Annotations_Image_Metadata_Sally.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "4c5d1d09-ce28-4ff3-8f76-f8df0a6af3f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "# Annotations and Metadata\n",
    "def load_json_files(folder_path):\n",
    "    data = []\n",
    "    for file_name in os.listdir(folder_path):\n",
    "        if file_name.endswith('.json'):\n",
    "            with open(os.path.join(folder_path, file_name), 'r') as f:\n",
    "                data.append(json.load(f))\n",
    "    return data\n",
    "\n",
    "# Folders\n",
    "annotations_folder = r\"B:\\annotations\\sample\\coco\\annotations_06\"\n",
    "metadata_folder = r\"B:\\annotations\\sample\\ocular-json\"\n",
    "\n",
    "# Load all annotations and metadata\n",
    "annotations = load_json_files(annotations_folder)\n",
    "metadata = load_json_files(metadata_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bcfa71c-fca9-4bdc-999f-94f1050a8242",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "5ed0d309-a62b-499a-a9e1-5c574c448510",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract timestamp from file name or file path\n",
    "def extract_timestamp(file_path):\n",
    "    match = re.search(r'Ocular_(\\d{4}-\\d{2}-\\d{2}_\\d{2}_\\d{2}_\\d{2})', file_path)\n",
    "    return match.group(1) if match else None\n",
    "\n",
    "metadata_list = []\n",
    "for meta in metadata:\n",
    "    if isinstance(meta, dict):\n",
    "        # Processing each key-value pair in the dictionary\n",
    "        timestamp = extract_timestamp(meta.get('file_path', None))\n",
    "        if timestamp:\n",
    "            metadata_list.append({\n",
    "                'timestamp': timestamp,\n",
    "                'zoom_ration': meta.get('zoom_ration', None),\n",
    "                'slide_name': meta.get('slide_name', None),\n",
    "                'username': meta.get('username', None),\n",
    "                'stain': meta.get('stain', None)\n",
    "            })\n",
    "        else:\n",
    "            print('No timestamp found')\n",
    "\n",
    "metadata_df = pd.DataFrame(metadata_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35520627-bcb3-4613-9315-495bae8bba6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "a1606fa8-835d-41a5-9759-992a9dbc17d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Based on the timestamp pattern in the file paths match annotations to metadata\n",
    "annotation_list = []\n",
    "for annotation_file in annotations:\n",
    "    for image in annotation_file['images']:\n",
    "        file_name = image['file_name']\n",
    "        image_id = image['id']\n",
    "        timestamp = extract_timestamp(file_name)\n",
    "        if timestamp:\n",
    "            matching_metadata = metadata_df[metadata_df['timestamp'] == timestamp]\n",
    "            if not matching_metadata.empty:\n",
    "                for annotation in annotation_file['annotations']:\n",
    "                    if annotation['image_id'] == image['id']:\n",
    "                        for category in annotation_file['categories']:\n",
    "                            category_name = category['name']\n",
    "                            category_id = category['id']\n",
    "                            if annotation['category_id'] == category_id:\n",
    "                             for _, row in matching_metadata.iterrows():\n",
    "                                annotation_list.append({\n",
    "                                'file_name': file_name,\n",
    "                                'category_id': annotation['category_id'],\n",
    "                                'category_name':category_name,\n",
    "                                'image_id': image_id,\n",
    "                                'zoom_ration': row['zoom_ration']\n",
    "                                })\n",
    "\n",
    "\n",
    "sally_annotations_df = pd.DataFrame(annotation_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "e2d2fc44-3651-4c6e-997b-65bcfc692983",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sally_annotations_df.to_csv('sally_annotations_df.csv', sep=',', header=True, index=False, na_rep='NA', float_format='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "878c3238-cd92-4cfe-93b1-a1def5ce44e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "c5b34f1e-5050-492c-90c7-9478ad06ef0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slide_name</th>\n",
       "      <th>zoom_ration</th>\n",
       "      <th>Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126_f_eu</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1487</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DSI AYE 013-G-NU</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         slide_name zoom_ration  Number\n",
       "0                 1         1.6       1\n",
       "1          126_f_eu         1.0      20\n",
       "2              1487         1.0       1\n",
       "3                 2         1.2       1\n",
       "4  DSI AYE 013-G-NU         2.8       1"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_2 = metadata_df.groupby(['slide_name','zoom_ration']).size().reset_index(name='count')\n",
    "grouped_2.columns = ['slide_name','zoom_ration','Number']\n",
    "#print(grouped_2)\n",
    "slides = pd.DataFrame(grouped_2)\n",
    "slides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32490746-66a5-4575-aa64-c6e7390f7ed7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "28d524e1-a275-4295-8d2e-51426cfa5626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>zoom_ration</th>\n",
