def parse(self, **kwargs):
"""Parse the retrieved files from a `MatdynCalculation`."""
try:
output_folder = self.retrieved
except exceptions.NotExistent:
return self.exit(self.exit_codes.ERROR_NO_RETRIEVED_FOLDER)
filename_stdout = self.node.get_option('output_filename')
filename_frequencies = MatdynCalculation._PHONON_FREQUENCIES_NAME
if filename_stdout not in output_folder.list_object_names():
return self.exit(self.exit_codes.ERROR_OUTPUT_STDOUT_READ)
if 'JOB DONE' not in output_folder.get_object_content(filename_stdout):
return self.exit(self.exit_codes.ERROR_OUTPUT_STDOUT_INCOMPLETE)
if filename_frequencies not in output_folder.list_object_names():
return self.exit(self.exit_codes.ERROR_OUTPUT_STDOUT_READ)
# Extract the kpoints from the input data and create the `KpointsData` for the `BandsData`
try:
kpoints = self.node.inputs.kpoints.get_kpoints()
kpoints_for_bands = self.node.inputs.kpoints.clone()
except AttributeError:
kpoints = self.node.inputs.kpoints.get_kpoints_mesh(
print_list=True)
kpoints_for_bands = orm.KpointsData()
kpoints_for_bands.set_kpoints(kpoints)
parsed_data = parse_raw_matdyn_phonon_file(
output_folder.get_object_content(filename_frequencies))
try:
num_kpoints = parsed_data.pop('num_kpoints')
except KeyError:
return self.exit(self.exit_codes.ERROR_OUTPUT_KPOINTS_MISSING)
if num_kpoints != kpoints.shape[0]:
return self.exit(
self.exit_codes.ERROR_OUTPUT_KPOINTS_INCOMMENSURATE)
output_bands = orm.BandsData()
output_bands.set_kpointsdata(kpoints_for_bands)
output_bands.set_bands(parsed_data.pop('phonon_bands'), units='THz')
for message in parsed_data['warnings']:
self.logger.error(message)
self.out('output_parameters', orm.Dict(dict=parsed_data))
self.out('output_phonon_bands', output_bands)
return
def crop_bands_inline(bands, kpoints):
"""
Crop a BandsData to the given kpoints by removing from the front.
"""
# check consistency of kpoints
kpoints_array = kpoints.get_kpoints()
band_slice = slice(-len(kpoints_array), None)
cropped_bands_kpoints = bands.get_kpoints()[band_slice]
assert np.allclose(cropped_bands_kpoints, kpoints_array)
cropped_bands = orm.BandsData()
cropped_bands.set_kpointsdata(kpoints)
cropped_bands_array = bands.get_bands()[band_slice]
cropped_bands.set_bands(cropped_bands_array)
return {'bands': cropped_bands}
def build_output_bands(self, parsed_bands, parsed_kpoints=None):
"""Build the output bands from the raw parsed bands data.
:param parsed_bands: the raw parsed bands data
:param parsed_kpoints: the `KpointsData` to use for the bands
:return: a `BandsData` or None
"""
if not parsed_bands or not parsed_kpoints:
return
# In the case of input kpoints that define a list of k-points, i.e. along high-symmetry path, and explicit
# labels, set those labels also on the output kpoints to be used for the bands. This will allow plotting
# utilities to place k-point labels along the x-axis.
try:
self.node.inputs.kpoints.get_kpoints()
parsed_kpoints.labels = self.node.inputs.kpoints.labels
except (AttributeError, ValueError, TypeError):
# AttributeError: input kpoints defines a mesh, not an explicit list
# TypeError: inputs kpoints do not define any labels
# ValueError: input kpoints labels are not commensurate with `parsed_kpoints`
pass
# Correct the occupation for nspin=1 calculations where Quantum ESPRESSO populates each band only halfway
if len(parsed_bands['occupations']) > 1:
occupations = parsed_bands['occupations']
else:
occupations = 2. * numpy.array(parsed_bands['occupations'][0])
if len(parsed_bands['bands']) > 1:
bands_energies = parsed_bands['bands']
else:
bands_energies = parsed_bands['bands'][0]
bands = orm.BandsData()
bands.set_kpointsdata(parsed_kpoints)
bands.set_bands(bands_energies,
units=parsed_bands['bands_units'],
occupations=occupations)
return bands
def window_search_builder(test_data_dir, code_wannier90): # pylint: disable=too-many-locals,useless-suppression
"""
Sets up the process builder for window_search tests, and adds the inputs.
