40 lines
1.7 KiB
Python
40 lines
1.7 KiB
Python
"""
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Schedules retrieve the specific usage schedules module for the given standard
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2020 Project Author Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
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contributors Pilar Monsalvete pilar_monsalvete@yahoo.es
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"""
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import pandas as pd
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from imports.occupancy_feeders.helpers.schedules_helper import SchedulesHelper
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class ComnetSchedules:
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def __init__(self, city, base_path):
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self._city = city
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self._comnet_schedules_path = base_path / 'comnet_archetypes.xlsx'
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xls = pd.ExcelFile(self._comnet_schedules_path)
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# todo: review for more than one usage_zones per building
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for building in city.buildings:
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schedules = dict()
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usage_schedules = pd.read_excel(xls, sheet_name=SchedulesHelper.comnet_pluto_schedules_function(building.function)
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, skiprows=[0, 1, 2, 3], nrows=39, usecols="A:AA")
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# todo: should we save the data type? How?
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number_of_schedule_types = 13
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schedules_per_schedule_type = 3
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day_types = dict({'week_day': 0, 'saturday': 1, 'sunday': 2})
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for schedule_types in range(0, number_of_schedule_types):
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data = pd.DataFrame()
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columns_names = []
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name = ''
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for schedule_day in range(0, schedules_per_schedule_type):
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row_cells = usage_schedules.iloc[schedules_per_schedule_type*schedule_types + schedule_day]
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if schedule_day == day_types['week_day']:
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name = row_cells[0]
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columns_names.append(row_cells[2])
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data1 = row_cells[schedules_per_schedule_type:]
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data = pd.concat([data, data1], axis=1)
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data.columns = columns_names
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schedules[name] = data
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building.usage_zones[0].schedules = schedules
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