def test_k3_peaks_periodic(self):
"""Tests the correct peak locations and intensities are found for the
k=3 term in periodic systems.
"""
scale = 0.85
desc = LMBTR(
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])[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([weight, weight])
# Check the X-H-H peaks
xhh_feat = features[desc.get_location(("X", "H", "H"))]
xhh_peak_indices = find_peaks(xhh_feat, prominence=0.01)[0]
xhh_peak_locs = x[xhh_peak_indices]
xhh_peak_ints = xhh_feat[xhh_peak_indices]
self.assertTrue(np.allclose(xhh_peak_locs, assumed_locs, rtol=0, atol=1e-1))
self.assertTrue(np.allclose(xhh_peak_ints, assumed_ints, rtol=0, atol=1e-1))
# Calculate assumed locations and intensities.
assumed_locs = np.array([45])
dist = 2+2*np.sqrt(2) # The total distance around the three atoms
weight = np.exp(-scale*dist)
assumed_ints = np.array([weight])
# Check the H-X-H peaks
hxh_feat = features[desc.get_location(("H", "X", "H"))]
hxh_peak_indices = find_peaks(hxh_feat, prominence=0.01)[0]
hxh_peak_locs = x[hxh_peak_indices]
hxh_peak_ints = hxh_feat[hxh_peak_indices]
self.assertTrue(np.allclose(hxh_peak_locs, assumed_locs, rtol=0, atol=1e-1))
self.assertTrue(np.allclose(hxh_peak_ints, assumed_ints, rtol=0, atol=1e-1))
# Check that everything else is zero
features[desc.get_location(("X", "H", "H"))] = 0
features[desc.get_location(("H", "X", "H"))] = 0
self.assertEqual(features.sum(), 0)
mbtr = LMBTR(
species=[11, 17],
k=[2, 3],
periodic=True,
virtual_positions=False,
grid={
"k1": {
"min": 10,
"max": 18,
"sigma": 0.1,
"n": 200,
},
"k2": {
"min": 0,
"max": 0.7,
"sigma": 0.01,
"n": 200,
},
"k3": {
"min": -1.0,
"max": 1.0,
"sigma": 0.05,
"n": 200,
}
},
weighting={
"k2": {
"function": "exponential",
"scale": decay_factor,
"cutoff": 1e-3
},
"k3": {
"function": "exponential",
"scale": decay_factor,
"cutoff": 1e-3
},
},
sparse=False,
flatten=False
)
default_k2 = {
"geometry": {"function": "inverse_distance"},
"grid": {"min": 0, "max": 1/0.7, "sigma": 0.1, "n": nk2},
"weighting": {"function": "exponential", "scale": 0.5, "cutoff": 1e-2},
}
default_k3 = {
"geometry": {"function": "angle"},
"grid": {"min": 0, "max": 180, "sigma": 2, "n": 50},
"weighting": {"function": "exponential", "scale": 0.5, "cutoff": 1e-2},
}
default_desc_k2 = LMBTR(
species=[1, 8],
k2=default_k2,
periodic=False,
flatten=True,
sparse=False,
)
default_desc_k3 = LMBTR(
species=[1, 8],
k3=default_k3,
periodic=False,
flatten=True,
sparse=False,
)
default_desc_k2_k3 = LMBTR(
species=[1, 8],
k2=default_k2,
def test_symmetries(self):
"""LMBTR: Tests translational and rotational symmetries for a finite system.
"""
desc = LMBTR(species=[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
},
},
virtual_positions=False,
flatten=True)
def create_1(system):
"""This function uses atom indices so rotation and translation
should not affect it.
"""
return desc.create(system, positions=[0])
def create_2(system):
"""This function uses scaled positions so atom permutation should
not affect it.
"""
desc.virtual_positions = True
return desc.create(system,
positions=[[0, 1, 0]],
scaled_positions=True)
# Rotational check
self.assertTrue(self.is_rotationally_symmetric(create_1))
# Translational
self.assertTrue(self.is_translationally_symmetric(create_1))
# Permutational
self.assertTrue(self.is_permutation_symmetric(create_2))
def test_k3_peaks_finite(self):
"""Tests the correct peak locations and intensities are found for the
k=3 term in finite systems.
