{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6eee3ddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import fitz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b50e3ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = fitz.open(\"data/~Moran~K~19910201~Spirometry Exam~20250729~20250729032843.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7e1c3ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3 image(s) on page 1\n",
      "Saved: page_1_image_1.png\n",
      "Saved: page_1_image_2.png\n",
      "Saved: page_1_image_3.png\n",
      "\n",
      "Total images extracted: 3\n",
      "Images saved in: extracted_images/\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Create directory to save images if it doesn't exist\n",
    "output_dir = \"extracted_images\"\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Extract all images from the PDF\n",
    "image_count = 0\n",
    "for page_num in range(len(file)):\n",
    "    page = file[page_num]\n",
    "    \n",
    "    # Get list of images on this page\n",
    "    image_list = page.get_images()\n",
    "    \n",
    "    if image_list:\n",
    "        print(f\"Found {len(image_list)} image(s) on page {page_num + 1}\")\n",
    "        \n",
    "        for img_index, img in enumerate(image_list):\n",
    "            # Get image reference number\n",
    "            xref = img[0]\n",
    "            \n",
    "            # Extract image data\n",
    "            base_image = file.extract_image(xref)\n",
    "            image_bytes = base_image[\"image\"]\n",
    "            image_ext = base_image[\"ext\"]\n",
    "            \n",
    "            # Create filename\n",
    "            image_filename = f\"page_{page_num + 1}_image_{img_index + 1}.{image_ext}\"\n",
    "            image_path = os.path.join(output_dir, image_filename)\n",
    "            \n",
    "            # Save image\n",
    "            with open(image_path, \"wb\") as image_file:\n",
    "                image_file.write(image_bytes)\n",
    "            \n",
    "            print(f\"Saved: {image_filename}\")\n",
    "            image_count += 1\n",
    "    else:\n",
    "        print(f\"No images found on page {page_num + 1}\")\n",
    "\n",
    "print(f\"\\nTotal images extracted: {image_count}\")\n",
    "print(f\"Images saved in: {output_dir}/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2af9631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error extracting tables from page 1: object of type 'TableFinder' has no len()\n",
      "\n",
      "Extracted text from 1 pages\n",
      "Found 0 tables total\n",
      "\n",
      "First 1000 characters of extracted text:\n",
      "\n",
      "--- Page 1 ---\n",
      "PRE#1\n",
      "PRE#2\n",
      "PRE#3\n",
      "Spirometry Results\n",
      "VISIT DATE 2025-07-29\n",
      "ID\n",
      "Last Name\n",
      "Moran\n",
      "First Name\n",
      "K\n",
      "Date of birth\n",
      "1991-02-01\n",
      "Origin\n",
      "Caucasian\n",
      "Age\n",
      "34\n",
      "Gender\n",
      "F\n",
      "Height\n",
      "163 cm\n",
      "Weight\n",
      "54 kg\n",
      "BMI\n",
      "20.3\n",
      "ACCEPTABILITY CRITERIA\n",
      "Quality Grade PRE F Variability FEV1=0.05(1.56%), FVC=0.07(1.68%)\n",
      "Acceptable trials 0\n",
      "LLN\n",
      "Predicted\n",
      "FVC\n",
      "FEV1\n",
      "FEV1/FVC\n",
      "-5\n",
      "-4\n",
      "-3\n",
      "-2\n",
      "-1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "Spirometry\n",
      "Parameters\n",
      "FVC\n",
      "FEV1\n",
      "FEV1/FVC\n",
      "PEF\n",
      "FEF2575\n",
      "FEF25\n",
      "FEF50\n",
      "FEF75\n",
      "PEFTime\n",
      "EVol\n",
      "FEV6\n",
      "L\n",
      "L\n",
      "%\n",
      "L/m\n",
      "L/s\n",
      "L/s\n",
      "L/s\n",
      "ms\n",
      "mL\n",
      "L\n",
      "L/s\n",
      "Best\n",
      "3.26\n",
      "76.89\n",
      "684\n",
      "2.74\n",
      "6.08\n",
      "3.06\n",
      "1.06\n",
      "79\n",
      "78.0\n",
      "4.24\n",
      "4.22\n",
      "LLN\n",
      "3.03\n",
      "2.53\n",
      "72.47\n",
      "222\n",
      "2.15\n",
      "0.0\n",
      "0.0\n",
      "0.71\n",
      "-\n",
      "-\n",
      "3.03\n",
      "Pred.\n",
      "3.79\n",
      "3.16\n",
      "384\n",
      "3.42\n",
      "0.0\n",
      "0.0\n",
      "1.41\n",
      "-\n",
      "-\n",
      "3.79\n",
      "83.78\n",
      "%Pred.\n",
      "112.0\n",
      "103.3\n",
      "91.8\n",
      "178.7\n",
      "80.2\n",
      "-\n",
      "-\n",
      "75.1\n",
      "-\n",
      "-\n",
      "111.4\n",
      "ZScore\n",
      "0.95\n",
      "0.28\n",
      "-1.05\n",
      "-\n",
      "-0.84\n",
      "0.0\n",
      "0.0\n",
      "-0.72\n",
      "-\n",
      "-\n",
      "-\n",
      "PRE#1\n",
      "4.24\n",
      "3.26\n",
      "76.9\n",
      "444\n",
      "2.74\n",
      "6.08\n",
      "3.06\n",
      "1.06\n",
      "79\n",
      "78.0\n",
      "4.22\n",
      "PRE#2\n",
      "4.17\n",
      "3.21\n",
      "77.0\n",
      "438\n",
      "2.68\n",
      "6.0\n",
      "1.12\n",
      "49\n",
      "77.0\n",
      "4.17\n",
      "3.1\n",
      "PRE#3\n",
      "0.94\n",
      "684\n",
      "4.15\n",
      "2.77\n",
      "197.0\n",
      "4.13\n",
      "75.7\n",
      "39\n",
      "2.48\n",
      "3.14\n",
      "5.53\n",
      "NOTE\n",
      "Spirobank Smart Z114689 Sent on 2025-07-29 15:28\n",
      "BTPS 1.111 21.0 °C \n"
     ]
    }
   ],
   "source": [
    "# Extract text and tables from the PDF\n",
    "text_content = \"\"\n",
    "tables_data = []\n",
    "\n",
    "for page_num in range(len(file)):\n",
    "    page = file[page_num]\n",
    "    \n",
    "    # Extract text from the page\n",
    "    page_text = page.get_text()\n",
    "    text_content += f\"\\n--- Page {page_num + 1} ---\\n\"\n",
    "    text_content += page_text\n",
    "    \n",
    "    # Try to find tables using PyMuPDF's table detection\n",
    "    try:\n",
    "        tables = page.find_tables()\n",
    "        if tables:\n",
    "            print(f\"Found {len(tables)} table(s) on page {page_num + 1}\")\n",
    "            for i, table in enumerate(tables):\n",
    "                table_data = table.extract()\n",
    "                tables_data.append({\n",
    "                    'page': page_num + 1,\n",
    "                    'table_index': i,\n",
    "                    'data': table_data\n",
    "                })\n",
    "                print(f\"Table {i+1} on page {page_num + 1}:\")\n",
    "                for row in table_data:\n",
    "                    print(row)\n",
    "                print(\"-\" * 50)\n",
    "    except Exception as e:\n",
    "        print(f\"Error extracting tables from page {page_num + 1}: {e}\")\n",
    "\n",
    "print(f\"\\nExtracted text from {len(file)} pages\")\n",
    "print(f\"Found {len(tables_data)} tables total\")\n",
    "\n",
    "# Display first 1000 characters of text content to see what we have\n",
    "print(\"\\nFirst 1000 characters of extracted text:\")\n",
    "print(text_content[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d95cd8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('data/SECA body comp for all patients.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bbc907f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (63, 147)\n",
      "Columns: ['MeasurementDate', 'Comment', 'ExternalDeviceId', 'ExternalPatientId', 'FirstName', 'LastName', 'BirthDate', 'Age', 'Ethnicity', 'Gender', 'Height', 'Height_Unit', 'Weight', 'Weight_Unit', 'WaistCircumference', 'WaistCircumference_Unit', 'PAL', 'Adult_BMI', 'Adult_BMI_Unit', 'Adult_FM', 'Adult_FM_Unit', 'Adult_FMP', 'Adult_FMP_Unit', 'Adult_FMI', 'Adult_FMI_Unit', 'Adult_ZFMI', 'Adult_FFM', 'Adult_FFM_Unit', 'Adult_FFMP', 'Adult_FFMP_Unit', 'Adult_FFMI', 'Adult_FFMI_Unit', 'Adult_TBW', 'Adult_TBW_Unit', 'Adult_TBWP', 'Adult_TBWP_Unit', 'Adult_ECW', 'Adult_ECW_Unit', 'Adult_ECWP', 'Adult_ECWP_Unit', 'Adult_ECWbyTBW', 'Adult_ECWbyTBW_Unit', 'Adult_SMM', 'Adult_SMM_Unit', 'Adult_SMMBMIIndependant', 'Adult_SMMBMIIndependant_Unit', 'Adult_SMMP', 'Adult_SMMP_Unit', 'Adult_SMI', 'Adult_SMI_Unit', 'Adult_SMoA', 'Adult_SMoA_Unit', 'Adult_SMoABMIIndependant', 'Adult_SMoABMIIndependant_Unit', 'Adult_ZSMI', 'Adult_SSMMRightArm', 'Adult_SSMMRightArm_Unit', 'Adult_SSMMLeftArm', 'Adult_SSMMLeftArm_Unit', 'Adult_SSMMRightLeg', 'Adult_SSMMRightLeg_Unit', 'Adult_SSMMLeftLeg', 'Adult_SSMMLeftLeg_Unit', 'Adult_SSMMTorso', 'Adult_SSMMTorso_Unit', 'Adult_SSMMRightArmBMIIndependant', 'Adult_SSMMRightArmBMIIndependant_Unit', 'Adult_SSMMLeftArmBMIIndependant', 'Adult_SSMMLeftArmBMIIndependant_Unit', 'Adult_SSMMRightLegBMIIndependant', 'Adult_SSMMRightLegBMIIndependant_Unit', 'Adult_SSMMLeftLegBMIIndependant', 'Adult_SSMMLeftLegBMIIndependant_Unit', 'Adult_SSMMTorsoBMIIndependant', 'Adult_SSMMTorsoBMIIndependant_Unit', 'Adult_ASMM', 'Adult_ASMM_Unit', 'Adult_ASMI', 'Adult_ASMI_Unit', 'Adult_ASMP', 'Adult_ASMP_Unit', 'Adult_R', 'Adult_R_Unit', 'Adult_XC', 'Adult_XC_Unit', 'Adult_BIVA_ZRh', 'Adult_BIVA_ZXcH', 'Adult_PhA', 'Adult_PhA_Unit', 'Adult_VAT', 'Adult_VAT_Unit', 'Adult_TBS', 'Adult_TBS_Unit', 'Adult_TBS_MuscleScore', 'Adult_TBS_MuscleScore_Unit', 'Adult_TBS_FatScore', 'Adult_TBS_FatScore_Unit', 'Adult_REE_Kcal', 'Adult_REE_MJ', 'Adult_TEE_Kcal', 'Adult_TEE_MJ', 'Child_BMI', 'Child_BMI_Unit', 'Child_FM', 'Child_FM_Unit', 'Child_FMP', 'Child_FMP_Unit', 'Child_FMI', 'Child_FMI_Unit', 'Child_ZFMI', 'Child_FFM', 'Child_FFM_Unit', 'Child_FFMP', 'Child_FFMP_Unit', 'Child_FFMI', 'Child_FFMI_Unit', 'Child_ZFFMI', 'Child_TBW', 'Child_TBW_Unit', 'Child_TBWP', 'Child_TBWP_Unit', 'Child_SMMByKim', 'Child_SMMByKim_Unit', 'Child_SMMP', 'Child_SMMP_Unit', 'Child_SMI', 'Child_SMI_Unit', 'Child_LSTLeftArm', 'Child_LSTLeftArm_Unit', 'Child_LSTRightArm', 'Child_LSTRightArm_Unit', 'Child_LSTLeftLeg', 'Child_LSTLeftLeg_Unit', 'Child_LSTRightLeg', 'Child_LSTRightLeg_Unit', 'Child_R', 'Child_R_Unit', 'Child_XC', 'Child_XC_Unit', 'Child_BIVA_ZRh', 'Child_BIVA_ZXcH', 'Child_PhA', 'Child_PhA_Unit', 'Child_REE_Kcal', 'Child_REE_MJ', 'Child_TEE_Kcal', 'Child_TEE_MJ']\n",
      "\n",
      "First few rows:\n"
     ]
    },
    {
     "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>MeasurementDate</th>\n",
       "      <th>Comment</th>\n",
       "      <th>ExternalDeviceId</th>\n",
       "      <th>ExternalPatientId</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>Age</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>Child_XC</th>\n",
       "      <th>Child_XC_Unit</th>\n",
       "      <th>Child_BIVA_ZRh</th>\n",
       "      <th>Child_BIVA_ZXcH</th>\n",
       "      <th>Child_PhA</th>\n",
       "      <th>Child_PhA_Unit</th>\n",
       "      <th>Child_REE_Kcal</th>\n",
       "      <th>Child_REE_MJ</th>\n",
       "      <th>Child_TEE_Kcal</th>\n",
