SmartCane/python_app/experiments/omnicloud/cloud_detection_step2_test_omnicloudmask.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2050f0a8",
   "metadata": {},
   "source": [
    "# Cloud Detection - Step 2: Test OmniCloudMask\n",
    "\n",
    "This notebook tests **OmniCloudMask** on cloudy Planet imagery and compares it with Planet's UDM1 mask.\n",
    "\n",
    "**OmniCloudMask Info:**\n",
    "- Deep learning model for cloud/shadow detection\n",
    "- Trained on Sentinel-2 (10m) but works with any resolution\n",
    "- Outputs: Cloud mask, shadow mask, confidence scores\n",
    "- Paper: https://www.mdpi.com/2072-4292/15/11/2945\n",
    "\n",
    "**Workflow:**\n",
    "1. Load candidate image from Step 1\n",
    "2. Prepare data for OmniCloudMask (RGB + NIR)\n",
    "3. Run OmniCloudMask inference\n",
    "4. Compare with Planet UDM1\n",
    "5. Visualize results\n",
    "6. Analyze differences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c370ce1",
   "metadata": {},
   "source": [
    "## 1. Setup and Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed2a96b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ OmniCloudMask imported successfully\n"
     ]
    }
   ],
   "source": [
    "# Install OmniCloudMask (uncomment if needed)\n",
    "# !pip install omnicloudmask\n",
    "# !pip install torch torchvision  # Required dependencies\n",
    "\n",
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from osgeo import gdal\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Import OmniCloudMask\n",
    "try:\n",
    "    from omnicloudmask import predict_from_array\n",
    "    print(\"✓ OmniCloudMask imported successfully\")\n",
    "except ImportError as e:\n",
    "    print(\"❌ OmniCloudMask not found. Install with: pip install omnicloudmask\")\n",
    "    print(f\"   Error: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8715e5",
   "metadata": {},
   "source": [
    "## 2. Load Summary from Step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "de402868",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Summary loaded from Step 1 (ESA project)\n",
      "\n",
      "Date range: 2024-12-01 to 2024-12-15\n",
      "Available images: 14\n",
      "Test candidates: 0\n"
     ]
    }
   ],
   "source": [
    "# Change project to 'aura' or 'esa' depending on which dataset you want to test\n",
    "project = 'esa'  # Change to 'aura' for Aura dataset\n",
    "\n",
    "BASE_PATH = Path('../laravel_app/storage/app') / project\n",
    "\n",
    "# Try ESA-specific summary first, then fall back to generic\n",
    "if project == 'esa':\n",
    "    summary_path = BASE_PATH / 'cloud_detection_summary_esa.json'\n",
    "else:\n",
    "    summary_path = BASE_PATH / 'cloud_detection_summary.json'\n",
    "\n",
    "# Load summary\n",
    "if summary_path.exists():\n",
    "    with open(summary_path, 'r') as f:\n",
    "        summary = json.load(f)\n",
    "    \n",
    "    print(f\"✓ Summary loaded from Step 1 ({project.upper()} project)\")\n",
    "    print(f\"\\nDate range: {summary['date_range']}\")\n",
    "    print(f\"Available images: {summary['available_dates']}\")\n",
    "    print(f\"Test candidates: {len(summary['test_candidates'])}\")\n",
    "    \n",
    "    if summary['test_candidates']:\n",
    "        print(\"\\nTop candidate:\")\n",
    "        top = summary['test_candidates'][0]\n",
    "        print(f\"  Date: {top['date']}\")\n",
    "        print(f\"  Cloud coverage (UDM1): {top['cloud_percentage']:.1f}%\")\n",
    "        print(f\"  Path: {top['path']}\")\n",
    "else:\n",
    "    print(f\"❌ Summary not found at: {summary_path}\")\n",
    "    print(f\"   Run cloud_detection_esa.ipynb or cloud_detection_step1_identify.ipynb first.\")\n",
    "    summary = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c368fb",
   "metadata": {},
   "source": [
    "## 3. Select Test Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32ce69b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected test image:\n",
      "  Date: 2024-12-03\n",
      "  Path: ../laravel_app/storage/app/esa/cloud_test_merged_tif/2024-12-03.tif\n"
     ]
    }
   ],
   "source": [
    "# Direct path to test image (since files already exist)\n",
    "target_date = '2024-12-03'  # Change this to your desired date\n",
    "test_image_path = f'../laravel_app/storage/app/esa/cloud_test_merged_tif/{target_date}.tif'\n",
    "\n",
    "# Verify file exists\n",
    "from pathlib import Path\n",
    "if not Path(test_image_path).exists():\n",
    "    raise FileNotFoundError(f\"Image not found: {test_image_path}\")\n",
    "\n",
    "test_date = target_date\n",
    "udm_cloud_pct = 0  # Will be calculated from actual data\n",
    "\n",
    "print(f\"Selected test image:\")\n",
    "print(f\"  Date: {test_date}\")\n",
    "print(f\"  Path: {test_image_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930788bd",
   "metadata": {},
   "source": [
    "## 4. Load and Prepare Image Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72a8f746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Image loaded: 4426 x 5102 pixels\n",
      "  Bands: 5\n",
      "\n",
      "Data ranges:\n",
      "  Red: 0.000 to 255.000\n",
      "  Green: 0.000 to 255.000\n",
      "  Blue: 0.000 to 255.000\n",
      "  NIR: 0.000 to 255.000\n",
      "  UDM1 unique values: [  0 255]\n",
      "  UDM1 unique values: [  0 255]\n"
     ]
    }
   ],
   "source": [
    "# Load GeoTIFF\n",
    "ds = gdal.Open(test_image_path)\n",
    "if ds is None:\n",
    "    raise FileNotFoundError(f\"Could not open: {test_image_path}\")\n",
    "\n",
    "print(f\"✓ Image loaded: {ds.RasterXSize} x {ds.RasterYSize} pixels\")\n",
    "print(f\"  Bands: {ds.RasterCount}\")\n",
    "\n",
    "# Read bands\n",
    "# Band 1: Red, Band 2: Green, Band 3: Blue, Band 4: NIR, Band 5: UDM1\n",
    "red = ds.GetRasterBand(1).ReadAsArray()\n",
    "green = ds.GetRasterBand(2).ReadAsArray()\n",
    "blue = ds.GetRasterBand(3).ReadAsArray()\n",
    "nir = ds.GetRasterBand(4).ReadAsArray()\n",
    "udm1 = ds.GetRasterBand(5).ReadAsArray()\n",
    "\n",
    "# Get geotransform for later\n",
    "geotransform = ds.GetGeoTransform()\n",
    "projection = ds.GetProjection()\n",
    "\n",
    "ds = None  # Close\n",
    "\n",
    "print(f\"\\nData ranges:\")\n",
    "print(f\"  Red: {red.min():.3f} to {red.max():.3f}\")\n",
    "print(f\"  Green: {green.min():.3f} to {green.max():.3f}\")\n",
    "print(f\"  Blue: {blue.min():.3f} to {blue.max():.3f}\")\n",
    "print(f\"  NIR: {nir.min():.3f} to {nir.max():.3f}\")\n",
    "print(f\"  UDM1 unique values: {np.unique(udm1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5e954a",
   "metadata": {},
   "source": [
    "## 5. Prepare Data for OmniCloudMask\n",
    "\n",
    "OmniCloudMask expects:\n",
    "- Shape: (bands, height, width) - **channels first format**\n",
    "- Channels: **Red, Green, NIR (3 bands)** - in that specific order\n",
    "- Values: 0-1 range (reflectance)\n",
    "- No NaN values\n",
    "\n",
    "**Note:** Blue band is NOT used - model is trained on R,G,NIR combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1603b0d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input array shape for OmniCloudMask: (5102, 4426, 3)\n",
      "Bands used: Red, Green, NIR\n",
      "Data type: uint8\n",
      "Value range (all pixels): 0.000 to 1.000\n",
      "Value range (valid data only): 1.000 to 1.000\n",
      "Contains NaN: False\n",
      "No-data pixels: 22,044,669 (97.6%)\n",
      "\n",
      "✓ Data prepared for OmniCloudMask (R, G, NIR)\n"
     ]
    }
   ],
   "source": [
    "# Stack bands: R, G, NIR (as recommended by OmniCloudMask)\n",
    "# OmniCloudMask expects Red, Green, NIR in that order\n",
    "image_array = np.dstack([red, green, nir])\n",
    "\n",
    "# CRITICAL FIX: Identify no-data pixels (black areas outside fields)\n",
    "# and set them to a specific no-data value that OmniCloudMask can handle\n",
    "no_data_mask = (red == 0) & (green == 0) & (nir == 0)\n",
    "\n",
    "# Handle NaN and clip to valid range\n",
    "image_array = np.nan_to_num(image_array, nan=0.0)\n",
    "image_array = np.clip(image_array, 0, 1)\n",
    "\n",
    "# IMPORTANT: OmniCloudMask treats 0 as valid data (causes it to think black = vegetation)\n",
    "# We need to keep no-data as 0, but tell OmniCloudMask to ignore it via no_data_value parameter\n",
    "# The model will handle this in the predict_from_array call\n",
    "\n",
    "print(f\"Input array shape for OmniCloudMask: {image_array.shape}\")\n",
    "print(f\"Bands used: Red, Green, NIR\")\n",
    "print(f\"Data type: {image_array.dtype}\")\n",
    "print(f\"Value range (all pixels): {image_array.min():.3f} to {image_array.max():.3f}\")\n",
    "print(f\"Value range (valid data only): {image_array[~no_data_mask].min():.3f} to {image_array[~no_data_mask].max():.3f}\")\n",
    "print(f\"Contains NaN: {np.isnan(image_array).any()}\")\n",
    "print(f\"No-data pixels: {np.sum(no_data_mask):,} ({np.sum(no_data_mask)/no_data_mask.size*100:.1f}%)\")\n",
    "print(\"\\n✓ Data prepared for OmniCloudMask (R, G, NIR)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f22127",
   "metadata": {},
   "source": [
    "## 5.1 Create Field Mask from GeoJSON\n",
    "\n",
    "Clip the image to only analyze pixels within actual field boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a0f237a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading field boundaries from: ../laravel_app/storage/app/esa/Data/pivot_2.geojson\n",
      "✓ Loaded 23 field polygons\n",
      "  CRS: EPSG:4326\n",
      "\n",
      "Creating field mask...\n",
      "✓ Field mask created: (5102, 4426)\n",
      "  Pixels inside fields: 482,388 (2.1%)\n",
      "  Pixels outside fields: 22,099,064 (97.9%)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✓ Ready to mask data to field boundaries only!\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "from rasterio.features import rasterize\n",
    "from rasterio.transform import from_bounds\n",
    "\n",
    "# Load field boundaries\n",
    "if project == 'esa':\n",
    "    geojson_path = '../laravel_app/storage/app/esa/Data/pivot_2.geojson'\n",
    "else:\n",
    "    geojson_path = '../laravel_app/storage/app/pivot.geojson'\n",
    "\n",
    "print(f\"Loading field boundaries from: {geojson_path}\")\n",
    "fields_gdf = gpd.read_file(geojson_path)\n",
    "print(f\"✓ Loaded {len(fields_gdf)} field polygons\")\n",
    "print(f\"  CRS: {fields_gdf.crs}\")\n",
    "\n",
    "# Create a raster mask from the field polygons\n",
    "# This mask will be 1 inside fields, 0 outside\n",
    "print(f\"\\nCreating field mask...\")\n",
    "\n",
    "# Get the geotransform from the image to align the mask\n",
    "# geotransform is (x_origin, pixel_width, 0, y_origin, 0, -pixel_height)\n",
    "from rasterio.transform import Affine\n",
    "transform = Affine.from_gdal(*geotransform)\n",
    "\n",
    "# Rasterize the field polygons\n",
    "field_mask = rasterize(\n",
    "    [(geom, 1) for geom in fields_gdf.geometry],\n",
    "    out_shape=(red.shape[0], red.shape[1]),\n",
    "    transform=transform,\n",
    "    fill=0,\n",
    "    dtype=np.uint8\n",
    ")\n",
    "\n",
    "print(f\"✓ Field mask created: {field_mask.shape}\")\n",
    "print(f\"  Pixels inside fields: {np.sum(field_mask == 1):,} ({np.sum(field_mask == 1)/field_mask.size*100:.1f}%)\")\n",
    "print(f\"  Pixels outside fields: {np.sum(field_mask == 0):,} ({np.sum(field_mask == 0)/field_mask.size*100:.1f}%)\")\n",
    "\n",
    "# Visualize the field mask\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# Show field mask\n",
    "axes[0].imshow(field_mask, cmap='RdYlGn', vmin=0, vmax=1)\n",
    "axes[0].set_title(f\"Field Mask\\n{np.sum(field_mask == 1):,} pixels inside fields\", fontsize=11)\n",
    "axes[0].axis('off')\n",
    "\n",
    "# Show RGB with field overlay\n",
    "rgb_display = np.dstack([red, green, blue])\n",
    "rgb_display = np.clip(rgb_display * 2.5, 0, 1)\n",
    "axes[1].imshow(rgb_display)\n",
    "# Overlay field boundaries\n",
    "axes[1].imshow(field_mask, cmap='Greens', alpha=0.3, vmin=0, vmax=1)\n",
    "axes[1].set_title(\"RGB with Field Boundaries Overlay\", fontsize=11)\n",
    "axes[1].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n✓ Ready to mask data to field boundaries only!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6371ee9c",
   "metadata": {},
   "source": [
    "## 5.2 Prepare Data for OmniCloudMask (Masked to Fields Only)\n",
    "\n",
    "Apply field mask and prepare data in the format OmniCloudMask expects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99c690db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== APPLYING FIELD MASK ===\n",
      "\n",
      "✓ Field mask applied:\n",
      "  Pixels inside fields:  482,388 (2.1%)\n",
      "  Pixels outside fields: 22,099,064 (set to 0, will be ignored)\n",
      "\n",
      "✓ Data prepared for OmniCloudMask:\n",
      "  Array shape: (5102, 4426, 3) (H, W, C)\n",
      "  Bands: Red, Green, NIR\n",
      "  Data type: uint8\n",
      "\n",
      "  Value ranges (field pixels only):\n",
      "    Red:   0.0000 to 255.0000 (mean: 84.4381)\n",
      "    Green: 0.0000 to 255.0000 (mean: 85.1551)\n",
      "    NIR:   0.0000 to 255.0000 (mean: 229.9071)\n",
      "\n",
      "  NIR/Red ratio: 2.72 (healthy vegetation = 3-8)\n",
      "    Red:   0.0000 to 255.0000 (mean: 84.4381)\n",
      "    Green: 0.0000 to 255.0000 (mean: 85.1551)\n",
      "    NIR:   0.0000 to 255.0000 (mean: 229.9071)\n",
      "\n",
      "  NIR/Red ratio: 2.72 (healthy vegetation = 3-8)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✓ Ready for OmniCloudMask inference (field areas only)!\n"
     ]
    }
   ],
   "source": [
    "# Stack bands: R, G, NIR (as recommended by OmniCloudMask)\n",
    "image_array = np.dstack([red, green, blue, nir])\n",
    "\n",
    "# CRITICAL: Apply field mask - set pixels outside fields to a no-data value\n",
    "# OmniCloudMask will be told to ignore these pixels\n",
    "print(\"=== APPLYING FIELD MASK ===\\n\")\n",
    "\n",
    "# Create a copy to preserve original data\n",
    "red_masked = red.copy()\n",
    "green_masked = green.copy()\n",
    "nir_masked = nir.copy()\n",
    "\n",
    "# Set pixels outside fields to 0 (will be treated as no-data)\n",
    "red_masked[field_mask == 0] = 0\n",
    "green_masked[field_mask == 0] = 0\n",
    "nir_masked[field_mask == 0] = 0\n",
    "\n",
    "# Stack the masked bands (R, G, NIR order for OmniCloudMask)\n",
    "image_array_masked = np.dstack([red_masked, green_masked, nir_masked])\n",
    "\n",
    "# Handle NaN and clip to valid range\n",
    "image_array_masked = np.nan_to_num(image_array_masked, nan=0.0)\n",
    "image_array_masked = np.clip(image_array_masked, 0, 1)\n",
    "\n",
    "# Statistics\n",
    "field_pixels = np.sum(field_mask == 1)\n",
    "non_field_pixels = np.sum(field_mask == 0)\n",
    "\n",
    "print(f\"✓ Field mask applied:\")\n",
    "print(f\"  Pixels inside fields:  {field_pixels:,} ({field_pixels/(field_pixels+non_field_pixels)*100:.1f}%)\")\n",
    "print(f\"  Pixels outside fields: {non_field_pixels:,} (set to 0, will be ignored)\")\n",
    "\n",
    "print(f\"\\n✓ Data prepared for OmniCloudMask:\")\n",
    "print(f\"  Array shape: {image_array_masked.shape} (H, W, C)\")\n",
    "print(f\"  Bands: Red, Green, NIR\")\n",
    "print(f\"  Data type: {image_array_masked.dtype}\")\n",
    "\n",
    "# Check values in field areas only\n",
    "field_data = image_array_masked[field_mask == 1]\n",
    "if len(field_data) > 0:\n",
    "    print(f\"\\n  Value ranges (field pixels only):\")\n",
    "    print(f\"    Red:   {red_masked[field_mask == 1].min():.4f} to {red_masked[field_mask == 1].max():.4f} (mean: {red_masked[field_mask == 1].mean():.4f})\")\n",
    "    print(f\"    Green: {green_masked[field_mask == 1].min():.4f} to {green_masked[field_mask == 1].max():.4f} (mean: {green_masked[field_mask == 1].mean():.4f})\")\n",
    "    print(f\"    NIR:   {nir_masked[field_mask == 1].min():.4f} to {nir_masked[field_mask == 1].max():.4f} (mean: {nir_masked[field_mask == 1].mean():.4f})\")\n",
    "    \n",
    "    # Check vegetation health\n",
    "    nir_vs_red = nir_masked[field_mask == 1].mean() / red_masked[field_mask == 1].mean() if red_masked[field_mask == 1].mean() > 0 else 0\n",
    "    print(f\"\\n  NIR/Red ratio: {nir_vs_red:.2f} (healthy vegetation = 3-8)\")\n",
    "\n",
    "# Visualize the masked data\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "\n",
    "axes[0].imshow(red_masked, cmap='Reds', vmin=0, vmax=np.percentile(red_masked[field_mask == 1], 98) if np.any(field_mask) else 0.3)\n",
    "axes[0].set_title(f\"Red (Masked)\\nNon-field = black\")\n",
    "axes[0].axis('off')\n",
    "\n",
    "axes[1].imshow(green_masked, cmap='Greens', vmin=0, vmax=np.percentile(green_masked[field_mask == 1], 98) if np.any(field_mask) else 0.3)\n",
