def evaluate(self, test_structures, ref_energies, ref_forces,
ref_stresses):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
"""
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
_, df_orig = convert_docs(predict_pool)
_, df_predict = convert_docs(pool_from(test_structures))
outputs = self.model.predict(inputs=test_structures, override=True)
df_predict['y_orig'] = df_predict['n'] * outputs
return df_orig, df_predict
def evaluate(self,
test_structures,
ref_energies=None,
ref_forces=None,
ref_stresses=None,
predict_energies=True,
predict_forces=True,
predict_stress=False):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
predict_energies (bool): Whether to predict energies of configurations.
predict_forces (bool): Whether to predict forces of configurations.
predict_stress (bool): Whether to predict virial stress of
configurations.
"""
if not which('quip'):
raise RuntimeError(
"quip has not been found.\n",
"Please refer to https://github.com/libAtoms/QUIP for ",
"further detail.")
xml_file = 'predict.xml'
original_file = 'original.xyz'
predict_file = 'predict.xyz'
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
with ScratchDir('.'):
_ = self.write_param(xml_file)
original_file = self.write_cfgs(original_file,
cfg_pool=predict_pool)
_, df_orig = self.read_cfgs(original_file)
exe_command = ["quip"]
exe_command.append("atoms_filename={}".format(original_file))
exe_command.append("param_filename={}".format(xml_file))
if predict_energies:
exe_command.append("energy=T")
if predict_forces:
exe_command.append("forces=T")
if predict_stress:
exe_command.append("virial=T")
p = subprocess.Popen(exe_command, stdout=open(predict_file, 'w'))
stdout = p.communicate()[0]
rc = p.returncode
_, df_predict = self.read_cfgs(predict_file, predict=True)
return df_orig, df_predict
def evaluate(self, test_structures, ref_energies, ref_forces,
ref_stresses):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
"""
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
_, df_orig = convert_docs(predict_pool)
data_pool = []
for struct in test_structures:
d = {'outputs': {}}
d['structure'] = struct.as_dict()
d['num_atoms'] = len(struct)
features = self.describer.describe(struct)
targets = self.predictor.predict(features.values)
d['outputs']['energy'] = 0
d['outputs']['forces'] = targets.reshape((-1, 3))
d['outputs']['virial_stress'] = [0., 0., 0., 0., 0., 0.]
data_pool.append(d)
_, df_pred = convert_docs(data_pool)
return df_orig, df_pred
def train(self,
train_structures,
energies,
forces,
stresses=None,
**kwargs):
"""
Training data with model.
Args:
train_structures ([Structure]): The list of Pymatgen Structure object.
energies ([float]): The list of total energies of each structure
in structures list.
energies ([float]): List of total energies of each structure in
structures list.
forces ([np.array]): List of (m, 3) forces array of each structure
with m atoms in structures list. m can be varied with each
single structure case.
stresses (list): List of (6, ) virial stresses of each
structure in structures list.
"""
train_pool = pool_from(train_structures, energies, forces, stresses)
_, df = convert_docs(train_pool)
ytrain = df['y_orig'] / df['n']
self.model.fit(inputs=train_structures, outputs=ytrain, **kwargs)
self.specie = Element(train_structures[0].symbol_set[0])
def describe(self, structure):
"""
Returns data for one input structure.
Args:
structure (Structure): Input structure.
"""
if not which('RuNNer'):
raise RuntimeError("RuNNer has not been found.")
if not which("RuNNerMakesym"):
raise RuntimeError("RuNNerMakesym has not been found.")
def read_functions_data(filename):
"""
Read structure features from file.
Args:
filename (str): The functions file to be read.
