Source code for propertyestimator.workflow.workflow

"""
Defines the core workflow object and execution graph.
"""
import copy
import json
import math
import uuid
from math import sqrt
from os import makedirs, path
from shutil import copy as file_copy

import pint

from propertyestimator.attributes import UNDEFINED, Attribute, AttributeClass
from propertyestimator.backends import ComputeResources
from propertyestimator.forcefield import (
    ForceFieldSource,
    ParameterGradient,
    SmirnoffForceFieldSource,
)
from propertyestimator.storage.attributes import FilePath, StorageAttribute
from propertyestimator.substances import Substance
from propertyestimator.utils.exceptions import EvaluatorException
from propertyestimator.utils.serialization import TypedJSONDecoder, TypedJSONEncoder
from propertyestimator.utils.utils import get_nested_attribute
from propertyestimator.workflow.protocols import Protocol, ProtocolGraph
from propertyestimator.workflow.schemas import ProtocolReplicator, WorkflowSchema
from propertyestimator.workflow.utils import ProtocolPath, ReplicatorValue


[docs]class Workflow: """Encapsulates and prepares a workflow which is able to estimate a physical property. """ @property def protocols(self): """tuple of Protocol: The protocols in this workflow.""" return {x.id: x for x in self._protocols} @property def final_value_source(self): """ProtocolPath: The path to the protocol output which corresponds to the estimated value of the property being estimated. """ return self._final_value_source @property def gradients_sources(self): """list of ProtocolPath: A list of paths to the protocol outputs which correspond to the gradients of the estimated property with respect to specified force field parameters. """ return self._gradients_sources @property def outputs_to_store(self): """dict of str and StorageBackend: A collection of data classes to populate ready to be stored by a `StorageBackend`. """ return self._outputs_to_store @property def schema(self): return self._get_schema() @schema.setter def schema(self, value): self._set_schema(value)
[docs] def __init__(self, global_metadata, unique_id=None): """ Constructs a new Workflow object. Parameters ---------- global_metadata: dict of str and Any A dictionary of the metadata which will be made available to each of the workflow protocols through the pseudo "global" scope. unique_id: str, optional A unique identifier to assign to this workflow. This id will be appended to the ids of the protocols of this workflow. If none is provided, one will be chosen at random. """ assert global_metadata is not None self._global_metadata = global_metadata if unique_id is None: unique_id = str(uuid.uuid4()).replace("-", "") self.uuid = unique_id self._protocols = [] self._final_value_source = UNDEFINED self._gradients_sources = [] self._outputs_to_store = {}
def _get_schema(self): """Returns the schema that describes this workflow. Returns ------- WorkflowSchema The schema that describes this workflow. """ schema = WorkflowSchema() schema.id = self.uuid schema.protocol_schemas = [copy.deepcopy(x.schema) for x in self._protocols] if self._final_value_source != UNDEFINED: schema.final_value_source = self._final_value_source.copy() schema.gradients_sources = [source.copy() for source in self._gradients_sources] schema.outputs_to_store = copy.deepcopy(self._outputs_to_store) return schema def _set_schema(self, schema): """Sets this workflow's properties from a `WorkflowSchema`. Parameters ---------- schema: WorkflowSchema The schema which outlines this steps in this workflow. """ # Copy the schema. schema = WorkflowSchema.parse_json(schema.json()) if schema.final_value_source != UNDEFINED: self._final_value_source = schema.final_value_source self._final_value_source.append_uuid(self.uuid) self._build_protocols(schema) self._gradients_sources = [] if schema.gradients_sources != UNDEFINED: for gradient_source in schema.gradients_sources: gradient_source.append_uuid(self.uuid) self._gradients_sources.append(gradient_source) self._outputs_to_store = {} if schema.outputs_to_store != UNDEFINED: for label in schema.outputs_to_store: self._append_uuid_to_output_to_store(schema.outputs_to_store[label]) self._outputs_to_store[label] = self._build_output_to_store( schema.outputs_to_store[label] ) def _append_uuid_to_output_to_store(self, output_to_store): """Appends this workflows uuid to all of the protocol paths within an output to store, and all of its child outputs. Parameters ---------- output_to_store: BaseStoredData The output to store to append the uuid to. """ for attribute_name in output_to_store.get_attributes(StorageAttribute): attribute_value = getattr(output_to_store, attribute_name) if not