{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1cb3d1e8-f930-4777-816f-bc5379118487",
   "metadata": {},
   "source": [
    "# Data Analysis Fundamentals: 1D Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed06ecb-5cc0-4978-bf46-a08361b8f4c1",
   "metadata": {},
   "source": [
    "This notebook introduces fundamental concepts in analyzing one-dimensional datasets. We'll explore:\n",
    "- Basic NumPy operations\n",
    "- Visualizing distributions with histograms and KDEs\n",
    "- Statistical summaries and outlier detection\n",
    "- Distribution transformations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "00a76bba-6439-44df-9bc8-b82f04bc5548",
   "metadata": {},
   "source": [
    "## 1. Setup and Installation\n",
    "\n",
    "If you haven't installed the required packages, run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24ce4246-3918-4b93-888d-45152480022f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (2.3.3)\n",
      "Requirement already satisfied: scipy in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (1.16.2)\n",
      "Requirement already satisfied: matplotlib in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (3.10.6)\n",
      "Requirement already satisfied: seaborn in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (0.13.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (4.60.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (1.4.9)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (25.0)\n",
      "Requirement already satisfied: pillow>=8 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (3.2.5)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: pandas>=1.2 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from seaborn) (2.3.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/aleksei/miniforge3/envs/env01/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy scipy matplotlib seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdc7ffa5-1171-489d-a0ea-529e1c9a32b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f338d7a-7bf7-451e-ae35-c12974f88479",
   "metadata": {},
   "source": [
    "## 2. NumPy Arrays Basics\n",
    "\n",
    "NumPy arrays are the foundation of numerical computing in Python. Let's explore different ways to create and manipulate them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16636eb3-9da0-451f-931e-d4222bc86bf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From list: [1 2 3 4 5]\n",
      "Using arange: [0 2 4 6 8]\n",
      "Using linspace: [0.  0.1 0.2 0.3 0.4] ...\n",
      "Random array shape: (100,)\n"
     ]
    }
   ],
   "source": [
    "# Different ways to create arrays\n",
    "arr_from_list = np.array([1, 2, 3, 4, 5])\n",
    "arr_range = np.arange(0, 10, 2)  # start, stop, step\n",
    "arr_linspace = np.linspace(0, 1, 11)  # start, stop, num_points\n",
    "arr_random = np.random.randn(100)  # 100 samples from standard normal\n",
    "\n",
    "print(\"From list:\", arr_from_list)\n",
    "print(\"Using arange:\", arr_range)\n",
    "print(\"Using linspace:\", arr_linspace[:5], \"...\")  # showing first 5\n",
    "print(\"Random array shape:\", arr_random.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af6fff06-af9d-4501-ba6b-2e064e159af0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.49671415 -0.1382643   0.64768854  1.52302986 -0.23415337 -0.23413696\n",
      "  1.57921282  0.76743473 -0.46947439  0.54256004 -0.46341769 -0.46572975\n",
      "  0.24196227 -1.91328024 -1.72491783 -0.56228753 -1.01283112  0.31424733\n",
      " -0.90802408 -1.4123037   1.46564877 -0.2257763   0.0675282  -1.42474819\n",
      " -0.54438272  0.11092259 -1.15099358  0.37569802 -0.60063869 -0.29169375\n",
      " -0.60170661  1.85227818 -0.01349722 -1.05771093  0.82254491 -1.22084365\n",
      "  0.2088636  -1.95967012 -1.32818605  0.19686124  0.73846658  0.17136828\n",
      " -0.11564828 -0.3011037  -1.47852199 -0.71984421 -0.46063877  1.05712223\n",
      "  0.34361829 -1.76304016  0.32408397 -0.38508228 -0.676922    0.61167629\n",
      "  1.03099952  0.93128012 -0.83921752 -0.30921238  0.33126343  0.97554513\n",
      " -0.47917424 -0.18565898 -1.10633497 -1.19620662  0.81252582  1.35624003\n",
      " -0.07201012  1.0035329   0.36163603 -0.64511975  0.36139561  1.53803657\n",
      " -0.03582604  1.56464366 -2.6197451   0.8219025   0.08704707 -0.29900735\n",
      "  0.09176078 -1.98756891 -0.21967189  0.35711257  1.47789404 -0.51827022\n",
      " -0.8084936  -0.50175704  0.91540212  0.32875111 -0.5297602   0.51326743\n",
      "  0.09707755  0.96864499 -0.70205309 -0.32766215 -0.39210815 -1.46351495\n",
      "  0.29612028  0.26105527  0.00511346 -0.23458713]\n"
     ]
    }
   ],
   "source": [
    "print(arr_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65b2b832-973f-4387-93ae-d07eba76910b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loop time: 0.2370 seconds\n",
      "Vectorized time: 0.0119 seconds\n",
      "Speedup: 19.9x\n"
     ]
    }
   ],
   "source": [
    "# Vectorization demonstration\n",
    "import time\n",
    "\n",
    "# Create a large array\n",
    "large_array = np.random.randn(1_000_000)\n",
    "\n",
    "# Method 1: Using a loop (slow)\n",
    "start = time.time()\n",
    "squared_loop = []\n",
    "for x in large_array:\n",
    "    squared_loop.append(x**2)\n",
    "loop_time = time.time() - start\n",
    "\n",
    "# Method 2: Vectorized operation (fast)\n",
    "start = time.time()\n",
    "squared_vec = large_array**2\n",
    "vec_time = time.time() - start\n",
    "\n",
    "print(f\"Loop time: {loop_time:.4f} seconds\")\n",
    "print(f\"Vectorized time: {vec_time:.4f} seconds\")\n",
    "print(f\"Speedup: {loop_time/vec_time:.1f}x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9191603d-e639-4dab-983b-ad8493bd3e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic statistics:\n",
      "Mean: 49.10\n",
      "Standard deviation: 10.43\n",
      "Minimum: 26.01\n",
      "Maximum: 73.66\n",
      "Median: 49.10\n"
     ]
    }
   ],
   "source": [
    "# Basic statistical methods\n",
    "sample_data = np.random.normal(50, 10, 200)\n",
    "\n",
    "print(\"Basic statistics:\")\n",
    "print(f\"Mean: {sample_data.mean():.2f}\")\n",
    "print(f\"Standard deviation: {sample_data.std():.2f}\")\n",
    "print(f\"Minimum: {sample_data.min():.2f}\")\n",
    "print(f\"Maximum: {sample_data.max():.2f}\")\n",
    "print(f\"Median: {np.median(sample_data):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27607860-7f51-42bb-b57b-c89a7436266d",
   "metadata": {},
   "source": [
    "## 3. First 1D Data Analysis\n",
    "\n",
    "Let's analyze a dataset step by step, building our intuition about distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f04f472-8cf4-4c89-a76a-b6731c86db2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 1000 test scores\n",
      "First 10 scores: [81.  73.3 82.8 93.3 72.2 72.2 94.  84.2 69.4 81.5]\n"
     ]
    }
   ],
   "source": [
    "# Generate sample data - imagine these are test scores\n",
    "np.random.seed(42)\n",
    "test_scores = np.random.normal(loc=75, scale=12, size=1000)\n",
    "\n",
    "# Ensure scores are in valid range [0, 100]\n",
    "test_scores = np.clip(test_scores, 0, 100)\n",
    "\n",
    "print(f\"Generated {len(test_scores)} test scores\")\n",
    "print(f\"First 10 scores: {test_scores[:10].round(1)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8d51042-8d62-499b-8e5c-9ca7102ed4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 30 bins\n",
      "Try changing 'bins' parameter to see how it affects the visualization!\n"
     ]
    }
   ],
   "source": [
    "# Visualize with histogram and KDE\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Histogram\n",
    "n, bins, patches = ax.hist(test_scores, bins=30, density=True, alpha=0.7, \n",
    "                          color='skyblue', edgecolor='black', label='Histogram')\n",
    "\n",
    "# KDE (Kernel Density Estimation)\n",
    "kde_xs = np.linspace(test_scores.min(), test_scores.max(), 200)\n",
    "kde = stats.gaussian_kde(test_scores)\n",
    "ax.plot(kde_xs, kde(kde_xs), 'r-', linewidth=2, label='KDE')\n",
    "\n",
    "ax.set_xlabel('Test Score')\n",
    "ax.set_ylabel('Density')\n",
    "ax.set_title('Distribution of Test Scores')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Discussion about bin selection\n",
    "print(f\"Using {len(bins)-1} bins\")\n",
    "print(\"Try changing 'bins' parameter to see how it affects the visualization!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bb1ac0f-a68a-49a8-b2b6-273df6bede67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentiles:\n",
      "  10th percentile: 60.06\n",
      "  25th percentile: 67.23\n",
      "  50th percentile: 75.30\n",
      "  75th percentile: 82.78\n",
      "  90th percentile: 90.67\n",
      "\n",
      "Interquartile Range (IQR): 15.55\n",
      "This means the middle 50% of scores fall within a range of 15.55 points\n"
     ]
    }
   ],
   "source": [
    "# Calculate quantiles and percentiles\n",
    "percentiles = [10, 25, 50, 75, 90]\n",
    "quantile_values = np.percentile(test_scores, percentiles)\n",
    "\n",
    "print(\"Percentiles:\")\n",
    "for p, v in zip(percentiles, quantile_values):\n",
    "    print(f\"  {p}th percentile: {v:.2f}\")\n",
    "\n",
    "# Calculate IQR (Interquartile Range)\n",
    "q1, q3 = np.percentile(test_scores, [25, 75])\n",
    "iqr = q3 - q1\n",
    "print(f\"\\nInterquartile Range (IQR): {iqr:.2f}\")\n",
    "print(f\"This means the middle 50% of scores fall within a range of {iqr:.2f} points\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cad696f3-45c8-447a-be17-32360d875aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot visualization\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "box_plot = ax.boxplot(test_scores, vert=True, patch_artist=True,\n",
    "                      tick_labels=['Test Scores'])\n",
    "\n",
    "# Customize box plot\n",
    "box_plot['boxes'][0].set_facecolor('lightblue')\n",
    "box_plot['medians'][0].set_color('red')\n",
    "box_plot['medians'][0].set_linewidth(2)\n",
    "\n",
    "# Add annotations\n",
    "ax.text(1.1, np.median(test_scores), f'Median: {np.median(test_scores):.1f}', \n",
    "        va='center')\n",
    "ax.text(1.1, q1, f'Q1: {q1:.1f}', va='center')\n",
    "ax.text(1.1, q3, f'Q3: {q3:.1f}', va='center')\n",
    "\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_title('Box Plot of Test Scores')\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7fb34b8c-c824-454a-bee9-1ef92129b4f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers (|z| > 2.5): 6\n",
      "Outlier values: [43.6 36.1 45.3 42.6 43.2 45.9]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlier detection using Z-scores\n",
    "mean = test_scores.mean()\n",
    "std = test_scores.std()\n",
    "\n",
    "# Calculate z-scores\n",
    "z_scores = (test_scores - mean) / std\n",
    "\n",
    "# Find outliers (typically |z| > 3, but let's use 2.5 for this example)\n",
    "outlier_threshold = 2.5\n",
    "outliers_mask = np.abs(z_scores) > outlier_threshold\n",
    "outliers = test_scores[outliers_mask]\n",
    "\n",
    "print(f\"Number of outliers (|z| > {outlier_threshold}): {len(outliers)}\")\n",
    "print(f\"Outlier values: {outliers.round(1)}\")\n",
    "\n",
    "# Visualize outliers\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "# Histogram with outliers marked\n",
    "ax1.hist(test_scores, bins=30, alpha=0.7, color='skyblue', edgecolor='black')\n",
    "ax1.scatter(outliers, np.zeros_like(outliers), color='red', s=100, \n",
    "            marker='^', label=f'Outliers (|z| > {outlier_threshold})')\n",
    "ax1.set_xlabel('Test Score')\n",
    "ax1.set_ylabel('Frequency')\n",
    "ax1.set_title('Distribution with Outliers Marked')\n",
    "ax1.legend()\n",
    "\n",
    "# Z-scores distribution\n",
    "ax2.hist(z_scores, bins=30, alpha=0.7, color='lightgreen', edgecolor='black')\n",
    "ax2.axvline(-outlier_threshold, color='red', linestyle='--', label=f'z = ±{outlier_threshold}')\n",
    "ax2.axvline(outlier_threshold, color='red', linestyle='--')\n",
    "ax2.set_xlabel('Z-score')\n",
    "ax2.set_ylabel('Frequency')\n",
    "ax2.set_title('Z-score Distribution')\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1efed29e-e7fb-4e09-90ca-f68534c74d1e",
   "metadata": {},
   "source": [
    "## 4. Creating a Reusable Analysis Function\n",
    "\n",
    "Let's wrap our analysis steps into a function. Fill in the TODO sections!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d97cfc33-bf48-46ae-bd11-632ac2e78495",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_1d_data(data, name=\"Data\", outlier_z=2.5, bins=30):\n",
    "    \"\"\"\n",
    "    Comprehensive analysis of 1D data\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    data : array-like\n",
    "        The data to analyze\n",
    "    name : str\n",
    "        Name for the dataset (for plot titles)\n",
    "    outlier_z : float\n",
    "        Z-score threshold for outlier detection\n",
    "    bins : int\n",
    "        The number of bins for the histogram\n",
    "        \n",
    "    Returns:\n",
    "    --------\n",
    "    stats_dict : dict\n",
    "        Dictionary containing statistical summaries\n",
    "    fig : matplotlib figure\n",
    "        Figure with visualizations\n",
    "    \"\"\"\n",
    "    # Convert to numpy array\n",
    "    data = np.array(data)\n",
    "    \n",
    "    # Calculate statistics\n",
    "    stats_dict = {\n",
    "        'mean': np.mean(data),\n",
    "        'std': np.std(data),\n",
    "        'median': np.median(data),\n",
    "        'q1': np.percentile(data, 25),\n",
    "        'q3': np.percentile(data, 75),\n",
    "        'iqr': np.percentile(data, 75) - np.percentile(data, 25),\n",
    "        'min': data.min(),\n",
    "        'max': data.max()\n",
    "    }\n",
    "\n",
    "    # Calculate outliers\n",
    "    z_scores = (data - stats_dict['mean']) / stats_dict['std']\n",
    "    outliers_mask = np.abs(z_scores) > 2.5\n",
    "    n_outliers = np.sum(outliers_mask)\n",
    "    \n",
    "    # Create visualizations\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "    \n",
    "    # Left plot: Histogram and KDE\n",
    "    ax1.hist(data, bins=bins, density=True, alpha=0.7, \n",
    "             color='skyblue', edgecolor='black', label='Histogram')\n",
    "    \n",
    "    # Add KDE\n",
    "    kde_xs = np.linspace(data.min(), data.max(), 200)\n",
    "    kde = stats.gaussian_kde(data)\n",
    "    ax1.plot(kde_xs, kde(kde_xs), 'r-', linewidth=2, label='KDE')\n",
    "    \n",
    "    ax1.set_xlabel('Value')\n",
    "    ax1.set_ylabel('Density')\n",
    "    ax1.set_title(f'{name}: Distribution')\n",
    "    ax1.legend()\n",
    "    ax1.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Right plot: Box plot\n",
    "    box_plot = ax2.boxplot(data, vert=True, patch_artist=True,\n",
    "                          tick_labels=['Test Scores'])\n",
    "    \n",
    "    # Customize box plot\n",
    "    box_plot['boxes'][0].set_facecolor('lightblue')\n",
    "    box_plot['medians'][0].set_color('red')\n",
    "    box_plot['medians'][0].set_linewidth(2)\n",
    "\n",
    "    ax2.set_ylabel('Value')\n",
    "    ax2.set_title(f'{name}: Box Plot')\n",
    "    ax2.grid(True, alpha=0.3)\n",
    "    \n",
    "    plt.suptitle(f'Analysis of {name} (n={len(data)}, outliers={n_outliers})')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return stats_dict, fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcab7809-0ea2-4f87-b390-b2269917b372",
   "metadata": {},
   "source": [
    "## 5. Analyzing Sophisticated Examples\n",
    "\n",
    "Now let's apply our function to more complex distributions! Summarize the features of each dataset with a sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "deeb55c8-01f1-4824-9077-20b4cbd5c6b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def string_to_dataset(s):\n",
    "    s_filtered = s.replace('[','').replace(']','').strip()\n",
    "    return np.array([float(x) for x in s_filtered.split() if x])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de87b1e-d965-4502-be5c-8eaa6153d1fc",
   "metadata": {},
   "source": [
    "## Dataset 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78738bcd-ddad-4660-8816-4a3a94159647",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_string_1 = '''\n",
    "[44.5  0.  50.  60.  59.3 50.4 48.5 36.1 45.7 72.  39.2 37.4 28.5 41.2\n",
    "  0.  60.6 33.8 32.3 62.6 41.  58.9 43.2 59.6 58.9 50.7 52.4 58.2 52.4\n",
    " 42.6 66.5 43.  49.  52.5 67.5  0.  31.1 46.7 51.7 60.7 48.9 48.1 53.4\n",
    " 48.8 58.7 47.5 22.1 43.3 48.4 54.7 32.4 48.  39.1 33.6 51.3 52.9 46.1\n",
    " 54.2 47.7 50.5 46.5 51.6 51.4 53.2 29.5 69.3 40.1 47.7 46.3 46.4 59.6\n",
    " 61.  43.3 60.7 46.5 46.4 61.6 64.9 48.7 37.5 55.9 44.2 37.6 32.5 57.5\n",
    " 48.3 51.8 66.1 42.5 38.5 49.6 61.8 43.1 42.  62.6 41.  41.3 51.2 57.7\n",
    " 45.9 60.4 48.6 57.7 44.1 47.2 48.  35.3 44.5 40.3 60.9 43.6 68.  68.1\n",
    " 59.8 55.7 65.  51.3 52.5 59.6 47.2 51.4 47.6 48.6 31.6 60.3 49.9 43.3\n",
    " 50.3  0.  53.8 40.8 49.9 50.  51.3 54.4 53.6 43.2 55.8 32.7 43.4 43.2\n",
    " 49.  53.9 53.8 47.5 60.7 55.3 60.4 51.5 56.7 49.9 76.  46.5 63.2 65.\n",
    " 42.4 35.7 43.3 37.5 56.1 56.9 67.4 59.7 36.7 48.8 36.7 50.9 46.4 66.\n",
    " 27.6 51.7 45.2 35.8 59.9 47.3  0.  57.5 35.8 44.  46.5 53.  59.5 57.5\n",
    " 63.3 64.7 39.6 43.7 53.8 40.4 72.1 51.2 40.7 58.5 41.5 60.5 44.5 67.2\n",
    " 47.7 51.  55.9 55.4 49.  43.4 49.6  0.  30.8 40.1 57.1 69.4 50.4 45.7\n",
    " 42.6 60.7 36.4 49.5 38.6 58.1 57.8 53.1 67.8 36.8 25.6  0.  32.9  0.\n",
    " 56.4 51.8 28.8 55.  40.7 47.  43.6 41.8 40.2 35.5 25.8 37.4 39.1 41.2\n",
    " 43.9 65.2 56.3 62.5 32.4 45.3 66.3 66.6 49.4 54.3 43.9 62.6 61.6 48.1\n",
    " 36.3 36.  28.3 61.4  0.  66.4 55.9 52.7 58.1 54.4 60.9 50.6 33.  47.5\n",
    " 44.5 42.7 63.2 59.3  0.  67.2 31.4 37.3 49.7 59.4 47.1 52.6 45.9 53.\n",
    " 63.3 29.6 62.7 54.5 60.8 38.  55.1 44.7 35.9 58.2 50.7 39.  79.6 46.\n",
    " 46.5 65.2 33.1 48.6 59.8 66.3 48.6 62.  37.4 75.6 35.8 49.1 49.7 61.1\n",
    " 43.2 46.  27.5 53.8 53.5 53.7 43.1 43.9 46.2 70.9 56.9 47.8 53.2 61.7\n",
    " 56.8 64.9 53.4 62.6 52.2 40.3 65.9 55.7 42.3 63.7 48.5 59.1 47.8 51.4\n",
    " 40.4 39.6 57.5 36.9 45.7 37.9 51.2  0.  63.1 41.7 37.3 54.  44.  50.7\n",
    " 43.2 44.5  0.  55.9 51.6 47.7 73.9 64.6 41.9 49.9 50.1 43.1 58.9  0.\n",
    " 57.5 51.4 42.4  0.   0.  39.5 48.7 55.6 38.2 36.7 34.7 61.7 46.9 45.5\n",
    " 59.1 61.6 64.2 54.7 39.9 46.5 64.  38.8 62.6 57.   0.  47.2 52.8 50.5\n",
    " 65.3 54.1 44.5 48.3 85.7 45.6 35.3  0.  51.1 22.1 57.  55.7 46.5 62.7\n",
    " 54.6 59.5 48.6 45.  65.2 52.8 58.7 52.8  0.  67.  58.9  0.  65.3 39.6\n",
    " 48.1 54.1 39.3 61.5 58.1 57.7  0.  49.8 36.4 43.8 46.6 53.7 50.2 38.8\n",
    " 56.3 55.2 57.4 39.5 41.3 29.7 35.2 36.3 46.8 33.4 49.4 56.  52.7 34.2\n",
    " 47.6 38.4 46.6 62.2 59.  36.  35.  47.9 57.5 47.7 46.8 53.9 49.1 49.6\n",
    " 37.4 66.5 39.2 49.7 49.3 49.4 59.8 36.2 50.   0.  44.9 67.4 56.7 45.9\n",
    " 50.3 52.  36.3 51.1 54.2 40.6 52.8 52.5 54.3 57.3 37.1 51.2 52.8 48.9\n",
    " 49.  34.8  0.  66.1 40.3 44.7 31.3 61.3 59.  52.8 22.  39.  25.7  0.\n",
    " 59.9 50.5  0.  69.9 56.4  0.  64.5 47.4 49.9 56.1 46.2  0.  56.8 49.7\n",
    " 68.7 30.2 49.9 45.7 64.1 42.7 38.7  0.  63.4 43.8 25.6 53.5 45.8 39.\n",
    " 50.3 60.1 60.3  0.  59.1 56.  40.  73.3 60.7 48.4 51.  42.4 42.1 28.1\n",
    " 68.6 43.6 51.3 45.8 51.9 40.5 51.3 52.5 50.8 52.  33.9 58.  57.  49.3\n",
    " 60.1 58.9 50.5 42.6 51.9 46.1 44.7 35.8 61.3 42.6 65.4 48.3 41.3 46.1\n",
    " 47.7 49.  58.9 44.5 51.4  0.  45.4 56.9 35.9 48.7 41.7 52.8 51.6 43.\n",
    " 46.9 34.1 48.4 63.1 70.6 55.  38.4 53.1 50.8 41.8 38.7 43.5 59.3 56.3\n",
    " 72.4 45.6 45.4 52.8 57.4 61.8 37.9 56.7 36.9 61.1 28.8 47.4 53.9 51.6\n",
    " 52.  69.8 55.8 62.2 33.5 44.2 43.2 55.6 59.7 39.1  0.   0.  48.7 54.8\n",
    " 56.7 48.4 48.3  0.  57.9 56.7 66.2 34.  44.1 47.5 39.1 53.2 54.1 33.9\n",
    " 49.2 72.5  0.  39.8 48.8 54.2 49.7 44.4 55.1 31.  66.  40.1  0.  32.3\n",
    " 53.4 41.8 45.5 56.9  0.  52.1 52.3 44.5 68.8 31.2 43.1 39.4 71.9 54.7\n",
    " 47.3 44.5 44.7 59.8 68.5 39.2 50.5 48.7 59.5 50.6 61.9 46.2 55.5  0.\n",
    " 67.2 52.9 34.  34.9 56.9 49.  49.2 51.8 55.8 44.3 54.2 48.8 41.5 51.2\n",
    " 55.9 37.9  0.  44.1 55.5 49.6 51.1 47.9 40.8 38.  46.7 47.3 40.9 36.5\n",
    " 42.1 39.2 57.4 42.8 45.3 56.4 52.8 67.9 54.1 63.4 50.9  0.  29.  41.2\n",
    " 41.4 62.3 62.6 52.3 53.1 57.6 17.7 62.2 44.7 55.8 64.9 45.7 51.6 51.7\n",
    " 53.1 41.6 58.8 44.  40.   0.  60.6 47.9 60.3 56.  53.6 52.8  0.  47.3\n",
    " 48.5 33.9 60.  56.9 60.6 44.5 23.7 50.7 30.7  0.  62.4 53.  47.5 45.1\n",
    " 57.5 53.6 42.7 46.2 46.5 51.5 50.4 34.4  0.  51.8  0.  47.5 42.3 47.3\n",
    " 50.   0.  43.4 53.3 50.3 65.2 34.3 36.9 46.8 50.2 34.2  0.  69.3 49.9\n",
    " 38.5 53.3 52.1 53.9 58.4 48.3 66.1 69.6 69.6  0.  63.8 50.3 42.5 48.3\n",
    " 44.  51.5  0.  48.1  0.  46.7 41.8 43.3 57.7 43.2 38.8 61.8 52.  44.2\n",
    " 41.8  0.  44.8 46.  66.1 37.3  0.  42.4 52.5 36.2 46.  65.2 46.  37.8\n",
    " 48.7 47.3 53.  50.9 47.9 24.4 38.3 58.1 57.9 59.9]\n",
    " '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97a0c4f9-a0a5-41ec-bc29-2fc690a9741a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean': np.float64(46.858705882352936),\n",
       "  'std': np.float64(15.270704771149642),\n",
       "  'median': np.float64(48.9),\n",
       "  'q1': np.float64(41.5),\n",
       "  'q3': np.float64(56.3),\n",
       "  'iqr': np.float64(14.799999999999997),\n",
       "  'min': np.float64(0.0),\n",
       "  'max': np.float64(85.7)},\n",
       " <Figure size 1400x500 with 2 Axes>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_1 = string_to_dataset(dataset_string_1)\n",
    "analyze_1d_data(dataset_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9fb66421-d268-4f1b-87cd-2f6e7b4b6d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_1_part_1 = dataset_1[dataset_1 <= 0]\n",
    "dataset_1_part_2 = dataset_1[dataset_1 > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e6392af1-986e-4bac-a543-ffc7b44f7a8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_288499/2672750811.py:39: RuntimeWarning: invalid value encountered in divide\n",
      "  z_scores = (data - stats_dict['mean']) / stats_dict['std']\n"
     ]
    },
    {
     "ename": "LinAlgError",
     "evalue": "The data appears to lie in a lower-dimensional subspace of the space in which it is expressed. This has resulted in a singular data covariance matrix, which cannot be treated using the algorithms implemented in `gaussian_kde`. Consider performing principal component analysis / dimensionality reduction and using `gaussian_kde` with the transformed data.",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mLinAlgError\u001b[39m                               Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/env01/lib/python3.12/site-packages/scipy/stats/_kde.py:235\u001b[39m, in \u001b[36mgaussian_kde.__init__\u001b[39m\u001b[34m(self, dataset, bw_method, weights)\u001b[39m\n\u001b[32m    234\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m235\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mset_bandwidth\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbw_method\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbw_method\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    236\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m linalg.LinAlgError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/env01/lib/python3.12/site-packages/scipy/stats/_kde.py:578\u001b[39m, in \u001b[36mgaussian_kde.set_bandwidth\u001b[39m\u001b[34m(self, bw_method)\u001b[39m\n\u001b[32m    576\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m578\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_compute_covariance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/env01/lib/python3.12/site-packages/scipy/stats/_kde.py:590\u001b[39m, in \u001b[36mgaussian_kde._compute_covariance\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    587\u001b[39m     \u001b[38;5;28mself\u001b[39m._data_covariance = atleast_2d(cov(\u001b[38;5;28mself\u001b[39m.dataset, rowvar=\u001b[32m1\u001b[39m,\n\u001b[32m    588\u001b[39m                                        bias=\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m    589\u001b[39m                                        aweights=\u001b[38;5;28mself\u001b[39m.weights))\n\u001b[32m--> \u001b[39m\u001b[32m590\u001b[39m     \u001b[38;5;28mself\u001b[39m._data_cho_cov = \u001b[43mlinalg\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcholesky\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_data_covariance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    591\u001b[39m \u001b[43m                                         \u001b[49m\u001b[43mlower\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m    593\u001b[39m \u001b[38;5;28mself\u001b[39m.covariance = \u001b[38;5;28mself\u001b[39m._data_covariance * \u001b[38;5;28mself\u001b[39m.factor**\u001b[32m2\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/env01/lib/python3.12/site-packages/scipy/_lib/_util.py:1233\u001b[39m, in \u001b[36m_apply_over_batch.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m   1232\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28many\u001b[39m(batch_shapes):\n\u001b[32m-> \u001b[39m\u001b[32m1233\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mother_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1235\u001b[39m \u001b[38;5;66;03m# Determine broadcasted batch shape\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/env01/lib/python3.12/site-packages/scipy/linalg/_decomp_cholesky.py:106\u001b[39m, in \u001b[36mcholesky\u001b[39m\u001b[34m(a, lower, overwrite_a, check_finite)\u001b[39m\n\u001b[32m     52\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m     53\u001b[39m \u001b[33;03mCompute the Cholesky decomposition of a matrix.\u001b[39;00m\n\u001b[32m     54\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    104\u001b[39m \n\u001b[32m    105\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m c, lower = \u001b[43m_cholesky\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlower\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlower\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite_a\u001b[49m\u001b[43m=\u001b[49m\u001b[43moverwrite_a\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m    107\u001b[39m \u001b[43m                     \u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcheck_finite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    108\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m c\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/env01/lib/python3.12/site-packages/scipy/linalg/_decomp_cholesky.py:39\u001b[39m, in \u001b[36m_cholesky\u001b[39m\u001b[34m(a, lower, overwrite_a, clean, check_finite)\u001b[39m\n\u001b[32m     38\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m info > \u001b[32m0\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m39\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\n\u001b[32m     40\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minfo\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m-th leading minor of the array is not positive definite\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     41\u001b[39m     )\n\u001b[32m     42\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m info < \u001b[32m0\u001b[39m:\n",
      "\u001b[31mLinAlgError\u001b[39m: 1-th leading minor of the array is not positive definite",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[31mLinAlgError\u001b[39m                               Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43manalyze_1d_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset_1_part_1\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 52\u001b[39m, in \u001b[36manalyze_1d_data\u001b[39m\u001b[34m(data, name, outlier_z, bins)\u001b[39m\n\u001b[32m     50\u001b[39m \u001b[38;5;66;03m# Add KDE\u001b[39;00m\n\u001b[32m     51\u001b[39m kde_xs = np.linspace(data.min(), data.max(), \u001b[32m200\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m kde = \u001b[43mstats\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgaussian_kde\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     53\u001b[39m ax1.plot(kde_xs, kde(kde_xs), \u001b[33m'\u001b[39m\u001b[33mr-\u001b[39m\u001b[33m'\u001b[39m, linewidth=\u001b[32m2\u001b[39m, label=\u001b[33m'\u001b[39m\u001b[33mKDE\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m     55\u001b[39m ax1.set_xlabel(\u001b[33m'\u001b[39m\u001b[33mValue\u001b[39m\u001b[33m'\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/env01/lib/python3.12/site-packages/scipy/stats/_kde.py:244\u001b[39m, in \u001b[36mgaussian_kde.__init__\u001b[39m\u001b[34m(self, dataset, bw_method, weights)\u001b[39m\n\u001b[32m    236\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m linalg.LinAlgError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m    237\u001b[39m     msg = (\u001b[33m\"\u001b[39m\u001b[33mThe data appears to lie in a lower-dimensional subspace \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    238\u001b[39m            \u001b[33m\"\u001b[39m\u001b[33mof the space in which it is expressed. This has resulted \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    239\u001b[39m            \u001b[33m\"\u001b[39m\u001b[33min a singular data covariance matrix, which cannot be \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   (...)\u001b[39m\u001b[32m    242\u001b[39m            \u001b[33m\"\u001b[39m\u001b[33manalysis / dimensionality reduction and using \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    243\u001b[39m            \u001b[33m\"\u001b[39m\u001b[33m`gaussian_kde` with the transformed data.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m linalg.LinAlgError(msg) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n",
      "\u001b[31mLinAlgError\u001b[39m: The data appears to lie in a lower-dimensional subspace of the space in which it is expressed. This has resulted in a singular data covariance matrix, which cannot be treated using the algorithms implemented in `gaussian_kde`. Consider performing principal component analysis / dimensionality reduction and using `gaussian_kde` with the transformed data."
