10_NIST_stats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import dataframe_image as dfi\n",
    "import numpy as np\n",
    "\n",
    "from src.database import *\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('custom_matplotlibrc')\n",
    "plt.rcParams[\"figure.figsize\"] = (20,5)\n",
    "fig_id = 0\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.colheader_justify', 'center')\n",
    "pd.set_option('display.precision', 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "database_path = Path('databases/3T_NIST_T1maps_database.pkl')\n",
    "remove_outliers = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(database_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare stats table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    '1',\n",
    "    '2',\n",
    "    '3',\n",
    "    '4',\n",
    "    '5',\n",
    "    '6',\n",
    "    '7',\n",
    "    '8',\n",
    "    '9',\n",
    "    '10',\n",
    "    '11',\n",
    "    '12',\n",
    "    '13',\n",
    "    '14'\n",
    "]\n",
    "\n",
    "col_vals = [\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None,\n",
    "    None\n",
    "]\n",
    "\n",
    "df_setup = {\n",
    "    'all submissions mean T1': col_vals,\n",
    "    'inter-submission mean T1': col_vals,\n",
    "    'intra-submission mean T1': col_vals,\n",
    "    'all submissions T1 STD': col_vals,\n",
    "    'inter-submission T1 STD': col_vals,\n",
    "    'intra-submission T1 STD': col_vals,\n",
    "    'all submissions COV [%]': col_vals,\n",
    "    'inter-submission COV [%]': col_vals,\n",
    "    'intra-submission COV [%]': col_vals\n",
    "}\n",
    "\n",
    "stats_table = pd.DataFrame.from_dict(df_setup, orient='index', columns=columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All submissions (phantom version 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_1 = np.array([])\n",
    "estimate_2 = np.array([])\n",
    "estimate_3 = np.array([])\n",
    "estimate_4 = np.array([])\n",
    "estimate_5 = np.array([])\n",
    "estimate_6 = np.array([])\n",
    "estimate_7 = np.array([])\n",
    "estimate_8 = np.array([])\n",
    "estimate_9 = np.array([])\n",
    "estimate_10 = np.array([])\n",
    "estimate_11 = np.array([])\n",
    "estimate_12 = np.array([])\n",
    "estimate_13 = np.array([])\n",
    "estimate_14 = np.array([])\n",
    "\n",
    "\n",
    "ii = 0\n",
    "for index, row in df.iterrows():\n",
    "    if index == 13.001: # missing data\n",
    "        break\n",
    "    if remove_outliers and (index == 12.001 or index == 2.001):\n",
    "            pass\n",
    "    else:\n",
    "        if df.loc[index]['phantom serial number'] is None:\n",
    "            df.at[index, 'phantom serial number'] = 999  # Missing phantom number was version 2, see discussion here https://github.com/rrsg2020/data_submission/issues/25#issuecomment-620045155\n",
    "        if df.loc[index]['phantom serial number']<42: # version 1\n",
    "            pass\n",
    "        else:\n",
    "            estimate_1 = np.append(estimate_1, np.mean(df.loc[index]['T1 - NIST sphere 1']))\n",
    "            estimate_2 = np.append(estimate_2, np.mean(df.loc[index]['T1 - NIST sphere 2']))\n",
    "            estimate_3 = np.append(estimate_3, np.mean(df.loc[index]['T1 - NIST sphere 3']))\n",
    "            estimate_4 = np.append(estimate_4, np.mean(df.loc[index]['T1 - NIST sphere 4']))\n",
    "            estimate_5 = np.append(estimate_5, np.mean(df.loc[index]['T1 - NIST sphere 5']))\n",
    "            estimate_6 = np.append(estimate_6, np.mean(df.loc[index]['T1 - NIST sphere 6']))\n",
    "            estimate_7 = np.append(estimate_7, np.mean(df.loc[index]['T1 - NIST sphere 7']))\n",
    "            estimate_8 = np.append(estimate_8, np.mean(df.loc[index]['T1 - NIST sphere 8']))\n",
    "            estimate_9 = np.append(estimate_9, np.mean(df.loc[index]['T1 - NIST sphere 9']))\n",
    "            estimate_10 = np.append(estimate_10, np.mean(df.loc[index]['T1 - NIST sphere 10']))\n",
    "            estimate_11 = np.append(estimate_11, np.mean(df.loc[index]['T1 - NIST sphere 11']))\n",
    "            estimate_12 = np.append(estimate_12, np.mean(df.loc[index]['T1 - NIST sphere 12']))\n",
    "            estimate_13 = np.append(estimate_13, np.mean(df.loc[index]['T1 - NIST sphere 13']))\n",
    "            estimate_14 = np.append(estimate_14, np.mean(df.loc[index]['T1 - NIST sphere 14']))\n",
    "\n",
    "        ii = ii +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_table['1']['all submissions mean T1'] = np.nanmean(estimate_1)\n",
    "stats_table['1']['all submissions T1 STD'] = np.nanstd(estimate_1)\n",
    "stats_table['1']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_1),np.nanmean(estimate_1)) * 100\n",
    "\n",
    "stats_table['2']['all submissions mean T1'] = np.nanmean(estimate_2)\n",
    "stats_table['2']['all submissions T1 STD'] = np.nanstd(estimate_2)\n",
    "stats_table['2']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_2),np.nanmean(estimate_2)) * 100\n",
    "\n",
    "stats_table['3']['all submissions mean T1'] = np.nanmean(estimate_3)\n",
    "stats_table['3']['all submissions T1 STD'] = np.nanstd(estimate_3)\n",
    "stats_table['3']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_3),np.nanmean(estimate_3)) * 100\n",
    "\n",
    "stats_table['4']['all submissions mean T1'] = np.nanmean(estimate_4)\n",
    "stats_table['4']['all submissions T1 STD'] = np.nanstd(estimate_4)\n",
    "stats_table['4']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_4),np.nanmean(estimate_4)) * 100\n",
    "\n",
    "stats_table['5']['all submissions mean T1'] = np.nanmean(estimate_5)\n",
    "stats_table['5']['all submissions T1 STD'] = np.nanstd(estimate_5)\n",
    "stats_table['5']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_5),np.nanmean(estimate_5)) * 100\n",
    "\n",
    "stats_table['6']['all submissions mean T1'] = np.nanmean(estimate_6)\n",
    "stats_table['6']['all submissions T1 STD'] = np.nanstd(estimate_6)\n",
    "stats_table['6']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_6),np.nanmean(estimate_6)) * 100\n",
    "\n",
    "stats_table['7']['all submissions mean T1'] = np.nanmean(estimate_7)\n",
    "stats_table['7']['all submissions T1 STD'] = np.nanstd(estimate_7)\n",
    "stats_table['7']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_7),np.nanmean(estimate_7)) * 100\n",
    "\n",
    "stats_table['8']['all submissions mean T1'] = np.nanmean(estimate_8)\n",
    "stats_table['8']['all submissions T1 STD'] = np.nanstd(estimate_8)\n",
    "stats_table['8']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_8),np.nanmean(estimate_8)) * 100\n",
    "\n",
    "stats_table['9']['all submissions mean T1'] = np.nanmean(estimate_9)\n",
    "stats_table['9']['all submissions T1 STD'] = np.nanstd(estimate_9)\n",
    "stats_table['9']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_9),np.nanmean(estimate_9)) * 100\n",
    "\n",
    "stats_table['10']['all submissions mean T1'] = np.nanmean(estimate_10)\n",
    "stats_table['10']['all submissions T1 STD'] = np.nanstd(estimate_10)\n",
    "stats_table['10']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_10),np.nanmean(estimate_10)) * 100\n",
    "\n",
    "stats_table['11']['all submissions mean T1'] = np.nanmean(estimate_11)\n",
    "stats_table['11']['all submissions T1 STD'] = np.nanstd(estimate_11)\n",
    "stats_table['11']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_11),np.nanmean(estimate_11)) * 100\n",
    "\n",
    "stats_table['12']['all submissions mean T1'] = np.nanmean(estimate_12)\n",
    "stats_table['12']['all submissions T1 STD'] = np.nanstd(estimate_12)\n",
    "stats_table['12']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_12),np.nanmean(estimate_12)) * 100\n",
    "\n",
    "stats_table['13']['all submissions mean T1'] = np.nanmean(estimate_13)\n",
    "stats_table['13']['all submissions T1 STD'] = np.nanstd(estimate_13)\n",
    "stats_table['13']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_13),np.nanmean(estimate_13)) * 100\n",
    "\n",
    "stats_table['14']['all submissions mean T1'] = np.nanmean(estimate_14)\n",
    "stats_table['14']['all submissions T1 STD'] = np.nanstd(estimate_14)\n",
    "stats_table['14']['all submissions COV [%]'] = np.divide(np.nanstd(estimate_14),np.nanmean(estimate_14)) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inter-submision stats (Version 1)\n",
    "One subject/measurement per submission were selected. The protocol closest to the proposed one was selected, preferring complex data over magnitude-only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Selecting one subject/measurement per submission, protocol closest to proposed, complex if available.\n",
    "ids_intersubmissions = (\n",
    "    2.001,\n",
    "    3.001,\n",
    "    5.002,\n",
    "    6.002,\n",
    "    8.001,\n",
    "    12.001,\n",
    ")\n",
    "\n",
    "\n",
    "estimate_1 = np.array([])\n",
    "estimate_2 = np.array([])\n",
    "estimate_3 = np.array([])\n",
    "estimate_4 = np.array([])\n",
    "estimate_5 = np.array([])\n",
    "estimate_6 = np.array([])\n",
    "estimate_7 = np.array([])\n",
    "estimate_8 = np.array([])\n",
    "estimate_9 = np.array([])\n",
    "estimate_10 = np.array([])\n",
    "estimate_11 = np.array([])\n",
    "estimate_12 = np.array([])\n",
    "estimate_13 = np.array([])\n",
    "estimate_14 = np.array([])\n",
    "print(estimate_1.size)\n",
    "ii = 0\n",
    "for index in ids_intersubmissions:\n",
    "    if index == 13.001: # missing data\n",
    "        break\n",
    "    if remove_outliers and (index == 12.001 or index == 2.001):\n",
    "            pass\n",
    "    else:\n",
    "        if df.loc[index]['phantom serial number']<42: # version 1\n",
    "            pass\n",
    "        else:\n",
    "            if df.loc[index]['phantom serial number'] is None:\n",
    "                df.at[index, 'phantom serial number'] = 999  # Missing phantom number was version 2, see discussion here https://github.com/rrsg2020/data_submission/issues/25#issuecomment-620045155\n",
    "            estimate_1 = np.append(estimate_1, np.mean(df.loc[index]['T1 - NIST sphere 1']))\n",
    "            estimate_2 = np.append(estimate_2, np.mean(df.loc[index]['T1 - NIST sphere 2']))\n",
