def test_flatten(self):
"""Tests that flattened, and non-flattened output works correctly.
"""
system = H2O
n = 10
n_species = len(set(system.get_atomic_numbers()))
# K1 unflattened
desc = MBTR(
species=[1, 8],
k1={
"grid": {"n": n, "min": 1, "max": 8, "sigma": 0.1},
"geometry": {"function": "atomic_number"}
},
periodic=False,
flatten=False,
sparse=False
)
feat = desc.create(system)["k1"]
self.assertEqual(feat.shape, (n_species, n))
# K1 flattened. The sparse matrix only supports 2D matrices, so the first
# dimension is always present, even if it is of length 1.
desc.flatten = True
feat = desc.create(system)
self.assertEqual(feat.shape, (1, n_species*n))
def test_flatten(self):
system = H2O
n = 10
n_species = len(set(system.get_atomic_numbers()))
# K1 unflattened
desc = MBTR(species=[1, 8],
k=[1],
grid={"k1": {
"n": n,
"min": 1,
"max": 8,
"sigma": 0.1
}},
periodic=False,
flatten=False,
sparse=False)
feat = desc.create(system)["k1"]
self.assertEqual(feat.shape, (n_species, n))
# K1 flattened. The sparse matrix only supports 2D matrices, so the first
# dimension is always present, even if it is of length 1.
desc = MBTR(species=[1, 8],
k=[1],
grid={"k1": {
"n": n,
"min": 1,
"max": 8,
"sigma": 0.1
}},
periodic=False)
feat = desc.create(system)
self.assertEqual(feat.shape, (1, n_species * n))
def test_k3_weights_and_geoms_finite(self):
"""Tests that all the correct angles are present in finite systems.
There should be n*(n-1)*(n-2)/2 unique angles where the division by two
gets rid of duplicate angles.
"""
# Test with water molecule
mbtr = MBTR([1, 8], k=[3], grid=default_grid, periodic=False)
mbtr.create(H2O)
geoms = mbtr._k3_geoms
weights = mbtr._k3_weights
assumed_geoms = {
(0, 1, 0): 1*[math.cos(104/180*math.pi)],
(0, 0, 1): 2*[math.cos(38/180*math.pi)],
}
self.dict_comparison(geoms, assumed_geoms)
assumed_weights = {
(0, 1, 0): [1],
(0, 0, 1): [1, 1],
}
self.dict_comparison(weights, assumed_weights)
# Test against system with different indexing
mbtr = MBTR([1, 8], k=[3], grid=default_grid, periodic=False)
mbtr.create(H2O_2)
weights2 = mbtr._k3_weights
geoms2 = mbtr._k3_geoms
self.assertEqual(weights, weights2)
self.assertEqual(geoms, geoms2)
def test_k3_geoms_finite_concave(self):
"""Test with four atoms in a "dart"-like arrangement. This arrangement
has both concave and convex angles.
"""
atoms = Atoms(
positions=[
[0, 0, 0],
[np.sqrt(2), np.sqrt(2), 0],
[2*np.sqrt(2), 0, 0],
[np.sqrt(2), np.tan(np.pi/180*15)*np.sqrt(2), 0],
],
symbols=["H", "H", "H", "He"]
)
# view(atoms)
mbtr = MBTR([1, 2, 10], k=[3], grid=default_grid, periodic=False)
mbtr.create(atoms)
angles = mbtr._k3_geoms
# In finite systems there are n*(n-1)*(n-2)/2 unique angles.
n_atoms = len(atoms)
n_angles = sum([len(x) for x in angles.values()])
self.assertEqual(n_atoms*(n_atoms-1)*(n_atoms-2)/2, n_angles)
assumed = {
(0, 1, 0): [math.cos(105/180*math.pi), math.cos(150/180*math.pi), math.cos(105/180*math.pi)],
(0, 0, 0): [math.cos(90/180*math.pi), math.cos(45/180*math.pi), math.cos(45/180*math.pi)],
(0, 0, 1): [math.cos(45/180*math.pi), math.cos(30/180*math.pi), math.cos(45/180*math.pi), math.cos(30/180*math.pi), math.cos(15/180*math.pi), math.cos(15/180*math.pi)]
}
self.dict_comparison(angles, assumed)
def test_k2_weights_and_geoms_finite(self):
"""Tests that the values of the weight and geometry functions are
correct for the k=2 term.
"""
mbtr = MBTR([1, 8], k=[2], grid=default_grid, periodic=False)
mbtr.create(H2O)
weights = mbtr._k2_weights
geoms = mbtr._k2_geoms
# Test against the assumed weights
pos = H2O.get_positions()
assumed_weights = {
(0, 0): [1],
(0, 1): [1, 1]
}
self.dict_comparison(weights, assumed_weights)
# Test against the assumed geometry values
pos = H2O.get_positions()
assumed_geoms = {
(0, 0): [1/np.linalg.norm(pos[0] - pos[2])],
(0, 1): 2*[1/np.linalg.norm(pos[0] - pos[1])]
}
self.dict_comparison(geoms, assumed_geoms)
# Test against system with different indexing
mbtr = MBTR([1, 8], k=[2], grid=default_grid, periodic=False)
mbtr.create(H2O_2)
weights2 = mbtr._k2_weights
geoms2 = mbtr._k2_geoms
self.dict_comparison(geoms, geoms2)
self.dict_comparison(weights, weights2)
def test_k1_weights_and_geoms_finite(self):
"""Tests that the values of the weight and geometry functions are
correct for the k=1 term.