       "      <th>Child_TEE_MJ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-09-05T14:56:27.0000000Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000001583275_0055003f5631501320313557</td>\n",
       "      <td>LD5163301170</td>\n",
       "      <td>Lucy</td>\n",
       "      <td>Dibenedetto</td>\n",
       "      <td>1997-08-28T00:00:00.0000000Z</td>\n",
       "      <td>28</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-09-03T13:16:22.0000000Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000001583275_0055003f5631501320313557</td>\n",
       "      <td>NS6479273340</td>\n",
       "      <td>Niyanta</td>\n",
       "      <td>Shah</td>\n",
       "      <td>1985-03-11T00:00:00.0000000Z</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-09-03T13:14:23.0000000Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000001583275_0055003f5631501320313557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985-03-11T00:00:00.0000000Z</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-08-27T20:57:32.0000000Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000001583275_0055003f5631501320313557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996-04-05T00:00:00.0000000Z</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-08-20T14:01:13.0000000Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000001583275_0055003f5631501320313557</td>\n",
       "      <td>MW4167267833</td>\n",
       "      <td>Monica</td>\n",
       "      <td>Wong</td>\n",
       "      <td>1985-02-17T00:00:00.0000000Z</td>\n",
       "      <td>40</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 147 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MeasurementDate  Comment  \\\n",
       "0  2025-09-05T14:56:27.0000000Z      NaN   \n",
       "1  2025-09-03T13:16:22.0000000Z      NaN   \n",
       "2  2025-09-03T13:14:23.0000000Z      NaN   \n",
       "3  2025-08-27T20:57:32.0000000Z      NaN   \n",
       "4  2025-08-20T14:01:13.0000000Z      NaN   \n",
       "\n",
       "                          ExternalDeviceId ExternalPatientId FirstName  \\\n",
       "0  10000001583275_0055003f5631501320313557      LD5163301170      Lucy   \n",
       "1  10000001583275_0055003f5631501320313557      NS6479273340   Niyanta   \n",
       "2  10000001583275_0055003f5631501320313557               NaN       NaN   \n",
       "3  10000001583275_0055003f5631501320313557               NaN       NaN   \n",
       "4  10000001583275_0055003f5631501320313557      MW4167267833    Monica   \n",
       "\n",
       "      LastName                     BirthDate  Age  Ethnicity  Gender  ...  \\\n",
       "0  Dibenedetto  1997-08-28T00:00:00.0000000Z   28  Caucasian  Female  ...   \n",
       "1         Shah  1985-03-11T00:00:00.0000000Z   40      Other  Female  ...   \n",
       "2          NaN  1985-03-11T00:00:00.0000000Z   40        NaN     NaN  ...   \n",
       "3          NaN  1996-04-05T00:00:00.0000000Z   29        NaN     NaN  ...   \n",
       "4         Wong  1985-02-17T00:00:00.0000000Z   40      Asian  Female  ...   \n",
       "\n",
       "   Child_XC Child_XC_Unit  Child_BIVA_ZRh Child_BIVA_ZXcH  Child_PhA  \\\n",
       "0       NaN           NaN             NaN             NaN        NaN   \n",
       "1       NaN           NaN             NaN             NaN        NaN   \n",
       "2       NaN           NaN             NaN             NaN        NaN   \n",
       "3       NaN           NaN             NaN             NaN        NaN   \n",
       "4       NaN           NaN             NaN             NaN        NaN   \n",
       "\n",
       "  Child_PhA_Unit  Child_REE_Kcal  Child_REE_MJ Child_TEE_Kcal  Child_TEE_MJ  \n",
       "0            NaN             NaN           NaN            NaN           NaN  \n",
       "1            NaN             NaN           NaN            NaN           NaN  \n",
       "2            NaN             NaN           NaN            NaN           NaN  \n",
       "3            NaN             NaN           NaN            NaN           NaN  \n",
       "4            NaN             NaN           NaN            NaN           NaN  \n",
       "\n",
       "[5 rows x 147 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Data shape:\", df.shape)\n",
    "print(\"Columns:\", df.columns.tolist())\n",
    "print(\"\\nFirst few rows:\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77d3b19a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for body composition related columns:\n",
      "Relevant columns: ['Adult_TBS_FatScore', 'Adult_TBS_FatScore_Unit']\n",
      "\n",
      "Looking for patient data:\n",
      "Patient column names: ['ExternalPatientId', 'FirstName', 'LastName']\n"
     ]
    }
   ],
   "source": [
    "# Let's look for relevant columns for body composition\n",
    "print(\"Looking for body composition related columns:\")\n",
    "relevant_cols = [col for col in df.columns if any(word in col.lower() for word in ['fat', 'lean', 'mass', 'percent', 'composition'])]\n",
    "print(\"Relevant columns:\", relevant_cols)\n",
    "\n",
    "# Let's also check if we have specific data for a patient like Keirstyn\n",
    "print(\"\\nLooking for patient data:\")\n",
    "if 'Name' in df.columns or 'Patient' in df.columns:\n",
    "    print(df[df.columns[df.columns.str.contains('name|patient', case=False, na=False)]].head())\n",
    "else:\n",
    "    print(\"Patient column names:\", [col for col in df.columns if 'name' in col.lower() or 'patient' in col.lower()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bd23e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "\n",
    "# Set the style for better-looking plots\n",
    "plt.style.use('default')\n",
    "sns.set_palette(\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebe2ef60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body composition visualization created!\n",
      "Fat Mass: 27.6 lbs (22.4%)\n",
      "Lean Mass: 95.4 lbs (77.6%)\n",
      "Total Body Mass: 123.0 lbs\n",
      "Body Fat Percentage: 22.4%\n"
     ]
    },
    {
     "data": {
      "image/png": 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++Ud9PgYNGmQdOHDA7dzY2NijnhceHm4tWLCg3o/HsizrlltuqfV6Tz/9dL2f14Z8ze699956/ywDAAAAgDfR3RI4iuDgYCUlJWngwIF68MEHtWHDBrdWZIf06tVLa9as0RNPPKHBgwcrISFBQUFBioqKUkZGhqZMmaJVq1bptttuq3Zuy5YttXz5ck2dOlUtW7ZUcHCwUlJSNHnyZK1cuVInnXRSvfPefPPNbuujR492m/XxePTr10/r16/Xe++9p0svvVQdO3ZUbGysAgMDFRMTo27duumKK67QnDlzdO2117qdO3z4cK1fv14PPPCATjvtNMXFxSkoKEgxMTHq1auXbr31Vq1bt04TJkw4oYwnaty4cVq+fLnOO+88JSQkKDQ0VJ07d9bDDz+sefPmVZvEIDQ0VDNnztRnn32miy66SK1bt1Z4eLhCQkLUokULjRs3Tu+8844WLlyopk2bup373//+V7fccosGDhyo9PR0RUdHKzAwULGxsTrppJN0ww03aPXq1RoyZMgxPYa//e1vevDBB9WlSxe3brLHqrG8ZgAAAADgSQ7LaoDpzAAY8/HHH7uNfzVv3rx6dbOzm0mTJrm6S0rSwoULdcYZZ5gLBAAAAAAwijHJAD+wZMkSLVmyRFlZWZoxY4Zre8+ePTVixAiDyQAAAAAAaBwokgF+4H//+5/uu+8+t23h4eF68cUX3WZhBAAAAAAANWNMMsDPNGvWTOecc46WLl3qNtMkAACAJzgcjhqXoKAgxcbGqkuXLpowYYLmzZtnJN+0adPccr366qtG7lvXcizjzXrDokWL6swbHR2tzp0764orrtB3331nOi6OU0FBgT766CPddtttOuOMM9ShQwc1adJEISEhSk5O1rBhw/T888+rrKysxvOXL1+uxx57TOedd566d++u5ORkhYSEKDY2Vj179tRNN93kNhP6iSosLFS7du2q/Txu27at2rEbNmzQH//4RyUlJSk4OFipqan685//rKysrFqvnZ6eLofDofvvv99jmeEHTM8cAAAAAKDx0DHMCn7ZZZd5PZ+pmZSPvG9dS48ePTxyzxkzZrhd99577z2u6yxcuLDe2R0Oh3Xbbbd5JL8deOo18oSZM2fW6zXu0qWLtWPHjmrnR0ZGHvXcsLAw6/XXX/dI3muuuabGe2zdutXtuI0bN7pmjw8ICLBSUlJcx6alpVWbad6yLOuGG26wJFkZGRlWaWmpR/LCP9DdEgAAAMBxGzVqlCIiIlReXq6VK1dqx44drn0zZszQBRdcoFGjRhlMaEZaWppOPvnkGve1bt3ay2mOTUREhOs1y8/P1/Lly5WdnS1JsixLjzzyiDp06KDJkyebjIkTEB4ert69eys6Olo//fSTMjMzXfvWrl2rP/zhD1qyZEmt53fp0kWtW7fWnj179MMPP7i2l5SUaNKkSTrppJOUkZFx3Pnmz5+v5557rl7H/u1vf1Nubq4k6cMPP9TZZ5+tZ555Rtdff722b9+uZ555Rvfcc4/r+O+//15PP/20AgIC9OKLLyokJOS4c8L/UCQDAAAAcNz+9a9/KT09XZJUXl6uAQMGuHXJW7BggS2LZGeccYbXunp6WmJiot577z3X+oEDB3TGGWdozZo1rm3/+Mc/KJI1Qh06dNDtt9+uP/7xjwoPD5fk/L297rrr9MILL7iOW7p0qVatWqUePXq4toWHh+uaa67RlClTXL/zkvT5559r9OjRKi8vlyRVVFTopZde0hNPPHFcGXNzc10/W7GxsXI4HMrJyan1+OXLl0uS4uLidPbZZ0uSLr30Ul1//fWS5PZ+VFlZqSuvvFKVlZWaMmWKTjvttOPKCP/FmGQAAAAAPCI4OFiDBg1y21ZcXFzjsQUFBXr66ac1bNgwNWvWzDW2Uffu3TV16lStW7eu1vscPHhQf/3rX5WWlqbQ0FC1atVKU6ZM0d69e2s9Z8mSJW7jGl1yySU1Hjd27Fi34w4vDDWEr7/+Wn/96181ePBgtW3bVnFxca7x3bp166ZrrrlGq1atcjvn1VdflcPh0GWXXea2/b777nPLPm3aNI9kjI+P14033ui2bd26dSooKHDbVlxcrOeff14jRoxwG6/q5JNP1n333acDBw7UeP3DM6enp6usrEyPPvqounfvrsjIyGoTUVmWpTlz5uiPf/yj2rZtq6ioKIWHh6tVq1YaNWpUrS2QvvrqK02cOFHt27dXVFSUwsLC1Lp1a02cONFVaDnSpEmT3PItWrRIP/74oy688EIlJSUpNDRU7dq10913363S0lLXed5+jerj9NNP15o1a3TZZZe5CmSS8/f2ySefVFCQexua9evXu62vWrVK06dPdyuQSdKwYcN04YUX1nnusbj++uu1a9cuSdLTTz+t2NjY477WkZ544gn9+OOPSk1N1UMPPeSx68KPmO7vCQAAAKDxUB3jA5WVlVl9+/Y96phgK1eutNLT0+sc2ygoKMj6xz/+Ue3cXbt2WW3atKnxnObNm1sXX3xxrffv16+fa3tISIiVlZXldu39+/dbwcHBrmMGDBhQ7+flyDHJJk6cWK/zpkyZctRxngIDA62XX37Zdc6R41zVttR3/KsjxyRLS0urdszHH39c7fq7d+927V+7dq3VoUOHOvMkJydbS5YsqXbtI1/DoUOHVjv3kL1791qnn356nfc5Mn95ebl12WWXHXWstbvvvrtatokTJ7odd8kll1iBgYE1XuOcc85psNfIGxISEtyyzZs3r97n3nzzzW7n/uEPfziuDLNnz3Zd47zzzrMsy7LS0tLqHJNs0qRJrn0fffSRZVmW9fTTT7u23X///ZZlWdbWrVutiIgIS5I1Z86c48oH/0d3SwAAAADH7dprr1VERIQqKiq0cuVKbd++3bVv4MCBuvjii92O379/v0aMGKFff/3VtS0+Pl69evVSZmam1q5dK8nZZevmm29WcnKyW6uvSZMmuc2gFxwcrL59+6qiokLLly/Xm2++WWvWW265Reeee64kqaysTC+++KLuuusu1/533nnH1WVMkq6++upjfTpcFi1apPPPP7/Gfdddd53OOOMM13pAQIA6dOigxMRExcXFqby8XNu2bXO1pjvUNWzUqFFq3ry50tPTNX78eG3fvl3ff/+96zqdO3dWly5dXOuHf3+iDh93SnI+7/Hx8ZKk7OxsDR8+3NX6R5LatWunjh076tdff3VlzMrK0pgxY/TTTz8pJSWlxvvs2bNHe/bsUWRkpHr16qWwsDBXK6/KykqNHj3a7TFLzi6E7dq1U35+frV9kvSXv/xFM2bMcK1HR0erb9++CggI0JIlS1RQUCDLsvTAAw8oJSWlztf9jTfeUGhoqPr376/s7GytXr3ate+DDz7QkiVL1K9fPyOv0YlYtmyZ9u/f71qPiIhQ375963VuZWVltdlshwwZcswZ9u3bp6uuukqSlJSUpOeff75e5/3f//2fZs+erdzcXI0bN07JycnavXu3JOfYgFOmTJEkXXPNNSoqKtIFF1ygMWPGHHM+2ITpKh0AAACAxkP1aB0jyWrbtq21ZcuWaufffvvtbsf17dvXys7Odu1/4IEH3Pa3aNHCqqystCzLsr7//nu3fcHBwdbSpUtd586bN89yOBy1tiSrrKx0a+2UmppqlZeXu/Yf3tIsISHBKikpqffzciyzWx6eadOmTVZOTk6N13zmmWfcznvuuefc9jfU7JaHt8TKz8+33n33XSsmJsbtmJEjR7qOueuuu9z2Pfzww27Xf/PNN932X3fddW77j3x+TjrpJGvXrl2u/Ydeh1deecXtuPDwcFfLocPz/uc//3Gtb9iwwQoICHCdc8opp1i5ubmu/b/++qvVsmVL1/74+Hi32Q6PbEkWGxtrrVy5stb99913n1seX5rdsjYHDhywunTp4pbzzjvvrPf5t912m9u57dq1s4qKio45x3nnnee6xgcffODafrSWZJZlWevXr7cuvPBCKzEx0QoKCrJSUlKsK664wtqzZ49lWb//DDZp0sS17YMPPrD+9Kc/WYMHD7bOPfdc65lnnrGKi4uPOTf8Cy3JAAAAAHjcli1b1L17d3300UcaPHiwa/ucOXPcjps2bZqaNGniWr/99tv13HPPuVqCZGZm6ocfftDJJ5+s+fPnu507fvx4nXrqqa71kSNHaujQofr8889rzBQQEKCbbrrJ1Vpl165d+uCDD3T++edr69atbrP5TZo0SaGhocf34I9BmzZt9N577+mdd97RypUrlZWVpeLiYlmWVe3YExnn6Vhs37692jhghwsPD9ff//531/rs2bPd9i9dutStFV1lZaXb/o8++khPP/10rdd/+umn1aJFC9f6odfh/fffdzvutttucw3UfkhUVJQmTJjgWp8zZ46qqqpc62VlZdUmHDj8uT5w4ICWLFni1tLvcFdffbXbYPZjx47Va6+95lo/fJZIT6qtVWJSUpL+9a9/Hfd1MzMzNWrUKFcLTkk699xzdd999x31XMuydMstt+ixxx5zyzN37ly3Mc/q4/XXX3e9vhMnTtS4ceOO6fyOHTvqnXfeqXFfdna2brjhBknS9OnTlZycrCuvvFIvvvii23GzZ8/Wyy+/rEWLFikmJuaY7g//QZEMAAAAwHHbunWr0tPTZVmWMjMzNX36dP3zn/+UJBUWFurSSy/V5s2bXYWObdu2uZ3frVs3t/WgoCB16dLFVSQ7dI+TTz7ZrStnTedKUteuXWstkknOWe/uvvtu1yD/zz77rM4//3y9/vrrrmMcDoerkHa8Jk6ceNTZLS3L0vjx4/XBBx/U65q5ubknlMkT2rRpoxkzZqhnz56ubVu3bnU75sMPP6zzGjt37lRlZaUCAwOr7QsJCVG/fv1qPO/wbraScyD6ozky28qVK7Vy5cqjnlNbkaxPnz5u60cOKn/44P2eNGvWrBq3p6WlHfc116xZo1GjRrl1k/3jH/+o//znPzW+NocrLS3VxIkT3QpTLVu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",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample data based on the image (you can replace with actual data from your dataset)\n",
