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.
DPF is a workflow-based framework which allows simple and/or complex evaluations by chaining operators. The data in DPF is defined based on physics agnostic mathematical quantities described in a self-sufficient entity called field. This allows DPF to be a modular and easy to use tool with a large range of capabilities. It’s a product designed to handle large amount of data.
ansys.dpf.core module provides a Python interface to
the powerful DPF framework enabling rapid post-processing of a variety
of Ansys file formats and physics solutions without ever leaving a
Opening a result file generated from MAPDL (or other of ANSYS solvers) and extracting results from it is as easy as:
from ansys.dpf.core import Model from ansys.dpf.core import examples model = Model(examples.simple_bar) print(model)
DPF Model ------------------------------ DPF Result Info Analysis: static Physics Type: mecanic Unit system: MKS: m, kg, N, s, V, A, degC Available results: U Displacement :nodal displacements ENF Element nodal Forces :element nodal forces ENG_VOL Volume :element volume ENG_SE Energy-stiffness matrix :element energy associated with the stiffness matrix ENG_AHO Hourglass Energy :artificial hourglass energy ENG_TH thermal dissipation energy :thermal dissipation energy ENG_KE Kinetic Energy :kinetic energy ENG_CO co-energy :co-energy (magnetics) ENG_INC incremental energy :incremental energy (magnetics) BFE Temperature :element structural nodal temperatures ------------------------------ DPF Meshed Region: 3751 nodes 3000 elements Unit: m With solid (3D) elements ------------------------------ DPF Time/Freq Support: Number of sets: 1 Cumulative Time (s) LoadStep Substep 1 1.000000 1 1
disp = model.results.displacement().X() model.metadata.meshed_region.plot(disp.outputs.fields_container())
See the Examples Gallery for detailed examples.
DPF is a modern framework and it has been developed by taking advantages of new hardware architectures. Thanks to continued development, new capabilities are frequently added.
DPF is physic agnostic. Thus, its use is not limited to a particular field, physics solution, or file format.
Extensibility and Customization
DPF is developed around a two core entities: data represented as a
field, and the
operator to act upon that data. Each DPF
capability is developed through operators which allows for a
componentization of the framework. DPF is also plugin based, allowing
new features or new formats to be easily added within the operators