isinstance(attribute_value, ProtocolPath): continue attribute_value.append_uuid(self.uuid) def _build_output_to_store(self, output_to_store): """Sets the inputs of a `BaseStoredData` object which are taken from the global metadata. Parameters ---------- output_to_store: BaseStoredData The output to set the inputs of. Returns ------- BaseStoredData The built object with all of its inputs correctly set. """ for attribute_name in output_to_store.get_attributes(StorageAttribute): attribute_value = getattr(output_to_store, attribute_name) if ( not isinstance(attribute_value, ProtocolPath) or not attribute_value.is_global ): continue attribute_value = get_nested_attribute( self._global_metadata, attribute_value.property_name ) setattr(output_to_store, attribute_name, attribute_value) return output_to_store def _build_protocols(self, schema): """Creates a set of protocols based on a WorkflowSchema. Parameters ---------- schema: WorkflowSchema The schema to use when creating the protocols """ self._apply_replicators(schema) for protocol_schema in schema.protocol_schemas: protocol = protocol_schema.to_protocol() # Try to set global properties on each of the protocols for input_path in protocol.required_inputs: value_references = protocol.get_value_references(input_path) for source_path, value_reference in value_references.items(): if not value_reference.is_global: continue value = get_nested_attribute( self._global_metadata, value_reference.property_name ) protocol.set_value(source_path, value) protocol.set_uuid(self.uuid) self._protocols.append(protocol) def _get_template_values(self, replicator): """Returns the values which which will be passed to the replicated protocols, evaluating any protocol paths to retrieve the referenced values. Parameters ---------- replicator: ProtocolReplicator The replictor which is replicating the protocols. Returns ------- Any The template values. """ invalid_value_error = ValueError( f"Template values must either be a constant or come " f"from the global scope (and not from {replicator.template_values})" ) # Get the list of values which will be passed to the newly created protocols. if isinstance(replicator.template_values, ProtocolPath): if not replicator.template_values.is_global: raise invalid_value_error return get_nested_attribute( self._global_metadata, replicator.template_values.property_name ) elif not isinstance(replicator.template_values, list): raise NotImplementedError() evaluated_template_values = [] for template_value in replicator.template_values: if not isinstance(template_value, ProtocolPath): evaluated_template_values.append(template_value) continue if not template_value.is_global: raise invalid_value_error evaluated_template_values.append( get_nested_attribute( self._global_metadata, template_value.property_name ) ) return evaluated_template_values def _apply_replicators(self, schema): """Applies each of the protocol replicators in turn to the schema. Parameters ---------- schema: WorkflowSchema The schema to apply the replicators to. """ if schema.protocol_replicators == UNDEFINED: return while len(schema.protocol_replicators) > 0: replicator = schema.protocol_replicators.pop(0) # Apply this replicator self._apply_replicator(schema, replicator) if schema.json().find(replicator.placeholder_id) >= 0: raise RuntimeError( f"The {replicator.id} replicator was not fully applied." ) def _apply_replicator(self, schema, replicator): """A method to create a set of protocol schemas based on a ProtocolReplicator, and add them to the list of existing schemas. Parameters ---------- schema: WorkflowSchema The schema which contains the protocol definitions replicator: `ProtocolReplicator` The replicator which describes which new protocols should be created. """ # Get the list of values which will be passed to the newly created protocols. template_values = self._get_template_values(replicator) # Replicate the protocols. protocols = {} for protocol_schema in schema.protocol_schemas: protocol = protocol_schema.to_protocol() protocols[protocol.id] = protocol replicated_protocols, replication_map = replicator.apply( protocols, template_values ) replicator.update_references( replicated_protocols, replication_map, template_values ) # Update the schema with the replicated protocols. schema.protocol_schemas = [ replicated_protocols[key].schema for key in replicated_protocols ] # Make sure to correctly replicate gradient sources. replicated_gradient_sources = [] if schema.gradients_sources != UNDEFINED: for gradient_source in schema.gradients_sources: if replicator.placeholder_id not in gradient_source.full_path: replicated_gradient_sources.append(gradient_source) continue for index, template_value in enumerate(template_values): replicated_source = ProtocolPath.from_string( gradient_source.full_path.replace( replicator.placeholder_id, str(index) ) ) replicated_gradient_sources.append(replicated_source) schema.gradients_sources = replicated_gradient_sources # Replicate any outputs. self._apply_replicator_to_outputs(replicator, schema, template_values) # Replicate any replicators. self._apply_replicator_to_replicators(replicator, schema, template_values) @staticmethod def _apply_replicator_to_outputs(replicator, schema, template_values): """Applies a replicator to a schema outputs to store. Parameters ---------- replicator: ProtocolReplicator The replicator to apply. schema: WorkflowSchema The schema which defines the outputs to store. template_values: List of Any The values being applied by the replicator. """ outputs_to_replicate = [] if schema.outputs_to_store != UNDEFINED: outputs_to_replicate = [ label for label in schema.outputs_to_store if label.find(replicator.id) >= 0 ] # Check to see if there are any outputs to store pointing to # protocols which are being replicated. for output_label in outputs_to_replicate: output_to_replicate = schema.outputs_to_store.pop(output_label) for index, template_value in enumerate(template_values): replicated_label = output_label.replace( replicator.placeholder_id, str(index) ) replicated_output = copy.deepcopy(output_to_replicate) for attribute_name in replicated_output.get_attributes( StorageAttribute ): attribute_value = getattr(replicated_output, attribute_name) if isinstance(attribute_value, ProtocolPath): attribute_value = ProtocolPath.from_string( attribute_value.full_path.replace( replicator.placeholder_id, str(index) ) ) elif isinstance(attribute_value, ReplicatorValue): if attribute_value.replicator_id != replicator.id: # Make sure to handle nested dependent replicators. attribute_value.replicator_id = attribute_value.replicator_id.replace( replicator.placeholder_id, str(index) ) continue attribute_value = template_value setattr(replicated_output, attribute_name, attribute_value) schema.outputs_to_store[replicated_label] = replicated_output @staticmethod def _apply_replicator_to_replicators(replicator, schema, template_values): """Applies a replicator to any replicators which depend upon it (e.g. replicators with ids similar to `other_id_$(replicator.id)`). Parameters ---------- replicator: ProtocolReplicator The replicator being applied. schema: WorkflowSchema The workflow schema to which the replicator belongs. template_values: List of Any The values which the replicator is applying. """ # Look over all of the replicators left to apply and update them # to point to the newly replicated protocols where appropriate. new_indices = [str(index) for index in range(len(template_values))] replicators = [] for original_replicator in schema.protocol_replicators: # Check whether this replicator will be replicated. if replicator.placeholder_id not in original_replicator.id: replicators.append(original_replicator) continue # Create the replicated replicators for template_index in new_indices: replicator_id = original_replicator.id.replace( replicator.placeholder_id, template_index ) new_replicator = ProtocolReplicator(replicator_id) new_replicator.template_values = original_replicator.template_values # Make sure to replace any reference to the applied replicator # with the actual index. if isinstance(new_replicator.template_values, ProtocolPath): updated_path = new_replicator.template_values.full_path.replace( replicator.placeholder_id, template_index ) new_replicator.template_values = ProtocolPath.from_string( updated_path ) elif isinstance(new_replicator.template_values, list): updated_values = [] for template_value in new_replicator.template_values: if not isinstance(template_value, ProtocolPath): updated_values.append(template_value) continue updated_path = template_value.full_path.replace( replicator.placeholder_id, template_index ) updated_values.append(ProtocolPath.from_string(updated_path)) new_replicator.template_values = updated_values replicators.append(new_replicator) schema.protocol_replicators = replicators
