def modify_monthly_multiplier(locator, config, measured_building_names):
##create building input schedules to set monthly multiplier
archetypes_mapper.archetypes_mapper(
locator,
update_architecture_dbf=False,
update_air_conditioning_systems_dbf=False,
update_indoor_comfort_dbf=False,
update_internal_loads_dbf=False,
update_supply_systems_dbf=False,
update_schedule_operation_cea=True,
buildings=[])
##calculate monthly multiplier based on buildings real consumption
for building_name in measured_building_names:
monthly_measured_data = pd.read_csv(locator.get_monthly_measurements())
fields_to_extract = ['Name'] + MONTHS_IN_YEAR_NAMES
monthly_measured_demand = monthly_measured_data[
fields_to_extract].set_index('Name')
monthly_measured_demand = monthly_measured_demand.loc[building_name]
monthly_measured_demand = pd.DataFrame({
'Month':
monthly_measured_demand.index.values,
'measurements':
monthly_measured_demand.values
})
monthly_measured_load = monthly_measured_demand.measurements / max(
monthly_measured_demand.measurements)
path_to_schedule = locator.get_building_weekly_schedules(building_name)
data_schedule, data_metadata = read_cea_schedule(path_to_schedule)
data_metadata["MONTHLY_MULTIPLIER"] = list(
monthly_measured_load.round(2))
# save cea schedule format
save_cea_schedule(data_schedule, data_metadata, path_to_schedule)
def schedule_to_dict(schedule_path):
schedule_data, schedule_complementary_data = read_cea_schedule(
schedule_path)
df = pandas.DataFrame(schedule_data).set_index(['DAY', 'HOUR'])
out = {
'SCHEDULES': {
schedule_type: {
day: df.loc[day][schedule_type].values.tolist()
for day in df.index.levels[0]
}
for schedule_type in df.columns
}
}
out.update(schedule_complementary_data)
return out
def get(self, building):
config = current_app.cea_config
locator = cea.inputlocator.InputLocator(config.scenario)
try:
schedule_path = locator.get_building_weekly_schedules(building)
schedule_data, schedule_complementary_data = read_cea_schedule(schedule_path)
df = pandas.DataFrame(schedule_data).set_index(['DAY', 'HOUR'])
out = {'SCHEDULES': {
schedule_type: {day: df.loc[day][schedule_type].values.tolist() for day in df.index.levels[0]}
for schedule_type in df.columns}}
out.update(schedule_complementary_data)
return out
except IOError as e:
print(e)
abort(500, 'File not found')
def fill_in_data(self):
get_list_uses_in_database = []
for file in os.listdir(self.path_database):
if file.endswith(".csv"):
get_list_uses_in_database.append(file.split('.')[0])
data_schedules = []
data_schedules_complimentary = []
for use in get_list_uses_in_database:
path_to_schedule = self.locator.get_database_standard_schedules_use(self.path_database, use)
data_schedule, data_metadata = read_cea_schedule(path_to_schedule)
data_schedules.append(data_schedule)
data_schedules_complimentary.append(data_metadata)
return dict(zip(get_list_uses_in_database, data_schedules)), \
dict(zip(get_list_uses_in_database, data_schedules_complimentary))
def fill_in_data(self):
occupancy_types = []
for file_name in os.listdir(self.locator.get_database_use_types_folder()):
if file_name.endswith(".csv"):
use, _ = os.path.splitext(file_name)
occupancy_types.append(use)
data_schedules = []
data_schedules_complimentary = []
for use in occupancy_types:
path_to_schedule = self.locator.get_database_standard_schedules_use(use)
data_schedule, data_metadata = read_cea_schedule(path_to_schedule)
data_schedules.append(data_schedule)
data_schedules_complimentary.append(data_metadata)
schedule_data = dict(zip(occupancy_types, data_schedules))
complementary_data = dict(zip(occupancy_types, data_schedules_complimentary))
return schedule_data, complementary_data