       "      <th>category_name</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>WBC</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>PF</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Trophozoite</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>WBC</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   image_id zoom_ration category_name  number\n",
       "0         1         1.0   Trophozoite      14\n",
       "1         1         1.0           WBC       2\n",
       "2         1         1.4            PF       5\n",
       "3         1         1.6   Trophozoite     114\n",
       "4         1         1.6           WBC      10"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_annotations = sally_annotations_df.groupby(['image_id','zoom_ration','category_name']).size().reset_index(name='number')\n",
    "#print(grouped_annotations)\n",
    "slides_2 = pd.DataFrame(grouped_annotations)\n",
    "slides_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9be2b3a6-9d7c-491c-9e1f-b2e3d3e9463d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "4e7550d4-63db-46f1-a941-e0216f00373c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image_id\n",
       "54     11\n",
       "44     11\n",
       "104    10\n",
       "49     10\n",
       "123    10\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_annotations = sally_annotations_df.groupby(['image_id','zoom_ration','category_name']).size().reset_index(name='number')\n",
    "#print(grouped_annotations)\n",
    "slides_2 = pd.DataFrame(grouped_annotations)\n",
    "#slides_2.head(26)\n",
    "slides_number = slides_2['image_id'].value_counts()\n",
    "slides_number.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7987bd1a-4b7c-484d-8a51-af1aa99e88a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "3c845074-4803-4cac-ac42-1835bbbab59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category_name\n",
       "Trophozoite    417\n",
       "WBC            362\n",
       "PF             261\n",
       "Artifact        66\n",
       "Gametocyte      56\n",
       "PV              31\n",
       "PM              24\n",
       "Schizont        13\n",
       "PO               7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the count of each category\n",
    "category_counts = slides_2['category_name'].value_counts()\n",
    "category_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb57e1dd-4867-459b-856b-23ff19a94317",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "2ee68de0-59c4-4e55-b1de-cfdbfa78e065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJrlnSve4ceNks9k0YMAA+5hhGIqOjlZISIi8vb1Vv3597d+/3+Fz6enp6tu3rwoXLiwfHx+1adNGJ0+evMvpAQAAAADI7p4o3Vu3btVHH32kKlWqOIyPHz9eEydO1NSpU7V161YFBQWpcePGSk1Ntc8ZMGCAlixZotjYWMXFxenChQtq1aqVMjMz7/ZmAAAAAADgwPLSfeHCBT3zzDP6+OOPVbBgQfu4YRiaPHmyRowYoXbt2ikiIkJz5szRpUuXtGDBAklScnKyZs6cqQkTJqhRo0aqXr265s2bp7179+q7776zapMAAAAAAJB0D5TuF198US1btlSjRo0cxuPj45WYmKgmTZrYxzw9PVWvXj1t2rRJkrR9+3ZduXLFYU5ISIgiIiLscwAAAAAAsIqblT88NjZWO3bs0NatW7MtS0xMlCQFBgY6jAcGBurYsWP2OR4eHg5HyK/Nufb5G0lPT1d6err9fUpKyh1vAwAAAAAAN2PZke4TJ06of//+mjdvnry8vG46z2azObw3DCPb2PVuNWfcuHHy9/e3v0JDQ3MWHgAAAACA22BZ6d6+fbuSkpJUs2ZNubm5yc3NTevXr9f7778vNzc3+xHu649YJyUl2ZcFBQUpIyND586du+mcGxk2bJiSk5PtrxMnTuTy1gEAAAAAYGHpbtiwofbu3atdu3bZX7Vq1dIzzzyjXbt2qXTp0goKCtKqVavsn8nIyND69esVGRkpSapZs6bc3d0d5iQkJGjfvn32OTfi6ekpPz8/hxcAAAAAALnNsmu6fX19FRER4TDm4+OjQoUK2ccHDBigsWPHqly5cipXrpzGjh2rfPnyqWPHjpIkf39/9ejRQ4MGDVKhQoUUEBCgwYMHq3LlytluzAYAAAAAwN1m6Y3UbmXIkCFKS0tTnz59dO7cOdWuXVsrV66Ur6+vfc6kSZPk5uamp59+WmlpaWrYsKFiYmLk6upqYXIAAAAAACSbYRiG1SGslpKSIn9/fyUnJ9/Tp5qHDV1mdYQ85ehbLa2OAAAAAMBJ3W6PtPw53QAAAAAAOCtKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJnGzOgCQF4QNXWZ1hDzj6FstrY4AAAAA3DM40g0AAAAAgEko3QAAAAAAmITSDQAAAACASSjdAAAAAACYhNINAAAAAIBJKN0AAAAAAJjE0tI9ffp0ValSRX5+fvLz81OdOnX0zTff2Jd37dpVNpvN4fXwww87rCM9PV19+/ZV4cKF5ePjozZt2ujkyZN3e1MAAAAAAMjG0tJdvHhxvfXWW9q2bZu2bdumBg0a6PHHH9f+/fvtc5o1a6aEhAT7a/ny5Q7rGDBggJYsWaLY2FjFxcXpwoULatWqlTIzM+/25gAAAAAA4MDNyh/eunVrh/dvvvmmpk+frs2bN6tSpUqSJE9PTwUFBd3w88nJyZo5c6Y+/fRTNWrUSJI0b948hYaG6rvvvlPTpk3N3QAAAAAAAP7GPXNNd2ZmpmJjY3Xx4kXVqVPHPr5u3ToVLVpU4eHhev7555WUlGRftn37dl25ckVNmjSxj4WEhCgiIkKbNm26q/kBAAAAALiepUe6JWnv3r2qU6eOLl++rPz582vJkiWqWLGiJKl58+Z66qmnVLJkScXHx+u1115TgwYNtH37dnl6eioxMVEeHh4qWLCgwzoDAwOVmJh405+Znp6u9PR0+/uUlBRzNg4AAAAAcF+zvHSXL19eu3bt0vnz5/XFF1+oS5cuWr9+vSpWrKj27dvb50VERKhWrVoqWbKkli1bpnbt2t10nYZhyGaz3XT5uHHjNGrUqFzdDgAAAAAArmf56eUeHh4qW7asatWqpXHjxqlq1ap67733bjg3ODhYJUuW1KFDhyRJQUFBysjI0Llz5xzmJSUlKTAw8KY/c9iwYUpOTra/Tpw4kXsbBAAAAADA/7G8dF/PMAyHU7//6syZMzpx4oSCg4MlSTVr1pS7u7tWrVpln5OQkKB9+/YpMjLypj/D09PT/piyay8AAAAAAHKbpaeXDx8+XM2bN1doaKhSU1MVGxurdevWacWKFbpw4YKio6P15JNPKjg4WEePHtXw4cNVuHBhPfHEE5Ikf39/9ejRQ4MGDVKhQoUUEBCgwYMHq3Llyva7mQMAAAAAYBVLS/fp06f17LPPKiEhQf7+/qpSpYpWrFihxo0bKy0tTXv37tXcuXN1/vx5BQcHKyoqSgsXLpSvr699HZMmTZKbm5uefvpppaWlqWHDhoqJiZGrq6uFWwYAAAAAgMWle+bMmTdd5u3trW+//faW6/Dy8tKUKVM0ZcqU3IwGAAAAAMA/ds9d0w0AAAAAgLOgdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJrG0dE+fPl1VqlSRn5+f/Pz8VKdOHX3zzTf25YZhKDo6WiEhIfL29lb9+vW1f/9+h3Wkp6erb9++Kly4sHx8fNSmTRudPHnybm8KAAAAAADZWFq6ixcvrrfeekvbtm3Ttm3b1KBBAz3++OP2Yj1+/HhNnDhRU6dO1datWxUUFKTGjRsrNTXVvo4BAwZoyZIlio2NVVxcnC5cuKBWrVopMzPTqs0CAAAAAECSxaW7devWatGihcLDwxUeHq4333xT+fPn1+bNm2UYhiZPnqwRI0aoXbt2ioiI0Jw5c3Tp0iUtWLBAkpScnKyZM2dqwoQJatSokapXr6558+Zp7969+u6776zcNAAAAAAA7p1rujMzMxUbG6uLFy+qTp06io+PV2Jiopo0aWKf4+npqXr16mnTpk2SpO3bt+vKlSsOc0JCQhQREWGfcyPp6elKSUlxeAEAAAAAkNssL9179+5V/vz55enpqV69emnJkiWqWLGiEhMTJUmBgYEO8wMDA+3LEhMT5eHhoYIFC950zo2MGzdO/v7+9ldoaGgubxUAAAAAAPdA6S5fvrx27dqlzZs3q3fv3urSpYt++ukn+3KbzeYw3zCMbGPXu9WcYcOGKTk52f46ceLEP9sIAAAAAABuwPLS7eHhobJly6pWrVoaN26cqlatqvfee09BQUGSlO2IdVJSkv3od1BQkDIyMnTu3LmbzrkRT09P+x3Tr70AAAAAAMhtlpfu6xmGofT0dJUqVUpBQUFatWqVfVlGRobWr1+vyMhISVLNmjXl7u7uMCchIUH79u2zzwEAAAAAwCpuVv7w4cOHq3nz5goNDVVqaqpiY2O1bt06rVixQjabTQMGDNDYsWNVrlw5lStXTmPHjlW+fPnUsWNHSZK/v7969OihQYMGqVChQgoICNDgwYNVuXJlNWrUyMpNAwAAAADA2tJ9+vRpPfvss0pISJC/v7+qVKmiFStWqHHjxpKkIUOGKC0tTX369NG5c+dUu3ZtrVy5Ur6+vvZ1TJo0SW5ubnr66aeVlpamhg0bKiYmRq6urlZtFgAAAAAAkiSbYRiG1SGslpKSIn9/fyUnJ9/T13eHDV1mdYQ85ehbLXNtXez725eb+x0AAAC4V91uj7znrukGAAAAAMBZULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADCJm9UBAOBmwoYuszpCnnL0rZZWRwAAAMB1ONINAAAAAIBJKN0AAAAAAJiE0g0AAAAAgEko3QAAAAAAmITSDQAAAACASSjdAAAAAACYxNLSPW7cOD344IPy9fVV0aJF1bZtWx08eNBhTteuXWWz2RxeDz/8sMOc9PR09e3bV4ULF5aPj4/atGmjkydP3s1NAQAAAAAgG0tL9/r16/Xiiy9q8+bNWrVqla5evaomTZro4sWLDvOaNWumhIQE+2v58uUOywcMGKAlS5YoNjZWcXFxunDhglq1aqXMzMy7uTkAAAAAADhws/KHr1ixwuH97NmzVbRoUW3fvl2PPfaYfdzT01NBQUE3XEdycrJmzpypTz/9VI0aNZIkzZs3T6Ghofruu+/UtGlT8zYAAAAAAIC/cU9d052cnCxJCggIcBhft26dihYtqvDwcD3//PNKSkqyL9u+fbuuXLmiJk2a2MdCQkIUERGhTZs23fDnpKenKyUlxeEFAAAAAEBuu2dKt2EYGjhwoB555BFFRETYx5s3b6758+drzZo1mjBhgrZu3aoGDRooPT1dkpSYmCgPDw8VLFjQYX2BgYFKTEy84c8aN26c/P397a/Q0FDzNgwAAAAAcN+y9PTyv3rppZe0Z88excXFOYy3b9/e/s8RERGqVauWSpYsqWXLlqldu3Y3XZ9hGLLZbDdcNmzYMA0cOND+PiUlheINAAAAAMh198SR7r59++qrr77S2rVrVbx48b+dGxwcrJIlS+rQoUOSpKCgIGVkZOjcuXMO85KSkhQYGHjDdXh6esrPz8/hBQAAAABAbrO0dBuGoZdeekmLFy/WmjVrVKpUqVt+5syZMzpx4oSCg4MlSTVr1pS7u7tWrVpln5OQkKB9+/YpMjLStOwAAAAAANyKpaeXv/jii1qwYIH+3//7f/L19bVfg+3v7y9vb29duHBB0dHRevLJJxUcHKyjR49q+PDhKly4sJ544gn73B49emjQoEEqVKiQAgICNHjwYFWuXNl+N3MAAAAAAKxgaemePn26JKl+/foO47Nnz1bXrl3l6uqqvXv3au7cuTp//ryCg4MVFRWlhQsXytfX1z5/0qRJcnNz09NPP620tDQ1bNhQMTExcnV1vZubAwAAAACAA0tLt2EYf7vc29tb33777S3X4+XlpSlTpmjKlCm5FQ0AAAAAgH/snriRGgAAAAAAzojSDQAAAACASSjdAAAAAACYhNINAAAAAIBJKN0AAAAAAJiE0g0AAAAAgEnuqHSXLl1aZ86cyTZ+/vx5lS5d+h+HAgAAAADAGdxR6T569KgyMzOzjaenp+u33377x6EAAAAAAHAGbjmZ/NVXX9n/+dtvv5W/v7/9fWZmplavXq2wsLBcCwcAAAAAQF6Wo9Ldtm1bSZLNZlOXLl0clrm7uyssLEwTJkzItXAAAAAAAORlOSrdWVlZkqRSpUpp69atKly4sCmhAAAAAABwBjkq3dfEx8fndg4AAAAAAJzOHZVuSVq9erVWr16tpKQk+xHwa2bNmvWPgwEAAAAAkNfdUekeNWqURo8erVq1aik4OFg2my23cwEAAAAAkOfdUemeMWOGYmJi9Oyzz+Z2HgAAAAAAnMYdPac7IyNDkZGRuZ0FAAAAAACnckel+7nnntOCBQtyOwsAAAAAAE7ljk4vv3z5sj766CN99913qlKlitzd3R2WT5w4MVfCAQAAAACQl91R6d6zZ4+qVasmSdq3b5/DMm6qBgAAAADAn+6odK9duza3cwAAAAAA4HTu6JpuAAAAAABwa3d0pDsqKupvTyNfs2bNHQcCAAAAAMBZ3FHpvnY99zVXrlzRrl27tG/fPnXp0iU3cgEAAAAAkOfdUemeNGnSDcejo6N14cKFfxQIAAAAAABnkavXdHfq1EmzZs3KzVUCAAAAAJBn5Wrp/uGHH+Tl5ZWbqwQAAAAAIM+6o9PL27Vr5/DeMAwlJCRo27Zteu2113IlGAAAAAAAed0dlW5/f3+H9y4uLipfvrxGjx6tJk2a5EowAAAAAADyujsq3bNnz87tHAAAAAAAOJ07Kt3XbN++XQcOHJDNZlPFihVVvXr13MoFAAAAAECed0elOykpSR06dNC6detUoEABGYah5ORkRUVFKTY2VkWKFMntnAAAAAAA5Dl3dPfyvn37KiUlRfv379fZs2d17tw57du3TykpKerXr19uZwQAAAAAIE+6oyPdK1as0HfffacKFSrYxypWrKgPPviAG6kBAAAAAPB/7uhId1ZWltzd3bONu7u7Kysr6x+HAgAAAADAGdxR6W7QoIH69++vU6dO2cd+++03vfzyy2rYsGGuhQMAAAAAIC+7o9I9depUpaamKiwsTGXKlFHZsmVVqlQppaamasqUKbmdEQAAAACAPOmOrukODQ3Vjh07tGrVKv38888yDEMVK1ZUo0aNcjsfAAAAAAB5Vo6OdK9Zs0YVK1ZUSkqKJKlx48bq27ev+vXrpwcffFCVKlXSxo0bTQkKAAAAAEBek6PSPXnyZD3//PPy8/PLtszf3189e/bUxIkTb3t948aN04MPPihfX18VLVpUbdu21cGDBx3mGIah6OhohYSEyNvbW/Xr19f+/fsd5qSnp6tv374qXLiwfHx81KZNG508eTInmwYAAAAAQK7LUenevXu3mjVrdtPlTZo00fbt2297fevXr9eLL76ozZs3a9WqVbp69aqaNGmiixcv2ueMHz9eEydO1NSpU7V161YFBQWpcePGSk1Ntc8ZMGCAlixZotjYWMXFxenChQtq1aqVMjMzc7J5AAAAAADkqhxd03369OkbPirMvjI3N/3++++3vb4VK1Y4vJ89e7aKFi2q7du367HHHpNhGJo8ebJGjBihdu3aSZLmzJmjwMBALViwQD179lRycrJmzpypTz/91H5N+bx58xQaGqrvvvtOTZs2zckmAgAAAACQa3J0pLtYsWLau3fvTZfv2bNHwcHBdxwmOTlZkhQQECBJio+PV2Jiopo0aWKf4+npqXr16mnTpk2SpO3bt+vKlSsOc0JCQhQREWGfAwAAAACAFXJUulu0aKHXX39dly9fzrYsLS1NI0eOVKtWre4oiGEYGjhwoB555BFFRERIkhITEyVJgYGBDnMDAwPtyxITE+Xh4aGCBQvedM710tPTlZKS4vACAAAAACC35ej08v/85z9avHixwsPD9dJLL6l8+fKy2Ww6cOCAPvjgA2VmZmrEiBF3FOSll17Snj17FBcXl22ZzWZzeG8YRrax6/3dnHHjxmnUqFF3lBMAAAAAgNuVoyPdgYGB2rRpkyIiIjRs2DA98cQTatu2rYYPH66IiAh9//332Y5K346+ffvqq6++0tq1a1W8eHH7eFBQkCRlO2KdlJRk/zlBQUHKyMjQuXPnbjrnesOGDVNycrL9deLEiRxnBgAAAADgVnJUuiWpZMmSWr58uf744w9t2bJFmzdv1h9//KHly5crLCwsR+syDEMvvfSSFi9erDVr1qhUqVIOy0uVKqWgoCCtWrXKPpaRkaH169crMjJSklSzZk25u7s7zElISNC+ffvsc67n6ekpPz8/hxcAAAAAALktR6eX/1XBggX14IMP/qMf/uKLL2rBggX6f//v/8nX19d+RNvf31/e3t6y2WwaMGCAxo4dq3LlyqlcuXIaO3as8uXLp44dO9rn9ujRQ4MGDVKhQoUUEBCgwYMHq3Llyva7mQMAAAAAYIU7Lt25Yfr06ZKk+vXrO4zPnj1bXbt2lSQNGTJEaWlp6tOnj86dO6fatWtr5cqV8vX1tc+fNGmS3Nzc9PTTTystLU0NGzZUTEyMXF1d79amAAAAAACQjaWl2zCMW86x2WyKjo5WdHT0Ted4eXlpypQpmjJlSi6mAwAAAADgn8nxNd0AAAAAAOD2ULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNYWro3bNig1q1bKyQkRDabTV9++aXD8q5du8pmszm8Hn74YYc56enp6tu3rwoXLiwfHx+1adNGJ0+evItbAQAAAADAjVlaui9evKiqVatq6tSpN53TrFkzJSQk2F/Lly93WD5gwAAtWbJEsbGxiouL04ULF9SqVStlZmaaHR8AAAAAgL/lZuUPb968uZo3b/63czw9PRUUFHTDZcnJyZo5c6Y+/fRTNWrUSJI0b948hYaG6rvvvlPTpk1zPTMAAAAAALfrnr+me926dSpatKjCw8P1/PPPKykpyb5s+/btunLlipo0aWIfCwkJUUREhDZt2nTTdaanpyslJcXhBQAAAABAbrunS3fz5s01f/58rVmzRhMmTNDWrVvVoEEDpaenS5ISExPl4eGhggULOnwuMDBQiYmJN13vuHHj5O/vb3+Fhoaauh0AAAAAgPuTpaeX30r79u3t/xwREaFatWqpZMmSWrZsmdq1a3fTzxmGIZvNdtPlw4YN08CBA+3vU1JSKN4AAAAAgFx3Tx/pvl5wcLBKliypQ4cOSZKCgoKUkZGhc+fOOcxLSkpSYGDgTdfj6ekpPz8/hxcAAAAAALktT5XuM2fO6MSJEwoODpYk1axZU+7u7lq1apV9TkJCgvbt26fIyEirYgIAAAAAIMni08svXLigw4cP29/Hx8dr165dCggIUEBAgKKjo/Xkk08qODhYR48e1fDhw1W4cGE98cQTkiR/f3/16NFDgwYNUqFChRQQEKDBgwercuXK9ruZAwAAAABgFUtL97Zt2xQVFWV/f+066y5dumj69Onau3ev5s6dq/Pnzys4OFhRUVFauHChfH197Z+ZNGmS3Nzc9PTTTystLU0NGzZUTEyMXF1d7/r2AAAAAADwV5aW7vr168swjJsu//bbb2+5Di8vL02ZMkVTpkzJzWgAAAAAAPxjeeqabgAAAAAA8hJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEktL94YNG9S6dWuFhITIZrPpyy+/dFhuGIaio6MVEhIib29v1a9fX/v373eYk56err59+6pw4cLy8fFRmzZtdPLkybu4FQAAAAAA3JilpfvixYuqWrWqpk6desPl48eP18SJEzV16lRt3bpVQUFBaty4sVJTU+1zBgwYoCVLlig2NlZxcXG6cOGCWrVqpczMzLu1GQAAAAAA3JCblT+8efPmat68+Q2XGYahyZMna8SIEWrXrp0kac6cOQoMDNSCBQvUs2dPJScna+bMmfr000/VqFEjSdK8efMUGhqq7777Tk2bNr1r2wIAAAAAwPXu2Wu64+PjlZiYqCZNmtjHPD09Va9ePW3atEmStH37dl25csVhTkhIiCIiIuxzbiQ9PV0pKSkOLwAAAAAActs9W7oTExMlSYGBgQ7jgYGB9mWJiYny8PBQwYIFbzrnRsaNGyd/f3/7KzQ0NJfTAwAAAABwD5fua2w2m8N7wzCyjV3vVnOGDRum5ORk++vEiRO5khUAAAAAgL+6Z0t3UFCQJGU7Yp2