Ansys 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.
The Python ansys-dpf-post package provides a high-level, physics-oriented
API for postprocessing. Loading a simulation (defined by its results files)
allows you to extract simulation metadata and results and then apply
postprocessing operations on them.
The latest version of DPF supports Ansys solver results files for:
- Mechanical APDL (
.rst,.mode,.rfrq,.rdsp) - LS-DYNA (
.d3plot,.binout) - Fluent (
.cas/dat.h5,.flprj) - CFX (
.cas/dat.cff,.flprj,.res)
For more information on file support, see the main page in the PyDPF-Core documentation.
PyDPF-Post 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 Ansys DPF.
Documentation for the latest stable release of PyPDF-Post is hosted at PyDPF-Post documentation.
In the upper right corner of the documentation's title bar, there is an option for switching from viewing the documentation for the latest stable release to viewing the documentation for the development version or previously released versions.
You can also view or download the PyDPF-Post cheat sheet. This one-page reference provides syntax rules and commands for using PyDPF-Post.
On the PyDPF-Post Issues page, you can create issues to report bugs and request new features. On the PyDPF-Post Discussions page or the Discussions page on the Ansys Developer portal, you can post questions, share ideas, and get community feedback.
To reach the project support team, email [email protected].
To install this package, run this command:
pip install ansys-dpf-post
You can also clone and install this package with these commands:
git clone https://github.com/ansys/pydpf-post
cd pydpf-post
pip install . --user
Provided you have Ansys 2023 R1 or later installed, a DPF server automatically starts 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 ansys.dpf.post import examples
>>> simulation = post.load_simulation(examples.download_crankshaft())
>>> displacement = simulation.displacement()
>>> print(displacement) results U (m)
set_ids 3
node_ids components
4872 X -3.4137e-05
Y 1.5417e-03
Z -2.6398e-06
9005 X -5.5625e-05
Y 1.4448e-03
Z 5.3134e-06
... ... ...>>> displacement.plot()
>>> stress_eqv = simulation.stress_eqv_von_mises_nodal()
>>> stress_eqv.plot()
To run PyDPF-Post with Ansys 2021 R1 through 2022 R2, use this code to start the legacy PyDPF-Post tools:
>>> from ansys.dpf import post
>>> from ansys.dpf.post import examples
>>> solution = post.load_solution(examples.download_crankshaft())
>>> stress = solution.stress()
>>> stress.eqv.plot_contour(show_edges=False)
PyDPF-Post is licensed under the MIT license. For more information, see the LICENSE file.
PyDPF-Post makes no commercial claim over Ansys whatsoever. This library extends the functionality of Ansys DPF by adding a Python interface to DPF without changing the core behavior or license of the original software.