plot_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Scripts for generating plots from the air quality data\n",
    "The first code cell has to be run before running other cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"Imports and helper functions required to run other cells\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "!pip install kaleido\n",
    "import kaleido\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "import plotly.express as px\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"browser\"\n",
    "!pip install pyarrow\n",
    "from collections import defaultdict\n",
    "import math\n",
    "import plotly.graph_objects as go\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import itertools as it\n",
    "import csv\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "# remove outliers by replacing values greater than X with the median value\n",
    "def replace_outliers_median(df, column: str):\n",
    "    Q = df[column].quantile(0.95)\n",
    "    historical_max = 352.6 # max historical data from https://powietrze.gios.gov.pl/pjp/archives/downloadFile/423\n",
    "    median = df[column].quantile(0.50)\n",
    "    df[column] = np.where(df[column] > historical_max, median, df[column])\n",
    "    return df\n",
    "\n",
    "# remove values greater than X\n",
    "def remove_outliers(df, column: str):\n",
    "    historical_max = 352.6 # max historical data from https://powietrze.gios.gov.pl/pjp/archives/downloadFile/423\n",
    "    df = df[df[column] <= historical_max]\n",
    "    return df\n",
    "\n",
    "# get dataframe by station id\n",
    "def get_all_sensor_data(dir_path, id: str):\n",
    "    sensor_dfs = []\n",
    "    for file_name in os.listdir(dir_path):\n",
    "        if file_name.startswith(id):\n",
    "            sensor_df = pd.read_feather(os.path.join(dir_path, file_name))\n",
    "            # print(f\"max before: {sensor_df['PM2.5[calibrated]'].max()}\")\n",
    "            sensor_df = remove_outliers(sensor_df, column='PM2.5[calibrated]')\n",
    "            # print(f\"max after: {sensor_df['PM2.5[calibrated]'].max()}\")\n",
    "            sensor_dfs.append(sensor_df)\n",
    "    return pd.concat(sensor_dfs)\n",
    "\n",
    "\n",
    "# get data from sensor by station id, year and month\n",
    "def get_sensor_data(dir_path, id: str, year = \"2020\", month = \"05\"):\n",
    "    for file_name in os.listdir(dir_path):\n",
    "        if file_name.startswith(id):\n",
    "            file_year = file_name[file_name.find('_') + 1: file_name.rfind('_')]\n",
    "            file_month = file_name[file_name.rfind('_') + 1: file_name.rfind('.')]\n",
    "            if file_year == year and file_month == month:\n",
    "                sensor_df = pd.read_feather(os.path.join(dir_path, file_name))\n",
    "                sensor_df.drop(sensor_df[sensor_df['timestamp'].dt.month != int(month)].index, inplace=True)  # drop rows with incorrect month\n",
    "                sensor_df = remove_outliers(sensor_df, column='PM2.5[calibrated]')\n",
    "                sensor_df['timestamp'] = pd.to_datetime(sensor_df['timestamp'])\n",
    "                sensor_df.set_index('timestamp', inplace=True)\n",
    "                return sensor_df\n",
    "\n",
    "\n",
    "def get_polish_aqi(x):\n",
    "    if x <= 13:\n",
    "        return \"Very good\"\n",
    "    elif x <= 35:\n",
    "        return \"Good\"\n",
    "    elif x <= 55:\n",
    "        return \"Moderate\"\n",
    "    elif x <= 75:\n",
    "        return \"Satisfactory\"\n",
    "    elif x <= 110:\n",
    "        return \"Bad\"\n",
    "    elif x > 110:\n",
    "        return \"Hazardous\"\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "\n",
    "def get_polish_aqi_num(x):\n",
    "    if x <= 13:\n",
    "        return 1\n",
    "    elif x <= 35:\n",
    "        return 2\n",
    "    elif x <= 55:\n",
    "        return 3\n",
    "    elif x <= 75:\n",
    "        return 4\n",
    "    elif x <= 110:\n",
    "        return 5\n",
    "    elif x > 110:\n",
    "        return 6\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "\n",
    "def get_eu_aqi(x):\n",
    "    if x <= 10:\n",
    "        return \"Good [1]\"\n",
    "    elif x <= 20:\n",
    "        return \"Fair [2]\"\n",
    "    elif x <= 25:\n",
    "        return \"Moderate [3]\"\n",
    "    elif x <= 50:\n",
    "        return \"Poor [4]\"\n",
    "    elif x <= 75:\n",
    "        return \"Very poor [5]\"\n",
    "    elif x > 75:\n",
    "        return \"Extremely poor [6]\"\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "\n",
    "def get_eu_aqi_num(x):\n",
    "    if x <= 10:\n",
    "        return 1\n",
    "    elif x <= 20:\n",
    "        return 2\n",
    "    elif x <= 25:\n",
    "        return 3\n",
    "    elif x <= 50:\n",
    "        return 4\n",
    "    elif x <= 75:\n",
    "        return 5\n",
    "    elif x > 75:\n",
    "        return 6\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "\n",
    "def dict_to_plotly(dictionary):\n",
    "    return {'z': [v for v in dictionary.values()],\n",
    "            'x': [t[0] for t in dictionary.keys()],\n",
    "            'y': [t[1] for t in dictionary.keys()]}\n",
    "\n",
    "# add column with AQI transition\n",
    "def add_aqi_columns(df, polish_aqi=True):\n",
    "    for column in df.columns:\n",
    "        if column.endswith('PM2.5[calibrated]'):\n",
    "            aqi_transitions_list = []\n",
    "            aqi_lvl_difference_list = []\n",
    "            for i in range(len(df)):\n",
    "                row_t = df.iloc[i]\n",
    "                if i < len(df) - 1:\n",
    "                    row_t1 = df.iloc[i + 1]\n",
    "                else:\n",
    "                    row_t1 = df.iloc[i]\n",
    "\n",
    "                if polish_aqi:\n",
    "                    row_t_aqi_num = get_polish_aqi_num(row_t[column])\n",
    "                    row_t1_aqi_num = get_polish_aqi_num(row_t1[column])\n",
    "                else:\n",
    "                    row_t_aqi_num = get_eu_aqi_num(row_t[column])\n",
    "                    row_t1_aqi_num = get_eu_aqi_num(row_t1[column])\n",
    "\n",
    "                # aqi_transitions_list.append((row_t_aqi, row_t1_aqi))\n",
    "                aqi_transitions_list.append((row_t_aqi_num, row_t1_aqi_num))\n",
    "                aqi_lvl_difference_list.append(abs(row_t1_aqi_num - row_t_aqi_num))\n",
    "\n",
    "            df[f'AQI_change'] = aqi_transitions_list\n",
    "            df[f'AQI_difference'] = aqi_lvl_difference_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\334467422.py:30: FutureWarning:\n",
      "\n",
      "iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Average pm25 heatmaps: each month of year, multiple sensors, at given timewindow\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. sensor_ids - list of sensor node IDs in form of strings, e.g. [\"1001\", \"1002\", \"1003\"]\n",
    "4. year - the year for which the plot should be generated, e.g. \"2019\"\n",
    "5. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "6. title - the title shown in the upper part of the plot\n",
    "\"\"\"\n",
    "\n",
    "def show_year_months_heatmap(input_dir, output_dir, sensor_ids, year =\"2021\", window=\"15min\", title=\"default title\"):\n",
    "    pio.renderers.default = \"browser\"\n",
    "    months = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\"]\n",
    "    dfs = []\n",
    "    for sensor_id in sensor_ids:\n",
    "        month_dfs = []\n",
    "        for month in months:\n",
    "            df = get_sensor_data(input_dir, sensor_id, year, month)\n",
    "            if df is not None:\n",
    "                df = df['PM2.5[calibrated]']\n",
    "                month_dfs.append(df)\n",
    "\n",
    "        if len(month_dfs):\n",
    "            all_months_df = pd.concat(month_dfs)\n",
    "            all_months_df = all_months_df.resample(window).mean()\n",
    "            dfs.append(pd.DataFrame(all_months_df))\n",
    "\n",
    "\n",
    "    all_days_df = pd.concat(dfs, axis=1)\n",
    "    all_days_df = all_days_df.mean(axis=1)\n",
    "    months_grouped_df = all_days_df.groupby(all_days_df.index.month).apply(list)\n",
    "\n",
    "\n",
    "    matrix = []\n",
    "\n",
    "    for month_number, day_data in months_grouped_df.iteritems():\n",
    "        day_data = [round(x, 0) for x in day_data]\n",
    "        matrix.append(day_data)\n",
    "\n",
    "    max_len = max([len(i) for i in matrix])\n",
    "    matrix = [row + [np.NaN] * (max_len - len(row)) for row in matrix]\n",
    "\n",
    "    fig = px.imshow(np.array(matrix), title=f\"{title} average PM<sub>2.5</sub> [µg/m<sup>3</sup>], year {year}\", text_auto=True, labels=dict(x=f\"Day\", y=\"Month\", color=\"PM_2.5\"), color_continuous_scale='rdylgn_r', template='plotly')\n",
    "    fig.update_layout(xaxis = dict(\n",
    "        tickmode = 'array',\n",
    "        tickvals = [x for x in range(31)],\n",
    "        ticktext = [x+1 for x in range(32)]\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "        tickmode = 'array',\n",
    "        tickvals = [x for x in range(12)],\n",
    "        ticktext = [str(x+1).zfill(2) for x in range(13)]\n",
    "    )\n",
    "    )\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "      os.makedirs(output_dir)\n",
    "\n",
    "    output_file = os.path.join(output_dir, f\"{output_dir}_{year}_{window}.html\")\n",
    "    fig.write_html(output_file, include_plotlyjs='cdn')\n",
    "    # fig.write_image(f\"{title}_{year}_{window}.png\", width=1000, height=500, scale=3)\n",
    "    # fig.show()\n",
    "\n",
    "sensor_ids_main = ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009'] # main campus\n",
    "sensor_ids_wittiga = ['1010'] # T-19 Wittiga\n",
    "sensor_ids_gdanska = ['1012'] # F-3 Gdanska\n",
    "sensor_ids_geo = ['1013'] # L-1 Na Grobli\n",
    "sensor_ids_dluga = ['1014'] # M-2 Dluga\n",
    "sensor_ids_prusa = ['1019'] # E-1 Prusa\n",
    "\n",
    "years = ['2019','2020', '2021', '2022']\n",
    "\n",
    "for year in years:\n",
    "    show_year_months_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='main',\n",
    "        sensor_ids=sensor_ids_main,\n",
    "        year = year,\n",
    "        window = '1D',\n",
    "        title='Main campus')\n",
    "\n",
    "    show_year_months_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='wittiga',\n",
    "        sensor_ids=sensor_ids_wittiga,\n",
    "        year = year,\n",
    "        window = '1D',\n",
    "        title='Wittiga St.')\n",
    "\n",
    "    show_year_months_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='bujwida',\n",
    "        sensor_ids=sensor_ids_gdanska,\n",
    "        year = year,\n",
    "        window = '1D',\n",
    "        title='Bujwida St.')\n",
    "\n",
    "    show_year_months_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='nagrobli',\n",
    "        sensor_ids=sensor_ids_geo,\n",
    "        year = year,\n",
    "        window = '1D',\n",
    "        title='Na Grobli St.')\n",
    "\n",
    "    show_year_months_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='dluga',\n",
    "        sensor_ids=sensor_ids_dluga,\n",
    "        year = year,\n",
    "        window = '1D',\n",
    "        title='Dluga St.')\n",
    "\n",
    "    show_year_months_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='prusa',\n",
    "        sensor_ids=sensor_ids_prusa,\n",
    "        year = year,\n",
    "        window = '1D',\n",
    "        title='Prusa St.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"PM2.5 changes heatmap for group of sensors\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. sensor_ids - list of sensor node IDs in form of strings, e.g. [\"1001\", \"1002\", \"1003\"]\n",
    "4. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "5. min_pm25 - the lower limit of PM2.5 concentration shown in the plot's axes\n",
    "6. divide - integer value by which all data will be divided to make the data points better visible\n",
    "7. log - boolean value. If True - a log operation will be performed on the z-axis  \n",
    "8. title - the title shown in the upper part of the plot\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def pm25_change_heatmap(input_dir, output_dir, sensor_ids, window=\"15min\", min_pm25=0, divide: int =0, log=False, title='default title'):\n",
    "    dfs = []\n",
    "    for sensor_id in sensor_ids:\n",
    "        sensor_df = get_all_sensor_data(input_dir, sensor_id)\n",
