plsc-df.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path as op\n",
    "from collections import OrderedDict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the main directory and target directory paths\n",
    "data_dir = \"./dset\"\n",
    "deriv_dir = \"./derivatives\"\n",
    "os.makedirs(deriv_dir, exist_ok=True)\n",
    "csv_sub_dir = op.join(deriv_dir, \"csv_subset_dir\")\n",
    "os.makedirs(csv_sub_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transfer the Data\n",
    "#### First, we want to select the ABCD variables from the large collection of ABCD csv files.\n",
    "#### Some of the variables will be selected at specific event years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables that don't need to be controlled for age\n",
    "# Define the lists of filenames and variables to extract\n",
    "abcd_var = {\n",
    "    \"abcd\": {\n",
    "        \"abcd_y_lt\": [\"interview_age\", \"rel_family_id\", \"site_id_l\"],\n",
    "        \"abcd_p_demo\": [\n",
    "            \"demo_sex_v2\",\n",
    "            \"demo_ethn_v2\",\n",
    "            #\"demo_race_a_*\",\n",
    "            \"demo_prnt_age_v2\",\n",
    "            \"demo_prnt_gender_id_v2\",\n",
    "            \"demo_prnt_ethn_v2\",\n",
    "            #\"demo_prnt_race_a_*\",\n",
    "            \"demo_prnt_ed_v2_2yr_l\",\n",
    "            \"demo_prtnr_ed_v2_2yr_l\",\n",
    "            \"demo_comb_income_v2\",\n",
    "            \"demo_prnt_income_v2_l\",\n",
    "            \"demo_origin_v2\",\n",
    "            \"demo_biomother_v2\",\n",
    "            \"demo_biofather_v2\",\n",
    "            \"demo_matgrandm_v2\",\n",
    "            \"demo_matgrandf_v2\",\n",
    "            \"demo_patgrandm_v2\",\n",
    "            \"demo_patgrandf_v2\",\n",
    "        ],\n",
    "    },\n",
    "    \"led_l\": {\n",
    "        \"led_l_coi\": [\"reshist_addr1_coi_r_coi_nat\"],\n",
    "        \"led_l_nbhsoc\": [\"reshist_addr1_nanda_disadv_fac\"],\n",
    "        \"led_l_gi\": [\"reshist_addr1_gstat_h_queen\"],\n",
    "    },\n",
    "    \"ph_y\": {\n",
    "        \"ph_y_anthro\": [\"anthroheightcalc\", \"anthroweightcalc\"],\n",
    "    },\n",
    "}\n",
    "\n",
    "\n",
    "# variables that need to be controlled for specific years\n",
    "# Define the lists of filenames and shared eventname\n",
    "\n",
    "abcd_var_age = {\n",
    "    \"ce_y\": {\n",
    "        \"ce_y_meim\": {\n",
    "            \"eventname\": \"3_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"meim_ss_exp\",\n",
    "                \"meim_ss_com\",\n",
    "            ],\n",
    "        },\n",
    "        \"ce_y_via\": {\n",
    "            \"eventname\": \"3_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"via_ss_hc\",\n",
    "                \"via_ss_amer\",\n",
    "            ],\n",
    "        },\n",
    "        \"ce_y_macv\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"macv_y_ss_fs\",\n",
    "                \"macv_y_ss_fo\",\n",
    "                \"macv_y_ss_fr\",\n",
    "            ],\n",
    "        },\n",
    "        \"ce_y_dm\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"dim_y_ss_mean\",\n",
    "            ],\n",
    "        },\n",
    "    },\n",
    "    \"ce_p\": {\n",
    "        \"ce_p_meim\": {\n",
    "            \"eventname\": \"3_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"meim_p_ss_exp\",\n",
    "                \"meim_p_ss_com\",\n",
    "            ],\n",
    "        },\n",
    "        \"ce_p_via\": {\n",
    "            \"eventname\": \"3_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"via_p_ss_hc\",\n",
    "                \"via_p_ss_amer\",\n",
    "            ],\n",
    "        },\n",
    "        \"ce_p_macv\": {\n",
    "            \"eventname\": \"2_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"macv_p_ss_fs\",\n",
    "                \"macv_p_ss_fo\",\n",
    "                \"macv_p_ss_fr\",\n",
    "            ],\n",
    "        },\n",
    "        \"ce_p_comc\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"comc_ss_cohesion_p\",\n",
    "                \"comc_ss_control_p\",\n",
    "            ],\n",
    "        },\n",
    "    },\n",
    "    \"ph_y\": {\n",
    "        \"ph_y_yrb\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"physical_activity1_y\",\n",
    "            ],\n",
    "        },\n",
    "        \"ph_y_resp\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"resp_wheeze_yn_y\",\n",
    "                \"resp_pmcough_yn_y\",\n",
    "                \"resp_diagnosis_yn_y\",\n",
    "                \"resp_bronch_yn_y\",\n",
    "            ],\n",
    "        },\n",
    "        \"ph_y_mctq\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"mctq_sdweek_calc\",\n",
    "                \"mctq_msfsc_calc\",\n",
    "            ],\n",
    "        },\n",
    "        \"ph_y_bp\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"blood_pressure_sys_mean\",\n",
    "                \"blood_pressure_dia_mean\",\n",
    "            ],\n",
    "        },\n",
    "    },\n",
    "    \"mh_p\": {\n",
    "        \"mh_p_cbcl\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"cbcl_scr_syn_internal_t\",\n",
    "                \"cbcl_scr_syn_external_t\",\n",
    "            ],\n",
    "        },\n",
    "    },\n",
    "    # here I select RS data that is available at year 4\n",
    "    \"mri_y\": {\n",
    "        \"mri_y_rsfmr_cor_gp_gp\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"copy_entire_file\": True,\n",
    "        },\n",
    "        \"mri_y_adm_info\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"mri_info_manufacturer\",\n",
    "            ],\n",
    "        },\n",
    "        \"mri_y_qc_motion\": {\n",
    "            \"eventname\": \"4_year_follow_up_y_arm_1\",\n",
    "            \"columns_to_extract\": [\n",
    "                \"src_subject_id\",\n",
    "                \"eventname\",\n",
    "                \"rsfmri_meanmotion\",\n",
    "            ],\n",
    "        },\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to recursively find files in directories\n",
    "def find_files(directory):\n",
    "    for dirpath, _, filenames in os.walk(directory):\n",
    "        for filename in filenames:\n",
    "            yield os.path.join(dirpath, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/2211116325.py:22: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_path)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved abcd_y_lt_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved abcd_p_demo_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved led_l_coi_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved led_l_nbhsoc_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved led_l_gi_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ph_y_yrb_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ph_y_resp_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ph_y_mctq_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ph_y_bp_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_y_meim_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_y_via_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_y_macv_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_y_dm_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_p_meim_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_p_via_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_p_macv_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved ce_p_comc_subset.csv to ./derivatives/csv_subset_dir\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/2211116325.py:22: DtypeWarning: Columns (124,128,132,136,140,144,148,152,156,160,164,168,172,176,180,184,188,192,196,200) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_path)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved mh_p_cbcl_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved mri_y_rsfmr_cor_gp_gp_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved mri_y_adm_info_subset.csv to ./derivatives/csv_subset_dir\n",
      "Saved mri_y_qc_motion_subset.csv to ./derivatives/csv_subset_dir\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Iterate through both dictionaries\n",
    "for category, files_and_vars in {**abcd_var, **abcd_var_age}.items():\n",
    "    for filename, vars_or_info in files_and_vars.items():\n",
    "        # Initialize processing parameters\n",
    "        found_file = False\n",
    "        copy_entire_file = False\n",
    "        columns_to_extract = []\n",
    "        eventname_filter = None\n",
    "\n",
    "        # Determine if it's from `abcd_var` or `abcd_var_age`\n",
    "        if isinstance(vars_or_info, dict):  # Logic for `abcd_var_age`\n",
    "            eventname_filter = vars_or_info[\"eventname\"]\n",
    "            copy_entire_file = vars_or_info.get(\"copy_entire_file\", False)\n",
    "            columns_to_extract = vars_or_info.get(\"columns_to_extract\", [])\n",
    "        else:  # Logic for `abcd_var`\n",
    "            vars_to_extract = vars_or_info\n",
    "\n",
    "        # Search for the file in all subdirectories of `data_dir`\n",
    "        for file_path in find_files(data_dir):\n",
    "            if filename + \".csv\" in file_path:\n",
    "                # Read the CSV file\n",
    "                df = pd.read_csv(file_path)\n",
    "\n",
    "                # Handle processing logic based on dictionary type\n",
    "                if eventname_filter is not None:  # `abcd_var_age` logic\n",
    "                    df_filtered = df[df[\"eventname\"] == eventname_filter]\n",
    "                    if not copy_entire_file and columns_to_extract:\n",
    "                        df_filtered = df_filtered[columns_to_extract]\n",
    "                    df_subset = df_filtered\n",
    "                else:  # `abcd_var` logic\n",
    "                    # Ordered dictionary to maintain the order of columns\n",
    "                    columns_to_keep = OrderedDict()\n",
    "                    columns_to_keep[\"src_subject_id\"] = True\n",
    "                    columns_to_keep[\"eventname\"] = True\n",
    "\n",
    "                    for col in vars_to_extract:\n",
    "                        if \"*\" in col:\n",
    "                            # Use regex to match pattern\n",
    "                            regex_pattern = col.replace(\"*\", \".*\")\n",
    "                            matched_columns = list(\n",
    "                                df.filter(regex=regex_pattern).columns\n",
    "                            )\n",
    "                            for matched_col in matched_columns:\n",
    "                                columns_to_keep[matched_col] = True\n",
    "                        else:\n",
    "                            columns_to_keep[col] = True\n",
    "\n",
    "                    # Create subset dataframe with desired columns\n",
    "                    df_subset = df[list(columns_to_keep.keys())]\n",
    "\n",
    "                # Construct the destination path and filename\n",
    "                output_filename = f\"{filename}_subset.csv\"\n",
    "                output_path = os.path.join(csv_sub_dir, output_filename)\n",
    "\n",
    "                # Save the subset dataframe to the target directory\n",
    "                df_subset.to_csv(output_path, index=False)\n",
    "\n",
    "                print(f\"Saved {output_filename} to {csv_sub_dir}\")\n",
    "\n",
    "                found_file = True\n",
    "                break\n",
    "\n",
    "        if not found_file:\n",
    "            print(f\"File {filename}.csv not found in {data_dir}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next, we want to foward fill some of the demographic data\n",
    "#### this ensures we have as much demo data as possible while making sure subject ID only appears once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered subject IDs: ['NDAR_INV00LH735Y', 'NDAR_INV00X2TBWJ', 'NDAR_INV028D3ELL', 'NDAR_INV030W95VP', 'NDAR_INV056V9RRJ', 'NDAR_INV06DX0E7R', 'NDAR_INV07CMT243', 'NDAR_INV0889M0JE', 'NDAR_INV08FUB58A', 'NDAR_INV09T2EBX4', 'NDAR_INV0AHJHA5D', 'NDAR_INV0DC9BJZK', 'NDAR_INV0DJJP33V', 'NDAR_INV0DKWEM1A', 'NDAR_INV0DVK13LU', 'NDAR_INV0EXY6KFW', 'NDAR_INV0FUC15ZB', 'NDAR_INV0G2N59GL', 'NDAR_INV0G42TBW1', 'NDAR_INV0GND16RW', 'NDAR_INV0LDK94T8', 'NDAR_INV0MPBK7TU', 'NDAR_INV0PDYPPJ7', 'NDAR_INV0PR0WY8F', 'NDAR_INV0RHLKA9M', 'NDAR_INV0T7889MV', 'NDAR_INV0TRD5ZK4', 'NDAR_INV0U23M45G', 'NDAR_INV0UERLJJY', 'NDAR_INV0UPVEC1J', 'NDAR_INV0W00RU2X', 'NDAR_INV0W4WA8RV', 'NDAR_INV0ZTJ2432', 'NDAR_INV1147ELD2', 'NDAR_INV114VT9DX', 'NDAR_INV137Y0WR8', 'NDAR_INV13BCLD41', 'NDAR_INV145EPB4G', 'NDAR_INV14UJARAJ', 'NDAR_INV15YZBNE0', 'NDAR_INV1AFNKM8M', 'NDAR_INV1AKMLL9A', 'NDAR_INV1B7G7LND', 'NDAR_INV1BGRYN2J', 'NDAR_INV1CC6HB4A', 'NDAR_INV1CMWHGWK', 'NDAR_INV1D8KBZHP', 'NDAR_INV1DDX454E', 'NDAR_INV1ET4JE3B', 'NDAR_INV1FT79EHP', 'NDAR_INV1H7JEJW1', 'NDAR_INV1HKCTUW1', 'NDAR_INV1JERB432', 'NDAR_INV1KZTEZF5', 'NDAR_INV1LT8UZYC', 'NDAR_INV1MF1L9LR', 'NDAR_INV1MYF7VFL', 'NDAR_INV1NBP7WNB', 'NDAR_INV1NV489TJ', 'NDAR_INV1P2BB9HY', 'NDAR_INV1PX9GGJP', 'NDAR_INV1R36G2UH', 'NDAR_INV1R4G3839', 'NDAR_INV1T4GDEXG', 'NDAR_INV1V7XPJKR', 'NDAR_INV1VCFJ6CR', 'NDAR_INV1VMB1ZYW', 'NDAR_INV1XH2YX3W', 'NDAR_INV1ZZAPY9K', 'NDAR_INV22E7G6BN', 'NDAR_INV22GRHDPN', 'NDAR_INV23E4LDJX', 'NDAR_INV24Z1RUE9', 'NDAR_INV263A3T6P', 'NDAR_INV273EJWMX', 'NDAR_INV29CHYRH1', 'NDAR_INV2AD1P5NV', 'NDAR_INV2B7X981Z', 'NDAR_INV2CK4MB02', 'NDAR_INV2CTVWFH6', 'NDAR_INV2E77XGT6', 'NDAR_INV2EJZCHYV', 'NDAR_INV2ERWUABP', 'NDAR_INV2FV7FT02', 'NDAR_INV2G3T7XUL', 'NDAR_INV2GPZ2973', 'NDAR_INV2H0H09CW', 'NDAR_INV2HLZM8RB', 'NDAR_INV2MH3HPHP', 'NDAR_INV2MMMWY0R', 'NDAR_INV2NLERKN7', 'NDAR_INV2NV4C2CL', 'NDAR_INV2PG9W3G7', 'NDAR_INV2RLDDFYE', 'NDAR_INV2T5K2NDJ', 'NDAR_INV2VJF8M3C', 'NDAR_INV2X040T48', 'NDAR_INV2X7YEHPB', 'NDAR_INV2XAHZZ2A', 'NDAR_INV2XGC3LN5', 'NDAR_INV2YV96ETT', 'NDAR_INV302FDTJF', 'NDAR_INV3062KMFL', 'NDAR_INV30BGMG0D', 'NDAR_INV352XG4XH', 'NDAR_INV35UXPP43', 'NDAR_INV37MKKJBX', 'NDAR_INV3807LYLG', 'NDAR_INV38MDKPJC', 'NDAR_INV39R1KUT6', 'NDAR_INV39Z29MG3', 'NDAR_INV3AEJYFGX', 'NDAR_INV3BL9Z315', 'NDAR_INV3CAV3A8F', 'NDAR_INV3CR6NXBG', 'NDAR_INV3DG9GH8G', 'NDAR_INV3ENJ6K37', 'NDAR_INV3GZNM6F8', 'NDAR_INV3HC4V43B', 'NDAR_INV3HJUHZX1', 'NDAR_INV3HV5ZJLJ', 'NDAR_INV3JUUH0FL', 'NDAR_INV3KBYKMJ2', 'NDAR_INV3MXVVRKB', 'NDAR_INV3N6L8Z9U', 'NDAR_INV3PNCV5A7', 'NDAR_INV3R6TY43Y', 'NDAR_INV3RTWYZX5', 'NDAR_INV3TUE7DV5', 'NDAR_INV3UPL5YML', 'NDAR_INV3UW7BCN8', 'NDAR_INV3VAXZUWK', 'NDAR_INV3WEDXU2N', 'NDAR_INV3XM1RJF0', 'NDAR_INV3XNH8EYM', 'NDAR_INV3XZ29M5N', 'NDAR_INV403JXRX5', 'NDAR_INV40TJWHRT', 'NDAR_INV415JRYRK', 'NDAR_INV4206Z9NR', 'NDAR_INV425E5RC6', 'NDAR_INV43VJ1LGA', 'NDAR_INV449WT7FF', 'NDAR_INV44AXV5FM', 'NDAR_INV455D0ZHD', 'NDAR_INV45BG08PF', 'NDAR_INV46KRVLWU', 'NDAR_INV47W6DHJC', 'NDAR_INV486GVBCD', 'NDAR_INV49D4EDYG', 'NDAR_INV49YTZ5Z4', 'NDAR_INV4AGK770Z', 'NDAR_INV4B0U03EB', 'NDAR_INV4BAP6ERL', 'NDAR_INV4EXN0D8M', 'NDAR_INV4GP0WREF', 'NDAR_INV4HGTZ5NC', 'NDAR_INV4HX3418K', 'NDAR_INV4JD9NHNZ', 'NDAR_INV4JHN1XBR', 'NDAR_INV4KMCR8NT', 'NDAR_INV4LPXB23R', 'NDAR_INV4NBKWH5N', 'NDAR_INV4P23T4RH', 'NDAR_INV4PR3H6ZL', 'NDAR_INV4R7NANWE', 'NDAR_INV4TL31WGV', 'NDAR_INV4UHNX2WX', 'NDAR_INV4UMAF27X', 'NDAR_INV4UZ9DDJH', 'NDAR_INV4V86FH9A', 'NDAR_INV4X6J651P', 'NDAR_INV4X8Z8RG2', 'NDAR_INV4Z6WBWBG', 'NDAR_INV50WZX903', 'NDAR_INV51EYYMEY', 'NDAR_INV51ZUKMA3', 'NDAR_INV52PY7ZPT', 'NDAR_INV53AA4BL6', 'NDAR_INV55R9F7EX', 'NDAR_INV56L41G67', 'NDAR_INV56VJH1CR', 'NDAR_INV57CKHLC8', 'NDAR_INV5B5DML2V', 'NDAR_INV5D3CU8ML', 'NDAR_INV5FUBUX54', 'NDAR_INV5G46RBZN', 'NDAR_INV5GUMR9J0', 'NDAR_INV5HRL29UE', 'NDAR_INV5K5J2WLT', 'NDAR_INV5KTH90ED', 'NDAR_INV5LC1UB7X', 'NDAR_INV5N40CFL4', 'NDAR_INV5NPRNJAU', 'NDAR_INV5UZDXP10', 'NDAR_INV5V2AYDE7', 'NDAR_INV5VT1M6LF', 'NDAR_INV5VTE9R5K', 'NDAR_INV5WHF8EZJ', 'NDAR_INV5XAZTBUE', 'NDAR_INV5XRG1L4T', 'NDAR_INV5XXGXM1J', 'NDAR_INV60VLVXBP', 'NDAR_INV61VX090A', 'NDAR_INV62G4MJ4G', 'NDAR_INV64GKZ96V', 'NDAR_INV654DRYXT', 'NDAR_INV65V1Z314', 'NDAR_INV66708YNC', 'NDAR_INV66PM75JX', 'NDAR_INV67CCUNV4', 'NDAR_INV67G76XVR', 'NDAR_INV67GRP56R', 'NDAR_INV67J2H4Y1', 'NDAR_INV68L0WZRB', 'NDAR_INV69EDG4B6', 'NDAR_INV69FLDML4', 'NDAR_INV6A2D8DTU', 'NDAR_INV6AP3YFJV', 'NDAR_INV6CG6LPAT', 'NDAR_INV6EPGL57G', 'NDAR_INV6F1LPHLT', 'NDAR_INV6F8VJE7N', 'NDAR_INV6G6L9NXY', 'NDAR_INV6J8LBGU5', 'NDAR_INV6JF8WUYT', 'NDAR_INV6JFY5LBB', 'NDAR_INV6JHNJJUB', 'NDAR_INV6JJFGCTX', 'NDAR_INV6KM1LGCB', 'NDAR_INV6L7B1GX3', 'NDAR_INV6LJL1R97', 'NDAR_INV6LW8CTG9', 'NDAR_INV6M1Y11R0', 'NDAR_INV6NLC9UXB', 'NDAR_INV6NUTPPMD', 'NDAR_INV6R35KCYL', 'NDAR_INV6TFE3J8G', 'NDAR_INV6TK12BD1', 'NDAR_INV6TM6V4CG', 'NDAR_INV6UH8VGPA', 'NDAR_INV6UWU0UNB', 'NDAR_INV6UZL0D8M', 'NDAR_INV6VUN0VRU', 'NDAR_INV6VXADERG', 'NDAR_INV6WV9X2KM', 'NDAR_INV6X19N1LP', 'NDAR_INV6YVYFKU7', 'NDAR_INV6ZNTXKJ1', 'NDAR_INV6ZU9NKBV', 'NDAR_INV70EJ1089', 'NDAR_INV725NP8B2', 'NDAR_INV72Z70LY1', 'NDAR_INV73D1F1AM', 'NDAR_INV753T05PF', 'NDAR_INV761E1JVD', 'NDAR_INV76NR9YPR', 'NDAR_INV78NACXBB', 'NDAR_INV78PUEPUH', 'NDAR_INV7AWGW55P', 'NDAR_INV7B2V2JHN', 'NDAR_INV7BKAZZAM', 'NDAR_INV7CBLTJJ7', 'NDAR_INV7CN47GEF', 'NDAR_INV7E36P23G', 'NDAR_INV7EGEBVF3', 'NDAR_INV7GDD5D2D', 'NDAR_INV7GGXBFFE', 'NDAR_INV7J8EL4J8', 'NDAR_INV7JGE0MH9', 'NDAR_INV7JUL4TDF', 'NDAR_INV7LM8LGFW', 'NDAR_INV7NW5NH08', 'NDAR_INV7PB55MX2', 'NDAR_INV7PLTAW3E', 'NDAR_INV7RC0NHDC', 'NDAR_INV7WT9690H', 'NDAR_INV7YFTLJBG', 'NDAR_INV804KTLAN', 'NDAR_INV80G295L2', 'NDAR_INV80VX1MHD', 'NDAR_INV81W1WMVM', 'NDAR_INV82L824HF', 'NDAR_INV82N7RUVA', 'NDAR_INV83VRUF8D', 'NDAR_INV840V0YZ8', 'NDAR_INV85UUUHN0', 'NDAR_INV87C4733D', 'NDAR_INV87L30DBB', 'NDAR_INV89DW5M7A', 'NDAR_INV8DPZJA7V', 'NDAR_INV8DZWHUBC', 'NDAR_INV8E3TY3RU', 'NDAR_INV8E597FU6', 'NDAR_INV8E73MX03', 'NDAR_INV8EUP2KFK', 'NDAR_INV8GWBGTAB', 'NDAR_INV8H3BZN6Y', 'NDAR_INV8J2PXGLH', 'NDAR_INV8JTY86NW', 'NDAR_INV8JX18G49', 'NDAR_INV8K9N9DEZ', 'NDAR_INV8LUWPYZD', 'NDAR_INV8P2LE3UR', 'NDAR_INV8PHEF3UH', 'NDAR_INV8RKX7N9Y', 'NDAR_INV8RNZXR25', 'NDAR_INV8U3ZUTV6', 'NDAR_INV8UF58XPK', 'NDAR_INV8Z3G152U', 'NDAR_INV8ZAB17TC', 'NDAR_INV90VTTWND', 'NDAR_INV92V7K5PE', 'NDAR_INV930295U4', 'NDAR_INV936LMN28', 'NDAR_INV9593TAX5', 'NDAR_INV95V2LL1W', 'NDAR_INV95YMR61C', 'NDAR_INV97DU7DER', 'NDAR_INV99GKTN71', 'NDAR_INV99YDLEBZ', 'NDAR_INV9B3TN6RL', 'NDAR_INV9BV5RW8J', 'NDAR_INV9C8A57E7', 'NDAR_INV9E57ARM0', 'NDAR_INV9FXBK9TB', 'NDAR_INV9G2Y1NLA', 'NDAR_INV9K086XZF', 'NDAR_INV9K1GRJNJ', 'NDAR_INV9K46ANFP', 'NDAR_INV9L5C55GA', 'NDAR_INV9L5C6ATX', 'NDAR_INV9LL2UB0W', 'NDAR_INV9LVF1WUU', 'NDAR_INV9NAL2XKV', 'NDAR_INV9NXP23VX', 'NDAR_INV9PTH3VTR', 'NDAR_INV9RRGXBK5', 'NDAR_INV9V1RV3V5', 'NDAR_INV9V46ARXX', 'NDAR_INV9X7P9U2G', 'NDAR_INV9XZMNP0M', 'NDAR_INV9YG9PWZ2', 'NDAR_INVA001KGR6', 'NDAR_INVA1MV25KY', 'NDAR_INVA354YMUE', 'NDAR_INVA3G711KJ', 'NDAR_INVA4ZHJBK2', 'NDAR_INVA5VVBY98', 'NDAR_INVA70NMJZP', 'NDAR_INVA79CZENJ', 'NDAR_INVA8U01EHN', 'NDAR_INVA9YBPFCH', 'NDAR_INVABCY2GG6', 'NDAR_INVABMADLKV', 'NDAR_INVAC8KCUT4', 'NDAR_INVADAE4UFN', 'NDAR_INVAF7KTP72', 'NDAR_INVAGGKXW0P', 'NDAR_INVAKCU5UPL', 'NDAR_INVAMW3EDUZ', 'NDAR_INVAP80JDC6', 'NDAR_INVAR03WFKR', 'NDAR_INVAU8LN4NP', 'NDAR_INVAV50Y5VX', 'NDAR_INVAVX9HYUA', 'NDAR_INVAW7EWP69', 'NDAR_INVAWBNZMY0', 'NDAR_INVAYDXW667', 'NDAR_INVAYHAR3EY', 'NDAR_INVAZ1GVXNZ', 'NDAR_INVAZPCL56V', 'NDAR_INVAZYU06NV', 'NDAR_INVB0DM4DHN', 'NDAR_INVB1BC9HDJ', 'NDAR_INVB1DEJVMN', 'NDAR_INVB1VMAP3Z', 'NDAR_INVB240DXDP', 'NDAR_INVB2KKR9FP', 'NDAR_INVB3L1EM9T', 'NDAR_INVB4Z249PC', 'NDAR_INVB5JG1BH6', 'NDAR_INVB6AN7YDK', 'NDAR_INVB6KYFLDG', 'NDAR_INVB7CBMZ2F', 'NDAR_INVB7CUAZH9', 'NDAR_INVB8W8N3ME', 'NDAR_INVBDGGPJYX', 'NDAR_INVBFZJK5R5', 'NDAR_INVBHCAN3DK', 'NDAR_INVBHNYRR2J', 'NDAR_INVBJM8ABYZ', 'NDAR_INVBJVF8AGB', 'NDAR_INVBKW33JVR', 'NDAR_INVBL9481WL', 'NDAR_INVBMTA765G', 'NDAR_INVBN6JZ5DM', 'NDAR_INVBPPLUGKN', 'NDAR_INVBR0R4R8M', 'NDAR_INVBR7WZCN7', 'NDAR_INVBTWHMF8T', 'NDAR_INVBUEAVPUZ', 'NDAR_INVBUEMRDVF', 'NDAR_INVBX1Z1PJ9', 'NDAR_INVBXDF81XE', 'NDAR_INVBXFPPY15', 'NDAR_INVBZ0JV1FF', 'NDAR_INVBZ1YG32T', 'NDAR_INVBZBW11DL', 'NDAR_INVBZZ8KWTC', 'NDAR_INVBZZGY7AM', 'NDAR_INVC4056LRF', 'NDAR_INVC624EF1K', 'NDAR_INVC6941X6U', 'NDAR_INVC6Z9A3VP', 'NDAR_INVC7BXUG5T', 'NDAR_INVC7HBE7N7', 'NDAR_INVC7P1CVEU', 'NDAR_INVC868NEEC', 'NDAR_INVCA6UX1YE', 'NDAR_INVCB3T3T8X', 'NDAR_INVCBV85UMM', 'NDAR_INVCFPMDG7R', 'NDAR_INVCGHNP1DK', 'NDAR_INVCGWV06JE', 'NDAR_INVCHUKUNFD', 'NDAR_INVCJW0RAFP', 'NDAR_INVCK8WM58K', 'NDAR_INVCL18941F', 'NDAR_INVCL748RXE', 'NDAR_INVCLPUR48B', 'NDAR_INVCNCRDPTT', 'NDAR_INVCP2K1NFW', 'NDAR_INVCP3Z2564', 'NDAR_INVCR43W7MR', 'NDAR_INVCTLNR3G6', 'NDAR_INVCU8GZZV0', 'NDAR_INVCU8K037T', 'NDAR_INVCW82V6Z6', 'NDAR_INVCX7YWXVP', 'NDAR_INVCXKTLU8R', 'NDAR_INVD0KNT1VK', 'NDAR_INVD1JBJBFY', 'NDAR_INVD1MG320G', 'NDAR_INVD25VAT32', 'NDAR_INVD5CN8JME', 'NDAR_INVD5DMDCCC', 'NDAR_INVD61Y7K88', 'NDAR_INVD9KW5TPY', 'NDAR_INVDB8CUMHL', 'NDAR_INVDC5LMY0K', 'NDAR_INVDCMB3LBH', 'NDAR_INVDCTNAUD1', 'NDAR_INVDDRLC2VM', 'NDAR_INVDEHN3PH7', 'NDAR_INVDG2H0995', 'NDAR_INVDGABKCP2', 'NDAR_INVDGVADKJA', 'NDAR_INVDHCAPYWM', 'NDAR_INVDKM7MF67', 'NDAR_INVDMT3XG3K', 'NDAR_INVDNGGNJ0B', 'NDAR_INVDR13TAXK', 'NDAR_INVDRM97DTU', 'NDAR_INVDU5LEWJK', 'NDAR_INVDV9BCU3J', 'NDAR_INVDZ1H9HTE', 'NDAR_INVDZLD38UM', 'NDAR_INVE1HR9837', 'NDAR_INVE1TKY79C', 'NDAR_INVE2GEK6A2', 'NDAR_INVE30MYZ4J', 'NDAR_INVEBVXV8NL', 'NDAR_INVEDNJMEJ0', 'NDAR_INVEEY2XYCA', 'NDAR_INVEGCJDH40', 'NDAR_INVEGFEVU9N', 'NDAR_INVEHLY59UX', 'NDAR_INVEK7BN7Y4', 'NDAR_INVEKL2JGYR', 'NDAR_INVEL2DU62H', 'NDAR_INVEPMR7U1F', 'NDAR_INVETDKN8PJ', 'NDAR_INVETNDZFKZ', 'NDAR_INVEU57U8JH', 'NDAR_INVEXAKNZ18', 'NDAR_INVEZNGVY2P', 'NDAR_INVF1W2BTPZ', 'NDAR_INVF20K5ZNE', 'NDAR_INVF2BU99TZ', 'NDAR_INVF4ADZ6G5', 'NDAR_INVF5JET3CJ', 'NDAR_INVF6JGRMBP', 'NDAR_INVF72J4L68', 'NDAR_INVF76ZR3W2', 'NDAR_INVF894WBDM', 'NDAR_INVF9RGL53U', 'NDAR_INVFAMRPW9C', 'NDAR_INVFBE9UXHG', 'NDAR_INVFBU7WX6N', 'NDAR_INVFCN9U0EN', 'NDAR_INVFCVZ66WZ', 'NDAR_INVFD34548C', 'NDAR_INVFDM0PRRA', 'NDAR_INVFJP3171C', 'NDAR_INVFMV7H24T', 'NDAR_INVFPDTRJ0D', 'NDAR_INVFPDZ4C13', 'NDAR_INVFR9N4HJY', 'NDAR_INVFX6XRA82', 'NDAR_INVFXG8Z1V0', 'NDAR_INVFYK5K5G7', 'NDAR_INVFYRF6WEB', 'NDAR_INVFZ4F6Y38', 'NDAR_INVG1ZW4G9G', 'NDAR_INVG269TZZX', 'NDAR_INVG2ZHGAWM', 'NDAR_INVG3RUAU07', 'NDAR_INVG4VG9M98', 'NDAR_INVG5PC310E', 'NDAR_INVG5V1RRFA', 'NDAR_INVG6JBZCCD', 'NDAR_INVG6NB3W1G', 'NDAR_INVG6VRYVW0', 'NDAR_INVG7B119A0', 'NDAR_INVG7UNU2FK', 'NDAR_INVG8VWC1TN', 'NDAR_INVG957G5YY', 'NDAR_INVG9JGX9UJ', 'NDAR_INVGAKHNMUU', 'NDAR_INVGBLP7T9X', 'NDAR_INVGC7NCM6H', 'NDAR_INVGCDY2UAK', 'NDAR_INVGEHCD5AD', 'NDAR_INVGFDT73J0', 'NDAR_INVGJ27R29V', 'NDAR_INVGK9ACPNW', 'NDAR_INVGM5D6MEW', 'NDAR_INVGNPD1GHE', 'NDAR_INVGPBCUX69', 'NDAR_INVGPH4CP68', 'NDAR_INVGPPUVVG4', 'NDAR_INVGR764V5U', 'NDAR_INVGRREN8LB', 'NDAR_INVGU0XFNRV', 'NDAR_INVGU3ADLYR', 'NDAR_INVGY65ZZBV', 'NDAR_INVGZJDGF8L', 'NDAR_INVH13A9F8Y', 'NDAR_INVH1CG52NU', 'NDAR_INVH2D9U5VD', 'NDAR_INVH3MZK10Z', 'NDAR_INVH5LXH4TB', 'NDAR_INVH68T1XNB', 'NDAR_INVH6HR9EFA', 'NDAR_INVH6MRC067', 'NDAR_INVH8FRH0UK', 'NDAR_INVH8J67ZNJ', 'NDAR_INVH93P807E', 'NDAR_INVH9ERT7NK', 'NDAR_INVH9F934NE', 'NDAR_INVHBGX2W56', 'NDAR_INVHBJ3VGCL', 'NDAR_INVHCCKW1DN', 'NDAR_INVHEABF6E2', 'NDAR_INVHEFP90K0', 'NDAR_INVHFVEFR9P', 'NDAR_INVHHJXG6Y1', 'NDAR_INVHJX7K8DN', 'NDAR_INVHKU16664', 'NDAR_INVHLRR37FK', 'NDAR_INVHM2TZ8AD', 'NDAR_INVHNPRTMWZ', 'NDAR_INVHPDP068K', 'NDAR_INVHPU5TKNF', 'NDAR_INVHRUJUH1Y', 'NDAR_INVHTAJ1KYF', 'NDAR_INVHTJ14CBX', 'NDAR_INVHU2D03RY', 'NDAR_INVHVHF0RNR', 'NDAR_INVHVYR57F5', 'NDAR_INVHW0PPV8W', 'NDAR_INVHX5PF696', 'NDAR_INVHXY3TJAT', 'NDAR_INVHY2NHY1C', 'NDAR_INVHYYUJ7LR', 'NDAR_INVJ1KU80DP', 'NDAR_INVJ27L7Z6G', 'NDAR_INVJ8DDAHGM', 'NDAR_INVJA30PYR6', 'NDAR_INVJAEV7W5J', 'NDAR_INVJDVAX1CU', 'NDAR_INVJDVWTD1U', 'NDAR_INVJDW2699M', 'NDAR_INVJF6WB6AE', 'NDAR_INVJFC8DZWR', 'NDAR_INVJGLXMGJ2', 'NDAR_INVJJA0RAMJ', 'NDAR_INVJJMH6ZDT', 'NDAR_INVJJWR8W0W', 'NDAR_INVJKFPZ3RV', 'NDAR_INVJKLMWC89', 'NDAR_INVJN2G8BWG', 'NDAR_INVJNMHDYGC', 'NDAR_INVJP6F56RT', 'NDAR_INVJPMRY00B', 'NDAR_INVJTHY8P5B', 'NDAR_INVJWJBC1ZT', 'NDAR_INVJXWWYJBU', 'NDAR_INVJZ4637B3', 'NDAR_INVJZJBNBTC', 'NDAR_INVJZRV5ANP', 'NDAR_INVK0AGJP6T', 'NDAR_INVK0BCW5KA', 'NDAR_INVK1HM1MWX', 'NDAR_INVK2KFJDG5', 'NDAR_INVK3DP06LU', 'NDAR_INVK3GPV04G', 'NDAR_INVK52CCTV1', 'NDAR_INVK5PEZ25W', 'NDAR_INVKA96L5HC', 'NDAR_INVKC7ZC9VX', 'NDAR_INVKD35P0MA', 'NDAR_INVKEP4C8W8', 'NDAR_INVKFWP4PX0', 'NDAR_INVKH00C10R', 'NDAR_INVKJ30FGZ0', 'NDAR_INVKJDAZKWA', 'NDAR_INVKMBXR817', 'NDAR_INVKNU295WW', 'NDAR_INVKPF9HV5P', 'NDAR_INVKX4KKBJR', 'NDAR_INVKY6C0Z8R', 'NDAR_INVKZBPXM31', 'NDAR_INVKZDAJBD7', 'NDAR_INVL36MC5HJ', 'NDAR_INVL3DBH8WC', 'NDAR_INVL3G13U80', 'NDAR_INVL3ND8XCF', 'NDAR_INVL49379MF', 'NDAR_INVL5H2T7U1', 'NDAR_INVL7GB4EN0', 'NDAR_INVL8KH6W61', 'NDAR_INVL8XGD85G', 'NDAR_INVLABCHP1W', 'NDAR_INVLCMDKYGP', 'NDAR_INVLDP9RT86', 'NDAR_INVLEND5NH4', 'NDAR_INVLEXJMGJ6', 'NDAR_INVLF41F4P6', 'NDAR_INVLHZWGFNU', 'NDAR_INVLJ8NB5W8', 'NDAR_INVLKWJTNDZ', 'NDAR_INVLM20BZVZ', 'NDAR_INVLN3392MM', 'NDAR_INVLNN6NNXL', 'NDAR_INVLTFB8A3W', 'NDAR_INVLVLHRL2N', 'NDAR_INVLZ0K08DY', 'NDAR_INVLZ0V0F16', 'NDAR_INVM007MAX7', 'NDAR_INVM05MNA24', 'NDAR_INVM0JXYXX1', 'NDAR_INVM1Z5GMD8', 'NDAR_INVM22URD3V', 'NDAR_INVM2KHVT0W', 'NDAR_INVM3LU9WJM', 'NDAR_INVM42YXRFM', 'NDAR_INVM6LGY18K', 'NDAR_INVM6LR22KX', 'NDAR_INVM91VXF6F', 'NDAR_INVMAV6PR9W', 'NDAR_INVMAZCGHX3', 'NDAR_INVMBU1DVRD', 'NDAR_INVMBVX3LHF', 'NDAR_INVMBWU2HEK', 'NDAR_INVMC7UEVZ1', 'NDAR_INVMCB04PW4', 'NDAR_INVMG3RV4AC', 'NDAR_INVMGH9KKCV', 'NDAR_INVMHPNB2Y1', 'NDAR_INVMJH6HGA1', 'NDAR_INVMJMFXLDG', 'NDAR_INVMK9LW258', 'NDAR_INVMKWH6RED', 'NDAR_INVMNXPRD46', 'NDAR_INVMT5RKZJE', 'NDAR_INVMTVMYZRH', 'NDAR_INVMTYZ9AH4', 'NDAR_INVMV3VPRF1', 'NDAR_INVMVF87GGA', 'NDAR_INVMWWHDNLT', 'NDAR_INVMX57RL1Z', 'NDAR_INVMXNTYPJU', 'NDAR_INVMY1X0RAH', 'NDAR_INVMY85AAAM', 'NDAR_INVMY8D3KPH', 'NDAR_INVMYKKZ4YM', 'NDAR_INVMZCA5Y9V', 'NDAR_INVN12PKFR0', 'NDAR_INVN2RB0FR4', 'NDAR_INVN394NEWK', 'NDAR_INVN3M2X26L', 'NDAR_INVN7FKUA2W', 'NDAR_INVNAJWH1CY', 'NDAR_INVNBMXW84N', 'NDAR_INVNDCR1NEX', 'NDAR_INVNDF1FD44', 'NDAR_INVNF2BWTVU', 'NDAR_INVNHZ8AAGB', 'NDAR_INVNJA6E3NT', 'NDAR_INVNKBLG2AL', 'NDAR_INVNKCFGVZJ', 'NDAR_INVNLN4DGBT', 'NDAR_INVNMRZ1KX9', 'NDAR_INVNTJ9TYDB', 'NDAR_INVNTUN0CBE', 'NDAR_INVNU5J10B7', 'NDAR_INVNUAGE7UT', 'NDAR_INVNUWHUFKV', 'NDAR_INVNVY8KZHN', 'NDAR_INVNWUL0U0R', 'NDAR_INVNXXDV17F', 'NDAR_INVNY9E48W0', 'NDAR_INVNZ90MJVP', 'NDAR_INVP0T27M9X', 'NDAR_INVP0UWHEP8', 'NDAR_INVP12BW7UL', 'NDAR_INVP1BMWBHG', 'NDAR_INVP38LCGFT', 'NDAR_INVP4GPP1NB', 'NDAR_INVP5A0M52P', 'NDAR_INVP8CFEA4E', 'NDAR_INVP9Y862GP', 'NDAR_INVPATP4RPG', 'NDAR_INVPBJTJ0Z3', 'NDAR_INVPBZ6UJ07', 'NDAR_INVPCJWLN13', 'NDAR_INVPDUZETD1', 'NDAR_INVPEUL1TU6', 'NDAR_INVPHL3D8GY', 'NDAR_INVPL13NA8X', 'NDAR_INVPRLTKVRN', 'NDAR_INVPVV52NXT', 'NDAR_INVPZDDVLGW', 'NDAR_INVR17JHZRJ', 'NDAR_INVR1JFZ70M', 'NDAR_INVR44LUUEZ', 'NDAR_INVR5EGEA6F', 'NDAR_INVR674GFP1', 'NDAR_INVR6YF8VHD', 'NDAR_INVRBNRB1DU', 'NDAR_INVRCHTJK34', 'NDAR_INVRCYZ8XZ2', 'NDAR_INVRDCUTM3A', 'NDAR_INVRDJX9GRE', 'NDAR_INVRE48TR17', 'NDAR_INVRF2ULZ5M', 'NDAR_INVRH8MPM9J', 'NDAR_INVRJ67E8PE', 'NDAR_INVRLETBAV0', 'NDAR_INVRMNMGK77', 'NDAR_INVRPYECTJJ', 'NDAR_INVRREA9RBW', 'NDAR_INVRUTNUAYE', 'NDAR_INVRVKDMDNH', 'NDAR_INVRW8117A7', 'NDAR_INVRWKJCGLY', 'NDAR_INVRXX916ZV', 'NDAR_INVRYT5RRUM', 'NDAR_INVRZL7PGK1', 'NDAR_INVT0JW7H6Z', 'NDAR_INVT1GJUT9Z', 'NDAR_INVT2JJ42KE', 'NDAR_INVT2YYABW3', 'NDAR_INVT3366FHE', 'NDAR_INVT4BR7L9V', 'NDAR_INVT4M6PYF4', 'NDAR_INVT77R03AY', 'NDAR_INVT7YJ84MG', 'NDAR_INVT9LPE86G', 'NDAR_INVTAP6MEMP', 'NDAR_INVTAX3MN8C', 'NDAR_INVTBL4GD57', 'NDAR_INVTEL34457', 'NDAR_INVTEL35LKE', 'NDAR_INVTFFA4ZV9', 'NDAR_INVTGZMNV19', 'NDAR_INVTHHL55VX', 'NDAR_INVTM2HD1WE', 'NDAR_INVTUL4PTMG', 'NDAR_INVTVGMJRN9', 'NDAR_INVU11WDWJ8', 'NDAR_INVU3XLKGUY', 'NDAR_INVU4BNFAZ9', 'NDAR_INVU5990AMT', 'NDAR_INVU7276YU8', 'NDAR_INVU7CWXB8U', 'NDAR_INVU7KXR08V', 'NDAR_INVU7WWPJ0M', 'NDAR_INVU8N8L34K', 'NDAR_INVU8VT1VB7', 'NDAR_INVU9ATRA9G', 'NDAR_INVU9C9ZMKM', 'NDAR_INVUA1GPU5Z', 'NDAR_INVUB4XK4NV', 'NDAR_INVUC80Z0WM', 'NDAR_INVUCF0VBM1', 'NDAR_INVUDKZF62L', 'NDAR_INVUF504K04', 'NDAR_INVUJBHEFTN', 'NDAR_INVUM3L54UF', 'NDAR_INVUNZGDT4H', 'NDAR_INVURLHBMZV', 'NDAR_INVUTEVFWND', 'NDAR_INVUTNUNCNK', 'NDAR_INVUTVE98D5', 'NDAR_INVUU88CLEU', 'NDAR_INVUW2UNAF1', 'NDAR_INVUWH8GXXW', 'NDAR_INVUXMACPNB', 'NDAR_INVUXWUN73X', 'NDAR_INVUYF7JD2E', 'NDAR_INVUYYEU9U1', 'NDAR_INVUZAZXB08', 'NDAR_INVV0BMK3B8', 'NDAR_INVV0ZY5YPC', 'NDAR_INVV18ZDFDE', 'NDAR_INVV1F9DNZH', 'NDAR_INVV23Y1DXH', 'NDAR_INVV37537GK', 'NDAR_INVV4YEXL6X', 'NDAR_INVV5X50ZMM', 'NDAR_INVV82L1HKC', 'NDAR_INVV874VP8D', 'NDAR_INVV8VK6RGX', 'NDAR_INVVE6MPVNE', 'NDAR_INVVF539UBP', 'NDAR_INVVFMTWZYL', 'NDAR_INVVH22P5KB', 'NDAR_INVVKV5RYVF', 'NDAR_INVVLGAGC69', 'NDAR_INVVMT7W436', 'NDAR_INVVNAD3K0E', 'NDAR_INVVR6JW9BP', 'NDAR_INVVU46PA0Y', 'NDAR_INVVV34F5T4', 'NDAR_INVVV5V2KXY', 'NDAR_INVVWUJFEDA', 'NDAR_INVVXH1GGXB', 'NDAR_INVVZYWE87C', 'NDAR_INVW01VXPJ7', 'NDAR_INVW04H37XX', 'NDAR_INVW0KP5575', 'NDAR_INVW1A2WG25', 'NDAR_INVW26YNDL9', 'NDAR_INVW3N4MZ02', 'NDAR_INVW58F28HT', 'NDAR_INVWAN79D4A', 'NDAR_INVWBAKLX5Y', 'NDAR_INVWCLDPKM7', 'NDAR_INVWELLZDW7', 'NDAR_INVWF9BVBHA', 'NDAR_INVWFEJT579', 'NDAR_INVWFN7K4C5', 'NDAR_INVWGYVBK6Y', 'NDAR_INVWM3FXZ4K', 'NDAR_INVWMKYTKBW', 'NDAR_INVWNKZ5Z4L', 'NDAR_INVWRETMHUC', 'NDAR_INVWT771AK6', 'NDAR_INVWTBPTVCP', 'NDAR_INVWVA9UY95', 'NDAR_INVWVM2TW33', 'NDAR_INVWWCYEZFZ', 'NDAR_INVWX3C6A3L', 'NDAR_INVWX4KD0B0', 'NDAR_INVWXGW2FKF', 'NDAR_INVX1697BGE', 'NDAR_INVX1VBLPUJ', 'NDAR_INVX4UYLW56', 'NDAR_INVX5JWUPX9', 'NDAR_INVX618B7BH', 'NDAR_INVX64TGR65', 'NDAR_INVX8D3JRAH', 'NDAR_INVXAPU8YP9', 'NDAR_INVXC2KNGXL', 'NDAR_INVXD6CMU32', 'NDAR_INVXDHB28GU', 'NDAR_INVXDX3UAWF', 'NDAR_INVXEKGFN5Y', 'NDAR_INVXF2K4GPX', 'NDAR_INVXFL1L8T2', 'NDAR_INVXFVACT0F', 'NDAR_INVXGFXPB14', 'NDAR_INVXGWM6XC8', 'NDAR_INVXHEEJUKX', 'NDAR_INVXHG35TUW', 'NDAR_INVXHJBRK7N', 'NDAR_INVXJGP5M4G', 'NDAR_INVXPE3RPDD', 'NDAR_INVXRNZ5M92', 'NDAR_INVXU8K8M57', 'NDAR_INVXUPMEDT7', 'NDAR_INVXVRX9PPJ', 'NDAR_INVXYLTMBZ9', 'NDAR_INVXZRDA6CJ', 'NDAR_INVY43C1PAY', 'NDAR_INVY463K3XX', 'NDAR_INVY5AB6YNB', 'NDAR_INVY61UGEKY', 'NDAR_INVY6FE3R8A', 'NDAR_INVY6HBB319', 'NDAR_INVYAEE6YN4', 'NDAR_INVYD28H9BD', 'NDAR_INVYE9WDNR8', 'NDAR_INVYEMHGFJF', 'NDAR_INVYH3BPP7A', 'NDAR_INVYJK3Y0NG', 'NDAR_INVYJPCD59K', 'NDAR_INVYNANG2B0', 'NDAR_INVYRYJZLMX', 'NDAR_INVYTRMF19U', 'NDAR_INVYUKDPT70', 'NDAR_INVYWCJTCBW', 'NDAR_INVYYA36DVL', 'NDAR_INVZ1WZW3KT', 'NDAR_INVZ2REMPTW', 'NDAR_INVZ3638RYW', 'NDAR_INVZ49U1FM4', 'NDAR_INVZ4KYLWJA', 'NDAR_INVZ4V1KGHH', 'NDAR_INVZ6TDLDW8', 'NDAR_INVZ6Z9DP4H', 'NDAR_INVZ6ZN350N', 'NDAR_INVZ7FR6UCT', 'NDAR_INVZ7U9A2DX', 'NDAR_INVZ8U8TUHJ', 'NDAR_INVZ95YA2ZB', 'NDAR_INVZDYWHP34', 'NDAR_INVZF8VDCAF', 'NDAR_INVZJUBWF3F', 'NDAR_INVZLD0PANU', 'NDAR_INVZM2Y9JCA', 'NDAR_INVZNW7Y6R1', 'NDAR_INVZP45TZD9', 'NDAR_INVZP49GXF4', 'NDAR_INVZPR155DG', 'NDAR_INVZR9NMJBR', 'NDAR_INVZT1J0KUC', 'NDAR_INVZTTGYL51', 'NDAR_INVZUGVEHNK', 'NDAR_INVZWRJXNHE', 'NDAR_INVZXL47HRG', 'NDAR_INVZYRTFYRP', 'NDAR_INVZZZNB0XC']\n",
      "Number of subject IDs w demo_ethn_v2 == 1 AND Y4 demographic data : N = 956\n"
     ]
    }
   ],
   "source": [
    "# clean up the demo files\n",
    "file_names = [\n",
    "    \"abcd_y_lt_subset.csv\",\n",
    "    \"abcd_p_demo_subset.csv\",\n",
    "]\n",
    "\n",
    "# Array to store subject IDs if demo_ethn_v2 == 1 AND year 4 data available\n",
    "subject_ids = []\n",
    "\n",
    "# Loop over each file name\n",
    "for file_name in file_names:\n",
    "    # Define the path to the CSV file\n",
    "    file_path = op.join(csv_sub_dir, file_name)\n",
    "\n",
    "    # Load the CSV file into a DataFrame\n",
    "    df = pd.read_csv(file_path)\n",
    "\n",
    "    # Forward fill within each 'src_subject_id' group\n",
    "    df = df.set_index(\"src_subject_id\").groupby(\"src_subject_id\").ffill().reset_index()\n",
    "\n",
    "    # Filter rows where 'eventname' is '4_year_follow_up_y_arm_1'\n",
