def launch_phono3py(cutoff_energy=350, is_nac=False):
"""Launch calculation."""
structure, forces_config, nac_config, phonon_settings = get_settings(
cutoff_energy, is_nac)
Phono3pyWorkChain = WorkflowFactory("phonopy.phono3py")
builder = Phono3pyWorkChain.get_builder()
builder.structure = structure
builder.calculator_settings = Dict(dict={
"forces": forces_config,
"nac": nac_config
})
builder.run_phono3py = Bool(False)
builder.remote_phono3py = Bool(False)
builder.code_string = Str("phonopy@nancy")
builder.phonon_settings = Dict(dict=phonon_settings)
builder.symmetry_tolerance = Float(1e-5)
builder.options = Dict(dict=forces_config["options"])
dim = phonon_settings["supercell_matrix"]
kpoints_mesh = forces_config["kpoints_mesh"]
label = "ZnTe phono3py %dx%dx%d kpt %dx%dx%d PBEsol %d eV" % (
tuple(dim) + tuple(kpoints_mesh) + (cutoff_energy, ))
builder.metadata.label = label
builder.metadata.description = label
future = submit(builder)
print(label)
print(future)
print("Running workchain with pk={}".format(future.pk))
def launch_aiida_bulk_modulus(structure,
code_string,
resources,
label="AlN VASP relax calculation"):
incar_dict = {
'PREC': 'Accurate',
'EDIFF': 1e-8,
'NELMIN': 5,
'NELM': 100,
'ENCUT': 500,
'IALGO': 38,
'ISMEAR': 0,
'SIGMA': 0.01,
'GGA': 'PS',
'LREAL': False,
'LCHARG': False,
'LWAVE': False,
}
kpoints = KpointsData()
kpoints.set_kpoints_mesh([6, 6, 4], offset=[0, 0, 0.5])
options = {'resources': resources, 'max_wallclock_seconds': 3600 * 10}
potential_family = 'PBE.54'
potential_mapping = {'Al': 'Al', 'N': 'N'}
parser_settings = {
'add_energies': True,
'add_forces': True,
'add_stress': True
}
code = Code.get_from_string(code_string)
Workflow = WorkflowFactory('vasp_bm.bulkmodulus')
builder = Workflow.get_builder()
builder.code = code
builder.parameters = Dict(dict=incar_dict)
builder.structure = structure
builder.settings = Dict(dict={'parser_settings': parser_settings})
builder.potential_family = Str(potential_family)
builder.potential_mapping = Dict(dict=potential_mapping)
builder.kpoints = kpoints
builder.options = Dict(dict=options)
builder.metadata.label = label
builder.metadata.description = label
builder.clean_workdir = Bool(False)
builder.relax = Bool(True)
builder.force_cutoff = Float(1e-8)
builder.steps = Int(10)
builder.positions = Bool(True)
builder.shape = Bool(True)
builder.volume = Bool(True)
builder.convergence_on = Bool(True)
builder.convergence_volume = Float(1e-8)
builder.convergence_max_iterations = Int(2)
builder.verbose = Bool(True)
node = submit(builder)
return node
def run_two_volumes(self):
self.report("run_two_volumes")
for strain, future_name in zip((0.99, 1.01), ('minus', 'plus')):
Workflow = WorkflowFactory('vasp.relax')
builder = Workflow.get_builder()
for key in self.ctx.inputs:
builder[key] = self.ctx.inputs[key]
if 'label' in self.ctx.inputs.metadata:
label = self.ctx.inputs.metadata['label'] + " " + future_name
builder.metadata['label'] = label
if 'description' in self.ctx.inputs.metadata:
description = self.ctx.inputs.metadata['description']
description += " " + future_name
builder.metadata['description'] = description
builder.structure = self.ctx['structure_%s' % future_name]
relax = AttributeDict()
relax.perform = Bool(True)
relax.force_cutoff = Float(1e-8)
relax.positions = Bool(True)
relax.shape = Bool(True)
relax.volume = Bool(False)
relax.convergence_on = Bool(False)
builder.relax = relax
future = self.submit(builder)
