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 calc_mixed_schedule(locator,
building_typology_df,
buildings,
list_var_names=None,
list_var_values=None):
"""
Builds the ``cea.inputlocator.InputLocator#get_building_weekly_schedules`` for each building in the zone,
combining the occupancy types as indicated in the inputs.
:param cea.inputlocator.InputLocator locator: the locator to use
:param building_typology_df: ``occupancy.dbf``, with an added column "mainuse"
:param buildings: the list of buildings to calculate the schedules for
:type buildings: list[str]
:param list_var_names: List of column names in building_typology_df that contain the names of use-types being caculated
:type list_var_names: list[str]
:param list_var_values: List of column names in building_typology_df that contain values of use-type ratio in respect to list_var_names
:type list_var_values: list[str]
:return:
"""
if list_var_names is None:
list_var_names = ["1ST_USE", '2ND_USE', '3RD_USE']
if list_var_values is None:
list_var_values = ["1ST_USE_R", '2ND_USE_R', '3RD_USE_R']
metadata = 'mixed-schedule'
schedule_data_all_uses = ScheduleData(locator)
building_typology_df = building_typology_df.loc[
building_typology_df['Name'].isin(buildings)]
# get list of uses only with a valid value in building_occupancy_df
list_uses = get_list_of_uses_in_case_study(building_typology_df)
internal_loads = pd.read_excel(locator.get_database_use_types_properties(),
'INTERNAL_LOADS')
building_typology_df.set_index('Name', inplace=True)
internal_loads = internal_loads.set_index('code')
occupant_densities = {}
for use in list_uses:
if internal_loads.loc[use, 'Occ_m2pax'] > 0.0:
occupant_densities[use] = 1.0 / internal_loads.loc[use,
'Occ_m2pax']
else:
occupant_densities[use] = 0.0
for building in buildings:
schedule_new_data, schedule_complementary_data = calc_single_mixed_schedule(
list_uses, occupant_densities, building_typology_df,
internal_loads, building, schedule_data_all_uses, list_var_names,
list_var_values, metadata)
# save cea schedule format
path_to_building_schedule = locator.get_building_weekly_schedules(
building)
save_cea_schedule(schedule_new_data, schedule_complementary_data,
path_to_building_schedule)
def schedule_dict_to_file(schedule_dict, schedule_path):
schedule_data = schedule_dict['SCHEDULES']
schedule_complementary_data = {
'MONTHLY_MULTIPLIER': schedule_dict['MONTHLY_MULTIPLIER'],
'METADATA': schedule_dict['METADATA']
}
data = pandas.DataFrame()
for day in ['WEEKDAY', 'SATURDAY', 'SUNDAY']:
df = pandas.DataFrame({'HOUR': range(1, 25), 'DAY': [day] * 24})
for schedule_type, schedule in schedule_data.items():
df[schedule_type] = schedule[day]
data = data.append(df, ignore_index=True)
save_cea_schedule(data.to_dict('list'), schedule_complementary_data,
schedule_path)
print('Schedule file written to {}'.format(schedule_path))
def dict_to_schedule(locator, building, schedule_dict):
schedule_path = locator.get_building_weekly_schedules(building)
schedule_data = schedule_dict['SCHEDULES']
schedule_complementary_data = {
'MONTHLY_MULTIPLIER': schedule_dict['MONTHLY_MULTIPLIER'],
'METADATA': schedule_dict['METADATA']
}
data = pandas.DataFrame()
for day in ['WEEKDAY', 'SATURDAY', 'SUNDAY']:
df = pandas.DataFrame({'HOUR': range(1, 25), 'DAY': [day] * 24})
for schedule_type, schedule in schedule_data.items():
df[schedule_type] = schedule[day]
data = data.append(df, ignore_index=True)
save_cea_schedule(data.to_dict('list'), schedule_complementary_data,
schedule_path)
def create_mixed_use_type(locator, internal_loads_df, indoor_comfort_df,
use_type_name, use_type_metadata,
use_type_ratios_dict):
"""
Takes a list of use-types and their respective ratios to aggregate and create a new use-type
with schedules, internal loads and indoor comfort data
:param schedules_path:
:param internal_loads_df:
:param indoor_comfort_df:
:param use_type_name:
:param use_type_metadata:
:param use_type_ratios_dict:
:return:
"""
list_uses = use_type_ratios_dict.keys()
list_var_names = [
COLUMN_VAR_NAME_TEMPLATE.format(i)
for i in range(len(use_type_ratios_dict))
]
list_var_values = [
COLUMN_VAR_VAL_TEMPLATE.format(i)
for i in range(len(use_type_ratios_dict))
]
# Creating required parameters
properties_dict = {}
for i, (k, v) in enumerate(use_type_ratios_dict.items()):
properties_dict[list_var_names[i]] = k
properties_dict[list_var_values[i]] = v
properties_df = pd.DataFrame([properties_dict])
occupant_densities = calculate_occupant_density(list_uses,
internal_loads_df)
print("Calculating internal loads...")
