def default_reweighting_schema(
absolute_tolerance=UNDEFINED,
relative_tolerance=UNDEFINED,
n_effective_samples=50,
):
"""Returns the default calculation schema to use when estimating
this property by reweighting existing data.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_effective_samples: int
The minimum number of effective samples to require when
reweighting the cached simulation data.
Returns
-------
ReweightingSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = ReweightingSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
data_replicator_id = "data_replicator"
# Set up a protocol to extract the dielectric constant from the stored data.
extract_dielectric = ExtractAverageDielectric(
f"calc_dielectric_$({data_replicator_id})"
)
# For the dielectric constant, we employ a slightly more advanced reweighting
# protocol set up for calculating fluctuation properties.
reweight_dielectric = ReweightDielectricConstant("reweight_dielectric")
reweight_dielectric.reference_dipole_moments = ProtocolPath(
"uncorrelated_values", extract_dielectric.id
)
reweight_dielectric.reference_volumes = ProtocolPath(
"uncorrelated_volumes", extract_dielectric.id
)
reweight_dielectric.thermodynamic_state = ProtocolPath(
"thermodynamic_state", "global"
)
reweight_dielectric.bootstrap_uncertainties = True
reweight_dielectric.bootstrap_iterations = 200
reweight_dielectric.required_effective_samples = n_effective_samples
protocols, data_replicator = generate_base_reweighting_protocols(
extract_dielectric, reweight_dielectric, data_replicator_id
)
# Make sure input is taken from the correct protocol outputs.
extract_dielectric.system_path = ProtocolPath(
"system_path", protocols.build_reference_system.id
)
extract_dielectric.thermodynamic_state = ProtocolPath(
"thermodynamic_state", protocols.unpack_stored_data.id
)
# Set up the gradient calculations
coordinate_path = ProtocolPath(
"output_coordinate_path", protocols.concatenate_trajectories.id
)
trajectory_path = ProtocolPath(
"output_trajectory_path", protocols.concatenate_trajectories.id
)
statistics_path = ProtocolPath(
"statistics_file_path", protocols.reduced_target_potential.id
)
reweight_dielectric_template = copy.deepcopy(reweight_dielectric)
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
reweight_dielectric_template,
ProtocolPath("force_field_path", "global"),
coordinate_path,
trajectory_path,
statistics_path,
replicator_id="grad",
effective_sample_indices=ProtocolPath(
"effective_sample_indices", reweight_dielectric.id
),
)
schema = WorkflowSchema()
schema.protocol_schemas = [
*(x.schema for x in protocols),
gradient_group.schema,
]
schema.protocol_replicators = [data_replicator, gradient_replicator]
schema.gradients_sources = [gradient_source]
schema.final_value_source = ProtocolPath("value", protocols.mbar_protocol.id)
calculation_schema.workflow_schema = schema
return calculation_schema
def default_simulation_schema(
absolute_tolerance=UNDEFINED, relative_tolerance=UNDEFINED, n_molecules=1000
):
"""Returns the default calculation schema to use when estimating
this class of property from direct simulations.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_molecules: int
The number of molecules to use in the simulation.
Returns
-------
SimulationSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = SimulationSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
# Define the protocol which will extract the average dielectric constant
# from the results of a simulation.
extract_dielectric = ExtractAverageDielectric("extract_dielectric")
extract_dielectric.thermodynamic_state = ProtocolPath(
"thermodynamic_state", "global"
)
# Define the protocols which will run the simulation itself.
use_target_uncertainty = (
absolute_tolerance != UNDEFINED or relative_tolerance != UNDEFINED
)
protocols, value_source, output_to_store = generate_base_simulation_protocols(
extract_dielectric, use_target_uncertainty, n_molecules=n_molecules,
)
