ansys-dpf-core
Data Processing Framework - Python Core
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
- Low friction install with a wheel distribution.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it straightforward to integrate into proprietary or open-source workflows.
last release 2026-06-08 (67 days) · last repo commit 2026-08-14 · 91 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 122,727 downloads/mo, #11,937 on PyPI
Alternatives
Verify before relying
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()- 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
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 on it.
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
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.
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.
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()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
| License | MIT permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesansys-tools-commongrpcioimportlib-metadatanumpypackagingprotobufpsutilsetuptoolstqdm |
| Maintenance | Actively maintained 67 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 122,727 / month, #11,937 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Information Analysis |
Evidence: ansys_dpf_core-0.16.1-py3-none-any.whl
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