self.to_context(**{future_name: future})
def launch_mae(structure, inpgen, calc_parameters, fleurinp, fleur,
wf_parameters, scf_parameters, parent_folder, daemon, settings,
option_node):
"""
Launch a mae workchain
"""
workchain_class = WorkflowFactory('fleur.mae')
inputs = {
'scf': {
'wf_parameters': scf_parameters,
'structure': structure,
'calc_parameters': calc_parameters,
'settings': settings,
'inpgen': inpgen,
'fleur': fleur
},
'wf_parameters': wf_parameters,
'fleurinp': fleurinp,
'remote': parent_folder,
'fleur': fleur,
'options': option_node
}
inputs = clean_nones(inputs)
builder = workchain_class.get_builder()
builder.update(inputs)
launch_process(builder, daemon)
def Distribute(req, prop):
"""
After the retrieval of the structure,
we proceed with the distribution of the task
according to the requested data
: input req workfunction node
: prop property to calculate
"""
if prop == 'band_gap':
# submit a bandgap workchain
xx = WorkflowFactory('ext_aiida.BandGap')
calcspecs = req.inputs.predefined['aiida']
structure = req.outputs.structure
pwcode = calcspecs['qe']
code = load_node(pwcode)
upfamily = calcspecs['upf']
wf = submit(xx, structure=structure, code=code) # aiida pk # code pk
if prop == 'band_structure':
xx = WorkflowFactory('quantumespresso.pw.band_structure')
calcspecs = req.inputs.predefined['aiida']
structure = req.outputs.structure
pwcode = calcspecs['qe']
code = load_node(pwcode)
print(('picc {}'.format(code)))
# upfamily = calcspecs['upf']
wf = submit(xx, structure=structure, code=code) # aiida pk # code pk
return wf
def launch_fleur(fleurinp, fleur, parent_folder, settings, daemon,
max_num_machines, max_wallclock_seconds,
num_mpiprocs_per_machine, option_node, with_mpi, launch_base):
"""
Launch a base_fleur workchain.
If launch_base is False launch a single fleur calcjob instead.
"""
process_class = CalculationFactory('fleur.fleur')
workchain_class = WorkflowFactory('fleur.base')
inputs = {
'code': fleur,
'fleurinpdata': fleurinp,
'parent_folder': parent_folder,
'settings': settings,
'metadata': {
'options': {
'withmpi': with_mpi,
'max_wallclock_seconds': max_wallclock_seconds,
'resources': {
'num_machines': max_num_machines,
'num_mpiprocs_per_machine': num_mpiprocs_per_machine,
}
}
}
}
if not launch_base:
inputs = clean_nones(inputs)
builder = process_class.get_builder()
builder.update(inputs)
else:
if option_node is None:
option_node = Dict(
dict={
'withmpi': with_mpi,
'max_wallclock_seconds': max_wallclock_seconds,
'resources': {
'num_machines': max_num_machines,
'num_mpiprocs_per_machine': num_mpiprocs_per_machine
}
})
inputs_base = {
'code': fleur,
'fleurinpdata': fleurinp,
'parent_folder': parent_folder,
'settings': settings,
'options': option_node
}
inputs_base = clean_nones(inputs_base)
builder = workchain_class.get_builder()
builder.update(**inputs_base)
launch_process(builder, daemon)
def submit(self, _=None):
assert self.input_structure is not None
builder = WorkflowFactory('quantumespresso.pw.relax').get_builder()
builder.base.pw.code = self.code_group.selected_code
builder.base.pw.parameters = load_default_parameters()
builder.base.pw.metadata.options = self.options
builder.base.kpoints_distance = Float(0.8)
builder.base.pseudo_family = Str(self.pseudo_family_selection.value)
builder.structure = self.input_structure
self.process = submit(builder)
def get_sub_workchain_builder(self, scale_factor, previous_workchain=None):
"""Return the builder for the relax workchain."""