# Make sure the input of the analysis protcol is properly hooked up.
extract_dielectric.system_path = ProtocolPath(
"system_path", protocols.assign_parameters.id
)
# Dielectric constants typically take longer to converge, so we need to
# reflect this in the maximum number of convergence iterations.
protocols.converge_uncertainty.max_iterations = 400
# Set up the gradient calculations. For dielectric constants, we need to use
# a slightly specialised reweighting protocol which we set up here.
coordinate_source = ProtocolPath(
"output_coordinate_file", protocols.equilibration_simulation.id
)
trajectory_source = ProtocolPath(
"trajectory_file_path",
protocols.converge_uncertainty.id,
protocols.production_simulation.id,
)
statistics_source = ProtocolPath(
"statistics_file_path",
protocols.converge_uncertainty.id,
protocols.production_simulation.id,
)
gradient_mbar_protocol = ReweightDielectricConstant("gradient_mbar")
gradient_mbar_protocol.reference_dipole_moments = [
ProtocolPath(
"dipole_moments",
protocols.converge_uncertainty.id,
extract_dielectric.id,
)
]
gradient_mbar_protocol.reference_volumes = [
ProtocolPath(
"volumes", protocols.converge_uncertainty.id, extract_dielectric.id
)
]
gradient_mbar_protocol.thermodynamic_state = ProtocolPath(
"thermodynamic_state", "global"
)
gradient_mbar_protocol.reference_reduced_potentials = statistics_source
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
gradient_mbar_protocol,
ProtocolPath("force_field_path", "global"),
coordinate_source,
trajectory_source,
statistics_source,
)
# Build the workflow schema.
schema = WorkflowSchema()
schema.protocol_schemas = [
protocols.build_coordinates.schema,
protocols.assign_parameters.schema,
protocols.energy_minimisation.schema,
protocols.equilibration_simulation.schema,
protocols.converge_uncertainty.schema,
protocols.extract_uncorrelated_trajectory.schema,
protocols.extract_uncorrelated_statistics.schema,
gradient_group.schema,
]
schema.protocol_replicators = [gradient_replicator]
schema.outputs_to_store = {"full_system": output_to_store}
schema.gradients_sources = [gradient_source]
schema.final_value_source = value_source
calculation_schema.workflow_schema = schema
return calculation_schema
def _get_reweighting_protocols(
id_suffix,
gradient_replicator_id,
data_replicator_id,
replicator_id=None,
weight_by_mole_fraction=False,
substance_reference=None,
n_effective_samples=50,
):
"""Returns the set of protocols which when combined in a workflow
will yield the molar volume of a substance by reweighting cached data.
Parameters
----------
id_suffix: str
A suffix to append to the id of each of the returned protocols.
gradient_replicator_id: str
The id of the replicator which will clone those protocols which will
estimate the gradient of the molar volume with respect to a given parameter.
data_replicator_id: str
The id of the replicator which will be used to clone these protocols
for each cached simulation data.
replicator_id: str, optional
The optional id of the replicator which will be used to clone these
protocols, e.g. for each component in the system.
weight_by_mole_fraction: bool
If true, an extra protocol will be added to weight the calculated
molar volume by the mole fraction of the component.
substance_reference: ProtocolPath or PlaceholderValue, optional
An optional protocol path (or replicator reference) to the substance
whose molar volume is being estimated.
n_effective_samples: int
The minimum number of effective samples to require when
reweighting the cached simulation data.
Returns
-------
BaseReweightingProtocols
The protocols used to estimate the molar volume of a substance.
ProtocolPath
A reference to the estimated molar volume.
ProtocolReplicator
The replicator which will replicate each protocol for each
cached simulation datum.
ProtocolGroup
The group of protocols which will calculate the gradient of the reduced potential
with respect to a given property.
ProtocolPath
A reference to the value of the gradient.
