149 lines
6.2 KiB
Python
149 lines
6.2 KiB
Python
"""
|
|
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._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):
|
|
holiday = []
|
|
winter_design_day = []
|
|
summer_design_day = []
|
|
custom_day_1 = []
|
|
custom_day_2 = []
|
|
weekday = []
|
|
weekend = []
|
|
day_types = []
|
|
schedules_day = {'None':[]}
|
|
compact_keywords = ['Weekdays', 'Weekends', 'Alldays', 'AllOtherDays', 'Sunday', 'Monday', 'Tuesday',
|
|
'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Holiday', 'Winterdesignday',
|
|
'Summerdesignday', 'Customday1', 'Customday2']
|
|
j = 0
|
|
for compact_schedule in idf[self._SCHEDULE_COMPACT_TYPE]:
|
|
j = j +1
|
|
if 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:
|
|
schedule_type = 'Unknown'
|
|
# todo: check this piece of code logic
|
|
days_index = []
|
|
for position, elements in enumerate(compact_schedule):
|
|
element_title = elements.title().replace('For: ', '')
|
|
if elements.title() != element_title:
|
|
days_index.append(position)
|
|
day_types.append(element_title)
|
|
days_index.append(len(day_types))
|
|
print(day_types)
|
|
print(days_index)
|
|
'''
|
|
for count, word_day_type in enumerate(day_type):
|
|
if word_day_type in compact_keywords:
|
|
index.append(count)
|
|
index.append(len(day_type))
|
|
'''
|
|
|
|
for i, day_index in enumerate(days_index):
|
|
if day_index == len(day_types):
|
|
break
|
|
schedules_day[f'{day_types[day_index]}'] = []
|
|
print(f'{day_types[day_index]}')
|
|
for hours_values in range(day_index+1, days_index[i + 1]-1, 2):
|
|
|
|
print(f'{day_types[hours_values]}# {day_types[hours_values+1]} ')
|
|
|
|
if j == 2:
|
|
return
|
|
|
|
'''
|
|
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': weekday,
|
|
'Weekends': weekend,
|
|
'Holiday': holiday,
|
|
'Winterdesignday': winter_design_day,
|
|
'Summerdesignday': summer_design_day,
|
|
'Customday1': custom_day_1,
|
|
'Customday2': custom_day_2,
|
|
})
|
|
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
|
|
'''
|