    "        sensor_df.index = sensor_df['timestamp']\n",
    "        dfs.append(sensor_df[['PM2.5[calibrated]']])\n",
    "\n",
    "    sensors_concatenated_df = pd.concat(dfs)\n",
    "    window_df = sensors_concatenated_df.resample(window).mean()\n",
    "    window_df = window_df[window_df['PM2.5[calibrated]'] >= min_pm25]\n",
    "    window_df['PM2.5[calibrated]'] = window_df['PM2.5[calibrated]'].fillna(0).astype('int')\n",
    "\n",
    "    if divide > 0:\n",
    "        new_df = pd.DataFrame()\n",
    "        new_df['PM2.5[calibrated]'] = window_df.groupby(np.arange(len(window_df))//divide).mean()\n",
    "        new_df['PM2.5[calibrated]'] = new_df['PM2.5[calibrated]'].astype('int')\n",
    "        window_df = new_df\n",
    "\n",
    "    d = defaultdict(int)\n",
    "\n",
    "    for row_current, row_next in zip(window_df.values[:], window_df.values[1:]):\n",
    "        pm_current = row_current[0]\n",
    "        pm_next = row_next[0]\n",
    "        d[pm_current, pm_next] += 1\n",
    "\n",
    "    if log:\n",
    "        d = {(k1, k2): math.log(v) for (k1, k2), v in d.items()} # calculate log of dictionary values\n",
    "\n",
    "    fill_axes(d)\n",
    "\n",
    "    # to show text labels add: texttemplate=\"%{text}\" as Heatmap argument\n",
    "    fig = go.Figure(data=go.Heatmap(dict_to_plotly(d), hoverongaps=False, text= [v for v in d.values()], hovertemplate=None))\n",
    "    fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=800,\n",
    "    height=800,\n",
    "    title=f\"Number of PM<sub>2.5</sub> value transitions, {title}, {window} window average\",\n",
    "    xaxis_title=\"PM<sub>2.5</sub> (t)\",\n",
    "    yaxis_title=\"PM<sub>2.5</sub> (t+1)\",\n",
    "    template=\"plotly\")\n",
    "\n",
    "    # fig.show()\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "      os.makedirs(output_dir)\n",
    "\n",
    "    # fig.write_image(f'{plot_dir}{window}_{title}_min{min_pm25}_no_stratification.png')\n",
    "    fig.write_html(os.path.join(output_dir, f'{output_dir}_{window}_min{min_pm25}.html'), include_plotlyjs='cdn')\n",
    "\n",
    "\n",
    "# fills x and y axes with \"Nones\". Prevents visual glitches\n",
    "def fill_axes(dictionary):\n",
    "    x = [t[0] for t in dictionary.keys()]\n",
    "    for i in range(min(x), max(x)):\n",
    "        if i not in x:\n",
    "            dictionary[i, i] = None\n",
    "\n",
    "\n",
    "\n",
    "sensor_ids_main = ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009'] # main campus\n",
    "sensor_ids_wittiga = ['1010'] # T-19 Wittiga\n",
    "sensor_ids_gdanska = ['1012'] # F-3 Gdanska\n",
    "sensor_ids_geo = ['1013'] # L-1 Na Grobli\n",
    "sensor_ids_dluga = ['1014'] # M-2 Dluga\n",
    "sensor_ids_prusa = ['1019'] # E-1 Prusa\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='main',\n",
    "    sensor_ids=sensor_ids_main,\n",
    "    window='15min',\n",
    "    min_pm25=0,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Main campus')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='main',\n",
    "    sensor_ids=sensor_ids_main,\n",
    "    window='15min',\n",
    "    min_pm25=50,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Main campus')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='wittiga',\n",
    "    sensor_ids=sensor_ids_wittiga,\n",
    "    window='15min',\n",
    "    min_pm25=0,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Wittiga St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='wittiga',\n",
    "    sensor_ids=sensor_ids_wittiga,\n",
    "    window='15min',\n",
    "    min_pm25=50,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Wittiga St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='bujwida',\n",
    "    sensor_ids=sensor_ids_gdanska,\n",
    "    window='15min',\n",
    "    min_pm25=0,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Bujwida St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='bujwida',\n",
    "    sensor_ids=sensor_ids_gdanska,\n",
    "    window='15min',\n",
    "    min_pm25=50,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Bujwida St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='nagrobli',\n",
    "    sensor_ids=sensor_ids_geo,\n",
    "    window='15min',\n",
    "    min_pm25=0,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Na Grobli St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='nagrobli',\n",
    "    sensor_ids=sensor_ids_geo,\n",
    "    window='15min',\n",
    "    min_pm25=50,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Na Grobli St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='dluga',\n",
    "    sensor_ids=sensor_ids_dluga,\n",
    "    window='15min',\n",
    "    min_pm25=0,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Długa St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='dluga',\n",
    "    sensor_ids=sensor_ids_dluga,\n",
    "    window='15min',\n",
    "    min_pm25=50,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Długa St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='prusa',\n",
    "    sensor_ids=sensor_ids_prusa,\n",
    "    window='15min',\n",
    "    min_pm25=0,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Prusa St.')\n",
    "\n",
    "pm25_change_heatmap(\n",
    "    input_dir='readings',\n",
    "    output_dir='prusa',\n",
    "    sensor_ids=sensor_ids_prusa,\n",
    "    window='15min',\n",
    "    min_pm25=50,\n",
    "    divide=0,\n",
    "    log=True,\n",
    "    title='Prusa St.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"Average PM25 heatmaps: each day of month, multiple sensors, at given timewindow\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. sensor_ids - list of sensor node IDs in form of strings, e.g. [\"1001\", \"1002\", \"1003\"]\n",
    "4. year - the year for which the plot should be generated, e.g. \"2019\"\n",
    "5. month - number of the month for which the plot should be generated, e.g. \"02\"\n",
    "6. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "7. title - the title shown in the upper part of the plot\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def show_month_heatmap(input_dir, output_dir, sensor_ids, year =\"2021\", month =\"02\", window=\"15min\", title=\"default_title\"):\n",
    "    pio.renderers.default = \"browser\"\n",
    "\n",
    "    dfs = []\n",
    "    for sensor_id in sensor_ids:\n",
    "        df = get_sensor_data(input_dir, sensor_id, year, month)\n",
    "        if df is None:\n",
    "            continue\n",
    "        df = df.resample(window).mean()\n",
    "        day_numbers = df.index.day.unique().to_list()\n",
    "        day_numbers.reverse()\n",
    "\n",
    "        new_df = pd.DataFrame()\n",
    "        for day in day_numbers:\n",
    "            day_df = df[df.index.day == day].reset_index()\n",
    "            new_df[f'{day}.{month}'] = day_df['PM2.5[calibrated]']\n",
    "\n",
    "        dfs.append(new_df)\n",
    "\n",
    "    mean_df = pd.concat(dfs)\n",
    "    mean_df = mean_df.groupby(mean_df.index).mean()\n",
    "    mean_df.index += 1\n",
    "    mean_df = mean_df.iloc[:, ::-1]  # reverse column order\n",
    "\n",
    "    mean_df.to_csv('test.csv', index=True)\n",
    "    mean_df = mean_df.T\n",
    "\n",
    "    int_df = mean_df.round(0)\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "      os.makedirs(output_dir)\n",
    "\n",
    "    fig = px.imshow(int_df, title=f\"{title} average PM<sub>2.5</sub> [µg/m<sup>3</sup>], {month}-{year}\", text_auto=True, labels=dict(x=f\"Window ({window})\", y=\"Day of month\", color=\"PM_2.5\"), color_continuous_scale='rdylgn_r', template='plotly')\n",
    "    fig.write_html(os.path.join(output_dir, f\"{output_dir}_{year}_{month}.html\"), include_plotlyjs='cdn')\n",
    "    # fig.write_image(f\"{title}_{year}_{month}.png\", width=1920, height=1080, scale=2)\n",
    "    # fig.show()\n",
    "\n",
    "years = ['2019','2020', '2021', '2022']\n",
    "\n",
    "sensor_ids_main = ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009'] # main campus\n",
    "sensor_ids_wittiga = ['1010'] # T-19 Wittiga\n",
    "sensor_ids_gdanska = ['1012'] # F-3 Gdanska\n",
    "sensor_ids_geo = ['1013'] # L-1 Na Grobli\n",
    "sensor_ids_dluga = ['1014'] # M-2 Dluga\n",
    "sensor_ids_prusa = ['1019'] # E-1 Prusa\n",
    "\n",
    "for year in years:\n",
    "    show_month_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='main',\n",
    "        sensor_ids=sensor_ids_main,\n",
    "        year = year,\n",
    "        month = \"01\",\n",
    "        window = '15min',\n",
    "        title='Main campus')\n",
    "\n",
    "    show_month_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='wittiga',\n",
    "        sensor_ids=sensor_ids_wittiga,\n",
    "        year = year,\n",
    "        month = \"01\",\n",
    "        window = '15min',\n",
    "        title='Wittiga St.')\n",
    "\n",
    "    show_month_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='bujwida',\n",
    "        sensor_ids=sensor_ids_gdanska,\n",
    "        year = year,\n",
    "        month = \"01\",\n",
    "        window = '15min',\n",
    "        title='Bujwida St.')\n",
    "\n",
    "    show_month_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='nagrobli',\n",
    "        sensor_ids=sensor_ids_geo,\n",
    "        year = year,\n",
    "        month = \"01\",\n",
    "        window = '15min',\n",
    "        title='Na Grobli St.')\n",
    "\n",
    "    show_month_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='dluga',\n",
    "        sensor_ids=sensor_ids_dluga,\n",
    "        year = year,\n",
    "        month = \"01\",\n",
    "        window = '15min',\n",
    "        title='Dluga St.')\n",
    "\n",
    "    show_month_heatmap(\n",
    "        input_dir='readings',\n",
    "        output_dir='prusa',\n",
    "        sensor_ids=sensor_ids_prusa,\n",
    "        year = year,\n",
    "        month = \"01\",\n",
    "        window = '15min',\n",
    "        title='Prusa St.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\747943170.py:28: FutureWarning:\n",
      "\n",
      "Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x2000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9cAAAPXCAYAAAAhd7gpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACvFklEQVR4nOzdd3iT9f7G8Ttt6IIWCrJEkF1A9kbZUxAHONmgoIgcfqKgoqgIguMAbpE9ReSgoIJHpWwQEAQR2SACouxRKN3t74+exKZN2zRP0iTN+3VdXEKekc+3TeJz5zseU1paWpoAAAAAAIDTAjxdAAAAAAAAvo5wDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADDI7OkCvNH589dcer6AAJOKFy+sS5dilZqa5tJzexPaWbDQzoLHX9pKO51XsmS4S84DAIA/ouc6HwQEmGQymRQQYPJ0KW5FOwsW2lnw+EtbaScAAPAEwjXgZrNnT1fLlo2z/GnTppm6deug4cMf1/fff2tzzN9//2Xd7+GH78vx/IcOHbTuO3v2dKfrvHEjVh9//J4efvg+tW3bXJ07t9OwYcN0+PAhh88xYcLLdtvasmVjTZv2gVN1ffvtNw61rWXLxnrggbvzdO5t237Uk08OUYMGDdSlS3s988y/dODAPqfqdJbldz1mzLP5+ryuYvn9LF26ONd9ly5drJYtG+vbb79xe10ffPCBmjdvqH/964ls97l27Zpatmys4cMfd1sdFy6c14IFczRkSH/dfXdntWvXQj173qVXXhmjHTu2ue158/J72bVrp1q2bKz33ptifeyFF15QVFSUDhw44NDzrVmzRk888YRatGih2rVrq2XLlnryySe1Zs0au/ufO3dOX3zxhWONMWDEiBG699577W5LTEzUbbfdpqioKLt/jh075rI6tm/frqioKE2cONFl5zQir79fR02cOFFRUVHavn27S88LAL6CYeFAPmnVqo2qVq1u/XdKSoouX76sdetWa8KEV3TixB96/PFhWY47ffpPHT16RFWrVrN73vXr7V+85kV8fLyGDRuio0cPq3btumrVqq0uXDin9evXavPmzXrnnY9Ut279XM9z9OgRFS9eQvfe2zPLNkeOz09ff71cb789USVLltQDDzygS5eu6IcfvtewYYP18cezVLPmbflSR5Ei4Ro0aIhuvbVivjyfq1WrVl2DBg1RrVp1PF2KXbt3/6yVK1eoe/f78v25162L1htvTNCNG7GqXj1K7dp1UFhYYf3991/atm2L1q5drW7d7tYLL7ysgADv+q67Y8eOKleunG666aZc950wYYIWLVqkcuXKqUOHDoqMjNTZs2e1YcMGrV27Vg899JAmTJhg3f/ixYu688471bx5c91///1ua8Ps2bP1/fffq0aNGna3HzlyRMnJyWrZsqXq16+fZXtkZKTLailXrpyGDx+uevXqueycRuTl9wsAcBzhGsgnrVq1VbduWXtWe/fup0cf7aNPP52ve+7poTJlylq3lShRQhcvXtSGDWtzDNehoWGKi7vhdG3/+c8SHT16WA888IiefnqUJMlsDtCRI/s0cOBATZnypubPX5LjOZKTk3XixB+6/fZWeuyx7HsLvcGZM2f03nuTVbFiJX3yySxVqnSLLl+O1d1399STTz6qadM+0Pvvf5IvtYSHh3v9zysn1apFqVq1KE+XkaOPPnpft9/eSsWLl8i359yxY7teeWWMihYtqokTP1KTJs1stl+5ckXjxr2ob7/9RtWrR+mBBx7Jt9oc0bFjR3Xs2DHX/bZv365FixapS5cumjp1qszmfy4rrl27pv79+2vp0qVq06aN9XxxcXGKjY11W+0pKSmaMmWKZs+eneN+hw6lj8rp3bu3OnTo4LZ6JOmWW27Rv/71L7c+R144+vsFAOSNd31VDvih8uUrqFWrtkpJSdH27VttttWseZtuuqmkNm5cb/fYo0eP6NSpk7rjjlaGati4ca1MJpOGDBlq83jTpk3VoEEjHTt2VOfPn8vxHH/8cVzJycmqUqWqoVryw6pVXykhIUH/93+jVKzYP71Tt91WW71791e1atVzOBq+pHr1KF27FqN3352cb8+ZlJSkt956XZL0xhtTsgRrSSpWrJhee22SChcurMWLFyotzTcXXlu/fr0kqU+fPjbBWkr/4ujZZ9OnO6xevTpf6tm3b5969uyp2bNnq2XLljnuawnXUVHe/eUQAMB3EK4BL3DTTSUlSTExV20eN5lMat26rY4dO6LTp//Mctz69WsUHBys22/P+SIyN/fee78ef3yYChcukmVbUFCQJOXaM37s2BFJyraHPSPLHE93znXNybZtWxQeHqFGjZpk2TZ06HD961/PuPw5H3jgbg0f/rhWrfpad9/dWZ06tdIHH7yTZc71u+/+Wy1bNs7yRYsk/fbbXrVs2ViTJ79pfSw29rqmTftADz10r9q1a6H77uuqyZPf0OXLl2yOnThxnJo3b6hff/1Vjzxyv9q3v11Dhz6aY6hbvfo7Pfnko7rzznbq1KmVBg/ur+XLl9kck93c3k2b1uuJJwapY8eW6tnzLs2fP1spKal2n+fixQuaPPlN9ejRTe3atdCDD96jjz9+XzduGO/d7NNngCpUuFVr167Wli2bHDrmxo0bmjdvlgYO7K1OnVqrffvb9cgjPfTxx+8pLi4u1+O3bduiM2f+1h13tFKdOtkPAy5atJj69h2ou+66RwkJCZL+mYM/a9Ynevfdf6tjx5a6664OWrs2WlL6POEFC+aob98H1apVMzVr1kyjR4/UwYP77T5Hamqq5s2bpZ4971L79ndoyJD+WrcuOtc2ODonNykpSZJ0+PBhu9sbN26sd999VwMHDpQkffnll9Ze4jVr1igqKkpffvllrvU4au3atTp58qRGjRqlGTNm5LjvoUOHVKRIEd1yyy25njcqKkovvfSSfvrpJ/Xu3Vv16tVTy5YtNXXqVKWkpOjo0aN67LHH1KBBA7Vq1UoTJkywea3Ym3Pdr18/tW/fXmfOnNGzzz6rZs2aqV69eurTp0+uc5bXr1+vqKgovf/++1naHxUVpd69e9s8fuXKFdWsWVPPPfecpKy/3z///FNRUVH64IMPtGbNGj3wwAOqW7euWrRoobFjx+rSJdvPE0latmyZ7rnnHtWrV0+dO3fWkiXZj2769ddfNWzYMDVr1kx16tRRt27d9MknnygxMVGSlJaWpttvv13t27e3OS4tLU3NmzdXVFSUTp48abNt2LBhqlu3bq7vyZ9//lnDhw9Xy5YtVbt2bTVp0kSDBg3Stm3/rHnw+uuvKyoqSps2Zf2M+OWXXxQVFaVx48ZZH7t+/bomT56sjh07qnbt2mrVqpVeffVVXbx40eZYy8/5119/Vbdu3VSnTh098sgj1s/QdevWafDgwWrevLluu+02NW/eXMOGDbP7vtu3b5+eeOIJNW3aVI0aNdLIkSN19uxZ1apVSy+88ILNvo7WB8D1CNeAFzh9+pSkf0J2Rm3bpl+IbtiwNsu29evXqHnz2xUSEmro+bt3v1f9+g3K8vilS5f0yy+7FRoaqjJlbs7xHJZwffLkCT355KPq1Km1unfvpEmTXtOFC+dt9i1b9mYNGjTE7jB5d0tLS9MffxzXrbdW1KVLFzV+/Ctq3ry52ra9Xc88M1xHjji+gFte/fHH75o69S21atVG7dp1VO3aWecpd+7cVZK0dm3Wnr41a36QJHXpkr7P9evX9eSTj+nTT+erbNlyevDBR1S7dh19/fVyDRkyQBcuXMhyjieffFK33FJe9957vxo2bCyTyf5K09HR3+u118bq8uUr6tatu+65p6euXYv53xSBnIfbfvPNCo0ZM0p//XVaXbp0U4MGDbVgwRwtWbIwy75nzpzRkCED9NVXXygqqoYeeqi3ype/VYsXL9Dw4Y87FGZzUqhQkJ577iWZTCZNmfKmbtzI+Uui5ORkPf30MM2ePV0lStykHj0eULdu6eF38eKFmjjx1Vyfc9OmDZKkli3b5Lpvv36D9NhjTygkJMTm8a+/Xq61a6N1330P6Lbb6ui222orISFBTz89TDNmfKyAgAD17PmAbr/9dm3fvlVDhz6qTZvWZzn/Z58t1MKFc9WsWQt17XqX/vrrtF5++QWtWLEs19occccdd0iS3nrrLU2YMEG7d+9WSkqKdXtISIi6du2qmjVrSpJq1qyp/v37S5IqVaqk4cOHW7e5Qrt27bR69WoNGTJEgYGBOe57+PBhlS1bVlOnTlXnzp2toW/+/Pl2v3Tas2ePHn30URUvXly9evVSUFCQpk+frldeeUW9evVSamqqevXqpaJFi2rRokV65513cq03NjZWvXv31sGDB3XfffepY8eO2rVrlx577DEdOXIk2+OaNWum4OBgm4AoyfrvvXv3Wr+wkaQtW7YoNTVVbdu2zbGedevWafjw4SpZsqT69eun0qVL6z//+Y+GDbNdD+Tdd9/VSy+9pOvXr+uBBx5QjRo1NH78eP33v//Ncs7o6Gj16tVLmzZt0u23365HHnlEgYGBeueddzRo0CAlJibKZDKpZcuWOn36tE2IPnTokC5fvixJ2rFjh/XxpKQkbdu2Tc2aNVNoaPb//4uOjla/fv30yy+/qGPHjhowYIAaNGigrVu36rHHHrOG2HvuuUeS7Na/atUqm32uXbumXr16aebMmbrlllvUv39/NWjQQEuXLtWDDz6oc+eyjvJ68sknVaFCBT3yyCNq1qyZTCaTFi1apKFDh+rEiRPq3r27+vfvr6pVq2rNmjXq06ePzXl2796tPn366Mcff1Tr1q31wAMPaNeuXerVq1eW16oz9QFwHeZcAx528OB+bd68UcHBwWre/I4s2+vVa6BixYpp48b16t27v/Xx48d/1x9/HNfAgYPdVtu///1v3bgRq/vue8Dag52do0ePSpLmzZulNm3aq1atOtq//zd9++032rFju6ZPn6tSpUpLSg/XeZ1nvHv3z4ZWQ7e4fv264uLilJiYoCFDBig0NFTdu3fXX3+d0fr1azVs2GB98MF01ahRy/BzZXblyhU9/fQom/m1f//9l80+tWrVVvnyFbRx43qNHv2idahtamqq1q2LVtmy5ay9odOnf6Tffz+mZ555Xj17Pmg9x+bNG/TCC8/qvfcma8KEN23O37BhQ40f/6aSk+33Ilt89tkihYaGas6chQoLKyxJevTRIerd+wEtW/a5Bgx4zG4wv3btmj766F2VKlVan3wyx/o7f/DBXnrqqSFZ9p8y5Q2dP39Ob731js0IjP/8Z4nee2+y5s6doWHD/i/HWnNTv35D3X33ffr66+WaMeMjPf306Gz3Xb9+jfbv/039+z9qs8DgsGH/0iOP9NSmTRsUHx+fJQxn9Oef6V+WVa5cxemaL1++pLlzF9uMBJk3b5Z+/fUXdet2t5577iWFhAQpMrKwtm7dqSeeeFQTJ76mL75obDMC5cqVK5o+fZ5q1EgPsH37DtTQoYP00UfvqUOHLgoPN3Zf63bt2qlXr1767LPPtGjRIi1atEhFihRRo0aNdPvtt+vOO+9UmTJlrPvXrFlTAwYM0IIFC1S5cmWXz0OuXbu2Q/udP39eFy9e1MWLFxUfH6/27dsrLi5O69ev16RJk3TgwAG9+abte+fIkSMaM2aMtRf+oYceUteuXbVs2TI9+uijev755yWl96i2adNGK1eu1IsvvphjHVeuXFGjRo303nvvqVChQpKkatWq6Z133tFXX32lUaNG2T0uNDRUTZo00fbt23Xjxg2FhYVJSg/XYWFhunHjhvbs2aOmTZtKkjZt2iSz2ZzrUPl9+/bp3XffVdeu6V/gPf300+rRo4d2796tY8eOqUqVKvrjjz80c+ZM1axZUwsWLFBERISk9GD+5JNP2pzv+vXrevHFFxUSEqIFCxbottvSF4pMTk7WCy+8oG+++UYzZ87UU089pTZt2uirr77S1q1bVaFCBWt7AgICFBwcrJ07d1oXwNu1a5diY2Nz/bJg8uTJCg8P14oVK2wWb5s5c6YmT56s//73v6pZs6bq1q2rihUrKjo6Wq+99pr1d5GamqrvvvtOt9xyixo2bChJmjp1qg4fPqxXXnlFffr0sZ5zzZo1GjZsmCZOnKj33nvPpo6GDRvqgw/+uWNGYmKi3nnnHVWsWFHLly+3/v4kady4cfrss8+0bt06Pfzww5KkV155RUlJSfr000+ti+8NGzZMDz30kFJTbT/LnakPgOvQcw3kk02b1mv27OnWP9Onf6SxY5/XsGFDlJKSoqeeetru6rSBgYFq1aqt9u3ba9MTuX79GgUFBev2243Nt87OnDmz9OWXX6pMmbJ2VzHPLDg4WLfcUkEzZy7QK69M0L/+NVLTps3WkCFP6vz5c4bnvP7yyy7NnTsz2z+Oio9P7wk9fPiQKlSoqAULPtPYsWM1ceJbmjjxbcXFxentt913uxzLSIScdO7cVdeuxeinn/7pldqzZ7cuXDivTp26SEq/OP3uu1WqVKmyTbCW0ntM69Spp40b1yk29nqmc3d2qM60tFQlJCTo99//uR1R4cJFNHPmfP3nP19n2+O9deuW//VmPWIN1pJUo0Ytde3a3WbfCxcuaNu2H9WixR1Zpjbcf/9DKlWqtL79dqVD9ebmySdHqESJm/Tll//Rvn2/Zbtf9eo19MILY/XQQ7bDasPCCqt69SilpKRkmb6RmWVIfnh4RJZtGzastfkcsPzJPGKiXLnyWaZYfPvtNwoJCdHTT4+ymd9co0ZN9ez5kK5fv6YNG9bZHNOlSzdrsJbSv9h64IFeiouLs9vT7Yxx48Zp+vTpatWqlQoVKqTr169rw4YNeuONN9ShQwdNmTIlSwDwtIsXL6patWrq0qWLvv32W7344ouaMGGCVq1apdq1a2v58uVZbiMWFBRkM9y6cuXK1s/sRx991Pp4kSJFVKVKFWtwz82jjz5qDXOS1KZN+oiH06dP53hcmzZtlJSUZO3RvXTpkg4fPqyHHnpIkm1P7+bNm9WwYUNrEM5O+fLlrcFakgoVKqQWLVrY1PPdd98pOTlZQ4cOtTlfu3btsoT36OhoXb16Vf3797cGa0kym83W0G25JVvLli0VGBiorVv/mRKzbds21ahRQ3Xq1MnSnow/K3tSU1P17LPP6u23386yKnqzZunrIGQcJn333Xfr6tWr2rJli/WxnTt36ty5c+rePf2zKzk5WStWrFC1atVsgqskdejQQQ0bNtTq1at1/XrOn7spKSmaMGGCJk6caBOsJVm/ELHUtm/fPh0+fFh33XWXzar2RYsW1fDhw22OdbY+AK5DzzWQTzZt2mAdLiqlX1wULVpMTZs2U8+eD6lp0+bZHtumTXt9880Kbdy4zhqkLEPCM/+P2RVmzfpE8+bNUrFixTR16vu5XpBJ0htv2A/P/foN0sqVX2vLlo02PSx5NWjQkBx7u1u2bOzQeUymf75THD78aZseyJYt26hBg0bavftnnTp1UuXLV3Cq1uwUKlTI7tD/zDp37qrZs6drzZofrKHznyHh3SSlD7+Pi7uh1NRUuz36iYmJSklJ0bFjR21ug+bI/FJJuueenpo8+Q0NHfqoqlSppubNb1eLFneobt36Od426ujR9Lm3GQOdRe3adbVixT/3Nj58+KDS0tJ09epVu20oVKiQzp07q/Pnz6lkyVIO1Z2d8PBwjRw5WmPHPq+3335ds2cvsrtfhQq3qkKFW5WQkKB9+37TqVMndPr0nzp06IB27/5ZknINihERRSVJ167FZNm2YcM6/fBD1qGnZcvebLPq+s03207DuHEjVn/9dVp16tSzjiTIqG7devrss4XWn7+FvTnftWqlh5zM+xrRtm1btW3bVrGxsdq5c6e2bt2qtWvX6sSJE5oxY4ZSU1M1enT2IwbyW40aNbRyZdYvbiIiIjR69GgNGDBAq1atsllFvGzZsllG8ISFhSkuLk4lS9q+r4ODgyWlvw9zGuUgSRUrVrT5d5EiRazH5qRNmzaaOHGitm3bpjZt2mj79u1KS0tTjx49tHLlSu3cuVOSdPDgQZ0/f16DBmWd+pNbLZKsoxss9Rw8eFCS/VECDRo0sJm3bNm3SZOs61sUL15clSpV0oEDB3Tt2jUVLVpU9erVs7YjNTVVO3bs0AMPPCCz2ayffvpJZ8+eVenSpbVp0yZVq1Ytx8+zgIAAderUSVL6FwNHjhzRyZMndfToUeuc9ozv5XvvvVcffPCBvv32W2uPeOYh4cePH9eNGzeUkpJi0xNtkZCQoJSUFB06dEiNGjWyPp65ztDQUHXr1s16zmPHjunkyZM6cuSI9csFS2179+6VJNWtWzfL81l60y2crQ+A6xCugXzy4ouvOj3HuHHjpipSJNwark+dOqljx46qT5+BLq0xJSVF//73JK1c+ZUiI4tr3ry5Kl26fK5DiHMSEBCgqlWr6e+/T+v8+XP5dj/npUsX69q1azaPNWjQSLVqpV8Qms1muyubV6tWXbt3/6zTp//MNlxv3Lg+S09j2bI35/r7tVxw56ZcuVtUu3Zdbd68QYmJiQoICND69WtVvXoN68/v+vX0tp048UeOPfcxMbYBL7cLfYv77rtfkZHFtWzZEv366y86duyIPv10vkqWLKXhw0eqQ4dOdo+z/MztBUBL6LSwtGHfvr3at29vjm0wGq6l9FEDrVq10aZNG/Tpp/PVs+dDWfZJTU3VwoVztWTJp9ZwHBlZXLVr11XZsjfrjz+O57qyd9myN2vfvr36888/ra83i1demaBXXvnnns9Lly7W++9PzXKOzK8Vy62rLMErM8uXNpl7SiMji2fZ1/IFl9H57PYULlxYbdq0UZs2bfT8889r2bJlevnll7Vo0SINHz48x/mx3sLSw/rnn7aLSGZXe8ZeZ2dkDuyWUSG5vc5uvfVWVaxY0RrGtm3bpmLFiikqKkqNGzfWxo0blZycrI0bN0pSrkOo7dWSsR4Ly2dK4cJZ3+PFihWz+belhzS7122pUqV04MABxcXFKTw8XG3atNGuXbt06NAhJSQk6Pr162ratKnMZrNmzZqlHTt2qFmzZjp48KAee+yxXNtz6NAhvf766/rpp58kpf+uqlSpotq1a+uPP/6w+RmXL19eDRo00Jo1a6yfuz/88INuu+02ValSxabtv//+uz788MNsn/fqVdvRLfY+d3fs2KE33nhD+/btk5T+nq9Ro4Zuu+02/f3339baLHPO7d2TvFQp289FZ+sD4DqEa8AHmM1m3XFHK0VHf6+YmKvWIeEtW7puSHhiYqJefvl5bdmySWXL3qz33vtYNWrU0OXLua/YHB8fr6NHjyg4ONjubawsC+vkNm/blZYu/Uxnzvyd5fGGDRvrpptK6tKli3Z7IJOTkyXlHEI3bVqv//7Xtterfv2GLl2grXPnrpo69S1t3/6jgoNDdOXKZfXtO8C6PTQ0PSB16dJNL7883mXPm1GbNu3Upk07Xbt2Tbt27dSWLRu1evV3eu21l1SpUiVVrpz1ywlLL1fm4ehS1hXnLWFl4MDBGjx4aJb93eGZZ57Xrl07NX/+HLu3yFqyZJFmzpymBg0aqU+fAapWrbpKlEi/qH322RH644/juT5Hq1ZtFB39vTZuXKvOne90Sd2WQHz+/Hm72y1fahQtWszmccsXGBlZFhi0N2w9L65fv66ePXuqUqVKmj4968gDk8mkBx98UN999502b96sM2fOqFKlSoae01XOnDmjEydOqFq1aipe3PYLCMsXFI5+GeZJrVq10qJFi3T58mX99NNPatKkiUwmk5o2barvvvtO+/bt0+bNm3XLLbdYA6JRlpFM169fzzKVKfP9yy0BPLtFtCxh0BLKW7durXfeeUdbt25VUlKSAgIC1KRJEwUEBCgwMFA7duxQcnKy0tLScv2y4Pr163r00Ud17do1Pf/887r99ttVuXJlBQUFac+ePXZHLtx9990aP368Nm7cqJCQEF26dEmPP/7PXS0s7bn33nv19ttv5/j8OTl9+rQGDx6skJAQTZgwQY0aNVLFihUVGBiob7/9VtHR/6zob/liwt5Q7syPuao+AM5jzjXgI9q2ba+UlBRt2bJJ69evVbNmze32DjojLS1Nr732krZs2aRKlSpr2rTZ1gVlHHHx4gUNHTpIr7/+SpZt8fHxOnz4oIoVi1SZMmVdUq8jli37Rps377T5YxlWXq9efaWmplqH+WZ06NBBBQYGqmLFytme+6WXxmU594cf5nzbn7zq0KGTzGazNm/eqHXrohUQEKCOHf8JahUq3KqgoCAdOnTQbg/X0qWLNW/eLF29eiXPz52UlKT582fr888/laT/9Si104svvqoBAx5Tamqq9u791e6xUVHpw8H37t2TZdvBg7a3l6lSpdr/Hrd/G6nZs6dr4cJ51ts9uULJkqX0xBPDlZiYoMmT38iyffXq7xQYGKg335yi5s1vtwbrtLQ0nTz5h/XvOWnZsrVKliyljRvX65dfduW4r6NzkQsXLqKyZcvp1KkT1p6sjCzPU6mS7evW3s/2t9/SRwlYflfOKlKkiK5du6Yff/zR7sr0GQUEBFiHTmc3Xz8/ff755+rfv7+++uqrLNt+/jn9c8HRxdE8qU2bNkpLS9N3332n33//3Tr8unnz9GlG69ev165duxzqtXaUpWff8nPK6LffbNczsKwEb2/f69ev68CBA7r11lutX7zWqlVLJUuW1LZt27Rr1y5FRUUpIiJCRYoU0W233aYdO3Zo06ZNKlq0qBo0aJBjndu2bdOFCxfUp08fPfroo6pRo4b1eY4dS19LIvN7uVu3bipUqJDWrl2r7777TgEBAbrrrrus2ytVqqSgoCDt27fP7ufAvHnz9PHHH9t9j2YUHR2t+Ph4jRgxQg899JCqVKliXd0+c22Wn/evv2b9zM38mKvqA+A8wjXgI5o2baHQ0DAtX75Mhw4dULt2HV127mXLPteGDet0yy3l9cEHMxyaF5xRuXK3qHr1Gjp27KjNfNK0tDR98skHunLlsnr0eMArLqql9PnEkjRt2vs2PS1r1vygffv26o47WmcZ3pjfihYtpmbNWmjbti3asmXT/3rc/xkWGBwcrPbtO+mPP37XkiWf2hy7a9dOffTRe1q16huneicLFSqk1au/16xZ07PcX92yunl2X5S0aHGHihWL1LJlS3Ty5Anr4ydO/KFvvllhs+/NN5dT/foNtW3bj1nuvfzdd6s0d+5Mbd/+o+Fht5n16PGA6tSpq8OHs952LSgoWCkpKbpy5YrN4/PmzbK23TK6ITvBwSHW0QRjxoyyexu95ORkrVz5lebPnyPJscDZrVt3JSQk6IMPptrUcPDgAX3xxecqUiRcd9xhO5rlu+9WWVcvl9J/D8uXL1OxYsVcMvKlT58+SkxM1IgRI+z2Tq5Zs0Y//vijOnXqZO2BsyzG5sovTfKqS5cuMplMmjNnjs2iVufOndM777yjQoUKWRcG82aWW1HNnDnT+m9JqlKlim666SYtXLhQSUlJLg3X3bp1U3BwsKZNm2YzkmLnzp1au9b2td6xY0eFh4dr8eLF1uHPUvrrf+LEiYqPj9e9995rc0zr1q21Y8cO7d6927q4l5S+0NexY8e0fv16tWzZ0mZRP3ssIw8y39v5r7/+sg6ZzvxejoyMVMuWLbVx40atW7dOzZs3txl6HRwcrG7duuno0aOaO3euzbHbt2/X22+/rS+++EJFi9pOgcmutsxfSh08eFALFiywqa1BgwaqXLmyvv76a5ufYUxMTJZVv11VHwDnMSwc8BHBwcFq0eIOrV27WkFBQVkuop2VmJio+fNnSZKqVKmqL774XJIUEGBSaGiQ4uISlZqapvvuu9/ai2eZz/zQQ72tw4Cfe+4l/etfT2jChFe0fv1alS1bVnv2/KKDB/erfv2GNvfR/vvvv/Ttt984NE/ZHRo1aqIHHnhEy5YtUZ8+D+rOO+/UqVOntW7dGhUvXkIjRjyT7zXZ07lzN23Zkr440NChw7Nsf+qpp/Xbb7/qo4/e1ebNG1SrVm2dO3dWGzeuU2CgWWPGvJzj4mM5GTr0KY0ZM0qPPdZX7dp1VHh4hA4dOqCff96h+vUb2h1SLaUPX37++Zc0duzzevzxAdbV0deti1axYpFZhik/99yLGjZsiF5++QU1b367KleuopMnT+jHHzcrIqKonn32Beu+rnrdmEwmPffcWD36aJ8sAa9z567at2+vnnzyMbVv31FmcyHt2rVThw8fVGRkcV2+fCnX1cKl9OkH//73e5ow4RW99NJzuuWWCmrQoJGKFi2q8+fP6aeftuny5UsKCQnR4MFD1alT7sPHe/fur59+2qoffvivjh07osaNm+j69RhFR0f/b/TJJJvbcElSsWKRGjJkgDp16qKEhAStX79GCQnxevXV1xUc7Nj8+5wMHTpUhw8f1vfff6/OnTurZcuWqlixopKTk7Vnzx7t2rVLlStX1rhx46zHREZGKigoSNu3b9cbb7yhTp06qXHjxjpw4ICio6NVs2ZNdez4z5eH8+bN07Vr1zRgwACHFld0RI0aNTRkyBDNmDFD3bt315133qnExEStXbtWly5d0quvvuo1Q9hzEhQUpObNm2vdunXW+dYWTZo00X//+1+FhYVZQ7crlCtXTs8//7zGjx+vHj16qGPHjrp+/bq+++47lS1b1uY+1UWKFNGkSZM0cuRIPfLII+rUqZNKlCihbdu26fDhw2rcuLGGDLG9RV+bNm2sK4hnDtezZs3S9evXc1wl3KJRo0YqV66cvvrqK12+fFk1atTQ33//rTVr1ig4OFgmkynLl2hS+uJl69alr7r/7LPPZtn+/PPPa/fu3Xrrrbe0Zs0a1a1bV2fPntUPP/wgs9msSZMm5fq5265dO02ZMkXTp0/X77//rgoVKujEiRNat26d9f+pltpMJpPGjx+vQYMGqXfv3urcubPCw8O1bt0667oJGZ/PFfUBcB7hGvAhbdq019q1q9WkSbMsF9HOOnHiuPV/4hs2rMtyKx+L1q3bZgjX6fOZu3W723ohUKNGTc2atUCzZn2iXbt2aOvWGypTpqwGDx6q3r3728y3/vvvvzR37kyXz1POi6efHqXq1aP05ZdL9dlnnyksLEydOnXRkCHD8nX4ek5atmytwoULKykpWW3atMuyPTIyUjNmzNOCBXO1ceM6LVu2RMWKReqOO1ppwIDBdue/O/7cbTR16gdatGiBtmzZpOvXr6l06TIaNGiI+vQZkOPFWatWbfXee9M0Z84MrVmzWiEhIbrnnp6qUaOWXn11jM2+FSpU1OzZCzVv3mxt27ZFP/+8QyVK3KQuXbpp4MDBKlfun1V2Xfm6qVSpsvr2HZhlMbj01fjTtHz5F/rmmxUqUiRc5ctX0LhxExUcHKwxY0Zp69Ytql0768q9mTVr1kKLFy/T99//V+vXr9HWrZt15cplhYdHqFKlynrkkT7q3v3eLPOksxMcHKx33/1Yn322SD/88F99+eUyRUREqGXL1urTZ4CqV6+R5ZghQ4bp8OGD+vbbb3TjRqxq1rxNQ4YMU7169R16ztyYzWa9//77Wr16tb7++mv9+uuv2rhxowoVKqRbb71Vzz77rPr372+zhkFQUJBeeeUVvf/++1q8eLHCw8Ot4frDDz+0BjaLBQsW6PTp0+rRo4fLwrWUHpyqVq2qhQsX6osvvpDZbFbt2rU1ZMgQtWrlnlscukPr1q21bt06NW7c2GYERLNmzfTf//5XzZs3d/l6F3369FHp0qU1ffp0LV++XJGRkRoxYoSCgoL0xhu20y06d+6sxYsXa9q0adq0aZMSExNVoUIFPffcc+rfv3+WkSl33HGHChUqpJSUFJtVxhs3biyz2azU1FSHfj9hYWGaO3euJk+erJ9//lk7d+5U2bJldc899+ipp57S448/rp07dyo2NtZmcbYOHTqoSJEiSkpKsnvrwuLFi2vp0qWaPn26Vq9erYULF6p48eJq3769hg0bpho1sr4PMytdurTmzp2rqVOnatu2bdq8ebNuvvlm9evXT0888YQ6d+6sTZs2KS0tTSaTSU2aNNGCBQv03nvvKTo6fZpQu3bt9OCDD6p///42i+25oj4AzjOl5TZ5zA+dP591ARgjzOYARUYW1uXLsYZWXfZ2tLNgoZ0FT0Fo66ZN67V8+TJNnZr9SrgFoZ2OcEc7S5YMd8l5XK1p06ZauXJlltWRgYIuISFB58+fV9myZa3zsi22bdumAQMGaNSoUVlGAADwDMaFAAB8QlpamqKjf1DVqtU8XQry0caNGxUUFGT3VkRAQRcbG6sOHTpo0KBBNouUpaSkaN68eZLk0mH/AIxhWDhQwFjmpTqqdeu2qlYtKvcdAQ87ffpPxcXFqX//3O9vi4Ljq6++0r///W/micIvFS9eXF26dNH333+v+++/X82aNVNKSop+/PFHHTlyRA8//LDq1s19mgqA/EG4BgoYy7xUR5UtezPhGj7hllvK6+233/F0GchnU6ZM8XQJgEdNnjxZDRo00IoVK/T55+mLjlauXFnjx4/3iVXtAX9CuAYKmIYNG2vz5p2eLgMAALhAUFCQBg0apEGDBuW+MwCPYowVAAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhw7QNMJpNMJpOnywAAAAAAZINw7eVMJpPmrNqvOav2E7ABAAAAwEuZPV0Achcbn+TpEgAAAAAAOaDnGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsvZzJ5ugIAAAAAQG4I117MZDJp+cbfPV0GAAAAACAXhGsvF5eY7OkSAAAAAAC5IFwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCtQ8xmSSTyeTpMgAAAAAAmRCufURYiFmzV+7XnFX7CdgAAAAA4GXMni4AjouNT5KU3oOdlubhYgAAAAAAVvRc+5iwELM+iz5C7zUAAAAAeBHCtQ+6kZDk6RIAAAAAABkQrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOHaR5lMkslk8nQZAAAAAAARrn1SaLBZs1fu15xV+wnYAAAAAOAFzJ4uAM6JjU/ydAkAAAAAgP+h5xoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDh2seZTJ6uAAAAAABAuPZhYSFmfRZ9RCYSNgAAAAB4FOHax91ISPJ0CQAAAADg9wjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcF0AmEySyWTydBkAAAAA4LcI1z4uNNis2Sv3a86q/QRsAAAAAPAQs6cLgHGx8UmeLgEAAAAA/Bo91wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOG6ADGZPF0BAAAAAPgnwnUBERZi1mfRR2QiYQMAAABAviNcFyA3EpI8XQIAAAAA+CXCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4LGJNJMplMni4DAAAAAPwK4boACQ02a/bK/Zqzaj8BGwAAAADykdnTBcC1YuOTPF0CAAAAAPgdeq4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXBdQJpNkMpk8XQYAAAAA+AXCdQEUFmLW7JX7NWfVfgI2AAAAAOQDs6cLgHvExid5ugQAAAAA8Bv0XAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcFnMnk6QoAAAAAoOAjXBdgYSFmfRZ9RAEBJplI2QAAAADgNoTrAi5NaZq9cr/mrNpPwAYAAAAANzF7ugC4X2x8kqT0IeJpaR4uBgAAAAAKIHqu/YRliDi91wAAAADgeoRrP3IjIcnTJQAAAABAgUS4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhw7WdMJslkMnm6DAAAAAAoUAjXfiQ02KzZK/drzqr9BGwAAAAAcCGzpwtA/oqNT/J0CQAAAABQ4NBzDQAAAACAQYRrL2UymcTIbQAAAADwDQwL90Imk0lzVu1XaLBZImADAAAAgNej59pLxcYn6UYC86MBAAAAwBcQrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES49lMm7p8NAAAAAC5DuPZDYSFmfRZ9RCYSNgAAAAC4BOHaT91ISPJ0CQAAAABQYBCuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwrUfM5kkk8nk6TIAAAAAwOcRrv1UaLBZs1fu15xV+wnYAAAAAGCQ2dMFwHNi45M8XQIAAAAAFAj0XAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA2ZTJLJZPJ0GQAAAADgswjXfi4sxKzZK/drzqr9BGwAAAAAcJLZ0wXA82LjkzxdAgAAAAD4NHquAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGlYsFg4AAAAAziFcQ1L6Lbk+iz7C7bgAAAAAwAmEa1jdSOCWXAAAAADgDMI1AAAAAAAGEa4BAAAAADCIcA0bJpOYdw0AAAAAeUS4hlVosFmzV+7XnFX7CdgAAAAAkAdmTxcA7xIbz6JmAAAAAJBX9FzDLjquAQAAAMBxhGtkwT2vAQAAACBvCNewi3teAwAAAIDjCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLhGtkwmyWQyeboMAAAAAPB6hGvYFRps1uyV+zVn1X4CNgAAAADkwuzpAuC9YuOTPF0CAAAAAPgEeq4BAAAAADCIcI1cMfcaAAAAAHJGuEaOwkKYew0AAAAAuWHONXJlmXttMklpaR4uBgAAAAC8ED3XcEhYiFmfRR+h9xoAAAAA7CBcw2E3Elg9HAAAAADsIVwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4hlNY2AwAAAAA/kG4Rp6ZTCYtWcPK4QAAAABgQbiGU1g5HAAAAAD+YfZ0AfAtdFYDAAAAQFaEazgsNNis2Sv3KzSYlw0AAAAAZERKQp7ExicpTWnMtwYAAACADJhzDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa7hNJNJ3O8aAAAAAES4hpNCg82avXK/5qzaT8AGAAAA4PfMni4Avis2PsnTJQAAAACAV6DnGgAAAAAAgwjXMIxR4QAAAAD8HeEahoSFmPVZ9BHmXQMAAADwa4RrGHYjgbnXAAAAAPwb4RoukX5bLk9XAQAAAACeQbiGYZbbcs38er+nSwEAAAAAj+BWXHCJ2Pgk5l0DAAAA8Fv0XMPlyNgAAAAA/A3hGi4TFmzW1MW7NPPr/fRiAwAAAPArDAuHS92IT1JycoqnywAAAACAfEXPNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBzLmGW6SvZ5a+qFlaWppHawEAAAAAdyNcw+XCgs1avvF3XYqJlyQ9elctAjYAAACAAo1wDbeIS0xWbHySpPRebLI1AAAAgIKMOddwq7AQsz6LPmK97zX3vwYAAABQEBGu4XY3Eiw92CYtWXOEgA0AAACgwCFcI19ZgjYAAAAAFCSEawAAAAAADCJcI9+ZTMy9BgAAAFCwsFo48oUlS4cGmzV75X5J3KILAAAAQMFBuIbbWQJ1aLBZMsl6iy4AAAAAKCgYFo58ERuflGUxM4aHAwAAACgoCNfwiLCQ9N7sOav2KyCAgA0AAADAtxGu4TGx8UlKU5o+i+be1wAAAAB8G+EaHse9rwEAAAD4OsI1AAAAAAAGEa7hFVjcDAAAAIAvI1zD4yy36mJxMwAAAAC+inANr8DiZgAAAAB8GeEaXuVGQhJDxAEAAAD4HLOnCwAysgwRl6THutdSWpqUlpbm4aoAAAAAIGf0XMPrWIaIW+Zh04sNAAAAwNvRcw2vFRuffv9rk0mi8xoAAACAN6PnGl4tLMTMImcAAAAAvB7hGl6PRc4AAAAAeDuGhcPrZVzk7NG7alkfZ6EzAAAAAN6CcA2fYJl/HRAgzfrmn6BNwAYAAADgDQjX8BlhIWZ9ueF3a9CW7A8VJ3ADAAAAyG+Ea/iUuMRk699NJmn2yv0KDTYrLiFZocFmmUwmPdKhGgEbAAAAQL4iXMOnWe6JfSM+WWlKY9EzAAAAAB7BauEocFhZHAAAAEB+I1zDZ9nLz5aVxees2k/ABgAAAJBvGBYOn2RZ3MweewueMQcbAAAAgDvRcw2flXFxM3tMJmnOKnqxAQAAALgfPdcosEwm215sAAAAAHAXeq5RIGUeNk7HNQAAAAB3IlyjwLIMGw8LMeuz6CMKCDAxPBwAAACAWxCu4RfSlMYq4gAAAADchjnX8BuW+dfp2ZpVxAEAAAC4DuEafiUsJP0+2KHBZplMJvXqWE328jWhGwAAAEBeEK7hd2Ljk5SmNIWFFLIG7dBgsy7FxFtD9yMdqhGwAQAAADiMcA2/ZgnaMv3zd3tzsk0mkzVsM2UbAAAAQGYsaAbYYTLJGrJNJpOWrElfbTwgwKRFPxzO5VjSNwAAAOBv6LkGMgkNTp+XLUmPda8l6Z/VxkODzYpPSpFkG8AtTCbps+gjdoeVW/ZluDkAAABQ8BCu85EljBGuvF9sfJLN4mcZh40XDg3S1MW7FBRo0o34pCxztuMSk60rkqelpQ8zN5lkE9gzvgR4PQAAAAC+j3Cdj2Z+vV9paWl69K5aBCofkXHxs4xuxCcpOdBkd862ZaE0SRp8dy3N+iY9oFsC+/KNv9ssnmZZsZzXBAAAAOC7CNf5KDY+iQDlJyxB+ssNv/8TwP8nLjHZbhDP2KPN6wQAAADwLYTrfGAySXO/2Wfzb7KTf4hLTM51n8w92lLWoeNS/gRu5oUDAAAAziFc55P4/4WssBCzzYJXGRfEItD4N0uPdsa53pa53JL9wC255nWTeV74o3fVMnxOAAAAwJ8Qrj3gRkKSpPRAs2JTzr2V8E+Z53Jnnqsdl5BsnbOd8Yua3IK2/Xt4yxrmY+OTrI9ZdrW3KrrEl0EAAABARoRrD7Fklcy9lVL6IlhAZhnnat+IT1aa/lmJ3GQy6bPoI9bF0ezJGKIzrm5uCdWWeeGWIH/5WoIKFw5SUKBJl65mDfX2nis/Azcr7wMAAMCbEK49wHIfZcstniwyLoIFOCLza8leeLYEYpsQbbLtHc/MEuQDAgOUWiggS6i3LMRmb+h6amp64LXX250dS0jO7ZiMtzbL7cuEjNMuMk/ByPy8GWUX2u3NR8+uXnv7ZK7HHqNz3pkzDwAA4DmEazuKFy/s8nOGBJkVXjhYocGBCg0yKzE5TYHmAOvfQ4MDZTabFRocqMDAAEUUCbHZN+P2jI9l93dX7puXc8UnpiokKCDfn9cT+4YEBSjQHOgVv5OMr6VAc4ACA9NrCzQHKDAl0PqYvddVjs8bYlZooUAl/O84e89rea7Q4EB9s/WE+nWtKUmasXyvQoICFRJs1pVrCdn+XZIe71FHkrQ0+rDdfeMTU6z7Wc5bOLSQlq47luu+mY+z97zzVu5Xv641JEkL/3vA2oaMMp7LImO98YkpCgkKlCnAZHN85hoynyO358gLR4+PiAh16vy+hnYCAID8ZEqjiwMAAAAAAEMCPF0AAAAAAAC+jnANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAaZPV2AN0pJSdWlS7EuO19AgEnFixfWpUuxSk1Nc9l5vQ3tLFhoZ8HjL22lnc4rWTI8x+3nz19zyfNI/J4KGtpZsNDOgsMf2ii5v525/f/RWofLnxlZBASYZDKZFBBg8nQpbkU7CxbaWfD4S1tpp2/w9fodRTsLFtpZsPhDO/2hjZL3tJNwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwyOPh+uLFixo9erSaN2+uBg0a6PHHH9exY8es28eOHauoqCibP+3bt7duT01N1fvvv69WrVqpfv36GjJkiE6dOuWJpgAAAAAA/JTHw/VTTz2lEydOaMaMGVq2bJlCQkI0cOBAxcXFSZIOHTqkoUOHavPmzdY/y5Ytsx7/8ccfa/HixZowYYKWLFmi1NRUDR48WImJiZ5qEgAAAADAz3g0XF+9elXlypXT66+/rrp166pKlSoaNmyYzp07pyNHjigtLU1Hjx5V7dq1VbJkSeuf4sWLS5ISExM1Z84cjRgxQm3btlWNGjX0zjvv6MyZM/rhhx882TSfsOfoFe05esXTZQAAAACAz/NouC5atKimTJmi6tWrS5IuXbqkefPmqUyZMqpatapOnjypGzduqHLlynaPP3jwoGJjY9WiRQvrYxEREapVq5Z27NiRL23wZfWqFlO9qsU8XQYAAAAA+DyzpwuwePnll7V06VIFBQVp2rRpCgsL0+HDhyVJCxcu1MaNGxUQEKDWrVtr5MiRCg8P15kzZyRJZcuWtTlXqVKlrNucZTa77nuHwMAAm/8WVLSzYKGdBY+/tJV2AgAAT/CacD1gwAA9/PDD+vTTT/XUU09p8eLFOnz4sAICAlSqVCl98sknOnnypN5++20dOXJE8+fPt87LDgoKsjlXcHCwrl696nQtAQEmRUYWNtQeeyIiQl1+Tm9EOwsW2lnw+EtbaScAAMhPXhOuq1atKkmaOHGi9uzZo0WLFmnixInq3bu3IiMjJUnVq1dXyZIl9dBDD2nv3r0KCQmRlD732vJ3SUpISFBoqPMXG6mpaYqJuWGgNbYCAwMUERGqmJg4paSkuuy83oZ2Fiy0s+Dxl7bSTue544tlAAD8hUfD9aVLl7R161Z16dJFZnN6KQEBAapatarOnTungIAAa7C2qFatmiTpzJkz1uHg586dU4UKFaz7nDt3TlFRUYZqS052/QVZSkqqW87rbWhnwUI7Cx5/aSvtBAAA+cmjE7UuXLigZ555Rlu3brU+lpSUpP3796tKlSp67rnnNHDgQJtj9u7dKym9p7tGjRoqUqSItm/fbt0eExOj/fv3q0mTJvnSBgAAAAAAPBquq1evrtatW+v111/Xjh07dPjwYb3wwguKiYnRwIED1aVLF23dulUffvihTp48qQ0bNujFF19U9+7dVaVKFQUFBalv376aPHmy1qxZo4MHD2rkyJEqU6aMOnfu7MmmAQAAAAD8iMfnXE+dOlVTpkzRyJEjde3aNTVu3Fiffvqpbr75Zt1888169913NWPGDM2cOVPh4eG6++679fTTT1uPHzFihJKTkzV27FjFx8erSZMmmj17tgoVKuS5RgEAAAAA/IrHw3V4eLjGjRuncePG2d3etWtXde3aNdvjAwMDNXr0aI0ePdpNFQIAAAAAkDNujgkAAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAII+H64sXL2r06NFq3ry5GjRooMcff1zHjh2zbj9w4ID69u2r+vXrq3379lqwYIHN8ampqXr//ffVqlUr1a9fX0OGDNGpU6fyuxkAAAAAAD/m8XD91FNP6cSJE5oxY4aWLVumkJAQDRw4UHFxcbp8+bIGDRqkChUq6IsvvtBTTz2lyZMn64svvrAe//HHH2vx4sWaMGGClixZotTUVA0ePFiJiYkebBUAAAAAwJ+YPfnkV69eVbly5fTEE0+oevXqkqRhw4bp3nvv1ZEjR7R161YVKlRI48ePl9lsVpUqVaxB/P7771diYqLmzJmjUaNGqW3btpKkd955R61atdIPP/yg7t27e7B1AAAAAAB/4dGe66JFi2rKlCnWYH3p0iXNmzdPZcqUUdWqVbVz5041bdpUZvM/3wE0b95cf/zxhy5cuKCDBw8qNjZWLVq0sG6PiIhQrVq1tGPHjnxvDwAAAADAP3m05zqjl19+WUuXLlVQUJCmTZumsLAwnTlzxhq8LUqVKiVJ+vvvv3XmzBlJUtmyZbPsY9kGAAAAAIC7eU24HjBggB5++GF9+umneuqpp7R48WLFx8crKCjIZr/g4GBJUkJCguLi4iTJ7j5Xr141VI/Z7LpO/cDAAJv/FlS0s2ChnQWPv7SVdgIAAE/wmnBdtWpVSdLEiRO1Z88eLVq0SCEhIVkWJktISJAkhYWFKSQkRJKUmJho/btln9DQUKdrCQgwKTKysNPHZyciwvmafAntLFhoZ8HjL22lnb5t2oq9evK+Op4uAwAAh3k0XF+6dElbt25Vly5drPOqAwICVLVqVZ07d05lypTRuXPnbI6x/Lt06dJKTk62PlahQgWbfaKiopyuKzU1TTExN5w+PrPAwABFRIQqJiZOKSmpLjuvt6GdBQvtLHj8pa2003nu+GLZWUnJBfd3BwAomDwari9cuKBnnnlGs2bNUqtWrSRJSUlJ2r9/v9q3b6+bbrpJS5YsUUpKigIDAyVJ27ZtU6VKlVSiRAmFh4erSJEi2r59uzVcx8TEaP/+/erbt6+h2pLd8D/1lJRUt5zX29DOgoV2Fjz+0lbaCQAA8pNHJ2pVr15drVu31uuvv64dO3bo8OHDeuGFFxQTE6OBAwfq/vvv1/Xr1/XSSy/p6NGj+vLLLzVv3jw98cQTktLnWvft21eTJ0/WmjVrdPDgQY0cOVJlypRR586dPdk0AAAAAIAf8fic66lTp2rKlCkaOXKkrl27psaNG+vTTz/VzTffLEmaNWuWJk6cqB49eqhkyZJ67rnn1KNHD+vxI0aMUHJyssaOHav4+Hg1adJEs2fPVqFChTzVJAAAAACAn/F4uA4PD9e4ceM0btw4u9vr1q2rzz//PNvjAwMDNXr0aI0ePdpNFQIAgPw0+9t9ni4BAIA84/4dAADAqzCHHADgiwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcA0AAAAAgEGEawAAAAAADCJcAwAAAABgEOEaAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgwjXAAAAAAAYRLgGAAAAAMAgwjUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AADwOuZAk6at2OvpMgAAcBjhugDYc/SK9hy94ukyAABwqaTkVE+XAACAw8yeLgDG1atazNMlAAAAAIBfo+caAAAAAACDCNcAAAAAABhEuAYAAAAAwCDCNQAAAAAABhGuAQAAAAAwiHANAAAAAIBBhGsAAAAAAAwiXAMAAAAAYBDhGgAAAAAAgzwerq9cuaJXXnlFrVu3VsOGDdWrVy/t3LnTun3QoEGKioqy+dOvXz/r9oSEBL322mtq0aKFGjRooGeffVaXLl3yRFMAAAAAAH7K7OkCnnnmGZ0/f15Tp05ViRIltHDhQj322GNavny5KleurEOHDmncuHHq2LGj9ZhChQpZ/z5u3Djt3LlTH3zwgYKCgvTqq69qxIgRWrRokSeaAwAAAADwQx4N1ydOnNCWLVu0ePFiNWrUSJL08ssva9OmTfrmm2/Ut29fXbx4UfXq1VPJkiWzHH/27FmtWLFCn3zyiRo3bixJmjp1qu68807t3r1bDRo0yNf2AAAAAAD8k0eHhUdGRmrGjBmqU6eO9TGTySSTyaSYmBgdOnRIJpNJlSpVsnv8zz//LElq3ry59bFKlSqpdOnS2rFjh3uLBwAAAADgfzzacx0REaE2bdrYPPb999/rxIkTevHFF3X48GGFh4dr/Pjx2rJli8LCwnTnnXdq2LBhCgoK0tmzZxUZGang4GCbc5QqVUpnzpwxVJvZ7LrvHQIDA2z+W1DRzoKFdhY8/tJW2gkAADzB43OuM9q1a5fGjBmjzp07q23btnrxxReVkJCgunXratCgQTpw4IDefvtt/fXXX3r77bcVFxenoKCgLOcJDg5WQkKC03UEBJgUGVnYSFPsiogIdfk5vZE3tjN66yl1bFHepef0xna6A+0sePylrbTT95kDTZ4uAQAAh3lNuI6OjtaoUaPUsGFDTZ48WZI0fvx4Pf/88ypatKgkqXr16ipUqJBGjhyp5557TiEhIUpMTMxyroSEBIWGOn+xkZqappiYG04fn1lgYIAiIkIVExOnlJRUl53X23hzOxvVKK7Ll2Ndci5vbqcr0c6Cx1/aSjud544vlgEA8BdeEa4XLVqkiRMn6s4779Rbb71l7Y02m83WYG1RrVo1SdKZM2dUpkwZXblyRYmJiTY92OfOnVPp0qUN1ZSc7PoLspSUVLec19vQzoKFdhY8/tJW2gkAAPKTxydqLV68WBMmTFCfPn00depUm5Dcr18/jRkzxmb/vXv3qlChQqpYsaIaNWqk1NRU68JmknT8+HGdPXtWTZo0ybc2AAAAAAD8m0d7ro8fP65JkyapU6dOeuKJJ3ThwgXrtpCQEHXp0kWTJk1S3bp11bJlS+3du1dvv/22HnvsMRUpUkRFihTRXXfdpbFjx2rSpEkKDQ3Vq6++qqZNm6p+/fqeaxgAAAAAwK94NFx///33SkpK0urVq7V69WqbbT169NCbb74pk8mkhQsXatKkSSpZsqQGDhyoxx9/3LrfhAkTNGnSJA0fPlyS1Lp1a40dOzZf2wEAAAAA8G8eDddDhw7V0KFDc9ynT58+6tOnT7bbw8LC9Prrr+v11193dXkAAAAAADjE43OuAQAAAADwdYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcO3j9hy94ukSAAAAAMDvEa59XL2qxTxdAgAAAAD4PcI1AAAAAAAGEa4BAAAAADCIcA0AALxSYKBJ01bs9XQZAAA4hHAN7Tl6hYXRAABeKSk51dMlAADgELOnC4DnsSgaAAAAABhDzzUAAAAAAAYRrgEAAAAAMIhwDQAAAACAQYRrAAAAAAAMIlwDAAAAAGAQ4RoAAAAAAIMI1wAAAAAAGES4hvYcvaI9R694ugwAAAAA8FlmTxcAz6tXtZinSwAAAAAAn0bPNQAAAAAABhGuAQAAAAAwiHDt5ZgLDQAAAADej3Dt5ZgP7Tos3AYAAADAXVjQDH6DLyoAAAAAuAs91wAAAAAAGES4BgAAAADAIMI1AAAAAAAGEa4BAAAAADCIcI0Ci5XBAQAAAOQXl4brY8eOqX///q48JeA0VgcHAAAAkF9cGq4TExP1559/uvKUAAAAAAB4PZeG65o1a2rt2rWuPCUAAAAAAF4vz+F6x44dio2NtbstJiZGq1atMlwUAAAAAAC+JM/hun///jp27Jjdbfv379eYMWMMFwW4GoubAQAAAHAnsyM7Pf/88/r7778lSWlpaRo3bpyKFCmSZb8//vhDN910k2srBFyAxc0AAAAAuJNDPdddunRRWlqa0tLSrI9Z/m35ExAQoPr16+uNN95wW7EAAAAAAHgjh3qu27dvr/bt20uS+vXrp3HjxqlKlSpuLQwAAAAAAF/hULjOaOHChVkeu3TpkooXL+6SggAAAAAA8DUOL2h26tQpTZgwQWvWrLE+Fh0drZYtW+qOO+5Qq1at9O2337qlSAAAAAAAvJlDPdenTp3Sgw8+qISEBNWqVUuSdPz4cT399NMqXry4XnjhBf3+++8aNWqUSpUqpcaNG7u1aAAAAAAAvIlD4fqTTz5R8eLFNX/+fJUsWVKSNHfuXKWkpGjy5Mlq2rSpJCkxMVEzZ84kXAMAAAAA/IpDw8J//PFHPfbYY9ZgLUkbN25UqVKlrMFakjp37qw9e/a4vkoAAAAAALyYQ+H6woULqlChgvXfp06d0pkzZ9SsWTOb/cLDwxUbG5unAq5cuaJXXnlFrVu3VsOGDdWrVy/t3LnTun3r1q3q2bOn6tWrpzvvvFOrVq2yOT4hIUGvvfaaWrRooQYNGujZZ5/VpUuX8lQDAAAAAABGOBSuCxcurJiYGOu/f/rpJ5lMJjVv3txmv1OnTqlYsWJ5KuCZZ57R7t27NXXqVH3xxReqWbOmHnvsMf3+++86duyYnnjiCbVq1UpffvmlHnzwQT333HPaunWr9fhx48Zp8+bN+uCDDzR//nz9/vvvGjFiRJ5qAAAAAADACIfmXNevX1/ffvutOnToIEn66quvFBgYqDZt2lj3SUtL09KlS1W3bl2Hn/zEiRPasmWLFi9erEaNGkmSXn75ZW3atEnffPONLl68qKioKI0cOVKSVKVKFe3fv1+zZs1SixYtdPbsWa1YsUKffPKJdZ731KlTdeedd2r37t1q0KCBw7UAAAAAAOAsh8L1kCFDNGDAAJ05c0apqanavXu3Hn74YZUoUUJS+tDt+fPn65dfftHcuXMdfvLIyEjNmDFDderUsT5mMplkMpkUExOjnTt3qmPHjjbHNG/eXBMnTlRaWpp+/vln62MWlSpVUunSpbVjxw7CNQAAAAAgXzgUrhs1aqSZM2dq+vTpunDhggYPHqz/+7//s24fNWqUbty4oXHjxmUZKp6TiIgIm95vSfr+++914sQJvfjii1q+fLnKlCljs71UqVKKi4vT5cuXdfbsWUVGRio4ODjLPmfOnHG4DnvMZodvAZ6rwMAAm/8WVLSzYKGdBY+/tJV2AgAAT3AoXEtSixYt1KJFC7vbpk2bpooVKyoiIsJQMbt27dKYMWPUuXNntW3bVvHx8QoKCrLZx/LvxMRExcXFZdkuScHBwUpISHC6joAAkyIjCzt9fHYiIkJdfk5vRDsLFtpZ8PhLW2knAADITw6H65zkZZ51dqKjozVq1Cg1bNhQkydPlpQekhMTE232s/w7NDRUISEhWbZL6SuIh4Y6f7GRmpqmmJgbTh+fWWBggCIiQhUTE6eUlFSXndfbeEs7fz6Yvlp8oxrF3XJ+b2mnu9HOgsdf2ko7neeOL5aNMgeaPF0CAAAOcUm4NmrRokWaOHGi7rzzTr311lvW3uiyZcvq3LlzNvueO3dOYWFhCg8PV5kyZXTlyhUlJiba9GCfO3dOpUuXNlRTcrLrL8hSUlLdcl5v4+l21qtaTJJ7focZebqd+YV2Fjz+0lbaCQAA8pPHJ2otXrxYEyZMUJ8+fTR16lSbkNy4cWP99NNPNvtv27ZNDRs2VEBAgBo1aqTU1FTrwmaSdPz4cZ09e1ZNmjTJtzYAAAAAAPybR8P18ePHNWnSJHXq1ElPPPGELly4oPPnz+v8+fO6du2a+vXrp19//VWTJ0/WsWPHNGfOHH333XcaPHiwJKl06dK66667NHbsWG3fvl2//vqrnnnmGTVt2lT169f3ZNMAAAAAAH7Eo8PCv//+eyUlJWn16tVavXq1zbYePXrozTff1Mcff6x///vfmj9/vm655Rb9+9//tllYbcKECZo0aZKGDx8uSWrdurXGjh2br+0AAAAAAPg3h8L1X3/9pZIlS6pQoUL666+/ct3/5ptvdujJhw4dqqFDh+a4T+vWrdW6detst4eFhen111/X66+/7tBzAgAAAADgag6F6w4dOujzzz9X3bp11b59e5lMOa/ceeDAAZcUBwAAAACAL3AoXE+aNEnly5e3/j23cA14iz1Hr0j6ZwVxAAAAAHAHh8J1jx49rH/v2bOn24oBXI1QDQC+LTDQpGkr9urJ++p4uhQAAHLkULhesWJFnk563333OVEKAABAVkncxxsA4AMcCtcvvPCCwyc0mUyE6wKCIdUAAAAA4BiHwvWaNWvcXQe8EKEaAAAAABzjULguV66c3cePHTummJgYlShRQhUqVHBpYXA/eqYBAAAAwDUcCteZLVy4UNOnT9fFixetj5UtW1bPPPOMunfv7rLi4F4FMVTvOXqlQLYLAAAAgHfLc7hetGiRJk6cqI4dO6pTp04qUaKELly4oJUrV2r06NEKDAxU165d3VErkCuCNQAAAABPyHO4XrBggfr27auxY8faPH7ffffppZde0ocffki4diF6YgEAAADA+wXk9YAzZ86offv2drd1795dp06dMlwU/kGwBgAAAADvl+dwXadOHW3dutXutv379ysqKspwUQAAAAAA+BKHhoXv2LHD+ve77rpLb7zxhuLi4tS1a1eVLFlSV65c0YYNG7Rw4UK9/vrrbisWAAAAAABv5FC47tevn0wmk/XfaWlpWrRokT799FObxyTp//7v/3TgwAEXlwkAAAAAgPdyKFwvWLDA3XUAAAAAAOCzHArXTZs2dXcdAAAAAAD4rDzfikuSjh8/rvfff18//fSTYmJiFBkZqcaNG+upp55SlSpVXF0jAAAAAABeLc/h+ujRo3rkkUcUGBio9u3b66abbtL58+e1bt06rV+/Xv/5z38I2PCoPUevSOI2ZgAAAADyT57D9eTJk3XLLbdo4cKFCg8Ptz5+7do1DRgwQO+8844+/PBDlxbpz/YcvUJIzCN+XgAAAADyW57vc71jxw4NHTrUJlhLUnh4uB5//HGb23bBOIIiAMDfmQNNmrZir6fLAAAgR3kO12azWcHBwXa3BQUFKTEx0XBRAAAAGSUlp3q6BAAAcpTncF2nTh0tXrzYel9ri7S0NH366aeqXbu2y4pD/thz9Ip1njIAAAAAIO/yPOf6//7v/9SrVy/dc889uvPOO1WyZEmdP39e3333nY4fP665c+e6o064QHbzt50Zes5ccAAAAAD4R57DdZ06dTRr1ixNmTJFH374odLS0mQymVS7dm3NnDlTTZo0cUedcAFXhmGCNQAAAAD8I8/hevny5br99tv1n//8R3FxcYqJiVFERIRCQ0PdUR8AAAAAAF4vz3Oux48fr19//VWSFBoaqtKlSxOs3Yi50AAAAADg/fIcrsuUKaPr16+7oxbYkd/Dr1ncDAAAAADyLs/Dwh9++GFNnDhRu3fvVlRUlAoXLpxln/vuu88VtcEDmEsNAAAAAHmX53D95ptvSpKWLl1qd7vJZCJc+7HorackEdIBAAAA+Jc8h+s1a9a4ow7kM3fdSqtji/K6fDlWycmpLj+3I7hFGAAAAABPyHO4LleunDvqQDbcFRbzck7LHGxfCK2+UCMAAACAgidP4frXX3/VjRs31Lx5c6WmpmrgwIE227t27apevXq5sj6/5w1h0RtqAAAAAABv5vBq4RMnTtTDDz+sRYsWSZLS0tL0008/6fr160pLS9PZs2f19ttv69y5c24rFq6V28rgrBwOAAAAAI5xqOd61apVWrRokZ577jn179/fZtuECRN022236erVq+rcubOWLl2q4cOHu6VYuFZuPdL0WAMAAACAYxzquf7Pf/6je++9V4MGDVJgYKDdfYoWLaqePXtqw4YNLi0Q3s8Xerd9oUYAAAAAvsuhcH3gwAF17tw51/2aNWum48ePGy4K//CFUOgLPdy+UCMAAAAA3+VQuI6Pj1dERITNY4GBgZo7d64qVapkfSwsLEwpKSmurdDPEQoBAAAAwPs5FK5LliypP//8M8vjLVq0UFhYmPXfx48fV+nSpV1XHQAAAAAAPsChcN2kSROtWLEi1/2WL1+uli1bGq0JAAAAAACf4lC47tWrl3bs2KFJkyYpMTExy/a0tDS9+eab2r9/P/e5BgAAAAD4HYduxVW3bl2NHj1ab731lqKjo9WpUyfdeuutkqTTp08rOjpap06d0tixY1WlShW3Fgz3yriAmr353pbtGbftOXqFueEAAAAA/JpD4VqSBg4cqJo1a+qjjz7SwoULlZqaat1Wv359vfzyywwJLwCcufc1wRoAAACAv3M4XEvpt9pq1qyZYmJidPr0aaWmpqps2bIqXry4u+qDHfQUAwAAAIB3yVO4toiIiMhyay7kH4I1AAAAAHgXhxY0g+dknAMNAAAAAPBOhGsvRy81AACSOdDk6RIAAMiRV4Xr6dOnq1+/fjaPjR07VlFRUTZ/2rdvb92empqq999/X61atVL9+vU1ZMgQnTp1Kr9LBwAAAAD4Ma8J159++qnefffdLI8fOnRIQ4cO1ebNm61/li1bZt3+8ccfa/HixZowYYKWLFmi1NRUDR482O79uOF/9hy9wtB6AAAAAG7n1IJmknTx4kUlJiYqLS1NUnoPclxcnHbu3KlevXo5fJ6zZ8/q1Vdf1fbt21WxYkWbbWlpaTp69Kgef/xxlSxZMsuxiYmJmjNnjkaNGqW2bdtKkt555x21atVKP/zwg7p37+5s87ySvXtMI2f8rAAAAADkhzyH64MHD2rUqFE6duyY3e0mkylP4Xrfvn0qVKiQvv76a3300Uc6ffq0ddvJkyd148YNVa5cOdtaYmNj1aJFC+tjERERqlWrlnbs2FHgwrU3BEUCPgAAAABkledw/fbbb+vq1at6/vnntW7dOgUFBaldu3bauHGjNm7cqAULFuTpfO3bt7eZQ53R4cOHJUkLFy7Uxo0bFRAQoNatW2vkyJEKDw/XmTNnJElly5a1Oa5UqVLWbc4ym103Yj4wMMDmv76sUY3s72lekNqZE9pZsPhLOyX/aSvtBAAAnpDncL1nzx6NGTNGDzzwgEJDQ/XNN9+od+/e6t27t0aMGKGFCxeqcePGLinu8OHDCggIUKlSpfTJJ5/o5MmTevvtt3XkyBHNnz9fcXFxkqSgoCCb44KDg3X16lWnnzcgwKTIyMKGarcnIiLU5ef0RrSzYKGdBY+/tJV2AgCA/JTncJ2YmGidG12xYkUdPHjQuq1nz5569dVXXVbck08+qd69eysyMlKSVL16dZUsWVIPPfSQ9u7dq5CQEGtNlr9LUkJCgkJDnb/YSE1NU0zMDWPFZxAYGKCIiFDFxMQpJSXVZef1NrSzYKGdBY+/tJV2Os8dXywDAOAv8hyub775Zp06dUqNGzdWxYoVdf36df3555+65ZZbFBQUZKjHOLOAgABrsLaoVq2aJOnMmTPW4eDnzp1ThQoVrPucO3dOUVFRhp47Odn1F2QpKaluOa+3oZ0FC+0sePylrbQTAADkpzxP1OrcubOmTJmi77//XqVLl1blypX17rvv6tChQ5ozZ47Kly/vsuKee+45DRw40OaxvXv3SpKqVq2qGjVqqEiRItq+fbt1e0xMjPbv368mTZq4rA4AAAAAAHKS53A9fPhwNWzY0Hqv6TFjxmj16tW67777tG3bNv3rX/9yWXFdunTR1q1b9eGHH+rkyZPasGGDXnzxRXXv3l1VqlRRUFCQ+vbtq8mTJ2vNmjU6ePCgRo4cqTJlyqhz584uqwMAAAAAgJzkeVh4cHCw3n//fSUlJUmSWrVqpZUrV+q3337TbbfdZjM826gOHTro3Xff1YwZMzRz5kyFh4fr7rvv1tNPP23dZ8SIEUpOTtbYsWMVHx+vJk2aaPbs2SpUqJDL6oDjoreeksStugAAAAD4lzyHa4uM4bV8+fIuGQ7+5ptvZnmsa9eu6tq1a7bHBAYGavTo0Ro9erTh54dxHVuU1+XLscz/AwAAAOBXHArXY8aMcfiEJpNJkyZNcrogAAAAAAB8jUPhOuOCYdm5fPmy4uLiCNcutufoFYZYAwAAAICXcyhcr127NtttycnJ+vjjjzVjxgzddNNNGjdunKtqg5i7DAAAAAC+wOk515J04MABjRkzRocOHdJdd92ll19+WUWLFnVVbQAAAAAA+ASnwnVycrI++ugjzZw5U8WKFdOHH36oDh06uLo2AACQyZ6jV6x/79iisOcKAQAANvIcrvfv32/trb7nnns0duxYRUREuKM2AACQiWW6kNkc4NlC8llgoEnTVuzVk/fV8XQpAADY5XC4Tk5O1ocffqhZs2YpMjJS06ZNU7t27dxZG3Jg6bkwMifbFecAALgPi1raSuI2jwAAL+ZQuN63b59eeOEFHT16VPfdd59efPFFhYeHu7s25MAVF1vOnMNXAzkXqAC8UW6fqfYe5/MMAADv5FC4fuihh5Samqrw8HCdPn1aTz31VLb7mkwmzZ8/32UFwj2cvTjz1Qs6X60bQMHmT5/DAAAUdA6F64YNG1r/npaWluO+uW1H3jgbgjMueGPveC7OAAAAAMB1HArXCxcudHcdyEZeQnDGIO7Icb46xBsAAAAAvI1/LTXqgzL2QOcmryG5XtViBGsAAAAAcAHCtZfz1fCbly8FAAD/4PMTAADfRLiGW/jqlwIA4Gl8fmbP/L97XQMA4I0I1wAAwGdwr2sAgLciXPuZPUevMOQQANyIz1kAAPyTQ6uFo+CwN9zQHauGO3sLMQDwdXz2AQDgn+i59lIZez7c0QuS+T7Yrr4Y5OISQEFALzQAAHAUPddeKmM4zRxUXdErTPgFAPsyjubhsxIAADiKnmsvZ6/HxJ0Xe67spaHHB4Avyi5U85kGAAByQs+1l3LHPGhH5PZ8eamLHh8ABYmRzzTWoQAAoOCj59pLeetwRG+tKzf0NgG+paC9Z33xcxMAAOQN4dqP+dMQRy5sAd/ibe9Zf/msBAAAziNc+7i8XPBl3jenXmguJAH4K3tfPHpb2AcAAN6HOdc+xN5857xc8LlrXwAoSPj8AwAAzqDn2odk7Gl2xZBufxoW7i/tBAAAAOAZhGsf5YqFxXx1cTJn+Es7AX9n74s0vlwDAAD5gWHhfirjxSbBE4A3cuaWhPb25TMOAADkB8K1n+JiE4C3s/c5lfF+0c6EbwAAAHdhWLgPYogjAH+VW5Dm8xEAAHgK4doHeVsvDRezQMHjrQseZp7Skvnz0Ns+HwEAgP8gXPsRd10oczELFDz5ueBhXj6b+LyBOdDk6RIAALCLcO1H8jKc0ht7rAD4tuw+VzJ+NnlrjzkAAEBuWNDMS2VcqCfzhaa7em4ynpfeIQCu5sjnCp89AADAVxGuvVR+BF1W2gXgbfhcAgAAvoph4V7O24ZHMmQTAAAAALIiXPsAdwXanBYsysvzEbYBuEp+LqQGAADgSgwL9wHuvtC0N787u+fkoheAN2I4OQAA8DTCtZfKLfC68kLS0XPsOXrF7r5czAJ5421B0BP1ZPd54ixv+VkCAAD/Rbj2UrktaJYfK4bntM3VF8aAP/G2905+jo7Jr+cEAADIb8y59kHeMMeZC2MAmeV0H2s+MwAAQEFHuPZBXKQC8EZ8NgEAAH9GuPZSnrrllbPPu+foFf188JLrCwIKEG5lBwAAUHARrr2Uo8MoXX2h7uzwzXpVi6lRjeIurQXwBkYCcebjCsLwaL4ggKcFBpo0bcVeT5cBAEAWLGjm5XJbOCy/L9S9bZVjwBFGFuAz8lo3cqy9YJ4f3HE7Pj434GpJyameLgEAgCwI114ut4vR/Fi1O+OFMRfH8EW++Lr1VM32njencOzIZ5Av/vwBAADyinDt4/LjopULY8B9vLVXN7cv1by1bgAAAE/xqjnX06dPV79+/WweO3DggPr27av69eurffv2WrBggc321NRUvf/++2rVqpXq16+vIUOG6NSpU/lZtltY5jXmdW4j8yEB3+KtI0Ls1ZXxs8Vb6wYAAPAUrwnXn376qd59912bxy5fvqxBgwapQoUK+uKLL/TUU09p8uTJ+uKLL6z7fPzxx1q8eLEmTJigJUuWKDU1VYMHD1ZiYmI+t8C1LBeueb14ze2YvIRvgjrgf3J63xOmAQAAsufxYeFnz57Vq6++qu3bt6tixYo225YuXapChQpp/PjxMpvNqlKlik6cOKEZM2bo/vvvV2JioubMmaNRo0apbdu2kqR33nlHrVq10g8//KDu3bvnf4PcILfhl3nZnpeLYy6kgX946zBoZ+rKeNu8zMd5W/sAAAB8hcd7rvft26dChQrp66+/Vr169Wy27dy5U02bNpXZ/M93AM2bN9cff/yhCxcu6ODBg4qNjVWLFi2s2yMiIlSrVi3t2LEj39rgDhl7jzKH4rze3ifjdnqjAefkNkzaUyx1Za4lp/d6oxrFGdYNAADgYh7vuW7fvr3at29vd9uZM2dUvXp1m8dKlSolSfr777915swZSVLZsmWz7GPZ5iyz2XXfOwQGBtj81xGWe0Zn7GGyPGbkftJ5Odby3PaO+fngpSyPO9NOX0Q7CxYj7XTlvd3tvaeM1JLx35b3ctPbbpLE77Sg8Jd2AgDgKzwernMSHx+voKAgm8eCg4MlSQkJCYqLi5Mku/tcvXrV6ecNCDApMrKw08dnJyIi1OF9o7emL8rWsUV5l9fhqI4tsv4MHKkrL+30ZbSzYMmPdtp7/1gek+Syz53oradsnsPyXnbX54o3fF7Zw2sXAADkJ68O1yEhIVkWJktISJAkhYWFKSQkRJKUmJho/btln9BQ5y82UlPTFBNzw+njMwsMDFBERKhiYuKUkpLq0DGWXqfLl2NdVkdGzvaS5VSXM+10t5x6353lje10B9rpevbePxlfm656vzeqUdzuuZredpNb2uruz6u84rXrPHd8sewO5kCTpq3Yqyfvq+PpUgAAsPLqcF2mTBmdO3fO5jHLv0uXLq3k5GTrYxUqVLDZJyoqytBzJye7/oIsJSXVLee12HP0Sq5zKDPO43ZXLe5uZ15Yfh6++Pv0FrQz/2R8fzq6UKFFXuZPe0Nb8wPtLNiS/LDNAADv5tXhukmTJlqyZIlSUlIUGBgoSdq2bZsqVaqkEiVKKDw8XEWKFNH27dut4TomJkb79+9X3759PVl6vnJ0teCM4duRIA7AOc6uLJ5xf3vHOrvyPwAAANzPq1dBuf/++3X9+nW99NJLOnr0qL788kvNmzdPTzzxhKT0udZ9+/bV5MmTtWbNGh08eFAjR45UmTJl1LlzZw9X7xoZe6eyW/3X0Qvt3C7cs3teoKDLOO85N/n93sjpzgGuOm9ejgEAAIB9Xt1zXaJECc2aNUsTJ05Ujx49VLJkST333HPq0aOHdZ8RI0YoOTlZY8eOVXx8vJo0aaLZs2erUKFCHqzcdewFYlf0Oud2Dlf2itFLDm/XsUV5h+cL5/WLrLyw1+Odl/d9XnrMnamR9zEAAED2vCpcv/nmm1keq1u3rj7//PNsjwkMDNTo0aM1evRod5bmVVxxgZuXIeTufi7AHl/4UsZojZmPN/KFF73KAAAAnuXVw8L9WU5DNh25iHZmyGdGjgQGo88B5MRTwTovr+mMNVreDzkdn5f3TF7fX5Yh45l/brxHUVCZA02eLgEAABte1XMNx2S+oLcXQvIjmHh7ryLgjLy8rjMG1+wWILM3rNuRwOvMEPCchpUDAADAvQjXPiDzBXPGi3QunAHPyS1Q53ZLrex6mZ2dM83nAQAAgOcQrr1UTj1P7ryAzq0nLuN+XMjD39nrjc5uZIm9L8Vc/T5y9hZgAAAAMI5w7QPycsGc21DR3M7l6EV5dvtZbmnExT18maPvuey2Zzc8O/Pj+fnFGQAAANyLcO0DcuoZy8iR+dfOXLznJdxbbmmUnJyap/MTKmCUp3pts7v3fObt2S025orb6mV3fgAAAOQfwrUPcbYXzR3Py626UJA5+pq09z7IPLUiL7fXymlaRnbbeP8AAAB4B8K1l3N0DrSRcztzXkdWLAfcLS+9ts683nPrlXbkvEYXKXNkGwAAADyPcO3lcrtgz2kfZ8+d3XM5+zyEb9hj9HWR+fic1htw9nmdCcwZ51a7AouUAQAA+AbCtQ/J73vYOhoQPDVcPS8I+N7H6O8jL4uBuXpF7txW+3blezS7+2TzegYAAPAuhGsf4upQndP9s7N7Dl/tRfO1egua/HjdGPnyKXrrKTWqUdx6npx6xI32tmeUl3MxzxoAAMC7Ea4LGCPzOx2Zs+rM4mb0siE/fv+5vQZz2s+yyr297a6snfcJAABAwUW49lLO9vQ5etuu7J4zt6G1zvQO5kdg8NUedX/njt+bM6/RjD3XrjwvAAAA/Afh2ku5ej6qo8fktCiUN99H11vr8hfu+DInL8cbXUCsY4vyit56yvrvvKw87mjbc+s9d8Vq4wAAAPAcwrWPc+VQ0uwWTOKiHrkxeju37OT2ZY/RGjJqVKO4kpNTHdrXXu+1s+E5L89F0AYAAPBehGsf5Y6LbGeHuzJXFO7iaEh19t7VeVlDwBumP/A+A/4RGGjStBV79eR9dTxdCgAAkgjXfssVgTi3gE8vGyxcca/0nI535PWc0znyMiTcWXwJBbhekoOjTQAAyA+Eax/lijnZeZ0rmtdhuAQJuFput8ly9LjMx1tWC3d0WLgzeD8AAAAUbIRrL+ULvcI51ZDd/G34J3f1WNt7jWW3qnd2j5vNAU7V5gxveN8CAADAPQjXPiDzSsjZrdqd1xDr6L65BZvctnkqSPhDqPe1NuY23znjPq5Ynd7eKuL2fmbO3oorr3zpdwUAAIC8IVx7qbyE09xuQ+RIoCmI/KGtvtZGR+p15pZW2b1fchoKnvnv9vZxxZdYAAAA8A+Eax+Xl+Ccl3nWjjyH0fmuKBhcuTielPNrLHMYzi0E2ztnRpZh4ZGRhXX5cmyO+/MaBgAAQE4I117KXo+asxf8zvSCu3JYt7OhHt7H6OsjrwuS5TSP2t4868zH5jaqA4BvM3M7LgCAFyFceylHFwNzda+w0YWnOrYo7NLzwv3y8hpyxRctOf07u/CdeZ/Mx+a0YBmAgo3bcQEAvAXh2su5Ohy4cv61J1ddhuu4877OOZ0/t6HgOR1rb7+cerkzYhoDAAAA3IFw7ePs9fzZezzz/vYWdXJnoGBYuH/IGEwdWQwsp57sPUev5DqsO7epEs687vL6ngIAAAAkwrVPcHT+tbPDe/MjNBBM/IMjvdQZw2p2c6nzel5ne6kdwWsXAAAAjiBc+4DcVkLOab+8opcOUvrrIKf7Pjt7T/Xseq6z66HmdQgAAABfQbj2ctmFmOx6+oyGY8KMb3DllyD2XmP1qhbTzwcvSbpkN2S7YoVwe+dzJLRnbDuvVwAAAHgLwrWXy+v8UUfDhrt6vpE/3LVCfMbXWKMaxbPc/9mRYdz2FhZzdvg3K4ADAADAVxCuvVx2PXnO9FzmttgU/JeR+0HnFpgz7+PMuZ3tqWeaA1DwmQNNni4BAABJhGuvl12PXXbzVnMKIwQMZJTb8OqMw8Izh+acQnROvdTZbc8N0xwAAADg7QjXBQhB2n+4okc2txERlmHh0VtP2V14LKfbuuW0r5GauQc1UPDN+OY3T5cAAIBTCNdeytHwlLm3OreVxd0RTAg8+c/VP297r7eMPdfpfzfOyEJoeT0egG9KTk5VIEO9AQA+iHDtpXLrVXQ2fLsDgSdnRnuZXXV8dufI7jWSsec68/F7jl5xen61o+3hdQUAAABfQrj2IbkN+7YXurM7xt96mz3ZXmcW5XL0dlOOtCu7BcccWYjM0fPmx3EAAACANyNcFwA59Uxmt83fAo43tDc/FvByRsYvZXKT25xqAAAAwF8RrguA3IZ957QYFRzjzp7v3FbXzklea8ptTn5Gjs655rUEAAAAEK4LlNyG+bp6WLg/hXV3ti+7If55ldffqb05/PZkF6ydfS4AAACgICJc+6jswkp+LhZFUMpfuYXnnKYE2Nue3X45cXZxvPx4rRDgAQAA4EkBni4ArmMv8GQXgty1cnj01lMuu22Tv7C38ra9x5wZAp7daAZX/P5zWjE8P9j7+RCsAf81bcVeT5cAAPBz9Fx7uex6K/NyuyN7PXp5uZ1XXnRsUV6XL8cqOTnVZef0hPzsBbV3n3JHnzfz68PeUH1HpgvkFpJzm7fvzG3ijMrvIE3POOCdAgNNmvHNb0ry8f/vAAB8H+HaB+UUuHOba+3I47A/xNoTYTHzPahzu62XvRCcW3DObnvG+1zn1ovu6Er0vnwLOF+tG/AHvv6FLgCgYGBYuJfL3DuY3dBvR++L7Os8NQzZyM808+/N2aH6mQNsbrddy/y87l7t3BE5zfv25BBzAAAAwCh6rr1UXkJRTrdwcmVPoTf0OrozILryZ5bTlx259exm3sfRVb0z7uvINkfb2KhG8Rx7hVgcDwAAACBce62c5lln7qm2sBcKnZ0Dm925CpLs5jjnFmwb1Sjutppy+73b25aXHm+LzK8he1/KuLOdAAAAQEHjE+H67Nmzat26dZbH33jjDfXs2VMHDhzQxIkT9dtvv6l48eIaOHCg+vfv74FK809ui1Rlt68z588PnlgsypHncnQucXbszZPOfK7ceqtzqisvQ6ntDRvPXKO9+gDA2wUGmpSckubpMgAAfs4nwvXBgwcVHBys6OhomUwm6+Ph4eG6fPmyBg0apPbt2+u1117TL7/8otdee02FCxfW/fff78GqXSO7Raxym2OdH3NtXckTNeb0MzIa9h1d4T278J1bjY6EakcXFrM3H7xe1WL/u6XaJXqwAQAAAAf4RLg+fPiwKlasqFKlSmXZNn/+fBUqVEjjx4+X2WxWlSpVdOLECc2YMaNAhOuMjPaiwlZOP0Nne6kzH2uvVzjzcHRXzanOKC9ztrObOmBZLfzy5Vi75wIAAADwD58I14cOHVKVKlXsbtu5c6eaNm0qs/mfpjRv3lzTp0/XhQsXdNNNN+VXmW7hyL2Ec7u3sa/KS5DLbV9PLcbmyC3T7H1pYm97bo9lPld29zd3tE4j+wEAAAD+xifC9eHDhxUZGak+ffro+PHjuvXWW/Xkk0+qdevWOnPmjKpXr26zv6WH+++//3Y6XJvNrrtLWWBggM1/8yJ9aG66jMNzs/u7Zf/cHnMHI+20Jy/15ravK9ueXTvt/cwzPp7xsYwyBmTLvhmPyW24eEaW1+3PB40P587p9+mK83sLV79uvZm/tJV2AgAAT/D6cJ2cnKzff/9dVatW1QsvvKAiRYpo1apVevzxxzV37lzFx8crKCjI5pjg4GBJUkJCglPPGRBgUmRkYcO1ZxYREerwvtFbT0mSOrYon+WxzI9n1LGFvbrTQ112bbL3XEbkpZ2+KHrrKXVsUT6Xdv4TpC2h2vLzze73GL31VLYBPCcZz5vxeFe9hu210/7rzLcV9NdtRv7SVtoJAADyk9eHa7PZrO3btyswMFAhISGSpNq1a+vIkSOaPXu2QkJClJiYaHOMJVSHhYU59ZypqWmKiblhrPAMAgMDFBERqpiYOKWkZH+/4IwsvYIZ57tm3Gbv8dx6ue0d48h2R2QMkHlpZ35yVQ9+09vSR0PExMTpp30XrI/b+5ln7OHNGKot7D2WFxlfC5nbZeT3KTn3unWl/Ood93Q785O/tJV2Os8dXywDAOAvvD5cS1Lhwln/Z1+tWjVt3rxZZcqU0blz52y2Wf5dunRpp58zOdn1F2QpKalOnTfz3Nnk5NRs59NaHrcESVfMkXVk7nO9qsWsQ5Kdbac7ZZxzbbS2nw9eUscW5ZWSkmoz9zm782b+XeTl9lnZsZzr54OXbM7rjjnRnvp91qtaLF+f1xtft+7iL22lnQAAID95fbg+cuSIHn74YU2bNk3NmjWzPv7bb7+patWqqlmzppYsWaKUlBQFBgZKkrZt26ZKlSqpRIkSnirbsNxutZWXhanccVspX+PIfaVzux+1RaMaxa09zvZW/c6Os6E6t1tmAQAAAPA8rw/XVapUUeXKlTV+/Hi99tprioyM1NL/b+/Ow5sqEzWAv0naspQdynJHNoVSutECLe0tCJTSBxAqIuICKAKiLHqHtVSrLAOCbCI7FSgDyJUrMIyIjogjyp1LoUWp7FBZBEeKUGhZ2yb57h8lIUlPcrK1yUnf3/P0oTnn5OT7ckiaN9/2P/+Do0ePYseOHWjYsCHWrVuHd955B6NHj8bPP/+MjRs3YtasWZ4uukvkQpO1mcOljrM3NFq7n61t9vDUTN1S5QAg2dJrWT5bz5mh5frmzbtmXfGdaZkWAFQyx9h73bzhOSYi8hQ/jQoZu49jzIBwTxeFiIiqKK8P12q1GmvWrMHixYvx5z//GUVFRQgNDUVmZqZxlvB169Zh7ty5eOaZZxAUFIRp06bhmWee8XDJ3cOekGtv4HIkfDnSWu7MuSy5syXW2rmkbjuz3BcgPVZaLlQLiW3WgrVcV29HvighIqoq2D2eiIg8yevDNQA0atQI8+bNs7o/MjIS27Ztq8QSVR5HA6AlpYQwR1rqK+JxbbViSx1raLk2fJCzp7VaroXa9FrLrWnuyPPALuREVNWs3nUMYwdGeLoYRERUxSgiXFdlUt26HQlZvhCoKqNbuVTvAMvtci3XhpZpuRBtjbV6Otv7wPI+3tI9n4ioopWyBZuIiDyA4dpLWZsNXOp3X2iZtPXFgaMtttaOcWRsujPjp+0N1dYmUrMsj+FYd11fJf//ICIiIiLydgzXXspWa6NlV29fCE1yrbP21tGeYCwXXKW2u3OSMstz2foiha3NRERERETKwHDt5dw5sZgpT4U2ucd1dZZyWyFZKtTaG8TlmAZqe1qw5VrX5Y6rbPa2+hMRERERVVUM115OakIyd4QZb11Sy51fJthTXlvhuyJJPZZpL4TKLIs9uAQYEREREZFtDNdeSi5cOTIm19p+R8fyuiPkunOWa0fGK9v7WLaYLqVl2TodZWUctbUJ6eQe25sC65HTBbLPM1nH54mIiIioamC49lK2Wqktl2ySm9xMbmmpimZP67GtFmtb+x2ZNV0uQMu1GhsCtdR61dbGUUudX2k6hTTg2rEuUOp1J1IijUaFjN3HPV0MIiKqohiuvZyjodHbP8jLteQ6MiZbKsBa229Py7TpMbaW1ZLaJje+2/T8Ul+W2MvWEmHuvPa5ebfQKaQBgLKWawNOukZE3o5fBhIRkacwXCuUtW7HUrNceyr4OBP6XF3n2Z6Qa2A6CZlla7TldmuTlNkzwZcz46cdCeIVcX1Nz9kppIFZwAbKzwXArs9E5E38NPYujkhEROQ+DNcK564lrNzNHV257TmX6TFS95NiGpilunirrPxuIBU2TctjbcZvR0K2t4VUuXHj3vBlDhERERGRJzFceylrs4Sb3pa6j70TTzkTgqxN1CXF1mPJdft2pDyW53R0PWoDR9o4TIO1tbHVjox9l1OZrcKOPJbU889gTURERERVFcO1l7J3gi9HJjSzZ9ZuW+HK0bHBjgQuuaAsN65cLlSbdgF3Zr+lpPjmuHnzrjFou/rFhS2VMb7a3mvlji8LiIiIiIh8EcO1l7K35drR8cim55daEqqiApNcOJYaNy7FkeW1AOlu3waOjsiTCv2V3RVf7prZuyyb5Tk5ZpqIfIlGo8LqXccwdmCEp4tCRERVCMO1l3J2QiypCc2kzulqiHK2FdvaftPz2qqv3DhqW+QmJ5MidR06hTTAvoOXrR5nemxFdwV3dIiAtfsxVBORrynlrOFERFTJGK4VxJ6u3K6EJmfGYBvu9yj0lu8m7WiLuuV2uRm/7WUrVFuGelst0kdOFxi7hRuWfHFkXLkt9i615eyyXhXdxZyIiIiIqKpiuFYAe2dqNuXMxFSOhizLMOnnp0b9+oFmodNwfmvjxe0plyWpQC0kNqpM0rQ9rdVy9ZdqubZnrLIjz6+93czdEYjlgnbZePIC43rXRERERERkHcO1l7LViurIJGb2cCaoybXy2jre2rhfa/tMWWupVkmkZ7m1qS0fSy4E2/s8OfoFgq0Z2B0dU20ve1raO4U0MH5ZQkREREREtjFcezmpyaZsraPsjtBlek5r2+w9j4Fc2LS367dlC7UjodqdLLuFA/Kzm7s6IZ1UaHf2erMrOBH5Oj9OakZERJWM4dpL2QqjtlpWpYK43NJVcsdaC9qG303PlRQfKFlGRyZlM5AbT+1It2976mVv4Cwbcx1o9Xz2PI/2crUVnYioKhNSY4aIiIgqCMO1l5KeLEx6vy32hDNrrdVyrc229jvabdyU5UchVz4b2dsC70gZrY1BdmQMub3dxt05wzsREREREVUchmsFsLWsltyYXVOOLHklFerkxk0DMJvoS6plG5BvkdYbDjBZO0vt4ORkhjIYSJXdnmWspOppGq7lunrb0w3ekWtGRET243rXRERUmRiuvZS9gdkdE5dZeyx7W1WttVxL7bcWrG3N9u3KrN/OtBZLncvyubGcLdzW5Ghya4+7q0WdiIjKE0IwYBMRUaVguFYAdy2rZRnq7O1Sbq2Lur3doAWkQ7HeJFAbwrUKgFptfpzc+tTWymjZmmxPi7XU71LbDBOalS1X5dh57FlKTep8DNpERM4pNZl8koiIqKIwXCuAM92GrYU6R1ptrbXG2ktI/C6ESUu0ePSPsZXa5D7OzPrtyEzqjq6/bbrf0HJt6zh3YagmIiIiIvJ+DNdeTq6Ls7WxzY6uPW25zZ4u5KZMhkeb3Tbuf7hBLwDxsAHB0EKthnS4toe9gV9uYjh7ziXVcq1lawgREREREYHhWhHsDbxS9zHdL9c668pYZWuhGigL1oYu4KZjq9VOLkhtq4Xe8ssGR9YAd2a5MLnu6I6ewxXuWOeciIiIiIicw3DtpUwDl9S4Yjmm97HWOmvvBF9S95ebmEwA0Js06hpbqVXmE5QBznX/tiyXPcdIHW/Z4m5J6rn281NL7ncm2LozEDNYExFJqxag5qRmRERU4RiuvZQjQcnZrtxS53E2oAmLlmm9MJmkzMWltBwZ++1o67MjodoRrq5DTkRE7sVJzYiIqKIxXHs508DrSLdmqTHZptstW7SljrW1HrVp9269ALS6st81JjN9+5v875IKsXKh356Jxez94sB0xnJr3ejlJo5zZFZvhmYiIiIioqqF4dpLOTOWV27stdRte/YZQqlePArcej1QqhMP96vgpynbbtb924FympLrqu3IJGQGUQ4+d5bPuaNd8RmuiYiIiIiqFoZrLyXXOutI66u9rbuAlQnJTP41tFiX6gTUDwdPa9QwhmsDW12/5coh1cJs7T62grizS4+52vLMYE1E5H38NM7O8EFERGQfhmsv5czkZdbuZ28wNVuX2qKVGnjYUv1wo7+fytgFXKWyPVu4aXlcWWdbiuVHJcvni0GXiIgAQKNRIWP3ceh0ghObERFRhWC49lLOzORtz3YDYe13k4nJdA9DdYn20RGGb/7V6vKzfjtTTnvKaPow1rpvO/L41rpvM4gTEfk2rVYPrc7WjCJERETOY7hWAGcDpRRr61Ab/jWsR11cKozb/f1UD/99NOu3XK42fRxHO+JJLT0mNc7aWvdtqdZ7W93liYiIiIiIXMVw7aVsdQu3nOTM3rBtrdu3QamuLFQDZSG7RsCjUA2YL6clx5FALdeFXWpWc2ucnUyMk5AREREREZErGK4VwNV1nU1DtN6klbpUW/b73QdlG9VqoGa1h4Fa82jmbwPLwCzVbdtaqLY18Zi1idrsmbTN1hhzR8KytwZrdywRRkREREREFY/hWgGcmQxMqvu3Xv8oXJfqBO48KPvdMIFqdX+TdaolUrDlOR1pnZY71t6AKDdhma8FTWeWXyMiInmrdx3jxGZERORWDNcK4EqoNg3U94oF7hWX3dBoVKhVrWy7YUy15XJaUuRmBTew7K5ua2kxuYAotwSZPff1ZAj1hjIQEZG5Uq3e00UgIiIfw3DtpZwJYqbrUBv/xaNx1AV3BGoElG0PrAYE+D9ap9pdkuKb4+bNuzhyusBsu72TjEmx7PLtSDdpZwOtOwOxp0M1wz0RERERUcVjuPZyUi29gPTyWXrx6Pc7Dx4F6mr+ZduC6qhQ7WGgVqkeTVAmTE6mMpkN3FY3cHu6hNuz1rYlufAsF7TlgrfcMdbOo+SxzUotNxFRRagWoEbG7uPGpSWJiIjcheHaS9kbRk0DtVb3KFT/UVTW3S2wmgp1a5R9gKjmrzJbm9rWOtX2LKUlFfr3Hbxsts/yWNPjpbqKWwuCUrOjOxoaXQmZpvc9croASfGBZuVy9fzkHLbKE5EztOwSTkREFYDh2kuZBk9DgDAEXr14dKNUBxTeK7uRl69Dkzplfbwfa1D2r6GlGigL04ZbavWjUG5swYbtFmnLgOzsettywVtuTWpPB6lOIQ2Mv3u6LFUdn38iIiIi8hYM1wqk15e1UgPAzbt6XPyj7Bv4oNpqNK5rmJzsUfdvA9NwDZRft9r0ptT62dbCtCHg+PmpUb9+oHHMtTPBh2Gp8rDVl4iIiIjIfRiuvZRpa7Vhtm/DxGSXbwj8fKUYANAmKADBzcqm+a5VXVVucjK12jxES832LdW9W26mb8uy2tMN3HK7rftTxeNzTkRVmUaj4nJcRETkVgzXXsoQfku1wE8XtQCA3BPHAQCRYeFICq0OoGxMtebhElpSIdqSvUHacrsUqf1yY67tCeKeDn3eUAYiIqp4XI6LiIjcieHaC+Xm3TKOh95/qgS5Xy8FAPR5fjIA4PHGalR/OJZarbZv5m7TcxvYWm/aViu14VipoG66FJfUJGWuLo0ldw53BGMGayIiIiIichTDtRewbAEWAH7+tWxQdc/QAPSOmGa233SZLFcWEpEbU21523QctvVjC4zHWjuPnKMPj7dWN2da1KVYzjzuWDnN1/FmICciUi52DyciInfwiXCt1+uxYsUKfPbZZ7h9+zZiYmLw3nvvoXnz5p4umiyptatVAF7p1dATxZFlK0SaTmhmusyJNwdPZ1rVrdWTiIiUid3DiYjIHdTyh3i/VatWYevWrfjLX/6CTz/9FHq9HqNHj0ZJSYmni2aVYSkr04nFotrUQ5RJd2siIiIiIiJSBsWH65KSEmzYsAFvvfUWevTogZCQEHz44Ye4evUq9u7d6+niSTKEakOw7sBATUREVOn8NCqs//IE/DQqZOw+7uniEBGRwik+XJ8+fRp3795FfHy8cVudOnUQGhqK7OxsD5bM3L6Dl5F9qgA/nSsL1qqHP1EM1URERB5jGN4jhMDqXcc8XBoiIlIyxY+5vnr1KgCgWbNmZtsbN25s3OcMPz/3fe+g0aih1QOqhzN0xYQ0cNu5vYnm4SLbGsvFtn0M6+lbqko9gapTV9aTnMWx10RE5ArFh+v79+8DAAICAsy2V6tWDYWFhU6dU61WoX79QJfLZqpPgvdPruYuderU8HQRKgXr6VuqSj2BqlNX1pOIiIgqk+LDdfXq1QGUjb02/A4AxcXFqFHDuQ8cer1AUdE9t5QPKGtVqFOnBoqK7kOn891vxVlP38J6+p6qUlfW03nu/mJZafw0rixwSUREVZ3iw7WhO/i1a9fQokUL4/Zr166hXbt2Tp+3IpZY0un0VWLpJtbTt7Cevqeq1JX1JGdx3WsiInKG4gdqhYSEoFatWjh06JBxW1FREU6ePImYmBgPloyIiIiURPNw1nAhhPzBREREFhQfrgMCAjBs2DAsWrQI3377LU6fPo2JEyeiadOmSE5O9nTxiIiISEFMewFw9nAiInKE4ruFA8Bbb70FrVaL9PR0PHjwADExMVi/fj38/f09XTQiIiJSKM4eTkREjvCJcK3RaDB16lRMnTrV00UhIiIiIiKiKkjx3cKJiIiIiIiIPI3hmoiIiMiEYWKzagFqjrsmIiK7MVwTERERWTBMbCaEQMbu48jYfdwsaDN0ExGRJZ8Yc01ERERUUYQQ0OkEtLqyJboydh/nZGdERFQOW66JiIiIHKDV6uGnUUnuy9h9vJJLQ0RE3oLhmoiIiMgO1QLUWP/lCQBl47KluoYbupOz2zgRUdXDbuFEREREdtKadAc3jMcGysK2Tieg0aiw/ssT7DZORFQFseWaiIiIyElCCGi1euMP8KjbOFuviYiqFoZrIiIi8pjlO372dBEqjBACq3Ydw/uZhz1dFCIiqgTsFk5ERESeIwRW7zqGsQMjfG4yMI1GBZ1Wj4Bqaizf8TPGPh0OoGzSM0M3cp1OmNXdcJuIiJSHLddERETkUWp1WeDU+vI4ZZPx2aZdyQ11N2wz3LZcV5uIiLwfwzURERF5nFarh8bK8la+wnQCNFOWXypotXoIIYzHr//yhDFoWwZuw21bx9i6HxERuY9KCCE8XQhvI4SAXu/ep0WjUUOn8+Fv5B9iPX0L6+l7qkpdWU/nz2eLu59TjUaN+ZsOQ6NWQacXkv8CsLpPKcf4qdXQ6vUOn8fy2Gp+GhRrdajmpzHuM9zW6vVWj9HpBQQEVFBh4osdsWzbT5LHyJWrRKvHlKGdAACLPjmCKUM7mf2b+nIMFm3JgU4vjMcZGI7zBXx/8S1VoZ5VoY5AxdZT7u+jAcM1ERERERERkYvYLZyIiIiIiIjIRQzXRERERERERC5iuCYiIiIiIiJyEcM1ERERERERkYsYromIiIiIiIhcxHBNRERERERE5CKGayIiIiIiIiIXMVwTERERERERuYjhmoiIiIiIiMhFDNdERERERERELmK4JiIiIiIiInIRwzURERERERGRixiuiYiIiIiIiFzEcF2B9Ho9li1bhm7duiEqKgqvvfYaLl++7OliuezWrVt477338OSTT6Jjx4548cUXkZOTY9x/8OBBDBo0CB06dECfPn2wZ88eD5bWPS5cuIDo6Gjs3LnTuO3UqVMYNmwYoqKikJiYiE2bNnmwhK7ZtWsX+vXrh4iICDz11FP46quvjPuuXLmC119/HR07dkTXrl2xdOlS6HQ6D5bWeVqtFh999BF69uyJ6OhoDB06FEePHjXu94VrunbtWgwfPtxsm1y9lPheJVXPf/7zn3j22WcRHR2NxMREfPDBB3jw4IFxf3FxMWbNmoX4+HhER0dj8uTJKCgoqOyiO0SqnqbS09ORmJhotk0J11MJZXRUfn4+2rVrV+7H8HeD7y/KudZS9UxPTy93bU1fe0qpp6uf4ZTwPipXx1dffbXctTS93kqoIwDcuHEDU6dORVxcHKKjozFmzBj88ssvxv2+8tqUq6fXvTYFVZjly5eLLl26iO+++06cOnVKjBw5UiQnJ4vi4mJPF80lr776qujfv7/Izs4W58+fF7NmzRKRkZHil19+EXl5eSIiIkIsWbJE5OXliXXr1onQ0FDxf//3f54uttNKSkrEoEGDRHBwsNixY4cQQoiCggLRpUsXkZaWJvLy8sT27dtFRESE2L59u4dL67hdu3aJ0NBQsWXLFnHp0iWxatUqERISIn788UdRUlIikpOTxZgxY8SZM2fEN998I2JjY8VHH33k6WI7ZdmyZSIhIUEcOHBAXLx4UbzzzjuiU6dOIj8/3yeu6ZYtW0RISIgYNmyYcZs99VLae5VUPbOzs0X79u3F6tWrxYULF8T+/fvFk08+KaZPn248Zvr06SIpKUlkZ2eL3NxcMXDgQDF06FBPVMEuUvU09c0334jg4GDRs2dPs+1KuJ5KKKOj9u/fLyIiIkR+fr64du2a8ef+/ft8f1HQtbb2uhs8eLBYsmSJ2bW9ceOGcb9S6unqZzglvI/aqqMQQsTHx4utW7eaXcubN28a76+EOgohxPPPPy+ee+45kZubK/Ly8sSbb74punbtKu7du+dTr01b9RTC+16bDNcVpLi4WERHR4tPPvnEuK2wsFBERkaK3bt3e7Bkrrl48aIIDg4WOTk5xm16vV4kJSWJpUuXinfffVcMHjzY7D6TJk0SI0eOrOyius3ixYvFyy+/bBau16xZI7p27SpKS0vNjktOTvZUMZ2i1+tFz549xfz58822jxw5UqxZs0bs3r1bhIeHi1u3bhn3ffrpp6Jjx45e9+Zrj5SUFDFv3jzj7du3b4vg4GDx9ddfK/qaXr16Vbz++usiKipK9OnTx+xDoVy9lPReZauekydPFiNGjDA7/m9/+5sICwsTxcXF4urVqyIkJETs37/fuP/8+fMiODhY/Pjjj5VWB3vYqqdBfn6+iIuLE8OGDTML10q4nkooozMyMjLEgAEDJPfx/cX7r7Wteur1ehEVFSX27t0reV+l1NPVz3BKeB+Vq+P169dFcHCwOHHihOT9lVBHIYS4deuWmDRpkjhz5oxx26lTp0RwcLDIzc31mdemXD298bXJbuEV5PTp07h79y7i4+ON2+rUqYPQ0FBkZ2d7sGSuqV+/PjIyMhAREWHcplKpoFKpUFRUhJycHLM6A0BcXByOHDkCIURlF9dl2dnZ2LZtG+bPn2+2PScnB7GxsfDz8zNui4uLw8WLF3H9+vXKLqbTLly4gN9++w0DBgww275+/Xq8/vrryMnJQVhYGOrWrWvcFxcXhzt37uDUqVOVXVyXNWzYEN999x2uXLkCnU6Hbdu2ISAgACEhIYq+pidOnIC/vz8+//xzdOjQwWyfXL2U9F5lq54jR45Eamqq2Ta1Wo3S0lLcuXMHR44cAVBWd4PWrVujSZMmiqonAAghMH36dDz99NOIjY0126eE66mEMjrjzJkzeOKJJyT38f3F+6+1rXr++uuvuHfvHh5//HHJ+yqlnq5+hlPC+6hcHc+cOQOVSoXWrVtL3l8JdQSAunXrYvHixQgODgYAFBQUYOPGjWjatCnatGnjM69NuXp642uT4bqCXL16FQDQrFkzs+2NGzc27lOiOnXqoHv37ggICDBu+/rrr3Hp0iV069YNV69eRdOmTc3u07hxY9y/fx83b96s7OK6pKioCNOmTUN6enq562itngDw+++/V1oZXXXhwgUAwL179zBq1CjEx8fjueeewz//+U8AvlNPg3feeQf+/v7o1asXIiIi8OGHH2LZsmVo0aKFouuamJiI5cuXo3nz5uX2ydVLSe9VtuoZGhqKkJAQ4+3S0lJs3LgR4eHhaNCgAfLz81G/fn1Uq1bN7H5KqycAbNy4EX/88QcmTZpUbp8SrqcSyuiMs2fPoqCgAEOHDsV//ud/4sUXX8QPP/wAQNnvpXx/Kbu2ALB582YkJiYiKSkJs2fPxu3btwEo5/+0q5/hlPA+KlfHs2fPonbt2pg9ezaefPJJ9OnTB0uXLkVJSQkAKKKOlt59913Ex8djz549mDt3LmrWrOlTr00DqXp642uT4bqC3L9/HwDMXtwAUK1aNRQXF3uiSBXixx9/RFpaGpKTk9GjRw88ePCgXJ0Ntw1vXEoxc+ZMREdHl2vVBSBZT8MbsZKu7507dwAAqamp6N+/PzZs2ICEhASMGzcOBw8e9Jl6GuTl5aF27dpYuXIltm3bhkGDBmHKlCk4deqUz9XVQK5evvhepdVqMW3aNJw7dw4zZswAUPaebFlHQHn1PH36NFasWIGFCxdK1kcJ11MJZXSUVqvF+fPnUVhYiDfffBMZGRmIiorCmDFjfPK91KCqvL+cPXsWarUajRs3xpo1azB9+nT87//+L8aNGwe9Xq/Yejr6GU6J76OWdTx79iyKi4sRGRmJdevWYezYsfjss8+Qnp4OQJl/K1555RXs2LED/fv3x/jx43HixAmffG1K1dMbX5t+8oeQM6pXrw6g7M3I8DtQ9h+6Ro0aniqWW+3btw9TpkxBx44dsWjRIgBl/1ktQ7ThtpLqvWvXLuTk5GD37t2S+6tXr16unoYXac2aNSu8fO7i7+8PABg1ahSeeeYZAED79u1x8uRJZGZm+kw9gbJvaidPnoyNGzeic+fOAICIiAjk5eVh+fLlPlVXU3L18rX3qjt37uDPf/4zDh8+jBUrViAyMhKA9PMAKKuexcXFmDJlCsaOHWvWSm9KCddTCWV0lJ+fHw4dOgSNRmOsU3h4OM6dO4f169fz/UXh13rs2LF46aWXUL9+fQBAcHAwgoKCMGTIEBw7dkyR9XTmM5zS3kel6jh79mykpqYah7sFBwfD398fEydOxLRp0xRXRwBo06YNAGDu3LnIzc3Fli1bfPK1KVXPuXPnet1rky3XFcTQ/eDatWtm269du4YmTZp4okhutWXLFrz55pvo2bMn1qxZY/w2rFmzZpJ1rlmzJmrXru2Jojplx44duHHjBnr06IHo6GhER0cDAGbMmIHRo0ejadOmkvUEoKjrayirYSyLQZs2bXDlyhWfqScA5ObmorS01GwcFgB06NABly5d8qm6mpKrly+9V127ds24vNr69evRvXt3476mTZvi1q1b5T5sKKmeubm5OHfuHFasWGF8X1q7di3+/e9/Izo6Gjk5OYq4nkooozMCAwPNPrwBQNu2bZGfn8/3F4Vfa7VabfzwbtC2bVsAZd1OlVZPZz/DKel91Fod/fz8zOaRAcyvpVLqWFBQgD179kCr1Rq3qdVqtGnTBteuXfOZ16ZcPb3xtclwXUFCQkJQq1YtHDp0yLitqKgIJ0+eRExMjAdL5rqtW7fiL3/5C4YOHYolS5aYdbXo3LkzDh8+bHZ8VlYWOnbsCLVaOf/dFi1ahC+//BK7du0y/gDAW2+9hblz5yImJgZHjhwxW+85KysLrVu3RsOGDT1UaseFhYUhMDAQubm5ZtvPnj2LFi1aICYmBidPnjR2HwfK6hkYGGi15cxbGcYenTlzxmz72bNn0apVK5+5ppbk6uUr71WFhYV45ZVXUFBQgE8++aRc2Tt16gS9Xm+crAYom3MgPz9fMfWMjIzE3r178fe//934vvTCCy+gcePG2LVrF8LDwxVxPZVQRkedO3cOHTt2NKsTABw/fhxt2rTh+4vCr/W0adMwYsQIs23Hjh0DUPZltJLq6cpnOKW8j9qq4/Dhw5GWlmZ2/LFjx+Dv749WrVoppo7Xr1/HpEmTcPDgQeO20tJSnDx5Ek888YTPvDbl6umVr80KmYOchBBCLFmyRMTGxop9+/aZratWUlLi6aI57fz58yIsLEyMHz/ebD25a9euiaKiInH27FkRFhYmFi5cKPLy8sT69esVv861gelSXNevXxcxMTEiNTVVnDt3TuzYsUNERESInTt3eriUjlu5cqWIjo4Wu3fvNlvnOisrSzx48EAkJSWJUaNGiVOnThnXuV6+fLmni+0wnU4nXnzxRdGnTx9x8OBBceHCBfHhhx+K9u3bi6NHj/rMNU1NTTVbQsaeeinxvcqynqmpqSIsLEwcPHiw3HuTVqsVQpQtKZOYmCiysrKMa5daW0PaW1jW09KyZcvKrXOthOuphDI6QqfTiWeffVb069dPZGdni7y8PPH++++L8PBwcebMGb6/KOxaW9Zz3759Ijg4WCxfvlxcunRJ7N+/XyQmJopJkyYZj1FCPd3xGc7b30fl6rh582bRvn17sXXrVvHrr7+KPXv2iC5duoglS5YYz+HtdTQYPXq0SE5OFocPHxZnzpwRkyZNEjExMeK3337zqdemrXp642uT4boCabVasWDBAhEXFyeioqLEa6+9Ji5fvuzpYrlk9erVIjg4WPInNTVVCCHE999/L/r37y/Cw8NFnz59xJ49ezxcavcwDddCCJGbmyuGDBkiwsPDRc+ePcXmzZs9WDrXbNiwQSQmJoqwsDCRkpIivvnmG+O+ixcvildffVVERESIrl27iqVLlwqdTufB0jrv1q1bYubMmaJHjx4iOjpaPP/88+LQoUPG/b5wTaXCmFy9lPheZVpPrVYrIiIirL43Gepy9+5d8c4774jOnTuLzp07i0mTJomCggJPVkOWM+FaCddTCWV01B9//CGmT58uEhISREREhHj++edFdna2cT/fX5RzraXq+eWXX4qBAweKyMhIkZCQIObPny8ePHhg3K+EerrjM5y3v4/aU8ctW7aIvn37Gv/Prl692uxzjbfX0aCoqEjMmDFDJCQkiMjISDFy5Ehx9uxZ435feW3K1dPbXpsqIRS4+DARERERERGRF1HOIFgiIiIiIiIiL8VwTUREREREROQihmsiIiIiIiIiFzFcExEREREREbmI4ZqIiIiIiIjIRQzXRERERERERC5iuCYiIiIiIpdxhV+q6hiuiRRq+vTpSExMtLo/MTER06dPlz1PSUkJlixZgh49eiAyMhIpKSn48ssv3VnUcoYPH47hw4dX6GO4wp7nTu75JyIi7zR8+HCEhobi2LFjkvvt/ftpr59//hlpaWno1asXIiMjERsbi1deeQVffPGF2x7j0KFDaNeuHQ4dOmT1mCtXrqBdu3bYuXMnAGDnzp1o164drly5YvPcR44cwRtvvIEuXbogPDwcPXr0wNtvv43Lly+bHfftt98iNTXV9coQKZifpwtARJ41ZcoU/Otf/8LkyZPRqlUr/P3vf8ekSZNQq1YtPPnkkxXymDNmzKiQ87rLihUrUKtWLU8Xg4iIKohOp0NaWhp27tyJgICACnucjRs3YsGCBejcuTMmTJiAxx57DIWFhdi7dy+mTJmCo0ePIj09vcIe35YePXpg27ZtaNy4sdVjDh48iNGjR6N3796YO3cuateujV9//RUbNmzA4MGD8dlnn6FFixYAyupKVNUxXBNVYTk5Ofj666+RkZGB7t27AwDi4+Nx6dIl/PDDDxUWrtu0aVMh53WX0NBQTxeBiIgqUO3atXHu3DmsXLkSEydOrJDHOHToEObPn49hw4aVC9BJSUkICQnBBx98gP79+yMqKqpCymBLgwYN0KBBA5vHrFmzBpGRkVi6dKlxW5cuXdC9e3f07t0bmZmZXv+FOVFlYrdwoirsH//4B1q0aGEM1gCgUqnw6aefuuWb9J07dyI0NBSfffYZEhISEBsbi7y8PLNu4SNHjsSgQYPK3XfcuHFISUkx3s7JycGwYcPQoUMHxMbGIjU1FQUFBbKPJeWLL75ASkoKIiMjERcXhylTpiA/P9+437JLYGFhIdLS0hAbG4uYmBgsXLgQer2+3Hn37duHQYMGISIiAgkJCZgzZw7u3bvn+BNHREQVqn379hg4cCDWrVuH48eP2zz2wYMHWLx4MZKTkxEeHo6OHTvi1VdfxalTp2zeb+XKlWjWrBmmTp0quf/ll19Gr169cP/+fQCPum1nZmaiT58+6NChA3bs2AEAOHbsGEaNGoUuXbqgY8eOeOONN3Du3Lly58zLy8NLL72EiIgI9O7dG5s3b7ZaPnu6hV+/fl1yHHXjxo2Rnp6OhIQEAGVd7Q8fPozDhw/Ldk8n8mUM10QKp9VqJX/scfr0abRt2xa7d+9G3759ERoair59+2Lfvn1uK59Op8OGDRswd+5cpKWl4YknnjDbn5KSghMnTuDSpUvGbUVFRfjhhx/w9NNPAwCys7MxYsQIVK9eHUuXLsXbb7+Nw4cP4+WXX8aDBw/sfiygbOzYtGnTkJycjI8//hhpaWnIysrC5MmTJcuv1+sxevRofP/990hNTcX8+fPx448/lhuXvnv3bowfPx6PP/44Vq5ciQkTJuDzzz/HuHHjOMELEZEXevvtt1G/fn2kpaWhpKTE6nHTpk3Djh07MGbMGGzYsAFpaWk4d+4cJk+ebPX9vbCwENnZ2ejVqxeqVasmeYyfnx9WrVqF+Ph4s+3Lly/Ha6+9hgULFiAhIQFZWVl48cUXAQDvv/8+5syZg99//x0vvPACfvnlF7P7zps3D1FRUVi9ejW6deuGOXPm4K9//asjT4uZHj164KeffsLw4cOxfft2s3HWzz33HJKSkgCUDfcKDQ1FaGgotm3bhrCwMKcfk0jJ2C2cSMF+++03l/6AFRQU4OLFizhx4gQmTpyIoKAgbN26FRMmTEBGRobbuoW/8cYb6NGjh+S+5ORkzJo1C1988QXGjx8PANi7dy90Oh369+8PAFi8eDFat26NtWvXQqPRAAA6dOiAp556Cjt27MDQoUPteiygLFxXr14dY8aMMY6zq1evHo4dOwYhBFQqldnxP/zwA37++Wd8/PHHxucjPj7ebDIzIQQWLVqEbt26YdGiRcbtrVq1wogRI/D999/bLBMREVW+unXrYvbs2Rg7dqzV7uElJSW4e/cu0tPT0a9fPwBAbGws7ty5g/nz5+P69esICgoqd7/ffvsNer0erVu3NtsuhIBOpzPbplKpjH/bAKBv37549tlnjbfffPNNtGzZEhkZGcbjunbtit69e2PZsmX46KOPjMcOGTIE06ZNMx6Tn5+PtWvXOj2J6H/913/h9u3b2L59Ow4fPgwAaNq0Kbp3744RI0bg8ccfB1A23MswV4knurgTeQu2XBMpWFBQELZv3y75Y/rHXq/XS7Zsl5aW4o8//sCqVaswcOBAJCQkYNmyZWjTpg1WrVpl9XEtzyXXMtu+fXur+2rWrImkpCSzluA9e/YgPj4eTZo0wf3795Gbm4vu3btDCGF8zObNm+OJJ57Av/71L7sfCwBiYmJw//599O/fH4sXL0ZOTg66du2KCRMmlAvWQFl3dH9/f3Tr1s2szKZd6c+fP4+rV68iMTHR7HmJiYlBrVq1ypWRiIi8Q2JiIlJSUrBu3TqcOHGi3P6AgACsX78e/fr1Q35+PrKysvDpp5/iu+++AwCrLd5SQ4eAsgnCwsLCzH5GjBhhdozp37F79+7h2LFj6Nu3r1kAr1OnDnr27GkMvAaGLwAMevfujRs3buD8+fPWnwQbAgICMHv2bHz//feYO3cuBgwYAL1ej23btiElJQV79+516rxEvoot10QKFhAQgIiICKv7DFauXIkVK1aY7T9z5gwCAwMRFBRk1vqt0WgQHx+Pbdu2SZ73ypUr6NWrl9m2efPmSY6bNqhZs6bNejz99NP4/PPPcfr0aTRq1AiHDh3C+++/D6Csi7her8fHH3+Mjz/+uNx9LbvbyT1WdHQ0MjIysHHjRmRmZiIjIwONGjXCG2+8IfnNfmFhIerVq1cueJt+eXHr1i0AwKxZszBr1qxy57h27ZrNMhERkeekp6fj4MGDSEtLM45xNnXgwAG8//77OH/+PAIDAxESEmL8W2Pty+X/+I//AIBy45kjIyOxfft2422pycBM/47dvn0bQgg0atSo3HGNGjXC7du3y20z1bBhQwBlf8uqV68uWVZ7BAUFYfDgwRg8eDAAICsrC1OnTsXMmTORlJQEtZrtdUQAwzVRlTBkyBDJbsktW7bE77//Xq47tFartfpHuHHjxmYfDADgsccec6l88fHxCAoKwldffYWgoCBUq1YNycnJAIDAwECoVCqMGDECTz31VLn71qhRw+HH69atG7p164b79+8jKysLmzZtwpw5c9ChQwdERkaaHVu/fn3cvHkTOp3OrNXAEKiBshYEoGxcXmxsbLnHq1u3rsNlJCKiylG3bl3MnDkT48ePL9dr69dff8X48eORlJSEtWvXonnz5lCpVPjkk09w4MABq+ds0KABoqOjsW/fPkyZMsX496NWrVpmX4oHBgbaLFvt2rWhUqlw/fr1cvv++OMP1KtXz2xbYWGh2W3D/Qwh2xG5ubkYO3YsFi5caJy4zCAuLg6jRo3CvHnzcPPmTafOT+SL+DUTURXQpEkTREREmP0AQPfu3XHr1i2zbsslJSU4cOAAOnXqJHkuQ2u56U/9+vVdKp9Go8GAAQPw3Xff4R//+AeSkpKM39zXqlULoaGhOH/+vNljtm3bFsuXL3d4RtIPPvgAzz77LIQQqFGjBnr27InU1FQAwL///e9yx8fHx0Or1ZpN8lZSUmL2nD3++ONo2LAhrly5YlbGJk2aYPHixTh58qQzTwsREVWSpKQk9O/fHxkZGWYrURw/fhzFxcUYM2YMWrRoYfwi2hCsbQ2LGj9+PC5fvowFCxZIHldYWCjbs6lmzZoIDw/HV199ZTZW+/bt29i/f3+5v9X79+83u71nzx40a9YMLVu2tPk4Ulq1aoX79+9j06ZNkt3cL1y4gKCgIONyXmy9JmLLNVGVNmDAAGzZsgVTpkzB5MmT0aRJE2zatAlXr141myClMjz99NPYsGED1Gp1ue7fkyZNwpgxYzB58mSkpKQYZwXPzc3FuHHjHHqcuLg4ZGZmYvr06UhJSUFpaSnWrVuHevXqIS4urtzx8fHx6Nq1K9LT03Hjxg386U9/wqZNm1BQUGD8pl6j0WDixIl47733oNFo0LNnTxQVFWHVqlXIz8/nrKlERArw7rvvIisry6yVOCwsDH5+fli4cCFGjhyJkpIS7Ny50xhibS232K1bN7z77ruYN28ejh49imeeeQatW7fGvXv3cPjwYezYsQPFxcV4+eWXbZZr8uTJGDVqFMaMGYOXXnoJpaWlyMjIQElJiXEiUIPNmzcjMDAQoaGh2LNnDw4cOIAFCxZIzikip27dukhNTcWMGTPw0ksvYciQIWjevDlu376Nb775Bn/729+waNEi47nr1KmDn376CQcPHkRoaCjq1q2LkydPIiAgAG3atHH48YmUiOGaqArz9/dHZmYmlixZgg8//BB3795FaGgoNm7cWOmBMCQkBMHBwbh582a5ZUm6du2K9evXY8WKFXjrrbfg7++PsLAwZGZmOjwraffu3bFo0SJs2LDBOIlZp06dsGnTpnLd6wxWrFiBRYsWYdmyZSguLka/fv0wZMgQfPvtt8ZjnnvuOQQGBmLdunXYtm0batasiY4dO2LRokVo3ry5o08HERFVsnr16mHmzJmYMGGCcVvLli2xePFirFixAmPHjkXdunURFRWFzZs3Y/jw4cjJyUG7du2snnPo0KGIjY3Ff//3fyMzMxNXr16FRqNB69atMWzYMDz//PNo0qSJzXLFx8cjMzMTy5Ytw6RJkxAQEIDOnTvjgw8+QNu2bc2OnTNnDtatW4elS5eiefPmWLJkieSQKnu98MILaNmyJTZt2oQlS5bg1q1bCAwMRGRkJP7617+iS5cuZnU9fvw4XnvtNcybNw8DBgzAhAkT8Kc//cnmettEvkQluAArERERERERkUs4OIKIiIiIiIjIRQzXRERERERERC5iuCYiIiIiIiJyEcM1ERERERERkYsYromIiIiIiIhcxHBNRERERERE5CKGayIiIiIiIiIXMVwTERERERERuYjhmoiIiIiIiMhFDNdERERERERELmK4JiIiIiIiInIRwzURERERERGRi/4fUNyvHUaN7ecAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"PM2.5 pairplots for given station ids\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. sensor_ids - list of sensor node IDs in form of a dictionary containing IDs of the sensor nodes as the keys and descriptions as the values, e.g. {\"1010\": \"T-19 - 1st floor\", \"1011\": \"T-19 - roof\"} \n",
    "4. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "# helper function\n",
    "def dict_to_csv(d: dict, filename: str):\n",
    "    with open(filename, 'w', newline='') as file:\n",
    "        writer = csv.writer(file)\n",
    "        writer.writerow(['pair', 'statistic', 'p_value', 'reject_h0'])\n",
    "        for k, v in d.items():\n",
    "            a, b = k\n",
    "            statistic, pvalue = v\n",
    "            writer.writerow([f'{a}_{b}', statistic, pvalue, pvalue < 0.05])\n",
    "\n",
    "\n",
    "def group_pairplots(input_dir, output_dir, sensor_ids: dict, window):\n",
    "    sensor_dfs = []\n",
    "    for sensor_id, description in sensor_ids.items():\n",
    "        if sensor_id == 'main':\n",
    "            main_campus_dfs = []\n",
    "            sensor_ids_main = ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009']\n",
    "            for i in sensor_ids_main:\n",
    "                main_campus_df = get_all_sensor_data(input_dir, i)\n",
    "                main_campus_df.index = main_campus_df['timestamp']\n",
    "                main_campus_df = main_campus_df[['PM2.5[calibrated]']]\n",
    "                main_campus_df = main_campus_df.resample(window).mean()\n",
    "                main_campus_dfs.append(main_campus_df)\n",
    "            sensor_df = pd.concat(main_campus_dfs, axis=1)\n",
    "            sensor_df = sensor_df.mean(axis=1, level=0)\n",
    "        else:\n",
    "            sensor_df = get_all_sensor_data(input_dir, sensor_id)\n",
    "            sensor_df.index = sensor_df['timestamp']\n",
    "        if sensor_df is not None:\n",
    "            sensor_df = sensor_df[['PM2.5[calibrated]']]\n",
    "            sensor_df = sensor_df.resample(window).mean()\n",
    "            sensor_df.rename(columns={'PM2.5[calibrated]': description}, inplace=True)\n",
    "            sensor_dfs.append(sensor_df[[description]])\n",
    "\n",
    "    concat_df = pd.concat(sensor_dfs, axis=1)\n",
    "    concat_df.dropna(inplace=True)\n",
    "\n",
    "    pl = sns.pairplot(concat_df, kind='hist', height=5, corner=True)\n",
    "    sns.set(font_scale=1)\n",
    "    pl.fig.suptitle(f\"PM_2.5: {', '.join([d for d in sensor_ids.values()])}, {window} window average\")\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "      os.makedirs(output_dir)\n",
    "\n",
    "    plt.savefig(os.path.join(output_dir, f\"{output_dir}_{window}.png\"), dpi=300)\n",
    "\n",
    "    combinations = it.combinations(concat_df.columns, 2)\n",
    "    kstest_dict = dict()\n",
    "    for (a, b) in combinations:\n",
    "        statistic, pvalue = stats.kstest(concat_df[a], concat_df[b], alternative='two-sided', mode='auto')\n",
    "        kstest_dict[a, b] = (statistic, pvalue)\n",
    "    dict_to_csv(kstest_dict, os.path.join(output_dir, f\"{output_dir}_{window}_kstest.csv\"))\n",
    "\n",
    "\n",
    "h6_river_street = {\"1007\": \"H-6 - street side\", \"1015\": \"H-6 - river side\"} # H-6 river vs street\n",
    "t19_floors = {\"1010\": \"T-19 - 1st floor\", \"1011\": \"T-19 - roof\"} # T-19 floors\n",
    "c5_floors = {\"1005\": \"C-5 - ground floor\", \"1018\": \"C-5 - 3rd floor\", \"1017\": \"C-5 - 6th floor\", \"1020\": \"C-5 - 9th floor\"} # C-5 floors\n",
    "d_buildings = {\"1002\": \"D-1\", \"1001\": \"D-2\", \"1009\": \"D-3\"} # D-1, D-2, D-3 buildings\n",
    "h4_sides = {\"1003\": \"H-4 - street side\", \"1016\": \"H-4 - yard side\"} # H-4 yard vs street\n",
    "river_sides = {\"1015\": \"H-6 - river side\", \"1013\": \"Na Grobli St.\"} # river sides\n",
    "e1_f3 = {\"1019\": \"E-1\", \"1012\": \"F-3\"} # E-1, F-3\n",
    "m2_campus = {\"1014\": \"M-2\", \"main\": \"Main campus\"} # M-2, main campus\n",
    "\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='h6',\n",
    "    sensor_ids=h6_river_street,\n",
    "    window='15min')\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='t19',\n",
    "    sensor_ids=t19_floors,\n",
    "    window='15min')\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='c5',\n",
    "    sensor_ids=c5_floors,\n",
    "    window='15min')\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='d',\n",
    "    sensor_ids=d_buildings,\n",
    "    window='15min')\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='h4',\n",
    "    sensor_ids=h4_sides,\n",
    "    window='15min')\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='river_sides',\n",
    "    sensor_ids=river_sides,\n",
    "    window='15min')\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='e1_f3',\n",
    "    sensor_ids=e1_f3,\n",
    "    window='15min')\n",
    "\n",
    "group_pairplots(\n",
    "    input_dir='readings',\n",
    "    output_dir='m2_campus',\n",
    "    sensor_ids=m2_campus,\n",
    "    window='15min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\943414231.py:33: FutureWarning:\n",
      "\n",
      "Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\943414231.py:33: FutureWarning:\n",
      "\n",
      "Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\943414231.py:33: FutureWarning:\n",
      "\n",
      "Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\943414231.py:33: FutureWarning:\n",
      "\n",
      "Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\943414231.py:33: FutureWarning:\n",
      "\n",
      "Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "\n",
      "C:\\Users\\jpawl\\AppData\\Local\\Temp\\ipykernel_11504\\943414231.py:33: FutureWarning:\n",
      "\n",
      "Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Calculate MAE for each campus and show as heatmap\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "# helper function\n",
    "def dict_to_csv(d: dict, filename: str):\n",
    "    with open(filename, 'w', newline='') as file:\n",
    "        writer = csv.writer(file)\n",
    "        writer.writerow(['pair', 'statistic', 'p_value'])\n",
    "        for k, v in d.items():\n",
    "            a, b = k\n",
    "            statistic, pvalue = v\n",
    "            writer.writerow([f'{a}_{b}', statistic, pvalue])\n",
    "\n",
    "\n",
    "def campus_MAE_heatmap(input_dir, output_dir, window=\"15min\"):\n",
    "    campuses_dict = {\n",
    "        'Main (1001-1009)': ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009'],\n",
    "        'Na Grobli (1013)': ['1013'],\n",
    "        'Wittiga (1010)': ['1010'],\n",
    "        'Dluga (1014)': ['1014'],\n",
    "        'Bujwida (1012)' : ['1012'],\n",
    "        'Prusa (1019)' : ['1019']}\n",
    "\n",
    "    campus_dfs = []\n",
    "    for campus_name, sensor_ids in campuses_dict.items():\n",
    "        sensor_dfs = []\n",
    "        for sensor_id in sensor_ids:\n",
    "            sensor_df = get_all_sensor_data(input_dir, sensor_id)\n",
    "            sensor_df.index = sensor_df['timestamp']\n",
    "            sensor_df = sensor_df[['PM2.5[calibrated]']]\n",
    "            sensor_df = sensor_df.resample(window).mean()\n",
    "            sensor_dfs.append(sensor_df)\n",
    "        campus_df = pd.concat(sensor_dfs, axis=1)\n",
    "        campus_df = campus_df.mean(axis=1, level=0)\n",
    "        campus_df.rename(columns={'PM2.5[calibrated]': campus_name}, inplace=True)\n",
    "        campus_dfs.append(campus_df)\n",
    "\n",
    "    concat_df = pd.concat(campus_dfs, axis=1)\n",
    "\n",
    "    combinations = [p for p in it.product(campuses_dict.keys(), repeat=2)]\n",
    "    mae_dict = dict()\n",
    "    kstest_dict = dict()\n",
    "\n",
    "    for (a, b) in combinations:\n",
    "        ab_df = concat_df[[a, b]].copy()\n",
    "        ab_df.dropna(inplace=True)\n",
    "\n",
    "        mae_dict[a, b] = mean_absolute_error(ab_df[a], ab_df[b])\n",
    "\n",
    "        statistic, pvalue = stats.kstest(concat_df[a], concat_df[b], alternative='two-sided', mode='auto')\n",
    "        kstest_dict[a, b] = (statistic, pvalue)\n",
    "\n",
    "    fig = go.Figure(data=go.Heatmap(dict_to_plotly(mae_dict), text= [v for v in mae_dict.values()], texttemplate=\"%{text:.2f}\"))\n",
    "    fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=800,\n",
    "    height=800,\n",
    "    title=f\"PM<sub>2.5</sub> MAE for campuses, {window} window average\",\n",
    "    template=\"plotly\")\n",
    "\n",
    "    # fig.show()\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "    fig.write_html(os.path.join(output_dir, f'{output_dir}_{window}.html'), include_plotlyjs='cdn')\n",