    "axes[1].set_title(f\"Green (Masked)\\nNon-field = black\")\n",
    "axes[1].axis('off')\n",
    "\n",
    "axes[2].imshow(nir_masked, cmap='gray', vmin=0, vmax=np.percentile(nir_masked[field_mask == 1], 98) if np.any(field_mask) else 0.5)\n",
    "axes[2].set_title(f\"NIR (Masked)\\nNon-field = black\")\n",
    "axes[2].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n✓ Ready for OmniCloudMask inference (field areas only)!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a491c0",
   "metadata": {},
   "source": [
    "## 6. Run OmniCloudMask\n",
    "\n",
    "**Note:** First run will download the pre-trained model (~100MB). This may take a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c97e8cbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OmniCloudMask inference...\n",
      "\n",
      "⏳ This may take a few minutes on first run (model download)...\n",
      "\n",
      "Transposed array shape: (3, 5102, 4426)\n",
      "Format: (bands=3, height=5102, width=4426)\n",
      "\n",
      "⚠️  CRITICAL: Checking input data values (field pixels only)...\n",
      "  Band 0 (Red):   min=0.000000, max=1.000000, mean=0.999994\n",
      "  Band 1 (Green): min=0.000000, max=1.000000, mean=0.999994\n",
      "  Band 2 (NIR):   min=0.000000, max=1.000000, mean=0.999994\n",
      "\n",
      "🔍 Running OmniCloudMask with no_data_value=0 (ignores non-field pixels)...\n",
      "\n",
      "Results shape: (1, 5102, 4426)\n",
      "Results type: <class 'numpy.ndarray'>\n",
      "Unique values: [0 1]\n",
      "\n",
      "Results shape: (1, 5102, 4426)\n",
      "Results type: <class 'numpy.ndarray'>\n",
      "Unique values: [0 1]\n",
      "✓ OmniCloudMask inference complete!\n",
      "\n",
      "Cloud mask shape: (5102, 4426)\n",
      "Shadow mask shape: (5102, 4426)\n",
      "Clear mask shape: (5102, 4426)\n",
      "\n",
      "✅ OmniCloudMask Results (FIELD AREAS ONLY):\n",
      "  Total field pixels: 482,388\n",
      "  Clear:         175 (  0.0%)\n",
      "  Clouds:    482,213 (100.0%)\n",
      "  Shadow:          0 (  0.0%)\n",
      "✓ OmniCloudMask inference complete!\n",
      "\n",
      "Cloud mask shape: (5102, 4426)\n",
      "Shadow mask shape: (5102, 4426)\n",
      "Clear mask shape: (5102, 4426)\n",
      "\n",
      "✅ OmniCloudMask Results (FIELD AREAS ONLY):\n",
      "  Total field pixels: 482,388\n",
      "  Clear:         175 (  0.0%)\n",
      "  Clouds:    482,213 (100.0%)\n",
      "  Shadow:          0 (  0.0%)\n"
     ]
    }
   ],
   "source": [
    "print(\"Running OmniCloudMask inference...\\n\")\n",
    "print(\"⏳ This may take a few minutes on first run (model download)...\\n\")\n",
    "\n",
    "try:\n",
    "    # Use the MASKED data (field pixels only, non-field = 0)\n",
    "    # OmniCloudMask expects shape: (bands, height, width) instead of (height, width, bands)\n",
    "    # Transpose from (H, W, C) to (C, H, W)\n",
    "    image_array_transposed = np.transpose(image_array_masked, (2, 0, 1))\n",
    "    \n",
    "    print(f\"Transposed array shape: {image_array_transposed.shape}\")\n",
    "    print(f\"Format: (bands={image_array_transposed.shape[0]}, height={image_array_transposed.shape[1]}, width={image_array_transposed.shape[2]})\")\n",
    "    \n",
    "    # Check data values being sent to OmniCloudMask\n",
    "    print(f\"\\n⚠️  CRITICAL: Checking input data values (field pixels only)...\")\n",
    "    field_pixels_only = field_mask == 1\n",
    "    print(f\"  Band 0 (Red):   min={image_array_transposed[0][field_pixels_only].min():.6f}, max={image_array_transposed[0][field_pixels_only].max():.6f}, mean={image_array_transposed[0][field_pixels_only].mean():.6f}\")\n",
    "    print(f\"  Band 1 (Green): min={image_array_transposed[1][field_pixels_only].min():.6f}, max={image_array_transposed[1][field_pixels_only].max():.6f}, mean={image_array_transposed[1][field_pixels_only].mean():.6f}\")\n",
    "    print(f\"  Band 2 (NIR):   min={image_array_transposed[2][field_pixels_only].min():.6f}, max={image_array_transposed[2][field_pixels_only].max():.6f}, mean={image_array_transposed[2][field_pixels_only].mean():.6f}\")\n",
    "    \n",
    "    # Run prediction with no_data_value parameter\n",
    "    # This tells OmniCloudMask to ignore pixels where all bands = 0 (non-field areas)\n",
    "    # Returns array with shape (1, height, width) containing class labels:\n",
    "    # 0 = Clear, 1 = Thick Cloud, 2 = Thin Cloud, 3 = Cloud Shadow\n",
    "    print(f\"\\n🔍 Running OmniCloudMask with no_data_value=0 (ignores non-field pixels)...\")\n",
    "    results = predict_from_array(\n",
    "        image_array_transposed,\n",
    "        no_data_value=0,\n",
    "        apply_no_data_mask=True\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nResults shape: {results.shape}\")\n",
    "    print(f\"Results type: {type(results)}\")\n",
    "    print(f\"Unique values: {np.unique(results)}\")\n",
    "    \n",
    "    # Extract classification result (remove batch dimension)\n",
    "    classification = results[0]  # Shape: (height, width)\n",
    "    \n",
    "    # Create separate binary masks\n",
    "    cloud_mask = np.logical_or(classification == 1, classification == 2).astype(int)  # Thick + Thin clouds\n",
    "    shadow_mask = (classification == 3).astype(int)  # Cloud shadow\n",
    "    clear_mask = (classification == 0).astype(int)  # Clear\n",
    "    \n",
    "    # For visualization, create confidence placeholder (all 1.0 for now)\n",
    "    confidence = np.ones_like(classification, dtype=float)\n",
    "    \n",
    "    print(\"✓ OmniCloudMask inference complete!\\n\")\n",
    "    print(f\"Cloud mask shape: {cloud_mask.shape}\")\n",
    "    print(f\"Shadow mask shape: {shadow_mask.shape}\")\n",
    "    print(f\"Clear mask shape: {clear_mask.shape}\")\n",
    "    \n",
    "    # Calculate statistics - USING FIELD MASK\n",
    "    # Only analyze pixels within field boundaries\n",
    "    \n",
    "    # Statistics for field pixels only\n",
    "    total_pixels_field = np.sum(field_mask == 1)\n",
    "    cloud_pixels_field = np.sum(cloud_mask[field_mask == 1] == 1)\n",
    "    shadow_pixels_field = np.sum(shadow_mask[field_mask == 1] == 1)\n",
    "    clear_pixels_field = np.sum(clear_mask[field_mask == 1] == 1)\n",
    "    \n",
    "    omni_cloud_pct = (cloud_pixels_field / total_pixels_field) * 100 if total_pixels_field > 0 else 0\n",
    "    omni_shadow_pct = (shadow_pixels_field / total_pixels_field) * 100 if total_pixels_field > 0 else 0\n",
    "    omni_clear_pct = (clear_pixels_field / total_pixels_field) * 100 if total_pixels_field > 0 else 0\n",
    "    \n",
    "    print(f\"\\n✅ OmniCloudMask Results (FIELD AREAS ONLY):\")\n",
    "    print(f\"  Total field pixels: {total_pixels_field:,}\")\n",
    "    print(f\"  Clear:  {clear_pixels_field:>10,} ({omni_clear_pct:>5.1f}%)\")\n",
    "    print(f\"  Clouds: {cloud_pixels_field:>10,} ({omni_cloud_pct:>5.1f}%)\")\n",
    "    print(f\"  Shadow: {shadow_pixels_field:>10,} ({omni_shadow_pct:>5.1f}%)\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"❌ Error running OmniCloudMask: {e}\")\n",
    "    import traceback\n",
    "    traceback.print_exc()\n",
    "    cloud_mask = None\n",
    "    shadow_mask = None\n",
    "    confidence = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41a97e4e",
   "metadata": {},
   "source": [
    "## 6.5 Diagnostic: Check OmniCloudMask Output\n",
    "\n",
    "Verify the classification results look reasonable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ba700f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== OMNICLOUDMASK CLASSIFICATION BREAKDOWN ===\n",
      "\n",
      "Classification values found: [0 1]\n",
      "  0 = Clear:       22,099,239 pixels ( 97.9%)\n",
      "  1 = Thick Cloud:    482,213 pixels (  2.1%)\n",
      "  2 = Thin Cloud:           0 pixels (  0.0%)\n",
      "  3 = Shadow:               0 pixels (  0.0%)\n",
      "\n",
      "=== IN VALID FIELD AREAS ONLY ===\n",
      "\n",
      "  0 = Clear:           54,570 pixels ( 10.2%)\n",
      "  1 = Thick Cloud:    482,213 pixels ( 89.8%)\n",
      "  2 = Thin Cloud:           0 pixels (  0.0%)\n",
      "  3 = Shadow:               0 pixels (  0.0%)\n",
      "  2 = Thin Cloud:           0 pixels (  0.0%)\n",
      "  3 = Shadow:               0 pixels (  0.0%)\n",
      "\n",
      "=== IN VALID FIELD AREAS ONLY ===\n",
      "\n",
      "  0 = Clear:           54,570 pixels ( 10.2%)\n",
      "  1 = Thick Cloud:    482,213 pixels ( 89.8%)\n",
      "  2 = Thin Cloud:           0 pixels (  0.0%)\n",
      "  3 = Shadow:               0 pixels (  0.0%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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NvXPeqvbll1/itddew3vvvYfXX38dDocDOTk5uPbaa3HXXXf5XHfWrFkYP348Jk2ahMOHD6NVq1aYN2+ex2TZ+eefj2effRZTp07FjBkzMGDAALzxxhseDyMHgGnTpmH06NEYN24cEhISMGLECPTu3Rv33Xefpf0bMGAAnnnmGUydOhXTp09Hjx49sGjRIo8HfZ9yyimIiorCQw89hP3796NWrVo499xz8fTTT/ts9/HHH0dRURGGDh2KRYsWVdmfStnZ2VizZg1efPFFfPjhh3j66acRFRWFtm3b4v7778ctt9wSsH77s19mjRs3DrVr18ZLL72EGTNmIDU1Fd26dXM90PzRRx/FgQMH8OijjwJwTjC+/PLLVSZSX331Vdx111244IILUFRUhOXLl6N3795VtnfHHXcgNjYWL774Il577TXUr18fEydOxPjx4y3vCxERERERUTjQhPu9QERERERERERERCbxGUtEREREREREROQXTiwREREREREREZFfOLFERERERERERER+4cQSERERERERERH5hRNLREREREQUsSZOnAhN01w/tWrVwllnnYXPP//c6/IFBQX4z3/+g86dOyMlJQUJCQlo0aIFbr31Vvz+++8ey7q3q2ka6tati4suuqjKct6MHDkS7dq1s2UfQ8n+/fuRmpqKP/74w/Xa4cOHMXjwYGRmZkLTNCxcuLD6Oqjjgw8+QE5ODmJjY9GpU6fq7k5APPXUU+jfv391d4PCDCeWIty3336LSy65BHXq1EFcXBwaNWqEq6++Gj/99JNrmWbNmmH06NHV2EtzevfuXeV/3vLPyJEjATj/J//888/rtjdjxgxomoaDBw+a7kMgPgBs3brVcL80TcOKFStM97l379648MILbe1nMJWXl2Pq1Kno2LEjUlJSkJmZiY4dO2L06NEoKSlxLWcmz1ZMnjwZmqYFrH0iIiIKjsTERKxZswZr1qzBW2+9heLiYlx00UVYvXq1x3IHDx5Et27d8Mwzz2DgwIGYP38+lixZgjvvvBPff/89zj333Cpt33HHHVizZg1Wr16NV155BTt37sS5556L3NzcIO1daHnqqafQu3dvj8/ML774IpYvX46ZM2dizZo16NWrVzX20Lv8/Hxcf/31OOuss7BixQq899571d2lgBg1ahR+/PFHLF++vLq7QmEkpro7QPb68ccfce211/p8/+mnn8bgwYMBAK+99hpGjx6Nvn37YsqUKWjYsCF27dqF999/H/3798eRI0eC1W1Du3btwjnnnOPz/dGjR+O1117D0aNHXa/dfvvtSEpK8phYqF27tultDhw4EGvWrEFGRoZffTbrlVdewSuvvOLz/aVLl2LNmjWu3/fs2YNLL70U//nPf9CnTx/X623atMHWrVtNbfO1115DdHS0330OFKNjsWzZMjRs2BB33nknpk+fjgcffBDdu3dHYWEhfvnlF7z33nt48sknER8fH8ReExERUbiLiopC165dXb+feeaZaNy4MWbOnInu3bu7Xr/tttuwefNm/PDDD2jbtq3r9T59+uD222/HO++8U6XtJk2aeLTdokULdOrUCatXr8aAAQMCtEehKT8/H++8806VSZm///4bHTp0wMUXX2x5G0VFRUhMTLR92a1bt6KkpATXXHMNevToYaWLEEKgtLQ0JD+zZmRkYMiQIZgyZYrHWINID69YqmEKCwtx1lln4e+//67yM3LkSOTl5QEAfvvtN9x111245ppr8L///Q/Dhw9Hz549MXz4cCxatKjaZuArKipQVlZW5fWysjI0atTI635NnDgRBw8eRJs2bdC1a1fXT1paGjIyMjxeO/nkk033pXbt2ujatStiYgI7/3rw4EE8/PDDXvetWbNmEEJ47EPnzp0BADk5OVX216w2bdqgZcuWgdolvxkdi7KyMhQWFuLtt9/Ggw8+iEcffRT9+vXDxRdfjEcffRT//PMP0tPTq3s3iIiIKMw1bNgQtWvXxvbt212vbdu2DfPnz8ftt9/uMalUKSoqCjfddJNh26mpqQDg9TOvnhUrVkDTNCxduhSXX345UlJS0KRJE3zwwQcAgJdffhlNmjRBVlYWbrzxRo+ruPfs2YPrr78ezZs3R2JiInJycjB+/HiPZQAgLy8PV199NVJTU1GnTh2MHz8eL7zwQpUrtHNzc3H77bejfv36iI+Px2mnnYYvv/zScB/mzZsHALjgggtcr2mahvnz52PVqlWuK/Erffzxx+jUqRMSEhLQoEED3HPPPSguLq5yTBYvXoyhQ4ciLS0Nl112mc/ta5qGSZMm4f7770e9evVQp04dAM6Jnueffx4tWrRAfHw8mjdvjpdeesm13sSJE9G+fXsAwDnnnANN0zBx4kQAQElJCcaPH4+mTZsiPj4erVu3duWkUuVdDZ9//jk6duyI+Ph4fPbZZwCANWvWoG/fvkhOTkZ6ejquvPJK7N+/37Vu5d0Ls2bNwujRo5GZmYn69etj7NixKC8v99jO+vXrcemllyIrKwtJSUno2LEjZs+e7XrfaD8rXXbZZVi8eLHSnRsU2TixFKGmTJmCqKgor/+jAGB4m5TRP4AA8MADD6B9+/ZISUlBw4YNMXz4cOzZs8djmcpbsmbOnImWLVsiPj4ev/76q/UdNMHhcGDixImoW7cusrOzcd1116GgoMD1vrfbykpKSvDwww+jefPmiI+PR6NGjVy31/naxo033ojs7GyP2wsDbceOHbjggguQnJyMnJwcvPvuux7vy7fCTZw4ESkpKfj9999x1llnISkpCe3atcPSpUuD1mezCgoKUFZWhvr163t9X/57Nsqz2Q9aR48exbXXXovU1FTUrl0b48aNq/I/c8D5oXPo0KFIT09HcnIyzjvvPI/nKDzxxBNo0qSJR/8yMjJcH2wq1a9fH88995z5A0NERES2yc/Px+HDh3HSSSe5Xlu5ciWEEF5vd9PjcDhQXl6OsrIybN26FePGjUN2djZ69+7tV99uu+02tGvXDgsWLEDXrl1xzTXX4P7778fSpUvxxhtv4PHHH8e7776LF154wbXOwYMHkZWVhRdffBFffPEFxo0bh5kzZ+LWW2/1aPu6667DokWL8Oyzz2LGjBlYv349pkyZ4rFMaWkp+vfvj0WLFuGpp57Cp59+ijZt2mDgwIGGz4766quvcOqppyIhIcH12po1a9CzZ0907tzZdTsiAHz66acYOnQo2rRpg4ULF2LcuHF44403cPXVV1dp9+abb8bJJ5+MBQsWYOzYsbp9mDJlCjZu3Ih33nkHs2bNAgDcddddePTRRzFixAgsXrwYI0eOxP3334833ngDAHDjjTe6Pk+/+uqrWLNmDW688UYAwOWXX44333wT9957LxYtWoTzzz8fV199NZYsWeKx3d27d+POO+/E3XffjS+++AKdOnXCmjVr0Lt3b6Snp+PDDz/Ef//7X6xduxaXXHJJlX4/9NBDiIqKwkcffYRbb70VL7zwAt5++23X+//88w+6deuGf/75By+//DI+/fRTXHfddR6To0b7Walbt26oqKjAihUrdI8lkYugGmX58uXihhtu8Pre008/LaZPny6EEOLkk08W3bt3N9Vm06ZNxahRo1y/r169WsTFxYlBgwaJzz77TMyZM0eccsopomvXrh7rXXfddeKDDz4QK1asEHPnzhVdu3YVOTk5oqyszLVMr169RN26dUXbtm3FnDlzxNKlS8XevXur9GHLli3inHPO8dq/2bNniwkTJlR5vVevXmLgwIFe1wEgGjduLK688kqxZMkSMWXKFBEXFyfuv/9+1zLTp08XAMSBAwdcrw0cOFAkJiaKJ554Qvzvf/8TH3zwgRg6dKjr/REjRoi2bdsKIYQoKysTV1xxhahXr574/fffvfZDCCEmTJgg3nvvPa/vnXfeeWLLli0er23ZskUAEHPnzq2yfGWfW7duLV588UXx5Zdfissuu0xomib++usvn8dmwoQJIi4uTrRv31689dZb4osvvhB9+vQRycnJ4uDBgz77bjezx6JJkyaiSZMmYvbs2eLw4cM+2zOT599++03ce++9YsGCBWLFihXiv//9r2jQoIEYOXKkR1tDhgwRKSkp4pVXXhGLFy8WAwcOFA0bNhTu/4wePXpUNGvWTDRv3lx88MEH4uOPPxannXaayMjIENu3bxdCCLFixQoBQGzevFkIIcTPP/8sYmNjRVRUlCtHGzZsEADE999/r34QiYiISMmECRNEcnKyKCsrE2VlZWLbtm1i2LBhIjMzU/z999+u5SZNmiQAeLwmhBAVFRWudd0/5wrh/Cwi/2RlZYlvvvnGsF/unyuFcH7OByDGjRvnei03N1dER0eLxo0bi9LSUtfrQ4YMEZ06dfLZdllZmXj//fdFTEyMKCgoEEII8eeffwoA4t133/XYt5ycHI/PO9OmTRMxMTHizz//9GjzzDPPFJdddpnuPrVo0cJjXFHpkksuEb169fJ4rXPnzqJbt24er7355psCgPjtt9+EECeOya233qq73UoARJs2bYTD4XC99u+//wpN08Sbb77psez9998v6tWrJyoqKoQQQqxbt04AEMuXL3ct8/XXXwsAYunSpR7rDhs2TJx++umu30eMGOH1s13Pnj1F9+7dPfrz559/Ck3TxOLFi4UQJz77y8e2V69eHuOjK6+8UtSuXVvk5eV53Xez+1mpadOmYuzYsV7bIpLxiqUItWvXLo+rJlQ88MAD6NKlCz7++GNceOGFGDZsGD755BP88MMPHt+eMW3aNAwfPhy9evXC4MGDMW/ePPzzzz/4+uuvPdo7fPgwlixZgmHDhuHcc89F3bp1Le2bWfXr18f777+P888/H3feeSeGDx/uujzXm//9739YvHgx3nnnHTz88MPo168fhg8fjrlz51ZZtqSkBEOGDMHq1auxcuXKoH+jx+jRo3H33Xejf//+mD59OhITEzF//nzddUpLSzFp0iTceOONOO+88zBt2jQUFBRUOdsSCmbOnImSkhIMHz4ctWrVQps2bfDggw96vVzXKM/t27fH888/j0GDBqFXr1647rrr8Nxzz2HWrFkoLCwEAPz111/4+OOPMWXKFIwaNQoDBgzAJ598gri4OI9tTZ8+Hdu2bcOiRYswfPhwDB48GF9++SXKysowefJkAM5nNsTHx2PlypUAnGc/Tz/9dHTs2BHffPON67Xk5GScdtppgTh8REREJCkoKEBsbCxiY2PRtGlTzJs3D++9957XRwfIV0dffPHFrnVjY2OrXKV+1113Ye3atVi7di0WL16Mbt264ZJLLsFvv/3mV1/dv7ErPT0dderUQc+ePREbG+t6vUWLFtixY4frdyEEJk+ejDZt2iAxMRGxsbG46qqrUF5ejs2bNwMA1q5d69qfSlFRUbjooos8tv/ll1+iffv2aNGiBcrLy10//fv3d7Xhy549e0w98zQ/Px+//PILhg4d6vH6sGHDADi/gMjdwIEDDdusdMEFF3jk8KuvvgIADBkyxGN/+vXrh71793ocR9mXX36JrKws9O3bt8qxWLduHSoqKlzL1qpVC2eeeabr98LCQnz33Xe47LLLUFFR4Vq3RYsWaNy4cZVjKV8p16ZNG+zcudP1+7Jly1y3A3qjup/Z2dlV7jYh8oUTSxHMn2+zUvkHcMmSJejevTvS09MRExODRo0aAQA2btzo0WaHDh3QuHFjazvjB/lrNOV/nGXLli1DUlISrrjiCt12i4qKcOGFF2L9+vVYtWoVcnJybOmvCvf/8SQnJ6Np06a6+wY4Pzj069fP9XuzZs2QmJhouJ4vZWVl+OyzzzBt2jRs27bNrzZ86d27NzZt2oS5c+filltuQUVFBSZNmoR27dph9+7dHssa5dnsBy0hhOvB9wAQHR2NQYMGebS9atUqtGvXDq1bt3a9lpWVhf79+7s+ACUkJOD000/3mFjq1asXevXq5TGx1K1bt4A/34uIiIicEhMTsXbtWvzwww+YNWsW6tevj2uvvdZjYN2gQQMAqPLZaPLkyVi7dm2V24kqNWrUCF26dEGXLl0wYMAAzJ8/HzExMXj88cf96qv8xTJxcXFeX3N/FtHkyZNx77334pJLLsEnn3yCH3/8Ea+++ioAuJbbs2cPYmNjqzyvUr5d/+DBg1i3bp3HZFpsbCyefPJJ3UmYym2ZeWB1bm4uhBBVTjinp6cjPj4ehw8f9nhd5cS0vOzBgwchhEB2drbH/lR+htTbp4MHD+Lw4cNVjsWNN96I8vJyj78febtHjhxBRUUF7r777irrb9++vcp2jXJ86NAh19+or76q7Gd8fDyKiop8tkfkjqOWCNWwYUOP+23Ncv8H8O67767yfuU/SGvXrsXFF1+MSy65BA888ADq1KkDTdPQtWtXj38AAbX/EdjJ2z/O8nN13B06dAj169c3nJA7cOAAduzYgVGjRvl9VZhVRv/j8SYxMbHKFThm1nNXWFiIG2+8EQsWLEDdunVdE0qJiYlYsmSJrV8dm5ycjKFDh7rOZL399tu46aab8Pzzz+PFF190LWeU58mTJ2Ps2LEYN24c+vTpg8zMTKxduxajRo2q8kErMzPToy1vHxC8/T3XrVsXf/zxh+v3Xr164cMPPwTgPON28803o6ioCKNHjwbgnFi64YYbVA8JERER+SkqKgpdunQBAJxxxhlo2bIlzjzzTDz++ON4/fXXAQA9e/aEpmn48ssv0bdvX9e6p5xyCgDnVTZmVD40+c8//7R5L3ybO3cuLr74Yjz99NOu1/766y+PZerXr4+ysjLk5eV5TC7Jz1HNyspChw4dvH4DnpGsrCzk5uYaLpeRkQFN06psOy8vDyUlJcjKyvJ4XeWEubxsVlYWNE3Dt99+W+WzMADdL7zJyspC7dq1Pe7acOc+KSdvt3Ifx48fX+VkJeC8YkhFrVq1qpxglfuqsp+5ubleH1JP5A0nliJU7969MWvWLBw+fLjKP8x6zP4DuGDBAqSnp+Ojjz5CVJTzwjhfV634c+VUdahVqxb27NkDIYRun5s0aYKJEyfiiiuuQHZ2Nh566KEg9rL6lJSU4LnnnnN984R7vouKinDHHXfg2WefRa9evUx/rauKG2+8Effffz/Wr1+vtJ7KB60jR454TC7t27fPY7msrCxs2LChyjb27dvnUWc9e/bEU089hWXLluHw4cPo0aMHSkpKsGfPHnz99dfYtm0bzj77bKX9ICIiIvt06dIFw4cPx/Tp0zFhwgTUq1cPTZs2xZAhQ/Dqq69ixIgRHlcoqyguLsamTZv8Xt8fRUVFVSYT3n//fY/fKyfWPvnkE1x77bUAnA8er/z2skr9+vXD559/jgYNGuheIeNNy5YtsWXLFsPlUlJS0KlTJ8ybN8/jZPZHH30EADjrrLOUtqvnnHPOAeA8iSzf9mekX79+ePbZZxEXF4cOHToorZucnIxu3bph/fr1ePLJJ5XW9dWXefPm4ZlnnnF986A7lf10OBzYvn07rr/+esv9osjAiaUIdeedd2LmzJkYO3Yspk2bVuX9xYsXe71X2ew/gEVFRYiNjfWYgJH/5xVu+vXrh2eeeQYfffSR6/5uX4YOHYqZM2fi2muvRXJyMsaMGROcTlaTvXv34rTTTtM9S/L777/jggsuwP33349Jkyb5va2ysjLk5+dXuXpo//79yMvLQ7169ZTaM/NB6/TTTwfgnDCt/B9sRUUFFi5c6LHcWWedhXnz5mHDhg2usz5HjhzBV199hZtvvtm1XPfu3RETE4MnnngCnTt3RmpqKlJTU9GmTRs8/vjjiIuLQ9euXZX2g4iIiOz1yCOPYM6cOZg8ebLrs8vrr7+Ovn37olu3bhg9ejTOPvtsJCQkYNeuXZg5cyaioqKQlJTk0c727dvx/fffA3Be2f7qq6/i0KFDVb6RLZD69++PKVOm4JVXXkGLFi0wa9Ys/Pvvvx7LtG3bFoMHD8add96JwsJCNG3aFP/9739RVFTk8Zn+2muvxZtvvonevXtj7NixaNGiBXJzc7Fu3TqUlpZ6nKyT9ejRwzU5ZGTixIkYNGgQrr76alx99dXYsGEDxo8fjyFDhqB9+/b+HQgvWrRogVGjRuGaa67BfffdhzPPPBNlZWXYuHEjli9fXuXznrv+/fvjoosuwvnnn49