UlGQ/+h0UFKSMjAydO3fupnNuxNPTU35+fg4vAAAAAABy2z1bukuVKqWgoCCtWrXKPpaRkaH169crMjJSklSzZk25u7s7zElISNC+ffvscwAAAAAAsIqldy+/cOGCDh8+bH8fHx+vXbt2KSAgQCVKlNCAAQM0duxYlStXTuXKldPYsWOVL18+dezYUZLk7++vHj16aNCgQSpUqJACAgI0ePBgVa5c2X43cwAAAAAArGJp6d62bZuioqLs7wcOHChJ6tKli2JiYjRkyBClpaWpT58+OnfunGrXrq2VK1fK19fX/plJkybJzc1NTz/9tNLS0tSwYUPFxMTI1dX1rm8PAAAAAAB/ZWnprl+/vgzDuOlym82m6OhoRUdH33SOl5eXpkyZoilTppiQEAAAAACAO3fPXtMNAAAAAEBeR+kGAAAAAMAklG4AAAAAAExC6QYAAAAAwCSUbgAAAAAATELpBgAAAADAJJRuAAAAAABMQukGAAAAAMAklG4AAAAAAExC6QYAAAAAwCSUbgAAAAAATELpBgAAAADAJJRuAAAAAABMQukGAAAAAMAklG4AAAAAAExC6QYAAAAAwCSUbgAAAAAATELpBgAAAADAJJRuAAAAAABMQukGAAAAAMAklG4AAAAAAExC6QYAAAAAwCSUbgAAAAAATELpBgAAAADAJJRuAAAAAABMQukGAAAAAMAklG4AAAAAAEziZnUAAMC9J2zoMqsj5BlH32ppdQQAAHAP40g3AAAAAAAmoXQDAAAAAGASSjcAAAAAACahdAMAAAAAYBJKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjcAAAAAACZxszoAAAD4U9jQZVZHyFOOvtXS6ggAANzSPX2kOzo6WjabzeEVFBRkX24YhqKjoxUSEiJvb2/Vr19f+/fvtzAxAAAAAAD/c0+XbkmqVKmSEhIS7K+9e/fal40fP14TJ07U1KlTtXXrVgUFBalx48ZKTU21MDEAAAAAAH+650u3m5ubgoKC7K8iRYpI+vMo9+TJkzVixAi1a9dOERERmjNnji5duqQFCxZYnBoAAAAAgDxQug8dOqSQkBCVKlVKHTp00K+//ipJio+PV2Jiopo0aWKf6+npqXr16mnTpk1WxQUAAAAAwO6evpFa7dq1NXfuXIWHh+v06dMaM2aMIiMjtX//fiUmJkqSAgMDHT4TGBioY8eO/e1609PTlZ6ebn+fkpKS++EBAAAAAPe9e7p0N2/e3P7PlStXVp06dVSmTBnNmTNHDz/8sCTJZrM5fMYwjGxj1xs3bpxGjRqV+4EBAAAAAPiLe/708r/y8fFR5cqVdejQIftdzK8d8b4mKSkp29Hv6w0bNkzJycn214kTJ0zLDAAAAAC4f+Wp0p2enq4DBw4oODhYpUqVUlBQkFatWmVfnpGRofXr1ysyMvJv1+Pp6Sk/Pz+HFwAAAAAAue2ePr188ODBat26tUqUKKGkpCSNGTNGKSkp6tKli2w2mwYMGKCxY8eqXLlyKleunMaOHat8+fKpY8eOVkcHAAAAAODeLt0nT57Uv//9b/3xxx8qUqSIHn74YW3evFklS5aUJA0ZMkRpaWnq06ePzp07p9q1a2vlypXy9fW1ODkAAAAAAPd46Y6Njf3b5TabTdHR0YqOjr47gQAAAAAAyIE8dU03AAAAAAB5CaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwiZvVAQAAAKwWNnSZ1RHyjKNvtbQ6AgDkKRzpBgAAAADAJJRuAAAAAABMQukGAAAAAMAklG4AAAAAAExC6QYAAAAAwCSUbgAAAAAATMIjwwAAAGAJHtWWMzyuDcibONINAAAAAIBJKN0AAAAAAJiE0g0AAAAAgEko3QAAAAAAmITSDQAAAACASSjdAAAAAACYhNINAAAAAIBJKN0AAAAAAJiE0g0AAAAAgEko3QAAAAAAmMTN6gAAAAAA7p6wocusjpCnHH2rpdURkMdxpBsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTcPdyAAAAALgLuHP87XOmu8ZzpBsAAAAAAJM4TemeNm2aSpUqJS8vL9WsWVMbN260OhIAAAAA4D7nFKV74cKFGjBggEaMGKGdO3fq0UcfVfPmzXX8+HGrowEAAAAA7mNOUbonTpyoHj166LnnnlOFChU0efJkhYaGavr06VZHAwAAAADcx/L8jdQyMjK0fft2DR061GG8SZMm2rRp0w0/k56ervT0dPv75ORkSVJKSop5QXNBVvolqyPkKbn575N9f/vY79Zh31uD/W4d9r012O/Wya19z37PGf43b417vZtJ/8toGMbfzrMZt5pxjzt16pSKFSum77//XpGRkfbxsWPHas6cOTp48GC2z0RHR2vUqFF3MyYAAAAAwAmdOHFCxYsXv+nyPH+k+xqbzebw3jCMbGPXDBs2TAMHDrS/z8rK0tmzZ1WoUKGbfgY3lpKSotDQUJ04cUJ+fn5Wx7mvsO+twX63DvveGux3a7DfrcO+twb73Trs+ztnGIZSU1MVEhLyt/PyfOkuXLiwXF1dlZiY6DCelJSkwMDAG37G09NTnp6eDmMFChQwK+J9wc/Pj/8ntQj73hrsd+uw763BfrcG+9067HtrsN+tw76/M/7+/reck+dvpObh4aGaNWtq1apVDuOrVq1yON0cAAAAAIC7Lc8f6ZakgQMH6tlnn1WtWrVUp04dffTRRzp+/Lh69epldTQAAAAAwH3MKUp3+/btdebMGY0ePVoJCQmKiIjQ8uXLVbJkSaujOT1PT0+NHDky2+n6MB/73hrsd+uw763BfrcG+9067HtrsN+tw743X56/ezkAAAAAAPeqPH9NNwAAAAAA9ypKNwAAAAAAJqF0AwAAAABgEko3AAAAAAAmoXQDAAAAAGASSjdybOPGjerUqZPq1Kmj3377TZL06aefKi4uzuJkzislJUVZWVnZxjMzM5WSkmJBIgDO7vz58/rkk080bNgwnT17VpK0Y8cO++99APinGjRooPPnz2cbT0lJUYMGDe5+IMAklG7kyBdffKGmTZvK29tbO3fuVHp6uiQpNTVVY8eOtTidc1qyZIlq1aqly5cvZ1uWnp6uBx98UEuXLrUgGWAuV1dXJSUlZRs/c+aMXF1dLUh0/9izZ4/Cw8P19ttv691337X/UbxkyRINGzbM2nBOqm3btvr6669v+AUr4KzWrVunjIyMbOOXL1/Wxo0bLUgEmMPN6gDIW8aMGaMZM2aoc+fOio2NtY9HRkZq9OjRFiZzXtOnT9eQIUOUL1++bMvy5cunV199VVOnTlXr1q0tSOfcOnfurA8++EC+vr6SpN27d6tixYpyd3e3ONn9wTCMG46np6fLw8PjLqe5vwwcOFBdu3bV+PHj7f/7l6TmzZurY8eOFiZzXmlpaWrbtq2KFi2qrl27qlu3bipXrpzVsZze8ePHb2teiRIlTE5yf9mzZ4/9n3/66SclJiba32dmZmrFihUqVqyYFdHuG7/99pu++OIL/fLLL7LZbAoPD1e7du3Y7yaxGTf7qwa4gXz58umnn35SWFiYfH19tXv3bpUuXVq//vqrKlaseMOjsfhnQkJCtGHDBpUtW/aGyw8fPqzHHntMp06dusvJnJ+rq6sSEhJUtGhRSZKfn5927dql0qVLW5zMub3//vuSpJdffllvvPGG8ufPb1+WmZmpDRs26OjRo9q5c6dVEZ2ev7+/duzYoTJlyjj8rj927JjKly/P73qTnDx5UrNnz9acOXMUHx+vunXr6rnnntNTTz0lb29vq+M5pb+eNXPtT2KbzeYwZrPZlJmZedezOTMXFxf7fr5RFfH29taUKVPUvXv3ux3tvjBt2jQNHDhQGRkZ8vf3l2EYSklJkYeHhyZOnKg+ffpYHdHpcKQbORIcHKzDhw8rLCzMYTwuLo4iYpJz587p6tWrN11+5coVnTt37i4mun9c/4cA31HeHZMmTZL05/6eMWOGwx/FHh4eCgsL04wZM6yKd1/w8vK64f0iDh48qCJFiliQ6P5QvHhxvfbaa3rttde0du1azZo1S3369FHfvn3VoUMHde/eXbVr17Y6plOx2WwqXry4unbtqtatW8vNjT+N74b4+HgZhqHSpUvrxx9/dPi94uHhoaJFi3IZkUmWLVumfv36acCAARo0aJCCg4MlSQkJCXrnnXfUv39/hYWFqUWLFhYndS78ZkGO9OzZU/3799esWbNks9l06tQp/fDDDxo8eLBef/11q+M5pbCwMG3btk0PPPDADZdv27ZNJUuWvMupAPPEx8dLkqKiorR48WIVLFjQ4kT3n8cff1yjR4/WokWLJP1ZTI4fP66hQ4fqySeftDjd/SEqKkpRUVFKTU3VggULNHz4cM2cOfNvv4RFzp08eVJz5sxRTEyMZsyYoU6dOqlHjx6qUKGC1dGc2rW/W7iHwd03fvx4DR06VGPGjHEYDw4O1sSJE5UvXz69/fbblO5cxunlyLERI0Zo0qRJ9tMLPT09NXjwYL3xxhsWJ3NOI0aM0Lx58/Tjjz8qMDDQYVliYqJq166tTp066c0337QoofNycXHRmjVrFBAQIOnPexcsWrRIxYsXd5hXpUoVK+IBpklJSVGLFi20f/9+paamKiQkRImJiapTp46WL18uHx8fqyPeF3799VfFxMQoJiZGp06dUqNGjbRixQqrYzmtuLg4zZ49W59//rkqVqyoHj16qEePHnJx4b7DZvrll1+0bt06JSUlZSvhHNDJfX5+ftq6davKly9/w+UHDx5UrVq1lJqaepeTOTdKN+7IpUuX9NNPPykrK0sVK1Z0uOYSuSs1NVV16tTR8ePH1alTJ5UvX142m00HDhzQ/PnzFRoaqs2bNzvc7Ai54+/+0LLZbFzrZ7J//etfqlWrloYOHeow/s477+jHH3/U559/blGy+8eaNWu0Y8cOZWVlqUaNGmrUqJHVkZxeWlqaPv/8c82ePVsbNmxQiRIl1K1bN3Xr1k2hoaFWx7svnD59Wv/+97+1fv16/f777/YvXpH7Pv74Y/Xu3VuFCxdWUFCQw/X0NptNO3bssDCdc8qfP7/27Nlz08tCf/31V1WpUkUXLly4y8mcG6UbOdK9e3e999572QrexYsX1bdvX82aNcuiZM4tOTlZw4YN08KFC+3XbxcsWFDt27fX2LFjVaBAAWsDOqljx47d1jxO7zdHkSJFtGbNGlWuXNlhfO/evWrUqJFOnz5tUTLnN3fuXLVv316enp4O4xkZGYqNjVXnzp0tSua8Nm3apNmzZ2vhwoW6cuWK2rZtqx49evBFx120adMmzZo1S59//rnKly+v7t2764UXXuBIt4lKliypPn366NVXX7U6yn2jdu3a6tChg15++eUbLp84caIWLlyoLVu23OVkzo3SjRy5/m7O1/zxxx8KCgriWjOTGYahP/74Q4ZhqEiRIg7fCCP3paWlafDgwfryyy915coVNWrUSO+//74KFy5sdbT7gre3t3bt2pXtFLiff/5Z1atXV1pamkXJnN/NftefOXNGRYsW5ewOE7i4uKhatWrq0aOHnnnmGb5MvUsSEhI0d+5czZ49W+fOndMzzzyjHj16qFKlSlZHuy/wVJC7b86cOerdu7feffddvfDCC/abB169elUffvihXnnlFU2bNk1du3a1NqiT4as73JaUlBQlJyfLMAylpqYqJSXF/jp37pyWL1