"""
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 openff.evaluator.attributes import UNDEFINED, Attribute, AttributeClass
from openff.evaluator.backends import ComputeResources
from openff.evaluator.forcefield import (
ForceFieldSource,
ParameterGradient,
SmirnoffForceFieldSource,
)
from openff.evaluator.storage.attributes import FilePath, StorageAttribute
from openff.evaluator.substances import Substance
from openff.evaluator.utils.exceptions import EvaluatorException
from openff.evaluator.utils.graph import retrieve_uuid
from openff.evaluator.utils.serialization import TypedJSONDecoder, TypedJSONEncoder
from openff.evaluator.utils.utils import get_nested_attribute
from openff.evaluator.workflow import Protocol, ProtocolGraph
from openff.evaluator.workflow.schemas import ProtocolReplicator, WorkflowSchema
from openff.evaluator.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, update_paths_only=False):
"""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.
update_paths_only: bool
Whether only update the `final_value_source`, `gradients_sources`
and `outputs_to_store` attributes, or to also update all of the
protocols in `protocols`.
"""
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
if not update_paths_only:
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] def to_graph(self):
"""Converts this workflow to an executable `WorkflowGraph`.
Returns
-------
WorkflowGraph
The graph representation of this workflow.
"""
graph = WorkflowGraph()
graph.add_workflows(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
[docs] def __init__(self):
super(WorkflowGraph, self).__init__()
self._workflows_to_execute = {}
self._protocol_graph = ProtocolGraph()
[docs] def add_workflows(self, *workflows):
"""Insert a set of workflows into the workflow graph.
Parameters
----------
workflow: Workflow
The workflow to insert.
"""
workflow_uuids = [x.uuid for x in workflows]
if len(set(workflow_uuids)) != len(workflow_uuids):
raise ValueError("A number of workflows have the same uuid.")
existing_uuids = [x for x in workflow_uuids if x in self._workflows_to_execute]
if len(existing_uuids) > 0:
raise ValueError(
f"Workflows with the uuids {existing_uuids} are already in the graph."
)
original_protocols = []
for workflow in workflows:
original_protocols.extend(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
for workflow in workflows:
if (
retrieve_uuid(
original_protocol
if isinstance(original_protocol, str)
else original_protocol.id
)
!= workflow.uuid
):
continue
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, True)
[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)