"""
desc = LMBTR(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])[0, :]
x = desc.get_k3_axis()
# Check the X-H-O peaks
xho_assumed_locs = np.array([38])
xho_assumed_ints = np.array([1])
xho_feat = features[desc.get_location(("X", "H", "O"))]
xho_peak_indices = find_peaks(xho_feat, prominence=0.5)[0]
xho_peak_locs = x[xho_peak_indices]
xho_peak_ints = xho_feat[xho_peak_indices]
self.assertTrue(
np.allclose(xho_peak_locs, xho_assumed_locs, rtol=0, atol=5e-2))
self.assertTrue(
np.allclose(xho_peak_ints, xho_assumed_ints, rtol=0, atol=5e-2))
# Check the X-O-H peaks
xoh_assumed_locs = np.array([104])
xoh_assumed_ints = np.array([1])
xoh_feat = features[desc.get_location(("X", "O", "H"))]
xoh_peak_indices = find_peaks(xoh_feat, prominence=0.5)[0]
xoh_peak_locs = x[xoh_peak_indices]
xoh_peak_ints = xoh_feat[xoh_peak_indices]
self.assertTrue(
np.allclose(xoh_peak_locs, xoh_assumed_locs, rtol=0, atol=5e-2))
self.assertTrue(
np.allclose(xoh_peak_ints, xoh_assumed_ints, rtol=0, atol=5e-2))
# Check the H-X-O peaks
hxo_assumed_locs = np.array([38])
hxo_assumed_ints = np.array([1])
hxo_feat = features[desc.get_location(("H", "X", "O"))]
hxo_peak_indices = find_peaks(hxo_feat, prominence=0.5)[0]
hxo_peak_locs = x[hxo_peak_indices]
hxo_peak_ints = hxo_feat[hxo_peak_indices]
self.assertTrue(
np.allclose(hxo_peak_locs, hxo_assumed_locs, rtol=0, atol=5e-2))
self.assertTrue(
np.allclose(hxo_peak_ints, hxo_assumed_ints, rtol=0, atol=5e-2))
# Check that everything else is zero
features[desc.get_location(("X", "H", "O"))] = 0
features[desc.get_location(("X", "O", "H"))] = 0
features[desc.get_location(("H", "X", "O"))] = 0
self.assertEqual(features.sum(), 0)
def test_parallel_sparse(self):
"""Tests creating sparse output parallelly.
"""
# Test indices
samples = [molecule("CO"), molecule("N2O")]
desc = LMBTR(species=[6, 7, 8],
k=[2],
grid={"k2": {
"n": 100,
"min": 0,
"max": 2,
"sigma": 0.1
}},
virtual_positions=False,
periodic=False,
flatten=True,
sparse=True)
n_features = desc.get_number_of_features()
# Multiple systems, serial job
output = desc.create(
system=samples,
positions=[[0], [0, 1]],
n_jobs=1,
).toarray()
assumed = np.empty((3, n_features))
assumed[0, :] = desc.create(samples[0], [0]).toarray()
assumed[1, :] = desc.create(samples[1], [0]).toarray()
assumed[2, :] = desc.create(samples[1], [1]).toarray()
self.assertTrue(np.allclose(output, assumed))
# Test when position given as indices
output = desc.create(
system=samples,
positions=[[0], [0, 1]],
n_jobs=2,
).toarray()
assumed = np.empty((3, n_features))
assumed[0, :] = desc.create(samples[0], [0]).toarray()
assumed[1, :] = desc.create(samples[1], [0]).toarray()
assumed[2, :] = desc.create(samples[1], [1]).toarray()
self.assertTrue(np.allclose(output, assumed))
# Test with cartesian positions. In this case virtual positions have to
# be enabled
desc = LMBTR(species=[6, 7, 8],
k=[2],
grid={"k2": {
"n": 100,
"min": 0,
"max": 2,
"sigma": 0.1
}},
virtual_positions=True,
periodic=False,
flatten=True,
sparse=True)
output = desc.create(
system=samples,
positions=[[[0, 0, 0], [1, 2, 0]], [[1, 2, 0]]],
n_jobs=2,
).toarray()
assumed = np.empty((2 + 1, n_features))
assumed[0, :] = desc.create(samples[0], [[0, 0, 0]]).toarray()
assumed[1, :] = desc.create(samples[0], [[1, 2, 0]]).toarray()
assumed[2, :] = desc.create(samples[1], [[1, 2, 0]]).toarray()
self.assertTrue(np.allclose(output, assumed))
def test_number_of_features(self):
"""LMBTR: Tests that the reported number of features is correct.
