""" MyClass module SPDX - License - Identifier: LGPL - 3.0 - or -later Copyright © 2020 Project """ import pandas as pd import parseidf class DoeIdf: """ This is a import factory to add Idf schedules into the data model """ idf_schedule_to_commet_schedule = {'BLDG_LIGHT_SCH': 'Lights', 'BLDG_OCC_SCH_wo_SB': 'Occupancy', 'BLDG_EQUIP_SCH': 'Equipment', 'ACTIVITY_SCH': 'Activity', 'INFIL_QUARTER_ON_SCH': 'Infiltration'} _SCHEDULE_COMPACT_TYPE = 'SCHEDULE:COMPACT' _SCHEDULE_TYPE_NAME = 1 def __init__(self, city, base_path): self._hours = [] panda_hours = pd.timedelta_range(0, periods=24, freq='H') for i, hour in enumerate(panda_hours): self._hours.append(str(hour).replace('0 days ', '').replace(':00:00', ':00')) self._city = city self._idf_schedules_path = base_path / 'ASHRAE901_OfficeSmall_STD2019_Buffalo.idf' with open(self._idf_schedules_path, 'r') as f: idf = parseidf.parse(f.read()) self._load_schedule(idf, 'small_office') def _load_schedule(self, idf, building_usage): schedules_day = {} for compact_schedule in idf[self._SCHEDULE_COMPACT_TYPE]: if compact_schedule[self._SCHEDULE_TYPE_NAME] in self.idf_schedule_to_commet_schedule: schedule_type = self.idf_schedule_to_commet_schedule[compact_schedule[self._SCHEDULE_TYPE_NAME]] else: continue days_index = [] days_schedules = [] for position, elements in enumerate(compact_schedule): element_title = elements.title().replace('For: ', '') if elements.title() != element_title: days_index.append(position) # store a cleaned version of the compact schedule days_schedules.append(element_title) days_index.append(len(days_schedules)) for i, day_index in enumerate(days_index): if day_index == len(days_schedules): break schedules_day[f'{days_schedules[day_index]}'] = [] hour_index = 0 for hours_values in range(day_index + 1, days_index[i + 1] - 1, 2): # Create 24h sequence for index, hour in enumerate(self._hours[hour_index:]): hour_formatted = days_schedules[hours_values].replace("Until: ", "") if len(hour_formatted) == 4: hour_formatted = f'0{hour_formatted}' if hour == hour_formatted: hour_index += index break else: entry = (hour, days_schedules[hours_values + 1]) schedules_day[f'{days_schedules[day_index]}'].append(entry) print(schedules_day[f'{days_schedules[day_index]}']) ''' for i in range(len(index) - 1): number_of_day_schedule = list(day_types[index[i] + 1:index[i + 1]]) hourly_values = list(range(24)) start_hour = 0 for num in range(int(len(number_of_day_schedule) / 2)): until_time = list(map(int, (number_of_day_schedule[2*num].split('Until: ')[-1]).split(":")[0:])) end_hour = int(until_time[0] + until_time[1] / 60) value = float(number_of_day_schedule[2 * num + 1]) for hour in range(start_hour, end_hour): hourly_values[hour] = value start_hour = end_hour print(f'{compact_schedule}') print(f'{building_usage} {schedule_type} {hourly_values}') if day_type[index[i]] == 'Weekdays': weekday.append(hourly_values) elif day_type[index[i]] == 'Weekends': weekend.append(hourly_values) elif day_type[index[i]] == 'Holiday': holiday.append(hourly_values) elif day_type[index[i]] == 'Winterdesignday': winter_design_day.append(hourly_values) elif day_type[index[i]] == 'Summerdesignday': summer_design_day.append(hourly_values) elif day_type[index[i]] == 'Customday1': custom_day_1.append(hourly_values) elif day_type[index[i]] == 'Customday2': custom_day_2.append(hourly_values) else: raise ValueError('Unknown schedule day type') idf_schedules = pd.DataFrame( {'week_day': schedules_day['Weekdays'], 'Weekends': schedules_day['Weekends'], 'Holiday': schedules_day['Holiday'], 'Winterdesignday': schedules_day['Winterdesignday'], 'Summerdesignday': schedules_day['Summerdesignday'], 'Customday1': schedules_day['Customday1'], 'Customday2': schedules_day['Customday2'] }) print(idf_schedules) ''' ''' for building in city.buildings: schedules = dict() for usage_zone in building.usage_zones: usage_schedules = idf_schedules # todo: should we save the data type? How? number_of_schedule_types = 5 schedules_per_schedule_type = 6 idf_day_types = dict({'week_day': 0, 'Weekends': 1, 'Holiday': 2, 'Winterdesignday': 3, 'Summerdesignday': 4, 'Customday1': 5, 'Customday2': 6}) for schedule_types in range(0, number_of_schedule_types): data = pd.DataFrame() columns_names = [] name = '' for schedule_day in range(0, len(idf_schedules)): row_cells = usage_schedules.iloc[schedules_per_schedule_type*schedule_types + schedule_day] if schedule_day == idf_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 '''