def process_successful_property(physical_property, layer_directory, **_):
"""Return a result as if the property had been successfully estimated."""
dummy_data_directory = path.join(layer_directory, "good_dummy_data")
makedirs(dummy_data_directory, exist_ok=True)
dummy_stored_object = StoredSimulationData()
dummy_stored_object.substance = physical_property.substance
dummy_stored_object.thermodynamic_state = physical_property.thermodynamic_state
dummy_stored_object.property_phase = physical_property.phase
dummy_stored_object.force_field_id = ""
dummy_stored_object.coordinate_file_name = ""
dummy_stored_object.trajectory_file_name = ""
dummy_stored_object.statistics_file_name = ""
dummy_stored_object.statistical_inefficiency = 1.0
dummy_stored_object.number_of_molecules = 10
dummy_stored_object.source_calculation_id = ""
dummy_stored_object_path = path.join(layer_directory,
"good_dummy_data.json")
with open(dummy_stored_object_path, "w") as file:
json.dump(dummy_stored_object, file, cls=TypedJSONEncoder)
return_object = CalculationLayerResult()
return_object.physical_property = physical_property
return_object.data_to_store = [(dummy_stored_object_path,
dummy_data_directory)]
return return_object
def create_dummy_simulation_data(
directory_path,
substance,
force_field_id="dummy_ff_id",
coordinate_file_name="output.pdb",
trajectory_file_name="trajectory.dcd",
statistics_file_name="statistics.csv",
statistical_inefficiency=1.0,
phase=PropertyPhase.Liquid,
number_of_molecules=1,
calculation_id=None,
):
"""Creates a dummy `StoredSimulationData` object and
the corresponding data directory.
Parameters
----------
directory_path: str
The path to the dummy data directory to create.
substance: Substance
force_field_id
coordinate_file_name
trajectory_file_name
statistics_file_name
statistical_inefficiency
phase
number_of_molecules
calculation_id
Returns
-------
StoredSimulationData
The dummy stored data object.
"""
os.makedirs(directory_path, exist_ok=True)
data = StoredSimulationData()
data.substance = substance
data.force_field_id = force_field_id
data.thermodynamic_state = ThermodynamicState(1.0 * unit.kelvin)
data.property_phase = phase
data.coordinate_file_name = coordinate_file_name
data.trajectory_file_name = trajectory_file_name
data.statistics_file_name = statistics_file_name
with open(os.path.join(directory_path, coordinate_file_name), "w") as file:
file.write("")
with open(os.path.join(directory_path, trajectory_file_name), "w") as file:
file.write("")
with open(os.path.join(directory_path, statistics_file_name), "w") as file:
file.write("")
data.statistical_inefficiency = statistical_inefficiency
data.number_of_molecules = number_of_molecules
if calculation_id is None:
calculation_id = str(uuid.uuid4())
data.source_calculation_id = calculation_id
return data
def generate_simulation_protocols(
analysis_protocol: S,
use_target_uncertainty: bool,
id_suffix: str = "",
conditional_group: Optional[ConditionalGroup] = None,
n_molecules: int = 1000,
) -> Tuple[SimulationProtocols[S], ProtocolPath, StoredSimulationData]:
"""Constructs a set of protocols which, when combined in a workflow schema, may be
executed to run a single simulation to estimate the average value of an observable.
The protocols returned will:
1) Build a set of liquid coordinates for the
property substance using packmol.
2) Assign a set of smirnoff force field parameters
to the system.
3) Perform an energy minimisation on the system.
4) Run a short NPT equilibration simulation for 100000 steps
using a timestep of 2fs.
5) Within a conditional group (up to a maximum of 100 times):
5a) Run a longer NPT production simulation for 1000000 steps using a
timestep of 2fs
5b) Extract the average value of an observable and it's uncertainty.
5c) If a convergence mode is set by the options, check if the target
uncertainty has been met. If not, repeat steps 5a), 5b) and 5c).
6) Extract uncorrelated configurations from a generated production
simulation.
7) Extract uncorrelated statistics from a generated production
simulation.
Parameters
----------
analysis_protocol
The protocol which will extract the observable of
interest from the generated simulation data.
use_target_uncertainty
Whether to run the simulation until the observable is
estimated to within the target uncertainty.
id_suffix: str
A string suffix to append to each of the protocol ids.
conditional_group: ProtocolGroup, optional
A custom group to wrap the main simulation / extraction
protocols within. It is up to the caller of this method to
manually add the convergence conditions to this group.
If `None`, a default group with uncertainty convergence
conditions is automatically constructed.
n_molecules: int
The number of molecules to use in the workflow.
Returns
-------
The protocols to add to the workflow, a reference to the average value of the
estimated observable (an ``Observable`` object), and an object which describes
the default data from a simulation to store, such as the uncorrelated statistics
and configurations.