structure = scale_structure(self.inputs.structure, scale_factor)
process_class = WorkflowFactory(self.inputs.sub_process_class)
builder = process_class.get_inputs_generator().get_builder(
structure,
previous_workchain=previous_workchain,
**self.inputs.generator_inputs
)
builder._update(**self.inputs.get('sub_process', {})) # pylint: disable=protected-access
return builder
def test_generate_inputs(castep_code, nacl, si): # pylint: disable=invalid-name
"""
Test for the generator
"""
protocol = CastepRelaxInputGenerator(process_class=CastepRelaxWorkChain).get_protocol('moderate')
override = {'base': {'metadata': {'label': 'test'}, 'calc': {}}}
output = generate_inputs(WorkflowFactory('castep.relax'), copy.deepcopy(protocol), castep_code, si, override)
assert output['calc']['parameters']['basis_precision'] == 'fine'
assert 'structure' not in output['calc']
output = generate_inputs(WorkflowFactory('castep.base'), copy.deepcopy(protocol), castep_code, si, override)
assert output['calc']['parameters']['PARAM']['basis_precision'] == 'fine'
assert 'structure' in output['calc']
def run_relax(self):
self.report("run_relax")
Workflow = WorkflowFactory('vasp.relax')
builder = Workflow.get_builder()
for key in self.ctx.inputs:
builder[key] = self.ctx.inputs[key]
if 'label' in self.ctx.inputs.metadata:
label = self.ctx.inputs.metadata['label'] + " relax"
builder.metadata['label'] = label
if 'description' in self.ctx.inputs.metadata:
description = self.ctx.inputs.metadata['description'] + " relax"
builder.metadata['description'] = description
future = self.submit(builder)
self.to_context(**{'relax': future})
def search_pk(uuid):
"""uuid can be pk."""
IterHarmonicApprox = WorkflowFactory('phonopy.iter_ha')
qb = QueryBuilder()
qb.append(IterHarmonicApprox,
tag='iter_ha',
filters={'uuid': {
'==': uuid
}})
PhonopyWorkChain = WorkflowFactory('phonopy.phonopy')
qb.append(PhonopyWorkChain, with_incoming='iter_ha')
qb.order_by({PhonopyWorkChain: {'ctime': 'asc'}})
pks = [n[0].pk for n in qb.all() if n[0].is_finished_ok]
return pks
def get_sub_workchain_builder(self, distance, previous_workchain=None):
"""Return the builder for the relax workchain."""
molecule = set_distance(self.inputs.molecule, distance)
process_class = WorkflowFactory(self.inputs.sub_process_class)
builder = process_class.get_inputs_generator().get_builder(
molecule,
previous_workchain=previous_workchain,
**self.inputs.generator_inputs
)
builder._update(**self.inputs.get('sub_process', {})) # pylint: disable=protected-access
distance_node = molecule.creator.inputs.distance
return builder, distance_node
def validate_inputs(value, _):
"""Validate the entire input namespace."""
if 'scale_factors' not in value:
if 'scale_count' not in value or 'scale_increment' not in value:
return 'neither `scale_factors` nor the pair of `scale_count` and `scale_increment` were defined.'
# Validate that the provided ``generator_inputs`` are valid for the associated input generator.
process_class = WorkflowFactory(value['sub_process_class'])
generator = process_class.get_input_generator()
try:
generator.get_builder(structure=value['structure'],
**value['generator_inputs'])
except Exception as exc: # pylint: disable=broad-except
return f'`{generator.__class__.__name__}.get_builder()` fails for the provided `generator_inputs`: {exc}'
def primitive_structure_from_cif(cif, parse_engine, symprec, site_tolerance):
"""Attempt to parse the given `CifData` and create a `StructureData` from it.
First the raw CIF file is parsed with the given `parse_engine`. The resulting `StructureData` is then passed through
SeeKpath to try and get the primitive cell. If that is successful, important structural parameters as determined by
SeeKpath will be set as extras on the structure node which is then returned as output.
