def test_lj():
def params_relation(params):
sigma = params.get_value("sigma")
epsilon = params.get_value("epsilon")
epsilon[0] = 2 * sigma[0]
params.set_value("epsilon", epsilon)
model = LennardJones(params_relation_callback=params_relation)
# set optimizing parameters
model.set_opt_params(sigma=[[1.1, "fix"]], epsilon=[[2.1, None, 3.0]])
model.echo_model_params()
model.echo_opt_params()
with warnings.catch_warnings(): # context manager to ignore warning
warnings.simplefilter("ignore")
# set model_params by reading from file (the same as set model_params directly)
filename = "tmp_lj_params.txt"
write_tmp_params(filename)
model.read_opt_params(filename)
delete_tmp_params(filename)
model.echo_model_params()
model.echo_opt_params()
path = (Path(__file__).parents[1].joinpath(
"configs_extxyz/MoS2/MoS2_energy_forces_stress.xyz"))
config = Configuration.from_file(path)
energy_forces_stress(model, config, True, False, False)
energy_forces_stress(model, config, True, True, False)
energy_forces_stress(model, config, True, True, True)
def test_desc():
test_file_path = Path(__file__).parents[1].joinpath("configs_extxyz")
conf = Configuration.from_file(test_file_path.joinpath("Si.xyz"))
cut_name = "cos"
cut_dists = {"Si-Si": 5.0}
hyperparams = {
"jmax": 2,
"rfac0": 1,
"diagonalstyle": 3,
"rmin0": 0,
"switch_flag": 1,
"bzero_flag": 0,
"weight": {
"Si": 1.0
},
}
desc = Bispectrum(cut_dists, cut_name, hyperparams)
zeta, dzeta_dr = desc.transform(conf, grad=True)
assert desc.get_size() == 14
assert np.allclose(zeta, zeta_ref)
assert np.allclose(dzeta_dr[0][:2], dzeta_dr_001)
assert np.allclose(dzeta_dr[-1][-2:], dzeta_dr_minus_121)
zeta, dzeta_dr = desc.transform(conf, grad=False)
assert np.allclose(zeta, zeta_ref)
assert dzeta_dr is None
def test_desc():
test_file_path = Path(__file__).parents[1].joinpath("configs_extxyz")
config = Configuration.from_file(test_file_path.joinpath("Si.xyz"))
desc = get_descriptor()
for fit_forces, fit_stress in itertools.product([False, True],
[False, True]):
zeta, dzetadr_forces, dzetadr_stress = desc.transform(
config, fit_forces, fit_stress)
assert np.allclose(zeta, zeta_ref)
if fit_forces:
assert np.allclose(dzetadr_forces[0], dzetadr_forces_ref)
if fit_stress:
assert np.allclose(dzetadr_stress[0], dzetadr_stress_ref)
def test_descriptor():
test_file_path = Path(__file__).parents[1].joinpath("configs_extxyz")
fname = test_file_path.joinpath("Si.xyz")
conf = Configuration.from_file(fname)
conf.identifier = str(fname)
configs = [conf, conf]
for normalize, fit_forces, fit_stress, serial in itertools.product(
[False, True], [False, True], [False, True], [False, True]):
desc = ExampleDescriptor(normalize)
desc.generate_fingerprints(configs,
fit_forces,
fit_stress,
use_welford_method=serial)
data = load_fingerprints("fingerprints.pkl")[0]
if normalize:
assert_mean_stdev(desc.mean, desc.stdev, _mean, _stdev)
assert np.allclose(data["zeta"], _normalized_zeta)
if fit_forces:
assert np.allclose(data["dzetadr_forces"],
_normalized_dzetadr_forces)
if fit_stress:
assert np.allclose(data["dzetadr_stress"],
_normalized_dzetadr_stress)
else:
assert_mean_stdev(desc.mean, desc.stdev, None, None)
assert np.allclose(data["zeta"], _zeta)
if fit_forces:
assert np.allclose(data["dzetadr_forces"], _dzetadr_forces)
if fit_stress:
assert np.allclose(data["dzetadr_stress"], _dzetadr_stress)
# check when normalize is True, if mean and stdev is provided by user, it has to be
# correct.
for normalize, fp_path, mean_std_path in itertools.product(
[False, True], ["fp.pkl"], [None, "ms.pkl"]):
delete_file("fingerprints.pkl")
delete_file("fingerprints_mean_and_stdev.pkl")
delete_file("fp.pkl")
delete_file("ms.pkl")
desc = ExampleDescriptor(normalize)
if normalize and mean_std_path is not None:
# will raise exception because the file mean_std_path does not exist
try:
desc.generate_fingerprints(
configs,
fingerprints_filename=fp_path,
fingerprints_mean_stdev_filename=mean_std_path,
)
except DescriptorError:
pass
else:
desc.generate_fingerprints(
configs,
fingerprints_filename=fp_path,
fingerprints_mean_stdev_filename=mean_std_path,
)
if fp_path is None:
fp_path = "fingerprints.pkl"
data = load_fingerprints(fp_path)[0]
if normalize:
assert_mean_stdev(desc.mean, desc.stdev, _mean, _stdev)
assert np.allclose(data["zeta"], _normalized_zeta)
else:
assert_mean_stdev(desc.mean, desc.stdev, None, None)
assert np.allclose(data["zeta"], _zeta)