[docs] def replace_protocol(self, old_protocol, new_protocol): """Replaces an existing protocol with a new one, while updating all input and local references to point to the new protocol. The main use of this method is when merging multiple protocols into one. Parameters ---------- old_protocol : Protocol or ProtocolPath The protocol (or its id) to replace. new_protocol : Protocol or ProtocolPath The new protocol (or its id) to use. """ new_id = ( new_protocol if not isinstance(new_protocol, Protocol) else new_protocol.id ) if new_id in [x.id for x in self._protocols]: raise ValueError( "A protocol with the same id already exists in this workflow." ) if isinstance(old_protocol, Protocol): self._protocols.remove(old_protocol) old_protocol = old_protocol.id if isinstance(new_protocol, Protocol): self._protocols.append(new_protocol) new_protocol = new_protocol.id for protocol in self._protocols: protocol.replace_protocol(old_protocol, new_protocol) if self._final_value_source != UNDEFINED: self._final_value_source.replace_protocol(old_protocol, new_protocol) for gradient_source in self._gradients_sources: gradient_source.replace_protocol(old_protocol, new_protocol) for output_label in self._outputs_to_store: output_to_store = self._outputs_to_store[output_label] for attribute_name in output_to_store.get_attributes(StorageAttribute): attribute_value = getattr(output_to_store, attribute_name) if not isinstance(attribute_value, ProtocolPath): continue attribute_value.replace_protocol(old_protocol, new_protocol)
@staticmethod def _find_relevant_gradient_keys( substance, force_field_path, parameter_gradient_keys ): """Extract only those keys which may be applied to the given substance. Parameters ---------- substance: Substance The substance to compare against. force_field_path: str The path to the force field which contains the parameters. parameter_gradient_keys: list of ParameterGradientKey The original list of parameter gradient keys. Returns ------- list of ParameterGradientKey The filtered list of parameter gradient keys. """ from openforcefield.topology import Molecule, Topology # noinspection PyTypeChecker if parameter_gradient_keys == UNDEFINED or len(parameter_gradient_keys) == 0: return [] with open(force_field_path) as file: force_field_source = ForceFieldSource.parse_json(file.read()) if not isinstance(force_field_source, SmirnoffForceFieldSource): return [] force_field = force_field_source.to_force_field() all_molecules = [] for component in substance.components: all_molecules.append(Molecule.from_smiles(component.smiles)) topology = Topology.from_molecules(all_molecules) labelled_molecules = force_field.label_molecules(topology) reduced_parameter_keys = [] for labelled_molecule in labelled_molecules: for parameter_key in parameter_gradient_keys: if ( parameter_key.tag not in labelled_molecule or parameter_key in reduced_parameter_keys ): continue contains_parameter = False for parameter in labelled_molecule[parameter_key.tag].store.values(): if parameter.smirks != parameter_key.smirks: continue contains_parameter = True break if not contains_parameter: continue reduced_parameter_keys.append(parameter_key) return reduced_parameter_keys
[docs] @staticmethod 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
[docs] def to_graph(self): """Converts this workflow to an executable `WorkflowGraph`. Returns ------- WorkflowGraph The graph representation of this workflow. """ graph = WorkflowGraph() graph.add_workflow(self) return graph
[docs] @classmethod def from_schema(cls, schema, metadata, unique_id=None): """Creates a workflow from its schema blueprint, and the associated metadata. Parameters ---------- schema: WorkflowSchema The schema blueprint for this workflow. metadata: dict of str and Any The metadata to make available to the workflow. unique_id: str, optional A unique identifier to assign to this workflow. This id will be appended to the ids of the protocols of this workflow. If none is provided one will be chosen at random. Returns ------- cls The created workflow. """ workflow = cls(metadata, unique_id) workflow.schema = schema return workflow
[docs] def execute( self, root_directory="", calculation_backend=None, compute_resources=None ): """Executes the workflow. Parameters ---------- root_directory: str The directory to execute the graph in. calculation_backend: CalculationBackend, optional. The backend to execute the graph on. This parameter is mutually exclusive with `compute_resources`. compute_resources: CalculationBackend, optional. The compute resources to run using. If None and no `calculation_backend` is specified, the workflow will be executed on a single CPU thread. This parameter is mutually exclusive with `calculation_backend`. Returns ------- WorkflowResult or Future of WorkflowResult: The result of executing this workflow. If executed on a `calculation_backend`, the result will be wrapped in a `Future` object. """ if calculation_backend is None and compute_resources is None: compute_resources = ComputeResources(number_of_threads=1) workflow_graph = self.to_graph() return workflow_graph.execute( root_directory, calculation_backend, compute_resources )[0]
