diff --git "a/haptix_dataset_analysis_2.ipynb" "b/haptix_dataset_analysis_2.ipynb"
new file mode 100644--- /dev/null
+++ "b/haptix_dataset_analysis_2.ipynb"
@@ -0,0 +1,481 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7c0d7108",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: azure-cognitiveservices-vision-customvision in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.1.1)\n",
+      "Requirement already satisfied: msrest>=0.6.21 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from azure-cognitiveservices-vision-customvision) (0.7.1)\n",
+      "Requirement already satisfied: azure-common~=1.1 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from azure-cognitiveservices-vision-customvision) (1.1.28)\n",
+      "Requirement already satisfied: azure-mgmt-core<2.0.0,>=1.2.0 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from azure-cognitiveservices-vision-customvision) (1.6.0)\n",
+      "Requirement already satisfied: azure-core>=1.32.0 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from azure-mgmt-core<2.0.0,>=1.2.0->azure-cognitiveservices-vision-customvision) (1.36.0)\n",
+      "Requirement already satisfied: requests>=2.21.0 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from azure-core>=1.32.0->azure-mgmt-core<2.0.0,>=1.2.0->azure-cognitiveservices-vision-customvision) (2.32.5)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.0 in c:\\users\\el081\\appdata\\roaming\\python\\python311\\site-packages (from azure-core>=1.32.0->azure-mgmt-core<2.0.0,>=1.2.0->azure-cognitiveservices-vision-customvision) (4.15.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from msrest>=0.6.21->azure-cognitiveservices-vision-customvision) (2025.10.5)\n",
+      "Requirement already satisfied: isodate>=0.6.0 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from msrest>=0.6.21->azure-cognitiveservices-vision-customvision) (0.7.2)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.5.0 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from msrest>=0.6.21->azure-cognitiveservices-vision-customvision) (2.0.0)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests>=2.21.0->azure-core>=1.32.0->azure-mgmt-core<2.0.0,>=1.2.0->azure-cognitiveservices-vision-customvision) (3.4.3)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests>=2.21.0->azure-core>=1.32.0->azure-mgmt-core<2.0.0,>=1.2.0->azure-cognitiveservices-vision-customvision) (3.10)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests>=2.21.0->azure-core>=1.32.0->azure-mgmt-core<2.0.0,>=1.2.0->azure-cognitiveservices-vision-customvision) (2.5.0)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\el081\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests-oauthlib>=0.5.0->msrest>=0.6.21->azure-cognitiveservices-vision-customvision) (3.3.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install azure-cognitiveservices-vision-customvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1b130986",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Endpoint: https://team1test.cognitiveservices.azure.com\n",
+      "Project ID: f39fce05-705b-4854-91f7-d361299f8a56\n",
+      "Training Key configured: Yes\n",
+      "Target Iteration Name: Iteration 2\n",
+      "Iteration ID: 3dcfe86e-5e09-46c5-af8a-840ea8417e5b\n",
+      "\n",
+      "--- Overall Performance (mAP) ---\n",
+      "Precision: 0.8333\n",
+      "Recall:    0.7206\n",
+      "Average Precision (AP): 0.8503\n",
+      "F1 Score:  0.7729\n",
+      "\n",
+      "--- Per-Tag Performance ---\n",
+      "Tag: E1N-Risky       | AP: 0.8473 | Precision: 0.8387 | Recall: 0.7039 | F1: 0.7654\n",
+      "Tag: E1P-Cozy        | AP: 0.8585 | Precision: 0.8199 | Recall: 0.7516 | F1: 0.7842\n",
+      "Tag: E2N-Dull        | AP: 0.8465 | Precision: 0.8594 | Recall: 0.6848 | F1: 0.7622\n",
+      "Tag: E2P-Energetic   | AP: 0.8479 | Precision: 0.8199 | Recall: 0.7613 | F1: 0.7895\n",
+      "Tag: E3N-Chaotic     | AP: 0.8987 | Precision: 0.8729 | Recall: 0.7885 | F1: 0.8286\n",
+      "Tag: E3P-Harmonic    | AP: 0.8512 | Precision: 0.8377 | Recall: 0.6904 | F1: 0.7570\n",
+      "Tag: E4N-Unpleasant  | AP: 0.8401 | Precision: 0.8166 | Recall: 0.7662 | F1: 0.7906\n",
+      "Tag: E4P-Pleasant    | AP: 0.7566 | Precision: 0.7990 | Recall: 0.6073 | F1: 0.6901\n",
+      "Tag: P1Hard          | AP: 0.7924 | Precision: 0.7889 | Recall: 0.6525 | F1: 0.7143\n",
+      "Tag: P1Soft          | AP: 0.8472 | Precision: 0.8314 | Recall: 0.6989 | F1: 0.7594\n",
+      "Tag: P2Cold          | AP: 0.8579 | Precision: 0.8283 | Recall: 0.7628 | F1: 0.7942\n",
+      "Tag: P2Warm          | AP: 0.8334 | Precision: 0.8508 | Recall: 0.6638 | F1: 0.7458\n",
+      "Tag: P3Delicate      | AP: 0.8618 | Precision: 0.8375 | Recall: 0.7172 | F1: 0.7727\n",
+      "Tag: P3Rough         | AP: 0.9096 | Precision: 0.8917 | Recall: 0.7879 | F1: 0.8366\n",
+      "Tag: P3Smooth        | AP: 0.8373 | Precision: 0.8019 | Recall: 0.7181 | F1: 0.7577\n",
+      "Tag: P4Dynamic       | AP: 0.8619 | Precision: 0.8466 | Recall: 0.7107 | F1: 0.7727\n",
+      "Tag: P5Static        | AP: 0.8764 | Precision: 0.8134 | Recall: 0.7781 | F1: 0.7954\n"
+     ]
+    }
+   ],
+   "source": [
+    "from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient\n",
+    "from msrest.authentication import ApiKeyCredentials\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "\n",
+    "# 1. Configuration: Load from user.env file\n",
+    "load_dotenv('user.env')\n",
+    "\n",
+    "ENDPOINT = os.getenv('training_endpoint').strip().rstrip('/')\n",
+    "TRAINING_KEY = os.getenv('training_key').strip()\n",
+    "PROJECT_ID = os.getenv('project_id').strip()\n",
+    "\n",
+    "print(f\"Training Endpoint: {ENDPOINT}\")\n",
+    "print(f\"Project ID: {PROJECT_ID}\")\n",
+    "print(f\"Training Key configured: {'Yes' if TRAINING_KEY else 'No'}\")\n",
+    "\n",
+    "def get_model_performance():\n",
+    "    # 2. Authentication\n",
+    "    # We act as a 'Trainer' to access the backend data, not a 'Predictor'.\n",
+    "    credentials = ApiKeyCredentials(in_headers={\"Training-key\": TRAINING_KEY})\n",
+    "    trainer = CustomVisionTrainingClient(ENDPOINT, credentials)\n",
+    "\n",
+    "    # 3. Get Iterations (History of your training)\n",
+    "    # Iterations are returned in a list. Usually, the first one is the latest.\n",
+    "    iterations = trainer.get_iterations(PROJECT_ID)\n",
+    "    \n",
+    "    if not iterations:\n",
+    "        print(\"No iterations found.\")\n",
+    "        return\n",
+    "\n",
+    "    # Let's pick the latest published iteration (or just the latest one)\n",
+    "    latest_iteration = iterations[0]\n",
+    "    print(f\"Target Iteration Name: {latest_iteration.name}\")\n",
+    "    print(f\"Iteration ID: {latest_iteration.id}\")\n",
+    "\n",
+    "    # 4. Fetch Performance Metrics\n",
+    "    # This is the core function. It pulls the specific math stats.\n",
+    "    # 'threshold' parameter can be adjusted, but usually defaults to 0.5 (50%)\n",
+    "    performance = trainer.get_iteration_performance(PROJECT_ID, latest_iteration.id, threshold=0.5)\n",
+    "\n",
+    "    # 5. Display Global Metrics (Overall Performance)\n",
+    "    print(\"\\n--- Overall Performance (mAP) ---\")\n",
+    "    print(f\"Precision: {performance.precision:.4f}\")\n",
