def test_api_optim(cleanup):
results_folder_path = pth.join(RESULTS_FOLDER_PATH, "api_optim")
configuration_file_path = pth.join(results_folder_path, "oad_process.yml")
# Generation of configuration file ----------------------------------------
api.generate_configuration_file(configuration_file_path, True)
# Generation of inputs ----------------------------------------------------
# We get the same inputs as in tutorial notebook
source_xml = pth.join(
root_folder_path,
"src",
"fastoad",
"notebooks",
"01_tutorial",
"data",
"CeRAS01_baseline.xml",
)
api.generate_inputs(configuration_file_path, source_xml, overwrite=True)
# Run optim ---------------------------------------------------------------
problem = api.optimize_problem(configuration_file_path, True)
assert not problem.optim_failed
# Check that weight-performances loop correctly converged
_check_weight_performance_loop(problem)
# Design Variable
assert_allclose(problem["data:geometry:wing:aspect_ratio"], 14.52, atol=1e-2)
# Constraint
assert_allclose(problem["data:geometry:wing:span"], 44.88, atol=1e-2)
# Objective
assert_allclose(problem["data:mission:sizing:needed_block_fuel"], 18900.0, atol=1)
def test_api_eval_mission(cleanup):
results_folder_path = pth.join(RESULTS_FOLDER_PATH, "api_eval_mission")
configuration_file_path = pth.join(results_folder_path, "oad_process.yml")
api._PROBLEM_CONFIGURATOR = MissionConfigurator()
# Generation of configuration file ----------------------------------------
api.generate_configuration_file(configuration_file_path, True)
# Generation of inputs ----------------------------------------------------
# We get the same inputs as in tutorial notebook
source_xml = pth.join(
root_folder_path,
"src",
"fastoad",
"notebooks",
"01_tutorial",
"data",
"CeRAS01_baseline.xml",
)
api.generate_inputs(configuration_file_path, source_xml, overwrite=True)
# Run model ---------------------------------------------------------------
problem = api.evaluate_problem(configuration_file_path, True)
api._PROBLEM_CONFIGURATOR = None
# Check that weight-performances loop correctly converged
_check_weight_performance_loop(problem)
assert_allclose(problem["data:handling_qualities:static_margin"], 0.05, atol=1e-2)
assert_allclose(problem["data:geometry:wing:MAC:at25percent:x"], 17.149, atol=1e-2)
assert_allclose(problem["data:weight:aircraft:MTOW"], 74846, atol=1)
assert_allclose(problem["data:geometry:wing:area"], 126.581, atol=1e-2)
assert_allclose(problem["data:geometry:vertical_tail:area"], 27.535, atol=1e-2)
assert_allclose(problem["data:geometry:horizontal_tail:area"], 35.848, atol=1e-2)
assert_allclose(problem["data:mission:sizing:needed_block_fuel"], 19495, atol=1)
def test_api_eval_breguet(cleanup):
results_folder_path = pth.join(RESULTS_FOLDER_PATH, "api_eval_breguet")
configuration_file_path = pth.join(results_folder_path, "oad_process.yml")
# Generation of configuration file ----------------------------------------
api.generate_configuration_file(configuration_file_path, True)
# Generation of inputs ----------------------------------------------------
# We get the same inputs as in tutorial notebook
source_xml = pth.join(DATA_FOLDER_PATH, "CeRAS01_notebooks.xml")
api.generate_inputs(configuration_file_path, source_xml, overwrite=True)
# Run model ---------------------------------------------------------------
problem = api.evaluate_problem(configuration_file_path, True)
# Check that weight-performances loop correctly converged
_check_weight_performance_loop(problem)
assert_allclose(problem["data:handling_qualities:static_margin"], 0.05, atol=1e-2)
assert_allclose(problem["data:geometry:wing:MAC:at25percent:x"], 17.149, atol=1e-2)
assert_allclose(problem["data:weight:aircraft:MTOW"], 74892, atol=1)
assert_allclose(problem["data:geometry:wing:area"], 126.732, atol=1e-2)
assert_allclose(problem["data:geometry:vertical_tail:area"], 27.565, atol=1e-2)
assert_allclose(problem["data:geometry:horizontal_tail:area"], 35.884, atol=1e-2)
assert_allclose(problem["data:mission:sizing:needed_block_fuel"], 19527, atol=1)
def _generate_conf_file(args):
"""Generates a sample TOML file."""
