"""
A set of utilities for setting up property estimation workflows.
"""
import copy
from collections import namedtuple
from propertyestimator import unit
from propertyestimator.protocols import analysis, forcefield, gradients, groups, reweighting, coordinates, simulation, \
storage
from propertyestimator.thermodynamics import Ensemble
from propertyestimator.utils.statistics import ObservableType
from propertyestimator.workflow import WorkflowOptions
from propertyestimator.workflow.plugins import available_protocols
from propertyestimator.workflow.schemas import ProtocolReplicator, WorkflowSimulationDataToStore
from propertyestimator.workflow.utils import ProtocolPath, ReplicatorValue
BaseReweightingProtocols = namedtuple('BaseReweightingProtocols', 'unpack_stored_data '
'analysis_protocol '
'decorrelate_statistics '
'decorrelate_trajectory '
'concatenate_trajectories '
'concatenate_statistics '
'build_reference_system '
'reduced_reference_potential '
'build_target_system '
'reduced_target_potential '
'mbar_protocol ')
BaseSimulationProtocols = namedtuple('BaseSimulationProtocols', 'build_coordinates '
'assign_parameters '
'energy_minimisation '
'equilibration_simulation '
'production_simulation '
'analysis_protocol '
'converge_uncertainty '
'extract_uncorrelated_trajectory '
'extract_uncorrelated_statistics ')
[docs]def generate_base_reweighting_protocols(analysis_protocol, mbar_protocol, workflow_options,
replicator_id='data_repl', id_suffix=''):
"""Constructs a set of protocols which, when combined in a workflow schema,
may be executed to reweight a set of existing data to estimate a particular
property. The reweighted observable of interest will be calculated by
following the passed in `analysis_protocol`.
Parameters
----------
analysis_protocol: AveragePropertyProtocol
The protocol which will take input from the stored data,
and generate a set of observables to reweight.
mbar_protocol: BaseReweightingProtocol
A template mbar reweighting protocol, which has it's reference
observables already set. This method will automatically set the
reduced potentials on this object.
workflow_options: WorkflowOptions
The options being used to generate a workflow.
replicator_id: str
The id to use for the data replicator.
id_suffix: str
A string suffix to append to each of the protocol ids.
Returns
-------
BaseReweightingProtocols:
A named tuple of the protocol which should form the bulk of
a property estimation workflow.
ProtocolReplicator:
A replicator which will clone the workflow for each piece of
stored data.
"""
assert isinstance(analysis_protocol, analysis.AveragePropertyProtocol)
assert f'$({replicator_id})' in analysis_protocol.id
assert f'$({replicator_id})' not in mbar_protocol.id
replicator_suffix = '_$({}){}'.format(replicator_id, id_suffix)
# Unpack all the of the stored data.
unpack_stored_data = storage.UnpackStoredSimulationData('unpack_data{}'.format(replicator_suffix))
unpack_stored_data.simulation_data_path = ReplicatorValue(replicator_id)
# The autocorrelation time of each of the stored files will be calculated for this property
# using the passed in analysis protocol.
if isinstance(analysis_protocol, analysis.ExtractAverageStatistic):
analysis_protocol.statistics_path = ProtocolPath('statistics_file_path', unpack_stored_data.id)
elif isinstance(analysis_protocol, analysis.AverageTrajectoryProperty):
analysis_protocol.input_coordinate_file = ProtocolPath('coordinate_file_path', unpack_stored_data.id)
analysis_protocol.trajectory_path = ProtocolPath('trajectory_file_path', unpack_stored_data.id)
