def test_input_lines(db_test_app, get_potential_data, potential_type,
file_regression):
potential = get_potential_data(potential_type)
node = EmpiricalPotential(type=potential.type,
structure=potential.structure,
data=potential.data)
file_regression.check(six.ensure_text(node.get_input_potential_lines()))
def test_input_lines(
db_test_app, # pylint: disable=unused-argument
get_potential_data,
potential_type,
file_regression,
):
"""Test that one can get the potential lines for a given aiida-lammps potential"""
potential = get_potential_data(potential_type)
node = EmpiricalPotential(
potential_type=potential.type,
data=potential.data,
)
file_regression.check(node.get_input_lines())
def test_potential_files(
db_test_app, # pylint: disable=unused-argument
get_potential_data,
potential_type,
file_regression,
):
"""Test that one can read the potential content."""
potential = get_potential_data(potential_type)
node = EmpiricalPotential(
potential_type=potential.type,
data=potential.data,
)
file_regression.check(node.get_object_content('potential.pot', 'r'))
def test_init(db_test_app, get_potential_data, potential_type,
data_regression):
potential = get_potential_data(potential_type)
node = EmpiricalPotential(type=potential.type,
structure=potential.structure,
data=potential.data)
data_regression.check(node.attributes)
def test_init(
db_test_app, # pylint: disable=unused-argument
get_potential_data,
potential_type,
data_regression,
):
"""Test that the potential can be generated"""
potential = get_potential_data(potential_type)
node = EmpiricalPotential(
potential_type=potential.type,
data=potential.data,
)
data_regression.check(node.attributes)
options.account = ''
options.qos = ''
options.resources = {
'num_machines': 1,
'num_mpiprocs_per_machine': 1,
'parallel_env': 'localmpi',
'tot_num_mpiprocs': 1
}
#options.queue_name = 'iqtc04.q'
options.max_wallclock_seconds = 3600
inputs.metadata.options = options
# Setup code
inputs.code = Code.get_from_string(codename)
# setup nodes
inputs.structure = structure
inputs.potential = EmpiricalPotential(structure=structure,
type='lennard_jones',
data={'1 1': '0.01029 3.4 2.5'})
inputs.parameters = Dict(dict=parameters_opt)
# run calculation
result, node = run_get_node(LammpsOptimizeCalculation, **inputs)
print('results:', result)
print('node:', node)
# submit to deamon
#submit(LammpsOptimizeCalculation, **inputs)
options = AttributeDict()
options.account = ''
options.qos = ''
options.resources = {
'num_machines': 1,
'num_mpiprocs_per_machine': 1,
'parallel_env': 'localmpi',
'tot_num_mpiprocs': 1,
}
# options.queue_name = 'iqtc04.q'
options.max_wallclock_seconds = 3600
inputs.metadata.options = options
# Setup code
inputs.code = Code.get_from_string(codename)
# setup nodes
inputs.structure = structure
inputs.potential = EmpiricalPotential(type=potential['pair_style'],
data=potential['data'])
inputs.parameters = Dict(dict=parameters_opt)
# run calculation
result, node = run_get_node(LammpsOptimizeCalculation, **inputs)
print('results:', result)
print('node:', node)
# submit to deamon
# submit(LammpsOptimizeCalculation, **inputs)
options.account = ''
options.qos = ''
options.resources = {'num_machines': 1, 'num_mpiprocs_per_machine': 1,
'parallel_env': 'localmpi', 'tot_num_mpiprocs': 1}
#options.queue_name = 'iqtc04.q'
options.max_wallclock_seconds = 3600
inputs.metadata.options = options
# Setup code
inputs.code = Code.get_from_string(codename)
# setup nodes
inputs.structure = structure
#inputs.potential = Dict(dict=potential)
inputs.potential = EmpiricalPotential(structure=structure,
type='eam',
data=eam_data)
print(inputs.potential.get_potential_file())
print(inputs.potential.atom_style)
print(inputs.potential.default_units)
inputs.parameters = Dict(dict=parameters_opt)
# run calculation
result, node = run_get_node(LammpsOptimizeCalculation, **inputs)
print('results:', result)
print('node:', node)
# submit to deamon
#submit(LammpsOptimizeCalculation, **inputs)
def test_list_potentials():
assert set(EmpiricalPotential.list_types()).issuperset(
["eam", "lennard_jones", "reaxff", "tersoff"])
def test_potential_files(db_test_app, get_potential_data, potential_type,
file_regression):
potential = get_potential_data(potential_type)
node = EmpiricalPotential(type=potential.type, data=potential.data)
file_regression.check(node.get_object_content("potential.pot", "r"))
def test_load_type():
EmpiricalPotential.load_type("eam")
'num_mpiprocs_per_machine': 1,
'parallel_env': 'localmpi',
'tot_num_mpiprocs': 1
}
#options.queue_name = 'iqtc04.q'
options.max_wallclock_seconds = 3600
inputs.metadata.options = options
# Setup code
inputs.code = Code.get_from_string(codename)
# setup nodes
inputs.structure = structure
#inputs.potential = Dict(dict=potential)
inputs.potential = EmpiricalPotential(structure=structure,
type='tersoff',
data=tersoff_gan)
inputs.parameters = Dict(dict=parameters_opt)
print(inputs.potential.get_potential_file())
print(inputs.potential.atom_style)
print(inputs.potential.default_units)
# run calculation
result, node = run_get_node(LammpsOptimizeCalculation, **inputs)
print('results:', result)
print('node:', node)
# submit to deamon
#submit(LammpsOptimizeCalculation, **inputs)
options = AttributeDict()
options.account = ""
options.qos = ""
options.resources = {
"num_machines": 1,
"num_mpiprocs_per_machine": 1,
"parallel_env": "localmpi",
"tot_num_mpiprocs": 1,
}
# options.queue_name = 'iqtc04.q'
options.max_wallclock_seconds = 3600
inputs.metadata.options = options
# Setup code
inputs.code = Code.get_from_string(codename)
# setup nodes
inputs.structure = structure
inputs.potential = EmpiricalPotential(type=potential["pair_style"],
data=potential["data"])
inputs.parameters = Dict(dict=parameters_md)
# run calculation
result, node = run_get_node(LammpsMDCalculation, **inputs)
print("results:", result)
print("node:", node)
# submit to deamon
# submit(LammpsOptimizeCalculation, **inputs)
def test_load_type():
"""Test that an specific potential can be loaded"""
EmpiricalPotential.load_type('eam')
def test_list_potentials():
"""Test that all the supported potential types are recognized."""
assert set(EmpiricalPotential.list_types()).issuperset(
['eam', 'lennard_jones', 'reaxff', 'tersoff'])