from os.path import exists
from os.path import join as join_path
from typing import Any, Iterable, NamedTuple, TextIO
from warnings import warn
import numpy as np
from ase import Atoms
from ase.io import read, write
from ase.stress import voigt_6_to_full_3x3_stress
from pandas import DataFrame
[docs]
def read_loss(filename: str) -> DataFrame:
"""Parses a file in `loss.out` format from GPUMD and returns the
content as a data frame. More information concerning file format,
content and units can be found `here
<https://gpumd.org/nep/output_files/loss_out.html>`__.
Parameters
----------
filename
input file name
"""
data = np.loadtxt(filename)
if isinstance(data[0], np.float64):
# If only a single row in loss.out, append a dimension
data = data.reshape(1, -1)
if len(data[0]) == 6:
tags = 'total_loss L1 L2'
tags += ' RMSE_P_train'
tags += ' RMSE_P_test'
elif len(data[0]) == 10:
tags = 'total_loss L1 L2'
tags += ' RMSE_E_train RMSE_F_train RMSE_V_train'
tags += ' RMSE_E_test RMSE_F_test RMSE_V_test'
else:
raise ValueError(
f'Input file contains {len(data[0])} data columns. Expected 6 or 10 columns.'
)
generations = range(100, len(data) * 100 + 1, 100)
df = DataFrame(data=data[:, 1:], columns=tags.split(), index=generations)
return df
def _write_structure_in_nep_format(structure: Atoms, f: TextIO) -> None:
"""Write structure block into a file-like object in format readable by nep executable.
Parameters
----------
structure
input structure; must hold information regarding energy and forces
f
file-like object to which to write
"""
# Allowed keyword=value pairs. Use ASEs extyz write functionality.:
# lattice="ax ay az bx by bz cx cy cz" (mandatory)
# energy=energy_value (mandatory)
# virial="vxx vxy vxz vyx vyy vyz vzx vzy vzz" (optional)
# weight=relative_weight (optional)
# properties=property_name:data_type:number_of_columns
# species:S:1 (mandatory)
# pos:R:3 (mandatory)
# force:R:3 or forces:R:3 (mandatory)
try:
structure.get_potential_energy()
structure.get_forces() # calculate forces to have them on the Atoms object
except RuntimeError:
raise RuntimeError('Failed to retrieve energy and/or forces for structure')
if np.isclose(structure.get_volume(), 0):
raise ValueError('Structure cell must have a non-zero volume!')
try:
structure.get_stress()
except RuntimeError:
warn('Failed to retrieve stresses for structure')
write(filename=f, images=structure, write_info=True, format='extxyz')
[docs]
def write_structures(outfile: str, structures: list[Atoms]) -> None:
"""Writes structures for training/testing in format readable by nep executable.
Parameters
----------
outfile
output filename
structures
list of structures with energy, forces, and (possibly) stresses
"""
with open(outfile, 'w') as f:
for structure in structures:
_write_structure_in_nep_format(structure, f)
[docs]
def write_nepfile(parameters: NamedTuple, dirname: str) -> None:
"""Writes parameters file for NEP construction.
Parameters
----------
parameters
input parameters; see `here <https://gpumd.org/nep/input_parameters/index.html>`__
dirname
directory in which to place input file and links
"""
with open(join_path(dirname, 'nep.in'), 'w') as f:
for key, val in parameters.items():
f.write(f'{key} ')
if isinstance(val, Iterable):
f.write(' '.join([f'{v}' for v in val]))
else:
f.write(f'{val}')
f.write('\n')
[docs]
def read_nepfile(filename: str) -> dict[str, Any]:
"""Returns the content of a configuration file (`nep.in`) as a dictionary.
Parameters
----------
filename
input file name
"""
int_vals = ['version', 'neuron', 'generation', 'batch', 'population',
'mode', 'model_type', 'charge_mode']
float_vals = ['lambda_1', 'lambda_2', 'lambda_e', 'lambda_f', 'lambda_v',
'lambda_q', 'lambda_shear', 'force_delta']
settings = {}
with open(filename) as f:
for line in f.readlines():
# remove comments - throw away everything after a '#'
cleaned = line.split('#', 1)[0].strip()
flds = cleaned.split()
if len(flds) == 0:
continue
settings[flds[0]] = ' '.join(flds[1:])
for key, val in settings.items():
if key in int_vals:
settings[key] = int(val)
elif key in float_vals:
settings[key] = float(val)
elif key in ['cutoff', 'n_max', 'l_max', 'basis_size', 'zbl', 'type_weight']:
settings[key] = [float(v) for v in val.split()]
elif key == 'type':
types = val.split()
types[0] = int(types[0])
settings[key] = types
return settings
[docs]
def read_structures(dirname: str) -> tuple[list[Atoms], list[Atoms]]:
"""Parses the output files with training and test data from a nep run and returns their
content as two lists of structures, representing training and test data, respectively.
