Widefield_10x_ROIs_KO_Pdgfrb_TDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c20959f6",
   "metadata": {},
   "source": [
    "# Topological Data Analysis (TDA) of PDGFRβ+ labeled nuclei (FISH)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d167ffd",
   "metadata": {},
   "source": [
    "## Preview\n",
    "\n",
    "This notebook reports the topological data analysis (TDA) for PDGFRβ+ nuceli (labeled with fluorescence in situ hybridization) in three ROIs of the ischemic hemisphere. Please note that this notebook runs Python code. \n",
    "\n",
    "**Parent dataset:** PDGFR-β(FISH) stained ischemic hemispheres imaged at 10x (with stitching). We took regions of interest (ROIs) from three different areas in the injure cortex/striatum to quantify PDGFR-β expression. Samples are grouped at 0 (Sham), and 14 days post-ischemia (DPI). The raw images and pre-processing scripts (if applicable) are available at the Zenodo repository (10.5281/zenodo.10553084) under the name `Widefield_10x_ROIs_KO_Fish_Pdgfrb.zip`. Please note that we excluded mice `KL31` because of an inefficient staining (the section dried during the staining procedure).\n",
    "\n",
    "**Working dataset**: The `Data_Processed/Widefield_10x_ROIs_KO_Fish_Pdgfrb/Widefield_10x_ROIs_KO_Fish_Pdgfrb_AreaInt.csv`data frame containing the individual xy coordinates of PDGFR-β+ cells generated in a Quarto [notebook](https://github.com/elalilab/Stroke_PDGFR-B_Reactivity/blob/main/Widefield_10x_Ipsilateral_KO_Fish_Pdgfrb.qmd).\n",
    "\n",
    "**Note:** This notebook runs using python code."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a309a3cb",
   "metadata": {},
   "source": [
    "## Load required libraries\n",
    "\n",
    "We import the required Python libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "baa28da0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "import csv\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.collections import LineCollection\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from matplotlib.patches import Circle\n",
    "\n",
    "import gudhi as gd\n",
    "from gudhi import RipsComplex, plot_persistence_diagram\n",
    "from gudhi import bottleneck_distance\n",
    "from gudhi.wasserstein import wasserstein_distance\n",
    "\n",
    "import itertools\n",
    "from ripser import ripser\n",
    "from persim import plot_diagrams\n",
    "from random import choices\n",
    "from scipy.spatial import ConvexHull\n",
    "from collections import defaultdict  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff6c5a8",
   "metadata": {},
   "source": [
    "## Load and handle the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7db456e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>x-coord</th>\n",
       "      <th>y-coord</th>\n",
       "      <th>MouseID</th>\n",
       "      <th>DPI</th>\n",
       "      <th>Genotype</th>\n",
       "      <th>Section</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>156</td>\n",
       "      <td>65.310231</td>\n",
       "      <td>26.653846</td>\n",
       "      <td>5.429487</td>\n",
       "      <td>KL26</td>\n",
       "      <td>14D</td>\n",
       "      <td>WT</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>19.471275</td>\n",
       "      <td>36.738462</td>\n",
       "      <td>8.984615</td>\n",
       "      <td>KL26</td>\n",
       "      <td>14D</td>\n",
       "      <td>WT</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>49.192050</td>\n",
       "      <td>168.807692</td>\n",
       "      <td>31.288462</td>\n",
       "      <td>KL26</td>\n",
       "      <td>14D</td>\n",
       "      <td>WT</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98</td>\n",
       "      <td>36.100435</td>\n",
       "      <td>95.336735</td>\n",
       "      <td>36.091837</td>\n",
       "      <td>KL26</td>\n",
       "      <td>14D</td>\n",
       "      <td>WT</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>235</td>\n",
       "      <td>88.383765</td>\n",
       "      <td>249.225532</td>\n",
       "      <td>37.953191</td>\n",
       "      <td>KL26</td>\n",
       "      <td>14D</td>\n",
       "      <td>WT</td>\n",
       "      <td>S1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  Intensity     x-coord    y-coord MouseID  DPI Genotype Section\n",
       "0   156  65.310231   26.653846   5.429487    KL26  14D       WT      S1\n",
       "1    65  19.471275   36.738462   8.984615    KL26  14D       WT      S1\n",
       "2   104  49.192050  168.807692  31.288462    KL26  14D       WT      S1\n",
       "3    98  36.100435   95.336735  36.091837    KL26  14D       WT      S1\n",
       "4   235  88.383765  249.225532  37.953191    KL26  14D       WT      S1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset\n",
    "file_path = 'Data_Processed/Widefield_10x_ROIs_KO_Fish_Pdgfrb/Widefield_10x_ROIs_KO_Fish_Pdgfrb_AreaInt.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Display the first few rows to check the data\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a38f0c",
   "metadata": {},
   "source": [
    "We then filter the 14D data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff0969e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5588 entries, 0 to 5918\n",
      "Data columns (total 8 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Area       5588 non-null   int64  \n",
      " 1   Intensity  5588 non-null   float64\n",
      " 2   x-coord    5588 non-null   float64\n",
      " 3   y-coord    5588 non-null   float64\n",
      " 4   MouseID    5588 non-null   object \n",
      " 5   DPI        5588 non-null   object \n",
      " 6   Genotype   5588 non-null   object \n",
      " 7   Section    5588 non-null   object \n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 392.9+ KB\n",
      "Unique MouseIDs: ['KL26' 'KL27' 'KL28' 'KL29' 'KL32' 'KL34' 'KL36' 'KL39' 'KL48' 'KL49'\n",
      " 'KL50' 'KL52' 'KL53' 'KL54' 'KL56' 'KL57']\n",
      "Unique Sections: ['S1' 'S2' 'S3']\n",
      "Unique Genotypes: ['WT' 'KO']\n"
     ]
    }
   ],
   "source": [
    "# Filter the data\n",
    "filtered_data = data[data['DPI'] == '14D']\n",
    "\n",
    "# Display the first few rows of the filtered data\n",
    "filtered_data.head()\n",
    "\n",
    "# Check the data types and any missing values\n",
    "filtered_data.info()\n",
    "\n",
    "# Check for unique values in 'MouseID', 'Section', and 'genotype'\n",
    "print(\"Unique MouseIDs:\", filtered_data['MouseID'].unique())\n",
    "print(\"Unique Sections:\", filtered_data['Section'].unique())\n",
    "print(\"Unique Genotypes:\", filtered_data['Genotype'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4b43aa",
   "metadata": {},
   "source": [
    "We group the datasets by animals and sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d06b531a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the filtered dataset by MouseID and Section, then extract the point clouds\n",
    "point_clouds_by_mouse_section = filtered_data.groupby(['MouseID', 'Section']).apply(lambda df: df[['x-coord', 'y-coord']].values).to_dict()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e856b2",
   "metadata": {},
   "source": [
    "## Calculate Vietoris-Rips Complex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6845c97",
   "metadata": {},
   "source": [
    "For information on this aspect please refer to the following Jupyther [notebook](https://github.com/elalilab/Stroke_PDGFR-B_Reactivity/blob/main/Widefield_20x_ROIs_Pdgfrb_TDA.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25f4619e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_vietoris_rips_complex(point_cloud, max_dim, max_radius):\n",
    "    \"\"\"\n",
    "    Calculate the Vietoris-Rips complex for a given point cloud.\n",
    "    \n",
    "    Parameters:\n",
    "    - point_cloud: numpy array of shape (n_points, n_dimensions) representing the point cloud.\n",
    "    - max_dim: int, the maximal dimension of the simplices in the Vietoris-Rips complex.\n",
    "    - max_radius: float, the maximal radius for simplices in the Vietoris-Rips complex.\n",
    "    \n",
    "    Returns:\n",
    "    - simplex_tree: a GUDHI SimplexTree object representing the Vietoris-Rips complex.\n",
    "    \"\"\"\n",
    "    rips_complex = gd.RipsComplex(points=point_cloud, max_edge_length=max_radius)\n",
    "    simplex_tree = rips_complex.create_simplex_tree(max_dimension=max_dim)\n",
    "    return simplex_tree\n",
    "\n",
    "# Parameters for Vietoris-Rips complex calculation\n",
    "max_dim = 3\n",
    "max_radius = 50\n",
    "\n",
    "# Calculate Vietoris-Rips complexes for each section of each mouse\n",
    "vietoris_rips_complexes = {}\n",
    "for (mouse_id, section), point_cloud in point_clouds_by_mouse_section.items():\n",
    "    key = (mouse_id, section)  # Use a tuple of MouseID and Section as the key\n",
    "    vietoris_rips_complexes[key] = calculate_vietoris_rips_complex(point_cloud, max_dim, max_radius)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efca6ce8",
   "metadata": {},
   "source": [
    "We plot a sample point cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "131f5c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a sample point cloud for one 'MouseID' and 'Section'\n",
    "sample_key = list(point_clouds_by_mouse_section.keys())[0]\n",
    "sample_point_cloud = point_clouds_by_mouse_section[sample_key]\n",
    "\n",
    "plt.scatter(sample_point_cloud[:, 0], sample_point_cloud[:, 1])\n",
    "plt.xlabel('x-coord')\n",
    "plt.ylabel('y-coord')\n",
    "plt.title(f\"Point Cloud for MouseID: {sample_key[0]}, Section: {sample_key[1]}\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64707055",
   "metadata": {},
   "source": [
    "Then, we save the point clouds in a designated folder:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce4a24a",
   "metadata": {},
   "source": [
    "## Save static Vietoris-rips complexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d566e99b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_range' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 55\u001b[0m\n\u001b[0;32m     53\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmouse_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msection\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_VietorisRips\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     54\u001b[0m point_cloud \u001b[38;5;241m=\u001b[39m point_clouds_by_mouse_section[(mouse_id, section)]\n\u001b[1;32m---> 55\u001b[0m plot_vietoris_rips_complex(point_cloud, simplex_tree, filename, output_dir, \u001b[43mx_range\u001b[49m, y_range)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'x_range' is not defined"
     ]
    }
   ],
   "source": [
    "def plot_vietoris_rips_complex(point_cloud, simplex_tree, filename, save_dir, x_range=None, y_range=None):\n",
    "    \"\"\"\n",
    "    Plot and save the Vietoris-Rips complex for a given 2D point cloud, with specified x and y axis ranges.\n",
    "\n",
    "    Parameters:\n",
    "    - point_cloud: numpy array of shape (n_points, 2) representing the 2D point cloud.\n",
    "    - simplex_tree: a GUDHI SimplexTree object representing the Vietoris-Rips complex.\n",
    "    - filename: str, the name of the file to save the image.\n",
    "    - save_dir: str, directory to save the image.\n",
    "    - x_range: tuple, the range for the x-axis (optional).\n",
    "    - y_range: tuple, the range for the y-axis (optional).\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    \n",
    "    # Plot points\n",
    "    ax.scatter(point_cloud[:, 0], point_cloud[:, 1], c='black', s=20, alpha=0.8)\n",
    "    \n",
    "    # Plot edges\n",
    "    edges = []\n",
    "    for simplex in simplex_tree.get_skeleton(2):  # Get up to 2-simplices (edges)\n",
    "        if len(simplex[0]) == 2:  # Edges\n",
    "            p1, p2 = point_cloud[simplex[0][0]], point_cloud[simplex[0][1]]\n",
    "            edges.append([p1, p2])\n",
    "    lc = LineCollection(edges, colors='green', linewidths=1, alpha=0.7)\n",
    "    ax.add_collection(lc)\n",
    "    \n",
    "    # Adjust plot settings\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "    \n",
    "    if x_range is not None:\n",
    "        ax.set_xlim(x_range)\n",
    "    if y_range is not None:\n",
    "        ax.set_ylim(y_range)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "    plt.savefig(os.path.join(save_dir, f\"{filename}.png\"))\n",
    "    plt.close()\n",
    "\n",
    "    \n",
    "    # Directory to save the images\n",
    "output_dir = \"D:/Research/Stroke_PDGFR-B_Reactivity/Images/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/VietorisRips_Static\"\n",
    "#x_range = (0, 500)  # Example x-axis range, update based on your data\n",
    "#y_range = (0, 550)  # Example y-axis range, update based on your data\n",
    "\n",
    "# Generate and save plots for each section of each mouse\n",
    "for (mouse_id, section), simplex_tree in vietoris_rips_complexes.items():\n",
    "    filename = f\"{mouse_id}_{section}_VietorisRips\"\n",
    "    point_cloud = point_clouds_by_mouse_section[(mouse_id, section)]\n",
    "    plot_vietoris_rips_complex(point_cloud, simplex_tree, filename, output_dir, x_range, y_range)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb8ed62b",
   "metadata": {},
   "source": [
    "## Save animated Vietoris-rips complexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48fb492e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def animate_vietoris_rips_mp4(point_cloud, simplex_tree, filename, save_dir, max_radius, step_size=1):\n",
    "    fig, ax = plt.subplots(figsize=(7, 7))\n",
    "    ax.scatter(point_cloud[:, 0], point_cloud[:, 1], c='black', s=20, alpha=0.8)\n",
    "\n",
    "    def update(threshold):\n",
    "        ax.cla()  # Clear current plot details\n",
    "        ax.scatter(point_cloud[:, 0], point_cloud[:, 1], c='black', s=10, alpha=0.7)\n",
    "        edges = []\n",
    "        for simplex in simplex_tree.get_skeleton(2):\n",
    "            if simplex[1] <= threshold:\n",
    "                if len(simplex[0]) == 2:  # Edges\n",
    "                    p1, p2 = point_cloud[simplex[0][0]], point_cloud[simplex[0][1]]\n",
    "                    edges.append([p1, p2])\n",
    "        lc = LineCollection(edges, colors='green', alpha=0.7)\n",
    "        ax.add_collection(lc)\n",
    "        \n",
    "        ax.set_title(f\"Threshold: {threshold:.2f}\", fontsize=20)\n",
    "        ax.set_xlabel('X', fontsize=20, labelpad=15)\n",
    "        ax.set_ylabel('Y', fontsize=20, labelpad=15)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "        ax.axis('equal')\n",
    "\n",
    "    ani = FuncAnimation(fig, update, frames=np.arange(0, max_radius, step_size), blit=False)\n",
    "    ani_file = os.path.join(save_dir, f\"{filename}_Animation.mp4\")\n",
    "    ani.save(ani_file, writer='ffmpeg', fps=2, extra_args=['-vcodec', 'libx264'])\n",
    "\n",
    "    plt.close()\n",
    "    return ani_file\n",
    "\n",
    "# Directory to save the animations\n",
    "output_dir = \"D:/Research/Stroke_PDGFR-B_Reactivity/Images/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/VietorisRips_Animated\"  # Update this path\n",
    "max_radius = 100  # Adjust as necessary\n",
    "step_size = 100  # Adjust as necessary\n",
    "\n",
    "# Generate and save animations for each section of each mouse\n",
    "for (mouse_id, section), simplex_tree in vietoris_rips_complexes.items():\n",
    "    filename = f\"{mouse_id}_{section}_VietorisRips\"\n",
    "    point_cloud = point_clouds_by_mouse_section[(mouse_id, section)]\n",
    "    ani_file = animate_vietoris_rips_mp4(point_cloud, simplex_tree, filename, output_dir, max_radius, step_size)\n",
    "    print(f\"Saved animation: {ani_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c5ec77",
   "metadata": {},
   "source": [
    "## Generate point clouds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea0f459",
   "metadata": {},
   "source": [
    "Now, we generate point clouds to perform pcalculations of persistent homology. First, we define the groups to generate the point clods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3ff1bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point clouds loaded: 47\n",
      "('KL26', 'S1') (211, 2)\n",
      "('KL26', 'S2') (153, 2)\n",
      "('KL26', 'S3') (236, 2)\n",
      "('KL27', 'S1') (74, 2)\n",
      "('KL27', 'S2') (188, 2)\n"
     ]
    }
   ],
   "source": [
    "# Group the filtered dataset by MouseID and Section, then extract the point clouds\n",
    "point_clouds_by_group = filtered_data.groupby(['MouseID', 'Section']).apply(\n",
    "    lambda df: df[['x-coord', 'y-coord']].values\n",
    ").to_dict()\n",
    "\n",
    "# Check if the point_clouds_by_group dictionary is empty\n",
    "if not point_clouds_by_group:\n",
    "    print(\"The point_clouds_by_group dictionary is empty.\")\n",
    "else:\n",
    "    print(\"Point clouds loaded:\", len(point_clouds_by_group))\n",
    "    \n",
    "    # Inspect the first few items to ensure they're structured correctly\n",
    "    for key, value in list(point_clouds_by_group.items())[:5]:\n",
    "        print(key, value.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9592b9d0",
   "metadata": {},
   "source": [
    "We generate and save the point clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e40761e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_point_clouds_as_npy(point_clouds_by_group, data_directory):\n",
    "    \"\"\"\n",
    "    Saves point clouds to .npy files in the specified directory.\n",
    "    \n",
    "    Parameters:\n",
    "        point_clouds_by_group (dict): Dictionary with keys as identifiers \n",
    "            (e.g., ('MouseID', 'Section')) and values as numpy arrays representing point clouds.\n",
    "        data_directory (str): Path to the directory where .npy files will be saved.\n",
    "    \"\"\"\n",
    "    # Ensure the directory exists\n",
    "    if not os.path.exists(data_directory):\n",
    "        os.makedirs(data_directory)\n",
    "    \n",
    "    # Iterate through the point clouds and save each as an .npy file\n",
    "    for identifier, point_cloud in point_clouds_by_group.items():\n",
    "        # Create a filename from the identifier\n",
    "        filename = \"_\".join(map(str, identifier)) + \".npy\"\n",
    "        filepath = os.path.join(data_directory, filename)\n",
    "        np.save(filepath, point_cloud)\n",
    "    \n",
    "    print(\"Point clouds saved as .npy files.\")\n",
    "    \n",
    "    # Directory to save the point clouds\n",
    "data_directory = \"PointClouds/Widefield_10x_ROIs_KO_Fish_Pdgfrb/\"  # Update this path\n",
    "\n",
    "# Call the function with your data and desired save location\n",
    "save_point_clouds_as_npy(point_clouds_by_group, data_directory)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb16382",
   "metadata": {},
   "source": [
    "## Calculate persistence homology"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52efa0c8",
   "metadata": {},
   "source": [
    "First, we disable latex to aviod problems in plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2c8908b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['text.usetex'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957e42cd",
   "metadata": {},
   "source": [
    "Next, we define an execute a function for the calculation of persistence homology. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e848af4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Persistence diagrams saved to D:/Research/Stroke_PDGFR-B_Reactivity/Images/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/PersistenceDiagrams.\n",
      "Persistence diagrams data saved to Data_Raw/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/PersistenceDiagrams.\n"
     ]
    }
   ],
   "source": [
    "def process_npy_for_persistence_diagrams(data_directory, save_directory, save_diag_directory, img_size=(6, 6), x_range=None, y_range=None):\n",
    "    \"\"\"\n",
    "    Process .npy files to compute and save persistence diagrams.\n",
    "\n",
    "    Parameters:\n",
    "    - data_directory: str, directory containing the .npy files.\n",
    "    - save_directory: str, directory to save the persistence diagram images.\n",
    "    - save_diag_directory: str, directory to save the persistence diagram data.\n",
    "    - img_size: tuple, size of the output images.\n",
    "    - x_range: tuple, the range for the x-axis (optional).\n",
