def test_weight_by_mole_fraction_protocol(component_smiles, value):
full_substance = Substance.from_components("C", "CC", "CCC")
component = Substance.from_components(component_smiles)
mole_fraction = next(
iter(full_substance.get_amounts(component.components[0].identifier))
).value
with tempfile.TemporaryDirectory() as temporary_directory:
weight_protocol = WeightByMoleFraction("weight")
weight_protocol.value = value
weight_protocol.full_substance = full_substance
weight_protocol.component = component
weight_protocol.execute(temporary_directory, ComputeResources())
assert weight_protocol.weighted_value == value * mole_fraction
def test_same_component_batching():
thermodynamic_state = ThermodynamicState(temperature=1.0 * unit.kelvin,
pressure=1.0 * unit.atmosphere)
data_set = PhysicalPropertyDataSet()
data_set.add_properties(
Density(
thermodynamic_state=thermodynamic_state,
substance=Substance.from_components("O", "C"),
value=0.0 * unit.kilogram / unit.meter**3,
),
EnthalpyOfVaporization(
thermodynamic_state=thermodynamic_state,
substance=Substance.from_components("O", "C"),
value=0.0 * unit.kilojoule / unit.mole,
),
Density(
thermodynamic_state=thermodynamic_state,
substance=Substance.from_components("O", "CO"),
value=0.0 * unit.kilogram / unit.meter**3,
),
EnthalpyOfVaporization(
thermodynamic_state=thermodynamic_state,
substance=Substance.from_components("O", "CO"),
value=0.0 * unit.kilojoule / unit.mole,
),
)
options = RequestOptions()
submission = EvaluatorClient._Submission()
submission.dataset = data_set
submission.options = options
with DaskLocalCluster() as calculation_backend:
server = EvaluatorServer(calculation_backend)
batches = server._batch_by_same_component(submission, "")
assert len(batches) == 2
assert len(batches[0].queued_properties) == 2
assert len(batches[1].queued_properties) == 2
def test_simulation_data_query():
substance_a = Substance.from_components("C")
substance_b = Substance.from_components("CO")
substance_full = Substance.from_components("C", "CO")
substances = [substance_a, substance_b, substance_full]
with tempfile.TemporaryDirectory() as base_directory:
backend_directory = os.path.join(base_directory, "storage_dir")
storage = LocalFileStorage(backend_directory)
for substance in substances:
data_directory = os.path.join(base_directory,
f"{substance.identifier}")
data_object = create_dummy_simulation_data(data_directory,
substance)
storage.store_object(data_object, data_directory)
for substance in substances:
substance_query = SimulationDataQuery()
substance_query.substance = substance
results = storage.query(substance_query)
assert results is not None and len(results) == 1
assert len(next(iter(results.values()))[0]) == 3
component_query = SimulationDataQuery()
component_query.substance = substance_full
component_query.substance_query = SubstanceQuery()
component_query.substance_query.components_only = True
results = storage.query(component_query)
assert results is not None and len(results) == 2
def test_calculate_reduced_potential_openmm():
substance = Substance.from_components("O")
thermodynamic_state = ThermodynamicState(298 * unit.kelvin,
1.0 * unit.atmosphere)
with tempfile.TemporaryDirectory() as directory:
force_field_path = path.join(directory, "ff.json")
with open(force_field_path, "w") as file:
file.write(build_tip3p_smirnoff_force_field().json())
build_coordinates = BuildCoordinatesPackmol("build_coordinates")
build_coordinates.max_molecules = 10
build_coordinates.mass_density = 0.05 * unit.grams / unit.milliliters
build_coordinates.substance = substance
build_coordinates.execute(directory, None)
assign_parameters = BuildSmirnoffSystem(f"assign_parameters")
assign_parameters.force_field_path = force_field_path
assign_parameters.coordinate_file_path = build_coordinates.coordinate_file_path
assign_parameters.substance = substance
assign_parameters.execute(directory, None)
reduced_potentials = OpenMMReducedPotentials(f"reduced_potentials")
reduced_potentials.substance = substance
reduced_potentials.thermodynamic_state = thermodynamic_state
reduced_potentials.reference_force_field_paths = [force_field_path]
reduced_potentials.system_path = assign_parameters.system_path
reduced_potentials.trajectory_file_path = get_data_filename(
"test/trajectories/water.dcd")
reduced_potentials.coordinate_file_path = get_data_filename(
"test/trajectories/water.pdb")
reduced_potentials.kinetic_energies_path = get_data_filename(
"test/statistics/stats_pandas.csv")