"""
mbtr = MBTR([1, 8], k=[1], grid=default_grid, periodic=False)
mbtr.create(H2O)
weights = mbtr._k1_weights
geoms = mbtr._k1_geoms
# Test against the assumed weights
assumed_weights = {
(0,): [1, 1],
(1,): [1]
}
self.dict_comparison(weights, assumed_weights)
# Test against the assumed geometry values
assumed_geoms = {
(0,): [1, 1],
(1,): [8]
}
self.dict_comparison(geoms, assumed_geoms)
# Test against system with different indexing
mbtr = MBTR(
[1, 8],
k=[1],
grid={"k1": {"min": 1, "max": 8, "sigma": 0.1, "n": 10}},
periodic=False
)
mbtr.create(H2O_2)
weights2 = mbtr._k1_weights
geoms2 = mbtr._k1_geoms
self.dict_comparison(weights, weights2)
self.dict_comparison(geoms, geoms2)
def test_k2_weights_and_geoms_periodic(self):
"""Tests that the values of the weight and geometry functions are
correct for the k=2 term in periodic systems.
"""
atoms = Atoms(
cell=[
[10, 0, 0],
[10, 10, 0],
[10, 0, 10],
],
symbols=["H", "C"],
scaled_positions=[
[0.1, 0.5, 0.5],
[0.9, 0.5, 0.5],
]
)
mbtr = MBTR(
[1, 6],
k=[2],
grid=default_grid,
periodic=True,
weighting={
"k2": {
"function": "exponential",
"scale": 0.8,
"cutoff": 1e-3
},
},
)
mbtr.create(atoms)
weights = mbtr._k2_weights
geoms = mbtr._k2_geoms
# Test against the assumed geometry values
pos = atoms.get_positions()
distances = np.array([
np.linalg.norm(pos[0] - pos[1]),
np.linalg.norm(pos[0] - pos[1] + atoms.get_cell()[0, :]),
np.linalg.norm(pos[1] - pos[0] - atoms.get_cell()[0, :])
])
assumed_geoms = {
(0, 1): 1/distances
}
self.dict_comparison(geoms, assumed_geoms)
# Test against the assumed weights
weight_list = np.exp(-0.8*distances)
# The periodic distances are halved
weight_list[1:3] /= 2
assumed_weights = {
(0, 1): weight_list
}
self.dict_comparison(weights, assumed_weights)
def test_k3_periodic_cell_translation(self):
"""Tests that the final spectra does not change when translating atoms
in a periodic cell. This is not trivially true unless the weight of
distances between periodic neighbours are not halfed. Notice that the
values of the geometry and weight functions are not equal before
summing them up in the final graph.
"""
# Original system with atoms separated by a cell wall
atoms = Atoms(
cell=[
[10, 0, 0],
[0, 10, 0],
[0, 0, 10],
],
symbols=["H", "H", "H", "H"],
scaled_positions=[
[0.1, 0.50, 0.5],
[0.1, 0.60, 0.5],
[0.9, 0.50, 0.5],
[0.9, 0.60, 0.5],
],
pbc=True
)
# Translated system with atoms next to each other
atoms2 = atoms.copy()
atoms2.translate([5, 0, 0])
atoms2.wrap()
mbtr = MBTR(
[1],
k=[3],
grid={
"k3": {
"min": -1,
"max": 1,
"sigma": 0.01,
"n": 200,
}
},
periodic=True,
weighting={
"k3": {
"function": "exponential",
"scale": 1,
"cutoff": 1e-3
},
},
)
# The resulting spectra should be indentical
spectra1 = mbtr.create(atoms).toarray()[0, :]
spectra2 = mbtr.create(atoms2).toarray()[0, :]
self.assertTrue(np.allclose(spectra1, spectra2, rtol=0, atol=1e-8))
def test_parallel_sparse(self):
"""Tests creating sparse output parallelly.
"""
# Test indices
samples = [molecule("CO"), molecule("N2O")]
desc = MBTR(
species=[6, 7, 8],
k={1, 2},
grid={
"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 1 / 0.7,
"sigma": 0.1,
"n": 100,
}
},
weighting={
"k2": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-2
}
},
periodic=False,
flatten=True,
sparse=True,
)
n_features = desc.get_number_of_features()
# Multiple systems, serial job
output = desc.create(
system=samples,
n_jobs=1,
).toarray()
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0]).toarray()
assumed[1, :] = desc.create(samples[1]).toarray()
self.assertTrue(np.allclose(output, assumed))
# Multiple systems, parallel job
output = desc.create(
system=samples,
n_jobs=2,
).toarray()
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0]).toarray()
assumed[1, :] = desc.create(samples[1]).toarray()
self.assertTrue(np.allclose(output, assumed))
def test_sparse(self):
"""Tests the sparse matrix creation.