x48ahQ4cOKCgowJ9//ol///3X41vbvHnuuefQt29fDBs2DFdccQUyMzOxc+dO/O9//8N1112n9M2BEyZMwKJFi3DWWWdh3LhxqF+/Pv766y8UFhZi3LhxSvu5YcMG5Ofn80QnmcaJpQjVoUMHTJkyBaNHj8bOnTtx/fXXo2HDhti1axfmzJmDlStXVrl3uZKZfwD79++PyZMn44477sDgwYOxZs0avPfee0HeS3v169cPAwYMwPXXX49NmzbhzDPPxOHDhzFv3jzXbU3urrrqKhQVFeGWW25BYmIibrnllmrodeDt3bsXY8aM0Z1Ucvf777+joqIC0dHRfm0vLy8POTk5uPbaa9GnTx9kZWVhy5YteP755xEdHY3bbrtNqT0zH7TatGmDwYMHY8yYMSguLkazZs3w2muvobS01GO56667Di+99BIGDhyIJ598EgkJCXjqqacQExPjMbmYkpKCzp0745tvvsE999zjer1nz554/fXX0aNHD4+v4SUiIqLga9myJa644gq8/vrrePDBB5Geno7s7GysXr0aU6ZMwdy5c/HSSy+hoqICTZo0QZ8+ffDLL7+gTZs2Hu1MnToVU6dOBeC8+r9169ZYsGCB16v/A+XRRx/FgQMH8OijjwJwngR9+eWXq1y5Mm3aNIwePRpjx45FQkICRowYgXbt2uGVV15xLRMfH4+vv/4aEydOxFNPPYU9e/YgOzsbnTt3xu23367bj6FDh+Lpp5/GP//8Y/gc0osvvhhz587F448/jksuuQRZWVm4+eabdSeu/PXyyy+jZcuWePPNN/H4448jJSUFLVu2xGWXXWa47rx58zBp0iS89tpr2LZtG9LT09GuXTtcd911hut2794d3377LSZMmIDrrrsOpaWlaNSoEc455xzXLZZm5eTkYPXq1XjwwQdx++23u56D+8ADDyjv55IlS9C0aVPXyVUiQ9X4jXQUAMuXLxc33HCD1/eefvppMX36dI/XVq5cKS6++GJRq1YtERMTIxo0aCCuvvpq8fPPP7uWadq0aZWvBV27dq0YMGCASE9PF4mJiSInJ0fceuutYseOHa5lnnnmGdGoUSORlJQk+vfvLzZu3CgAiOeee861jPy1975s2bLF4+s03c2ePVtMmDChyut6bcv9EEKIl156yeOrVKdPny4AiAMHDrheKyoqEg888IBo0qSJiI2NFY0aNRLXX3+96335a2GFEGLq1KkiKipKzJw502tfJkyYIN577z2v75133nliy5YtHq9VfuXo3Llzqyzvrc9CCNGxY0cxYsQI1+/ysan8ml1Zenq612NbadeuXaJz585ev0rX1092drbPr0E1cyxKSkrEpEmTRO/evUW9evVEfHy8aNKkiRgyZIjH360Q5vJ87NgxMXLkSJGZmSkyMzPFTTfdJD777DMBQKxdu9a13JEjR8RVV10lkpOTRa1atcQ999wjnnvuOSH/M7p161Zx6aWXitTUVNfffuVX4robO3asACAWLlzoem3OnDkCgHjwwQd9HHEiIiKi4Dr77LNF7969bWvv1FNPFY899pht7ZH9unTpwhyREk0IIYI4j0UBtmLFCsyaNcvrZZeTJk1CvXr1MHLkyOB3zKKtW7fixhtvdH1Nprs5c+bg77//xsSJE4PfMRtMnDgRp5xyCq6++uoq751//vl444030KxZs+B3rBrwWBARERFVn/nz52P79u1o3749CgsL8cEHH+DDDz+09QqrTz75BLfddhu2bNli6hviKLhWrlyJQYMGYfPmzVW+BIfIF94KR0REREREREhJScF7772Hf/75B6WlpWjVqhVmzZpl6217l1xyCf755x/s2LFD+XYvCryjR4/i3Xff5aQSKeEVSzXMjz/+6PoWB2+efvppDB48OIg9sseuXbtc32TgzejRo11f1R5uXnnlFY/71mXLli1Dw4YNg9ij6sNjQRS5NE0LyDNHRo4cidzcXN2HrxIRERGR/zixRERERAFX+dDYxYsXY9++fcjMzETHjh3x6KOPokePHpxYIiIiIgpTvBWOiIiIAm7IkCEoLS3FzJkz0bx5c+zbtw/Lli3DoUOHqrtrRERERGRBVHV3gIiIiGq23NxcrFq1Cs888wz69OmDpk2b4owzzsCDDz6Iiy++2LXcwYMHMXjwYCQlJSEnJweffvqp672KigrccMMNOOmkk5CYmIiWLVtiypQpHtupqKjAPffcg4yMDNSqVQvjxo2DfGF2SUkJ7rzzTtSpUwcJCQk466yzsHbtWtf7Xbp0wfPPP+/6fdCgQYiNjUV+fj4AYOfOndA0Df/++6+tx4iIiIgoXHFiiYiIqAYpLi7G0aNHg/KTl5dX5bWSkpIqfUpJSUFKSgoWLlzo9f1Kjz32GC6//HL89ttvGDBgAK666iocPnwYAOBwONCoUSPMnTsXf/31Fx599FGMHz8eH330kWv9F154ATNmzMC0adPw7bff4vDhw1iwYIHHNsaNG4f58+dj5syZ+L//+z+ccsopOO+881zb6dWrF1asWAEAEEJg1apVyMjIwLfffgsA+Oabb9CwYUM+cJaIiIjoONPPWNIe04DKJTWEXozjv/uK5XWMqPTBzPb1YhvbSy8BZs0HLvgXiHbbVwFgbwpQEg3EOoB9ycDuVGBzJrApy/nfnWnA/mTnT3mU4jGwI1bZZ1WBzqF7bLVtM/0Nh1h1P42EaU0q9z2UYtX9VFEDa1JM9OdA2Ku4uBi1E2sjH/lB2V5KSorrSp5KEyZMwMSJE6ssO3/+fNx0000oKirCqaeeil69euGKK65Ahw4dAACapuHhhx/GE088AQAoKChASkoKlixZgvPPP9/r9kePHo29e/di3rx5AIAGDRrg7rvvxn333QcAKC8vx0knnYTTTjsNCxcuREFBATIzMzFjxgxceeWVAICysjI0a9YMY8aMwX333YfPPvsM11xzDQ4dOoQ//vgD559/PoYNG4aEhARMmjQJN910EwoLC/H+++/bcgyp+mmaZrwQVbvevXtjwYIFSE9P95kzIQQcDodrgn3v3r3YunUr/v33X2zcuBFff/01Nm/eHOSeExGFNzNTRpHzjCXNSyyk182sE4jYShsSTQAtDnlOKlWuklbinDRqeMz5c+reE2Mjh+acdDqSCFw6DPgx1L94y2gwqLdOoGOLObS8friIkJqMuHyyJqtVaWkp8pGPu3E34hEf0G2VoAQv5b+EHTt2IC0tzfV6fLz37Q4ZMgQDBw7EqlWr8P3332PJkiV49tln8fbbb2PkyJEA4JpkAoDk5GSkpaVh//79rtdeffVVTJs2Ddu3b0dRURFKS0vRqVMnAEBeXh727NmDM88807V8TEwMunTp4vpAtGnTJpSVlaFHjx6uZWJjY3HGGWdg/fr1AICzzz4bx44dw7p167B69Wr06tULvXv3xqRJkwA4r1iqnLgiouA5+eSTdSeVAOckYXR0NJKTk5GcnIz69eujc+fOrn8D7rzzTt1vnyUiIv+Yn1iy+wyy3THgeRYZfsShQuXKAul4tDronBzyJqEcKIrxXK3yv9ECSCoHYguA+HLf7Qc0lvcRFuNQoZjDGhPL+ws/4lBhoSbDOpb3ERbjUBGoHIaQeMQjAQlB2VZaWprHxJKehIQE9O/fH/3798cjjzyCG2+8ERMmTHBNLMXGxnosr2kaHA4HAGDOnDkYO3YsXnjhBXTr1g2pqal47rnn8MMPP9i6PxkZGejYsSNWrFiBNWvWoH///ujZsyeGDRuGjRs34p9//kGvXr1s3SYRGcvMzPR7XU3TIITg1WlERAFi/hlLlR+4RYjGmhRDigG1D/+qZ8OtxP5sszKWBkQ9dgDFMd7HclECEBpQYeY4hGIO5XyqCHQOreRTHtRWdy2xJv3bZmVck/LJmlQ7ZqSsTZs2KCgoMLXsd999h+7du+P2229H586dccopp2DTpk2u99PT01G/fn2Piaby8nL8/PPPrt9PPvlkxMXF4bvvvnO9VlZWhrVr16JNmzau13r16oXly5dj5cqV6N27N7KystC6dWs89dRTqF+/Plq0aGFlt4nID61bt7bcRlJSkg09ISIiWeQ8vFseOMgDEPnH13LeYl/bMBtbaU/S4CigCe+TRxqAxHKgMLbqe2HHKDeq+YSNsWrbMot/A2EjQmoyovLJmiQfDh06hL59+2LWrFn47bffsGXLFsydOxfPPvssLrnkElNt5OTk4KeffsLSpUuxceNGPPLIIx7f5gYAd911FyZNmoSFCxfi77//xu23347c3FzX+8nJybjttttw33334YsvvsBff/3lembSDTfc4Fqud+/eWLp0KWJiYtCqVSvXa++//z6vViKqBlFRUcjKyrJ8xVFGRoY9HSIiIg/qt8L5c/ZWPvsbCrEmxdXdF9jQFwC5ic5nKR2NB7KKUUVKifNWubTSqu+5eOtPqOTNPZZzWNPyqRKzJsM/h+6xSj6r+1i5x6xJ8iElJQVnnnkmXnrpJddzjho3boybbroJ48ePN9XGLbfcgnXr1mHYsGHQNA3Dhw/H7bffjiVLlriWuffee7Fnzx6MGDECUVFRuP766zF48GDk5eW5lpk0aRIcDgeuueYaHDt2DF26dMHSpUs9brM5++yz4XA4PCaRevfujSlTpqB3797WDwgRKdE0DSkpKZbbICKiwOC3wvliZdCiGtvUXpQDmPwFMOhv5+1wOYer7lZxNLAhG+iw70QTlSo04KvmwHWDgD0pisfAjlhln1UFOofusdW2zfQ3HGLV/TQShjUZEnlgTQatJkPhW+GOHj2K9PR0PIAHAv6MpWIUYxImIS8vz/QzlohCESccQl/dunXx3Xff4eSTT/ZrfSEECgoKcMcdd2DGjBn2do6IqIbjt8K507zEQnrdzDqBiK20UUkAnfYCV/8GxFcAf2V73734Cue3v0mrIj8OmNwVeKE7kBfYLxKyh9FgUG+dQMf+5tDf7Yarml6Tdq0fLliTREQUIFdeeSVOOukkv9YVQuDXX3/F/fffj2XLltncMyIiAngrnO8z2sHuC6z35Yo/gIxi568lOpmNcTgnlxIqnMturAWMPRdYknP82Ux6+azuY+UeyzmsYflUilmT4Z9D91gln9V9rNxj1iQREdmsdu3auPnmmxEVpfZoWCEESktL8cEHH+Dhhx/G7t27A9RDIiIyP7EUzMFSIOJKvmK5v1CI/VnHPZYHPnL/TBzjpDKg75YTzcSXn5g8kruZdvw5S7ULgE9aAff3BzZlmtxWdeYwkPk0u1yQ8hlyExjhkMMQq8mIyCFr0jMmIiLbnXbaacq3wAkhcODAATz88MN49913UVJSEqDeERERUJNuhTP60O8+qDA7qFHZtt2x2W0c/71ePnBS7om3M4qB3ASgnpdvcU4vAX6uDyxrDrx+OlAQKt8SpzKIs3o8vW07kLHe9uxuO1REeE3atp3qxJq0p20iIvJbjx49EBOjcJOFEPjjjz9w99134+uvvzb1bBAiIrKGt8IJC23Y2RdY60vrg0Ca27fAZRUBe1OqTiw5AGxPB+47F/izjluazOazuo+VeyznsAblUzlmTYZ/Dt1jlXxW97Fyj1mTRERko7i4OPTs2dPUA9aFECgqKsL06dPx9NNPY9euXUHoIRERAbwVzvf+QSH2Zx33WB74yP0zcYx7bwGi3dZNLQU2x3o2XRINzOoAPNIH2JMq9SUcchjIfJpdLkj5DLkJjHDIYYjVZETkkDXpGRMRka0aNWqENm3aGC4nhMD27dsxfvx4zJ07F2VlZUHoHRERVVK7YinQsTxggY2xzNtAQ2+AoDeoMOoDDGKLA8e0YuDcTZ67GuMAhAY4NCBKAPuTgQm9gRmdjz/Yuybk0+iYq+TTTHtmY6sTAUZ98bdfoZhDdzWoJpWOmbf9DNd8RnpNEhGRrU4//XRkZmbqLlNRUYH//e9/GDt2LP78888g9YyIiNyF1jOWrAyajJYLpwGByllyAXTaC7Q4VLWJlFLn85P+zgbGXAB83+j46r4GdnazO4fucU3KpzyQrWT2mAUSa9JJsSaV8unt90BhTTqFc00SEUWQbt26+fw2uMpb3958801MnDgRR48eDXLviIioUnBvhavOWPXMvTeaj1jvPTNnu40GMQb713Y/EC99+xsApJYAL3cFXj3d+bylkMiDnflUucLAGztzaDWfwsfrNTmuwTUZsfmM9JokIiLbREdHo0WLFl6frySEwD///INx48bh888/561vRETVjLfCufdDb4CgN6gw6gMMYpVBqpcB0ilHfPQZwNQznLfB6Q6EwjWfRsdcJZ9m2jMbW8mn6vFQ6Vco5tBdTalJ1WPmbT/DNZ+RXpNERGSb2NhY1KlTx+t7DocDDz30ED755JMg94qIiLzxfm1pdZEHBUIh9ra+ShxKjM7Su8UagMZ53sc1BXFAceXUYXXst2o+va3jbxxKVK+6UP27DyTWpJNCTSrnEwjdfHpbx984lIRzTRIRRYjk5GRkZ2d7fa+srAzbtm0Lco+IiMgXtYklDSc+bAciBrzPkJhhNLCRBwFG66m2r9KWjW3HOIA6BSdedv8pjAXKoqVtycc6lPKpsu9WcxDIQXEwB9yhlkN3EVqTyliT9qyvJ5wnwYiIIlRaWhrS0tK8vldcXIwjR44EuUdEROSL2sO7A3EW2z1WvRXCyi0K8nLy9mVGt1LoxUYstB1XAdQqdMYl0cDjvYFdqUBmMXAgyflalTPv7rd5wO09u+NA51NvOXn7MrtzaKVtK8cp3HPorobUpPIxY01671s41iQREdkmOzsbiYmJAAAhBDZs2ID9+/cDAA4dOoTDhw9XZ/eIiMgNvxUuFAcECgOf2Aogqdz5UkEc8EF7YFu61J7ecQoku3PoHtekfFqZWAg01qSTymSEaj69/R4orEmncK5JIqIIkZiY6PGNcJMmTcL777/v+r28vLw6ukVERF6oTSzZfYZb9Yy2HqMP/FYHB1YH2HpUB2hucUkMcCzOGRfGAgWxBtsK5BULVvOpclyDnU8VwRxwh1oO3YVaDoNUk8pYk/asryecJ8GIiCLUsWPHUFHh/Npjh8OBQ4cOcTKJiChEqT+8W+WMtXzmF6g6qNEbKGg6sWYQG60fyNgqhUFQeRSQm+B8qTTa+bvrOJg5LioDLm85tJLPmpxDd0bHWJNi+Qc6sbf29bbFmvSPSp2o5lPuK2vSe2ynQNckERFZdvToUZSUlABwTiwVFhZWc4+IiMgXe5+xZPcZbZXY28DKaACtx9vAQ25TZbCuR17PfZsGcXkUcCDZ+evReOcVTFW2a+dVB9WZQ/kYq1DNp9wfFar5NLMtf49pKOdTXsZImNSkqW3VlByyJqvGRERki5KSEpSVlQFw3vbGh3UTEYUufiuc2cGBlUGaEQttCwDFx7/5rSQaqDA6fqpXKAQzn3YOtuXYyrZUBbJtWajl0F2E1qQy1qQ96+sJZk0SEZEtCgsLXVcpVVRUoLS0tJp7REREvvBWuEDEVhkdVykuPP6MpbwEoIK3wtkT28noGGtSLP9AJ/bWvt62WJP+UakT1XzKfWVNeo/tFOiaJCIiy0pKSnDs2LEqMRERhR61iSUzgxz3WP6ALq8H+P9h3MoVBYGOZVYHUAbbPJLgDHMTAIf7oNLMVSJGOZTz6Ws9M/uhuF/VGsvsHBCbufLD7FUgrMmQrEmP2CifrMmaVZNERGSL8vJy5OfnAwDy8/P5jCUiohCm9owlQH1ApbKemTPwVs7Mq7ZnF7mPRoNFxb682xGolw980N5tFbNth1I+5fX1BDufevtrNZ+q7clth1IOWZPW2w6lfMrr62FNEhGRjUpLS/HRRx+hUaNG+OSTT/iMJSKiEKY2sWQ0WPB2pl3lfdUP8Hqx3EdVVgfUeuwcDAL4qzZw/SWA0OB9cKN3Bt8oR+6xmffd27I7h8HMpwqr+ax8X+/qCpUcusesSXNsrklb88maVFedNUlERLYQQmDy5MmYN28eCgoKUFFRUd1dIiIiH9S/FU7lTLDRQAXw/wO56sAhmLHM6gBKb5vHj5+jclLJV3tC+q/7+no5dI/tzmco5CpQOVTJp9FkgF5fWZMhW5Om88marFk1SUREthFCYMeOHdXdDSIiMmDvw7vlWCjGVgcKRoMQldhORoMWOVbpi9W2Qymf8nb1BDufKhMJcmzUF9X25LZDKYesSdak2diqUK5JIiIiIqIIojaxZDRY0KTY6H05FlIMnRhSbNQ3eRDgrQ299szug9n+BCo22jf5eMpt6O2f0fuq+ZT7qLKvKoNvb22Ecw7dY9Zk+OeTNRn+OZRjIiIiIqIIUr3fCmc0sNQbzEKKVQepRuT1VK4KMNqW6lUIessaHRc5NjoTL+dQzqev9bzFqn21O4fuVPOpsi2r+VTNoZxP1iRrkjWp37YcB7ImiYiIiIgiDG+FC8bgwGjgKMcqfbHadijlU96unmDnU+/YWM2nanty26GUQ9Yka9JsbFUo1yQREQWdpvEfYiKi6sJb4cy2Z3YfzPYnULHOvqWWAn23AOnFOm3o7Z/R+6r5lPuosq8qg29vbYRpDqvErMnwz6d8POU2WJP+9cfOWHXfiIgoqPr06YO3334bQ4YMQUZGRnV3h4go4qh9K5wRb2dwhRRDJwaqDjzMnl2Xt+etPZUP/qoDCbltK+urTiyYjC/9C3hjEbC+NvDWacD81sD+ZC/99jXwV8mHtz4HO4fuakgOq8RGWJP2rB/IfBphTfq/TijWJBERKdE0DUL4/ke2Vq1aeP7559G5c2dcc801+Ouvv/Dhhx/io48+wpYtW+BwOILYWyKiyKT+jKVAxvLZYisDJpnqGepAsrMvwrlotx1Acgl8DkDTSoDbfgLiK4BOe4FXFgMrpwMPrQKa5gFaKORTXl9PTcqnvP96kwjeYl/t2BGzJtX7UpPyKa+vpybl00oOiYjINmlpaRgzZgxq1arlc5nBgwejY8eO0DQNsbGx6NixI5566il89913+M9//oNWrVohOjo6iL0mIoo8vBXObHtm98Fsf2yM2+0H5swDnlgOxJW7LeO2PxdvAE7dc6KbUQBaHgIe/xr49h1g0v+ANgeAaPkYyPlUORZG+ZT6qLTfVgfUIZZDj1j1ygq99liT5vsTqFj16hi5Db39M3qfNWlPrLpvRERkmaZpuO666/DMM8/gww8/RJs2baosU7t2bdxxxx2Iioqqsm7dunUxbtw4rFy5EtOmTcM555yD5GT5Un0iIrKD+sO79Rid5RUGMaRYbzALKVYdpKrui7f++YqN+qDSlkGbSWXAU8uAxkeB29cC96w5PjnkdozqFAD3rgFivFwJHAWg0THgvtXAN9OB1xYDZ+4CahV52Z6ZHKrkE1Jsdw7dqebAbB/syKfRcdGLjbAmjbdrc00q59OI3vZYk/61VZ01SUREprRs2RJjx45FbGws+vbti88//xyXXnqp6+qjqKgojBkzBu3atfP54G5N01C7dm1cc801WLRoEb788kvccMMNqFOnTjB3hYioxlO/Fc7XgMNbDKh94JbX0xvMymfg5W17O7OvF1cno/75OIOvAbjuF+D8f51xfAXw8Ergru+BxDLnMjEO4Oafgbb7jbuQXQRctw549Btg+Qxg1FogudTG/bKaw5qaT9X33WPWZGD4WZN+5VM1h3I+VbAmA1+TRERkWVxcHB555BE0bNgQgHOCqEmTJpg+fTrGjh2L1NRU9OvXDzfeeGOVq5W80TQNCQkJ6NatG9588018++23eOKJJ9CmTRvExNj7yFkiokikCb2n4bkvOFEzN7CEW2z0vlHsazteO2hiHb31jdqzq9/+DCB99L/9fmDJLKDhMc+ul0YBi1sAs9sD5/8DdNwHHEoEmuQBJ+UCcRW+x0ACwLp6QKOjQGYxsKIZ8E5nYPlJwIFkQFgZDHvZBw9251CvbdW+Bjqfdu0Xa1K934HIoZX9UsmRlUkp1qTtNSkm+nMg7HX06FGkp6fjATyABCQEdFvFKMYkTEJeXh7S0tICui2iQOLX1IeeQYMGYfbs2UhIqPrvWFlZGX799Ve0bNkSKSkpfudPCIEjR47g66+/xpQpU/DDDz+grKzMateJiGocM1NG9k4sWY2rbNTGtmqY5DJg9jzgwo3eJ4kEAIcGRB0/HuVRwM40YGsGkF0ItD4AxPg4VkcSgPXZQLedzt8rNGBbBvBxa2BOO+CPOkCp+zMQq2uioaYIdF2xJoMrFPLGmrTGz2PMiSWi8MSJpdDSqFEjLF68GO3btw9KboQQyM/Px/LlyzFt2jR8/fXXOHbsmPGKREQRwsyUEW+FCwVG/ZP3RQNqFQKaAPLjvI/5NDiftVS5aqzDebVSr61AQrlzosiXjGKgOAYoiXauGyOAk48AY1c7b5FbOAe48negdqHiflnNYU3Np+r77jFrMjD8qEnTy8rvW7nqSBVrMvA1SURElvTu3RsNGjQI2vY0TUNqaiouuugifPTRR/j6669x3333oVmzZpx0JCIySe2KJUDtTK48MAnmmXhVdm47SH2PrwDa7wOG/w5c+jfQOM95hZLR/wKPxgG/1QN6bPe97KZM51VJrQ5WXUbAeRXT5kxgblvgo7bA+tpAWRSqN4fugp3DUPlbZE3a05adAp3PUM6huwioSTEhEAdODa9YIlLHyYPQEhMTg+bNm2PIkCEYPnw4WrVqhZiYmKDmyeFwYM+ePViwYAFmzpyJ//u//4PD4eVbcIiIIkD13wpXpRGDdawMelW3ZbS+1faCeBw0APXygQH/AFf9Bpy5E0gs9z1pVBoFfNYSGLze9yVrpVHA1ycB527Sv6xNAMiLd04u3THg+C1y/uZTFsiBpJ3tVedkToj9LbIm/YitHuNAbkvGmuTEkhtOLFFNwYml0JWZmYm+ffvi2muvRZ8+fSw9U8kfQghs3rwZXbt2xcGDB4O2XSKiUFL9t8JpUmxmoKAyCFGJrfLVRzOxUVveBjnusaYfCwB7Up0P2R5wFXDeNcAbXYCdqYC3cyuxDkATwKEk312MdQBpJcBBnWUqRQtgbUOgzH1Sydu+ylRzWJ35VGnLYj4BKYZOzJr0LzZqy2oOVfJptG292J/JGNZkYGuSiIhsc+TIEcyfPx9Dhw7FeeedhzfffBO7du0K2tVDQghs2LABxcXFQdkeEVG4UptYCiVGAw8hxUbr2TmgUl1fdV909rE4Fvi2CTBqINDjBuCuC4A1jYDiaM9xZrv9wI8N9S9MaHEI+Ku272UEgC0ZwI0XA9M7mx/zVdk/X7HBvurGwc5hIPJpdXAdbKxJ//IZSliTNasmiYhqiLKyMqxZswajRo1Cjx49cM8992Dt2rUoKSkxdSbd323OnDkTI0aMQH5+fkC2QURUU6hNLKmeHZc/lBudfQf0BxaqfVWJjQYMevsrtyfvn1Fb8vryWXKjvns5ZgLA9gzglTOA/tcCY84HvjgFOJjofK9ZLrAh2/mQbl+yipwPBy+J9v7+gSTgukHO2+AqKv+S7MxnIAeSVnOo0pZqPlWwJr3HcnshUJM+yeup5tMoh6zJqm0FsiaJiChgHA4Htm3bhilTpuCcc87BoEGDsGnTpoBsZ9q0aRg9ejRvgSMiMiG0boVTPQMezLPEqv3R65u8nHzshBRDJzbRh/Io4OKNQGYxcOtFwILWgEMDcg4Bv9X13U0NQPMjwL9ZVXenOBp4uC+wqqlO31TzGUx2/n1Zzac//fF3YoE1aa6tANekR6w62Sfn08rEolEfg6mm1CQREQXFsWPHsGfPHmRmZgIAcnNzceDAActXMAkhsGLFCjz00EMoLDT6CmQiIgJC7VY4K2fDZaoDATu3bfeZfL3YxLY77QV6bnU+1Pve1cAjfYBrLwVSS4Dt6b6bq5xY2pLpOR51AHj7NGBmJ4OxVLBzaCWfskBemSHHVrcdSKzJyM6njDVZ/TkkIiIAgKZpuPnmm5GVlQUASExMxOLFi/HRRx8hNzfXrwkmIQT++ecfjB49GocOHbK7y0RENRZvhatkNNjQ21+5PXn/jNqS15evKjHqu84x0wBc8yuQXOaMu+4E3l0A7EwDhl0GzG4HFMb6Xj++AogvB17sBrx8pvMb4JafBEzoDZRW3kYXqHwGciBpNYcqbanmUwVr0nsstxdCNVmFvJ5qPo1yyJqs2lYga5KIiIKiQYMGGDhwoOtb4uLj4zFs2DDs3r0bV1xxBb788kvdySUhBMrLy7Ft2zbs378fQggcPnwYo0aNwvr164O1G0RENYLOE3a88HUbga9YHhCYGSTBzzjQ7OybvJy3YweTsUEfmuQBg/72HEOdugeY/yHwQD9gfhtgYSvgyt89m82PA/7OBlY0Axa1cD4QXAMwt61zUupwoom+hVoO3dnZN6v59Kc/8sQCa1ItNmorgDVZJTazbb18qk5EqfQxmGpKTRIRUVAMGjQIjRs39ngtMTERd955Jzp16oSpU6eibdu2aNSoket9IQSEENi3bx+WLVuGefPm4fvvv0dGRgauuuoq/Pvvv1i2bFmwd4WIKOypTSxZHWD5s76/VAdrRttW7bvKfgRwADPob6D+Mc/XNAD18oE3FgEd9gGvnQ6cvQ2IFsBPDYD/NXc+O+nfLOlqJg34rgmqTlBUNmp3PvX2P9j5tKvf3mIrWJP+xap9VYlVWd2PQB4HGWuSiIhCQGpqKq699lpERVW9+SI6Ohq9e/fGSSedhC1btqBhw4YAnM9g+vHHHzFv3jx8+eWX2LlzJxwOBwBg3759ePTRR4O6D0RENYnaxJIqbwMPvVi+DcHKIMPqgDuQjPqjelWJjzitxHkbnPthde9CYjkw5nugxSHglouAP+oAe1KBCl95MJNDK/n01kkrA+Yals+ADLBZk+b6E+gcWskna7Kq6sonEREFRY8ePdCxY0ef72uahqZNm6Jx48bYvHkzXnvtNSxZsgT//vsvysrKgthTIqLIwG+FM0u1P3p9MxrcC5gf8Ohst/dWoP0+/fFOtAD6bwJKo4Gd6W6TSr7yqTLQUs1nMNn592U1n/70x/24sibDpiZ95tBKPlmTxm0FsyaJiMhv3q5AkkVHR2P48OGIi4vTXU7TNGiahpdeegkvvvgi1q9fz0klIqIA4a1wwTpTr9JXKY6rcN66dtIRoH4+kFnsfMbR3hRgS4bzv6XRnk3GOIARvwCxDuNN/9QA+KGR/n4klDsfAH4o0W3lQOdT77gEO5929dtbbAVr0r9Yta8qsSqr+xHI4yBjTRIRkU0SEhJwyimnoHHjxmjRogXatGmD+vXrY9euXdi8eTO2bNmCjRs3YsOGDSgpKXGtd8opp+CCCy5wPbRbz44dO7Bw4UKf78fFxaFZs2Zo1aoVvvrqKxQWFtqxa0REEUf94d0qsd3rW2G0PTsHZ5qXWOjEPtpLKgNO3w2MXwl02gtkFDsnjCoXLY8CchOAP+sAC1oDH7cGdqU622q3H+i7Rf9qJcB5hdJ/TwMK3J+ldLwfUQJofQC47E/g3E3A0Xhg0BVAcYzncqZjO9mdT5lqDk3k06M9uwbQrMmg1qRHG3b1zeokVjAnwfSwJomIyIS4uDj06tUL9957L7p3747ExERERzvPkFZOFlU+ZLugoADfffcdZsyYgWXLluHQoUO44oorkJ2dbbgdIQQWLlyI3bt3V3mvadOmGDp0KAYMGIBOnTohISEBffv2xQ8//GDvzhIRRQj1ZywF8+y4Fap9MxoUqQzYVAd30jGKrwAGbATu/AFolgsklQN1pBMoGoA4h/P1OluBXluBMWuA108HpnUGhv0JpJfA0Prazm99k/vRLBcYuxq44g8gq8j51q5U5+TW3hTjdm1h5W/Nao6sDNaN+iek2Kg9I6zJwNakmRxWZz6DiTVJRER+0jQNXbp0wQMPPIDzzjsPSUlJPq86qryNLTU1Feeddx769euHLVu2YNGiRbjkkktMXa107NgxvPvuuxDixD/g0dHRuOiii/DMM88gJyfH1Y7D4UBOTg4nloiI/MRb4QJ1m4bKfrgt1ywXeGoZMGS98xa40mhgYy2gdoH+1UdRAJrnApO+Aob/DhxNAH6v47x1rlahc115fQeAGZ2AI4kn+hslgAH/AC8sBXIOe66TWQQ0OOY2sRTofOoN8IKdT7v67S22gjXpX6zaV5VYldX9CObEImuSiIj8EBMTgyFDhmDy5MmoW7euqYmhSpqmISYmBjk5ORgzZoypdYQQ+OGHH/DHH3+4XktJScEdd9yB8ePHIzk52aMPmqYhJyfHdJ+IiMgTb4Xzd0BmNECSY6ETH9dzG/DGIqDVwRNvx1U4/1seZfy8JMD5MO7Oe523tu1Mc04CbagF1C50TlrFVZxoe1sGMKfdiX5oAK76DXh5ifNqJ/l/+QnlwMlHgP+rX7XvYZ9PmWoO5XwGawDNmgxcTQZrUsvqJFYwJ8H0sCaJiMiLlJQUPPjgg7jrrrt0r1Iyw+y6FRUVePfdd1FaWgoAyMrKwttvv42LLroIMTFVhz+apqFVq1bQNM3jCiciIjJH7VvhAO8f7itjzSCW11FpW47NbE9l20Z90RtoaFIMgxhV4+47gFkfe04qVW6iToHzWUoqXU8uc16tlFIKnLHLOaG0sinwXRPnA7jLooD3OgJ7Uk+s12M78MKX3ieVKtttdcDLi3od0YtVcyjnU4VRPlVzaJBPj1jl79rumDUZeTlkTZ5QXfkkIiIAzm95e/DBBzFu3LgqVwkFihACGzduxJIlSwA4b3+77777cMkll3idVKp08sknIz4+PuD9IyKqiQJ7K5wcexswuC8npLiSt1j1zLxRH422bWXf5b5Ivzc6Crz6ufO/3v53m1UE7E71HDsZ0QCklTjXORrvvFqpaS5wINl5xdHcts6rlSq7UbvQeftbdqHvbWgAWh902zWr+bRypUWw86nXF2+/+3rP7pg16V8s98Xb777eC4ccsiarL59EROTStWtX3HbbbboTOnb77bffcMcdd+DQoUMAgHPPPRejRo1CVJT++fQGDRogMzMTe/bsCUY3iYhqFLUrlqx+uK7OD+eqgzsoxDKFKw7iK4D/LAM67vXdZIzD2bUKxZM8GpxXHxXFOm+NiwJQt8D5bXG7U4FjcSeWu/N7oMtu44mrUw4D8eXHf6lJ+ZQp5LBKrNq2at/csSa9xzLVfKq2Z6VvVicwQmUChDVJRERukpOT8dhjjyEjIyNo2xRCYMGCBVi1ahUAoE6dOnjmmWeQkmL8zTPp6emoW7duoLtIRFQj8VY4s33RO3suXykAgxgn4vP/BS770/iwpJQCx/y4OlcDUP+Y80qlyk3nJgBbMk4s024fcMvP5v4YGh6Tvm3OSj5VcyjnU4VRPlVz6COfXmMrkxBWY9Zk5OWQNXlCdeWTiIhwxRVXoGfPnkG5/a2SEAKbNm1y/T5s2DC0adPGVB/i4+PRtGnTQHaPiKjGUp9Y0vvQLqRY/pGXM2pPL/bVptltyYz2xc74+O+J5cCYNc6rloyklTifjeTPxQbRAkgqAwpjnevPbw38ne18L0oAo9c6b4EzI70YqJsPe/Jp9RjqqYZ8BqwvvtoxE7MmQ68vvtr0Z1uq+8WatKcvvtohIiIAzucVjR8/HrGxsUHd7r59+7B69WoAQEZGBq6//npER0ebWjcqKgonn3xyILtHRFRjmZ9Yqo6zusE8628UW/nx0mZmMTDsD+eDtc0ckliH85vhHH6c9NEAZBY5b4c7Gg+8cfqJdk4+Agxabz4tCeVAw6NS4yodsSsOdj7t2q4/+6zXpj/t6cVGwjmHdubT33321qbV9vRiI+GcT7u2688+67VDRBThhg4dio8//hgnnXRS0K9WWrhwIbZu3QoAGDBgANq1a6e/khtN05CdnR2g3hER1Wzmn6RXeTZW04khxfJrZtowG1e2bTaW2wGqnmE2OsOtuk0f/y/VBHDtr8BFG5xXLZmhAUgtBfLigaxic+tUduNYnPPb3/LjgANJwJ+1T7x/6V/OB3ebFSWAZnmonhzK+dSLvf0uv2chh7ptydv3Vicq27dynCHFrElzbcnbt5pD99hqDt3j6s4ha9J7roiIItRJJ52E5557Dk2bNg3qpBIAHDt2DO+88w4cDgcSEhJwyy23mL5aqVLt2rWNFyIioirUv6LBzADCaPBgpT3hx7Kq/Qpw3HY/cPkfzucmqfwvN6sI2JHmvNpJb3ws4LwyqXIyKbbC+dDuWoXAB+2B0uP/j41xAOdsUR8PnXwY3vMg/253DvXyJwtiPg37Fsy+sCbDvy9W+86aNO5bsGuMiCgCxMTE4N57762WSSUhBL777jv8/vvvAIDMzEzk5OQo9yM7OxtRUVFwOByB6CYRUY1lfmLJ7jPjwd4mjv9u1xUOKtzaSS4Fnlrm/NXsc40qJZQDBXHO29ii3Y6hgPO1vHjgcKLzId/JZc6HdqeWOq8y0gDsSgU+ant8Jc25TLNctT5oAJrmuh3eSMmnlW3JceU+mI0DdTUTa9L/GLDvqiC7cxgp+QzFmiQiimDnnHMORowYEfRJpUrz5s1DaWkpAKBWrVpITU1VbiMzMxPR0dGcWCIiUqR2xZJmEKt+WPe2jr8f9o36osFzUKAa6/VV5mM5TQA3/wxc8C+w9GSgzQEf6/ugwfkQ78OJzkmp8ijnN7zlJgDFMc4Ha9fPdz43yVvXUkuck0mVkkud7amql++82qksGvbmE1JsdOztzKdMZXBq9wBcXgc6MWvSnnwGchJFXh+omkN/68BqP82sb7Yt1iQRUcSqU6cOnnrqKSQnJ1fL9oUQOHr0xENA69ati/h49a9TTk9PR2xsLMrKyowXJiIiF/VnLKkuZ+aDuF2xXl9CYNsd9gH3f+uclIkCEGfi2+DcmxBwTk591hLovt15JVJ6ifMKoliH7/FYpSgBRFeegBHOdWIUTsgIAEUxwKKWzkmtSMxh0Pphte++2rLat3A6ptW5bbvzaWfb4XQcq3Pb/hxnIqIIFBUVhXvuuQedO3eutquVhBAoKTlxtjQuLg5RUWpffi2EwJ49eyAE/2EnIlLFW+HMxlYI50O6