The Data Processing Framework (DPF) provides numerical simulation users and engineers with a toolbox for accessing and transforming simulation data. With DPF, you can perform complex preprocessing or postprocessing of large amounts of simulation data within a simulation workflow.

DPF is an independent, physics-agnostic tool that you can plug into many apps for both data input and data output, including visualization and result plots. The following table shows an exhaustive list of supported apps by DPF and their related format:


File format

Server version

DPF examples


.rst, .mode
.rfrq, .rdsp
1.0 and later
(Ansys 2021 R1)

Basic DPF-Core usage


.d3plot, .binout

4.0 and later
(Ansys 2022 R2)

Results extraction and analysis from LS-DYNA sources


CFF restart files

7.0 and later
(Ansys 2024 R1 pre0)

Explore Fluids models

Explore Fluids mesh

Project files

Explore Fluids results


CFF files

7.0 and later
(Ansys 2024 R1 pre0)

Explore Fluids models

Explore Fluids mesh

Project files

Explore Fluids results

Visualisation is ensured by VTK and leverage of PyVista tools.

Using the many DPF operators that are available, you can manipulate and transform this data. You can also chain operators together to create simple or complex data-processing workflows that you can reuse for repeated or future evaluations.

The data in DPF is defined based on physics-agnostic mathematical quantities described in self-sufficient entities called fields. This allows DPF to be a modular and easy-to-use tool with a large range of capabilities.

DPF flow

The ansys.dpf.core package provides a Python interface to DPF, enabling rapid postprocessing of a variety of Ansys file formats and physics solutions without ever leaving the Python environment.

Brief demo#

Here is how you open a result file generated by MAPDL (or another ANSYS solver) and extract results:

>>> from ansys.dpf.core import Model
>>> from ansys.dpf.core import examples
>>> model = Model(examples.find_simple_bar())
>>> print(model)

Here is how you plot displacement results:

>>> disp = model.results.displacement().X()
>>> model.metadata.meshed_region.plot(disp.outputs.fields_container())

For comprehensive examples of how you use PyDPF-Core, see Examples.

Key features#

Computational efficiency

DPF is a modern framework based on new hardware architectures. Thanks to continued development, new capabilities are frequently added.

Generic interface

DPF is physics-agnostic, which means that its use is not limited to a particular field, physics solution, or file format.

Extensibility and customization

DPF is developed around two core entities:

  • Data represented as a field

  • An operator to act upon this data

Each DPF capability is developed through operators that allow for componentization of the framework. Because DPF is plugin-based, new features or formats can be easily added.

Accessing and enriching DPF capabilities#

Most of the DPF capabilities can be accessed using the operators. For more information about the existing operators, see the Operators tab.

The following sections are summaries. For more detailed content, see DPF capabilities and scripting languages.

Accessing DPF Server files

DPF capabilities are accessible when DPF Server files are available. These files can be accessed using:

  • The Ansys installer. To use it, download the standard installation using your preferred distribution channel,

and install Ansys following the installer instructions. For information on getting a licensed copy of Ansys, visit the Ansys website.

It is independent of the Ansys installer.

Accessing capabilities with scripting

Enriching DPF capabilities