     ]
    },
    {
     "data": {
      "image/png": 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I8vLy2GuvvWKPPfaIiRMnRmNjY1aLBgAAAOB9nQp3Fi9eHOXl5bFkyZJ46KGHYtOmTXHyySdHS0tLe59p06bFb3/727j77rtj8eLF8frrr8fXv/71rBcOAAAAQES/znRetGhRh8dz586NIUOGxNKlS+OLX/xiNDU1xa233hrz5s2LL3/5yxERcdttt8Whhx4aS5YsiS984QvZqxwAAACAHbvnTlNTU0REDBo0KCIili5dGps2bYrS0tL2PiNHjozhw4dHXV3dVtfR2toazc3NHRYAAAAAtk+Xw522tra45JJL4rjjjovDDz88IiIaGhoiNzc3Bg4c2KFvYWFhNDQ0bHU9VVVVUVBQ0L4UFxd3tSQAAACAT50uhzvl5eXxwgsvxPz583eogMrKymhqampfVq9evUPrAwAAAPg06dQ9dz4wderU+N3vfhePPfZY7Lvvvu3tRUVFsXHjxli3bl2Hq3caGxujqKhoq+vKZDKRyWS6UgYAAADAp16nrtxJkiSmTp0aCxYsiEceeST222+/Ds+PGTMmdtttt6ipqWlvW7ZsWaxatSpKSkqyUzEAAAAA7Tp15U55eXnMmzcvfvOb38SAAQPa76NTUFAQ/fv3j4KCgjjvvPOioqIiBg0aFPn5+XHxxRdHSUmJb8oCAAAA6AadCndmzZoVEREnnnhih/bbbrstzj333IiI+OlPfxp9+vSJiRMnRmtra5SVlcXNN9+clWIBAAAA6KhT4U6SJJ/YJy8vL6qrq6O6urrLRQEAAACwfbr8bVkAAAAA7HzCHQAAAIAUE+4AAGRZdXV1jBgxIvLy8mLs2LHx1FNPfWz/devWRXl5eQwdOjQymUwcfPDBsXDhwh6qFgBIu07dcwcAgI935513RkVFRcyePTvGjh0bM2fOjLKysli2bFkMGTJki/4bN26Mr371qzFkyJC45557Yp999om//vWvMXDgwJ4vHgBIJeEOAEAW3XTTTXH++efHlClTIiJi9uzZ8cADD8ScOXPi8ssv36L/nDlzYu3atfHEE0/EbrvtFhERI0aM6MmSAYCU87EsAIAs2bhxYyxdujRKS0vb2/r06ROlpaVRV1e31TH3339/lJSURHl5eRQWFsbhhx8e119/fWzevHmb22ltbY3m5uYOCwDw6SXcAQDIkjVr1sTmzZujsLCwQ3thYWE0NDRsdcwrr7wS99xzT2zevDkWLlwYV111VfzkJz+JH/zgB9vcTlVVVRQUFLQvxcXFWX0dAEC6CHcAAHaitra2GDJkSPziF7+IMWPGxKRJk+KKK66I2bNnb3NMZWVlNDU1tS+rV6/uwYoBgN7GPXcAALJk8ODB0bdv32hsbOzQ3tjYGEVFRVsdM3To0Nhtt92ib9++7W2HHnpoNDQ0xMaNGyM3N3eLMZlMJjKZTHaLBwBSy5U7AABZkpubG2PGjImampr2tra2tqipqYmSkpKtjjnuuONi+fLl0dbW1t725z//OYYOHbrVYAcA4KOEOwAAWVRRURG33HJL3H777fHSSy/FRRddFC0tLe3fnnXOOedEZWVle/+LLroo1q5dG9/97nfjz3/+czzwwANx/fXXR3l5+c56CQBAyvhYFgBAFk2aNCneeuutmD59ejQ0NMSRRx4ZixYtar/J8qpVq6JPn3/8fa24uDgefPDBmDZtWowaNSr22Wef+O53vxuXXXbZznoJAEDKCHcAALJs6tSpMXXq1K0+V1tbu0VbSUlJLFmypJurAgB2VT6WBQAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIoJdwAAAABSTLgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAAAAKSbcAQAAAEgx4Q4AAABAigl3AAAAAFJMuAMAAACQYsIdAAAAgBQT7gAAAACkmHAHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUky4AwAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIoJdwAAAABSTLgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAAAAKdbpcOexxx6L8ePHx7BhwyInJyfuu+++Ds+fe+65kZOT02E55ZRTslUvAAAAAB/S6XCnpaUlRo8eHdXV1dvsc8opp8Qbb7zRvvz617/eoSIBAAAA2Lp+nR0wbty4GDdu3Mf2yWQyUVRU1OWiAAAAANg+3XLPndra2hgyZEgccsghcdFFF8Xbb7+9zb6tra3R3NzcYQEAAABg+2Q93DnllFPiV7/6VdTU1MSPfvSjWLx4cYwbNy42b9681f5VVVVRUFDQvhQXF2e7JAAAAIBdVqc/lvVJvvWtb7X/+4gjjohRo0bFAQccELW1tfGVr3xli/6VlZVRUVHR/ri5uVnAAwAAALCduv2r0Pfff/8YPHhwLF++fKvPZzKZyM/P77AAAAAAsH26Pdx57bXX4u23346hQ4d296YAAAAAPnU6/bGsd955p8NVOCtXroz6+voYNGhQDBo0KK655pqYOHFiFBUVxYoVK+LSSy+NAw88MMrKyrJaOAAAAABdCHeefvrpOOmkk9off3C/nMmTJ8esWbPiueeei9tvvz3WrVsXw4YNi5NPPjmuu+66yGQy2asaAAAAgIjoQrhz4oknRpIk23z+wQcf3KGCAAAAANh+3X7PHQAAAAC6j3AHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUky4AwAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIoJdwAAAABSTLgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAFlWXV0dI0aMiLy8vBg7dmw89dRT2zVu/vz5kZOTExMmTOjeAgGAXYpwBwAgi+68886oqKiIGTNmxDPPPBOjR4+OsrKyePPNNz923Kuvvhrf+9734oQTTuihSgGAXYVwBwAgi2666aY4//zzY8qUKXHYYYfF7NmzY/fdd485c+Zsc8zmzZvjrLPOimuuuSb233//HqwWANgVCHcAALJk48aNsXTp0igtLW1v69OnT5SWlkZdXd02x1177bUxZMiQOO+887ZrO62trdHc3NxhAQA+vYQ7AABZsmbNmti8eXMUFhZ2aC8sLIyGhoatjnn88cfj1ltvjVtuuWW7t1NVVRUFBQXtS3Fx8Q7VDQCkm3AHAGAnWb9+fZx99tlxyy23xODBg7d7XGVlZTQ1NbUvq1ev7sYqAYDert/OLgAAYFcxePDg6Nu3bzQ2NnZob2xsjKKioi36r1ixIl599dUYP358e1tbW1tERPTr1y+WLVsWBxxwwBbjMplMZDKZLFcPAKSVK3cAALIkNzc3xowZEzU1Ne1tbW1tUVNTEyUlJVv0HzlyZDz//PNRX1/fvnzta1+Lk046Kerr633cCgDYLq7cAQDIooqKipg8eXIcffTRccwxx8TMmTOjpaUlpkyZEhER55xzTuyzzz5RVVUVeXl5cfjhh3cYP3DgwIiILdoBALZFuAMAkEWTJk2Kt956K6ZPnx4NDQ1x5JFHxqJFi9pvsrxq1aro08fF0wBA9gh3AACybOrUqTF16tStPldbW/uxY+fOnZv9ggCAXZo/GwEAAACkmHAHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUky4AwAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIoJdwAAAABSTLgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAAAAKSbcAQAAAEgx4Q4AAABAinU63Hnsscdi/PjxMWzYsMjJyYn77ruvw/NJksT06dNj6NCh0b9//ygtLY2//OUv2aoXAAAAgA/pdLjT0tISo0ePjurq6q0+f+ONN8Z//dd/xezZs+PJJ5+Mf/qnf4qysrLYsGHDDhcLAAAAQEf9Ojtg3LhxMW7cuK0+lyRJzJw5M6688so4/fTTIyLiV7/6VRQWFsZ9990X3/rWt3asWgAAAAA6yOo9d1auXBkNDQ1RWlra3lZQUBBjx46Nurq6rY5pbW2N5ubmDgsAAAAA2yer4U5DQ0NERBQWFnZoLywsbH/uo6qqqqKgoKB9KS4uzmZJAAAAALu0nf5tWZWVldHU1NS+rF69emeXBAAAAJAaWQ13ioqKIiKisbGxQ3tjY2P7cx+VyWQiPz+/wwIAAADA9slquLPffvtFUVFR1NTUtLc1NzfHk08+GSUlJdncFAAAAADRhW/Leuedd2L58uXtj1euXBn19fUxaNCgGD58eFxyySXxgx/8IA466KDYb7/94qqrrophw4bFhAkTslk3AAAAANGFcOfpp5+Ok046qf1xRUVFRERMnjw55s6dG5deemm0tLTEBRdcEOvWrYvjjz8+Fi1aFHl5edmrGgAAAICI6EK4c+KJJ0aSJNt8PicnJ6699tq49tprd6gwAAAAAD7ZTv+2LAAAAAC6TrgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAAAAKSbcAQAAAEgx4Q4AAABAigl3AAAAAFJMuAMAAACQYsIdAAAAgBQT7gAAAACkmHAHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUky4AwAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIoJdwAAAABSTLgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAAAAKSbcAQAAAEgx4Q4AAABAigl3AAAAAFJMuAMAAACQYsIdAIAsq66ujhEjRkReXl6MHTs2nnrqqW32veWWW+KEE06IPffcM/bcc88oLS392P4AAB8l3AEAyKI777wzKioqYsaMGfHMM8/E6NGjo6ysLN58882t9q+trY0zzjgjHn300airq4vi4uI4+eST429/+1sPVw4ApJVwBwAgi2666aY4//zzY8qUKXHYYYfF7NmzY/fdd485c+Zstf8dd9wR3/nOd+LII4+MkSNHxi9/+ctoa2uLmpqaHq4cAEgr4Q4AQJZs3Lgxli5dGqWlpe1tffr0idLS0qirq9uudbz77ruxadOmGDRo0Db7tLa2RnNzc4cFAPj0Eu4AAGTJmjVrYvPmzVFYWNihvbCwMBoaGrZrHZdddlkMGzasQ0D0UVVVVVFQUNC+FBcX71DdAEC6CXcAAHqJG264IebPnx8LFiyIvLy8bfarrKyMpqam9mX16tU9WCUA0Nv029kFAADsKgYPHhx9+/aNxsbGDu2NjY1RVFT0sWN//OMfxw033BAPP/xwjBo16mP7ZjKZyGQyO1wvALBrcOUOAECW5ObmxpgxYzrcDPmDmyOXlJRsc9yNN94Y1113XSxatCiOPvronigVANiFuHIHACCLKioqYvLkyXH00UfHMcccEzNnzoyWlpaYMmVKREScc845sc8++0RVVVVERPzoRz+K6dOnx7x582LEiBHt9+bZY489Yo899thprwMASA/hDgBAFk2aNCneeuutmD59ejQ0NMSRRx4ZixYtar/J8qpVq6JPn39cPD1r1qzYuHFjfOMb3+iwnhkzZsTVV1/dk6UDACkl3AEAyLKpU6fG1KlTt/pcbW1th8evvvpq9xcEAOzS3HMHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUizr4c7VV18dOTk5HZaRI0dmezMAAAAARDd9FfpnP/vZePjhh/+xkX6+cR0AAACgO3RL6tKvX78oKirqjlUDAAAA8CHdcs+dv/zlLzFs2LDYf//946yzzopVq1Zts29ra2s0Nzd3WAAAAADYPlkPd8aOHRtz586NRYsWxaxZs2LlypVxwgknxPr167fav6qqKgoKCtqX4uLibJcEAAAAsMvKergzbty4+Jd/+ZcYNWpUlJWVxcKFC2PdunVx1113bbV/ZWVlNDU1tS+rV6/OdkkAAAAAu6xuv9PxwIED4+CDD47ly5dv9flMJhOZTKa7ywAAAADYJXXLPXc+7J133okVK1bE0KFDu3tTAAAAAJ86WQ93vve978XixYvj1VdfjSeeeCL++Z//Ofr27RtnnHFGtjcFAAAA8KmX9Y9lvfbaa3HGGWfE22+/HXvvvXccf/zxsWTJkth7772zvSkAAACAT72shzvz58/P9ioBAAAA2IZuv+cOAAAAAN1HuAMAAACQYsIdAAAAgBQT7gAAAACkmHAHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUky4AwAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIoJdwAAAABSTLgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAAAAKSbcAQAAAEgx4Q4AAABAigl3AAAAAFJMuAMAAACQYsIdAAAAgBQT7gAAAACkmHAHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUky4AwAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIoJdwAAAABSTLgDAAAAkGLCHQAAAIAUE+4AAAAApJhwBwAAACDFhDsAAAAAKSbcAQAAAEgx4Q4AAABAigl3AAAAAFJMuAMAAACQYt0W7lRXV8eIESMiLy8vxo4dG0899VR3bQoAoFfp7HnQ3XffHSNHjoy8vLw44ogjYuHChT1UKQCwK+iWcOfOO++MioqKmDFjRjzzzDMxevToKCsrizfffLM7NgcA0Gt09jzoiSeeiDPOOCPOO++8ePbZZ2PChAkxYcKEeOGFF3q4cgAgrbol3Lnpppvi/PPPjylTpsRhhx0Ws2fPjt133z3mzJnTHZsDAOg1Onse9LOf/SxOOeWU+I//+I849NBD47rrrovPfe5z8d///d89XDkAkFb9sr3CjRs3xtKlS6OysrK9rU+fPlFaWhp1dXVb9G9tbY3W1tb2x01NTRER0dzcnO3SIiJi7dq1sW7dum5ZN7Btq1evjtYNG+L1FX+Ov7e806mx/9fwevz93XfjxRdfjPXr13dThbBrGzhwYAwaNCjr6/3g/TpJkqyvO406ex4UEVFXVxcVFRUd2srKyuK+++7b5nZ6+vwJAMiO7jp3ynq4s2bNmti8eXMUFhZ2aC8sLIyXX355i/5VVVVxzTXXbNFeXFyc7dKAXmDJ4ke6PPZr/1ubtTqA7Fq/fn0UFBTs7DJ2us6eB0VENDQ0bLV/Q0PDNrfj/AkA0u3tt9/O6rlT1sOdzqqsrOzw16q2trZYu3Zt7LXXXpGTk7MTK+sdmpubo7i4OFavXh35+fk7u5xdkjnuGea5Z5jn7meOO0qSJNavXx/Dhg3b2aV8qnz0/GndunXxmc98JlatWiVk28kcI3oH+6H3sC96D/uid2hqaorhw4dn/YrqrIc7gwcPjr59+0ZjY2OH9sbGxigqKtqifyaTiUwm06Ft4MCB2S4r9fLz8/0H7GbmuGeY555hnrufOf4HYcI/dPY8KCKiqKioU/0jtn7+FPH+vvBz2Ts4RvQO9kPvYV/0HvZF79CnT3ZvgZz1Gyrn5ubGmDFjoqampr2tra0tampqoqSkJNubAwDoNbpyHlRSUtKhf0TEQw895LwJANhu3fKxrIqKipg8eXIcffTRccwxx8TMmTOjpaUlpkyZ0h2bAwDoNT7pPOicc86JffbZJ6qqqiIi4rvf/W586Utfip/85Cdx2mmnxfz58+Ppp5+OX/ziFzvzZQAAKdIt4c6kSZPirbfeiunTp0dDQ0MceeSRsWjRoi1uFsgny2QyMWPGjK1eek12mOOeYZ57hnnufuaYT/JJ50GrVq3qcCn2scceG/PmzYsrr7wyvv/978dBBx0U9913Xxx++OHbvU0/l72HfdE72A+9h33Re9gXvUN37YecxHeXAgAAAKRW1u+5AwAAAEDPEe4AAAAApJhwBwAAACDFhDsAAAAAKSbc6WXWrl0bZ511VuTn58fAgQPjvPPOi3feeWe7xiZJEuPGjYucnJy47777urfQlOvsPK9duzYuvvjiOOSQQ6J///4xfPjw+Ld/+7doamrqwap7v+rq6hgxYkTk5eXF2LFj46mnnvrY/nfffXeMHDky8vLy4ogjjoiFCxf2UKXp1pl5vuWWW+KEE06IPffcM/bcc88oLS39xP1C53+WPzB//vzIycmJCRMmdG+BfCo5xvYejsO9g2N179HZfbFu3booLy+PoUOHRiaTiYMPPtgxKks6uy9mzpzZ/jtOcXFxTJs2LTZs2NBD1e6aHnvssRg/fnwMGzZsu383r62tjc997nORyWTiwAMPjLlz53Z+wwm9yimnnJKMHj06WbJkSfK///u/yYEHHpicccYZ2zX2pptuSsaNG5dERLJgwYLuLTTlOjvPzz//fPL1r389uf/++5Ply5cnNTU1yUEHHZRMnDixB6vu3ebPn5/k5uYmc+bMSf70pz8l559/fjJw4MCksbFxq/3/8Ic/JH379k1uvPHG5MUXX0yuvPLKZLfddkuef/75Hq48XTo7z2eeeWZSXV2dPPvss8lLL72UnHvuuUlBQUHy2muv9XDl6dHZOf7AypUrk3322Sc54YQTktNPP71niuVTwzG293Ac7h0cq3uPzu6L1tbW5Oijj05OPfXU5PHHH09WrlyZ1NbWJvX19T1c+a6ns/vijjvuSDKZTHLHHXckK1euTB588MFk6NChybRp03q48l3LwoULkyuuuCK59957t+t381deeSXZfffdk4qKiuTFF19Mfv7znyd9+/ZNFi1a1KntCnd6kRdffDGJiOSPf/xje9vvf//7JCcnJ/nb3/72sWOfffbZZJ999kneeOMN4c4n2JF5/rC77roryc3NTTZt2tQdZabOMccck5SXl7c/3rx5czJs2LCkqqpqq/2/+c1vJqeddlqHtrFjxyb/+q//2q11pl1n5/mj3nvvvWTAgAHJ7bff3l0lpl5X5vi9995Ljj322OSXv/xlMnnyZL8wkHWOsb2H43Dv4Fjde3R2X8yaNSvZf//9k40bN/ZUiZ8and0X5eXlyZe//OUObRUVFclxxx3XrXV+mmzP7+aXXnpp8tnPfrZD26RJk5KysrJObcvHsnqRurq6GDhwYBx99NHtbaWlpdGnT5948skntznu3XffjTPPPDOqq6ujqKioJ0pNta7O80c1NTVFfn5+9OvXrzvKTJWNGzfG0qVLo7S0tL2tT58+UVpaGnV1dVsdU1dX16F/RERZWdk2+9O1ef6od999NzZt2hSDBg3qrjJTratzfO2118aQIUPivPPO64ky+ZRxjO09HId7B8fq3qMr++L++++PkpKSKC8vj8LCwjj88MPj+uuvj82bN/dU2bukruyLY489NpYuXdr+0a1XXnklFi5cGKeeemqP1Mz7svWe7bfSXqShoSGGDBnSoa1fv34xaNCgaGho2Oa4adOmxbHHHhunn356d5e4S+jqPH/YmjVr4rrrrosLLrigO0pMnTVr1sTmzZujsLCwQ3thYWG8/PLLWx3T0NCw1f7buw8+jboyzx912WWXxbBhw7Z4A+F9XZnjxx9/PG699daor6/vgQr5NHKM7T0ch3sHx+reoyv74pVXXolHHnkkzjrrrFi4cGEsX748vvOd78SmTZtixowZPVH2Lqkr++LMM8+MNWvWxPHHHx9JksR7770XF154YXz/+9/viZL5/23rPbu5uTn+/ve/R//+/bdrPa7c6QGXX3555OTkfOyyvScEH3X//ffHI488EjNnzsxu0SnUnfP8Yc3NzXHaaafFYYcdFldfffWOFw495IYbboj58+fHggULIi8vb2eXs0tYv359nH322XHLLbfE4MGDd3Y5QC/nOLxzOFb3Lm1tbTFkyJD4xS9+EWPGjIlJkybFFVdcEbNnz97ZpX3q1NbWxvXXXx8333xzPPPMM3HvvffGAw88ENddd93OLo0ucOVOD/j3f//3OPfccz+2z/777x9FRUXx5ptvdmh/7733Yu3atdv8uNUjjzwSK1asiIEDB3ZonzhxYpxwwglRW1u7A5WnS3fO8wfWr18fp5xySgwYMCAWLFgQu+22246WvUsYPHhw9O3bNxobGzu0NzY2bnNOi4qKOtWfrs3zB3784x/HDTfcEA8//HCMGjWqO8tMtc7O8YoVK+LVV1+N8ePHt7e1tbVFxPtXBC5btiwOOOCA7i2aXZ5jbO/hONw7OFb3Hl35PzF06NDYbbfdom/fvu1thx56aDQ0NMTGjRsjNze3W2veVXVlX1x11VVx9tlnx7e//e2IiDjiiCOipaUlLrjggrjiiiuiTx/XgvSEbb1n5+fnb/dVOxGu3OkRe++9d4wcOfJjl9zc3CgpKYl169bF0qVL28c+8sgj0dbWFmPHjt3qui+//PJ47rnnor6+vn2JiPjpT38at912W0+8vF6jO+c54v0rdk4++eTIzc2N+++/31/cPiQ3NzfGjBkTNTU17W1tbW1RU1MTJSUlWx1TUlLSoX9ExEMPPbTN/nRtniMibrzxxrjuuuti0aJFHe41xZY6O8cjR46M559/vsMx+Gtf+1qcdNJJUV9fH8XFxT1ZPrsox9jew3G4d3Cs7j268n/iuOOOi+XLl7cHbBERf/7zn2Po0KGCnR3QlX3x7rvvbhHgfBC6vX8vYHpC1t6zO3evZ7rbKaeckhx11FHJk08+mTz++OPJQQcd1OErul977bXkkEMOSZ588sltriN8W9Yn6uw8NzU1JWPHjk2OOOKIZPny5ckbb7zRvrz33ns762X0KvPnz08ymUwyd+7c5MUXX0wuuOCCZODAgUlDQ0OSJEly9tlnJ5dffnl7/z/84Q9Jv379kh//+MfJSy+9lMyYMcPX9G6Hzs7zDTfckOTm5ib33HNPh5/b9evX76yX0Ot1do4/yjew0B0cY3sPx+HewbG69+jsvli1alUyYMCAZOrUqcmyZcuS3/3ud8mQIUOSH/zgBzvrJewyOrsvZsyYkQwYMCD59a9/nbzyyivJ//zP/yQHHHBA8s1vfnNnvYRdwvr165Nnn302efbZZ5OISG666abk2WefTf76178mSZIkl19+eXL22We39//gq9D/4z/+I3nppZeS6upqX4W+K3j77beTM844I9ljjz2S/Pz8ZMqUKR3e/FeuXJlERPLoo49ucx3CnU/W2Xl+9NFHk4jY6rJy5cqd8yJ6oZ///OfJ8OHDk9zc3OSYY45JlixZ0v7cl770pWTy5Mkd+t91113JwQcfnOTm5iaf/exnkwceeKCHK06nzszzZz7zma3+3M6YMaPnC0+Rzv4sf5hfGOgujrG9h+Nw7+BY3Xt0dl888cQTydixY5NMJpPsv//+yQ9/+EN/MM2SzuyLTZs2JVdffXVywAEHJHl5eUlxcXHyne98J/m///u/ni98F7Kt3x0/mPvJkycnX/rSl7YYc+SRRya5ubnJ/vvvn9x2222d3m5OkrjeCgAAACCt3HMHAAAAIMWEOwAAAAApJtwBAAAASDHhDgAAAECKCXcAAAAAUky4AwAAAJBiwh0AAACAFBPuAAAAAKSYcAcAAAAgxYQ7AAAAACkm3AEAAABIMeEOAAAAQIr9fwjIi9WyqQ+mAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_1d_data(dataset_1_part_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5aad3025-6f0e-49b3-8fdb-0ca31ce50ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.True_"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(dataset_1_part_1 == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d15b22-aebb-4266-829c-ece040e59ed6",
   "metadata": {},
   "source": [
    "The first part of Dataset 1 consists entirely of zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8ff5c17c-167d-4c6f-a8b4-05c539f9a069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean': np.float64(49.787375000000004),\n",
       "  'std': np.float64(10.097441661598001),\n",
       "  'median': np.float64(49.7),\n",
       "  'q1': np.float64(43.2),\n",
       "  'q3': np.float64(56.9),\n",
       "  'iqr': np.float64(13.699999999999996),\n",
       "  'min': np.float64(17.7),\n",
       "  'max': np.float64(85.7)},\n",
       " <Figure size 1400x500 with 2 Axes>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_1d_data(dataset_1_part_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3b3877-7bdf-4847-b1d2-7e49a4ce00de",
   "metadata": {},
   "source": [
    "The second part looks like a sample from a normal distribution with the mean around 50 and the standard deviation around 10."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8219838-38e7-4e3d-944b-a126682d5be0",
   "metadata": {},
   "source": [
    "## Dataset 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "612f13a1-1c8d-49e2-b738-df09a1db66f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_string_2 = '''\n",
    "[39.7 79.6 38.  74.6 28.6 69.5 68.5 23.8 28.8 73.2 71.6 68.2 39.2 28.4\n",
    " 27.6 69.5 34.9 76.3 25.1 60.8 36.9 66.1 36.  71.  73.4 74.7 66.6 32.9\n",
    " 28.4 32.2 31.5 77.5 71.  33.7 29.6 31.5 25.2 27.3 39.1 27.7 68.6 31.3\n",
    " 27.2 73.7 34.3 72.4 30.1 68.8 27.3 28.5 58.5 33.4 72.4 72.4 40.7 70.8\n",
    " 68.3 32.8 27.5 72.9 65.6 31.8 67.2 36.  62.3 76.9 29.2 26.8 74.6 69.4\n",
    " 42.  24.4 31.9 63.9 31.3 60.6 29.6 62.8 68.  67.2 74.1 79.2 77.2 74.5\n",
    " 72.5 63.8 64.  28.8 42.9 39.6 73.4 61.2 25.8 73.6 34.5 61.7 25.6 37.7\n",
    " 80.3 71.3 62.7 27.3 37.4 65.  24.2 72.8 67.9 76.5 26.7 78.4 62.9 70.1\n",
    " 62.7 67.6 72.  65.7 26.7 69.  30.7 30.1 64.9 69.1 59.3 69.5 24.3 30.5\n",
    " 73.4 36.  26.8 24.6 74.8 72.5 34.4 32.  35.7 67.9 68.8 28.1 68.4 64.1\n",
    " 72.7 66.8 71.  73.1 61.  30.4 19.9 75.7 35.7 35.7 68.2 66.  68.8 78.7\n",
    " 28.8 79.7 76.9 78.2 64.5 23.9 32.1 64.2 29.6 74.1 73.  21.3 34.7 26.1\n",
    " 76.3 32.2 34.6 28.4 30.7 22.5 33.5 62.7 62.  28.  25.7 70.8 24.9 31.2\n",
    " 74.6 70.6 30.5 71.7 33.  69.5 36.8 32.9 25.5 75.7 18.9 25.7 40.1 41.4\n",
    " 25.  15.3 71.1 64.3 72.9 33.4 65.6 30.4 72.4 33.4 70.1 28.9 67.7 69.7\n",
    " 70.5 65.3 26.4 67.5 25.  65.4 68.6 24.8 58.2 18.4 27.5 71.3 23.5 76.2\n",
    " 21.3 73.7 33.1 72.3 63.5 73.7 26.  38.1 27.6 74.8 71.7 65.6 34.3 70.4\n",
    " 34.4 67.  66.3 30.2 61.7 25.5 67.  60.6 28.4 25.8 31.9 69.2 26.1 30.6\n",
    " 31.8 20.5 19.8 65.6 28.5 33.8 66.5 68.4 34.  23.5 28.6 24.2 64.9 57.2\n",
    " 60.6 35.4 28.5 69.6 70.7 77.  72.9 71.1 84.1 29.5 25.9 71.2 31.  30.4\n",
    " 33.1 38.6 71.  31.8 67.2 35.7 35.9 74.5 69.8 29.3 65.4 77.1 35.7 74.\n",
    " 29.9 72.2 35.5 29.5 79.1 75.4 23.  68.8 70.8 28.7 67.5 34.7 35.1 65.2\n",
    " 75.6 24.7 33.2 64.6 35.8 20.4 72.5 20.9 28.2 69.7 29.7 81.7 29.4 35.8\n",
    " 71.3 31.8 71.  72.6 29.4 32.8 68.9 71.3 29.3 33.7 33.9 67.2 31.5 74.8\n",
    " 20.9 26.4 26.1 72.8 66.8 36.6 77.3 74.6 29.9 28.3 64.  33.2 24.8 28.2\n",
    " 67.9 30.2 21.6 70.8 62.8 73.3 77.4 23.9 26.3 73.2 30.4 30.6 35.5 68.8\n",
    " 34.3 31.2 22.8 66.9 76.6 67.6 75.5 70.2 30.  64.5 66.7 28.3 77.1 26.\n",
    " 30.4 36.2 41.6 20.7 26.2 72.4 65.5 29.4 26.6 63.7 32.9 77.  29.7 65.5\n",
    " 36.9 79.8 35.9 67.  69.6 30.3 73.6 57.5]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "14706add-bcd8-4ef7-87f6-a95bede2ba0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean': np.float64(50.014250000000004),\n",
       "  'std': np.float64(20.574176093771047),\n",
       "  'median': np.float64(50.05),\n",
       "  'q1': np.float64(29.975),\n",
       "  'q3': np.float64(70.1),\n",
       "  'iqr': np.float64(40.12499999999999),\n",
       "  'min': np.float64(15.3),\n",
       "  'max': np.float64(84.1)},\n",
