def test_score_uio():
harmonic, equivalencies = get_harmonic('uio66_ff')
mapping = Mapping(harmonic.atoms.get_masses(), equivalencies)
# contains reference values on the number of reductions as a function of
# the maximum number of equivalent clusters to be considered
cutoff = 4
tol = 1e-1
reductions = generate_reductions(
mapping,
harmonic,
cutoff=cutoff,
tol=tol,
max_num_equiv_clusters=6,
)
reduction = reductions[0]
reduction.apply(mapping)
masses = np.repeat(harmonic.atoms.get_masses(), 3)
mass_matrix = 1 / np.sqrt(np.outer(masses, masses))
hessian_mw = mass_matrix * harmonic.hessian
smap = score(mapping, hessian_mw=hessian_mw, temperature=300)
smap_ = score(mapping, harmonic=harmonic, temperature=300)
assert smap == smap_
assert smap > 0
# construct mapping that groups all atoms into same bead
clusters = np.zeros((mapping.natoms, mapping.natoms), dtype=np.int32)
clusters[0, :] = 1
mapping.update_clusters(clusters)
mapping.update_identities()
entropy = score(mapping, harmonic=harmonic, temperature=300)
print(entropy)
def test_generate_uio():
harmonic, equivalencies = get_harmonic('uio66_ff')
mapping = Mapping(harmonic.atoms.get_masses(), equivalencies)
cutoff = 3.0
tol = 1e-1
environments, radii = generate_environments(mapping, harmonic, cutoff, tol)
# check whether each cluster receives precisely one environment
_all = []
for _, envs in environments.items():
for env in envs:
_all.append(env.indices[0]) # get index of central atom
assert np.allclose(np.sort(np.array(_all)), np.arange(mapping.nclusters))
# verify that envs of the same cluster_type are 'equal'
# verify that each env generates the same number of templates
for cluster_type, envs in environments.items():
reference = envs[0]
templates = reference.generate_templates()
n = len(templates)
for env in envs:
assert reference.equals(env, tol)
assert len(env.generate_templates()) == n
# discard those which cannot be matched uniquely
i = 0
while i < len(templates):
if len(reference.match_template(templates[i], tol)) > 1:
templates.pop(i)
else:
i += 1
for env in envs:
for template in templates:
assert len(env.match_template(template, tol)) == 1
def test_uio():
harmonic, equivalencies = get_harmonic('uio66_ff')
mapping = Mapping(harmonic.atoms.get_masses(), equivalencies)
#write('test.xyz', harmonic.atoms)
# contains reference values on the number of reductions as a function of
# the maximum number of equivalent clusters to be considered
cutoff = 4
tol = 1e-1
reductions = generate_reductions(
mapping,
harmonic,
cutoff=cutoff,
tol=tol,
max_num_equiv_clusters=1,
)
assert len(reductions) == 10
reductions = generate_reductions(
mapping,
harmonic,
cutoff=cutoff,
tol=tol,
max_num_equiv_clusters=2,
)
assert len(reductions) == 18
reductions = generate_reductions(
mapping,
harmonic,
cutoff=cutoff,
tol=tol,
max_num_equiv_clusters=3,
)
assert len(reductions) == 21
reductions = generate_reductions(
mapping,
harmonic,
cutoff=cutoff,
tol=tol,
max_num_equiv_clusters=4,
)
assert len(reductions) == 24
reductions = generate_reductions(
mapping,
harmonic,
cutoff=cutoff,
tol=tol,
max_num_equiv_clusters=5,
)
assert len(reductions) == 24
reductions = generate_reductions(
mapping,
harmonic,
cutoff=cutoff,
tol=tol,
max_num_equiv_clusters=6,
)
assert len(reductions) == 27
def test_frequencies():
name = 'uio66_ff'
harmonic, _ = get_harmonic(name)
frequencies, _ = harmonic.compute_eigenmodes()
entropy, _ = get_entropy(frequencies, 300)
assert np.isclose(np.sum(entropy), 5.328, atol=1e-3) # reference value
# check whether largest frequencies are below 4000 and above 3800 cm^-1
frequencies_invcm = frequencies * ase.units.s / (ase.units._c * 100)
assert np.all(frequencies_invcm[3:] < 4e3)
assert np.any(frequencies_invcm[3:] > 3.8e3)
def test_write_load(tmp_path):
harmonic, equivalencies = get_harmonic('uio66_ff')
mapping = Mapping(harmonic.atoms.get_masses(), equivalencies)
clusters = Mapping.merge(
mapping.clusters,
[(0, 1, 2, 5), (4, 8, 33, 6, 100)],
)
mapping.update_clusters(clusters)
mapping.update_identities(validate=True)
path_npz = tmp_path / 'mapping.npz'
mapping.write(path_npz)
loaded = Mapping.load(path_npz)
assert np.allclose(loaded.masses, mapping.masses)
for i in range(mapping.natoms):
assert loaded.atom_types[i] == mapping.atom_types[i]
assert np.allclose(loaded.clusters, mapping.clusters)
def test_algorithm_uio():
harmonic, equivalencies = get_harmonic('uio66_ff')
mapping = Mapping(harmonic.atoms.get_masses(), equivalencies)
min_neighbors = 3
max_num_equiv_clusters = 6
temperature = 300
algorithm = Algorithm(
harmonic,
mapping,
temperature,
min_neighbors,
max_num_equiv_clusters,
)
cutoff = 4
tol = 1e-1
threshold = 450 # performs only one reduction step
algorithm.run(threshold, cutoff, tol)
def test_init():
name = 'uio66_ff'
harmonic, _ = get_harmonic(name)
assert harmonic.periodic