# Decorrelate the frames of the stored trajectory and statistics arrays.
decorrelate_statistics = analysis.ExtractUncorrelatedStatisticsData('decorrelate_stats{}'.format(replicator_suffix))
decorrelate_statistics.statistical_inefficiency = ProtocolPath('statistical_inefficiency', analysis_protocol.id)
decorrelate_statistics.equilibration_index = ProtocolPath('equilibration_index', analysis_protocol.id)
decorrelate_statistics.input_statistics_path = ProtocolPath('statistics_file_path', unpack_stored_data.id)
decorrelate_trajectory = analysis.ExtractUncorrelatedTrajectoryData('decorrelate_traj{}'.format(replicator_suffix))
decorrelate_trajectory.statistical_inefficiency = ProtocolPath('statistical_inefficiency', analysis_protocol.id)
decorrelate_trajectory.equilibration_index = ProtocolPath('equilibration_index', analysis_protocol.id)
decorrelate_trajectory.input_coordinate_file = ProtocolPath('coordinate_file_path', unpack_stored_data.id)
decorrelate_trajectory.input_trajectory_path = ProtocolPath('trajectory_file_path', unpack_stored_data.id)
# Stitch together all of the trajectories
join_trajectories = reweighting.ConcatenateTrajectories('concat_traj' + id_suffix)
join_trajectories.input_coordinate_paths = ProtocolPath('coordinate_file_path', unpack_stored_data.id)
join_trajectories.input_trajectory_paths = ProtocolPath('output_trajectory_path', decorrelate_trajectory.id)
join_statistics = reweighting.ConcatenateStatistics('concat_stats' + id_suffix)
join_statistics.input_statistics_paths = ProtocolPath('output_statistics_path', decorrelate_statistics.id)
# Calculate the reduced potentials for each of the reference states.
build_reference_system = forcefield.BuildSmirnoffSystem('build_system{}'.format(replicator_suffix))
build_reference_system.force_field_path = ProtocolPath('force_field_path', unpack_stored_data.id)
build_reference_system.substance = ProtocolPath('substance', unpack_stored_data.id)
build_reference_system.coordinate_file_path = ProtocolPath('coordinate_file_path', unpack_stored_data.id)
reduced_reference_potential = reweighting.CalculateReducedPotentialOpenMM('reduced_potential{}'.format(
replicator_suffix))
reduced_reference_potential.system_path = ProtocolPath('system_path', build_reference_system.id)
reduced_reference_potential.thermodynamic_state = ProtocolPath('thermodynamic_state', unpack_stored_data.id)
reduced_reference_potential.coordinate_file_path = ProtocolPath('coordinate_file_path', unpack_stored_data.id)
reduced_reference_potential.trajectory_file_path = ProtocolPath('output_trajectory_path', join_trajectories.id)
reduced_reference_potential.kinetic_energies_path = ProtocolPath('output_statistics_path', join_statistics.id)
# Calculate the reduced potential of the target state.
build_target_system = forcefield.BuildSmirnoffSystem('build_system_target' + id_suffix)
build_target_system.force_field_path = ProtocolPath('force_field_path', 'global')
build_target_system.substance = ProtocolPath('substance', 'global')
build_target_system.coordinate_file_path = ProtocolPath('output_coordinate_path', join_trajectories.id)
reduced_target_potential = reweighting.CalculateReducedPotentialOpenMM('reduced_potential_target' + id_suffix)
reduced_target_potential.thermodynamic_state = ProtocolPath('thermodynamic_state', 'global')
reduced_target_potential.system_path = ProtocolPath('system_path', build_target_system.id)
reduced_target_potential.coordinate_file_path = ProtocolPath('output_coordinate_path', join_trajectories.id)
reduced_target_potential.trajectory_file_path = ProtocolPath('output_trajectory_path', join_trajectories.id)
reduced_target_potential.kinetic_energies_path = ProtocolPath('output_statistics_path', join_statistics.id)
# Finally, apply MBAR to get the reweighted value.
mbar_protocol.reference_reduced_potentials = ProtocolPath('statistics_file_path', reduced_reference_potential.id)
mbar_protocol.target_reduced_potentials = [ProtocolPath('statistics_file_path', reduced_target_potential.id)]
if (isinstance(mbar_protocol, reweighting.ReweightStatistics) and
mbar_protocol.statistics_type != ObservableType.PotentialEnergy and
mbar_protocol.statistics_type != ObservableType.TotalEnergy and
mbar_protocol.statistics_type != ObservableType.Enthalpy and
mbar_protocol.statistics_type != ObservableType.ReducedPotential):
mbar_protocol.statistics_paths = ProtocolPath('output_statistics_path', decorrelate_statistics.id)
elif isinstance(mbar_protocol, reweighting.ReweightStatistics):
mbar_protocol.statistics_paths = [ProtocolPath('statistics_file_path', reduced_target_potential.id)]
mbar_protocol.frame_counts = ProtocolPath('number_of_uncorrelated_samples', decorrelate_statistics.id)
# TODO: Implement a cleaner way to handle this.
if workflow_options.convergence_mode == WorkflowOptions.ConvergenceMode.NoChecks:
mbar_protocol.required_effective_samples = -1
base_protocols = BaseReweightingProtocols(unpack_stored_data,
analysis_protocol,
decorrelate_statistics,
decorrelate_trajectory,
join_trajectories,
join_statistics,
build_reference_system,
reduced_reference_potential,
build_target_system,
reduced_target_potential,
mbar_protocol)
