Analysis_for_UBDC_Report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8ee9580-7ec3-46ce-a9c6-1471318e56ce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e45d34ad-fb7e-4984-93bc-830af3688f3b",
   "metadata": {},
   "source": [
    "# Prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7c7f0b-c37a-4d06-a2fd-8e2be7521271",
   "metadata": {},
   "source": [
    "## Read survey and epc data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33122a86-8cad-41fa-9b3b-e12b008eb105",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read 'SensorID_EPC_Survey_Open_Data' file\n",
    "file_path_ID ='Data/SensorID_EPC_Survey_Open_Data.csv'\n",
    "df_ID_time = pd.read_csv(file_path_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc5da4c6-bb8c-45d8-9804-6af8b36385d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ID_list=df_ID_time['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a97531d6-2b35-4563-a28d-e04000dd5c2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert field to datetime type \n",
    "df_ID_time['Battery Charging Start Time'] = pd.to_datetime(df_ID_time['Battery Charging Start Time'], format=\"%d/%m/%Y %H:%M\")  \n",
    "df_ID_time['Battery Charging End Time'] = pd.to_datetime(df_ID_time['Battery Charging End Time'], format=\"%d/%m/%Y %H:%M\")  \n",
    "\n",
    "df_ID_time['Recording Start Time (Participant)'] = pd.to_datetime(df_ID_time['Recording Start Time (Participant) '], format=\"%d/%m/%Y %H:%M\")  \n",
    "df_ID_time['Recording End Time (Participant)'] = pd.to_datetime(df_ID_time['Recording End Time (Participant) '], format=\"%d/%m/%Y %H:%M\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd5a31b0-c611-484e-8daf-d8c4c9d3bcc4",
   "metadata": {},
   "source": [
    "## Generate statistics dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cac94317-b519-44cf-993b-4fee390af4d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      count       mean       std    min     25%    50%    75%    max  \\\n",
      "0   54906.0  24.414297  2.019142  20.52  23.050  23.97  25.39  30.25   \n",
      "1   70281.0  25.562214  1.266366  20.54  24.760  25.46  26.32  29.19   \n",
      "2   56718.0  23.537636  1.763632  20.00  22.160  23.37  24.70  30.70   \n",
      "3   56919.0  25.728024  1.696041  22.02  24.270  25.77  26.90  30.40   \n",
      "4   64961.0  25.067441  1.384199  18.98  24.260  25.23  25.83  28.86   \n",
      "5    1905.0  22.838919  0.794798  21.45  22.160  22.95  23.49  24.27   \n",
      "6   67838.0  22.689428  1.106300  20.57  21.960  22.53  23.38  25.96   \n",
      "7   52662.0  23.812827  2.498582  18.27  21.550  23.90  25.56  31.76   \n",
      "8   55489.0  23.142175  1.767700  18.30  21.840  23.12  24.18  28.12   \n",
      "9   69039.0  23.306204  2.355669  18.47  21.460  23.10  24.81  31.45   \n",
      "10  64624.0  24.214287  1.262814  19.94  23.490  24.32  24.98  27.27   \n",
      "11  50095.0  24.828931  1.462743  21.89  23.510  24.94  25.82  28.07   \n",
      "12  52417.0  23.534161  2.378468  19.20  21.700  23.45  24.77  29.74   \n",
      "13  10265.0  26.724364  1.851601  23.34  24.470  27.51  28.02  30.21   \n",
      "14  74880.0  24.371238  1.781025  20.55  23.020  24.43  25.31  28.77   \n",
      "15  64030.0  21.726997  1.356011  18.93  20.730  21.52  22.27  25.93   \n",
      "16  61895.0  25.059203  1.853706  20.62  23.730  24.82  25.96  30.82   \n",
      "17  68287.0  22.969706  1.332545  20.28  21.960  22.95  23.61  26.62   \n",
      "18  54673.0  23.459063  1.767827  20.27  22.180  23.32  24.29  28.23   \n",
      "19  53645.0  27.098864  1.282466  24.23  26.090  27.11  28.00  30.15   \n",
      "20  74549.0  22.078413  1.746572  18.41  20.720  21.94  23.02  26.95   \n",
      "21  49610.0  24.320605  1.984207  19.98  23.080  24.42  25.51  29.41   \n",
      "22  52128.0  21.814331  1.856223  17.18  20.530  21.65  22.76  27.06   \n",
      "23  63230.0  24.752530  1.552284  19.84  23.720  24.64  25.76  29.16   \n",
      "24  56382.0  23.851260  1.862667  19.40  22.640  23.66  24.96  29.03   \n",
      "25  56260.0  23.701920  0.919710  21.35  23.060  23.57  24.17  26.11   \n",
      "26  61782.0  23.956082  2.220351  19.83  22.350  23.69  25.31  30.60   \n",
      "27  62551.0  25.116307  1.704536  21.72  23.940  25.15  26.06  29.58   \n",
      "28  59512.0  25.874806  1.711160  22.59  24.510  25.44  27.18  30.91   \n",
      "29  54435.0  24.603406  1.708003  21.82  23.120  24.53  25.59  29.38   \n",
      "30  62165.0  24.910916  1.289801  22.48  23.880  25.09  25.85  28.30   \n",
      "31  56226.0  23.757599  1.826940  20.13  22.560  23.39  24.24  29.19   \n",
      "32  55158.0  24.647445  4.368370  16.93  21.760  23.73  26.49  47.40   \n",
      "33  55357.0  23.473933  1.737512  18.40  22.380  23.21  24.33  27.92   \n",
      "34  33276.0  24.074923  1.101605  21.18  23.390  24.20  24.80  26.55   \n",
      "35  57488.0  22.567211  1.634150  19.47  21.285  22.51  23.27  27.93   \n",
      "36  55640.0  22.568058  1.708477  18.32  21.240  22.27  23.73  27.03   \n",
      "37  56528.0  23.726614  2.153434  17.98  22.210  23.53  25.02  29.49   \n",
      "38  54743.0  23.129297  2.078675  19.31  21.550  23.05  24.42  28.71   \n",
      "39  50731.0  21.798360  1.451028  18.82  20.730  21.69  22.52  26.41   \n",
      "\n",
      "          ID  heat_period  recored_periods  \n",
      "0   BL007-02            8               40  \n",
      "1   BL007-04           29               49  \n",
      "2   BL007-07            7               41  \n",
      "3   BL007-09           26               40  \n",
      "4   BL007-13           18               46  \n",
      "5   BL007-14            0                2  \n",
      "6   BL007-15            0               48  \n",
      "7   BL007-17            8               39  \n",
      "8   BL007-18            5               39  \n",
      "9   BL007-19            6               49  \n",
      "10  BL007-22            6               47  \n",
      "11  BL007-23           16               36  \n",
      "12  BL007-25            8               38  \n",
      "13  BL007-28            5                9  \n",
      "14  BL007-31            9               53  \n",
      "15  BL007-32            0               45  \n",
      "16  BL007-33           18               44  \n",
      "17  BL007-34            4               48  \n",
      "18  BL007-35            7               43  \n",
      "19  BL007-36           38               40  \n",
      "20  BL007-39            5               53  \n",
      "21  BL007-40            8               35  \n",
      "22  BL007-42            0               37  \n",
      "23  BL007-43           14               45  \n",
      "24  BL007-44            7               40  \n",
      "25  BL007-45            4               40  \n",
      "26  BL007-46            9               44  \n",
      "27  BL007-47           18               44  \n",
      "28  BL007-48           21               42  \n",
      "29  BL007-49            9               39  \n",
      "30  BL007-50           23               44  \n",
      "31  BL007-51            7               39  \n",
      "32  BL007-52           10               40  \n",
      "33  BL007-54            7               39  \n",
      "34  BL007-56            0               24  \n",
      "35  BL007-57            5               42  \n",
      "36  BL007-59            0               39  \n",
      "37  BL007-64            6               40  \n",
      "38  BL007-66            6               39  \n",
      "39  BL007-67            0               37  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_17896\\4207018955.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n"
     ]
    }
   ],
   "source": [
    "description_dfs = []\n",
    "\n",
    "\n",
    "# Iterate over each sensor\n",
    "for sensor_id in ID_list:\n",
    "    try:\n",
    "        df = pd.read_csv('Data/Household Indoor Sensor Measurement Data/'+sensor_id+'.csv')\n",
    "\n",
    "        # Convert field to datetime type\n",
    "        df['TIME'] = pd.to_datetime(df['TIME'])\n",
    "        df['TIME'] = df['TIME']\n",
    "\n",
    "        selected_sensor = df_ID_time[df_ID_time['ID'] == sensor_id]\n",
    "            \n",
    "        if pd.notnull(selected_sensor['Battery Charging Start Time']).any():\n",
    "            start_date = selected_sensor['Battery Charging Start Time'] + pd.Timedelta(hours=1)\n",
    "            # Delay 1 hour to allow the sensor settled in the indoor condition and improve the accuracy of indoor temperature measurements\n",
    "            end_date = selected_sensor['Battery Charging End Time']\n",
    "        else:\n",
    "            start_date = selected_sensor['Recording Start Time (Participant) ']\n",
    "            end_date = selected_sensor['Recording End Time (Participant) '] \n",
    "            \n",
    "        start_date_value = start_date.iloc[0]\n",
    "        end_date_value = end_date.iloc[0]\n",
    "\n",
    "        sliced_df = df[(df['TIME'] >= start_date_value) & (df['TIME'] <= end_date_value)]\n",
    "\n",
    "        # Extract the date, time, and hour from the timestamp\n",
    "        sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
    "        sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
    "        sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
    "        \n",
    "\n",
    "        # Temperature statistics\n",
    "        description = sliced_df['TEMP'].describe()\n",
    "        description_df = pd.DataFrame(description)\n",
    "        description_df = description_df.T\n",
    "        description_df.reset_index(drop=True, inplace=True)\n",
    "        description_df['ID'] = sensor_id\n",
    "        \n",
    "        # Add heat period days in DataFrame\n",
    "        # Calculate rolling mean over a 3-day window\n",
    "        rolling_mean = sliced_df['TEMP'].rolling(1440, center=True).mean()\n",
    "        # Define a threshold temperature for identifying heat periods\n",
    "        threshold_temp = 25\n",
    "        # Create a mask to identify heat periods where rolling mean is greater than threshold_temp\n",
    "        heat_period_mask = rolling_mean > threshold_temp\n",
    "\n",
    "        heat_periods = []\n",
    "        current_period = None\n",
    "        total_heat_period_duration = 0  \n",
    "\n",
    "        for i, is_heat_period in enumerate(heat_period_mask):\n",
    "            if is_heat_period:\n",
    "                if current_period is None:\n",
