def test_api_eval(cleanup):
results_folder_path = pth.join(RESULTS_FOLDER_PATH, "api_eval")
configuration_file_path = pth.join(results_folder_path, "oad_process.toml")
# 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",
"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)
# Check that weight-performances loop correctly converged
assert_allclose(
problem["data:weight:aircraft:OWE"],
problem["data:weight:airframe:mass"] +
problem["data:weight:propulsion:mass"] +
problem["data:weight:systems:mass"] +
problem["data:weight:furniture:mass"] +
problem["data:weight:crew:mass"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MZFW"],
problem["data:weight:aircraft:OWE"] +
problem["data:weight:aircraft:max_payload"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MTOW"],
problem["data:weight:aircraft:OWE"] +
problem["data:weight:aircraft:payload"] +
problem["data:mission:sizing:needed_block_fuel"],
atol=1,
)
assert_allclose(problem["data:handling_qualities:static_margin"],
-0.071146,
atol=1e-3)
assert_allclose(problem["data:geometry:wing:MAC:at25percent:x"],
16.0,
atol=1e-2)
assert_allclose(problem["data:weight:aircraft:MTOW"], 76796, atol=1)
assert_allclose(problem["data:geometry:wing:area"], 131.26, atol=1e-2)
assert_allclose(problem["data:geometry:vertical_tail:area"],
27.49,
atol=1e-2)
assert_allclose(problem["data:geometry:horizontal_tail:area"],
33.99,
atol=1e-2)
assert_allclose(problem["data:mission:sizing:needed_block_fuel"],
20708,
atol=1)
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.toml")
# 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",
"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
assert_allclose(
problem["data:weight:aircraft:OWE"],
problem["data:weight:airframe:mass"] +
problem["data:weight:propulsion:mass"] +
problem["data:weight:systems:mass"] +
problem["data:weight:furniture:mass"] +
problem["data:weight:crew:mass"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MZFW"],
problem["data:weight:aircraft:OWE"] +
problem["data:weight:aircraft:max_payload"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MTOW"],
problem["data:weight:aircraft:OWE"] +
problem["data:weight:aircraft:payload"] +
problem["data:mission:sizing:needed_block_fuel"],
atol=1,
)
# Design Variable
assert_allclose(problem["data:geometry:wing:MAC:at25percent:x"],
17.076,
atol=1e-3)
# Constraint
assert_allclose(problem["data:handling_qualities:static_margin"],
0.05,
rtol=1e-5)
# Objective
assert_allclose(problem["data:mission:sizing:needed_block_fuel"],
20837,
atol=1)
class GenerateFiles:
"""
Generate the configuration file
"""
DATA_FOLDER_PATH = "data"
WORK_FOLDER_PATH = "workdir"
CONFIGURATION_FILE = pth.join(WORK_FOLDER_PATH, "oad_process.yml")
SOURCE_FILE = pth.join(DATA_FOLDER_PATH, "CeRAS01_baseline.xml")
# For having log messages on screen
logging.basicConfig(level=logging.INFO, format="%(levelname)-8s: %(message)s")
# For using all screen width
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:95% !important; }</style>"))
oad.generate_configuration_file(CONFIGURATION_FILE, overwrite=True)
def run_non_regression_test(
conf_file,
legacy_result_file,
result_dir,
use_xfoil=False,
vars_to_check=None,
tolerance=5.0e-3,
check_weight_perfo_loop=True,
):
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)
problem = FASTOADProblemConfigurator(configuration_file_path).get_problem()
# Next trick is needed for overloading option setting from TOML file
if use_xfoil and (system() == "Windows" or xfoil_path):
problem.model.aerodynamics_landing._OPTIONS["use_xfoil"] = True
if system() != "Windows":
problem.model.aerodynamics_landing._OPTIONS["xfoil_exe_path"] = xfoil_path
# BTW we narrow computed alpha range for sake of CPU time
problem.model.aerodynamics_landing._OPTIONS["xfoil_alpha_min"] = 18.0
problem.model.aerodynamics_landing._OPTIONS["xfoil_alpha_max"] = 22.0
# Generation and reading of inputs ----------------------------------------
ref_inputs = pth.join(DATA_FOLDER_PATH, legacy_result_file)
get_problem_after_setup(problem).write_needed_inputs(ref_inputs, VariableLegacy1XmlFormatter())
problem.read_inputs()
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 that weight-performances loop correctly converged
assert_allclose(
problem["data:weight:aircraft:OWE"],
problem["data:weight:airframe:mass"]
+ problem["data:weight:propulsion:mass"]
+ problem["data:weight:systems:mass"]
+ problem["data:weight:furniture:mass"]
+ problem["data:weight:crew:mass"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MZFW"],
problem["data:weight:aircraft:OWE"] + problem["data:weight:aircraft:max_payload"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MTOW"],
problem["data:weight:aircraft:OWE"]
+ problem["data:weight:aircraft:payload"]
+ problem["data:mission:sizing:fuel"],
atol=1,
)
ref_var_list = VariableIO(
pth.join(DATA_FOLDER_PATH, legacy_result_file), formatter=VariableLegacy1XmlFormatter(),
).read()
row_list = []
for ref_var in ref_var_list:
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:
row = df.loc[df.name == name]
assert_allclose(row.ref_value, row.value, rtol=tolerance)
# assert np.all(df.abs_rel_delta.loc[df.name == name] < tolerance)
else:
assert np.all(df.abs_rel_delta < tolerance)
def run_non_regression_test(
conf_file,
legacy_result_file,
result_dir,
use_xfoil=False,
vars_to_check=None,
tolerance=5.0e-3,
check_weight_perfo_loop=True,
):
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 that weight-performances loop correctly converged
assert_allclose(
problem["data:weight:aircraft:OWE"],
problem["data:weight:airframe:mass"] +
problem["data:weight:propulsion:mass"] +
problem["data:weight:systems:mass"] +
problem["data:weight:furniture:mass"] +
problem["data:weight:crew:mass"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MZFW"],
problem["data:weight:aircraft:OWE"] +
problem["data:weight:aircraft:max_payload"],
atol=1,
)
assert_allclose(
problem["data:weight:aircraft:MTOW"],
problem["data:weight:aircraft:OWE"] +
problem["data:weight:aircraft:payload"] +
problem["data:mission:sizing:needed_block_fuel"],
atol=1,
)
ref_var_list = VariableIO(pth.join(DATA_FOLDER_PATH,
legacy_result_file), ).read()
row_list = []
for ref_var in ref_var_list:
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:
row = df.loc[df.name == name]
assert_allclose(row.ref_value, row.value, rtol=tolerance)
# assert np.all(df.abs_rel_delta.loc[df.name == name] < tolerance)
else:
assert np.all(df.abs_rel_delta < tolerance)