"""
if replicator_id is not None:
id_suffix = f"{id_suffix}_$({replicator_id})"
full_id_suffix = id_suffix
if data_replicator_id is not None:
full_id_suffix = f"{id_suffix}_$({data_replicator_id})"
if substance_reference is None:
substance_reference = ProtocolPath("substance", "global")
extract_volume = analysis.ExtractAverageStatistic(
f"extract_volume{full_id_suffix}"
)
extract_volume.statistics_type = ObservableType.Volume
reweight_volume = reweighting.ReweightStatistics(f"reweight_volume{id_suffix}")
reweight_volume.statistics_type = ObservableType.Volume
reweight_volume.required_effective_samples = n_effective_samples
(protocols, data_replicator) = generate_base_reweighting_protocols(
analysis_protocol=extract_volume,
mbar_protocol=reweight_volume,
replicator_id=data_replicator_id,
id_suffix=id_suffix,
)
# Make sure to use the correct substance.
protocols.build_target_system.substance = substance_reference
value_source = ProtocolPath("value", protocols.mbar_protocol.id)
# Set up the protocols which will be responsible for adding together
# the component molar volumes, and subtracting these from the full system volume.
weight_volume = None
if weight_by_mole_fraction is True:
weight_volume = miscellaneous.WeightByMoleFraction(
f"weight_volume{id_suffix}"
)
weight_volume.value = ProtocolPath("value", protocols.mbar_protocol.id)
weight_volume.full_substance = ProtocolPath("substance", "global")
weight_volume.component = substance_reference
value_source = ProtocolPath("weighted_value", weight_volume.id)
# Divide by the component molar volumes by the number of molecules in the system
number_of_molecules = ProtocolPath(
"total_number_of_molecules",
protocols.unpack_stored_data.id.replace(f"$({data_replicator_id})", "0"),
)
number_of_molar_molecules = miscellaneous.MultiplyValue(
f"number_of_molar_molecules{id_suffix}"
)
number_of_molar_molecules.value = (1.0 / unit.avogadro_constant).to(unit.mole)
number_of_molar_molecules.multiplier = number_of_molecules
divide_by_molecules = miscellaneous.DivideValue(
f"divide_by_molecules{id_suffix}"
)
divide_by_molecules.value = value_source
divide_by_molecules.divisor = ProtocolPath(
"result", number_of_molar_molecules.id
)
value_source = ProtocolPath("result", divide_by_molecules.id)
# Set up the gradient calculations.
reweight_volume_template = copy.deepcopy(reweight_volume)
coordinate_path = ProtocolPath(
"output_coordinate_path", protocols.concatenate_trajectories.id
)
trajectory_path = ProtocolPath(
"output_trajectory_path", protocols.concatenate_trajectories.id
)
statistics_path = ProtocolPath(
"statistics_file_path", protocols.reduced_target_potential.id
)
gradient_group, _, gradient_source = generate_gradient_protocol_group(
reweight_volume_template,
ProtocolPath("force_field_path", "global"),
coordinate_path,
trajectory_path,
statistics_path,
replicator_id=gradient_replicator_id,
id_suffix=id_suffix,
substance_source=substance_reference,
effective_sample_indices=ProtocolPath(
"effective_sample_indices", protocols.mbar_protocol.id
),
)
# Remove the group id from the path.
gradient_source.pop_next_in_path()
if weight_by_mole_fraction is True:
# The component workflows need an extra step to multiply their gradients by their
# relative mole fraction.
weight_gradient = miscellaneous.WeightByMoleFraction(
f"weight_gradient_$({gradient_replicator_id})_"
f"by_mole_fraction{id_suffix}"
)
weight_gradient.value = gradient_source
weight_gradient.full_substance = ProtocolPath("substance", "global")
weight_gradient.component = substance_reference
gradient_group.add_protocols(weight_gradient)
gradient_source = ProtocolPath("weighted_value", weight_gradient.id)
scale_gradient = miscellaneous.DivideValue(
f"scale_gradient_$({gradient_replicator_id}){id_suffix}"
)
scale_gradient.value = gradient_source
scale_gradient.divisor = ProtocolPath("result", number_of_molar_molecules.id)
gradient_group.add_protocols(scale_gradient)
gradient_source = ProtocolPath("result", gradient_group.id, scale_gradient.id)
all_protocols = (*protocols, number_of_molar_molecules, divide_by_molecules)
if weight_volume is not None:
all_protocols = (*all_protocols, weight_volume)
return (
all_protocols,
value_source,
data_replicator,
gradient_group,
gradient_source,
)
def default_simulation_schema(
absolute_tolerance=UNDEFINED, relative_tolerance=UNDEFINED, n_molecules=1000
):
"""Returns the default calculation schema to use when estimating
this class of property from direct simulations.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_molecules: int
The number of molecules to use in the simulation.
Returns
-------
SimulationSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = SimulationSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
use_target_uncertainty = (
absolute_tolerance != UNDEFINED or relative_tolerance != UNDEFINED
)
# Define the protocol which will extract the average density from
# the results of a simulation.
extract_density = analysis.ExtractAverageStatistic("extract_density")
extract_density.statistics_type = ObservableType.Density
# Define the protocols which will run the simulation itself.
protocols, value_source, output_to_store = generate_base_simulation_protocols(
extract_density, use_target_uncertainty, n_molecules=n_molecules,
)
# Set up the gradient calculations
coordinate_source = ProtocolPath(
"output_coordinate_file", protocols.equilibration_simulation.id
)
trajectory_source = ProtocolPath(
"trajectory_file_path",
protocols.converge_uncertainty.id,
protocols.production_simulation.id,
)
statistics_source = ProtocolPath(
"statistics_file_path",
protocols.converge_uncertainty.id,
protocols.production_simulation.id,
)
reweight_density_template = reweighting.ReweightStatistics("")
reweight_density_template.statistics_type = ObservableType.Density
reweight_density_template.statistics_paths = statistics_source
reweight_density_template.reference_reduced_potentials = statistics_source
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
reweight_density_template,
ProtocolPath("force_field_path", "global"),
coordinate_source,
trajectory_source,
statistics_source,
)
# Build the workflow schema.
schema = WorkflowSchema()
schema.protocol_schemas = [
protocols.build_coordinates.schema,
protocols.assign_parameters.schema,
protocols.energy_minimisation.schema,
protocols.equilibration_simulation.schema,
protocols.converge_uncertainty.schema,
protocols.extract_uncorrelated_trajectory.schema,
protocols.extract_uncorrelated_statistics.schema,
gradient_group.schema,
]
schema.protocol_replicators = [gradient_replicator]
schema.outputs_to_store = {"full_system": output_to_store}
schema.gradients_sources = [gradient_source]
schema.final_value_source = value_source
calculation_schema.workflow_schema = schema
return calculation_schema
def _get_simulation_protocols(
id_suffix,
gradient_replicator_id,
replicator_id=None,
weight_by_mole_fraction=False,
component_substance_reference=None,
full_substance_reference=None,
use_target_uncertainty=False,
n_molecules=1000,
):
"""Returns the set of protocols which when combined in a workflow
will yield the molar volume of a substance.
Parameters
----------
id_suffix: str
A suffix to append to the id of each of the returned protocols.
gradient_replicator_id: str
The id of the replicator which will clone those protocols which will
estimate the gradient of the molar volume with respect to a given parameter.
replicator_id: str, optional
The id of the replicator which will be used to clone these protocols.
This will be appended to the id of each of the returned protocols if
set.
weight_by_mole_fraction: bool
If true, an extra protocol will be added to weight the calculated
molar volume by the mole fraction of the component.
component_substance_reference: ProtocolPath or PlaceholderValue, optional
An optional protocol path (or replicator reference) to the component substance
whose enthalpy is being estimated.
full_substance_reference: ProtocolPath or PlaceholderValue, optional
An optional protocol path (or replicator reference) to the full substance
whose enthalpy of mixing is being estimated. This cannot be `None` if
`weight_by_mole_fraction` is `True`.
use_target_uncertainty: bool
Whether to calculate the observable to within the target
uncertainty.
n_molecules: int
The number of molecules to use in the simulation.