    "    df = df[df[\"eventname\"] == \"4_year_follow_up_y_arm_1\"]\n",
    "\n",
    "    # Additional filtering for the 'abcd_p_demo_subset.csv' file\n",
    "    if file_name == \"abcd_p_demo_subset.csv\":\n",
    "        df = df[df[\"demo_ethn_v2\"] == 1]\n",
    "\n",
    "        # Save the subject IDs to the array\n",
    "        subject_ids = df[\"src_subject_id\"].tolist()\n",
    "\n",
    "    # Reset index and ensure 'src_subject_id' is preserved\n",
    "    df = df.reset_index(drop=True)\n",
    "\n",
    "    # Save the processed DataFrame\n",
    "    df.to_csv(file_path, index=False)\n",
    "\n",
    "# Print the array of subject IDs\n",
    "print(\"Filtered subject IDs:\", subject_ids)\n",
    "\n",
    "# Print the count of subject IDs\n",
    "print(\n",
    "    f\"Number of subject IDs w demo_ethn_v2 == 1 AND Y4 demographic data : N = {len(subject_ids)}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique subject IDs with RS data: 589\n",
      "Subject IDs with RS data: ['NDAR_INV028D3ELL', 'NDAR_INV030W95VP', 'NDAR_INV056V9RRJ', 'NDAR_INV06DX0E7R', 'NDAR_INV0889M0JE', 'NDAR_INV09T2EBX4', 'NDAR_INV0AHJHA5D', 'NDAR_INV0DC9BJZK', 'NDAR_INV0DJJP33V', 'NDAR_INV0DKWEM1A', 'NDAR_INV0EXY6KFW', 'NDAR_INV0FUC15ZB', 'NDAR_INV0G2N59GL', 'NDAR_INV0G42TBW1', 'NDAR_INV0MPBK7TU', 'NDAR_INV0PDYPPJ7', 'NDAR_INV0RHLKA9M', 'NDAR_INV0T7889MV', 'NDAR_INV0U23M45G', 'NDAR_INV0UERLJJY', 'NDAR_INV0W00RU2X', 'NDAR_INV0W4WA8RV', 'NDAR_INV114VT9DX', 'NDAR_INV137Y0WR8', 'NDAR_INV145EPB4G', 'NDAR_INV14UJARAJ', 'NDAR_INV15YZBNE0', 'NDAR_INV1AFNKM8M', 'NDAR_INV1B7G7LND', 'NDAR_INV1BGRYN2J', 'NDAR_INV1CC6HB4A', 'NDAR_INV1CMWHGWK', 'NDAR_INV1DDX454E', 'NDAR_INV1ET4JE3B', 'NDAR_INV1FT79EHP', 'NDAR_INV1H7JEJW1', 'NDAR_INV1HKCTUW1', 'NDAR_INV1KZTEZF5', 'NDAR_INV1LT8UZYC', 'NDAR_INV1MYF7VFL', 'NDAR_INV1NV489TJ', 'NDAR_INV1P2BB9HY', 'NDAR_INV1PX9GGJP', 'NDAR_INV1R36G2UH', 'NDAR_INV1R4G3839', 'NDAR_INV1VCFJ6CR', 'NDAR_INV1ZZAPY9K', 'NDAR_INV22E7G6BN', 'NDAR_INV23E4LDJX', 'NDAR_INV24Z1RUE9', 'NDAR_INV263A3T6P', 'NDAR_INV273EJWMX', 'NDAR_INV2AD1P5NV', 'NDAR_INV2B7X981Z', 'NDAR_INV2CK4MB02', 'NDAR_INV2CTVWFH6', 'NDAR_INV2E77XGT6', 'NDAR_INV2FV7FT02', 'NDAR_INV2G3T7XUL', 'NDAR_INV2H0H09CW', 'NDAR_INV2MH3HPHP', 'NDAR_INV2VJF8M3C', 'NDAR_INV2X040T48', 'NDAR_INV2X7YEHPB', 'NDAR_INV2XAHZZ2A', 'NDAR_INV2XGC3LN5', 'NDAR_INV302FDTJF', 'NDAR_INV3062KMFL', 'NDAR_INV35UXPP43', 'NDAR_INV39R1KUT6', 'NDAR_INV3BL9Z315', 'NDAR_INV3CAV3A8F', 'NDAR_INV3CR6NXBG', 'NDAR_INV3DG9GH8G', 'NDAR_INV3GZNM6F8', 'NDAR_INV3HJUHZX1', 'NDAR_INV3HV5ZJLJ', 'NDAR_INV3JUUH0FL', 'NDAR_INV3MXVVRKB', 'NDAR_INV3N6L8Z9U', 'NDAR_INV3R6TY43Y', 'NDAR_INV3RTWYZX5', 'NDAR_INV3TUE7DV5', 'NDAR_INV3UPL5YML', 'NDAR_INV3WEDXU2N', 'NDAR_INV403JXRX5', 'NDAR_INV40TJWHRT', 'NDAR_INV43VJ1LGA', 'NDAR_INV455D0ZHD', 'NDAR_INV45BG08PF', 'NDAR_INV47W6DHJC', 'NDAR_INV486GVBCD', 'NDAR_INV4AGK770Z', 'NDAR_INV4B0U03EB', 'NDAR_INV4EXN0D8M', 'NDAR_INV4GP0WREF', 'NDAR_INV4HX3418K', 'NDAR_INV4JD9NHNZ', 'NDAR_INV4JHN1XBR', 'NDAR_INV4LPXB23R', 'NDAR_INV4PR3H6ZL', 'NDAR_INV4UMAF27X', 'NDAR_INV4V86FH9A', 'NDAR_INV4X6J651P', 'NDAR_INV50WZX903', 'NDAR_INV51EYYMEY', 'NDAR_INV51ZUKMA3', 'NDAR_INV52PY7ZPT', 'NDAR_INV53AA4BL6', 'NDAR_INV56L41G67', 'NDAR_INV57CKHLC8', 'NDAR_INV5B5DML2V', 'NDAR_INV5D3CU8ML', 'NDAR_INV5G46RBZN', 'NDAR_INV5GUMR9J0', 'NDAR_INV5K5J2WLT', 'NDAR_INV5N40CFL4', 'NDAR_INV5NPRNJAU', 'NDAR_INV5UZDXP10', 'NDAR_INV5V2AYDE7', 'NDAR_INV5WHF8EZJ', 'NDAR_INV5XAZTBUE', 'NDAR_INV60VLVXBP', 'NDAR_INV62G4MJ4G', 'NDAR_INV64GKZ96V', 'NDAR_INV654DRYXT', 'NDAR_INV65V1Z314', 'NDAR_INV66708YNC', 'NDAR_INV67G76XVR', 'NDAR_INV67GRP56R', 'NDAR_INV69EDG4B6', 'NDAR_INV69FLDML4', 'NDAR_INV6A2D8DTU', 'NDAR_INV6AP3YFJV', 'NDAR_INV6CG6LPAT', 'NDAR_INV6EPGL57G', 'NDAR_INV6F1LPHLT', 'NDAR_INV6F8VJE7N', 'NDAR_INV6G6L9NXY', 'NDAR_INV6J8LBGU5', 'NDAR_INV6JF8WUYT', 'NDAR_INV6JHNJJUB', 'NDAR_INV6JJFGCTX', 'NDAR_INV6KM1LGCB', 'NDAR_INV6L7B1GX3', 'NDAR_INV6M1Y11R0', 'NDAR_INV6NLC9UXB', 'NDAR_INV6NUTPPMD', 'NDAR_INV6R35KCYL', 'NDAR_INV6TFE3J8G', 'NDAR_INV6UH8VGPA', 'NDAR_INV6UWU0UNB', 'NDAR_INV6UZL0D8M', 'NDAR_INV6VXADERG', 'NDAR_INV6WV9X2KM', 'NDAR_INV6YVYFKU7', 'NDAR_INV6ZNTXKJ1', 'NDAR_INV6ZU9NKBV', 'NDAR_INV70EJ1089', 'NDAR_INV725NP8B2', 'NDAR_INV72Z70LY1', 'NDAR_INV73D1F1AM', 'NDAR_INV753T05PF', 'NDAR_INV761E1JVD', 'NDAR_INV76NR9YPR', 'NDAR_INV78NACXBB', 'NDAR_INV7AWGW55P', 'NDAR_INV7BKAZZAM', 'NDAR_INV7CBLTJJ7', 'NDAR_INV7CN47GEF', 'NDAR_INV7GDD5D2D', 'NDAR_INV7J8EL4J8', 'NDAR_INV7JUL4TDF', 'NDAR_INV7LM8LGFW', 'NDAR_INV7NW5NH08', 'NDAR_INV7PB55MX2', 'NDAR_INV7RC0NHDC', 'NDAR_INV7WT9690H', 'NDAR_INV7YFTLJBG', 'NDAR_INV82N7RUVA', 'NDAR_INV83VRUF8D', 'NDAR_INV840V0YZ8', 'NDAR_INV85UUUHN0', 'NDAR_INV89DW5M7A', 'NDAR_INV8DPZJA7V', 'NDAR_INV8DZWHUBC', 'NDAR_INV8E3TY3RU', 'NDAR_INV8E73MX03', 'NDAR_INV8EUP2KFK', 'NDAR_INV8GWBGTAB', 'NDAR_INV8JTY86NW', 'NDAR_INV8LUWPYZD', 'NDAR_INV8RNZXR25', 'NDAR_INV8UF58XPK', 'NDAR_INV8Z3G152U', 'NDAR_INV8ZAB17TC', 'NDAR_INV92V7K5PE', 'NDAR_INV930295U4', 'NDAR_INV936LMN28', 'NDAR_INV9593TAX5', 'NDAR_INV95V2LL1W', 'NDAR_INV95YMR61C', 'NDAR_INV97DU7DER', 'NDAR_INV99YDLEBZ', 'NDAR_INV9B3TN6RL', 'NDAR_INV9BV5RW8J', 'NDAR_INV9C8A57E7', 'NDAR_INV9E57ARM0', 'NDAR_INV9FXBK9TB', 'NDAR_INV9G2Y1NLA', 'NDAR_INV9K086XZF', 'NDAR_INV9K1GRJNJ', 'NDAR_INV9L5C55GA', 'NDAR_INV9L5C6ATX', 'NDAR_INV9LVF1WUU', 'NDAR_INV9RRGXBK5', 'NDAR_INV9V46ARXX', 'NDAR_INV9X7P9U2G', 'NDAR_INV9XZMNP0M', 'NDAR_INV9YG9PWZ2', 'NDAR_INVA354YMUE', 'NDAR_INVA3G711KJ', 'NDAR_INVA79CZENJ', 'NDAR_INVA8U01EHN', 'NDAR_INVABCY2GG6', 'NDAR_INVABMADLKV', 'NDAR_INVAC8KCUT4', 'NDAR_INVADAE4UFN', 'NDAR_INVAF7KTP72', 'NDAR_INVAGGKXW0P', 'NDAR_INVAKCU5UPL', 'NDAR_INVAR03WFKR', 'NDAR_INVAVX9HYUA', 'NDAR_INVAW7EWP69', 'NDAR_INVAYHAR3EY', 'NDAR_INVAZ1GVXNZ', 'NDAR_INVAZPCL56V', 'NDAR_INVAZYU06NV', 'NDAR_INVB1BC9HDJ', 'NDAR_INVB1DEJVMN', 'NDAR_INVB1VMAP3Z', 'NDAR_INVB2KKR9FP', 'NDAR_INVB3L1EM9T', 'NDAR_INVB5JG1BH6', 'NDAR_INVB6AN7YDK', 'NDAR_INVB7CBMZ2F', 'NDAR_INVB7CUAZH9', 'NDAR_INVBHCAN3DK', 'NDAR_INVBL9481WL', 'NDAR_INVBN6JZ5DM', 'NDAR_INVBPPLUGKN', 'NDAR_INVBR0R4R8M', 'NDAR_INVBR7WZCN7', 'NDAR_INVBTWHMF8T', 'NDAR_INVBX1Z1PJ9', 'NDAR_INVBXDF81XE', 'NDAR_INVBXFPPY15', 'NDAR_INVBZ1YG32T', 'NDAR_INVBZZ8KWTC', 'NDAR_INVC624EF1K', 'NDAR_INVC7BXUG5T', 'NDAR_INVC868NEEC', 'NDAR_INVCA6UX1YE', 'NDAR_INVCGWV06JE', 'NDAR_INVCHUKUNFD', 'NDAR_INVCK8WM58K', 'NDAR_INVCL748RXE', 'NDAR_INVCLPUR48B', 'NDAR_INVCP2K1NFW', 'NDAR_INVCP3Z2564', 'NDAR_INVCR43W7MR', 'NDAR_INVCU8GZZV0', 'NDAR_INVCU8K037T', 'NDAR_INVCW82V6Z6', 'NDAR_INVCX7YWXVP', 'NDAR_INVCXKTLU8R', 'NDAR_INVD1JBJBFY', 'NDAR_INVD1MG320G', 'NDAR_INVD25VAT32', 'NDAR_INVD5CN8JME', 'NDAR_INVD5DMDCCC', 'NDAR_INVD61Y7K88', 'NDAR_INVD9KW5TPY', 'NDAR_INVDC5LMY0K', 'NDAR_INVDDRLC2VM', 'NDAR_INVDEHN3PH7', 'NDAR_INVDG2H0995', 'NDAR_INVDGVADKJA', 'NDAR_INVDHCAPYWM', 'NDAR_INVDRM97DTU', 'NDAR_INVDU5LEWJK', 'NDAR_INVDV9BCU3J', 'NDAR_INVDZLD38UM', 'NDAR_INVE2GEK6A2', 'NDAR_INVEBVXV8NL', 'NDAR_INVEDNJMEJ0', 'NDAR_INVEEY2XYCA', 'NDAR_INVEHLY59UX', 'NDAR_INVEL2DU62H', 'NDAR_INVETDKN8PJ', 'NDAR_INVETNDZFKZ', 'NDAR_INVEU57U8JH', 'NDAR_INVF1W2BTPZ', 'NDAR_INVF20K5ZNE', 'NDAR_INVF2BU99TZ', 'NDAR_INVF5JET3CJ', 'NDAR_INVF72J4L68', 'NDAR_INVF76ZR3W2', 'NDAR_INVF894WBDM', 'NDAR_INVF9RGL53U', 'NDAR_INVFBE9UXHG', 'NDAR_INVFBU7WX6N', 'NDAR_INVFCN9U0EN', 'NDAR_INVFD34548C', 'NDAR_INVFDM0PRRA', 'NDAR_INVFJP3171C', 'NDAR_INVFMV7H24T', 'NDAR_INVFYRF6WEB', 'NDAR_INVFZ4F6Y38', 'NDAR_INVG269TZZX', 'NDAR_INVG5V1RRFA', 'NDAR_INVG6VRYVW0', 'NDAR_INVG7B119A0', 'NDAR_INVG7UNU2FK', 'NDAR_INVG8VWC1TN', 'NDAR_INVG957G5YY', 'NDAR_INVG9JGX9UJ', 'NDAR_INVGAKHNMUU', 'NDAR_INVGBLP7T9X', 'NDAR_INVGEHCD5AD', 'NDAR_INVGFDT73J0', 'NDAR_INVGJ27R29V', 'NDAR_INVGK9ACPNW', 'NDAR_INVGM5D6MEW', 'NDAR_INVGPBCUX69', 'NDAR_INVGPPUVVG4', 'NDAR_INVGR764V5U', 'NDAR_INVGU0XFNRV', 'NDAR_INVGY65ZZBV', 'NDAR_INVGZJDGF8L', 'NDAR_INVH13A9F8Y', 'NDAR_INVH1CG52NU', 'NDAR_INVH6HR9EFA', 'NDAR_INVH8J67ZNJ', 'NDAR_INVH9ERT7NK', 'NDAR_INVHBJ3VGCL', 'NDAR_INVHEABF6E2', 'NDAR_INVHHJXG6Y1', 'NDAR_INVHKU16664', 'NDAR_INVHM2TZ8AD', 'NDAR_INVHNPRTMWZ', 'NDAR_INVHPU5TKNF', 'NDAR_INVHTAJ1KYF', 'NDAR_INVHTJ14CBX', 'NDAR_INVHU2D03RY', 'NDAR_INVHVYR57F5', 'NDAR_INVHW0PPV8W', 'NDAR_INVHX5PF696', 'NDAR_INVHXY3TJAT', 'NDAR_INVHYYUJ7LR', 'NDAR_INVJ27L7Z6G', 'NDAR_INVJ8DDAHGM', 'NDAR_INVJA30PYR6', 'NDAR_INVJAEV7W5J', 'NDAR_INVJDVAX1CU', 'NDAR_INVJDVWTD1U', 'NDAR_INVJDW2699M', 'NDAR_INVJF6WB6AE', 'NDAR_INVJJMH6ZDT', 'NDAR_INVJN2G8BWG', 'NDAR_INVJNMHDYGC', 'NDAR_INVJTHY8P5B', 'NDAR_INVJXWWYJBU', 'NDAR_INVJZRV5ANP', 'NDAR_INVK0BCW5KA', 'NDAR_INVK1HM1MWX', 'NDAR_INVK2KFJDG5', 'NDAR_INVK3GPV04G', 'NDAR_INVK5PEZ25W', 'NDAR_INVKC7ZC9VX', 'NDAR_INVKD35P0MA', 'NDAR_INVKEP4C8W8', 'NDAR_INVKH00C10R', 'NDAR_INVKJDAZKWA', 'NDAR_INVKNU295WW', 'NDAR_INVKX4KKBJR', 'NDAR_INVKY6C0Z8R', 'NDAR_INVKZBPXM31', 'NDAR_INVKZDAJBD7', 'NDAR_INVL3DBH8WC', 'NDAR_INVL3G13U80', 'NDAR_INVL49379MF', 'NDAR_INVL8KH6W61', 'NDAR_INVL8XGD85G', 'NDAR_INVLABCHP1W', 'NDAR_INVLCMDKYGP', 'NDAR_INVLEND5NH4', 'NDAR_INVLEXJMGJ6', 'NDAR_INVLF41F4P6', 'NDAR_INVLHZWGFNU', 'NDAR_INVLJ8NB5W8', 'NDAR_INVLNN6NNXL', 'NDAR_INVLTFB8A3W', 'NDAR_INVLVLHRL2N', 'NDAR_INVLZ0K08DY', 'NDAR_INVLZ0V0F16', 'NDAR_INVM007MAX7', 'NDAR_INVM05MNA24', 'NDAR_INVM22URD3V', 'NDAR_INVM42YXRFM', 'NDAR_INVM6LR22KX', 'NDAR_INVM91VXF6F', 'NDAR_INVMAV6PR9W', 'NDAR_INVMAZCGHX3', 'NDAR_INVMBVX3LHF', 'NDAR_INVMBWU2HEK', 'NDAR_INVMC7UEVZ1', 'NDAR_INVMCB04PW4', 'NDAR_INVMG3RV4AC', 'NDAR_INVMNXPRD46', 'NDAR_INVMT5RKZJE', 'NDAR_INVMTVMYZRH', 'NDAR_INVMTYZ9AH4', 'NDAR_INVMV3VPRF1', 'NDAR_INVMVF87GGA', 'NDAR_INVMXNTYPJU', 'NDAR_INVMY85AAAM', 'NDAR_INVN394NEWK', 'NDAR_INVN7FKUA2W', 'NDAR_INVNAJWH1CY', 'NDAR_INVNBMXW84N', 'NDAR_INVNDF1FD44', 'NDAR_INVNJA6E3NT', 'NDAR_INVNKBLG2AL', 'NDAR_INVNLN4DGBT', 'NDAR_INVNMRZ1KX9', 'NDAR_INVNTJ9TYDB', 'NDAR_INVNTUN0CBE', 'NDAR_INVNU5J10B7', 'NDAR_INVNUAGE7UT', 'NDAR_INVNUWHUFKV', 'NDAR_INVNVY8KZHN', 'NDAR_INVNWUL0U0R', 'NDAR_INVNZ90MJVP', 'NDAR_INVP0T27M9X', 'NDAR_INVP38LCGFT', 'NDAR_INVP4GPP1NB', 'NDAR_INVP5A0M52P', 'NDAR_INVP8CFEA4E', 'NDAR_INVP9Y862GP', 'NDAR_INVPATP4RPG', 'NDAR_INVPBZ6UJ07', 'NDAR_INVPCJWLN13', 'NDAR_INVPDUZETD1', 'NDAR_INVPHL3D8GY', 'NDAR_INVPRLTKVRN', 'NDAR_INVPZDDVLGW', 'NDAR_INVR17JHZRJ', 'NDAR_INVR1JFZ70M', 'NDAR_INVR44LUUEZ', 'NDAR_INVR5EGEA6F', 'NDAR_INVRBNRB1DU', 'NDAR_INVRCYZ8XZ2', 'NDAR_INVRDJX9GRE', 'NDAR_INVRE48TR17', 'NDAR_INVRJ67E8PE', 'NDAR_INVRLETBAV0', 'NDAR_INVRMNMGK77', 'NDAR_INVRVKDMDNH', 'NDAR_INVRW8117A7', 'NDAR_INVRWKJCGLY', 'NDAR_INVRXX916ZV', 'NDAR_INVRYT5RRUM', 'NDAR_INVRZL7PGK1', 'NDAR_INVT0JW7H6Z', 'NDAR_INVT1GJUT9Z', 'NDAR_INVT2JJ42KE', 'NDAR_INVT2YYABW3', 'NDAR_INVT3366FHE', 'NDAR_INVT4BR7L9V', 'NDAR_INVT4M6PYF4', 'NDAR_INVT77R03AY', 'NDAR_INVT7YJ84MG', 'NDAR_INVT9LPE86G', 'NDAR_INVTAP6MEMP', 'NDAR_INVTAX3MN8C', 'NDAR_INVTBL4GD57', 'NDAR_INVTGZMNV19', 'NDAR_INVTM2HD1WE', 'NDAR_INVU11WDWJ8', 'NDAR_INVU3XLKGUY', 'NDAR_INVU4BNFAZ9', 'NDAR_INVU5990AMT', 'NDAR_INVU7276YU8', 'NDAR_INVU7WWPJ0M', 'NDAR_INVU8VT1VB7', 'NDAR_INVU9ATRA9G', 'NDAR_INVU9C9ZMKM', 'NDAR_INVUA1GPU5Z', 'NDAR_INVUF504K04', 'NDAR_INVUJBHEFTN', 'NDAR_INVUM3L54UF', 'NDAR_INVURLHBMZV', 'NDAR_INVUTEVFWND', 'NDAR_INVUTNUNCNK', 'NDAR_INVUTVE98D5', 'NDAR_INVUXMACPNB', 'NDAR_INVUYYEU9U1', 'NDAR_INVV18ZDFDE', 'NDAR_INVV1F9DNZH', 'NDAR_INVV23Y1DXH', 'NDAR_INVV37537GK', 'NDAR_INVV5X50ZMM', 'NDAR_INVV82L1HKC', 'NDAR_INVV874VP8D', 'NDAR_INVV8VK6RGX', 'NDAR_INVVE6MPVNE', 'NDAR_INVVF539UBP', 'NDAR_INVVKV5RYVF', 'NDAR_INVVLGAGC69', 'NDAR_INVVMT7W436', 'NDAR_INVVR6JW9BP', 'NDAR_INVVU46PA0Y', 'NDAR_INVVV34F5T4', 'NDAR_INVVV5V2KXY', 'NDAR_INVVXH1GGXB', 'NDAR_INVVZYWE87C', 'NDAR_INVW01VXPJ7', 'NDAR_INVW04H37XX', 'NDAR_INVW26YNDL9', 'NDAR_INVW3N4MZ02', 'NDAR_INVW58F28HT', 'NDAR_INVWAN79D4A', 'NDAR_INVWBAKLX5Y', 'NDAR_INVWF9BVBHA', 'NDAR_INVWFN7K4C5', 'NDAR_INVWM3FXZ4K', 'NDAR_INVWMKYTKBW', 'NDAR_INVWT771AK6', 'NDAR_INVWTBPTVCP', 'NDAR_INVWVA9UY95', 'NDAR_INVWVM2TW33', 'NDAR_INVWX4KD0B0', 'NDAR_INVWXGW2FKF', 'NDAR_INVX1697BGE', 'NDAR_INVX8D3JRAH', 'NDAR_INVXD6CMU32', 'NDAR_INVXDHB28GU', 'NDAR_INVXDX3UAWF', 'NDAR_INVXEKGFN5Y', 'NDAR_INVXFL1L8T2', 'NDAR_INVXGFXPB14', 'NDAR_INVXGWM6XC8', 'NDAR_INVXHEEJUKX', 'NDAR_INVXHG35TUW', 'NDAR_INVXHJBRK7N', 'NDAR_INVXPE3RPDD', 'NDAR_INVXU8K8M57', 'NDAR_INVXUPMEDT7', 'NDAR_INVXYLTMBZ9', 'NDAR_INVXZRDA6CJ', 'NDAR_INVY43C1PAY', 'NDAR_INVY5AB6YNB', 'NDAR_INVYAEE6YN4', 'NDAR_INVYD28H9BD', 'NDAR_INVYE9WDNR8', 'NDAR_INVYJPCD59K', 'NDAR_INVYRYJZLMX', 'NDAR_INVYTRMF19U', 'NDAR_INVYUKDPT70', 'NDAR_INVYWCJTCBW', 'NDAR_INVZ1WZW3KT', 'NDAR_INVZ2REMPTW', 'NDAR_INVZ49U1FM4', 'NDAR_INVZ4KYLWJA', 'NDAR_INVZ6Z9DP4H', 'NDAR_INVZ7FR6UCT', 'NDAR_INVZ7U9A2DX', 'NDAR_INVZ8U8TUHJ', 'NDAR_INVZ95YA2ZB', 'NDAR_INVZM2Y9JCA', 'NDAR_INVZP45TZD9', 'NDAR_INVZP49GXF4', 'NDAR_INVZPR155DG', 'NDAR_INVZR9NMJBR', 'NDAR_INVZT1J0KUC', 'NDAR_INVZTTGYL51', 'NDAR_INVZYRTFYRP', 'NDAR_INVZZZNB0XC']\n",
      "Filtered and saved mri_y_adm_info_subset.csv with RS subject IDs\n",
      "Filtered and saved ph_y_mctq_subset.csv with RS subject IDs\n",
      "Filtered and saved ph_y_resp_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_p_macv_subset.csv with RS subject IDs\n",
      "Filtered and saved ph_y_bp_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_y_macv_subset.csv with RS subject IDs\n",
      "Filtered and saved ph_y_yrb_subset.csv with RS subject IDs\n",
      "Filtered and saved mri_y_qc_motion_subset.csv with RS subject IDs\n",
      "Filtered and saved led_l_coi_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_p_meim_subset.csv with RS subject IDs\n",
      "Filtered and saved abcd_y_lt_subset.csv with RS subject IDs\n",
      "Filtered and saved led_l_nbhsoc_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_y_meim_subset.csv with RS subject IDs\n",
      "Filtered and saved mh_p_cbcl_subset.csv with RS subject IDs\n",
      "Filtered and saved led_l_gi_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_y_dm_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_p_via_subset.csv with RS subject IDs\n",
      "Filtered and saved mri_y_rsfmr_cor_gp_gp_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_y_via_subset.csv with RS subject IDs\n",
      "Filtered and saved ce_p_comc_subset.csv with RS subject IDs\n",
      "Filtered and saved abcd_p_demo_subset.csv with RS subject IDs\n"
     ]
    }
   ],
   "source": [
    "# use subject IDS to filter CSV files\n",
    "\n",
    "rs_subject_ids = []  # Array to store unique subject IDs with RS data\n",
    "\n",
    "for file_name in os.listdir(csv_sub_dir):\n",
    "    if file_name.endswith(\".csv\"):  # Ensure we're only processing CSV files\n",
    "        file_path = os.path.join(csv_sub_dir, file_name)\n",
    "\n",
    "        # Load the CSV file into a DataFrame\n",
    "        df = pd.read_csv(file_path)\n",
    "\n",
    "        # Keep only rows where 'src_subject_id' is in the subject_ids list\n",
    "        df_filtered = df[df[\"src_subject_id\"].isin(subject_ids)]\n",
    "\n",
    "        # Save the filtered DataFrame back to the same file\n",
    "        df_filtered.to_csv(file_path, index=False)\n",
    "\n",
    "        # print(f\"Filtered and saved {file_name}\")\n",
    "\n",
    "        if file_name == \"mri_y_rsfmr_cor_gp_gp_subset.csv\":\n",
    "            # Get the unique subject IDs\n",
    "            df_filtered = df_filtered.dropna()\n",
    "            \n",
    "            rs_subject_ids = df_filtered[\"src_subject_id\"].unique().tolist()\n",
    "\n",
    "            # Get the number of unique subject IDs\n",
    "            subject_ids_count = len(rs_subject_ids)\n",
    "\n",
    "            # Print the number of unique subject IDs\n",
    "            print(\n",
    "                f\"Number of unique subject IDs with RS data: {subject_ids_count}\"\n",
    "            )\n",
    "\n",
    "# Print the array of unique subject IDs\n",
    "print(f\"Subject IDs with RS data: {rs_subject_ids}\")\n",
    "\n",
    "for file_name in os.listdir(csv_sub_dir):\n",
    "    if file_name.endswith(\".csv\"):  # Ensure we're only processing CSV files\n",
    "        file_path = os.path.join(csv_sub_dir, file_name)\n",
    "\n",
    "        # Load the CSV file into a DataFrame\n",
    "        df = pd.read_csv(file_path)\n",
    "\n",
    "        # Filter rows where 'src_subject_id' is in the unique RS subject IDs list\n",
    "        df_filtered = df[df[\"src_subject_id\"].isin(rs_subject_ids)]\n",
    "\n",
    "        # Save the filtered DataFrame back to the same file\n",
    "        df_filtered.to_csv(file_path, index=False)\n",
    "\n",
    "        print(f\"Filtered and saved {file_name} with RS subject IDs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we have filtered data, we want to create measure dataframes that will be used in PLSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mri_y_adm_info_subset.csv',\n",
       " 'ph_y_mctq_subset.csv',\n",
       " 'ph_y_resp_subset.csv',\n",
       " 'ce_p_macv_subset.csv',\n",
       " 'ph_y_bp_subset.csv',\n",
       " 'ce_y_macv_subset.csv',\n",
       " 'ph_y_yrb_subset.csv',\n",
       " 'mri_y_qc_motion_subset.csv',\n",
       " 'led_l_coi_subset.csv',\n",
       " 'ce_p_meim_subset.csv',\n",
       " 'abcd_y_lt_subset.csv',\n",
       " 'led_l_nbhsoc_subset.csv',\n",
       " 'ce_y_meim_subset.csv',\n",
       " 'mh_p_cbcl_subset.csv',\n",
       " 'led_l_gi_subset.csv',\n",
       " 'ce_y_dm_subset.csv',\n",
       " 'ce_p_via_subset.csv',\n",
       " 'mri_y_rsfmr_cor_gp_gp_subset.csv',\n",
       " 'ce_y_via_subset.csv',\n",
       " 'ce_p_comc_subset.csv',\n",
       " 'abcd_p_demo_subset.csv']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(csv_sub_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mri_y_adm_info_subset.csv\n",
      "ph_y_mctq_subset.csv\n",
      "ph_y_resp_subset.csv\n",
      "ce_p_macv_subset.csv\n",
      "ph_y_bp_subset.csv\n",
      "ce_y_macv_subset.csv\n",
      "ph_y_yrb_subset.csv\n",
      "mri_y_qc_motion_subset.csv\n",
      "led_l_coi_subset.csv\n",
      "ce_p_meim_subset.csv\n",
      "abcd_y_lt_subset.csv\n",
      "led_l_nbhsoc_subset.csv\n",
      "ce_y_meim_subset.csv\n",
      "mh_p_cbcl_subset.csv\n",
      "led_l_gi_subset.csv\n",
      "ce_y_dm_subset.csv\n",
      "ce_p_via_subset.csv\n",
      "mri_y_rsfmr_cor_gp_gp_subset.csv\n",
      "ce_y_via_subset.csv\n",
      "ce_p_comc_subset.csv\n",
      "abcd_p_demo_subset.csv\n",
      "Sociocultural DataFrame:\n",
      "     src_subject_id  macv_p_ss_fs  macv_p_ss_fo  macv_p_ss_fr  macv_y_ss_fs  \\\n",
      "0  NDAR_INV028D3ELL      3.833333           3.2           3.6      3.333333   \n",
      "1  NDAR_INV030W95VP      4.333333           4.6           3.2      4.000000   \n",
      "2  NDAR_INV056V9RRJ      4.500000           4.0           4.0      3.333333   \n",
      "3  NDAR_INV06DX0E7R      3.000000           2.4           2.4      4.500000   \n",
      "4  NDAR_INV0889M0JE      5.000000           3.8           3.0      1.833333   \n",
      "\n",
      "   macv_y_ss_fo  macv_y_ss_fr  reshist_addr1_coi_r_coi_nat  meim_p_ss_exp  \\\n",
      "0           2.4           2.8                         36.0       2.333333   \n",
      "1           3.8           3.8                         61.0       1.000000   \n",
      "2           3.4           3.2                         30.0       3.000000   \n",
      "3           4.8           4.6                         78.0       2.000000   \n",
      "4           2.8           2.2                         64.0       3.666667   \n",
      "\n",
      "   meim_p_ss_com  ...  meim_ss_exp  meim_ss_com  reshist_addr1_gstat_h_queen  \\\n",
      "0       3.333333  ...     4.000000     4.000000                     0.807848   \n",
      "1       1.000000  ...     2.666667     3.333333                    -0.342727   \n",
      "2       3.333333  ...     3.000000     3.000000                     6.223984   \n",
      "3       2.000000  ...     2.666667     3.333333                    -1.338352   \n",
      "4       2.000000  ...     4.000000     4.000000                    -0.996478   \n",
      "\n",
      "   dim_y_ss_mean  via_p_ss_hc  via_p_ss_amer  via_ss_hc  via_ss_amer  \\\n",
      "0       1.285714        6.375          7.750      4.875          5.5   \n",
      "1       1.000000        6.125          9.000      6.125          6.0   \n",
      "2       1.000000        5.250          5.250        NaN          NaN   \n",
      "3       1.000000          NaN            NaN        NaN          NaN   \n",
      "4       4.000000        6.125          6.625      5.875          5.5   \n",
      "\n",
      "   comc_ss_cohesion_p  comc_ss_control_p  \n",
      "0                 3.8                3.6  \n",
      "1                 3.0                3.0  \n",
      "2                 3.0                3.0  \n",
      "3                 3.4                3.6  \n",
      "4                 3.4                5.0  \n",
      "\n",
      "[5 rows x 21 columns]\n",
      "\n",
      "Covariate DataFrame:\n",
      "     src_subject_id    mri_info_manufacturer  rsfmri_meanmotion  \\\n",
      "0  NDAR_INV028D3ELL  Philips Medical Systems           0.066058   \n",
      "1  NDAR_INV030W95VP  Philips Medical Systems           0.155444   \n",
      "2  NDAR_INV056V9RRJ       GE MEDICAL SYSTEMS           0.173794   \n",
      "3  NDAR_INV06DX0E7R       GE MEDICAL SYSTEMS           0.423909   \n",
      "4  NDAR_INV0889M0JE                  SIEMENS           0.131859   \n",
      "\n",
      "   interview_age  rel_family_id site_id_l  demo_sex_v2  demo_ethn_v2  \\\n",
      "0          157.0         9360.0    site01          2.0           1.0   \n",
      "1          179.0         7063.0    site19          2.0           1.0   \n",
      "2          170.0        10216.0    site10          2.0           1.0   \n",
      "3          160.0        10937.0    site18          1.0           1.0   \n",
      "4          161.0         4724.0    site21          2.0           1.0   \n",
      "\n",
      "   demo_prnt_age_v2  demo_prnt_gender_id_v2  ...  demo_prtnr_ed_v2_2yr_l  \\\n",
      "0              40.0                     2.0  ...                    18.0   \n",
      "1              30.0                     2.0  ...                    14.0   \n",
      "2              38.0                     2.0  ...                    12.0   \n",
      "3              33.0                     2.0  ...                    13.0   \n",
      "4              41.0                     2.0  ...                    19.0   \n",
      "\n",
      "   demo_comb_income_v2  demo_prnt_income_v2_l  demo_origin_v2  \\\n",
      "0                  9.0                    9.0           189.0   \n",
      "1                  4.0                    5.0           189.0   \n",
      "2                  5.0                    6.0           189.0   \n",
      "3                  8.0                    7.0           189.0   \n",
      "4                  NaN                    NaN           189.0   \n",
      "\n",
      "   demo_biomother_v2  demo_biofather_v2  demo_matgrandm_v2  demo_matgrandf_v2  \\\n",
      "0              189.0              189.0              189.0              189.0   \n",
      "1               24.0               41.0               24.0               24.0   \n",
      "2              189.0              111.0              111.0              111.0   \n",
      "3               84.0              189.0              111.0              189.0   \n",
      "4              189.0              189.0               37.0              189.0   \n",
      "\n",
      "   demo_patgrandm_v2  demo_patgrandf_v2  \n",
      "0              111.0              111.0  \n",
      "1               41.0               41.0  \n",
      "2              111.0              111.0  \n",
      "3              189.0              189.0  \n",
      "4              189.0              189.0  \n",
      "\n",
      "[5 rows x 22 columns]\n",
      "\n",
      "Physical Health DataFrame:\n",
      "     src_subject_id  mctq_sdweek_calc  mctq_msfsc_calc  resp_wheeze_yn_y  \\\n",
      "0  NDAR_INV028D3ELL            8.9869          29.2440               0.0   \n",
      "1  NDAR_INV030W95VP            9.3677          28.7685               0.0   \n",
      "2  NDAR_INV056V9RRJ            8.7353          26.3704               0.0   \n",
      "3  NDAR_INV06DX0E7R            9.1411           6.5014               0.0   \n",
      "4  NDAR_INV0889M0JE            3.9715          32.0024               0.0   \n",
      "\n",
      "   resp_pmcough_yn_y  resp_diagnosis_yn_y  resp_bronch_yn_y  \\\n",
      "0                0.0                  0.0               NaN   \n",
      "1                0.0                  0.0               0.0   \n",
      "2                0.0                  0.0               0.0   \n",
      "3                0.0                  0.0               0.0   \n",
      "4                0.0                  0.0               0.0   \n",
      "\n",
      "   blood_pressure_sys_mean  blood_pressure_dia_mean  physical_activity1_y  \\\n",
      "0               104.333333                56.333333                   6.0   \n",
      "1               105.500000                66.000000                   2.0   \n",
      "2                93.000000                46.333333                   3.0   \n",
      "3               102.500000                57.000000                   3.0   \n",
      "4               100.666667                67.666667                   7.0   \n",
      "\n",
      "   cbcl_scr_syn_internal_t  cbcl_scr_syn_external_t  \n",
      "0                     39.0                     40.0  \n",
      "1                     62.0                     40.0  \n",
      "2                     43.0                     40.0  \n",
      "3                     47.0                     34.0  \n",
      "4                     71.0                     52.0  \n",
      "\n",
      "RSFC DataFrame:\n",
      "     src_subject_id  rsfmri_c_ngd_ad_ngd_ad  rsfmri_c_ngd_ad_ngd_cgc  \\\n",
      "0  NDAR_INV028D3ELL                0.237271                 0.050704   \n",
      "1  NDAR_INV030W95VP                0.424266                 0.320737   \n",
      "2  NDAR_INV056V9RRJ                0.283040                 0.123300   \n",
      "3  NDAR_INV06DX0E7R                0.195883                 0.176239   \n",
      "4  NDAR_INV0889M0JE                0.260193                 0.170786   \n",
      "\n",
      "   rsfmri_c_ngd_ad_ngd_ca  rsfmri_c_ngd_ad_ngd_dt  rsfmri_c_ngd_ad_ngd_dla  \\\n",
      "0               -0.097674               -0.066317                -0.020173   \n",
      "1                0.125510               -0.117286                 0.067547   \n",
      "2               -0.153066               -0.030460                 0.004602   \n",
      "3               -0.119832               -0.025027                 0.004940   \n",
      "4               -0.065887               -0.039431                -0.004096   \n",
      "\n",
      "   rsfmri_c_ngd_ad_ngd_fo  rsfmri_c_ngd_ad_ngd_n  rsfmri_c_ngd_ad_ngd_rspltp  \\\n",
      "0               -0.080367              -0.017838                   -0.064739   \n",
      "1               -0.112132               0.006792                    0.028030   \n",
      "2               -0.016012              -0.004500                   -0.029544   \n",
      "3                0.023253              -0.002502                   -0.039661   \n",
      "4                0.009387              -0.017920                   -0.067054   \n",
      "\n",
      "   rsfmri_c_ngd_ad_ngd_sa  ...  rsfmri_c_ngd_vs_ngd_dt  \\\n",
      "0               -0.092830  ...               -0.243057   \n",
      "1                0.128417  ...               -0.178131   \n",
      "2               -0.022439  ...               -0.155399   \n",
      "3                0.084849  ...               -0.086269   \n",
      "4                0.087728  ...               -0.173222   \n",
      "\n",
      "   rsfmri_c_ngd_vs_ngd_dla  rsfmri_c_ngd_vs_ngd_fo  rsfmri_c_ngd_vs_ngd_n  \\\n",
      "0                -0.026573               -0.189696              -0.042605   \n",
      "1                 0.055936               -0.151272              -0.046745   \n",
      "2                 0.044107               -0.077020              -0.033758   \n",
      "3                 0.035431                0.001731               0.010144   \n",
      "4                 0.001335               -0.162735              -0.105122   \n",
      "\n",
      "   rsfmri_c_ngd_vs_ngd_rspltp  rsfmri_c_ngd_vs_ngd_sa  \\\n",
      "0                   -0.068369               -0.281293   \n",
      "1                    0.024615                0.043603   \n",
      "2                   -0.020566               -0.032887   \n",
      "3                    0.096410               -0.033476   \n",
      "4                    0.209847               -0.062999   \n",
      "\n",
      "   rsfmri_c_ngd_vs_ngd_smh  rsfmri_c_ngd_vs_ngd_smm  rsfmri_c_ngd_vs_ngd_vta  \\\n",
      "0                 0.074834                 0.187627                -0.037641   \n",
      "1                -0.003773                 0.131326                -0.033680   \n",
      "2                -0.007562                 0.012954                -0.071637   \n",
      "3                -0.165801                -0.018897                -0.111714   \n",
      "4                -0.060484                -0.031723                -0.082378   \n",
      "\n",
      "   rsfmri_c_ngd_vs_ngd_vs  \n",
      "0                0.555925  \n",
      "1                0.377985  \n",
      "2                0.236013  \n",
      "3                0.291132  \n",
      "4                0.506870  \n",
      "\n",
      "[5 rows x 170 columns]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Initialize empty DataFrames for each category\n",
    "sociocult_df_Nan = pd.DataFrame()\n",
    "covariate_df_Nan = pd.DataFrame()\n",
    "rsfc_df_Nan = pd.DataFrame()\n",
    "phyhealth_df_Nan = pd.DataFrame()\n",
    "\n",
    "\n",
    "# Function to merge CSV files on subject ID and remove 'eventname' column if it exists\n",
    "def merge_csv(file_path, df):\n",
    "    temp_df = pd.read_csv(file_path).replace([777.0, 999.0], np.nan)\n",
    "\n",
    "    # Remove 'eventname' column if it exists\n",
    "    if \"eventname\" in temp_df.columns:\n",
    "        temp_df = temp_df.drop(columns=[\"eventname\"])\n",
    "\n",
    "    # Merge on subject ID (assuming 'subject_id' is the column name for subject ID)\n",
    "    if df.empty:\n",
    "        return temp_df\n",
    "    else:\n",
    "        return pd.merge(df, temp_df, on=\"src_subject_id\", how=\"outer\")\n",
    "\n",
    "\n",
    "# Iterate through files in the directory\n",
    "for files in os.listdir(csv_sub_dir):\n",
    "    print(files)\n",
    "    file_path = os.path.join(csv_sub_dir, files)\n",
    "\n",
    "    if files.startswith(\"ce\") or files.startswith(\"led\"):\n",
    "        sociocult_df_Nan = merge_csv(file_path, sociocult_df_Nan)\n",
    "    elif files.startswith((\"abcd\", \"mri_y_adm\", \"mri_y_qc\")):\n",
    "        covariate_df_Nan = merge_csv(file_path, covariate_df_Nan)\n",
    "    elif files.startswith(\"mri_y_rsfmr\"):\n",
    "        rsfc_df_Nan = merge_csv(file_path, rsfc_df_Nan)\n",
    "    elif files.startswith((\"mh\", \"ph\")):\n",
    "        phyhealth_df_Nan = merge_csv(file_path, phyhealth_df_Nan)\n",
    "\n",
    "# After merging, you can print or save the DataFrames as needed\n",
    "# Display the first few rows of each DataFrame\n",
    "print(\"Sociocultural DataFrame:\")\n",
    "print(sociocult_df_Nan.head())\n",
    "\n",
    "print(\"\\nCovariate DataFrame:\")\n",
    "print(covariate_df_Nan.head())\n",
    "\n",
    "print(\"\\nPhysical Health DataFrame:\")\n",
    "print(phyhealth_df_Nan.head())\n",
    "\n",
    "print(\"\\nRSFC DataFrame:\")\n",
    "print(rsfc_df_Nan.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrames have been saved successfully.\n"
     ]
    }
