def test_load_berlin_format_data(self):
data_actual_file_locations = load_hbefa_cold_berlin_format_data(
berlin_format_link_data=f'{self.init_path}/test_data/berlin_format_data/shape_data.csv',
berlin_format_fleet_composition=f'{self.init_path}/test_data/berlin_format_data/fleet_comp_data.csv',
berlin_format_emission_factors=f'{self.init_path}/test_data/berlin_format_data/hbefa_cold_starts_ef.csv',
berlin_format_cold_starts_data=f'{self.init_path}/test_data/berlin_format_data/cold_starts.csv',
output_folder=f'{self.init_path}/temp_for_hbefa_cold_data_loading_test'
)
yeti_format_emission_factors_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/hbefa_cold_starts_ef.csv')
yeti_format_vehicle_data_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/vehicle_data.csv')
yeti_format_link_data_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/link_data.csv')
yeti_format_cold_starts_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/cold_starts.csv')
self.assertTrue(df_equal(
pd.read_csv(data_actual_file_locations["yeti_format_emission_factors"]),
yeti_format_emission_factors_expected))
self.assertTrue(df_equal(
pd.read_csv(data_actual_file_locations["yeti_format_vehicle_data"]),
yeti_format_vehicle_data_expected))
self.assertTrue(df_equal(
pd.read_csv(data_actual_file_locations["yeti_format_link_data"]),
yeti_format_link_data_expected))
self.assertTrue(df_equal(
pd.read_csv(data_actual_file_locations["yeti_format_cold_starts_data"]),
yeti_format_cold_starts_expected))
def test_use_hbefa_ef_works_correctly(self):
data_loader = HbefaDataLoader(
link_data_file=f'{self.init_path}/test_data/berlin_format_data/shape_data.csv',
fleet_comp_file=f'{self.init_path}/test_data/berlin_format_data/fleet_comp_data.csv',
emission_factor_file=f'{self.init_path}/test_data/berlin_format_data/hbefa_ef_data.csv',
los_speeds_file=f'{self.init_path}/test_data/berlin_format_data/los_speed_data.csv',
traffic_data_file=f'{self.init_path}/test_data/berlin_format_data/traffic_data.csv'
)
(link_data, vehicle_data, traffic_data, los_speeds_data,
emission_factor_data, missing_ef_data) = data_loader.load_data()
link_data.Length = link_data.Length.apply(lambda val: round(val, 4))
yeti_format_data = load_hbefa_hot_yeti_format_data(
yeti_format_emission_factors=f'{self.init_path}/test_data/yeti_format_data/hbefa_ef_data.csv',
yeti_format_vehicle_data=f'{self.init_path}/test_data/yeti_format_data/vehicle_data.csv',
yeti_format_link_data=f'{self.init_path}/test_data/yeti_format_data/link_data.csv',
yeti_format_traffic_data=f'{self.init_path}/test_data/yeti_format_data/traffic_data.csv'
)
link_data_pre = yeti_format_data["link_data"]
vehicle_data_pre = yeti_format_data["vehicle_data"]
traffic_data_pre = yeti_format_data["traffic_data"]
emission_factor_data_pre = yeti_format_data["emission_factor_data"]
self.assertTrue(df_equal(link_data, link_data_pre))
self.assertTrue(df_equal(vehicle_data, vehicle_data_pre))
self.assertTrue(los_speeds_data is None)
self.assertTrue(df_equal(traffic_data, traffic_data_pre))
self.assertTrue(df_equal(emission_factor_data, emission_factor_data_pre))
def test_yeti_format_data_to_emissions(self):
StrategyInvoker().calculate_and_save_emissions(
save_interval_in_rows=2,
emissions_output_folder="temp",
pollutants=["PollutantType.NOx"],
Strategy=CopertStrategy,
only_hot=True,
link_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/link_data.csv"),
traffic_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/traffic_data.csv"
),
vehicle_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/vehicle_data.csv"
),
emission_factor_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/emission_factor_data.csv"
),
los_speeds_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/los_speeds_data.csv"
))
