def test_relax_wc(fresh_aiida_env, vasp_params, potentials, mock_vasp):
# def test_relax_wc(fresh_aiida_env, vasp_params, potentials, mock_vasp, mock_relax_wc):
"""Test submitting only, not correctness, with mocked vasp code."""
from aiida.orm import Code
from aiida.plugins import WorkflowFactory
from aiida.engine import run
workchain = WorkflowFactory('vasp.relax')
mock_vasp.store()
create_authinfo(computer=mock_vasp.computer, store=True)
structure = PoscarParser(file_path=data_path('test_relax_wc', 'inp', 'POSCAR')).structure
kpoints = KpointsParser(file_path=data_path('test_relax_wc', 'inp', 'KPOINTS')).kpoints
parameters = IncarParser(file_path=data_path('test_relax_wc', 'inp', 'INCAR')).incar
parameters['system'] = 'test-case:test_relax_wc'
parameters = {'incar': {k: v for k, v in parameters.items() if k not in ['isif', 'ibrion', 'nsw', 'ediffg']}}
parameters['relax'] = {}
parameters['relax']['perform'] = True
parameters['relax']['algo'] = 'cg'
parameters['relax']['force_cutoff'] = 0.01
inputs = AttributeDict()
inputs.code = Code.get_from_string('mock-vasp@localhost')
inputs.structure = structure
inputs.kpoints = kpoints
inputs.parameters = get_data_node('dict', dict=parameters)
inputs.potential_family = get_data_node('str', POTCAR_FAMILY_NAME)
inputs.potential_mapping = get_data_node('dict', dict=POTCAR_MAP)
inputs.options = get_data_node('dict',
dict={
'withmpi': False,
'queue_name': 'None',
'max_wallclock_seconds': 1,
'import_sys_environment': True,
'resources': {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
},
})
inputs.max_iterations = get_data_node('int', 1)
inputs.clean_workdir = get_data_node('bool', False)
inputs.verbose = get_data_node('bool', True)
results, node = run.get_node(workchain, **inputs)
assert node.exit_status == 0
assert 'relax' in results
relax = results['relax']
assert 'structure' in relax
sites = relax['structure'].sites
assert sites[0].kind_name == 'Si'
assert sites[1].kind_name == 'Si'
np.testing.assert_allclose(sites[0].position, [4.8125, 4.8125, 4.8125])
np.testing.assert_allclose(sites[1].position, [0.6875, 0.6875, 0.715])
def relax_input_structure(self):
'''
Run a relax/vc-relax calculation to find the ground state structure
NOTE: I would really like to modify this to be flexible (LAMMPS or QE)
'''
inputs = AttributeDict(self.exposed_inputs(PwRelaxWorkChain,
namespace='initial_relax'))
inputs.structure = self.inputs.structure
inputs.clean_workdir = self.inputs.clean_workdir
future = self.submit(PwRelaxWorkChain, **inputs)
self.report('Launching PwRelaxWorkChain<{}>'.format(future.pk))
self.to_context(initial_relax_workchain=future)
def test_vasp_wc_chgcar(fresh_aiida_env, vasp_params, potentials, vasp_kpoints,
vasp_structure, mock_vasp):
"""Test submitting only, not correctness, with mocked vasp code, test fetching of the CHGCAR."""