       " <Figure size 1400x500 with 2 Axes>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O/VqYcyMtLa3QCeLchPrmzZtx8+ZNfPvtt3keJKdQKPDgwQPY2dnBxMQEjo6OUCqViI+Ph52dnbpeZmYmnj59CicnJwCAtbU1DA0NERMTk2e/uWW5dUtDQYnHwjwkrihUKhUaNmyIwMDAfNe/PH8skH+y2s7ODhcvXsTBgwfx119/4a+//kJISAiGDh2KH3/8UR13QkJCoWKytrZWj4p2dHTE48eP89Qp6HuQ+0eAV+c/JiIiIiptTNwSERFRmdq6dSvs7Oywdu3aPOt27dqF3bt3Izg4uEgjD589e4awsDDMmzcPc+bMUZffvn270G0MHToUfn5+iImJwbZt29C9e/c8UxtIpVJ07twZnTt3RmBgIL755ht89dVXOHLkyGtH/tWoUQNffPEFvvjiC9y+fRtNmjTB8uXLsWXLlnzrV6tWDQBw8+bNPOtu3LiBypUrl8oIyp07dyItLQ1eXl4aZT4+Pli+fLm6LD09HUlJSRrbFpQUPHr0KJ4+fYpdu3ahXbt26vKXR1u/Tp06ddT1GzVqVNhD0fDLL7/A19e3UHVzE75RUVHIyspC69at89TZvHkzNm/ejN27d8Pb2xtNmjQBAJw7dw7dunVT1zt37hxUKpV6vVQqRcOGDXHu3Lk8bZ4+fRrVq1cv8oPJinKuVKpUKc/3Dfhv1O7LSjK6tEaNGrh06RI6d+5conYMDAzQo0cP9OjRAyqVCmPGjMG3336L2bNno2bNmnj48GG+o3Xzc+TIEXTo0AEA0KRJExw5cgRyuVzjAWW5U2Lkfr9y5Z6ruQ/bIyIiIiorTNwSERFRmUlLS8OuXbvQr18/9O3bN896Jycn/Pzzz9i7dy8GDBhQ6HZzR12+PMoSAIKCggrdxqBBg/DFF19g4sSJuHfvHpYuXaqxPjExUWMOUeC/BE9+H7sGgNTUVEilUvXcmUBOUsvc3LzAbYCcEYFNmjTBjz/+iBkzZqjn171y5QoOHTqEwYMHF/q4CuvSpUuYNGkSKlWqhLFjx6rLZTJZnn5dvXp1nlGaucnBVxOD+X1vMjMzsW7dukLF5eHhASAnCVrcxG1x5rgdOHBgngQeAPTu3RvdunXDyJEj4e7uDiBn7lZra2usX79eI3G7fv16mJiYoHv37uqyvn37Yvr06Th37hyaN28OICfpGh4ejilTphT52IpyrtSoUQPPnz/H5cuX1X0ZExOD3bt352nX1NQ03yRvYfTv3x/79+/Hd999h1GjRmmsS0tLg0qleuMfHp4+fQobGxv1slQqVcec+7NT3Dlu+/bti2XLlmHDhg3qPs/IyEBISAjc3d3zjAg+f/48JBKJ+lwkIiIiKitM3BIREVGZ2bt3LxQKBXr27Jnv+pYtW8LW1hZbt24tUuLWwsIC7dq1w5IlS5CVlYUqVarg0KFDhR7VCQC2trbo2rUrduzYASsrK41kGwDMnz8fx48fR/fu3VGtWjXEx8dj3bp1qFq1Ktq0aZNvm7du3ULnzp3Rv39/1KtXD3p6eti9ezfi4uIwcODA18azdOlSfPDBB/Dw8MDw4cORlpaG1atXw9LSEnPnzi30ceXn77//Rnp6OpRKJZ4+fYoTJ05g7969sLS0xO7duzXm3/3www/x008/wdLSEvXq1cOpU6dw+PBhjaQakJPElslkWLx4MZ4/fw5DQ0N06tQJrVq1QqVKleDj44MJEyZAIpHgp59+ypMMLkj16tXRoEEDHD58GJ9++mmxjrc4c9zWqVNHPdr3VW5ubvD29lYvGxsbY8GCBRg7diz69esHLy8v/P3339iyZQsWLlyokfAfM2YMvvvuO3Tv3h1TpkyBvr4+AgMDYW9vjy+++EJjPx06dMCxY8fe2FeFPVcGDhyIadOmoXfv3pgwYQJSU1Oxfv16vPPOO4iIiNBos1mzZjh8+DACAwPh5OQENzc3daL6TYYMGYJff/0Vn332GY4cOYLWrVtDqVTixo0b+PXXX3Hw4EF10rogI0aMQGJiIjp16oSqVasiMjISq1evRpMmTdQjX4s7x627uzv69euHGTNmID4+HjVr1sSPP/6IBw8e4IcffshTPzQ0FK1bt85zzhMRERGVOoGIiIiojPTo0UMwMjISUlJSCqwzbNgwQV9fX3jy5Ilw//59AYCwdOnSPPUACP7+/urlR48eCb179xasrKwES0tLoV+/fkJ0dHSeeiEhIQIA4f79+3na/PXXXwUAwqhRo/KsCwsLE3r16iU4OTkJBgYGgpOTkzBo0CDh1q1b6jq58YaEhAiCIAhPnjwRxo4dK9SpU0cwNTUVLC0tBXd3d+HXX399c2cJgnD48GGhdevWgrGxsWBhYSH06NFDuHbtmkadI0eOCACEHTt2vLG93Lq5L319fcHW1lZo166dsHDhQiE+Pj7PNs+ePRN8fX2FypUrC2ZmZoKXl5dw48YNoVq1aoKPj49G3e+++06oXr26IJPJBADCkSNHBEEQhBMnTggtW7YUjI2NBScnJ+HLL78UDh48qFHndQIDAwUzMzMhNTVVXVaUc0ObAAhjx47Nd92GDRuE2rVrCwYGBkKNGjWEFStWCCqVKk+9hw8fCn379hUsLCwEMzMz4cMPPxRu376dp16zZs0EBweHQsVVmHNFEATh0KFDQoMGDQQDAwOhdu3awpYtWwR/f3/h1bcFN27cENq1aycYGxsLANTf6/x+ftq3by+0b99eY/vMzExh8eLFQv369QVDQ0OhUqVKQrNmzYR58+YJz58/V9crqD937twpdOnSRbCzsxMMDAwEFxcXYfTo0UJMTEyh+uNN0tLShClTpggODg6CoaGh8N577wkHDhzIUy8pKUkwMDAQvv/+e63sl4iIiKgoJIJQyOEOREREROXc77//Dm9vbxw/fhxt27YVOxx64fnz56hevTqWLFmC4cOHix1OmVAoFLC2tkZQUJDG1BVUtoKCgrBkyRLcvXu3SPNuExEREWmDVOwAiIiIiHTFd999h+rVqxc49QGJw9LSEl9++SWWLl0KlUoldjhl4vjx46hSpQpGjhwpdigVVlZWFgIDAzFr1iwmbYmIiEgUHHFLREREFd727dtx+fJlBAQEYOXKlZgwYYLYIRERERERUQXHxC0RERFVeBKJBGZmZhgwYACCg4Ohp8fntxIRERERkbj4roSIiIgqPP4dm4iIiIiIdA3nuCUiIiIiIiIiIiLSMUzcEhEREREREREREekYJm6JiIiIiIiIiIiIdAwTt0REREREREREREQ6holbIiIiIiIiIiIiIh3DxC0RERERERERERGRjmHiloiIiIiIiIiIiEjHMHFLREREREREREREpGOYuCUiIiIiIiIiIiLSMUzcEhEREREREREREekYJm6JiIiIiIiIiIiIdAwTt0REREREREREREQ6holbIiIiIiIiIiIiIh3DxC0RERERERERERGRjmHiloiIiIiIiIiIiEjHMHFLREREREREREREpGOYuCUiIiIiIiIiIiLSMUzcEhEREREREREREekYJm6JiIiIiIiIiIiIdAwTt0REFYSrqyuGDRtW6vt58OABJBIJNm3apC4bNmwYzMzMSn3fuSQSCebOnVtm+yMiIiKiimnu3LmQSCRih0FE5RQTt0RUYWzatAkSiUT9MjIygpOTE7y8vLBq1SooFIpit33y5EnMnTsXSUlJ2gv4NTp06KA+DqlUCgsLC9SuXRtDhgxBaGio1vazf/9+nU2A6nJsRERERGWhvN7fSiQSGBgYwM3NDaNGjcLDhw/LJIaXDRs2TCMeCwsLNG7cGMuXL0dGRoZW9rFu3TqNwQ5ERK/SEzsAIqKyNn/+fLi5uSErKwuxsbE4evQoJk2ahMDAQOzduxeNGjUqcpsnT57EvHnzMGzYMFhZWWk/6HxUrVoVAQEBAICUlBTcuXMHu3btwpYtW9C/f39s2bIF+vr66vo3b96EVFq0v9ft378fa9euLVKCtFq1akhLS9PYd2l4XWxpaWnQ0+OvOCIiIqoYyuP9bWZmJq5du4bg4GAcPHgQ169fh4mJSZnEkcvQ0BDff/89ACApKQm//fYbpkyZgrNnz2L79u0lbn/dunWoXLlymXwqjojeTnxXS0QVzgcffIDmzZurl2fMmIHw8HB8+OGH6NmzJ65fvw5jY2MRIywcS0tLDB48WKNs0aJFmDBhAtatWwdXV1csXrxYvc7Q0LBU48nOzoZKpYKBgQGMjIxKdV9vIvb+iYiIiMpSeb6/dXNzw7hx43DixAm8//77ZRqPnp6eRjxjxoyBu7s7fvnlFwQGBsLJyalM4yGiiodTJRARAejUqRNmz56NyMhIbNmyRV1++fJlDBs2DNWrV4eRkREcHBzw6aef4unTp+o6c+fOxdSpUwHk3FjmfpzqwYMHAICQkBB06tQJdnZ2MDQ0RL169bB+/fo8MTx//hw3btzA8+fPi30cMpkMq1atQr169bBmzRqNtl6d4zYrKwvz5s1DrVq1YGRkBBsbG7Rp00Y91cKwYcOwdu1aAND4mBjw3zy2y5YtQ1BQEGrUqAFDQ0Ncu3Yt3zluc927dw9eXl4wNTWFk5MT5s+fD0EQ1OuPHj0KiUSCo0ePamz3apuviy237NWRuBcuXMAHH3wACwsLmJmZoXPnzvj333816uR+3PDEiRPw8/ODra0tTE1N0bt3byQkJLz5G0BERESkI8rL/a2DgwMA5Pk01Zvu7cLDwyGVSjFnzhyN7bZt2waJRJJvvG8ilUrRoUMHAFD3RX6ys7OxYMEC9T2yq6srZs6cqTHFgqurK65evYpjx46p+ze3bSKiXBxxS0T0wpAhQzBz5kwcOnQII0eOBACEhobi3r178PX1hYODA65evYoNGzbg6tWr+PfffyGRSPDRRx/h1q1b+Pnnn7FixQpUrlwZAGBrawsAWL9+PerXr4+ePXtCT08Pf/zxB8aMGQOVSoWxY8eq97979274+voiJCSkRB+XkslkGDRoEGbPno1//vkH3bt3z7fe3LlzERAQgBEjRqBFixaQy+U4d+4cIiIi8P7772P06NGIjo5GaGgofvrpp3zbCAkJQXp6OkaNGgVDQ0NYW1tDpVLlW1epVKJr165o2bIllixZggMHDsDf3x/Z2dmYP39+kY6xMLG97OrVq2jbti0sLCzw5ZdfQl9fH99++y06dOiAY8eOwd3dXaP++PHjUalSJfj7++PBgwcICgrCuHHj8MsvvxQpTiIiIiIxvW33t0qlEk+ePAGQM8jg+vXr8Pf3R82aNdG6dWt1vcLc23Xq1AljxoxBQEAAvL290bRpU8TExGD8+PHw9PTEZ599Vqw+vXv3LgDAxsamwDojRozAjz/+iL59++KLL77A6dOnERAQgOvXr2P37t0AgKCgIIwfPx5mZmb46quvAAD29vbFiomIyjGBiKiCCAkJEQAIZ8+eLbCOpaWl8O6776qXU1NT89T5+eefBQDC8ePH1WVLly4VAAj379/PUz+/Nry8vITq1avnG19ISMgbj6V9+/ZC/fr1C1y/e/duAYCwcuVKdVm1atUEHx8f9XLjxo2F7t27v3Y/Y8eOFfL7VXH//n0BgGBhYSHEx8fnu+7l4/Dx8REACOPHj1eXqVQqoXv37oKBgYGQkJAgCIIgHDlyRAAgHDly5I1tFhSbIAgCAMHf31+97O3tLRgYGAh3795Vl0VHRwvm5uZCu3bt1GW53wNPT09BpVKpyydPnizIZDIhKSkp3/0RERERiaG83d8CyPOqW7eucO/ePY26hb23S0lJEWrWrCnUr19fSE9PF7p37y5YWFgIkZGRb4zHx8dHMDU1FRISEoSEhAThzp07wjfffCNIJBKhUaNG6nr+/v4a96QXL14UAAgjRozQaG/KlCkCACE8PFxdVr9+faF9+/ZvjIWIKi5OlUBE9BIzMzONp+++PBdYeno6njx5gpYtWwIAIiIiCtXmy208f/4cT548Qfv27XHv3j2Nj40NGzYMgiBo5eEEZmZmAPDaJwlbWVnh6tWruH37drH306dPH/XIi8IYN26c+muJRIJx48YhMzMThw8fLnYMb6JUKnHo0CF4e3ujevXq6nJHR0d8/PHH+OeffyCXyzW2GTVqlMbUC23btoVSqURkZGSpxUlERERUGt6m+1tXV1eEhoYiNDQUf/31F4KCgvD8+XN88MEH6mmrinJvZ2Jigk2bNuH69eto164d9u3bhxUrVsDFxaVQ8aSkpMDW1ha2traoWbMmZs6cCQ8PD/Wo2fzs378fAODn56dR/sUXXwAA9u3bV6h9ExEBnOOWiEhDcnIyzM3N1cuJiYmYOHEi7O3tYWxsDFtbW7i5uQFAoefqOnHiBDw9PWFqagorKyvY2tpi5syZRWqjqJKTkwFA41heNX/+fCQlJeGdd95Bw4YNMXXqVFy+fLlI+8nti8KQSqUaN9cA8M477wB4/RxhJZWQkIDU1FTUrl07z7q6detCpVLh4cOHGuWv3sxXqlQJAPDs2bNSi5OIiIioNLxN97empqbw9PSEp6cnunbtiokTJ2Lv3r24efMmFi1aBKDo93atW7fG559/jjNnzsDLywuffvppoeMxMjJSJ5KPHz+Ohw8f4sSJE3nuaV8WGRkJqVSKmjVrapQ7ODjAysqKAwGIqEg4xy0R0QuPHj3C8+fPNW6y+vfvj5MnT2Lq1Klo0qQJzMzMoFKp0LVr1wLncn3Z3bt30blzZ9SpUweBgYFwdnaGgYEB9u/fjxUrVhSqjeK4cuUKAOS5YXxZu3btcPfuXfz+++84dOgQvv/+e6xYsQLBwcEYMWJEofaj7acTvzzK9WVKpVKr+3kTmUyWb7nw0oPUiIiIiHRdebi/bdasGSwtLXH8+PFibZ+RkaF+8O3du3eRmpoKExOTQm0rk8ng6elZrP0WdF9LRFQUTNwSEb2Q+5ArLy8vADmjK8PCwjBv3jyNp9HmN7VAQTdmf/zxBzIyMrB3716NUZxHjhzRZugalEoltm3bBhMTE7Rp0+a1da2treHr6wtfX18kJyejXbt2mDt3rjpxq80bTpVKhXv37qlH2QLArVu3AOR8LA74b2RrUlKSxrb5jUwobGy2trYwMTHBzZs386y7ceMGpFIpnJ2dC9UWERER0dukPN3f5n6irKj3dv7+/rh+/TqWLVuGadOmYfr06Vi1alWpxVqtWjWoVCrcvn0bdevWVZfHxcUhKSkJ1apVU5cxuUtEb8KpEoiIAISHh2PBggVwc3PDJ598AuC/UZevjrIMCgrKs72pqSmAvAnH/Np4/vw5QkJC8rTx/Plz3Lhxo0QfL1MqlZgwYQKuX7+OCRMmwMLCosC6T58+1Vg2MzNDzZo1kZGRoS4r6LiKa82aNeqvBUHAmjVroK+vj86dOwPIudGVyWR5RlSsW7cuT1uFjU0mk6FLly74/fffNaZkiIuLw7Zt29CmTZvX9hMRERHR26i83N8eOXIEycnJaNy4sXr/hb23O336NJYtW4ZJkybhiy++wNSpU7FmzRocO3as2PG8Sbdu3QDk7dPAwEAAQPfu3dVlpqamWrvPJqLyiSNuiajC+euvv3Djxg1kZ2cjLi4O4eHhCA0NRbVq1bB3714YGRkBACwsLNCuXTssWbIEWVlZqFKlCg4dOoT79+/nabNZs2YAgK+++goDBw6Evr4+evTogS5dusDAwAA9evTA6NGjkZycjO+++w52dnaIiYnRaGP37t3w9fVFSEhIoR7g8Pz5c2zZsgUAkJqaijt37mDXrl24e/cuBg4ciAULFrx2+3r16qFDhw5o1qwZrK2tce7cOezcuVPjAWK5xzVhwgR4eXlBJpNh4MCBb4wtP0ZGRjhw4AB8fHzg7u6Ov/76C/v27cPMmTPVDziztLREv379sHr1akgkEtSoUQN//vkn4uPj87RXlNi+/vprhIaGok2bNhgzZgz09PTw7bffIiMjA0uWLCnW8RARERHpivJ4f5udnY2bN29i/fr1MDY2xvTp09X1CnNvl56eDh8fH9SqVQsLFy4EAMybNw9//PEHfH198b///U+dnNamxo0bw8fHBxs2bEBSUhLat2+PM2fO4Mcff4S3tzc6duyortusWTOsX78eX3/9NWrWrAk7Ozt06tRJ6zER0VtMICKqIEJCQgQA6peBgYHg4OAgvP/++8LKlSsFuVyeZ5tHjx4JvXv3FqysrARLS0uhX79+QnR0tABA8Pf316i7YMECoUqVKoJUKhUACPfv3xcEQRD27t0rNGrUSDAyMhJcXV2FxYsXCxs3btSo83J8ISEhbzyW9u3baxyLmZmZUKtWLWHw4MHCoUOH8t2mWrVqgo+Pj3r566+/Flq0aCFYWVkJxsbGQp06dYSFCxcKmZmZ6jrZ2dnC+PHjBVtbW0EikQi5vzbu378vABCWLl2aZz+5614+Dh8fH8HU1FS4e/eu0KVLF8HExESwt7cX/P39BaVSqbF9QkKC0KdPH8HExESoVKmSMHr0aOHKlSt52iwoNkEQ8v3+RERECF5eXoKZmZlgYmIidOzYUTh58qRGndzvwdmzZzXKjxw5IgAQjhw5km/fEhEREYmhPN/fSiQSwdraWujZs6dw/vz5PPXfdG83efJkQSaTCadPn9bY7ty5c4Kenp7w+eefvzae3PvXN/H39xdeTa1kZWUJ8+bNE9zc3AR9fX3B2dlZmDFjhpCenq5RLzY2Vujevbtgbm4uABDat2//xv0RUcUiEQQ+aYWIiIiIiIiIiIhIl3COWyIiIiIiIiIiIiIdw8QtERERERERERERkY5h4paIiIiIiIiIiIhIxzBxS0RERERERERERKRjmLglIiIiIiIiIiIi0jFM3BIRERERERERERHpGD2xA3hbqVQqREdHw9zcHBKJROxwiIiIiOglgiBAoVDAyckJUmnFGavAe1QiIiIi3VaU+1QmbospOjoazs7OYodBRERERK/x8OFDVK1aVewwygzvUYmIiIjeDoW5T2XitpjMzc0B5HSyhYWFyNGIR6VSISEhAba2thVqNIu2sR+1g/2oHexH7WA/agf7UTsqYj/K5XI4Ozur79kqCt6jElF5UxF/hxFR+VaU+1Qmbosp96NnFhYWFfqmWKVSIT09HRYWFvwlWgLsR+1gP2oH+1E72I/awX7UjorcjxVtugDeoxJReVORf4cRUflWmPtUXvWIiIiIiIiIiIiIdAwTt0REREREREREREQ6holbIiIiIiIiIiIiIh3DOW5LmVKpRFZWlthhlBqVSoWsrCykp6dXqPmG9PX1IZPJxA6DiIiIiIiIiIjKKSZuS4kgCIiNjUVSUpLYoZQqQRCgUqmgUCgq3MM/rKys4ODgUOGOm4iIiIiIiIiISh8Tt6UkN2lrZ2cHExOTcpvcEwQB2dnZ0NPTK7fH+CpBEJCamor4+HgAgKOjo8gRERERERERERFRecPEbSlQKpXqpK2NjY3Y4ZSqipi4BQBjY2MAQHx8POzs7DhtAhERERERERERaVXFmZS0DOXOaWtiYiJyJFSacr+/5XkOYyIiIiIiIiIiEgcTt6WoIo1ArYj4/SUiIiIiIiIiotLCxC0RERERERERERGRjuEct2UoISEBcrm8zPZnYWEBW1tbrbe7adMmTJo0CUlJSVpvm4iIiIiIiIiIiJi4LTMJCQn4dNRnUKSll9k+zY2NsHFDcJGSt8OGDUNSUhL27NmjUX706FF07NgRz549w4ABA9CtW7dCtcckLxERERERERERUdExcVtG5HI5FGnp6DDkc9g4Vi31/T2NeYSjP62HXC7X+qhbY2NjGBsba7VNbcjMzISBgYHYYRAREREREREREZWYTiRu165di6VLlyI2NhaNGzfG6tWr0aJFiwLr79ixA7Nnz8aDBw9Qq1YtLF68uMARoJ999hm+/fZbrFixApMmTVKXJyYmYvz48fjjjz8glUrRp08frFy5EmZmZto+PA02jlXhUM2tVPdR2l4dRXvp0iVMnToV586dg0QiQa1atfDtt98iOTkZvr6+AP57kJe/vz/mzp2LZ8+eYeLEifjjjz+QkZGB9u3bY9WqVahVq5Z6P9999x3mz5+Pp0+fwsvLC23btsX8+fPV+507dy727NmDcePGYeHChYiMjIRKpcKBAwfw9ddf48qVK5DJZPDw8MDKlStRo0YNAMCDBw/g5uaGX375BatXr8a5c+fQoEEDbN26Fc+fP8fnn3+OGzduoG3btti8eXOpTDdBRERERERUXqSmpuLGjRul1valS5fQuHFjmJiYlMo+6tSpU2ptExGVhOiJ219++QV+fn4IDg6Gu7s7goKC4OXlhZs3b8LOzi5P/ZMnT2LQoEEICAjAhx9+iG3btsHb2xsRERFo0KCBRt3du3fj33//hZOTU552PvnkE8TExCA0NBRZWVnw9fXFqFGjsG3btlI71vLKx8cHTZs2xfr16yGTyXDx4kXo6+ujVatWCAoKwpw5c3Dz5k0AUCfGhw0bhtu3b2Pv3r2wsLDAtGnT0K1bN1y7dg36+vo4ceIEPvvsMyxevBg9e/bE4cOHMXv27Dz7vnPnDn777Tfs2rULMpkMAJCSkgI/Pz80atQIycnJmDNnDnr37o2LFy9CKv3veXz+/v4ICgqCi4sLPv30U3z88ccwNzfHypUrYWJigv79+2POnDlYv3691vqqoHmOBUGAQqFAcnKyOsn9OqU1fzEREREREVFR3bhxA82aNRM7jGI7f/48mjZtKnYYRER5iJ64DQwMxMiRI9UjM4ODg7Fv3z5s3LgR06dPz1N/5cqV6Nq1K6ZOnQoAWLBgAUJDQ7FmzRoEBwer6z1+/Bjjx4/HwYMH0b17d402rl+/jgMHDuDs2bNo3rw5AGD16tXo1q0bli1blm+ityL5888/84w8ViqVBdZ/+PAhpk6dijp16gCAxqhZS0tLSCQSODg4qMtyE7YnTpxAq1atAABbt26Fs7Mz9uzZg379+mH16tX44IMPMGXKFADAO++8g5MnT+LPP//U2HdmZmaeUbF9+vTRqLNx40bY2tri2rVrGsn9KVOmwMvLCwAwceJEDBo0CGFhYWjdujUAYPjw4di0adPrO6sIXjfPsUQigZuLM+5HPYQgCG9sqzjzFxMREREREZWGOnXq4Pz586XS9rVr1zBkyBD89NNPqFevXqnsI/e9LBGRrhE1cZuZmYnz589jxowZ6jKpVApPT0+cOnUq321OnToFPz8/jTIvLy+Nh2mpVCoMGTIEU6dORf369fNtw8rKSp20BQBPT09IpVKcPn0avXv3LuGRvd06duyYZ5Tp6dOnMXjw4HzrT5w4ESNHjsSWLVvg6emJfv36qaclyM/169ehp6cHd3d3dZmNjQ1q166N69evAwBu3ryZ5/vQokWLPInbatWq5Ule3r59G3PmzMHp06fx5MkTqFQqAEBUVJRG4rZRo0bqr+3t7QEADRs21CiLj48v8DiK6rXzHAsCjJVpeFdmDLxhxG1pzl9MRERERERUVCYmJqU2YjX3/VydOnU4KpaIKhxRE7dPnjyBUqlUJ81y2dvbFzg/TmxsbL71Y2Nj1cuLFy+Gnp4eJkyYUGAbr07DoKenB2tra412XpaRkYGMjAz1cu7H3VUqlfoXSS6VSgVBENQvAIUaRVkaXo6hsExNTfMkXh8+fJinvdz/Z8+ejcGDB2Pfvn04cOAA/P398fPPP6N37975Hv/LZa/G9mr7BW2X+7+pqWmeNnr06IFq1aphw4YNcHJygkqlQsOGDZGRkaHRpp6eXp5tXy3L/V7mJ7et/M6BgupLJBLYOFSBg0u1V1dCokiEpbn1GxO3eNFO7r7pP7nfL/ZLybAftYP9qB3sR+2oiP1YkY6ViIiIiMon0adK0Lbz589j5cqViIiIKNRcoYUVEBCAefPm5SlPSEhAerrmR9+zsrKgUqmQnZ2N7OxsAEB2dnaeZG5pyt3PyzEURm4S8tVtcqdKyM7OVr8Ryj0mpVIJNzc3jB8/HuPHj8fgwYOxceNG9OjRAzKZDEqlUqO9WrVqITs7GydPnoSHhwcA4OnTp7h58yZq166N7Oxs1KpVC2fOnNHY7syZM+r95saae4y5cttZv3492rRpAwA4ceKE+hhe/Z7kfv3y8b3c/sv7e1VuXzx9+hT6+vpv7FuFQgE3F2cYK9MgUSS+slaAJE0BSIAX/xTIWJkGNxdnKBQKrY4ILg9UKhWeP38OQRA05jOmomE/agf7UTvYj9pREftRoVCIHQIRERERUYmImritXLkyZDIZ4uLiNMrj4uI05kR9mYODw2vr//3334iPj4eLi4t6vVKpxBdffIGgoCA8ePAADg4OeRJe2dnZSExMLHC/M2bM0JiiQS6Xw9nZGba2trCwsNCom56eDoVCAT09Pejp5XSxnp4eJBKJ+lXacvfzcgyFIZVKIZVK82yT++AvPT099Rs+PT09pKWl4YsvvkD//v3h5uaGR48e4fz58/joo4+gp6eHGjVqIDk5GceOHVM/BbRu3bro1asXPv/8cwQHB8Pc3BwzZsxAlSpV1NtNmDAB7du3x6pVq9CjRw+Eh4fj4MGD6mPKjfXlZQCwtbWFjY0NNm7ciKpVqyIqKko9FYdMJsvzPcn9+uXje7n93LL85PaFjY0NjIyM3ti3ycnJuB/1EO/KjHNG1r5MEAABEMzePOI2LVGO+1EPYW5unu8D/CoylUoFiUQCW1vbCpOYKA3sR+1gP2oH+1E7KmI/FuZ3MxERERGRLhM1cWtgYIBmzZohLCwM3t7eAHLeWISFhWHcuHH5buPh4YGwsDBMmjRJXRYaGqoeuTlkyBB4enpqbOPl5YUhQ4aoH4Dm4eGBpKQknD9/Xv3ky/DwcKhUKo15V19maGgIQ0PDPOW5ic5Xy15N0ub+/zTm0eu6RGty91PcRPGr27x8HC9/LZPJkJiYCB8fH8TFxaFy5cr46KOPMH/+fEgkErRu3RqfffYZBg4ciKdPn8Lf3x9z585FSEgIJk6ciB49eiAzMxPt2rXD/v37YWBgAABo06YNgoODMW/ePMyePRteXl6YPHky1qxZk6dPX45VJpNh+/btmDBhAho2bIjatWtj1apV6NChQ77fk/zael37r/aJRCLJ9xwoqL4gCDmJWcmr9VUvyvNbl6ch9bQLFeXNd1EU5XtCBWM/agf7UTvYj9pR0fqxohwnEREREZVfok+V4OfnBx8fHzRv3hwtWrRAUFAQUlJS1EnWoUOHokqVKggICACQ8yCs9u3bY/ny5ejevTu2b9+Oc+fOYcOGDQByHnJlY2OjsQ99fX04ODigdu3aAIC6deuia9euGDlyJIKDg5GVlYVx48Zh4MCBcHJyKpXjtLCwgLmxEY7+tP7NlbXE3Ngoz2jgN9m0aVO+5R06dFBP8TBs2DAMGzYMQE7yfcuWLeoRxflZv359noedVapUCZs3b35tLCNHjsTIkSM1lmvWrKlenjt3LubOnZtnO09PT1y7dk2j7OXpKVxdXfNMV/Hy8eV6+TiJiIiIiIiIiIjKkuiJ2wEDBiAhIQFz5sxBbGwsmjRpggMHDqgfQBYVFaUxYqJVq1bYtm0bZs2ahZkzZ6JWrVrYs2cPGjRoUKT9bt26FePGjUPnzp0hlUrRp08frFq1SqvH9jJbW1ts3BCsfqhZWbCwsICtrW2Z7U/bli1bhvfffx+mpqb466+/8OOPP2LdunVih0VERERERERERFTqRE/cAsC4ceMKnBrh6NGjecr69euHfv36Fbr9Bw8e5CmztrbGtm3bCt2GNtja2r7VidSydubMGSxZsgQKhQLVq1fHqlWrMGLECLHDIiIiIiIiIiIiKnU6kbglys+vv/4qdghERERERERERESi4FMbiIiIiIiIiIiIiHQME7dEREREREREREREOoaJWyIiIiIiIiIiIiIdw8QtERERERERERERkY5h4paIiIiIiIiIiIhIxzBxS0RERERUBpRKJWbPng03NzcYGxujRo0aWLBgAQRBUNcRBAFz5syBo6MjjI2N4enpidu3b4sYNRERERGJhYlbIiIiIqIysHjxYqxfvx5r1qzB9evXsXjxYixZsgSrV69W11myZAlWrVqF4OBgnD59GqampvDy8kJ6erqIkRMRERGRGJi4JQ3Dhg2Dt7e3RtnOnTthZGSE5cuXY9iwYZBIJJBIJNDX14eDgwM++OADbNy4ESqVSmM7V1dXdd2XX4sWLSrDIyIiIiLSDSdPnkSvXr3QvXt3uLq6om/fvujSpQvOnDkDIGe0bVBQEGbNmoVevXqhUaNG2Lx5M6Kjo7Fnzx5xgyciIiKiMqcndgCk277//nuMHTsWwcHB8PX1xbBhw9C1a1eEhIRAqVQiNjYW+/fvx6RJk/Dbb79h79690NP777SaP38+Ro4cqdGmubl5WR8GERGVEwkJCZDL5SVux8LCAra2tlqIiKjwWrVqhQ0bNuDWrVt45513cOnSJfzzzz8IDAwEANy/fx+xsbHw9PRUb2NpaQl3d3ecOnUKAwcOzNNmRkYGMjIy1Mu5Px8qlSrPH9WJiN5GudcyXteIqLwoyrWMiVsq0JIlS+Dv74/t27ejd+/e6nJDQ0M4ODgAAJycnNCoUSO0atUKnp6e2LRpE0aMGKGua25urq5LRERUEgkJCfh01GdQpJX8I+PmxkbYuCGYyVsqU9OnT4dcLkedOnUgk8mgVCqxcOFCfPLJJwCA2NhYAIC9vb3Gdvb29up1rwoICMC8efPylCckJHB6BSIqF549e6b+Pz4+XuRoiIhKTqFQFLouE7eUr2nTpmHdunX4888/0blz5zfW79SpExo3boxdu3ZpJG6JiIi0RS6XQ5GWjg5DPoeNY9Vit/M05hGO/rQecrmciVsqU7/++iu2bt2Kbdu2oX79+rh48SImTZoEJycn+Pj4FKvNGTNmwM/PT70sl8vh7OwMW1tbWFhYaCt0IiLRVKpUSf2/nZ2dyNEQEZWckZFRoesycVtWmjcHChgpUaocHIBz54q0yV9//YXff/8dYWFh6NSpU6G3q1OnDi5fvqxRNm3aNMyaNStP+23bti1STERERLlsHKvCoZqb2GEQFdnUqVMxffp09ZQHDRs2RGRkJAICAuDj46P+lFJcXBwcHR3V28XFxaFJkyb5tmloaAhDQ8M85VKpFFIpH2dBRG+/3GsZr2tEVF4U5VrGxG1ZiY0FHj8WO4pCadSoEZ48eQJ/f3+0aNECZmZmhdpOEARIJBKNsqlTp2LYsGEaZVWqVNFWqERERERvjdTU1Dw36jKZTD3PmZubGxwcHBAWFqZO1Mrlcpw+fRqff/55WYdLRERERCJj4rasiDXPazH2W6VKFezcuRMdO3ZE165d8ddffxXqgWLXr1+Hm5vmCKjKlSujZs2aRY6BiIiIqLzp0aMHFi5cCBcXF9SvXx8XLlxAYGAgPv30UwCARCLBpEmT8PXXX6NWrVpwc3PD7Nmz4eTkBG9vb3GDJyIiIqIyx8RtWSnidAViq1atGo4dO6ZO3h44cOC1ydvw8HD873//w+TJk8swSiIiIqK3x+rVqzF79myMGTMG8fHxcHJywujRozFnzhx1nS+//BIpKSkYNWoUkpKS0KZNGxw4cKBIc6ERERERUfnAxC0VyNnZGUePHkXHjh3h5eWFAwcOAAAyMjIQGxsLpVKJ2NhY7N+/H0uWLMGHH36IoUOHarShUCjyPAXZxMSED8sgIiKiCsfc3BxBQUEICgoqsI5EIsH8+fMxf/78sguMiIiIiHQSZ/am16patSqOHj2KJ0+ewMvLC3K5HAcOHICjoyNcXV3xwQcf4NixY1i5ciV+//13yGQyje3nzJkDR0dHjdeXX34p0tEQERERERERERG9HTjiljRs2rQpT1mVKlVw69atfOsLgoDs7Gzo6enleTDZgwcPSiFCIiIiIiIiIiKi8o8jbomIiIiIiIiIiIh0DBO3RERERERERERERDqGiVsiIiIiIiIiIiIiHcPELREREREREREREZGOYeKWiIiIiIiIiIiISMcwcVuKVCqV2CFQKeL3l4iIiIiIiIiISoue2AGURwYGBpBKpYiOjoatrS0MDAwgkUjEDqtUCIKA7Oxs6OnpldtjfJUgCMjMzERCQgKkUikMDAzEDomIiIiIiIiIiMoZJm5LgVQqhZubG2JiYhAdHS12OKVKEASoVCpIpdIKk7jNZWJiAhcXF0ilHLhORERERERERETaxcRtKTEwMICLiwuys7OhVCrFDqfUqFQqPH36FDY2NhUqgSmTySrUKGMiIiIiIiIiIipbTNyWIolEAn19fejr64sdSqlRqVTQ19eHkZFRhUrcEhERERERERERlSZm2oiIiIiIiIiIiIh0DBO3RERERERERERERDqGiVsiIiIiIiIiIiIiHcPELREREREREREREZGO0YnE7dq1a+Hq6gojIyO4u7vjzJkzr62/Y8cO1KlTB0ZGRmjYsCH279+vsX7u3LmoU6cOTE1NUalSJXh6euL06dMadVxdXSGRSDReixYt0vqxERERERERERERERWV6InbX375BX5+fvD390dERAQaN24MLy8vxMfH51v/5MmTGDRoEIYPH44LFy7A29sb3t7euHLlirrOO++8gzVr1uB///sf/vnnH7i6uqJLly5ISEjQaGv+/PmIiYlRv8aPH1+qx0pERERERERERERUGKInbgMDAzFy5Ej4+vqiXr16CA4OhomJCTZu3Jhv/ZUrV6Jr166YOnUq6tatiwULFqBp06ZYs2aNus7HH38MT09PVK9eHfXr10dgYCDkcjkuX76s0Za5uTkcHBzUL1NT01I9ViIiIiIiIiIiIqLCEDVxm5mZifPnz8PT01NdJpVK4enpiVOnTuW7zalTpzTqA4CXl1eB9TMzM7FhwwZYWlqicePGGusWLVoEGxsbvPvuu1i6dCmys7NLeEREREREREREREREJacn5s6fPHkCpVIJe3t7jXJ7e3vcuHEj321iY2PzrR8bG6tR9ueff2LgwIFITU2Fo6MjQkNDUblyZfX6CRMmoGnTprC2tsbJkycxY8YMxMTEIDAwMN/9ZmRkICMjQ70sl8sBACqVCiqVqvAHXc6oVCoIglCh+6CwBEGARCIBBAEQVK+u/O+FN/Tli3bY73nxfNQO9qN2sB+14+V+fO11tCgq4HW0Ip6PFelYiYiIiKh8EjVxW5o6duyIixcv4smTJ/juu+/Qv39/nD59GnZ2dgAAPz8/dd1GjRrBwMAAo0ePRkBAAAwNDfO0FxAQgHnz5uUpT0hIQHp6eukdiI5TqVR4/vw5BEGAVCr6zBs6TaFQwM3FGcbKNEgUia+sFSBJUwAS4MU/BTJWpsHNxRkKhaLAuaArKp6P2sF+1A72o3a83I+vv44WnrEyDVWdHHDv3j0oFIoSx2hiYgJLS8sSt1OaKuL5qI3vLRERERGRmERN3FauXBkymQxxcXEa5XFxcXBwcMh3GwcHh0LVNzU1Rc2aNVGzZk20bNkStWrVwg8//IAZM2bk2667uzuys7Px4MED1K5dO8/6GTNmaCR75XI5nJ2dYWtrCwsLi0Idb3mkUqkgkUhga2tbYd4IFldycjLuRz3EuzJjWJpba64UBEAABDNrQPL6xG1aohz3ox7C3Nxc/YcIysHzUTvYj9rBftSOl/sxNTW14OtoEcTff4Dw8KO4ez8y3z/WFpW5sRG+D16n8ckeXVMRz0cjIyOxQyAiIiIiKhFRE7cGBgZo1qwZwsLC4O3tDSDnjUVYWBjGjRuX7zYeHh4ICwvDpEmT1GWhoaHw8PB47b5UKpXGVAevunjxIqRSaYGJMENDw3zf3Eml0grzBqggEomE/VAIuR/LhUQCSF7tK9WL8vzW5WlI/XFh9nlePB+1g/2oHexH7cjtx9dfRwsvPTUVkMnQbvBnqOJao0SxPY15hKM/rYdCodD5P6ZVtPOxohwnEREREZVfok+V4OfnBx8fHzRv3hwtWrRAUFAQUlJS4OvrCwAYOnQoqlSpgoCAAADAxIkT0b59eyxfvhzdu3fH9u3bce7cOWzYsAEAkJKSgoULF6Jnz55wdHTEkydPsHbtWjx+/Bj9+vUDkPOAs9OnT6Njx44wNzfHqVOnMHnyZAwePBiVKlUSpyOIiIioTNk4OMGhmpvYYRAREREREeVL9MTtgAEDkJCQgDlz5iA2NhZNmjTBgQMH1A8gi4qK0hgx0apVK2zbtg2zZs3CzJkzUatWLezZswcNGjQAAMhkMty4cQM//vgjnjx5AhsbG7z33nv4+++/Ub9+fQA5o2e3b9+OuXPnIiMjA25ubpg8ebLGVAhEREREREREREREYhE9cQsA48aNK3BqhKNHj+Yp69evn3r07KuMjIywa9eu1+6vadOm+Pfff4scJxEREREREREREVFZ4ORfRERERERERERERDqGiVsiIiIiIiIiIiIiHcPELREREREREREREZGO0Yk5bol0WUJCAuRyeYnbiYyMRHZ2thYiIiIiIiIiIiKi8o6JW6LXSEhIwKejPoMiLb3EbaWlpiA6Ng5ZWZlaiIyIiIiIiIiIiMozJm6JXkMul0ORlo4OQz6HjWPVErV1++JZ/LZuGZRKpZaiIyIiIiIiIiKi8oqJW6JCsHGsCodqbiVqIyH6oZaiISIiIiIiIiKi8o4PJyMiIiIiIiIiIiLSMUzcEhEREREREREREekYJm6JiIiIiIiIiIiIdAwTt0REREREREREREQ6holbIiIiIqIy4OrqColEkuc1duxYAEB6ejrGjh0LGxsbmJmZoU+fPoiLixM5aiIiIiISCxO3RERERERl4OzZs4iJiVG/QkNDAQD9+vUDAEyePBl//PEHduzYgWPHjiE6OhofffSRmCETERERkYj0xA6AiIiIyreEhATI5fJibSsIAhQKBZKTkxEVFYXs7GwtR0dUdmxtbTWWFy1ahBo1aqB9+/Z4/vw5fvjhB2zbtg2dOnUCAISEhKBu3br4999/0bJlSzFCJiIiIiIRMXFLREREpSYhIQGfjvoMirT0Ym0vkUjg5uKM+1EPkZqSjOjYOGRlZWo5SqKyl5mZiS1btsDPzw8SiQTnz59HVlYWPD091XXq1KkDFxcXnDp1qsDEbUZGBjIyMtTLuX8kUalUUKlUpXsQRPTWiYqKwpMnT8QOo0iuXbum8f/bpnLlynBxcRE7DCLSIUW5R2PiloiIiEqNXC6HIi0dHYZ8DhvHqkVvQBBgrEzDuzJj3L50Dr+tWwalUqn9QInK2J49e5CUlIRhw4YBAGJjY2FgYAArKyuNevb29oiNjS2wnYCAAMybNy9PeUJCAtLTi/cHEyIqnx49eoR27dohLS1N7FCKxcfHR+wQisXY2BjHjx9H1arFuA8ionJJoVAUui4Tt0RERFTqbByrwqGaW9E3FFSQKBJhaW6NhJhH2g+MSCQ//PADPvjgAzg5OZWonRkzZsDPz0+9LJfL4ezsDFtbW1hYWJQ0TCIqRx49eoS0tDRMXLoGVavXFDucQsvMSEf844ewq+IMA0MjscMpkkf37mDl1HEAADs7O5GjISJdYWRU+GsZE7dERERERGUoMjIShw8fxq5du9RlDg4OyMzMRFJSksao27i4ODg4OBTYlqGhIQwNDfOUS6VSSKV8DjER/Sf3mlC1ek1Ur99I5GiKpk7TFmKHUCK8JhPRy4pyPeCVg4iIiIioDIWEhMDOzg7du3dXlzVr1gz6+voICwtTl928eRNRUVHw8PAQI0wiIiIiEhlH3BIRERERlRGVSoWQkBD4+PhAT++/W3FLS0sMHz4cfn5+sLa2hoWFBcaPHw8PD48CH0xGREREROUbE7dERERERGXk8OHDiIqKwqeffppn3YoVKyCVStGnTx9kZGTAy8sL69atEyFKIiIiItIFTNwSEREREZWRLl26QBCEfNcZGRlh7dq1WLt2bRlHRURERES6iIlbKpcSEhIgl8tL3E5kZCSys7O1EBEREREREREREVHhMXFL5U5CQgI+HfUZFGnpJW4rLTUF0bFxyMrK1EJkREREREREREREhcPELZU7crkcirR0dBjyOWwcq5aordsXz+K3dcugVCq1FB0REREREREREdGbMXFL5ZaNY1U4VHMrURsJ0Q+1FA0REREREREREVHhScUOgIiIiIiIiIiIiIg0MXFLREREREREREREpGOYuCUiIiIiIiIiIiLSMUzcEhEREREREREREekYJm6JiIiIiIiIiIiIdAwTt0REREREREREREQ6Rk/sAIhIXAkJCZDL5Vppy8LCAra2tlppi4iIiIiIiIioItOJxO3atWuxdOlSxMbGonHjxli9ejVatGhRYP0dO3Zg9uzZePDgAWrVqoXFixejW7du6vVz587F9u3b8fDhQxgYGKBZs2ZYuHAh3N3d1XUSExMxfvx4/PHHH5BKpejTpw9WrlwJMzOzUj1WIl2SkJCAT0d9BkVaulbaMzc2wsYNwUzeEhERERERERGVkOiJ219++QV+fn4IDg6Gu7s7goKC4OXlhZs3b8LOzi5P/ZMnT2LQoEEICAjAhx9+iG3btsHb2xsRERFo0KABAOCdd97BmjVrUL16daSlpWHFihXo0qUL7ty5o04offLJJ4iJiUFoaCiysrLg6+uLUaNGYdu2bWV6/ERiksvlUKSlo8OQz2HjWLVEbT2NeYSjP62HXC5n4paIiIiIiIiIqIRET9wGBgZi5MiR8PX1BQAEBwdj37592LhxI6ZPn56n/sqVK9G1a1dMnToVALBgwQKEhoZizZo1CA4OBgB8/PHHefbxww8/4PLly+jcuTOuX7+OAwcO4OzZs2jevDkAYPXq1ejWrRuWLVsGJyen0jxkIp1j41gVDtXcxA6DiIiIiIiIiIheEPXhZJmZmTh//jw8PT3VZVKpFJ6enjh16lS+25w6dUqjPgB4eXkVWD8zMxMbNmyApaUlGjdurG7DyspKnbQFAE9PT0ilUpw+fbqkh0VERERERERERERUIqKOuH3y5AmUSiXs7e01yu3t7XHjxo18t4mNjc23fmxsrEbZn3/+iYEDByI1NRWOjo4IDQ1F5cqV1W28Og2Dnp4erK2t87STKyMjAxkZGerl3Ic5qVQqqFSqQhxt+aRSqSAIgk71gSAIkEgkgCAAQknjEiCVSku/LUH474U37OfF8Wmj37XaV1qMq7h08Xx8G7EftYP9mKPE1xmN66O2rslavLbrwLWvMCri+ViRjpWIiIiIyifRp0ooLR07dsTFixfx5MkTfPfdd+jfvz9Onz6d77y5hREQEIB58+blKU9ISEB6unYe7PQ2UqlUeP78OQThxZtgHaBQKODm4gxjZRokisQStWUpA+rXqQ1TIbOU2xIgSVMAEuDFPwUyVqbBzcUZCoUC8fHxJYpJm32lzbiKSxfPx7cR+1E72I85Sn6d+e/6qK1rsjav7bpw7SuMing+KhQKsUMgIiIiIioRURO3lStXhkwmQ1xcnEZ5XFwcHBwc8t3GwcGhUPVNTU1Rs2ZN1KxZEy1btkStWrXwww8/YMaMGXBwcMjz5io7OxuJiYkF7nfGjBnw8/NTL8vlcjg7O8PW1hYWFhaFPubyRqVSQSKRwNbWVmfeCCYnJ+N+1EO8KzOGpbl1idp6rgSu3riJzhIDCKXZliAAAiCYWQOS1ydu0xLluB/1EObm5sX+Q0QubfaVNuMqLl08H99G7EftYD/mKPF15qXro7auydq8tuvCta8wKuL5aGRkJHYIREREREQlImri1sDAAM2aNUNYWBi8vb0B5LyxCAsLw7hx4/LdxsPDA2FhYZg0aZK6LDQ0FB4eHq/dl0qlUk914OHhgaSkJJw/fx7NmjUDAISHh0OlUsHd3T3f7Q0NDWFoaJinXCqVVpg3QAWRSCQ61Q+5H1mFRAJIShqTJOejlqXeVm55Ifbz4vhy+71EEWmzr7QYV8nC0K3z8W3FftQO9qM2rjMvXR+1dk3W4rVdR659hVHRzseKcpxEREREVH6JPlWCn58ffHx80Lx5c7Ro0QJBQUFISUmBr68vAGDo0KGoUqUKAgICAAATJ05E+/btsXz5cnTv3h3bt2/HuXPnsGHDBgBASkoKFi5ciJ49e8LR0RFPnjzB2rVr8fjxY/Tr1w8AULduXXTt2hUjR45EcHAwsrKyMG7cOAwcOBBOTk7idAQRERERERERERHRC6InbgcMGICEhATMmTMHsbGxaNKkCQ4cOKB+AFlUVJTGiIlWrVph27ZtmDVrFmbOnIlatWphz549aNCgAQBAJpPhxo0b+PHHH/HkyRPY2Njgvffew99//4369eur29m6dSvGjRuHzp07QyqVok+fPli1alXZHjxRMWVmZiAyMrLE7URGRiI7O1sLERERERERERERkTaJnrgFgHHjxhU4NcLRo0fzlPXr1089evZVRkZG2LVr1xv3aW1tjW3bthUpTiJdoEhKxP279/DVgm/ynb6jKNJSUxAdG4esrEwtRUdERERERERERNqgE4lbIiq89NQUSPX10X7I56jiWqNEbd2+eBa/rVsGpVKppeiIiIiIiIiIiEgbmLglekvZODjBoZpbidpIiH6opWiIiIiIiIiIiEib+LhdIiIiIiIiIiIiIh3DxC0RERERERERERGRjmHiloiIiIiIiIiIiEjHMHFLREREREREREREpGOYuCUiIiIiIiIiIiLSMXpiB0BERETakZCQALlcrpW2LCwsYGtrq5W2iIiIiIiIqOiYuCUiIioHEhIS8Omoz6BIS9dKe+bGRti4IZjJWyIiIiIiIpEwcUtERFQOyOVyKNLS0WHI57BxrFqitp7GPMLRn9ZDLpczcUtERERERCQSJm6JiIjKERvHqnCo5iZ2GERERERERFRCfDgZERERERERERERkY5h4paIiIiIiIiIiIhIxzBxS0RERERERERERKRjmLglIiIiIiIiIiIi0jFM3BIRERERERERERHpGCZuiYiIiIjKyOPHjzF48GDY2NjA2NgYDRs2xLlz59TrBUHAnDlz4OjoCGNjY3h6euL27dsiRkxEREREYtETOwAiIiLSPZmZGYiMjCxxO5GRkcjOztZCRERvv2fPnqF169bo2LEj/vrrL9ja2uL27duoVKmSus6SJUuwatUq/Pjjj3Bzc8Ps2bPh5eWFa9euwcjISMToiYiIiKisMXFLREREGhRJibh/9x6+WvANDA0NS9RWWmoKomPjkJWVqaXoiN5eixcvhrOzM0JCQtRlbm5u6q8FQUBQUBBmzZqFXr16AQA2b94Me3t77NmzBwMHDizzmImIiIhIPEzcEhERkYb01BRI9fXRfsjnqOJao0Rt3b54Fr+tWwalUqml6IjeXnv37oWXlxf69euHY8eOoUqVKhgzZgxGjhwJALh//z5iY2Ph6emp3sbS0hLu7u44depUvonbjIwMZGRkqJflcjkAQKVSQaVSlfIREdHbhNcE8fCaTEQvK8r1gIlbIiIiypeNgxMcqrm9ueJrJEQ/1FI0RG+/e/fuYf369fDz88PMmTNx9uxZTJgwAQYGBvDx8UFsbCwAwN7eXmM7e3t79bpXBQQEYN68eXnKExISkJ6erv2DIKK3VmJiotghVFiJiYmIj48XOwwi0hEKhaLQdZm4JSIiIiIqAyqVCs2bN8c333wDAHj33Xdx5coVBAcHw8fHp1htzpgxA35+fupluVwOZ2dn2NrawsLCQitxE1H5YG1tLXYIFZa1tTXs7OzEDoOIdERRnlvAxC0RERERURlwdHREvXr1NMrq1q2L3377DQDg4OAAAIiLi4Ojo6O6TlxcHJo0aZJvm4aGhvnORS2VSiGVSrUUORGVB7wmiIfXZCJ6WVGuB7xyEBERERGVgdatW+PmzZsaZbdu3UK1atUA5DyozMHBAWFhYer1crkcp0+fhoeHR5nGSkRERETi44hbIiIiIqIyMHnyZLRq1QrffPMN+vfvjzNnzmDDhg3YsGEDAEAikWDSpEn4+uuvUatWLbi5uWH27NlwcnKCt7e3uMETERERUZlj4paIiIiIqAy899572L17N2bMmIH58+fDzc0NQUFB+OSTT9R1vvzyS6SkpGDUqFFISkpCmzZtcODAgSLNhUZERERE5QMTt0REREREZeTDDz/Ehx9+WOB6iUSC+fPnY/78+WUYFRERERHpIs5xS0RERERERERERKRjmLglIiIiIiIiIiIi0jFM3BIRERERERERERHpGCZuiYiIiIiIiIiIiHQME7dEREREREREREREOoaJWyIiIiIiIiIiIiIdoxOJ27Vr18LV1RVGRkZwd3fHmTNnXlt/x44dqFOnDoyMjNCwYUPs379fvS4rKwvTpk1Dw4YNYWpqCicnJwwdOhTR0dEabbi6ukIikWi8Fi1aVCrHR0RERERERERERFQUoiduf/nlF/j5+cHf3x8RERFo3LgxvLy8EB8fn2/9kydPYtCgQRg+fDguXLgAb29veHt748qVKwCA1NRUREREYPbs2YiIiMCuXbtw8+ZN9OzZM09b8+fPR0xMjPo1fvz4Uj1WIiIiIiIiIiIiosIQPXEbGBiIkSNHwtfXF/Xq1UNwcDBMTEywcePGfOuvXLkSXbt2xdSpU1G3bl0sWLAATZs2xZo1awAAlpaWCA0NRf/+/VG7dm20bNkSa9aswfnz5xEVFaXRlrm5ORwcHNQvU1PTUj9eIiIiIiIiIiIiojfRE3PnmZmZOH/+PGbMmKEuk0ql8PT0xKlTp/Ld5tSpU/Dz89Mo8/Lywp49ewrcz/PnzyGRSGBlZaVRvmjRIixYsAAuLi74+OOPMXnyZOjp5d8lGRkZyMjIUC/L5XIAgEqlgkqlet1hlmsqlQqCIOhUHwiCAIlEAggCIJQ0LgFSqbT02xKE/154037KKKYiN5XT72KeD7p4Pr6N2I/aUdb9+FZe+wq1+cvXR23FVb6ufYVREX+uK9KxEhEREVH5JGri9smTJ1AqlbC3t9cot7e3x40bN/LdJjY2Nt/6sbGx+dZPT0/HtGnTMGjQIFhYWKjLJ0yYgKZNm8La2honT57EjBkzEBMTg8DAwHzbCQgIwLx58/KUJyQkID09/bXHWZ6pVCo8f/4cgvDiTbAOUCgUcHNxhrEyDRJFYonaspQB9evUhqmQWcptCZCkKQAJ8OIfHYipaIyVaXBzcYZCoShwqpPSpovn49uI/agdZd2Pb+e1rzD+uz5qK67ydu0rjIr4c61QKMQOgYiIiIioRERN3Ja2rKws9O/fH4IgYP369RrrXh6126hRIxgYGGD06NEICAiAoaFhnrZmzJihsY1cLoezszNsbW01EsIVjUqlgkQiga2trc68EUxOTsb9qId4V2YMS3PrErX1XAlcvXETnSUGELTU1vvZEtj+73+wunoJFnduwvz+Hegr5JBmZkCiEpBhbYNUp6pIreKCp+++h3j3NsiyqlSqMWmjrbREOe5HPYS5uTns7OxK1FZx6eL5+DZiP2pHWfejrl/7it2WIAACIJhZay2u8nbtK4yK+HNtZGQkdghERERERCUiauK2cuXKkMlkiIuL0yiPi4uDg4NDvts4ODgUqn5u0jYyMhLh4eFvTK66u7sjOzsbDx48QO3atfOsNzQ0zDehK5VKK8wboIJIJBKd6ofcj6xCIgEkJY1JkvNRyxK2JcnORr0zJ7Eu+hG6fNwNhmmpBdY1exQJm8sR6mVBIsGz+o0R1as/Ir0Hai2mF5Fpr60X/Z57PohF187HtxX7UTvKsh918dqnnbZyt5VoMa7yd+0rjIr2c11RjpOIiIiIyi9RE7cGBgZo1qwZwsLC4O3tDSBnREhYWBjGjRuX7zYeHh4ICwvDpEmT1GWhoaHw8PBQL+cmbW/fvo0jR47AxsbmjbFcvHgRUqlUp0fL0NtHT/Ec1X/ZjJqbv4Vp9KMC62WZmkGQySDIZDBIegaJIKjXSQQB1lcuwvrKRTRcOg/V3vPA0czMsgifiKhMydJSYXP+NKz/dwEmj6Ng+vgh9J8/AwQVBANDNFIqUfVJPFz374Y0PQ3PGjQBZDKxwyYiIiIiIioVok+V4OfnBx8fHzRv3hwtWrRAUFAQUlJS4OvrCwAYOnQoqlSpgoCAAADAxIkT0b59eyxfvhzdu3fH9u3bce7cOWzYsAFATtK2b9++iIiIwJ9//gmlUqme/9ba2hoGBgY4deoUTp8+jY4dO8Lc3BynTp3C5MmTMXjwYFSqVCn/QImKQJKdDbdffkSDoIUwfKY5f2KauQXiO3VFXJuOeF6rLhTVa0FpbAyJIhGCuTUkWdkwiY2GxZ0bsP33b9ifOgar61cAALLMDDQ5cRThAK6uCkDU7MVId3AS4QiJiLRD/3kSXH7/Fc5/7YbNxXOQZmUVWLcygNoAsHYpsHYpMipZI65NJzz8oBdiOn0AoYAHjBIREREREb2NRH+HM2DAACQkJGDOnDmIjY1FkyZNcODAAfUDyKKiojQ+6taqVSts27YNs2bNwsyZM1GrVi3s2bMHDRo0AAA8fvwYe/fuBQA0adJEY19HjhxBhw4dYGhoiO3bt2Pu3LnIyMiAm5sbJk+erDGHLVFx2UScRtM5frC6cVWj/GaT9+AfF43m3/6MavUaaW700lPNBQMDpLi4IsXFFTGdugIAzO/cRPVffkS13T/DMOkZ9AA0PvgH6h8Pw/+mzMGdIaMAfiSUiN4iljeuoPaGlah6YC9kmRnFasPwWSJc/tgJlz92ItXeCfcGDcPdj4cj0/rNn7QhIiIiIiLSdaInbgFg3LhxBU6NcPTo0Txl/fr1Q79+/fKt7+rqmjPH32s0bdoU//77b5HjJHodSVYW6q1dgrrrlkOi+i8RG9WjL65OmI5TsdH4e+4UNJMV/cdOUbM2Ln31Da5M/goW86ehxa5tsFCpoJeWincXTEfVA3txdtFapFRz0+YhERFpndm926i/MgAu+3blWaeoVh3xHu2Q0KI1kt1qIsXJGZmVKkGiSASMLXA/dD/OLJ6NUb0GoO69W7A/cQz6yXIAgElcNBoEfYPa36/BjdGTcHvYZ1Aam5T14REREREREWmNTiRuid52Jg8foOWkEbC5dE5dltigCS7OXoSnzVrmFMRGl3g/ShNTHO/VH5POncJP9Rrh3f27AQC2Z0/i/Z5tcXbJOjz26lni/RARaZteRgYaLpmLd35YDalSqS7PsKqESO8BuN9vCOS16+fdUMh5iJigrw+FtQ1OGpugxYd9kFS7HiRZWbA7eQw1tv0ApyMHIVGpoJ8sR8Pl81Fjy/e4ODsAj7v2KsOjJCIiIiIi0h5+tpqohGzOnYLnR53VSVuVTIb/+c1C2G9h/yVtteyZTIbDY6fi6E97kexcDQCgn5KMVmOHon7g18BLSREiIrG1TEuFz7ihqLMhSJ20TbeujItffYM//7mGS7MW5Z+0fQNBXx9x7T1x8tufsT/8Au4N8IHwYtoYk7hotBrnA/fJI6Cf9Eyrx0NERERERFQWmLglKoFqu39G+yG9YPjsKQAg2cUN4b8ewo0xU8rkSecJHu1w6M8TiOz539Qh9dYtQ+vPPoYsLbXU909E9FpKJTr+tg07oh/BOvphTpG+Aa6On4b9Ry7itu8YqIyMtbKr1KrVcH7hShzcdxIxHbqoy13+2AmvD1rC9t+/tbIfIiIiIiKislKsxO29e/e0HQfRW6d28Aq0mPo5ZFmZAIC41h1wePcRPGvcrEzjUJqa4czyDbg4cyFUL5LFTkcOot2wj6D/PKlMYyEiymWQ+BRth/dDxz3b1TcbT5q6I/SPv3Ft4gwoTc1KZb+KWnXwz3e/4PTyDci0sAQAGCfEoZ2PN2r+GAy8YR58IiIiIiIiXVGsxG3NmjXRsWNHbNmyBenp6dqOiUi3CQLqBX2DRsvmqYvufDwcf3+/A1mWVuLEJJHg9qdj8XfILmSZWQAAKp//Fx0+7gaj+FhxYiKiCsv89g14ereHwz/hAAAlgOM+n+HI9r+gqFm79AOQSBDVqz8O/vUvYtt0AgBIlUq8u2A63ps2BrKsrNKPgYiIiIiIqISKlbiNiIhAo0aN4OfnBwcHB4wePRpnzpzRdmxEukcQ0HDpXNRfs0RddHnqXFyYtwyCvr6IgeWIb9UeR7f+gXQbWwCA1c1r6PDJhzB8Ei9yZERUUVQ+exKdBnjBNPoRACDZwhIDnaridP+hgLRsZ2hKt3fE3z/swPXRk9Vlrrt+xuDl82GiUpVpLEREREREREVVrHdQTZo0wcqVKxEdHY2NGzciJiYGbdq0QYMGDRAYGIiEhARtx0mkE+qv/AZ1NqxUL1+YvQg3R08CJBLxgnpFUv3GOPLLAaRUdQEAmN+/g/ZDvWHwLFHkyIiovKtycC/a+fSGgfw5AOBZ/cZY/3UQThqbiBeUTIYrU/1xatUmZL+YT7fG1Uv4JfoRjF7ESUREREREpItKNPRFT08PH330EXbs2IHFixfjzp07mDJlCpydnTF06FDExMRoK04i0dXctB711ixVL5/7Ogh3fD4TMaKCJbvWwNGtfyLFqSoAwPLWNbQb1hv68iRxAyOicqvqvt1oOcEXsswMAEBs2844uvVPKCrZiBxZjkfdvHF88x71vLdNM9IxaNoYfiKBiIiIiIh0VokSt+fOncOYMWPg6OiIwMBATJkyBXfv3kVoaCiio6PRq1cvbcVJJKp64Qfw7tcz1MsXZi/C/YHDxAuoEFKruODY5r1Is3MAAFS6egmtPh8MSWamyJERUXlTdf8euPuNgFSpBAA86D0Q/2zYjmwzc5Ej0/S0qTuO/LwfcitrAEDlqPto5+MN/aRnIkdGRERERESUV7ESt4GBgWjYsCFatWqF6OhobN68GZGRkfj666/h5uaGtm3bYtOmTYiIiNB2vERlrlVaKroGLVQvXx33pc6OtH1Vimt1HNv8O9KtKwMA7E7/g+Yzx/Op6kSkNVUO/A73ycPVSdt7/Yfi7OJ1OjHvd37ktevjh9mL8FhPD0DOXODtfD+CnoLTJhARERERkW4pVuJ2/fr1+PjjjxEZGYk9e/bgww8/hPSVB47Y2dnhhx9+0EqQRGKxjovGhthoyF4kJO58MgLXJs54w1a6RVGzNk5s2A6loREAwHXPL6i3apHIURFReWB76jjcJ4/USNqe/zqozB9CVlTP7BwwwLEqkl9M42D9vwtoM3IgpBnpIkdGuiw7OxuHDx/Gt99+C4VCAQCIjo5GcnKyyJERERERUXlVrHdWoaGhmDZtGhwdHTXKBUFAVFQUAMDAwAA+Pj4lj5BIJPryJHwS+DUqvXjyeEyHLrgwZ7FOPYissBKbNMfpwA0QXsRef/ViOO/dIXJURPQ2s7z+P7T+fDBkWTnTr9zv8/FbkbTNdd/AAL8uXImMSjnTJtieO4X3vhwDvLjmE70sMjISDRs2RK9evTB27Fj1g3gXL16MKVOmiBwdEREREZVXxXp3VaNGDTx58iRPeWJiItzc3EocFJHolEq4TxoB2+hHAIAnLm74d8X3gEwmcmDF99irJy5NX6Bebj5zAhwi74kYERG9rUweR6Ht8H7QT5YDAKI7euH8wlVvTdI219Nq1XE8ZBeyTUwBAC77dqHhsvkiR0W6aOLEiWjevDmePXsGY2NjdXnv3r0RFhYmYmREREREVJ4V6x2WUMD8mMnJyTAyMipRQES6oN7apXA8fhgAkCiVYtecJcg2txA5qpK7/elY3Os3BACgl56GgSsDYPXiI85ERIWhl6xA61EDYRwfCwB42uQ9/LsqBMKLOWPfNkkNmuDUyo0QXiSd62wIgtv2TeIGRTrn77//xqxZs2BgYKBR7urqisePH4sUFRERERGVd0V6l+Xn5wcAkEgkmDNnDkxMTNTrlEolTp8+jSZNmmg1QKKyZvfPEdRbvRgAoJJIMdrBCS0cq6CSyHFphUSCC3OXwurmVVhfjoB1QhzWJitwjslbIioMlQotpn4Gq5vXAAAKt5r4Z8N2KI1N3rChbovt6IULc5ag6dycj7w3nTsF8pq18bS5h8iRka5QqVRQ5vO78tGjRzA3NxchIiIiIiKqCIo04vbChQu4cOECBEHA//73P/XyhQsXcOPGDTRu3BibNm0qpVCJSp9xzGO0nDwCkhejysP7foyTb3lC4lUqQyOcXLMZ6daVAQAd0lLR8tfNIkdFRG+D+qsCUCV0HwAg09wC/3z7MzKtbUSOSjvuDh6BW75jAADS7Gx4jB8Go7gYkaMiXdGlSxcEBQWplyUSCZKTk+Hv749u3bqJFxgRERERlWtFGnF75MgRAICvry9WrlwJC4u3/6PjRGpKJdwnj4Dhs6cAch5G9veHfYGIMyIHpn1pTlXx76oQtBvSC1JBhVbbfsCx7r3x5L1WYodGRDqqysG9qLdmKQBAkErx78oQJFevJXJU2nV52nxY3rgC+1PHYZwQB49xPji69U8Ir3w8niqe5cuXw8vLC/Xq1UN6ejo+/vhj3L59G5UrV8bPP/8sdnhEREREVE4Va47bkJAQJm2p3Knz7QrYnjsFAEhxqoozS4PVcx6WRwkt2+Ko9wAAgFSlgrvfKOgnPRM5KiLSRaYP7uG9aePUy5emL0Bcu84iRlQ6BD09/Bu0ESlOVQEAlS+cQZNvvhI5KtIFVatWxaVLlzBz5kxMnjwZ7777LhYtWoQLFy7Azs5O7PCIiIiIqJwq9Ijbjz76CJs2bYKFhQU++uij19bdtWtXiQMjKkuVLp1H/ZUBAHJGkp1e8T0yK1mLHFXpO+bdH0Z/7UGr9DSYxDzCezPG4eS6LYBEInZoRKQjpOlpaDV+KPST5QCAqB59cfvFlALlUaZNZZxa+xM6DugKWWYGam75DnGt2iG6Sw+xQyOR6enpYfDgwWKHQUREREQVSKETt5aWlpC8SOZYWlqWWkBEZU2Wkgx3v5GQvnjoyPUxX+Bps5YiR1U2BKkME+wd8HfiUxgr5KgSug+uO37Cg/5DxQ6NiHTEu/Onwer6FQCAvMY7OPd1ULn/486zhu/i4uxFaDZ7MgDgvenjEFq/MVKruIgcGYll8+bXzwU/dCh/bxIRERGR9hU6cRsSEpLv10Rvu8aLZsM88h4A4Gnj5rg29kuRIypbMXr6ODBxJnp/PR0A0GThTMS3ao/UqtVEjoyIxFbnWCiqv3h4YbaRMU6t3gSlqZnIUZWNewOHwe7EUTgf+B0G8udw9xuVM9+tXpEeD0DlxMSJEzWWs7KykJqaCgMDA5iYmDBxS0RERESlolgTeKalpSE1NVW9HBkZiaCgIBw6dEhrgRGVBbsTR1Hj55w/RGSbmOL08g0Q9PVFjqrs3fFoh/t9PwEA6Kck470vxwAqlchREZGYqmRl4f21S9XLEQsCIX+nnogRlTGJBOe/WYmUqjmjbCuf/xd1X+oPqliePXum8UpOTsbNmzfRpk2bIj2cbO7cuZBIJBqvOnXqqNenp6dj7NixsLGxgZmZGfr06YO4uLjSOCQiIiIiegsUK3Hbq1cv9UfGkpKS0KJFCyxfvhy9evXC+vXrtRogUWnRS1ag+YyXHrYzbT5SXKuLGJG4Ln4VgJQqzgAAuzMnUOvHYJEjIiKxSFRKrIqPhVFKMgAgsmd/RPYeJHJUZS/Lwgr/Bm2E6sUo27rrlqHS5QiRoyJdUatWLSxatCjPaNw3qV+/PmJiYtSvf/75R71u8uTJ+OOPP7Bjxw4cO3YM0dHRb3y2BBERERGVX8VK3EZERKBt27YAgJ07d8LBwQGRkZHYvHkzVq1apdUAiUpLoyX+MI1+BACIb9kW9wb5ihyRuLLNLXB20Vr1csNl82H24K6IERGRWNr++RtapqcBAFKqOCNiXsUdaZrYpDmujZ0KAJAqlWgxZTSkL/qGSE9PD9HR0UXexsHBQf2qXLkyAOD58+f44YcfEBgYiE6dOqFZs2YICQnByZMn8e+//5ZG+ERERESk44o1UVtqairMzc0BAIcOHcJHH30EqVSKli1bIjIyUqsBEpUG21PHUGPbRgA5UyScDVgNSIv1d4xyJcGjHW4N+wzvbAqGLCMdzWZOwLEtf7BviCoQqysX0XFXzke/VVIpziz7FtnmFfuhpDc+84PTkYOwvhwBi3u30XDZPFyatUjssKgM7d27V2NZEATExMRgzZo1aN26dZHaun37NpycnGBkZAQPDw8EBATAxcUF58+fR1ZWFjw9PdV169SpAxcXF5w6dQotW1aMB6cSUemaDGD0yMHQq4DTw4khOysLRfvzHhGRpmIlbmvWrIk9e/agd+/eOHjwICZPznnqcnx8PCwsLLQaIJG2SdPT0GzWZPXy5alzkersKl5AOuaK32xUObwfpo+iYHfmBNx+3Yz7A4eJHRYRlQFJZibemzYGMqUSAHC63xA8ea+VyFGJT9DXx5mlwXi/ZzvIMtLxzqZgRHt2R0LLtmKHRmXE29tbY1kikcDW1hadOnXC8uXLC92Ou7s7Nm3ahNq1ayMmJgbz5s1D27ZtceXKFcTGxsLAwABWVlYa29jb2yM2NrbANjMyMpCRkaFelsvlAACVSgUV56snopeoVCpYADBPfCJ2KBWKBXhNJiJNRbkeFCtxO2fOHHz88ceYPHkyOnfuDA8PDwA5o2/ffffd4jRJVGbqrlsO88h7AIAnzVri7ifDRY5ItyhNTHFu4Sq09/EGADReNAcxHbog3cFJ3MCIqNTVW7sEVjevAQCuGhji5KBP4SJyTLpCUeMdXP5yLt5dMB0A0OyriQj98583bEXlhbbebH/wwQfqrxs1agR3d3dUq1YNv/76K4yNjYvVZkBAAObNm5enPCEhAenp6cWOlYjKn8TERMgBKKwrc8RtGcnOyoI88QkSExMRHx8vdjhEpCMUCkWh6xYrcdu3b1+0adMGMTExaNy4sbq8c+fO6N27d3GaJCoTFreuo86GIACASl8f574O4jQA+Yhv3QH3+34Ct51boZ8sR9O5U3AyeJvYYRFRKar0vwuoE7wCAKCUyTDJzgFd+KZOw50ho1D1r99he+4UzCPvod6qRXg8sGLPj04lY2VlhXfeeQd37tzB+++/j8zMTCQlJWmMuo2Li4ODg0OBbcyYMQN+fn7qZblcDmdnZ9ja2vKTcESkwdraGisAOH23BdXrNxI7nArh3tXLWNGnKz62toadnZ3Y4RCRjjAyMip03WIlbgGoH6jwshYtWhS3OaLSp1Kh2axJkGZnAwBujJwIRa06Igeluy7NWAjHo6EwehKPKof3w+HIQcR29BI7LCIqBTlTJIyF9MUUCcd6DcC1S+fQReS4dI5UinPfrEKXD9tAlpmB2j+swaV3ee9TXr2cDH2TwMDAYu0jOTkZd+/exZAhQ9CsWTPo6+sjLCwMffr0AQDcvHkTUVFR6k+35cfQ0BCGhoZ5yqVSKaT84zQRvYTXBPHwmkxELyvK9aBYiduUlBQsWrQIYWFhiI+Pz/PxsXv37hWnWaJS5bpzCypHnAYAKKpVx/UxX4gckW7LsrTCxZkL0dJvJADg3fnTcLBVe6gMC/+XISJ6O9T+YQ0sb+VMkfCsXiMc79EXuHRO5Kh0U3L1Wrg6YToaLZsHiUqFzkvnYUPNGmKHRaXgwoULhaonkUgK3eaUKVPQo0cPVKtWDdHR0fD394dMJsOgQYNgaWmJ4cOHw8/PD9bW1rCwsMD48ePh4eHBB5MRERERVVDFStyOGDECx44dw5AhQ+Do6FikG1YiMegnPUOjpf/N/xYxPxAqo+LNJVeRPOzRF9W3b4LdmRMwe/gAtTesxPXx08QOi4i0yDTyPuqtWQIAEF6MKFUp5CJHpdtuDR8H5/27UenaZVS+fxt99WVih0Sl4MiRI1pv89GjRxg0aBCePn0KW1tbtGnTBv/++y9sbW0BACtWrIBUKkWfPn2QkZEBLy8vrFu3TutxEBEREdHboViJ27/++gv79u1D69attR0PUaloEPg1DJ89BQA87NYb8a07iBvQ20IiwYW5y/B+jzaQKpWoG7wCUb0GIMXFVezIiEgbBAFN506BLCPnAUa3fT5DUoMmwKnj4sal4wR9fZz7ZhU8e3eERBAw7O5txMXEADU48pZeb/v27a9db2RkhLVr12Lt2rVlFBERERER6bJiJW4rVaoEa2trbcdCVCqsrlxEjZ83AgCyTUxxacbXIkf0dpG/Uxe3h32O2j+sgSwjHU2+no4TG17/xpOI3g7O+3bB4e8wAECqQxVcmTRT5IjeHkkNmuDux8NRc+v3MFYqUTkgANi3T+ywqBSdO3cOv/76K6KiopCZmamxbteuXSJFRURERETlWbFmx16wYAHmzJmD1NRUrQSxdu1auLq6wsjICO7u7jhz5sxr6+/YsQN16tSBkZERGjZsiP3796vXZWVlYdq0aWjYsCFMTU3h5OSEoUOHIjo6WqONxMREfPLJJ7CwsICVlRWGDx+O5ORkrRwP6RCVCu/O+xISQQAAXBv3JdIcq4gc1Nvn2vhpSLN3BAA4hR+AY9hfIkdERCWlL09Ck69nqJcv+C+B0tRMxIjePlf8ZiHN0goAYLZ/P3D4sLgBUanZvn07WrVqhevXr2P37t3IysrC1atXER4eDktLS7HDIyIiIqJyqliJ2+XLl+PgwYOwt7dHw4YN0bRpU41XUfzyyy/w8/ODv78/IiIi0LhxY3h5eSE+Pj7f+idPnsSgQYMwfPhwXLhwAd7e3vD29saVK1cAAKmpqYiIiMDs2bMRERGBXbt24ebNm+jZs6dGO5988gmuXr2K0NBQ/Pnnnzh+/DhGjRpVnO4gHebyx05UvpDzhwB5jXdwa9jnIkf0dso2M9cYqdzk6+mQpqeJGBERlVTDZfNh9CTnd+3j97sj+v3uIkf09smytMKJUZP+Kxg3DsjIEC0eKj3ffPMNVqxYgT/++AMGBgZYuXIlbty4gf79+8PFxUXs8IiIiIionCrWVAne3t5aCyAwMBAjR46Er68vACA4OBj79u3Dxo0bMX369Dz1V65cia5du2Lq1KkAckb/hoaGYs2aNQgODoalpSVCQ0M1tlmzZg1atGiBqKgouLi44Pr16zhw4ADOnj2L5s2bAwBWr16Nbt26YdmyZXByctLa8ZF49NLT0PClB5JdnLUIgoGBiBG93R52/yjnQWX//g2zh5Go820Qrk2c8eYNiUjnWEecQY1tOVPIZJma4cLsxSJH9Pa63qUH7L9biYZJz4CbN4EVK4B87l/o7Xb37l10757zxw0DAwOkpKRAIpFg8uTJ6NSpE+bNm/eGFoiIiIiIiq5YiVt/f3+t7DwzMxPnz5/HjBn/JX+kUik8PT1x6tSpfLc5deoU/Pz8NMq8vLywZ8+eAvfz/PlzSCQSWFlZqduwsrJSJ20BwNPTE1KpFKdPn0bv3r3ztJGRkYGMl0bRyOU5T9xWqVRQqVRvPNbySqVSQRAEneoDQRAgkUjQZMcWmMQ+BgDEtH8fcW06AEJR4xQglUoBQSjGtkVoSxD+e+FN+ymjmPIRMWcxuvRsB2l2Nup8G4TIXv2RUs1NfQwSiUTU80EXz8e3EftRO8q6H3OvfW/6eZZkZaH5rInq5asTZyDN0emVbcS7zuTd/OXro7bi0uLxSSRYWa8+vvv3JCQqFYQFCyAMHAjo2CjMivhzrc1jrVSpEhQKBQCgSpUquHLlCho2bIikpCStTR1GRERERPSqYiVuASApKQk7d+7E3bt3MXXqVFhbWyMiIgL29vaoUqVwc4g+efIESqUS9vb2GuX29va4ceNGvtvExsbmWz82Njbf+unp6Zg2bRoGDRoECwsLdRt2dnYa9fT09GBtbV1gOwEBAfmOpkhISEB6enr+B1gBqFQqPH/+HILw4k2wDlAoFGhS2RrNt4cAAFQyGS6P/wISRWKR27KUAfXr1IapkFms7QvflgBJmgKQAC/+0YGY8kp2sMPtj31Re/N3kGVmoPHCGTi1fB0AwFiZBjcXZygUigKnOiltung+vo3Yj9pR1v2oUCjg5uIMY2Xaa3+e3/nxO1jeug4AeFanHu5698lTX8zrTF7/XR+1FZc2j89YmQZVgwZIcKkKu+3bIUlNRcbYsUj64YcStattFfHnOjfRWhJXrlxBgwYN0K5dO4SGhqJhw4bo168fJk6ciPDwcISGhqJz585aiJaIiIiIKK9iJW4vX74MT09PWFpa4sGDBxg5ciSsra2xa9cuREVFYfPmzdqOs1iysrLQv39/CIKA9evXl6itGTNmaIz0lcvlcHZ2hq2trTohXBGpVCpIJBLY2trqzBvB5ORkeB49Cv0Xc7DeG+QLeeMWxWrruRK4euMmOksMIJhblyiu17YlCIAACGbWgOT1idsyi6kAV/1mw+WvvTBOiEOVI4dgc/0GnrRohbREOe5HPYS5uXmeP4yUFV08H99G7EftKOt+TE5Oxv2oh3hXZgzLAn6eDRPiUPf7tQAAQSLB+YWroKqU9+dV7OuMhpeuj9qKS5vHl3vtS144H7ZHjkASFwej/fthFxEBdO1aora1qSL+XBsZGZW4jUaNGuG9996Dt7c3+vXrBwD46quvoK+vj5MnT6JPnz6YNWtWifdDRERERJSfYiVu/fz8MGzYMCxZsgTm5ubq8m7duuHjjz8udDuVK1eGTCZDXFycRnlcXBwcHBzy3cbBwaFQ9XOTtpGRkQgPD9dIrjo4OOQZEZidnY3ExMQC92toaAhDQ8M85VKptMK8ASqIRCLRqX4wvHEDHzx6CADINLfA1fHTAUlxY5PkfNRSIilBG4VpK7e8MPspq5jypzSzwNVJX6H5VxMAAI0XzUbYb2HAi2kScs8Hseja+fi2Yj9qR1n2Y+5UJa/7eW4Y+DX0U5IBAPcGDsOzxs3zrSf2dUbTS9dHrcWlxeN70e+CpSUkS5cCQ4cCAKSTJgFXrgA6NLd6Rfu51sZxHjt2DCEhIQgICMDChQvRp08fjBgxIt/nMBARERERaVux7mjPnj2L0aNH5ymvUqVKgVMN5MfAwADNmjVDWFiYukylUiEsLAweHh75buPh4aFRHwBCQ0M16ucmbW/fvo3Dhw/DxsYmTxtJSUk4f/68uiw8PBwqlQru7u6Fjp90kCDA5ptv1Cf29bFTkWlTWdSQyqP7fT9BUu16AADr/12Ayx87RY6IiN6k0uUIuO3cCiDnj1pXJn0lckTl0ODBQJs2OV/fvg0EB4sbD5VY27ZtsXHjRsTExGD16tV48OAB2rdvj3feeQeLFy8u0n0vEREREVFRFStxa2hoqH4418tu3boFW1vbIrXl5+eH7777Dj/++COuX7+Ozz//HCkpKfD19QUADB06VOPhZRMnTsSBAwewfPly3LhxA3PnzsW5c+cwbtw4ADlJ2759++LcuXPYunUrlEolYmNjERsbi8zMTABA3bp10bVrV4wcORJnzpzBiRMnMG7cOAwcOBBOTk7F6RLSFX/+CZMXD7Z77lgVd4aMEjmgckomw+XpX6sXGy6bD1lGxZ3rmUjnCQKaLPhvhOC1CdP5R63SIJEAK1b8tzxvHvDsmXjxkNaYmprC19cXx44dw61bt9CvXz+sXbsWLi4u6Nmzp9jhEREREVE5VazEbc+ePTF//nxkZWUByPnoXVRUFKZNm4Y+ffoUqa0BAwZg2bJlmDNnDpo0aYKLFy/iwIED6geQRUVFISYmRl2/VatW2LZtGzZs2IDGjRtj586d2LNnDxo0aAAAePz4Mfbu3YtHjx6hSZMmcHR0VL9Onjypbmfr1q2oU6cOOnfujG7duqFNmzbYsGFDcbqDdEVWFjBlinrxxKiJUOUzvQVpR1zbTohp5wkAMIl5hCa/bRU5IiIqiPMfO1H5whkAgLzGO7gzeKTIEZVjzZvnjLwFgMREYOFCceMhratZsyZmzpyJWbNmwdzcHPv27RM7JCIiIiIqp4o1x+3y5cvRt29f2NraIi0tDe3bt0dsbCw8PDywsBhvUMaNG6ceMfuqo0eP5inr16+f+gERr3J1dc2Z4+8NrK2tsW3btiLFSTouOBi4dQsAcKlSJdxt2xn5z1hM2nJ5+gI4/BMOiUqF5j+HoFKLlmKHRESvkKWmoNFif/XyxZnfQNDXFzGiCuCbb4CdO4H0dGD1auDzz4EaNcSOirTg+PHj2LhxI3777TdIpVL0798fw4cPFzssIiIiIiqnipW4tbS0RGhoKE6cOIFLly4hOTkZTZs2haenp7bjIyocuTznI6kvrKtdFw0kEhEDqhjk79TFvf5DUWP7JhikpsD3zi2xQyKiV9TZEASTuGgAQHRHL8S15+/qUufsDPj55SRwMzOB6dOBHTvEjoqKKTo6Gps2bcKmTZtw584dtGrVCqtWrUL//v1hamoqdnhEREREVI4VOXGrUqmwadMm7Nq1Cw8ePIBEIoGbmxscHBzUT5QnKnPLlwNPnwIAFD174mZGNhqIHFJFcXXSTLj8sRP6Kcno/ughHt+6xZFlRDrC5HEUan+3GgCg0tPDpZn82H6ZmT4d+P57ID4+Z/TtiRNA69ZiR0VF9MEHH+Dw4cOoXLkyhg4dik8//RS1a9cWOywiIiIiqiCKNMetIAjo2bMnRowYgcePH6Nhw4aoX78+IiMjMWzYMPTu3bu04iQqWHx8TuIWAPT0kDhpkqjhVDQZle1wY/QkAIAMgM3ixaLGQ0T/abTYX/3gwNs+nyHZrabIEVUg5ubAggX/Lfv5ASqVePFQsejr62Pnzp149OgRFi9ezKQtEREREZWpIiVuN23ahOPHjyMsLAwXLlzAzz//jO3bt+PSpUs4fPgwwsPDsXnz5tKKlSh/CxcCKSk5X48ejWwXF3HjqYBu+46BwjbngYKmx44B4eEiR0RENudOwXn/bgBAunVlXBs3VeSIKqBPPwXq18/5+swZ4JdfxI2Himzv3r3o1asXZDKZ2KEQERERUQVUpMTtzz//jJkzZ6Jjx4551nXq1AnTp0/H1q18sjyVoQcPgPXrc742MQFmzRI1nIpKaWyCfz8d+1/BjBn4f3t3HldFvf9x/HVQNhVQEMRdMnPJXcuwUq9aWObV8pqZuf+0TE0lLfd9L9fcS2kxszSz3a5508qt1Kxcr1tiV0EKFURZ5Mzvj5GDKCr7HOD9fDxOfWfOnO/5zDgMh8/5zudLBiYJFJFcYhjUmz7Wsbg/dCxXvXwsDKiQKloUXn89dXnkSHPCMhERERERkQzIVI3b3377jVmzZt3y+ccee4wFCxZkOyiRDBs/HpKSzPbQoRAYCMePWxtTIXWk1eNUXTSLuy5dMkeWbdgAKp8iYokKX3+K36+7AbhYrSYnOz1ncUQFW2JiAqdOnUr/yWrVKPvwwxT74Qc4dYq/x4/nQr9+t+zL29sbf3//XIpURERERETyk0wlbqOjoylTpswtny9Tpgznz5/PdlAiGbJ/P7z3ntn29YXhug3YSkaRIrxZrTrTf9ljrhg9Gtq1M0eciUiecUlKos7rEx3Lv706EXSbd66JvRDNyeMnGD15Gu7u7uluE5QMKzBvc3KdM5fBu3/hkqtrutt6eXqwcvlSJW9FRERERCRzidvk5GSK3iYJU6RIEa5evZrtoEQyZPTo1NvxR44EH90GbLUd/gFcadgQz7174dAhM7Heq5fVYYkUKrW/WEeJ8JMAnHvgYSKaP2JxRAVb/OU4XFxdad6tP+WrVL3ldkdmjqPmvz/H+2oS4wMrsbPPwJu2+fvsn2x5bwkxMTFK3IqIiIiISOYSt4Zh0LNnz1uOKElISMiRoETuaPt2+Owzs12+PAwYcPvtJW/YbEQPH075Ll3M5QkToEsX8PCwNCyRwqJ4UhL3v7vcsfzriMlgs1kYUeHhF1iOwMpBt3z+xKgp3PPdNxRJSqTBJx8QMXA48QGBeRihiIiIiIjkN5manKxHjx4EBATg4+OT7iMgIIDu3bvnVqwiJsOAESNSlydMAE9Py8KRtOLvuw8ef9xcCA+HpUutDUikEOly8gSeMRcAOPXPTlyoXd/SeCTV5QqVOd61NwBFr1ym5qLX7/AKEREREREp7DI14jYsLCy34hDJuI0b4YcfzPY990DPnpaGI+mYNg2++spsT50KvXuDt7e1MYkUcEXOnqXTKbNEQrKrG/tDx1gckdzocP+XCVq7Cte4S9z14dv8t/cA4m4zSldERERERAq3TI24FbGcYcC4canLU6Zo8itnVK8ePPus2f7rL5gzx9p4RAoB33nzcLfbATjWvR+XK1S2OCK5UYKfP//tbZb2cbl6lXvnT7M4IhERERERcWZK3Er+8uWXsHu32a5fH/71L0vDkduYNCk1qT57Npw7Z208IgXZb7/htX49APElvDjU/2WLA5Jb+W/vASSU8gOg0ufr8Dn0u8URiYiIiIiIs1LiVvIPwzDr2aYYP16T7jizqlWhXz+zfemSWT5BRHLHq69iMwwAdnf9P5JKlrI4ILmVq17eHOofCoDNMKg9Z4rFEYmIiIiIiLNS4lbyj88/hz17zHaDBtC+vbXxyJ2NGQPFipntJUvgzz+tjUekINq61az9DUR4ePJbh84WByR3crxrHy6XrQBAue++ofTP2y2OSEREREREnJESt5I/3DjadsIEjbbND8qWhUGDzHZiojlRmYjkHMOA0aMdi2F3VyPZzd3CgCQj7O4eHBg8wrFc5/VJ5r+liIiIiIjIdZS4lfzhs8/gl1/MdsOG0K6dtfFIxg0fDl5eZnvFCvjjD0vDESlQNm6EbdsASKxalU3lylsckGTUqQ7PEFO1OgCl9+wkcOsmiyMSERERERFno8StOD+Nts3f/PxgyBCznZQEkydbGo5IgWG3pxltGz10KHZdG/MNo2hR9g8d5Vi+d940jboVEREREZE0lLgV57dhA+zbZ7YbN4YnnrAyGsmK0FAoWdJsv/MOHD1qaTgiBcLHH6feidCoEXEhIdbGI5n2v0fbcb5mHQB89+8jaPsWawMSERERERGnosStODe7HSZOTF3WaNv8qWRJePlls52cnPbfVEQy7+pVGDs2dXnKFF0b8yMXFw4MSR11+8DbS7Bp1K2IiIiIiFyjxK04tw0b4NdfzfZ998Hjj1sajmTD4MFm2QSA1avh4EFr4xHJz1atgiNHzPbDD4NG2+ZbZ1u2IbpuQwBKnzhKs8gIiyMSERERERFnocStOC+7XbVtCxIvL3jlFbN9Y91iEcm4hIS0Pz9Tp+ramJ/ZbOwfnDrqtuexo+adCSIiIiIiUugpcSvO65NP4PffzXaTJvDYY9bGI9k3YAAEBJjttWtTR1OLSMa9+SacOmW227QxR9xKvhbZrBV/NWwCQFDcJUp8+aXFEYmIiIiIiDNQ4lack0bbFkzFi8PIkanL48dbF4tIfhQXZ9azTXF9W/Ivm439Q1NH3ZZasMCsYywiIiIiIoWaErfinD7+GPbvN9tNmqh+Y0HywgtQrpzZ/vRT2L3b2nhE8pOFCyEy0mz/61/QqJG18UiOiQpuzp/1GgPg9scf8P771gYkIiIiIiKWU+JWnI/dDhMnpi5PnKjRtgWJhweMHp26PG6cdbGI5CcXLsDMmWbbxQUmTbI0HMl5u3r2T12YNAmSkqwLRkRERERELKfErTifdevgwAGzHRwMjz5qbTyS8/r0gYoVzfbXX8OOHdbGI5IfzJ4N58+b7W7doGZNa+ORHHembkN+9ittLpw4AW+/bWk8kvtmzJiBzWZjyJAhjnXx8fEMGDAAPz8/SpQoQceOHYlMGWkvIiIiIoWKErfiXJKT0462VW3bgsndHcaOTV2+vi0iNzt3DubONduurqoPXYCF3V0tdWHyZEhIsC4YyVU///wzy5Yto27dumnWDx06lM8//5y1a9eydetWzpw5w1NPPWVRlCIiIiJiJSVuxbmsXQsHD5rtpk3hkUesjUdyT8+ecNddZnvzZti61dJwRJzajBnmxGQA/fpBUJC18UiuOViyFHEtWpgLp0/DihWWxiO549KlS3Tt2pU333yTUqVKOdZfvHiRFStWMGfOHFq2bEmjRo0ICwtj+/bt7Ny508KIRURERMQKStyK87hxtK1q2xZsrq5p69uOHQuGYV08Is7qzz9h8WKz7emZtka0FEjR1902z9SpcOWKZbFI7hgwYABt27aldevWadbv2bOHpKSkNOtr1KhBpUqV2KGyQiIiIiKFTlGrAxBx+OgjOHzYbD/0ELRqZW08kvu6doVp0+C//4UffoBvv9Uoa5EbXX+7/KBBULastfFIrkusXRs6dIANG+DMGVi2DK5P5kq+tmbNGvbu3cvPP/9803MRERG4ublRsmTJNOvLlClDREREuv0lJCSQcF1JjZiYGADsdjt2uz3nAheRfE/XBOvomiwi18vM9UCJW3EOyclpZ0hXbdvCoWhR89/62WfN5XHjoHVr/duLpDh2LPVWeW9veOUVa+ORvDNxopm4BZg+Hfr2heLFLQ1Jsu/06dMMHjyYTZs24eHhkSN9Tp8+nYnX37F0TVRUFPHx8TnyHiJSMERHR1sdQqEVHR3NuXPnrA5DRJxEbGxshrdV4lacw5o1qaNtH34YWra0Nh7JO08/bd4KfOAA7NwJGzfCY49ZHZWIcxg/3vxiC+Dll8HPz9p4JO/UrQudOpm138+dg0WLlLgvAPbs2cO5c+do2LChY11ycjLff/89Cxcu5JtvviExMZELFy6kGXUbGRlJYGBgun2OHDmS0NBQx3JMTAwVK1bE398fb2/vXNsXEcl/fH19rQ6h0PL19SUgIMDqMETESWTmC3zLE7eLFi3itddeIyIignr16vHGG29w//3333L7tWvXMnbsWP744w+qVavGzJkzefzxxx3Pr1+/nqVLl7Jnzx6io6P55ZdfqF+/fpo+WrRowdYbJkJ6/vnnWbp0aY7um2TQjaNtVdu2cClSxBx126mTuTxuHLRpo3NA5Pff4YMPzHbp0jB0qLXxSN6bMAHWrTPrf8+aBf37g5eX1VFJNrRq1Yrff/89zbpevXpRo0YNXn31VSpWrIirqyubN2+mY8eOABw5coTw8HCCg4PT7dPd3R13d/eb1ru4uODiouksRCSVrgnW0TVZRK6XmeuBpVeODz/8kNDQUMaPH8/evXupV68eISEht7yFYPv27XTp0oU+ffrwyy+/0KFDBzp06MD+/fsd28TFxfHQQw8xc+bM27533759OXv2rOMxa9asHN03yYQPPjBrnAI0awYps2lL4fHUU1CvntnevRs+/9zaeEScwfUT9o0cqYRdYVSrFnTpYrb//hsWLrQ2Hsk2Ly8vateuneZRvHhx/Pz8qF27Nj4+PvTp04fQ0FC+++479uzZQ69evQgODuaBBx6wOnwRERERyWOWJm7nzJlD37596dWrF7Vq1WLp0qUUK1aMlStXprv9/PnzadOmDcOHD6dmzZpMnjyZhg0bsvC6P2S6devGuHHjbpql90bFihUjMDDQ8dCtZBa5elWjbQVcXMx/+xTjxoGK90thtmsXfPqp2S5f3hxpKYXTuHHmNRLg9dfh2sRTUnDNnTuXJ554go4dO9KsWTMCAwNZv3691WGJiIiIiAUsK5WQmJjInj17GDlypGOdi4sLrVu3ZseOHem+ZseOHWlqeAGEhISwIWXyjkx4//33WbVqFYGBgbRr146xY8dSrFixW26vGXvTZ7fbMQwj68dg1Spcjh4FwGjRAqNZs2wn7AzDwGazmSPVjOz+2xjmEPbc7sswUh/c6X3yKKZMd2Ue9yyfD088ga1RI2x79sCvv2Jfv94ciZsJ2T4fBdBxzCnZOY620aNJ+QrLPno0uLvf8dqYL699GXr59dfHnIrLifYvTVfpXEerVcPWtSu2996D6Gjs8+fD6NGZ7row/lznl33dsmVLmmUPDw8WLVrEokWLrAlIRERERJyGZYnbv/76i+TkZMqUKZNmfZkyZTicMknVDSIiItLdPiIiIlPv/eyzz1K5cmXKlSvHb7/9xquvvsqRI0duO5pBM/amz263c/HiRQzDyHzNnqtXKT1pkmPYd/SgQSTlwEybsbGxBFWqiGfyFWyx2Zs51acI3FujOsWNxFzuy8B2JRYzU3P7Ecd5F1PmeCZfIahSRWJjY7M8Y6rb0KH4PvccAMljx/J306apI80yIFvnozjoOOaMrB5Htx9/xHfzZgCuVq7MX088YU5OdQf589qXEanXx5yKy7n2L9WtrqNF+ven9OrV2JKTYfZsop5+GsPHJ1N9F8af68zM1isiIiIi4owsn5zMCv369XO069SpQ9myZWnVqhXHjx+natWq6b5GM/amz263Y7PZ8Pf3z/wfgu+8g8vJkwAY//gHpTp0yJGYLl26xMnw0zQo4omPV/ZmTr2YDAcOH6GVzQ0jN/syDDDAKOF7x1IReRZTJl2JjuFk+Gm8vLyyPmPqM89gvPEGtl27cD18mICtW6Fz5wy/PFvnozjoOOaMLB1Hw8A2e7Zj0WXyZALKl8/QS/PltS8jrrs+5lRcTrV/17nldTQgALp1g7ffxuXiRfw/+MAsoZAJhfHnOjOz9YqIiIiIOCPLErelS5emSJEiREZGplkfGRlJYGBguq8JDAzM1PYZ1aRJEwCOHTt2y8StZuy9NZvNlvnjcPUqTJ2a2sfEidhy6Dim3GaKzQa27PZpM2+1zPW+UtZn5H3yKqbMdmVz3KqdrZ+JSZMgJAQAl0mT4OmnoUiRTISRhfNRbqLjmDMyfRw//xx27jTb996Ly7PPZnjUef689mXEddfHHIvLmfbv+q5ucx0dOxZWrYKrV3GZOxcGD4ZSpTLZfeH6uS4s+ykiIiIiBZdln2jd3Nxo1KgRm6/dDgrmaJDNmzcTHByc7muCg4PTbA+wadOmW26fUfv27QOgbNmy2epHMmHVKjh+3Gy3agUPP2xtPOI8HnkEHnzQbB8+DGvWWBuPSF6x22HMmNTlKVMy9aWFFHB33QU9e5rtmBiYM8fScEREREREJPdZOhQhNDSUN998k3feeYdDhw7Rv39/4uLi6NWrFwDdu3dPM3nZ4MGD2bhxI7Nnz+bw4cNMmDCB3bt3M3DgQMc20dHR7Nu3j4MHDwJw5MgR9u3b56iDe/z4cSZPnsyePXv4448/+Oyzz+jevTvNmjWjbt26ebj3hVhSEkyenLo8YYJloYgTstnSnh8TJ5ojtEUKuo8+gt9+M9v33Qft21sbjzif0aPB1dVsz58Pf/9tbTwiIiIiIpKrLE3cdu7cmddff51x48ZRv3599u3bx8aNGx0TkIWHh3P27FnH9k2bNmX16tUsX76cevXqsW7dOjZs2EDt2rUd23z22Wc0aNCAtm3bAvDMM8/QoEEDli5dCpgjfb/99lseffRRatSowcsvv0zHjh35/PPP83DPC7n33oMTJ8x269bw0EPWxiPO5x//gBYtzPbRo+YIbZGCLCnJvBU+xdSpd6x3LYVQlSrQu7fZjo2F6+ohi4iIiIhIwWP55GQDBw5MM2L2elu2bLlpXadOnejUqdMt++vZsyc9U24lTEfFihXZunVrZsOUnJKUZN7+m2LiROtiEec2aRI0a5ba7to1daSZSEHzzjtw7JjZbtHC/FJLJD2jRkFYGCQmwoIFMHQo+PtbHZWIiIiIiOQCzdogeevdd+HkSbP96KPQtKm18Yjzevhhs94tmOfM229bGo5IromPT/sllkbbyu1UqgR9+5rtuDh47TVr4xERERERkVyjxK3kncTEtKNtVdtW7uT6ZNaUKZCQYF0sIrll2TL480+z/cQT+kJL7mzkSHB3N9uLFkFkpLXxiIiIiIhIrlDiVvLOO+/AH3+Y7ZAQCA62NBzJB4KD4bHHzHZ4OKxcaW08Ijnt0iVzhG2K6yfmE7mV8uXh+efN9uXLMGuWtfGIiIiIiEiuUOJW8saNo21V21YyatKk1PbUqeZt5SIFxfz5EBVltjt3hvr1LQ1H8pERI8DDw2wvXgzXTeYqIiIiIiIFgxK3kjfeftscMQnmCMomTSwNR/KRxo3hn/802//7Hyxfbm08Ijnl/PnU+qRFiqT9kkLkTsqWhf79zXZ8PMycaW08IiIiIiKS45S4ldyXmJj2VmDVtpXMun6E9vTp5q3BIvnda6/BxYtmu0cPuOcea+OR/OfVV8HT02wvXWp+uSUiIiIiIgWGEreS+1asSB1t+/jjcP/91sYj+U/9+tCxo9mOiDATFCL5WUSEWSYBwM0Nxo2zNh7Jn8qUgYEDzXZCgvnFloiIiIiIFBhK3Eruio/XaFvJGRMmgM1mtmfMgLg4S8MRyZZp01JHjr/wAlSubG08kn8NHw7Fi5vtN9+E06etjUdERERERHKMEreSu5YtS71185//hPvuszYeyb9q14annzbbUVGwcKG18Yhk1cmTqaPGixWDUaOsjUfyN39/GDTIbCcmml8KiIiIiIhIgVDU6gCkALt8Oe1tm3eYeCcqKoqYmJhsv+2pU6e4evVqtvuRzEtMTODUqVM50pe3tzf+/v5pV06YAGvXgt0Os2aZE/N4e+fI+4nkmfHjISnJbA8dat7uLpIdw4aZX2ZdumSWJxoxQqO4RUREREQKACVuJfcsWgSRkWb7X/+CevVuuWlUVBS9+71A7JX4bL/tlctxnImIJCkpMdt9ScbFXojm5PETjJ48DXd392z35+XpwcrlS9Mmb2vUgGefhVWrIDoa3ngDRo/O9nuJ5JnffjPPXwBfX/M2d5Hs8vODwYPN0kRJSeb/ly+3OioREREREckmJW4ld8TGwsyZZttmu2Nt25iYGGKvxNOiW3/8ylbI1lsf3fczHy9+neTk5Gz1I5kTfzkOF1dXmnfrT/kqVbPV199n/2TLe0uIiYm5edTtuHHwwQeQnAyvvw4DBkDJktl6P5E8M3o0GIbZHjUKfHysjUcKjtBQ88usmBgICzNH3d51l9VRiYiIiIhINihxK7ljwQL4+2+z3aUL3Htvhl7mV7YCgZWDsvXWUWc0MYuV/ALLZfvf8LaqVYPu3c3ExIULMG+eJr2T/OHHH+GLL8x2hQrmlw4iOcXXF4YMMcsSXb0KU6bAypVWRyUiIiIiItmgyckk5124YI6EBHBxMes5iuSkMWOg6LXvnebONcsmiDgzwzBHQKaYOBE8PKyLRwqmoUNTR3G/+y4cO2ZtPCIiIiIiki1K3ErOmzvXTN6COTLynnssDUcKoLvugl69zHZMDLz2mrXxiNzJF1/Atm1mu0YN89ooktNKloSXXzbbyckwebKl4YiIiIiISPYocSs56++/zcQtmCMix42zNh4puMaMATc3sz1/Ppw9a208IreSnGzWs00xdWrqiHGRnDZ4MJQqZbZXrYIjR6yNR0REREREskyJW8lZr79uTkwG0Ls3BOVirVMp3CpVgv79zfaVK2Y9RxFn9P77sH+/2b7/fnjySWvjkYLN2xuGDTPbdrtZ81ZERERERPIlJW4l55w7Z05KBuZIyDFjrI1HCr5Ro6B4cbO9fDmcOGFtPCI3SkjAdv3keTNmgM1mWThSSAwaBH5+ZvuDD+D3362NR0REREREskSJW8k5M2fC5ctm+/nnoWJFa+ORgi8gAEJDzfbVq2kTZCJOoNi772I7dcpcCAmBf/zD2oCkcPDySp0MzzBg7Fhr4xERERERkSxRkT3JGWfOwOLFZtvDA0aOtDYeKTxefhkWLYLoaFi9mqJ9+pgJXRGrxcZSYt681OXp09PdLCoqipiYmGy/3alTp7h69Wq2+5ECYsAAs+b8mTPw6aewa5fKF4mIiIiI5DNK3EqOsE2fDvHx5sKAAVC2rLUBSeHh42OWTBg2DJthUGLGDGje3OqoRLDNmYMtOtpceOYZaNDgpm2ioqLo3e8FYq/EZ/v9rlyO40xEJElJidnuSwoAT09zgtAXXgDANnasOVmZiIiIiIjkG0rcSra5/PknvPWWuVC8OLz6qrUBSeHz4ovmyLL//Q+Pf/8b+/bt8NBDVkclhdm5czBnDgBG0aLYJk9Od7OYmBhir8TTolt//MpWyNZbHt33Mx8vfp3k5ORs9SMFSO/eMGsWnDiBbfNm3H74ATp2tDoqERERERHJICVuJdtKzJuHLfHaCK+XXgJ/f2sDksLH0xPGj4d+/QCwjRkD332nSaDEOlOnYrt0yWz36QN3333bzf3KViCwcvZuY486czpbr5cCyNUVJk2C554DMO9IeOopi4MSEREREZGM0uRkkj1HjuC5Zo3Z9vKCYcOsjUcKr169MKpVA8C2dSv8+98WBySF1smTsGQJAIaHB4YmhhIrPfMM1K4NgNvevfD55xYHJCIiIiIiGaXErWSLbdw4bCm35Q4fDr6+1gYkhVfRohgTJ6YujxoFdrt18UjhNWoUJCUBENe3r2p+i7WKFIEpUxyLtrFjdW0UEREREcknlLiVrNu9G9u6dQAYZcrA0KEWBySFXqdOJF0bWcbevfDhh9bGI4XPrl1w7S4Eo3Rp4gYOtDggEeCf/8Ro0gQA2/79jnNUREREREScmxK3knUjRjiaxpgxUKKEhcGIAC4uxI4enbo8ahQkJFgXjxQuhgEvv5y6OH48hre3hQGJXGOzYVw36pZx4xyjwkVERERExHkpcStZs2kTbN4MwNXKleH//s/igERMiS1aYLRubS788QcsXGhpPFKIrF8P27aZ7erVoW9fa+MRuV7LliQ8/LDZPn4cVqywNh4REREREbkjJW4l8+x2GDnSsXjplVfAzc3CgETSMmbNApvNXJgyBaKjrQ1ICr7ERHj11dTlWbPA1dW6eETScem6O2WYMAEuXbIsFhERERERuTMlbiXz1q2DPXsAMOrXJ75DB2vjEblRvXrQvbvZvnABpk2zNBwpBBYvNkcxArRoAe3aWRqOSHqSGjbEeOopcyEyEubMsTYgERERERG5LSVuJXOSkuC6GqLG1KngotNInNDkyeDhYbbfeANOnrQ2Him4zp+HSZNSl19/PXXEt4iTMaZOhaJFzYVZs8wEroiIiIiIOCVl3CRzVqyAY8fMdvPmEBJibTwit1KxIgwZYrYTE9N84SCSo6ZONZO3AN26QaNG1sYjcjv33AP9+pntuDiYONHaeAqZJUuWULduXby9vfH29iY4OJivv/7a8Xx8fDwDBgzAz8+PEiVK0LFjRyKVXBcREREptJS4lYy7fDntH3gzZmhUmTi3ESOgdGmz/cEHsHu3tfFIwXPihDmiG8wR3lOnWhuPSEaMGwclSpjt5cvhyBFr4ylEKlSowIwZM9izZw+7d++mZcuWtG/fngMHDgAwdOhQPv/8c9auXcvWrVs5c+YMT6WUtxARERGRQsfyxO2iRYuoUqUKHh4eNGnShJ9++um2269du5YaNWrg4eFBnTp1+Oqrr9I8v379eh599FH8/Pyw2Wzs27fvpj40miGL5s6FiAiz/eST8MAD1sYjcic+PmaCIsWwYWAY1sUjBc+IEeaIboDQUHOkt4izK1MGhg8328nJMGqUtfEUIu3atePxxx+nWrVq3HPPPUydOpUSJUqwc+dOLl68yIoVK5gzZw4tW7akUaNGhIWFsX37dnbu3Gl16CIiIiJiAUsTtx9++CGhoaGMHz+evXv3Uq9ePUJCQjh37ly622/fvp0uXbrQp08ffvnlFzp06ECHDh3Yv3+/Y5u4uDgeeughZs6cecv31WiGLIiIgOnTzXaRIhpVJvnH88/D3Xeb7a1b4csvrY1HCo6tW2HtWrMdEACvvmptPCKZERoKgYFme/162LHD2ngKoeTkZNasWUNcXBzBwcHs2bOHpKQkWrdu7dimRo0aVKpUiR369xEREREplIpa+eZz5syhb9++9OrVC4ClS5fy5ZdfsnLlSkaMGHHT9vPnz6dNmzYMvzZKZPLkyWzatImFCxeydOlSALp16wbAH3/8ke57poxmWL16NS1btgQgLCyMmjVrsnPnTh7QKNL0jRtn1sIDszZezZrWxiOSUW5uZlmPf/3LXB42zKzN7OpqbVySv129Ci+9lLo8ZQp4e1sXj0hmlSgBEybACy+Yy8OHww8/qARSHvj9998JDg4mPj6eEiVK8Mknn1CrVi327duHm5sbJUuWTLN9mTJliEi54ykdCQkJJCQkOJZjYmIAsNvt2O32XNkHEcmfUq4Jf544ZnEkmZOYEM+5/50moHxF3Nw9rA4nU1KOta7JInK9zFwPLEvcJiYmsmfPHkaOHOlY5+LiQuvWrW85qmDHjh2EhoamWRcSEsKGDRsy/L53Gs1wq8Rtof5Q/Pvv2FaswAYY3t4Y48bBtX222+0YhpHtY2AYBjabzbyN3cju8TRwcXHJX30ZRuqDO71PPty/THdlng+ZPbdueT526ICtaVNs27fDkSPYFy6EwYOzF2MBllM/1wXasmW4/PYbAEaDBhg9ezquiykychwL/bUvQy+//vqYU3E50f6l6Spr176MSPd87NUL29y52I4cgW3bsG/YAO3b5+j7WslZr2HVq1dn3759XLx4kXXr1tGjRw+2bt2a5f6mT5/OxHQmmYuKiiI+Pj47oYpIAeTp6cn84QOtDqNQ8fT0BLjlncUiUvjExsZmeFvLErd//fUXycnJlClTJs36MmXKcPjw4XRfExERke72txuFkF4fWRnNUGg/FBsGpV56Cfdrf/xcGjSIOIBrv3TsdjsXL17EMK794ZpFsbGxBFWqiGfyFWyx0dkK2acI3FujOsWNxHzUl4HtSizY4Np/nCAm6/ryTL5CUKWKxMbGZuoDzu3Ox6Jjx+L3+OPYDAMmTCDqkUcwUiYukzRy6ue6oLKdP4//2LGO5egJE0j6+++btsvIcdS1LyNSr485FZdz7V+qrF77MuJW56P7q69Sqndvc5vhw/mrceMCc0dCZj4Q5yU3NzfuvlbCp1GjRvz888/Mnz+fzp07k5iYyIULF9J8To2MjCQwpaxFOkaOHJlmYENMTAwVK1bE398fb90JICLXCQgI4ODBg/z1119Wh5IpBw8epEePHrzzzjvUqlXL6nAyrXTp0lSqVMnqMETEiXh4ZPzuAUtLJeQnhfZD8ddf4/L99wAYlStTfNQoil93gkVFRXHlyhWKFi1qjhrLoujoaI6eOEmDIp74ePlmK+SLyXDg8BFa2dww8ktfhgEGGCV873ibar7cv0y6Eh3DyfDTeHl5ERAQkOHX2e12bDYb/v7+NyfKHn0UevaEsDBcYmIIWLAA41qJFUnrtsdRsE2ejO38eQCMLl0o9cQT6W6XkeN46dIlToafLrzXvoy47vqYU3E51f5dJ6vXvoy45fnYvTvGihXYtm2j6PHjBGzYAAMG5Oh7WyUzH4itZLfbSUhIoFGjRri6urJ582Y6duwIwJEjRwgPDyc4OPiWr3d3d8fd3f2m9S4uLrqGi8hNqlSpQpUqVawOI0tq1apF48aNrQ5DRCTbMvMZzbLEbenSpSlSpAiRkZFp1t9uVEFgYGCmtr9VH1kZzVAoPxRfvQqvvOJYtM2cia1YMcdyVFQUffsPoLS/PyfDT2MYRpbf6srlOM5ERJJ0NQls2T2eNvP2SJstH/WVsj4j75Mf9y+zXdkct5Bn9ucr5TXpvm7aNFi3DmJjsb31FrYXX4T69bMXawF12+NYmP3+O6Qk/IsXx/baa9huc4zudBxTbot3up9Bp+rruutjjsXlTPt3fVdZv/ZlrPtbnI+zZ8O1UlEuEyZA167gm70ktDNwxuvXyJEjeeyxx6hUqRKxsbGsXr2aLVu28M033+Dj40OfPn0IDQ3F19cXb29vBg0aRHBwsOZgEBERESmkLEvcurm50ahRIzZv3kyHDh0Ac8TB5s2bGTgw/Zo7wcHBbN68mSFDhjjWbdq06bajEG6U1dEMhdJbb8HBg2b7gQfg6afTPB0TE0PslXha/ONxGvhXyNaEJkf3/czHi18nOTk5OxGL3F5gIIwda34hYRjm5FJbt2oyHsmYlHMmpW7mqFFQvry1MYnkhCZN4LnnYNUqiI42Jy1bsMDqqAqkc+fO0b17d86ePYuPjw9169blm2++4ZFHHgFg7ty5uLi40LFjRxISEggJCWHx4sUWRy0iIiIiVrG0VEJoaCg9evSgcePG3H///cybN4+4uDh69eoFQPfu3SlfvjzTp08HYPDgwTRv3pzZs2fTtm1b1qxZw+7du1m+fLmjz+joaMLDwzlz5gxgJmXBHGkbGBio0QwZFRMD48alLs+Zc8vklrevHz6Vq2RrpFHUmdNZfq1IpgweDG++CUePmjOor11705cSIun6+GPYssVs33UX3DBZpki+NmMGrF8Ply/D4sXwwguQD+sIOrsVK1bc9nkPDw8WLVrEokWL8igiEREREXFmlt5D1rlzZ15//XXGjRtH/fr12bdvHxs3bnRMQBYeHs7Zs2cd2zdt2pTVq1ezfPly6tWrx7p169iwYQO1a9d2bPPZZ5/RoEED2rZtC8AzzzxDgwYNWHpdLcu5c+fyxBNP0LFjR5o1a0ZgYCDr16/Po73OJ6ZPh6gos925M2g0shQUbm7mFxEphg0zExUit3P5Mrz8curynDmQT+pnimRI+fIwYoTZTk5Oe76LiIiIiIglLJ+cbODAgbcsjbAlZWTTdTp16kSnTp1u2V/Pnj3p2bPnbd9Toxnu4MQJmDvXbLu5mUlckYKkbVto0wY2boTTp+G112D8eKujEmc2cyaEh5vtRx6Bf/7T2nhEcsOwYeYdCadPm9fHr76Cxx+3OioRERERkULL+WZtEOsNHQoJCWZ7yBAICrI0HJEcZ7OZX04Uvfbd1fVJOZEbHTli3kYO5jkzb57qIkvB5OkJs2alLoeGQlKSdfGIiIiIiBRyStxKWl99BZ99ZrbLlYMxY6yNRyS31KgBgwaZ7StXzNq3IjcyDLPWZ2Kiufzyy6r7KQVb587w4INm+8gR0N1JIiIiIiKWUeJWUsXHmzOmp3j9dfDysi4ekdw2bhxcq6nNhg2pX1qIpHjvvdQJyapUSTtpo0hBZLOZo8pTTJwIf/1lWTgiIiIiIoWZ5TVuxYnMmQPHj5vtZs3gmWesjUckt5UsaZZMePZZc3nQIGjVCooXtzQscRJ//512gqZFi6BYMevikUIhMTGBU6dO5Uhf3t7e+Pv7Z/6FjRtDz57w9ttw4QKMHQtLluRITCIiIiIiknFK3IopPBymTDHbRYrAwoWq4SiFwzPPwMqV8O235s/BxIlpazxK4TViROpIw3/9S5M0Sa6LvRDNyeMnGD15Gu7u7tnuz8vTg5XLl2YteTttGqxbB5cuwbJl0KePmdAVEREREZE8o8StmF5+2azzCTBwINSpY208InnFZoPFi81zPiHBHHnerZt+Bgq7H3+Et94y215eMH++tfFIoRB/OQ4XV1ead+tP+SpVs9XX32f/ZMt7S4iJicla4rZsWRg/HoYPN2s99+8PO3eaX+6KiIiIiEieUOJWzJGG69aZ7YAAmDDB0nBE8ly1ajBqlJmkSE6G5583E3cuKgNeKCUmmhOSpZg61ZysUSSP+AWWI7BykNVhmJM2vv02HDgAu3fD8uVmAldERERERPKEshKFXWKiWdczxaxZZt1PkcLm1VfhnnvM9o4dsGKFtfGIdebMMRNVYN4a/uKL1sYjYhVX17S1bUeOhMhI6+IRERERESlklLgt7ObPh8OHzXZwsHmLuEhh5O6eNkHx6qtw7px18Yg1jh+HSZPMtouLWdtTt4ZLYfbww+ZEZQAXL5qlE0REREREJE8ocVuYnTyZWhbBZjMnJNOt4VKYtWwJzz1nts+fN2s/S+Fht0Pv3qn1vgcNgoYNrY1JxBnMmgWlSpnt996DrVutjUdEREREpJBQlq6wMgyzhuPly+bygAFKUIgAzJ6dWi5k1Sr4+mtLw5E8tHAhfP+92Q4KgilTrI1HxFn4+8P06anLL75olloSEREREZFcpcRtYbVqFfz732a7YkWYNs3aeEScRUAAvPZa6nLfvnDhgmXhSB45dgxGjEhdXrkSSpSwLh4RZ9O3L9x/v9k+eBDmzbM0HBERERGRwkCJ28Lo3DkYMiR1efFi8PKyLBwRp9OnDzz6qNn+3/8gNNTaeCR32e3Qq1dqiYQBA6BFC0tDEnE6Li5mHfCUkkoTJ8KpU9bGJCIiIiJSwClxWxgNHQrR0Wb7mWfgiSesjUfE2dhs8OabqV9ohIXBV19ZG5PkngUL4McfzXZQEMyYYW08Is6qYUPziw0wSy298IJZeklERERERHKFEreFzddfw+rVZrtUKd3qKHIrlSrB3Lmpy337mhOWScFy9CiMGpW6rBIJIrc3eTKUL2+2N26Ed9+1Nh4RERERkQJMidvC5NIlc3RMijlzoEwZ6+IRcXa9e0ObNmb7zBlztLoUHMnJaUskDByoEgkid+LjA0uXpi4PGWJeH0VEREREJMcpcVuYjBkD4eFmu1Ur6NHD2nhEnF1KyQRvb3P5nXfgyy+tjUlyzoIFsG2b2b7rLpVIEMmoJ56A554z2xcuQP/+KpkgIiIiIpILlLgtLHbuNJMUAJ6esGyZmZQSkdurUCFtSRGVTCgYfv0VRo5MXQ4Lg+LFrYtHJL+ZNw8CAsz2Z5/Bhx9aGo6IiIiISEGkxG1hEBcH3bqljoaZOBGqVrU2JpH8pGdPePxxs332rHlLvUaX5V9xcebEjAkJ5vKQIdCsmaUhieQ7fn6weHHq8sCBcO6cdfGIiIiIiBRAStwWBsOHw7FjZrtJE9XpFMksmw2WLzdrO4I5wZ8m5Mm/hgyBw4fNdv36KpEgklUdO0KnTmb7779h0CBr4xERERERKWCUuC3ovv4aliwx28WKwXvvQdGi1sYkkh+VL2+WGEnx4otw5Ih18UjWfPghvPWW2S5eHNasAXd3a2MSyc/eeMMcfQvw0Uewfr218YiIiIiIFCBK3BZkf/0FvXunLs+eDdWqWRePSH7XuTP83/+Z7cuXzeX4eGtjkow7eRL69UtdXrgQqle3Lh6RgqBMmdQa+mB+qRUVZV08IiIiIiIFiIZeFlSGAS+8ABER5vJjj8Hzz1sbk0gmJCYmcOrUqUy9xjAMYmNjuXTpErZrk+95e3vj7++fc4HNnw/btsGhQ+YEV8OHmyPOxLklJcGzz0JMjLncpQv06JGtLqOioohJ6S8d6Z2PNzp16hRXr17NVhwiluvSxRzN/tlnEBkJffrAp59qElQRERERkWxS4ragWrUKPv7YbPv5wYoV+gNK8o3YC9GcPH6C0ZOn4Z6J29htNhtBlSpyMvw0xrXJw7w8PVi5fGnOJW+LFTMTFPffb462XbgQWrWCDh1ypn/JHRMmwM6dZvuuu2Dp0mxdE6Oioujd7wVir9x6xHV65+ONrlyO40xEJElJiVmORcRyNptZSmbHDnO07eefm2WaXnzR6shERERERPI1JW4LovBwc3bnFMuWQdmy1sUjkknxl+NwcXWlebf+lK9SNeMvNAw8k6/QoIgn2Gz8ffZPtry3hJiYmJwddVunDsybZ45qB7MkScOGUKlSzr2H5Jxvv4Xp08120aLwwQfg7Z2tLmNiYoi9Ek+Lbv3xK1sh/Y1uOB/Tc3Tfz3y8+HWSk5OzFY+I5QIDISwMnnjCXH75ZWjeHO6919q4RERERETyMSVuC5rkZPP235Tbd7t1M2d9FsmH/ALLEVg5KOMvMOzYYqPx8fIFWy6X8O7XDzZtMke2nz8PXbvCd99p8j9nEx5u3sadMuJ16lRztHQO8Stb4dbnaAbOx6gzp3MsFhHLtW1rfnG8cKF5R8Kzz8KuXeDhYXVkIiIiIiL5kiYnK2gmT4YtW8x2pUqqvSmSW2w2ePNNqFzZXP7xRxg1ytqYJK34ePjXv8yJGsGs9T1smLUxiRR0s2ZB7dpm+7ffYORIa+MREREREcnHlLgtSP79b5g0yWy7uMB774GPj7UxiRRkpUqZt92njLJ97TX46CNrY5JUL70EP/9stoOCzNrfLvq1J5KrPD1h9WpIqU8+bx5s3GhpSCIiIiIi+ZX+gi0o/vzTvFX7+tuBmzWzNiaRwiA4GObMSV3u3Rv277cuHjEtX26OiAbzNu3168HX19qYRAqLOnXML7JS9OwJ585ZFo6IiIiISH6lxG1BkJQEnTun3g7cti288oq1MYkUJgMHQvfuZjsuDp580qx7K9bYsgUGDEhdXrYM6te3KhqRwmngQHj8cbMdGWnW37fbrY1JRERERCSfUeK2IBg1CrZvN9uVKsE77+h2YJG8ZLPB0qXQsKG5fOyYWVs1KcnauAqjEyfMCRmvXjWXQ0NTk+oikndsNggLg4AAc3njRpg2zdqYRERERETyGWX38rtPP4XXXzfbrq6wdi34+Vkbk0hh5Olp3o6fkqT4z39g0KDU8iWS+y5ehHbtIDraXG7TxpwoSUSsERBg1rtN+TJ53Dj49ltrYxIRERERyUecInG7aNEiqlSpgoeHB02aNOGnn3667fZr166lRo0aeHh4UKdOHb766qs0zxuGwbhx4yhbtiyenp60bt2ao0ePptmmSpUq2Gy2NI8ZM2bk+L7lqhMnzFsPU8yeDfffb108IoVd5crwySfg5mYuL1sGCxZYG1NhkZholqg4eNBcrlED1qyBIkWsjUvEQomJCZw6dYrjx49z4sQJzp49y4kTJzh+/HimHlFRUVkPolWr1IlTDQO6dDHr8ouIiIiIyB0VtTqADz/8kNDQUJYuXUqTJk2YN28eISEhHDlyhICUkWvX2b59O126dGH69Ok88cQTrF69mg4dOrB3715q164NwKxZs1iwYAHvvPMOQUFBjB07lpCQEA4ePIiHh4ejr0mTJtG3b1/HspeXV+7vcE6Ji4OnnjJHmAF06mTWkxMRazVtCitXwnPPmctDh5olTJ580tq4CjLDgD594LvvzOXSpeHzz8HHx9q4RCwUeyGak8dPMHryNNzd3bHZbARVqsjJ8NMYmbwTwMvTg5XLl+Lv75+1YEaONEs6ffWVWY//6afNWtQpX3KJiIiIiEi6LE/czpkzh759+9KrVy8Ali5dypdffsnKlSsZMWLETdvPnz+fNm3aMHz4cAAmT57Mpk2bWLhwIUuXLsUwDObNm8eYMWNo3749AO+++y5lypRhw4YNPPPMM46+vLy8CAwMzIO9zGF2uznS9tdfzeVq1eCtt8x6ciJiva5d4dAhmDo1dYTZt9/CQw9ZHVnBNHYsrFpltj08zKTt3XdbG5OIxeIvx+Hi6krzbv0pX6UqGAaeyVdoUMQzU58X/j77J1veW0JMTEzWE7cuLvDee2Yd8FOnoHZtTVQmIiIiIpIBlpZKSExMZM+ePbRu3dqxzsXFhdatW7Njx450X7Njx4402wOEhIQ4tj958iQRERFptvHx8aFJkyY39Tljxgz8/Pxo0KABr732GldTJrNxdpMnw8cfm20vL7POrbe3tTGJSFqTJ0O3bmY7IQH++c/U2/gl58ybZybIwUxGrV4NDzxgaUgizsQvsByBlYMIrFyFkv5lCKxc5dpyxh5+ZSvkTCC+vrBunTlh2fLl5pcsIiIiIiJyW5aOuP3rr79ITk6mTJkyadaXKVOGw4cPp/uaiIiIdLePiIhwPJ+y7lbbALz00ks0bNgQX19ftm/fzsiRIzl79ixz5sxJ930TEhJISEhwLMfExABgt9ux5+WokXXrcJkwAQDDZsN4/32oXt2SkSuGYWCz2cwRhYYBZCcGAxcXl2t9ZXdf8mFfKccwQ8cxH+5fXvV143G8do4ahpG3P6cpli/HFhGBbdMmOH8eIyQEY+tWqFIl72PJBLvdbt0xy4ywMFyGDnUs2ufPh/bt8+R6mPb6d4v3y9DPtRP83Dh7X2mOY07F5UT7l1d9Zer3zPXd5OB1tGFD85FH1xZnvIZNnz6d9evXc/jwYTw9PWnatCkzZ86kevXqjm3i4+N5+eWXWbNmDQkJCYSEhLB48eKbPtuKiIiISMFneakEq4SGhjradevWxc3Njeeff57p06fj7u5+0/bTp09n4sSJN62PiooiPj4+V2NN4bp7N77XTUZ2afRo4u67D86dy5P3v1FsbCxVKlXEzZ6I7VI0kPVSDT5F4N4a1SluJGKLjc5WXPmzLwPbldhrh/D2xzF/7l9e9ZX2OHomXyGoUkViY2M5Z9HPiW3xYnyffBLX/fux/fknyS1bEr1hA3YnLtNit9u5ePEihnEt8eOE3L/4gpLPP+9Yjh02jLhOnfLsehgbG0tQpYp4Jl+5zTl6559r5/i5cfa+Uo9jTsXlXPuXV31l/PfM9ZzhOppVsbGxVodwk61btzJgwADuu+8+rl69yqhRo3j00Uc5ePAgxYsXB2Do0KF8+eWXrF27Fh8fHwYOHMhTTz3Ftm3bLI5eRERERPKapYnb0qVLU6RIESIjI9Osj4yMvGXt2cDAwNtun/L/yMhIypYtm2ab+vXr3zKWJk2acPXqVf744480ox5SjBw5Mk2yNyYmhooVK+Lv7493XpQpOH4cW69e2K4liY1u3Sg+YQLFLaxre+nSJf4IP02iixueJXyzVWP3YjIcOHyEVjY3DC/fbMWVL/syDDDAyMBxzJf7l1d93XAcr0THcDL8NF5eXulOdpgnAgJg0yaMFi2wHTlC0VOn8H/2WYzvvoOs1ovMZXa7HZvNhr+/v3MmbjdswNa/P7Zro+mMl16i+IwZeXo9vHTpEifDT9OgiCc+tzpHM/Bz7RQ/N87e13XHMaficqr9y6u+MvF75npOcR3NIg8nLMewcePGNMtvv/02AQEB7Nmzh2bNmnHx4kVWrFjB6tWradmyJQBhYWHUrFmTnTt38oBKwYiIiIgUKpYmbt3c3GjUqBGbN2+mQ4cOgJkw2Lx5MwMHDkz3NcHBwWzevJkhQ4Y41m3atIng4GAAgoKCCAwMZPPmzY5EbUxMDLt27aJ///63jGXfvn24uLjc8o8Sd3f3dEfiuri45H5iIzoa2rUzZ2IG+Mc/sL31FrYiRXL3fe8g5dZJbLZrj+wcB5t5S2O2+8mvfdkzcRzz4/7lVV83HMdr56jNZrM2ARkYCJs3w8MPw8mT2A4dwvbII+aEZU6aCEk5Zk6XuN2wATp3hpSa5D17Yps7F1sex5n2+ner987Iz7Uz/Nw4e1/XHccci8uZ9i+v+srM75nru3GS62gW5Id4L168CICvr5mo37NnD0lJSWnmaqhRowaVKlVix44dStyKiIiIFDKWl0oIDQ2lR48eNG7cmPvvv5958+YRFxdHr169AOjevTvly5dn+vTpAAwePJjmzZsze/Zs2rZty5o1a9i9ezfLly8HzD+mhwwZwpQpU6hWrRpBQUGMHTuWcuXKOZLDO3bsYNeuXfzjH//Ay8uLHTt2MHToUJ577jlKlSplyXG4JcOAZ56BI0fM5Zo1Yf16cHOzNi4RyZzy5VOTt//7H/z+O7RoYa677u4AuY3169Mmbbt1g7feMmesFxHJZ+x2O0OGDOHBBx+kdu3agDlXg5ubGyVLlkyz7Y1zNVzPaeZhEBHJJSnXMl3XRKSgyMy1zPLEbefOnYmKimLcuHFERERQv359Nm7c6JiAITw8PM2IiaZNm7J69WrGjBnDqFGjqFatGhs2bHB84AV45ZVXiIuLo1+/fly4cIGHHnqIjRs3Om6Zc3d3Z82aNUyYMIGEhASCgoIYOnRomlIITsNmg1Gj4OefzWTtV1/BDR/mReTWEhMTOHXqVI705e3tjX92yhsEBcF330HLlvDnn3DoUGrytkIOzdxeUIWFwf/9X+qkRt26mesyeedBVFSUI6mRHadOneJqSgJZRCQLBgwYwP79+/nxxx+z1Y8zzMMgIpKbzp8/7/h/fqu3LiKSnszMxWB54hZg4MCBtyyNsGXLlpvWderUiU6dOt2yP5vNxqRJk5g0aVK6zzds2JCdO3dmKVZLtGgB27dDXJzTz0Yv4kxiL0Rz8vgJRk+elm6pk8zy8vRg5fKl2UveVqsG338P//gHnDoF//0vNG0K33xjjqiXm82dC9d/sdajB6xYkaWkbe9+LxB7JfuJjCuX4zgTEUlSUmK2+xKRwmfgwIF88cUXfP/991S47ou7wMBAEhMTuXDhQppRt7eb/8HyeRhERHJZyl2xpUqVynf11kVE0pOZuRicInErGaCEjkimxV+Ow8XVlebd+lO+StVs9fX32T/Z8t4SYmJispe4BXPk7dat0KoVHD8Op0/DQw/BF1/AtXrdgjm6dsQIeO211HWDB8OcOVkqjxATE0PslXhadOuPX9nsjXA+uu9nPl78OsnJydnqR0QKF8MwGDRoEJ988glbtmwhKCgozfONGjXC1dWVzZs307FjRwCOHDlCeHi4Yz6HG1k6D4OISB5IuZbpuiYiBUVmrmVK3IpIgecXWI7AykF33jAvVa4M27bBY4/BL7+YkxC2agWrVsFTT1kdnfUuX4bu3eHjj1PXTZoEY8Zcm6Qq6/zKVsj2+RB15nS2Xi8ihdOAAQNYvXo1n376KV5eXo66tT4+Pnh6euLj40OfPn0IDQ3F19cXb29vBg0aRHBwsCYmExERESmE9HWViIhVypSBLVvMhC3AlSvQsSNMnWpOTFhYnTljlpJISdoWKQJLlsDYsdlO2oqIWGnJkiVcvHiRFi1aULZsWcfjww8/dGwzd+5cnnjiCTp27EizZs0IDAxk/fr1FkYtIiIiIlbRiFsRESt5e8OXX5oTb61aZa4bMwYOHIA334Tixa2NL6/98AN06gSRkeaylxd89BG0aWNtXCIiOcDIwJdyHh4eLFq0iEWLFuVBRCIiIiLizDTiVkTEau7u8O67MH166ojSDz6AJk3g0CFrY8srhgELFkDLlqlJ24oV4ccflbQVERERERGRQkmJWxERZ2CzmRNxffIJlChhrjtwAO67L3UkbkEVFQXt25sTj129aq5r1Qr27IG6da2NTURERERERMQiStyKiDiT9u1h926oXdtcjouDbt3gmWfg77+tjS03fPONmZz9/PPUda+8Ahs3gr+/dXGJiIiIiIiIWEyJWxERZ1O9OuzaBT17pq778EOoUwe++MKysHLU+fPQu7dZBuHarOr4+5v7N3MmFFUJdhERERERESnc9JdxIRUVFUVMTEy2+jh16hRXU25rFpGcVawYhIWZic3+/c1E59mz0K4dPPkkzJ9v1oDNbwzDTEIPHZqasAV49FF45x0IDLQuNhEREREREREnosRtIRQVFUXvfi8QeyU+W/1cuRxHxLkoJW9FclPnzvDww9Cnj1k+AMw6uP/+N4waBUOGmEne/GDPHrOO7bZtqeu8veG11+D//g9cdBOIiIiIiIiISAolbguhmJgYYq/E06Jbf/zKVshyP0f3/cwnS+dgt9tzMDoRuUm5cvDVV7B6NYSGwrlzZu3b0aNh0SKYNAl69HDe8gKHD5sxrlljjrhN0a4dLF4MFbJ+HRIREREREREpqJz0r3zJC35lKxBYOSjLr486czoHoxGR27LZoGtXePxxM2G7bBnY7XDmjDladcoUGD4cevUCT0+rozX9+iu8/rqZcL7+C5577oE5c8x9sdmsi09ERERERETEiem+VBGR/KRUKXOU6u+/wz//mbr+jz9gwACoXBlGjIATJ6yJLzHRLOXQsiXUrw+rVqUmbUuXNhO2v/8ObdsqaSsiIiIiIiJyG0rciojkR7Vqwaefwo8/mhOYpYiKgpkzoWpVaNXKHJl77lzuxpKcDNu3w6BBZlmHp56C775Lfd7XF6ZPh5MnzUnJ3NxyNx4RERERERGRAkClEkRE8rMHH4Svv4ZffoFZs+DjjyEpyXzuP/8xHy++CA88YCZyW7WC++7L3oRmhgFHj8KOHbB5s/n+f/1183b33GNORta9O5QokfX3ExERERERESmElLgVESkIGjSADz4wR9eGhcFbb8GxY+Zzdrs5Inb7dpg8GVxcoHp1s5TBXXeZ5RXKlwdvb/NRpAhFz52DP/+EixfNPs+eNZO1hw+bpQ7+/jv9ODw8oEMH6NkTHnnEfC8RERERERERyTQlbkVECpKAAHj1VXjlFdi3Dz76yKw5e+RI6jZ2Oxw6ZD7S4QKUzsx7ligBISHQrp2ZtPXxyXr8IiIiIiIiIgIocSsiUjDZbOYo3AYNzPqyf/5pljX4/nuzrML+/aklFTIrIMAst9C0aepDdWtFREREREREcpQStyIihUGFCtCjh/kASEw0SymcOmU+IiIgNhZiYzESE7ly9SqeXl7YSpY0E7UBAWZZherVzcnGRERERERERCRXKXErIlIYublBrVrm4waG3U7MuXN4BARgU41aEREREREREUsocSsiIiIiIiIiWXb58mUOHz6cK32n9Hv48GFccmlQQY0aNShWrFiu9C0ikh1K3IqIiIiIiIhIlh0+fJhGjRrl6nt069Yt1/res2cPDRs2zLX+RUSySolbEREREREREcmyGjVqsGfPnlzp+/Lly/z666/Uq1cv10bF1qhRI1f6FRHJLiVuRURERERERCTLihUrlmsjVu12O3fffTcBAQG5VipBRMRZ6aonIiIiIiIiIiIi4mSUuBURERERERERERFxMkrcioiIiIiIiIiIiDgZ1bjNR6KiooiJicl2P6dOneLq1as5EJFI4ZKYmMCpU6dypC9vb2/8/f1zpC8Rkdyka5+IiIiIiDWUuM0noqKi6N3vBWKvxGe7ryuX4zgTEUlSUmIORCZSOMReiObk8ROMnjwNd3f3bPfn5enByuVLlcAQEaema5+IiIiIiHWUuM0nYmJiiL0ST4tu/fErWyFbfR3d9zMfL36d5OTkHIpOpOCLvxyHi6srzbv1p3yVqtnq6++zf7LlvSXExMQoeSEiTk3XPhERERER6yhxm8/4la1AYOWgbPURdeZ0DkUjUvj4BZbL9s+giEh+o2ufiIiIiEje0+RkIiIiIiIiIiIiIk5GiVsRERERERERERERJ+MUidtFixZRpUoVPDw8aNKkCT/99NNtt1+7di01atTAw8ODOnXq8NVXX6V53jAMxo0bR9myZfH09KR169YcPXo0zTbR0dF07doVb29vSpYsSZ8+fbh06VKO75uIiIiIiIiIiIhIZlmeuP3www8JDQ1l/Pjx7N27l3r16hESEsK5c+fS3X779u106dKFPn368Msvv9ChQwc6dOjA/v37HdvMmjWLBQsWsHTpUnbt2kXx4sUJCQkhPj7esU3Xrl05cOAAmzZt4osvvuD777+nX79+ub6/IiIiIiIiIiIiIndieeJ2zpw59O3bl169elGrVi2WLl1KsWLFWLlyZbrbz58/nzZt2jB8+HBq1qzJ5MmTadiwIQsXLgTM0bbz5s1jzJgxtG/fnrp16/Luu+9y5swZNmzYAMChQ4fYuHEjb731Fk2aNOGhhx7ijTfeYM2aNZw5cyavdl1EREREREREREQkXZYmbhMTE9mzZw+tW7d2rHNxcaF169bs2LEj3dfs2LEjzfYAISEhju1PnjxJREREmm18fHxo0qSJY5sdO3ZQsmRJGjdu7NimdevWuLi4sGvXrhzbPxEREREREREREZGsKGrlm//1118kJydTpkyZNOvLlCnD4cOH031NREREuttHREQ4nk9Zd7ttAgIC0jxftGhRfH19HdvcKCEhgYSEBMfyxYsXAbhw4QJ2u/22+5kTYmJiSE6+ypnjR7hyKTZbfZ0LPwmGwZmTxzCuXs12P1Fn/uRSsgvYbJbHlG/7Mgw8k+O5UuR/dzyO+XL/8qqvG46jU8SUjvORZ4i/coUDBw4QExOTrb5yS2xsLGfPnrU6jBx1+vRpEhMS8u46moGfa2c9R52qr+uOY07//nKK/curvjLxeya3YjofeYbk5KvExMRw4cKFbPWVESnXV8Mwcv29nEnK/jrr7xcRkcyy2+3Exsbi4eGBi4vlNw2LiGRbpj6nGhb63//+ZwDG9u3b06wfPny4cf/996f7GldXV2P16tVp1i1atMgICAgwDMMwtm3bZgDGmTNn0mzTqVMn4+mnnzYMwzCmTp1q3HPPPTf17e/vbyxevDjd9x0/frwB6KGHHnrooYceeuiRjx6nT5/O2AfTAuL06dOWH3M99NBDDz300EMPPe78yMjnVEtH3JYuXZoiRYoQGRmZZn1kZCSBgYHpviYwMPC226f8PzIykrJly6bZpn79+o5tbpz87OrVq0RHR9/yfUeOHEloaKhj2W63Ex0djZ+fH7ZsjDTN72JiYqhYsSKnT5/G29vb6nDyLR3HnKHjmDN0HHOGjmPO0HHMGYXxOBqGQWxsLOXKlbM6lDxVrlw5Tp8+jZeXV6H+jCoiBUdh/B0mIgVbZj6nWpq4dXNzo1GjRmzevJkOHToAZkJ08+bNDBw4MN3XBAcHs3nzZoYMGeJYt2nTJoKDgwEICgoiMDCQzZs3OxK1MTEx7Nq1i/79+zv6uHDhAnv27KFRo0YA/Oc//8Fut9OkSZN039fd3R13d/c060qWLJnFPS94vL299Us0B+g45gwdx5yh45gzdBxzho5jzihsx9HHx8fqEPKci4sLFSpUsDoMEZEcV9h+h4lIwZbRz6mWJm4BQkND6dGjB40bN+b+++9n3rx5xMXF0atXLwC6d+9O+fLlmT59OgCDBw+mefPmzJ49m7Zt27JmzRp2797N8uXLAbDZbAwZMoQpU6ZQrVo1goKCGDt2LOXKlXMkh2vWrEmbNm3o27cvS5cuJSkpiYEDB/LMM88UulEZIiIiIiIiIiIi4nwsT9x27tyZqKgoxo0bR0REBPXr12fjxo2OycXCw8PTFCBv2rQpq1evZsyYMYwaNYpq1aqxYcMGateu7djmlVdeIS4ujn79+nHhwgUeeughNm7ciIeHh2Ob999/n4EDB9KqVStcXFzo2LEjCxYsyLsdFxEREREREREREbkFyxO3AAMHDrxlaYQtW7bctK5Tp0506tTplv3ZbDYmTZrEpEmTbrmNr68vq1evznSskpa7uzvjx4+/qYyEZI6OY87QccwZOo45Q8cxZ+g45gwdRxERya/0O0xECjObYRiG1UGIiIiIiIiIiIiISCqXO28iIiIiIiIiIiIiInlJiVsRERERERERERERJ6PErYiIiIiIiIiIiIiTUeJWMmT69Oncd999eHl5ERAQQIcOHThy5EiabeLj4xkwYAB+fn6UKFGCjh07EhkZaVHEzmnJkiXUrVsXb29vvL29CQ4O5uuvv3Y8r2OYeTNmzMBmszFkyBDHOh3HO5swYQI2my3No0aNGo7ndQwz7n//+x/PPfccfn5+eHp6UqdOHXbv3u143jAMxo0bR9myZfH09KR169YcPXrUwoidT5UqVW46H202GwMGDAB0PmZUcnIyY8eOJSgoCE9PT6pWrcrkyZO5fjoDnY8iInIr6f0uvv4xYcKEbPW9YcOGO263detWWrZsia+vL8WKFaNatWr06NGDxMTELL+3iEh+psStZMjWrVsZMGAAO3fuZNOmTSQlJfHoo48SFxfn2Gbo0KF8/vnnrF27lq1bt3LmzBmeeuopC6N2PhUqVGDGjBns2bOH3bt307JlS9q3b8+BAwcAHcPM+vnnn1m2bBl169ZNs17HMWPuvfdezp4963j8+OOPjud0DDPm/PnzPPjgg7i6uvL1119z8OBBZs+eTalSpRzbzJo1iwULFrB06VJ27dpF8eLFCQkJIT4+3sLIncvPP/+c5lzctGkTAJ06dQJ0PmbUzJkzWbJkCQsXLuTQoUPMnDmTWbNm8cYbbzi20fkoIiK3cv3v4nnz5uHt7Z1m3bBhw3L1/Q8ePEibNm1o3Lgx33//Pb///jtvvPEGbm5uJCcn58p7GobB1atXc6VvEZEcYYhkwblz5wzA2Lp1q2EYhnHhwgXD1dXVWLt2rWObQ4cOGYCxY8cOq8LMF0qVKmW89dZbOoaZFBsba1SrVs3YtGmT0bx5c2Pw4MGGYehczKjx48cb9erVS/c5HcOMe/XVV42HHnrols/b7XYjMDDQeO211xzrLly4YLi7uxsffPBBXoSYLw0ePNioWrWqYbfbdT5mQtu2bY3evXunWffUU08ZXbt2NQxD56OIiGRcWFiY4ePjk2bdm2++adSoUcNwd3c3qlevbixatMjxXEJCgjFgwAAjMDDQcHd3NypVqmRMmzbNMAzDqFy5sgE4HpUrV073PefOnWtUqVLljrH9+OOPRvPmzQ1PT0+jZMmSxqOPPmpER0cbhmEY8fHxxqBBgwx/f3/D3d3dePDBB42ffvrJ8drvvvvOAIyvvvrKaNiwoeHq6mp89913RnJysjFt2jSjSpUqhoeHh1G3bt00nz2io6ONZ5991ihdurTh4eFh3H333cbKlSszejhFRLJMI24lSy5evAiAr68vAHv27CEpKYnWrVs7tqlRowaVKlVix44dlsTo7JKTk1mzZg1xcXEEBwfrGGbSgAEDaNu2bZrjBToXM+Po0aOUK1eOu+66i65duxIeHg7oGGbGZ599RuPGjenUqRMBAQE0aNCAN9980/H8yZMniYiISHMsfXx8aNKkiY7lLSQmJrJq1Sp69+6NzWbT+ZgJTZs2ZfPmzfz3v/8F4Ndff+XHH3/kscceA3Q+iohI1r3//vuMGzeOqVOncujQIaZNm8bYsWN55513AFiwYAGfffYZH330EUeOHOH999+nSpUqgHlnDUBYWBhnz551LN8oMDCQs2fP8v33398yjn379tGqVStq1arFjh07+PHHH2nXrp1jRO4rr7zCxx9/zDvvvMPevXu5++67CQkJITo6Ok0/I0aMYMaMGRw6dIi6desyffp03n33XZYuXcqBAwcYOnQozz33HFu3bgVg7NixHDx4kK+//ppDhw6xZMkSSpcuna1jKiKSEUWtDkDyH7vdzpAhQ3jwwQepXbs2ABEREbi5uVGyZMk025YpU4aIiAgLonRev//+O8HBwcTHx1OiRAk++eQTatWqxb59+3QMM2jNmjXs3bs33Q99OhczpkmTJrz99ttUr16ds2fPMnHiRB5++GH279+vY5gJJ06cYMmSJYSGhjJq1Ch+/vlnXnrpJdzc3OjRo4fjeJUpUybN63Qsb23Dhg1cuHCBnj17AvqZzowRI0YQExNDjRo1KFKkCMnJyUydOpWuXbsC6HwUEZEsGz9+PLNnz3aUKgoKCuLgwYMsW7aMHj16EB4eTrVq1XjooYew2WxUrlzZ8Vp/f38ASpYsSWBg4C3fo1OnTnzzzTc0b96cwMBAHnjgAVq1akX37t3x9vYGzJI/jRs3ZvHixY7X3XvvvQDExcWxZMkS3n77bceXlm+++SabNm1ixYoVDB8+3PGaSZMm8cgjjwCQkJDAtGnT+PbbbwkODgbgrrvu4scff2TZsmU0b96c8PBwGjRoQOPGjQEcSWkRkdymxK1k2oABA9i/f3+aepiScdWrV2ffvn1cvHiRdevW0aNHD8c3uXJnp0+fZvDgwWzatAkPDw+rw8m3Uj7MAtStW5cmTZpQuXJlPvroIzw9PS2MLH+x2+00btyYadOmAdCgQQP279/P0qVL6dGjh8XR5U8rVqzgscceo1y5claHku989NFHvP/++6xevZp7772Xffv2MWTIEMqVK6fzUUREsiwuLo7jx4/Tp08f+vbt61h/9epVfHx8AOjZsyePPPII1atXp02bNjzxxBM8+uijmXqfIkWKEBYWxpQpU/jPf/7Drl27mDZtGjNnzuSnn36ibNmy7Nu3z1ED/0bHjx8nKSmJBx980LHO1dWV+++/n0OHDqXZNiUBC3Ds2DEuX77sSOSmSExMpEGDBgD079+fjh07snfvXh599FE6dOhA06ZNM7V/IiJZoVIJkikDBw7kiy++4LvvvqNChQqO9YGBgSQmJnLhwoU020dGRt72W9XCyM3NjbvvvptGjRoxffp06tWrx/z583UMM2jPnj2cO3eOhg0bUrRoUYoWLcrWrVtZsGABRYsWpUyZMjqOWVCyZEnuuecejh07pnMxE8qWLUutWrXSrKtZs6aj7ETK8YqMjEyzjY5l+k6dOsW3337L//3f/znW6XzMuOHDhzNixAieeeYZ6tSpQ7du3Rg6dCjTp08HdD6KiEjWXLp0CTBHr+7bt8/x2L9/Pzt37gSgYcOGnDx5ksmTJ3PlyhWefvpp/vWvf2Xp/cqXL0+3bt1YuHAhBw4cID4+nqVLlwLk2ACD4sWLO9op+/fll1+m2b+DBw+ybt06wBz0cOrUKYYOHcqZM2do1apVrk/WJiICStxKBhmGwcCBA/nkk0/4z3/+Q1BQUJrnGzVqhKurK5s3b3asO3LkCOHh4Y7bTSR9drudhIQEHcMMatWqFb///nuaD1WNGzema9eujraOY+ZdunSJ48ePU7ZsWZ2LmfDggw9y5MiRNOv++9//Om4PDAoKIjAwMM2xjImJYdeuXTqW6QgLCyMgIIC2bds61ul8zLjLly/j4pL2o12RIkWw2+2AzkcREcmaMmXKUK5cOU6cOMHdd9+d5nH934Xe3t507tyZN998kw8//JCPP/7YUVvW1dXVUYc2M0qVKkXZsmWJi4sDzDvFrv89dr2qVavi5ubGtm3bHOuSkpL4+eefb/qi/Xq1atXC3d2d8PDwm/avYsWKju38/f3p0aMHq1atYt68eSxfvjzT+yMiklkqlSAZMmDAAFavXs2nn36Kl5eXoxaej48Pnp6e+Pj40KdPH0JDQ/H19cXb25tBgwYRHBzMAw88YHH0zmPkyJE89thjVKpUidjYWFavXs2WLVv45ptvdAwzyMvLy1FbOUXx4sXx8/NzrNdxvLNhw4bRrl07KleuzJkzZxg/fjxFihShS5cuOhczYejQoTRt2pRp06bx9NNP89NPP7F8+XLHB3mbzcaQIUOYMmUK1apVIygoiLFjx1KuXDk6dOhgbfBOxm63ExYWRo8ePShaNPXjic7HjGvXrh1Tp06lUqVK3Hvvvfzyyy/MmTOH3r17AzofRUQk6yZOnMhLL72Ej48Pbdq0ISEhgd27d3P+/HlCQ0OZM2cOZcuWpUGDBri4uLB27VoCAwMdNeqrVKnC5s2befDBB3F3d6dUqVI3vceyZcvYt28fTz75JFWrViU+Pp53332XAwcO8MYbbwDm31N16tThxRdf5IUXXsDNzY3vvvuOTp06Ubp0afr378/w4cPx9fWlUqVKzJo1i8uXL9OnT59b7puXlxfDhg1j6NCh2O12HnroIS5evMi2bdvw9vamR48ejBs3jkaNGnHvvfeSkJDAF198Qc2aNXPlWIuIpGGIZACQ7iMsLMyxzZUrV4wXX3zRKFWqlFGsWDHjySefNM6ePWtd0E6od+/eRuXKlQ03NzfD39/faNWqlfHvf//b8byOYdY0b97cGDx4sGNZx/HOOnfubJQtW9Zwc3Mzypcvb3Tu3Nk4duyY43kdw4z7/PPPjdq1axvu7u5GjRo1jOXLl6d53m63G2PHjjXKlCljuLu7G61atTKOHDliUbTO65tvvjGAdI+NzseMiYmJMQYPHmxUqlTJ8PDwMO666y5j9OjRRkJCgmMbnY8iIpIRYWFhho+PT5p177//vlG/fn3Dzc3NKFWqlNGsWTNj/fr1hmEYxvLly4369esbxYsXN7y9vY1WrVoZe/fudbz2s88+M+6++26jaNGiRuXKldN9z7179xrPPfecERQUZLi7uxt+fn5Gs2bNjM8++yzNdlu2bDGaNm1quLu7GyVLljRCQkKM8+fPG4ZhfmYYNGiQUbp0acPd3d148MEHjZ9++snx2u+++84AHNunsNvtxrx584zq1asbrq6uhr+/vxESEmJs3brVMAzDmDx5slGzZk3D09PT8PX1Ndq3b2+cOHEiC0dWRCRzbIZhGNaljUVERERERERERETkRqpxKyIiIiIiIiIiIuJklLgVERERERERERERcTJK3IqIiIiIiIiIiIg4GSVuRURERERERERERJyMErciIiIiIiIiIiIiTkaJWxEREREREREREREno8StiIiIiIiIiIiIiJNR4lZERERERERERETEyShxKyIitGjRgiFDhlgdhoiIiIiIiIhco8StiEg+165dO9q0aZPucz/88AM2m43ffvstj6MSERERERERkexQ4lZEJJ/r06cPmzZt4s8//7zpubCwMBo3bkzdunUtiExEREREREREskqJWxGRfO6JJ57A39+ft99+O836S5cusXbtWjp06ECXLl0oX748xYoVo06dOnzwwQe37dNms7Fhw4Y060qWLJnmPU6fPs3TTz9NyZIl8fX1pX379vzxxx85s1MiIiIiIiIihZwStyIi+VzRokXp3r07b7/9NoZhONavXbuW5ORknnvuORo1asSXX37J/v376devH926deOnn37K8nsmJSUREhKCl5cXP/zwA9u2baNEiRK0adOGxMTEnNgtERERERERkUJNiVsRkQKgd+/eHD9+nK1btzrWhYWF0bFjRypXrsywYcOoX78+d911F4MGDaJNmzZ89NFHWX6/Dz/8ELvdzltvvUWdOnWoWbMmYWFhhIeHs2XLlhzYIxEREREREZHCTYlbEZECoEaNGjRt2pSVK1cCcOzYMX744Qf69OlDcnIykydPpk6dOvj6+lKiRAm++eYbwsPDs/x+v/76K8eOHcPLy4sSJUpQokQJfH19iY+P5/jx4zm1WyIiIiIiIiKFVlGrAxARkZzRp08fBg0axKJFiwgLC6Nq1ao0b96cmTNnMn/+fObNm0edOnUoXrw4Q4YMuW1JA5vNlqbsApjlEVJcunSJRo0a8f7779/0Wn9//5zbKREREREREZFCSolbEZEC4umnn2bw4MGsXr2ad999l/79+2Oz2di2bRvt27fnueeeA8But/Pf//6XWrVq3bIvf39/zp4961g+evQoly9fdiw3bNiQDz/8kICAALy9vXNvp0REREREREQKKZVKEBEpIEqUKEHnzp0ZOXIkZ8+epWfPngBUq1aNTZs2sX37dg4dOsTzzz9PZGTkbftq2bIlCxcu5JdffmH37t288MILuLq6Op7v2rUrpUuXpn379vzwww+cPHmSLVu28NJLL/Hnn3/m5m6KiIiIiIiIFApK3IqIFCB9+vTh/PnzhISEUK5cOQDGjBlDw4YNCQkJoUWLFgQGBtKhQ4fb9jN79mwqVqzIww8/zLPPPsuwYcMoVqyY4/lixYrx/fffU6lSJZ566ilq1qxJnz59iI+P1whcERERERERkRxgM24sYigiIiIiIiIiIiIiltKIWxEREREREREREREno8StiIiIiIiIiIiIiJNR4lZERERERERERETEyShxKyIiIiIiIiIiIuJklLgVERERERERERERcTJK3IqIiIiIiIiIiIg4GSVuRURERERERERERJyMErciIiIiIiIiIiIiTkaJWxEREREREREREREno8StiIiIiIiIiIiIiJNR4lZERERERERERETEyShxKyIiIiIiIiIiIuJk/h+dz1KyF30oVwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_2 = string_to_dataset(dataset_string_2)\n",