   ],
   "source": [
    "# Define file paths for saving\n",
    "sociocult_save_path = os.path.join(deriv_dir, \"sociocult_Nan.csv\")\n",
    "covariate_save_path = os.path.join(deriv_dir, \"covariate_Nan.csv\")\n",
    "phyhealth_save_path = os.path.join(deriv_dir, \"phyhealth_Nan.csv\")\n",
    "rsfc_save_path = os.path.join(deriv_dir, \"rsfc_Nan.csv\")\n",
    "\n",
    "# Save each DataFrame to a CSV file\n",
    "sociocult_df_Nan.to_csv(sociocult_save_path, index=False)\n",
    "covariate_df_Nan.to_csv(covariate_save_path, index=False)\n",
    "phyhealth_df_Nan.to_csv(phyhealth_save_path, index=False)\n",
    "rsfc_df_Nan.to_csv(rsfc_save_path, index=False)\n",
    "\n",
    "print(\"DataFrames have been saved successfully.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a plot that shows percentage of missing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_data_summary(df, df_name):\n",
    "\n",
    "    # Calculate the number of non-NaN values for each column (except the first column)\n",
    "    non_nan_counts = df.iloc[:, 1:].notna().sum()\n",
    "    # Calculate the percentage of missing data for each column (except the first column)\n",
    "    missing_percentage = df.iloc[:, 1:].isna().mean() * 100\n",
    "\n",
    "    # Create the summary DataFrame\n",
    "    summary = pd.DataFrame(\n",
    "        {\n",
    "            \"Measure\": df.columns[1:],  # Exclude the first column\n",
    "            \"Number of Subjects\": non_nan_counts,  # Number of non-NaN values\n",
    "            \"Percentage Missing\": missing_percentage,\n",
    "        }\n",
    "    )\n",
    "\n",
    "    # Determine if an asterisk should be added\n",
    "    summary[\"Above 5%\"] = summary[\"Percentage Missing\"].apply(\n",
    "        lambda x: \"*\" if x > 5 else \"\"\n",
    "    )\n",
    "\n",
    "    # Create a formatted string for the DataFrame summary\n",
    "    summary_str = f\"\\n{df_name} Missing Data Summary:\\n\"\n",
    "    summary_str += (\n",
    "        summary.to_string(\n",
    "            index=False,\n",
    "            columns=[\"Measure\", \"Number of Subjects\", \"Percentage Missing\", \"Above 5%\"],\n",
    "        )\n",
    "        + \"\\n\"\n",
    "    )\n",
    "\n",
    "    return summary, summary_str\n",
    "\n",
    "\n",
    "# Generate missing data summaries for each DataFrame\n",
    "sociocult_summary, sociocult_summary_str = missing_data_summary(\n",
    "    sociocult_df_Nan, \"Sociocultural DataFrame\"\n",
    ")\n",
    "covariate_summary, covariate_summary_str = missing_data_summary(\n",
    "    covariate_df_Nan, \"Covariate DataFrame\"\n",
    ")\n",
    "phyhealth_summary, phyhealth_summary_str = missing_data_summary(\n",
    "    phyhealth_df_Nan, \"Physical Health DataFrame\"\n",
    ")\n",
    "rsfc_summary, rsfc_summary_str = missing_data_summary(rsfc_df_Nan, \"RSFC DataFrame\")\n",
    "\n",
    "# Combine all summaries into one string\n",
    "all_summaries = (\n",
    "    sociocult_summary_str\n",
    "    + covariate_summary_str\n",
    "    + phyhealth_summary_str\n",
    "    + rsfc_summary_str\n",
    ")\n",
    "\n",
    "# Write the combined summaries to a text file\n",
    "with open(\"PLSC-missing_data.txt\", \"w\") as file:\n",
    "    file.write(all_summaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/3734573659.py:6: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  barplot = sns.barplot(\n",
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/3734573659.py:19: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  plt.legend()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/3734573659.py:6: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  barplot = sns.barplot(\n",
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/3734573659.py:19: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  plt.legend()\n"
     ]
    },
    {
     "data": {
      "image/png": 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SJQtOTk6p1nmZcyS1vX//Pvb29oby69evc/nyZUqXLo2ZmRkdO3ZMMXlN7XVVaTF48GAuXLjA8uXLcXFxIUOGDERFRbF69WpDnTx58hgS6b+7d+9esnF8+eWXRl8gJElKxv4pa9asKT67fOfOHYAUl5WnRY4cOahSpQpbt27F2dmZM2fOpLo0/O/+vuT7yJEjrFq1igULFlC8eHEaNmzIe++9x2effcbYsWM5c+YMW7duZdGiRWTPnv2FXvmVM2dO0qdPn+waJyXfL2P//v1kzpyZUqVKvfDvUGRkJN7e3jg6OrJp0ybs7e0xNzfnhx9+YNu2bS8Vn4j8t2mTMxERead16tSJhw8f4ufnl+zYnTt3WLp0KUWKFKFUqVKGRPGfG5ht2rSJuLg4ypcvbyhr1qwZN2/eZO7cuVhYWBgtdz5y5AhFihShRYsWhuT61q1bnDt3jvj4+DTH/uDBA8LCwmjZsiVOTk6kS5f4vfmPP/4IYOgraSl8Ent7e3LmzMnVq1dxcnIy/HnvvfeYPn26Yafzl/Ey53B2diZ9+vSG5bZJli5dysCBA8mcOTMlS5bkwoULRn0XLVoUf39/w/LetEqaOU1y5MgR6tWrR+XKlcmQIQOQ/JpWrFiR3377zZD0QuKs/G+//Wb4nDTr/eDBA6M479+/z6xZs56aKFasWJFr167x66+/GpVv2LCB9OnTG5Zmv4ik3cTXrl1L+fLl0/TqrOXLl1O7dm1iYmLIkCEDVatWZfz48UDilx6//vor1apV4/jx45iZmVGiRAkGDBhAsWLFUl0dkhoLCwvKlSvHrl27jMq///57o+X2z+v06dPs2rWLFi1akDFjxhf+Hbpw4QIPHz6kffv2FClSxHAP/bOdiLx7NIMtIiLvtLJly9KvXz/8/PwIDQ3F09OT7Nmzc/78eZYsWUJ0dLQh+S5SpAjNmzdn9uzZREVFUbFiRU6fPs2cOXOoXLkyrq6uhn6LFStGiRIl+Prrr2nYsKHR8mlnZ2fmzZvHwoULKVu2LJcuXeKLL74gJibmuXYfzpkzJ/ny5WPlypXkyZMHGxsbfvrpJ8NO5El9JSXxe/bsIWvWrBQvXpwBAwYwZswYLCwsqF27NuHh4cybN49bt26luPT5eVlYWLzwOXLkyEH79u1Zvnw5GTJkoFKlShw7doxvvvmGoUOHYm5ubtg4bNCgQTRt2pS4uDiWLl3KsWPHjDarSgsbGxuOHj3KL7/8QoUKFXB2diY4OJhSpUqRJ08ejh49ysKFCzEzMzNc0/bt27Ny5Uo6d+5s2Lxu3rx5PHnyxPDMsKOjI02bNmX06NFcu3aN0qVLExYWxsyZM8mfPz92dnYpxuPl5cXXX39Nr1696Nu3L/nz5+f777/nu+++o3fv3oaZ8RdRt25dxo4dy/Lly402cUtNlSpVmDZtGr169aJt27ZYWFjw7bffkiFDBmrXrk2+fPmwtLRk6NCh9OnTB1tbW/bv38/p06cNr7d6EX379qVdu3b07duXli1bcv36dWbNmgVg9Fz20yR92ZGQkMDjx485ceIEy5cvx87Ojn79+gEv/jtUuHBhrKysWLBgAenSpSNdunRs27bN8Co17SIu8u5Sgi0iIu+8Hj16ULJkSVauXMnnn3/Oo0ePyJs3L25ubnTv3p28efMa6k6cOJFChQrx3XffsWjRInLnzk379u3p2bNnspnQZs2aMXnyZMPmZkm6devGgwcPWLFiBXPnziVv3rw0a9bMsLt0eHh4mpOoefPmMXHiRIYPH06GDBkoUqQI8+fP5/PPP+fw4cO0a9eOokWL0rhxY1auXMlPP/3Exo0b+fDDD8mSJQuLFy9m1apVZM6cmXLlyjFt2jTDa7Ze1sucY8iQIeTMmZNvv/2WxYsXkz9/fkaPHk3r1q2BxKX9S5YsYc6cOfTt25f06dNTqlQpli1b9ty7N3fv3p158+bRpUsXNm/ezOTJkxk/frxhltbOzo5x48axYcMGDh8+DCQm5StWrGDixIkMHTqULFmy0KZNGzJlymT03PykSZP44osv+Pbbb7l58yY5c+bEw8OD/v37J5sVTZIpUya++uorpk+fzqxZs4iMjMTe3p6JEyc+92ME/2RjY0ONGjX46aefUnztW0qKFy/OggULmDt3LgMHDiQuLo7SpUuzdOlSwxL+pUuXMn36dCZOnEh4eDh2dnb4+Pjg5eX1wrFWqFABf39/Zs2aRc+ePcmXLx+jR49mwIABKS65/6ePPvrI8G9LS0sKFCjAxx9/jLe3t9EXXi/6OzRv3jx8fX3p168fWbJkoUSJEgQEBNClSxcOHz6Mu7v7C49dRP67zBK0C4OIiIjIczl27BgPHz402s07NjYWNzc3w67y8nJ27dpFnjx5jFY7nD9/nsaNGzNv3jw++OCDNxidiEjKNIMtIiIi8pyuX7/OgAED6NWrF5UqVSIqKopVq1YRERFBq1at3nR4b4W9e/eyefNmBg8eTOHChbl16xbz58/H3t6eGjVqvOnwRERSpBlsERERkRfwzTff8PXXX3PlyhXSp09PmTJl6Nev3zN3WJe0+eOPP5g1axbbtm3j9u3bZMuWDVdXVwYNGmR4ZZmIyL+NEmwRERERERERE9BrukRERERERERMQAm2iIiIiIiIiAkowRYRERERERExASXYIiIiIiIiIiag13SJvAYJCQnEx2s/QTENc3Mz3U9iUrqnxNR0T4mp6Z4SUzE3N8PMzOyV9a8EW+Q1MDMzIzz8d2Jj4990KPIfly6dOdmzZ9H9JCaje0pMTfeUmJruKTGlHDmyYGHx6hJsLREXERERERERMQEl2CIiIiIiIiImoARb5DV5lc96iIiIiIjIm6cEW+Q1MTdXgi0iIiIi8jZTgi0iIiIiIiJiAtpFXERERERERP714uPjiYuLTbWOhUU6zM3f3DyyEmwRERERERH510pISCA8/D5RUZFpqp8pkxU2NjneyB5IWiIu8obt3r2LevVqUajQe1So4MTcubNJSEhItU1g4BpcXStRsGBuqlevwLffrkxW57ffjuLp6YGdXV6cnIoxceI4YmJijOpMnjyekiXtKVeuVLI+EhISqFu3FmvXrnr5QYqIiIiIvKCk5NrKKjs5cuQhZ868Kf7JkSMPVlbZiYqKJDz8/huJVTPYIm/Q4cOHaNu2Fc2aeTF8+KccPHgAH5/RxMXF0rfvwBTbBAcH0aOHN1279qB27Tps2bKJvn17kDFjRpo3bwnAxYthtGzZjAoVKrJ48XLOnTvHpEk+PHjwgGnT/ADYsWMr8+b5M2OGPw8fPmDgwD6ULVuO4sVLALBu3Vri4uJo0aLVa7kWIiIiIiL/FB8fZ0iuraxsnlk/Q4aMAERGPsDaOvtrXy6uBFvkDfL1/RwnJ2fmzVsEgLt7XZ48icXPbzpduvQgU6ZMydp8/vk4mjZtzvjxk/9sU4eHDx8wefIEQ4Lt7++HlZUVK1Z8S4YMGahTpz6ZMmVixIjB9O8/iPz5C/Djj3uoWdONli0/AiAgYAX79v1E8eIliImJ4fPPx+PrO12vFxMRERGRNyYuLg74K3FOi6S6cXGxmJtneCVxPY2WiD/FwYMHcXR05OrVq2mqHxgYiKOj43OdY9y4cbi4uFC+fHnu3r37ImH+Z/zwww+4u7vj5OTEihUrXrq/33//nZUrky+L/i+Jjo5m//69eHg0MSpv0qQZkZERHDx4IFmby5cvERoagodH42RtwsIucOFCCAB79uyiTp36ZMiQ4W91PImPj2f37l1/lphhaflXAp8hQ3ri4xP/A7Zs2SIKFCiAu3tdUwxVREREROSlPM+kz5ucIFKC/RQuLi7s3buXvHnzpqm+h4cHe/fuTXP/Z86c4euvv2bYsGEEBQVha2v7oqH+J/j5+VG4cGG2bNmCl5fXS/e3dOlSlixZYoLI3pxLly4SExODvX0Ro/LChe0BCA09n6zN+fNnAXBwSLlNSMh5oqKiuHLlcrI6tra2WFvbEBKS2G/FipXYv/8nQkPPc+TIL5w+fYpKlaoQHv4IP79pjB49zjQDFRERERF5R2iJ+FNkyJCBXLlypbm+paUllpaWaa4fHh4OQPXq1cmfP/9zx/df8+jRI2rXrm2ysT5rE7D/gvDwRwBYW1sblVtZJX6OiIhIoU24UZ0kWbJYGdok1flnv4ntrIiMTOy3SRNPfvzxB1xdK5M+fXqGDfuUMmVcmDhxHFWr1qBMGRdGjx7Brl3bKV3aiUmTppMzZ86XGbKIiIiIyFvtrZzBdnR0ZNWqVbRp0wYnJycaNmzI0aNHWbVqFW5ubpQrV47+/fvzxx9/AInLu+vWrcuECRMoX748PXv2fOkl4o6Ojqxdu5aOHTvi7OxMjRo1mDNnjqFuu3btAKhTpw7Dhw8HIDQ0lO7du1O5cmXKly9P3759uXbtWprHffDgQUqWLMmOHTuoX78+zs7OtG/fnhs3bjBhwgQqVKhA1apVmT9/vqFNTEwMU6ZMwd3dndKlS1OpUiX69evH/fuJu+5dvXoVR0dHtm3bxocffkjp0qVxd3dn1aq/dpYePny4YTwplTk6OnLt2jXmzp1ruEbXr19nwIABVK1alVKlSlGzZk2mTp1KfHy8oY/jx4/TsWNHXFxcqFatGmPHjiUqKgp/f3/mzJnDtWvXDD+jZ8WQNI4vvviC6tWr88EHHxAZGUlERASjR4+mSpUqlC9fnvbt23PixAlDH/7+/rRt25YBAwZQrlw5xo8fn+afx7PEx6f+JUFKGzL8/fo8rU1CQup1zMzM//zbjGnT/AgLu8GFC9fp3bsfN25cZ8mShYwcOYalSxfy44+7Wbo0AHNzC4YM6Z/6gERERERE3nFvZYINMHPmTLy9vQkKCsLa2pru3buzbds2Fi5cyKRJk9i5cydr1qwx1L98+TK3b99m/fr1DBgwwCQxTJkyhebNm7Np0ybatm2Lv78/v/zyCx4eHvj7+wOwZs0aRo0axbVr1/joo4/IkCEDX375JUuXLuXOnTu0bduWyMi0ve8NEjcBmD9/PtOmTePLL7/kzJkzNGvWjPTp07NmzRpat26Nn58fZ88mLjX29fVl+/btTJ48mW3btjF58mR+/vlnoyQcYNKkSXTv3p0tW7bg5ubGZ599xpUrV9IU0969e8mTJw+dOnUyLKPv0aMHERERLFu2jK1bt9KpUycWL17M999/D8CVK1fo0KEDuXPnZtWqVfj7+7Nv3z7GjRtHp06d6NSpE3ny5HmuZfwA69at48svv8TPz48sWbLQpUsXrly5whdffMHq1aspW7YsH3/8MadOnTK0+eWXX7C1tSUoKChZEv8ybGwSd0H85883IiJpBjr5LolJbR4/Nm6TNCttY2NjmN1OKjPuO8LQR5KMGTNiYWEBwJQpE2nevCVFihQlODiIli1bU7x4Cbp27cHWrZsMm0yIiIiIiEhyb22C3aJFC9zd3bG3t6dZs2Y8evSIMWPGUKxYMerXr0+JEiU4f974GdeePXtSoEABihYtapIYPD09adasGQUKFKB79+7Y2Nhw9OhRLC0tyZo1KwA5cuTA2tqar7/+msyZMzNt2jSKFy9OmTJlmD17Nvfu3SMoKOi5ztuvXz+cnJxwcXGhSpUqZMqUiaFDh1K4cGG6desGYBi7k5MTU6ZMoVKlSuTLlw93d3eqVavGuXPnjPrs2LEjH3zwAQUKFGDAgAHEx8dz7NixNMWTK1cuLCwsyJw5M7ly5eKPP/6gWbNmjB8/nuLFi1OgQAE6duyIra2tIfFfvXo12bJl4/PPP6dYsWKUL1+eCRMmUKhQIbJkyULmzJmxsLAw9J1Wbdq0oUiRIjg5OfHzzz/z22+/4efnR5kyZXBwcGDgwIGULVs22UZsffv2pUCBAtjZ2aX5XM9iZ1cYCwsLwsIuGJUnfS5WLPmmeQ4ORY3q/LNN0aKOWFlZkTfv+8nq3Llzh8jIiBT7BThz5jRBQesYMiRxRcXdu3fInj07ANmyZSM2NpZ79+497zBFRERERF7a8zwi+iYfJ31rn8EuVKiQ4d9JrzoqWLCgoczS0pKYmBijNqZMngAcHByMPltbW/PkyZMU6547d47SpUsb7fqcK1cuChcunCzZfZa/jz1z5szkz5/fsJNe0nPiSWNv1qwZ+/fvZ9q0aVy8eJELFy4QFhZGhQoVnjqWpGd7nzaWZ7G0tKRt27Zs3bqV48ePc+nSJc6ePcvdu3cNS6DPnTtHqVKlSJfur1u0SpUqVKlS5YXOmeTv1+bkyZMkJCRQu3ZtozoxMTFER0cbPufMmTPF55lflqWlJVWrVmfTpg306tXX8DPauHEDNjZZcXEpn6yNvb0DBQvaERwcRNOmzQ3lGzdu+PNY4vjc3NzZsWMbPj7RZMyY8c86QVhYWFCjRs0U4xk/fgydO3clT57EFQG2trm4ffsWALdu3cLCwoIcOXKY7gKIiIiIiDxD0mRaTEx0ml/VFRMT/Wfb15/uvrUJ9t8TsyTPesn482xSlhZ/T5aTPO3blKeVx8fHkz59+uc67z/Hntq4x4wZw7Zt2/D09MTd3Z1evXqxZMkSbt26ZVTvecYCEBsb+9Rjv//+O23btuWPP/6gQYMGNG/eHGdnZz755JOnjuFFpBTD33/G8fHxWFlZERgYmKze38dr6vvi7wYMGELLlk3x9u5AmzZt+eWXg8ydO4tPPx1H5syZiYgI5+zZM9jZ2Rt2mh88eBh9+/Yge/bsNGjgwZYtmwkKCmThwmWGfnv37s+6dWv5+OMWdO/ei9DQUD7/fBzt2nUkf/4CyeLYv38vhw8fYv78xYayunUbsGzZIpycnFm0aAF16tQzyc9FRERERCStzM0tyJTJisjIB0DiO66f9hquhIQEYmKiiYx8QKZMVs/M/14F/d/yv4SjoyMbNmwgJibGkNzdvXuXS5cu0aZNm1dyzgcPHrBq1SpmzpyJh4eHofzChQtkzpw5zf2kT58+2XPEly5dempiunfvXk6ePMm+ffsMSePDhw+5d++eIWkvUqQIwcHBxMXFGb612rFjB5MmTWLLli3JfqmeNwaAYsWKERkZyZMnTyhS5K9XWn366acUL16ctm3bpvEKvDhX11osXRqAr+/ndOjQhjx53mfs2An07NkHgOPHj9G8eSNmz55P69aJX0C0bv0J0dHRzJs3m2++CaBQITvmzPkCT88Whn6LFi3G6tXrGTfuUzp3bk+OHDnp1q0Xw4aNSjEOH5/R9O07CBubrIayLl26c+bMKbp396ZMmbLMmjXvFV4JEREREZGU2dgkrqJMSrKfJVMmK0Ob100J9r/Exx9/zDfffMOQIUPo0aOHYXfv7Nmz06hRo1dyTisrK6ytrdm1axelSpXijz/+ICAggJMnT1KmTJk091O2bFnWrl3Lhg0bcHFxYcOGDZw7dw5nZ+cU6+fJkweADRs2UL9+fW7cuMGMGTN48uSJYel6mzZtWLFiBWPHjuX//u//uH//Pr6+vlSpUoWMGTOSOXNmHj16RFhYGPnz53/uGABcXV0pUaIEAwYMYNSoUeTNm5evv/6awMDA1/qO7UaNmtCoUZMUj1Wv7srt2+HJyjt06ESHDp1S7bdKlWps2fJ9mmLYunV3sjJLS0vmzl2YpvYiIiIiIq+KmZkZWbPmxNo6O3FxT18pC4nLwt/EzHWSt3aTs/+a/PnzExAQQHh4OB999BGdO3cmV65cfPPNN8l2fTaV9OnTM2vWLM6dO0eTJk3w9vYmKiqKgQMHEhISQlRUVJr6adq0KZ988gkTJkygWbNmXL9+nQ4dOjy1vrOzMyNGjGDFihU0bNiQESNGULFiRRo3bmx4RdZ7773H0qVLuXDhAp6engwYMIDatWszZswYAOrVq0euXLlo2rQpp06deu4YIPF5jqVLl1K6dGn69+9P06ZN+eWXX5gzZw5Vq1ZN41UUEREREZHXwdzcnPTpM6T6500m1wBmCW9yizWRd0h4eBTR0al/4ybyLOnSmZM9exYePHhMbGzq7zwXSQvdU2JquqfE1HRPiSnlyJEFC4tXl4RrBltERERERETEBPQM9jPcunWLBg0apFrHyckp2XuTTa1ChQrExcU99XjOnDnZuXPnK41BREREREREnk4J9jPY2tqyfv36VOskvWf4VQoMDEz1tVhJO22LiIiIiIjIm6EE+xksLCwoVKjQmw6DggULvukQREREREREJBV6BltERERERETEBJRgi4iIiIiIiJiAEmyR1yQ+Xm/EExERERF5mynBFnlN9Mp5EREREZG3mxJsERERERERERNQgi0iIiIiIiJiAkqwRURERERERExACbaIiIiIiIiICSjBFhERERERETEBJdgir4mZmdmbDkFERERERF4hJdgir4m5uRJsEREREZG3mRJsERERERERERNQgi0iIiIiIiJiAkqwRURERERERExACbaIiIiIiIiICSjBFnnDdu/eRb16tShU6D0qVHBi7tzZJCQkpNomMHANrq6VKFgwN9WrV+Dbb1cmq/Pbb0fx9PTAzi4vTk7FmDhxHDExMUZ1Jk8eT8mS9pQrVypZHwkJCdStW4u1a1e9/CBFRERERN4BSrBF3qDDhw/Rtm0rihQpxrJlAbRo8SE+PqPx95/51DbBwUH06OGNm5s7y5d/TbVqrvTt24N169Ya6ly8GEbLls2wtLRk8eLl9OjRhwUL5jBy5FBDnR07tjJvnj8+PpPo2bMPAwf24cyZ04bj69atJS4ujhYtWr2awYuIiIiIvGWUYL9iP/zwA+7u7jg5ObFixQqT9Hn16lUcHR05ePCgSfp721y/fp1NmzYZPru7u+Pv7/8GI3o6X9/PcXJyZt68Rbi712XEiDH06tUPP7/pREVFpdjm88/H0bRpc8aPn4y7ex2mTp1Js2ZeTJ48wVDH398PKysrVqz4ljp16tOzZx98fCYRELCcq1evAPDjj3uoWdONli0/wtu7O8WKFWffvp8AiImJ4fPPx/Ppp2P1/m4RERERkTRSgv2K+fn5UbhwYbZs2YKXl5dJ+sybNy979+7FxcXFJP29bYYNG8ZPP/1k+Lx27Vo6der0BiNKWXR0NPv378XDo4lReZMmzYiMjODgwQPJ2ly+fInQ0BA8PBonaxMWdoELF0IA2LNnF3Xq1CdDhgx/q+NJfHw8u3fv+rPEDEvLTIbjGTKkJz4+DoBlyxZRoEAB3N3rmmKoIiIiIiLvBCXYr9ijR48oU6YM+fPnx8rKyiR9WlhYkCtXLqPkSZ4uR44cZMmS5U2HkcylSxeJiYnB3r6IUXnhwvYAhIaeT9bm/PmzADg4pNwmJOQ8UVFRXLlyOVkdW1tbrK1tCAlJ7LdixUrs3/8ToaHnOXLkF06fPkWlSlUID3+En980Ro8eZ5qBioiIiIi8I5RgPwdHR0dmz55N7dq1qVGjBhcvXuT48eO0adMGFxcXKlasSJ8+fbh+/bqh/rVr15g7dy6Ojo5A4nLlhQsX0rVrV8qUKYO7uzs7d+5k586d1K9fn7Jly9K5c2fu3bsHwMGDBylZsiQLFy6kcuXKeHl5ceXKledaIp7Ux44dO6hfvz7Ozs60b9+eGzduMGHCBCpUqEDVqlWZP3++UbvvvvuOhg0b4uzsTMOGDfnyyy+Jj483HD98+DDt27enXLlylC5dmoYNGxIUFGQ4Pnz4cIYPH86UKVOoWrUqZcqUoVu3bty6dQv4a6n7pk2b8PT0xMnJCS8vL0JDQ5k7dy7VqlWjUqVKjBs3zmjTrz179tCqVStcXFyoUaMGkyZN4o8//gCgXbt2HDp0iHXr1uHu7m645n9fIp5a+6Sf29q1a+nYsSPOzs7UqFGDOXPmpOlaP4/w8EcAWFtbG5VbWSV+joiISKFNuFGdJFmyWBnaJNX5Z7+J7ayIjEzst0kTTzw8muLqWhkvr8YMG/YpZcq44O/vR9WqNShTxoXRo0dQrVp5unbtaLgnRUREREQkZUqwn9PXX3/N7NmzmTNnDgUKFKBbt25UrFiRDRs2sHz5cq5fv87IkSMB2Lt3L3ny5KFTp07s3bvX0Me8efPw8PAgODiY4sWLM3ToUBYsWMDUqVNZsGABJ06cYNGiRYb6cXFx/PDDD6xatYqJEye+0DOxcXFxzJ8/n2nTpvHll19y5swZmjVrRvr06VmzZg2tW7fGz8+Ps2cTZ0hXrVqFr68vvXv3ZtOmTfTv359FixYxbdo0AG7dukXnzp1xcnJi3bp1rF+/HmdnZ0aNGsXdu3cN5924cSMPHz4kICCARYsWcfLkSfz8/IximzlzJiNHjmTNmjWEh4fz8ccfc/HiRb766isGDBjA119/ze7duwHYsWMHPXr0wM3NjcDAQMaNG8fmzZsZOHAgAP7+/ri4uNCwYUPWrl3LPz2rfZIpU6bQvHlzNm3aRNu2bfH39+eXX3557uuemvj41HcKNzdP/uv59y84ntYmISH1OmZm5n/+bca0aX6Ehd3gwoXr9O7djxs3rrNkyUJGjhzD0qUL+fHH3SxdGoC5uQVDhvRPfUAiIiIiIu+4dG86gP+aZs2a4eTkBCQu/37w4AG5c+cmX758FChQAD8/P8NMX65cubCwsCBz5szkypXL0Iebmxuenp4AtGrVil27djFgwACcnZ0BqFatGufPGy8P7tSpE3Z2dkDizO+L6NevnyH2KlWqcOzYMYYOHYqZmRndunVj3rx5nD9/HkdHR+bNm0ePHj1o1KgRAAUKFCAyMpJx48bRr18/oqOj6dOnD507dzYk/F27dmX9+vVcvHgRW1tbIHEW1cfHh/Tp0+Pg4ICHhwc//PBDsrFVqlQJgLp16/LVV1/h4+NDpkyZcHBwwN/fn/Pnzxtm/+vWrUvPnj0BKFy4MAkJCfTq1YuQkBCKFClC+vTpsbS0JEeOHMmuQVraA3h6etKsWTMAunfvzpIlSzh69CgVK1Z8oWufEhsbGwAiIyONyiMikmagbZ7a5vFj4zZJs9I2NjaG2e2kMuO+Iwx9JMmYMaPh31OmTKR585YUKVKUgQP70LJla4oXL0HXrj1o3LgecXFxWFhYPNc4RURERETeFUqwn1OhQoUM/86aNSve3t6MHz+e2bNnU6VKFWrVqkXDhg3T3EemTImbTBUsWNBQZmlpmWw5blJybarYM2fOTP78+Q3JsaWlJZC4e/T9+/e5efMmM2bMYNasWYY28fHxREdHc/XqVRwcHPDy8mLFihWcO3eOy5cvc+bMGSBxtjxJwYIFSZ8+veGztbU1T548STUuW1tbw3VJii3p/c3nzp0zJP1JkpLzc+fOGRLkp0lrewcHB6M6KcX9suzsCmNhYUFY2AWj8qTPxYo5Jmvj4FDUUMfJqUyyNkWLOmJlZUXevO8n6/fOnTtERkak2C/AmTOnCQpax4EDRwC4e/cO2bNnByBbtmzExsZy7949cufO/SLDFRERERF562mJ+HNKSkSTDB48mO+//57+/fuTkJDA+PHjadGihSEhTEm6dMm/13jWsu+/zzK+qH+eN6UlyPDXMuQRI0awfv16w58NGzawfft2ChQoQEhICA0aNGDPnj3Y2dnh7e3NkiVLkvWVlo3Y0hoXYPQs9j/jTem6vmj7lOJOqe3LsLS0pGrV6mzatMGo740bN2BjkxUXl/LJ2tjbO1CwoB3BwUFG5Rs3bvjzWOKXFW5u7uzYsY3o6Oi/1QnCwsKCGjVqphjP+PFj6Ny5K3ny5AXA1jYXt28nPi9/69YtLCwsUlwVICIiIiIiiZRgv4QLFy4wduxYcubMyccff8zs2bNZvHgxoaGhhtnc/6KcOXOSI0cOrly5QqFChQx//v789LfffkvOnDlZtmwZXbp0oVatWoZnr02diP6do6MjR48eNSo7fPgwkHzW+VW0N7UBA4Zw9OhhvL07sGvXdiZPHs/cubPo128QmTNnJiIinMOHDxk91z548DCCggIZOnQA33+/gyFDBhAUFMjw4Z8a6vTu3Z+7d+/w8cct2L59C/Pnz2HMmBG0a9eR/PkLJItj//69HD58iL59BxjK6tZtQEDAl+zYsZWZM6dSp069NH2JISIiIiLyrlKC/RKyZ8/Opk2bGDNmDKGhoYSFhbFu3TqyZs2Kvb39mw7vhZmZmdGlSxe++uorAgICuHz5Mjt27OCzzz7D0tKSDBkykCdPHm7evMkPP/zAtWvX2L59O5999hlAqrP3L8vb25vt27czb948wsLC2L17N+PHj6d27dqGBDlLlixcu3aNmzdvvlD718nVtRZLlwYQEnKeDh3asHbtGsaOnUCfPv0BOH78GB4eddi5c5uhTevWnzB1qh8//LCbDh3acODAXubM+QJPzxaGOkWLFmP16vVERf1O587tWbBgDt269WLiRN8U4/DxGU3fvoOwsclqKOvSpTtVq1ane3dvYmNj8fWd+WougoiIiIjIW0LTUS8he/bsLFq0iOnTp9OqVSvi4uIoW7Ysy5YtM9k7r9+UTp06kTFjRr766ismT56Mra0trVq1om/fvgC0b9+eCxcuMHToUGJiYrCzs2PgwIHMnj2bEydOULNmysuQX1b9+vWZMWMG8+fPZ968eeTIkYPGjRsb4gJo3bo1w4YNo2nTphw4cOC5279ujRo1oVGjJikeq17dldu3w5OVd+jQiQ4dOqXab5Uq1diy5fs0xbB16+5kZZaWlsyduzBN7UVEREREBMwSXuV6XhExCA+PIjo69k2HIf9x6dKZkz17Fh48eExsbOqvZBNJC91TYmq6p8TUdE+JKeXIkQULi1e3kFtLxEVERERERERMQEvE/+MqVKhg9Fqsf8qZMyc7d+58jRGJiIiIiIi8m5Rg/8cFBgamumu3hYXFa4xGRERERETk3aUE+z+uYMGCbzoEERERERERQc9gi4iIiIiIiJiEEmwRERERERERE1CCLfKaxMfrjXgiIiIiIm8zJdgir4leOS8iIiIi8nZTgi0iIiIiIiJiAkqwRURERERERExACbaIiIiIiIiICSjBFhERERERETEBJdgiIiIiIiIiJqAEW+Q1MTMze9MhiIiIiIjIK6QEW+Q1MTdXgi0iIiIi8jZTgi0iIiIiIiJiAkqwRURERERERExACbaIiIiIiIiICSjBFhERERERETEBJdgib9ju3buoV68WhQq9R4UKTsydO5uEhIRU2wQGrsHVtRIFC+amevUKfPvtymR1fvvtKJ6eHtjZ5cXJqRgTJ44jJibGqM7kyeMpWdKecuVKJesjISGBunVrsXbtqpcfpIiIiIjIO0AJtsgbdPjwIdq2bUWRIsVYtiyAFi0+xMdnNP7+M5/aJjg4iB49vHFzc2f58q+pVs2Vvn17sG7dWkOdixfDaNmyGZaWlixevJwePfqwYMEcRo4caqizY8dW5s3zx8dnEj179mHgwD6cOXPacHzdurXExcXRokWrVzN4EREREZG3jFnCs6bK5F/L0dGRSZMm4eXllab658+f59q1a7i5ub2ymNq1a0e+fPmYPHnyKzvHf1V4eBTR0bFGZa1aeRIe/oitW3cbynx8xrB8+RJOngwhU6ZMyfqpWrUcpUs7s2jRckNZly4dOX78Nw4e/A2AQYP6sWvXdg4dOkaGDBkAWLZsMSNGDObw4RPkz1+A0aOHExZ2gYCA1QC4uVWjXbuOdO7clZiYGKpVq4Cv73Tc3eua+ErIy0iXzpzs2bPw4MFjYmPj33Q48hbQPSWmpntKTE33lJhSjhxZsLB4dfPMmsH+D9u7dy8eHh5prt+tWzdOnDjxCiMCf39/Ro0a9UrP8baIjo5m//69eHg0MSpv0qQZkZERHDx4IFmby5cvERoagodH42RtwsIucOFCCAB79uyiTp36huQ6sY4n8fHx7N69688SMywt/0rgM2RIT3x8HADLli2iQIECSq5FRERERJ6DEuz/sFy5cmFpafmmwzCSLVs2rK2t33QY/wmXLl0kJiYGe/siRuWFC9sDEBp6Plmb8+fPAuDgkHKbkJDzREVFceXK5WR1bG1tsba2ISQksd+KFSuxf/9PhIae58iRXzh9+hSVKlUhPPwRfn7TGD16nGkGKiIiIiLyjlCC/R/m6OhIYGAgAMOHD2f48OFMmTKFqlWrUqZMGbp168atW7cAcHd359q1a8yZM4d27doBEBERwejRo6lSpQrly5enffv2RjPc/v7+tG3blgEDBlCuXDlGjBiBi4sLX3/9tVEcc+bMwc3Njfj4eNq1a8fw4cMNx44ePconn3yCs7Mzbm5ujBs3jsjISAD69OlD9+7dDXXPnDmDo6MjS5YsMZR99dVX1K2b9lnUNWvW0KRJE5ydnSlbtixt2rQxGlNUVBRjx46lcuXKlCtXjlGjRjFo0KA0x2xK4eGPAJJ9IWFllfg5IiIihTbhRnWSZMliZWiTVCelLzqsrKyIjEzst0kTTzw8muLqWhkvr8YMG/YpZcq44O/vR9WqNShTxoXRo0dQrVp5unbtyL17915muCIiIiIibz0l2G+RjRs38vDhQwICAli0aBEnT57Ez88PgLVr15InTx46deqEv78/CQkJdOnShStXrvDFF1+wevVqypYty8cff8ypU6cMff7yyy/Y2toSFBREt27daNCgARs3bjQ6b3BwMM2aNcPc3Ph2OnPmDP/3f/+Hq6srGzZsYNq0aZw8eZJOnTqRkJBA7dq1OXToELGxic8l79u3DzMzMw4ePGjoY8+ePXzwwQdpGv+OHTvw8fHB29ubLVu2sHz5cqKjo/n0008NdYYNG8a+ffuYOXMm3377LREREWzatCnNMZtSfHzq/f3zeia2Sf25I3NzcxISUq9jZmb+599mTJvmR1jYDS5cuE7v3v24ceM6S5YsZOTIMSxdupAff9zN0qUBmJtbMGRI/9QHJCIiIiLyjlOC/RaxtrbGx8cHBwcHKlWqhIeHB0ePHgUgR44cWFhYkDlzZrJly8bPP//Mb7/9hp+fH2XKlMHBwYGBAwdStmxZVqxYYdRv3759KVCgAHZ2djRv3pyjR49y7do1AI4fP87FixdT3GhtyZIlVK9ene7du2NnZ0eFChWYPn06x44d49ChQ7i5uREVFcVvv/0GwP79+/nggw84fPgwsbGx/P777xw6dCjNCXa2bNmYOHEizZo1I1++fJQtW5aWLVty7tw5AK5cucK2bdsYO3Ys1apVo1ixYkydOhVbW9s0x2xKNjY2AMlmxyMikmagbZ7a5vFj4zZJs9I2NjaG2e2kMuO+Iwx9JMmYMSMWFhYATJkykebNW1KkSFGCg4No2bI1xYuXoGvXHmzduom4uLjnHqeIiIiIyLsi3ZsOQEynYMGCpE+f3vDZ2tqaJ0+epFj35MmThlnkv4uJiSE6OtrwOWfOnEZLjStWrEj+/PnZuHEj3bp1Y8OGDZQrV45ChQolO8epU6e4dOkSLi4uyY6FhoZSuXJlypQpw759+3B2dubw4cN89dVX7Nmzh//973/cu3ePzJkzU65cuTSNv2LFioSGhjJ37lwuXLjApUuXOHv2rGHWN2lm/u/xZMyYEWdn5+eK2VTs7ApjYWFBWNgFo/Kkz8WKOSZr4+BQ1FDHyalMsjZFizpiZWVF3rzvJ+v3zp07REZGpNgvwJkzpwkKWseBA0cAuHv3DtmzZwcSv7yIjY3l3r175M6d+0WGKyIiIiLy1lOC/Rb5+47RzxIfH4+VlZXhGe6n9fPPTdTMzMzw9PQkODjYsBS7f//+Tz1HkyZNjJ6zTpIjRw4g8dnwnTt3UqlSJWxsbHB2dsbJyYmDBw9y7do1ateubZhdfZbg4GCGDx9OkyZNKFeuHK1bt+bcuXP4+PgAGPpJbZl1WmI2FUtLS6pWrc6mTRvo1asvZmZmAGzcuAEbm6y4uJRP1sbe3oGCBe0IDg6iadPmhvKNGzf8eSzxiw43N3d27NiGj080GTNm/LNOEBYWFtSoUTPFeMaPH0Pnzl3JkycvALa2ubh9O/EZ/lu3bmFhYWHyayAiIiIi8jbREvF3VLFixYiMjOTJkycUKlTI8GfRokXs2rUr1bbNmzcnJCSEb7/9lsePH9OwYcMU6xUtWpSQkBCj/mNjY5k0aRI3btwAEhPs//3vf+zYsYOqVasCUK1aNX7++efnev4aYOHChbRs2ZLJkyfzySefULFiRa5cuQJAQkICjo6OmJmZGZakQ+KM/cmTJ58rZlMaMGAIR48extu7A7t2bWfy5PHMnTuLfv0GkTlzZiIiwjl8+BB37941tBk8eBhBQYEMHTqA77/fwZAhAwgKCmT48L+eNe/duz93797h449bsH37FubPn8OYMSNo164j+fMXSBbH/v17OXz4EH37DjCU1a3bgICAL9mxYyszZ06lTp16pEun7+RERERERJ5GCfY7JEuWLFy8eJG7d+/i6upKiRIlGDBgAD///DOXLl1i0qRJBAYG4uDgkGo/+fLlo3LlykyfPp06depgZWWVYr1OnTpx6tQpxo0bR2hoKL/++iuDBg3i4sWL2NnZAVCkSBHy5cvHmjVrDAl21apV+fnnn3n48CHVq1dP8/jy5s3L0aNHOXnyJJcvX2b58uUEBAQAiYl0gQIFaNiwIePHj+fAgQOEhIQwatQobt68aZg9TkvMpuTqWoulSwMICTlPhw5tWLt2DWPHTqBPn/4AHD9+DA+POuzcuc3QpnXrT5g61Y8ffthNhw5tOHBgL3PmfIGnZwtDnaJFi7F69Xqion6nc+f2LFgwh27dejFxom+Kcfj4jKZv30HY2GQ1lHXp0p2qVavTvbs3sbGx+PrONPn4RURERETeJpqOeoe0a9eOKVOmcP78eTZs2MDSpUuZOnUq/fv3JyoqCgcHB+bMmWNIdFPj5eXFzz//nOLmZknKli3L4sWLmTVrFs2bNydz5sxUrVqVYcOGGS1Dr127Nl9++SVVqlQxtLO0tKRy5cpkzpw5zeMbPXo0Y8aMoW3btmTIkIHixYvj6+vLgAEDOHHiBBUqVGD8+PFMmDCBPn36kJCQQJMmTXBxcTE8u57WmE2pUaMmNGrUJMVj1au7cvt2eLLyDh060aFDp1T7rVKlGlu2fJ+mGLZu3Z2szNLSkrlzF6apvYiIiIiIgFmCqd89JPIvFR0dzU8//USVKlWMZt3r169P06ZN6dWr1ys9f3h4FNHRsa/0HPL2S5fOnOzZs/DgwWNiY1N/JZtIWuieElPTPSWmpntKTClHjixYWLy6hdyawZZ3RoYMGRg3bhyVKlWiZ8+eWFhYsHbtWq5fv06DBg3edHgiIiIiIvIfpwRb/vVu3br1zATYyckp2fu7/8nMzIyFCxcydepUPvroI+Li4ihZsiRLly595nPnIiIiIiIiz6IEW/71bG1tWb9+fap1kl5F9SwlSpRg6dKlJohKRERERETEmBJs+dezsLCgUKFCbzoMERERERGRVOk1XSIiIiIiIiImoARbRERERERExASUYIu8JvHxeiOeiIiIiMjbTAm2yGuiV86LiIiIiLzdlGCLiIiIiIiImIASbBERERERERETUIItIiIiIiIiYgJKsEVERERERERMQAm2iIiIiIiIiAkowRZ5TczMzN50CCIiIiIi8gopwRZ5TczNlWCLiIiIiLzNlGCLiIiIiIiImIASbBERERERERETUIItIiIiIiIiYgJKsEVERERERERMQAm2yBu2e/cu6tWrRaFC71GhghNz584mISEh1TaBgWtwda1EwYK5qV69At9+uzJZnd9+O4qnpwd2dnlxcirGxInjiImJMaozefJ4Spa0p1y5Usn6SEhIoG7dWqxdu+rlBykiIiIi8g5Qgi3yBh0+fIi2bVtRpEgxli0LoEWLD/HxGY2//8yntgkODqJHD2/c3NxZvvxrqlVzpW/fHqxbt9ZQ5+LFMFq2bIalpSWLFy+nR48+LFgwh5Ejhxrq7NixlXnz/PHxmUTPnn0YOLAPZ86cNhxft24tcXFxtGjR6tUMXkRERETkLaMEW5IJDAzE0dHxudrMnz+fSpUq4eLiwokTJ15JXI6OjgQGBgIwfPhw2rVr90r6Tompz5fE1/dznJycmTdvEe7udRkxYgy9evXDz286UVFRKbb5/PNxNG3anPHjJ+PuXoepU2fSrJkXkydPMNTx9/fDysqKFSu+pU6d+vTs2Qcfn0kEBCzn6tUrAPz44x5q1nSjZcuP8PbuTrFixdm37ycAYmJi+Pzz8Xz66Vi9v1tEREREJI2UYMtLi4iIYNasWbRp04aNGzdSvHjxV3KevXv34uHh8Z/r+2mio6PZv38vHh5NjMqbNGlGZGQEBw8eSNbm8uVLhIaG4OHROFmbsLALXLgQAsCePbuoU6c+GTJk+FsdT+Lj49m9e9efJWZYWmYyHM+QIT3x8XEALFu2iAIFCuDuXtcUQxUREREReScowZaXFh4eTkJCAlWqVCFfvnykT5/+lZwnV65cWFpa/uf6fppLly4SExODvX0Ro/LChe0BCA09n6zN+fNnAXBwSLlNSMh5oqKiuHLlcrI6tra2WFvbEBKS2G/FipXYv/8nQkPPc+TIL5w+fYpKlaoQHv4IP79pjB49zjQDFRERERF5RyjBfss5Ojoye/ZsateuTY0aNbh4MTGpmzp1Kq6urri4uNCqVSv27t37Qv0fPHgQd3d3ADp06GBYRn348GHat29PuXLlKF26NA0bNiQoKMjQbvjw4QwdOpQJEyZQoUIFKlWqxOzZswkNDaVNmzY4OzvTpEkTjh07ZjSWlJZx9+rVi/bt2xuVXbhwAUdHR86fT56kpuTvfSckJDBv3jxq1qxJ2bJlGTFiBNHR0c93YdIgPPwRANbW1kblVlaJnyMiIlJoE25UJ0mWLFaGNkl1/tlvYjsrIiMT+23SxBMPj6a4ulbGy6sxw4Z9SpkyLvj7+1G1ag3KlHFh9OgRVKtWnq5dO3Lv3r2XGa6IiIiIyFtPCfY74Ouvv2b27NnMmTMHOzs7RowYwb59+5g2bRrr1q2jYcOGdO/enT179jx33y4uLqxZswYAf39//P39uXXrFp07d8bJyYl169axfv16nJ2dGTVqFHfv3jW03bx5MxYWFgQGBtKxY0fmzp1L9+7d6dy5M2vWrCFjxoyMG/fsWVQvLy8OHTrEjRs3DGXr16/HycmJokWLPveYFi5cyOLFixk6dCiBgYHY2NiwefPm5+7nWeLjU98p3Nw8+a9nfHz8M9skJKRex8zM/M+/zZg2zY+wsBtcuHCd3r37cePGdZYsWcjIkWNYunQhP/64m6VLAzA3t2DIkP6pD0hERERE5B2nBPsd0KxZM5ycnChbtiyXLl1i48aNTJo0icqVK2NnZ8f//d//0ahRI5YsWfLcfWfIkIEcOXIAkDVrVrJly0Z0dDR9+vRh8ODBFCpUiCJFitC1a1eePHnCxYsXDW2zZcvGsGHDKFiwIB07dgTAw8ODDz74AEdHR7y8vDh37twzY6hVqxa2trZs2LABSExCg4KCaN68+XOPJyEhga+++or27dvTuHFj7O3tGTFiBCVKlHjuvp7FxsYGgMjISKPyiIikGWibp7Z5/Ni4TdKstI2NjWF2O6nMuO8IQx9JMmbMiIWFBQBTpkykefOWFClSlODgIFq2bE3x4iXo2rUHW7duIi4u7rnHKSIiIiLyrkj3pgOQV69QoUKGf586dQqANm3aGNV58uRJssTrRRUsWBAvLy9WrFjBuXPnuHz5MmfOnAEwStDy589vmKXNnDkzAAUKFDAct7S05MmTJ888X7p06WjatClBQUF069aNn3/+mfv379O4ceNntv2nBw8ecOfOHZycnIzKy5YtS2ho6HP3lxo7u8JYWFgQFnbBqDzpc7FiyXdyd3Aoaqjj5FQmWZuiRR2xsrIib973k/V7584dIiMjUuwX4MyZ0wQFrePAgSMA3L17h+zZswOJX4bExsZy7949cufO/SLDFRERERF56ynBfgf8ffOuhITEZckrV64kS5YsRvVSWpL8IkJCQmjTpg2lSpWiWrVq1KtXj+zZs/Phhx8a1UtpM7QXjaFFixYsWbKE//3vf2zYsIEPPviArFmzPnc/Sa+kSrpOSdKlM/2viqWlJVWrVmfTpg306tXXcO6NGzdgY5MVF5fyydrY2ztQsKAdwcFBNG361wz9xo0b/jyW+GWKm5s7O3Zsw8cnmowZM/5ZJwgLCwtq1KiZYjzjx4+hc+eu5MmTFwBb21zcvn0LgFu3bmFhYWFYrSAiIiIiIskpwX7HJD2TfOfOHUqWLGkonzlzJubm5vTr1++lz/Htt9+SM2dOli1bZij7/vvvgeSJq6k4ODjg4uLCli1b2LVrF9OnT3+hfrJnz07evHk5cuQIderUMZT/73//eyW7ow8YMISWLZvi7d2BNm3a8ssvB5k7dxaffjqOzJkzExERztmzZ7Czs8fW1haAwYOH0bdvD7Jnz06DBh5s2bKZoKBAFi7863r37t2fdevW8vHHLejevRehoaF8/vk42rXrSP78BZLFsX//Xg4fPsT8+YsNZXXrNmDZskU4OTmzaNEC6tSp90q+aBAREREReVvoGex3TNGiRalduzZjx47l+++/58qVKyxatIgvvviCggULmuQcefLk4ebNm/zwww9cu3aN7du389lnnwEQExNjknOkpEWLFgQEBGBpaUn16tVfuJ8uXbqwcuVK1qxZQ1hYGH5+fhw/ftyEkf7F1bUWS5cGEBJyng4d2rB27RrGjp1Anz79ATh+/BgeHnXYuXOboU3r1p8wdaofP/ywmw4d2nDgwF7mzPkCT88WhjpFixZj9er1REX9TufO7VmwYA7duvVi4kTfFOPw8RlN376DsLH5a9a/S5fuVK1ane7dvYmNjcXXd+YruQYiIiIiIm8LTUe9g2bOnMnMmTMZM2YMjx49omDBgkycOPGFNgVLSfv27blw4QJDhw4lJiYGOzs7Bg4cyOzZszlx4gQ1a6a8RPllNWzYkPHjx+Pp6WnYtOtFfPLJJ8THxzN//nzu3r2Lq6srLVu2JCwszITR/qVRoyY0atQkxWPVq7ty+3Z4svIOHTrRoUOnVPutUqUaW7Z8n6YYtm7dnazM0tKSuXMXpqm9iIiIiIiAWcKrWrMr8ppduXKFevXqsWXLFuzs7N50OMmEh0cRHR37psOQ/7h06czJnj0LDx48JjY29VeyiaSF7ikxNd1TYmq6p8SUcuTIgoXFq1vIrRls+c+7ceMGx48f5+uvv8bV1fVfmVyLiIiIiMjbTwm2PNWtW7do0KBBqnWcnJxYsWLFa4ooZQ8ePGD48OHY2dkxZ84co2Pdu3fn4MGDqbYPDAykcOHCrzJEERERERF5B2iJuDxVXFwcV69eTbVOxowZyZMnz2uK6PndunWLP/74I9U677///ivZIfyftERcTEHL5MTUdE+JqemeElPTPSWmpCXi8sZYWFhQqFChNx3GS3nvvffedAgiIiIiIvKO0Gu6RERERERERExACbaIiIiIiIiICSjBFhERERERETEBJdgir0l8vPYTFBERERF5mynBFnlNtGG/iIiIiMjbTQm2iIiIiIiIiAkowRYRERERERExASXYIiIiIiIiIiagBFtERERERETEBJRgi7wmZmZmbzoEERERERF5hZRgi7wm5uZKsEVERERE3mZKsEVERERERERMQAm2iIiIiIiIiAkowRYRERERERExASXYIiIiIiIiIiagBFvkDdu9exf16tWiUKH3qFDBiblzZ5OQkJBqm8DANbi6VqJgwdxUr16Bb79dmazOb78dxdPTAzu7vDg5FWPixHHExMQY1Zk8eTwlS9pTrlypZH0kJCRQt24t1q5d9fKDFBERERF5ByjBFnmDDh8+RNu2rShSpBjLlgXQosWH+PiMxt9/5lPbBAcH0aOHN25u7ixf/jXVqrnSt28P1q1ba6hz8WIYLVs2w9LSksWLl9OjRx8WLJjDyJFDDXV27NjKvHn++PhMomfPPgwc2IczZ04bjq9bt5a4uDhatGj1agYvIiIiIvKWUYItr9XBgwdxdHTk6tWrADx48IA1a9aY9Bzt2rVj+PDhTz0eGBiIo6Njmvt73vrPw9f3c5ycnJk3bxHu7nUZMWIMvXr1w89vOlFRUSm2+fzzcTRt2pzx4yfj7l6HqVNn0qyZF5MnTzDU8ff3w8rKihUrvqVOnfr07NkHH59JBAQs5+rVKwD8+OMeatZ0o2XLj/D27k6xYsXZt+8nAGJiYvj88/F8+ulYvb9bRERERCSNlGDLa+Xi4sLevXvJmzcvAL6+vmzYsMGk5/D392fUqFEm7fNViI6OZv/+vXh4NDEqb9KkGZGRERw8eCBZm8uXLxEaGoKHR+NkbcLCLnDhQggAe/bsok6d+mTIkOFvdTyJj49n9+5df5aYYWmZyXA8Q4b0xMfHAbBs2SIKFCiAu3tdUwxVREREROSdoARbXqsMGTKQK1cuLCwsAJ75rPGLyJYtG9bW1ibv19QuXbpITEwM9vZFjMoLF7YHIDT0fLI258+fBcDBIeU2ISHniYqK4sqVy8nq2NraYm1tQ0hIYr8VK1Zi//6fCA09z5Ejv3D69CkqVapCePgj/PymMXr0ONMMVERERETkHaEEW16JH374AS8vL8qUKUPVqlUZPnw4jx49MloiPnz4cNatW8ehQ4cMS7ATEhJYtGgRH3zwAWXKlKFZs2bPPcP9zyXiO3bsoEmTJjg5OdGmTRuuX79u0rG+qPDwRwDJvgywskr8HBERkUKbcKM6SbJksTK0SaqT0pcMVlZWREYm9tukiSceHk1xda2Ml1djhg37lDJlXPD396Nq1RqUKePC6NEjqFatPF27duTevXsvM1wRERERkbeeEmwxufv379O7d29atGjB5s2bmTNnDr/88gu+vr5G9UaNGkXDhg0Ny8YBZs6cyTfffMPo0aMJDg6mffv2fPbZZ6xcmXyX7LQ4evQoffr0oX79+mzYsIHmzZuzcOHClx6jKcTHpz57b26e/NczPj7+mW0SElKvY2Zm/uffZkyb5kdY2A0uXLhO7979uHHjOkuWLGTkyDEsXbqQH3/czdKlAZibWzBkSP/UByQiIiIi8o5L96YDkLfPrVu3iImJ4f333ydfvnzky5ePBQsWEBcXx6NHjwz1rK2tsbS0JH369OTKlYvff/+d5cuXM2PGDNzc3AAoWLAg165dY8mSJXzyySfPHUtAQADlypWjd+/eABQuXJhz586xYsUKk4z1ZdjY2AAQGRlpVB4RkTQDbfPUNo8fG7dJmpW2sbExzG4nlRn3HWHoI0nGjBkN/54yZSLNm7ekSJGiDBzYh5YtW1O8eAm6du1B48b1iIuLMyzvFxERERERY0qwxeRKlChB48aN6d69O7ly5aJ69eq4ublRt25djhw58tR2ISEhREdHM2jQIKPZ29jYWGJiYvjjjz+wtLR8rljOnTtH9erVjcpcXFz+FQm2nV1hLCwsCAu7YFSe9LlYseQ7lzs4FDXUcXIqk6xN0aKOWFlZkTfv+8n6vXPnDpGRESn2C3DmzGmCgtZx4EDiz+ju3Ttkz54dSHyuPTY2lnv37pE7d+4XGa6IiIiIyFtPS8TllZg+fTpbtmzB29ubBw8eMGTIEDp37pxqm6QNz/z8/Fi/fr3hz8aNG9m+fbvRjthpZWZmlmxZdfr06Z+7n1fB0tKSqlWrs2nTBqPN3jZu3ICNTVZcXMona2Nv70DBgnYEBwcZlW/cuOHPY4UAcHNzZ8eObURHR/+tThAWFhbUqFEzxXjGjx9D585dyZMncYd3W9tc3L59C0hclWBhYUGOHDlebtAiIiIiIm8xzWCLyR07doxNmzYxcuRI7O3t6dixIxs2bGDIkCF89NFHRnX//o5le3t70qVLx/Xr16ldu7ahfMWKFYSEhODj4/PcsRQvXpxff/3VqOx///vfc/fzqgwYMISWLZvi7d2BNm3a8ssvB5k7dxaffjqOzJkzExERztmzZ7Czs8fW1haAwYOH0bdvD7Jnz06DBh5s2bKZoKBAFi5cZui3d+/+rFu3lo8/bkH37r0IDQ3l88/H0a5dR/LnL5Asjv3793L48CHmz19sKKtbtwHLli3CycmZRYsWUKdOPdKl038yRERERESeRjPYYnJWVlZ8/fXXTJ06lUuXLnHu3Dk2b96MnZ2dYclxksyZM3P79m2uXLmCtbU1rVu3ZtasWQQFBXHlyhXWrl3L1KlTX3hZcqdOnThz5gxTpkwhLCyMDRs2EBAQYIphmoSray2WLg0gJOQ8HTq0Ye3aNYwdO4E+ffoDcPz4MTw86rBz5zZDm9atP2HqVD9++GE3HTq04cCBvcyZ8wWeni0MdYoWLcbq1euJivqdzp3bs2DBHLp168XEib7/DAEAH5/R9O07CBubrIayLl26U7Vqdbp39yY2NhZf35mv5iKIiIiIiLwlzBJexYuI5Z23e/du5syZw4ULFzA3N6dKlSoMGzaMGzdu0L59e3bt2kX+/Pk5ceIEvXr14tGjR2zfvp2cOXOyYMECAgMDuX37Nnnz5qVVq1Z4e3sbzXanpl27duTLl4/JkycDcODAAaZOncr58+cpWrQoDRs2ZNq0aZw9ezZN/QUGBjJixIg013+a8PAooqNjX6oPkXTpzMmePQsPHjwmNjb1HeNF0kL3lJia7ikxNd1TYko5cmTBwuLVzTMrwRZ5TZRgiynofzLE1HRPianpnhJT0z0lpvSqE2wtERcRERERERExAe1YJP8ZixYtYt68eanWGTlyJB9++GGa+rt16xYNGjRItY6Tk9O/4pVeIiIiIiLy76cEW/4zWrVqRb169VKtkzNnzjT3Z2try/r161OtkzFjxjT3JyIiIiIi7zYl2PKfkTVrVrJmzfrsimlkYWFBoUKFTNafiIiIiIi82/QMtoiIiIiIiIgJKMEWERERERERMQEl2CIiIiIiIiImoARb5DWJj9cr50VERERE3mZKsEVek4QEJdgiIiIiIm8zJdgiIiIiIiIiJqAEW0RERERERMQElGCLiIiIiIiImIASbBERERERERETUIIt8pqYmZm96RBEREREROQVUoIt8pqYmyvBFhERERF5mynBFhERERERETEBJdgiIiIiIiIiJqAEW0RERERERMQElGCLiIiIiIiImIASbJE3bPfuXdSrV4tChd6jQgUn5s6dTUJCQqptAgPX4OpaiYIFc1O9egW+/XZlsjq//XYUT08P7Ozy4uRUjIkTxxETE2NUZ/Lk8ZQsaU+5cqWS9ZGQkEDdurVYu3bVyw9SREREROQdoARb5A06fPgQbdu2okiRYixbFkCLFh/i4zMaf/+ZT20THBxEjx7euLm5s3z511Sr5krfvj1Yt26toc7Fi2G0bNkMS0tLFi9eTo8efViwYA4jRw411NmxYyvz5vnj4zOJnj37MHBgH86cOW04vm7dWuLi4mjRotWrGbyIiIiIyFvGLOFZU2Xyn+Lo6MikSZPw8vJ606H8Zx09epSZM2dy6tQpMmfOTM2aNRkyZAjZsmV7qX7Dw6OIjo41KmvVypPw8Eds3brbUObjM4bly5dw8mQImTJlStZP1arlKF3amUWLlhvKunTpyPHjv3Hw4G8ADBrUj127tnPo0DEyZMgAwLJlixkxYjCHD58gf/4CjB49nLCwCwQErAbAza0a7dp1pHPnrsTExFCtWgV8fafj7l73pcYtppUunTnZs2fhwYPHxMbGv+lw5C2ge0pMTfeUmJruKTGlHDmyYGHx6uaZNYMt8jdhYWF07twZR0dHVq9ezcyZMzl+/Dj9+vUz+bmio6PZv38vHh5NjMqbNGlGZGQEBw8eSNbm8uVLhIaG4OHROFmbsLALXLgQAsCePbuoU6e+IblOrONJfHw8u3fv+rPEDEvLvxL4DBnSEx8fB8CyZYsoUKCAkmsRERERkeegBFvkb9avX0/u3LkZNWoUDg4OVKhQgbFjx/Lzzz9z5coVk57r0qWLxMTEYG9fxKi8cGF7AEJDzydrc/78WQAcHFJuExJynqioKK5cuZysjq2tLdbWNoSEJPZbsWIl9u//idDQ8xw58gunT5+iUqUqhIc/ws9vGqNHjzPNQEVERERE3hFKsP/Dbt68SY8ePXBxcaFmzZoEBwcbHd+9ezdeXl44OztTt25d/Pz8jDa5cnR0ZNWqVbRp0wYnJycaNmzI0aNHWbVqFW5ubpQrV47+/fvzxx9/GNr8+uuvtG/fnvLly1O5cmVGjBjBgwcP0hxzXFwcU6dOpVatWpQuXZoGDRrwzTffGNX57rvvaNiwIc7OzjRs2JAvv/yS+PjE5UBLly6lZMmSHD9+HID4+HjatGmDl5dXsg28UjJ8+HA+/PBDo7Jr165RvHhx9u/fT9OmTZkyZQpmZmaG40n/fvToUZrHmRbh4Yn9WVtbG5VbWSV+joiISKFNuFGdJFmyWBnaJNX5Z7+J7ayIjEzst0kTTzw8muLqWhkvr8YMG/YpZcq44O/vR9WqNShTxoXRo0dQrVp5unbtyL17915muCIiIiIibz0l2P9RsbGxeHt78+DBAwICApg1axZLliwxHP/xxx/p378/rVq1YuPGjYwdO5YtW7YwZMgQo35mzpyJt7c3QUFBWFtb0717d7Zt28bChQuZNGkSO3fuZM2aNQAcP36cdu3aUbRoUVavXs2sWbM4duwYnTt3Ji4uLk1xf/3112zdupWZM2eybds22rZty2effcbhw4cBWLVqFb6+vvTu3ZtNmzbRv39/Fi1axLRp0wDo2LEj5cuXZ9SoUTx58oRFixZx+vRpZsyYYbQc+mm8vLw4fvw4ly9fNpQFBweTJ08eqlSpgoODA2XLljVqs2jRInLlyoWjo2OaxphW8fGpb39gbp781zPpi4bU2iQkpF7HzMz8z7/NmDbNj7CwG1y4cJ3evftx48Z1lixZyMiRY1i6dCE//ribpUsDMDe3YMiQ/qkPSERERETkHacE+z/qwIEDnD9/Hl9fX0qVKoWLiwuTJk0yHF+wYAGtWrWidevWFCxYkBo1ajBu3Di2bt3K1atXDfVatGiBu7s79vb2NGvWjEePHjFmzBiKFStG/fr1KVGiBOfPJy4pXrp0KY6OjowePRoHBweqVKnCjBkzOHnyJHv37k1T3JcvXyZz5szkz5+ffPny0bZtW5YtW0bhwoUBmDdvHj169KBRo0YUKFCA+vXrM2DAAAICAoiOjsbc3JzJkydz/fp1Ro0ahb+/P2PGjMHOzi5N569YsSIFChRgw4YNhrLg4GCaNWuWYkI7ZcoU9uzZw2effUb69OnTdI60srGxASAyMtKoPCIiaQba5qltHj82bpM0K21jY2OY3U4qM+47wtBHkowZM2JhYQHAlCkTad68JUWKFCU4OIiWLVtTvHgJunbtwdatm9L8RYqIiIiIyLso3ZsOQF7MuXPnyJo1KwULFjSUlShRAktLSwBOnTrF8ePHWbv2r1c3JW0YHxoaSv78+QEoVKiQ4XjSjtV/79PS0tKw9PrcuXNUr17dKI7ixYtjbW3N2bNnqVWr1jPj/uSTT9i5cye1atWiRIkSVK9enUaNGpEzZ07u37/PzZs3mTFjBrNmzTK0iY+PJzo6mqtXr+Lg4EC+fPkYOXIkI0eOpE6dOjRv3jxtF43EWVtPT0+Cg4Pp3bs3p06dIiQkhHnz5hnVe/LkCWPGjGH9+vWMHz+eOnXqpPkcaWVnVxgLCwvCwi4YlSd9LlYs+Yy5g0NRQx0npzLJ2hQt6oiVlRV5876frN87d+4QGRmRYr8AZ86cJihoHQcOHAHg7t07ZM+eHYBs2bIRGxvLvXv3yJ0794sMV0RERETkracE+z/KzMwsxeXC6dIl/kjj4+Px9vZOMfnMlStXsvp/l9JMLvyVoKdUntbZXTs7O7Zv386hQ4fYt28fe/bsYdGiRUyaNAlXV1cARowYQbVq1ZK1zZs3r+Hf//vf/0iXLh0nTpzg0aNHZM2aNU3nB2jevDlz5szhxIkTbN68mXLlyhl90RAZGUnv3r05fPgwM2bMoGHDhmnu+3lYWlpStWp1Nm3aQK9efQ3Pem/cuAEbm6y4uJRP1sbe3oGCBe0IDg6iadO/frYbN27481jiONzc3NmxYxs+PtFkzJjxzzpBWFhYUKNGzRTjGT9+DJ07dyVPnsTrbGubi9u3bwFw69YtLCwsyJEjh+kugIiIiIjIW0ZLxP+jSpQoQUREhGH5NsDFixcNy42LFi1KWFgYhQoVMvy5efMmvr6+PH78+IXO6ejoyJEjR4zKzpw5Q2RkJA4ODmnqY8WKFWzfvp3q1aszdOhQgoODqVq1Kps3byZnzpzkyJGDK1euGMV98uRJ/Pz8DH389NNPfPPNN8ydO5dMmTIxbtzz7XadL18+KleuzLZt29iyZYvRO8NjYmLo1q0bx48fZ8mSJa8suU4yYMAQjh49jLd3B3bt2s7kyeOZO3cW/foNInPmzEREhHP48CHu3r1raDN48DCCggIZOnQA33+/gyFDBhAUFMjw4Z8a6vTu3Z+7d+/w8cct2L59C/Pnz2HMmBG0a9eR/PkLJItj//69HD58iL59BxjK6tZtQEDAl+zYsZWZM6dSp069FL+QERERERGRREqw/6MqV65MmTJlGDp0KL/99hsnTpxg6NChhtnnLl26sG3bNubMmUNYWBgHDhxgxIgRREREGM1gP4//+7//4+zZs4wfP57Q0FAOHjzI4MGDKVmyJFWrVk1TH/fv38fHx4ddu3Zx7do1fvrpJ06fPo2LiwtmZmZ06dKFr776ioCAAC5fvsyOHTv47LPPsLS0JEOGDDx8+JCRI0fSqlUr3NzcmDhxIps3b2bjxo3PNZbmzZvz9ddf8/DhQ6Mk+osvvuDIkSOMHz8ee3t77ty5Y/iTll3Kn5eray2WLg0gJOQ8HTq0Ye3aNYwdO4E+ffoDcPz4MTw86rBz5zZDm9atP2HqVD9++GE3HTq04cCBvcyZ8wWeni0MdYoWLcbq1euJivqdzp3bs2DBHLp168XEib4pxuHjM5q+fQdhY/PXSoAuXbpTtWp1unf3JjY2Fl/fmSYfv4iIiIjI28Qs4WnrfuVf78GDB0yYMIHdu3djaWlJt27dWLhwIYMGDcLLy4stW7bwxRdfEBISQrZs2XB3d2fw4MGGTa4cHR2ZNGmSYQY3MDCQESNGcPbsWcM52rVrR758+Zg8eTKQuLman58fp06dwsrKijp16jBo0CCyZcuWpphjY2OZOXMmmzdv5s6dO+TKlQtPT0969+5t2Ghr5cqVfPXVV1y9ehVbW1saN25M3759yZAhA/369ePYsWNs3LgRK6vEV1P5+PiwceNGgoODee+999IUR1RUFNWrV8fd3d2wQzlA/fr1uXjxYoptVqxYQeXKldPUf0rCw6OIjo594fYiAOnSmZM9exYePHhMbGzqO8aLpIXuKTE13VNiarqnxJRy5MiChcWrm2dWgi3ymijBFlPQ/2SIqemeElPTPSWmpntKTOlVJ9haIi4iIiIiIiJiAtqxSEzi1q1bNGjQINU6Tk5OrFix4pXF0L17dw4ePJhqncDAQMM7t0VERERERExJCbaYhK2tLevXr0+1TtLrol6VcePG8ccff6Ra5/3333+lMYiIiIiIyLtLCbaYhIWFhdG7pN+EtG5wJiIiIiIi8iroGWwRERERERERE1CCLSIiIiIiImICSrBFRERERERETEAJtshrEh+vV86LiIiIiLzNlGCLvCYJCUqwRURERETeZkqwRURERERERExACbaIiIiIiIiICSjBFhERERERETEBJdgiIiIiIiIiJqAEW0RERERERMQElGCLvCZmZmZvOgQREREREXmFlGCLvCbm5kqwRURERETeZkqwRURERERERExACbaIiIiIiIiICSjBFhERERERETEBJdgib9ju3buoV68WhQq9R4UKTsydO5uEhIRU2wQGrsHVtRIFC+amevUKfPvtymR1fvvtKJ6eHtjZ5cXJqRgTJ44jJibGqM7kyeMpWdKecuVKJesjISGBunVrsXbtqpcfpIiIiIjIO0AJtsgbdPjwIdq2bUWRIsVYtiyAFi0+xMdnNP7+M5/aJjg4iB49vHFzc2f58q+pVs2Vvn17sG7dWkOdixfDaNmyGZaWlixevJwePfqwYMEcRo4caqizY8dW5s3zx8dnEj179mHgwD6cOXPacHzdurXExcXRokWrVzN4EREREZG3jFnCs6bK5F/J0dGRSZMm4eXl9aZDeWEPHjxg586dfPjhhwC0a9eOfPnyMXny5Dca13fffcfy5cu5cuUKuXPnpmXLlnTu3BkLC4uX6jc8PIro6FijslatPAkPf8TWrbsNZT4+Y1i+fAknT4aQKVOmZP1UrVqO0qWdWbRouaGsS5eOHD/+GwcP/gbAoEH92LVrO4cOHSNDhgwALFu2mBEjBnP48Any5y/A6NHDCQu7QEDAagDc3KrRrl1HOnfuSkxMDNWqVcDXdzru7nVfatxiWunSmZM9exYePHhMbGz8mw5H3gK6p8TUdE+JqemeElPKkSMLFhavbp5ZM9jyxvj6+rJhw4Y3HYaRDRs2MHbsWNq2bcuGDRvo378/X3zxBfPnzzf5uaKjo9m/fy8eHk2Myps0aUZkZAQHDx5I1uby5UuEhobg4dE4WZuwsAtcuBACwJ49u6hTp74huU6s40l8fDy7d+/6s8QMS8u/EvgMGdITHx8HwLJliyhQoICSaxERERGR56AEW96Yf+PiiW+++QZPT08++ugjChYsiIeHB506dWLt2rXPbvycLl26SExMDPb2RYzKCxe2ByA09HyyNufPnwXAwSHlNiEh54mKiuLKlcvJ6tja2mJtbUNISGK/FStWYv/+nwgNPc+RI79w+vQpKlWqQnj4I/z8pjF69DjTDFRERERE5B2hBPs/4ObNm/To0QMXFxdq1qxJcHCw0fHdu3fj5eWFs7MzdevWxc/Pz2gzK0dHR1atWkWbNm1wcnKiYcOGHD16lFWrVuHm5ka5cuXo378/f/zxh6HNr7/+Svv27SlfvjyVK1dmxIgRPHjw4LniPnr0KJ988gnOzs64ubkxbtw4IiMjARg+fDjr1q3j0KFDODo6Gto8fvyYESNGUKFCBcqXL8/w4cP5/fffATh48CAlS5bkhx9+oHHjxpQuXZoGDRqwc+fONMWTkJDABx98wNSpU43K169fT9myZYmMjGTw4MF07tzZ6Li5uTmPHj16rrGnRXh4Yp/W1tZG5VZWiZ8jIiJSaBNuVCdJlixWhjZJdf7Zb2I7KyIjE/tt0sQTD4+muLpWxsurMcOGfUqZMi74+/tRtWoNypRxYfToEVSrVp6uXTty7969lxmuiIiIiMhbTwn2v1xsbCze3t48ePCAgIAAZs2axZIlSwzHf/zxR/r370+rVq3YuHEjY8eOZcuWLQwZMsSon5kzZ+Lt7U1QUBDW1tZ0796dbdu2sXDhQiZNmsTOnTtZs2YNAMePH6ddu3YULVqU1atXM2vWLI4dO0bnzp2Ji4tLU9xnzpzh//7v/3B1dWXDhg1MmzaNkydP0qlTJxISEhg1ahQNGzbExcWFvXv3Gtpt376d3LlzExgYiK+vL5s3b2bRokWG43FxcUydOpVRo0axceNGihUrxrBhw3j8+PEzYzIzM6N58+Zs3rzZaPZ8w4YN1KlTBysrK8qXL0/hwoUNxyIiIvjmm29wdXVN07ifR3x86jP45ubJfz3j41N/7sjc3JyEhNTrmJmZ//m3GdOm+REWdoMLF67Tu3c/bty4zpIlCxk5cgxLly7kxx93s3RpAObmFgwZ0j/1AYmIiIiIvOOUYP/LHThwgPPnz+Pr60upUqVwcXFh0qRJhuMLFiygVatWtG7dmoIFC1KjRg3GjRvH1q1buXr1qqFeixYtcHd3x97enmbNmvHo0SPGjBlDsWLFqF+/PiVKlOD8+cSlw0uXLsXR0ZHRo0fj4OBAlSpVmDFjBidPnjRKhlOzZMkSqlevTvfu3bGzs6NChQpMnz6dY8eOcejQIaytrbG0tCR9+vTkypXL0M7Z2ZkBAwZQsGBBPvjgA6pXr87//vc/o7779+9P1apVsbOzo2fPnkRGRnLu3Lk0xeXp6cmNGzc4fPgwAHfu3OHnn39OcbO4x48f07NnT6Kjoxk6dGiy4y/LxsYGwDCrnyQiImkG2uapbR4/Nm6TNCttY2NjmN1OKjPuO8LQR5KMGTMaNnCbMmUizZu3pEiRogQHB9GyZWuKFy9B16492Lp1U5q/YBEREREReRele9MBSOrOnTtH1qxZKViwoKGsRIkSWFpaAnDq1CmOHz9u9Ixw0uxsaGgo+fPnB6BQoUKG40k7U/+9T0tLS8Oy8nPnzlG9enWjOIoXL461tTVnz56lVq1az4z71KlTXLp0CRcXl2THQkNDqVy5cort7OzsjD5nzZqVa9euGZXZ29sb/m1llbg0+smTJ8+MCSB//vxUqlSJ4OBgKlasyKZNm8idOzdVqlQxqnfnzh26devG1atXWbJkieE6mpKdXWEsLCwIC7tgVJ70uVgxx2RtHByKGuo4OZVJ1qZoUUesrKzIm/f9ZP3euXOHyMiIFPsFOHPmNEFB6zhw4AgAd+/eIXv27ABky5aN2NhY7t27R+7cuV9kuCIiIiIibz0l2P9yZmZmKS4LTpcu8UcXHx+Pt7c3zZs3T1bn7zPDSfX/LqUlyPD0zccSEhJInz59muKOj4+nSZMmdO/ePdmxHDlyPLVdWl6F9fedsf8eW1p5eXnx+eef8+mnn7JhwwaaNWtmdC1CQ0Px9vYmPj6elStXUrRo0TT3/TwsLS2pWrU6mzZtoFevvpiZmQGwceMGbGyy4uJSPlkbe3sHCha0Izg4iKZN//qZb9y44c9jiV+kuLm5s2PHNnx8osmYMeOfdYKwsLCgRo2aKcYzfvwYOnfuSp48eQGwtc3F7du3ALh16xYWFhap/uxERERERN51WiL+L1eiRAkiIiIMy7cBLl68aFhWXLRoUcLCwihUqJDhz82bN/H19U3Tc8kpcXR05MiRI0ZlZ86cITIyEgcHhzT1UbRoUUJCQoziio2NZdKkSdy4cQPAkFC+bvXr1yc2NpY1a9Zw8uRJo+XhV65coUOHDmTKlIlvv/32lSXXSQYMGMLRo4fx9u7Arl3bmTx5PHPnzqJfv0FkzpyZiIhwDh8+xN27dw1tBg8eRlBQIEOHDuD773cwZMgAgoICGT78U0Od3r37c/fuHT7+uAXbt29h/vw5jBkzgnbtOpI/f4Fkcezfv5fDhw/Rt+8AQ1ndug0ICPiSHTu2MnPmVOrUqZfiFzUiIiIiIpJICfa/XOXKlSlTpgxDhw7lt99+48SJEwwdOtQw49qlSxe2bdvGnDlzCAsL48CBA4wYMYKIiAijGezn8X//93+cPXuW8ePHExoaysGDBxk8eDAlS5akatWqaeqjU6dOnDp1inHjxhEaGsqvv/7KoEGDuHjxomEZeObMmbl9+zZXrlx5oThfVKZMmWjQoAHTp0+nXLlyRsvnR44cSUxMDDNmzCBdunTcuXPH8OdVcHWtxdKlAYSEnKdDhzasXbuGsWMn0KdPfwCOHz+Gh0cddu7cZmjTuvUnTJ3qxw8/7KZDhzYcOLCXOXO+wNOzhaFO0aLFWL16PVFRv9O5c3sWLJhDt269mDjRN8U4fHxG07fvIGxsshrKunTpTtWq1ene3ZvY2Fh8fWe+kmsgIiIiIvK20HTUv5y5uTlffPEFEyZMoFOnTlhaWtKtWzfDc8kNGjRg5syZfPHFFyxYsIBs2bLh7u7O4MGDX/icZcqUYfHixfj5+eHp6YmVlRV16tRh0KBBaV4iXrZsWRYvXsysWbNo3rw5mTNnpmrVqgwbNsywxNvT05MdO3bQuHFjtm/f/sLxvggvLy++++47o9nrW7ducejQIQCaNWuWrM3Zs2dfSSyNGjWhUaMmKR6rXt2V27fDk5V36NCJDh06pdpvlSrV2LLl+zTFsHXr7mRllpaWzJ27ME3tRUREREQEzBKe5+FVEXlh4eFRREfHvukw5D8uXTpzsmfPwoMHj4mNTf2VbCJpoXtKTE33lJia7ikxpRw5smBh8eoWcmuJuIiIiIiIiIgJaIm4PJdbt27RoEGDVOs4OTmxYsWK1xTRXypUqJDqe5pz5szJzp07X2NEIiIiIiLyLlGCLc/F1taW9evXp1on6bVQr1tgYGCqr+tKyyvAREREREREXpQSbHkuFhYWRrtu/5sULFjwTYcgIiIiIiLvMD2DLSIiIiIiImICSrBFRERERERETEAJtoiIiIiIiIgJKMEWeU3i4/XKeRERERGRt5kSbJHXJLUdzkVERERE5L9PCbaIiIiIiIiICSjBFhERERERETEBJdgiIiIiIiIiJqAEW0RERERERMQE0r3pAETeFRYWKX+fFR+foB3GRURERETeAkqwRV6DhIQEbGwypXgsLi6ehw9/V5ItIiIiIvIfpwRb5DUwMzPjs+XruHjzrlG5XR5bPuvYHHNzMyXYIiIiIiL/cUqwRV6Tizfvcu7qzTcdhoiIiIiIvCLa5ExERERERETEBJRgi4iIiIiIiJiAEmyR/4Dr169RpEgB9u376Zl1AwPX4OpaiYIFc1O9egW+/XZlsjobNqyjXr1aFC78PmXLlqBv3x7cvn3bqM7kyeMpWdKecuVKJesjISGBunVrsXbtqpcbmIiIiIjIW0QJtsi/3LVrV2nVypPw8EfPrBscHESPHt64ubmzfPnXVKvmSt++PVi3bq2hzrp1a/H27oCzswtLl37FiBGj2bv3R1q0aMwff/wBwI4dW5k3zx8fn0n07NmHgQP7cObMaaM+4uLiaNGilekHLCIiIiLyH6VNzkT+peLj41m9+hs++2wUCQlp22H888/H0bRpc8aPnwyAu3sdHj58wOTJE2jevCUAfn7TqVOnHtOm+RnaFSlSlIYNP2DHjq00aeLJjz/uoWZNN1q2/AiAgIAV7Nv3E8WLlyAmJobPPx+Pr+90zMzMTDtoEREREZH/MM1gvyRHR0cCAwPfdBhvxJEjRzh8+PCbDsPkHj58yJgxY6hZsyblypXj448/fiPjPHnyfwwZ0p8PP/yYuXMXPrP+5cuXCA0NwcOjsVF5kybNCAu7wIULIcTHx1OrVm3atfs/ozpFixYDICws7M8SMywt/3pvd4YM6YmPjwNg2bJFFChQAHf3ui8xOhERERGRt48SbHlhbdq04fLly286DJMbOHAgv/76KzNmzOC7776jRIkSdO7cmQsXLrzWOPLnz8/Bg78xfvwkMmXK/Mz658+fBcDBoYhReeHC9gCEhJzH3NwcH5/PadiwkVGdzZs3AlC8eHEAKlasxP79PxEaep4jR37h9OlTVKpUhfDwR/j5TWP06HEvPT4RERERkbeNEmyRv7l06RL79u3js88+o0KFChQuXJjRo0eTO3dugoODX2ss2bPn4P3386W5fnh4OABWVtZG5VmyWAEQERGRYruwsAuMG/cppUs7U6dOfQCaNPHEw6Mprq6V8fJqzLBhn1KmjAv+/n5UrVqDMmVcGD16BNWqladr147cu3fvRYYoIiIiIvJWUYL9HG7evEmPHj1wcXGhZs2ayRKu3bt34+XlhbOzM3Xr1sXPz4+YmBjDcUdHR1atWkWbNm1wcnKiYcOGHD16lFWrVuHm5ka5cuXo37+/YaMpgF9//ZX27dtTvnx5KleuzIgRI3jw4EGaYw4MDKRmzZqsXr2aGjVq4OLiQq9evbh165ahjru7O1OmTMHDw4PKlStz6NAh2rVrx7Rp0xg5ciQVKlSgXLlyDBo0iMjISMNYAEaMGMHw4cPTFEtMTAxTpkzB3d2d0qVLU6lSJfr168f9+/cNdS5fvkyXLl1wcXHB1dWVZcuWUbduXaNl+N999x0NGzbE2dmZhg0b8uWXXxIfH5+mGA4ePIijoyOhoaFG5e3bt2fw4MFkz56dhQsX4uTkZDhmZmaGmZmZIYH9t3rWNTA3T/7rfv78Oby8GmNhkY4lS1YY6piZmTFtmh9hYTe4cOE6vXv348aN6yxZspCRI8ewdOlCfvxxN0uXBmBubsGQIf1fxZBERERERP5TlGCnUWxsLN7e3jx48ICAgABmzZrFkiVLDMd//PFH+vfvT6tWrdi4cSNjx45ly5YtDBkyxKifmTNn4u3tTVBQENbW1nTv3p1t27axcOFCJk2axM6dO1mzZg0Ax48fp127dhQtWpTVq1cza9Ysjh07RufOnYmLi0tz7Pfv3+fLL7/Ez8+PL7/8khs3buDt7U1sbKyhTkBAAJ9++imLFy+mbNmyACxfvhxbW1vWrl3L1KlT2bVrF8uXLwdg7969AIwcOZJRo0alKQ5fX1+2b9/O5MmT2bZtG5MnT+bnn39m/vz5AERFRdGxY0fi4+P55ptvmDlzJoGBgVy5csXQx6pVq/D19aV3795s2rSJ/v37s2jRIqZNm5amGCpVqkT+/PmNvhy5efMmv/zyC15eXtjY2FCrVi0yZMhgOL5t2zYuXbqEq6trms7xptjY2ADw+HGkUXlkZITR8ST79v1Eo0Z1AFi3bpNhKfnfZcyYEQsLCwCmTJlI8+YtKVKkKMHBQbRs2ZrixUvQtWsPtm7d9Fz3pIiIiIjI28gkCfadO3c4efLkW/0/2AcOHOD8+fP4+vpSqlQpXFxcmDRpkuH4ggULaNWqFa1bt6ZgwYLUqFGDcePGsXXrVq5evWqo16JFC9zd3bG3t6dZs2Y8evSIMWPGUKxYMerXr0+JEiU4f/48AEuXLsXR0ZHRo0fj4OBAlSpVmDFjBidPnjQkuGnx5MkTpkyZQoUKFXB2dmbq1KmcO3eOAwcOGOrUqlWLatWq4eTkZEguixQpwsCBA7Gzs+ODDz6gevXq/PrrrwDkypULAGtra6ytrZOfNAVOTk5MmTKFSpUqkS9fPtzd3alWrRrnzp0DYPPmzdy/f5/p06dTvHhxKlSowNSpU4120J43bx49evSgUaNGFChQgPr16zNgwAACAgKIjo5+ZgxmZmY0b96cjRs3Gso2btzIe++9R5UqVZLVP3r0KCNGjKBevXq4ubmlaZxvioNDUSBxyfffJX0uWtTRUBYYuIZWrTzJmzcfmzfvNGxy9jRnzpwmKGgdQ4Ykrla4e/cO2bNnByBbtmzExsZqmbiIiIiIvPOeO8GOjIxkxIgRrFy5EoAtW7ZQu3ZtWrZsSePGjblx44bJg/w3OHfuHFmzZqVgwYKGshIlSmBpaQnAqVOn+Oabb3BxcTH86d69O4DRcuRChQoZ/p0pU+IuzX/v09LS0rCs/Ny5c5QrV84ojuLFi2Ntbc3Zs2fTHHuWLFkoXbq04bODgwNZs2Y1JLb/jCuJvb3xjKa1tbXRkvfn1axZM2JiYpg2bRq9e/fGw8ODrVu3GpY2nzp1isKFC5MtWzZDm6TxQuJM/M2bN5kxY4bRdfbx8SE6Otroi4zUeHp6cvXqVcOXBRs2bKBZs2bJllDv3LmTTp06UbZs2TTPkL9J9vYOFCxoR3BwkFH5xo0b/jyW+DPeuXMbvXp1pWLFymzcuI28ed9/Zt/jx4+hc+eu5MmTFwBb21zcvp34mMGtW7ewsLAgR44cJh6RiIiIiMh/y3O/B3v69Ols27aN6tWrAzBt2jSKFy9Ojx498PPzY9q0aUyfPt3kgb5pZmZmKT7jmi5d4iWMj4/H29ub5s2bJ6uTNNv79/p/l9KzscBT332ckJBA+vTp0xQ3kGLduLg4w9JfwPBFwd/9fZm0KYwZM4Zt27bh6emJu7s7vXr1YsmSJYbnwS0sLFJ9jjjp2IgRI6hWrVqy43nz5k1THPnz56dSpUoEBweTJUsWzp49i7+/v1GdgIAAJk6cSIMGDZgyZYrJr4UpRESEc/bsGezs7LG1tQVg8OBh9O3bg+zZs9OggQdbtmwmKCiQhQuXAfDHH38wYEAfrKys6d9/MGfPnjHq8/338yXbWG3//r0cPnyI+fMXG8rq1m3AsmWLcHJyZtGiBdSpUy/Fe1tERERE5F3y3P9HvGvXLoYPH07jxo353//+x7Vr1xg6dCgffPABsbGxjB079lXE+caVKFGCiIgIzp8/T9GiiUtxL168aNj0q2jRooSFhRnNBB88eJAVK1bw2WefkTnzs1+z9E+Ojo4cOXLEqOzMmTNERkbi4OCQ5n4ePnzIlStXKFCgAADnz58nMjKSkiVLPndML+rBgwesWrWKmTNn4uHhYSi/cOGC4doUL16c1atX8/DhQ8MsdmhoqGH365w5c5IjRw6uXLlidJ03b97Mjh07mDJlyv+zd9/xNZ7/48dfJ4PITkRri5kEScSKrQ1qhojWCDEiRlFiJ4KQqNgJQmN+0agajRE1irZ0aNSoGCVpUKO2IAmZ53z/yMep0wTncGK+n4/Hefyc677u637fd+7P99f3uZbW8Xh7ezNnzhwsLCyoXbu2RntfffUVYWFh+Pr6EhwcjEKheJFbLzQJCcfp3Lk9CxZ8QffuPQHo3r0nmZmZLF68gHXrYqhQwZ6oqCV4eXUB4Pff47l+/RoAXbt65WtzzJhAxo2boFEWGjqJ4cNHY2lppS4bMGAwZ86cZvBgf1xdazF//uJCukshhBBCCCHeHDon2Hfv3lUPHd6/fz9GRkbq3mwrKyut5sG+idzd3XF1dWXcuHGEhIRgaGhIWFiYuvd5wIABBAQEEBUVRfv27bl27RrBwcGULVtWowdbF/369cPHx4ewsDB8fHy4desWYWFhVK9enYYNG+rU1tixY5k4cSI5OTlMnToVNzc36tWr91xxPWJqakpycjIpKSnq+bhPYm5ujoWFBfv27aNGjRpkZGQQExPDqVOncHV1BaBDhw4sXLiQMWPGMGbMGDIyMggNDQX+Xcl7wIABREREULp0aZo1a8bZs2eZMmUKLVq00KmXuXXr1oSGhvLll18SFBSkLj9//jzTp0+nVatWDBo0iFu3bqmPmZiYaD3fXN8aN27KjRv3n1kG0KePH336+BXYTtOmzQs852l27fohX5mJiQmLFi3VqR0hhBBCCCHedjrPwS5Tpox6/u/evXupVasW5uZ5++zu37+fsmXL6jfC14SBgQFLliyhUqVK+Pn5MWjQINq3b6+ed9qmTRsiIiLYu3cvnp6ejB07liZNmhAVFfXc13R1dWX58uWcPHkSLy8vAgICcHNz4//+7/90GiIO4OnpycCBA/H396dq1aosWbLkhXtm/fz8iImJ0UhQn8TY2Jj58+eTmJiIp6cn/v7+PHz4kFGjRvHXX3/x8OFDihQpwvLly8nOzqZr16589tlndOnSRX3+o2sGBgYSExNDu3bt+Pzzz+natStTp07VKfZixYrRtm1blEolbdu2VZfv3r2b7Oxs9uzZQ5MmTTQ+n3/+uU7XEEIIIYQQQrxbFKonTfR9gtWrVzNnzhzKlSvHuXPnmDdvHu3atWPYsGHs27ePiRMn0rNnz8KKV+goNjaWoKAgnRZFe1UuX77MhQsXaNKkibrs+vXrNGvWjLVr11K3bt1XGN2L6ztjGYmXr2mUVStbklWBA0hJSScnR7u9vMW7zcjIABsbM3lnhN7IOyX0Td4poW/yTgl9srU1w9Cw8Har1nmIeJ8+fShevDi///67eiVoyOthnDJlCt26ddN7kOLdkJmZycCBAxk9ejQfffQRqampREZGYm9vrx5GLoQQQgghhBCvq+da9rdDhw506NBBoywiIkIvAQntXL9+nTZt2jy1jrOzM15eXoUey44dOwgODn5qnX79+jF8+PCn1qlcuTLz5s0jOjqaBQsWYGJiQsOGDbUeEq+vOIQQQgghhBDieeg8RBzy9iNesWIFv/76Kzdv3mT58uXs3bsXR0dHWrZsWRhxiv/Izc195r7PRYsWpWTJkoUeS3p6usZiYAWxtLR85kJob0scTyJDxIU+yDA5oW/yTgl9k3dK6Ju8U0KfXrsh4pcuXaJHjx5kZmZSp04dzpw5Q25uLufPn2fx4sUsXryYDz74oBBCFY8zNDTU2FrqVTIzM8PMzOxVh/HaxCGEEEIIIYR4N+mcYM+cOZPixYvz5ZdfYmpqSs2aNQGYO3cumZmZREdHS4IthBBCCCGEEOKdo3OCffDgQaZPn46lpSW5ubkax7p160ZAQIC+YhPirWJf0k6rMiGEEEIIIcSb6bkWOTMyKvi0rKysF95bWYi3kUqlYkrfzgUey81VolTqvBSCEEIIIYQQ4jWjc4Jdt25dlixZQsOGDSlatCgACoUCpVLJunXrqF27tt6DFOJNp1AouH//Ibm5+RfmUCpVkmALIYQQQgjxFtA5wR49ejQ9evTgo48+wt3dHYVCwYoVK0hOTubvv//mq6++Kow4hXjj5eYqZeVLIYQQQggh3mI6r09erVo1vvnmG9zd3YmPj8fQ0JBff/2V8uXL8/XXX+Pk5FQYcQohhBBCCCGEEK81nXuwN2/eTKNGjZg7d25hxCOEEEIIIYQQQryRdO7BDg0NJSEhoTBiEUIIIYQQQggh3lg6J9glS5YkLS2tMGIR4q0mK+wLIYQQQgjxdtN5iHi3bt34/PPPOXbsGA4ODpiZmeWr4+XlpY/YhHhrqFQqLCxMuHv3gawYLoQQQgghxFtKoVKpdPqvfUdHx6c3qFDw559/vlBQQrytUlLSZSVx8UKMjAywsTGTd0nojbxTQt/knRL6Ju+U0CdbWzMMDXUeyK01nXuw9+3bVxhxCCGEEEIIIYQQbzSdE+wyZcoURhxCCCGEEEIIIcQbTecEOyoq6pl1hg0b9lzBCCGEEEIIIYQQbyq9Jtjm5ua89957kmALIYQQQgghhHjn6Dy7+8yZM/k+R48eZenSpVhaWjJp0qTCiFOIt9oPP+zjo4+aU6HC+9St68yiRQvQdv3BEyeOU7q0LRcv/q0uu3jxb957z/KJn+HDP1XXnTEjjOrVK1G7dg2+/nqtRtsqlYpWrZqzadN6/dyoEEIIIYQQbzGde7ALYmpqSrNmzRg6dCizZs1i8+bN+mhWiHfC4cOH6NWrK506eRMYOJH4+IOEhk4iNzeH4cNHPfXcP/88jY/PJ+Tk5GiUv/9+SXbs2Juv/sqVy9i6NRYfn94A7Nmzi8WLFzJv3kLu3k1h1KjPqFWrNo6OTgBs3ryJ3NxcunTpqqe7FUIIIYQQ4u2l1/XJS5cuTXJy8gu34+DgQGxsrB4ievMcOXKEw4cPv+ownuny5cs4ODgQHx9fqNdZuHAhHh4eLzWO77//ni5duuDm5oaHhwczZ84kIyNDb+3/16xZ03F2dmHx4mV4eLQiKGgyQ4eOIDJyLg8fPizwnKysLBYvXkjbth5kZWXmO160aFHq1q2v8TE2Nmbr1lgmTAihQYOGABw48CPNmn3Axx93w99/MNWqOfLLLz+przF9ehgTJ4agUCgK7f6FEEIIIYR4W+glwVapVFy9epXly5fLKuMvyMfHh4sXL77qMF4bfn5+bNq06aVd7/DhwwwbNoxWrVqxefNmQkJC2LFjB1OnTi2U62VmZvLrrz/Trp2nRrmnZyfS0lKJjz9Y4Hl7937HnDkzGDFiNJMmhT7zOiqVisDA0VSr5sjgwUMfO6LAxKSY+luRIsYolbkA/N//LaNcuXJ4eLTS/caEEEIIIYR4B+k8RNzR0fGJvVkqlYpZs2a9cFBCPGJmZoaZmdlLu97XX3+Nu7s7gwcPBsDe3p6RI0cyceJEpk6dSpEiRfR6vb//vkBWVhaVKlXRKK9YsRIAyclJfPBB/h58N7faHDlyAhsb23zzpguyZcs3HDlymM2bv8XQ0FBdXq9efQIDR5OcnMTdu3f588/T1K/fgPv37xEZOYe1aze+4B0KIYQQQgjx7tA5wR46dGiBCba5uTkffPAB9vb2OrV37do1pk6dym+//YaFhQVjx47VOP7DDz+wcOFC/vrrL95//33at2/PkCFD1ImOg4MDoaGhbN26lRMnTlC2bFk+//xzkpKS+OKLL7h//z7NmjVjxowZmJiYAHDs2DEiIiI4deoURkZGeHh4MG7cOGxsbLSKOTY2lsjISIYNG8aCBQtIT0+nUaNGTJ48mffffx8ADw8PWrduzf79+7l9+zYLFy5k4cKFuLq6cufOHb777juUSiUffvghU6dOxdzcHAcHBwCCgoI4dOgQM2bM0CqeVatWsWbNGm7dukWdOnWoU6cOsbGxfP/99wBcv36dGTNm8NNPP2FoaIibmxuBgYHqv1VgYCAANjY2bNmyhQcPHtCgQQNCQ0PV95OYmMi0adNISEigRIkSDBw4MF8c33zzDcuXL+fKlSuUKVOG7t274+vri4GBAZcvX6ZFixaMGjWKNWvWYGJiwtatWzE3N3/qvS1cuJDNmzer70WbOJ7G19eX4sWLExkZqS77/fff6dWrF9999x1+fn4YGGgO7DAwMCA7O5u0tDRsbW11ut6z3L9/DwALCwuNcnPzvO+pqakFnleqVGmdrrNo0QLq129A48ZNNco9Pb04cGA/TZu6Y2xszPjxE3F1dePzz6fSsGETXF3dmDQpiH37vqNmTWfCw+dSvHhxna4thBBCCCHEu0LnBPuzzz7T28VzcnLw9/fH3NycmJgYsrKyNIbiHjhwgICAAIKCgmjUqBEXL14kLCyM8+fPM3/+fHW9iIgIpk+fjr29PYGBgQwePJiaNWuydOlSzp8/z+jRo9m4cSO+vr4kJCTg6+tLt27dCAkJ4ebNm4SGhtK/f382btyo0bv3NHfu3GH16tVERkZSpEgRpkyZgr+/P5s3b8bIKO+xxsTEsGTJEiwsLNTJ86pVq9TDnpOTkxk9ejQVK1Zk2LBh/PzzzzRp0oQJEybg7e2tVRxr164lIiKCSZMmUadOHXbt2sWCBQsoVaoUAA8ePMDX15caNWoQExODgYEB//d//0fXrl2Ji4tTJ9Dbt2/H09OTmJgYbt++zahRo4iMjCQ8PJzU1FT69u2Lm5sbGzdu5MaNG/lWi1+/fj3z5s1j8uTJuLi4cPr0acLCwrh+/Trjxo1T19u8eTOrV6/m4cOHz0yu/0ubOJ7F29ubKVOmkJaWpr7+tm3bqF27NhUqVMhXPzs7m1WrVlGzZk29J9cASuXTVwr/b7L/PA4diich4Q9Wr16X75hCoWDOnEg+/3wmRkZGGBoacvXqP6xYsZTvvvuRlSuXcuDAD6xcGUNk5BzGjg1g5covXzgmIYQQQggh3kbP9V/vhw4d4o8//gDg6tWrDB48GE9PTxYtWqRTOwcPHiQpKYlZs2ZRo0YN3NzcCA8PVx+Pjo6ma9eudO/enfLly9OkSROmTp3Krl27uHz5srpely5d8PDwoFKlSnTq1Il79+4xefJkqlWrRuvWrXFyciIpKQmAlStX4uDgwKRJk6hcuTINGjRg3rx5nDp1ip9//lnr2LOzs5k5cyZ169bFxcWF2bNnk5iYyMGD/86Zbd68OY0aNcLZ2Vnd416lShVGjRqFvb09LVq0oHHjxhw7dgyAEiVKAHm9mf/t0XySFStW0Lt3bz7++GMqVqzIp59+SosWLdTHv/32W+7fv8/s2bNxdHSkWrVqfP7555ibm7NhwwZ1PQsLC0JDQ6lcuTL169enXbt2HD16VN3Gw4cPmTFjBlWrVqVx48ZMmDBBI47Fixfz6aef0r59e8qVK0fr1q0ZOXIkMTExZGb+uwiXj48PVapUwdnZWetn/fi9PCuOZ2ndujUGBgbs3Zu3wnZWVha7d+8u8AeNnJwcxo0bR1JSEiEhITrHqw1LS0sA0tLSNMpTU+8DYGFh+cLXiIvbgrW1NS1bfvTEOkWLFlX/uDRz5ud07vwxVapUJS5uKx9/3B1HRycGDvyUXbu+JTc394VjEkIIIYQQ4m2kcw/2li1bCAoKws/Pj1q1ajFp0iSOHDlC48aNiY6OxtjYWOthu4mJiVhZWVG+fHl1mZOTk3oo9+nTp0lISNBY5OrR3sDJycmULVsWQKPnsVixvAWbHm/TxMSErKws9TUbN26sEYejoyMWFhacPXuW5s2baxW7mZkZNWvWVH+vXLkyVlZWJCYm0rRp03xxPVKpUiWN7xYWFty/f1+ra/5XSkoKV65coVatWhrldevW5fTp00DeM7x37x716tXTqJOZmamx4nv58uUxNjbWiCs7OxvIe2b29vYaSb+bm5v633fu3OHatWvMmzdPY2SBUqkkMzOTy5cvU7RoUaDgZ6KtZ8WhDVNTU9q0aUNcXBxeXl7s37+frKws2rZtq1EvLS2NgIAADh06RFRUFC4uLs8d99PY21fE0NCQ8+fPaZQ/+l6tmsMLX2PPnl20bdtB4+/7JGfO/MnWrZs5ePAIALdu3VRPnbC2tiYnJ4fbt2/z3nvvvXBcQgghhBBCvG10TrBXrVpF586dGTt2LDdv3uTXX39l9OjR9O/fn5UrV7J+/XqtE2yFQoFSqcwf1P+GWCuVSvz9/encuXO+Oo96ex+v/7gnDa19lKAXVK5NAvJIQXVzc3M1hpg/+qHgcfpcJOvRfT/pniDvGVasWJEvvvgi3zFTU1Ot4iro7/T4M3907NFQ/v8qVaoUN27cAAp+Jtp6Vhza8vb2pk+fPty6dYu4uDhatmypMVz9xo0bDBgwgCtXrrBixYp8P07ok4mJCQ0bNubbb7cxdOhw9foG27dvw9LSCje3Oi/UfkrKHc6dS+azz0ZqVT8sbDL9+w+kZMm8KQZ2diW4ceM6kDeX39DQsFCGygshhBBCCPE20HmI+Llz5/Dy8gJg//79qFQq9ZBkZ2dnrl69qnVbTk5OpKamqodvA1y4cEE9XLZq1aqcP3+eChUqqD/Xrl1j1qxZpKen6xo6kLco2pEjRzTKzpw5Q1paGpUrV9a6nbt373Lp0iX196SkJNLS0qhevfpzxfU8LCwsKFOmjHq4/iOPf69WrRr//PMPFhYW6mdYunRp5s6dy++//67VdRwdHblw4QJ37txRl508eVL97+LFi2Nra8ulS5c0/lanTp3SWEzsRT0rDm3VrVuXMmXKsHXrVn788UeN4eH37t2jT58+3Llzh7Vr1xZqcv3IyJFjOXr0MP7+fdi37ztmzAhj0aL5jBgxGlNTU1JT73P48CFu3bqlc9t//pk3kkGbnvBff/2Zw4cPMXz4v8l4q1ZtiIlZzZ49u4iImE3Llh89148aQgghhBBCvAt0TrAtLS3VCfBPP/1E6dKl1atRX7x4UeuVuAHc3d1xdXVl3Lhx/PHHH5w4cYJx48ape58HDBjA7t27iYqK4vz58xw8eJCgoCBSU1M1erB10a9fP86ePUtYWBjJycnEx8czZswYqlevTsOGDXVqa+zYsZw8eZI//viDcePG4ebm9sIJmampKcnJyaSkpGhVf8CAAcTExBAbG8vff//NihUr2L17t/p4x44dsbKyYvjw4Rw/fpzk5GQCAwM5cOCAeuG1Z2nfvj3Fixdn9OjRnDlzhkOHDvH555+rjysUCgYMGMCXX35JTEwMFy9eZM+ePUyZMgUTExO99do/Kw5tKRQKvLy8WLRoEba2tjRo0EB9LDw8nEuXLjF79mxsbW25efOm+lNYc4+bNm3OypUx/PVXEn36+LBp00ZCQqbx2WcBACQkHKddu5bs3bv76Q0V4ObNvJED1tbP/t9laOgkhg8fjaWllbpswIDBNGzYmMGD/cnJyWHWrAidYxBCCCGEEOJdoXNXlLu7O1FRUfz111/s27ePfv36AbB7927mz59PkyZNtG7LwMCAJUuWMG3aNPz8/DAxMWHQoEFcuXIFgDZt2hAREcGSJUuIjo7G2toaDw8PxowZo2vYaq6urixfvpzIyEi8vLwwNzenZcuWjB49Wqch4gCenp4MHDiQrKwsPDw8CA4OfuIe4dry8/Nj+fLlJCcnEx0d/cz6PXr04N69e0RGRpKSkkL9+vXp3LmzupfewsKCmJgYZs2aRf/+/cnNzaVGjRqsXLlS6x57U1NTVq9eTVhYGD169FAn7EFBQRpxFy1alC+//JIZM2ZgZ2dH165dGT58+PM9iOeMQ1udO3cmKipKvY0Y5A3x37FjB9nZ2fTp0yffOfv27VPP+9e39u09ad/es8BjjRs35caNJ8/T7969J9279yzwWKdO3nTqpN2K9Lt2/ZCvzMTEhEWLlmp1vhBCCCGEEO86heppE3gLcOfOHcaOHcvvv/+Ou7s7ERER6j2wS5YsyaJFi976fXJjY2MJCgri7NmzrzoUDhw4QJUqVShd+t99kSdNmsTFixdZvXr1K4xMFCQlJZ2cnPzrDgihLSMjA2xszORdEnoj75TQN3mnhL7JOyX0ydbWDEPDF98K90l07sG2tbVlxYoV+cq/+uorjSRPvBxbt24lOTmZKVOmUKJECX7//Xe2bdtWaNtKCSGEEEIIIYQo2HOvVnT79m2ysrLUK1grlUqSkpI4fPgwPXr00FuAL9P169dp06bNU+s4OzurF3krTDt27CA4OPipdfr168ekSZOYMWMGQ4cO5f79+1SoUIEJEyYUuK/z6+TYsWP4+fk9tU7r1q2ZMWOG1m127NhRY+G5gsTHx+t1JXchhBBCCCGEeETnIeJnzpxhzJgxGnsoazSoUKj3YH7T5Obmcvny5afWKVq0KCVLliz0WNLT05+5arSlpaVOi8q9TjIzM7l27dpT65iZmWFnZ6d1m//884967+4nKV++/AvPk38RMrRJvCgZJif0Td4poW/yTgl9k3dK6NNrN0R81qxZ3Lt3j/Hjx/PDDz9QpEgRPvzwQw4cOMCBAwdYs2ZNYcT5UhgaGlKhQoVXHQaQl1yamZm96jAKTdGiRfX+rGWKghBCCCGEEOJV0jl1P378OCNGjKBv3760a9eOhw8f4uPjQ3R0NC1btuTLL78sjDiFEEIIIYQQQojXms4JdlZWlnrfa3t7e86cOaM+5u3tzR9//KGv2IQQQgghhBBCiDeGzgl26dKl1QtJ2dvbk5aWpp63XKRIEe7du6ffCIV4C6hUKnJzlSiVOi15IIQQQgghhHiD6Jxgf/TRR8ydO5fdu3fz/vvvU6lSJSIjIzl79iwrV66kXLlyhRGnEG80hUJBamqGJNhCCCGEEEK8xXROsIcNG0bt2rXZtGkTAEFBQezZswcvLy9+++03PvvsM70HKcTbQMcF+4UQQgghhBBvGJ236XokOzsbY2NjAC5dusTJkyepUaMG5cuX12uAQrwtZGsJoQ+yVYnQN3mnhL7JOyX0Td4poU+v3TZdjxgaGnLmzBlu3LhB7dq1adiwIdbW1noMTQghhBBCCCGEeHM8V4K9detW5s6dy40bNzAwMGDjxo0sXLgQY2Nj5s6dS5EiRfQdpxBCCCGEEEII8VrTuW98x44djB8/ngYNGhAREYFSmTdMo1WrVuzfv5/FixfrPUgh3gaGhgYYGWl+DAwUrzosIYQQQgghhJ7o3IMdHR1N9+7dmTJlCrm5ueryLl26cOfOHTZs2EBAQIA+YxTijadSqbC0LJavPDdXyd27D2R1cSGEEEIIId4COifY58+fZ/z48QUec3V1ZeHChS8clBBvG4VCwcQNsZy/eUtdVrGEHdO6emNgoJAEWwghhBBCiLeAzgl28eLFSU5OpnHjxvmOJScnU7x4cb0EJsTb5vzNW5z559qrDkMIIYQQQghRSHSeg92uXTsWLFjArl27yMrKAvJ6506ePMnixYtp06aN3oMUQgghhBBCCCFedzr3YAcEBJCYmEhAQAAGBnn5ua+vLw8ePKBu3bqMGDFC70EKIYQQQgghhBCvO50T7CJFirB8+XJ++eUXDh48yL1797CwsKB+/fo0b94chUJWRRZCCCGEEEII8e7ReYj4I40bN2bMmDGEhYUxbtw4PvjgA0muhdCjH37Yx0cfNadChfepW9eZRYsWoFI9eTG0nJwcFiyYh7t7LeztS/Lhh43ZsuWbfPVcXR157z3LfJ/bt28DkJ2dzZgxAVStWp6GDWuzb993Guc/fPiQWrWciI//Tb83LIQQQgghxBtOqx7sqKgorRtUKBQMHTr0uQMSQsDhw4fo1asrnTp5Exg4kfj4g4SGTiI3N4fhw0cVeM7s2dOZP38eo0ePx929ITt2xDFwYD8MDY3w9OwEwO3bt7l69R9CQqbh7t5A43wrKysA1qz5P3bs2Mb8+Ys5fvwoAwb049Ch49jZ2QGwdOliXFxc850vhBBCCCHEu06helqX2P84Ojqqe6efVV2hUPDnn3/qJ7rXjIODA+Hh4Xh7e7/qUF66I0eOoFKpqFu3rt7aTEpK4sqVK3zwwQfA6/F8MzIyWLRoEd9++y0pKSlUrFiRoUOH0qJFixduu+eipRqriDuWLsnaoQNJSUknJ0epUbdrVy/u37/Hrl0/qMtCQyezatUKTp36i2LF8u+p7excjaZNm7N48TJ1Wdu2LTAxMWHz5m8B2L//Bz75pBPx8X9QsWKlAuPs08eH0qVLEx4+B5VKRdWq5fnii2W0atWGO3du07BhbbZu3YWjo9MLPQ/xfIyMDLCxMSvwvRHiecg7JfRN3imhb/JOCX2ytTXD0PC5B3I/k1Ytu7i4oFKpcHR0ZOzYsfz444+cOXOmwM/bmly/63x8fLh48aJe2xw0aBAnTpzQa5svatq0acTFxRESEsKWLVto2bIlw4YNIz4+/qXFkJmZya+//ky7dp4a5Z6enUhLSyU+/uATzsvAwsJCo8zW1pY7d+6ov588eQJzcwvs7Ss+8foKhQITk2LqfxsbG5Gbm/f/mc2bN4vWrdtJci2EEEIIIUQBtEqwN2zYwL59+2jbti3bt2/Hw8ODnj17sm7dOlJSUgo7RiFeiocPH7JlyxZGjRpF8+bNqVChAkOGDKF+/fp8803+ucyF5e+/L5CVlUWlSlU0yh/1OCcnJxV43sCBQ9iw4Wu+/34Pqan32bRpPd9/v5dPPumurnPyZAI2Njb4+flSuXJZ7O1LMWBAX65f/7dnvW7d+uzZs4urV/9hx47tpKenU6uWG3//fYF169YyfnxwIdy1EEIIIYQQbz6t+8bLlCnDwIED2bx5M9u3b6dhw4bExMTQtGlT+vfvz+bNm0lLSyvMWF+qa9eu8emnn+Lm5kazZs2Ii4vTOP7DDz/g7e2Ni4sLrVq1IjIyUr0vOOQNd16/fj0+Pj44OzvTtm1bjh49yvr16/nggw+oXbs2AQEBZGRkqM85duwYvXv3pk6dOri7uxMUFKTTDxixsbE0a9aMDRs20KRJE9zc3Bg6dCjXr19X1/Hw8GDmzJm0a9cOd3d3Dh06hK+vL3PmzGHChAnUrVuX2rVrM3r0aPXf08HBAYCgoCACAwO1juebb76hbdu2uLi40LZtW1avXo1SqVTHceXKFaKiovD19VWfc/78efr27YuzszNNmzZlyZIl6mMLFy6kb9++LF26lGbNmuHs7EyvXr1ITk7WKp5Lly7h6OjI/v37NcqDgoLo0aMHCoWC6OhomjVrpnHcwMCA+/fva33fL+r+/XsA+Xqjzc3zvqemphZ43uDBQ3F3b0D37l2oXLksQ4YMoFs3H4YN+3frvFOnTnD16j+4utZi7doNhIZO59dff6ZTp7akp6cD0L//QKpUqYabW3VGjBjC3LkLKFmyFOHhofTq1QcbG1s++2wwDRvWZsyYAB48eFAYj0EIIYQQQog3znMNPq9UqRLDhg3j22+/ZdOmTdSsWZPo6GgaN27MZ599pu8YX7qcnBz8/f1JSUkhJiaG+fPns2LFCvXxAwcOEBAQQNeuXdm+fTshISHs3LmTsWPHarQTERGBv78/W7duxcLCgsGDB7N7926WLl1KeHg4e/fuZePGjQAkJCTg6+tL1apV2bBhA/Pnz+f48eP079+f3NxcrWO/c+cOq1evJjIyktWrV3P16lX8/f3JyclR14mJiWHixIksX76cWrVqAbBq1Srs7OzYtGkTs2fPZt++faxatQqAn3/+GYAJEyYQHKxd7+X69euZNWuW+j0JCAhg2bJlzJkzB4BNmzZRsmRJ/Pz8WLhwoUZsXl5e7Nixgx49ejBv3jwOHvx3SPThw4c5cuQIS5cu5auvvuL27dtMnTpVq5jKlStHvXr12L59u7osMzOT7777Dm9vb0xMTGjSpAnW1tbq4wkJCfz22280bdpUq2vog1L59HUOHu0//7jMzEw8PduQkHCc2bMj2bJlB8HBIWzZ8g3BwePU9ebOXcC33+4hIGAMDRo0onfvfqxcGcO5c8ls2LAOgGLFirFq1VrOn79KYuLffPJJd44fP8b33+8lIGA04eFh/PPPP6xZ8zXnzv3FzJmf6/cBCCGEEEII8YZ64dndlSpVwsXFBRcXF3Jzc/nhhx+efdJr7uDBgyQlJTFr1ixq1KiBm5sb4eHh6uPR0dF07dqV7t27U758eZo0acLUqVPZtWsXly9fVtfr0qULHh4eVKpUiU6dOnHv3j0mT55MtWrVaN26NU5OTiQl5Q33XblyJQ4ODkyaNInKlSvToEED5s2bx6lTp9QJrjays7OZOXMmdevWxcXFhdmzZ5OYmKiRpDZv3pxGjRrh7OxMkSJFAKhSpQqjRo3C3t6eFi1a0LhxY44dOwZAiRIlgLwe1f/2qj7J4sWL+fTTT2nfvj3lypWjdevWjBw5kpiYGDIzM7G1tcXQ0BBTU1ONhNbHxwcvLy/KlSvHkCFDsLCw4OTJk+rjOTk5zJo1C0dHR5ydnenevTtHjx7V+vl4e3uzd+9eHj58CMD3339Pbm4ubdu2zVf33LlzDB06FBcXF7p27ar1NV6UpaUlQL4RIampeb3oFhaW+c7Zvn0rp06d4IsvltOnjx+NGjVhxIjRTJ4cxrJl0fz552kA6tVzx82tjsa57u4NsLS04tSpkxrlxYoVUy9uGBo6maFDR2BjY8v27Vvp3bsvVatWo08fP779dpt+blwIIYQQQog3nFbbdP1XVlYWBw4cYNeuXfz44488fPiQ2rVrM2HCBNq0aaPvGF+6xMRErKysKF++vLrMyckJExMTAE6fPk1CQgKbNm1SH3+0unpycjJly5YFoEKFCurjj1Z9frxNExMT9bDyxMREGjdurBGHo6MjFhYWnD17lubNm2sVu5mZGTVr1lR/r1y5MlZWViQmJqp7YR+P65FKlTRXlLawsHjuYdF37tzh2rVrzJs3j/nz56vLlUolmZmZXL58mcqVKxd4rr29vcZ3S0tLMjMz1d/t7OzU20k9ijM7O1vr2Fq3bk1oaCj79u2jQ4cObNu2jZYtW2Jubq5R7+jRowwZMoSSJUsSHR2NsbGx1td4Ufb2FTE0NOT8+XMa5Y++V6vmkO+cy5cvAVC/vubWWQ0b5r1TZ8/+SZkyZdi+fRtubnVwcqqurqNUKsnOzsLOrniB8Xz//R4SE8/y5ZfrAbh16ybW1jYAWFlZc+PG9QLPE0IIIYQQ4l2jdYL936Q6PT0dV1dXhg8fTps2bXjvvfcKM86XSqFQqOcKP87IKO9xKZVK/P396dy5c746j3p7H6//uIKG98KTtz9TqVQ6JXcF1c3NzcXQ0FD9/dEPBY971JOtD4+eXVBQEI0aNcp3vFSpUk889/E4H3n82bxonKamprRp04a4uDiaNGnCTz/9xNKlSzXqfPfdd4wZMwZXV1cWL16sda+9vpiYmNCwYWO+/XYbQ4cOV/cib9++DUtLq3w90ABVqlQD4LfffuXDD//dUuzQod8AqFDBniJFihIUNIa2bTsQHf3vlIddu3bw8OFDGjfWnHsOeX/L0NAQxowJxNTUFAA7uxLqpPr69WvY2ZXId54QQgghhBDvIq0S7DFjxqiT6po1azJkyBDatm371ETpTebk5ERqaipJSUlUrVoVgAsXLqiH7FatWpXz589r9ATHx8ezZs0apkyZok5EdOHg4MCRI0c0ys6cOUNaWtoTe3sLcvfuXS5dukS5cuWAvL2m09LSqF69+jPO1J/ixYtja2vLpUuXNJ7Rjh072LNnDzNnznxpsRSkS5cu9O3bly1btmBnZ0eDBv/2+n7//feMHDmSFi1aMGfOHL3+8KCLkSPH8vHHHfH374OPTy9+/z2eRYvmM3HiVExNTUlNvc/Zs2ewt6+EnZ0dbdq0o06dugwdOoCxYydQtWo1jh49zLx5s2nTpp06Kf/ss5HMmjWdEiXeo2XLj/jzz1PMnj2DNm3a07Rp/lESGzasIzMzg549e6vLWrVqTXT0IooXL87SpV/Qpk27l/ZchBBCCCGEeJ1plWBv374dQ0ND6tatS9myZUlKSlLPHf4vhULB9OnT9Rrky+bu7o6rqyvjxo0jJCQEQ0NDwsLC1L3PAwYMICAggKioKNq3b8+1a9cIDg6mbNmyGj3YuujXrx8+Pj6EhYXh4+PDrVu3CAsLo3r16jRs2FCntsaOHcvEiRPJyclh6tSpuLm5Ua9eveeK6xFTU1OSk5NJSUnBxsbmqXUVCgUDBgwgIiKC0qVL06xZM86ePcuUKVNo0aKFOmk1MzPjwoUL3Lp1Czs7uxeKTxd169alVKlSLFiwAF9fX/Xf9d69e4wfP54aNWoQHBzMvXv31OcYGxtrzBUvbE2bNmflyhhmzZpOnz4+lCxZmpCQaQwZkreIYELCcTp3bs+CBV/QvXtPDA0N2bBhC9OnhzJv3izu3k2hQgV7Ro0ay+DBw9Ttjho1juLF7fi//1vG6tUrsLGxpU8fP8aODcoXQ0ZGBjNnfk5o6HSN0RhBQZMYNmwQAwb0o2nT5gQGTiz8ByKEEEIIIcQbQKsEu3Tp0gBcuXKFK1euPLXuo+GsbzIDAwOWLFnCtGnT8PPzw8TEhEGDBqnvvU2bNkRERLBkyRKio6OxtrbGw8ODMWPGPPc1XV1dWb58OZGRkXh5eWFubk7Lli0ZPXq0zvN/PT09GThwIFlZWXh4eBAcHPzCfxc/Pz+WL19OcnIy0dHRWtUvWrQoX375JTNmzMDOzo6uXbsyfPhwdR1fX19mzpxJUlIS27a93IWyOnfuzPz58/H29laXHThwgPv373P8+PF8W3XVr1+fL7/88qXG2L69J+3bexZ4rHHjpty4oTlH3sLCkvDwOYSHz3limwYGBvTr50+/fv7PvL6JiQnHjp3OV25jY8vatRufeb4QQgghhBDvGoXqSZN/xRsnNjaWoKAgzp49+6pDEQXouWgpZ/65pv7uWLoka4cOJCUlnZyc/HP+hSiIkZEBNjZm8t4IvZF3SuibvFNC3+SdEvpka2uGoeELb6b1RIXXshBCCCGEEEII8Q55rm26xMt1/fr1Z25/5uzsjJeXV6HHsmPHDoKDg59ap1+/fhpDwV+GY8eO4efn99Q6rVu3ZsaMGS8pIiGEEEIIIcS7RhLsN4CdnR1btmx5ap2iRYtSsmRJjTnFhaF58+bPjMXS0rJQYyhI9erVnxmXmZnZywlGCCGEEEII8U6SBPsNYGhoqLHd1atkZmb2WiaqRYsWfW2ekRBCCCGEEOLdJHOwhRBCCCGEEEIIPXjuHuzk5GR++eUXbty4ga+vL5cuXcLR0RFzc3N9xifEW6NiCbunfhdCCCGEEEK82XROsJVKJZMnT+abb75BpVKhUCho27Ytixcv5uLFi8TExFCyZMnCiFWIN5ZKpWJa1/zz43NzlSiVslOeEEIIIYQQbwOdh4gvXryYuLg4pk2bxi+//MKjbbTHjh2LUqkkIiJC70EK8aZTKBTcv/+QlJR0jc/duw8kwRZCCCGEEOItoXOC/c033zB8+HC6dOmCtbW1utzJyYnhw4fzyy+/6DM+Id4aublKcnI0P5JcCyGEEEII8fbQOcG+desWTk5OBR57//33uX///gsHJYQQQgghhBBCvGl0TrArVKjA/v37Czx26NAh2SpJCCGEEEIIIcQ7SedFzvr06cPkyZPJzs7mww8/RKFQ8PfffxMfH8/KlSsJDAwsjDiFEEIIIYQQQojXms4J9ieffMKdO3f44osvWLduHSqVilGjRmFsbIy/vz89evQojDiFeOMZGuYNGFEqVTL3WgghhBBCiLeQQvVoGXAdpaWlcezYMe7evYulpSWurq4ai54JIf71aEs7yFvsTFYPF8/LyMgAGxszUlLSyclRvupwxFtA3imhb/JOCX2Td0rok62tmbrjqzDo3HJQUBCXLl3C3Nycpk2b4unpSfPmzbG2tubcuXMMHjy4MOIU4o2mUCiYsOUbJmz5BkNDAwwMFK86JCGEEEIIIYSeaTVE/J9//lH/e8uWLbRs2RJDQ8N89Q4cOMCvv/6qv+iEeIucu3XrVYcghBBCCCGEKERaJdhTp07lwIED6u/Dhg0rsJ5KpaJx48b6iUwIIYQQQgghhHiDaJVgh4aG8uuvv6JSqZgwYQKffvop5cuX16hjYGCApaUl7u7uhRKoEEIIIYQQQgjxOtMqwX7//ffp3LkzkDeXtHnz5tja2hZqYEIIIYQQQgghxJtE50XOOnfujJmZGQkJCRw+fJjff/+d33//nfj4eH788UfmzJlTGHEK8U744Yd9fPRRcypUeJ+6dZ1ZtGgBT1vo/9y5ZN57zzLfp1mzgkeS/PPPFapUKccvv/ykUZ6dnc2YMQFUrVqehg1rs2/fdxrHHz58SK1aTsTH//biNymEEEIIIcRbSud9sOPj4xkxYgT37t0r8LiZmRljxox54cCEeNccPnyIXr260qmTN4GBE4mPP0ho6CRyc3MYPnxUgeecOnUCgG++iaNYsWLq8mLFTPPVvXLlMt26deb+/fz/212z5v/YsWMb8+cv5vjxowwY0I9Dh45jZ2cHwNKli3FxccXdvYE+blUIIYQQQoi3ks492BEREdjY2LBgwQJatmzJRx99RHR0ND4+PigUCpYtW1YYcebj4OBAbGzsS7nW6+bIkSMcPny4UK9x+fJlHBwciI+P11ub33//PV26dMHNzQ0PDw9mzpxJRkaGTm188803eHp6UqtWLT766COWLl1Kbm6u3mIsyMt612bNmo6zswuLFy/Dw6MVQUGTGTp0BJGRc3n48GGB55w8mUDp0mVo2rQ5devWV39q1KiprqNUKvn667W0aNGEmzdvFNjOgQM/0qmTN+3adSAwcBIGBgYcO5b3jt25c5vFixcwYUKI/m9aCCGEEEKIt4jOCfbZs2cZNmwYrVq14sMPP+Tq1as0b96cSZMm8fHHH/PFF18URpziMT4+Ply8ePFVh6GTw4cPq9+bzZs3ExISwo4dO5g6darWbWzbto2QkBB69erFtm3bCAgIYMmSJW/FO5eZmcmvv/5Mu3aeGuWenp1IS0slPv5ggeedPHmCmjWdn9r2qVMnGTs2gE8+6cGiRUsLrKNQKDAxKab+t7GxEbm5SgDmzZtF69btcHR00vW2hBBCCCGEeKfonGArlUref/99ACpUqEBSUpL6WOvWrTl9+rT+ohNvja+//hp3d3cGDx6Mvb09zZs3Z+TIkcTFxZGVlaVVG+vWrcPLy4tu3bpRvnx52rVrh5+fH5s2bSrk6Avf339fICsri0qVqmiUV6xYCYDk5KSCTuPkyROkpaXRrl1LypUrQY0aVQgLCyE7O1tdp2zZssTH/0FYWHiBQ8cB6tatz549u7h69R927NhOeno6tWq58fffF1i3bi3jxwfr6U6FEEIIIYR4e+mcYJcvX56zZ88CULFiRR4+fMi5c+cAyMnJIT09Xb8RAteuXePTTz/Fzc2NZs2aERcXp3H8hx9+wNvbGxcXF1q1akVkZKRG0ubg4MD69evx8fHB2dmZtm3bcvToUdavX88HH3xA7dq1CQgI0BiufOzYMXr37k2dOnVwd3cnKCiIlJQUrWOOjY2lWbNmbNiwgSZNmuDm5sbQoUO5fv26us6jYdLt2rXD3d2dQ4cO4evry5w5c5gwYQJ169aldu3ajB49mrS0NPW9AAQFBREYGKh1PN988w1t27bFxcWFtm3bsnr1apRKpfp4YmIivXv3platWrRq1YqDBwvuMX0SX19fAgICNMp+//13HBwc+Pvvv/Hz82P8+PEaxw0MDMjOzlbf27OMGTOG/v3752vj0XoAXl5eBAUFaRz/6aefcHZ25u7duwQGBjJ8+HD8/PyoXbv2S5vOoI1H86ItLCw0ys3N876npqbmO+f27dtcvfoPSUmJ9Onjx/r1m/H17cuSJYv47LPB6no2NraULl3mqdfv338gVapUw82tOiNGDGHu3AWULFmK8PBQevXqg42NLZ99NpiGDWszZkwADx48eNFbFkIIIYQQ4q2jc4Lt6enJnDlziImJwdbWlpo1axIWFsb333/PokWLqFKlyrMb0UFOTg7+/v6kpKQQExPD/PnzWbFihfr4gQMHCAgIoGvXrmzfvp2QkBB27tzJ2LFjNdqJiIjA39+frVu3YmFhweDBg9m9ezdLly4lPDycvXv3snHjRgASEhLw9fWlatWqbNiwgfnz53P8+HH69++v03zfO3fusHr1aiIjI1m9ejVXr17F39+fnJwcdZ2YmBgmTpzI8uXLqVWrFgCrVq3Czs6OTZs2MXv2bPbt28eqVasA+PnnnwGYMGECwcHa9SquX7+eWbNmMWzYML799lsCAgJYtmyZesX31NRU+vbti4WFBRs3bmTKlCk6D7v29vbmhx9+0EiWt23bRu3atalQoQLVq1fH0dFRfSw7O5tVq1ZRs2ZNrbd8q1OnDhUrVlR/T01NZd26dTRt2lQdw+7duzV+KNmyZQseHh5YW1sDsHv3bho1asQ333xDhw4ddLrHwqRUPnmlcMj7IeG/TE1N2bBhCzt37qNbNx8aNWpCYOBERo8eT2zsRhITz2p9/WLFirFq1VrOn79KYuLffPJJd44fP8b33+8lIGA04eFh/PPPP6xZ8zXnzv3FzJmf63yPQgghhBBCvO10TrD9/f3p3r07x48fByAkJIQ///yTIUOGcO7cOcaNG6fXAA8ePEhSUhKzZs2iRo0auLm5ER4erj4eHR1N165d6d69O+XLl6dJkyZMnTqVXbt2cfnyZXW9Ll264OHhQaVKlejUqRP37t1j8uTJVKtWjdatW+Pk5KQe7r5y5UocHByYNGkSlStXpkGDBsybN49Tp06pE1xtZGdnM3PmTOrWrYuLiwuzZ88mMTFRo3e4efPmNGrUCGdnZ4oUKQJAlSpVGDVqFPb29rRo0YLGjRtz7NgxAEqUKAHk9XT+t7fzSRYvXsynn35K+/btKVeuHK1bt2bkyJHExMSQmZnJt99+y8OHD5kxYwZVq1alcePGTJgwQev7hLzpAQYGBuzduxeArKwsdu/ejbe3d766OTk5jBs3jqSkJEJCnm/hrPT0dIYMGUJmZqb6nfP09CQrK0sdQ1paGnv37tWIwcrKCn9/fypWrEipUqWe69qFwdLSEiBfb35q6n0ALCws851TrFgxPvjAg/LlK2iUt2zZGvh3hXFdFCtWDIVCAUBoaN4iazY2tmzfvpXevftStWo1+vTx49tvt+ncthBCCCGEEG87nbfpMjAw0Bjq6+zszN69ezl37hyVKlXC3NxcrwEmJiZiZWVF+fLl1WVOTk6YmJgAcPr0aRISEjTm4T7aNzg5OZmyZcsCefPFH3m0ndHjbZqYmKiHlScmJtK4cWONOBwdHbGwsODs2bM0b95cq9jNzMyoWfPf1ZwrV66MlZUViYmJ6l7Xx+N6pFKlShrfLSwsuH//vlbX/K87d+5w7do15s2bx/z589XlSqWSzMxMLl++TGJiIvb29hoJu5ubm07XMTU1pU2bNsTFxeHl5cX+/fvJysqibdu2GvXS0tIICAjg0KFDREVF4eLiovM93bx5k0GDBnH58mVWrFih/hvb2NjQokULtmzZQocOHdi5cycWFhY0adJEfW5Bz/t1YG9fEUNDQ86fP6dR/uh7tWoO+c45d+4vfvrpAF5e3lhZWavLMzLyVhwvXtzuueP5/vs9JCae5csv1wNw69ZNrK1tALCysubGjetPO10IIYQQQoh3ks4JdkHMzc2fK1HShkKh0Jgr/IiRUV7oSqUSf39/OnfunK/Oo97ex+s/rqBht/Bvgl5QubGxsVZxAwXWzc3NxdDQUP390Q8Fj3vUk60Pj55dUFAQjRo1yne8VKlSBT7jgp7Xs3h7e9OnTx9u3bpFXFwcLVu21PjB5caNGwwYMIArV66wYsUK6tWrp/M1kpOT8ff3R6lUsnbtWqpWrapxvEuXLgwePJjbt2+zbds2OnXq9Mzn/TowMTGhYcPGfPvtNoYOHa7uRd6+fRuWlla4udXJd87169cZOzYAAwMDfH37qsu3bo3FwsISV9dazxWLUqkkNDSEMWMCMTXNWxTNzq6EOqm+fv0adnYlntaEEEIIIYQQ7yStsignJyfWr1+Pi4sLjo6O6v/4L4hCodDrSuJOTk6kpqaSlJSkTqYuXLigHkpbtWpVzp8/r9EzGR8fz5o1a5gyZYo6QdCFg4MDR44c0Sg7c+YMaWlpVK5cWet27t69y6VLlyhXrhwASUlJpKWlUb16dZ1jel7FixfH1taWS5cuaTyjHTt2sGfPHmbOnImjoyObNm3izp076vnQJ0+e1PladevWpUyZMmzdupUff/yR6Oho9bF79+7Rp08f0tLSWLt2rXqxNl1cunSJPn36YGlpyYoVKwoc4t2kSRNKlCjBhg0bOHz4MFOmTNH5Oq/KyJFj+fjjjvj798HHpxe//x7PokXzmThxKqampqSm3ufs2TPY21fCzs4Od/eGNG36ASEhwWRkPKRaNUf27t3NsmXRhIZO1+jV1sWGDevIzMygZ8/e6rJWrVoTHb2I4sWLs3TpF7Rp005Pdy2EEEIIIcTbQ6sEe+jQoeqtuYYNG1aoAf2Xu7s7rq6ujBs3jpCQEAwNDQkLC1P3Pg8YMICAgACioqJo3749165dIzg4mLJly2r0YOuiX79++Pj4EBYWho+PD7du3SIsLIzq1avTsGFDndoaO3YsEydOJCcnh6lTp+Lm5vZcPbePMzU1JTk5mZSUFGxsbJ5aV6FQMGDAACIiIihdujTNmjXj7NmzTJkyhRYtWlCkSBHat2/PF198wejRoxk/fjz379/n8891X8RKoVDg5eXFokWLsLW1pUGDBupj4eHhXLp0ieXLl2Nra8vNmzfVx2xtbTV6mZ9kwoQJZGVlMW/ePIyMjDTaePS3NjAwwMvLi+joaJydnXX6QeRVa9q0OStXxjBr1nT69PGhZMnShIRMY8iQzwBISDhO587tWbDgC7p374mBgQGrVsUwe/YMlixZzPXr17C3r8jcuQvo1avPc8WQkZHBzJmfExo6XWMUQ1DQJIYNG8SAAf1o2rQ5gYET9XLPQgghhBBCvE20SrAfT6rLlClDo0aN1Al3YTMwMGDJkiVMmzYNPz8/TExMGDRoEFeuXAGgTZs2REREsGTJEqKjo7G2tsbDw4MxY8Y89zVdXV1Zvnw5kZGReHl5YW5uTsuWLRk9erROQ8Qhb+GtgQMHkpWVhYeHB8HBwU8dAaANPz8/li9fTnJyskYv8dPqFy1alC+//JIZM2ZgZ2dH165dGT58OJCXsK9evZqwsDB69OiBlZUVw4cPz7fllTY6d+5MVFQUvr6+6h9BcnNz2bFjB9nZ2fTpkz/x27dvn3oe9ZNcv36dQ4cOAdCpU6d8xx9tHQd5Q9Wjo6MLXGDtdde+vSft23sWeKxx46bcuKE5F9/CwpLQ0OmEhk7Xqv2C2niciYkJx47lH4FiY2PL2rUbtbqGEEIIIYQQ7yqF6kkTjp/Azc2NWbNm0apVq8KK6a0QGxtLUFCQRuInXo74+HgGDRrETz/9pPVK6y9D9+VLAPjafxApKenk5ORfW0CIZzEyMsDGxkzeIaE38k4JfZN3SuibvFNCn2xtzTA01HkzLa3pvJJVyZIl820lJMTrIDk5mcTERKKjo+ncufNrlVwLIYQQQggh3n46J9jdunXj888/59ixYzg4OGBmZpavjpeXlz5iey1dv36dNm3aPLWOs7PzS3kGO3bsIDg4+Kl1+vXrpx4K/rw6duzIpUuXnlonPj7+uVc/19d9/P333wQFBeHq6srIkSOfed3BgwcTHx//1DqxsbFUrFjxmW0JIYQQQgghhM5DxB0dHZ/eoELBn3/++UJBvc5yc3O5fPnyU+sULVqUkiVLFnos6enp3Lp166l1LC0tn7kQ2rP8888/ZGdnP7VO+fLln3tu+cu6j/+6fv06GRkZT61TunRpnefdP4kMERf6IMPkhL7JOyX0Td4poW/yTgl9eu2GiO/bt68w4nhjGBoaamx39SqZmZkVOIJA30qXLl2o7b+s+/ivl7VQnxBCCCGEEOLdoHOCXaZMmace17FDXAghhBBCCCGEeCvonGBD3pzZQ4cOkZWVpU6oVSoVDx484I8//uDAgQN6DVIIIYQQQgghhHjd6ZxgR0VFERUVhYWFBTk5ORgbG2NkZMSdO3cwMDDgk08+KYw4hXjjVbKze9UhCCGEEEIIIQqRzrO7N2/ejJeXF4cOHaJv3758+OGH/Prrr2zatAlra2uqVq1aGHEK8UZTqVRM9+rCdK8u5OYqUSplKoUQQgghhBBvG50T7OvXr+Pp6YlCocDJyYljx44BULNmTQYPHszGjRv1HqQQbzqFQsH9+w9JSUnn7t0HkmALIYQQQgjxFtI5wTY1NVVvx1ShQgUuX76s3urIycnpmVtYCfGuys1VkpMjvddCCCGEEEK8rXROsJ2dndmyZQsAFStWxNDQkIMHDwKQnJxMkSJF9BqgEEIIIYQQQgjxJtB5kbPBgwfTr18/7t+/T3R0NB07dmT8+PG4u7vz888/07Jly8KIUwghhBBCCCGEeK3pnGDXq1ePTZs2cfbsWQAmT56MgYEBR48epU2bNgQGBuo9SCHeBoaGOg8YEa8RpVIlw/uFEEIIIcRTKVSPNrLW0p07d7C1tS2seIR4K6lUKvXaBeLNlKtUcjfl1S9QZ2RkgI2NGSkp6eTkKF9pLOLtIO+U0Dd5p4S+yTsl9MnW1qxQO7507sFu1qwZTZs2pVOnTnh4eMicayG0oFAoCPp2E+du33rVoYjnUKm4HeHtP8bAQPHKE2whhBBCCPH60jnBHjNmDDt27CAgIAALCwvatGlDp06dqFu3bmHEJ8Rb49ztW5y5cfVVhyGEEEIIIYQoJDon2H379qVv375cunSJ7du3s2PHDjZu3Ejp0qXp2LEjnp6eVK5cuTBiFUIIIYQQQgghXlvPPfi8XLlyfPrpp8TFxREXF8cHH3zAsmXL6NChgz7jE0IIIYQQQggh3gg692A/7vbt2+zcuZOdO3dy7NgxrK2tadeunb5iE0IIIYQQQggh3hg692CnpqayadMm+vXrR/PmzZk9ezbvvfceixcv5qeffmLixImFEacQQrx2DP65QvEq5TD+5SeNcusOH1HiPct8H6M/jj65MaUSk1UrsGneEDv7UtjWdcFsUiCK1Psa1YwO/gJNm2JdvhS2btUxCx6HIi1Vo47JymXYujhQvEYVis2fm+9Sln17Yhox+/lvXAghhBBCFEjnHuyGDRuiVCqpU6cOU6ZMoU2bNpibmxdGbEII8doyuHIZq26dMbh/T/OASoXh6VM8GDyMzI5eGodyqjo8sb1iUZGYhYfxcOgIspo2xzD5L8xmTsPozz+5t3ELKBQYnvkTc++O0KQJaSvXwJUrmIVOwvDvC9yP2QCA4elTmE8YS9q0GaisbbAY9Rk5zq5ke7QEwOj3eIyO/M79xcv0+TiEEEIIIQTPkWB/9tlneHp6Urp06cKIp0AODg6Eh4fj7e390q75Mjx48IDNmzfTs2fPVx1KgfT93I8ePUpERASnT5/G1NSUZs2aMXbsWKytrbVu4/vvv2fRokWcO3cOGxsbWrduzYgRIzAxMdFLjAXx8PCgc+fOfPbZZ4V2DfEGUSopumEd5lOCQZV/yy6D8+cwSEslq+VH5NStr3Wbpgsjyejdj/SJUwDIbv4hKltbLAf2w+j4MXJq1cbkmw2gUMCWLeRkK/L2As3JwWJsAAaXLqIsV54iP+8n18GRDP/BABTdtpkiB35UJ9jmUyfxYPR4MDXVy+MQQgghhBD/0nmI+KBBgyhdujTJycmsW7eOpUuXsnHjRpKTkwsjvrfaypUrWbFixasO46U4f/48/fv3x8HBgQ0bNhAREUFCQgIjRozQuo3Dhw8zbNgwWrVqxebNmwkJCWHHjh1MnTq1ECMXQpPhqZNYjA0g45MepC5amu+40akTAOTUdNa6TUXqfTI+6UaGd1eN8pwq1fKueeF8XkFGBhgZayTHSltbAAxS7vyvMQWqx39wMi4CubkAFNmxHcWtm2T06qN1bEIIIYQQQns692CrVCpCQkLYuHEjqsd6bxQKBZ07d2b69Ol6DfBtpiqg9+tttWXLFt577z2Cg4NRKBQAhISE0LNnTy5dukS5cuWe2cbXX3+Nu7s7gwfn9czZ29szcuRIJk6cyNSpUylSpEih3oMQAMqyZbkT/wfK0mXyzb0GMDqZgNLMHPMpEyny3U4U6elkNWlGetgMcqtULbBNlZU16dPzz4kuunM7ADkOTgBk+PhSbO0aGDUKxWejMPznGmZzZpDjVIOcGnkJfXbd+piFTsbo6GFUVlYY//oTGbPnQ24uZp9PIX3CZDB6ofUthRBCCCHEE+jcg718+XK++eYbhg8fzr59+0hISGDv3r0MGzaMbdu2sWrVqhcK6Nq1a3z66ae4ubnRrFkz4uLiNI7/8MMPeHt74+LiQqtWrYiMjCQrK0t93MHBgfXr1+Pj44OzszNt27bl6NGjrF+/ng8++IDatWsTEBBARkaG+pxjx47Ru3dv6tSpg7u7O0FBQaSkpGgdc2xsLM2aNWPDhg00adIENzc3hg4dyvXr19V1PDw8mDlzJu3atcPd3R1fX1+ioqK4cuUKDg4OXL58mcDAQAIDA5k5cyYNGzbE1dWVQYMGqdu5fPkyDg4OLFmyhMaNG9OiRQvS0tK0ivFZz+1Zz/1ZAgMD+eSTTzTKrly5gqOjI7/++isdO3Zk5syZ6uQaUP/73r3/zGF9Aj8/P8aPH69RZmBgQHZ2NmlpaQwdOpTevXtrHD937hwODg4kJSWxcOFCevXqxciRI6lduzZhYWE63aMQACobW5SlyzzxuNHJExikp6G0sub+qq9InbcQw3PJWHdsjcG1q1pfx+jI75gujCCzdVtynaoDkOtUnQdTw2DhQqyrVMC2mTuKtDTufbURDA0ByHGrw4OAMVh3aotNswZkeH9CVoeOmKxdg8rMjCxPL4rNn4tNk3pY+nyMwd8XXuh5CCGEEEKIf+mcYG/atAl/f38+/fRTypQpQ5EiRShbtixDhw7F39+fDRs2PHcwOTk5+Pv7k5KSQkxMDPPnz9cYQn3gwAECAgLo2rUr27dvJyQkhJ07dzJ27FiNdiIiIvD392fr1q1YWFgwePBgdu/ezdKlSwkPD2fv3r1s3LgRgISEBHx9falatSobNmxg/vz5HD9+nP79+5P7v2GV2rhz5w6rV68mMjKS1atXc/XqVfz9/cnJyVHXiYmJYeLEiSxfvpzFixfj5+dHyZIl+fnnnylVqhQA27dv5+7du8TExLBs2TJOnTpFZGSkxrU2b96svpY2C8w967k967lrw9vbm4SEBC5evKgui4uLo2TJkjRo0IDKlStTq1YtjXOWLVtGiRIlcHB48sJPj6tevTqOjo7q79nZ2axatYqaNWtia2uLt7c3hw4d4urVf5OYLVu24OzsTNWqeT2Hv//+O3Z2dmzduhVfX1+d7lEIbaQHTebu1p2kh4WT3aARmZ905976zSju36fY0i+0asMo/jesuncht3wFUucvVpcXWzAPs9EB8OmnpG7Zzv1lq1CZm2PVxRPFjRvqeg9Gj+fWuX+4df4q6Z/PgvR0TGeHkz5xKkV276TY8iWkLl5GbjVHLAfIcHEhhBBCCH3ROcG+evUqDRo0KPCYu7s7ly9ffu5gDh48SFJSErNmzaJGjRq4ubkRHh6uPh4dHU3Xrl3p3r075cuXp0mTJkydOpVdu3ZpXLdLly54eHhQqVIlOnXqxL1795g8eTLVqlWjdevWODk5kZSUBOTNg3ZwcGDSpElUrlyZBg0aMG/ePE6dOsXPP/+sdezZ2dnMnDmTunXr4uLiwuzZs0lMTOTgwYPqOs2bN6dRo0Y4OztjYWGBqakphoaGlChRAsP/9T5ZWFgQGhpK5cqVqV+/Pu3atePoUc2tfXx8fKhSpQrOztrN8XzWc3vWc9dGvXr1KFeuHNu2bVOXxcXF0alTJwwM8r9mM2fO5Mcff2TKlCkYGxvrdC3I+1Fg3LhxJCUlERISAuQ9Xzs7O3UMSqWSrVu30rlzZ41zhw8fTrly5bC3t9f5ukI8S25NZ7IbNtYoU9pXJLeqg3p+9tMU3fIN1p90RFmmLPe+iUNlWzzvQE4OZvNmkflJN4iKIqfZB2R28ubupjgMrl/HdNF8zYaMjeF/0yZMo6PIdapOdrMPKBq3hay27clxqcWDoSMw/uMYBpcuIoQQQgghXpzOCXaZMmU4e/ZsgcfOnDmD7f8W3HkeiYmJWFlZUb58eXWZk5OTeoXo06dPs27dOtzc3NSfR/NxH19krUKFCup/FytWDECjTRMTE/Xw6MTERGrXrq0Rh6OjIxYWFk+8z4KYmZlRs2ZN9ffKlStjZWVFYmJigXE9Sfny5TUSTgsLC7KzszXqaNPO45713J713LWhUCjw8vJSDy0/ffo0f/31V74VyLOzswkKCmLVqlWEhYXRsmVLne4FIC0tjcGDB7Nv3z6ioqJwcXEBwMjIiI4dO7J161YAfvvtN+7cuUOHDh3U5xYvXhwLCwudrymEVnJyKPr1Wox+j89/LOMhyuJ2Tz292KIFWAzyI7tufe5u24ny/ZLqYwa3b6F48IAcd80fOFUlSpBbpSpGZ/8ssE3FrVsUW7xQvTq5wa2bKG1s8s793wr+BjeuF3iuEEIIIYTQjc4r3XTo0IGFCxfy/vvv06ZNGxQKBSqVip07dxIVFUW3bt2eOxiFQoFSqcwf5P8W5FEqlfj7++frkQQoUaJEvvqPK6gXFZ680JhKpdKpZ7Wgurm5ueqeaUCrhFWbhbp03ZLqWc8tOTn5qc9dW507dyYqKooTJ06wY8cOateurfFjQFpaGsOGDePw4cPMmzePtm3b6tQ+wI0bNxgwYABXrlxhxYoV1KtXT+N4ly5dWLFiBSdPnmTbtm20aNECKysr9fHC3M5LCIyMMJszE2XJktzd/t2/xQl/YHj+HA8/G/nEU01Wr8R86kQyvLxJjVqq7n1+RGlXAqWNDcYHfwUC1OWK27cxTP6LzNp1CmzXbE44WS1bkeNSS92Owf+Gkxtcv6YuE0IIIYQQL07nHuwBAwbg4uLCyJEjcXZ2pmnTpjg7OzN69Ghq1qyp07ZL/+Xk5ERqaqp6+DbAhQsX1At5Va1alfPnz1OhQgX159q1a8yaNYv09PTnuqaDgwNHjhzRKDtz5gxpaWlUrlxZ63bu3r3LpUuX1N+TkpJIS0ujevXqTzzn8QW/CtOzntuznru2ypQpg7u7O7t372bnzp0avddZWVkMGjSIhIQEVqxY8VzJ9b179+jTpw937txh7dq1+ZJryBs54Obmxs6dO9m3b99bt3e6eP2ljw3E+NBvWAwdiPGP32MSsxrLnl3JqelCRjefvEqZmRgdPoTBP1cAUFy/jvnkIHLLV+Ch3yCMEv7A6PAh9Udx6xYYGpI+bgJFvtkIgwdjtP+HvOHkH3cEQ0MefJp/n3aDc8mYfL2W9MBJ6rKsj9pQNG4rReK2YjY9lJwazijL6zYqRgghhBBCFEznHuwiRYrwf//3f+zfv5/ff/+de/fuYWVlRb169WjevPkLBePu7o6rqyvjxo0jJCQEQ0NDwsLC1L3PAwYMICAggKioKNq3b8+1a9cIDg6mbNmyGj3YuujXrx8+Pj6EhYXh4+PDrVu3CAsLo3r16jRs2FCntsaOHcvEiRPJyclh6tSpuLm5FZgEPmJqasq9e/c4f/48ZcuWfa74tfGs51a8ePGnPndddO7cmdDQUJRKpUYSvWTJEo4cOcLcuXOpVKkSN2/eVB+zsrLSquc+PDycS5cusXz5cmxtbTXasLW1VY8W6NKlC9OmTcPS0pLGjRs/qTkhCkVmNx/um5hQLGo+Vn19UJmaktnWk/SJIeqVvg2uX8OmXUvSxwTyYNwEiu77DsXDhxhe/Bubjq3ztXl/wRdkdu9JRv9BGNjYYBYdhfn//R9K2+JkN2hI+qq1KCvY5zvPbHooGd17oqxY6d/4PL0wOnIYi9GfkVuhIve/WA4v6cc+IYQQQoi33XNvhtq8eXN1Qn3nzp0Xmnv9iIGBAUuWLGHatGn4+flhYmLCoEGDuHIlr5enTZs2REREsGTJEqKjo7G2tsbDw4MxY8Y89zVdXV1Zvnw5kZGReHl5YW5uTsuWLRk9erTOi295enoycOBAsrKy8PDw0NjzuSAfffQRGzZsoGPHjsTExDz3PTzLs57bs567Llq3bk1oaCgtW7bUWOF8+/btqFQqRo0ale+cNWvW4O7u/tR2c3Nz2bFjB9nZ2fTpk3/V43379ql/pGjbti1hYWF4eXlpDNEXQt+yGzfl5o37+cozO3mT2enJoyeU5StonJfh40uGj3ar2md17Y7ZoP7cTUknJyf/1I7HpS5fnb/QwID00Omkh07X6npCCCGEEEJ7CtWTJiH/x/Xr1/n8889xc3OjX79+6vIHDx7QoEEDGjduzLRp0yhevHihBfu6io2NJSgoSKdF0UThuXTpEh999BE7d+58rVYK77YmmjM3tN8HWbw+HN8rxfreg0nRIqktbEZGBtjYmL0WsYi3g7xTQt/knRL6Ju+U0CdbWzMMDXUfqastrVq+c+cOPXv25KefflKvyv1ITk4O3bp149ChQ/To0YN79+4VSqBCPMvVq1fZvXs3EydOpGnTpq9Vci2EEEIIIYR4+2k1RHz58uVkZmayZcuWfFtEWVpaEhwcTLdu3ejZsycrV65k5Mgnr5T7Jrl+/Tpt2rR5ah1nZ2e8vLxeTkD/cezYMfz8/J5ap3Xr1syYMeOFrjN48GDi4wvYdugxsbGxVKxY8bna19d9pKSkEBgYiL29PVFRUc+8bmhoKJs3b35qnUWLFtGoUaNntiWEEEIIIYQQWg0Rb9OmDT179sTX9+lzBKOjo4mLi+Pbb7/VW4CvUm5uLpcvX35qnaJFi1KyZMmn1iksmZmZXLt27al1zMzMsLN7+t67z3L9+nUyMjKeWqd06dI6z1l/5GXdx3/duXOH1NTUp9Z577338o3aeF4yRPzNJUPExdtM3imhb/JOCX2Td0roU2EPEdeqB/vatWs4ODg8s56rqytLlix54aBeF4aGhvl67F8nRYsWfSnxvf/++4Xa/su6j/+ytbXVy+J8QgghhBBCCAFazsG2tLTk7t27z6yXnp6OmZnZi8YkhBBCCCGEEEK8cbRKsF1dXdm1a9cz6+3evZvKlSu/cFBCCCGEEEIIIcSbRqsh4j4+Pvj5+eHm5vbEedgxMTFs376dOXPm6DVAId4WlYrrdw65eHnkbyeEEEIIIbShVYLdsGFD+vfvz+eff86GDRv44IMPKFu2LLm5ufzzzz8cOHCApKQkPv74Y9q3b1/YMQvxxlGpVIS3//hVhyFeQK5SiVL5zDUhhRBCCCHEO0yrBBtgzJgxODk5sXTpUpYtW6YuVygU1KhRg3nz5tG2bdtCCVKIN51CoeD+/Yfk5srKl28qpVIlCbYQQgghhHgqrRNsgPbt29O+fXtu3brF1atXMTIyolSpUlhbWxdSeEK8PXJzlbK1hBBCCCGEEG8xnRLsR+zs7PS+J7EQQgghhBBCCPEmK7wdtoUQQgghhBBCiHfIc/VgCyF0Z2gov2e9DDJXWgghhBBCvCqSYAvxEqhUKiwti73qMN4JuUold1MeSJIthBBCCCFeOkmwhXgJFAoFE/ds4nzKzVcdylutok0JprX6GAMDhSTYQgghhBDipdM5wc7MzOTEiRPcvHkThUJByZIlqV69OkWKFCmM+IR4a5xPucmZW1dfdRhCCCGEEEKIQqJ1gn337l3mzZvHli1byM7ORqXK6x1SKBSYmprSpUsXhg8fjrm5eaEFK4QQQgghhBBCvK60SrDv3btHjx49uHz5Mh999BGNGzfGzs4OlUrF9evX+eWXX/jqq6/47bffWLduHWZmZoUdtxBCCCGEEEII8VrRKsGOjo7m5s2bfPXVVzg7O+c73rVrV/7880/69u3LypUr+eyzz/QeqBBCCCGEEEII8TrTat+g77//noEDBxaYXD/i5ORE3759+e677/QWnBBCvCiDf65QvEo5jH/5SaPc+Kf9WHVqS/Gq5SleowqW/XphcP7cM9srsm0z1h81p3jF0tjWcsJi+KcobtzQqGOU8AeWPbpQ3KkixR0qYPVJJ4wS/tBs59s4bOu6UNyhAmYTx0NursZxs0lBmI+SHyuFEEIIId4kWiXY165de2py/YiLiwuXL19+4aCEEEIfDK5cxqqrFwb372mUG8X/hlVXL1S2xUn9Yhmp4bMxPJeMTYePUNy+/cT2im7ehJV/H3Jc3Li/8kvSgyZh/PMBrLt0gIyMvGueS8aqUzsUDx+SGrGI1AXRkJWFtWdrDP9KAkBx6xaWQweQ0eVjUiMWYRK7EZOY1f/GffFvTL5ey4OxQYXwVIQQQgghRGHRKsHOzMzE1NT0mfXMzMzI+N9/ZBYmBwcHYmNjC/06b5LAwEB8fX1feRtvg6NHj+Lr60udOnVo2rQpwcHB3L1791WHJXShVFL067XYtGiCwc0b+Q6bRkWQW82R+yvWkNWyNVkdO3NvfSyK27cw+XrtE5s1jZxLZsuPSJsTSfaHLcjs5sP9ZaswOnuGInt2AVBseTQUK8a9tRvJatOOrNZtuffVJlSmpnnHAOPf41EZGvEgcBJZ7TqQ0fljihz4UX0ds/AwMnr3Q1mqtH6fixBCCCGEKFRaJdiQt1q4PuqI11dwcDALFy581WG8UufPn6d///44ODiwYcMGIiIiSEhIYMSIEa86NKEDw1MnsRgbQMYnPUhdtDTf8ZzadXk4aAgY/Pt/ApUlS6GytMTwwvmCG1UqyWr+IRm+/TSKc6tWy7vm+fP/++7AgyHD4fHFHs3MyC1V5t+2FQooWiTv/wUwLqIeIm504jhFftjLg+Ejn+fWhRBCCCHEK6TzPtji7WVhYfGqQ3jltmzZwnvvvUdwcLD6B6OQkBB69uzJpUuXKFeu3CuOUGhDWbYsd+L/QFm6TL651wAPRo7NV2b8688Y3L1LjqNjwY0aGJAeOj1fcZEd2wHI/d95Gf388596LhmjM6d52LQ5ADmutVDcv0+Rnd+SU8uNInt2kdGrLwBmUyfzYNhIVFbW2tyqEEIIIYR4jWjdg92tWzecnJye+unevbveA7x27Rqffvopbm5uNGvWjLi4OI3jP/zwA97e3ri4uNCqVSsiIyPJyspSH3dwcGD9+vX4+Pjg7OxM27ZtOXr0KOvXr+eDDz6gdu3aBAQEaAxtP3bsGL1796ZOnTq4u7sTFBRESkqKTnEnJCTQt29f3NzcaNSoESEhITx8+BCA3NxcVq1aRevWrXF2dqZ169asW7dOfW58fDzVq1dnz549tG7dGhcXF3r37s3Vq1eZNm0adevWpWHDhnzxxRca18zJySEsLIzatWvj7u5OaGgomZmZWsf8+BDxRzHs37+fDh06ULNmTdq0acPevXvV9VUqFatXr1bH2L59e7Zv364+fvXqVcaMGUPjxo2pVasW/fv358yZMxrXGzdunPqe6tevz4IFC0hOTsbHxwcXFxc8PT05fvy4+pzU1FQmTZpEgwYNqFOnDr179+bEiRM63eMnn3yiUXblyhUcHR359ddf6dixIzNnztQYjfHo3/fuac7jFa8vlY0tytJltK6vuH0b81GfkVuyFJndfLQ+z+D8OcynTiS7pgtZLVsXXOnhQyw/G4yqqAkP/QcBoCxVmrRZEVgMHYht7Rrk1KjJQ78BGP+wD8O/EnnoPwiTr77EpnlDrLzaYXTieMFtCyGEEEKI14pWPdjDhg0r7DgKlJOTg7+/P+bm5sTExJCVlcXUqVPVxw8cOEBAQABBQUE0atSIixcvEhYWxvnz55k/f766XkREBNOnT8fe3p7AwEAGDx5MzZo1Wbp0KefPn2f06NFs3LgRX19fEhIS8PX1pVu3boSEhHDz5k1CQ0Pp378/GzduxNDQ8JlxX7p0iT59+tCqVSvWr19Pamoq48ePZ+rUqcyYMYMZM2awdetWJk2ahLOzMwcOHODzzz8nMzOTvn37AnlJ+BdffMGcOXPIyclh0KBBdOrUiS5durBx40a2bdtGZGQkHh4eODg4AHlzh99//33Wr1/P5cuXmThxIgYGBkycOPG5nn9ubi6zZ88mODiYUqVKMW/ePMaPH8+BAwcwMzNj+fLlLFq0iODgYNzd3dm/fz/jxo3Dzs6OmjVr0qNHD8qVK8cXX3xBkSJFWLhwIb169WLr1q2UKZOX/OzYsYOePXsSGxvL9u3bmT9/PnFxcQQGBlK2bFmCg4OZOnUqsbGxqFQqBgwYgImJCUuWLMHc3JytW7fSo0cPNmzYQPXq1Z95T97e3vj6+nLx4kXKly8PQFxcHCVLlqRBgwYYGOT/zWnZsmWUKFFC/ZzF28Xg+jWsunbG4Pp17m3aispcu5EchkmJeQulGRpxf8UajeHmjyjSUrHs44PRsSPcX/ElynLl1ccyfHzJ6N4TsrLAxASUSszCQngwNgjDv5IwnzCOe+s2YXTiOJa9unHn0HEoWlRv9y2EEEIIIfTvtU6wDx48SFJSEnv27FEnQ+Hh4Xh5eQF5+3N37dpV3XNevnx5pk6dSp8+fbh8+TJly5YFoEuXLnh4eADQqVMnQkNDmTx5Mvb29lSrVo3ly5eTlJS3uu/KlStxcHBg0qRJAFSuXJl58+bRqVMnfv75Z5o3b/7MuDds2IC1tTXTp0/HyCjvEU+bNo1jx46RlpbGunXrCAwMxNPTEwB7e3suX77M0qVL6dOnj7qdESNGqFdvb9CgAcePH2fcuHEoFAoGDRrE4sWLSUpKUid+JUqUYObMmRQtWpSqVasyYsQIQkNDGT16NMWKFXuuv0FAQAANGzYEYMiQIezevZvExERq1arF6tWr6d27t7pH2NfXl4yMDHJycti2bRspKSnExsZia2sLwNy5c2nZsiVr165l3LhxAFhbWzN+/HgMDAzo27cv8+fPp127drRo0QLIS4inT88blvvbb7/xxx9/8Ntvv2FtbQ3AqFGjOHr0KGvWrGHGjBnPvJ969epRrlw5tm3bpn6v4+Li6NSpU4HJ9cyZM/nxxx+JiorC2Nj4uZ6heH0Znj6FVa+uKNJSufd1LDl16ml1nvEvP2HZrycqM3Pubf4WZcVK+eoYXLmMVc+uGCYncX/pKrLats/fkIFBXnINFN20HkVWJhnde2I6O5ysho3IbtiYbPeGmE0Pw/jI72Q3avJC9yuEEEIIIQqX1kPEAe7cucPFixfzla9Zs4Zbt27pLahHEhMTsbKyUifXkLfftsn//oP09OnTrFu3Djc3N/Vn8ODBACQnJ6vPqVChgvrfjxLNx9s0MTFRDytPTEykdu3aGnE4OjpiYWHB2bNntY67Ro0a6uQa8hLkTz/9lHPnzpGdnU2dOnU0zqlfvz63b9/m9mNbBD0et6mpKWXLllUPV370DB4fDl+zZk2KPtbD5eLiQnZ2NhcuXNAq7oJUqvRv4mBubg5AdnY2KSkp3Lx5E1dXV436AwYMoEmTJiQmJmJvb69Orh/F7OLiQmJiorqsbNmy6sT20Ur1j89zNjExITs7G4BTp06hUqn48MMPNf7mx44d0/h7P41CocDLy0s91eD06dP89ddfeHt7a9TLzs4mKCiIVatWERYWRsuWLbVqX7w5jH8+gLVna1CpuLttNznuDbQ6r2jsRqy6eqEsVYa7O/aqFzl7nOHpU1i38cDgymXubdhCVoeOT280MxOzmZ+TPiEEDA0xuHULlbVN3jEDA5SWlhjcuK7rLQohhBBCiJdM60XOtmzZwpQpU/Dx8VH3PkLeHOnp06czZ84cZs6cSdu2bfUWnEKhQKlU5it/lLgqlUr8/f3p3LlzvjolSpTIV/9xBfVWQt684ieVa9uDWdD1ntX+o/t8/Nz/tvOkmB/57/D1R20WKVLkqec9TUHnavMsnnafj99XQe086T6VSiXm5uYFbtGmyz127tyZqKgoTpw4wY4dO6hdu7bGjxlpaWkMGzaMw4cPM2/ePL2+0+L1YHTiOFa9upJbvgL3NmxBWbKUVucV2bsbi6EDyXZvyP0vv0ZlYZmvjsGVy1h/3BGVkRF3t39HrsMTFk17TLHlS1C+X5Ksdh0AUNrZYXz+XN7BrCwMUu6gtCvxlBaEEEIIIcTrQKse7CNHjjBhwgTq1atHp06dNI6VLFmSzZs3U69ePUaPHq3TglPP4uTkRGpqqnr4NsCFCxdIS0sDoGrVqpw/f54KFSqoP9euXWPWrFmkp6c/1zUdHBw4cuSIRtmZM2dIS0ujcuXKWrVRpUoVTp8+Te7/tt0B2LNnDx4eHlSuXBljY+N81zh8+DAlSpTAysrqueIG+PPPPzV+kDhy5AgmJiaFsvK1hYUF7733Xr6/9/DhwwkPD8fBwYELFy5o9MhnZmZy8uRJqlSp8lzXrFatGmlpaWRnZ2v8zZctW8a+ffu0bqdMmTK4u7uze/dudu7cqdF7nZWVxaBBg0hISGDFihWSXL+lzAOGQXY26WMnYHD5EkaHD6k/Bo8SW9D8npGB+cjPUJlb8CBgDIZnz2ie98+VvLYnjMPg1k0ejB6PIvW+Rh3Ds2fyxaK4m4Lp/DmkTw5Vl2V91Abjgz9T9Ou1mE0PRWVpRbaWw9eFEEIIIcSro1WCvWzZMho0aMCyZcsKXOjJycmJZcuWUbNmTZYsWaK34Nzd3XF1dWXcuHH88ccfnDhxgnHjxql7OAcMGMDu3buJiori/PnzHDx4kKCgIFJTUzV6sHXRr18/zp49S1hYGMnJycTHxzNmzBiqV6+unov8LD4+PqSkpBASEkJycjK///47s2bNokGDBpibm9OtWzcWLFjA9u3b+fvvv1m7di1fffUVfn5+L7SX+NWrV5kwYQJJSUns3r2bhQsX4u/v/0I92E8zcOBAVq9ezdatW7l48SJr1qxh3759tGjRAk9PT6ytrQkICCAhIYEzZ84wZswYHjx4QLdu3Z7rek2bNsXJyYmRI0fy22+/8ffffxMeHk5sbKzWP3480rlzZ7766ivu3r2rkUQvWbKEI0eOEBYWRqVKlbh586b68/hwfPHmMrhwHuMTx1FkZ2PV3xebdi01PmbzZqnrPv7d+Pd4DK9fw+DeXay7euU7zyRmNWRlUWTPLgAsxgbkq2M+flS+eEznzyO7njvZDRqpy3Jq1yU9eArmoZMo8t1O7i9fDc+5joIQQgghhHh5tBoifurUqWeuRG1gYEDPnj2JiIjQS2CP2lyyZAnTpk3Dz88PExMTBg0axJUreT1Fbdq0ISIigiVLlhAdHY21tTUeHh6MGTPmua/p6urK8uXLiYyMxMvLC3Nzc1q2bMno0aO1HiL+/vvvs3LlSmbPno2XlxdWVla0a9eOUaPy/uM6KCgIGxsb5syZw61bt7C3t2fy5Ml07dr1ueMGaNGiBYaGhnTt2pVixYrRo0cPhgwZ8kJtPk2vXr3IyMhg/vz53Lx5E3t7eyIiIqhfvz4AMTExzJgxQ70yep06dVi3bt1z96gbGhqqn2tAQAAPHz6kcuXKREVFaf3jxyOtW7cmNDSUli1bqueWA2zfvh2VSqX+Wz1uzZo1uLu7P1fs4tXJbtyUmzfuq78r7StqfH+ax+tlN22u1Xm3/rmjU3zpIWEFlj8cOpyHQ4fr1JYQQgghhHi1FKonTZZ9TK1atViyZMkzk4tDhw4xcOBA/vjjD33FJ8Rbo+eGLzhz6+qrDuOt5mhXirVdPyUlJZ2cnPzrN7wNjIwMsLExe6vvUbxc8k4JfZN3SuibvFNCn2xtzTA01Gmtb51o1XKpUqU4f/78M+tduHABOzu7Fw5KCCGEEEIIIYR402g1RPzDDz/kyy+/xNvb+4nzeTMzM/nyyy/f6iG0169fp02bNk+t4+zszJo1a15SRNrZsWMHwcHBT63Tr18/hg9/s4ejDh48mPj4+KfWiY2NpWLFii8pIiGEEEIIIcS7RKsEu2/fvmzevJm+ffsyYcIEatasqXH81KlThIeHc/nyZSIjIwsjzteCnZ0dW7ZseWqdx/ehfl00b978mXFbWubfbuhNM3XqVDIyMp5ap3Tp0i8pGiGEEEIIIcS7RqsE+7333iMqKoqRI0fyySefULx4ccqWLUtubi5Xr17l9u3bFC9enKioKJ1Xc36TGBoaauyX/KYwMzPDzMzsVYdR6N5///1XHYIQQgghhBDiHaZVgg15K0DHxcWxefNmfv75Z65du4ahoSHOzs40adKETp06aazGLIQQQgghhBBCvEu0TrABrKys6Nu3r3rbJSGEEEIIIYQQQuTRKcEuyJ9//smFCxewt7fHyclJHzEJ8VaqaFPiVYfw1pNnLIQQQgghXiWtE+yDBw+yfv16FAoFPj4+1KtXj7Fjx7J9+3ZUKhUKhYImTZqwYMECihUrVpgxC/HGUalUTGv18asO452Qq1SiVKpedRhCCCGEEOIdpFWCvWfPHoYPH07p0qWxsLCgX79+dO3ald27dzNixAhq1qzJ8ePH+eKLL1i8eDGjR48u7LiFeKMoFAru339Ibq7yVYfy1lMqVZJgCyGEEEKIV0KrBHv58uV06NCB2bNnA7BmzRrCw8MZOnQogwcPBqBJkyYoFAq2bdsmCbYQBcjNVZKTIwm2EEIIIYQQbysDbSr99ddfdOjQQf29Y8eOqFQq6tWrp1HP3d2df/75R78RCiGEEEIIIYQQbwCtEuz09HSsrKzU3x9tx/XfbbmMjIzIzs7WY3hCCCGEEEIIIcSbQetFzgwM/s3FFQpFoQQjxNvM0FCr37PESyJztYUQQgghhL690DZdkmgLoR2VSoWlpayu/zrJVSq5m/JAkmwhhBBCCKE3WifYQ4cOpUiRIhplgwcPxtjYWP09KytLf5EJ8RZRKBRMPbCBv+/efNWhCKCCdQlCmnXFwEAhCbYQQgghhNAbrRLszp07a92gUimrJAtRkL/v3iTxjiwCKIQQQgghxNtKqwQ7PDz8mXVu3LjBhg0b2LRp0wsHJYQQQgghhBBCvGleaA42wE8//cTXX3/N/v37ycnJoVy5cvqISwghhBBCCCGEeKM8V4J9584dNm3axIYNG7hy5Qrm5uZ07tyZTp06UbduXX3HKIQQQgghhBBCvPZ02jfot99+Y+TIkTRv3pzIyEhKlSoFwKJFiwgLC5PkWgjxxjL45wrFq5TD+JefNMqL7NmF9UfNsbMviW3tGpjO/ByetaBjRgZ2pW0p8Z6lxsfOvpRm2zu2Y92yGXb2pbCt74rp7PB8bZvOCKN49UrY1q5B0a/Xal5HpcK6VXOKblr/3PcthBBCCCH0R6se7FWrVrF+/XrOnz9PhQoVGDJkCJ07d8bU1JT69evLdl1CiDeawZXLWHXrjMH9exrlxj/sw9K3OxndfEgPnoLhX4mYTZuKwY3rpM1d8MT2jM6cRpGTw/3Fy8i1r/jvAUPDf9v+8Xss+/Uk08ub9IlTMDr7J2afT8Xgzm3SwucAecm96eKFpM5biOJuChajPuN+nTrQqB4ARTdvgtxcMrt01ePTEEIIIYQQz0urBHvGjBk4ODiwZs0a6tevry5PTU0ttMCexMHBgfDwcLy9vV/6tV+1I0eOoFKptB4pEBgYyJUrV/jyyy8LObLX29GjR4mIiOD06dOYmprSrFkzxo4di7W1tVbnL1y4kM2bN/P9998XbqDi5VMqKfr1WsynBIMq/3ZdpgvmkeNai7T5iwHIbv4hBrdvYxoxm7TQcDAzK7BZo5MnUBkZkenpBUWLFljH5OsYlGXLkbp4ORgakv2BBwY3b1IsOiqvbWNjjA/8SFazD8j8uBsAxWLW5PWwN6oHWVmYTQ8jddZckB85hRBCCCFeC1oNEW/fvj1///03gwYNYsiQIezZs4ecnJzCjk38h4+PDxcvXtS6fnBwMAsXLizEiF5/58+fp3///jg4OLBhwwYiIiJISEhgxIgRrzo08RowPHUSi7EBZHzSg9RFS/MdT42IInXRMs3CIkVAqUSRk/3Edo1OJpBbtdoTk2sARUYmKlNTjV5tpY0tiqwsFGmPfrxUoDIppj6uKmIMubkAFF2xjNxy5cj2aKXFnQohhBBCiJdBqx7suXPnkpaWRlxcHLGxsXz22WfY2NjQsmVLFAqFDBF/TVlYWLzqEF65LVu28N577xEcHKx+T0NCQujZsyeXLl2SVe/fccqyZbkT/wfK0mXyzb0GUD42vFuReh/j/T9SbPECMjt/jMrK+ontGp08gcrQCKtPOmH8ezyqIkXI9OxM+tRpqMzz/nf50G8AVj26UGzRAjJ69cYwKZFiSxeT2fIjVDa2AGTXq49F4GgMk5NQ3L2L0Z+neejeEO7dw2TeLO6t3ajfByKEEEIIIV6I1oucmZub06NHDzZu3EhcXBydOnXi+++/R6VSMWHCBObPn89ff/2l1+CuXbvGp59+ipubG82aNSMuLk7j+A8//IC3tzcuLi60atWKyMhIsh5bIMjBwYH169fj4+ODs7Mzbdu25ejRo6xfv54PPviA2rVrExAQQEZGhvqcY8eO0bt3b+rUqYO7uztBQUGkpKRoHXNsbCzNmjVjw4YNNGnSBDc3N4YOHcr169fVdTw8PJg5cybt2rXD3d2dQ4cO4evry5w5c5gwYQJ169aldu3ajB49mrS0NPW9AAQFBREYGKhVLIGBgfj6+gIQHx9P9erV2b9/Px06dKBmzZq0adOGvXv3quurVCpWr15N69atcXFxoX379mzfvl19/OrVq4wZM4bGjRtTq1Yt+vfvz5kzZzSuN27cOKZNm0bdunWpX78+CxYsIDk5GR8fH1xcXPD09OT48ePqc1JTU5k0aRINGjSgTp069O7dmxMnTmj9vAMDA/nkk080yq5cuYKjoyO//vorHTt2ZObMmRo/Aj369717mvNtxbtHZWOLsnSZZ9YzuH4Nu8plsfLrhcrKmvQJk5/SqArD06cwPH+OzDbtubfuGx4EjKXo5k1Y9fgYlEoAsps258GwEZhPnYhd1fLYtGuJ0q4EqdEr1E1leXqR2a4jNk3dsfbuQPr4ieTWcoOZM8lp1IQcVzfMJgVh06gOFgP7orh9+4WfiRBCCCGEeH46rSL+SNWqVQkMDGT//v0sXLiQSpUqsWzZMjw9PenYsaNeAsvJycHf35+UlBRiYmKYP38+K1b8+x+eBw4cICAggK5du7J9+3ZCQkLYuXMnY8eO1WgnIiICf39/tm7dioWFBYMHD2b37t0sXbqU8PBw9u7dy8aNeb1ACQkJ+Pr6UrVqVTZs2MD8+fM5fvw4/fv3J/d/wzK1cefOHVavXk1kZCSrV6/m6tWr+Pv7awyrj4mJYeLEiSxfvpxatWoBeYvJ2dnZsWnTJmbPns2+fftYtWoVAD///DMAEyZMIDg4+HkeKbm5ucyePZvg4GC2b99OtWrVGD9+POnp6QAsX75c/by2b99O9+7dGTduHL/99htpaWn06NGD69ev88UXX/D1119jYmJCr169uHLlivoaO3bswNDQkNjYWPr27cuiRYsYPHgw/fv3Z+PGjRQtWpSpU6cCeQn9gAEDuHTpEkuWLGHDhg3UqlWLHj16cPr0aa3uydvbm4SEBI2h83FxcZQsWZIGDRpQuXJl9fN9ZNmyZZQoUUL9o4UQz6IyMeHuN3HcW74aVdGi2LRtgcHVf55QWcX9L7/m7s59ZPQfSHbDxjwc8hlps+ZhHH+QIj/k/ahlPnYkplHzSR81lrubv+X+gi8wuJuCVTdvePAgry2FgrQ5kdw6f5Vb5/7h4bARKP75B6KieDgxhGIrl1LkwA/cXxkDBoZYjA14OQ9ECCGEEEIU6LkS7EeMjIxo1aoV0dHR/Pjjj4waNUpvc7MPHjxIUlISs2bNokaNGri5uREeHq4+Hh0dTdeuXenevTvly5enSZMmTJ06lV27dnH58mV1vS5duuDh4UGlSpXo1KkT9+7dY/LkyVSrVo3WrVvj5OREUlISACtXrsTBwYFJkyZRuXJlGjRowLx58zh16pQ6wdVGdnY2M2fOpG7duri4uDB79mwSExM5ePCguk7z5s1p1KgRzs7OFClSBIAqVaowatQo7O3tadGiBY0bN+bYsWMAlChRAsgb9v0iQ78DAgJo2LAh9vb2DBkyhLS0NBITE9W917179+aTTz6hfPny+Pr6MnLkSHJycti2bRspKSnMnz8fFxcXHB0dmTt3LiYmJqxd++/WQdbW1owfP57y5cvTt29fANq1a0eLFi1wcHDA29ubxMREIG/btz/++IPIyEhcXV2pXLkyo0aNolatWqxZs0ar+6lXrx7lypVj27Zt6rJHIywMDPK/3jNnzuTHH39kypQpGBsbP/dzFO8WlZU12U2bk9WxM/e+2oTi1k1M1j7hHTUwILtxU3IdnTSKs1q1BvLmfRtc/QeTL/+PB8NG8CBwEtmNm5LZvSf3vtqE8ZHfMVn3n4UJixZVz9UuFj4NevRAWbUaReK2kvFxd3IdnXg48FOK7PpWPUdbCCGEEEK8fFrNwdaGnZ0dAwYMYMCAAXppLzExESsrK8qXL68uc3JywsTEBIDTp0+TkJDApk2b1MdV/1sFODk5mbJlywJQoUIF9fFixfIWC3q8TRMTE/Ww8sTERBo3bqwRh6OjIxYWFpw9e5bmzZtrFbuZmRk1a9ZUf69cuTJWVlYkJibStGnTfHE9UqlSJY3vFhYW3L9/X6trauvxa5ibmwN5PwikpKRw8+ZNXF1dNeo/+ntOmTIFe3t7bG1t1cdMTExwcXFRJ8wAZcuWVSe2pqamABrznE1MTMjOzlsc6tSpU6hUKj788EONa2ZlZZGZmanV/SgUCry8vIiLi2PYsGGcPn2av/76i8WLF2vUy87OZvLkyWzZsoWwsDBatmypVfviHZabS9HtW8mtVJkc53//d6EsXwGVjQ0G164VeJrBtasU2bObrA9boCz777uv+N9UFFVxOwwuX0KhUpFTv4HmJR0cUdraYvTY1IvHGZ75kyJbY+Hs2bxr3bqJysYmr11raxQ5OShu30b13nvPf99CCCGEEOK56S3B1jeFQoHyf3MVH2dklBeyUqnE39+fzp0756vzqLf38fqPK6hnE/5N0Asq16W3s6C6ubm5GD62WvCjHwoe96gnuzAVdA1t7u9Jz0apVGo844LaedLzViqVmJubExsbq1WcT9K5c2eioqI4ceIEO3bsoHbt2ho/YKSlpTFs2DAOHz7MvHnzaNu2rdZti3eYoSFmYVPIrVSJexu2qIuNEv7A4M4dcqrXKPi8nBwsRg8nPWAMDx6bq110yzeoDA3JbtAQpZUNKkNDjH87SFaLj/695F9JGNy5Q24F+wKbNgubTIb/IIqVLg0p6SjtSmBwI299B4Pr11EZGqJ67EcwIYQQQgjxcr3QEPHC5OTkRGpqqnr4NsCFCxfUi35VrVqV8+fPU6FCBfXn2rVrzJo1Sz2nWFcODg4cOXJEo+zMmTOkpaVRuXJlrdu5e/culy5dUn9PSkoiLS2N6tWrP1dcL4OFhQXvvfdevgXGhg8fTnh4OA4ODly4cIHbjy2ilJmZycmTJ6lSpcpzXbNatWqkpaWRnZ2t8XdctmwZ+/bt07qdMmXK4O7uzu7du9m5c6fGHulZWVkMGjSIhIQEVqxYIcm10En62ECK/Pg95mNHYnzgR0y+XIVlz67kOFUno0evvEqZmRgdPoTBP3lrESjLluNhj16YLpqP6bxZGB/4EdPZ4ZiFhfCw/0ByK1dFZWfHw4FDKLZoPmafT8X45wMU/XotVt29yS1XngzfPvliMf71Z4wPHyIjYJS6LKtVG0xiVlNkzy5MI2aT1fIjKOBHRSGEEEII8XK8tgm2u7s7rq6ujBs3jj/++IMTJ04wbtw4dW/ogAED2L17N1FRUZw/f56DBw8SFBREamqqRg+2Lvr168fZs2cJCwsjOTmZ+Ph4xowZQ/Xq1WnYsKFObY0dO5aTJ0/yxx9/MG7cONzc3KhXr95zxfWIqakpycnJOq1qrouBAweyevVqtm7dysWLF1mzZg379u2jRYsWeHp6Ym1tTUBAAAkJCZw5c4YxY8bw4MEDunXr9lzXa9q0KU5OTowcOZLffvuNv//+m/DwcGJjY3X6QQPyerG/+uor7t69q5FEL1myhCNHjhAWFkalSpW4efOm+vP4ivNCFCSzmw/3VqzB6I+jWPXujll4GFmt23F3607435QTg+vXsGnXEpOY1erz0mZF8GD0eIpu/Bqrnp9gsvFr0scFkx767zoS6VOmkR4SRpFvt2HV3Ruz2eFkNf+QlN0/FrgFmFnoJB4MHw2WVuqyhwMGk92wMRaD/SEnh7RZEYX3MIQQQgghxDO9tl0dBgYGLFmyhGnTpuHn54eJiQmDBg1Sr1jdpk0bIiIiWLJkCdHR0VhbW+Ph4cGYMWOe+5qurq4sX76cyMhIvLy8MDc3p2XLlowePVrnBbE8PT0ZOHAgWVlZeHh4aOzD/Lz8/PxYvnw5ycnJREdHv1BbBenVqxcZGRnMnz+fmzdvYm9vT0REBPXr1wfyVj6fMWOGevGyOnXqsG7duufeS9rQ0JCVK1cye/ZsAgICePjwIZUrVyYqKkrnHzRat25NaGgoLVu2VM8tB9i+fTsqlYpRo0blO2fNmjW4u7s/V+zi7ZPduCk3b+Rf8yDL04ssT68nnqcsXyH/eUWL8mDUOB6MGvfkCyoUPBw0lIeDhmoV391dPwD/+T/aJiakLlqq1flCCCGEEKLwKVRPmlwrnktsbCxBQUGc/d8iREI84rdtEYl3nrC1k3ipqtmWZmXHoaSkpJOTk3+th9eZkZEBNjZmb2Ts4vUk75TQN3mnhL7JOyX0ydbWDEPDwhvI/doOERdCCCGEEEIIId4kr+0Q8dfN9evXadOmzVPrODs74+XlVeix7Nixg+Dg4KfW6devH8OHDy/0WArT4MGDiY+Pf2qd2NhYKlas+FztHzt2DD8/v6fWad26NTNmzHiu9oUQQgghhBDvFkmwtWRnZ8eWLVueWqdo0aKULFlSYxXrwtC8efNnxmJpaVmoMbwMU6dOJeN/ewc/SenSpZ+7/erVqz/zOZqZmT13+0IIIYQQQoh3iyTYWjI0NNTYW/lVMjMzeycSv/fff79Q2y9atOhr8zcVQgghhBBCvPlkDrYQQgghhBBCCKEHkmALIYQQQgghhBB6IEPEhXhJKliXeNUhiP+Rv4UQQgghhCgMkmAL8RKoVCpCmnV91WGIx+QqlSiVqlcdhhBCCCGEeItIgi3ES6BQKLh//yG5ucpXHYr4H6VSJQm2EEIIIYTQK0mwhXhJcnOV5ORIgi2EEEIIIcTbShY5E0IIIYQQQggh9EASbCGEEEIIIYQQQg8kwRZCCCGEEEIIIfRAEmwhXhKFQvGqQxBCCCGEEEIUIkmwhXgJVCoVFpYmGBhIki2EEEIIIcTbShJsIV4ChUKBoYGBJNhCCCGEEEK8xSTBFkIIIYQQQggh9EASbCGEEEIIIYQQQg8kwRZCCCGEEEIIIfRAEmwhXgM//LCPjz5qToUK71O3rjOLFi1ApVJpde6JE8cpXdqWixf/VpddvPg3771n+cTP8OGfquvOmBFG9eqVqF27Bl9/vVajbZVKRatWzdm0ab1+blQIIYQQQoi3mNGrDkCId93hw4fo1asrnTp5Exg4kfj4g4SGTiI3N4fhw0c99dw//zyNj88n5OTkaJS//35JduzYm6/+ypXL2Lo1Fh+f3gDs2bOLxYsXMm/eQu7eTWHUqM+oVas2jo5OAGzevInc3Fy6dOmqp7sVQgghhBDi7SU92K8pBwcHYmNjX3UYeufr60tgYKDW9T08PFi4cGEhRqQpIyODuXPn4uHhgZubG97e3uzbt69Qrzlr1nScnV1YvHgZHh6tCAqazNChI4iMnMvDhw8LPCcrK4vFixfStq0HWVmZ+Y4XLVqUunXra3yMjY3ZujWWCRNCaNCgIQAHDvxIs2Yf8PHH3fD3H0y1ao788stP6mtMnx7GxIkhsoe3EEIIIYQQWpAEW7xUCxcuJDg4WOv6mzZtws/PrxAj0jRt2jTi4uIICQlhy5YttGzZkmHDhhEfH18o18vMzOTXX3+mXTtPjXJPz06kpaUSH3+wwPP27v2OOXNmMGLEaCZNCn3mdVQqFYGBo6lWzZHBg4c+dkSBiUkx9bciRYxRKnMB+L//W0a5cuXw8Gil+42J/2fvvuOqrv4Hjr/uvewNYmopDlJABcRN4kJNnCimmStF3A0sNUlxVm4RV0Bq5qhwBOWexAAAmCZJREFUEIoLM+3rzzLLiblAxJniQva89/7+IG/eQMVEcbyfj8d9KOecz/m87+Ej8r7nfM5HCCGEEEK8hCTBFk+VjY0NlpaWJW5vZ2eHubn5E4zoH9nZ2URHR/PRRx/RsmVLqlatysiRI2ncuDEbN258Iue8cOE8eXl51Kjxul559eo1AEhMTCj2OA+P+hw6dJzRo8diYPDwOz2iozdy6NBBPvtsJiqVSlfeqFFjfv31/0hMTODQoT84deokjRs3JS0tlQUL5hIcPPUx3p0QQgghhBAvF0mwnwHXrl1jxIgReHh40KJFC2JiYvTq9+zZg5+fH25ubrRr144FCxaQl5enq3dyciIyMpI+ffrg6upKhw4dOHz4MJGRkbRq1Yr69esTGBhITk6O7pgjR44wYMAAGjRoQJMmTQgKCiIlJeWR4n5YH97e3syaNYuOHTvSpEkTfv/99yJLxPft20f37t1xdXWlc+fObNy4EScnJy5fvqzr4+4S8UWLFjFw4EAiIiJo0aIFrq6u9OvXj8TExBLFe+nSJZydnfnf//6nVx4UFMQ777yDQqEgLCyMFi1a6NUrlUrS0tIeaWxKKi0tFaDIhw4WFoVfp6enF3tcpUqvYmtrV+LzLFmykMaNm9KsWXO98i5dutGxY1eaN2+Cn19nPvlkIu7uHixatABPTy/c3T0IDg7ijTcaMHToQG7duvUob08IIYQQQoiXiiTYZaygoICAgABSUlJYs2YNoaGhLF++XFe/d+9eAgMD6dWrF1u2bGHy5Mls376dsWPH6vUTEhJCQEAAmzZtwtLSkuHDhxMbG0tERAQzZsxg165drF+/HoC4uDj69+9PzZo1WbduHaGhoRw7dozBgwejVqtLFHdJ+1izZg0TJ05k2bJl1KtXT6+PU6dOMWzYMDw9Pdm0aRMjRoxg1qxZDzzvwYMHOXToEBEREXz77bfcunWLqVNLNstapUoVGjVqxJYtW3Rlubm57Ny5Ez8/P0xMTPDy8sLGxkbvff722280b968mB4fn0bz4J3ClcrH/yf6++8HiIs7yqhRHxapUygUzJ27gKSkq5w79xfvvfchV6/+xfLlEXz66SRWrIhg7949rFixBqVSxdixgY8djxBCCCGEEC8qSbDL2P79+0lISGD27NnUqVMHDw8PZsyYoasPCwujV69e9O7dGwcHB7y8vJg6dSo7duzQzfIC9OjRA29vb2rUqIGvry+pqalMmjSJWrVq0b59e1xcXEhIKFxuvGLFCpycnAgODsbR0ZGmTZsyf/58Tpw4wb59+0oUd0n7aNmyJW+88Qaurq4YGRnp9bFy5Urq1q3LuHHjqFGjBp06deK999574HkLCgqYPXs2zs7OuLq60rt3bw4fPlyimAH8/PzYtWuXbvOw3bt3o1ar6dChQ5G2586dY9SoUbi5udGr15PZRdvKygqAjIwMvfL09MIZc0tLq8c+R0xMNDY2NrRt++Z92xgbG+uWjs+a9Tndu7/F66/XJCZmE2+91RtnZxeGDh3Bjh1bS/whjBBCCCGEEC8bSbDLWHx8PNbW1jg4OOjKXFxcMDExAeDkyZN89913eHh46F7Dhw8H0FsaXbVqVd3fTU0LN626t08TExPdsvL4+Hjq16+vF4ezszOWlpacOXOmxHGXpI974/q3kydPFpnVbtSo0QPPa29vj7W1te5rS0tL8vPzSxQzQPv27QF0O4Nv3ryZtm3bYmFhodfu8OHD9OnTh3LlyhEWFoahoWGJz/EoqlWrjkqlIinpnF753a9r1XJ67HP8+OMOOnToXKL3cPr0KTZt+oGxYwuX8d+8eQNbW1ug8P75goICWSYuhBBCCCHEfchzsMuYQqFAo9EUKb+7cZVGoyEgIIDu3bsXaVO+fPki7e91v+XFWm3xy5K1Wm2JE8mS9nH3g4LiqFSqYt/7g/x7FvxRmZmZ4ePjQ0xMDF5eXvzf//0fERERem127tzJmDFjcHd3Z+nSpY+0KdujMjExwdOzGVu3bmbUqA90j8PasmUzVlbWeHg0eKz+U1Juc+5cIu+/P7pE7adPn8TgwUOpWLESAPb25bl+PRmA5ORkVCoVdnYlv/dbCCGEEEKIl4nMYJcxFxcX0tPTdcu3Ac6fP69bMlyzZk2SkpKoWrWq7nXt2jVmz55NZmbmfzqnk5MThw4d0is7ffo0GRkZODo6PrU+nJ2diYuL0ys7cuRIiY59HD169OCXX34hOjoae3t7mjZtqqvbvXs3o0ePplWrVixfvvyJJtd3jR49lsOHDxIQ8C4//bSTmTOns2RJKB9++DFmZmakp6dx8ODv3Lx585H7PnXqJFCymfBff93HwYO/88EH/yTj7dr5sGbNN/z44w5CQubQtu2bJdq1XAghhBBCiJeRJNhlrEmTJri7uzNu3DiOHj3K8ePHGTdunG72eciQIcTGxrJ48WKSkpLYv38/QUFBpKen681gP4pBgwZx5swZpk+fTmJiIgcOHGDMmDHUrl0bT0/Pp9aHv78/x48fZ+7cuSQlJfHjjz+ycOFCAN1M7pPQsGFDKlWqxMKFC/H19dWNdWpqKp988gl16tRhwoQJpKamcuPGDW7cuMGdO3eeWDzNm7dkxYo1nD2bwLvv9mHDhvVMnvwZ778fCEBc3DE6dmzLrl2xj9z3jRvXAbCxsX1o22nTgvngg4+xsvpnCf6QIcPx9GzG8OEBf9//HvLIMQghhBBCCPGykKmoMqZUKgkPD+ezzz7D398fExMThg0bxpUrVwDw8fEhJCSE8PBwwsLCsLGxwdvbmzFjxvznc7q7u7Ns2TIWLFhAt27dsLCwoG3btnz88cclXiJeGn3UqlWLxYsXM3/+fFauXEn16tXp168fixYtemL3PN/VvXt3QkND8fPz05Xt3buXtLQ0jh07VuRRXY0bN2b16tVPLJ5OnbrQqVOXYuuaNWvO9ev3f0xY79596d27b7F1vr5++Pr6FVv3bzt27ClSZmJiwpIlEcW0FkIIIYQQQvybQnu/m2mFeMLi4uIwMDCgdu3aurKYmBg+/fRTjhw58kIuRU5JyaSg4NHuOxfiXgYGSmxtzeVaEqVGrilR2uSaEqVNrilRmuzszFGpntxCblkiLsrMqVOnGDBgAD/99BN//fUX+/fvZ9GiRXTq1OmFTK6FEEIIIYQQLzbJYoSe5ORkfHx8HtjG1dWVVatWPfa5evXqxY0bN/jiiy9ITk6mXLlydOrUiQ8++OCR+zpy5Aj+/v4PbNO+fXtmzpz5X8MVQgghhBBCiAeSJeJCj1qt5vLlyw9sY2xsTMWKFZ9SRCWTm5vLtWvXHtjG3Nwce3v7pxRR8WRpk3hcskxOlDa5pkRpk2tKlDa5pkRpetJLxGUGW+hRqVRUrVq1rMN4ZMbGxs9l3EIIIYQQQogXh9yDLYQQQgghhBBClAJJsIUQQgghhBBCiFIgCbYQQgghhBBCCFEK5B5sIZ4CrVaLRqtFo5E9BYUQQgghhHhRyQy2EE+BQqEgPS1HEmwhhBBCCCFeYJJgC/GUyBPxhBBCCCGEeLFJgi2EEEIIIYQQQpQCSbCFEEIIIYQQQohSIAm2EEIIIYQQQghRCmQXcSGeEpVKPs8Sj+/udfQsXU8ajeyQL4QQQggBkmAL8VRotVqsrEzLOgzxAnmWrie1RsOdlCxJsoUQQgjx0pMEW4inQKFQEH5sDVczkss6FCFKVSWLCgxz74dSqZAEWwghhBAvPUmwhXhKrmYkcyHtSlmHIYQQQgghhHhCnp2b+IQQQgghhBBCiOeYJNhCCCGEEEIIIUQpkARbCCHE49NoMFm5HNuWnthXq4RdQzfMg8ejSE8rtrlpxFLKv2KF8uKF+3apvHiB8q9Y3fdl+cEIXVuDPw5g3b0T9lUrUK52DSw+HIni+nW9/gwO/IZt88aUe70KlqOGQmamfkxffYn1W76PMQhCCCGEeNnJPdhCCCEem/HCEEw/n0b2qA/Ja94SVeJZzGd9hsGpU6SujwaFQtdWlZiA+edTH9qnpkJFUrbtKlJuuuIrjDdFkd1nAAAGhw9i070T6ppOpC0KAxMTTJcuwrZjW1J2/x9aK2vIy8NqyLvktX2TvE5dMJ/wCeYhc8icOAUARXoaZiFzSf1+Y6mMhxBCCCFeTpJgCyGEeDwaDSahIeQMGKRLWPNbtkZrZ4fV0EEYHDtCQb36hW3VaizfH4HG1g5V9kM2/TM2pqBhY70ig2NHMN4UReankylo6gmA2YK5aK2suPPDFrQ2tgDkebXErllDTBeHkvXpJFTxZ1Bdu0rmxClo7cqhSojHOGo9UBiv6eIF5Hk1p8CtXikNihBCCCFeRrJE/CGcnJyIiooq6zDuKyoqCicnpwe28fb2ZtGiRU8pouKlpKSwfv163df9+/dn/PjxZRjR/e3evZsePXrg4eGBt7c3s2bNIicnp6zDEuLZlZZG3tu9yfHrpVdc8HotAFTnk3RlpksWorxxnawPPnr082i1WIz/GHUtZ7KHj9IVq+LPkN/YU5dcA2BmRkH9hhj/GFv49d8z6FqTwueHaw2NQK0BQHntKqbLvyJzfPCjxySEEEIIcQ+ZwX4JbNiwAWNj4zKNYfbs2Vy+fJmePXuWaRwPc/DgQd577z0++OADfHx8uHDhApMmTeLOnTvMmDGjrMMT4tlkY0P2zLkUFGj0io23bwGgwMkFANXpU5jPnUHq91EPvPf6foyjN2J46CB3ftgKKpWuXGtXDuXlS0XaK88nobxwHgB1DUc05cph8t1qcru9hXFMNPlNmgJgNvsLcnv0RFPD8ZFjEkIIIYS4l8xgvwTs7OwwNzcv0xi0Wm2Znr+kvv/+e5o0acLw4cOpVq0aLVu2ZPTo0cTExJCXl1fW4Qnx3DA49Admi0LIbd8BtUttKCjA8r1h5PQdQP4bXv+pT9MlC8lv3JT8Zs31ynP69Mcw7ijmEz9Bee0qiuRkzKdNwiD+NIqsvzcyMzUlfeGXmM+bRbnaNcDAkKyxQajiz2C86QcyP/oEo53bsWnXEhuf1hj+b8/jDoEQQgghXkKSYN/j2rVrjBgxAg8PD1q0aEFMTIxe/Z49e/Dz88PNzY127dqxYMECvaTLycmJyMhI+vTpg6urKx06dODw4cNERkbSqlUr6tevT2BgoN5y4yNHjjBgwAAaNGhAkyZNCAoKIiUl5ZFjX7duHc2bN8fd3Z3hw4dz5co/9zb+e4n4zz//TK9evfDw8MDLy4sZM2boxVTa72P8+PH88MMP/P7773rL2TMzMwkKCqJhw4Y0aNCA8ePHk5WVpatPTExkyJAhujg//vhjbty4oavv378/wcHB9OzZk4YNG7J58+aHjlP//v0JDAzUK/vjjz9wcnLiwoUL+Pv788knn+jVK5VK8vPzycjIeGj/QojC3bqte/dA7VCV9NClAJiFzEGZeoeMiQ/f3KzYPn8/gGHcUbJGfVikLqffu2RM/QLTNaso5+ZEObdaKC9eILv/QLSmZrp2ee18uHXyHDeTrpK6YRNaWzvMp08ie8gwUCqxCniXrMAxZI0KxPrdPiju+XkjhBBCCFESkmD/raCggICAAFJSUlizZg2hoaEsX75cV793714CAwPp1asXW7ZsYfLkyWzfvp2xY8fq9RMSEkJAQACbNm3C0tKS4cOHExsbS0REBDNmzGDXrl26e5Hj4uLo378/NWvWZN26dYSGhnLs2DEGDx6MWq1+pPhXr15NaGgoa9euJSUlhVGjRhU7a/zjjz8yYsQIWrVqRVRUFFOnTmXbtm189JH+/ZCl+T4mTJhAhw4d8PDwYN++fbpz7Ny5k1deeYWoqChmz57Ntm3b+OqrrwBITk6mT58+VK1alQ0bNhAWFkZGRgZvv/22XhK+fv16BgwYwLfffkvz5vqzWsXx8/Njz549esny5s2bqV+/PlWrVqV27do4Ozvr6vLz81m5ciV169bFzs6uhN8NIV5extEbsenZFc1rlUndGIPWrhwGx49hFjqP9HkLwdgYCgpQaP5eTq5WF74e1m9MNBobG/LavllsffaI97iZcJHbvxzk1olE0pd9gzI1Fa2tbdHGZoVJt+Fvv2J48HeyR32I8Y87UDtUJa9TF/K6+KKuXBmjn3b+53EQQgghxMtJEuy/7d+/n4SEBGbPnk2dOnXw8PDQu+c2LCyMXr160bt3bxwcHPDy8mLq1Kns2LGDy5cv69r16NEDb29vatSoga+vL6mpqUyaNIlatWrRvn17XFxcSEhIAGDFihU4OTkRHByMo6MjTZs2Zf78+Zw4cUIvES2JOXPmUL9+ferWrcusWbM4deoU+/fvL9IuIiKCdu3aMXLkSKpXr06bNm2YPHkyP/30E2fPnn0i78PS0hITExMMDQ0pX7687hxubm6MHj0aBwcH2rRpQ7Nmzfjzzz8B+O6776hYsSITJ07E0dGRunXrsmDBAm7dusWOHTt0fbi4uNClSxdq1aqFbXG/SP9L+/btUSqV7NpV+OifvLw8YmNj8fPzK9K2oKCAcePGkZCQwOTJk0v4nRDi5WW6ZCGWw/zJb9iYO5u3o6lQEQCj7VtR5OVh81ZXyr9qR/lX7bAMLNykrFyTelj36PLQvo1+3EFuh85gaFikzuDoYYy2bAZDQ9Q1a6G1ty8sP36MAlf3+/ZpPjWYrMAxaC2tUNy8obdJmtbaBuW/nqMthBBCCPEwssnZ3+Lj47G2tsbBwUFX5uLigomJCQAnT54kLi6ODRs26OrvzhAnJiZSuXJlAKpWraqrNzUt3K323j5NTEx0y8rj4+Np1qyZXhzOzs5YWlpy5swZWrZsWaLYzc3N9WZdq1WrhrW1NfHx8bzxxhtF3menTp30yho3bqyre/3115/a+6hWrZre19bW1rql7SdPniQhIQEPDw+9Nrm5uSQmJuq+vjfOkjAzM8PHx4eYmBi6devG//73P/Ly8ujQoYNeu4yMDAIDA/n9999ZvHgxbm5uj3QeIV42RiuXYz51Ijnd/EhfHAFGRrq6nAGDyHvTR7/9zh2Yz51J6upI1I6vP7BvRcptDM4lkv3+6GLrDX/Zh/msz7jVPB6ttU1h2c+7MTh9iqyRHxQfb0w0yuvJZA8MAEBrXx7l9WRdvTI5Gc09HwgKIYQQQpSEJNh/UygUaDSaIuUGBoVDpNFoCAgIoHv37kXa3Dsre7f9vZTK4hcK3G/jL61Wi2ExszT3o7pnN927NBoNRvf8gvugc9593/fG/jTeR3Fx3xtT06ZNi505trS01P397gcgj8LPz493332XmzdvEhMTQ9u2bbGwsNDVX79+nSFDhnDlyhWWL19Oo0aNHvkcQrxUrl3DbMJ41A5VyfYfhkHcUb1qdbUa/zwH+2+q06cAKHCpjcbhnw/KDA7+jqacPZrqNf4pO3WysG2t4h9JmPPW25gtnFd4D/WoD1FduYz5pE/Jb9yU3J69ix5QUID551PJ/GRC4ZJ1IK+VNxZjAzFdWrhfhfL6NfJbt3m0cRBCCCHES0+WiP/NxcWF9PR03bJngPPnz+vu1a1ZsyZJSUlUrVpV97p27RqzZ88mMzPzP53TycmJQ4cO6ZWdPn2ajIwMHB1L/riYtLQ0Ll68qPv6zJkzpKenU6tWrWLPefjwYb2ygwcPAjzSOf/d58Peh+LvZ9CWVM2aNUlMTKRSpUq68ba2tuaLL74gPj7+P8V5V8OGDXnttdfYtGkTP//8s97y8NTUVN59911u377N2rVrJbkWoiS2bUORnY3q4gVsu7bHtmNbvZfRrtgSd2XbsS3m82frlSlvFC7V1nvO9T20FSqQui4a8vKwGtQPszkzyHmnL6nfb9R7nNddJqu+BmMTct96W1emefU10kOXYhq+BNNlYaQtiUBTsVKJ4xZCCCGEAEmwdZo0aYK7uzvjxo3j6NGjHD9+nHHjxulmbYcMGUJsbCyLFy8mKSmJ/fv3ExQURHp6ut4M9qMYNGgQZ86cYfr06SQmJnLgwAHGjBlD7dq18fT0LHE/SqWSwMBAjh49ytGjRxk3bhyNGzemYcOGRdoGBASwc+dOli5dSlJSEnv27GH69Om0bt36PyfYJXkfZmZmXL9+nUuXij6rtjh9+vQhPT2dMWPGcPr0aU6fPs3o0aM5fvx4sR8cPAqFQkG3bt1YsmQJdnZ2NG3aVFc3Y8YMLl26xJw5c7Czs+PGjRu616NuPCfES8Pfn5TbGdy4nlbsK7d33yKH5Pbuy43raXqz1wA3rqeRvihMv62vHzeup6Guef9/+wXuHqRu2s6tpL+4ffQUmZ/NQmthWWzbHP8hpPxvP/xrVU7uW29z+9hpbh8+QV6XbiV880IIIYQQ/5AE+29KpZLw8HBq1KiBv78/w4YNo1OnTrqdo318fAgJCWHXrl106dKFsWPH4uXlxeLFi//zOd3d3Vm2bBl//vkn3bp1IzAwEA8PD77++utHWiJuZ2eHr68vI0eOZNCgQTg6OhIaGlps2/bt2zN//ny2b99Oly5dmDx5Mp06dWLBggVP9H1069aN7OxsOnfuTHJy8kN6hCpVqrBmzRoyMzN555136NevH4aGhqxatapUdvPu3r072dnZ+Pr66j5EUavVbNu2jfz8fN599128vLz0XlevXn3s8wohhBBCCCFeXArt/W6gFUKUqim/zONC2pWHNxTiOVLV6jWmNPuYlJRMCgqK7mMhnn0GBkpsbc3leyhKjVxTorTJNSVKk52dOSrVk5tnlhlsIYQQQgghhBCiFMgu4s+o5ORkfHx8HtjG1dWVVatWPaWInn1du3Z96D3eBw4cKHZ3dSGEEEIIIYR4XJJgP6Ps7e2Jjo5+YBvjvx8vIwqFhYWRn5//wDaPcm+7EEIIIYQQQjwKSbCfUSqViqpVqz68odB59dVXyzoEIYQQQgghxEtM7sEWQgghhBBCCCFKgcxgC/GUVLKoUNYhCFHq5LoWQgghhPiHJNhCPAVarZZh7v3KOgwhngi1RoNGI098FEIIIYSQBFuIp0ChUJCWlo1aLc9uFI9HpVJiZWX6TF1PGo1WEmwhhBBCCCTBFuKpUas1FBQ8GwmReP7J9SSEEEII8eyRTc6EEEIIIYQQQohSIAm2EEIIIYQQQghRCiTBFkIIIYQQQgghSoHcgy3EU6JSyedZ4vHdvY6et+tJNkITQgghxMtAEmwhngKtVouVlWlZhyFeIM/b9aTWqLmTki1JthBCCCFeaJJgC/EUKBQKvj35NdezrpV1KEI8da+YVaRP7UEolQpJsIUQQgjxQpMEW4in5HrWNa5kXCrrMIQQQgghhBBPyPN1E58QQgghhBBCCPGMkgRbCCGEEEIIIYQoBZJgCyGEEEIIIYQQpUASbCGEEE+HRoPJyuXYtvTEvlol7Bq6YR48HkV6mq6JTec3Kf+KVZGXwdHDD+zXdMlC7Bq7Y1+lPLbNGmKyPLxIM9XJE1j39qNcLQfs6tbE8r1hKK5f12tjtDUGu4ZulHOqivnET0Ct1qs3Dw7C4qP3H28chBBCCPHCkk3OhBBCPBXGC0Mw/Xwa2aM+JK95S1SJZzGf9RkGp06Ruj4aKEyCs4a/R27XbnrHFtR0um+/5pM/xSx8KdnvDia3Y2dU55Mwn/UZqgsXyJz2BQCK69ex6dEZ9auVSV8YhiInG/Ppk7F+pwd3duwGQ0MUN29iNWoIWcNGUuBeH8sxH6Cu6UTOu/4AKC9ewOT7taTs/e2JjI8QQgghnn8yg/2YnJyciIqKKusw7isqKgonp/v/Ygrg7e3NokWLStzn8ePH6dChA3Xr1mXWrFklOubQoUMcPHgQgMuXL+Pk5MSBAwdKfM6nJScnh3nz5uHt7Y2Hhwd+fn789NNPZR2WEM8/jQaT0BByBgwic+IU8lu2Jsd/CBmz5mO0dw8Gx46gTDqHMiOdvLZvUtCwsd4Lc/Niu1XcuoXpsnCy+71LxpwQ8lu3IWdQAOkLv8Q0YimqhHgAjHdsRXnrFmnLviHPpyO53XqQPmcBhsePYfhH4c8iwz8OoFUZkDU+mLyOncnp/hZGe3/Wnct8xnRyBgxCU+nVJz5cQgghhHg+yQy2YMOGDRgbG5e4fXh4OIaGhmzbtg1LS8sSHdOnTx9mzJhBw4YN/2uYT8Vnn33Gvn37mDp1KtWqVWPr1q289957rFy5kiZNmpR1eEI8v9LSyHu7Nzld/PSKC16vBYDqfBJKlaqwrK5ribtVJZ5FoVaT92YHvfK8Zi1QaDQY7f6R7Jq1UOTmAKC1tNK10drZAaC4fbuwQKEAY6PCPwEMjXRLxA2OH8Nozy5uHzha4tiEEEII8fKRGWyBnZ0d5veZHSpOamoqLi4uODg4YGtr+wQje7qys7OJjo7mo48+omXLllStWpWRI0fSuHFjNm7cWNbhCfF8s7Ehe+ZcCpo01Ss23r4FgAInFwz+jENjboHFlImUc66GfZXyWL3TA9XZhPt2qy1XmCQrL1/UK1edTyosv3AegJyufqgrVMQiaAzK5GsoL5zHfGow6goVyW/ZqjAG93oo0tIw2r4V5dW/MPpxB/lNPAEwnzqJrPdGo7W2edyREEIIIcQLTBLsR3Dt2jVGjBiBh4cHLVq0ICYmRq9+z549+Pn54ebmRrt27ViwYAF5eXm6eicnJyIjI+nTpw+urq506NCBw4cPExkZSatWrahfvz6BgYHk5OTojjly5AgDBgygQYMGNGnShKCgIFJSUh459nXr1tG8eXPc3d0ZPnw4V65c0dX9e4n4zz//TK9evfDw8MDLy4sZM2boYvL29ub3338nOjoaJycnLl++TGpqKhMnTqR58+bUqVMHT09PJk6cSHZ2tu59AwQFBTF+/HjdeY4dO0bPnj2pW7cubdq0KZLEbty4kQ4dOuDm5kaHDh345ptv0Gg0wD/LzMPDw2nWrBlt2rQhIyPjgWNw6dIlnJ2d+d///qdXHhQUxDvvvINCoSAsLIwWLVro1SuVStLS0hBClC6DQ39gtiiE3PYdULvUxuDP4ygzM9BY25C28lvS5y9CdS4Rm67tUV67Wmwfasea5DfxxHz2FxhtjUGRlorB8WNYjh6F1tgYRVYWANoKFciYswDjndsp51qLco3cMDhxnNTvNupmtTWVXiVjdgiWo4ZiV78OBXXqku0/BMM9P6E6G092wDBMvl2NbUtPrLt1xOD4sac2VkIIIYR4PkiCXUIFBQUEBASQkpLCmjVrCA0NZfny5br6vXv3EhgYSK9evdiyZQuTJ09m+/btjB07Vq+fkJAQAgIC2LRpE5aWlgwfPpzY2FgiIiKYMWMGu3btYv369QDExcXRv39/atasybp16wgNDeXYsWMMHjwY9b92tn2Y1atXExoaytq1a0lJSWHUqFFotdoi7X788UdGjBhBq1atiIqKYurUqWzbto2PPvoIKFxO7uHhQYcOHdi3bx+VKlVi/PjxnDx5ksWLFxMbG0tQUBDR0dFERkYCsG/fPgA+/fRTJkyYoDvXN998w4gRI9i2bRvNmzdn4sSJXLhwAYDIyEhmz57Ne++9x9atWwkMDOSrr75i7ty5evH+8MMPfPPNNyxYsAALC4sHjkGVKlVo1KgRW7Zs0ZXl5uayc+dO/Pz8MDExwcvLCxsbG119XFwcv/32G82bN3+E0RZCPIzBgd+w7t0DtUNV0kOXApAZNIk7m7aTOX0G+U3fILdnb1Ijf0CRloZpxJf37St1+WrymzbDelBf7F+vgrVfF3L6D0JjawempgAYb1yH1cA+5LbvwJ3IH0hd9T0FzrWx6dVNd582QE6f/tw6e4mbSVdJj1gJhoaYT59M1tggVGcTsPh0HBkz55LXsTNW/d6G3NwnOk5CCCGEeL5Igl1C+/fvJyEhgdmzZ1OnTh08PDyYMWOGrj4sLIxevXrRu3dvHBwc8PLyYurUqezYsYPLly/r2vXo0QNvb29q1KiBr68vqampTJo0iVq1atG+fXtcXFxISChcDrlixQqcnJwIDg7G0dGRpk2bMn/+fE6cOKFLWktqzpw51K9fX7cx2alTp9i/f3+RdhEREbRr146RI0dSvXp12rRpw+TJk/npp584e/YsdnZ2GBoaYmJiQvny5VGpVDRr1owZM2bg7u5O5cqV6dq1K7Vr1yY+vvCX1vLlywNgaWmpd8/2qFGj8Pb2xsHBgdGjR6PRaDhx4gQAS5cuZcSIEXTq1IkqVarQvn17Ro8ezZo1a8i95xfaPn368Prrr+PqWrJ7Nv38/Ni1a5dudn337t2o1Wo6dOhQpO25c+cYNWoUbm5u9OrVq4QjLYR4GOPojdj07IrmtcqkboxBa1cOAHVdV/I9m+m11VSrjrqmEwYnjt+3P+0rr5C26jtuJlzk9v/9zq0/E8h5px/K68lobApvYzGbM4OCRk1Ij1hJfus25Pl0JDXyB7SmppjPmK7foVIJJiaFsW6IRJGXS07vvhhv2USe5xvkezYjO2A4ytRUDA/9UYojI4QQQojnnSTYJRQfH4+1tTUODg66MhcXF0z+/iXs5MmTfPfdd3h4eOhew4cPByAxMVF3TNWqVXV/N/17ZuXePk1MTHTLyuPj46lfv75eHM7OzlhaWnLmzJkSx25ubo6zs7Pu62rVqmFtba1LgP/9Pv99zsaNG+vqitOnTx8uXbrEzJkzGT58OG3btiUuLk63nPt+qlevrvu7tbU1UDijfPv2ba5du8b8+fP1xnPatGnk5ubqfWBx73iWRPv27QF0O4Nv3ryZtm3bFpn9Pnz4MH369KFcuXKEhYVhaGj4SOcRQhTPdMlCLIf5k9+wMXc2b0dToWJhRUEBxt+vxeCPYp4ukJONppz9ffs0/mEDqhN/orW2Qe3kDMbGGPwZh0KjocCtHgCqy5fIb/SvjQpNTSlw90B15lTxHefmYj7rczI/nQwqFcqbN9H+nbCjVKKxskJ5PfkRR0AIIYQQLzLZRbyEFApFsQmjgUHhEGo0GgICAujevXuRNndncO9tfy+lsvjPOYpbwn23/FESPtXfO/PeS6PRYGRkVKJz3n3fxcWu0WgYNmwYCQkJdO7cmY4dO1KnTh2Cg4MfGldx71ur1erOFxQUxBtvvFGkTaVKlbh+/TqA7gOOkjIzM8PHx4eYmBi8vLz4v//7PyIiIvTa7Ny5kzFjxuDu7s7SpUtLvFO6EOLBjFYux3zqRHK6+ZG+OALu/RlkYID53FloKlbkzpad/xTHHUWVdI7s90fft1+zkDkUuNQmPfxrXZlp2BI0VtbkN/MCQP16LQx//w202n92Cc/JwSDuGAW1ahXbr+mycDQVKpLXsTMAGnt7DJPOFVbm5aFMuY3GvnyxxwohhBDi5SQJdgm5uLiQnp5OQkICNWvWBOD8+fO6jbVq1qxJUlKS3ozqgQMHWLVqFVOmTMHMzOyRz+nk5MShQ4f0yk6fPk1GRgaOjo4l7ictLY2LFy/qZsrPnDlDeno6tYr5pdLJyYnDhw8zcOBAXdnd51cXd85Tp06xd+9e1q1bh7u7OwD5+flcvHiRKlWqlDjGe5UrVw47OzsuXbqkN57btm3jxx9/LPGzt++nR48eDBw4kOjoaOzt7Wna9J9djXfv3s3o0aNp06YNc+fOLfZDCCHEf3DtGmYTxqN2qEq2/zAM4o7qVaur1SBz7His3h+O5aih5PTsjeryJcxmfU5BXTdy3u5T2DA3F4Pjx9C8+hqaV18DIDtgOBZjA1E71ya/UROMf9iISdR60meHoLUqXB2TOX4iVu++g1XAu2T37Y8iNw/T8CUor/1FVtiyIuEq7qRgFjqXtFXf68ry3vTBbOH8wpn206fQWlmT36DRkxkvIYQQQjyXJMEuoSZNmuDu7s64ceOYPHkyKpWK6dOn62ZhhwwZQmBgIIsXL6ZTp05cu3aNCRMmULlyZb0Z7EcxaNAg+vTpw/Tp0+nTpw83b95k+vTp1K5dG09PzxL3o1QqCQwMZNKkSQBMnjyZxo0bF/tM6oCAAD788EOWLl1Khw4dOH/+PNOnT6d169bFJtj29vYYGBiwfft27OzsuHPnDmFhYdy4cUNvB3UzMzMSExNLtAO6QqFgyJAhhISE8Oqrr9KiRQvOnDnDlClTaNOmzWMnvQ0bNqRSpUosXLiQ/v37676HqampfPLJJ9SpU4cJEyaQmpqqO8bQ0FBv8zMhxCPatg1Fdjaqixew7dq+SHXawi/J7d2XNBMTTBeHYj2wD1ozM3I7dCFzYuESbQBl8jVsO7Ylc8x4ssZ9CkDOgEEocrIxXRaOWeg8Chxrkha2nFy/nrr+83w6kvrdBsznzcZ6YF+0FhbkezQgJfZn1HXqFonHLHQ++Y2akN/0n1U0BfUbkjlhChbTgtHY2JK27BvdJmpCCCGEECAJdokplUrCw8P57LPP8Pf3x8TEhGHDhuked+Xj40NISAjh4eGEhYVhY2ODt7c3Y8aM+c/ndHd3Z9myZSxYsIBu3bphYWFB27Zt+fjjjx9pibidnR2+vr6MHDmS7OxsWrduzcSJE4tt2759e+bPn8+XX37J0qVLsbOzo3PnznzwwQfFtq9QoQIzZ85k0aJFrF27lvLly9OqVSsGDhzI7t27de38/f1ZtmwZiYmJ9z33vfz9/TE2Nmb16tXMnDkTe3t7evXqdd84HlX37t0JDQ3Fz89PV7Z3717S0tI4duxYkUd1NW7cmNWrV5fKuYV4Kfn7k9L9bQoKHrw3Q66vH7m+fvet1zhU5cb1oo/Nyx46kuyhIx/Yd753O+54tytRuJmTpxdbnj3qA7JHlc7PISGEEEK8eBTa+93oK4QoVQsOzuBKxqWyDkOIp+41iyoENgwiJSXzoQm2ePoMDJTY2prL90eUGrmmRGmTa0qUJjs7c1SqJ7fXt+wiLoQQQgghhBBClAJZIv6cSk5OxsfH54FtXF1dWbVq1VOKqGwdOXIEf3//B7Zp3749M2fOfEoRCSGEEEIIIV42kmA/p+zt7YmOjn5gG2Nj46cTzDOgdu3aDx0Pc3PzpxOMEEIIIYQQ4qUkCfZzSqVS6T3C6mVnbGws4yGEEEIIIYQoU3IPthBCCCGEEEIIUQpkBluIp+QVs4plHYIQZUKufSGEEEK8LCTBFuIp0Gq19Kk9qKzDEKLMqDVqNBp5KqQQQgghXmySYAvxFCgUCtLSslGr5dmN4vGoVEqsrEyfu+tJo9FKgi2EEEKIF54k2EI8JWq1hoKC5ychEs82uZ6EEEIIIZ49ssmZEEIIIYQQQghRCiTBFkIIIYQQQgghSoEk2EIIIYQQQgghRCmQe7CFeEpUKvk8Szy+u9fRs3Q9yQZmQgghhBCFJMEW4inQarVYWZmWdRjiBfIsXU8ajZqUlGxJsoUQQgjx0pMEW4inQKFQEHNmKbey/irrUIQoVeXMXqWL00iUSoUk2EIIIYR46UmCLcRTcivrL5Izz5d1GEIIIYQQQogn5Nm5iU8IIYQQQgghhHiOSYIthBBCCCGEEEKUAkmwhRBCCCGEEEKIUiAJthBCiMen0WCycjm2LT2xr1YJu4ZumAePR5GeVmxz04illH/FCuXFC/ftUnnxAuVfsbrvy/KDEbq2Bn8cwLp7J+yrVqBc7RpYfDgSxfXrev0ZHPgN2+aNKfd6FSxHDYXMTP2YvvoS67d8H2MQhBBCCPGyk03OhBBCPDbjhSGYfj6N7FEfkte8JarEs5jP+gyDU6dIXR8NCoWurSoxAfPPpz60T02FiqRs21Wk3HTFVxhviiK7zwAADA4fxKZ7J9Q1nUhbFAYmJpguXYRtx7ak7P4/tFbWkJeH1ZB3yWv7JnmdumA+4RPMQ+aQOXEKAIr0NMxC5pL6/cZSGQ8hhBBCvJxe6hlsJycnoqKiyjqMp+7QoUMcPHiwrMMo1qJFi/D29i7TGBISEhg6dChNmjTB09OTDz74gL/+ksdrCXFfGg0moSHkDBhE5sQp5LdsTY7/EDJmzcdo7x4Mjh35p61ajeX7I9DY2j28X2NjCho21nthaIjxpigyP51MQVNPAMwWzEVrZcWdH7aQ17U7eW92IPXbDaAuwHRxKACq+DOorl0lc+IU8tq8Sc7AwRju3aM7leniBeR5NafArV5pjowQQgghXjIvdYL9surTpw8XL14s6zCeSSkpKQwaNAgTExNWr17NV199xe3btwkICCA3N7eswxPi2ZSWRt7bvcnx66VXXPB6LQBU55N0ZaZLFqK8cZ2sDz569PNotViM/xh1LWeyh4/SFaviz5Df2BOtje0/bc3MKKjfEOMfYwu//nsGXWtiWvinoRGoNQAor13FdPlXZI4PfvSYhBBCCCHuIUvEhbjHrl27yMrKYvbs2ZiYmAAwZ84cWrVqxeHDh/H09CzjCIV4BtnYkD1zLgUFGr1i4+1bAChwcgFAdfoU5nNnkPp91APvvb4f4+iNGB46yJ0ftoJKpSvX2pVDeflSkfbK80koL5wHQF3DEU25cph8t5rcbm9hHBNNfpOmAJjN/oLcHj3R1HB85JiEEEIIIe710sxgX7t2jREjRuDh4UGLFi2IiYnRq9+zZw9+fn64ubnRrl07FixYQF5enq7eycmJyMhI+vTpg6urKx06dODw4cNERkbSqlUr6tevT2BgIDk5Obpjjhw5woABA2jQoAFNmjQhKCiIlJSUEsccFRVFu3bt+P7772nVqhXu7u588MEHJCcnM2bMGN172bBhg+6Y1NRUJk6cSPPmzalTpw6enp5MnDiR7Oxs3fsACAoKYvz48QBcvHiRIUOG4OHhQfPmzfn6669p166dbvn8+PHj+eCDD/D396d+/fp89dVXaDQawsPDad++PXXr1qV+/foEBATozYw7OTmxYcMGBg4ciJubG15eXixevFjvPUZGRtKuXTvc3NwYPnw4qampevX/ZdwfZNGiRXh5eaHR/JMIZGdn4+Hhwfr16/H09GTp0qW65BpAqSz8Z5KWVvxmTUKIogwO/YHZohBy23dA7VIbCgqwfG8YOX0HkP+G13/q03TJQvIbNyW/WXO98pw+/TGMO4r5xE9QXruKIjkZ82mTMIg/jSLr743MTE1JX/gl5vNmUa52DTAwJGtsEKr4Mxhv+oHMjz7BaOd2bNq1xManNYb/21NMBEIIIYQQD/ZSJNgFBQUEBASQkpLCmjVrCA0NZfny5br6vXv3EhgYSK9evdiyZQuTJ09m+/btjB07Vq+fkJAQAgIC2LRpE5aWlgwfPpzY2FgiIiKYMWMGu3btYv369QDExcXRv39/atasybp16wgNDeXYsWMMHjwYtVpd4tj/+usvduzYQUREBAsXLuSnn36iS5cu1KlTh40bN9KiRQumTJmiS9zHjx/PyZMnWbx4MbGxsQQFBREdHU1kZCQA+/btA+DTTz9lwoQJZGdnM3DgQDQaDd999x0hISFERUVx6ZL+bFBsbCxvvPEGGzdupHPnzqxatYrly5czfvx4YmNjWbJkCefPn2fmzJl6x82aNYvu3buzdetW+vXrx6JFi/jjjz8A2LJlC9OmTWPgwIFs2rSJ+vXrs3bt2iJj8Cjj/jDdunXj5s2bHDhwQFe2a9cutFotHTp0oHLlyjRt2lTvmIiICExMTGjUqFGJziHEy87gwG9Y9+6B2qEq6aFLATALmYMy9Q4ZEx++uVmxff5+AMO4o2SN+rBIXU6/d8mY+gWma1ZRzs2Jcm61UF68QHb/gWhNzXTt8tr5cOvkOW4mXSV1wya0tnaYT59E9pBhoFRiFfAuWYFjyBoViPW7fVDcuPHfBkAIIYQQL62XIsHev38/CQkJzJ49mzp16uDh4cGMGTN09WFhYfTq1YvevXvj4OCAl5cXU6dOZceOHVy+fFnXrkePHnh7e1OjRg18fX1JTU1l0qRJ1KpVi/bt2+Pi4kJCQgIAK1aswMnJieDgYBwdHWnatCnz58/nxIkTuiS3JAoKCggODqZWrVq0bNkSZ2dnatSowaBBg3R/5ufnc/78eQCaNWvGjBkzcHd3p3LlynTt2pXatWsTHx8PQPny5QGwtLTE0tKSbdu2cfv2bebNm4ezszMNGzZkzpw5aLVavTisra0JCAigevXqVKpUCQcHB2bNmkXr1q157bXX8PT0xMfHR3eeu7p164avry9VqlRh+PDhWFlZcfjwYQBWr15Nx44d6du3L9WrV2fo0KG0bt26yBg8yrg/TJUqVWjUqBGbN2/WlcXExNC2bVssLCyKtF+9ejVr1qxhzJgx2NmVYFMmIV5yxtEbsenZFc1rlUndGIPWrhwGx49hFjqP9HkLwdgYCgpQ3F1FolYXvh7Wb0w0Ghsb8tq+WWx99oj3uJlwkdu/HOTWiUTSl32DMjUVra1t0cZmhUm34W+/Ynjwd7JHfYjxjztQO1Qlr1MX8rr4oq5cGaOfdv7ncRBCCCHEy+mluAc7Pj4ea2trHBwcdGUuLi66ZcAnT54kLi5Ob6n13QQzMTGRypUrA1C1alVdvalp4UY59/ZpYmKiW1YeHx9Ps2bN9OJwdnbG0tKSM2fO0LJlyxLHf+85zMzMqFSpku5rY2NjAN15+/Tpw+7du/nhhx84f/48Z8+e5fLly9SoUaPYvk+ePEn16tWxsbEpEue97n3vAN7e3hw7dozQ0FCSkpJISkri7NmzVKhQQa+do6P+PY2Wlpbk5+cDhWPUqVMnvXoPDw9Onz5933M/bNxLokePHkyfPp0pU6aQmZnJL7/8wldffaXXRqvVEhoaypdffsmIESPo379/ifsX4mVlumQh5tOCyW/WnLSVawsfjwUYbd+KIi8Pm7e6FjmmXJN65L3hRWr0tgf2bfTjDnI7dAZDwyJ1BkcPo7x8mbzOXVHXrPVP+fFjFLi637dP86nBZAWOQWtpheLmDb1N0rTWNij/9RxtIYQQQoiHeSkSbIVCoXfP7V0GBoVvX6PREBAQQPfu3Yu0uTvje2/7e929P/ff/j0DfG+5YTG/ID7Iv9vf75wajYZhw4aRkJBA586d6dixI3Xq1CE4+P4746pUqmLH5t/uvScZCpdNL1myhO7du+Pp6cnAgQP56aef2Lp1q147IyOjIn3dOzb/PndxY/Mo414Sb775JlOnTmXPnj3cvHmT8uXL6y0Lz8/PJygoiC1bthAUFMTAgQP/87mEeFkYrVyO+dSJ5HTzI31xBNzzbz9nwCDy3vTRb79zB+ZzZ5K6OhK14+sP7FuRchuDc4lkvz+62HrDX/ZhPuszbjWPR2ttU1j2824MTp8ia+QHxccbE43yejLZAwMA0NqXR3k9WVevTE5Gc8/PfyGEEEKIkngpEmwXFxfS09NJSEigZs2aAJw/f56MjAwAatasSVJSkt5M6YEDB1i1ahVTpkzBzMys2H4fxMnJiUOHDumVnT59moyMjCKzuqXl1KlT7N27l3Xr1uHuXjhrk5+fz8WLF6lSpUqxxzg7O7Nu3Tru3Lmjm8VOTEwkPT39gecKCwtj1KhRDB06VFe2fPny+36wUBwXFxcOHz6sl8AeP368xMf/V2ZmZnTo0IGdO3dy9epVfH199RL2cePG8eOPPzJv3rwiM+xCiGJcu4bZhPGoHaqS7T8Mg7ijetXqajUoqFdfr0x1+hQABS610Tj887PX4ODvaMrZo6n+z6obg1MnC9vWcir29DlvvY3ZwnmF91CP+hDVlcuYT/qU/MZNye3Zu+gBBQWYfz6VzE8mFC5ZB/JaeWMxNhDTpYsAUF6/Rn7rNo82DkIIIYR46b0U92A3adIEd3d3xo0bx9GjRzl+/Djjxo3TJVVDhgwhNjaWxYsXk5SUxP79+wkKCiI9PV1vBvtRDBo0iDNnzjB9+nQSExM5cOAAY8aMoXbt2k/sUU/29vYYGBiwfft2Ll26xPHjxwkMDOTGjRt6S6jNzMxITEwkJSWFzp07Y2try5gxYzh9+jRHjx7Vbe6m+Pu5scWpVKkSv/zyC2fPnuXcuXOEhISwc+fOR1qqPXToUH788UeWLVvG+fPnWb16NbGxsf99AB6Bn58fe/bs4ejRo/j5+enKo6Ki2LZtG6NHj6Zx48bcuHFD9yrpTuVCvHS2bUORnY3q4gVsu7bHtmNbvZfRrpL/u7bt2Bbz+bP1ypQ3Cpdq6z3n+h7aChVIXRcNeXlYDeqH2ZwZ5LzTl9TvN+o9zusuk1Vfg7EJuW+9rSvTvPoa6aFLMQ1fgumyMNKWRKCpWKnIsUIIIYQQD/JSJNhKpZLw8HBq1KiBv78/w4YNo1OnTrpNq3x8fAgJCWHXrl106dKFsWPHFvtIqUfh7u7OsmXL+PPPP+nWrRuBgYF4eHjw9ddfP/IS8ZKqUKECM2fOZPfu3XTs2JEPP/yQChUqMHDgQP78809dO39/f9asWUNQUBBGRkYsW7aM/Px8evXqxfvvv0+PHj2A4pdr3zV79mxycnLo0aMH/fr1Iz4+nqlTp3Lr1i3++uuvEsXbqlUr5s2bx8aNG+nSpQs7d+7E39//8QahhBo2bEj58uWpV6+e3sqFLVsKn9s7e/ZsvLy89F7btj34HlEhXlr+/qTczuDG9bRiX7m9+xY5JLd3X25cT9ObvQa4cT2N9EVh+m19/bhxPU3v/up/K3D3IHXTdm4l/cXto6fI/GwWWgvLYtvm+A8h5X/74V+3muS+9Ta3j53m9uET5HXpVsI3L4QQQgjxD4X2Udb0ihfO5cuXOX/+PF5e/zyXNjk5mRYtWrB27VoaNmxYhtG9WFYemUhy5vmyDkOIUlXBvBoDPT4jJSWTgoKH7+cgnj0GBkpsbc3leyhKjVxTorTJNSVKk52dOSrVk5tnfilmsMX95ebmMnToUJYvX86lS5c4efIkwcHBVKtWTXcftxBCCCGEEEKIh3spNjl71iQnJ+Pj4/PANq6urqxateqJx+Lo6Mj8+fMJCwtj4cKFmJiY4Onp+USXsj8p06ZN44cffnhgmyVLlvDGG288pYiEEEIIIYQQLxNJsMuAvb090dHRD2xz9/nWT4OPj89DE/7nwXvvvce77777wDavvPLKU4pGCCGEEEII8bKRBLsMqFQqvY21ROmws7PTbVwnhBBCCCGEEE+b3IMthBBCCCGEEEKUApnBFuIpKWf2almHIESpk+taCCGEEOIfkmAL8RRotVq6OI0s6zCEeCI0GjUajTzxUQghhBBCEmwhngKFQkFaWjZqtTy7UTwelUqJlZXpM3U9aTRaSbCFEEIIIZAEW4inRq3WUFDwbCRE4vkn15MQQgghxLNHNjkTQgghhBBCCCFKgSTYQgghhBBCCCFEKZAEWwghhBBCCCGEKAVyD7YQT4lKJZ9nicd39zp61q4n2ehMCCGEEEISbCGeCq1Wi5WVaVmHIV4gz9r1pNGoSUnJliRbCCGEEC81SbCFeAoUCgX/S5hDavalsg5FiFJnbVqFljXHolQqJMEWQgghxEtNEmwhnpLU7Evcykws6zCEEEIIIYQQT8izdROfEEIIIYQQQgjxnJIEWwghhBBCCCGEKAWSYAshhBBCCCGEEKVAEmwhhBClQ6PBZOVybFt6Yl+tEnYN3TAPHo8iPa3Y5qYRSyn/ihXKixce2rXx92uxbdEE+yrlsWvohtncmVBQoN8oNxfzz6di51Ebe4dXsG31BsabovSaGBz4DdvmjSn3ehUsRw2FzEz9mL76Euu3fB/tfQshhBBC/E0SbCGEEKXCeGEIFkFjyGvXntRvviVr5PuYrPsOq0H9Qau/u7gqMQHzz6eWqF/TiKVYfTACdU0n0lauJXPseEy+W4PVkIF67axGDsFkxVdkffARqasjKajriuXQQRju/rGwQV4eVkPeJb9RE9LDl2Nw6A/MQ+bojlekp2EWMpfMSSWLSwghhBDi3yTBfgAnJyeioqIe3vAFc+jQIQ4ePFjWYRRr0aJFeHt7674+fvw4HTp0oG7dusyaNatUzrFx40a6dOlCvXr1ePPNN4mIiECtVpdK30K8sDQaTEJDyBkwiMyJU8hv2Zoc/yFkzJqP0d49GBw78k9btRrL90egsbV7eL9qNWbzZpHXsjVpy1eR1+ZNct/uQ+qadRhv3Yzhz7sBMPztV4xjokmPWEHOoADyW7YmfVEYBQ0bY/RTYYKtij+D6tpVMidOIa/Nm+QMHIzh3j26U5kuXkCeV3MK3OqV5sgIIYQQ4iUiCbYook+fPly8eLGswyiR8PBwDA0N2bZtG0OHDn3s/jZv3szkyZPp168fmzdvJjAwkPDwcL788stSiFaIF1haGnlv9ybHr5deccHrtQBQnU/SlZkuWYjyxnWyPvjood0qb1xHmZJC3ps+euVql9poypXDaFcsAEYx0airVSevzZv/NFIouLP1RzI/n637GkBrYlr4p6ERqDWF57l2FdPlX5E5PvgR3rQQQgghhD5JsMVzLTU1FRcXFxwcHLC1tX3s/r777ju6devG22+/jYODAx07dsTf358NGzaUQrRCvMBsbMieOZeCJk31io23bwGgwMkFANXpU5jPnUF66FK0ZmYP7VZjZY3WwADlpUt65Yo7KSju3EF14TwABn8ep8C5NsYb12HbrCH2lWyxbdYQo21bdMeoaziiKVcOk+9Wo7h1C+OYaPL/jtds9hfk9uiJpobjfx4CIYQQQghJsP927do1RowYgYeHBy1atCAmJkavfs+ePfj5+eHm5ka7du1YsGABeXl5unonJyciIyPp06cPrq6udOjQgcOHDxMZGUmrVq2oX78+gYGB5OTk6I45cuQIAwYMoEGDBjRp0oSgoCBSUlJKHHNUVBTt2rXj+++/p1WrVri7u/PBBx+QnJzMmDFjdO/l3uQwNTWViRMn0rx5c+rUqYOnpycTJ04kOztb9z4AgoKCGD9+PAAXL15kyJAheHh40Lx5c77++mvatWunWz4/fvx4PvjgA/z9/alfvz5fffUVGo2G8PBw2rdvT926dalfvz4BAQF6M+NOTk5s2LCBgQMH4ubmhpeXF4sXL9Z7j5GRkbRr1w43NzeGDx9Oamqqrs7b25vff/+d6OhonJycuHz58gPHS6vV0qZNG+bMmaNXHh0dTb169cjIyGDMmDEMHjxYr16pVOqdVwhRMgaH/sBsUQi57TugdqkNBQVYvjeMnL4DyH/Dq2SdmJmR6+uH6YoITL5djeJOCqqzCVgN8wcDAxRZWQAob93E4MghzKdPLrwH+7uNqF+vhdWgvv/cg21qSvrCLzGfN4tytWuAgSFZY4NQxZ/BeNMPZH70CUY7t2PTriU2Pq0x/N+eBwQmhBBCCFGUJNhAQUEBAQEBpKSksGbNGkJDQ1m+fLmufu/evQQGBtKrVy+2bNnC5MmT2b59O2PHjtXrJyQkhICAADZt2oSlpSXDhw8nNjaWiIgIZsyYwa5du1i/fj0AcXFx9O/fn5o1a7Ju3TpCQ0M5duwYgwcPfqT7ff/66y927NhBREQECxcu5KeffqJLly7UqVOHjRs30qJFC6ZMmaJL3MePH8/JkydZvHgxsbGxBAUFER0dTWRkJAD79u0D4NNPP2XChAlkZ2czcOBANBoN3333HSEhIURFRXHpX7NJsbGxvPHGG2zcuJHOnTuzatUqli9fzvjx44mNjWXJkiWcP3+emTNn6h03a9YsunfvztatW+nXrx+LFi3ijz/+AGDLli1MmzaNgQMHsmnTJurXr8/atWt1x27YsAEPDw86dOjAvn37qFSp0gPHSqFQ0L17d7Zt24b2ng2XNm/eTNu2bbGwsKBBgwZUr15dV5eens53331H8+bNS/w9EUIU7tZt3bsHaoeqpIcuBcAsZA7K1DtkTHy0TcTS5ywg5623sRj9Hva1qmLbxov8Rk0ocKuH1rRwubciLw9V8jXSVn1H7tt9yG/lTdqK1aidnDGf+8/+DHntfLh18hw3k66SumETWls7zKdPInvIMFAqsQp4l6zAMWSNCsT63T4obtwovUERQgghxAtPEmxg//79JCQkMHv2bOrUqYOHhwczZszQ1YeFhdGrVy969+6Ng4MDXl5eTJ06lR07dujNmvbo0QNvb29q1KiBr68vqampTJo0iVq1atG+fXtcXFxISEgAYMWKFTg5OREcHIyjoyNNmzZl/vz5nDhxQpfklkRBQQHBwcHUqlWLli1b4uzsTI0aNRg0aJDuz/z8fM6fPw9As2bNmDFjBu7u7lSuXJmuXbtSu3Zt4uPjAShfvjwAlpaWWFpasm3bNm7fvs28efNwdnamYcOGzJkzRy9BBbC2tiYgIIDq1atTqVIlHBwcmDVrFq1bt+a1117D09MTHx8f3Xnu6tatG76+vlSpUoXhw4djZWXF4cOHAVi9ejUdO3akb9++VK9enaFDh9K6dWvdsXZ2dhgaGmJiYkL58uVRqVQPHa9u3bpx9epV3SZuN27c4LfffsPPz69I28zMTEaOHElubi7jxo0r4XdECGEcvRGbnl3RvFaZ1I0xaO3KYXD8GGah80iftxCMjaGgAIWm8P5n1OrC1/1YWJCxYAk3E69we+8Bbp5IJGvMeJR/XUFrU3hriMbCEnWFivoblKlU5LVohcGfcUX7/Ht5uuFvv2J48HeyR32I8Y87UDtUJa9TF/K6+KKuXBmjn3aW0qgIIYQQ4mVgUNYBPAvi4+OxtrbGwcFBV+bi4oKJiQkAJ0+eJC4uTm+p9d0EMzExkcqVKwNQtWpVXb3p37Mq9/ZpYmKiW1YeHx9Ps2bN9OJwdnbG0tKSM2fO0LJlyxLHf+85zMzM9GZyjY2NAXTn7dOnD7t37+aHH37g/PnznD17lsuXL1OjRo1i+z558iTVq1fHxsamSJz3uve9Q+Hy7WPHjhEaGkpSUhJJSUmcPXuWChUq6LVzdNS/39HS0pL8/HygcIw6deqkV+/h4cHp06fvOxYPU7lyZRo3bkxMTAyNGjVi69atvPLKKzRtqn/f6I0bNxg2bBiXL19m+fLluu+xEOLBTJcsxHxaMPnNmpO2ci1aK2sAjLZvRZGXh81bXYscU65JPfLe8CI1eluxfRrt3I7G2paCJk1ROxfey624cQPlX1cocHMHCu+vVv11ufBxYH9vZgagyM9H+/fP8uKYTw0mK3AMWksrFDdv6BJ2AK21Dcrr1x99EIQQQgjx0pIEm8Klw5q7Myn3MDAoHB6NRkNAQADdu3cv0ubujO+97e+lVBa/SODfM8D3lhsaGpYo7rv+3f5+59RoNAwbNoyEhAQ6d+5Mx44dqVOnDsHB9981V6VSFTs2/2byr19gIyIiWLJkCd27d8fT05OBAwfy008/sXXrVr12RkZGRfq6d2z+fe5HHZvi+Pn58cUXXzBx4kQ2b96Mr6+v3pglJiYSEBCARqNh7dq11KxZ87HPKcTLwGjlcsynTiSnmx/piyPgnn/fOQMGFdkJ3GjnDsznziR1dSRqx9fv26/JNytQ3r7Nne0/6crMIpaCSkVuu8I+89q+icnmHzD8eTf5rdsUNsrLw2j3LvKbvlF8vDHRKK8nkz0wAACtfXmU15N19crkZDT3/IwXQgghhHgYSbApnK1OT08nISFBl0ydP3+ejIwMAGrWrElSUpLeLO2BAwdYtWoVU6ZMwawEO+H+m5OTE4cOHdIrO336NBkZGUVmdUvLqVOn2Lt3L+vWrcPdvXDWJz8/n4sXL1KlSpVij3F2dmbdunXcuXNHN4udmJhIenr6A88VFhbGqFGj9B6dtXz58vt+sFAcFxcXDh8+zMCBA3Vlx48fL/Hx99O+fXumTZvG+vXrOXHiBCEhIbq6S5cu8e6772JlZcXy5csfel+3EOJv165hNmE8aoeqZPsPwyDuqF61uloNCurV1ytTnT4FQIFLbTQO//x8NTj4O5py9miqF66syQ4Yjs3b3TEPHk9e+44Y/t/PmIXOI+v90bo2uT16kb8sHKuRAWROmIL61Vcxi/gS5dW/yFq+qmi8BQWYfz6VzE8mFC5ZB/JaeWMxNhDTpYsAUF6/9k+yLoQQQghRAnIPNtCkSRPc3d0ZN24cR48e5fjx44wbN043qzlkyBBiY2NZvHgxSUlJ7N+/n6CgINLT0/VmsB/FoEGDOHPmDNOnTycxMZEDBw4wZswYateujaenZ2m+PR17e3sMDAzYvn07ly5d4vjx4wQGBnLjxg29HdHNzMxITEwkJSWFzp07Y2try5gxYzh9+jRHjx7Vbe6muGcZ5r9VqlSJX375hbNnz3Lu3DlCQkLYuXOn3nkeZujQofz4448sW7aM8+fPs3r1amJjY//7APzN1NQUHx8f5s2bR/369fU+OPn000/Jy8tj/vz5GBgYcOPGDd1LCPEA27ahyM5GdfECtl3bY9uxrd7r7vOqS8K2Y1vM58/WfZ3fug1pYcsx+nk31n17YrxlM+lfzCYz+J7N0gwNSd2widzO3TD/YhrWg/qhyMggdcNm/fuy/2ay6mswNiH3rbd1ZZpXXyM9dCmm4UswXRZG2pIINBXlQzYhhBBClJzMYFO4pDo8PJzPPvsMf39/TExMGDZsGFeuXAHAx8eHkJAQwsPDCQsLw8bGBm9vb8aMGfOfz+nu7s6yZctYsGAB3bp1w8LCgrZt2/Lxxx+XyjLo4lSoUIGZM2eyaNEi1q5dS/ny5WnVqhUDBw5k9+7dunb+/v4sW7aMxMREwsLCWLZsGdOmTaNXr15YW1szfPhwTpw48cA4Z8+ezbRp0+jRowfm5ua4u7szdepUpkyZwl9//cWrr7760HhbtWrFvHnzWLRoEaGhodSrVw9/f3+2bNny0GMfxs/Pj40bN+ptbpacnMzvv/8OgK+vb5Fjzpw589jnFeKF5e9PSve3KSh4+C0ld+X27suN3n2LlN+4nla0rV9Pcv16PrA/rY0tGXNCyJgT8sB2ADn+Q8jxH1L0PG+9rZd0CyGEEEI8CoX2UdbsipfO5cuXOX/+PF5e/zyzNjk5mRYtWrB27VoaNmxYhtE9XzbHfcCtzMSyDkOIUlfO3JGubgtJScl8pARbPBsMDJTY2prL90+UGrmmRGmTa0qUJjs7c1SqJ7eQW5aIiwfKzc1l6NChLF++nEuXLnHy5EmCg4OpVq2a7j5uIYQQQgghhBCyRPyZlJycjI+PzwPbuLq6smpVMRv3lDJHR0fmz59PWFgYCxcuxMTEBE9PT77++usntpT9v2rYsCHqBzxLt1y5cuzatespRiSEEEIIIYR4mUiC/Qyyt7cnOjr6gW3uPt/6afDx8Xlowv8siIqKeuAu5SqV6ilGI4QQQgghhHjZSIL9DFKpVHo7W4uScXBwKOsQhBBCCCGEEC8xuQdbCCGEEEIIIYQoBZJgCyGEEEIIIYQQpUCWiAvxlFibVinrEIR4IuTaFkIIIYQoJAm2EE+BVqulZc2xZR2GEE+MRqNGo7n/JoNCCCGEEC8DSbCFeAoUCgVpadmo1ZqyDkU851QqJVZWps/c9aTRaCXBFkIIIcRLTxJsIZ4StVpDQcGzkxCJ55tcT0IIIYQQzx7Z5EwIIYQQQgghhCgFkmALIYQQQgghhBClQJaIC/GUqFTyeZZ4fHevo+f9epJ7toUQQgjxIpIEW4inQKvVYmVlWtZhiBfI8349aTRqUlKyJckWQgghxAtFEmwhngKFQsHhs1PJyL5Q1qEIUeYsTKtS//XJKJUKSbCFEEII8UKRBFuIpyQj+wKpWfFlHYYQQgghhBDiCXm+b+ITQgghhBBCCCGeEZJgCyGEEEIIIYQQpUASbCGEEEIIIYQQohRIgi2EEKJsaDSYrFyObUtP7KtVwq6hG+bB41Gkp+maKK/+heXwwZRzqkq5Gq9h3aMrBsePPdJpzIPHU/4VqyLlBn8cwLp7J+yrVqBc7RpYfDgSxfXr+m0O/IZt88aUe70KlqOGQmamXr3pV19i/ZbvI8UjhBBCiBeXJNhCCCHKhPHCECyCxpDXrj2p33xL1sj3MVn3HVaD+oNWiyIjHZuuPhj8GUf63FDSw5ahyEzHuqcvyuRrJTqH4f5fMI34ski5weGD2HTvhPLOHdIWhZG+YAmqC+ex7dgWRVpqYaO8PKyGvEt+oyakhy/H4NAfmIfM0fWhSE/DLGQumZOmlsp4CCGEEOL5Jwn2PZycnIiKiirrMJ64PXv2cPbs2bIOo1jjx4+nf//+ZRrD4cOH6d+/Pw0aNKB58+ZMmDCBO3fulGlMQrxwNBpMQkPIGTCIzIlTyG/Zmhz/IWTMmo/R3j0YHDuCafhSFCkppG6MIa9LN/Le7EDqN9+jNTLG8Jf/e/g5MjKw/GAEmkqvFqkyWzAXrZUVd37YQl7X7oV9f7sB1AWYLg4FQBV/BtW1q2ROnEJemzfJGTgYw717dH2YLl5AnldzCtzqldaoCCGEEOI5Jwn2S+bKlSsMHz6cW7dulXUoz6SkpCQGDx6Mk5MT69atIyQkhLi4OD788MOyDk2IF0taGnlv9ybHr5deccHrtQBQnU/COGYTuV180VSoqKvXVqjA7bgz5Pr1fOgpLKYGo3mlAjm9+xapU8WfIb+xJ1ob238KzcwoqN8Q4x9jC79WKArPaWJa+KehEag1ACivXcV0+Vdkjg8u+XsWQgghxAtPEuyXjFarLesQnmnR0dG88sorTJgwAUdHRxo2bMjkyZP57bffuHTpUlmHJ8SLw8aG7JlzKWjSVK/YePsWAApqOaOKP43asSZmM6djV7cm9q/aYd29E6rTpx7aveHPuzFZ/x3pC5eCsuh/dVq7cigvF/03rTyfhPLCeQDUNRzRlCuHyXerUdy6hXFMNPl/x2s2+wtye/REU8PxUd+5EEIIIV5gL22Cfe3aNUaMGIGHhwctWrQgJiZGr37Pnj34+fnh5uZGu3btWLBgAXl5ebp6JycnIiMj6dOnD66urnTo0IHDhw8TGRlJq1atqF+/PoGBgeTk5OiOOXLkCAMGDKBBgwY0adKEoKAgUlJSShxzVFQULVq0YN26dXh5eeHh4cGoUaNITk7Wtfnrr78YPXo0np6e1KlThxYtWjBnzhw0Gg2XL1+mTZs2AAwYMIBFixYB8Oeff9K3b1/c3d1p06YNmzdvpnbt2hw4cACA/v37ExwcTM+ePWnYsCGbN28mLy+PWbNm4e3tTd26dWncuDEffvght2/fBuDy5cs4OTkRGxtLz549qVu3Lt7e3kRGRupi1Wq1LF26lBYtWlCvXj2CgoLIzc3V1d/tY+vWrXTr1g1XV1f8/PxITExkyZIlvPHGGzRu3JipU6eW+IOD8ePH07On/szXlStXcHZ25tdff6Vr167MmjULxd8zV4Du76mpqSX+XgkhHp3BoT8wWxRCbvsOaMq/gqKgANPwJRjt+z8yQhaRFrES5a2b2HTrgPLa1fv2o0hLxXL0e2SOm4DasWaxbXL69Mcw7ijmEz9Bee0qiuRkzKdNwiD+NIqsvzcyMzUlfeGXmM+bRbnaNcDAkKyxQajiz2C86QcyP/oEo53bsWnXEhuf1hj+b0+x5xJCCCHEy+OlTLALCgoICAggJSWFNWvWEBoayvLly3X1e/fuJTAwkF69erFlyxYmT57M9u3bGTt2rF4/ISEhBAQEsGnTJiwtLRk+fDixsbFEREQwY8YMdu3axfr16wGIi4ujf//+1KxZk3Xr1hEaGsqxY8cYPHgwarW6xLHfvn2bb775hgULFvDNN99w9epVAgICKCgoAGDEiBGkp6fz9ddfs2PHDvz9/Vm2bBm7d++mUqVKungWLVqEv78/ycnJvPvuu7z22mts3LiRSZMmERISUiSm9evXM2DAAL799luaN2/O7Nmz2blzJzNnziQ2NpaZM2fy22+/8eWX+psJzZgxg+HDh7N9+3ZatWrFlClTdDPBERERLFu2jHHjxhEVFYWVlRXbtm0r8p5DQkL49NNPWb9+PWlpabzzzjucP3+e1atXM3r0aL799lv27CnZL7Z+fn7ExcVx8eJFXVlMTAwVK1akadOmODo6Uq9ePb1jvvrqK8qXL4+Tk1OJziGEeHQGB37DuncP1A5VSQ9diiL/nw8073wfRV47H/I6dyX12w0oMjIwXR5x374sJo5H8+prZA8fdd82Of3eJWPqF5iuWUU5NyfKudVCefEC2f0HojU107XLa+fDrZPnuJl0ldQNm9Da2mE+fRLZQ4aBUolVwLtkBY4ha1Qg1u/2QXHjRukMiBBCCCGeSy9lgr1//34SEhKYPXs2derUwcPDgxkzZujqw8LC6NWrF71798bBwQEvLy+mTp3Kjh07uHz5sq5djx498Pb2pkaNGvj6+pKamsqkSZOoVasW7du3x8XFhYSEBABWrFiBk5MTwcHBODo60rRpU+bPn8+JEyfYt29fiWPPz89n1qxZNGzYEDc3N+bMmUN8fDz79+8nJycHX19fpk+fjrOzM1WqVGHgwIHY29tz5swZVCoVdnZ2AFhbW2Nubk5kZCSWlpZ8/vnnvP7667Rs2ZKJEycWOa+LiwtdunShVq1a2Nra4urqyqxZs2jcuDGvvfYa3t7evPHGG8THx+sdN3DgQNq0aUOVKlUYPXo0Go2GY8eOodVqWb16NQMGDKBz587UqFGDoKAgXFxcipzb39+fxo0b4+zsTLt27cjKymLatGk4OjryzjvvUK5cOd04P0yjRo2oUqUKmzdv1pXFxMTg6+uLsphlpLNmzeLnn39mypQpGBoalugcQohHYxy9EZueXdG8VpnUjTFo7cqhtbAAIL+ZF/z9dwBN5Sqoazrd91FdRju3Yxy9kfR5C0GjgYKCwj9B/+9A9oj3uJlwkdu/HOTWiUTSl32DMjUVra1t0Y7NCpNuw99+xfDg72SP+hDjH3egdqhKXqcu5HXxRV25MkY/7SylURFCCCHE88igrAMoC/Hx8VhbW+Pg4KArc3FxwcTEBICTJ08SFxfHhg0bdPV3lyAnJiZSuXJlAKpWraqrNzUt3ATn3j5NTEx0y8rj4+Np1qyZXhzOzs5YWlpy5swZWrZsWaLYzc3NqVu3ru5rR0dHrK2tiY+Pp3nz5vTr148dO3YQFxfHhQsXOHPmDDdv3kRzzy+V9zp58iR169bVSx4bNWpUpN297xXA19eXX3/9lblz53L+/HnOnTtHUlISDRs21Gvn6PjP/YmWlpZA4YcEKSkp3LhxA1dXV7329erVIzEx8b7nNjMzw97eXjfeoD/OD6NQKOjWrRsxMTG89957nDx5krNnz7J06VK9dvn5+UyaNIno6GimT59O27ZtS9S/EOLRmC5ZiPm0YPKbNSdt5Vq0VtYAaK2s0djbo8gt5t92Qb5u47F/M47ZhCInB7sWTYrUlX/Vjpy3+5C+KAyDo4dRXr5MXueuqGvW0rUxOH6MAlf3+8ZrPjWYrMAxaC2tUNy8obdJmtbaBuW/nqMthBBCiJfLS5lgKxSKYhNOA4PC4dBoNAQEBNC9e/cibcqXL1+k/b2KmwWF+28uptVqH2lmtLi2arUalUpFVlYW/fr1IycnBx8fH7p3746bmxt9+xbdQfculUp13+T7Xnc/fLhr0qRJxMbG0q1bN7y9vRk1ahTLly/Xux8cwMjIqEhfWq1Wd1/zv8eluDH9d9n9xrikunfvzuLFizl+/Djbtm2jfv36ekl8RkYG7733HgcPHmT+/Pl06NDhsc4nhCie0crlmE+dSE43P9IXR8C/fl7ktXkTo21bUNy6hbZcOQBUZxNQnU0gp++AYvvMHBtE9uChemUmq1diunolKTt/RmNX2I/hL/swn/UZt5rHo7W2KSz7eTcGp0+RNfKD4uONiUZ5PZnsgQEAaO3Lo7z+z888ZXIymnv+jxBCCCHEy+elTLBdXFxIT08nISGBmjULN8A5f/48GRkZANSsWZOkpCS9pOvAgQOsWrWKKVOmYGZmVmy/D+Lk5MShQ4f0yk6fPk1GRobeLO/D3Llzh0uXLlGlShUAEhISyMjIoHbt2uzbt48TJ07wyy+/YG9vr2t/69YtXSJ77+ZdUDiLvnHjRvLz83XJ+5EjRx4YQ0pKCpGRkYSEhNCxY0dd+blz50o8Nra2tlSqVIlDhw7pzQ7/+eefT3wp9muvvUaTJk2IjY1l+/btjBr1z32aeXl5DBs2jFOnTrF8+XKaNCk6CyaEKAXXrmE2YTxqh6pk+w/DIO6oXrW6Wg0yP/4Eo+1bse7VjayPP0GRn4fZF9PQvFaZnH7v6toaHPwdTTl7NNVroHGoisZBf8WNZucOAArq1deV5bz1NmYL5xXeQz3qQ1RXLmM+6VPyGzclt2fvovEWFGD++VQyP5kAxsYA5LXyxmJsIKZLCzeMVF6/Rn7rNqUxOkIIIYR4Tr2U92A3adIEd3d3xo0bx9GjRzl+/Djjxo3TzYwOGTKE2NhYFi9eTFJSEvv37ycoKIj09HS9GexHMWjQIM6cOcP06dNJTEzkwIEDjBkzhtq1a+Pp6flIfY0dO5Y///yTo0ePMm7cODw8PGjUqBEVKxY+K3bz5s1cuXKFgwcPMnLkSPLz83VLqO8mwPHx8aSnp9OnTx/S0tIIDg4mMTGRX3/9lenTpwNFk/G7LCwssLS05KefftItQw8ODubEiRMlXqoNheO8du1a1q9fT1JSEgsWLCAuLu6RxuK/6t69O99++y137tzRm6EODw/n0KFDTJ8+nRo1anDjxg3d61HemxDiIbZtQ5GdjeriBWy7tse2Y1u9l9GuWDTVqnNn649oKlXCctRQLD7+EHVdN+5s3oHWwlLXlW3HtpjPn/1Ip9dWqEDqumjIy8NqUD/M5swg552+pH6/EVSqIu1NVn0NxibkvvW2rkzz6mukhy7FNHwJpsvCSFsSgaZipf88JEIIIYR4/r2UM9hKpZLw8HA+++wz/P39MTExYdiwYVy5cgUAHx8fQkJCCA8PJywsDBsbG7y9vRkzZsx/Pqe7uzvLli1jwYIFdOvWDQsLC9q2bcvHH3/8yDO2Xbp0YejQoeTl5eHt7c2ECRNQKBS4ubkRFBTEypUrWbBgARUqVKBjx45UqlSJ48ePA4Uzxz169GD27NlcuHCBiRMnsmzZMr744gt8fX2pWLEi77zzDrNnz75vXIaGhoSGhjJz5ky6dOmCtbU1TZo04aOPPiI8PJzs7OwSvY++ffui0Wj48ssvuXnzJs2bN+ett94iKSnpkcbjv2jfvj3Tpk2jbdu2WNyzgdKWLVvQarV89NFHRY5ZtWqVzGgLUVr8/Unp/jYFBQ++RUXt5EzamnUPbHPjetoD67PGfUrWuE+LlBe4e5C6afvDYwVy/IeQ4z+kSHnuW2/rJd1CCCGEeLkptCV9gLAoc1FRUQQFBXHmzJlS6/Ps2bOkpqbSoEEDXdnhw4d55513+Pnnn6lUSWZjSsve4/6kZsU/vKEQLzhrs1q0cF1BSkrmQxNs8eQZGCixtTWX74coNXJNidIm15QoTXZ25qhUT24h90u5RFz849q1awwYMIDo6GiuXLnCkSNHmDFjBo0bN5bkWgghhBBCCCEewUu5RPxZk5ycjI+PzwPbuLq60q1bt1I/t5eXFxMmTCA8PJzg4GAsLS0fezl8WRk+fDgHDhx4YJuoqCiqV6/+lCISQgghhBBCvExkifgzQK1Wc/ny5Qe2MTY21m1iJoqXnJxMTk7OA9u8+uqrT3yX8vuRJeJCFJIl4s8WWXopSptcU6K0yTUlStOTXiIuM9jPAJVKpfdIMPHfVKhQoaxDEEIIIYQQQrzE5B5sIYQQQgghhBCiFEiCLYQQQgghhBBClAJZIi7EU2JhKrcBCAHyb0EIIYQQLy5JsIV4CrRaLfVfn1zWYQjxzNBo1Gg0ssemEEIIIV4skmAL8RQoFArS0rJRq2XnS/F4VColVlamz/31pNFoJcEWQgghxAtHEmwhnhK1WiOPlhClRq4nIYQQQohnj2xyJoQQQgghhBBClAJJsIUQQgghhBBCiFIgS8SFeEpUKvk8Szy+u9fRi3I9yb3YQgghhHiRSIItxFOg1WqxsjIt6zDEC+RFuZ40GjUpKdmSZAshhBDihSAJthBPgUKhIDFxItk5SWUdihDPDFOT6jg6foZSqZAEWwghhBAvBEmwhXhKsnOSyMo6XdZhCCGEEEIIIZ6QF+MmPiGEEEIIIYQQooxJgi2EEEIIIYQQQpQCSbCFEEIIIYQQQohSIAm2EEKIsqXRYLJyObYtPbGvVgm7hm6YB49HkZ6ma6K8+heWwwdTzqkq5Wq8hnWPrhgcP/bQro22bcGmbYvCfhu7YzZnBuTl6bUx+OMA1t06Uq76q9jVrYn5p2NRZKTrtTFZ8RV2bk6Uq/M6pqHzipzHamBfzELm/McBEEIIIcSLQhJsIYQQZcp4YQgWQWPIa9ee1G++JWvk+5is+w6rQf1Bq0WRkY5NVx8M/owjfW4o6WHLUGSmY93TF2Xytfv2a/jzbqwG9UXt6EjqyrVkDx6K2aIQLCZ/qmujOvEnNm91RWthQdqK1WROnILJph+w8u//T5uTJ7D4dCxZH4wmY+rnmM+bheHuXbp6gz8OYHDoD7KGjXoyAySEEEKI54Yk2I/AycmJqKiosg7jiduzZw9nz54t6zCKNX78ePr3/+cX3//97394e3vj6urKqlWrHrt/jUbDsmXLaN++PfXq1aNTp06sX7/+sfsVQtyHRoNJaAg5AwaROXEK+S1bk+M/hIxZ8zHauweDY0cwDV+KIiWF1I0x5HXpRt6bHUj95nu0RsYY/vJ/9+3a5Ps1aCpXIX3pMvJbeZM9bBTZQ0disnol5OcDYBa+BI2NLWkr1pDfug25vfuSETwVo593ozqbAIDRvv+hdnImJ2A4uW+9TV4rb4z2/qw7j8XUYLI+/gTMzJ7kSAkhhBDiOSCP6RJ6rly5wvDhw1m1ahWvv/56WYfzUAsWLKB69eqsWrUKGxubx+4vPDycFStWMHXqVOrWrcv+/fuZMmUKhoaGdOvW7bH7F0L8S1oaeW/3JqeLn15xweu1AFCdT8I4ZhO5XXzRVKioq9dWqMDtuDMP7FqRk4vWzAxUKl2ZxtYORV4eiox0tLZ2ZAYFoxg2CoyM/jnQ0LDwz5ycvztSoDUxuafeCNRqoHAJuuLmDXL6vfuo71wIIYQQLyCZwRZ6tFptWYfwSFJTU3F3d6dy5cpYWFg8dn/fffcd/v7+dOzYEQcHB95++218fX1lFluIJ8XGhuyZcylo0lSv2Hj7FgAKajmjij+N2rEmZjOnY1e3Jvav2mHdvROq06ce2HW2/xBU5xIxXbIQReodDA7+jmnEUnLbvonW1g4ATaVXUdepW3hAZiaG/9uD+RfTyG/cFHVdVwDyGzbG4OQJDA4fRJWYgOGv/0d+E09QqzH/fAqZn04CA/m8WgghhBCSYN/XtWvXGDFiBB4eHrRo0YKYmBi9+j179uDn54ebmxvt2rVjwYIF5N2zcY6TkxORkZH06dMHV1dXOnTowOHDh4mMjKRVq1bUr1+fwMBAcu7OkABHjhxhwIABNGjQgCZNmhAUFERKSkqJY46KiqJFixasW7cOLy8vPDw8GDVqFMnJybo2f/31F6NHj8bT05M6derQokUL5syZg0aj4fLly7Rp0waAAQMGsGjRIgD+/PNP+vbti7u7O23atGHz5s3Url2bAwcOANC/f3+Cg4Pp2bMnDRs2ZPPmzeTl5TFr1iy8vb2pW7cujRs35sMPP+T27dsAXL58GScnJ2JjY+nZsyd169bF29ubyMhIXaxarZalS5fSokUL6tWrR1BQELm5uXpjfOXKFZYsWYKTk9NDxyczMxMPDw++/fZbvfLFixfTqlUrNBoNs2bNonv37nr1SqWStLQ0hBBPh8GhPzBbFEJu+w5oyr+CoqAA0/AlGO37PzJCFpEWsRLlrZvYdOuA8trV+/aT37wlWe99iMXUidjXdMC2Y1s09uVJD1tetLFWi71LdWx6+qLIzCBjxj8blhV4NCArcAw2vh2wbdGUHL+e5HXuisnaVWjNzcnr0g3T0HnYejXCqs9bKC+cfwKjIoQQQojngSTYxSgoKCAgIICUlBTWrFlDaGgoy5f/8wvZ3r17CQwMpFevXmzZsoXJkyezfft2xo4dq9dPSEgIAQEBbNq0CUtLS4YPH05sbCwRERHMmDGDXbt26WZG4+Li6N+/PzVr1mTdunWEhoZy7NgxBg8ejPrvpYglcfv2bb755hsWLFjAN998w9WrVwkICKCgoACAESNGkJ6eztdff82OHTvw9/dn2bJl7N69m0qVKuniWbRoEf7+/iQnJ/Puu+/y2muvsXHjRiZNmkRISEiRmNavX8+AAQP49ttvad68ObNnz2bnzp3MnDmT2NhYZs6cyW+//caXX36pd9yMGTMYPnw427dvp1WrVkyZMoVLly4BEBERwbJlyxg3bhxRUVFYWVmxbds23bH79u2jYsWK+Pv7s2/fvoeOjbm5OT4+PmzZskWvPCYmBl9fX5RKJZ6enlSs+M8y1L/++outW7fi5eVV4u+BEOK/MzjwG9a9e6B2qEp66FIU+f98cHnn+yjy2vmQ17krqd9uQJGRgenyiPv2ZTF2NGaLQ8n8aCx3fthK2sIvUd5JwfptP8jK0m9cUEDqqu9JXR2JusbrWHftgOrP47rqrI8/4ea5v7iZdJXMz2dDZiZmc2aQOXEqRrHbMV0WTvrSr1DXcsZqiCwXF0IIIV5WkmAXY//+/SQkJDB79mzq1KmDh4cHM2bM0NWHhYXRq1cvevfujYODA15eXkydOpUdO3Zw+fJlXbsePXrg7e1NjRo18PX1JTU1lUmTJlGrVi3at2+Pi4sLCQmFm+isWLECJycngoODcXR0pGnTpsyfP58TJ06UKHm8Kz8/n1mzZtGwYUPc3NyYM2cO8fHx7N+/n5ycHHx9fZk+fTrOzs5UqVKFgQMHYm9vz5kzZ1CpVNjZFS6btLa2xtzcnMjISCwtLfn88895/fXXadmyJRMnTixyXhcXF7p06UKtWrWwtbXF1dWVWbNm0bhxY1577TW8vb154403iI+P1ztu4MCBtGnThipVqjB69Gg0Gg3Hjh1Dq9WyevVqBgwYQOfOnalRowZBQUG4uLjoji1fvjwqlQozMzPKly9fovHp3r07hw8f5sqVK0DhBxvnz5/Hz8+vSNubN28yZMgQypUrx4gRI0r8PRBC/DfG0Rux6dkVzWuVSd0Yg9auHNq/b/3Ib+YF99wGoqlcBXVNp/s+qkt59S9MVn9N1nsfkjU+mPxmzcnt3ZfUbzdgeOgPTL5brX+AoSH5rbzJa9+B1O83goEBpl99WaTN3Xu1zcIWo3apTX6LVhjHRJPXoRMFbvXIGvUhhkePoLx0sfQGRgghhBDPDblprBjx8fFYW1vj4OCgK3NxccHk701uTp48SVxcHBs2bNDV3713OTExkcqVKwNQtWpVXb2pqSmAXp8mJia6ZeXx8fE0a9ZMLw5nZ2csLS05c+YMLVu2LFHs5ubm1K1bV/e1o6Mj1tbWxMfH07x5c/r168eOHTuIi4vjwoULnDlzhps3b6LRaIrt7+TJk9StWxfDu5v+AI0aNSrS7t73CuDr68uvv/7K3LlzOX/+POfOnSMpKYmGDRvqtXN0dNT93dLSEij8kCAlJYUbN27g6uqq175evXokJiaWaCyK06hRIypXrsyWLVsYNmwYmzdvpn79+kXiP3fuHEOHDkWtVrNq1SqsrKz+8zmFEA9numQh5tMKE+G0lWvRWlkDoLWyRmNvjyI3r+hBBfloTUyL7U95+RIKrZaCxvr3dqudnNHY2WFw+jQARrHb0VpZke/5z89frZU16mrVUd1n+bni5k1Mly4i9YfC1TDKmzfI//vnvvbvzRaV15PRVHEo9nghhBBCvLhkBrsYCoWi2ITT4O9NbDQaDQEBAURHR+temzZtYufOnXrJp0Exm94olcUP+f02F9NqtXrJ7cMU11atVqNSqcjKyqJ3796EhYVhZWVF9+7d+fbbb/WWRP+bSqW6b/J9L5N7d9gFJk2axOjRo8nPz8fb25t58+bRqVOnIscZ3btz79+0Wi0KhUL393sVN6aPQqFQ0K1bN2JiYlCr1Wzfvr3I7PWhQ4fo3bs3pqamfP/991SpUuWxzimEeDCjlcuxmDqRXN/upH4fpUuu78pr8yaGe39GceuWrkx1NgHV2QTym3oW26e6uiNalQrD3/brlavOJqC8fRt11WoAmIYvwWLcaN2u4ADKv65gEH+agtp1KY753BnktW1HgVs9ADT25VFev1547N/P5dbYl2xVjRBCCCFeLDKDXQwXFxfS09NJSEigZs2aAJw/f56MjAwAatasSVJSkt6s54EDB1i1ahVTpkzB7D88C9XJyYlDhw7plZ0+fZqMjAy9Wd6HuXPnDpcuXdIlhQkJCWRkZFC7dm327dvHiRMn+OWXX7C3t9e1v3Xrli6RvZvY3uXs7MzGjRvJz8/XJe9Hjhx5YAwpKSlERkYSEhJCx44ddeXnzp0r8djY2tpSqVIlDh06RNu2bXXlf/755yN94FCc7t27s3jxYr7//nsyMzPp0KGDri4uLo6AgABq167Nl19+KTPXQjxp165hNmE8aoeqZPsPwyDuqF61uloNMj/+BKPtW7Hu1Y2sjz9BkZ+H2RfT0LxWWe/xWAYHf0dTzh5N9Rpo7e3JHjoS0yWhAOS1bI3y8iXM585EXcWBnP6Fx2V9NA7rnr5YDRlIdv+BKG/dxGz+bDTWNmSNeL9IuMpziZh8v5bbe37VleW96YPFxx+S590O4+1bKKjjisahapFjhRBCCPHikxnsYjRp0gR3d3fGjRvH0aNHOX78OOPGjdPNPg8ZMoTY2FgWL15MUlIS+/fvJygoiPT09BLfC/xvgwYN4syZM0yfPp3ExEQOHDjAmDFjqF27Np6exc/Q3M/YsWP5888/OXr0KOPGjcPDw4NGjRrpZqo3b97MlStXOHjwICNHjiQ/P1+3VP1uAhwfH096ejp9+vQhLS2N4OBgEhMT+fXXX5k+fTpQNBm/y8LCAktLS3766SfdMvTg4GBOnDiht9P6wwwZMoS1a9eyfv16kpKSWLBgAXFxcY80FsV57bXXaNKkCfPmzaNt27a6x3sVFBQwZswYypUrx8yZM8nNzeXGjRvcuHFDt/u5EKKUbduGIjsb1cUL2HZtj23Htnovo12xaKpV587WH9FUqoTlqKFYfPwh6rpu3Nm8A62Fpa4r245tMZ8/W/d15pTPyJw8HaOtm7Hu7Yf5nBnktWxNSuzPaK1tAMj3akHq+k0ob1zHavAALD4dS4GbO3e2/4T2lVeKhGv+xTRyevdFU72Griy3Szdy+vTH8uP3USXEk/blMrjPz0chhBBCvNhkBrsYSqWS8PBwPvvsM/z9/TExMWHYsGG6jbF8fHwICQkhPDycsLAwbGxs8Pb2ZsyYMf/5nO7u7ixbtowFCxbQrVs3LCwsaNu2LR9//PEjz9h26dKFoUOHkpeXh7e3NxMmTEChUODm5kZQUBArV65kwYIFVKhQgY4dO1KpUiWOHy/cLdfW1pYePXowe/ZsLly4wMSJE1m2bBlffPEFvr6+VKxYkXfeeYfZs2ffNy5DQ0NCQ0OZOXMmXbp0wdramiZNmvDRRx8RHh5OdnZ2id5H37590Wg0fPnll9y8eZPmzZvz1ltvkZSU9EjjURw/Pz9+++03veXhd+9LB/RmzaEwKd+9e/djn1cI8S/+/qR0f5uCggffiqJ2ciZtzboHtrlx/V+P01MoyB42iuxhox54XL5XC+54tShRuOnLvilaqFSSOe0LMqd9UaI+hBBCCPHiUmjvd/OveO5ERUURFBTEmTNnSq3Ps2fPkpqaSoMGDXRlhw8f5p133uHnn3+mUqVKpXauF92fJ/qSlXW6rMMQ4plhZuZM3TprSUnJfGiCLZ4cAwMltrbm8n0QpUauKVHa5JoSpcnOzhyV6skt5JYl4uKBrl27xoABA4iOjubKlSscOXKEGTNm0LhxY0muhRBCCCGEEOIeskT8OZCcnIyPj88D27i6utKtW7dSP7eXlxcTJkwgPDyc4OBgLC0tH3s5/JNQ0jFatWrVU4pICCGEEEII8bKRJeLPAbVazeXLlx/YxtjY+IGP23rRPQ9jJEvEhdAnS8SfDbL0UpQ2uaZEaZNrSpSmJ71EXGawnwMqlUrvkWCiKBkjIYQQQgghRFmTe7CFEEIIIYQQQohSIAm2EEIIIYQQQghRCmSJuBBPialJ9bIOQYhnivybEEIIIcSLRhJsIZ4CrVaLo+NnZR2GEM8cjUaNRiN7bQohhBDixSAJthBPgUKhIC0tG7Vadr4Uj0elUmJlZfrCXE8ajVYSbCGEEEK8MOQxXUI8JS9CMiSeDSqVUq4nUarkmhKlTa4pUdrkmhKlRalUoFAonlj/kmALIYQQQgghhBClQHYRF0IIIYQQQgghSoEk2EIIIYQQQgghRCmQBFsIIYQQQgghhCgFkmALIYQQQgghhBClQBJsIYQQQgghhBCiFEiCLYQQQgghhBBClAJJsIUQQgghhBBCiFIgCbYQQgghhBBCCFEKJMEWQgghhBBCCCFKgSTYQgghhBBCCCFEKZAEWwghhBBCCCGEKAWSYAshhBBCCCGEEKVAEmwhnhCNRsPChQtp3rw59erVY8iQIVy6dKmswxLPiTt37jBp0iRatGhB/fr1eeeddzh48KCufv/+/fj5+eHu7o6Pjw9bt24tw2jF8yYpKQkPDw+ioqJ0ZadOnaJfv37Uq1cPb29vVq1aVYYRiudFdHQ0HTt2xNXVlU6dOrF9+3Zd3eXLlxk2bBj169fHy8uLBQsWoFaryzBa8awrKCggNDSU1q1b4+HhQd++fTl69KiuXn5OiZIKDw+nf//+emUPu35K63d3SbCFeEKWLl3Kt99+y/Tp0/n+++/RaDQEBASQl5dX1qGJ58BHH33EkSNHmD9/Phs3bsTFxYXBgwdz7tw5EhMTGTZsGM2bNycqKoqePXsybtw49u/fX9Zhi+dAfn4+Y8aMISsrS1eWkpLCoEGDcHBwYOPGjYwaNYq5c+eycePGMoxUPOs2bdrEhAkT6Nu3L1u3bqVz5866n135+fkMHjwYgO+//54pU6bw3XffsWTJkjKOWjzLvvzyS9avX8/06dOJjo6mevXqBAQEcP36dfk5JUps7dq1LFiwQK+sJNdPaf3ublAab0IIoS8vL48VK1YwZswYWrVqBUBISAjNmzdn586ddO7cuWwDFM+0Cxcu8Msvv/Dtt9/SoEEDAIKDg/m///s/YmJiuHXrFk5OTowePRoAR0dHTp48ybJly/D09CzL0MVzYNGiRVhYWOiVrVu3DkNDQ6ZNm4aBgQGOjo5cuHCBiIgIevToUUaRimeZVqslNDSUAQMG0LdvXwBGjBjBwYMH+f3337ly5Qp//fUX69atw9ramlq1anHr1i1mz57N8OHDMTIyKuN3IJ5Fu3btonPnznh5eQEwfvx41q9fz9GjR0lKSpKfU+KBkpOTmTx5MgcOHKBatWp6dQ/7f640f3eXGWwhnoDTp0+TmZmpl+xYWVlRu3Zt/vjjjzKMTDwPbG1tiYiIwNXVVVemUChQKBSkpaVx8ODBIol006ZNOXToEFqt9mmHK54jf/zxB5GRkcycOVOv/ODBgzRu3BgDg38+d2/atCnnz5/n5s2bTztM8RxISkriypUrdOnSRa98+fLlDBs2jIMHD1KnTh2sra11dU2bNiUjI4NTp0497XDFc6JcuXLs2bOHy5cvo1ariYyMxMjICGdnZ/k5JR7qxIkTGBoasnnzZtzd3fXqHnb9lObv7pJgC/EEXLt2DYBKlSrplb/yyiu6OiHux8rKipYtW+rN8MTGxnLhwgWaN2/OtWvXqFixot4xr7zyCtnZ2aSkpDztcMVzIi0tjXHjxjFx4sQiP5vud00BXL169anFKJ4fSUlJAGRlZTF48GA8PT3p2bMnu3fvBuSaEv/NhAkTMDQ0pE2bNri6uhISEsLChQtxcHCQa0o8lLe3N4sWLaJKlSpF6h52/