Workflow¶
Interface to underlying gRPC Workflow
- class ansys.dpf.core.workflow.Workflow(workflow=None, server=None)¶
- A class used to represent a Workflow:
a workflow is a black box containing operators and exposing only the necessary operator’s inputs and outputs to compute a given algorithm
- Parameters
server (server.DPFServer, optional) – Server with channel connected to the remote or local instance. When
None, attempts to use the global server.workflow (workflow_pb2.Workflow) –
Examples
Create a generic Workflow computing the minimum of displacement by chaining the
'U'and'min_max_fc'operators.>>> from ansys.dpf import core as dpf >>> disp_op = dpf.operators.result.displacement() >>> max_fc_op = dpf.operators.min_max.min_max_fc(disp_op) >>> workflow = dpf.Workflow() >>> workflow.add_operators([disp_op,max_fc_op]) >>> workflow.set_input_name("data_sources", disp_op.inputs.data_sources) >>> workflow.set_output_name("min", max_fc_op.outputs.field_min) >>> workflow.set_output_name("max", max_fc_op.outputs.field_max)
>>> from ansys.dpf.core import examples >>> data_src = dpf.DataSources(examples.multishells_rst) >>> workflow.connect("data_sources", data_src) >>> min = workflow.get_output("min", dpf.types.field) >>> max = workflow.get_output("max", dpf.types.field)
- connect(pin_name, inpt, pin_out=0)¶
Connect an input on the workflow using a pin name.
- Parameters
pin_name (str) – Name of the pin to connect. This name should be exposed before with wf.set_input_name
inpt (str, int, double, bool, list of int, list of doubles, Field, FieldsContainer, Scoping, ScopingsContainer,) –
MeshedRegion – Object you wish to connect.
MeshesContainer – Object you wish to connect.
DataSources – Object you wish to connect.
Operator – Object you wish to connect.
pin_out (int, optional) – In case of the input is an Operator, this is the output pin of the input Operator. Defaults to 0.
Examples
Create a generic Workflow computing the minimum of displacement by chaining the
'U'and'min_max_fc'operators.>>> from ansys.dpf import core as dpf >>> disp_op = dpf.operators.result.displacement() >>> max_fc_op = dpf.operators.min_max.min_max_fc(disp_op) >>> workflow = dpf.Workflow() >>> workflow.add_operators([disp_op,max_fc_op]) >>> workflow.set_input_name("data_sources", disp_op.inputs.data_sources) >>> workflow.set_output_name("min", max_fc_op.outputs.field_min) >>> workflow.set_output_name("max", max_fc_op.outputs.field_max)
>>> from ansys.dpf.core import examples >>> data_src = dpf.DataSources(examples.multishells_rst) >>> workflow.connect("data_sources", data_src) >>> min = workflow.get_output("min", dpf.types.field) >>> max = workflow.get_output("max", dpf.types.field)
- get_output(pin_name, output_type)¶
Returns the output of the operator on the pin number.
- Parameters
pin_name (str) – Name of the pin to get. This name should be exposed before with wf.set_output_name
output_type (core.type enum) – The requested type of the output.
