Idf parser and standardization is done (correct version)_need to be updated
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Soroush Samareh Abolhassani
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1d0dd11409
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@ -1,65 +1,105 @@
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import pandas as pd
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from imports.schedules.helpers.schedules_helper import SchedulesHelper
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import parseidf
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class IdfParser:
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_OCCUPANCY_SCHEDULE= ("BLDG_OCC_SCH_wo_SB","Occupancy")
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_Activity_Schedule= "ACTIVITY_SCH"
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_Electrical_Equipent_Schedule= "BLDG_EQUIP_SCH"
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_Lighting_Schedule="BLDG_LIGHT_SCH"
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_Infiltration_Ventilation_Schedule="INFIL_QUARTER_ON_SCH"
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def __init__(self, city, base_path, type):
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self._city = city
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self._idf_schedules_path = base_path / 'ASHRAE901_OfficeSmall_STD2019_Buffalo.idf'
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with open(self._idf_schedules_path, 'r') as f:
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idf = parseidf.parse(f.read())
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class IDFParser:
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_OCCUPANCY_SCHEDULE = ("BLDG_OCC_SCH_wo_SB", "Occupancy")
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_Activity_Schedule = "ACTIVITY_SCH"
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_Electrical_Equipent_Schedule = "BLDG_EQUIP_SCH"
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_Lighting_Schedule = "BLDG_LIGHT_SCH"
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_Infiltration_Ventilation_Schedule = "INFIL_QUARTER_ON_SCH"
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for building in city.buildings:
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schedules = dict()
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for usage_zone in building.usage_zones:
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usage_schedules = pd.Datafram(idf['SCHEDULE:COMPACT'])
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def __init__(self, city, base_path):
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self._city = city
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self._idf_schedules_path = base_path / 'ASHRAE901_OfficeSmall_STD2019_Buffalo.idf'
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with open(self._idf_schedules_path, 'r') as f:
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idf = parseidf.parse(f.read())
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print(idf.keys()) # lists the object types in the idf file
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print(idf['SCHEDULE:COMPACT']) # lists all the Output:Variable objects in the idf file
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Holiday = []
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Winterdesignday = []
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Summerdesignday = []
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Customday1 = []
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Customday2 = []
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Weekday = []
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Weekend = []
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dayType = []
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compactKeywords = ['Weekdays', 'Weekends', 'Alldays', 'AllOtherDays', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
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'Thursday', 'Friday', 'Saturday', 'Holiday', 'Winterdesignday', 'Summerdesignday', 'Customday1',
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'Customday2']
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for val in idf['SCHEDULE:COMPACT']:
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if val[1] == 'BLDG_LIGHT_SCH':
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count = 0
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for words in val:
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dayType.append(words.title().split('For: ')[-1])
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index = []
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for count, word_dayType in enumerate(dayType):
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if word_dayType in compactKeywords:
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index.append(count)
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index.append(len(dayType))
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for i in range(len(index) - 1):
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numberOfDaySch = list(dayType[index[i] + 1:index[i + 1]])
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hourlyValues = list(range(24))
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startHour = 0
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for num in range(int(len(numberOfDaySch) / 2)):
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untilTime = list(map(int, (numberOfDaySch[2 * num].split('Until: ')[-1]).split(":")[0:]))
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endHour = int(untilTime[0] + untilTime[1] / 60)
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value = float(numberOfDaySch[2 * num + 1])
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for hour in range(startHour, endHour):
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hourlyValues[hour] = value
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print(hour, value)
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startHour = endHour
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if dayType[index[i]] == 'Weekdays':
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Weekday.append(hourlyValues)
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elif dayType[index[i]] == 'Weekends':
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Weekend.append(hourlyValues)
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elif dayType[index[i]] == 'Holiday':
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Holiday.append(hourlyValues)
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elif dayType[index[i]] == 'Winterdesignday':
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Winterdesignday.append(hourlyValues)
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elif dayType[index[i]] == 'Summerdesignday':
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Summerdesignday.append(hourlyValues)
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elif dayType[index[i]] == 'Customday1':
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Customday1.append(hourlyValues)
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elif dayType[index[i]] == 'Customday2':
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Customday2.append(hourlyValues)
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else:
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print('its none of them')
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idf_schedules = pd.DataFrame(
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{'week_day': Weekday,
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'Weekends': Weekend,
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'Holiday': Holiday,
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'Winterdesignday': Winterdesignday,
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'Summerdesignday': Summerdesignday,
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'Customday1': Customday1,
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'Customday2': Customday2,
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})
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print(idf_schedules)
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Small_Office_Occupancy = []
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Small_Office_OccupancyActivity = []
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Small_Office_ElectricalEquipment = []
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Small_Office_Lighting = []
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Small_Office_Infiltration = []
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for building in city.buildings:
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schedules = dict()
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for usage_zone in building.usage_zones:
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usage_schedules = idf_schedules
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for i in usage_schedules:
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if i[1] == self._Occupancy_Schedule:
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return Small_Office_Occupancy.append(i)
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elif i[1] == 'ACTIVITY_SCH':
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Small_Office_OccupancyActivity.append(i)
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elif i[1] == 'BLDG_EQUIP_SCH':
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Small_Office_ElectricalEquipment.append(i)
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elif i[1] == 'BLDG_LIGHT_SCH':
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Small_Office_Lighting.append(i)
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elif i[1] == 'INFIL_QUARTER_ON_SCH':
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Small_Office_Infiltration.append(i)
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else:
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print("The schedule name is not standard")
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# todo: should we save the data type? How?
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number_of_schedule_types = 5
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schedules_per_schedule_type = 6
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idf_day_types = dict({'week_day': 0, 'Weekends': 1, 'Holiday': 2, 'Winterdesignday': 3, 'Summerdesignday': 4, 'Customday1': 5, 'Customday2': 6})
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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, len(idf_schedules)):
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row_cells = usage_schedules.iloc[schedules_per_schedule_type*schedule_types + schedule_day]
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if schedule_day == idf_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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usage_zone.schedules = schedules
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'''
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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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usage_zone.schedules = schedules
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'''
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