ansys-dpf-core

What it is and what it does

ansys-dpf-core is a Python wrapper around Ansys Data Processing Framework, a physics-agnostic tool for accessing and transforming simulation results. It reads output from Mechanical APDL, LS-DYNA, Fluent, CFX, and other Ansys solvers, as well as neutral formats like CSV, HDF5, and VTK. The package represents data as mathematical fields and chains operators together to build reusable preprocessing or postprocessing workflows.

The library automatically starts a local DPF service in the background or can connect to a remote instance. It depends on grpcio for service communication, numpy for numerical operations, protobuf for data serialization, and optional PyVista for visualization. The package is actively maintained and supports current Python versions (3.10+).

Use it for:

• Extract displacement, stress, or other results from Mechanical APDL .rst files and perform post-analysis calculations in Python
• Chain DPF operators to compute derived quantities (e.g., stress norms, energy sums) from raw solver output without manual data extraction
• Read LS-DYNA .d3plot or Fluent .cas/.dat.h5 results and integrate them into a larger simulation workflow or optimization loop
• Convert Ansys result data to neutral formats (VTK, HDF5) for visualization or sharing with non-Ansys tools
• Automate batch postprocessing of multiple simulation runs by scripting DPF model loading and result evaluation

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Provides a Python interface to Ansys Data Processing Framework (DPF) for reading, transforming, and postprocessing simulation data from Ansys solver result files and neutral formats like CSV, HDF5, and VTK.

Yes, if you have access to a compatible Ansys installation or ansys-dpf-server. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers low-friction installation. It is the official Python interface to DPF and is well-suited for automating postprocessing workflows. Not suitable as a standalone tool—DPF server availability is a hard prerequisite.

Install

ansys-dpf-core on PyPI

pip install ansys-dpf-core

uv add ansys-dpf-core

poetry add ansys-dpf-core

Installing ansys-dpf-core

Before you install

Low friction install with a wheel distribution. Requires a compatible Ansys version or the standalone ansys-dpf-server package to function; the Python package alone is not sufficient. Active maintenance with recent releases.

License in practice

MIT license permits commercial and private use with minimal restrictions, making it straightforward to integrate into proprietary or open-source workflows.

Quickstart

pip install ansys-dpf-core

from ansys.dpf import core as dpf
from ansys.dpf.core import examples
model = dpf.Model(examples.find_simple_bar())
result = model.results.displacement.eval()

Requires DPF server to be available—either a compatible Ansys installation (2024 R1 or later for current version) or the standalone ansys-dpf-server package (8.0 or later). Python 3.10 or later required.

Verify before relying

• Whether ansys-dpf-server can be installed independently via pip or requires separate procurement from Ansys
• Performance characteristics when processing large simulation datasets (memory usage, processing speed)
• Availability and completeness of operator library for domain-specific workflows beyond the documented file formats

Package facts

Evidence: ansys_dpf_core-0.16.1-py3-none-any.whl

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