"""
with zopen(filename, 'rt') as f:
lines = f.read()
block_pattern = re.compile(
r'(\n\s+\d+\n|^\s+\d+\n)(.+?)(?=\n\s+\d+\n|$)', re.S)
points_features = []
for (num_neighbor, block) in block_pattern.findall(lines):
point_features = pd.DataFrame([
feature.split()[1:] for feature in block.split('\n')[:-1]
],
dtype=np.float32)
points_features.append(point_features)
points_features = pd.concat(points_features,
keys=range(
len(block_pattern.findall(lines))),
names=['point_index', None])
return points_features
dmin = sorted(set(structure.distance_matrix.ravel()))[1]
r_etas = self.operator.generate_eta(dmin=self.dmin,
r_cut=self.cutoff,
num_symm2=self.num_symm2)
atoms_filename = 'input.data'
mode_output = 'mode.out'
with ScratchDir('.'):
atoms_filename = self.operator.write_cfgs(filename=atoms_filename,
cfg_pool=pool_from(
[structure]))
input_filename = self.operator.write_input(mode=1,
r_cut=self.cutoff,
r_etas=r_etas,
a_etas=self.a_etas,
scale_feature=False)
p = subprocess.Popen(['RuNNer'], stdout=open(mode_output, 'w'))
stdout = p.communicate()[0]
descriptors = read_functions_data('function.data')
return pd.DataFrame(descriptors)
def test_pool_from(self):
test_pool = pool_from(self.test_structures, self.test_energies,
self.test_forces, self.test_stresses)
for p1, p2 in zip(test_pool, self.test_pool):
self.assertEqual(p1['outputs']['energy'], p2['outputs']['energy'])
self.assertEqual(p1['outputs']['forces'], p2['outputs']['forces'])
self.assertEqual(p1['outputs']['virial_stress'],
p2['outputs']['virial_stress'])
def evaluate(self, test_structures, ref_energies, ref_forces,
ref_stresses):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
"""
if not which('nnp-predict'):
raise RuntimeError("NNP Predictor has not been found.")
original_file = 'input.data'
predict_file = 'output.data'
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
with ScratchDir('.'):
_, _ = self.write_param()
original_file = self.write_cfgs(original_file,
cfg_pool=predict_pool)
_, df_orig = self.read_cfgs(original_file)
input_filename = self.write_input()
dfs = []
for data in predict_pool:
_ = self.write_cfgs(original_file, cfg_pool=[data])
p = subprocess.Popen(['nnp-predict', input_filename],
stdout=subprocess.PIPE)
stdout = p.communicate()[0]
rc = p.returncode
if rc != 0:
error_msg = 'RuNNer exited with return code %d' % rc
msg = stdout.decode("utf-8").split('\n')[:-1]
try:
error_line = [
i for i, m in enumerate(msg)
if m.startswith('ERROR')
][0]
error_msg += ', '.join([e for e in msg[error_line:]])
except Exception:
error_msg += msg[-1]
raise RuntimeError(error_msg)
_, df = self.read_cfgs(predict_file)
dfs.append(df)
df_predict = pd.concat(dfs, ignore_index=True)
return df_orig, df_predict
def evaluate(self, test_structures, ref_energies=None,
ref_forces=None, ref_stresses=None, **kwargs):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
kwargs: Parameters of write_param method.
"""
if not which('mlp'):
raise RuntimeError("mlp has not been found.\n",
"Please refer to http://gitlab.skoltech.ru/shapeev/mlip ",
"for further detail.")
fitted_mtp = 'fitted.mtp'
original_file = 'original.cfgs'
predict_file = 'predict.cfgs'
predict_pool = pool_from(test_structures, ref_energies,
ref_forces, ref_stresses)
dataset = predict_pool[0]
if isinstance(dataset['structure'], dict):
structure = Structure.from_dict(dataset['structure'])
else:
structure = dataset['structure']
symbol = structure.symbol_set[0]
with ScratchDir('.'):
self.write_param(fitted_mtp=fitted_mtp, Abinitio=0, Driver=1,
Calculate_EFS=True, Write_cfgs=predict_file,
Database_filename=original_file, **kwargs)
original_file = self.write_cfg(original_file, cfg_pool=predict_pool)
_, df_orig = self.read_cfgs(original_file, symbol=symbol)
p = subprocess.Popen(['mlp', 'run', 'mlip.ini'], stdout=subprocess.PIPE)
stdout = p.communicate()[0]
rc = p.returncode
if rc != 0:
error_msg = 'MLP exited with return code %d' % rc
msg = stdout.decode("utf-8").split('\n')[:-1]
try:
error_line = [i for i, m in enumerate(msg)
if m.startswith('ERROR')][0]
error_msg += ', '.join([e for e in msg[error_line:]])
except Exception:
error_msg += msg[-1]
raise RuntimeError(error_msg)
_, df_predict = self.read_cfgs(predict_file, symbol=symbol)
return df_orig, df_predict
def evaluate2(self,
test_structures,
ref_energies=None,
ref_forces=None,
ref_stresses=None):
"""
Evaluate energies, forces and stresses of structures with trained
interatomic potential.