    "            estimate_3 = np.append(estimate_3, np.mean(df.loc[index]['T1 - NIST sphere 3']))\n",
    "            estimate_4 = np.append(estimate_4, np.mean(df.loc[index]['T1 - NIST sphere 4']))\n",
    "            estimate_5 = np.append(estimate_5, np.mean(df.loc[index]['T1 - NIST sphere 5']))\n",
    "            estimate_6 = np.append(estimate_6, np.mean(df.loc[index]['T1 - NIST sphere 6']))\n",
    "            estimate_7 = np.append(estimate_7, np.mean(df.loc[index]['T1 - NIST sphere 7']))\n",
    "            estimate_8 = np.append(estimate_8, np.mean(df.loc[index]['T1 - NIST sphere 8']))\n",
    "            estimate_9 = np.append(estimate_9, np.mean(df.loc[index]['T1 - NIST sphere 9']))\n",
    "            estimate_10 = np.append(estimate_10, np.mean(df.loc[index]['T1 - NIST sphere 10']))\n",
    "            estimate_11 = np.append(estimate_11, np.mean(df.loc[index]['T1 - NIST sphere 11']))\n",
    "            estimate_12 = np.append(estimate_12, np.mean(df.loc[index]['T1 - NIST sphere 12']))\n",
    "            estimate_13 = np.append(estimate_13, np.mean(df.loc[index]['T1 - NIST sphere 13']))\n",
    "            estimate_14 = np.append(estimate_14, np.mean(df.loc[index]['T1 - NIST sphere 14']))\n",
    "    \n",
    "        ii = ii +1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_table['1']['inter-submission mean T1'] = np.nanmean(estimate_1)\n",
    "stats_table['1']['inter-submission T1 STD'] = np.nanstd(estimate_1)\n",
    "stats_table['1']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_1),np.nanmean(estimate_1)) * 100\n",
    "\n",
    "stats_table['2']['inter-submission mean T1'] = np.nanmean(estimate_2)\n",
    "stats_table['2']['inter-submission T1 STD'] = np.nanstd(estimate_2)\n",
    "stats_table['2']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_2),np.nanmean(estimate_2)) * 100\n",
    "\n",
    "stats_table['3']['inter-submission mean T1'] = np.nanmean(estimate_3)\n",
    "stats_table['3']['inter-submission T1 STD'] = np.nanstd(estimate_3)\n",
    "stats_table['3']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_3),np.nanmean(estimate_3)) * 100\n",
    "\n",
    "stats_table['4']['inter-submission mean T1'] = np.nanmean(estimate_4)\n",
    "stats_table['4']['inter-submission T1 STD'] = np.nanstd(estimate_4)\n",
    "stats_table['4']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_4),np.nanmean(estimate_4)) * 100\n",
    "\n",
    "stats_table['5']['inter-submission mean T1'] = np.nanmean(estimate_5)\n",
    "stats_table['5']['inter-submission T1 STD'] = np.nanstd(estimate_5)\n",
    "stats_table['5']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_5),np.nanmean(estimate_5)) * 100\n",
    "\n",
    "stats_table['6']['inter-submission mean T1'] = np.nanmean(estimate_6)\n",
    "stats_table['6']['inter-submission T1 STD'] = np.nanstd(estimate_6)\n",
    "stats_table['6']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_6),np.nanmean(estimate_6)) * 100\n",
    "\n",
    "stats_table['7']['inter-submission mean T1'] = np.nanmean(estimate_7)\n",
    "stats_table['7']['inter-submission T1 STD'] = np.nanstd(estimate_7)\n",
    "stats_table['7']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_7),np.nanmean(estimate_7)) * 100\n",
    "\n",
    "stats_table['8']['inter-submission mean T1'] = np.nanmean(estimate_8)\n",
    "stats_table['8']['inter-submission T1 STD'] = np.nanstd(estimate_8)\n",
    "stats_table['8']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_8),np.nanmean(estimate_8)) * 100\n",
    "\n",
    "stats_table['9']['inter-submission mean T1'] = np.nanmean(estimate_9)\n",
    "stats_table['9']['inter-submission T1 STD'] = np.nanstd(estimate_9)\n",
    "stats_table['9']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_9),np.nanmean(estimate_9)) * 100\n",
    "\n",
    "stats_table['10']['inter-submission mean T1'] = np.nanmean(estimate_10)\n",
    "stats_table['10']['inter-submission T1 STD'] = np.nanstd(estimate_10)\n",
    "stats_table['10']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_10),np.nanmean(estimate_10)) * 100\n",
    "\n",
    "stats_table['11']['inter-submission mean T1'] = np.nanmean(estimate_11)\n",
    "stats_table['11']['inter-submission T1 STD'] = np.nanstd(estimate_11)\n",
    "stats_table['11']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_11),np.nanmean(estimate_11)) * 100\n",
    "\n",
    "stats_table['12']['inter-submission mean T1'] = np.nanmean(estimate_12)\n",
    "stats_table['12']['inter-submission T1 STD'] = np.nanstd(estimate_12)\n",
    "stats_table['12']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_12),np.nanmean(estimate_12)) * 100\n",
    "\n",
    "stats_table['13']['inter-submission mean T1'] = np.nanmean(estimate_13)\n",
    "stats_table['13']['inter-submission T1 STD'] = np.nanstd(estimate_13)\n",
    "stats_table['13']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_13),np.nanmean(estimate_13)) * 100\n",
    "\n",
    "stats_table['14']['inter-submission mean T1'] = np.nanmean(estimate_14)\n",
    "stats_table['14']['inter-submission T1 STD'] = np.nanstd(estimate_14)\n",
    "stats_table['14']['inter-submission COV [%]'] = np.divide(np.nanstd(estimate_14),np.nanmean(estimate_14)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_1_inter = estimate_1\n",
    "estimate_2_inter = estimate_2\n",
    "estimate_3_inter = estimate_3\n",
    "estimate_4_inter = estimate_4\n",
    "estimate_5_inter = estimate_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intra-submision stats\n",
    "All unique T1 maps from Site 6 were used for the stats calculations. One single vendor, 7 sites. Complex data selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting one submission\n",
    "ids_intrasubmissions = (\n",
    "6.002,\n",
    "6.004,\n",
    "6.006,\n",
    "6.008,\n",
    "6.010,\n",
    "6.012,\n",
    "6.014,\n",
    ")\n",
    "\n",
    "\n",
    "estimate_1 = np.array([])\n",
    "estimate_2 = np.array([])\n",
    "estimate_3 = np.array([])\n",
    "estimate_4 = np.array([])\n",
    "estimate_5 = np.array([])\n",
    "estimate_6 = np.array([])\n",
    "estimate_7 = np.array([])\n",
    "estimate_8 = np.array([])\n",
    "estimate_9 = np.array([])\n",
    "estimate_10 = np.array([])\n",
    "estimate_11 = np.array([])\n",
    "estimate_12 = np.array([])\n",
    "estimate_13 = np.array([])\n",
    "estimate_14 = np.array([])\n",
    "\n",
    "ii = 0\n",
    "for index in ids_intrasubmissions:\n",
    "    \n",
    "    if index == 13.001: # missing data\n",
    "        break\n",
    "    if remove_outliers and (index == 12.001 or index == 2.001):\n",
    "            pass\n",
    "    else:\n",
    "        estimate_1 = np.append(estimate_1, np.mean(df.loc[index]['T1 - NIST sphere 1']))\n",
    "        estimate_2 = np.append(estimate_2, np.mean(df.loc[index]['T1 - NIST sphere 2']))\n",
    "        estimate_3 = np.append(estimate_3, np.mean(df.loc[index]['T1 - NIST sphere 3']))\n",
    "        estimate_4 = np.append(estimate_4, np.mean(df.loc[index]['T1 - NIST sphere 4']))\n",
    "        estimate_5 = np.append(estimate_5, np.mean(df.loc[index]['T1 - NIST sphere 5']))\n",
    "        estimate_6 = np.append(estimate_6, np.mean(df.loc[index]['T1 - NIST sphere 6']))\n",
    "        estimate_7 = np.append(estimate_7, np.mean(df.loc[index]['T1 - NIST sphere 7']))\n",
    "        estimate_8 = np.append(estimate_8, np.mean(df.loc[index]['T1 - NIST sphere 8']))\n",
    "        estimate_9 = np.append(estimate_9, np.mean(df.loc[index]['T1 - NIST sphere 9']))\n",
    "        estimate_10 = np.append(estimate_10, np.mean(df.loc[index]['T1 - NIST sphere 10']))\n",
    "        estimate_11 = np.append(estimate_11, np.mean(df.loc[index]['T1 - NIST sphere 11']))\n",
    "        estimate_12 = np.append(estimate_12, np.mean(df.loc[index]['T1 - NIST sphere 12']))\n",
    "        estimate_13 = np.append(estimate_13, np.mean(df.loc[index]['T1 - NIST sphere 13']))\n",
    "        estimate_14 = np.append(estimate_14, np.mean(df.loc[index]['T1 - NIST sphere 14']))\n",
    "\n",
    "        ii = ii +1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_table['1']['intra-submission mean T1'] = np.nanmean(estimate_1)\n",
    "stats_table['1']['intra-submission T1 STD'] = np.nanstd(estimate_1)\n",
    "stats_table['1']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_1),np.nanmean(estimate_1)) * 100\n",
    "\n",
    "stats_table['2']['intra-submission mean T1'] = np.nanmean(estimate_2)\n",
    "stats_table['2']['intra-submission T1 STD'] = np.nanstd(estimate_2)\n",
    "stats_table['2']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_2),np.nanmean(estimate_2)) * 100\n",
    "\n",
    "stats_table['3']['intra-submission mean T1'] = np.nanmean(estimate_3)\n",
    "stats_table['3']['intra-submission T1 STD'] = np.nanstd(estimate_3)\n",
    "stats_table['3']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_3),np.nanmean(estimate_3)) * 100\n",
    "\n",
    "stats_table['4']['intra-submission mean T1'] = np.nanmean(estimate_4)\n",
    "stats_table['4']['intra-submission T1 STD'] = np.nanstd(estimate_4)\n",
    "stats_table['4']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_4),np.nanmean(estimate_4)) * 100\n",
    "\n",
    "stats_table['5']['intra-submission mean T1'] = np.nanmean(estimate_5)\n",
    "stats_table['5']['intra-submission T1 STD'] = np.nanstd(estimate_5)\n",
    "stats_table['5']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_5),np.nanmean(estimate_5)) * 100\n",
    "\n",
    "stats_table['6']['intra-submission mean T1'] = np.nanmean(estimate_6)\n",
    "stats_table['6']['intra-submission T1 STD'] = np.nanstd(estimate_6)\n",
    "stats_table['6']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_6),np.nanmean(estimate_6)) * 100\n",
    "\n",
    "stats_table['7']['intra-submission mean T1'] = np.nanmean(estimate_7)\n",
    "stats_table['7']['intra-submission T1 STD'] = np.nanstd(estimate_7)\n",
    "stats_table['7']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_7),np.nanmean(estimate_7)) * 100\n",
    "\n",
    "stats_table['8']['intra-submission mean T1'] = np.nanmean(estimate_8)\n",
    "stats_table['8']['intra-submission T1 STD'] = np.nanstd(estimate_8)\n",
    "stats_table['8']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_8),np.nanmean(estimate_8)) * 100\n",
    "\n",
    "stats_table['9']['intra-submission mean T1'] = np.nanmean(estimate_9)\n",