:param cif: the `CifData` node
:param parse_engine: the parsing engine, supported libraries 'ase' and 'pymatgen'
:param symprec: a `Float` node with symmetry precision for determining primitive cell in SeeKpath
:param site_tolerance: a `Float` node with the fractional coordinate distance tolerance for finding overlapping
sites. This will only be used if the parse_engine is pymatgen
:return: the primitive `StructureData` as determined by SeeKpath
"""
CifCleanWorkChain = WorkflowFactory('codtools.cif_clean') # pylint: disable=invalid-name
try:
structure = cif.get_structure(converter=parse_engine.value,
site_tolerance=site_tolerance.value,
store=False)
except exceptions.UnsupportedSpeciesError:
return CifCleanWorkChain.exit_codes.ERROR_CIF_HAS_UNKNOWN_SPECIES
except InvalidOccupationsError:
return CifCleanWorkChain.exit_codes.ERROR_CIF_HAS_INVALID_OCCUPANCIES
except Exception: # pylint: disable=broad-except
return CifCleanWorkChain.exit_codes.ERROR_CIF_STRUCTURE_PARSING_FAILED
try:
seekpath_results = get_kpoints_path(structure, symprec=symprec)
except ValueError:
return CifCleanWorkChain.exit_codes.ERROR_SEEKPATH_INCONSISTENT_SYMMETRY
except SymmetryDetectionError:
return CifCleanWorkChain.exit_codes.ERROR_SEEKPATH_SYMMETRY_DETECTION_FAILED
# Store important information that should be easily queryable as attributes in the StructureData
parameters = seekpath_results['parameters'].get_dict()
structure = seekpath_results['primitive_structure']
# Store the formula as a string, in both hill as well as hill-compact notation, so it can be easily queried for
extras = {
'formula_hill':
structure.get_formula(mode='hill'),
'formula_hill_compact':
structure.get_formula(mode='hill_compact'),
'chemical_system':
'-{}-'.format('-'.join(sorted(structure.get_symbols_set()))),
}
for key in [
'spacegroup_international', 'spacegroup_number', 'bravais_lattice',
'bravais_lattice_extended'
]:
try:
extras[key] = parameters[key]
except KeyError:
pass
structure.set_extra_many(extras)
return structure
def main(code_string, datafiles, parameters):
"""Main method to setup the calculation."""
# First, we need to fetch the AiiDA datatypes which will
# house the inputs to our calculation
dict_data = DataFactory('dict')
# Then, we set the workchain we would like to call
workchain = WorkflowFactory('logger.gc_example')
# Set inputs for the following WorkChain execution
inputs = AttributeDict()
# inputs.metadata = {'options': {'resources': {'num_machines': 1, 'num_mpiprocs_per_machine': 1},
# 'parser_name': 'logger',
# 'withmpi': False,
# 'output_filename': 'logger.out'}}
# Set code
inputs.code = Code.get_from_string(code_string)
# Set datafiles
inputs.datafiles = datafiles
# Set parameters
inputs.parameters = dict_data(dict=parameters)
# Set workchain related inputs, in this case, give more explicit output to report
inputs.verbose = Bool(True)
# Submit the requested workchain with the supplied inputs
run(workchain, **inputs)
def test_eosworkchain_inpgen(aiida_profile, fixture_code, generate_structure):
"""Test the validation of subclasses of `InputsGenerator`."""
from aiida_siesta.utils.protocols_system.input_generators import EosWorkChainInputGenerator
inp_gen = EosWorkChainInputGenerator(WorkflowFactory("siesta.eos"))
structure = generate_structure()
protocol = inp_gen.get_default_protocol_name()
code = fixture_code("siesta.siesta")
code.store()
calc_engines = {
"siesta": {
'code': code.uuid,
'options': {
"resources": {
"num_mpiprocs_per_machine": 1
},
"max_wallclock_seconds": 360
}
}
}
build = inp_gen.get_filled_builder(structure,
calc_engines,
protocol,
relaxation_type="atoms_only")
assert "parameters" in build
def launch_workflow(code, calculation, clean_workdir, max_num_machines,
max_wallclock_seconds, with_mpi, daemon):
"""Run the `Q2rBaseWorkChain` for a previously completed `PhCalculation`."""
from aiida.orm import Bool
from aiida.plugins import WorkflowFactory
from aiida_quantumespresso.utils.resources import get_default_options
expected_process_type = 'aiida.calculations:quantumespresso.ph'
if calculation.process_type != expected_process_type:
raise click.BadParameter(
'The input calculation node has a process_type: {}; should be {}'.
format(calculation.process_type, expected_process_type))
inputs = {
'q2r': {
'code': code,
'parent_folder': calculation.outputs.remote_folder,
'metadata': {
'options':
get_default_options(max_num_machines, max_wallclock_seconds,
with_mpi),
}
}
}
if clean_workdir:
inputs['clean_workdir'] = Bool(True)
launch.launch_process(WorkflowFactory('quantumespresso.q2r.base'), daemon,
**inputs)
class FleurCommonRelaxWorkChain(CommonRelaxWorkChain):
"""Implementation of `aiida_common_workflows.common.relax.workchain.CommonRelaxWorkChain` for FLEUR."""
