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DPF Framework 2026 R1

invariant:principal invariants (field)

Last update: 31.07.2026

Version: 0.0.0

Description

Computes the element-wise Eigen values of a tensor field.

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 field Required field

field (Pin 0)

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

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 field_eig_1 field
1 field_eig_2 field
2 field_eig_3 field

field_eig_1 (Pin 0)

  • Expected type(s): field

first eigen value field

field_eig_2 (Pin 1)

  • Expected type(s): field

second eigen value field

field_eig_3 (Pin 2)

  • Expected type(s): field

third eigen value field

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.

num_threads

  • Expected type(s): int32
  • Default value: 0

Number of threads to use to run in parallel

run_in_parallel

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

Loops are allowed to run in parallel if the value of this config is set to true.

Scripting

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

Category: invariant

Plugin: core

Scripting name: principal_invariants

Full name: invariant.principal_invariants

Internal name: invariants

License: None

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("invariants"); // operator instantiation
op.connect(0, my_field);
ansys::dpf::Field my_field_eig_1 = op.getOutput<ansys::dpf::Field>(0);
ansys::dpf::Field my_field_eig_2 = op.getOutput<ansys::dpf::Field>(1);
ansys::dpf::Field my_field_eig_3 = op.getOutput<ansys::dpf::Field>(2);
CPython
import ansys.dpf.core as dpf

op = dpf.operators.invariant.principal_invariants() # operator instantiation
op.inputs.field.connect(my_field)
my_field_eig_1 = op.outputs.field_eig_1()
my_field_eig_2 = op.outputs.field_eig_2()
my_field_eig_3 = op.outputs.field_eig_3()
IPython
import mech_dpf
import Ans.DataProcessing as dpf

op = dpf.operators.invariant.principal_invariants() # operator instantiation
op.inputs.field.Connect(my_field)
my_field_eig_1 = op.outputs.field_eig_1.GetData()
my_field_eig_2 = op.outputs.field_eig_2.GetData()
my_field_eig_3 = op.outputs.field_eig_3.GetData()

Changelog

  • Version 0.0.0: Initial release.

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