new_internal_loads_df = calculate_mixed_loads(
properties_df, internal_loads_df, occupant_densities, list_uses,
use_type_name, list_var_names, list_var_values)
print("Calculating indoor comfort...")
new_indoor_comfort_df = calculate_mixed_loads(
properties_df, indoor_comfort_df, occupant_densities, list_uses,
use_type_name, list_var_names, list_var_values)
prop_df_c = properties_df.copy()
prop_df_c[
'Name'] = '0' # Set a `Name` column as index for function to work
prop_df_c.set_index('Name', inplace=True)
schedule_data_all_uses = ScheduleData(locator)
internal_loads = internal_loads_df.set_index('code')
print("Calculating schedules...")
schedule_new_data, schedule_complementary_data = calc_single_mixed_schedule(
list_uses, occupant_densities, prop_df_c, internal_loads, '0',
schedule_data_all_uses, list_var_names, list_var_values,
use_type_metadata)
print("Writing to disk...")
use_type_properties_path = locator.get_database_use_types_properties()
with pd.ExcelWriter(use_type_properties_path) as writer:
new_internal_loads_df.to_excel(writer,
sheet_name='INTERNAL_LOADS',
index=False)
new_indoor_comfort_df.to_excel(writer,
sheet_name='INDOOR_COMFORT',
index=False)
schedule_path = os.path.join(locator.get_database_use_types_folder(),
'{}.csv'.format(use_type_name))
save_cea_schedule(schedule_new_data, schedule_complementary_data,
schedule_path)
def put(self):
form = api.payload
config = current_app.cea_config
locator = cea.inputlocator.InputLocator(config.scenario)
tables = form['tables']
geojsons = form['geojsons']
crs = form['crs']
schedules = form['schedules']
out = {'tables': {}, 'geojsons': {}}
# TODO: Maybe save the files to temp location in case something fails
for db in INPUTS:
db_info = INPUTS[db]
location = getattr(locator, db_info['location'])()
file_type = db_info['file_type']
if len(tables[db]) != 0:
if file_type == 'shp':
from cea.utilities.standardize_coordinates import get_geographic_coordinate_system
table_df = geopandas.GeoDataFrame.from_features(
geojsons[db]['features'],
crs=get_geographic_coordinate_system())
out['geojsons'][db] = json.loads(
table_df.to_json(show_bbox=True))
table_df = table_df.to_crs(crs[db])
table_df.to_file(location,
driver='ESRI Shapefile',
encoding='ISO-8859-1')
table_df = pandas.DataFrame(
table_df.drop(columns='geometry'))
out['tables'][db] = json.loads(
table_df.set_index('Name').to_json(orient='index'))
elif file_type == 'dbf':
table_df = pandas.read_json(json.dumps(tables[db]),
orient='index')
# Make sure index name is 'Name;
table_df.index.name = 'Name'
table_df = table_df.reset_index()
cea.utilities.dbf.dataframe_to_dbf(table_df, location)
out['tables'][db] = json.loads(
table_df.set_index('Name').to_json(orient='index'))
else: # delete file if empty
out['tables'][db] = {}
if os.path.isfile(location):
if file_type == 'shp':
import glob
for filepath in glob.glob(
os.path.join(
locator.get_building_geometry_folder(),
'%s.*' % db)):
os.remove(filepath)
elif file_type == 'dbf':
os.remove(location)
if file_type == 'shp':
out['geojsons'][db] = {}
if schedules:
for building in schedules:
schedule_dict = schedules[building]
schedule_path = locator.get_building_weekly_schedules(building)
schedule_data = schedule_dict['SCHEDULES']
schedule_complementary_data = {
'MONTHLY_MULTIPLIER': schedule_dict['MONTHLY_MULTIPLIER'],
'METADATA': schedule_dict['METADATA']
}
data = pandas.DataFrame()
for day in ['WEEKDAY', 'SATURDAY', 'SUNDAY']:
df = pandas.DataFrame({
'HOUR': range(1, 25),
'DAY': [day] * 24
})
for schedule_type, schedule in schedule_data.items():
df[schedule_type] = schedule[day]
data = data.append(df, ignore_index=True)
save_cea_schedule(data.to_dict('list'),
schedule_complementary_data, schedule_path)
print('Schedule file written to {}'.format(schedule_path))
return out
def calc_mixed_schedule(locator,
building_occupancy_df,
buildings):
metadata = 'mixed-schedule'
schedule_data_all_uses = ScheduleData(locator)
building_occupancy_df = building_occupancy_df.set_index('Name')
building_occupancy_df = building_occupancy_df.loc[buildings]
# get list of uses only with a valid value in building_occupancy_df
list_uses = get_short_list_of_uses_in_case_study(building_occupancy_df)
internal_DB = pd.read_excel(locator.get_archetypes_properties(), 'INTERNAL_LOADS')
internal_DB = internal_DB.set_index('Code')
occupant_densities = {}
