSJYwdOxa1Wk1MTAwzZsygWLFiujiaNGnC6tWradu2LaDJ8fv8+XPq1atHbGwsP/30E82bN6dnz54A+Pj4MG3aNKpUqcLMmTO5d+8eO3bs4NixY5QqVQrQpKorWrRossdL+8NIYsvz5MnD7t27dcvatGmjW+bl5cW0adMoX748z58/x87OTtfx5eTkpMuJHhsbS7169fTaXbBgAR4eHvz9998ULlw4pVOooygKcXFxAEZJI/GpiI2N1aXuMTc311v26tUrI0WVNO3IZ61x48Yxb948Tpw4gaenJ0uWLGH16tW6uzaWLVtGgQIFOHHiBOXKlftwgU2bpnmkpGRJ+P/oby3TBg0ghfzfAPTvr3m8I0VR2LdvH7t27aJXr17cu3ePZcuWce/ePd1n1cCBA9m5cyfLli3jp59+AjSf+XPnztX7rNK+t93d3XVlI0eOZOrUqXzzzTeA5vPpypUrLFiwgLZt27J69WrCwsI4ffq0LnVW7ty5devb2dnpRpin5OLFi9jZ2emeFyxYkFOnTunVcXR0xMLCAhsbm1S1KYQQQgiR3hmtE93FxQVTU1ODWz1DQkLe+kIrMDCQ0NBQSpYsqSuLi4vj0KFDzJ49m6ioKExNTQ3WGzZsGP3jXRy/fPmS7Nmz4+rqioODQ5piUKvVqFQqXF1dpbMnA5Pz+GmQ8/hpkPOoLzIyklevXmFmZqY3MZ2JiclHPT4mJiapnhhP+3+viYmJQYehthzQ26fy5cvrtV+hQgVmzJiBmZkZTZs2ZdasWeTLl4+AgABq1qxJnTp1MDMz4+rVq8TFxVGoUCG9bURFReHi4oKZmRkmJiZYWFhQsmRJvY7gVq1aUb58eUJDQ/Hw8GDdunXUqlULFxcXQNNx9Pfff7NmzRrdOtrRxPfv3+eff/7BzMyMMmXK6PapcOHCZMqUKdnjpU3JkNhybRoE7bLAwEBd2oVnz57pfjh59OgRBQsW1B3rhK+Pf/75h5EjR3Ly5EkeP36st17x4sUTjSs5iZ1HkXra16Gzs7PBwI+0DAQxhri4ONavX09ERATly5cnMDCQmJgYvTSK+fPnx8vLi+PHj3/YTvSXL+Hhw5TrZc9uWPb4MarUrPv/O1Tf1rZt27Czs9P9ANqiRQtGjRrFgQMHiIuLI2/evHr1o6Ki9CZatrCwoGjRosluIyIigps3b9KxY0c6d+6sK4+NjdX9sHb+/HlKlCjxXuaeyJcvny4lDZCmyY+FEEIIITIqo3WiW1hY4Ovry759+3S589RqNfv27dON8EqrKlWqcPHiRb2y9u3bkz9/foYMGZJoBzpoLvwSu/h72w4J7eRk0tmTscl5/DTIefw0yHn8j7bDVfswptRuP0+ePKhUKl26toTrXbt2jcyZM+Pm5qZblnD/4pd7eXlx/fp19u7dy549e+jRowdTpkzh4MGDREREYGpqSmBgoMH/+3Z2drp2ra2tDV5PZcqUIVeuXKxbt47u3buzadMmli9frtt2eHg4Xbt21cu/ruXl5cU///yTaOxJlSW2bwldu3YNHx8fVCoVERER1KhRg4CAAFatWoWrqyv37t0jICBAN8I/qeNXt25dcuTIwaJFi/Dw8ECtVlO4cOFE7wxIjqIoycYrUkd7fhL7XEuvn3MXL16kfPnyREZGYmdnx6ZNmyhYsCDnz5/HwsJCNzpaK0uWLAQHByfZXlrSKSaZxsreHrJlSzl4V1dImH7IxQUlNeva2xuumwaVKlVi7ty5WFhY4OHhoftx69WrV5iamnLmzJlEP6u0+2ptbQ3op09KmFJJe/fCwoULKVu2rF5bpqamKIqi+3EmrWmlEqtnYWFBrly5El0/sb+TazNhaiNJ4ZZ+yblJ3+T8pF9ybtIvOTfp28c8P6ndhlHTufTv35+2bdtSqlQpypQpw/Tp04mIiKB9+/aA5pblbNmyMX78eECTB/TKlSu6vx8+fMj58+exs7Mjd+7c2NvbG9ySbGtri7Ozc5puVRZCCCHE++Ps7Ey1atVYsGABAwYMwMbGRrcsODiYVatW0aZNG71O2RMnTui1ceLECQoUKKB7bm1tTZ06dahTpw49evQgf/78XLx4kRIlShAXF0doaChffvllmmNt2bIlq1atwtPTExMTE2rVqqVbVrJkSa5cuaKXAiG+/PnzExsbS2BgIKVLlwbg+vXrBpOVpta1a9fYuXMnw4YN0z1/8uQJEyZMIPv/R9aeOXNGbx0LCwsAXcoVgCdPnnD9+nUWLVqkOyZHjhx5q5jE5ytfvnycP3+eFy9esGHDBtq2bcvBgwffur20pFNMKo0VvXtrHqkRbz1FUYhbvx5TU9PU/RiUytRVCanVaqytrfH29o7XlKatIkWKEBcXR1BQEBUrVkxkk7G6L3QJUz1p39/acmdnZzw8PPj3339p2rRpom0VKlSIJUuWEBoamuhodDMzM8Pjm8Q+JZV+StsJrl1mbm6eYpsJUxtJCrf0S85N+ibnJ/2Sc5N+yblJ3z7m+UltOkWjdqI3bdqUsLAwRowYQXBwMMWLF2fnzp26yUbv3bund6AePXpEiRIldM+nTJnClClT8PPz05vJXgghhBDpy6xZs/jiiy+oUaMGY8eOxcfHh8uXLzNo0CCyZcvGuHHj9OofPXqUSZMmUb9+ffbs2cP69evZvn07AMuXLycuLo6yZctiY2PDypUrsba2JkeOHDg7O9OyZUvatGnD1KlTKVGiBGFhYezbt4+iRYvqdYonpmXLlowaNYpx48bRqFEjvTvVhgwZQrly5ejZsyedOnXC1taWK1eusGfPHmbPnk2+fPmoUaMGXbt2Zd68eZiZmdG3b1/dSNLkxMbGEhwcrOtMOnDgAGPHjqV48eIMGjQI0Ix2t7CwYNasWXTr1o1Lly7pJhzVypEjByqVim3btlGzZk2sra3JnDkzzs7OLFy4kKxZs3Lv3j2GDh2aqvMmhJaFhYXuByRfX19Onz7NjBkzaNq0KdHR0Tx//lxvNHpKKRrTkk4xqTRW7+pDpyXS3mmQWMwFCxakZcuWdOjQgSlTpiT6WRU/1VV88dM2aY0aNYo+ffqQOXNmatSoQVRUFGfOnOHZs2f079+fVq1aMWnSJBo3bsxPP/1E1qxZOXfuHB4eHpQvX56cOXNy584dLl26hKenJ/b29kneqZtU+qmE++vt7c3p06d58OABdnZ2ODk5GXwJTpjaSFK4pV9ybtI3OT/pl5yb9EvOTfr2Mc9PatMpGn1i0Z49eyaZviVhx7i3t3eKtxim1IYQb+v1a4g3eBKAKVNgxw6oWxfatYP/p50UQgiRQJ48eTh+/Dhjx46lSZMmPH36FHd3d+rXr8/IkSMNRkYOGDCAM2fOMHr0aBwcHJg2bRoBAQGAZlK9CRMm0L9/f+Li4ihSpAh//PGHLo/wsmXLGDt2LAMGDODhw4e4uLhQrlw5ateunWKcuXPnpkyZMpw6dYrp06frLStatCgHDx7ku+++48svv0RRFHLlyqU38nPZsmV06tQJPz8/smTJwtixY/nhhx9S3O7ly5fJmjUrpqamODo6UrBgQYYNG0b37t11HVmurq4sX76c4cOHM3PmTEqWLMmUKVOoW7eurp1s2bIxevRohg4dSvv27WnTpg3Lly9n7dq19O7dm8KFC5MvXz5mzpyJv79/inEJkRS1Wk1UVBS+vr6Ym5uzb98+GjZsCGjuwLh37x7ly5dPcv20pFN832msPnZaoqS2of2sGjhwoN5nVZ06dQzSMyXWXvzyzp07Y2try+TJkxk8eDC2trYUKVKEvn37olKpsLS0ZPfu3QwYMIBatWoRGxtLwYIFmTNnDiqVikaNGrFp0yYqV67M8+fPWbZsGe3atUtyX5I7btplgwYNom3bthQqVIg3b95w+/ZtvVH52roJUxtJCrf0S85N+ibnJ/2Sc5N+yblJ3z7W+Ult+yolrb3Sn4GXL1/i6OjIixcv3mpi0dDQUNzc3ORNmIHFP4/PnpkwdCicPg3nzkH87wydOsGSJZq/7e1h5EjNXcUy31r6IO/HT4OcR32RkZHcvn0bHx8fvV/MFyxYQHh4+EeLw87Ojq5du6a6vvb2fzMzM8mlnYHJeXw/knofw7tdh34ow4YN4+uvv8bLy4tXr16xevVqJk6cyK5du6hWrRrdu3fnzz//ZPny5Tg4ONCrVy8Ajh07luptJLffyR2vtyGv4/Qj4bmV//PTLzk36Zucn/RLzk36JefGyCIj4dUrzdw1ifiY5ye1199GH4kuRHp25gw0bgz37mmeHz0K8dNWvn7939+vXsHAgbBhA/z2G/w/Xa0QQnwUaenQFkKItAgNDaVNmzYEBQXh6OhI0aJFdR3oAD///DMmJiY0bNiQqKgoAgICmDt3rpGjFkIIIYQQ6dI//0CTJprRqPv3w3tM1/chyU8tQiTh6FELKldW6TrQHRzg6VP9OqtWwaVL0KHDfyPUT5yA0qXh/PmPGq4QQgghxAexZMkS7ty5Q1RUFKGhoezdu1fXgQ6aPJJz5szh6dOnREREsHHjxmTzoQshhBBCiM/UmjVQsqSm0+zwYRgzxtgRpZp0oguRiBMnoFWrzEREaHrGy5eHK1c0uc/jU6mgUCFNSpfjx0Gb5jEkBPz94ezZjxq2EEIIIYQQQgghhBDpy5s30LUrtGgB2jSk+fNr0j9kENKJLkQCd+5A/foqIiM1Heh16sBff0G2bMmvV7asJv1LuXKa5y9eQECA5i4VIYQQQgghhBBCCCE+O9evazrLFi78r6x1a83kg0WKGC+uNMoYSWeE+Eiio6FpUwgL03SgV6qksGGDCguLxOsnNpFfnTpmhIQ05PZtTx4/hiFD9vHFF+cN1k3rpHxCCCGEEEIIIYQQQmQYq1drRqBr+86srWHOHGjX7r+8yBmEdKILEc/IkXDqlOZvb+9Y1q83wcIi6Td1eHg4zZo1Myhv0EBF69ZRfPttOAEB+YH8BnXWrl37vsIWQgghhPjkqdVqY4cg3jNFUYwdghBCCCE+BLUaunWDRYv+KytQANav1+RFzoCkE12I/3vx4r/3toWFwvz5z8mc2emt2nJwUNi8+XFG+1FNCCGEECLdsbCwwMTEhEePHuHq6oqFhQWqd7jIUhSF2NhYzMzM3qkd8W4URSEsLAyVSoW5ubmxwxFCCCHE+2Rigl5ah7ZtNSPQbW2NF9M7kk50If7P0REuXIBvv4UyZRSKFYt9p/bkO5kQQgghxLszMTHBx8eHoKAgHj169M7tKYqCWq3GxMREOtGNTKVS4enpiampqbFDEUIIIcT7NmWKpqOtY0dN+pYMTjrRhYgnWzbYvBni4uDx4/fXrqLAunU27NplxZIlTzGRKX2FEEIIIVLNwsICLy8vYmNjiYuLe6e21Go1T548wdnZGRO5KDMqc3Nz6UAXQgghPgWvX8O5c/DFF/+VWVnBoUOfzChT6UQXIgGVivfeyT14cCbWrNHcsrJihS1t20a83w0IIUQGd+fOHXx8fDh37hzFixdP1TrLly+nb9++PH/+/L3HEx0dTcGCBfn111+pUKHCe29fGMfOnTsZOnQoZ8+elc7TDEib9uNdU3+o1WrMzc2xsrKS14EQQgghxLu6dg0aN4Zbt+D0aShY8L9ln0gHOoBcNYrP3pkz8OZNCpWCg2H6dPjmGyheHPLlgwoVqPL771jt2gXR0cmuXr/+a93fP/3kwKNHMuJGCCHSs/nz5+Pj46PXga5SqXQPBwcHSpcuzZYtW/TWW758OSqVigIFChi0uX79elQqFd7e3rqyuLg4JkyYQP78+bG2tsbJyYmyZcuyePFiXZ127drpbVv7qFGjRpr2KTIykh49euDs7IydnR0NGzYkJCQk2XUURWHEiBFkzZoVa2trqlWrxj///KNX5+nTp7Rs2RIHBwcyZcpEx44dCQ8P1y2/fv06lSpVIkuWLFhZWZEzZ06+//57YmJidHX8/f0T3cdatWrp6iS2XKVSMXnyZIO4o6KiKF68OCqVivPnz+vKa9Sogbm5OatWrUrTsRNCCCGEEEIkYsUKKFUKLl3SjEZv316TjuETJJ3o4rMWHg41aoCPD8yYkUiFoCCedmnNiq89eDWkH2zapMnndOMGHD9OoZPHce7YEfdy5bBdvBjidQjE98UX0bRooRl9HhFhwsSJDh9wr4QQQrwLRVGYPXs2HTt2NFi2bNkygoKCOHPmDF988QWNGjXi4sWLenVsbW0JDQ3l+PHjeuVLlizBy8tLr2z06NH8/PPP/Pjjj1y5coW//vqLLl26GIyur1GjBkFBQXqPNWvWpGm/+vXrxx9//MH69es5ePAgjx494ptvvkl2nUmTJjFz5kzmz5/PyZMnsbW1pXbt2kRGRurqtGzZksuXL7Nnzx62bdvGoUOH6NKli265ubk5bdq0Yffu3Vy/fp3p06ezaNEiRo4cqauzceNGvX27dOkSpqamNG7cWFcn4f4vXboUlUpFw4YNDeIePHgwHh4eie5Tu3btmDlzZqqPmxBCCCGEECKB16+hQwdo0wYi/p9toVAhWL78kxp9Hp90oovP2rx58OQJhIRo7jiJL3TzL3Tsn5OsWVbSpr7CSc//LzA3BwcHXliCxwDoXguC34SSadQo3GrWxOzKlUS3NXz4CzJlUgPw++82PHjg9gH3TAgh0pedO3fi7+9P5syZcXZ2pnbt2ty8eTPJ+gcOHEClUrF9+3aKFi2KlZUV5cqV49KlSwZ1d+3aRYECBbCzs9N1NmudPn2aatWq4eLigqOjI35+fpw9ezbZWAMDA7l586beKGitTJky4e7uTt68efnxxx+JjY3lr7/+0qtjZmZGixYtWLp0qa7swYMHHDhwgBYtWujV3bp1K99++y2NGzfGx8eHYsWK0bFjRwYOHKhXz9LSEnd3d71H5syZk92P+F68eMGSJUuYNm0alStXxtfXl2XLlnHs2DFOnDiR6DqKojB9+nS+//576tWrR9GiRfnll1949OgRmzdvBuDq1avs3LmTxYsXU7ZsWSpWrMisWbNYu3atbgLInDlz0r59e4oVK0aOHDmoW7cuLVu25PDhw7ptOTk56e3bnj17sLGx0etET7j/W7ZsoVKlSuTMmVMv7h07drB7926mTJmS6H7VqVOHM2fOJPv6E0IIIYQQQiThyhUoUwaWLfuvrGNHOHUKErkj91MhnejisxURAdo7wFUq+O47zd+KWs28sfUo92AoywtGE/3/mQNOtvCDv/+GyEh48YK1+6bz2Bbml4aW/x8EZ371Km516mC9caPB9jJlUujb96Xu+R9/+H2qd7gIIYSBiIgI+vTpw+nTp9m3bx8mJiY0aNAAtVqd7HqDBg1i6tSpnD59GldXV+rUqaOXBuT169dMmTKFFStWcOjQIe7du6fXAf3q1Svatm3LkSNHOHHiBHny5KFmzZq8evUqyW0ePnyYvHnzYm9vn2Sd2NhYlixZAmgmPEyoQ4cO/Pbbb7x+rUnntXz5cmrUqEGWLFn06rm7u7N//37CwsKSPQ4padeuHf7+/kkuDwwMJCYmhqpVq+rK8ufPj5eXl8GIea3bt28THByst46joyNlypTRrXP8+HEyZcpEqVKldHWqVq2KiYkJJ0+eTLTdf//9l507d+Ln55dkvEuWLKFZs2bY2tomujwkJITt27cb3C0QEhJC586dWbFiBTY2Nomu6+XlRZYsWfQ68YUQQgghhBCp8MsvULo0XL6seW5rq0npsngxJHH9/amQiUXFZ2vhQtD2WTRpovmxLComks5jSrHC7DL8f86qTHHmtC/ZkVrluoJ7Ed36ESZxWGKJ2kTN6FrTiTk5G/OrV1FFReHUuzcvwsII79pVb5tt2kSwbJkdd++acfOmFx07bqJQoVtvFb+dnR1dE7QvhPhMlSqlmbvhY3N310wskQoNGzYkNjYWMzMzVCoVS5cuxdXVlStXrlC4cOEk1xs5ciTVqlUD4JdffsHT05NNmzbRpEkTAGJiYpg/fz65cuUCoGfPnowZM0a3fuXKlfXaW7hwIZkyZeLgwYPUrl070W3evXs3yVQgzZs3x9TUlDdv3qBWq/H29tbFEl+JEiXImTMnGzZsoHXr1ixfvpxp06Zx65b+Z/60adNo1KgR7u7uFCpUiAoVKlCvXj2+/vprvXrbtm3Dzs5Or2z48OEMHz4cgKxZsyb7g0RwcDAWFhZkypRJrzxLliwEJ/Ha0ZYn7Ph3c3PT5VIPDg7GzU3/ziozMzOcnJwM2q1QoQJnz54lKiqKLl266J2n+E6dOsWlS5d0P1Ik5pdffsHe3l4vHY2iKLRr145u3bpRqlQp7ty5k+T6Hh4e3L17N8nlQgghhBBCiAQGD/5vNCpA4cKwfj3kz2+8mD4i6UQXnyW1GubM+e/5999DVGwU30wowZ9m13Tl3UzLMX7wn2SyNrxlvn/5/oQfDcenog+5s1cm9I8AMv3wA7b/z1Hr+OOPKObmRHTooFvHwkKT1qVrV2cAjh6tzejRoZi8xT0ha9euTftKQohPU3AwPHxo7CiS9c8///DDDz9w+vRpHj9+rOvwvXfvXrKd6OXLl9f97eTkRL58+bh69aquzMbGRteBDprO5NDQUN3zkJAQvv/+ew4cOEBoaChxcXG8fv2ae/fuJbnNN2/eYGVlleiyn3/+mapVq3Lr1i369evHzJkzcXJySrRuhw4dWLZsGV5eXkRERFCzZk1mz56tV6dgwYJcunSJwMBAjh49yqFDh6hTpw7t2rXTm1y0UqVKzJs3T2/d+NsdP358kvuTXqxbt45Xr15x4cIFBg0axJQpUxg8eLBBvSVLllCkSBHKlCmTZFtLly6lZcuWeudp1qxZvHr1imHDhqUYi7W1te4uASGEEEIIIUQq1KoFU6dqOtU6ddJMLviJjz6PTzrRxWdpzx7QpkKtWhUKFlLTZGZl/lRrOtCtYmCBTUtaDf0Vk2R6uG1VtlTO/v9RjlZWPJ80iWgPd2ad+ZluZ8B15EjUrq68qVNHt07NmpGULBnN2bMW3Lhhzp9/WlG7dmQSWxBCiFRwd0/3261bty5eXl4sXLiQbNmyoVarKVy4MNHR0e8Ugrm5ud5zlUqFEi9XVtu2bXny5AkzZswgR44cWFpaUr58+WS36+LiYjBZqJa7uzu5c+cmd+7cLFu2jJo1a3LlyhWD0digmXBz8ODBjBo1itatW2Nmlvhll4mJCaVLl6Z06dL07duXlStX0rp1a7777jt8fHwAzWSluXPnTvF4JMXd3Z3o6GieP3+uNxo9JCQE9yTOo7Y8JCSErFmz6spDQ0MpXry4rk78Hy1Ak+rm6dOnBu1mz54d0PxwEBcXR5cuXRgwYACmpqa6OhEREaxduzbJUeqgSbdz/fp11q1bp1e+f/9+jh8/jqWlpV55qVKlaNmyJb/88ouu7OnTp7i6uia5DSGEEEIIIUQCfn4wcSJkzQotWxo7mo9OOtHFZ2nu3P/+/vZb+H5TL35/fgwA6xjYbt6eAh1+SnO7kXFRdC16g+32sDUf/LVcIXOfPsQ5OxNdoQKgyb/ev/9LWrfOTOPGkRQuHJNCq0IIkYJUplQxlidPnnD9+nXmzZuHv78/KpWKI0eOpGrdEydO4OXlBcCzZ8+4ceMGBdIwWc3Ro0eZO3cuNWvWBOD+/fs8fvw42XVKlCjBvHnzUBQFVTIzy5cpUwZfX1/GjRvHjBkzDJY7OTlRt25dfvvtN+bPn5/qmAsWLAhoOpTfF19fX8zNzdm3bx8NG2om8rh+/Tr37t3TG+0fn4+PD+7u7uzbt0/Xaf7y5UtOnTpF9+7dAc2dAs+fPycwMBBfX19A05mtVqspW7ZskvGo1WpiYmJQq9V6nejr168nKiqKVq1aJbnukiVL8PX1pVixYnrlM2fOZOzYsbrnjx49IiAggHXr1unFEhkZyc2bNylRokSS2xBCCCGEEOKzFhGhyYPcpw966RPizT/1uZFOdPHZuXcPtm3T/J0tG5BnM+N/1/Sqm6jh90cV8Vu0iNC3mOTtedRzzoaeBSDQAxo2hW2ro3H69ltCd+9G/f+Rin5+UTRoMIBp0wa9l30SQoj0LHPmzDg7O7N48WI8PT25f/8+Q4cOTdW6Y8aMwdnZmSxZsvDdd9/h4uJC/fr1U73tPHnysGLFCkqVKsXLly8ZNGgQ1tbWya5TqVIlwsPDuXz5crKpZgD69u1LgwYNGDx4MNmyZTNYvnz5cubOnYuzs3Oi6zdq1IgvvviCChUq4O7uzu3btxk2bBh58+Ylf7zcglFRUQY5xs3MzHBxcQFg2LBhPHz4kF9//TXR7Tg6OtKxY0f69++Pk5MTDg4O9OrVi/Lly1OuXDldvfz58zN+/HgaNGiASqWib9++jB07ljx58uDj48MPP/yAh4eH7hwUKFCAGjVq0LlzZ+bPn09MTAw9e/akWbNmurzyq1atwtzcnCJFimBpacmZM2cYNmwYTZs2NbiTYMmSJdSvXz/J4/Xy5UvWr1/P1KlTDZZpf2zR0uaQz5UrF56enrryEydO6O5IEEIIIYQQQiRw6RI0bgzXrkF0NAwZYuyI0oW3yMQsRMa2cKEmfRNAs86P6LTxv1tQpp1x4uuft2mGi78Fd1t3VtVYhaOlIwB7csGgamD6+DGZe/WCuDhA07yt7dN32xEhhMggTExMWLNmDWfPnqVIkSL069ePyfEnpEnGhAkT6NOnD76+vgQHB/PHH39gYWGR6m0vWbKEZ8+eUbJkSVq3bk3v3r0TTb0Sn7OzMw0aNGDVqlUptl+jRg18fHwYN25cosutra2T7BAGCAgI4I8//qBOnTrkzZuXtm3bkj9/fnbv3q2X/mXnzp1kzZpV71GxYkXd8qCgoGTzvIMmn3vt2rVp2LAhX331Fe7u7mzcuFGvzvXr13nx4oXu+eDBg+nVqxddunShdOnShIeH88cff+jlIl+1ahX58+enSpUq1KxZk4oVK7Jw4ULdcjMzMyZOnEiZMmUoWrQoo0ePpmfPnno537XbPnLkCB07dkxyH9auXYuiKDRv3jzZfU3OmjVraNmyJTafUf5GIYQQQgghUqQosGQJlCmj6UAHTfqW58+NGlZ6oVLiJw4VgGaUk6OjIy9evMDBwSFN66rVakJDQ3Fzc0s2l7Ywnlu3YP58WLECfEfVZnvwdgAaXIXfexxC9eWXqT6PU6dOpVmzZgblZ0LO0PTPpkTFRQGwaS3UvwYvBw3iVZ8+gGbU4LtMBLd27VoGDBjw1ut/DuT9+GmQ86gvMjKS27dv4+Pjk+Tkl+mRoijExsZiZmaWbIoUrQMHDlCpUiWePXuml8P7Y/n777+pVq0aN2/e1I1oFmk/j+nN48ePyZcvH2fOnNHlmzeG5N7H73IdmpF9zP2W/1fSLzk36Zecm/RNzk/6Jecm/ZJzk0B4OHTvDitX/ldWrBj89hvkzfvRw/mY5ye116HyKhGfnZw5YdIkWHxwp64D3TUCFrt0QPXll+9lG6WylGJk2ZG65+3rwV1HsJ86FfO//9ar++aNihUrbBg6NNN72bYQQoh3V7RoUSZOnMjt27eNHYp4j+7cucPcuXON2oEuhBBCCCFEunLxIpQqpd+B3q0bnDhhlA709Eo60cVn6U3MG3pv6aR7PuWYHU7jpr3XbbQu0JpaPrUAeG4NLRuCoqjJNHgwxMbq6jVu7MKwYZlZudKWq1dlmgIhhEgv2rVrR5EiRYwdhniPSpUqRdOmTY0dhhBCCCGEEManKLBokSZ9y/XrmjJ7e1i7FubNgwx05/PHIJ3o4rM04ch4br1+CIDfHWjdZBw4Or7XbahUKiZVnISXvWais6NeMK8UWFy6hF28PLDffPNa9/fixZIyQAghAPz9/VEUxSipXIQQQgghhBDik7dwIXTpApGRmufFi0NgIMigk0TJsFfx2di5U3OHSrNmCsfPbwPALA4mHsvM9PxvUE+dqlffxsaG169fJ9aUzpUrV5Jd7mjpyNSvptJ4e2MAhlWFutch25QpuNaoAUDTpq+ZMsWBly9N2LTJhiFDXuLmpn7b3RRCCCGEEEIIIYQQInktW8L06ZpJRLt3h2nTZPR5MqQTXXw2Zs6EHTtgyBA4mzsnIepz3HCGkLK1adKqlV5dRVF48eIFjo6OyU6cNmzYsBS3Wz5reZrna86a62t4ZQk9asGWNZG0PHMGAFtbhZYtI5g3z57oaBW//GLLoEGv3m1nhRBCCCGEEEIIIYRIip2dZuLQK1dk9HkqSDoX8VkIDYXduzV/Z3eNpOg/Gwm4Cb2U0vxTtOgH3/53Zb7DzdoNgD/yweb8UPLBAyyOHQOgfftwzMwUAFavtiU6+oOHJIQQQgghhBBCCCE+B69eaVK33LqlX16kiHSgp5J0oovPwvr1EBen+buF1SZM0HRYM3QoJDPS/H3JZJmJMRXG6J4Pqg7RpuD444+gVuPhoSYgQJODKizMlF27rD94TEIIIYQQQgghhBDiE3fhAvj6aiYRbdYMGbn5dqQTXXwWNm4EbEPAZx/N7o3XFObJA/XqfbQYannXonzW8gDcdILZZcDi4kWsN24EoE2bcF3dFStsP1pcQgghhBBCCCGEEOIToyiwYAGULQv//KMpu34dLl0yblwZlHSii0/e06dw8CBQei60rUqv9pe45gIMGACmph8tDpVKxciyI1GhGfk+xg8e24DjhAnw5g0VKkSTM2cMAMeOWfLvvzJlgRBCCCGEEEIIIYRIo5cvoXlz6NYNoqI0Zb6+cPYslCxp3NgyKOlEF5+87dshjmgotQCAY9nBzt4Z2rT56LEUdilMk7xNAHhhpelINw0OxnbVKlQqaN06AgAzM4WzZy0+enxCCPEhdezYkQYNGgCaCZy7dOmCk5MTKpWK8+fPv3W77dq1o379+u8nyI/ohx9+oEuXLsYOQ7xnzZo1Y+rUqcYOQwghhBBCfK7OndN0mK9b919Zr15w9CjkymW8uJJx+zacOKH5N72STnTxyduy5f9/7PuJ3MG2NLgKnh36grVx8o4P8h2EtZk1mSJtKPdAU2Y/dy68eUOjRq8ZMuQFp04F06TJa6PEJ4QQH8q0adNYtmwZADt37mT58uVs27aNoKAgChcubOTo3t2dO3dS/YNAcHAwM2bM4LvvvtOVtWvXDpVKhUqlwtzcHB8fHwYPHkxkZKTeuto6J06c0CuPiorC2dkZlUrFgQMHdOUHDx6kcuXKODk5YWNjQ548eWjbti3R/8+FeODAAV2bCR/BwcFpOgbr168nf/78WFlZUaRIEf78888U1zlw4AAlS5bE0tKS3Llzs3z5coM6c+bMwdvbGysrK8qWLcupU6f0lnft2pVcuXJhbW2Nq6sr9erV49q1a3p19u3bR4UKFbC3t8fd3Z0hQ4YQGxurW379+nUqVapElixZsLKyImfOnHz//ffExMTo6ly+fJmGDRvi7e2NSqVi+vTpBrF+//33jBs3jhcvXqS470IIIYQQQrw3igLz5kG5cvDvv5oyR0f4/XeYORMsLY0bXxJ+/13Txz94sObf3383dkSJk0508Ul78wZ27gTiLHA9X5er81+zbJspdO5stJjcbd35vfbv1HrYgAbZawJgGhqK7cqVZM6s0KtXOG5uaqPFJ4QQH4qjoyOZMmUC4ObNm2TNmpUKFSrg7u6OmdnnlcJq8eLFVKhQgRw5cuiV16hRg6CgIG7dusXPP//MggULGDlypMH62bNn1/0gobVp0ybs7Oz0yq5cuUKNGjUoVaoUhw4d4uLFi8yaNQsLCwvitDNu/9/169cJCgrSe7i5uaV6n44dO0bz5s3p2LEj586do379+tSvX59LyeRcvH37NrVq1aJSpUqcP3+evn370qlTJ3bt2qWrs27dOvr378/IkSM5e/YsxYoVIyAggNDQUF0dX19fli1bxtWrV9m1axeKolC9enXdPl64cIGaNWtSo0YNzp07x7p169i6dStDhw7VtWFubk6bNm3YvXs3169fZ/r06SxatEjv+L9+/ZqcOXMyYcIE3N3dE92nwoULkytXLlauXJnqYyeEEEIIIcQ7O3sWvv32v4lDS5XSlH3zjXHjSsbt27Bsmab/P29ezb/LlsGdO8aOzJB0ootP2r59EKHJkEIdZQtmKNjVrA9Zshg1rqIuRTHBhJf9+unK7OfMQfVaRp8LIT5d2nQu7dq1o1evXty7dw+VSoW3t3eK627YsIEiRYpgbW2Ns7MzVatWJUL7AZ/A6dOncXV1ZeLEiQA8f/6cTp064erqioODA5UrV+bChQsAvHjxAlNTU86cOQOAWq3GycmJcuXK6dpbuXIl2bNnTzFGHx8fAEqUKIFKpcLf3z/JumvXrqVOnToG5ZaWlri7u5M9e3bq169P1apV2bNnj0G9tm3bsnbtWt68eaMrW7p0KW3bttWrt3v3btzd3Zk0aZKuc7dGjRosWrQI6wR3ZLm5ueHu7q73MDFJ/aXijBkzqFGjBoMGDaJAgQL8+OOPlCxZktmzZye5zvz58/Hx8WHq1KkUKFCAnj170qhRI37++WddnWnTptG5c2fat29PwYIFmT9/PjY2NixdulRXp0uXLnz11Vd4e3tTsmRJxo4dy/3797nz/6vvdevWUbRoUUaMGEHu3Lnx8/Nj0qRJzJkzh1evXgGQM2dO2rdvT7FixciRIwd169alZcuWHD58WLed0qVLM3nyZJo1a4ZlMiN56tSpw9q1a1N97IQQQgghhHhnvr4waJDm7z594MgRyJnTuDGlICREk749a1bNtIVZs2qeh4QYOzJD0okuPmlly2omIv7a5TQN+f/9IEYchZ5QbIECvK5dGwDTx4+xTTBqLSYGIiJUxghNCCE+mBkzZjBmzBg8PT0JCgri9OnTydYPCgqiefPmdOjQgatXr3LgwAG++eYbFEUxqLt//36qVavGuHHjGDJkCACNGzcmNDSUHTt2EBgYSMmSJalSpQpPnz7F0dGR4sWL69KfXLx4EZVKxblz5wgPDwc06VD8/PxS3C9tipG9e/cSFBTExo0bE6339OlTrly5QqlSpZJt79KlSxw7dgwLC8M5Mnx9ffH29ub3/9/reO/ePQ4dOkTr1q316rm7uxMUFMShQ4dSjD852pQvd5IZEnL8+HGqVq2qVxYQEMDx48ffep3o6GgCAwP16piYmFC1atUk242IiGDZsmX4+PjofvyIiorCyspKr561tTWRkZEEBgYm2s6///7Lzp07U3XuEypTpgynTp0iSjuJkxBCCCGEEO+bomge8Y0bB3v2wPTp6TZ9S3xZsoCDAwQFQVyc5l8HB6OPfU3U53XvtPgkLViwQNfRkVCkEsnel5v4ySqQcsCLzJlZ/PffEO/W8itXrnykSBN3pksD5lhtY+x+yLlwIeHt2hHyzIoFC+zYuNGGVq0iGDjwlVFjFEKkf9OmaR4pKVkStm7VL6tbV3OXX0r699c83pWjoyP29vaYmpommRIjvqCgIGJjY/nmm2906U+KFCliUG/Tpk20adOGxYsX07RpUwCOHDnCqVOnCA0N1Y0cnjJlCps3b2bDhg106dIFf39/Dhw4wMCBAzlw4ADVqlXj2rVrHDlyhBo1anDgwAEGDx6cYpyurq4AODs7J7tf9+7dQ1EUPDw8DJZt27YNOzs7YmNjiYqKwsTEJMmR3B06dGDp0qW0atWK5cuXU7NmTV0MWo0bN2bXrl34+fnh7u5OuXLlqFKlCm3atMHBwUGvrqenp97zHDlycPnyZQBsbGzIly8f5ubmSe5XcHAwWRJc7WbJkiXZvOpJrfPy5UvevHnDs2fPiIuLS7ROwpznc+fOZfDgwURERJAvXz727Nmj+wEiICCA6dOns2bNGpo0aUJwcDBjxowBNK+v+CpUqMDZs2eJioqiS5cuunpp4eHhQXR0NMHBwQYpe4QQQgghhHhnL15Ap05QuTJ07/5fubk5JBikkp75+ED79poULjduaDrQO3QAb2+Il70xXZBOdJHhhYeH06xZs0SXrb2+lgOHA6nQCb4/CIN8O9KsRQu9OsOGDfsYYSZqx50ddDnbBaUwZIqEBduCsdmyBXXFpixebIdarWLDBhv6939FGu6oF0J8hl6+hIcPU66XWFaSsLDUrfvyZdrjeh+KFStGlSpVKFKkCAEBAVSvXp1GjRqROXNmXZ2TJ0+ybds2NmzYQP369XXlFy5cIDw8HGdnZ70237x5w82bNwHw8/NjyZIlxMXFcfDgQapXr467uzsHDhygaNGi/Pvvv8mmZkkrbQqWhCOjASpVqsS8efOIiIjg559/xszMjIYNGybaTqtWrRg6dCi3bt1i+fLlzJw506COqakpy5YtY+zYsezfv5+TJ0/y008/MXHiRE6dOkXWrFl1dQ8fPoy9vb3uefwO8zJlynDt2jUURdGbjDM9admyJdWqVSMoKIgpU6bQpEkTjh49ipWVFdWrV2fy5Ml069aN1q1bY2lpyQ8//MDhw4cNUtasW7eOV69eceHCBQYNGsSUKVNS9SNKfNpUOa8lTZsQQgghhHjfzpyBpk3h1i344w/NRKIlShg7qrfWsKFmsFdIiGYEuo8PqNPhVIHSLSc+aRv+2aD7u9a/KiL+PzIxvaiQtQK25rYA/JEPokzBbv58smaJxc9Pcwv4gwdmHDuW/m/BEUIYl4MDZMuW8iPBQGVAU5aadRMMXP5oTE1N2bNnDzt27KBgwYLMmjWLfPnycfv2bV2dXLlykT9/fpYuXUpMTIyuPDw8nKxZs3L+/Hm9x/Xr1xn0/3yBX331Fa9eveLs2bMcOnQIf39/3ej0gwcP4uHhQZ48ed7b/ri4uADw7Nkzg2W2trbkzp2bYsWKsXTpUk6ePMmSJUsSbcfZ2ZnatWvTsWNHIiMj+frrr5PcZrZs2WjdujWzZ8/m8uXLREZGMn/+fL06Pj4+5M6dW/dI6whqd3d3QhIkLwwJCUl2VH5S6zg4OGBtbY2LiwumpqapatfR0ZE8efLw1VdfsWHDBq5du8amTZt0y/v378/z58+5d+8ejx8/pl69eoAmF3p82bNnp2DBgjRv3pwJEyYwatQog0lYU/L06VMAgzsDhBBCCCGEeGuKArNmQYUKmg50AGtrzaioDM7HR/NbwP+nmUqXpBNdfLJ+Xv6YE8EnAMj3GIrl/BJ1vBF36YGjpSN9S/RlVLlRXDxaEss4ML9+Hav9+2na9L8J89atszFilEKIjKB/f3jwIOVHwlQuoClLzbrvI5XL21KpVHzxxReMHj2ac+fOYWFhoddB6uLiwv79+/n3339p0qSJriO9ZMmSBAcHY2ZmptdBnDt3bl1ndqZMmShatCizZ8/G3Nyc/Pnz89VXX3Hu3Dm2bduW6pzY2tQhKXW45sqVCwcHhxTTiZmYmDB8+HC+//57vQlE4+vQoQMHDhygTZs2mJqapirOzJkzkzVr1iQnZn1b5cuXZ9++fXple/bsoXz58m+9joWFBb6+vnp11Go1+/btS7ZdRVFQFMUgJ7lKpcLDwwNra2vWrFlD9uzZKVmyZJLtqNVqYmJiUKdxKMylS5fw9PTUvcaEEEIIIYR4J8+fQ6NG0Lu3ZgI90EwEeO4cVK9u1NA+F9KJLj5Jr16p+HnfZt3ztuchsm49o8WTnG5Fu9GpcCfo0lNXZjdvHtWqReLoqPnSvmuXFa9fywSjQojPkzYFyZkzZ7h37x4bN24kLCyMAgUK6NVzc3Nj//79XLt2jebNmxMbG0vVqlUpX7489evXZ/fu3dy5c4djx47x3XffcebMGd26/v7+rFq1Stdh7uTkRIECBVi3bl2qO9Hd3NywtrZm586dhISE8OLFi0TraSfGPHLkSIptNm7cGFNTU+bMmZPo8ho1ahAWFpZk3u4FCxbQvXt3du/ezc2bN7l8+TJDhgzh8uXL1KlTR69uaGgowcHBeg/tjxGnTp0if/78PEwm70+fPn3YuXMnU6dO5dq1a4waNYozZ87Qs+d//78NGzaMNm3a6J5369aNW7duMXjwYK5du8bcuXP57bff6Nevn65O//79WbRoEb/88gtXr16le/fuRERE0L59ewBu3brF+PHjCQwM5N69exw7dozGjRtjbW1NzZo1de1MnjyZixcvcvnyZX788UcmTJjAzJkzdT8+rFq1it9++42rV69y69YtfvvtN4YNG0bTpk11qW2io6N1dzNER0fz8OFDzp8/z7///qt3LA4fPkx1+TIjhBBCCCHeh9OnNflONm78r2zAADh0SJM8XHwU0okuPklHjligLrJC80RR0fKqOW9q1DBuUCmIrFqVmP+nC7A8eRK7639Tq5Zm5OHr1ybs3m2YO1cIIT4HDg4OHDp0iJo1a5I3b16+//57pk6dmmj6End3d/bv38/Fixdp2bIlarWaP//8k6+++or27duTN29emjVrxt27d/Umq/Tz8yMuLk4v97m/v79BWXLMzMyYOXMmCxYswMPDQ5cuJDGdOnVi7dq1KY5wNjMzo2fPnkyaNCnRkeMqlQoXFxfdKPiEypQpQ3h4ON26daNQoUL4+flx4sQJNm/ebPDjQL58+ciaNaveIzAwENDk9r5+/bpeqpyEKlSowOrVq1m4cCHFihVjw4YNbN68mcKFC+vqBAUFce/ePd1zHx8ftm/fzp49eyhWrBhTp05l8eLFBAQE6Oo0bdqUKVOmMGLECIoXL8758+fZuXOn7vxZWVlx+PBhatasSe7cuWnatCn29vYcO3YMNzc3XTs7duzgyy+/pFSpUmzfvp0tW7bo5c83MzNj4sSJlClThqJFizJ69Gh69uzJ4sWLdXUePXpEiRIlKFGihC73eokSJejUqZOuTmRkJJs3b6Zz585JHishhBBCCCFSpCgwYwZ88QVoU1lmzgxbtsCUKZDEdwDxYagURVGMHUR68/LlSxwdHXnx4gUOaUwAq1arCQ0Nxc3NzWCiKvFhTJ061WBi0Y4jb7Are2UAit1y5nhIKZ4mkVN22LBhjB8/Xq9MURRevHiBo6MjKlXSI8ATWze1ElvXZuVK1KOH4hAF0Q0bs6PJHBo31uRTrVIlkl9+eaKru3btWgYMGPBW2/5cyPvx0yDnUV9kZCS3b9/Gx8cn0Ykp0ytFUWjWrBnm5uasXLnS2OEYnaIolC1bln79+tG8eXNjh5Nq2olFzczMkv3/8XM2b948Nm3axO7du5Osk9z7+F2uQzOyj7nf8v9K+iXnJv2Sc5O+yflJv+TcpF8Z4ty8fAlFi8Ldu5rn5crB2rWQxrmLMqKPeX5Sex2aTl8lQrw9RYFjz//QPe/29yveJDMaML248/IOvbKdxXMAbCgINlu2UD7XI7JmjQXg4EFLnj6Vt6wQIuOJjY3lypUrnDx5koIFCxo7nHRBpVKxcOFCYmNjjR2KeM/Mzc2ZNWuWscMQQgghhBAZnYMDrFsH5uYwcKAmfctn0IGeXkmPnPjk3LplyqvsmjxRqjhTGt0zJbJaNSNHlbKgiCBW3fyNSDOYUQ5U0dHYr1lFvXqalC6xsSq2bbM2cpRCCJF2ly5donTp0hQsWJBu3boZLL937x52dnZJPuKn/zCmn376KckYE0stk5LixYvTunXrDxCpMKZOnTqRL18+Y4chhBBCCCEyGkWBV6/0y8qWhX/+gcmTNZ3pwmjMjB2AEO/bmoPXIJPmVpfcdzyx+bI4Udbpv/O5nHs5CjoV5MrTK5z0hBOeUPrXX/lmaR/mz7fHxSUOGbAohMiIihcvTkREhC4NSEIeHh6cP38+yfU9PDw+YHSp161bN5o0aZLoMusM8P+MEEIIIYQQIp169gzatdN0ou/ZA6am/y2T0efpgnSii0/O7vvbIZvm76ZX4njTtZZxA0ollUpFx8IdGXBIk+d8RllY83sIJW9v4bffGlOmTDSJ9D0JIUSGZ2ZmRu7cuY0dRoqcnJxwcnIydhhCCCGEEEKIT8nJk9C06X+5z3/8EUaNMmpIwpCkcxGflLg4hTs2WzRP1CZ8ezOMKD8/4waVBvVy1sPZyhnQ5EV/aA92q1dRoYJ0oAshhBBCCCGEEEJkRLdvw4kTmn91FAWmTYOKFf/rQHdygtKljRKjSJ50ootPys6z/6LO/A8ALncLktm3IoqtrZGjSj0rMyta5W8FQKwpLCkJlseOYXrrlpEjE0IIIYQQQgghhBBp9fvv0KsXDB6s+ff334GnT6F+fRgwAF3u3goV4Px5qJUxMip8bqQTXXxSjr/4Q/e3/xVn3lSvbsRo3k6L/C0wUWnemotLQpwKbNes0S0PCzMhLEzeukIIIYQQQgghhBDp2e3bsGyZZtB53ryaf49NO0FskRKwdet/FYcMgQMHIHt2o8Uqkic9ceKTcvrVLt3fP187SGS1akaM5u1ks8tGZc/KANx3hB15wOa337h5TaFlS2dKlXJn6VI7I0cphBBCCCGEEEIIIZITEgIvX0LWrGBqotD28VQmHv8Ss0f3NBWcnWH7dpgwAczNjRusSJZ0ootPRlBEEJefXAbA9xG45vVF7eZm5KjeTqsCrXR/L/AF0ydPcAncz6FDlsTFqdi2zRpFMWKAQgghhBBCCCGEECJZWbKAgwMEBUGcWoVd0D+YKf9P31KxoiZ9S82aRo1RpI50ootPxl/3/9L9XesGRGbAVC5alTwr4WHrAcCfeeC+A+TctpiyZaMBuH3bjKAgF2OGKIQQ6dpXX33F6tWrjR2GeI8eP36Mm5sbDx48MHYoQgghhBBCpIqPD7RvDyoV3LgBiwv9zHOfEjBsGPz1F3h6GjtEkUrSiS4+GVmiy9Dj31wUC4Za/0BkQICxQ3prpiamNM/XHAC1iSY3utXhw9Qp/1BX5++/8xorPCGE+OiWL19OpkyZUlV369athISE0KxZM12Zt7c3KpUKlUqFjY0NRYoUYfHixXrrHThwAJVKRebMmYmMjNRbdvr0ad368S1atIhixYphZ2dHpkyZKFGiBOPHj9ctHzVqlG69+I/8+fOnaf8VRWHEiBFkzZoVa2trqlatyj///JPienPmzMHb2xsrKyvKli3LqVOn9JZHRkbSo0cPnJ2dsbOzo2HDhoSEhCTa1pMnT8iePTsWFhY8f/480TpHjx7FzMyM4sWLGyx7+PAhrVq1wtnZGWtra4oUKcKZM2cSbadbt26oVCqmT5+uK3NxcaFNmzaMHDkyxf0WQgghhBDC6NRquHKFhg1h1iyYNAmmzrUm09Xj8NNPYGZm7AhFGkgnuvhkrJzsy5yV/xIz/xLZKElsrlzGDumdNMvXDFOVKaDpRI81gUbPlqBSafK4/P13XknpIoQQiZg5cybt27fHxET/MmfMmDEEBQVx6dIlWrVqRefOndmxY4fB+vb29mzatEmvbMmSJXh5eemVLV26lL59+9K7d2/Onz/P0aNHGTx4MOHh4Xr1ChUqRFBQkN7jyJEjadqnSZMmMXPmTObPn8/JkyextbUlICDAoLM/vnXr1tG/f39GjhzJ2bNnKVasGAEBAYSGhurq9OvXjz/++IP169dz8OBBHj16xDfffJNoex07dqRo0aJJbu/58+e0adOGKlWqGCx79uwZX3zxBebm5uzYsYMrV64wdepUMmfObFB306ZNnDhxAg8PD4Nl7du3Z9WqVTx9+jTJOIQQQgghhDC6x4+hTh0oUwauX8fHB8qV04xMx9LS2NGJtyCd6OKToFbDqZOaCRhCccO+chHNvTIZWFbbrFT1qgrAIwfYngdybV9Ead8oAEJDnblyxZgRCiFE6lWqVIl+/foxePBgnJyccHd3Z9SoUXp1nj9/TteuXcmSJQtWVlYULlyYbdu2ceDAAdq3b8+LFy90I7kTrqsVFhbG/v37qVOnjsEye3t73N3dyZkzJ0OGDMHJyYk9e/YY1Gvbti1Lly7VPX/z5g1r166lbdu2evW2bt1KkyZN6NixI7lz56ZQoUI0b96ccePG6dUzMzPD3d1d7+HikvqUXIqiMH36dL7//nvq1atH0aJF+fXXX3n06BGbN29Ocr1p06bRuXNn2rdvT8GCBZk/fz42Nja6fXvx4gVLlixh2rRpVK5cGV9fX5YtW8axY8c4ceKEXlvz5s3j+fPnDBgwIMntdevWjRYtWlC+fHmDZRMnTiR79uwsW7aMMmXK4OPjQ/Xq1cmV4Afvhw8f0qtXL1atWoV5IhMrFSpUCA8PD4MfOYQQQgghhEg3jh6FEiXgzz8hIgKaNoW4OGNHJd6RdKKLT8L162a8eK35Ja8iR4jx/8rIEb0fLfO31P29oBSYhoVRP+8FXdn69caISggh3s6KFSuwtbXl5MmTTJo0iTFjxug6sdVqNV9//TVHjx5l5cqVXLlyhQkTJmBqakqFChWYPn06Dg4OupHcAwcOTHQbR44cwcbGhgIFCiQZh1qt5vfff+fZs2dYWFgYLG/dujWHDx/m3r17APz+++94e3tTsmRJvXru7u6cOHGCu3fvvu0hATQpX7y9vZNcfvv2bYKDg6lataquzNHRkbJly3L8+PFE14mOjiYwMFBvHRMTE6pWrapbJzAwkJiYGL06+fPnx8vLS6/dK1euMGbMGH799VeD0f1ay5Yt49atW0mmWtm6dSulSpWicePGuLm5UaJECRYtWqRXR61W07p1awYNGkShQoWSPB5lypTh8OHDSS4XQgghhBDCKNRqmDgR/PxAO4+Pq6smj4upqXFjE+9Mku+IT8K8k+vA6Wt4mocKJseJqtDB2CG9F37Z/PC08+RB+AN25YKH9tAoeC7DKQvAhg2QxGBMIcRnZtrxaUw7Pu2d21n5zUr8vf11zw/cOUCrja0A6F++P/3L93/rtosUKcLIkSNRqVTkyZOH2bNns2/fPqpVq8bevXs5deoUV69eJW9ezZwPOXPm1K3r6OiISqXC3d092W3cvXuXLFmyJNrZO2TIEL7//nuioqKIjY3FycmJTp06GdRzc3Pj66+/Zvny5YwYMYKlS5fSoYPh/ysjR47km2++wdvbm7x581K+fHlq1qxJo0aN9LZ/8eJF7Ozs9NZt1aoV8+fPBzS5vhOOyI4vODgYgCxZsuiVZ8mSRbcsocePHxMXF5foOteuXdO1a2FhYZBrPn67UVFRNG/enMmTJ+Pl5cXNmzcNtvXPP/8wdOhQDh8+jFkSeR1v3brFvHnz6N+/P8OHD+f06dP07t0bCwsL3Qj/iRMnYmZmRu/evZM8FgAeHh6cO3cu2TpCCCGEEEJ8VI8fQ5s2ED9dpJ8frF4NiaQpFBmPdKKLDO+x+jEbYydA7z7wd0vKXrdCsbc3dljvhamJKY3zNObncz+jNoEVxWDIkbWUKj6XM+dtuXwZrl6FZAZcCiE+Ey+jXvLw1cOUK6YgKjbK4Lm23ZdRL9+p7SJFiug9z5o1qy4/9/nz5/H09NR1oL+tN2/eYGVlleiyQYMG0a5dO4KCghg0aBDffvstuXPnTrRuhw4d6NOnD61ateL48eOsX7/eYPRz1qxZOX78OJcuXeLQoUMcO3aMtm3bsnjxYnbu3KnrSM+XLx9bt27VW9fBwUH3d8+ePenZs+e77PYHM2zYMAoUKECrVq0SXR4XF0eLFi0YPXp0sudOrVZTqlQpfvrpJwBKlCjBpUuXmD9/Pm3btiUwMJAZM2Zw9uxZg8lbE7K2tub169dvv1NCCCGEEEK8T0eOQLNm8PD/38dUKvjuOxg5UiYP/YQYPZ3LnDlz8Pb2xsrKirJly3Lq1Kkk616+fJmGDRvi7e2NSqVi+vTpBnXGjx9P6dKlsbe3x83Njfr163P9+vUPuAfC2P6J+0f3t1loXgrUTH6UYkbTKE8jTFQm+EV6UCgUVNHR1M+myVVbrhw8f27c+IQQ6YODpQPZ7LO988PSTH+SG0szS90yB0uHJLaeOglzXKtUKtRqNaDpGH0fXFxcePbsWZLLcufOzZdffsn69evp3bs3V5KYXOLrr7/mzZs3dOzYkTp16uDs7JzkNgsXLsy3337LypUr2bNnD3v27OHgwYO65RYWFuTOnVvv4ebmlup90o6+DwkJ0SsPCQlJcmS+i4sLpqamya7j7u5OdHQ0zxP8RxK/zv79+1m/fj1mZmaYmZnpUr+4uroycuRIXr16xZkzZ+jZs6euzpgxY7hw4QJmZmbs378f0PzgULBgQb3tFChQQJcy5/Dhw4SGhuLl5aVr5+7duwwYMMAg1c3Tp09xdXVNzaETQgghhBDiw5o7F/z9/+tAd3ODXbvgxx+lA/0TY9SzuW7dOvr378/8+fMpW7Ys06dPJyAggOvXryf65fL169fkzJmTxo0b069fv0TbPHjwID169KB06dLExsYyfPhwqlevzpUrV7C1tf3QuySMwP1NEdhfCnLupchND5TvPI0d0nuVwyEHgc0D8bgdituEAAA63h9L5Pc3+fHHLkaOTgiRXrxrqpWk+Hv786D/g/febkJFixblwYMH3LhxI9ERzRYWFsSlYjKeEiVKEBwczLNnz8icOXOS9bJnz07Tpk0ZNmwYW7ZsMVhuZmZGmzZtmDRpEjvi35KZAm1HcURERKrXSYmPjw/u7u7s27eP4sWLA/Dy5UtOnjxJ9+7dE13HwsICX19f9u3bR/369QHNaPB9+/bpRr37+vpibm7Ovn37aNiwIQDXr1/n3r17uslBf//9d968eaNr99SpU3Ts2JFDhw6RO3duHBwcuHjxot62586dy/79+9mwYQM+Pj4AfPHFFwaDGm7cuEGOHDkATR76+LnZAQICAmjdujXt27fXK7906RL+/v6pOXRCCCGEEEJ8WPnza3Khg6YzffVqyJrVqCGJD8OonejTpk2jc+fOui9H8+fPZ/v27SxdupShQ4ca1C9dujSlS5cGSHQ5wM6dO/WeL1++HDc3NwIDA/nqq09jskmhL/xmMThSH44MpZr1DGKSmYwso3K1cSWmkCsxBQpgfvUq7n8fwOfrssYOSwgh3hs/Pz+++uorGjZsyLRp08idOzfXrl1DpVJRo0YNvL29CQ8PZ9++fRQrVgwbGxtsbGwM2ilRogQuLi4cPXqU2rVrJ7vNPn36ULhwYc6cOUOpUqUMlv/4448MGjQoyVHo3bt3x8PDg8qVK+Pp6UlQUBBjx47F1dVV1wkNEBsba5C7XKVS6fKVz549m02bNrFv375Et6NSqejbty9jx44lT548+Pj48MMPP+Dh4aHrIAeoUqUKDRo00HWS9+/fn7Zt21KqVCnKlCnD9OnTiYiI0F13OTo60rFjR/r374+TkxMODg706tWL8uXLU65cOQCDXO1hYWGAZhS59keKwoUL69Vxc3PDyspKr7xfv35UqFCBn376iSZNmnDq1CkWLlzIwoULAXB2djY4zubm5ri7u5MvXz5d2evXrwkMDNSlhRFCCCGEEMKoKleG0aMhLg5++EEmEP2EGa0TPTo6msDAQIYNG6YrMzExoWrVqhw/fvy9befFixcAODk5JVknKiqKqKj/csC+fKnJ+apWq3W3maeWWq1GUZQ0ryfeXshFR93fZYqHo6hUoCipXl+lUqEkqK8oiu6R1nXfZbspiWjYkExjxwJQIDBQXmcpkPfjp0HOoz7t8UjNZ1R6o403sc9cgA0bNjBw4ECaN29OREQEuXPnZvz48SiKQvny5enatStNmzblyZMnjBgxglGJzKxsYmJCu3btWLVqFbVq1TLYTvxtFyhQgOrVqzNixAi2b9+uF5+iKJibm+s6duOvq/23SpUqLFu2jHnz5vHkyRNcXFwoX748e/fuxcnJSbfO5cuXyZpgNIqlpaVuhHdYWBg3b95M9nwOGjSI8PBwunTpwvPnz6lYsSI7duzA0tJSt97NmzcJCwvTPW/SpAmhoaGMGDGC4OBgihcvzo4dO3Bzc9PVmTZtGiqVioYNGxIVFUVAQABz5sxJ1WsrqTqJnedSpUqxceNGhg8fzpgxY/Dx8eHnn3+mRYsWyW4r4TnbvHkzXl5eVKxYMcO9/uPT7ldi15ryWSeEEEIIkU6p1fDbb9C0qSbvudYPPxgvJvHRqBQjfQN59OgR2bJl49ixY3qjtQYPHszBgwc5efJksut7e3vTt29f+vbtm2QdtVpN3bp1ef78OUeOHEmy3qhRoxg9erRB+Y0bN7BP4wSVarWaFy9e4OjoqJtQTHxYnctcZOd9f2Ix5+KwKahafp2m9X/99VfatGmjV6YoCq9fv8bGxibZCc4SW/ddtpsSVVgIDzpXxT5SofBrJyIuXkRRmXD7tik5c6ac5uBzI+/HT4OcR30xMTG8ePGCHDlyJDmBZnqkKApxcXGYmpqmOHHku9J2GJ88eVKXLkS8Hx/zPCamYsWK9OjRg+bNm3/0bb9PkZGR3L17F0dHR4O5Al69ekXevHl58eKF3gS0n7qXL1/i6Oj4UfZbrVYTGhqKm5ub/L+Szsi5Sb/k3KRvcn7SLzk36Veaz01oKLRuDbt3w5QpMGDAhw/yM/Yx3zupvQ79pDPc9+jRg0uXLiXbgQ4wbNgw+vf/L4/sy5cvyZ49O66urmm+iFer1ahUKlxdXeUD8iNYc2kNXd2HsPrBE64oRXGuM504R8eUV4wnODgYxwTraH9bcnR0TLaTILF132W7yXnw6gHfnGlFUCuFGv/AjlVPmTf6NXN35eL+fXj4UCEN89R9FuT9+GmQ86gvMjKSV69e6SZfzGgSdhh+CJ6enixevJhHjx4ZpCMR78fHOI8JPX78mG+++YZWrVoZpQP/fTIzM8PExARnZ2eDH8My0o9jQgghhBCfhYMHoXlzCArSPP/uO2jRQnKff2aM9u3bxcUFU1NTQkJC9MpDQkJwd3d/5/Z79uzJtm3bOHToEJ6eyU80aWlpiaWlpUG5iYnJW3XYqFSqt15XpF5kbCSd/uhEZK1IivnCmT+DCfHyIq1fqxVFSfTLuEql0j3Suu67bDcp2eyzYarS5NbanQse2sOz49e5ezc3AH/+qaJDh7cK5ZMm78dPg5zH/5iYmKTq8ym9if+Z9zHibtCgwQffxufoY5/H+FxdXRkyZMhH3eaHon3/Jva5Jp9zQgghhBDpRFwcjB8PI0f+N3loliwyeehnymhX6RYWFvj6+upNoqVWq9m3b59eepe0UhSFnj17smnTJvbv34+Pj8/7CFekQ8fuHyMyNhIA30cQ9Q6vm4zARGVCs3zNqJqtEmu3WeHyGurdnKZbvmWLEYMTQgghhBBCCCGE+FSEhECNGpp859oO9CpV4Px5zWSi4rNj1PvA+/fvT9u2bSlVqhRlypRh+vTpRERE0L59ewDatGlDtmzZGD9+PKCZjPTKlSu6vx8+fMj58+exs7Mjd27NaNwePXqwevVqtmzZgr29PcHBwYAmLYe1tbUR9lJ8KHtv7dX9XeU2RHWqYMRoPo4+xfugUqnItHcAlmfXUeb1X7hnekPwc2t274aICLC1NXaUQgghhBBCCCGEEBnUX39p0rX8v08RExMYNQqGDwdTU6OGJozHqPeLNm3alClTpjBixAiKFy/O+fPn2blzJ1myZAHg3r17BGnzDaGZjLREiRKUKFGCoKAgpkyZQokSJejUqZOuzrx583jx4gX+/v5kzZpV91i3bt1H3z/xYR28c1D398PbLYmq8Ol3omtvnX9Trx4AJijUzXwYgMhI2LPHaKEJIYQQQgghhBBCZGybN0PVqv91oLu7w969mhHp0oH+WTP6jGQ9e/akZ8+eiS47cOCA3nNvb2/dhI9JSWm5+DS8jnnN6UenNU8e52V3bFdafEb5qKIqVCDCzg7b8HDq3Z/DQqoDms/6+vWNGpoQ4iNRa28pFEJkOPL+FUIIIYRIp6pUgVy54J9/oFo1WLFCkwddfPaM3okuxNs4+eAkMeoYzZO7X1Ei1xMgp1Fj+piiVWoWVfXkRsw1pvy5EzurGMIjzfnjD4iNBTN5ZwvxybKwsMDExIRHjx7h6uqKhYVFhphgVFEUYmNjMTMzyxDxisTJeXw3iqIQHR1NWFgYJiYmWFhYGDukVBk/fjwbN27k2rVrWFtbU6FCBSZOnEi+fPl0dfz9/Tl48KDeel27dmX+/PkfO1whhBBCiLdnbw/r18P27TB0qCaVixBIJ7rIoA7dPfTfk7tfUfzLjPEl9H3pdaAX24tfA6DFxWhqRJ1gQ+SXPH0KR4+Cn5+RAxRCfDAmJib4+PgQFBTEo0ePjB3O/9i777gqy/+P469zmKKCogwHCi5wj9TcWplaNkxz9NUy7WdLbVhZWGaapk21cjQ1W1qZmpqWWWrOcuDCkYo5QVABAZnn/P649SA5AgPuA7yfjwcP7vs6932fN90e83y4zufKNbvdjs1mw2q1qvhahOk+5g8vLy+qVauGtYi8KVu9ejVDhw6lRYsWZGZmMmrUKLp06UJkZCSlL1mMZciQIYwbN86x7+XlZUZcERERkdzJyoKJE2HAAKhePXu8cWPjS+QSKqJLkbTmSM4iesN3Es0LY4Iu1bqwNGopAF81hB4/fsh3tAeMli4qoosUb+7u7lSrVo3MzEyysrLMjpMrNpuN06dPU6FChSJTOJTL6T7+dy4uLkVuJv/y5ctz7M+ePRt/f3+2bNlChw4dHONeXl4EBgYWdjwRERGRvIuOpnzfvljXrYPFi2HNGiginxIUc6iILkVOelY6G45uMHbiq1PtHPiG+ZobqpB1rd4VN9zIIINv6sMryxbjYrFRpqxVrVxESgiLxYKbmxtubm5mR8kVm82Gm5sbnp6eKr4WYbqPApCQkACAr2/Of399+eWXfPHFFwQGBnLnnXcyevToq85GT0tLIy0tzbGfmGhMiLDZbAXeM95mszk+VSHORffGeeneODfdH+ele+OkfvkFy/3343HqFAD2P//Evnq10Q9dnEJhvnZy+xwqt0mRs+XEFs5nnjd2/u5AXZ/9QD1TMxW2Mu5lqO9an4jMCE57wZ81E9hY+hEabfpIvzgVERGRAmOz2Xjqqado27YtDRo0cIz/73//o3r16lSuXJkdO3bw/PPPs2/fPr7//vsrXmfixImMHTv2svHY2FhSU1MLLD8YP0NCQgJ2u12/DHIyujfOS/fGuen+OC/dGyeTlUWZd96h9OTJWOx2YygwkPjp08lo2BAuFNXFfIX52jl37lyujlMRXYqcf/ZDr1btBCWtiA7Q1LUpEZkRgNHS5cvvP4bo0VCtmrnBREREpNgaOnQou3btYu3atTnGH374Ycd2w4YNqVSpErfccgsHDx6kZs2al10nPDycESNGOPYTExMJCgrCz88Pb2/vgvsBMN6UWSwW/Pz8VNBwMro3zkv3xrnp/jgv3RsncvIklgEDsKxa5RhK7dQJ16++onxAgHm55IoK87Xj6emZq+NURJci55/90Ct0/cW8MCYKdQnFt5QvZ86fYWEYJLlDmW++gWefNTuaiIiIFEPDhg1jyZIlrFmzhqpVq17z2BtvvBGAAwcOXLGI7uHhgYeHx2XjVqu1UIoMFoul0J5L8kb3xnnp3jg33R/npXvjBFasMBYPvTjT3MUF26uvEj9wIP4BAbo3TqqwXju5vb7+lEiRkmXLYu2RCzOfkgKwng7Bp0G6uaFM4mpxpXe93gCkuMPiOsC33xr7KcaXiIiIyH9lt9sZNmwYCxYs4NdffyUkJORfz4mIiACgUqVKBZxORERE5Br274du3bIL6FWqwKpV8PzzoOK55IH+tEiRsjt2N4lpxsJTjY+UITx0IW7uWSanMk+/Bv0c29/Whz//sNH95hQqVIAvvzQxmIiIiBQbQ4cO5YsvvuCrr76ibNmyREdHEx0dzfnzxho1Bw8e5NVXX2XLli0cPnyYH374gQceeIAOHTrQqFEjk9OLiIhIiVanDjz5pLF9++0QEQHt2pkaSYomFdGlSNlwdINje+CRg4x/+KiJaczXvlp7Akobvbt+rA3J7ln8+JsXqamwZInJ4URERKRYmDFjBgkJCXTq1IlKlSo5vubNmweAu7s7v/zyC126dCEsLIxnnnmGXr16sXjxYpOTi4iIiACTJsGsWbB4MVSsaHYaKaLUE12KlPXH1ju2Wx/D+O3h77+bF8hkLlYX7q13L9P+nEaaKxwL3UbA3tPEZFTgl18gNRVyuT6CiIiIyBXZ7fZrPh4UFMTq1asLKY2IiIjIVWRmwpgxEBwMQ4Zkj7u7w4MPmpVKignNRJci5dDZQwB4ZELTeE9o2tTkRObrU7+PY/u7+nB7xiLA6Il+yaLTIiIiIiIiIiLF0/HjcPPN8Npr8MQTsGOH2YmkmFERXYqUNZ2/4u3J9/Dq1w1Ia3IzuLmZHcl0bYPaUqmMsWjXslpws8cCx2Nq6SIiIiIiIiIixdry5dCkSXangowM2LLF1EhS/KiILkVK2u9/8nzCPEYe3EnbfZ+YHccpXGzpApDuCudDV+BmyQBg6VL4l09gi4iIiIiIiIgUPZmZEB4Ot90GcXHGWFAQrFkDgwaZm02KHRXRpUjZ9eMRMjFmnzdrkGFyGudxaUuXxfXT6GhfBcDhwxAZaU4mEREREREREZECcewY3HSTsWjoRXfcAdu2QZs25uWSYktFdClStmzMLpw3u7mceUGcTJugNlQuWxmAn2rCzZ7zHY+ppYuIiIiIiIiIFBvLlhntW9auNfZdXeGtt+CHH6BCBVOjSfGlIroUCRlZGdw0qxPTa/0CNX8G4IZOZU1O5TysFiu96/UGjJYurqHZfdGXLjUrlYiIiIiIiIhIPkpLg8ceg9Onjf1q1Yxe6M88AxaLudmkWFMRXYqEHTE7WHVkNTta/wxNZmPBRpMmZqdyLn3q96FN1TZM2erPgIOnCGUvAAkJxpoaIiIiIiIiIiJFmocHfP21Mfv8zjuN9i2tWpmdSkoAFdGlSIiIjsjeOdqa0IB4ypQxLY5TahPUhnUPrePJRg9TKQk+4SEOvTKH7dvBzc3sdCIiIiIiIiIi1yEzM+d+69awYQMsWgS+vuZkkhJHRXQpEh5q9hArtz4JXy6BvT1o1sRudiTn1dto69KW9YSsmmVyGBERERERERGR65CRASNHwm23QVZWzseaN1f7FilUKqJLkRG1sRT81R0Sg7jhZh+z4zivhg2hdm1je80aiIkxN4+IiIiIiIiISF4cOQIdO8Kbb8Ivv8CECWYnkhJORXQpGuLi2HqqqmO3WUtXE8M4NzuwqXdrnukCZzxssMBYZPSfn34SEREREREREXE6S5ZA06ZGyxYwetSWK2dqJBEV0aVo2LSJAGIIYw9Wi42mTc0O5LzGrh5LK/c5vNMGvqjjzfOv+1K/Prz4otnJRERERERERESuIiMDnn3WWDD0zBljLDgY1q2DJ54wNZqIiuji9D6L+IzH/hhDUJNXWVeqHuc+W4CPurlcVZeaXRzby+qe583D9xIZafwiV0RERERERETE6fz9N3ToAG+/nT12zz2wbRu0aGFeLpELVEQXp7dw30JmWrcwuAecKg1eHZqbHcmptaraiv4N+/NpUmc+/yGDG9kEQGSk0VJMRERERERERMRp/PCD0b5l40Zj380Npk6F+fPVxkWchoro4vT+OP4HAN6pUMfuC9WqmZzIuVktVr7o+QWDur9IxRS4jWWOx5Ytu8aJIiIiIiIiIiKFbfFiOHvW2A4JyW7fYrGYm0vkEiqii1M7nnicE+dOAND8BFibt9BfornVrh1UqMDt/OgYUhFdRERERERERJzK1KnQoAH06gVbt6p9izglFdHFqf154k/H9o7jD9H/2Oukp5sYqChxdYU776QZW/EnBoCVK9F/PxERERERERExT3R0zn0vL1i9Gr79Vu1bxGmpiC5O7c/j2UX0uBO38+uJUNzdTQxUhGRkZfDzLcE8foedRiHvAJCUBGvXmhxMREREREREREqe9HR4+mmoUwf++ivnY76+6jwgTk1FdHFqf5z4I3vneEsaN7KbF6aIWX5gOV0PvsIHzSGj0beO8R9/vMZJIiIiIiIiIiL5LSrKaDs7ZQqcOwd9+kBamtmpRHJNRXRxWna7nc0nNhs75ypBYhUat/Q0N1QRcmvNWyntVhqAHaGHsViN/zmpL7qIiIiIiIiIFJoFC6BpU/jzQrcBd3cYMgS1GpCiREV0cVpR8VHEp8YbOyduACw0aqyP9uSWp6sn3et0B+Csl52w6h8CcPAgxMWZmUxEREREREREir30dHjqKejZExISjLGaNWHDBnj8cbVvkSJFRXRxWltPbs3eOdkMgMaNTQpTRPUM6+nYrln3LZb4D+bMaTsVK5oYSkRERERERESKt0OHoG1bmDo1e6xPH9i6FZo1My+XyHVyNTuAyNVsO7kte+dkM9xdswgNdTEvUBF0e+3bcXdxJz0rna1hR7ht2Sysfz3BB5s2kZSUdF3XLFOmDI888kg+JxURERERERGRYmHxYhgwABITjX0PD6MX+iOPaPa5FFkqoovT2hp9yUz06KbUC7Xh5qYiel6U9ShLl5pdWLJ/CSe84Y8q0GrhQpLKlqVfv37Xdc25c+fmc0oRERERERERKTY8PY3FQwFq1YJvvjF6oosUYWrnIk7Jbrdnt3NJqQAJQTS6wc3cUEXUpS1dvq+LsaCHiIiIiIiIiEhBuPVWeOkl6NsXtmxRAV2KBc1EF6d0Mukkp5JPXdhpBljUD/063RV6Fy4WF7LsWcyva6HuiibM/eRWYmLK8uST58yOJyIiIiIiIiJF2e+/Q7t2OVu1vPKKsV+I7VuioiAmBgICICSk0J5WSgjNRBendOmion1P7uOdrj/RpYuJgYqwCl4V6BTcCYBDvnYGBzzN5j2N+OGHUuYGExEREREREZGiKzUVhg2DDh1yLiAKYLUWagF9/nwYPhxGjjS+z59faE8tJYSK6OKULi2i33PyCE8Pz6RBAxMDFXE962a3dKlS930A9u1z48QJ9ZgXERERERERkTw6cADatIFp04z9kSPhr79MiRIVBbNmgd0OdeoY32fNMsZF8ouK6OKUtkVvc2w3OwnccIN5YYqBHmE9HNsZdRc7tn/91cOENCIiIiIiIiJSZH3zDTRrBtsu1G48PY1ieq1apsSJiYHERKhUCVxcjO+Jica4SH5REV2ckqeLB2XToGwa1PSqAoGBZkcq0iqXrUzrqq0BOBVwCirsB+C33zzNjCUiIiIiIiIiRUVqKjz+uLFg6LkLa6zVqQObNsGQIYXavuVSAQHg7Q0nT0JWlvHd29sYF8kvKqKLU/q60Th+n1Sf8PcHsDFkAMnJZicq+i5t6eIV+iUAa9d6kJ5uViIRERERERERKRL++gtat4YZM7LH+veHzZuhUSPzcmEsIjpokFHD37/f+D54sBYXlfylIro4p4gIltnvYNS5z2n7+yQWLjQ7UNF3d+jdjm2vsK8BSE628uef7mZFEhERERERERFnt3at0b4lIsLY9/SEjz+Gzz+HsmVNjXZRr17w3nvwxhvG9549//0ckbxQEV2cU0QE22ns2DX5l5rFQu0KtannVw+A00F/QWmjOZhauoiIiIiIiIjIVTVqlN0bJSwM/vgDHnrItPYtVxMSAq1aaQa6FAwV0cU5RUSwA6Ny7u5uJyzM5DzFRI/QHgDYLUDoIkBFdBERERERERG5Bm9v+PZbo0fKn39Cw4ZmJxIpdCqii9Np92k7ulT6jT0dvwGgXj1wczM5VDHRI6wHLSs159XfrDSKqgjAvn1unDihvwpEREREREREBJg3D44ezTnWtCl88gmUKWNOJhGTqXImTiUxLZF1R9exoloq9jrLAGjUyLk+HlSUtajSgk0P/0nf0/V49uwCXmckq6b+RqVKNrOjiYiIiIiIiIiZzp+HRx6Bfv2Mr4wMsxOJOA1XswOIXOpIwhEquvoQl5kAJ5sB+pRQQThYvz737/oCgHP7U0i0hJucSERERERERERMs28f9OkDO3YY++vXw4IFxpiIaCa6OJcG/g045TaKx94Jh9+Nwm79+iaHKoYO1a2L/cICIJ4rVpicRkRERERERERM8+WXcMMN2QX0UqVg1iwV0EUuoZno4nQs27dzJPE+oDoADRqYm8dZRURE8Pbbb1/XubuP/M3mm+qxImM3I9ftxyUqiiwtXy0iIiIiIiJScqSkwBNPGL3OL6pXD775RjMaRf5BRXRxPhER7GICAN7edqpWVU/0K7FYLPTr1++6zr357ZuZ1WE/AOWP1uHY6HS8u3kxYEBKfkYUEREREREREWe0dy/07g27dmWPDRoE770HpUubl0vESamILs7l/Hmy9uynGVtxc7cS0LAaFtXQ851/qj/7fYwi+uNh3WD5zdSNyVARXURERERERKS4O34cmjeH5GRj38sLpk+HgQPNzSXixNQTXZzGvrh93PxJe57pmsnw4F789b8xrF5tdqriqUpKFTpW6cjUjRVotP5WAPbscSM6Wn8liIiIiIiIiBRrVarA4MHGdv368OefKqCL/AtVzMRpbD25ld9Ob2FqK9gQBDRpgouL2amKJ3ebO1/e9iWDqt/L3YlbHeOrVnmamEpERERERERECsWbb8K4cfDHH0YfdBG5JhXRxWnsiNnh2G4UAzRpYlqWkiL11lvpxnLH/qpVHiamEREREREREZF8N2cOfP55zjEPDxg92mjlIiL/Sj3RxWnsOPWPInqjRuaFKSHSmzenuc9+yiec4Sy+/L7Gg8xMcNXfDCIiIiIiIiJFW3IyDBsGs2dDqVLQtCk0aGB2KpEiSTPRxWlcnIluTS3LXSk7Gf5yeZMTFX9ZVgur7qqPX5cHwOcICYkubNvmbnYsEREREREREfkvdu+Gli2NAjrA+fOwYIGpkUSKMhXRxSmcOX+GY4nHALDFNGF7RgN27jQ5VAkwc+dMutZcw/42SyFsIaCWLiIiIiIiIiJF2uzZ0KIFREYa+6VLG+1cRo82NZZIUWZ6EX3atGkEBwfj6enJjTfeyB9//HHVY3fv3k2vXr0IDg7GYrEwZcqU/3xNcQ47Yy6pmMcYbVzq1zcpTAlyc9DN2TuhiwD47VctLioiIiIiIiJS5CQnw8CBMGiQMfMcoGFD2LwZBgwwN5tIEWdqEX3evHmMGDGCMWPGsHXrVho3bkzXrl05derUFY9PSUmhRo0aTJo0icDAwHy5pjiHSxcVvVhEV5uughdWPozqZasbO8GrwfMsO3a6Exdn+u/XRERERERERCS3du0yZp/PmZM9NmQIbNoEYWHm5RIpJkytlL3zzjsMGTKEQYMGUa9ePWbOnImXlxeffvrpFY9v0aIFb775Jv369cPD48otJ/J6TXEOVyqiayZ6wbNYLHSp3sXYsWbRrs7zfHLHF3h52c0NJiIiIiIiIiK5k5UFvXrBnj3Gfpky8OWX8OGHxoKiIvKfmVZET09PZ8uWLXTu3Dk7jNVK586d2bBhg9NcUwrHjlOXFNFPGVPQVUQvHF2rd3VsB4Z+xICjb6mILiIiIiIiIlJUuLjAJ58Y3xs1Mtq3/O9/ZqcSKVZczXriuLg4srKyCAgIyDEeEBDA3r17C/WaaWlppKWlOfYTExMBsNls2Gy2PGWw2WzY7fY8n1eSZdmy2BWzCwDrmWBs6WUIDLRTvryd3P5ntNuvv+hrsVguO99utzu+8nruf3newjr/0nNv8L+B8h7lOZt2lmW1wbZgO5YTJ7BVqnTV84vKn2+9HosH3cfiQfexeNB9LHj6bysiIiK5YreDxZK9364dLFkCHTtq9rlIATCtiO5MJk6cyNixYy8bj42NJTU1NU/XstlsJCQkYLfbsVrVVzo3DiUcIiUzBQBbTFMA6tRJ59Sps7k638vLi4SEhOt+/sDAwMvOt9vtpKQYmSyX/k8pF+f+l+ctrPP/eW7HwI4s/Hshye7wawg0WbKEhD59rniul5dXkVljQK/H4kH3sXjQfSwedB8L3rlz58yOICIiIs7MbodPPzUK5vPnw6X/JuvWzbxcIsWcaUX0ihUr4uLiQkxMTI7xmJiYqy4aWlDXDA8PZ8SIEY79xMREgoKC8PPzw9vbO08ZbDYbFosFPz8/vbnMpd9P/569c6EfepMm7vj7++fq/JSUFHx8fK77+aOjoy87/+IsbR8fn2sW0a907n953sI6/5/ndq/VnYV/LwTg21A3Er6H1OAAbr318l8ipaSk5PremE2vx+JB97F40H0sHnQfC56np6fZEURERMRZJSXBo48a/c4BJk2CUaPMzSRSQphWRHd3d+eGG25g5cqV9OjRAzDemK1cuZJhw4YV6jU9PDyuuFCp1Wq9rjeIFovlus8tiXad2pW9c6GI3rChBav16sXrf7pWofvf2O32K55vsVgcX3k99788b2Gc/89zOwV1wtPFk9SsVGaHVmD20jE0ezeNLl3Srnh+Ufqzrddj8aD7WDzoPhYPuo8FS/9dRURE5Ip27IDevWH//uyx6OjL27qISIEw9V/pI0aM4KOPPuKzzz5jz549PPbYYyQnJzNo0CAAHnjgAcLDwx3Hp6enExERQUREBOnp6Rw/fpyIiAgOHDiQ62uK87l0UdFPYybz1rgUOnY0MVAJVMq1FB2qdDB2ykZDlT/Ytt2Ts2f1Rl5ERERERETENHY7fPgh3HhjdgG9bFmYNw/efVcFdJFCYmpP9L59+xIbG8vLL79MdHQ0TZo0Yfny5Y6FQY8cOZJjNs6JEydo2rSpY/+tt97irbfeomPHjqxatSpX1xTnsyPGKKJ7pcNAryiso71MTlQydQ3uys9HfjZ2whZiP9aKNWs8uPvu8+YGExERERERESmJzp2DRx6Br7/OHmvaFL75BmrVMi+XSAlk+sKiw4YNu2qrlYuF8YuCg4Mdvaqv95riXM6lnePQ2UMANDwF1oaNTE5UcnUO6ozVYsVmt0HoIvhlEr/96q4iuoiIiIiIiEhh277daN/y11/ZY0OHwltvQRFbQyUqCmJiICAAQkLMTiNyfUwvokvJFpcSx42lQ9l5dh+NYoAGDcyOVGJVKFWBFgEt2BS9Cfz2QoV9rF5ZHZst52LfIiIiIiIiIlLApk3LLqB7e8Mnn8C995qb6TrMnw+zZkFiovFjDBoEvXqZnUok71QaE1OFlA9ho20wWyfWpMlP/dlY6iaSksxOVXJ1qdYleydsEbHxnuze7WZeIBEREREREZGSaPJkqFcPmjWDrVuLZAE9KsoooNvtUKeO8X3WLGNcpKhREV3Mt3Mnv9i7MjT9C1qPu42vvjI7UMnVpfolRfTQRQCsWuVhUhoRERERERGREiI5Oed+6dKwbBmsXw81a5qT6T+KiTFmoFeqBC4uxvfERGNcpKhREV3Mt3Mne6jr2K1b9xrHSoEK8QkhtHyosRO0AUrHsHqZuZlEREREREREii27HWbMgBo14ODBnI9VqwYehT+xLSoKNm787zPGAwKMFi4nT0JWlvHd29sYFylqVEQXc2VkwJ49KqI7kS7VulDP7ofv7w8D8OfOMiQkWExOJSIiIiIiIlLMJCZCv37w+ONw6hT06QNpaaZGmj8fhg+HkSON7/PnX/+1QkKMHugWC+zfb3wfPFiLi0rRpIVFxTSnU05zw7RG1L83nc17t8HWWyhdOoXPPpuRp+tERkYWUMKS6ZkbnuFF33t5fexmfucInav8SFZWN8BudjQRERERERGR4mHrVqNofuns8/btjUqzSf7Zw/zkSWO/WbPrL3z36mWcHxNjzEBXAV2KKhXRxTS7Y3fzd8oJ/q4DxB8GoEEDF/r165en64SHh+d/uBLM1epKZs2aTAwZjEfUM9ijXYi2RGCjvNnRRERERERERIo2ux2mT4cRIyA93Rjz8TGq1ffcY2q0iz3M69TJ7mG+f78x/l+K3yEhKp5L0ad2LmKa2ORYytrdjZ1TDQCoXTvTxERyqfQunQGwZGXh8euvJqcRERERERERKeISEozZ58OGZRfQW7SAbdtML6CDepiLXIuK6GKaXvV6kRDRjTff6Q27+wBQq1aGyankotQuXdhfAX6sDZ4rVpgdR0REREwyceJEWrRoQdmyZfH396dHjx7s27cvxzGpqakMHTqUChUqUKZMGXr16kVMTIxJiUVERJzQ1q1GX5Pvvssee+opWLvWaaZpq4e5yNWpnYuYyrJzFzGJjwK+gGaiOwu73U7Xk6+ybThUSIFfpx3n2GZo3NzsZCIiIlLYVq9ezdChQ2nRogWZmZmMGjWKLl26EBkZSenSpQF4+umnWbp0Kd9++y0+Pj4MGzaMnj17sm7dOpPTi4iIOIlTp+DQIWO7XDmjfUuPHmYmuiL1MBe5MhXRxTxJSXDoEJHUcwzVrq2Z6M7AYrFQpWxVtsVFcNoLGlecTLtRrsz9Wb/kEBERKWmWL1+eY3/27Nn4+/uzZcsWOnToQEJCAp988glfffUVN998MwCzZs2ibt26bNy4kVatWpkRW0RExLl06wbh4bByJcybB8HBuT41KirvRe3rOeci9TAXuZyK6GKeyEgAKnOCyu4nSPIMpFIlm8mh5KLbgm8j9the/lp6L2fO1GbT0QokJZ0yO5aIiIiYLCEhAQBfX+OThFu2bCEjI4POnTs7jgkLC6NatWps2LDhikX0tLQ00tLSHPuJiYkA2Gw2bLaC/fegzWbDbrcX+PNI3uneOC/dG+em++Ok9u/HVrNmznvzyivw8svg7g65vF8LFsDs2caCn97e8OCD/94+/XrOKWn0unFuhXl/cvscKqKLKTYc3cDYX4dQvwv83+6Hua/53QS/Mw2LxexkctHdNe+mR8AtTBj9K9NtlckA1q11NzuWiIiImMhms/HUU0/Rtm1bGjQwFoaPjo7G3d2dcuXK5Tg2ICCA6OjoK15n4sSJjB079rLx2NhYUlNT8z33pWw2GwkJCdjtdqxWLRHlTHRvnJfujXPT/XEydjten3xC2XHjSBozhviePa/73sTEGBPXq1YFX184c8bYr1Xr6ot9Xs85JZFeN86tMO/PuXPncnWciuhiis0nNvNT2m5+agNhcVAuMJBQD7NTyT/Zy5ThlnpHmL7L2F+9IJVGN5mbSURERMwzdOhQdu3axdq1a//TdcLDwxkxYoRjPzExkaCgIPz8/PD29v6vMa/JZrNhsVjw8/PTm2Yno3vjvHRvnJvujxOJj8fy0ENYFi4EwGfcODJat6Z87drXdW+iomD3bqhTB06fhqwsY8HPlBTw98+/c0oivW6cW2HeH09Pz1wdpyK6mGLXqV2O7fqxsD0w0MQ0ci039iyP+6400vFg1e9eNOxkdiIRERExw7Bhw1iyZAlr1qyhatWqjvHAwEDS09OJj4/PMRs9JiaGwKv8G8/DwwMPj8tnUFit1kJ5I2uxWArtuSRvdG+cl+6Nc9P9cQJ//AF9+8Lhw9ljw4eTdaGA/s97k5ue5YGBULYsnDgBlSrByZPGfmAgXO1WX885JZVeN86tsO5Pbq+vPyViit2xux3b9TPKkVKmjIlp5Fost3egceWPoOX7HEn05dQpX7MjiYiISCGy2+0MGzaMBQsW8OuvvxLyj3f6N9xwA25ubqxcudIxtm/fPo4cOULr1q0LO66IiEjhstthyhRo1y67gO7rC4sXY3/jDXBzu+yU+fNh+HAYOdL4Pn/+lS8dEgKDBoHFYswmt1hg8OBrL/p5PeeIyL/TTHQpdHa7nd0XZqJ7JZTn1qR12ObauL27BW9vu8np5J/+t+sF/nx4tbGztwdREfrUgIiISEkydOhQvvrqKxYtWkTZsmUdfc59fHwoVaoUPj4+PPTQQ4wYMQJfX1+8vb0ZPnw4rVu3vuKioiIiIsXG2bNGxXrRouyx1q1h7lyoVu2Ki4dGRcGsWUbtvU4dY6b4rFnQrNmVC929ehmP/dus9f96johcm4roUuhOJp0kPi0BAEtsPf5MqotLRCZeXjEmJ5MraebXjNXHLhTRQ3/g0Pa25gYSERGRQjVjxgwAOnXqlGN81qxZPPjggwBMnjwZq9VKr169SEtLo2vXrkyfPr2Qk4qIiBSiyEi4/Xb4++/ssZEjYfx4oo65EbPR6EHu5ZXztJgYSEw0CuguLkbLlf37jfGrFbtDQvJeCL+ec0Tk6lREl0J3aT/0lFMtAfD3P4ur/jQ6pa7VuzJ522RjJ3QROzf/H8nJULq0ublERESkcNjt//5JQU9PT6ZNm8a0adMKIZGIiIgTqFw5u8m4ry/MmQPduzN/vjGzPDERfHyMieo9emSfFhAA3t7GDPSLPcu9vY1xEXFe6okuhW73qex+6PZTDQHw9z9jVhz5F/Ur1KdK6SrGTshv3OrxDWeOpZgbSkRERERERKSAREXBxo3G96s+vrccxyd/AzfdBBER0L37Za1a7Hb45Zeca42qZ7lI0aS5v1LoLl1UlNj6APj7nwa0uKgzslgsdKnehVmRs8Algwdq3k/Q3rIQerfZ0URERERERETy1aUzyb29jYJ3r14YVfWgIOZvrHLJ480Z9PhKegVZgCu3aklJMcZr1Mh+DvUsFyl6NBNdCl3OIno9AAICNBPdmXWt3tWxvSgM+OEH88KIiIiIiIiIFIArzSSf9amd0y++De3bk3rPfXz2SWbOx2dbHDPWL23VkpVlfPfyunKrlpAQaNVKBXSRokJFdClUdrudyFORAJQ+WxHSjdnnAQGnzYwl/+LGSjfi4+4NwNLakP7j4iuuMi4iIiLOKzExkYULF7Jnzx6zo4iIiDilizPJK1UyZpLXKn+a8I13UeG1ZyEzE88/f6fDX584Hq9UyTg+JsY4/0qtWjp3huBgU38sEckHauciher4ueMkpicC4BIbChjrcPj5nTUzlvwLN6sbNwfdwoKDC0j0hBVeZ2myZBtV7rrB7GgiIiJyFX369KFDhw4MGzaM8+fP07x5cw4fPozdbmfu3Ln06tXL7IgiIiJO5dKZ5G2tG/i/X/rin3rU8Xj8Y+GsOfwQWddYFPTSVi3+/sZMdBEp+jQTXQrVntjsmU8psc0Boy+Yq2uWWZEkly5t6dIjbDAPjyhtYhoRERH5N2vWrKF9+/YALFiwALvdTnx8PO+++y7jx483OZ2IiIjzCQmBQQNt9Dr0JiOXdsguoFesCMuWUW76awx8yPVfFwW92KpFM9BFio88z0SfNWsWffv2xUu/SpPrsCcuu4ieGdcIgHr1zEojedGpaifcrW6k2zLIDF3Gr8smk5oKnp5mJxMREZErSUhIwNfXF4Dly5fTq1cvvLy86N69O88995zJ6URERJzQ6dP0+mwg7F2aPda+PXz9NVSpAmhRUJGSKs8z0V944QUCAwN56KGHWL9+fUFkkmIsMjbSsf1G7Ke8/mIiAwaYGEhyrYx7GdpUbmvs+BwlNXAva+adNDeUiIiIXFVQUBAbNmwgOTmZ5cuX06VLFwDOnj2Lp34LLiIiktPZs9C0KSy9UEC3WODFF+HXXx0F9Iu0KKhIyZPnIvrx48f57LPPiIuLo1OnToSFhfH6668THR1dEPmkmLl0JvqQ9J2MfLUsvXubGEjypFv1btk7YQtZNkuvexEREWf11FNP0b9/f6pWrUqlSpXo1KkTYLR5adiwobnhREREnE358nDXXcb2hfYtjB8PrlpOUESuo4ju6urKPffcw6JFizh69ChDhgzhyy+/pFq1atx1110sWrQIm81WEFmlGNhzypiJHngOytWsb/xmV4qMztU6Z++ELmL5n77mhREREZFrevzxx9mwYQOffvop69atw2o1/ulfo0YN9UQXEREBoqJg40bjOwBvvQWPPQYREdC167VOFZES5j8tLBoQEEC7du1o3bo1VquVnTt3MnDgQGrWrMmqVavyKaIUFykZKVRyLYdHJtSNA+rXNzuS5FFg6UCCrEEXdnaw193G4R2J5oYSERGRq2revDndu3fn+PHjZGZmAtC9e3fatm1rcjIRERFzrRq/lrn3fsvIkTB8OMyfj7Ho1/Tpl7VvERG5riJ6TEwMb731FvXr16dTp04kJiayZMkSoqKiOH78OH369GHgwIH5nVWKOC83L7b7vkjyBOj/zd1sLnsT586ZnUryqoFrg+yd0B9YPnWfeWFERETkqlJSUnjooYfw8vKifv36HDlyBIDhw4czadIkk9OJiIiYxGbjzMhJtH+5E09vf5COfpHY7TBr1iUz0kVE/iHPRfQ777yToKAgZs+ezZAhQzh+/Dhff/01nTsbbR5Kly7NM888w9GjR/M9rBQDu3cTb/fl/84vpMXk/3H33WYHkryq73rJJwjCFrHsJ7XkERERcUbh4eFs376dVatW5VhItHPnzsybN8/EZCIiIoXrYtuWvzfHQvfu+L4Zjos9C8+sFG6NnEqlSpCYCDExZicVEWeV59UR/P39Wb16Na1bt77qMX5+fkTp13dyJZGR7CPUsRsWZmIWuS4BlgBqla/FoYOZ2I634JfjoaQnZ+Be2s3saCIiInKJhQsXMm/ePFq1aoXlknVo6tevz8GDB01MJiIiUnjmzzdmmQcf/Z2x+/pB2gkAbFiYV+slfmv9MidPgrc3BASYHFZEnFaeZ6J37NiRZs2aXTaenp7OnDlzALBYLFSvXv2/p5PiJzKSvWRXzkNDr3GsOCWLxcKG/9vAfT/MgV9epyonOPL9ZrNjiYiIyD/Exsbi7+9/2XhycnKOorqIiEhxFRUFsz+10fuv13h3ZycqXCigZ1XwZ93LP/Nl6Dj2HnDFYoHBgyEkxOTAIuK08lxEHzRoEAkJCZeNnzt3jkGDBuVLKCmebpnViR6tDjO7/XHHmGaiF00VvSry8hOJHKQG+wij1tZvzI4kIiIi/9C8eXOWLl3q2L9YOP/444+v+alSERGR4uL0nlOMXn8bA/e/iNVuA2Crz01EzI6g/djOvPcevPEGvPce9OxpclgRcWp5budit9uvOHPl2LFj+Pj45EsoKX6S05P59chqCIPyZQ7D78a4ZqIXXXUGtYWRRyETWLQI3nkHNKtNRETEabz22mvcdtttREZGkpmZydSpU4mMjGT9+vWsXr3a7HgiIiIFy26n4Qvd8Yg3Pjltw8LXtV5mXp3RTK3vAhgzzzX7XERyI9dF9KZNm2KxWLBYLNxyyy24umafmpWVRVRUFN26dSuQkFL0/Z3wN6Us7py3p5MV2wAAT0+oVs3kYHL9ypWDjh05+udKss5GERwZCfXr/+tpIiIiUjjatWtHREQEkyZNomHDhvz88880a9aMDRs20LBhQ7PjiYiIFCyLBY/33sJ+883Eu/kxts5XHKh2s9q2iMh1yXURvUePHgBERETQtWtXypQp43jM3d2d4OBgevXqle8BpXio51ePpOQnOfDhO9RjIgB16oA1zw2FxFnsP72f+zrtY2t7eHgzvL9gCW4qoouIiDiVmjVr8tFHH5kdQ0REpFBERUFMjLFAaEgI0LEjli++4FzNm+hnC8weFxHJo1wX0ceMGQNAcHAwffv2xdPTs8BCSfFkjdyDPb4mWdQB1MqlqKvqXZU9xAHwaWhF4iY3Z/5LJocSEREp4RITE3N9rLe3dwEmERERKVxrxv5G2sefMz74Y8r6WBk0CHr1Au67j2qAPggvIv9FnnuiDxw4sCBySEmwezf7aODY1aKiRZuXmxe31uzK0tWHydzbkxWJ9ck4FoNb1QCzo4mIiJRY5cqVu+L6RZe6uMZRVlZWIaUSEREpQFlZnH12Am2njMUFGwmVwphlH8msWdCsmWaei0j+yFUR3dfXl/3791OxYkXKly9/zX+YnzlzJt/CSTGSnAyHD5NCSyq7neJEhr9mohcD3/b5hoHj9zA3sjHngI3vL6X9pO5mxxIRESmxfvvtN7MjiIiI5LvL2rRcGDuzJ4Z6E/pTfv1Kx7Fh0b9RueGz7PvLSkyMiugikj9yVUSfPHkyZcuWdWz/2+wWkUtlZGXQ+/PuhN5ip92ReRxvU4rEqbNwzfPnIMTZuLu4061XGeZGGvvLv0+h/SRzM4mIiJRkHTt2NDuCiIhIvpo/H2bNgsRE8PaGQYOM8S1v/sozW/tTKiMagCysfFlnLL+3DedEtBVvb6PoLiKSH3JVxry0hcuDDz5YUFmkiPvggw9ISkq6bDzGFsOilNXQDv7eBd5xcWz+6O0cx0RGRhZWTMln3R4LgVeN7WUHajPh/HkoVcrcUCIiIiXUjh07aNCgAVarlR07dlzz2EaNGhVSKhERkesTFWUU0O12qFMHTp6EGe9nMeDweMYfHosVOwCnPSrx+2Nf881fHUk8YBTbBw/WLHQRyT95ngu8detW3NzcaNiwIQCLFi1i1qxZ1KtXj1deeQV3d/d8DylFQ1JSEv369bts/MeoH+HCJ6vqxkL9wb2pdcstOY4JDw8vjIhSAAIqWWla4TDbXJLY5p5K9Px1BA7obHYsERGREqlJkyZER0fj7+9PkyZNsFgs2O32y45TT3QRESkKYmKMGeh16oCLC4T6RNN3TX9uTPrVcczuyrcSHvQFo/r6817A5W1fRETyQ56L6I888ggvvPACDRs25NChQ/Tt25eePXvy7bffkpKSwpQpUwogphRlB+IPOLbrxkFmrVomppH8Fp8az+GHO4LHETjSlp8+eYiBA8xOJSIiUjJFRUXh5+fn2BYRESnKAgKMWeUnT0KlStBp/WuOAnoWVhbd8Cqf+r8AVqujcK7iuYgUBGteT9i/fz9NmjQB4Ntvv6Vjx4589dVXzJ49m/nz5+d3PikG/or/y7H9WtznPDW1EWvWeJiYSPJTOc9yeJfzMnaC1rNghxVsNnNDiYiIlFDVq1d3rF/0999/U6VKFapXr57jq0qVKvz9998mJxUREbm6