_process_class = WorkflowFactory('fleur.base_relax')
_generator_class = FleurCommonRelaxInputGenerator
def convert_outputs(self):
"""Convert the outputs of the sub workchain to the common output specification."""
outputs = self.ctx.workchain.outputs
if 'optimized_structure' in outputs:
self.out('relaxed_structure', outputs.optimized_structure)
output_parameters = outputs.output_relax_wc_para
out_para_dict = output_parameters.get_dict()
if 'total_magnetic_moment_cell' in out_para_dict:
if out_para_dict.get('total_magnetic_moment_cell',
None) is not None:
self.out('total_magnetization',
get_total_magnetization(output_parameters))
self.out('total_energy',
get_total_energy(outputs.output_relax_wc_para))
self.out('forces',
get_forces_from_trajectory(outputs.output_relax_wc_para))
def launch_workflow(code, datum, kpoints_mesh, clean_workdir, max_num_machines,
max_wallclock_seconds, with_mpi, daemon):
"""Run the `MatdynBaseWorkChain` for a previously completed `Q2rCalculation`."""
from aiida.orm import Bool
from aiida.plugins import WorkflowFactory
from aiida_quantumespresso.utils.resources import get_default_options
inputs = {
'matdyn': {
'code': code,
'kpoints': kpoints_mesh,
'force_constants': datum,
'metadata': {
'options':
get_default_options(max_num_machines, max_wallclock_seconds,
with_mpi),
}
}
}
if clean_workdir:
inputs['clean_workdir'] = Bool(True)
launch.launch_process(WorkflowFactory('quantumespresso.matdyn.base'),
daemon, **inputs)
def get_parameters(previous_workchain):
"""
Extracts the FLAPW parameter for inpgen from a given previous workchain
It finds the last Fleur Calcjob or Inpgen calc and extracts the
parameters from its fleurinpdata node
:param previous_workchain: Some workchain which contains at least one
Fleur CalcJob or Inpgen CalcJob.
:return: Dict node of parameters ready to use, or None
"""
from aiida.plugins import WorkflowFactory
from aiida.common.exceptions import NotExistent
from aiida_fleur.tools.common_fleur_wf import find_last_submitted_workchain
fleur_scf_wc = WorkflowFactory('fleur.scf')
# Find Fleurinp
try:
last_base_relax = find_last_submitted_workchain(previous_workchain)
last_relax = find_last_submitted_workchain(
orm.load_node(last_base_relax))
last_scf = find_last_submitted_workchain(orm.load_node(last_relax))
last_scf = orm.load_node(last_scf)
except NotExistent:
# something went wrong in the previous workchain run
#.. we just continue without previous parameters but defaults.
return None
if last_scf.process_class is fleur_scf_wc:
fleurinp = last_scf.outputs.fleurinp
else:
return None
# Be aware that this parameter node is incomplete. LOs and econfig is
# currently missing for example.
parameters = fleurinp.get_parameterdata_ncf(
) # This is not a calcfunction!
return parameters
def launch_workflow(code, calculation, kpoints_mesh, clean_workdir,
max_num_machines, max_wallclock_seconds, with_mpi, daemon):
"""Run the `PhBaseWorkChain` for a previously completed `PwCalculation`."""
from aiida.orm import Bool, Dict
from aiida.plugins import WorkflowFactory
from aiida_quantumespresso.utils.resources import get_default_options
inputs = {
'ph': {
'code': code,
'qpoints': kpoints_mesh,
'parent_folder': calculation.outputs.remote_folder,
'parameters': Dict(dict={'INPUTPH': {}}),
'metadata': {
'options':
get_default_options(max_num_machines, max_wallclock_seconds,
with_mpi),
}
}
}
if clean_workdir:
inputs['clean_workdir'] = Bool(True)
launch.launch_process(WorkflowFactory('quantumespresso.ph.base'), daemon,
**inputs)
class QuantumEspressoCommonRelaxWorkChain(CommonRelaxWorkChain):
"""Implementation of `aiida_common_workflows.common.relax.workchain.CommonRelaxWorkChain` for Quantum ESPRESSO."""
