"""
Schedules retrieve the specific usages schedules module for the given standard
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
contributors Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
import pandas as pd
from imports.schedules.helpers.schedules_helper import SchedulesHelper


class ComnetSchedules:

  def __init__(self, city, base_path):
    self._city = city
    self._comnet_schedules_path = base_path / 'comnet_archetypes.xlsx'
    xls = pd.ExcelFile(self._comnet_schedules_path)
    for building in city.buildings:
      schedules = dict()
      for usage_zone in building.usage_zones:
        usage_schedules = pd.read_excel(xls,
                                        sheet_name=SchedulesHelper.comnet_from_usage(usage_zone.usage),
                                        skiprows=[0, 1, 2, 3], nrows=39, usecols="A:AA")
      # todo: should we save the data type? How?
        number_of_schedule_types = 13
        schedules_per_schedule_type = 3
        day_types = dict({'week_day': 0, 'saturday': 1, 'sunday': 2})
        for schedule_types in range(0, number_of_schedule_types):
          data = pd.DataFrame()
          columns_names = []
          name = ''
          for schedule_day in range(0, schedules_per_schedule_type):
            row_cells = usage_schedules.iloc[schedules_per_schedule_type*schedule_types + schedule_day]
            if schedule_day == day_types['week_day']:
              name = row_cells[0]
            columns_names.append(row_cells[2])
            data1 = row_cells[schedules_per_schedule_type:]
            data = pd.concat([data, data1],  axis=1)
          data.columns = columns_names
          schedules[name] = data
        usage_zone.schedules = schedules