from aiida.orm import Code
from aiida.plugins import WorkflowFactory
from aiida.engine import run
workchain = WorkflowFactory('vasp.vasp')
mock_vasp.store()
create_authinfo(computer=mock_vasp.computer, store=True)
kpoints, _ = vasp_kpoints
inputs = AttributeDict()
inputs.code = Code.get_from_string('mock-vasp@localhost')
inputs.structure = vasp_structure
inputs.parameters = get_data_node('dict',
dict={'incar': vasp_params.get_dict()})
inputs.kpoints = kpoints
inputs.potential_family = get_data_node('str', POTCAR_FAMILY_NAME)
inputs.potential_mapping = get_data_node('dict', dict=POTCAR_MAP)
inputs.options = get_data_node('dict',
dict={
'withmpi': False,
'queue_name': 'None',
'resources': {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
},
'max_wallclock_seconds': 3600
})
inputs.settings = get_data_node('dict',
dict={
'ADDITIONAL_RETRIEVE_LIST': ['CHGCAR'],
'parser_settings': {
'add_chgcar': True
}
})
inputs.max_iterations = get_data_node('int', 1)
inputs.clean_workdir = get_data_node('bool', False)
inputs.verbose = get_data_node('bool', True)
results, node = run.get_node(workchain, **inputs)
assert node.exit_status == 0
assert 'chgcar' in results
assert results['chgcar'].get_content() == 'This is a test CHGCAR file.\n'
def test_vasp_wc(fresh_aiida_env, vasp_params, potentials, vasp_kpoints,
vasp_structure, mock_vasp):
"""Test submitting only, not correctness, with mocked vasp code."""
from aiida.orm import Code
from aiida.plugins import WorkflowFactory
from aiida.engine import run
workchain = WorkflowFactory('vasp.vasp')
mock_vasp.store()
create_authinfo(computer=mock_vasp.computer, store=True)
kpoints, _ = vasp_kpoints
inputs = AttributeDict()
inputs.code = Code.get_from_string('mock-vasp@localhost')
inputs.structure = vasp_structure
inputs.parameters = get_data_node('dict',
dict={'incar': vasp_params.get_dict()})
inputs.kpoints = kpoints
inputs.potential_family = get_data_node('str', POTCAR_FAMILY_NAME)
inputs.potential_mapping = get_data_node('dict', dict=POTCAR_MAP)
inputs.options = get_data_node('dict',
dict={
'withmpi': False,
'queue_name': 'None',
'resources': {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
},
'max_wallclock_seconds': 3600
})
inputs.max_iterations = get_data_node('int', 1)
inputs.clean_workdir = get_data_node('bool', False)
inputs.verbose = get_data_node('bool', True)
results, node = run.get_node(workchain, **inputs)
assert node.exit_status == 0
assert 'retrieved' in results
assert 'misc' in results
assert 'remote_folder' in results
misc = results['misc'].get_dict()
assert misc['maximum_stress'] == 22.8499295
assert misc['total_energies']['energy_extrapolated'] == -14.16209692
def compute_deformed_structures(self):
'''
Run relax workflows for each deformed structure
NOTE: I would really like to modify this to be flexible (LAMMPS or QE)
'''
self.report('Computing deformed structures')
deformed_structures = self.ctx.deformed_structures
for key_index in range(len(deformed_structures)):
inputs = AttributeDict(self.exposed_inputs(PwRelaxWorkChain,
namespace='elastic_relax'))
inputs.structure = deformed_structures[key_index]
inputs.clean_workdir = self.inputs.clean_workdir
future = self.submit(PwRelaxWorkChain, **inputs)
deformation_key = 'deformation_{}'.format(key_index)
self.report('launching PwRelaxWorkChain<{}>'.format(future.pk))
self.to_context(**{deformation_key: future})
def test_converge_wc(fresh_aiida_env, potentials, mock_vasp):
"""Test submitting only, not correctness, with mocked vasp code."""