    "fat_mass = 27.6  # lbs\n",
    "lean_mass = 95.4  # lbs\n",
    "body_fat_percent = 22.4\n",
    "\n",
    "# Calculate total body mass\n",
    "total_mass = fat_mass + lean_mass\n",
    "\n",
    "# Data for pie chart\n",
    "sizes = [fat_mass, lean_mass]\n",
    "labels = [f'Fat Mass ({fat_mass}lbs)\\n{fat_mass/total_mass*100:.1f}%', \n",
    "          f'Lean Mass ({lean_mass}lbs)\\n{lean_mass/total_mass*100:.1f}%']\n",
    "colors = ['#ff9999', '#ffcc99']  # Light red/orange colors\n",
    "\n",
    "# Create the figure\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))\n",
    "\n",
    "# 1. Body Composition Pie Chart\n",
    "wedges, texts, autotexts = ax1.pie(sizes, labels=labels, colors=colors, autopct='', \n",
    "                                   startangle=90, pctdistance=0.85)\n",
    "\n",
    "# Add a circle at the center to create a donut chart\n",
    "centre_circle = plt.Circle((0,0), 0.70, fc='white')\n",
    "ax1.add_artist(centre_circle)\n",
    "\n",
    "ax1.set_title('Body Composition', fontsize=16, fontweight='bold', pad=20)\n",
    "ax1.axis('equal')\n",
    "\n",
    "# 2. Body Fat Percentage Bar Chart\n",
    "ax2.barh([0], [body_fat_percent], color='orange', height=0.3, alpha=0.7)\n",
    "ax2.set_xlim(0, 50)\n",
    "ax2.set_ylim(-0.5, 0.5)\n",
    "ax2.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n",
    "ax2.set_title(f'Body Fat Percent - {body_fat_percent}%', fontsize=16, fontweight='bold')\n",
    "ax2.set_yticks([])\n",
    "\n",
    "# Add percentage ranges for context\n",
    "ranges = [(0, 15, 'red'), (15, 20, 'yellow'), (20, 25, 'lightgreen'), \n",
    "          (25, 30, 'yellow'), (30, 50, 'red')]\n",
    "for i, (start, end, color) in enumerate(ranges):\n",
    "    ax2.axvspan(start, end, alpha=0.3, color=color, ymin=0.3, ymax=0.7)\n",
    "\n",
    "# Add a marker for current value\n",
    "ax2.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2)\n",
    "ax2.text(body_fat_percent, 0.2, f'{body_fat_percent}%', ha='center', va='bottom', \n",
    "         fontsize=12, fontweight='bold')\n",
    "\n",
    "print(f\"Body composition visualization created!\")\n",
    "print(f\"Fat Mass: {fat_mass} lbs ({fat_mass/total_mass*100:.1f}%)\")\n",
    "print(f\"Lean Mass: {lean_mass} lbs ({lean_mass/total_mass*100:.1f}%)\")\n",
    "print(f\"Total Body Mass: {total_mass} lbs\")\n",
    "print(f\"Body Fat Percentage: {body_fat_percent}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "622f0788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body Fat Percent Master Chart created!\n",
      "Patient's body fat percentage (22.4%) is marked on the chart.\n"
     ]
    }
   ],
   "source": [
    "# Create the Body Fat Percent Master Chart\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))\n",
    "\n",
    "# Age groups and corresponding y-positions\n",
    "age_groups = ['20-39', '40-59', '60-79']\n",
    "y_positions = [2, 1, 0]\n",
    "\n",
    "# Body fat percentage ranges and colors (similar to the image)\n",
    "# Red: 0-10%, 40-50% (high risk)\n",
    "# Yellow: 10-15%, 15-20%, 35-40% (moderate risk) \n",
    "# Green: 20-35% (healthy range)\n",
    "\n",
    "ranges_male = [\n",
    "    (0, 5, '#ff6b6b'),    # Red - too low\n",
    "    (5, 10, '#ffd93d'),   # Yellow - low\n",
    "    (10, 15, '#6bcf7f'),  # Green - healthy\n",
    "    (15, 20, '#6bcf7f'),  # Green - healthy\n",
    "    (20, 25, '#ffd93d'),  # Yellow - moderate\n",
    "    (25, 30, '#ff6b6b'),  # Red - high\n",
    "    (30, 35, '#ff6b6b'),  # Red - very high\n",
    "    (35, 40, '#ff6b6b'),  # Red - extremely high\n",
    "    (40, 45, '#ff6b6b'),  # Red - extremely high\n",
    "    (45, 50, '#ff6b6b')   # Red - extremely high\n",
    "]\n",
    "\n",
    "ranges_female = [\n",
    "    (0, 10, '#ff6b6b'),   # Red - too low\n",
    "    (10, 15, '#ffd93d'),  # Yellow - low\n",
    "    (15, 20, '#ffd93d'),  # Yellow - moderate\n",
    "    (20, 25, '#6bcf7f'),  # Green - healthy\n",
    "    (25, 30, '#6bcf7f'),  # Green - healthy\n",
    "    (30, 35, '#6bcf7f'),  # Green - healthy\n",
    "    (35, 40, '#ffd93d'),  # Yellow - moderate\n",
    "    (40, 45, '#ff6b6b'),  # Red - high\n",
    "    (45, 50, '#ff6b6b')   # Red - very high\n",
    "]\n",
    "\n",
    "# Plot Male chart\n",
    "ax1.set_title('Body Fat Percent Master Chart', fontsize=16, fontweight='bold', pad=20)\n",
    "for i, age_group in enumerate(age_groups):\n",
    "    y = y_positions[i]\n",
    "    for start, end, color in ranges_male:\n",
    "        width = end - start\n",
    "        rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, edgecolor='black', linewidth=0.5)\n",
    "        ax1.add_patch(rect)\n",
    "\n",
    "ax1.set_xlim(0, 50)\n",
    "ax1.set_ylim(-0.5, 2.5)\n",
    "ax1.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n",
    "ax1.set_ylabel('Age (M)', fontsize=12)\n",
    "ax1.set_yticks(y_positions)\n",
    "ax1.set_yticklabels(age_groups)\n",
    "ax1.set_xticks(range(0, 51, 5))\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Add vertical lines for reference\n",
    "for x in range(0, 51, 5):\n",
    "    ax1.axvline(x=x, color='black', linewidth=0.5, alpha=0.3)\n",
    "\n",
    "# Plot Female chart  \n",
    "for i, age_group in enumerate(age_groups):\n",
    "    y = y_positions[i]\n",
    "    for start, end, color in ranges_female:\n",
    "        width = end - start\n",
    "        rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, edgecolor='black', linewidth=0.5)\n",
    "        ax2.add_patch(rect)\n",
    "\n",
    "ax2.set_xlim(0, 50)\n",
    "ax2.set_ylim(-0.5, 2.5)\n",
    "ax2.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n",
    "ax2.set_ylabel('Age (F)', fontsize=12)\n",
    "ax2.set_yticks(y_positions)\n",
    "ax2.set_yticklabels(age_groups)\n",
    "ax2.set_xticks(range(0, 51, 5))\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Add vertical lines for reference\n",
    "for x in range(0, 51, 5):\n",
    "    ax2.axvline(x=x, color='black', linewidth=0.5, alpha=0.3)\n",
    "\n",
    "# Mark the current patient's value (assuming female, 20-39 age group)\n",
    "patient_age_group = 0  # 20-39 age group\n",
    "ax2.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2)\n",
    "ax2.plot(body_fat_percent, y_positions[patient_age_group], 'v', color='black', markersize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Body Fat Percent Master Chart created!\")\n",
    "print(f\"Patient's body fat percentage ({body_fat_percent}%) is marked on the chart.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "732c3859",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_221522/2454516635.py:109: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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AdHQ0Ll++jAMHDuDgwYOS5IK+vj6WLVtWpJ4zERHlHyYYiIioWLGyssL06dOzLGdoaIjjx49jwoQJKFOmDAwNDeHs7IwhQ4bg9u3baNGiRZb7OHDgAEaOHIkyZcpovJEqCAsWLMD8+fNRtWpVcWrJ3Gjfvj0ePHiAefPmoVGjRrCxsYGBgQEsLS1Rp04dfPnll7h//77kF+jCZGNjg23btuH27duYOnUqvL294eDgAENDQ8jlcjg6OqJhw4YYOXIk/vrrL4SEhKglhRo2bIizZ8+iQ4cOsLS0hLGxMapXr44ff/wRp06dktwk55ShoSH279+PJUuWwMvLC3K5HLa2tujcuTPOnTuXZYIKULQGuHnzJsaPH4+aNWvC0tIS+vr6MDc3R+XKldGnTx+sWrUKr169ynWcBaFPnz54+PAh5s6di6ZNm8LOzg4GBgYwNjaGm5sbOnTogHnz5uH27dto2rSpWG/58uWYMWMG2rZtCw8PD1hZWUFPTw/m5ubw8vLCyJEjce3aNQwcOFCHz46IiAqSTCiI4aaJiIiIiIiI6IPCFgxERERERERElGdMMBARERERERFRnjHBQERERERERER5xgQDEREREREREeUZEwxERERERERElGdMMBARERERERFRnjHBQEQ5IpPJJI+AgAC1MgEBAWrlMtvesmXLwgme8o2fn5/kHA4ZMiTX+3r79i2++eYbVKtWDaampjAxMUGZMmXQvHlzTJ06FY8ePcpTrLNnz1a7HjMeJiYmcHFxQYsWLTBv3jyEhYXl6Vi5pRyTu7u7TmJ4+fIl5syZg9atW8PFxQXGxsYwNjaGq6srOnTogEWLFuHly5c6ie1DMGTIEMl14Ofnl6v9bNy4UbKf2bNn52uchalHjx5q79nevXvrOiwiIsoEEwxEVOQwAfHhePnyJWrVqoWFCxfi7t27SEhIQGJiIl69eoVz587hxx9/xIULFwrs+ImJiXj9+jXOnj2Lb7/9FpUrV8bNmzcL7HhFUVJSEj777DOUL18es2fPxunTp/H69WskJSUhKSkJQUFBOHbsGKZNm4ZatWrpOtwPVn4lIIqLsLAwHDp0SG39gQMHEBkZqYOIqKhp2bJllj94EFHhM9B1AERE9OGaPHmy5FdxFxcX1KlTBzExMbhx4wZiYmLy/Zhubm6oV68eACAiIgLnzp1DWloaACAyMhKjR4/G5cuX8/24RVFiYiLatWuH8+fPS9ZbWFigXr16MDc3R2hoKPz9/ZGYmIj09HQdRVry1a9fH7GxseKyg4NDrvbj7u6OXr16ictVq1bNc2y68McffyAlJUVtfXJyMv766y+MHz9eB1EREVFWmGAgIiKdOXbsmPi3hYUFHjx4AAsLCwCKG4n9+/fD0dExX4/ZsmVLbNy4UVzev38/unfvLi5fuXIFMTExYhwl2fjx4yXJBZlMhm+//RbTpk2DsbGxuD4hIQFbtmzB8uXLdRDlh2HcuHEYN25cnvfTsmXLEtHq6/fffxf/1tfXR3p6OgRBAKDoBsIEAxFR0cQuEkTFiLu7e4noV6tNRteIcuXKSdafOXNGa5cJ5ddEJpNBEASsW7cODRs2hKWlpaTZ5MGDBzFu3Dg0bdoU7u7usLKygqGhIWxsbFC3bl1MmTIFz5490xhbREQEZs+ejYYNG8LW1haGhoawtLSEh4cH2rRpg6+++gpnz57VWPfq1asYMWIEKleuDAMDAzFWV1dXbNiwAcnJyWp1tPWjVm0Sqq3fvqb6ISEh+Oyzz1CuXDnI5XI4OTlh6NChCAoK0hh3amoqli9fjho1asDY2Bj29vbo2bNnvnYhUL6JTUxMRFRUlLhsZGSE3r17o3nz5vl2PE3atGmjti4xMVFjWeVzaWFhASMjIzg7O8PHx0frucxw8OBBtGzZEhYWFrC0tESzZs2wc+fOTGNr3769eA719PQ0jkdx48YNybnu06dPFs9Y4c6dO9iwYYNk3Zw5czB79mzJeQEAExMTDBs2DFevXtW4r5MnT+KTTz5B+fLlYWZmBmNjY5QtWxY9e/bErl27NLZ80HSNPnjwAP369YODgwPMzMzQsGFD7Nq1S6xz/PhxtGnTBlZWVjA3N0fz5s1x9OhRtX1r6maVmJiIhQsXwsvLCyYmJrCzs0OvXr3g7++v9TWKiIjAwoUL0bRpU9jb24ufF/Xq1cP06dMRGBiotV5OPy+0dYHIWK98ww0ArVq10lg+u2Mw5Nc5y+nnSnbcvHlTcl7atm2LZs2aicvXr1/HnTt3tNbfunUrunbtijJlysDY2BhyuRwuLi6oW7cuRowYgTVr1oitljJcvnwZvr6+qFSpEszMzGBoaAgHBwdUrVoVffv2xeLFixESEqJ2rNTUVPz111/o1q0bXF1dYWxsDAsLC1SvXh1Tp07Fq1evNMaYm2skNTUVa9euRbt27eDs7Ay5XC6OWePt7Y2xY8fijz/+kNQpyPdZhoSEBKxZswYdOnSAk5MTjIyMYGVlhXr16mHOnDmIiIjQWE/1/7D09HSsX78e3t7eMDc3h7m5OZo1a4bDhw9L6mX8P3jmzBnJ+nLlyrHLBFFRIBCRVqdPnxYAqD309PQES0tLoUaNGsK4ceOEhw8fFko8bm5uYgyzZs0qlGOeP39eGDlypODl5SVYWVmpvRZ+fn5qdZ4/f65WLrPtLVq00FpP0yOjvCBIXxMAwqBBg9TKP3/+XBAEQejcuXOW+zYxMREOHz4siTcsLEztOJoevXr1ktRLT08XJk2alGW9mjVrCi9evBBatGghrmvSpImkTMb5Vi6j/HBzc5Mce8OGDZLtPj4+gp2dnda6b9++ldRPSUkRfHx8NJY3NDQUPvvsM8k6X1/fbF1Pqnx9fSX7adasmZCQkJCrfWkza9asTGPdt29fpq+lIOT8XKpauHCh1jpffPGF1uMfO3ZMsm3ixIlq+1aN68SJE9l6XWbMmCGp5+DgICQmJmarboakpCShX79+Wb4urVq1UrvGVK/Rdu3aCaamphrrr1q1Sli2bJkgk8nUtunp6Ql79+6V7Fv1s6RWrVpCvXr1NO5bLpervecFQRBOnDgh2NvbZ/q8TE1NhT///FNSL7efF6rvhdOnT2tcr+2RUV71dVX9vyI/z1lOP1eya8KECZJ9bdy4Ufjll18k66ZMmaKx7rhx47L1esXExIh1tm3bJujp6WVZ58CBA5JjBQcHCw0aNMi0joWFhbBv3z5JvdxcI+np6ULXrl2zrGNnZ5fpOcvP95kgCMK9e/eEihUrZhqTk5OTcOHCBbW6ymVKlSoltG/fXmN9mUwm7N69W6yn7f9B1UfG//1EVLiYYCDKhLYEg+rD1NRUuHLlSoHHU5gJhsjISKFHjx5ZPveGDRuq1c1tgiE0NFTo1auX0KlTJ8l2e3t7oVevXuLj22+/Ffel6UuaXC4XGjRoIHTs2FEoVaqUJMFgaGgoVK9eXWjZsqXQvXt3oUOHDkK5cuXUvgwp3+Sq3hy6u7sLXbp0Edq1aydUrVpVMDExUfsyKAiCMG/ePLXY3N3dhQoVKghyuVyyvmrVqkKzZs3EZW0Jhq1btwqLFy8WFi9eLNmeVYIh41G7dm2hWbNmgr6+vmT9ggULJPXnz5+vVrdGjRpCmzZtBAsLC7VtuU0w/Pnnn2r76tKli5CcnJyr/WmimmBwc3MTr6WWLVtKXgsDAwNh//79avvQdC5r166t8fWoWrWqkJSUJNY9e/as2hf2MmXKCB06dBCcnZ3V9qt6LmvWrClus7KyEmJjY8Vtqampkn1UrFhRSE9Pz9br0qpVK8lx+/fvn+PXdvjw4ZJ9GBgYCA0bNhSaN28uGBsbS7a1bdtWUlfTNWpoaCg0bdpUqF69umS9sbGxoKenJ5iamgqtW7cW3N3dJdsrVqwo2be2ZGXFihWFdu3aCba2tpL1NjY2wps3b8T69+/fF8zMzCRlXFxchA4dOggeHh6S9fr6+pJEa24/L7QlGH7++WehV69eap91zZs3l3wu3rlzR+Prqvp/RX6fs5x8rmRHcnKyJLFjbGwsREVFCWFhYYKBgYHkczolJUVSNygoSPJeMzMzE1q3bi107dpVqFevnuDo6ChuU04wKN8g6+npCQ0bNhS6desmNGnSRHB3dxf3qZxgSE5OFmrVqiV5vq6uroKPj4/QpEkTScLC2NhYuHXrVp6ukQsXLqhds+3btxc6d+4s1K5dW7yms0ow5Of7LDIyUnB1dZWUqVChgtC5c2e1hJ6dnZ0QFBQkqa/pWnJ2dhbatWunltzz9PQU63377bdCr1691Mp06tRJ8p4IDQ3N8fVHRHnHBANRJlQTDP369RMWL14sfPfdd8JHH30k2da9e/cCj6ewEgyxsbFqXw6cnJyE0aNHq30ZyM8EQ3a3K1P90u3m5ibcu3dP3J6amiqkpqYKgqD4pSUuLk7jflR/RVb+RXPkyJGSL1gZ+8uQlJQknDx5Uti5c6e4LjIyUvySmPH466+/xO0vX75U+/Lm6ekp/q0twaBM9Xkr0/SlcsOGDVq3t2rVSvJ8VG/AfvzxR3H78+fP1W6Mc5NgmDFjhsZfygAIffv2FdLS0sSyq1atErdZWlrm6DiqCYbMHvPnz5ccVxBydy7XrFkjbu/SpYtkW8+ePcUESlxcnNC6detMz+Uff/wh2f7LL7+I21RbOCifp6xUrVpVUnfatGk5eVmFe/fuSc6fgYGBcObMGXH77du31Vo9HTlyRNyueg3KZDKx9UVaWprQsGFDyXYzMzPh33//FV831WtQueWIps+gqVOnitvDwsKEatWqSbbPmTNH3N6/f3/Jtm7duolJx7S0NGHUqFGS7d7e3mLd3HxeCIL2BEN2t2t7XZU/O/L7nOXkcyW7du3aJdmH8k22aqsq1RYF//zzj2T72bNn1fZ///59YcWKFZIkoKGhoVhn7ty5anVCQkKETZs2Cffv3xfXrV+/XnKssWPHSj47/vnnH8lr3aVLF3Fbbq4R1WTsy5cvJXXS09OFGzduCKtWrZKsL8j3mWorqO+//15y7L/++kuyffz48ZLtqtdSx44dhfj4ePE1V04IqR5bENRbMrDFAlHRwAQDUSZUEwzKX6QEQZB8Qa1UqZJa/dTUVOHXX38VWrduLdjZ2QkGBgaCra2t0LJlS2Ht2rVqv75kWLt2rVCtWjVBLpcLpUuXFiZPnixER0drTDD89ttv4joTExPh3bt3kn29fftW8uVp69atWT7vadOmSZ539+7dxRtz1S8E8+fPV6uvekMEKH7h+vbbb4WIiAi1L//Kv+bPmjVL7QtR2bJlxRuyVatWCZUrVxbkcrlQrlw5wdraWlJ28+bNki/iLVq0EIKCggRfX1/BwcFBMDAwECwtLQV7e3u1m0blh7u7u2BtbS0YGhpKfsk0NDQUvvzyS2H79u3C9evXhZiYGGHfvn1Chw4dBEdHR8HAwECwsLBQ+2KUkYhR/kKk+sUus8dnn32mVl/5kXFT+vDhQ2H06NFCqVKlJNsdHR0lX44jIiLU9tGiRQshODhY6Natm2S9tbW12hdg1RYOOU0wKP+CZ2BgIOzYsUNo06aNZJ9Dhw4Vf41XTm41btw4R8fKSYIBgNCjRw9JC4rt27drPJfKlBMgAITOnTsLgqD4DFBtjvzgwQNJ3fPnz2s8lxlSUlKEsmXLiturVasmbhs8eLC43tjYWAgPD8/266KaYPjqq6+yXVcQBOGHH36Q1O/Xr59amalTp0rKjBs3Ttym+j5v3bq1pO7nn38u2T5s2DDJ9p49e0q2KzfBVv2MsbCwUEsuqn5ONW3aVBAExU2XaqsU5feOICg+V42MjMTtMplM/LVU+b2h6fNCm8JIMOT3OVN9L6h+rqj+4p0dqt0AlG+yN2/eLNmm2grk1atXku3t27cX1q9fL/j5+an9eq6sQoUKYp1KlSoJy5cvFw4fPiw8fvxY7bMvg2qXu3bt2kl+Pe/Vq5fk/za5XC52QcrNNaL6OdGvXz/h999/F86fPy9pfaOqIN9nXl5ekm3du3eXPH/VVpCqn22qn72q7zPVa0G1mwUTDERFExMMRJnQlmBITU0VLl68KFhaWorbVH9lj42NFZo3b57pjUzTpk3Vvkyo3txnPOrVqye5acz40piQkCDpA6v664VyAsLGxibLPtbJycmSL9dOTk6SJtmq/VSfPXsmqT958uRMn3Pp0qWFo0ePStYpfwmrXbu2xnq+vr5q/XI1PQIDAyVfxCtWrCiULl06y3q5fWj7BV71MWLECEEQpF+IVLuCZPbIToJh+/btak2cVV/nLVu2iNew6nYPDw+NTfYBCL/++qvkPO/du1ft/GRXeHi45Kb7008/FQRBEGJiYtSSLhMnThTCwsIkiaRFixZl+1iCkPkYDLGxscKJEyckN/Cq7yPVm7KMc6lM9ct/1apVBUFQ/AqnvN7IyEitC8O7d+/UzqWqpUuXSsr4+fkJ8fHxkvfqwIEDc/S6qHaR0HSzmZmxY8dK6mcn2ejj4yNuU73xybjGM8ycOVOyfenSpZLtmd1wqyYYateurRbbrVu3JGXKlCkjCIKiq1ZW50wQpDelAMRucm/evFG7npQ/L6pUqSJMmzZNrfl2YSQY8vucqf4irfq5oulazkxISIikG4SlpaWku1pMTIwkMWxkZCRERERI9qGppV3Gw97eXujbt69ay4a//vpL62e5iYmJ0Lp1a2HTpk2S60A1QZedR8b/l7m9RjL7P8PFxUUYMmSI4O/vL6lTkO8zbWM5aHvo6elJEjbK28zNzdWuh4EDB2Z6zTPBQFQ0cRYJohwYOnQoZDIZDAwM0KhRI0RHRwMA9PT0MHXqVEnZiRMnSkaAbt++PWbNmoUOHTqI686fP4+JEyeKy1evXsWiRYvEZScnJ3z55ZcYO3Ysbt++jTdv3qjFZGxsjJEjR4rL69evl2zfsWOH+PfHH38MuVye6XO8evUqYmJixOV+/frBzMxMXLa2tpaUf/v2rfj35s2bsXTpUrV9yuVy6OvrAwCCgoIwevRorce/efMmPD091db//vvvWLlyJWrXro2ZM2dqLAMALi4ukuVHjx4hNjZWMgJ5BplMhnbt2qFXr16oV6+e2vbevXvj22+/RaNGjSTrjYyMxL+F99OmZayfPn06Zs6cibp166odKyuGhobi36qzQihv0yQ1NRWDBg0SZz9QPmcZkpKS4Ovri8ePH4vnQ9mzZ8/w9u1btGvXTm3bDz/8kGX82XXlyhXEx8eLyzVq1AAAmJub4/Dhw6hevbq47aeffkLt2rXx7t07AIClpSWGDh2ab7GYmZmhTZs2mDNnjmS98vtG+RwD2TuX+W3kyJGS997PP/+Mffv2Sd6rmb2vNGnSpIlk+eTJk0hKSsp2/fx+XVQ/W/T0pF9RbGxs8rT/7FJ9Xjnl6OiIW7duYcGCBWjcuLHkvSgIAu7fv4/vv/8eDRo0EP8PKSz5fc7s7Owky5o+V3Lijz/+QGpqqricmJiIChUqwNXVFa6urqhcubJke3JyMv766y/JPn755Rfs2rULPXv2hLOzs2RbeHg4tm/fjhYtWmDv3r3i+gEDBuDKlSsYOXIkPD09JddeQkICTp06hcGDB2PKlCl5en5xcXEAcn+NHDhwABs2bICPjw/s7e0l+w4ODsbGjRvRoEEDXLlyRWsMunyfpaenIyEhQeM21WsJyPv1RES6wQQDUT6YP38+OnfuLC5HRERIphTr27cvjh49itmzZ+PIkSPo27evuG3Tpk3iFE6//vqr+AVQX18fZ86cwaJFi7Bq1Sr89ttvWo8/ZswY8T/imzdv4saNGwAUN/8nTpwQyw0bNizL56I6tVjlypUzXT537pz495IlSzTus379+li5cqW4/Pz5c63Ht7Ozw+7duzVuc3R0xLlz5zB37lzJ/pSpflkCgEOHDmn80iQIAho0aICdO3fC29tbsq1x48bYsWMH5syZg3PnzkmSDGZmZnj9+jXOnTsHS0tLcX1ycjIqVqyIuXPn4quvvpLs7/bt22rHj4yMlCxbWFiIf5cpU0ayLTY2VtPTFUVHR4s3h3p6epg2bZrGcsnJyVi1apXW/WzdulXtZhsAHj58KLmZvXv3bqbxZEb1uSh/GbaxscGxY8dQoUIFcZ3yNG9LliyBg4NDro+tjeqX7tevX4t/q06bqulc/vvvv5LljDr29vYwNTUV1ycnJ+Px48eSstl5Lc3NzSUJhL1792LZsmXicvXq1dUSBlnp16+f5P0SHh6eZSJJOQGRl9elsD19+lTtxkb1dXdzcwOgOGfm5ubi+uTkZLXpQd+9e4eXL1+Ky6pTxdrY2ODrr7/GP//8g5iYGLx58wbnzp3DRx99JJYJCAjQ+lmnSX4ktor6OVOdijM5ORlBQUGSR0pKiqTMxo0b1faTMdVmcHAwYmNjcefOHaxYsUL8f1IQBCxfvlxSp169eli7di0ePXqEhIQEPH36FDt27JAkrVevXi0mcVVfl0uXLkFQtAzW+qhWrZpYPjfXiL6+PoYMGYK///4bYWFhePfuHW7evImZM2eKZZKSkrB69erMXuZ8o/wayGQyBAcHZ/kaKL+38koXyV4iyhoTDEQ50K9fPyxevBjff/89Bg0aBAMDAwDA119/jblz54rlrly5Iplj29fXV7If5eW0tDTxBuvatWvi+nr16qFixYqSY2v7Fbts2bLo3r27uLxu3ToAipuQjC9jNWrUQJ06dXL2hDVQTqQAwNy5c7F3715ERERonU++c+fOGDx4cLb237VrVzg6OmrdT8YvPeXLl8/W/jw8PNC4cWO1L6UZrl+/jkePHmHTpk2S9bVq1RL/Pnv2rKTFxNu3b/H27Vs0bdpU7TVdsGABxo0bh+fPn8PY2Fhcf+nSJWzfvl1SVvUGR/kXHOWbUgA4ePCg1vnUAemNX926deHq6irZrvwl+eLFixr34eLigu7du6Nu3boaEzIZrVVevnyZaZIiK7Vr15Ysb9q0SdLyxsnJCT/++KNavVq1amH48OG5Pq42SUlJ4nsmg/Lr1aZNG5iYmIjLqucyKCgIixcvltTv0qULAMUNQcuWLSXbvvnmG/F6TEhIwKxZs7IV58SJE8XWM6mpqZLETE5bLwBAtWrVMGTIEMm6WbNmYc6cOeJNVIaEhAT8+uuvqF+/vriuc+fOki/4u3btwj///CMu37t3D2vXrpXsJ+N1KWzR0dGSz+iIiAh8//33kjJt27YFoEjQ+fj4SLZNmzZNfI+lp6dj+vTpSE5OFrc3aNBATHydPn0amzdvFhOIMpkMjo6OaNq0KTp16iTZb0hISLafg/I1CKgng7OjKJ+z69eva0x4ZKfenTt3AADx8fFYsGCBuAwoEsJeXl4YNGiQ5DNZ+bX/6aef4OfnJ7aOMDIygoeHB3r27Cn5vyYpKUlsTdWtWzdJHJMmTUJoaKhafE+ePMGiRYsk119urpGXL19i2bJlePbsmbjNysoKtWrVwqBBgzTWKWjKr4EgCBg3bpzGVjn//vsvZs6ciTVr1uTr8fPjPUFEBaCQumIQFUtZDfKo3LfbwMBAePXqlSAI6n1Yr127Jql37do1yfaMedTLly8vruvatataPE5OThr71QqCIPj5+YnbrKyshLi4OKFjx47iuuXLl2frOauOwq3aXzMyMlJwcXHR2G9UdR2gGHMhY+BJc3NzjWWUx2D4+uuvBUEQ1GYxABSDb/Xq1Us4fPiw1mnoBEHaZzRjdPc5c+ZoLG9hYSEYGRmpxa884vyyZcvU6jVv3lzw9vZWm5Ytq4e216By5cqSaSo/+eQTyRgfgGIwMNXpLZWvv4y/u3TpotbvVnmGiozpvlT3oTx44ty5c9W2V6lSRWjbtm2+TFOpOko/oJjezMfHR6hVq5bW60n5vGRXZtNUduzYUfK+yngozwKhaR8AhDp16ght2rRRO0+VK1eWjHXi5+en9nzKli0rdOjQQeN7KbN+60OHDtV4TUVFReX4dREEQYiPjxeaNm2q8X3RunVroVu3boK3t7c4toeVlZWkvmr/bAMDA6FRo0ZCixYt1AZRVZ1RIKvpFFVfc9XP35yMwaD8GdK+fXvJuDWAYiDTkJAQsf7du3fV+pdrm6ZST09POHXqlFg34/NCX19fqFKlitChQwehR48eGj8v9u7dm63nIwiCsGLFCrVz5OPjI/Tq1UsYOnRotl/XgjxngpD57DaZGTdunKTulClTtJadOHGixrJv374V1zk5OQktWrQQpyNW/T+lR48e4v4ypoK1tLQUGjZsKHTt2lXo0qWL2hTG9vb24hgCSUlJaoMcyuVyoXHjxkL37t2FVq1aSd7fyp+RublGbt68Kfn8aN26tdCjRw+hTZs2av+vfP7559k+Z3l5n4WHh6t9fpqbmwvNmzcXunXrJjRv3lwylaTqsbO6VrJ6T0yaNEmy3cHBQejSpYvQq1cv4csvv9R6/RBRwWKCgSgTWSUY/v77b8n2PXv2CIIgCIcOHZKs//vvvzOtd+jQIUEQBKFu3briOtXRuVNTUyWzQWj6Yqc8p/XSpUvF8kZGRkJYWFi2nrPqII/Ozs5qo6/fuHFD6wBVyg93d3dxwKnY2Fit5VRnkRAE9ZHMlR8rV67MdoLBw8NDEARFYkQ5gaP8sLOzE9q3by9ZN3bsWPH5akowaHrUrl1bmDdvnvDJJ5/kaPCratWqCc+fP5cMWOXr65vt42ac44y/69evr/alUvmLboMGDQRBUE8wKA9UmpycLDRo0EDjsfT09NS++OU0wRAXF6c2wrimh4WFhdqX5+wmyzLkdBaJTz75RG2qyvT0dGH8+PHZPpeqNCVsMh7Dhg2TLGd2U3b37l21ZMXIkSNz9HqoSkxMFCZMmJCtZJmNjY1a3d69e2dZr3nz5mqD8RVmgqF+/fpCy5Yttb53Dh48qPa6HD16VGOiU/lhYmIibNq0SVIvu+9bHx8fyXWW1c1UcHCwWjIr42FnZ5ft17Ugz5kg5C7BoGlq3KtXr2otr5oId3JyElJSUiQJhswednZ2wp07d8T9ZSQYMnvo6+sLmzdvlsQRGBioNqWztsfw4cPFerm5RpQTDJk93N3dheDg4Gyfs7y8zwRBMbWpcgI7s8e8efMkdbO6VrI69q1btyTJdeVH3bp1NV06RFQI2EWCKA+uXr0qWc7oFtGgQQPJ4ESq/UqVl/X19dGgQQMAkAw0eO3aNUm/323btmlt5p9hwoQJ4t9ff/21WL5r165qA0JpY2hoiLFjx4rLr1+/xqBBgyT9l2vXro27d+9i/vz5qFq1KpydnSGXyyVNb21tbXHlyhVxAD/VLghZWbBgAebPn5+jOpo8e/YMFy5cgI2NDS5evCjpSgIouo7cunVLrf/6rVu3xL+7d+8u6V8tk8lgaGgIuVwOJycn+Pj44Pfff8fVq1cxY8YM/PHHHxgxYoRYvkmTJhg2bBgqVqwo6fPu4uKCdevW4dq1a3B3d5d0gYmPj8fnn3+OzZs3o27dujA2NoaFhQWsrKw0Pk/lwTuvX7+u1lQ0ODhY/Ltx48aZvGIKhoaGWLBggWSdtbU1OnfujHPnzqk1rc8pU1NT7NmzB4cOHULfvn1RtmxZyOVyGBkZoXTp0ujUqRNWrlyJly9fYvv27ZL306RJk9S6m+SWTCaDubk5KlWqhIEDB+LIkSP4448/1MbykMlkWLlyJS5evCieSzMzMxgaGqJUqVLo0KGD5FyqmjlzJvbt24dmzZrBzMwMZmZmaNiwITZu3Ihff/012/FWrVpVrQl1brpHKJPL5fjpp5/w9OlTzJo1Cy1atICTk5PkfLRr1w7fffed5H2RUXfHjh04evQoBgwYgHLlysHExESs1717d2zbtg2nT5+Gra1tnuLMC1NTUxw7dgyLFi2Cl5cXjI2NYWNjgx49euDSpUtqXb8AxcC8Dx48wLx589CoUSPY2NjAwMAAlpaWqFOnDr788kvcv39frXl6z549sWLFCvTv3x9eXl4oVaqU+HlRpkwZdO7cGb///jv279+vccwYbZydnXH69Gnx8zwndZUVxXO2f/9+yZg0FSpU0DjwboZGjRpJxqkJCQnBkSNHYGFhgS1btmDChAnw9vZG2bJlYWZmBgMDA9ja2qJBgwb45ptvcOfOHXh5eYn1ly9fjhkzZqBt27bw8PCAlZUV9PT0YG5uDi8vL4wcORLXrl3DwIEDJXG4urri0qVL2Lp1Kz766COULVsWxsbGMDQ0hL29PRo0aIBx48Zh//79+OWXX8R6ublGPD09sXHjRowaNQp169ZF6dKlYWxsDAMDAzg4OKBp06bie1R1cMuCVK1aNfj7+2PdunXw8fGBi4sL5HK5+NnYpEkTTJkyBSdPnsTXX3+dr8euWbMmjhw5gjZt2sDa2ppjMhAVFbrOcBAVZaotGPr16ycsXrxY+P7774XBgwdLMuf6+vpCYGCgWHf48OGSuu3btxdmzZoldOjQQbJeuWnr5cuXJb9OOjk5CV999ZUwduxYtabxmn45iouLE2xsbNQy+Zp+nctMTEyMUKdOHck+nJ2dhbFjxwoLFy4Upk+fLnTo0EEwMDCQ/OqtOke5l5eXMGPGDGHYsGGSX0crVqwopKSkiPXc3Ny0Pi9tz1n1F0rlXzZUf/WwsrISJk2aJEyePFky3aGBgYHw+PFjsV6bNm0k9Xr37i3MmjVLaNSokWT9nDlzxDodOnQQbGxshN69ewuTJk0SFi5cKIwfP14wMzMTyytPH6jaSkGZctympqbCV199JSxevFjyi5K2+o8fP5ZcI3Z2dsLkyZOFKVOmSJqoGhkZSZ6z8jFVp1pVvf45BZjupaenC/Xr1xfPiWpLJ1JQ/XxQvbaJiIioYDDBQJQJ1RuszB7KN52CoOgS0Lx580zrNGnSRIiJiZHU09Y1wMvLK9O+jBm++OILtcSA8rzT2RUeHi506dIly+et+sV98uTJmZZ3cXGRNE0VhIJNMFStWlVwd3fXGMuiRYskx3r9+nWWc5v36tVLkhxRTRipPoyNjYUrV66I5TNLMOzbt0/ruc9O/e3bt4t95TU95HK5sGXLFkkdJhiKh8WLFwsLFixQ68qT0S2LpJhgICIi0g12kSDKJblcDjc3N/Tu3RtHjhzBt99+K9luZmaGkydPYv369WjVqhVsbW1hYGAAGxsbtGjRAv/73//g5+enNmXTDz/8gDVr1qBq1aowMjKCs7Mzxo0bh3PnzknmytZm3LhxkqazgwcPztVc0nZ2djhw4ADOnDmD4cOHo0qVKrC0tIS+vj5sbW3RtGlT/PDDD2pdH5YsWYLjx4+jV69ecHFxgaGhIczNzVGrVi3MnDkT//77r6RpakFzcHDApUuXMGzYMDg6OkIul6NWrVr4888/8eWXX0rKOjk54erVq1iyZAkaNWoEKysrsflpx44dsXXrVuzcuVOcPQQApk6dis8++wze3t4oXbo0jIyMIJfL4eHhAV9fX1y5ckUy8n5munXrhp9//hlVqlQRZwvIiT59+uDWrVsYPXo0KlSoAGNjYxgbG6N8+fIYOXIkbt68if79++d4v6R7U6dOxTfffINjx46J6/r27YsePXroLigiIiIiFTJBEARdB0FE+ScxMRFOTk6IiooCADx48ACVKlXScVSFa8iQIeI4Fy1atICfn59uAyLKo4y+xcbGxvDw8MDQoUPx2WefaZ269kMXEBCAcuXKicv8HCAiIiocBlkXIaLi4NKlS3j37h02bdokJhfatm37wSUXiEoi/haQM+7u7nzNiIiIdIAJBqISon///njx4oW4bGRkhB9++EGHERERERER0YeEYzAQlTAWFhZo3rw5Tpw4gdq1a+s6HCIiIiIi+kBwDAYiIiIiIiIiyjO2YCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM8MdB0AEX2gBAFISwJSEzU/0tMACIpy6UZAYBKgpwfIZIp/9fUBExPpw9QUkMsVZYiIiIiIqFAxwUBEBUcQgORYICkKSHz3/vH+75R4AEL29mNkDzwIz15ZmQwwNlYkG8zMABsbwM4OsLUFrK0VyQkiIiIiIsp3MkEQsvkNn4goC0nRQGyI4hEXpkgspKfmfb9G9sDFbCYYMqOnp0gy2Nr+l3RwcFAkJIiIiIiIKE+YYCCi3BHSgfgIpYTCm/etEgpAfiUYtLG3B0qXBlxdAScnRfcLIiIiIiLKESYYiCj7UhOBqEAg6gUQ/QpISy6c4xZ0gkGZgQHg7PxfwsHWtnCOS0RERERUzDHBQESZS4kH3j4H3j0HYl4j2+Mm5KfCTDCoMjUF3N2BihUBR0fdxEBEREREVAwwwUBE6tJTgcinQMQjRfcHXSQVlOkywaDM2hrw9FQ8zM11HQ0RERERUZHCBAMR/SchEgi7B0Q+KbzuD9lRVBIMGWQyRTeKihWBcuUAQ0NdR0REREREpHNMMBB96DJaK4TfB+JCdR2NZkUtwaDMwAAoXx6oWVPRwoGIiIiI6APFBAPRhyolAXjzryKxUJRaK2hSlBMMGWQywM0NqFWLYzUQERER0QeJCQaiD01KPBDir0gspKfqOprsKQ4JBmUuLopEg6urriMhIiIiIio0BroOgIgKSXIc8MYfCLsPCGm6