def test_mft_time_series_to_modelica_and_run(self):
project_name = "time_series_massflow"
self.data_dir, self.output_dir = self.set_up(os.path.dirname(__file__),
project_name)
# load in the example geojson with a single office building
filename = os.path.join(self.data_dir, "time_series_ex1.json")
self.gj = UrbanOptGeoJson(filename)
# load system parameter data
filename = os.path.join(self.data_dir,
"time_series_system_params_massflow_ex1.json")
sys_params = SystemParameters(filename)
# create the load, ETSes and their couplings
time_series_mft_load = TimeSeriesMFT(sys_params, self.gj.buildings[0])
geojson_load_id = self.gj.buildings[0].feature.properties["id"]
heating_indirect_system = HeatingIndirect(sys_params, geojson_load_id)
ts_hi_coupling = Coupling(time_series_mft_load,
heating_indirect_system)
cooling_indirect_system = CoolingIndirect(sys_params, geojson_load_id)
ts_ci_coupling = Coupling(time_series_mft_load,
cooling_indirect_system)
# create network stubs for the ETSes
heated_water_stub = NetworkHeatedWaterStub(sys_params)
hi_hw_coupling = Coupling(heating_indirect_system, heated_water_stub)
chilled_water_stub = NetworkChilledWaterStub(sys_params)
ci_cw_coupling = Coupling(cooling_indirect_system, chilled_water_stub)
# build the district system
district = District(root_dir=self.output_dir,
project_name=project_name,
system_parameters=sys_params,
coupling_graph=CouplingGraph([
ts_hi_coupling,
ts_ci_coupling,
hi_hw_coupling,
ci_cw_coupling,
]))
district.to_modelica()
root_path = os.path.abspath(
os.path.join(district._scaffold.districts_path.files_dir))
mo_file_name = os.path.join(root_path, 'DistrictEnergySystem.mo')
# set the run time to 31536000 (full year in seconds)
mofile = Model(mo_file_name)
mofile.update_model_annotation({"experiment": {"StopTime": 31536000}})
mofile.save()
self.run_and_assert_in_docker(
mo_file_name,
project_path=district._scaffold.project_path,
project_name=district._scaffold.project_name)
# Check the results
results_dir = f'{district._scaffold.project_path}_results'
mat_file = f'{results_dir}/time_series_massflow_Districts_DistrictEnergySystem_result.mat'
mat_results = Reader(mat_file, 'dymola')
# hack to get the name of the loads (rather the 8 character connector shas)
timeseries_load_var = None
coolflow_var = None
heatflow_var = None
for var in mat_results.varNames():
m = re.match("TimeSerMFTLoa_(.{8})", var)
if m:
timeseries_load_var = m[1]
continue
m = re.match("cooInd_(.{8})", var)
if m:
coolflow_var = m[1]
continue
m = re.match("heaInd_(.{8})", var)
if m:
heatflow_var = m[1]
continue
if None not in (timeseries_load_var, coolflow_var, heatflow_var):
break
(time1, ts_hea_load) = mat_results.values(
f"TimeSerMFTLoa_{timeseries_load_var}.ports_aChiWat[1].m_flow")
(_time1, ts_chi_load) = mat_results.values(
f"TimeSerMFTLoa_{timeseries_load_var}.ports_aHeaWat[1].m_flow")
(_time1,
cool_q_flow) = mat_results.values(f"cooInd_{coolflow_var}.Q_flow")
(_time1,
heat_q_flow) = mat_results.values(f"heaInd_{heatflow_var}.Q_flow")
# if any of these assertions fail, then it is likely that the change in the timeseries massflow model
# has been updated and we need to revalidate the models.
self.assertEqual(ts_hea_load.min(), 0)
self.assertAlmostEqual(ts_hea_load.max(), 51, delta=1)
self.assertAlmostEqual(ts_hea_load.mean(), 4, delta=1)
self.assertEqual(ts_chi_load.min(), 0)
self.assertAlmostEqual(ts_chi_load.max(), 61, delta=1)
self.assertAlmostEqual(ts_chi_load.mean(), 4, delta=1)
self.assertAlmostEqual(cool_q_flow.min(), -51750, delta=10)
self.assertAlmostEqual(cool_q_flow.max(), 354100, delta=10)
self.assertAlmostEqual(cool_q_flow.mean(), 3160, delta=10)
self.assertAlmostEqual(heat_q_flow.min(), -343210, delta=10)
self.assertAlmostEqual(heat_q_flow.max(), 39475, delta=10)
self.assertAlmostEqual(heat_q_flow.mean(), -23270, delta=10)
y = removeRepeats((time, yRaw), 0, True)
yint, tint = uniformData(t, y, tstart, tstop, nt)
data[val] = yint
data['time'] = tint
return data
if __name__ == "__main__":
r = Reader('dsres.mat', 'dymola')
varNames_param_base = []
varNames_var_base = []
for i, val in enumerate(r.varNames()):
if np.size(r.values(val)) == 4:
varNames_param_base.append(val)
else:
varNames_var_base.append(val)
varNames_param = varNames_param_base
varNames_var = varNames_var_base
params = cleanDataParam(r, varNames_param)
data = cleanDataTime(r, varNames_var, 0, 1, 201)
# Normalize data
data_norm = {}
for i, val in enumerate(data):
data_norm[val] = data[val] / max(abs(data[val]))