    "    - y_range: tuple, the range for the y-axis (optional).\n",
    "    \"\"\"\n",
    "    if not os.path.exists(save_directory):\n",
    "        os.makedirs(save_directory)\n",
    "    if not os.path.exists(save_diag_directory):\n",
    "        os.makedirs(save_diag_directory)\n",
    "    \n",
    "    npy_files = [f for f in os.listdir(data_directory) if f.endswith('.npy')]\n",
    "    \n",
    "    for file in npy_files:\n",
    "        points_array = np.load(os.path.join(data_directory, file))\n",
    "        \n",
    "        # Compute persistence diagrams using Ripser\n",
    "        diagrams = ripser(points_array, maxdim=1)['dgms']\n",
    "        \n",
    "        # Save persistence diagram data\n",
    "        for dim, diag in enumerate(diagrams):\n",
    "            diag_filename = os.path.splitext(file)[0] + f\"_persistence_diag_dim{dim}.npy\"\n",
    "            np.save(os.path.join(save_diag_directory, diag_filename), diag)\n",
    "        \n",
    "        # Plot and save persistence diagrams\n",
    "        fig, ax = plt.subplots(figsize=img_size)\n",
    "        plot_diagrams(diagrams, show=False, ax=ax)\n",
    "        ax.set_title(\"\")\n",
    "        ax.set_xlabel('Birth', fontsize=25)\n",
    "        ax.set_ylabel('Death', fontsize=25)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=25)\n",
    "        \n",
    "        legend = ax.legend()\n",
    "        if legend:\n",
    "            for label in legend.get_texts():\n",
    "                label.set_fontsize(20)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        \n",
    "        if x_range is not None:\n",
    "            ax.set_xlim(x_range)\n",
    "        if y_range is not None:\n",
    "            ax.set_ylim(y_range)\n",
    "        \n",
    "        filename = os.path.splitext(file)[0] + \"_persistence_diagram.png\"\n",
    "        fig.savefig(os.path.join(save_directory, filename))\n",
    "        plt.close()\n",
    "    \n",
    "    print(f\"Persistence diagrams saved to {save_directory}.\")\n",
    "    print(f\"Persistence diagrams data saved to {save_diag_directory}.\")\n",
    "    \n",
    "    # Directory containing the .npy point cloud files\n",
    "data_directory = \"PointClouds/Widefield_10x_ROIs_KO_Fish_Pdgfrb/\"  # Update this path\n",
    "\n",
    "# Directory to save the persistence diagram images\n",
    "save_directory = \"D:/Research/Stroke_PDGFR-B_Reactivity/Images/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/PersistenceDiagrams\"  # Update this path\n",
    "\n",
    "# Directory to save the persistence diagram data\n",
    "save_diag_directory = \"Data_Raw/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/PersistenceDiagrams\"  # Update this path\n",
    "\n",
    "# Optional: Set x and y axis ranges\n",
    "# x_range = (0, 500)  \n",
    "# y_range = (0, 500)\n",
    "\n",
    "# Call the function to process .npy files for persistence diagrams\n",
    "process_npy_for_persistence_diagrams(data_directory, save_directory, save_diag_directory, img_size=(6, 6), x_range=None, y_range=None)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17646ee1",
   "metadata": {},
   "source": [
    "## Calculate bett curves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d3f142",
   "metadata": {},
   "source": [
    "### For 0D homology"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8aa828",
   "metadata": {},
   "source": [
    "We load the genotype mapping from our .csv file: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "077cc284",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_genotype_mapping(csv_path):\n",
    "    df = pd.read_csv(csv_path)\n",
    "    mouse_id_to_genotype = dict(zip(df['MouseID'], df['Genotype']))\n",
    "    return mouse_id_to_genotype\n",
    "\n",
    "csv_path = \"Data_Processed/Widefield_10x_ROIs_KO_Fish_Pdgfrb/Widefield_10x_ROIs_KO_Fish_Pdgfrb_AreaInt.csv\"  # Update this path\n",
    "mouse_id_to_genotype = load_genotype_mapping(csv_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46959ce1",
   "metadata": {},
   "source": [
    "Next, we load the persistence diagrams for 0D homology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b1a6869",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_persistence_diagrams(dim0_path, mouse_id_to_genotype):\n",
    "    files = [f for f in os.listdir(dim0_path) if 'dim0' in f]\n",
    "    data = {}\n",
    "    for file in files:\n",
    "        parts = file.split('_')\n",
    "        mouse_id, section = parts[0], parts[1]\n",
    "        genotype = mouse_id_to_genotype.get(mouse_id, 'Unknown')\n",
    "        diagram = np.load(os.path.join(dim0_path, file))\n",
    "        key = (genotype, mouse_id, section)\n",
    "        data[key] = diagram\n",
    "    return data\n",
    "\n",
    "dim0_path = \"Data_Raw/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/PersistenceDiagrams\"  # Update this path\n",
    "dim0_data = load_persistence_diagrams(dim0_path, mouse_id_to_genotype)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e60de6",
   "metadata": {},
   "source": [
    "We calculate the Betti curves for 0D homology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da6ff89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_betti_0_curve(persistence_diagram, max_filtration_value, n_steps):\n",
    "    betti_0_curve = []\n",
    "    filtration_values = np.linspace(0, max_filtration_value, n_steps)\n",
    "    for value in filtration_values:\n",
    "        betti_0 = sum(1 for point in persistence_diagram if point[0] <= value and (point[1] > value or point[1] == float('inf')))\n",
    "        betti_0_curve.append(betti_0)\n",
    "\n",
    "    # Normalize the curve to start at 1\n",
    "    if betti_0_curve[0] != 0:  # Ensure there is no division by zero\n",
    "        initial_value = betti_0_curve[0]\n",
    "        normalized_betti_0_curve = [b / initial_value for b in betti_0_curve]\n",
    "    else:\n",
    "        # Handle the case where the initial value is 0\n",
    "        normalized_betti_0_curve = betti_0_curve\n",
    "\n",
    "    return normalized_betti_0_curve\n",
    "\n",
    "n_steps = 100\n",
    "max_filtration_value = 100\n",
    "betti_0_curves = {key: compute_betti_0_curve(diagram, max_filtration_value, n_steps) for key, diagram in dim0_data.items()}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f562a338",
   "metadata": {},
   "source": [
    "Before plotting, we aggregate the Betti curves by genotype. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "514ccaa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genotype: WT, Number of curves: 20\n",
      "Genotype: KO, Number of curves: 27\n"
     ]
    }
   ],
   "source": [
    "aggregated_curves_by_genotype = defaultdict(list)\n",
    "for (genotype, mouse_id, section), curve in betti_0_curves.items():\n",
    "    aggregated_curves_by_genotype[genotype].append(curve)\n",
    "\n",
    "# Print some intermediate results to verify aggregation\n",
    "for genotype, curves in aggregated_curves_by_genotype.items():\n",
    "    print(f\"Genotype: {genotype}, Number of curves: {len(curves)}\")\n",
    "\n",
    "# Calculate the mean and standard deviation of the Betti-0 curves for each genotype\n",
    "mean_curves_by_genotype = {genotype: np.mean(curves, axis=0) for genotype, curves in aggregated_curves_by_genotype.items()}\n",
    "std_curves_by_genotype = {genotype: np.std(curves, axis=0) for genotype, curves in aggregated_curves_by_genotype.items()}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f232e8",
   "metadata": {},
   "source": [
    "Now, we plot and save the Betti curves. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e0f3250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved Betti-0 curve plot at Plots/Widefield_10x_ROIs_KO_Fish_Pdgfrb\\Betti0_Curves_KO_WT.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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JkiUsXbqUpUuX8vvvv/Pcc8953n/wwQf58ssvWbRoEVu3bqV///6ce+65FBcXEx8fz5dffgnAvn37yMnJ4eWXX+bKK6/E5XLx7bffevopLCxk6dKl3HzzzZ7XkpOT+e9//8t3333HDz/8wLZt27yWti1YsIBHH32Up59+mj179vDMM88we/ZsFi1a5LOfpxDt1a2CWCF8aWBUAIoCUwZGEBtsptKp4U3NNagohBRswpZ7gGq7E/J2gbv7lNETQnRvycnJqKraoMx4eHg4/v7++Pv789BDD7Fy5Up27NjB559/zrhx4xgwYAD//ve/CQ4O9trU63a7WbhwIcOHD+eUU07hhhtu4JdffgGgqqqKN998kxdeeIGpU6cydOhQFixYgNls5t1330Wr1RIaGgpAZGQk0dHRBAUFYTabufbaa3n//fc91/noo4+Ii4vjtNNO87xms9lYtGgRo0ePZsqUKbz66qt8+umn5ObmAvDPf/6T//znP0ybNo0+ffowbdo07r33Xt5+++3O+vEK0WrdJoi12WwAPssOIIS/UUdMsBmtRuEvE2o32L2T25+MkIkARB9aQlapFZw2KDxwNIcqhDgGHVmpcuPGjWzbto1hw4ZRU1PDli1bqKysJCwszBPc+vv7k5qaSkpKiue83r17ExBwuJJgr169yM+vvUOUkpKCw+Fg8uTJnvf1ej0TJkxgz549zY7v1ltvZcWKFWRlZQHw/vvvc9NNN3mNOyEhgbi4OM/xpEmTcLvd7Nu3j4KCAjIyMrjlllu8xj937lyv8QtxtPhkY5cv1P1jDAkJOcojET1Jvwh/csttDI4O5ISEYLakl/JU9ZXMVzYRWLqH/LzdlPiPIYQ0CIwBU+DRHrIQopvr378/iqKwd+9er9f79u0L1Ob8htoZ1l69evHbb7816CM4ONjz/MgCN4qi4Ha7ATxL7I4MmFVVbbHc+5gxYxg1ahQffPAB5557Ljt27OC7775r9py6PuuPYcGCBUycONGrnVarbbYfIbrCUZ+JraysZO7cuaSnp6MoCsOHDz/aQxI9iEGnITHUD4ArTohHr4Gfy3qxK7C2tG+vQ9+QU2Kt/bLO3SG5Y4UQLQoLC+Pss8/mtddeo6qqqsl2Y8eOJTc3F51OR//+/b0e4eHhrbpW//79MRgMrFmzxvOaw+Fg8+bNDBkyBACDwQDU5iQ/0l//+lfef/993nvvPc466yxP2sc66enpZGdne47Xr1+PRqNh4MCBREVFERsby8GDBxuMv37xHyGOljbPxNb9pdmY33//vdn361NVFavVSmFhoddmLklJInwtLsSPQ0XVRAQYOXdoJEt35vNg2TSWatZgqTiIqXA7+QETiNaUQ1EyhA842kMWQnRzb7zxBpMnT2bcuHHMmTOHkSNHotFo2LRpE3v37uWEE07grLPOYtKkSVx66aU8//zzDBo0iOzsbJYvX86ll17KuHHjWryOxWLhjjvu4IEHHiA0NJSEhAT+9a9/UV1dzS233AJAYmIiiqKwdOlSzj//fMxmM/7+/kBtKfNZs2axYMECTzrM+kwmE9OnT+ff//435eXl3HPPPVx11VVER0cDtYV77rnnHgIDA5k6dSo1NTVs3ryZkpIS7rvvPh/+RIVouzYHsWlpaY3ewlBVFZvNxqFDh1rd15GZCIYPH+75RymErxh0GmKCzWQUVzN1RCzrkgvYYwtjQ8RZTK74nl5p33AgdDihfgYMRSngHyklaYUQzerXrx9//vknzzzzDI888giZmZkYjUaGDh3KrFmzuPPOO1EUheXLl/Poo48yY8YMCgoKiI6OZsqUKURFRbX6Ws899xxut5sbbriBiooKxo0bx48//uhZfhcbG8uTTz7Jww8/zM0338yNN97IwoULAQgMDOTyyy9n2bJlXHrppQ367t+/P9OmTeP888+nuLiY888/nzfeeMPz/l//+lf8/Px44YUXePDBB7FYLIwYMYL/+7//68iPTwifUNQ25rTSaHy/AiEyMpJrr72WOXPmEBgoaxLbo7y8nKCgIMrKyuRn2Aibw8Xa5EJUFVbtyeaDTdkMMJXzg/b/0LpspA26BTXhJPqEW8DgD4mToRP+Wxc9i81mIzU1lT59+simVNFtnX322QwZMoRXXnnF6/U5c+awZMkStm3bdnQGJo4p7fm+6+zYpM0zsStXrvQ6VlWVM844A0VRGD9+PM8//3yr+tFoNFgsFqKjo4mJiWnrMIRoE5NeS1SgidwyGxP7R/HltmwO2ALZGnce4wuX0OvQt+wJG0uBUUdEAFC4HyIHH+1hCyFEuxUXF7NixQp+/fVXXnvttaM9HCF8rs1B7Kmnntro66qqEhoa2uT7QhxtiWF+5JbZMOq1TOkXyvd7i/lP1VQ+1P+C0VZAaP56srUnYzFq8StJq11W4Bd6tIcthBDtMnbsWEpKSjzrcYXoaXySYuvxxx9HURT69+/vi+6E6BQBJj1h/gaKKu2cOjSWH/YWs6EkgH19pzI8+79Epy+jJHIi6UVWBkRp0ebugN4ng0ZSyQghjj1paWnNvj9nzhzmzJnTJWMRojP4JIiVfwTiWNE7zEJRpZ1wfyNjY/3YklXNW7azeNHwEwZ7CeE5v1MQexZZpVYSQhXI3wPRkvZNCCGE6G58snPljDPO4IwzzuDhhx9udx+zZ8/mjDPO4Mwzz/TFkIRoVIjFQJBfbWLx04fVVqn5KdfCodgLAIjK+B6ts5riKjsl1XYoy4CyrKM2XiGEEEI0ziczsb/99huKonRod+7OnTs9/QjRmRLD/EiqLmNQVADxgToyyp184jiVB80/Y7LmEpn5Izm9LyOjpBo/gw5j3i4w+kvaLSGEEKIbkRxC4rgTGWDCz6hFURROH1qbGWNZlpnMxMsAiMj+FX1NCW43HCqqQnU7IftPcDmO5rCFEEIIUU+3CWKbqg8tRGeIC64tRTuxbxgWvUKeVcOv7rFUBvZH43YQnV5bX7za7iKn3AYOK2Rvk7K0QgghRDfRbYLYkpISoLbEnhCdLTLQiKKAUafllAG1NcyXZhrJ7j0NgNC89ZiqatfCFpTXUGlzQnUhFB44amMWQgghxGHdIogtLy9n69atKIpCbGzs0R6OOA6Y9FqC6zZ4DY5GAZKKdezV9Kc0bAwKKr3SlgCgAoeKq3C63VCcAhV5R23cQgghhKjV5o1dq1atavK94uLiZt+vT1VVrFYrKSkpvPfee1RVVXmqfgnRFSIDTJRUOQjzNzI2PoAtGRV8nmpkQL9LCCraTlDJDixl+6kKGojDpZJRbK0tS5u7o3aTl17KjAohhBBHS5uD2NNOO63RdauqqrJp0yZOP/30Dg1oxowZHTpfiNaKCjSxP68CVYULRsWzNWM3q/P0bI+PJSL6ZMJzVxGT+hUHRj0EikKZ1UFhZQ3h/kBuEsRPONofQQghhDhutXs5gaqqnkdjr7X1oSgKjz76qJStFV3GoNMQYjEAkBDqx8l9AgF4/4CRnPgLcWmMWCrTCC7c4jknu9SKzeGC6iIoTj0q4xZCCCFEO2ZiExISGszEHjp0CEVRMBqNREVFtaofjUaDxWIhOjqasWPH8pe//IVRo0a1dThCdEhUoIniSjsAl5zQm43pSewr0/FbSSgRsWcRnbGMXmlLKAsbharR41YhraiagZH+aAr3g18YmAKP8qcQQgghjj9tDmIbq8Ws0dRO6J522mksX768w4MSoqtEBhjZpwG3G4L9DFwwOIivdpXxQbKJEyecTVjeGow1hURk/0p+3LkA2BwucsttxASba5cVJJwEmm6xR1IIIYQ4bvjsN68q+TPFMUiv1RBqMXqOzxzZlzCTSoFNw7fZgeQkXgLUlqPV2cs97fIraii3OaCmAgr3dfm4hRBCiOOdT4LY1NRUUlNTWbhwoS+687l169Yxc+ZMhg4dSlBQEIGBgQwdOpTbbruNtWvXduq1bTYbH374IVdeeSX9+/cnMDAQg8FAeHg448aN46677mL9+vWdOgbRvOjAw1kGjHotV44IBuCLVCMHgyZRbUlA67J5CiDUySiuxuFyQ0kaVBV14YiFEEII4ZMgNjExkcTERCIjI33Rnc9UVVVxyy23MHnyZObPn8+ePXsoLy+noqKCPXv2sGDBAk4++WRmzJhBVVWVz6//888/M3DgQG688Ua++OILUlJSqKiowOFwUFRUxJYtW3jjjTc46aSTuPDCC8nLk/yjR0O4vwGt5vA677ED+9A/0I3VpfBxqpmsvlcCEJa7xlMAAcDhUskssdYe5G4Hp71Lxy3E8cTlchEYGIiiKIwdO7bZtqqqEhYWhqIoKIrCe++912z7zz77zNM2ODjY87y9jzlz5vjwkwshmtIlC/kqKirIzMwkPT29Ky4H1H7hTZs2zevLy2w2M27cOE488UQCAw9vxnn//feZNm0aLpfLZ9dfunQpU6dOJSMjw/NaYGAgJ554ImeccQZDhw71rCUGWLZsGaeeeiqFhYU+G4NoHZ1WQ5i/wXOs0eq4dlQQAD9n6dmlHeQpgBCT+qVX6dm6tFs4ayBvR5ePXYjjhVar5aSTTgJg+/btlJWVNdl2165dFBcXe45byl++evVqz/MpU6Z0cKRCiK7SKUHsoUOH+Mc//sGkSZMwmUwEBweTmJhI3759G23/wQcfMH/+fObPn4/d7pvZrNmzZ7NixQrP8a233kpmZiabNm1i/fr1ZGdnM3v2bM/7K1as4PHHH/fJtUtLS5kxYwZOpxOAgIAA3nvvPQoLC1m/fj2//PILu3bt4tChQ1x77bWe8/bt28esWbN8MgbRNlGB3oULEhP6cFKkAzcKHxyoLUfrVnQElu4moGSnV1tP2q3K/NqlBUKITlEXYLrdbtatW9dku7qgVKvVeh231D4wMJA333yTHTt2NPqYO3eu55y5c+c22e7OO+/s0OcUQrROm7MTNMdut/Pggw/y+uuv43a7gdZt+Fq3bh0LFiwAIDg4mKuuuqpD48jOzuall17yHN9www3Mnz/fq43FYuGpp55CVVXPF9OLL77IXXfdRUxMTIeu/+mnn1JQUOA5/uSTT7jgggsatIuLi+Ojjz6iurqaJUuWAPDxxx8zb948goODOzQG0Tbh/ka0WgWXq/a/V1Vr4KoRgfzxazVbi/T8aYsmPOZ0IrN+Ijb1S/YGDwVN7S/IurRb/SMt6Ar2gTlU0m4J0Qnqz5KuWrWKqVOnNtquLii98sor+fTTTzl48CBZWVmNljUvLS1l587aP0wnT55MbGxsk+XPN2/e7HkeGxvL8OHD2/1ZhBAd57OZ2Orqak4//XReffVVXC5Xg0IIzbnnnns8bT/++OMOj2XevHnYbDYA/Pz8mDdvXpNtZ8+eTXx8PFC7Cevll1/u8PXr/9U/fPjwRgPY+h599FHPc4fDwaZNmzo8BtE2Wo1ChL/R67WgyN6cHesA4MNkIzlx5+PU+WOy5hKe63170uZwkVpYVbskJWcbuH23NEUIUWvChAmYTLV3TZqbXa1777LLLmPQoEHNtl+zZo1n0kWWEghxbPFZEHvLLbewfv16VFVFp9Nxxx13sGbNGkpLSzn33HObPXfo0KEMHz4cVVVZuXJlh9emfv31157nV111FaGhoU22NRgM3HzzzZ7jr776qkPXBrxmYVvzl/qRbeqfL7pOXIjZ69ilt3DZID8MGpW9ZTr+KA0gJ/EiAKIylqNx2bzaV9W4SCuqwm2rhLxdXTZuIY4XBoOBiRMnArBp0ybPZEV9aWlpZGZmAnDyySdz8sknA02vi5X1sEIcu3wSxK5fv96zu9Pf35+VK1fy+uuvc9JJJ3ltoGrOWWedBUBlZSU7drR/g8y+fftITk72HJ933nktnlP/llRycjL79nUs76e/v7/neWvW+NbU1Hgdh4SEdOj6on2C/QwE++m9XjOFJ3JRQu3/h4uTjeRHnkyNKQK9o4Lw7JUN+qiwOckoqUYty4Ty7C4ZtxDHk7pA026388cffzR4vy4o7du3LzExMZ4gtqmZ2Lrgtm7jrxDi2OGTIPbDDz/0PJ83bx6TJ09ucx9jxozxPN+7d2+7x7J9+3av40mTJrV4ztixYzEYDu9OT0pKavf1ofaWV53169d7Nng15ffff/c81+v1XueLrpUQ5ud17DCGcFF/AxadSnqVllX5JnIS/jcbm7kCrbNharaSakdt6q28XWArb/C+EKL9jlwXe6S6YLUueK373yMzFgBYrVa2bNkCwIknnuj1e0AI0f35JIj97bffgNqdndOnT29XH9HR0Z7nHcmXumfPHs9zg8HgWe/anCPb1e+jPaZPn46fX20wlJOTw9NPP91k29LSUh555BHP8U033URYWFiHri/aLzLAhJ9R6/WaNjiey3vXzpZ/kmKkIGwcVr9YtC4rkZk/NdpPUZWd7JIKyNpSm35LCOETkyZNQq+vvWPS2Oxq3WunnHIKAP379yc6OhpVVVmzZo1X2w0bNuBw1K57l6UEQhx7fBLEZmdnoygKw4YN86Q0aau6oA/oUOGBtLQ0z/O4uDgURWm6cT0JCQmN9tEevXr14r333vN80c6ZM4drrrmGVatWUVFRgdPpJDMzk4ULF3LCCSewe/duAE477TT+/e9/d+jaouMSwyxexzXmSKYmKoQY3OTbNKzINpKTeDEA4dm/orM3nq8yv7yGsor/BbKy0eu4p6oq1XZnj390dglyi8XiKXZw5J2ugoICz528uiAW8NwdPHLmVtbDCnFs80mKrbp1nx25FVM/cXVAQEC7+6moqPA8DwoKavV59dfu1u+jva6++mqioqK488472bNnD5999hmfffZZo23DwsK49dZbefLJJ1v9M6ypqfFaS1teLretfaVXoImU/Ersztodyyga3IGxXNM3gzf3mvlvqpEzJo0kKqAPlopUojK+J6vfNY32lVFcjZ+hBH3OdohtvsqQ6NmsDhdDH//xaA+j0+1+6lz8DD7N3tjAlClT+OOPP6isrOTPP/9k/PjxAJ6Z1oiICE9WAqhdUvDll182mLmtC2oNBkOrlp4JIboXn8zERkZGoqqqZ0doe9Rfy9qR8rWVlZWe53WpWFrDbD68M71+Hx1x2mmnsXz5ci688MIm2+j1embMmMEdd9zRpj8Cnn32WYKCgjyP1iybEK2j0SjEh3qvjbX5xXBGrIteZhdldg1LM43kJF4CQFjuagy2xiutOd0q6cXVUJkHBfs7fexCHA/qz7LWn109cj3ske23bt3qudPndDrZsGEDAOPGjfP6HSCEODb45M/lIUOGkJ6eTkpKCocOHSIxMbHNfXzxxRee53UpVNqj/q0lna71H69+27o1Uh1htVp54IEHePvttz1j8vPzY/jw4fj7+1NQUMDu3btxOBy88MILzJs3jzlz5vCPf/yjVf0/8sgj3HfffZ7j8vJyCWR9KC7ETFpRlVfxA5clmmv65fPSTj+Wphu4OGEwUcGDCSjdS1T6MjIGNr4evMLmJL/CRiQpYPCDoLiu/CiimzDrtex+qvl0gz2BWd++JWVtccopp6DRaHC73axevZr7778faDqIHT16NP7+/lRWVrJ+/XrOOussr4BWlhIIcWzyyUzs+eef73n+z3/+s83nL168mD///BNFURgwYAC9e/du91jqr61tLIdgU+q3tVgszbRsmd1u54ILLuD111/H6XQSFBTEu+++S0lJCX/88Qe//PILSUlJ5OXl8cADD6AoCg6Hg0cffdSr8EFzjEYjgYGBXg/hO3qththg75kZqyWekyOdRJjclDk0/Jaj98zGhuZvwFid02R/OWU2qu1OyNsNjtb/dyl6DkVR8DPoevyjtfsQOiI4ONiTX3vNmjWoqupZWgDeM7VQW372xBNPBA7P3NafwZUgVohjk0+C2Ouvv96T2/T999/3Kvnakm+//ZaZM2d6juv+om6v+jlarVZrq8+rrq5utI/2mDt3LitX1uYQNZvNrFy5khkzZjRYLhAWFsa//vUvXnnlFc9rzz77LBs3buzQ9YVvJIT6Uf/3sUtvwWUO8+SN/TbdQKV/H0pDR6Gg0ittSZN9qSocKqrG5XJC0YFOHrkQPV9d4FlUVMTu3btZt24dLpcLi8XilbKxzpH5Yuv+V6vVtistpBDi6PNJEBsSEsLcuXM9u1JnzZrFeeedx/fff+8VHNax2+389NNPXH755UybNg2r1YqiKIwePZoZM2Z0aCzh4eGe5zk5Tc+MHSk3N9fzvCMpro4sXXvbbbc1+oVa3913382oUaOA2h3Mr776aruvL3zHpNcSFei9rtpqiefsGDt+OpWsai2bC3XkJl6Ciobg4u34lzZdKKPG6Sar1AplWWBvfwYOIUTDfLF1QemJJ57Y6FKyuiD2jz/+oKamhrVr1wIwatQouZMlxDHKZ2Vn77jjDu655x5PIPvTTz9x4YUXEhAQwK+//upp169fP/z9/TnvvPNYsmSJp2Z1r169+Oabb9qdoqtO/R2pRUVFjQbRjcnIyPA8Hzx4cLuvv3HjRq9MARdffHGrzrvooos8z5sqjyi63pEbvBymUPQmf86NrZ2N/eaQAZslhsJetbcvYw/+F9Sm02kVV9mptDmgoGNV4YQ43tUPYlevXt3ketg6dcGt1Wpl0aJFFBUVNehHCHFs8VkQC7XVul599VWMRiOqqnoeTqfTs04qLS3Ns9GpLuCdNGkSGzduJC6u4xtehgwZ4nW8bdu2Fs/JysqioKCgyT7aIisry+u4tZut6rerPyssjq4gs55As3cpWqsljgvj7WgVlV2lOg6UachNuAinzg9zdRZhuWub7TOz1IpakQvW0k4cuRA9W1RUFAMHDgRqC+7ULcM6cj1sHYvFwujRowF4/vnnPa9LECvEscunQSzAXXfdxYEDB3jggQeIiYkB8Apo6wJXRVE48cQT+fTTT1mzZo2nbUdNmDABo9HoOT6yQktj6ucONJlMHSr7Wv/a0Pp1ufVnjCXVS/cSH+r9/0eNOYpQs5Yp0bVZLL5JN+LS+5P7v3K0vQ5902g52jo2h4vCSjsUSsotITqiLgDNycnBarWi0+k8G7gaUzdLe/DgQaD291BTQa8QovvzeRALEBsby/PPP09mZiYpKSl8++23vP/++7z22mssXryYFStWUFRUxLp167jqqqt8upvV39+fM88803P80UcftXhO/TZnnnlmh7IT9OrVy+u4ri53S+q3i42Nbff1he9FBZjQ6+r9U9FosfnFcPH/Nnity9ORZ1UojJ6CzRyNzllFVPqyZvvMLbfiqCiAqsbzywohWnbkLOqYMWOa/f4+cqnBkCFDvPZRCCGOLZ0SxNbXp08fLrzwQqZPn86dd97Jtddey1lnndWmalptddNNN3meJyUl8d133zXZduvWrXz//feNntseJ5xwgteX6BtvvOFZ99uUjIwMvvzyS8/xqaee2qExCN/SaBRig4/c4BVDnwA3o0KduFFYmm4AjZasvlcCEJHzG8bqppeFuNy1abdkbawQ7XdkENvUetg6R866ylICIY5tnR7EHg1XXHGFZ7c/wMyZMz31tOvLycnh+uuvx+Wq3YgzevRoLr/88kb7TEtLQ1EUz2POnDmNtjMYDFx33XWe482bN3P77bc3WUAhOzubSy65xGvZQUczNAjfiwvxTrfl1vlhN4ZxaWJt6d+fsg1UOqAiZBhlISNQVDexqZ8322dxlZ2qimIob30WDSHEYYmJiV5L1V588cVm29dVl6x7vPnmm2263k033eQ5t6MTHkKIjuuRQayiKCxYsMCztjQnJ4eJEyfy8MMPs3z5clasWMHcuXMZM2YMe/bsAWrXoc6fP98nSxvmzJlDVFSU53jBggUMGzaMZ555hh9++IE1a9bw9ddfc++99zJ06FBPgm6Av/71r4wbN67DYxC+ZdJrCff3Xu9ss8QyOtRFor8Lm0vhh8zaPMDZfa/ArWgJLNlFQPHOZvvNKrHWro11OZttJ4QQQghvPik72xYul4vCwkLMZnOn5uYbP348ixcv5vrrr8dqtVJeXs7zzz/vtSu1jtlsZvHixYwfP94n1+7Vqxc//PADF110EZmZmQAcOHCgxWpcV111FW+88YZPxiB8Ly7ETEFFjefYbgzDrTNxaYKdl3eb+SLNyMlRDqL9oiiMOZ3IrJ+JTf2cvcFDQNN46rhqu4ui0lLCDNsg9gTogmpHQgghRE/QZTOxX375JaeddhoWi4WYmBhCQkIIDQ3luuuuIykpqVOuOW3aNLZs2cJZZ53V6AyroiiceeaZbN68mWnTpvn02qNHj2bHjh3MmjWL0NDQZtuecMIJfPrpp3z22Wfo9fpm24qjJ8zfiJ+xXjCqKNgscZzay8GwYCc2l8Iru824VMiNvwCHPgCTNY/w3Obz/uaUWrGX5UHerk7+BEIIIUTPoah1Oa/aaNmyZbzzzjts3LiRoqIi/P39GT58ONdffz0zZsxAo6mNj51OJzfeeCOfffYZcDg3rGcAioJGo+HFF1/kb3/7Wwc/TtMyMjJYu3atJ49rbGwskydPbnUe145wOp1s376dpKQkioqKqKmpITAwkNjYWMaPH++TMZSXlxMUFERZWZlUn+lEGcXV7Mut8BwrbgeheWvJr1b5+wZ/bC6FmwbYuDTRTljOKuJTPsap82PPCf/EpW9617RJp6V/lAVd1FAI7dMVH0X4gM1mIzU1lT59+mAymVo+QQghjlHt+b7r7NikzUGs2+1mxowZfPjhh4B3UFo32zllyhSWL1+O2Wzmzjvv5K233kJRlEYD2Lo+FEXh888/9/mM6PFCgtiu4XS5WZ1ciMt1+L9l/5LdmKy5rMjS88YeM3qNyn8mVJFgcTDoz2cwV2dR0Ot0svpd3Wzf/kYdfSP80cSOhYCoZtuK7kGCWCHE8aI7BrFtXk7w6KOP8sEHHzQISOFwUYNVq1Yxe/ZsduzY4RXAjhgxgjvuuIN//OMf3HHHHQwbNswTwKqqyt13343dbvfJBxOiM+i0GnoFHZluq3Ym/ewYB2PDHDjcCvN2mXGqh1Nuhef8jrG6+SwElTVOMoqrIGe7VPMSQgghWtCmjV3p6en85z//8QSder2e888/n8GDB2O329mwYQPr1q0DYP78+Z4qVEajkYULF3LVVVc16PPTTz/l5ptvpqamhry8PL7++muuvrr5GSshjqZeQWYyiw+nRHMZAnDoA9E7yrl7qI171us4WKHlizQD1/QdTFnoSIKKk4hJ/ZLUYXc323dJtQN9aSUxmi2QcCIY2l94QwghhOjJ2jQTu2jRIpzO2lRAiYmJ7Nixg6+++opnnnmGf//736xZs4Yvv/wSrVZLVVUVCxcuRFEUnnjiiUYDWIBrrrnGK+fq8uXL2/9phOgCQWY9Jr13tgGbJQ6AUKPKzMG1Ae7nqUaSyzVk9bkct6IlqGQnASUtb97KL6+hqLwS0jdATaXvP4AQQgjRA7QpiF29erXn+euvv86AAQMatLnsssu44447UFUVm82GVqvljjvuaLbf22+/HZ2udlJ469atbRmSEEdFVKB3ztgacxRube1rp0Q7mRzlwKUqvL7bjM0URWGv0wGISf0CVFeL/WcWV1NWWQUZG8BW7vsPIIQQQhzj2hTE7t69G4CQkBDOP//8JtvVVaxSFIVBgwa1uJg3KCiIQYMGoaoqOTlSvUh0f5EBRyxqVxSqLQmew5mDbPhpVVIrtazO1ZGXcD5OnQVzdQ5huatpiQocKqqiqtoKGRvBVubjTyCEEEIc29oUxJaWlnoC0+YMGTLE8zwyMrJVfde1q6ioaKGlEEdfkF8jSwr8YnBraqt2BRpULutdWxjhoxQTNo0fOYkXAxCdvgzF1fIGRrcKBwsrsdXYIGMTWEt8/CmEEEKIY1ebgti6jVotzawGBAR4nhuNxmZaHmYw1P7yr1tzK0R3F3nEkgI0Wqz/WxsLcFGCnRCDm3ybhh8zDRRFn0yNKRy9o4LwVszGArjccLCgCru9BjI3y9ICIYQQ4n/aVbGrsepXQhxvIgMa/oFms8ShKrXru01auKZv7Wzsf1MNVLu05MWdV3tu1goUt6NV17G73BwsqMLptEPONnDJH3pCCCFEl5WdFaKnCfYzYNR7/xNSNTqslljP8VkxDmL8XJQ7NCw5ZKQk8kTsxhD09jJC89a2+lo2p4vUwirctkrI2+mzzyCEEEIcqySIFaIDogIbVi2xWuJRldp/WloNXN+vdjb2m0MGih168mPPrT0380cUd+tnVatqXOSU26AiB0rTfTB6IYQQ4tglQawQHdDYkgJVa8DmF+M5nhTpZECgixq3wn9TjRRFT8ZhCMJQU0JI/oY2Xa+gooYKmxPy90jGAiGEEMe1NlXsqpOcnMxTTz3l07bJycntGYoQR1XdkoIah9vrdaslAVNVFgoqigLTB9h4bIuFFVl6LkowEh57NrGpXxCV+QPFUZNA0TZxhYbSi6sYFK1Bl70NEieDtl3/jIUQQohjWrt++6WkpPDkk08226Zu81dr2gpxLIsMMJFRXO31mltnosYchcmaC8DwEBdjwxxsLdKzONlI7LApRGb+iNFWSEj+JkqiTmz19RwulcxiK73DNZCbBLFjffp5hBBCiGNBu5cTqKra7KMtbY9sL8SxpLElBQBW/0Sv4xv716Cgsi5fz75KEwWxZwEQlfk9qO7GumhSqdVBcZUdKvOgLKt9AxdCCCGOYW2aiU1