for use in list_uses:
if internal_DB.loc[use, 'Occ_m2pax'] > 0.0:
occupant_densities[use] = 1 / internal_DB.loc[use, 'Occ_m2pax']
else:
occupant_densities[use] = 0.0
for building in buildings:
schedule_new_data = {}
main_use_this_building = building_occupancy_df['mainuse'][building]
monthly_multiplier = np.sum([np.array(
schedule_data_all_uses.schedule_complementary_data[use]['MONTHLY_MULTIPLIER']) * building_occupancy_df.loc[
building, use] for use in list_uses], axis=0)
for schedule_type in VARIABLE_CEA_SCHEDULE_RELATION.values():
current_schedule = np.zeros(len(schedule_data_all_uses.schedule_data['HOTEL'][schedule_type]))
normalizing_value = 0.0
if schedule_type in ['HEATING', 'COOLING']:
schedule_new_data[schedule_type] = schedule_data_all_uses.schedule_data[main_use_this_building][
schedule_type]
else:
for use in list_uses:
if building_occupancy_df[use][building] > 0.0:
current_share_of_use = building_occupancy_df[use][building]
if schedule_type in ['OCCUPANCY'] and occupant_densities[use] > 0.0:
# for variables that depend on the number of people, the schedule needs to be calculated by number
# of people for each use at each time step, not the share of the occupancy for each
share_time_occupancy_density = current_share_of_use * occupant_densities[use]
normalizing_value += share_time_occupancy_density
current_schedule = np.vectorize(calc_average)(current_schedule,
schedule_data_all_uses.schedule_data[use][
schedule_type],
share_time_occupancy_density)
if schedule_type in ['WATER'] and occupant_densities[use] > 0.0 and (
internal_DB.loc[use, 'Vw_lpdpax'] + internal_DB.loc[use, 'Vw_lpdpax']) > 0.0:
# for variables that depend on the number of people, the schedule needs to be calculated by number
# of people for each use at each time step, not the share of the occupancy for each
share_time_occupancy_density = current_share_of_use * occupant_densities[use] * (
internal_DB.loc[use, 'Vw_lpdpax'] + internal_DB.loc[use, 'Vw_lpdpax'])
normalizing_value += share_time_occupancy_density
current_schedule = np.vectorize(calc_average)(current_schedule,
schedule_data_all_uses.schedule_data[use][
schedule_type],
share_time_occupancy_density)
elif schedule_type in ['APPLIANCES'] and internal_DB.loc[use, 'Ea_Wm2'] > 0.0:
share_time_occupancy_density = current_share_of_use * internal_DB.loc[use, 'Ea_Wm2']
normalizing_value += share_time_occupancy_density
current_schedule = np.vectorize(calc_average)(current_schedule,
schedule_data_all_uses.schedule_data[use][
schedule_type],
share_time_occupancy_density)
elif schedule_type in ['LIGHTING'] and internal_DB.loc[use, 'El_Wm2'] > 0.0:
share_time_occupancy_density = current_share_of_use * internal_DB.loc[use, 'El_Wm2']
normalizing_value += share_time_occupancy_density
current_schedule = np.vectorize(calc_average)(current_schedule,
schedule_data_all_uses.schedule_data[use][
schedule_type],
share_time_occupancy_density)
elif schedule_type in ['PROCESSES'] and internal_DB.loc[use, 'Epro_Wm2'] > 0.0:
share_time_occupancy_density = current_share_of_use * internal_DB.loc[use, 'Epro_Wm2']
normalizing_value += share_time_occupancy_density
current_schedule = np.vectorize(calc_average)(current_schedule,
schedule_data_all_uses.schedule_data[use][
schedule_type],
share_time_occupancy_density)
elif schedule_type in ['SERVERS'] and internal_DB.loc[use, 'Ed_Wm2'] > 0.0:
share_time_occupancy_density = current_share_of_use * internal_DB.loc[use, 'Ed_Wm2']
normalizing_value += share_time_occupancy_density
current_schedule = np.vectorize(calc_average)(current_schedule,
schedule_data_all_uses.schedule_data[use][
schedule_type],
share_time_occupancy_density)
if normalizing_value == 0.0:
schedule_new_data[schedule_type] = current_schedule * 0.0
else:
schedule_new_data[schedule_type] = np.round(current_schedule/normalizing_value, 2)
# add hour and day of the week
DAY = {'DAY': ['WEEKDAY'] * 24 + ['SATURDAY'] * 24 + ['SUNDAY'] * 24}
HOUR = {'HOUR': range(1, 25) + range(1, 25) + range(1, 25)}
schedule_new_data.update(DAY)
schedule_new_data.update(HOUR)
# calculate complementary_data
schedule_complementary_data = {'METADATA': metadata, 'MONTHLY_MULTIPLIER': monthly_multiplier}
# save cea schedule format
path_to_building_schedule = locator.get_building_weekly_schedules(building)
save_cea_schedule(schedule_new_data, schedule_complementary_data, path_to_building_schedule)