def main():
locator = InputLocator(REFERENCE_CASE)
gv = GlobalVariables()
weather_path = locator.get_default_weather()
weather_data = epwreader.epw_reader(weather_path)[[
'drybulb_C', 'relhum_percent', 'windspd_ms', 'skytemp_C'
]]
building_properties = BuildingProperties(locator, gv)
date = pd.date_range(gv.date_start, periods=8760, freq='H')
list_uses = building_properties.list_uses()
schedules = schedule_maker(date, locator, list_uses)
usage_schedules = {'list_uses': list_uses, 'schedules': schedules}
print("data for test_calc_thermal_loads_new_ventilation:")
print building_properties.list_building_names()
bpr = building_properties['B01']
result = calc_thermal_loads('B01', bpr, weather_data, usage_schedules,
date, gv, locator)
# test the building csv file
df = pd.read_csv(locator.get_demand_results_file('B01'))
expected_columns = list(df.columns)
print("expected_columns = %s" % repr(expected_columns))
value_columns = [
u'Ealf_kWh', u'Eauxf_kWh', u'Edataf_kWh', u'Ef_kWh', u'QCf_kWh',
u'QHf_kWh', u'Qcdataf_kWh', u'Qcref_kWh', u'Qcs_kWh', u'Qcsf_kWh',
u'Qhs_kWh', u'Qhsf_kWh', u'Qww_kWh', u'Qwwf_kWh', u'Tcsf_re_C',
u'Thsf_re_C', u'Twwf_re_C', u'Tcsf_sup_C', u'Thsf_sup_C', u'Twwf_sup_C'
]
print("values = %s " % repr([df[column].sum()
for column in value_columns]))
print("data for test_calc_thermal_loads_other_buildings:")
# randomly selected except for B302006716, which has `Af == 0`
buildings = {
'B01': (81124.39400, 150471.05200),
'B03': (81255.09200, 150520.01000),
'B02': (82176.15300, 150604.85100),
'B05': (84058.72400, 150841.56200),
'B04': (82356.22600, 150598.43400),
'B07': (81052.19000, 150490.94800),
'B06': (83108.45600, 150657.24900),
'B09': (84491.58100, 150853.54000),
'B08': (88572.59000, 151020.09300),
}
for building in buildings.keys():
bpr = building_properties[building]
b, qcf_kwh, qhf_kwh = run_for_single_building(building, bpr,
weather_data,
usage_schedules, date,
gv, locator)
print("'%(b)s': (%(qcf_kwh).5f, %(qhf_kwh).5f)," % locals())
def main():
locator = InputLocator(REFERENCE_CASE)
gv = GlobalVariables()
weather_path = locator.get_default_weather()
weather_data = epwreader.epw_reader(weather_path)[['drybulb_C', 'relhum_percent', 'windspd_ms', 'skytemp_C']]
building_properties = BuildingProperties(locator, gv)
date = pd.date_range(gv.date_start, periods=8760, freq='H')
list_uses = building_properties.list_uses()
schedules = schedule_maker(date, locator, list_uses)
usage_schedules = {'list_uses': list_uses,
'schedules': schedules}
print("data for test_calc_thermal_loads_new_ventilation:")
print building_properties.list_building_names()
bpr = building_properties['B01']
result = calc_thermal_loads('B01', bpr, weather_data, usage_schedules, date, gv, locator)
# test the building csv file
df = pd.read_csv(locator.get_demand_results_file('B01'))
expected_columns = list(df.columns)
print("expected_columns = %s" % repr(expected_columns))
value_columns = [u'Ealf_kWh', u'Eauxf_kWh', u'Edataf_kWh', u'Ef_kWh', u'QCf_kWh', u'QHf_kWh',
u'Qcdataf_kWh', u'Qcref_kWh', u'Qcs_kWh', u'Qcsf_kWh', u'Qhs_kWh', u'Qhsf_kWh', u'Qww_kWh',
u'Qwwf_kWh', u'Tcsf_re_C', u'Thsf_re_C', u'Twwf_re_C', u'Tcsf_sup_C', u'Thsf_sup_C',
u'Twwf_sup_C']
print("values = %s " % repr([df[column].sum() for column in value_columns]))
print("data for test_calc_thermal_loads_other_buildings:")
# randomly selected except for B302006716, which has `Af == 0`
buildings = {'B01': (81124.39400, 150471.05200),
'B03': (81255.09200, 150520.01000),
'B02': (82176.15300, 150604.85100),
'B05': (84058.72400, 150841.56200),
'B04': (82356.22600, 150598.43400),
'B07': (81052.19000, 150490.94800),
'B06': (83108.45600, 150657.24900),
'B09': (84491.58100, 150853.54000),
'B08': (88572.59000, 151020.09300), }
for building in buildings.keys():
bpr = building_properties[building]
b, qcf_kwh, qhf_kwh = run_for_single_building(building, bpr, weather_data, usage_schedules,
date, gv, locator)
print("'%(b)s': (%(qcf_kwh).5f, %(qhf_kwh).5f)," % locals())
def main(output_file):
import cea.examples
archive = zipfile.ZipFile(
os.path.join(os.path.dirname(cea.examples.__file__),
'reference-case-open.zip'))
archive.extractall(tempfile.gettempdir())
reference_case = os.path.join(tempfile.gettempdir(), 'reference-case-open',
'baseline')
locator = InputLocator(reference_case)
gv = GlobalVariables()
weather_path = locator.get_default_weather()
weather_data = epwreader.epw_reader(weather_path)[[
'drybulb_C', 'relhum_percent', 'windspd_ms', 'skytemp_C'
]]
# run properties script
import cea.demand.preprocessing.properties
cea.demand.preprocessing.properties.properties(locator, True, True, True,