Args:
test_structures ([Structure]): List of Pymatgen Structure Objects.
ref_energies ([float]): List of DFT-calculated total energies of
each structure in structures list.
ref_forces ([np.array]): List of DFT-calculated (m, 3) forces of
each structure with m atoms in structures list. m can be varied
with each single structure case.
ref_stresses (list): List of DFT-calculated (6, ) viriral stresses
of each structure in structures list.
"""
predict_pool = pool_from(test_structures, ref_energies, ref_forces,
ref_stresses)
_, df_orig = convert_docs(predict_pool)
efs_calculator = EnergyForceStress(ff_settings=self)
efs_results = efs_calculator.calculate(test_structures)
assert len(test_structures) == len(efs_results)
data_pool = []
for struct, (energy, forces, stresses) in zip(test_structures,
efs_results):
d = {'outputs': {}}
d['structure'] = struct.as_dict()
d['num_atoms'] = len(struct)
d['outputs']['energy'] = energy
d['outputs']['forces'] = forces
d['outputs']['virial_stress'] = stresses
data_pool.append(d)
_, df_pred = convert_docs(data_pool)
return df_orig, df_pred
def train(self,
train_structures,
energies=None,
forces=None,
stresses=None,
**kwargs):
"""
Training data with agni method.
Args:
train_structures ([Structure]): The list of Pymatgen Structure object.
energies ([float]): The list of total energies of each structure
in structures list.
energies ([float]): List of total energies of each structure in
structures list.
forces ([np.array]): List of (m, 3) forces array of each structure
with m atoms in structures list. m can be varied with each
single structure case.
stresses (list): List of (6, ) virial stresses of each
structure in structures list.
"""
train_pool = pool_from(train_structures, energies, forces, stresses)
_, _, features, targets = self.sample(train_pool, **kwargs)
gamma = self.fit(features, targets)
return 0
def train(self,
train_structures,
energies=None,
forces=None,
stresses=None,
default_sigma=[0.0005, 0.1, 0.05, 0.01],
use_energies=True,
use_forces=True,
use_stress=False,
**kwargs):
"""
Training data with gaussian process regression.
Args:
train_structures ([Structure]): The list of Pymatgen Structure object.
energies ([float]): The list of total energies of each structure
in structures list.
energies ([float]): List of total energies of each structure in
structures list.
forces ([np.array]): List of (m, 3) forces array of each structure
with m atoms in structures list. m can be varied with each
single structure case.
stresses (list): List of (6, ) virial stresses of each
structure in structures list.
default_sigma (list): Error criteria in energies, forces, stress
and hessian. Should have 4 numbers.
use_energies (bool): Whether to use dft total energies for training.
Default to True.
use_forces (bool): Whether to use dft atomic forces for training.
Default to True.
use_stress (bool): Whether to use dft virial stress for training.
Default to False.
kwargs:
l_max (int): Parameter to configure GAP. The band limit of
spherical harmonics basis function. Default to 12.
n_max (int): Parameter to configure GAP. The number of radial basis
function. Default to 10.
atom_sigma (float): Parameter to configure GAP. The width of gaussian
atomic density. Default to 0.5.
zeta (float): Present when covariance function type is do product.
Default to 4.
cutoff (float): Parameter to configure GAP. The cutoff radius.
Default to 4.0.
cutoff_transition_width (float): Parameter to configure GAP.
The transition width of cutoff radial. Default to 0.5.
delta (float): Parameter to configure Sparsification.
The signal variance of noise. Default to 1.
f0 (float): Parameter to configure Sparsification.
