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.
ansys.dpf.post module provides an simplified Python
interface to DPF, thus enabling rapid post-processing, without ever
leaving a Python environment.
This module leverages the DPF-Core module
and can be found by visiting DPF-Core GitHub. Use
building more advanced and customized workflows using Ansys’s DPF.
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])
See the Examples for more detailed examples.
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.