    "stats_table['9']['intra-submission T1 STD'] = np.nanstd(estimate_9)\n",
    "stats_table['9']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_9),np.nanmean(estimate_9)) * 100\n",
    "\n",
    "stats_table['10']['intra-submission mean T1'] = np.nanmean(estimate_10)\n",
    "stats_table['10']['intra-submission T1 STD'] = np.nanstd(estimate_10)\n",
    "stats_table['10']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_10),np.nanmean(estimate_10)) * 100\n",
    "\n",
    "stats_table['11']['intra-submission mean T1'] = np.nanmean(estimate_11)\n",
    "stats_table['11']['intra-submission T1 STD'] = np.nanstd(estimate_11)\n",
    "stats_table['11']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_11),np.nanmean(estimate_11)) * 100\n",
    "\n",
    "stats_table['12']['intra-submission mean T1'] = np.nanmean(estimate_12)\n",
    "stats_table['12']['intra-submission T1 STD'] = np.nanstd(estimate_12)\n",
    "stats_table['12']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_12),np.nanmean(estimate_12)) * 100\n",
    "\n",
    "stats_table['13']['intra-submission mean T1'] = np.nanmean(estimate_13)\n",
    "stats_table['13']['intra-submission T1 STD'] = np.nanstd(estimate_13)\n",
    "stats_table['13']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_13),np.nanmean(estimate_13)) * 100\n",
    "\n",
    "stats_table['14']['intra-submission mean T1'] = np.nanmean(estimate_14)\n",
    "stats_table['14']['intra-submission T1 STD'] = np.nanstd(estimate_14)\n",
    "stats_table['14']['intra-submission COV [%]'] = np.divide(np.nanstd(estimate_14),np.nanmean(estimate_14)) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: center;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all submissions mean T1</th>\n",
       "      <td>1925.0</td>\n",
       "      <td>1359.5</td>\n",
       "      <td>949.3</td>\n",
       "      <td>669.9</td>\n",
       "      <td>481.9</td>\n",
       "      <td>343.5</td>\n",
       "      <td>242.7</td>\n",
       "      <td>204.8</td>\n",
       "      <td>142.1</td>\n",
       "      <td>95.6</td>\n",
       "      <td>64.5</td>\n",
       "      <td>70.6</td>\n",
       "      <td>74.6</td>\n",
       "      <td>118.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter-submission mean T1</th>\n",
       "      <td>1953.7</td>\n",
       "      <td>1383.1</td>\n",
       "      <td>936.9</td>\n",
       "      <td>641.5</td>\n",
       "      <td>476.6</td>\n",
       "      <td>341.7</td>\n",
       "      <td>244.0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>149.5</td>\n",
       "      <td>117.8</td>\n",
       "      <td>99.1</td>\n",
       "      <td>137.5</td>\n",
       "      <td>130.3</td>\n",
       "      <td>208.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intra-submission mean T1</th>\n",
       "      <td>1960.3</td>\n",
       "      <td>1420.8</td>\n",
       "      <td>962.3</td>\n",
       "      <td>696.7</td>\n",
       "      <td>487.1</td>\n",
       "      <td>312.7</td>\n",
       "      <td>243.1</td>\n",
       "      <td>163.2</td>\n",
       "      <td>120.2</td>\n",
       "      <td>69.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>33.4</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all submissions T1 STD</th>\n",
       "      <td>122.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>45.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>57.7</td>\n",
       "      <td>26.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>34.8</td>\n",
       "      <td>53.2</td>\n",
       "      <td>54.7</td>\n",
       "      <td>98.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter-submission T1 STD</th>\n",
       "      <td>91.7</td>\n",
       "      <td>42.7</td>\n",
       "      <td>59.1</td>\n",
       "      <td>82.1</td>\n",
       "      <td>34.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>30.1</td>\n",
       "      <td>36.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>55.3</td>\n",
       "      <td>57.4</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intra-submission T1 STD</th>\n",
       "      <td>96.2</td>\n",
       "      <td>49.4</td>\n",
       "      <td>24.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all submissions COV [%]</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>28.2</td>\n",
       "      <td>18.6</td>\n",
       "      <td>22.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>75.4</td>\n",
       "      <td>73.4</td>\n",
       "      <td>83.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter-submission COV [%]</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>16.3</td>\n",
       "      <td>24.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.2</td>\n",
       "      <td>44.1</td>\n",
       "      <td>43.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intra-submission COV [%]</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>24.5</td>\n",
       "      <td>54.2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>53.4</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            1       2      3      4      5      6      7      8      9      10     11    12     13     14  \n",
       "all submissions mean T1   1925.0  1359.5  949.3  669.9  481.9  343.5  242.7  204.8  142.1   95.6  64.5   70.6   74.6  118.9\n",
       "inter-submission mean T1  1953.7  1383.1  936.9  641.5  476.6  341.7  244.0  184.5  149.5  117.8  99.1  137.5  130.3  208.3\n",
       "intra-submission mean T1  1960.3  1420.8  962.3  696.7  487.1  312.7  243.1  163.2  120.2   69.4  27.0   25.7   33.4   58.7\n",
       "all submissions T1 STD     122.3    49.0   38.5   45.9   18.5    9.8    8.1   57.7   26.4   21.0  34.8   53.2   54.7   98.9\n",
       "inter-submission T1 STD     91.7    42.7   59.1   82.1   34.9   18.0   16.5   30.1   36.8   19.0  30.8   55.3   57.4   91.5\n",
       "intra-submission T1 STD     96.2    49.4   24.8   14.3    7.7    3.8    3.2    3.3    3.1   17.0  14.6   16.0   17.9   35.0\n",
       "all submissions COV [%]      6.4     3.6    4.1    6.9    3.8    2.8    3.3   28.2   18.6   22.0  54.0   75.4   73.4   83.2\n",
       "inter-submission COV [%]     4.7     3.1    6.3   12.8    7.3    5.3    6.8   16.3   24.6   16.1  31.0   40.2   44.1   43.9\n",
       "intra-submission COV [%]     4.9     3.5    2.6    2.0    1.6    1.2    1.3    2.0    2.6   24.5  54.2   62.0   53.4   59.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(stats_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.943727894663505\n",
      "6.096387617633078\n",
      "2.919468062099153\n"
     ]
    }
   ],
   "source": [
    "all_sub_mean_COV = [\n",
    "    stats_table['1']['all submissions COV [%]'],\n",
    "    stats_table['2']['all submissions COV [%]'],\n",
    "    stats_table['3']['all submissions COV [%]'],\n",
    "    stats_table['4']['all submissions COV [%]'],\n",
    "    stats_table['5']['all submissions COV [%]']\n",
    "]\n",
    "print(np.mean(all_sub_mean_COV))\n",
    "\n",
    "inter_sub_mean_COV = [\n",
    "    stats_table['1']['inter-submission COV [%]'],\n",
    "    stats_table['2']['inter-submission COV [%]'],\n",
    "    stats_table['3']['intra-submission COV [%]'],\n",
    "    stats_table['4']['inter-submission COV [%]'],\n",
    "    stats_table['5']['inter-submission COV [%]']\n",
    "]\n",
    "print(np.mean(inter_sub_mean_COV))\n",
    "\n",
    "intra_sub_mean_COV = [\n",
    "    stats_table['1']['intra-submission COV [%]'],\n",
    "    stats_table['2']['intra-submission COV [%]'],\n",
    "    stats_table['3']['intra-submission COV [%]'],\n",
    "    stats_table['4']['intra-submission COV [%]'],\n",
    "    stats_table['5']['intra-submission COV [%]']\n",
    "]\n",
    "print(np.mean(intra_sub_mean_COV))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_1_intra = estimate_1\n",
    "estimate_2_intra = estimate_2\n",
    "estimate_3_intra = estimate_3\n",
    "estimate_4_intra = estimate_4\n",
    "estimate_5_intra = estimate_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[1127/120717.955615:WARNING:angle_platform_impl.cc(49)] RendererVk.cpp:1962 (initialize): ANGLE VMA version: 2003000\n",
      "88722 bytes written to file /var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/tmpw7tgjw7k/temp.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/Users/mathieuboudreau/neuropoly/github/analysis/plots/10_nist_stats/nist_stats.png')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd = os.getcwd()\n",
    "folder_path = Path(\"plots/10_nist_stats/\")\n",
    "dfi.export(stats_table, 'nist_stats.png')\n",
    "\n",
    "shutil.move('nist_stats.png', Path(pwd) / folder_path / 'nist_stats.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l1/1xswjffd73l8yp7dd7pq9lyw0000gn/T/ipykernel_58494/1943462326.py:63: FutureWarning: \n",
      "\n",
      "The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='count'` for the same effect.\n",