+e7Y8z5D6bzaYiRYqoSJEiWrt2rZYtW8ady030+uuvKyYmRi1btlSHDh20atUq9e7d2+pY942IiAgtXLgw23hsbKwqVqxoQaL7x7VLJ6538uRJ+fv7W5DI+cXFxenhhx/W2LFjFR4ero4dO+qPP/6wOpbTK1mypKZPn6727dtr+fLl6tq1qzIzM7Vnzx6HF8zx1FNPaeXKlVbHuK906dJFffr00UsvvaRChQqpRo0aqlGjhgoVKqR+/fqpZ8+eFG4TcKQbt+Wvz1O8EZvNplGjRmnEiBF3MdX94fz58+rfv7927Nihhx9+WBMmTFCLFi20adMmSX+egrtq1Spu5mWCMmXK6M0331SHDh0kST/++KPq1q2ry5cv8yiTu+Crr77Sk08+qY4dO6pBgwaSpNWrV+uzzz7T559/rrZt21ob0AlVr15dNptNu3fvVqVKlRwen5SZman4+Hg1a9bMfldz5J5rR5eeeeYZeXl56bPPPlP9+vW5d4HJ/nrq+LV7dVyPe3eYZ9y4cZo4caJatmypypUry93d3WF5v379LErm/LZs2aLPPvtMv/zyiyQpPDxcHTp00MMPP2xxMudE6cZtWb9+vQzDUIMGDfTFF1843FTEw8NDJUuWVEhIiIUJnddzzz2nDRs2qHPnzvr666/l4uIiwzA0efJkubi4aMiQIcqfP7+WLl1qdVSn4+Hhofj4eBUrVsw+5u3trV9++YUbGt0ly5Yt09ixY7Vr1y55e3urSpUqGjlypOrVq2d1NKc0atQo+/8dNGiQw00yrz0j/cknn5SHh4dVEZ0WX/JZg3t3WKtUqVI3XWaz2fTrr7/exTT3h0uXLumVV16xXzrXsGFDTZkyhUvnTEbpRo4cO3ZMJUqU4Friu6hYsWJasGCB6tWrp99++02hoaFas2aN6tevL+nPP8zatGmjxMREa4M6IVdXVyUmJqpIkSL2MV9fX+3Zs+dv/1AA8ro5c+aoQ4cO2W6kBvPwJZ81uHcH7jd/PavG29tbCxYs4Kyau8Dt1lNwv9uzZ48iIiLk4uKi5ORk7d2796ZzOcU5950+fVrh4eGS/izgXl5eDn+AlShRQr///rtV8ZyaYRjq2rWrQ/G4fPmyevXq5fCc4sWLF1sRDzDN2rVrVapUKT322GNWR7lvZGZmZjuDwM3NjRuUmuzavTv+elp/7969KSAWuHYckAM75lq8eLFmzpxpP6vmmWeeUd26dZWZmclZNSaidOOWqlWrpsTERBUtWlTVqlXjmqe7LCsry+GXoKura7bnWMIcXbp0yTbWqVMnC5LcnzIzMzVp0iQtWrRIx48fV0ZGhsPys2fPWpTM+aWmpqpJkyYKDQ1Vt27d1KVLF4cjsMh9fMlnjesLSKdOnSggd9ncuXP1zjvv6NChQ5L+vLb4lVde0bPPPmtxMud04sQJPfroo/b3Dz30kNzc3HTq1CnOqjERpRu3FB8fbz+9Nj4+3uI096dPPvnEfm3l1atXFRMTYz/1LTU11cpoTm327NlWR7ivjRo1Sp988okGDhyo1157TSNGjNDRo0f15Zdf6vXXX7c6nlP74osvdObMGc2bN08xMTEaOXKkGjVqpB49eujxxx/PdrMj/HN8yWcNCoi1Jk6cqNdee00vvfSS6tatK8Mw9P3336tXr176448/bvosadw5zqqxBtd0A/e4sLCw2zqazRcicDZlypTR+++/r5YtW8rX11e7du2yj23evFkLFiywOuJ9Y+fOnZo1a5b9C8BOnTqpT58+KleunNXRgH+Ee3dYq1SpUho1apQ6d+7sMD5nzhxFR0fzt40JXFxc1Lx5c4ezapYuXaoGDRpwVo2JONKNHDty5IgmT56sAwcOyGazqUKFCurfv7/KlCljdTSn9N1336ls2bJWxwDuusTERFWuXFmSlD9/fiUnJ0uSWrVqpddee83KaPeVhIQErVy5UitXrpSrq6tatGih/fv3q2LFiho/fjxHopCncVq/tRISEhQZGZltPDIyUgkJCRYkcn6cVWMNSjdy5Ntvv1WbNm1UrVo1+2lAmzZtUqVKlbR06VI1btzY6ohOJzw8XMWKFVNUVJQaNGigqKgoHl2C+0Lx4sWVkJCgEiVKqGzZslq5cqVq1KihrVu3cldtk125ckVfffWVZs+erZUrV6pKlSp6+eWX9cwzz8jX11eSFBsbq969e1O6kadRQKxVtmxZLVq0SMOHD3cYX7hwIWfSmIRL56zB6eXIkerVq6tp06Z66623HMaHDh2qlStXaseOHRYlc14bN27U+vXrtW7dOv3www+6fPmySpQoYS/gUVFR3OAITmno0KHy8/PT8OHD9d///lf//ve/FRYWpuPHj+vll1/O9nsIuadw4cLKysrSv//9bz3//POqVq1atjnnzp1TjRo1OP0TwB374osv1L59ezVq1Eh169aVzWZTXFycVq9erUWLFumJJ56wOiKQKyjdyBEvLy/t3bs327ePv/zyi6pUqaLLly9blOz+cOXKFf3www9at26d1q1bp82bNys9PV1ly5bVwYMHrY4HmGrLli36/vvvVbZsWbVp08bqOE7t008/1VNPPSUvLy+rowBwctu3b9ekSZN04MABGYahihUratCgQapevbrV0YBcQ+lGjoSGhmrixIl66qmnHMYXLVqkwYMH6/jx4xYlu7+kpaUpLi5O3377rT7++GNduHCBx7XBKdSoUUOrV69WwYIFNXr0aA0ePFj58uWzOtZ9Jzk5WZmZmQoICHAYP3v2rNzc3OTn52dRMgAA8h4XqwMgb3n++ef1wgsv6O2339bGjRsVFxent956Sz179tQLL7xgdTyndfnyZa1Zs0avvfaaHn30URUsWFD9+vXThQsXNH36dL7sgNM4cOCALl68KOnPR4ZduHDB4kT3pw4dOig2Njbb+KJFi+zPMwaAf6pBgwYaNWpUtvFz586pQYMGFiQCzMGRbuSIYRiaPHmyJkyYoFOnTkmSQkJC9Morr6hfv3639Wgr5Ey9evW0detWlSlTRo899pjq1aunevXqKTAw0OpoQK6rU6eO8ufPr0ceeUSjRo3S4MGD7c+ovx7P6jZPQECAvv/+e1WoUMFh/Oeff1bdunV15swZi5IBcCYuLi4qVKiQ6tatq/nz59vvGH/69GmFhIRwFh+cBqUbdyw1NVWS7HeyhTnc3d0VHBystm3bqn79+nrsscdUuHBhq2MBpjh48KBGjhypI0eOaMeOHapYsaLc3LI/aMNms3HjRhP5+Pho8+bN9ke2XbN3717Vrl1bly5dsigZAGfi4uKinTt3qmfPnrp48aKWLl2qsLAwSjecDqUbOTJ69Gg98sgj2U75uXjxoiZMmMCRJxNcvHhRGzdu1Lp167R27Vrt2rVL4eHhqlevnurXr6969eqpSJEiVscEcp2Li4sSExNVtGhRq6Pcd+rXr6/KlStrypQpDuMvvvii9uzZo40bN1qUDIAzufZ73t/fX927d9fKlSv1+eefq0KFCpRuOBVKN3LExcVF7u7uGjdunAYOHGgf5xvJuyc1NVVxcXFau3at1q1bp927d6tcuXLat2+f1dEAOInvv/9ejRo10oMPPqiGDRtKklavXq2tW7dq5cqVevTRRy1OCMAZuLq6KiEhwf7l6pgxYzRmzBi9+uqrGjNmDH9XwmlkP2cPuIW5c+fqpZde0p49e/TRRx/Jw8PD6kj3FR8fHwUEBCggIEAFCxaUm5ubDhw4YHUsIFd89dVXat68udzd3fXVV1/97VweG2aeunXr6ocfftD48eO1aNEieXt7q0qVKpo5c2a2R0YCwJ26/tjff/7zH1WoUEFdunSxKBFgDo50I0eunQaUmpqq1q1bq0CBAlqyZIkkcaTbJFlZWdq2bZv99PLvv/9eFy9eVLFixRQVFWV/lSxZ0uqowD/211PKXVxu/oANm83G7xsAyOOOHTum0NDQbL/v9+3bp+3bt1O+4TQo3ciRv54GlJKSoqefflr79+/XjBkz1KZNG/4INoGfn58uXryo4OBg1a9fX/Xr11dUVJTKlCljdTQATiwzM1NffvmlDhw4IJvNpooVK6pNmzZydXW1OhoAAHkKpRs5cv2NjbKysjRgwABNnz5dWVlZlG4TfPjhh4qKilJ4eLjVUYC7au7cuWrfvr08PT0dxjMyMhQbG6vOnTtblMz5HT58WC1bttTJkydVvnx5GYahX375RaGhoVq2bBlf+gG4Y+3atVNMTIz8/PzUrl27v527ePHiu5QKMBelGzkyZ84cdejQIdsfwbNnz9aGDRs0e/Zsi5IBcDbX32DnmjNnzqho0aJ8yWeiFi1ayDAMzZ8/XwEBAZL+3O+dOnWSi4uLli1bZnFCAHlVt27d9P7778vX11fdunX727n8XQlnQekGANyTXFxcdPr06WyPxNu9e7eioqJ09uxZi5I5v5s9p3v37t2qW7euLly4YFEyAADyHu5ejhxbv3693n33Xft1fhUqVNArr7zCI2QA5Irq1avLZrPJZrOpYcOGcnP733+qMjMzFR8fr2bNmlmY0Pl5enoqNTU12/iFCxd4YgUAADlE6UaOzJs3T926dVO7du3Ur18/GYahTZs2qWHDhoqJiVHHjh2tjgggj2vbtq0kadeuXWratKny589vX+bh4aGwsDA9+eSTFqW7P7Rq1UovvPCCZs6cqYceekiStGXLFvXq1YtHtQHINadPn9bgwYO1evVqJSUlZXuEGJcRwVlwejlypEKFCnrhhRf08ssvO4xPnDhRH3/8Mc+LBpArMjMz9emnn6pp06YKDg62Os595/z58+rSpYuWLl0qd3d3SdLVq1fVpk0bzZ49WwUKFLA2IACn0Lx5cx0/flwvvfSSgoODZbPZHJY//vjjFiUDchelGzni6emp/fv3q2zZsg7jhw8fVkREhC5fvmxRMgDOxsvLSwcOHFCpUqWsjnLfOnz4sA4cOCDDMFSxYsVsv/sB4J/w9fXVxo0bVa1aNaujAKZyufUU4H9CQ0O1evXqbOOrV69WaGioBYkAOKvKlSvr119/tTrGfWn06NG6dOmSypYtq9atW6tNmzYqW7as0tLSNHr0aKvjAXASoaGh2U4pB5wRR7qRI9OnT9eAAQPUvXt3RUZGymazKS4uTjExMXrvvffUs2dPqyMCcBIrV67Uq6++qjfeeEM1a9aUj4+Pw3I/Pz+Lkjk/HtcG4G5YuXKlJkyYoA8//FBhYWFWxwFMQ+lGji1ZskQTJkywX7997e7lXHcDIDe5uPzvZKy/XudnGIZsNhvFz0Q3e1zbmjVr1L59e/3+++8WJQOQ1xUsWNDhd/rFixd19epV5cuXz34PiWt4NCScBXcvR4498cQTeuKJJ6yOAcDJrV279qbLdu7ceReT3D+u/TFss9kUHh7u8IdxZmamLly4oF69elmYEEBeN3nyZKsjAHcdR7pxRzIyMpSUlKSsrCyH8RIlSliUCICzS05O1vz58/XJJ59o9+7dHOk2wZw5c2QYhrp3767JkyfL39/fvuza49rq1KljYUIAAPIeSjdy5NChQ+revbs2bdrkMM7pngDMsmbNGs2aNUuLFy9WyZIl9eSTT+rJJ59U9erVrY7mtNavX6+6devKzY0T4gCYZ/ny5XJ1dVXTpk0dxleuXKnMzEw1b97comRA7uK/psiRrl27ys3NTV9//fUNn6cIALnh5MmTiomJ0axZs3Tx4kU9/fTTunLlir744gtVrFjR6nhOr169ejpy5Ihmz56tI0eO6L333lPRokW1YsUKhYaGqlKlSlZHBOAEhg4dqrfeeivbeFZWloYOHUrphtPgSDdyxMfHR9u3b9cDDzxgdRQATqpFixaKi4tTq1at9Mwzz6hZs2ZydXWVu7u7du/eTem+C9avX6/mzZurbt262rBhgw4cOKDSpUtr/Pjx+vHHH/Xf//7X6ogAnIC3t7cOHDiQ7c7lR48eVaVKlXTx4kVrggG5jOd0I0cqVqyoP/74w+oYAJzYypUr9dxzz2nUqFFq2bKlXF1drY503xk6dKjGjBmjVatWycPDwz4eFRWlH374wcJkAJyJv7+/fv3112zjhw8fzvaYSCAvo3TjllJSUuyvt99+W0OGDNG6det05swZh2UpKSlWRwXgBDZu3KjU1FTVqlVLtWvX1tSpU3lE1V22d+/eGz6lokiRIjpz5owFiQA4ozZt2mjAgAE6cuSIfezw4cMaNGiQ2rRpY2EyIHdRunFLBQoUUMGCBVWwYEE1btxYmzdvVsOGDVW0aFH7+LU5APBP1alTRx9//LESEhLUs2dPxcbGqlixYsrKytKqVauUmppqdUSnV6BAASUkJGQb37lzp4oVK2ZBIgDO6J133pGPj48eeOABlSpVSqVKldIDDzygQoUK6d1337U6HpBruKYbt7R+/frbnluvXj0TkwC4Xx08eFAzZ87Up59+qvPnz6tx48b66quvrI7ltIYMGaIffvhBn3/+ucLDw7Vjxw6dPn1anTt3VufOnTVy5EirIwJwEoZhaNWqVdq9e7e8vb1VtWpVPfroo1bHAnIVpRsAkGdkZmZq6dKlmjVrFqXbRFeuXFHXrl0VGxsrwzDk5uamzMxMdezYUTExMVxnD+Af2bJli86ePetwd/I5c+Zo5MiRunTpktq2baspU6bI09PTwpRA7qF0I8fOnTunmTNn6sCBA7LZbKpQoYK6deumgIAAq6MBAHLRkSNHtHPnTmVlZal69eoqV66c1ZEAOIHmzZurfv36evXVVyX9eR+JmjVrqkuXLqpQoYLeeecd9ezZU9HR0dYGBXIJpRs5sn79erVp00b+/v6qVauWJGn79u06f/68vvrqK04vBwAAwN8KDg7W0qVL7X9LjhgxQuvXr1dcXJwk6fPPP9fIkSP1008/WRkTyDVuVgdA3vLiiy+qffv2mj59uv30wszMTPXp00cvvvii9u3bZ3FCAMA/ZRiG/vvf/2rt2rVKSkpSVlaWw/LFixdblAyAMzh37pwCAwPt79evX69mzZrZ3z/44IM6ceKEFdEAU3D3cuTIkSNHNGjQIIfr+VxdXTVw4ECHxz0AAPKu/v3769lnn1V8fLzy588vf39/hxcA/BOBgYGKj4+XJGVkZGjHjh2qU6eOfXlqaqrc3d2tigfkOo50I0dq1KihAwcOqHz58g7jBw4cULVq1awJBQDIVfPmzdPixYvVokULq6MAcELNmjXT0KFD9fbbb+vLL79Uvnz5HO5YvmfPHpUpU8bChEDuonQjR/r166f+/fvr8OHDevjhhyVJmzdv1gcffKC33npLe/bssc+tUqWKVTEBAP+Av7+/SpcubXUMAE5qzJgxateunerVq6f8+fNrzpw58vDwsC+fNWuWmjRpYmFCIHdxIzXkiIvL31+RYLPZZBiGbDabMjMz71IqAEBumjNnjlasWKFZs2bJ29vb6jgAnFRycrLy58+f7TGEZ8+eVf78+R2KOJCXUbqRI8eOHbvtuSVLljQxCQDALJcuXVK7du30/fffKywsLNu1lTt27LAoGQAAeQ+nlyNHKNIA4Py6du2q7du3q1OnTgoMDJTNZrM6EgAAeRZHupFjR44c0eTJk3XgwAHZbDZVqFBB/fv354YXAOAkfHx89O233+qRRx6xOgoAAHkejwxDjnz77beqWLGifvzxR1WpUkURERHasmWLKlWqpFWrVlkdDwCQC0JDQ+Xn52d1DAAAnAJHupEj1atXV9OmTfXWW285jA8dOlQrV67kOj8AcALLli3TlClTNGPGDIWFhVkdBwCAPI3SjRzx8vLS3r17Va5cOYfxX375RVWqVNHly5ctSgYAyC0FCxbUpUuXdPXqVeXLly/bjdTOnj1rUTIAAPIebqSGHClSpIh27dqVrXTv2rVLRYsWtSgVACA3TZ482eoIAAA4DUo3cuT555/XCy+8oF9//VWRkZGy2WyKi4vT22+/rUGDBlkdDwCQC7p06WJ1BAAAnAanlyNHDMPQ5MmTNWHCBJ06dUqSFBISoldeeUX9+vXjsTIA4GTS0tJ05coVhzFusgYAwO2jdOO2Xb16VfPnz1fTpk0VFBSk1NRUSZKvr6/FyQAAuenixYt69dVXtWjRIp05cybb8szMTAtSAQCQN/HIMNw2Nzc39e7dW+np6ZL+LNsUbgBwPkOGDNGaNWs0bdo0eXp66pNPPtGoUaMUEhKiuXPnWh0PAIA8hdKNHKldu7Z27txpdQwAgImWLl2qadOm6V//+pfc3Nz06KOP6j//+Y/Gjh2r+fPnWx0PAIA8hRupIUf69OmjQYMG6eTJk6pZs6Z8fHwcllepUsWiZACA3HL27FmVKlVK0p/Xb197RNgjjzyi3r17WxkNAIA8h9KN29K9e3dNnjxZ7du3lyT169fPvsxms8kwDNlsNq7zAwAnULp0aR09elQlS5ZUxYoVtWjRIj300ENaunSpChQoYHU8AADyFG6khtvi6uqqhIQEpaWl/e28kiVL3qVEAACzTJo0Sa6ururXr5/Wrl2rli1bKjMzU1evXtXEiRPVv39/qyMCAJBnULpxW1xcXJSYmKiiRYtaHQUAcJcdP35c27ZtU5kyZVS1alWr4wAAkKdwejluG8/gBgDnlpaWptWrV6tVq1aSpGHDhtmfWCFJmzdvVvny5eXl5WVVRAAA8hyOdOO2uLi4yN/f/5bF+9rNdgAAec+HH36or7/+WkuXLpX056MhK1WqJG9vb0nSzz//rCFDhujll1+2MiYAAHkKR7px20aNGiV/f3+rYwAATDJ//vxshXrBggUqXbq0JGnevHn64IMPKN0AAOQApRu3rUOHDlzTDQBO7JdfflF4eLj9vZeXl1xcXOzvH3roIb344otWRAMAIM+idOO2cD03ADi/5ORkubn970+D33//3WF5VlaWwzXeAADg1lxuPQWQuPQfAJxf8eLFtW/fvpsu37Nnj4oXL34XEwEAkPdRunFbsrKyOLUcAJxcixYt9Prrr+vy5cvZlqWlpWnUqFFq2bKlBckAAMi7uHs5AACQJJ0+fVrVqlWTh4eHXnrpJYWHh8tms+nnn3/W1KlTdfXqVe3cuVOBgYFWRwUAIM+gdAMAALv4+Hj17t1bq1atsl9aZLPZ1LhxY02bNs1+J3MAAHB7KN0AACCbs2fP6vDhw5KksmXLKiAgwOJEAADkTZRuAAAAAABMwo3UAAAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwCAXJSYmKi+ffuqdOnS8vT0VGhoqFq3bq3Vq1ff9jpiYmJUoEAB80LeJdHR0bLZbOrVq5fD+K5du2Sz2XT06FFrggEAcBdRugEAyCVHjx5VzZo1tWbNGo0fP1579+7VihUrFBUVpRdffNHqeHfsypUrd/xZLy8vzZw5U7/88ksuJgIAIO+gdAMAkEv69Okjm82mH3/8Uf/6178UHh6uSpUqaeDAgdq8ebN93sSJE1W5cmX5+PgoNDRUffr00YULFyRJ69atU7du3ZScnCybzSabzabo6GhJUkZGhoYMGaJixYrJx8dHtWvX1rp16xwyfPzxxwoNDVW+fPn0xBNPaOLEidmOmk+fPl1lypSRh4eHypcvr08//dRhuc1m04wZM/T444/Lx8dHY8aMUdmyZfXuu+86zNu3b59cXFx05MiRm+6T8uXLKyoqSv/5z39uOiczM1M9evRQqVKl5O3trfLly+u9995zmNO1a1e1bdtWY8eOVWBgoAoUKKBRo0bp6tWreuWVVxQQEKDixYtr1qxZDp/77bff1L59exUsWFCFChXS448/zhF2AMBdRekGACAXnD17VitWrNCLL74oHx+fbMv/WnxdXFz0/vvva9++fZozZ47WrFmjIUOGSJIiIyM1efJk+fn5KSEhQQkJCRo8eLAkqVu3bvr+++8VGxurPXv26KmnnlKzZs106NAhSdL333+vXr16qX///tq1a5caN26sN9980yHHkiVL1L9/fw0aNEj79u1Tz5491a1bN61du9Zh3siRI/X4449r79696t69u7p3767Zs2c7zJk1a5YeffRRlSlT5m/3zVtvvaUvvvhCW7duveHyrKwsFS9eXIsWLdJPP/2k119/XcOHD9eiRYsc5q1Zs0anTp3Shg0bNHHiREVHR6tVq1YqWLCgtmzZol69eqlXr146ceKEJOnSpUuKiopS/vz5tWHDBsXFxSl//vxq1qyZMjIy/jYzAAC5xgAAAP/Yli1bDEnG4sWLc/zZRYsWGYUKFbK/nz17tuHv7+8w5/Dhw4bNZjN+++03h/GGDRsaw4YNMwzDMNq3b2+0bNnSYfkzzzzjsK7IyEjj+eefd5jz1FNPGS1atLC/l2QMGDDAYc6pU6cMV1dXY8uWLYZhGEZGRoZRpEgRIyYm5qbbNXLkSKNq1aqGYRhGhw4djAYNGhiGYRg7d+40JBnx8fE3/WyfPn2MJ5980v6+S5cuRsmSJY3MzEz7WPny5Y1HH33U/v7q1auGj4+P8dlnnxmGYRgzZ840ypcvb2RlZdnnpKenG97e3sa33357058NAEBu4kg3AAC5wDAMSX+emn0ra9euVePGjVWsWDH5+vqqc+fOOnPmjC5evHjTz+zYsUOGYSg8PFz58+e3v9avX28/vfvgwYN66KGHHD53/fsDBw6obt26DmN169bVgQMHHMZq1arl8D44OFgtW7a0n7799ddf6/Lly3rqqaduub2SNGbMGG3cuFErV6684fIZM2aoVq1aKlKkiPLnz6+PP/5Yx48fd5hTqVIlubj870+XwMBAVa5c2f7e1dVVhQoVUlJSkiRp+/btOnz4sHx9fe37KyAgQJcvX/7bU+IBAMhNblYHAADAGZQrV042m00HDhxQ27Ztbzrv2LFjatGihXr16qU33nhDAQEBiouLU48ePf72hmVZWVlydXXV9u3b5erq6rAsf/78kv4s/teX/mtfBvzVjeZcP3ajU+Sfe+45Pfvss5o0aZJmz56t9u3bK1++fDfN/FdlypTR888/r6FDh2rmzJkOyxYtWqSXX35ZEyZMUJ06deTr66t33nlHW7ZscZjn7u6ebTtuNJaVlSXpz31Ws2ZNzZ8/P1ueIkWK3FZuAAD+KUo3AAC5ICAgQE2bNtUHH3ygfv36ZSut58+fV4ECBbRt2zZdvXpVEyZMsB+1vf7aZQ8PD2VmZjqMVa9eXZmZmUpKStKjjz56wwwPPPCAfvzxR4exbdu2ObyvUKGC4uLi1LlzZ/vYpk2bVKFChVtuY4sWLeTj46Pp06frm2++0YYNG275mb96/fXXVaZMGcXGxjqMb9y4UZGRkerTp499LDeORNeoUUMLFy5U0aJF5efn94/XBwDAneD0cgAAcsm0adOUmZmphx56SF988YUOHTqkAwcO6P3331edOnUk/XnE9+rVq5oyZYp+/fVXffrpp5oxY4bDesLCwnThwgWtXr1af/zxhy5duqTw8HA988wz6ty5sxYvXqz4+Hht3bpVb7/9tpYvXy5J6tu3r5YvX66JEyfq0KFD+vDDD/XNN984HMV+5ZVXFBMToxkzZujQoUOaOHGiFi9ebL9Z299xdXVV165dNWzYMJUtW9a+TbcrMDBQAwcO1Pvvv+8wXrZsWW3btk3ffvutfvnlF7322ms3velaTjzzzDMqXLiwHn/8cW3cuFHx8fFav369+vfvr5MnT/7j9QMAcDso3QAA5JJSpUppx44dioqK0qBBgxQREaHGjRtr9erVmj59uiSpWrVqmjhxot5++21FRERo/vz5GjdunMN6IiMj1atXL7Vv315FihTR+PHjJUmzZ89W586dNWjQIJUvX15t2rTRli1bFBoaKunPa7NnzJihiRMnqmrVqlqxYoVefvlleXl52dfdtm1bvffee3rnnXdUqVIlffjhh5o9e7bq169/W9vYo0cPZWRkqHv37ne0j1555RX76fDX9OrVS+3atVP79u1Vu3ZtnTlzxuGo953Kly+fNmzYoBIlSqhdu3aqUKGCunfvrrS0NI58AwDuGptxo4u9AACAU3j++ef1888/a+PGjbmyvu+//17169fXyZMnFRgYmCvrBADAmXFNNwAATuTdd99V48aN5ePjo2+++UZz5szRtGnT/vF609PTdeLECb322mt6+umnKdwAANwmTi8HAMCJ/Pjjj2rcuLEqV66sGTNm6P3339dzzz33j9f72WefqXz58kpOTraf7g4AAG6N08sBAAAAADAJR7oBAAAAADAJpRsAAAAAAJNQugEAAAAAMAmlGwAAAAAAk1C6AQAAAAAwCaUbAAAAAACTULoBAAAAADAJpRsAAAAAAJNQugEAAAAAMMn/B5+MfvMF5CGXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the