"""
# K = 1
n = 100
atomic_numbers = [1, 8]
n_elem = len(atomic_numbers) + 1 # Including ghost atom
lmbtr = LMBTR(
species=atomic_numbers,
k=[1],
grid={"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 100,
}},
virtual_positions=False,
periodic=False,
flatten=True)
n_features = lmbtr.get_number_of_features()
expected = n_elem * n
self.assertEqual(n_features, expected)
# K = 2
lmbtr = LMBTR(species=atomic_numbers,
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": n,
}
},
virtual_positions=False,
periodic=False,
flatten=True)
n_features = lmbtr.get_number_of_features()
expected = n_elem * n + 1 / 2 * (n_elem) * (n_elem + 1) * n
self.assertEqual(n_features, expected)
# K = 3
lmbtr = LMBTR(species=atomic_numbers,
k={3},
grid={
"k1": {
"min": 1,
"max": 8,
"sigma": 0.1,
"n": 100,
},
"k2": {
"min": 0,
"max": 1 / 0.7,
"sigma": 0.1,
"n": n,
},
"k3": {
"min": -1,
"max": 1,
"sigma": 0.1,
"n": n,
}
},
virtual_positions=False,
periodic=False,
flatten=True)
n_features = lmbtr.get_number_of_features()
expected = n_elem * 1 / 2 * (n_elem) * (n_elem + 1) * n
self.assertEqual(n_features, expected)
def test_positions(self):
"""Tests that the position argument is handled correctly. The position
can be a list of integers or a list of 3D positions.
"""
decay_factor = 0.5
lmbtr = LMBTR(species=[1, 8],
k=[1, 2],
grid={
"k1": {
"min": 10,
"max": 18,
"sigma": 0.1,
"n": 200,
},
"k2": {
"min": 0,
"max": 0.7,
"sigma": 0.01,
"n": 200,
},
"k3": {
"min": -1.0,
"max": 1.0,
"sigma": 0.05,
"n": 200,
}
},
weighting={
"k2": {
"function": "exponential",
"scale": decay_factor,
"cutoff": 1e-3
},
"k3": {
"function": "exponential",
"scale": decay_factor,
"cutoff": 1e-3
},
},
periodic=True,
virtual_positions=True,
flatten=False,
sparse=False)
# Position as a cartesian coordinate in list
lmbtr.create(H2O, positions=[[0, 1, 0]])
# Position as a cartesian coordinate in numpy array
lmbtr.create(H2O, positions=np.array([[0, 1, 0]]))
# Position as a scaled coordinate in list
lmbtr.create(H2O, positions=[[0, 0, 0.5]], scaled_positions=True)
# Position as a scaled coordinate in numpy array
lmbtr.create(H2O,
positions=np.array([[0, 0, 0.5]]),
scaled_positions=True)
# Positions as lists of vectors
positions = [[0, 1, 2], [0, 0, 0]]
desc = lmbtr.create(H2O, positions)
# Position outside range
with self.assertRaises(ValueError):
lmbtr.create(H2O, positions=[3])
# Invalid data type
with self.assertRaises(ValueError):
lmbtr.create(H2O, positions=['a'])
# Cannot use scaled positions without cell information
with self.assertRaises(ValueError):
H = Atoms(
positions=[[0, 0, 0]],
symbols=["H"],
)
lmbtr.create(H, positions=[[0, 0, 1]], scaled_positions=True)
# Non-virtual positions
lmbtr = LMBTR(species=[1, 8],
k=[3],
grid=default_grid,
virtual_positions=False,
periodic=False,
flatten=True)
# Positions as a list of integers pointing to atom indices
positions = [0, 1, 2]
desc = lmbtr.create(H2O, positions)
def main(fxyz, dictxyz, prefix, output, per_atom, config_path, periodic):
"""
Generate the LMBTR Representation.