jqZkCw5WPBwcFF0nypVTtHAgIiIiIirB2IKBqKRLiQde3wDCHxbfxEJxa8GgysYGaNSILRqIiIiIqERjgoGopBLSgTe3FcmF9BRdR5M3xT3BkMHNTZFosLTUdSRERERERPmOCQaikigqEAi8ACRF6TqS/FFSEgwAoK8PVK8O1K7N6S2JiIiIqERhgoGoJEmKBgIvAlEvdB1J/ipJCYYMZmZAgwaAp6euIyEiIiIiyhdMMBCVBOlpQMhNxewQxXWchcyUxARDhlKlgKZNATs7XUdCRERERJQnTDAQFXfx4cDz00DiW11HUnBKcoIBAPT0gPr1gRo1ONsEERERERVbTDAQFVdCumIAx9c3AZTwt3FJTzBkcHEBWrYEzM11HQkRERERUY4xwUBUHCVFA89PAXGhuo6kcHwoCQYAkMsVXSbKl9d1JEREREREOcIEA1FxE/EIePlP8Z96Mic+pARDBk9PoEkTwMhI15EQEREREWWLga4DIKJsSk8DXp5TJBio5Hv8GAgJAVq3VgwESURERERUxOnpOgAiyoaUeODRASYXPjQxMcD+/cC9e7qOhIiIiIgoS2zBQFTUxYUBT48BKXG6joR0QRCA8+eByEigcWPFjBNEREREREUQEwxERVnEY+DFWUBI03UkpGv37gHv3gFt2wLGxrqOhoiIiIhIDX8KIyqKBAF4dQkIOM3kAv0nOBjYtw+IjtZ1JEREREREaphgICpqhHTg2Qngzb+6joSKoqgoRZIh9AOZopSIiIiIig0mGIiKkvRU4MlR4N1zXUdCRVlCAnDwIPDiha4jISIiIiISMcFAVFSkpQBPjgDRgbqOhIqD1FTg2DHgOZNRRERERFQ0MMFAVBSkJgGPDwExwbqOhIoTQQBOngQCAnQdCREREREREwxEOpeaCDw6CMS90XUkVBylpwMnTgAvX+o6EiIiIiL6wDHBQKRLqYnAwwNAQoSuI6HiLD0dOH4cCGT3GiIiIiLSHSYYiHQlLQV4fBhIfKvrSKgkSEtTjMnw6pWuIyEiIiKiDxQTDES6kDEVZXyYriOhkiQtDTh6FAjmWB5EREREVPiYYCAqbIIABJzhbBFUMNLSgCNHgNBQXUdCRERERB8YJhiIClvQFSDysa6joJIsYwrLuDhdR0JEREREHxAmGIgK05vbwBt/XUdBH4L4eEWSITVV15EQERER0QeCCQaiwvL2OfDqoq6joA9JWBhw5oyuoyAiIiKiDwQTDESFIfEdEOCn6yjoQ/T0KXDzpq6jICIiIqIPABMMRAUtLRl4egxIT9F1JPShunoVCAjQdRREREREVMIxwUBU0AL8FC0YiHTp9GkgMlLXURARERFRCcYEA1FBenMbeBeg6yiIgJQU4OhRxb9ERERERAWACQaighIXCgRd1nUURP+JiQEucqBRIip8MplM48PAwABWVlaoWrUqBg0ahMOHD+skvtmzZ0vi2rhxo06Om9mjVq1ahRJTdvn5+WUar4WFBapUqYIRI0bgypUrug6Xcik2NhYHDhzAV199hZYtW6JixYqwtraGkZERnJyc0LZtW6xZswbJycka61+9ehVLlixBz549UaNGDTg5OcHIyAhWVlaoXbs2pkyZgmfPnuVbvHFxcahQoYLa9Rigoavow4cP0b9/fzg6OsLQ0BCurq4YOXIkQkJCtO7b3d0dMpkMc+fOzbeYSxomGIgKQloy8OwkIKTrOhIiqQcPgJcvdR0FEREAIC0tDdHR0bh//z7++OMP+Pj4YNiwYboOq0TbuHGj5MZr9uzZBXKc2NhYPHjwAL/++iu8vb0xbdq0AjlOSVRY5yg7jhw5gm7duuGHH37AmTNn8PjxY0RFRSElJQVv3rzByZMnMWbMGNSuXRuBgYFq9Vu1aoUvvvgCe/bswe3bt/HmzRukpKQgOjoat27dwtKlS+Hl5YU///wzX+KdOnUqnj59mmW5x48fo2HDhti2bRsiIiLg6OiIoKAgrF+/Ht7e3ojU0K10xowZePHiBby8vHg9Z8JA1wEQlUivLgPJMbqOgkizs2eB3r0BY2NdR0JEH6hOnTrB1NQUKSkpuHXrFl4qJT43bNiAPn36oFOnTjqMUDfc3NxQr149jdvKlStXyNHkjKmpqXjOYmJicPXqVbx9+xYAIAgCFi1ahIoVKzKBVIyZmJigbt26sLCwwL///ougoCBx271799CvXz9cuHBBa/2qVauiXLlyeP36NW7cuCGuT0xMxJAhQ1CrVi14eXnlOr7jx4/jl19+yVbZBQsWICoqCgCwb98+dOnSBT///DMmTJiAFy9e4Oeff8a3334rlr927RpWrlwJPT09rFu3DkZGRrmOs6RjgoEov8UEA+H3dR0FkXbx8cD580DbtrqOhIg+UKtXr4a7uzsAICUlBU2bNpU0oz958uQHmWBo2bJloXXPyG8ODg7YuXOnuBwREYGWLVvizp074roff/yRCYZiqGLFipg2bRr69+8PExMTAIr37fjx47F27Vqx3MWLF+Hv74+aNWuK60xMTDBmzBiMGzdOfM8DwIkTJ+Dj44OU92NDpaamYv369Vi2bFmuYoyKihKvLSsrK8hkMrx7905r+atXrwIAbGxs0KVLFwDA4MGDMWHCBACQfB6lpaVh1KhRSEtLw7hx49CoUaNcxfihYBcJovyUngq8OKvrKIiy9uwZ8OSJrqMgIoKhoSGaN28uWZeQkKCxbGxsLFauXIm2bduiVKlSYl/uGjVqYOLEibh/X3uCPzIyEpMmTYKbmxvkcjnKli2LcePGITQ0VGudCxcuSJqqf/LJJxrLdevWTVJO+aa6IJw/fx6TJk1Cq1atUL58edjY2IjjWVSvXh1jxoyBv7+/pE5Gs/uhQ4dK1s+ZM6dAmuPb2dlh8uTJknX3799HbGysZF1CQgLWrFmDDh06SPrn16tXD3PmzEFERITG/SvH7O7ujuTkZPzwww+oUaMGzMzMIJPJJOUFQcD+/fvRv39/lC9fHubm5jAxMUHZsmXRqVMnrb98nzt3Dr6+vvD09IS5uTmMjY1Rrlw5+Pr6ijepqoYMGSKJz8/PDzdv3kTfvn3h6OgIuVyOChUqYObMmUhKShLrFfY5yo4WLVrgzp07GDp0qJhcABTv2+XLl8PAQPp79YMHDyTL/v7+WLx4sSS5AABt27ZF3759M62bExMmTMCrV68AACtXroSVlVWu96Vq2bJluHnzJlxdXfHdd9/l235LKrZgIMpPQVeApGhdR0GUPf/8Azg7A2Zmuo6EiD5gKSkpOHfunGRd/fr11cr5+/ujR48eaoO1paSk4Pbt27h9+zZ++eUXfP/995gyZYqkTFBQEJo3by4ZTC4wMBCrV6/Gnj170KpVK42xNW7cGI0bNxabfe/cuRNLly5FqVKlxDIRERE4cuSIuNy0aVNUq1Yte08+l7Zu3YpVq1aprY+OjsadO3dw584drFu3DmvXrtVpiwHl1ylDTEwMzM3NASgSDj169MCjR48kZVJSUnD9+nVcv34da9aswe7duzP91Tg5ORk+Pj44efKkxu1hYWHo06cPzpw5o7YtMDAQgYGBuH//PsaMGSOuT01NxahRo7Bhwwa1OgEBAQgICMDmzZsxY8aMLAf8W79+PbZu3Yq0tDRx3dOnTzF//nzcuXMHe/bsybS+Ljk4OGjdZmJiAmtra4SHh4vrVG/sXVxctNZ3dnaWLOc2KbB3715s3rwZANCzZ08MGjQIM2fOzLROgwYNcO/ePbx9+xYHDx5Ely5dsGnTJnF7w4YNASjO9axZswAoWl5ZWFjkKsYPCRMMRPklNgQIvavrKIiyLylJMR7DB9gMmYh0a+zYsTA1NUVqaipu3bqFFy9eiNuaNWuGjz/+WFI+PDwcHTp0wJs3b8R1dnZ2qFOnDoKCgnDv3j0AipvCL774Ak5OTpLWBkOGDJEkFwwNDdGwYUOkpqbi6tWr+Ouvv7TGOnXqVHz00UcAFDey69atw4wZM8Tt27ZtE5t5A8Do0aNz+nKI/Pz80Lt3b43bxo8fj5YtW4rLenp6qFixIhwcHGBjY4OUlBQEBASIrTgymnN36tQJzs7OcHd3R69evfDixQtcu3ZN3E+VKlVQtWpVcVn577xS7mcPKF53Ozs7AMDbt2/Rvn178VdnAKhQoQIqVaqEN2/eiDGGhISga9eu+Pfff7XerL5+/RqvX7+GmZkZ6tSpA2NjY7F1QVpaGnx8fCTPGVA0+69QoQJiYmLUtgHAZ599JkkuWFhYoGHDhtDT08OFCxcQGxsLQRAwb948uLi4ZHre//zzT8jlcjRp0gRv377F7du3xW179+7FhQsX0LhxY52co7y4fPmyJLlgamoq3phnJS0tTW3WmNatW+c4hrCwMHz66acAAEdHR6xZsyZb9b7++mvs2bMHUVFR6N69O5ycnBAcHAxAMRbKuHHjAABjxoxBfHw8+vTpg65du+Y4vg8REwxE+SE9FQg4A0DQdSREORMYCAQEACpNF4mICpK26SjLly+PjRs3qg2gtmTJEklyoWHDhjhy5Aisra0BAPPnz5f8YvnVV19hwIAB0NPTw/Xr13HixAlxm6GhIc6ePQtvb28AilHyfXx8IAia/w/v1q0bKlasKP7K/r///Q/Tpk0Tm4Yrj35vb2+vNUGQHS9evJAkW5Rl9BMHgM8//xwLFizQ+IvvqlWrMH78eACKwfP27duH0aNHo2XLluIYD8pN8Pv27ZvvTe5jY2Nx+PBhLF68WLK+TZs24rldunSpJLnw/fff46uvvhKXt2zZIiaaIiIi8N1332HlypVaj1mrVi0cPHgQpUuXBgCx68GmTZskN+smJibYvn275PWMjY2VtCJ49OiR5Ea1QYMGOH78OCwtLQEAoaGhqFevnjhrwowZMzBs2DCtA/9ZWVnhzJkz4tgEQ4YMwe+//y5uP3HiBBo3blyo5yivIiMj1VrHTJo0CTY2Ntmq/8033+Du3f9+mKtQoQIGDRqU4zhGjx4tdnNau3Ztpi0ulHl6euLy5cv49ttvcfr0aYSGhsLFxQU+Pj6YN28ebG1tsWXLFvFz5qeffgKgGBBy586dCAoKgrW1Ndq0aYPhw4fDmANni5hgIMoPb24DSVG6joIody5fBsqWBfQ4LA8R6dbTp09Ro0YNHDhwQNJtYf/+/ZJys2fPFpMLADBt2jT88ssv4i+QQUFBuHHjBurVq4fjx49L6vbq1UtMLgBAx44d0aZNG0kSQpmenh6mTJki/kr66tUr7N27F71798bz588lo+YPGTIEcrk8d08+Bzw8PLBz505s27YNt27dQkhICBISEjQmSfLSrz0nXrx4oTbugTITExMsXLhQXFbtFnDx4kVJcka5OwEAHDhwINMEw8qVK8XkAgDxPOzevVtS7quvvpIkFwDA3NxccnO7f/9+pKf/N9V4cnKy2s208msdERGBCxcuSFqYKBs9erRk4MNu3bpJEgzKszHkJ23JLkdHR6xevTrX+w0KCkKnTp3ElkMA8NFHH2HOnDlZ1hUEAVOnTsWSJUsk8Rw8eFAyxkN2/PHHH+L59fX1Rffu3XNUv1KlSti2bZvGbW/fvsXnn38OAFi8eDGcnJwwatQorFu3TlJuz549+PXXX+Hn5ycmoD50TDAQ5VVKAhByS9dRlCAywMgcMJADMj1Apg/oWQCl5UB6OpCSAiQkKB5K//lTHkRFAXfvAtWr6zoSIvpAPH/+HO7u7hAEAUFBQVi8eLH4C2FcXBwGDx6MJ0+eiDeJquMuVFf5vDIwMEDVqlXFBEPGMerVq6fWIkC1LgBUq1ZNa4IBUIwuP3PmTPGX0lWrVqF37974448/xDIymUxMQuSWr69vlrNICIKAXr16Ye/evdnaZ8ZUfLrk4eGBDRs2oHbt2uK658+fS8rs27cv030EBgYiLS0N+vr6atuMjIzQuHFjjfWUu8YAikELs6Ia261bt3Dr1q0s62hLMKiOKaLa8kR5oMf8tGvXLo3r3dzccr3PO3fuoFOnTpLWJ/3798emTZs0nhtlSUlJ8PX1ldzUlylTBkePHkWlSpVyFEdiYqI440PZsmXFz4/88sUXXyA0NBQtW7bE8OHDceDAATG54Ovri2XLlmHbtm0YM2YMbt68idmzZ2Pp0qX5GkNxxQQDUV69vg6kp2RdjqT0DAFj6/cPq//+llsCeho+mjw17CMpSTHl4tu3QGQkEBGh+DcmpkBDL5Fu3AA8PQE28SOiQiSTyeDq6ooVK1Zg//79YiLh1atXuHTpkngzqPrLfGa/lBcEY2NjTJgwQeyG4efnh7t370q6R7Rp0wYVKlQo8Fh27dqlllyoXr06ypUrB0NDQ4SFheHs2f9mtNLW9SO/mZqailOLymQymJubo0yZMmjcuDHatWuX5c1nVtLT05GQkCAOEKmsVKlS0NNxK7y4uDit2zLGnciQ19dCV86cOYMePXpIpn+cOnUqFi1alOV7MioqCj169ICfn5+4rmbNmvj7778lLU+yKzExUYwjNDQUHh4eku1v376VLNepUwd6enr4+eef0b9//0z3febMGWzYsAHGxsZYu3YtZDKZZArWb7/9FjY2Nhg9ejQWLlyIwMBA7N69mwmG95hgIMqLxHdAmPYpsUiJTA8wKwVYlQGsygImtnnfp1yueNjYAMr/scTHA69eKR5BQYrWDpS5pCRFkkHLL0BERAVN9Vfd169fi3+XK1dO0hz79u3bkgH/UlNTJdsz6gCKXzeVaZpCUrkvuDZjx47F999/L95Ijho1Cg8fPhS352Vwx5xQnXFj0aJF+PLLL8XlLVu2SBIMqgoqOePg4CC5CctKuXLlxNddJpMhKChIbVaB7MosueDh4SG5Ns6cOaO1pYFybMpUx4coaPl1jvIzubRt2zb4+vqKrS309fWxcuVKycwb2rx69QqdOnWSvPc6dOiAHTt25MusDImJiUhMTMy0TEbCIatySUlJ+PTTTyEIAmbOnAlPT8UvXMqto8qUKSP+7erqisDAQMn2Dx073BLlxavL4MCOmZDpAZZlAPeWQM3BQKWugFOt/EkuZMbUFKhYEWjdGhg0CPjoI6BaNf46n5V79wClXyWIiAqLn5+f2o2/cgJBtc/8nDlzJE3/Fy9eLPmC7+Ligjp16gAA2rZtK6m7a9cuXL58WVw+fvx4pt0jMtja2koG3lMee8HFxSXH/b9zS3nGCkDRciBDSEgI5s+fn2l91X7uBdX/PyvdunUT/xYEAePGjUN0tPpU3//++y9mzpyZ7dkBVPXo0UOyvGjRIhw8eFCyLiEhQdIapUuXLpKb/CVLlqjNiAEoZjfZuHGj2qwneVVUzlGGpUuXYsCAAWJywczMDPv27ctWcuH27dto1KiR5P09cuRIHDx4MMvkQkBAAGQymfjIKjGUHxYsWICHDx+iRo0amDp1qrheecyXsLAwtb+zO7jlh4AtGIhyK+Y1EKV5pOcPnpE5UKoGYFsBMCgCN/UODoqHt7di1oSHD4EXL4BCajZabKSnA5cuAR076joSIirhMqapzBiD4cqVK5JfW8uWLYtGjRqJy1OmTMGGDRvEL/MXL15EhQoVxGkqVVsgfPfdd+Kv2vXr10fr1q1x6tQpAIoB+5o3b44GDRogLS1N7diZmTx5Mn755Re1AQiHDx8uzipR0Ly9vfHLL7+Iy5999hm2b98OuVyOS5cuZdpUHwAqV64sWd6wYQOePHkiNuNftmyZ5BfagpJxTkNCQgAoBss7fvw46tSpA2tra7x79w737t0Tp0GcNWtWro7j6+uLVatW4ebNmwAUyYSuXbuiYsWK8PT0RGxsLK5fvw47OztxatPKlStjxIgRYp/7sLAw1K1bFzVr1kTZsmWRlJSEgIAAPHnyBOnp6Xka00CTonKOAMU0mlOmTJGsc3d3x4YNGyTTeGZQnU61WbNmkmSgmZkZIiMjNXZT8PLyytZAkYDihj+z9627u7tk/JWMcV8yc+/ePSxatAh6enpYt24dDA0NxW1t27YVW+isWbMGc+fOxalTp/DkyRMAii5SpMAEA1Fuvbqk6wiKHiMLRQsF+0qK1gtFjZ4e4OameERFAf7+wKNHHCxS2cuXQHAwoGWucSKi/KBtmkpA0VJg69atki/3jo6OOHLkCD766CO8fPkSgOLX42PHjknq6uvrY8GCBRg8eLBk/caNG9G8eXNxjIfk5GScP39ePF6zZs2yHGQQUDSd79WrF7Zv3y455siRI7Osm18GDBiA1atXi60w0tPTxW4TJiYmmDt3rmTKTlU1atRAgwYNcOXKFQCK2RqU+8XPnj27UG5e7ezscPz4cfTs2ROPHz8GoJguUlv3jtwmcAwMDHD48GH07t1bPOeAYirKjKlHM+JRtmrVKiQlJWHTpk3iOn9/f/j7++dbbNoUlXMEQDLeQoa7d+9q7Vak2tpIdZDRuLg4rYNPZiSTdEEQBIwaNQrJycn47LPP0KBBA8n2IUOGYN26dbh+/TrmzZuHtWvXSlovzJ07VxdhF0lF8A6AqBiICgTiw7Iu96GQWwFuLYBq/QCHKkUzuaDKygpo3hwYMEAxewKnaPxPFiNlExHlJ0NDQzg6OqJZs2aYP38+Hj58KGm9kKFOnTq4c+cOli1bhlatWsHe3h4GBgYwNzeHl5cXxo0bB39/f4195cuUKYOrV69i4sSJKFOmDAwNDeHi4oJhw4bh1q1bqFWrVrbj/eKLLyTLPj4+hXazByher5MnT+LLL7+Eu7s7DA0N4eDggN69e+Pq1ato2rRplvs4cOAARo4ciTJlyhRaywtNqlWrBn9/f6xbtw4+Pj5wcXGBXC6HoaEhSpUqhSZNmmDKlCk4efIkvv7661wfp1SpUjhz5gx2796NPn36wN3dHSYmJpDL5XB1dUWHDh0k41gAitf5999/x/nz5zFs2DBUqVIF5ubm0NfXh6WlJapVq4aBAwfit99+w9WrV/P6UqgpKufoQ7F27Vr8888/KFu2rMZuRnK5HKdPn8bkyZPh5uaGiIgI2Nraol+/frh8+XKhDPBaXMiEwhpalqgkeXQQiOFgLpBbAc51ANvyxSOpkJmoKODiRcUv+AT07g3YFvBYGURExdDff/8t+ZX28OHD6MiuZUREAJhgIMq5+HDg/m5dR6FjMsUYCy51NU8pWZwFBgIXLigSDh8yT0+gVStdR0FEVCRcuHABFy5cQEhICDZs2IDIyEgAQO3atXH9+vVCnzaTiKioKmF3BkSFIES9790HxdgGcG8BmDnqOpKCUaYM0KsXcPkykI1py0qsp0+BBg0AMzNdR0JEpHPHjh1TG3zOxMQE69atY3KBiEhJMW/TTFTIkmKAt890HYXuOFQFqnxUcpMLGQwMgCZNFLMpqEwV9cFITwc0zBVPRPShK1WqFHr06IGLFy+ibt26ug6HiKhIYRcJopwIvACEfoA3XfpywK05YFNO15EUvoQE4PRp4NUrXUdS+IyMgI8/VvxLRERERJQFtmAgyq7URCD8ga6jKHxyK6BKzw8zuQAoWjB07KiYaeJDk5wMPPgAr3kiIiIiyhUmGIiyK+IRkJ6q6ygKl4ktUKkrILfQdSS6pacHNGoENG0KfGh9be/cUXSXICIiIiLKAhMMRNkV/lDXERQuUwegYhfA0FTXkRQdVasC7dsD+vq6jqTwxMYCwZySlYiIiIiyxgQDUXbEhQKJb3UdReExdwIqdgYMjHUdSdHj5ga0bato1fChePJE1xEQERERUTHwAX1DJsqDiEe6jqDwWLoCnj6APgf20+pDSzI8fw6kfmDdg4iIiIgoxz6Qb8dEeSCkA5FPdR1F4bAqC5TvAOgZ6DqSos/dHWjd+sMYkyElBXj5UtdREBEREVERxwQDUVaiAoG0JF1HUfBMbIFybQC9D2h8gbzy8AAaNNB1FIWD3SSIiIiIKAtMMBBlJfIDuLHSlwPl2wP6hrqOpPipWRPw9NR1FAUvMFAxbSURERERkRZMMBBlJj0ViHqh6ygKmAzwaAvILXUdSPHVvDlQqpSuoyhYaWnAs2e6joKIiIiIijAmGIgyE/NakWQoyZzrAJaldR1F8aavD7RrB8jluo6kYD39QMYiISIiIqJcYYKBKDPRgbqOoGCZOysSDJR3pqZA06a6jqJgBQcDiYm6joKIiIiIiigmGIgyE1WCEwz6cqDcBzILQmEpX14x8GNJJQjA69e6joKIiIiIiigmGIi0SYoBkqJ0HUXBKV0fMDLTdRQlT9OmitYMJVVQkK4jICIiIqIiigkGIm1KcvcIY2vAvrKuo8izjz/+GDKZDBs2bNB1KP8xNgYaNoT7119D9umnmH3gAADA7+FDyD79FLJPP0VAeHiud5+Wng6Pb76BwZgxeBASkl9RZx9bMBARUT5JTU3F1atXdR0GEeUjJhiItNFB94iWo5ZAVu9TjY+9freyvZ+NBy6I9TQq3QCQ/ff2l8lk4mPixImSoocOHZJsP3LkSG6eWr67d+8etm3bhlKlSuGTTz4BAMTExODzzz9H3bp1YW9vDxMTE1SsWBEzZ85ETEyMWHfIkCGS56T6yI7169ejfv36MDMzg7m5OapVq/ZfoqNCBUCvYD5e9fX0MKlNG6Slp4vJi0L19i0QH1/4xyUiohJn2rRpaNCgAfz9/XUdChHlEyYYiDQR0oGYYJ0d3sjQAA2rlZM8bC3zqdm9uTNg7a518++//47Y2FhxeeXKlflz3Hy2Zs0apKeno0+fPjAyMgIAREREYMWKFbh79y5cXV1hbm6Ox48fY/78+ejXr59Yt3z58mjYsKHkYWam6C7i5OSU5bEnTJiAkSNH4tq1a7C3t4enpyfCwsLwzz//KArIZAU6o0S/evWgr6eHXTduIDQ6usCOoxVbMRARUT548uQJAODdu3e6DYSI8g0TDESaxIcD6Sk6O7yzvRUubZwmeTSvUxEA4DtrAzw/mgmL5hNh5D