True)
building_properties = BuildingProperties(locator, gv)
date = pd.date_range(gv.date_start, periods=8760, freq='H')
list_uses = building_properties.list_uses()
archetype_schedules, archetype_values = schedule_maker(
date, locator, list_uses)
usage_schedules = {
'list_uses': list_uses,
'archetype_schedules': archetype_schedules,
'occupancy_densities': archetype_values['people'],
'archetype_values': archetype_values
}
print("data for test_calc_thermal_loads:")
print(building_properties.list_building_names())
bpr = building_properties['B01']
result = calc_thermal_loads('B01', bpr, weather_data, usage_schedules,
date, gv, locator)
# test the building csv file
df = pd.read_csv(locator.get_demand_results_file('B01'))
expected_columns = list(df.columns)
print("expected_columns = %s" % repr(expected_columns))
config = ConfigParser.SafeConfigParser()
config.read(output_file)
value_columns = [
u'Ealf_kWh', u'Eauxf_kWh', u'Edataf_kWh', u'Ef_kWh', u'QCf_kWh',
u'QHf_kWh', u'Qcdataf_kWh', u'Qcref_kWh', u'Qcs_kWh', u'Qcsf_kWh',
u'Qhs_kWh', u'Qhsf_kWh', u'Qww_kWh', u'Qwwf_kWh', u'Tcsf_re_C',
u'Thsf_re_C', u'Twwf_re_C', u'Tcsf_sup_C', u'Thsf_sup_C', u'Twwf_sup_C'
]
values = [float(df[column].sum()) for column in value_columns]
print("values = %s " % repr(values))
if not config.has_section("test_calc_thermal_loads"):
config.add_section("test_calc_thermal_loads")
config.set("test_calc_thermal_loads", "value_columns",
json.dumps(value_columns))
print values
config.set("test_calc_thermal_loads", "values", json.dumps(values))
print("data for test_calc_thermal_loads_other_buildings:")
buildings = ['B01', 'B03', 'B02', 'B05', 'B04', 'B07', 'B06', 'B09', 'B08']
results = {}
for building in buildings:
bpr = building_properties[building]
b, qcf_kwh, qhf_kwh = run_for_single_building(building, bpr,
weather_data,
usage_schedules, date,
gv, locator)
print("'%(b)s': (%(qcf_kwh).5f, %(qhf_kwh).5f)," % locals())
results[building] = (qcf_kwh, qhf_kwh)
if not config.has_section("test_calc_thermal_loads_other_buildings"):
config.add_section("test_calc_thermal_loads_other_buildings")
config.set("test_calc_thermal_loads_other_buildings", "results",
json.dumps(results))
with open(output_file, 'w') as f:
config.write(f)
print("Wrote output to %(output_file)s" % locals())
def main():
import zipfile
import cea.examples
import tempfile
archive = zipfile.ZipFile(
os.path.join(os.path.dirname(cea.examples.__file__),
'reference-case-open.zip'))
archive.extractall(tempfile.gettempdir())
reference_case = os.path.join(tempfile.gettempdir(), 'reference-case-open',
'baseline')
locator = InputLocator(reference_case)
gv = GlobalVariables()
weather_path = locator.get_default_weather()
weather_data = epwreader.epw_reader(weather_path)[[
'drybulb_C', 'relhum_percent', 'windspd_ms', 'skytemp_C'
]]
# run properties script
import cea.demand.preprocessing.properties
cea.demand.preprocessing.properties.properties(locator, True, True, True,
True)
building_properties = BuildingProperties(locator, gv)
date = pd.date_range(gv.date_start, periods=8760, freq='H')
list_uses = building_properties.list_uses()
archetype_schedules, archetype_values = schedule_maker(
date, locator, list_uses)
usage_schedules = {
'list_uses': list_uses,
'archetype_schedules': archetype_schedules,
'occupancy_densities': archetype_values['people'],
'archetype_values': archetype_values
}
print("data for test_calc_thermal_loads:")
print building_properties.list_building_names()
bpr = building_properties['B01']
result = calc_thermal_loads('B01', bpr, weather_data, usage_schedules,
date, gv, locator)
# test the building csv file
df = pd.read_csv(locator.get_demand_results_file('B01'))
expected_columns = list(df.columns)
print("expected_columns = %s" % repr(expected_columns))
value_columns = [
u'Ealf_kWh', u'Eauxf_kWh', u'Edataf_kWh', u'Ef_kWh', u'QCf_kWh',
u'QHf_kWh', u'Qcdataf_kWh', u'Qcref_kWh', u'Qcs_kWh', u'Qcsf_kWh',
u'Qhs_kWh', u'Qhsf_kWh', u'Qww_kWh', u'Qwwf_kWh', u'Tcsf_re_C',
u'Thsf_re_C', u'Twwf_re_C', u'Tcsf_sup_C', u'Thsf_sup_C', u'Twwf_sup_C'
]
print("values = %s " % repr([df[column].sum()
for column in value_columns]))
print("data for test_calc_thermal_loads_other_buildings:")
# randomly selected except for B302006716, which has `Af == 0`
buildings = ['B01', 'B03', 'B02', 'B05', 'B04', 'B07', 'B06', 'B09', 'B08']
for building in buildings:
bpr = building_properties[building]
b, qcf_kwh, qhf_kwh = run_for_single_building(building, bpr,
weather_data,
usage_schedules, date,
gv, locator)
print("'%(b)s': (%(qcf_kwh).5f, %(qhf_kwh).5f)," % locals())