    "                    current_period = [i]\n",
    "                else:\n",
    "                    current_period.append(i)\n",
    "            elif current_period is not None:\n",
    "                if len(current_period) > 4380:  # Check if it's more than 3 days\n",
    "                    start_date = df['TIME'].iloc[current_period[0]].strftime('%Y-%m-%d')\n",
    "                    end_date = df['TIME'].iloc[current_period[-1]].strftime('%Y-%m-%d')\n",
    "                    start_date_timestamp = pd.to_datetime(start_date)\n",
    "                    end_date_timestamp = pd.to_datetime(end_date)\n",
    "                    period_duration = (end_date_timestamp - start_date_timestamp).days\n",
    "                    total_heat_period_duration += period_duration  # Accumulate the duration\n",
    "\n",
    "                current_period = None\n",
    "\n",
    "        description_df['heat_period'] = total_heat_period_duration\n",
    "        \n",
    "        # Add monitoring days for each sensor in DataFrame\n",
    "        periods = sliced_df['Date'].iloc[-1] - sliced_df['Date'].iloc[0]\n",
    "        periods = periods.days\n",
    "        description_df['recored_periods'] = periods \n",
    "\n",
    "        # Append the description DataFrame to the list\n",
    "        description_dfs.append(description_df)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"Error in {str(e)}\")\n",
    "\n",
    "# Concatenate the description DataFrames for all sensors into a single DataFrame\n",
    "description_df = pd.concat(description_dfs, ignore_index=True)\n",
    "\n",
    "\n",
    "print(description_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "280c812a-75bb-4720-82ab-01a4a5c69ea2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "file_path_ID ='Data/SensorID_EPC_Survey_Open_Data.csv'\n",
    "df = pd.read_csv(file_path_ID)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe9b43d-c792-4e6a-9e7a-c1d8251f7272",
   "metadata": {},
   "source": [
    "## Merge two DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80d8d10e-edf8-4a97-b50d-65a8f46d140e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.merge(description_df, on='ID', suffixes=('_left', '_right'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62927177-9494-4f4d-817d-d068e5a2a195",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculate the sustained heat period proportion of whole monitoring days\n",
    "df['perc_heat']=round(df['heat_period']/df['recored_periods']*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f369bd-32ff-4fbd-a0e2-04a21bb14f4d",
   "metadata": {},
   "source": [
    "# Two-sampled t-test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1ce4ad-e926-410a-bd1e-57c9e29019b1",
   "metadata": {},
   "source": [
    "## EPC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "abee2740-209a-4032-864d-29c45d729e96",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of EPC\n",
    "counts = df['Current EPC Rating '].value_counts()\n",
    "\n",
    "category_order=[\"B\",\"C\",\"D\",'E']\n",
    "colors =[\"#2C9F29\", \"#A0D20E\", \"#F8F400\", \"#F1C300\"]\n",
    "plt.figure()\n",
    "sns.barplot(x=counts.index, y=counts.values, order=category_order, palette=colors)\n",
    "\n",
    "\n",
    "for i, count in enumerate(counts):\n",
    "    plt.text(i, count, str(count), ha='center', va='bottom', fontsize=12, color='black')\n",
    "\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Counts of Household with Various EPC', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4deb684-9b40-4222-b340-dc4e1c38f457",
   "metadata": {},
   "source": [
    "### Maximum temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04c711fc-93b9-4a82-b95d-5e9e94953a4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.set(style=\"whitegrid\")\n",
    "category_order=[\"B\",\"C\",\"D\",'E']\n",
    "ax = sns.boxplot(x='Current EPC Rating ', y='max', data=df, palette=colors, order=category_order)\n",
    "sns.stripplot(x='Current EPC Rating ', y='max', data=df, jitter=True, order=category_order, color='black', alpha=0.5)\n",
    "\n",
    "# Calculate the median and mean\n",
    "medians = df.groupby('Current EPC Rating ')['max'].median()\n",
    "means = df.groupby('Current EPC Rating ')['max'].mean()\n",
    "\n",
    "mean_points = [means[category] for category in category_order]\n",
    "plt.scatter(ax.get_xticks(), mean_points, marker='o', color='darkred', s=50, label='Mean')\n",
    "# Annotate the median and mean\n",
    "for xtick in ax.get_xticks():\n",
    "    ax.text(xtick, medians[category_order[xtick]]+0.05, f'Median: {medians[category_order[xtick]]:.2f}', horizontalalignment='center', color='black', fontsize=11)\n",
    "    ax.text(xtick+0.28, means[category_order[xtick]]-0.1, f'Mean: {means[category_order[xtick]]:.2f}', horizontalalignment='center', color='darkred', fontsize=11)\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Temperature (°C)')\n",
    "plt.title('Boxplot of Max Temperature with Various EPC', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15bb5d7-fab7-44f0-9d4d-55d7b78a8d21",
   "metadata": {},
   "source": [
    "### T-test for maximum temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f78c240-8dd3-4009-9699-db12cd873430",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "B=df[df['Current EPC Rating ']=='B']['max']\n",
    "C=df[df['Current EPC Rating ']==\"C\"]['max']\n",
    "D=df[df['Current EPC Rating ']==\"D\"]['max']\n",
    "E=df[df['Current EPC Rating ']==\"E\"]['max']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7413672d-45f8-4843-849c-4d2240dd8040",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# ANOVA\n",
    "f_statistic, p_value = stats.f_oneway(B,C,D,E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc6dc047-de11-4fd1-a4f5-0cabe2d9c9ba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12276839591763096"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4de7a7a-2479-4fed-9463-47b38ead10e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0938591511106885"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88ea647a-7433-4f44-b47a-86e543bfc9fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4782363671838442"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(B, C)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e6c26c0d-a119-4dc1-a387-cffd921224d6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2556483474913897"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(B, D)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70c35c84-af42-4e52-adad-07f18b616e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009490695445608559"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(B, E)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "deb3cd06-593d-41ee-ba86-24a210a2d8b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.794714920161521"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(C, D)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "407db791-1218-4295-87d2-411384940f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14020326697481958"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(C, E)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6491d429-8645-4f18-a757-1a7bb3dc19e6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11985489324277886"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(D, E)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32277e68-9d7a-4670-a628-52ee30d0781b",
   "metadata": {},
   "source": [
    "### Minimum temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43238724-5908-4389-9a12-459acbd4a175",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.set(style=\"whitegrid\")\n",
    "category_order=[\"B\",\"C\",\"D\",'E']\n",
    "ax = sns.boxplot(x='Current EPC Rating ', y='min', data=df, palette=colors, order=category_order)\n",
    "sns.stripplot(x='Current EPC Rating ', y='min', data=df, jitter=True, order=category_order, color='black', alpha=0.5)\n",
    "\n",
    "# Calculate the median and mean\n",
    "medians = df.groupby('Current EPC Rating ')['min'].median()\n",
    "means = df.groupby('Current EPC Rating ')['min'].mean()\n",
    "\n",
    "mean_points = [means[category] for category in category_order]\n",
    "plt.scatter(ax.get_xticks(), mean_points, marker='o', color='darkred', s=50, label='Mean')\n",
    "# Annotate the median and mean\n",
    "for xtick in ax.get_xticks():\n",
    "    ax.text(xtick, medians[category_order[xtick]]+0.05, f'Median: {medians[category_order[xtick]]:.2f}', horizontalalignment='center', color='black', fontsize=11)\n",
    "    ax.text(xtick+0.28, means[category_order[xtick]]-0.1, f'Mean: {means[category_order[xtick]]:.2f}', horizontalalignment='center', color='darkred', fontsize=11)\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Temperature (°C)')\n",
    "plt.title('Boxplot of min Temperature with Various EPC', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4199f439-cb45-4cfc-891e-f858e8309b18",
   "metadata": {},
   "source": [
    "### T-test for minimum temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e3f9163-e4c9-4777-bcba-dbd3459f860d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "B=df[df['Current EPC Rating ']=='B']['min']\n",
    "C=df[df['Current EPC Rating ']==\"C\"]['min']\n",
    "D=df[df['Current EPC Rating ']==\"D\"]['min']\n",
    "E=df[df['Current EPC Rating ']==\"E\"]['min']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87dba7c0-9d6d-4a71-b8bb-92aca4085b9d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# ANOVA\n",
    "f_statistic, p_value = stats.f_oneway(B,C,D,E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca72c1b0-9ede-4127-8a8b-d4b9fc4149ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0391350763144506"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "48f37b6a-302b-4f4b-9a8b-2870a6df4ec6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1691950466817467"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d54eb41b-1968-4a6a-9ef7-dc1bda4099c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1552248793813648"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(B, C)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "074a53d7-b199-4651-9117-5e833e2d1033",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1393861944581774"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(B, D)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "293ef390-2c95-4b63-9883-3a8044b31b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08555721879222279"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(B, E)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d6907bdb-02d0-45af-8f9f-454bde3edcaa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8370578964036777"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(C, D)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "60fe4f14-e3fd-4e5d-b79e-04b51fa81dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.036931296113387"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(C, E)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3080b98d-87dd-47b1-9212-ac1b444f3142",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004262694787445767"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(D, E)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9dcfbb-9854-4023-a123-08518b499031",