0Wbl2mY+LirYiOTQAADJm9EUPn/C7uK6Mlw+z/vf+129Vb63ENDQ0RHR2N339X1H/8+DGOHj0KQ0NDtbKvXr2Cj48PypQpAxMTE5iYmKBatWpYvnw5BEEQyx06dAiNGjWCtbU1TE1NUaFCBfTr1w9v374FADx8+BDdunWDo6Mj5HI5XF1d0alTJ1y5ckVrnIIgYOvWrQCArl27iuuNjY2xePFihIWF4datWwgMDIS3t+L5Hj58WDzmzJkzcenSJfGxe/dupKQozveECRO0HhcALl68iJ9//hl6enrYvXs3Xrx4gZs3b+LNmzdYtmzZfwX19bXu40FICFouWQLjceNQYcYM7Lx+XdwWm5iIMX/+iTLTpkE+bhwcpkxBkx9+wO8XL4plHC0tUd/NDanp6dh540am8RYIjsNARERERBowwUCkSXzu+8gXtH1n/PE2Og7lXR1QppQNXoZEYuW20xg+bxMAoLyrAzxK24vlM1pAuJayAWzKA2YOWvf90UcfQU9PDz///DMEQRD/7d27t1rZ8PBwHD58GABQpUoVWFpa4u7du5g0aRJWr14NAAgLC8NHH32ES5cuwcrKCp6enoiIiMD27dsRFaUYQHPAgAE4cOAAUlNT4eXlhfT0dBw5cgT37t3TGuejR48QFhYGAKhfv7643snJCV988QUsLCwAKBIOGdv19PRgYGCgcX8//fQTkpOTYWZmhjFjxmg9LgBs374dAFC6dGn89ttvsLKyQtmyZTFhwgRJYiUzvdeuxeuoKMgNDPA0LAz91q3DzZcvAQDf7t+PNWfPIiwmBl7OzrAwNsbl589x+uFDyT4alCsHADj3/tefQsUWDERERESkgeZv20QfurgwnR7+xesItfEThGv/AwCcWTsFNSuWEdfPWL0XC347jL1+t5CYlIKZIzqjTCkbsRXDpY3TFAVleoqxFzJRrlw5dO7cGQcOHMCePXuwceNG2NjYYODAgdiyZYta2efPn8Pd3R0AkJ6ejlatWuHs2bPYunUrxo0bh5cvXyI5ORkWFhZ48OABTExMIAgCrl27BgcHRaLj8ePHAIADBw6gSZMmAIDnz59nOhZCRh1LS0vY2NhoLRcaGopdu3YBAPr37y8mHpTFxsbif/9TvLbDhw/PdH+AosUFAAQGBiI8PBweHh64d+8efv75ZwQEBOCA6rgIGo45oVUrfPfRRwiJikKV2bPxLj4ei44exdaRI/E4NBQAMLNzZ3zj4wMAiIyLQ2BkpGQfbra2AIAn78sXqqgoIC4OMOMsJERERET0H7ZgINIkXrcJBk1jMGQ4ceUBqvWdA5Mm4yGr9ykW/KZoRZCalo6wtzHadglYuQFy9ZtdVRldBIYOHYro6GgMGzYMphqmXTQwMMAPP/wANzc3GBoaQl9fH2fPngUABAcrxq/w8vKCh4cHYmJi4OjoiDp16mDIkCF4/fq1OOZBRheHVq1aoUqVKujVqxeOHDkCZ2dnrTFmtH7QlDDI8PTpUzRt2hTBwcFo0qQJ1qxZo7HcunXr8O7dO+jr62PSpElZvTxITU0V/z527Bju3LmDOXPmAAAOHjyIgIAAaQUNYzoMeN+qwsnKCq0qKrq+3H7f7aBrjRoAgJn798Nt+nR0WLECK0+dQilLS8k+LE1MAABRCQlZxlwg3rzRzXGJiIiIqMhiCwYiVempQMJbnYaQMQaDqj8PX8YXy3eKZcpUsEH4u1g8C1J06UhLT9e+U4cq2Tp227ZtUblyZTx48AB6enoYN24cXrx4oVbu888/x/r16wEAnp6esLW1xdOnTxEeHo60tDQAii4K169fx+bNm3H58mXcu3cPmzdvxqZNm7B9+3b06dMHmzZtQrdu3eDn54d79+7h0KFD2L17N+7cuYNVq1ZpjNHy/c228mCUyi5evIhu3bohPDwcXbt2xdatWzUmSVJTU7F8+XIAQJ8+fcTWGJkpXbq0+HdG94sGDf5rGRIQECDdj6Ul8L61QXaMat4clZ2csP/ff3E7KAjXX7zAsXv3sOPGDdyZNUssF/0+sWBpbJztfeeryEjAw0M3xyYiIiKiIoktGIhUxUcAyF5f+sJ26fYzAICFmTGe71+Ay79PR3vvqmrlTI2NxL/jEpIAIwvAorRaOU1kMhnGjx8PAOjcuTPKlSunsdylS5cAAO3bt8ejR4/g5+cnufkGgOjoaNy/fx/jx4/HH3/8gRs3bqB9+/YAILZ2OHfuHD766COsWbMGZ8+exaz3N9EZ2zXx9PQEoGjJoDry9M6dO9G6dWuEh4djwoQJ2Lt3r8bkAqAYT+Hl+7EPvvjiC7XtP//8MypXrozKlSuL69q2bSv+fe3aNcm/MpkMFSpUUD+QUn0A2Pa+fGh0NPwePQIAVH//2l15/hxeLi74sXdvHP3sMxx8fy7uBgcjQimh8uJ9lwnPUqU0PrcCFxGhm+MSERERUZHFFgxEqnTcPSIzNTxdAQAxcYnw6D4DciMDRMWqN5Gv7P5fs/yqfWbD2dkFS1aWFcc4yMro0aPRr18/sRuDxlhq1MCdO3dw7NgxVKpUCZGRkUhXaUERGhqKxo0bw8bGBq6urkhOThbHMKjxvivAoEGDEBkZiTJlysDU1BR3796VbNekUqVKcHBwQFhYGK5fv442bdoAUHTN6Nu3LwRBgJGREa5cuYLGjRuL9VavXo06deqIy0uWLAGg6J5Rt25dteOEh4eL8Wbo27cvli9fjmvXrqF9+/bw8PAQYx46dChcXV3VAy5fXjF15XsrTp3Crhs3EBIdjaiEBOjJZPiyQwcAwE+nTmHbtWtwtbGBrZmZOMZCaWtr2CqdjyvPnwMAmmlKaBQGlTEhiIiIiIjYgoFIVXzR/WV2ePcmmPxJW9hbmyMmPhEt61TE3E+7qpWr4emKmSM6o5SdJV6GROLyzTviFI3Zoa+vD3t7e5i87+evydKlS9G9e3eYm5sjJiYGU6dOlUwZCQB2dnYYMmQISpUqhefPnyMwMBCVK1fGwoULMWLECACKm3IvLy+Eh4fj3r17cHJywqhRo/Dzzz9rPbZMJkP//v0BQDKoYnJysjiTQ3JyMi5fvix5REdHi2VPnTqFG++neNTUekEbQ0NDHDt2DJ9++iksLS3x5MkTeHl5Yfny5Vi7dq3mSiYmgP1/M3vsHDUKpSwtkZiSAg97e2wZMQJ1ypYFAHSuXh3NPD2RkJKC20FBMDY0RNcaNXBowgRx4MvQ6GhcffECBnp66K2UMCkUMgB25oCTMZCmu6lciYiIiKjokQnZnVeN6EPx8AAQW4Km4TOxA6r20nUU+e7evXuoXr06HB0d8eLFCxgZGWVdSZcePQL8/PJlVz+dOoXPtm1D//r1seV9oibf6ckAewvA2hgw1weM0gAkAamxgKAYYwNVegKm9pnuhoiISJsePXpg37598PPzQ4sWLXQdDhHlA7ZgIFKVnMlMDMWRbXldR1Agqlatin79+iEkJAR//vmnrsPJmru7pJtEbqWlp2P5yZPQk8kwq0uXvMdloAc4WwFVSgF1nQBvR8DbAqgNoEw0YBEKyF4DKaFAStR/yQUASHyX9+MTERERUYnBFgxEyoR04MavKKqDPOYKf2UuOvbsAcJ0NMaHsSFgbw5YGgGmeoBhKpAeD6TG5X6fznUBF/WxK4iIiLT59ddfEfp+fKGffvoJISEhGDFiBDzez0zUpEkTNG/eXJchElEeMMFApCwpBrizRddR5B+ZPlB7KCBjY6Ui4fJlwN+/YI9hJgfszRSJBBMA+invEwnqg4Hmma0nUK5V/u+XiIhKpNevX8PFxQUAYGBggPT0dKSnp8PAQDHufGpqKmrVqoWbN2/qMkwiygPOIkGkrKR1jzCxZXKhKHFxyb8Eg5UJYGsKWBoCcgD6yUBaHJCWBCBJUSbt/aOgpMQX4M6JiKikcXZ2xrhx47B69WqkpqaK6zP+lslk+P7773UVHhHlAyYYiJQllbAEg5mDriMgZU5OinEYsttwTAbAxkyRSDDXB+QCoJesGGgxPQHA+1YJBZ1I0CY1UQcHJSKi4mzp0qW4fv06rl69irQ06X9e8+bNQ4f30zYTUfHEBAORspLWgoFjLxQthoaApSUQFSVdrydTTP1oYwyYGQDydACJ72dsiAPwfpyEVNUd6lhBdLsgIqISzcjICLt370aNGjUQGRmJ9PR06Ovrw8fHB9OnT9d1eESUR0wwECkraU2+TdmCochxdwaS5ICZPmCYBiBBMdCiEAPgfYIrRZcB5gBbMBARUS44Oztj79694tSUbm5u2Lx5M/T02K2TqLjju5hIWWqSriPIPzJ9wMSmwHZ/8+ZNTJs2DY0bN0bp0qVhZGQEBwcHdO3aFefOnZOU3bx5M3x9feHl5QUbGxvI5XJUqFABn332GcLDw3N1/OfPn8Pc3BwymQwymQze3t6S7S9evECvXr1gb28Pe3t79O7dG4GBgZIyqamp8PLygqOjI6JUWxUUFFcTwCwUQMbUjzGK2UuKIyG9ZL1niIio0DRp0gSrV69G7dq1sX//flhZWek6JCLKB5xFgkjZo7+BmCBdR5E/zByByj0KbPejR4/G//73P43b9PT0sGPHDvTs2RMAYGxsjKQkzTei5cqVw7Vr12Bra5uj47dv3x7Hjx8Xlxs2bIhLly4BANLT01G3bl34+/tj8+bNAIBBgwahdu3auHr1qvgLyYoVK/D5559j3bp1GDFiRI6On2vhD4EXZwrnWIXBqy9gbK3rKIiIiIioCGALBiJlacm6jiD/GJoV+CGcnJzwzTff4PDhw/jrr79QqVIlAIob/MmTJ4vlZDIZmjZtijVr1uD48eOYN28ejIyMAChaIqxYsSJHx/39999x/PhxGBsba9z++PFj3Lp1C9WrV8cnn3yCTz75BNWqVcONGzfw9OlTAEB4eDhmz56NOnXqYNiwYbl5+rkjtyy8YxWGFI7DQEREREQKHIOBSFl6cen8ng0G8gLd/cCBA7F06VKYmpqK66pWrYpatWoBUHRRCA0NhaOjI/bt24f27duL5dq2bYuIiAgsX74cAHD16tVsHzc0NBSTJ0+GTCbDjBkzMGPGDLUyGa0lMpIYACCXK16PxETFuAHffPMN3r17h59++qlw+3zKLQrvWIWhJCXliIiIiChPmGAgUpaui7n+CoiB5l/380vTpk3V1nl6ekqWM5IPyskFTWXNzLLf2mLixImIjIzEuHHj0KRJE41lKleuDBcXF/j7++PWrVuQyWTw9/eHq6srKlWqhFu3bmH9+vX4+OOPte6jwOiVtI9d9rIjIiIiIgV2kSBSll7U5gHMA/2CbcGgya5du8S/mzVrBnNzc61ld+/eLf7dqVOnbO3/wIED2LZtG8qUKYPvvvtOazkjIyPs2LED7u7uqF27NmrVqgV3d3fs2LEDRkZGmDhxIkxMTPDDDz8AAOLj4xEZGZmtGPJOVkjHKSQcxoeIiIiI3mOCgUhZSUow6OkX6uGuX7+OCRMmAFB0R1i2bJnWsjNmzMDJkycBAN7e3hg8eHCW+4+JicHYsWMBAGvWrIGFReZdDRo3boxHjx4hODgYwcHBePToEby9vbF161acO3cO06dPh1wuR/fu3WFhYQE7OztUr14dt27dyuYzziVZCUswsAUDEREREb3HBANRSSUrvLf3+fPn0bp1a0RFRcHAwABbtmxB3bp1NZb94osvsGDBAgCKrgz79++HgUHW3Qa+++47vHr1CgMGDICPj0+2Y3N2doazszMARUuFL7/8EuXKlcOUKVMwceJE7N+/H/3798e8efNw9+5d9OzZU+uMF/mjhCUY2IKBiIiIiN4raZ2BifKmEG/KC1whPZdjx47ho48+Qnx8PORyObZt24bu3burlUtPT8eYMWOwdu1aAECNGjVw7NgxODg4ZOs4wcHBAIAtW7Zgy5YtatsvX74MmUyGZcuW4fPPP9e4j++//x6BgYHYvXs3DA0NsXPnThgZGWHt2rUwMzPD4cOHceHCBVy5cgXNmjXL5iuQQ2zBQER5sHTWLMS9eaPrMIq1+0+eoEqFCroOI1P3Xz9Alepuug5Dq0v3g1Dm/cxRRdGTB09QoXLRPsdFPcbQR/dQu3JZXYeRqfvXA1HFraKuw9CqOHzWzFyzJt/3yQQDkTI9faDEjPNY8Deye/bsQf/+/ZGcnAwzMzPs27cPbdq0USuXmpoKX19f/PXXXwAU3SIOHToEGxubAo8xQ0BAABYvXow2bdrgo48+QmhoKFJSUuDi4iIOMunu7o4LFy4gJCSk0OIq9tiCgahQxb15g5nvZ+uh3Blw40aRfw0HvPwHM8d46zoMrZqPuY9aIwp5kOQcuPH5zSIdH1D0Yzwy6Rxmjmmo6zAyNaDfnSL9Xi4OnzUFgQkGImWywh23oEClJhTo7nfs2IEBAwYgLS0NMpkMs2bNglwux/nz58Uy9evXh1wuR69evbB//34AQJkyZTB79mzcvXtXLGdlZYXq1auLy7L3v/K7ubkhICAAAPDxxx+LU2BmePLkCVatWiWW/fzzz9GiRQuN8X7xxRdITU3FihUrAAAODg4wNTXFu3fvkJqaCgMDA4SFhYn7KjhswUBEREREJRMTDETKCnlgxAKVEl+gu//777+RlqZo7iEIAr788ku1Ms+fP4e7u7uYXACAwMBAdOzYUVKuRYsW8PPzy/R47du3V5vu0s/PT0wwODk5ae0acfr0aezatQsTJkyAl5cXAEUSY+TIkVixYgUWLlyImjVr4syZM6hTpw7q1auXaSykpCQl5YiIiIgoT5hgIFJWkm6WkuN0HUGRkJaWhs8++wx2dnaYM2eOZNv3338PPT09rF27FvHx8fDx8cHKlSuhp1eA41eklLDzYlD406ESERERUdHEBAORshLVgqFgb2Q3btyIjRs3ZquskMN++tkt37JlyyzL6uvr499//9W4zdjYGEuXLsXSpUtzFF+eJMcW3rEKgz4TDERERESkUIKGzCfKB2zBQAWtpCUY2IKBiIiIiN5jgoFIWUm6WUqJ5wj/RVFSjK4jyF/6RrqOgIiIiIiKCCYYiJQZmuk6gnwkFPhMEpQLiW91HUH+YhcJIiIiInqPCQYiZUYlKcEAICla1xGQqoQSlGDQNwJkJW3aTSIiIiLKLSYYiJSVqBYMAGJDdB0BKRPSgaQoXUeRf9h6gYiIiIiUMMFApKyktWBggqFoiY9QJBlKipL2fiEiIiKiPGGCgUiZoamuI8hfsW840GNREvVC1xHkLyMLXUdAREREREUIEwxEykpaF4m0JCAhQtdRUIZ3JSzBILfUdQREREREVIQwwUCkTN+w5E27F/VS1xEUusaNG0Mmk+H06dO6DuU/STFAQgTcu34NWb1PMft/BwAAftceQlbvU8jqfYqA4PBc7z4tLR0e3b+BQcMxeBBQSF1j5GzBQERERET/YYKBSJWxTYHuvuWoJZDV+xTuXb8u0OOIogJzVS0gIAAymUx8LF26VLJ99erVku0PHjzIj2jz7MiRI7h48SJq1aqFVq1aietv376NXr16oXTp0jA2NkaNGjWwYcMGSV0/Pz/Jc1J+nDhxItsxrFq1Sqzn5OSkWFnA3SP09fUwaUAbpKWli8mLAmdsXTjHISIiIqJigQkGIlWmdrqOIH/FhQKpiXnezerVq5Ge/t8AhT///HOe91kQVq9eDQAYOHCguO7evXvw9vbG7t27kZiYCE9PT9y+fRvDhg3D8uXL1fZhZGSEhg0bSh5WVlbZOv69e/cwdepU9Q2F0D2iX/t60NfXw65TNxAaWQhTlDLBQERERERKmGAgUmViq+sIkJScgln/2w/Pj2bCyHssHNt9gWFzfkf4u1ixzK2HgWgzZimcO0yFvNE4mDWdgPqDF+KPQ5ck+5LVGwWZoQmWLFmCgQMHwsLCAqVLl8b8+fOzHY+hoSGePn2Kw4cPAwBOnDiB+/fvw9DQUK3srVu30KZNGzg7O0Mul8PMzAz169fHH3/8ISm3adMm1KpVCxYWFrCwsECVKlUwaNAgcfulS5fQpk0b2NnZwdjYGO7u7ujRoweePn2qNc6oqCgxxq5du4rrN27ciPj4eMjlcjx+/Bi3b9/G118rWpDMnj0bCQkJkv04Ozvj0qVLkkf9+vWzfJ2Sk5Px8ccfw8TEBG3atPlvQ1oyEPs6y/oPAkLQctQSGDcehwo9ZmDnievittj4RIz57k+U6TwN8kbj4NB2CpoM+wG/H7wolnG0tUT9qm5ITUvHzpM3sjxenhialrzuRERERESUJ0wwEKky0X0Lhp5T12Duur/xPDgcVco5Iyk5BRsOXECLUT8iITEZABDwOgJ+1x9BbmQILw9nyI0McO3eCwz6dgP+Pn9bbZ/Tp0/HqVOnYGxsjODgYMycORPHjx/PVjy9evUCAKxcuVLyb+/evdXKBgQEwM/PD3K5HF5eXpDL5bh27RoGDRqEv//+GwDg7++PIUOGwN/fH05OTnB3d8erV6/EJER6ejq6dOmCU6dOwdDQEFWqVEF8fDz27duHwEDtXT4uXbqE1NRUWFtbw9PTU1yv3PJCJpMBAPT0FB9/UVFRuHr1qmQ/wcHBsLa2hrW1Nby9vbFz585svU7Tp0+Hv78/1q1bB1dX1/82RL3M1vSUvb9ai9fhUZAbGuDpqzD0+3odbj5QjKHx7Zr9WLPrLMLexsDLwxkWZsa4fPc5Tl97KNlHA69yAIBzN59kK+ZcK+CuRERERERU/DDBQKRKxy0Yzlx/hEP/3AEAnPplEvy3zMSDXXNhIjfEvWev8deRKwAA72rlEHz4BwQcWIgbf85A8OEfUKGMIwBg69GravutV6s6AgICJC0PTp48ma2YWrRogerVq+PYsWM4evQoDh48iEqVKqFdu3ZqZb29vREcHIyAgADcuHEDwcHBqFChgiKurVsBAE+ePIEgCKhYsSIePnyI27dv4927dzhz5gwA4O3bt4iIUMx+cf36ddy8eROhoaG4c+cOqlatqjXOx48fAwDKli0rJhIAoGfPntDX10dSUhI8PT1Ro0YNLFiwQNweFBQk2Y+joyPc3NyQmJiIy5cvo0+fPvjll18yfY1OnDiBZcuWYcSIEejZs6d0Y9j9TOtmmNCvFR7unouHu+bC2sIU6ekCFv1+VPHcAkMBADOHd8aNP2fg2b4FCD32IyZ93EayDzcnxfX75FVoto6Za2YOBbt/IiIiIip2mGAgUqVvqNPp967cfS7+3eL9gJAuHb9EQlIKAODSHcV2mUyGKct3wKXjlzBoOAYmTcbjyfub0ODwd2r77du2DoyMjGBvbw9HR0Ui4s2bN9mOa8KECRAEAX379kV6ejrGjx8vuYnPIJPJMGXKFLi4uMDAwAAmJiZ48kTxa3pwcDAAoEmTJrCxscGjR49gZ2eHhg0bYuzYseI+7Ozs0KhRIwBAhQoVUL16dQwYMAA3b96Evb291hijoqIAABYW0tkNGjdujH379qFhw4ZISkpCREQEBg8eLG7PSLh4eXnhyZMnePXqFfz9/fHo0SOUKlUKALBkyRKtx42Li4Ovry8qVqyIFStWSDcK6dnqHgEAAzooumE42VuhVb2KAIDbTxXJj67NagAAZq7ZD7cu09Fh/Aqs3HYKpWyl16qluYnitYiVdvvId2alCnb/RERERFTsMMFApEkR6CYBAA2rlVN7ONkpbigHzvwNfx6+gpCIaFR2c0LDauVgYWYMAEhLE9T2ZW2UAiREAgAMDAwAAIKgXk6bTz75BDY2NoiOjoaFhQV8fX01lhs4cCD+/PNPhISEoHLlymjYsKF4w5+WlgYAcHJywt27d7Fo0SK0b98eMTExWLt2LVq1aoXLly8DULSuWL9+PQYMGAAzMzPs3LkTgwYNyvRG39JS8drExsaqbevcuTMuXbqEmJgYBAUFoUOHDuK2SpUqAQAcHBxQvnx5cX3ZsmXRtGlTAMDLl9qn+wwLC0NwcDCePXsGR0dHmJub488//wQAhIaFw7zZRBw896/W+tkxqmdznFk7BZM/aYvK7k64fv8FZq89iLZjl0vKRb9PLFi+vxYKjDkTDEREREQkxQQDkSam2n8lzy+CICAxKUXySEtLR/2q7mKZ6UM64tLGabi0cRrOr5+K2aO6YHj3JgCAS3eeAQBG9miKO9tn4dCKCTA3kWd+0DfqYzNkl6mpKYYPHw4AGDJkiForgQyXLikGmRw5ciTu3LmDQ4cOwdzcXFImODgYYWFh+PLLL7Ft2zbcu3cPlStXRnp6Os6fPw9BEHDhwgUMGTIEv/32Gy5duiQe++zZs1pjzBh3QVMyIKP7BQAEBgZi9uzZABStFqpVqwZAMfBkRoIDAF69eoXz588DANzd3cX106dPR+XKlaUDOQJISUlBXFwc4uLikJqaCkBxnuMSkpCalvUYDNuOXQMAhEZGw+/6IwBA9fKlAQBX7jyHl4cLfvy8N47+/BkOLh8PALj7LBgRSoN/vghRJJE8yxRgAkBuBRgUcAKDiIiIiIodA10HQFQkWTgX+CFehkTCpMl4ybplk/vg84/bokOjqjh68R56fPELKrmVgr6+Hl68jkRcQhJOr5kMdxd71Kjgigv/PsX6fedx3v8JgsOioKHHglTkE8C1Ya5jXrBgAb766iuxpYAmNWrUwIULF7B+/XqcP38ewcHBal0p7t27h3bt2sHBwQEuLi6Ijo7G8+eKrh/Vq1dHWloa2rZtCwsLC5QpUwZ6enq4d++euH9tGjVqBAMDA7x9+xZPnz6VtEbo3LkzTE1NUapUKTx+/BhJSUkwNTXFunXrxPhOnToFX19f2Nvbw8XFBY8ePUJiomKKz2+++Ubc1+vXr/Hw4UNxm7u7u1prkCFDhuD3339HKTtLhBxdnOVrCwArtp7CrlM3EBIRjajYBOjpyfClr6KlxU/bTmHbsWtwLWUDW0szsTtMaUdr2FqZifvI6GLTrHaFbB0