emissions_actual = pd.read_csv("temp/PollutantType.NOx_emissions.csv")
shutil.rmtree("temp")
emissions_expected = pd.read_csv(
f'{self.init_path}/test_data/emissions_expected.csv')
self.assertEqual(len(emissions_expected), len(emissions_actual))
emissions_actual = emissions_actual.round(5)
emissions_expected = emissions_expected.round(5)
self.assertTrue(df_equal(emissions_actual, emissions_expected))
def test_load_builder_data(self):
data_actual = load_hbefa_cold_yeti_format_data(
yeti_format_emission_factors=f'{self.init_path}/test_data/yeti_format_data/hbefa_cold_starts_ef.csv',
yeti_format_vehicle_data=f'{self.init_path}/test_data/yeti_format_data/vehicle_data.csv',
yeti_format_link_data=f'{self.init_path}/test_data/yeti_format_data/link_data.csv',
yeti_format_cold_starts_data=f'{self.init_path}/test_data/yeti_format_data/cold_starts.csv'
)
yeti_format_emission_factors_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/hbefa_cold_starts_ef.csv')
yeti_format_vehicle_data_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/vehicle_data.csv')
yeti_format_link_data_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/link_data.csv')
yeti_format_cold_starts_expected = pd.read_csv(f'{self.init_path}/test_data/yeti_format_data/cold_starts.csv')
self.assertTrue(df_equal(data_actual["link_data"], yeti_format_link_data_expected))
self.assertTrue(df_equal(data_actual["vehicle_data"], yeti_format_vehicle_data_expected))
self.assertTrue(df_equal(data_actual["traffic_data"], yeti_format_cold_starts_expected))
self.assertTrue(df_equal(data_actual["emission_factor_data"], yeti_format_emission_factors_expected))
def test_load_data(self):
ef_data, missing_ef_data = self.loader.load_data()
ef_data_expected = pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/emission_factor_data.csv"
)
self.assertEqual(sorted(list(ef_data_expected.columns)),
sorted(list(ef_data.columns)))
self.assertTrue(df_equal(ef_data, ef_data_expected))
def test_link_data_loader(self) -> None:
if os.path.isfile("./tests/test_data/berlin_format_data/shape_data.csv"):
self.init_path = "./tests"
else:
self.init_path = ".."
link_data = LinkDataLoader(
link_data=pd.read_csv(f"{self.init_path}/test_data/berlin_format_data/shape_data.csv")
).load_data()
link_data_with_speed = LinkDataLoader(
link_data=pd.read_csv(f"{self.init_path}/test_data/berlin_format_data/shape_data_with_speed.csv")
).load_data()
link_data_expected = pd.read_csv(f"{self.init_path}/test_data/yeti_format_data/link_data.csv")
link_data_with_speed_expected = pd.read_csv(f"{self.init_path}/test_data/yeti_format_data/link_data_with_speed.csv")
self.assertTrue(df_equal(link_data, link_data_expected))
self.assertTrue(df_equal(link_data_with_speed, link_data_with_speed_expected))
def test_load_nh3_ef_data(self):
nh3_ef_data = self.loader.load_nh3_ef_data()
nh3_ef_data_expected = pd.DataFrame({
"VehicleName": ["pc vehicle_a", "lcv vehicle_b"],
"EF": [1, 2.5],
"Pollutant": ["PollutantType.NH3", "PollutantType.NH3"]
})
self.assertTrue(df_equal(nh3_ef_data, nh3_ef_data_expected))
def test_data_is_equal(self):
(link_data, vehicle_data, traffic_data, los_speeds_data,
emission_factor_data, missing_ef_data) = self.loader.load_data()
link_data.Length = link_data.Length.apply(lambda val: round(val, 4))
yeti_format_data = load_copert_hot_yeti_format_data(
yeti_format_emission_factors=f'{self.init_path}/test_data/yeti_format_data/emission_factor_data.csv',
yeti_format_los_speeds=f'{self.init_path}/test_data/yeti_format_data/los_speeds_data.csv',
yeti_format_vehicle_data=f'{self.init_path}/test_data/yeti_format_data/vehicle_data.csv',
yeti_format_link_data=f'{self.init_path}/test_data/yeti_format_data/link_data.csv',
yeti_format_traffic_data=f'{self.init_path}/test_data/yeti_format_data/traffic_data.csv'
)
link_data_pre = yeti_format_data["link_data"]