reduced_potentials.high_precision = False
reduced_potentials.execute(directory, ComputeResources())
assert path.isfile(reduced_potentials.statistics_file_path)
final_array = StatisticsArray.from_pandas_csv(
reduced_potentials.statistics_file_path)
assert ObservableType.ReducedPotential in final_array
def _setup_dummy_system(directory):
force_field_path = path.join(directory, "ff.json")
with open(force_field_path, "w") as file:
file.write(build_tip3p_smirnoff_force_field().json())
substance = Substance.from_components("C")
build_coordinates = BuildCoordinatesPackmol("build_coordinates")
build_coordinates.max_molecules = 1
build_coordinates.mass_density = 0.001 * unit.grams / unit.milliliters
build_coordinates.substance = substance
build_coordinates.execute(directory, None)
assign_parameters = BuildSmirnoffSystem(f"assign_parameters")
assign_parameters.force_field_path = force_field_path
assign_parameters.coordinate_file_path = build_coordinates.coordinate_file_path
assign_parameters.substance = substance
assign_parameters.execute(directory, None)
return build_coordinates.coordinate_file_path, assign_parameters.system_path
def test_build_tleap_system():
with tempfile.TemporaryDirectory() as directory:
force_field_path = path.join(directory, "ff.json")
with open(force_field_path, "w") as file:
file.write(TLeapForceFieldSource().json())
substance = Substance.from_components("C", "O", "C(=O)N")
build_coordinates = BuildCoordinatesPackmol("build_coordinates")
build_coordinates.max_molecules = 9
build_coordinates.substance = substance
build_coordinates.execute(directory, None)
assign_parameters = BuildTLeapSystem(f"assign_parameters")
assign_parameters.force_field_path = force_field_path
assign_parameters.coordinate_file_path = build_coordinates.coordinate_file_path
assign_parameters.substance = substance
assign_parameters.execute(directory, None)
assert path.isfile(assign_parameters.system_path)
def _build_input_output_substances():
"""Builds sets if input and expected substances for the
`test_build_coordinate_composition` test.
Returns
-------
list of tuple of Substance and Substance
A list of input and expected substances.
"""
# Start with some easy cases
substances = [
(Substance.from_components("O"), Substance.from_components("O")),
(Substance.from_components("O",
"C"), Substance.from_components("O", "C")),
(
Substance.from_components("O", "C", "CO"),
Substance.from_components("O", "C", "CO"),
),
]
# Handle some cases where rounding will need to occur.
input_substance = Substance()
input_substance.add_component(Component("O"), MoleFraction(0.41))
input_substance.add_component(Component("C"), MoleFraction(0.59))
expected_substance = Substance()
expected_substance.add_component(Component("O"), MoleFraction(0.4))
expected_substance.add_component(Component("C"), MoleFraction(0.6))
substances.append((input_substance, expected_substance))
input_substance = Substance()
input_substance.add_component(Component("O"), MoleFraction(0.59))
input_substance.add_component(Component("C"), MoleFraction(0.41))
expected_substance = Substance()
expected_substance.add_component(Component("O"), MoleFraction(0.6))
expected_substance.add_component(Component("C"), MoleFraction(0.4))
substances.append((input_substance, expected_substance))
return substances
def test_duplicate_simulation_data_storage(reverse_order):
substance = Substance.from_components("CO")
with tempfile.TemporaryDirectory() as base_directory_path:
storage_directory = os.path.join(base_directory_path, "storage")
local_storage = LocalFileStorage(storage_directory)
# Construct some data to store with increasing
# statistical inefficiencies.
data_to_store = []
for index in range(3):
data_directory = os.path.join(base_directory_path, f"data_{index}")
coordinate_name = f"data_{index}.pdb"
data_object = create_dummy_simulation_data(
directory_path=data_directory,
substance=substance,
force_field_id="ff_id_1",
coordinate_file_name=coordinate_name,
statistical_inefficiency=float(index),
calculation_id="id",
)
data_to_store.append((data_object, data_directory))
# Keep a track of the storage keys.
all_storage_keys = set()
iterator = enumerate(data_to_store)
if reverse_order:
iterator = reversed(list(iterator))
# Store the data
for index, data in iterator:
data_object, data_directory = data
storage_key = local_storage.store_object(data_object,
data_directory)
all_storage_keys.add(storage_key)
retrieved_object, stored_directory = local_storage.retrieve_object(
storage_key)