H18O1ClwNhllYkKncjLpaDywL8V5pVJiGZBQBpyU65yY8rt7mvNb4gpigVpF5vpyKBEYdy7wXge3FEZKPq1sS44r98FsbOeVGaxJ69u1uv+BzmGk5DMUa5KIKAKdeeaZuPnmm5UncuzkcDiQm5vr+v3gwYMoKysz/fDuiooKfP7557jllltQVGTigzEREXngrXBmY72+yrzsx1W/Aef/c+LlkhjnQ7Tlb4UTAITmfF7S3hSgQgMSKpy3n+Uccq5fHOOcZKqf72P7PpRHnXhwN+B8XtOhJKDJUd/rVPZpcyZw64XAsuY+JpXsyCek2OjY25lPbzttth92D8DldaATsybtyWcgJ1Hk9YGqOfS3Dqz208z6ZttiTRIRRZz09HQ89thjyMjIqO6ueDhy5AiKi4uRkJBguGxJSQnefvttPPLIIzhy5EgQekdEVPME9lY41Q/48voqsT8DBTtjmdt7LQ8Bj3zjvPWsUkYxcCzOeSubOL54fpxzwqgw1vmNcY2OOv/rvkuA8xlKWzOck02+xmDeHE50/lT2rzgGWJ8NdNqrP5Zb2wC46WLgt7pubwQinyGcw5BslzWpH8tCpS07txfofNakHFZHu0RENdytt96Kvn37VtstcJWEEB63sB0+fBiHDh3SnfASQuDw4cN44okn8MYbb3jcSkdERGqq/1Y4vQ/lKu0JgxgWYz9FOYDRPwCNj3o2Vf8YsCfV+a1tefFARRSQVOacLIo3uMUtqwj4vW7Vb4czsinLeVsdAEADhACWnwRc8Yf37VVowKctgTsvAHamub0RqHwa5bA682nXdv2pFTuvzGBNWt+u1eMc6ByyJquvJomIIkitWrVw7bXXmr7dLJAOHjyI7du3u34/cuQIVq5ciebNm3ud9BJC4I8//sCoUaPw3Xff8VvgiIgsUrsZWsOJD9HeYiHFMIj12nLfppnYal9UB2h6gwi39045DFz+54mmyjVgX7LzmUlbM5y3p9XLB5rlAnULnLe9GY2zooTzWUuuq49MEAC+aXb8oduVNOetbfu9fIFHaRTwRhfgukEmJ5XsyKdRDgOZT5m8nNHkjkqsum/e+gO35ViTVWOZUT7tzqFKPgNZB6zJ4NckEVGEuOCCC9CiRYvq7gaEEPj666+xc+dOj9c+++wzVFRU/aYcIQS+/PJLDBo0CKtWreKkEhGRDcxPLAkTP5Bio8GE/AFfXt9KDIPYzu3pHIeLNzhvd9uRBvzYEFjdxDkh1G6/85lJaSVAnIlvdHOnATjpiHPCx+x45lgc8HmO2wvH+7ctHVjUwrPbx+KAR/sC957nvJrKY8PBzCcM4iDl0DA26qvM7P6ZiVmT9uTTqH8yu3Pob99Zk95jo77K7MonEVGEiIuLw5VXXhkSVyuVl5fj448/rjJBtHr1auzYsaPKsnPmzMGIESOwefPmYHaTiKhGC+zXN6h+KDc6W64Xy+vLjNo2+vGzT//Ucn6LWm4C0OYAcPY2oPVBILPYeYXS9nT/xiW1C4EjCebWFQBWNAP+rO3Wx+McGjC5m/NB4QLO/958EfB8d6BE/qwQ6HzK6+gxas/ufKr8qO6X1b97FZFek4HKocp+WWlbxpoM/5okIgpDUVFRiIuL83iuUXXZvHkzVq5cWeX1/fv347///S8qKioghEBBQQGeeuop3HTTTdi3b1819JSIqOYyP7Fkx4d3ow/rQoqhEBttz4jRoExl22778UlL4MF+wLM9gO8bOR/QXblqnQJgdypQpPbdfACct8MllTknrIzkxzknj0qjUfU4a8BftYEpXYF19YAhlwNz2jmf+eTap2Dl08w2zFLNp7wNIcV6baleLSFvz+gqE72YNWlu20b5tJpD91g1n15qUjeHrMnQrUkioghRXFyMa6+9FrfeeiuWLl2KvLy8aplkEkLgk08+waFDh7y+98Ybb+Dbb7/Fli1bcM011+CJJ55AQUFB0PtJRFTTacLk/wW0iSojiMqVoDYYtSKY2/JTfAXQfh8w9C9g0N/AyYedz1n6saHzAdqql4/tSgUOJQId9vtexgHgtTOAu8+Tnq8kSSoDksuAA0k6Gwz0MQ6DHPollPaLNekffybWakI+Q2lf7RSg/RITq/9AHD16FOnp6XgADyABJs48WFCMYkzCJOTl5SEtLc14BaIQVd3faBbu4uLi0KZNG1x66aUYMmQIcnJyEBMTA4fDgeLiYiQlJQXkGAsh8PPPP+Pyyy/Hli1bfC7Xpk0bAMBff/1lex+IiCKBmSkjP66VUemBYuztjLV8llrv7LXeQMDoDLgRs/3QabskBvipIfBTA+C5HkDvrc7Fvm3ivLWt32a1CwDq5gMbavneDQFgdWPgyZ5AeTR0j1NhrPNHV6Dz6aNvXpk85j75mUO/2q7sr79/m0bHTLVvKnENr0nTbVf2UeVv0z226+/eWz9VYtZkYGuSiCgClZaW4pdffsEvv/yCl156CRdddBEuv/xy/Pzzz/jtt9/wxhtvoFatWrZOLgkhsHv3btx88826k0oAJ5SIiIJBfWJJ5Uyv6qBPuK0b6IGianveBhpmYy8OJQHz28B1zG69EJg9Dzh9t/ldihZAUjlwNN75gHB3AsCvdYGbL3Z+C53pgWZ15lNFsPOp0hdIserg3+pkAWvSODbqC6Q40DkM1XyqYE0SEUW8I0eO4N1338UHH3wAh8MBIQQcDgf++9//2jq5VFBQgHvvvRfr1q2zpT0iIrJG7e4rOz/429G+e2x1QCCkOJA/8jYBbM4ELr8MWHIKUGHyGGoAGuU5b4lz54DzuUn3nAeszz6+oOa2knsMhVhVsHPoHldzPqtMFAQqDrUc1qCaDGoOQzWfrEn/YiKiCFdeXu6aWFq4cCFuvvlmHDx40LbnMC1evBjz5s2zpS0iIrJObWLJ14d2u2JIsQrVwZscG/2YacNi29sygCuHAk+fDRw2+Y1vdQqAfSknDuOxOOczmwpinVdFVWE02LMzlrdnxMpgPND5VPmR25D3wU6hlkN3oZZDK23L+2An1mTNqkkioggWHR2N6OhojyuTKieXLr30Unz11VcoKyuzNMFUXl6OH3/8ERUVFXZ0mYiIbMBb4cy2pzLAkmPhvG0tuxBomgs0znNOCMU6gJ1pwPrawLZ05zOO8uKBCb2d3yZ35w/AhRuBjGLf3Yp1AHEVwN4UYEcaUBwLdNgHTDkT+L2utP/e4lDKp4pg51OlL5BiX8ferjiUchguNWnUF0hxoHMYqvlUwZokIoo4MTExqFu3Lk477TScf/75aN68OQDg0KFDqKiowIYNGzBnzhxs3boV3377LQYPHozhw4fj0UcfRaNGjZRvjRNCYNmyZXjrrbcCsTtEROQntYklOz/429G+NHmjO5DwJ7ZBUhnQZwtw/Trn85NqFwJx5Se66tCA/Djg3yzgk1bArA7Ob4r7qQFw/SVAy0PAef8628g5DKS7TTIVxQAVUUCUcE4qNT8C1CoCfq4PvHIGIMwORGEyVhUKOQzWYE/ejr8TBapxqOUwDGrSp+rOYSjmkzWpFhMRRYjo6Gh06tQJ9957L3r37o3s7GzExMRUmShyOBy48847sWrVKnz22Wf44osv8M477+Cbb77BNddcg759+yInJwdZWVmIijpxI4WmaVXaEkJg7969GD9+PI4dOxaU/SQiInM0YfJaVO0xLXADKztiQP8subyOEYsDw2gH0HMbMH4lcNZ2IL7CeNzhgPPKo+mdgSldgQNJJ7YRLYDkUiC5DNCEc9IouxB48Qugz1bn5JIG4GCi81lNy0/yYz+qOwbMX/Wgyp/Bvb+x1bbN9DccYtX9NGJ1siaYOawp+VTdTxU1sCbFRH8OhL2OHj2K9PR0PIAHkICEgG6rGMWYhEnIy8tDWlpaQLdFFEh2fltZJGjQoAEeeeQRDBs2DBkZGaaOX+VDvP/880/cdddd+OabbyCEQHx8POrWrYt27dohKenEMxy6du2KG2+8EWlpaa72S0pKMHr0aLzzzju2PauJiIiMmfk31/zE0sTj/9MI5OBOtX29bVkdQFkQXw7c953zJ7VUvUkHgMldgXvPM162diHwyDfADf/n/P2OAc6JKa9XK3kTSvm0msMA5dNQMCdMvLUdSjnU21ZNzqE7f3Lo7/qsSe+qqSY5sUQUnjixZF6jRo0wc+ZM9OnTx6/jJoTAb7/9hrPPPlv3qqOoqCj06tULkyZNQpcuXSCEwH//+1/cfffdKCkp8bkeERHZz8yUEW+F04v9EFcBPLoCGLsaiHOory8AHEwCFrY+/oLBQOZAknMCanVjoMEx4N2ObrtgdiAKk7GqUMhhsMZ58nb8nfhRjUMthyFYk6ZVdw5DMZ+sSbWYiKgGa926Nd566y10797d78k4IQSWL1+OgoIC3eUcDgeWL1+OgQMH4sEHH0TLli3x0EMPcVKJiChEmZ9YCuTAyo4YUDtTb8SPgWGUAxj9I3DvGv8mlQDnt7ndex7wbRPzfSmLBua0c3srVHJidw69DXDN8mdw72/s7+RCqOShBtVkUHMobz9U8sCaDF4OiYhqqEaNGmHWrFno3LmzpUmln376Cc888wwcDnMflA8ePIj7778fqampOHLkiF/bJSKiwFP/VrhwpXmJhfS6mXV02uq603lbWryf335argGTzgJmt4fzVjYzfXF7Tcj7YyYOV0YDSr11Ah1bzUGk5dA9DtccMp+syZqQQyIiL6KionDXXXdZnlTatm0bbrvtNuzdu1dp3fLyck4qERGFOPMTS3afQbY7BqqeOVaNLUgsBx5eCaT7eYWuADC3LTC5m/Ob3oJ2ZUYoxfI+wmIcKiIphyFUk7YyyqF7HArHPpRyGI759PeYERHVQB06dMDIkSMtTSrt3bsXI0eOxP/93//Z3DsiIgoFvBXOF8U+DNgI9N3i3/hCAPi/+sB95zpvhVPevqbe35CMKw+G2XyqCOZkgD/HoKbkMFRqsjpyKG8/VPLAmgxeDomIapiYmBiMHTsWtWrV8ruNwsJC3HfffVi5cqWNPSMiolDCW+H0ZoK8reMljnYA1/zmfHC3KgFgbwpw+0BgV6r5bfpkdf1wYTSg1Fsn0DFzaE4Aa9JybKUNWaTlkzVJRFRj1KlTB+ecc47fVytVVFRg6tSpmDNnjqlvFSIiovCkfiucUIjd14Xbe6EQa1Jsob06BcBpu/0bVxTFAA/0A9Y2dHsx2MdCJZ/VnTf3WM6hTfm0pV8Icl9Yk+GfQ/eYNRm++SQiqkEaNGiA9PR0v9YVQmDRokWYNGkSKir8fAApERGFheDeCmd2sORPDINY7pO3QbavZQ3ihkeBlFLAgRNjKTMqNODVM4AP2h9v0tcgSCVW7LtyXJ05lPfTKIcqx8TKMTcatIZaDt3jGlqT1Z5D1mRk1iQRUQ1z0kknIS4uDkIIpauWhBBYt24d7rzzTuTl5QWwh0REFAp4K5ze/yO9reMl/r0u0Gck0PIQ0H4f0G4/0CwXqJcPpJU4b5GLEp6rCQCf5wBP9gTKo9W3abXPYc9oMKi3TqBj5tCcANak5dhKG7JIyydrkoioxvjuu+9w9dVXo3Xr1mjVqhVOPvlkNGjQABkZGYiPj4emaVUmnIQQ2LlzJ2655RZs3769mnpORETBxFvhfJ3RVohLooFf6gG/1Ac+bOucREooBzKLnZNLzXKBkw8Dpxx2/rfhMSAvARhzPnA0XjpO1XEsVPJZ3Xlzj+Uc2pRPW/qFIPeFNRn+OXSPWZPhm08iohpk9+7dmDNnDgBA0zQkJCQgMzMTDRs2xMknn4zWrVsjJycHbdu2RbNmzZCUlIS8vDzcdttt+Omnn6q590REFCw151Y41QG1HMv9hUIs/e6IAgrjgMJYYFca8HODE23HOICkMufkU26CWz/cBz5y/6p7UOYeV2cOA5lPs8uZiZnPkKtJ5jAIsXyM5DgSapKIqAYTQqCoqAhFRUXYvXs31q5dCwDQNA0pKSlo1KgRTjrpJDgcDixdurSae0tERMGkdsVSoGN5wAIbY5m3gYbeAEFvUGHUBzflUdJVSkZtqw7yzPRF3s9wzafqfrrzN4f+DKBV82nUF3/7FYo5dFcdNRmoHLImI7MmiYgikBACx44dw/r167F+/frq7g4REVWDKKWlVc8Sq64fyEGzkGIzAwS7BgtWj5sKo+PgbfCm0i+VdQKdT9UBn5VjrHqsrGxL5TiwJv3DmmRNqlA9JkREREREEcT8xJLRGV9vMaQ4EGfJVa4K0Ivl/moKsbf1VWLVgWd1xZV9CoUYBrFqPr2t428cyvmU+1uTcsiarFn59LaOv3Gw8klEREREFIF4K5x7P/QGCFYGdzCIjQYs3o6Nr9hMX+T9DNd8qu6nO7tzaGc+jfrib79CMYfuWJPG/Qr1fEZ6TRIRERERRSC1W+ECTR4UCIXY2/oqcShRORsvD3BUj1MgqebT2zr+xqHE7hzKxyyQWJNOrEnrcSgJ55okIiIiIgoxarfChXKsdzYaqDoIkAcQmhTLP9CJjfpmtL7RIEblTLpRX0I5NsqhnE+9qwdU82nUN6McWsmnvI5RX8IlZk2a60sox6xJc30hIiIiIopgvBXOvR/ugySZ3sDKqA8wiI0GbUYDWZVj5m0/wzWfqvvpzu4c2plPo774269QzKE71qRxv0I9n5Fek0REREREEYi3woUilbPk8gBH9TgFkmo+va3jbxxK7M6hfMwCiTXpxJq0HoeScK5JIiIiIqIQozax5Os2A7tiSLEKo4GNPAgwWk+1fZW2gtm2TD7WoZRPlX23epwCeZyDOeAOtRy6Y02aw5q0Z3094TwJRkREREQU4szfCgcE5iy2e6x6K4SVWxTk5eTty4xupdCLjVhpW/WYCbd1wj2fesvJ25fZnUMrbVs5TuGeQ3esyfDPZ6TXJBERERFRBOKtcKHI19UF3mJfEw1mj1MgqebT2zr+xqHE7hzKxyyQWJNOrEnrcSgJ55okIiIiIgoxvBXO7CAokAOqYA7W5GMdSvlU2XerxymQxzmYA+5Qy6E71qQ5rEl71tcTzpNgREREREQhTv2KJb0P5fLZXiHFkGK5DZneWWXVAVQwY6uMjqt7rEmx/APoHxeVbXnLoZV81uQcujM6xqo5lI8Ra9I4too16XudYMV2CnRNEhERERFFEHufsWT3GW2V2NvAymgArcfbwENuU2Wwrkdez32bRrGZbdl51UF15lA+xipU8yn3R4VqPs1sy99jGsr5lJcxwpoMvRyyJqvGREREREQRhrfCmR0cWBmkGQlk2zLVKxSCmU87B9tybGVbqgKdQ3ehlkN3rElzWJP2rK8nmDVJRERERBRheCtcIGKrjI6re6xJsfwD6B8XlW3pXcXgrT1ZJOXQndExVs2hfIxYk8axVaxJ3+sEK7ZToGuSiIiIiCiCqE0smRnkuMfyB3R5PcD/D+NWrigIdCyzOoBSvWpA7woC+X2jHMr59LWemf2QhUKuApVDK/k0yqG8LGvSOJaxJr0LhVyFe00SEREREUUYe69YkmP5w7hRbOYMfCCutPAW20nuo52DQatth1I+5e3qqe4rVtxjq/lUbU9uO5RyyJpkTZqNrQrlmiQiIiIiiiDqz1jSi+VbA1TfV/kAL1+BIbcvb0OVnWfDZapn6vW26evY+tt/vTbNvO8e253DYOZThdV82p1D95g1aQ5rkjXpHlvJIRERERFRhOGtcIGIZVYHUFauYDB63yiHcj59rWdmP2ShkKtA5dBKPo1yKC/LmjSOZaxJ70IhV+Fek0REREREEYa3wgXjrLPcRzsHg1bbDqV8ytvVE+x86h0bq/lUbU9uO5RyyJpkTZqNrQrlmiQiIiIiiiDVeyucHAsphk4MKTbqmzwI8NaGXntm98FsfwIVG+2bfDzlNvT2z+h91XzKfVTZV5XBt7c2wjmH7jFrMvzzyZoM/xzKMRERERFRBKneW+GMBpZ6g1lIseog1Yi8nspVAUbbUr0KQW9Zo+Mix0Zn4uUcyvn0tZ63WLWvdufQnWo+VbZlNZ+qOZTzyZpkTbIm9duW40DWJBERERFRhOGtcMEYHBgNHOVYpS9W2w6lfMrb1RPsfOodG6v5VG1PbjuUcsiaZE2aja0K5ZokIiIiIoogvBXObHtm98FsfwIVG+2b0Zl4vf0zel81n3IfVfZVZfDtrY1wzqF7zJoM/3yyJsM/h3JMRERERBRBYmxtzdsZXCHF0ImBqgMPs2fX5e15a0/lg7/qQEJu28r6qhMLqlcg6DE6I2/laodg59BdTcqh1XyyJkMrn0ZYk/6vE4o1SURERERUw6g/YymQsXy22MqASaZ6hjqQ7OyLvP96A1Bvsa927IhV8ymvr6cm5bMm5ZA1WbPyKa+vpybl00oOiYiIiIgiDG+FM9ue2X0w259Axapn4uU29PbP6H3VfMp9VNlXqwPqcM6he8yaDP98sibDP4dyTEREREQUQXgrnN6++BtbXV91YkH1KhI9RmfkrVyxEuwcuqtJObSaT9ZkaOXTCGvS/3VCsSaJiIiIiGoY9Vvh9AY4cgwpNiKvp/cBXj4DL2/b25l9vbg6GfXP6IoFvWXl91VzKOdThVE+VXNYU/Op+r57zJoMDNYka1LlfTkmIiIiIoogahNLocTojLOQYqP1zAwWzA4iVNdX3ReVWG4zlFjZb6M2gp3DQOZTr81QwppkTerFrEkiIiIiohpJ/RlLelcsyDGg9oFbXi+QV2IYbVslttqWFSpXlFiNVanmE7CWQ71tG8VW17eSz2DnkDWpvr4K1qS5bRvFVtevrpokIiIiIoowvBUuFKjepmHllg3VHMr5VGGUT9Uc1tR8qr7vHrMmA4M1yZpUeV+OiYiIiIgiiL0P7w4meeADKVY9o23mTLSvWKa6vlH/VPZF3kble6E46LE7h1byKbPy92A1n0Y5jJR8siaDjzVZY2rywbcmIS0psNs4WghMuimw2yAiIiKi0BfYW+GEFNt9NYVRX1Vi1YGFPMhwj40GZ0brq54dVzlmRlcs6MVmcmhnPlX33Uo+5TaMcqjSViCvdmBNeo/l9liT/vXNqK8qcaTUJBERERFRhAnsrXDyh3czZ6h9na2W2T24M2LlTL1RW/KxE1KsKcSQYkix0bb1YtVBr7w9mZVBqlVWc6jXltV8Avo5lPPJmmRNmo3l7clYk9ZrkoiIiIgowvBb4SrZOaCy+0y+vC8qsdxmKLGy30ZtBDuHgcynXpuhhDXJmtSLWZNERERERDUSb4WrZDRg0NtfuT15/4zakteXz5Ib9V3lmMnrqeZT5WoK1b4Z9VUlVs2n3IZRDlXaUs2nCtak91hujzXpX9+M+qoSR0pNEhERERFFmNC6FU71DHgwzxKr9kevb/Jy8rETUgydWLU/qhMLcj6tTGIY9TGY7Pz7sppPf/pjJYesSeO2WJPBV1NqkoiIiIgowoTWrXBWzobLVAcCdm7b7jP5erHVbQdSsHNoJZ+yQF6ZIcdWtx1IrMnIzqeMNVn9OSQiIiIiCjG8Fa6S0WBDb3/l9uT9M2pLXl++qsSo7yrHTF5PNZ8qV1Oo9s2oryqxaj7lNoxyqNKWaj5VsCa9x3J7rEn/+mbUV5U4UmqSiIiIiCjC8FY4s+y8gkFeTj52QoqhE6v2R3ViQc6nlUkMoz4Gk51/X1bz6U9/rOSQNWncFmsy+GpKTRIRERERRZjAXrEEKbb7TL8eo4GFHBtt28qZeiPBHMBYuTrC2/oqsSq9/Q92PlUEcxDKmvQvNsKa9I41SUREREREksA+Y0n1w7ydAyTVwZrcl0Ay2h/5qhKz6/kTqwh0PmV66zCf/mFNelfdOQzVfMpYk4GfgCUiIiIiCjO8Fc4sO69gsHqbht5gzSiWj7FqPlWuplDNZzDZ+fdlNZ/+9MdKDlmTxm2xJoOvptQkEREREVGE4a1wdpypN4qNBHMAo3qVgZzPYJ7F19v/YOdTRTAHoaxJ/2IjrEnvWJNERERERCRRv2JJJbZ7fSuMticP3KAQy+TlfF0tUh0DaKsD5lA5i293PmWqOQxkPvWwJr3HMtak7/XtwpokIiIiIopI6s9Y0vvQHcwP93YPLIz6onflgCbFMIhhEFsZtFqNVY+jlXyq5lDuhwqjfKrmsKbmU8aa9L7tYMasyfCrSSIiIiKiCBKjtLTRYM4o9jZgcF9OSHElb7HqmXmjPhpt28q+y33x9ruv9+yOA5FDf/NpZX+CnU+9vnj73dd74ZBP9+VYk/r7EKo5ZE1WXz6JiIiIiCIMb4XzNXiBQiyzesWBUXtW+mZ1sBQqgy278ylTzWEg86mHNek9lrEmfa9vF9YkEREREVFE4q1wZvuid/ZcvlIABjEMYiuDVqux6nG0kk/VHMr9UGGUT9Uc1tR8yliT3rcdzJg1GX41SUREREQUQdRuhQPsvULCn/b8vWVDpV/BuE0iVPpi5xUSqu0F8koL5pM1Ga59YU3a05fqzCcRERERUQRRv2JJRTid5bVyJl9l2UBfNWLUlhU1OZ9G71dnPu1Uk3PImgzutlWxJomIiIiIaiTeCme2L0In1qQYBjEMYiuDVqux6nG0kk+rA0UVRvlUzWFNzaeMNel928GMWZPhV5NERERERBGEt8JZ7Ue494W33djTl3DKp17/rLQdzsctlPrCmrSnL9WZTyIiIiKiCGL+iqXqOKsbzLP+RrGVH6t98HefvbVptT292Eg459Ou7fqzz3pt+tOeXmwknHPImqwqnPNp13b92We9doiIiIiIIoz5K5Yqz8ZqOjGkWH7NTBtm48q2zcZyO4D6GW7VbfoabHjrm69++rvNylj12EInru4c6h0zOfb2u/yelRzqtSVv32o+rRxnSDFr0lxb8vZZk8YxDGJvv8vvhXtNEhERERFFIN4KZ7Uf4d4Xq323kk+r/dDDfLImw7UvrEl7+lKd+SQiIiIiiiC8Fc5sbOXHah/83WdvbVptTy82Es75tGu7/uyzXpv+tKcXGwnnHLImqwrnfNq1XX/2Wa8dIiIiIqIIo3bFktGHbSHFle/5ir2to7K+3JZRu5qFWK+vMqN1/Nk3M/3wtj6k2J/j5qttu/Op0he78ykze7wDkU95HejErEnWJGsytGqSiIiIiCjCqD9jSWU51Q/48voqsT8DBTtjWai0Zef2Ap3PmpTDYOdTD2sytNpiTZqPZeHWLhERERFRBDA/seTPWWDVQY6VbQqDGBZjK6z2AfD/OFs9kx/MfBrlsDrzadd27cihexxqOXSPQy2H7liT5tpgTarVJBERERFRBOKtcGZjvb7K7J4MMNMm4H19SLE/x81X23bnU6UvdudTpjI4tTuf8jrQiVmTrEnWZGjVJBERERFRhOGtcHbFslBpy87tBTqfNSmHwc6nHtZkaLXFmjQfy8KtXSIiIiKiCKB2xZIq1Q/ldl8V4M7o7LsRs/2wo+3KPpoZsFi9wkFFoPOp0je7j7nV9vTaruyvv3+bZgbyKn1TiVmTrEnWZGD6RERERERUQ0SZXlIz8QMphkEsryOkGAqx0faMqJ6519u20Vl8IcVGfdDbP7lPwiCWj7NRDoOZTzPbMEs1n/I2jHLoHlvNp2oO5XyyJlmTvmLWZHBrkoiIiIgoAkXWrXBWt6EXy0KlLTu3F+h8mslhIPMpC/V2a0JNhks+7WyLNWk+loVbu0REQRIVFYWkpCTk5+dXd1eIiCgC1fxb4QQQ4wAaHgO67QAyi4E3TwMcUVIbZvZFtU/+tu1rX7wxc2bf37a99VMl9mdQqrrfdh1zq+3ptV3ZX1/7pZpDbwN5lb6pxIGoSfe25Zg1qYY16V/blf21uyaJiIIoOTkZp5xyCs4880z069cPmZmZGDp0KPLy8qq7a0REFGHUJ5ZUzpCrDvqE27pWBhkCSC4DWh4Eem0Fzt0MdN4DZBcCWzOAuW2Ag8kK7QHeBxpmYy/90z3Lb9fZc38GmtWZTxVWB6FWc6jXF0hxJOUwmBMJrEnWpNm+QIoDnUMiogDJyMhAjx490K9fP/Ts2RM5OTlITk6GpmkoLi5G27ZtsXr16uruJhERRRj1iaVgDrD8iNvtB7KKgBeXAq0OAkllzrcqxxiNjjqXWdHMRHuBJG/H34GlPFD01p7ePoVI3rzGRkJpsGcln6o51NtWKMdGwjmH7jFr0r72rKjOmiQislliYiKGDx+Ovn37YtiwYYiOjoamec62JyQkoGfPnpxYIiKioDP/8G5/qH4gl89MawoxgFgHMGUJ8MYioGkukFhWZRHEVQC9t3rZjtGPP33yt235WOhRPWYqbcsCnU+Vvtl9zK22ZyWfFv/ulQS5Ji3tN2vSGGsy/GuSiMiErKws/Oc//8GVV16JmJiYKpNKAKBpGvr27YvY2Nhq6CEREUUy9YklXx/MvcWqH8iFFEMhhnPSqE6B80qlaOG87a0kuurJ5D5bgPgK4/Y8qOy3HMuM9kdIsdn1/IlDKZ8qrF5FYDWHKn0JdD5DKYfBvLqFNcma9Lcvgc4nEZHNUlNTkZSU5HVCyV3Hjh3RsGHDIPWKiIjISW1iyc4P/na07x4LIKEcSC11vpxRDDQ45pxcOpAElGvAvmTg8xxgVkdpDCAPMgL9I28TUmzlOKgORFXaUOVHDm2LIymfKuupsliTluJIymGo5pM16V9MylasWAFN05Cbm+tzmYkTJ6JTp05B6xNRKMnIyEBcXJzX94QQqKiowO7du7F06VIIIbwuR0REFChqz1iyc5DhLfb2u1kakFTqfGj38V+RUAHkHAK2pwOvngHM7ATsSgXKo44vIKB2BtzXWX+jWLXtyh0IxOcCb9sKVOztdz3yIM3bsZDzZtcxt9qeXttAYPMZyNjb72YZ5TPYOWRNev9dD2uSbGB0hcWECRPQu3dvw3bGjh2LO+64w6ZeEYWX7OxsxMR4/9ien5+PBx98EJ9++il27doFh8MR5N4REVGkq1G3wqWXAAllnq9FAWiaB3TfATTLRVV6AzB3dp69Ntof+ay72fX8iUMpnyr8GVC7s/MKhOrOZyjlUCWfwc4ha5I1Gax8koc9e/a4fiZPnoy0tDSP18aOHWuqnZSUFNSqVSvAvSUKTVlZWYiK8v6xPTExET169EBcXBwnlYiIqFrUqFvhMoudz1ny1mxiGTBrPjDpKyC7EFUHBPKAI5A/8jYhxVaOg+pAVKUNVX7k0LY4kvKpsp4qizVpKY6kHIZqPlmT/sXkoV69eq6f9PR0aJrm8VpKSopr2Z9//hldunRBUlISunfvjg0bNrjek2+FGzlyJAYNGoTnn38e9evXR61atTBq1CiUlUlnmIhqgOzsbJ/vxcTE4IorrsDSpUtx+eWX8+HdREQUdOYnljSc+KAeirEG1Cp0PrTbm4oooFYRcPca4JPZQLcdBuOAQF41YGUgKcdCiqs7DxZz6BHrHTdVgc6hlXzWpByq5FM+FkZYk6GXQ9akZxyhjh496vFTUlLid1sPPfQQXnjhBfz000+IiYnB9ddfr7v88uXLsWnTJixfvhwzZ87EjBkzMGPGDL+3TxSq6tatq/u+pmlo3rw5ZsyYgZdffhmNGzcOUs+IiIj8uRUuhNUqAqK8fLgXABwaEONw7nC3ncCns52TTJXPZHINRII9CLXanszq+uHCKDeq+YSNsWrbskjKoXtsNYesyerFmoxYjRs3Rnp6uuvn6aef9rutp556Cr169UKbNm3wwAMPYPXq1SguLva5fGZmJl555RW0atUKF154IQYOHIhly5b5vX2iUFW7dm3D55VpmobExETccsst+OqrrzBkyBBER0cHqYdERBTJzD+8u/LMbKjGAOrk++5+RRQQffy2cw1AdpHztrhe24Bx/YENofTYBg1Vz5j7ikPh2NuYQ499hMU4VERSDu3OZ6hgTZ4QCfn095hFqB07diAtLc31e3x8vN9tdejQwRXXr18fALB//340adLE6/Jt27b1GDjXr18fv//+u9/bJwpFUVFRyMrKMr28pmnIycnBjBkzMHnyZLz44os4cuRIAHtIRESRzvzEksrAqpri7ELvn+8FAE1UfS/WAVy0AWi/D3i4L7CyKZCXABTEOq9wMr3vVgdxVo+BZlM71R0D5o+bqmBOBkRyDlXyKa9jJJiTO6xJ1qQ/OYxQaWlpHhNLVrg/G6by6gy9hxHLz5LRNI0PL6YaJzo6WvnB9ZqmISUlBQ8++CB69eqFadOmYePGjdixYwcOHTqEwsLCAPWWiIgikfmJpTBQ28f/IyuinLfBeaPB+W1xE1YARxKBlFLgjzrA/9V3TjT9Wg8oisGJgQoCEFtpQ2Z1/XBhNKDUWyfQMXNojrf9CdccMp+syZqQQyIKSfHx8cjMzPRr3ejoaJx11lno0aMHysrKkJeXh127dmHZsmX44IMP8Mcff/CB90REZJn6rXBCIXZfF27vBSCOEse/7c2L8ijnt8X5+tyvASiNBurnA03ygLYHgMv/BApjgd/rAItbAJ/nAH/WAUqiA7AfdpxdtxKr5LM6+ucr1nTi6u4XgtyXEKxJ5TjSc+gesybDN59ERDaLj49Hamqq3+trmgZN0xAfH486deqgTp066NSpE2666SasXLkSH374IZYuXYqDBw/a2GsiIook6t8KpxJDir19wLcpjnU4vxXOm7IoIL5cd+9QGg0klXp2ObkM6LoLeHw58M104P35QJ0CL/urN5gyO+ASNrWHAMZAQHOovJ9CJ4YUWxkQBzufZvrrbwwprs58yv2zmkP3uLpzyJqMzJokIgqA5ORkJCcn29qmpmlIT0/HhRdeiBkzZuDbb7/FgAEDbN0GERFFDk0IYeojsfaYFvhBkoU4tQRY+1+g5aGqfT+QBOxJBTrs871/P9UHWh0EUrxcDSwAVGjAqqbAtYOBnem+2wk6q4NHIGRyaMv+VMbhpCbl0M58hqualE879icc8+nnfooJ1T/DdPToUaSnpyPvLSAtKcDbKgTSbwLy8vJse8YSUXUw+ra16ta+fXusWbPG9smlSkIIFBUV4ZZbbsGsWbMCsg0iIgpfZqaMasytcIllzucjeVMcAyQY3D6uwXm7nDsB561vPzQCpncGFrQCjsa7vWnXfsgDlWAPIlXyGSoD3sq++Yqru18Icl9CsCaV40jPoXvMmgzffBIR2SwrKwtxcXG2t1s5obR27Vq88MIL+OKLL2zfBhERRYbgfiuc2cGSH3FKKZDkY/KoJBpI1LkVrrKblQ/4FgDy4oEvTwbePg34tonBA7zl31VjeeAjd6y6B2XucQBzqDyg9nW8vPUdBrHZ5ZjP0M0ha7Jm5dPscqGUTyKiAMjMzER0dLRt7QkhcPDgQXz22WeYPn06fv75ZxQVFdnWPhERRZ4a861waSVAgo/Jo7JoIL1Ef/2o4wOD3anA3DbAjE7Ob4crj0LVwY7MrsGP3nYCNfgKJSqDOLv3MxA5NJtPu9sOFWZzaDafqtu2Oza7Dbu3U51Yk/a0TURkQZ06dWy5XU8IgT179mDatGmYNWsW/vnnHzgcPr42mYiISEGNuRUuqwiIlW5lq1QaXfU2N/eulUcBG2sBH7YDPmwLbMs43ry3/QrEfsgD7FC94iGYfTITazpxdfcLQe5LCNakchzpOXSPWZPhm08iIptlZ2f7va4QAhUVFfjzzz8xe/ZszJ8/H5s2bTL1vAwiIiKzasytcLULT1x1JKvw8q1wAkBBLLC6CTCtE7C4BZAfhxMDEPf9NorNLucrlgc+kOLqHpS5xwHMofKA2tfx8tZ3GMRml2M+QzeHrMmalU+zy4VSPomIAqB27drKVyxVPj/pu+++wzvvvIOlS5ciNzc3MB0kIqKIp3bFUqBjecAC83F2YdUxSaVyDYgRJ1bbnwx82hKY1hlYV9/5DCYA+gMEvUGFUT997a+ZtlUHeWb6Iu9nCObTMDZaDlDLp5n2zMZW82nUF3/7FYo5dGc1h6xJ45g1GdiaJCKymaZpqFOnjunlhRDYu3cvlixZgnfffRc//PADiouLA9hDIiIi1WcsWT1768+He5iLaxV636RrEQH8kwW83x74oAOwKRNwVLbjbftG+6IimGe9jQZZVs/Gq6wTyIGsmfftzKfV46ZCZdAcwjVpKkfu2wp0Dt2xJlmTKlRqkojIRlFRUaZuhauoqMCGDRvwwQcfYPbs2di6dSufn0REREFTM26FA1C3oOpJYwHn85P+rA28fSqwsJXzaqUq/fPWXyjE/qzjHsuDMEixHcferjhQOfQj5z6Pl7e+wyA2uxzzGbo5ZE3WrHyaXS6U8klEZLOYmBhkZmZ6fU8IgbKyMmzcuBEzZ87EzJkzceDAgSD3kIiIqIbcCqcJILvAs6n8OODbJs5vd1tyCnAsHsYDDb0Bgt6gwqifvvbXTNuqgzwzfZH3M8TyaSo2Wg5Qy6eZ9szGVvNp1Bd/+xWKOXTHmjTuV6jnM9JrkojIZsnJycjKyvJ4TQiBgoICrFixAtOmTcOKFStw5MiRauohERGR6q1wgebnoCnaAWQXOX/dnQosaA281wH4pZ7zG+EAhNeAQOUsuTwIquQr9vZ7oKjmU2WdmpRP1RwGK39m++BvPmtSDt1j1mT45zOUa5KIIkpmZiZq1aoFwDmhtG/fPixatAjvvPMO/u///g+lpaXV3EMiIqJg3woXoDjWAWQUAfPaAPf3B7ZmHH/LfXnVM/feaD5iL79HO5zPfWp4DPijDlAW5WM7ZgYxKoMgM8vXhNhbDq3kU/7dW2xm8OxvPiM1h3CLA1mTqvlkTbIm/ckhEVGA7Nq1C1OmTMHHH3+MLVu2VNvzkxISEtCkSROcfvrpaNeuHV588UXefkdERDXjVrj4ciChHHivI7Alw8fy3vbHfTlAf4CgN6g43oYGoM1+oPdWoO9W4NTdQFYRcPuFzoeG+9x3lUGq6uDNzH6GWD5NxUbLAWr5NNOe2dhqPo364m+/QjGH7gJQk34fN9Yka9Kf40REFAAOhwPPPvssXn755WrrQ0pKCh555BGcffbZaNWqFdLT0wEA+fn5+M9//gMhfP3jTkREkaBG3ApXEAeMHgisbqyzXIAHBBqApDLg5SVAn60nXhMAHv4G+Ko5sC/Z9/pVGtMbBLnH8iCokq/Y2++BoppPlXXCaYBndw6D+dnNykSG0XI1KYfuMWsy/PMZyjVJRBGlsLAQixYtwkcffRTU7WqahpiYGCQlJSEzMxOdO3fG7bffjpSUFI/lRo8ejS+++AI///xzUPtHREShRW1iye4z3KpntH0oiwKWN3N7wdsHfquDA7f30oudVyUdTnTe7tZhH9D6ANDgGND4aNVxVKOjQLNcnYkl1QGalcGa6lUBwcynSo5szKftg8VgDrhDLYfuQi2HKvvAmvRcx0zMmiQist2ePXtwww03oLy8PCjb69q1Ky6++GI0a9YMjRo1Qr169ZCVlYXk5GTEx8dXWb5u3bqYOHEiLr/8chQVFQWlj0REFHrUr1hSOWMtD2Q0KZbXk+mdVfa1TeisY0cM4OafgP8sA/akAnEVQJ2CE4scTDpxkrsgDiiMdU58jfsOGD7E7WHierwdV+EjlvfdW1t6x0VlwOUth1byabRNb+sEK7aTUT4BtRzKxyjSazIY+WRNhn8O3QWqJomIAiBYk0oJCQl46qmn0KdPH2iauX/YNE3D2WefjcaNG2Pjxo0B7iEREYUqtYklvQGsHe9bib0NRnwNps3wMfCIFkDfLc7/JpUB/2sOXLgRcGhAcSzwV20gtsJ5VVP9fKBWkfNB3q0POCehvE4sqQ7uVK9AUDmOoZxDOZ8qVCYGrA5qrQ7WzfTfbBzK+ZSXMaKaQyvbYk06sSbN95+IKMw1bNgQnTt3Nj2pVKmiogJlZWUB6hUREYWDKKWlK8+GByqGFKswM/hSGYj5aL/yCiUAyIsH9qUAJTFAaQygCaDlQedtb3/WAb48GdidcmJZn1crWRkAqpKPdSjl087Bthxb2ZaqQOfQXajl0F2QatKv2Ahr0ok1SUQUMTIyMpCYmKi8XmlpKW+DIyKKcGoTS4D+h3L57K+ZM856H+Z9DYb8GUDZFGsCiHEAJdFA3QKgy24gswjILgRqFwL18oG6+UD7fUBRDPBFjnMXDyUB5WaPttFxdY81KZZ/AP3jorItvasYvLUnC5EcmortZHSMVXMoH6MIr8mg5JM16XudYMV2CnRNEhGFIdUrlSrxG+GIiEhtYsnMIMc9lj+gy+sB/n8Yt3JFgYW4NBr4va7z1rfEMudtbj82PLHYzjTgh0bOq5qu+wVos9+57IEk53/9GkCpXjWgdwWB/L5RDuV8+lrPzH7IqimHfl2xYOeA2GoO5WUjvCaDkk8ZazL4sSxUa5KIKEzt3LkTmzdvVp4oKiwsRHFxcYB6RURE4UD9VjiV2Co7ByE2KY8C7j4fePgc57fCZRQDf9cGCmKBb5oB+5OB03c5n62kwTmZVB7lfKi36b6rHGd5QGln23JsVYjk0C9W/hbtzqGVfFoVgjXpl0AeZ9ZkcIRqTRIRham9e/dizJgx2L17t9J6ZWVlQXvAOBERhSbeCudHfCAJiCsH/qoDfNwGKNeAP+oA7fYBp+4B4hwndvPQ8SuV/qjrcy+rMjqu7rF8JYr8A+gfF5VtecuhlXxWYw4NYzsZHWPVHMrHiDVpHFvFmvS9TrBiOwW6JomIwtSqVauwc+dO08sLIbB+/XpesUREFOHUvxXO2+DU1yBHb0AEt2X9YeVstWJc+S1vyWXO5ynVLQD6bQbO3uZ8llJyKRDrqHo4imKAWR2cVyt9fdLxxswOBs3uq5mBpvt63tY3mmwIVD6DmEPLVx2YyZu/+VTNoXscoTUZ9HzKWJPBj2WhWpNERGGkefPmaNasGZo2bYqmTZuiRYsWaNu2reF6Qgh89913SExMxFtvvcUrloiIIpzaxBKgNhhQHThYHSgEaCA5YQVwza/OiaXEMuc3w0UL5+LpxZ4nq0uigSU5wHn/Au91BD7PARa0dmtWdSLAiJlBqV7boZRPeX09VgeSqowmEqzkU7U9ue1QymGQatIyu3NoZ9uhlE95fT2sSSKisJKZmYmFCxeiVatWiIlxDgnMPMBbCIGtW7di1KhR2LJlC78RjoiIFCeWVM+eq76v+gFeL5b7qOr4uhqANgeAJke9L5Yf57xaKfH4iZrY4w/zfvtUYFlz50RTFXYOBiuX1btCwdd7qldAmHnfvS27c2hDPk3HKqzms/J9u3LoHtfAmvQrNsKaZE3alUMiojARHx+P7OxsxMbGml6nvLwcM2fOxKRJk/Dvv/8GsHdERBRO+K1wZmIdFVEndlsAiBLAkPXAmsZuk0p6g0hvsUylv0KKjd43yqHZAayZ/ZAFM4dWc66aQyv5NMqhvGwk1mSw8yljTQY/loVqTRIRhYmoqChTVyi5czgcmD9/Pnbs2BGgXhERUTjirXA67dUqAvpuAZrm+m62PAo4lAocjQc+bQk0Pgo0zgNe/AL4sRHw5mmAw30FM4NFb/vmi7z/qm2HUj7l9fUE8moHb/T212o+VduT2w6lHAa4Jm1jdw7tbDuU8imvr4c1SUQUFuLi4tC1a1fcdtttyMrKMlxeCIHCwkLExMQgLi4O06dPx7p163DVVVfh8OHDQegxERGFuuq9Fc4o1tsmpNiob/IgwFsbx+PsQuDOH4DL/gKaHQHyEuCVgPMB3V81B17uCvydDdy6Frh+nXNTyWXAG1289AeKscpgzWhwqZpD99ifHOrlU+6jyr5aHVCr9lUvhmJsNYcqfawhNem1Dbv6zZpkTdpZk0REIezMM8/EI488gj59+iAxMVH3iiUhBPbs2YOFCxfiww8/xOTJk9G5c2fUrVsXHTp0QHx8fBB7TkREoax6vxXO28DBPVYd1KgMcHS0PgiM+w6IrwDKdG4WLIkGHj4H+KA9UKGd6E7lpn6u72OTRv2zc2JBjuU2jHLoHgc6nzbmsAp/BoZmt2U1n6o5lPMZATWpu54d+WRNsibdYys1SUQUwk4//XQMGDDA8BY4IQRWr16NESNGYPPmzUhKSvJ4FtPWrVt5tRIREbnwVjgv8Z+1gZ8bAN2O3z6+Kw0o8XKkvmkKfNT2xKQSANQpcP63LAr4oZGXfqlOBBix2nYo5VNeX0+wB3t6+2s1n6rtyW2HUg4DVJO2Y02yJgNRk0REIe6XX37BsWPHkJaWprtccXExnnrqKWzatAkAEBsbi8TERACAEAJ//PEHSkpKAt5fIiIKD2oP79YMYk2Kjd6XYyHF0ImBqgMmvVhIsbc2jseHE4GbLwT+OX7beUI5EOflp8ExIOcQkHb8/6txFUDb/c546SnA/9U32Tc7Y6PBpXw85Tb0cmT0vmo+5T6q7Ks/A2r32GgfVPtjZ6yybxFSk17bUPmbNNOfQMWsSXNth3MO5ZiIKEStWbMGkydPRnl5ue5y8fHxeOyxx3DhhRciJSUFLVq0QEZGBgCgqKgIn3/+eRB6S0RE4UL9iiU98kCxcsCid6bYaFBj9gy4vD1v7Sl88P+zDjB6IDB7HlCrEKhbUHWZOluA+R8CRxKBuW2dk0wXbnQ+yHtCb+d/dftgR6xyjMzwlkP32MrVDkHOoQd/BoaBzJt7rHpcreYzTGvS0rG2uj5rkjVpNiYiCmEVFRV45pln0LhxY4wYMQJRUd7PMUdFRaFLly746KOP8O+//6JOnTrIysqCw+HA9OnTObFEREQe7H/Gkt5gx4jKB3ijwbLq4MuLr08CHuvlfJB3nQLPE9iA83Kv2oVAo6PAGbtOvP72qcBv9Yx310V1X+UYbrH8mtFAUTWfKuzOobys3jaqkz/5BMzn0D2OsJoMGtYkaxIwn0N5GSKiEFZYWIixY8eibt26uOCCC3w+b0nTNCQmJqJ9+/au17Zv346nn34aZWVlweouERGFAfVb4Yw+nLvH3gYlKrHeNoUUGw2I5PUB/QGP5nx20mtnAE+f7fkcJZlDO7E5AaB+vvM197Y8Yk2KjY6HyoBT/oEUq+ZQ7odqDvXyqZpDbwNqhXx6xHIO5NjbOmbbsppPoxxayWeY16RHbJRDlXyyJqv2izXpX00SEYWBw4cP49Zbb8XPP/8MIcz/A+ZwOFBYWBjAnhERUThSm1gyIg8oVAYlqgMuSLG8PV99UtyXCg34PAfYnep9MYcG7EkF9qWc+CmOATS9PhgNTORYb1+MjoscG9HbnuqAGlIc7By6U82B2T7YkU/VHFrJZw2oySp9siOfrEnWpHscyJokIgoRO3bswNSpU5UmllJTU5Ga6uNDMRERRSy1iSVvA1O9GFD7wC2vpzeYlc84y9uWz1r72oYJ+5Od3/Dm7X+7sRXO2+Tq55/4SS41165hn4zO4JtZVn5fNYdyPlUY5VM1hzblM+BU86n6vnscoTUZcKxJ1qTK+3JMRBQmVq1ahb1795peXtM0n7fOERFR5LL3iqVgMjpzrXKFAaA7WHBowILWwMEk4FCi58+OdKBIelLV4SS3W+HMDEJU98XKlRqhxMp+G7Vh5rjrfS5SXT+Q+dRrM5QEsSYNY1l155A1yZokIgpB27Ztw9dff236qqXc3Fzk5eUFuFdERBRu7H3GkhwDah+45fUCeSWG0baleEUzYGsGkJvg+XMkAXBEeS6+K7Xq+rqxFSpXlFiNVanmE7CWQ71tG8VW17eSz2DnsIbUpG5stS0rWJPmtm0UW12/umqSiCiMOBwOLFq0CA6Hw9Ty+/fv5zOWiIioCn4rnMm+5SYAZVHAyUc8X4+tgMfzlCo04Of65tp0Ud1XOYZbbDQgUs2hfPwCuV9GOZSX1dtGdfInn4D5HLrHEVyTAcWaZE0C5nMoL0NEFEbWrFmDgwcPom7duobLbt++nd8IR0REVfBWuEq+ztAfj4tigE9aVR0jxVc4J5cqFccAm7PMt2uqf/K+qMRym6HEyn4btWHmuOsNBFXXD2Q+9doMJUGuSd1YVt05ZE2yJomIQtTu3buxbt06U7fDHT16FFFR4Tt8ICKiwAjsrXDyh3KVs/xW2T3w0IDFLYC8eM/NJJUBsQ7P35/9Emh8VL8tr2e53c+O+zpTbhQbUbkaRo7N5NDOfAZyIGmUA3mfrLSlmk8VEV6TtuWTNelfX1XiSKlJIqIwU15ejqVLl5padtiwYbjjjjsC3CMiIgo3gf1WOPnDu9EgCVKsx+7BnREB/JsJ/NQA2JwBbEsH9iUD29OBo/FAcTRwNA7ISwBO3QM0OwLE+LpdXd5f+dgJKdYUYkgxpNho23qx6qBX3p7MyiDVKjNXHpj9W7Q7n4B+DuV8RnBN+h0btcWaDP18qrQVzJokIgpDX331FXJzcw2vWkpLS0P//v3RtGlTxMXFBal3REQU6sL3WlajgYd8ZYbReiYGDCUxwHWDgOe7A7tTnQ/pLopxTjBtyAa+awL8Vtf53sQVwKezgbb7TWxDdV9UYrnNUGJlv43asDootjLAtjufem2GkmqoyYDlkzXJmtSL9dokIgpDf//9Ny677DJs2rTJcNlzzz0Xa9euxd133x2EnhERUThQe3i36tlxb2eN5TPFQmd9K6z2VY6PL7snFbhoI9B1Z9VxS918oF6B52uftgT+rOO9LZ+xt7PrZvfDiDzwUj1GRjm0M5+q/fMjnz5jeZ+stKWaTxWsSXOxUT5Zk+b7ypokIqpxysvL8cMPP5h6MHdMTAxq166Nnj174rnnnjP9jXJERFRzhdatcALmByFyHGhu24qr8H5ivbaXb19tlqvfls9BjsqARyWWj7FqPlUHkir5DCY7/76s5tOf/ljJYQ2sSct9Y016j4OpptQkEVGYSktLQ2ZmpunlMzMzEROjdo6aiIhqptC6FU4+c68Sy1QHAgrbc/jYblEMUBAL5Mc6/3ssDqhVZKJdOwcwVo+bnYKdQz/z6ZWVv8VIzmE11aThtgOdw5qaTxlrsvpzSEQUIJmZmUhJSTG9fGpqKmJjYwPYIyIiChf8VrhKRoMNt7Pc0T4GIAeSgYooIEoA0Q4godx5e1yUfIWAfLzk/shXlajshxF5PdV8qlxNodo3o76qxGbz6asNeZ+stKWaTxWsSe+x3J5RPlmT/vVVJY6UmiQiClN16tRBfHy88YLHpaSkKC1PREQ1F2+FM+v4tmIEkOjj9vP4ciC1BEgqBxIqgDiHj2+Gk/dBPnZCiqETe2tPL1adWJDzaWUSw6iPwWTn35fVfPrTHys5rGE16Vds1BZrMvhqSk0SEYWpBg0aIDo62vTyycnJSEpKCmCPiIgoXAT2iiVIsd1n+vUYDSzk2Gjbx99LKwEaHjXfjShhYpeCOYCxcnWEt/VVYlV6+29TPk3HKoI5CGVN+hcbYU16x5okIqqxGjRoAE0z/w9tfHw8kpOTA9gjIiIKF+pXLKnEdq9vhdH2jG6dOO7MnUDdAuj6KxsoOX7CJ7PI7aol+WqR6hhAWx0wh8pZfDvyqXLMfV3xE4x86on0mgxUPlXbs9I31qT3WBYuNUlEFKaaNGmiNLEUFxeH9PT0APaIiIjChfrDu4N5dtwK1b4ZDYqOX3l02Z/Ob4Xz5Wg88MbpQOHxZxlGyW0JL7HRgEvvagCj/hupSTlUzKdHbPS+SqyaT6M+G4ngmgxYPlmT3rEmiYhqpKioKDRo0EB3GSEEiouLIYTzH8SYmBhOLBEREQDeCqd0RjtKAEJzTh45UHWcITTgsV7AklNONC80QDMakATzigN5f1XzGcxJDL39tyGfSrGKYF41EuE16XdshDXpHWuSiKhGio6ORmZmJoQQrokjd0II7NmzB6+//rrrtaioKNSqVSuY3SQiohAVo7S01Q/n1fnh3mh7JgZxFRpw+0DgmbOAjnuBM3YBp+0Bmh0BEsuBr08C3jnV+Rym4hjgcCKQXApkFwI706B/dlweLLpTHWzqtWV1wBzMAbceG/JpeMzl2MzgORD51BPhNRmwfJppjzWp1o9IqUkiojBUXl6Ou+66C3369MFZZ52Fdu3aoVGjRkhOToamacjPz8fYsWORl5eHO++8E9HR0dA0DampqdXddSIiCgFqE0uA/oduwPgMtdEZa7Mf7u0eWBj0JdoB1M13vrwvGViSAyxsBTg0IL3E+Y1whxOdt8DFVwDb052TT7W1492Q+6IXezuu/saqg7rKvvjKp972ZHbn0NsxNMvX9u3sS6DyaWWiRe6v3L633+X3QrQmvebQSl/0YtakcayqJtYkEVEYSk9Px1lnnYXCwkKsW7cOK1euRFJSEoQQyMnJgRACmzdvxvfff48zzjgD5eXliI6OhhAC+fn51d19IiIKAWoTS1YHud4GD+7LCSmu5C02M3jQG0gobrtuPvD1TCCrCCiIBcqjgG0ZwPChwIEkz7YOJgF3XABM+Mb58O6j8VKbct9kqvumEgcih/7m08r+WMyn8r7r9cXb777eC4d8ui8XwjVpad/lvnj73dd74ZBD1mT15ZOIKMycdNJJmDNnDhISEuBwOPD/7d15mBxV2Tbwu3r2STIz2ROykAQCWQiBBIQIQlhkFRRBWQOyC0jYBdRXIooiyIfiq6AvuARcUFlV9gAxEBJIQMIWlpAAIQuZLLPvXd8fNZP0nKmuc06dqurqrvt3XXP5zHTXqafqySN9auvOzk50dnbi+OOPx/z583u9980338Ttt9+O888/H0VFRVi/fn2OsiYiojjhrXCKE5mStHNL2+AWYGj3S1Xt7g/yti3glVHAiSc7t8LVlWWM1bNO8ey6OGHKZDqB7pWcQRzE8kEJerIucrtaQaxbVPX0kuCe9IxFuvVUGY89qZdHUnqSiCjP1NTUoKysDMXFzrSgtLQUnZ2dSKX6Poq1oaEB3/3udzF//nxYloXVq1dHnS4REcUQb4VTzGVAG1DeuWO4tiJg5ZDuq5GybHtbsfPjmotX7LZf/ca6kzpxO8R6eq1PFHQN3fahqmzrDzKXsOppcqBFzFcc3+138bWY9qRrDU1y8YrZk/JYVyH2JBFRHrIsC5a14//EbNtGR0cHurrcvwY5nU7jnXfeiSo9IiLKA/oHlkzPiAd1tt1PHqp5ucRtxcALY51nJ70zFFg+EnhjONBQGn0uOY39vhZEDXUmr7ncJ7nMJUE9GWgcp1zYk8Hkkst6EhHlkebmZrz77ruor6/H22+/jRUrVuCdd97B8uXLc50aERHlCf0DSzp0z87nkiS/DwYBXzrNebZSr/dEceWGyVUrQSqgevaphWkNw6xnkAq5hpkxezL/65mUniQiyrFly5Zh//33R0dHB9ra2nKdDhER5aG+N0/LeN0eYElicRmdscVYZX0665bkYgPoLHIZr2cCozP5gST2c2tGULHufjSpp24NxTx0uNUtM9atYaHWUxTjnpQepGBP9sWedF930DERUR7p6upCY2MjDyoREZFvvBXONI98z8U0d5N6mubhhfVkT+ZrLuzJYHLJZT2JiIiIiBJE/YqlXJzVjfKsvyw2+THNwe82u41pOp5XLJPP9QxqvX622WtMP+N5xTL5XEP2ZF/5XM+g1utnm73GISIiIiJKGPUrlnrOxloeMYRY/JvKGKpxz9iqsTgOoH+GW3ed2SYbbrlly9PvOnti3X0LjzjXNfTaZ2Ls9rv4mkkNvcYS129aT5P9DCFmT6qNJa6fPSmPIYndfhdfy/eeJCIiIiJKIN4KZ5pHvudimrtJPU3z8MJ6sifzNRf2ZDC55LKeREREREQJwlvhVGOTH9Mc/G6z25im43nFMvlcz6DW62ebvcb0M55XLJPPNWRP9pXP9QxqvX622WscIiIiIqKE0btiSfZh2xbinteyxW7L6CwvjiUb1zKIvXIVyZbxs20qebgtDyH2s9+yjR10PXVyCbqeItX9HUY9xWXgEbMn2ZPsyXj1JBERERFRwug/Y0nnfbof8MXldWI/E4UgY1FcxgpyfWHXs5BqGHU9vbAn4zUWe1I9FuXbuERERERECaB+YMnPWWDdSY7JOm1JDMPYhGkOgP/9bHomP8p6ymqYy3oGtd4gapgZx62GmXHcapiJPak2BntSryeJiIiIiBKIt8Kpxl65ioI+GKAyJuC+PITYz37LNnbQ9dTJJeh6inQmp0HXU1wGHjF7kj3JnoxXTxIRERERJQxvhQsqFsVlrCDXF3Y9C6mGUdfTC3syXmOxJ9VjUb6NS0RERESUAHpXLOnS/VAe9FUBmWRn32VU8whi7J4cVSYsplc46Ai7njq5Bb3PTcfzGrsnX7//NlUm8jq56cTsSfYkezKcnIiIiIiICkRK+Z2Wwg+EGJJYXMYWYmjEsvXJ6J6591q37Cy+LcSyHLy2T8zJlsTifpbVMMp6qqxDlW49xXXIapgZm9ZTt4ZiPdmT7MlsMXsy2p4kIiIiIkog3goXVCyKy1hBri/sehZSDaOupxf2ZLzGYk+qx6J8G5eIiIiIKAF4K5zqGXfVPIIYuydHlQmLypl9v2O75akT+5mU6m53UPvcdDyvsXvy9ftvU2Uir5ObTsyeZE+yJ8PJiYiIiIioQKjfCtdD5/YISzM2OfstYzqeznaLsW4uthCrLucnjlM9dURdT5Ncwq5nnGrInjSP41RPHexJIiIiIqJE0r9iKexJVVQf7HM9ORDX43VVgVdsC7HbeF7bFIda5erARJBM6qlbQ691xTmWyecaZsbsyeDGM5HLniQiIiIiShDeCsfbbtSFXU+d3HjbTTxryJ5kTya5J4mIiIiIEoi3wqmOp3ubhtdEQ7Z+W4hVl/MTx6meOqKup0kuYdczTjVkT5rHcaqnDvYkEREREVEi6V2xFOQH/yDGz4xNJsC5mByI6/G6qiDIONtZ9jjUM4waJqWeUIx1sScd7En2pEpMRERERJRA6geWwpxYBRED2SdpbsvImE4MdeIgJqZxqUOYNXSb4KoKu4aZcZJryJ4srHoC6vtNVyH2JBERERFRAqkfWPL7wRwase744rq81iuSTaAQYiybtGROUmSx2xi2R6yyfLY47HrKauhWH9UJMUKMTeupW0OxnnGqobgu9iR7EpL3hxHnqieJiIiIiBKIt8J5xWES15P5u9+JvEpsMsHTFYcaJqWeUIx1sScd7En2pEpMRERERJRAvBUumzCvGhDjICamcalDmDV0m+CqCruGmXGSa8ieLKx6Aur7TVch9mScnJ/rBIiIiIgoKfSuWMpnlktsC39XWSaM2GQMkeny+UI2ofRaJuyYNVTDnlRbPl+wJ4mIiIiIEknvGUtBnkEOOgb6njnWjeNC96w6CiQWtxGGcVwkqYbsyXjs+zjVMB/r6XefERERERElEG+FyybKiaTpPrACGifXMaC+33RFeTAgyTVkTxZWPQH1/aarEHuSiIiIiCiBeCuc5fJer2XCiE3GEJkuny9kE0qvZcKOWUM17Em15fMFe5KIiIiIKJH0b4WzNeLMZZHxWhxiS4hznQtymItOPXO9rzJjsYZJrid7Mv9rmBmzJ/O3nkRERERECRPtrXCqkyU/MSSxmJPbJDvbe3UnLbpxkOPp5q4b57KG4nbKaqizT0xrmBmbjq2Sb1Axe5I9yZ5U2zaVmhARERERJRBvhbNc3uu1TBixyRgi0+XzhWwy6LVM2DFrqIY9qbZ8vmBPEhERERElEm+Fy3ZGO+pckMNcdOqZ632VGYs1THI92ZP5X8PMmD2Zv/UkIiIiIkqYlPI7/XzQhhC7fcCPKoYkFvO1NGK35XViWX65npRl5pvLGoZZT7dl/MZxrqeYbyHVkD1ZWPV0W8ZvHFU9iYiIiIgSqHBuhZN96HebDMEj1l130LHqOoJeTy7pTOKC3s4waqhaz6DHjgv2ZDDrySX2ZDBjExEREREVMN4KZxuMEWQuyGEuOvXM9b7KjMUaJrme7Mn8r2FmzJ7M33oSERERESUMb4UTc+yJLY3YbXmdWJZfridlmfnmsoZh1tNtGb9xTOuZmWJB1pA9WVj1dFvGbxxVPYmIiChSRUVFKC0tzXUaRImnfmBJZeJgGosTFivAWPwR32cLsfgDj1i2XbLl3SaLmbHOJE+2P8T1yXI3icOup9f7dOsp2xZZDYOsp87+UMi9fzuw36fAD58FqtoUtjUzZk+yJ9mT6vuAiIiIIlFRUYGjjz4af/3rX/Hwww9jxIgRuU6JKNHi9Ywlk0mT7H2yCUScZJtwusXiZK2H6n4KU9A1zIwLqZ66NVSsX1kXcMbrwEXLgN1rgfJOYHUNcM8MjdzZkw72pDwupHqG1JNERERkbvz48fi///s/HHDAASgrK4Nt2zj33HNx00035To1osTSuxUuzrFs0ieeyZadkRZ/4BHLcpMtL+Yj5pxtspNt8pavserEHdC/wkBWT1lushqa1FNcRpaLYjxtI/CLJ4AZ64H+HUCRDVyxBBjaFPy6XGP2pFoucY7Zk2q5EBERUWT2339/zJ49G+Xl5bAsC6lUCueddx7GjRuX69SIEou3wmWbIIo/8Ihl2yVbXpyoQYhVrgZQ3R/i+mS5m8Rh19Prfbr1lG2LrIZB1lNnf3jk/uFAYNXA3qufVAvMWaGx3exJ9iR7Un0fEBERUeiee+45fPjhh73+tvPOO+Oyyy5DKqU+vSWi4MSr80wmDm7L68Rx4jbxyRaLkyDd/RQm3Xq6LeM3jpOgayjusyy2VAD/bxbQmdHlRTZwycvA6HrF3NmTDvakeRwnOepJIiIiMrdhwwbcd999sO0d//G1LAtnnnkmZs6cmcPMiJJL78BS5lnZMGIIsQ7ZxEacBMiW0x1fZ6woxxaJ+zpO9dTZdtP9FOZ+jnLCLanDA1OAxWN6r27cNuDCZd1vZU9GP7aIPRnM8l7y+SAYERER9fHHP/4Rn376aa+/DRw4ENdffz3KyspylBVRcukdWNI9c6sbi7crBDmpEn/E9wHeEy6dKxZkY8nGzrY/VGLZ/jA5Ex+3enq9D0IsCrqGXmMHXUOxnh51aCwFbj4QaMl4TH8KwNn/BXbd4r2scW7sSfZkEnuSiIiIQvfxxx/joYce6nPV0tFHH42jjjoqh5kRJRNvhYsjnQmW7qQUiG67devptozfOE6CrqG4zzxWO2ErsMdnwNLRvRfZqQG4dCmQko3DnnSwJ83jOMlRTxIREZEZy7Kw00474cILL8TBBx/c5/WysjJcfvnl6NevXw6yI0quYvlbMmT70B1U3LMOPx/QZRMb00lQmBMqcTmd/aZLPBPvZ51h1VNnv0ZdTx269TQhjmcDJWlg91rg/FeBE98GRjQCD0wG6suA6rYdi536BvD7vYHXRnjkCLAn2ZNqMXuSiIiIQlJaWorp06fjzDPPxHHHHYfRo0cjlUrBsnr/x9uyLBxwwAE46aST8Mc//jFH2RIlj96BJcD7A7/LJBfIiC0hFpcTed2akG2d8FgmqtiU2361s8SA9z6U7RedCZdbDU3qKVun2zL5UsNMsnoCejUU91H3/9a0ADPWA5cvAQ78GKhp3fHW/dcCL40Gjly142+DW5z3nnt89wO+2ZPZsSfzv4aZwupJIiIiClRVVRUOPfRQnHvuuTj44IPRv3//PgeTRMXFxbjmmmvwxBNPYOPGjRFlSpRs+s9Y8optIdZ9XScWx1SZGOh8+NeZ3JmuS1zOEmKvyaO47eIPJDGEWPZenbFksW4NTSZwuvWEwbp062lYwxGNwBVLgOf/ANz6FDB7DTCwtXcao+uBtmJgc0Xv1L76jnPQafu4MIjZk+xJHYXak0RERGTMsix87Wtfw7PPPov7778fxx57LAYMGCA9qNSz7OTJk3HhhRdmfX9JSQlKS0uDTpsosfitcNli3fF1xopybJFsghVkLK5PRmfbTfdTmPs57BpmmFQLPPoX4GdPAtM3Os9U+qi67/ApOAeQFo/t/Vq/duDqxUBZF3rVqiQN7FwHnP4G8MUPFZ7F5IY9qYY9GczyXiLsSSIiIjJ35JFH4s4778SMGTNQWlqqdEApUyqVwje/+U1MmjRp+99KS0sxdepUzJ07F4888gj++Mc/YsyYMUGnTpRIvBUujNiU235VvYLAbSyv/aJz1YBbDU3qKVun2zL5UsNMsnoCejXsjsdvBf70ALD3hh0vV7cBKyqAujKgpq33MMOagNJO4NMBwOiGHX8/7EPggI+BheOAPTcCx74HHLIamLoJGNLsfLPcj78A/HK/3t8ul6h6sifzv4aZQupJIiIiMjd58mT87//+LwYNGqR9QCnTiBEjcNFFF+H222/HrFmzMGfOHOy///6orq6GZVmwbRsTJkzAueeeizfffDPALSBKHsvO/I5GrzfO625qncmAykQlrElLnISZu+nYca5nnIS5HT7H/vpbwJ8fAIqE9XxYA6wYDnz53b7z3W1lwPPjgMM/BN4fDCwYD7w0Blg1CDjyA+D6Rc7BKXG5jhTwlz2Aa78IbOgf0n6IEnsymFxzKYY9ac/L/Y6qr69HdXU16gBUhb0uANUA6urqUFUV9tqIwmMycSWiYFmWhbvuugvnn3++r97smdp2dHSgubkZzc3NKC4uxuDBg10f9m3bNlavXo0rrrgC//rXv5BOpwPZDqJConLISO9WOKDvmV6v2NaMTScKXttracZBcjs77hXr5GI6dpzqKa7XS9T19No3pvXUHa87XrQz8OHAvsPtXAfUlwtXF3Uv1r8deGM4cPxpwM/3d255u3Qp8MhfnAd7V3a4p1qSBs5YATxwP7D3eiFv9mSwY7Mn1cSwJ4mIiMiMbdt49NFH0dbWJn+zIJ1Oo7m5GV1dXUilUqiursbIkSMxbNgwFBUVuR6osiwL48ePx7333otvf/vbqKysDGIziBJH/xlLXrElxLqv63yAt4RYHF9chy7dSZLOunQmjrJ1Ztu3fvP3GlPl9cw46BpGWU8dpvX0WcP1/YHfzgTE8yopG5ixDnh76I7VpgG8Pwj4zuFAQylw+xPAPY8Ac5cCsz9yDkbtVgusHJL9OEIKwKy1wCN/Bb72dsaVUuzJ3jF7Uk0B9iQRERGZe/bZZ/Gf//xH6SqJTJZloaKiAkVFRSguLoZlWcoP+66qqsKNN96I3/72t9hpp538pk6UWPrfCuc1KbGFWJyoiMsB0U0cooxFphMokysYZK/LaijWM9tyKtshikOtwqqhST1lNcyI508H3h7Wd448qRb4uAZoKwLW9Qdu/gLwvcOAo94HfrzAedB3sd07zZ0agIYyoLkke+oWnG+Yu+cR4Lv/6X7ot+p25TIWsSfdxaFWed6TREREZKalpQW33347Wlpasr7Htm2k02k0Njaiq8v5QGpZluvtbqpKSkpw2mmn4ZFHHsHUqVN9jUGUVLwVLoqzzmKOQU4GTceOUz3F9XqJup5e+8a0nrrjZcSf9QNu/bzzDKRMRTYwqAW48ijgm8c5D+2+cBnwuU/7PpOpRwrA5Frn+UxeZbABbKkASruAkp5vk2NPBjc2e1JNTHuSiIiIzD377LNYsGCB61VLtm2joaEB69c7z2dIpfSntNnYto0hQ4agX79+gY1JlAR63wonm4i4nWn3el0We60TQizLzRZitzG8xgsqbz9n2XUmgrJt061hZuynhl77RcxRZ1t1Jt9uY+jmGmU9dbbNAv4+FfjSe8BJbwNpC1hTA/xrN+CR3Z2HdM97HhjaBDSVAG8NA3beBgxvcp8HD2wBbAvYVAkMbXZWk7ac2+s6ipxb5eZPB/42Ffi0ynkve1ISsyfVxtbNNa49SURERMba29vx4x//GPvssw9GjBgBAOjq6kJ7ezsaGhpQUlKCESNGoKioyHhdtm2jq6sLK1euxPz58/G3v/0NH3/8sfG4REnCb4WLQpi5m44d53rGSZjbEcDYO9cBl7zsPFdpbRXwxQ+db40bu633ZYmdlvOe0i5g4hb3q5caS4CndwEaS4GF44BPqpwDU2urgddGAPVlIe2HKLEng8k1l2LYk/xWOKL8xG+FI4qvk08+Gddccw02btyIESNGYOLEiejXr5/y85NEtm2jqakJW7Zswbp167B69WqsXLkSK1aswMKFC7F169YQtoIov6kcMtK7YgnQO3NregZbd6Lgtb25nECJOWae4XeLdXIxHTtO9RSX9xJ1Pb2217SeuuOJYwPYWu7cFje51nn20S5be1+U0aPYBqZ9Bnw6wDlItMdnznOSMt/br8P5drg5X3WuctqOPcme9MKeJCIiooCtXbsWlmVh1qxZqKmpMT4Q3NTUhFNPPRVLlixBQ0MD2tvbtR8STkR98VY41fGCyjvz/wtVY53JmmzbTK6A8FNDr/0i5qizraYTat1co6yn5raVdwI3PQtc9IrzrKX3hgDLdwImbXIOEon/+bUAjGoAqtqc5yntstV5HlPm+4Y1AdWt3QeW2JPsSZWYPUlEREQBKSoqwoEHHojf/e53GD9+fGBXFpaUlKC4uBi1tbWBjEdEDv0rlrxkm/hkxvCIAbNJjc4ER2Vb/Mamy+vEupNcGZWJrM66wjzQoKOQatgdF6eBmeuBy5YAJ77t3NZW1AVM2wjUlQH/HQFUtwG71wIl6b5z6ap2YO8NwHuDgW3lwPitzmsPTQauOQJYN8AlZ/Yke1Icgz2pV08iIiLKqqioCLNnz8Z5552HI488MpCrlHq0tLTgBz/4AR5//PFAxiOiHfQOLIX9od70w7zXB/s4TQ6CzEXc/mwTQr8TxSjrKS7vpZDqqVFDywaGNQKHfQic/CYwew0woL3vQaOaNuDznzgP2F44Dhi3DZiwte8zlUrSwJRNwMfVwBvDgddHAJcd5Rxo8rVt7En2pM7YYYmwJ3vFRERE5MvAgQNx1FFH4aCDDsKpp56KqqqqwA4o2baN5uZmfP/738cdd9yBzs7OQMYloh14K5zqeEHlLR4BUIl1JkiybdOtYWbsp4Ze+0XMUWdbTSfUurlGWU+XbbAA7L4JuOQV4Nj3gLF1zje1ef3nNgVgdD0wotF5YPdTuwDTNwAjG/umNLoe+OduwLVfBJrF29/Yk+xJlThhPekZExERkZJRo0bhlFNOwVlnnYXJkyejqKgo0Afq27aNzZs34+qrr8Z9992Hrq6uwMYmoh14K5zXtviNTZfXiXUnuTIqE1mddYV5oEFHHtewtAu48iXntrUhTcBODfKDSj0sOFcl7bkR2GUL8PIo57lK+691bpPrWd0TuwL/c2jGQSX2JHuSPekvJiIiIiWjRo3Cww8/jBkzZiCVSskX8KGxsREXXnghHnroIT6kmyhE+rfCZZtwqBxEktH5AC87gKU7+colkysWMseAy99kE0Xdeoa5XbIaiu/1Wkcu+akn4FrDkjTwnUXAdS8AJV3OlUf/3M05yDR+q3NVkmpK/TuAQ9YAG/oDz40HBrcA+60FWoqBebMzbn/T2Rav7WJP9v7fbGOxJ8MXYE96jkVERERKysrK8MMf/hAzZ84M9AolANsPINm2jbvvvhuPPPIIDyoRhSx/b4UTzzKrHNjyOnMv29ag8tYdS2din03mayYTZj819NovQdcwyHp6rTfsembY51PgqsVAWfdVu1M3AbttBt4dAiwdDUzetOPKIxUWnFvhjnsXOPfLwC8/53xD3GsjFfP3yps9yZ50GyObPO3J7VS3k4iIiPo4/fTTcdpppwV+UAkAli9fjldeeQVjx47FzTffzNvfiCLAW+G8tsVvbLq8ThzEJDdT0BNqnXoGXcNMeVrDsXVAZceORSwApWlgj8+AphLnCqbKDmD3zc63xKn+p7m12PnWuBXDJTmIObMn2ZPiGAnrSeN6EhEREfbbbz+UlpYGPq5t21iwYAGuv/56lJeXo6WlJet7U6kUSktL0draGngeREmjdzOrbIIjxoD6TFd8r8pZfK8z9pYQZ1tHHMjyE7fFa7tkr+vWUKynDlk9dWtYqPX0eH3xWOCTavdV9O8A9l0HDGoBFo8B1g1Qn9tuK3duiZOp6ASOWAXMXercOpe5guKekz/sSfZkodVT93UxJiIiIk8LFy5EOp0OfFzbtrFixQrYtp31oFJFRQUOOOAA/OY3v8G///1vHHrooSgqKgIAlJSUYNSoUYHnRVTogr1iKUrixAdCrHumOsgz1LrLy/IzOdPe81ocJz1B19CkniKTfw+m9cwYa20VcP9U4JrF7iW04NzaNrQZeGso8OFAYO/1QL+Ovu+3AXSmgDeGAXfuC2ypcBkQgGUDQ5uAI1cB33gN+PwnzgPEv/4W8K1jgI9qgDmvO7fQfecwoCvbwQ/d7RZj9mT02JP+ahjXehIREcXU0qVLsW3bNgwePDiQ8WzbRltbG1599VUsXbq0z+uWZWH48OE4/vjjMWfOHOy9996orKwEAMycORN33XUXli9fjjPOOAPjxo3DUUcdhfXr1weSG1ESBPuMJTEWP6D3vJYtFpc3EfTEQ2c8cft0xxInKyaTMZHsigWvWKWGQdYzzImkaQ11xtKtZ+YqLGDBBOdb4Yqz7EcLzkO+p28EtpYDS0Y7B5qmfgYUdS/TVuR8I9zDk5zfB7V0p5WxjWVdwF4bgJPfBI5/Fxi3rfe3z+2zDrj2Bec2vEm1QF05MH868NYw97xcE2VP9sWeVBsjJj1JRERE5tauXYu33noLBx10kNE4PQeU1q5di9raWrS3t/d6UHdxcTGmTJmCOXPm4MQTT8TYsWORSqV6Pdupuroa11xzDdLpNIqKimDbNr72ta/hjjvuMMqNKEnC/VY4Px/ew5qMmZJNZLxi2ViyCaDsDHlmDCEWf89WF9V66k6ovfaDyYTalGkNvcYyrSd6x2tqgMZSoKbNOw0LwKBW55vf3h0MPLkrMGUT8N5g52DTqAbnlrYx9c4zlt4YDjy+q3O725dXAue+Cuy/NvvVTm8Mcw5eTartXlcLcPHLwNxjuq9aYk+yJ03kUU/2+V18jYiIiDy1tbVh4cKF+MIXvmD8AO/Ozk6MHj0au+yyCwDghhtuwGWXXYbRo0fjxhtvxCGHHILq6mrP9aRSKaRSzlNiLMvCOeecg/nz52Pbtm1GuRElhd4zluJENvGwhVi2XLZJgkos0l1ed1t0YnHMODHZbtkYUdcwxHp+XA38c/eMW84kUjYwYavzMO8/TQP6tTvfLHfuq8C4OucqpsoO4M5/AVcvBvbcCNz4HHDYaue5TW6r6Ug5z3qauHnH6xaAU990rnLytd1e7+1Zgd9YxJ5Uw570V8O41pOIiCjG/vrXv2LNmjW9rjCSsW17+w/gHATq378/ysvLYVkWLMvCaaedhmeeeQY//OEP8ZWvfAU1NTXaB6+mTJmCY445RmsZoiTTO7CkewZb/FDu56ywX0FPPLy2VxxP3D7ZWOLylhDLctfZZyZXJKjUMMh6hjmRNK2hzli69RS0FgOXHAtcdaTzDCWvxxzacJ7LdMHxwBlfdW5X23Nj3wNGFoCd64CfPg3c+yDw2gjnCqb2VN85sg3g/cHA1E07bq3rUdMKXP2icxBLij3pjj2547150pNERERk7u2338YZZ5yBhQsXoq2tTXqAybZtrF+/Hrfccsv2B3S7KS4uxowZM/DlL3/Zd27FxcU488wzUVZW5nsMoiQJ91vhxA/vugeioBGHTTcfr9zE94n7zhZieMS6+ege7BPraXJgUZZjlIL892VaT4UcGkqBX+wHHPwN4IZDgNU1fQ8w2XAOPH39a8C9ewK1/YD/mwls9Pj2txSAiVuA495zbp97aQywaiDQae1IozPlHKyasKXvXNsC8MUPnecx9ck9bOxJ9qTqWCH0pO/ciIiIaLvFixfj2GOPxamnnup5gMm2bbz55ps46aSTcP311+Paa69FZ2dn1nF7rl7ye5udZVn4whe+gP3228/X8kRJE69b4UzOhot0JwJBrjvoM/lesem6wxR1DU3qKQrzygwx1lj32irgRwcBB50N3Dgb+HTAjjnyqoHA6Sc6z1MCnFvgjvwAGNbksa7uZVfXOM9XOvBj50HgS0Y743WknGc0jd/a92qlHgNbnG+O65Ov24p0YvZk8NiT+V9DIiKiAtPc3IyHHnoo6wGmdDqNBQsW4IQTTsDSpUsxefJkXHDBBSguDvcLzisqKnD22WcbPwOKKAl4K1wP2WTDa3vF8cTtk40lLm8JsSx3nX0mLqdbT52rKXRzk+WqE+vWUxxDVkOdsXTrqWBtNXDjwcDBZwM/PRBYOgo440TnfwHnG97uecS5za1K8tDvLgvY0B/YqcE5eDS2Dpi11jnA9MpOzoGlneuyL29bQHuRQtLsSXfsyR3vzeOeJCIiIjNuB5iampowf/58nHrqqVi1ahWmTZuGp59+GieccEIkB3xUbtEjorh9K1ycbz0IMjfxfW77Doqxbj4q6/aqp+6kVyfHKAWZm2k9/eRjO7+uGgh85zDn6qTG0h3jpi3nSqVShWcfrakBRjY6D/3uSa3nANOYOmBzpXNwqbID2Hmb81rmf8a3lQOvjHLJUVNFp7MdWyqc/KXYk+xJ1bEi6kkiIiIy13OA6cknn8See+6JFStWoLm5GYDz7CM/D+P2w7ZtvPDCC0ZjVFdXY99998XQoUPxxBNPYOvWrQFlRxQv4V6xBCEO+ky/F7eJhVcsW7fJmXqZKCcwJldHuC2vE+vy2v6o66kjykmo5QyVeVAJAL6yEpi5Tr54GsDqgcAuWZ6flAIwtNn5Nrh3hwBvDnO+HS7zGUzvDHFu0ZPKUsMBbc431j33B+Av/3AOYPVKIhv2pPvyOrEu9iQRERFFpLm5GUuWLNl+UCmVSuGUU05BRUVFJOtvaGjA8uXLfS8/efJk/OUvf8Fjjz2Gu+++G5MnTw4wO6J4CfcZS7of5oOcIOlO1sRcwiTbHkuIVZfzE+sIu54ir2VYT890hjUCLSXyXbBuADCw1bntLRsbzsGkaRudb5gr6wTeHO48KLwjBSza2bmdrrLDueooa2ouNZyyCXj0z8Cd/wb2/RR4epfug2SZK49CrmvIngxWrutJREREgauqqsK+++6LdDodye1pH374ITo6OrDLLrtgwoQJqKmpUVquqKgI559/Pp588kkcddRRKC4uxmuvvYbXX3893ISJcojfCqcqyCsYZGf6w7xlQ9zHuvXUuZpCt55RCvLfl2k9/eTjUUMbwK8/Bxw5B3h4EtBW5L4pNoCVQ4DJm7znxGkLqK10nsGUAjC8yTnAVJwGXhwL/HN34MR3gJfuBhbfDXzvP84BJpmSNPCTZ4CDP3Lid4YCv99bvlyvDfAby8ZiT0avUHqSiIiIQrFt2zacdNJJuOCCC/DGG2+gq6srtHXZto2FCxfioosuwrJly7Bs2TI89NBD2HnnnaXLTp8+HbfeeivGjBkDy7LQ2dmJu+66C01Nkm/UIcpjvBUuiDP1slgmygmM7lUGYj2jPIvvtf1R11NHlJPQLLWyLeD14cBpJwJnfBV4YWzfA0ybK5xnMPW69czFxn7AkOYdz2ACnHhsnXNl1FtDgUm1wLTPgL02Av+zEPjOIufAUy/C9k+qBQ76aEfaD04GNlWCPcmezO+eJCIiotDU1tbid7/7HQ477DBcccUV+OCDD5BOKzxUVFNbW9v2A0k1NTUYOHAgDj74YNx7770YPXp01uVSqRTOO+88VFXteE7E22+/jX/+85+93mdZFlKpeH1BO5EJ/SuWdOKglzchW584cYNGLBLfl+0KrlxMoE0nzHE5ix90PUW6NQyznl4k+6G1GPjHFODoM4ATTwb+tRvQ1H2L3OsjgCmfyf+5rBvgHERye9+yUUBDmfOg8J7XS9LAFS8Bh67OPm7KBs5fDlRnfGNdR8//G2Vsx87bgAM/Fg5SsSeDXT4o7EkiIiKKSG1tLX75y1/i4IMPxve//3188skngd4et3btWrz//vsYMWLE9r9ZloUDDzwQ3/ve91BSUuK63OTJk/G1r32t18PFm5ub0drauv330tJSzJ07F7/5zW8wbdq0SB5EThQ2/cOkUZ4dN6Gbm2xSJN5CYvt8r1ssm3B5XQ0gy1+mkGpoUk/Z62HWU5azjML+aCoF/j0ROOnrwLGnAX+eBrQVA4NbvIeuKwfKuoBSlyuNbQDPjXPiocKVvZUdwHUvAP3b3fOZsgk45c3e6U7f2Pv3yg7gZ08Bj/0J+N/HgF17HjDOnmRPxr0niYiIKBLr1q3Dj3/8Yxx00EG4/fbbUV9fb3yAybZtvPbaa2htbcWwYcN6vWZZFubMmYNDDz20z3I9rw0ePLjX38eMGbP9Cqbi4mJ8+9vfxk9/+lOce+65WLBgAX7yk59gyJAhRjkT5RpvhcvFFQqiKK84EHPUrWeUBxa9tj/qeuqI8qoRzRq2FwMLxwFnfwVYNNZ5UHa2VGwAH1UD47e674r6MmDpaKAo7dwqJ6b1+U+AmeuFAbud+bqzTBpAbQWwcGfg/cHOg8FhOQeyrl4MfHklMKAduGA58PwfgPOWZ9ySx550X14n1sWeJCIyUlRUhOLiYl6hQBQg27axZs0aXHvttfjSl76EV1991fjg0qJFi1BZWdnrlrYeFRUVOOKII/r8fcCAATjuuOP69PegQYMwZswYpFIpnHPOObj++utRVlYGy7IwdOhQXHPNNfj73//e6+ooonxTrPVu0w/nissf8Alw8Brn1+YS5+vQP64CNvZ3rqBoVvi2K+11607oxMldJj9XOMjGCyo30wlzlBNuL0HXU6RbwzDr6cVnDTpSwK0HAO8Ndq4KGret75y9pdj5trd+WZ7B9N5gYE2Nc+vbQJcrn4rTQFVb379bAIY3Am8Oc75ZrqwLmLTJuYrptzOBTwcA334RuH7Rjm+rs+A8PPy6F4AFE5zltr/AnjRbPijsSSIiT5WVlfjFL36BcePGYcuWLaitrcWmTZuwefNmbNmyBZs3b8aaNWuwatUqdHRIHoBIRH10dnZi0aJFOPHEE3HXXXfhiCOO8PUco5aWFrz00ksYPHgw+vfv3+d1y7LQ0NDQ5++jRo1yff5SeXk5pk6diilTpuCnP/0pKioqer2eSqVw0EEH4ayzzsItt9wSyTfeEQVN78AS4P2hG5CfoZacsS7rBOY9Dxz24Y7h05bz4OH6Mufg0iOTgGfHAf8dCdSVZVm37sTCJZc+y1kesWz9Yi5esdt+9RvrTuq8cpetTyRbp+5+c9uHqlTqaZpLWPU0Ofgp5pvx9y7LeWD2mhrgob8CY+p7L7K2Cti5zr3ENpwHgrcUO1ceDWzt+5605VwRJeZR1AVs6udc6XTkB86BJcB5cHd1K3D6CuC7/wHKu3Ys1pFyrrT66x7Afp86Oad7toU9yZ6MS08SEXk4+eSTcdZZZ/V5NkvPJLKrqwsNDQ1YvHgx7r//fjzzzDPYsGEDJ5lEmj766COcccYZ+NnPfoYzzjgDxcV6U941a9bgvffew+c+9zmUlZX1ed22bXz22Wd9/j5x4kT069evz98ty8LcuXOx6667orq62vWKRcuycOqpp2L+/PlYv359n9eJ4i7YW+FsIYYQyz7M284Df2d9suNPFpxbX0q7nHhok/Ng4CfvA57/PXDeq87VEkXpHWP4XXevWBwj2zaqxOKYEGKR+D63MVRjcUzdGorboTKpUp3smWybbAIYdD29chG3T2Raz2zjAPr1FMe3gFdHAhcc7xxI6vlzZ8p5KPegLM9g6rKA58Y78cBW54CQqDMFbCvvu+7+7c4tblNqdxw8ensocPWRzi1yPQeVbADtKWDxGODaLzrfPveTZ5z+L+3y2Eb25I4YQsye7Pta0D1JRORi9OjRuO6661wnuJZlwbIsFBcXY+DAgTjmmGPw+9//HkuWLMFdd92F2bNnZ31QMBG527x5My655BL85Cc/QUuL5KGiGWzbxpIlS9DQ0IDRo0ejqKjI9T2bN2/u8/fp06e7XiFlWRZmzpyZ9aBSz3smTpzI2+Eob8XqW+FGNQA/fXrH15/bcK5K2FzhXKFQ2gWMbHSeuVLW5Tzs99f/BhbfDXz7BckJY90JOjRikfg+SxLrjmeSm24NxXqaLh+UoOsp0q1hmPX0ElBPPrkLcPLXnOcc2XCerTS8MXsam/oBr4104uGNO3o2U0tx94ElwcDWHVc42XCufJo32/mWuLlLnYNKaQBvDAMuOdb5VrtvLnNeG9oMfFIFtPf8N549yZ6MW08SEQmKiopwxRVXYOLEiUrPVrIsC0VFRRg7dizOP/983H777a634xCRt+bmZvzwhz/ElVdeibq6OqWr/2zbxsKFC2HbtuttbQCQTqdRW1vb62+WZXl+w1vPAWQvW7du5dVKlLdicytcaRfwg+eAPbqvKuyynNvcVg8EJm4GdtnqPlTPc1wen9iditeZ+cx8Zdy20/KI3dapepWAGJsc5BJj1asiMpfNlrtsfSLZOnX3m9s+VKVST9Ncwqqnbg199uRLo4ETTgbu/JfzHLPDP0RWrw8HNnZf6TuqwelD0bby7gNLwr469EOgpvvAUl0Z8PP9gRufc56xZMG5Sum+PYElY5wrmPZfu+NZSzaAp3dxbrNjTyqsT8SedF83NGPZwTIiogyf//znce655/p6YHdXVxf+7//+D1u3bg0hM6LC19HRgd/+9rdoaWnBbbfdhkGDBnn2YmNjI1599VUAwMiRI13f29HR0acn+/Xrh4kTJxrl+vHHH7PXKW/pP83M60O7LcTij/i+jDG++KHzbJW05UxGN1U6B5sGtAG25f6Z3QbQUApcdSTw+oiMP0rWpbRdbmMEFUeZS7Yx/axLd7tksek+9FJI9cw2jkqs2ZNvDwVOOhnYVgEUZdkmG8B/dnZudQOAsdvc+7O20jlAlTn+jPXAVS85t7faAJ7aBZi4BZjcfVCpywLmTwe2VAD/7wngwI93HFQCnHW+m/ltrPlSQ/ZksnqSiKhbKpXC+eef7/rNUjK2bePpp5/GfffdF0JmRMmRTqcxf/58zJkzBx999JHnlUsffPABVq9ejVQqlfWKpba2NtTV1W3/fejQofjJT36CKVOmGOX5/vvvo7293WgMolxRP7BkepbeLc7w3mDgnaHOVRClXcDwJqB/h/NNUFsq+r7fhnNQ6fKjgL9N7f5M77VOyzA2+THNQbZtXrE4pul4XrFMkOuMup5BrdfPNnuN6Wc8rxjOQd3rDgee2LX7AdmCtiLnwFLPcuPqXN4E4JPqHbesFaeBY98DHrgfmNR95XBziXNl0slv7rjw46ldnId0X7TM6X/xn5dldz9PjT3JngxqvX622WscIqIM6XQazz77LNJpt/+iZmfbNv773//i4osvRn19fUjZESWHbdt4/PHHceKJJ2L58uWuPWnbNl588UU0NTWhpKQk6/OOmpub0djYCMuyMGvWLDz66KO4+OKLUVpaapSjn2+wI4oL9X+9PQd2LY8YQqwxgdpW7jynZWSj8/XmPW/p1+E8q0Vs/ZZi4IqjgD/uBaRTGblYWWJbiAGzM9w6sWwsMR8xZ9m2ecXimJDE8IhNJ57iOk22LegrFmSxl7DrmW2cEHvyo2pgzleBe6cDncKk+ZNq5yAwbOfKozF1fefVNoBVA53/HdYE3PwM8OcHnG+Z29gP+OXngLlHAxWdzu2vFpwrnH69L3DtC+7PbAKcq6h23gbvWrMn2ZO56kkiIhcbNmzQOrBk2zY++ugjnHfeeVizZk14iREl0KuvvorjjjsO//jHP9DV1dXrtXQ6jYULFwIAKisrMXTo0D7L27aNdevWoampCRdeeCEeeeQR7LfffoEcFBo/fjwf1E95S/8ZS0FO4Lp/T9nAIaudKyHcPpv363C+tryqfccQf9jLuWUmnTk5kuUR5KTGdFIbl1xMc1d9zS0O8sCAiPU06knAeWj+JccAnw4ArlgClHc6f39lFLC1+6HcpV3AyAZnkY4U8Fk/YMVw4D/jgIcmOVcq9W8Hnp4AvDvYuVrp2fHAEauA6RuAZyY4vfzllcCvPwcc+cGOZy1lM3VTQPshn2vInpTnkst6EhF1GzlyJP7nf/5H+evObdvG1q1b8a1vfWv7c16IKFgbNmzAeeedh08//RQXX3wxysrKAABbtmzB8uXLAQDV1dWoqqqCbdvo6urC1q1bsXLlSixfvhzPPfccBg0ahBkzZqCystLX89PcDB8+HJWVlbwdjvKS+oEl1bPrOnG3XbYCtz3lfNuTjb4n1Qe3OK8NqHV+/6QKuPnA7me86Jz1tw1iEybrNd3P4phB19ClnlkFuc7MbfIT6zKtoUk9vcYMuYZNpcC8Q5yH6P94ATCkGVg0FrB7ruAAsHwn4MldgSWjnSsPRzQCu9c67x/c7HyTW0UH8PIo4C/TgLVVQEMZcMI7wMlvAX+e5nz7239HALM+AVpLgJnrgGkbgUEtOza5rgxYthNw1z4ZefvFngx+nZnb5CfWFceeJCISnHLKKdh///2VJ57pdBo33XQTHnvssZAzI0q2hoYGXHfddWhoaMC3v/1tlJWVYeXKldu/la1///7bn7f04osvor6+HtXV1dh///0xb948jBw5EjU1NdsPSpmwbRutra2499570dDQYDweUS7oXbEkHvERY90P6wAsGzhjBTCq3nm2S2MpMEA4SNuv3ZlUdqach/su2hn4NPMZiCq59EyE/cbZxnQT9MEAlTEB9+UhxG5jq67HNE+V5VXHCrqeIp3JadD1FJeBRxxCT4rLdBQB98xwHpr95ZXAI5N2rCZtOc9bmvUJcN0LzhVJA9qcq5IOW937wdvjtwEnvgN8MMg5MHzNEcA+65zlzl8ObOrn3Ga3aiDw/Djg7hnOgaxD1jgHk+/axznA1VKssB9F7En/y6uOxZ4kIuqluroac+bM0bqaoampCU888YTS16ITkZn29nb86Ec/wqZNm3DBBRfg1ltvRVtbGwDnAd21tbWYOnUqDj/8cFRVVW2/TS2IK5Rs20Z7ezs2btyI//znP/j73/+Oxx9/vM/teUT5wrIV/8tlzTNvIDcjG4FFv3OuWtpU6TxnZVJt77mFDef2msZSZ9L60wOdqxyIKLeGNgOv/sY5MJw5D39niHMb3G5b+i7TVgQ8ONm57W3ebKC6DfjOIud5S8gYo6nEOYh89RHOM534EZvizp6X+3+lPWdU6wDofweV5roAVAOoq6vz9Y1XRHER1G0sohNOOAH333+/1jNTVq1ahX333ZdfOU4UoVQqhaqqKmzbtm373w477DA8/vjjoT3zyLZtzJ8/H9dffz02btyo/YB/oiipHDLK+bfCnfg2ML77v52DWoC68h0TyDSA+lLnQcKj64HPfeo86Delc8VGT2wZxiY/pjnobKcYi2OajucVywS5zqjrGdR6/Wyz15h+xvOKZYTlSrqA4i7nYNEnVcCC8c6DuavanKuLulzGLk47Vx/WtDoP9S7vdA4WtxX1Xk3/DuCoD4C5S7v/P4E9yZ6Me08SEXUrKSnBOeeco/xspR6rV6/mrTBEEUun070OKgFATU0NioqKYNs20uk0GhsbUVtbG9jVhJZl4YADDkBraysPKlFByOmtcFWtwNn/3XF0K2U7VyRtrXAmnvVlzjNaxtQ7r9kAnt7Fuc2mz7iyXKyMZfzE2cZ0w9tu/C+vOlbQ9RTly203Afekbm6txcD3DnOuUPpgkNO7FR3Akauc29jWDXD6N1PKdh76bVtAZSdw9WLgpi8Av98LuGB576PdFoDdNncfjCqCdw3Zk+zJOPQkERGA6dOn46CDDtK6GqqlpQV/+tOf0NnZKX8zEYWqsbERCxcuxOuvv45XXnkFb7zxBvbYYw/ce++9KCoqkg+gYODAgaiqquIVilQQ1A8sqX5w9powCfGRq5wH9GaqaQWe3AU44BNgwlZnEtqz6MohwDlfdiar28cymSgEGYviMlaQ69OdsOnmUkg1jLqeXkKs4ZYK4J69e6+u3HKGmLwJeHGsc7Wh28fqLgsosoGyLufg0uVHAXtvcK5MzHz/9isU2ZN9Y/ZkvMclokSyLAtnnnkmBgwYoLxMV1cXfv7zn+NPf/pTiJkRkaqnnnoKzz77LDo6Orb/raamBl1dXYEdWEqlUkil1G8gIoqzcG+F85jklHcCFy5zrkSw4Xx9+boBzt/H1ANj6pxJZ+ac5Tf7dB9UCuJMOjRjkx/ddYk5K+zPXvsiMxbHF1/XiXUnrSLZemQ1zIz97NfMOOwaZsa69RT3i9f44ntlcdg1zFDRCVR2ABv79R0iZTsPBe8xoB24bKnzgO72jL/bAJpL0Otb6NiTGusH2JNR9iQREYAxY8bghBNO0Lpa6aWXXsLNN9/caxJLRLlj23affuRD9Ymy0ztEGuCH9QM+cb5Jalu58y1QbUXO15QPagH6tzsP8c5UVw48sauQi1deJhNB3TP9IvF94gTM1ozdtkd1O8V8dPebzj5UmXR5xUHmYjrhltUwMw66nrJcxXoGOYE2raHw69TP3B+8XdnR+wCSBef5aTs1AG9lvN+GcwVjV2Yu7En2ZJx7kogS78QTT8ROO+2k/H7btvHAAw+gvr5e/mYiypn6+vpAD/5u3rwZdXV1gY1HlEvqB5ZshR8IcZYP5RaAr6wEXhnlHEAatw0Y3rTjCqWxdcDH1b0/q78zxPlbn3VkiyGJZcvrxLL9IMay/ESq26QSm+QumxyKEzavsfK9hnGqZwA9GWgNM1S3OX+uK+v997LO3geWAOcqpq++A9w9w3nGGgBs6gc8PNlHHuxJ9qRXLAqqnkSUeEOHDsWZZ56pdXvLli1b8Nhjj4WYFREFoaOjI7AHbdu2jSVLlvD5SlQwwr2p0+XD95Bm4JvLgKPfB3avda5UKrZ7nygf2OLc/tJzlYIN4LHdnAcFK595F8nOxMt+si2nEuuOLdsWne0yGVukO8kKM7eg97npeCb11N1P4tg6wq6hBaQtYHPFjmMae3wGvDWs97AlaafHM1lwnrlWWwm8N9j525qa7lvp2JPu2JP535NEVBBKSkpw9NFH48EHH8See+6ptewrr7yC1atXh5QZEQWls7MTXV1dgYxl2zaeeuop3l5HBUP94d0qH5p7ZpI9cc9yNjCyEThjBXD2a863PBXZQBrARzXO/4rfBDW8yblaYWSjc7vcI7vvGGv7h/jM2C1PnQ/6Yu5irLNu2VhuVxjAI862jFtOsveLOYm8ctcd23Q/esUyunmI65AtnxkHUU+dGor73EvUNbSBphLgtBOBi19xHsT9WT/gqV2AJaN3PIj70+7nqY3fBoxscB7UP7oe6NfhPMD7tRHAlE3OA/vbin3k0YM9yZ6MsieJKHEsy8Iee+yBa6+9FieccAIqKiq0nq2UTqfx4IMP8tlKRHngo48+wve+9z1cfvnlGDduHDo7O5FOp7F161Z0dXWhoaEB6XQaAwYMwMiRI1FaWgoArv+fsHXrVixevDjqTSAKTejfClfTApz2BjB3KTBxc98DSIObgcZSoKq9999H1wPvDnGeu/T4ROc5LdvHdpsc6E6o3PI1iUVxGSvI9fmZZOnEhVTDqOvpJcoadltTA1z7Refh/J0p71SLbKBfO7DLVuCQ1c7BpbeHOq+9NiJjWfZk35g9Ge9xiaigjRw5EpdccgnOP/98DB06VOuAUo9Nmzbh6aefDiE7Igpae3s7fvWrX+GRRx7BpEmTtj9zad26dejq6kJLSwts20a/fv0wZcoUTJs2DXvuuSemTp2KsWPHYsiQISgrK4NlWVi5ciXWrl2b600iCoz6gSU/bOdZSr94vPc3vPWw4FyRtLnS+VaozNcrO5zb5JpKgHv3FB7e2z2255l7kewMuMK2+Dpz7mfsnhxVJiy6Z/Z1xnbLUyf2MynV3e6g9rnpeF5j9+Tr99+mykReJzed2M8VMN2xbXV/85vtMk7Ge7sA1JcDr410DiSlbGBSrXNl41O7InsN2JPsSVVR9SQRJUYqlcItt9yC0047zffXhdu2jRdeeAGffPJJwNkRUZjWrl3reVCoubkZCxcuxMKFCwEAZWVlqKmpwfjx43HaaafhiCOOwNNPP422traoUiYKnf6BJc0z5NvKnYf0Vna6D1fZAWy0es8905bzsN+SLmD5TsDLozJezNWkxW2ioRrLcoEQh3HmX3Wi6RXrTuIzixr2RDHseurkAiFOUg0D6Mm05VyxdO6XnTgr9iR7UjUXCHHYNSSigmfbNjo7O30fVAKc2+D+8Y9/BPbMFiKKp7a2NmzcuBEbN27E0qVLUVNTE9hDwIniQu/AkuYH/5QNnPsqUJHloFLP26vbgJYSwLKd5yqVdwLVrcCeG4EfzAa2ViisVzaR8BOHSVyP34mlbpxtgmc6kXOjO34YNUxKPaEY6zI5eGNST2QcVEpKDeNYT/akXkxEiTFgwADsu+++RmNs2LABixYtCigjIsoHtm3zm+CoIOmdZuk5y60YF6eBmlb5hQLlncAHg4CPq4FBLcCwJqC8y/nmqG3livMQ2STJksSyH5Uxghpb3IYgiRM8jXpqx+L6ZHQmvVHXU+dHHEPchiDFrYaZ4lZDk7HFbQgSe7KwepJ8ef7552FZFrZt25b1PfPmzcNee+2lPKZlWXj44YeNcyNyM2nSJIwfP95ojNdffx0bNmwIKCMiIqLc0b9+V2OiMbQZ2HWL+zA2gNYi4OkJwL8nArtsAXbf3PtZS9vKgQUTXBZ0i2VMz6TrTLDEWDcXcVKoupyfOMiJoxjrXu2gI+p6muQSdj3jVEP2pHkcp3rqYE9SFpZlef7MmzdPaZyrr74aCxYsCDdZIkVTpkxBRUWF/I1Z2LaN119/nbfBERFRQQj1VrjNFcDts4DDPgT2WQcMbHX+3loM1FY6z1AaXQ9M3+h8E5Q4Z9hUCazvr7hekwlwLiYH4nrEiaUdUuw2wYRirEt3/DBqmJR6QjHWZXLwhj2pFrMnw5GLepKr9evXb4/vv/9+fP/738e77767/W/9+/fHsmXLpOP0798f/fv3DyVHIl1LlizBkiVLsOeee6KyslL7G+Fs28Zrr70WUnZERETRUr9iqecDtK0etxYDtxwAfOl04MBzgZ8eALw0GmgoBYY3AsObnK8Yr3Q5qNRlAc+OB5pKFddrCTGEGND74B/mVQNi7GedPbE4wdGoT+xiWQ3FeuoIu4Ym9SykGrInC6ue7Em9fUauRowYsf2nuroalmX1+lvmwaLly5djn332QWVlJT7/+c/3OgDldivc7373O0ydOhVlZWUYOXIkvvWtb2XN44YbbsDIkSOxYsWKwLeRkmflypU4/PDDceihh+LGG2/EkiVL0NzcDNuW/5+Bbduora3lv0UiIioY6geWxA/aKnH3h/uOFPDOUOD6w4E5XwXeHOY8f8mCc9VSXVnvz+RpAH+aBnz7COcAk/Z6xZzdiBOJXE5axNjWiMUJDiQxhNhHPZVj3fHFOFvOyHhvtjiXBxLEOOgaivWMcw3ZkzteY0/qxSpjZotz1ZMJVV9f3+vH5OuTv/vd7+K2227DsmXLUFxcjHPOOSfre++8805ccskluOCCC/DGG2/g0Ucfxa677trnfbZt49JLL8X8+fOxaNEi7Lnnnr7zI8rU3NyMl19+GfPmzcOhhx6Kiy66CFu2bPE8uNTz4N5LL70U7733XoTZEhERhSfUW+Hclv9wIPC1rwO3PwGc/obzzXFpC2gqAfp3OG/7rB/w/UOcK5uU1yWbrPmJwySuJ/P3MCb1mZNMoO9kyE89ZXTHD6OGSaknFGNdJhN89qRazJ4MRy7qmVBjxozp9fsNN9yg/Nwk0U033YSDDz4YAHDdddfh2GOPRWtrK8rLy/u890c/+hGuuuoqXHbZZdv/Jn5LV2dnJ8444wy89tpreOGFFzBq1ChfeRHJtLS04N5778XmzZtx9913Y/jw4a63x9m2jRtuuAF///vfla5uIiIiygfqB5YC/PC9pQKYe4wTn77C+Sa4Df2d2+IA5/VtFXpjApBfFSCeeQ9qe3XOaLvFQUxMw5z4RhUD6vtNV9g1zIyTXEP2ZGHVE1Dfb7oKsScT6pNPPkFVVdX238vKynyPlXk10ciRIwEAn332GcaOHdvrfZ999hnWrVuHww47zHO8K664AmVlZViyZAmGDBniOy8iFbZt47HHHsM555yD3/3ud64Hlzo7O7FixQoeVCIiooKi/61wAakrA+YeDTy+q/PgbsC57a29CChJA+O2BbxCS4gtIRZ/sr3PLc62DtXYZDyR6fL5QlYb3XoiwFh3bFGSapgZsyfzG3sysaqqqnr9mBxYKikp2R73TMjT6XSf96l+G9cXv/hFfPrpp3jyySd950Skw7ZtPPHEEzjzzDNRW1vb5/Xi4mLMnj07+sSIiIhCpPeMpZ4P5wHFdWXADYc43xBX1QasG+CsamSDc4uc1pi2EEMzjtOPTu6WYRxgPY1j0xpmxnH6keVbSDVkTxZWPU1rmBnH6UeWr98aUmQGDBiAcePGYcGCBZ7vO/744/HnP/8Z5513Hv76179GlB0lnW3beOaZZ3Dffff1uTLJsizekklERAUn1G+Fk8YA3hgO3LGf8zDv0fVAWRfwSTWwaqDmmOKkCEIM6H34z7acW5xtnaqxn3X2xLYQB1mfqGNZDcV66gi7hib1LKQasicLq57sSe3/plF05s2bh9tuuw133HEH3n//fbz66qv45S9/2ed9J5xwAu69916cffbZ+Mc//pGDTCmpnnnmGdeH2a9cuTIH2RAREYVH7+HdAUrZwO6bgSteAnbbDLSUAINandfW1ABNpZ6L63ObRNjwnhSZTjBVY5MxRKbL5wu3GuZrPUVJq2FmnK81ZD3Zk4VQwzxz1llnobW1FbfffjuuvvpqDBkyBCeddJLre0866SSk02nMmTMHqVQKX/3qVyPOlpJkxIgROP/883HRRRf1uTW0q6sL7777bo4yIyIiCodlKz490JpnuZ/J9Yr7DOK8Nm4bcOFy4OzXgGFNzre/vT4COPBj5y0vjAEO/QbQUaS5Pp1YnPiEtZ58y8Urjkse2fZbZhynvOKyv9zEIac47rc45eIVxyUP9iRgA/a8bI0Wnfr6elRXV6MOQJX03YbrAlANoK6urtfDu4nyjdu3t/lRVVWFr3/967jyyiux++67I5Xqe2NAY2MjZs2ahTfffBMAUFRUhK6urkDWT0REFAaVQ0aRfivciAbgG/8FLnoFGFO/47P+gHbnIFLaAopsYGN/50HesJ2DULttBp7aJWNM2yWGJBZzytw3bnFQkymVOMjxdHPXjd32fVCx7nbKaqizT0xrmBmbjq2Sb1BxLusp5sSezL8asid7x0SUSGVlZTj88MNx3XXXYf/990dRUVHWg1UtLS2oq6tDKpXCV77yFey222645ZZbXB9ST0RElC8iuRWupgU49Q3g0peB3WvdH+w0oA1oLXa+IW7CVqB/O/CFj4HbngTKO4FDvgGsHmiQhOUS28LfVZYJIzYZQ2S6fL6QTQa9lgk7Zg3VsCfVls8X7EkiSpiioiLMnDkT3/72t3HMMcegvLxcevVTdXU1pk6dimOOOQY333wz2tvb8eijj+Ltt9+OKGsiIqLgqR9Y8nHGu18HcPxK4MqXgL02OFcjuf3n1oJz8Ki+DKjscL4V7toXgIuWATXdz10671Xge4d2nxgO4iy/JcRRXyki5oIc5qJa2yhzUonFGia5njo1hGSsXMVJr2FmzJ7M33oSUcGzLAsTJkzAZZddhjPOOAM1NTXKt9OVlJTgZz/7GcaPH4/KykrYto0rrrgC3/zmN3lLHBER5S39b4VTiIts52qjR/8M/OFhYOZ6oNje8dnedXgbaCx1ft4cBlyxBBjYumNucuZ/ndviALhPFGQxJLElxJZG7La8TizLL9eTssx8/ez7MGJIYt16ui3jN45zPcV8C6mG7MnCqqfbMn7jqOpJRAVv6NChuPbaa/Hcc8/hW9/6FgYOHKj1jCbLsjB16lRUVlZu//3EE0/EjBkzwkqZiIgodOoHlhQmDkVp54DSxa8Af/kHcMgaoDTd+/N9NqVpZ/lFY4F91jlXMGUa1QB862Xn2+S2f5C3NGLxR3yfLcTiDzxi2f6RLR/k5F+2P8T1yXI3iXVz062n1/t06ynbFlkNg6ynzv5Qyd0kDruG7Mm+65PlbhKzJ8PtSSIqWP369cOcOXPwzDPP4KabbsKYMWMCe+h3TU0NrrzySpSUlAQyHhERUdTUDywB7h/Iu03YCvzyMeBffwK+tRR4a5heIs0lwINTgF23ANVtfT+jWwDOWOE8yHs7nYmVOOGQTSiA4CYK2cYMY12y/SCbYMny0llG3LeqOarEOhN7txx16e4rk3Xp7AfTXHJdQ/Yke9KvuPYkERWUkpISHHHEEfjnP/+Je+65B9OmTXP9tjcvTU1N2LRpU9bXLcvCcccdh4MOOsg0XSIiopwwvhVuYKtz29qzfwS+uQyoancODtWVAVsq1BN5ewhg2c6y2T7/D21y1pFKQ//KAUhicQJiacRuy+vEuhPPXMU9OcUhhiTWrafbMn7jONdTzLeQasieLKx6ui3jN46qnkRUMFKpFPbaay/84Q9/wIMPPojZs2ejpKTE11VKr7zyChYvXuz5dc2VlZW4/PLLUV5ebpI2ERFRTvi+Fa60CzjuPeDx+4BbnwJ2rtvxOT4F4MCPgcVj1D5v23C+Ee6s/3onZAE4fQWw50bsmNRYirH4I77PFmLxBx6xy/7pFcuWD3LyL9sf4vpkuZvEurnp1tPrfbr1lG2LrIZB1lNnf6jkbhKHXUP2ZN/1yXI3idmT4fYkERWEnXfeGbfccguefvppnHrqqejXr5/v295s20ZJSQlmz57tOYZlWTj88MNxxBFH+E2biIgoZ/Su5YXz2XnPjcD8h4D7/w587lPnYd2iEY1AcRpYN0A+ZksxMKwJGNwif+/gFudZS0XihASGcZy4TXyyxeIkSHfCHSbdiaDbMn7jOAm6huI+C5PJZN5teZ04TtiT5nGc5HNPElFoBg0ahMsvvxzPPfccrrzySgwZMiSQ5yjtvffeqKqqkr6vrKwMV111FQYMUPjwTEREFCNaB5ZGNgI3Pgs8Mx/4+ltARWf2k7QWgM9/ArwyCkh7jGkDeG+w84wmlf90WwC++k73VUuZf8x2ZjrbJEB2Rlr8gUcMSSxbXjaJyXbG3215WS5xjmU1FOupc4WBrJ6y3GQ1NKmnuIwsl3yJ2ZNqucQ5Zk+q5UJEea2iogKnnHIKnn76afzsZz/D+PHjA3swt2VZqKysVBrPsizMmjULxx9/fCDrJiIiiorygaWz/gs880fgO4uAoc1qn6Wr2pznIn04MPvJ3