The signal mean of noise. Default to 0.0.
n_sparse (int): Parameter to configure Sparsification.
Number of sparse points.
covariance_type (str): Parameter to configure Sparsification.
The type of convariance function. Default to dot_product.
sparse_method (str): Method to perform clustering in sparsification.
Default to 'cur_points'.
sparse_jitter (float): Intrisic error of atomic/bond energy,
used to regularise the sparse covariance matrix.
Default to 1e-8.
e0 (float): Atomic energy value to be subtracted from energies
before fitting. Default to 0.0.
e0_offset (float): Offset of baseline. If zero, the offset is
the average atomic energy of the input data or the e0
specified manually. Default to 0.0.
"""
if not which('teach_sparse'):
raise RuntimeError(
"teach_sparse has not been found.\n",
"Please refer to https://github.com/libAtoms/QUIP for ",
"further detail.")
atoms_filename = 'train.xyz'
xml_filename = 'train.xml'
train_pool = pool_from(train_structures, energies, forces, stresses)
exe_command = ["teach_sparse"]
exe_command.append('at_file={}'.format(atoms_filename))
gap_configure_params = [
'l_max', 'n_max', 'atom_sigma', 'zeta', 'cutoff',
'cutoff_transition_width', 'delta', 'f0', 'n_sparse',
'covariance_type', 'sparse_method'
]
preprocess_params = ['sparse_jitter', 'e0', 'e0_offset']
target_for_training = ['use_energies', 'use_forces', 'use_stress']
if len(default_sigma) != 4:
raise ValueError(
"The default sigma is supposed to have 4 numbers.")
gap_command = ['soap']
for param_name in gap_configure_params:
param = kwargs.get(param_name) if kwargs.get(param_name) \
else soap_params.get(param_name)
gap_command.append(param_name + '=' + '{}'.format(param))
exe_command.append("gap=" + "{" + "{}".format(' '.join(gap_command)) +
"}")
for param_name in preprocess_params:
param = kwargs.get(param_name) if kwargs.get(param_name) \
else soap_params.get(param_name)
exe_command.append(param_name + '=' + '{}'.format(param))
default_sigma = [str(f) for f in default_sigma]
exe_command.append("default_sigma={%s}" % (' '.join(default_sigma)))
if use_energies:
exe_command.append('energy_parameter_name=dft_energy')
if use_forces:
exe_command.append('force_parameter_name=dft_force')
if use_stress:
exe_command.append('virial_parameter_name=dft_virial')
exe_command.append('gp_file={}'.format(xml_filename))
with ScratchDir('.'):
self.write_cfgs(filename=atoms_filename, cfg_pool=train_pool)
p = subprocess.Popen(exe_command, stdout=subprocess.PIPE)
stdout = p.communicate()[0]
rc = p.returncode
if rc != 0:
error_msg = 'QUIP exited with return code %d' % rc
msg = stdout.decode("utf-8").split('\n')[:-1]
try:
error_line = [
i for i, m in enumerate(msg) if m.startswith('ERROR')
][0]
error_msg += ', '.join([e for e in msg[error_line:]])
except Exception:
error_msg += msg[-1]
raise RuntimeError(error_msg)
def get_xml(xml_file):
tree = ET.parse(xml_file)
root = tree.getroot()
potential_label = root.tag
gpcoordinates = list(root.iter('gpCoordinates'))[0]
param_file = gpcoordinates.get('sparseX_filename')
param = np.loadtxt(param_file)
return tree, param, potential_label
tree, param, potential_label = get_xml(xml_filename)
self.param['xml'] = tree
self.param['param'] = param
self.param['potential_label'] = potential_label
return rc
def train(self,
train_structures,
energies=None,
forces=None,
stresses=None,
**kwargs):
"""
Training data with moment tensor method.
Args:
train_structures ([Structure]): The list of Pymatgen Structure object.
energies ([float]): The list of total energies of each structure
in structures list.
energies ([float]): List of total energies of each structure in
structures list.
forces ([np.array]): List of (m, 3) forces array of each structure
with m atoms in structures list. m can be varied with each
single structure case.
stresses (list): List of (6, ) virial stresses of each
structure in structures list.
kwargs: Parameters in write_input method.
