serialization:export symbolic workflow
Last update: 31.07.2026Version: 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)
- Expected type(s):
data_sources,string
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.