count of each category\n",
    "category_counts = slides_2['category_name'].value_counts()\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(category_counts.index, category_counts.values)\n",
    "plt.xlabel('Category Name')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Category Counts')\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7361494-f2d6-4b68-b407-e8768082e7c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "47f729e0-7507-4882-a666-d01d30f90ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262\n",
      "4513\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Thick and Thin in annotations_df\n",
    "sally_annotations_df['is_thick'] = sally_annotations_df['file_name'].str.contains('THICK', case=False)\n",
    "sally_annotations_df['is_thin'] = sally_annotations_df['file_name'].str.contains('THIN', case=False)\n",
    "annotation_thick = annotations_df['is_thick']\n",
    "annotation_thin = annotations_df['is_thin']\n",
    "\n",
    "thick_annotations = sally_annotations_df[annotation_thick]\n",
    "thick_annotations = thick_annotations.drop('is_thick', axis = 1)\n",
    "thick_annotations = thick_annotations.drop('is_thin', axis = 1)\n",
    "thick_annotations = thick_annotations.drop('category_id', axis = 1)\n",
    "thick_annotations= thick_annotations.drop('category_name', axis = 1)\n",
    "thick_annotations= thick_annotations.groupby(['file_name','image_id']).size().reset_index(name='count')\n",
    "length_thick_annotations = len(thick_annotations)\n",
    "print(length_thick_annotations)\n",
    "\n",
    "thin_annotations = sally_annotations_df[annotation_thin]\n",
    "thin_annotations = thin_annotations.drop('is_thick', axis = 1)\n",
    "thin_annotations = thin_annotations.drop('is_thin', axis = 1)\n",
    "thin_annotations = thin_annotations.drop('category_id', axis = 1)\n",
    "thin_annotations = thin_annotations.drop('category_name', axis = 1)\n",
    "length_thin_annotations = len(thin_annotations)\n",
    "print(length_thin_annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f58314c-2b10-4704-82cf-addd51e304f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "a96b499e-98b2-4d85-8eb7-d28d46eaed5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2661\n",
      "2472\n"
     ]
    }
   ],
   "source": [
    "# Thick and Thin in metadata_df\n",
    "metadata_df['is_thick'] = metadata_df['slide_name'].str.contains('THICK', case=False)\n",
    "metadata_df['is_thin'] = metadata_df['slide_name'].str.contains('THIN', case=False)\n",
    "metadata_thick = metadata_df['is_thick']\n",
    "metadata_thin = metadata_df['is_thin']\n",
    "\n",
    "thick_meta = metadata_df[metadata_thick]\n",
    "thick_meta = thick_meta.drop('is_thick', axis = 1)\n",
    "thick_meta = thick_meta.drop('is_thin', axis = 1)\n",
    "thick_meta = thick_meta.drop('stain', axis = 1)\n",
    "thick_meta = thick_meta.drop('username', axis = 1)\n",
    "thick_meta = thick_meta.groupby(['timestamp','slide_name','zoom_ration']).size().reset_index(name='Number')\n",
    "thick_meta = thick_meta.drop('Number', axis = 1)\n",
    "length_thick_meta = len(thick_meta)\n",
    "print(length_thick_meta)\n",
    "\n",
    "thin_meta = metadata_df[metadata_thin]\n",
    "thin_meta = thin_meta.drop('is_thick', axis = 1)\n",
    "thin_meta = thin_meta.drop('is_thin', axis = 1)\n",
    "thin_meta = thin_meta.drop('stain', axis = 1)\n",
    "thin_meta = thin_meta.drop('username', axis = 1)\n",
    "thin_meta = thin_meta.groupby(['timestamp','slide_name','zoom_ration']).size().reset_index(name='Number')\n",
    "thin_meta = thin_meta.drop('Number', axis = 1)\n",
    "length_thin_meta = len(thin_meta)\n",
    "print(length_thin_meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "735d2ce9-9504-4ed7-a433-14030f4e1798",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "a5f3ee27-2552-4618-a5b3-a73581ba7c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "trophozoite_annotations = sally_annotations_df[sally_annotations_df['category_id'] == 1]\n",
    "\n",
    "# Create a mapping of category_id to category_name\n",
    "category_mapping = sally_annotations_df.groupby('category_id')['category_name'].first()\n",
    "\n",
    "# Replacing category_id with category_name in the groupby column\n",
    "trophozoite_annotations.loc[:,'category_id'] = trophozoite_annotations['category_id'].map(category_mapping)\n",
    "\n",
    "trophozoite_annotations = trophozoite_annotations.groupby(['image_id','category_id']).size().reset_index(name='Number')\n",
    "trophozoite_annotations.columns = ['Image_ID','Category_name','Number']\n",
    "#sally_trop_annot = trophozoite_annotations\n",
    "#sally_trop_annot.head()\n",
    "#sally_trop_annot_number = len(sally_trop_annot)\n",
    "#sally_trop_annot_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a517f8df-e13b-41f5-8bc1-4b7a65878eb7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "36dbbf9d-ef5e-4ac5-aa9b-c8345ebcd721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Thick\n",
