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DPF Python client

Project description

DPF - Ansys Data Processing Framework

PyAnsys Python pypi freq-PyDPF-Core GH-CI docs MIT pypidl cov codacy

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.

The Python 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 Python environment.


Visit the DPF-Core Documentation for a detailed description of the library, or see the Examples Gallery for more detailed examples.


DPF requires an Ansys installation and must be compatible with it. Compatibility between PyDPF-Core and Ansys is documented here.

Starting with Ansys 2021R2, install this package with:

pip install ansys-dpf-core 

For use with Ansys 2021R1, install this package with:

pip install ansys-dpf-core==0.2.1

You can also clone and install this repository with:

git clone
cd pydpf-core
pip install . --user

Running DPF

See the example scripts in the examples folder for some basic example. More will be added later.

Brief Demo

Provided you have ANSYS 2021R1 or higher installed, a DPF server will start automatically once you start using DPF.

To open a result file and explore what's inside, do:

>>> from ansys.dpf import core as dpf
>>> from ansys.dpf.core import examples
>>> model = dpf.Model(examples.simple_bar)
>>> print(model)

    DPF Model
    Static analysis
    Unit system: Metric (m, kg, N, s, V, A)
    Physics Type: Mechanical
    Available results:
         -  displacement: Nodal Displacement
         -  element_nodal_forces: ElementalNodal Element nodal Forces
         -  elemental_volume: Elemental Volume
         -  stiffness_matrix_energy: Elemental Energy-stiffness matrix
         -  artificial_hourglass_energy: Elemental Hourglass Energy
         -  thermal_dissipation_energy: Elemental thermal dissipation energy
         -  kinetic_energy: Elemental Kinetic Energy
         -  co_energy: Elemental co-energy
         -  incremental_energy: Elemental incremental energy
         -  structural_temperature: ElementalNodal Temperature
    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               

Read a result with:

>>> result = model.results.displacement.eval()

Then start connecting operators with:

>>> from ansys.dpf.core import operators as ops
>>> norm = ops.math.norm(model.results.displacement())

Starting the Service

The ansys.dpf.core automatically starts a local instance of the DPF service in the background and connects to it. If you need to connect to an existing remote or local DPF instance, use the connect_to_server function:

>>> from ansys.dpf import core as dpf
>>> dpf.connect_to_server(ip='', port=50054)

Once connected, this connection will remain for the duration of the module until you exit python or connect to a different server.

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