The Ansys Data Processing Framework (DPF) is designed to provide numerical simulation users and engineers with a toolbox for accessing and transforming simulation data.

The Python ansys-dpf-post package provides a high-level, physics-oriented API for postprocessing. Loading a simulation (defined by its result files) allows you to extract simulation metadata as well as results and then apply postprocessing operations on it.

The latest version of DPF supports Ansys solver result files for:

  • MAPDL (.rst, .mode, .rfrq, .rdsp)

  • LS-DYNA (.d3plot, .binout)

  • Fluent (.cas/dat.h5, .flprj)

  • CFX (.cad/dat.cff, .flprj)

See the PyDPF-Core main page for more information on file support.

This module leverages the PyDPF-Core project’s ansys-dpf-core package, which is available at PyDPF-Core GitHub. Use the ansys-dpf-core package for building more advanced and customized workflows using DPF.

Brief demo#

Provided you have Ansys 2023 R1 installed, a DPF server starts automatically once you start using PyDPF-Post.

To load a simulation for a MAPDL result file to extract and postprocess results, use this code:

>>> from ansys.dpf import post
>>> from import examples
>>> simulation = post.load_simulation(examples.download_crankshaft())
>>> displacement = simulation.displacement()
>>> print(displacement)
       results         U
        set_id         3
node      comp
4872         X -3.41e-05
             Y  1.54e-03
             Z -2.64e-06
9005         X -5.56e-05
             Y  1.44e-03
             Z  5.31e-06
>>> displacement.plot()
>>> stress_eqv = simulation.stress_eqv_von_mises_nodal()
>>> stress_eqv.plot()

To run PyDPF-Post with Ansys versions 2021 R1 and 2022 R2, use this code to start the legacy PyDPF-Post tools:

>>> from ansys.dpf import post
>>> from import examples
>>> solution = post.load_solution(examples.download_crankshaft())
>>> stress = solution.stress()
>>> stress.eqv.plot_contour(show_edges=False)

For comprehensive how-to information, see Examples.

Key features#

Computational efficiency

PyDPF-Post is based on DPF, whose data framework localizes loading and postprocessing on the DPF server, enabling rapid postprocessing workflows because they are written in C and FORTRAN. Because PyDPF-Post presents results in a Pythonic manner, you can rapidly develop simple or complex postprocessing scripts.

Easy to use

The PyDPF-Post API automates the use of chained DPF operators to make postprocessing easier. The PyDPF-Post documentation describes how you can use operators to compute results. This allows you to build your own custom, low-level scripts to enable fast postprocessing of potentially multi-gigabyte models using PyDPF-Core.