Target and predicted data are included in the :attr:`info` dict of the :class:`Atoms`
objects.
Parameters
----------
dirname
Directory from which to read output files.
"""
path = join_path(dirname)
if not exists(path):
raise FileNotFoundError(f'Directory {path} does not exist')
# fetch model type from nep input file
nep_info = read_nepfile(f'{path}/nep.in')
model_type = nep_info.get('model_type', 0)
# set up which files to parse, what dimensions to expect etc
# depending on the type of model that is parsed
if model_type == 0:
charge_mode = int(nep_info.get('charge_mode', 0))
if charge_mode not in [0, 1, 2]:
raise ValueError(f'Unknown charge_mode: {charge_mode}')
# files to parse: (sname, size, includes_target, per_atom)
files_to_parse = [
('energy', 1, True, False),
('force', 3, True, True),
('virial', 6, True, False),
('stress', 6, True, False),
]
if charge_mode in [1, 2]:
# files to parse: (sname, size, includes_target, per_atom)
files_to_parse += [
('charge', 1, False, True),
('bec', 9, True, True),
]
elif model_type == 1:
# files to parse: (sname, size, includes_target, per_atom)
files_to_parse = [('dipole', 3, True, False)]
elif model_type == 2:
# files to parse: (sname, size, includes_target, per_atom)
files_to_parse = [('polarizability', 6, True, False)]
else:
raise ValueError(f'Unknown model_type: {model_type}')
# read training and test data
structures = {}
for stype in ['train', 'test']:
filename = join_path(dirname, f'{stype}.xyz')
try:
structures[stype] = read(filename, format='extxyz', index=':')
except FileNotFoundError:
warn(f'File {filename} not found.')
structures[stype] = []
continue
n_structures = len(structures[stype])
# loop over files from which to read target data and predictions
for sname, size, includes_target, per_atom in files_to_parse:
infile = f'{sname}_{stype}.out'
ts, ps = _read_data_file(path, infile, includes_target=includes_target)
if ts is not None:
if ts.shape[1] != size:
raise ValueError(f'Target data in {infile} has unexpected shape:'
f' {ts.shape} (expected: (-1, {size}))')
if ps.shape[1] != size:
raise ValueError(f'Predicted data in {infile} has unexpected shape:'
f' {ps.shape} (expected: (-1, {size}))')
if per_atom:
# data per-atom, e.g., forces, per-atom-virials, Born effective charges ...
n_atoms_total = sum([len(s) for s in structures[stype]])
if len(ps) != n_atoms_total:
raise ValueError(f'Number of atoms in {infile} ({len(ps)})'
f' and {stype}.xyz ({n_atoms_total}) inconsistent.')
n = 0
for structure in structures[stype]:
nat = len(structure)
if ts is not None:
structure.info[f'{sname}_target'] = \
np.array(ts[n: n + nat]).reshape(nat, size)
structure.info[f'{sname}_predicted'] = \
np.array(ps[n: n + nat]).reshape(nat, size)
n += nat
else:
# data per structure, e.g., energy, virials, stress
if len(ps) != n_structures:
raise ValueError(f'Number of structures in {infile} ({len(ps)})'
f' and {stype}.xyz ({n_structures}) inconsistent.')
for k, structure in enumerate(structures[stype]):
assert ts is not None, 'This should not occur. Please report.'
t = ts[k]
assert np.shape(t) == (size,)
structure.info[f'{sname}_target'] = t
p = ps[k]
assert np.shape(p) == (size,)
structure.info[f'{sname}_predicted'] = p
# special handling of target data for BECs
# The target data for BECs need not be complete. In this case nep writes
# zeros for every component (not optimal). If we encounter such a case we set
# all components to nan instead in order to be able to quickly filter for
# this case when analyzing data.
for s in structures[stype]:
if 'bec_target' in s.info and np.allclose(s.info['bec_target'], 0):
nat = len(s)
size = 9
s.info['bec_target'] = np.array(size * nat * [np.nan]).reshape(nat, size)
return structures['train'], structures['test']
def _read_data_file(dirname: str, fname: str, includes_target: bool = True):
"""Private function that parses *.out files and
returns their content for further processing.