ZTm7+6SYAvxBGRnZwMUWQJnbGxMbm5uWYQknmOHDx8mKCiIN998k1atWpGTk1Pk2rr7tdzjL+5nypQpeHh4FJlxBIq9poyNjQHkZ5YoVkZGBgCffPIJnTt3ZsWKFTRr1oyRI0eyf/9+uabEf3L27FksLS1ZsmQJkZGR+Pn5MWbMGE6dOiXXlHgsD7t+SvN3d7kHW4gnwMTEBChMdu7+HQr/AZuampZVWOI5tGvXLsaMGUP9+vWZO3cuUPjD/t+J9N2v5foSxYmOjubgwYPExMQUW29iYlLkmrr7C4WZmdkTj088fwwNDQEYPHgw3bt3B8DFxYWTJ0/y9ddfyzUlHtnVq1f5+OOPWblyJQ0bNgTA1dWVs2fPsmjRIrmmxGN52PVTmr+7ywy2EE/A3eUl169f1yu/fv06FSpUKIuQxHNozZo1vP/++7Ru3ZqwsDDdJ62VKlUq9toyMzPD0tKyLEIVz7iNGzdy69YtWrVqhYeHBx4eHgBMnjyZgIAAKlasWOw1BcjPLFGsu9dFrVq19Mpff/11Ll++LNeUeGTHjh0jPz9fb/8RAHd3dy5cuCDXlHgsD7t+SvN3d0mwhXgCnJ2dsbCw4MCBA7qytLQ0Tp48SaNGjcowMvG8uPuYiL59+zJ//ny9JUsNGzbk999/12v/22+/Ub9+fZRK+bEuipo7dy7btm0jOjpa9wL44IMP+Pzzz2nUqBGHDh3Se0bxb7/9RvXq1SlXrlwZRS2eZXXq1MHc3Jxjx47plcfHx+Pg4ECjRo04efKkbik5FF5T5ubmODs7P+1wxXPg7v2xZ86c0SuPj4+nWrVq8nNKPJaHXT+l+bu7/CYmxBNgZGREv379mDt3Lj/99BOnT59m9OjRVKxYkTfffLOswxPPuKSkJL744gvatWvHsGHDuHnzJjdu3ODGjRukp6fTv39/4uLimDt3LomJiaxYsYIdO3YQEBBQ1qGLZ1SFChWoWrWq3gsKd+ytUKECPXr0ICMjgwkTJnD27FmioqJYuXIlw4YNK+PIxbPKxMSEgIAAlixZwpYtW7h48SJffvklv/zyC4MGDaJt27aUL1+ewMBATp8+za5du5g/fz7+/v7yiC5RLDc3Nxo0aMAnn3zCb7/9xvnz51mwYAH79+9n6NCh8nNKPJaHXT+l+bu7QivPdBHiiVCr1cyfP5+oqChycnJo1KgRkyZNonLlymUdmnjGhYWFERISUmxd9+7dmTlzJnv37uX/27vzqKrK9YHjXyBABJwHSsycQmUKlIOKyJgiahkXS4Fc5BAJUorzkDORCmIyCQgiQko5pKZRcsvhesUxaVBvV6NfpKYoCqYg4+8P1tnLw+yNMu35rOVassd373cf5dnP875nzZo1/PTTT5iamhIcHIynp+ef3FLxODMzMyMsLAwvLy8AvvnmG0JDQzl37hwdO3Zk4sSJ+Pn5PeJWir+6TZs2kZaWxrVr1+jZsyfBwcG4u7sD8H//938sW7aMU6dO0bp1a7y9vQkODpZKG1GvwsJC1q1bx8GDByksLOT5558nJCQElUoFyL9TounmzZvH5cuX2bJli7KsseenuX53lwBbCCGEEEIIIYRoBvIKUQghhBBCCCGEaAYSYAshhBBCCCGEEM1AAmwhhBBCCCGEEKIZSIAthBBCCCGEEEI0AwmwhRBCCCGEEEKIZiABthBCCCGEEEII0QwkwBZCCCGEEEIIIZqBBNhCCCHEY6SqqupRN0H8CaSfHy/SX0IINQmwhRBCPJFef/11zMzMNP5YWFjg7OzMsmXLKCwsfNRNfGixsbEkJSU96mZoUN/btWvX1rm+srISR0dHzMzM2LlzJwDHjx/HzMyM48ePN2s7oqKimu14TfHWW2/x8ccfN/txm6ufy8vLmTdvHjY2Ntja2pKdnV3ndpGRkbU+K2ZmZhptKCgoYNGiRTg6OjJgwAD8/f05d+6cxnFKS0uJiIjAyckJKysrXnnlFfbt2/e7r6MpoqKiMDMza3CbP+K5A/j4449ZtWpVk7e/desWzs7O5OXlNWs7hBB/DU896gYIIYQQf5R+/fqxZMkS5eeysjK+//571q5dy/nz59m6dStaWlqPsIUP54MPPmDatGmPuhm1aGtrk5mZSUhISK11J0+e5Pr16xrLzM3NycjIoFevXs3WhoyMDExMTJrteI3ZuXMn165d4x//+EezH7u5+vnIkSPs2rWLwMBABg8eTL9+/erc7sKFC6hUKmbOnKmx/JlnngGqs7PBwcFcunSJWbNm0alTJzZu3Iifnx+7d++ma9euAMyYMYODBw8yceJEBg0axHfffcfChQspKCjg9ddf/93X83v9Ec8dQFxcHCqVqsnbt23bFn9/fxYsWEBqaupj9W+QEKJxEmALIYR4YhkZGfHCCy9oLLOzs+Pu3busX7+enJycWuvFw7O1teXUqVOcO3euVhC3b98++vbty/nz55VldfXL7/Vn9mNJSQnh4eEsWbIEbe2/bjHg7du3AfDy8lKC4LqcP38eLy+veu/hTz/9xKlTp1i5ciXe3t5AdZ8PHDiQ3bt3M23aNM6dO0dWVhbTp09n6tSpAAwePJiWLVsSERHByy+/TKtWrZr1+h7WH/Hc/a98fHyIi4vjwIEDDBs27FE3RwjRjP66/ysIIYQQfxALCwsArly5oizLysrCy8sLS0tLHBwcWLlyJffu3VPWR0VF8eKLLxIdHY1KpWLIkCEUFhZSVVVFSkoKI0aMwMrKihdffJGkpCSNMZmnTp3Cz88Pa2trVCoVc+fOpaCgQFm/c+dO+vXrR05ODq+99hqWlpa4uLholOiqy1+jo6M1SmGzsrLw8fHBxsYGCwsLPDw8SE9P17jeS5cuMWXKFGxtbRk8eDCRkZHMnz9fI6tYWVlJQkICL774IhYWFgwfPpwtW7Y06X7a2dnRoUMHMjMzNZaXl5fzxRdfMHLkSI3lNUt1S0pKWLp0KUOHDlWuoWaJ9ObNm/Hw8MDS0hJHR0eWLl3Kb7/9pnF/1CXi6uMfO3aMiRMnYm1tjYODA2vWrKGiokLZ57fffmPx4sUMGjQIGxsbZsyYQUpKSqOlxjt27OD+/fu4uLhoLD969Cg+Pj70798fe3t7Zs6cydWrV5X19ZUxP9j2+vq5poqKCtLT0xk9ejRWVlY4OzsTHh7O/fv3AZg3bx7z5s0DwN3dvd4MckFBAdeuXaNv3771nkt9TCMjI2VZy5Yt0dfXV4L4S5cuAdS6J/b29ty7d48TJ07Ue/yjR4/y6quvYmNjg52dHVOnTlWOB+Dq6qpci9rOnTsxMzPjl19+0VielZXF8OHDsbS0ZOzYsRw7dkxZV1eJ+A8//EBAQAC2trbY2toSFBRUq3T7+vXrzJ07V3lO/Pz8+Prrr5W2Xb58mV27dintqaysJDIyEldXVywsLHB1dSUiIoKysjLlmHp6egwfPpz4+Ph674sQ4vEkAbYQQoi/ndzcXAAlq7d3716CgoLo0aMHMTExTJs2jT179hAYGKgRKF+5coVDhw4pAWrr1q1ZvXo1q1evxtXVlQ0bNuDt7U14eDgJCQlAdYm0v78/LVq0YN26dSxYsIATJ04wYcIESkpKlGNXVlYyffp0PD09SUhIwNbWltWrV3PkyBGgugQawNvbW/n7wYMHCQoKwtzcnNjYWKKioujatSvLly8nJycHqA6g/Pz8uHr1KmFhYSxatIjMzEw+/fRTjXuydOlS1q9fz0svvcSGDRvw8PDgvffeIyYmptH7qaOjw/Dhw2sF2MeOHeP+/fu4uro2uP97773H4cOHmTt3LklJSbi5ubF69Wp27NgBwKeffsqaNWvw9fUlKSmJoKAgdu/ezYoVKxo87qxZs+jfvz8bNmxg1KhRbNy4UWPMdGBgIJ999hnBwcFERkZy9+5dIiIiGr3ePXv24OzsjJ6enrLsk08+YeLEiTz99NOsXbuW+fPn8/XXX/Paa69x8+bNRo+pVlc/12Xx4sWEhYXh7u5OXFwcvr6+pKWlKc9sYGCgkkmOjo7WGCrxoAsXLgDVz5KLiwvm5uaMGTOGQ4cOKdv06dOHgQMHEhsbyw8//MDt27d5//33KSkpwdPTE6guewbNl1YAP//8M0C9443z8vIIDAzEwsKCuLg4QkNDyc3N5c0336SysrLR+1XTwoULmTBhAlFRURgaGjJlyhS+/fbbOrfNzc1l3Lhx3Lx5k1WrVhEaGkpeXh7jx49X+uzu3buMHz+e48ePM3v2bKKjo9HX12fixIn89NNPREdH07FjR5ycnMjIyKBTp04kJiaydetWgoKCSE5OZvz48SQlJREXF6dxfg8PD7777jvl3yMhxJNBSsSFEEI8saqqqigvL1d+Liws5MSJE8TFxSkZ36qqKsLDw3F0dCQ8PFzZ9rnnnsPf359Dhw7h7OwMVGdk586dy4ABAwAoKioiNTUVPz8/Zs+eDVSXxebn53Py5EkCAgKIiIige/fuxMfHo6OjA4C1tTUjR45kx44d+Pr6Km0NDAxk7NixAPTv358DBw5w8OBBHB0dldJWExMT5e8XL17klVdeYeHChUq7bWxssLe35/jx41hbW7Nlyxbu3r3LJ598QufOnZXzDx8+XNknNzeXjz76iJCQEN58800AhgwZgpaWFvHx8fj4+CgBVH08PT1JT0/XKBPfv38/bm5u6OvrN7jviRMncHBwUDLd9vb2tGzZkvbt2yvrTU1N8fX1RVtbG5VKRcuWLRudqG7s2LEEBQUBMGjQILKysjh48CDjxo3j2LFjHD9+nKioKKVEd+jQoYwaNUoje1rTb7/9xrfffsuIESOUZZWVlYSHhzNkyBCNAN3W1hZPT0+SkpKYM2dOg21Vq6ufa7p48SLbt29n5syZSn85ODjQqVMn5syZw+HDh3FycuLZZ58FoG/fvpiamtZ5LHXpfn5+PitXrqS0tJS0tDTeeustEhIScHR0BKpfwEyePJnRo0cDoKWlRVhYGLa2tgCoVCq6du3KypUrMTAwwNLSkgsXLhAeHo6WlpZGNciDvvnmG0pKSggICFCeTxMTE/75z39y7949jax5UyxbtgwPDw+gus/d3NxITExk/fr1tbaNjo7GwMCAlJQU5TyDBg3C3d2djRs3MnfuXHbt2qVkqNVZfltbW8aMGcPJkycZO3Ysenp6tGvXTumvEydOYGFhoYzPV6lUGBgYYGxsrHF+S0tLoPpFVPfu3R/qOoUQf10SYAshhHhinTx5EnNzc41l2traDB48mOXLl6OlpcWlS5f49ddfCQgI0AjG7ezsMDIy4ujRo0qADWiU0p49e5by8vJaYygXLVoEQHFxMTk5OUyaNEkj2O/atSs9e/bk6NGjSoAN1cGxmvqX9voCE4DJkycD1Vm23Nxcfv75ZyVbV1paCkB2djY2NjZK8ALQpUsXjXNlZ2dTVVWFq6urxj1wdXUlLi6O06dP4+7uXm87oPqFQOfOncnMzKRfv36UlpaSlZXFmjVrGtwPqgPqbdu28euvv+Lk5ISTk5MSGAMMHDiQjIwMvLy8cHd3x8nJidGjRzc6OdSD1wjVgZv6fmZnZ6Orq6txXdra2nh6ejY4G/nVq1epqKjQCFhzc3PJz8+vNUnYs88+i42NTYPl0f8L9fFqlt6PHDmS+fPnc/z4cZycnJp0rBEjRtCjRw+GDh2qvAAaMmQIL7/8MuvXr8fR0ZFLly4xfvx4unTpwvr16zE2Nuazzz5j0aJFtGjRghEjRqCnp0dSUhILFizA398fgI4dO7Jo0SKmT5+OgYFBnee3trZGX18fb29vPDw8GDp0KPb29lhZWT30fdHV1dX4LOrr6zN06FC++uqrOrfPzs5GpVLRokUL5bk3MjJiwIAB/Pvf/wbg9OnTmJqaanzuDQwM+Pzzz+tth729PREREfj4+ODq6oqzszN+fn61tjM2NqZVq1a1ytyFEI83CbCFEEI8sczNzVm2bBlQnXHT19fn6aef1siKqceQLlu2TNn2QTVnwDY0NKy1b7t27eo8f1FREZWVlSQmJpKYmFhrfc3MbosWLTR+1tbWbvD7dQsKCliyZAlZWVloaWnRrVs3Jbuu3q+goKDWSwaADh06cOPGDY3rqBmwqV27dq3eNqhpaWnh4eGhzCZ+5MgRtLW1cXBwaHT/hQsXYmJiwp49e1ixYgUrVqzAxsaGpUuX0qdPHzw9PamsrOTDDz9USuG7dOnCrFmzlBLlujR0P2/dukWbNm1qTVKmzprX586dO0D1GGQ19f3r0KFDre07dOhQ6+usfi915r5jx44ay5966inatm2rtLEpnnnmGWW2cDVdXV0cHBzYtm0bACkpKVRUVJCcnKxUMgwePJiioiKWL1+Oh4eH8vylp6dz8+ZNbt++Tbdu3bh69SpVVVW0bt26zvObmpqSlpZGQkIC27dvJzU1lVatWuHj48P06dMfaobttm3b1tmfRUVFdW5/+/Zt9u/fz/79+2utU3+mb9++3egzUdPkyZMxNDRkx44dhIeHs2bNGnr37s2iRYsYOHCgxrYGBgYacwkIIR5/EmALIYR4YhkaGiplmPVRz2w8Z86cOr9qp77A4MF9CwoK6NGjh7L8ypUr/Pzzz1hYWKClpYW/v3+dwWt9Wb2mmjVrFj/++CMpKSnY2Nigp6dHcXExH330kbKNiYmJEkg/6MFxwerr2Lx5s8YLBLWaAVh9PD092bx5M+fPn2f//v0MGzYMXV3dRvfT09Nj6tSpTJ06lStXrvDVV18RGxvLzJkzle9RHjVqFKNGjeLOnTv861//IjExkdmzZyuZ84fVuXNnbt26RWVlpUZQ1th4aXWA+WDQ1qZNG4A673N+fr6yjzpYrKioULLFd+/efei2q5/J/Px8unTpoiwvKyvj1q1bjZbzP+jQoUOUlJRoDBmA6onN1EHmlStX6NGjR63j2tnZkZmZyc2bNzEyMuLzzz/H1taWrl27KkHp999/D1DnSx41KysroqOjKS0t5fTp02RkZLBhwwb69OmjlOI/ODkdUGdlx507d6iqqtIIym/cuFHvCzBjY2MGDx7MG2+8UWvdU089pWxTV4b5zJkztG7dmp49e9Zap62tja+vL76+vty8eZNDhw6xYcMGgoODOXr0qMbY/aKioofqLyHEX59MciaEEOJvrUePHrRv355ffvkFS0tL5U/nzp2JiIhoMPtoZWWFrq5urRLU5ORkQkJCaNmyJf369ePHH3/UOHbv3r2JiorSmM24KWpm506fPs2wYcOwt7dXfmk/fPgwgDJBlJ2dHWfPniU/P1/Z7/r165w9e1b5WZ31vnXrlkY7CwoK+OCDD5QMbWNeeOEFunTpwu7du/nyyy/rzYg/SB3cJScnA9XBvK+vLyNHjlQmzJo+fbpSMm5sbMyIESMIDAykvLy8VoVBU6lUKsrLy/nyyy+VZVVVVWRlZTW4X+fOndHR0eHXX39VlnXv3p2OHTvWmjguLy+Ps2fPKuOU1ZUTD+57+vTpWudo7Ku/1C+C1C8f1Pbt20dFRQX9+/dvcP8HZWZmMn/+fI0+vnfvHgcPHsTe3h6ovr6LFy/Weg7OnDmDsbExbdq0QVdXlxUrVmi83CkvLyctLY1nn32W559/vs7zp6Sk4OLiQmlpKXp6egwaNEiZvE7d/0ZGRhr3DOq+b8XFxWRnZys/3717V+M6alKpVFy8eJG+ffsqz7yFhQUpKSkcOHAAqP5s5OXl8d///lfZ7/79+wQHB7N9+3agdn+NGzeOlStXAtUZdC8vL3x9fSkqKtLIVhcWFlJcXNzkF1hCiMeDZLCFEEL8reno6DBjxgwWL16Mjo4OLi4uFBUVERsby7Vr1xrMvLVr144JEyaQkpKCnp4eKpWKnJwctm7dypw5c9DW1lYmDps5cyYvvfSSUmqbk5NDYGDgQ7W1VatWnDlzhpMnTzJgwACsrKzYu3cv5ubmmJiYcObMGRISEtDS0qK4uBiACRMmkJ6ezqRJk5QgNTY2lrKyMiXTZ2ZmxksvvcS7777L5cuXsbCwIDc3l8jISExNTXnuueea3EYPDw9SU1Np06ZNnRUBNbVo0QJzc3Oio6PR1dXFzMyM3Nxcdu3apWRVBw4cyJIlS1i1ahVDhw6lqKiI6OhonnvuOfr06fNQ91DNzs4OBwcHFi5cyI0bN3jmmWfYvn07//nPfxosS27ZsiW2tracPn1aGWus7uf58+cr/Xzr1i2io6Np3bq1kiF1cnIiLCyMxYsXM2nSJK5evUpMTEytqoGa/VyzPb169eKVV15h/fr1FBcXY2dnx/nz54mOjsbe3l6ZmKwpJk+eTGZmJlOmTCEgIEAZ0lBcXExwcDAAb7zxBnv37sXf35+AgACMjY354osv2LdvH/Pnz1eyvT4+PmzevBkTExO6d+9Oeno6Z86cISYmpt6XBgMHDiQ8PJygoCD8/PzQ0dFh27Zt6OnpKV/55eLiQnx8PPHx8VhbW/Pll19qBNJqurq6LFiwgJCQEIyMjEhISKCkpKTez1lgYCDjxo0jICCA8ePHo6+vT0ZGBllZWcqkaF5eXmzZsoWpU6fy9ttv07ZtW1JTUykrK8PHx0fpr3PnznHixAmsrKyws7MjOTmZDh06YGNjw7Vr19i0aRMqlUojm65+STBkyJAm95cQ4q9PAmwhhBB/e2PHjsXQ0JCNGzeSkZGhBFHh4eHKV3nVZ/bs2bRv355t27axceNGTE1Neffddxk3bhxQ/ctzUlIS0dHRvP322+jq6mJubs6mTZvqnSW6Pm+99RaxsbFMmTKF/fv38/777ytjlqF65vNly5axZ88eTp06BVT/8p+amkpoaChz5szB0NAQHx8fDAwMNMYRh4WFER8fr0w21r59ezw9PZk+fbpSztwU6lmzR4wY0WgmVm358uWsW7eO5ORk8vPzad++Pd7e3rzzzjtAdUawrKyMbdu28eGHH9KiRQsGDRrE7Nmzm1SCXp/IyEjef/99IiIiKC8vx83NjfHjx/PJJ580uN/w4cOJiori/v37yjh6Ly8vDA0NiY+PJygoCCMjIxwdHQkJCVHGSnfv3p1Vq1YRFxfHm2++Sc+ePTX6T61mP9eV4QwNDaVbt27s2LGDxMREOnXqxIQJEwgMDGzyfQfo2bMnaWlprF27loULF1JaWoqdnR2hoaHKs9+lSxe2bt3K2rVreffdd6msrKRXr14aM7ADBAcHo6WlRWJiIoWFhfTp04eEhIQGA8g+ffqwYcMGYmJiCAkJoaKiAgsLC5KTk5VhFwEBARQUFJCUlERZWRnOzs6EhoYqX0Om1q5dO2bOnMnatWvJz8/H2tqatLQ0jeEbNc+dnp5OZGQkc+bMoaqqiueff56YmBjc3NyA6ux5Wloaq1evZsWKFVRWVvLCCy+Qmpqq3J+JEyfy3nvvMWnSJDZt2sQ777yDnp4eO3bsICYmBmNjY1xdXWtNgnf48GGsrKw0yvyFEI8/raqGZk8RQgghxGMtJyeH27dva8wqXV5ejrOzszLr9N/R5cuXOXv2LG5ubhqTob399tvk5eWxa9euevctLi7G3d2d2bNnM2bMmD+htaI5HD58mClTpvDxxx//T7OUN6d79+7h6OjIqlWrGp2hXwjxeJEMthBCCPEEu3LlCjNmzCAoKAiVSkVxcTEZGRncuXOHV1999VE375HR1tZm3rx5uLm54e3tjY6ODkeOHOGLL74gLCyswX0NDAwIDg4mKSmJ0aNHP1SGXzwaBw4cYO/evejo6NT7neB/pm3bttG7d28lUy6EeHJIBlsIIYR4wm3dupUPP/yQvLw8dHV1sba25p133ml0hvUnXXZ2NjExMZw/f57y8nJ69uzJG2+8wahRo5q0/5QpU3Bzc1OGA4i/ptLSUlxcXKisrGTSpEnK98c/KgUFBYwZM4YtW7bQrVu3R9oWIUTzkwBbCCGEEEIIIYRoBvI1XUIIIYQQQgghRDOQAFsIIYQQQgghhGgGEmALIYQQQgghhBDNQAJsIYQQQgghhBCiGUiALYQQQgghhBBCNAMJsIUQQgghhBBCiGYgAbYQQgghhBBCCNEMJMAWQgghhBBCCCGagQTYQgghhBBCCCFEM/h/MhEZ4ii5iIQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/3734573659.py:6: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  barplot = sns.barplot(\n",
      "/var/folders/b0/6xxmhnys7hj9g_47_dsjm7c00000gn/T/ipykernel_13599/3734573659.py:19: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  plt.legend()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(color_codes=True)\n",
    "\n",
    "\n",
    "def plot_missing_data_summary(summary, df_name, deriv_dir):\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    barplot = sns.barplot(\n",
    "        x=\"Percentage Missing\", y=\"Measure\", data=summary, palette=\"viridis\"\n",
    "    )\n",
    "    plt.title(f\"{df_name} Percentage of Missing Data\")\n",
    "    plt.xlabel(\"Percentage Missing (out of 589 subjects)\")\n",
    "    plt.ylabel(f\"ABCD {df_name} Measures\")\n",
    "    plt.xlim(0, 100)\n",
    "\n",
    "    # Add vertical lines\n",
    "    # plt.axvline(x=5, color=\"green\", linestyle=\"--\", label=\"5% Missing\")\n",
    "    # plt.axvline(x=10, color=\"red\", linestyle=\"--\", label=\"10% Missing\")\n",
    "\n",
    "    # Add custom legend with total number of subjects\n",
    "    plt.legend()\n",
    "\n",
    "    for index, row in summary.iterrows():\n",
    "        percentage_missing = format(row[\"Percentage Missing\"], \".2f\")\n",
    "        text_color = \"red\" if row[\"Percentage Missing\"] > 5 else \"black\"\n",
    "        barplot.annotate(\n",
    "            f\"{percentage_missing}%\",\n",
    "            xy=(row[\"Percentage Missing\"], index),\n",
    "            xytext=(20, 0),  # Move text slightly more to the right\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            color=text_color,\n",
    "        )\n",
    "\n",
    "    # Save the figure\n",
    "    file_path = os.path.join(deriv_dir, f\"{df_name.replace(' ', '_')}_missing_data.png\")\n",
    "    plt.tight_layout()  # Ensure a tight layout with some padding\n",
    "    #plt.savefig(file_path)\n",
    "    #plt.close()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Plot and save the bar plots\n",
    "plot_missing_data_summary(sociocult_summary, \"Sociocultural\", deriv_dir)\n",
    "plot_missing_data_summary(covariate_summary, \"Covariate\", deriv_dir)\n",
    "plot_missing_data_summary(phyhealth_summary, \"Physical Health\", deriv_dir)\n",
    "#plot_missing_data_summary(rsfc_summary, \"RSFC DataFrame\", deriv_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we will remove all subjects with missing data to make final csvs for the PLSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Filtered Variables: 92\n"
     ]
    }
   ],
   "source": [
    "# specify covariates we want to include\n",
    "\n",
    "cov_list = [\n",
    "    \"src_subject_id\",\n",
    "    \"interview_age\",\n",
    "    \"site_id_l\",\n",
    "    \"demo_sex_v2\",\n",
    "    \"demo_prnt_age_v2\",\n",
    "    \"demo_prnt_gender_id_v2\",\n",
    "    \"demo_prnt_ed_v2_2yr_l\",\n",
    "    \"demo_prtnr_ed_v2_2yr_l\",\n",
    "    \"demo_comb_income_v2\",\n",
    "    \"demo_prnt_income_v2_l\",\n",
    "    \"demo_origin_v2\",\n",
    "    \"mri_info_manufacturer\",\n",
    "    \"rsfmri_meanmotion\",\n",
    "]\n",
    "covariate_df_Nan_filtered = covariate_df_Nan[cov_list]\n",
    "\n",
    "# Define the list of sites to exclude\n",
    "exclude_sites = [\"site07\", \"site14\", \"site15\"]\n",
    "\n",
    "# Filter the DataFrame to exclude rows with the specified sites\n",
    "'''covariate_df_Nan_filtered = covariate_df_Nan_filtered[\n",
    "    ~covariate_df_Nan_filtered[\"site_id_l\"].isin(exclude_sites)\n",
    "]'''\n",
    "\n",
    "\n",
    "# specify sociocult we want to include\n",
    "soc_list = [\n",
    "    \"src_subject_id\",\n",
    "    \"meim_ss_exp\",\n",
    "    \"meim_ss_com\",\n",
    "    \"via_ss_hc\",\n",
    "    \"via_ss_amer\",\n",
    "    \"macv_y_ss_fs\",\n",
    "    \"macv_y_ss_fo\",\n",
    "    \"macv_y_ss_fr\",\n",
    "    \"meim_p_ss_exp\",\n",
    "    \"meim_p_ss_com\",\n",
    "    \"via_p_ss_hc\",\n",
    "    \"via_p_ss_amer\",\n",
    "    \"macv_p_ss_fs\",\n",
    "    \"macv_p_ss_fo\",\n",
    "    \"macv_p_ss_fr\",\n",
    "    \"dim_y_ss_mean\",\n",
    "    \"comc_ss_cohesion_p\",\n",
    "    \"comc_ss_control_p\",\n",
    "]\n",
    "\n",
    "# Filter out rows where 'comc_ss_control_p' is greater than 100\n",
    "sociocult_df_Nan = sociocult_df_Nan[sociocult_df_Nan[\"comc_ss_control_p\"] <= 100]\n",
    "\n",
    "sociocult_df_Nan_filtered = sociocult_df_Nan #[soc_list]\n",
    "\n",
    "# Original list\n",
    "rsfc_list = [\n",
    "    \"src_subject_id\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_ad\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_cgc\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_ca\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_dt\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_dla\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_fo\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_n\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_ad_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_cgc\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_ca\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_dt\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_dla\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_fo\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_n\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_cgc_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_ca\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_dt\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_dla\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_fo\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_n\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_ca_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_dt\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_dla\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_fo\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_n\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_dt_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_dla\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_fo\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_n\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_dla_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_fo\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_n\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_fo_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_n_ngd_n\",\n",
    "    \"rsfmri_c_ngd_n_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_n_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_n_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_n_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_n_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_n_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_rspltp_ngd_rspltp\",\n",
    "    \"rsfmri_c_ngd_rspltp_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_rspltp_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_rspltp_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_rspltp_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_rspltp_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_sa_ngd_sa\",\n",
    "    \"rsfmri_c_ngd_sa_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_sa_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_sa_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_sa_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_smh_ngd_smh\",\n",
    "    \"rsfmri_c_ngd_smh_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_smh_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_smh_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_smm_ngd_smm\",\n",
    "    \"rsfmri_c_ngd_smm_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_smm_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_vta_ngd_vta\",\n",
    "    \"rsfmri_c_ngd_vta_ngd_vs\",\n",
    "    \"rsfmri_c_ngd_vs_ngd_vs\",\n",
    "]\n",
    "\n",
    "# Filter the list to exclude items containing \"_ad\", \"_smh\", \"_smm\", or \"_vs\"\n",
    "'''rsfc_list = [\n",
    "    var\n",
    "    for var in rsfc_list\n",
    "    if all(keyword not in var for keyword in [\"_ad\", \"_smh\", \"_smm\", \"_vs\", \"d_n\"])\n",
    "]'''\n",
    "\n",
    "print(\"Number of Filtered Variables:\", len(rsfc_list))\n",
    "\n",
    "rsfc_df_Nan_filtered = rsfc_df_Nan[rsfc_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       src_subject_id  macv_p_ss_fs  macv_p_ss_fo  macv_p_ss_fr  macv_y_ss_fs  \\\n",
      "0    NDAR_INV028D3ELL      3.833333           3.2           3.6      3.333333   \n",
      "1    NDAR_INV030W95VP      4.333333           4.6           3.2      4.000000   \n",
      "4    NDAR_INV0889M0JE      5.000000           3.8           3.0      1.833333   \n",
      "7    NDAR_INV0DC9BJZK      4.666667           3.6           3.0      3.666667   \n",
      "9    NDAR_INV0DKWEM1A      4.666667           3.6           3.8      4.000000   \n",
      "..                ...           ...           ...           ...           ...   \n",
      "583  NDAR_INVZPR155DG      4.666667           4.2           4.4      4.500000   \n",
      "584  NDAR_INVZR9NMJBR      3.500000           3.4           3.2      4.166667   \n",
      "585  NDAR_INVZT1J0KUC      5.000000           4.4           3.4      4.000000   \n",
      "586  NDAR_INVZTTGYL51      4.500000           3.6           3.8      3.666667   \n",
      "587  NDAR_INVZYRTFYRP      5.000000           4.4           5.0      3.500000   \n",
      "\n",
      "     macv_y_ss_fo  macv_y_ss_fr  reshist_addr1_coi_r_coi_nat  meim_p_ss_exp  \\\n",
      "0             2.4           2.8                         36.0       2.333333   \n",
      "1             3.8           3.8                         61.0       1.000000   \n",
      "4             2.8           2.2                         64.0       3.666667   \n",
      "7             3.8           3.8                          8.0       4.000000   \n",
      "9             3.6           3.6                         45.0       2.333333   \n",
      "..            ...           ...                          ...            ...   \n",
      "583           4.0           4.0                         54.0       2.000000   \n",
      "584           4.2           3.8                         78.0       2.666667   \n",
      "585           3.8           3.8                         62.0       4.000000   \n",
      "586           3.8           3.0                          4.0       5.000000   \n",
      "587           3.6           3.4                         24.0       3.333333   \n",
      "\n",
      "     meim_p_ss_com  ...  meim_ss_exp  meim_ss_com  \\\n",
      "0         3.333333  ...     4.000000     4.000000   \n",
      "1         1.000000  ...     2.666667     3.333333   \n",
      "4         2.000000  ...     4.000000     4.000000   \n",
      "7         5.000000  ...     3.333333     3.333333   \n",
      "9         2.666667  ...     3.333333     3.333333   \n",
      "..             ...  ...          ...          ...   \n",
      "583       5.000000  ...     3.000000     4.000000   \n",
      "584       2.666667  ...     3.000000     3.333333   \n",
      "585       4.000000  ...     4.000000     4.000000   \n",
      "586       5.000000  ...     4.000000     4.666667   \n",
      "587       3.666667  ...     4.333333     4.333333   \n",
      "\n",
      "     reshist_addr1_gstat_h_queen  dim_y_ss_mean  via_p_ss_hc  via_p_ss_amer  \\\n",
      "0                       0.807848       1.285714        6.375          7.750   \n",
      "1                      -0.342727       1.000000        6.125          9.000   \n",
      "4                      -0.996478       4.000000        6.125          6.625   \n",
      "7                       1.041787       1.142857        7.625          7.000   \n",
      "9                      -0.028830       1.000000        2.375          3.625   \n",
      "..                           ...            ...          ...            ...   \n",
      "583                     4.471116       1.000000        6.500          6.375   \n",
      "584                    -0.215563       1.000000        5.625          6.375   \n",
      "585                     3.587465       1.285714        5.875          4.875   \n",
      "586                    -0.101596       1.000000        8.750          9.000   \n",
      "587                     3.343483       1.000000        9.000          9.000   \n",
      "\n",
      "     via_ss_hc  via_ss_amer  comc_ss_cohesion_p  comc_ss_control_p  \n",
      "0        4.875        5.500                 3.8                3.6  \n",
      "1        6.125        6.000                 3.0                3.0  \n",
      "4        5.875        5.500                 3.4                5.0  \n",
      "7        6.125        7.500                 3.4                3.4  \n",
      "9        4.750        4.625                 3.8                4.8  \n",
      "..         ...          ...                 ...                ...  \n",
      "583      7.125        6.750                 3.0                3.0  \n",
      "584      6.000        5.750                 4.6                4.6  \n",
      "585      7.750        7.750                 5.0                5.0  \n",
      "586      7.250        7.750                 3.0                3.4  \n",
      "587      7.250        6.500                 3.6                4.4  \n",
      "\n",
      "[366 rows x 21 columns]\n",
      "       src_subject_id  interview_age site_id_l  demo_sex_v2  demo_prnt_age_v2  \\\n",
      "0    NDAR_INV028D3ELL          157.0    site01          2.0              40.0   \n",
      "1    NDAR_INV030W95VP          179.0    site19          2.0              30.0   \n",
      "2    NDAR_INV056V9RRJ          170.0    site10          2.0              38.0   \n",
      "3    NDAR_INV06DX0E7R          160.0    site18          1.0              33.0   \n",
      "5    NDAR_INV09T2EBX4          159.0    site13          2.0              39.0   \n",
      "..                ...            ...       ...          ...               ...   \n",
      "582  NDAR_INVZP49GXF4          153.0    site03          1.0              32.0   \n",
      "584  NDAR_INVZR9NMJBR          158.0    site02          1.0              42.0   \n",
      "585  NDAR_INVZT1J0KUC          168.0    site03          2.0              40.0   \n",
      "587  NDAR_INVZYRTFYRP          164.0    site10          1.0              49.0   \n",
      "588  NDAR_INVZZZNB0XC          157.0    site03          2.0              31.0   \n",
      "\n",
      "     demo_prnt_gender_id_v2  demo_prnt_ed_v2_2yr_l  demo_prtnr_ed_v2_2yr_l  \\\n",
      "0                       2.0                   19.0                    18.0   \n",
      "1                       2.0                   13.0                    14.0   \n",
      "2                       2.0                   12.0                    12.0   \n",
      "3                       2.0                   18.0                    13.0   \n",
      "5                       2.0                   16.0                    23.0   \n",
      "..                      ...                    ...                     ...   \n",
      "582                     2.0                   14.0                    13.0   \n",
      "584                     2.0                   18.0                    10.0   \n",
      "585                     2.0                   17.0                    17.0   \n",
      "587                     2.0                   16.0                     8.0   \n",
      "588                     2.0                   19.0                    13.0   \n",
      "\n",
      "     demo_comb_income_v2  demo_prnt_income_v2_l  demo_origin_v2  \\\n",
      "0                    9.0                    9.0           189.0   \n",
      "1                    4.0                    5.0           189.0   \n",
      "2                    5.0                    6.0           189.0   \n",
      "3                    8.0                    7.0           189.0   \n",
      "5                    7.0                    7.0           189.0   \n",
      "..                   ...                    ...             ...   \n",
      "582                  5.0                    3.0            44.0   \n",
      "584                  8.0                    1.0           189.0   \n",
      "585                  6.0                    3.0           189.0   \n",
      "587                  6.0                    2.0           189.0   \n",
      "588                  3.0                    6.0           189.0   \n",
      "\n",
      "       mri_info_manufacturer  rsfmri_meanmotion  \n",
      "0    Philips Medical Systems           0.066058  \n",
      "1    Philips Medical Systems           0.155444  \n",
      "2         GE MEDICAL SYSTEMS           0.173794  \n",
      "3         GE MEDICAL SYSTEMS           0.423909  \n",
      "5         GE MEDICAL SYSTEMS           0.054791  \n",
      "..                       ...                ...  \n",
      "582                  SIEMENS           0.107982  \n",
      "584                  SIEMENS           0.073203  \n",
      "585                  SIEMENS           0.041093  \n",
      "587       GE MEDICAL SYSTEMS           0.153301  \n",
      "588                  SIEMENS           0.103553  \n",
      "\n",
      "[390 rows x 13 columns]\n",
      "       src_subject_id  rsfmri_c_ngd_ad_ngd_ad  rsfmri_c_ngd_ad_ngd_cgc  \\\n",
      "0    NDAR_INV028D3ELL                0.237271                 0.050704   \n",
      "1    NDAR_INV030W95VP                0.424266                 0.320737   \n",
      "2    NDAR_INV056V9RRJ                0.283040                 0.123300   \n",
      "3    NDAR_INV06DX0E7R                0.195883                 0.176239   \n",
      "4    NDAR_INV0889M0JE                0.260193                 0.170786   \n",
      "..                ...                     ...                      ...   \n",
      "584  NDAR_INVZR9NMJBR                0.224798                 0.130237   \n",
      "585  NDAR_INVZT1J0KUC                0.187419                 0.096684   \n",
      "586  NDAR_INVZTTGYL51                0.446860                 0.251059   \n",
      "587  NDAR_INVZYRTFYRP                0.231318                 0.133982   \n",
      "588  NDAR_INVZZZNB0XC                0.363400                 0.090317   \n",
      "\n",
      "     rsfmri_c_ngd_ad_ngd_ca  rsfmri_c_ngd_ad_ngd_dt  rsfmri_c_ngd_ad_ngd_dla  \\\n",
      "0                 -0.097674               -0.066317                -0.020173   \n",
      "1                  0.125510               -0.117286                 0.067547   \n",
      "2                 -0.153066               -0.030460                 0.004602   \n",
      "3                 -0.119832               -0.025027                 0.004940   \n",
      "4                 -0.065887               -0.039431                -0.004096   \n",
      "..                      ...                     ...                      ...   \n",
      "584               -0.053828               -0.011020                -0.031037   \n",
      "585               -0.038299               -0.035381                 0.007394   \n",
      "586                0.094042               -0.074721                 0.034349   \n",
      "587               -0.059732               -0.001253                -0.004477   \n",
      "588               -0.231313               -0.150431                -0.010488   \n",
      "\n",
      "     rsfmri_c_ngd_ad_ngd_fo  rsfmri_c_ngd_ad_ngd_n  \\\n",
      "0                 -0.080367              -0.017838   \n",
      "1                 -0.112132               0.006792   \n",
      "2                 -0.016012              -0.004500   \n",
      "3                  0.023253              -0.002502   \n",
      "4                  0.009387              -0.017920   \n",
      "..                      ...                    ...   \n",
      "584               -0.021513              -0.002398   \n",
      "585                0.005280              -0.005596   \n",
      "586               -0.052045               0.017108   \n",
      "587               -0.029146              -0.001025   \n",
      "588               -0.140455              -0.063671   \n",
      "\n",
      "     rsfmri_c_ngd_ad_ngd_rspltp  rsfmri_c_ngd_ad_ngd_sa  ...  \\\n",
      "0                     -0.064739               -0.092830  ...   \n",
      "1                      0.028030                0.128417  ...   \n",
      "2                     -0.029544               -0.022439  ...   \n",
      "3                     -0.039661                0.084849  ...   \n",
      "4                     -0.067054                0.087728  ...   \n",
      "..                          ...                     ...  ...   \n",
      "584                   -0.077600               -0.036138  ...   \n",
      "585                   -0.043423               -0.041989  ...   \n",
      "586                    0.025911                0.058948  ...   \n",
      "587                    0.040491                0.076065  ...   \n",
      "588                    0.018483               -0.151073  ...   \n",
      "\n",
      "     rsfmri_c_ngd_smh_ngd_smh  rsfmri_c_ngd_smh_ngd_smm  \\\n",
      "0                    0.340691                  0.219597   \n",
      "1                    0.231254                  0.082087   \n",
      "2                    0.252366                  0.140852   \n",
      "3                    0.277711                 -0.047412   \n",
      "4                    0.230610                  0.141163   \n",
      "..                        ...                       ...   \n",
      "584                  0.284258                  0.013019   \n",
