The 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.
DPF is an independent, physics-agnostic tool that you can plug into many apps for both data input and data output, including visualization and result plots. It can access data from solver result files and other neutral formats, such as CSV, HDF5, and VTK files.
Using the many DPF operators that are available, you can manipulate and transform this data. You can also chain operators together to create simple or complex data-processing workflows that you can reuse for repeated or future evaluations.
The data in DPF is defined based on physics-agnostic mathematical quantities described in self-sufficient entities called fields. This allows DPF to be a modular and easy-to-use tool with a large range of capabilities.
ansys.dpf.post package leverages the
ansys.dpf.core package, which
is available at PyDPF-Core GitHub. With
PyDPF-Core, you can build more advanced and customized DPF workflows.
Here is how you open and plot a result file generated by Ansys Workbench or MAPDL:
>>> 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)
Here is how you extract the raw data as a
.. code:: python
>>> 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])
For comprehensive demos, see Examples.
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 DPF-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 DPF’s chained 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.