def test_python_serial_workflow_class():
model = PythonModel(model_script='python_model.py',
model_object_name='SumRVs')
model_python_serial_class = RunModel(model=model)
model_python_serial_class.run(samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_serial_class.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
def test_python_serial_workflow_function_vectorized():
model = PythonModel(model_script='python_model.py',
model_object_name='sum_rvs')
model_python_serial_function = RunModel(model=model)
model_python_serial_function.run(samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_serial_function.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
def test_python_parallel_workflow_function():
model = PythonModel(model_script='python_model.py',
model_object_name='sum_rvs')
model_python_parallel_function = RunModel(model=model, ntasks=3)
model_python_parallel_function.run(samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_parallel_function.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
shutil.rmtree(model_python_parallel_function.model_dir)
def test_append_samples_true():
model = PythonModel(model_script='python_model.py',
model_object_name='SumRVs')
model_python_serial_class = RunModel(model=model, samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_serial_class.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
model_python_serial_class.run(x_mcs_new.samples, append_samples=True)
assert np.allclose(
np.array(model_python_serial_class.qoi_list).flatten(),
np.sum(np.vstack((x_mcs.samples, x_mcs_new.samples)), axis=1))
def test_third_party_parallel():
names = ['var1', 'var11', 'var111']
model = ThirdPartyModel(model_script='python_model_sum_scalar.py',
fmt="{:>10.4f}",
delete_files=True,
input_template='sum_scalar.py',
var_names=names,
model_object_name="matlab",
output_script='process_third_party_output.py',
output_object_name='read_output')
m = RunModel(model=model, ntasks=3)
m.run(x_mcs.samples)
assert np.allclose(np.array(m.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1),
atol=1e-4)
shutil.rmtree(m.model.model_dir)
# %%
d_n = Normal(loc=50, scale=400)
d_u = Uniform(location=2.50e8, scale=1.75e7)
x_mcs = MonteCarloSampling(distributions=[d_n, d_u],
samples_number=100,
random_state=987979)
# %% md
#
# Running simulations using the previously defined model object and samples
# %%
sample_points = x_mcs.samples
abaqus_sfe_model.run(samples=sample_points)
# %% md
#
# The outputs from the analysis are the values of the performance function.
# %%
qois = abaqus_sfe_model.qoi_list
# %% md
#
# Save the samples and the qois in a dictionary called results with keys 'inputs' and 'outputs'.
# %%
results = {'inputs': sample_points, 'outputs': qois}