invariant:principal invariants (field)
Last update: 31.07.2026Version: 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.