zV8wcC27fRERERFRssQUDkSamDoCe7vJve38ci29HdoZnWUc8CwpHSEQ0qpRzwozhPqhWQfGL9sbZvmhVrxKMjQwRn5iM5VP6okYF18x3LKQBr2/mOq6MMRyMjLRPT7hx40a0atUKxsbGiI+Px/Lly9WSAh4eHujfvz8sLS3x6NEjhIWFoWbNmli7di3at28PfX19jB49GuXKlUNQUBCePHkCd3d3fPHFF/j222+1HtvKygqdOnUCABw4cECyrWvXrjAwMMDDhw9hZmaGnj174uLFi+JYDwAwaNAg9OnTB+bm5nj06BGsrKzQtm1bHD9+XGuXEK3SknNWHsDORaNQys4Sickp8Chtjy0LRqBO5bIAgM5NqqNZbU8kJKXg9pMgGMsN0bVZDRxaMUFMkIRGRuPqvRcw0NdD7zZ1cnz8bGP3CCIiIiLSQCbkpBM20Yfk0d9ATFDW5YodGVC1N2BSMqcZPHLkCDp16oQ6derg+vXrugvk2Qng7bNCPeRPW0/hsx+3oX/7+tiycETBHahKL8C0aIxTQvShmTd6NGbWqqXrMIq1Ab/9hi3Dhuk6jEwN2L8KW9Z11nUYWjUfcwofL/9c12Fo9dvnP2PY8vFZF9Shoh7jkUnfY+9qH12HkakB/Y5jy8CRug5Dq+LwWYPRo/N9l+wiQaSNhUsJTTAIQOA/QMUuug6kQHTs2BGNGzfGhQsXcPr0abRq1arwg4h6WejJhbS0dCzfchJ6ejLMGlWA51ZfXmKTU0TFgVmpUph365auwyjWZJaWRf41lBnaYd4v2ZuBSBcsDC1xa/0/ug5DK0sDiyIdH1D0Y9Qr4tcgAMgsrIr0e7k4fNbMLIB9sgUDkTZxYcCDPbqOouCUaw3YFmA//Q9VaiJwdweQWsDTROqKbQXFtUNEREREpIJjMBBpY2pfskfKD7wApJTQm2Bdenm+5CYXAMDaXdcREBEREVERxQQDkTYyGWCZxaCJxVlqIvDynK6jKFkinxR614hCJdMr2e8JIiIiIsoTJhiIMlPSf619F6C4Kaa8S44DXhbdvpT5wsIF0Nc+gwgRERERfdiYYCDKjFVZQM9Q11EUrIAzQFyorqMo/l6cBdKSdB1FwbJy03UERERERFSEMcFAlBk9A8C6hN9UCWnAk6NAUrSuIym+Qm4B0YG6jqLglfT3AhERERHlCRMMRFn5EGZaSE0AnhwBUrP/C7y7uztkMlmmDz8/P/j5+WVZzt3dPUfhHjhwAB07doSdnR3kcjlcXV3RtWtXnD17Vixz+/ZttG/fHtbW1nB2dsbw4cMRGRkp2U9UVBQcHR3h5eWF1NTUHMUginwCBF3JXd3ixMQOMDLXdRREREREVIQZ6DoAoiLP0hXQl5f85u+J74CnxwBPH0BPP192aWhoiJSUlGyVy64vvvgCS5YskawLCgpCUFAQGjZsiObNmyMmJgYdO3bEu3fvsGPHDty9exdffvklIiIisHfvXrHe7NmzERYWhr/++gsGBrn4OIwOAgL8cl6vOLKrqOsIiIiIiKiIkwmCIOg6CKIi78VZIPyBrqMoHLYVgHKtsyx27do1JCYmStY9ePAAI0eOBAA4OzvjxYsXiI+Px+3bt9XqL126FHv27AGgSBosXrw4y2Nu27YN/fv3BwCULl0aU6ZMgZeXF2JjY+Hv748qVaqgf//+OHr0KDp27Ihu3bph3759EAQBNjY2iImJQVxcHIyNjXH//n3UqFEDXbp0EePIkbhQ4NHfQHrWCZRiT6YP1PikZE/bSkRERER5xhYMRNlhW+HDSTBEPgEMzQDXhpkWq1evntq6bdu2iX+PGjUKhoaGsLKyQtOmTSXlkpKScO6cYopMPT09jBkzJluhzZ07FwBgbGyM06dPw9PTU9zWs2dPyf4BwMhIMeOBTCaDoaEh0tPTkZycDGNjY3z++efQ19dXaw2RLXFhwONDH0ZyAVDMpsLkAhERERFlgWMwEGWHufOH1f/8jb+i1YaQnu0qcXFx2LRpEwDAwMAAo0aN0lp2+/btCA8PBwB07NgRHh4eWe7/2bNnuHfvHgCgSpUqWLx4MVxdXWFiYoKGDRviwIEDYtmGDRvCzMwMfn5+ePnyJQ4dOoTw8HA0aNAAlpaW2L9/P44dO4YpU6Zk69gS8eGK5EJacs7qFWf2lXUdAREREREVA+wiQZRdIf5A0GVdR1G4rNwAjzaK2TSy8L///Q+jR48GAPTp0wfbt2/XWrZRo0a4dOkSAODvv/+Gj49Plvs/ePAgunbtqnW7TCbDpk2bMHDgQADA/v37MXr0aLx+/RqAosXFn3/+CTc3N3h5eSExMREPHz6EmZkZoqMVM2hYWlpmHsTbZ4ppPT+UlgsAYGQBVOsPyGS6joSIiIiIiji2YCDKLvvK2brRLlGiXgAPDwDJcVkWXb16tfj3uHHjtJa7efOmmFzw8PBAx44dsxXKu3fvJMtt27bF33//jYkTJwIABEHA5MmTxUElu3XrhqCgILx8+RJhYWG4evUqKlasiGXLluHp06dYtGgRQkND0axZM1hbW8Pa2hpNmzbF8+fP1Q8upAOvLgHPTnxYyQUAsK/E5AIRERERZQsTDETZZSAHbD2zLlfSxIcBD/YAsW+0Fjl//jz+/fdfAICXlxdatGihteyqVavEv8eMGQM9vex9DMnlcsny6tWr4ePjg+XLl6N06dIAgLCwMDEOQNGqoUyZMrC3twcAvH79GgsWLEDjxo3x8ccfY+DAgTh//jwmTpyIiRMn4p9//hFbQIhSEhRdIt78iw+PDLCrpOsgiIiIiKiYYIKBKCccq+k6At1IiQceHQDC7gEaelVlt/XCu3fvsGXLFgCAiYkJhg0blu0QypYtK1l2c3MD8F8SIUNGdwdNvvrqK8THx+Onn35CYGAgLly4AFdXVyxfvlxMVFy4cAGBgYGKCnGhwP3dQExwtuMsUWzKAUZmuo6CiIiIiIoJJhiIcsLEBrB01XUUuiGkAy/PAw/3AwmR4urQ0FDs2rULgGIMg0GDBmndxcaNGxEfHw8A6N+/P2xtbbN9+Jo1a8LM7L+b3ZcvXyrCEgS8evVKXK+cbFB26dIl/PHHHxg6dCjq1q2LkJAQANLERUbSIiQkRJFMebgfSMm6e0iJ5VRL1xEQERERUTHCBANRTn2orRgyxL0B7u1SjEmQloJ169YhOVkxo8LgwYNhbq55tg1BEPDLL7+Iy5m1dJDJZJDJZHB3dxfXGRsbS5IX48aNw+HDhzF58mQxwVCzZk1UqFBB47EnTpwIS0tLLFy4EADEfYeFhYnlMv52S7mvSKbkYBaNEseiNGBqr+soiIiIiKgY+cBGrCPKB5ZlAGMbIPGtriPRIQF48y/Swp9g7Zr/ukeMHTtWa40TJ07g0aNHABTTSNatWzfHR12wYAFOnz6Nhw8f4tixYzh27Ji4zdzcHOvWrdNYb+PGjbh69SqWLFkCR0dHAICjoyO6d++Offv2Yf369ZClp+Lx48fo0bIWHOWJOY6txGHrBSIiIiLKIbZgIMopmQxwyfnNcUl08OQFvHylGJ+gdcvmqFKlitay2R2nITO2tra4cOECPv/8c7i5ucHQ0BAODg7o378/rl69ivr166vViYmJwfTp01GpUiVMmDBBsm3Dhg0Y4jsY30z/Cl9P/wq+XRrht28H5yq2EsWsFGBZWtdREBEREVExIxMEDSO2EVHW7u9RzLBA78kAazegVE3AvJSug8laWjIQ8QgIuaUYxJL+4+nz4Y41QkRERES5xgQDUW5Fv1JMX0jqzEoBDlUAGw9Arwj1xBIEICYICH8IvAsAhDRdR1T0mDkClXvoOgoiIiIiKoaYYCDKi0cHP9wpDLND3wiwrQDYVQRMHRTdS3QhMUrRWiHi0Yc9K0R2VOwCWLjoOgoiIiIiKoaYYCDKi7hQ4MFeXUdRPOgbAeZOiptXCxfAxK7gEg7paYruKzGvgehAIDakYI5T0liXA8q303UURERERFRMMcFAlFdPjgJRL3QdRfGjLwcsnN8nG2wBI3PFQ5bDsWfT0xQzesSHAwmRin/jwtj9Iadk+oBXX0BuoetIiIiIiKiYYoKBKK8S3gL3dwFCuq4jKQFkgKEpYGiiSEAYyBX/CmlAWopiYMaMR/r75fRUXQddMjjVBkqrz8JBRERERJRdnKaSKK9MbADHarqOooQQFGMkxIcrBmN8+wwIv68YO+Hdc8W6+DAgKUox8wOTC/nD0Axwrq3rKIjovcePH6Nx48aoWLEi6tevj7t37yIlJQU9evRAzZo10bNnT6SmKj7/EhMT0bx5c7x9+7ZQY3R3d0elSpVQq1Yt1KpVC9u2bdNpjBMnToS7uztkMhlu3bolrtf0WgIo9Fi1xafpddRFfImJiejRowcqVqyImjVrol27dnjy5AkAIDQ0FB07doSnpyeqVauGs2fPivU+/fRTVK9eHa1bt0ZUVBQAQBAEdOrUCU+fPi20GFu2bIly5cqJr+OyZct0EmP79u1Ro0YN1KpVC82aNcPNmzcBFJ3rMLMYi8q1mGHDhg2QyWTYu3cvgKJzHWqLr6hcg4D2c1lY1yETDET5wbmuonk/UXFUukHRmu2D6AP36aefYtSoUXj06BG++uorDBkyBEePHoWtrS38/f1hbW2NI0eOAADmzZuH8ePHw8bGptDj3LZtG27duoVbt26hX79+Oo2xd+/eOH/+PNzc3CTrNb2WAAo9Vm3xAeqvoy7iA4BRo0bh4cOH8Pf3R/fu3TFixAgAwLRp0+Dt7Y3Hjx9jw4YN+Pjjj5GSkoI7d+7g8ePHuH37Nlq2bInNmzcDANavX49WrVqhfPnyhRYjACxbtkx8HSdNmgQAhR7j9u3b8e+//+LWrVuYPHmyeL0VleswsxiBonMtBgQEYN26dfD29hbXFaXrUFN8QNG4BjNoOpeFdR0ywUCUH/QNgTKNdR0FUc6ZlQLsPHUdBRG9FxoaimvXrmHgwIEAgF69eiEwMBBBQUGIj48HAMTHx8PIyAj//vsvHjx4gL59++oyZJGhoaHOYmzevDlcXV0l67S9lk+ePCn0WDXFl5nCjs/Y2Bg+Pj6QvR982dvbGwEBAQAUN6SjR48GANSvXx8uLi44c+YMDA0NkZSUhPT0dMTFxcHIyAivX7/Gli1bMHny5EKNUZvCjtHa2lr8OyoqCjKZrEhdh9pizExhx5ieno4RI0Zg5cqVkMvl4vqich1qi0+bwo5Pm8K8DplgIMov1u6KB1FxIdMH3JrrOgoiUhIYGAhnZ2cYGChaFclkMpQtWxaenp6wsLBAzZo1YWVlhdatW2Py5MlYsWKFzmIdPHgwqlevjuHDhyMsLAzt2rUrUjFqey1fvnxZpGJVfR0B6Dy+FStWoHv37oiIiEBKSgqcnJzEbe7u7nj58iUqVaqEVq1aoU6dOnj27BkGDhyISZMmYfHixeJrXhgxZpg2bRqqV6+Ofv364dmzZwCgkxgHDx6MMmXKYObMmdi8eXORvA5VY1Rer+trcenSpWjSpAnq1q0rritK16Gm+DIUlWsQUD+XhXkdsk0sUX4q2xSICVYMPkhU1JWurxhDhIiKhXXr1ol/L1++HD169EBqaio+/vhjJCUlYdy4cWjdunWhxHL27FmULVsWKSkpmDFjBnx9fXHo0KEiFWNm9PT0ikSs2l5HXca3cOFCPHnyBCdPnkRCQkKmZefPn4/58+cDAPbt24cyZcrA3d0dQ4cORXR0NPr27Ss2zy6oGPF/9u47Poo6/+P4e5JssoH0kEIooQTpCgICohRF0IhKUfEUQURQjN5RREWw4R2cotgOK1IVUA/BO0WkKIhKE7EgiqKACIYSSIGQsGTn90cu+2NJQptkNpu8no/Hanbafvazw2b3ne/MSJozZ47q1Kkj0zQ1depU9erVS1u2bPFJjbNnz5YkzZo1Sw888ICeeOKJUpf11et8co2LFy+uEPvi5s2btWDBAq/zK5wJu17jU9VXkfbBkl5LO/dDriIBlLUDP0k7z+6NEbBdWE3pvF7SaYZGArDXvn37lJKSooMHDyooKEimaapmzZr6/PPPlZKSIknauXOnhg4dqiVLlmjQoEEaMmSI2rRpow4dOnhO2mWnP//8U+edd55ycnI803xVY7169bRo0SK1atXqjHppd60n1neykvpod31PP/205s+fr+XLl3uG0levXl2//vqr56/HF110kSZOnKju3bt71svOztbVV1+tjz/+WJMmTVLDhg01YMAAXXDBBfrqq68UGhparjWezOl0avfu3YqNjfVJjUVCQ0O1Y8cONWrUqELthyfX+Mcff3j1ylf74ssvv6wJEyZ4Dj1IT09XRESEHn/8cd13330+3w9PVd/w4cO9lq0o+2DRa/nrr7/a9n7IIRJAWavRhEMlULEFOKR6XQkXgAooPj5eF154od58801J0oIFC1S7dm2vD4B/+9vf9OyzzyogIEBHjhyRYRien+1w5MgRZWZmeu7PmzdPrVt7X4nG1zVKZ9ZLX9Z6Jn20s74pU6Zo3rx5WrZsmdcX9xtuuEGvvPKKJGnDhg3avXu3unTp4rXugw8+qEceeUTVqlXz1GgYhlwul44dK7tRnSXVePz4ce3du9ezzIIFC5SQkOD1xc6OGjMzM7Vnzx7P/UWLFik2NrZC7Yel1eh0OivEvjh8+HD9+eef2rFjh3bs2KEOHTrotdde0/DhwyvEflhafUOHDq0Q+6BU+vuKnfshh0gA5SG5S+GlFo8d9nUlQHF1Okoh4b6uAkApXn31Vd12222aOHGiIiIiNGPGDM+8uXPn6oILLlDz5s0lFX5gHTp0qI4dO6aHH37Ylvr27t2rfv36qaCgQKZpqkGDBp4h176q8c4779SHH36o9PR09ezZU+Hh4dq2bdspe2lnrSXVt3Tp0lP20c76/vjjD40ePVoNGjRQt27dJEkhISFat26dnnzySd16661q1KiRgoOD9eabb8rhcHjW/eKLL3T06FFdccUVkqS0tDT95S9/8awXGRlZrjV+8sknuvrqq5Wfn6+AgADVqFFD//nPf7zWtaPGrKws3XDDDTp69KgCAgIUFxenDz74QIZhVJj9sLQaT/dv2s4aS1NR9sOS5OfnV4h9UDr1+7Nd+yGHSADl5fBeaet/JPFPDBVIZF0p5UpfVwEAAIBKiEMkgPISllB4Ej2gonBU56oRAAAAKDcEDEB5SrhAijjz614D5cYIlBpeITmq+boSAAAAVFIEDEB5MgypXje+1MH36l4iVY/3dRUAKqhJkyapXbt2Cg8PV3x8vHr37q2tW7d6LZOXl6e0tDTFxsYqLCxM/fr18zqxWUm2bt2qbt26KSEhQU6nUw0aNND48ePlcrk8y7hcLk2YMEENGzaU0+nUBRdcoCVLlpTL8wQAlC8CBqC8OUKl+pdLBv/c4CNxzaQajX1dBYAKbNWqVUpLS9PatWu1bNkyuVwu9ejRw+vM4SNHjtR///tfvfvuu1q1apX27Nmjvn37nnK7DodDAwcO1NKlS7V161Y999xzev311/Xoo496lhk/frxeffVVvfjii9qyZYvuuusu9enTR5s2bSq35wsAKB+c5BGwy8Ft0vZPfF0FqpqwmtJ5VxNwATgr+/fvV3x8vFatWqXOnTsrKytLcXFxmjt3rq6//npJ0k8//aSmTZtqzZo16tChwxlve9SoUdqwYYNWr14tSUpKStK4ceOUlpbmWaZfv34KDQ31XFINAOAf+MQJ2CUmRUpq6+sqUJU4qksNuhMuADhrWVlZkqSYmBhJ0saNG+VyudS9e3fPMk2aNFHdunW1Zs2aM97utm3btGTJEq/r1+fn58vpdHotFxoaqs8//9zKUwA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",
      "text/plain": [
       "<Figure size 1200x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete body composition analysis visualization created!\n"
     ]
    }
   ],
   "source": [
    "# Create a combined visualization matching the original image more closely\n",
    "fig = plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Create custom grid layout\n",
    "gs = fig.add_gridspec(3, 2, height_ratios=[2, 1, 2], width_ratios=[1, 1], hspace=0.3, wspace=0.3)\n",
    "\n",
    "# 1. Body Composition Pie Chart (top left)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "\n",
    "# Create donut chart\n",
    "sizes = [fat_mass, lean_mass]\n",
    "colors = ['#ff9999', '#ffcc99']  # Light coral and peach colors\n",
    "wedges, texts = ax1.pie(sizes, colors=colors, startangle=90, counterclock=False, \n",
    "                        wedgeprops=dict(width=0.5))\n",
    "\n",
    "# Add labels inside the chart\n",
    "ax1.text(0, 0.2, f'Fat Mass ({fat_mass}lbs)', ha='center', va='center', fontsize=10, fontweight='bold')\n",
    "ax1.text(0, 0, f'{fat_mass/total_mass*100:.1f}%', ha='center', va='center', fontsize=12, fontweight='bold')\n",
    "ax1.text(0, -0.2, f'Lean Mass ({lean_mass}lbs)', ha='center', va='center', fontsize=10, fontweight='bold')\n",
    "ax1.text(0, -0.4, f'{lean_mass/total_mass*100:.1f}%', ha='center', va='center', fontsize=12, fontweight='bold')\n",
    "\n",
    "ax1.set_title('Body Composition', fontsize=14, fontweight='bold', pad=20)\n",
    "\n",
    "# 2. Body Fat Percentage indicator (top right)\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "ax2.text(0.5, 0.7, f'Body Fat Percent - {body_fat_percent}%', ha='center', va='center', \n",
    "         fontsize=16, fontweight='bold', transform=ax2.transAxes)\n",
    "\n",
    "# Create horizontal bar for current age group (20-39 F)\n",
    "bar_ranges = [(0, 10, '#ff6b6b'), (10, 15, '#ff6b6b'), (15, 20, '#ffd93d'), \n",
    "              (20, 25, '#6bcf7f'), (25, 30, '#6bcf7f'), (30, 35, '#6bcf7f'),\n",
    "              (35, 40, '#ffd93d'), (40, 45, '#ff6b6b'), (45, 50, '#ff6b6b')]\n",
    "\n",
    "y_pos = 0.4\n",
    "bar_height = 0.1\n",
    "for start, end, color in bar_ranges:\n",
    "    width = (end - start) / 50  # Normalize to 0-1 range\n",
    "    x_start = start / 50\n",
    "    rect = plt.Rectangle((x_start, y_pos), width, bar_height, facecolor=color, \n",
    "                        alpha=0.7, edgecolor='black', linewidth=0.5, transform=ax2.transAxes)\n",
    "    ax2.add_patch(rect)\n",
    "\n",
    "# Add percentage labels\n",
    "for pct in [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]:\n",
    "    x_pos = pct / 50\n",
    "    ax2.text(x_pos, 0.3, f'{pct}%', ha='center', va='top', fontsize=8, \n",
    "             transform=ax2.transAxes)\n",
    "\n",
    "# Add arrow pointing to current value\n",
    "current_x = body_fat_percent / 50\n",
    "ax2.arrow(current_x, 0.6, 0, -0.05, head_width=0.02, head_length=0.02, \n",