"""
build_coordinates = coordinates.BuildCoordinatesPackmol(
f"build_coordinates{id_suffix}"
)
build_coordinates.substance = ProtocolPath("substance", "global")
build_coordinates.max_molecules = n_molecules
assign_parameters = forcefield.BaseBuildSystem(f"assign_parameters{id_suffix}")
assign_parameters.force_field_path = ProtocolPath("force_field_path", "global")
assign_parameters.coordinate_file_path = ProtocolPath(
"coordinate_file_path", build_coordinates.id
)
assign_parameters.substance = ProtocolPath("output_substance", build_coordinates.id)
# Equilibration
energy_minimisation = openmm.OpenMMEnergyMinimisation(
f"energy_minimisation{id_suffix}"
)
energy_minimisation.input_coordinate_file = ProtocolPath(
"coordinate_file_path", build_coordinates.id
)
energy_minimisation.parameterized_system = ProtocolPath(
"parameterized_system", assign_parameters.id
)
equilibration_simulation = openmm.OpenMMSimulation(
f"equilibration_simulation{id_suffix}"
)
equilibration_simulation.ensemble = Ensemble.NPT
equilibration_simulation.steps_per_iteration = 100000
equilibration_simulation.output_frequency = 5000
equilibration_simulation.timestep = 2.0 * unit.femtosecond
equilibration_simulation.thermodynamic_state = ProtocolPath(
"thermodynamic_state", "global"
)
equilibration_simulation.input_coordinate_file = ProtocolPath(
"output_coordinate_file", energy_minimisation.id
)
equilibration_simulation.parameterized_system = ProtocolPath(
"parameterized_system", assign_parameters.id
)
# Production
production_simulation = openmm.OpenMMSimulation(f"production_simulation{id_suffix}")
production_simulation.ensemble = Ensemble.NPT
production_simulation.steps_per_iteration = 1000000
production_simulation.output_frequency = 2000
production_simulation.timestep = 2.0 * unit.femtosecond
production_simulation.thermodynamic_state = ProtocolPath(
"thermodynamic_state", "global"
)
production_simulation.input_coordinate_file = ProtocolPath(
"output_coordinate_file", equilibration_simulation.id
)
production_simulation.parameterized_system = ProtocolPath(
"parameterized_system", assign_parameters.id
)
production_simulation.gradient_parameters = ProtocolPath(
"parameter_gradient_keys", "global"
)
# Set up a conditional group to ensure convergence of uncertainty
if conditional_group is None:
conditional_group = groups.ConditionalGroup(f"conditional_group{id_suffix}")
conditional_group.max_iterations = 100
if use_target_uncertainty:
condition = groups.ConditionalGroup.Condition()
condition.right_hand_value = ProtocolPath("target_uncertainty", "global")
condition.type = groups.ConditionalGroup.Condition.Type.LessThan
condition.left_hand_value = ProtocolPath(
"value.error", conditional_group.id, analysis_protocol.id
)
conditional_group.add_condition(condition)
# Make sure the simulation gets extended after each iteration.
production_simulation.total_number_of_iterations = ProtocolPath(
"current_iteration", conditional_group.id
)
conditional_group.add_protocols(production_simulation, analysis_protocol)
# Point the analyse protocol to the correct data sources
if not isinstance(analysis_protocol, analysis.BaseAverageObservable):
raise ValueError(
"The analysis protocol must inherit from either the "
"AverageTrajectoryObservable or BaseAverageObservable "
"protocols."
)
analysis_protocol.thermodynamic_state = ProtocolPath(
"thermodynamic_state", "global"
)
analysis_protocol.potential_energies = ProtocolPath(
f"observables[{ObservableType.PotentialEnergy.value}]",
production_simulation.id,
)
# Finally, extract uncorrelated data
time_series_statistics = ProtocolPath(
"time_series_statistics", conditional_group.id, analysis_protocol.id
)
coordinate_file = ProtocolPath(
"output_coordinate_file", conditional_group.id, production_simulation.id
)
trajectory_path = ProtocolPath(
"trajectory_file_path", conditional_group.id, production_simulation.id
)
observables = ProtocolPath(
"observables", conditional_group.id, production_simulation.id
)
decorrelate_trajectory = analysis.DecorrelateTrajectory(
f"decorrelate_trajectory{id_suffix}"
)
decorrelate_trajectory.time_series_statistics = time_series_statistics
decorrelate_trajectory.input_coordinate_file = coordinate_file
decorrelate_trajectory.input_trajectory_path = trajectory_path
decorrelate_observables = analysis.DecorrelateObservables(
f"decorrelate_observables{id_suffix}"
)
decorrelate_observables.time_series_statistics = time_series_statistics
decorrelate_observables.input_observables = observables
# Build the object which defines which pieces of simulation data to store.
output_to_store = StoredSimulationData()
output_to_store.thermodynamic_state = ProtocolPath("thermodynamic_state", "global")
output_to_store.property_phase = PropertyPhase.Liquid
output_to_store.force_field_id = PlaceholderValue()
output_to_store.number_of_molecules = ProtocolPath(
"output_number_of_molecules", build_coordinates.id
)
output_to_store.substance = ProtocolPath("output_substance", build_coordinates.id)
output_to_store.statistical_inefficiency = ProtocolPath(
"time_series_statistics.statistical_inefficiency",
conditional_group.id,
analysis_protocol.id,
)
output_to_store.observables = ProtocolPath(
"output_observables", decorrelate_observables.id
)
output_to_store.trajectory_file_name = ProtocolPath(
"output_trajectory_path", decorrelate_trajectory.id
)
output_to_store.coordinate_file_name = coordinate_file
output_to_store.source_calculation_id = PlaceholderValue()
# Define where the final values come from.
final_value_source = ProtocolPath(
"value", conditional_group.id, analysis_protocol.id
)
base_protocols = SimulationProtocols(
build_coordinates,
assign_parameters,
energy_minimisation,
equilibration_simulation,
production_simulation,
analysis_protocol,
conditional_group,
decorrelate_trajectory,
decorrelate_observables,
)
return base_protocols, final_value_source, output_to_store