"""
# Dense
desc = MBTR([1, 8], k=[1], grid=default_grid, periodic=False, flatten=True, sparse=False)
vec = desc.create(H2O)
self.assertTrue(type(vec) == np.ndarray)
# Sparse
desc = MBTR([1, 8], k=[1], grid=default_grid, periodic=False, flatten=True, sparse=True)
vec = desc.create(H2O)
self.assertTrue(type(vec) == scipy.sparse.coo_matrix)
def test_properties(self):
"""Used to test that changing the setup through properties works as
intended.
"""
# Test changing species
a = MBTR(
k1=default_k1,
k2=default_k2,
k3=default_k3,
periodic=False,
species=[1, 8],
sparse=False,
flatten=True,
)
nfeat1 = a.get_number_of_features()
vec1 = a.create(H2O)
a.species = ["C", "H", "O"]
nfeat2 = a.get_number_of_features()
vec2 = a.create(molecule("CH3OH"))
self.assertTrue(nfeat1 != nfeat2)
self.assertTrue(vec1.shape[1] != vec2.shape[1])
# Test changing geometry function and grid setup
a.k1 = {
"geometry": {"function": "atomic_number"},
"grid": {"min": 5, "max": 6, "sigma": 0.1, "n": 50},
}
vec3 = a.create(H2O)
self.assertTrue(not np.allclose(vec2, vec3))
a.k2 = {
"geometry": {"function": "distance"},
"grid": {"min": 0, "max": 10, "sigma": 0.1, "n": 50},
"weighting": {"function": "exponential", "scale": 0.6, "cutoff": 1e-2},
}
vec4 = a.create(H2O)
self.assertTrue(not np.allclose(vec3, vec4))
a.k3 = {
"geometry": {"function": "angle"},
"grid": {"min": 0, "max": 180, "sigma": 5, "n": 50},
"weighting": {"function": "exponential", "scale": 0.6, "cutoff": 1e-2},
}
vec5 = a.create(H2O)
self.assertTrue(not np.allclose(vec4, vec5))
def test_k3_peaks_periodic(self):
"""Tests that the final spectra does not change when translating atoms
in a periodic cell. This is not trivially true unless the weight of
angles is weighted according to the cell indices of the involved three
atoms. Notice that the values of the geometry and weight functions are
not equal before summing them up in the final graph.
"""
scale = 0.85
desc = MBTR(
species=["H"],
k3={
"geometry": {"function": "angle"},
"grid": {"min": 0, "max": 180, "sigma": 5, "n": 2000},
"weighting": {"function": "exp", "scale": scale, "cutoff": 1e-3},
},
normalize_gaussians=False,
periodic=True,
flatten=True,
sparse=False
)
atoms = Atoms(
cell=[
[10, 0, 0],
[0, 10, 0],
[0, 0, 10],
],
symbols=3*["H"],
scaled_positions=[
[0.05, 0.40, 0.5],
[0.05, 0.60, 0.5],
[0.95, 0.5, 0.5],
],
pbc=True
)
features = desc.create(atoms)[0, :]
x = desc.get_k3_axis()
# Calculate assumed locations and intensities.
assumed_locs = np.array([45, 90])
dist = 2+2*np.sqrt(2) # The total distance around the three atoms
weight = np.exp(-scale*dist)
assumed_ints = np.array([4*weight, 2*weight])
assumed_ints /= 2 # The periodic distances ar halved because they belong to different cells
# Check the H-H-H peaks
hhh_feat = features[desc.get_location(("H", "H", "H"))]
hhh_peak_indices = find_peaks(hhh_feat, prominence=0.01)[0]
hhh_peak_locs = x[hhh_peak_indices]
hhh_peak_ints = hhh_feat[hhh_peak_indices]
self.assertTrue(np.allclose(hhh_peak_locs, assumed_locs, rtol=0, atol=1e-1))
self.assertTrue(np.allclose(hhh_peak_ints, assumed_ints, rtol=0, atol=1e-1))
# Check that everything else is zero
features[desc.get_location(("H", "H", "H"))] = 0
self.assertEqual(features.sum(), 0)
def test_properties(self):
"""Used to test that changing the setup through properties works as
intended.
"""
# Test changing species
a = MBTR(
k=[1, 2, 3],
grid=default_grid,
periodic=False,
species=[1, 8],
sparse=False,
)
nfeat1 = a.get_number_of_features()
vec1 = a.create(H2O)
a.species = ["C", "H", "O"]
nfeat2 = a.get_number_of_features()
vec2 = a.create(molecule("CH3OH"))
self.assertTrue(nfeat1 != nfeat2)
self.assertTrue(vec1.shape[1] != vec2.shape[1])
def test_k2_peaks_periodic(self):
"""Tests the correct peak locations and intensities are found for the
k=2 term in periodic systems.