      "  ax = sns.violinplot(x='', y='T1 (ms)', hue='Type', data=df, split=True, inner=\"quartile\", scale=\"count\", palette=\"Set2\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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5zW8YNWrUMUeH1NbW8t577/H++++zY8cOPB4PMTExXH755dx99920b9/+uGtRwCAiIm63m6+//pqpU6dSXV2N0RqCo3MGoR3TMVqOaxbF00IBw5nD7/ezpqSeKdsqqWr0kpmZyZ133klqamqwSxMREREREZGTpIBBjpsCBhGR85ff72fJkiV8/PHHlJSUYLSYCe2SiaNTOsaQMydYOEABw5mnzuPj020VLN1fR4jZzE9vvJFhw4Ydcc0qEREREREROfOdyD3joN1F+Oabb1i9ejVer5cLL7yQXr16BasUERGR80Z+fj7vvvsu+fn5GExGHF0ycXbNPCOmQpKzR6jZyI0doukRa+fjrRVMmjSJNWvWcOeddxIRERHs8kREREREROQ0abWAoaqq6rD7w8PDm/1eVlbGT37yExYsWNBs/w033MA777yD1WptrZJERETkOzU1NXz88cfMnTsXv9+PLSOJsAs6YHbag12anMW6RNt56AILH20tZ+3atTzy8MP8329/S9euXYNdmoiIiIiIiJwGrRYwREZGNltDIDk5mWuvvZZXXnklsM/r9TJq1KjDDqn45JNPqK+v53//+19rlSQiInLe8/v9LF26lHfffZeqqirMUWFE9OuCJT4q2KXJOcJpMXF7lxgWFNTw2fYqXnjhBa6//npGjx6tKZNERERERETOca06RZLf7ycpKYmXXnqJMWPGYDKZmh3/85//zJIlSzAYDPj9fvr3789Pf/pTKioqeOedd/jss8/473//y09+8pPWLEtEROS8VFlZyYQJE1i+fDkGk4mw3h1xdErHoJu+0soMBgMXpYSRGW7lnY2l/Pvf/2bbtm3ceeed2O0aJSMiIiIiInKuatWAISkpiRUrVpCUlHTIsZ07d/L8888HRjnceOONvP/++4Hf7777bvr27ctbb72lgEFEROQkrVy5krfeeouqqiosCdFEDOyKOSw02GWdsL01bp5dvg+Aa7MiyY6x81Lufuo9vkCb4Wnh9E1w8I+1xZQ1eAL7hyY7uSgljHc2lrKnpjGwv2+8g+Hp4Xy8tZytFa7A/uxoO9e2jeSz7RWsKakP7M8Kt/LzjtHM2l1FTmFtYH9SaAi3Z8eyaF8Nc/ZUB/ZHWEz8rkc8q4rrmL6jMrA/xGjgkd6JbCp3MTmvvNl1Pto7kYJaN+9tKm22//c94mnw+vnn+uJm++/IjsUZYuTl1UXN9t/UMZr0MAvPryhstv8nbSPpEm3nxVX7cXm/f+2uTA+nT/yhr93FKWFcmOxkwrel7K39/rXrl+BgWFo4H20pI6+yIbC/a7SdBy5I4KXc/axYsYI77riDV199lagojZgRERERERE5F7XqVxgffvjhw4YLAPfffz8ulwu/309iYiJvvvlmsymVoqOjeeSRR1ixYkVrliQiInJeaWho4O233+avf/0r1bU1hPfpRPTlfc/qcEHOLo4QIz1i7VhNBrxeL08//UdKSkqCXZaIiIiIiIicAga/3+9vjY6MRiPLli2jT58+hxybPXs2l19+eSBQGDduHHfeeech7VasWEH//v3xer2tUZIcxcCBA8nJyWm2b8CAAYddH0NERM4Ou3bt4rXXXmPv3r2Yo8KIHNKDkEhnsMs6afsmzSAtLIT7eiYEuxRpAb/fz6zd1Xyxs4qkpCSeeeYZHA5HsMsSERERERGRIziRe8atNoLBYrE0G5FwgNfr5fe//z3Q9EEzMzOTO+644/DFaE5oERGRFvP5fHz55Zf84fHH2bt3L47OGcReOfCcCBfk7GUwGLgiLZwfpYaxb98+3n333WCXJCIiIiIiIq2s1e7oJyUlsWrVqkP2/+1vf+Pbb78Fmj5o/vnPfz5k8ecD1qxZ01rliIiInBdKS0v585//zKRJk/CbTUT/qDfhfTphMCm0lzPDiIxw0sMsLFq0iL179wa7HBEREREREWlFrXb3YfDgwTz//PPk5+cH9r3//vs8/vjjGAwGDAYDV155Jdddd91hz/f5fLzxxhutVY6IiMg5zefzMWfOHB586CHWr1+PNTWe2FGDsSbHBbs0kWaMBgNDkpqmRjrwpRMRERERERE5N5hbq6O7776bjz76iOzsbDp37kxVVRU7duwAmqZGSktLO+LQeK/Xy5133smKFSsOO82SiIiIfG/37t1MmDCBzZs3Y7SEEDGoG/asZP0NlTNWo69pyS9NhykiIiIiInJuabWAoX///vzxj3/kqaeeOmSqo8zMTGbOnElsbGyz/S6XiylTpvCnP/2JjRs3tlYpIiIi56Sqqir++9//Mnv2bHw+H7b0RML7dMIUagt2aSJHVFLvYeauakLMZnr27BnsckRERERERKQVtVrAAPDEE0/Qr18/3nrrLTZu3IjNZmPkyJHcf//9hIeHH9L+4osvpra2FoPBQJcuXVqzFBERkXNGXV0dX375JdOnT8flcmGOcBDZp9N5NR3S3ho3++vcrC91sbiwhq7Rdq5MD+fF3P0A/K57PIv21bCyuI7ecaEMTnby6poiAB68IIEvd1axvqyeQYlOusXYeHNDCQCP90nkk7wKtla4uCQljPRwC+9uLMViNPBw70Te21TKrupGhqWFE2Mz8+GWMsJCTNzTM563NpRQWOfmqsxIzEb4b34FsTYzd3aLY9zaYsobPIxpF0Wtx8f0HZWkOEK4tUssf83dT53Hx00do9lf52HW7ioyw63c1DGaP60oxOv3c1uXWLZWuFhQUEOnKBvXZEXywspCAO7qFsfKojqW7q+lR6ydH6WG8fLqpmu9t2c8c/fUsLqkjn4JDvrEh/L6umIAHu2dyNRtFWwsd3FhspOOkTb+9W0JRoOBP/RJ5IPNZWyrauCyNuEkhZqZtLkMu9nIAxckMOHbUvbWNjIiIwKH2ci/88qJtJq4u3s8b6wrpsTl4dq2kXh98On2ChJCQ/h1lxhyS+r5T34FdW4fv/zlL4mJiTnd/+mIiIiIiIjIKdSqAQPAsGHDGDZs2HG1zcnJae2nFxEROWdUVlYyc+ZMZs6cSX19PSa7lfB+XQhtn4pBU83IGcrvh+pGL6+sKWZHdSMhISHcccftXHzxxcEuTURERERERFqZwe/3+4NdhJx+AwcOPCTgGTBgAEuWLAlSRSIicsDu3buZMWMGCxcuxOPxYLRZcXTJwNExDYPZFOzyTrt9k2aQFhbCfT0Tgl2KHEVJvYdVxXUs3V9HqcsDQL9+/fjZz35GfHx8kKsTERERERGRYzmRe8atPoLhZGzcuJERI0awbdu2YJciIiJyWjU2NrJ8+XJmz57Npk2bADCFhRLeuQOhbVPOy2BBzmx+v599301btb60nl01bgCsViuXXjqU4cOHk5qaGuQqRURERERE5FQ6owKG/Px8du7cGewyRERETgu/309+fj4LFy5k0eLF1NXWAmBNjiW0YxrWlDgMBkOQqxT5XrnLQ15lA1sqGthc0UBVoxcAk8lEjx49GDRoEH369MFutwe5UhERERERETkdzoiAwefzsX37dl566aVglyIiInLKFRQUsHjxYhYvXkxhYdPCvSa7FWe3ttjbpmAOCw1yhSLg8/vZX+dhW1UD26sa2V7VQKnLGzgeGRHBkH7d6NWrF927dyc0VP/dioiIiIiInG9aPWDYvHkzEydO5JtvviEvL4+KigpcLtdxnev3+/VNTREROSft27ePpUuXkpOTw65duwAwmE3YM5OxZyVjSYzBYNTfQAkOv99PRYOX3TVudlY3squ6kV01bhq8vkAbp9NJ376d6dy5M126dKFNmzZ63yYiIiIiInKea9WA4Q9/+ANjx47F52v6MKr1o0VE5Hzl9/vZtWsXy5cvZ9myZezZswcAg8mINTUee2YS1pQ4jCFnxGBCOY/4/H5KXV721jSyt9bN7ppG9tS4qXF/HyYYDAZSUlJo164dHTp0oEOHDiQlJSlQEBERERERkWZa7a7GhAkTeOGFF4CmD6UKF0RE5Hzj8XjYvHkzK1euZMXKFZQUlwBgMJmwtonHnp6INSUeo0WhgpweDV4f+2rd7K11UxB4eJqNTABIiI+na1YWmZmZZGVlkZWVpXUURERERERE5Jha7Q7HP/7xD6ApXLBYLFx88cV07tyZsLAwjEbjUc91uVxs3LiRadOmKZgQEZGzSl1dHWvXrmXFihWsXr2auro6AIyWEOyZyVjT4rEmxWqkgpxSXr+f4noP+2rdTY+6pjDh4DUTAExGI8kpKWRkZJCenh746XA4glS5iIiIiIiInM1a7W7Hpk2bMBgMdOjQga+++oo2bdq0uI+HH35YCz2LiMgZ7+6776ahoYGkpCTy8vIC4bjRYcMQYsZgNhEzvD/1eXupXrkZT2Y1oR3aUDpzKQCxIwZTs2Yrrj1FhHZog61NAmWzVwAQd/VQKnM20FhYiqNLJpbYCMoXrMZgNhE3agjlC1fjLqnE2b0dJqedysXrMNqtxA4fQNmclXgqawjv3QlMRqqWfYspLJSYy/pS+tUyvLX1RPTPxtfopjp3C+aocKIvvoCS6YvxNbqJHNIdT0UtNevzscRHETm4O0WfLgSfj6iLe9FYWErtpp1Yk2MJ79OZ4s8WAhB9eV/qt+2jPn8PtvREnNmZlHyxpOlarxxIzbfbce0sxN42BXtWMmWzljdd6+gLqVqxiYaCYhyd0rEkxlA+bxUYDcRfNZSKRWtpLCo/3f+8ZzSf30+Zy0thXVOIUFjrZl+dh/11Hrw/+JJGZEQE3dqnkZaWRps2bUhPTyclJQWzWWGXiIiIiIiItI5W+4TpdDpxuVw888wzJxQuAFx55ZW8+OKLrVWSiIhIq/D7/ezYsYMVK1awcuVKSktLAdi6dSvGUBt4PFhT4gjv14XS6YsBMBxj9J7I0fj9fiobveyr9QTChH21bvbXeWj0NQ8SbFYrmW3b0qZNm2aP8PDwIFUvIiIiIiIi5wuDv5XmJLrwwgtZvHgxa9asoWvXrifUR0NDA5s2baJHjx6tUZIcxcCBA8nJyWm2b8CAASxZsiRIFYmInFl8Ph+bN29m2bJlrFixIhAqGExGLIkx2NrEY02Nx2S3BrnSc9++STNICwvhvp4JwS7llKhu9H43GsHT9LPOTWGdh3pP83USQkJCSElJITU1lTZt2pCamkpqaiqxsbFafFlERERERERO2oncM261EQzXX389ixcvpqKi4oT7sFqtChdERCRoDoQKS5YsYdny5VRVVgJgtIZgz0rG1iYBS1KM1lOQE9Lg9VFY56HgwDoJ362VUONuHiSYTCaSkpIDIxFSUlJo06YN8fHxx1zXSkREREREROR0arU7JLfccgvPPfccixcvZsiQISfUx9atWxkxYgRbtmxprbJERESOaffu3SxcuJBvFi2iorxpzn+j3dq0PkJaIpaEKE15JMfN7/dT1uBlb03TQssHHqUuDwcPGzUYDCQkJND5u9EIB0YlJCYmap0EEREREREROSu02qfXyMhI/va3v/Hwww9z++23Ex0d3eI+tmzZQn5+fmuVJCIickQNDQ0sWbKEr7/+mm3btgFNIxVC27fBlpGIJT4ag1HTzsjReX1+9te72V3jZm+Nm701jeyt9eDyNh+VEB4eTna7tMCohLS0NFJSUrBaNcWWiIiIiIiInL1a9etxP//5z1m+fDkjRoxg2rRpxMTEHPe5+/btY+zYsa1ZjoiIyCEqKyuZOXMmX82aRV1tLRgMWFPjCW2bgjUlDoNJIxXk8Hx+P4V1HnZVN7K7pulRUOvBc9Ciy0ajkeTkZNLT00lLSyM9PZ309HQiIiKCWLmIiIiIiIjIqdHq4++fffZZevXqRfv27YmMjDyuc2praykpKcHv92uRQhEROSVcLhfTpk1j2rRpNDY2YrRZcPZoR2i7VEyhtmCXJ2eg6kYv26sa2VndyI6qBnbXumn0fh8mhJjNpGVkkpnZ9MjIyCA1NRWLxRLEqkVEREREREROn1YNGP773//ym9/8hvLycvx+/0kt+CwiItJaNm/ezLPPPovP5wMDGKwhhPfvgj0tkeLpi/E3ugNtnT3aEZqVQumsZXhr6gP7HZ3ScXTOoHx+Lu6yqsB+e1YKYT3aUbFkPY2FpYH91tR4Ivp2pmrlZly7CgP7LfFRRA7uTs26fOry9gT2myPDiL6kF7VbdlG7YXtgvzHURuyw/tTv2Ed17vdrFBlMJuJGD6GhoITKpRuaXW/c6Atxl1dTsXB1s/0xwwfgd3som72i2f7oS3tjtFko+WJJs/2Rg7sTEhdJ8dQFzfaH9+uCLSWO4mmL8Ls9B7127QnNSqb0q2V4aw967Tpn4OiUfuhr1zaFsO7tqFi8jsb9ZYH9ttR4wvt2pmrlJly79nM6+P1+Sl1e8iobyK9sYHtVIyWu76/NYDDQpk0b2rZtS9u2bcnKyiI1NVVrJYiIiIiIiMh5rdU+Fc+bN48bbrgBn8+HwWDAYDDg9/uPfeJ3WtpeRETkeCxfvpxXX3216e9TiBmjJaQpZNCizee9ygYvmytcbKloIK+ygYoGb+CY0+Ggd+8etG/fnvbt25OZmYnNppEuIiIiIiIiIgcz+Fvprv7ll1/O7NmzA0FB+/bt6dKlC+Hh4RiPcRPH4/Gwe/duFi1ahM/nw+v1HrW9nLyBAweSk5PTbN+AAQNYsmTJEc4QETn7FBcX8+CDD+Lx+4i8pBfWhOhglyQnYN+kGaSFhXBfz4ST6sfn97OzupH1pS42lrsoqP1+5EpkRASdu3ShS5cudOzYkeTk5GO+fxERERERERE5l5zIPeNWG8GQm5uLwWAgLS2NTz75hH79+rW4j5dffpkHH3ywtUoSEZHz3MKFC2lsbCRiUDeFC+cpn99PfmUDucX1rCtzUd3Y9CUGiyWEnj170q1bN7p160ZKSorWgRIRERERERFpoVYLGBobG4GmkOBEwgWAoUOHapokERFpNXV1dQBU527BEhuBa08RdVt2Y0uNx9mzPSXTFgEQM6w/dVt2U7+9AHtmMqEd2lA6cykAsSMGU7NmK649RYR2aIOtTUJgDYO4q4dSmbOBxsJSHF0yscRGUL5gNQazibhRQyhfuBp3SSXO7u0wOe1ULl6H0W4ldvgAyuasxFNZQ3jvTmAyUrXsW0xhocRc1jewhkFE/2x8jW6qc7dgjgon+uILKJm+GF+jm8gh3fFU1FKzPj+wrkPRpwvB5yPq4l40FpZSu2kn1uRYwvt0pvizhQBEX96X+m37qM/fgy09EWd2ZmDthdgrB1Lz7XZcOwuxt03BnpVM2azlTdc6+kKqVmyioaAYR6d0LIkxlM9bBUYD8VcNpWLRWhqLynF2zcIcGUbFN2swWEKIGzEosPZC2AUdMFpDqMzZgMlhI+aK/pR+vRxvdR3hfTuDz0/Vyk2YI5xEX9qbkhk5+OobTujfvrjeTU5hHSuK6qj8LlSIjIjgRxf2oVevXmRnZ2sxZhEREREREZGT1GoBQ5s2bdi0aRPt2rU74T4yMzN59tlnW6skERE5z11wwQV88cUX+Boa8R60kLOcm/x+PxvLXczfW8PmiqZgwulw8KMLBzBo0CA6duyoaY9EREREREREWlGrrcHwu9/9jn/84x9Mnz6d4cOHt0aXcgppDQYROV+89957zJw5E5PDRnj/bGwpccEuSVpo36QZOEOM3Nsz/ohtCmrdfLmzKrCuQnZ2Nj/60Y/o3bs3ISEhp6tUERERERERkbNWUNdguO2223j99deZM2fOCQcMpaWlvPnmmzz22GOtVZaIiJznbrnlFiIjI/nPf/5D+ZyVWJJicHZriyU+SnPun0Vq3D6eXV541DYGg4EhQ4YwcuRI0tLSTlNlIiIiIiIiIuevVgsYevTowf333897773HM888g81ma3Ef27dv54knnlDAICIircZgMHDVVVfRu3dv3n//fdauXUvZvlLMUWGEdmiDPSMJo0XfcD+jGQ2EOZz07t37iE3sdjuXXnopKSkpp7EwERERERERkfNbqwUMAC+88ALffvst99xzD//85z9bfP6cOXNasxwREZGA1NRUHnnkEfLy8vjiiy9YtnwZVUu/pXrFJqwpcdgykrAmx2IMadU/jdIKDEYj8fHx3HHHHcEuRUREREREREQO0mp3Uf7yl7/Q0NBA165d+fvf/05+fj5t2rQ5rnP9fj+7d+9m7ty5rVWOiIjIYbVr147f/e53VFZWsmDBAhYtWsSuXbtw7dqPwWTEkhiDNTUOW0ocJoc92OWKiIiIiIiIiJyxWi1gmDZtGosXLwaaAoMTGY3g9/s1H7aIiJwWERERjBo1ilGjRrFnzx6WLVvG8hUr2LljBw17i6kCzBFOrMkxWJJiscRHaXSDiIiIiIiIiMhBWu1OyZgxY1i0aBEGgyEQEvj9/uM6V6GCiIgEU2pqKqmpqVx77bUUFxezZs0a1qxZw/r166nduJPajTvBYCAkNgJLQjTWhGhC4iIVOIiIiIiIiIjIea1VA4Z7770Xn8+HyWQiJiYGu/34ppZwuVwUFRW1VikiIiInLC4ujssuu4zLLrsMj8fD1q1b2bBhA+vXrycvP4/a4gpq129rChyiw7HERxISF4UlPgqT3Rrs8kVERERERERETptWCxgSEhLo378/kZGRfPjhh0RERLTo/JKSEm688UYt9CwiImcMs9lM586d6dy5M9dddx0ul4utW7eyceNGNm3aRF5eHrWllbBxJwAmpx1LXBQhcZFY4iIxRzoxGI1BvgoRERERERERkVOjVed2GDVqFE6ns8XhAkBsbCwPPPCAAgYRETlj2Ww2unXrRrdu3QBobGxk+/btbNmyhc2bN7NlyxZqthdQv70AAIPZREh0OCFxkU3TK8VGYgq1BfMSRERERERERERaTasGDNdddx3r168/4fM7duzI0KFDW7EiERGRU8disdCxY0c6duzIqFGj8Pv9FBYWsnXr1sBj9+7dNBaVB84xhdoIiY0gJCai6Wd0BEaL1nIQERERERERkbNPq97RaNeuHe3atTvh8zMyMpg7d24rViQiInL6GAwGkpKSSEpKCgTmLpeL7du3k5eXR15eHvn5+ZTt2o9r1/7AeeYIJyEx4YTENo10CIkMw2DS1EoiIiIiIiIicmY7roBh/vz5DB06FIPBcKrrOapVq1bRvXt3zGZ901NERM4ONpstsI7DAeXl5YGwIT8/n23bt1G/rYD6bd9NrWQ0Yo4KazbSwRzuCPrfYRERERERERGRgx3Xnfovv/ySSZMm8a9//etU13NECxYs4Oc//zm5ubnExsYGrQ4REZGTFRUVRd++fenbty8APp+PwsLCprBh2zby8/PZsWMHdaWVgXMMIeamUQ4xTWs5hMRGaD0HEREREREREQmq4woYnn76aXr27Mn999/PX//611Nd0yFycnK46qqreO211xQuiIjIOcdoNJKcnExycjIXXnghAB6Ph127djULHfbu3UtjYRm1351nCrVhjonAEhsRGO1gDNEoPxERERERERE5PY7rLoTVamXixIlcdNFFlJaW8vbbb2MymU51bQBMnTqVm266iZEjR3LTTTedlucUEREJNrPZTFZWFllZWYF99fX1bN++PTC1Ul5+PmW799Ow+7v1HAxgjgxrGuUQF4klLhKTplYSERERERERkVPkuL/m2LdvXyZMmMDPfvYztm/fzqRJk0hLSztlhTU0NPDYY4/x6quv0r9/fyZOnHjKnktERORsYLfb6dKlC126dAnsKy8vbwobDlpEur58D/V5ewAwWkKaRjfER2GJi9QoBxERERERERFpNS26w/DTn/6UXbt28cgjj9C9e3eeeuop7rzzTmy21psD2u/38/HHH/P000+zdetWevXqxWeffYbFYmm15xARETlXREVF0adPH/r06QM0reewZ88etm7dytatW9mydQuFBYU0FJQ0nWAwEBIdjiU+Ckt8FCHxUZhs+hsrIiIiIiIiIi3X4q8wPvTQQ0RERPB///d/PPDAA4wdO5Ybb7yRa6+9ln79+p1QENDQ0MDy5cv57LPP+M9//sPOnTvx+/2MGDGCTz75hNDQ0Bb3KSIicj4yGo2kpaWRlpbGj370IwCqqqqawoYtW9iyZQv5+fnUllZSu3EHAOYIJ5aEaCyJ0VjiozDZrUG8AhERERERERE5W5zQHAm/+c1vSEtL49Zbb2X//v288sorvPLKK5jNZrp06UL79u1JSUkhMTERh8OB1WrFYrHQ0NBAQ0MDNTU17Nu3j71797JlyxY2b96M1+sFmkYw2Gw2nnrqKR588EGMRmOrXrCIiMj5Jjw8nN69e9O7d28AGhsb2bZtG5s3b2bTpk1s3ryZui27qNuyCwBzpBNLYgzWxBgsCdEYLZpSSUREREREREQOdcJ3DK688ko2bNjA3Xffzccff4zf78ftdrN27VrWrl3bor78fn9ge9iwYbzyyit06NDhREsTERGRo7BYLHTq1IlOnTpx1VVX4fV62bFjBxs3bmTDhg1s2rSJuk07qdu0EwyGpgWjk2KxpsQSEh2uRaNFREREREREBDiJgAEgOjqaDz74gMcee4xnnnmGqVOn4na7A8ePdgPi4FDBYrEwatQoHnnkkcC3K0VEROT0MJlMtG3blrZt2zJy5Eg8Hg95eXmsX7+edevWkZeXR2NROTVrtmK0W7Emx2JLjceSFKMFo0VERERERETOY61yVyA7O5tPPvmEyspKPv30U7788ktyc3PJy8vD5/Md0t5oNNK+fXt69+7N8OHDueqqqwgPD2+NUkREROQkmc3mwAiH6667jtraWjZs2MDq1avJzc2lMn8v9fl7MZhMWJJisKUlYGsTj9ESEuzSRUREREREROQ0atWvHUZERHDLLbdwyy23AOByuSgsLKSyspKamhpCQ0OJjIwkISFBCzeLiIicJRwOB/369aNfv374fD527NjBqlWrWL58Obt376ZhTxFVRiOW5BjsmcnYUuMxmE3BLltERERERERETrFTOq+BzWYjIyPjVD7FSSkqKiI+Pj7YZQTs37+fiRMn8uCDDwa7FBERkcMyGo1kZWWRlZXFddddx/79+1m6dClLcnLYuWMHDXuKMYSYsaUnEtq+DSExWrNBRERERERE5FxlDHYBwbBjxw7uuOMOUlNTj6u9z+fj448/ZsSIESQkJGCxWIiNjeWyyy7j3XffxePxHFc/mzZtwmg0YjAYDvtITEw86vmNjY2MHz+eiy++mLi4OKKjo+nQoQP33nsvO3bsOK4aREREWlNCQgKjR4/mhT/9iZdeeomrr76amIhI6vP2UPrlEkqmL6Zu6x78Hm+wSxURERERERGRVnZeBQzbtm3j9ttvp3379rz11lvNFqQ+kqqqKoYNG8aNN97IF198QVFREW63m9LSUmbPns2vfvUrfvSjH1FRUXHMvsaOHdtscesfslgs/PKXvzzssYKCAgYMGMBvfvMbLr/8cnbv3k1JSQmvvvoqH330ET169OCzzz47Zg0iIiKnSnJyMmPGjOHvf/87jz32GAMGDMBfVUdlznqKpsyjem0evobGYJcpIiIiIiIiIq3klE6RdKYoLS3lxRdfpKysjJqamuMeceD3+xkzZgxLly7lj3/8I4MHDyY+Pp5du3bxn//8h0mTJuHz+ViwYAE//elPmTFjxhH7Kigo4IMPPqBNmzaHXX/CaDQyZswY4uLiDjlWU1PDiBEjWL16Nffddx9/+MMfAseGDx/Op59+ysCBA7n++uuZO3cugwYNOq7rExERORWMRiNdu3ala9eulJeXM3v2bL6aNYuaNXnUfrsDR+d0HJ0zMVrOi7chIiIiIiIiIues8+KTvd1u57nnnsNsNuPz+ZgxYwaVlZXHPO+DDz4gNzeX1atXk5WVFdjfvXt3Ro4cyVVXXcV1112Hz+dj5syZzJgxg+HDhx+2r7///e84nU6+/fZbnE5ni+q/6667WL16NQ6HgyeeeOKQ4/379+e6665j8uTJ3HjjjWzZsgWr1dqi5xARETkVoqKiuO666xg5ciRz587ls88+o3JtPnVbdhPWsz32dqlao0FERERERETkLHVeTJEUGhqK2dyUpRiNRiIjI4/rvL/+9a+8+uqrzcKFg11zzTX86le/Cvz+6aefHrZdVVUVb775Jvfee2+Lw4W8vDw+/PBDAEaMGHHE2m+++WYAdu3axbhx41r0HCIiIqeazWbjyiuv5O9//zs//elPsWCkMmcDpTOX4qmsCXZ5IiIiIiIiInICzouA4URs374dgBtuuOGo7a6//vrA9pEWWv7nP/9JVVUViYmJgX6P1yuvvILX27Qw5mWXXXbEdpdeeilGY9M/5/jx41v0HCIiIqeL1Wpl9OjRvPzyywwaNAh3cQUl05dQt3VPsEsTERERERERkRY6L6ZIOhFGo5HXXnvtmO0yMjIC2xaL5ZDjjY2NvPLKKwD8+te/BpoWwbzsssu4/vrrufLKKzGZTEfsf/bs2YHtXr16HbGdw+GgY8eObNy4kS1btrBmzRp69OhxzPpFRESCITIykt/+9rf079+f8ePHU5mznoZ9JZjCDl2nyO/xBqFCERERERERETkWjWA4gvT0dIYMGXLMdlVVVYHtrl27HnL8gw8+oKCgoNm+goICJk6cyKhRo+jWrRs5OTmH7bu8vJxNmzYFfs/MzDxqLQcfX7Zs2TFrFxERCba+ffvy5z//mYyMDFw7C6ldv+2QB0BERESQKxURERERERGRH9IIhpO0a9euwPaoUaMOOd6vXz8mTJhAWVkZ69atY9GiReTl5QWOb9y4kYsuuohPPvmEq6++utm5W7Zswe/3A01TSkRHRx+1luTk5MD22rVrT+RyRERETruYmBieeeYZdu3ahc/nO2ybNm3anOaqRERERERERORYFDCcpDlz5gBN38AcMGDAIcezs7PJzs5utm/RokW8+OKLgUWhGxsbufHGG1m4cCF9+vQJtCsrKwtsh4WFHbOW8PDwwHZRUVHLLkRERCSIzGYzWVlZwS5DRERERERERFpAUySdBLfbzSeffALASy+9dNznDR48mKlTp/K///0vEAq4XC7uuOOOZu3Ky8sD2zab7Zj9Wq3WwPbBUzcdrKGhgaqqqsDC0SIiIiIiIiIiIiIiJ0IBw0kYP348paWl3HXXXQwdOrTF51999dXMnDmTkJAQAHJzc8nNzQ0cNxgMh90+ErP5+wEpR5pi4oUXXiAiIoLly5e3uF4RERERERERERERkQMUMJygPXv28Pjjj9O3b19efvnlE+5nwIAB3HPPPYHfD77xf/C0SC6X65h91dfXB7YPni7pYI8++iiVlZX07dv3BKoVEREREREREREREWmigOEEuN1ufvaznxEVFcVnn33WbGqiE3HrrbcGtktKSgLbKSkpge3a2tpj9lNXVxfYTkpKOmwbq9VKeHg4JpPpREoVEREREREREREREQEUMJyQO+64g7y8PGbNmkViYuJJ99epUydCQ0MBcDqdgf3t27cPTI1UV1d3xHUVDigoKAhs/3BhaRERERERERERERGR1nRGBQybNm064tQ+Z4oHH3yQzz//nFmzZtG2bdtW6zciIgKgWWDhdDrp1q1b4PcdO3YctY+dO3cGtgcOHNhqtYmIiIiIiIiIiIiI/NAZFTCUlJQc11RAwfLMM88wfvx4ZsyY0aojBBoaGigpKcFgMDBkyJBmx4YPHx7YPngB6MP1sWHDBgDS0tLo3r17q9UnIiIiIiIiIiIiIvJDZ1TAsGLFimCXcER///vf+ctf/sK0adPo06fPEdtVVlayffv2FvX91Vdf4Xa7ueSSS0hOTm527NZbbw1MkzR37twj9rFw4UIaGxsBuP3221v0/CIiIiIiIiIiIiIiLXVGBAwNDQ3MmjWLP//5z6fl+Xw+X4vav/322zzyyCNMmTKFCy+88Khtf//73+PxeADw+/2sWbOG/fv3H7G92+3mj3/8I1arlddee+2Q4x07duSaa64BYOrUqdTU1By2n0mTJgFNUy3deeedx3VdIiIiIiIiIiIiIiInynw8jS644ALWrl17qmvB7/cHvq1/qrhcLvbt2xf4vby8nKioqCO2/+STT7jjjjt49tlnSU9PZ9OmTYdtV1RUxFtvvUVJSQnt27cH4JFHHmHs2LEAjBo1itdee4309PTAOT6fj//3//4f3377LR999BFdunQ5bN+vv/46CxcupLi4mBdeeIHnn3++2fE1a9bwwQcfAPDGG28QGxt7HK+EiIiIiIiIiIiIiMiJO66A4Z577uFXv/rVqa7llCsvL+fhhx8OjDAAePzxx3n++eeJjIw8pP20adO4+eab8fl8/OEPf+APf/jDMZ9j+vTpge2Dw5LPP/+c+fPn88ADD3DJJZewZ88eXn31VSorK5kzZ85RF2VOSEhg4cKFjBgxghdeeAGn08k999yDzWZj3rx53HzzzZhMJt544w1uvPHG43w1REREREREREREREROnMHv9/uP1cjn89GpUyfy8/OBppEGrV6IwRAYweD1elu1b6/XS3x8PGVlZUdsExUVxZIlS+jYsSMAK1euZMiQIbhcruN+nrZt27J169ZAsODxeHjjjTeYPHkyGzdupKKiArvdTmpqKn369OHaa69l5MiRmM3HlfNQW1vLe++9x/vvv8+OHTvweDzExMRw+eWXc/fddwdGThyPgQMHkpOT02zfgAEDWLJkyXH3ISIiIiIiIiIiIiLnhhO5Z3xcAQPAxIkT+eUvfwnA4MGDycrKwmhsnSUcysrKWLBgAZWVlackYJBDKWAQERERERERERERkQNO5J7x8X11Hrjpppt4/vnnGTx4MBMmTDjxKo+grKyMoUOHsnHjxlbvW0REREREREREREREWtdxD0EwGo08+eSThISEnJJCoqOjueeee05J3yIiIiIiIiIiIiIi0rpaNMfRjTfeyLZt205VLXTv3v2U9S0iIiIiIiIiIiIiIq2nRQGD0Wjk17/+Nbm5uaekmPT0dO66665T0reIiIiIiIiIiIiIiLSe416D4YAxY8acijoASEhI4LXXXjtl/YuIiIiIiIiIiIiISOto0QgGERERERERERERERERUMAgIiIiIiIiIiIiIiInQAGDiIiIiIiIiIiIiIi0mAIGERERERERERERERFpseNe5Pkvf/kLNTU1zR61tbXceuutXH311aewRBEREREREREREREROdMcd8Dw6KOPYjAYAr8PHjyYK6+8kp49e56KukRERERERERERERE5Ax23AHDAUOGDOHNN9+kU6dOp6IeERERERERERERERE5C7QoYMjIyGDWrFlYLJZTVY+IiIiIiIiIiIiIiJwFWrTI8y233KJwQUREREREREREREREWhYw9OrV61TVISIiIiIiIiIiIiIiZ5EWBQwpKSmnqg4Atm7dSlZW1il9DhEREREREREREREROXktChhCQ0NPVR0AlJaWsnPnzlP6HCIiIiIiIiIiIiIicvJaFDAYjS1q3mLr1q07pf2LiIiIiIiIiIiIiEjraFFiYDAYTlUdrF69mpdeeumU9S8iIiIiIiIiIiIiIq3H3JLGl156KSEhIa1agNfrpby8nNraWvx+/ykNMUREREREREREREREpHW0KGAoKCjA7/efqlpEREREREREREREROQs0aKAAU7tNEkKL0REREREREREREREzg4tChgUAIiIiIiIiIiIiIiICLQwYPjTn/5EUlJSqxbQ0NBAcXExixYtYsaMGa3at4iIiIiIiIiIiIiInBotChiuvfZaOnTocKpq4a677uLNN988Zf2LiIiIiIiIiIiIiEjrMAa7gIONHj062CWIiIiIiIiIiIiIiMhxaFHA0NDQcKrqACA5OVnrPIiIiIiIiIiIiIiInAVaFDBUVVWdqjoAaNu2LZ9//vkpfQ4RERERERERERERETl5LQoY8vLyTlUdADgcDkaMGHFKn0NERERERERERERERE5eiwKGmTNnnqo6RERERERERERERETkLNKigGHy5MnMnj37VNUiIiIiIiIiIiIiIiJniRYFDD6fjyuvvJLbbruNxYsXa0FmEREREREREREREZHz1HEHDGazGb/fj8fj4Z133mHIkCFERkbyySefnMr6RERERERERERERETkDGQ+3oaNjY14PB6qq6upqamhpqaG2tpaUlJSTmV9IiIiIiIiIiIiIiJyBjrugAGaRjFERUURFRV1quoREREREREREREREZGzQIvWYBAREREREREREREREYEWjmAQERERERE5mx2Y6vVIoqKisFgsp7EiEREREZGzlwIGERERERE5L3zzzTf8881/4vP6jtgmJTWFF8e+eBqrEhERERE5eylgEBERERGRc978+fMZP348BosZe2byYds07N5PSXHJaa5MREREROTspYBBRERERETOWX6/n08//ZR///vfGK0Woi/rQ0h0+GHblpRXQ43rNFcoIiIiInL2UsAgIiIiIiLnpPr6esaPH8/SpUsxOe1E/6gP5nBHsMsSERERETlnKGAQEREREZFzTn5+Pq+99hpFRUVYEqKJGtoTo02LN4uIiIiItCYFDCIiIiIics5obGxkypQpTJs2DZ/Ph7NrFs4e7TAYjcEuTURERETknKOAQUREREREzglr1qzhvffeo7CwEJMzlOhBXbEmRAe7LBERERGRc5YCBhEREREROavt3buXjz/+mJUrV4LBgKNLBmE92mMwm4JdmoiIiIjIOU0Bg4iIiIiInJVKSkr43//+x7x58/D7/VgSownv25mQyLBglyYiIiIicl5QwCAiIiIiImeVoqIiPvvsM+bPn4/X68UcGUZYrw5Yk2MxGAzBLk9ERERE5LyhgEFERERERM4K27ZtY9q0aSxduhS/3485wkFkt7bY0pMwGBUsiIiIiIicbgoYRERERETkjOXxeFixYgUzZ85k8+bNAJijw3FmZ2JLS1SwICIiIiISRAoYRERERETkjFNaWsrcuXOZO3cu5eXlAFhT4nB0zsCSGK2pkEREREREzgAKGERERERE5IzgdrvJzc1l/vz5rF69Gr/fj9FixtEpndCOaZjDHcEuUUREREREDqKAQUREREREgsbv95Ofn8/ChQtZvHgxtbW1AITERRLavg329EQMZlOQqxQRERERkcNRwCAiIiIiIqfd3r17Wbx4MYsWLaKoqAgAk92KIzsTe9sUQiKcQa5QRERERESORQGDiIiIiIicFvv27WPp0qXk5OSwa9cuAAxmE/asZOyZSVgSY7Vos4iIiIjIWUQBg4iIiIiInBJ+v589e/awfPlyli1b9n2oYDJibROPPSMJW2q8pkASERERETlLKWAQEREREZFW4/P5yMvLY+XKlSxfvpzCwkLgu1AhNR57RiLWlHiMFn0UERERERE52+ldvYiIiIiInJSGhgbWr1/PqlWrWLlyJVVVVUDT9Ee29ERs6YlYk2Mxhujjh4iIiIjIuUTv8EVEREREpMXKysrIzc1l1apVrF+/HrfbDYDRbiW0fRusbeKxJkZjMGn6IxERERGRc5UCBhEREREROaYDUx/l5uaSm5sbWE8BwBwVhjM1DWtqHCExERgMWqhZREREROR8oIBBREREREQOq7KykrVr17JmzRrWrFlDbW0t8N16CilxWFPisKXGYXLYg1ypiIiIiIgEw3kdMBQVFREfHx/sMkREREREzgg+n4/8/HzWrFnD6tWr2b59O36/HwCTw05ohzZNwUJiDAazpj4SERERETnfnZcBw44dO/jTn/7Eu+++S2Nj43GdU1JSwmuvvcann35KQUEBHo+HjIwMbrzxRu68806cTucx+8jJyWHcuHEsWLCAuro6LBYLvXv35q677uLKK6885vmNjY28++67fPjhh2zYsAGv10tsbCwjRozg97//PRkZGcd1LSIiIiIiB1RWVgZGKKxduzYwSgGjAUtCNNaUWKwpcZjDHZr6SEREREREmjmvAoZt27bxpz/9iffeew+Px3Pc5y1YsIDrrrsucIN/9OjRuN1u/vnPf3L//fczYcIEpk6dSseOHY/Yx8MPP8yLL77I0KFDmTt3Lm3btmXXrl3cdddd/PjHP+b222/njTfewGw+/D9JQUEBI0eOJDc3l+eee44ZM2ZgsVj46quv+OUvf8mECROYNGkSo0ePbvHrIiIiIiLnD6/XS15eXmCUwo4dOwLHAqMUkuOwJEZjDDmvPi7gczXiaWzkqaeeOuRYUlISffv2pWvXrlit1iBUJyIiIiJy5jkvPjGUlpby4osvUlZWRk1NTYvChQ0bNjB69GgqKyv57LPPGDVqFABWq5Xf//73NDY28tBDDzF8+HCWL19ObGzsIX0899xzjB07loyMDD7//HPCwsIASEtLY8qUKQwYMIB//etfWK1Wxo0bd8j5NTU1jBgxgtWrV3Pffffxhz/8IXBs+PDhfPrppwwcOJDrr7+euXPnMmjQoJa+RCIiIiJyDisvLw+MUli3bh11dXVA01oKlqQYrMlxWFNiz/tRCr6GRvx+Pzvy85rt9wNbt25lwYIFhJjNtO/QgS5dutC+fXvatm1LaGhocAoWEREREQkyg//ApKrnsAPTEZnNZnw+H9HR0VRWVgJwtMt3u9106tSJbdu2MWjQIBYtWnRIm8bGRtq2bcuePXu44YYb+Pjjj5sdnz17Npdffjl+v5/x48fz61//+pA+vvjiC0aMGAHAl19+yfDhw5sdv+WWW5g0aRIOh4M9e/YQGRl5SB9jxoxh8uTJpKWlsWXLlmN+q2rgwIHk5OQ02zdgwACWLFly1PNERERE5Mzn8/nIy8tj9erVh45SCAvFmtw07ZE1IVprKRyk8KNZhPh8jB2c0my/3+9nd42bdaX1bCp3safGzYFPEQaDgaSkJDIyMsjIyCAtLY20tDQiIiLO67BGRERERM4+J3LP+LwYwXDwN4qMRiORkZGBgOFo3n//fbZt2wbATTfddNg2FouFMWPG8PLLL/PJJ5/wwAMP0KdPn8DxZ555Br/fH2h3OMOGDSM+Pp6ioqLAaIgD8vLy+PDDDwEYMWLEYcMFgJtvvpnJkyeza9cuxo0bx/3333/M6xMRERGRc0dNTQ1r1qwhNzeXNWvWBNZSMJiMTYFC8vdrKUjLGAwG0sIspIVZGJERQZ3bx/bqBnZUNbKzupHdRYUUFBSwePHiwDlOh4OU1FSSk5NJSUkhOTmZpKQk4uLiMBqNQbwaEREREZHWc14EDCfq5ZdfDmxfdtllR2x3+eWXB9q+9dZbgYBh1apVLFiwAIA+ffoQERFx2PNNJhOXXHIJn3zyCevWrWPJkiUMHDgQgFdeeQWv13vMGi699FKMRiM+n4/x48crYBARERE5x/n9fgoKCli1ahWrVq1iy5YtgdG5Jqed0I5pGqVwioSGGMmOtpMdbQea/i3KGrzsrXFTUOtmX62bfXUNbN2ymc2bNzc712w2Ex8fT2JiIomJiSQkJAS2Y2JiFD6IiIiIyFlFAcMRFBUVsX79egDCwsJo167dEdv26tUrsP2f//yHN954A6PRyNdff33YNkfq45NPPgHg3//+dyBgmD179nH14XA46NixIxs3bmTLli2sWbOGHj16HPU5RUREROTs4vP52LJlCytXrmTlypUUFhY2HTAYsMRFYk2Nw5oSjzni/F5L4XQzGAzE2MzE2Mx0j7UH9nt8fkpcHorqPOyvd1Nc37RdXNw04uGHTCYT8fFxxMcnBMKHA4/4+HjMZn18ExEREZEzi96hHsHBw5szMjKO+gEtPj4eh8NBbW0tZWVl5Ofn0759+2Z9ZGZmHvX5Dj6+bNkyoGkxvk2bNrWoj40bNwb6UMAgIiIicvbzeDysX7+e5cuXs3LlSqqqqgAwhJixpSdiS43HmhKL0WoJcqXyQ2ajgcTQEBJDQwB7s2N1bh8lLg/F9R5Kv/tZ4vJQXFLEvn2FrFmzpll7g8FAbGwMCQmJgREPSUlJJCYmEhcXp/BBRERERIJC70KP4OChzMnJycdsn5SURF5eHgBr166lffv2Lerj4OPr1q0DaDbM3Wq1Eh0dfdx9rF279pg1i4iIiMiZ6UCokJOTw4oVK6irqwPAaLcS2qENtjYJWBKiMZg0nc7ZKjTESFpI07oOP+Ty+Ch1NQUOJfVeil0eSus9FFeVs764JDDS+gCT0Uh8QjzJySmBNR8OPGw22+m6JBERERE5DylgOIKysrLAdlhY2DHbh4eHB7aLiopa3MfB51dXV1NfX98qNfxQQ0MDDQ0NgXUdREREROTM4Pf72bJlC9988w1LcnKo+26RZpPDhqNLBra0BEJiIzX10XnAZjaS4rSQ4jw0fGj0+gMjHpoeborqPRSVFLFyXyErV65s1j42NpY2bdrQpk0b0tPTSU9PJzExUWs9iIiIiEirUMBwBOXl5YHt4/nWj9VqDWwfGLbekj4OPv9AH61Rww+98MILPP3008fsS0REREROj+LiYubPn8/ChQspLi4GwBRqw9E5A1t6IiGxEQoVJMBiMpDkCCHJEXLIsVq3j6J6N4V1Hgrr3Oyvc7Ovqpzc3BJyc3MD7axWK+np6WRkZJCRkUFmZiYpKSmaZklEREREWkzvII/g4A9xx/OB7uA34z6fr8V9/PDNvM/na5UafujRRx/lvvvu47LLLmP58uXH7FNEREREWp/X62XlypXMmTOHdevW4ff7MYaYsbdNwZ6VjCU+GoNRoYK0jCPESGaIlczw5l9eqnX7KKhtpKDWTUGtm701bvK3bmXLli2BNiEhIaSnp5OZmUlmZiYZGRmkpqYqdBARERGRo9K7xSM4eEoil8t1zPb19fWB7QNTFYWFhVFaWnpcfRx8/oE+WqOGH7JarVitVkwm0zH7ExEREZHWVVNTw9y5c/nqq68C7xMt8VHY26diT0vEYNZ7NGl9jhAj7SNttI/8flS0x+dnX52bPdWN7K5xs7umke35+YF15QBMJhMpKSmkp6eTlpYWmGopMlJTdYmIiIhIEwUMR5CSkhLYrv1u/tujObDwHjQt+HygjwMfHI/Vx8Hnh4WF4XA4WqUGEREREQm+qqoqvvzyS2bOnInL5cIQYia0YxqOjmmYI5zBLk/OQ2ajgTZOC22cFgZ+t8/j81NY52ZPjZs9NY3srXWzd+9udu3a1ezc0NDQwGLSycnJJCYmkpSURHx8PBbLoetGiIiIiMi5SwHDEXTo0CGwvW/fvmO2LygoCGxnZ2cH+li7du1x9XG489u3b4/BYMDv91NXV0dVVdURRyYcqQ8RERERCZ7Gxka++OILPv3sUxpcDZjsVsJ6dyS0XSpGy6Fz6EvwNfr8PLt8H3dkx7K+1MXiwhq6Rtu5Mj2cF3P3A/C77vEs2lfDyuI6eseFMjjZyatrigB48IIEvtxZxfqyegYlOukWY+PNDSUAPN4nkU/yKtha4eKSlDDSwy28u7EUi9HAw70TeW9TKbuqGxmWFk6MzcyHW8oICzFxT8943tpQQmGdm6syIzEb4b/5FcTazNzZLY5xa4spb/Awpl0UtR4f03dUkuII4dYusfw1dz91Hh83dYxmf52HWburyAy3clPHaP60ohCv389tXWLZWuFiQUENnaJsXJMVyazdTWu6PXRBPAsLallZXEeczYwjxMjG8jry8vKajXY4wGg0YjKZGDhwIAUFBRQVFTFo0CCuvvpqwsLCNPJBRERE5ByjgOEIBgwYgNFoxOfzsWPHjqO2raqqoqKiAoDY2Fjat28PwKBBg/jPf/4DcMw+du7cGdgeOLDpO0ROp5Nu3boFQoodO3bQvXv3FvUhIiIiIsGxevVq3n77bUpLSzHarIT360xou1QMmqpSziIGgwFHiBGryUBmhIUfpYbx8uoi/H74RacY5uypYmtlAzE2M2aDgZ3Vjbh9PhYsWBDoY8aMGcyYMQOr1UpcXBxxcXHExsYSGxsb+D0uLk4BhIiIiMhZyOD3+/3BLuJ0y8jICNyMP9rl9+/fn2XLlgFNN/fT09MP227u3LlceumlANxyyy289957AGzcuJEuXboAcPHFFzN37twjPtcvfvELJk6cCMCcOXO45JJLAHj44YcZO3YsAO+++y6/+MUvDnt+Q0MD4eHhNDY2kpaW1ixsOJyBAweSk5PTbN+AAQNYsmTJUc8TERERkaNzu91MnDiR2bNnYzAaCe2SgbNrFsYQfbfnTFf40SxCfD7GDk45dmM5Io/PT3mDh5J6L6UuD6UuD2UuL6UNHsobvNS6fYc9z2qxEPOD0CE+Pp74+HgSEhIIDQ09zVciIiIicn45kXvG+pRzFLfddlsgYJg7dy6//OUvD9tu9uzZge3bb789sN25c2cGDhzIkiVLWLZsGXV1dYd9U+z3+wPhQ7t27bjooosCx2699VZefPHFQJsjBQwLFy6ksbHxkBpERERE5PSpqanh//7v/3C73ZjCHViTY3Ht2AceL86e7SmZtgiAmGH9qduym/rtBdgzkwnt0IbSmUsBiB0xmJo1W3HtKSK0QxtsbRIom70CgLirh1KZs4HGwlIcXTKxxEZQvmA1BrOJuFFDKF+4GndJJc7u7TA57VQuXofRbiV2+ADK5qzEU1lDeO9OYDJStexbTGGhxFzWl9KvluGtrSeifza+RjfVuVswR4UTffEFlExfjK/RTeSQ7ngqaqlZn48lPorIwd0p+nQh+HxEXdyLxsJSajftxJocS3ifzhR/thCA6Mv7Ur9tH/X5e7ClJ+LMzqTki6YPKLFXDqTm2+24dhZib5uCPSuZslnLm6519IVUrdhEQ0Exjk7pWBJjKJ+3CowG4q8aSsWitTQWlePsmoU5MoyKb9ZgsIQQN2IQ5fNzcZdVEXZBB4zWECpzNmBy2Ii5oj+lXy/HW11HeN/O4PNTtXIT5ggn0Zf2xu/xglHfoD9ZZqOBOHsIcfbDTwPm8vgoa/BS7vJQ1uCl7Luf5S4PZcWFzaZ+PZjT6SQxMZHExESSk5NJTk6mTZs2JCQkYDQaT+UliYiIiMgRnJcBg893+G/M/NBNN93Ec889x+7du3n//fcPGzB4vV4++ugjAAYPHsyFF17Y7Pijjz7K6NGjqaurY8qUKdx0002H9DFv3jx2794NNI1YOPjNcceOHbnmmmuYMmUKU6dOpaamBqfz0IUAJ02aBEBERAR33nnncV2fiIiIiLQet9vNiy++iNvtxmA2ETW0Jw0FxcEuS1rowBoMB1ybFUl2jJ2XcvdT7/n+c8TwtHD6Jjj4x9piyho8gf1Dk51clBLGOxtL2VPTGNjfN97B8PRwPt5aztYKV2B/drSda9tG8tn2CtaU1Af2Z4Vb+XnHaGbtriKnsDawPyk0hNuzY1m0r4Y5e6oD+yMsJn7XI55VxXVM31EZ2B9iNPBI70Q2lbuYnFfe7Fof7Z1IQa2b9zaVNtv/+x7xNHj9/HN98/9+78iOxRli5OXVRc3239QxmvQwC8+vKGy2/ydtI+kSbefFVftxeb9/7a5MD6dPvIP/5Vc0e+0uTgnjwmQn/9pQwu4aNz6/H68f4u1mnCFG8iprD7v2w4F1H4YPH063bt3o3LkzJk1FJiIiInJanHcBg8vlarbgcnl5OVFRUYdtGxoayoQJExg2bBizZ8/mq6++4oorrmjW5p///Cfbtm3D4XDwzjvvHNLHqFGjuPnmm5k0aRLPPPMM1157bbNRDB6Ph8ceewyAYcOGcdtttx3Sx+uvv87ChQspLi7mhRde4Pnnn292fM2aNXzwwQcAvPHGG8TGxh7nqyEiIiIireXf//43W7duxZ6VTMSgbhgMBkKiwnBmZwXaxF/z/UjVsJ7tCevZ/rDHwvt2bvqG/WGORQ7s2ux5Dz4WdWHPIx6LvrR3s2O2g47FXNGv2TF7RlJgO3bEoMC2JS6K0Pap3/d/1fdfrgmJCsPROeOwzx3WvS1h3dse9lh4r46E9+p42GMR/bsc8XoiB3c/4rGoiy444rGYy/o2O2ZLSwhsG8ymplEMElRGgwGzEaBpNEnnaBvD0sL5aEsZWysa8AFen594u5lYu5kNZS5q3G4+//xzPv/8c+Li4rj//vtJS0sL5mWIiIiInBfOq3Gk5eXl/O53v8Pj+f5bMo8//nhggebDueyyy5gyZQoOh4MbbriBqVOn4vP5cLvdjB8/nnvvvZekpCS+/vrrwOLOP/T222/zq1/9iq1btzJy5Ejy8/MB2LdvHzfccAM5OTmMHj2ayZMnH3ZRs4SEBBYuXEjbtm154YUXeOGFF6ivrw9MmzRixAhMJhPjx4/nxhtvPLkXSURERERarLKykpkzZ2JyhhIxIFsL1Z4D7siOZVCikynbKvhffgW/7RYXOPa77vEU13t4dvk+MsMt/K5HfOBY/4Smb+bvqWlkUKKT32Q3fflneVFts/XfLkkJ45edY9hQVs9fVhYyOjOStDALAMPSwhmQ6ODZ5fvYUOriib5JJIY2TTfUL8HBt2X1zNlTTazNzBN9k4iymqls9LK5/PuRESmOEJ7om0SI0cCzy/dhNRm4vE04AJnhVp7om8TYVft5b1Mpt3WJZWhy0yjpTlE2Qs3GwOiFu7rF0T/BAcDS/d+PpAC4t2c8PWNDeX9zGbN2V3PXQa/Ro70T+bbMxbPL99E3IZTbuzS9DkaDgT7xDj7YXEZZg4fL2oRzc8fow/Y/IiOCMe2iWLa/ltfWFnFjh2ji7GZMBvhphyguSgkjr7KBNs4Q7usZzwVxdgCKi4uZP39+C/61RUREROREnRcBg9frJSYmhujoaN56661mx15//XWioqKIjo5m8+bNhz3/qquuYvPmzfy///f/eOqpp0hKSiI1NZXx48fz7LPPsn79egYMGHDE5w8JCWHChAnMnTuX6OhorrjiCuLj4+nfvz8ul4tPP/2UqVOnEhYWdsQ+OnbsyJo1axg3bhyff/45bdu2JSEhgbvuuotrr72W9evX8+tf//rEXiAREREROSmbNm3C4/EQ2qENBk3NItKq/H7w+aCg1k2N24fL42dpYS1z9lRT0eBlY3kDL68uIre4aYqpdu3aMWzYsCBXLSIiInJ+MPgP/hqNnDdOZEVwERERETm8r776infffZfIC3s0m15Izi6FH80ixOdj7OCUYJdyzvP7/dR7/dS6fdS4vdS4fdQ0eql2+6hu9FJ14Gejl8pGH27f4T+2htrtJKekkJaWRmZmJl26dCEpSf8bFBERETkRJ3LP+Lxbg0FEREREpLXFxzdNkeMurVLAIOcdr99PndtHncdH7YGfHl+zfbUHfrq91Hr81Lq9eI/xVTeT0Uh4RARpKdFER0cTFRVFTEwMMTExxMfHEx8fT1hYmKYkExEREQkiBQwiIiIiIicpOzub8PBwavL24OicjinUFuySRFrE7/fT6GsKCmo9Puo9TeFAnef78CDwcDcdr/3ueIPXd1zPYTAYcDodOCPCSAoPJywsjLCwMMLDwwkPDyciIoKwsDAiIyOJiIjA6XRiNJ4Xs/qKiIiInLUUMIiIiIiInKSQkBBuuOEG3nrrLYr+twCT3Qrffak6vG9nbKnxFE9fjL/RHTjH2aMdoVkplM5ahremPrDf0SkdR+cMyufn4i6rCuy3Z6UQ1qMdFUvW01hYGthvTY0nom9nqlZuxrWrMLDfEh9F5ODu1KzLpy5vT2C/OTKM6Et6UbtlF7Ubtgf2G0NtxA7rT/2OfVTnbgnsN5hMxI0eQkNBCZVLNzS77rjRF+Iur6Zi4epm+2OGD8Dv9lA2e0Wz/dGX9sZos1DyRfMh1pGDuxMSF0nx1AXN9of364ItJY7iaYvwuz0HvXbtCc1KpvSrZXhrD3rtOmfg6JR+6GvXNoWw7u2oWLyOxv1lgf221HjC+3amauUmXLv24/d4wXh2fxve5/fj8n4fFBwcDhwYXVDvOXRfncfHEWYhOqxQux1HuJNkpxOHw4HT6SQsLCywffAjLCwM53ftFBiIiIiInFsUMIiIiIiItIKLL76Yb7/9lkWLFuF1NWCyWUBTt8hJ8Pv9NHj9VH83tVCN20eN+8CUQ97vphw6aPqh78KD4w0KzGYzToeDiGgnKQcFBQ6H44jbTqeT0NBQTFrMXERERETQIs/nLS3yLCIiItL6vF4v//jHP8jJycEcFkrkRRcQEhUW7LLkOJ2uRZ4bvT4qG71UNR746aW60Ue1u+lnjbtpseMatw/PcaQFJpOJMKcT53cjBQ6MJPjhz4NDAqfTicVi0foFIiIiIhKgRZ5FRERERILIZDLx29/+lsTERKZOnUrpF0tw9myPo3MGhrN86h05Pj6/n6pGL6UuL6UuDxUNXsoavFQ0NG1XNjaNMjgam81GRGQUceERgfUJDqxVcODnwVMP2e12BQUiIiIiEhQKGEREREREWpHRaGTMmDF06NCB8ePHU7FqM/XbC4jo1wVLfFSwy5NW4vH52V/nZl+dm8I6D/vr3BTXeyhxeY846sDpdBKfEkNUVFTgcWBB48jIyMC2xWI5zVcjIiIiInJiFDCIiIiIiJwCPXv2ZOzYsXz44YfMmzeP0plLsWUkEdazPeaw0GCXJ0fQ6PPz2faKIx53efzsrmmkoNaD9wezzTodDtIzE0lISCA+Pp64uDhiY2OJjY0lJiZGwYGIiIiInHMUMIiIiIiInCJOp5M77riDSy65hIkTJ5Kfn0/DrkLs7dvg7JqFKdQW7BLlYN9NMzRnT81Rm4WYzWS2bUtGRgZpaWmkpqaSnJxMeHj46ahSREREROSMoYBBREREROQUa9++Pc888wxLly7lk08+Yf/mXdTn7WkKGrIzFTScIUxhoRiq63nqqaeO2MZsNpOcnIzZrI9SIiIiIiJ6VywiIiIichoYDAYGDBhA3759WbhwIf/73/8o3rST+i27sGWl4MzOxBzuCHaZ5zWDwYDRaCQrKyvYpYiIiIiInBUUMIiIiIiInEYmk4mLL76YIUOGsHjxYj777DMK8vZQn7cHW1oCjuxMLLGRwS5TRERERETkmBQwiIiIiIgEgdlsZujQoQwZMoQVK1bw+eefk5+fj2vXfizxUTi6ZGBNjcfw3boAIiIiIiIiZxoFDCIiIiIiQWQ0GunXrx99+/Zl06ZNTJs2jdzcXBqLyjGFheLonIE9KxljiN66i4iIiIjImUWfUkREREREzgAGg4HOnTvTuXNn9u7dyxdffME333xD1bJvqVm9FXv7VByd0rUgtIiIiIiInDEUMIiIiIiInGFSUlL49a9/zQ033MDXX3/NV199RdWG7dR+uwN7RhINhaUYTEaiL+2Na08RdVt2Y0uNx9mzPSXTFgEQM6w/dVt2U7+9AHtmMqEd2lA6cykAsSMGU7NmK649RYR2aIOtTQJls1cAEHf1UCpzNtBYWIqjSyaW2AjKF6zGYDYRN2oI5QtX4y6pxNm9HSanncrF6zDarcQOH0DZnJV4KmsI790JTEaqln2LKSyUmMv6UvrVMry19UT0z8bX6KY6dwvmqHCiL76AkumL8TW6iRzSHU9FLTXr87HERxE5uDtFny4En4+oi3vRWFhK7aadWJNjCe/TmeLPFgIQfXlf6rftoz5/D7b0RJzZmZR8saTpWq8cSM2323HtLMTeNgV7VjJls5Y3XevoC6lasYmGgmIcndLxe7yn+59aREREROSspoBBREREROQMFR4ezrXXXsvIkSNZvHgxX3zxBXu272k6aDLSsL8cvz+4NYqIiIiIyPnL4PfrI8n5aODAgeTk5DTbN2DAAJYsWRKkikRERETkWPx+P+vXr2f69OmsXbsWAHOkE0d2JvaMJAxGY5ArPLuVfLEEY42Ld955J9iliIiIiIicdidyz1gjGEREREREzhIGg4Fu3brRrVs3du3axfTp01m0eBGVi9ZRs3orji6ZhLZLxWA2BbtUERERERE5D+grTiIiIiIiZ6G0tDTuvPNOXvn7K/z4xz/G7PFTtXwjRf+bT836bfjcnmCXKCIiIiIi5zgFDCIiIiIiZ7GYmBhuuukmXn31VX7yk59gM4VQnbuF4v/Np2ZdvoIGERERERE5ZRQwiIiIiIicA8LCwvjJT37Ca6++yg033EBoiJXq1VspnrqA2o078Ht9wS5RRERERETOMQoYRERERETOIXa7nauuuopXXnmF66+/HovBRNWKTRR/tpD67fvw+/3BLlFERERERM4RChhERERERM5Bdruda665hldfeYWRI0dicLmp+GYNpTNyaCypDHZ5IiIiIiJyDlDAICIiIiJyDnM6nfzsZz/jr3/9K4MGDcJdUknpl0uoWLIeX0NjsMsTEREREZGzmAIGEREREZHzQFxcHL/97W958sknSUtLoz5vD8WffUP99gJNmyQiIiIiIidEAYOIiIiIyHmkU6dOPP/889x0002E+A1UfLOW8vmr8dY3BLs0ERERERE5yyhgEBERERE5z5hMJn784x8zduxYsrOzadi9n5Jpi3DtLQ52aSIiIiIichZRwCAiIiIicp6Ki4vj0Ucf5ZZbbsHo8VE+ZyVVKzfj9/mCXZqIiIiIiJwFFDCIiIiIiJzHjEYjw4cP57nnniMpOZnab7dTNmu5pkwSEREREZFjUsAgIiIiIiKkpaXx/HPPMWDAABqLyin9Mgd3eXWwyxIRERERkTOYAgYREREREQHAZrNx991389Of/hRfnYvSmUu1LoOIiIiIiByRAgYREREREQkwGAyMHj2ae+65B7PBQPncVdTl7wl2WSIiIiIicgZSwCAiIiIiIofo27cvTzz+BE6Hg8rF66nZsD3YJYmIiIiIyBlGAYOIiIiIiBxWu3bt+OMf/0h0dDTVqzZTlbsFv98f7LJEREREROQMYQ52ASIiIiIicuZKTk7m6aef5vnnn6dw/Tb8bg/hfTtjMBiCXZqIyBln5syZTJ8+Ha/Xe8y28fHx/OEPf8Bs1q0ZERE5e2kEg4iIiIiIHFVMTAxPPfUUaWlp1G3eReWS9fh9GskgInKw2bNn895771FaWUGVt+Goj4qaKjZv3kxFRUWwyxYRETkpislFREREROSYIiIiePzxx/nLX/5Cfn4+fo+XyMHdMZj0nSURkblz5zJhwgSMNisxw/phDncctX3F4nXU5+89TdWJiIicOvo0ICIiIiIix8XpdPLYY4/RpUsXXDsLKZ+3Cr/n2NOAiIicy2bMmMFbb72FwRpC9GV9jhkuiIiInEsUMIiIiIiIyHGz2+089NBD9OrVi4aCEkq/Xo6voTHYZYmInHY+n48PPviAiRMnYgq1EnNFP0KiwoJdloiIyGmlgEFERERERFrEYrFwzz33MHToUNzFFZTOXIqnui7YZYmInDb19fW88sorTJ8+HXOEk+hhAzBHOINdloiIyGmngEFERERERFrMbDbzm9/8htGjR+OprKV0Rg6NxRXBLktE5JTbu3cvjz/xBMuXL8eSGEPM8P6YnfZglyUiIhIUWuRZREREREROiMFg4Kc//SlxcXG88847lM1aRsSArtizkoNdmohIq/P7/cyfP5/33nuPhoYGHNmZhPVsj8Go726KiMj5SwGDiIiIiIiclB/96EfEx8fzyiuvULFoLe6yKsJ6ddBNNxE5Z1RVVfHOO++wdOlSjJYQoi66AFtaQrDLEhERCTq94xcRERERkZPWrVs3nn32WVJSU6nduIOyWcvx1rmCXZaIyEnLycnhwQcfZOnSpVgSoogdOUjhgoiIyHcUMIiIiIiISKtISkri2WeeYcCAATQWlVMyfTENBcXBLktE5ISUlJTw8ssv8+qrr1JTV0d4n05EX9YPk0PrLYiIiBygKZJERERERKTV2Gw27r77bjp37sykSZMom70SR5cMwnp2wGDS95tE5Mzn8Xj44osvmDJlCo2NjVgSookY2BVzWGiwSxMRETnjKGAQEREREZFWZTAYuPzyy+nQoQOvvfYaBd/uoGFfKZGDuxMSFRbs8kREDsvv97NixQo+/PBD9u/fj9FmJXJwd2yZSRgMhmCXJyIickZSwCAiIiIiIqdEeno6zz//PB9++CGzZs2i9IvFOLu3w5GdqQWgReSMkpeXx4cffsimTZswGA04Oqfj7N4OoyUk2KWJiIic0RQwiIiIiIjIKWO1WvnVr35F7969eXP8eMpXb8W1az8Rg7ppNIOIBN3u3buZPHkyK1asAMCWlkDYBR0whzuCXJmIiMjZQQGDiIiIiIicct27d2fsX/7CBx98wLx58yj9YjGO7Cyc3bIwmEzBLk9EzjN79uxh6tSpLFmyBL/fjyU+irALOmCJjwp2aSIiImcVBQwiIiIiInJaOBwO7rjjDgYMGMC//vUvStbl49pZSPiAbKwJ0cEuT0TOA7t27eJ///sfy5Ytw+/3ExITTljPDliSYrTOgoiIyAlQwCAiIiIiIqdV9+7dGTt2LJMnT2bGjBmUfbUMe9sUwnt3xGi1BLs8ETnH+P1+Nm3axOeff87q1asBCImNwNm9HdbkWAULIiIiJ0EBg4iIiIiInHY2m42bb76ZwYMH869//Ysd+Tto2FNMWO+O2LOSdcNPRE6az+djxYoVTJs2jby8PAAsCdE4u2ZpxIKIiEgrUcAgIiIiIiJBk5WVxbPPPstXX33Fv//9byoXr6M+fy8R/btgjnAGuzwROQvV1dUxf/58ZsyYQXFxMdC0eLOjSyaWuMjgFiciInKOUcAgIiIiIiJBZTKZuPLKK+nXrx8TJ05k+fLllExb1LQIdNcsDGYtAi0ix1ZQUMCsWbOYP38+LpcLg9lEaMc0HJ3SMYc7gl2eiIjIOUkBg4iIiIiInBFiYmK49957WbVqFe+++y4l6/Kp37GPiP5dsCbFBrs8ETkD+Xw+Vq9ezcyZM1m3bh0AplAbYb06ENquDUZrSJArFBERObcpYBARERERkTNKr1696NKlC1OmTOGLL76g7OsV2DOTCevTCZNNi0CLCFRUVDBv3jxmz55NaWkpAJbEaBwd07GmxmEwGoNcoYiIyPlBAYOIiIiIiJxxbDYbP/vZzxgyZAhvvfUW+fn5NBQUE9a7kxaBFjlP+Xw+NmzYwOzZs1mxcgU+rw9DiJnQDmmEdmxDSGRYsEsUERE57yhgOEO4XC7ef/99LrvsMjIyMoJdjoiIiIjIGSEtLY2nn36ar7/+mo8//pjKxetw7dhHeP9szE57sMsTkdOgvLyc+fPnM3fu3MCizebocMI6tMGWkYQxRLc2REREgkV/hY/h8ccf5/nnnz/u9g6Hg5qamsMeGzhwIDk5OUc8t0uXLtx+++1HPJ6Tk8O4ceNYsGABdXV1WCwWevfuzV133cWVV1553DWKiIiIiJxNjEYjV1xxBb1792bChAnk5uZSMm1R0xzr7dtoNIPIOcjj8bB69WrmzZvH6tWr8fl8TYs2t0/F3q4NltiIYJcoIiIigCYlPAqXy8Wbb77ZonNGjBhx2P0LFiw4argAcMcddxzx2MMPP8ygQYPYs2cPc+fOpaSkhJycHPx+Pz/+8Y/59a9/jcfjaVGtIiIiIiJnk5iYGB544AHuuusu7BYrVUu/pWz2Crx1rmCXJiKtZO/evXz44Yf89u67efnll1m1ahWm6DAiBnQl/rpLiBjQVeGCiIjIGUQjGI7igw8+wOVy8cILLzB06FCio6OP2PbnP/85q1at4rrrrjvs8bFjxxIeHk5SUtJhjycmJvKLX/zisMeee+45xo4dS0ZGBp9//jlhYU3zSqalpTFlyhQGDBjAv/71L6xWK+PGjWvhVYqIiIiInD0MBgNDhgwhOzubf/3rX02jGT5fRPiAbOzpicEuT0ROQF1dHUuWLGH+/Pnk5eUBYLRacHROx942lZAora0gIiJyplLAcBTjxo1jypQpXH755UdtV1RUxJo1a7Db7fz4xz8+5Pi3337LF198wQcffMCNN97Yohpmz57Nk08+CcBjjz0WCBcOsFgsPPfcc4wYMYJ//OMfjBw5kuHDh7foOUREREREzjZRUVE88MADzJs3j4kTJ1KxYDUN7VKJ6NsZg9kU7PJE5Bh8Ph/ffvst8+fPZ9ny5bgbG8FgwJoSh71tCrbUeAwmTbogIiJyplPAcAQ7d+7k6quvPma4ADBlyhS8Xi9XXnklDofjkONjx46lffv2jBkzpsV1PPPMM/j9fiwWyxHPHzZsGPHx8RQVFfHQQw8pYBARERGR84LBYOCSSy6hU6dOvPbaa+zI24G7tJKooT0xhx/6vlxEgm///v3Mnz+fhQsXUlpaCoA53EFYdgb2rGRMobYgVygiIiItoa8DHEF6ejpPPfXUcbWdPHkywGGnRzowf2SPHj3Izc2lsbHxuGtYtWoVCxYsAKBPnz5ERBx+nkmTycQll1wCwLp161iyZMlxP4eIiIiIyNkuKSmJp59+miuuuAJPeTWlXy7Btbc42GWJyHfq6+uZN28eTz/9NPfeey9Tp06lvLqS0PZtiBnen9jRQ3B2zVK4ICIichbSCIaTVFRUxPz587HZbIwcOfKQ43/7299wu91MnjyZyZMnY7fbGThwIKNHj+bnP/85sbGxR+z766+/Dmz36tXrqHX06tWLTz75BIB///vfDBw48ASvSERERETk7BMSEsIvf/lLOnTowJvj36R8zkrCenfC0Tkdg8EQ7PJEzjs+n4+NGzc2TYG0bFngy3aWpBhC26ZiaxOv6cxERETOAQoYTtKB6ZFGjBhxyPoIlZWVjB8/vtm++vp65syZw5w5c3jsscd48skneeihhw77oWfx4sWB7czMzKPWcfDxZcuWnciliIiIiIic9QYNGkRSUhIvvfQS5Ss34a2pI7xvZ4UMIqdJUVERCxYsYMGCBZSUlABgCncQ1iUde1YKJodGKYiIiJxLFDCcpKNNj2QymXjzzTcpLS1l165dLF68mGXLluF2uwGoq6vjkUceYc2aNUyaNAmTqfm3NzZv3hzYTk5OPmodBx9ft27dCV+PiIiIiMjZLjMzk2effZaxY8eya/MufA1uIgd3w2DUDLEip0JDQwNLly5l/vz5bNy4EQBjiBl7u1RC26UQEhupkE9EROQcpYDhJBQXFzN//nwsFgujR48+5LjT6eTGG29stq+wsJDx48fz4osvUlNTA8BHH31ESkoKL774YrO2ZWVlge0fjo74ofDw8MB2dXU19fX12O32Fl+TiIiIiMi5IDo6mieffJIXX3yRzZs3U+H3Ezmku0IGkVbi9/vJz89n3rx5LF68GJfLBYAlMYbQtinY0hI0BZKIiMh5QO+uT8J///tfvF4vl1122REXYP6hxMREnnzySdavX8+QIUMC+19++WVWr17drG15eXlg22Y7+jBSq9Xa7PeqqqrDtmtoaKCqqgqv13tc9YqIiIiInK1CQ0N55JFH6NKlC66dhVQuWY/f7w92WSJntZqaGr788ksefvhhnnzySebMmYPbZMDZvR1x1wwl5vK+2LOSFS6IiIicJxQwnISjTY90LOnp6cycOZP+/fsDTQtgvfPOO83aHDyE9FjDSc3m5oNRfD7fYdu98MILREREsHz58hbXLCIiIiJytrFarTzwwAO0b9+e+m0FVOduCXZJImcdv9/Pxo0b+cc//sFdd93FpEmT2FtQgC0jkejL+hB3zVDCerTD7AwNdqkiIiJymilgOEEHpkcym81cddVVJ9RHaGgob7/9diA8+OFN/4OnRTow3PRI6uvrm/1+8JRJB3v00UeprKykb9++J1KyiIiIiMhZx2az8eCDD5KUnEzthu3Ubdsb7JJEzgq1tbXMmDGDBx98kGeffZZFixaBw0Z4n07EX3cxURf2xJoUq/UVREREzmNag+EETZkyBa/XyxVXXEF0dPQJ95OdnU2/fv1YunQpJSUlzY6lpKRQWloKNL2xO5q6urrAdlhYGA6H47DtrFYrVqv1kAWlRURERETOZU6nk4cefJA/PP44VTnfEhIVTkjU0dc5Ezlf7dixg6+++opFixfjbmzEYDJiz0omtH0bQuK0YLOIiIh8TwHDCfr3v/8NnNj0SD/Uq1cvli5ditPpbLa/Q4cOrF27FoB9+/YdtY+CgoLAdnZ29knXJCIiIiJyrklISOCuO+/kpZdeouKbNYR2SGt23Odq0BBvOW95vV5WrlzJjBkz2LRpEwCmsFDCumUS2jYFo9US5ApFRETkTKSA4QQcmB7JZDJx9dVXn3R/kZGRQNMC0AcbNGgQ//nPf4Cmb5Aczc6dOwPbAwcOPOmaRERERETORb169eLyyy9n1qxZVC379pDjYVFRQahKJHjq6+uZO3cuX375ZWAEvTUljtCOaViTNf2RiIiIHJ0ChhNwYHqkSy+9lLi4uJPub8+ePQAMHTq02f7hw4dz3333AZCbm3vUPlauXBnYHjVq1EnXJCIiIiJyrvrFL35B7969D7vOWWpqahAqEjn9KioqmDFjBl9//TV1dXUYQsyEdkzD0Skdc/jhp9wVERER+SEFDCdg8uTJQOtMj9TY2MisWbMwm82MGTOm2bHOnTszcOBAlixZwrJly6irqyM0NPSQPvx+P3PnzgWgXbt2XHTRRSddl4iIiIjIucpoNNK9e/dglyESFGVlZXz++efMmTMHt9uNyW4l7IIOhLZvg9EaEuzyzhu+hkYA3n//faxWa7NjJpOJ+Ph4EhMTSUhIIDY2FqfTqdEkIiJyRlLA0ELFxcXMmzcPo9HItddee9S2e/bsobq6mo4dO2I0Hn4213HjxlFYWMgTTzxBVlbWIccfffRRRo8eTV1dHVOmTOGmm246pM28efPYvXs3AA8//PARn0tERERERETOTxUVFUydOpU5c+bg8XgwOUOJ6N0Be1YKBpM+Q55unspaAJYtW3Zc7S2WEKKioomKiiIyMpLIyEjCw8MDj7CwMMLCwnA6nTidTt0XEBGR00YBQwsdmB5p6NChJCQkHLHd+vXr6dmzJ16vl7S0NF599VWuuuqqZm2++uorHnvsMX7+85/z5JNPHrafUaNGcfPNNzNp0iSeeeYZrr322majGDweD4899hgAw4YN47bbbmuFqxQREREREZFzQX19PdOnT2f69Ok0NDRgCgsloltn7JlJGHQTOuh+3yOOKKup2T63D4rr3RTXeyhzeSlv8FLR6KGyvIQtRfvx+Y/ep8FgIDTUjtMZhsPhOOQRGhoa+Hlg2263B/aFhIRotISIiBw3BQwtdLzTIx38x3jXrl1cffXV/OQnP+G2227DYrEwdepUJk6cyFNPPcUjjzxy1D/eb7/9NmazmXfeeYeRI0fy1ltv0bZtW/bt28dvf/tbcnJyGD16NO+//77eBIiIiIiIiAh+v58FCxbw0UcfUVVVhcluJWJAV+xtkxUsnEEiLSYirYfemomzH/52jc/vp9bto6rRS43bR43bR7XbS91323UeH7VuH3UeN7UVJVSUFNHgPUYi8QNms5lQu53Q7wKHg8OHA9sH7/thWGGz2TSCQkTkPKKAoQUOTI9kMBiOOT1SdnY2c+fO5Y033uCbb76huLiY6dOnk5OTQ9euXbniiivYunUr8fHxx3zekJAQJkyYwC233MK4ceO44oorqK6uxmaz0a1bNz799FNGjRqlcEFERERERETYvXs3EyZMYPPmzRhCzIT1bI+jcwYGs+nYJ8sZzWgwEGYxEWY5/n9Lr89PvacpfKj3+pt+un3Ue33Ue3zUe/y4vtt2efzf7a+nvqyO8mJfiwMKg8EQCCB++DgwhdPBj4OndzKZ9N+oiMjZRgFDC8TFxeHxeI67/YUXXsiFF17Yas9/8cUXc/HFF7dafyIiIiIiInLu8Pl8TJs2jcmTJ+P1erGlJxLepxOmUFuwS5MgMhkNOC0mnC0IJQ7m9ftxHRRC1Ht8uLz+wHb9D47Ve/zUedzUV5Wxv7yUeo/vuJ/L4QglPDyCsLAwIiIiCA8PJyIiotkjMjKSiIgIbDb9dy0iciZQwCAiIiIiIiJylisrK2PcuHFs2rQJU6iNqAE9saXEBbssOYZX1hZzZ9dY1pe6WFxYQ9doO1emh/Ni7n4Aftc9nkX7alhZXEfvuFAGJzt5dU0RAA9ekMCXO6tYX1bPoEQn3WJsvLmhBIDH+yTySV4FWytcXJISRnq4hXc3lmIxGni4dyLvbSplV3Ujw9LCibGZ+XBLGWEhJu7pGc9bG0oorHNzVWYkZiP8N7+CWJuZO7vFMW5tMeUNHsa0i8JtNLCgoIYURwi3donlr7n7qfP4uKljNPvrPMzaXUV2tI2fdYjiTysK8fhgVGYEeZUu1pW6iLObaRdhYUFBLX4/pDhCKHG5KNxXx759+4752hkAo8lE27ZtqaiooLq6mo4dO9KuXTtmzZpFSEgITz/9NGFhYZjNuv0lInKq6P9hRURERERERM5i27Zt48WXXqKyogJbeiIR/btgtFqCXZYI0DStk9FgwGT0k+QIocbtZUtFA8mOEIalRbCquB6AmzpFs7KojqX7a+kRY2dQopNx64rx4efKtHByi+vZXdNIjM2MzWxge1UjPq+XrVu2cGASp9WrV7N69erAc//f//0fAE6nMzAC4oejIsLDwwMPjYwQEWk5BQwiIiIiIiIiZ6k777yTyspKAKwpcbhLK6lZm4+zZ3tKpi0CIGZYf+q27KZ+ewH2zGRCO7ShdOZSAGJHDKZmzVZce4oI7dAGW5sEymavACDu6qFU5mygsbAUR5dMLLERlC9YjcFsIm7UEMoXrsZdUomzeztMTjuVi9dhtFuJHT6Asjkr8VTWEN67E5iMVC37FlNYKDGX9aX0q2V4a+uJ6J+Nr9FNde4WzFHhRF98ASXTF+NrdBM5pDueilpq1udjiY8icnB3ij5dCD4fURf3orGwlNpNO7EmxxLepzPFny0EIPryvtRv20d9/h5s6Yk4szMp+WJJ07VeOZCab7fj2lmIvW0K9qxkymYtb7rW0RdStWITDQXFODqlY0mMoXzeKjAaiL9qKBWL1tJYVI6zaxbmyDAqvlmDwRJC3IhBlM/PxV1WRdgFHTBaQ6jM2YDJYSPmiv6Ufr0cb3Ud4X07g89P1cpNmCOczf4Nx3836uDarEiyY+y89N3oBYBX1xYxPC2cJ/om8Y+1xawsLgocW7q/lmvaRlLR6GVxYQ2LC2sA6BvvaLZG49y91bAXsqPtXNs2ks+2V7CruhGAmbuqyAq38kTfJGbtruLZ5d+PHFi2v5bbs2Mpb/AyZ0914FiExUTHKBuriusA2Fvr5tnl/7+9O4+Pqrr/P/6efUlmsofdsC+iIEJYRbDuUhDEpSputa3Vute6VsG9imKrtlprxX5r6w9rVai4K4iyJSxhDwQI+5Z93yZzfn8ERiJhCSSZJLyej8c8cufce8/9zCR+Bu9nzjm75bBa9GhyO6XnlevL7YWSpMzCCj2ZulsPDWyrXSVV+vva7FD/6XnlKg0E9evTEvT66iz9ZVVWaN+QthHyOCyyWSWbLPpqR5Ek6VenxSvJ59TTS/Yo2lUz5ZMx0pjOfrWLcOittTkqqzYyxihopLZeu5w2q7YUlmhXcbF27tx5tP+k5HI6ZbFaFQgEZLVaZbVa1bFjR51zzjlasWKFNm7cqGnTpsnppIgHABIFBgAAAAAAWqS1a9eGigv+IX1lqqoUKCgOc1RA07JYJI/dqvYRTrlsVhkFVTOBknRW+0gNSozQn1dmKbciIGMkI2lggkc9ot36eEuBcspr2oOS/E6bPDajPaUVCgR/WNx648aN2rhxY+j5TTfdpISEBCUlJemKK65Qx44dm/ZFA0AzYjHGmKMfhtZm2LBhWrRoUa22oUOHauHChWGKCAAAAABwrPbt26cHH3pIFRUVivnJmXK1iw93SKiHfR/NU3VRqaJdtha7BkNJIKjZWwqOuAZDF79Lk3rF6pkle1RtjG4+NV4Z+eWat6tYvWPcmtA1Ws8u3SNJuu30hB+mSIr36NyOPk1Lq3mt95yRqDk7ipWWXarBbSI0KNEbGvHw0MC2+mhzvtbllWtk+0j1inbrzbXZslosemRQW/1rfa42F1bovE5+tfPa9c/1ufLYrbpvQBu9tTZHO0sqNaZzlCLsVs3IyJPfadOkXrH65/pc5ZQH1DvapZyKam0rqlR1HXfQ+vfvrwceeKCx/2QAoEkczz1jRjAAAAAAANCCBINB/fnPf1Z5WZmiR/SjuICTgpGRMVJV0KioslrVwZrRCNuKKlVQWa3KaqNdJVUKBI3KA0YWi9GX2wq1o7hSJVVBpe4tkcNqUWFlUMVVQb2Utk97S6tUWW00IyNPVdU1Yx9yK6r11JI9oesu2b9GxMFsNpsSEhLUoUMHXXbZZU33JgBAM8QIhpMUIxgAAAAAoGVauHChXnnlFbk7t1PMyP7hDgfH4cAIhsnJbRXjbpnf/TTGqCooVQWDqgqa0KOy2tR6XhU0qqo2qvxxW1ChfYccf9B5VeaHYxryBpbT6ZDL5ZLb7ZHH45Hb7ZbH4wk9vF6vvF6vIiMjFRERIZ/PJ5/Pp5iYGPl8Plmt1gaMBgCaB0YwAAAAAADQyn3yySeSxSLfGT3CHQqaueqgUXm1UUV1UBX7f1ZWG1UEjSqqjSr3t1fuLwxUBo2qqoO1n9faV3PD/0ARoTE4nQ45HU453E65nE75nE45f/RwOGqKAz9uP9D2459ut1sul6vWcwoEANAwKDAAAAAAANBCFBcXa9OmTXK2i5Pd5w13OGgCQWNUGgiqqDKo4qpqlVQFVRIIqrQqqNJAzaMsYFQWCKq8uma7fH/hoCGKAA67XU6XU063Sy6nU/4j3Ng/cPP/x/vrehx83IFth8Mhi8XSAO8aAKCpUGAAAAAAAKCF2L59uyTJERcV5kjQEB5P3SO79cg31IPG6FjrBG63W16vV1EHTfVzYPqfgx81UwP98K3+ur7hf2Cbb/oDAI6EAgMAAAAAAC1EMBiUJFlstjBHghNhi/SouqhUXbp0kcPhOOKxVqtVfr9fUVFR8vv98vv98vl8ioiIUEREhCIjI0PrBVAMAAA0NQoMAAAAAAC0ED6fT5JUXVIW5khwImxetyTpnnvuUXx8fJijAQDg+FFgAAAAAACghejYsaMiIyNVvGmnKvfkhNr9yX3k7piorNkLZCqrQu2R/bvL27WDcr5MUXXxD0WJiN5JiujTWXnfLldVbmGo3dO1g3z9uyt/4epa/bs6JioquY8Kl65X+bY9oXZnYoyiR/RT8apNKt24I9Ruj/Yp9pwzVbJhm0rWZIbarV634i8corItu1W0fEOo3WKzKWHcWarYla2CxWtqveaEcSNVlVek/O/SarXHXTRUpiqg3K+X1GqP/clAWd1OZX+ysFZ79Ih+ciREK+ujebXa/YNPlbtDgrI+ni9TFTjoveshb9f2yvkipVZBJ6JPZ0X0Tjr0vevWQb5+3ZW/YJUq9+aG2t0dE+VP7qPCpekq37ZXkhQsrxQAAK0BY+cAAAAAAGghrFarBg8eLBkjE6gOdzgAAOAkZzHGHONSQWhNhg0bpkWLFtVqGzp0qBYuXHiYMwAAAAAAzUFWVpbuufceWTxuxf90uKwOJidoafIXrFLZpp16+eWXmSIJANBsHM89Y0YwAAAAAADQgiQkJOjScZequrhUBYvXiO8NAgCAcKHAAAAAAABAC3PZZZepZ8+eKs/creKVG8MdDgAAOElRYAAAAAAAoIWx2Wy655571KZtWxWv3KTi1ZsZyQAAAJocBQYAAAAAAFqgqKgoPfzQQ4qLi1PR8g0qWpJOkQEAADQpCgwAAAAAALRQCQkJevzxx9WpUyeVpG9V3txlClZWhTssAABwkqDAAAAAAABACxYbG6vHHntM/fr1U8WOLOV8slBVuYXhDgsAAJwEKDAAAAAAANDCRURE6P7779f48eMVKCpVzqeLVLw2kymTAABAo6LAAAAAAABAK2C1WnXllVfqgQcekN/nU9HS9cr9KlWBotJwhwYAAFopCgwAAAAAALQi/fv313PPPafk5GRV7slV9sfzVbJui0yQ0QwAAKBhUWAAAAAAAKCV8fv9uvvuu3XHHXco0uNV4ZJ05Xy2iLUZAABAg6LAAAAAAABAK2SxWDRs2DBNnTpVZ511lqpyCpT9yUIVpK5TsLIq3OEBAIBWgAIDAAAAAACtmN/v12233aZHHnlE7du1U2n6VmXN/E6lG3ewCDQAADghFBgAAAAAADgJ9O3bV3/4wx909dVXy2EsKli4WjmfLlJlVn64QwMAAC0UBQYAAAAAAE4SdrtdY8eO1bRp0zRixAhV5RQo57NFyv9+papLysMdHgAAaGEoMAAAAAAAcJKJiYnRb37zG02ZMkVdu3ZVWeYuZc38TkUrMhSsCoQ7PAAA0EJQYAAAAAAA4CTVs2dPPfHEE/r1r3+tKL9PxSs3KXvW9yrdvJP1GQAAwFFRYAAAAAAA4CRmtVp19tlna9qL03TZZZfJGgiqYP4q5Xy6UJX78sIdHgAAaMYoMAAAAAAAALndbl1++eWa9uKLOuuss1SVU6iczxcrb16aAsWl4Q4PAAA0Q/ZwBwAAAAAAAJqPuLg43Xbbbbrwwgv1z3/+Uxs2bFDFjn3y9ums8szdkkWK/clAle/Yp9IN2+XumKjIM3oo++P5NedfOESlG7arLHOXPF3ay9uzk3I+XyxJih8zQsUrMlS+Y5+8PTvJ3amNcr9eIklKGH+2ChatUeWeHEWc2kXO+CjlzUuTxW5TwtizlPddmqqyCxTZr7tskR4VLFglq8el+IuGKvebpQoUFMs/sLdks6owZa1sPq/izktWzhcpqi4pU9SQvgpWVqlo+QbZY/yKHT1A2bMXKFhZpeiz+imQX6Li1ZvkTIxR9Ih+2jfzOykYVMzoM1W5J0cl6Vvlah8v/6A+ypr1nSQp9vxklW3erbJNO+ROaqvIvl2U/cnCmtd68TAVr81U+dY98nTrIE/X9sr9MlWS5EiMaepfKwAAjYIRDAAAAAAA4BDdunXT5MmTdeeddyouJlYlqzerurRMwapqsTwDAACQJIth1aaT0rBhw7Ro0aJabUOHDtXChQvDFBEAAAAAoLmqrKzU7NmzNXPmTFVWVsqREK2o5D5yxEWFO7QWKX/BKpVt2qmXX35Z8fHx4Q4HAABJx3fPmBEMAAAAAADgiJxOpyZMmKAXX3xRw4YNU1VWvrI/XaSClLUKVlSFOzwAABAmFBgAAAAAAMAxiYuL0x133KFHH31UHdq3V+n6bcqa9Z1KN+8SEyQAAHDyocAAAAAAAADqpU+fPnr22Wd17bXXymEsKpi/UrlfL1GgqDTcoQEAgCZEgQEAAAAAANSb3W7XmDFjNHXqVA0YMECVu3OU/b/5Klm3RSbIaAYAAE4GFBgAAAAAAMBxi4+P13333ac777xTvogIFS5JV84XixUoLAl3aAAAoJFRYAAAAAAAACfEYrFo6NChev755zV8+PCaRaBnL1BpxnbWZgAAoBWjwAAAAAAAABqE3+/X7bffrrvuuksel1sFi9Yo79vlClZUhTs0AADQCCgwAAAAAACABjVkyBA9/9xz6tu3ryq271P2JwtUmV0Q7rAAAEADo8AAAAAAAAAaXGxsrB566CFdfvnlCpaUK/fzRSrN2B7usAAAQAOiwAAAAAAAABqF1WrVZZddpocfflgREREqWLRGBSlrZYLBcIcGAAAaAAUGAAAAAADQqPr27aunn3pap5xyikrXb1Pe3OUKVgXCHRYAADhB9nAHAAAAAAAAWr+EhARNmTJFr7zyipYvX67cL1IUe+4gWd3OcIcGAM1eMBjUunXrNH/+fBUU1G9Nm4iICN14443yer2NFB1OZhQYAAAAAABAk3C73br33nv11ltvac6cOcr5sqbIYPO6wx0aADQ7xhhlZmZq8eLFWrBggXJyco67rxEjRqh///4NGB1QgwIDAAAAAABoMjabTb/4xS/k9Xo1e/Zs5X6ZqtgLBsvmcYU7NAAIu8rKSq1bt05paWlasmRJqKhgdTrk6d5R3u4d5Ij1H3N/xWu2qHhFhowxjRUyTnIUGAAAAAAAQJOyWCy65pprZLPZNGvWLOV+vURx5yfL6mK6JAAnl2AwqG3btmn16tVavXq11qWnq6qyUpJkdTnk6dpe7lPaytU+XhZb/ZfTPZ5zgPqgwAAAAAAAAJqcxWLRVVddpcrKSn322WfKm7tcseclczMMQKtWVVWlzMxMbdiwQenp6UpPT1dpaWlovz3Gp4geHeTukCBHQrQsVnIimjcKDAAAAAAAICwsFosmTZqkwsJCLViwQPkLVin6rH6yWCzhDg0ATpgxRtnZ2dq0aZM2btyojRs3avPmzQoEAqFjbD6vPN07ytU2Vs62cUwXhxaHAgMAAAAAAAgbq9WqW265Rbm5uUpPT1dJrE+RfbuGOywAqBdjjHJzc5WZmaktW7Zo06ZN2rx5s4qKin44yGKRI8Ynb2KMnAnRcibGsMg9WjwKDAAAAAAAIKwcDofuuusuPfLII8pdniFHXJRcbePCHRYA1CkQCGj37t3aunWrtm7dqm3btilzyxYVH1xMUM3oBHdSWznio+SMj5Yj1i+L3RamqIHGQYEBAAAAAACEXVRUlO655x5NmTJFBd+vVNxPR8jmZtFnAOFjjFFeXp62b9+u7du3a9u2bdq2bZt27dpVa5oj6aBiQpxf5VRqoQAAPXhJREFUjhi/HHF+Fq7HSYECAwAAAAAAaBa6deumq6++Wu+8844KFq5WzOgBrMcAoEkUFhZqx44d2r59u3bs2KGdO3dq+/btKikpqXWcxW6TPTpS3hif7DF+OWJ9skf7ZHVwmxUnJ/7yAQAAAABAs3HRRRcpLS1Nq1evVtnmnfJ26xjukAC0EsYYFRYWaufOnaEiwo4dO7Rjx47aayVIksUiuz9C7qS2skf75IiOlD3GJ1ukh8IncBAKDAAAAAAAoNk4sOjz7+6/X0VL1svVLp5FUAHUizFG+fn5oSLCgceOHTtUXFxc+2BLzfRGrk5taooIUZGyR0fK7o+QxWYNzwsAWhAKDAAAAAAAoFmJi4vTtddco7///e8qTF2nmFEDwh0SgGbIGKPc3NzQKIRQIWHnDpWVltU+2GKpWSfhlDY1RYSoCNmjfRQSgBNEgaGBGGO0Zs0aLV26VDfccEO