ISJD0WkIcIdhPj0Gnwe70DkRdegt2YyiGmmIAege4Oa2Xg5U5BhYdMDFo1KlEZq7AZM0juHALpRHj23TdrNJq/I06DAV7wT8StHqffSYhhBCiu2tTEJuWltZJwxDi2KUoCpGBRjKLrQ3es1niPEEswLX9aliTp2dXqY6Npf5ExpxBr/TviE5fSnnoSNza1hUHgdpCCOnFVfSLUFAK9kH0cJ98HiGEEOJYINkJhPCBmGBzo6/bjWG4tIffizCpXBRfW3L2g2QjedGn49AHYrLmEZvyaZuvW1njIr24GrU0XcrSCiGOKSNHjkRRFNLTG6YMXL16NdHR0ej1el5++WXP60OHDkVRFPLz81vdf1OPpUuXNnrev/71L0+bDRvalkHGV9rzs6nz559/cvfddzN48GD8/f3x9/dn1KhRPPHEE5SWlnbB6LuObGsWwgcCTXoiA43kl9d4v6Eo2CyxWMoPZ9+Y1ruGn7L1ZFRp+akgCP9Bt9Bv5zzC8tdTFTSA4qiT2nTtkmoHbrWaRMNONH1OBlnyI4To5mw2G3v27CE8PJyEhASv91599VXuu+8+QkND+fnnnzn11FMBqK6uZv/+/cTGxhIZGdmq/iMjI7njjjsabXPSSQ2/a/fs2cPjjz+OxWKhqqqqnZ+uY9rzswFwOBzce++9vPnmm+j1es4991wuvPBCXC4XP//8M0899RQffPABv/76K3369Onqj9UpJIgVwkf6R/pTUFHDkfsUbX698KtIRVFdAPjr4co+dt7bb+LjFCOnTB6EJfEieh36lriUT6j2T8RWr+pXa5RZHaRm59I7KA1tWM/4chJC9Fzbt2/H6XRywgkneF6zWq3MnDmTDz/8kAkTJvDll18SFxfneX/btm24XC7Gjm05I0td/1OmTGHOnDmtGpPL5WL69OmMGjWKgQMHsnjx4jZ/Ll9oz8/Gbrczbdo0li1bxkUXXcS7775LRESE531VVbn33nt5+eWXufrqq9m4cWOXfqbOIssJhPARP4OO2JCGywpUjR6bOdrrtalxdqLMbkrsGr49ZCAv7jzKg4eicTvovXcBGpetzdevsDlJ2fMnjprqlhsLIUQH7dixg5kzZzJo0CAsFgsmk4k+ffowffp0kpKSmj1369atAJ5ALS0tjcmTJ/Phhx9yyy23sGrVKq8grf45rQli69qOGzeu1Z/n+eefZ/v27bz33ntota3P3e1r7fnZ/OMf/2DZsmVcdtllfPPNN14BLNTu3XjuueeIj49n06ZNrF3b+rLn3ZkEsUL4UJ9wC1pNw9v5Nov3F45eA9f3qw1UvzpkpNSh5dCgm7EbgjFZc4lL/ogGU7qtUG2rYe/2PxpkShCip3nzzTebXOtYF0zdcMMNTc44NXW+RqMhKCiIxMREzj33XGbPns2ePXvaPc6CggJefPFFLr74Ynr37k1AQABGo5HIyEgmTpzInXfeybJly3A4HO36rI09/vzzz1afb7FYGDx4MLfffnubPue//vUvxowZw/z589m/fz/V1dXU1NSQlpbGBx98wMSJE1m1alWT52/ZsgWoDdR++uknxo0bx65du3jzzTd55513MBobbnKtO6c1QWxd29YGsTt37uTJJ5/kscceY9iwYa06p7O09Weze/duXnrpJaKionjvvfeazCJlMpk4+eSTAfjjjz8690N0EQlihfAho05LQphfg9fr0m3VNznKSf9AFzaXwmcHjbj0ARwa9FdUNIQWbCIsb027xuAqz2XPgQOSf1n0aNu2bWvyvbpgavHixUyaNIk333yz1eerqkp5eTnp6emsWLGCuXPnMnToUC666CKyslqfk9lqtTJr1iwSEhK4//77+e677zh06BCVlZXY7XYKCgrYuHEjb775JhdeeCFxcXG8+OKLjQazzX3WIxmNRoYP985U0tz51dXV7Nu3j7fffpsxY8bw2WeftXiN9957j4ceegiXy8WgQYOYP38+mzZt4ueff+a+++5Dq9Vis9l45JFHmuyjbrbx559/5rzzzsNoNPLbb79x++23t3hOW2Zily1bxpw5c7weX3/9tVdbp9PJTTfdxJAhQ3j44Ydb7LuztfVn8+9//xu32829995LcHBws33XzeCWlfWMsuWyJlYIH0sM9SOzxIrjiNnQI9NtaRSY3t/G7K0WfszUc3aMnb5B/cnpfQkxaV8Tm/IZ1ZYErAGJR16iRa6cHaQEhdE/JrzDn0eI7qguMAsODmb16tWe110uF2lpabz99tt8//33uN1u7rnnHs466ywGDBjQ4vlut5uKigrS09PZsGEDn3zyCQUFBSxdupSxY8eydu1a+vfv3+zYMjMzOeecczwzm4GBgVx22WWcc845xMbGYrFYyM/PZ/v27Xz33XesX7+e/Px8HnzwQe688070eu+cz3VjjYiI4Ndff2322gaDocnzg4KCWLPm8B/HdrudlJQU5s2bx7p166ipqeHmm2/m5JNPJja28XX5JSUl3H///QBMnDiRX375BYvF4nn/zDPPJD8/n8WLF7Np06ZG+7Db7ezcuROonSXu06cP69atIzo6utH2ULvZaffu3URERDS4ld5c/y+99FKD959++mkuu+wyz/EzzzzD9u3b+eOPPxr87LpaW382breb7777DoCrr766xf6LiooACAsL89GIj67jIohdt24dixYtYvXq1WRlZaGqKnFxcZx88slMnz6dyZMnd/oYHA4HK1as4PPPP2fz5s3k5ORQXV1NVFQUvXr1Yty4cZx++umcfvrphISEdPp4ROfRaTX0DbewL7fC63W7KRyX1ozWdTif7IhQF5OjHKzN0/PmXjPPja8iP/ZsLOUpBBUn0Wfv2+wb/Q9cev82jUHjdlCcspU8/ylEBZp88rmE6C7cbrfnF/2oUaMazDyOGjWKSy65hIsuuoilS5fidDr5+OOPeeKJJxqcP3LkyAbnA0yePJm//OUvPPPMM/ztb3/j/fffJz8/n0suuYSkpKQm10zm5ORwxhlncODAAQCmT5/Oyy+/TFBQUIO2559/Po888girV6/m73//Ow6HA5PJ+99ra8banPrnjxgxosH5Y8eO5fLLL2fy5Mls2LABq9XKJ598wqxZsxrtb9GiRZ40Te+++65XAFtn0KBBwOFqnUfe3k5KSsLhcDBu3DiKiopIS0tjw4YNXHrppU1+jqSkJJxOZ6tmYev6v+aaa/jkk0+abbt9+3bmzp3LrFmzWtV3Y8LDwz3BYWusXLmS0047rdH32vqzycjIoLCwkPDwcHr37t3itetSho0ZM6bV4+3OenQQW1VVxT333MN7773X4L09e/awZ88eFixYwM0338yrr77a6D9GX1i3bh233XYbu3btavDeoUOHOHToEBs2bOC1117jrrvu4rXXXuuUcYiuExtsJr24Gqvd5fX6kem2AG4ZaOPPQh0HyrX8mKnn/HgH6QNvYuC2ZzHaCkjc9x4Hh90NSttW/xhqikg5sAe/YcMJMEk1L9Fz7Nu3j+rq2g2Mo0ePbrLdLbfc4skFunfv3kbPHzVqVLPXslgsvPPOO6Snp/PLL7+we/duPvzwQ2666aYGbaurqznzzDM9Aey//vUvHnjggRY/zymnnMLatWv59ttvG7xXf6xtDWCPPH/kyJGNttFoNNxxxx2eAKex31V1vvnmGwAmTZrU5NrR7OxsABITExtdn1l3u/yMM87gxhtvZNKkSVx//fWsXr26yeCqPUsJmvtvo8706dPp169fqzMYNOYvf/kLFRUVLTf8n+ZmnNv6s6kLnkNDQxu8d6QdO3awe/duIiMjmTRpUqvH25312CDW5XIxbdo0VqxY4XnNbDYzbNgwdDodu3fvpry8HID333+frKwsli9f7vMdiR988AE333wzbvfhW8vBwcH07duX4OBgysrK2Lt371HLRyc6h0aj0C/Cn51Z3uuObH4xXum2AEKNKtf3tzF/n5kPk02cGOkk1OhH6pDbGbj9OQJLdxOdvpTcxIvbPA5z6X52pYVxwoA49FpZAi96hvprPJsLVGJiYjzPXa7D/+bqn99SEAu1Qd5jjz3GL7/8AsBXX33VaBD79NNPe5YQ/O1vf2tVAFvHbDY3eju4/lhHjBjR6v7aen5i4uFlS06ns9E2TqfTE+g2NZPodrv5/vvvAbxymNZXf+PSsGHD+OSTT7j44ou5+OKL2bhxI7169WpwTluC2Lr+WxPEbt++HaDBDHidumDv66+/bnI29NVXX23xOq3V1p9N3Qx/3R8OzXn88ccBuPfee9Hpekb412N/q82ePdsrgL311lvJzMxk06ZNrF+/nuzsbGbPnu15f8WKFZ7/g33liy++8Apgx4wZw48//khBQQFbtmzhl19+YfPmzZSXl/PHH38wa9asHrNORUBUoBGT3vuPIlWjw+bXcK3ZuXEOBgS6sLoU3tlX+2Vqs8SS0f96AKIzlhNY1HzKmsYoqhtt3g52ZpbKRi/RY7Q2iK2/Eav+L/+2BrFQu7ygLtBZv359g/f379/Pv//9b6A2IHzuueda1W9L6o+1PTOx9c9vaiYWIC8vz/O8qUT4e/bswWarzarSVDD54YcfekrUX3755Y22OTIgveCCC3j++efJzMzk4osvxmptWMK7s2Zib7nllkYfdeunL774Ym655ZZW3ar3hbb+bPr27UtkZCSVlZXNZhxYsGABS5YsYeTIkfzf//1fp42/q/WMUPwI2dnZXou5b7jhBubPn+/VxmKx8NRTT6GqKnPnzgXgxRdf5K677vL667298vLyuO222zwB7OWXX86nn37a6F8/Go2GCRMmMGHChA5fV3QfiqIQG2ImJb/S63WrJQ5zVQZwOKjUKnDHECuzNlpYl69nS6GDE8KdlEROxK8ijYiclSTuf599ox/Bbm6+Us2R9I5yqnP3s984jEHRAb74aEIcVXWBmV6vZ+jQoU22q19WdMqUKQ3O12q1rU6npNfriYqK4tChQxQVFeFyubzu3M2dOxe7vbak9Jw5c/Dza5ilpD3qxqooSrtSP7U2CF6yZInn+SWXXNJiX40Fkz/99BN33303UDtpc/755zdo43A42LFjB0FBQfTr18/z+qxZs9i1axcLFy7kxhtv5L///a9nKULdOXV3MZtT1zY6OpqoqKhm2wK88847jb5+0003ceDAAR555BFOPPHEFvvxhfb8bBRF4e677+bxxx/n7rvv5ueff/Zaf22323nxxRd59NFHiY+P58svv2xy1vlY1COD2Hnz5nn+WvTz82PevHlNtp09ezaLFi0iIyMDm83Gyy+/zPPPP9/hMdx///2UlNTWsh84cCAfffRRj5m+F60XG2wmtbCSeqtJcOtM1JgjMVrzvNr2DXBzUbydb9KNvL3XxKuTKjFqIbvP5Zir0vEvT6HPnrfZP/oRVE3b/lvyq0gjNycYnTaOfhFt2yQmRHdTdwt46NChGAyGRtusWrWKDz/8EKhNK1Q/oKo7f8CAAZjNDQuUNKXuWqqq4nA4PEFsZWUln3/+OVC7XKw1u8Rbq26sUVFRnhnOxpjNZq/A58jze/fuTWBgYKPnLlmyxJNa64orrvCqFFVf/SwHffr0oaKigl27drF//36++uorvv32W1RVJTg4mE8//bTRPnbt2kVNTQ2TJk1qsF727bff5sCBA3zxxRfMnj3bM8G0c+dO7HY7AQEB/PWvf22031NOOYXp06d7+m/NLGx3056fDcAjjzzC5s2b+fbbbxkwYACXXHIJERERZGVl8eOPP5KXl8ekSZP4+OOPu2xGuav0yKiqfg64q666qtkFzwaDgZtvvpmnnnoKqF3r1NEgNjc31yvX3r/+9a9GEzeLns+g0xAdaCa71PsWULUloUEQC3BN3xrW5unJt2n47KCRGwfUoGp0pA26lUHbnsZcnUVE1s/kx5/XxpGoBBZvJ1NV0WsSG81lK8SxIDc313Pr+8hAxWazkZyczKeffsq///1vT6D51ltveYLV+ue3dilBnbqJCX9/f6/ZrJUrV3omTqZOndpsYLx//37PjO2REhMTCQg4fLek/lhzc3ObXdN68cUXezZdNXb+kefW1NSwf/9+3n//fV555RXcbjcnn3xyoxuh69QVURg9ejSKorBo0SL+9re/ebWJjY3lt99+azINWXMFCwwGA1999RUTJkzg6aefZvDgwVx//fWeW+xFRUW8++67jfY7ePBgr/6PxSC2PT8bAJ1Ox9dff82HH37I+++/zxdffEF1dbWnoMa1117LVVdd1WQRhGNZjwti9+3bR3Ly4d3f553X8i/7qVOneoLY5ORk9u3b50kR0h4LFy70LIzv1asXF154Ybv7Ese++NCGQazLEIDDEIzeXur1ulkHtw228cx2P5YcMjA0xMW4cCdOYzDZvS8n8cBCojKWUxIxAYep5d2o9Smqm8Di7aSpTrTa/sQGt34GSviAqoLjOCgJrPeDTvxlWb8a1aJFi1i0aFGTbUNCQnj33Xe54IILGj2/LUGs0+n0pJY68jZ1/bWIzaVsdDgcjBw5kpqamkbf37Jli1cAc2TlreY0Vpmq/vnfffddk0HMCSecwIwZM7jtttuavWNYN6tbt0u+sTywWVlZXH311SxevJghQ4Y0eL9uzWlTIiMjG8w4t3ROe9s2Z+HChSxcuLDD/bRFe342dTQaDdOnT2f69OmdNLruqccFsXX/yOq0Jo3E2LFjMRgMnr+Ok5KSOhTE1t9Qdt555x3VGszi6Asw6Qmx6Cmp8q7EY/VPQF9c2qD9hAgnZ8fa+SnLwL93mHluXBW9A9yURE4kLG8N/uXJxKZ+TtqQmW0ei4JKYMkuUlPc6AYMkhyyXclRDc90fL19t/ePbDB0TrpCaLl6lVarZcSIEUybNo2ZM2cSGem9hrw9m7rqzqubnBg/frzXe3UptYBGA7c6u3fvbjKA1ev1zVba+vnnnznzzDNbPd4jz29OZWUlU6dObTaATU9Pp7i4tlhLXaD9/vvv89prr5Gens6mTZt4//33WbVqFVu3buXMM89k9+7dLVaQEqIjWh3EzpgxozPH4aEoSpO3C1qjfu1ng8FAfHx8i+fUtUtJSWnQR1upquq5JQB4FoSnpqYyf/58li5dSnp6Oi6XyzPVP23aNC6//HI0mh6bLOK4Fx/iR0mVd7otuykcl84PrbPh7NzMQTZyqzXsKNExd5sfL0yoIsQImf3+wqA/nya46E8CSnZREdKeGt8q/iW7SdnvRDd4OGH+stRFHDvqAjONRsOmTZs861Q1Gg0BAQFEREQ0u3GlvUFs/UpXZ599ttd7dcsMoPlKSKNGjWqQJSQxMZH09HSGDRvWYH1va7MwNKX++b/99ptnbNXV1ezfv58XX3yRP//8k3379jF9+nRWrVrVqr7qZmLrfubDhg1j2LBh3HTTTdx55528+eab5OTk8Pnnn3Prrbe2edxCtFarg9iFCxd22XqKjgSx9afa4+LiWj3mhIQETxDb3OL5lqSmpnryz0LtxoE33niDWbNmNUiNkZqaSmpqKp9++imjRo3iv//9LwMHDmz3tUX3FRFQm27L5vAufmC1xONftq9Be50GHhpZzYObLGRXa3lmux9zT6gCSywFMWcQmf0zcSmfsnfs46ia9hUy8Cvdz4F9Kn4jxmA2yN2CTqf3q52l7On0nbveui6Y6tevX7sqLNWdHxoa2mRp1cbUrRXV6/VMnTrV6736d9vaUra0pKSE9PR0oPEKSnVjjY+Pb1f6xbrzw8PDG+RsnTBhAldccQXjxo1j165drF69mi1btrS4qctkMnnWnzbm4Ycf5s033wTwfDYhOkubpv7qSsi15tHSOU2931H1q2Y0VuavKfV3bbal8saRjiw9t2TJEu666y5PAJuYmMhpp53GuHHjvP7q3r59O5MmTWL37t2tuk5NTQ3l5eVeD9F9KYpCXEjDNag2v164NY3vrvbXw2OjrQTo3Rwo1/LKbjNuFXITLsRuCMJoKyAyc0Wj57aWqeQAe/fuxOWWHLKdTlFqb7P39EcnTnZUV1d79jw0l/O0Nee3ZRZ22bJl7NixA6hN2XhkwvmIiAjP89zc3Fb321zKqvpjbc8sbP3zm6qCZTKZeOyxxzzHixcvbnGsI0aMaHbZQf2Jo+YqUwnhC62eia2rOd0cp9PJ22+/TVFREaqqotVqPVUnwsLCMBqNVFRUkJqaypYtWzwVJhRF4ZJLLmnzTtHGVFYezsnZllxo9XeT1u+jreoW/td55ZVXgNpa0gsWLOCUU07xvFdWVsaTTz7pyWlbXFzMFVdcwbZt25pMG1Pn2Wef5cknn2z3OEXXiwk2k1pY5R0wKhpsllj8KlIbP8fPzUMjrczZ6sfaPD2xfm6u7QfZfa6k9753iMr8gZLICdhNEY2e3xpK/m4O7DcweHDTa/mE6A62b9/uyb3dniC2/vmt/X1TWFjouSVuMBh4+OGHG7Q54YQTPOm8Vq1axRlnnNGqvhu7Rd/YWNtT577++c0FwZdccgn+/v5UVlby1VdfeeVYr69uk1hLs99r1671PK//+06IzuCzILawsJCLL76YwsJCLBYLDz74ILfffrvXX6hH+v3333nsscdYu3YtP/zwAxdccEGHdxXWL5fXlrys9ds6HI5mWjavsUX7MTExrFq1qsEGg6CgIF588UUsFosn59uePXtYtGhRi+uIHnnkEe677z7PcXl5eavW/4qjx6DTEBVoapCpwOoXi6kqE4278f/uhoe4uGOIjVd3m/lvqpFJkQ76hJ9ARe5qAsr2EZvyX1KH3tmhGTBb5nYyTAbiezfMMylEd9He9aztPT8vL48LLriAnJwcoLYgTl0lp/ouvPBC7rvvPtxuNwsWLOD+++/3SpXV0ng0Gk2D8fhyPWxzQbDZbOass85iyZIlpKenk5SU1OAPhLKyMs8yu5bucNYtJRg4cGC7yuQK0RY+2Unkdru55ppr2LBhA1FRUaxdu5bZs2c3G8BCbV3l1atXc9ddd2Gz2bj99tv56aefOjSW+lVS6vL2tUb9thZL+3fWNnbu008/3SCAre+JJ57wCkCby9NXx2g0EhgY6PUQ3V98aMMlBarWQGVQ82uhz4xxMDmqNsj9OMUIikJmv7/gVrQElewgqHh7s+e3TKUoZRPFebKGTXRfrS2h2przmwtiVVXl008/ZcKECZ6NujNmzOCuu+5qtH2/fv245pprgNqKkVdddVWLS7ySk5M9m8UGDBiAv793ERJfBrEtnV8/Bdl3333XbF9vvPGGZ2nFkV544QXP5rCHHnqoR+YlFd2LT1JsffHFF/z6668oisJbb73V5i+Xl19+mQ0bNrBlyxbuueeeDmUHqP9F0Fj95aZUVx/eIX7kl0l7rw+1i/yvuuqqZs/R6XRcc801vPDCCwBs3ryZqqqqDgXTonsKMOmJDDSSX+49Y283R1FjLcBoy2/y3Gv71rA+T8emQj37yuwMCoomP/ZsojN/IDblMyqCB+PWdiBllqqSuWs9ZqMJc3DbStsK0RXqgqmAgIB2VR6qX8LV7Xazc+dOoHYipqSkhPz8fNauXcuyZcs860kVReGpp57yWjvamNdee41NmzZx4MABfvjhBwYOHMiMGTM4+eSTiY6Oxu12k5+fT1JSEj/++COrVq3y3O5vLMdr3ViDg4Pp06dPuz+rn59fixuGzz//fBRFQVVVli5dyqOPPtpoX1C73O6UU07h3nvv5bTTTiMgIIDk5GQWLVrE8uXLgdqZ6ZtvvrnNYxairXwSxNYlm46Ojm6y5nJzNBoNf/3rX9myZQv79+9n48aNTJgwoV1jCQ8P9zyvuwXUGvUX47dnF2idI2efBw8e3Koa2vXXGTmdTjIyMprdASqOXX0j/CmoqOHIfYyVQQPR20vRuBuv5hNrcXN6jINfsg0sTjbyzxOqyYs/n5CCzRhrColOX0p2nys6NDa3203aznUMnHAuWoMUQxDdR/2gc8SIEW2e5at/vqqqjQaO9SmKwsUXX8yjjz7aIC9sY0JCQlizZg3XXXcdP//8M3l5eTz77LPNnjNo0CD+7//+r0HAV3+s7Vk2ceTPqqVc5TExMYwZM4atW7eyceNG8vPzve4e1gWxZrMZq9VKWVkZc+bMabSvG264gfnz58ssrOgSPllOsGvXLhRF6VB6qPoBW2t36DemfpGCoqIirxnW5mRkZDQ6lrZKSEjwClqbK3lb35GBc/28g6Jn8TfqGi0yULusoPkiG1f3qUGnqOwo0bG9WIuqNZDZr/Y2ZkTWr5gqMzs8vhqblbSd62kQZQtxFO3fv9/zfd6epQT1zz9SQEAAiYmJjBkzhhtvvJEFCxaQnJzMkiVLWhXA1omMjOSnn37i119/5ZZbbmHw4MEEBgai0+kICQlh4MCBXH755TzzzDMkJSWxd+9ebr/99gZlyeuPtT1LCdpzft2SArfbzbJly7zeqwtiL7vsMr7++mvOOOMMQkND0Wq1+Pv7M3ToUG699VbWr1/PBx980KZN1UJ0hE9mYutqM1dVVbW7j/rn1vXXHkdWS9m2bRsnnXRSs+dkZWVRUFDQZB9todFoGDx4sKfWc1PVWY505Ppd+RLo2fqEW8grtzWIE+3mCGps0RitjafpiTSrnBtnZ1mGkcXJJkaOr6IidDgl4WMJKdxKfMpHHBj5ACgd+/u0ojiP7JSdxPSXjRmiexg8eHCH0jB29Py2OP300zn99NPbff7R+KxPPfWUp/x6fQ6HwzOxNHLkSC699FIuvfTSdo9NCF/yyUxsaGgoqqqyY8cOysrKWj6hEfUrhbR29rIxEyZM8Pqrtn6VlaasXr3a89xkMrV7KUOd+kmlU1MbT510pCPbHVmbW/QsliZmYwEqgwbg1jRdReuK3naMGpUD5Vo2Fdb+HZrd50pcWhOWilTCclv+b7418g/torSw9TkvhRA9z65duzwl2dszAy5EZ/JJEFv3H7bdbm9VPtkjpaen89Zbb3mOO5KWw9/f36u+9EcffdTiOfXbnHnmmR3eUDVt2jTP87y8vCZ3cta3YsXhpPXx8fHExBwHNdaPc30jLI1mxVI1eiqCm17SEmJUuTCh9pfKRylG3Co4jCHkJF4MQK+0JejsPih+oaqk71qPzdb6DZJCiJ6l/qYuSZkluhufBLHXXXed5/mrr77Kww8/3Opcq0lJSZxxxhmUl5ejKAr9+vXjxBNP7NB4brrpJq/+G0sZUmfr1q18//33jZ7bXieddJLXutqWFvdv27bNawzt2Rwnjj1+Bh3RQY3PxjpMYdSYm56NvyyxBj+dyqFKLWvyamdjC3udRrV/AjpXNTGpX/hkjG6HjZSkdbilopcQx6X6ZXrj4uKO7mCEOILPgtiTTjrJswbnhRdeYPDgwTz77LNs3bq1wbrQ7OxsvvnmG6699lpOOOEEr1vpr776aofHc8UVV3jt6Jw5cyZ79+5t0C4nJ4frr78el6u2nv3o0aO5/PLLG+0zLS0NRVE8j6Z2ZkLtuthnnnnGc/zJJ580WQUlPT2dK6+80pNqxWAwMGvWrBY/o+gZ+ob7o2niX2Fl4ABUpfFl6/762kAW4JMUI3YXoGjI6HcdKgqhBRsJKGn/Bsn6HOX5HNyf5JO+hBDHlvrlZoXobnwSxCqKwueff86QIUM8gWxqaiqPPfYY48ePx8/Pj4CAAE/p2fj4eKZNm8Znn32Gy+XyCn7PPfdcn4xnwYIFnlKyOTk5TJw4kYcffpjly5ezYsUK5s6dy5gxYzw5ac1ms0/Tglx22WVcf/31nuP77ruPM888k4ULF/L777+zfPlyHn74YUaMGOHJRwgwb948EhMTfTIG0f2ZDVqiAxtPZaVqDVQG9m/y3Avj7QQb3ORYtczfVzujaw1IpLDXaQDEJX+ExtW6jYUtqczcQ16OFEIQ4nizfXttIRVZDyu6I59kJwDo1asXa9as4Z577vGsMa2/O7KqqsorA0FdYuW6c1977TUuu+wyXw2H8ePHs3jxYq6//nqsVivl5eU8//zzPP/88w3ams1mFi9e3KZUKq3x7rvvUl5ezrfffgvAr7/+yq+//tpoW0VRePrpp7njjjt8OgbR/fWNsJBbbuV/k/FeaiwxmKy56O2lDd4z6+DeYVae/NOPn7MNDAxycU6sg5zelxBUvB1jTRHRh74lu++VPhilSu7ejVj8g/APaL7spBCi55B0j6I788lMbJ2QkBA+/PBD/vjjD2bMmOFVeKDBhTUaJkyYwKuvvsq+fft8GsDWmTZtGlu2bOGss85qdIZVURTOPPNMNm/e7LUZy1cMBgPffPMN8+fPb7biyimnnMJvv/3GI4884vMxiO7PpG96NhagIngwKo3fIRgV5uK6/rWzrfP3mthfpsGtNZHRv3adekT2r/hVtC5DRktUl4O07atwOhovxiCEEEJ0JUXt5MR5aWlppKSkUFJSgt1uJzAwkMjISEaMGOG53d8VMjIyWLt2LVlZWQDExsYyefJk4uPju2wMmzZtYteuXeTm5mI0GunVqxennHIKsbGxHe67vLycoKAgysrKCAwM9MFoRVeqtjtZn1LUZH0Bv4rUJoNRVYXnksz8UaAn3OjmPxOrCDKoJOx7n9CCP7D6xbJ/9COoGt/cePEPi6X/mCk+6etYZ7PZSE1NpU+fPpLbWQjRo7Xn+66zY5NOD2JF15Ag9ti3M6uM3DJb42+qbkIKNqJ1Nl5xqMoJD2y0kF2tZUSIkzljqjG4KhmyZQ46ZyU5CReTl3C+z8Ya2XsYMf1ljZwEsUKI40V3DGJ9upxACNF+vcObyU+saKgIajp3rEUHD4+0YtLWlqRdnGLEpfcns+9VAERlLMdY7bvCBQWHdlGe3/ESt0IIIUR7SRArRDfhb9QRGdh0pS6nMZgaU0ST7yf4u7l7SG1hgq8PGUkq1lIaMZ6ykOFoVCfxyYtBbWT3WDuoKqTu2kB+calP+hNCCCHaqtOC2BUrVvDII49w7rnncsIJJ9C/f3/69evXaNsDBw6wf/9+9u/f31nDEeKY0OxsLFAd0PQGQYCTo52cF1u78eqtvSbsboXMftfi0hrxL08mMvNHn41VdTlI2/UHyfkVXVaTXgghhKjjsxRbdZYsWcKDDz5ISkqK1+uqqjaZg/Wee+7xlF1duXIlU6bIphFxfAo06QnzN1BU2XgGAJfenxpTBEZbQZN93DDAxh8FOrKrtXyeZuS6fqFk9b2ahAMf0OvQt1QH9KUyeJBPxmuoKSYvbS/ltv4MjwnCoJObO0IIIbqGT3/j3HfffVx++eWkpKSgqqrXozmzZs3ytPnggw98OSQhjjl9Ojgba9HBrYNqN4h9nWYgo1JDcdRJFEWdhIJK4r530NWU+mq4+JWnUFZSzMbUYsptrSs33dPITLQQoqfrjt9zPgtin332WebNm+cJWocOHcrcuXNZsmRJi0UEzjjjDHr16oWqqnz//fe+GpIQx6RgPwMhFn2T79fOxkY228ekSCfjwh04VYU39phwq5DZ9xqsfrHoHRX03vcOqC6fjFdBJaBkNza7g62HSqg4jgJZzf9qBrsbq1QhhBA9iMtV+ztD01St9KPAJyM5dOgQTz31FFBbQODZZ59l586d/OMf/+Diiy8mNDS02fMVRWHq1KkA5ObmcuDAAV8MS4hjVp9w/2bfrw7oDU0UQABQFJg52IZJq7KnTMdPWXpUrYHUIbfh0prwL0+mV9o3PhuvzlmJX8VBnC6VbRml2By+CZC7O71ej1ar9apGKIQQPVF1dTVarRa9vulJlq7mkyD2nXfeoaamBkVRuO+++3jooYfa3Me4ceM8z3fv3u2LYQlxzAq1GIgJbroYiEvvT425+dnYCJPKtf1qq3ktSjZRUqNgN0eRPuBGAKKyVhBYtM1nY/arzEBfU0KNw83W9BLszp4/O6koCgEBAZSXl3fLW21CCOELqqpSXl5OQEBAk/ubjgafBLE//li741mv1zN79ux29ZGQkOB5npGR4YthCXFMGxwdQKC56b94q/1709xsLMAF8Xb6Bbiodiq8u782OXVZ+FjyY84AIHH/IgzWpjeJtY1KYPEOdPZyqmtcbM8sxeXu+YFdUFAQDoeD7OxsCWSFED2OqqpkZ2fjcDgICgo62sPx4pPsBGlpaSiKwogRI9pdkaH+D6aiosIXwxLimKbRKIyMC2JjanGjs5ouvYUacyRGa16TfWgVuHOIlQc2WliTp+fECAcnRzvJ6T0NS0UaloqD9NnzFvtHPYSqNXR4zIrqJKjoT8pDR1BGKEmZpYyOD+5Wf7n7mp+fH3FxcWRmZmK1WgkMDMTPzw+tVtujP7cQoudSVRWXy0V1dTXl5eU4HA7i4uLw8/M72kPz4pMgtry8HIDg4OB291F/TZnR2HTCdyGOJya9lhGxQWxNL6GxSb5q/94YrflA0zOA/QLdTOtt54s0I6/tMZMYUEW8RUfa4FsZuO0ZzNVZxCcvJn3gzbWLaTtIUV0EFm2nImQ4RUSwK7ucYTGBPTqgCwgIIDExkbKyMkpLSykqKjraQxJCiA7TarUEBAQQFBTU7QJY8FEQGxoaSl5eHoWFhe3uo35e2fDwcF8MS4geIcRiYEBkAPvzGt6hcOktWC2xmKuaLwH7l7417CvTsqNEx/PbzbwwoQqMIaQNvpX+O+YRWrCR6oA+FMac7pMxK6gEluykQh1MLr1wulVGxAah1fTcQNbPzw8/Pz+io6NxOBySsUAIcUzTaDTo9fpuPQHhkyC2d+/e5ObmsmvXLioqKggICGhzH8uWLfM8HzFihC+GJUSPkRDmR7nNQW6ZrcF71QF9MVoL0Lhrmjxfq4H7R1i57w8LmdVaXt1t5oERVqqCBpLdZxqxqV8Qm/o5Vks8VUH9fTRqlYDSPSiqi0Li2Jpewuj4YPTa7pOepTMoioLB0PGlGUIIIZrnk98m55xzDlCbQ+zNN99s8/lbtmzx5IcNCwtjzJgxvhiWED3KkF6BGPUN/8mqGh2VrQg8gw0qD46wolVU1uXr+S6jNtAqiDmTkvBxKKqb3nvn+7QQAoB/2QF09jLKqh1sTis5btJvCSGE6Fw+CWKvu+46dLraSd05c+awfv36Vp976NAhrrjiCk9Z2ltvvdUXQxKix9FqlCbTbtnNUdiNYS32MTjYxc0DamdzFx0wsrtUC4pCxoAbsPrFoHeU03vvfBS304cjVwko2YXidlJV42RTWjGVNb7sXwghxPHIJ0HsgAEDuO2221BVFZvNxllnncVzzz1HWVlZk+dUVVXx6quvMm7cONLT01EUhbCwMGbNmuWLIQnRI8UGm5vce1UZNBBVafmf9AXxDk6JcuBSFV5IMlNSo+DWGkkdMrO2EELFQaLTv/PpuLUuG/5l+wGocbjZnFZMfkXDpRFCCCFEaymqjxIb1tTUcPbZZ7NmzRrPImC9Xs+oUaNIT08nLy8PRVG46aabOHDgAJs2bcJut3vyKur1en766SemTJnii+Ecd8rLywkKCqKsrKzdac7EsSEps5T88sbXv5orDmGpSGn0vfqsTnhok4X0Ki0jQpzMGVuNVoGgwq302TsfFYUDI2dRHdjPp2OvCB5KjV+057h3uB/9Ivy79cYBIYQQ7dPZsYnPdlgYjUaWLVvmWRqgqip2u53NmzeTn5/v+SW1cOFC1q5dS03N4V/CoaGhLF26VAJYIVohLqTpNCdW/3hcOkuLfZh18OBIKyatyo4SHf89WJvWrix8LMURE1FQSdy/EI2r6c1i7eFftg+N0+o5TiusZmt6KTVOWScrhBCibXy6TTggIID//ve/fP7550ycOBHAE9Ae+QAwGAzcfvvtbN++nbPPPtuXQxGixwq1GPAzaht/U9FQETSoVf3EWdzcMbg2oPxvqoFtRbV9ZvW7GrshGKOtgF5pX/tkzJ7hqS4CSndTP+ltSZWdjanFlFbbfXotIYQQPZvPlhM0Jjk5mdWrV7Nr1y6KioqoqqoiODiYyMhITjzxRKZMmSK3vn1ElhMcXzKKq9mX23RlO//SPZiqc1rV1+t7TPyUZSBI7+alE6sINaoElOym365XAEge/n9UBg/2ybjrVPv3pjqwr9drGg0MjwkiMtDk02sJIYQ4Ojo7NunUIFZ0HQlijy9Ol5vVyYW4XI3/81VcdkLzN6CoLWcBqHHVro9Nq9QyLNjJU2Or0WogLvljwnNXYTeGsHfM47h1jWdGaK/ykGHYzVHe41ZgaEwgvYJ8ey0hhBBd75hZEyuE6Do6rYboZmYsVa2BqiNmOpti1MIDI2rXx+4q1fHJ/9bHZve5nBpTBIaaEuIOfuaTcdcXULK7QU5aVYVdWeVkllT7/HpCCCF6FglihThGxYU0P1tp84vFqfNvVV+xFjd3D6ldH/tFmpHNhTrcWiPpA6ejohCav4Ggwj87POb6FFSCipPQOBsGrHtzKkgvkkBWCCFE03wSxPbt29fzePTRR9vVx0033UTfvn3p18+3KX2E6KkCTHqC/fRNN1AUKoMGtrq/k6OdTI2r3Vz10k4zOdUKVYH9yY+rrcgXn/wh+priDo25wRBVJ0FF21BcDTd17c+r4GBBpU+vJ4QQoufwSRCblpbGoUOHSEtL47nnnuOyyy6jqqqqTX3k5eWRlpZGWlqaL4bkZd26dcycOZOhQ4cSFBREYGAgQ4cO5bbbbmPt2rU+v15LnE4no0aNQlEUz+O0007r8nGIY19z6bYAnMZgaszRzbapb8ZAG4OCnFQ5FZ5L8sPmgtyEi6jyT0TnrCZx33ug+jYdltZlI7A4CdwN+z1YUEVaYdu+S4QQQhwfOmU5wbfffsvkyZPJyMjojO5braqqiltuuYXJkyczf/589uzZQ3l5ORUVFezZs4cFCxZw8sknM2PGjDYH3R3xwgsvkJSU1GXXEz1XZIARk76JdFv/UxnYH1Vpvk0dvQYeGmkl2ODmUKWW13ebcSs6Dg36a201r/JkotOX+2Lo3td1lBNYussr9Vad5PxKMoplaYEQQghvPg1iQ0JCPEUNkpKSGDduHOvWrfPlJVrN5XIxbdo03nvvPc9rZrOZcePGceKJJ3rtknv//feZNm0aLlfnJ1xPTk7mqaee6vTriOODRqMwMLr5da+q1kB1QJ9W9xlqVHlwpBWtorI6T8+36Qbs5ggy+l0LQFTGciz/KyHrSwZbYYMcsnX25VaQU2Zt5CwhhBDHK58GsRMnTuTbb7/F37+2jGRBQQFnnnkmixYt8uVlWmX27NmsWLHCc3zrrbeSmZnJpk2bWL9+PdnZ2cyePdvz/ooVK3j88cc7fVy33XYbNpsNk8nE5MmTO/16oueLDDAREWBsto3V0rpKXnWGBruYMdAGwKJkIzuKtZRGTqAoclJtNa9976F1+H69qtGaR0DpnkYD2d3Z5eRX2Hx+TSGEEMcmny8nOP/881m3bh29e/cGoKamhhkzZvDggw/SVSlps7OzeemllzzHN9xwA/Pnzyc0NNTzmsVi4amnnuKxxx7zvPbiiy+SnZ3daeN69913WblyJQD/+Mc/6N+/f6ddSxxfBkUHoNUqTTdo4yYvgPPjHJwWbcetKryww0yBTSGr39XYzFEY7KUkHPig0WCzo4zWXPwbCWRVFXZmlVFU6dtSuEIIIY5NnbImdtiwYWzatIlTTz0VqC09+5///IeLL76YysrO3208b948bLbaGRs/Pz/mzZvXZNvZs2cTHx8PgM1m4+WXX+6UMeXl5fHAAw8AMHjwYB566KFOuY44Ppn0WvqFN7+swGEMoeaI4gLNURS4Y4iNvgEuyh0aXtllxqkxcWjQLbgVHUHFSYTn/N7RoTfKZM3Fv2xvg9fdbkjKLKNQAlkhhDjudVqe2NDQUH766Sduu+02z2vLly9n0qRJnZKBoL6vvz5c7/2qq67ymoE9ksFg4Oabb/Ycf/XVV50ypr/97W+UlJQA8NZbb2EwGDrlOuL4FR9qJsCka7ZNVRs2eUFdIYRqTFqVHSU6lqYbsPonkN37MgBi0r7EVJXVoXE3xVSd0+iMrMutkpRZSm6ZLC0QQojjWacWO9DpdLz11lu88soraLW1vzh37drF+PHjWbVqVadcc9++fSQnJ3uOzzvvvBbPmTp1qud5cnIy+/bt8+mYvvvuOz7//HOgNh9u3Qy1EL6kKAqDewWiNLOqwK01tmmTF0AvP5WbB9QGjB+mGEmv1FAYcwblIcPQuB0k7nsXxe3oyNCbZKrOIahwS4OCCG537dICKYgghBDHry6p2HX33XezfPlygoODURSFoqIizjnnHN555x2fX2v79u1ex5MmTWrxnLFjx3rNjPoy/VVFRQV33nknAGFhYbzwwgs+61uIIwWZ9S3mjm3rJi+Ac2IdjA1z4HArzNtlxqEqpA+YjkMfgLk6m5jUzrmDAbXpt0IKNmGsarhefX9eBcn5UhBBCCGOR11Wdvass85iw4YNDBxYu7nEbrczc+ZM7r33Xp9u+NqzZ4/nucFg8Kx3bc6R7er30VGPPPIImZmZQG1+2PDwcJ/1LURj+kVYMOqb+afdjk1eigJ3D7URoHdzsELLf1ONOA2BpA+4EYCInJUEFO/oyLCbv77qIqBsLwHFOxrM+qYVVrE7u7zLNo4KIYToHrosiAUYMGAAf/zxB+eeey5Qu+HrlVdeYerUqZSVlfnkGvXX28bFxXny1rYkISGh0T46Yv369bz55psATJkyhZtuuskn/QrRHJ1WQ++w5mda27rJC2rzx94+uHZZwZepBvaVaakIHUFBr9MBSDjwATp7efsG3UpGWwEh+RsbpPfKLrWyM0sCWSGEOJ50aRALEBgYyLJly/j73//uee2nn37ijz/+8En/FRUVnudBQUFtGldjfbSXw+Hg1ltvxe12YzAYeOutt1odULdGTU0N5eXlXg8h6sQGmzEbmt/A1dZNXgCTo5ycGm3HjcK8nSZsLsjuMw2rXyx6RwUJBxZ1Stqt+jTuGoKKtqF1eFfZyyu3kZRZhtstgawQQhwPujyIBdBoNLz00ku888476PV6n/ZdP4WXyWRq9Xlms7nRPtrr2WefZdeuXQA8+OCDDBkypMN9Htl/UFCQ59GaZRPi+KHRKPQJb3421q01UhXYr8193zrIRpjRTY5Vy7ydZpzoOTRoBm5FR2DJLiIzf2zvsFtN47YTVLStwYavgooatmeWSiArhBDHAZ8Fse25jTdjxgx+/vlnwsPDfXYb0Ol0ep7rdM2nG6qvfluHo2M7rffu3cszzzwDQL9+/Xj00Uc71F9jHnnkEcrKyjyPjIwMn19DHNt6BZnwMzY/02qzxOEwBLepX3893Dfcik5R2VCg57U9Jqr9YsnqeyUAMYeWEJrX+eWmNe4aggv/bBDIFlXa+TOjFJcEskII0aO1Psprhtvtbve5J598Mps3b+bXX3/1xVDw8zu8M7uu4EFr1G9rsbRt53Z9qqpy2223UVNTm4z9jTfeaNOMcGsZjUaMxuZLjYrjm6Io9IvwZ0dm8+vNK4KHEFLwB4ra+n/Hw0JcPDDCyvM7zKzMMWDWwq0Dp2C0FRCZ9TPxBxbj1FkoDxvV0Y/RLI27huCiPykNG4tbd/huSkmVnT/TSxgdH4xOe1RuOAkhhOhk3eLbPT4+nunTp3seHeHvf7hqkdVqbfV51dWHZ3Pq99FWb7/9NqtXrwbgL3/5C+ecc067+xKio6ICTS0WQHDrzFQFtH1ZwcRIJ38fakVBZXmmgcUHTWT3vpziyBNRcNN77wIsZfvbO/RW07hqCC7aisbp/e+9tNrBNpmRFUKIHqtbBLG+VD+FVU5OTqvPy83N9TwPCwtr17VtNhsPP/wwAMHBwbz00kvt6kcIX+oX2fIfZTb/+DYvKwA4tZfzcMaCNCNfHjKSPuAGykJHolGd9N39BubK9Db321YaVw3BhVsbZC0orXbIGlkhhOihelwQO2jQIM/zoqIirxnW5tRfUzp48OB2Xdtms3lShZWWlhIdHY2iKE0+Fi1a5Dn3999/93pv4cKF7RqDEEcK9zcS7NfyBsqK4CGoStu/Es6NczC9//8qeiWb+C7DTNqgv1IZ2B+ty0bfXa9isOa3ud+2qs1a8Cdau3d2keJKO0lZkrVACCF6mh4XxB6ZBWDbtm0tnpOVlUVBQUGTfQhxrOsX0fJsbHuXFQBc1tvOlX1q14G/u9/Elxn+HBx6F1ZLHHpHBX13v97gdn9n0LgdBBdtRVdT6vV6YUUNO7LKJI+sEEL0IK3e2NW3b1/Pc0VRSElJafS9jjqy77aaMGECRqPRs7FqzZo1nHTSSc2eU7eGFWrTck2YMKFd11YUpU25aaurqz2ZELRardda3PplcIXoqBCLgfAAI4UVNc22s/nHY7QVoLeXtvka1/atQQN8lmrkg2QTNS6F64f+jUHbn8VkzSNx33ukDr0D2jHb2xaK6iKoeBvlISNwmA4vDSqoqGFXdjnDYgJ9mrNZCCHE0aGorZya0Gg0KIqCqqooioLL5WrwXkc11nd7XHDBBSxfvhyAkSNHsn379mbbX3TRRSxdutRzbt3zznbTTTd5lhSceuqp/Pbbb+3uq7y8nKCgIMrKyrwKNwhRx+Zwsf5gES5X8//kNc5qQvL/QKF9s5ZfpRn4ILk2I8eliTXcHrWfgTteQKM6yY2fSm7iJe3qt61UFCpCR2A3eZd6DvM3MDw2CL1kLRBCiE7V2bFJm77Fm4t3VVXt8MNX6pd3TUpK4rvvvmuy7datW/n+++8bPVeInsSk1zIwKqDFdm6dH9UBfdp9nWm97fx1YO3SgSWHjLycPYj0/tcDEJ3xPUGFW9rdd1soqAQU70BvK/Z6vajSzqbUYqpqnE2cKYQQ4ljQ6uUEK1eubNd7R8MVV1zBqFGjPDOwM2fOZMCAAQ02bOXk5HD99dd7Zn5Hjx7N5Zdf3mifaWlp9Olz+Bf7E088wZw5czrnAwjRSWKDzeSV2yiutDfbzuqfgMmah9ZZ1Wy7plyY4MCohTf2mPg+04CqnsZTMRlEZv9Cwv5FHDBHYbPEtavvtlBQCSxJoix0NE5jsOf1aruLjWnFDI8JIiJA8i0LIcSxqNVB7Kmnntqu944GRVFYsGABp556KlarlZycHCZOnMgdd9zBlClT0Ol0bNy4kddee428vDygtuzs/PnzZa2c6PGG9gpseVmBoqEieDDBhVuhncsKzo51YNCozNtl5ocsA6F9ruGR4CwCSvfSZ/db7B/9MC59+3Myt5aiugkq3k5Z2BichsO3s1wulaTMUvpG+LdYolcIIUT302MXhY0fP57FixdjNtdW8SkvL+f555/nggsu4Nxzz2X27NleAezixYsZP3780RyyEF3CpNcyoBW5Y52GIKx+MR261qm9nNz2vzyyH6daeC/gDmqM4RhrCumz520Ud8dKPLeWoroIKtrWII+sqkJKfiUpBZVNnCmEEKK76rFBLMC0adPYsmULZ511VqMzrIqicOaZZ7J582amTZt2FEYoxNERF+JHiKXlDBjVgf1wazp2u31qnIOr/pd+6+UD4XwTfTcurQn/8gMk7nsP2lDutiMU1UlQ0Z/o7OUN3kstqKKghcwNQgghupdWZyc41mVkZLB27VqysrIAiI2NZfLkycTHxx/lkfmGZCcQbWW1u9hwsKjFsqwGawGBJTs6dC1VrV0f+1O2Ab1G5a2BWzj90CtoVCcFvU4lq+810EVLeVQUqgP7YfVP8Hpdp1WY0CcUP0OrV1kJIYRoRmfHJsdNENvTSRAr2iOr1Mqe7IYzk0cKKN6B0VbQYrvmuNzwfJKZjYV6LDqV9/v9zvhDC1BQyUm4mLyE8zvUf1vZjWFUhAxF1RyuZuZv0jG+dyhajayNF0KIjuo2QeyqVat8fvGmTJkypcuu1VNIECvaa3tGaYu30hWXndD8DShqx9JS1bjgia1+7C3T4adVmR+zlJMLPgEgvf/1FEef3KH+28qtNVIePMwrc0F0kInhsa0vWiKEEKJx3SaI9VVBg5YoioLTKfkb20qCWNFedqebP1KLqHE0vzbVWJ1LQOnuDl+vwgHPbPdjT2ntbft3wz7izKplqCikDZlJWdjoDl+jbRSqjlheMDAqgIQwvy4ehxBC9CzdqtgB+KaoQVcVPRBCtMyg0zAspuWZxxq/6AbVr9ojQA9Pja3mgvja2d9biq7lZ90UFFR6751PcP7GDl+jbVQs5cn4l+6tXbwLHMivILOkusX1wkIIIY6eVs/E9u7du8tyqKampnbJdXoSmYkVHXUgr4JDRdXNtvHVsoI6K3P0vLnHhMvt5nXTm5zHOgAy+15NYczpPrlGW9iNoVSEDPOsk9VpFWKDzcSF+GE2aLt8PEIIcSzrNssJRPcmQazoKLdbZVNaMRW25gNUXy0rqHOwXMNzSX4U2GCu/gOu064AIDf+AnITLuyyrAV1XDo/ykJH4tYdXk6gKBBqMdA33J8gP30zZwshhKjT7ZYTCCF6Jo1GYXhsUIs78321rKBO30A3/5lYyZBgN486pvOys7b0c3TGMmIPftpleWTraJ3VBBduQW8r9rymqlBUaWfzoWIOFbWvFK8QQgjfkiBWCOFhMeoYFB3QYruKoMGoiu/yqQbo4Ykx1YwLd/KS83Ied9yEikJEzu/03rsArbP5ZQ6+pnE7CCrehn/pXhT34ZlpVYUDeZVszyjF4era4FoIIYQ3CWKFEF5igs3EhZqbbaNqDVQED/LpdY1aeHiklVOj7XzgOod77HfhQktw0Z8M+nMufuUpPr1ea5iqswku2Ii+psTr9YKKGjamFlNu65qyuUIIIRqSIFYI0cDAyACCW1j7aTdHYfPr5dPr6jTw92E2Loiv4Tv3SVxe8wTF2nAMNcUMSPoPURnLu355gctGUNGfWMoOgNvled1qd7E5rZjMkq6dJRZCCFFLglghRAN162ON+ua/IioDB+LS+TafqkaBvw6s4eo+NWxT+zOl6jn+NJ2Igpteh76l3855DWZGu4K5KoOQgj8w2Ao9r7ndsDengt3Z5bglHZcQQnSpTslOUFxczIoVK9i0aRNZWVmUlZVRU9N8RSDPgBSFX375xddD6vEkO4HoDKXVdraml+BuZvJT66gkuGATCr4P4r5OM7Ao2QSoPBe1kisrPkTrrsGhD+DgsLux+if6/JqtYTeGURk0ELfu8LKLAJOOUfHBmPSSiksIIeAYS7FVWVnJAw88wAcffIDNZmvz+aqqoigKLper5cbCiwSxorNkFFezL7ei2Tamqkz8y/Z3yvW/OWTg/QMmAG6PO8Tfq1/DXJ2FS2MkbchtVIQM65TrtkRFweqfSHVAb1BqZ6z1Og3DYwIJ8zcelTEJIUR3csyk2MrPz2fs2LHMnz8fq9XapspcUqlLiO4rPtSP6CBTs21sljhqTBGdcv1LEu3MGFj7R/FbmYn80/9RKoIGo3XX0Hf364TkbeiU67ZEQcWvMo2Q/D/Q2csBcDjdbMso5WBBpXynCSFEJ/NZjpwrr7yS5ORkAPR6Pddccw0TJ07krbfeYufOnSiKwnvvvUdFRQXZ2dmsW7eO1atXe2Zfb7rpJqZMmeKr4QghfGhQdADFVXbszqbXFVQGD0ZfUI7G1bqlQ21xcYIdDSrv7DfzcXowVXH383j4AsIKN5J4YCF6ewn5ced1eWEEAK3LSnDhFqoDelPt3xsVhYMFVRRW2hkWE4jF6LtUZEIIIQ7zyXKCFStWcN5556EoCkFBQfz000+ccMIJAEydOpUff/yx0WUCBw8e5O9//zvLli1Dp9Mxb9487rzzzo4O57gkywlEZ8sts7Ezq6zZNjp7GUGFWztlfSzA8gw98/fVrkMdGODgrdCP6JtXW+GroNepZPW92nNr/2hw6AOpCBnqqfal0UC/CH8SQv26rGy3EEJ0F8fEcoLPP//c8/zZZ5/1BLAt6du3L9999x233347TqeTv//976xcudIXQxJC+Fh0kIlQf0OzbZyGICqDfJs/tr7z4x08OKIai05lf4We8zOnszLsL57CCAn73/dKg9XV9I5yQgo2Yq5MB7cLt7u2OMKWQyVY7bLWXwghfMknQey6desAMBgM3HjjjW0+/5VXXmHw4MG4XC7uvfdeXwxJCNEJhkQHtlyW1hKD1S+208ZwUpSTeSdWMizYic2lcHPWRbzpNxO3oiG0YBN99r6N4j56RQgU1Y2lPJnQ/PWYK9JQ3A5Kqx38kVpEWbUURxBCCF/xSRCbm5uLoigMGTIEs9m70k/9W2hNpdnS6XTccsstAOzYsYOdO3f6YlhCCB8zG7T0Cbe02K4qaAAOQ1CnjSPCpPLUCdX8pa8NjaLyr+IpzFLvxaXoCCpOou+u19C42p4hxZc0bjuWioOE5q3DUpaMy17D1vQSiip9v2ZYCCGORz4JYisqatPvhIWFNXjPz+9wIvSysqbX040aNcrzPCkpyRfDEkJ0gsQwP/xNLWxWUjSUh4zArem8VFNaBa7ua+eZE6qJMLn5ynYC0+0PUqMYCSjbR7+dL6N1VnXa9VtLUV2Yq9IJzd+AxlrE9sxScsuOboAthBA9gU+CWIuldmbGbrc3eC84ONjzPC0trck+DIbDa+1ycnJ8MSwhRCdQFIUh0YEtJgJQtQbKQ0eg0rkbmgYHu3hpYiXjwh2scQ3nKtujVCoWLBWp9E96Ea2j+Ry3XUVRnQQWbcdQkcWu7DIyiqVcrRBCdIRPgtj4+HhUVSU/P7/Be4MGHd7ksX79+ib72LVrly+GIoToAkF+euJCWi436zQEUhk8pNPH46+Hf4yycmN/GzvUfkyzPU4RQZirs+i/4yVPHtejTUHFv2wffqUH2JdTTnJ+hZSrFUKIdvJJEDt8+HAAUlJSGszGnnjiiZ7n7777bqPVuBwOB2+++abnuE+fPr4YlhCiEw2I9G95WQFQ4xdNVWD/Th+PRoFpve08dUI1BfpYrqyZTb4ajLk6m347u08gC2CuyiCwOIlD+eWsP1hEfoUsLxBCiLbySRBbV6TA5XLx+++/e7138sknEx8fD9TOtl599dWkp6d73k9JSeGSSy7xbOYyGo2cfvrpvhiWEKITaTQKI+OC0GpbXi5g9U+g2r935w8KGB7i4sWJVZiCIrnaPptcNQRzdQ79d7yIzt58ntuuZKgpIrhgE/bKUpIyythyqITKGufRHpYQQhwzfBLETp061ZOF4IsvvvB6T1EU/vnPf3pKMH799df06dOHqKgooqOjGThwID/++KOn7V133UVISIgvhuWxbt06Zs6cydChQwkKCiIwMJChQ4dy2223sXbtWp9eq47dbue3335j9uzZTJ06lcTERCwWCyaTiV69enHqqafy6KOPeqqcCXEs8jPoGNqrdQmsqwP7dmrqrfpCjCpPjq0mJjKcq+2zyVZDMVlz/7e0oPsEsrXVvjZjrkijpLKGPw4WcSCve6zhFUKI7s4nFbsApk2bxo4dO7BYLKxfv75Bqq2//e1vvP766yiK0mRN8fPOO49vv/0Wnc43ZRqrqqq45557eO+995ptd/PNN/Pqq696Nqh1hM1m46677uKrr76itLS0xfaKonDLLbfw4osvEhAQ0O7rSsUucTTtzS0ns9jaqrYBJbswWvM6eUS13CosPGBkW0YpnxjmEqsUYTNFkDZ4Jjb/uC4ZQ2s5DMFUBA/FrTMRG2JmSCv/OBBCiO6qs2MTnwWxrfHpp5/y/PPPs337dq/XExMT+fvf/84999yDRuObkpEul4vzzz+fFStWeF4zm80MGzYMnU7H7t27KS8/vEbunHPOYfny5Wi12g5dt7CwkIiIiAavJyQkEBMTg8lkIi0trUGmhrFjx/Lrr78SFNS+3JoSxIqjye1W2ZRWTIWtFbfDVZXA4iQMNUWdP7D/+faQgZ+Sy/jE8DRxSiFuRUdO70spiDnjqJapPZKq6KgMGkiNXzTxoX4Mim7/H7ZCCHG0HRNlZ1vrmmuu4c8//yQvL4/Nmzezfv160tLSSE1N5f/+7/98FsACzJ492yuAvfXWW8nMzGTTpk2sX7+e7OxsZs+e7Xl/xYoVPP744z67PsDEiROZP38+mZmZHDp0iPXr17Ny5UpSU1PZunUrkydP9rTdunUrt912m0+vL0RX0WgURrRyfSyKQnnIcByG4E4fV52LE+1cPSyAafZ/8rNrDBrVSWzqF/Td9Vq3Wl6gqE4CSndjKdtPRlGlLC0QQohmdOlMbFfJzs6mX79+2Gy1O35vuOEGPvjgg0bbzp49m7lz5wJgMplISUkhJiam3dcuKiri6quv5p///CeTJk1qtq3D4eDcc89l5cqVnte2bt3KmDFj2nxdmYkV3UFRZQ1JWWW4XC1/rShuJ0FFf6Lrwjyue0q1vLDDxPnOX3lMtxiT4sChDyBjwA2Uh47ssnG0hsMQRHnICBKjQugf6X+0hyOEEG3Wo2Ziu8q8efM8Aayfnx/z5s1rsu3s2bM92RNsNhsvv/xyh64dFhbGzz//3GIAC6DX63nrrbe8Xvvqq686dH0hjqYwfyPje4fiZ2h5WY6q0VEWOgqXruV8s74yJNjFixOq2R54Ohfan2a3OxG9o4K+u98gNuUzFLejy8bSEr29jJCCTWRmZ3OwoPJoD0cIIbqdHhnEfv31157nV111FaGhoU22NRgM3HzzzZ7jrg4iBw4cyMCBAz3He/fu7dLrC+Fr/kYd4/uEEupvaLGtqjVQFjYGl9bUBSOrFWxUmTOmmlGJEVxqf4p3nFMBiMhZycBtz2Gszu6ysbRE464hqHAr2Wn72JlZKoURhBCinh4XxO7bt88rbdV5553X4jlTp071PE9OTmbfvn2dMram1A+y6282E+JYpddqGBMfTEJYy7Osbq2RsrDRuDUtB72+otXADf1rmDXKwYtcz032BykhAHN1FoO2PUtYziroJiutaqt87cd+4FeSdmzDVlNztIckhBDdgm9yWR0hNTWVdevWsXfvXkpKSrBarU2m1TqSoii8++677b72kZkPWnNbf+zYsRgMBk+1saSkJK9yuZ3t0KFDnueRkZFddl0hOpOiKAyMCsCs17Ivt/l1r26dH2Vhowkq3o7G1XVB2oQIJ/8aX8XjW0dyju15XjW9yYnuHcSnfExA6W4y+12L09A91phrXDVQsJcDJSkk9BlIUMwA0JtbPlEIIXoonwax69ev58EHH2TdunUd6qcjQeyePXs8zw0Gg2e9a3Pq2qWkpDToo7Nt2LCBnJwcz3Frgm4hjiXxoX5U1jjJKmk+j6xL709J+HgCS3ag78KMAXEWN8+cUMXjWwP5i+0h/m5azj3KZwQXbcO/dD/ZfS6jOGpyt0nF5XI6SDuwi14FqUTE9UMJHwA649EelhBCdDmffSu//fbbnHLKKaxbtw5VVdv96Kj6+Vfj4uI8lcRakpCQ0Ggfne3JJ5/0PDeZTFx22WVddm0husqgqAACTC3/zVy3RtZmju6CUR0W7afyzLgqYvxU5tku5FrXk5SaE9C5qklI/oj+O/6Dqar7rJVVgezSavbs3UVB0gpcBcngdh3tYQkhRJfyyUzsli1buPvuu3G73Z7XBg0axIQJE4iOjsbPr+t2H1dUHL5t2ZbCAfVTP9TvozMtXryYH374wXN855130qtXr1adW1NTQ029tXGyllZ0ZxqNwsi4YP5ILcLZUvotRUNlyFBcen8s5SnUhmydL9yk8vS4auZs9WNDZV+muOfyTOhyplZ+jX95CgO3PU1+3Dnkx52Luws3ojXH7nSTVVxJXulmQoP3E544DENIHGg6VrRFCCGOBT4JYl988UVcLheKohAbG8uHH37Iqaee6ouu26yy8nAqGpOp9b9o6pfJrd9HZ0lKSmLmzJme4/79+zNnzpxWn//ss896zeIK0d2ZDVqGxgSSlNG6pQJW/wScOguBJbtQ1FZUAvOBYIPKP0+oYu42P/aV6bg7/2ISNZN4xfI+oxzbiM74nrDcteQmXEhR1ORuEyw63Sr5xWUUlKzDz2jAFByFOSQG/7CYNn0PCiHEscQnywlWrVrlef7tt98etQAWwOk8/MtOp2t9jF6/rcPRubkic3NzueSSS6iurgbAaDTy8ccfExDQ+hKTjzzyCGVlZZ5HRkZGZw1XCJ+JDDCR2IqMBXUcpjBKIibg0Hfd5qoAPTx9QjX3Dq+mb4CLQ+4ILql4gJn2e8lVItA7yolP+ZjBW58kqHBLt8liALVDqbLZKcrNIHPPH+xZs4Tt638iLTsfl6TnEkL0MD6Zic3Pz0dRFIYNG8bo0aN90WW71V+6UFfwoDXqt7VYLD4dU32lpaWcd955nnW3Wq2Wjz76iPHjx7epH6PRiNEomznEsad/pD9lVgel1a37Y9GtM1EWPhZL+UHMVemdPLpaOg2cGu1kSpSTXaVavj1kYEXhOH61juF67S/cb/wKf1s+ffYuoMq/N9l9rqAqqH+XjK0tFFTUqkJK9vxOTvEoEuLiiQkytXqvgBBCdGc+mYmtm0HsDumh/P0Pl2e0WpvfDV1f3azokX34UlVVFRdccIEnDVhdOrHLL7+8U64nRHekKLXrY0Ms+jacpKEqqD/loSNxa9pwXgcpCgwPcfGP0VZem1TFiDB433UuE6vn8R6X4dAYsVSmMWDHv0ncOx+DrbDLxtYWiurElLuVg8n7+CO1mMJKyTUrhDj2+SSIHTlyJKqqeqWKOlrCw8M9z9syntzcXM/zsLAwn44Jamd6L7nkEq/0Y6+99hrTp0/3+bWE6O4MOg1j4kOIC21bnlO7KZzS8PFdurygTqzFzezR1dw/vBqdwchTtiuZVD2P342noaIQUriVwVvm0CttCRpn6+8CdRUFlYDSPbjz97EtvZQNB4vIKK7G6XK3fLIQQnRDPglir7nmGqC2ZOrBgwd90WW71S9SUFRU5DXD2pz6a0oHDx7s0zE5HA6uuOIKfvnlF89rL7zwAnfeeadPryPEsUSjURgcHciQmEA0bfgmqlteYLW0nAPa1xQFTol28vqkSs6OtVNIENPLbmOa8xkOGoegUZ1EZf7AkC2PE5H5Expn6+8GdRW/ykMElOzCWlHKvpxyVicXsju7nHJb5+4FEEIIX1NUHyRndTgcjB07lv9v777Do6ry/4G/b5mayaSHBAKhGQgoKEoTaYIUKXZRRIEvKL+VXd3F3ohtRRbLqqxtVcC2rlFEl6KCNEGUJggSEEJCEghpJJNMMvXe8/tjkstM6tQkEz6v55kn986ce+6594Thk3NPOXLkCCZOnIi1a9eC9+V/pSDavHkzxo0bp+zv3LkTV155ZbPHnD59GikpKR55jB07NijlkSQJM2bMwJdffqm89+yzz+Kpp54KSv51KisrERUVBZPJ5DFdGCHhoKLGjt8KTLA7fWsVVFuKEVmRBY61zRypRyoEvHtUi1yzAIBhhm4vHhc/RZSjCAAgCTqUJo9CSeer4VR7P+Vfa2HgIIt6OMUISKoIcPoY6KISEKlTI1IrwqhVQatqHzMwEELCT6hjk6AEsQCQnZ2NMWPG4MyZM5g8eTLeffdddO7cORhZ+8RsNiM+Pl6ZQ3Xp0qV4+OGHmz3ms88+w+233w7ANS1XaWlpUAZ3ybKMu+66C5988ony3qOPPoolS5YEnHd9FMSScGd1SPg1rwLVNt+m0+KdNTCeOwzRGfqp8RojMeCHMyp8ckIDk4OHCk7cb9yG2dw6RNpc3ZRkTkR54lAUdZ0Muza+hRzbFuMEODQxsGviYNfEgVProBZ510tw/YwzqJEYSVN3EUKaFzZBLAAUFRVh/vz5WLduHdRqNSZMmIBhw4YhMTERarXa63zuuuuugMoxZcoUrF+/HoCrv27dQKqmTJs2DWvXrlWOrdsOBGMM99xzD9577z3lvb/+9a949dVXA867MRTEko7AIck4mF/h9cwFClmCseL3Nh1YVe0EPj+pwbp8NZyMgwAJixL2YhZbiyiza0lrmRNRmjwKRV2vhaQKzQDSYHOoo1FjSIVD6zlWID5Sg75JkdRSSwhpUlgFsSaTCa+++ipefPFF2O12v6Zx4TjOY65Xf2RmZuLWW29V9r/55htMmzat0bT79+/HkCFDIEmScuzNN98c0PkBV8D62muvKfsLFizA22+/HXC+TaEglnQUksxw+LQJJVU+jqBnDJEVR6CxFIWmYF46Xc3jwxMa/FLimkVBKzD8NekwbnN+hWjTUQCAJGhRnDIBJZ3HQRbCY6o8p2iAxdANNl0nV+dgAALPoWdCBLrF6mnaLkJIA2ETxB4+fBgTJkxAUZHnfyC+Zs9xnBJQ+osxhssuu0xpgU1OTsbmzZsbDNgqLCzEuHHjkJWVBQC49NJLsX///ka/jHNzc9GjRw9lPyMjo8kVtp566ik8//zzyv6cOXPwwQcfhPRLnoJY0pEwxpBVWIUzFT4OjGIMBtMxaGvOhKZgPvi9XMDK41ocr3S1VMaoZTzXdR+urvgC+mrXQFKHytjuVv9qiSRoYdcmgPEiZE4E41WI0GnQq2tnRNF3DyHETVgEsYWFhRgwYADKysrOZ8xxSElJQVJSkscCBN7YsmVLoEXCnj17MHr0aGWuWKPRiD/96U8YNWoURFHE7t27sXz5ciXo1ul02LZtW5OLDngbxO7evRtDhw5V9nmex7hx47we6NapUyesWrXK28tUUBBLOqITxWbkllb7fJy+8gT05tZZGKE5jAE7i0R8lK1FkcX1HTA1xYK/xf6ElLyvoant/mDTJqCw2zRUJFwBcG0zKDZgHIfITr3Q9aIBUNNCLIQQhD42CcqKXUuXLkVZWRk4joNKpcITTzyBu+++G0lJScHI3i+DBw/Gxx9/jFmzZsFisaCyshJLly7F0qVLG6TV6XT4+OOPfV41qzH1p/SSZRkbN270+vjU1NSAy0BIR9E70QCNyOPY2Sqfjqsx9gbjRERUte2UfxwHXJXkxNBEMz4+ocHXeRqsLdDhiGkMHup/OS6p3IZO+RugsZag+x8fwFLwHQq7X4fKmEuUR/ZhgzFUnT2BoyWnkNCjP5K6pYELk9ZlQkh4Csqf/OvWrVO2V61ahaeeeqpNA9g6N954I/bt24fx48c3+iif4ziMGzcOe/fuxY033tgGJSSEtKRrrB4Xd4nyaS5ZALBEdoc5Ki00hfKRigfmptnw5KU1iFTJOFkl4G97ovAlfw2yrngOZ1KvgyTooKs5jZ5H3kSvw/+EpuZsyxm3Q7LkQNGJAzj68waYzmQDtraZNYIQ0vEFpTuBVquFw+FASkoKTp06FYxyBV1+fj527tyJ06dPAwC6dOmCESNGoGvX1p8wPRSoOwHp6ErNNhwqMEGSffvK0tScRWRFFoCgjWENSKmVwyuHdThS4XoQdlmcEzd1t2FARCU6nf4OCYVbwMsOyJyA4i7XoKjrtWCC97O7tDcakUe0QY/Y+ARoDPGALhrQxYRNH2BCiP/Cok9sYmIiysrKMGbMGI9VqUjroSCWXAgqauw4kF8Bp+Tb15baUoLI8sPg2kkgK8nAf3M0+CJXDZm5nhL1iXLipu52XGkoRNeT/0VU+WEAgF0Ti4Ket6EybkBbFjkoDBoRUToVVCIPMSIGKkMcVIZ48PoYQFC1dfEIIUEWFkHs4MGDsW/fPlxxxRXYvXt3MMpFfERBLLlQmG1OZBVWwuTjXLIq2zkYz/0Gjvm2Klgona3hsOaUBj8UquCQXcFstwgJM3taMUHYh5Sc/0JtKwcAVBu6ozxxKCriL4dT3bH+jWtEHokxRsRGR4PTGACVHtAYAX0cfO5HQghpN8IiiH3hhRfw5JNPQq1Wo7i4mIKoNkBBLLnQFFdZkV1c7dMKX6LdBOO538DLPi6mEGLlNg7/y1NjQ4EaFskVzF4S48TdvUwYWrEWiac3KsE3A4+qmHSUJwyGKe5SyELHWTlLI/JIjtIiWl/bfUJQA8YuQFQKoAmPxSEIIeeFRRBbUlKC/v37o6ysDIsWLcKyZcuCUTbiAwpiyYWIMYazlVacLKmGxe7d/NKcZEdkRRbUtrKWE7cyswP4+pQGX+epYZc5cGAY38WBuV2L0c20BzEluxFhPj/uQOZVMMVegor4waiMvRiM7xiP5HUqAcnRWhi1bteji3EFtBoDIOoAVccJ3gnpqMIiiAWAzZs3Y+rUqbDZbHj++efx2GOPBSNb4iUKYsmFTJYZCsotOFlq9rq/rKb6DAyVx8GxwBZXCYViC4cPT2ixo8gVxOkEhhu72zCtmx1GexFiSvYgpng3tNZi5RhJ0KIi7jKUJw51zcoQrvPNutFrBCQZ6wWzdTgeUOkAVQRgSAAik6lfLSHtTFgEsXl5rknF9+/fj/nz56O8vBz9+vXD3LlzMWzYMCQmJkKt9n50bbdu3QIt0gWHglhCALtTRk5pNQrKa+DNNxvvtCCy4ghUdlPoC+eHrAoB7/+hxYnaVb+i1DJu6W7HxBQ7VByDrjoPMSV7EV2yF2p7uXKcTROPc0lX4lzicDg0MW1V/KCJ0Ajo1FQwW4fjAX08YEwGDJ1o9gNC2oGwCGJ5nveYh7UuS3+WWeU4Dk6n933ciAsFsYScV21z4nixGaVVtpYTMwZtdQEiqnLAsfb33SMz4MezIv5zUouztat+xWtkzOhpw9hkB0QeAJMRUXkSMSW7EVOyB4LkWqmQgUNlzMUoSxqBythLAC68AzudSoAouP5fqfv/pdH/ZXgessoApo6EpIoAUxvAaQxI7RQHrSq87wEh4SSsgljGWKOBqzencD9ektrf4732joJYQho6a7Ii62wlJC+6GHCSHYbKE9BY2uciA04Z+OGMCp/naFBmcwWzESLDpXFOXB7nxKA4J6I1DJxkR3TZfsSd3QlD5XHleIc6CmWJw3EuaQTs2oS2uow2JQoCUjvFItJgAERt7UtT+6rdFjSAEJTFLAm54IVNEBssFMT6h4JYQhpXY3fiUIEJVVbvWllFWwUMpj8gOtvnSlN2Cfi2QI3Vp9SosHt+9/Y2SrisNqBNM0rQW88itmgnYot/hspxfuneqqi+KE8cArPxIti18eG3xG0AOACdo3VIiNQ0k0hw9a8V1LUvVe3Ass7U75YQH4RFEEvaHgWxhDRNlhmOF5uRf67GuwPaeRcDAJAYcNwkYF+ZiH2lIk5WeT4m14sMA2KcGBTvxOBYK7pXH0Tc2R2IrMjyWPTBoY6C2dgL1cbeqDb2hiWiS9h3O/BGjF6FlBg9BN6HAJ7jgYgE15RfEQkXVPBPiD8oiCVeoSCWkJYVV1pxpLDS6xkMOMkOfVUOdDVn0F6WrW1KuY3D/jIRv5aJOHhOQJXjfCstD4YBsRLGJtsxyngWyaW7YKjIgt58Cny92RkkXoOayO6oNvZyvSJ7QhZ1rX05rUKrEhCtV0Er8tCoBGgEHry3Qa2gAjRRrsCW42p/8q4BZbxY+xIAXgVoIgEtfS+TC09YBLEffvihsj1p0iQkJiYGmiXxEQWxhHjH6pBw+LQJFT6s+CU4zDCYjkPlNgNAeyYxILuSx69lIvaXiThmOt/HUyswjEh0YHgnJ/oaLOhky0VE5QkYKk8govKkMiisDuN4mI1pMMUNhCl2IBza2Na+nFbDAVCLPPRqAXq1iAiNAJ1K8GuQcgP6eCC2BxARH3hehISJsAhi6wZ2GQwGFBUVQaulSahbGwWxhHiPMYbskmqcKqv2aiquOmprKXRVuVA5KkNXuBAorOGwtVCNrWdVKLJ49qPtpJORZpRwUZSE3pEOpAsFiK85iYjKbERUZkNjLfFIXxPRFVXRfeHQxMKhjoZDHQWHOgpOdRQY3/EGRPE8oFeJiNKpEBOhghjoGBCN0RXMRiZTdwTS4YVFEKvX62Gz2XDFFVfgl19+CUa5iI8oiCXEd+eq7fj9jAk2h+zTcSrbOeirToVNy2wdxoAsk4CthSocKRdQUNN439cErYzuBgndDTIu1hRioO1XdK8+gKiqEx79ad3JnAhz1EWoiumPyph+sOk6XpDGc0CUToW4CA0M2gADdl4F6KJdA8Z0MYA22hUxE9KBhEUQ26tXL+Tm5mLUqFHYsmVLMMpFfERBLCH+sTtlHD1bieJKL+aUrUe0m6A3n4LaWob23me2MWYHkF0p4I9KAX+YBORUCSi1NR1IJQsmXKfZj8uEHHQTy5HIlcMgmaCym8DXGwBn18SgKrofzFEXodrYG3ZNXIcKajUij0SjFrF6VXC6G3CCK6g1JLoWa1B1zH7I5MISFkHs1KlTsX79eqSmpiInJycY5SI+oiCWkMAUV1lx7GyVz62yAMA7a6CrLoC2prBdLmPrC7MDOGUWkGvmkVsl4EwNj7MWXpmbtj6twJBmdGKMoQAT1QfRtfowDKY/Gga16mhlBoRqY09Y9F06xKpaWlFA55gWVhPzhybSFczq4xp+xnGuoJcXan/WDiLrQH8kkI4hLILYjz76CLNnzwbHcdi9ezcuv/zyYJSN+ICCWEIC55RkZJd4v2xtfZzsgLamENrqAgiSNfgFbEN2CSix8ii08Dhl5pFVISCrQkS10zNw6hkpYVyiGdfqj6BLTRYiKrOhN58Cxzz/OJB5FWoMqaiO7ImayB6wGLqGdWutQSOic7QWenUb9QvmeEAdAWijXC+N0fWiLgqkDYVFEGuz2TBkyBAcOnQIQ4YMwZYtW6DT0aOQ1kRBLCHBY6px4FhRFSot3s9g4IExqK3F0JvzILotMtDRyAzIr+ZxpELAvlLX9F4Sq1sOliE9WkK/aAnpkTW4nM9GJ8txV1BblQtRajhnr1PQw2JIgSUiBZaIrqiOql2MIYxoxIZBIwcO4Fx9asEBPACB56FT8dCpRehUAtSNHBcwXgXE9QJiuoftHwckvIVFEAsAf/zxB6655hoUFBTgiiuuwLvvvouBAwcGI2viBQpiCQm+MrMNuWXVKK/2M5gFoLKVQ2fOg9pWFsSStU8mO4efikVsP6tCVkXDFskErYxekRISNE5cJBSij5yN7o5sJFlPItJW2GDOWgCwaeJgju6Lqqg+MEf3gVMd1RqX0upEnoNWJYDnOfConXIWHASBg0YUoBF5aEQeKsGPYFdtABL7ARGNdE0gJITCIoitmye2tLQUzzzzDMxm13KNgwYNwlVXXYWePXsiMjLS6+Vp77rrrkCLdMGhIJaQ0KmosSO3rAalVb4P/qrDO2ugtpVDZa+AylYBXvY/r3BQbOHwa5mIPyoFHDcJyK/mwdB0a2Ck4MBYQx5GaHNxMZ+Lro4cRFbnNuiG4BT0cGhiYNfGwa6JhV0TC2tEF9QYUiGpDKG+rDYn8hzUIq80rJ6/ow3vLc9ziNapEK1TuRZxMHQCEtNp0BhpNWERxNbNE+uOMebXiE2O4+B0ts9lHtszCmIJCb0qqwM5pdV+zWRQH++0QGUrh8ZaDLWtHOE4u4EvLE7gRKWAXLOAchuHcjuHchuPcjuHYgsPi9Tw/4vu2mpco83CCP4I+klHkGDLb3KKLwCwaWJhMXRDjSHV9Yrs0WFXG/OFyHOINagRH6GBWiW6FlyI7OyaCaEDDK4j7VfYBLHBwnEcJCm8R/e2BQpiCWk9VVYHcktrUFQZnMFbnGSHxloMjaUIKrspKHmGk7q+tUcrBBw1CThaIaDQ0jC40sOKLlwpunAl6MKVoQtXim5cMdK5U+jFFzaSL4ccJOMweuF39EaNJgGJRh1SorXoFqOFqNZeUH1FOQAGrQidWoCK56FWq6GKSoI6ujN4TUTtLAcqJbD1egleQpoQFkHsmDFjgjNPXi2aa9Z3FMQS0vrMNidyS6tRUmWDJAenJZV3WqCtOQ1tTSF42f++uOGu0s4hr9o1E8Ips4BTZh5nanjYJA4yA5zM8/+cSNSgP5+Li7kcDOBPYiCXjVS+uNlzOCHAwulhFyIgqSIAlQ6CJgKyKgJO0QCn6vzLoXZ1YQDX8Uf7M3BgvAimMQIRCRAiE6DSRUKrEhCpFRGjV/vXN5dccMIiiCVtj4JYQtqOU5JRXGVDocmKihq7X9NzNSBL0FiKoKsugOg0ByHDjkdigCQDsrLNQWKAk7lad0V7FSKrcxFVnYOo6lzwNhNEZzX0shlazvc/ECROhFWbCIe+E6y6TnBoYsB4FWRe7XoJKsiCFk7RFRRLgq7DBL2yoIFdEwdJ1IPxIiL0ehgjdIiOjIBGowUvqsFx5/vqqgWeWnIJBbHB8NNPP2HVqlX48ccfcfr0aTDGkJKSgquuugqzZ8/GiBEjQnr+kydPYuXKlVi3bh3y8vJgNpvRuXNnDBgwAHfccQeuv/56iGJgcwtSEEtI+2B1SCg0WXG63AKrIzhdo0RbBTTWUqjs5R16yq7WwhhQVO1EbpkFFdUWWCwWOG01kO01MLBqxHBmxKAKcVwlYrgqxKIKXbhSnwNfBg6SGAGH2gi7Jg52bTzs2jjYtPFwaGJdrbyiAUxQh+hKWxMHmRfB6gJ6UQtBHw1VRDS0EdHQa9WuAWnnkwMAeI6DyHPgeQ4Cx0HgOQqAOxAKYgNQXV2N++67Dx988EGz6ebOnYs33ngDERERQS/Da6+9hkceeQQ2W9MDQYYNG4ZPPvkEPXv29Ps8FMQS0r4wxlBSZUN+eU1AU3TVx0l2qOzlUNvKITirIThrLuhuB8EkM6DMxqHCxqHKUffiUengcLYaqK4qh8F2Fj25QvTiziCeq4QWdmg5O3SwI1KwIxIWGJgZOnjfX1riVZBqA1qnKhJOtRFOVSQcqkhIKgMY6rXmchwkQQtJ1EMSdZAEPSRRD1nUBvmOBAvnKqugafAJ48TaLhuua5UFDXgeMGhUiNarEKVzvbQqGoAWjiiI9ZMkSbj22mvx/fffK+/pdDr0798foijiyJEjqKysVD6bMGEC1q9fD0EI3j+U5557DosXL1b2eZ5Hv379EBsbi+PHj6Ow8PxAhJSUFOzevRvJycl+nYuCWELaL7PNibyyGhSaLMHpalAPJzsgOC0QnDXQWIouiDlp24pVci3Le7LKtSxv3fK8Ntmz5VANB6JgRixXhUSuAl25EnTlipWfydw5RKMKai54A5klQVvb4hun/LREdIElopurz28YkHmV0g2DcTwADuA4CKIInchDo+KhVYnQqlzbGlGAyqPVlgNUekClrf2pAxqboYLjLqhBfW2lQwSxZrMZ5eXlsNlsiI6ORkxMTFCDxcY8/vjjWLJkibJ/991348UXX0RsbCwAVyvt0qVL8dxzz3kc8/e//z0o5//uu+8wefJk1N3e4cOHY+XKlUhLSwMAyLKMzMxMzJ8/X5lXd8SIEdixY4df56MglpD2z2xz4nhRFcrM9pCeR3CYoTfnQWMpQkefuqs9kBhwtoZHrplHuY2HzFzvyXC17jplDg4ZsMuAQ+ZglwGzg0OJhYPZaoNWrkYsqhDLVSGeMyEOlYjnTIjnTIiGucEMsCpOgoGzwIgaRKIGRq4aajQ/NWWlGI8aQ1fIxq6w6xLgUEfBoY6CUx0NuZEW0nAi8IBacK16phL4BrEpB0Al1AW9PFS8e3cFztVvmeMbm2o3COqCZbefTSbl3dLyPgbZXL1jm0gjagBBA4hqQFAD2mhXwB8iYRnEWq1WfPLJJ/j666/xyy+/oLS01ONzQRBwySWXYOTIkZg3bx4uueSSoJ7/zJkz6NWrF6xW1+OcO++8U1mQob6nnnoKzz//PABAq9UiOzsbnTt3Duj8jDFcdtllOHjwIACgT58+2L9/P/R6fYO0mzZtwjXXXKPsr169GjfccIPP56QglpDwUWa24XixGWZraOfE5p1W6KrzIDgtYLwImVeBcSIYL4KXbFBbSyBIwZkmjPiHMcDsBIosPEqtvNKNobLup51DhZ1HhZ1DhZ2DQ248sNHBis5cGbpyJejClSKFK0EqV4R+3Cl054uaLUMNtKjhdHBABQdUsEMFB6dCNaeHmTeimo9EtRAJi2AEp9JCq1YjQiPCoFEhUidCo9ZAFtSQeG1YzDtbF9QGM2jlANfqagIPlchDLfBQizz4RgJRngPUIg8xiNOT+q1TfyC6W8iyD7sg9sMPP8SiRYtQXl4OAGgqe/cpuaZNm4a3334bSUlJQSnDww8/jGXLlgEA9Ho98vPzlRbY+ux2O3r37o38/Hzl2KVLlwZ0/vXr12PKlCnK/rfffouJEyc2mf62227Df//7XwDAkCFD8Msvv/h8TgpiCQkvjDEUmqw4U2FBRU3b9WkVHGaoraVQW0uhclSBWm7bL8aAGsk1/ZiTcZBk10wMEuPglBumd8qu+XfPVlqhripAgu0U+nKnkMydQyLKkcSVQ88Fd+U4BxNQw2lhgwY2TgM7p4aT08DBq8E40dVQiNoXB0icChbRCJsqGjaVEQ51NKCOQISKR4Sah1YUAN7VUtrcim8KjqudQUEV1OsKBZ5zBdMqgXMtGlXv88auVgmdOIADB1eDsv/ReGTqQEQl9/L7+JaEVRC7cOFCvP3228pqXb5knZCQgK1btyI9PT3gclx00UU4ceIEAGDOnDlYsWJFs+kzMjLw7LPPAgB69+6N48ePB3T++fPn4/333wcA9OjRA9nZ2c3Oo7tlyxZcffXVyn5+fj5SUlJ8OicFsYSErxq7E4UmK4pMVtTY23CxFyZDcNZAdJghOKsh1g0cc1qaXSmLhAenDBRaeFicHGwS4JAYmNMG0WGCKNshMgcE2QGROSEwO1RSDTTOSmidVdBJVdBLlRAka20aG1TMAR1s0MEGVRD79gaDg9fALhhgEyJgFyLAcRw4DuDhCgR5DmAcDwYejBNqX7V9cP1Ul2/9HBgnABznOh/HA/UH6tUefL4c7uXx7sQMPBh//thGg36OhyToagcEugYFxvYdgeSeA0LWPzjUsUlg8zq5efHFF/HWW28pwRrP8xg3bhymTp2KAQMGID4+HhqNBpWVlcjOzsbPP/+Mzz//HAUFBeA4DiUlJZgwYQIOHjzYZKupN44dO6YEsAAwadKkFo+ZPHmyEsSeOHECx44dQ58+ffwuw7p165TtiRMntrgQxMiRIxEREYHq6mrl+AULFvh9fkJIeNGrRfRKMKBXggEmiwPVNidsThlWh1T7klFjd4ZkUJgHjoekMkBSGTzfZwy8ZIHorIHgrKmdFcEKQaoBL9lBrbfhQeSBrhH1m2xVAOK9Or6mkfesElDjdC0XLzvtkB02MKcdstMGp9Phes9pB5w2MFmCXDuHrwzXohWibINBMiFSrkCUbEK0XAEDqiEwCSKcECBDBQkCvAuSRc51fSrZBpVsQ4SDBjk2aw+Qd+kD6Hb94pbTtkNBCWILCgrw3HPPKcHakCFD8P7776N///6Npr/ssstw880348UXX8Rrr72Gxx9/HE6nE2fOnMHTTz+N119/3e+y1PVDrTN8+PAWjxk0aBDUajXsdtdgi99++83vILa4uBhnz5716fyiKGLw4MHYunWrcn5CyIWpbkqh+pySjAqLAxU1DlTU2FFpdUBu5BFySHAcZFEPu9iwX7+r9dYCXq59LO0Wz3LMAZWtAmp7BQRndeuUlbQqrQBoBQZoBAC62pf/KmtfAGCTgEoHB5Odg8nOw+wEqmv7Clc7OZgdHKyS62VxAhaJg90puwa7wYwomGGEGQbUQGbMtRCGfL7rhcDJECF5vIKNhwwBrPanDIGTGn2qIcBVFpVSFicEL/845CFD4GSo4Gz2WgTIMHLViEI1orhqGFEDkZORX6NC6HrFhlZQgtgVK1bAYrGA4zgMGzYMmzZtgk7X8i+yKIp44IEH0Lt3b2Uw04oVK7Bs2TJoNP6NlszKylK21Wo1unbt2uIxdemys7Mb5BHI+QGgVy/v+pr06tVLCWIDOT8hpGMSBR7xBg3iDa7vRllmsNS11Na22lrsEspr7LA5Wiu6RW3rbQQkND6Fk13XCdWom9+2Aip7BURHFURHFTjWiuUkYUcjAAkCQ4KWwTXXg7c4AJG1r8ZJcnCfHzC4ZqSwS+dnorDLrtbmBudm52epsEuun07mSi/Vzmwh1bZYNzwPV3s8lKBc8vdCGENMlBGTrwi8G2dbCUoQ++233yrb//73v70KYN1dd911uOWWW5CZmYmamhps27YNEyZM8Kssubm5ynZKSkqLj/LrdOvWTQli3fMI5Px1+Xp7/qbyIISQ+nieQ4RGRISm4dd4RY0dRZU2FFVaYW9sxE8bYIIadl0i7LrE2jdYbf9bV0Ar2k2uwJa6JpBWIIRgYgAValulFe3/d7nTRd2QnOrbGJz2JChBbE5ODjiOQ1paGvr16+dXHjfddBMyMzOV/PxVVXV+ScaoqCivj3PvcOyeRyDn96UMvp7fZrN5rALmvnADIeTCFq1XI1qvRlonA8prHCivsaPK6kSV1dG6rbTN4ThXC64qAjbUzkwjS1A5KiHaTVA5Kl3dFCRLWLTYijwHnVqAWDv/qMxcIQxjDIwxVz9QGZAZq320zULfxznIGDiAE5Q9z591u65915RuGsiC6yUJWteAo0Z4/OHCWO2ZmLINMHA+3Su59neGgavLo7ZcPA+IPA+Bdw30gtvvVqC/ZzzHQeABoTZ/kXddnce1MAaeYxA4DhwPCBznmoaLA5gsu15MhizLAGMN/qhjyu+V63cJcP1ugcn17pl39OqgDY1qE0Ep/blz5wAgoPlV3Veqqpueyx91CwcArnlfveXeeuyeRyDn96UMvp5/yZIleOaZZ3wrHCHkgsJxHGIj1IiNUCvv2Z2yK5h1ymBwdUtw/cfIYHfKMNucqLG7uia0Ol6AQxMDhyYGFre3OckOQbKClywQJBt4yVq7bwMv2cDJDp9bcPVqAUatCI5z9aO0S7LyE3AFpQLPQ+Q5iDwHnueUaaE4zrUtChy0KgE6leCad9RHsuwKZmXGIMkMTpnBITE4nBLsEoNDkl0zCNSmYQyQUVtffgTAHFcbNPEceEF0rWilNoBT6wFObJAWogqcSgte1AIqLQRRA6H2XrjyceUHQCm/VPsC0CAtB+58QF97TbIMSIxBkmXXlGGyDMZc99a9DuoWJ+DcysfBlS/PcUow2NTUq66prFzBZYfX5C8IA2QJYJLrp+x0rWgWxoISxEZHR6OkpATFxcV+51FSUqJs+9KCWp/TeX7ycFH0/vLc0zoc/s/Z6H5+X8rg6/kfe+wxLFq0SNmvrKz0qv8vIeTCphZ5xBlaHnMgyQzVdieqbU6YrU6Yba5XXUuuUNvyaNCI0KtdQZxdkmFzyLA5JdicrqDQPajxB8e5/siP0ERCp3a15NUFP0pgxxgkWQKTnWCSBCY5wNsqIVrLINoqwDltYGDQq0UYdSoYtaJfQWew8TyHhrODAuBFV3Ch0gFiIw0hjIFJrpH/TKqdEUByeLQqepxDHQFeZwSnjQI0UYA6IqSrNJE21tySumGwGIUvghLEduvWDcXFxcjKysKpU6eQmprqcx5r1671yM9f7qti1a3Y5Q33tBER/q8xXX9VLqvV2uhKXYGeX6PR+D34jRBCWiLwHIxaFYxaFeDWruCQZDglBq2K93rMAWPnW+kcbi2eNsf5FlBXy5trpSNR4CAKHCLUInQqwW2JUD/ZzEBNGeC0nW+FUn7K9fYlQHICsjeNGRx87/fIuYJIdYQrQBXUtUuA1i4FqtK7lgb14swNwhFZdrWusdpWNiYDqghACO9HxoQ0JSi/2RMmTMDevXvBGMPChQvxv//9z+svNwD46aef8NFHHwFwzRQwZswYv8tiMJyf39BisTST0lNNzfkZ8NzzCOT8dWXwJogN1vkJISSUXI9lfTuG4zioBA4qAdD6enAwaAyuly8YAyR77as2oOVF10tQuX5yXO1j2dqgse7ltAGSDXDaAWdtA4XWCGiMgDYqdK1hPA/w6pbTEdJBBOV5yl133aU8Dt+wYQOuu+46j7lSm/PFF1/g2muvhSRJ4DgOM2bMCKglND7+/KTNhYWFXh/nXt64uLignN+XMgTr/IQQQoKA41wtoppIQB/remmNgFrvCmLrGmp4wdWCqta7PtfHAsZkIKY7kJAGJA9wvWK6uz7rYI9zCWlLQQli09LScP/99yvLzK5btw5paWn4v//7P2RmZuLo0aMoLS1FZWUlTp8+jR07duDll1/GFVdcgRkzZigj66OiovDCCy8EVBb3RQrKyso8Wjibk5+fr2z37ds3KOcHgLy8vFY9PyGEEELIhSBoHWWWLl2KkydP4quvvgLHcTCbzVi1ahVWrVrl1fF6vR5r164NaIYDAEhP95y098CBA7jyyiubPeb06dMeA8vq5+GLiy66CKIoKgO8Dhw4gGuvvbbF43799degnJ8QQggh5EIQtOGZPM8jMzMTL7zwAtRqV5+cupbZunnymnoNGTIEe/fubTHY9MaQIUM8Bjzt2LGjxWN+/PFHZVur1WLIkCF+n1+tVmPo0KE+nf/s2bM4ceKEsj9q1Ci/z08IIYQQciEI6hwjPM/j0UcfxalTp/Dcc89h8ODBUKkargEOuOaUnTFjBr777jv8/PPPQXuEbjAYMG7cOGX/k08+afEY9zTjxo0LqE8u4FqBrM6mTZtQVFTk9fmjo6MpiCWEEEIIaQHHmD9TJnvPbrcjPz8fFRUVsNlsiIqKQkJCAhITE0N2zszMTNx6663K/jfffINp06Y1mnb//v0YMmQIJElSjr355psDOn9BQQF69+6trKi1aNEivPzyy42mNZvN6N+/v9J3duHChVi+fLnP56ysrERUVBRMJpPH6l+EEEIIIW0h1LFJyGd7VqvV6NWrFy6//HJceeWV6N+/f0gDWAC4+eabMXDgQGV/wYIFOHr0aIN0hYWFmDVrlhLAXnrppbjpppsazTM3N9e1Skvt6+mnn27y/CkpKViwYIGy/9prr+HLL79skM7hcGDu3LlKAKvT6fD44497dY2EEEIIIReyDjkDMsdx+Pe//43Ro0fDYrGgsLAQQ4cOxZ/+9CeMGjUKoihi9+7dWL58ufKoX6fT4d133/VpftvmPP3009iwYQOOHz8OSZJw6623YubMmbj++usRGxuLY8eO4a233sJvv/2mHLNs2bKAB7YRQgghhFwIQt6doC2tXr0as2bNanHRA51Oh48//hg33nhjk2lyc3PRo0cPZT8jI6PZ1lgA+OOPPzB+/HiP6bOa8vDDD2Pp0qUtpmsKdScghBBCSHsS9t0J2tKNN96Iffv2Yfz48Y22sHIch3HjxmHv3r3NBrD+SktLw2+//YZ58+ZBp9M1miY9PR1ff/11QAEsIYQQQsiFxqeW2GeffTaUZVEsXrw46Hnm5+dj586dOH36NACgS5cuGDFiBLp27Rr0czWmqqoKmzdvRn5+Pqqrq5GcnIxLLrkEl112WVDyp5ZYQgghhLQnoY5NfApieZ4PWp/R5tQNtCLeoyCWEEIIIe1JqGMTvwZ2hbIbbWsEyR1RXZ3ULeFLCCGEENKW6mKSUMWNPgWxQ4YMCXqQWVhYiLy8PHAcF9LguKOrqqoCgFbrHkEIIYQQ4o2qqipERUUFPd82m52guLgYL7zwAt555x3Y7XYArkid4zjqTuAHWZZx5swZREZGhqw1u7KyEl27dkV+fj51WWgnqE7aH6qT9onqpf2hOml/gl0njDFUVVWhc+fO4PngzyXQ6vPEVlRU4B//+AfeeOMN1NTUKO8zxtC3b18888wzrV2kDoHneaSkpLTKuYxGI33htDNUJ+0P1Un7RPXS/lCdtD/BrJNQtMDWabUg1mw249VXX8Urr7yCyspKj64DPXr0QEZGBmbNmhWSSJ0QQgghhHQsIQ9irVYrli9fjn/84x8oKyvzCF67dOmCJ598EvPmzYModsjFwwghhBBCSAiELHJ0Op1455138MILL+Ds2bMewWtiYiIeffRR/OlPf4JGowlVEUiQaTQaZGRkUJ21I1Qn7Q/VSftE9dL+UJ20P+FWJ0Ef2CXLMlatWoVnn30WeXl5ymAtxhhiYmLw4IMP4v7774derw/maQkhhBBCyAUkqC2xn332GTIyMnDixAmP9w0GA+6//348+OCD1HmbEEIIIYQELChB7Ndff43Fixfj8OHDynuMMeh0OixcuBCPPPII4uLignEqQgghhBBCAgtiv//+ezz11FPYu3ev8h5jDGq1GnfffTeeeOIJJCUlBVxIQgghhBBC3PkVxO7YsQNPPPEEduzYAeD8cmKCIGD27NlYvHgxunXrFrxSEkIIIYQQ4sanSVn37t2LSZMmYfTo0dixYwcYY2CMged53H777cjKysJ7771HAWwH8tNPP2HBggXo168foqKiYDQa0a9fP9xzzz3YuXNnWxcvrFVUVOCrr77Cfffdh1GjRiEpKQkajQYGgwHdunXDtGnT8M9//hPl5eV+5X/o0CEsWrQIAwYMQGxsLAwGA/r06YM77rgD3377bZCv5sKQm5uLiIgIcBynvJ5++mmf8qB6CVxFRQVWrFiBadOmIS0tDZGRkTAajUhLS8PVV1+Np556Clu2bIHNZvMqP6oT/xUXF2PZsmWYNGkSUlJSoNfrodVqkZycjLFjx2Lx4sXIycnxOV+qE08lJSXYsGEDnn32WUyfPh3Jycke30MrV670O+9Q3euTJ09i8eLFuPzyy5GQkACdTodevXrhhhtuwBdffAGn0+l33grmA47jGM/zjOM4ZfvGG29kv//+uy/ZkDBgNpvZ//3f/zEAzb7mzp3LzGZzWxc3rGRlZbGpU6cytVrd4v0FwPR6PXv11VeZLMte5e9wONhjjz3GeJ5vNt8pU6aw4uLiEF9txzJx4sQG9zEjI8OrY6legmPFihUsLi7Oq387mZmZzeZFdRKYV199lel0uhbrQRAE9tBDDzG73d5inlQnngoLC1lqamqL93jFihU+5x3Ke/3Pf/6TaTSaZvMdNmwYy87O9rnc7nyaYovneXAcp+z37t0bAwcO9PZwr3Ach//+979BzZP4RpIkXHvttfj++++V93Q6Hfr37w9RFHHkyBFUVlYqn02YMAHr16+HIAhtUdyw88UXX+CWW27xeE8QBPTu3RudOnWCJEnIysrCuXPnPNLMnz8f7777rse/wcbMmzcPH3zwgbKvUqnQr18/GAwGHD16FGVlZcpnAwYMwM6dO2EwGIJwZR3bxx9/jDvvvLPB+xkZGV61xlK9BIYxhnvuuQfvvfeex/udO3dGt27doNPpUFxcjGPHjiktPJmZmbj55pubzJPqxH+PPvooli5d6vFe586d0bt3b3Ach5ycHOTl5Xl8fvPNN+Pzzz9v9juM6sRTbm4uevTo0WK6FStWYM6cOT7lHap7/dxzz2Hx4sXKPs/z6NevH2JjY3H8+HEUFhYqn6WkpGD37t1ITk72qewKnyLe2tbXUL3q8idt67HHHvP4a+nuu+9mZWVlyudms5k99dRTHmkef/zxNixxeMnMzGQAmCiK7Prrr2dr1qxhJpPJI40sy2zNmjWsS5cuHvf5zTffbDbvd955xyP99OnTWUFBgfK53W5nb7zxBhNFUUkzc+bMkFxnR1JSUsLi4+MZAJaens46d+7sU0ss1Uvg/vKXv3jcw5tvvpkdPHiwQTqLxcLWr1/PZs6cyb7++usm86M68d+PP/7oce/S0tLY1q1bG6Tbs2cPGzRokNcthlQnDeXk5CjXmpCQwCZNmsSefPJJtmbNmoBaYkN1r7/99lvGcZxyzPDhw9mxY8eUzyVJYp999hkzGAxKmhEjRvhUdnc+B7GhflEQ27ZOnz7NtFqt8st15513Npn2ySefVNJptVp2+vTpVixp+FqzZg2bP38+O3XqVItp8/LyWFJSknKf4+Pjm3wkV11d7ZF2zJgxzOl0Npr2vffeU9JxHMf27dsX0DV1dLNmzVLu17Zt2zwe77UUxFK9BO67777z+A/3lVdeCSg/qpPAzJgxQ7knUVFRzX73V1RUePx7ueKKKxpNR3XSOJPJxDIzM1lubm6Dz/wNYkN1r2VZZgMHDlTS9+nTh1VXVzeaduPGjR7lX716tdfld+dTd4K5c+d6mzQgK1asaJXzkIYefvhhLFu2DACg1+uRn5+P2NjYRtPa7Xb07t0b+fn5yrH1Hy+RwL377rtYsGCBsr9p0yaMGzeuQbo333wTCxcuBODqlvP7778jPT29yXyHDRuGX375BQBw6623UjeeJnz//feYOHEiANd34AcffIDu3bvj1KlTAFruTkD1EhiHw4G0tDTk5uYCAGbPnh3QIBaA6iRQXbp0wZkzZwAAf/7zn/HGG280m/6ll17CQw89BMB1v61WK9RqtUcaqhPfuXfL8KU7Qaju9fr16zFlyhRl/9tvv1W+Oxtz2223KXkNGTJEOYdP/Ap9SYfVu3dv5S+jOXPmtJh+8eLFSvrevXu3QgkvPGfOnPH4i3X58uWNphs3bpzHX9Yt+eCDD5T0BoOBWa3WYBc97FVXV7MePXooreClpaWMMeZTSyzVS2Dqut8AYGq1mhUVFQWcJ9VJYFQqlXI/3n777RbTb9iwweM7rLGWW6oT37nfU19aYkN1r+fNm6ek69GjR4uDkTdv3uxxDfn5+V5fQx2fptgiHduxY8c8lgyeNGlSi8dMnjxZ2T5x4gSOHTsWkrJdyOq3hLsPqqtjNpuxfft2Zd/XujObzdi6dav/heygnnrqKWV6oJdeesnnlQepXgLnPpDruuuuQ2JiYkD5UZ0Ezn1wj91ubzG9+1RnHMchKirK43Oqk9YTynu9bt06ZXvixIktDkIeOXIkIiIiGj3eWxTEEsXBgwc99ocPH97iMYMGDfJ4LPTbb78FvVwXurrH1nUa+0/8yJEjcDgcyr43dZeUlITu3bsr+1R3nvbt24fXXnsNADB69GjMnj3b5zyoXgLjcDg8/sOcOnVqwHlSnQRuyJAhyrZ7QNSUbdu2KduXXnqpR+ACUJ20plDd6+LiYpw9e9anfEVRxODBg5vNtyUUxBJFVlaWsq1Wq9G1a9cWj6mfzj0PEhyrV6/22G/sy6H+fe/Vq5dXebuno7o7z+l0Yv78+ZAkCWq1Gm+//bZf+VC9BObgwYMerXjDhg0D4FqEZe7cuejduzd0Oh1iYmLQv39/3Hvvvfj555+bzZPqJHD33nuvsr169Wps3ry5ybQHDhzAO++8o+w/+OCDDdJQnbSeUN3rtqpDCmKJom7gBOCau62lRwF13Fdoc8+DBM5kMimtgYBrrr5+/fo1SOd+30VR9HrOPaq7xr388ss4cOAAAOCRRx5B3759/cqH6iUw7i0zPM+jS5cuWLBgAUaMGIGVK1ciOzsbVqsVFRUVOHLkCN566y0MHz4ct956K8xmc6N5Up0Ebvr06fjLX/4CAJBlGZMnT8bjjz+OQ4cOwWq1wmaz4ejRo/j73/+OkSNHoqamBgCwaNEizJw5s0F+VCetJ1T3uv573q7cGmgdij4fQTqsqqoqZbt+n6XmGI3GRvMggXvggQc8HtE8//zzjaZzv++RkZHgee/+PqW6ayg7OxvPPPMMANeCLo8//rjfeVG9BMZ9svWEhATMmzdPGc3McRz69euHxMREFBUV4ciRI0razMxMZGdn48cff4Rer/fIk+okOF5//XVcdNFFeO6551BSUoIlS5ZgyZIljabt06cPHn300SZHz1OdtJ5Q3ev673kbQwRah9QSSxTuLRdardbr43Q6XaN5kMC89957eP/995X9GTNmYNq0aY2mpboLngULFsBisQAA3nrrLZ/uZ31UL4GpqKhQtouKipQA9tprr8XJkydx+PBhbN68Gb///jtOnDiBCRMmKOn379+vtBa6ozoJnr/85S9YvXp1s08qEhMTsXDhwmZXTqM6aT2hutf13/M270DrkIJYoqhbqhFwPWbwlnta9w7jxH/bt29X5vEDgB49enj0K6uP6i44VqxYgR9++AEAcMcdd2D8+PEB5Uf1Ehj3/rB1Jk6ciG+++cZjoAng6lu3du1ajB07Vnlv5cqVDWZMoToJjry8PEyYMAEjR47E0aNHAbgC1hEjRmDMmDHo2bMnANeAn/vuuw89e/bE+vXrG82L6qT1hOpeu+frS96B1iEFsUTh/tjNarV6fZx72vqjTonvDhw4gOnTpytT1yQmJuLbb79t9vEM1V3giouLlUEnMTExeOWVVwLOk+olMPWvned5vPnmmxAEodH0KpUKb775ptKfX5blBgsjUJ0ELjc3F1deeSU2btwIAEhPT8cPP/yAoqIi7NixA1u2bEF2djaysrKUye9LSkpw3XXXYcOGDQ3yozppPaG61/W77Xibd6B1SEEsUbjP/Vf3ONUbdZ326+dBfHfs2DFMnDgRJpMJgCuY+v7775GWltbscVR3gbvvvvtw7tw5AMCLL74Y8HykANVLoOpf+/Dhw5UWvqb07dsXV1xxhbLvPr1T/TypTvxz11134fTp0wBc/V137dqFq6++ukG6vn374n//+x9uueUWAK7Wurlz56K6utojHdVJ6wnVva7/nrd5B1qHFMQSRXx8vLJdWFjo9XHuA498nQyenJeTk4Px48ejuLgYgKvT/YYNGzBw4MAWj3WvO7PZ7HXfIqo7l127din9LYcPH4677747KPlSvQQmISHBY3/QoEFeHeee7uTJkx6fUZ0EZteuXfjxxx+V/RdffLHZp0Qcx+H1119X5hMvKirCZ5995pGG6qT1hOpeu+cLeB9DBFqHFMQSRZ8+fZTtsrIyj7+QmpOfn69s+zsV0YWuoKAA48aNQ0FBAQDXo5m1a9di6NChXh3vXneAq7+aN6juXIqKipTtXbt2ged5cBzX5Mt9AYpnnnnG4zP3aWKoXgJT/9rrr17XFPf/DMvLyz0+ozoJTF0XAsDVfcObFZ+SkpKaXSCB6qT1hOpet1UdUhBLFOnp6R77dfNkNuf06dMoKSlpMg/SsqKiIowfP15Z3lSj0WDNmjUYNWqU13n4U3cOhwO///57k3mQwFG9BKb+nMiNDfRqjHs/u/qjpKlOAlPXjQBwtZR7OwrdfVEc99Y3gOqkNYXqXl900UUeg7S8yRcAfv3112bzbQkFsUQxZMgQaDQaZX/Hjh0tHuP+WEmr1Xr8tU1aVlZWhvHjxysjqFUqFb744gtcc801PuXTs2dPpKSkKPve1N2+ffs8Wtt9CZo7GpVKhaioKK9f7guBaDQaj8/c512kegmM0WjEZZddpuzX/aHXEvd0nTp18viM6iQw7v9H+Nun0n1aJYDqpDWF6l6r1WqPJ4fe5Hv27FmcOHGi2XxbQkEsURgMBowbN07Z/+STT1o8xj3NuHHjaISoD0wmEyZOnIjDhw8DAARBwKeffur3+vDTp09XtjMzM5XZDZriXnf9+/f3epnAjmjKlCmoqKjw+uW+ysyjjz7a5GcA1UugbrzxRmV769atLU7DY7PZPB5X1y1V647qxH/uKzyVl5d7/YfF/v37le0uXbo0+JzqpPWE6l5fd911yvamTZs8umm1lG90dLR/f4gwQtx8/vnnDIDy+uabb5pMu2/fPiYIgpI2MzOzFUsa3sxmMxsxYoRy73ieZx9//HFAee7evduj7l5//fUm0+bn57PIyEgl7bJlywI694UmNTVVuXcZGRnNpqV6CUxubi5Tq9XKPXnnnXeaTf/666973O8vvviiQRqqE//t2LHD49797W9/a/GYzMxMj2P++9//NkhDdeI79/u1YsUKr48L1b3Oz89nGo1GSbto0aIm01ZVVbFu3bopaRcuXOh1+d1REEs8yLLMBg4cqPxiJScns6ysrAbpzpw5w9LT05V0l156KZNluQ1KHH6sVisbP368cu84jmPvv/9+UPKePn26kq/BYGA7duxokMZkMrGRI0cq6ZKSklhNTU1Qzn+h8CWIZYzqJVD33Xefx/3btm1bo+k2bdrE9Hq9kjY9PZ1JktRoWqoT/0iS5PHd39L3108//cRiYmKU9J06dWJms7nRtFQnvvE3iGUsdPfa/d+qIAiN/hFpt9vZzTffrKTT6XTs9OnTPpW/DscYY1402JILyJ49ezB69Gilv5PRaMSf/vQnjBo1CqIoYvfu3Vi+fLnyqECn02Hbtm0YPHhwWxY7bPzjH//AI488ouzHxMT41Jf4mmuuwQMPPNDoZ7m5uRg8eDBKS0sBuPqvzZs3DxMmTIDBYMBvv/2GN954Q3kEyPM81qxZ0+RytqRx3bt3V2YoyMjIwNNPP91seqqXwJhMJowYMUIZXCIIAu68805MnToViYmJKCoqwjfffINPPvkEsiwDcPXR37lzZ5PTclGd+G/jxo2YPHkyJElS3hs9ejRmzJiBtLQ0qFQq5OXlYd26dcjMzPRI99FHH2HWrFmN5kt10ri7774bH330UYP33Qc6iqLY6CIgTS06EKp7XV5ejqFDh+L48ePKcTNnzsT111+P2NhYHDt2DG+99RZ+++035Zjly5d7rFDpE79CX9Lhffnll0yn03n8pdfYS6fTsS+//LKtixtWMjIyWryvzb1mz57dbP47d+5ksbGxLeYjCAJ74403WueiOxhfW2IZo3oJ1KlTp1haWppX/0aioqLYd99912KeVCf+++ijj5hWq/X6e0sURfbyyy+3mC/VSUOzZ8/2+/+L5oTqXh87dox17drVq/I9/PDDAd0bCmJJk44cOcLGjx/POI5r8IvHcRwbN24c+/3339u6mGEn1EEsY4wVFBSwm266iYmi2GgegwcPZjt37gz9xXZQ/gSxjFG9BKqmpoY9+OCDLDo6uslA6Y477mAnT570Ok+qE/8dOXKE3X777R59luu/eJ5n06dPZ7t37/Y6X6oTT6EKYhkL3b0uLy9n8+bNa7IxLD09nX399df+3A4P1J2AtCg/Px87d+5U5gfs0qULRowY4THvH2mfSkpKsH37dhQUFMBut6Nz58644oorGkxMTVoX1Utg7HY7tm3bhpycHJSWlsJoNKJ79+4YNWoUjEajX3lSnfjPbDZjz549+OOPP5TFJaKiotCrVy8MGTIE0dHRfuVLddJ6QnWvq6qqsHnzZuTn56O6uhrJycm45JJLPKbOCwQFsYQQQgghJOzQPLGEEEIIISTsUBBLCCGEEELCDgWxhBBCCCEk7FAQSwghhBBCwg4FsYQQQgghJOxQEEsIIYQQQsIOBbGEEEIIISTsUBBLCCGEEELCDgWxhBBCCCEk7FAQSwghhBBCwg4FsYQQQgghJOxQEEsICamtW7eC4zhwHIfu3bs3mW7OnDlKuqeffrrVyhcucnNzlfvDcVxbF4c0w72ecnNz27o4hHRYFMQSQprkHlj68oqOjm7ropMOoKysDBqNRvm9evzxxwPKb/jw4Upew4cPD1IpCSFthYJYQkjY6Citkd27d1euYevWrW1dnHYrLi4OU6dOVfY//vhjyLLsV17Hjx/Hzz//rOzPnj074PIRQtqW2NYFIISEh5iYGAwZMsSrtAaDIcSlIReK2bNnY/Xq1QCA/Px8bN26FVdffbXP+Xz44YfKtkajwYwZM4JWRkJI26AglhDilQEDBuDbb7/1+bgxY8aAMRaCEl1YunfvfkHex2uvvRYJCQkoKSkB4ApGfQ1iGWP4+OOPlf3p06cjJiYmqOUkhLQ+6k5ACCGk3RJFETNnzlT2v/zyS9TU1PiUx/bt2z0GWFFXAkI6BgpiCSGEtGtz5sxRts1ms9K9wFvuXQk6deqEiRMnBqtohJA2REEsISSkvJ1iqzkrV64Ex3Ho0aOHx/tNzY5Qf4qup59+WvnMPSD6/vvvcdddd6Fv376Iiopq8HmdoqIirFixAnPmzMGgQYMQGxsLlUqF6OhopKWlYdasWcjMzGx20JH7oLRTp04p748dO7bRaxgzZkyTx3s7qM1iseCdd97BlClTkJqaCp1Oh+joaKSnp+Oee+7Bpk2bvMqn7v7XL9ehQ4ewcOFCpKenw2AwwGg0YsCAAXj44Ydx9uxZr/L2xqWXXooBAwYo++5BaUssFgu++OILZf+OO+6AKHr2pMvKysKrr76Km266CX379oXRaIRKpUJ8fDwuvfRS/PnPf8auXbsCvxA3/kwp58+AwD/++AMZGRkYPnw4kpOTodFokJCQgMGDB+PJJ5+kKcBIWKM+sYSQC05lZSXmz5+PzMzMFtP+9a9/xfLlyyFJUoPPTCYTTCYTjh8/jk8++QQXX3wxvvzyS6SlpYWi2D759ttvcc899yA/P9/jfavVCpPJhKNHj+Lf//43Jk6ciFWrVqFTp05e580Yw5IlS7B48eIG9+XQoUM4dOgQ3nnnHXz11Vd+DcJqzOzZs/HAAw8AAH744QecOXMGnTt3bvG4NWvWoLKy0iMfd5dffjn279/f6LFlZWUoKyvDwYMH8a9//Qs33ngjVq5cicjIyACupHVYrVY8+OCDeOedd+B0Oj0+Ky0tRWlpKfbu3YuXXnoJixcvDnj6MkLaAgWxhJB2r0uXLpg4cSIsFgu2b9+uvN/UY+HevXs3mRdjDDNnzsS6desAALGxsejTpw94nseJEycapD98+LASqNW1JicnJ0On06GiogJZWVlKH83Dhw9j+PDh+PXXX9GtWzePfHQ6nVLebdu2wWq1AgAGDx6M2NjYBud1b3n0VWZmJmbOnOkRvHTq1AlpaWmwWCw4fPiwcv7vvvsOV111FbZs2YKUlBSv8n/mmWfwzDPPAACMRiPS09Oh1Wpx9OhRFBUVAXD9oTB9+nT8/vvvSE1N9fta6txxxx145JFH4HQ6IcsyPv74Yzz88MMtHufealu/RRcADh48qGyrVCpcdNFFiI+PhyAIKC4uxtGjR5X6X716NQoLC7F9+/YGrbntSWVlJaZOnYoff/xReU8QBPTv3x/x8fGoqKjAoUOH4HA4YLPZ8MQTT6C4uBj//Oc/267QhPiDEUJIE2bPns0AMABs9OjRfuWxZcsWJY/U1FSvzpWRkdFompycHCWNL19fGRkZyjGRkZEMAEtKSmKff/45czqdSjpJktjJkyc9jp00aRK79dZb2erVq1llZWWDvG02G/v0009Z586dlXNMmDCh2fKkpqYqabds2eLVNXh77SdOnGARERFKuqSkJPbVV18xSZKUNCaTiT322GOM4zgl3dixY5ksy43muWLFCiVdbGws4ziORUdHsxUrVjC73a6kk2WZrVy5kqnVaiX9rFmzvLo+b0ydOlXJ9+KLL24xfWFhIRMEQTnm1VdfbZAmLi6O3X///Wz79u0e11Ln3Llz7Pnnn2cajUbJ54UXXmj2vO71lJOT02gab37f6/P29+aWW25R0mk0GvbCCy+wiooKjzTl5eVs0aJFHmX98ssvvSoHIe0FBbGEkCZ1xCAWADMajezYsWNeHWs2m71Kl5OTw6Kjo5VzHDp0qMm0oQxir7vuOiVNVFQUy8rKajLtSy+95JHnp59+2mg69yAWANPpdGz//v1N5vvyyy97pK2qqvLqGluSmZnpUY59+/Y1m979+kRRZEVFRQ3SeFu/a9asUfJKTk5uNOCt05ZB7BdffKGk0Wq17Mcff2w2zyVLlijpe/Xq5fHHDiHtHQ3sIoR4Zdu2bV4tOXvppZe2dVFblJGR4XW/1YiICK/Sde/eHX/5y1+U/W+++cavsgUiLy8Pa9euVfafeeYZ9O3bt8n0ixYtwtChQ5X95cuXe3Wexx57DJdddlmTny9YsAA6nQ6Aa2DVr7/+6lW+LZk2bZrH/K4tDfBy/3zy5MlITExskMbb+r3uuuswcuRIAEBhYSH27Nnj1XGtbdmyZcr2k08+iauuuqrZ9I888gj69esHAMjOzsb3338f0vIREkwUxBJCLiiiKDY6A0EwuAeEbRHkrF27Vum/qdfrMX/+/GbTcxyH+++/X9n/6aeflEUFmnPPPfc0+3lERITHHzNHjx5tMU9vaDQa3Hbbbcr+f/7znwaDluocOnQIv/32m7IfjLlh27p+W3L8+HH88ssvAFz9e++9994Wj+E4zmMe3i1btoSsfIQEW/vtmU4IaVe8XXa2V69erVAa//Xr16/RgVTe2LNnD3bu3IkjR46gvLwc1dXVHtNqnTt3Ttk+ffp0wGX1VV0AAwCjRo3yqpVxypQp4DhOWQ1s9+7dmDJlSpPpe/To4dVMBu6DxCoqKlpM763Zs2fjrbfeAgAUFxfju+++a7S8q1atUrZjY2Mxbdq0ZvN1OBzYvHkz9uzZgxMnTqCyshIWi8VjlTT3gX9tUb8tcR/INXDgQK9XJbv44ouV7aZmaiCkPaIglhDiFX+XnW1vevbs6fMx69evxwMPPOBTi6LJZPL5PIHKzs5Wti+55BKvjjEajUhNTVXmC3XPozFJSUle5avX65VtX1fYas7QoUPRt29fpS4+/PDDBkGsJEn49NNPlf3bb78darW60fwkScJrr72GJUuWoLS01OtytEX9tuT3339XtvPy8jBp0iSvjnP/48uXe0BIW6MglhByQfF1js+XXnoJDz30kM/nsdlsPh8TKPcWz/j4eK+Pi4+PV4LY8vLyZtM2FQw2x701Mxhmz56Nxx57DICr77HJZEJUVJTy+caNG1FYWOiRvjFOpxO33HIL1qxZ43MZ2qJ+W1JWVqZs17VS+6o9BueENIX6xBJCLig87/3X3s8//+wxF2lqair+/ve/Y/v27cjPz0d1dTUkSQJzzfTS5v0J3QMrX4JNjUbTaB7t1Z133qnUo9Vqxeeff+7xufuArvT0dAwePLjRfF566SWPAHb48OF46623sHfvXhQXFyvdCepeGRkZwb+YIKqurg44j+ZWnSOkvaGWWEIIacKLL76otCIOGzYMGzduhMFgaDJ9VVVVaxWtUe6tkb6UxX1Fq+jo6GAWKSS6dOmCcePGYePGjQBcQevdd98NwHXd7oFpU62wkiThpZdeUvb//Oc/44033mj2vG1dvy0FmO51N2fOHKxYsSLEJSKkbVFLLCGENIIxhk2bNin7S5YsaTaABYAzZ86EuljNSkhIULZzcnK8OoYx5pHWPY/2zH2GiZ07d+LkyZMAXKuVWSwWAK5W91mzZjV6/P79+5XH73q9HkuXLm3xnMGsX/eWcofD4dUxLXX1cJ9C7Pjx4/4VjJAwQkEsISRs1O8KEOy+lu7OnTvn8Xi2qUfS7n766Sev8na/jmBew6BBg5Rt95kKmnP48GGYzeZG82jPbrjhBhiNRgCue/jRRx8B8OxKMH78eHTp0qXR4/Py8pTtfv36eQxEa8quXbsCKbIH977ZLQWnAHDq1CmPemrMsGHDlO09e/a0ecsxIaFGQSwhJGzUnzKqrsUtFOq3jnEc12z6qqoqfPXVV17l7X4dwbyGusn4AeDIkSM4ePBgi8d88sknynZ0dLTHdEvtmU6nwy233KLsf/TRR8jNzcX27duV95qbG9a9fluqWwDYunUrTp065WdpG+rWrZuyfejQoRbTf/311y2mGTVqlNK/2W6344MPPvC/gISEAQpiCSFhIzo6GlqtVtlvaTqoQMTFxUGlUin7O3fubDb9Y4895nXLl/s0VcG8hmuuuQZdu3b1KFNz8vPzPVbpmjNnDgRBCFp5Qs09SM3OzsbChQuVlm2j0YgbbrihyWOTk5OV7cOHD3v0C67P4XDgb3/7WxBKfJ57i/euXbuanXfWZDJ5rMTVlKioKMydO1fZf/bZZ73uVkJIOKIglhASNgRBwIABA5T91157LWSjqVUqFa688kpl/+GHH2400GGM4YUXXsC//vUvr/N2D2A++OCDoE1rJAgCHnnkEWV/w4YNeOihhxq9R2fPnsXUqVOVLhMRERH461//GpRytJaRI0d6LK6xfv16ZfuWW25Rlr5tzJAhQzyWxl20aFGjXTvMZjNuvfVWHDhwIHgFB3DllVcqi0ZIkoSFCxc2Wk8mkwk33HADCgoKvMr3iSeeUBbzOHfuHMaOHYu9e/c2e4wsy/jhhx8wadIkHDt2zMcrIaTt0OwEhJCwMnPmTOzevRsA8P7772PdunW4+OKLPR7R33bbbR7Lk/rr/vvvx7Zt2wAABw4cwIABA/DnP/8Zl19+ORhjyMrKwqpVq5QlSOfPn4/33nuvxXxvv/12LFu2DIwxHDhwAF26dMGgQYMQExOjPNq++OKL8fzzz/tc5nvvvRdfffUVfvjhBwCuaaS2bt2KefPmoU+fPrBardi5cyfefvttj3lFX3nlFaSmpvp8vrZ21113NTr1VUvLzOp0Otx99914/fXXAbh+l7KysjB//nz07t0b1dXV2L17N/7973+joKAABoMBU6dOxWeffRaUcguCgL/97W949NFHAbi6CwwfPhz/7//9P/Tq1Qtmsxm7du3Cu+++i+LiYowZMwbHjx9vcaWwlJQU/Oc//8GUKVPgdDpx6tQpDBkyBJMnT8aUKVPQq1cvREREwGQyIS8vD3v37sWGDRuUeXVD2c+ckKBjhBDShNmzZzMADAAbPXq0X3ls2bJFySM1NdWrc2VkZDSZzm63s7FjxyppG3vVPz4jI0P5bPbs2T6Vf/78+c2eq+714IMPen2tjDH25JNPNptf/fudk5Pj8XlzKisr2ZgxY7wqNwC2bNmyZvNbsWKFz78H3tZnoHJychjHcR7X07NnTybLcovHms1mNnDgwBbvj0ajYV9++aXXv0fux+bk5DSZzm63s9GjR7d4/vT0dFZcXMxSU1OV97Zs2dLstW3ZsoXFxcV5/TtQ98rKymrxvhHSXlB3AkJIWFGpVNi4cSNWrlyJqVOnomvXrs0+Ng7Uu+++iyVLligj4evr2bMnPv30U6/6LLp77rnnsHnzZsyaNQt9+vSBwWDwaoCRNyIjI7Fx40a89tprHn0/6xsxYgR27tyJBx98MCjnbQvdu3fHqFGjPN676667vLqXERER2L59O2bPnt1kX+Dhw4dj165duPHGG4NSXncqlQrr16/Hvffe2+j5NRoN5s2bh927d/s89dmYMWNw7NgxPProo4iLi2s2bVJSEubOnYstW7agT58+Pp2HkLbEMUbPDgghpCVVVVXYunUrjh8/DrvdjqSkJKSnp2Po0KFtXbRmMcawZ88eHDp0CCUlJdBoNEhKSsLIkSORkpLS1sVrN86cOYMtW7agoKAAoiiic+fOGDx4MHr37t0q5y8tLcWmTZuQn58PQRDQrVs3jB07tsUA1BuyLOPXX3/FoUOHUFpaCpvNhsjISHTt2hX9+vWjwJWELQpiCSGEEEJI2KHuBIQQQgghJOxQEEsIIYQQQsIOBbGEEEIIISTsUBBLCCGEEELCDgWxhBBCCCEk7FAQSwghhBBCwg4FsYQQQgghJOxQEEsIIYQQQsIOBbGEEEIIISTsUBBLCCGEEELCDgWxhBBCCCEk7FAQSwghhBBCwg4FsYQQQgghJOxQEEsIIYQQQsLO/we3MspgFylzUgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define custom legend labels\n",
    "legend_labels = {\n",
    "    'WT': 'WT',\n",
    "    'KO': '$PDGFRβ^{KLF4-KO}$'\n",
    "}\n",
    "\n",
    "def plot_aggregated_betti_curves(mean_curves_by_genotype, std_curves_by_genotype, save_directory, legend_labels):\n",
    "    if not os.path.exists(save_directory):\n",
    "        os.makedirs(save_directory)\n",
    "    \n",
    "    plt.figure(figsize=(7, 4))\n",
    "    \n",
    "    for genotype, mean_curve in mean_curves_by_genotype.items():\n",
    "        std_curve = std_curves_by_genotype[genotype]\n",
    "        label = legend_labels.get(genotype, genotype)  # Get custom label or use the genotype as default\n",
    "        plt.plot(mean_curve, label=label)\n",
    "        plt.fill_between(range(len(mean_curve)), mean_curve - std_curve, mean_curve + std_curve, alpha=0.3)\n",
    "    \n",
    "    plt.xlabel(\"Filtration Value\", fontsize=25)\n",
    "    plt.ylabel(\"Normalized Betti-0\", fontsize=25)\n",
    "    plt.xticks(fontsize=25)\n",
    "    plt.yticks(fontsize=25)\n",
    "    plt.legend(title=\"Genotype\", fontsize=20)\n",
    "    plt.title(\"\", fontsize=25)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plot_path = os.path.join(save_directory, \"Betti0_Curves_KO_WT.png\")\n",
    "    plt.savefig(plot_path)\n",
    "    print(f\"Saved Betti-0 curve plot at {plot_path}\")\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "\n",
    "save_directory = \"Plots/Widefield_10x_ROIs_KO_Fish_Pdgfrb\"  # Update this path\n",
    "plot_aggregated_betti_curves(mean_curves_by_genotype, std_curves_by_genotype, save_directory, legend_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32b22141",
   "metadata": {},
   "outputs": [],
   "source": [
    "### For 1D homology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84dc68f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_persistence_diagrams(dim1_path, mouse_id_to_genotype):\n",
    "    files = [f for f in os.listdir(dim1_path) if 'dim1' in f]\n",
    "    data = {}\n",
    "    for file in files:\n",
    "        parts = file.split('_')\n",
    "        mouse_id, section = parts[0], parts[1]\n",
    "        genotype = mouse_id_to_genotype.get(mouse_id, 'Unknown')\n",
    "        diagram = np.load(os.path.join(dim1_path, file))\n",
    "        key = (genotype, mouse_id, section)\n",
    "        data[key] = diagram\n",
    "    return data\n",
    "\n",
    "dim1_path = \"Data_Raw/Widefield_10x_ROIs_KO_Fish_Pdgfrb_TDA/PersistenceDiagrams\"  # Update this path\n",
    "dim1_data = load_persistence_diagrams(dim1_path, mouse_id_to_genotype)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a765b3",
   "metadata": {},
   "source": [
    "Compute the Betti curves for 1D homology:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a00f77ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_betti_1_curve(persistence_diagram, max_filtration_value, n_steps):\n",
    "    betti_1_curve = []\n",
    "    filtration_values = np.linspace(0, max_filtration_value, n_steps)\n",
    "    for value in filtration_values:\n",
    "        betti_1 = sum(1 for point in persistence_diagram if point[0] <= value and (point[1] > value or point[1] == float('inf')))\n",
    "        betti_1_curve.append(betti_1)\n",
    "    return betti_1_curve\n",
    "\n",
    "n_steps = 100\n",
    "max_filtration_value = 200\n",
    "betti_1_curves = {key: compute_betti_1_curve(diagram, max_filtration_value, n_steps) for key, diagram in dim1_data.items()}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca19608",
   "metadata": {},
   "source": [
    "We aggregate the curves by genotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d5ad63ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genotype: WT, Number of curves: 20\n",
      "Genotype: KO, Number of curves: 27\n"
     ]
    }
   ],
   "source": [
    "aggregated_curves_by_genotype = defaultdict(list)\n",
    "for (genotype, mouse_id, section), curve in betti_1_curves.items():\n",
    "    aggregated_curves_by_genotype[genotype].append(curve)\n",
    "\n",
    "# Print some intermediate results to verify aggregation\n",
    "for genotype, curves in aggregated_curves_by_genotype.items():\n",
    "    print(f\"Genotype: {genotype}, Number of curves: {len(curves)}\")\n",
    "\n",
    "# Calculate the mean and standard deviation of the Betti-1 curves for each genotype\n",
    "mean_curves_by_genotype = {genotype: np.mean(curves, axis=0) for genotype, curves in aggregated_curves_by_genotype.items()}\n",
    "std_curves_by_genotype = {genotype: np.std(curves, axis=0) for genotype, curves in aggregated_curves_by_genotype.items()}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f02b78d5",
   "metadata": {},
   "source": [