[docs]class WorkflowResult(AttributeClass): """The result of executing a `Workflow` as part of a `WorkflowGraph`. """ workflow_id = Attribute( docstring="The id of the workflow associated with this result.", type_hint=str, ) value = Attribute( docstring="The estimated value of the property and the uncertainty " "in that value.", type_hint=pint.Measurement, optional=True, ) gradients = Attribute( docstring="The gradients of the estimated value with respect to the " "specified force field parameters.", type_hint=list, default_value=[], ) exceptions = Attribute( docstring="Any exceptions raised by the layer while estimating the " "property.", type_hint=list, default_value=[], ) data_to_store = Attribute( docstring="Paths to the data objects to store.", type_hint=list, default_value=[], )
[docs] def validate(self, attribute_type=None): super(WorkflowResult, self).validate(attribute_type) assert all(isinstance(x, ParameterGradient) for x in self.gradients) assert all(isinstance(x, tuple) for x in self.data_to_store) assert all(len(x) == 2 for x in self.data_to_store) assert all(all(isinstance(y, str) for y in x) for x in self.data_to_store) assert all(isinstance(x, EvaluatorException) for x in self.exceptions)
[docs]class WorkflowGraph: """A hierarchical structure for storing and submitting the workflows which will estimate a set of physical properties.. """ @property def protocols(self): """dict of str and Protocol: The protocols in this graph.""" return self._protocol_graph.protocols @property def root_protocols(self): """list of str: The ids of the protocols in the group which do not take input from the other grouped protocols.""" return self._protocol_graph.root_protocols @property def dependants_graph(self): """dict of str and str: A dictionary of which stores which grouped protocols are dependant on other grouped protocols. Each key in the dictionary is the id of a grouped protocol, and each value is the id of a protocol which depends on the protocol by the key.""" return self._protocol_graph.dependants_graph
[docs] def __init__(self): super(WorkflowGraph, self).__init__() self._workflows_to_execute = {} self._protocol_graph = ProtocolGraph()
[docs] def add_workflow(self, workflow): """Insert a workflow into the workflow graph. Parameters ---------- workflow : Workflow The workflow to insert. """ if workflow.uuid in self._workflows_to_execute: raise ValueError( f"A workflow with the uuid {workflow.uuid} is already in the graph." ) original_protocols = [*workflow.protocols.values()] self._workflows_to_execute[workflow.uuid] = workflow # Add the workflow protocols to the graph. merged_protocol_ids = self._protocol_graph.add_protocols( *original_protocols, allow_external_dependencies=False ) # Update the workflow to use the possibly merged protocols for original_id, new_id in merged_protocol_ids.items(): original_protocol = original_id new_protocol = new_id if original_protocol in workflow.protocols: # Only retrieve the actual protocol if it isn't nested in # a group. original_protocol = workflow.protocols[original_id] new_protocol = self._protocol_graph.protocols[new_id] workflow.replace_protocol(original_protocol, new_protocol)
[docs] def execute( self, root_directory="", calculation_backend=None, compute_resources=None ): """Executes the workflow graph. Parameters ---------- root_directory: str The directory to execute the graph in. calculation_backend: CalculationBackend, optional. The backend to execute the graph on. This parameter is mutually exclusive with `compute_resources`. compute_resources: CalculationBackend, optional. The compute resources to run using. If None and no `calculation_backend` is specified, the workflow will be executed on a single CPU thread. This parameter is mutually exclusive with `calculation_backend`. Returns ------- list of WorkflowResult or list of Future of WorkflowResult: The results of executing the graph. If a `calculation_backend` is specified, these results will be wrapped in a `Future`. """ if calculation_backend is None and compute_resources is None: compute_resources = ComputeResources(number_of_threads=1) protocol_outputs = self._protocol_graph.execute( root_directory, calculation_backend, compute_resources ) value_futures = [] for workflow_id in self._workflows_to_execute: workflow = self._workflows_to_execute[workflow_id] data_futures = [] # Make sure we keep track of all of the futures which we # will use to populate things such as a final property value # or gradient keys. if workflow.final_value_source != UNDEFINED: protocol_id = workflow.final_value_source.start_protocol data_futures.append(protocol_outputs[protocol_id]) if workflow.gradients_sources != UNDEFINED: for gradient_source in workflow.gradients_sources: protocol_id = gradient_source.start_protocol data_futures.append(protocol_outputs[protocol_id]) if workflow.outputs_to_store != UNDEFINED: for output_label, output_to_store in workflow.outputs_to_store.items(): for