qCjYuNHYHjQILBbYvx/mhL1GYpUwzvtWZlSr35jiNQqsVrVuEZECl+eZ6Ha7HduFAtkvv/zCHXfcAUBQUBBxcXH5m06KhQNnjSK6xWZhe1xvtn/jQd16mXTokGZyMskvfZv24I31k8Bi5+fqiWRs2opb6+ZmxxIRESnRbrrpJk6ePIm/v3+O8YSEBG666Sa1cxEREad06UKi5cpmMXCwC++9d7FNSxm8MxZBuXI8muzPPWrdIiKFJM8z0Zs3b8748eP5/PPPWb16Nd27dweMj4sGaNlj+Qeb3eZo5+Jz1heyjBnotWplmhlL8lnPuj0c22lhy9kze5N5YURERAQwJr9cnJV+qdOnT1O6dGkTEomIiFzbpQuJ9ii9gumr6/HjNKM9WatWF4rldeqAvz8hIZeMiYgUsDzPRJ8yZQr9+/dn4cKFvPjii9S60N/6u+++o02bNvkeUIq2E0knOG9LBcAzroZjvGZNFdGLkxZVWlDRLYC4jBjcav5IyI9/A0PNjiUiIlIi9ezZEzAWD33wwQfx8Mhuo5eVlcWOHTv073YREXFKMTGQnJDJiKSxdI+YgBU7z/7Zl1PH1hIS4m52PBEpwfJcRG/UqBE7d+68bPzNN9/ExcUlX0JJ8XFpP/T02EYAeHjYqVJFHx8uTqwWK30a92L65umkucKytN30iYrSlAARERET+Pj4AMZM9LJly1KqVCnHY+7u7rRq1YohQ4aYFU9EREqgqKiL7ViMt4lX2+fECd6N/B8Nz6x2nJtcqgKB3imAiugiYp48F9EvSk9P59SpU47+6BdVq1btP4eS4uNiKxeA+LjWANSokYk1z42ExNn1rNuT6ZunA/B9XeizcCE8/bS5oUREREqgWbNmARAcHMyzzz6r1i0iImKqS3uce3tD3bqwZ8/l+6F//8zo/QMolx4LQCYufB42AZ9Xn6N5YxURRMRcef5baP/+/bRv355SpUpRvXp1QkJCCAkJITg4mBDNOpV/uHQmui22AQA1a2aYFUcKUIfqHfB1N2a+La0NqT98b3IiERGRkm3MmDEqoIuIiKku7XFepw4kJ8OcOcb3i/tffpZJ350v8eaubo4CeppfVfbOXE2nH5+n570qoIuI+fI8E33QoEG4urqyZMkSKlWqdMXFikQuOnBJEZ24MED90IsrNxc37gy7h892zCbJA7ocb8Oa2Fjw8zM7moiISIkUExPDs88+y8qVKzl16hR2uz3H41lZaq8nIiIF42J7lhMnjBnndeqAiwuULQupqcZ3Fxeo7nqc8Wfuo23W745zN5S/Hbc5n9G8W0UTfwIRkZzyXESPiIhgy5YthIWFFUQeKWYOnDWK6N6JZUhMM2Ypq4hefPWs25PPdswGYF3dGJK+XUaZxx8wN5SIiEgJ9eCDD3LkyBFGjx6tyS8iIlLgLhbOt22DpUuN4rmrqzHb/ORJqFQJzp0DT0/ju78/BMZsdxTQsywuzA6dyMIaz/BuqGafi4hzyXMRvV69esTFxRVEFilmzqae5Ux6PACl46qSeGFcRfTiq0utW3HN8iLTJQVb2BJ+nh1Cz8fNTiUiIlIyrV27lt9//50mTZqYHUVERIq5i33PT52Cv/+GqlWhaVOjeJ6QAOfPw/79Rg/0Bx4weqDv3w/R1W5nledz1Ns5l1dC53KkahsGDzYWGxURcSZ5LqK//vrrjBw5ktdee42GDRvi5uaW43Fvb+98CydF28GEg47tlnGJNOiyhj1lmquIXox5unrSukJ3fo//FrxO88npZHomJUGZMmZHExERKXGCgoIua+EiIiKS3y7tex4YCAcOQGwspKRkzz4fNgwqV4ZKbnFUb1aBqMMWYmIgIABCqk7g710v8ECar7GvArqIOKE8fz6mc+fObNy4kVtuuQV/f3/Kly9P+fLlKVeuHOXLly+IjFJEHYg/4Ni++fQJnhxyinffPUuZMnozV5z9X/ueju1VoXHYly03MY2IiEjJNWXKFF544QUOHz5sdhQRESnGYmKM1i2VKkHp0sYcqqQk4+vkSWP2edOm0OrsMqp3DYOZMwkJgVatLhTM3dyo3tQ3e19ExAnleSb6b7/9VhA5pBi6dCZ6aBxk1qplYhopLD3q3451kTs2azopdX9my6xKNO99r9mxRERESpy+ffuSkpJCzZo18fLyuuwTpGfOnDEpmYiIFCcBAUah/GLf84oVIS0NoqONvucPPZBByAej4fXXjROefhpatwa1GxORIiTPRfSOHTsWRA4phpIzknG1QaYV6qSVxVZRK2uXBN4e3tTz6MyujB/B+zgf7jlD8/R0cHc3O5qIiEiJMmXKFLMjiIhIMXdxMdHu3Y3FRPfvN9q2DBlizD6vnHWUaiP7wfr12Sd16QLVqpkXWkTkOuS5iA7w+++/88EHH3Do0CG+/fZbqlSpwueff05ISAjt2rXL07WmTZvGm2++SXR0NI0bN+a9996jZcuWVz3+22+/ZfTo0Rw+fJjatWvz+uuvc/vttzseT0pK4oUXXmDhwoWcPn2akJAQnnjiCR599NHr+VHlP5jY9CU+vm8Ov5evTJxfW8qnWihVyuxUUhgeaNmLket+BGBJrWRYvRpuvdXkVCIiIiXLwIEDzY4gIiLF0MXC+bZtRuE8MdGYiX777dCsGdl9zZcuNVYRvfjJJ1dXeOMNeOopsFjM/BFERPIszz3R58+fT9euXSlVqhRbt24lLS0NgISEBF577bU8XWvevHmMGDGCMWPGsHXrVho3bkzXrl05derUFY9fv3499913Hw899BDbtm2jR48e9OjRg127djmOGTFiBMuXL+eLL75gz549PPXUUwwbNowffvghrz+q/EeuBw/iZoOfTj9By73fUKdOZdav12zkkmBQm7vAZoXEKpxMrUvMVyvNjiQiIlKipaamkpiYmONLREQkr+bPh+HD4Ykn4JVXjBYudeoYi4r++OPFhUIzYORIuOOO7AJ69eqwdq3RykUFdBEpgvJcRB8/fjwzZ87ko48+ytFXsW3btmzdujVP13rnnXcYMmQIgwYNol69esycORMvLy8+/fTTKx4/depUunXrxnPPPUfdunV59dVXadasGe+//77jmPXr1zNw4EA6depEcHAwDz/8MI0bN+aPP/7I648q/5HrAWNh0X2EAmC3WwgKyjIzkhSSil4VGXJ+K0w+AmvD+WVhEthsZscSEREpUZKTkxk2bBj+/v6ULl2a8uXL5/gSERG5lqgo2LjR+B4VBd9/D9OmGQXzwEDIyIDYWEhJMXqhJybCmV0noGNHePPN7Avdfbcxbf3GG837YURE/qM8t3PZt28fHTp0uGzcx8eH+Pj4XF8nPT2dLVu2EB4e7hizWq107tyZDRs2XPGcDRs2MGLEiBxjXbt2ZeHChY79Nm3a8MMPPzB48GAqV67MqlWr2L9/P5MnT851Nskf/yyie3jYqVJFRfSiLiIigrfffvtfjyub5ceIir8xMPZTGsbv5MunXUiqV49HHnmkEFKKiIjIyJEj+e2335gxYwb3338/06ZN4/jx43zwwQdMmjTJ7HgiIuKkoqJg7lxYsQIyMyE52RjPyoKjR41e535+UKYMJCUZXwkJRkuXilU94cQJ4wQ3N6OY/sQTmn0uIkVenovogYGBHDhwgODg4Bzja9eupUaNGrm+TlxcHFlZWQQEBOQYDwgIYO/evVc8Jzo6+orHR0dHO/bfe+89Hn74YapWrYqrqytWq5WPPvroioX/i9LS0hxtaQDHx1ttNhu2PM6etdls2O32PJ9XXNjtdgC+++s7frd/Q+hNFv7a5gLxUKNGBhaLnQuH5GCxWBzn5tV/Ofdq59vtdsdXQT13QeQujHOtVit9+/bN1bFelebi+9xOAO7MyuKjpKRCfW2U9NdjcaH7WDzoPhYPuo8FLz//2y5evJg5c+bQqVMnBg0aRPv27alVqxbVq1fnyy+/pH///vn2XCIiUrRd2uf8228hIsJoYV67tlE4B2jRwqiP79gBN90EFStCWhpER4O/PwweDNWb+sK8eUYv9C++ME4SESkG8lxEHzJkCE8++SSffvopFouFEydOsGHDBp599llGjx5dEBnz5L333mPjxo388MMPVK9enTVr1jB06FAqV65M586dr3jOxIkTGTt27GXjsbGxpKam5un5bTYbCQkJ2O12rNY8d8sp0ry8vEhISABgzZE1LPA9CR2BvecgHqpVO+94/J8CAwOv+ti/+S/nXu18u91OSkoKYBScC+K5CyK3s52bdOONlLdaOVXKhu2XxXg9+cJV1zwoCCX59Vic6D4WD7qPxYPuY8E7d+5cvl3rzJkzjkku3t7enLnQl7Zdu3Y89thj+fY8IiJStFwsmF+co3hx1nlSEvz9N5QvD+7uxkTygweN9i0uLsZ+w4ZGoX3/fqhSBZ7udYR6jd3wrV/JWEwUjLYtu3cbVXgRkWIiz3+jvfDCC9hsNm655RZSUlLo0KEDHh4ePPvsswwfPjzX16lYsSIuLi7ExMTkGI+JiSEwMPCK5wQGBl7z+PPnzzNq1CgWLFhA9+7dAWjUqBERERG89dZbVy2ih4eH52gTk5iYSFBQEH5+fnh7e+f6ZwLjzaXFYsHPz6/EvblMSUnBx8cHgBPnj2c/cLoOAKGhFsfj/xQdHX3Vx/7Nfzn3audfnKXt4+NzzSK6s+V2tnMj0qP4v2GlWV/+HG+sOEKFw4fx9/e/rue9HiX59Vic6D4WD7qPxYPuY8Hz9PTMt2vVqFGDqKgoqlWrRlhYGN988w0tW7Zk8eLFlCtXLt+eR0REioYrtWlJSzNml7u6Gmt/ZmQYvc1dXIxzsrKMYy0W8PAwHm/SBIYNgzZnFhM4ciA0bgy//AK4ZD+ZCugiUszk+W81i8XCiy++yHPPPceBAwdISkqiXr16lClTJk/XcXd354YbbmDlypX06NEDMN6YrVy5kmHDhl3xnNatW7Ny5Uqeeuopx9iKFSto3bo1ABkZGWRkZFz2ps7FxeWaH4318PDAw8PjsnGr1XpdbxAtFst1n1vUXSw4f1f/Dc6/2InXfXvxSXpZAGrVyrxqQdput1+zWH0t/+Xca51vsVgcXwXx3AWV25nO9fHwYZ2vMaNuYt06LNi5q9BfFyX59Vic6D4WD7qPxYPuY8HKz/+ugwYNYvv27XTs2JEXXniBO++8k/fff5+MjAzeeeedfHseERFxbheL5z/8APv25WzTkpFh7Ht4GMV0T09ITYUaNeDIEbDZjBnnHh5w8qTR93z4I+ncsy4cLv6/ZNUqmDwZnn3W1J9TRKQgXdevBu12O4mJiQQEBFCvXr3rfvIRI0YwcOBAmjdvTsuWLZkyZQrJyckMGjQIgAceeIAqVaowceJEAJ588kk6duzI22+/Tffu3Zk7dy6bN2/mww8/BIyPqXbs2JHnnnuOUqVKUb16dVavXs2cOXP0RqGQeUb9TdUzwJlujrGaNTPNCySmqOFTA6/EeqSk2TgT1YusyCsvGiwiIiL57+mnn3Zsd+7cmb1797JlyxZq1apFo0aNTEwmIiKFZf58mDbN6HF+cVksD4/sNi2urjlnnfv4GN+tVmjQALp0gYtLYsXEQJXMvwl6ti9s2pT9JD17wv/9X6H+XCIihS1PRfTo6GhGjhzJDz/84OjX6O3tzT333MPEiRMvW/Tz3/Tt25fY2FhefvlloqOjadKkCcuXL3dc58iRIzlm47Rp04avvvqKl156iVGjRlG7dm0WLlxIgwYNHMfMnTuX8PBw+vfvz5kzZ6hevToTJkzg0UcfzVM2+W/cDhwAYB+hjjEV0Uum4d5Lef2VYAA2MIqbjh83pjKIiIhIoapevTrVq1c3O4aIiBSSqCiYNcuYWe7ubrRkiY83ZptfbNPi5mbMOj9wwBirUgWeegqaNjV6pjv6nAMhOxbBgw8aFwHjom+9ZfR2+Q+fshYRKQpyXURPTEykTZs2JCUlMWjQIMLCwrDb7URGRvL111+zdu1atm7dmue2LsOGDbtq+5ZVq1ZdNta7d2969+591esFBgYya9asPGWQ/Od6oYh+Bl8AAgKyKFPGbmYkMcndXTx5/RVj+wfuYtQPP4AWMxMRESlw48aNu+bjL7/8ciElERERM8TEGP3Ng4Lg+HGjNYu7u1FUt1qz27S4uuacdX5p4RyA9HR4/nmYMiV7rEYN+OYbuOGGwvyRRERMk+si+tSpU3FxcWH37t34+fnleOyll16ibdu2vPvuu4waNSrfQ0rRsuDAAo5bV1O/Eazb34Djv+0jJuX6F9CUoq1atSzqhpxjT1RZNtGKk3OnUElFdBERkQK3YMGCHPsZGRlERUXh6upKzZo1VUQXESnmAgKMHuZJSVCrFuzcaRTM69SBu+/O2abln7POHc6fh06d4I8/ssfuvRc+/tjo/SIiUkLkuoi+dOlSRo0adVkBHcDf35/w8HA++ugjFdGFJVFL+Kn2CagNf31ZkXJBpSmHWrmUZF3utLPnXcD3ADP+ymJcfDyUK2dyKhERkeJt27Ztl40lJiby4IMPcs8995iQSEREClNICAwaZLR0yci4+mzzKxbPLypVyujt8scfxjT2d96Bxx9X+xYRKXFyXUTfv38/bdq0uerjbdq04VmtxCzAwTP7AfDMgEoBocSbG0ecQPObjkD6AAjczsy/mjFuyRIYMMDsWCIiIiWOt7c3Y8eO5c477+T+++83O46IiBSgqCijZcvIkUb9+6qzzf/N5MkQGwsvvgjNmuV7ThGRoiBPPdHLXWPmaLly5UhMTMyPTFKEZdgy+DvpKAC1z4C9Zi2TE4kz6NCsHNbfz2ADYmvs4Oi33xGkIrqIiIgpEhISSEhIMDuGiIgUkKgomDsXVqwwFg/19jZmpLdqlcuT9+6F227LHitVCubPL7C8IiJFQa6L6Ha7HavVetXHLRYLdrsWjizpjp47SobdaN2SFVePJw4Pp/rHpRkwIBlPT5PDiWlcXCyEZt3NHt4Hl0xeP36c98+dg7JlzY4mIiJSbL377rs59u12OydPnuTzzz/ntkuLIyIiUqQdPgxHj8K+fbB+Pfzwg7Ht6gqNGoHdbrR0adbsX2aif/89DB5sVN63bIHQ0ML6EUREnF6eiuh16tTBcpW+VyqgC8DB+IOO7Zi49sz68wY8dtgZNCjZxFTiDHrX7864E+8DsDAsjfd//DF7JRsRERHJd5MnT86xb7Va8fPzY+DAgYSHh5uUSkRE8svFGee//GKs8bl2rbEOqOuFSo+HB/z1lzED/eRJYwHRKxbR09Lguefgvfeyx8LDjaK6iIgAeSiiz5o1qyBzSDFxMCG7iB5/2visWEhIJi4uZiUSZ3H/TQ14/cMA0krHcKrGTs4u+JryKqKLiIgUmKioKLMjiIhIAbhYPL8449zFxZhxnpUF6eng6QlJScZ3u92Ype7ra/REv8zBg8bkpi1bssf69oUPPyy0n0dEpCjIdRF94MCBBZlDiokD8Qcc21lxDQCoWTPTrDjiREqVsnCjd23WZMWQ4QKLopbxYEoKeHmZHU1ERKTYio+P58CBA7i7uxMSEkJZtVITESnS5s+HadMgIsIokINRRD9/HsqUMYrnFouxkGhqKlitRjF98OArzEL/7jt46CG4uL6dhwdMnQoPP2xcREREHK7e5FzkOhw6m11E53QdAGrWzDApjTibRu5NHNvf1kqHn34yL4yIiEgxdvjwYbp3707FihW58cYbadq0KRUrVuS+++4jJibGcVxaWpqJKUVEJC/WrIF33oH4eKNI7ulpFMovFtMvLjuVlWW0dAkLM7q0fPIJ9Ox5yYVSU2HYMOjdO7uAXrs2bNwIjzyiArqIyBXkeia6SG4cvDAT3ftcaRLTvAHNRJds1azVqOpekWPpcayoCfHff0W5e+4xO5aIiEixcvToUVq1aoWbmxuvvvoqdevWBSAyMpIZM2bQqlUrtm3bxpo1a9izZw/PP/+8yYlFROTfzJ9vFNB37YLSpY1CuaenUUzPzDRmo7u4gJ8fBAZCly5GV5Yr9kDv1Qt+/DF7/7774IMPjAq8iIhckYrokm9S7CmczogHoHRcVS78PltFdHGwWqzcXe8+pkW8R4YLfLj/D0ampRkfGxQREZF88corrxAaGspPP/2Ep6enY7xHjx48/fTTdOvWjTvvvJPNmzczd+5cE5OKiEhuREXBrFlGwdzHJ3v2eUqKMeM8NNQomDdsaLy1Cgi4SvH8omeegWXLjIPffRf+7/80+1xE5F+oiC755pTtlGPbHhfm2FYRXS5l3d8XMFZ9n13TjZErVsAdd5gbSkREpBhZvnw58+bNy1FAv6hUqVK8+uqrdOrUiY8//pi7777bhIQiIpIXMTFG15U6dYyi+b59kJAAtWoZHVn69DGWmvL3N3qg/6ubb4bp06FNG2NFUhER+Vd57on+22+/FUQOKQZibbGO7XNxzQEICMiibFm7WZHECQ2/pzUkVgFgb80jJHz/tcmJREREipe4uDiCg4Ov+niNGjVwdXVl8ODBhRdKRESuW0AAeHvDyZNQvTrUrAkNGsDkyfDCC3CNv/Lhr79g5MjsxukXPfqoCugiInmQ55no3bp1o2rVqgwaNIiBAwcSFBRUELmkCLp0JnryaaOIXqOGZqFLtoiICGAypQ7fzvlGH2F3zeDTHT/BG29gc3H51/PLlCnDI488UuA5RUREirJKlSoRGRlJ1apVr/j4rl27qFy5ciGnEhGR6xUSAt27wxdfQGysMeN8+HDo0OFfTpw3D4YMMVYcDQyEESMKJa+ISHGU5yL68ePH+fzzz/nss88YO3YsN998Mw899BA9evTA3d29IDJKERF7SRF9WPp6jtzVgXr1MkxMJM7GYrHQr18/1h/bzfd8BMBndbz4pWpV0jp2/Nfz1bdVRETk3/Xo0YNnn32WlStX4ufnl+OxU6dO8fzzz9OjRw9zwomISJ7Nnw9Ll0JGBri5we23Q8+e1zghNRWefhpmzswemz3bqLy7uRV0XBGRYinP7VwqVqzI008/TUREBJs2baJOnTo8/vjjVK5cmSeeeILt27cXRE4pAk5nxgDgmQETQzcwffpZhg1LMjmVOKP7OzWCRGMG3M6aJ0lftsDkRCIiIsXHmDFjSE1NpWbNmjz++OO8++67TJ06lUcffZRatWpx/vx5Xn755Vxfb82aNdx5551UrlwZi8XCwoULczz+4IMPYrFYcnx169Ytn38qEZGSac0aeOcdSE42uq+UKwc//mgsNnpF+/dDq1Y5C+gDBsD69Sqgi4j8B3kuol+qWbNmhIeHM2zYMJKSkvj000+54YYbaN++Pbt3786vjFIEZNoyOcUZAGqfAXvNWiYnEmd2Q7MsSkXdA4DNNZOlB3+FTLX+ERERyQ/ly5dn06ZN9O/fn7lz5/LUU0/x9NNP88033/C///2PDRs24Ovrm+vrJScn07hxY6ZNm3bVY7p168bJkycdX19/rTVPRET+q/nzITwcdu2CgwfhyBGoVMlYZDQm5gonfP013HADXJzcWKoUfPIJzJkDZcoUanYRkeLmuoroGRkZfPfdd9x+++1Ur16dn376iffff5+YmBgOHDhA9erV6d27d35nFSd2NOEodmwAhMVBZu3aJicSZ2a1Qjvfuxz7H1X3wH3TJhMTiYiIFC/ly5dnxowZnD59mujoaKKjozl9+jQzZ86kQoUKebrWbbfdxvjx47nnnnuueoyHhweBgYGOr/Lly//XH0FEpESLioJZs8DdHXx8jA4te/caxXRvb2OxUYfz5/F+7jmsAwZA0oVPg4eFwR9/wODBYLGY8jOIiBQneS6iDx8+nEqVKvHII49Qp04dtm3bxoYNG/i///s/SpcuTXBwMG+99RZ79+4tiLzipELKh7BhxY3smgYPrarC+WAV0eXa7u/UGM4FQnJFEhMaUOrHH82OJCIiUuxYLBb8/f3x9/fHUoBFlFWrVuHv709oaCiPPfYYp0+fLrDnEhEpCbZtg2PHjEVEQ0PB0xMSEiA93aiLh4RkH2uZMAGvL77IHnjgAfjzT2jQoPCDi4gUU3leWDQyMpL33nuPnj174uHhccVjKlasyG+//fafw0nREhgTR+VYF5pyCFsfN26+OZVZs86YHUucVLu2mZR+8VeSj9XmpC0ZF/8G8OqrxjR1ERERKTK6detGz549CQkJ4eDBg4waNYrbbruNDRs24OLicsVz0tLSSEtLc+wnJiYCYLPZsNlsBZrXZrNht9sL/Hkk73RvnJfuTeFasACmTzeK6NHR0LAh1KplFNAnTID27eHSW2F77jmYNw+Xkyexv/8+PPjghQd0v8ym147z0r1xboV5f3L7HHkuoo8ZM4Y2bdrg6prz1MzMTNavX0+HDh1wdXWlY8eOeb20FHG+p04RRQgZuEOW8bEzkatxd4eHelTB/Ydv6XP4HTxPHcN982bSW7Y0O5qIiIjkQb9+/RzbDRs2pFGjRtSsWZNVq1Zxyy23XPGciRMnMnbs2MvGY2NjSU1NLbCsYLxRSkhIwG63Y9Uv752K7o3z0r0pPDExsHIl1KljzDY/fBiysozt7t2NWemnTuU8x2azkfrOO5Tx9sZWt+7lB4hp9NpxXro3zq0w78+5c+dydVyei+g33XQTJ0+exN/fP8d4QkICN910E1lZWXm9pBQH8fGUOXeOvWT/8qRWrQwTA0lRMHLkOUrVOIXvU5sBKLV4sYroIiIiRVyNGjWoWLEiBw4cuGoRPTw8nBEjRjj2ExMTCQoKws/PD29v7wLNZ7PZsFgs+Pn56U2zk9G9cV66N4UnKgp27zaK6K6uULEi7N9vFNB79AD27sXy+OPYZ82C6tUB4/7EtmyJr+6P09Frx3np3ji3wrw/np6euTouz0V0u91+xX6Kp0+fpnTp0nm9nBQTj8x/kCod4fBJV9hvjNWokWluKCkSUrt0we7uzmnXdKLXL6J81itwlY9+i4iIiPM7duwYp0+fplKlSlc9xsPD44qtIa1Wa6G8kbVYLIX2XJI3ujfOS/emcAQGGm+Hdu+GoCBjndDKlaFpU7B+9QU8+igkJ2O57z5Ys8bxEXDdH+ele+O8dG+cW2Hdn9xeP9dF9J49ewLGD/Dggw/m+EdvVlYWO3bsoE2bNnmMKcXB2fNn+fDYIrgJKkXtcRTRa9ZUEV3+XYInDHrMm1+942h/5AzzNm0iXX+XiIiIXJd3330318c+8cQTuTouKSmJAwcOOPajoqKIiIjA19cXX19fxo4dS69evQgMDOTgwYOMHDmSWrVq0bVr1zznFxEp6bZuNRYQjYqCffsgOBhefjaFkAlPwCefZB+YlGS0bala1bSsIiIlSa6L6D4+PoAxE71s2bKUKlXK8Zi7uzutWrViyJAh+Z9QnN6+0/sc2/bToY5tFdElN8q6lWVvORcyLbAqGBJ+nEcpFdFFRESuy+TJk3Psx8bGkpKSQrly5QCIj4/Hy8sLf3//XBfRN2/ezE033eTYv9iGZeDAgcyYMYMdO3bw2WefER8fT+XKlenSpQuvvvrqFWeai4jI1UVFwaxZxmz0WrXg6FGok7WH217pA/t3ZR84eDC89x54eZkXVkSkhMl1EX3WrFkABAcH8+yzz6p1izg0q9SMXXtuYu+O33go8XEAAgKyKFvWbnIyKQqSk60cWzsEGn1GmV2347HnG3gl02gAKCIiInkSFRXl2P7qq6+YPn06n3zyCaGhxkSHffv2MWTIEB555JFcX7NTp07Y7Vf/d91PP/10/YFFRMRh2zY4dsxYPLRcOegSPYf/bXoMd1uKcYCXF8ycCfffb2pOEZGSKM9VqjFjxhREDinC3F3cqb/tOIH7fUnA+NiuZqFLbpUpY6eNbQRrp4zlnN1KDJsJXr+etA4dzI4mIiJSpI0ePZrvvvvOUUAHCA0NZfLkydx7773079/fxHQiIiVXVBTExEB6OsTFGWO7d8PChXD4MMQfT2am2zC6nZydfVKDBvDNN1C3rgmJRUQkV0X0Zs2asXLlSsqXL0/Tpk2vuLDoRVu3bs23cFJEpKfDwYPso6VjqGbNDBMDSVFzR1dY+5uxkMMC7mHUDz+oiC4iIvIfnTx5kszMyyc2ZGVlERMTY0IiERGZP99o2XLwIJw4ATYbZGVBRgZ4eICnJzRNWUe3lNnZJ/3f/8HUqWrfIiJiolwV0e+++25HT8MePXoUZB4pig4ehKws/qK2Y0gz0SUvunZNJTzcjt1u4Xt68uqyN4l/7TXHSvMiIiKSd7fccguPPPIIH3/8Mc2aNQNgy5YtPPbYY3Tu3NnkdCIiJc/FnufJycbioampxvjFQrrNZrwF2uTehdmeIxiQ8gGuH38A+uSQiIjpclVEv7SFi9q5yKUybZm8tnocofWhZcwc/uoVzPZbhxEUlGV2NClC/PxstGyZzqZNHuz1LsNrNUrx6O+/k3bLLWZHExERKbI+/fRTBg4cSPPmzXFzcwMgMzOTrl278vHHH5ucTkSk5ImJgVOnjBnn58+D1WoUzktZUknGg8xMC66ukJQEs0IncstrjxF0Uy2zY4uICNfRE13kUlFnoxgTMxd6w7274YPGgbRqlW52LCmCbrvtPJsqPg0tZjAauP3nLwlQEV1EROS6+fn58eOPP7J//3727t0LQFhYGHXq1DE5mYhIybR1K/z9tzEDPTXVKKDXZzdfZ/ZmiuVpPrMOITHRKLL3e8BdBXQRESeSqyJ6+fLlr9kH/VJnzpz5T4GkaNl3ep9jO/Q0ZNTS/+Tl+nTrlsory7Pf1C+MW8UjqalGU0ARERG5bsHBwdjtdmrWrImrq+bQiIiYISoKfvwRqlQxFhPNzLDTL2M279mH4sV5ptif4JjfjcRXa8SAAfDYY2YnFhGRS+XqX9FTpkwp4BhSVO2Lyy6i1zkNmSEhJqaRoqxq1Szq2nqwxz4CLHa+qG3nidWrSeva1exoIiIiRVJKSgrDhw/ns88+A2D//v3UqFGD4cOHU6VKFV544QWTE4qIlAxRUbByJRw9CtWrQ63AJB7d+Tg3H/vccUxWrVAmTPDEpwXobbWIiPPJVRF94MCBBZ1Diqi9cXsc2wsSXuDYNxVp2TKNunW1sKjk3T2dy7PncEcIWUVUxQz2//I51VVEFxERuS7h4eFs376dVatW0a1bN8d4586deeWVV1REFxEpBPPnG4uJ7tkDx46Bx1+7+DKjN7Uz92Yf9MgjlJk8mSalSpkXVERErsmam4MSExNzbF/rS0qWfSd3ObYXnnyBF18sx9Kl+h+/XJ877jhPnbRejv2F8euMFXdEREQkzxYuXMj7779Pu3btcrRmrF+/PgcPHjQxmYhIybBmDbz2Ghw+DOdT7Pyf5RNWnW/pKKCne5SBr7+GmTNBBXQREaeW657oJ0+exN/fn3Llyl2xP7rdbsdisZCVlZXvIcV5XeyJXvZcac6l+QBQs6Zmocv1qVYti+/Gd6TZ5xYyLXbmhWbw3MpfSL/jTrOjiYiIFDmxsbH4+/tfNp6cnJzr9Y5ERCTvoqJg7lz46COjhUtpexLTbI/R3/6F45j9pRqTMvsbmvTRYs8iIkVBrorov/76K76+vgD89ttvBRpIio741HhOZcQDUPp0Vc5dGK9VS0V0uX6+nr50LN2IlSnbOVIOdqz6nDAV0UVERPKsefPmLF26lOHDhwM4Cucff/wxrVu3NjOaiEixFBUF06fDsmVw5AikpoLFAp6WdNpnrXYct6jyo8xuNJl3WniamFZERPIiV0X0jh07XnFbSrZLFxW1xYU5tmvUUBFd/ps7bxjIyt9HAPBdyiZGJydjL13a5FQiIiJFy2uvvcZtt91GZGQkmZmZTJ06lcjISNavX8/q1av//QIiIpIrF4vnc+dCdDTY7ca4xQJWK5xz9eV/mXNZZL+TMX7TOdy0L4MHawFREZGiJFdF9H86e/Ysn3zyCXv2GItK1qtXj0GDBjlmq0vJcLGVC8C5080BqFQpk9Kl7WZFkmLi1mrd8ch6njSXDOaG2Rnz8zKy7rnX7FgiIiJFSrt27YiIiGDSpEk0bNiQn3/+mWbNmrFhwwYaNmxodjwRkSItKgpiYmDFCmPh0CNHICvLKJyXczmHa2Yqp61+xkx0T9jt3oa7gg7zzCtladpUBXQRkaImz0X0NWvWcOedd+Lj40Pz5kbh9N1332XcuHEsXryYDh065HtIcU5747JXEz8f1xRQP3TJH9984U/a7t7Q6CvivWz8vnYWbVREFxERybOaNWvy0UcfmR1DRKRYmT/fKJzv2WP0PM/KApvNeKyhfTvfZPbhmCWIrrafsFtdyMqCmjXhmRfL0rOnudlFROT6WPN6wtChQ+nbty9RUVF8//33fP/99xw6dIh+/foxdOjQgsgoTmpf9O7snQvtXFREl/zQtWsq7Bjg2P/WvgPr2bMmJhIRESl6OnfuzOzZs0lMTDQ7iohIsREVBdOmwYkTkJBgFM/tdrBgZwgfsokbCWU/t9hX8rLH6wwaBJ9+Ct99hwroIiJFWJ6L6AcOHOCZZ57BxcXFMebi4sKIESM4cOBAvoYT57YvehcArplWiA8GtKio5I9q1bJoVKYjJPsBsKgOpC/+zuRUIiIiRUv9+vUJDw8nMDCQ3r17s2jRIjIyMsyOJSJSpM2dC9u3w+HDcO4cuLhAWXsiX1n+x4c8gidpAOxyb0bN8D58+KFRPFf7FhGRoi3PRfRmzZo5eqFfas+ePTRu3DhfQonzy7JlcSD5KAA+Z/zAbvxSRTPRJb/c2T0Ddt4HQLqrneWb55icSEREpGiZOnUqx48fZ+HChZQuXZoHHniAgIAAHn74YS0sKiJyHaKi4IcfjNnnLi7GoqENMraxmRvoZ5/rOG5d02GU3r6e/mNqmZhWRETyU656ou/YscOx/cQTT/Dkk09y