vehicle_data_pre = yeti_format_data["vehicle_data"]
traffic_data_pre = yeti_format_data["traffic_data"]
los_speeds_data_pre = yeti_format_data["los_speeds_data"]
emission_factor_data_pre = yeti_format_data["emission_factor_data"]
self.assertTrue(df_equal(link_data, link_data_pre))
self.assertTrue(df_equal(vehicle_data, vehicle_data_pre))
self.assertTrue(df_equal(los_speeds_data, los_speeds_data_pre))
self.assertTrue(df_equal(traffic_data, traffic_data_pre))
self.assertTrue(
df_equal(missing_ef_data, pd.read_csv(f"{self.init_path}/test_data/other_data/missing_ef_data.csv")))
# avoid error due to floating point inaccuracy
for colname in ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon','Zita', 'Hta', 'Thita', 'ReductionPerc']:
emission_factor_data[colname] = pd.Series(
np.around(emission_factor_data[colname].values, 5))
emission_factor_data_pre[colname] = pd.Series(
np.around(emission_factor_data_pre[colname].values, 5))
self.assertTrue(df_equal(emission_factor_data, emission_factor_data_pre))
def test_load_speed_dependent_ef_data(self):
speed_dependent_ef_data = self.loader.load_speed_dependent_ef_data()
speed_dependent_ef_data_expected = pd.read_csv(
f"{self.init_path}/test_data/other_data/speed_dependent_ef_data.csv"
)
self.assertFalse(speed_dependent_ef_data_expected.empty)
self.assertFalse(speed_dependent_ef_data.empty)
self.assertTrue(
df_equal(speed_dependent_ef_data,
speed_dependent_ef_data_expected))
def test_load_data(self):
if os.path.isfile(
"./tests/test_data/berlin_format_data/shape_data.csv"):
self.init_path = "./tests"
else:
self.init_path = ".."
loader = PMNonExhaustDataLoader(
link_data_file=
f'{self.init_path}/test_data/berlin_format_data/shape_data.csv',
fleet_comp_file=
f'{self.init_path}/test_data/berlin_format_data/fleet_comp_data.csv',
los_speeds_file=
f'{self.init_path}/test_data/berlin_format_data/los_speed_data.csv',
traffic_data_file=
f'{self.init_path}/test_data/berlin_format_data/traffic_data.csv')
(link_data, vehicle_data, traffic_data, los_speeds_data, _,
_) = loader.load_data()
expected_data = load_pm_non_exhaust_yeti_format_data(
yeti_format_los_speeds=
f'{self.init_path}/test_data/yeti_format_data/los_speeds_data.csv',
yeti_format_vehicle_data=
f'{self.init_path}/test_data/yeti_format_data/vehicle_data.csv',
yeti_format_link_data=
f'{self.init_path}/test_data/yeti_format_data/link_data.csv',
yeti_format_traffic_data=
f'{self.init_path}/test_data/yeti_format_data/traffic_data.csv')
self.assertTrue(
df_equal(link_data, expected_data["link_data"].round(5)))
self.assertTrue(df_equal(vehicle_data, expected_data["vehicle_data"]))
self.assertTrue(df_equal(traffic_data, expected_data["traffic_data"]))
self.assertTrue(
df_equal(los_speeds_data, expected_data["los_speeds_data"]))
def test_load_data(self):
if os.path.isfile(
"./tests/test_data/berlin_format_data/fleet_comp_data.csv"):
init_path = "./tests"
else:
init_path = ".."
vehicle_data = VehicleDataLoader(fleet_comp_data=pd.read_csv(
f"{init_path}/test_data/berlin_format_data/fleet_comp_data.csv")
).load_data()
vehicle_data_expected = pd.read_csv(
f"{init_path}/test_data/yeti_format_data/vehicle_data.csv")
self.assertTrue(df_equal(vehicle_data, vehicle_data_expected))
def test_convert_dir_and_day_type(self):
df = pd.DataFrame({
"XY": [1, 2, 3, 4],
"Dir": ["R", Dir.L, "L", "R"],
"DayType": [DayType.FRI, "1", "2", "3"]
})
expected_output = pd.DataFrame({
"XY": [1, 2, 3, 4],
"Dir": [Dir.R, Dir.L, Dir.L, Dir.R],
"DayType":
[DayType.FRI, DayType.MONtoTHU, DayType.FRI, DayType.SAT]
})
actual_output = convert_dir_and_day_type(df)
self.assertTrue(df_equal(actual_output, expected_output))
def test_load_data(self):
if os.path.isfile(
"./tests/test_data/berlin_format_data/hbefa_ef_data.csv"):
init_path = "./tests"
else:
init_path = ".."