"""
atoms = Atoms(
cell=[
[10, 0, 0],
[10, 10, 0],
[10, 0, 10],
],
symbols=["H", "C"],
scaled_positions=[
[0.1, 0.5, 0.5],
[0.9, 0.5, 0.5],
]
)
desc = MBTR(
species=["H", "C"],
k2={
"geometry": {"function": "distance"},
"grid": {"min": 0, "max": 10, "sigma": 0.5, "n": 1000},
"weighting": {"function": "exp", "scale": 0.8, "cutoff": 1e-3},
},
normalize_gaussians=False,
periodic=True,
flatten=True,
sparse=False
)
features = desc.create(atoms)[0, :]
x = desc.get_k2_axis()
# Calculate assumed locations and intensities.
assumed_locs = np.array([2, 8])
assumed_ints = np.exp(-0.8*np.array([2, 8]))
assumed_ints[0] *= 2 # There are two periodic distances at 2Å
assumed_ints[0] /= 2 # The periodic distances ar halved because they belong to different cells
# Check the H-C peaks
hc_feat = features[desc.get_location(("H", "C"))]
hc_peak_indices = find_peaks(hc_feat, prominence=0.001)[0]
hc_peak_locs = x[hc_peak_indices]
hc_peak_ints = hc_feat[hc_peak_indices]
self.assertTrue(np.allclose(hc_peak_locs, assumed_locs, rtol=0, atol=1e-2))
self.assertTrue(np.allclose(hc_peak_ints, assumed_ints, rtol=0, atol=1e-2))
# Check that everything else is zero
features[desc.get_location(("H", "C"))] = 0
self.assertEqual(features.sum(), 0)
def create(data):
"""This is the function that is called by each process but with different
parts of the data.
"""
i_part = data[0]
samples = data[1]
mbtr = MBTR(
atomic_numbers=atomic_numbers,
k=[1, 2],
periodic=True,
grid={
"k1": {
"min": min(atomic_numbers) - 1,
"max": max(atomic_numbers) + 1,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 1 / min_distance,
"sigma": 0.01,
"n": 100,
},
},
weighting={
"k2": {
"function": lambda x: np.exp(-0.5 * x),
"threshold": 1e-3
},
},
flatten=True,
)
n_samples = len(samples)
n_features = int(mbtr.get_number_of_features())
mbtr_inputs = lil_matrix((n_samples, n_features))
# Create descriptors for the dataset
for i_sample, sample in enumerate(samples):
system = sample.value
mbtr_mat = mbtr.create(system)
mbtr_inputs[i_sample, :] = mbtr_mat
# Return the list of features for each sample
return {
"part": i_part,
"mbtr": mbtr_inputs,
}
def test_k3_peaks_finite(self):
"""Tests that all the correct angles are present in finite systems.
There should be n*(n-1)*(n-2)/2 unique angles where the division by two
gets rid of duplicate angles.
"""
desc = MBTR(
species=["H", "O"],
k3={
"geometry": {"function": "angle"},
"grid": {"min": -10, "max": 180, "sigma": 5, "n": 2000},
"weighting": {"function": "unity"},
},
normalize_gaussians=False,
periodic=False,
flatten=True,
sparse=False
)
features = desc.create(H2O)[0, :]
x = desc.get_k3_axis()
# Check the H-H-O peaks
hho_assumed_locs = np.array([38])
hho_assumed_ints = np.array([2])
hho_feat = features[desc.get_location(("H", "H", "O"))]
hho_peak_indices = find_peaks(hho_feat, prominence=0.5)[0]
hho_peak_locs = x[hho_peak_indices]
hho_peak_ints = hho_feat[hho_peak_indices]
self.assertTrue(np.allclose(hho_peak_locs, hho_assumed_locs, rtol=0, atol=5e-2))
self.assertTrue(np.allclose(hho_peak_ints, hho_assumed_ints, rtol=0, atol=5e-2))
# Check the H-O-H peaks
hoh_assumed_locs = np.array([104])
hoh_assumed_ints = np.array([1])
hoh_feat = features[desc.get_location(("H", "O", "H"))]
hoh_peak_indices = find_peaks(hoh_feat, prominence=0.5)[0]
hoh_peak_locs = x[hoh_peak_indices]
hoh_peak_ints = hoh_feat[hoh_peak_indices]
self.assertTrue(np.allclose(hoh_peak_locs, hoh_assumed_locs, rtol=0, atol=5e-2))
self.assertTrue(np.allclose(hoh_peak_ints, hoh_assumed_ints, rtol=0, atol=5e-2))
# Check that everything else is zero
features[desc.get_location(("H", "H", "O"))] = 0
features[desc.get_location(("H", "O", "H"))] = 0
self.assertEqual(features.sum(), 0)
def create(system):
desc = MBTR(
atomic_numbers=[1, 8],
k=[1, 2, 3],
periodic=False,
grid={
"k1": {
"min": 10,
"max": 18,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 0.7,
"sigma": 0.01,
"n": 100,
},
"k3": {
"min": -1.0,
"max": 1.0,
"sigma": 0.05,
"n": 100,
}
},
weighting={
"k2": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-3
},
"k3": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-3
},
},
flatten=True
)
return desc.create(system)
def test_k2_peaks_finite(self):
"""Tests the correct peak locations and intensities are found for the
k=2 term in finite systems.