+    "    print(f\"Recall:    {performance.recall:.4f}\")\n",
+    "    print(f\"Average Precision (AP): {performance.average_precision:.4f}\")\n",
+    "    \n",
+    "    # Calculate F1 Score\n",
+    "    precision = performance.precision\n",
+    "    recall = performance.recall\n",
+    "    if precision + recall > 0:\n",
+    "        f1_score = 2 * (precision * recall) / (precision + recall)\n",
+    "        print(f\"F1 Score:  {f1_score:.4f}\")\n",
+    "    else:\n",
+    "        print(f\"F1 Score:  N/A (Precision and Recall are both 0)\")\n",
+    "\n",
+    "    # 6. Display Per-Tag Metrics (Class-wise Performance)\n",
+    "    print(\"\\n--- Per-Tag Performance ---\")\n",
+    "    for tag_perf in performance.per_tag_performance:\n",
+    "        tag_precision = tag_perf.precision\n",
+    "        tag_recall = tag_perf.recall\n",
+    "        \n",
+    "        # Calculate F1 score for each tag\n",
+    "        if tag_precision + tag_recall > 0:\n",
+    "            tag_f1 = 2 * (tag_precision * tag_recall) / (tag_precision + tag_recall)\n",
+    "        else:\n",
+    "            tag_f1 = 0.0\n",
+    "        \n",
+    "        print(f\"Tag: {tag_perf.name:<15} | AP: {tag_perf.average_precision:.4f} | Precision: {tag_precision:.4f} | Recall: {tag_recall:.4f} | F1: {tag_f1:.4f}\")\n",
+    "\n",
+    "# Execute\n",
+    "get_model_performance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ee3fef33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1360x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualization complete for 17 tags\n",
+      "\n",
+      "--- Statistical Analysis ---\n",
+      "Precision - Mean: 0.8326, Std Dev: 0.0259\n",
+      "Recall    - Mean: 0.7202, Std Dev: 0.0501\n",
+      "\n",
+      "Interpretation:\n",
+      "Lower standard deviation indicates more consistent performance across tags.\n",
+      "Precision std dev (0.0259) vs Recall std dev (0.0501) shows relative variance.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def visualize_tag_performance():\n",
+    "    \"\"\"\n",
+    "    Visualize per-tag Precision and Recall as grouped vertical bar charts\n",
+    "    \"\"\"\n",
+    "    # Authentication\n",
+    "    credentials = ApiKeyCredentials(in_headers={\"Training-key\": TRAINING_KEY})\n",
+    "    trainer = CustomVisionTrainingClient(ENDPOINT, credentials)\n",
+    "\n",
+    "    # Get iterations\n",
+    "    iterations = trainer.get_iterations(PROJECT_ID)\n",
+    "    \n",
+    "    if not iterations:\n",
+    "        print(\"No iterations found.\")\n",
+    "        return\n",
+    "\n",
+    "    # Get latest iteration performance\n",
+    "    latest_iteration = iterations[0]\n",
+    "    performance = trainer.get_iteration_performance(PROJECT_ID, latest_iteration.id, threshold=0.5)\n",
+    "\n",
+    "    # Extract tag names and metrics\n",
+    "    tag_names = []\n",
+    "    precisions = []\n",
+    "    recalls = []\n",
+    "    \n",
+    "    for tag_perf in performance.per_tag_performance:\n",
+    "        tag_names.append(tag_perf.name)\n",
+    "        precisions.append(tag_perf.precision)\n",
+    "        recalls.append(tag_perf.recall)\n",
+    "    \n",
+    "    if not tag_names:\n",
+    "        print(\"No tag performance data available.\")\n",
+    "        return\n",
+    "    \n",
+    "    # Calculate statistics\n",
+    "    precision_mean = np.mean(precisions)\n",
+    "    precision_std = np.std(precisions)\n",
+    "    recall_mean = np.mean(recalls)\n",
+    "    recall_std = np.std(recalls)\n",
+    "    \n",
+    "    # Create figure\n",
+    "    fig, ax = plt.subplots(figsize=(max(12, len(tag_names) * 0.8), 8))\n",
+    "    \n",
+    "    x = np.arange(len(tag_names))\n",
+    "    width = 0.35\n",
+    "    \n",
+    "    # Create bars\n",
+    "    bars1 = ax.bar(x - width/2, precisions, width, label='Precision', color='#2E86AB', alpha=0.8)\n",
+    "    bars2 = ax.bar(x + width/2, recalls, width, label='Recall', color='#A23B72', alpha=0.8)\n",
+    "    \n",
+    "    # Add labels and title\n",
+    "    ax.set_xlabel('Tags', fontsize=13, fontweight='bold')\n",
+    "    ax.set_ylabel('Score', fontsize=13, fontweight='bold')\n",
+    "    ax.set_title(f'Precision and Recall by Tag - {latest_iteration.name}', \n",
+    "                 fontsize=15, fontweight='bold', pad=20)\n",
+    "    ax.set_xticks(x)\n",
+    "    ax.set_xticklabels(tag_names, rotation=45, ha='right')\n",
+    "    ax.set_ylim(0, 1.1)\n",
+    "    ax.legend(fontsize=11, loc='upper right')\n",
+    "    ax.grid(axis='y', alpha=0.3, linestyle='--')\n",
+    "    \n",
+    "    # Add value labels on bars\n",
+    "    for bar in bars1:\n",
+    "        height = bar.get_height()\n",
+    "        ax.text(bar.get_x() + bar.get_width()/2., height + 0.02,\n",
+    "                f'{height:.3f}', ha='center', va='bottom', fontsize=8)\n",
+    "    \n",
+    "    for bar in bars2:\n",
+    "        height = bar.get_height()\n",
+    "        ax.text(bar.get_x() + bar.get_width()/2., height + 0.02,\n",
+    "                f'{height:.3f}', ha='center', va='bottom', fontsize=8)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print(f\"\\nVisualization complete for {len(tag_names)} tags\")\n",
+    "    print(f\"\\n--- Statistical Analysis ---\")\n",
+    "    print(f\"Precision - Mean: {precision_mean:.4f}, Std Dev: {precision_std:.4f}\")\n",
+    "    print(f\"Recall    - Mean: {recall_mean:.4f}, Std Dev: {recall_std:.4f}\")\n",
+    "    print(f\"\\nInterpretation:\")\n",
+    "    print(f\"Lower standard deviation indicates more consistent performance across tags.\")\n",
+    "    print(f\"Precision std dev ({precision_std:.4f}) vs Recall std dev ({recall_std:.4f}) shows relative variance.\")\n",
+    "\n",
+    "# Execute\n",
+    "visualize_tag_performance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f21bc8cc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "14ca44bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RERERqRwKoyIiIiIiIiIiIlI5FEZFRERERERERESkciiMioiIiIiIiIiISOVQGBUREREREREREZHKoTAqIiIiIiIiIiIilUNhVERERERERERERCqHwqiIiIiIiIiIiIhUjr7d/QZERERERERERETaiYEX3RJK5fLd1+3ut9A2GDEqIiIiIiIiIiIilUNhVERERERERERERCqHwqiIiIiIiIiIiIhUDoVRERERERERERERqRwKoyIiIiIiIiIiIlI5FEZFRERERERERESkciiMioiIiIiIiIiISOVQGBUREREREREREZHKoTAqIiIiIiIiIiIilUNhVERERERERERERCqHwqiIiIiIiIiIiIhUDoVRERERERERERERqRwKoyIiIiIiIiIiIlI5FEZFRERERERERESkciiMioiIiIiIiIiISOVQGBUREREREREREZHKoTAqIiIiIiIiIiIilUNhVERERERERERERCqHwqiIiIiIiIiIiIhUDoVRERERERERERERqRwKoyIiIiIiIiIiIlI5FEZFRERERERERESkciiMioiIiIiIiIiISOVQGBUREREREREREZHKoTAqIiIiIiIiIiIilUNhVERERERERERERCpHWwqj5513XlhooYXCtNNOG1ZeeeVw//33T/D1V155ZVhiiSXi65deeunw17/+tf7c6NGjw49+9KN4fIYZZgjzzDNPGDRoUHj11Vcbfgd/r1evXg2PU045ZbKdo4iIiIiIiIiIiHQfbSeM/v73vw+HHHJIOOaYY8LDDz8cll122bDhhhuGN998s+Xrhw0bFgYOHBj22GOP8Mgjj4Qtt9wyPp544on4/EcffRR/z9FHHx3//9Of/hSefvrpsPnmm4/3u37yk5+E1157rf444IADJvv5ioiIiIiIiIiIyJSn7YTRM888M+y5555h9913D0suuWS44IILwvTTTx+GDh3a8vVnn3122GijjcJhhx0WvvKVr4Tjjz8+LL/88uHcc8+Nz88888zhpptuCtttt11YfPHFwyqrrBKfe+ihh8LLL7/c8LtmnHHGMNdcc9UfRJiKiIiIiIiIiIhIefQNbcT//ve/KFgefvjh9WO9e/cO6623Xrjnnnta/gzHiTDNIcL0mmuu6fDvjBw5MqbK9+vXr+E4qfMIqwsssEDYcccdw8EHHxz69m19iT755JP4SLz33nvx/3HjxsUHpJT8Wq0WH4nPO55+flKPc82af/fEHu/wPYZaqIVe8f+cT7+bnMd71f+dHMfjc5PwOeXvs93OqSufU/MY7uzYy99Ru53TpB8ffwx09n5qHB/tdU5d+