def _process_collections(self, description, data_type_text, unit):
"""Convert the raw data in the hidden _data property to data collections."""
data_type = Power() if unit == 'W' else MassFlowRate()
collections = []
for i, col_head in enumerate(self._headers):
if data_type_text in col_head:
metadata = {
'type': description,
'Zone': col_head.split(':')[0]
}
head = Header(data_type, unit, self._a_period, metadata)
collections.append(
HourlyContinuousCollection(
head, [float(val) for val in self._data[i]]))
return collections
def data_to_load(data_colls, data_type, analysis_period):
"""Convert data collections output by EnergyPlus to a single load collection.
Args:
data_colls: A list of monthly data collections for an energy term.
data_type: Text for the data type of the collections (eg. "Cooling").
analysis_period: AnalysisPeriod object describing the date and timestep
of the design day.
"""
if len(data_colls) != 0:
total_vals = [sum(ts_vals) for ts_vals in zip(*data_colls)]
else: # just make a "filler" collection of 0 values
total_vals = [0] * (24 * analysis_period.timestep)
meta_dat = {'type': data_type}
total_head = Header(Power(), 'W', analysis_period, meta_dat)
return HourlyContinuousCollection(total_head, total_vals)
def heat_loss_convection(self):
"""Data Collection of heat loss by convection in [W]."""
return self._get_coll('_hl_convection_coll',
self._heat_loss_convection,
Power('Heat Loss From Convection'), 'W')
def heat_loss_dry_respiration(self):
"""Data Collection of heat loss by dry respiration in [W]."""
return self._get_coll('_hl_dry_respiration_coll',
self._heat_loss_dry_respiration,
Power('Heat Loss From Dry Respiration'), 'W')
def heat_loss_latent_respiration(self):
"""Data Collection of heat loss by latent respiration in [W]."""
return self._get_coll('_hl_latent_respiration_coll',
self._heat_loss_latent_respiration,
Power('Heat Loss From Latent Respiration'), 'W')
def heat_loss_sweating(self):
"""Data Collection of heat loss by sweating in [W]."""
return self._get_coll('_hl_sweating', self._heat_loss_sweating,
Power('Heat Loss From Sweating'), 'W')
pv = val[0]['size_kw']
elif key == 'storage' and len(val) != 0:
storage = [val[0]['size_kw'], val[0]['size_kwh']]
elif key == 'generator' and len(val) != 0:
generator = val[0]['size_kw']
# parse the CSV results of the simulation if successful
if os.path.isfile(re_csv):
data = [] # final list of data to be collected
# parse the data and figure out the timeseries properties
csv_data = csv_to_matrix(re_csv)
csv_header = csv_data.pop(0)
a_period = extract_analysis_period(csv_data)
for col, col_name in zip(zip(*csv_data), csv_header):
if col_name.startswith('REopt:'):
# figure out the type of object to write into the metadata
base_name = col_name.replace('REopt:', '').split(':')
end_name, units_init = base_name[-1].split('(')
units_init = units_init.replace(')', '')
if units_init == 'kw':
units, data_type = 'kW', Power()
elif units_init == 'pct':
units, data_type = 'fraction', Fraction()
else:
continue
metadata = {'type': ':'.join(base_name[:-1] + [end_name])}
# create the final data collections
result_vals = [float(val) for val in col]
header = Header(data_type, units, a_period, metadata)
data.append(HourlyContinuousCollection(header, result_vals))