from aiida.orm import Code
from aiida.plugins import WorkflowFactory
from aiida.engine import run
workchain = WorkflowFactory('vasp.converge')
mock_vasp.store()
create_authinfo(computer=mock_vasp.computer, store=True)
structure = PoscarParser(
file_path=data_path('test_converge_wc', 'inp', 'POSCAR')).structure
parameters = IncarParser(
file_path=data_path('test_converge_wc', 'inp', 'INCAR')).incar
parameters['system'] = 'test-case:test_converge_wc'
parameters = {
k: v
for k, v in parameters.items()
if k not in ['isif', 'ibrion', 'encut', 'nsw']
}
restart_clean_workdir = get_data_node('bool', False)
restart_clean_workdir.store()
inputs = AttributeDict()
inputs.code = Code.get_from_string('mock-vasp@localhost')
inputs.structure = structure
inputs.parameters = get_data_node('dict', dict={'incar': parameters})
inputs.potential_family = get_data_node('str', POTCAR_FAMILY_NAME)
inputs.potential_mapping = get_data_node('dict', dict=POTCAR_MAP)
inputs.options = get_data_node('dict',
dict={
'withmpi': False,
'queue_name': 'None',
'resources': {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
},
'max_wallclock_seconds': 3600
})
inputs.max_iterations = get_data_node('int', 1)
inputs.clean_workdir = get_data_node('bool', False)
relax = AttributeDict()
converge = AttributeDict()
converge.relax = get_data_node('bool', False)
converge.compress = get_data_node('bool', False)
converge.displace = get_data_node('bool', False)
converge.pwcutoff_samples = get_data_node('int', 3)
converge.k_samples = get_data_node('int', 3)
relax.perform = get_data_node('bool', True)
inputs.relax = relax
inputs.converge = converge
inputs.verbose = get_data_node('bool', True)
results, node = run.get_node(workchain, **inputs)
assert node.exit_status == 0
converge = results['converge']
assert 'data' in converge
conv_data = converge['data']
try:
conv_data.get_array('pw_regular')
except KeyError:
pytest.fail('Did not find pw_regular in converge.data')
try:
conv_data.get_array('kpoints_regular')
except KeyError:
pytest.fail('Did not find kpoints_regular in converge.data')
assert 'pwcutoff_recommended' in converge
try:
_encut = converge['pwcutoff_recommended'].value
except AttributeError:
pytest.fail('pwcutoff_recommended does not have the expected format')
assert 'kpoints_recommended' in converge
try:
_kpoints = converge['kpoints_recommended'].get_kpoints_mesh()
except AttributeError:
pytest.fail('kpoints_recommended does not have the expected format')
def test_converge_wc_pw(fresh_aiida_env, vasp_params, potentials, mock_vasp):
"""Test convergence workflow using mock code."""
from aiida.orm import Code
from aiida.plugins import WorkflowFactory
from aiida.engine import run
workchain = WorkflowFactory('vasp.converge')
mock_vasp.store()
create_authinfo(computer=mock_vasp.computer).store()
structure = PoscarParser(file_path=data_path('test_converge_wc/pw/200',
'inp', 'POSCAR')).structure
parameters = IncarParser(
file_path=data_path('test_converge_wc/pw/200', 'inp', 'INCAR')).incar
parameters['system'] = 'test-case:test_converge_wc'
parameters = {
k: v
for k, v in parameters.items()
if k not in ['isif', 'ibrion', 'encut', 'nsw']
}
kpoints = KpointsParser(file_path=data_path('test_converge_wc/pw/200',
'inp', 'KPOINTS')).kpoints
restart_clean_workdir = get_data_node('bool', False)
restart_clean_workdir.store()
inputs = AttributeDict()
inputs.code = Code.get_from_string('mock-vasp@localhost')
inputs.structure = structure
inputs.kpoints = kpoints
inputs.parameters = get_data_node('dict', dict={'incar': parameters})
inputs.potential_family = get_data_node('str', POTCAR_FAMILY_NAME)
inputs.potential_mapping = get_data_node('dict', dict=POTCAR_MAP)
inputs.options = get_data_node('dict',
dict={
'withmpi': False,
'queue_name': 'None',
'resources': {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
},
'max_wallclock_seconds': 3600
})
inputs.max_iterations = get_data_node('int', 1)
inputs.clean_workdir = get_data_node('bool', False)
relax = AttributeDict()
converge = AttributeDict()
relax.perform = get_data_node('bool', False)
converge.relax = get_data_node('bool', False)
converge.testing = get_data_node('bool', True)
converge.compress = get_data_node('bool', False)
converge.displace = get_data_node('bool', False)
converge.pwcutoff_samples = get_data_node('int', 3)
converge.k_samples = get_data_node('int', 3)
inputs.relax = relax
inputs.converge = converge
inputs.verbose = get_data_node('bool', True)
results, node = run.get_node(workchain, **inputs)
assert node.exit_status == 0
assert 'converge' in results
converge = results['converge']
assert 'data' in converge
conv_data = converge['data']
try:
conv_data = conv_data.get_array('pw_regular')
except KeyError:
pytest.fail('Did not find pw_regular in converge.data')
conv_data_test = np.array([[200.0, -10.77974998, 0.0, 0.0, 0.5984],
[250.0, -10.80762044, 0.0, 0.0, 0.5912],
[300.0, -10.82261992, 0.0, 0.0, 0.5876]])
np.testing.assert_allclose(conv_data, conv_data_test)
assert 'pwcutoff_recommended' in converge
try:
_encut = converge['pwcutoff_recommended'].value
np.testing.assert_equal(_encut, 300)
except AttributeError:
pytest.fail('pwcutoff_recommended does not have the expected format')
def test_bands_wc(fresh_aiida_env, potentials, mock_vasp):
"""Test with mocked vasp code."""