Tzi2J+E+e3Td9Oe59SZ4x29l0mdy9Pva8dz6vJxxv9Erk9tf04dHv/895hfh86uuZ+Nj/Y8p64cn5Q1t93PaUqvuckea9dzmtJr7sR83lVYcz99N+17TlN6ze2cvd4z76d8fHR2zc3faTue04SPT/g9duYaNN83zfZ6u51TV44ne2zK2+vteT81rKMTba+35zl15Xh8bnLpRr26XwtrrVX0AGH0rbfeCmPHjg1zzjlnw3G+f+qpp1r+zOuvv97y9RxvxahRo2LNUdLvZ5pppvrxAw88MEaazjrrrDE9H3GWdHoiWFtx8sknh+OOO26848OHD49/A6abbroYsYpo+vHHH9dfQyQq0anvvvtuFIMTvB+iY995550wZsyY+vFZZpklTDPNNPF354Onf//+oU+fPuOVGZhjjjnidXz77bfrxxg0XBf+Hn83gfA722yzxfeXxF2Yeuqp47X44IMPwocfflg/PnOfcWHE2D7x/+l7f/Ze3h/bO7w/rleYtc+4ME12fMTY3uGjcb3C7H3Hhr7Z3f/2mD7hk1oIc001tmFSeHN0nzA21MLcU41tOKfXRvcJfTi37Dh/5bXRfcM0vULo3/ez42NqIbw5pm98f/36fHYzfDKuV3h7bJ8wY+9amDE7zvuDSfmc8vfZbufUlc8pjamJHXtMtu16Tl35nLgfJ+V+Sr+rHc+pK58Tn3tHc8SE5j1o13Pqyuc0qXN5Ood2PKeufk6c98SuT+1+Tl35nNJ1mJg1l7/TzufUlc9pUtbcdj+nKb3mcn7tfE5Tes3Nf087ntOUXnM593Y+pym95ubvvx3PqSufE+NjYveE0M7n1JXPKb8GnV1z099o13PqyufEeU+sHgHtfE5d+Zzy8dHZNTedW7ueU1c+J4TDyaUbTdcGWtj7778feqQwOrmhERMp9VzQ888/v+G5POp0mWWWiR/w3nvvHQVQPohmEE7zn+EDn3/++cPss89en1DSwsP3fPiJdJwPuZWHhoGVk47zu5uP8+DDb1bJWx0HzqvVcQYuzaua/+aXvvSlhpICI8f2rv8/Mruv0lm8w/Mtjg8fwzQy/vHXR7c63itOPI3He4UxoTbeceDmb3WcyeLjceMfZ3L5oMXxSfmc8r/bjuc0qZ9TGiMTO/ba+Zy68jmxEEzK/dT8u9rpnLryOXHOHc0Rnzfvtes5deVzmtS5PL2ndjynrn5O3DMTuz61+zl15XNqPt/OrLn572vHc5rSa25POKcpuebyftv5nKb0mtvqXKu85nLu7XxOU3rNHd9eb69z6srnlMaHa+6n5zQp+9zm39Vu59SVzynpEq65n55TPj46u+Z+Zq+35zl15XOanLpRrzbQwvL32aOEURRoVN833nij4TjfU/OzFRzvzOuTKPrSSy+FW2+9tSFatBUrr7xyVKpffPHFWJu0GcTSVoIpH8SnKYHjf2jNdHS8+ecn5fjE/s3OHk9B360T4yb38VbB31/c8Un5nFq9z3Y6p0m97s1jauLGZHueU1eOT+p9M/7vap9z6srnkX/uE3tt2vWcOnO8o/cyqXN58+9rp3Pq6vF0PSZmfWr3c5rw8Qm/x0mxC/K/047n1NXj3TN3lLPmfvY72/OcpvSaOzGfdzXW3Clhr/ecNbfz9nrPu5/y83bN7TVJ++LO2WM9836qp8a75sbjrcbHxNnr7XdOXT0+uXSjdtDCOvqZlr8ntBGo0iussEK45ZZbGsJ7+X7VVVdt+TMcz18PNFvKX59E0WeffTbcfPPNMez283j00UfjhWylkouIiIiIiIiIiEjPpq0iRoH09F133TWsuOKKYaWVVgpnnXVWrFVAl3oYNGhQmHfeeWOKOxx00EFhzTXXDGeccUbYdNNNwxVXXBEefPDBcOGFF9ZF0W9/+9vh4YcfDtddd12sN5DqjxKmixhLA6f77rsvrL322jHMl+9pvLTzzjvHEF8REREREREREREpi7YTRrfffvtYWHXw4MFRwFxuueXCDTfcUG+w9PLLLzeExK622mrhsssuC0cddVQ44ogjwqKLLho70n/1q1+Nz//3v/8N1157bfya35Vz2223hbXWWiumxCOoHnvssbHT/IABA6Iw2tztXkRERERERERERMqg7YRR2H///eOjFbfffvt4x7bddtv4aMVCCy3UUNS1FXSjv/feeyfx3YqIiIiIiIiIiEhPo61qjIqIiIiIiIiIiIhMCRRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FUREREREREREREakcbSmMnnfeeWGhhRYK0047bVh55ZXD/fffP8HXX3nllWGJJZaIr1966aXDX//614bna7VaGDx4cJh77rnDdNNNF9Zbb73w7LPPNrzmnXfeCTvttFOYaaaZQr9+/cIee+wRPvjgg8lyfiIiIiIiIiIiItK9tJ0w+vvf/z4ccsgh4ZhjjgkPP/xwWHbZZcOGG24Y3nzzzZavHzZsWBg4cGAUMh955JGw5ZZbxscTTzxRf81pp50WhgwZEi644IJw3333hRlmmCH+zlGjRtVfgyj65JNPhptuuilcd9114Y477gh77bXXFDlnERERERERERERmbL0DW3GmWeeGfbcc8+w++67x+8RM6+//vowdOjQ8OMf/3i815999tlho402Cocddlj8/vjjj4/i5rnnnht/lmjRs846Kxx11FFhiy22iK+5+OKLw5xzzhmuueaasMMOO4R///vf4YYbbggPPPBAWHHFFeNrzjnnnLDJJpuE008/Pcwzzzydfv8ffvhh6NOnTyiVMZ98HEqEz21S8Ho04vVoxOvRiNejGtdjUq+J16MRr0cjXo9GvB6NeD0a8Xo04vVoxOvRiNejEa9HI16PipxfrY345JNPan369KldffXVDccHDRpU23zzzVv+zPzzz1/72c9+1nBs8ODBtWWWWSZ+/fzzz9c4zUceeaThNWussUbtwAMPjF//+te/rvXr16/h+dGjR8f38qc//anl3x01alRt5MiR9cfLL78c/44PHz58+PDhw4cPHz58+PDhw4cPHz5Ctz5GjBjxuVpkW6XSv/XWW2Hs2LExmjOH719//fWWP8PxCb0+/f95r5ljjjkanu/bt2+YddZZO/y7J598cph55pnrjwUWWGCiz1dERERERERERES+eN5///2el0rfUzj88MNjLdTEuHHjYgOn/v37h169enXreyuB9957L8w///zhlVdeiQ2xqo7XoxGvRyNej0a8Ho14PRrxejTi9WjE69GI16MRr0cjXo/x8Zo04vVoxOvRiNejEa/HFwtlNRFFO1Mas62E0dlmmy3W53zjjTcajvP9XHPN1fJnOD6h16f/OUZX+vw1yy23XP01zc2dxowZE4XOjv7uNNNMEx85dLOXLxYmBCeFz/B6NOL1aMTr0YjXoxGvRyNej0a8Ho14PRrxejTi9WjE6zE+XpNGvB6NeD0a8Xo04vX44iC7uzO0VSr91FNPHVZYYYVwyy23NERi8v2qq67a8mc4nr8eaL6UXj9gwIAobuavQYmnO316Df+PGDEiPPTQQ/XX3HrrrfFvr7zyyl/4eYqIiIiIiIiIiEj30lYRo0B6+q677hq7w6+00kqxozzdpFKX+kGDBoV555031viEgw46KKy55prhjDPOCJtuumm44oorwoMPPhguvPDC+Dxp7d///vfDCSecEBZddNEolB599NExnHbLLbeMr/nKV74SO9vvueeesZP96NGjw/777x871k9MR3oRERERERERERHpGbSdMLr99tuH4cOHh8GDB8fGR6S733DDDfXmSS+//HLo3fuzQNfVVlstXHbZZeGoo44KRxxxRBQ/r7nmmvDVr361/pof/vCHUVzda6+9YmTo6quvHn/ntNNOW3/N7373uyiGrrvuuvH3b7PNNmHIkCFT+OwlQZmCY445ZrxyBVXF69GI16MRr0cjXo9GvB6NeD0a8Xo04vVoxOvRiNejEa/H+HhNGvF6NOL1aMTr0YjXo/voRWv6bvz7IiIiIiIiIiIiIlOctqoxKiIiIiIiIiIiIjIlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVREREREREREREQqh8KoiIiIiIiIiIiIVA6FURFpK8aNG9fdb0HalDFjxnT3WxARERGpNLVarbvfgojIF4rCqHTrQurCKolrr702vPDCC6F3796OCxmPk08+OQwZMiR8+OGH3f1WRNoe5lDnURGRL9Zp77z6KW+99Vb832AGESkFhVGZ7OSLZq9eveL/L774Yvjf//5X/16qbWw9/PDD4cgjjwyHH354ePnll+O4qPL1SKRrMHr06FB1MMJ/8IMfhEsuuURxtGle9V75FK9DCGPHjo3/5+vrc889F95+++1ufmciIj0XnPbwyiuvhKrzhz/8IXzjG98Ijz/+eLwuiqMKxDnPP/98/euhQ4eGkSNHduv7EeksCqMy2WHR/L//+7+w2267xe+vvvrqsPXWW0cBrMqkTfwnn3wSv66ySLz88suH733ve+GNN96I4uhLL71UeXE0jYmbbropnHbaaeFf//pXqDJnnHFGGDx4cNh///2jOPree++FKhvgaZP2+9//Plx11VVh1KhRoeqkOfSXv/xluPfee0MVef/99+Njm222CR9//HH4y1/+EtZaa60wfPjwUGXytaTK60o6fzfxjaQxwTr76KOPVn6MJAfLO++8U/m5I+e6664Lq622WvjPf/4Tqsz0008fFl544Wi3K4422mSPPfZYeO2110JVufvuu8OOO+4YbdODDz44fPe7343zSNXJ15g333yzu9+OdIDCqEyRyYCFgg3aGmusETdshx56aFhkkUVC1UWv66+/Puy+++5h8803D//4xz8qJ/b84he/CGeffXb8er/99gvbbbdd9MYfccQRlRdHOfc//elP0Ynw0Ucfhb59+4aqkiJmjz322Hi/HHXUUdHo+uCDD0IVSQb4YYcdFufSESNGaHj+f/773/+GCy+8MNx1110NG/wqwHhgo3rjjTdGcXSVVVaJ6y1OhSWWWCJUfb29+eabo+Nt0003DZdffnl44oknQhXhWqQ55Lbbbgt33nln+Oc//1l/vmprbhofrLcbbbRRHCfYH1UDG/2hhx6KX/fp0yf88Y9/DJtssklYccUVo8CRnqsy0003XZhpppnCq6++Gr+vqhi42WabhR/96EdhrrnmisLXU089VVlxlPkjzafsXfbZZ584p1Y1s2mxxRYLCyywQMzwIlr0kUceCQMGDKiULdbRGkNg2MYbbxz3vlXdv7Q9NZEpxGGHHVbr1atXbeWVV64fGz16dK2q3HnnnbUvfelLte9+97u11Vdfvda/f//aaaedVnv99ddrVeDtt9+u7bbbbrUvf/nLtQsvvLB+/Oc//3ntm9/8Zm3HHXesvfjii/HYuHHjalXjySefrM0333y1X/3qV939VtqCK664orb88svXdtppp3ivTDvttHGsfPDBB7WqkN8Hv/zlL2tzzTVX7d57763k/TEhDj300NrCCy9cqfXl0ksvrU0zzTT1OfPUU0+N6+0SSyxRGz58eDw2ZsyYWlX505/+FNfbAw44oLbPPvvUvva1r9XWXXfd2n/+859aVdh///1rZ599dv37gw8+uDbnnHPWZp999toKK6xQO+OMM+rPVW1OueWWW2ozzDBD7fzzz6+98847tSrBZ/3CCy/E+4P19d///nft0Ucfrc0xxxy1wYMHx7lkoYUWqm2xxRa1m2++uVYVxo4d2/L4RhttVFtjjTVqVSWtq9ioP/nJT2qzzTZbbbXVVqv961//muB1K53jjjsuzqV///vfa++++26tiiQb48wzz4zzKevs7373u/qYqOrYAMbFdNNNF233ZKdJ+2HEqEx2UvQBHiTSYEnLIYoFiIJrrp9YlWgFUnF++MMfxrRPvItETJ577rnhoosuiinlpTPrrLNGjzOeZyKaiPICvK0DBw6sVOQokcPvvvtuw7HXX389zDzzzGGDDTaoe+Gr6I0HUhv32muveI8QYUx0AuPkwAMPDBdffHHxntc77rgj/p+X27jnnntipPnKK69cP1615nbN55ciEphXieo577zzQlUg24CabwsuuGC4/fbbw5xzzhl+8pOfxEgNos4pZ0MU2JgxY0LVYA055phj4jpDA7fTTz89PPvss+HrX/96mHfeeUMVIMKNOm8XXHBB+M1vfhMbHd5yyy3hb3/7W4wMJIqFufWEE06Iry95zWVtTaRzvOKKK8K2224bI65nmWWWSq23fNYLLbRQHAesK2eddVaMmmW9Pe644+J8SqQTY4j7h3FTBVIUIBk7OUcffXS0ObhGUOp90hHs26gxSnQ1+5ivfe1rcT4hm4co/CpEjuZjgs+f6/DnP/85nHPOOdFm79evX/25KpA+b2wMWHXVVcOtt94ao0fPP//8uK/NI2urBteHLLddd901Rlhjp6Xj0l5Uc4TKFKF5QUAUZWNyyimnhAcffLAujk411VR18aPkhkzpepBWQMoSDYdmm222+vMYoNRh/fnPfx7FnpJr1KTFgPTOvffeOxoSZ555ZofiaKkNmbgODzzwQNhhhx1irdnm4uVs6Oebb75oTCBoJKOCtMeSa442d9TmXphjjjnChhtuGPr37x/vG8YLGzdSyTHSSy3uftBBB8W03/x6IACSMp7GQxIEuUdwNCGMUXO01LkU8rrMpGtxrySBfMYZZ4x1i0kprwoIoayhGN7rrLNOmGeeeWLJCRwKU089dVxbaHqYSnKwBpdafqF5ncCu4BhrCo2oFl988fj1ySefHJ8fNmxYsdciwXigweH6668fhWEEUGrPImp885vfDPvuu28cK6T4nXjiifFnSpw/sLNwGKT1Np0jjgOcKfl8mubXZ555pjjbI8F5cb48sMNw1N9www3hpJNOanDQL7fcclFUZ93B4cRrSiUXK1hbqKXJmHn66afjsaWXXjruWxCLS71PJnRdEAFZWyivwHzBOotgjhhYhbR67PV8/PP5Y59zb1BaANJ8wXOsP6n0Qun1VZ988snoeEIQXWmlleKeH9sdZ9xvf/vb+s8wv1Dup0Qo18N9kMMYoBQJtmkiF4pL3b/0SLo7ZFXKJKVh3XDDDbW99947pkXffffd8dgnn3xSu/LKK2sLLrhgbauttqq9//77taOOOqq20kor1d56661ayfzxj3+MKcCLLbZYTHNcf/31xwupJx2DFISf/exnxaY+NqfpPfbYYzHFcfHFF6/94he/qB8nVXrttdeubbbZZrWXX365VnJZAXj66afr9wApngMGDKgddNBB9delNJS99torpj2WnpbC/cI1ue222+J98+qrr8bjH3/8cfyfe2emmWaK99Kvf/3rItM/n3322dr//ve/+DXXIk8Xp6RA85zJffKd73wnptiXSv45P//887UVV1wx3itbbrll7a9//Wt9bHB9LrnkklpV2G677WpTTz11bZtttql99NFH9ePXXHNNXGu+8Y1v1B566KHaMcccE1Nj33zzzVoptJoLn3vuuVhq4/HHH6999atfjenBjBPK16TXP/zww/H7f/7zn7VSye0I0l8PPPDAWIYD+yuH+fWEE06IttkPf/jDWolgkz7xxBPxa2zPBDbqMsssM941e+2112rHH398/WdKIY3/tJYCc8OHH34YbfX555+/Pl/kcL9Q/mjgwIHxtaWRzyPDhg2Ln//hhx9e22STTaJdzn3x4IMP1u64445YhqLkdRZ++tOf1k4++eTxbBLS59Nam5c74jhlBrDpS+Xoo4+O+1hIthnjZOaZZ477tmSjJDvlnnvuiXuZ9957r1Yy3CcLLLBAvezG/fffH4//97//rX3729+O5RYo57LppptG26zU/S0p863sCexy5pFULi+Nj6eeeqp2xBFHFK9/9BQURmWy8be//a02/fTTx80qEyIbtiR6saj8+c9/jjUUmUgx0tMkWiqvvPJKrN/ENaCGFcbGUkstFQWOZnGU55555playWBYLrfccvXv2byyaDaLowiAG2+8cZG14NLCiIHA+SHwMR4YHxhcxx57bO3rX/96bd99940bfGo44UTAqKAOWMmw+eB6MBbYrLBJW2uttRpqimJw7bnnnrUf/OAH9fpWpQqA1JCkPvMf/vCHuphOzVVqSP7f//1fHDNvvPFGvFeYb0sVzfPzYtO233771a8P9wnrzPbbbx/nUJwtjA/upVKvR5o/ECkYD6y3bNAQc5IjAa6//vraBhtsEDeu1F8tcb3lPkAcTmIwYijiKCAMM5/gVMr50Y9+FO8rNrZVgbWWe4WakvlaC1wHrgnjqDRHUz4H3HrrrbVdd921Lvzdd9990WHN3NG82ccmKXF8YHN85Stfif9jrzNvIOKkmqvY5lwjHAo5fM+9Vhr5+KB2Zr9+/Wp33XVX/B5RCycbTnocB9hl8847b+2ss86Kz5co8rCmMBdQF3HIkCH14zjUVl111dgTobmON6Io44j6zaNGjSpqDmm2Ic4999x4XVJNYgTTueeeu3bZZZfVX4PtwbrLfVTStYD8fG688cZ4PyAKIg5vvfXWcT7FuQDYIt///vejKMrakgTlku0yrgkOpAT1q7G9sFvzXiLUcWYeLnGN6YkojMpkgcLTJ510UpwIckOjT58+0XOWYCKgKULJ0YDwyCOPxMWAzVku8DFBIg6yYLz00ku1KkFUAkZE3oyLDVuKHKVAdaIqhczZpE411VRxM4bhMGLEiCgMY2DgZOC6EK1B1EbJEG3AtcgjFf7yl7/UVlllldiYiyhBvKxHHnlkHD8Y4KXDZ44wjPCJ6AN4pTlG1OwiiywSI55oolKi0UnDHJxLkDZjzKmnnHLKeE3t2KBgaCKE0ZAoiealbUyaSRFwNEthTCCO5sY2G1oEoBKdTEn8ZU0h+4TPnqYPCe4VHAZLLrlkHCPYHTQfmnHGGYuNFs3v/4svvjjODQnuCRyROFaaG/wRuZLulZ5+z+TXIM2LiFhkISB84TjBych5/uY3v4njg3UWJzYi2CyzzBLttxIhepioYRrGYHeQoZGLfDfddFMUAXfZZZdi75FELmwSUU10aB4BmMAhiZ2KA4Zrg3hcsn2KgMM6wjyZRGDYY4894n2C+JPWYwJeBg0aFK9ZFUQeoszJQCBbCRuUABfsFK4VWQg4bddcc83ooEtzT0+fT1uBUxpbPB8fRFITJYpdmsRRsliIUE/XoJTmmB3Z2dgj2CHMnwkizgmIwm7nHmL+xVYrdY3piSiMyhcOKUekvbIxTdFNuTjau3fv2gUXXFC8mJEmf9JOSE9bdtlla7POOut4kaCIo3ifMdBLF4ibFxOiExD98g0bRidCMcY6G5XSx0cymBIYWSymP/7xj6OhyXVC8Lj66qujqJFHgZUI9wBiJ9FMzBcJrhOpW0QkIHbheSXSnMjS0ujI0CJSZ7311osOFoTiBBEKF110UUxlSxu8UoxOYJPFPIFTIB//GJdEbTTfU1w/ol3YoDG3Eq3RfJ/1dNK5st4S3UXEV56KlcRR1p4qbFITbNCYP/NsBK4V98MDDzxQ23DDDWNkC/YJUU3N0XAlziHMm0R+cV3y9PnkiEQcZd1pppRNPFHDiIBw1VVX1Q455JD6dSFlfLfddqtHFiOSImjsvPPO8XU44EoGG4txgUicbNM8wh5xFHEDJxTjpXSwPRHDGQeMCzJ0WkWEcn2IusfZktagUu6XVuIoJb4Q/HDUJyhzhThKaRb2epR9YqyU6HjD9s73bJTkAMQtzhnnEmsMwQy//e1vo51K1CQie7LFSrLJEswZq6++enQkYHM0X7Ntt902jhEyBHNKu1fIXksC8OWXXx7nBM4Ru4y5Nc9EQEhGIF1nnXXi+Ehrk7QHCqMyWSASAWMreZDySfDEE0+Mzw0dOrRWOnjgF1100bj5ImJj6aWXrm2++eZRLM1BAGIhzcPrS6Q50hHjkshRRA/qBCbwnrFwpM1KaaT7gU0HgjiLI+JGEm+SOErkaNXqzpAqz7zBmGDT0Yrbb789Gl0pgrAk8rmS1D2MTWoyp3HAvYGggziaInyaKTGtj7mAaGEEcYxQwCBPGQhsOtJmPj9/Ut0QydiwlAbjgtR41hXmC6JT8owMUh0pu0H9quHDh9dKJn32bFApR4LYt9FGG3VYl5bo2ZEjR9ZKh2uBCHzYYYdFURjnLPNHArELQYPNW+5sKQUc8ER2sXE/88wz432CcJFH9SRxtEob1DRHEtnFnLHDDjvEmpmptEByyiY7hTkmzbulQsAG4yNFxxL1l1JhWznWuIbUc8bxVrpzBbEziaMIg4kUFck9xDrbXJO2BCgbwfyICMo8SmmB3EnA3JHE0ZS10SyCliiKJiiLx/6V2qJ5HXzAeYAAmErclAZ2BXsWyiUghCd9IzkaeR4HHOOHObZ5/ihNIC4BhVGZbAsqNaxI/73uuuvGe56FtcSagJAmOhZI0kpOP/30+nOIwRgReI/YnLVqwFMKzRM+oi/GE4JO8+KAKIjBxeKSSMXNS4XNBqUliEwhJYuaTZSfSOfNwkp6GxEMqYZRVcDQIJUecZSUxmSglxb1NyEQxRG9SO0k9YZIrxSJgTjKfURafZ4uXPrmDHEUsRwDHKGTefScc86JzyEcp01J7lBBECSyONWKKwUipRG5KDmCyMfmDcOb9SWvG0kaJOmepQujzZtQyk3glEQMbHbO5U13SoaSAWRe4EhKay1R5VyXfB1mPkE0LNGhAqS4fu1rX6v17ds3rrHAOptslCSO4qTMMxBK3LTmdc3z8yNCkqhQ5so8ippNPetx3qSpRLgWRN/ndjmlNprnDyKO82tH6jR2a0n1NNN5UFc1dx5RMoDo0GZxlLUY26TUkgJkn5CxxX6Wc09jJG9wiDiKvYo42myvlzIuJlSaiTmUbCZ6ATSLo+z1Syrr1AoiYnHGIopSyiknF0fztHppTxRG5QtLF6cuYHNaK4YmnvpW4mjpGxLq/bFJbe5aycKJdw3Bp8QGSx0tgBjWv//972M6LAZ4DgYG0V8sKlybkoyJVhDpiOf5vPPOi9+z8cCRgDiKkJHEUZ5nMS2pe3RO+oyZO9iwX3vttfVoaoxyzp8NLcZEvqErkXTfcJ4IN9RnInKFsUDkKHMJkV65OEqEBpu3UsnnktQBGQGQUgts4KknSQM/DFJE5HnmmSeKpvn8guBD1GRPTe9rngfTNcHJxhrDdUmvoQwF44YNSi5klOZ0S6TzJkqJdZVrkjZlXBciWdisIgIinJMWyz1TFZGYzTxjn6Zs+Vp74YUXxpJGeVp9mldLnF8Z/2Sk4GRivkjRXnnKOBtXbFUi4Ep1yqb7hfROnErYXAh7lJhIDqVUcxTHApHmzKsllniakFCTynyRDovQk64dAihza/pZ1mAa3pVUHzCNEWwxxgdOWcrRYJ9hk2GrJnE0rylZOtwzzA84I/MGfvk6S9dxrgvXrvR61WQZULaGNSbBess9wj3Tam9bsjjKGkstWTKa2Nu3Kh3AGGrVAFLaC4VR6fICysTIZMDmlNqiRLjlhhTiKOJOR2mfJcImnE0rk2BKT8sXhbSpZSEtKQounSN1AGlusffee8dFgK/T5pyviWBqFkcRBhlLRHeUDBFLbNRZRClcn3vd2ZTRYIh0jGScl5gCnM8fzAtE63A9ELwQLlI9J6IVEEcxzEvsktxRVCTiDiJoXiqAtGmcBmzMksiH8VmqsZmfF+Im90TqhMx5k7bE/EptM4QOmqngkCL6PBd3iCjtqY1D8vmUiIw8eod0YES/JPKlVD3SgbkueYRsifdNPn8giCNQUGoBUSd11kYEZDNChCR1RXmQ2lcirT5j7hMckWxkc5g/sNkQTWkwVDpcG+4T5lW6IjMOcMZBbn9xz5TorM5BtJl66qmjbUYd81SyJnXSZv0h02mOOeaIolgSTUsWeaglS2doxL8csjFwtBEByLhhHmm210ssdcScyRjhmlCDFhGdOpE47XHacj34mnWG8gMl0mxXsZa88MIL0Q7j3th9991bvpZI2hKdSwlKnDF/MiYoM4GjKW+yjDhKJhPzSokOlQlBqRHsT/Yx1FZtJY6y5yu9bnVPR2FUugQFqOncyMSIJ5GIQBZLarHkAhcpfkymvKYqsPlA5EEAZUFtVT+wJBEwGQekIxGdgbCDYc3XjIktttiivilN4ihRcKQmUZMWo7P0GlZJ5GAzSlpOc8oFHnnqjeJkSKlKJQpf6T649dZbowc+GVZElpPuiOH5j3/8o35NKPjPWCl9fLBRRdghsomIyOYoR+4V6jXRyC2PIi5xjCSIrOaasIHNGy8RWUxEC9eqVUR1T3c45fMpUdOU3EAcTSAGU4rjlFNOafg5hA3m3eZ6ziXCHIG4RwQksL6w1jC3/v3vf4/H2KQiXhARWGJN4maoSZyc0AgY3/rWt2qbbLJJLN2SYB5lY0sZBhxSzU0yS1lfSA+nIQab1QTHELnY0HNvAfcQ80yJDoQE50aqM9FcOJkSjBGcTGSrJLGYuYf5tdRMlea1hTqhnD92B4JxLhBS5ghnNUJPWlPyetYlwVxJtDRRwzRly8FWpY4mkcTAXMpcU6LIk3+2OEsQ+1hbkmMSWwT7jBq0Cez2m2++uf59ieIo6wWOgpQFiXMWe52AKOqZJ9AByGQq8Rok0lpBwA/7+HzMEAyFOIrukcRR7p+8/ru0LwqjMslgZFF0HM8hEM1DdAKGOBMl4eR5vZ5Su2mnCZJQeh4IOQmEDRofEPH20ksvNby+JNKiQG0qhHIMzrzOHxGydPokzYLNCNeA6C5EUwxONmdV2Mjn9VZJeV1ppZXGa0KG8cX1S2J6CbSqEYqThAjZ1PWVzTpiORt2GpSRwpeEdO6pEuus5nMBnmQ262w8MDJxqCCQN0ekYJRz3UrcmDVz0UUXxXT5tGFPKdKpyzrzK9FONEMoKXInjQvET+ZNNhmtanITTc3GhM7zGOfcI6S3sZkvdb3NI3iIaOKRxgLzB5E82B6MieaIjdJhI4pNRpp8Sudk3LBJo4M0jhfmF77GTmNTR7Q+zR9Lu3eI7EK8wL7AgYBDKWXvII7iqOXeQQTiepWUDt0RpP2yrqQu6mk9xpZH8PjBD35QqwpkGLC2YIcmu4uIUexXRK60pyE1mmj0XBQtgWQ/MCaa107sdKJoIWUuAXZZKnUFJQtf8KMf/SjeFziiscVwMGGTpvJGCOk4GphbWHNLGRutYBywfhCkAKwv3CuI48wbzKV55GjJYyStMQjm2J80rWM9SZ3ogbWGDBb2/owb1phSs1VKQ2FUJhlSKkg1QcBhYWXxIFIUaP7AREAnuiQIlkgyLlgkiA4leonIWCIik0CaxFE80iWJXc0geH7pS1+qb1TTgpiuEWOF9Jxm45t0i9LTxRGMMbopFZA87AgXRGoQ9dYsjpYonqf6ZXnnXzzPbEzYlKywwgr12jtcJyK/mEPSxqVkSEfDm3z22WfXj3HeGF04DToS/UoyOtlgXHrppQ3HiGxKJTeIYEIIJKUPoScJ6qS+fu973yvqWgAiJ9cEJ0mrTUpq/EBtTURANmZs3kgrL7EzcKt5kZIbRASy1jIm0vzBvcP8wYOo9FJp5RxhbCDsTDPNNPXoLlLIKXHE+MBGIZsj1cZba6216umwpaw7jAvKN9HAEHGLtQcxmKwDIoeTXcYmn2yVUhuB5p8pYwVnJMIojrX0XBL8KHmV15wt/X5hrSFLKRf++Jq9C2sM9ixz7OWXX15fW0oRvtJ1wBYlEwHhl5T5NFZ23HHHeCyRau7SSJY1qadnY3QGRD4cK8n+pN4uNUbJkkxjhaZ2OOG4n9I1KcUOye+V9PkT+IMdxj6WtYQSR0BEPns71luiSKsA2W2Mh5NPPjkKnowD5hPq76b7COfLcccdF3WRfN8j7Y3CqHSadLNzg6d6kam7K5szFswUpYLQQ/pJq3TQ0hYNJki8yimthE066X1s4lPqBdFwXAs8i6UYV/m4YJO19NJLx4idPFKU5/LNFvVpEE9LHBMdQeQK0Y+I49R1I80zCUCMCzYjbNpKT7PgXIncoctr80aUNM/ccYBgSrojG/wSU7VyiNYhVRqjMncapKhq5tXm1PnSwPDGoM43qXDsscfG+4YGIVwDHG1ELZCWxOa1uRRJKZsSYAPC5iN3DNx9990xOhRxi40rEQvA/YRzDodCqXW90jrCNUC8yGG+IPuASMAUactYOfTQQ4sWvRKpuVK6RtxHCH5sVtMYwe5gnc7nEe4l7JJUu7cUEHoZD5xvstWIMieCh2i4UueMnHwsQBJuSHPF4Ujt5RzsENLISxHHcxCwKE10+OGH18vxkCJNpFcq2ZPAiY09n9d/L2mcpPuBLAwiZomKZI7I115seAI8cNznIPBglzWv0yVC5gFiKLCuzjTTTHUHElkrrZzVpezt8v0tgjmPPGOLaEgCgdI1wBGFwE5JllLukwlBsBf6Rmo+hmOW+wUblUAGxNA0j/J/FbK7SkJhVDpFusmJQCBdnmgdjM50nAUEQzRFSbLYIo6WuIASfZGMSjahNENJ9VWou0MdHiZHRKAhQ4ZE4QMQjXPRsDSIxGBs7LbbbtG4TDBG0mKJsYmBUYW0NaDpC6mw1OYhKpYNKMY5aSdEIgCGOmOIdItSI2fT54+QRfoRUUq5B5VrwWYtHWM+obB7cryUDoIOdWdJpc8j7Ll3EIJIB8XwrAKUZkk18FhjqHXGZgzHQRLJ8cSTolSygwVxD2GUcgLA+VN6g2g/GtUR1UNU4IMPPlirCtTYxcmEwyRfY9io4VhIIij2CWOGDWwVrgnnntbUZJMR7cbmvpXIw5hBCGO+KbGEDZFtOA8SyQ5FDMIuQ0gvUQBs1X2edYM5g4jh9Fmnxjk0/6SMAvMJDusSo5rYh2CX4ijIm5DhhMXpiNiXN+fDTsfJn9fkLQ32LVwTxkROEnD4nzmV+YEIY2x6ShwRIddTGxlOiOa5gPOnPiSCKOVYuDeSKIr4iWOOgIc8craU+SQ/D7JVcJwhjOa2FusJtgdZgIijrLWsNelnSxSImx35ZBuwn8PhRqQocyj6B3sbRFKcsqWMiaqhMCqdhogUaoeyKDSnx7NRxeDEG7/RRhtF8SuvC1ea0Y1RiZjBAkD6K1EY1I1kI5sKcqfOnrw+rztaImkBIM2EqFEMqfzzTwsMkcU0BkkRx6WDcUlkV95NOkXOkuqXhHLGTkmNQXKDIomiaXPK9xhVCJ9JyEAcppMl14R0RwzwEuePZkMrLzdBhCAbNaKK8/qQ3Ftciyp44jE42agzv6ZaVpx/LnAh+CAi40go0ROfby6oQzxgwIBY54x0eRxwqWEMojHHiaotjTzaIkG6GvNDc7RoGhPUzGTckFLPRjYXTkuGdYNzp4lMOud0XyDupJICRMg1C0aldmBHJMYeTRE9CUQdIs1LFACbIQqQdZZakbvsskvM1EAkx9EGiD6UaiGKlrm0RMGL6FhsCUSsVmINJa/ISKD5EqnT7GPWX3/9WNanxPU2zac46rE1JtTMkjmEfR7lSRAJq5AOnGcnIWyR3cW6iwCYYO9C5l9z08PSIKiHiOLmupiU42BsEF0+1VRTRXsV8TyJxKWJgdwjDzzwQPyaTD9S5SHt6QniYP5IAVB8j9OBerwpk0N6Fgqj0imIZEPwTIsBGxEmDCInMSaALrBMECygRLuUCiIoEX5MfES0pRQD0jsRd1LUH5GBbFaIcCmxcczniaM0gciNbRZO6lgRpVBiJHFH0Txs0JIBmoxt7g+aXqR7p0SIyBg+fHhDjaIU+YZwgfOEEgJEYAPiH+luGBYlps/nIh5dtDEsifqjFEcCoZzNKoZVq+Y5pW3W2Hhxznn6N4Y3tasQc1INK8C5QHkS5tjcEO/p4mh6/7n4m86NeZLNPRtZhPMcxgciYIo8L4kk3uTw2SPuIHCktSa/H9iw8hoE9VIFv47GOvMs9gjZCbkgTGQoUWE0hUjCUE+/XzpqfMl6wzqTxgSljSglwHjATsMuw0ZjbsURWTLMlTTJyZtqMddif9EwJdllyQ5L9YpLgnuC6K1mx1FzeSfWX9Zh7BFK2hBdW1q9yGaI7kMQb0W6NrnNVuq1yOdC0sC5Jn/84x/r9wRzKns45hfmD+x49sGsu6VERXYEkeYHHXRQ/BrbAzsDQZi9b9rfs07jYCmtBm+6DxgDZMLSWAntA5u02TFLbVEckwmyWX72s5/V9z7S81AYlU6BYUlqEik4GFN40/A4EmaP8UkkIGBQlGR4J5rPiQ0YBnZaRNNCwiSZFgk89dSHKzU6stkzmKeVkMLVLI7iacMDmerAlUYrTynpJ9SiIc0iF7rYmJEezXUqEaLZSK9hfkgdxAHjgs07zWFwMFDji81Lc13akqHMCClqRFVTtB9jizStdN5cCwx0Il1K6rTeDF1dicyhHjP3CHNqPn5SZH6K+sLoJtKaGs7JAO/phnhaV5gTiVTCkZJo3pg2g8hDhkJpzQ2J4kKgYOzn6y7rB9kGqWlQPk9Q46z0+SO/FojlCH+IfimiBzEsiaM8f88998TGZWSuJHr6/dJReSdqu2FvUPeOuRQRg/uHOQT7lKgmxg7rTamNyXJYW0nnzJs6cr0QjxE3uJcYT8lWLfGewbmKqEXARiua7wVsMuy10tKBW0HNbtaOtMa0+vxJnceBmyhtjOTzKWXAsMVwGuCYTs5q5lZsE0RzIs0RyRBFSxbOuS48sLMQQXGuYKOTpUPkOSIgjunmLLhSrkXzOCdwgyAWbNEULZrmB64TzYZpoIsgyj6PMVRaze6qoTAqnYbua6SyEe2FwZ0WTSZLUnFKmRibYdOKyEMKYx7pyKLBApGgrhnX5sgjj4wCKWlLKRquJEMCkbw51TF99qT1pVTPFDnKAkt5AcowlLopSdeBzSg1AdmYJti80liIaI3nn38+ep2JimSxLblGIk0NiAhNtd4Qvti05/XuGC+IhJQbSFGipRngOTQYont4EjO4RzC4qK+ap2ZxLYgoLXVORbwgRY30Re4VNhukjOfRbkkcJeIaD3yag1pFC/ZkECuoS41ATLQSERiJVk5Gxg5GOGtxifUhWW/TvJhHEmNvIGyREp6vP2xQiIBK9QNLnj+ABm2MFaJnuR6IgalJCPcM60wSA9nIl9xB+vrrr492FnMnawnOJuZXnAbJIY09Qo085pnmZm0lg5BB9lZznW6EDUp0lM6wYcPiHJmcJq3WC5y2NCBrju4qJbijo7mQ8l+IfcyZSQDOrw/ZC0TR5o66Ukl1NE866aQ4b8wzzzxRCMwzebhOzCGUxiotOrKjsc75U0IBu4z5Ndka2G7s90s5/1bXgghh5g/S47kXKFnEWsv1oD51DusOtjqCOsFiVSnhUzIKo9Kp9CTgfyYLoiRZHNIkQmo0hc1L2ag2w0YD8QLxhtTOVJSda8NkiacxwYaVTT5e+RInSBYBRGIEnjRO0saLTQc1eRCI03N464l8QwQpcROfg9FEzR02o4wXmlukKFGEHRZNjhPdQjRHqSJxPg+QasN5I4iygU21eiAZVkS8ET1bWuRbMzhV2JCkIv6MFzYnpEknARDxuHkzU9q8etlll8X7II+WJrKJYwgdOaTVp8jRPGW8BPGLc2DuJF2NSD+EG0rRUGO3I3EUIZm1BYdDaeVq2HzmY536wwjGeZ1I5hKO0WWaTQuiD6IgzhWcTqXDnMHGPW3OiIwkVZg154QTTmiosUlpkjR2StzEImpxHySHEkIoG1gaXyIKI3JUocZb3mwrRVMD4wHHPQ6FvEwHTvtkr5cwj3YE9jlrap5K33y+ZC2wfynReZDufSLvmQ/I3Er3A+OECGqy3po70nONiILjPsodUyXCGsp8ioM6wbUiApBghlwcLdEmy20L9vXYn2QipPsB+6s54xFRlP4Apc0d6VpQR5c9PLV1abyVIvA5ju2BjUYATCtK7yVSFRRG5XPTk/ifzXyzVxXDg+hIRI8UJVgiTIqIgWxIidbAm0j0LGB0sYjmUXBMjrmBWhIYWaSWsCG54oor6uMFARCvK9GhaYFJzyGOlVrzLZ0n50wpBerPsAnBCKW2LGMmNVViXCAGUV6hpEjRVjUSc8ORCC/uH8TglH7T7HUvxdDMaWU4It6QZsOGg41HioREJKdRBgJg3jm3NBCzEHHYsCejE8hA4NxJ20IkveWWWxp+jqL3pUZo4CxIdauIBt16663HE0fTWGIDe+utt8Y1qSS4J9igEnWRb1CIxGADTy1zYAyw9hBtj23C2otzskSnWz5/pGtCrVA6Zuf2BWPhgAMOiDUlW23MSpxbAVGcKC4csgg+ONcYL0BEJOsN9VVLu1dagbjFfZHbpkCmDuOFSCcyV3Dy46AsvYlOuncIWsBZjTOuee5lLmWupeRVaaTzxDnC588cSvQsghZ1h5MtgjDKfcI1wBGFUMw9hMO2xMCO/PNnDmXdwamWHLLpeex3Ajq4n1qtwyWQnwvlnVhPcTximzU3qMNup9wAWQqMp9IaLaXz4H5Bz8BZzX2SjqdxgUMScXTbbbeNQWJAxPlxxx3Xje9evmgURqVT6Ul44vHAJyOTpjEIQUwSeNhKBgMKTxrRoIhaXB9EL4wuugRzbVhYSqU56oQxwALJpjSJo4ifRNPmC2Upi2ZnIqu5JkQu5Z3UcRwwTkiPbW6cUgoTqpGY1y9jfDBe6ACbohZyoau0sZIb4Hjcmxs+cD0wMJPDgE0JxhhGeCkCYEdw7mziEUMRR7fbbrtoiBOlQq1q6o7yPSnCbOSSYwF6+rVJ4wLHCHMn3W6bIz+JRiAqAaErbco47+busCXBRouNF05Y6uum68R9gbiF6IUomNchZU3GicA8W6oTEqEzT4XGQUBpklTDLM0p2GA4FtJmrTQ6Wh+SPcrGlBTx1BmY7xE8aJRSujDKZn3WWWeN4jg17ihZRG33BHYZQjHiGOn1JXaf7wjuB8QtnI4EdyDwEAXHNcOGZb5Ja0ppNghlvHAakSrO18wdOJ7IWklwbzAmCGpg/mAvw31UWjZCM9haNMdNTVBT1HmeCck6xD1D5lfJgT+MB3o/pAw2MlcYC9hgaW/PnEHJPKLNS6nv3gyBXzjtW+3lsU9S4AdzB2swAUIbbLBBzJLsKIJUeiYKo9Lp9CS8i4ijGBcYoHipS6zZhFhBcXKiupJnjIWBiTAZFdSJxLOKMUqxZRaS5iinnk4StvJIwLxQPYsCHsa8AVVpxuXngRBIEXvuESIT8LjnsGnH6FhppZWKFUcnVCOxOa0eYxOPdN6QqWROPPHEOG8gGpMSnFLWmCtSejieeSJqEQoTpRmdrTasjAXECyIV8mwE0oOZX3A8UTuylIi3PBqFVF+cBKR6Mn8gkjZfn5RWzxzDOsN4Yd0tbY7Nm52Q0oitgRMljxxFHGWjliJHS4fxgKDDxpwxkMRfrgXRXFwPBJ4Ex5lXc8dcSeMjzQEIGWxC82ZbwFyB0JVEZJqDEk1aoija7GTDWZ+XUWBtodY9Qkb+Ouz2PGW6VJqbStHMEGGY+ZP6szircb6RzVNqEx3mBj7/XCAH1pEkBqf5lXPHWc099c4774xXk7YE8vuAORKbg0ymVJ6GGu8IxwnKUnD9sOcZL6SXl+p8QyROEdXs6VlfyGTCMYttkiKHS29Mxnlie+SBXmTzsOfHHsHJlsRjXkMgDCVJSo++ryIKozJR6Umkux188MFFd1qnjEDfvn2jAXH88cfXoxA4TnoF0bLpWhHxROQsRleJ3daZ9PEmMwYoTs5GI4kYLKp4m6nHwsKaDK1SCtd/HqkGHp0KabhEkXI2szQdyiG6h9eVVkOzszUS800Hm1oiJREL80Y6pZCfD+nRRPJgZBKFQc0qUhuTo4H0NeYNHE504S4tPakj0vkRAYnBiZh+8803159vtUnt6RvXXBQlwuCHP/xhdK5Rw4wNOp8/G/j8s+deYc1lzSG9q+SI0bxeNeVGiMhoJY4iFBIBVzKpMRm1dYlqYn7geqR199prr42COmnRzLOUEUAUZB0uae3F3uCRuPLKK6MTmnWWewLbLEGEKMIwdgpiEM9zP5Xe6JGIadI6ibbPSeIo9TNLFLryJoUIw8yrzeWrsEmJBuQ4ojBCGHMHD2z40urv5vc+nzn2BfdMvn4SaY8onM45/5mS5o6OQOAkIhDHSX7e2PDYYjhieQ4blowVYA3OndY9mVa2JX0gsEW4h1hrUmYG2SxcE+bcvBRaqfYpJYpwnFC/HKj9T+APdjtzLEIxonEKcEkl1KQ8FEZlPPAaTig9CSO8RE98s8eVEgIsjkT2EO2GcYFnHk9ic1Hy5vqrpcBnzuKIh5WNB4YmhhV1rPCmIY4SOco4yWuOlg6pNdTXzetTsRFD9MOzmBbXRCnG96TWSEyGOT/Lhq7ESPMcNl7UHspToanxhnjBZjVt4rgWRAeW1um0o/HSbEwyXogcZf7IGyDkQmgpcwprBtEnGNk5NEdByGnlWGNDRm24UlP50mfL502UTjqGUE60Si6O4ojCsfD1r3+9bpOUBk0Nm7NPiEphc5qnQCMeE91CNgLXifk2OVZK2KwxHqg3zLUgYofvWVtx2iMEs96QoYGTPr9ONAbB0VB6ujjiONcGW4xoNxyNpEs3r0G8hmjaUubQHMYCmTqzzz57PE8cJymyGgGQcmCphndH51/CvdJcogUHNVlcREImkl1BIAdNZGmkm65J6Q2WEthcOE0YKxtvvPF4z5OZgR2Co5Zgl9R0mGyfEkqlNY/1ZmczwR0EdqRSV0SPEmFMX42e7pju6Fqwz09f83mTSs8eF0ckJUnY76eIWfZ3rLdpryPlojBaYdLCSHoSQg6LaZ42Tb2iqqQn5Z0bUydxNhps0IiGo0YRIhieeTZn1EODlJpUouGZrgHRTZw/mzHqqxKpQTTtHHPMUVtvvfXiZgRPGtE8RNWWSvqMSQPHeMLwbjaw8KyygSOS9qabbhrvZ3s6XamRmIr+l0j++eJ5xplCdH0ePYxhTrQK4mgeOZoozfgkmicXxPPrxPxJh21AGGasbLHFFtEYLxU27Yh6rCdEOSVo3Idgmos5KYoF0aPUGt75WBg0aFCcNynVk+ZNrktzWj1jijILJYJAQeQwQmee8owNxmaeuYO5JWUe8BrGVKnd55kvf/rTn8ZzxwlJeaO8fAAC0NRTTx0zFhJchySwl0be6JK6zL/+9a9j5hZOe6JDcbg019tFCCsxk4loLj57xBzsDSIBGSdDhgyJ1wS7tCoCRnOJFkqOYK8zj+RlrpKgTnBLuo9S81i+L8VGnZDoTQAL+zgyAtNeJc9cyudd7DMi9tnnsO6Uci3ISCC4h0jYvFEb+zrKYbEX5oFInAvCpdin6VqwH8G+oAlZOjfWDpwplBIkSyW/J3gd+96095dyURitKOmGZ+FkA0/UDpszDIrUFZloQSaC0tOTWnVubO5yS31AojLwJCUPfWl05D1ns0X0LIsm4iiQmsQGjY0snegxNBg/qSlEqZCehbFAWhaGA4Yo4mAOqRYYnxgeJW3SrJH4+dAoCIMaZwIGNQJofr5s9plLSAfN00RLg1pMiDwY282iDWsOYyGvx8vmlvHEdSsZHCdElBNlTwQkTkcinlqdN4Z7qZGiCYRwSgtQr4sNG6lrzCeIOanmKDYI63Mp0V0dweYMxzNOAtYWzh9bhAgWhEHmEuZTRLC99torRlTm9PTrk94/DafIWkqZS0TaE71DmmdzujTiKHYpZQVKo9U6idCJAEpUbJ7eir3KdcDmKD0bI0VVU587h8g+7h3IG/aVTGdKtCDo5GnTlHUCHG9EXae6iSWRz4WcP2spYl+qV7zHHntEYR3nW/769D8/QyAQdnxJjknGB/t9spmwQ7mPCIAC5luEQuYR5lq+TlkIpZA+X6JAWUfJMujs3HvUUUfFa8L9JWWjMFox8hue7mqIXXieU0osE2Xe4IBO7ETFlZqe9HmdG3MvGYsrRjpiENeJqLnSFgzEzqFDh8bzTEZDep76OyyarTxmbOBLbaiT7hm8zdSgwaBIAijRPBjkf/jDHxp+BgdCbpD2dKyR+PkgahApnIxMBHRSc3Ae5NcFsRwhuRQPfCs4f+YLHG5E86TxQ4OH1CG41RxS8jVJIGiwpiJycV+QAppojqwtGTJPiFZKay2iF/ZIXp4EmGOo81W64AOIoTgMsEG4FkR/5ZHlpPsxz5DqiQjU08XQRB4RjPBHCQmEG8B5QN077C4iSJvBkY8Nl1JAS7oenBPraBoDzJ8IGwhazdGACDg49nG6lFbPPIf01uRYw0GfYNzQxLAKDaYmtUQLY4l1hzWHdbjELJ587cThRukASpFgqxPkwxzLnMLXOFzyzK4EaxG2bk8vM5CvD3z2CJ44VwCnI2OAcZIgcvi8886Lc2qp3ef5XMn2S85oxgu2CM6U5Ixrtku5X7DVShLJpWMURisC6a4pfSJNlmxOMSQAUZBJk6iENFkk73yp6UkT6txIbdG0IDRvUhHESuownsYDkbMIf0TtEBmL8Y13Pof6iHjaUuRoVcCIIMKPdD42KWlMYHRicBFpTWRkyVgjsXN131KaNEYWDpeVVlppPHE0UZIQmNeLBKKDiSZH1CI1iXkG8TxvtJT/XInXZELiKA4F5tu83EKpgmir80LUYNOKA4nHvPPO2xD5h2OOtGHGQ55CXRJsUhGGSd8jJRo4X7IQuG+o+ZZKGeUb1Fz86eniaG5/sL5Q0445Io9WwhalGRXza6obmUOUaSnkDccou0J0MHNEfp0WXXTR6IQjuCGH4Abuo5Kc9q0geguh/Oc//3ndecL3RERWjYkp0UJQA/cQ9lqJkaI52BycP+VHmB9wJiGIpXsG+wRxlOtRmtOeOTStG2neoHQAtmiqwcs+LjmouRZ5jfdSbTHGAZlcZIema7PDDjvEDFDEc/ZxeTk4xhD3Fo5K5l2pBgqjFYA6f3QWpx5PHn3AZj1tRDCmMMDSJIrIQ72eVIC6RDrbuTHf2JXmPUuQAs8mnSg3Nl1ER1JKgUhAIkHzjS3iKN22S64HmMM9gecZA4oInrRJT+OFKBccDCygJddYtUZirWVkX3rATjvtFNO0klGKwYlnGpEjr4VXIkQpNRvS1Htj84FBjrDRnLJWZXCukVaPY4XSHCUyIaGXCFDKJyACkt6JUzaNC67Njjvu2JC1UBq/+tWvol1GBBeNDdmcIYYB1yFlr1DDm/sImlMbSxHSEfJYW/Nu0c3zRC6OYpuWSPo82YQTocSakUf1peeJHOWeodEhX+eUGjGZ0qAT2GSkQ7OH4VqlzLcqri2dLdFCyQXq45dYdzYfJyNGjIhZGalcD4I5YjC1aSE1n8I5R0ZLSfs61lXscbKV8v1+anSJIw6BOM/aodkfNd5LCvhJNM8HrLvTTTddrK+LEMr9QAYXxykZx32T7A7mUva5qe+IVAOF0YpAvT/qdBEBmCZLUghYLPAcESqeG9lE+rAxKTFSw86N44s7iOQIXnlkMIsDG7dWiwJRg9Q+K3F8JPL7gfpEFCpH6EupJ7mhTkQk91jJKWxgjcQJRyrRIIVOuUnIyCMT2MCVImQ0Q3ovgsXaa69d23777WMzv7T5wrhkg4/wc+KJJ9bvmdKiESb1fiLyi2tDqluJ6wvNklKHW+YJagOmtZbIFsbNVltt1fCziB6IhaXWCuQaIOZQgoX7gA7iZOwwpzan1ZPBQc1R1qBSwTmN84TI4QkJW9hkSRzNbbeSwCmNqNGqEzbCeLLfmS+IHKURU+5YKW2NSc6CVuIozlfGArV4Sw7i+KJKtACiYekwFpg3iaAmk4c9bpoveA4bPs/UgJLEURwr1MMkwCfNF2SFUn4FZ0IqU5IcTgR2sN8vbe5o1SyWNZbgJ+YNHI/MtwmEYfY21B4t1cEkn4/CaKG0iszBgKKxAZtYBA0MDCImCS1PNRKpacSmBI9TbqSXgp0baw3jgsWSTRnd5hEtcjCgED87SiEo1YvWnBKcC1zcG4ijqeFSbqiXVqi8I6paIzGPDAWcTMwT/J9HYOBUSSVJEsy36WdLvDakHGFokvpJVDENc2aaaaZYqoTuwJRyoWQJBjjChqLoZzB2qMVbklMlzQM4RoiWZv2kPA2ZKanJIxtUNh+kslHrjY0r0Sz77bdfjGihQUKJsElj7qSOdw7XCOEYJyUbt7RZZ61ZbLHFoohcKtSyxyZrRZov2eAncZg6+CXap8C4R9TIMy0QeLg3GCM4JlMaNNGi1KIdNGjQeI2pSgDHAOsK46MjcRT7nOafRMDlEXJVZEIlWkq0O6CVI4VxQHQk6wzZbankQsqOQ/xqbppa4l6XkmjYo+m+wAGFzYojgQhr9v1cI4KmkjBcSsT1hJrFkkVL6RrGAK/L7w0ChFKJQakmCqMFkhdux4DK09HwCjExpOLKpAGT+olhwcSBp5qOyc1d2UvAzo2N4wPDm4hhjIZWZQMQghFG83NvrmlVGuncqXHGZgPB4vDDD68/zzWhoyPiaOqKWrLg1RFVq5HYimHDhkWxAqOLiD8EYsQtGoTghErRbvkmruTrQw1enE043hDEMD4RiDHEEUv5n/kXEYyIffmMkqKd8s6v1O0iPRo7IznbGCdEmSOYsu5wjDUZYYxxwpxbcj0v5gCyUqi3myCNkWvCuZONgRiU6nszf2DDlexMOPLII6NdSqR9R+eJLXb88ccXP49SE5F1lah7IP2X6DfuF8YGUV84nVLaK3ZcPpZKAeGCOYFMHBwlRJx3JI4yflh7cLqVKBBPDFUo0ZLIRTwcjGQnpGwdxg+iKKnSKXCBtYbSJDimSptPm+dEvmfPy74ep2QSRynRwjzC2kwNazJ8UlBHKddkQs1iyWpKzWKb972cP47aPKJWqofCaGHkBdrxOi+wwALRyMYLkgqyMylihOKNZSJgckDcoKg9KW8lpo3buXH8TSsLRqt0LWBMYGTQATVdGwRCxhId/EoGQQejgVqqbMRS5Fvqeos4iiHOtUj1aatIlQxwInaIUiLSAMGczWoyqpgfEDFIhV1//fVj+hJjY+jQobXSad6gMp+QqkVn0wRG91/+8pdYigIDnWtUigEurUEYRwBHBG0G4YLoLtZjxI60oSOlrdRGj0BdZqJEKbdBTbfcBmHOIHsjsfvuu0cBLO+6DaXeN6y5zJmXXXZZywg3Iu6JOMehXxKtorO4P4jywilNxBf3ETSlg6UAACR+SURBVJ3YUwQ1G/q55567oUZgieCwp/wMThUi26gLOCFxlEhzogRLFs07S8klWlpBJhfOBJzUlGVJgT301SCIgXGRHmSzlCoEAlkYeYkz1mL2+5x3EkeZY9jf5WttSaUEJrTnJ4OpVbNYrhnZGpSPS45cqSYKowWRe0kwIhCyMMCprUHKK9GizWn1GJqpUUjJ2LlxfC8axkQO1wKDCjAu8bwijJJ68pOf/CTW6Smpe2MrAxqHAtHCSdjBeGChxLhCFEv1aLhnaE5VajpfZ6mCAc4ciZOJlCOaXTBvMmcgXOSRfhibeOP33XffGIFPzbwSnUyfV/eNeYWUJVKjm+uZ4VTpqFSFlFN3F2cJNke6P9KmK0+LJlKFFPEk/pUceY8jhYhQIlVYR3Gi5E61d955p+H1++yzT4werdKYITKQ7BycKM0MHjw4dhKmsUiJ9e9odkoUPQ64dH8goFOihLk2vyfIbkLkwE4tEUpbEe2H7ZUylLhW7GOaxVHIawGWPIdMLCWWaGklBNJNnX3K3/72txgxTPQ9NloqRcF+h70wmV44E9JaVJoQCOzTcDyzh2F/QlYTMIcQOcr606ovRIn3y8Ts+em3guONNbrEbFmZOBRGC4ziQtRjEcghNLxfv34NKeNsTGhwgAel1CiNHDs3fuZFQxjPITKSCIX8nBF6kiBEFFhJkbPJECAKNG22OIaRRTQokApNWQk2qRjopHPtvPPOtddee63hd1Sdkg1wDGkidkgPZ6MGCDkY4NRmJr2xVW1ZIlxowlRiJG1n6r4hjqa6b60M8VLqWEmt5WdL/TKyLkhbTHUh0/hIG1LuDYQwIlpKnksRRVk/Eb6ws0iRJnKJxg8piyeH+wU7BTGwRJo/6zQXsMZSnoUxQfkF6qzSKZgNK7ZrXnOztPp3lLBivaBG8+c5n4866qhol5EaWho40/j8KSfQDPNHsziKo42o9OSoK7mWZtVLtLSCGpFE+VF/N4EYuOWWW0anW7qXmkXQUpyyuR1FRg71htnLHXbYYTHaHAdlcjSxzrLfp6RRVcpNdHbPjy1Cjw0jRQUURgsDgyIJo3hS06b93HPPjV4kRJ18kaCJCtFeVehUCFXv3Jh70ZJow4KKWIrHNYFxidDFWELgyL1rpUAzJWrskLZIVCxgMLABYyyQkkMKPfcLERxsXlIHZYWd8g1w0uP5vPO017TpQuAgqhhxNJWjaN6UIQrttNNORW3UJqXuG+nDVTHEq0iruZBxgICO+Md9kCIi8/Fx1llnxc1biXNHgsaGzCFE70CaC0477bSYiZCXpWGdwXFJBD6p1KVENKXx0aoBXRoPOCixzXCyMZ9SyoaMHSK/WG/Z1JcG5zrnnHNGWx17k7HC+RK91AquAfYqdmtJInECJxq2Zmo81gruCRwMZDyRZk8zKvY12mPVAPsjzZmUHmF9YZ7AUZ1D9hLZPURJlprJlMPcSb17amjmAjFzJ42E0v6G4BaCO0oRhru658+vQ1Wa58rnozBaINQJJUJh//33j98jcFGEOo9AyCeEVlELJVPFzo2tvGiIoxiXeNBoONUMXnhSg/O02dIg2gChmOi31MwgRQWSCp06V2J4EzlKU6YSGx1IIwgUpB2xUU2brjRn5s3JuI8YPznp9UQtMGZKMkKt+yaflw6cMgtSR/UkjqamGBxnE7L33nvHBw7cUscHay3jn/TO3NY49dRTY6Rgyj7gmnA/sYnFUV1aDTyyLzg3nE3ps07nSMYGUflJPAaiIVmPEdRLzGZCJP7Wt75V22uvvRqOc5+QPk+UMbZGgmOsM9xLJTYmo84uexZqzeZwHZKok2D88HocDthoaRwpjpYNtgefeT7+r7nmmtiYjNIkeTZkikBHDKOZTsngSEml3yhDkYM4ihjYytlQytrSGWwWKxODwmihsGCQkkKjEFJ02KAnrPFWrcYxrcDbSi0ahI3kbc2FYdK1aNyVamqWRnPkEtGgCDxE1KaNC3WLdtttt9rjjz8eU1O4j0pvPCWfjQ828SkdOIk6zenACD9EN7366qv1jRn/IxxirJZSr8i6bzIx6cCpNjevwbnWLI5SamHeeeetROpans7H10SgE0ndvIkl7ZN6xs1zTAlQTgHBl47AzBlpLmDeJHL2e9/7XvH2aLNwR9me++67r/49qZysGWSxcJ3yBn7MpwQ8cL1Kj6pOEDmN8Nlcf5fvaVZGI7+S60VKYzQxwnnznAnXXnttFL2o+d4sjhJlXQXB/Nhjj63fQ9wLua3FvMv+pepUfc8vnUdhtGAwpOiojTetRK97V6lC45gJQeQjmzXSDO644476cWr2sHErqaZoK+MoT+FkwSRyFoEnNaCiuQGNMqgzyia+FJFLOjdO+DqPeGtOB+b5Qw45JBparWje0PVUrPsmXUkHZqOW0upJb+SeIRW2SvNpSucjRZ57CbE43T+t7o0SRMI0lybRinmBuZL05ySO4myhyVDp80MeWU1U9eWXX97QECQ1kEHkIQKSB/cJtURLFENbRVWTwZSaT1GqhXqIyVGdO9eo980YSpGiiqJlQ9QwzRybo4nztHH2uvRCQBxtjjCGUsTR/Dyaxz3iJ2sLDv1UuoimdqzHlG4R9/zSORRGeyDNRmT+fbMHlcLLeNoOOuigomt5TSolN46ZmGgWNixsVEnxI62tJFE0j5IlUonoi9zA+OlPfxprrJIizMYVgScZV6Q6YqynxjtSLjT6YMNFPdmOxNEU8ZY2+ogdJ5xwQsPvKSlC0rpv0pV04FSiJaXVU/OtNKfbxKbzIXalqOtS5olWom4+jyZYR3HGkv6aR3+Vdg0mFFnNPcCcimBB+YREc/Q0HabZwJcgknfWDqXcAusHZWyaRVHAFuP7ZtFdygQxlEjIK6+8suE46w4ZbXkvCJwKzC3cXyU6E/L7ALuMLFCaKqdADjj44IOjiExzXfY6XKell17a+pkZVd/zy+ejMNrDSAYBniCMzDy1NxlQGBSkSVPQPqXVU39kjz32UBxtQdWvSfKi0UgGj2Opm1YiQDGyKL6dDAU28NTgSdFwP/vZz2KKFgZGFdI85VODkw0WTYUYH2xG807qzKuIOnSgR/RBEMob16XNWWmbe+u+yReRDsx4SfcRdUiT4FHldD4eJafzES1Oijw1ZIkex05NZXlIq8feoOTCZZdd1lCCpGRRFMcR3ZCpn0q0GyI5AmBHdgY9Aijlk5clKd0OJeqP0jRJMM7HBM571pZEaeutjM/1119fbyacIJoYGz2tI7njgPWFwIbSnAn5WCddnv38wIEDY0Yb80huoyGIcs2YY7FbSyzN0lWqvueXCaMw2oNIRgJGJ6knpMjTVImi1CmSiUL2pP0SuZNPpkT0kPJm5Ju0AuOcVKYSu7/m4FVG7KFcABs27p8bb7yx4TVDhgyJtfLojquntTrzKhuvww8/vLbuuuvGGmbvvvtu/TXMpaR/EumEFx7RByG1tCYpCeu+yReZDpyaDMmnAhCRcdRkRTArEeYN5g+iukiJnn/++eP8cPLJJ8eoP8RRoruYS/Oao6U28iMjZdttt204jiMJgYMIphwcb9R452dKbnzZUXkn1mGcjnmTFL5fbLHFtMcq3EwYpwKiKOsJ+1xI8wZrU3MT4dJsMmA+IBqUEiTAfp46+NijeVYPdiyZf6zTpV4LkcmFwmgP25Q8+uij0UuEV+ycc86pfec734mLRqpZxaaVQvatDM0U6STSiqoYnXhX2ZCwcSOKNJEbD9RVbFWrSMrlyCOPjGI5RiebeaJBR44cGTeppN0wByMGkSbO8yXXOLPum+SYDvzF8q9//SuKxiVGSQLzADXvKJvwt7/9LUZ+HX/88bVll102ZqYQGUj0PTXwl1pqqZjVVCrMl3SUZy7NHQk4ZBE/c3GczBVKU8w999yVqsHbKq2e8UFUNXWJc1HUtaV6YLPPOOOMMaMtpY7ne1yiJikXVxqpVmjakxBlT1mnvFQAtikZGs2Ro4ceemjc51BzVEQ6j8JoD/MW4QUi2i2HSRFvPAwbNqxo77vIFwGbNe4ljIc8Vc0NfHUhymnQoEHxa8pJpEYpCDv5+KCxTBXSk6z7JmA68OSlp4ujHb1/ju+66661/v37x/UWcDSxkceGxYGPuI44WLoTMs2lRMkiihOkQLNH7qUcas/SjCqvG1jlqGrW3zwzw7WluhDEgKMFh1LeTJhxgrOutMCOW265pbbDDjvEtTbZWTgiEYjJymiOSifTiXqit99+e/04cyyZopTeE5HOoTDagzj22GNjlFvqIJ6MBIowMylaN0OqzsQ0JqP5g43JqgmRO6effnrtjDPOqM+njzzySDSyk6ceY7tfv35R+Mk991US0a37JmA6sLQizQXUTkXs3HnnneOYyOeD3XffPY6RPDsjQemeqpRZYC4lJZzUV+qaU+u81Vqi+PcpzClkxlmapVrk6fFpfknZjqSLY7MjjuJsI6q41Ghi+h3gnGf+ZP0F1lKyNTjvlE6fwGmNI7LZLk29RkSkcyiM9iCY8KgdiseIbvOAN4nv6awtUmVsTCadgWYwROtgdFKWhNR4ivYzlxJ5zyaf50glJzWJ9KUFF1ywIUqhSlj3TUwHlglFEvNZ41TaYostYldk0j6bxVHm2hQ5WlUQR4myZj3J51IdShOmJMFLWq8vjz/+eEOn8HRPYJvR+DJ1oMdmJ3OBaOIll1yySFE0cd5558W69mQyJXEUBz6Ro9RnbhZHq+S0F5lcKIz2AJHnww8/bHgOEYcIHuq6DRgwIEaMJjSwpIrYmEw6K4oSccBnjtBJ2hHjhXT5p59+urbGGmvE1E+K2SdDnKYy1HKusrFp3TcxHViawYlEgyWafaQ1mPWVaOFmKKnAmkwzlapfM+4jnE3MpSJV5pJLLonlErC7CFI466yz6rYXEaI4VLDjczjO/VOq/ZHbmvQSQRzdZZdd6uIovUaIHN1yyy2jI1JEvjgURtsYNuo0uxg6dOh44ijGJ2n1GFgJRVGpIjYmk0nptJ7GwWmnnRYjnhBKSW8kEjJPP8pTx6sujlr3rdqYDixpPmT+pEY3jvq81Ajp9IwRbNMjjjgiRjglKMVAh/oPPvigVvX7aLPNNosOuXvuuae7345It/CLX/wi2uj8T2TogQceGO0LMnUIaED847kJUcpaw/6lVZZbLo6yf0kBHI899lh0NDU7JkWkayiMtiFpw06XZDbybNrxquWpnGxK99tvvygEEdEkUmVsTCad7bROumeexnjqqafG1MZ33303fp9v2h0vjVj3TUwHri5JFMVpf//998fuyHmzjxNOOKHWu3fvaJvigKLMwlZbbdVQbiPvqFz1ufTb3/52Q/qwSFW4+OKL4/42byTEPEEwEJGQaZ6pwpz6yiuvxGtBjdDhw4e3FEcpl0dzJa5b4vnnn6+0s15kctA3SNvRq1ev+P9aa60V/x8zZkzYY489wtixY8OgQYPi81NNNVU499xzw0cffRR23HHHcNFFF4XNN9+8m9+5SPdw5ZVXhk8++SSsv/769Xumb9++4Stf+Up45plnwv/+97+w6qqrdvfblG5k0UUXDb/+9a/DgQceGE488cQw99xzh1deeSUMHjw4XHbZZaFfv37xdTPMMMN4c7F8yhJLLBGGDBnScI9J9e6jX/ziF+GAAw4IJ510UujTp0/4xje+4b1SOOPGjQu9e/cOjz76aFh99dXDqaeeGr7+9a/HeRRefPHF8O9//ztcf/31YaONNorHNtxww7juPvTQQ2GVVVaJx9Lrqw5z6e9+97sw9dRTd/dbEZniMCdA//79CdCKX7OvZX6YccYZ47HFFlsslA5z6nzzzRfngl133TVMM8004cc//nGYbbbZ4trKvp//f/CDH4Rbb7012qq77LJLnI8XXnjh+DvSa0Sk6/T+An6HfAEwybXi6quvDqecckrYfffdwz777BOuvfbasPfee4ezzjorPs9Gf9111w37779/+PDDD6fwuxZpD4466qjoPNh0003DddddFwWbV199NVx66aVxk+bmQ5Kog7CHEbnDDjvEscEcuvXWW0fjUjqPomh1WWSRReJ9xEaWDdu9997b3W9JpoAo+s9//jOK4Iji++23X8NrFlpooXDOOefEORVRg58ZPXp0WHrppRVDO0C7RKrKmWeeGW12nCd/+9vfomON/e1NN90Udtttt8o42v5/5m4YOHBgFEe5Luz533rrrfg8tiqBHbDiiiuGmWaaKX7NfJxQFBX54nBn00ZG57PPPhsjmNZZZ514fOWVV45eI6JCL7jgghjJtM0220Rv2o033hhfw+Lxhz/8Ibz22msNkU4ipd8vORgGv/zlL+NzO++8czj77LPDcccdF79m4w4YH1UxtmTC4ijj43vf+15YfPHFo8iTxpBjRKTz99FPf/rTcPTRR4d55pmnu9+OTOb19rHHHgurrbZa+P73vx8j7hMIGQsssECcS1PUPXMoD6JHZ5lllvpmXkQEmFPIPGB+2WmnnaKj5fzzzw8XXnhh3AO3svNLhHkyiaPbbrttPLb99tvH7w8++OAYTYoDhYy4e+65Jyy77LLd/ZZFiqYX+fTd/SaqTJ6e9M1vfjOcdtppMTI0QarSySefHNZbb72w1157hcsvvzx64X/729/G1PnpppuuW9+/SHfcL88991y4+OKLwwsvvBDWWGONsOeee9Zfw33yq1/9qu6JBgUvaYYxRORTijgmEkpEJg6iWYx8Kxsc9ssvv3wULH7/+9/Xj59wwglRyMBRT2p4gnV56NCh0QF19913x6hREakupIEPGzYsRkIyj6TSb9jm++67bxRJEUeJOq8C+Z4kRdinyE+CnUirJ4qU8mDzzz9/1AHeeOONmJ1hto7I5KN8d0wPSk8iHT4XRYEaKwihBx10UPS+P/nkkzHSiQnzL3/5S702i0iVIlcQQx9++OHw/vvvx3uGiOrEeeedFw2su+66K/z973+PxxRFpaN0YIxRoqAYVyIycSiKlg9lRgYMGBBGjRoVhU4g3RPhE2E0F0X/9a9/hcMPPzxcccUV4Y477lAUFak4lCtiz3rfffdFu33LLbeMQT7JNqdfBnY8AT833HBDKN3JlETRfP+OHcp+BTt0u+22C1dddVV4/vnno236ox/9KK6zRIwiilr2SWTyYcRoG4g8FKdvTk9iglxuueVioeVDDz00zDXXXLH+CjVG4LDDDov1WXKDVKQKUX7U1CX1hmgV7iEiROecc85w/PHHN7yWurzcMzYmkwlBwxAijEkLrkLqlojIxEKpJxrXsUFnvb3mmmtiDe8NNtig4XWPPPJILOs07bTTxhR7EakuZG1RM5RIUAQ/SsNR5urBBx+MPTSYK1Kk5He/+93wxz/+MdrsiKelgaOI0l4EOtE0GXE0NU7iWlAqjzqjiMjw9ttvhw8++CCKoZSr4fU2vRSZvCiMtml6EmkFt9xySwydv+SSS2KqAUKp3eekqk4EpiocAiNGjIgeZjZeQIdGDAie536iTg/3CmCIkXqC+GUNXvk8qlLXSkRkYnnmmWdiZhPZGDgicdqnLQSb9sGDB0dR4/HHH6/XGxWRaoKIR/YWNtXPf/7zeuYW+12iILHLm8vB0QiTTDBqF5fGm2++GfcnXA8yP9mfcE3uvPPOWPqLpsoEenSE9qnI5EdhtBt58cUX48RIx84f/vCHMZ2e9KQzzjgj1k/ceOON4+vwGH3pS1+KXzsxSpVI450N2ciRI2Mhcr5ec8014/NEWbMZIw1njjnmiF5pavVieNExGWhMZldcERGRrkF6J456HPSkzLPeAuswNfJJtV9hhRW6+22KSDfypz/9Kcw888zRbqcO9Q477NBQboP9LWn1s84663ilrkra56ZzSZGew4cPj8EcH374YXQy0WjpH//4R3jvvffCt771re5+uyKVR2G0jdOT8uhQFpIll1yyu9+uSLc0Jlt99dXDqaeeGr3PuWOBpjl0nt9oo43iMWoYUZqCIu+rrLJKN757ERGRcu1Wtg80BSG665hjjomRpIqiItWGmv+kizMv4DjJmwzx9UsvvRRteux0GgsBZa/ykleliKOvv/56LIWXQybooEGDYtATqfVbbbVVPG6TWJHup+fPOj2cRRddNBaw//jjj6MgSnoBoigTZFoU8MQTZk8KsTq2VLExGd3Dc1EUFlpooRghiiiaujrSqIxmD0aIioiITB67lcZ1ZGWw/uKgVBQVEcrAYa/TXIkmqbnQlxoOES1J4E8qt0HfgORoSZQgilIGj9qg1BM95JBDYo8ESugRBEVzuv79+8fa9vQSYf/C9eF/Eek+ev7MUwB0nj///POjZ426otQbYYJMNZuYOIkkZRHRmyRVaky22mqrxcZkRKUk8EI//fTT8etkWHFf8DPXX399mGWWWcJMM83Ube9fRESkdHH09NNPj5kZNFxSFBWpNr/85S+jwHnllVfGWqEJmlu+8MIL9e9JKydLkkAG0seJqiQKvblTe0+HbDdgv0KJkfXXXz+KxQRA0fuAff/0008f/vCHP8RrlgdEiUj3YCp9G2F6ksjnNya78MILw4033hiWWGKJ+nGMrqFDh8boawwQokZFRERk8oG4kep5i0g1uf3226O9fuyxx8aAngTCJ02Hbrjhhhi0kOx7BELEUUTSp556Ks4hpXVc53x+/OMfx9IC1113XRRBH3jggRjohGhKeTzS6QkCWXDBBaOwvPbaa3f32xapNLom2gjTk0Q+hTSbAQMGhFGjRkWhE2hMhvCJMJqLotTfpQkEqSl33HGHoqiIiMgUQFFUROadd95YN/Shhx4KDz74YDz27W9/O7z88ssxuAFRNMVh0WiIOqN0pCeasiRRFNEXsRM4HxrScR2IoH3rrbdiSTAy2zhvGi9RRoAmVVw/xGIR6V6MGG1DmDDpUn/SSSeFpZZaqrvfjkjbNSbLIY1vhhlmCNNOO21YYIEFuu39ioiIiIhU1WanaTDd6KklSnd6+gGkxkL8f9ttt4WPPvooBgAhHpYiipIOv++++4YzzjgjiqFEiCb22GOPKBDzmo033rjh52gky96FNPq86bKITHkURtsU05NEQnjmmWfC/vvvHyOnjz/++HDooYfWvc6pBu9FF10UHn/88Xq9URERERERmbLiKOIgKeOkhm+77bYNHeYRBRFNyQTDhi9BFE2i71577RXrqc4222zhzDPPDNtss02Mik3stttu4Y9//GO46qqrYkPlZvLrJCLdg8KoiLQ1zz//fDS08KKSMk+TMkAUJU0FA8tyEyIiIiIi3WuzkzKOyEeNzZQivskmm8TO7E8++WQM/EmCYk8nCZp0l3/77bfD8OHD497kF7/4Rdh1110bXvvd7343iqO/+c1vwhZbbNFt71lEWqMwKiJtj43JRERERER6hs2OYHjEEUfECMonnngiPkqqKZpDqjx9EIYNGxajRyn9ReMlImf5fpdddol7GNLsqbPKPkZE2guFURHpMYbWIYccEu6///7w7rvvhnvuuUdRVERERESkzWz2gw8+ONx4441h4YUXjiWvShJFqZ8600wzhfXWWy8KnkTK7r777uHOO++Mz++zzz6xWSyNlW+55ZbYYClh2rxIe+JdKSI9AoyL008/Payyyiqx4ZKiqIiIiIhIe9rs3/ve94qLFCUSdODAgfVeIJQEWGSRRcLHH38cXnjhhXjsvvvuC3PPPXd4+eWXwx133BEbTiUQRRFHRaS9UBgVkR7D4osvHguXL7XUUt39VkREREREpAVLLLFEGDJkSFHd56kdesABB4QrrrgirLnmmvFYSr5FCH3sscfCqquuGmaeeebwn//8J9YV3WmnneqRpAkjRkXaD+9KEelRJA+tiIiIiIi0NyWIotQLpXbqlVdeGbbaaquG42+88UZYZpll4vFpp502XH755fE5hOGf/vSnYd111+3Gdy4incEaoyIiIiIiIiIiTdx+++1hnXXWCccee2wYPHhw/fi3vvWtKIrefPPNsQcC/Q9otjTnnHOGsWPHhj59+tRfW0rUrEipKIyKiIiIiIiIiLRoJrXHHnuEWWaZJRx99NFhxRVXjB3mOX711VfHBlOQi6E2WRLpWSiMioiIiIiIiIi0ABGUVHqEz5EjR4YPP/wwdqdfaKGFGgTRu+66K6y++urd/XZFZCJRGBURERERERERmYA4uu+++4YHHngg1hbddtttGyJDN9544zBixIgwbNiw2K1eRHoOCqMiIiIiIiIiIhPg+eefD/vtt18UQ3/84x+HNdZYIx7fZJNN4nNPPPGEjWJFeiAKoyIiIiIiIiIinUyrRxw94ogjwplnnhkF0SSK2mhJpOdhRWARERERERERkc9h0UUXDUOGDInp8muvvXZ48sknFUVFejhGjIqIiIiIiIiIdJKnnnoq/PznP48Ro4ihiqIiPReFURERERERERGRSUBRVKRnozAqIiIiIiIiIiIilcMaoyIiIiIiIiIiIlI5FEZFRERERERERESkciiMioiIiIiIiIiISOVQGBUREREREREREZHKoTAqIiIiIiIiIiIilUNhVERERERERERERCqHwqiIiIiIiIiIiIhUDoVREREREelxLLTQQqFXr16dftx+++3d/ZZFREREpM1QGBUREREREREREZHK0atWq9W6+02IiIiIiEwMDz74YBg1alT9+6FDh4aLLroofj3XXHOFK6+8suH1Sy+9dJh55pmn+PsUERERkfbFiFERERER6XGsuOKKYfXVV68/Flhggfpz00wzTf34tNNOGy644IL49eyzzx6mmmqqMOOMM4blllsuHHPMMeGDDz4Y73c/9thjYaONNgozzDBDmHXWWcPAgQPDf/7zn4b0/Tw1/6mnngo77bRTmH/++cPUU08df47XbrbZZmHIkCFT7JqIiIiIyMTRdyJfLyIiIiLSoyJLf/e73zUcQwz95z//GR/XX399uPfee0Pfvp+axU888UT45je/Gd577734/UcffRSuuOKKcM8994T3339/vN//9ttvh2984xvhnXfeqR8bPXp0eOmll+LjueeeCwceeOBkP08RERERmXgURkVERESkWJZZZplwxhlnhIUXXjhGivbp0ye89dZb4bTTTgsPPPBAeOihh8LVV18dtt122/j6gw46qC6Kzj333OGUU06JEaCDBw+OQmczt912W10UXXvttcOhhx4ao1L/+9//hmHDhoX/+7//m8JnLCIiIiKdRWFURERERIplpZVWCg8//HA4/fTTw7/+9a8wcuTIMG7cuIbXEDGKMIpgeuutt9aPn3feeWGrrbaKXy+xxBLhq1/96ni/P69bipC6+OKLhwEDBkQBdvfdd5+s5yYiIiIiXcMaoyIiIiJSLN/5znfCAQccEO6+++7w7rvvjieKAseBtPccUuQTSy21VOjXr994P0vaPc/BZZddFhZddNEw/fTTRxF13333jWKsiIiIiLQnCqMiIiIiUiSks19yySX177///e+HG2+8Mdx5551h0KBB9eNJLKWpUk7z962guROiKxGpm266afjyl78cxo4dG5588slw/vnnh9VWWy28/PLLX+h5iYiIiMgXg8KoiIiIiBTJK6+8Uv+6f//+4Wc/+1lYf/31Y4d6RNNmFllkkQYxlBT7BE2ZRowYMd7P1Gq1mE5PbdHrrrsuRp1So3SbbbaJz5O6/9e//nUynJ2IiIiIdBVrjIqIiIhIkdBwKe8ef+KJJ4YVV1wxXHXVVeGWW24Z7/WIp+uss079uf322y8Km9NNN1045phjWv6N+++/P+y5556xFin1Reeaa67YjImI0cSoUaMmy/mJiIiISNdQGBURERGRIpljjjnCDjvsEK644or4/VFHHRX/pzEStUFJqW/m7LPPDquuump4//33Y8TpLrvsEo/PP//8YdZZZ613oM8jRh9//PH4aMWMM85Yb+AkIiIiIu2FqfQiIiIiUiy/+tWvYm3R+eabL0Z+rrzyyjG1ncjQVtBI6a677gobbLBBbKJEmjwd66kjmjdummGGGerp90ceeWRYc801wzzzzBOmmWaaMNVUU4UFFlgg7LzzzuG+++4LCy644BQ7XxERERHpPL1quLlFRERERKRDiAhdZpll4te9e/cOb731Vphlllm6+22JiIiISBcwYlREREREJKsHusoqq4RLL700Nlx66aWXwrXXXhu22267+ms222wzRVERERGRAjBiVEREREQkE0ZJue+IRRddNNx+++0xbV5EREREejZGjIqIiIiI/H+oD3rAAQeE5ZZbLjZb6tu3b+jXr19syHTqqaeGRx55RFFUREREpBCMGBUREREREREREZHKYcSoiIiIiIiIiIiIVA6FUREREREREREREakcCqMiIiIiIiIiIiJSORRGRUREREREREREpHIojIqIiIiIiIiIiEjlUBgVERERERERERGRyqEwKiIiIiIiIiIiIpVDYVRERERERERERERC1fh/lco6hgDtzUoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1360x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Visualization complete for 17 tags\n",