"""
desc = MBTR(
species=[1, 8],
k2={
"geometry": {"function": "distance"},
"grid": {"min": -1, "max": 3, "sigma": 0.5, "n": 1000},
"weighting": {"function": "unity"},
},
normalize_gaussians=False,
periodic=False,
flatten=True,
sparse=False
)
features = desc.create(H2O)[0, :]
pos = H2O.get_positions()
x = desc.get_k2_axis()
# Check the H-H peaks
hh_feat = features[desc.get_location(("H", "H"))]
hh_peak_indices = find_peaks(hh_feat, prominence=0.5)[0]
hh_peak_locs = x[hh_peak_indices]
hh_peak_ints = hh_feat[hh_peak_indices]
self.assertTrue(np.allclose(hh_peak_locs, [np.linalg.norm(pos[0] - pos[2])], rtol=0, atol=1e-2))
self.assertTrue(np.allclose(hh_peak_ints, [1], rtol=0, atol=1e-2))
# Check the O-H peaks
ho_feat = features[desc.get_location(("H", "O"))]
ho_peak_indices = find_peaks(ho_feat, prominence=0.5)[0]
ho_peak_locs = x[ho_peak_indices]
ho_peak_ints = ho_feat[ho_peak_indices]
self.assertTrue(np.allclose(ho_peak_locs, np.linalg.norm(pos[0] - pos[1]), rtol=0, atol=1e-2))
self.assertTrue(np.allclose(ho_peak_ints, [2], rtol=0, atol=1e-2))
# Check that everything else is zero
features[desc.get_location(("H", "H"))] = 0
features[desc.get_location(("H", "O"))] = 0
self.assertEqual(features.sum(), 0)
def test_periodic_supercell_similarity(self):
"""Tests that the output spectrum of various supercells of the same
crystal is identical after it is normalized.
"""
decay = 1
desc = MBTR(
species=["H"],
periodic=True,
k1={
"geometry": {"function": "atomic_number"},
"grid": {"min": 0, "max": 2, "sigma": 0.1, "n": 100},
},
k2={
"geometry": {"function": "inverse_distance"},
"grid": {"min": 0, "max": 1.0, "sigma": 0.02, "n": 200},
"weighting": {"function": "exponential", "scale": decay, "cutoff": 1e-3},
},
k3={
"geometry": {"function": "cosine"},
"grid": {"min": -1.0, "max": 1.0, "sigma": 0.02, "n": 200},
"weighting": {"function": "exponential", "scale": decay, "cutoff": 1e-3},
},
flatten=True,
sparse=False,
normalization="l2_each",
)
# Create various supercells for the FCC structure
a1 = bulk('H', 'fcc', a=2.0) # Primitive
a2 = a1*[2, 2, 2] # Supercell
a3 = bulk('H', 'fcc', a=2.0, orthorhombic=True) # Orthorhombic
a4 = bulk('H', 'fcc', a=2.0, cubic=True) # Conventional cubic
output = desc.create([a1, a2, a3, a4])
# Test for equality
self.assertTrue(np.allclose(output[0, :], output[0, :], atol=1e-5, rtol=0))
self.assertTrue(np.allclose(output[0, :], output[1, :], atol=1e-5, rtol=0))
self.assertTrue(np.allclose(output[0, :], output[2, :], atol=1e-5, rtol=0))
self.assertTrue(np.allclose(output[0, :], output[3, :], atol=1e-5, rtol=0))
def test_k1_peaks_finite(self):
"""Tests the correct peak locations and intensities are found for the
k=1 term.
"""
desc = MBTR(
species=[1, 8],
k1={
"geometry": {"function": "atomic_number"},
"grid": {"min": 0, "max": 9, "sigma": 0.5, "n": 1000}
},
normalize_gaussians=False,
periodic=False,
flatten=True,
sparse=False
)
features = desc.create(H2O)[0, :]
x = desc.get_k1_axis()
# Check the H peaks
h_feat = features[desc.get_location(("H"))]
h_peak_indices = find_peaks(h_feat, prominence=1)[0]
h_peak_locs = x[h_peak_indices]
h_peak_ints = h_feat[h_peak_indices]
self.assertTrue(np.allclose(h_peak_locs, [1], rtol=0, atol=1e-2))
self.assertTrue(np.allclose(h_peak_ints, [2], rtol=0, atol=1e-2))
# Check the O peaks
o_feat = features[desc.get_location(("O"))]
o_peak_indices = find_peaks(o_feat, prominence=1)[0]
o_peak_locs = x[o_peak_indices]
o_peak_ints = o_feat[o_peak_indices]
self.assertTrue(np.allclose(o_peak_locs, [8], rtol=0, atol=1e-2))
self.assertTrue(np.allclose(o_peak_ints, [1], rtol=0, atol=1e-2))
# Check that everything else is zero
features[desc.get_location(("H"))] = 0
features[desc.get_location(("O"))] = 0
self.assertEqual(features.sum(), 0)
k3={
"geometry": {"function": "cosine"},
"grid": {"min": -1, "max": 1, "n": 100, "sigma": 0.1},
"weighting": {"function": "exponential", "scale": 0.5, "cutoff": 1e-3},
},
periodic=False,
normalization="l2_each",
)
# Create
from ase.build import molecule
water = molecule("H2O")
# Create MBTR output for the system
mbtr_water = mbtr.create(water)
print(mbtr_water)
print(mbtr_water.shape)