attribute_name in output_to_store.get_attributes( StorageAttribute ): attribute_value = getattr(output_to_store, attribute_name) if not isinstance(attribute_value, ProtocolPath): continue data_futures.append( protocol_outputs[attribute_value.start_protocol] ) if len(data_futures) == 0: data_futures = [*protocol_outputs.values()] if calculation_backend is None: value_futures.append( WorkflowGraph._gather_results( root_directory, workflow.uuid, workflow.final_value_source, workflow.gradients_sources, workflow.outputs_to_store, *data_futures, ) ) else: value_futures.append( calculation_backend.submit_task( WorkflowGraph._gather_results, root_directory, workflow.uuid, workflow.final_value_source, workflow.gradients_sources, workflow.outputs_to_store, *data_futures, ) ) return value_futures
@staticmethod def _gather_results( directory, workflow_id, value_reference, gradient_sources, outputs_to_store, *protocol_result_paths, **_, ): """Gather the data associated with the workflows in this graph. Parameters ---------- directory: str The directory to store any working files in. workflow_id: str The id of the workflow associated with this result. value_reference: ProtocolPath, optional A reference to which property in the output dictionary is the actual value. gradient_sources: list of ProtocolPath A list of references to those entries in the output dictionaries which correspond to parameter gradients. outputs_to_store: dict of str and WorkflowOutputToStore A list of references to data which should be stored on the storage backend. protocol_results: dict of str and str The result dictionary of the protocol which calculated the value of the property. Returns ------- CalculationLayerResult, optional The result of attempting to estimate this property from a workflow graph. `None` will be returned if the target uncertainty is set but not met. """ return_object = WorkflowResult() return_object.workflow_id = workflow_id try: results_by_id = {} for protocol_id, protocol_result_path in protocol_result_paths: with open(protocol_result_path, "r") as file: protocol_results = json.load(file, cls=TypedJSONDecoder) # Make sure none of the protocols failed and we actually have a value # and uncertainty. if isinstance(protocol_results, EvaluatorException): return_object.exceptions.append(protocol_results) return return_object # Store the protocol results in a dictionary, with keys of the # path to the original protocol output. for protocol_path, output_value in protocol_results.items(): protocol_path = ProtocolPath.from_string(protocol_path) if ( protocol_path.start_protocol is None or protocol_path.start_protocol != protocol_id ): protocol_path.prepend_protocol_id(protocol_id) results_by_id[protocol_path] = output_value if value_reference is not None: return_object.value = results_by_id[value_reference] for gradient_source in gradient_sources: return_object.gradients.append(results_by_id[gradient_source]) return_object.data_to_store = [] for output_to_store in outputs_to_store.values(): unique_id = str(uuid.uuid4()).replace("-", "") data_object_path = path.join(directory, f"data_{unique_id}.json") data_directory = path.join(directory, f"data_{unique_id}") WorkflowGraph._store_output_data( data_object_path, data_directory, output_to_store, results_by_id, ) return_object.data_to_store.append((data_object_path, data_directory)) except Exception as e: return_object.exceptions.append(EvaluatorException.from_exception(e)) return return_object @staticmethod def _store_output_data( data_object_path, data_directory, output_to_store, results_by_id, ): """Collects all of the simulation to store, and saves it into a directory whose path will be passed to the storage backend to process. Parameters ---------- data_object_path: str The file path to serialize the data object to. data_directory: str The path of the directory to store ancillary data in. output_to_store: BaseStoredData An object which contains `ProtocolPath`s pointing to the data to store. results_by_id: dict of ProtocolPath and any The results of the protocols which formed the property estimation workflow. """ makedirs(data_directory, exist_ok=True) for attribute_name in output_to_store.get_attributes(StorageAttribute): attribute = getattr(output_to_store.__class__, attribute_name) attribute_value = getattr(output_to_store, attribute_name) if not isinstance(attribute_value, ProtocolPath): continue attribute_value = results_by_id[attribute_value] if issubclass(attribute.type_hint, FilePath): file_copy(attribute_value, data_directory) attribute_value = path.basename(attribute_value) setattr(output_to_store, attribute_name, attribute_value) with open(data_object_path, "w") as file: json.dump(output_to_store, file, cls=TypedJSONEncoder)