The Data Processing Framework (DPF) is designed to provide numerical simulation users/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 apply postprocessing operations on it.

This module leverages the PyDPF-Core project’s ansys-dpf-core package which can be found by visiting PyDPF-Core GitHub. Use ansys-dpf-core for building more advanced and customized workflows using Ansys DPF.

Brief demo#

Provided you have Ansys 2023 R1 installed, a DPF server starts automatically once you start using PyDPF-Post. Loading a simulation for a MAPDL result file to extract and post-process results:

>>> 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 starting from 2021 R1 to 2022 R2, use the following 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 demos, 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.