    "          fc='black', ec='black', transform=ax2.transAxes)\n",
    "\n",
    "ax2.text(0.1, 0.2, '20-39\\n(F)', ha='center', va='center', fontsize=10, \n",
    "         transform=ax2.transAxes)\n",
    "ax2.set_xlim(0, 1)\n",
    "ax2.set_ylim(0, 1)\n",
    "ax2.axis('off')\n",
    "\n",
    "# 3. Full Body Fat Percent Master Chart (bottom)\n",
    "ax3 = fig.add_subplot(gs[2, :])\n",
    "\n",
    "age_groups = ['20-39', '40-59', '60-79']\n",
    "y_positions = [2, 1, 0]\n",
    "\n",
    "# Male ranges (top)\n",
    "for i, age_group in enumerate(age_groups):\n",
    "    y = y_positions[i] + 3\n",
    "    for start, end, color in ranges_male:\n",
    "        width = end - start\n",
    "        rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n",
    "                        edgecolor='black', linewidth=0.5)\n",
    "        ax3.add_patch(rect)\n",
    "\n",
    "# Female ranges (bottom)\n",
    "for i, age_group in enumerate(age_groups):\n",
    "    y = y_positions[i]\n",
    "    for start, end, color in ranges_female:\n",
    "        width = end - start\n",
    "        rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n",
    "                        edgecolor='black', linewidth=0.5)\n",
    "        ax3.add_patch(rect)\n",
    "\n",
    "ax3.set_xlim(0, 50)\n",
    "ax3.set_ylim(-0.5, 5.5)\n",
    "ax3.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n",
    "ax3.set_title('Body Fat Percent Master Chart', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Set y-axis labels\n",
    "y_labels = ['20-39', '40-59', '60-79', '20-39', '40-59', '60-79']\n",
    "y_tick_positions = [0, 1, 2, 3, 4, 5]\n",
    "ax3.set_yticks(y_tick_positions)\n",
    "ax3.set_yticklabels(y_labels)\n",
    "\n",
    "# Add age group labels\n",
    "ax3.text(-3, 1, 'Age (F)', ha='center', va='center', fontsize=10, fontweight='bold')\n",
    "ax3.text(-3, 4, 'Age (M)', ha='center', va='center', fontsize=10, fontweight='bold')\n",
    "\n",
    "ax3.set_xticks(range(0, 51, 5))\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Mark patient's position (assuming 20-39 female)\n",
    "ax3.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2, alpha=0.8)\n",
    "ax3.plot(body_fat_percent, 2, 'v', color='black', markersize=12)  # Triangle pointing down\n",
    "\n",
    "plt.suptitle('Nutrition Guidelines\\nUltrasound & Body Composition Assessment', \n",
    "             fontsize=16, fontweight='bold', y=0.95)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Complete body composition analysis visualization created!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cff16154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\n==================================================\n",
      "SAMPLE USAGE:\n",
      "==================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_221522/3354465480.py:138: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results: {'total_mass': 123.0, 'body_fat_percent': 22.439024390243905, 'fat_mass_percent': 22.439024390243905, 'lean_mass_percent': 77.5609756097561}\n"
     ]
    }
   ],
   "source": [
    "def create_body_composition_chart(fat_mass_lbs, lean_mass_lbs, age_group='20-39', gender='F'):\n",
    "    \"\"\"\n",
    "    Create a comprehensive body composition visualization\n",
    "    \n",
    "    Parameters:\n",
    "    - fat_mass_lbs: Fat mass in pounds\n",
    "    - lean_mass_lbs: Lean mass in pounds  \n",
    "    - age_group: Age group ('20-39', '40-59', '60-79')\n",
    "    - gender: Gender ('M' or 'F')\n",
    "    \"\"\"\n",
    "    \n",
    "    # Calculate derived values\n",
    "    total_mass = fat_mass_lbs + lean_mass_lbs\n",
    "    body_fat_percent = (fat_mass_lbs / total_mass) * 100\n",
    "    \n",
    "    # Create the visualization\n",
    "    fig = plt.figure(figsize=(12, 10))\n",
    "    gs = fig.add_gridspec(3, 2, height_ratios=[2, 1, 2], width_ratios=[1, 1], \n",
    "                         hspace=0.3, wspace=0.3)\n",
    "\n",
    "    # 1. Body Composition Pie Chart\n",
    "    ax1 = fig.add_subplot(gs[0, 0])\n",
    "    sizes = [fat_mass_lbs, lean_mass_lbs]\n",
    "    colors = ['#ff9999', '#ffcc99']\n",
    "    wedges, texts = ax1.pie(sizes, colors=colors, startangle=90, counterclock=False, \n",
    "                            wedgeprops=dict(width=0.5))\n",
    "\n",
    "    # Add labels\n",
    "    ax1.text(0, 0.2, f'Fat Mass ({fat_mass_lbs}lbs)', ha='center', va='center', \n",
    "             fontsize=10, fontweight='bold')\n",
    "    ax1.text(0, 0, f'{fat_mass_lbs/total_mass*100:.1f}%', ha='center', va='center', \n",
    "             fontsize=12, fontweight='bold')\n",
    "    ax1.text(0, -0.2, f'Lean Mass ({lean_mass_lbs}lbs)', ha='center', va='center', \n",
    "             fontsize=10, fontweight='bold')\n",
    "    ax1.text(0, -0.4, f'{lean_mass_lbs/total_mass*100:.1f}%', ha='center', va='center', \n",
    "             fontsize=12, fontweight='bold')\n",
    "    ax1.set_title('Body Composition', fontsize=14, fontweight='bold', pad=20)\n",
    "\n",
    "    # 2. Body Fat Percentage Bar\n",
    "    ax2 = fig.add_subplot(gs[0, 1])\n",
    "    ax2.text(0.5, 0.7, f'Body Fat Percent - {body_fat_percent:.1f}%', ha='center', va='center', \n",
    "             fontsize=16, fontweight='bold', transform=ax2.transAxes)\n",
    "\n",
    "    # Color ranges for body fat percentage\n",
    "    if gender == 'F':\n",
    "        bar_ranges = [(0, 10, '#ff6b6b'), (10, 15, '#ff6b6b'), (15, 20, '#ffd93d'), \n",
    "                      (20, 25, '#6bcf7f'), (25, 30, '#6bcf7f'), (30, 35, '#6bcf7f'),\n",
    "                      (35, 40, '#ffd93d'), (40, 45, '#ff6b6b'), (45, 50, '#ff6b6b')]\n",
    "    else:  # Male\n",
    "        bar_ranges = [(0, 5, '#ff6b6b'), (5, 10, '#ffd93d'), (10, 15, '#6bcf7f'), \n",
    "                      (15, 20, '#6bcf7f'), (20, 25, '#ffd93d'), (25, 30, '#ff6b6b'),\n",
    "                      (30, 35, '#ff6b6b'), (35, 40, '#ff6b6b'), (40, 50, '#ff6b6b')]\n",
    "\n",
    "    y_pos = 0.4\n",
    "    bar_height = 0.1\n",
    "    for start, end, color in bar_ranges:\n",
    "        width = (end - start) / 50\n",
    "        x_start = start / 50\n",
    "        rect = plt.Rectangle((x_start, y_pos), width, bar_height, facecolor=color, \n",
    "                            alpha=0.7, edgecolor='black', linewidth=0.5, transform=ax2.transAxes)\n",
    "        ax2.add_patch(rect)\n",
    "\n",
    "    # Add percentage labels\n",
    "    for pct in [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]:\n",
    "        x_pos = pct / 50\n",
    "        ax2.text(x_pos, 0.3, f'{pct}%', ha='center', va='top', fontsize=8, \n",
    "                 transform=ax2.transAxes)\n",
    "\n",
    "    # Add arrow for current value\n",
    "    current_x = body_fat_percent / 50\n",
    "    ax2.arrow(current_x, 0.6, 0, -0.05, head_width=0.02, head_length=0.02, \n",
    "              fc='black', ec='black', transform=ax2.transAxes)\n",
    "\n",
    "    ax2.text(0.1, 0.2, f'{age_group}\\\\n({gender})', ha='center', va='center', fontsize=10, \n",
    "             transform=ax2.transAxes)\n",
    "    ax2.set_xlim(0, 1)\n",
    "    ax2.set_ylim(0, 1)\n",
    "    ax2.axis('off')\n",
    "\n",
    "    # 3. Master Chart\n",
    "    ax3 = fig.add_subplot(gs[2, :])\n",
    "    \n",
    "    age_groups = ['20-39', '40-59', '60-79']\n",
    "    y_positions = [2, 1, 0]\n",
    "    \n",
    "    # Male and female ranges\n",
    "    ranges_male = [(0, 5, '#ff6b6b'), (5, 10, '#ffd93d'), (10, 15, '#6bcf7f'), \n",
    "                   (15, 20, '#6bcf7f'), (20, 25, '#ffd93d'), (25, 30, '#ff6b6b'),\n",
    "                   (30, 35, '#ff6b6b'), (35, 40, '#ff6b6b'), (40, 50, '#ff6b6b')]\n",
    "    \n",
    "    ranges_female = [(0, 10, '#ff6b6b'), (10, 15, '#ff6b6b'), (15, 20, '#ffd93d'), \n",
    "                     (20, 25, '#6bcf7f'), (25, 30, '#6bcf7f'), (30, 35, '#6bcf7f'),\n",
    "                     (35, 40, '#ffd93d'), (40, 45, '#ff6b6b'), (45, 50, '#ff6b6b')]\n",
    "\n",
    "    # Plot male ranges (top)\n",
    "    for i, age_group_name in enumerate(age_groups):\n",
    "        y = y_positions[i] + 3\n",
    "        for start, end, color in ranges_male:\n",
    "            width = end - start\n",
    "            rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n",
    "                            edgecolor='black', linewidth=0.5)\n",
    "            ax3.add_patch(rect)\n",
    "\n",
    "    # Plot female ranges (bottom)\n",
    "    for i, age_group_name in enumerate(age_groups):\n",
    "        y = y_positions[i]\n",
    "        for start, end, color in ranges_female:\n",
    "            width = end - start\n",
    "            rect = Rectangle((start, y-0.3), width, 0.6, facecolor=color, alpha=0.7, \n",
    "                            edgecolor='black', linewidth=0.5)\n",
    "            ax3.add_patch(rect)\n",
    "\n",
    "    ax3.set_xlim(0, 50)\n",
    "    ax3.set_ylim(-0.5, 5.5)\n",
    "    ax3.set_xlabel('Body Fat Percentage (%)', fontsize=12)\n",
    "    ax3.set_title('Body Fat Percent Master Chart', fontsize=14, fontweight='bold')\n",
    "\n",
    "    # Set labels\n",
    "    y_labels = ['20-39', '40-59', '60-79', '20-39', '40-59', '60-79']\n",
    "    y_tick_positions = [0, 1, 2, 3, 4, 5]\n",
    "    ax3.set_yticks(y_tick_positions)\n",
    "    ax3.set_yticklabels(y_labels)\n",
    "\n",
    "    ax3.text(-3, 1, 'Age (F)', ha='center', va='center', fontsize=10, fontweight='bold')\n",
    "    ax3.text(-3, 4, 'Age (M)', ha='center', va='center', fontsize=10, fontweight='bold')\n",
    "\n",
    "    ax3.set_xticks(range(0, 51, 5))\n",
    "    ax3.grid(True, alpha=0.3)\n",
    "\n",
    "    # Mark patient's position\n",
    "    patient_y = age_groups.index(age_group) + (0 if gender == 'F' else 3)\n",
    "    ax3.axvline(x=body_fat_percent, color='black', linestyle='--', linewidth=2, alpha=0.8)\n",
    "    ax3.plot(body_fat_percent, patient_y, 'v', color='black', markersize=12)\n",
    "\n",
    "    plt.suptitle('Nutrition Guidelines\\\\nUltrasound & Body Composition Assessment', \n",
    "                 fontsize=16, fontweight='bold', y=0.95)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return {\n",
    "        'total_mass': total_mass,\n",
    "        'body_fat_percent': body_fat_percent,\n",
    "        'fat_mass_percent': (fat_mass_lbs/total_mass)*100,\n",
    "        'lean_mass_percent': (lean_mass_lbs/total_mass)*100\n",
    "    }\n",
    "\n",
    "# Example usage with the sample data\n",
    "print(\"\\\\n\" + \"=\"*50)\n",
    "print(\"SAMPLE USAGE:\")\n",
    "print(\"=\"*50)\n",
    "result = create_body_composition_chart(fat_mass_lbs=27.6, lean_mass_lbs=95.4, \n",
    "                                     age_group='20-39', gender='F')\n",
    "print(f\"Results: {result}\")"
   ]
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