_process_class = WorkflowFactory('quantumespresso.pw.relax')
_generator_class = QuantumEspressoCommonRelaxInputGenerator
def convert_outputs(self):
"""Convert the outputs of the sub workchain to the common output specification."""
outputs = self.ctx.workchain.outputs
result = extract_from_parameters(outputs.output_parameters).values()
forces, stress = extract_from_trajectory(
outputs.output_trajectory).values()
try:
total_energy, total_magnetization = result
except ValueError:
total_energy, total_magnetization = list(result)[0], None
if 'output_structure' in outputs:
self.out('relaxed_structure', outputs.output_structure)
if total_magnetization is not None:
self.out('total_magnetization', total_magnetization)
self.out('total_energy', total_energy)
self.out('forces', forces)
self.out('stress', stress)
def test_relax_generator(castep_code, nacl, with_otfg):
"""Test for generating the relax namespace"""
CastepCommonRelaxWorkChain = WorkflowFactory('castep.relax') # pylint: disable=invalid-name
protocol = CastepCommonRelaxInputGenerator(
process_class=CastepCommonRelaxWorkChain).get_protocol(
'moderate')['relax']
override = {
'base': {
'metadata': {
'label': 'test'
},
'calc': {
'parameters': {
'cut_off_energy': 220
},
'metadata': {
'label': 'test'
}
}
}
}
otfg = OTFGGroup.objects.get(label='C19')
generated = generate_inputs_relax(protocol, castep_code, nacl, otfg,
override)
assert 'structure' in generated
paramd = generated['calc']['parameters']
assert 'basis_precision' not in paramd
assert 'kpoints_spacing' in generated['base']
assert 'kpoints' not in generated['calc']
assert generated['calc']['metadata']['label'] == 'test'
assert generated['base']['metadata']['label'] == 'test'
def run_scf(self):
"""
Run the SCF calculation
"""
base_work = WorkflowFactory(self._base_wk_string)
inputs = AttributeDict(self.exposed_inputs(base_work, namespace='scf'))
inputs.metadata.call_link_label = 'scf'
inputs.calc.structure = self.ctx.current_structure
# Ensure that writing the check/castep_bin
param_dict = inputs.calc.parameters.get_dict()
if 'PARAM' in param_dict:
ensure_checkpoint(param_dict['PARAM'])
else:
ensure_checkpoint(param_dict)
# Update if changes are made
if param_dict != inputs.calc.parameters.get_dict():
self.report(
"Updated the PARAM to make sure castep_bin file will be written"
)
inputs.calc.parameters = orm.Dict(dict=param_dict)
running = self.submit(base_work, **inputs)
self.report('Running SCF calculation {}'.format(running))
self.to_context(workchain_scf=running)
def find_latest_uuid():
IterHarmonicApprox = WorkflowFactory('phonopy.iter_ha')
qb = QueryBuilder()
qb.append(IterHarmonicApprox)
qb.order_by({IterHarmonicApprox: {'ctime': 'desc'}})
qb.first()
return qb.first()[0].uuid
def post(self, prop):
"""
Route to manage the requests from ext
Access is through a JSON file passed to the serveri
containing the input required for calculation
Data is handled and responded accordingly
:input prop is the quantity we required for calculation
"""
# workfunction to process the incoming json dictionary
# here it needs a validation by
cao = 'cao\n'
print((
cao,
cao,
'data',
request.data,
cao,
'args',
request.args.getall(),
cao,
'form',
request.form,
cao,
'files',
request.files,
cao,
request.values,
cao,
request.base_url,
cao,
request.date,
cao,
))
xx = WorkflowFactory('ext_aiida.ProcessInputs')
wf = submit(
xx,
request=Dict(dict=request.get_json()),
predefined=Dict(dict=CALCULATION_OPTIONS),
property=Str(prop),
)
sleep(2)
if not wf.is_finished_ok:
msg = 'Structure retrieval error. See node uuid={} for more specific report'.format(
wf.uuid)
return {
'error': wf.exit_message,
'message': msg,
'stored_request': wf.inputs.request.get_dict(),
}
else:
exwf = Distribute(wf, prop)
msg = ' Successful retrieval of structure, {}, workflow at uuid {}'.format(
exwf.inputs.structure.pk, exwf.pk)
return {
'error': wf.exit_message,
'message': msg,
'stored_request': wf.inputs.request.get_dict(),
}
def launch_banddos(fleurinp, fleur, wf_parameters, parent_folder, daemon,
settings, option_node):
"""
Launch a banddos workchain
"""
workchain_class = WorkflowFactory('fleur.banddos')
inputs = {
'wf_parameters': wf_parameters,
'fleur': fleur,
'remote': parent_folder,
'fleurinp': fleurinp,
'options': option_node
}
inputs = clean_nones(inputs)
builder = workchain_class.get_builder()
builder.update(inputs)
launch_process(builder, daemon)
def export_isotherm(
sample,
node,
file_name: str = None,
aiidalab_instance: str = "unknown",
):
"""Export Isotherm object."""