- set_input_name(name, *args)¶
Name an input pin of the workflow to expose it for future connection
- Parameters
name (str) – Name of the pin to connect. This name should be exposed before with wf.set_input_name
*args (core.Operator, core.Input, int) – operator with it’s input pin number or input to name
Examples
>>> from ansys.dpf import core as dpf
>>> workflow = dpf.Workflow() >>> workflow.add_operators([disp_op,max_fc_op]) >>> workflow.set_input_name("data_sources", disp_op.inputs.data_sources)
>>> from ansys.dpf.core import examples >>> data_src = dpf.DataSources(examples.multishells_rst) >>> workflow.connect("data_sources", data_src)
- set_output_name(name, *args)¶
Name an output pin of the workflow to expose it for future connection
- Parameters
name (str) – Name of the pin to connect. This name should be exposed before with wf.set_input_name
*args (core.Operator, core.Output, int) – operator with it’s outpt pin number or output to name
Examples
>>> from ansys.dpf import core as dpf
>>> workflow = dpf.Workflow() >>> workflow.add_operators([disp_op,max_fc_op]) >>> workflow.set_output_name("contour", disp_op.outputs.fields_container)
>>> fc = workflow.get_output("contour", dpf.types.fields_container)
- add_operators(operators)¶
Add operators to the list of operators of the workflow
- Parameters
operators (dpf.core.Operator, list of dpf.core.Operator) – operators to add to the list
Examples
>>> from ansys.dpf import core as dpf
>>> workflow = dpf.Workflow() >>> disp_op = dpf.Operator("U") >>> max_op = dpf.Operator("min_max") >>> workflow.add_operator([disp_op,max_op])
- add_operator(operator)¶
Add an operator to the list of operators of the workflow
- Parameters
operator (dpf.core.Operator) – operator to add to the list
Examples
>>> from ansys.dpf import core as dpf
>>> workflow = dpf.Workflow() >>> disp_op = dpf.Operator("U") >>> workflow.add_operator(disp_op)
- record(identifier=None, transfer_ownership=True)¶
Add the workflow to DPF’s internal registry with an id returned by this method. The workflow can be recovered by dpf.core.Workflow.get_recorded_workflow(id)
- Parameters
identifier (str, optional) – name given to the workflow
transfer_ownership (bool) – if the owner ship is not transferred, the workflow is removed from the internal registry as soon as the workflow has been recovered by its id
Examples
>>> from ansys.dpf import core as dpf
>>> workflow = dpf.Workflow() >>> disp_op = dpf.Operator("U") >>> workflow.add_operator(disp_op) >>> # ... >>> id = workflow.record() >>> workflow_copy = dpf.Workflow.get_recorded_workflow(id)
- static get_recorded_workflow(id, server=None)¶
Recover a workflow registered (with workflow.record())
- Parameters
id (int) – id given by the method “record”
- Returns
workflow – workflow registered in dpf’s registry (server side)
- Return type
core.Workflow()
Examples
>>> from ansys.dpf import core as dpf
>>> workflow = dpf.Workflow() >>> disp_op = dpf.Operator("U") >>> workflow.add_operator(disp_op) >>> # ... >>> id = workflow.record() >>> workflow_copy = dpf.Workflow.get_recorded_workflow(id)
- property info¶
Returns a dictionary with the operator names and the exposed input and output names
- Returns
info – dictionary with “operator_names”, “input_names” and “output_names” key
- Return type
dictionarry str->list str
- property operator_names¶
Returns a list of the names of operators added in the workflow
- Returns
names
- Return type
list str
- property input_names¶
Returns a list of the input names exposed in the workflow with set_input_name
- Returns
names
- Return type
list str
- property output_names¶
Returns a list of the output names exposed in the workflow with set_output_name
- Returns
names
- Return type
list str
- chain_with(workflow, input_output_names=None)¶
Chain 2 workflows together so that they become one workflow with all the operators, inputs and outputs exposed in both workflows
- Parameters
workflow (core.Workflow) – This second workflow’s inputs will be chained with this workflow’s outputs
input_output_names (str tuple, optional) – the input name of this workflow will be chained with the output name of the second workflow If nothing is specified, this workflow’s outputs with the same names as the second workflow’s inputs will be chained
Examples
+-------------------------------------------------------------------------------------------------+ | INPUT: | | | |input_output_names = ("output","field" ) | | ____ ______________________ | | "data_sources" -> |this| -> "stuff" "field" -> |workflow_to_chain_with| -> "contour" | |"time_scoping" -> | | "mesh_scoping" -> | | | | |____| -> "output" |______________________| | | OUTPUT | | ____ | |"data_sources" -> |this| -> "stuff" | |"time_scoping" -> | | -> "contour" | |"mesh_scoping" -> |____| -> "output" | +-------------------------------------------------------------------------------------------------+