"""
builder = WindowSearch.get_builder()
input_folder = orm.FolderData()
input_folder_path = test_data_dir / 'wannier_input_folder'
for filename in os.listdir(input_folder_path):
input_folder.put_object_from_file(
path=str((input_folder_path / filename).resolve()), key=filename
)
builder.wannier.local_input_folder = input_folder
builder.wannier.code = code_wannier90
builder.code_tbmodels = orm.Code.get_from_string('tbmodels')
builder.model_evaluation_workflow = BandDifferenceModelEvaluation
# print(builder.model_evaluation.dynamic)
builder.model_evaluation = {
'code_bands_inspect': orm.Code.get_from_string('bands_inspect'),
}
builder.reference_bands = read(test_data_dir / 'bands.hdf5')
initial_window = orm.List()
initial_window.extend([-4.5, -4, 6.5, 16])
builder.initial_window = initial_window
builder.window_tol = orm.Float(1.5)
a = 3.2395 # pylint: disable=invalid-name
structure = orm.StructureData()
structure.set_pymatgen_structure(
pymatgen.Structure(
lattice=[[0, a, a], [a, 0, a], [a, a, 0]],
species=['In', 'Sb'],
coords=[[0] * 3, [0.25] * 3]
)
)
builder.structure = structure
wannier_parameters = orm.Dict(
dict=dict(
num_wann=14,
num_bands=36,
dis_num_iter=1000,
num_iter=0,
spinors=True,
mp_grid=[6, 6, 6],
)
)
builder.wannier.parameters = wannier_parameters
builder.wannier.metadata.options = {
'resources': {
'num_machines': 1,
'tot_num_mpiprocs': 1
},
'withmpi': False
}
builder.symmetries = orm.SinglefileData(
file=str((test_data_dir / 'symmetries.hdf5').resolve())
)
slice_idx = orm.List()
slice_idx.extend([0, 2, 3, 1, 5, 6, 4, 7, 9, 10, 8, 12, 13, 11])
builder.slice_idx = slice_idx
k_values = [
x if x <= 0.5 else -1 + x
for x in np.linspace(0, 1, 6, endpoint=False)
]
k_points = [
list(reversed(k)) for k in itertools.product(k_values, repeat=3)
]
wannier_bands = orm.BandsData()
wannier_bands.set_kpoints(k_points)
# Just let every energy window be valid.
wannier_bands.set_bands(np.array([[0] * 14] * len(k_points)))
builder.wannier_bands = wannier_bands
return builder
def inner(window_values, slice_, symmetries):
builder = RunWindow.get_builder()
input_folder = orm.FolderData()
input_folder_path = test_data_dir / 'wannier_input_folder'
for filename in os.listdir(input_folder_path):
input_folder.put_object_from_file(path=str(
(input_folder_path / filename).resolve()),
key=filename)
builder.wannier.local_input_folder = input_folder
builder.wannier.code = code_wannier90
builder.code_tbmodels = orm.Code.get_from_string('tbmodels')
builder.model_evaluation_workflow = BandDifferenceModelEvaluation
builder.reference_bands = read(test_data_dir / 'bands.hdf5')
builder.model_evaluation = {
'code_bands_inspect': orm.Code.get_from_string('bands_inspect'),
}
window = orm.List(list=window_values)
builder.window = window
k_values = [
x if x <= 0.5 else -1 + x
for x in np.linspace(0, 1, 6, endpoint=False)
]
k_points = [
list(reversed(k)) for k in itertools.product(k_values, repeat=3)
]
wannier_kpoints = orm.KpointsData()
wannier_kpoints.set_kpoints(k_points)
builder.wannier.kpoints = wannier_kpoints
wannier_bands = orm.BandsData()
wannier_bands.set_kpoints(k_points)
# Just let every energy window be valid.
wannier_bands.set_bands(
np.array([[-20] * 10 + [-0.5] * 7 + [0.5] * 7 + [20] * 12] *
len(k_points)))
builder.wannier_bands = wannier_bands
a = 3.2395 # pylint: disable=invalid-name
structure = orm.StructureData()
structure.set_pymatgen_structure(
pymatgen.Structure(lattice=[[0, a, a], [a, 0, a], [a, a, 0]],
species=['In', 'Sb'],
coords=[[0] * 3, [0.25] * 3]))
builder.structure = structure
wannier_parameters = orm.Dict(dict=dict(
num_wann=14,
num_bands=36,
dis_num_iter=1000,
num_iter=0,
spinors=True,
mp_grid=[6, 6, 6],
))
builder.wannier.parameters = wannier_parameters
builder.wannier.metadata.options = {
'resources': {
'num_machines': 1,
'tot_num_mpiprocs': 1
},
'withmpi': False
}
if symmetries:
builder.symmetries = orm.SinglefileData(
file=str(test_data_dir / 'symmetries.hdf5'))
if slice_:
slice_idx = orm.List()
slice_idx.extend([0, 2, 3, 1, 5, 6, 4, 7, 9, 10, 8, 12, 13, 11])
builder.slice_idx = slice_idx
return builder