Returns
-------
BaseSimulationProtocols
The protocols used to estimate the molar volume of a substance.
DivideValue
The protocol used to calculate the number of molar molecules in
the system.
ProtocolPath
A reference to the estimated molar volume.
WorkflowSimulationDataToStore
An object which describes the default data from a simulation to store,
such as the uncorrelated statistics and configurations.
ProtocolGroup
The group of protocols which will calculate the gradient of the reduced potential
with respect to a given property.
ProtocolReplicator
The protocol which will replicate the gradient group for every gradient to
estimate.
ProtocolPath
A reference to the value of the gradient.
"""
if replicator_id is not None:
id_suffix = f"{id_suffix}_$({replicator_id})"
if component_substance_reference is None:
component_substance_reference = ProtocolPath("substance", "global")
if weight_by_mole_fraction is True and full_substance_reference is None:
raise ValueError(
"The full substance reference must be set when weighting by"
"the mole fraction"
)
# Define the protocol which will extract the average molar volume from
# the results of a simulation.
extract_volume = analysis.ExtractAverageStatistic(f"extract_volume{id_suffix}")
extract_volume.statistics_type = ObservableType.Volume
# Define the protocols which will run the simulation itself.
(
simulation_protocols,
value_source,
output_to_store,
) = generate_base_simulation_protocols(
extract_volume, use_target_uncertainty, id_suffix, n_molecules=n_molecules
)
# Divide the volume by the number of molecules in the system
number_of_molecules = ProtocolPath(
"output_number_of_molecules", simulation_protocols.build_coordinates.id
)
built_substance = ProtocolPath(
"output_substance", simulation_protocols.build_coordinates.id
)
number_of_molar_molecules = miscellaneous.DivideValue(
f"number_of_molar_molecules{id_suffix}"
)
number_of_molar_molecules.value = number_of_molecules
number_of_molar_molecules.divisor = (1.0 * unit.avogadro_constant).to(
"mole**-1"
)
extract_volume.divisor = ProtocolPath("result", number_of_molar_molecules.id)
# Use the correct substance.
simulation_protocols.build_coordinates.substance = component_substance_reference
simulation_protocols.assign_parameters.substance = built_substance
output_to_store.substance = built_substance
conditional_group = simulation_protocols.converge_uncertainty
if weight_by_mole_fraction:
# The component workflows need an extra step to multiply their molar volumes by their
# relative mole fraction.
weight_by_mole_fraction = miscellaneous.WeightByMoleFraction(
f"weight_by_mole_fraction{id_suffix}"
)
weight_by_mole_fraction.value = ProtocolPath("value", extract_volume.id)
weight_by_mole_fraction.full_substance = full_substance_reference
weight_by_mole_fraction.component = component_substance_reference
conditional_group.add_protocols(weight_by_mole_fraction)
value_source = ProtocolPath(
"weighted_value", conditional_group.id, weight_by_mole_fraction.id
)
if use_target_uncertainty:
# Make sure the convergence criteria is set to use the per component
# uncertainty target.
conditional_group.conditions[0].right_hand_value = ProtocolPath(
"per_component_uncertainty", "global"
)
if weight_by_mole_fraction:
# Make sure the weighted uncertainty is being used in the conditional comparison.
conditional_group.conditions[0].left_hand_value = ProtocolPath(
"weighted_value.error",
conditional_group.id,
weight_by_mole_fraction.id,
)
# Set up the gradient calculations
coordinate_source = ProtocolPath(
"output_coordinate_file", simulation_protocols.equilibration_simulation.id
)
trajectory_source = ProtocolPath(
"trajectory_file_path",
simulation_protocols.converge_uncertainty.id,
simulation_protocols.production_simulation.id,
)
statistics_source = ProtocolPath(
"statistics_file_path",
simulation_protocols.converge_uncertainty.id,
simulation_protocols.production_simulation.id,
)
reweight_molar_volume_template = reweighting.ReweightStatistics("")
reweight_molar_volume_template.statistics_type = ObservableType.Volume
reweight_molar_volume_template.statistics_paths = statistics_source
reweight_molar_volume_template.reference_reduced_potentials = statistics_source
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
reweight_molar_volume_template,
ProtocolPath("force_field_path", "global"),
coordinate_source,
trajectory_source,
statistics_source,
replicator_id=gradient_replicator_id,
substance_source=built_substance,
id_suffix=id_suffix,
)
# Remove the group id from the path.
gradient_source.pop_next_in_path()
if weight_by_mole_fraction:
# The component workflows need an extra step to multiply their gradients by their
# relative mole fraction.
weight_gradient = miscellaneous.WeightByMoleFraction(
f"weight_gradient_by_mole_fraction{id_suffix}"
)
weight_gradient.value = gradient_source
weight_gradient.full_substance = full_substance_reference
weight_gradient.component = component_substance_reference
gradient_group.add_protocols(weight_gradient)
gradient_source = ProtocolPath("weighted_value", weight_gradient.id)
scale_gradient = miscellaneous.DivideValue(f"scale_gradient{id_suffix}")
scale_gradient.value = gradient_source
scale_gradient.divisor = ProtocolPath("result", number_of_molar_molecules.id)
gradient_group.add_protocols(scale_gradient)
gradient_source = ProtocolPath("result", gradient_group.id, scale_gradient.id)
return (
simulation_protocols,
number_of_molar_molecules,
value_source,
output_to_store,
gradient_group,
gradient_replicator,
gradient_source,
)
def default_reweighting_schema(
absolute_tolerance=UNDEFINED,
relative_tolerance=UNDEFINED,
n_effective_samples=50,
):
"""Returns the default calculation schema to use when estimating
this property by reweighting existing data.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_effective_samples: int
The minimum number of effective samples to require when
reweighting the cached simulation data.
Returns
-------
ReweightingSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = ReweightingSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
data_replicator_id = "data_replicator"
# The protocol which will be used to calculate the densities from
# the existing data.
density_calculation = analysis.ExtractAverageStatistic(
f"calc_density_$({data_replicator_id})"
)
density_calculation.statistics_type = ObservableType.Density
reweight_density = reweighting.ReweightStatistics(f"reweight_density")
reweight_density.statistics_type = ObservableType.Density
reweight_density.required_effective_samples = n_effective_samples
protocols, data_replicator = generate_base_reweighting_protocols(
density_calculation, reweight_density, data_replicator_id
)
# Set up the gradient calculations
coordinate_path = ProtocolPath(
"output_coordinate_path", protocols.concatenate_trajectories.id
)
trajectory_path = ProtocolPath(
"output_trajectory_path", protocols.concatenate_trajectories.id
)
statistics_path = ProtocolPath(
"statistics_file_path", protocols.reduced_target_potential.id
)
reweight_density_template = copy.deepcopy(reweight_density)
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
reweight_density_template,
ProtocolPath("force_field_path", "global"),
coordinate_path,
trajectory_path,
statistics_path,
replicator_id="grad",
effective_sample_indices=ProtocolPath(
"effective_sample_indices", protocols.mbar_protocol.id
),
)
schema = WorkflowSchema()
schema.protocol_schemas = [
*(x.schema for x in protocols),
gradient_group.schema,
]
schema.protocol_replicators = [data_replicator, gradient_replicator]
schema.gradients_sources = [gradient_source]
schema.final_value_source = ProtocolPath("value", protocols.mbar_protocol.id)
calculation_schema.workflow_schema = schema
return calculation_schema