from aiida.orm import Code, Log, RemoteData
from aiida.plugins import WorkflowFactory
from aiida.engine import run
workchain = WorkflowFactory('vasp.bands')
mock_vasp.store()
create_authinfo(computer=mock_vasp.computer, store=True)
structure = PoscarParser(file_path=data_path('test_bands_wc', 'inp', 'POSCAR')).structure
parameters = IncarParser(file_path=data_path('test_bands_wc', 'inp', 'INCAR')).incar
parameters['system'] = 'test-case:test_bands_wc'
# Make sure we replace encut with pwcutoff
del parameters['encut']
parameters = {'vasp': parameters}
parameters['electronic'] = {'pwcutoff': 200}
inputs = AttributeDict()
inputs.code = Code.get_from_string('mock-vasp@localhost')
inputs.structure = structure
inputs.parameters = get_data_node('dict', dict=parameters)
inputs.potential_family = get_data_node('str', POTCAR_FAMILY_NAME)
inputs.potential_mapping = get_data_node('dict', dict=POTCAR_MAP)
inputs.options = get_data_node('dict',
dict={
'withmpi': False,
'queue_name': 'None',
'resources': {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
},
'max_wallclock_seconds': 3600
})
inputs.max_iterations = get_data_node('int', 1)
inputs.clean_workdir = get_data_node('bool', False)
inputs.verbose = get_data_node('bool', True)
# Also set the restart folder as we assume a bands data will start from
# a previous calculation that is sitting in the restart folder
inputs.restart_folder = RemoteData(computer=inputs.code.computer, remote_path=data_path('test_bands_wc', 'inp'))
results, node = run.get_node(workchain, **inputs)
assert node.exit_status == 0
assert 'bands' in results
kpoints = results['bands'].get_kpoints()
test_array = np.array([[0., 0., 0.], [0.02272727, 0., 0.02272727], [0.04545454, 0., 0.04545454], [0.06818182, 0., 0.06818182],
[0.09090909, 0., 0.09090909], [0.11363636, 0., 0.11363636], [0.13636364, 0., 0.13636364],
[0.15909091, 0., 0.15909091], [0.18181818, 0., 0.18181818], [0.20454545, 0., 0.20454545],
[0.22727273, 0., 0.22727273], [0.25, 0., 0.25], [0.27272727, 0., 0.27272727], [0.29545455, 0., 0.29545455],
[0.31818182, 0., 0.31818182], [0.34090909, 0., 0.34090909], [0.36363636, 0., 0.36363636],
[0.38636364, 0., 0.38636364], [0.40909091, 0., 0.40909091], [0.43181818, 0., 0.43181818],
[0.45454545, 0., 0.45454545], [0.47727273, 0., 0.47727273], [0.5, 0., 0.5], [0.51785714, 0.03571429, 0.51785714],
[0.53571429, 0.07142857, 0.53571429], [0.55357143, 0.10714286, 0.55357143], [0.57142857, 0.14285714, 0.57142857],
[0.58928571, 0.17857143, 0.58928571], [0.60714286, 0.21428571, 0.60714286], [0.625, 0.25, 0.625],