   "metadata": {},
   "source": [
    "There is a statistically significant difference between EPC C/D houses and EPC E house at about 2.5 Celsius degree. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50bad2af-a8d9-4c4b-a973-da5505ae47be",
   "metadata": {},
   "source": [
    "## Communal heating system"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8789de6b-b26d-4024-950a-8e4a27992342",
   "metadata": {},
   "source": [
    "_**Note that the rest of the code cannot run directly because the data for 'if you are communal heating system' is not available at the moment.**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d23db403-115e-4829-b4fc-a22d686827ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "counts = df['if_communal'].value_counts()\n",
    "colors = ['#1289A7', '#EE5A24','#FFC312']\n",
    "\n",
    "plt.bar(counts.index, counts.values,color=colors)\n",
    "\n",
    "for i, count in enumerate(counts.values):\n",
    "    plt.text(i, count-1, str(count), ha='center', va='bottom', fontsize=12, color='black')\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Counts of Household on Communal Heating System', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b82fbe-e620-468d-9619-824015a78ad2",
   "metadata": {},
   "source": [
    "### Mean temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "517bc110-401e-4434-9d3b-fe46abaeaf35",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "colors = ['#1289A7', '#EE5A24','#FFC312']\n",
    "\n",
    "ax = sns.boxplot(x='if_communal', y='mean', data=df_survey, palette=colors, order=category_order)\n",
    "sns.stripplot(x='if_communal', y='mean', data=df_survey, jitter=True, order=category_order, color='black', alpha=0.5)\n",
    "\n",
    "medians = df.groupby('if_communal')['mean'].median()\n",
    "means = df.groupby('if_communal')['mean'].mean()\n",
    "\n",
    "mean_points = [means[category] for category in category_order]\n",
    "plt.scatter(ax.get_xticks(), mean_points, marker='o', color='darkred', s=50, label='Mean')\n",
    "\n",
    "for xtick in ax.get_xticks():\n",
    "    ax.text(xtick, medians[category_order[xtick]]+0.05, f'Median: {medians[category_order[xtick]]:.2f}', horizontalalignment='center', color='black', fontsize=11)\n",
    "    ax.text(xtick+0.28, means[category_order[xtick]], f'Mean: {means[category_order[xtick]]:.2f}', horizontalalignment='center', color='darkred', fontsize=11)\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Temperature (°C)')\n",
    "plt.title('Boxplot of Mean Temperature with Communal Heating System or Not', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f29082-bf2b-466e-b3e6-d70ff11c6a53",
   "metadata": {},
   "source": [
    "### T-test for mean temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5b4fc904-7ddd-409a-9bde-4a607e80d4b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# ANOVA\n",
    "f_statistic, p_value = stats.f_oneway(communal_no, communal_yes_off,communal_yes_on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cdf217f0-7450-4b32-8d80-ba8d912032a8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07443489671607653"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "84c2871f-f3e7-4555-b58e-cf2c8c1f3cb5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.930235216746733"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "eb050327-2294-403f-883e-50f85a2c0f73",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2158274945716955"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(communal_no, communal_yes_off)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bf1739fb-2025-426e-aa40-e67294992617",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.011658834158933998"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(communal_no, communal_yes_on)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dcfb6017-cf32-427d-aa8a-eb1695257744",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5583323292287945"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(communal_yes_on, communal_yes_off)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30cc5810-7936-40d0-b6cb-543abe5b5ae8",
   "metadata": {},
   "source": [
    "A statistically significant mean temperature difference at 1.1 Celsius degrees between households without communal heating system and households that cannot turn the communal heating off. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5c8953-a381-42dc-b762-fc51a1c8dc66",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Heat periods proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "17c3e2e4-51a6-4e2a-b15e-a6f3fca3dbd9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B2dkZ9+/fx4ABAzB06FC0adNG7nPX1NQUHTp0kP1dr1491KxZE7du3UJkZKTcNUIikeCvv/6Cubk53N3dkZWVhdGjRyMgIACenp5o164d4uLicObMGVy/fh0bNmxAy5Yt4ebmhi+//BI7d+6ElZUV+vTpI7vfLSYmBl988QVevHiBxo0bo3379oiNjcWZM2dw6dIlbN68GQ0bNpTbr+PHj+PJkyfo0KEDGjVqhDNnzmDr1q1ISEjAjRs3YGlpib59++Lhw4e4fPkywsLCcOLECYVfqd5VlPdzfp8f7yMSibBw4UJ0794dmzdvRrt27T5YNpKSkoKhQ4ciICAAYrEYAwYMwKtXr3Dx4kWMHTsW3333nexXifw+OwpE+AhWrVoliMViYciQIcKqVatk/5YuXSpMmDBBqFWrljB06FAhNTVVbrlRo0YJYrFYWLNmjVz7gwcPBBcXF6Fu3bpCZGSkrP3MmTOCWCwWunXrJmRnZwsbN24UxGKx8NVXX8ktP2TIEEEsFguenp5CfHy8wnrd3NyElJQUQRAE4cWLF4JYLBYGDhwom+/kyZOCWCwW+vbtK7f869evhZEjRwpisVjYuHGjrP3w4cOCWCwWli9f/sFjde/ePUEsFgtt27YVXrx4IWvPzs4W/ve//wlisVj45ZdfZO3S+IYMGfLBdb+97//73//kXot3/4nFYkEsFsuWy8jIEJo3by64uLgIDx8+lFvn3r17BbFYLHz//feytrlz5wpisVg4evSo3Lzp6emCh4eHIBaLhcePH8vapds8ePDgB/dBus9t2rTJd54NGzYIYrFY6Nevn6xt+/btglgsFqZNmyZkZ2fL2pOSkoROnToJtWrVEp4+fSprlx6D/fv3K6xfLBYL7u7usr8Le04MHz5cEIvFwpEjR2Rt2dnZwtSpU2Xblca4a9cupcfm2LFjglgsFn7++ef3Hi9BEGTnzuHDh5VOj4+PF1q1aiWIxWLhypUrgiAU/jWXvoYeHh5CRkaG3PwnTpwQxGKxMGzYMNl7SxAEITQ0VGjQoIHQrFkzITs7u8jbdHNzk3u/JCUlCW5ubkKtWrWEmJgYheNw/fp1WZv0uvHjjz8qHJfJkycLYrFY2L17t6xt4sSJglgsFrZt2yY3r/ScF4vFslhiYmIEZ2dnoXnz5kJ4eLjc/MuWLSvQdUF63v75558KMdevX19o37693Pxt2rQRPDw85P5++3zKr016LBs2bCg8e/ZM1p6RkSF4enoKYrFY8Pf3f2+s0tdo3rx5751PmYiICKFu3bqCm5ubEBAQIGuXSCTC8uXLBbFYLIwePVohXldXVyEiIkIhhnffu0lJSYKLi4vg5OQkSCQSQRD+u5aIxWJh586dsnkTEhKEevXqya6tWVlZsmlTpkwRxGKxcOLECVmb9LoaEhKisF/vHuv8tikIgvDjjz8KYrFY2LRpk6xt3LhxglgsFm7duiU3b1xcnNCgQQPB0dFR7j317rUpPzdv3pRdR9/3WSDdt1WrVsmWLcy1NDY2Vqhdu7bQpUsXIS0tTS4G6Wv19ddfy7Ur2wdl713pcVy8eLHcvKtXrxbEYrEwe/ZsWduCBQsEsVgsLFiwQG7eLVu2yNZz8+bNDx63p0+fCu7u7rJlpMfwhx9+EI4ePSokJycrLCPdxvr16+Xar169KojFYuHXX38VBEEQLl68KIjFYmHFihVy892+fVsQi8XCyJEjZW3KchNB+O/6dODAAbn2R48eCU5OTkKbNm1kr5n0HBCLxcKFCxfk9lHaPmXKFNn7RSKRCIMGDRLEYrFw586d9x6nor6flX1+5OfdvGHnzp2CWCwWunbtKmRmZsrmk547b7++s2bNkn1+vn0Oh4WFCS1bthQcHByEe/fuKazj7fOvID5qucitW7fwxx9/yP5t2rQJFy5cQG5uLkxNTeV+jnz58iW8vLxQrVo1TJw4UW49derUwahRo5CVlSXrDQXy7kju3r07goKCMHfuXKxcuRJWVlaYP3++0ni+/fZbuR6xOnXqYMCAAUhKSnpvXar0p8iZM2fKLa+np4fZs2dDTU0NBw4cKNzBeWfd3333HWxtbWXtGhoamD59OoyMjHD06NFi1x0dPXpU7rV499+7Ll68iNjYWAwaNEihN3fQoEGoWrUqzpw5I/sptkuXLpgzZw66d+8uN6+Ojg5cXFwA5PXQFkdycjJWr14t92/x4sUYNGgQfvvtNxgYGGDOnDmy+Q8ePAh1dXX8/PPPct/AK1SogAkTJiA3N1fufJLq2LHjB2MpzDkRExMDb29v1KlTR643T0NDAzNmzICmpqbcuoU3Pdt+fn7Izs6WtXfu3Bnnz5/HrFmzPhif1Pnz5+WO1++//44ff/wRnTp1QlRUFHr06CErgynsay7l4eGh0DN77NgxAHk/+b7dw2lnZ4effvoJI0eORFpaWpG32a1bN7n3S4UKFdCgQQPk5uZ+sPTIwcEBCxcuxDfffKMwTTrSjfRcTUxMxKVLl2Bvb6/w8+93332n0LN5/PhxZGRkYMKECQq/Mn399dfQ19f/4JB5Hh4eACD3c/H169ehr6+P7t27IyQkBNHR0QCAp0+fIiIiQmkvdkH16NED9vb2sr+1tbXh7u4OALJfh/KTnJwMIK/Gv7D++usvZGVlYdSoUXK/zIhEInz99deoUqUKrl69iqioKLnlunTpItc7KO2lMzY2Rv/+/WXtFSpUQI0aNZCZmYnY2Fi5dejr6+OLL76Q/V2xYkVUq1YNQN6No2+/Jxs0aAAAspKbojIyMpLbJvDfa/32cR46dCiWLFmCRo0ayc1ramqKmjVrQiKR5Fu6UBARERHv/Sy4deuWwjKFuZZqaGjg119/xbx586Crqyu3Hun7qyClSO8zduxYub/fPY65ubk4fvw4KlSogG+//VZu3mHDhqFq1aoF3lb16tVx8uRJfPvtt6hRowaAvGN4/Phx/O9//0Pr1q2xY8cOuWV69OgBTU1NHD9+XK5del2U3owuvdYHBAQgLS1NNp+rqyvOnTuHdevWvTe2uLg4XLhwAU5OTnLnPgDUqlULPXr0QEREhEKJXvXq1WXHTPq3sbExAGD06NGyX69EIhHq168v2+f3Ker7WdnnR0ENGTIEjRo1+mDZSFZWFv766y8YGRlhxowZcudw5cqVMXnyZAiCUOQ87m0ftVzkq6++khuVITMzEzExMTh58iRWrlwJX19fHDt2DKamprKfQt+9sEi5uroCgMJYyLNmzcLt27dx4MABqKmpYenSpahQoYLSdSgb2q1BgwbYtm0bHj16pJAgSj18+BA6OjpwcnJSmFa5cmVYWlrixYsXSE1NLfQwb9J6PGX7bWRkBAcHB9y+fRvPnj0rVonAzp070bhx43ynv1tzJK1BfP78udLxUtXV1ZGTk4OgoCA0bNgQDRo0QIMGDZCamoqgoCCEhYUhLCwMAQEBsrq34o64kpKSovCFQFdXF1ZWVhg0aBBGjhwp+xkwLS0NT58+hYGBgdIheOLj4wFAoSa6QoUK+Z4/byvMOREQEABBEJTeeW5sbIwaNWrIndcdO3bE2rVrcejQIZw7dw5NmjRBixYt4O7uXujhrC5cuCBXA6mhoQEjIyPUqlUL3bp1kxt5pLCv+dv7+66AgADo6ekprWXr27dvsbep7ENSmvC+/cVEmapVq6Jq1arIzs7Go0ePEBISgvDwcDx+/Fh2j4a0FvPhw4fIzc1FvXr15IbEAwADAwM4OjrKJSXS/bl//77SL5V6enqIjY2VKwV5V+XKlVGjRg3cuHEDgiBAJBLB29sbrq6ucHNzw759+3Dz5k307NlTVtdZnCRb2bGUfnF8u8ZdGekN2Mru0fgQ6XtP+rP32zQ0NFC/fn2Ehobi0aNHsvsHlMWrp6cHALC1tVUobZHWf7/bSVG5cmWFoQSlXxTeLSXIbx2FpWybhoaGAOTP2ebNmwPIq7N9+1r68OFD2T0ZxbmWurm5YdeuXflOX716tdx1trDXUmNjY1k5U0hICIKDgxEeHo7g4GD8+++/AKD0npWCMjY2VrjxX3ocpa9RWFgYkpKS0KhRI4UvgOrq6qhfv36hbpA3NDTExIkTMXHiRERHR8PHxwc+Pj64fPky4uPjsXDhQuTk5GDUqFEA8r4QtWrVCufPn4e/vz+cnJyQmpqK8+fPw8XFRVbK2qxZM1StWhWXL19G8+bN0bhxYzRr1gzu7u5yX3zz8/DhQwiCgKysLKXXT+mX8UePHqFVq1aydmXveX19fSQlJRX5/C/q+7k4QzQWtGwkJCREVtamLKHPL78silKtydbW1kblypUxfvx4JCYmYvv27di5cye+++47pKSkAPjvzfIu6QfS2zcCSudv3rw5/vzzTxgZGcm+ab5LU1MTZmZmCu3SpFjaI6NMamoqKlSooPAh+3ZskZGRSEtLK3SSLe2hK+x+lzTp8bh48eJ7e/mlH66pqalYunQpjh07Jrsxx8TEBPXq1YOdnR0CAwMVao8Ly8bGpsAjYUjPp9TUVKU99e/GL/XuTVn5Kcw5IT2W+b3G0h4EKXNzcxw5cgQbNmzA+fPncfbsWZw9exZAXk/Q7NmzC3QBBoBFixYpHcJPmcK+5lLv9lYBeQlCxYoVPzj+dlG3qexCKd3Wh84zQRCwbds2bNmyRTZMk56eHmrXro1atWohNjZWtg5pj5u5ubnSdVlYWCjdH2mPVX6SkpLyTbIBoE2bNti0aRMCAgJgZGSEsLAwDBo0CE2aNIFIJIKPjw969uyJK1euwNjYWKHmsjAKes4rI/2ALMiNUXFxcVBXV5clSEW95ufXa67sRvr8SBPz4q6nMAp6zkpvID979qzs5slKlSqhQYMGqFSpEsLDw4t9LS2MolxLL1++jOXLl8tuItbQ0ECNGjXg5OSEp0+fFiv+9x1HqQ+9b9/33vsQS0tL9OjRAz169EBWVhbWrFmD9evXY/369RgxYoTsM6FPnz44f/48jh8/DicnJ5w6dQoZGRly12MdHR0cOHAAGzdulNVQX7p0CUDeL+2zZ89+77CA0mMeFBSk9Ibtd+eTKonzv6jvZ2WfH4VhZ2eH77//HgsWLMBPP/2k9OF8BY3t7V8TiqpM3PgI5H2D2759u+xpOtLkNL/hnaQfXu8mJD4+Pjh8+DCMjY2RlJSE2bNnY+XKlQrL5+TkIDs7W+GneenJ9+6NVW8zMDBAYmIisrKylJ6A0nW8G1tBvL3fVapUUem6i0P6QbZixQqlIx28a9q0abhw4QI6d+6MAQMGoGbNmjA1NQWQ97P6x37injT+6tWrl8jT5gpzTkhfu/y+yCl7Y1tZWWH27Nn45Zdf8PjxY3h7e+Pvv//GzZs3MX78eJw5c0blD5Ap7Gv+oXW9fv1a1hP7tszMTGhoaEBdXV2l2yyoHTt2YMmSJahTpw7mzJkDR0dH2NjYQCQSYd++fXKjJUjjk16k3/VuT690/kOHDhVrzFxpkn39+nXZ+dOkSROYmJhALBbDx8cHKSkpuHPnDrp06fLBG5JKiqurKwwNDfHvv/8iLS3tvR/eK1aswOHDhzFlyhSMGTNG7tqn7KFkpXXtKwxlPcrF+aAWBAFjx45FQEAABg8ejC5duqBGjRqyX9f69+9f7JGYCquw11J/f39MnDgRRkZGmD9/PlxcXFClShVoaWkhODhYNmpDSfrQ+7ag400vX74cR44cwdKlS5X+Eq6lpYXvvvsOZ86cQUhICGJjY2UJW6tWrWBubo7Tp09j+vTpOHHiBHR0dBSuc8bGxpg2bRqmTZuG0NBQ3LhxA2fPnoW3tzdGjx6Nixcv5tt5J93PQYMGYfbs2QXap5JSmu/noUOH4ty5c7h9+7bSspGC5peqGBq5TAzhB/z3TVO689J6zHv37il9vLS05EB65zCQ90b56aefoKGhgR07dsDNzQ1nzpzBiRMnFJYXBAH3799XaJf+fCWtG1amdu3akEgksnnf9vLlS4SEhKBq1aqyZKswyY90v2/fvq0wLTMzE35+ftDX1y/RUUSUkd7hq+yYAcDatWuxbt06JCUlITk5GRcvXoSNjQ1WrFiBJk2ayBJsIK9uFJDvrSnpJwwaGBigcuXKCAsLU1r/9/DhQyxZskTWa1BYhTkn6tSpA3V1ddy5c0dh3rS0NDx58kSu7dSpU/jll1+QkpICkUgEBwcHDB8+HAcOHEDVqlUREhKCmJiYIsX9PoV5zT/EwcEBaWlpePz4scK0ZcuWoV69evD19VXpNpVRdp5Je5lXrlwpG81EOp/0XJWqU6cORCKRwhCiQN5P3u8O7SbdH2XDUEkkEvz666/YuHHjB0ta6tevj4oVK+L69eu4desWjI2NZeViTZs2RUREBPbv34+cnJxilYoUl6amJnr27InMzExs2LAh3/kiIyNlD4mS1nu/79oH5N3TIxKJULNmTRVHXXzSa/27X7KSkpKKVS8dFBSEgIAANGrUCLNmzULDhg1lCXZ2drasxOFj9mQX9lp64sQJ5Obm4ocffkC/fv1Qs2ZN2fFS9llQEqpVqwY9PT08ePBAaT5R0EehGxgYIDY2tkBjYGtqasp11qmrq6NHjx6Ii4vD5cuXcefOHbRt21auN1U6GpX0l6AqVapg0KBB2L59Oxo3boxXr17JrqHKrmUfun6ePHkSv//++0fp5CrN97O0bERXVxebN29W2N9q1apBV1cXjx8/VtrZpSy/LGqOUiaS7LS0NOzcuRMAZI8dt7S0RIsWLfDixQvZeK9SgYGB2LJlC7S0tOS+Bc6fP1/2EARHR0fMnz8fOjo6mDdvntJvLMuXL5f7Bnv//n38+eefsLKykl34lZGOf/nrr7/KXWTS09Pxyy+/QCKRyP0EJO1VUvbmzm/da9askeuhyMnJwYIFC5CcnIyuXbuW2E+Y+Wnbti2MjY2xZ88ehYTh1KlTWLlyJY4dOwYjIyNoaWlBTU1N6ROpNm7cKLtIvH08pMfoQ8lGcfTt2xfZ2dmYM2eOXD1ZWloafvnlF2zduvW9ZULvU5hzwsTEBJ6ennj8+LHcDTKCIGD58uUKPV+BgYHYv38/du/eLdeenJyMpKQk6Ovrl8jDiArzmn+I9AbPpUuXyv08GB4ejuPHj8PAwAD16tVT6TaVUXaeScsjpLWkUrdu3ZLdlCidv1KlSmjVqhUCAwMVblhcs2aNwlPBpDc8rV27VqHmc8uWLdiyZQtu3Lih8Ivau9TU1NCqVSv8+++/8Pb2hpubm+xnaGmP2saNG6GlpYUWLVp88DhIt1eQa1JhffPNNzAzM8PGjRuxZcsWhXrbFy9eYMKECUhNTUXfvn1lXxa6d+8OTU1N7NixQ+FDce3atXj27BmaN29erJ/2S4r0Jsl3v6T/8ccfKimFePXqldxrlZubi0WLFsl6A0vidXyfwlxL83t/RUVFYfny5QAU49fU1FTpPmlqaqJXr15ITExUKHE5fPiw0rHJlenXrx8MDQ1x8OBB7Ny5U+GXC2npWUhICHr16qXwvpZ+TsyfPx85OTmyv6UiIyOxa9cubNq0Sa5derOumpqarINNWV5hbW2N5s2b4+HDhwo3Xz579gxz5szBxo0bC13GWhSl/X6Wlo3k5OQo1FZramqie/fuSE1NxaJFi+SOYXh4OFasWAGRSCQbIhooeo7yUX9TvHXrllwxviAIiIuLwz///IOEhAR4enrKjS85d+5cDB48GGvWrMH169fh4uKCly9f4sKFC5BIJJg3b56sBvD8+fOyMTHHjRsHIO9b4Ndff42lS5fip59+wpYtW+S+jQQFBaFHjx5o06YNEhMTce7cOairq2PRokXvvbu1a9eu8PLywrFjx9CtWze0atUKmpqauHbtGl68eAF3d3fZDQ/Afw/5kN5Z3KNHD7lvSG+rX78+Jk2ahDVr1qBnz57w8PCAkZERfHx88PjxY9SpUwfTpk0r7KEvNgMDA/z666/46quvMGjQIHh4eKBKlSoIDg7GlStXoKenh8WLF0NNTQ06Ojro1KkT/v77b/Tu3Rtt2rQBkPf6P3r0CGZmZoiLi5NLRqXHaMeOHYiKisKQIUNU/uYbNWoUbt68idOnTyMgIADNmzeHmpoazp8/j6ioKHTs2BFdu3Yt0roLe07MnDkTDx48wMKFC3Hp0iU4ODjgzp07CAoKgqWlpewGFSDv7ve///4bv//+O3x8fFC7dm2kp6fjn3/+QVJSEn7++ecS+dJVmNf8Q3r16oULFy7g/Pnz6NGjB9zd3ZGdnY1Tp07h9evX2LhxIzQ1NaGpqamybSojPc9WrVoFX19fTJo0Cb1798bdu3cxZswYdOrUCUZGRggMDMSNGzdQsWJFZGZmyvVGzpgxA35+fpg5cybOnDmDmjVr4v79+/Dz84ORkRGSk5NlN7TZ2tril19+waxZs9CjRw+0bdsWlpaWePDgAW7evAlzc3P88ssvBYq9TZs2OHbsGGJjY2WjMgB5JRoaGhpITk5Gq1atCjSyh5WVFUJCQjBt2jTZQ0RUxcjICDt27MCYMWPw66+/Ys+ePWjWrBmMjIwQEhKCq1evIjs7G+3atcPPP/8sW87GxkZ2rPr16ycbV/fu3bvw8/ODra1tviNFlbYBAwZg3759WLt2LZ48eYLKlSvj9u3beP78ORwdHYvcc1i1alU0aNAAd+7cQd++fdG0aVNkZ2fDy8sLISEhMDU1RXx8fLF6y4uiMNfSLl26YNu2bfj999/x8OFD2NnZITIyEhcvXpS959+N38rKCmFhYZg+fTrc3Nzkkp2i+uabb3Dt2jWsW7cOPj4+qFevHp4+fQovLy9UrFgRiYmJCjeivqtixYpYv349JkyYgAULFmD79u1o2rQpzMzMkJycDB8fHwQHB6N+/fqyh+i8rVq1aqhfvz7u3r0La2trufcxkJeYHjhwAIcOHUJQUBBcXV2RnZ2Nq1evIjQ0FCNHjpR9LpqYmEBbWxuPHj3C/Pnz0bRpU3h6emLevHkYPHgwFi5ciLNnz6JevXpISkrCmTNnkJaWhhkzZsiNxFRSysL7eejQoTh79ix8fX0Vpv3www+4e/cujhw5ggcPHqBx48ayX+FTUlLw7bffykYSApR/dhTk/pWPnmS/fee9uro6DA0NIRaL0bVrV/Tt21cuCbaxscGRI0ewfv16XLhwAXv27EGFChXg4eGBUaNGyWocExISMGvWLKirq2PBggVyCceIESNw+vRpXL9+HXv37sXgwYNl09asWYO9e/fiyJEj0NTUROvWrTFp0qQCjdqxePFiuLm54eDBgzh58iTU1NRQvXp1jBo1CgMGDJBLAqRPkzx69Ch2796NqlWr5ptkA3kXg9q1a2Pnzp04f/48JBIJ7OzsMGXKFAwfPvyj92JLtWrVCocOHcLGjRtld1JbWFigW7duGDdunFzd1bx582BjY4PTp09j3759MDY2RpUqVbB06VJYW1tj8ODBuHz5suzi2alTJ3h5eeHChQvYvXs33NzcVJ5ka2pqYtOmTdizZw+OHz+Ow4cPQ1NTE1WqVMGECRPQp0+fD15k36cw50SlSpVw4MAB/PHHH7h06RLu3LmDWrVqYevWrVi5cqVckm1qaoq9e/di48aNuH79Ou7evSsrO5k7d67c0EuqVpjX/H1EIhFWrVqFPXv24MiRIzh06BDU1dVRr149TJw4Ue4OdFVtU5kvvvgCd+/exa1btxAaGooePXqgf//+UFNTw65du3DixAloaWnJHtoyZMgQtGrVClevXkVubi7U1dVhZ2eHgwcPYsWKFbhx4wZu3bqFOnXqYOvWrViyZAkePnwod/NOv379YG9vj82bN+PatWtIS0uDlZUVBg8ejLFjx8oelvQhLVq0gKamJrKzs+U+nA0MDODk5IS7d+8W+FyYOnUqpk+fjosXLyIgIEClSTYA1KhRAydOnMCRI0dw+vRpXLlyBYmJiTAwMEDTpk3Rt29fuQ4VKemx2rRpE27cuIH09HTY2Nhg/PjxGD16dL43KpW2GjVqYOvWrVi9ejWuXr0KDQ0NNG7cGIsXLy7Wz/MikQhr1qzBqlWrcPXqVezevRtmZmaoXr06pk+fjqSkJEybNg2XLl2SjYjwMRTmWurg4IBt27Zh1apVuHnzJq5evQorKyt0794dEyZMwI8//ghvb28EBwfL3tuzZs3CvHnz8NdffyEiIkIlSbaxsTH27t2LlStX4uLFi/D390f16tWxcuVKnDx5EufOnSvQTXeurq44e/Ys9uzZg2vXrsmSMn19fYjFYgwbNgx9+/bN97OkW7duuHv3Lnr27KnQWaCrq4utW7di27ZtOH/+PPbv3w8g7xhOmDBB7jhoampi7ty5+P3337F//368fv0anp6esrxpw4YNuHDhAnbt2iUb0nTkyJGy0Wo+htJ+P4tEIixatAjdu3dXOkjG/v37sWXLFpw+fRr79++Hvr4+6tevj+HDhyscJ2WfHfkNrCEXg/Axi7nKiKFDh+LWrVs4d+6c0psLiYiUEQQBoaGhsLa2Vvplt2XLlrIbEEv6PgMiKrjQ0FBUqlRJae/jwIEDcffuXXh7e7930ANVkD4Z+J9//inWcHVUPpSJmmwiovJAJBLJfvp89ya348eP4+XLl7Jh9Yio7Pj222/h5uamcH+Wr68v7t27BwcHhxJPsJ8+fYqTJ08W6RkHVD6VmSH8iIjKg8GDB2PdunXo2rUr2rRpAz09PTx+/BheXl4wNjZWWotJRKVr8ODBmDlzJnr27Cm7wTo0NBQXL16Ejo5Oge+NKIoVK1bg8uXLCA4OhkQiUfp0Wfo0MckmIiqEyZMno0aNGti/fz9Onz6N1NRUmJubo3///pgwYUKBa6yJ6OPp168fKlWqhJ07d+LSpUtISkqCqakpOnfujLFjxxaovraopE/8rVSpEqZMmaL0ycD0afosa7KJiIiIiEoSa7KJiIiIiFSMSTYRERERkYp9ljXZd+/ehSAIH3zKGhERERGVjuzsbIhEItSvX7+0QymSzzLJFgShWI+6JSIiIqKSVd5ztc8yyZb2YPMOXyIiIqKyyd/fv7RDKBbWZBMRERERqRiTbCIiIiIiFWOSTURERESkYkyyiYiIiIhUjEk2EREREZGKMckmIiIiIlIxJtlERERERCrGJJuIiIiISMXKTJJ97NgxdO7cGU5OTujSpQtOnz4tmxYQEIAhQ4bAxcUFrVu3xpYtW0oxUiIiIiKi9ysTSfbx48cxffp0DBgwAH///Tc6d+6M77//Hnfv3kViYiJGjBiBqlWr4vDhw/j666+xcuVKHD58uLTDJiIiIiJSqtQfqy4IAlauXIlhw4Zh2LBhAIBJkybhzp07uHXrFm7dugUtLS3Mnj0bGhoaqF69OkJDQ7Fp0yb06dOnlKMnIiIiIlJU6j3Zz549Q0REBLp16ybXvmXLFowbNw6+vr5o1KgRNDT++z7QpEkTPH/+HPHx8R87XCIiIiKiDyr1nuyQkBAAQFpaGkaNGoVHjx7B1tYWEyZMgIeHB6KjoyEWi+WWsbCwAABERkbC1NS0SNsVBAFpaWnFip2IiIiISoYgCBCJRKUdRpGVepKdmpoKAPjf//6Hr776ClOnTsXZs2cxceJEbNu2DRkZGdDS0pJbRltbGwCQmZlZ5O1mZ2cjICCg6IETERERUYl6NwcsT0o9ydbU1AQAjBo1Cr169QIA1KpVC48ePcK2bdugo6ODrKwsuWWkybWenl6xtlujRo0iL09EREREJefp06elHUKxlHqSbWlpCQAKJSE1atTA5cuXYWNjg5iYGLlp0r8rVapU5O2KRKJiJelEREREVHLKc6kIUAaS7Nq1a0NfXx9+fn5wdXWVtT9+/Bh2dnZo0KAB9u/fj9zcXKirqwMAvL29YW9vX+R6bCKiT01gYCC8vLwQExMDCwsLuLu7w9HRsbTDIiL6bJV6kq2jo4PRo0djzZo1qFSpEpydnXHy5Elcv34d27dvR40aNbB582bMmDEDo0ePxv3797Fjxw7MmTOntEMnos+AIAjFuv/jYwgKCsLBgwcB5PX8hIaGIiwsDH379oWDg0MpRydPW1u73PdOEREVhEgQBKG0gwCAbdu2Yffu3Xj58iWqV6+Or7/+Gm3btgUA3L9/HwsWLMCjR49gbm6OkSNHYsiQIUXelr+/PwDAyclJJbET0adJEATMnDkTQUFBpR3Kez1//hzp6ekK7bq6urC3ty+FiPLn6OiIefPmMdEmog8q7/laqfdkS40YMQIjRoxQOs3Z2RkHDhz4yBEREZWPmsD8etrLeg88EdGnrMwk2UREZY1IJMK8efPKfLK6YcMGbNiwAQDQvXt32cO7rK2tMXLkyNIMTQHLRYjoc8Ekm4joPUQiEXR0dEo7jPdq3bq1LMnW0NCAhoYGRCIRPDw8ynzsRESfKibZRETlnIODA2xtbREfHw8tLS1YWVlxdBEiolLGJJuI6BNgZGQEIyMjTJs2jb3XRERlgFppB0BERERE9Klhkk1EREREpGJMsomIiIiIVIxJNhERERGRijHJJiIiIiJSMSbZREREREQqxiSbiIiIiEjFmGQTEREREakYk2wiIiIiIhVjkk1EREREpGJMsomIiIiIVIxJNhERERGRijHJJiIiIiJSMSbZREREREQqxiSbiIiIiEjFmGQTERHRZ8nX1xcTJkyAr69vaYdCnyAm2URERPTZyczMxMaNGxEbG4uNGzciMzOztEOiT4xGaQdARERE9LEdOXIEiYmJAIDExEQcPXoUAwcOLPDygYGB8PLyQkxMDCwsLODu7g5HR8eSCpfKIfZkExER0WclKioKx44dgyAIAABBEHD06FFERUUVaPnAwEDs378fERERyM7ORkREBA4cOIDAwMCSDJvKGSbZRERE9NkQBAGbN2+WJdgfalfGy8tL6XqVtdPni0k2ERERfTYiIiLg5+cHiUQi1y6RSODn54eIiIgPriMmJkZpe2xsrEpipE8Dk2wiIiL6bNjY2KBevXpQU5NPgdTU1ODi4gIbG5sPrsPCwkJpu7m5uUpipE8Dk2wiIiL6bIhEIowePRoikahA7cq4u7srXd7d3V2lsVL5xiSbiIiIPitWVlbo2bOnLFEWiUTo1asXLC0tC7S8o6MjBgwYABsbG2hpacHGxgYDBgzg6CIkh0P4ERER0Wend+/euHTpEhISEmBiYoJevXoVanlHR0cm1fRe7MkmIiKiz462tjbGjh0Lc3NzjBkzBtra2qUdEn1i2JNNREREnyVXV1e4urqWdhj0iWJPNhERERGRijHJJiIiIiJSMSbZREREREQqxiSbiIiIiEjFmGQTEREREakYk2wiIiIiIhVjkk1EREREpGJMsomIiIiIVIxJNhERERGRijHJJiIiIiJSMSbZREREREQqxiSbiIiIiEjFmGQTEREREakYk2wiIiIiIhXTKO0AiIiobAkMDISXlxdiYmJgYWEBd3d3ODo6lnZYRETlCpNsIioVgiAgMzOztMP4JGRkZCj9/6IICgrCoUOHZH+HhoYiLCwMffv2hYODQ7HWXZ5oa2tDJBKVdhhEVI4xySaiUpGZmYkhQ4aUdhifnNGjRxdr+efPnyM9PV2h/dSpU7C3ty/WusuT3bt3Q0dHp7TDIKJyjDXZREQkk9+vC/zVgYiocNiTTUSlblEddWjxK3+xCIIAAMUucdiXpYPoZMWebEsjHQxyUi/Wusu6LAnw08Pc0g6DiD4RTLKJqNRpqQHa6qx/LR7VHL/m9hb4yz8Mwjtrbm5v8Rm8RsKHZyEiKiD2HRERkUxNcyN0d7KDpZEuNNXVYGmki+5OdqhpblTaoRERlSvsySYiIjk1zY2YVBMRFRN7somIiIiIVIxJNhERERGRijHJJiIiIiJSMSbZREREREQqxiSbiIiIiEjFmGQTEREREalYmUiyIyIi4ODgoPDv0KFDAICAgAAMGTIELi4uaN26NbZs2VLKEVNJ8fX1xYQJE+Dr61vaoRAREREVWZkYJzsoKAja2to4f/683COBDQ0NkZiYiBEjRqBt27aYM2cO7t27hzlz5sDY2Bh9+vQpxahJ1TIzM7Fx40YkJCRg48aNcHJygra2dmmHRURERFRoZSLJfvz4Mezt7WFhYaEwbceOHdDS0sLs2bOhoaGB6tWrIzQ0FJs2bWKSXU4EBgbCy8sLMTExsLCwgLu7OxwdHRXmO3LkCBITEwEAiYmJOHr0KAYOHPixwyUiIiIqtjJRLhIUFIQaNWoonebr64tGjRpBQ+O/7wNNmjTB8+fPER8f/7FCpCIKDAzE/v37ERERgezsbERERODAgQMIDAyUmy8qKgrHjh2DIAgAAEEQcPToUURFRZVG2ERERETFUmZ6ss3NzfHFF18gJCQEVapUwcSJE+Hu7o7o6GiIxWK5+aU93pGRkTA1NS3SNgVBQFpaWrFjp/e7cOECsrOzlbbb2dkByHstNmzYIEuwpaTtP/zwg1wZEX0aMjMzSzsEonylp6dDIpGUdhhEnzVBEMr153+pJ9lZWVkICQmBrq4upk2bBj09Pfz1118YM2YMtm3bhoyMDGhpacktI63TLc6HdHZ2NgICAooVO33Yw4cPkZOTo9CelJQkO/5xcXF48OCBwjwSiQQPHjzAtWvXYGZmVuKx0seVlZVV2iEQ5SswMFDhs4eIPr7y/D4s9SRbS0sLt2/fhoaGhuxA1q1bF8HBwdiyZQt0dHQUPoylybWenl6Rt6upqZlviQqpTp06dRAZGanQbm1tjVq1agHI+6bq7e2NR48eyfUcqampoU6dOmjRokW5/iZLyrEnm8oyR0dH3nhNVMqePn1a2iEUS6kn2YDyZFksFuPatWuwtLRETEyM3DTp35UqVSryNkUiUbGSdCoYT09PHDhwQK4URCQSwdPTU+74jxs3DpMnT5ZbViQSYdy4cdDX1/9Y4dJHpKZWJm4JIVJKV1cXOjo6pR0G0WetvHewlfqnXGBgIOrXr68wLvKDBw9Qo0YNNGrUCP/++y9yc3Nl07y9vWFvb1/kemz6eBwdHTFgwADY2NhAS0sLNjY2GDBggMLoIlZWVujZs6fsDSUSidCrVy9YWlqWRthERERExVLqPdlisRg1a9bEnDlz8Msvv6BixYo4ePAg7t27hz///BNmZmbYvHkzZsyYgdGjR+P+/fvYsWMH5syZU9qhUwE5OjoqHbLvXb1798alS5eQkJAAExMT9OrV6yNER0RERKR6pd6TraamhvXr18PJyQmTJ09Gr1694Ofnh23btsHBwQGmpqbYvHkznj9/jl69euGPP/7AtGnTmIB9grS1tTF27FiYm5tjzJgxrIckIiKicqvUe7IBwMTEBAsXLsx3urOzMw4cOPARI6LS4urqCldX19IOg4iIiKhYSr0nm4iIiIjoU8Mkm4iIiIhIxZhkExERERGpGJNsIiIiIiIVY5JNRERERKRiTLKJiIiIiFSMSTYRERERkYoxySYiIiIiUjEm2UREREREKsYkm4iIiIhIxZhkExERERGpGJNsIiIiIiIVY5JNRERERKRiTLKJiIiIiFSMSTYRERERkYoxySYiIiIiUjEm2UREREREKsYkm4iIiIhIxZhkExERERGpGJNsIiIiIiIVY5JNRERERKRiTLKJiIiIiFSMSTYRERERkYoxySYiIiIiUjEm2UREREREKsYkm4iIiIhIxZhkExERERGpGJNsIiIiIiIVY5JNRERERKRiTLKJiIiIiFSMSTYRERERkYoxySYiIiIiUjEm2