# Locations
# The locations of specific element combinations can be retrieved like this.
h_loc = mbtr.get_location(("H"))
ho_loc = mbtr.get_location(("H", "O"))
hoh_loc = mbtr.get_location(("H", "O", "H"))
# These locations can be directly used to slice the corresponding part from an
# MBTR output for e.g. plotting.
mbtr_water[0, h_loc]
mbtr_water[0, ho_loc]
mbtr_water[0, hoh_loc]
grid={"k2": {
"min": 0,
"max": 1,
"n": n,
"sigma": 0.1
}},
weighting=None)
# Creating an atomic system as an ase.Atoms-object
from ase.build import molecule
import ase.data
water = molecule("H2O")
# Create MBTR output for the system
mbtr_water = mbtr.create(water)
print(mbtr_water)
print(mbtr_water.shape)
from ase.build import bulk
nacl = bulk("NaCl", "rocksalt", a=5.64)
# Optionally we can preserve the tensorial nature of the data by specifying
# "Flatten" as False. In this case the output will be a list of
# multidimensional numpy arrays for each k-term. This form is easier to
# visualize as done in the following.
import matplotlib.pyplot as plt
decay = 0.5
mbtr = MBTR(atomic_numbers=[11, 17],
def test_parallel_dense(self):
"""Tests creating dense output parallelly.
"""
samples = [molecule("CO"), molecule("N2O")]
desc = MBTR(
species=[6, 7, 8],
k={1, 2},
grid={
"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 1 / 0.7,
"sigma": 0.1,
"n": 100,
}
},
weighting={
"k2": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-2
}
},
periodic=False,
flatten=True,
sparse=False,
)
n_features = desc.get_number_of_features()
# Multiple systems, serial job
output = desc.create(
system=samples,
n_jobs=1,
)
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0])
assumed[1, :] = desc.create(samples[1])
self.assertTrue(np.allclose(output, assumed))
# Multiple systems, parallel job
output = desc.create(
system=samples,
n_jobs=2,
)
assumed = np.empty((2, n_features))
assumed[0, :] = desc.create(samples[0])
assumed[1, :] = desc.create(samples[1])
self.assertTrue(np.allclose(output, assumed))
# Non-flattened output
desc._flatten = False
output = desc.create(
system=samples,
n_jobs=2,
)
assumed = []
assumed.append(desc.create(samples[0]))
assumed.append(desc.create(samples[1]))
for i, val in enumerate(output):
for key in val.keys():
i_tensor = val[key]
j_tensor = assumed[i][key]
self.assertTrue(np.allclose(i_tensor, j_tensor))
def test_basis(self):
"""Tests that the output vectors behave correctly as a basis.
"""
sys1 = Atoms(symbols=["H"], positions=[[0, 0, 0]], cell=[2, 2, 2], pbc=True)
sys2 = Atoms(symbols=["O"], positions=[[0, 0, 0]], cell=[2, 2, 2], pbc=True)
sys3 = sys2*[2, 2, 2]
desc = MBTR(
atomic_numbers=[1, 8],
k=[1, 2, 3],
periodic=True,
grid={
"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 50,
},
"k2": {
"min": 0,
"max": 1/0.7,
"sigma": 0.1,
"n": 50,
},
"k3": {
"min": -1,
"max": 1,
"sigma": 0.1,
"n": 50,
}
},
weighting={
"k2": {
"function": "exponential",
"scale": 1,
"cutoff": 1e-4
},
"k3": {
"function": "exponential",
"scale": 1,
"cutoff": 1e-4
}
},
flatten=True
)
# Create normalized vectors for each system
vec1 = desc.create(sys1).toarray()[0, :]
vec1 /= np.linalg.norm(vec1)
vec2 = desc.create(sys2).toarray()[0, :]
vec2 /= np.linalg.norm(vec2)
vec3 = desc.create(sys3).toarray()[0, :]
vec3 /= np.linalg.norm(vec3)
# The dot-product should be zero when there are no overlapping elements
dot = np.dot(vec1, vec2)
self.assertEqual(dot, 0)
# The dot-product should be rougly one for a primitive cell and a supercell
dot = np.dot(vec2, vec3)
self.assertTrue(abs(dot-1) < 1e-3)
def test_k3_geoms_and_weights_periodic(self):
"""Tests that the final spectra does not change when translating atoms
in a periodic cell. This is not trivially true unless the weight of
distances between periodic neighbours are not halfed. Notice that the
values of the geometry and weight functions are not equal before
summing them up in the final graph.