      "585                  0.254235                  0.049280   \n",
      "586                  0.523051                  0.391244   \n",
      "587                  0.295387                  0.090491   \n",
      "588                  0.407501                  0.318553   \n",
      "\n",
      "     rsfmri_c_ngd_smh_ngd_vta  rsfmri_c_ngd_smh_ngd_vs  \\\n",
      "0                   -0.015784                 0.074834   \n",
      "1                   -0.043124                -0.003773   \n",
      "2                   -0.003845                -0.007562   \n",
      "3                    0.049361                -0.165801   \n",
      "4                    0.023122                -0.060484   \n",
      "..                        ...                      ...   \n",
      "584                  0.006484                -0.127016   \n",
      "585                 -0.011353                -0.061906   \n",
      "586                  0.049429                -0.034149   \n",
      "587                  0.043314                -0.144720   \n",
      "588                 -0.023083                 0.174555   \n",
      "\n",
      "     rsfmri_c_ngd_smm_ngd_smm  rsfmri_c_ngd_smm_ngd_vta  \\\n",
      "0                    0.570781                  0.028428   \n",
      "1                    0.419668                  0.068082   \n",
      "2                    0.438947                  0.028452   \n",
      "3                    0.316955                  0.075131   \n",
      "4                    0.441094                  0.068266   \n",
      "..                        ...                       ...   \n",
      "584                  0.431063                  0.047040   \n",
      "585                  0.496075                 -0.073670   \n",
      "586                  0.852260                  0.012615   \n",
      "587                  0.484635                  0.026944   \n",
      "588                  0.780099                 -0.011418   \n",
      "\n",
      "     rsfmri_c_ngd_smm_ngd_vs  rsfmri_c_ngd_vta_ngd_vta  \\\n",
      "0                   0.187627                  0.156074   \n",
      "1                   0.131326                  0.207021   \n",
      "2                   0.012954                  0.233519   \n",
      "3                  -0.018897                  0.170449   \n",
      "4                  -0.031723                  0.224965   \n",
      "..                       ...                       ...   \n",
      "584                 0.091964                  0.291122   \n",
      "585                 0.247621                  0.264634   \n",
      "586                 0.063949                  0.195726   \n",
      "587                 0.082411                  0.249235   \n",
      "588                 0.119748                  0.152578   \n",
      "\n",
      "     rsfmri_c_ngd_vta_ngd_vs  rsfmri_c_ngd_vs_ngd_vs  \n",
      "0                  -0.037641                0.555925  \n",
      "1                  -0.033680                0.377985  \n",
      "2                  -0.071637                0.236013  \n",
      "3                  -0.111714                0.291132  \n",
      "4                  -0.082378                0.506870  \n",
      "..                       ...                     ...  \n",
      "584                -0.080669                0.515854  \n",
      "585                -0.162295                0.594432  \n",
      "586                -0.075519                0.407961  \n",
      "587                -0.137141                0.494742  \n",
      "588                -0.010881                0.392232  \n",
      "\n",
      "[589 rows x 92 columns]\n"
     ]
    }
   ],
   "source": [
    "# Remove rows with any NaN values\n",
    "sociocult_df_cleaned = sociocult_df_Nan_filtered.dropna()\n",
    "covariate_df_cleaned = covariate_df_Nan_filtered.dropna()\n",
    "rsfc_df_cleaned = rsfc_df_Nan_filtered.dropna()\n",
    "\n",
    "print(sociocult_df_cleaned)\n",
    "print(covariate_df_cleaned)\n",
    "print(rsfc_df_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'NDAR_INV6M1Y11R0', 'NDAR_INV64GKZ96V', 'NDAR_INVZR9NMJBR', 'NDAR_INVL49379MF', 'NDAR_INVH13A9F8Y', 'NDAR_INV753T05PF', 'NDAR_INV0G42TBW1', 'NDAR_INVAZPCL56V', 'NDAR_INVM91VXF6F', 'NDAR_INV0G2N59GL', 'NDAR_INVLEXJMGJ6', 'NDAR_INVP0T27M9X', 'NDAR_INVKEP4C8W8', 'NDAR_INVMT5RKZJE', 'NDAR_INVLTFB8A3W', 'NDAR_INV1FT79EHP', 'NDAR_INVD25VAT32', 'NDAR_INVLABCHP1W', 'NDAR_INV62G4MJ4G', 'NDAR_INV030W95VP', 'NDAR_INV4LPXB23R', 'NDAR_INVF20K5ZNE', 'NDAR_INVRW8117A7', 'NDAR_INVF76ZR3W2', 'NDAR_INV7CN47GEF', 'NDAR_INVU8VT1VB7', 'NDAR_INVRMNMGK77', 'NDAR_INVBX1Z1PJ9', 'NDAR_INV0DKWEM1A', 'NDAR_INV6JHNJJUB', 'NDAR_INVYWCJTCBW', 'NDAR_INV67G76XVR', 'NDAR_INVXDHB28GU', 'NDAR_INVB3L1EM9T', 'NDAR_INVNUWHUFKV', 'NDAR_INVYRYJZLMX', 'NDAR_INVHEABF6E2', 'NDAR_INVZYRTFYRP', 'NDAR_INV725NP8B2', 'NDAR_INVBR7WZCN7', 'NDAR_INVV82L1HKC', 'NDAR_INV1CMWHGWK', 'NDAR_INV6VXADERG', 'NDAR_INVDHCAPYWM', 'NDAR_INV5K5J2WLT', 'NDAR_INV2FV7FT02', 'NDAR_INV028D3ELL', 'NDAR_INV35UXPP43', 'NDAR_INVP5A0M52P', 'NDAR_INV6UH8VGPA', 'NDAR_INVNUAGE7UT', 'NDAR_INVMV3VPRF1', 'NDAR_INVHNPRTMWZ', 'NDAR_INV6EPGL57G', 'NDAR_INVRWKJCGLY', 'NDAR_INV6F8VJE7N', 'NDAR_INV2X7YEHPB', 'NDAR_INVA354YMUE', 'NDAR_INVK5PEZ25W', 'NDAR_INVGY65ZZBV', 'NDAR_INVNBMXW84N', 'NDAR_INV7WT9690H', 'NDAR_INVXGWM6XC8', 'NDAR_INV6NLC9UXB', 'NDAR_INVB1BC9HDJ', 'NDAR_INVHBJ3VGCL', 'NDAR_INVG7B119A0', 'NDAR_INV7JUL4TDF', 'NDAR_INV4UMAF27X', 'NDAR_INVFDM0PRRA', 'NDAR_INVM42YXRFM', 'NDAR_INVP4GPP1NB', 'NDAR_INVL8KH6W61', 'NDAR_INV1BGRYN2J', 'NDAR_INV8Z3G152U', 'NDAR_INVG269TZZX', 'NDAR_INVDGVADKJA', 'NDAR_INV50WZX903', 'NDAR_INV2AD1P5NV', 'NDAR_INVU9ATRA9G', 'NDAR_INVD61Y7K88', 'NDAR_INVG5V1RRFA', 'NDAR_INVBZZ8KWTC', 'NDAR_INVNU5J10B7', 'NDAR_INV22E7G6BN', 'NDAR_INV99YDLEBZ', 'NDAR_INVB5JG1BH6', 'NDAR_INVTAP6MEMP', 'NDAR_INVGBLP7T9X', 'NDAR_INVCK8WM58K', 'NDAR_INV654DRYXT', 'NDAR_INVFYRF6WEB', 'NDAR_INV85UUUHN0', 'NDAR_INV4PR3H6ZL', 'NDAR_INV6R35KCYL', 'NDAR_INV1R4G3839', 'NDAR_INVB2KKR9FP', 'NDAR_INVLJ8NB5W8', 'NDAR_INVJZRV5ANP', 'NDAR_INV6J8LBGU5', 'NDAR_INVMY85AAAM', 'NDAR_INV1B7G7LND', 'NDAR_INVVLGAGC69', 'NDAR_INVZP49GXF4', 'NDAR_INV3UPL5YML', 'NDAR_INV6YVYFKU7', 'NDAR_INVVXH1GGXB', 'NDAR_INVWMKYTKBW', 'NDAR_INV6ZU9NKBV', 'NDAR_INVCP2K1NFW', 'NDAR_INV0MPBK7TU', 'NDAR_INVMAZCGHX3', 'NDAR_INVXHEEJUKX', 'NDAR_INVZT1J0KUC', 'NDAR_INVY5AB6YNB', 'NDAR_INV5NPRNJAU', 'NDAR_INVJTHY8P5B', 'NDAR_INV6KM1LGCB', 'NDAR_INVYUKDPT70', 'NDAR_INV47W6DHJC', 'NDAR_INV4AGK770Z', 'NDAR_INVFZ4F6Y38', 'NDAR_INV7AWGW55P', 'NDAR_INV56L41G67', 'NDAR_INV5GUMR9J0', 'NDAR_INV8E3TY3RU', 'NDAR_INV3JUUH0FL', 'NDAR_INVH1CG52NU', 'NDAR_INV1VCFJ6CR', 'NDAR_INVC868NEEC', 'NDAR_INV5N40CFL4', 'NDAR_INV2B7X981Z', 'NDAR_INV2MH3HPHP', 'NDAR_INVV1F9DNZH', 'NDAR_INVT2JJ42KE', 'NDAR_INV273EJWMX', 'NDAR_INVA79CZENJ', 'NDAR_INVT7YJ84MG', 'NDAR_INVF9RGL53U', 'NDAR_INVK0BCW5KA', 'NDAR_INVT0JW7H6Z', 'NDAR_INV930295U4', 'NDAR_INV1R36G2UH', 'NDAR_INV6UWU0UNB', 'NDAR_INV114VT9DX', 'NDAR_INV0RHLKA9M', 'NDAR_INV9G2Y1NLA', 'NDAR_INV7BKAZZAM', 'NDAR_INVPATP4RPG', 'NDAR_INV1CC6HB4A', 'NDAR_INVH9ERT7NK', 'NDAR_INVWF9BVBHA', 'NDAR_INV43VJ1LGA', 'NDAR_INVBPPLUGKN', 'NDAR_INVZ49U1FM4', 'NDAR_INVK2KFJDG5', 'NDAR_INV69EDG4B6', 'NDAR_INVLCMDKYGP', 'NDAR_INVFCN9U0EN', 'NDAR_INV89DW5M7A', 'NDAR_INVBL9481WL', 'NDAR_INVRZL7PGK1', 'NDAR_INV0DC9BJZK', 'NDAR_INVPZDDVLGW', 'NDAR_INV97DU7DER', 'NDAR_INV145EPB4G', 'NDAR_INVEHLY59UX', 'NDAR_INVP9Y862GP', 'NDAR_INVNDF1FD44', 'NDAR_INVM22URD3V', 'NDAR_INVGR764V5U', 'NDAR_INVM007MAX7', 'NDAR_INV1PX9GGJP', 'NDAR_INVC624EF1K', 'NDAR_INVMBVX3LHF', 'NDAR_INVJJMH6ZDT', 'NDAR_INVU7WWPJ0M', 'NDAR_INV4JHN1XBR', 'NDAR_INVUTVE98D5', 'NDAR_INVAZ1GVXNZ', 'NDAR_INV0UERLJJY', 'NDAR_INVADAE4UFN', 'NDAR_INVZM2Y9JCA', 'NDAR_INV6TFE3J8G', 'NDAR_INV6JJFGCTX', 'NDAR_INVPDUZETD1', 'NDAR_INVWX4KD0B0', 'NDAR_INVDG2H0995', 'NDAR_INV6CG6LPAT', 'NDAR_INVKZDAJBD7', 'NDAR_INVMNXPRD46', 'NDAR_INV2CK4MB02', 'NDAR_INV5B5DML2V', 'NDAR_INVJDW2699M', 'NDAR_INVNTUN0CBE', 'NDAR_INV486GVBCD', 'NDAR_INVWTBPTVCP', 'NDAR_INVDDRLC2VM', 'NDAR_INVR5EGEA6F', 'NDAR_INVJNMHDYGC', 'NDAR_INVG9JGX9UJ', 'NDAR_INVMTYZ9AH4', 'NDAR_INVWT771AK6', 'NDAR_INVU3XLKGUY', 'NDAR_INV0W00RU2X', 'NDAR_INVUM3L54UF', 'NDAR_INVHKU16664', 'NDAR_INV53AA4BL6', 'NDAR_INV2XAHZZ2A', 'NDAR_INVJ8DDAHGM', 'NDAR_INV9RRGXBK5', 'NDAR_INV82N7RUVA', 'NDAR_INVW04H37XX', 'NDAR_INVXGFXPB14', 'NDAR_INVEDNJMEJ0', 'NDAR_INVAR03WFKR', 'NDAR_INVHPU5TKNF', 'NDAR_INVAYHAR3EY', 'NDAR_INVTGZMNV19', 'NDAR_INV95YMR61C', 'NDAR_INVGFDT73J0', 'NDAR_INV23E4LDJX', 'NDAR_INVCX7YWXVP', 'NDAR_INV2XGC3LN5', 'NDAR_INVXUPMEDT7', 'NDAR_INVUTNUNCNK', 'NDAR_INVURLHBMZV', 'NDAR_INVB1VMAP3Z', 'NDAR_INV4B0U03EB', 'NDAR_INVKH00C10R', 'NDAR_INVW26YNDL9', 'NDAR_INVPHL3D8GY', 'NDAR_INVV8VK6RGX', 'NDAR_INVDRM97DTU', 'NDAR_INVG8VWC1TN', 'NDAR_INVUJBHEFTN', 'NDAR_INVT77R03AY'}\n",
      "Number of common subject IDs: 237\n",
      "     macv_p_ss_fs  macv_p_ss_fo  macv_p_ss_fr  macv_y_ss_fs  macv_y_ss_fo  \\\n",
      "0        3.833333           3.2           3.6      3.333333           2.4   \n",
      "1        4.333333           4.6           3.2      4.000000           3.8   \n",
      "7        4.666667           3.6           3.0      3.666667           3.8   \n",
      "9        4.666667           3.6           3.8      4.000000           3.6   \n",
      "12       4.000000           3.2           3.0      3.666667           4.0   \n",
      "..            ...           ...           ...           ...           ...   \n",
      "580      4.166667           4.2           4.2      4.000000           3.6   \n",
      "582      5.000000           5.0           4.8      4.333333           4.8   \n",
      "584      3.500000           3.4           3.2      4.166667           4.2   \n",
      "585      5.000000           4.4           3.4      4.000000           3.8   \n",
      "587      5.000000           4.4           5.0      3.500000           3.6   \n",
      "\n",
      "     macv_y_ss_fr  reshist_addr1_coi_r_coi_nat  meim_p_ss_exp  meim_p_ss_com  \\\n",
      "0             2.8                         36.0       2.333333       3.333333   \n",
      "1             3.8                         61.0       1.000000       1.000000   \n",
      "7             3.8                          8.0       4.000000       5.000000   \n",
      "9             3.6                         45.0       2.333333       2.666667   \n",
      "12            3.2                         53.0       3.000000       4.000000   \n",
      "..            ...                          ...            ...            ...   \n",
      "580           3.8                         75.0       2.333333       5.000000   \n",
      "582           4.8                         63.0       3.000000       3.000000   \n",
      "584           3.8                         78.0       2.666667       2.666667   \n",
      "585           3.8                         62.0       4.000000       4.000000   \n",
      "587           3.4                         24.0       3.333333       3.666667   \n",
      "\n",
      "     reshist_addr1_nanda_disadv_fac  meim_ss_exp  meim_ss_com  \\\n",
      "0                          0.111666     4.000000     4.000000   \n",
      "1                          0.151376     2.666667     3.333333   \n",
      "7                          0.273194     3.333333     3.333333   \n",
      "9                          0.115622     3.333333     3.333333   \n",
      "12                         0.074931     3.333333     5.000000   \n",
      "..                              ...          ...          ...   \n",
      "580                        0.098939     4.000000     4.333333   \n",
      "582                        0.107454     4.333333     4.333333   \n",
      "584                        0.039215     3.000000     3.333333   \n",
      "585                        0.089180     4.000000     4.000000   \n",
      "587                        0.124226     4.333333     4.333333   \n",
      "\n",
      "     reshist_addr1_gstat_h_queen  dim_y_ss_mean  via_p_ss_hc  via_p_ss_amer  \\\n",
      "0                       0.807848       1.285714        6.375          7.750   \n",
      "1                      -0.342727       1.000000        6.125          9.000   \n",
      "7                       1.041787       1.142857        7.625          7.000   \n",
      "9                      -0.028830       1.000000        2.375          3.625   \n",
      "12                      3.131624       1.000000        6.500          5.250   \n",
      "..                           ...            ...          ...            ...   \n",
      "580                     3.412597       1.142857        7.250          7.125   \n",
      "582                     3.971429       1.428571        8.000          7.000   \n",
      "584                    -0.215563       1.000000        5.625          6.375   \n",
      "585                     3.587465       1.285714        5.875          4.875   \n",
      "587                     3.343483       1.000000        9.000          9.000   \n",
      "\n",
      "     via_ss_hc  via_ss_amer  comc_ss_cohesion_p  comc_ss_control_p  \n",
      "0        4.875        5.500                 3.8                3.6  \n",
      "1        6.125        6.000                 3.0                3.0  \n",
      "7        6.125        7.500                 3.4                3.4  \n",
      "9        4.750        4.625                 3.8                4.8  \n",
      "12       7.250        6.750                 3.4                4.2  \n",
      "..         ...          ...                 ...                ...  \n",
      "580      6.250        6.500                 5.0                4.8  \n",
      "582      8.750        8.125                 4.2                5.0  \n",
      "584      6.000        5.750                 4.6                4.6  \n",
      "585      7.750        7.750                 5.0                5.0  \n",
      "587      7.250        6.500                 3.6                4.4  \n",
      "\n",
      "[237 rows x 20 columns]\n",
      "       src_subject_id  interview_age site_id_l  demo_sex_v2  demo_prnt_age_v2  \\\n",
      "0    NDAR_INV028D3ELL          157.0    site01          2.0              40.0   \n",
      "1    NDAR_INV030W95VP          179.0    site19          2.0              30.0   \n",
      "7    NDAR_INV0DC9BJZK          182.0    site09          1.0              42.0   \n",
      "9    NDAR_INV0DKWEM1A          176.0    site02          1.0              38.0   \n",
      "12   NDAR_INV0G2N59GL          157.0    site10          2.0              38.0   \n",
      "..                ...            ...       ...          ...               ...   \n",
      "580  NDAR_INVZM2Y9JCA          168.0    site03          1.0              33.0   \n",
      "582  NDAR_INVZP49GXF4          153.0    site03          1.0              32.0   \n",
      "584  NDAR_INVZR9NMJBR          158.0    site02          1.0              42.0   \n",
      "585  NDAR_INVZT1J0KUC          168.0    site03          2.0              40.0   \n",
      "587  NDAR_INVZYRTFYRP          164.0    site10          1.0              49.0   \n",
      "\n",
      "     demo_prnt_gender_id_v2  demo_prnt_ed_v2_2yr_l  demo_prtnr_ed_v2_2yr_l  \\\n",
      "0                       2.0                   19.0                    18.0   \n",
      "1                       2.0                   13.0                    14.0   \n",
      "7                       1.0                   19.0                    10.0   \n",
      "9                       2.0                   23.0                    23.0   \n",
      "12                      2.0                   18.0                    18.0   \n",
      "..                      ...                    ...                     ...   \n",
      "580                     2.0                   19.0                    19.0   \n",
      "582                     2.0                   14.0                    13.0   \n",
      "584                     2.0                   18.0                    10.0   \n",
      "585                     2.0                   17.0                    17.0   \n",
      "587                     2.0                   16.0                     8.0   \n",
      "\n",
      "     demo_comb_income_v2  demo_prnt_income_v2_l  demo_origin_v2  \\\n",
      "0                    9.0                    9.0           189.0   \n",
      "1                    4.0                    5.0           189.0   \n",
      "7                    7.0                    5.0           189.0   \n",
      "9                    8.0                    6.0           189.0   \n",
      "12                   8.0                    7.0           189.0   \n",
      "..                   ...                    ...             ...   \n",
      "580                  5.0                   10.0           189.0   \n",
      "582                  5.0                    3.0            44.0   \n",
      "584                  8.0                    1.0           189.0   \n",
      "585                  6.0                    3.0           189.0   \n",
      "587                  6.0                    2.0           189.0   \n",
      "\n",
      "       mri_info_manufacturer  rsfmri_meanmotion  \n",
      "0    Philips Medical Systems           0.066058  \n",
      "1    Philips Medical Systems           0.155444  \n",
      "7                    SIEMENS           0.039169  \n",
      "9                    SIEMENS           0.140203  \n",
      "12        GE MEDICAL SYSTEMS           0.104586  \n",
      "..                       ...                ...  \n",
      "580                  SIEMENS           0.133359  \n",
      "582                  SIEMENS           0.107982  \n",
      "584                  SIEMENS           0.073203  \n",
      "585                  SIEMENS           0.041093  \n",
      "587       GE MEDICAL SYSTEMS           0.153301  \n",
      "\n",
      "[237 rows x 13 columns]\n",
      "     rsfmri_c_ngd_ad_ngd_ad  rsfmri_c_ngd_ad_ngd_cgc  rsfmri_c_ngd_ad_ngd_ca  \\\n",
      "0                  0.237271                 0.050704               -0.097674   \n",
      "1                  0.424266                 0.320737                0.125510   \n",
      "7                  0.261305                 0.121087               -0.042363   \n",
      "9                  0.269920                 0.148276               -0.005116   \n",
      "12                 0.355387                 0.188577               -0.122491   \n",
      "..                      ...                      ...                     ...   \n",
      "580                0.227564                 0.064915               -0.051506   \n",
      "582                0.277297                 0.159869                0.004499   \n",
      "584                0.224798                 0.130237               -0.053828   \n",
      "585                0.187419                 0.096684               -0.038299   \n",
      "587                0.231318                 0.133982               -0.059732   \n",
      "\n",
      "     rsfmri_c_ngd_ad_ngd_dt  rsfmri_c_ngd_ad_ngd_dla  rsfmri_c_ngd_ad_ngd_fo  \\\n",
      "0                 -0.066317                -0.020173               -0.080367   \n",
      "1                 -0.117286                 0.067547               -0.112132   \n",
      "7                 -0.084039                -0.076729               -0.096828   \n",
      "9                 -0.074434                 0.012974               -0.017872   \n",
      "12                -0.104594                -0.006964               -0.080065   \n",
      "..                      ...                      ...                     ...   \n",
      "580               -0.065964                 0.002798               -0.041336   \n",
      "582               -0.040502                -0.044507               -0.025343   \n",
      "584               -0.011020                -0.031037               -0.021513   \n",
      "585               -0.035381                 0.007394                0.005280   \n",
      "587               -0.001253                -0.004477               -0.029146   \n",
      "\n",
      "     rsfmri_c_ngd_ad_ngd_n  rsfmri_c_ngd_ad_ngd_rspltp  \\\n",
      "0                -0.017838                   -0.064739   \n",
      "1                 0.006792                    0.028030   \n",
      "7                 0.006874                   -0.009298   \n",
      "9                -0.010245                   -0.062839   \n",
      "12               -0.012657                   -0.005448   \n",
      "..                     ...                         ...   \n",
      "580               0.008236                    0.004348   \n",
      "582               0.003349                   -0.025186   \n",
      "584              -0.002398                   -0.077600   \n",
      "585              -0.005596                   -0.043423   \n",
      "587              -0.001025                    0.040491   \n",
      "\n",
      "     rsfmri_c_ngd_ad_ngd_sa  rsfmri_c_ngd_ad_ngd_smh  ...  \\\n",
      "0                 -0.092830                 0.186005  ...   \n",
      "1                  0.128417                 0.134004  ...   \n",
      "7                 -0.042871                 0.190731  ...   \n",
      "9                  0.008085                 0.093294  ...   \n",
      "12                 0.035477                 0.192790  ...   \n",
      "..                      ...                      ...  ...   \n",
      "580               -0.051343                 0.198616  ...   \n",
      "582                0.039535                 0.124384  ...   \n",
      "584               -0.036138                 0.108352  ...   \n",
      "585               -0.041989                 0.093601  ...   \n",
      "587                0.076065                 0.108119  ...   \n",
      "\n",
      "     rsfmri_c_ngd_smh_ngd_smh  rsfmri_c_ngd_smh_ngd_smm  \\\n",
      "0                    0.340691                  0.219597   \n",
      "1                    0.231254                  0.082087   \n",
      "7                    0.539698                  0.272221   \n",
      "9                    0.418728                  0.235362   \n",
      "12                   0.373671                  0.172014   \n",
      "..                        ...                       ...   \n",
      "580                  0.380891                  0.284173   \n",
      "582                  0.333172                  0.155052   \n",
      "584                  0.284258                  0.013019   \n",
      "585                  0.254235                  0.049280   \n",
      "587                  0.295387                  0.090491   \n",
      "\n",
      "     rsfmri_c_ngd_smh_ngd_vta  rsfmri_c_ngd_smh_ngd_vs  \\\n",
      "0                   -0.015784                 0.074834   \n",
      "1                   -0.043124                -0.003773   \n",
      "7                   -0.022411                 0.015080   \n",
      "9                   -0.020216                -0.039554   \n",
      "12                   0.080026                -0.016562   \n",
      "..                        ...                      ...   \n",
      "580                 -0.041324                 0.044926   \n",
      "582                  0.047045                -0.092238   \n",
      "584                  0.006484                -0.127016   \n",
      "585                 -0.011353                -0.061906   \n",
      "587                  0.043314                -0.144720   \n",
      "\n",
      "     rsfmri_c_ngd_smm_ngd_smm  rsfmri_c_ngd_smm_ngd_vta  \\\n",
      "0                    0.570781                  0.028428   \n",
      "1                    0.419668                  0.068082   \n",
      "7                    0.763860                  0.043816   \n",
      "9                    0.524231                 -0.009040   \n",
      "12                   0.654482                  0.132204   \n",
      "..                        ...                       ...   \n",
      "580                  0.626764                 -0.052651   \n",
      "582                  0.658899                  0.046049   \n",
      "584                  0.431063                  0.047040   \n",
      "585                  0.496075                 -0.073670   \n",
      "587                  0.484635                  0.026944   \n",
      "\n",
      "     rsfmri_c_ngd_smm_ngd_vs  rsfmri_c_ngd_vta_ngd_vta  \\\n",
      "0                   0.187627                  0.156074   \n",
      "1                   0.131326                  0.207021   \n",
      "7                   0.025647                  0.218380   \n",
      "9                  -0.005047                  0.156693   \n",
      "12                 -0.016446                  0.306943   \n",
      "..                       ...                       ...   \n",
      "580                 0.144667                  0.290985   \n",
      "582                 0.044293                  0.152802   \n",
      "584                 0.091964                  0.291122   \n",
      "585                 0.247621                  0.264634   \n",
      "587                 0.082411                  0.249235   \n",
      "\n",
      "     rsfmri_c_ngd_vta_ngd_vs  rsfmri_c_ngd_vs_ngd_vs  \n",
      "0                  -0.037641                0.555925  \n",
      "1                  -0.033680                0.377985  \n",
      "7                  -0.036514                0.256233  \n",
      "9                  -0.109029                0.362436  \n",
      "12                 -0.067291                0.303463  \n",
      "..                       ...                     ...  \n",
      "580                -0.163966                0.392957  \n",
      "582                -0.064930                0.440777  \n",
      "584                -0.080669                0.515854  \n",
      "585                -0.162295                0.594432  \n",
      "587                -0.137141                0.494742  \n",
      "\n",
      "[237 rows x 91 columns]\n"
     ]
    }
   ],
   "source": [
    "# Get the intersection of subject IDs in both DataFrames\n",
    "# Get the intersection of subject IDs in all three DataFrames\n",
    "common_ids = (\n",
    "    set(sociocult_df_cleaned[\"src_subject_id\"])\n",
    "    .intersection(set(covariate_df_cleaned[\"src_subject_id\"]))\n",
    "    .intersection(set(rsfc_df_cleaned[\"src_subject_id\"]))\n",
    ")\n",
    "print(common_ids)\n",
    "\n",
    "# Count the number of common IDs\n",
    "print(\"Number of common subject IDs:\", len(common_ids))\n",
    "\n",
    "# Filter both DataFrames to keep only the rows with common subject IDs\n",
    "sociocult_df_final = sociocult_df_cleaned[\n",
    "    sociocult_df_cleaned[\"src_subject_id\"].isin(common_ids)\n",
    "]\n",
    "sociocult_df_final = sociocult_df_final.drop(columns=[\"src_subject_id\"])\n",
    "\n",
    "covariate_df_final = covariate_df_cleaned[\n",
    "    covariate_df_cleaned[\"src_subject_id\"].isin(common_ids)\n",
    "]\n",
    "rsfc_df_final = rsfc_df_cleaned[rsfc_df_cleaned[\"src_subject_id\"].isin(common_ids)]\n",
    "# Drop the src_subject_id column from rsfc_df_final\n",
    "rsfc_df_final = rsfc_df_final.drop(columns=[\"src_subject_id\"])\n",
    "\n",
    "\n",
    "print(sociocult_df_final)\n",
    "print(covariate_df_final)\n",
    "print(rsfc_df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrames have been saved successfully.\n"
     ]
    }
   ],
   "source": [
    "# Define file paths for saving\n",
    "sociocult_save_path = os.path.join(deriv_dir, \"sociocult.csv\")\n",
    "covariate_save_path = os.path.join(deriv_dir, \"covariate.csv\")\n",
    "rsfc_save_path = os.path.join(deriv_dir, \"rsfc.csv\")\n",
    "\n",
    "# Save each DataFrame to a CSV file\n",
    "sociocult_df_final.to_csv(sociocult_save_path, index=False)\n",
    "covariate_df_final.to_csv(covariate_save_path, index=False)\n",
    "rsfc_df_final.to_csv(rsfc_save_path, index=False)\n",
    "\n",
    "print(\"DataFrames have been saved successfully.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here, I want to combine some measures for the reduced PLSC\n",
    "\n",
    "#### to get to 8 variables:\n",
    "##### meim_y = (meim_ss_exp + meim_ss_com) / 2\n",
    "##### via_y = (via_ss_hc + via_ss_amer) / 2\n",
    "##### macv_y = (macv_y_ss_fs + macv_y_ss_fo + macv_y_ss_fr) / 3\n",
    "##### meim_p = (meim_p_ss_exp + meim_p_ss_com) / 2\n",
    "##### via_p = (via_p_ss_hc + via_p_ss_amer) / 2\n",
    "##### macv_p = (macv_p_ss_fs + macv_p_ss_fo + macv_p_ss_fr) / 3\n",
    "##### dim_y_ss_mean  \n",
    "##### comc_p = (comc_ss_cohesion_p, comc_ss_control_p) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'# Create a new DataFrame with the calculated columns\\nnew_sociocult_df = pd.DataFrame()\\n\\n# Assign the calculated columns to the new DataFrame\\nnew_sociocult_df[\"meim_y\"] = (\\n    sociocult_df_final[\"meim_ss_exp\"] + sociocult_df_final[\"meim_ss_com\"]\\n) / 2\\nnew_sociocult_df[\"via_y\"] = (\\n    sociocult_df_final[\"via_ss_hc\"] + sociocult_df_final[\"via_ss_amer\"]\\n) / 2\\nnew_sociocult_df[\"macv_y\"] = (\\n    sociocult_df_final[\"macv_y_ss_fs\"]\\n    + sociocult_df_final[\"macv_y_ss_fo\"]\\n    + sociocult_df_final[\"macv_y_ss_fr\"]\\n) / 3\\nnew_sociocult_df[\"meim_p\"] = (\\n    sociocult_df_final[\"meim_p_ss_exp\"] + sociocult_df_final[\"meim_p_ss_com\"]\\n) / 2\\nnew_sociocult_df[\"via_p\"] = (\\n    sociocult_df_final[\"via_p_ss_hc\"] + sociocult_df_final[\"via_p_ss_amer\"]\\n) / 2\\nnew_sociocult_df[\"macv_p\"] = (\\n    sociocult_df_final[\"macv_p_ss_fs\"]\\n    + sociocult_df_final[\"macv_p_ss_fo\"]\\n    + sociocult_df_final[\"macv_p_ss_fr\"]\\n) / 3\\nnew_sociocult_df[\"dim_y_ss_mean\"] = sociocult_df_final[\\n    \"dim_y_ss_mean\"\\n]  # Assuming dim_y_ss_mean is already in your DataFrame\\nnew_sociocult_df[\"comc_p\"] = (\\n    sociocult_df_final[\"comc_ss_cohesion_p\"] + sociocult_df_final[\"comc_ss_control_p\"]\\n) / 2\\n\\n\\nprint(new_sociocult_df)\\n\\nsociocult_save_path = os.path.join(deriv_dir, \"sociocult_8.csv\")\\nnew_sociocult_df.to_csv(sociocult_save_path, index=False)'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''# Create a new DataFrame with the calculated columns\n",
    "new_sociocult_df = pd.DataFrame()\n",
    "\n",
    "# Assign the calculated columns to the new DataFrame\n",
    "new_sociocult_df[\"meim_y\"] = (\n",
    "    sociocult_df_final[\"meim_ss_exp\"] + sociocult_df_final[\"meim_ss_com\"]\n",
    ") / 2\n",
    "new_sociocult_df[\"via_y\"] = (\n",
    "    sociocult_df_final[\"via_ss_hc\"] + sociocult_df_final[\"via_ss_amer\"]\n",
    ") / 2\n",
    "new_sociocult_df[\"macv_y\"] = (\n",
    "    sociocult_df_final[\"macv_y_ss_fs\"]\n",
    "    + sociocult_df_final[\"macv_y_ss_fo\"]\n",
    "    + sociocult_df_final[\"macv_y_ss_fr\"]\n",
    ") / 3\n",
    "new_sociocult_df[\"meim_p\"] = (\n",
    "    sociocult_df_final[\"meim_p_ss_exp\"] + sociocult_df_final[\"meim_p_ss_com\"]\n",
    ") / 2\n",
    "new_sociocult_df[\"via_p\"] = (\n",
    "    sociocult_df_final[\"via_p_ss_hc\"] + sociocult_df_final[\"via_p_ss_amer\"]\n",
    ") / 2\n",
    "new_sociocult_df[\"macv_p\"] = (\n",
    "    sociocult_df_final[\"macv_p_ss_fs\"]\n",
    "    + sociocult_df_final[\"macv_p_ss_fo\"]\n",
    "    + sociocult_df_final[\"macv_p_ss_fr\"]\n",
    ") / 3\n",
    "new_sociocult_df[\"dim_y_ss_mean\"] = sociocult_df_final[\n",
    "    \"dim_y_ss_mean\"\n",
    "]  # Assuming dim_y_ss_mean is already in your DataFrame\n",
    "new_sociocult_df[\"comc_p\"] = (\n",
    "    sociocult_df_final[\"comc_ss_cohesion_p\"] + sociocult_df_final[\"comc_ss_control_p\"]\n",
    ") / 2\n",
    "\n",
    "\n",
    "print(new_sociocult_df)\n",
    "\n",
    "sociocult_save_path = os.path.join(deriv_dir, \"sociocult_8.csv\")\n",
    "new_sociocult_df.to_csv(sociocult_save_path, index=False)'''"
   ]
  }
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