    "And finally, we plot the Betti curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c605f485",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved Betti-1 curve plot at Plots/Widefield_10x_ROIs_KO_Fish_Pdgfrb\\Betti1_Curves_KO_WT.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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f2j1+55138vbbb7Nu3bqBGZgwpPTm/S6VMdKwW8TVdoGV3x9/v/i2bf9aE/AFIR6KquELyaRbjD1v3MFgW8QVllUCYQWLUSzQEIShrrm5mY8//pjPP/+cxx57bKCHIwhJNewC2BEjRkRvOxyO6LeFnqxZsyZ6u6SkJCVjE4an+pYAVQ4/88bkJLSfomqEBkkN2La8QVkEsIIwDMydOxeHw8EDDzzQqWatIAx1wy4HduHChe3uP/rooz3u8/rrr7er53rMMcckfVzC8FXjCuD0hal1BRLab7DNvrbyBgfnuARBSEx5eTkul4sbb7wx5vN33nmnSB8QhqxhF8AuWLAgWloE4K9//SvPPfdcl9uvWLGCn//859H7hYWFnH766SkdozB8BMIKDm8IgJ31LShq/CnlgzWAFS1lBUEQhMFu2KUQ6HQ6HnnkERYtWoSiKGiaxpVXXsk///lPLrroIiZNmoTRaKSiooL333+f1157rV3tvIceeqjL3taC0FGtK0DrMshgWKW8ycv4/LS49h1MXbjaEgGsIAiCMNgNuwAWIisuX3jhBX72s58RCEQu637xxRd88cUXXe5jMBh44IEH+PGPf9xfwxSGgZoOaQN7m7wUZ1qxmnrOIQ2EB1/+K4hasIIgCMLgN+xSCFr9+Mc/Zs2aNfzwhz+MNimIRafTcdZZZ/H111/zm9/8ph9HKAx17kC4U91UVY2kEsRjsKYQKIo26DqECYIgCEJbw3IGttXUqVN5+eWX8Xg8rFq1ih07duBwOADIzMxk/PjxzJ8/v11PakGIV40z9qKteneQZm+IHHvXX5xg8AawEEkjiGcWWRAEQRAGwrAOYFulpaVx3HHHcdxxxw30UIRhQlU1at1dVx3YUdfC4WNzkCSpy20Gaw4sRALY/HRzzxsKgiAIwgAYtikEgpBKjd4g4W5quHoCMtXdlNVSVY3gIM2BBdFSVhAEQRjcRAArCL0QT83X7rYJyIN39hVEJQJBEARhcBMBrCAkKKyoNHqCPW7n9IUIK7FnWQf7IilfSEbT4q9pKwiCIAj9SQSwgpCgOncANY6r/5oGTZ5QzOcCg7CFbFuqCr5BHmQLgiAIBy8RwApCgjrWfu1OQ0vsmdrBPgMLIo1AEARBGLxEACsICfCFZFy+cNzbN3qDqDHayw7mElqtvB73QA9BEARBEGISAawgJGB3gzeh7RVFw+HrnEYwFAJYuaEMgp6BHoYgCIIgdCICWEGIk8sfjqv6QEcNMRZ8DdY2slGaht9RTUPtvoEeiSAIgiB0IgJYQYjTzrr4WsR21DEPVtM0goO8jJYx2Iwmhyjfu4fVe5tFPqww4BRFISMjA0mSmDt3brfbappGbm4ukiQhSRLPPfdct9u/+uqr0W2zsrKit3v7c+eddybxlQuCEIsIYAUhDnXuAM4Ecl/bCoZVXP4D+wbCKoO9QpXZXweAMeTG6fby7e4mdta1IHdRFkwQUk2v1/O9730PgPXr1+NyubrcdvPmzTQ3N0fvL1u2rNtjL1++PHp74cKFfRypIAj94aBoJSsIfaGqGrvq+5YL2ugJkmk1AqnJf9UpQVR9klq/airmQEPrHUzBJoL6Eext8lHrDjBvdA5Wkz455xKEBCxcuJCPPvoIVVX5+uuvWbRoUcztWgNSvV6PoijtAtTuts/IyOCJJ57g3nvvjbndf//7X26//XYA7r77bs4+++yY2xUUFMT1egRB6D0RwApCDyqafX0ue9XQEmR8fhoA/iQHsJISwu7eRUv29KQczxRoQtIOjNEcaCBoGwFEZpOd/hBWkzUp5xKERLSdHV22bFmPAewFF1zAv//9b3bv3k1VVRUlJSWdtnU6nWzatAmAI488kpKSkpjbAXz33XfR2yUlJcyYMaPXr0UQhL4RKQSC0I2grLCnKbHKA7F4AnI0CE72DKw50IDJX4+kJidPtTV9oJUx2AzagdQBT0DkwwoDY/78+VgsFoBuZ1Vbnzv33HOZPHlyt9t/+eWXqPs7k4j0AUEYOkQAKwjdKKv3oijJSVhtXcyV7BlYs78WCQ1ToLHvB1MVTMH2x5E0FVPwQD5hi1jQJQwQk8nE4YcfDsCqVasIBDpXBSkvL6eyshKAo446iqOOOgroOg9W5L8KwtAkAlhB6EJLIEyNy5+047WW00rmDKxODmAMRRazdJw57Q1zoAFJ67xQq21wLGZghYHUGmSGQiG+/fbbTs+3BqTjxo2juLg4GsB2NQPbGtharVbmzZuXiiELgpACIoAVhC7sbfIltVqA0xcirKhJrQFr9tdGbxuDzUhq7yolRI8XqI/5uCnQFL0dktVBXwZMGL465sF21Bqotgaurf/tWJkAwO/3s3r1agCOOOIITCZTSsYsCELyiQBWELrgTfKlck2LVCNIZvBnaTPrKqFh9scOQOMhqWGMbQLVtnRqEEPoQGtZMQsrDJQFCxZgNEYqesSaVW197OijjwZgwoQJFBUVoWkaX375Zbttv/nmG8LhyJc+kT4gCEOLqEIgCF0IyMmveVrp8KMm6bD6sAe93H6BmdlfT8AeewV1T0z+BiS6nnI2BRqQTRkAeIMKuWm9Os1BS9O0pOc/D0ZWox5JklJ2fLvdzty5c/n2229ZsWIFsixjMEQ+yhoaGti2bRtwIICFSHWBN954g2XLlnHWWWdFHxf5r4IwdIkAVhBiUFWNcAoCWFcvmyHEYvHVdnrMGHL2uiZsV+kDrUyBJnwZ4wFoCSbvdRws/GGFaXd8NNDDSLktd52CzZTaj5aFCxfy7bff4vF4WLt2LYcddhhAdIY1Pz8/Wn0AImkEb7zxRqcZ29YUBJPJxIIFC1I6ZkEQkkukEAhCDIHBnuOpaV0s2urq8e5JSghT0NHtNgbZg06OLGoTKQTCQGo7u9o2D7Zj/mvH7desWYPXG7lqIcsy33zzDQDz5s3DahW1jQVhKBEzsIIQQzIXWqWCMeRApwZjPmfy1+NPG5XQ8SKzrz2vWDMHGvCnjcIbktE0LaWXiocbq1HPlrtOGehhpJzVmPoubUcffTQ6nQ5VVVm+fDk33HAD0HUAe8ghh5CWlobH42HFihWceOKJ7YJZkT4gCEOPCGAFIYZUtHtNpu5mWY1hNzrZj2qIc0ZJU7F69sW1qSnQhD9tFKoK3pBCmlm8hcRLkqSUX1o/WGRlZTFjxgw2bNjAl19+iaZpeL1e1q5dC7SfoYVIS9kjjjiCTz/9lGXLlnHiiSe2m7kVAawgDD0ihUAQYhgMAazdtROLt7LzE5qK2d/Q7b6JVCOweKvQK/HVuzWGnNFSXSKNQBhIrUFnU1MTW7Zs4euvv0ZRFOx2O3PmzOm0fcd6sK3/1ev1HHnkkf00akEQkkUEsIIQw2BIITD760lz7SCzcS06+UDHIVOgEUnrPniMNw9WUmVsnvIERqVhCLUA4BEduYQB1LEebGtAesQRR0SrErTVGsB+++23BINBvvrqKwBmz55NRkZGP4xYEIRkEgGsIMQw0Iu4DCF3NMfVGHKQ3fAtZm81EF9wapA96MPeHrezevaiS7D5gUH2ACKAFQZW2wB2+fLlXea/tmoNbP1+P4sXL6apqanTcQRBGDpEACsIMQQHeAbWFGifIiBpCumubWQ0rWvX1rU7PQW6OiWI1Rtf7mtbrbVnWwKilJYwcAoLC5k0aRIAS5cuZeXKlUDn/NdWdrudQw45BIAHHngg+rgIYAVhaBIBrCDEMNAzsOYuglRTsLnbZgNtWb37uuysBWBzlyFpiQfqetkHRIL8sDLwqRbCwas1+KypqcHv92MwGDjiiCO63L51dnb37t1AZGFdVwGvIAiD20ETwDqdTp5//nnOPPNMJk2aRHp6OhkZGUyaNInjjz+e3//+9yxZsoRgMHZpIuHgEVZUFCW+ILE7bfNWE9vP36nDVm9ImkJm8wYs3qpOz+nDnnZtaBNhaJOaIBZyCQOp4+zpnDlzsNvtXW7fMb1g6tSp5OXlpWRsgiCk1kFR0+WFF17gxhtvjOY8tdXS0sLOnTtZsmQJd999N6+99hrnn3/+AIxSGCySVYEgzbUdf9oowubshPYzB7qvMJAYjTTXdnSKH1/GhOijdvcu4qn7GoukydFuX56gTLbdlKSxCkJiOgawXeW/tuo42yrSBwRh6BrWAaymafz85z/nmWeeafd4cXExo0aNwmq1Ul9fz/bt25FlMZMkRCSrAoFB9pDu2Iwz/7CEWruaurns31s2TwV6JUhL1lSMQSemYHOfjqcPe1H1ZlrEDKwwgEaPHo2mxf9FrKCgIKHtO7rsssu47LLLer2/IAjJM6wD2F//+tftgtfzzz+f3//+98yaNavddoFAgCVLlvDSSy9hMonZpINdUmZgVQWdEklHyWjegDPvUJB6ztiR1DDGkLPv54/B7K9DpwSQ1L6/Pr3sJUyOqEQgCIIgDIhhG8B+/PHHPProo9H7Dz/8MNdff33MbS0WC4sWLWLRokX9NTxhEAvKfZ+BbV3oBGAIt5Dm2oEna0qP+0VmX/uef9sVY8iVlOMY9ufoeoOipawgCILQ/4blIq5wOMxVV10VvX/ppZd2GbwKQkfJmIE1dFiEZfFVR+u4difeElkDrXWRmaJq+AdB1zJBEATh4DIsA9j//ve/lJeXA2AymfjTn/40sAMShpRgEkpoxaoikObajiHk7nonTcUUTH7+ayqISgSCIAjCQBqWAWzbvNezzz6bgoKCARyNMNQkYxFXxxlYAAmNjOaN6GR/zH2MQSeSNjRmMyVNRlJCALSIPFhBEAShnw27ADYcDrN06dLo/TPOOGPgBiMMOZqmJWcGtos2rjo1SFbjmpjPd+y+NdhFW8qKGVhBEAShnw27AHb9+vXtmhG0dmX5+uuvufzyy5kwYQJWq5Xs7GymT5/O1VdfzTfffDNQwxUGmaCsovZ1AlZT0StdNzHQqUEym9aiD3vaPd5V963BqjUIF5UIBEEQhP427KoQbNiwIXpbp9NRUlLCVVddxT/+8Y922wUCAZxOJ1u2bOGJJ57gggsu4LnnniMtLa2/hywMIsEkpA9EKhB0X0lAp4bIalyDM3cOiikdfagFnTq0usC1pkn4QwqyomLQD7vvw4IgCMIgNewC2LbdtvLz87nyyit59dVXgUjf62nTplFQUEBdXR1btmyJbvvaa69RVlbG8uXLsdlsXR4/GAy2m+F1u7tZlCMMOalawBWLpMlkNa3FlTOrz40FBkLbUmGeoEyWTdRQFgRBEPrHsJsycTqd0dt1dXXR4PW0005j9+7dbNq0ic8//5zNmzeza9cuTj755Oj2a9as4dprr+32+Pfddx+ZmZnRn5EjR6bkdQgDIykLuLrIf41F0mQym9dh8dX0+bz9rW2gLtIIBEEQhP407ALYtrOjrU455RTeeecdxowZ0+7x8ePH895773HcccdFH3vhhRfYvn17l8e/9dZbcblc0Z99+/YlbezCwAskZQbW1/NGbUiaOuTSBwB0ajhaicDlDw/waARBEISDybALYO12e7v7Op2Oxx9/HL1eH3N7o9HI448/Hu0kpKoqL7zwQpfHN5vNZGRktPsRho9UNDEYzlpfa0NLEFVNXQcxQRAEQWhr2AWwHRdhLViwgHHjxnW7z5QpU5g3b170/hdffJGSsQmDX59TCDQNXYIzsENZaxqBrGg0eUMDPBpBEAThYDHsAtj8/Px29+fOnRvXfm232717d1LHJAwdfZ2B1cs+pB4qEAwnbevZ1rm7Lh0mCIIgCMk07ALYKVOmtLufk5MT1365ubnR2w6HI6ljEoYGVdUIyX2bgU00/3Woa5su0egRaQSCIAhC/xh2Aey0adPa3Y+1qCuWQODA7JHFYknqmIShITkLuA6e/Fdo/3pFGoEgCILQX4ZdAJuRkcGcOXOi9/fs2RPXfm23KywsTPq4hMEvGU0MDqYFXNC+EgGINAJBEAShfwzKAPbUU0/l+OOP54QTTujV/uedd1709tKlSwmHuy/xEwwGWbZsWfR+a/tZ4eAyECW0hoO2QXuDSCMQBEEQ+sGgDGC/+OILli5dytKlS3u1/yWXXILJFOkKVFdXx/PPP9/t9v/4xz/adfA6++yze3VeYWhLRgWCgy2FANqnESiKRqN36NW0FQRBEIaWQRnA9tXo0aP5xS9+Eb1/ww03tJthbeuzzz7jlltuid6fOnUq5557bsrHKAw+fa1AoFMCSFrf0xCGmo6zzvVuEcAKgiAIqWUY6AGkyl133cVnn33G5s2b8Xg8HH/88VxyySWcccYZFBQUUFdXxzvvvMO//vUvVDUSdFgsFl566SV0umEZ1ws96GsAe7Dlv7bq2Dq3wRNEUTX0OmmARiQIgiAMd8M2UsvMzOSDDz5g0qRJACiKwgsvvMD555/PwoULueCCC3jxxRejwWtmZib//e9/464bKww/fU0hOBjzX6Fz5QVF0WjyiFlYQRC6N2vWLCRJoqKiotNzy5cvp6ioCKPRyCOPPBJ9fNq0aUiSRH19fdzH7+rnvffei7nfn/70p+g233zzTe9fYB/05nfTau3atVxzzTVMmTKFtLQ00tLSmD17Nn/4wx9wOp39MPr+0esZ2Fi/1GTRtOQsAhk1ahTr1q3jjjvu4Jlnnon5P85gMHDRRRfxf//3f4wdOzYp5xWGpr4u4tKHPUkaydCiU0NIahhNZ4w+VucOUpAhytEJghBbIBBg69at5OXlMWrUqHbPPfroo/zmN78hJyeHTz/9lGOOOQYAn8/Hjh07KCkpoaCgIK7jFxQU8Mtf/jLmNt/73vc6PbZ161buuOMO7HY7Xu/AXFXrze8GIBwOc/311/PEE09gNBo55ZRTOOOMM1AUhU8//ZS77rqLf/7zn3z++efDIt7pdQA7ZswYJGnwXyK0Wq08+OCD3HPPPXzxxRfs2bOHxsZGMjIyGDNmDAsXLiQjI2OghykMMFlRUZS+fXE6WGdgIdKRSzZnRe83ijQCQRC6sX79emRZ5tBDD40+5vf7ueqqq3jxxReZP38+b7zxBqWlpdHn161bh6IocV0pbT3+woULufPOO+Mak6IoXHrppcyePZtJkybx0ksvJfy6kqE3v5tQKMR5553H+++/z5lnnsmzzz7brjOppmlcf/31PPLII1x00UWsXLmyX19TKvQpBzZZM6UdpSIwNplMnHTSSUk/rjA8BPrYgQsO3hxYiLz2tgGsomo0eoIUillYQRi2Nm7cyGOPPcbSpUuprKxEURRGjBjBwoULueGGG5g1a1aX+65ZswYgGqSVl5dz3nnnsXbtWq688kr+/ve/YzabY+4TTwDbuu28efPifj0PPPAA69evZ82aNTz44INx75dsvfnd/O53v+P999/n3HPP5Y033ugUR0mSxP3338+bb77JqlWr+OqrrzjyyCP75wWlSJ9yYFtzRARhqOt7BYIgktb3OrJDlTHk6vSYaGogpNITTzzRZW6jxWJh7NixXHLJJV3ONHW1v06nIzMzk9GjR3PKKafw+9//nq1bt/Z6nA0NDTz88MOcddZZjBkzhvT0dMxmMwUFBRx++OFcffXVvP/++93WK+/utcb6Wbt2bdz72+12pkyZwi9+8YuEXuef/vQn5syZwz/+8Q927NiBz+cjGAxSXl7OP//5Tw4//PAuq/8ArF69GogEaZ988gnz5s1j8+bNPPHEEzzzzDOdArS2+8QTwLZuG28Au2nTJv74xz9y++23M3369Lj2SZVEfzdbtmzhL3/5C4WFhTz33HNdxmUWi4WjjjoKgG+//Ta1L6If9HoGNj09HY8nkvP3pz/9icMOOywpA9I0jVNOOaXH5gOCkEx9DWD14YN39hXA7K/Dmz4G1WCLPtboCbJ+nxObSY/VpMdq1GMzGbAYdeKLr9Bn69at6/K51kCqvLycl19+mccee6xTHmRX+2uahtvtxu12U1FRwccff8zdd9/NGWecwZNPPklJSUlc4/P7/fz+97/n73//e7tW5a0aGhpoaGhg5cqVPPHEExQUFHDzzTdz7bXXYjQa223b3WvtyGw2M2PGjLj39/l8bN++ne3bt/PCCy+wePFiLrroom7P8dxzz3HzzTcDMHnyZG644QbmzJmDy+Xigw8+4JFHHiEQCHDrrbfy1VdfxTxG6yzjp59+ylNPPUVRURHvvvsuCxYs6PK8vZmBff/991m+fHm752bPnt2uXKYsy1x22WVMnTq1XVnNgZLo7+ahhx5CVVWuv/56srKyuj12a9qBy9V50mGo6XUAe+ihh7J06dLoB1HbROK+EmWshP7W1woEBvngXMB1gIatpRxP9rToI6oKDS2dqxFk2YzMHpmFUS/+zoXeaw3KsrKy2gUoiqJQXl7OU089xf/+9z9UVeW6667jxBNPZOLEiT3ur6oqLS0tVFRU8M033/DKK6/Q0NDAe++9x9y5c/nqq6+YMGFCt2OrrKzk5JNPjs5oZmRkcO6553LyySdTUlKC3W6nvr6e9evX8+6777JixQrq6+v57W9/y9VXX91lAJufn8/nn3/e7blNJlOX+2dmZvLll19GHw+FQpSVlfHXv/6Vr7/+mmAwyOWXX85RRx3VZaDucDi44YYbADj88MP57LPPsNvt0edPOOEE6uvreemll1i1alXMY4RCITZt2gREZofHjh3L119/TVFRUZevKxAIsGXLFvLz89vlfvZ0/L/85S+dnr/nnnvaBbD33nsv69ev59tvv+30u+tvif5uVFXl3XffBejxiwcQbdqUm5ubpBEPnF4HsPPmzYt2yvruu++SNR5BGBB9noE9iBdwtbL46/B1mIWNxekLs2avgzmjsjEZRBArJE5V1eiH/OzZszvNOM6ePZuzzz6bM888k/feew9Zlnn55Zf5wx/+0Gn/WbNmddof4Mgjj+SHP/wh9957L9deey3PP/889fX1nH322WzYsAG9Xh9zbDU1NRx//PHs3LkTgEsvvZRHHnmEzMzMTtuedtpp3HrrrSxfvpxf//rXhMNhLJb2eePxjLU7bfefOXNmp/3nzp3L97//fY488ki++eYb/H4/r7zyCjfeeGPM4y1evDha0efZZ59tF7y2mjx5MhCZzdY0rdMVlw0bNhAOh5k3bx5NTU2Ul5fzzTffcM4553T5OjZs2IAsy3HNvrYe/wc/+AGvvPJKt9uuX7+eu+++mxtvvLHXZTTz8vLadfPsyZIlSzj22GNjPpfo72bfvn00NjaSl5fHmDFjejx3a1mwOXPmxD3ewarXAWzblAERwApDXbCvJbQO4gVcB2jYW8ppaTML25WWgMzqvQ7mjMrCYowdCAhCV7Zv347PF/nSeMghh3S53ZVXXhmt9blt27aY+8+ePbvbc9ntdp555hkqKir47LPP2LJlCy+++CKXXXZZp219Ph8nnHBCNHj905/+xE033dTj6zn66KP56quveOeddzo913asiQavHffvalGVTqfjl7/8ZTS42bx5c5fH++9//wvAggULuswVra6uBiJdMWOlC7VeIj/++OP5yU9+woIFC/jxj3/M8uXLuwysepM+0N2/jVaXXnop48ePj7tSQSw//OEPaWlpiXv77maaE/3dtAbOOTk5PZ5348aNbNmyhYKCgm5TNYaKPs3AttqzZw9Op7PH3AtBGKyCoolBUpj9dfjSRqMYO8/KdOQNyqzZ62Du6OzEglhVBZFmdFBrm9PZXZBSXFwcva0oB76ktt2/pwAWIgHe7bffzmeffQbAm2++GTOAveeee6JpA9dee21cwWsrq9Ua8xJw27HOnDkz7uMluv/o0aOjt2VZjrmNLMvRILerGURVVfnf//4HdJ1a2HaR0vTp03nllVc466yzOOuss1i5ciUjRozotE8iAWzr8eMJYNevXw/Qaea7VWug99Zbb3U5C/roo4/2eJ54Jfq7aZ3Zb/3S0J077rgDgOuvvx6DYeg3Yu31Kxg7diw5OTk0NzcjSRKrVq1KWpmqUaNGEQqFknIsQYhHX5oY6GQfOlUsOozQsHnKacmObxWvL6SwqryZQ0dnYzPFeDtqqYOQB0LeAz+aAvY8SC+GtALQiRncg028AWxVVVX0dtsP/kQDWIikFFgsFgKBACtWrOj0/I4dO3jooYeASDB4//33x3XcnrQda29mYNvu311Zq7q6uujtrorcb926NbogratA8sUXX6S8vByA73//+zG36RiMnn766TzwwAPcdNNNnHXWWSxbtgyr1drtPt1JZAb2yiuvjPn4smXL2LlzJ2eddRb5+flxXZ5PhkR/N+PGjaOgoID6+nq+/fZbDj/88JjHffrpp3n77beZNWsW/+///b+Uv47+0KcQ/J///CcNDQ0ASf2fu3379qQdSxC6o6ga22tbUPswAWsMuZM3oGHA7K/fPwubFtf2wbDK+n0uFozvsKjAUw/Va2Lv5KmP/OgMkSA2cyTYer6EJgwPrUGZ0Whk2rSuU1batgpduHBhp/31en3cJZOMRiOFhYXs3buXpqYmFEVplwd79913Ryde7rzzTmy27nPB49U6VkmSelXeKd4A+O23347ePvvss3s8VqxA8pNPPuGaa64BIjmWp512WqdtwuEwGzduJDMzk/Hjx0cfv/HGG9m8eTMvvPACP/nJT/jPf/4TTT9o3ScrK4tx48Z1+RrabltUVERhYWG32wI888wzMR+/7LLL2LlzJ7feeitHHHFEj8dJht78biRJ4pprruGOO+7gmmuu4dNPP22Xbx0KhXj44Ye57bbbGDlyJG+88UaXs81DTZ8C2Fj/OAVhqPAGZTZWufAEYl8ui5chRg3Ug1ukIkFLTvyzRd6gjMMbIttuOvBg856ed1RlcFeDtwEmnNiLsQpDUetl32nTpmEymWJus2zZMl588UUgUjqo7edV6/4TJ07sNNPXndZzaZpGOByOBrAej4fXXnsNiFQ1iGc1eLxax1pYWBid2YzFarW2C3o67j9mzJguu06+/fbbvPrqqwCcf/757TpAtdW2msHYsWNpaWlh8+bN7NixgzfffJN33nkHTdPIysri3//+d8xjbN68mWAwyIIFCzrlxz711FPs3LmT119/nd///vfcfffdQKRGaygUIj09nZ/+9Kcxj3v00Udz6aWXRo8fz+zrYNOb3w3Arbfeynfffcc777zDxIkTOfvss8nPz6eqqoqPPvqIuro6FixYwMsvv9xvM8n9YegnQQhCL9S4/GyraUFR+95NLlYR/8HKEwZZk8gypaaLXitzoB5f2BP3LCxApcN/IID1O8HfHP8JlTCEfGBKzqyXMHjV1tZGL3d3DFICgQC7du3i3//+Nw899FA0yHzyySejgWrb/eNNH2jlcDgASEtLazeLtWTJkuil9UWLFnUbFO/YsaPLFLnRo0eTnp4e87XW1tZ2m8N61llnRRdYxdq/477BYJAdO3bw/PPP87e//Q1VVTnqqKN47rnnujxHa4OEQw45BEmSWLx4Mddee227bUpKSli6dGmXpca6a0ZgMpl48803mT9/Pvfccw9Tpkzhxz/+cfSyelNTE88++2zM406ZMqXd8YdiANub3w2AwWDgrbfe4sUXX+T555/n9ddfx+fzRZtlXHzxxVx44YXDrv62CGCFg4qiamyrdVPjTE6XKEmVh0wL2YaAxE0r7XjCEldMCrCoNEwq38/sLbtx53Sdc9dpfJ4AQTkNs0EPjjhmXzsKuAZ3AKtpED4IFvsZbaTyH1bbLlOLFy9m8eLFXW6bnZ3Ns88+y+mnnx5z/0QCWFmWo+WjOl6abtvVqLv2nOFwmFmzZhEMdq6PDJEApm3w0rGjVndidZxqu/+7777bZQBz6KGHcsUVV/Dzn/+828U9rbO5ravhY9V5raqq4qKLLuKll15i6tSpnZ6/8soru8w7BSgoKOg009zTPr3dtjsvvPACL7zwQp+Pk4je/G5a6XQ6Lr30Ui699NIUjW7wEQGscFCpaPYlLXgFMITdQGpnM5MhpMADG2w4Q5HV+//YbmWr08DVU/1YU/QuYAo0og9746pIAJHiAtXOAGMzpMjirUQFXJDRefXyoBH2wb3FPW831P2uGkzx/T/vjZ66Uun1embOnMl5553HVVddRUFBQZf7JxLArlu3Lro6v2PnydayWUDMoK3Vli1bugxejUZjtx20Pv30U0444YS4x9tx/+54PB4WLVrUbfBaUVFBc3PkqkhrkP3888/z2GOPUVFRwapVq3j++edZtmwZa9as4YQTTmDLli2iOpGQMiKAFQ4q3mDf8l07MgyBBVyaBk9tt7DLrSfdqLKoNMwb5SaW1xkpa9Hx21l+xqT1rYxYV6zeCjxZXX+gd1Tl8DNGrkXqzZeC4OD/fyH0XWtQptPpWLVqVTQvVafTkZ6eTn5+freLVHobwLbtYNWx4k5ragF03+Fo9uzZaFr7f9ujR4+moqKC6dOnd8rnjbfaQlfa7r906dLo2Hw+Hzt27ODhhx9m7dq1bN++nUsvvZRly5bFdazWGdjW3/n06dOZPn06l112GVdffTVPPPEENTU1vPbaa/zsZz9LeNyCEI8BCWAXLVrExx9/jCRJXdabE4RUSHYAOxTyXz+uMvJZtQkdGjfM8HNIrsLcXJkHN1qp9un57Uo7V00JcEJx8kuBmX21eNPHo+ljL7TpKBjw4/KVk2XpRWmswCD/f2G0RWYnhztjatM4WgOp8ePH96pzUuv+OTk5XbZLjaU1N9RoNLJo0aJ2z7WtRpBIK1KHw0FFRQUQuzNS61hHjhzZq9afrfvn5eV1qsk6f/58zj//fObNm8fmzZtZvnw5q1ev7nEBl8ViieabxnLLLbfwxBNPAERfmyCkwoDNwHb8FioI/cHXx5axHUVSCAav7S49T2+PzEb9eEKQQ3Ijr39KlsJfDvfy181W1jQZeHSLFbNO46ii5Ab4EhpW7z58GZ1XR8di9VXRFPaTZYl/8VeUKkPQA+Ze7NsfJCmll9YPBj6fj127dgHd1zSNZ/9EZl/ff/99Nm7cCMAll1zSqZh8fn5+9HZtbW23pb3a6q4sVdux9mb2te3+XXW3slgs3H777fzwhz8E4KWXXuoxgJ05c2a3qQZt82y76zglCH0l2tkIB41AWEFRkvfFabA3MHAEJR7YYEXWJBYUhDl3dPuVzxkmjdsP8XFySeTxZbXxzxwlwuqtAjWOLw6qgsVbSUtA7n1r38E+Cyv0yfr161H3F23uTQDbdv94A9jGxsboZXCTycQtt9zSaZu2QV93l+E7inVZPtZYe9O3vu3+3QXAZ599NmlpkS99b775ZpfbtS4I62nW+6uvvorePvroo+MdriAkTASwwkEj+ekDg2P2tSEg8V6FiXf2tv+5f4OV5qCOUrvCddP8MReG6yQ4dX8Au8FhoI8ddWOSNBmLr+dL5xZ/bfQLQZOnl534RB7ssNbb/NXe7l9XV8epp55KTU0NAA8//DATJ07stN0ZZ5yBbn9746effpqWlpaExqPT6TqNJ5n5r90FwFarlRNPjNRQrqioYMOGDZ22cblc0dXvbYvkx9KaPjBp0qRetb4VhHiJAFY4aPhCSU4fGCT5r3/ZZOWZHRae29n+Z7vLgE2vceus7isNjElXyTKpBBSJbc7UtGW1evdFVpN1RdOweg7kyzV7Q6i9qdErZmCHtXjbosazf3cBrKZp/Pvf/2b+/PnR2pxXXHEFv/rVr2JuP378eH7wgx8AkZ70F154IW5391+mdu3aFV0YNnHixOgsaKyx9jWA7Wn/tmXG3n333W6P9fjjj0fTKTp68MEHozPQN99887CrOyoMLqIKgXDQ8IaG3wKuCo+OLU4DOknjqEK53ep9gwSnlIYosXc/raqTYE6uzJIaE6ubDMzMSW6gD6BXApgCDYSsBTGft/iq0Sv+6H1Z1XD6w+TY41v8FRVwRwJl8cE5LLUGUunp6b3qKNS2LauqqmzatAkAVVVxOBzU19fz1Vdf8f7770fzRyVJ4q677uL222/v9tiPPfYYq1atYufOnXz44YdMmjSJK664gqOOOoqioiJUVaW+vp4NGzbw0UcfsWzZsugl/lg1XFvHmpWVxdixY3v9Wm02G5MmTep229NOOw1JktA0jffee4/bbrst5rEgUnLr6KOP5vrrr+fYY48lPT2dXbt2sXjxYj744AMgMiN9+eWXJzxmQUiECGCFg4Y3mLzAbLA0MPikKpK3eliezG9m+HvYumutAezaJgOXTYxdp7KvrJ6KzgGspmFrKcPm6bxaudEbTDyA1RQItoAldstMYehqG3DOnDkz4dm9tvtrmhYzaGxLkiTOOussbrvttk51X2PJzs7myy+/5Ec/+hGffvopdXV13Hfffd3uM3nyZP7f//t/nYK9tmPtTapEx99V2yoJsRQXFzNnzhzWrFnDypUrqa+vb1c/tzWAtVqt+P1+XC4Xd955Z8xjXXLJJfzjH/8Qs69CyokAVjho+JI4AzsYGhiEFFi6f+HVSSV9W0x2SI6ChMZej56mgESuJfmvzRh2Ywg6kc1ZAEhqmHTHZkzB2C1jfUEFX0jGZkrwbSrgEgHsMLRjxw58vkgns96kD7Tdv6P09HRycnLIyclh5syZHH300Rx//PGMGzcuoXMUFBTwySefsGTJEv71r3/x1VdfUV1djc/ni9aonTlzJoceeihnnHFGlzmibcfam/SB3ux/+umns2bNGlRV5f33328XVLcGsOeeey4XXHABjz76KOvWrcPlcmG1Whk1ahRHHnkkV1xxBUcccUTC4xWE3hABrHBQkBWVYBJXKA2GBgbfNBhoCevIM6vMye1bcJ5h0piYobDDbWBtk4ET+xgQd8XmrcBtzkIf9pDRvLFd2kAsTl848QBWLOQalqZMmdKn8ot93T8Rxx13HMcdd1yv9x+I13rXXXdx1113dXo8HA6zZcsWIPLF4ZxzzuGcc87p9dgEIVkGJIBdvHgxfn/vL3cKQqK8SV7ANRjyXz+pilxeP7EkhD4JV+vm5EYC2DUpDGBNgSasngpsLbuRtJ6/UDh8IUZkWhK7HCkWcglC0mzevJlQKFIVpDcz34KQKgMSwHbsTS0IqZbM9AEY+AYG1T4dGx0GdGhJ66A1N0/m1T1m1jcbUFTQp6RGiYbdvSvurcOKRktQJsOSQI3aYAuoKuhEkRVB6Ku2C7hEWSxhMBHv8MJBIZkLuAZDA4PWxVtzcmXyk5SvOiFDId2o4pUldrhTU06rNxzenmvCNnlDhJX9M7qaKtIIBCFJ2rbeLS0tHdjBCEIbB2UAW15ejt1uR5Kk6E9XKyqF4SGZTQwGuoFBWIXPa5KzeKstvRRZzAWwpmnwpMe7/GEUtet0A0XVqHH6qW9pUz1BBLCCkBRtW8gKwmByUAawv/jFL7pcjSoMT8msATvQDQxWNRhwhXRkm1Tm5SU3NaJ1MdjaQRTAqho4/V2/zmZvCFnVaPQECcn7A12RBysISbF+/XpA5L8Kg0+vPqXalhaRJImysrKYz/VVx2Mnw0svvcRHH32U1GMKg5umaQTCyUshGOgFXJ9URxZvnVAcxpDkr6CtAewutx5nSCLLNLClwlo5vEFyY9SEVVSNOncAiPQvqHH5GZ1rFwGsICSJw+EY6CEIQky9CmDLy8ujXTs6rg5ufa6vYh27rxobG7n++usBmDp1Ki6Xi+rqnnu0C0ObL6TQzRXohAx0A4M6v8S6pkh+6onFPeeGJirbrDEuXWF3i551TQaOHTGwub6tPEGFkKxi6hCxt86+tnL6wuSny9jwgKqAbvDk8gqCIAjJ0+v5m+5qzGma1uefVLj++utpbGwE4Mknn8RoTGBlszBkJTV9YIAbGHxabUJDYnaOTJEtNeM4kEYwuIK/Zl/7gL3t7GsrDah1ByO3xCysIAjCsNWrGdglS5b06rmB9PHHH/PSSy8BcPnll7Nw4cIBHpHQX3xJrEBgDA7c5bTGgMSnVa2Lt5I/+9pqbq7MG+Vm1jYZUDXQDZKOkA5viKIMS/R+kzfYbva1ldsfxhOUSQu4wJbTn0MUBEEQ+kmvAthjjjmmV88NFJ/Pxy9+8QsA8vLyePDBBwd4REJ/SuYMbFdtT1NtbZOev2yy4t7feevw/OQu3mprcqaCTa/hDusoc+uYmJm8DmZ9EZRVvEEZu9mAomrUu4Ndblvt8jMpX1QiEARBGK4Gz1LjFPr973/Pnj17AHjooYfIzc0d4BEJ/cmXpC5ckhrGEPYk5VjxUjT4z24z/9kTSR0Yl65w00wfxhTWDzHoYFaOzKoGHV/VGcgwtc+DTTdq2AboncPhC2E3G7qcfW3lCyo4mhvIHtGPgxMEQRD6zbAPYFevXs0jjzwCRGaHL7300gEekdDfklUDNpI+0H/5r86gxMObrWxojvyZnlIS4spJAUz9kJp6UsZennD/ia9rpnFVxf9r95xFr/GXwz2MSFEObnccvhBFmZZuZ19b1TY0kaWEkfQi110QBGG4SXoAe8UVVwCRosetK/4T9dhjj7FmzRokSeLZZ5/t9VhkWeanP/0piqJgMpl48skne30sYWgKygqykpxAqz/TB3a5ddyzzoYjpMOi1/jlFD/HjEhd2kA7msKFjmdIlzycrl/JS9p21mqTAJBVCCgS71aY+fmUQA8HSj5FhfJGb7ezr62CssqOlZ9QMHI82UVjwdC5DJcgCIIwNCU9gH3hhReQJIlTTjml1wHsZ599xn//+98+B7B//vOfo11Ebr75ZqZMmdLrYwlDUzJbyPbXAq4yt44/rLHjlSVG2RVumuVnpL3/8lALKj8m3VcRvf9k/hvsmX4NAOub9fxhjZ3Pa4xcPD5A2gBMbnoS+H/q97rYu20NjXs2kjdiFNkjxoEtF5Jcoi9VlVMEQRAGi8H2PjdsO3GVlZXxxz/+EYAJEybwu9/9boBHJAyEZKUP6GQ/esWflGN1Z3fLgeB1SqbMA4d5+zV4tXirKKp4D4DakaejIZHp2ITVEwloZ2UrjLIrBBSJT6uHzoymNxhmb3kZO1Z/TvOOrwkryfmd6nSRt1A1WYWGBUEQBilFiUwetL7vDbTBMYou9KWRwVVXXYXfHwk4nnjiCSwWSw97xCcYDOJ2u9v9CINXshZw9Uf6QHmLjj+sseGRJSZnytwxx4e1P7PUNYVROxej0xRc2TOpHXUGjvz5ABTu+wCITFyeOSpSwuv9fSaSFAf2G19IoWJfBV9v3Mk3u5vYVuum1hXodac2o9GIXq/H6x245haCIAj9wefzodfrB00N/UEZwLYGhTabrVf7P//883z22WcA/OhHP+LEE09M2tjuu+8+MjMzoz8jR45M2rGF5EtWCS1jigPYvR4dd6yx0RLWMTFD4Y45vn5f6V9Q+TE2TwWy3sa+CT8CSaJu5CloSGQ1rcPirQLgmKIwGUaVhoCObxqG5jpQm3s3noBMZbOfTVUuvtzZSEsg8a5jkiSRnp6O2+0edJfXBEEQkkXTNNxuN+np6Unvktpbgy6ADYVCrFu3DkmSKCwsTHj/+vp6brzxRgCys7N5+OGHkzq+W2+9FZfLFf3Zt29fUo8vJFdSmhhoGqYU5r/u8+i4Y7UNd1jHhAyFP8zxYu/nuLBt6kDV+AuRzVkABG3FuHLnAFBY+SEAJj2cWhoJ9t6tGDppBG0ZQ85OX0p21veuRFpmZibhcJjq6moRxAqCMOxomkZ1dTXhcJjMzMyBHk5Unz4mKyoqunzO7/d3+3xbmqbh9/spKyvj8ccfx+FwIEkSc+fOTXhM1113Hc3NkQ+m+++/n4KCgoSP0R2z2YzZbE7qMYXUUFSt15eG2zKEW5C01FUA+MtmK66wjnHpCnfO8fb/wqgOqQOO/MPbPV03chFZTWvIaviO2lFnELQWsqg0xJvlJra5DOxw6Zg0SJodJMLWsgeX+UCnrmZPiGZviBx7YkG5zWajtLSUyspK/H4/GRkZ2Gw29Hr9oJmpEARBSISmaSiKgs/nw+12Ew6HKS0t7fWV8VToUwA7ZsyYmG/QmqaxbNkyxo4d25fDc/HFFye0/YoVK3j11VcBWLBgAT/72c/6dH5haBsK6QMNAYndLXp0aNxxiK/3waum9W5lvaZRsueNTqkDbfnTRuLKmUVm8wYK9n3IvkmXkm3WOLoozJIaE+9WmLlhZuoXuCWbMeTCGGgibDnQ2GRXvYf5YxNvP5uens7o0aNxuVw4nU6ampqSOVRBEIQBodfrSU9PJzMzc1AFr5CkMlqxLpv19VLaj3/8Y84+++yE9qmrq4veXrFiRUIr5f74xz9GqxYA7NmzhzFjxiR0fmFwSUr6AKldwLW2KfInOClTIcvcu7+Zor3vklP/NRUTf4Ina2r8O2oqpbteJq/uSwAqJ/wwmjrQUd3IRWQ2byCn/lvqRp1OyJLHmSNDLKkx8XW9gUsDEnmWoXf53N6yG2ebANbtD1PnDlCYkfiiT5vNhs1mo6ioiHA4LCoTCIIwpOl0OoxG46C9ktTnADYZOV+SJGG32ykqKmLu3LlcfPHFnHXWWX0+rnBwS8oMrKpgCLn6fpwurGmM/AnOye3dWNOc2yja9z4AY7c+xa6ZN+BP63lhoaTKjNrxAtmN36EhsW/Cj3HmH9bl9r70sbizppLh3EpB5cdUTriYcRkq07NkNjsN/K/SxCUTeu6ONdgYwi2Y/A2ErPnRx8rqPRSkm3v9pi1JEibT0MwNFgRBGCr6FMDGmmHQ6XTRRgYffPBBXw6fMKPRmFCCcduVw2azuV2prcFS50zovWTUgDWGnEgpah8rq0TbxM7tRQCrkwOM2vlPABS9Bb0SYNzmR9k5+7eELHld7icpIcZue4oMx2ZUSc/eyVfgyju0x/PVjTyNDOdWcuq+pm7kIsLmbM4cFWKz08BHlSYuHBvE3A9tbpPN1rKnXQDrCylUOvyMzBlcl8sEQRCEA1ISpQ3UStzTTz8dp9MZ98+oUaOi+95yyy1dPicMTcmoAZvK9IHtLj0+RSLDqDI+I/HLzcXlb2IKNhM057Ft7h/w20owht2M2/wo+nDsFfU62c/4zX+LBK86I3umXR1X8ArgzZyIJ2MCOk0mr+YLAA7Llym0qnhkic9rBkdtwEQZZA8mf327x/Y0elHiaFcrCIIgDIykB7BLlixhyZIlPPDAA8k+tCAkxJ+ECgSpDGDXNB1IH9AleLU6zbmNvNplAOybeAlhcza7p19DyJyNxV/H2C2PIymh6PbGYDMF+/7H5LV3k+behaK3UDb917RkT0/ovA0lkZrKubXL0ClB9BKcOTJynrfKzchDNO3T3rKn3f2QrLK3STQnEARBGKySXm3ymGOOSfYhBSFhIVlFUfo2g6ZTgujlnoOYoAJhlYQrCKyNBrCJBdptUwcaRhyDJ2syQCSInXYtEzY8RFrLbkbveA5n7lxy6leQ7twWTYUIGzPYPf0a/GmJX2Vw5cwiaMnHHGggu34FTSOO5aSSEK+Vm6gP6FhaY+TEksQbAgw0vexFJ/tQDQfSBvY2+yjNtmEyiHQiQRCEwSbp78x6vR69Xs/pp5/e62Oce+656PV6DIah2eVHGHj+JKQPGONoXiCrcONKO7/4Kp1aX/zTqI5gpHwWwCEJ5r+2TR2oGXNuu+cC9mL2TPslqmQgq2kdY3Y8R4ZzKxIaLZmT2DvxUrbOu6tXwSsAko6G4uMBKKj6DDQVsx7OHR2ZhX2t3Dzk2su2MgXal75SFI3vyptp9oa62EMQBEEYKEkPYDVNi/4k4ziC0BtJSR8INPa4zTf1BvZ59Xhkib9vtRJv2mTr7Ov4dIUsU/z/zjumDqj6zuWevJkT2Tv5clTJEAlyR53Blnl3UzbzNzgKF8TcJxHNhQtQ9FbMgQYymjcCcGppiAyjSp1fxxe1QzMXNla6iC+ksGavg83VLsJDNTIXBEEYhlIyxTlYa4YJB4++BrDGQDPmQH2P273TppXqRoeBT6qMnFLa8yX01gB2bl43s6+aijnQgNWzD6u3Eqt3H3Z3GdA+dSAWV96hbMyeiaYzgJTc76mq3kJj0dEUVn1MfvVnuHNnY9HDOaND/HOXhdfLTRxTFEY/xK68G0MO0NSYv68aZ4AmT4hJhekUZfbtC4AgCILQd4PyGr2iRIIPvT61NXnKy8tTenxh4PQlhUBSw6Q7t/a43XaXnh1uAwZJ45zRIV4vN/PCTgtz82Tyuynqr2iwrjnyb7ur8ll2107GbnkCg+Lr9JzfVtIpdSAWTZ+6WqSNxcdRUP0p6a4dWD0V+NNGsag0xFt7TVT79CyvM3LsiKGVCytpKsagk7AldieukKyyqcpFkzfI9OLB0w9cEAThYDQo50hqa2uBSHtGQeiNvszApjm3o1N7LsrfOvt6TFGYH44PMiVTxq9I/H2Lle6yX3a59bSEddgMGpMyYo8zv+pTDIoPVWfEmz6WxqKj2Tf+YnbM+i07Drm1z2kAfRU2Z+PYX34rv+ozAKwGOGvU/lzYPSb6uIZuQMRTdaLGGaAlMLSCc0EQhOFm0AWwW7duZe3atUiSxLhx4wZ6OMIQ1dsZWLOvNq7UgYaAxIr6yAWMM0aF0Etw7bQARp3GumZDtzVRW7tvHZIjx7zMrg97yXBsBmDH7FvYOftmKif8iKYRC/FljIukBQwCDcWRklrZjauiC95OHxkizaBR5dPzVd3gGGciTMGmnjcCyhs7z4wLgiAI/adPnzB33XVXl8/t2rWr2+fb0jQNv99PWVkZH330EYqiIEkSCxcu7MvwhIOUqmoE5cQDWJ0SJM21I65t399nQtUkZmbLjE2PLO4psav8cFyQf+6y8OwOC4fkyOTGSCVY29R9+kBW0xp0mozfVkLAXpLw6+gv/vTReDImkObeRV7NUmrGnIvNAGeOCvLKbgv/2WPmqMLEa9wOJL3sRacEUfXmbrerbwngDdqxm4dekC4IgjAc9Ond984774y5YEvTNMrKyvjjH//Y62NbLBZ++ctf9mV4wkEqICvdXsLvSppzK5LWc0krvwyfVEXSB84c1b7E0tmjQqyoN7LTreeJbRZum+2n7Z+IOySx0x0JYOd0EcBm168EwFEwP/EX0c8aSk4kzb2L3Nrl1I08DVVv5oxRIf5bYabSq+fregNHFfa9pW9/MgaaCNqLu91G0yLdumaUiFxYQRCEgdDnFIK2ZbPalr3q+HgiPwUFBfznP/9hwoQJfR2ecBDqTfqAxbMv7q5bS2qMeGWJEVaFeR2qCOh1cO00PwZJ47tGI49strSrD7uuWY+GxOg0JebsrDHQTJp7JxoSjvzDEn4d/a21sYFB9jFhw0OUlL3CyMYv+UXhdsyEeHGXhSrvoMtU6la8/w7q3IGk1BsWBEEQEtenGdhLL72002OLFy9GkiSKi4s58cQT4zqOTqfDbrdTVFTE3LlzOeGEEzAah2YtSWHgJbqAS1JC2FvK4tpW1eC9fZHZ1zNGhWJeHh+VpnLJhCDP77SwtNbE8jojx48Ic/7YYJvuW13MvjauAsCbMYGwOfZq+EFF0lFXeiqjdr2IzbsPm3cfANcBV5t1vBFeyI0rf8qvpgWGzEysKdgcmWLtoRygpkF5k5epIzL6aWSCIAhCqz4FsM8//3ynxxYvXgzAzJkzYz4vCKkWSDCANQWbkbT4itSvaTJQ7dNjM2gc302ZqLNHh5iapfDKbjNrmwx8Um1iSY0xGvAe2kP6QPMQSB9o1Vx0JN6M8dg8e6P1aq2eSgyyh4sMS/k6NI2HNh7FFkeIyycFMA7yCVlJkzGEXMjmrB63rXH5GZtnx2JMbck/QRAEob2kr0AYNWoUkiRRWFiY7EMLQlx8CV7WNcZ5yRgOlM46uSSEtYe/nkmZCn+Y42ObU88ru82sbzaABha9xpSszmO0eKuw+qpQJQOuvLkJvYaBFrQVEbQV4eDwyAOaRmHl/xix9x3+z/IvPvPN5YNKGzvcem6a6aPQ2n2SsqSGGb39ecz+WgL2Evz2Uvz2kfjtpcim1M94moLNcQWwqgoVzT4mFYqSf4IgCP0p6QGsaA4gDLRE8xJN+0tA9WSzQ8+GZgM6NE4rDfW8w35TshT+ONfHZoeej6pMzM6RY85CZjdEZl/d2dNRDPa4jz8oSRL1JSeRU7eCjEADLxe/xsX1P2GXW89vvk3j3nleRqd1MeutqYze/jxZTWsAsPqqyW5YFX06ZMrCmTeX5oLvEUgrTcnwTcEmfMRXxq/K4WdMrh2TYZBPLQuCIAwj4h1XGHYSyYHVhz1xNS3Y7tJz9zobAAuLwhT0MIPYkTHo4DjvBzwp/56f+P8JWocxamo0gB0K1QfioemMVI67EICZjk94cvYuJmQoeGWJv222osSKXzWNkj2vk9W0BlXSs2/8D6kefQ6OvEMJWAvRkDCFnBRUf86UdXczae095FV9hj7cktSxG8IeJCW+LymKqlHRLOrCCoIg9Kd+K2Lo9XrZsGEDjY2NuFwuVFXlJz/5SX+dXjhIhBUVOYEWUPGsON/h0vHHNTb8SqTu6y+nBuI6tqSGyWxaT079CtIdW5CIjMvmqcAUdFI+5adoushiRbu7DFPQgaK34M6ZFff4B7uWnJm4cmaR2byB2VWv8LtZ/49rv0mnrEXP2xUmvj+mfZCYX/Up+dWfA1Ax6TKcHSox6JQAaa4d5NR9Q0bzhsjCsT37KCl/g+ox59FQEt/C0Z5pmILNBG1FcW1d6fCRYzeRY09d+15BEAThgJQGsIqi8PLLL/PEE0/w3XffoSjtZ51iBbC//vWvaWhoAOCxxx4jJ2cIrMQWBo1EKxAYe0gf2OXWcedaOz5FYnqWzG2H+DDHsV7H7trB2C1PYlAOzMx5MibgyZxMQeVHZDavZ9zmx9gz9ZeoBkt09tWZOzca1A4XVWMvIN2xhXTnNsZ61nLlpPn8bYuVV8rMzM+XGWmPTMVmNayipPyNyD5jvt8peAVQ9wf47pxZ6MNeshpXkVu3AptnLyP2/pemoqN7bEIQL2MCAaysaKzZ68Bm1lOaZWNElgVjrDZrgiAIQlKkLIDdtWsXP/zhD1mzJpLHpnWoLB+rAQJAYWEhjz76KJIkccQRR3DdddelaojCMBRIJP9VUzGGug5gy9w6/rDGjk+WmJolc/shPixxLjYvLn8Lg+IjZM6hueAImguOIGQtAMCTOYmxWx8n3bWd8Zv+yp5pvySrcTUwfNIH2gpZ86kvPZmifR9Qsuc1jp8znS/rDKxpMvLYFgv3zvOR6drGqB0vAFBffHxcM6mK0U7TiGNpKjqGqd/9HnOwkXTHJlx5hyZl3PHmRrflCyrsqGuhrMFDYYaFwgwzug7vdZIEmVZjl++BgiAIQs9SMkVQVlbG0UcfzZo1a6LNCWw2G4cccgjZ2dnd7vuzn/0MvT4SJbz22mupGJ4wjCVSgcAYcnZZPqu8JRK8emWJKZkydxzi67HqQCuTvwF7yx40JHbMvpna0WdFg1cAT9Zkds24Htlgx+4pZ8qauzDIPsKmTDyZk+Ie/1BSV3oqIXMOpmAzRVUf8uvxdZxsWMsJnvcxr3mWsVufQKcpOPLmUj32/B5rsLYjSTjzI0FrdsPqpI1ZpwbRh3qXW6uoGtVOP2srnKze62j38125g6/Lmqh0+FDVXrSMEwRBEJIfwGqaxoUXXkhdXR2apjFy5EheffVVnE4na9asYf787meY8vPzWbhwIZqm8e233+LxeJI9RGEYSySFoLvyWYt3WfDIEpMzZe6YE3/wCgeqCbRkTUE2xW416k8fw66ZNxA2ZWKQvQA48g4DaXhedtb0JqrGng9A0b4POGbDb/mH4UF+a3yVGf5V6JUgnoyJVEy6vFe/A+f+smMZjo3olJ4X5cUrs3kDhqAzacdr5Q8pbKtp4auyRvY1i0BWEAQhUUn/tHzttddYu3YtkiQxceJEVq9ezQUXXIDBEH8EcNRRRwGRHNr169cne4jCMJZIAGsKxA5gw2qkZBbA1VMD2BJJtNG0A9UE8rv/shawF7Nz1o0ELXloko7mwgUJnGjoceXOwZU9AwANCb91BF/oj+C+8A+5zXgTO2b8v17n//rtowia89CpYdIdm5I2Zp0aJLNpLRbPvqQds61gWGV7bQtf7mqk3h3f4kBBEAQhBTmwb7zxRvT2P/7xD/Ly8hI+xowZM6K3d+zYwZFHHpmUsQnDX7w5sJISwiDHnt0vc+sJqRIZRpVR9vg6dLWyeiuw+OtQdUZcuYf0uH3Iks+2OXdgCLcQtuQmdK4hR5Ion3oVZn89QUs+mt5EwC+x+Js0Ai0Sxn0Bzhkdf33djsd25h9KYeVHZDesTloeLICERpp7J8ZwCy2Zk0GX/K5bIVllQ6WL8QUKY/OGeA1gQRCEfpD0GdhVqyIFx4uLiznmmGN6dYy2QW9zc/xdkoSDm6ZpBOT4Atjuymdt2j/7Oi1LSSgVEw60gnXlzEI1WOPaR9Obhn/wup+mMxKwl6DpI+WmCq0aP5kQmXl8YaeFT6p6X4EhVWkErcz+WrIaV6OT/Uk/dquyeg+bqlwipUAQBKEHSQ9g6+rqoukDvdU23SAU6uWMjHDQCcoqapwTpt0HsJF/fzOy5cQGoKlkN34H9Jw+IBywqDTMGSMjAefjWy18Vj240gjaMsgeMpo3puTYrWpdAVZXOAjG+WVMEAThYJT0ALY1+AyHw70+RmsdWKDHqgWC0CqhCgRdlEiSVdjmiszAzshOLIBIc23HGHIhG+y0ZE9PaN+DmSTBlZOCnFYaQkPisS0WlvQmiE1RNYKODLIHQ8idsuMDuHxhVu1x0BLo/fuoIAjCcJb0ALawsBBN0ygrK+v1Mb755pvo7ZKSkmQMSzgIxLuAq7v2sbtb9AQUiXSjyqi0xPJfc+q/BSKXsjVdvzW5GxYkCX42OcCp+4PYv22x8EVN4r/DVKcRtLL4qlN27FaBsMLqvQ6cPnEVShAEoaOkB7CHHRbpnlNXV8e3336b8P6hUIiXX34ZAJ1OF61IIAg98cc5AxtP/uvULAVdIqVIlRCZTesAkT7QW5IEP58c4OSSSBD7yGYry2oTC2LbphGk8lK/2V8Pauov8cuKxtoKJ02e1AXjgiAIQ1HSA9gzzzwzevumm25CjTcpcb/f/e53VFdXI0kSRx55pEghEOIWiHMGtrv6r60BbKLpA5nNG9ArAULmHLwZ4xPaVzhAJ8EvpgQ4qTiEisTfNlup8ibwNtUmjSCrcU2KRgmSJmMONPS8YRIoqsb6Sif1LaLMliAIQqukB7AXXHABkyZFugl99dVXXHjhhbS09NzNJhQKccMNN/CXv/wl+thtt92W7OEJw1hcObCa2mX+q6LCVuf+BVxZiS3galf7dZg2I+gvOgl+OTXA3FwZWZN4Yac5of2HUxpBK1WFjZUualypq4AgCIIwlCT9k1av1/Pkk09iNEYWYbz11luMHz+em2++mf/973/tgtnKyko+//xzbrvtNsaNG8df//pXNE1DkiR+9KMfcdJJJyV7eMIwFk8OrDHoRCJ2iaI9Hh1+RcJm0BidHv+VA33YS7pjMyDSB5JFJ8EVkwLoJY1VjUbWN8dfe7W/0giMISc62Zey43ekabC5ys2+5v47pyAIwmCVkqmiY489lsWLF0eD2MbGRh566CHOOOMMVqxYAURqdo4ePZqTTjqJ+++/n+rqA7MZxx9/PM8880wqhiYMU7KiEpZ7DjqNIWeXz7WWz5qWJaNPIP81q3E1Ok3Bby8lYC+Of0ehW6V2lVNLIwuYnt9hQemmNGqNTyLY+v2ln9IIACy+mpQeP5bttS1sr20RtWIFQTiopexa50UXXcSKFSuYPj1STkjTNDQt8oYrSRKSJEUfa33cYDDw29/+lg8//BCTyZSqoQnDULwVCLrqvgW9z39tTR9oFrOvSXfR2BB2g0a5R8/nXZTW+mCfkV9+nc4N39rx7s/8aJtGkNWwCklJzUp+i682MjXaz/Y1+1hT4Yg771sQBGG4SWmy3pw5c9iwYQMffPABF198MSNHjuwUtOp0OubOncstt9zC7t27uf/++9Hrk9+qURjeEimhFYuiwRZn4g0MLN4q0ty70JBw5h8W935CfDJMGheNjeSx/qvMjL/D/5oPK438Y3uk41mlT88jm62oWiSNwG8vRaeGGbP9WWasvJnSXf/C5t6d1IBTpwa7XRSYSk5fmJV7mkWZLUEQDkr9Uqzy1FNP5dRTTwXA4/HQ1NSE1+slKyuLvLy8lMy2Op1OlixZwpIlS1i3bh07duzA4XBgNBrJyclh9uzZnHDCCVx66aWi0sEwEAj1nD4gqTJ6JfZK7r0tOnyyhFWvMTaB+q/51Z8B4Mo9hLBZ/DtKhUUjQ/yv0kiNX88b5WZ+PCES0H5UaeTJbZHg9ZiiEF/XG1nZYOT1PQoXjgtRNv068mqWklO/AlPQQV7tcvJqlxOwFlEx6VJ86WOTMj6Lr3rAWgGHZJU1FQ4m5KczKtc2IGMQBEEYCJKmDcD1rxTatm0bN910Ex9//HFcbWhtNhv33HMPv/71r5ESbXwPuN1uMjMzcblcZGRk9GbIQhJsq3VT2dz9Cm1D0ElWU+ycyHf2mnhup4W5uWHumBPfSm9DyM20Vb9Dp8nsmHUTPlE+K2W+qTdw/wYbRp3G3xd4WN9s4O9bI8HrWaOCXD4xyGfVRh7bakVC4/ZD/Byat3+6VlNJc+0gp24FWU1r0KlhQuYcts25A9Vg6fPYNCSaC49E0w9s2tPEwjRG59oHdAyCIAhtpTJGGnb1fjZt2sR7773XLnjV6/VMnjyZhQsXcuSRR5KTkxN9zufzcf311/Pzn/+cYRbLH1TiaWLQbf6rM/H817yaL9BpMt70sfjSx8W9n5C4w/NlpmfJhFWJu9fZeHxrJPA8Y2QkeJUkOLEkzCn7myA8vMlKjW//F1JJhydrChWTL2fz/PsJmvMwBZspLn8jKWOT0LD4a5NyrL7Y3egVObGCIBw0BiyA9fv9LF++nFdffZV33nmH9evXJ/X4BoOBc845h7fffpvm5ma2bdvGF198wZdffkljYyNvv/12uza1zzzzDE8++WRSxyD0n3hyYA1d5L+qGmxxtOa/xhcASEqI3NplADQUnxBpIyWkjCTBlZMCSGhUePVoSJw+MsiVk4LtfvU/nRxgcqaMV5Z4YIPtQGWC/RSDnX0TLwEgr3Y5ac6tSRnfQFQj6EhRNHbVd/0lTRAEYThJWgCrqio7d+7km2++YdOmTV1evm9paeHaa68lPz+fY489losvvphzzz2XuXPnUlJSwuOPP96nmVCj0chPf/pTysrKeOuttzj77LM7TVtLksTZZ5/NihUrKCoqij5+xx13EA6He31uYeDEM/PU1QKuvR4dHlnCotcYlx5fAJvdsBJjuIWQOQdn3pyExir0zrgMlVNLI3+fi0pD/LRD8Apg1MFvZ/rJNKmUe/Q8tiWyqKstT9ZkGkYcA8ConS+ik/veHEAve7G7dgxIRYK2al0BHF6xqEsQhOGvzwGsx+PhpptuorCwkClTpnDkkUcye/ZsMjMz+fGPf0xlZWV025qaGubPn8/jjz+Oz+drV5FA0zRqamq49tpr+cEPfoCi9O5S2Nlnn83TTz/NqFGjetx25MiR/PGPf4zeb2xsZNmyZb06rzBwAmGFHjsWa1qXKQSb98++TslUMMTzF6Fp0cVbDSOOA0lUzegvP58c4KkjW7hqSqDLSe9ci8ZvZ/rRSxrL64z83zob7lD7jWvGnNsmleDNpIzN6q2MNLTQEmufnWzb61pEOpQgCMNenwLYxsZGvve97/Hwww/T1NTULhgNBoO88sorHH744VRUVADwgx/8gO3bt3d5vNbasK+//jr33ntvX4YWtzPPPLPd/W3btvXLeYXk2VHXc6tineJH6iKwOFD/Nb7yWenOLVh9NSh6M01FR8U/UKHPJAkKrT0HZ9OzFf7fdD8mncbaJgPXf2tnq/PAFw1Vb2mfSuDYkpTxmQP1ZDatR1ITa0WcTJ6ATKVDtJwVBGF461MA+9Of/pRNmzYBxFzBr2katbW1XH/99fzvf/9j+fLlSJKE3W7n2muv5dVXX+Xjjz/m1Vdf5ZprrsFms0WD2HvvvZfGxsa+DC8ubRd0QWTFnDB07Kpvod7dc7/7rvJf93l1bE5wAVd+VWT2tanwSFSDNc6RCv3t6CKZB+d7KbEpNAV13Lbaxtt7TdGr/O1SCXa9lJRUAgBjyEFm4xp0Ss//LlOlrMFDUBYLugRBGL56XQd21apVvPPOO9GAc8qUKfziF79gypQphEIhVqxYwZNPPonD4eCdd96J5pYWFxfzxRdfMG5c+1XbF1xwAddddx3HHXccVVVVhEIhXn75Za677rq+vcIe7N27t939goKClJ5PSJ4qp5/yxvj6whvC7Wdpq7w6Xt1jZnmtAQ2JbJPK+IyeP/At3ioynFvQkGgsPr5X4xb6z+g0lYfme3l8q5XldUZe2Glhi1PP1VMDZJk0asacS0bzZszBRorL36Rywo+Scl6D7CGzcTUtWdNQ9SZAB5KEhgSSDk2X2hLc8v4FXdOLM1N6HkEQhIHS63fRf/3rX9HbxxxzDB9++CFmszn62BlnnMGVV17JEUccQVNTE++//z6SJPHwww93Cl5bTZgwgT//+c/84Ac/AGDp0qUpD2DffLN9/tuCBQtSej4hOZo8QbbVxD9b3joDW+3T8Z/dJpbVGlGJXDU4Ij/MjycEMcZxPaJt44KQJS/xgQv9zmqA38zwMy1b5tntFlY2GFnfZOD0kSHOGS1RMeknTNz4MLm1X9I44hgC9tKknFevBGLWHdYkHY6CBah6c4y9kqfGGaA0y0amLXYLXkEQhKGs1ykE3377bfT2o48+2i54bTVu3DhuvvnmaF6szWbjvPPO6/a45513HjZbpKPMhg0beju8uLhcLh555JHo/VmzZjFt2rSUnlPoO09QZkOVK6EF3wbZw2aHnmtX2Flaa0JF4rC8MH+e7+GW2X5K7T0vvDGE3GTXrwSgvuTE3g5fGACSBItKw9x/mJcJGQpBVeLNvWZ+/lUaTzXMpCFnHhIaI8r/m/qxaCq2lt0pPw/A1lo3ascyDIIgCMNArwPY3bsjb8ClpaXMmDGjy+1OP/10IJIjO2XKFPT67ldsGwwGpk2bhqZpKc+BveGGG6itPVCA/O677+5xn2AwiNvtbvcj9J+grLCuwomixP+hLKlhdEqQ/1WaUDSJKZkyDx7m4bZD/IzPiH/FeEHlR6JxwRA3IUPlwcO83Dbbx7h0hYAi8Xq5mUvqfoiCnkzHRuyunSkfh8VX22VZt2TyBGR2NYjasIIgDD+9DmBdLheSJDF2bPf9xMeMGRO93XHBVFeysrKASImuVHnmmWd49tlno/cvuuiiThUJYrnvvvvIzMyM/owcOTJlYxQ621XvSbjbkCHsIazCmsZIxsxlE4NMzEys1JEx0ERezRcA1I46QzQuGMIkCQ7Ll/nzfC+3zPIxJk1hmzKCV+RjARhR/lY/1HPVsLvLUnyOiIomH02egVtQJgiCkAq9DmBbGxW0Xu7visVyoNd4T7OvHbdLVS3DZcuW8atf/Sp6f+zYsTz11FNx7XvrrbficrmiP/v27UvJGIXYmntRpF0f9rDFocenSGSaVCZmJr46u6jiPXSaTEvmZFqyRJrJcCBJcESBzMOHe7lsYoC/yefh10yktewmozm16UsApmATxmBzys8DsLnaPSBVCaqcfr4rb2ZjpYuddS1UNPmodwcIyQNbK1cQhKEvtUthB6F169Zx1llnRQPwgoICPvzwQzIz41utazabY+b7CqnnDcoEw4l/8BlkD6v2z77Oy5PRJzh5avFWk1P/DQA1Y84Rs6/DjE6Cc0aHACvP7TmVXxnewb7zHdw5M0FKbbdtu7sMZ358V6a6I6kyetmLbIr9PhaSVbZUu5kzKrvP54qXyx9me617f5OR9h0OizItzCgRFRIEQei91L47DzLbt2/nlFNOweVyAZCdnc3HH3/MpEmTBnhkQjx6M/sKoA95WNkQWYk9Py/xAvMj9r6NhIYzdw6+9O5TZoSh65zRIVyjTsap2SmUqyjb/F3Kz2kIt2D21fa8YXfHCDrJalhJVuNqbO6yLtMfmjwh9jXHV3aur0KyyoZKZ5cd8urcAVoCom23IAi9d9AEsHv27OHEE0+kvr4egPT0dP73v/8xe/bsAR6ZEK9eBbCaRpXTT31Ah0mnMTs3sQDW5i4js3kDGhI1o89O/PzCkHLaOAPfZJ4GwOGOd3hrt0Sqr3bbWnb3rv2spmJ37SKraS16JRA5lmcvmU1ru2yisLO+JeWBo6ZpbKxydXu1RNOgrMGb0nEIgjC8HRQpBJWVlZxwwglUVlYCkbzd9957j8MPP3yARybES9M0HL5e5L/KPlY1RL6nzcqRscSXht16UorL3wKgufB7BG1FCZ9fGHpGTD8a17efUqo2Yti7nB/vXcTULIWZ2TIzsmXGp6vok/jVX68EsHor8aeNin+fsId0xxYMcueFrsZQZEa2JWsaYUtuu+dUFTZVuRlfYO+0n81kIM3c94+EXfUeHHF82WxsCeL0hciymfp8TkEQDj59frdauXIlxx8fX0eieLdNZv3Xuro6TjzxRPbs2QNEcljffvttFi5cmLRzCKnnDsjICZTOaqWX26QP5Cc2+5rh2ESaexeqZIhUHhAOCprehGvc6WTueolrDG/zcehQ1jYVsrYp8nZp02ucNTrIBWNDCedTd8Xq2YtsSEOnhiI/SgidGkRSw0gxUgIMIScSXf896NQwmc3r8dtH4reXoBoOLLb1BmU27HN13kcHU4oyKM7qfXvkeneAvU3xpynsrPdw2Ji+5wALgnDw6XMA63A4+OKLL7rdRtq/6CWebZOpqamJE088ke3btwNgNBp5/fXXOemkk/ptDEJy9Db/taXFzU53ZNp1Xhf5r7k1yzAHGvDbS/HbSwnYigCJEeVvA9BYfBxhc/8tfhEGXnPhAgqqPiXbX8tSy018m3YiT2ln850zA68s8e/dFjY7DNwww0+Wue/VUiIB57q+D7wDq3cfVu8+ZEMaQWsBIUs+irHz7CtEZme3VLvxBmUmFKRF37fj5Q3KbE6gOx6AyxemviVAQbql540FQRDa6FMAm6oyV8ngcrk45ZRT2LRpExApzfXyyy9zxhliJm0o6m0Au6Eqcol1YoZCToxAw9qyl5FlL7d7TJUMhCy5WPx1KHordaWn9urcwhAm6dk97WpGlr1MunMb32v5kPmGL6medAZvcTxPbLez0WHg+m/t3DDTz4zs/i9RlQiD7MHQ4sHeshvFYMdnH0nQNiJmRY29TT68IYUZxRkY4syV8ARl1lY4Emow0qqs3kt+mjnhgFkQhINbrwPYSy+9NJnjSCqv18vpp5/O6tWrAdDpdCxevJjzzz9/gEcm9Iaqarj8vQtgV9eGAD2H5cVeuNLanMBvK0YxWLF6K9ErQSz+OgDqSk/pcsZKGN5C1gLKpv+adMcmSva8gcVfy6jd/+Yq61LmzLiS23dNocKr547VNi4eH+S8MSF0QyAG08te0l3bsHn34c0YT8iS12mbxpYgq8odHDIyC6up+8Rxpy/Eun3OXqX4QGTmtsYV6FPqgiAIBx9JG8zTqL0QDAY544wz+PTTT4FI+sIzzzzDFVdckZLzud1uMjMzcblcZGRkpOQcB7smT5C1Fc6E9wuFgvy//2wgpEr89XAPY9Lbr4rWy16mr7wFnRpm56wb8WZMAE3FFGjC6t2HXgnQXHA4SIms/BKGJVUht245I/a+h0H2EDTnsm72H3hyZwZLaiKLkCZnyszPl5mZrTA+XelxoZeqQYVXx6ZmPZscBvyKxJRMhZk5MpMzFYz9VCMmbMrCmzEB2dT5/cuglxiTa2dkjg19jOi8oSXIpioXitq3jxGLUc/3xueiGwrfAARBiFsqY6RhV4XgkUceiQavEGlL+5///If//Oc/ce1/0kknccMNN6RqeEIv9Kb6AMD2ynpCqkS+RWV0WueSPjl136BTw/htJXjTx0celHSErPmErPl9GbIw3Oj0NI04Fmf+YUxeczfmYBOjaj/mumlnMC1L4entFra7DGx3Rd5SLXqNaVmRQNTc4fuPokGZW88mhx53uH2Uur7ZwKt7zJh0GpMzFWblyBw3IkyeJXXzDMaQk6zG7wjYRuDJnNyueYOsaOyq91DR7GN0ro3S7AOBbLXTz9Yad1K67gbCCnubfYzNE1c7BEGIz7ALYH2+9itgHQ4HH330Udz7FxWJUkmDTbO3d3Ur1+9faT0/P9w51U/TyK1dBkDjiIWiu5YQF8Vgp2rc+Yzd9jSFlR/hKDiCk0rymJUjs6rByEaHns0OAx5ZYk2TkTVNxm6PZ9ZpkRJdOTJ2g8Zmh4GNDj3OkI6NDgMbHQZe3W3mlNIQ3x8TipnHnSwWXw36sBd3ziw0ffvSViFZZWedh71NPsbk2lE0jbL6ziW8+qKs3kOTJ8i4/DRy7KK0liAI3Rt2AawwvIQVtVeF11VNY21tpLh7rO5baa7t+xdpmXHki3rAqRA2ZiBpasxapUOZK3cuLZmTSXdtp3jP65RP/QWFVo0zRoU4Y1RkhnVvi45NDgN7PDpiXV0vsanMzFGYkNE+VeDU0jCaBlU+HRsdepbXGtniNPD+PjOfVJk4pSTEeWNCZKcokDWG3WQ3rMKdMzNmSkFIVtlR15KScwM4fWHW7HWQZTMyNs9Obppo2y0IQmzDLge2v4kc2NSqdwfYUNm5ZmVPymsauPuTvdj0GouPaemUTzhm6z/IalpDY9FCKidcnKTRCq00JJz5h6FJBrIav0On9i4NZLCyeKuZvPZuJFTKpl9LS/b0lJxH02CjQ88rZWa27k9PMOk0FpWGOHdMiCxTat6+NUmHJ3PKgDfvyLIZGZVjIz9dVCkQhKEolTHSQdNKVhiamhPNf9VUbO7dbN25A4A5eXKn4NUQckVrbjaOEA0t+sqolxifn4apzaolf9pIFGMaqsESuSQtDa+3moC9mIbi4wAo2f0fJDWxJhnxkiSYlaNw7zwff5jjZXKmTEiV+G+Fmau+TGPxTjPuUPIDO0lTSXduweYuS/qxE+H0hdlQ6eLLXY2UNXgIhAd3uTJBEPrP8PpUEYadZk/8Aawh6CS7YSXWlnK+qY/MVs2PUT4rt/ZLJE3Fkz6OgL00aWMdqgw6CWMvW0oZ9RITCtJItxgYX2DHoJNQ9BZ8aWOj28imDFqypgPDawatdtQZhI0ZWPx15Fd/ltJzSRLMyVW4f56P3x/iY2KGQlCVeGuvmZ9/lcaLu1ITyNo8ezF7q5N+3EQFwyp7Grx8tauR9fucuHuRViQIwvAicmCFQSsQVvCF4phxURXS3Duw+GoA2OLSU+XTY9ZpHNaxfaymkFv7JQBNI45J9pAHPUmC/HQzVoMek1GH2aDDoNMRViK5jeEEanm2Bq9mQ2SZvdmgZ1y+nbXqBNC1X3ofsubjVcZjd+9K6usZSKrBSvWYcxm9czGFFR/gyJ+f8o5tkgSH5snMzZX5rtHAK7vN7G7R80a5mQ/2mThjVIizRwVJ637tWELSXNtRjGkxc2L7m6ZFSnc1e0PMKMkkP13kyArCwUrMwAqDVrzdt6y+qmjwCvBxZeTT+6iiMLYOX9EymjdiCjmQDXaceXOTNtahItduojjTSrbdhN1kwKCLvAUY9TpG59rjLsbQMXhtZcsdyfSJ42PWDPWnjSJgK+7zaxhMHAWH400fh14NpjSVoCNJgsPyZf4838uts3yMSVPwKxKv7THz8y/TeaXMjCdJk5QSGhmOjUjK4MljVlSNDZVOqp3+gR6KIAgDRASwwqAVVwCraVi8ldG7njB8XR8JYE8u6fwJnlcTKZ3VVPg9NF0Sp6mGAJ1Etz3n08wGijN77obUVfCKzggFU8mymZhRkhkzGPZkTiZgK0YbLukEko7K8T9AQyKraS1T1vyRzKZ1JKU4ajynl+DwApmHD/dy8ywfo+wKPkXi1T1mrvoqnVd3m/AlIabWKUHSnVv67XXFQ9NgS7WbvU3egR6KIAgDQASwwqAkh8O4HA09bmcKNKJXAtH7X9QaCakSo9MUJmW0Tz8w+RvIcG4BoKno4Fu8lZtmxmTo/k8+P91Mtq3rwN5u1scOXgHyJ4HBHD3OtOIYl5wlCU/WFJoLj8SbPg5VN/QvAfvTRrF38hWEjRmYAw2M3fokEzY+jNVT0W9j0EmwoEDmr0d4uWmmj5F2Ba8s8cpuC1d9lcZre0z4+xjImoLN2Fp2J2fASbSzzsPOFJb2EgRhcBI5sMKgtH33HkzNu/DnHdrtdtY2s6+aBp9URQqgn1Qc6jQDmFP3FQDurGkHXactnQ4K4swXLM224Q+3X/Ft0EkUZ1m7LjBvzYasUe0eGpFpxe2X2dfs67S5pjfhTx+DP200pkA9Vm81aAqazoAmGdB0ejTJiCHswhhKvIxaf3PmH4Y7eyYFVR9RUPUpae6dTFp3H46Cw6kZfXbKc2Nb6SQ4slDmiAKZr+siTRAqfXr+VWbhnQoT544Osag0hLWX7/w2z15kY8ag+/vZ2+QjpKhMLcoQ7WgF4SAhAlhh0Nnb5MXTWIU55MLkb+jyw1If9mAMOaL3d7p1lHv0mHQax47okD6gaWQ3rAKgufB7KRv7YJWXZsaoj++Ci14nMTbPzo46N6oambktyjRH82U7kXRQOCPmUxML0nD4QngCXUz/SRIhayEha2GX4zEGmrC7ywZ9QwTVYKF29Nk0FR7NiL1vk9Owkpz6b8hsXENDyUnUl56Mqu+fGWe9BEcXyXyvUObL2kh72mqfnn/usvDfvSaOKJDpWHjCatA4fkSYEnvntsttpTu34FUnIBvTkA1pnRbsAUiqjF72og970Ss+9EoInRJEpwTQqUHC5hzcObOS+ZKpcQbwhRRmlmRiMca4QiAIwrAiGhn0kWhkkFwOb4g1e5vIqfkSSZNRDHYc+fNjtnpNc25tt3jr71ssfFJt4piiENfPCLTb1u7excQND6HozWya/2CnVpnDmV4HU0dkdB2AdsETkNHpwGbq4Xtu7kTIm9Dl096gzMo9zSixWlIlwOyrxdayB70yNBbu2Fr2ULznddL211INmzKpGX02zQVHRIL+fqSosKzWyKt7zNT6uz63Do1jRoS5cGyQEbb4/n8peguKMQ1Fb0Wv+NGHPe3SerriSxuFL6Prfze9ZTLomFmSSXai7WhbakEJgzkdzBmRyxaCIPRJKmMkMQMrDBqBsMLGKheGgANJi8zY6WUvZn8tQduIdttKahizvy563y/D8rquF29l168EwJU756AKXgHy07qZPe1GmiWOtwdzOuSM63YTu9nApKJ0tla7Ex5DW0FbEUFrAWZfLVZfFYbw4M579KWPZdfMG8lsWkPxnrcwBxsZtfOf5FUvoXL8D/FldP97Sya9Do4rDnN0UZiv6g1UejvPUO5p0fFdo5ElNSa+qDVyXFGYC8YGKeohkNUrgbgC1o5sngoUQ1rSu32FZJU1FQ4mFqQzKtfW8w7uGmjaBaG2M/wSmOyRf98me+d9JB3YciKpM13RNPA7IOyD9GIREAtCkokAVhgUNE1jU5WLkKySFqhv95y9ZTdBa2G7WSuLtxpJO3Cpc1mtkYAiUWJTmJbVfvGWpMpkNa4GiMzmHkQMOin+WpmSDvRGMFhAb47cbqkFratavBIUzYzrg7kky0qzJ0SdO/FAp+MYg/ZigvZi9KEWLL5qLP5apC7HOMAkCVfeobhzZpFXvYSiff/D5t3HhE1/oXzKz3HnzOzX4Rh0cEyRDMRO6djhCvLv3WbWNBn5rMbE0lojx4+IBLIF1tiBrF+G9/aZWF5rJBwj+6DAqjEjW2ZmtsKEDIW26wjTnFtR9BZkc1afXpdO9mN3lyGhIhvSkI3p7Kry4w5kM21EF3mxMQPXVlrk8ZjPtaE3QVph5MeWG3nM2wCeOvDWR2Z0ARp3Qu54yCgVgawgJIlIIegjkUKQHDvrWtjb5ANNI6fuS3Rq+1lUb8YE/Gn7FwlpGjn1X6NTgtHnb1xpZ5dbz2UTA5wzun35rYymDYzb+jhhYwab598H0vDLjyvJshKUFZy+MHKbS/XFWZb2pbP0RsifEil5pTOA3hD5r84Ihhgz034nVK8BOdj5ueyxUDAl7jGGFZVvdzcnvx2oqmD215Hm2o7E4H4704c9jNqxmEzHRjRJx95Jl+PMP2ygh9XJdpeeV8rMrGuOzHEYJI0TisOcPzZIviXyO/bL8EGlibf3mmgJxxeUWfQaU7MUDssLc0ppGL0Eqs6EM28eqqHrEm9d0lRsnr1YW8pj/r/XJAMj8rIoyuhwbFUGuY9fpjrSGSKzrt19mTJaIXcCZJTETIvqRFUiQbQls/fjCnrAnNb7/QWhD0QKgTBsbKh0EpRV9DoJg06KFryvcUY+TIwhR6fgFcDq2RupH6ozYAo0tAted7fo2OXWY5A0juu4eAvIboikDzjz5w3L4NWgk8hLMyFJEiVZGp6ggtMXwhtSyLV3mH3NmwyZCbTPtWbBqAVQtRqCbS7ZG22QNzGhcRr1OmaUZLB6ryO55UR1eoL2yL+NDMdmGMRBrGJMY8/UXzBq52JyGlYyevtz6GU/TSMGV1m3yZkKd871sdWp55XdZjY0G/ioysRn1UZOKgmTb1F5e68J9/7AtdimcP6YECNs7adgVQ3KPTo2NhvY7NTTEtaxtsnA2iYDjQEdP5kYRKeGyGjegCtvLpou/o8kU6ARu2tHt+kLkibT2NREvikdfapnPuNpYhH2Q+1GaCqLzNhasyLBqTn9wDZyKDJ721IHvkbQVLDmQP7kyPbxCrigYUfkGJkjoWCamP0VhhURwAr9xhOUqXfHmMlrw+SPXftVp4axeirwZYzD6t3X7rnW0lmHF8hkmtoHLzo5QGbzegCah2n6QLrFgLR/NkeSJNItBtJj5a9asyFrZOInMFph5BFQsz7ywQr7UwcS/zKQZTNRmm2LWVqrr0LWAjxqiDTXjqQfO6l0eiomXYZisJJf8wUjy15Gr/ipLz1loEfWydQshbvm+tjsiASymxwG/ld5YKa+yKpy0bggCwvDdFXkYnq2wukjw6gaVHh0fNNg4N+7Lby518yEDIXvFcoYZA+ZTWuRjfHNFOrlQLsKJN2RVY2GliBFcTTp6DdhH7h84Nr/XqYzRALZ1rzZjl/C/M1QsSKSqpA/OXZebquQDxp3QMuBBa649kHQDcVzIn/PgjAMiABW6DfxtH00Bxq7fM7q3UfYlNWuLmhLONK8AODk4s6duzKb1qFTwwSshfjTRvdi1INfhjWOjmKSDgqn9/4kegOUzIWGbZHLmracXh9qdK6NKqcPtftqTb0SsJeiU4LYPHuTf/BkknRUjfsBit5KUeWHFJe/hTHYjDNvHn57KaohziBDi6RPWD2V6DQZZ96hKSnVNT1b4e5DfWxs1vOfPWYcIYlzR4c4tqjrwLUjnQRj0lXGpIcIKBJv7zXzty1WRtq9jExTMYRbUrYwr94TJC+9d4sZ+4Uqg6+p5+08deCpj1xFscS4HBtsAVdlZNa2o4AL9n4FIw4Be16fhywIA00EsEK/UFWNGlf3OWeGoBOd2vUMraQpZDg2RO97wnDnGjs+WaLYpjAzp3PuWWv6QFeluIY6CWLPtnaUPab9ZcpenUyCgql9bidqMeopyrCmrI+9L2M8OiWIxV+bkuMnjSRRO+YcVIOV4vK3yK/5gvyaLwAImvPwp5USsBWj6jrmJmuYA41YvZVYfNXtUm6K9r5DzehzcBTMT0mprpk5CjNz+j57fsn4IGVuPRsdBu7bYOXB+V7sKfw0UlWobwnG1Sp58NMiM6q96e+hhKHyu0j6T+74pI9MEPqTCGCFftHgCRKWu59yMwd6bh3bWnnAK8Mf19opa9GTYVS5eZafjguNDSEX6c6tADgG4UKZZLCb9T3PKrUuHEmWJHwRGJNno8blT24ubBuerKno1DCmYByzWgOsvvQUgpY8cuq/xeqtxBRsxhxsxBxshKZ1Pe6v6M347aWYgg5MwWZG73yB/OrPqR77fTxZk1P/AnpBr4MbZ/q54Vs71T49j2y2ckuMv+FkamwJkp9AQ4/hS4ukGAScUDQ7cnVFEIYg8S9X6BdVcaUP1Pe4DYBPhrvW2tjp1pNuVLnrUB+j0zoHx1kN3yGh4U0fS8hakPCYh4K40gcKpvUqXzWVbCYDhRkWanuYle81SaIlezp21w7MgYbBW2ZrP1feobj2t03Wh71YvZVYvfsw+2rblYtrFTZl4E8bid8+kpAlDyQdkhomv/pzCvf9D5u3ggmb/oIzZzaOwgX47aWEzLmD6ipEpknjltk+bv3OzsoGI6/vUbhwXOc0oGRRNahzBynNHg6zsEngqY/k1ZbM7T6nVhAGKRHACinnDyk0e7r/YDKE3O0qC3R5rP3B63aXIRK8zvUxJkbwCh3SB4apDEsPAWxaQeRnEBqTZ09dAAtoOgOe7Gl4VAVzoAGzvw5TsJnBXKUAQDHa8WRNTnj2VNMZqS89heaCBRRVvEdu7ZdkNa8na/8iRkVvxW8v3R/4luK3jyRgG5HQyv9km5ChctXkAI9ttfLKbjN+RWJOrszkTAVzCr5zNXmDFKSbMRkO9lnY/UIe2LsCRsyGtNgtuwVhsBIBrJBy1a6eZ19Nccy++mW4a52NbS4DaQaNP87xMTY9dvBq8tdh9+xFQ4dz/8zWcGMy6Lrv+a4zQEEfFm6lWJrZQH66mYaWnr+49IlOH+niZStCUkJYvfsG/yKvPpBNGVROuJiG4mMpqPoMq6cCi68aveInzb2TNPfO6LaqpCdoGxENaCMBbimKof9m5E4sCbPLrefDKhNv7TXz1l4zBkljcqbCjGyFw/PDjMtIzoo/TYNad4BROXF06DpYqOFImTyRFysMMSKAFVJK07T4qg90UT6rrWd3WNjqNGA3aNw519vth1rO/taxLdlTkU3Ds8FEt7OvJjsUzgBjL4rD96MxefbUB7BtaHoTvozxaJIBe0tZv513IARtxeybeAkQ6UZn9tVi9e7b/1OJ1VOJQfHtT1eoBL6J7hsy50QDWmfeXAL2kpSO9WdTAkzOUljXFFnY1RzUsdlpYLPTwKt7zMzLC/ODcUEmJCGQdXhDFGaYMRsGV1rNwNqfF+uq7JxuJOkjpbv6UHlEEFJBBLBCSjV5QwRj9ZdsQx9qQa90H+Tuduv4rDoSsN0629ftB1maczv51Z8Cwzx9wBrrz1eCnHGRRVuDtWRQG5lWIzlpph5TTJLNnz4aSVOwecr79bwDRdMZCKSVEkgrxcGC/Q9qGIPNbQLayH/NwSZMwWZMwWYym9dTuO8Dmgu/R83os5BNfegI1Q29BMeNCHPciDCaBtU+HZscetY1G/i23sB3jUa+azQyf38g25cZWQ3YWeeJNlFpJSGRZjGQn25KOLj1BmUaPUF8ofhyrSVJItNqINc+yNIZwl1UmNi3MvKekjt+UOVRCwc3EcAKKVXl6D4wNQYdpDs2d7uNpsFzOy1oSBxdGGZGdtcfEhnNGxiz9R/oNJmWzMnDNn1AJ0GaqcOfrzkj0mAgVn3IQWxsrr1TAGsz6SnOsuINydEubcnmyxiHpKlYvRUpOf6gJ0mELbmELbm4cw+JPqyXfVj2B7Tpru1kNm8gt+4rshq+o37kKdQXn4imj9F2OHnDosSuUmJXOaU0TJVXx3/2mFhea2RlY+Rnfn6YI/JlZubI0da2iZBVrV3L5VYBj0KjJ0i6xUBumolMizHaJKQjVdVw+MM0tgTx96I9ciCsUO8OkmE1kp9mJi2ecngDRoOmnZGGCkWzBv2VHeHgIGlaqgrZHBxS2ed3qAvKCl/tauyyYL3VU4HdXUZPi2q+rTdw3wYbJp3GYws8FFhjb59Vv5LRO19A0lRcObMpn/JTNF0cq/SHoEyrkbF5dtAbwZwZKUyePWbIzo58V96MOxAmL81MSZaV3LQDxfgrmnzsrG9JWcktu3M7Vl9Vag4+DNjduyje/Tr2/bPVIXM2daWL8GaMJ2Atil3hQlWw+GuxeitR95f56msVhEqvjld3m/myzoDGgeMUWlVmZsvMzJY5LF/GlsQ40GTQkREjsFQ1cPvDMYPg3rIY9ORnmMm2GtGlsp5YX+mNkfJbQ3nRlxyKdCYTDR1SLpUxkghg+0gEsF3b2+RlZ52n8xOqQrprG2Z/XY/HCKtw3Qo7NX49548J8uMJsfMlc2u+oLTs30hoNOcfTsXEnwy60lHJoOrM+O0ljBs5ghH5BcNmJsQblDHopS4v3TZ6gmyqciErqXm7SnNsGfyNDwaSppLV8B3Fe9/CFDzQwlWVDARsxfjTRhK05GMO1GP17G+woMntDtG2CoI3fSzO3Lm9+hvd59GxpMbIJoeeXS16VE3CiMyxunVYTXpOnFLIqLz4WtJGXkQk2Lb4qlF1Jvz2kYTN2QP2ZdColyhIt5BjN6IfzGlA9nzIKI60tx0q77VhPzTv2d+tTImkW+UPzlrJw4UIYAcxEcB27euyRnzB9pfWdLKPjOZNGOQYgW0bZl8NpkATr7TM4pmdaWSZVJ74noeOaZ+SEqKg6hNGVLwLQMOIY6gad1FKuhANNFVnwpk3F9Vg46iJed1XIBiGvEGZ9fuccecZJkTT0Ms+9LIHQ9iLXvZiCHvQKwEGe9mt/iQpIfJrlpDRvBGrt3L/7yc2RW/Bv7+1bySgbf//LWAtonrs93Fnz+h1sOgLa3gqN3FI7esUKAe+EHt0GagZkWA51syvTglh8VVj9e7D4u0cbMsGW3QRW9Ba0Kf3k9YgP2AbkVDqhV4HeWlmcu3mBNr1Sl2mPKSMpI+U6ssoicxoDsarQMEWaN4N7ho6/T2nF0VmlAfzl4UhTASwg5gIYDtr9obYVe/B7Q+3e1xSQmQ3rOq2XSyAMdDMlLV3oVcCOLQ03laORDfqcGaPHRF5c9Q0bC17yKlfQXbDd9EFYLUjT6N21JmD8w20jzTJgDNvLooxjXSLgcPH5Q70kAZEWFEpa/CgqBp6nYROkvZ/aEO109/jgsFESUoIU6ARi78OY8jJUA9mLUY9uXYT1hhfflQ0gmEVX1gmEFIJyEr3aRuaiinYhNUTabpgCjQQshREZ1nbBo6SKmP21+7ftoKc+pXRL7EtWVOoGns+AXtpQq/F6qmgeM/rpLt2ABAypNOgpjFCqUUnJfb/KRJsl6CXA1j8NTGbR/SVho6ArWh/ybLSaCMKxZjAbHEPsqxGxuQNYFMCSQcMwvffnhqZWLKg5FAwpC63+2AlAthBTASwB7QEwuyq99DUxYryjOYNmAKN3R9E0xi3+W9kOLeiIqFrEzD4bSW0ZE0hw7EJS5v0g5A5h7qRi2gqOjopr2Ow0SQ9rtw50XJgY/PtjM9P3ofecBFWVHbWeeIq29YbkhLCHKjH7KvFGHan5BypoNdBts1Ejt2ErePCv25omoY/rNDkCdHsCyU1B1kv+yjY9z/yq5eg02Q0JJoLv4c3fVxc+6e5d5Jd/y0SGqpkoKHkROpKT0HRW/l4r8bK3fVMliqYZ9zDERnN2AwdBi/p9weTI/GntebnRmbgJDWMxVcTrcxgDDb37bUqAazeqi6vOoVMWe3H0SEA1CQdQVsRfltJXDO4RZkWijIskTxP1z7w1EVqvNqHcM5qfzBaoWQemMV7azKJALaPvv76axYvXszy5cupqqpC0zRKS0s56qijuPTSSznyyCN7fWwRwEZW05Y1eKh1Bbr8kLN4q0hzbe/xWLm1yxm5618okpFTg/dQTBN35n3O6Ja17S7zKToTrry5NBcswJM5cVimDEDkw8uVcwiyOSv62GFjcsi0Dc/FacnQ6Amytcad9NnYtqwte7G37GYwz8jqdTAi00qOzdTnRUEhWaXWHcCR5EDWFGhkRPlbZDeu7tX+jvzDqB59DmFL+ysSO1x6HtxopSGgw6jT+NnkACcVhwfu4oymYQw5oy2CW2etzYGe619HD4FE0FoY7aQmG9M7baMPe7B6K8kMVKH31EDbmeTC6TD2GBg5HwzDI3c+6XSG1PxujFbIGj20F771kghge8nr9XLdddfx3HPPdbvd5ZdfzqOPPordnvilFxHAwtoKR5ezrhDp7Z7VuKrdZTmvDFscBjY69Gxz6gkoEoVaI4vVW7ER4CH1RzwWOp15eWFuP8SPXvaS1bAae8tuPBkTceYdijrM34Q1JNw5swlbDhQQT7MYOOIgTR9IRKpnYwGMwWbSHZvRqeGeN+5n6RYDI7NtSa8xGpRV6twBPIHIojujXodRL2HQ61BVjfpeNqWwu3eRV/MFOjm+kmmKwULjiOPwZXQ9Y9sShkc2W/muMfJl79iiEL+YGsAyiFLHdXIAq68yGtAaQp1n9nVqJF/XGG5J/ATmdLDmgrP8wGMGC4w6AiacJDpv9TdTGuSMhfTigybnVgSwvaAoCqeddhoff/xx9DGr1cr06dMxGAxs2bIFt/vAm8XJJ5/MBx98gF6f2LvbwR7AuvxhVv3/9u48PKrq/AP49947eyYzk5XsCYQtgIAoS6RsgqAFse4VUeAnauvWFhXFqqi1xRbUurTWVgt1qxVFtIpaVDYRC4JAWAshCQlL9kwyk8x6z++PIZeZZJY7SSaZSd7P88zDncy5Z87MGybvnHuWkiCX2JgIU81uKJxNONvM4fNTKhyoV+BEIw/R51IZwxvKZzBZKMIucTBudDwOBc/hufFWZMVFrictmlkNA9Giz5Huq5U8xuYl9rnJW51RXteMo2c78IdfJt5tR3xdUZcPKeB5wKhRwqBRggFwiwwuUYRbZHCLDC1OEXanu13/L88DmSYdkuJ6ZixfWZ0V9dboSehFBqwvU+GtYjVExiE7zo2lF7QgWx9jnymMQeFslDab0ForwPuZQCcKatjiMtESlw2XIQf9s7OgVAiApQoo3QaUbPEct8opBEbd5JmERbqPQu3pkU3Ii50VHDqIEtgOeOSRR7BixQrp/u23345nnnkGiYme3iyr1Yrf//73+M1vfuNzzm9/+9uwnqevJ7B7yxtQE6TXRdd4HDrLSWyvVODlQ1q0uM8nrRk6N4YnuDEiwYVx1i0oPPMmXJwS/x7wOJrUaUjViEjT9cpfz5AYx6Ou30RpHVtB4HBRbkLw7WOJXydrm/G/ysglsWAi4szHOr2WbGvSatQpYVCHXgvULYqwOtxotrtgdbjBcRyyTNoe3dlJFBmOVVk6tLB/JB2sF7CqSIt6Bw81z/CzoTaMTHS1K2dUMXT07bO7AYtT3hgFjvM8lxDhIQ1xagH5yfrzv0uMAdVHgOKvgNLtABjAK4EhVwDDfwIodZFtEPGlUAPJgz0rOPTCyccAJbBhO336NPLz82Gzeb6h3nLLLXjjjTf8ln3sscfw9NNPAwA0Gg2Ki4uRkZEh+7n6cgLbaHNi54nAva9Kex101fuw5pgKn5Z7FqYvMLowK8uJEQkuJJ/bQUdpqz236oAdp/pfh+rMGd3S/mhm02XAYhoKwPO5NirbhGSvxf1JeAKuSdyFOLcDSocZSocZCqcZCmeT7NnsBq0SuYm6dtubxiK7S8T/KhvhjrJOzgY7h+cOarG/LvBENjXPMNTk9myMkOjGwHh3wCWs7G7gSIOAonoFDtQLONYowM3kxy9OwTDc5MKIRM/z5epFRCL88RqF36s2ysaTMBx5F5rawwAAtyoelv5XwK0xtSnJwaVNgSs+C0wR/DNIpxSQ0EO9/4E43SKqm+ztrlZEVVvV8UBKARDX+4aHUQIbpqVLl2LlypUAAJ1Oh/LycqnntS2Hw4GBAweivLxcOvf3v/+97OfqywnsvvIGVDfZobA3+J1ha6kqw7P7FDjW6PnwvDbPjnkD7Of/IDARKlstso+/jXjzEVgM+Th+wf29dkJWOOpTxknL6wxNj0dWAvWMdFZJjRXFVZFNYn0wEeqWKsQ3HEawyV4GrRJ5ibro3n0pTI02J0qqrVE3xc3NgPdOqPHvkyo42iTYIgCxTQKqERj669snsXY3h5ImHq425QWOyVpESmRoM4QKiFeKGG7yXJG6INGNnDgx8p1yjMFQX4SMkg98VnbxWxQc7NpUaX1caTKZyiiV4QDkp+qhV0fPtrjF1RY02dr3tnMcMDBFj7goaiviUvyvFqGOB3T+c5hoRwlsmAYNGoTjx48DABYuXIjVq1cHLb98+XI89dRTAICBAwfi2LFjsp+rryawTTYn/nuiDgqHGcaaPeC8/lRZnMCuGiVeP6qBxcVBr2D45YgWjDc2wFi7zzML99w4rtaF0EVeiaMXPgq7tl9PvaSo4VSZYE4eAwDIS9ZhYGr72cakY4qrLSiptnbrc2qsFdCfW6u0LeO5ntfelLy2Ottow1mzvElZ0UBkQLmVR1GdgAP1ChxsENDkDP5lOkktSr21IxJc6Bdgm+u23CJwook/13urwKFzE1m9GZUihie4UWByQyPIq9ekYhiT5JK98YFEdCP57DYY6va3u2rAMTfULZUBx3k7lQYpobVp0yEoVEg3aaLiakJDsxMNzf4nGDNOgDs+A3l5+VAooiiJ9YfjgayxMZnEUgIbhqNHj2Lo0KHS/XfffRc33nhj0HO+++47FBYWSvePHDmCIUPkbS/XVxPY/RUNqKmrh6lmN2wOJw41eC6jFdUpUNJ0foLWIIMbS4ebMaz+a6SVfyZtOtCqdZeaszmz0Zg0qideStRpTBgBhzYVyfFqjM429XRzep2eSGJ1jSegs5T6/Kw3J6+tSmqsMLdEz6SucIgMKLXwON3cPhvkAfSPdyNNy7qkl9QlAscbBc9naL0ChxsEOMSOVZyhc+PGAXb8qJ+rS8fYKhxmqeOhdTKZuqXSp/MiFom8CnxCjmdClSnX09vZ1QQVYMoGdJ3YqUxQeibdqXpwo4oOiGSOFOVfO8K3b98+n/veiWkgY8aMgUqlgsPh+aa2f/9+2QlsX9Rkc6K6vgnOin14tVjAV6c17S6lZejcmJjqxJ2GHcg+9CHUds8GBi26DDQlDJMuQ9m0aT0+C9MtaMGLTnCs/WWm7iYKajg0nktIGcbevUxYT8lP8exmdvB0I9zurvvjy/OAGGDcZ7NhgLQcEtA3klcAyEnUosYioM7qgN0VZYNiQ+A5YEC8iAHxkW+3ggeGmtwYanLjuv4OOEXgWKOAA3UCTjQJkPNrygAcNQs43Szg+QM6rC1x46f97bikn6tLxta6VEY0qYxoShgu/ax1m2DvhLZ1uIxawUMVdldw1xABtNhdCBY5T9vPQBAdQO1xzy3SlHFAQq7nZsrzJM2GTECQkYq5nUDFLk8SG2Iscl/R6xLYw4cPS8cqlQrZ2dkhz2ktV1xc3K4O0t73J2rw3rb9+KpcQBpqMIk7hRS1A7l6EblxbuToRRgUTiSf2Qz9Wc976lQZcSb3KtSlToiqMa6MU6AxcSRU9lrENXbDB1gILTrPbFSOQ/RMMOiFUuM10PdXYH+FGRY/4+PCYdIpMSg1HkadEk63CIfLc7O7RDjd4vmF/1MvhqpqHxQtVV2yuUAsEHge/Qwa9DNoYLG5UGu1o6HF2aWbIfRGSh4YZnJjmCm81RxaXMAn5Sp8VKZGhVXAqgM6ZJV4xtNGhhaACcAwAJ5EfKDBMyktL17EkH76sHZ/6yplNVacanTiwLkJdnX29n9zFApgcKYTE3SnkY+TyEIllE0VgDMCw16cVqDxlOffqkOeWyteAAxZ55PauOT25/MKwJjlGR97ajeQPb7HO36iQa9LYEtLS6XjrKwscDK763NycqQE1ruOaOFyOlC06b0ebQMDUHKyHE1lP+D/uDKsVJ2EgWs+X8By7uY1F0DklajKnImqrJkQhWj71sihMWE43Mo4tCi00FrLwbs7thC7XC26THAQoWk+0+4xBg42nWcFDKNWCWUP9V70FTqVAmPzEnH4TGOHxmpqVQIGpeqRajjfU+5Z2J9HXKBf9cQJnl6Uls5tTxqL9BoF9BoFMkURVU12z8xwSmS7lFYBXN/fgR9nO/DJSRU+OulJZCus3ZfsbD3rWeovXiliZKKI6bkisvTd82XNIQLfnnJhxxkVSi0ahJpSt/WsEq9hIAzKAbggUcRleQpkxEWmrZzoQlzLKRispYi3liHeWop460ko3Vagocxzw9agdTgFLZrictFkGIymtPFwqkydahOvUGDUpT/tVB09qdclsE1N59d7NBqNQUr68h6b4V1HW3a7HXb7+STHezOESHLYW3Dht3d3y3MFMwYAvD4LRU4Buy4Nbj/JqU2XicrsK+BUJ3Rb+8JhNeSf34KS42GNz0d8w6HgJ3UCA4eW+DyIghoir4LOUubzuEPbT9rrPJF6X7uFwHMYkWmEUatERX0LWpyugMMAWikVPPKSdMhO6MAQAP7cZAxXCyC6AdHl2e5TdAG2RqDhJBCFO3t1JQXPI8OohUmrRHldS9StGdsbxCmAGwc4MDvbgf9WK2HvprfY6uJwuMEzKa3JyWN7JY/tlUD3brl8/g9Uls6NEYlu5MS1fwOsLg6HGjzjjRu7pa0CgJxzt1YMmajBML4Mw7gyDOdLkcC1zz/iYEc+dwpqdwsSG48gsfEIUPFxp1vUCB1ACWz0sFjOL5Oj0cgfQ6jVav3W0daKFSvw5JNPdqxxncDzAo4oCrr9edsSFRrAkIE4Y8q5Wac9P4a1I+zaNJ9drgDArkvzbOfYkS0bZbDpMqRe6GZDPkReBX3j+RUvWuKypOMkWvO1W2Un6pCdqANjDM0ON6x2Fyx2FxxuERqFAK1KgEYpQKsUOr9RAM/7n4gRnwYkDvD0xNSXAu7A2zP3BjqVAoP76VHZZEdlo416YyNArwSmZ3T3FyKHNCntaKMSxy0qdNfmbBwH5MS5MSDOjhEJbiSoQ/1SnW/rkUYliruxrR4cgBTUIgXbcDG2BSkpMBcyxdPoL5ahPytHrlgBNevcFUOnoMUFnaqhZ/W6BNblOj+eLZylMbzLOp2Bf4OXLVuGJUuWSPcbGxtljbPtLI1Oj6GPfhfx55GjxeHGwaI9UNQXo3u/WXcNlzIeTaahfh+zGvJhrN3b5c/Z2vvqzabPBuOV0DcchksZD5fKcxVAqeBh0PS6/5oxgeM4xKkViFMr0CObawoKz/70Cf2BxgpPj6y/RNblQI/83+MET/LNcYC9ydN7HLAs75nR7bB6epj9FeE4pBk053rAm+E4N9GrNZllYLI2RHCqTGiO7w8AUDibIDgtUDgtEFzWmJ8lH4sUPDA+ncf1w9U9sJwWD3OLZ1vjUFdTgJ5uazhUAPoDyTM9nxGk9yWwOt35Bd9bd+KSw7tsXFzgZSrUajXU6r7dO6ZVCRh2wYXYd9QAdfUB8GL39BSJvApWw8BOXeYXeTUaE0cGnEjmVCfCoU6Cyl7b4efwx7v31ZtdlwaRV/msgJAUp5I9dpv0UjwPmHI8N39cds+e9pZKoLk2eCLZYZynV1ib4ElaVXGA8vyVKjDmSWJtZsDeCDiaAZUO0BgBtcGTvHKcZ/Z0fSlQXxZweIRWKWBQgPWOLTYXKupbYHO1vwzsUsbDGp8Pp+b8+pg+Q5aYCN7fczIRvNsOwW0D77ZBcLdAcNkguKxhf56Jghp2TQocmmSAASpbNdS2GvBiZMfTR7PUeDXSjZoe+xwzapUYmKJHaU0zHCG+AaUa1MgwaoOWiQqCCkgf3St36+qoXpfA6vV66bilpSVISV/NzecnI3nXQfzTqRQYNSQfe5RxUFUfguBqbleGd9u6rPeDcQLMiaPgVsV71iOUse+8U2mAWxkHt6CDW6GDW6GFW6ELuQqC1ZAPVXUduqqHi4FDiz43cDs1votT0/hXEpJC7VlX0pQNuF2AtRpoOuP5t7PJLK88V3cuoAwyDIvjAI3BcwtGUALJgzy9yg1lQH2JJ6n197x+6HVKDNFpUN1kxxmzDaLI4BY0aI7Pg0ObCq1KQK5JizSjBhX1LSirtZ4fjsDxASePigot/PULc24HFE4LFM4mKFwW8K6Wdp9jjOPhVCXArkmBW+WbeDs1ibBiCBSORqhs1VA6GuEW1BAVWrgFredzSNCCFx0QXFYonFYIrmYoXNZzn6Ox22PMAchK1CEpCj7DdCoFBvXTo6TGimZH+y8/HAdkJURHWyW8wpOotqXUAWkXBP//2Af1ugQ2Ofn8EhRnzrSf6R3I2bNnpeOkJPqGI4dOpcCF/dOwm1dLl/58iG4oHQ1Q2eugstf6TXLlYODQmHCB9Iei2ZAfsofDru3ns15hONxKPWy6NL8rBXSEXZfuGTssEyWwJCyCAjCke25up2e5HvMpT69oOJQ6z1I+hix561J2pJ1J+Z61L1vqPWPnBZUncRWUQRd45wCkAjA43TheZUFdow1JejWyErQ+VywGpuqRGKfCwdNm2J3+E3mO8yx9xvt5PrfI4HAJcKjUaHGH3vWI4zwrT6gEHioF16bO5HM3/39oXSKD02WE3S1K6xFzohMqWw3Utmoo7XXtdsUCzu0gpdCBce1r5ZgLgqsZSk6EViVAp/KM2+6unlClwPXIslmBKAUe+Sl6WOztv6r0aFt5JaBQea5qqA3nbvqY26Sgp0XPb1oX8d6AoLa2Fs3NzT7DCgIpLy+Xjr138iLBxakVmDAgCTaX29MzIjK4GYMoAqW1Vlj4JDg1SbBiEHiXDSpbNbTNp8JKZi2mAp9eSsYrYDEOhqG+yG95lzIeTcbOxbA5foBnl5lO9mYxcGjW58kur9cooFHG3qQ4EiUEpSdBTMjzrGrQdAZwtgDMfW7Vg3MrHwhKQKX3/NFUGzzHim764sQL/te6lEGjFDAi04ihafFQBFhmLjFOhfH9k3DwtBm1lvPDAdRKHhkmLTJNWln/x0SRweEW4RLb94hyABQCB5XAd0ly2PpcTrcIUezn+Qx1uwBrDdBcC1GhAVPFQVTpAUXgy90Knke8RgENHJ6xx/Ymz7+A58oTL3j+5XjPY9bqgOOTA1LHA/HpnqEijAFgnl5/xiI0lKVzBABGmxkwV3j+HwSi1HomUSojkERynOfLmqAK+WWNyNfrEtiCAt+Z+nv37sUll1wS9JxTp06huro6YB0kOJWC9zszOyFOid1l9Wg+t4aLqNDAps+GTZ8Npb0OGuspqG01CHbJzBo/AHZdWrufO7QpcLQkQ2Wr8fm5yCvRmHBBp1dGEAU16lPGgWMiGMcDnADG8WDg/X74KB1m6JpOQOkw+/zcrksLq/c1qi5nkdgm5/J+jAqUvLZSKXhcmJOAk7XNqLHakWXSIiVeHVayyfMcNN20wkrrc/km1irA0HbZJbm0noQs1BcFUfSMobac9Yyn9je0A/AMWYnPAAwZsfk7ZcwEkgaeG8LSZiy2Kg5IzPe8NkosY0qvS2DHjRsHtVotrdX6zTffhExgt207v3iFRqPBuHHjItrGvkKtEDAmJwG7y+rR0mYMklOdCKc6EVa3HermM1A4LeBFJ3jRAU50gBddaNFltJu5781iHIIEe4PXBCgOTQkjwkoYgxEVoXvuWznVCTCrL4LSVou4phPnluIKr/cVoOWzCOlKOUk65CTJ/3/c5/A8oE/x3NgIT69suw4F7vyEvFimUHnGYicO8KzuYanyTJKMT4v919ZH9boEVq/XY/r06diwYQMA4O2338bSpUuDnvP2229Lx9OnTw+6CgEJj0bpSWK/L6vzOyYtOcGA/oPSceBUI5q8xykxFvJDRRTUsBoGQG/+HwDAYhjY45smODVJaNAkQdVSCYWrGWKQy31tCTwHk9b/RBZCCImo1kl5vR0vAIn9PTcS03rlXpULFy6Ujvfv349///vfAcvu2bMHn332md9zSdfQqjxJrNJrmIFBq8TFeQkYmWVCvEaJkVlGCIJXwirzG7EtLgtOlRE2bRps+sivxyuXQ9tPWpfSm04lQCH4f20mnTL8nZ0IIYSQPqhXJrDXXXcdRo0aJd2/8847ceTIkXblzpw5g/nz58Pt9lzeHj16NK699tpua2dfEqdWYEyOCXqNAiMyjRjXPxEmncrn8WHpHfv232QaBkuAjQmiTf+UOFyYneCbrJ+TTMMHCCGEEFk4xnrnBn67du3ClClTpLVgDQYDfv7zn2Py5MlQKBTYuXMnXn75ZVRWVgLwbCW7ZcsWjB07NqznaWxshNFohNlshsHQBy6/RNjRs00orwtvuS21kg+4ZE40USt5TMxPBs9zaGh24IeTDXB7zXAuzE9CnLrXjeohhBDSR0UyR+q1CSwArFu3DvPnzw+5oYFWq8Vbb72Fa665JuznoAS2a4kiw56T9WholrchtUmnxMgsE/acrIfFFuZyMN1sYKoeecnnx1fXWx3YW+5JYrUqARMHdmxpIUIIISQaRTJH6pVDCFpdc8012L17N2bMmOF3+RSO4zB9+nR8//33HUpeSdfjeQ4jMo0+42UDSTNqMCYnASoFj0xTdG8FKPAcMhN825gQp8KobBMEnqPNCwghhJAw9OoeWG/l5eXYvn07Tp3ybEGamZmJiRMnIju7cxN/qAc2MuqsDvxwsh6Bfjv7p8QhP+X8lr8ut4htx2p8Lsl3F0HgoFEIsPrZ7aVVdqIOQ9L87/Vea7GDgcbAEkII6V1oCEEUowQ2clxuEQ0tTjQ0O1Df7ESTzTOsoCDdgHRj+x7XQ6cbcboh+HCRSMhP1aOfQY3/ltRJW0J64zjgkvxkaFW0wxYhhJC+I5I5Es0YIVFLIfBI1qulnknPPuViwEQw06Tt9gRWreSRk6iDwHMYlm5AUYW5XZmUeDUlr4QQQkgX6tVjYEnvIvBc0ETQqFNCr+ne72QDUvQQzq3d2s+gQXZi+11/cvz8jBBCCCEdRwks6VW6czKXTi0gw+i7be2gVD0MXrtpGXVKn/VuCSGEENJ5lMCSXiXdqJF6RCNtYKq+3eoWPM/hgkyjtNsW9b4SQgghXY8SWNKrKAQeqYbIz+Y36ZRIjdf4fUyrEjA8wwitSkBqPK0sQAghhHQ1msRFep0skw5nGmwRfY5Bqf6XxGqVEq9GnFrwu/4wIYQQQjqHemBJrxPOZC61kkeaURPW5K9UgxpGnTJkOZ2Kvh8SQgghkUB/YUmvlGnS4ujZpnY/FwQOCToVEnUqJOpV0Ks9/wUYY6iob0FxtQUuP2u5tuI4z9hXQgghhPQcSmBJr5Rm1KC42gKtUoBBq4RBq4RRq0Scyv9lfY7jkJ2oQz+DBserLDhjbvHZBUynEpAQp0JKvJp6VgkhhJAeRn+JSa+kFHhMGZwS9hhUlYLHsAwDMhM8myIYtUokxqmgUdJGBIQQQki0oASW9FqdmUBlPNdjSwghhJDoQ5O4CCGEEEJITKEElhBCCCGExBRKYAkhhBBCSEyhBJYQQgghhMQUSmAJIYQQQkhMoQSWEEIIIYTEFEpgCSGEEEJITKEElhBCCCGExBRKYAkhhBBCSEyhBJYQQgghhMQUSmAJIYQQQkhMUfR0A2IdYwwA0NjY2MMtIYQQQgiJHq25UWuu1JUoge2kpqYmAEB2dnYPt4QQQgghJPo0NTXBaDR2aZ0ci0Ra3IeIoojTp08jPj4eHMdF9LkaGxuRnZ2N8vJyGAyGiD4X6RyKVWyheMUOilVsoXjFjkjEijGGpqYmZGRkgOe7dtQq9cB2Es/zyMrK6tbnNBgM9EEQIyhWsYXiFTsoVrGF4hU7ujpWXd3z2oomcRFCCCGEkJhCCSwhhBBCCIkplMDGELVajeXLl0OtVvd0U0gIFKvYQvGKHRSr2ELxih2xFiuaxEUIIYQQQmIK9cASQgghhJCYQgksIYQQQgiJKZTAEkIIIYSQmEIJLCGEEEIIiSmUwEa5b7/9FnfeeSeGDRsGo9EIg8GAYcOG4Y477sD27dt7unm9VkNDAz788EPcd999mDx5MtLS0qBWq6HX65GTk4Mrr7wSf/zjH1FfX9+h+ouKirBkyRKMHDkSiYmJ0Ov1GDJkCG6++WZ8/vnnXfxq+q7S0lLExcWB4zjp9sQTT4RVB8UqshoaGrB69WpceeWVGDx4MOLj42EwGDB48GBceumleOyxx7Bp0ybY7XZZ9VG8ul5VVRVWrlyJyy+/HFlZWdDpdNBoNEhPT8e0adPw+OOPo6SkJOx6KVbyVFdX47PPPsNTTz2FuXPnIj093eczbc2aNR2uO1IxOHHiBB5//HFcdNFFSElJgVarRX5+Pq6++mq8//77cLlcHa5bwkhUslgs7P/+7/8YgKC3RYsWMYvF0tPN7TUOHz7M5syZw1QqVcj3HgDT6XTs+eefZ6Ioyqrf6XSyZcuWMZ7ng9Y7e/ZsVlVVFeFX2/vNmjWr3Xu7fPlyWedSrCJv9erVLCkpSdb/tbVr1wati+IVGc8//zzTarUh4yMIAnvwwQeZw+EIWSfFSp4zZ86w3NzckO/96tWrw647kjH44x//yNRqddB6J0yYwIqLi8NutzdKYKOQy+ViM2fO9Am2VqtlF198MZswYQIzGAw+j82cOZO5XK6ebnavsHbtWr8fzEOGDGGTJ09mEydOZImJie3KLF68WFYS2/ZLiVKpZKNGjWITJ05s94d85MiRrKmpqRtede/05ptv+v3glJvAUqwiRxRFtnjx4naxycjIYBMmTGDTpk1jw4cPZwqFQnYCS/Hqeg899JDfGE2ePJlNmTKF5eTktHv8uuuuC/lZSLGSp6SkRNaXu44ksJGKwVNPPeVzLs/zbMSIEWzy5MksPT3d57GsrCx2+vTpsNveihLYKLRs2TKfIN9+++2strZWetxisbDHHnvMp8wjjzzSgy3uPVoTWIVCwX7yk5+w9evXM7PZ7FNGFEW2fv16lpmZ6RODP//5z0HrfvXVV33Kz507l1VUVEiPOxwO9tJLL/n80Z43b15EXmdvV11dzZKTkxkAVlBQwDIyMsJKYClWkXXvvfe2S3r27dvXrlxLSwvbsGEDmzdvHvvoo48C1kfx6nrbtm3zeU8HDx7MNm/e3K7crl272JgxY2QnVBQr+bwT2JSUFHb55ZezRx99lK1fv75TCWykYvD5558zjuOkcwoLC9nRo0elx91uN3v33XeZXq+XykycODGstnujBDbKnDp1imk0Gim4t9xyS8Cyjz76qFROo9GwU6dOdWNLe6f169ezxYsXs7KyspBlT548ydLS0qQYJCcnB7x8ZrVafcpOnTo1YK/5a6+9JpXjOI7t3r27U6+pL5o/f770Hm7ZssXnMlyoBJZiFVlffPGFzx/P5557rlP1Ubwi48Ybb5TeK6PRGPTvS0NDg8//sYsvvthvOYpVeMxmM1u7di0rLS1t91hHE9hIxUAURTZq1Cip/JAhQ5jVavVbduPGjT7tX7dunez2e6MENso8+OCDUlB1Op1Pz2tbdrudZWdnS+WXLl3ajS0ljLX/Jvvll1/6LfenP/3J54Pg0KFDQesdP368VP6GG26IRNN7Le8EadGiRYwxFlYCS7GKHIfDwfLy8qT3a8GCBZ2uk+IVGd5XLe65556Q5VeuXOkTB7vd3q4MxarrdDSBjVQMPv30U582ff7550Hr9f6CNG7cONnt90arEESZDz/8UDq+4YYbkJiYGLCsSqXCokWLpPvr1q2LaNtIe1deeaXP/SNHjvgt5x2bKVOmoKCgIGi9d955p3S8YcMG2TOw+7rm5mb87Gc/AwAkJydj5cqVYddBsYqcjz76CKWlpQA8n19/+MMfOl0nxSsyqqurpeMRI0aELO9dhjGGmpqadmUoVj0vUjHwrrd///6YOXOm7Hp37tyJioqKoOX9oQQ2ihw9ehTHjx+X7l9++eUhz7niiiuk4+PHj+Po0aMRaRvxr+0XjMbGxnZlLBYLtm7dKt0PN64WiwWbN2/ueCP7kMcee0xazmfVqlVISkoK63yKVWS99tpr0vFVV12F1NTUTtVH8YocvV4vHTscjpDlvRMbjuNgNBp9HqdY9bxIxuDTTz+VjmfNmgWO44LWO2nSJMTFxfk9Xy5KYKPIvn37fO4XFhaGPGfMmDFQqVTS/f3793d5u0hgZWVlPvf9/UE+dOgQnE6ndF9OXNPS0pCXlyfdp7iGtnv3brzwwgsAPD0LCxYsCLsOilXkOJ1Onz9+c+bM6XSdFK/IGTdunHTsnfQEsmXLFul49OjRPskJQLGKBpGKQVVVFc6ePRtWvQqFAmPHjg1abyiUwEaRw4cPS8cqlQrZ2dkhz2lbzrsOEnlth234+4/bNib5+fmy6vYuR3ENzuVyYfHixXC73VCpVPjLX/7SoXooVpGzb98+n166CRMmAPBs1rJo0SIMHDgQWq0WCQkJGD58OO666y589913QeukeEXOXXfdJR2vW7cOX3/9dcCye/fuxauvvirdf+CBB9qVoVj1vEjFoKdiSwlsFGkdGwYAWVlZIbvgW+Xk5Pitg0SW2WyWevwAYOTIkRg2bFi7ct4xUSgUSE9Pl1U/xVW+Z599Fnv37gUAPPTQQxg6dGiH6qFYRY53DwvP88jMzMSdd96JiRMnYs2aNSguLobNZkNDQwMOHTqEV155BYWFhbjhhhtgsVj81knxipy5c+fi3nvvBQCIoogrrrgCjzzyCIqKimCz2WC323HkyBH89re/xaRJk9Dc3AwAWLJkCebNm9euPopVz4tUDNr+zLt8Z+oNRRH2GSRimpqapOO244eCMRgMfusgkXX//ff7XDZ5+umn/Zbzjkl8fDx4Xt73RoqrPMXFxXjyyScBAAMHDsQjjzzS4booVpFTW1srHaekpOC2227Dv/71LwCeMZPDhg1DamoqKisrcejQIans2rVrUVxcjG3btkGn0/nUSfGKrBdffBGDBg3Cb37zG1RXV2PFihVYsWKF37JDhgzBww8/jIULF/p9nGLV8yIVg7Y/k5u/dDa21AMbRbx7GTQajezztFqt3zpI5Lz22mt4/fXXpfs33nhjuxUJWlFcI+vOO+9ES0sLAOCVV14J6z1ui2IVOQ0NDdJxZWWllLz++Mc/xokTJ3DgwAF8/fXXOHjwII4fP+4zi3nPnj1Sb6A3ilfk3XvvvVi3bl3Qqxqpqam4++67cd111wUsQ7HqeZGKQdufya27s7GlBDaKuFwu6VihkN857l3We4A2iYytW7fi7rvvlu7379/fZ/xXWxTXyFm9ejW++uorAMDNN9+MGTNmdKo+ilXk+Ft+Z9asWfj44499JokAnrFxn3zyCaZNmyb9bM2aNe1WWaF4RdbJkycxc+ZMTJo0SVoiMDU1FRMnTsTUqVMxYMAAAJ5JPPfddx8GDBiADRs2+K2LYtXzIhUD73rDqbuzsaUENop4Xx6z2Wyyz/Mu23bmJ+lae/fuxdy5c6VlZVJTU/H5558HvWRCcY2MqqoqabJIQkICnnvuuU7XSbGKnLbvC8/z+POf/wxBEPyWVyqV+POf/yzNBRBFEWvWrPEpQ/GKnNLSUlxyySXYuHEjAKCgoABfffUVKisr8c0332DTpk0oLi7G4cOHMXv2bACetWOvuuoqfPbZZ+3qo1j1vEjFoO3QHrl1dza2lMBGEe9191ovicrROni+bR2kax09ehSzZs2C2WwG4Ema/vOf/2Dw4MFBz6O4RsZ9992Huro6AMAzzzzT6TVFAYpVJLV9XwoLC6UevECGDh2Kiy++WLrvvVRT2zopXl3r1ltvxalTpwB4xrfu2LEDl156abtyQ4cOxb///W9cf/31ADy9cYsWLYLVavUpR7HqeZGKQdufya27s7GlBDaKJCcnS8dnzpyRfZ73RKJwF24n8pSUlGDGjBmoqqoC4BkA/9lnn2HUqFEhz/WOq8VikT3Wh+Ia2I4dO6QxlIWFhbj99tu7pF6KVeSkpKT43B8zZoys87zLnThxwucxildk7NixA9u2bZPuP/PMM0GvMnEchxdffFFak7yyshLvvvuuTxmKVc+LVAy86wXk5y+djS0lsFFkyJAh0nFtba3Pt5NgysvLpeOOLh9EAquoqMD06dOlre50Oh0++eQTjB8/Xtb53nEFPOPK5KC4BlZZWSkd79ixAzzPg+O4gDfvDSeefPJJn8e8l2+hWEVO2/cl2DbZ3rz/sNXX1/s8RvGKjNZhA4BnKIecHZvS0tKCbn5Asep5kYpBT8WWEtgo0nZP4tZ1LYM5deqUz57VofY1JuGprKzEjBkzpO1J1Wo11q9fj8mTJ8uuoyNxdTqdOHjwYMA6SGRQrCKn7RrJcve19x4n13Z2M8UrMlqHDgCennO5s8q9N9Xx7l0DKFbRIFIxGDRokM+ELDn1AsAPP/wQtN5QKIGNIuPGjYNarZbuf/PNNyHP8b7Mo9FofL4Bk86pra3FjBkzpJnPSqUS77//Pi677LKw6hkwYACysrKk+3Liunv3bp8e+HAS5r5AqVTCaDTKvnlvCqJWq30e814LkWIVOQaDARdeeKF0v/VLYSje5fr16+fzGMUrMrz/DnV0rKT3EkkAxSoaRCoGKpXK54qknHrPnj2L48ePB603FEpgo4her8f06dOl+2+//XbIc7zLTJ8+nWZpdhGz2YxZs2bhwIEDAABBEPDOO+90eP/2uXPnSsdr166VVjEIxDuuw4cPl701X18xe/ZsNDQ0yL557/jy8MMPB3wMoFhF0jXXXCMdb968OeTSOXa73edSdOv2s94oXl3Pe4em+vp62V829uzZIx1nZma2e5xi1fMiFYOrrrpKOv7yyy99hnmFqtdkMnXsywkjUeW9995jAKTbxx9/HLDs7t27mSAIUtm1a9d2Y0t7L4vFwiZOnCi9rzzPs7feeqtTde7cudMnri+++GLAsuXl5Sw+Pl4qu3Llyk49N2EsNzdXej+XL18etCzFKnJKS0uZSqWS3q9XX301aPkXX3zRJxbvv/9+uzIUr673zTff+Lynv/rVr0Kes3btWp9z/vWvf7UrQ7HqOt7v4+rVq2WfF6kYlJeXM7VaLZVdsmRJwLJNTU0sJydHKnv33XfLbr83SmCjjCiKbNSoUVJg09PT2eHDh9uVO336NCsoKJDKjR49momi2AMt7l1sNhubMWOG9L5yHMdef/31Lql77ty5Ur16vZ5988037cqYzWY2adIkqVxaWhprbm7ukufvy8JJYBmjWEXSfffd5/PebtmyxW+5L7/8kul0OqlsQUEBc7vdfstSvLqW2+32+fsS6nPw22+/ZQkJCVL5fv36MYvF4rcsxaprdDSBZSxyMfD+vy0Igt8vnA6Hg1133XVSOa1Wy06dOhVW+1txjDEmo6OWdKNdu3ZhypQp0tgjg8GAn//855g8eTIUCgV27tyJl19+Weqi12q12LJlC8aOHduTze4V/vCHP+Chhx6S7ickJIQ1rviyyy7D/fff7/ex0tJSjB07FjU1NQA848xuu+02zJw5E3q9Hvv378dLL70kXa7jeR7r168PuEUtkS8vL09aiWD58uV44okngpanWEWO2WzGxIkTpYkhgiDglltuwZw5c5CamorKykp8/PHHePvttyGKIgDP+P7t27cHXHqL4tX1Nm7ciCuuuAJut1v62ZQpU3DjjTdi8ODBUCqVOHnyJD799FOsXbvWp9ybb76J+fPn+62XYhWe22+/HW+++Wa7n3tPglQoFH43BAm0oUCkYlBfX4/x48fj2LFj0nnz5s3DT37yEyQmJuLo0aN45ZVXsH//fumcl19+2Wdny7B0KO0lEffBBx8wrVbr8y3L302r1bIPPvigp5vbayxfvjzkex7stmDBgqD1b9++nSUmJoasRxAE9tJLL3XPi+4Dwu2BZYxiFUllZWVs8ODBsv5PGY1G9sUXX4Ssk+LV9d58802m0Whkf/4pFAr27LPPhqyXYiXfggULOvz3KJhIxeDo0aMsOztbVvuWLl3aqfeGEtgodujQITZjxgzGcVy7wHMcx6ZPn84OHjzY083sVSKdwDLGWEVFBbv22muZQqHwW8fYsWPZ9u3bI/9i+5COJLCMUawiqbm5mT3wwAPMZDIFTIZuvvlmduLECdl1Ury63qFDh9hNN93kM3a57Y3neTZ37ly2c+dO2fVSrOSJVALLWORiUF9fz2677baAnXAFBQXso48+6sjb4YOGEMSA8vJybN++XVqbLzMzExMnTvRZc4/EnurqamzduhUVFRVwOBzIyMjAxRdf3G5RaNLzKFaR43A4sGXLFpSUlKCmpgYGgwF5eXmYPHkyDAZDh+qkeHU9i8WCXbt24X//+5+0oYTRaER+fj7GjRsHk8nUoXopVj0vUjFoamrC119/jfLyclitVqSnp+OCCy7wWU6vMyiBJYQQQgghMYXWgSWEEEIIITGFElhCCCGEEBJTKIElhBBCCCExhRJYQgghhBASUyiBJYQQQgghMYUSWEIIIYQQElMogSWEEEIIITGFElhCCCGEEBJTKIElhBBCCCExhRJYQgghhBASUyiBJYQQQgghMYUSWEJIUJs3bwbHceA4Dnl5eQHLLVy4UCr3xBNPdFv7YkVpaan0/nAc19PNIUF4x6m0tLSnm0MI8YMSWEJ6Me+kMpybyWTq6aaTXqC2thZqtVr6vXrkkUc6VV9hYaFUV2FhYRe1khASiyiBJYR0m97SC5mXlye9hs2bN/d0c6JWUlIS5syZI91/6623IIpih+o6duwYvvvuO+n+ggULOt0+QkjsUvR0Awgh3SMhIQHjxo2TVVav10e4NaSvWLBgAdatWwcAKC8vx+bNm3HppZeGXc8bb7whHavVatx4441d1kZCSOyhBJaQPmLkyJH4/PPPwz5v6tSpYIxFoEV9S15eXp98H3/84x8jJSUF1dXVADyJaLgJLGMMb731lnR/7ty5SEhI6NJ2EkJiCw0hIIQQEjEKhQLz5s2T7n/wwQdobm4Oq46tW7f6TKai4QOEEEpgCSGERNTChQulY4vFIg0pkMt7+EC/fv0wa9asrmoaISRGUQJLCAlK7jJawaxZswYcx6F///4+Pw+0CkLbZbieeOIJ6THvZOg///kPbr31VgwdOhRGo7Hd460qKyuxevVqLFy4EGPGjEFiYiKUSiVMJhMGDx6M+fPnY+3atUEnGHlPQCsrK5N+Pm3aNL+vYerUqQHPlzuBraWlBa+++ipmz56N3NxcaLVamEwmFBQU4I477sCXX34pq57W979tu4qKinD33XejoKAAer0eBoMBI0eOxNKlS3H27FlZdcsxevRojBw5UrrvnZCG0tLSgvfff1+6f/PNN0Oh8B39dvjwYTz//PO49tprMXToUBgMBiiVSiQnJ2P06NG45557sGPHjs6/EC8dWTauI5P//ve//2H58uUoLCxEeno61Go1UlJSMHbsWDz66KO0zBfps2gMLCEk5jQ2NmLx4sVYu3ZtyLK//OUv8fLLL8Ptdrd7zGw2w2w249ixY3j77bcxYsQIfPDBBxg8eHAkmh2Wzz//HHfccQfKy8t9fm6z2WA2m3HkyBH87W9/w6xZs/CPf/wD/fr1k103YwwrVqzA448/3u59KSoqQlFREV599VV8+OGHHZpw5c+CBQtw//33AwC++uornD59GhkZGSHPW79+PRobG33q8XbRRRdhz549fs+tra1FbW0t9u3bhz/96U+45pprsGbNGsTHx3filXQPm82GBx54AK+++ipcLpfPYzU1NaipqcH333+PVatW4fHHH+/0EmWExBpKYAkhEZeZmYlZs2ahpaUFW7dulX4e6FLwwIEDA9bFGMO8efPw6aefAgASExMxZMgQ8DyP48ePtyt/4MABKUlr7UVOT0+HVqtFQ0MDDh8+LI3JPHDgAAoLC/HDDz8gJyfHpx6tViu1d8uWLbDZbACAsWPHIjExsd3zevc4hmvt2rWYN2+eT+LSr18/DB48GC0tLThw4ID0/F988QV+9KMfYdOmTcjKypJV/5NPPoknn3wSAGAwGFBQUACNRoMjR46gsrISgOdLwty5c3Hw4EHk5uZ2+LW0uvnmm/HQQw/B5XJBFEW89dZbWLp0acjzvHtr2/bkAsC+ffukY6VSiUGDBiE5ORmCIKCqqgpHjhyR4r9u3TqcOXMGW7dubdeLG00aGxsxZ84cbNu2TfqZIAgYPnw4kpOT0dDQgKKiIjidTtjtdvz6179GVVUV/vjHP/ZcownpbowQ0mstWLCAAWA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      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_aggregated_betti_curves(mean_curves_by_genotype, std_curves_by_genotype, save_directory, legend_labels):\n",
    "    if not os.path.exists(save_directory):\n",
    "        os.makedirs(save_directory)\n",
    "    \n",
    "    plt.figure(figsize=(7, 4))\n",
    "    \n",
    "    for genotype, mean_curve in mean_curves_by_genotype.items():\n",
    "        std_curve = std_curves_by_genotype[genotype]\n",
    "        label = legend_labels.get(genotype, genotype)  # Get custom label or use the genotype as default\n",
    "        plt.plot(mean_curve, label=label)\n",
    "        plt.fill_between(range(len(mean_curve)), mean_curve - std_curve, mean_curve + std_curve, alpha=0.3)\n",
    "    \n",
    "    plt.xlabel(\"Filtration Value\", fontsize=25)\n",
    "    plt.ylabel(\"Betti-1\", fontsize=25)\n",
    "    plt.xticks(fontsize=25)\n",
    "    plt.yticks(fontsize=25)\n",
    "    plt.legend(title=\"Genotype\", fontsize=20)\n",
    "    plt.title(\"\", fontsize=25)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plot_path = os.path.join(save_directory, \"Betti1_Curves_KO_WT.png\")\n",
    "    plt.savefig(plot_path)\n",
    "    print(f\"Saved Betti-1 curve plot at {plot_path}\")\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "# Define custom legend labels\n",
    "legend_labels = {\n",
    "    'WT': 'WT',\n",
    "    'KO': '$PDGFRβ^{KLF4-KO}$'\n",
    "}\n",
    "\n",
    "save_directory = \"Plots/Widefield_10x_ROIs_KO_Fish_Pdgfrb\"  # Update this path\n",
    "plot_aggregated_betti_curves(mean_curves_by_genotype, std_curves_by_genotype, save_directory, legend_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5189934",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "left": "10px",
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     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}