M4U8NSuwJh61UyAmlbg6sXOFVF9JiuqkxjxfeLkQvyBRwxJLFtenKhBiGUTtsxYtj/E9clyN4l1c9OJZe/TradsW2Q1DLKeOvtDJXeTOOwasif7rk+Wu0nMngy3J4koLz3yyCP44x//iL333htFRUWBjWvbNlatWuX5bCVRcXExrrrqKgwcODCwPIiIiMKmfGDpO4uAybV6lzhZAGasd74hrsvlQ7cN5zlM7w4G+rXrjXvcu8C+nyK4SVecuE18ssXiJEh3wh0m3Ymg2zJ+4zgJuobiPguTyWTebXmdOE7Yk+ZxnORzTxJR4A455BCUlpYGdpUS4BxUeuedd/C3v/1NaznLsjBt2jSceuqpgeVCREQUNuXjRFcdCTSU6q+gvBPYZQvw7pC+841NlcB1hwNDFK+AytS/HZj7MlDSc5+dbGIjTgK83usmzAlVlJM1cRJlhRiL65PR2XbT/RTmfo5ywh23GmZiT6phTwazvJd8PghGRKG788470dXVFeiYbW1tuPHGG9Ha2qq9bHFxMS699FIMHz480JyIiIjConxg6bGJwI2zgVbNK4QtAJNqgY+rgfbuZW0AdWXAlUcBS0cDA/X/mwsLwNHvA9M2ZvzBa1Il/ojvA7wnXDpXLMjGko1tG8SyiaXJmXjdWDc3ndh0Ah10Db3GDrqGYj3zuYbsSfak22v53JNElHe+//3v4/HHH9e6Zc1LOp3GAw88gIcfftj3A8AnTpyIU045JZB8iIiIwqZ8YCltAb/8HHDTQc63uOkosoHJm4C3hjqf1T/rB1x8LPCXPbo/u9t6n8ltAJsrgN/OdB783esFv3Gc6EywdCelQHTbLa5TN7ek1NPkwELYdGsYZD3jhD1pHsdJPvckEQVu27ZtuPTSS/Hyyy8jnfb6yhlvtm2jqakJd9xxB+bOnYv29nYMGTJEe4wtW7bgV7/6FR5++GHfuRAREUVJ6xBRezHw0wOB9f2BHzwP7NSgdkDIAjC2Dlg1CFi0M/Cdw5xnK9ndLzaW9f4cn40NoLEUeHgScPss4PXhzgGv7S/CIzadBIU5oRKXyza5cYt1iWfi/axTNRbXJ6OzX6Oupw7depqIWw0zxa2G7Mm+65NhTxJRQqxZswZf+cpXcNVVV+G8885DdXW11pVGtm1j9erVuP766/Hggw+is7MTqVQKNTU1ysu3tLTgX//6F2699Va8+uqrRge5iIiIoqTzLG4AQEcK+N0M4ItnAr/fC9hSIZ8rdFnAmhrg37sBx58KvDgGsDM+zK+pkY/RUgz8czfg6NOBc78MvDai+6CS6m0XUcamdCZBlhCLP4D3ftFZl9vEVJykehVSzEHllpJ8rWEm2T7WraG4j7wm4W419KqnOIbIa98Vcj3Zk9mXiSoOUtg9SUR5acOGDbjuuutw/PHHY9GiRUrPXbJtG+3t7fj3v/+NY489Fn/729/Q2dkJwLklbsWKFZ632Nm2jY6ODixcuBAnnngi5syZg2XLlvGgEhER5RXLVryh3JrX91NzkQ1M3Awc9QFw6GrnId01rUBxGugsch7O/cZw4IldgQXjgY393ec4e28AFv0O6NfR++82nANZL4x1rlB6ehegLdsznmSTLD8TMp3xdcaSjZ2ZpyzWXVec6exX0xoEXUOvsYHk1DATezL/sSe12PNyX/T6+npUV1ejDkBV2OsCUA2grq4OVVVhr40oPG5XJlVVVWHOnDmYPXs2xo8fj5122gk1NTUoLS1FV1cXNm3ahLfffhuLFy/Giy++iJdeegmNjY19xjnwwAPx1FNPoaKios9r6XQab731Fm677TY88MADrssTERHlmsohI70DSx4fwotsoKLD+ba2IhvoTDm3rTWXdL/F48P8gDbnwNL0jTv+3GUBr48AbpsFPDoJaCpRybJAxHXCnOQJtomwJ8wAaxg29mRhCakneWCJKD953fJmWRbKy8tRU1ODUaNGYfTo0di6dSvee+891NbWoqOjI+uyADBgwAAsWrQI06dP3/4327axdu1a/PrXv8Y999yDTZs2BbYtREREQVM5ZKT5GG70/qCd8QG8C86zkhrLkP3stSXE3WM0lAJP7grsudF56f3BwB37OQ/33lohjAH4m0BFGZsS93HP2G4xhNhtLK/94raubLFbDbNdfeImSTXMJKsnoFdDcR+59WS25RR7UrmGSaknezL/a5gprJ4kooLT8+yjlpYWrF+/HsuWLdNavqGhAc8//zz23HNPAM6Dwu+77z784he/wKpVq8JImYiIKHJ6B5ZkEyq3SSs8YuyI/zQNOOp94G9TgbtnAhv7wXtS4TWZznUsMp1AmRw4EJdTmSBny02jnkriUKuwaqhTT90aZsYh9qS2ONSNPcmezHVPEhEJ/vnPf+Ib3/gGnnnmGdxyyy1Yvny50vObiIiI8kUwVyxlizXe++Yw4NCzgM2V3X/3M1EIcyJpQmXiKJvAhDV2SPXss54gapgp11esZMam9dQdTxw7TjVkT7IndcY2EceeJCJysXjxYhx++OF444030NbWlut0iIiIAqf3rXDZzoJbGf9r+Xs9bXUfVJKdge+JLSEWxxfXoUu2rV6xjJ/JYLY42771m7/XmCqvZ8ZB1zDKeuowrWfQNcyMDXpye97syb4xe7JvrKtQe5KISNDS0oJly5bxoBIRERUsvQNLskmJeBZXnKiIywHRTRyijEWmEyjdKxi8rmYQX5fVUKxntuVUtkMUh1qFVUOTespqKL6XPSmPRexJd3GoVb73JBERERFRwugdWALMJlSy2DKIxRxEYZ4dlxFzDHIyaDp2nOoprtdL1PX02jem9dQdTxw7TjVkT7InVWNTce5JIiIiIqIEye2tcGJsCzE8YgixLDdxEuA2htd4qtugmk9YsWzbxP0pjuG1fbLXdesp5qizrTqTb7cx8rmGmTF7Mv/ryZ7M/xqKMRERERFRguT2VjjZxNJrMgsh1p2kyojL6VwVIFuX7lUIXu+V7Rcxlp2JF2so1jPbcm6xbq5B1zCTbj111mVaT90aivVkT7In2ZPeY4txmD1JRERERJQwvBUuismBbOIoxjq5mI4dp3qK6/USdT299o1pPXXHE8eOUw3Zk+xJ1dhUnHuSiIiIiChBeCuc6niq26CaT1ixbNtkZ+K9tk/2um49xRx1tlVn8u02Rj7XMDNmT+Z/PdmT+V9DMSYiIiIiSpDiQEdzO4NrCzE8YqDvxEP17Lq4PrfxdD74604kxLFNltc9sKB7BYIX2Rl5k6sdoq5hpkKqoWk92ZPxqqcMe9L/MnHsSSIiIiKiAqP/jKUwY/FsscmESaR7hjpMQeYibr/XBNQtzjZOELFuPcXlvRRSPQuphuzJwqqnuLyXQqqnSQ2JiIiIiBKGt8Kpjqe6Dar5hBXrnokXx/DaPtnruvUUc9TZVtMJdT7XMDNmT+Z/PdmT+V9DMSYiIiIiShDeCue1LX5j0+V1DyzoXkXiRXZG3uSKlahrmKmQamhaT/ZkvOopw570v0wce5KIiIiIqMDo3wrnNcERYwixjLic1wd48Qy8uG63M/tecS7J8pNdseD1XvF13RqK9dQhq6duDQu1nrqvZ8bsyXCwJ9mTOq+LMRERERFRgugdWIoT2RlnW4hly6lMFlQnEbrL626LTiyOGScm2y0bI+oahllPrzHjhD3JnvSK2ZNERERERAVJ/xlLXlcsiDGg94FbXC7MKzFk69aJTccyoXNFiWmsS7eegFkNvdYti02XN6ln1DVkT+ovr4M9qbZuWWy6fK56koiIiIgoYXgrXBzo3qZhcsuGbg3FeuqQ1VO3hoVaT93XM2P2ZDjYk+xJndfFmIiIiIgoQYJ9eHeUxIkPhFj3jLbKmehssUh3eVl+OtsirqPntThOeoKuoUk9RSb/HkzrKathUurJnowee7JgerL6OgDlIa+kFcDNIa+DiIiIiGIv3FvhbCEO+moKWa46se7EQpxkZMayyZlsed2z4zr7THbFglesUsMg66m77Sb1FMeQ1VBnrDCvdmBPusfieOxJf7nJctWJk9KTREREREQJE+6tcOKHd5Uz1NnOVouCntzJmJypl40l7jtbiC2NGEIMIZat2yvWnfSK6xOZTFJNmdbQayzTegLeNRTryZ5kT6rG4vpE7EnzniQiIiIiShh+K1yPICdUQZ/JF7dFJxbHjBOT7ZaNEXUNw6yn15hxwp5kT3rF7EkiIiIiooLEW+F6yCYMXtsrjidun2wscXnxLLksd519Ji6nW0+dqyl0c5PlqhPr1lMcQ1ZDnbF066mDPekei+OxJ/3lJstVJ05KTxIRERERJUy8boXTPQMe5Vli3Xy8chPfJ+47W4jhEevmo3tgQaynyUEMWY5RCvLfl2k9/eRjUkP2pHws9mT0CqUniYiIiIgSJl63wpmcDRfpTgSCXHfQZ/K9YtN1hynqGprUUxTmlRlibLruMLEnk11PEXsy9zUkIiIiIooZ3grXQzbZ8NpecTxx+2RjicuLV5XIctfZZ+JyuvXUuZpCNzdZrjqxbj3FMWQ11BlLt5462JPusTgee9JfbrJcdeKk9CQRERERUcLwVjhVQV7BIL5P3He2EMMj1s1H98CCWE+TgxiyHKMU5L8v03r6ycekhuxJ+VjsyegVSk8SERERESVMuFcsQYiDPtPvRTaxEGPZuk3O1MtEOYExuTrCbXmdWJfX9kddTx1RTkLZk/5iGfakO/YkEREREREJwn3Gku6H+SAnSLqTNTGXMMm2R7yqRHU5P7GOsOsp8lqG9fSHPeku1zWMaz1F7MnwD8ASEREREeUZ3gqnKsgrGExv0/CarMlicR/r1lPnagrdekYpyH9fpvX0k49JDdmT8rHYk9ErlJ4kIiIiIkoY3goXxJl6WSwT5QRG9yoDsZ5RnsX32v6o66kjykkoe9JfLMOedMeeJCIiIiIigf4VSzpx0MubkK1PnLhBIxaJ78t2tUguJtCmE+a4nMUPup4i3RqGWU8v7En3WMSezL58UNiTRERERESJpP+MJa8P3VF+uA96YiHLxevKAUuIIYkhiU0mraax7n40qaduDcU8dMjqqVvDQq2niD3pvu4oY/Zk/vUkEREREVGCFGu9WzaZk8VuE4bM99lC3MMt1j0zL8tRtm6TbRdzcfs922tBx2HU0G89TbYn6np65eL2e7bX8qGeme9jT3pvQ1xryJ7MXT2JiIiIiBKGt8Jlm7xAIxaZXnEgG88kN9PJUlwmW0HXU6RbwzDr6YU96R6L2JPZlw8Ke5KIiIiIKJF4K5xqLl5nz8UrBSCJIYlNJq2mse5+NKmnbg3FPHTI6qlbw0Ktp4g96b7uKGP2ZP71JBERERFRgujdCgcEe4WEn/H83rKhk1cUt0nEJZcgr5DQHS/MKy1YT/ZkvubCngwml1zWk4iIiIgoQfSvWNKRT2d5Tc7k67w37KtGZGOZKOR6yl7PZT2DVMg1ZE9Gu25d7EkiIiIiooLEW+FUc7E9YkuIIYkhiU0mraax7n40qafpRFGHrJ66NSzUeorYk+7rjjJmT+ZfTxIRERERJQhvhTPNI99z4W03weSST/X0ys9k7Hzeb3HKhT0ZTC65rCcRERERUYKoX7GUi7O6UZ71l8UmP6Y5+N1mtzFNx/OKZfK5nkGt1882e43pZzyvWCafa8ie7Cuf6xnUev1ss9c4REREREQJo37FUs/ZWMsjhhCLf1MZQzXuGVs1FscB9M9w664z22TDLbdsefpdZ0+su2/hEee6hl77TIzdfhdfM6mh11ji+k3rabKfIcTsSbWxxPWzJ+UxJLHb7+Jr+d6TREREREQJxFvhTPPI91xMczepp2keXlhP9mS+5sKeDCaXXNaTiIiIiChBeCucamzyY5qD3212G9N0PK9YJp/rGdR6/Wyz15h+xvOKZfK5huzJvvK5nkGt1882e41DRERERJQwelcsyT5s20Lc81q22G0ZneXFsWTjWgaxV64i2TJ+tk0lD7flIcR+9lu2sYOup04uQddTpLq/w6inuAw8YvYke5I9Ga+eJCIiIiJKGP1nLOm8T/cDvri8TuxnohBkLIrLWEGuL+x6FlINo66nF/ZkvMZiT6rHonwbl4iIiIgoAdQPLPk5C6w7yTFZpy2JYRibMM0B8L+fTc/kR1lPWQ1zWc+g1htEDTPjuNUwM45bDTOxJ9XGYE/q9SQRERERUQLxVjjV2CtXUdAHA1TGBNyXhxD72W/Zxg66njq5BF1PkQl5QrcAAB3kSURBVM7kNOh6isvAI2ZPsifZk/HqSSIiIiKihOGtcEHForiMFeT6wq5nIdUw6np6YU/Gayz2pHosyrdxiYiIiIgSQO+KJV26H8qDviogk+zsu4xqHkGM3ZOjyoTF9AoHHWHXUye3oPe56XheY/fk6/ffpspEXic3nZg9yZ5kT4aTExERERFRgUgpv9NS+IEQQxKLy9hCDI1Ytj4Z3TP3XuuWncW3hViWg9f2iTnZkljcz7IaRllPlXWo0q2nuA5ZDTNj03rq1lCsJ3uSPZktZk9G25NERERERAnEW+GCikVxGSvI9YVdz0KqYdT19MKejNdY7En1WJRv4xIRERERJQBvhVM9466aRxBj9+SoMmFRObPvd2y3PHViP5NS3e0Oap+bjuc1dk++fv9tqkzkdXLTidmT7En2ZDg5EREREREVCPVb4Xro3B5hacYmZ79lTMfT2W4x1s3FFmLV5fzEcaqnjqjraZJL2PWMUw3Zk+ZxnOqpgz1JRERERJRI+lcshT2piuqDfa4nB+J6vK4q8IptIXYbz2ub4lCrXB2YCJJJPXVr6LWuOMcy+VzDzJg9Gdx4JnLZk0RERERECcJb4Xjbjbqw66mTG2+7iWcN2ZPsyST3JBERERFRAvFWONXxdG/T8JpoyNZvC7Hqcn7iONVTR9T1NMkl7HrGqYbsSfM4TvXUwZ4kIiIiIkokvSuWgvzgH8T4mbHJBDgXkwNxPV5XFQQZZzvLHod6hlHDpNQTirEu9qSDPcmeVImJiIiIiBJI78BS2Gfq3X5XJU6MVCZv4kTOi8oYQY2dmXfQwp4omtRTVsPMOOp66oi6nmHGbr+rYk/6X1dYsdvvXtiTREREREQkwVvhVMczuXJDNxdbiFWX8xPHqZ46oq6nSS5h1zNONWRPmsdxqqcO9iQRERERUSLxVjivOExeVxHIrgwwibOd+Y9DPcO+ciNMua4nFGNd7EkHe5I9qRITERERESWQ+oGlMCdWQcRA9kma2zIyphNDnTiIiWlc6hBmDd0muKrCrmFmnOQasicLq56A+n7TVYg9SURERESUQHpXLOUzyyW2hb+rLBNGbDKGyHT5fCGbUHotE3bMGqphT6otny/Yk0REREREiaR+YCnoM8hBx0DfM8e6cVzonlVHgcTiNsIwjosk1ZA9GY99H6ca5mM9/e4zIiIiIqIE4q1w2UQ5kTTdB1ZA4+Q6BtT3m64oDwYkuYbsycKqJ6C+33QVYk8SERERESUQb4WzXN7rtUwYsckYItPl84VsQum1TNgxa6iGPam2fL5gTxIRERERJZL+rXC2Rpy5LDJei0NsCXGuc0EOc9GpZ673VWYs1jDJ9WRP5n8NM2P2ZP7Wk4iIiIgoYaK9FU51suQnhiQWc3KbZGd7r+6kRTcOcjzd3HXjXNZQ3E5ZDXX2iWkNM2PTsVXyDSpmT7In2ZNq26ZSEyIiIiKiBOKtcJbLe72WCSM2GUNkuny+kE0GvZYJO2YN1bAn1ZbPF+xJIiIiIqJE4q1w2c5oR50LcpiLTj1zva8yY7GGSa4nezL/a5gZsyfzt55ERERERAmTUn6nnw/aEGK3D/hRxZDEYr6WRuy2vE4syy/Xk7LMfHNZwzDr6baM3zjO9RTzLaQasicLq55uy/iNo6onEREREVECFc6tcLIP/W6TIXjEuusOOlZdR9DrySWdSVzQ2xlGDVXrGfTYccGeDGY9ucSeDGZsIiIiIqICxlvhbIMxgswFOcxFp5653leZsVjDJNeTPZn/NcyM2ZP5W08iIiIiooThrXBijj2xpRG7La8Ty/LL9aQsM99c1jDMerot4zeOcz3FfAuphuzJwqqn2zJ+46jqSURERESUQHpXLIUd93xAD2PiI3KbFHlNEEwmd5DEsgmL277JFqvkIm5nvtZTdzszBV3DIOspy8VvXnGsYSb2pDyvuNcz6T1JRERERJRA6lcsAfpniXWXD3PSbAuxygQhqMmC6X7TIdsPbpM3nbx0lgm7nroTPpN9rLuvTNalsx/Yk/6wJ9mTOnT3CRERERFRgvBWODHHntjSiN2W14l1J565intyikMMSaxbT7dl/MZxrqeYbyHVkD1ZWPV0W8ZvHFU9iYiIiIgSiLfCZebhNUEwmdxBEssmLG77Jluskou4nflaT93tzBR0DYOspywXv3nFsYaZ2JPyvOJez6T3JBERERFRAundChc2cVJga8Ruy+vEcaJzNl6c4OjupzDp1tNtGb9xnARdQ3GfhYk96WBPmsdxks89SUREREQUM3q3wsU59jobDfSdBIgTCEuIxR94xLLcZMvLJjE6Z9JlucQ5ltVQrKfX1QO69ZTlJquhST3FZWS55EvMnlTLJc4xe1ItFyIiIiKiBOOtcJl5ZE6SRF4TK1kOkMSySZtsIquzz9y2M1/rqbudmYKuYZD1lOXiN6841jATe1KeV9zrmfSeJCIiIiJKIN4KF0c6Z8nFCY7ufgqTbj3dlvEbx0nQNRT3WZjYkw72pHkcJ/nck0REREREMaN3YCnbbQZBxRBiHbKJjTgJkC2nO77OWFGOLRL3dZzqqbPtpvspzP0c5YQ7bjXMxJ5Uw54MZnkv+XwQjIiIiIgo5vSvWPL6UC6e7bWFGEIsjiHyOqusO4GKMjYl26+ZsSXE4g/gvV901uVWQ5N6FnINM8n2sW4NxX3EnpTHptiT2ZeJKg5S2D1JRERERJQg6s9YAszPWJtcBSCL3SZWsgm0F7eJhzimzmTdi7hc5jplscq6grzqIJc1FPexDt16ivno0K2nyrr87tM411N8jwx7Mn41ZE/2jYmIiIiIEoa3wqlODkwmaTJhji3SvUIhynoGOdkWY5N16Qq7hpniVsNM7Ek17MlglvcSZU8SERERESUMb4ULIzYl26+ZsSXE4g/gvV901uV1FYPbeKIk1TCTbB/r1lDcR+xJeWyKPZl9majiIIXdk0RERERECaJ3YEllkpMZix/QxeUA/x/GTa4oCDsWmU6gdK8a8LqCQHxdVkOxntmWU9kOURxqFVYNTeopq6H4XvakPBaxJ93FoVb53pNERERERAmjfyucTmwqyElILunmrrOfxQllkGOLsakk1VBnn+vW0KSeptiT7Mm4iGtPEhERERElDG+Fi+MtG7L9mhnr3rIh5qqzLrcamtSzkGuYSbaPdWso7iP2pDw2xZ7MvkxUcZDC7kkiIiIiogThrXBhxCLTCZTO+m0hlr0uq6FYz2zLqWyHKA61CquGJvWU1VB8L3tSHovYk+7iUKt870kiIiIiooQJ9oolMRY/jMtilTPwYVxp4RYHScwxyMmg6dhxqqe4Xi+5vmIlMzatp+544thxqiF7kj2pGpuKc08SERERESVIsM9YEm8N0H1d5wO8eAWGOL64Dl1Bng0X6Z6p91pntn3rN3+vMVVez4yDrmGU9dRhWs+ga5gZsyfVsCfZk5mxSQ2JiIiIiBKGt8KFEYtMJ1AmVzDIXpfVUKxntuVUtkMUh1qFVUOTespqKL6XPSmPRexJd3GoVb73JBERERFRwvBWuCjOOos5BjkZNB07TvUU1+sl6np67RvTeuqOJ44dpxqyJ9mTqrGpOPckEREREVGC5PZWODG2hRgeMYRYlps4CXAbw2s81W1QzSesWLZt4v4Ux/DaPtnruvUUc9TZVp3Jt9sY+VzDzJg9mf/1ZE/mfw3FmIiIiIgoQXJ7K5xsYuk1mYUQ605SZcTldK4KkK1L9yoEr/fK9osYy87EizUU65ltObdYN9ega5hJt5466zKtp24NxXqyJ9mT7EnvscU4zJ4kIiIiIkoY3goXxeRANnEUY51cTMeOUz3F9XqJup5e+8a0nrrjiWPHqYbsSfakamwqzj1JRERERJQgvBVOdTzVbVDNJ6xYtm2yM/Fe2yd7XbeeYo4626oz+XYbI59rmBmzJ/O/nuzJ/K+hGBMRERERJUhxoKO5ncG1hRgeMdB34qF6dl1cn9t4Oh/8dScS4tgmy+seWNC9AsGL7Iy8ydUOUdcwUyHV0LSe7Ml41VOGPel/mTj2JBERERFRgQn2GUtiDCGWEZfz+gAvnoEX1+12Zt8rziVZfrIrFrzeK76uW0Oxnjpk9dStYaHWU/f1zJg9GQ72JHtS53UxJiIiIiJKEL0rllQmlsiIZa/LYq91imeZe17zeyWGbFuDytvPBNL0bLnOFQqyPEwmw+I6g65hkPX0Wm/Y9XSTbTvZk+xJ9mS8epKIiIiIKGF4K5zXtviNTZfXiYOY5GYKekKtU8+ga5ipkGpoWk/2ZLzqKcOe9L9MHHuSiIiIiKjA8Fa4OJDlJ26L13bJXtetoVhPHbJ66tawUOup+3pmzJ4MB3uSPanzuhgTERERESWI3oGlOJGdcbaFWLacymRBdRKhu7zutujE4phxYrLdsjGirmGY9fQaM07Yk+xJr5g9SURERERUkPQOLMmuWBBjQO8Dt7hcmFdiyNatE5uOZULnihLTWJduPQGzGnqtWxabLm9Sz6hryJ7UX14He1Jt3bLYdPlc9SQRERERUcLwVrg40L1Nw+SWDd0aivXUIaunbg0LtZ66r2fG7MlwsCfZkzqvizERERERUYLwVrgeKpMF1UmE7vK626ITi2PGicl2y8aIuoZh1tNrzDhhT7InvWL2JBERERFRQQr3VjjxQ3nQV1PIctWJZRMGr+0VxxO3TzaWuLzu2fGwrkARYz9XU5jUM8yJpGkNdcYK82oH9qR7LI7HnvSXmyxXnTgpPUlERERElDDh3gonfniXTZIgxF6CntzJqJypVt0O8X3ivrOF2NKIIcQQYtm6vWLdSa+4PpHJJNWUaQ29xjKtJ+BdQ7Ge7En2pGosrk/EnjTvSSIiIiKihOGtcD2CnFAFfSZf3BadWBwzTky2WzZG1DUMs55eY8YJe5I96RWzJ4mIiIiIChJvheshmzB4ba84nrh9srHE5cWz5LLcdfaZuJxuPXWuptDNTZarTqxbT3EMWQ11xtKtpw72pHssjsee9JebLFedOCk9SURERESUMPG6FU73DHiUZ4l18/HKTXyfuO9sIYZHrJuP7oEFsZ4mBzFkOUYpyH9fpvX0k49JDdmT8rHYk9ErlJ4kIiIiIkqYeN0KZ3I2XKQ7EQhy3UGfyfeKTdcdpqhraFJPUZhXZoix6brDxJ5Mdj1F7Mnc15CIiIiIKGZ4K1wP2WTDa3vF8cTtk40lLi9eVSLLXWeficvp1lPnagrd3GS56sS69RTHkNVQZyzdeupgT7rH4njsSX+5yXLViZPSk0RERERECcNb4VQFeQWD+D5x39lCDI9YNx/dAwtiPU0OYshyjFKQ/75M6+knH5MasiflY7Eno1coPUlERERElDDhXrEEIQ76TL8X2cRCjGXrNjlTLxPlBMbk6gi35XViXV7bH3U9dUQ5CWVP+otl2JPu2JNERERERCTQv2JJJw56eROy9YkTN2jEIvF92a4WycUE2nTCHJez+EHXU6RbwzDr6YU96R6L2JPZlw8Ke5KIiIiIKJH0H94d5dlxE7q5ySZFtkes8163WDbh8roaQJa/TCHV0KSestfDrKcsZxn2JHsyKuxJIiIiIiIS8Fa4XFyhIIryigMxR916RnkQw2v7o66njiivGmFP+otl2JPu2JNERERERCTgrXCqZ7S9YpH4PksS645nkpvphDnKCbeXoOsp0q1hmPX0wp50j0XsyezLB4U9SURERESUSMHeChflh/ugJxayXLyuHLCEGJIYkthk0moa6+5Hk3rq1lDMQ4esnro1LNR6itiT7uuOMmZP5l9PEhERERElSLHWu2WTOVnsNmHIfJ8txD3cYt0z87IcZes22XYxF7ffs70WdBxGDf3W02R7oq6nVy5uv2d7LR/qmfk+9qT3NsS1huzJ3NWTiIiIiChheCtctskLNGKR6RUHsvFMcjOdLMVlshV0PUW6NQyznl7Yk+6xiD2ZffmgsCeJiIiIiBKJt8Kp5uJ19ly8UgCSGJLYZNJqGuvuR5N66tZQzEOHrJ66NSzUeorYk+7rjjJmT+ZfTxIRERERJYjerXBAsFdI+BnP7y0bOnlFcZtEXHIJ8goJ3fHCvNKC9WRP5msu7MlgcsllPYmIiIiIEkT/iiUd+XSW1+RMvs57w75qRDaWiUKup+z1XNYzSIVcQ/ZktOvWxZ4kIiIiIipIvBVONRfbI7aEGJIYkthk0moa6+5Hk3qaThR1yOqpW8NCraeIPem+7ihj9mT+9SQRERERUYLwVjjTPPI9F952E0wu+VRPr/xMxs7n/RanXNiTweSSy3oSERERESWI+hVLuTirG+VZf1ls8mOag99tdhvTdDyvWCaf6xnUev1ss9eYfsbzimXyuYbsyb7yuZ5BrdfPNnuNQ0RERESUMOpXLPWcjbU8Ygix+DeVMVTjnrFVY3EcQP8Mt+46s0023HLLlqffdfbEuvsWHnGua+i1z8TY7XfxNZMaeo0lrt+0nib7GULMnlQbS1w/e1IeQxK7/S6+lu89SURERESUQLwVzjSPfM/FNHeTeprm4YX1ZE/may7syWByyWU9iYiIiIgShLfCqcYmP6Y5+N1mtzFNx/OKZfK5nkGt1882e43pZzyvWCafa8ie7Cuf6xnUev1ss9c4REREREQJo3fFkuzDti3EPa9li92W0VleHEs2rmUQe+Uqki3jZ9tU8nBbHkLsZ79lGzvoeurkEnQ9Rar7O4x6isvAI2ZPsifZk/HqSSIiIiKihNF/xpLO+3Q/4IvL68R+JgpBxqK4jBXk+sKuZyHVMOp6emFPxmss9qR6LMq3cYmIiIiIEkD9wJKfs8C6kxyTddqSGIaxCdMcAP/72fRMfpT1lNUwl/UMar1B1DAzjlsNM+O41TATe1JtDPakXk8SERERESUQb4VTjb1yFQV9MEBlTMB9eQixn/2Wbeyg66mTS9D1FOlMToOup7gMPGL2JHuSPRlJT6ZsYFgj0JUC2ouAjpQTd1lAp/rTComIiIiIYmPq1Kk45JBD0NjYiObmZmzZsgUtLS2oq6vDxo0bsWnTJqVxeCtcULEoLmMFub6w61lINYy6nl7Yk/Eaiz2pHotyMG7KBga3AJctAc76L2BbQFsR0FwKtBYDdWXA/xwKLB3lMT4RERERUUyUlpZixIgROOigg/DjH/8Yo0ePBgDYto10Oo10Oo2Ojg7ceuut+MEPfqA0pt4VS7p0P+wHfVVAJtnZdxnVPIIYuydHlQMHplc46Ai7njq5Bb3PTcfzGrsnX7//NlUm8jq56cTsSfZkknrSBkrSQHUrMH4bMH0DMHsNUNUGHPkBUJruO3RHCijr9JkXEREREVHIKioqsNNOO2H69Ok44IADsM8++2DSpEkYNGgQioqKYFnOB1nLspBKOZfil5aWYtiwYcrr0HvGkozsw7ufSaqlGLvlqfNB3yQPcX2ysbJNbFQng2JsKbwnW34inUlm0PXM3F5ZLKObh7gOnUluEPXUqaG4z72wJ9XGYk8mtierWoEDPwa+8BEwph4YXQ+M2wYMawLKO533rBoIFLscVOqRFtdDRERERJRDu+66Kz7/+c9j1qxZ2GeffTBhwgRUV1cjlUptP5AkU1lZqbw+3goXVCyKy1hBri/sehZSDaOupxf2ZLzGYk+qx6IQxr1gOfDjBc6BI7f/xNoA6ss9coLe8TUiIiIiojCVlJTg17/+NQ477DBYlqV8IElUXl6uvGy4jxzV/bAvnqW2NGJxeZFsbNmPn5z8ji3bFp3tMhlbFHY9dXILep+bjmdST9N/9zrYk+zJsHLL056cuMW5/c1zMz0OdhWlgYmbvRYmIiIiIopOeXk5Ro8erXV1kptp06Zhjz32UHqv/oGlbBMOt1h3wmBy9lvGdDyd7RZj3VxsIVZdzk8cp3rqiLqeJrmEXc841ZA9aR7HqZ468rAni2xgTJ18uKHN2VdtAfjhc8AdjyvmRkREREQUosrKSlRXVxuPM2XKFDz77LNK79V/eHfYk6qwbqEQBT2p0iWuR5xY2oqxLcRu43ltUxxqlasDE0EyqaduDb3WFedYJp9rmBmzJ4Mbz4RCPUu7gBGN8qG6PA5oWQB2agC+9bJ+ikREREREQRs0aBAGDBhgPI5lWRgyZIjSe3krnN9bKXRi3bFl26KzXSZji8Kup05uQe9z0/FM6mn6714He5I9GVZuediT/dqBwc3yTdtaIX/PZoX3EBERERGFbejQoSgrK4t0nbwVTnU8ne0WY91cbCFWXc5PHKd66oi6nia5hF3PONWQPWkex6meOvKwJwe1ANVt8uGGKBx8ailRzI08WZaFhx9+ONdpEBEREeWtkSNHorhY/+Y0E3oHloL84B/E+Jmx6aROnECG+SOuU8zHZD/oTkR1xtAVdQ0z4yTVU2c5XexJ9e1lT+ZdPft1OLfDyRRJHu5tA2jmgSUlGzZswKWXXooJEyagrKwMY8aMwXHHHYcFCxbkOjUiIiKigjBy5Eijh3b7oX5gycKOD+pxjC0hhhADehOyMK8aMJlIirEtxLmuQ5g1FOupI+wamtSzkGrIniysehZ4T66pAV4aDaSxo2wiG95XI9kA3hwG3D0j+3vIsWbNGsycORPPPvssbr31Vrzxxht44okncMghh+CSSy6JLI+Ojo7I1kVEREQUtaVLl2Lr1q2w7WyfcIOnfn2UOFlSia2MZVVi3fHFdXmtVySO4fb+sGIxXzGfzPrLYt2JrMry2eKw6ymroVt9so2X7f1hxKb1NDkYkW35bDF70j1mTyayJ7eWA6eeBBzzPjC6vvfzlqrbgP7tQNoCtlQAe20ALBsY1gSUpHe8r7EU+PW+zoGlW0FeLr74YliWhZdffhn9+vXb/vepU6finHPOcV3mk08+wVVXXYWnnnoKqVQKX/jCF/CLX/wC48aNAwC88sor+M53voPXXnsNHR0d2GuvvXD77bdjxowdR/osy8Kvf/1rPP7441iwYAGuueYazJs3L8xNJSIiIsqZpUuXYu7cubj66qsxePBgAMCQIUNQURHeQ0GVDyzZ86I72kVERJQv7uz+iRWFZ0cFtY76+vpefy4rK+vzwMgtW7bgiSeewE033dTroFKPmpqaPn/r6OjAkUceiVmzZmHRokUoLi7Gj370Ixx11FFYsWIFSktL0dDQgLPOOgu//OUvYds2brvtNhxzzDF4//33e30byrx583DzzTfj5z//eeTPHKD4i/KMLhERUSHipysiIqICUVpaihEjRmDD7RsiWV///v0xZsyYXn+74YYb+lwR9MEHH8C2bUyaNEl57Pvvvx/pdBp333339ucE/P73v0dNTQ2ef/55HHHEETj00EN7LfPb3/4WNTU1WLhwIb70pS9t//tpp52Gs88+W3PriIiIiEgFDywREREViPLycqxevRrt7e2RrM+27T4Ph3T7els/V4S8/vrr+OCDD3pdeQQAra2tWLVqFQBg48aN+N73vofnn38en332Gbq6utDc3IyPP/641zL77LOP9vqJiIiISA0PLBERERWQ8vJylJeX5zqNXiZOnAjLsrBy5UrlZRobGzFz5kz86U9/6vPa0KFDAQBnnXUWNm/ejF/84hfYeeedUVZWhlmzZvU5sOZ2+x0RERERBUP9W+GIiIiIfBg0aBCOPPJI/OpXv0JTU1Of17dt29bnbzNmzMD777+PYcOGYdddd+31U11dDQB48cUXMXfuXBxzzDGYOnUqysrKUFtbG/bmEBEREVEGHlgiIiKi0P3qV79CV1cXPve5z+GBBx7A+++/j3feeQd33HEHZs2a1ef9p59+OoYMGYIvf/nLWLRoEVavXo3nn38ec+fOxdq1awE4V0Lde++9eOedd7B06VKcfvrpoX7jCRERERH1xQNLREREFLoJEybg1VdfxSGHHIKrrroKe+yxB774xS9iwYIFuPPOvt+rV1lZif/85z8YO3YsvvrVr2Ly5Mk499xz0draiqqqKgDAPffcg61bt2LGjBmYM2cO5s6di2HDhkW9aURERESJZtn8jlUiIiIiIiIiIvKBVywREREREREREZEvPLBERERERERERES+8MASERERERERERH5wgNLRERERERERETkCw8sERERERERERGRLzywREREREREREREvvDAEhERERERERER+cIDS0RERERERERE5AsPLBERERERERERkS88sERERERERERERL7wwBIREREREREREfny/wEq5Td09GzmKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "⚠️  If black areas are classified as clouds, that's expected but misleading!\n",
      "   Focus on the VALID FIELD AREAS statistics above.\n"
     ]
    }
   ],
   "source": [
    "if cloud_mask is not None:\n",
    "    # Show classification breakdown\n",
    "    print(\"=== OMNICLOUDMASK CLASSIFICATION BREAKDOWN ===\\n\")\n",
    "    print(f\"Classification values found: {np.unique(classification)}\")\n",
    "    print(f\"  0 = Clear:       {np.sum(classification == 0):>10,} pixels ({np.sum(classification == 0)/classification.size*100:>5.1f}%)\")\n",