"""
# Original system with atoms separated by a cell wall
atoms = Atoms(
cell=[
[10, 0, 0],
[0, 10, 0],
[0, 0, 10],
],
symbols=3*["H"],
scaled_positions=[
[0.05, 0.40, 0.5],
[0.05, 0.60, 0.5],
[0.95, 0.5, 0.5],
],
pbc=True
)
# view(atoms)
scale = 0.85
mbtr = MBTR(
[1],
k=[3],
grid={
"k3": {
"min": -1,
"max": 1,
"sigma": 0.01,
"n": 200,
}
},
periodic=True,
weighting={
"k3": {
"function": "exponential",
"scale": scale,
"cutoff": 1e-3
},
},
)
mbtr.create(atoms)
weights = mbtr._k3_weights
geoms = mbtr._k3_geoms
# Test against the assumed geometry values
angle_list = np.cos(np.array([45, 45, 90, 90, 45, 45])*np.pi/180)
assumed_geoms = {
(0, 0, 0): angle_list
}
self.dict_comparison(geoms, assumed_geoms)
# Test against the assumed weights
distance = 2+2*np.sqrt(2) # The total distance around the three atoms
distances = np.array(6*[distance])
weight_list = np.exp(-scale*distances)
# The periodic distances are halved
weight_list /= 2
assumed_weights = {
(0, 0, 0): weight_list
}
self.dict_comparison(weights, assumed_weights)
def test_gaussian_distribution(self):
"""Check that the broadening follows gaussian distribution.
"""
# Check with normalization
std = 1
start = -3
stop = 11
n = 500
mbtr = MBTR(
[1, 8],
k=[1],
grid={
"k1": {
"min": start,
"max": stop,
"sigma": std,
"n": n
}
},
periodic=False,
normalize_gaussians=True,
flatten=False,
sparse=False)
y = mbtr.create(H2O)["k1"]
k1_axis = mbtr._axis_k1
# Find the location of the peaks
peak1_x = np.searchsorted(k1_axis, 1)
peak1_y = y[0, peak1_x]
peak2_x = np.searchsorted(k1_axis, 8)
peak2_y = y[1, peak2_x]
# Check against the analytical value
gaussian = lambda x, mean, sigma: 1/(sigma*np.sqrt(2*np.pi))*np.exp(-(x-mean)**2/(2*sigma**2))
self.assertTrue(np.allclose(peak1_y, 2*gaussian(1, 1, std), rtol=0, atol=0.001))
self.assertTrue(np.allclose(peak2_y, gaussian(8, 8, std), rtol=0, atol=0.001))
# Check the integral
pdf = y[0, :]
dx = (stop-start)/(n-1)
sum_cum = np.sum(0.5*dx*(pdf[:-1]+pdf[1:]))
exp = 2
self.assertTrue(np.allclose(sum_cum, exp, rtol=0, atol=0.001))
# Check without normalization
std = 1
start = -3
stop = 11
n = 500
mbtr = MBTR(
[1, 8],
k=[1],
grid={
"k1": {
"min": start,
"max": stop,
"sigma": std,
"n": n
}
},
periodic=False,
normalize_gaussians=False,
flatten=False,
sparse=False)
y = mbtr.create(H2O)["k1"]
k1_axis = mbtr._axis_k1
# Find the location of the peaks
peak1_x = np.searchsorted(k1_axis, 1)
peak1_y = y[0, peak1_x]
peak2_x = np.searchsorted(k1_axis, 8)
peak2_y = y[1, peak2_x]
# Check against the analytical value
gaussian = lambda x, mean, sigma: np.exp(-(x-mean)**2/(2*sigma**2))
self.assertTrue(np.allclose(peak1_y, 2*gaussian(1, 1, std), rtol=0, atol=0.001))
self.assertTrue(np.allclose(peak2_y, gaussian(8, 8, std), rtol=0, atol=0.001))
# Check the integral
pdf = y[0, :]
dx = (stop-start)/(n-1)
sum_cum = np.sum(0.5*dx*(pdf[:-1]+pdf[1:]))
exp = 2/(1/math.sqrt(2*math.pi*std**2))
self.assertTrue(np.allclose(sum_cum, exp, rtol=0, atol=0.001))
def test_unit_cells(self):
"""Tests that arbitrary unit cells are accepted.
"""
desc = MBTR(
atomic_numbers=[1, 8],
k=[1, 2, 3],
periodic=False,
grid={
"k1": {
"min": 10,
"max": 18,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 0.7,
"sigma": 0.01,
"n": 100,
},
"k3": {
"min": -1.0,
"max": 1.0,
"sigma": 0.05,
"n": 100,
}
},
weighting={
"k2": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-3
},
"k3": {
"function": "exponential",
"scale": 0.5,
"cutoff": 1e-3
},
},
flatten=True
)
molecule = H2O.copy()
molecule.set_cell([
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]
])
nocell = desc.create(molecule)
molecule.set_pbc(True)
molecule.set_cell([
[2.0, 0.0, 0.0],
[0.0, 2.0, 0.0],
[0.0, 0.0, 2.0]
])
cubic_cell = desc.create(molecule)
molecule.set_cell([
[0.0, 2.0, 2.0],
[2.0, 0.0, 2.0],
[2.0, 2.0, 0.0]
])
triclinic_smallcell = desc.create(molecule)
def test_periodic_images(self):
"""Tests that periodic images are handled correctly.
