def test_thermoml_mole_constraints():
"""A collection of tests to ensure that the Mole fraction constraint is
implemented correctly alongside solvent constraints."""
# Mole fraction
data_set = ThermoMLDataSet.from_file(
get_data_filename("test/properties/mole.xml"))
assert data_set is not None
assert len(data_set) > 0
# Mole fraction + Solvent: Mass fraction
data_set = ThermoMLDataSet.from_file(
get_data_filename("test/properties/mole_mass.xml"))
assert data_set is not None
assert len(data_set) > 0
# Mole fraction + Solvent: Mole fraction
data_set = ThermoMLDataSet.from_file(
get_data_filename("test/properties/mole_mole.xml"))
assert data_set is not None
assert len(data_set) > 0
# Mole fraction + Solvent: Molality
data_set = ThermoMLDataSet.from_file(
get_data_filename("test/properties/mole_molality.xml"))
assert data_set is not None
assert len(data_set) > 0
def test_gradient_reduced_potentials(use_subset):
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())
reduced_potentials = OpenMMGradientPotentials(f"reduced_potentials")
reduced_potentials.substance = substance
reduced_potentials.thermodynamic_state = thermodynamic_state
reduced_potentials.statistics_path = get_data_filename(
"test/statistics/stats_pandas.csv")
reduced_potentials.force_field_path = force_field_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.use_subset_of_force_field = use_subset
reduced_potentials.enable_pbc = True
reduced_potentials.parameter_key = ParameterGradientKey(
"vdW", "[#1]-[#8X2H2+0:1]-[#1]", "epsilon")
reduced_potentials.execute(directory, ComputeResources())
assert path.isfile(reduced_potentials.forward_potentials_path)
assert path.isfile(reduced_potentials.reverse_potentials_path)
def test_thermoml_from_files():
"""A test to ensure that ThermoML archive files can be loaded from local sources."""
data_set = ThermoMLDataSet.from_file(
get_data_filename("properties/single_density.xml"),
get_data_filename("properties/single_dielectric.xml"),
get_data_filename("properties/single_enthalpy_mixing.xml"),
)
assert data_set is not None
assert len(data_set) == 3
data_set = ThermoMLDataSet.from_file("dummy_filename")
assert data_set is None
def test_build_docked_coordinates_protocol():
"""Tests docking a methanol molecule into alpha-Cyclodextrin."""
if not has_openeye():
pytest.skip("The `BuildDockedCoordinates` protocol requires OpenEye.")
ligand_substance = Substance()
ligand_substance.add_component(
Component("CO", role=Component.Role.Ligand),
ExactAmount(1),
)
# TODO: This test could likely be made substantially faster
# by storing the binary prepared receptor. Would this
# be in breach of any oe license terms?
with tempfile.TemporaryDirectory() as temporary_directory:
build_docked_coordinates = BuildDockedCoordinates("build_methanol")
build_docked_coordinates.ligand_substance = ligand_substance
build_docked_coordinates.number_of_ligand_conformers = 5
build_docked_coordinates.receptor_coordinate_file = get_data_filename(
"test/molecules/acd.mol2")
build_docked_coordinates.execute(temporary_directory,
ComputeResources())
docked_pdb = PDBFile(
build_docked_coordinates.docked_complex_coordinate_path)
assert docked_pdb.topology.getNumResidues() == 2
def test_statistics_object():
statistics_object = StatisticsArray.from_openmm_csv(
get_data_filename("test/statistics/stats_openmm.csv"),
1 * unit.atmosphere)
statistics_object.to_pandas_csv("stats_pandas.csv")
statistics_object = StatisticsArray.from_pandas_csv("stats_pandas.csv")
assert statistics_object is not None
subsampled_array = StatisticsArray.from_existing(statistics_object,
[1, 2, 3])
assert subsampled_array is not None and len(subsampled_array) == 3
if os.path.isfile("stats_pandas.csv"):
os.unlink("stats_pandas.csv")
reduced_potential = np.array([0.1] * (len(statistics_object) - 1))
with pytest.raises(ValueError):
statistics_object[ObservableType.ReducedPotential] = reduced_potential
reduced_potential = np.array([0.1] * len(statistics_object))
with pytest.raises(ValueError):
statistics_object[ObservableType.ReducedPotential] = reduced_potential
statistics_object[ObservableType.ReducedPotential] = (reduced_potential *
unit.dimensionless)
assert ObservableType.ReducedPotential in statistics_object