+      "\n",
+      "Positive values: Precision is higher than Recall\n",
+      "Negative values: Recall is higher than Precision\n",
+      "\n",
+      "Tags are sorted by absolute difference (largest to smallest)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def visualize_precision_recall_difference():\n",
+    "    \"\"\"\n",
+    "    Visualize the difference (Precision - Recall) for each tag as a diverging bar chart\n",
+    "    \"\"\"\n",
+    "    # Authentication\n",
+    "    credentials = ApiKeyCredentials(in_headers={\"Training-key\": TRAINING_KEY})\n",
+    "    trainer = CustomVisionTrainingClient(ENDPOINT, credentials)\n",
+    "\n",
+    "    # Get iterations\n",
+    "    iterations = trainer.get_iterations(PROJECT_ID)\n",
+    "    \n",
+    "    if not iterations:\n",
+    "        print(\"No iterations found.\")\n",
+    "        return\n",
+    "\n",
+    "    # Get latest iteration performance\n",
+    "    latest_iteration = iterations[0]\n",
+    "    performance = trainer.get_iteration_performance(PROJECT_ID, latest_iteration.id, threshold=0.5)\n",
+    "\n",
+    "    # Extract tag names and calculate differences\n",
+    "    tag_data = []\n",
+    "    \n",
+    "    for tag_perf in performance.per_tag_performance:\n",
+    "        diff = tag_perf.precision - tag_perf.recall\n",
+    "        tag_data.append({'name': tag_perf.name, 'diff': diff})\n",
+    "    \n",
+    "    if not tag_data:\n",
+    "        print(\"No tag performance data available.\")\n",
+    "        return\n",
+    "    \n",
+    "    # Sort by absolute difference (largest to smallest)\n",
+    "    tag_data_sorted = sorted(tag_data, key=lambda x: abs(x['diff']), reverse=True)\n",
+    "    \n",
+    "    # Extract sorted data\n",
+    "    tag_names = [item['name'] for item in tag_data_sorted]\n",
+    "    differences = [item['diff'] for item in tag_data_sorted]\n",
+    "    \n",
+    "    # Create figure\n",
+    "    fig, ax = plt.subplots(figsize=(max(12, len(tag_names) * 0.8), 8))\n",
+    "    \n",
+    "    x = np.arange(len(tag_names))\n",
+    "    \n",
+    "    # Color bars based on positive/negative values\n",
+    "    colors = ['#2E86AB' if d >= 0 else '#E63946' for d in differences]\n",
+    "    \n",
+    "    # Create bars\n",
+    "    bars = ax.bar(x, differences, color=colors, alpha=0.8)\n",
+    "    \n",
+    "    # Add labels and title\n",
+    "    ax.set_xlabel('Tags', fontsize=13, fontweight='bold')\n",
+    "    ax.set_ylabel('Precision - Recall', fontsize=13, fontweight='bold')\n",
+    "    ax.set_title(f'Precision vs Recall Difference by Tag - {latest_iteration.name} (Sorted by Absolute Difference)', \n",
+    "                 fontsize=15, fontweight='bold', pad=20)\n",
+    "    ax.set_xticks(x)\n",
+    "    ax.set_xticklabels(tag_names, rotation=45, ha='right')\n",
+    "    ax.axhline(y=0, color='black', linestyle='-', linewidth=1.5)\n",
+    "    ax.grid(axis='y', alpha=0.3, linestyle='--')\n",
+    "    \n",
+    "    # Add value labels on bars\n",
+    "    for i, (bar, diff) in enumerate(zip(bars, differences)):\n",
+    "        height = bar.get_height()\n",
+    "        label_y = height + 0.01 if height >= 0 else height - 0.01\n",
+    "        va = 'bottom' if height >= 0 else 'top'\n",
+    "        ax.text(bar.get_x() + bar.get_width()/2., label_y,\n",
+    "                f'{diff:+.3f}', ha='center', va=va, fontsize=8, fontweight='bold')\n",
+    "    \n",
+    "    # Add legend\n",
+    "    from matplotlib.patches import Patch\n",
+    "    legend_elements = [\n",
+    "        Patch(facecolor='#2E86AB', alpha=0.8, label='Precision > Recall'),\n",
+    "        Patch(facecolor='#E63946', alpha=0.8, label='Recall > Precision')\n",
+    "    ]\n",
+    "    ax.legend(handles=legend_elements, fontsize=11, loc='upper right')\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print(f\"\\nVisualization complete for {len(tag_names)} tags\")\n",
+    "    print(\"\\nPositive values: Precision is higher than Recall\")\n",
+    "    print(\"Negative values: Recall is higher than Precision\")\n",
+    "    print(\"\\nTags are sorted by absolute difference (largest to smallest)\")\n",
+    "\n",
+    "# Execute\n",
+    "visualize_precision_recall_difference()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f453343b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============================================================\n",
+      "PROJECT & MODEL INFORMATION\n",
+      "============================================================\n",
+      "Project Name: HaptixEmoSet_251115_20_20\n",
+      "Project Description: Haptix Emotional EmoSet Image Classification\n",
+      "Created: 2025-11-15 11:18:19.623000+00:00\n",
+      "Last Modified: 2025-11-15 14:50:14.412000+00:00\n",
+      "\n",
+      "--- Domain Information ---\n",
+      "Domain ID: a8e3c40f-fb4a-466f-832a-5e457ae4a344\n",
+      "Domain Name: General [A1]\n",
+      "Domain Type: Classification\n",
+      "Exportable: False\n",
+      "Enabled: True\n",
+      "\n",
+      "--- Model Configuration ---\n",
+      "Classification Type: Multilabel\n",
+      "\n",
+      "--- Training Iterations ---\n",
+      "Total Iterations: 2\n",
+      "\n",
+      "Iteration 1:\n",
+      "  Name: Iteration 2\n",
+      "  Status: Completed\n",
+      "  Created: 2025-11-15 12:39:15.800000+00:00\n",
+      "  Training Type: Advanced\n",
+      "  Training Time: 428 minutes\n",
+      "  Published As: HaptixEmoSet_251115_20_20\n",
+      "  Prediction Resource ID: N/A\n",
+      "\n",
+      "Iteration 2:\n",
+      "  Name: Iteration 1\n",
+      "  Status: Completed\n",
+      "  Created: 2025-11-15 11:18:19.626000+00:00\n",
+      "  Training Type: Regular\n",
+      "  Training Time: 51 minutes\n",
+      "\n",
+      "============================================================\n",
+      "NOTE: Azure Custom Vision does not expose:\n",
+      "  - Exact parameter count\n",
+      "  - Detailed model architecture (e.g., ResNet-50, EfficientNet)\n",
+      "  - Model size in MB\n",
+      "These details are abstracted by the Azure service.\n",
+      "============================================================\n"
+     ]
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}