[0.375, 0.375, 0.75], [0.35869565, 0.35869565, 0.7173913], [0.3423913, 0.3423913, 0.68478261],
[0.32608696, 0.32608696, 0.65217391], [0.30978261, 0.30978261, 0.61956522], [0.29347826, 0.29347826, 0.58695652],
[0.27717391, 0.27717391, 0.55434783], [0.26086957, 0.26086957, 0.52173913], [0.24456522, 0.24456522, 0.48913043],
[0.22826087, 0.22826087, 0.45652174], [0.21195652, 0.21195652, 0.42391304], [0.19565217, 0.19565217, 0.39130435],
[0.17934783, 0.17934783, 0.35869565], [0.16304348, 0.16304348, 0.32608696], [0.14673913, 0.14673913, 0.29347826],
[0.13043478, 0.13043478, 0.26086957], [0.11413044, 0.11413044, 0.22826087], [0.09782609, 0.09782609, 0.19565217],
[0.08152174, 0.08152174, 0.16304348], [0.06521739, 0.06521739, 0.13043478], [0.04891304, 0.04891304, 0.09782609],
[0.0326087, 0.0326087, 0.06521739], [0.01630435, 0.01630435, 0.0326087], [0., 0., 0.],
[0.02631579, 0.02631579, 0.02631579], [0.05263158, 0.05263158, 0.05263158], [0.07894737, 0.07894737, 0.07894737],
[0.10526316, 0.10526316, 0.10526316], [0.13157895, 0.13157895, 0.13157895], [0.15789474, 0.15789474, 0.15789474],
[0.18421053, 0.18421053, 0.18421053], [0.21052632, 0.21052632, 0.21052632], [0.2368421, 0.2368421, 0.2368421],
[0.26315789, 0.26315789, 0.26315789], [0.28947368, 0.28947368, 0.28947368], [0.31578947, 0.31578947, 0.31578947],
[0.34210526, 0.34210526, 0.34210526], [0.36842105, 0.36842105, 0.36842105], [0.39473684, 0.39473684, 0.39473684],
[0.42105263, 0.42105263, 0.42105263], [0.44736842, 0.44736842, 0.44736842], [0.47368421, 0.47368421, 0.47368421],
[0.5, 0.5, 0.5], [0.5, 0.48333333, 0.51666667], [0.5, 0.46666667, 0.53333333], [0.5, 0.45, 0.55],
[0.5, 0.43333333, 0.56666667], [0.5, 0.41666667, 0.58333333], [0.5, 0.4, 0.6], [0.5, 0.38333333, 0.61666667],
[0.5, 0.36666667, 0.63333333], [0.5, 0.35, 0.65], [0.5, 0.33333333, 0.66666667], [0.5, 0.31666667, 0.68333333],
[0.5, 0.3, 0.7], [0.5, 0.28333333, 0.71666667], [0.5, 0.26666667, 0.73333333], [0.5, 0.25, 0.75],
[0.5, 0.225, 0.725], [0.5, 0.2, 0.7], [0.5, 0.175, 0.675], [0.5, 0.15, 0.65], [0.5, 0.125, 0.625],
[0.5, 0.1, 0.6], [0.5, 0.075, 0.575], [0.5, 0.05, 0.55], [0.5, 0.025, 0.525], [0.5, 0., 0.5]])
np.testing.assert_allclose(kpoints, test_array)
bands = results['bands'].get_bands()
assert bands.shape == (1, 98, 20)
np.testing.assert_allclose(bands[0, 0, 0:3], np.array([-6.0753, 6.0254, 6.0254]))
np.testing.assert_allclose(bands[0, 2, 0:3], np.array([-6.0386, 5.7955, 5.8737]))
np.testing.assert_allclose(bands[0, 97, 0:3], np.array([-1.867, -1.867, 3.1102]))