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 test_extract_average_statistic():
statistics_path = get_data_filename("test/statistics/stats_pandas.csv")
with tempfile.TemporaryDirectory() as temporary_directory:
extract_protocol = ExtractAverageStatistic("extract_protocol")
extract_protocol.statistics_path = statistics_path
extract_protocol.statistics_type = ObservableType.PotentialEnergy
extract_protocol.execute(temporary_directory, ComputeResources())
def test_concatenate_trajectories():
import mdtraj
coordinate_path = get_data_filename("test/trajectories/water.pdb")
trajectory_path = get_data_filename("test/trajectories/water.dcd")
original_trajectory = mdtraj.load(trajectory_path, top=coordinate_path)
with tempfile.TemporaryDirectory() as temporary_directory:
concatenate_protocol = ConcatenateTrajectories("concatenate_protocol")
concatenate_protocol.input_coordinate_paths = [
coordinate_path, coordinate_path
]
concatenate_protocol.input_trajectory_paths = [
trajectory_path, trajectory_path
]
concatenate_protocol.execute(temporary_directory, ComputeResources())
final_trajectory = mdtraj.load(
concatenate_protocol.output_trajectory_path, top=coordinate_path)
assert len(final_trajectory) == len(original_trajectory) * 2
def test_extract_uncorrelated_trajectory_data():
import mdtraj
coordinate_path = get_data_filename("test/trajectories/water.pdb")
trajectory_path = get_data_filename("test/trajectories/water.dcd")
original_trajectory = mdtraj.load(trajectory_path, top=coordinate_path)
with tempfile.TemporaryDirectory() as temporary_directory:
extract_protocol = ExtractUncorrelatedTrajectoryData(
"extract_protocol")
extract_protocol.input_coordinate_file = coordinate_path
extract_protocol.input_trajectory_path = trajectory_path
extract_protocol.equilibration_index = 2
extract_protocol.statistical_inefficiency = 2.0
extract_protocol.execute(temporary_directory, ComputeResources())
final_trajectory = mdtraj.load(extract_protocol.output_trajectory_path,
top=coordinate_path)
assert len(final_trajectory) == (len(original_trajectory) - 2) / 2
assert (extract_protocol.number_of_uncorrelated_samples ==
(len(original_trajectory) - 2) / 2)
def test_concatenate_statistics():
statistics_path = get_data_filename("test/statistics/stats_pandas.csv")
original_array = StatisticsArray.from_pandas_csv(statistics_path)
with tempfile.TemporaryDirectory() as temporary_directory:
concatenate_protocol = ConcatenateStatistics("concatenate_protocol")
concatenate_protocol.input_statistics_paths = [
statistics_path, statistics_path
]
concatenate_protocol.execute(temporary_directory, ComputeResources())
final_array = StatisticsArray.from_pandas_csv(
concatenate_protocol.output_statistics_path)
assert len(final_array) == len(original_array) * 2
def test_extract_uncorrelated_statistics_data():
statistics_path = get_data_filename("test/statistics/stats_pandas.csv")
original_array = StatisticsArray.from_pandas_csv(statistics_path)
with tempfile.TemporaryDirectory() as temporary_directory:
extract_protocol = ExtractUncorrelatedStatisticsData(
"extract_protocol")
extract_protocol.input_statistics_path = statistics_path
extract_protocol.equilibration_index = 2
extract_protocol.statistical_inefficiency = 2.0
extract_protocol.execute(temporary_directory, ComputeResources())
final_array = StatisticsArray.from_pandas_csv(
extract_protocol.output_statistics_path)
assert len(final_array) == (len(original_array) - 2) / 2
assert (extract_protocol.number_of_uncorrelated_samples ==
(len(original_array) - 2) / 2)
def build_tip3p_smirnoff_force_field():
"""Combines the smirnoff99Frosst and tip3p offxml files
into a single one which can be consumed by the property
estimator.
Returns
-------
SmirnoffForceFieldSource
The force field containing both smirnoff99Frosst-1.1.0
and TIP3P parameters
"""
from openforcefield.typing.engines.smirnoff import ForceField
smirnoff_force_field_path = "smirnoff99Frosst-1.1.0.offxml"
tip3p_force_field_path = get_data_filename("forcefield/tip3p.offxml")
smirnoff_force_field_with_tip3p = ForceField(smirnoff_force_field_path,
tip3p_force_field_path)
return SmirnoffForceFieldSource.from_object(
smirnoff_force_field_with_tip3p)