4wwEAAAAAoEU755xzNH/+fKWnp6t8xz65OyaGOyQAYXJgRMKB9REOfpSXl9c+2GqR3bd/aqODRiNQSAAaBwWGenj99dd16623HvGYlJSUOtuzs7P1yiuvaObMmaGV5jt37qyrr75at956qyIjI496/UWLFunVV1/VvHnzVFpaKqfTqYEDB+q2227TxRdffFyvCQAAAACA5shqtermm2/Wgw8+qMLUdXK1jZPFbgt3WAAaWWlpqbZv337I45DFlq1W2aIi5G4bHSokOKIiZfN5ZbFSSACaCgWGYxQMBjVt2rQjHnPGGWcoOTn5kPZ58+bp8ssvV2Vlpd5++22NGzdOVVVVev311/Xb3/5Wb731lj766CP16tXrsH0/8MADmjp1qs4++2zNmTNH3bp107Zt23Tbbbfpkksu0S9+8Qu99tprstv5lQIAAAAAWocOHTpozJgxmjVrlopXb5bvjB7hDglAAzHGKCcnR1u2bNGWLVu0bds2bd26VVlZWbUPrLXYMoUEoLnhbvQx+uCDD5SRkXHYIoDD4dDjjz9+SPuaNWs0btw4FRQUaNasWRo7dqwkyeVy6a677lJlZaXuv/9+XXTRRUpNTVV8fPwhfTz11FN6/vnn1blzZ/3vf/+Tz+eTJJ1yyin64IMPNHToUL355ptyuVx69dVXG/BVAwAAAAAQXuPHj9f8+fOVuyZTnq7tZfdHhDskAPV0YK2EzZs3a9OmTcrMzFRmZuYhCy5bPS652sfLHuOTPdoXWnSZqY2A5osCwzGaOnWqLrjgAn3++efHfE5VVVWouDB8+PBQceFgd911l15++WVt2bJFt99+u/7f//t/tfZ//fXXeuyxxyRJDz/8cKi4cIDT6dRTTz2lMWPG6M9//rN++tOf6qKLLjqOVwgAAAAAQPPjdrt1/fXX66WXXlJBylrFnjtIFosl3GEBOIJAIKDMzEytX79eGRkZyti4Ufl5ebWOsfm8cie1lSPOL0eMX/YYn2weV5giBnC8KDAcg7lz5yolJUXfffddvc575513tHnzZknSpEmT6jzG6XTqyiuv1LRp0zRjxgzdd999GjRoUGj/E088IWNM6Li6XHjhhUpMTNS+fftCoyEAAAAAAGgtBg0apDPOOENpaWkq37ZXnqS24Q4JwEGCwaAyMzO1atUqrVmzRhsyMlRVWRnab/O65T6ljRzx0TUFhVi/rE5HGCMG0FAoMByD559/XomJiSopKVF2dnad0xjV5eA1G84777zDHnf++eeHjv3b3/4WKjAsW7ZM8+bNk1Tzj6moqKg6z7fZbDrnnHM0Y8YMrVq1SgsXLtSwYcOOKUYAAAAAAJo7i8WiG264QWvWrFFR6jq52sVxcxIIs/LycqWlpWnZsmVKS0urNd2RPcYnb5e2craJlTMhWjavO4yRAmhMFBiOYvXq1fr0008lKTQyoHfv3rr44ot17bXXauDAgXWet2/fPq1evVqS5PP51L1798Ne48wzzwxtv//++3rttddktVr11Vdf1XnM4fqYMWOGJOm9996jwAAAAAAAaFXatGmjyy67TDNmzFDR8g2KGtI33CEBJ5277rpLVVVV6tKli1auXKnq6mpJNWsnWOw2Wew2xZ6frPIte1SWuUtWp0POhGjt+/BbSVL8mBEqXpGh8h375O3ZSe5ObZT79RJJUsL4s1WwaI0q9+Qo4tQucsZHKW9emix2mxLGnqW879JUlV2gyH7dZYv0qGDBKlk9LsVfNFS53yxVoKBY/oG9JZtVhSlrZfN5FXdesnK+SFF1SZmihvRVsLJKRcs3yB7jV+zoAcqevUDByipFn9VPgfwSFa/eJGdijKJH9NO+md9JwaBiRp+pyj05KknfKlf7ePkH9VHWrJpZTmLPT1bZ5t0q27RD7qS2iuzbRdmfLKx5rRcPU/HaTJVv3SNPtw7ydG2v3C9Ta17ruJEqXJKuil1ZiuidJGfbOOXNXSZZLUq89Gzlz1+pyn15ijytq+zRPuV/v0IWp0MJY4Yr79vlqsotlG9AT1ldDhUsWiNbhFtxFwxRzlepqi4qlT+5jxQ0KlyaLu2fUm7FihXKzc2t9fu0Wq2Kj49XXFyc4uLi5HQ6G/+PCK0OBYajmDp16iFt6enpSk9P10svvaRLLrlEb775ptq1a1frmAULFoS2O3fufMT5IRMTExUREaGSkhLl5uZq06ZN6tGjR60+unTpcsQ4D96fkpJy1NcFAAAAAEBLM2bMGC1YsEDbN2yXu3M7udrEhjsk4KQQDAaVkpKi3NxcVVdXa/ny5bK4HLLYLHJ1SJB/8KnKmV1zH4vRRc2LqQpI0jGtK+v1ehUdHa2oqCj5fD75/X75fD5FRkYqMjJSERERtR4ej0cul4t1cU5yFmOMCXcQzdk333yjTZs2KSsrS0uWLNH333+vrKysWsd06NBBc+bMUY8ePUJtzz33nB588EFJNWskfPbZZ0e8To8ePbRx40ZJNaMYJk6cqD59+ig9PV2S9O677+pnP/vZYc+fP3++zjrrLEk1IyYKCwuPeL1hw4Zp0aJFtdqGDh2qhQsXHvE8AAAAAADCafPmzXr00UdljfQofsxwWR0t77uT+QtWqWzTTr388svHPA0zEC5ZWVl67bXXau5RWS3ydG4nb89OcsRHc2O5Bcj9ZqkqdmYpOdGrPrG1p6oKBI1yy6uVXxFQfmW1CiuDKqysVklVUMd6w9hmtcrj9cq7/+HxeI74cLvddba53W5ZrdaGfwNQL8dzz7jlfQo3sZ/85Cf6yU9+EnpeVVWlDz/8UE8//bRWrlwpSdq5c6cuvvhiLV26NLROwsFDjnw+31Gv4/f7Q9v79u2rdx8Hn19UVKSysjJ5PJ6jXhcAAAAAgJaka9euGjdunGbOnKmiZeuZKgloRHv27NGUKVNUWFgo9ylt5Duzl+w+b7jDwnEYkODRqbHHdq8waIxKA0EVVwVVWhVUSaDmZ2kgqLL9j9KAUXn1gecVKi8o097cbJUHjr048WNul0ueIxQpDhQwvAcVNLxeryIiIkI/7XZudzc13vF6cjgcuvLKK3XZZZfp6aef1pQpUyRJmzZt0uOPPx5arDkvLy90jtt99IVsXC5XaPvA6IP69HHw+Qf6qKvAUFFRoYqKitAceQAAAAAAtDQTJ05UWlqatm6omRPd3alNuEMCWqU33nhDhYWF8g/uo4heSeEOB03EarEo0mFTpMNW73ONMaoMGpUHjMqqg6qoNioP7P950PPyaqOK6pqf5dVBVewvWJSXF6m4pFDZgaAqg/UvVbicTkXsn87p4KmdDp7qKTIyUj6fL/SIiIiQzVb/14oaFBiOk91u1+TJkxUZGan77rtPkjR9+nS98MILslqttYaIHctwsYOra8Fg8JDzjtbHj6tzB/r4sWeffVaPP/74UeMBAAAAAKC5stvtuv322/XII4+oYMFqOX7qly2CUfxAQyouLlZ6erqcbeMoLrQC727I0+397FqdU64Fe4p1WqxHFyf5NXX5XknSnf0SNX93sZZmlWpgglcj2kfq5RU1s6z8bkAbfbq1UKtzyzS8baROj3Prr2uyJUm/H9RWMzbmKyO/XOd08CnJ79Tb63LktFr0wMC2+kd6jrYVVerCU/yKc9v17w258jlsuvuMRP1tTbb2lFbp0i7Rslul/27KV7zbrltPT9CrK7OUWx7QpV2jVFhRra92FCvebdNPOvn03435qqg2OiPerdyKam0urJTXblWix65NBRXKz8tVaUGetlcfe4HCYrHIarXqlFNOUUlJiQoKCtSpUyd1795d33//vaxWq26++WZ9//332rRpk8455xwNGDBAXbt2bfhfVgtDgeEE3XvvvXrvvfeUkpKi/Px8bdy4UT179qw1pVF5eflR+ykrKwttH5juyOfzKScn55j6OPj8g/v4sYceekj33nuvzjvvPKWmph41LgAAAAAAmqMOHTroxhtv1BtvvKG8b9MUd+EQWWzM3w00FJvNJovFIsMsGAgTi0Vy26yqdkp2qxThsKpvrEefOQplsQQ1qE2E9pYGtK8soC5+lyb1itUzS/ao2hjdfGq8NuSV69tdxerqd2pEu0hNX5cjI2l42whtLqjQ9pIqRTltirRbtbW4Uqa6Wtu3ZCqwvy6xcePG0Jq5kvTSSy+Ftv/73//qv//9r+x2u7wej7z7p2n68RROR3veGqZ1atnRNwMWi0U33XSTUlJSJEnZ2dnq2bOnOnToEDqmpKTkqP2UlpaGttu1ayep5h9LBwoMR+vj4PMPDO2pi8vlksvlYtgPAAAAAKDFGz16tDZs2KC5c+eqIGWtoob2ZdFZoIF4PB7169dPK1asUNnWPfIktQ13SDhBb+wfdXBZ12j1jfPohf2jFyTp5ZX7dNEpfj2a3E5/XpmlpVn7QvsW7y3RhG7Ryq+s1oI9xVqwp1iSlJwYUSvnztlZJO2U+sZ6dFm3aM3KzNe2okpJ0ufbCtXV79Kjye305fZCPZm6O3Reyt4S/aJvvPIqqvXNjqLQviinTb1i3FqWVXPfc2dJlZ5M3S2H1aJHk9spPa9cX26vmWo+s7BCT6bu1kMD22pXSZX+vrbmtVot0paiSl3a1a47+yfq9dVZWrq/v0iHVTefGqdIh1XT0n54vcZI47tGKc5t119XZyu4v83IqGe0W5EOq5ZmlSoQlEx1QCXFRaoqLVaupMp6jJo42IEZcfx+v7p166a9e/cqJydHV1xxheLi4tSuXTu1b9++WX7GUWBoAGeeeWZoOzIyUpLUs2fPUNvu3bsPOefHdu3aFdru27dvqI8DC0kfrY+6zgcAAAAAoLW78cYbtXXbNmVu3CxHjE8RvZnKBWgo1113ndLT01WwYJWsLodcbePCHRKOU3FVUFEWmyqDQf2/jDydmV8hY6S8ipoRKlFOm1Zkl+mzbYX7b6ZLBZU1+wJBow835WtdbpnsVoucVqsKq6r17a4iXXiKT5sLKpRXUS2PzSq7taZgkJFfrj6xbhVVBhUwRh67VUVV1Xoydbeqg5LNKhVWBlVtjCLsVq3NLdMnWwpUGTTyO20qqKhWQUW11ueVK7Owpn+7xSKf06rs8oCeTN2tke0jQ2s9HNj33NI9+9dusKgqWLPGg8NqUSBo9OeV+1RYFZTfaVPF/vUgvtlRpPM6+mq9D2WBoN7flK/hbSNktVpUvP99iHbaVFkd1LriSjmtFjmtFhVV1eyb2D1a6/MqtDyrVE6bRVaLRSVVQUU4rPpZjxj9a31uKBZJqjZGHps19D4cmO4+Ly9PS5YsCf3e/vGPf4S2+/btq0ceeaRR/06OBwWGBhAdHR3abtu2ppo7dOhQWa1WBYNBbdmy5YjnFxYWKj8/X5IUHx+vHj16SJKGDx+u999/X5KO2sfWrVtD28OGDavfCwAAAAAAoIVyOp367b331qzHsCRdNp9X7g4J4Q4LaBXat2+vu+++Wy+8+KLyvlkqi8Mui/2HWTH8yX3k7piorNkLZCqrQu2R/bvL27WDcr5MUXXxD9N6R/ROUkSfzsr7drmqcgtD7Z6uHeTr3135C1erck9OqN3VMVFRyX1UuHS9yrftCbU7E2MUPaKfildtUunGHaF2e7RPseecqZIN21SyJjPUbvW6FX/hEJVt2a2i5RtC7RabTQnjzlLFrmwVLF5T67UnjBupqrwi5X+XVqs97qKhMlUB5X69pFZ77E8Gyup2KvuThbXao0f0kyMhWlkfzavV7h98qtwdEpT18XyZqsBB710Pebu2V84XKaouOei969NZEb2TDn3vunWQr1935S9Ypcq9uaF2d8dE+ZP7KFBQM9og0mHVrafHh9ZgkKQ7+iUcsgbD7tIqDUqsvQbDiHaR+nRroXxO2yFrMEhS1yiXqo2ptQaDJI3rEq28iupD1mCIdtVeg+G8TjVTvbvtVnU8aA2GvIqa96WL36W1ueXqEOHQz0+N14vL96o0EFSSz6mxXaL05fbCWlMk2a3SzafGKyO/XPN2Fat3jFt+p002q0UxLptuOz1BS/eVavHeEkU4rPI6rIpx1fxd39U/QXN2FCstu0yVQaPre8Xo/9LzZGQ0qVes5uwsUmW1Ud9Yt9p47fp6R7EsksoCRrtLq+SwWZTkc8pts2ptXrmCxmjJvlLZrRbZgkZRTquCqinslFUHZTdWHeuYh8PNWBNuFBgawI4dNYmsV69eSkxMlCTFxcVp0KBBSklJUV5enrZu3aqkpLq/RbF06dLQ9iWXXCKrtWbOyIsuukj33nuvJGn58uVHjOHgPsaOHXv8LwYAAAAAgBYmNjZWv/vd7/TEE08of16a4i4YLEdcVLjDAlqF/v376/7f/U4vvfSSysvLZXHaZXU6wh0W6unqnjFq43Wojdehczv9sHbso8ntQtuXdI7SJZ2j6tw3oVu0JnSLrnPfz3rE1LrWwftu6B132H2/7BsvSTLGKGhqFpMOBI3yKgK6qke0AkGpKljzrf+ruseoyhityC7V6A4+VQWNdhZXKWCMhrWNUFXQ6H+ZBeoT61YgaPTtziIFglLHSKfyK6r1tzXZinbZFAhKb63NUSBYc1t/6b4yLd5bqkDQKBA0emzxD4Wsb3YU65sdxaHnL6/MCm0v2vvDdPVSzQLVB6zLq6i1Ly37h0JRfrVVLpdLMRE109i73e7QT4/HI7fbrYj96zlERkbK7/crOjpabdq0Oeyau+FGgaEBzJ49W5J07bXX1mq/+eabQ2szzJkzRzfeeGOd53/99deh7V/84heh7T59+mjYsGFauHChUlJSVFpaKq/Xe8j5xhjNmTNHktS9e3eNGjXqhF4PAAAAAAAtTdeuXXXHHXdo2rRpyv1mqeIuHCK7v3l+2xNoaU4//XQ9/vjjmjp1qrKzs+VoG6eo4afL6qi5tZgwZnid58WdP7jO9phRA+psjx52Wp3t/oG95B/Y65D2yNO7KfL0boe0R/Q8RRE9Tzmk3dO5nTyd2x3S7mo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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Identify common lengths for each group\n",
    "lengths = [len(estimate_1_inter), len(estimate_1_intra),\n",
    "           len(estimate_2_inter), len(estimate_2_intra),\n",
    "           len(estimate_3_inter), len(estimate_3_intra),\n",
    "           len(estimate_4_inter), len(estimate_4_intra),\n",
    "           len(estimate_5_inter), len(estimate_5_intra)]\n",
    "\n",
    "# Find the minimum length\n",
    "min_length = min(lengths)\n",
    "\n",
    "# Slice arrays to the minimum length\n",
    "estimate_1_inter = estimate_1_inter[:min_length]\n",
    "estimate_1_intra = estimate_1_intra[:min_length]\n",
    "\n",
    "estimate_2_inter = estimate_2_inter[:min_length]\n",
    "estimate_2_intra = estimate_2_intra[:min_length]\n",
    "\n",
    "estimate_3_inter = estimate_3_inter[:min_length]\n",
    "estimate_3_intra = estimate_3_intra[:min_length]\n",
    "\n",
    "estimate_4_inter = estimate_4_inter[:min_length]\n",
    "estimate_4_intra = estimate_4_intra[:min_length]\n",
    "\n",
    "estimate_5_inter = estimate_5_inter[:min_length]\n",
    "estimate_5_intra = estimate_5_intra[:min_length]\n",
    "\n",
    "# Combine data into a single DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'T1 (ms)': np.concatenate([\n",
    "        estimate_1_inter, estimate_1_intra,\n",
    "        estimate_2_inter, estimate_2_intra,\n",
    "        estimate_3_inter, estimate_3_intra,\n",
    "        estimate_4_inter, estimate_4_intra,\n",
    "        estimate_5_inter, estimate_5_intra\n",
    "    ]),\n",
    "    'Group': ['Sphere 1'] * min_length * 2 +\n",
    "             ['Sphere 2'] * min_length * 2 +\n",
    "             ['Sphere 3'] * min_length * 2 +\n",
    "             ['Sphere 4'] * min_length * 2 +\n",
    "             ['Sphere 5'] * min_length * 2,\n",
    "    'Type': ['Inter'] * min_length + ['Intra'] * min_length +\n",
    "            ['Inter'] * min_length + ['Intra'] * min_length +\n",
    "            ['Inter'] * min_length + ['Intra'] * min_length +\n",
    "            ['Inter'] * min_length + ['Intra'] * min_length +\n",
    "            ['Inter'] * min_length + ['Intra'] * min_length,\n",
    "    '': ['Sphere 1'] * min_length * 2 +\n",
    "                ['Sphere 2'] * min_length * 2 +\n",
    "                ['Sphere 3'] * min_length * 2 +\n",
    "                ['Sphere 4'] * min_length * 2 +\n",
    "                ['Sphere 5'] * min_length * 2,\n",
    "})\n",
    "\n",
    "# Set the color palette\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "# Create a violin plot using seaborn\n",
    "plt.figure(figsize=(16, 8))\n",
    "ax = sns.violinplot(x='', y='T1 (ms)', hue='Type', data=df, split=True, inner=\"quartile\", scale=\"count\", palette=\"Set2\")\n",
    "\n",
    "# Make the legend larger\n",
    "ax.legend(loc='upper right', title=\"Legend\", prop={'size': 30})\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#66c2a5\n",
      "#fc8d62\n"
     ]
    }
   ],
   "source": [
    "# Set the color palette\n",
    "sns.set_palette(\"Set2\")\n",
    "pal = sns.color_palette()\n",
    "print(pal.as_hex()[0])\n",
    "print(pal.as_hex()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "vscode": {
   "interpreter": {
    "hash": "021e32979ba0b507138b043ad23cc6ba88d193e810337e0e3a2521ed1faaaccb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}