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DPF Framework 2026 R1

serialization:export symbolic workflow

Last update: 31.07.2026

Version: 0.0.0

Description

Transforms a Workflow into a symbolic Workflow and writes it to a file (if a path is set in input) or string

Inputs

This table lists the input pins for this operator. Input pins define the data that the operator requires to perform its operation. Some inputs are required, while others are optional and provide additional configuration. Each parameter is detailed in the sections that follow the table.

Pin number Name Status Expected type(s)
0 workflow Required workflow
1 path string
2 format int32
3 options int32

workflow (Pin 0)

  • Required: Yes
  • Expected type(s): workflow

path (Pin 1)

  • Required: No
  • Expected type(s): string

format (Pin 2)

  • Required: No
  • Expected type(s): int32

0 is ASCII format and 1 is binary, default is 0.

options (Pin 3)

  • Required: No
  • Expected type(s): int32

1 copies connections with its data, 2 forwards named inputs and outputs names, 7 copies connections of named inputs and ouputs with their data. default is 7.

Outputs

This table lists the output pins for this operator. Output pins provide the results of the operator's computation and can be connected to inputs of other operators or retrieved for further processing. Each output is detailed in the sections that follow the table.

Pin number Name Expected type(s)
0 data_sources data_sources, string

data_sources (Pin 0)

Configurations

This operator supports configuration options that modify its behavior.

mutex

  • Expected type(s): bool
  • Default value: false

If this option is set to true, the shared memory is prevented from being simultaneously accessed by multiple threads.

Scripting

This operator can be accessed through scripting interfaces using these identifiers.

Category: serialization

Plugin: core

Scripting name: None

Full name: None

Internal name: export_symbolic_workflow

License: any_dpf_supported_increments

Examples

These examples demonstrate how to use this operator in different programming environments. Each example shows how to instantiate the operator, connect the required inputs, and retrieve the output.

C++
#include "dpf_api.h"

ansys::dpf::Operator op("export_symbolic_workflow"); // operator instantiation
op.connect(0, my_workflow);
op.connect(1, my_path);
op.connect(2, my_format);
op.connect(3, my_options);
ansys::dpf::DataSources my_data_sources = op.getOutput<ansys::dpf::DataSources>(0);
CPython
import ansys.dpf.core as dpf

op = dpf.operators.serialization.None() # operator instantiation
op.inputs.workflow.connect(my_workflow)
op.inputs.path.connect(my_path)
op.inputs.format.connect(my_format)
op.inputs.options.connect(my_options)
my_data_sources_as_data_sources = op.outputs.data_sources_as_data_sources()
IPython
import mech_dpf
import Ans.DataProcessing as dpf

op = dpf.operators.serialization.None() # operator instantiation
op.inputs.workflow.Connect(my_workflow)
op.inputs.path.Connect(my_path)
op.inputs.format.Connect(my_format)
op.inputs.options.Connect(my_options)
my_data_sources = op.outputs.data_sources.GetData()

Changelog

  • Version 0.0.0: Initial release.

Connect with Ansys