try:
api.generate_configuration_file(args.conf_file, args.force)
except FastFileExistsError:
if _query_yes_no(
'Configuration file "%s" already exists. Do you want to overwrite it?'
% args.conf_file):
api.generate_configuration_file(args.conf_file, True)
else:
print("No file written.")
def run_non_regression_test(
conf_file,
legacy_result_file,
result_dir,
use_xfoil=False,
global_tolerance=1e-2,
vars_to_check=None,
specific_tolerance=5.0e-3,
check_weight_perfo_loop=True,
):
"""
Convenience function for non regression tests
:param conf_file: FAST-OAD configuration file
:param legacy_result_file: reference data for inputs and outputs
:param result_dir: relative name, folder will be in RESULTS_FOLDER_PATH
:param use_xfoil: if True, XFOIL computation will be activated
:param vars_to_check: variables that will be concerned by specific_tolerance
:param specific_tolerance: test will fail if absolute relative error between computed and
reference values is beyond this value for variables in vars_to_check
:param global_tolerance: test will fail if absolute relative error between computed and
reference values is beyond this value for ANY variable
:param check_weight_perfo_loop: if True, consistency of weights will be checked
"""
results_folder_path = pth.join(RESULTS_FOLDER_PATH, result_dir)
configuration_file_path = pth.join(results_folder_path, conf_file)
# Copy of configuration file and generation of problem instance ------------------
api.generate_configuration_file(configuration_file_path) # just ensure folders are created...
shutil.copy(pth.join(DATA_FOLDER_PATH, conf_file), configuration_file_path)
configurator = FASTOADProblemConfigurator(configuration_file_path)
configurator._set_configuration_modifier(XFOILConfigurator(use_xfoil))
# Generation of inputs ----------------------------------------
ref_inputs = pth.join(DATA_FOLDER_PATH, legacy_result_file)
configurator.write_needed_inputs(ref_inputs)
# Get problem with inputs -------------------------------------
problem = configurator.get_problem(read_inputs=True)
problem.setup()
# Run model ---------------------------------------------------------------
problem.run_model()
problem.write_outputs()
om.view_connections(
problem, outfile=pth.join(results_folder_path, "connections.html"), show_browser=False
)
if check_weight_perfo_loop:
_check_weight_performance_loop(problem)
ref_data = DataFile(pth.join(DATA_FOLDER_PATH, legacy_result_file))
row_list = []
for ref_var in ref_data:
try:
value = problem.get_val(ref_var.name, units=ref_var.units)[0]
except KeyError:
continue
row_list.append(
{
"name": ref_var.name,
"units": ref_var.units,
"ref_value": ref_var.value[0],
"value": value,
}
)
df = pd.DataFrame(row_list)
df["rel_delta"] = (df.value - df.ref_value) / df.ref_value
df["rel_delta"][(df.ref_value == 0) & (abs(df.value) <= 1e-10)] = 0.0
df["abs_rel_delta"] = np.abs(df.rel_delta)
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", 1000)
pd.set_option("display.max_colwidth", 120)
print(df.sort_values(by=["abs_rel_delta"]))
if vars_to_check is not None:
for name in vars_to_check:
assert_allclose(df.ref_value, df.value, rtol=global_tolerance)
row = df.loc[df.name == name]
assert_allclose(row.ref_value, row.value, rtol=specific_tolerance)
else:
assert np.all(df.abs_rel_delta < specific_tolerance)