source_info = {
"uuid": node.uuid,
"url": aiidalab_instance,
"name": "Isotherm simulated using the isotherm app on AiiDAlab",
}
# Workaround till the Isotherm object is not ready.
isotherm_wf = WorkflowFactory("lsmo.isotherm")
query = (orm.QueryBuilder().append(orm.Dict,
filters={
"uuid": node.uuid
},
tag="isotherm_data").append(
isotherm_wf,
with_outgoing="isotherm_data",
tag="isotherm_wf").append(
orm.Str,
with_outgoing="isotherm_wf",
project="attributes.value"))
adsorptive = query.all(flat=True)
adsorptive = adsorptive[0] if adsorptive else None
meta = {
"adsorptive": adsorptive,
"temperature": node["temperature"],
"method": "GCMC",
}
jcamp = from_dict(
{
"x": {
"data": node["isotherm"]["pressure"],
"unit": node["isotherm"]["pressure_unit"],
"type": "INDEPENDENT",
},
"y": {
"data": node["isotherm"]["loading_absolute_average"],
"unit": node["isotherm"]["loading_absolute_unit"],
"type": "DEPENDENT",
},
},
data_type="Adsorption Isotherm",
meta=meta,
)
sample.put_data(
data_type="isotherm",
file_name=f"{node.uuid}.jcamp"
if file_name is None else f"{file_name}.jcamp",
file_content=jcamp,
metadata=meta,
source_info=source_info,
)
def run_workchain(
number=1,
code="sleep@slurm",
time=1,
payload=100,
output_dict=100,
output_array=100,
fail=False,
submit=False,
):
"""Run the `SleepWorkChain`
:param number: Number of children `SleepCalculation`
:param code: code label
:param time: seconds for which each `SleepCalculation` runs `sleep`
:param payload: number of fields in `payload` input dictionary of CalcJob
:param output: number of fields in output dictionary of CalcJob
:param output_array: Size of output array
:param fail: Intentionally fail all `SleepCalculation`
:param submit: whether to submit to daemon, otherwise run
:return: workchain node
"""
from aiida.engine import run_get_node
from aiida.engine import submit as submit_func
from aiida.orm import load_code
from aiida.plugins import WorkflowFactory
builder = WorkflowFactory("sleep").get_builder()
builder.children = number
builder.calcjob.code = load_code(code)
builder.calcjob.time = time
builder.calcjob.payload = {
f"input_key_{i}": f"value_{i}"
for i in range(payload)
}
builder.calcjob.metadata.options.fail_calcjob = fail
builder.calcjob.metadata.options.output_dict_size = output_dict
builder.calcjob.metadata.options.output_array_size = output_array
if submit:
node = submit_func(builder)
else:
node = run_get_node(builder).node
return node
def validate_sub_process_class(value, _):
"""Validate the sub process class."""
try:
process_class = WorkflowFactory(value)
except exceptions.EntryPointError:
return f'`{value}` is not a valid or registered workflow entry point.'
if not inspect.isclass(process_class) or not issubclass(process_class, CommonRelaxWorkChain):
return f'`{value}` is not a subclass of the `CommonRelaxWorkChain` common workflow.'