yeti_format_hbefa_ef_data, _ = HbefaEmissionFactorDataLoader(
ef_data=pd.read_csv(
f"{init_path}/test_data/berlin_format_data/hbefa_ef_data.csv")
).load_data()
yeti_format_hbefa_ef_data_expected = pd.read_csv(
f"{init_path}/test_data/yeti_format_data/hbefa_ef_data.csv")
self.assertTrue(
df_equal(yeti_format_hbefa_ef_data,
yeti_format_hbefa_ef_data_expected))
def test(self):
sys.argv = f"run_yeti.py -c tests/test_data/acceptance_test_data/acceptance_test_config.yaml".split(
)
execfile(f"run_yeti.py")
emissions_actual = pd.read_csv(
"tests/test_acceptance_tests/output/PollutantType.NOx_emissions.csv"
)
emissions_expected = pd.read_csv(
"tests/test_data/acceptance_test_data/EMoutput_artificialdata.csv")
emissions_actual = emissions_actual.sort_values(
by=["LinkID", "Dir", "DayType", "Hour"])
emissions_expected = emissions_expected.sort_values(
by=["LinkID", "Dir", "DayType", "Hour"])
emissions_expected = emissions_expected.round(3)
emissions_actual = emissions_actual.round(3)
self.assertTrue(df_equal(emissions_actual, emissions_expected))
def test_load_data(self):
if os.path.isfile(
"./tests/test_data/berlin_format_data/traffic_data.csv"):
init_path = "./tests"
else:
init_path = ".."
traffic_data = TrafficDataLoader(
fleet_comp_data=pd.read_csv(
f"{init_path}/test_data/berlin_format_data/fleet_comp_data.csv"
),
link_data=pd.read_csv(
f"{init_path}/test_data/berlin_format_data/shape_data.csv"),
traffic_count_data=pd.read_csv(
f"{init_path}/test_data/berlin_format_data/traffic_data.csv")
).load_data()
traffic_data_expected = pd.read_csv(
f"{init_path}/test_data/yeti_format_data/traffic_data.csv")
self.assertTrue(df_equal(traffic_data, traffic_data_expected))
def test_determine_missing_ef_data(self):
ef_data = pd.DataFrame({
"VehicleName": ["pc vehicle_a", "pc vehicle_a", "pc vehicle_b"],
"Pollutant":
[PollutantType.NOx, PollutantType.NH3, PollutantType.CO]
})
missing_ef_data_expected = pd.DataFrame({
"Pollutant": [
PollutantType.CO, PollutantType.VOC, PollutantType.PM_Exhaust,
PollutantType.NOx, PollutantType.CO, PollutantType.NH3,
PollutantType.VOC, PollutantType.PM_Exhaust
],
"VehicleName": [
"pc vehicle_a", "pc vehicle_a", "pc vehicle_a",
"lcv vehicle_b", "lcv vehicle_b", "lcv vehicle_b",
"lcv vehicle_b", "lcv vehicle_b"
]
})
missing_ef_data = self.loader.determine_missing_ef_data(ef_data)
self.assertTrue(df_equal(missing_ef_data, missing_ef_data_expected))
def test_berlin_format_data_to_emissions(self):
if os.path.isfile("./tests/test_data/emissions_expected_hbefa_ef.csv"):
self.init_path = "./tests"
else:
self.init_path = "."
StrategyInvoker().calculate_and_save_emissions(
save_interval_in_rows=5,
emissions_output_folder="temp",
pollutants=["PollutantType.NOx"],
Strategy=HbefaStrategy,
only_hot=True,
link_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/link_data.csv"),
traffic_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/traffic_data.csv"
),
vehicle_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/vehicle_data.csv"
),
emission_factor_data=pd.read_csv(
f"{self.init_path}/test_data/yeti_format_data/hbefa_ef_data.csv"
))
emissions_actual = pd.read_csv("temp/PollutantType.NOx_emissions.csv")
shutil.rmtree("temp")
emissions_expected = pd.read_csv(
f'{self.init_path}/test_data/emissions_expected_hbefa_ef.csv')
self.assertEqual(len(emissions_expected), len(emissions_actual))
emissions_actual = emissions_actual.round(5)
emissions_expected = emissions_expected.round(5)
self.assertTrue(df_equal(emissions_actual, emissions_expected))