# Create the replicator object.
component_replicator = ProtocolReplicator(replicator_id=replicator_id)
component_replicator.template_values = ProtocolPath('full_system_data', 'global')
return base_protocols, component_replicator
[docs]def generate_base_simulation_protocols(analysis_protocol, workflow_options, id_suffix='',
conditional_group=None):
"""Constructs a set of protocols which, when combined in a workflow schema,
may be executed to run a single simulation to estimate a particular
property. The observable of interest to extract from the simulation is determined
by the passed in `analysis_protocol`.
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: AveragePropertyProtocol
The protocol which will extract the observable of
interest from the generated simulation data.
workflow_options: WorkflowOptions
The options being used to generate a workflow.
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.
Returns
-------
BaseSimulationProtocols
A named tuple of the generated protocols.
ProtocolPath
A reference to the final value of the estimated observable
and its uncertainty (an `EstimatedQuantity`).
WorkflowSimulationDataToStore
An object which describes the default data from a simulation to store,
such as the uncorrelated statistics and configurations.
"""
assert isinstance(analysis_protocol, analysis.AveragePropertyProtocol)
build_coordinates = coordinates.BuildCoordinatesPackmol(f'build_coordinates{id_suffix}')
build_coordinates.substance = ProtocolPath('substance', 'global')
build_coordinates.max_molecules = 1000
assign_parameters = forcefield.BuildSmirnoffSystem(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 = simulation.RunEnergyMinimisation(f'energy_minimisation{id_suffix}')
energy_minimisation.input_coordinate_file = ProtocolPath('coordinate_file_path', build_coordinates.id)
energy_minimisation.system_path = ProtocolPath('system_path', assign_parameters.id)
equilibration_simulation = simulation.RunOpenMMSimulation(f'equilibration_simulation{id_suffix}')
equilibration_simulation.ensemble = Ensemble.NPT
equilibration_simulation.steps = 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.system_path = ProtocolPath('system_path', assign_parameters.id)
# Production
production_simulation = simulation.RunOpenMMSimulation(f'production_simulation{id_suffix}')
production_simulation.ensemble = Ensemble.NPT
production_simulation.steps = 1000000
production_simulation.output_frequency = 3000
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.system_path = ProtocolPath('system_path', assign_parameters.id)
# 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 workflow_options.convergence_mode != WorkflowOptions.ConvergenceMode.NoChecks:
condition = groups.ConditionalGroup.Condition()
condition.left_hand_value = ProtocolPath('value.uncertainty',
conditional_group.id,
analysis_protocol.id)
condition.right_hand_value = ProtocolPath('target_uncertainty', 'global')
condition.condition_type = groups.ConditionalGroup.ConditionType.LessThan
conditional_group.add_condition(condition)
conditional_group.add_protocols(production_simulation, analysis_protocol)
# Point the analyse protocol to the correct data source
if isinstance(analysis_protocol, analysis.AverageTrajectoryProperty):
analysis_protocol.input_coordinate_file = ProtocolPath('coordinate_file_path', build_coordinates.id)
analysis_protocol.trajectory_path = ProtocolPath('trajectory_file_path', production_simulation.id)
elif isinstance(analysis_protocol, analysis.ExtractAverageStatistic):
analysis_protocol.statistics_path = ProtocolPath('statistics_file_path', production_simulation.id)
else:
raise ValueError('The analysis protocol must inherit from either the '
'AverageTrajectoryProperty or ExtractAverageStatistic '
'protocols.')
# Finally, extract uncorrelated data
statistical_inefficiency = ProtocolPath('statistical_inefficiency', conditional_group.id, analysis_protocol.id)
equilibration_index = ProtocolPath('equilibration_index', 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)
statistics_path = ProtocolPath('statistics_file_path', conditional_group.id, production_simulation.id)
extract_uncorrelated_trajectory = analysis.ExtractUncorrelatedTrajectoryData(f'extract_traj{id_suffix}')
extract_uncorrelated_trajectory.statistical_inefficiency = statistical_inefficiency
extract_uncorrelated_trajectory.equilibration_index = equilibration_index
extract_uncorrelated_trajectory.input_coordinate_file = coordinate_file
extract_uncorrelated_trajectory.input_trajectory_path = trajectory_path
extract_uncorrelated_statistics = analysis.ExtractUncorrelatedStatisticsData(f'extract_stats{id_suffix}')
extract_uncorrelated_statistics.statistical_inefficiency = statistical_inefficiency