UREREREKsYkm4iIiIhIxZhkExERERGpGJNsIiIiIiIVY5JNRERERKRiTLKJiIiIiFSMSTYRERERkYoxySYiIiIiUjEm2UREREREKsYkm4iIiIhIxZhkExERERGpGJNsIiIiIiIVY5JNRERERKRiTLKJiIiIiFSMSTYRERERkYoxySYieqPRbydg9fMBrL8epHT6tL98YfXzASy7+KDY25Gu48Cd57D6+UCx1ldcU47dRqPfTsi1SSQCll54gPq//gX7uX9iwPbLeB6f8t71hCakYthuL9RaeBTOS45j5sk7SMvKUTqvd0gMbGYdVNk+EBGVNUyyiYjeoqmuhr8fvlBoz8mV4OTDcIhEqt1ed6fK8JvWXbUrLYTTj8Kx999nCu0rLj/EzttPsaynK/4e4wmRSITBO68iKydX6XqSM7LQY/MFJKZnYd+XLbFziDv8IhIwYu81hXlvPI/BiD3XIBEEle8PEVFZwSSbiOgt7tUq4U54PCJepcm1X3seAz0tdVgb6al0e7qaGrAw1FXpOgvqZUo6fvjLF02rmsu1Z+XkYv2NIEz1qAtPsTXqWFXEhv5NEZWSjlOPIpSu6+DdECRnZGPrF83hYmsKFxsTrB/QDF7PXuJmSCyAvC8qM0/ewYDtl2FnYlDi+0dEVJo0SjsAIqKypL6tCZ7EJuPEgxcY39xB1v6Xfxh61LXD8QdhcvPfDovDgnP34ReRABN9bbR3sMb0ds4w1NEEkNfDO/PkXZwNjICmuhq+aVlLbvkDd55j8tFbiJo3AAAQ9PIVFp33x63QWKRm5cCmgh5GNqmJMU3FAIBlFx/gxvMYeIqtsOXmEySkZcK1shkWd2uIGuZGAIBvj/jgxvMY3J7SLd/9FAQB3xz2Qd96VWCgrYkDd5/Lpj2MTkJqZg5aVKska6ugqwUnq4q4GRqDns52CusLjktBTXMjmOnryNpsKujBRE8b3iExaFLVHK+zcnA/MgEHhrfGi8TXmHz01vtfDCKicow92URE7+het7JcyUhWTi5OB0Sgh5N8cvkoOgn9t11GqxqVcOGrDljbrwnuRyZg4I7LEN6UQow94I274fHYMdgd+4e1wj9BkQhPku8ll0rLykH/7Zehr6WBY6M9ceXrjuhWtzJmnbqLB1GJsvn+fREP75BY7BrijoPDWyM86TV++vtf2fR5nevj9Ph2793HDTceIyYlAz+1c1aYFvkqHQBgXUG+h93SUBcR+cRuYaiDmJQM5EoksrbUzGwkpWchLjUDQF6i/teYtmhmb/He2IiIPgVMsqlM8fX1xYQJE+Dr61vaodBnrFvdynIlI1eCX8JUTxtO1hXl5lt7LRAtqlngu9Z1UM3UEI2rmGNd/6a4E54A75BYPI1NxpWn0VjYtSGaVDVHXauKWNuvKbQ1lF9607JzMKapGIu7NYTYwgj2poaY2qYOACDg5SvZfNkSCVb3aYw6VhXhVsUco5qKcSs0TjbdSEdLrkf5XY+ik7D80kOs6dcE2hrqCtPTs/NuVtRWl5+mraGGzByJwvwA0NPJDknpWZh16i5eZ2bjVXoWph33hQhAZq7yZYiIPmUsF6EyIzMzExs3bkRCQgI2btwIJycnaGtrl3ZY9BmqZ2OCKhUNZCUjx/3DlJZI+Ecm4nlCKqrPO6ww7UlsMhJeZwIAXGxMZO3mBjqwq6i8HtlMXwfDG9fAMf8wPIpOwrP4VDx804Mtkfx3k6C5vg4q6v333jDS1kRWARPZjOxcTDzkjcmta6O2pbHSeXQ085LrzNxc6Kr99zGRmSOBnpZiUg4A9qaG2DyoGaYd98W2W0+ho6GOUU3EqGdjAiNtzQLFRkT0KWGSTWXGkSNHkJiYl1AkJibi6NGjGDhwYIGWDQwMhJeXF2JiYmBhYQF3d3c4OjqWZLj0iev2pmRkuFsNnA2MwKlxiuUXEkFAb2c7fNuqtsI0U31tXH36Ujbf2zTVlA9REpuagfbrzkFDTQR7E0PYmxrga3dH9N12WW4+rXx6wgviTng8gmKSseziA/x26SEAIDtXghyJBNXnHcav3RuimqkhAOBlSgaqvnWDYnRKOurkk5gDgKfYGv/+0B0xKekw1NGErqYGnBYfw4D69kWOl4iovGKSTWVCVFQUjh07JqtjFQQBR48eRatWrWBlZfXeZQMDA7F//37Z3xEREThw4AAGDBjARLucyJSUjaHcBAHIkQjIzBXQsZYt/rgagF2+wahsbAA7E0Nk5gpy84gtKiDwZTKsjf9LRIPjkrHwnB+meTpDXMkYAHAjJBYeYmsAwKv0LDxPSJWtI/vNvmfmClh7LRAJrzMxtpkD1N8k4sf882rDs97MnyPJiyEz979j9vY6PqSOVUVc/rqTXNt2nyc4ExCB/cNbw8xAB5rqajDU1sTV4JewqqAvi9s/MhFDG9VQuh3fsDj8esEfu4e2RAW9vFKVK8EvkZCWhab2FgrLFCbmj6WsnIdE9Glgkk2lThAEbN68WZZgv9s+c+ZMiN4zOLGXl5fSdXp5eTHJLsPefr2nPywbNbuJ2cC5GAHh/rkAjKBvYIC55/zh6OiIKf65CvOkWdTA/YAraLPjNmrUqIGcnBzcvXsXOTk5WBelC3V1ddjY2OCrY3fQoAGgo6ODBw8eICNHIltHSHjevk/xz8WdyCxk5UpwJPgV9PT0kJmZiZcv83rD94XlwEcjF49iBCRmQxYPALl1AEB2djYkEkk+5VYiAPLDED5K0URyrgirI/9rt65aDT+fuY9jsZrQ19fH/fv3oaGji0sSK1zxz4UgCMjMzISmpibU1dWRlaWPe1Gv0HHvXdSsWROvX7+Gr68vqtrb4/dwXSBcfnztd2Mua969HhERFRaTbCp1ERER8PPzU2iXSCTw8/NDREQEbG1t810+JiZGaXtsbKzKYqTPk62tLQIDA/M9/0xNTdGiRQs8evQIFy5cgIaGBszNzeHs7Az1NzcNNmrUCPfv38etW7cgCALs7e2RlZWldH26urowNTXFy5cvIZFIoKmpCWNjY6SmpiIhIQHVqlUrUNx+fn6IjY1Fp06dPjxzPurUqQNBEHDnzh3k5ubCzMwMLVq0gJpaXqlKWloazpw5g4YNG6Jq1arQ0tJC8+bN4efnh/Pnz0NbWxvVqlXjF10i+myJhM/w67q/vz8AwMnJqZQjISCvx2j+/Pnw9/eH5K3hv9TU1ODs7IwZM2a8tyd706ZNiIhQfECGjY0NxowZUyIxU/FlZGRgyJAhAICFddSgnU+d8udk37/BiE5OV2i3NNLFoIbVSyGiz0umRJD9qrJ7927o6OQ/QgsRlbzynq+VuZ7s58+fo3fv3vj555/Ru3dvAEBAQAAWLFiABw8ewNjYGEOHDsWoUaNKOVJSFZFIhNGjR2Py5MlK29+XYAOAu7s7Dhw4IPfzrkgkgru7e0mESyVAW00EbXUm2c3tLfCXfxje7vkQvWnn8SEiKl/K1DjZ2dnZmDp1KtLS/nvYQWJiIkaMGIGqVavi8OHD+Prrr7Fy5UocPqw4ZBaVX1ZWVujZs6csoRaJROjVqxcsLS0/uKyjoyMGDBgAGxsbaGlpwcbGhjc9UrlU09wI3Z3sYGmkC011NVga6aK7kx1qvnmSIxERlR9lqid79erV0NfXl2s7ePAgtLS0MHv2bGhoaKB69eoIDQ3Fpk2b0KdPn1KKlEpC7969cenSJSQkJMDExAS9evUq8LKOjo5MqumTUNPcqNSS6qO/ncDrpDQ06OiC2m89Ul7K5y9fPLkdDKc2dVDPo24pRJi/qOBo3Dvvj6SXr6Ctq4WqzlVQz7Mu1N962E7AjSAE+TxFeko6jMwMUc/TCbYO1vmuMyk4GJe+/x7hV64AAKp27IjWy5bB8K0a/bCLF3Fl2jTEP3wIA1tbNJ01C3WGDi25HSWicqPM9GTfvn0bBw4cwJIlS+TafX190ahRI2ho/Pd9oEmTJnj+/Dni4+M/dphUgrS1tTF27FiYm5tjzJgxfBANUSlQU1dD2FuPlJeS5EoQ9jA8r36ljEmMSsSlXV6wrmGFrl91RNNebnjuF4o7Z/+7oTr4zjP4XXiA+u2c0fWrjrB1sMaVvdeQ8Nbj6t+Wk5GBg56eAIBB169jwOXLeB0VhSNdu8pK0+IDA3GkSxfYd+6ML+/dQ71x43BmxAiEXrhQ8jtNRGVemejJTk5OxrRp0zBz5kyFMZGjo6MhFovl2iwsLAAAkZGRMDU1LdI2BUGQK0uhsqF27dr47bffAICvzycuMzOztEMgJSyrVULk0yi8fpUG/Qr/DekX/TwGGlrq0NDUe8/SpeP1qzRUb2CPep55veuGJgaoUrcyop5Gy+Z5ERAJqxqWqFK3MgCgnqcTgnyeIvrZS5hYVVRYZ9yTJzBv2BAtV6yArpkZAMBp0iScHjAACWFh0DU3h8/SpTB1ckKDH38EANSZOBGRt27h5uLFMG/atKR3m+iTJwjCB+/LKsvKRJI9e/ZsuLi4oFu3bgrTMjIyoKWlJdcm7eEszod0dnY2AgICirw8ERVPfsPYUekytTXBq9hkhD54IVcyEuofhip17RD6IExu/tiwONw9dx/xEQnQ1teGrYM1XNo5Q0sn71Hqr1+l4e65+4gOjkZmehZ0DXRQzaUq6nk6QaQmQvCd57h/6QGcPerC//IjpL1Kg7FlBTTq3ADmdnnJrd/FB/C/9BBD5g1QGrOtow1sHW0A5H0ox4cn4MWjcFSu/V9Zh7aeFsIDI5EYnQTjShUQ+uAFsjOyYWajvKMmKicHdtOnIyQ2FoiNRXpkJIKWL4eBoyOex8ZCFBeHkEuXYNa6tdxniZqDAyKWLcOjR4/KdXJAVFa8mwOWJ6WeZB87dgy+vr44ceKE0uk6OjoKH8bS5FpPr+g9KpqamqhRo0aRlyei4mFPdtlVpW5lhD38L8nOzcnFi4AItB3eWi7JToxOwvltl1G3VS006dUIGakZuHPmHi7uuIwOY9tCJBLh8m4v6Ohrw2NYK2hqayLicSR8T96Fqa0pKtfKS4zTUzLw5HYwmvdtDDV1Ndw68S9uHPZB98mdIRKJULu5A8SNPjyEoUQiwYH5R5CbnYuKVsZwblNHNs3Zoy5exSbj5JqzEKmJIAgC3Lo2hEVVc6XrcnR0lHXo/NWtG8IvXoR2xYrocfIkzGrXBgBci4tD1Xr1UKtWLdlyeqGhCMrIgL2FhawHnIiK5unTp6UdQrGUepJ9+PBhxMfHo3Xr1nLtv/zyC7Zs2QJra2uFh41I/65UqVKRtysSiYqVpBNR8UgfakJlT5W6lfHoeqCsZCQq+CW09bRhYi1fVvHoWiAsq1nAqXVeMmtkaogW/Zvi2PKTeBkSCzNbE9i7VIFdncowMM67qd2xiRgPrwYiMTpJlmRLciVw69ZQVrZRt2VtXNl7DempGdAz1IWmtiY0tTU/HLgAtB/lgcy0TPieuouLu66iw2hPiNRESE1IBQA069MYxhYVEPYoHL6n78LQxABWNRRHMdLV1ZWNk+3x22/IzcyE9/z5ONGtG4bevQujypWRk5YGXUNDuc8SfWNjAICWmho/Y4iKqbz/GlTqSfayZcuQkZEh19a+fXt888036Ny5M06ePIn9+/cjNzdX9gQ1b29v2NvbF7kem4joU/EkNhk+obGIS82EmYE2GlcxL/boJKY2JjCoaCArGQn1D0NVZzuF+RIiE5GSkIr98xSHVE2OTYalvQUcGtdE2MMXCLzxGCnxKUiMTkJ6SrrCY8srvBWz5ptSE0muBIWhpq4GUxsTAECz3lo4s/E8YsJiYWJVEVf2XYdr5/qo5lIVAGBiXRGvk17jzlk/dFGSZL/NwsUFANDtwAFsqFwZD7ZtQ7NZs6Chq4vcd36RyXnzeab5zkhZRPT5KfUkO7/eaFNTU9jY2KBPnz7YvHkzZsyYgdGjR+P+/fvYsWMH5syZ85EjJaKSkiUBgLL58FlBEJBVNkNDcFwyTj34bySQ14lpCEsKRac6lVHdrPCJtkQAMnMFJGdLYFXLFs/9w2DVoBpeBETAfbQnkrMlcvPkCgJs6tqhpnsthXVp62kjIS0L17dfRm52DqxrV4alUxU4d6qPy9suIVciIDNXQI4k7+DmiNSQk5v3/9lv/puVkzfPhyRGJiAnKwfmVS1kbbpv9j85KR0SNXVkpWfB0LKi3PqMbUwQ9ihc1pb1Vk6fHBqKsAcPIH5rqFhNPT1UqFYNqW+eMGtYuTJSIyPlYkmNjISmgQG0K1T4YNxE9Gkr9ST7Q0xNTbF582YsWLAAvXr1grm5OaZNm1aoMZSJqGz76WFuaYdQLj1/Hov0dMUk9HpKLOztDQq9vvrZwJNYATsfSqCnYwOniED8ceoZLHT1seilAfBSIjdPTW0jRIe9wqGo/8oidFJSUOX+fYTVrQud1FQ4RCXi3y5dkP2m9EL9WRYapmTgXIyArf65MA+XoDqAKf7/nQNGsRLUBrAgKBeZ+h8+N6r4haBiZCTudewIvPl52SgmFrUBbErSR3aGNhoCWO2biATb/3qY7QOSYKBnILdtqTg/P5waMACjnz6FcfW8evCMpCQkBgWh9pAhAIDKLVvixeXLcsuFXbgAm+bNIWI5FNFnr0wm2UFBQXJ/Ozs748CBA6UUDRFR2ZTfzaOquKk0zdgYGQYGqPzgASLzedBTVM2aqH3lCuzv3EF0jRpQz8lB1bt3oZ6TgwwDA6jl5iWvZmFhiLexgXZ6Oio/eAA1QYAot+BfrNRycqCekyNL1N/1slo1WISEwP7OHUSJxdB+/Rr2d+8i3sYGaW9qpOMqV0ZVPz8IampIMzJCxehomIeE4Kmbm2w9mhkZyNXQgERDA1U6dIC5szNODR0Kzz/+gCAIuDptGnTNzOA0ciQAoP7XX2Nn/fq4+uOPqDN8OJ6dPInHf/6JvmfPFnjfiOjTJRLeLYz7DPj7+wMAnJycSjkSos+XIAjlYoSRshznjh078OT2bbz09UXSkyeyMWWrOTnh+yVLUKlx40Ktb7+zM2p+8QUavhn32XfBAtxbtgwD/PxgaGendJ7Iq1fhu2AB4u/fh4aeHqxbtkTjuXNhUDlvPGr/tWvxYO1aZMTHQ9/KCjX79UNySAjSoqPR6+xZBOzahQtjx+Kr9HRZHOFXr+JYhw74MjAQRlWqwGf+fNxesEBunne99PXF9enTEXvnDjQNDSEeMABN58yB+psRQnLS0+G7ZAmeHDqEtJcvYSwWo+EPP6DGW7+K/qGri0YzZqDxzJnQ1tbG6+hoXJ4yBaH//IPczExU7dABrZcvh9GbfQOA52fO4Mq0aUh8/BgV7O3RbM4cOPbvX6jjTkTKlfd8jUk2EVE5dfSXX7Bu7ty8Eom3LuX11dVhLpGg7dq1cBk/vhQjJCIquvKer7FojIioHAq/dg3B8+bBBUAFQYA6gAoAXACY5+YCgoDzEyci4vr10gyTiOizVSZrsomI6P18ly+HSF0dFjk5sMhnHpG6OnxXrIBN8+YfNTYiImJPNhFRuZOdno7g48ch5OS8dz4hJwdPjx5F9ntqmYmIqGQUqSc7JycHt27dgre3N8LDw5GSkoKKFSvC2toaLVu2RIMGDcr9U3qIiMqqrORkCJKCPahFkEiQlZwMTV3dEo6KiIjeVqgkOysrC/v27cO2bdsQHR2NChUqwNraGrq6uoiOjsbly5exYcMGWFhYYMyYMRgwYAC0tLRKKnYios+SlpERRGpqBUq0RWpq0DIq3hMgiYio8AqcZN+/fx//+9//oK6ujkGDBqFTp06ws1N8zG5QUBCuXLmCXbt2YefOnfj1119Rv359lQZNRPQ509TVRfUePRB84sR7S0ZEGhqo0aMHe7GJiEpBgYfw69ChA6ZOnYp27doVeOWnTp3CypUrcbaMDcxf3oeEISIKv3YN+1u2lBu6T4FIhEFeXrzxkYjKpfKerxU4yc7KyipS6UdRlytJ5f1FIyICgHvr1+P8xIkQqavL9WiLNDQg5OZynGwiKtfKe75W4NFFipool7UEm4joU+EyfjwGeXmhRo8eEKnlXc5Famqo0aMHBnl5McEmIipFhbrxMTs7G3v27IGVlRU6dOgga8/NzUX37t3Ro0cPjB49GmpqHBmQiOhjsGneHDbNmyM7PR1ZycnQMjJiDTYRURlQ4Gw4KysL48ePx5IlS2Td91KJiYnQ1tbG8uXLMXHiROR8YOxWIiJSLU1dXehXqsQEm4iojChwkn3gwAH4+vpixYoVmDp1qtw0MzMzHDlyBMuWLcO1a9fw559/qjxQIiIiIqLyosBJ9tGjRzF8+HB07Ngx33m6du2K/v37M8kmIiIios9agZPs0NBQNG7c+IPztWzZEqGhocUKioiIiIioPCtwkq2hoYHs7OwCzcdHqhMRERHR56zASXbNmjXh4+Pzwfl8fHxga2tbrKCIiIiIiMqzAifZPXr0wL59+3D//v185/H398eePXvQqVMnlQRHRERERFQeFXic7L59++Lvv//G0KFD0bdvX7Ru3Rq2traQSCSIiIjA1atXcfDgQTg4OGDo0KElGTMRERERUZlW4MeqA0BGRgYWLlyII0eOIDc3V9YuCAI0NDTQr18/fP/99zA0NCyRYFWlvD+mk4iIiOhTV97ztUIl2VIJCQm4efMmoqOjoaamBhsbGzRp0qTMJ9dS5f1FIyIiIvrUlfd8rVCPVZcyMTFB586dVR0LEREREdEnocBJtq+vL/z9/WFnZwdPT8+SjImIiIiIqFwr0Ogif/zxB2bNmgVNTU3s2LEDX3/9dUnHRURERERUbhWoJ3v79u1YtWoVmjVrhk6dOqFFixYIDw/neNhEREREREoUqCfbzs4Od+7cAQDcuXMHenp6MDExKdHAiIiIiIjKqwL1ZC9fvhw///wzDh8+DGNjY6xZswZ6enolHRsRERERUblUoCS7atWq2LVrV0nHQkRERET0SSjwY9WfPXtWpA0EBwcXaTkiIiIiovKqwEn22LFjsXDhQsTGxhZo/qioKMydOxdjx44tcnBEREREROVRgZPsY8eOITk5GW3atMGoUaNw6NAhBAYGIjU1FRKJBElJSQgMDMSePXswbtw4tGvXDq9evcKRI0dKMn4iIiIiojKn0I9Vf/jwITZs2ICLFy8iNzdXYbq2tjZatmyJsWPHom7duioLVJXK+2M6iYiIiD515T1fK/Rj1evUqYNVq1YhLS0Nvr6+ePHiBVJTU1GxYkVYW1vD1dUVOjo6JRErEREREVG5UOgkW0pPTw8tW7ZUZSxERERERJ+EAtdkExERERFRwTDJJiIiIiJSMSbZREREREQqxiSbiIiIiEjFip1kp6SkIDg4GFlZWUqH9CMiIiIi+twUOcn28fFBv3794Obmhm7duuHJkyeYMmUKFi9erMr4iIiIiIjKnSIl2d7e3hg1ahR0dHQwdepUSJ9nU7t2bezcuRPbtm1TaZBEREREROVJkZLs33//HZ6enti1axeGDRsmS7LHjh2L0aNH49ChQyoNkoiIiIioPClSkh0QEIA+ffoAAEQikdy05s2bIyIioviRERERERGVU0VKsg0NDREbG6t0WlRUFAwNDYsVFBERERFReVakJNvT0xMrVqyAv7+/rE0kEiE6Ohrr169H69atVRUfEREREVG5o1GUhaZMmQI/Pz/0798fZmZmAIDvv/8e0dHRsLKywvfff6/SIImIiIiIypMiJdkVKlTAoUOHcOzYMdy8eRNJSUkwNDTE0KFD0bt3b+jq6qo6TiIiIiKicqNISfbs2bPRo0cP9O/fH/3791d1TERERERE5VqRarJPnDiBjIwMVcdCRERERPRJKFKS7eTkhKtXr6o6FiIiIiKiT0KRykUcHBywa9cunD17FjVq1ICpqancdJFIhIULF6okQCIiIiKi8qZISfY///wDCwsLAMDTp0/x9OlTuenvPqCGiIiIiOhzUqQk++LFi6qOg4iIiKjMCwwMhJeXF2JiYmBhYQF3d3c4OjqWdlhUBhUpySYiIqLSIQgCMjMzSzuMDyovcRbG48ePceTIEdnfKSkpePbsGXr16gWxWFyi29bW1i4XlQLlJc6PoUhJ9pdffvnBeXbu3FmUVRMREdF7ZGZmYsiQIaUdxmfp+fPnSE9PV2g/d+4c7O3tSyGismf37t3Q0dEp7TDKhCIl2YIgKLSlpaUhODgYenp6aN++fbEDIyIiIipL8uuZ/9R67Ek1ipRk79q1S2n7q1evMG7cOFSrVq1YQREREdGHLeqtBa0yWvgpCAKycks7CtU6eFEPLxPTFNorVdRDfw/NEt22lnrZHVgiKwf46UhWaYdR5qj0rVmhQgWMGTMGCxYsKFBJCRERERWdlgagrVE2Ey9ABJ2SzTs/ulbOFvjrRpjcL/oikQitnC1gpFOkR498IhQrHKiID6N5H0EQEB8fX6hl4uPj8cMPP6BJkyaoX78+xo4dKzcsYEBAAIYMGQIXFxe0bt0aW7ZsUXXYRERERO9V07YCujezg6WJHjQ11GFpoofuzexQ07ZCaYdGZVCRerJv376t0Jabm4vo6Gj88ccfqFOnTqHWN2HCBKipqWHTpk3Q09PDypUrMXz4cPzzzz/IyMjAiBEj0LZtW8yZMwf37t3DnDlzYGxsjD59+hQlfCIiIqIiqWlbgUk1FUiRkuyhQ4cqrQsSBAFWVlaYPn16gdeVmJgIW1tbTJgwATVr1gQATJw4ET169MCTJ0/g7e0NLS0tzJ49GxoaGqhevTpCQ0OxadMmJtlEREREVCYVKclWNjyfSCSCgYEBHBwcoKZW8CqUihUrYvny5bK/4+LisGXLFlhaWqJGjRpYvXo1GjVqBA2N/0Jt0qQJNmzYgPj4eIVHuhMRERERlbYiJdkikQi1a9eGvr6+wrTk5GR4eXmhS5cuhV7vzz//jIMHD0JLSwvr1q2Dnp4eoqOjFQZ4lz7SPTIysshJtiAISEtTvEOYiIioLONwcVSWpaenQyKRqGRdgiCU2RFVCqLID6M5cOAAnJ2dFaY9evQIP/30U5GS7GHDhmHAgAHYt28fJk2ahL179yIjIwNaWlpy82lrawMo3oUmOzsbAQEBRV6eiIioNGRlcag0KrsCAwMV8rbiUOW6PrYCJ9n/+9//EBUVBSDvm8Xs2bNhYGCgMF9ISAjMzMyKFEyNGjUAAPPmzcO9e/dkTw1694IiTa719PSKtB0A0NTUlG2PiIiovGBPNpVljo6Oss7Q4np7pLnyqMBJdocOHbBt2za5tnef/Kiurg4XFxcMHjy4wAHEx8fD29sbnTp1grq6OgBATU0N1atXR0xMDCwtLRETEyO3jPTvSpUqFXg77xKJRMVK0omIiEpDYe57IvrYdHV1VfZY9fJcKgIUIsn28PCAh4cHgLzRRWbPno3q1asXO4CYmBhMmTIFpqamaNq0KYC8Uo5Hjx7Bw8MDZmZm2L9/P3Jzc2VJuLe3N+zt7XnTIxERERGVSUX6Orxr1673JtjBwcEFXpejoyNatGiBOXPmwNfXF48fP8b//vc/JCcnY/jw4ejTpw9SU1MxY8YMPH36FEeOHMGOHTswbty4ooRORERERFTiinTj46tXr/Dbb7/h9u3byM7OlpWNSEfsePXqVYFvKhSJRPj999/x22+/YfLkyUhJSYGrqyv27NkDa2trAMDmzZuxYMEC9OrVC+bm5pg2bRp69epVlNCJiIiIiEpckZLshQsX4u+//0bLli3x7Nkz6OrqomrVqvj333+RnJyMuXPnFmp9hoaGmD17NmbPnq10urOzMw4cOFCUUImIiIiIProilYt4eXnhq6++wrp16zBw4EBYWlri999/x5kzZ+Dg4FDu7wYlIiIiIiqOIiXZycnJaNiwIQCgZs2aePDgAQBAX18fI0eOxOXLl1UWIBERERFReVOkJLtixYpISUkBAFSpUgXx8fFITEwEkDes3suXL1UXIRERERFROVOkJLtp06ZYv349wsPDYWtrC2NjYxw5cgQAcOnSJVSsWFGlQRIRERERlSdFSrK//fZbxMfH48cff4RIJMLYsWOxdOlSuLm5Yfv27ejTp4+q4yQiIiIiKjeKNLqIjY0NTp06hZCQEADAiBEjYGZmhjt37sDZ2ZnD6xERERHRZ61ISfb48ePx5ZdfolmzZrK2bt26oVu3bioLjIiIiKg8ODr+OF7HvkaDYfVRu3sthek+G27hybmncOpfF/UGOJdChPmLDYzF3b1+SHyeCA0dDVjXt0aDoS7QNtSWzRNxJxJ++/yQ9OIV9Ez0UKubIxw6ifNdZ05GBi5PmYKgQ4eQk54O+44d4blmDfQtLGTzHGjTBi/eGSjDpnlzDLp2TeX7WFqKVC5y+/Zt2SPOiYiIiD53ahpqCPMOU2iX5EoQdvMFICqFoD4gOTIZF+ZdgknViui4uAPcv2+O+CfxuLrsv0T35cOXuLzoCqxcrNDt9y6o07M2fLf+i9Drofmu958JExBy7hx6HDmC/hcuICk4GCf69ZObJ87fH23XrcOEqCjZv55//VVi+1oaipRkN2/eHIcOHUJmZqaq4yEiIiIqdyydKiHuSTxex72Wa4/2fwkNbQ3om+qVUmT5e3b5OXRNdNFwRANUsDGCRS0LuI11xcsHL5ESnQoA8Dvgj8qNbVF/sAsMLQ1Rs30NVGttj5ePYpSuMzUiAo927oTn6tWwbdECVm5u6Lp/P8KvXkXkzZt580RGIj0+HtZNmkDf0lL2T9fE5KPt+8dQpHIRbW1tnD59Gv/88w9sbW1hamoqN10kEmHHjh0qCZCIiIiorDOtaYpXEckIvREmVzISej0UVZrbIfS6fC93bGAs7u6+h/jgBGgbacPW1QYug12gpacJAHgdn4a7u+8h2i8amamZ0DXWRbVW9qg3yBkiNRGCLz7D/YP+cB7gBP8/HyAtLg3GVYzRaFRDmDuYAwD8DtyH/8EHGHL4C6Ux27esChtXG4hEit3sWamZyMnUQWxALNyntpCb1nRSk3yPQ5S3NwCgcuvWsjYTsRgGNjYIv3oV1k2aIPb+fYjU1GDi6PieI1r+FaknOzo6GvXr14ezszNMTEwgCILcP4lEouo4iYiIiMq0Ks3s5EpGcrNz8eJWOKo2ryI3X2JIIs7PuQgrFyt0+a0TWkxuhoTgBFycexGCIAAALi+8jMxXGfD4uQ26r+qG2j1r4cGRhwj3jZCtJz0xHU/OPUHzb5uhw6L2EKmJcGOVt2wdtbvXQp/N+Q9GUcG2AszFZnJtD48+go6xDipWrYiUqBQIEgFqaiJcXeqFQyMO4+SUU3h6PjjfdaZGREDXzAwaOjpy7QbW1kgOyzs2sf7+0DY2xrlx47De1hZba9XCtZ9/Rs4nViFRpJ7sXbt2qToOIiIionKtSrMqeHQ8AK/jXkPfTB9RftHQNtKGSTX5MohHxwNg6VQJTn3rAgCMrI3Q4rvmODbxL7x8GAOzmqawb2UPuyZ2MLDQBwA4dnbAw6OPkBiaiMputgAASY4EbmPdYGKf93ySur3r4MqSq0hPyoBeRV1o6mpCU1ezwPH/u/0OIu5EouVUd6hpqCE7PRsAcHP9LdTtXQd1+9bFy4cvcWvTbUAE1PCsrrCOnPR0qGtrK7Rr6OggNyMDABD34AFyMjJg6+6ORj/8gJi7d3F5yhQkh4ai886dBY63rCtSki316tUr+Pr6IiYmBh06dEBSUhLs7e2V/uxARERE9CkzrW4Cg0oGspKR0OuhqNqiisJ8Cc8SkRKdgv2DDypMSw5PhmXdSnDoJEaYdxgCTyYgJSoFiSFJSE9IhyAR5OavYGsk+3/NN6UmkuzCVRRIciS4uf4Wnl1+BrcxjWDXpDKAvJs5AaBaa3s4dnEAAJjY5/VwB/wVqDTJ1tDRQa6SHumcjAxo6ud9Yei4dSs8V62CdoUKAADzunWhrqmJvwcNQqulS6FfqVKh4i+ripxkr1u3Dhs2bEBGRgZEIhGcnZ2xYsUKJCUlYevWrTAyMvrwSoiIiIg+IdKSEYeOYoTfDkfHxR0U5hEEAVXdq8KpTx2FadpG2sjJzMG5n88jJyMHVZrZoVore5iONsW5mecV5lfXVDbam6CkTbns9GxcWXIVMQGxaDG5Gaq2qCqbpvfmZk1jO2O5ZSpUroDgi8+Urs/A1hbp8fHIzcqCupaWrD01MhIGtnk98Grq6rIEW8rMyQkAkBIe/skk2UWqyd69ezdWr16NESNG4ODBg7Lan2HDhuHFixdYuXKlSoMkIiIiKg+qNKuCuCfxeHohGAaVDFDBtoLCPMZ2FfDqRRIMrQxl/yQSAb7b/kVafBoi70YhITgB7eZ6ot5AZ1RpXgWauprIeJVRmPz5g3Kzc3FpwWXEByfAY2YbuQQbAPRM9GBgaYC4x3Fy7UmhSTC0NFS6TqtmzSBIJAj38pK1JQQFITUiArbu7gCAfS1a4NzYsXLLRd++DXUtLVSsWVMFe1Y2FCnJ3rVrF8aOHYtvv/0Wder89y3M3d0dkydPxsWLF1UWIBEREVFJyMnMQXpSOnIyc1S2ThP7ijC0MsS9vX6o0lyxVAQAanWvhYTnifDZcAtJL14h7nEcrv9+HcmRKTC0MoSeqS4A4PnVEKTGvEZMQAwuL7kKSY6kUKUg2enZSE9Mz3f6gyMPERMYi8bjGqGCrRHSE9Nl/3KzcwEAzv2d8OSfpwg6/Rgp0al4cu4pgi8+Q+0e/40MkpGUDrWcvGNoYG2NWoMG4dyYMQi7fBlRt2/j70GDULl1a1g3yRuVxHHQIDzYtg1+Gzci6dkzBB44gCs//ADXqVOh/QlVQhSpXCQyMhJubm5Kp1WrVg1xcXFKpxERERGVtpiAGAScCMSLW+F5PcMioLKbLWp1rwULR/Nir79KMzs8+POh0npsADAXm8Hz5zbw23cfp6edgYa2BirVrYSGw+pDXVMdZjXN0HB4AwT8HQi/ffeha6KLqs2rQN9UD3FPCp5jPfor4L1D+IV4hQICcG3FDYVpbed4wrJuJVRrZQ8AeHjkIf7dfgf6FvpoNMYV1VpXk817YtwxWNeqhfDatQEA7TZuxKXJk3G8V97IJvadOsFz9WrZ/PUnTYJIXR3//v47Ln7zDfStrNDwu+/Q+McfC7xv5YFIkNZ6FEL79u3Ro0cPTJo0Cbm5uahTpw4OHz6MOnXqYMuWLThw4ADOnTtXEvGqhL+/PwDA6U39DxERUXmRkZGBIUOGAAB+668FbQ0ONlAYj888wa1NtyFSE8ndRCj9221sI4g7fDolCx9DZo6AKQezAOSVFOu8M3xfUZX3fK1IPdl9+/bF6tWroaOjg9ZvBhtPS0vD2bNnsWHDBowYMUKVMRIREREVW0xATN7wc4DCKB3Sv29tvA3jKsYq6dGmz1uRkuwxY8YgPDwcy5Ytw7JlywAAX375JQCgW7duGDdunOoiJCIiIlKBgBOBCj3Y7xKpiRBwIpBJNhVbkZJskUiEuXPnYsSIEfDx8UFSUhIMDQ3h5uaGmp/QXaFERET0acjJzPmvBvs9BImAFz4vkJOZAw3tYj1OhD5zxTp77O3ziuGTk5NhYmKCypUrqyQoIiIiIlXKTs8u+PB3Qt78TLKpOIp89uzZswfr1q1DfHy8rM3a2hpTpkxB586dVRIcERERkSpo6moCIhQs0RahUI8jJ1KmSEn2nj17MG/ePHh6eqJ9+/YwNTVFXFwczpw5gylTpkBLSwtt27ZVdaxERERERaKhrYHKbrYIvx3xwZpsWzdb9mJTsRXpDNqxYwe++OILzJo1S669Z8+emDVrFtasWcMkm4iIiMqUWt0c8cIn/L3zCBIBtbo5vnceooIo0hMfX758CU9PT6XTOnTogGfPlD/PnoiIiKi0WNSygNvYRgDyeqzfJv3bbWwjjixCKlGknmwnJyd4eXmhefPmCtPu3r0LBweHYgdGREREpGriDjVhXMU474mPPi9kT3y0dbNFrW6OTLBJZYqUZE+YMAHff/89UlNT0b17d1hYWCApKQkXL17E9u3bMX36dNy+fVs2f6NGjVQWMBEREVFxWDiaw8LRHDmZOchOz4amriZrsEnlinRGjRo1CgDw559/4vDhw7J26RPa58yZI/tbJBIhICCguHESERERqZSGtgaTayoxRTqzdu7cqeo4iIiIiIg+GUVKst3c3FQdBxERERHRJ6PIv5H4+/vj7t27SE5OVpgmEokwadKkYgVGRERE5UOj8ccRHvsavwyrj/HdaylMn7bhFnade4op/eti6gDnYm1nQBt7TB3gjAMXn2HympuIOvxFcUIvtHO3w7H80AM8iUiGiaE2ujW1ww8DnaD7puwk7lUGZm+/g0v3opCRlYumtS3w85f14VC5Qr7rTEjJxMwtvrh4JxIA0LWpHeaOaAg9nf/StCNeIVj550OExaSisoU+JvaohYEe1Ut2Z6lYijxO9uLFi2U12O9ikk1ERPR50dRQw9/eYQpJdk6uBCdvvoBIlM+CRdS9uR3a1LdS7Uo/4OajGIz81Qv/G+SMrk3t8Dw6BT+sv4WElEz8/lUTAMCwRVegpibCnhmtoaejgV/33ceAORdxY0036OVT/z1mqRcysnJxcLYnkl9n4bs1Pvhx022s+ropAOCafzS+Xe2NBaNd0crZCpfuRWLKulswNdJBO1ebj7b/VDhFGid727Zt8PT0xM2bNxEYGKjwjzc6EhERfV7cnSrhzpN4RMS9lmu/5v8SetoasDbVU+n2dLU1YFFRV6Xr/JBd/zxFc6dK+Lp3HdhbGcKjvjV++qIeDl99jszsXCSkZMLOwgDLxrvBpYYpxLYV8F2/uniZmI7HL14pXadvUCxuPIzB7181gXM1E7RwssTS8W7488pzRCekAQDO3o5ArSrG+LJ9TVSxNMDwjmLUrmKMy/eiPubuUyEVqSf71atXGDRoEIyNjVUcDhEREZVH9Wua4klEMk7ckO/N/ut6KHo0t8Px62Fy898OjMWC3ffgF5wAEyNttHe1wfTBLjDU0wQAJL/Owsyt/+LsrXBoaqjhmz515JZ/t1wkKCwJi/bex62AGKRm5MDGTA8jO4kxpmve0xuXHbiPGw9j4NnAGltOBiEhJROuDuZYPLYRatgYAQC+Xe2NGw9jcHt9D6X7OL6bI9TUFLvkc3IFpKZnw9RIB+u+/+8ZIrFJ6Vh3PADWpnoQ2yovF/EJiEWlirqo+db0ZnUsIBKJcCsgFt2bV0FFQy08CU/Gdf+XaFbXAt4PY/A0Ihnju/PJlGVZkZLsFi1a4O7du0ofRkNERESfp+7N7ORKRrKyc3H6VjgO/uIhl2Q/CklE/zkX8U2fOlg+sTFiX2Vg7o67GDj3Iv5e1B4ikQhjf7uGiLg07PipFQx0NTFnxx2Ex75Wut20zBz0n3MRLZwtcWxBO2iqq2HfxWDM2nYHTetUQl37igCAf4PioKetgV0zWuN1Rg6+WeWNnzbdxqHZeU+xnjeyIbJyJPnun1M1E7m/s7JzseFEIJyqVYSpkY7ctKnrfLDnfDC0NdWw/cdWcvXVb4uKT4O1mXwvv5amOioaaCEiLq8ne3RnB9x7Eo++sy9AXU2EXImAr3rWRp+W9vnGSqWvSEn2rFmz8OWXXyIiIgLOzs7Q1VX8uaZnz57FjY2IiIjKkW7NqmDt8QBExL2GjZk+rvhFw9RIWyE5XXs8AC2cKuG7vnUBANWsjbDuu+ZoPPEveD+MgUVFXVzxi8bBXzzQpLZF3jKTm6HR+ONKt5uWkYMxXR0xrENNWU/41P5OWH3kEQJCk2RJdnauBKu/aYqKhtoAgFGdxZi/655sPUb6WgXe15xcCb5e5Y3HL17h6Ly2CtPHdHXE0PY1sfPsE4xYchXHF7SD8zvHAQDSM3OhpaFYvautpY6M7FwAQGR8GpJSs7BojCtcHcxxzT8aS/bdRw0bIwzwqFbgmOnjKlKSffnyZYSFheH58+c4evSownSRSMQkm4iI6DNTr7oJqlQykJWMHL8eip4tqijM5/8sEc+jU1B98EGFaU/Ck5GQkgkAcKlhKms3N9aFXSUDpds1q6CD4R1r4ti1UDwKScSzqBQ8DEkCAEgk/w3SYF5BR5ZgA4CRntZ7e67zk5qejbG/XcONBy+xaWoLNBCbKcwjHU1k6Xg3+AbFYdvpx1gxqYnCfDpa6kpjyMzKld0oOXrpNfRuWQXDO4oBAHXtK+LV6yzM2XkX/VrbKy1hodJXpCR77dq1aNy4Mb799luYmSmeWERERPR56vamZGR4RzHO3g7HqcUdFOaRCAJ6u1fFt+/UWQOAqZE2rt6Pls33Nk115eM1xCalo8tP52BiqI0ObrZo4WwJlxqmaDj2mNx8WprqRdyr/7xMTMfg+ZcQFvMae2a0QXOnSrJpca8y4HU/Gt2b2UH9TaxqaiKIKxshKj5N6fqszfRw5la4XFtWdi4SU7NgbaqHuFcZCI5MlvvCAQANxWb4/c+HSEjJhFkF+VIVKhuKlGQnJCRg4cKFqFevnqrjISIionKse7Mq+OPoI+y7EIwqlQzkbuiTcrSrgKAXSbC3MpS1PY1IxtwddzB9iIusvON2YCzaNswbou7V6yw8j05Rus3DV0OQmJKJ66u7QfNN6cWZWy8AAGd9w5GdK0F8cmax9y0pNQt9f7mA1PRsHJ/fDrWqGMtNj05Ix8Tfb8DcWActnCwBANk5Evg/S0T7RsqH2mtS2wLzd93D86gU2fG4/uAlAMDVwQwmhtrQ1VZHQGgSPOpby5YLCE1CBX1NJthlWJGS7Hr16uHx48do2rSpquMhIiKiAsrMUf68io9NAJAjyYunZmVj2FsaYtEeP0zoWVsW49vzjOzsiP6zz2PKulsY0UmM1PRs/LzFF68zcmBtbgBtTXV0aVIZP23yhUikBnNjHfy6zw9Z2RLZOrLflIFk5giwqKiHtIxcHPEKRaNa5vC6H41f9/kBADKyJAiPS0NAaBKycyRyx+ztdQBAcloWsnMkCjcxSs3c+i/CXqZi+0+tYaCvhRdx//VOmxppo4ZtBbSsZ4n/bbyNxWPdUEFfC38cfYik1CwM7+SAzBwBuZK8hN9ITxM6WhqoY28CVwczjFt+HfNHueJ1Rg6mbbiN3u5VYVJBF9kSYGRnB/z+5wOYGOmgkaM5bgXEYtWRh/i6d90ycQ6UhRjKIpGQ3xNl3uPmzZuYMmUK+vbtCxcXFxgYKNZINWrUSCUBlgR/f38AgJOTUylHQkREVDjp6ekYOnRoaYch5/Tp06hSpQpq164NAHj48CECAwPRsWNH6OvrK50nJiYGjx49QmJiIjQ0NGBubg5nZ2fo6eWNtJGbm4v79+8jPDwcgiDA3t4ecXFxqFSpEmrXro2QkBD8+++/6NOnDwRBwIMHDxAaGoqcnBxoamrCyMgIqamp0NTUhLW1NWJjY5GcnIxu3brJ4n57HQDg6+uL2NhYdOrUSWEfBUHAsWPHIJEor+GW7mt2djYePHiAyMhIZGdnw9TUFPXq1YORUd4wga9fv8aZM2fQsGFDVK1aFQCQkZGBe/fuITo6Gurq6rC1tYWzszPU1dVl237y5AlCQkKQlpYGfX19VK9eHfb29hCp+ik/xbRr1y6lA2IURXnP14qUZDs6yo/L+PYLLAgCRCJRmX4gTXl/0YiI6PNVFpPssiYwMFBpMqympqaQw5BqMcn+T5HKRXbu3KnqOIiIiKgA3u7YWthbE9oaZasnsyzYd0EP0QnpCu2WJroY5FnwYfqoYDJzBEw/kg0AZa5nvTQVKcl2c3NTdRxERERUSNoaIibZSjSvY4G/boTh7R/rRSIRmtex4PGij6ZISTaQN8LIli1bcOPGDcTGxmLz5s04f/48HB0d0bat4qDsRERERB9DTdsK6N7MDj4BsYhPzoSpkTYa1zJXOtIJUUkpUpL94sULDBo0CJmZmWjYsCECAwORm5uL58+fY+3atVi7di1at26t4lCJiIiICqambQUm1VSqipRkL1myBKampti1axf09PRQt27eY1F/++03ZGZmYv369UyyiYiIiOizpfzRSR/g7e2NiRMnwsjISKHAfcCAAXjy5IlKgiMiIiIiKo+KlGQDkI3d+K6srCzeWUpEREREn7UiJdmurq7YuHEj0tL+e9KRSCSCRCLBvn370KBBA5UFSERERERU3hSpJnvKlCkYNGgQ2rdvj8aNG0MkEmHLli0IDg5GaGgo9u7dq+o4iYiIiIjKjQL3ZHt6eiIwMBAAIBaLcfjwYTRu3Bg+Pj5QV1fHjRs3YGdnh/3796NWrVolFjARERERUVlX4J7siIgIZGVlyf6uWrUqfvvttxIJisqPwMBAeHl5ISYmBhYWFnB3d+cja4mIiOizV+SH0VD5IwgCMjMzVba+oKAgHDp0SPZ3aGgowsLC0LdvXzg4OBRpndKnc5X1m2e1tbXLfIxERERUephkfyYEQcDMmTMRFBSksnU+f/4c6enpCu2nTp2Cvb29yrZTFjk6OmLevHlMtImIiEipQiXZkyZNgpaW1gfnE4lEOH/+fJGDopKh6oQwv15xVfaWExEREZVHhUqya9euDRMTk5KKhUqQSCTCvHnzVJoAb926FZGRkQrt1tbWGDlyZKHXl5GRgdGjRwMANm/eDB0dnWLHWFJYLkJEVL49CX8Fn4BYxL3KgFkFHTSuZc7HsJNKFbon29nZuaRioRImEolUmrh6eHjgwIEDsjpq6TY8PDyKvR0dHZ1irWNj1apIDg1F699+g+v33ytM/2f8ePht2ICmv/yC5rNnFyPSkpOdno49jRvD9fvvUXf4cLlpvitW4N6aNUiNioKJgwOaz52L6l27vnd9t5ctw721a/E6Kgqmdeqg1dKlsGvTRjY95Nw5eE2fjviAAOhbWqLeuHFo9MMP/DJBVIZl5QCA8KHZSoUgCMjKLe0olAuOTMYp71DZ369jXiMsJg2dmtqhurVRKUaWPy31snu/Ut55SO8qEzXZSUlJWL58OS5fvozU1FQ4ODhgypQpcHV1BQAEBARgwYIFePDgAYyNjTF06FCMGjWqlKMmR0dHDBgwAF5eXoiNjYW5uXmZGl1ETVMTQYcOKSTZkpwcPD58GCijFysASE9IwIl+/RDn768wzX/bNlz/+Wd03L4dlRo0wIPt23G8Vy8MuX0bFi4uStfnPX8+fBYtQvuNG2Hl5gbfFStwtFs3DPP3h7G9PeIDA3G0e3c0nj4d3Q4eRLSvL84MHw4NPT00+OqrEt5bIiqqn45kfXgmUvD8eTTS0yUK7ddDomFvr1sKEdGnqEwk2d9//z3i4+OxfPlymJiYYO/evRg1ahSOHDkCExMTjBgxAm3btsWcOXNw7949zJkzB8bGxujTp09ph/7Zc3R0LDNJ9buqtG2L52fOIPnFCxhVrixrD7t4EZr6+tDQ0yvF6PL39K+/cGHSJOhVqqR0evDx46jaoQMc+vYFALSYOxf3/vgDoRcuKE2ys16/xq3Fi9Hq119Re/BgAIDn6tWIvH4dEdeuwdjeHiFnzkBTXx/NZs0CABhXq4ag/fsRcvYsk2wi+uTwniL6GAqcZPfq1QsVK1ZUeQChoaG4fv263OPYZ8yYgatXr+Lvv/+Gjo4OtLS0MHv2bGhoaKB69eoIDQ3Fpk2bmGTTe1m6uSE+IACP3+nNDjxwAA4DBiDowAG5+SNu3IDXjz8i+vZt6Jqbo3q3bnBftAjaRnk/HaaEh+Pqjz8i9J9/kJGQAH1LS9T+8ku0mDcPIjU1PNi+HTdmz0azOXNwc948pLx4ATNnZ3iuWgXrpk0BANdnz4b3nDmYKuT/8+6zv/9G/W++QcNvv8UKbW2F6Tqmpgj+6y/E3r8PMycnBB08iMxXr2Dl5qZ0fRFeXshOS4PjoEGyNjV1dQzz85NbZ0ZCAgL27YPjwIGIe/AA4deuoeG33xbgSBPRx6StrY3du3eXdhgfpOphY1Vpx44diIqKUmi3srLCsGHDSiGiDysv9wJpK/nc+lwVOMletGhRiQRQsWJFbNy4EXXr1pW1iUQiCIKAV69e4cGDB2jUqBE0NP4LtUmTJtiwYQPi4+NhampaInHRp8Ghf3+5kpHcrCw8PXoU/S5ckEuyY+/fxyFPTzSeMQMdtmzB65cvcWXqVPzZvj2+8PaGSCTCka5doWdhgb5nz0LLyAjPTp7ExW++gZWbG2r06AEASI2MhN/69ei8ezfUtbTwz4QJOPXllxj1+DFEIhEaTZ0Kl/Hj3xtz+40b3zu9+Zw5SAgIwI569SBSV4cgkaDt2rWwdXdXOn/i48fQqVgRsffv4/rMmUh4/BimtWvDfeFC2DRrBgBwHDgQLy5dwqkhQ3Bq6FAIublwHDQIjadPL/CxJqKPQ9X315QkXd2yWXrRoUMHpfcUdejQAcbGxqUXGH1SSr1cxMjICK1atZJrO336NMLCwtCiRQusWLECYrFYbrqFhQUAIDIysshJtiAISEtLK1rQVCLe7vFIT0+HRKJYL1dQgiAgOzsbVbt3x+2lS/Hy8WMY2toi5PRp6JiZwdDBQTZPWloavBctgk3r1qj3JhnXtrGB59at2F2nDp6ePYtKjRqh5oABqN6rFwzs7AAAjqNGwWfRIkT++y+s27VDVmYmJNnZcF++HMZvbhCuP2UKTg8YgLhnz6BvZQWoqUFkZFSocy8rM1Nu/uiHD5GbmwvPzZthUqsWnv31Fy5NngxdGxtU9vRUWD41Ph456ek4O2YMmsydC0M7OzzasgUHPTzQ78YNmDg6IjUiAknPn6PRzz+jSvv2iPPzw43p03H155/RiIk2EX1i7Ozs0L17d9y4cQNxcXEwMzNDs2bNYGdnx9ygDBEEoVz03uen1JPsd/3777+YPn06PD094eHhgUWLFimMzS39KaI4P0NlZ2cjICCgWLGSamVl/XcDT2BgYIHGZM9PdnY24mJjYaijA10bG9xYtw52Q4bg0ZYtMG7dGgEBAbJ5AgICEH7rFtLDwrDBzExhXQFXriDZ3ByarVvD988/kfLoEdJfvEDqkyfIfPkSMdHRCAgIkP30+BJA7JtzKzExEQDwOCAAOklJRdqXyKgoCG/Wl5OWBu8BA1Dz++8hcXFBHACjfv1gdv8+Lk2dikZ79yosH5eYiJz0dFT5+mtkicWIB2AxcSJCr1yB1+LFEP/vf7j31VfQsbCAfrduiAMANzdU/eor+C5eDK02baDFnh0i+gQ1adJE9v8SiYR5QRlUnFygtJWpJPv8+fOYOnUq6tWrh+XLlwPIG8rt7eQL+C+51ivGjWuampqoUaNG0YMllXv7S5Ojo2Ox6rp8NTVhZm6OWrVq4dXAgYi4ehU1f/4Z165dQ98rV1DRwUFunruamrAbOBANp01TWJeOmRnUNDVxrH17ZKeloUbv3jDp1AkWrq442q6dbB0iX18EAqhTr55s2YjYWNwDUL16dRhVqVLo/bgEwNrKCo61agEAXv77L3JevYJLt26o+Naj63PatYP3lSuo9Wa+t6nXr49nAFw6dYKBjY2s/YWLC7JfvUKtWrXgdf8+nBYtklu+kiAgaP58WGppoZKS9RIREZWkp0+flnYIxVJmkuzdu3djwYIFaNeuHZYtWyb75mJpaYmYmBi5eaV/V8pn9IWCEIlExUrSSfXU1NRk/6+rq1usmkORSARNTU3o6emh7uDBuPPbbwjetw/G1arBpn59hXksnJyQFBQEKycn2ToSgoJweepUuC9ahMTHjxF79y4mREdD/815l56QgPSYGGhqaEBPTw9ab74UvH1eab/ZB11d3SKfb1ra2rJlLWrWBACkPHki2w8ASA4KQsWaNZVuo1rbtoBIhEQ/P9nygiAgKTAQdp6e0NPTg1HlyngVGCi3fNjTp4BIBKu6daHD9woREX1k5blUBCgjSfbevXsxb948DB06FNOnT5dLtho1aoT9+/cjNzcX6urqAABvb2/Y29vzpsdPWHZ6uspu7LFwcUHFmjXhNX06Gv/0k9J5XKdMwX53d/wzfjzqf/MNslNScH7iRGSlpKBizZrIeVOj92j3boj79kXKixfw+uknSLKzkVuIsqWs1FRkp6ZC39KySPuib2mJWl98gUuTJ0NDRwdmdevi2d9/w3/LFnR5q1TkdXQ0NA0MoGVgAKPKleE0ciQufvMNNPX0ULFmTdxZtQqvnj9H/UmTZPt/fuJEmDo6olq3boi7fx+XvvsOLhMmQKcERhUiIiL61Kl9eJaS9fz5cyxcuBDt2rXDuHHjEB8fj9jYWMTGxiIlJQV9+vRBamoqZsyYgadPn+LIkSPYsWMHxo0bV9qhkwqFX7uGv7/4Qvb3ukqVcKx3b0Rcv66S9Tv074+s5GQ4DhyodLp1kyboe/YsYv39sbthQxzp0gUVqldHv/PnoaGtDSs3N7Revhx3Vq7ENkdHnBk+HJVbtYLjoEGI8vEpcBy3ly3DOiurYu1L+82b4TRyJC5//z12ODvj4Y4d6Lp/Pxz69ZPNs87KCreXLZP93XbdOtQdMQLnxozBDmdnvPz3X/T75x+YvCk5cR4zBh22bIH/1q3YXrcurvzwA1wmTUKb338vVqxERESfK5EgvGfA3o9g/fr1WLFihdJpvXr1wuLFi3H//n0sWLAAjx49grm5OUaOHIkhQ4YUeZv+b56i5/RWaQCVnnvr1uH8pEmQaGnh1ptHgzc6dgwaIhGE3Fy0Xbv2g8PeERER0aelvOdrpZ5kl4by/qJ9SsKvXcP+li0BQUCuujpu9+wJIC/JVs/NzZtJJMIgLy/YNG9eeoESERHRR1Xe87VSLxehz5vv8uUQvam1z49IXR2++fzaQURERFQWMcmmUpOdno7g48ch5OS8dz4hJwdPjx5Fdnr6R4qMiIiIqHiYZFOpyUpOhlDApzoKEgmykpNLOCIiIiIi1WCS/YmqWrUqRCKR7KE+7xo/fjxEIhFmz55d7O3Mnj0bgYGBGDHi/+3de3zPdf/H8ed3h+93cxg2W2bY5LDlYlpoQyvN4XKIiCsS/WoV5Uo/JX4dyKGoiC6RSpSua105zsKtlFx0TYW4KMwsYuzAMDOz2fH7+2Pte+1rw7SPfTce99utW/b+nF7fw3t7ft/f9+fzeUwmk0kfffSR4uPjr7qt2cNDJqeKvQVNTk4ye3hccZ1Tp07J19dXW7ZssbVt2bJFJpOp3P9uvfXWy+7rzJkzevjhh9WgQQM1aNBAo0aN0oULF/5bz2X2aTKZdOzYsQo9JgAAcOOqFtfJxvXh6uqqlStX6vnnn7drLygo0OrVqw27yPvp06e1bNkyNW7cWOPHj1dycrKWL1+uTp06KTk5WWlpafLx8VF4eLiCgoL+W5+7u1rcf78Or1t3xSkjJhcXtbz/frm6u192nWPHjql///46ceKEpOLbssfGxiolJUWzZ89WaGioWv1+I5a9e/eqb9++evnlly+7vyFDhignJ0ebNm1SRkaGIiMjNWbMGH366aeSZLuFeokLFy6oW7duuueee9SsWbOrP2kAAOCGRsiuJKvVanc78OrCarUqIiJC33zzjX799Vc1bdrUtuzbb79VrVq15O7uroKCAl28eLFSxzly5Ijq168vk8kkNzc3FRQU6PTp03rvvfcU8vtdCRMTE3Xs2DENGTJEgaVuBx7817/qUEzMlY9RWKjgMWMuW+fSpUv18ssvq3nz5pKKb8P67bff2pZnZGTom2++kYeHh2699VaNGzdOAwcO1IgRI8rd57Zt27Rlyxbt2bPHVuuCBQs0YMAATZkyRY0bN1b9+vXttpk0aZJcXFz07rvvVur5vB4sFkuNv2sWAAA1DZfwq6SLFy9W6prd18tXX30lf39/JSYmqkWLFmrdurVt2a5du2Q2m5WUlCR/f3+1adNGUvEUiX379ik9PV0Wi0W+vr5q27atXF1dJUn5+fnas2ePUlNT5eTkpMDAQB06dEi1a9dWw4YNlZGRodTUVN122206e/ascnNzlZ+fr5ycHBUWFsrV1VW33HKLQkNDJUlxcXE6deqUWprNOpCSoguSGnh7KyQkRBGbNslUVKQVVqsSzGb16t//so/1hx9+kK+vr3x8fLRhwwa1atVKLi5lPz+WfKiIi4tTr1695H6ZkfGDBw/q0KFD6tevn62tqKhIa9asUWhoqJo0aWK3fkZGhjZt2qQuXbrIt5I3mrkeoqKiDLt7JgAAVYVL+KFaa9KkiZKTk20/FxUVKTk52W5kW5LOnTunf//73/Lx8VGPHj105513KiMjQ1u3blXJ57Bt27bp7Nmz6tKli+666y6lpqYqOzvbdrv70goKCnTu3Dk5OTnJ399fLVq0kIeHh5KSkpSRkWFbLz09XUeLinR3hw4a6uWlnPPntXfHDpkKC3XW11etu3bVPb16XfExdunSxTaKLRV/GChPTk6O4uPj1apVq8sG7JL1Ll3u5OQki8Wi7N9vr17a/v371bBhw2oZsAEAgGMwXcRAOX3GSs6uji5DkmTd8r3yW3fWLbd3VcL0J5Te+WG5e92ik3u+l9lrpywjpsu6Z7DyW3dWzn2PK27RdHm1C1PAuNmSit8YwadStGXiX5Tc6l5Z6nkpbfVq3Tlhnmq36ShJCj6Xrs0vDJa5TRcV1G2gwmMJUmqq8lt3lpz2yaPeLfIMDJGTq1mSVL+oUGfWfarTt3aRpUtv5RcsUVF8vNq+8okK63jorKRmX36m+OhFiv3wXyoyWyRJWamJSk/4WXmZZ2X2aCDP1u1Vx9e/zGO+eDpV2rBBCuysfLeyITr7VLIKTUflN2aOcurUu+xzl5v2huR8XDn32c9lN23eqtyWYcq571FbW1Zqok5ER6vTc3OU0y70Wl6i66swX+5fzXd0FQAA3LQI2UZydpVczI6uopjJJDk5q17LYNXy9lPqf/6tW/s8rNSdm+XbuXdxnb+vIxezMo8l6MKJ4/r6qR5ldpV1Ikl52cVX1qjXKtj2GC1ejVTLx0/mel7yDeul7PSTkiQ3r0YKaBmsswf3KDPpsPIyzygv65zyzqVLkqwyFe/DyVkWD0+Z6ze0HculfkMVFRaoqFbd4mOnHFXKT5ttyy+eS1fqT1vkG9ZTdRoH2Bf6+wccD/9A5ZxKkVX/nQllkklZKYlq1ClC5vreV3zqnN1qqaigoMxrWZSfJ2f3OnbtKdu/lbtXIzW8/a7i5xMAAECE7JuCb2gPndixSf7dhyht17/VZdrSMutYrVY17tJbLe+PLLPMXLeBTu/bXvxDkf0UfpNz8VuoTuMAeQXdoeTY9Wp27yDlnjujvR/PkJOzi2rd0lT1Am6Tb2hPbX/jKbvtS0a5Lyf94O6ytcqq9IO7y4bs37k3bKT6Lf6k9IO7lXc+Q+a69VXH71b9GrNYrYc8Ve42pbl53qLcXVvs2ooK8pWXdU5unj527Sd2fSff0J6cWAgAAOwwJ/sm4BvaQxmH9+n4d1/I3adxueG0rl8LZSX/ptq3NLX9Zy0q1IGoubp45oQ8/IuvsnH2159t2+RfOK/sk8fLPWbyDxtUmJOtDs/PlW9oD7l5+ujMgZ2SZDfCfDV5mWfLbz+fUaYt+2RS8bG3fqn0g7vlGRiilgMeU7N7Byn/fIZMJpO8gjpc9ZieQSG6mJ6mC6Ue25m44tobtAq2teVnZykr+Td5/T59BgAAoAQj2UYqKP+EO4ewWqWiQqkgTx5+zVX7liZKWLlQLfqNlAryyqzT/M8PatsbY7R3yesK6PEXFV7M1r5/zFHBxWzVathIzq5mNeoUof2fzpKTySRLPS8dXPWBigrybftQ0e/Xui7Ik3t9LxXkZuvXVR/IzesW5Weds42G56Ql/75+YXENJfVIdvuQJGezRfnpGXK22M+xNtf2tNsuKzVRJ/+zpXgXhfm6eOaEUn/YIN/Q7qrj66/Mowfk7t1Yzs5O9seTZC0qVN75DLm415Gz2aL6/q3VoFWwdi94SW1HTlBhbrb2ffKG/Lr2lptHfdv254/ESVar6vo2K7NPh6tO70UAAG5ChOxKKn0FRPcN1edEM1NOplwTfpT7+nOSpKb1ayv+xHE1zzoq9/Vzy6zjLumuLl0Ut3ervv/3Orm4uMjb21vBdwSr1tcLJElhfh765Yy79rw7UVarVc2bN1dB/Xq2fbgePSpJcl8/V82tVqX4+Oj0z1tVVFQkV1dX1a9fX1mFtZQeGyN3l3NyTYiTKSfTVo8ku31IUsYPP+j06dNq2bKl3ePzbdJE7usP2X4+ceSIXM4VP1aX4/vlml68n6zju+XdvLkKf9ktt/wLdscqceHCBW3asEEdOnRQQECAJKlL66bas2ePts8cLWdnZzVp0kTBDc1yLrW9Nal45Nzj30vLvcJKdXETXqUTAACH4zrZlZSTk6ORI0dWej83ovj4eBUVFZVpd3Jysrvz49VkZmbqzJkzys3NlcVikZeXlzwuucW6Uce6Ef3jH/+44iULAQCojmr6dbIZya6k0ie85fQeK7lUj0v4VQemumuVf/ZUmXa3Bt7KuWdAhffjKqnRJW051+lYN4yCfNs3K5yUCQBA1SNkG8mlGl3CrxrwvK2jUrdtLHMpPc/bOhr+PFXlsQAAAK6Gq4vguqnTOEC+YT3l1sBbTi6ucmvgXf71rWvYsQAAAK6GkWxcV3UaB1RZ0K3KYwEAAFwJI9kAAACAwQjZAAAAgMEI2QAAAIDBCNkAAACAwTjxETVOVspRpR/crbzMszJ7NJBnYAgnPAIAgGqFkWzUKFkpR5Wy7RtdPHtKRYUFunj2lFK3bVRWylFHlwYAAGBDyEaNkn5wd5k2q6zltgMAADgKIRs1Sl7m2fLbz2dUbSEAAABXQMhGjWL2aFB+e936VVsIAADAFRCyUaN4BobIJJNdm0kmeQaGOKgiAACAsgjZqFHqNA6Qb1hPuTXwlpOLq9waeMs3rCdXFwEAANUKl/BDjVOncQChGgAAVGuMZAMAAAAGI2QDAAAABmO6CKoN7uQIAABuFIxko1rgTo4AAOBGQshGtcCdHAEAwI2EkI1qgTs5AgCAGwkhG9UCd3IEAAA3Ek58NFJhvqMrqLE8W7ZV6vZNslqLihtMpuI7ObZsKxXkOba4moj3IgAADkXINpD7V/MdXUKN5S7Jkp2pM2fOKDc3VxaLRV5eXvLYtdrRpQEAAFwzQjaqDQ8PD3l4eDi6DAAAgEojZFeSxWJRVFSUo8uo9g4ePKjvv/9ep06dkre3t7p27arAwEC7dS5evKgnnnhCkrR48WK5ubk5otQbjsVicXQJAADcdAjZlWQymQiDVxEfH681a9bYfk5LS1NMTIyGDh2qoKCgcrdxc3PjeQUAADUWVxfBdRcbG1umzWq1ltsOAABwIyBk47pLS0srt/3UqVNVXAkAAEDVYLoIKi0+Pl6xsbFKS0uTj4+PwsPD7aaB+Pj4KDk5ucx23t7eVVkmAABAlWEkG5USHx+vZcuWKTk5Wfn5+UpOTtby5csVHx9vWyc8PFwmk8luO5PJpPDw8KouFwAAoEoQslEpFZlvHRQUpKFDh8rPz09ms1l+fn5XPOkRAACgpmO6CCqlovOtg4KCCNUAAOCmwUg2KsXHx6fcduZbAwCAmxkhG5XCfGsAAICyCNmoFOZbAwAAlMWcbFQa860BAADsMZINAAAAGIyQDQAAABiMkA0AAAAYjJANAAAAGIyQDQAAABiMkA0AAAAYrNqF7IULF2rkyJF2bQcOHNCIESN0++23q1u3blqyZImDqgMAAACurlqF7KVLl+rdd9+1azt79qwee+wxBQQEaPXq1Ro7dqzmzZun1atXO6hKAAAA4Mqqxc1oTp48qVdeeUW7du1S8+bN7ZatWLFCZrNZU6dOlYuLi1q0aKHExER99NFHGjx4sIMqBgAAAC6vWoxk79+/X/Xq1dPatWvVvn17u2U7d+5Up06d5OLy388DYWFhOnLkiM6cOVPVpQIAAABXVS1GsiMiIhQREVHushMnTqh169Z2bT4+PpKklJQUeXl5/aFjWq1WZWdn/6FtcX3k5uba/p2Tk6OioiIHVgMAABzJarXKZDI5uow/rFqE7Cu5ePGizGazXZvFYpFkH8quVX5+vg4cOFCp2mCsvLw827/j4+PLvO4AAODmUpOzQLUP2W5ubnbhS/pvuK5Vq9Yf3q+rq6tatmxZqdpgrNIfmoKCgmwfpgAAwM3n0KFDji6hUqp9yG7UqJHS0tLs2kp+vuWWW/7wfk0mU6VCOozn5PTfUwTc3d3l5ubmwGoAAIAj1eSpIlI1OfHxSjp16qRdu3apsLDQ1vbjjz+qefPmf3g+NgAAAHA9VfuQPXjwYGVlZemVV17RoUOHFB0drU8//VSjR492dGkAAABAuap9yPby8tLixYt15MgRDRo0SAsWLNDEiRM1aNAgR5cGAAAAlKvazcl+8803y7QFBwdr+fLlDqgGAAAAuHbVfiQbAAAAqGkI2QAAAIDBqt10EaBEfHy8YmNjlZaWJh8fH4WHhysoKMjRZQEAAFwVIfsmYrVaK3WXzOvt4sWLtn//8ssvWrt2re3nxMREHTt2TEOGDFFgYKAjyrNjsVhq/PU7AQDA9UPIvklYrVZNmjRJBw8edHQpFfL0008rJyenTPuXX36p5s2bO6Aie0FBQXrttdcI2gAAoFzMyb6J1KRAeLkR9+o8Eg8AAFCCkeybhMlk0muvvVbtQ6rVapUkffLJJ0pJSSmzvHHjxoqMjKzqsspguggAALgSQvZNxGQyyc3NzdFlVEhERISWL19uC91Scf0RERE15jEAAICbF9NFUC0FBQVp6NCh8vPzk9lslp+fn4YOHcrVRQAAQI3ASDaqraCgIEI1AACokRjJBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxWY0J2UVGR3n33XYWHh6t9+/aKjIxUYmKio8uCwXbu3Kmnn35aO3fudHQpQI1C3wGuHf0G11ONCdkLFy7UsmXL9Prrr2v58uUymUx68sknlZeX5+jSYJDc3FwtWrRIp06d0qJFi5Sbm+vokoAagb4DXDv6Da43F0cXUBF5eXn6+OOPNWHCBN1zzz2SpHfeeUfh4eHauHGj+vXr5+AKYYTo6GidPXtWknT27FmtWbNGw4YNs1snPj5esbGxSktLk4+Pj8LDwxUUFOSIcoFqoyJ9R6L/AKVVtN+URh/CtagRI9nx8fG6cOGCwsLCbG0eHh5q06aNfvrpJwdWBqOkpqYqJiZGVqtVkmS1WrVmzRqlpqba1omPj9eyZcuUnJys/Px8JScna/ny5YqPj3dU2YDDVaTvSPQfoLSK9pvS6EO4VjViJPvEiROSJF9fX7t2Hx+fK3aIK7FarcrOzq50bag8q9WqDz/80PbL7tL2CRMmyGQyadOmTcrPzy+z/aZNm9SsWbOqKheoNiradyTRf4DfXUu/KY0+VPWsVmu5r0VNUSNCdk5OjiTJbDbbtVssFp07d+4P7TM/P18HDhyodG2ovNOnT2vfvn1l2ouKirRv3z5t3bpVDRs21P79+1VQUFBmvYyMDF5L3JQq2nck0X+A311LvymNPuQYl2a/mqRGhGw3NzdJxXOzS/4tFZ+04O7u/of26erqqpYtWxpSHyrHarXqxx9/VFxcnIqKimztTk5O+tOf/qS77rpLJpNJf/rTn5SSklJm+8aNG+u2226rypKBaqGifUcS/Qf43bX0m9LoQ1Xv0KFDji6hUmpEyC6ZJpKWlmb3lUxaWtofPuHAZDKpVq1ahtSHyhs9erTGjRtn12YymTR69GjVrl1bktS9e3ctX77c7is+k8mk7t2781riplWRviPRf4DSKtpvSqMPVb2aPFVEqiEnPgYFBalOnTravn27rS0zM1NxcXHq2LGjAyuDUXx9fTVw4EBbhzKZTBo0aJAaNWpkWycoKEhDhw6Vn5+fzGaz/Pz8NHToUM7sxk2tIn1Hov8ApVW035RGH8K1qhEj2WazWSNGjNDbb78tT09P+fn5afbs2WrUqJF69uzp6PJgkAceeECbN29Wenq6PD09NWjQoDLrBAUF8QsNuERF+o5E/wFKq2i/KY0+hGtRI0ayJenZZ5/VkCFDNGnSJD300ENydnbWkiVLavSEeNizWCwaNWqUvL299eSTT8pisTi6JKBGoO8A145+g+vNZL30GjY3gb1790qS2rVr5+BKAAAAUJ6antdqzEg2AAAAUFMQsgEAAACDEbIBAAAAgxGyAQAAAIMRsgEAAACDEbIBAAAAgxGyAQAAAIMRsgEAAACDEbIBAAAAgxGyAQAAAIMRsgEAAACDEbIBAAAAgxGyAQAAAIO5OLoAR8jPz5fVatXevXsdXQoAAADKkZeXJ5PJ5Ogy/rCbMmTX5BcMAADgZmAymWp0ZjNZrVaro4sAAAAAbiTMyQYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyAYAAAAMRsgGAAAADEbIBgAAAAxGyEaVioiIULdu3ZSVlVVm2YsvvqiRI0c6oCpUZxMmTFBwcLCOHj1aZtmZM2cUGhqq559//rrXMX/+fEVERNh+/vXXX7VlyxZJUnR0tAIDA5WUlHTd66ioiIgIzZ8/39FlwCDVtR/8UZs3b9ahQ4cuuzwwMFDR0dGSpPz8fC1dutS2LCIiQi+++GKla7ic2NhY9ejRQ23bttVbb72l/fv367777lPbtm31v//7v2XWL+n/wKUI2ahyqampevPNNx1dBmqISZMmycPDQ5MnT5bVarVbNn36dJnNZk2ZMuW61xEZGalVq1bZfh49erT27t0rSTKZTHb/rw5WrVqlyMhIR5cBg1SXfmCE5ORkPfXUUzpz5sxl19m6dav69u0rSVq/fr3eeOMNu+XXs6/NmTNHTZs21YYNGzRq1CgtXLhQJpNJ69ev1+TJk8us37dvX23duvW61YOai5CNKte0aVOtXLlSsbGxji4FNUC9evU0bdo07dixwy7kbty4URs2bNDMmTNVr169615H7dq15enpWe6yhg0bysnJSQ0bNrzudVSUp6enateu7egyYJDq0g+McOmHhPJ4e3vLzc2t3PW9vb3l7e19XWqTpMzMTIWEhKhJkyZq0KCBMjMz1aZNGwUEBJTbx93c3K5rPai5CNmocgMGDFDnzp01efLkcqeNSFJGRoamTZume+65R8HBwXrooYe0c+fOKq4U1UX37t113333adasWTpz5oyysrI0bdo0DR8+XOHh4ZKkw4cP68knn1RISIjuuusujR8/XqdOnbLt4+jRo3r88cfVoUMHhYSE6PHHH9fBgwcrXEPpr8kjIiKUnJysBQsWaOTIkWrZsqWaNGkii8UiSYqJiVG/fv3Url07hYeHa8aMGcrLy7vsvlNTU/XCCy+oa9euuv3228vU9uKLL2rChAl666231LlzZ7Vv315jxoyxe3yXKj1dZP78+Ro5cqQ++ugj3X333WrXrp0eeeQR/fbbb5fd/mrPV+mv88s7ZnR0tHr27Kkvv/xSERERCg4O1uOPP66TJ09qxowZ6tSpk7p06aIPP/zQ7nG+9NJLeueddxQaGqoOHTrotdde04kTJ/TUU0+pffv26tWrl7777jvbNnl5eZo9e7bCw8MVEhKiBx980G5UMTo6WhEREZoxY4Y6duyop5566rKPubqrDv2gxMKFCxUWFqYOHTro1VdfVXZ2tm3Zld4bSUlJ6t69uyTpkUceueyUppJ9REdH66WXXrK1bd++XS1atFCLFi0kFU+VefbZZxUaGqrg4GANGzZMO3bsuGLtMTExGjBggIKDgxUREaEPPvhARUVFtmMkJyfrvffeU2BgoCIiIrRjxw7FxMTYjn+pS6eLBAYGasWKFXrssccUHBys8PBwu/d5eZYsWWKbohIREaH33nvP9uGivCk6lx4zIiJC//jHPzR27Fi1b99ed999t1auXKndu3dr4MCBat++vYYNG6Zjx45JkpKSkhQYGKjvvvtODzzwgNq1a6f+/ftrz549Wrlype69917dcccdGj9+vHJzc23H+c9//qOHH35YwcHB6tatm6ZNm2b3dzwiIkIzZ85U3759FRoaqm3btl3xcd/oCNmociaTSTNmzFBmZmaZrwAlqbCwUJGRkdq5c6feeustrVmzRkFBQXr00UdtX8/j5jN58mRZLBbNnj1b8+bNU+3atTVx4kRJ0smTJzV8+HA1bdpUq1at0gcffKCsrCwNGzbM9sf/+eefl4+Pj1avXq2VK1fKyclJzzzzzB+qZdWqVWrUqJEiIyM1f/58+fr6auPGjZKk+Ph4TZo0SWPHjtXXX3+tmTNn6osvvtDixYvL3VdWVpYeeughnTx5Uu+//76WLVumWrVqacSIEUpJSbGt99VXXykjI0NRUVFasGCBdu3apXfeeafCNe/evVs//fSTFi1apKVLlyolJUXTpk277PpGPF+pqan6/PPPtXDhQn3yySfau3evBgwYIBcXF61YsULDhg3T3LlzlZCQYNtm3bp1On/+vFasWKGXXnpJUVFRGjJkiHr37q3o6GjdeuutevHFF20B5KWXXlJsbKxmz56tNWvWqE+fPnrqqads8+Wl4ukJJ0+e1Jo1azR+/PhregzVTXXoB8nJyfrxxx/18ccf6/3339f27dsr/Lz6+vpq5cqVkorD49WmNPXt21cvv/yypOIpJCEhIZo5c6buv/9+SdLUqVN18eJFRUVFad26dWrevLnGjBljF/pLW7p0qSZPnqyhQ4dq7dq1eu6557RkyRLNmjXLdoySvr1161atWLFCISEh6tOnj+34FTFr1iwNHDhQX3zxhQYPHqy5c+dedqDoX//6lz744ANNmzZN33zzjV544QW9//77Wrt2bYWOVWLOnDkKDw/X+vXr1a1bN02dOlVTpkzRiy++qKioKJ06dUpvv/223TbTp0/XCy+8oJiYGLm5uWnUqFH66quv9MEHH+jNN9/U119/bXu94uPj9eijj6pr165au3at3n77be3fv1+RkZF23zZ8/vnnmjRpkhYvXqw77rjjmh7DjcbF0QXg5uTn56cJEyZo6tSp6t27t20URir+Jbd//36tW7dOrVu3liS9+uqr+vnnn7VkyRL97W9/c1DVcKT69etr6tSpeuaZZ+Ti4qKoqCi5u7tLKv6l7uPjo1dffdW2/t/+9jeFhYVpw4YNeuCBB3Ts2DF17dpVTZo0kYuLi2bOnKnffvtNRUVFcnK6tvEGT09POTs7q1atWqpfv77dsqSkJJlMJjVp0kSNGzdW48aNtWTJEtWpU6fcfa1du1Znz55VdHS0bTrK22+/rR49euizzz7ThAkTJEl16tTR9OnT5erqqhYtWuj++++3G9G9moKCAs2aNctW78iRIzV79uzLrm/E85Wfn6/Jkyfb+nHnzp21Z88eTZw4USaTSaNHj9Z7772nX3/91baOh4eHXnnlFTk7O8vf319z5sxRWFiYBg4cKEkaPny4Nm/erNOnTys7O1vr16/XqlWr1K5dO0nSY489pvj4eC1ZskTdunWz1TJmzBg1bdq0ws9XdVUd+oHZbNY777xjmzrx6quvKjIyUomJifL397/its7Ozrb3eb169a46pcnNzU1169aVpHKnZBw7dkytW7dWs2bNZLFY9Morr6h///5ydnYus67VatVHH32kESNG6OGHH5YkBQQEKCMjQ2+99Zb++te/ytvb29a3S47n6up6zVNCBg0aZPsgMG7cOP3zn//Url271LFjx3Ifg8Visfud4ePjo8aNG1f4eJJ0991368EHH5RU/C3B8uXLNXLkSIWFhUmS+vTpo2+//dZum8cee0xdunSRJA0cOFDTp0/XlClT5O/vr8DAQLVp08b2IXjJkiXq3LmzxowZI6n4uZszZ4569OihHTt2KDQ0VJJ0zz332PZ5syNkw2GGDRumr7/+WpMnT9b69ett7QkJCapbt67tj65UPPrdsWNH5nHf5Eq+TvXz89Ptt99ua4+Li9Phw4fLjDLl5ubq8OHDkqTnnntOM2fO1Oeff66wsDCFh4erT58+1xywr6Zk2sLgwYMVEBCgLl26qHv37mrbtm256yckJCggIMBuvrfFYlFwcLDd1/j+/v5ydXW1/Vy3bl3l5+dXuK6GDRvafSC42vZGPV/Nmze3/dvd3V1NmjSxnbRWMr2m9NfRzZo1swtI7u7uduG49DZxcXGSigNFafn5+fLw8LBrCwgIuKa6qzNH94NL5ya3b99eUvEVd64Wso32zDPPaMKECdq4caM6duyou+66S3379rW9T0pLT0/X6dOn1aFDB7v2Tp06KT8/X7/99pvtsVRWyXSWEnXq1LlsfxswYIBWr16tXr16KTAwUF27dlXPnj2vOWSX7msl89mbNGlia7NYLGWmrV3aPyWV6W8l28TFxSkxMbHc0fzDhw/bQnZVvweqM0I2HKZk2kj//v3tpo1YrdZyzxwvKiqSiwtv2Zudu7u77Y9BiaKiIoWFhZV7dYWSUbCHH35YvXv31nfffacff/xRc+fO1fz58xUTE2PoCYsWi0V///vfFRcXp61bt2rr1q1atmyZBg4cWO70qMu93wsLC+3e72azuVJ1Xev2FXm+Lj0hrbwQUfqDgaSrhrlL17/SNiXH/+yzz8qMiF66TUnouFE4sh9cOkpcWFgoyf61q8h7wwg9e/ZUbGysYmNj9cMPP2jx4sWaN2+eVqxYoVatWtmte7kTLkvqN/LvS3n97XLH9/T01BdffKHdu3fr+++/19atW/Xxxx9r7Nixtqk8l25bUFBQZj/l1X+1/nYt2xQVFal///7lntdQepDgRutrlcGcbDiUn5+fJk6cqFWrVtnmqwUGBiozM9NunqYk7dq1Sy1btnREmajmWrVqpcOHD8vX11f+/v7y9/dXvXr1NHPmTCUkJOj06dOaPn268vPz9cADD2j27Nlau3atTp06ddWTpK7Vd999pwULFqhNmzYaNWqU/v73v+vZZ5/Vl19+We76rVu31pEjR+wuZ5abm6t9+/Y57P1ekefL1dVV58+ft22TlZWl9PT0Kq2zJESlpaXZXnd/f39FR0dr9erVVVpLdVBV/eDo0aN2J7vt2rVLJpPJ9n692nvjWi+/d7n18/Ly9MYbb+j48ePq27evXn/9dW3cuFFOTk52c/JLeHl5ycvLS7t27bJr37lzp1xdXdWsWbNrqssoX3zxhT7//HN16NBBzz77rFasWKG//OUvtt8Zrq6uysrKsgvaiYmJVV5nq1atbN9WlPxXWFioN954Q6mpqVVeT01AyIbDDRs2TF26dNHx48clSV27dlVgYKDGjx+v7du36/Dhw5o2bZoSEhL0P//zPw6uFtXR8OHDdf78eT3//PM6cOCA4uPjNX78eP3yyy9q1aqV6tevry1btmjSpEk6cOCAjh8/rn/+859ydXW1TeM4f/78NYXE2rVr6+jRozp9+rRdu4uLi9577z0tXbpUx48f1969e7V58+bLnjDVv39/eXh4aNy4cfrll18UHx+vCRMmKDs7W0OHDv3jT0olVOT5CgkJ0fLly7V//34lJCRo4sSJVf5NU6tWrXTvvfdqypQp2rRpk44fP64lS5boww8/vCHmX1+rquoHubm5GjdunOLi4vT999/rtdde08CBA+Xn5yfp6u+NWrVqSSqeKlU6jF9Oyfr79u3TxYsXbe1ms1k///yzJk+erD179igpKUnR0dG6cOFCuf3NZDIpMjJSUVFR+uyzz5SYmKh169ZpwYIFGjp0qG20v6rl5ubqrbfeUkxMjJKSkrRz507t2LHD9hjuuOMOZWZmatGiRUpKStK6devKXL2lKkRGRurAgQN69dVXdejQIf3888964YUXdOTIkRtqOpaRCNmoFl5//XXb170uLi765JNPdNttt2ns2LEaPHiwEhIStHTpUrv5h0CJpk2bKioqSjk5ORo+fLhGjBghk8mkTz/9VF5eXnJxcdFHH30kJycnPfroo+rXr5+2bdumRYsW2UavZsyYoSFDhlT4mCNHjtSWLVvKXB2ha9eumjFjhlatWqX77rtPTzzxhAICAjR37txy9+Ph4aGoqCjVrVtXjz76qIYPH66cnBx9/vnnDguKFXm+pk6dKm9vbw0bNkxPPvmk7rzzzgpfecFI77zzjv785z9rypQp6tu3r1avXq3XXntNgwcPrvJaHK2q+kHbtm1122236ZFHHtG4ceN09913201Rudp7o0GDBho8eLBmzZqlefPmXfVxhYWF2S5Bt3nzZrtl8+bNU9OmTfX000+rd+/eWr58uebMmVPuCYaS9MQTT2jChAn69NNP1a9fP82bN09PPvmk7QomjvDggw9q7NixWrhwofr06aNx48YpPDxckyZNkiTdeeedeu655xQVFaW+ffsqJiZG//d//1fldd5+++1avHixEhIS9MADD2jUqFFq2rSpPvnkk0pPZ7tRmawVuSo8ANzgCgsLNWzYMNvlqoCbEf0AMA4j2QAgafHixerVq5ejywAcin4AGIeRbABQ8UlUfOWJmx39ADAOIRsAAAAwGNNFAAAAAIMRsgEAAACDEbIBAAAAgxGyAQAAAIMRsgEAAACDEbIBAAAAgxGyAQAAAIMRsgEAAACD/T/RwnMXnqBuXAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.set(style=\"whitegrid\")\n",
    "colors = ['#34ace0', '#ff793f','#ffb142']\n",
    "category_order=[\"No\",\"Yes, it's on in summer\",\"Yes, but it's off in summer\"]\n",
    "ax = sns.boxplot(x='if_communal', y='perc_heat', data=df, palette=colors, order=category_order)\n",
    "sns.stripplot(x='if_communal', y='perc_heat', data=df, jitter=True, order=category_order, color='black', alpha=0.5)\n",
    "\n",
    "\n",
    "medians = df.groupby('if_communal')['perc_heat'].median()\n",
    "means = df.groupby('if_communal')['perc_heat'].mean()\n",
    "\n",
    "mean_points = [means[category] for category in category_order]\n",
    "plt.scatter(ax.get_xticks(), mean_points, marker='o', color='darkred', s=50, label='perc_heat')\n",
    "\n",
    "for xtick in ax.get_xticks():\n",
    "    ax.text(xtick, medians[category_order[xtick]]+0.05, f'Median: {medians[category_order[xtick]]:.2f}', horizontalalignment='center', color='black', fontsize=11)\n",
    "    ax.text(xtick+0.28, means[category_order[xtick]]+0.6, f'Mean: {means[category_order[xtick]]:.2f}', horizontalalignment='center', color='darkred', fontsize=11)\n",
    "\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Temperature (°C)')\n",
    "plt.title('Boxplot of Heat Periods Percentage with Communal Heating System or Not', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d67812-46b6-44ee-a73c-f85057a2137c",
   "metadata": {},
   "source": [
    "### T-test for heat periods proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d7c23bfd-75d1-4656-a166-3d5ef697cbe3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "communal_no=df_survey[df_survey['if_communal']=='No']['perc_heat']\n",
    "communal_yes_off=df_survey[df_survey['if_communal']==\"Yes, but it's off in summer\"]['perc_heat']\n",
    "communal_yes_on=df_survey[df_survey['if_communal']==\"Yes, it's on in summer\"]['perc_heat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "73e7e1de-a930-4480-9f66-2badb9fca08e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# ANOVA\n",
    "f_statistic, p_value = stats.f_oneway(communal_no, communal_yes_off,communal_yes_on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "12b81737-c7ad-4a5c-bf49-dc3b36c11517",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08291145230420803"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b528f95d-91c6-43cb-928d-683e1fae1e6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.794325060762251"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "387b42f2-26bf-49ef-a3c0-d148e5ac4e7c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3308736810532007"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(communal_no, communal_yes_off)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "25e953c0-6d53-4256-b089-68064ca8a847",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022382294711848487"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(communal_no, communal_yes_on)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5aaa8e1c-7eb0-426e-a836-903979f1711f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3954524734808068"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_statistic, p_value = stats.ttest_ind(communal_yes_on, communal_yes_off)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1117de78-da2e-412b-8ef7-2e294cf1f036",
   "metadata": {},
   "source": [
    "A statistically significant sustained heat periods proportion difference between households without communal heating system and households that cannot turn the communal heating off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b044a9bd-8d00-4c34-aaa0-92645036245c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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