    "trophozoite_annotations['is_thick'] = metadata_df['slide_name'].str.contains('THICK', case=False)\n",
    "sally_trophozoite_thick = trophozoite_annotations['is_thick']\n",
    "\n",
    "# New column indicating whether the slide is thick or thin\n",
    "trophozoite_annotations.loc[:, 'Slide_Name'] = np.where(trophozoite_thick, 'Thick','Thin')\n",
    "trophozoite_annotations_thick = trophozoite_annotations[trophozoite_thick]\n",
    "trophozoite_annotations_thick = trophozoite_annotations_thick.drop('is_thick', axis = 1)\n",
    "trophozoite_annotations_thick.head()\n",
    "trophozoite_annotations_number_thick  = len(trophozoite_annotations_thick)\n",
    "trophozoite_annotations_number_thick "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "155ab48d-3098-4267-9a46-3ca96b913f6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "6a36e085-08aa-4062-b983-9e84fd031bfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Thin\n",
    "trophozoite_annotations['is_thin'] = metadata_df['slide_name'].str.contains('THIN', case=False)\n",
    "trophozoite_thin = trophozoite_annotations['is_thin']\n",
    "# For Zoomratio\n",
    "#trophozoite_annotations['zoom_ration'] = metadata_df['zoom_ration']\n",
    "\n",
    "# New column indicating whether the slide is thick or thin\n",
    "trophozoite_annotations.loc[:, 'Slide_Name'] = np.where(trophozoite_thin, 'Thin','Thick')\n",
    "trophozoite_annotations_thin = trophozoite_annotations[trophozoite_thin]\n",
    "trophozoite_annotations_thin = trophozoite_annotations_thin.drop('is_thin', axis = 1)\n",
    "trophozoite_annotations_thin = trophozoite_annotations_thin.drop('is_thick', axis = 1)\n",
    "trophozoite_annotations_thin.head()\n",
    "trophozoite_annotations_number_thin  = len(trophozoite_annotations_thin)\n",
    "trophozoite_annotations_number_thin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2a1fc9e-4704-4840-abfd-fe34d939fd72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "e229b42a-abac-4eb0-a40c-37ddacc4a025",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Image_ID  Number Category_name\n",
      "0        20       1      Schizont\n",
      "1        78       1      Schizont\n",
      "2        85       2      Schizont\n",
      "3       157       1      Schizont\n",
      "4       158       1      Schizont\n"
     ]
    }
   ],
   "source": [
    "schizont_annotations = sally_annotations_df[sally_annotations_df['category_id'] == 2]\n",
    "\n",
    "schizont_annotations = schizont_annotations.groupby(['image_id']).size().reset_index(name='Number')\n",
    "schizont_annotations['category_id'] = 2\n",
    "\n",
    "category_mapping = sally_annotations_df[['category_id', 'category_name']].drop_duplicates()\n",
    "schizont_annotations.loc[:,'category_id'] = schizont_annotations['category_id'].replace(category_mapping.set_index('category_id')['category_name'])\n",
    "schizont_annotations.columns = ['Image_ID','Number','Category_name']\n",
    "print(schizont_annotations.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04363955-804d-4495-b814-79ac0749e4ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "fe8ec230-79a2-46d6-ab9b-97ff99293f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image_ID</th>\n",
       "      <th>Number</th>\n",
       "      <th>Category_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gametocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gametocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Gametocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Gametocyte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>Gametocyte</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Image_ID  Number Category_name\n",
       "0         3       1    Gametocyte\n",
       "1         4       1    Gametocyte\n",
       "2         5       1    Gametocyte\n",
       "3        12       1    Gametocyte\n",
       "4        13       1    Gametocyte"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gametocyte_annotations = sally_annotations_df[sally_annotations_df['category_id'] == 3]\n",
    "\n",
    "gametocyte_annotations = gametocyte_annotations.groupby(['image_id']).size().reset_index(name='Number')\n",
    "gametocyte_annotations['category_id'] = 3\n",
    "\n",
    "category_mapping = sally_annotations_df[['category_id', 'category_name']].drop_duplicates()\n",
    "gametocyte_annotations.loc[:,'category_id'] = gametocyte_annotations['category_id'].replace(category_mapping.set_index('category_id')['category_name'])\n",
    "gametocyte_annotations.columns = ['Image_ID','Number','Category_name']\n",
    "gametocyte_annotations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43c7587a-e04b-4c7c-8681-783b53058a1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "41dc0daf-c623-45a8-93ee-040c04eb1440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   image_id  category_id  count\n",
       "0         1            4     16\n",
       "1         2            4     18\n",
       "2         3            4     13\n",
       "3         4            4     11\n",
       "4         5            4     13"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wbc_annotations = annotations_df[annotations_df['category_id'] == 4]\n",
    "wbc_annotations = wbc_annotations.drop('is_thick', axis = 1)\n",
    "wbc_annotations = wbc_annotations.drop('is_thin', axis = 1)\n",
    "wbc_annotations = wbc_annotations.groupby(['image_id','category_id']).size().reset_index(name='count')\n",
    "wbc_annotations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b2e05c4-de99-4fa7-968c-e2ea7c07fde4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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