extract_uncorrelated_statistics.equilibration_index = equilibration_index
extract_uncorrelated_statistics.input_statistics_path = statistics_path
# Build the object which defines which pieces of simulation data to store.
output_to_store = WorkflowSimulationDataToStore()
output_to_store.total_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 = statistical_inefficiency
output_to_store.statistics_file_path = ProtocolPath('output_statistics_path', extract_uncorrelated_statistics.id)
output_to_store.trajectory_file_path = ProtocolPath('output_trajectory_path', extract_uncorrelated_trajectory.id)
output_to_store.coordinate_file_path = coordinate_file
# Define where the final values come from.
final_value_source = ProtocolPath('value', conditional_group.id, analysis_protocol.id)
base_protocols = BaseSimulationProtocols(build_coordinates,
assign_parameters,
energy_minimisation,
equilibration_simulation,
production_simulation,
analysis_protocol,
conditional_group,
extract_uncorrelated_trajectory,
extract_uncorrelated_statistics)
return base_protocols, final_value_source, output_to_store
[docs]def generate_gradient_protocol_group(template_reweighting_protocol,
reference_force_field_paths,
target_force_field_path,
coordinate_file_path,
trajectory_file_path,
statistics_file_path='',
replicator_id='repl',
perturbation_scale=1.0e-4,
substance_source=None,
id_suffix='',
enable_pbc=True,
use_subset_of_force_field=True,
effective_sample_indices=None):
"""Constructs a set of protocols which, when combined in a workflow schema,
may be executed to reweight a set of existing data to estimate a particular
property. The reweighted observable of interest will be calculated by
following the passed in `analysis_protocol`.
Parameters
----------
template_reweighting_protocol: BaseMBARProtocol
A template protocol which will be used to reweight the observable of
interest to small perturbations to the parameter of interest. These
will then be used to calculate the finite difference gradient. This
utility takes care of setting the target and reference reduced potentials.
In the case that the template is of type `ReweightStatistics` and the
observable is an energy, the statistics path will automatically be pointed
to the energies evaluated using the perturbed parameter as opposed to the
energy measured during the reference simulation.
reference_force_field_paths: ProtocolPath or list of ProtocolPath
The paths to the force field parameters which were used to generate
the trajectories from which the observables of interest were calculated.
target_force_field_path: ProtocolPath
The path to the force field parameters which the observables are being
estimated at (this is mainly only useful when estimating the gradients
of reweighted observables).
coordinate_file_path: ProtocolPath
A path to the initial coordinates of the simulation trajectory which
was used to estimate the observable of interest.
trajectory_file_path: ProtocolPath
A path to the simulation trajectory which was used
to estimate the observable of interest.
statistics_file_path: ProtocolPath, optional
A path to the statistics where were generated from
the trajectory passed to the `trajectory_file_path`
parameter. This is optional in cases where multiple
reference force fields are passed to this method.
replicator_id: str
A unique id which will be used for the protocol replicator which will
replicate this group for every parameter of interest.
perturbation_scale: float
The default amount to perturb parameters by.
substance_source: PlaceholderInput, optional
An optional protocol path to the substance whose gradient
is being estimated. If None, the global property substance
is used.
id_suffix: str
An optional string to append to the end of each of the
protocol ids.
enable_pbc: bool
If true, periodic boundary conditions are employed when recalculating
the reduced potentials.
use_subset_of_force_field: bool
If True, any reduced potentials will only be calculated from a subset
of the force field which depends on the parameter of interest.
effective_sample_indices: ProtocolPath, optional
A placeholder variable which can be used to make the gradient protocols
dependant on an MBAR protcol to ensure gradients aren't calcuated when
the MBAR protocol failed due to insufficient samples.
Returns
-------
ProtocolGroup
The protocol group which will estimate the gradient of
an observable with respect to one parameter.
ProtocolReplicator
The replicator which will copy the gradient group for
every parameter of interest.
ProtocolPath
A protocol path which points to the final gradient value.
