city_retrofit/imports/occupancy_feeders/usage_schedules.py

"""
Schedules retrieve the specific usage 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 pilar_monsalvete@yahoo.es
"""
import pandas as pd
from imports.occupancy_feeders.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)
    # todo: review for more than one usage_zones per building
    for building in city.buildings:
      schedules = dict()
      usage_schedules = pd.read_excel(xls, sheet_name=SchedulesHelper.comnet_pluto_schedules_function(building.function)
                                      , 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
      building.usage_zones[0].schedules = schedules