# Handle the case where we haven't reversed the order of
# the data to store. Here only the first object in the list
# should be stored an never replaced as it has the lowest
# statistical inefficiency.
if not reverse_order:
expected_index = 0
# Handle the case where we have reversed the order of
# the data to store. Here only the each new piece of
# data should replace the last, as it will have a lower
# statistical inefficiency.
else:
expected_index = index
assert retrieved_object.json(
) == data_to_store[expected_index][0].json()
# Make sure the directory has been correctly overwritten / retained
# depending on the data order.
coordinate_path = os.path.join(stored_directory,
f"data_{expected_index}.pdb")
assert os.path.isfile(coordinate_path)
# Make sure all pieces of data got assigned the same key if
# reverse order.
assert len(all_storage_keys) == 1
def test_density_dielectric_merging(workflow_merge_function):
substance = Substance.from_components("C")
density = evaluator.properties.Density(
thermodynamic_state=ThermodynamicState(
temperature=298 * unit.kelvin, pressure=1 * unit.atmosphere
),
phase=PropertyPhase.Liquid,
substance=substance,
value=10 * unit.gram / unit.mole,
uncertainty=1 * unit.gram / unit.mole,
)
dielectric = evaluator.properties.DielectricConstant(
thermodynamic_state=ThermodynamicState(
temperature=298 * unit.kelvin, pressure=1 * unit.atmosphere
),
phase=PropertyPhase.Liquid,
substance=substance,
value=10 * unit.gram / unit.mole,
uncertainty=1 * unit.gram / unit.mole,
)
density_schema = density.default_simulation_schema().workflow_schema
dielectric_schema = dielectric.default_simulation_schema().workflow_schema
density_metadata = Workflow.generate_default_metadata(
density, "smirnoff99Frosst-1.1.0.offxml", []
)
dielectric_metadata = Workflow.generate_default_metadata(
density, "smirnoff99Frosst-1.1.0.offxml", []
)
density_workflow = Workflow(density_metadata)
density_workflow.schema = density_schema
dielectric_workflow = Workflow(dielectric_metadata)
dielectric_workflow.schema = dielectric_schema
workflow_merge_function(density_workflow, dielectric_workflow)
density_workflow_graph = density_workflow.to_graph()
dielectric_workflow_graph = dielectric_workflow.to_graph()
dependants_graph_a = density_workflow_graph._protocol_graph._build_dependants_graph(
density_workflow_graph.protocols, False, apply_reduction=True
)
dependants_graph_b = dielectric_workflow_graph._protocol_graph._build_dependants_graph(
dielectric_workflow_graph.protocols, False, apply_reduction=True
)
merge_order_a = graph.topological_sort(dependants_graph_a)
merge_order_b = graph.topological_sort(dependants_graph_b)
for protocol_id_A, protocol_id_B in zip(merge_order_a, merge_order_b):
if (
protocol_id_A.find("extract_traj") < 0
and protocol_id_A.find("extract_stats") < 0
):
assert (
density_workflow.protocols[protocol_id_A].schema.json()
== dielectric_workflow.protocols[protocol_id_B].schema.json()
)
else:
assert (
density_workflow.protocols[protocol_id_A].schema.json()
!= dielectric_workflow.protocols[protocol_id_B].schema.json()
)
def test_substance_len():
substance = Substance.from_components("C", "CC", "CCC", "CCC")
assert len(substance) == 3
def generate_default_metadata(
physical_property,
force_field_path,
parameter_gradient_keys=None,
target_uncertainty=None,
):
"""Generates the default global metadata dictionary.
Parameters
----------
physical_property: PhysicalProperty
The physical property whose arguments are available in the
global scope.
force_field_path: str
The path to the force field parameters to use in the workflow.
parameter_gradient_keys: list of ParameterGradientKey
A list of references to all of the parameters which all observables
should be differentiated with respect to.
target_uncertainty: pint.Quantity, optional
The uncertainty which the property should be estimated to
within.
Returns
-------
dict of str, Any
The metadata dictionary, with the following
keys / types:
- thermodynamic_state: `ThermodynamicState` - The state (T,p) at which the
property is being computed
- substance: `Substance` - The composition of the system of interest.
- components: list of `Substance` - The components present in the system for
which the property is being estimated.
- target_uncertainty: pint.Quantity - The target uncertainty with which
properties should be estimated.