4MABWrVqBcDGjRuZNm0akyZNKpiU4nT+TvibNLsxkyn4tDsdW59k3SE3zUSXfNO9+3kmzBoArd4FYF7ZKPqdOWNyKhERkaLFarXSpUsXunTpwsyZM1m8eDETJkzgk08+ISsry+x4IiJFyowZsHu30b4l9bydh20zed3+tGP2+Xl3bxLf+YS2Q7Wek4hIcZOrInqTJk2wWCzY7XbH2MiRIy877n//+x99+/bNv3TitC5dVPTWuKNMnJPM24sWUblyPxNTSXFSrVoWjfwasSOuDlTcz6pgeHz7BrNjiYiIFEnR0dHMnTuXL774gh07dtCyZUuzI4mIFBlRUfDaa0Yrl9RUcLdm8qW9Pz0zvnEck9bgBkotnEepmjVNTCoiIgUlV0X0qKiogs4hRcy+uH2O7dB4FwgONi+MFFt33pHKjt8HwM0vY7fA9tQ/0ZwOERGR3ElMTGT+/Pl89dVXrFq1iho1atC/f3/mzZtHTRV5RESuKSoKtm2D5cth2TI4edJo42K1QobdlfisstkHDx+Ox5tvgoeHeYFFRKRA5aqIXr169YLOIUXMvtjsvvhhXtXANVd/lETypHv380yY3h/avkH5PZ24bfcS2LEDGjUyO5qIiIjTCwgIoHz58vTt25eJEyfSvHlzsyOJiBQJ8+cbM88jI42Z5xdZLEYrF6sVnnd7l6aWfVR+82kCHutpXlgRESkU1135jIyM5MiRI6Snp+cYv+uuu/5zKHF+b9ceyiOPfMTOim7UrdvE7DhSTFWrlkWT6lWJeDOWs5me+BIGX36pIrqIiMi/sNvtvPvuu/Tv3x8vLy+z44iIFBlr1sCYMfD335B5YckvbxJoxA7W2ttjsUBGBmSW8mLjhDU89pjF3MAiIlIo8lxEP3ToEPfccw87d+7M0SfdYjH+x6EFikqG0n/9TdNoWB79DAF7xlGrAXTqVNnsWFIM3X9/Mk3/zOL/vulMqG0ffP01TJxoTP8QERGRK7Lb7QwdOpROnTpRu3Zts+OIiBQJ8+fDiy/CgQNwsbRxA1uYRx/8OUUztnKI2pQpAyNGwGOPq4AuIlJS5LkK9eSTTxISEsKpU6fw8vJi9+7drFmzhubNm7Nq1aoCiChOaa+xsOhewkjLcmP3bnB3zzA5lBRHffum8OpbaTTr5IEF4OhR+P13s2OJiIg4NavVSu3atTl9+rTZUUREioR58+C554y3G1YrWLAzlPdZRxtqcoiyJPEhjxAcDJMmwejRZicWEZHClOci+oYNGxg3bhwVK1bEarVitVpp164dEydO5IknniiIjOKMLhTR9xHqGPLzO2tWGikBUu65hyR3mN0E/vh2itlxREREnN6kSZN47rnn2LVrl9lRRESc2nPPweDBxmKiKSlQOjOBby29eZ/heGC0sI3waMnGhz/ll1/gscdMDiwiIoUuz+1csrKyKFvWWIW6YsWKnDhxgtDQUKpXr86+ffvyPaA4n/VH17MmbSV1wiDySEVIgerVwd090+xoUoxtal6Z3s9Csjv8b++PfJmeDu7uZscSERFxWg888AApKSk0btwYd3d3SpUqlePxM2fOmJRMRMR5rFkDs2YZfc4tFmhm38w8e19qcshxTMQtI/CZNpHwUL3/EBEpqfJcRG/QoAHbt28nJCSEG2+8kTfeeAN3d3c+/PBDatSoURAZxcn8uH8pE+ocgTrAV3tgfy1CQ//1NJH/pIJbM2w2LyCF72umk7j4O7x7/c/sWCIiIk5rypQpZkcQEXFqUVEwdy6cO2e0b3nK5X0mZT6DO0ar0gRrOSKfm03rSXebnFRERMyW5yL6Sy+9RHJyMgDjxo3jjjvuoH379lSoUIF58+ble0BxPvtO7MjeOW1Uz8PCTAojJcbLLwZwPuNpKHWatyL+oOye70BFdBERkasaOHCg2RFERJxSVBRMn24sJHr8OKSnwwwe41E+cBwT4XkjabPn0rpvsHlBRUTEaeS5iN61a1fHdq1atdi7dy9nzpyhfPnyWCxambokGFehN73eWMKXFZqx5GwIAKGhcP68ycGkWLv77vP89Ph4ACL4CMvioXD6NFSoYHIyERER53Xw4EFmzZrFwYMHmTp1Kv7+/ixbtoxq1apRv359s+OJiBSqqCiYOBEWLjTeSthsRgsXgO+4l4f5ECt2Pij7DP4fvcY9fdW+RUREDHleWPRSR48e5ejRo/j6+qqAXoLUPZJCv11Qe3V/sLkBmokuBe/WW1NxdzcW9ZlPL9IyLMZnL0VEROSKVq9eTcOGDdm0aRPff/89SUlJAGzfvp0xY8aYnE5EpHDNnw8dOsBHH0FsrFFAB7DbwcUF1rh1ZrTrJAb5LsJv9lsqoIuISA55LqJnZmYyevRofHx8CA4OJjg4GB8fH1566SUyMjIKIqM4m717jW9kV87VE10KWqlSdurXPwjAWXz5ia7w2WcmpxIREXFeL7zwAuPHj2fFihW4X7IY980338zGjRtNTCYiUrjWrIHwcDh50tgvx1me5U3ADkBWljE+03skfze+i6ZNzckpIiLOK89F9OHDh/Phhx/yxhtvsG3bNrZt28Ybb7zBJ598whNPPFEQGcXZXCii78OonJcpA5UrmxlISoqmTfcYG6XOMK5FRe6u+SdZu3eZG0pERMRJ7dy5k3vuueeycX9/f+Li4kxIJCJS+GbMgAED4MABo1jegj/YSjPeZCRPMtVxnJsbBAfDsGEQEmJeXhERcU557on+1VdfMXfuXG677TbHWKNGjQgKCuK+++5jxowZ+RpQnMvmE5s5mrKFUD/4Jf0ODn4bSdxpC+rmI4WhTp2/8fWFM10GsyVsEVuA376aQOcJX5sdTURExOmUK1eOkydPEvKPatC2bduoUqWKSalERArP9OkwahScOwd2u52nmMIbjMSNTACe53UWBwzhlrtK060bNG2qArqIiFxZnmeie3h4EBwcfNl4SEhIjo+J5ta0adMIDg7G09OTG2+8kT/++OOax3/77beEhYXh6elJw4YN+fHHHy87Zs+ePdx11134+PhQunRpWrRowZEjR/KcTS736R8f0POWOOoPhZOtXeh8q4V+/cxOJSWFq6uN3r2B7fc7xmb/vSj785ciIiLi0K9fP55//nmio6OxWCzYbDbWrVvHs88+ywMPPGB2PBGRArVmDUyYYBTQy9nOsJAeTGaEo4C+3tKGl279g182lObDD6FnTxXQRUTk6vJcRB82bBivvvoqaWlpjrG0tDQmTJjAsGHD8nStefPmMWLECMaMGcPWrVtp3LgxXbt25dSpU1c8fv369dx333089NBDbNu2jR49etCjRw927cpu53Dw4EHatWtHWFgYq1atYseOHYwePRpPT8+8/qhyBXuPRzi2Qys3NC+IlFj33QfsvwNSfAH4Pvg8CT8vNjeUiIiIE3rttdcICwsjKCiIpKQk6tWrR4cOHWjTpg0vvfSS2fFERArM/Pnw4INw4gS0sG1kK025mx8cj3/gM5Jjn6/ik5+DVDgXEZFcyVU7l549e+bY/+WXX6hatSqNGzcGYPv27aSnp3PLLbfk6cnfeecdhgwZwqBBgwCYOXMmS5cu5dNPP+WFF1647PipU6fSrVs3nnvuOQBeffVVVqxYwfvvv8/MmTMBePHFF7n99tt54403HOfVrFkzT7nk6vadNRZ2rJACFeo0MTeMlEjt20NQZQ+O7vwf3Pg+593g28WT+L/bepgdTURExKm4u7vz0Ucf8fLLL7Nz506SkpJo2rQptWvXNjuaiEiBiYqC8ePhxHE7I3iHSbzgmH0eRwWeD5xD92m3848yh4iIyDXlqoju4+OTY79Xr1459oOCgvL8xOnp6WzZsoXw8HDHmNVqpXPnzmzYsOGK52zYsIERI0bkGOvatSsLFy4EwGazsXTpUkaOHEnXrl3Ztm0bISEhhIeH06NHj6tmSUtLyzGzPjEx0XE9m82Wp5/LZrNht9vzfF5RcC7tHCeyzgJQIa4iH5zrQthqG61aGYuwgNFn7npYLBZTzr3a+Xa73fFVUM9dELmL87nZbIwaBQfP9eetlPcBmJ3xJ4Pj48HbO+eRxfj1WJLoPhYPuo/Fg+5jwSuI/7ZBQUEEBQWRlZXFzp07OXv2LOXLl8/35xERMduaNTB8OOzcCU/b3+FtnnU8tpa2POX/Ne/MC6JDBxNDiohIkZSrIvqsWbPy/Ynj4uLIysoiICAgx3hAQAB79+694jnR0dFXPD46OhqAU6dOkZSUxKRJkxg/fjyvv/46y5cvp2fPnvz222907NjxitedOHEiY8eOvWw8NjaW1NTUPP1cNpuNhIQE7HY7Vmueu+U4te2x2x3bZ+Ja8+gPTbF+YOfgwRg8PcHLy4uEhITrunZgYKAp517tfLvdTkpKCmAUfgviuQsid3E+F4w/Y6dOnaJHD7Dbq/Pj++WIdI9nXVUbWz9+naoDnsxxfHF+PZYkuo/Fg+5j8aD7WPDOnTuXb9d66qmnaNiwIQ899BBZWVl07NiR9evX4+XlxZIlS+jUqVO+PZeIiFmiomDbNmMR0TVrICPDGP+IITzGDGpxkEm8wFve43j1FTcV0EVE5Lrkqoh+JbGxsezbtw+A0NBQ/Pz88i3U9bo4c+fuu+/m6aefBqBJkyasX7+emTNnXrWIHh4enmOGe2JiIkFBQfj5+eH9j5mtuclgsVjw8/Mrdm8uY2NiHdsJp1sAxsIr1ar5A5CSknLZpxZyKzo62pRzr3b+xdnSPj4+1yyiO1vu4nwuGH/G/P39HfuDG9zPs/vfA2Deni943X9CjuOL8+uxJNF9LB50H4sH3ceCl5/r+Hz33XcMGDAAgMWLF3Po0CH27t3L559/zosvvsi6devy7blERMwwfz6MGQP792cXzy86hzd9+IYAYtgWcBuvjoHHHjMnp4iIFH15LqInJyczfPhw5syZ4yhau7i48MADD/Dee+/h5eWVq+tUrFgRFxcXYmJicozHxMQQGBh4xXMCAwOveXzFihVxdXWlXr16OY6pW7cua9euvWoWDw8PPDw8Lhu3Wq3X9QbRYrFc97nObH9c9icEMuIaAVC3rgWrNbvIfK2C87XY7XZTzr3W+RaLxfFVEM9dULmL67kXXfq6euDOlwh/4z0yXGCO7zEmHPoL91qhOY4vrq/Hkkb3sXjQfSwedB8LVn7+d42Li3P8O/nHH3+kT58+1KlTh8GDBzN16tR8ex4RkcIWFQVffAFTp8LZs1DOdpopPMUoXuMY2e1mt9EMLy/4dCr07WtiYBERKfLy/K/0ESNGsHr1ahYvXkx8fDzx8fEsWrSI1atX88wzz+T6Ou7u7txwww2sXLnSMWaz2Vi5ciWtW7e+4jmtW7fOcTzAihUrHMe7u7vTokULxwz5i/bv30/16tVznU2ubN/Rbdk7cWEA/OP3FSKF6u+9/lQ/1RmAU2VgyeejTU4kIiLiPAICAoiMjCQrK4vly5dz6623AsYnu1xcXExOJyJyfWbMgLZt4eWX4fRpuNG2ngiacD9fMJd+uJI9Jd3VFfr3VwFdRET+uzzPRJ8/fz7fffddjh6Kt99+O6VKlaJPnz7MmDEj19caMWIEAwcOpHnz5rRs2ZIpU6aQnJzMoEGDAHjggQeoUqUKEydOBODJJ5+kY8eOvP3223Tv3p25c+eyefNmPvzwQ8c1n3vuOfr27UuHDh246aabWL58OYsXL2bVqlV5/VHlH/ad2gOANcuK7WwNAOrWNTORlHTjx8OBXc/B/b8A8PHxxfTMygIVBkRERBg0aBB9+vShUqVKWCwWOnc2fvG8adMmwsLCTE4nIpJ3I0YYvc/T0sCCjWd5i9cYhStZANTmL2pxgL3UpXx5ePppGK15NiIikg/yXERPSUm5bHFPAH9/f8dCjLnVt29fYmNjefnll4mOjqZJkyYsX77ccf0jR47k+EhrmzZt+Oqrr3jppZcYNWoUtWvXZuHChTRo0MBxzD333MPMmTOZOHEiTzzxBKGhocyfP5927drl9UeVS9jsNvafPwZA2bN+JNjcABXRxVwPPACL7u0M8dWh3N8sr5rKkaVfUe2u+82OJiIiYrpXXnmFBg0acPToUXr37u1oX+ji4sILL7xgcjoRkdyLioIhQ+DXX8FuhwrEMYcHuJ1ljmNW04H7+Jos/8oMuRvCw401vERERPJDnovorVu3ZsyYMcyZM8ex8NH58+cZO3bsVduwXMuwYcMYNmzYFR+70uzx3r1707t372tec/DgwQwePDjPWeTqjiUe4/yFj8W5nK7pGNckJjFT9+5QvpyVs1sfgptfxm6BWT9NYoyK6CIiIgDce++9l40NHDjQhCQiItdnxgwYNw6io439tqxlLv2oynEAbFiYwIu87jGG/z3gquK5iIgUiDwX0adMmUK3bt2oWrUqjRs3BmD79u14enry008/5XtAcQ57L1lUNCWuKQCVK4OPj1mJpCSKiIjg7bffzjEWFnYLGyIehE6vgNXGp+6RVBj7CmllygLg5eVFSkoKZcqU4ZFHHin80CIiIiZauXIlkydPZs8eoy1f3bp1eeqppxytXUREnNmrr8KECdntW0byBuN5ydG+5RR+3M8X7KrchTdfgsceMzmwiIgUW3kuojds2JC//vqLL7/8kr17jcLqfffdR//+/SlVqlS+BxTnsO+SInrqhSK6FhWVwmaxWOjXr1+Osdq13bj7bn840A3q/MiRclDJfoBW/V7HbreTkJCAj48P8+bNMye0iIiISaZPn86TTz7Jvffey5NPPgnAxo0buf3225k8eTJDhw41OaGIyJWtWWPMPl+1CrKMejltWcckwh3HrKIjj5b9igEjKzOzv2afi4hIwcpTET0jI4OwsDCWLFnCkCFDCiqTOKF9x7Y7tttkZnCqFlzSil7ENM2aZRASkknU1iFQ50cAvjy6iFb2SSYnExERMddrr73G5MmTc7ROfOKJJ2jbti2vvfaaiugi4nTWrIEXX4QNG7KL5xetpT1TeYLhvMd4XuKjwJeZOs2Vnj3NySoiIiWL9d8Pyebm5kZqampBZREntu/4Dsf2wk6b+esveOcdEwOJXGCxwL33psD+7pBkLEq8qVwSWVs2mZxMRETEXPHx8XTr1u2y8S5dupCQkGBCIhGRq3vuObjtNli71iigW7AB9hzHjOQNbnZZw55+41izXgV0EREpPHkqogMMHTqU119/nczMzILII05qX8JBAMqfh4p1mgBG8VLEGfTsmQI2N/hlEgHfvMuhqVDhm+/NjiUiImKqu+66iwULFlw2vmjRIu644w4TEomIXNmaNfDBB3D+vLHvxymWcRsPMjvHcRYPD24Z046vv1b7FhERKVx57on+559/snLlSn7++WcaNmxI6dKlczz+/fcqXBU3yenJHLWdBSA0Dixd1AxdnEtQUBbt2qWydu2DuFmOEWN/k6o//ED8mDFmRxMRESlU7777rmO7Xr16TJgwgVWrVtG6dWvA6Im+bt06nnnmGbMiiohc5sUX4dw5Y7sDq/ma+6jMSdrzO3/Sgt00oFIlGD1ai4eKiIg58lxEL1euHL169SqILOKkPFw92LirNfv+2oBnBjAuzOxIIpcZMeIcDz+cxN1Ln8d73lFIglJLlhDftavZ0URERArN5MmTc+yXL1+eyMhIIiMjHWPlypXj008/5aWXXirseCIilxk3zuiBbsFGOBMZx8u4YAMgEW/8XM5yV3eYMkWzz0VExDx5LqLPmjWrIHKIE3O1unLj5pPUPVyWSpxkYncvevY0ZgGIOIuWLdMBSPXpi/e8rwDI+O4LUBFdRERKkKioKLMjiIjkWlSU0cbFN+sUXzCALqxwPPab5WZm3/olY18MpEMHE0OKiIiQh57oNpuN119/nbZt29KiRQteeOEFzl9sWCbFW0oK/P03ewkjhdJERFg4csTsUCJXltGsGb+2rco9fSH45m3E79tsdiQRERFTxcXFERcXZ3YMEZEcoqJgyBCofWIVETRxFNBtWHjV5RViv/yZz35SAV1ERJxDrovoEyZMYNSoUZQpU4YqVaowdepUhg4dWpDZxFn89RfY7USS3Qu9ntqii7OyWFjaOZiFdSHVDZasnmJ2IhERkUIXHx/P0KFDqVixIgEBAQQEBFCxYkWGDRtGfHy82fFEpISbMQNa3pBFq5XjWcktVOYkACcJpDO/sOWOMfS5z8XklCIiItly3c5lzpw5TJ8+nUceeQSAX375he7du/Pxxx9jtea6Fi9F0OyNM6lYBzbE+oOxvih165qbSeRqfv/dg7VrPoAODfBLciEwYi/oUzMiIlKCnDlzhtatW3P8+HH69+9P3Qv/cIuMjGT27NmsXLmS9evXU758eZOTikhJ9OqrMH48lE5P4FFmOvqfr6AzA/gCa2AA6yf/y0VEREQKWa6L6EeOHOH222937Hfu3BmLxcKJEyeoWrVqgYQT89nsNh4/+THn/wel476E998AVEQX53XokCt//FYPDq6hz7E/eNL2DGeWLjU7loiISKEZN24c7u7uHDx4kICAgMse69KlC+PGjbtsEVIRkYI2YgS89x5kZkI6vvRjLiu5hfG8xGuMwm5x4dn7tYCoiIg4n1xPIc/MzMTT0zPHmJubGxkZGfkeSpzHkYQjnLdkAmCPM3q4lC4NQUFmphK5uh49UvDwsMORdnxtG0ga7pT+/HOzY4mIiBSahQsX8tZbb11WQAcIDAzkjTfeYMGCBSYkE5GSKioK2rfJ4uPJiWRmZo+vox01OMR4RmPDherV4bHHzMspIiJyNbmeiW6323nwwQfx8PBwjKWmpvLoo49SunRpx9j333+fvwnFVOU8yzHnz6rsOn+SN2MfACAsDNTBR5yVj4+d228/z4IFXpyhAgvpQd+t3+DXsaPZ0URERArFyZMnqV+//lUfb9CgAdHR0YWYSERKqjVr4O23YftP0XyS1p9MXLmNZdgvmc93gioAVKpkHKtZ6CIi4oxyXQodOHAg/v7++Pj4OL4GDBhA5cqVc4xJ8VLOtQz3rzhF/1/rYd95P6BWLuL8+vVLdmx/wkP87QPnDiwzMZGIiEjhqVixIocPH77q41FRUfj6+hZeIBEpkfr1g5tvhqQfVrIxrQm38Ctd+ZkXmJTjOKvVOHbdOujZ06SwIiIi/yLXM9FnzZpVkDnEWR06BOnp7CG7cq4iuji71q3TqVYtkyNHXFjR5wNqhEH51L08dDYWz/J+ZscTEREpUF27duXFF19kxYoVuLu753gsLS2N0aNH061bN5PSiUhJ0K8ffDsvi5cZx2hexYodgBNUYh1tHce5u8NLL8Ho0WYlFRERyZ1cF9GlhNq9G4BI6jmG6tW72sEizsFqhb59U3jzTW/I8sBmhdNe8N1nzzPgqU/NjiciIlKgxo0bR/PmzalduzZDhw4lLCwMu93Onj17mD59OmlpaXyu9UJEpICsWQN/LDrJCvpzM785xn+iC/fzObH4A1CuHLz2mnqgi4hI0aAiulzTj7sWUMcXHj47nVbP38Qevw60aGF2KpF/16dPMu+8U5asPx+Dhl8DMOPQPAbYPwGLxeR0IiIiBadq1aps2LCBxx9/nPDwcOx2YwaoxWLh1ltv5f333ydIq8SLSAGZN+QXNqT2J4BTAGRh5SXG8zrPO3qht2kDX3yh/uciIlJ0qIguVxWbHEt32+fwBHQ9cIrl/X25rYHZqURyp1IlG507p/LTT+0gpgEE7GJ9hRS2rphDsy4DzY4nIiJSoEJCQli2bBlnz57lr7/+AqBWrVrqhS4iBSbqkJ1f2o7hvejxjvYtx6jCfXzNWtoDUL48PP202reIiEjRk+uFRaXk2RO3x7Fd+4wFatc2MY1I3jbHDDMAAH8OSURBVN1/fxJgoVZUf8fYuyteNS+QiIhIIStfvjwtW7akZcuWKqCLSIF59VVo0NCCa/RRRwF9Gd1oyjbW0p6AAJg+HbZsUQFdRESKJhXR5ar2xOx2bNe1+IGHh4lpRPKuffs0Fi/+i2Xv3E7588bY154HiTm219xgIiIiIiLFQFQUtG0LL78MKSkwjPfZTiOeZxLdWUocfgCMGWP0Plf7FhERKapURJer2hP1h2P7cHpfNm6EpCQTA4nkkdUKISHpeJb15c6YqgCku8KHc540OZmIiIiISNE2/pVM+tbbyfr12WMplKY5m3njkv7ngYHQrZtJIUVERPKJiuhyVXtPbHdsv7lhFK1bw5IlJgYS+Q/qB96B1WZsT09YSXr6eXMDiYiIiIgUUX3bHafD2JtZkdqOGhzM8Vgmbo5tX1+YNk0z0EVEpOhTEV2uak9iFABuqV6QFABA3bpmJhK5fhbfWrSOCQUg2iuL77580eREIiIiRcOaNWu48847qVy5MhaLhYULF+Z43G638/LLL1OpUiVKlSpF586dHYuZikjxM6z2T7y/rgkd+B0fEvmCAXChD/qlbrgBNm+Gnj0LP6OIiEh+UxFdrigpPYkjxAPgHlsTsGC1QmioqbFErktsrJXXXx/MuuUfOsbe3fWJiYlERESKjuTkZBo3bsy0adOu+Pgbb7zBu+++y8yZM9m0aROlS5ema9eupKamFnJSESlIMccz+bzai7x/oBt+xAFwhCCe4W3A4jjOxQX69jUK6JqBLiIixYWK6HJF++L2ObZT45oAULs2eHqaFEjkP6hY0Ya7eyb83R6iGwOwyTuRTWu+MjmZiIiI87vtttsYP34899xzz2WP2e12pkyZwksvvcTdd99No0aNmDNnDidOnLhsxrqIFF1fTDoGN/di4PFJjrEldKcp29hAG8dYaCj8+ivMnWtGShERkYLjanYAcR4ffPABSRdWDt2avtkxnhXXAAB39/28/fbiq54fGRlZsAFFrpPFAq1abWfBgs6w8UnoMRiAd5eO4csO/zM5nYiISNEVFRVFdHQ0nTt3doz5+Phw4403smHDBvr163fF89LS0khLS3PsJyYmAmCz2bDZbAWa2WazYbfbC/x5JO90b5zP4cPw2X3LGfbHQMfs8wxcGWV5jck8jd1ixYoNqxXuvRe+/NI4T7ewcOm147x0b5yX7o1zK8z7k9vnUBFdHJKSkhxvdI7+ugsOXXgg1miEfsstgVd9IwQQHh5e0BFFrtsNN+zh5587k7zrPrj1OSh9mm88DvDmkd1Urlbf7HgiIiJFUnR0NAABAQE5xgMCAhyPXcnEiRMZO3bsZeOxsbEF3gbGZrORkJCA3W7HatUHc52J7o3z2LULFi6Eej+8x9ijrznGo90r82LND9hZpjnNLhTVy5WDkSOhQQM4dcqcvCWdXjvOS/fGeeneOLfCvD/nzp3L1XEqossVHYjelb0TZxTRQ0MzTUoj8t95eqYzYAB88IEnbHkUOkwg0wVmfvEU40atMDueiIhIiRIeHs6IESMc+4mJiQQFBeHn54e3t3eBPrfNZsNiseDn56c3zU5G98Y59O8P331nzCbvam/L4xfG15S7lXvPfcHpfRUdx1asaLRuCQ42JapcoNeO89K9cV66N86tMO+PZy57V6uILld0IPkwWMCa6YrtrLEaTGhohrmhRP6jxx6DDz4A/nwM2k0CaxYzzv1KeGoSpTzLmB1PRESkyAkMDAQgJiaGSpUqOcZjYmJo0qTJVc/z8PDAw8PjsnGr1Voob2QtFkuhPZfkje6NedasgQcfhKio7LFl3M5YXibR4s3vtf7H6a0VsdmMe1O+PMyYATVqmJNXctJrx3np3jgv3RvnVlj3J7fX158SuUyGLYODnAbA7XQw2F1wc7MTEqKZ6FK0NW4MHTsC56rA7j7w/+3dd3gU1dvG8e9uekiBkJCEHnrvXbooYEWRqoKIWFEQReVVQEDFBgpYwF5+QBBFUFF6l15C74Se0JOQnuzO+8fCQqSXZDbJ/bmuudg5c87Ms3uy4eyTs2eAk952Zk4ZYW5gIiIiuVRERARhYWHMnz/fWZaQkMCqVato3LixiZGJyI3o2hXubJFB0+ifACPLsbcZxqeWlx03Gjrnzjth3Tp4+OEcDlRERMQkmokulziYcJBMi2PgFHwyBEuxDPz8wcPD5MBEboN+/WDxYmD5q4S672fa8hU0Kb8WepodmYiIiGtKTExkz549zv3o6GiioqIICgqiZMmS9O/fn3feeYfy5csTERHB4MGDKVq0KB06dDAvaBG5bvfdB5tmHmQxXWnCCgJI4HP6Xrau1Qq9e8NXX+VwkCIiIiZTEl0usfvMLufjXmlbeXbVcdLTTQxI5DZ64AEoVQoOHKjDsSnLKUYpOLQANm50TFUXERGRLNauXUurVq2c++fXMu/Zsyc//PADr732GklJSTz99NPExcXRtGlTZs2add3rS4qIeXr3Bmb+xQZ6UpjTALzPG0ymG6cpnKVuqVLw0UfnvtkpIiKSzyiJLpfYe2iD83E5n+IAeHqaFY3I7eXmBq+8Ahs2wEuhUyj1/kHHgdGj4ccfzQ1ORETEBbVs2RLDMK543GKxMHz4cIYPH56DUYnIrXrwngya/vN/fMvHzrL9lKILU7Ik0Nu0gbfegooVoUgRMyIVERExn9ZEl0vsjdnkfFwuuLKJkYhkjxdfhO++g1pv3ee4IxJgTJrI2eidJkcmIiIiIpK9oqPhrgoHeOOf5gy8KIH+Ox2ozQZW0xAAX1/44guYOxeaNTMrWhEREdegJLpcou/ZanwzA15ZDqUj6pkdjkj2KVCAzOefZVJ1qNvbxhPfP2B2RCIiIiIi2ea33+D1qn8yZXdtGrMSgHQ8eIkxPMw04nBMMLnvPtiyBZ57zsxoRUREXIeWc5FL1NmVQLMNUJ/VzI8uR41NHrz3XrzZYYlkC/tzzzIwdSRH/SHK2MWeg1GUK1nL7LBERERERG6rceNg3cv/4xfb486yaErTmV9YS31n2YABMGqUGRGKiIi4Ls1El0u4795NKl5soDZRuwuxbp0WRJe8KSkJvv2jJBkb/w+AOjFw5pefTI5KREREROT2qlsXXnoJptvuYx8RAPzGw9RmQ5YEepcuSqCLiIhcjmaiS1aGgceuXWymIrZzPx4VK2aaHJRI9li8GJ5/HvAeSK09hVmz/xUspaZB/w/BXb8eRURERCR3W7IEHngA4s99sTiegnTmFxqzgs/oC1gAsFqhXz8YPdq8WEVERFyZZqJLFtv3/Mvc4HgWB5YGDAAqVMgwNSaR7NKuHZQvD6QWJGr/ADZTHQ4ccCwWKSIiIiKSiz3aKZ2lLd6kQPyRLOXrqMdnvMj5BLq3N3z2mRLoIiIiV6MkumTxv43f0u5x6PfyH1BiOQCVKmkmuuRNVqvja63nfcyr5x58DIZhTlAiIiIiIreoY51o+v3alDd5j8l0w43Lf6bz94fZs3UDURERkWvRegWSxe643Rd2TlQBoGJFzUSX3C8qKopRl1ngMS3NHV/fp0lO9mEy3XiH/yP6xFpmDGhPseJ3AeDn58czzzyT0yGLiIiIiNywZ4r8zrcnelEQxxouDVlFPdayikZZ6vn7ww8/QPPmJgQpIiKSyyiJLlk8G1uSJnv3M97/ARJTC+Hra6dYMZvZYYncMovFQteuXS977PjxDMaM8SHTzaBJD0+OlIKwtIUse+RzvN29iYyMzOFoRURERERuzNSJ6Rzr+RoTbGOcZXsoS2d+YQN1nGVeXo77Ar34IkREmBGpiIhI7qPlXCSLzutTeXuuL4nTZgBQoUImVv2USB7Xq1cSXl4G2DyJTaoOQKxXOr8tHWdyZCIiIiIiV7dkCTQsEk2px5rS96IE+i90oi7rsiTQixeH1FTH+udKoIuIiFw/pUflAsPAY8cOtlHFWaSlXCQ/CA6206lTMgC2pUOc5V9s+5oMu94DIiIiIuJ6oqOhbl34pMXvzD5RmwasASANT57jC7owhQQCnfXvuQcOHTIrWhERkdxNSXRx8ouPx5qQwBaqOcsqVNBNRSV/ePrps1gsBsTUxXNPSwAOeCXzx6pvzQ1MREREROQ/fvsNKlWCzPUb+Z2Hneuf76YcjVjJeJ4DLM76ffrAzJkmBSsiIpIHKIkuTsknt7K/INxr+YNf2n/OG2/E06xZqtlhieSIMmVstGuXioeHQcvDF9ZO/3zDWOyG3cTIREREREQuGDcOunSB9HTYRE0+4wUAIulCXdYRRW1nXT8/WLwYvvrKrGhFRETyBt1YVJy+L7CIAf3BL+0024ok0aRTotkhieSot96KZ8SIOMK9m9L1EytLS9jZ5ZnAnoTVZocmIiIiIkLdurB+fdayV/mYf7mDSLpy8ezzChVg586cjU9ERCSv0kx0AcAwDA54JQAQmAZelWuaHJFIzitVykZYmB2jYEFe9r/XWb44eRaGYZgYmYiIiIjkdxVLp/HE+hfpwY9ZytPwJpJunE+gW63w8stKoIuIiNxOSqILALGJsSR42gCoegIyypUzOSIRczXsMYy6MY4PIjv9E5m1forJEYmIiIhIfhQdDfWD9jLxwB28yGd8wfNUZttl6953H+zZA6NH53CQIiIieZyS6ALAtmObnY+Ns3XZsseftDQTAxIxWbJ/KFWO9nPuD//zVc1GFxEREZEcNWIEvF5mKvPO1KEe6wBww0ZVtmap5+npWPv8zz8hIsKMSEVERPI2JdEFgK3bFzsfzz3wHO3aFeHPP31MjEjEPDt3utO4cSg//zUKv2OlAVhpOcLszb+bG5iIiIiI5BuNaqUSNOQFfqEzgTiW3txJBRqyil/p5Kzn4wOTJ0Pz5mZFKiIikvcpiS4AbI1ec2HnRFUAqlTJMCkaEXOVKZOJj48BhpXERR87y4dM76fZ6CIiIiKSrZYsgSqee/hiYxNe4Atn+US6U4+1bOLC/atat4atW+Hhh82IVEREJP9QEl0A2Hp6x4WdE1VwdzcoVy7TvIBETOThAS+8cNaxs+MhAmId