The Data Processing Framework (DPF) is designed to provide numerical simulation users/engineers with a toolbox for accessing and transforming simulation data. DPF can access data from solver result files as well as several neutral formats (csv, hdf5, vtk, etc.). Various operators are available allowing the manipulation and the transformation of this data.
The Python ansys-dpf-post package provides a simplified Python
interface to DPF, thus enabling rapid postprocessing without ever
leaving a Python environment.
This module leverages the DPF-Core project's ansys-dpf-core package and can
be found by visiting PyDPF-Core
GitHub. Use ansys-dpf-core for
building more advanced and customized workflows using Ansys's DPF.
Visit the DPF-Post Documentation for a detailed description of the package, or see the Examples Gallery for more detailed examples.
Install this repository with:
pip install ansys-dpf-post
You can also clone and install this repository with:
git clone https://github.com/pyansys/pydpf-post
cd pydpf-post
pip install . --user
Provided you have ANSYS 2021R1 installed, a DPF server will start automatically once you start using DPF-Post. Should you wish to use DPF-Post without 2020R1, see the DPF Docker documentation.
Opening and plotting a result file generated from Ansys workbench or MAPDL is as easy as:
>>> from ansys.dpf import post
>>> from ansys.dpf.post import examples
>>> solution = post.load_solution(examples.multishells_rst)
>>> stress = solution.stress()
>>> stress.xx.plot_contour(show_edges=False)
Or extract the raw data as a numpy array with:
>>> stress.xx.get_data_at_field(0)
array([-3.37871094e+10, -4.42471752e+10, -4.13249463e+10, ...,
3.66408342e+10, 1.40736914e+11, 1.38633557e+11])Computational Efficiency
The DPF-Post module is based on DPF Framework that been developed with a data framework that localizes the loading and post-processing within the DPF server, enabling rapid post-processing workflows as this is written in C and FORTRAN. At the same time, the DPF-Post Python module presents the result in Pythonic manner, allowing for the rapid development of simple or complex post-processing scripts.
Easy to use
The API of DPF-Post module has been developed in order to make easy post-processing steps easier by automating the use of DPF's chained operators. This allows for fast post-processing of potentially multi-gigabyte models in a short script. DPF-Post also details the usage of the operators used when computing the results so you can also build your own custom, low level scripts using the DPF-Core module.
PyDPF-Post is licensed under the MIT license. For more information, see the
LICENSE.