    "analyze_1d_data(dataset_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ddf45b0-9ca5-4eec-8c18-4f4f6076eb7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_2_part_1 = dataset_2[dataset_2 <= 50]\n",
    "dataset_2_part_2 = dataset_2[dataset_2 > 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6a87734-4c51-411d-badf-2cceaccde42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean': np.float64(30.0865),\n",
       "  'std': np.float64(5.088336442296245),\n",
       "  'median': np.float64(29.95),\n",
       "  'q1': np.float64(26.4),\n",
       "  'q3': np.float64(33.7),\n",
       "  'iqr': np.float64(7.300000000000004),\n",
       "  'min': np.float64(15.3),\n",
       "  'max': np.float64(42.9)},\n",
       " <Figure size 1400x500 with 2 Axes>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_1d_data(dataset_2_part_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5843da81-b7b8-4b0a-a989-087ad9421934",
   "metadata": {},
   "source": [
    "The first part of Dataset 2 is approx normal around the mean 30 with std 5, skewed to the right tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3c1241b3-1b7b-4c27-82db-fb69cb872eac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean': np.float64(69.94200000000001),\n",
       "  'std': np.float64(5.14507881377924),\n",
       "  'median': np.float64(70.1),\n",
       "  'q1': np.float64(66.45),\n",
       "  'q3': np.float64(73.4),\n",
       "  'iqr': np.float64(6.950000000000003),\n",
       "  'min': np.float64(57.2),\n",
       "  'max': np.float64(84.1)},\n",
       " <Figure size 1400x500 with 2 Axes>)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_1d_data(dataset_2_part_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1fd7ab-9590-4493-ad4d-c09048caa602",
   "metadata": {},
   "source": [
    "The second part of Dataset 2 is approx normal around the mean 70 with std 5, skewed to the left tail."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd20189b-f764-495f-b5b6-cc53641a45ae",
   "metadata": {},
   "source": [
    "## Dataset 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b723918-4189-42fd-ad35-160812b56179",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_string_3 = '''\n",
    "[  97.9  117.8  102.6  116.2   87.7  137.    52.8   83.3   92.5  103.3\n",
    "  124.   122.    74.7  101.   103.9  137.9   93.6   84.7  130.2  111.1\n",
    "  116.5  100.2   92.2   52.8   78.2   67.9  112.2  119.    92.9  114.7\n",
    "   93.6  101.   106.2   99.5   95.7   77.    62.3   96.5  106.8  118.3\n",
    "  116.5   40.2   75.9  111.9   26.8  102.5  107.6  121.4   96.6  111.6\n",
    "   83.2   50.9   -5.6   48.6  109.5   80.8  111.1  125.5  107.7  131.3\n",
    "   91.7  110.9  127.6   71.1   85.2  113.7  137.7  117.8   60.8   86.1\n",
    "   45.7   88.3  101.2   62.2  135.1  121.1  104.4  112.5  128.2  115.9\n",
    "   92.8   92.1  102.4   91.8  148.2  123.9  115.3  109.7   66.8  138.6\n",
    "  119.   102.8   47.5   36.2  104.    87.7   58.7  107.9  119.    87.1\n",
    "   99.3  140.9   80.2   62.2  111.6  106.6   95.3  122.4   97.   110.7\n",
    "  354.4   83.7   88.   101.   100.7   87.    88.3  106.5   99.2  116.1\n",
    "   69.2   87.7  112.5   97.9  122.8   77.1   49.7  140.2  112.1   32.2\n",
    "  104.1  102.8  107.5  147.3   68.3  137.9   87.8  162.7  100.5  128.8\n",
    "  138.3   79.8  106.   110.2   93.9  118.3  104.2   69.5   97.1  146.\n",
    "   36.6   99.1   87.2  106.6  126.   102.   156.5   79.8   70.3  101.2\n",
    "   98.9  145.   105.1  120.5  109.6   86.2  140.9   89.9   89.   110.7\n",
    "  104.   113.4   67.2   40.   105.1  106.7   -7.8  122.3  169.1   72.1\n",
    "  121.   119.4  111.7   98.   111.9   83.1  110.6  134.   116.9  125.6\n",
    "  113.9   87.4   91.4   79.6  104.5  143.4   83.9  143.4   90.5   97.\n",
    "  109.    62.4  111.8  120.9  118.6  109.   114.5   97.7  120.8  103.2\n",
    "   69.3  104.5  100.9  117.8   94.1  136.6  119.5   87.7  185.4  115.6\n",
    "  177.3   62.9  124.6   67.    32.7   99.4  117.5   98.4  210.7  108.3\n",
    "  112.5  110.4   87.3   60.6  180.    74.    97.4  233.    96.7   83.5\n",
    "   79.6   84.3  103.6   91.4   84.9   78.7  105.8  117.2   78.    75.8\n",
    "  111.1   97.6   95.7   95.1  112.9  100.2   96.1  120.8   79.9   89.9\n",
    "  230.8  131.1   62.   117.5  147.2  121.9   98.2  113.    87.5 -147.1\n",
    "   91.2   97.3  244.8  110.4  101.   103.8   78.5  111.6   78.7   81.5\n",
    "  105.3  109.6  102.1  102.    50.3  103.3   74.8  103.8   80.7  266.2\n",
    "  -20.2   82.7   32.1   73.6   95.5   96.5   60.4   84.2  107.3   66.2\n",
    "   80.1  147.    70.2  135.7   81.3  108.3   83.2  108.7   62.7  108.1\n",
    "  102.8   91.8   75.5  210.5  107.1   35.3  115.9  115.1   89.8  105.5\n",
    "  101.2   89.7   66.5  116.7   85.7   92.1   86.5  124.9  104.7  107.\n",
    "   92.   116.1   55.3  113.4   78.5  127.2  117.5  109.9   95.5  100.9\n",
    "  125.7   96.2  103.2  104.4   91.8  120.3  106.    65.7   91.2   79.9\n",
    "  139.6  126.2  107.2   77.2  101.9   22.6  118.    99.    99.8  107.1\n",
    "   88.5   87.7  100.    82.7  110.2   91.1   95.2   54.7   90.3   86.1\n",
    "   93.8  193.3   98.7  122.5   86.7  103.1  113.4  129.7  126.5   72.3\n",
    "   97.6   20.   107.2  111.4  126.3   61.1  107.6  107.7  125.2   72.6\n",
    "  107.1   88.2  117.2  106.    82.3  141.7   81.3  115.8  118.3  120.\n",
    "   84.   115.6   74.2   96.9  118.8   14.4   80.7   96.5  113.4  102.6\n",
    "   70.5  137.3  123.1   83.6   82.7   71.3   86.    86.6  126.2   88.7\n",
    "  125.8  102.5   88.   131.8  104.2  100.7  100.1   65.1  108.7   68.1\n",
    "  100.6  110.3  107.2  168.1   73.1   47.4   99.2  100.   103.6   87.1\n",
    "  106.6  181.5  114.2   93.4  105.3   97.1   82.4  117.7   64.1  124.3\n",
    "  120.    97.7  124.4  103.2   99.7   92.3  145.9  111.7  109.4  107.\n",
    "  104.7  155.9   73.7   94.8   60.6   96.2   74.3   88.1   77.3  116.7\n",
    "   96.4   88.4  189.6  102.3   83.5  102.5  126.6   45.5  110.5   87.9\n",
    "   94.9  209.8  125.   124.1   75.4   94.8   80.2   63.9   -6.3   91.7\n",
    "  101.2   91.6   84.2  118.1   50.4  111.5  111.3   97.4   66.5   86.7\n",
    "  103.8  104.5   78.6   51.4   82.2   64.   135.3  221.7  111.2   92.5\n",
    "  123.2   51.8  114.6   79.6   65.2   91.2  113.2  119.2  121.4  103.7\n",
    "   73.1   94.3  142.9  111.3  122.4  123.    19.6  111.4  103.9   97.8\n",
    "   59.8  120.6  139.8  133.    86.    71.3   89.1  101.   129.2   93.7\n",
    "  104.1  165.3  131.3  119.2   79.1  103.8   90.   119.6   89.8   57.5\n",
    "  105.6   15.2   93.9   96.1   89.    80.8  109.   100.4   36.9   80.7\n",
    "  119.9  116.4  121.8  111.5   80.   103.3   88.5  121.7  106.   114.2\n",
    "   89.8   91.3  115.1   83.5  115.   126.    75.2  110.6  150.7   87.9\n",
    "  105.3   76.6  116.9   95.7   80.1  132.9  115.4  132.5   59.5   79.6\n",
    "  111.8   93.9  199.7   85.4  110.   112.3  111.4   99.1  139.4  101.2\n",
    "   89.6  127.2   45.7  239.2  106.9   67.2  110.6  112.4   84.1   43.3\n",
    "  124.9  122.    66.2   42.7   45.3   84.8  111.8   89.5  134.5   49.7\n",
    "  111.8  105.3   90.2  114.8  110.8   94.5   99.4   99.6  128.6   40.5\n",
    "   70.9  106.3   94.8   70.4   90.4  108.    70.    94.1  134.    91.3\n",
    "   85.3  126.6  127.3  110.1  122.8  118.5  100.8  127.4   94.7   85.2\n",
    "  127.    80.7  100.2  119.7   67.3   92.4   94.3   75.3  104.6  102.5\n",
    "  104.7   94.6   73.7  106.6  109.7   89.2  109.1  104.1  121.1   83.3\n",
    "  105.3  105.5  124.6  141.4   91.    83.8  109.9   71.9   97.1   93.7\n",
    "  108.2  123.1  105.8   43.1  109.5   89.5  148.    90.4   74.9  110.8\n",
    "  180.4  105.4  102.9  117.3  120.2   92.5  105.1   76.6   71.    85.5\n",
    "  106.3   97.5   68.2  103.4  109.6   85.2  104.7   87.    43.6  127.3\n",
    "   85.2  115.8  111.1   92.   115.4   99.2   65.8   46.4  129.5  103.4\n",
    "  106.8   90.3   54.9   74.9  101.8  106.    63.3  103.5   82.    96.6\n",
    "  101.3  152.3   59.1   86.4   80.1  118.5  101.6  108.7  128.   125.1\n",
    "   87.3  101.8  110.4   66.9  116.4  114.2   91.    78.    90.8  125.7\n",
    "  130.4   86.6    5.2  102.2  137.1  100.6  110.1  119.7   95.    50.3\n",
    "  110.6   78.5   92.5  137.1   77.4   77.8  110.1   79.4  143.9  129.3\n",
    "  101.6  178.4  109.8   50.   118.5  101.7  119.3   75.   100.9  103.6\n",
    "  171.7  117.7  134.5   79.9  102.5  166.5  148.    81.8   87.1   48.8\n",
    "  111.9   65.7  101.3  142.9   69.2  129.9   62.8  176.6  111.6  104.\n",
    "   86.    85.7  113.2   95.1   56.3  106.2   77.1  118.8   95.9  111.6\n",
    "   99.5   84.8  100.3  140.6   73.2   93.6   83.3  110.3  114.5  117.7\n",
    "   69.1   99.3   69.8  100.5   98.9   67.6  110.7  104.1  110.1  115.4\n",
    "  109.3  104.5  108.5  116.1   72.2   97.6  120.   150.7  124.2   81.9\n",
    "  119.1   24.5   84.4  147.5   86.7   96.9  116.4   93.2   72.3  108.6\n",
    "  115.6  105.1   89.    69.7   21.    92.5   99.1   95.4  124.5   88.7\n",
    "   45.8  110.1  127.4   81.4   84.    94.4   98.5  140.9  117.9   66.1\n",
    "   62.7  124.8   93.9   80.8   90.6  105.5  104.9   86.5   94.9  112.8\n",
    "  108.5   77.9   94.6  112.6   -2.3   87.6  110.8  106.9  130.8  109.7\n",
    "   73.6  159.1   98.5  117.4   80.9   92.  -173.   120.1  113.3  128.5\n",
    "   65.    63.1   97.9  102.5  114.   120.6  141.4  111.   111.6  129.7\n",
    "   80.1   85.8   56.9  116.3   80.    91.8   17.7  101.1   90.6   91.2\n",
    "  109.4   77.2   68.6  130.1  228.8   86.4   91.3   85.7  131.6  128.2\n",
    "  109.9  132.    74.5  112.1   36.3  145.4   85.2   48.5   72.2  101.7\n",
    "  104.1  104.1   92.1   66.7  103.3  113.3   95.   102.9   63.7  105.6\n",
    "   88.1  180.1   98.3   36.2   82.1  115.9  101.6   91.    89.5   94.4\n",
    "   68.2  165.9   44.1  111.7  110.4  154.1  119.8  100.7  130.5  106.7\n",
    "   38.6   85.3  145.   101.7   99.5  124.8   86.8   99.6  103.8   92.6\n",
    "  109.3   35.2  142.4   57.8   96.2   92.   111.1  157.6  103.5   88.7\n",
    "  111.7  -27.1  151.8   82.6  119.7  120.5   93.3   94.4  105.7   97.2]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "768da806-0e7d-4883-b6a1-6ea4c450fada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'mean': np.float64(99.9245),\n",
       "  'std': np.float64(33.461668663561895),\n",
       "  'median': np.float64(101.2),\n",
       "  'q1': np.float64(85.2),\n",
       "  'q3': np.float64(114.725),\n",
       "  'iqr': np.float64(29.52499999999999),\n",
       "  'min': np.float64(-173.0),\n",
       "  'max': np.float64(354.4)},\n",
       " <Figure size 1400x500 with 2 Axes>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_3 = string_to_dataset(dataset_string_3)\n",
    "analyze_1d_data(dataset_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fdee526-675e-4cf3-9d17-38a67f856db9",
   "metadata": {},
   "source": [
    "Dataset 3 represents a distribution with heavy tails."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b55867-5509-472d-b364-239d4cfe913d",
   "metadata": {},
   "source": [
    "## 6. Distribution Transformation Challenge\n",
    "\n",
    "Sometimes we need to transform data to make it easier to work with. Let's explore this concept!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c1fc07df-b5a4-4fb9-afb6-d4c01bbc4595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notice how the log transformation turned a skewed distribution into a bell curve!\n"
     ]
    }
   ],
   "source": [
    "# Demonstration: Log transformation\n",
    "np.random.seed(111)\n",
    "\n",
    "# Generate log-normal data (common in nature: income, city sizes, etc.)\n",
    "log_normal_data = np.random.lognormal(mean=3, sigma=0.5, size=1000)\n",
    "\n",
    "# Apply log transformation\n",
    "transformed_data = np.log(log_normal_data)\n",
    "\n",
    "# Visualize before and after\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Original data\n",
    "ax1.hist(log_normal_data, bins=40, alpha=0.7, color='coral', edgecolor='black')\n",
    "kde = stats.gaussian_kde(log_normal_data)\n",
    "kde_xs = np.linspace(log_normal_data.min(), log_normal_data.max(), 200)\n",
    "ax1.plot(kde_xs, kde(kde_xs) * len(log_normal_data) * (kde_xs[1] - kde_xs[0]), \n",
    "         'r-', linewidth=2)\n",
    "ax1.set_xlabel('Value')\n",
    "ax1.set_ylabel('Frequency')\n",
    "ax1.set_title('Original: Log-Normal Distribution (skewed)')\n",
    "\n",
    "# Transformed data\n",
    "ax2.hist(transformed_data, bins=40, alpha=0.7, color='lightgreen', edgecolor='black')\n",
    "kde = stats.gaussian_kde(transformed_data)\n",
    "kde_xs = np.linspace(transformed_data.min(), transformed_data.max(), 200)\n",
    "ax2.plot(kde_xs, kde(kde_xs) * len(transformed_data) * (kde_xs[1] - kde_xs[0]), \n",
    "         'g-', linewidth=2)\n",
    "ax2.set_xlabel('log(Value)')\n",
    "ax2.set_ylabel('Frequency')\n",
    "ax2.set_title('Transformed: Normal Distribution!')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Notice how the log transformation turned a skewed distribution into a bell curve!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f46b7036-9137-4127-8b18-d2d313d93d91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You've been given a mystery dataset!\n",
      "Range: [0.0, 99.4]\n",
      "Mean: 33.2\n",
      "\n",
      "Your task: Find a transformation that makes this data roughly uniform or normal!\n"
     ]
    },
    {
     "data": {
      "image/png": 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jihUrOjyOHz+eZ/9atWrp0KFDSk9Pv2k9Fy5c0D333KM9e/Zo4sSJ+uyzz7R27VpNmzZNknLMa+xM/Ve1b99eERERDp/LyMhIxcTE3HTd6xX18czt81cU+8oV35X8yus7lVedhTnWBXX27FlJUsWKFV2+bVgbF5QBuejevbsefvhhffvttw7TW+UmLCxMnTt31qJFixQbG6stW7bkOjF5QECA+vTpoz59+ig9PV09evTQ5MmTNX78ePn5+eX5g7l69eoyxig6OjrPi7Xya9CgQRo3bpxOnjypxYsXq3PnzvZRwsL417/+JZvNpj//+c837OfsPnDW4cOHHUamjhw5ouzsbPtFNVff+/U3L8htJLAgtVWtWlUHDx7M0X71T9SumGy/fv36Wrt2rUNbZGRknv27dOmirVu36pNPPlG/fv1uuO2NGzfq7NmzWrZsmVq3bm1vj4+PL1zRuShVqpT69++vBQsWaNq0aVqxYoWGDRvmEKKuznWanJzsMHp7/f4syPHMjTOfv6LaVzf7ruTm6n7I67NXrly5Ipvqyxk3298NGjTQiBEjHObPBvKDkVsgF4GBgZo3b55eeukldenS5ab9Bw4cqP379+upp55SqVKl1LdvX4flV0cgrvLx8VGdOnVkjFFGRoYk2X/oXP+DuUePHipVqpRefvnlHKMjxpgc276Rfv36yWazafTo0fr5559zzFvrjKlTp+qLL75Qnz59cpwGcK3C7ANnzZ071+H57NmzJcl+VXpwcLDKlSunzZs3O/R76623cmyrILV16tRJ3333nbZu3Wpvu3Tpkt555x1Vq1atQOcN56Vs2bKKiYlxeNxoHtFHHnlEFStW1BNPPKFDhw7lWH769GlNmjRJ0v+Nzl37eUtPT891v7jCwIEDdf78eT388MP2G0xcq1OnTsrKytKcOXMc2t944w3ZbDanjmdunPn8FcW+ys93JTcVK1ZUgwYN9P777zu8h7179+qLL75Qp06dnK6pKAQEBOQ4beNa9evX18iRIwt0rjggMXIL5Gnw4MH57tu5c2eFh4dr6dKl6tixoypUqOCw/L777lNkZKRatmypiIgIHThwQHPmzFHnzp3t/3FfvVvUc889Zz/VoUuXLvZbT44fP15Hjx5Vt27dFBQUpPj4eC1fvlzDhw/Xk08+ma86y5cvrw4dOmjp0qUKDQ1V586d8/0eMzMz7X86Tk1NVUJCglauXKkffvhBbdu21TvvvHPD9QuzD5wdbYqPj9cDDzygDh06aOvWrfrggw/Uv39/1a9f397nr3/9q6ZOnaq//vWvatKkiTZv3pxr+CtIbc8++6x9OrlRo0YpLCxM77//vuLj4/XJJ5+45W5mZcuW1fLly9WpUyc1aNDA4Q5lu3bt0ocffmi/gKhFixYqW7asBg8erFGjRslms+lf//pXkfzpWZIaNmyounXraunSpapdu7YaNWrksLxLly5q27atnnvuOR09elT169fXF198oU8//VRjxoxR9erV7X3zezxzU716dYWGhmr+/PkKCgpSQECAmjVrdsNzvYtiX+Xnu5KX1157TR07dlTz5s01dOhQ+1RgISEhHncDhMaNG2vJkiUaN26c7rrrLgUGBjoMJsyZM0cvv/yy4uPjbzrtH+Cg2OdnADzQtVOB3cj1U4Fd6+o0U4sXL86x7O233zatW7c24eHhxtfX11SvXt089dRT5uLFiw79XnnlFVOpUiXj5eWVY9qpTz75xLRq1coEBASYgIAAU6tWLTNixIgcN0642RRCH3/8sZHkcIOJm7l+0vgyZcqYatWqmZ49e5p///vf9qmtrnX9VGCF3Qf6/zdxyI3ymAps//79plevXiYoKMiULVvWjBw50uEmDsb8Po3T0KFDTUhIiAkKCjK9e/c2p0+fznXqqLxqu9FNHEJDQ42fn59p2rRpnjdxuH46pxtNaVVYJ06cMGPHjjU1a9Y0fn5+pkyZMqZx48Zm8uTJDsdiy5Yt5k9/+pP9RgNPP/20fTqoa6dzyuszN3jw4JtO83St6dOnG0nm1VdfzXV5cnKyGTt2rImKijKlS5c2NWrUyHETB2MKdjxz8+mnn9pvEnLtMbjRd8vV+yo/35W8PjvGGPPll1+ali1bGn9/fxMcHGy6dOmS500czpw5k2P9vP6fy+07ePWz+tprr+W6b66v99r9kZKSYvr3729CQ0NzvYnD1Rpzm34PuBGbMUX0qzhwixk7dqzee+89JSYmuu2WnPnx6aefqlu3btq8ebN9yinA3d58802NHTtWR48ezXGlPgAUBOEWcIHU1FRVrlxZ999/v+Li4txdzg3df//9OnDggI4cOeLyC7gAZxhjVL9+fYWHh+eYtxkACopzboFCOH36tL788kv9+9//1tmzZzV69Gh3l5Snjz76SD/88IP+85//6M033yTYwu0uXbqklStXasOGDfrxxx/16aefurskABbAyC1QCBs3blTbtm1VoUIFTZgwQSNHjnR3SXmy2WwKDAxUnz59NH/+fHl787st3Ovo0aOKjo5WaGioHnvsMU2ePNndJQGwAMItAAAALIN5bgEAAGAZhFsAAABYBifd6fd7f584cUJBQUFcZAMAAOCBjDFKTk5WVFTUDW+GQ7iVdOLECVWuXNndZQAAAOAmjh8/rttuuy3P5YRbyX47w+PHjys4ONjN1QAAAOB6SUlJqly58k1vQ024leynIgQHBxNuAQAAPNjNTiHlgjIAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAluHt7gJuVWfOnFFSUlKB1wsODlb58uWLoCIAAICSj3DrBmfOnNGQ/kOUfDa5wOsGhQcpbnEcARcAACAXhFs3SEpKUvLZZLX2ba1w//B8r3f2ylltPrtZSUlJhFsAAIBcEG7dKNw/XBEBEQVbKa1oagEAALACLigDAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBluDbebN29Wly5dFBUVJZvNphUrVuTZ95FHHpHNZtPMmTMd2s+dO6fY2FgFBwcrNDRUQ4cOVUpKStEWDgAAAI/k1nB76dIl1a9fX3Pnzr1hv+XLl+vbb79VVFRUjmWxsbHat2+f1q5dq1WrVmnz5s0aPnx4UZUMAAAAD+btzhfv2LGjOnbseMM+v/76qx5//HF9/vnn6ty5s8OyAwcOaM2aNdq+fbuaNGkiSZo9e7Y6deqkv//977mGYQAAAFiXR59zm52drYEDB+qpp57SHXfckWP51q1bFRoaag+2khQTEyMvLy9t27Ytz+2mpaUpKSnJ4QEAAICSz6PD7bRp0+Tt7a1Ro0blujwxMVEVKlRwaPP29lZYWJgSExPz3O6UKVMUEhJif1SuXNmldQMAAMA9PDbc7ty5U2+++aYWLFggm83m0m2PHz9eFy9etD+OHz/u0u0DAADAPTw23H711Vc6ffq0qlSpIm9vb3l7eyshIUFPPPGEqlWrJkmKjIzU6dOnHdbLzMzUuXPnFBkZmee2fX19FRwc7PAAAABAyefWC8puZODAgYqJiXFoa9++vQYOHKghQ4ZIkpo3b64LFy5o586daty4sSRp/fr1ys7OVrNmzYq9ZgAAALiXW8NtSkqKjhw5Yn8eHx+v77//XmFhYapSpYrCw8Md+pcuXVqRkZH64x//KEmqXbu2OnTooGHDhmn+/PnKyMjQyJEj1bdvX2ZKAAAAuAW59bSEHTt2qGHDhmrYsKEkady4cWrYsKFeeOGFfG9j0aJFqlWrltq1a6dOnTqpVatWeuedd4qqZAAAAHgwt47ctmnTRsaYfPc/evRojrawsDAtXrzYhVUBAACgpPLYC8oAAACAgiLcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAy3BruN28ebO6dOmiqKgo2Ww2rVixwr4sIyNDzzzzjOrVq6eAgABFRUVp0KBBOnHihMM2zp07p9jYWAUHBys0NFRDhw5VSkpKMb8TAAAAeAK3httLly6pfv36mjt3bo5lly9f1q5duzRhwgTt2rVLy5Yt08GDB/XAAw849IuNjdW+ffu0du1arVq1Sps3b9bw4cOL6y0AAADAg3i788U7duyojh075rosJCREa9eudWibM2eOmjZtqmPHjqlKlSo6cOCA1qxZo+3bt6tJkyaSpNmzZ6tTp076+9//rqioqCJ/DwAAAPAcJeqc24sXL8pmsyk0NFSStHXrVoWGhtqDrSTFxMTIy8tL27Zty3M7aWlpSkpKcngAAACg5Csx4TY1NVXPPPOM+vXrp+DgYElSYmKiKlSo4NDP29tbYWFhSkxMzHNbU6ZMUUhIiP1RuXLlIq0dAAAAxaNEhNuMjAz17t1bxhjNmzev0NsbP368Ll68aH8cP37cBVUCAADA3dx6zm1+XA22CQkJWr9+vX3UVpIiIyN1+vRph/6ZmZk6d+6cIiMj89ymr6+vfH19i6xmAAAAuIdHj9xeDbaHDx/Wl19+qfDwcIflzZs314ULF7Rz50572/r165Wdna1mzZoVd7kAAABwM7eO3KakpOjIkSP25/Hx8fr+++8VFhamihUrqlevXtq1a5dWrVqlrKws+3m0YWFh8vHxUe3atdWhQwcNGzZM8+fPV0ZGhkaOHKm+ffsyUwIAAMAtyK3hdseOHWrbtq39+bhx4yRJgwcP1ksvvaSVK1dKkho0aOCw3oYNG9SmTRtJ0qJFizRy5Ei1a9dOXl5e6tmzp2bNmlUs9QMAAMCzuDXctmnTRsaYPJffaNlVYWFhWrx4sSvLAgAAQAnl0efcAgAAAAVBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBluDXcbt68WV26dFFUVJRsNptWrFjhsNwYoxdeeEEVK1aUv7+/YmJidPjwYYc+586dU2xsrIKDgxUaGqqhQ4cqJSWlGN8FAAAAPIVbw+2lS5dUv359zZ07N9fl06dP16xZszR//nxt27ZNAQEBat++vVJTU+19YmNjtW/fPq1du1arVq3S5s2bNXz48OJ6CwAAAPAg3u588Y4dO6pjx465LjPGaObMmXr++efVtWtXSdLChQsVERGhFStWqG/fvjpw4IDWrFmj7du3q0mTJpKk2bNnq1OnTvr73/+uqKioXLedlpamtLQ0+/OkpCQXvzMAAAC4g8eecxsfH6/ExETFxMTY20JCQtSsWTNt3bpVkrR161aFhobag60kxcTEyMvLS9u2bctz21OmTFFISIj9Ubly5aJ7IwAAACg2HhtuExMTJUkREREO7REREfZliYmJqlChgsNyb29vhYWF2fvkZvz48bp48aL9cfz4cRdXDwAAAHdw62kJ7uLr6ytfX193lwEAAAAX89iR28jISEnSqVOnHNpPnTplXxYZGanTp087LM/MzNS5c+fsfQAAAHDr8NhwGx0drcjISK1bt87elpSUpG3btql58+aSpObNm+vChQvauXOnvc/69euVnZ2tZs2aFXvNAAAAcC+3npaQkpKiI0eO2J/Hx8fr+++/V1hYmKpUqaIxY8Zo0qRJqlGjhqKjozVhwgRFRUWpW7dukqTatWurQ4cOGjZsmObPn6+MjAyNHDlSffv2zXOmBAAAAFiXW8Ptjh071LZtW/vzcePGSZIGDx6sBQsW6Omnn9alS5c0fPhwXbhwQa1atdKaNWvk5+dnX2fRokUaOXKk2rVrJy8vL/Xs2VOzZs0q9vcCAAAA93NruG3Tpo2MMXkut9lsmjhxoiZOnJhnn7CwMC1evLgoygMAAEAJ47Hn3AIAAAAFRbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACW4VS4/fnnn11dBwAAAFBoToXbP/zhD2rbtq0++OADpaamuromAAAAwClOhdtdu3bpzjvv1Lhx4xQZGamHH35Y3333natrAwAAAArEqXDboEEDvfnmmzpx4oT++c9/6uTJk2rVqpXq1q2rGTNm6MyZM66uEwAAALipQl1Q5u3trR49emjp0qWaNm2ajhw5oieffFKVK1fWoEGDdPLkSVfVCQAAANxUocLtjh079Nhjj6lixYqaMWOGnnzySf30009au3atTpw4oa5du7qqTgAAAOCmvJ1ZacaMGYqLi9PBgwfVqVMnLVy4UJ06dZKX1+9ZOTo6WgsWLFC1atVcWSsAAABwQ06F23nz5ukvf/mLHnroIVWsWDHXPhUqVNB7771XqOIAAACAgnAq3B4+fPimfXx8fDR48GBnNg8AAAA4xalzbuPi4rR06dIc7UuXLtX7779f6KIAAAAAZzgVbqdMmaJy5crlaK9QoYJeffXVQhcFAAAAOMOpcHvs2DFFR0fnaK9ataqOHTtW6KIAAAAAZzgVbitUqKAffvghR/uePXsUHh5e6KIAAAAAZzgVbvv166dRo0Zpw4YNysrKUlZWltavX6/Ro0erb9++rq4RAAAAyBenZkt45ZVXdPToUbVr107e3r9vIjs7W4MGDeKcWwAAALiNU+HWx8dHS5Ys0SuvvKI9e/bI399f9erVU9WqVV1dHwAAAJBvToXbq2rWrKmaNWu6qhYAAACgUJwKt1lZWVqwYIHWrVun06dPKzs722H5+vXrXVIcAAAAUBBOhdvRo0drwYIF6ty5s+rWrSubzebqugAAAIACcyrcfvTRR/r444/VqVMnV9cDAAAAOM2pqcB8fHz0hz/8wdW1AAAAAIXiVLh94okn9Oabb8oY4+p6AAAAAKc5dVrC119/rQ0bNmj16tW64447VLp0aYfly5Ytc0lxAAAAQEE4FW5DQ0PVvXt3V9cCAAAAFIpT4TYuLs7VdQAAAACF5tQ5t5KUmZmpL7/8Um+//baSk5MlSSdOnFBKSorLigMAAAAKwqmR24SEBHXo0EHHjh1TWlqa/vznPysoKEjTpk1TWlqa5s+f7+o6AQAAgJtyauR29OjRatKkic6fPy9/f397e/fu3bVu3TqXFQcAAAAUhFPh9quvvtLzzz8vHx8fh/Zq1arp119/dUlh0u+3+Z0wYYKio6Pl7++v6tWr65VXXnGYgswYoxdeeEEVK1aUv7+/YmJidPjwYZfVAAAAgJLDqXCbnZ2trKysHO2//PKLgoKCCl3UVdOmTdO8efM0Z84cHThwQNOmTdP06dM1e/Zse5/p06dr1qxZmj9/vrZt26aAgAC1b99eqampLqsDAAAAJYNT4fa+++7TzJkz7c9tNptSUlL04osvuvSWvN988426du2qzp07q1q1aurVq5fuu+8+fffdd5J+H7WdOXOmnn/+eXXt2lV33nmnFi5cqBMnTmjFihUuqwMAAAAlg1Ph9vXXX9eWLVtUp04dpaamqn///vZTEqZNm+ay4lq0aKF169bp0KFDkqQ9e/bo66+/VseOHSVJ8fHxSkxMVExMjH2dkJAQNWvWTFu3bs1zu2lpaUpKSnJ4AAAAoORzaraE2267TXv27NFHH32kH374QSkpKRo6dKhiY2MdLjArrGeffVZJSUmqVauWSpUqpaysLE2ePFmxsbGSpMTERElSRESEw3oRERH2ZbmZMmWKXn75ZZfVCQAAAM/gVLiVJG9vbw0YMMCVteTw8ccfa9GiRVq8eLHuuOMOff/99xozZoyioqI0ePBgp7c7fvx4jRs3zv48KSlJlStXdkXJAAAAcCOnwu3ChQtvuHzQoEFOFXO9p556Ss8++6z69u0rSapXr54SEhI0ZcoUDR48WJGRkZKkU6dOqWLFivb1Tp06pQYNGuS5XV9fX/n6+rqkRgAAAHgOp8Lt6NGjHZ5nZGTo8uXL8vHxUZkyZVwWbi9fviwvL8fTgkuVKqXs7GxJUnR0tCIjI7Vu3Tp7mE1KStK2bdv06KOPuqQGAAAAlBxOhdvz58/naDt8+LAeffRRPfXUU4Uu6qouXbpo8uTJqlKliu644w7t3r1bM2bM0F/+8hdJv8/SMGbMGE2aNEk1atRQdHS0JkyYoKioKHXr1s1ldQAAAKBkcPqc2+vVqFFDU6dO1YABA/S///3PJducPXu2JkyYoMcee0ynT59WVFSUHn74Yb3wwgv2Pk8//bQuXbqk4cOH68KFC2rVqpXWrFkjPz8/l9QAAACAksNl4Vb6/SKzEydOuGx7QUFBmjlzpsOcutez2WyaOHGiJk6c6LLXBQAAQMnkVLhduXKlw3NjjE6ePKk5c+aoZcuWLikMAAAAKCinwu3157PabDaVL19e9957r15//XVX1AUAAAAUmFPh9upsBQAAAIAncer2uwAAAIAncmrk9tq7e93MjBkznHkJAAAAoMCcCre7d+/W7t27lZGRoT/+8Y+SpEOHDqlUqVJq1KiRvZ/NZnNNlQAAAEA+OBVuu3TpoqCgIL3//vsqW7aspN9v7DBkyBDdfffdeuKJJ1xaJAAAAJAfTp1z+/rrr2vKlCn2YCtJZcuW1aRJk5gtAQAAAG7jVLhNSkrSmTNncrSfOXNGycnJhS4KAAAAcIZT4bZ79+4aMmSIli1bpl9++UW//PKLPvnkEw0dOlQ9evRwdY0AAABAvjh1zu38+fP15JNPqn///srIyPh9Q97eGjp0qF577TWXFggAAADkl1PhtkyZMnrrrbf02muv6aeffpIkVa9eXQEBAS4tDgAAACiIQt3E4eTJkzp58qRq1KihgIAAGWNcVRcAAABQYE6F27Nnz6pdu3aqWbOmOnXqpJMnT0qShg4dyjRgAAAAcBunwu3YsWNVunRpHTt2TGXKlLG39+nTR2vWrHFZcQAAAEBBOHXO7RdffKHPP/9ct912m0N7jRo1lJCQ4JLCAAAAgIJyauT20qVLDiO2V507d06+vr6FLgoAAABwhlPh9u6779bChQvtz202m7KzszV9+nS1bdvWZcUBAAAABeHUaQnTp09Xu3bttGPHDqWnp+vpp5/Wvn37dO7cOW3ZssXVNQIAAAD54tTIbd26dXXo0CG1atVKXbt21aVLl9SjRw/t3r1b1atXd3WNAAAAQL4UeOQ2IyNDHTp00Pz58/Xcc88VRU0AAACAUwo8clu6dGn98MMPRVELAAAAUChOnZYwYMAAvffee66uBQAAACgUpy4oy8zM1D//+U99+eWXaty4sQICAhyWz5gxwyXFAQAAAAVRoHD7888/q1q1atq7d68aNWokSTp06JBDH5vN5rrqAAAAgAIoULitUaOGTp48qQ0bNkj6/Xa7s2bNUkRERJEUBwAAABREgc65NcY4PF+9erUuXbrk0oIAAAAAZzl1QdlV14ddAAAAwJ0KFG5tNluOc2o5xxYAAACeokDn3Bpj9NBDD8nX11eSlJqaqkceeSTHbAnLli1zXYUAAABAPhUo3A4ePNjh+YABA1xaDG4uPSNdCQkJBV4vODhY5cuXL4KKAAAAPEeBwm1cXFxR1YF8SElPUXxCvJ57/Dn5+PoUaN2g8CDFLY4j4AIAAEtz6iYOcI/UrFR5ZXqplU8rVQqtlO/1zl45q81nNyspKYlwCwAALI1wWwKV9SuriIACzi2cVjS1AAAAeJJCTQUGAAAAeBLCLQAAACyDcAsAAADL8Phw++uvv2rAgAEKDw+Xv7+/6tWrpx07dtiXG2P0wgsvqGLFivL391dMTIwOHz7sxooBAADgLh4dbs+fP6+WLVuqdOnSWr16tfbv36/XX39dZcuWtfeZPn26Zs2apfnz52vbtm0KCAhQ+/btlZqa6sbKAQAA4A4ePVvCtGnTVLlyZYf5daOjo+3/NsZo5syZev7559W1a1dJ0sKFCxUREaEVK1aob9++xV4zAAAA3MejR25XrlypJk2a6MEHH1SFChXUsGFDvfvuu/bl8fHxSkxMVExMjL0tJCREzZo109atW/PcblpampKSkhweAAAAKPk8Otz+/PPPmjdvnmrUqKHPP/9cjz76qEaNGqX3339fkpSYmChJiohwnPM1IiLCviw3U6ZMUUhIiP1RuXLlonsTAAAAKDYeHW6zs7PVqFEjvfrqq2rYsKGGDx+uYcOGaf78+YXa7vjx43Xx4kX74/jx4y6qGAAAAO7k0eG2YsWKqlOnjkNb7dq1dezYMUlSZGSkJOnUqVMOfU6dOmVflhtfX18FBwc7PAAAAFDyeXS4bdmypQ4ePOjQdujQIVWtWlXS7xeXRUZGat26dfblSUlJ2rZtm5o3b16stQIAAMD9PHq2hLFjx6pFixZ69dVX1bt3b3333Xd655139M4770iSbDabxowZo0mTJqlGjRqKjo7WhAkTFBUVpW7durm3eAAAABQ7jw63d911l5YvX67x48dr4sSJio6O1syZMxUbG2vv8/TTT+vSpUsaPny4Lly4oFatWmnNmjXy8/NzY+UAAABwB48Ot5J0//336/77789zuc1m08SJEzVx4sRirAoAAACeyKPPuQUAAAAKgnALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDK83V0APNuZM2eUlJRU4PWCg4NVvnz5IqgIAAAgb4Rb5OnMmTMa0n+Iks8mF3jdoPAgxS2OI+ACAIBiRbhFnpKSkpR8NlmtfVsr3D883+udvXJWm89uVlJSEuEWAAAUK8ItbircP1wRAREFWymtaGoBAAC4ES4oAwAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlsEFZbeI9Ix0JSQkFGidhIQEZWZmFlFFAAAArke4vQWkpKcoPiFezz3+nHx8ffK93pW0Kzpx/ITSQ9KLsDoAAADXIdzeAlKzUuWV6aVWPq1UKbRSvtc7fP6wlmcuZ/QWAACUGITbW0hZv7IFmq/2tyu/FWE1AAAArscFZQAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAso0SF26lTp8pms2nMmDH2ttTUVI0YMULh4eEKDAxUz549derUKfcVCQAAALcpMeF2+/btevvtt3XnnXc6tI8dO1afffaZli5dqk2bNunEiRPq0aOHm6oEAACAO5WIcJuSkqLY2Fi9++67Klu2rL394sWLeu+99zRjxgzde++9aty4seLi4vTNN9/o22+/dWPFAAAAcIcSEW5HjBihzp07KyYmxqF9586dysjIcGivVauWqlSpoq1bt+a5vbS0NCUlJTk8AAAAUPJ5u7uAm/noo4+0a9cubd++PceyxMRE+fj4KDQ01KE9IiJCiYmJeW5zypQpevnll11dKgAAANzMo0dujx8/rtGjR2vRokXy8/Nz2XbHjx+vixcv2h/Hjx932bYBAADgPh4dbnfu3KnTp0+rUaNG8vb2lre3tzZt2qRZs2bJ29tbERERSk9P14ULFxzWO3XqlCIjI/Pcrq+vr4KDgx0eAAAAKPk8+rSEdu3a6ccff3RoGzJkiGrVqqVnnnlGlStXVunSpbVu3Tr17NlTknTw4EEdO3ZMzZs3d0fJAAAAcCOPDrdBQUGqW7euQ1tAQIDCw8Pt7UOHDtW4ceMUFham4OBgPf7442revLn+9Kc/uaNkAAAAuJFHh9v8eOONN+Tl5aWePXsqLS1N7du311tvveXusgAAAOAGJS7cbty40eG5n5+f5s6dq7lz57qnIAAAAHgMj76gDAAAACgIwi0AAAAso8SdlgBrO3PmjFN3jAsODlb58uWLoCIAAFCSEG7hMc6cOaMh/Yco+WxygdcNCg9S3OI4Ai4AALc4wi08RlJSkpLPJqu1b2uF+4fne72zV85q89nNSkpKItwCAHCLI9zC44T7hysiIKJgK6UVTS0AAKBk4YIyAAAAWAYjtygS6RnpSkhIKNA6CQkJyszMLKKKAADArYBwC5dLSU9RfEK8nnv8Ofn4+uR7vStpV3Ti+Amlh6QXYXUAAMDKCLdwudSsVHlleqmVTytVCq2U7/UOnz+s5ZnLGb0FAABOI9yiyJT1K1ugC8N+u/JbEVYDAABuBVxQBgAAAMsg3AIAAMAyCLcAAACwDM65xS3tzJkzSkpKcmrd4OBg7ogGAICHIdzilnXmzBkN6T9EyWeTnVo/KDxIcYvjCLgAAHgQwi0swdmbRpw/dV73BtyrcP/wAq179spZfZn4pX788UdVrVq1QOsy4gsAQNEh3KLEK+xNI4LqBxVoyrLCvKbEiC8AAEWJcIsSzx03jXD2Nc9eOavNZzcrKSmJcAsAQBEg3MIy3HHTiIK+piQpzbnXcvbiN06DAADcSgi3QAlQmIvfOA0CAHArIdwCJUBSUpKSzyartW/rAl38xmkQAIBbDeEWKEHC/cOL7TQIAABKIu5QBgAAAMsg3AIAAMAyCLcAAACwDM65BZArph4DAJREhFsAOTD1GACgpCLcAhaXnpGuhISEAq2TkJCg86fO696Ae5l6DABQohBuAQtLSU9RfEK8nnv8Ofn4+uR7vStpV3Ti+AkF1Q9i6jEAQIlCuAUsLDUrVV6ZXmrl00qVQivle73D5w9reeZyZWZmFmF1AAC4HuEWuAWU9StboBHY36785vRrOXMahGT9C9GcvUBPsv6+AQBXItwCcBlnT4OQrH0hWmEu0JOsvW8AwNUItwBcxtnTIKx+IVpSUpKSzyartW/rAl2gJ7ln3zANHICSjHALwOUKehqEpFviQrRw//CC7xepWPcN08ABKOkItwAAO2dHma0++g6g5CDcAgBycGqU+RYYfQfg+bzcXQAAAADgKh49cjtlyhQtW7ZM//vf/+Tv768WLVpo2rRp+uMf/2jvk5qaqieeeEIfffSR0tLS1L59e7311luKiHDivDYAtwwumkJx4bMGFC+PDrebNm3SiBEjdNdddykzM1N/+9vfdN9992n//v0KCAiQJI0dO1b/+c9/tHTpUoWEhGjkyJHq0aOHtmzZ4ubqAXgqLppCceGzBhQ/jw63a9ascXi+YMECVahQQTt37lTr1q118eJFvffee1q8eLHuvfdeSVJcXJxq166tb7/9Vn/6059y3W5aWprS0v7v5DBnJ1YHnOHMTQ4SEhK4W1genBkVS0hI0PlT53VvwL1cNIUixQV6QPHz6HB7vYsXL0qSwsLCJEk7d+5URkaGYmJi7H1q1aqlKlWqaOvWrXmG2ylTpujll18u+oKB6zh7k4MraVd04vgJpYekF2F1JY+zo2JX92dQ/SAumkKx4AI9oPiUmHCbnZ2tMWPGqGXLlqpbt64kKTExUT4+PgoNDXXoGxERocTExDy3NX78eI0bN87+PCkpSZUrVy6SuoFrOXuTg8PnD2t55nJGb6/j7KgY+xMArKvEhNsRI0Zo7969+vrrrwu9LV9fX/n6+rqgKsA5Bb3JwW9XfivCakq+go6KsT8BwLpKxFRgI0eO1KpVq7Rhwwbddttt9vbIyEilp6frwoULDv1PnTqlyMjIYq4SAAAA7ubRI7fGGD3++ONavny5Nm7cqOjoaIfljRs3VunSpbVu3Tr17NlTknTw4EEdO3ZMzZs3d0fJAACUWExbBivw6HA7YsQILV68WJ9++qmCgoLs59GGhITI399fISEhGjp0qMaNG6ewsDAFBwfr8ccfV/PmzfO8mAwAAOTEtGWwCo8Ot/PmzZMktWnTxqE9Li5ODz30kCTpjTfekJeXl3r27OlwEwcAAJB/TFsGq/DocGuMuWkfPz8/zZ07V3Pnzi2GigAAnoI/oRcNK09bxmfm1uDR4RYAgNzwJ3QUFJ+ZWwfhFoBH4M5tJZ8zx1CS0tPT5eOT/5uaSNxl7kacHZ0sSd8n7kyIGyHcAnA77txW8jl7DNMz0nX8xHFVrVRV3t75/5HEXeZyV5jRyZLyfeLOhLgZwi0At+PObSVfYY5hwpUEtSjVgmPvAs5eFCaVnH3KnQlxM4RbAB6jJNy5jdMnbszZY1gSjn1J4sxFYSVtn3JnQtex2oV2hFsAyCdOnwBgNVa80I5wCwD55K7TJ5y9UMtTR1VgPcV9MeGt8peQ4mDF+Y0JtwBQQMX5J3RnR4slzx1VgbW462JC/hLiWlaa35hwCwAezNnRYk8eVYG1cDFh3qx2LmtJQbgFgBKgoKPFkjx2VAXWxMWEjqx4LmtJQbgFAABwMSuey1pSEG4BAABuoDBTAIaHFvxc1vQULiItDMItAABAHop7CkAuIi08wi0AAPngzMVBTFtV8hX3FICFuYj0y8Qv9eOPP6pq1ar5Xs+Kn1HCLQAAN+HsxUFMW2UdxX3hW0Ffj5vM/B/CLQAAN+HsxUEladoqlGzuusmMJyLcAgCQTwWd6L4wo3eFuYgJty6rTq1WEIRbAAA8DH9iBpxHuAUA3FJKwogof2IGnEe4BQDcMkraiCh/YgYKjnALALhlMCIKWB/hFgBwy2FEFLAuL3cXAAAAALgKI7cAYFEl4cIpAHA1wi0AWFBJu3AKAFyFcAsAFsSFUwBuVYRbALAwLpwCcKvhgjIAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlmGZcDt37lxVq1ZNfn5+atasmb777jt3lwQAAIBiZolwu2TJEo0bN04vvviidu3apfr166t9+/Y6ffq0u0sDAABAMbJEuJ0xY4aGDRumIUOGqE6dOpo/f77KlCmjf/7zn+4uDQAAAMXI290FFFZ6erp27typ8ePH29u8vLwUExOjrVu35rpOWlqa0tLS7M8vXrwoSUpKSiraYv+/5ORkZWZl6kTyCaVmpuZ7vVOXTinbZCvxUqK8Suf/9xLWc+16JalW1ivZ65WkWlmvZK9XkmplPc9Y79yVc8rMylRycnKx5aerr2OMuXFHU8L9+uuvRpL55ptvHNqfeuop07Rp01zXefHFF40kHjx48ODBgwcPHiXscfz48RtmwxI/cuuM8ePHa9y4cfbn2dnZOnfunMLDw2Wz2Yr89ZOSklS5cmUdP35cwcHBRf56cD2OoTVwHEs+jmHJxzG0huI4jsYYJScnKyoq6ob9Sny4LVeunEqVKqVTp045tJ86dUqRkZG5ruPr6ytfX1+HttDQ0KIqMU/BwcF8kUs4jqE1cBxLPo5hyccxtIaiPo4hISE37VPiLyjz8fFR48aNtW7dOntbdna21q1bp+bNm7uxMgAAABS3Ej9yK0njxo3T4MGD1aRJEzVt2lQzZ87UpUuXNGTIEHeXBgAAgGJkiXDbp08fnTlzRi+88IISExPVoEEDrVmzRhEREe4uLVe+vr568cUXc5wagZKDY2gNHMeSj2NY8nEMrcGTjqPNmJvNpwAAAACUDCX+nFsAAADgKsItAAAALINwCwAAAMsg3AIAAMAyCLfFbO7cuapWrZr8/PzUrFkzfffdd+4uCXmYMmWK7rrrLgUFBalChQrq1q2bDh486NAnNTVVI0aMUHh4uAIDA9WzZ88cNxSBZ5k6dapsNpvGjBljb+M4er5ff/1VAwYMUHh4uPz9/VWvXj3t2LHDvtwYoxdeeEEVK1aUv7+/YmJidPjwYTdWjOtlZWVpwoQJio6Olr+/v6pXr65XXnlF117XznH0LJs3b1aXLl0UFRUlm82mFStWOCzPz/E6d+6cYmNjFRwcrNDQUA0dOlQpKSlFWjfhthgtWbJE48aN04svvqhdu3apfv36at++vU6fPu3u0pCLTZs2acSIEfr222+1du1aZWRk6L777tOlS5fsfcaOHavPPvtMS5cu1aZNm3TixAn16NHDjVXjRrZv3663335bd955p0M7x9GznT9/Xi1btlTp0qW1evVq7d+/X6+//rrKli1r7zN9+nTNmjVL8+fP17Zt2xQQEKD27dsrNTXVjZXjWtOmTdO8efM0Z84cHThwQNOmTdP06dM1e/Zsex+Oo2e5dOmS6tevr7lz5+a6PD/HKzY2Vvv27dPatWu1atUqbd68WcOHDy/awg2KTdOmTc2IESPsz7OyskxUVJSZMmWKG6tCfp0+fdpIMps2bTLGGHPhwgVTunRps3TpUnufAwcOGElm69at7ioTeUhOTjY1atQwa9euNffcc48ZPXq0MYbjWBI888wzplWrVnkuz87ONpGRkea1116zt124cMH4+vqaDz/8sDhKRD507tzZ/OUvf3Fo69Gjh4mNjTXGcBw9nSSzfPly+/P8HK/9+/cbSWb79u32PqtXrzY2m838+uuvRVYrI7fFJD09XTt37lRMTIy9zcvLSzExMdq6dasbK0N+Xbx4UZIUFhYmSdq5c6cyMjIcjmmtWrVUpUoVjqkHGjFihDp37uxwvCSOY0mwcuVKNWnSRA8++KAqVKighg0b6t1337Uvj4+PV2JiosMxDAkJUbNmzTiGHqRFixZat26dDh06JEnas2ePvv76a3Xs2FESx7Gkyc/x2rp1q0JDQ9WkSRN7n5iYGHl5eWnbtm1FVpsl7lBWEvz222/KysrKcde0iIgI/e9//3NTVciv7OxsjRkzRi1btlTdunUlSYmJifLx8VFoaKhD34iICCUmJrqhSuTlo48+0q5du7R9+/YcyziOnu/nn3/WvHnzNG7cOP3tb3/T9u3bNWrUKPn4+Gjw4MH245Tb/68cQ8/x7LPPKikpSbVq1VKpUqWUlZWlyZMnKzY2VpI4jiVMfo5XYmKiKlSo4LDc29tbYWFhRXpMCbdAPowYMUJ79+7V119/7e5SUEDHjx/X6NGjtXbtWvn5+bm7HDghOztbTZo00auvvipJatiwofbu3av58+dr8ODBbq4O+fXxxx9r0aJFWrx4se644w59//33GjNmjKKiojiOcClOSygm5cqVU6lSpXJcgX3q1ClFRka6qSrkx8iRI7Vq1Spt2LBBt912m709MjJS6enpunDhgkN/jqln2blzp06fPq1GjRrJ29tb3t7e2rRpk2bNmiVvb29FRERwHD1cxYoVVadOHYe22rVr69ixY5JkP078/+rZnnrqKT377LPq27ev6tWrp4EDB2rs2LGaMmWKJI5jSZOf4xUZGZnjovnMzEydO3euSI8p4baY+Pj4qHHjxlq3bp29LTs7W+vWrVPz5s3dWBnyYozRyJEjtXz5cq1fv17R0dEOyxs3bqzSpUs7HNODBw/q2LFjHFMP0q5dO/3444/6/vvv7Y8mTZooNjbW/m+Oo2dr2bJljmn4Dh06pKpVq0qSoqOjFRkZ6XAMk5KStG3bNo6hB7l8+bK8vBxjR6lSpZSdnS2J41jS5Od4NW/eXBcuXNDOnTvtfdavX6/s7Gw1a9as6IorskvVkMNHH31kfH19zYIFC8z+/fvN8OHDTWhoqElMTHR3acjFo48+akJCQszGjRvNyZMn7Y/Lly/b+zzyyCOmSpUqZv369WbHjh2mefPmpnnz5m6sGvlx7WwJxnAcPd13331nvL29zeTJk83hw4fNokWLTJkyZcwHH3xg7zN16lQTGhpqPv30U/PDDz+Yrl27mujoaHPlyhU3Vo5rDR482FSqVMmsWrXKxMfHm2XLlply5cqZp59+2t6H4+hZkpOTze7du83u3buNJDNjxgyze/duk5CQYIzJ3/Hq0KGDadiwodm2bZv5+uuvTY0aNUy/fv2KtG7CbTGbPXu2qVKlivHx8TFNmzY13377rbtLQh4k5fqIi4uz97ly5Yp57LHHTNmyZU2ZMmVM9+7dzcmTJ91XNPLl+nDLcfR8n332malbt67x9fU1tWrVMu+8847D8uzsbDNhwgQTERFhfH19Tbt27czBgwfdVC1yk5SUZEaPHm2qVKli/Pz8zO23326ee+45k5aWZu/DcfQsGzZsyPXn4ODBg40x+TteZ8+eNf369TOBgYEmODjYDBkyxCQnJxdp3TZjrrk1CAAAAFCCcc4tAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAFhMmzZtNGbMGHeXAQBuQbgFAA/SpUsXdejQIddlX331lWw2m3744YdirgoASg7CLQB4kKFDh2rt2rX65ZdfciyLi4tTkyZNdOedd7qhMgAoGQi3AOBB7r//fpUvX14LFixwaE9JSdHSpUvVrVs39evXT5UqVVKZMmVUr149ffjhhzfcps1m04oVKxzaQkNDHV7j+PHj6t27t0JDQxUWFqauXbvq6NGjrnlTAFCMCLcA4EG8vb01aNAgLViwQMYYe/vSpUuVlZWlAQMGqHHjxvrPf/6jvXv3avjw4Ro4cKC+++47p18zIyND7du3V1BQkL766itt2bJFgYGB6tChg9LT013xtgCg2BBuAcDD/OUvf9FPP/2kTZs22dvi4uLUs2dPVa1aVU8++aQaNGig22+/XY8//rg6dOigjz/+2OnXW7JkibKzs/WPf/xD9erVU+3atRUXF6djx45p48aNLnhHAFB8CLcA4GFq1aqlFi1a6J///Kck6ciRI/rqq680dOhQZWVl6ZVXXlG9evUUFhamwMBAff755zp27JjTr7dnzx4dOXJEQUFBCgwMVGBgoMLCwpSamqqffvrJVW8LAIqFt7sLAADkNHToUD3++OOaO3eu4uLiVL16dd1zzz2aNm2a3nzzTc2cOVP16tVTQECAxowZc8PTB2w2m8MpDtLvpyJclZKSosaNG2vRokU51i1fvrzr3hQAFAPCLQB4oN69e2v06NFavHixFi5cqEcffVQ2m01btmxR165dNWDAAElSdna2Dh06pDp16uS5rfLly+vkyZP254cPH9bly5ftzxs1aqQlS5aoQoUKCg4OLro3BQDFgNMSAMADBQYGqk+fPho/frxOnjyphx56SJJUo0YNrV27Vt98840OHDighx9+WKdOnbrhtu69917NmTNHu3fv1o4dO/TII4+odOnS9uWxsbEqV66cunbtqq+++krx8fHauHGjRo0aleuUZADgyQi3AOChhg4dqvPnz6t9+/aKioqSJD3//PNq1KiR2rdvrzZt2igyMlLdunW74XZef/11Va5cWXfffbf69++vJ598UmXKlLEvL1OmjDZv3qwqVaqoR48eql27toYOHarU1FRGcgGUODZz/YlYAAAAQAnFyC0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDL+H94OemuNf96uAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Challenge: Mystery distribution\n",
    "np.random.seed(222)\n",
    "\n",
    "# Generate a mystery distribution\n",
    "uniform_base = np.random.uniform(0, 1, 1000)\n",
    "mystery_data = uniform_base ** 2 * 100  # This creates a specific shape\n",
    "\n",
    "print(\"You've been given a mystery dataset!\")\n",
    "print(f\"Range: [{mystery_data.min():.1f}, {mystery_data.max():.1f}]\")\n",
    "print(f\"Mean: {mystery_data.mean():.1f}\")\n",
    "print(\"\\nYour task: Find a transformation that makes this data roughly uniform or normal!\")\n",
    "\n",
    "# Visualize the mystery data\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.hist(mystery_data, bins=40, alpha=0.7, color='purple', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Mystery Distribution - Can you transform it?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "087fe3e5-0f0c-4f42-a1a0-01aca4113108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformed = np.sqrt(mystery_data)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.hist(transformed, bins=40, alpha=0.7, color='blue', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('The transformed data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ad308941-a367-422c-8502-6bd58b41d891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformed = np.log(mystery_data + 3)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.hist(transformed, bins=40, alpha=0.7, color='red', edgecolor='black')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Mismatched transformation example')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}