34ldYxxm5ubfTIxMRERERPKyrl3h8xZTWJlRhzpsACAFb57iax7jfyTi76w7fDjMn6/lW0RERHKCkuiCYRhstcc6duJLQFoA5cpl4ulpblwiZurUKZnw8EwwrCQsunBnJs1GFxEREZHs0Lo1/DUlkdEMIADHhI4dVKQhq/iWpwDHTe/d3OCLL2DwYBODFRERyWeURBdiEmOIczu3dMtxx1IulStrKRfJ37y84PnnEx07Ox6kYIxjis8G4ygzoiJNjExERERE8pK33wY/P1i4EJLwozuTsGHlZx6jHmvZTA1n3fBw2L0bnnvOvHhFRETyIyXRha2H11/Y0XroIk5duyZRpIgNsBC3cKyzfOifA7AbdvMCExEREZE8oVAheG9YOklJF8qW0II6rKcHP5GEn7P83nvh6FEt3yIiImIGd7MDEPNt3browo5moos4+fjAs88mMnx4IOy6l6AjZYgP30fd7adJOhWDf3Cx6zrPhAkTSExMvKkY/Pz8eOaZZ26qrYiIiIi4puhoqFkhhY8y+1Ocw9zPnxgXzXG7+Oahvr7wzz/QvLkZkYqIiAi4SBL9888/56OPPiI2NpaaNWsybtw4GjRocMX6U6dOZfDgwezfv5/y5cvzwQcfcM8991y27rPPPsuECRP45JNP6N+/fzY9g9xt64G1F3ZOKIkucrHHHkti1CgPkpJ8OfPXL/yT9ghtT++HGt/DW29d1zkSExPp2rXrTV0/MlJLx4iIiIjkFdHR0Ls3HFm4k6V0piabAHiVj/mI1y6pX7UqbNmS01GKiIjIf5m+nMuUKVMYMGAAQ4cOZf369dSsWZO2bdty/Pjxy9Zfvnw53bp1o3fv3mzYsIEOHTrQoUMHtlxmZPH777+zcuVKihYtmt1PI1fbGr/7ws6JKgQF2ShSREtViAD4+hq0aLEOAK/TtTl15g7HgVGjIC7OvMBEREREJFcZMADKlIGwhZNYSz1nAj0ZH45T5JL61aopgS4iIuIqTE+ijx49mj59+tCrVy+qVKnC+PHj8fX15bvvvrts/TFjxtCuXTsGDhxI5cqVGTFiBHXq1OGzzz7LUu/IkSO8+OKLTJw4EQ8Pj5x4KrmSYRhsNRx/sPCJK0JYoCdVqmRgsZgcmIgLadJkA4MGQfR+K92f8HQUxsXBmDGmxiUiIiIiuUOZMvDlJylM4Gkm8Sj+OJb620Zl6rOGH3kiS/1774XNm00IVERERC7L1CR6eno669ato02bNs4yq9VKmzZtWLFixWXbrFixIkt9gLZt22apb7fbefzxxxk4cCBVq1bNnuDziCNnj5DgnglA81MnWbv+OD/+eMrkqERci7d3Bu+9B2FhOJZwcXcnzQ3GLhzJz8vHmx2eiIiIiLio6Gjw8gLP6B2soiFP87Xz2Pc8QX3WsI0Ln1m7doXFi+Gvv8yIVkRERK7E1DXRT548ic1mIzQ0NEt5aGgoO3bsuGyb2NjYy9aPjY117n/wwQe4u7vz0ksvXVccaWlppKWlOfcTEhIARzLebr+xZU3sdjuGYdxwO7Ns3rvS+bh0ij8G4OlpYBg3fi6LxYJxMw1NbHul9oZhOLfsunZ2xJ2X295M+4v78Vav7XxPly7N2V7dqePzE/uC0gid8yoP1umOn6ffNWO55WvnU7nt96pcnvoxb1A/Zj+9tiJ5w/m1zxcuhO5MZALP4EcSAEn48jxf8BM9nfX9/eHcx1ARERFxQS5xY9Hbad26dYwZM4b169djuc41SUaOHMmwYcMuKT9x4gSpqak3dH273U58fDyGYWC1mr5azjWtWnthikMpSyjx8fE3fa6wsLCbbm9W2yu1NwyD5ORkgKv+HLla3Hm57c20v7gfb+Xavr6+We7TkNitH4UnLGJf0EGOWZL4bdG3tK/V7artb9e186Pc9ntVLk/9mDeoH7Pf2bNnzQ5BRG7RiBEwZMiF/bbMdibQt1KFTkxlO1Wcx0uUgIMHczpKERERuRGmJtGDg4Nxc3Pj2LFjWcqPHTtGWFjYZduEhYVdtf7SpUs5fvw4JUuWdB632Wy88sorfPrpp+zfv/+Scw4aNIgBAwY49xMSEihRogQhISEEBATc0HOy2+1YLBZCQkJyxYfLrsklKfonbAyF8IBSBAYG3vS5YmNjb7q9WW2v1P78rOHAwMCrJtFdLe683PZm2l/cj7dy7eTkZIoUcdzs6euvYdCgUM5Y59DYvQGfLUig9om9GHdfejOoi9vfjmvnV7nt96pcnvoxb1A/Zj9vb2+zQxCRW9C1K0yZkrXseb6gPmtYQWNeZBzJFADA0xM+/hhefNGEQEVEROSGmJpE9/T0pG7dusyfP58OHToAjg9n8+fPp2/fvpdt07hxY+bPn0///v2dZXPnzqVx48YAPP7445ddM/3xxx+nV69elz2nl5cXXl5el5Rbrdab+oBosVhuum1Oq7QlhrLr3AknhrlhCWwbG0C/fjc3A+r8khm5qe3V2lssFueWHdfOrrjzatubbX++D2/12uffz3Y7nDkDUBG/ydOoY28DEyZgefllKFv2qnHc6rXzs9z0e1WuTP2YN6gfs5deV5Hcq3dvRwK9GIc5QnFneRJ+NGYF8RR0lj35JHz7rQlBioiIyE0xfTmXAQMG0LNnT+rVq0eDBg349NNPSUpKcia8e/ToQbFixRg5ciQA/fr1o0WLFowaNYp7772XyMhI1q5dy1dffQVA4cKFKVy4cJZreHh4EBYWRsWKFXP2yeUGGzeyjSqcIphTscHs2pVsdkQiLq13b/jwQ9i/H+ba72QxzWmRuQQGD4ZJk8wOT0RERERyWHQ0tG0Lh3cn8w0v0pHfqM0G9hPhrHM+ge7hATt3QkTEFU4mIiIiLsn0qS5dunTh448/ZsiQIdSqVYuoqChmzZrlvHnowYMHiYmJcdZv0qQJkyZN4quvvqJmzZr8+uuvTJ8+nWrVqpn1FHIvmw22bGE9dZxF1atnmBiQiOvz9ISLb6HwpvuHGIAtcjKL53xtWlwiIiIikvNGjIAyZcB99zZW04DefEdB4plCF9zIzFI3PBzS05VAFxERyY1Mn4kO0Ldv3ysu37Jo0aJLyjp16kSnTp2u+/yXWwddYMe62RwOT2HZsXKcu88N1aunmxuUSC7w6KPw/vuwfTv8m9mQd8vWZXLbdWxf/jQbajSgZlhNs0MUERERkWx2330wcyb04Ee+4HkK4PhWbyIFGMtL2M593HZzg08+0drnIiIiuZnpM9HFPD+umsBdPeC7gW9BuX8AqFpVM9FFrsXNDYYPv7A/PrwV24qAYYFXIns5b2gqIiIiInlT166wcGYS39GLH3nCmUDfTDXqsZaJPAZAYCBkZiqBLiIiktspiZ6PbT6x9cLOiaoEBcVRsKCSfyLX4+GHoc65lZCOrBxByJkCAMyP38BfO/8wMTIRERERyU4dO8LmKVtZTQN68YOz/Cv60IDV7KQSAEFBEBdnTowiIiJyeymJno/12htAzxWFYOd9EF+CYsWOmx2SSK5htcIHH5zbyfTGmP+J89gr054l3aalkURERETykilTwN0dPKdNZg31qco2AM7iR3cm8gxfkYoPANWqwalTZkYrIiIit5OS6PlYx0XHuHt2O5j8J2BREl3kBrVpA+3bOx7bDvSg9n4/AHZnxPL58k/NC0xEREREbqs2bRxLuNhskEAAvqQAsJEa1GMtk+kOOJb9i4yEzZvNjFZERERuNyXR86sTJ+DoUTZQ21lUrNgxEwMSyZ0+/BBeeQX27vfkm9OVsZxbEWnYgqGcTD5pbnAiIiIicstatYL58y/s/829fMhAxvMMjVjJLioCEBzsWP+8SxeTAhUREZFsoyR6frVxIwDrqeMsKl5cM9FFblS1avDxx1AoyEKdEd/wRJSjPJ5Uhs4caGpsIiIiInJrioQYWBfNB7LeO+p1PuA5xjuXb/HygtWrTQhQREREcoSS6PnUlvWzOBwA39OT319cwMiR4O+fbHZYIrlbjRq8W/QxCpxbDn381h/ZcnyLuTGJiIiIyA1bsgQCrImMOtmD+bThGSb8p4bF+cjdHSZNgoiInI1RREREco6S6PlUnzM/UmIA1H7tCG17+PDGG2ZHJJI3WF/4hDtX3AGA3WIw4Nc+GIZxjVYiIiIi4iruvx9eaLGZ1UY9Hud/AHxKf4pz6JK6nTrBrl3w8MM5HaWIiIjkJCXR8yG7YWeL22kAAtPAp3qda7QQkevxxRdQrlEwfyydS2icFwBzT6xkW+ZWkyMTERERkWuZMgV8fQxC//qG1TSgEjsBSMCfnvzIYUo46wYHg2HAL79oBrqIiEh+oCR6PrT/+E4SPewAVE/xdyzgJyK3zNsbEhOBTB98F77jLJ+Z+CspmSnmBSYiIiIiVzRlCgQEwFNdz/J16mN8Qx98SAVgPbWpyzp+4cLdQsPC4MQJs6IVERERMyiJng9tWj/L+diS2pTt2x2zKETk1vTsCTVqOB5Hb3yFitHhABz3SOSztZ+aF5iIiIiIXFbFitC1K0Sc3cha6vEok5zHPud5mrCcPZR3ljVqBDExZkQqIiIiZlISPR/atHOJ8/GMdU/QrJmJwYjkIW5uMGbM+T0LsbOn42Fz7H25eTz74veZFZqIiIiIXOTtt8FicaxnfjezWUVDKrILcCzf0olf6MvnpOHtbDN8OKxYYVLAIiIiYiol0fOhzScuWp/5WA3q1HEMIEXk1rVs6ZjNBBAf24DqK+4CIN1iY/CCgbrJqIiIiIiJoqOhQAEYNuxC2WoaEEsYAOuoQx3W8yudnMd9fWHfPhg8OKejFREREVehJHo+tCnzsONBhjecLkft2ubGI5LXfPSR48MZwIYlvxEW7wHA4lOrmHtwromRiYiIiORfX34JZcpAcnLW8jgK0ZlfGMNLNGE5eynnPNaqFSQl6eahIiIi+Z2S6PlMYtpZdvucu8HhiSpgd6dOHXNjEslriheHt95yPDbS/QmYMwzvDHhnPrTdZTM3OBEREZF86Isv4PnnAQye5FvCOZrl+Boa0J8xpOMFQOHCsHgxLFiQ87GKiIiI61ESPZ/ZFDUb4/zSLTGO7LmS6CK338svQ/lz96DatfUNPhrzMG8uhSJDR0BKirnBiYiIiOQjU6bACy+APwlMphvf8hQTeRQrl05u8PODyEg4eRKaNzchWBEREXFJSqLnM+uj/rmwE1ubgAAoW9a8eETyKi8vGDvW8TggIJHQYo6MuvvBg/h/9pmJkYmIiIjkH61bO+5XU4sNrKMuXZkCQCsW0ZbZWeq+/DKcPQtdupgRqYiIiLgyJdHzmQ2H117YialD/fpg1U+BSLZo1w6+/x5ee+17mo9vh+HhWBvd/8sv2bNpPja7lnYRERERyS7+/rBwocGzfMlKGlGePQDEEcjD/MY/3ANA7dqOG4eOHm1mtCIiIuLKlD7NZzakRjse2K1wrAYNGpgbj0he98QT4O2dQWb58iQ+/TQp7jCoeTp3ruzJ91u/Nzs8ERERkTzJYgFrYjxT6MKXPI8X6QCsoR51WM/vPIybmyN5vn69bhwqIiIiV6ckej6SnpnGFp+zjp1TFSHDl/r1zY1JJD85268fa6sF80FTsFnhw1XvcfjsYbPDEhEREckz3n7bkUCvzXrWUZfOTHUe+5R+NGUZ0ZTB3R0yM5U8FxERkevjbnYAknO2bpxHhpvjcfjp0qQWQjPRRXLQkTh/3jHWccfq+1lddxMD17oT9pCn2WGJiIiI5AmFC8Pp0xDBPlbQ2Dn7PI5AevE903kIcCxnmZFhZqQiIiKS22gmej6yYcNM5+NXymdw6hQUK2ZiQCL5yKlTVu68swhzNpZkw8I5/D0+nLf/SSZkxHtmhyYiIiKS6/n7OxLoANGU4Xt6AbCa+tRmgzOB7ucHNt2WRkRERG6Qkuj5yPqDq5yP65RvgcViYjAi+UzhwnYeeigZgOSUUEaf+R4D8P31V7wWLjQ3OBEREZFcrEABSEzMWvYyn/AaH9CUZezHsWZLq1Zw9qwJAYqIiEiupyR6PrIhaa/zca3GHcwLRCSfeuONBEJDHVOf/slsyy90BqDgG29w5NhOM0MTERERyZU8PQyeTB5HVyZnKU/Fh494jQwcS+ctXgwLFpgRoYiIiOQFWhM9j5kwYQKJ/52GAWAYvD89leWFC3A0JJ3vCswCy+wsVbZt25ZDUYrkTwEBBu+8E0efPoUBeMnjS5obs/m84hE++KMNX7SZwD0R95gcpYiIiEjuEO4Tx+TM3nRkGokUYD112EXFLHU8PCA93aQARUREJM9QEj2PSUxMpGvXrpeUux06RNjANF7bt5BtbtWpkeDOzz+fwsPjQp1BgwblYKQi+VP79qm0bZvC7Nk+HM8IonvVx1nU/DPAYNCiV6kfWp8Q3xCzwxQRERFxSVOmwIcfgnX9Gv6lC2WIBsCPJNoxK0sSvVw52L3brEhFREQkL9FyLvmEx6ZNpOPBBmqTYPPjyBH3LAl0Eck577wTh5+fHYBFW8fSeHsoAKdsCby2+BUMwzAzPBERERGXEx0NRYtC164GTdeP4V/ucCbQT1OIB5jBWPo56zdpogS6iIiI3D5KoucTnps3s5nqpOENQM2a+k6jiFnCw+0MGRJ/bs/C3n8WEpLouNPv3CML+GX3L+YFJyIiIuJifvsNypSBlJgzTONhxtAfTzIAWEEjarOBP3nAWf+OO+Dff82KVkRERPIiJdHziT9iF/Bj6RLgHQdAnTpKoouYqVu3ZFq1SgXgeEJlOs5s6zw2dNlbHDp7yKzQRERERFxGv37wyCNQn9VsoDYPMd157CNepTlLOEgpZ9lLL8GyZSYEKiIiInmakuj5gGG381LZ7Yx7Yga8UBmAunWVRBcxk8UCH310hvLlM/juu1O837EevTY4jiXaUxiw4CXsht3cIEVERERMVLgwjB0LHqTzK49QmgMAnCKI+/iT1/iITC6sUdmmDYwZY1a0IiIikpcpiZ4PxO5dz2mfc2ssx9bG29tOlSoZ5gYlIoSF2Zk//zh3351KYp8+fHCyLqXiHMdWnFjDd1u/MzU+ERERETMsXQru7nD6tGM/A0968T12LCynMbXZwEzuy9Lm5Zdh7lwTghUREZF8QUn0fCBo535Gz/CH1S/A9oeoWTNDNxUVcRHW87+F3dywffwZ3832dh4bufIdjtqOmhOYiIjIDXj77bexWCxZtkqVKpkdluRCb74JrVuDzZb1RusLuJO2zKYFizlEScDxzb577oF9+2D0aDOiFRERkfxCSfR8IGTrXiI2tIa/P4P1fbQeuoiLyixegl21f+eeFRUASCOTSck/kZSeZHJkIiIi11a1alViYmKc2zItTC03qHBh2LzJoL/xCb/yCJA1kT6Pu5zLt4wdC3Y7zJwJEREmBCsiIiL5irvZAUj284iKYvlFX3fUeugiric9HZ57LojZs9tRwHMFlSLC2RGWTqzlJP1n9ePrB74xO0QREZGrcnd3JywszOwwJBdasgRatIDCltN8vOcJmhtzABjAaEbzSpa6Hh6OcZOIiIhITlISPa+z2/GMimI7A51FSqKLuB5PTwgMdNxINCk9CPdpkRR46mGSPOGbDd/SpsxddKnWxeQoRURErmz37t0ULVoUb29vGjduzMiRIylZsuRl66alpZGWlubcT0hIAMBut2O3Z++Nte12O4ZhZPt15PrUrw9RUdDEsoJJRndKxR10HitIHFbrhX4qVw62b3fMQJecpfeNa1P/uC71jetS37i2nOyf672Gkuh5XPz2dWwIP8vX9gc4VbcXy7p/REiIfkGIuKIRI+JZs8aL6Gh3thx/iPZ/d+KfDlMBeHrGUzQo1oCIQvq+soiIuJ6GDRvyww8/ULFiRWJiYhg2bBjNmjVjy5Yt+Pv7X1J/5MiRDBs27JLyEydOkJqamq2x2u124uPjMQwDq1WrW5pl6VL46CNwt9oZXXQCLxx5D3cyAYhzL8SQMp+xMrA1dTkOQNOm8NprcPy4mVHnX3rfuDb1j+tS37gu9Y1ry8n+OXv27HXVUxI9j1u14Xee7O54/HZmNE/dl70fSkTk5hUoYPDFF6d54IEQMjIs/BM1hVZllrKwRiwJmYl0/aUzy55ajoeb7gwsIiKupX379s7HNWrUoGHDhpQqVYpffvmF3r17X1J/0KBBDBgwwLmfkJBAiRIlCAkJISAgIFtjtdvtWCwWQkJC9KHZJLVqwdatEGSc4nvjKe5jpvNYlF8DOqRM4dAex7cYPD1h1ixo1sykYAXQ+8bVqX9cl/rGdalvXFtO9o+3t/d11VMSPY/bFLMWCjkel41oYG4wInJN1atn8H//F8+wYQUBCxv//pfSxSuwP8jG6ti1vLXgLT646wOzwxQREbmqggULUqFCBfbs2XPZ415eXnh5eV1SbrVac+SDrMViybFrSVb+/pCYCI1ZTiRdKckh57GRvMEflV7g0Pqi2O1WqlWDzZtNDFay0PvGtal/XJf6xnWpb1xbTvXP9Z5fPyV53PqM/c7H1eo+YF4gInLdnnoqidatHd8aOZ1ahrBpY/CwgZsdwrcfukZrERER8yUmJrJ3717Cw8PNDkVcxJIlYLE4EugAzzLemUA/QTDt+Ie3rO9iszjmeUVGKoEuIiIirkNJ9DzMnniW9YHJAPidLcT2bRVNjkhErofFAqNHn6FIERsAKw+/wP0z27LwB+g/aAbs2GFugCIiIv/x6quvsnjxYvbv38/y5ct56KGHcHNzo1u3bmaHJiZbsgTCw6FFi6zlL/A5uyjPEppRiyhm0w4AHx/IyIAuup+6iIiIuBAt55KHHVg7i8Rz35JNPNSK8Wv8aN48zdygRHKhqKgoRo0addPtt23bdsNtgoPtjBlzhu7dC2MYFjbt+ZH6CaWAZOjcGVatcnzKFBERcQGHDx+mW7dunDp1ipCQEJo2bcrKlSsJCQkxOzQxUevWsHCh47EfZ0nkwk1mE/GnNQuIJQzbuY+lvXrBO++YEamIiIjI1SmJnodt3rHgQg8frU+DRummxiOSW1ksFrp27XrT7QcNGnRT7Zo1S6NNm5W4uzfmf1/54922LGzb5vhu87PPsvH9l6kRVhOLxXLTsYmIiNwOkZGRZocgLqZMGYiOBgt2XuNDBjCa+qzhIKWcdY5QHIDAQNiwAUqVguPHzYpYRERE5Mq0nEseFnVq44WdI/Vp2FCz0EVym7vuWs5ff0FQcV/45RcoUAC7Bd7Z/xO1v6rD52s+NztEERERkSyefNKRQA/mBDO5l/cZRBFOMIUueJB1Yk/duhAXBxER5sQqIiIicj2URM/D1ltinI/dT9SlVi3NRBfJbaxWxwZA1arw3XcsLQGDW4OBwcuz+rPl+BZTYxQRERE5Lzoavv8emrKUDdSmPbMAsGNhDndjv+gjaGQkrF1rVqQiIiIi109J9Dwq49B+NgVlOHZOVqRWJR8tnyySB8Q278wQ+x7a/1sZiwFvr/KhSkZBs8MSERERIToaKpSz8wYjWUgrinMEgGMU4W7mMJTh2HAnJAQMQzcPFRERkdxDSfQ8aufqGaSfXw/9SAMaNNAsdJHc7vhxqFMHlhwuy8IFq/n+23K8+Xci1k6dIV3vcRERETFHdDQ0aQINyxznT/s9jOT/cMcGwEJaUoso5tMGNzcYO1brnouIiEjuoyR6HrVh7+ILO4eaaD10kTygSBG47z7H41SbH4NjFhFLKKxYAf37mxqbiIiI5E+9eztuIuq2wrF8SztmA47lW95mKG2YRyzhFC8OmZnw4osmBywiIiJyE5REz6PWJu+8sHO4EfXqaZaqSF4wdiw0bOh4fMhWjA6WGaTiBV9+ybwx/Xh/2fvmBigiIiL5wpQp4OUF333n2C/EGYpxFIBYQrmLuQzjbey4YbHAkiUmBisiIiJyi9yvXUVyG0tSEssDEhw7aX5ULlyRwMDT5gYlIreFtzdMnw4NGsChQ7DKaMiTfEezeo/y4umx2OZDycCSdK/e3exQRUREJI968EH444+sZX/yAKN5mZps5FEmcoww57HPP4eIiBwOUkREROQ20kz0POjE6rnEBBgAFD9Tjo4PaRa6SF4SFub44FqggGN/Mt35070NtnO/0Z+c3otVh1eZF6CIiIjkWR07OsYhVdgKGFmOvc4H3M2cLAn0L76A557L4SBFREREbjMl0fOgDVtmOR93KV2EZ59NNDEaEckOtWrB//4HFotj/5+Vc7hzXWkA0uzpPDDpPvae3mtafCIiIpK3vP02uLnB9Gk23mIEm6jBU3yTpU4mHthxA8DXF/btUwJdRERE8gYl0fOgtSc3OB/Xrny3iZGISHbq0AFGjjy/Z+HfvzdSf78PAMdTTtL257s5nnTcrPBEREQkjyhUCIYNg2D7MWbRjhEMwQ0743iR8uzKUtdqhaFDISlJS7iIiIhI3qEkeh5jsdtZb41x7tesqiS6SF722mvQo4fj8dM9rfyzsCBVzuXN98bt475J95GUnmRegCIiIpJrRUeDhwfExUFLFhJFLe5iHgA2rLzDW+ylrLN+p05gszlmrYuIiIjkJUqi5zHBMTGM/KE2g76/m+G7G1HIO8jskEQkG1ks8NVXjqVdPv3Gj8LT/mLWb94UO3dv4TVH19D5185k2DLMDVRERERylREjoEwZsGfaGMxw5tGGcGIBiCGMO5nPu7yFHTfn0i2//GJy0CIiIiLZREn0PKbY/v1MzniKkQdmM2TiCpYv9zQ7JBHJZl5e8Oij59ZHr1OHEl9N4Z9JFgJTHcf/3v03z/71LIZhXPU8IiIiIgB9+sCQIVCEY8ymLcMZiht2AOZwF7WIYjEtAShcWEu3iIiISN7nbnYAcqkJEyaQmHhzNwNtFhXFfD4AwMPdTq1amn0qkptFRUUxatSoG27nWeU16kR68O9j75DuDt9FfUeYXxjv3vluNkQpIiIiecWSJfDNN1Cb9czkXufscxtWhjCckQzCODcXq1MnzT4XERGR/EFJdBeUmJhI165db7yhYXDmzQnsoTwAdeqk4+urmaciuZnFYrnh3weLF3vx1IogUlKsNPk9gRWPjMWwwHvL3sPfy583mr6RTdGKiIhIbnfPPY5/D1CKDDwAOEo43ZjMEloA0Lq1I9Gu2eciIiKSX2g5lzzEunsXT99jg4ZjocgWmtyRbnZIImICPz+D8yu3LN86hsb/dHEeGzR/EJ+t/sykyERERMRVRUeDr69jaRaA0xSmC1P4i3upRRRLaIGHh2Pt8/nzlUAXERGR/EVJ9Dxk34o/WFFzH7TvB62GcMcdaWaHJCImqFs3nfHjz2C1OtYuXb46kibz7nMef/GfF/l+w/dmhSciIiIuZsAA6F1mAQEpsVnKV9KY+/mLExTB3x/S05U8FxERkfzJJZLon3/+OaVLl8bb25uGDRuyevXqq9afOnUqlSpVwtvbm+rVq/P33387j2VkZPD6669TvXp1ChQoQNGiRenRowdHjx7N7qdhujV7Fjofex69gzp1NBNdJL9q0yaVbt3+cdxsFFi+7E8aL2nlPP7UH08xZcsUk6ITERERV9GsiY3AT4Yyjzb8j8ewYrukjqcnbNxoQnAiIiIiLsL0JPqUKVMYMGAAQ4cOZf369dSsWZO2bdty/Pjxy9Zfvnw53bp1o3fv3mzYsIEOHTrQoUMHtmzZAkBycjLr169n8ODBrF+/nmnTprFz504eeOCBnHxaOc8waDg9GMZvgH/G0KDgXXh6mh2UiJipdu0dfPfdhf0VC+bTaEVDAOzY+WLJx9gNu0nRiYiIiNk6Nolh+Io2DGU4VgzaMJ/uTLqk3qefaga6iIiI5G+mJ9FHjx5Nnz596NWrF1WqVGH8+PH4+vry3cWZn4uMGTOGdu3aMXDgQCpXrsyIESOoU6cOn33mWOM3MDCQuXPn0rlzZypWrEijRo347LPPWLduHQcPHszJp5aj3PfuZcGZRhBbC1a9xL1NSpgdkoi4gCeegC+/PL9nYeXsFdRfV4NW0TDz/UNY9+4zMToRERExw5QpcL/3XL5YUYtWLAIgEzfeYCQTeTRL3eHD4bnnTAhSRERExIW4m3nx9PR01q1bx6BBg5xlVquVNm3asGLFisu2WbFiBQMGDMhS1rZtW6ZPn37F68THx2OxWChYsOBlj6elpZGWdmH98ISEBADsdjt2+43N0rTb7RiGccPt/ss4f1fA6+S5fDmzaOfcb9489YbPYbFYbrjN7WpvVtsrtTcMw7ll17Xz4+ud08/54n7MTXHfzmvb7XaefhpSUmDAACtgYe1fa9jgVg2/zN0YrVtjLFoEpUvf9DWy2+36vSrmUj/mDerH7KfXVrJTdDTc3TqTHvuHMYN3seIYYxymGF2J5F+aZqn/xRdKoIuIiIiAyUn0kydPYrPZCA0NzVIeGhrKjh07LtsmNjb2svVjY2MvWz81NZXXX3+dbt26ERAQcNk6I0eOZNiwYZeUnzhxgtTU1Ot5Kk52u534+HgMw8BqvbmJ/r6+vsTHx99QG/8lS3iY43iSzpESdQkMPM0NnoKwsLAbvu7tam9W2yu1NwyD5ORkwJHEzI5r58fXO6ef88X9mJvivl1tfX19nUtjdesGJ08WYORIP8a8c5wq/3OH7WA5dAh78+ZsnziBVR6x3Fvm3pu6Vna6Hb9XxXzqx7xB/Zj9zp49a3YIkkeNGAHjhxxlIt1pyWJn+d+0pwc/cYpgZ1lgIGzYoCVcRERERM4zNYme3TIyMujcuTOGYfDlhfUMLjFo0KAss9sTEhIoUaIEISEhV0y8X4ndbsdisRASEnLTHy6Tk5MJDAy8/gaGwQTbUtKbz+KNw+MoM2sHXj430P6c2NjYG7vubWxvVtsrtT8/8zcwMPCqSXRXizsvt72Z9hf3Y26K+3a1TU5OpkiRIs79ESPgkUcMatYsCk8uwGjdGsv27ZyKO0KXaR3YHmTj6/u/pletXjd1vexyO36vivnUj3mD+jH7eXt7mx2C5EFdu8LCKcfYTC2KcAJwLN/yf7zHx7yKcdEqnw0bwsqVZkUqIiIi4ppMTaIHBwfj5ubGsWPHspQfO3aMsLCwy7YJCwu7rvrnE+gHDhxgwYIFV02Ge3l54eXldUm51Wq9qQ+IFovlpttefI7r5b5jB19UTeJAQfDKtLHdy+2G2p93fsmLm3Ur7c1qe7X2FovFuWXHtfPj623Gcz7fh7kt7tt17f/+Hqpd+9yDsDBYsADuuoshgYlsC9oPwLvzh9K9end8PHxu+prZ4Xb8XhXzqR/zBvVj9tLrKrdLdDSMGweRkRATAxDKHzzAU3zLIYrTlUiWc0eWNjVqKIEuIiIicjmmjtI9PT2pW7cu8+fPd5bZ7Xbmz59P48aNL9umcePGWeoDzJ07N0v98wn03bt3M2/ePAoXLpw9T8BFxC79gwMFHY/Lxvnj6eZpajwikkuEhfHjsyuY8O8+qqxsQ/F4mPdZAj7rN5kdmYiIiNyC336DqlXhk0/OJ9AdXmIsY3iJWkRlSaB7esLYsbBxownBioiIiOQCpk91GTBgAF9//TU//vgj27dv57nnniMpKYlevRzLCfTo0SPLjUf79evHrFmzGDVqFDt27ODtt99m7dq19O3bF3Ak0B955BHWrl3LxIkTsdlsxMbGEhsbS3p6uinPMbv9se1f5+PgtGImRiIiucnBg9DnZT/AwrZZc6g64UfCDmZCmzawePE124uIiIjriY6Gnj2hecosHmJalmMp+NKfMZzmwiSjVq0gLQ1efDGnIxURERHJPUxPonfp0oWPP/6YIUOGUKtWLaKiopg1a5bz5qEHDx4k5qLpE02aNGHSpEl89dVX1KxZk19//ZXp06dTrVo1AI4cOcIff/zB4cOHqVWrFuHh4c5t+fLlpjzHbJWayhTrhZufphxvZWIwIpKblCwJEyeChweAhdnJPWjDPE4lekLbtqROncw7S94hLTPN7FBFRETkOt3dOpM3kwYxi/b8wBOUY/dl6xUu7Pib+YIFORygiIiISC7kEjcW7du3r3Mm+X8tWrTokrJOnTrRqVOny9YvXbq082aC+YHHmtXsKH3EsZMaSHE3D3MDEpFcpVMnKFQIHnoIEhNhBU1oyjL+ymjLq791Z3plWLR/EdO6TCPA68ZutCwiIiI5q+edh/lufzeasQyAAM7yNF/xGh9lqVenDqxbZ0aEIiIiIrmT6TPR5dYsnTcXm288AAGxTfArcMbkiEQkt2nTBpYsgXNfAGIHlWlc5BtmlXPc0HR+9HyafteUg/EHTYxSRERErmbzh/8wakEtZwI9A3de4WNe48Ms9e64Qwl0ERERkRvlEjPR5eb9fOQwlHA8rhfUDE5sNzcgEXEZUVFRjBo16rrrP/lkAF9/3ZGTJ4M4EXs3vj/Nxq/7vST6ZLD5+GaqfVqNJ32epIRbiWuey8/Pj2eeeeZWwhcREZHrkZFB3IuDqT7hA2fRAUrShSmsohEAFgvceSc89RR06WJWoCIiIiK5l5LouZj15EmWBZ9y7ndr1oIl05REFxEHi8VC165db6hN165p9OiRzsaNniQfuovQb+ZQ5NHW7AsyOMtZJqRPYFyrcbQv3f6q54mMjLyV0EVEROR6HDrEybu6Erzzwr2fZvAAvfieMwQB4OUFkybBww+bFaSIiIhI7qflXHKxhDlLOVXKkTS3ng2nbd2SJkckIrld4cJ2pk49yT33pODmls7Pz8Sx8icPmh5wHE+1pfL0vKf5ctOX+er+EyIiIi7HbiepeTtnAj0Dd15mNB2Y7kygA3zyiRLoIiIiIrdKSfRc7KcFm8EjBYAyKY2wWi0mRyQieYGvr8H48adp334YVZ6vDz/8wuyZhXlso+O4gcG7q9+l/+L+pGSmmBusiIhIPhQdDV27W3lo/6fYsbCfUjRlGZ/yMnDhM0Hp0tCunWlhioiIiOQZSqLnVpmZ/GY/7txtU6WVicGISF5jtUKhQocASK9Xj4Q/ZjJ+RzVqLezgrPPbnt946M+HOHT2kElRioiI5D8DBkClSjBlCszlLrozidpsYDUNs9Tz84NRoyAiwqRARURERPIQJdFzqYxlG9hVdrdjx7DQu3VzcwMSkTzNVrw4bzRbTNTi33H/5X94pTtuqbHl1BbumX4Py44sMzlCERGRvG3pa38y2acXn3xikJ5+oXwKXYmjUJa6JUrApk1axkVERETkdlESPZc6vGAWRuhWAILjqxEeEGxyRCKSl6Wnw4p1BQDI3PYoad9swP90CABn0s7Q/Z/ujN80Xuuki4iI3GbRuzL4IfhVmn30AN1Sf6AfY65av04dOHhQM9BFREREbicl0XOpTTHTnI+71tEsdBHJXp6eMHXqSXr2THQUHK/G2a92ErDrDgDs2Hln9Tv0ntubM6lnTIxUREQk7/hn/AFOVmnOE6dGOcsasRK4/B+tq1WDdetyKDgRERGRfERJ9FzILTqaWQVPOPfvqXGvidGISH7h6QnvvhvP2LGn8fa2Q2ohEiYvwXPxQGedOQfn0P2fbpqRLiIicouWDvyDRs/Vpr5tJQDpePASY+jGZC6+eSiAxQL33gubN5sQqIiIiEg+oCR6bjR/FvPPfT0z1ChAtcLVzI1HRPKVhx9O4c8/TxARkQmGlfSFH8LEmXgl+wEwYlY6AXFx5gYpIiKSS0XvTGdqqVdo9vGDFMLx7a59RHAH/zKOl7g4ge7mBm3awKJF8Ndf5sQrIiIikh8oiZ4LzZ9xmsfXeVP+FLQKa4bVom4UkZxVuXImf/99nIcfTnYU7L6HtPHbefCPe3hw5i56jB4NU6eaG6SIiEgu87939nO6ajM6HRztLPuVjtRhPWupn6VukyawezfMnQvNtbqjiIiISLZS9jWXid+fwJMbPuGb2Wep/+1vvNNunNkhiUg+5e9vMHbsGcaMOY2fn53qwX5MPLYDAO+UFOjcGaP3k/SY2p2pW5VQFxERuZoBAyBz6Ajq2lYDkIYnL/AZnZhKPAWd9SpWhMWL4d9/dfNQERERkZzibnYAcmP+GXWYdCoDUKhsAN4ePiZHJCL5XceOKdStm47dDvGF/8Tyf/+H74wZAIzZGMnPJVP4edtk+h/qzyftPjE5WhEREdfTpw98/z0UsI+mKYuxYNCZX1hP3Sz1OneGKVNMClJEREQkH1MSPZeZMq+Y83HHpzxMjERE5ILSpW0AGARy5rPPWOztTdEZ+xlYvDzwPwDaLDkMLVPB29vESEVERFxIZiZTfnNn4kSw2SCBQO5lJrGEkUCgs5rFAnfeqQS6iIiIiFm0nEsusnddEmsKxkHgQWp4bKXCw2XNDklE5FIWC5tqN+SJiEVkzvgJfvsfYcu7ETFyK9StC2vWmB2hiIiI+aZNI7FEJT4ZcIiUlAvFu6joTKBbrVCpEkye7Fj7XERERETMoZnoucjvY+Lggd4QsoMzceVIt8/B083T7LBERC7Dwl33ebNxh4Gx+VFiNz9KLdIZuO0j3mrUCu83XqJn7QN0qNqRhys/bHawThMmTCAxMfGm2vr5+fHMM8/c5ohERCTPSUuD116DsWPxAz6xdqU5i8gk67dMg4Lgiy+gSxdzwhQRERGRC5REzyVsNpi4Ix2aO27aVyTUWwl0EXFZ7u42PvwQHnrIwhNPwK5dkIEn7/EmkfauPPT3g/zsuZWft07i4coP81n7zwj3Dzc7bBITE+natetNtY2MjLzN0YiISJ6zb59jYfN165xFB+3F8SQ9SxK9SBH48kt42HX+ziwiIiKSr2k5l1xi2axMTiSUgwUjCIgty/21OpodkojINTVuDBs3wuDB4OFhALCPsowqfeFGadO2T6PSZxUZu2osmfZMs0IVERHJXr/9BrVrOxPoqXjxLF/SlUiSKQA41j4vXRqmTlUCXURERMSVKImeS0wcmwGJYbDkLb7YPYAnq/U2OyQRkevi7Q3Dh8PGjRaaNTtXOP0H+HUyHkmONV8T0s/Sb1Y/6k6oy7KDy0yLVURE5HaL3pHG3ntehEcegYQEAHZTjsasYALPAhZnXW9veOYZaN7cpGBFRERE5LKURM8FjhxxY9bWCACKcoRWfUtitajrRCR3qVwZFi2Cb791rPPKlq7M/KEGT134Rjubjm+i2ffN6Dm9J7GJsWaFKiIictOio2HlSliyBN7vs5fEmndQ9p/PnMcj6UJd1hFF7SztvL2hZk2tgS4iIiLiipSJzQWCkw8ygWeoxQaeKjQVo14ts0MSEbkpVis8+STs3Gnhp5/gri2/8nXgY6z4BuocvVDvp40/UX5MOd5Z8g7JGcnmBSwiInIN0dEwbZpjGzECHnvMsd17L6z+djPV0y8s3/IM4+nGZM4SkOUcFguULAkDB0JEhBnPQkRERESuRkn0XKDw31M50fRbfipShxd7H3eMskVEcrHgYHj8cRx3Tvv5Zxr9vJDlK2tT7K//g5SCACRmJjF44WAqfVaRKTunYLPbTI1ZRETkv377zbFKy1NPORLnw4fDhg1w6BCkpMDvRgc+pR87qUBDVvEVz3Dx8i0AXl5w//0wa5bWQRcRERFxVUqiuzrDYMuiifxfG6jxPLxcebvZEYmI3H4tWzLppbUcWfsujNsFa54DuxsARxKP0n9Rf+p/XY95++aZHKiIiIhDdDR8/rkjYe7lBTYbBGfGYrcZ2O1gtzvmvrzOh9RjLZuomaW9xQJt2sCcOTBjhmagi4iIiLgyJdFdnOf69UwKiXHu145odpXaIiK5V/fHrHzyCRT2Lgwzv4AvNsPO+53HNx7fxF0/30W7/7VlzZE1JkYqIiJyYca5YYCnJ3SyT2EnFXg883usVke5xQI2N08S8Xe2s1jA19cxe33uXN1EVERERCQ3cDc7ALmyNWs8+f7lwizu7AbY8MGDe0rfY3ZYIiLZwssL+veHJ5+08vHHMHpURZIm/wGlF8Ldr0LR9QDM3juHRsmFqd9nopa3EhGRHBcdDZGR8McfcPw4GCmpjEwYwFOZXwIw1v4C6y0N2ehWFS8v8PBw3FC7TRsoUcKx36SJkuciIiIiuYmS6C7s8zE+zPM+Ab6OdYDblmqLv6f/NVqJiORuAQGONWVfeMHKO+/AhPEtyPh6DVSfBK3fwtPrBP37TYYf9sOQIdC2LQZgUUJdRESySXQ0HDvmmH0+dSpERYG7OzQN3c17eztT0x7lrDvHryNBVUsy9F6oWtVRVru2lmsRERERyc2URHdRO3a4M2+RH3T5yVn2cKXOJkYkInL9oqKiGDVq1E219fPz45lnniE0FMaNg379YOjQVH6Z0p3MrY8wsUhlCqbuhxUroH17qFuX/3uyJLsKwxtNB7H+r/UkJibe1LW3bdt2U+1ERCRvOZ80Dw2F9evh++8ds84PHIBChRzLt3TMiOSDPX3wMxz/52S4exM94DNCOzzJV2EWJc1FRERE8hAl0V3U+PF+4H8UKv4BQBGPIJoX03c+RSR3sFgsdO3a9abaRkZGZtkvUwZGjUrg3Xe9mTfXi0eCR8Nbb8G5hPeHO6rx0ZFIbCfS+GfnTN70+j+eeOypm7r2oEGDbqqdiIjkHb/95kiaJyQ4ZpvHx0NYmGPbswfS41P46OzLPJ48wdnmUIFKWKb+QoX21U2MXERERESyi24s6oJOn/Zn+u8+UOdrsDqWculW9XHcrfqbh4jkXyVLwlN9LPDQQ7B5M/z6K7Zadfm0cDtsaYUAKLTyXiLe2Y33N99jOTcb3TAMM8MWEREXFx0NK1c6/o2OdiTQDQMqVIDUVNi/H/z8oEABqOwdzYwTjbMk0OeEPs76CWsorgS6iIiISJ6lrKwLmjevMZmGDep+BYAVC49WetTkqEREXIjVCh07srvKw2Q0yoAxHaDG/zi6rw2PJpam7Nt7eGHkN9zVLYEu1ebSoXJnulXsRiHvQmZHLiIiLuTiWecBAXDHHY7HFSqAm5vjRqA7dsChQ1ClCngF+1P42EkAUq0+zH/oc6p8+AQRZXRfDhEREZG8TDPRXczOnbBmTVXHMi4BRwG4q0QbivoVNTkyERHXU6myhYOxnnz1hTeVTnaHuNIA7KUcA9Lep/auYmxL3MN7a96j/sS6vLrkVbad0rrnIiLimGF+8axzw4C5cx1LuMTEgM0GiYmOG4J6e8OuXeBbMphV/SNJKludk3+v4d5feymBLiIiIpIPaCa6ixkyBAzDCvW/cJb1rNrLxIhERFybjw/06QO9e/syaxYMfG4X2w5WACAzONpZL9VIJ3JXJJG7ImkYXJcnajxF21Jt8XTzvC1x3MrNVOHCDVVFRCRnHDuWddZ5eLgjUd6+Pfz7r+NxZetOnu9biCotizhvNBoR0RQ+iqKAVfORRERERPILJdFdyIYN8MsvQMg2KLMAgDIFStK0WFNzAxMRyQWsVrjnHtj+0p/UqfMYE79xY+pv40hY9jrVGjzLwVoLSPB21F11ch2rFqwjyC2ARyp3oWvFblQoVOGWrn8rN1OFS2+oKiIi2Ss01LGES0yMI4EeE+PY79rVsWX+NJGyHz6D1esO6PkPEREXJc2VQBcRERHJVzT6cyGZmVC36FG44wNnWY/qT2K1qJtERG5EhQqZDPswjXVbzjDq9YJMqNWLfQtq8cVfUOX4hXqnbQl8teVrWv/Wmgd/a89u/90kpieaF7iIiOSY0qWhVy+wWByzzi0WePJJiAhNJuLdpyj/9mNYk5NgzhwYP97scEVERETERJqJ7kLqV0liqmc1ytWIww4UdPenW8VuZoclIpJr+fgYdHk0HWhFat9WPLx9O49NmsjgyfGMqeYFlaeBezoA685shiJQ+6eatC/djg4VO9KsWDM8rB7mPgkREck2HTtCnTpcWKolbQc07ARbtlyo1KsX9OxpXpAiIiIiYjpNcXYl33zDqCpnsFsNAHpVf4oCHgVMDkpEJO/IrFyZhBHv0OTNcdy1/T0sow7BP2PgWHVnnRTSmLZ/Bj1m96DO9zWZukPLrIiI5GUREdCoEUQs+xnq1buQQPf1hR9/hO++gwIak4uIiIjkZ5qJ7irS04n9/AO+6eLY9bV682TVJ82NSUQkj7qjNdzR2pNjx2zM+Ppefv2lI9u8jkKdb6HqFPCJA+CMkUC5gW9TsPJ6Uu69l7QmTch0s+BmccNisZj7JERE5PZIToa+feH77y+UVa0KU6dC5crmxSUiIiIiLkMz0V3I3GfuIt3N8fixKj0o5F3I3IBERPK40FA7T7/lzZyNsPDn4rTe3oQKX62BydNhS2cKxvnTdmMiBSZNIvjRRwmtVYd+z79H/W/r8fayIUTHR5v9FEREXMbnn39O6dKl8fb2pmHDhqxevdrskK4tPh4aNMiaQO/dG1avVgJdRERERJyURHcVnp48/sqPbH1mI42NWvSp3sfsiERE8pXyFe0UazKPBZt9mP9JbQYmD2TZ5odx8/Jx1omKL8MM/6PEcoxvdnzHBz3XsHTYWlKPnDExchER802ZMoUBAwYwdOhQ1q9fT82aNWnbti3Hjx+/dmMzBQZC/fqOxwUKwM8/wzffOJZyERERERE5R8u5uJjK4TXo6P8Y4QXCzQ5FRCTfqljDjYpfhQPvEpvyFl4LFuDz99/MmFkfWAU2d0gO4a91/fhrrRWvr1Op4/EUfewjOFN1K/fXbU/rkq0p4V/C7KciIpIjRo8eTZ8+fejVqxcA48ePZ+bMmXz33Xe88cYbJkd3DZ99BklJMHw4VKpkdjQiIiIi4oKURBcREbkKw8eH1HvvJfXee3notXQKjW/B3z/0Z4ndgwzD8YWuNLxZkdEcPAZD+jJWrlgGK6C8WygtS99Fy/LtaRjWEG93b5OfjYjI7Zeens66desYNGiQs8xqtdKmTRtWrFhhYmTXqUAB+OUXs6MQERERERemJLqIiMh1Ci3lSZeREXQBkhNsrPhhOQv+tLNwdwQHjRAI2pul/m7bMXbv/R9f7/0f7plelM2sQcuKLbm3RjNqhNTA3Zr7/xueMGECiYmJ11XX19eX5ORk576fnx/PPPNMdoUmIjnk5MmT2Gw2QkNDs5SHhoayY8eOy7ZJS0sjLS3NuZ+QkACA3W7HbrdnX7DnrmEYRrZfR26c+sZ1qW9cm/rHdalvXJf6xrXlZP9c7zVy/6d3ERERE/gGuHHnS6W48yUwDBjc62Uq7ZrAzD2xFA/9lINlt7CiONjP3X0k0z2Nne5r2HlgDRMOfIR7RgHKZdSkZbnm3F+vBXYjdw7eEhMT6dq16zXrGYZBfHw8gYGBWCwWACIjI7M7PBFxUSNHjmTYsGGXlJ84cYLU1NRsvbbdbic+Ph7DMLBadYsoV6K+cV3qG9em/nFd6hvXpb5xbTnZP2fPnr2uekqii4iI3CKLBWzhZ3lsZC0eAywpLfBcs4bkZfP4d988vvOyM6+sAQFHnG0yPZLY4bGcHUeXM/6P93FLK8D4V6bTr+XDvHT/K+Y9GRGRGxQcHIybmxvHjh3LUn7s2DHCwsIu22bQoEEMGDDAuZ+QkECJEiUICQkhICAgW+O12+1YLBZCQkL0odnFqG9cl/rGtal/XJf6xnWpb1xbTvaPt/f1LbuqJLqIiMhtZvj4kNa8OW7Nm9Oc4TSOPcOWyD3MnnOABbZD7Cq+n8yIZeB3IeFk80pin9dydn6xGl4cB40aQf36RBVsyZ++23mwWWOqFyvjnMUtIuIqPD09qVu3LvPnz6dDhw6A44PP/Pnz6du372XbeHl54eXldUm51WrNkQ+yFoslx64lN0Z947rUN65N/eO61DeuS33j2nKqf673/Eqii4iIZDOPsELU7l+f2v3r8wZgPxTDjun7mbn4AAsyD7A7bB+pJdeBfywtDmXCgQOObcoUhgWMYfqAfgzZBX677uWeHZ9Su7E3tdqGUruBB/9ZglhExBQDBgygZ8+e1KtXjwYNGvDpp5+SlJREr169zA5NREREROSWKYkuIiIuJSoqilGjRmUp++8NKa9m27Zt2RHWbWUtEU6VF8OpQmMGAsQn8Fmftyns7k71jOVkuB/GIzMTgBXFL8w8T4ypzS/ry/HLeuBzwGLH+lIZCsSHUsWtENVKBFDYN4JgSzBWy/X9NV039xSR26FLly6cOHGCIUOGEBsbS61atZg1a9YlNxsVEREREcmNlEQXERGXYrFYstyo8nI3pLyaQYMGZWd42SMwgCNlveg7ciQAxzMzcd+1C4/1G3hqdhz/rOjM3sJHSD7YgIyL2xXehb3QAc4WOsAqYBVAMnjbrJS1BXPmaFNKe1egRqka3FG5Kg2rFMbXN+uldXNPEbld+vbte8XlW0REREREcjMl0UVERFyNuzuZVaqQWaUKzz0GzwGkpGDdvJ39875g26p0tuwLYLHfGaKSgjEKnMzSPNXNzla341B6GkeB5WkwPgpYHoTnyUoUTilDcc/SVAirTNrpAqSm2fD2csv55ykiIiIiIiKSCyiJLiIikhv4+GBvUIeSDepQEmgHvJqaituOyox5bwyValrYc3Yr24xYthTKZHeQBcNqZD2H72nSSy4nhuXEAGsAikGZPu/yAa2gbFkoW5YZMbVZlHqCelWqU61cEKVLWwgMzPFnLCIiIiIiIuISlEQXERHJrby9sdWqyZGyofR9cyRtAAwDt5gY0jZvZOWmLSw7eoSNtpPsLXCKE0ViyPA/eclpKm8+DFE/O/e/LvQpM/v1hy3A/3rC9B8o6JVC6SJJlC5hx63aSipERNCueYWceqYiIiIiIiIiplESXUREJC+xWLAVLYp70aI0bduepufLDQPr8eMk7NjAxm1RrDl0mK0pJzjqfohasQeznGJH8EU7Z8MBiEvzIeqQD1GHbXBnR0jLZOQcC+GJFn4f/S4l3III8wnj7w3tCfYKp3hQSSKKlqJMeCHCw+2EhWXi6Zk11MvdRPZ66YaoIiIiIiIiklOURBcREckPLBbsoaH4hbbjjhbtuONc8aBBgyiydAjHDhzAff9+3A8cYOD2bUzcU4/DHgmUjI3HygL2U5pDlCAz4Ci4ZZ47p0GMv0EMZ4AzwF6o+y+7Lr7uIS/YWhziS0BCMQqkhBJkD2ZQCyslz5yhe5s22IOCiD3uTnS0O8HBdoKDbRQsaHC1+8jqhqgiIiIiIiKSU5REFxERyecMHx8yK1Uis1IlAO47twGQkYHb4cO4H1iBJXoKW/fG8mV0Y/aRQLLvQWL9znKywFVO7p4GQXsdG5B0bpu53o+/ZibCzJkY7u78VaAfz3ZYBxYDTlTG459xhBRIIiQwleCgDHyKHyI4xIsyEUV48qms2fWzZ8HbGzw8bvMLIyIiIiIiIoKS6CIiInI1Hh7YIiKwRURASygLfHzR4Tdff53Bz/Um9sBmYmN3En3sMLvjTnMgPZ4jbkkc904kzv8s6d4pWU5bMjHd+diSmcmJeE8o+S+4ZYB3HBl2d46eDeTo2UA4DNR8EiLmAvDR51Ao1Urky0MojC+bTzTg6KnyeKUH4mcLJNASSJBnQUJ8gwgLDKFokTBCixaiYRM3GjS4EINhQFwcBASAm1t2vYAiIiIiIiKS2ymJLiIiIjfNbrXiXboCpUtXoDTQ6HKVMjJIOhpN9O51JCQe5cipA1Sp6saivRtoVKIE1mPHqHZ8E56ZVtLdoHCiO8WJ4hihnCAEG+7gF+s83VkvOOtl5yDJQDIU/BvKQxqO7RSw778xnPDFf4o7cZ0CsRYKgqAgUv2LELS7MxRdi2dGAQrv7kyQpTAF/W0E+hsUKJyEb1AKIQWDeeEZL0pXLQDujqHTyZOwbx/4+4Of34VNs+FFRERERETyHiXRRUREJHt5eOBbsjzFA4sQGBiI5dxi54NODKL8yJEA1Af22WyknjxK6vGjFHlgN9bTq7AcP0n80VSG4MueY+VIdDtLusdpTntkcNob7NbrjMEzmQy7J9aDCXDwEADxhMIDflDnW9KBmB0PEHM86EKbWj9A414AjPnVQsAkA78MC/6ZbqRmhrAnsRqk+0Gav+PfdH/cMnzwsvngbfhQAB8Ke3ky84lEigYWhwIFoEABpiwLZeshP/wCvPEt6IlPgAe+flZ8fMDX17H5+EBoKBQvfnu6QERERERERG6eSyTRP//8cz766CNiY2OpWbMm48aNo8HF37f+j6lTpzJ48GD2799P+fLl+eCDD7jnnnucxw3DYOjQoXz99dfExcVxxx138OWXX1K+fPmceDoiIiJyM9zc8A4tgXdoCdIvKvYE3ufZLFUjIyN5ud9LxMVEc/JYNKdOHuTk6SOcio/hRMIxjpyN52jKWY5nJnHSSCbOLYWgjAwILQSnT0NGBnashHrv4Ni5cxZJTeUsyaTg6yjwindeL8Pd4JQ7nMIAMoEYCI255CnY4Pz8eE4Dh4B5o6DHxgt1vg0bydxnB4HNA+a+ALM/yXqSx+8Cw42SlgSa+e3Fx+KJr8ULHzcvPtv6ILZ0PzwNT7zsXnjjhbfFE283D3zcPPB298Tb3ZOn70yiY90QRzbe25tDZ/z49g9/fHw88SngjpevG17n//Wx4u0NXl6OrVEj54R7AJKSID3dccyMmfYTJkwgMTHxptpu27aNKlWq3FRbPz8/nnnmmZtqKyIiIiIieYvpSfQpU6YwYMAAxo8fT8OGDfn0009p27YtO3fupEiRIpfUX758Od26dWPkyJHcd999TJo0iQ4dOrB+/XqqVasGwIcffsjYsWP58ccfiYiIYPDgwbRt25Zt27bh7e2d009RREREsoHV3YOgEhUIKlHh+ht9hmMx9ORkip45w5Kjmzl0Yg/xCSe49+s9eCVuJv10Igkn0piauJHI+ErEGSngfoYkazpnrRkkutlIvoFksl961v2zHueGX24ZYPxnKr3FDmXnAXAQmPjfkzXbBkDqNa4ZvaMQHV89c2GfZgzrnQGBKyHFE95KyXrtJh9D9YmQ6U3TnzfiC3gZbnjhxo7kymxJrOxI+ts9sNjdcLO7YzXccLO74Wa44Y47EX5nmVk1g3D3go5su7s73WfVYbP7fjws7hRKrEjBjGJ4uDsOWz3TSQ7cg6ebB3fVSqBlzRQ8PL3x8PAm0+bNd3+VxMvTnY1RhahZuyWeHlbcva24eVhx97Tg5mnFzdON+g3SCQhyA6sVLBZOn7Zy8KAbbm6waNEYHnvsMdzcwM3NwN0drFbHvx4eUKiQPcvrZhhw7osSREZGXuNVFhERERGR/ML0JPro0aPp06cPvXo5vi49fvx4Zs6cyXfffccbb7xxSf0xY8bQrl07Bg4cCMCIESOYO3cun332GePHj8cwDD799FPeeustHnzwQQB++uknQkNDmT59Ol27ds25JyciIiKux2JxLq1SoXhx/puC9wSCgefObf81atQoOnXuRHJGEkkJJ0k+e4rkpNMkJZ0hOTmO5NR4klITSEpLIDE1kZJdKhKf7Is1OZndUVG8VGI3CXGlSCWDJj47aR4xkuQUCympFuINg/dvw1P0ysy6n4YXuCWde/7Gpcn7wAMQHgXAskvOtvLc5nB+Lv5/bQAOfwXhRy+ULav+Hoc6/p9j558xsL7phYMF90P/+wCYZAPW/+eE51fWaXrujwmGBTLcIM0dDDewu4PdjY5/Wvj1r1OO2NzcmGfpyRM9doL3GQgsyV+tQ7Oet9VgKLEcT8NOa5/VWA0L7lhxw8rapBocSi2OxbDy6H4L7OgDX399mWcrIiIiIiL5ialJ9PT0dNatW8egQYOcZVarlTZt2rBixYrLtlmxYgUDBgzIUta2bVumT58OQHR0NLGxsbRp08Z5PDAwkIYNG7JixQol0UVEROSWuVnd8PcKwD8kAELKXLP++cVIFkZG8sorr9DtCvUMw+Bt28skZySTkp5EcuIZUpLiHcn5pDhSUs6SnJJAcupZUlLPkpyWRFJaIslpaSSlpZOSnkZKRjpVi/vB6zUgJQVSU6kV50FrVnIwrgQWu5VBVT4mLcNKaoaVtAw3pvlsYq3NDbub7ZZeF4+sE7vJtBoXdmz/mb7v9p8p+tdiMcAt07FdJM0j8EIVmw0bBgTvAN9T4J526XnCoqDMAtKBWZccXAo4/lCQcrQprE64sRhFRERERCRPMjWJfvLkSWw2G6GhWWcIhYaGsmPHjsu2iY2NvWz92NhY5/HzZVeq819paWmkpV34kBUf71gDNS4uDrvdftk2V2K320lISMDT0xOr9XrvdpZVamoqCQk396EtIyPDlLZmXjs74jYMg7Nnz2KxWJw3wLvd186Pr3dOP+eL+zE3xe0Kbc289n/bXu/7MTuunVNtzbx2TsV9uX68lWunpqYSFxd3U21v1a38P329cVuw4IsfvgX8oECJm7rWxVfxAH67+OCwrHWf4XEA7IaddFs6aZlpzn/TbKmkp6eSkZpMemoSp08ex8fbg8yMNDLTU8jISCMjPZWM9FQKflaTOLzAZoOMDCYnHGBOQifSMjK5o8NRKt3/NRlpdjLSDWLSz/BV/B2kG5l4uqXi4Z5KhpFJht1Gmt3OsRQvbNhJTk/D6u6GDTs2i4Ed27l/7RhWOyEevpyoUxZLRgbYbIQnJeKXZJBq88IvKZV7fL8n07Bit1uxGW4ssx3mxLXWwwF8MhKIB4wc/jk7/7NlGMY1auYt55/vrfw+ul52u52zZ8/i7e190+N1yR7qG9elvnFt6h/Xpb5xXeob15aT/XPd42/DREeOHDEAY/ny5VnKBw4caDRo0OCybTw8PIxJkyZlKfv888+NIkWKGIZhGP/++68BGEePHs1Sp1OnTkbnzp0ve86hQ4caOCYdadOmTZs2bdq0adNm+nbo0KGbHWLnSocOHTL9NdemTZs2bdq0adOWf7drjb9NnYkeHByMm5sbx44dy1J+7NgxwsLCLtsmLCzsqvXP/3vs2DHCw8Oz1KlVq9Zlzzlo0KAsS8TY7XZOnz5N4cKFr2vW48USEhIoUaIEhw4dIiAg4IbaiutQP+YN6se8Qf2YN6gf8wb1Y/Yzzn1ro2jRomaHkqOKFi3KoUOH8Pf3v+Hx943Sz7HrUt+4LvWNa1P/uC71jetS37i2nOyf6x1/m5pE9/T0pG7dusyfP58OHToAjgT2/Pnz6du372XbNG7cmPnz59O/f39n2dy5c2ncuDEAERERhIWFMX/+fGfSPCEhgVWrVvHcc5e7PRh4eXnh5eWVpaxgwYK39NwCAgL0JswD1I95g/oxb1A/5g3qx7xB/Zi9AgMDzQ4hx1mtVooXL56j19TPsetS37gu9Y1rU/+4LvWN61LfuLac6p/rGX+bmkQHGDBgAD179qRevXo0aNCATz/9lKSkJHr16gVAjx49KFasGCNHjgSgX79+tGjRglGjRnHvvfcSGRnJ2rVr+eqrrwCwWCz079+fd955h/LlyxMREcHgwYMpWrSoM1EvIiIiIiIiIiIiInI9TE+id+nShRMnTjBkyBBiY2OpVasWs2bNct4Y9ODBg1kWkG/SpAmTJk3irbfe4v/+7/8oX74806dPp1q1as46r732GklJSTz99NPExcXRtGlTZs2ahbe3d44/PxERERERERERERHJvUxPogP07dv3isu3LFq06JKyTp060alTpyuez2KxMHz4cIYPH367QrxuXl5eDB069JLlYSR3UT/mDerHvEH9mDeoH/MG9aPkBfo5dl3qG9elvnFt6h/Xpb5xXeob1+aK/WMxDMMwOwgREREREREREREREVdkvXYVEREREREREREREZH8SUl0EREREREREREREZErUBJdREREREREREREROQKlES/zT7//HNKly6Nt7c3DRs2ZPXq1WaHJDfg7bffxmKxZNkqVapkdlhyDUuWLOH++++naNGiWCwWpk+fnuW4YRgMGTKE8PBwfHx8aNOmDbt37zYnWLmia/XjE088ccn7s127duYEK5c1cuRI6tevj7+/P0WKFKFDhw7s3LkzS53U1FReeOEFChcujJ+fHx07duTYsWMmRSyXcz392LJly0vej88++6xJEYtcP43VXZPG4K5D42rXpvGya9IY2LVpbOu6vvzyS2rUqEFAQAABAQE0btyYf/75x3nc1d43SqLfRlOmTGHAgAEMHTqU9evXU7NmTdq2bcvx48fNDk1uQNWqVYmJiXFuy5YtMzskuYakpCRq1qzJ559/ftnjH374IWPHjmX8+PGsWrWKAgUK0LZtW1JTU3M4Urmaa/UjQLt27bK8PydPnpyDEcq1LF68mBdeeIGVK1cyd+5cMjIyuPvuu0lKSnLWefnll/nzzz+ZOnUqixcv5ujRozz88MMmRi3/dT39CNCnT58s78cPP/zQpIhFro/G6q5NY3DXoHG1a9N42TVpDOzaNLZ1XcWLF+f9999n3bp1rF27ltatW/Pggw+ydetWwAXfN4bcNg0aNDBeeOEF577NZjOKFi1qjBw50sSo5EYMHTrUqFmzptlhyC0AjN9//925b7fbjbCwMOOjjz5ylsXFxRleXl7G5MmTTYhQrsd/+9EwDKNnz57Ggw8+aEo8cnOOHz9uAMbixYsNw3C89zw8PIypU6c662zfvt0AjBUrVpgVplzDf/vRMAyjRYsWRr9+/cwLSuQmaKzuujQGd00aV7s2jZddl8bArk1jW9dWqFAh45tvvnHJ941mot8m6enprFu3jjZt2jjLrFYrbdq0YcWKFSZGJjdq9+7dFC1alDJlyvDoo49y8OBBs0OSWxAdHU1sbGyW92ZgYCANGzbUezMXWrRoEUWKFKFixYo899xznDp1yuyQ5Cri4+MBCAoKAmDdunVkZGRkeT9WqlSJkiVL6v3owv7bj+dNnDiR4OBgqlWrxqBBg0hOTjYjPJHrorG669MY3PVpXJ07aLxsPo2BXZvGtq7JZrMRGRlJUlISjRs3dsn3jbspV82DTp48ic1mIzQ0NEt5aGgoO3bsMCkquVENGzbkhx9+oGLFisTExDBs2DCaNWvGli1b8Pf3Nzs8uQmxsbEAl31vnj8muUO7du14+OGHiYiIYO/evfzf//0f7du3Z8WKFbi5uZkdnvyH3W6nf//+3HHHHVSrVg1wvB89PT0pWLBglrp6P7quy/UjQPfu3SlVqhRFixZl06ZNvP766+zcuZNp06aZGK3IlWms7to0Bs8dNK52fRovm09jYNemsa3r2bx5M40bNyY1NRU/Pz9+//13qlSpQlRUlMu9b5REF7lI+/btnY9r1KhBw4YNKVWqFL/88gu9e/c2MTIR6dq1q/Nx9erVqVGjBmXLlmXRokXceeedJkYml/PCCy+wZcsWrWmby12pH59++mnn4+rVqxMeHs6dd97J3r17KVu2bE6HKSK5nMbgIreHxsvm0xjYtWls63oqVqxIVFQU8fHx/Prrr/Ts2ZPFixebHdZlaTmX2yQ4OBg3N7dL7hJ77NgxwsLCTIpKblXBggWpUKECe/bsMTsUuUnn3396b+Y9ZcqUITg4WO9PF9S3b1/++usvFi5cSPHixZ3lYWFhpKenExcXl6W+3o+u6Ur9eDkNGzYE0PtRXJbG6rmLxuCuSePq3Efj5ZylMbBr09jWNXl6elKuXDnq1q3LyJEjqVmzJmPGjHHJ942S6LeJp6cndevWZf78+c4yu93O/Pnzady4sYmRya1ITExk7969hIeHmx2K3KSIiAjCwsKyvDcTEhJYtWqV3pu53OHDhzl16pTeny7EMAz69u3L77//zoIFC4iIiMhyvG7dunh4eGR5P+7cuZODBw/q/ehCrtWPlxMVFQWg96O4LI3VcxeNwV2TxtW5j8bLOUNjYNemsW3uYrfbSUtLc8n3jZZzuY0GDBhAz549qVevHg0aNODTTz8lKSmJXr16mR2aXKdXX32V+++/n1KlSnH06FGGDh2Km5sb3bp1Mzs0uYrExMQsfyGOjo4mKiqKoKAgSpYsSf/+/XnnnXcoX748ERERDB48mKJFi9KhQwfzgpZLXK0fg4KCGDZsGB07diQsLIy9e/fy2muvUa5cOdq2bWti1HKxF154gUmTJjFjxgz8/f2da9UFBgbi4+NDYGAgvXv3ZsCAAQQFBREQEMCLL75I48aNadSokcnRy3nX6se9e/cyadIk7rnnHgoXLsymTZt4+eWXad68OTVq1DA5epEr01jddWkM7jo0rnZtGi+7Jo2BXZvGtq5r0KBBtG/fnpIlS3L27FkmTZrEokWLmD17tmu+bwy5rcaNG2eULFnS8PT0NBo0aGCsXLnS7JDkBnTp0sUIDw83PD09jWLFihldunQx9uzZY3ZYcg0LFy40gEu2nj17GoZhGHa73Rg8eLARGhpqeHl5GXfeeaexc+dOc4OWS1ytH5OTk427777bCAkJMTw8PIxSpUoZffr0MWJjY80OWy5yuf4DjO+//95ZJyUlxXj++eeNQoUKGb6+vsZDDz1kxMTEmBe0XOJa/Xjw4EGjefPmRlBQkOHl5WWUK1fOGDhwoBEfH29u4CLXQWN116QxuOvQuNq1abzsmjQGdm0a27quJ5980ihVqpTh6elphISEGHfeeacxZ84c53FXe99YDMMwsic9LyIiIiIiIiIiIiKSu2lNdBERERERERERERGRK1ASXURERERERERERETkCpREFxERERERERERERG5AiXRRURERERERERERESuQEl0EREREREREREREZErUBJdREREREREREREROQKlEQXEREREREREREREbkCJdFFRERERERERERERK5ASXQRERe0aNEiLBYLcXFxt3SeJ554gg4dOtyWmMzQsmVL+vfvf816zZs3Z9KkSdkf0EW6du3KqFGjcvSaIiIiIrnF7RrP5jSLxcL06dNv2/lKly7Np59+etvOZ5b9+/djsViIiooCcq5/H3/8cd57773rrn8zcb399tvUqlXrxoO7yMmTJylSpAiHDx++pfOIiOtSEl1EJBuNHz8ef39/MjMznWWJiYl4eHjQsmXLLHXPD/j27t1LkyZNiImJITAwMNtj/Prrr6lZsyZ+fn4ULFiQ2rVrM3LkyGy/7u3yxx9/cOzYMbp27Xpbzvfjjz/StGnTa9Z76623ePfdd4mPj78t1xURERHJLSwWy1W3t99+2+wQr+lKidOYmBjat2+fo7GcPn2a/v37U6pUKTw9PSlatChPPvkkBw8ezNE4zrvcRJwSJUoQExNDtWrVciyOjRs38vfff/PSSy/l2DVvVnBwMD169GDo0KFmhyIi2URJdBGRbNSqVSsSExNZu3ats2zp0qWEhYWxatUqUlNTneULFy6kZMmSlC1bFk9PT8LCwrBYLNka33fffUf//v156aWXiIqK4t9//+W1114jMTExW697O40dO5ZevXphtd6e/9JmzJjBAw88cM161apVo2zZsvzvf/+7LdcVERERyS1iYmKc26effkpAQECWsldffdW02NLT02+pfVhYGF5eXrcpmms7ffo0jRo1Yt68eYwfP549e/YQGRnJnj17qF+/Pvv27cuxWK7Gzc2NsLAw3N3dc+ya48aNo1OnTvj5+eXYNW9Fr169mDhxIqdPnzY7FBHJBkqii4hko4oVKxIeHs6iRYucZYsWLeLBBx8kIiKClStXZilv1aqV8/HFX0P84YcfKFiwILNnz6Zy5cr4+fnRrl07YmJinO1tNhsDBgygYMGCFC5cmNdeew3DMK4a3x9//EHnzp3p3bs35cqVo2rVqnTr1o13333XWef8TJRhw4YREhJCQEAAzz77bJYPKHa7nZEjRxIREYGPjw81a9bk119/zXKtLVu20L59e/z8/AgNDeXxxx/n5MmTzuNJSUn06NEDPz8/wsPDr2uplBMnTrBgwQLuv//+LOUWi4UJEyZw33334evrS+XKlVmxYgV79uyhZcuWFChQgCZNmrB3794s7VJTU5kzZ44zif7FF19Qvnx5vL29CQ0N5ZFHHslS//777ycyMvKacYqIiIjkJWFhYc4tMDAQi8WSpezipOe6deuoV68evr6+NGnShJ07d2Y514wZM6hTpw7e3t6UKVOGYcOGZfkW58GDB3nwwQfx8/MjICCAzp07c+zYMefx8zPKv/nmGyIiIvD29gYgLi6Op556yjl+bd26NRs3bgQcY+thw4axceNG5+z5H374Abh0OZfDhw/TrVs3goKCKFCgAPXq1WPVqlUA7N27lwcffJDQ0FD8/PyoX78+8+bNu6HX8s033+To0aPMmzeP9u3bU7JkSZo3b87s2bPx8PDghRdecNa93NIwtWrVyjLzf/To0VSvXp0CBQpQokQJnn/++SwTZK71ueLtt9/mxx9/ZMaMGc7XZtGiRZcs53I5y5Yto1mzZvj4+FCiRAleeuklkpKSnMevNba+mM1m49dff71knP/zzz9Tr149/P39CQsLo3v37hw/fvyK5zn/fKdPn+68dtu2bTl06NAldX/++WdKly5NYGAgXbt25ezZs85js2bNomnTps7PWvfdd98lnyWqVq1K0aJF+f33368Yj4jkXkqii4hks1atWrFw4ULn/sKFC2nZsiUtWrRwlqekpLBq1SpnEv1ykpOT+fjjj/n5559ZsmQJBw8ezDLLZ9SoUfzwww989913LFu2jNOnT19zABcWFsbKlSs5cODAVevNnz+f7du3s2jRIiZPnsy0adMYNmyY8/jIkSP56aefGD9+PFu3buXll1/mscceY/HixYDjQ0zr1q2pXbs2a9euZdasWRw7dozOnTs7zzFw4EAWL17MjBkzmDNnDosWLWL9+vVXjWvZsmXOJPl/jRgxgh49ehAVFUWlSpXo3r07zzzzDIMGDWLt2rUYhkHfvn0veZ7FihWjUqVKrF27lpdeeonhw4ezc+dOZs2aRfPmzbPUb9CgAatXryYtLe2qcYqIiIjkV2+++SajRo1i7dq1uLu78+STTzqPLV26lB49etCvXz+2bdvGhAkT+OGHH5wTOux2Ow8++CCnT59m8eLFzJ07l3379tGlS5cs19izZw+//fYb06ZNcyZ5O3XqxPHjx/nnn39Yt24dderU4c477+T06dN06dKFV155hapVqzpnz//3nOBYhrFFixYcOXKEP/74g40bN/Laa69ht9udx++55x7mz5/Phg0baNeuHffff/91L8Nit9uJjIzk0UcfJSwsLMsxHx8fnn/+eWbPnn1DM5utVitjx45l69at/PjjjyxYsIDXXnstS52rfa549dVX6dy5szOxHhMTQ5MmTa553b1799KuXTs6duzIpk2bmDJlCsuWLXOOt69nbH2xTZs2ER8fT7169bKUZ2RkMGLECDZu3Mj06dPZv38/TzzxxFVjS05O5t133+Wnn37i33//JS4u7pKlIPfu3cv06dP566+/+Ouvv1i8eDHvv/++83hSUhIDBgxg7dq1zJ8/H6vVykMPPeT8WTivQYMGLF269Jqvl4jkQoaIiGSrr7/+2ihQoICRkZFhJCQkGO7u7sbx48eNSZMmGc2bNzcMwzDmz59vAMaBAwcMwzCMhQsXGoBx5swZwzAM4/vvvzcAY8+ePc7zfv7550ZoaKhzPzw83Pjwww+d+xkZGUbx4sWNBx988IqxHT161GjUqJEBGBUqVDB69uxpTJkyxbDZbM46PXv2NIKCgoykpCRn2Zdffmn4+fkZNpvNSE1NNXx9fY3ly5dnOXfv3r2Nbt26GYZhGCNGjDDuvvvuLMcPHTpkAMbOnTuNs2fPGp6ensYvv/ziPH7q1CnDx8fH6Nev3xXj/+STT4wyZcpcUg4Yb731lnN/xYoVBmB8++23zrLJkycb3t7eWdr16dPHePXVVw3DMIzffvvNCAgIMBISEq54/Y0bNxqAsX///ivWEREREcnLvv/+eyMwMPCS8vPj2Xnz5jnLZs6caQBGSkqKYRiGceeddxrvvfdelnY///yzER4ebhiGYcyZM8dwc3MzDh486Dy+detWAzBWr15tGIZhDB061PDw8DCOHz/urLN06VIjICDASE1NzXLusmXLGhMmTHC2q1mz5iVxA8bvv/9uGIZhTJgwwfD39zdOnTp1na+GYVStWtUYN26cc79UqVLGJ598ctm6sbGxBnDF49OmTTMAY9WqVVc8V82aNY2hQ4deMZ6pU6cahQsXdu5fz+eKnj17XvIZIjo62gCMDRs2GIZx6eeV3r17G08//XSWNkuXLjWsVquRkpJyXWPri/3++++Gm5ubYbfbr1pvzZo1BmCcPXv2snGdf74rV650ttm+fXuW13Xo0KGGr69vltgGDhxoNGzY8IrXPXHihAEYmzdvzlL+8ssvGy1btryu5ygiuYtmoouIZLOWLVuSlJTEmjVrWLp0KRUqVCAkJIQWLVo410VftGgRZcqUoWTJklc8j6+vL2XLlnXuh4eHO7+6GB8fT0xMDA0bNnQed3d3v2Tmxn+Fh4ezYsUKNm/eTL9+/cjMzKRnz560a9cuy6yKmjVr4uvr69xv3LgxiYmJHDp0iD179pCcnMxdd92Fn5+fc/vpp5+cX3HcuHEjCxcuzHK8UqVKgGPWx969e0lPT88Sf1BQEBUrVrxq/CkpKc6v7P5XjRo1nI9DQ0MBqF69epay1NRUEhISADAMgz///NO5lMtdd91FqVKlKFOmDI8//jgTJ04kOTk5yzV8fHwALikXEREREYeLx2Th4eEAzjHsxo0bGT58eJYxYp8+fYiJiSE5OZnt27dTokQJSpQo4TxHlSpVKFiwINu3b3eWlSpVipCQEOf+xo0bSUxMpHDhwlnOHR0dfckSHFcTFRVF7dq1CQoKuuzxxMREXn31VSpXrkzBggXx8/Nj+/btN3xDUOMaSzB6enpe97nmzZvHnXfeSbFixfD39+fxxx/n1KlTWcarV/tccbM2btzIDz/8kOX1btu2LXa7nejo6OsaW18sJSUFLy+vS+4RtW7dOu6//35KliyJv78/LVq0ALjqa+7u7k79+vWd+5UqVbrkZ6h06dL4+/s79//7muzevZtu3bpRpkwZAgICKF269GWv6+Pjo88GInlUzt0RQkQknypXrhzFixdn4cKFnDlzxjnQK1q0KCVKlGD58uUsXLiQ1q1bX/U8Hh4eWfYtFss1B9zXq1q1alSrVo3nn3+eZ599lmbNmrF48eKrLi9z3vk1FmfOnEmxYsWyHDt/U6bExETuv/9+Pvjgg0vah4eHs2fPnpuKOzg4mDNnzlz22MWv1/nB9+XKzv+xYPXq1WRmZjq/rurv78/69etZtGgRc+bMYciQIbz99tusWbOGggULAji/WnvxhzYRERERueBq46/ExESGDRvGww8/fEm7K02UuJwCBQpk2U9MTLzkvkTnnR/HXY/zEyau5NVXX2Xu3Ll8/PHHlCtXDh8fHx555JHrvrlpSEjIJcnci23fvh13d3ciIiIAx1It/x3/Z2RkOB/v37+f++67j+eee453332XoKAgli1bRu/evUlPT3dOismOzxWJiYk888wzvPTSS5ccK1myJJ6entccW18sODiY5ORk0tPTnX9ESEpKom3btrRt25aJEycSEhLCwYMHadu27S3fUPZyr8nFk4ruv/9+SpUqxddff03RokWx2+1Uq1btkuuePn1anw1E8igl0UVEckCrVq1YtGgRZ86cYeDAgc7y5s2b888//7B69Wqee+65mz5/YGAg4eHhrFq1yrm2YGZmpnP9xxtRpUoVgCw3Adq4cSMpKSnODxIrV67Ez8+PEiVKEBQUhJeXFwcPHnT+geC/6tSpw2+//Ubp0qVxd7/0v56yZcvi4eHBqlWrnLPxz5w5w65du654ToDatWsTGxvLmTNnKFSo0A09z/+aMWMG9957L25ubs4yd3d32rRpQ5s2bRg6dCgFCxZkwYIFzg96W7ZsoXjx4gQHB9/StUVERETyozp16rBz507KlSt32eOVK1fm0KFDHDp0yDkbfdu2bcTFxTnHrFc6b2xsLO7u7s4Zw//l6emJzWa7anw1atTgm2++4fTp05edjf7vv//yxBNP8NBDDwGORPL+/fuves6LWa1WOnfuzMSJExk+fHiWddFTUlL44osveOihhwgMDAQcSffzNwAFSEhIIDo62rm/bt067HY7o0aNwmp1LDzwyy+/XHc8513Pa/NfderUYdu2bVfsS7j22PpitWrVAhz9ff7xjh07OHXqFO+//77z52Ht2rXXjC0zM5O1a9fSoEEDAHbu3ElcXNxl76t0OadOnWLnzp18/fXXNGvWDHDcm+lytmzZQsuWLa/rvCKSu2g5FxGRHNCqVSuWLVtGVFRUlqRwixYtmDBhAunp6dc16/tq+vXrx/vvv8/06dPZsWMHzz//PHFxcVdt89xzzzFixAj+/fdfDhw4wMqVK+nRowchISE0btzYWS89PZ3evXuzbds2/v77b4YOHUrfvn2xWq34+/vz6quv8vLLL/Pjjz+yd+9e1q9fz7hx4/jxxx8BeOGFFzh9+jTdunVjzZo17N27l9mzZ9OrVy9sNht+fn707t2bgQMHsmDBArZs2cITTzzhHPxfSe3atQkODubff/+9pdcO4I8//nAu5QLw119/MXbsWKKiojhw4AA//fQTdrs9yxIzS5cu5e67777la4uIiIjkR0OGDOGnn35i2LBhbN26le3btxMZGclbb70FQJs2bahevTqPPvoo69evZ/Xq1fTo0YMWLVpcddnCNm3a0LhxYzp06MCcOXPYv38/y5cv580333QmXUuXLk10dDRRUVGcPHnysjeK79atG2FhYXTo0IF///2Xffv28dtvv7FixQoAypcv77yZ6caNG+nevfslN5q8lnfffZewsDDuuusu/vnnHw4dOsSSJUto27YtVquVMWPGOOu2bt2an3/+maVLl7J582Z69uyZZQJIuXLlyMjIYNy4cezbt4+ff/6Z8ePH31A851+bTZs2sXPnTk6ePJlltvuVvP766yxfvpy+ffsSFRXF7t27mTFjhvPGotcztr5YSEgIderUyZKsPj+j/fzz++OPPxgxYsQ1Y/Pw8ODFF19k1apVrFu3jieeeIJGjRo5k+rXUqhQIQoXLsxXX33Fnj17WLBgAQMGDLikXnJyMuvWrdPnA5E8Skl0EZEc0KpVK1JSUihXrpxzfW5wJNHPnj1LxYoVnWtE3qxXXnmFxx9/nJ49e9K4cWP8/f2ds2KupE2bNqxcuZJOnTpRoUIFOnbsiLe3N/Pnz6dw4cLOenfeeSfly5enefPmdOnShQceeIC3337beXzEiBEMHjyYkSNHUrlyZdq1a8fMmTOdXz0tWrQo//77Lzabjbvvvpvq1avTv39/ChYs6EyUf/TRRzRr1oz777+fNm3a0LRpU+rWrXvV+N3c3OjVqxcTJ068yVfNYe/evezZs4e2bds6ywoWLMi0adNo3bo1lStXZvz48UyePJmqVasCkJqayvTp0+nTp88tXVtEREQkv2rbti1//fUXc+bMoX79+jRq1IhPPvmEUqVKAY4lNWbMmEGhQoVo3rw5bdq0oUyZMkyZMuWq57VYLPz99980b96cXr16UaFCBbp27cqBAwecY/GOHTvSrl07WrVqRUhICJMnT77kPJ6ensyZM4ciRYpwzz33UL16dd5//31n4nr06NEUKlSIJk2acP/999O2bdsb/hZocHAwK1eupFWrVjzzzDNERETQokULbDYbUVFRWT4jDBo0iBYtWnDfffdx77330qFDhyxrm9esWZPRo0fzwQcfUK1aNSZOnMjIkSNvKB6APn36ULFiRerVq0dISMh1TVipUaMGixcvZteuXTRr1ozatWszZMgQihYtClx7bH05Tz31VJZxfkhICD/88ANTp06lSpUqvP/++3z88cfXjM3X15fXX3+d7t27c8cdd+Dn53fNn6GLWa1WIiMjWbduHdWqVePll1/mo48+uqTejBkzKFmypHO2uojkLRbjdi2oKyIiedITTzxBXFwc06dPNzuUy4qNjaVq1aqsX7/e+YHrRo0ePZp58+bx999/X3ebL7/8kt9//505c+bc1DVFRERERC7n22+/5fnnn2fKlCl06NDB7HBMk5KSQsWKFZkyZUqWb8neiB9++IH+/ftf8xu6t0OjRo146aWX6N69e7ZfS0Rynmaii4hIrhYWFsa3337LwYMHb/ocxYsXZ9CgQTfUxsPDg3Hjxt30NUVERERELqd3795ERkayfft2UlJSzA7HND4+Pvz000+cPHnS7FCu6eTJkzz88MN069bN7FBEJJtoJrqIiFyVq89EFxERERERuZycnIkuInmbkugiIiIiIiIiIiIiIleg5VxERERERERERERERK5ASXQRERERERERERERkStQEl1ERERERERERERE5AqURBcRERERERERERERuQIl0UVERERERERERERErkBJdBERERERERERERGRK1ASXURERERERERERETkCpREFxERERERERERERG5AiXRRURERERERERERESu4P8BE8ZlowgJFMQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Detailed Comparison of Top 3 Distributions (by RMSE, p > 0.05):\n",
      "================================================================================\n",
      "Distribution         RMSE         AIC      KS p-val   Parameters\n",
      "================================================================================\n",
      "alpha                0.003774     5224.7   0.9999     (3 params)\n",
      "jf_skew_t            0.003931     5230.2   0.9990     (4 params)\n",
      "nct                  0.004464     5225.8   0.9981     (4 params)\n",
      "\n",
      "💡 Note: These distributions were selected by lowest RMSE and filtered by p > 0.05\n"
     ]
    }
   ],
   "source": [
    "# Get top 3 distributions from comprehensive analysis (filtered by p > 0.05)\n",
    "top_3_dists = [dist for dist, _ in good_fit_comprehensive[:3]]\n",
    "colors = ['red', 'blue', 'green']\n",
    "linestyles = ['-', '--', '-.']\n",
    "\n",
    "print(f\"Visualizing top 3 distributions (by RMSE, p > 0.05): {top_3_dists}\")\n",
    "\n",
    "# Create comparison plot\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Plot 1: Histogram with fitted PDFs\n",
    "ax1.hist(wind_data, bins=40, density=True, alpha=0.6, color='lightgray', \n",
    "         label='Observed Data', edgecolor='black', linewidth=0.5)\n",
    "\n",
    "# Generate smooth curves for top distributions\n",
    "x_smooth = np.linspace(wind_data.min(), wind_data.max(), 500)\n",
    "\n",
    "for i, dist_name in enumerate(top_3_dists):\n",
    "    # Get the fitted adjuster for this distribution\n",
    "    result = dict(good_fit_comprehensive)[dist_name]\n",
    "    \n",
    "    # Create temporary processor and fit\n",
    "    temp_processor = ma.read_data(wind_data)\n",
    "    temp_processor.fit_distribution(dist_name)\n",
    "    \n",
    "    # Plot PDF\n",
    "    pdf_smooth = temp_processor.pdf(x_smooth)\n",
    "    ax1.plot(x_smooth, pdf_smooth, color=colors[i], linestyle=linestyles[i], \n",
    "            linewidth=2, label=f'{dist_name} (RMSE: {result[\"rmse\"]:.6f})')\n",
    "\n",
    "ax1.set_xlabel('Wind Speed (m/s)')\n",
    "ax1.set_ylabel('Probability Density')\n",
    "ax1.set_title('Top 3 Distributions - PDF Comparison (by RMSE, p > 0.05)')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 2: Q-Q plot for best distribution\n",
    "best_dist_name = top_3_dists[0]\n",
    "temp_processor = ma.read_data(wind_data)\n",
    "temp_processor.fit_distribution(best_dist_name)\n",
    "\n",
    "# Generate theoretical quantiles\n",
    "sorted_data = np.sort(wind_data)\n",
    "n = len(sorted_data)\n",
    "theoretical_quantiles = temp_processor.ppf(np.arange(1, n+1) / (n+1))\n",
    "\n",
    "ax2.scatter(theoretical_quantiles, sorted_data, alpha=0.6, s=10, color='blue')\n",
    "# Add reference line\n",
    "min_val = min(theoretical_quantiles.min(), sorted_data.min())\n",
    "max_val = max(theoretical_quantiles.max(), sorted_data.max())\n",
    "ax2.plot([min_val, max_val], [min_val, max_val], 'r--', linewidth=2, label='Perfect Fit')\n",
    "\n",
    "ax2.set_xlabel(f'Theoretical Quantiles ({best_dist_name})')\n",
    "ax2.set_ylabel('Observed Quantiles')\n",
    "ax2.set_title(f'Q-Q Plot - {best_dist_name} (Best RMSE)')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print goodness-of-fit metrics for top 3\n",
    "print(\"\\n📊 Detailed Comparison of Top 3 Distributions (by RMSE, p > 0.05):\")\n",
    "print(\"=\" * 80)\n",
    "print(f\"{'Distribution':<20} {'RMSE':<12} {'AIC':<8} {'KS p-val':<10} {'Parameters'}\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "for dist_name in top_3_dists:\n",
    "    result = dict(good_fit_comprehensive)[dist_name]\n",
    "    params_str = f\"({len(result['parameters'])} params)\"\n",
    "    print(f\"{dist_name:<20} {result['rmse']:<12.6f} {result['aic']:<8.1f} {result['ks_pvalue']:<10.4f} {params_str}\")\n",
    "\n",
    "print(\"\\n💡 Note: These distributions were selected by lowest RMSE and filtered by p > 0.05\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64466d98",
   "metadata": {},
   "source": [
    "## Summary and Best Practices\n",
    "\n",
    "### Key AutoFitter Features:\n",
    "\n",
    "1. **Multiple Testing Strategies**:\n",
    "   - **Default**: 16 curated, stable distributions (fast, reliable)\n",
    "   - **Comprehensive**: All 113 SciPy distributions (thorough, slower)\n",
    "   - **Custom**: Domain-specific distribution subsets\n",
    "\n",
    "2. **Selection Criteria**:\n",
    "   - `rmse`: Root Mean Square Error (good for overall fit)\n",
    "   - `aic`/`bic`: Information criteria (balance fit vs complexity)\n",
    "   - `ks_pvalue`/`chi2_pvalue`: Statistical test p-values\n",
    "\n",
    "3. **Memory Efficiency**:\n",
    "   - Lazy initialization - distributions fitted only when tested\n",
    "   - Failed fits don't crash the process\n",
    "   - Comprehensive error handling\n",
    "\n",
    "### Recommended Workflow:\n",
    "\n",
    "```python\n",
    "# 1. Start with default (fast screening)\n",
    "auto_fitter = processor.get_auto_fitter()\n",
    "best_default = auto_fitter.fit_best_distribution()\n",
    "\n",
    "# 2. If needed, do comprehensive search\n",
    "all_dists = AutoFitter.get_all_available_distributions()\n",
    "auto_fitter_all = processor.get_auto_fitter(candidates=all_dists)\n",
    "best_comprehensive = auto_fitter_all.fit_best_distribution()\n",
    "\n",
    "# 3. Use the best distribution\n",
    "best_adjuster = auto_fitter.get_best_adjuster()\n",
    "percentile_95 = best_adjuster.ppf(0.95)\n",
    "```\n",
    "\n",
    "### When to Use What:\n",
    "\n",
    "- **Default AutoFitter**: Quick analysis, most real-world cases\n",
    "- **Comprehensive AutoFitter**: Research, when you need the absolute best fit\n",
    "- **Custom candidates**: Domain expertise, specific requirements\n",
    "- **Different criteria**: AIC/BIC for model selection, p-values for statistical validity\n",
    "\n",
    "**AutoFitter makes distribution selection effortless while giving you complete control when needed!** 🎯"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "magica",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}