"""
if not which('nnp-train'):
raise RuntimeError("NNP Trainer has not been found.")
train_pool = pool_from(train_structures, energies, forces, stresses)
atoms_filename = 'input.data'
with ScratchDir('.'):
atoms_filename = self.write_cfgs(filename=atoms_filename,
cfg_pool=train_pool)
output = 'training_output'
input_filename = self.write_input(**kwargs)
p_scaling = subprocess.Popen(['nnp-scaling', input_filename])
stdout = p_scaling.communicate()[0]
p_train = subprocess.Popen(['nnp-train', input_filename],
stdout=open(output, 'w'))
stdout = p_train.communicate()[0]
rc = p_train.returncode
if rc != 0:
error_msg = 'RuNNer exited with return code %d' % rc
msg = stdout.decode("utf-8").split('\n')[:-1]
try:
error_line = [
i for i, m in enumerate(msg) if m.startswith('ERROR')
][0]
error_msg += ', '.join([e for e in msg[error_line:]])
except Exception:
error_msg += msg[-1]
raise RuntimeError(error_msg)
with zopen(output) as f:
error_lines = f.read()
energy_rmse_pattern = re.compile(
r'ENERGY\s*\S*\s*(\S*)\s*(\S*).*?\n')
forces_rmse_pattern = re.compile(
r'FORCES\s*\S*\s*(\S*)\s*(\S*).*?\n')
self.train_energy_rmse, self.validation_energy_rmse = \
np.array([line for line in energy_rmse_pattern.findall(error_lines)],
dtype=np.float).T
self.train_forces_rmse, self.validation_forces_rmse = \
np.array([line for line in forces_rmse_pattern.findall(error_lines)],
dtype=np.float).T
weights_filename_pattern = 'weights*{}.out'.format(self.epochs)
weights_filename = glob.glob(weights_filename_pattern)[0]
self.suffix = weights_filename.split('.')[1]
with open(weights_filename) as f:
weights_lines = f.readlines()
params = pd.DataFrame(
[line.split() for line in weights_lines if "#" not in line])
params.columns = [
'value', 'type', 'index', 'start_layer', 'start_neuron',
'end_layer', 'end_neuron'
]
self.params = params
for layer_index in range(1, len(self.layer_sizes)):
weights_group = params[
(params['start_layer'] == str(layer_index - 1))
& (params['end_layer'] == str(layer_index))]
weights = np.reshape(
np.array(weights_group['value'], dtype=np.float),
(self.layer_sizes[layer_index - 1],
self.layer_sizes[layer_index]))
self.weights.append(weights)
bs_group = params[(params['type'] == 'b') &
(params['start_layer'] == str(layer_index))]
bs = np.array(bs_group['value'], dtype=np.float)
self.bs.append(bs)
with open('scaling.data') as f:
scaling_lines = f.readlines()
scaling_params = pd.DataFrame(
[line.split() for line in scaling_lines if '#' not in line])
scaling_params.column = [
'e_index', 'sf_index', 'sf_min', 'sf_max', 'sf_mean',
'sf_sigma'
]
self.scaling_params = scaling_params
return rc
def train(self, train_structures, energies=None, forces=None, stresses=None,
unfitted_mtp=None, **kwargs):
"""
Training data with moment tensor method.
Args:
train_structures ([Structure]): The list of Pymatgen Structure object.
energies ([float]): The list of total energies of each structure
in structures list.
energies ([float]): List of total energies of each structure in
structures list.
forces ([np.array]): List of (m, 3) forces array of each structure
with m atoms in structures list. m can be varied with each
single structure case.
stresses (list): List of (6, ) virial stresses of each
structure in structures list.
unfitted_mtp (str): Define the initial mtp file. Default to
the mtp file stored in .params directory.
kwargs: Parameters in write_ini method.