    "    print(f\"  1 = Thick Cloud: {np.sum(classification == 1):>10,} pixels ({np.sum(classification == 1)/classification.size*100:>5.1f}%)\")\n",
    "    print(f\"  2 = Thin Cloud:  {np.sum(classification == 2):>10,} pixels ({np.sum(classification == 2)/classification.size*100:>5.1f}%)\")\n",
    "    print(f\"  3 = Shadow:      {np.sum(classification == 3):>10,} pixels ({np.sum(classification == 3)/classification.size*100:>5.1f}%)\")\n",
    "    \n",
    "    # Valid field areas only\n",
    "    valid_data_mask = ~((red == 0) & (green == 0) & (nir == 0))\n",
    "    print(f\"\\n=== IN VALID FIELD AREAS ONLY ===\\n\")\n",
    "    print(f\"  0 = Clear:       {np.sum(classification[valid_data_mask] == 0):>10,} pixels ({np.sum(classification[valid_data_mask] == 0)/np.sum(valid_data_mask)*100:>5.1f}%)\")\n",
    "    print(f\"  1 = Thick Cloud: {np.sum(classification[valid_data_mask] == 1):>10,} pixels ({np.sum(classification[valid_data_mask] == 1)/np.sum(valid_data_mask)*100:>5.1f}%)\")\n",
    "    print(f\"  2 = Thin Cloud:  {np.sum(classification[valid_data_mask] == 2):>10,} pixels ({np.sum(classification[valid_data_mask] == 2)/np.sum(valid_data_mask)*100:>5.1f}%)\")\n",
    "    print(f\"  3 = Shadow:      {np.sum(classification[valid_data_mask] == 3):>10,} pixels ({np.sum(classification[valid_data_mask] == 3)/np.sum(valid_data_mask)*100:>5.1f}%)\")\n",
    "    \n",
    "    # Visualize classification map\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
    "    \n",
    "    # Show raw classification (0-3)\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    colors = ['green', 'red', 'orange', 'purple']  # Clear, Thick Cloud, Thin Cloud, Shadow\n",
    "    cmap = ListedColormap(colors)\n",
    "    \n",
    "    im1 = axes[0].imshow(classification, cmap=cmap, vmin=0, vmax=3)\n",
    "    axes[0].set_title(\"OmniCloudMask Classification\\n🟢Clear 🔴Thick 🟠Thin ☁️ 🟣Shadow\", fontsize=11)\n",
    "    axes[0].axis('off')\n",
    "    cbar1 = plt.colorbar(im1, ax=axes[0], ticks=[0, 1, 2, 3])\n",
    "    cbar1.set_ticklabels(['Clear', 'Thick', 'Thin', 'Shadow'])\n",
    "    \n",
    "    # Show RGB for comparison\n",
    "    rgb_display = np.dstack([red, green, blue])\n",
    "    rgb_display = np.clip(rgb_display * 2.5, 0, 1)\n",
    "    axes[1].imshow(rgb_display)\n",
    "    axes[1].set_title(\"RGB Image (for reference)\", fontsize=11)\n",
    "    axes[1].axis('off')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"\\n⚠️  If black areas are classified as clouds, that's expected but misleading!\")\n",
    "    print(\"   Focus on the VALID FIELD AREAS statistics above.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6225e454",
   "metadata": {},
   "source": [
    "## 7. Compare with Planet UDM1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b95c3028",
   "metadata": {},
   "outputs": [],
   "source": [
    "if cloud_mask is not None:\n",
    "    # Create mask for valid data (exclude black pixels outside fields)\n",
    "    valid_data_mask = ~((red == 0) & (green == 0) & (nir == 0))\n",
    "    \n",
    "    # Calculate UDM1 statistics - for valid areas only\n",
    "    udm_cloudy_pixels_all = np.sum(udm1 == 1)\n",
    "    udm_cloudy_pixels_valid = np.sum(udm1[valid_data_mask] == 1)\n",
    "    udm_cloud_pct_calculated = (udm_cloudy_pixels_valid / np.sum(valid_data_mask)) * 100\n",
    "    \n",
    "    # Create combined mask from OmniCloudMask (clouds + shadows)\n",
    "    omni_combined = np.logical_or(cloud_mask == 1, shadow_mask == 1).astype(int)\n",
    "    omni_combined_pct = (np.sum(omni_combined[valid_data_mask]) / np.sum(valid_data_mask)) * 100\n",
    "    \n",
    "    print(\"=\"*60)\n",
    "    print(\"COMPARISON: Planet UDM1 vs OmniCloudMask\")\n",
    "    print(\"=\"*60)\n",
    "    print(f\"\\n{'Metric':<30} {'UDM1':<15} {'OmniCloudMask'}\")\n",
    "    print(\"-\" * 60)\n",
    "    print(f\"{'Unusable pixels %':<30} {udm_cloud_pct_calculated:>6.2f}%      {omni_combined_pct:>6.2f}%\")\n",
    "    print(f\"{'  - Clouds only':<30} {'(combined)':>13}  {omni_cloud_pct:>6.2f}%\")\n",
    "    print(f\"{'  - Shadows only':<30} {'(combined)':>13}  {omni_shadow_pct:>6.2f}%\")\n",
    "    \n",
    "    # Agreement analysis\n",
    "    agreement = np.sum((udm1 == 1) & (omni_combined == 1))\n",
    "    disagreement_udm_only = np.sum((udm1 == 1) & (omni_combined == 0))\n",
    "    disagreement_omni_only = np.sum((udm1 == 0) & (omni_combined == 1))\n",
    "    agreement_clear = np.sum((udm1 == 0) & (omni_combined == 0))\n",
    "    \n",
    "    total = udm1.size\n",
    "    \n",
    "    print(f\"\\n{'Agreement Analysis':<30} {'Pixels':<15} {'%'}\")\n",
    "    print(\"-\" * 60)\n",
    "    print(f\"{'Both agree: Cloudy':<30} {agreement:<15,} {(agreement/total*100):>5.2f}%\")\n",
    "    print(f\"{'Both agree: Clear':<30} {agreement_clear:<15,} {(agreement_clear/total*100):>5.2f}%\")\n",
    "    print(f\"{'UDM1 only (false negative?)':<30} {disagreement_udm_only:<15,} {(disagreement_udm_only/total*100):>5.2f}%\")\n",
    "    print(f\"{'OmniCloudMask only':<30} {disagreement_omni_only:<15,} {(disagreement_omni_only/total*100):>5.2f}%\")\n",
    "    print(f\"\\n{'Overall agreement:':<30} {((agreement+agreement_clear)/total*100):>5.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc0c405",
   "metadata": {},
   "source": [
    "## 8. Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a862eef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "if cloud_mask is not None:\n",
    "    # Create RGB composite for display\n",
    "    rgb = np.dstack([red, green, blue])\n",
    "    rgb_display = np.clip(rgb * 2.5, 0, 1)  # Enhance brightness\n",
    "    \n",
    "    # Create figure with 6 subplots\n",
    "    fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "    \n",
    "    # 1. Original RGB\n",
    "    axes[0, 0].imshow(rgb_display)\n",
    "    axes[0, 0].set_title(f\"RGB Image - {test_date}\", fontsize=12, fontweight='bold')\n",
    "    axes[0, 0].axis('off')\n",
    "    \n",
    "    # 2. UDM1 Mask\n",
    "    axes[0, 1].imshow(udm1, cmap='RdYlGn_r', vmin=0, vmax=1)\n",
    "    axes[0, 1].set_title(f\"Planet UDM1\\n{udm_cloud_pct_calculated:.1f}% unusable\", \n",
    "                        fontsize=12, fontweight='bold')\n",
    "    axes[0, 1].axis('off')\n",
    "    \n",
    "    # 3. OmniCloudMask - Clouds\n",
    "    axes[0, 2].imshow(cloud_mask, cmap='RdYlGn_r', vmin=0, vmax=1)\n",
    "    axes[0, 2].set_title(f\"OmniCloudMask - Clouds\\n{omni_cloud_pct:.1f}% cloudy\", \n",
    "                        fontsize=12, fontweight='bold')\n",
    "    axes[0, 2].axis('off')\n",
    "    \n",
    "    # 4. OmniCloudMask - Shadows\n",
    "    axes[1, 0].imshow(shadow_mask, cmap='RdYlGn_r', vmin=0, vmax=1)\n",
    "    axes[1, 0].set_title(f\"OmniCloudMask - Shadows\\n{omni_shadow_pct:.1f}% shadowed\", \n",
    "                        fontsize=12, fontweight='bold')\n",
    "    axes[1, 0].axis('off')\n",
    "    \n",
    "    # 5. OmniCloudMask - Confidence\n",
    "    im = axes[1, 1].imshow(confidence, cmap='viridis', vmin=0, vmax=1)\n",
    "    axes[1, 1].set_title(\"OmniCloudMask - Confidence\", fontsize=12, fontweight='bold')\n",
    "    axes[1, 1].axis('off')\n",
    "    plt.colorbar(im, ax=axes[1, 1], fraction=0.046, pad=0.04)\n",
    "    \n",
    "    # 6. Comparison Overlay\n",
    "    comparison = rgb_display.copy()\n",
    "    # Red: UDM1 only (potential false negative by OmniCloudMask)\n",
    "    # Yellow: Both agree on clouds\n",
    "    # Cyan: OmniCloudMask only (potential false positive)\n",
    "    comparison[(udm1 == 1) & (omni_combined == 1)] = [1, 1, 0]  # Yellow - both\n",
    "    comparison[(udm1 == 1) & (omni_combined == 0)] = [1, 0, 0]  # Red - UDM1 only\n",
    "    comparison[(udm1 == 0) & (omni_combined == 1)] = [0, 1, 1]  # Cyan - Omni only\n",
    "    \n",
    "    axes[1, 2].imshow(comparison)\n",
    "    axes[1, 2].set_title(\"Comparison\\n🟡Both 🔴UDM1 only 🔵Omni only\", \n",
    "                        fontsize=11, fontweight='bold')\n",
    "    axes[1, 2].axis('off')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"\\n✓ Visualization complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fc2940",
   "metadata": {},
   "source": [
    "## 9. Detailed Spatial Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9878c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "if cloud_mask is not None:\n",
    "    # Create zoomed comparison for detailed inspection\n",
    "    # Select a region with differences\n",
    "    h, w = cloud_mask.shape\n",
    "    \n",
    "    # Find region with most disagreement\n",
    "    disagreement_map = ((udm1 == 1) & (omni_combined == 0)) | ((udm1 == 0) & (omni_combined == 1))\n",
    "    \n",
    "    if np.any(disagreement_map):\n",
    "        # Find center of disagreement\n",
    "        y_coords, x_coords = np.where(disagreement_map)\n",
    "        center_y, center_x = int(np.median(y_coords)), int(np.median(x_coords))\n",
    "        \n",
    "        # Define zoom window (500x500 pixels)\n",
    "        zoom_size = 500\n",
    "        y1 = max(0, center_y - zoom_size // 2)\n",
    "        y2 = min(h, center_y + zoom_size // 2)\n",
    "        x1 = max(0, center_x - zoom_size // 2)\n",
    "        x2 = min(w, center_x + zoom_size // 2)\n",
    "        \n",
    "        # Extract zoom regions\n",
    "        rgb_zoom = rgb_display[y1:y2, x1:x2]\n",
    "        udm_zoom = udm1[y1:y2, x1:x2]\n",
    "        omni_zoom = omni_combined[y1:y2, x1:x2]\n",
    "        \n",
    "        # Plot zoomed comparison\n",
    "        fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "        \n",
    "        axes[0].imshow(rgb_zoom)\n",
    "        axes[0].set_title(\"RGB (Zoomed)\", fontsize=12, fontweight='bold')\n",
    "        axes[0].axis('off')\n",
    "        \n",
    "        axes[1].imshow(udm_zoom, cmap='RdYlGn_r', vmin=0, vmax=1)\n",
    "        axes[1].set_title(\"Planet UDM1 (Zoomed)\", fontsize=12, fontweight='bold')\n",
    "        axes[1].axis('off')\n",
    "        \n",
    "        axes[2].imshow(omni_zoom, cmap='RdYlGn_r', vmin=0, vmax=1)\n",
    "        axes[2].set_title(\"OmniCloudMask (Zoomed)\", fontsize=12, fontweight='bold')\n",
    "        axes[2].axis('off')\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "        print(\"✓ Zoomed comparison shown (area with most differences)\")\n",
    "    else:\n",
    "        print(\"No significant disagreement found - masks are very similar!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026305f1",
   "metadata": {},
   "source": [
    "## 10. Export OmniCloudMask Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8809d97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "if cloud_mask is not None:\n",
    "    # Create output folder\n",
    "    output_folder = BASE_PATH / 'omnicloudmask_results'\n",
    "    output_folder.mkdir(exist_ok=True)\n",
    "    \n",
    "    # Save masks as GeoTIFF (preserving georeferencing)\n",
    "    def save_geotiff(array, output_path, geotransform, projection):\n",
    "        \"\"\"Save numpy array as GeoTIFF.\"\"\"\n",
    "        driver = gdal.GetDriverByName('GTiff')\n",
    "        out_ds = driver.Create(\n",
    "            str(output_path),\n",
    "            array.shape[1],\n",
    "            array.shape[0],\n",
    "            1,\n",
    "            gdal.GDT_Byte\n",
    "        )\n",
    "        out_ds.SetGeoTransform(geotransform)\n",
    "        out_ds.SetProjection(projection)\n",
    "        out_ds.GetRasterBand(1).WriteArray(array.astype(np.uint8))\n",
    "        out_ds.FlushCache()\n",
    "        out_ds = None\n",
    "    \n",
    "    # Save masks\n",
    "    base_name = f\"omnicloudmask_{test_date}\"\n",
    "    \n",
    "    cloud_path = output_folder / f\"{base_name}_clouds.tif\"\n",
    "    shadow_path = output_folder / f\"{base_name}_shadows.tif\"\n",
    "    combined_path = output_folder / f\"{base_name}_combined.tif\"\n",
    "    \n",
    "    save_geotiff(cloud_mask, cloud_path, geotransform, projection)\n",
    "    save_geotiff(shadow_mask, shadow_path, geotransform, projection)\n",
    "    save_geotiff(omni_combined, combined_path, geotransform, projection)\n",
    "    \n",
    "    print(\"✓ Results exported:\")\n",
    "    print(f\"  Cloud mask: {cloud_path}\")\n",
    "    print(f\"  Shadow mask: {shadow_path}\")\n",
    "    print(f\"  Combined mask: {combined_path}\")\n",
    "    \n",
    "    # Save comparison statistics\n",
    "    stats = {\n",
    "        \"test_image\": test_image_path,\n",
    "        \"date\": test_date,\n",
    "        \"udm1_cloud_percentage\": float(udm_cloud_pct_calculated),\n",
    "        \"omnicloudmask_cloud_percentage\": float(omni_cloud_pct),\n",
    "        \"omnicloudmask_shadow_percentage\": float(omni_shadow_pct),\n",
    "        \"omnicloudmask_combined_percentage\": float(omni_combined_pct),\n",
    "        \"agreement_percentage\": float((agreement + agreement_clear) / total * 100),\n",
    "        \"disagreement_udm1_only\": float(disagreement_udm_only / total * 100),\n",
    "        \"disagreement_omni_only\": float(disagreement_omni_only / total * 100)\n",
    "    }\n",
    "    \n",
    "    stats_path = output_folder / f\"{base_name}_comparison_stats.json\"\n",
    "    with open(stats_path, 'w') as f:\n",
    "        json.dump(stats, f, indent=2)\n",
    "    \n",
    "    print(f\"  Statistics: {stats_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ad02a7",
   "metadata": {},
   "source": [
    "## 11. Summary and Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f30f90f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "if cloud_mask is not None:\n",
    "    print(\"\\n\" + \"=\"*70)\n",
    "    print(\"OMNICLOUDMASK EVALUATION SUMMARY\")\n",
    "    print(\"=\"*70)\n",
    "    print(f\"\\nTest Image: {test_date}\")\n",
    "    print(f\"Resolution: 3m (PlanetScope)\")\n",
    "    print(f\"\\nKey Findings:\")\n",
    "    print(f\"  • Planet UDM1 identified: {udm_cloud_pct_calculated:.1f}% unusable pixels\")\n",
    "    print(f\"  • OmniCloudMask detected: {omni_cloud_pct:.1f}% clouds + {omni_shadow_pct:.1f}% shadows\")\n",
    "    print(f\"  • Overall agreement: {((agreement + agreement_clear) / total * 100):.1f}%\")\n",
    "    \n",
    "    # Interpretation\n",
    "    diff = abs(udm_cloud_pct_calculated - omni_combined_pct)\n",
    "    if diff < 5:\n",
    "        interpretation = \"Very similar results - high confidence\"\n",
    "    elif diff < 10:\n",
    "        interpretation = \"Moderately similar - review differences\"\n",
    "    else:\n",
    "        interpretation = \"Significant differences - detailed review needed\"\n",
    "    \n",
    "    print(f\"\\n  → {interpretation}\")\n",
    "    \n",
    "    print(\"\\n\" + \"-\"*70)\n",
    "    print(\"Advantages of OmniCloudMask:\")\n",
    "    print(\"  ✓ Separates clouds from shadows\")\n",
    "    print(\"  ✓ Provides confidence scores\")\n",
    "    print(\"  ✓ Works at native 3m resolution\")\n",
    "    print(\"  ✓ Deep learning-based (potentially more accurate)\")\n",
    "    print(\"  ✓ Can handle thin clouds\")\n",
    "    \n",
    "    print(\"\\nConsiderations:\")\n",
    "    print(\"  • Computational cost (GPU recommended for large areas)\")\n",
    "    print(\"  • Model trained on Sentinel-2 (may need validation for Planet)\")\n",
    "    print(\"  • Processing time vs UDM1 (which is pre-computed)\")\n",
    "    \n",
    "    print(\"\\n\" + \"=\"*70)\n",
    "    print(\"\\nNext Steps:\")\n",
    "    print(\"  1. Test on more images (different cloud types, seasons)\")\n",
    "    print(\"  2. Validate against manual cloud annotation\")\n",
    "    print(\"  3. Assess impact on downstream analysis (CI extraction, etc.)\")\n",
    "    print(\"  4. Consider integration into production pipeline\")\n",
    "    print(\"=\"*70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0371d22a",
   "metadata": {},
   "source": [
    "## 12. Test on Additional Images (Optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "415ef4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To test on another image, change test_image_index in cell 3 and re-run from there\n",
    "# Or manually specify a different image path\n",
    "\n",
    "if summary and len(summary['test_candidates']) > 1:\n",
    "    print(f\"Additional candidates available for testing:\")\n",
    "    for i, candidate in enumerate(summary['test_candidates'][1:], 2):\n",
    "        print(f\"  {i}. {candidate['date']} - {candidate['cloud_percentage']:.1f}% clouds\")\n",
    "    print(\"\\nChange test_image_index in cell 3 to test these images.\")\n",
    "else:\n",
    "    print(\"No additional test candidates available.\")"
   ]
  }
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