- per_component_uncertainty: pint.Quantity - The target uncertainty divided
by the sqrt of the number of
components in the system + 1
- force_field_path: str - A path to the force field parameters with which the
property should be evaluated with.
- parameter_gradient_keys: list of ParameterGradientKey - A list of references to all of the
parameters which all observables
should be differentiated with respect to.
"""
components = []
for component in physical_property.substance.components:
component_substance = Substance.from_components(component)
components.append(component_substance)
if target_uncertainty is None:
target_uncertainty = math.inf * physical_property.value.units
target_uncertainty = target_uncertainty.to(
physical_property.value.units)
# +1 comes from inclusion of the full mixture as a possible component.
per_component_uncertainty = target_uncertainty / sqrt(
physical_property.substance.number_of_components + 1)
# Find only those gradient keys which will actually be relevant to the
# property of interest
relevant_gradient_keys = Workflow._find_relevant_gradient_keys(
physical_property.substance, force_field_path,
parameter_gradient_keys)
# Define a dictionary of accessible 'global' properties.
global_metadata = {
"thermodynamic_state": physical_property.thermodynamic_state,
"substance": physical_property.substance,
"components": components,
"target_uncertainty": target_uncertainty,
"per_component_uncertainty": per_component_uncertainty,
"force_field_path": force_field_path,
"parameter_gradient_keys": relevant_gradient_keys,
}
# Include the properties metadata
if physical_property.metadata != UNDEFINED:
global_metadata.update(physical_property.metadata)
return global_metadata
def test_storage_retrieval():
# Create some dummy properties
methane = Substance.from_components("C")
methanol = Substance.from_components("CO")
mixture = Substance.from_components("C", "CO")
# Add extra unused data to make sure the wrong data isn't
# Being retrieved.
unused_pure = Substance.from_components("CCO")
unused_mixture = Substance.from_components("CCO", "CO")
data_to_store = [
(methane, PropertyPhase.Liquid, 1000),
(methanol, PropertyPhase.Liquid, 1000),
(methanol, PropertyPhase.Gas, 1),
(mixture, PropertyPhase.Liquid, 1000),
(unused_pure, PropertyPhase.Liquid, 1000),
(unused_mixture, PropertyPhase.Liquid, 1000),
]
storage_keys = {}
state = ThermodynamicState(temperature=1.0 * unit.kelvin)
properties = [
# Properties with a full system query.
Density(
value=1.0 * unit.gram / unit.litre,
substance=methanol,
thermodynamic_state=state,
),
DielectricConstant(
value=1.0 * unit.dimensionless, substance=methane, thermodynamic_state=state
),
# Properties with a multi-component query.
EnthalpyOfVaporization(
value=1.0 * unit.joule / unit.mole,
substance=methanol,
thermodynamic_state=state,
),
# Property with a multi-phase query.
EnthalpyOfMixing(
value=1.0 * unit.joule / unit.mole,
substance=mixture,
thermodynamic_state=state,
),
ExcessMolarVolume(
value=1.0 * unit.meter ** 3, substance=mixture, thermodynamic_state=state
),
]
expected_data_per_property = {
Density: {"full_system_data": [(methanol, PropertyPhase.Liquid, 1000)]},
DielectricConstant: {
"full_system_data": [(methane, PropertyPhase.Liquid, 1000)]
},
EnthalpyOfVaporization: {
"liquid_data": [(methanol, PropertyPhase.Liquid, 1000)],
"gas_data": [(methanol, PropertyPhase.Gas, 1)],
},
EnthalpyOfMixing: {
"full_system_data": [(mixture, PropertyPhase.Liquid, 1000)],
"component_data": [
[(methane, PropertyPhase.Liquid, 1000)],
[(methanol, PropertyPhase.Liquid, 1000)],
],
},
ExcessMolarVolume: {
"full_system_data": [(mixture, PropertyPhase.Liquid, 1000)],
"component_data": [
[(methane, PropertyPhase.Liquid, 1000)],
[(methanol, PropertyPhase.Liquid, 1000)],
],
},
}
force_field = SmirnoffForceFieldSource.from_path("smirnoff99Frosst-1.1.0.offxml")
with tempfile.TemporaryDirectory() as base_directory:
# Create a storage backend with some dummy data.
backend_directory = os.path.join(base_directory, "storage_dir")
storage_backend = LocalFileStorage(backend_directory)
force_field_id = storage_backend.store_force_field(force_field)
for substance, phase, n_mol in data_to_store:
data_directory = os.path.join(base_directory, substance.identifier)
data = create_dummy_simulation_data(
data_directory,
substance=substance,
force_field_id=force_field_id,
phase=phase,
number_of_molecules=n_mol,
)
storage_key = storage_backend.store_object(data, data_directory)
storage_keys[(substance, phase, n_mol)] = storage_key
for physical_property in properties:
schema = registered_calculation_schemas["ReweightingLayer"][
physical_property.__class__.__name__
]
if callable(schema):
schema = schema()
# noinspection PyProtectedMember
metadata = ReweightingLayer._get_workflow_metadata(
base_directory, physical_property, "", [], storage_backend, schema,
)
assert metadata is not None
expected_data_list = expected_data_per_property[physical_property.__class__]
for data_key in expected_data_list:
assert data_key in metadata
stored_metadata = metadata[data_key]
expected_metadata = expected_data_list[data_key]
assert len(stored_metadata) == len(expected_metadata)
if isinstance(stored_metadata[0], list):
# Flatten any lists of lists.
stored_metadata = [
item for sublist in stored_metadata for item in sublist
]
expected_metadata = [
item for sublist in expected_metadata for item in sublist
]
metadata_storage_keys = [
os.path.basename(x) for x, _, _ in stored_metadata
]
expected_storage_keys = [storage_keys[x] for x in expected_metadata]
assert sorted(metadata_storage_keys) == sorted(expected_storage_keys)
def test_to_pandas():
"""A test to ensure that data sets are convertable to pandas objects."""
source = CalculationSource("Dummy", {})
pure_substance = Substance.from_components("C")
binary_substance = Substance.from_components("C", "O")
data_set = PhysicalPropertyDataSet()
for temperature in [
298 * unit.kelvin, 300 * unit.kelvin, 302 * unit.kelvin
]:
thermodynamic_state = ThermodynamicState(temperature=temperature,
pressure=1.0 *
unit.atmosphere)
density_property = Density(
thermodynamic_state=thermodynamic_state,
phase=PropertyPhase.Liquid,
substance=pure_substance,
value=1 * unit.gram / unit.milliliter,
uncertainty=0.11 * unit.gram / unit.milliliter,
source=source,
)
dielectric_property = DielectricConstant(
thermodynamic_state=thermodynamic_state,
phase=PropertyPhase.Liquid,
substance=pure_substance,
value=1 * unit.dimensionless,
uncertainty=0.11 * unit.dimensionless,
source=source,
)
data_set.add_properties(density_property)
data_set.add_properties(dielectric_property)
for temperature in [
298 * unit.kelvin, 300 * unit.kelvin, 302 * unit.kelvin
]:
thermodynamic_state = ThermodynamicState(temperature=temperature,
pressure=1.0 *
unit.atmosphere)
enthalpy_property = EnthalpyOfMixing(
thermodynamic_state=thermodynamic_state,
phase=PropertyPhase.Liquid,
substance=binary_substance,
value=1 * unit.kilojoules / unit.mole,
uncertainty=0.11 * unit.kilojoules / unit.mole,
source=source,
)
excess_property = ExcessMolarVolume(
thermodynamic_state=thermodynamic_state,
phase=PropertyPhase.Liquid,
substance=binary_substance,
value=1 * unit.meter**3 / unit.mole,
uncertainty=0.11 * unit.meter**3 / unit.mole,
source=source,
)
data_set.add_properties(enthalpy_property)
data_set.add_properties(excess_property)
data_set_pandas = data_set.to_pandas()
required_columns = [
"Temperature (K)",
"Pressure (kPa)",
"Phase",
"N Components",
"Source",
"Component 1",
"Role 1",
"Mole Fraction 1",
"Exact Amount 1",
"Component 2",
"Role 2",
"Mole Fraction 2",
"Exact Amount 2",
]
assert all(x in data_set_pandas for x in required_columns)
assert data_set_pandas is not None
assert data_set_pandas.shape == (12, 21)
data_set_without_na = data_set_pandas.dropna(axis=1, how="all")
assert data_set_without_na.shape == (12, 19)
def test_build_ligpargen_system(requests_mock):
force_field_source = LigParGenForceFieldSource(