"""
if not which('mlp'):
raise RuntimeError("mlp has not been found.\n",
"Please refer to http://gitlab.skoltech.ru/shapeev/mlip ",
"for further detail.")
train_pool = pool_from(train_structures, energies, forces, stresses)
atoms_filename = 'train.cfgs'
with ScratchDir('.'):
atoms_filename = self.write_cfg(filename=atoms_filename, cfg_pool=train_pool)
if not unfitted_mtp:
unfitted_mtp = 'MTP.mtp'
shutil.copyfile(MTP_file_path, os.path.join(os.getcwd(), unfitted_mtp))
save_fitted_mtp = '.'.join(
[unfitted_mtp.split('.')[0] + '_fitted', unfitted_mtp.split('.')[1]])
self.write_ini(Abinitio=1, MLIP=unfitted_mtp, Driver=1, Fit=True,
Save=save_fitted_mtp, Database_filename=atoms_filename, **kwargs)
p = subprocess.Popen(['mlp', 'run', 'mlip.ini'], stdout=subprocess.PIPE)
stdout = p.communicate()[0]
rc = p.returncode
if rc != 0:
error_msg = 'MLP exited with return code %d' % rc
msg = stdout.decode("utf-8").split('\n')[:-1]
try:
error_line = [i for i, m in enumerate(msg)
if m.startswith('ERROR')][0]
error_msg += ', '.join([e for e in msg[error_line:]])
except Exception:
error_msg += msg[-1]
raise RuntimeError(error_msg)
param = OrderedDict()
with open(save_fitted_mtp, 'r') as f:
lines = f.readlines()
param['safe'] = [line.rstrip() for line in lines[:-2]]
for line in lines[-2:]:
key = line.rstrip().split(' = ')[0]
value = json.loads(
line.rstrip().split(' = ')[1].replace('{', '[').replace('}', ']'))
param[key] = value
self.param = param
return rc
def describe(self, structure):
"""
Returns data for one input structure.
Args:
structure (Structure): Input structure.
"""
if not which('quip'):
raise RuntimeError(
"quip has not been found.\n",
"Please refer to https://github.com/libAtoms/QUIP for ",
"further detail.")
atoms_filename = 'structure.xyz'
exe_command = ['quip']
exe_command.append('atoms_filename={}'.format(atoms_filename))
descriptor_command = ['soap']
descriptor_command.append("cutoff" + '=' + '{}'.format(self.cutoff))
descriptor_command.append("l_max" + '=' + '{}'.format(self.l_max))
descriptor_command.append("n_max" + '=' + '{}'.format(self.n_max))
descriptor_command.append("atom_sigma" + '=' +
'{}'.format(self.atom_sigma))
atomic_numbers = [
str(num) for num in np.unique(structure.atomic_numbers)
]
n_Z = len(atomic_numbers)
n_species = len(atomic_numbers)
Z = '{' + '{}'.format(' '.join(atomic_numbers)) + '}'
species_Z = '{' + '{}'.format(' '.join(atomic_numbers)) + '}'
descriptor_command.append("n_Z" + '=' + str(n_Z))
descriptor_command.append("Z" + '=' + Z)
descriptor_command.append("n_species" + '=' + str(n_species))
descriptor_command.append("species_Z" + '=' + species_Z)
exe_command.append("descriptor_str=" + "{" +
"{}".format(' '.join(descriptor_command)) + "}")
with ScratchDir('.'):
atoms_filename = self.operator.write_cfgs(filename=atoms_filename,
cfg_pool=pool_from(
[structure]))
descriptor_output = 'output'
p = subprocess.Popen(exe_command,
stdout=open(descriptor_output, 'w'))
stdout = p.communicate()[0]
rc = p.returncode
if rc != 0:
error_msg = 'QUIP exited with return code %d' % rc
msg = stdout.decode("utf-8").split('\n')[:-1]
try:
error_line = [
i for i, m in enumerate(msg) if m.startswith('ERROR')
][0]
error_msg += ', '.join([e for e in msg[error_line:]])
except Exception:
error_msg += msg[-1]
raise RuntimeError(error_msg)
with zopen(descriptor_output, 'rt') as f:
lines = f.read()
descriptor_pattern = re.compile('DESC(.*?)\n', re.S)
descriptors = pd.DataFrame([
np.array(c.split(), dtype=np.float)
for c in descriptor_pattern.findall(lines)
])
return descriptors