    "    dict_to_csv(kstest_dict, os.path.join(output_dir, f'{output_dir}_{window}_kstest.csv'))\n",
    "\n",
    "\n",
    "campus_MAE_heatmap(input_dir='readings', output_dir='campuses_mae', window='15min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing sensor 1001\n",
      "Processing sensor 1002\n",
      "Processing sensor 1003\n",
      "Processing sensor 1004\n",
      "Processing sensor 1005\n",
      "Processing sensor 1006\n",
      "Processing sensor 1007\n",
      "Processing sensor 1008\n",
      "Processing sensor 1009\n",
      "Processing sensor 1010\n",
      "Processing sensor 1011\n",
      "Processing sensor 1012\n",
      "Processing sensor 1013\n",
      "Processing sensor 1014\n",
      "Processing sensor 1015\n",
      "Processing sensor 1016\n",
      "Processing sensor 1017\n",
      "Processing sensor 1018\n",
      "Processing sensor 1019\n",
      "Processing sensor 1020\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Calculate MAE for each station pair and show as heatmap\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "# helper function\n",
    "def dict_to_csv(d: dict, filename: str):\n",
    "    with open(filename, 'w', newline='') as file:\n",
    "        writer = csv.writer(file)\n",
    "        writer.writerow(['pair', 'statistic', 'p_value'])\n",
    "        for k, v in d.items():\n",
    "            a, b = k\n",
    "            statistic, pvalue = v\n",
    "            writer.writerow([f'{a}_{b}', statistic, pvalue])\n",
    "\n",
    "\n",
    "def sensors_MAE_heatmap(input_dir, output_dir, window=\"15min\"):\n",
    "    sensor_ids = ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009', '1010', '1011', '1012', '1013', '1014', '1015', '1016', '1017', '1018', '1019', '1020']\n",
    "\n",
    "    sensor_dfs = []\n",
    "    for sensor_id in sensor_ids:\n",
    "        print(f'Processing sensor {sensor_id}')\n",
    "        sensor_df = get_all_sensor_data(input_dir, sensor_id)\n",
    "        sensor_df.index = sensor_df['timestamp']\n",
    "        sensor_df = sensor_df[['PM2.5[calibrated]']]\n",
    "        sensor_df.rename(columns={'PM2.5[calibrated]': sensor_id}, inplace=True)\n",
    "        sensor_df = sensor_df.resample(window).mean()\n",
    "        sensor_dfs.append(sensor_df[[sensor_id]])\n",
    "\n",
    "    concat_df = pd.concat(sensor_dfs, axis=1)\n",
    "\n",
    "    combinations = [p for p in it.product(sensor_ids, repeat=2)]\n",
    "    mae_dict = dict()\n",
    "    kstest_dict = dict()\n",
    "\n",
    "    for (a, b) in combinations:\n",
    "        ab_df = concat_df[[a, b]].copy()\n",
    "        ab_df.dropna(inplace=True)\n",
    "\n",
    "        mae_dict[a, b] = mean_absolute_error(ab_df.iloc[:, 0], ab_df.iloc[:, 1])\n",
    "\n",
    "        statistic, pvalue = stats.kstest(ab_df.iloc[:, 0], ab_df.iloc[:, 1], alternative='two-sided', mode='auto')\n",
    "        kstest_dict[a, b] = (statistic, pvalue)\n",
    "\n",
    "    fig = go.Figure(data=go.Heatmap(dict_to_plotly(mae_dict), text= [v for v in mae_dict.values()], texttemplate=\"%{text:.2f}\"))\n",
    "    fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=800,\n",
    "    height=800,\n",
    "    title=f\"PM<sub>2.5</sub> MAE for pairs of sensor nodes, {window} window average\",\n",
    "    template=\"plotly\")\n",
    "\n",
    "    # fig.show()\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "    fig.write_html(os.path.join(output_dir, f'{output_dir}_{window}_no_stratification.html'), include_plotlyjs='cdn')\n",
    "    dict_to_csv(kstest_dict, os.path.join(output_dir,f'{output_dir}_{window}_kstest_no_stratification.csv' ))\n",
    "\n",
    "\n",
    "sensors_MAE_heatmap(input_dir='readings', output_dir='sensors_mae', window='15min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing sensor 1001\n",
      "Processing sensor 1002\n",
      "Processing sensor 1003\n",
      "Processing sensor 1004\n",
      "Processing sensor 1005\n",
      "Processing sensor 1006\n",
      "Processing sensor 1007\n",
      "Processing sensor 1008\n",
      "Processing sensor 1009\n",
      "Processing sensor 1010\n",
      "Processing sensor 1011\n",
      "Processing sensor 1012\n",
      "Processing sensor 1013\n",
      "Processing sensor 1014\n",
      "Processing sensor 1015\n",
      "Processing sensor 1016\n",
      "Processing sensor 1017\n",
      "Processing sensor 1018\n",
      "Processing sensor 1019\n",
      "Processing sensor 1020\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Calculate MAE for each station pair and show as heatmap, with stratification according to Polish AQI\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "# helper function\n",
    "def dict_to_csv_stratification(d: dict, filename: str):\n",
    "    with open(filename, 'w', newline='') as file:\n",
    "        writer = csv.writer(file)\n",
    "        writer.writerow(['pair', 'split_number', 'statistic', 'p_value'])\n",
    "        for k, v in d.items():\n",
    "            a, b, split_num = k\n",
    "            statistic, pvalue = v\n",
    "            writer.writerow([f'{a}_{b}', split_num, statistic, pvalue])\n",
    "\n",
    "\n",
    "def sensors_mae_heatmap_stratified(input_dir, output_dir, window=\"15min\"):\n",
    "    sensor_ids = ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009', '1010', '1011', '1012', '1013', '1014', '1015', '1016', '1017', '1018', '1019', '1020']\n",
    "\n",
    "    sensor_dfs = []\n",
    "    for sensor_id in sensor_ids:\n",
    "        print(f'Processing sensor {sensor_id}')\n",
    "        sensor_df = get_all_sensor_data(input_dir, sensor_id)\n",
    "        sensor_df.index = sensor_df['timestamp']\n",
    "        sensor_df = sensor_df[['PM2.5[calibrated]']]\n",
    "        sensor_df = sensor_df.resample(window).mean()\n",
    "        sensor_df[f'{sensor_id}_AQI'] = sensor_df['PM2.5[calibrated]'].apply(get_polish_aqi)\n",
    "        sensor_df.rename(columns={'PM2.5[calibrated]': sensor_id}, inplace=True)\n",
    "        sensor_dfs.append(sensor_df[[sensor_id, f'{sensor_id}_AQI']])\n",
    "\n",
    "    concat_df = pd.concat(sensor_dfs, axis=1)\n",
    "\n",
    "    skf = StratifiedKFold(n_splits=10)\n",
    "\n",
    "    combinations = [p for p in it.product(sensor_ids, repeat=2)]\n",
    "    mae_dict = dict()\n",
    "    kstest_dict = dict()\n",
    "\n",
    "    for (a, b) in combinations:\n",
    "        ab_df = concat_df[[a, f'{a}_AQI', b, f'{b}_AQI']].copy()\n",
    "        ab_df.dropna(inplace=True)\n",
    "\n",
    "        a_x = ab_df.iloc[:, 0].values # first element of pair - pm2.5\n",
    "        a_y = ab_df.iloc[:, 1].values # first element of pair - aqi\n",
    "\n",
    "        b_x = ab_df.iloc[:, 2].values # second element of pair - pm2.5\n",
    "        b_y = ab_df.iloc[:, 3].values # second element of pair - aqi\n",
    "\n",
    "        sum_mae = 0\n",
    "        split_num = 0\n",
    "        for (a_train_index, a_test_index), (b_train_index, b_test_index) in zip(skf.split(a_x, a_y), skf.split(b_x, b_y)):\n",
    "            split_num += 1\n",
    "            split_mae = mean_absolute_error(ab_df.iloc[:, 0][a_test_index], ab_df.iloc[:, 2][b_test_index])\n",
    "            sum_mae += split_mae\n",
    "\n",
    "            statistic, pvalue = stats.kstest(ab_df.iloc[:, 0][a_test_index], ab_df.iloc[:, 2][b_test_index], alternative='two-sided', mode='auto')\n",
    "            kstest_dict[a, b, split_num] = (statistic, pvalue)\n",
    "        avg_mae = sum_mae/10\n",
    "        mae_dict[a, b] = avg_mae\n",
    "\n",
    "\n",
    "    fig = go.Figure(data=go.Heatmap(dict_to_plotly(mae_dict), text= [v for v in mae_dict.values()], texttemplate=\"%{text:.2f}\"))\n",
    "    fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=800,\n",
    "    height=800,\n",
    "    title=f\"PM<sub>2.5</sub> MAE for pairs of sensor nodes, {window} window average. Stratified data.\",\n",
    "    template='plotly')\n",
    "\n",
    "    # fig.show()\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "    fig.write_html(os.path.join(output_dir, f'{output_dir}_{window}_stratification.html'), include_plotlyjs='cdn')\n",
    "    dict_to_csv_stratification(kstest_dict, os.path.join(output_dir,f'{output_dir}_{window}_kstest_stratification.csv' ))\n",
    "\n",
    "\n",
    "sensors_mae_heatmap_stratified(input_dir='readings', output_dir='sensors_mae', window='15min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing sensor 1001\n",
      "Processing sensor 1002\n",
      "Processing sensor 1003\n",
      "Processing sensor 1004\n",
      "Processing sensor 1005\n",
      "Processing sensor 1006\n",
      "Processing sensor 1007\n",
      "Processing sensor 1008\n",
      "Processing sensor 1009\n",
      "Processing sensor 1010\n",
      "Processing sensor 1011\n",
      "Processing sensor 1012\n",
      "Processing sensor 1013\n",
      "Processing sensor 1014\n",
      "Processing sensor 1015\n",
      "Processing sensor 1016\n",
      "Processing sensor 1017\n",
      "Processing sensor 1018\n",
      "Processing sensor 1019\n",
      "Processing sensor 1020\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Calculate number of AQI transitions and show as heatmap\n",
    "\n",
    "The function takes in the following arguments:\n",
    "1. input_dir - path to the input directory with .feather files containing readings from the sensor nodes from different months and years\n",
    "2. output_dir - path to the output directory which will contain output plot files\n",
    "3. window - a time window the data will be averaged with, e.g. '15min'. Other possible windows: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
    "4. years - list containing years for which the plot will be generated, e.g. ['2019', '2020']\n",
    "5. polish_aqi - boolean value. If True - Polish AQI will be used, if False - EU AQI will be used\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def aqi_transition_heatmap(input_dir, output_dir, window='15min', years=None, polish_aqi=False):\n",
    "    if years is None:\n",
    "        years = ['2019']\n",
    "\n",
    "    sensor_ids = ['1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009', '1010', '1011', '1012', '1013', '1014', '1015', '1016', '1017', '1018', '1019', '1020']\n",
    "\n",
    "    if polish_aqi:\n",
    "        aqi_labels = ['Very good [1]', 'Good [2]', 'Moderate [3]', 'Satisfactory [4]', 'Bad [5]', 'Hazardous [6]']\n",
    "    else:\n",
    "        aqi_labels = ['Good [1]', 'Fair [2]', 'Moderate [3]', 'Poor [4]', 'Very poor [5]', 'Extremely poor [6]']\n",
    "\n",
    "    sensor_dfs = []\n",
    "    for sensor_id in sensor_ids:\n",
    "        print(f'Processing sensor {sensor_id}')\n",
    "        sensor_df = get_all_sensor_data(input_dir, sensor_id)\n",
    "        sensor_df['timestamp'] = pd.to_datetime(sensor_df['timestamp'])\n",
    "        sensor_df = sensor_df[sensor_df['timestamp'].dt.year.isin(years)]\n",
    "        sensor_df.index = sensor_df['timestamp']\n",
    "        sensor_df = sensor_df[['PM2.5[calibrated]']]\n",
    "        sensor_df = sensor_df.resample(window).mean()\n",
    "\n",
    "        if len(sensor_df):\n",
    "            sensor_dfs.append(sensor_df)\n",
    "\n",
    "    concat_df = pd.concat(sensor_dfs, axis=0)\n",
    "    concat_df.dropna(inplace=True)\n",
    "\n",
    "    add_aqi_columns(concat_df, polish_aqi=polish_aqi)\n",
    "\n",
    "    value_counts_dict = concat_df['AQI_change'].value_counts().to_dict()\n",
    "    ser = pd.Series(list(value_counts_dict.values()),\n",
    "                  index=pd.MultiIndex.from_tuples(value_counts_dict.keys()))\n",
    "\n",
    "    matrix = ser.unstack().fillna(0).T\n",
    "\n",
    "    fig = px.imshow(matrix, text_auto=True, labels=dict(x=\"AQI(t)\", y=\"AQI(t+1)\"), x=aqi_labels, y=aqi_labels, template='plotly')\n",
    "    aqi_name = \"Polish\" if polish_aqi else \"EU\"\n",
    "    fig.update_layout(title=f'AQI changes frequency ({aqi_name} AQI)')\n",
    "    # fig.show()\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "    # fig.write_image(f'{plot_dir}{window}_{aqi_name}_{\"_\".join(map(str,years))}.png', height=800, width=800)\n",
    "    fig.write_html(os.path.join(output_dir, f'{output_dir}_{window}_{aqi_name}_{\"_\".join(map(str,years))}.html'),include_plotlyjs='cdn')\n",
    "\n",
    "\n",
    "aqi_transition_heatmap(input_dir='readings', output_dir='aqi_transitions', window='15min', years=[2018, 2019, 2020, 2021], polish_aqi=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}