request_url="http://testligpargen.com/request",
download_url="http://testligpargen.com/download",
)
substance = Substance.from_components("C", "O")
def request_callback(request, context):
context.status_code = 200
smiles = re.search(r'"smiData"\r\n\r\n(.*?)\r\n',
request.text).group(1)
cmiles_molecule = load_molecule(smiles, toolkit="rdkit")
smiles = mol_to_smiles(cmiles_molecule,
isomeric=False,
explicit_hydrogen=False,
mapped=False)
assert smiles == "C"
return 'value="/tmp/0000.xml"'
def download_callback(_, context):
context.status_code = 200
return """
<ForceField>
<AtomTypes>
<Type name="opls_802" class="H802" element="H" mass="1.008000" />
<Type name="opls_804" class="H804" element="H" mass="1.008000" />
<Type name="opls_803" class="H803" element="H" mass="1.008000" />
<Type name="opls_800" class="C800" element="C" mass="12.011000" />
<Type name="opls_801" class="H801" element="H" mass="1.008000" />
</AtomTypes>
<Residues>
<Residue name="UNK">
<Atom name="C00" type="opls_800" />
<Atom name="H01" type="opls_801" />
<Atom name="H02" type="opls_802" />
<Atom name="H03" type="opls_803" />
<Atom name="H04" type="opls_804" />
<Bond from="0" to="1"/>
<Bond from="0" to="2"/>
<Bond from="0" to="3"/>
<Bond from="0" to="4"/>
</Residue>
</Residues>
<HarmonicBondForce>
<Bond class1="H801" class2="C800" length="0.109000" k="284512.000000"/>
<Bond class1="H802" class2="C800" length="0.109000" k="284512.000000"/>
<Bond class1="H803" class2="C800" length="0.109000" k="284512.000000"/>
<Bond class1="H804" class2="C800" length="0.109000" k="284512.000000"/>
</HarmonicBondForce>
<HarmonicAngleForce>
<Angle class1="H801" class2="C800" class3="H802" angle="1.881465" k="276.144000"/>
<Angle class1="H801" class2="C800" class3="H803" angle="1.881465" k="276.144000"/>
<Angle class1="H801" class2="C800" class3="H804" angle="1.881465" k="276.144000"/>
<Angle class1="H802" class2="C800" class3="H803" angle="1.881465" k="276.144000"/>
<Angle class1="H803" class2="C800" class3="H804" angle="1.881465" k="276.144000"/>
<Angle class1="H802" class2="C800" class3="H804" angle="1.881465" k="276.144000"/>
</HarmonicAngleForce>
<PeriodicTorsionForce>
<Improper class1="C800" class2="H801" class3="H802" class4="H803" k1="0.000000" k2="0.000000" k3="0.000000"
k4="0.000000" periodicity1="1" periodicity2="2" periodicity3="3" periodicity4="4" phase1="0.00"
phase2="3.141592653589793" phase3="0.00" phase4="3.141592653589793"/>
<Improper class1="C800" class2="H801" class3="H802" class4="H804" k1="0.000000" k2="0.000000" k3="0.000000"
k4="0.000000" periodicity1="1" periodicity2="2" periodicity3="3" periodicity4="4" phase1="0.00"
phase2="3.141592653589793" phase3="0.00" phase4="3.141592653589793"/>
</PeriodicTorsionForce>
<NonbondedForce coulomb14scale="0.5" lj14scale="0.5">
<Atom type="opls_803" charge="0.074800" sigma="0.250000" epsilon="0.125520" />
<Atom type="opls_802" charge="0.074800" sigma="0.250000" epsilon="0.125520" />
<Atom type="opls_800" charge="-0.299400" sigma="0.350000" epsilon="0.276144" />
<Atom type="opls_804" charge="0.074800" sigma="0.250000" epsilon="0.125520" />
<Atom type="opls_801" charge="0.074800" sigma="0.250000" epsilon="0.125520" />
</NonbondedForce>
</ForceField>
"""
requests_mock.post(force_field_source.request_url, text=request_callback)
requests_mock.post(force_field_source.download_url, text=download_callback)
with tempfile.TemporaryDirectory() as directory:
force_field_path = path.join(directory, "ff.json")
with open(force_field_path, "w") as file:
file.write(force_field_source.json())
build_coordinates = BuildCoordinatesPackmol("build_coordinates")
build_coordinates.max_molecules = 8
build_coordinates.substance = substance
build_coordinates.execute(directory, None)
assign_parameters = BuildLigParGenSystem(f"assign_parameters")
assign_parameters.force_field_path = force_field_path
assign_parameters.coordinate_file_path = build_coordinates.coordinate_file_path
assign_parameters.substance = substance
assign_parameters.execute(directory, None)
assert path.isfile(assign_parameters.system_path)