"""
assert isinstance(template_reweighting_protocol, reweighting.BaseMBARProtocol)
id_suffix = f'_$({replicator_id}){id_suffix}'
# Set values of the optional parameters.
substance_source = ProtocolPath('substance', 'global') if substance_source is None else substance_source
effective_sample_indices = effective_sample_indices if effective_sample_indices is not None else []
# Define the protocol which will evaluate the reduced potentials of the
# reference, forward and reverse states using only a subset of the full
# force field.
reduced_potentials = gradients.GradientReducedPotentials(f'gradient_reduced_potentials{id_suffix}')
reduced_potentials.substance = substance_source
reduced_potentials.thermodynamic_state = ProtocolPath('thermodynamic_state', 'global')
reduced_potentials.reference_force_field_paths = reference_force_field_paths
reduced_potentials.reference_statistics_path = statistics_file_path
reduced_potentials.force_field_path = target_force_field_path
reduced_potentials.trajectory_file_path = trajectory_file_path
reduced_potentials.coordinate_file_path = coordinate_file_path
reduced_potentials.parameter_key = ReplicatorValue(replicator_id)
reduced_potentials.perturbation_scale = perturbation_scale
reduced_potentials.use_subset_of_force_field = use_subset_of_force_field
reduced_potentials.enable_pbc = enable_pbc
reduced_potentials.effective_sample_indices = effective_sample_indices
# Set up the protocols which will actually reweight the value of the
# observable to the forward and reverse states.
template_reweighting_protocol.bootstrap_iterations = 1
template_reweighting_protocol.required_effective_samples = 0
template_reweighting_protocol.reference_reduced_potentials = ProtocolPath('reference_potential_paths',
reduced_potentials.id)
# We need to make sure we use the observable evaluated at the target state
# if the observable depends on the parameter being reweighted.
use_target_state_energies = (isinstance(template_reweighting_protocol, reweighting.ReweightStatistics) and
(template_reweighting_protocol.statistics_type == ObservableType.PotentialEnergy or
template_reweighting_protocol.statistics_type == ObservableType.ReducedPotential or
template_reweighting_protocol.statistics_type == ObservableType.TotalEnergy or
template_reweighting_protocol.statistics_type == ObservableType.Enthalpy))
template_reweighting_schema = template_reweighting_protocol.schema
# Create the reweighting protocols from the template schema.
reverse_mbar_schema = copy.deepcopy(template_reweighting_schema)
reverse_mbar_schema.id = f'reverse_reweight{id_suffix}'
reverse_mbar = available_protocols[reverse_mbar_schema.type](reverse_mbar_schema.id)
reverse_mbar.schema = reverse_mbar_schema
reverse_mbar.target_reduced_potentials = [ProtocolPath('reverse_potentials_path', reduced_potentials.id)]
forward_mbar_schema = copy.deepcopy(template_reweighting_schema)
forward_mbar_schema.id = f'forward_reweight{id_suffix}'
forward_mbar = available_protocols[forward_mbar_schema.type](forward_mbar_schema.id)
forward_mbar.schema = forward_mbar_schema
forward_mbar.target_reduced_potentials = [ProtocolPath('forward_potentials_path', reduced_potentials.id)]
if use_target_state_energies:
reverse_mbar.statistics_paths = [ProtocolPath('reverse_potentials_path', reduced_potentials.id)]
forward_mbar.statistics_paths = [ProtocolPath('forward_potentials_path', reduced_potentials.id)]
# Set up the protocol which will actually evaluate the parameter gradient
# using the central difference method.
central_difference = gradients.CentralDifferenceGradient(f'central_difference{id_suffix}')
central_difference.parameter_key = ReplicatorValue(replicator_id)
central_difference.reverse_observable_value = ProtocolPath('value', reverse_mbar.id)
central_difference.forward_observable_value = ProtocolPath('value', forward_mbar.id)
central_difference.reverse_parameter_value = ProtocolPath('reverse_parameter_value', reduced_potentials.id)
central_difference.forward_parameter_value = ProtocolPath('forward_parameter_value', reduced_potentials.id)
# Assemble all of the protocols into a convenient group wrapper.
gradient_group = groups.ProtocolGroup(f'gradient_group{id_suffix}')
gradient_group.add_protocols(reduced_potentials, reverse_mbar, forward_mbar, central_difference)
# Create the replicator which will copy the group for each parameter gradient
# which will be calculated.
parameter_replicator = ProtocolReplicator(replicator_id=replicator_id)
parameter_replicator.template_values = ProtocolPath('parameter_gradient_keys', 'global')
return gradient_group, parameter_replicator, ProtocolPath('gradient', gradient_group.id, central_difference.id)