"""
decay = 1
desc = MBTR(
species=[1],
periodic=True,
k1={
"geometry": {"function": "atomic_number"},
"grid": {"min": 0, "max": 2, "sigma": 0.1, "n": 21}
},
k2={
"geometry": {"function": "inverse_distance"},
"grid": {"min": 0, "max": 1.0, "sigma": 0.02, "n": 21},
"weighting": {"function": "exp", "scale": decay, "cutoff": 1e-4}
},
k3={
"geometry": {"function": "cosine"},
"grid": {"min": -1.0, "max": 1.0, "sigma": 0.02, "n": 21},
"weighting": {"function": "exp", "scale": decay, "cutoff": 1e-4},
},
normalization="l2_each", # This normalizes the spectrum
flatten=True
)
# Tests that a system has the same spectrum as the supercell of
# the same system.
molecule = H.copy()
a = 1.5
molecule.set_cell([
[a, 0.0, 0.0],
[0.0, a, 0.0],
[0.0, 0.0, a]
])
cubic_cell = desc.create(molecule)
suce = molecule * (2, 1, 1)
cubic_suce = desc.create(suce)
diff = abs(np.sum(cubic_cell[0, :] - cubic_suce[0, :]))
cubic_sum = abs(np.sum(cubic_cell[0, :]))
self.assertTrue(diff/cubic_sum < 0.05) # A 5% error is tolerated
# Same test but for triclinic cell
molecule.set_cell([
[0.0, 2.0, 1.0],
[1.0, 0.0, 1.0],
[1.0, 2.0, 0.0]
])
triclinic_cell = desc.create(molecule)
suce = molecule * (2, 1, 1)
triclinic_suce = desc.create(suce)
diff = abs(np.sum(triclinic_cell[0, :] - triclinic_suce[0, :]))
tricl_sum = abs(np.sum(triclinic_cell[0, :]))
self.assertTrue(diff/tricl_sum < 0.05)
# Testing that the same crystal, but different unit cells will have a
# similar spectrum when they are normalized. There will be small
# differences in the shape (due to not double counting distances)
a1 = bulk('H', 'fcc', a=2.0)
a2 = bulk('H', 'fcc', a=2.0, orthorhombic=True)
a3 = bulk('H', 'fcc', a=2.0, cubic=True)
triclinic_cell = desc.create(a1)
orthorhombic_cell = desc.create(a2)
cubic_cell = desc.create(a3)
diff1 = abs(np.sum(triclinic_cell[0, :] - orthorhombic_cell[0, :]))
diff2 = abs(np.sum(triclinic_cell[0, :] - cubic_cell[0, :]))
tricl_sum = abs(np.sum(triclinic_cell[0, :]))
self.assertTrue(diff1/tricl_sum < 0.05)
self.assertTrue(diff2/tricl_sum < 0.05)
# Tests that the correct peak locations are present in a cubic periodic
desc = MBTR(
species=["H"],
periodic=True,
k3={
"geometry": {"function": "cosine"},
"grid": {"min": -1.1, "max": 1.1, "sigma": 0.010, "n": 600},
"weighting": {"function": "exp", "scale": decay, "cutoff": 1e-4}
},
normalization="l2_each", # This normalizes the spectrum
flatten=True
)
a = 2.2
system = Atoms(
cell=[
[a, 0.0, 0.0],
[0.0, a, 0.0],
[0.0, 0.0, a]
],
positions=[
[0, 0, 0],
],
symbols=["H"],
)
cubic_spectrum = desc.create(system)[0, :]
x3 = desc.get_k3_axis()
peak_ids = find_peaks_cwt(cubic_spectrum, [2])
peak_locs = x3[peak_ids]
assumed_peaks = np.cos(np.array(
[
180,
90,
np.arctan(np.sqrt(2))*180/np.pi,
45,
np.arctan(np.sqrt(2)/2)*180/np.pi,
0
])*np.pi/180
)
self.assertTrue(np.allclose(peak_locs, assumed_peaks, rtol=0, atol=5*np.pi/180))
# Tests that the correct peak locations are present in a system with a
# non-cubic basis
desc = MBTR(
species=["H"],
periodic=True,
k3={
"geometry": {"function": "cosine"},
"grid": {"min": -1.0, "max": 1.0, "sigma": 0.030, "n": 200},
"weighting": {"function": "exp", "scale": 1.5, "cutoff": 1e-4}
},
normalization="l2_each", # This normalizes the spectrum
flatten=True,
sparse=False
)
a = 2.2
system = Atoms(
cell=[
[a, 0.0, 0.0],
[0.0, a, 0.0],
[0.0, 0.0, a]
],
positions=[
[0, 0, 0],
],
symbols=["H"],
)
angle = 30
system = Atoms(
cell=ase.geometry.cellpar_to_cell([3*a, a, a, angle, 90, 90]),
positions=[
[0, 0, 0],
],
symbols=["H"],
)
tricl_spectrum = desc.create(system)
x3 = desc.get_k3_axis()
peak_ids = find_peaks_cwt(tricl_spectrum[0, :], [3])
peak_locs = x3[peak_ids]
angle = (6)/(np.sqrt(5)*np.sqrt(8))
assumed_peaks = np.cos(np.array([180, 105, 75, 51.2, 30, 0])*np.pi/180)
self.assertTrue(np.allclose(peak_locs, assumed_peaks, rtol=0, atol=5*np.pi/180))