Parameters
----------
fxyz: string giving location of xyz file
dictxyz: string giving location of xyz file that is used as a dictionary
prefix: string giving the filename prefix
output: [xyz]: append the representations to extended xyz file; [mat] output as a standlone matrix
input_path': string Specify the Kn parameters using a json file. (see https://singroup.github.io/dscribe/tutorials/lmbtr.html)
periodic: string (True or False) indicating whether the system is periodic
"""
periodic = bool(periodic)
per_atom = bool(per_atom)
fframes = []
dictframes = []
# read frames
if fxyz != 'none':
fframes = read(fxyz, ':')
nfframes = len(fframes)
print("read xyz file:", fxyz, ", a total of", nfframes, "frames")
# read frames in the dictionary
if dictxyz != 'none':
dictframes = read(dictxyz, ':')
ndictframes = len(dictframes)
print("read xyz file used for a dictionary:", dictxyz, ", a total of",
ndictframes, "frames")
frames = dictframes + fframes
nframes = len(frames)
global_species = []
for frame in frames:
global_species.extend(frame.get_atomic_numbers())
if not periodic:
frame.set_pbc([False, False, False])
global_species = np.unique(global_species)
print("a total of", nframes, "frames, with elements: ", global_species)
if config_path:
try:
with open(config_path, 'r') as config_file:
config = json.load(config_file)
except Exception:
raise IOError('Cannot load the json file for parameters')
if config_path:
rep_atomic = LMBTR(species=global_species,
periodic=periodic,
flatten=True,
**config)
else:
rep_atomic = LMBTR(species=global_species,
flatten=True,
periodic=periodic)
if config_path:
foutput = prefix + '-' + config_path
desc_name = "LMBTR" + '-' + config_path
else:
foutput = prefix
desc_name = "LMBTR"
# prepare for the output
if os.path.isfile(foutput + ".xyz"):
os.rename(foutput + ".xyz", "bck." + foutput + ".xyz")
if os.path.isfile(foutput + ".desc"):
os.rename(foutput + ".desc", "bck." + foutput + ".desc")
for i, frame in enumerate(frames):
fnow = rep_atomic.create(frame)
frame.info[desc_name] = fnow.mean(axis=0)
# save
if output == 'matrix':
with open(foutput + ".desc", "ab") as f:
np.savetxt(f, frame.info[desc_name][None])
if per_atom or nframes == 1:
with open(foutput + ".atomic-desc", "ab") as fatomic:
np.savetxt(fatomic, fnow)
elif output == 'xyz':
# output per-atom info
if per_atom:
frame.new_array(desc_name, fnow)
# write xyze
write(foutput + ".xyz", frame, append=True)
else:
raise ValueError('Cannot find the output format')
import numpy as np
from dscribe.descriptors import LMBTR
from ase.build import bulk
from ase.visualize import view
import matplotlib.pyplot as mpl
# Setup
lmbtr = LMBTR(
species=["H", "O"],
k2={
"geometry": {"function": "distance"},
"grid": {"min": 0, "max": 5, "n": 100, "sigma": 0.1},
"weighting": {"function": "exponential", "scale": 0.5, "cutoff": 1e-3},
},
k3={
"geometry": {"function": "angle"},
"grid": {"min": 0, "max": 180, "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 = lmbtr.create(water, positions=[0])