"""
path = join_path(dirname, fname)
if not exists(path):
raise FileNotFoundError(f'File {path} does not exist')
with open(path, 'r') as f:
lines = f.readlines()
target, predicted = [], []
for line in lines:
flds = line.split()
if includes_target:
if len(flds) % 2 != 0:
raise ValueError(f'Incorrect number of columns in {path} ({len(flds)}).')
n = len(flds) // 2
predicted.append([float(s) for s in flds[:n]])
target.append([float(s) for s in flds[n:]])
else:
predicted.append([float(s) for s in flds])
target = None
if target is not None:
target = np.array(target)
predicted = np.array(predicted)
return target, predicted
[docs]
def get_parity_data(
structures: list[Atoms],
property: str,
selection: list[str] = None,
flatten: bool = True,
) -> DataFrame:
"""Returns the predicted and target energies, forces, virials or stresses
from a list of structures in a format suitable for generating parity plots.
The structures should have been read using :func:`read_structures
<calorine.nep.read_structures>`, such that the `info` object is
populated with keys of the form `<property>_<type>` where `<property>`
is, e.g., `energy` or `force` and `<type>` is one of `predicted` or `target`.
The resulting parity data is returned as a tuple of dicts, where each entry
corresponds to a list.
Parameters
----------
structures
List of structures as read with :func:`read_structures <calorine.nep.read_structures>`.
property
One of `energy`, `force`, `virial`, `stress`, `bec`, `dipole`, or `polarizability`.
selection
A list containing which components to return, and/or the norm.
Possible values are `x`, `y`, `z`, `xx`, `yy`,
`zz`, `yz`, `xz`, `xy`, `norm`, `pressure`.
flatten
if True return flattened lists; this is useful for flattening
the components of force or virials into a simple list
"""
voigt_mapping = {
'x': 0, 'y': 1, 'z': 2, 'xx': 0, 'yy': 1, 'zz': 2, 'yz': 3, 'xz': 4, 'xy': 5,
}
if property not in ('energy', 'force', 'virial', 'stress', 'polarizability', 'dipole', 'bec'):
raise ValueError(
"`property` must be one of 'energy', 'force', 'virial', 'stress',"
" 'polarizability', 'dipole', or 'bec'."
)
if property in ['energy'] and selection:
raise ValueError('Selection cannot be applied to scalars.')
if property != 'stress' and selection and 'pressure' in selection:
raise ValueError(f'Cannot calculate pressure for `{property}`.')
data = {'predicted': [], 'target': []}
if property in ['force', 'bec'] and flatten:
size = 3 if property == 'force' else 9
data['species'] = []
for structure in structures:
if 'species' in data:
data['species'].extend(np.repeat(structure.symbols, size).tolist())
for stype in ['predicted', 'target']:
property_with_stype = f'{property}_{stype}'
if property_with_stype not in structure.info.keys():
raise KeyError(f'{property_with_stype} not available in info field of structure')
extracted_property = np.array(structure.info[property_with_stype])
if selection is None or len(selection) == 0:
data[stype].append(extracted_property)
continue
selected_values = []
for select in selection:
if property in ['force', 'bec']:
# flip to get (n_components, n_structures)
extracted_property = extracted_property.T
if select == 'norm':
if property == 'force':
selected_values.append(np.linalg.norm(extracted_property, axis=0))
elif property in ['virial', 'stress']:
full_tensor = voigt_6_to_full_3x3_stress(extracted_property)
selected_values.append(np.linalg.norm(full_tensor))
elif property in ['dipole']:
selected_values.append(np.linalg.norm(extracted_property))
else:
raise ValueError(
f'Cannot handle selection=`norm` with property=`{property}`.')
continue
if select == 'pressure' and property == 'stress':
total_stress = extracted_property
selected_values.append(-np.sum(total_stress[:3]) / 3)
continue
if select not in voigt_mapping:
raise ValueError(f'Selection `{select}` is not allowed.')
index = voigt_mapping[select]
if index >= extracted_property.shape[0]:
raise ValueError(
f'Selection `{select}` is not compatible with property `{property}`.'
)
selected_values.append(extracted_property[index])
data[stype].append(selected_values)
if flatten:
for stype in ['target', 'predicted']:
value = data[stype]
if len(np.shape(value[0])) > 0:
data[stype] = np.concatenate(value).ravel().tolist()
if property in ['force']:
n = len(data['target']) // 3
data['component'] = ['x', 'y', 'z'] * n
elif property in ['virial', 'stress']:
n = len(data['target']) // 6
data['component'] = ['xx', 'yy', 'zz', 'yz', 'xz', 'xy'] * n
elif property in ['bec']:
n = len(data['target']) // 9
data['component'] = ['xx', 'xy', 'xz', 'yx', 'yy', 'yz', 'zx', 'zy', 'zz'] * n
df = DataFrame(data)
# In case of flatten, cast to float64 for compatibility
# with e.g. seaborn.
# Casting in this way breaks tensorial properties though,
# so skip it there.
if flatten:
df['target'] = df.target.astype('float64')
df['predicted'] = df.predicted.astype('float64')
return df
