https://github.com/ansys/pydpf-core
Data Processing Framework - Python Core
https://github.com/ansys/pydpf-core
Last synced: 4 days ago
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Data Processing Framework - Python Core
- Host: GitHub
- URL: https://github.com/ansys/pydpf-core
- Owner: ansys
- License: mit
- Created: 2020-08-31T16:27:18.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-05-10T13:03:39.000Z (10 days ago)
- Last Synced: 2025-05-10T14:20:08.019Z (10 days ago)
- Language: Python
- Homepage: http://dpf.docs.pyansys.com/
- Size: 1.31 GB
- Stars: 71
- Watchers: 38
- Forks: 25
- Open Issues: 278
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
- Authors: AUTHORS
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README
# DPF - Ansys Data Processing Framework
[](https://docs.pyansys.com/)
[](https://pypi.org/project/ansys-dpf-core/)
[](https://pypi.org/project/ansys-dpf-core)
[](https://github.com/ansys/pydpf-core)
[](https://github.com/ansys/pydpf-core/actions/workflows/ci.yml)
[](https://dpfdocs.pyansys.com)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/ansys-dpf-core/)
[](https://codecov.io/gh/ansys/pydpf-core)
[](https://www.codacy.com/gh/ansys/pydpf-core/dashboard?utm_source=github.com&utm_medium=referral&utm_content=ansys/pydpf-core&utm_campaign=Badge_Grade)
Ansys 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.
The latest version of DPF supports Ansys solver results files for:
- Mechanical APDL (`.rst`, `.mode`, `.rfrq`, `.rdsp`, `.rth`)
- LS-DYNA (`.d3plot`, `.binout`)
- Fluent (`.cas/dat.h5`, `.flprj`)
- CFX (`.cas/dat.cff`, `.flprj`, `.res`)
For more information on file support, see the [main page](https://dpf.docs.pyansys.com/version/stable/index.html)
in the PyDPF-Core documentation.
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.
The ``ansys.dpf.core`` package provides a Python interface to DPF, enabling
rapid postprocessing of a variety of Ansys file formats and physics solutions
without ever leaving the Python environment.
## Documentation and issues
Documentation for the latest stable release of PyDPF-Core is hosted at
[PyDPF-Core documentation](https://dpf.docs.pyansys.com/version/stable/).
In the upper right corner of the documentation's title bar, there is an option for switching from
viewing the documentation for the latest stable release to viewing the documentation for the
development version or previously released versions.
You can also [view](https://cheatsheets.docs.pyansys.com/pydpf-core_cheat_sheet.png) or
[download](https://cheatsheets.docs.pyansys.com/pydpf-core_cheat_sheet.pdf) the
PyDPF-Core cheat sheet. This one-page reference provides syntax rules and commands
for using PyDPF-Core.
On the [PyDPF-Core Issues](https://github.com/ansys/pydpf-core/issues) page,
you can create issues to report bugs and request new features. On the
[PyDPF-Core Discussions](https://github.com/ansys/pydpf-core/discussions) page or the [Discussions](https://discuss.ansys.com/)
page on the Ansys Developer portal, you can post questions, share ideas, and get community feedback.
To reach the project support team, email [[email protected]](mailto:[email protected]).
## Installation
PyDPF-Core requires DPF to be available. You can either have a compatible Ansys version installed
or install the standalone ``ansys-dpf-server`` server package. For more information, see
[Getting Started with DPF Server](https://dpf.docs.pyansys.com/version/stable/getting_started/dpf_server.html)
in the PyDPF-Core documentation.
For the compatibility between PyDPF-Core and Ansys, see
[Compatibility](https://dpf.docs.pyansys.com/version/stable/getting_started/compatibility.html) in
the PyDPF-Core documentation.
To use PyDPF-Core with the ``ansys-dpf-server`` server package or with Ansys 2022 R2 or later,
install the latest version with this command:
```con
pip install ansys-dpf-core
```
PyDPF-Core plotting capabilities require `PyVista `_ to be installed.
To install PyDPF-Core with its optional plotting functionalities, use this command:
```con
pip install ansys-dpf-core[graphics]
```
For more information on PyDPF-Core plotting capabilities, see [Plot](https://dpf.docs.pyansys.com/version/stable/user_guide/plotting.html) in the PyDPF-Core documentation.
To use PyDPF-Core with Ansys 2022 R1, install the latest compatible version
with this command:
```con
pip install ansys-dpf-core<0.10.0
```
To use PyDPF-Core with Ansys 2021 R2, install the latest compatible version
with this command:
```con
pip install ansys-grpc-dpf<0.4.0; pip install ansys-dpf-core<0.10.0
```
To use PyDPF-Core with Ansys 2021 R1, install the latest compatible version
with this command:
```con
pip install ansys-grpc-dpf<0.3.0; pip install ansys-dpf-core<0.3.0
```
### Brief demo
Provided you have DPF available, a DPF server automatically starts once you start using PyDPF-Core.
To open a result file and explore what's inside, use this code:
```pycon
>>> from ansys.dpf import core as dpf
>>> from ansys.dpf.core import examples
>>> model = dpf.Model(examples.find_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 this command:
```pycon
>>> result = model.results.displacement.eval()
```
Then, start connecting operators with this code:
```pycon
>>> from ansys.dpf.core import operators as ops
>>> norm = ops.math.norm(model.results.displacement())
```
### Starting the service
The ``ansys.dpf.core`` library 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`` method:
```pycon
>>> from ansys.dpf import core as dpf
>>> dpf.connect_to_server(ip='10.0.0.22', port=50054)
```
Once connected, this connection remains for the duration of the
module. It closes when you exit Python or connect to a different server.
## License and acknowledgments
PyDPF-Core is licensed under the MIT license. For more information, see the
[LICENSE](https://github.com/ansys/pydpf-post/raw/master/LICENSE) file.
PyDPF-Core makes no commercial claim over Ansys whatsoever. This library
extends the functionality of Ansys DPF by adding a Python interface
to DPF without changing the core behavior or license of the original
software.