PyEnSight is a Python wrapper for EnSight, the Ansys simulation postprocessor. It supports Pythonic access to EnSight so that you communicate directly with it from Python. With PyEnSight, you can perform these essential actions:
- Start a new EnSight session or connect to an existing one.
- Read simulation data from any supported solver output format into the session.
- Generate complex postprocessing results in a Pythonic fashion.
- Visualize the processed data, extract it, or get a widget to embed it in an external app.
Documentation for the latest stable release of PyEnSight is hosted at PyEnSight documentation.
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 or download the PyEnSight cheat sheet. This one-page reference provides syntax rules and commands for using PyEnSight.
On the PyEnSight Issues page, you can create issues to report bugs and request new features. On the PyEnSight Discussions page or the Discussions page on the Ansys Developer portal, you can post questions, share ideas, and get community feedback.
To reach the project support team, email pyansys.core@ansys.com.
To use PyEnSight, you must have a locally installed and licensed copy of
Ansys EnSight 2022 R2 or later. The ansys-pyensight-core package supports
Python 3.9 through Python 3.12 on Windows and Linux.
Two modes of installation are available:
- User installation
- Developer installation
Install the latest release from PyPI with this command:
pip install ansys-pyensight-core
If you plan on doing local development of PyEnSight with GitHub, consider using a virtual environment.
To clone PyEnSight and then install it in a virtual environment, run these commands:
git clone https://github.com/ansys/pyensight cd pyensight pip install virtualenv virtualenv venv # create virtual environment source venv/bin/activate # (.\venv\Scripts\activate for Windows shell) pip install .[dev] # install development dependencies
A developer installation allows you to edit ansys-pyensight files locally.
Any changes that you make are reflected in your setup after restarting the
Python kernel.
To build and install PyEnSight, run these commands:
python -m build # build # this will replace the editable install done previously. If you don't want to replace, # switch your virtual environments to test the new install separately. pip install .[tests] # install test dependencies pytest # Run the tests
pre-commit is a multi-language package manager for pre-commit hooks.
To install pre-commit into your git hooks, run this command:
pre-commit install
pre-commit then runs on every commit. Each time you clone a project,
installing pre-commit should always be the first action that you take.
If you want to manually run all pre-commit hooks on a repository, run this command:
pre-commit run --all-files
A bunch of formatters run on your source files.
To run individual hooks, use this command, where <hook_id> is obtained from
from the .pre-commit-config.yaml file:
pre-commit run <hook_id>
The first time pre-commit runs on a file, it automatically downloads, installs, and runs the hook.
Simulating GitHub Actions on your local desktop is recommended. After installing the
act package, you can run a job. For
example, this command runs the docs job defined in the ci_cd.yml file:
act -j docs
Deploy and upload steps must always be ignored. If they are not ignored, before
running a job, add if: ${{ !env.ACT }} to the workflow step (and commit if required).
You can use this code to start the simplest PyEnSight session:
>>> from ansys.pyensight.core import LocalLauncher
>>> session = LocalLauncher().start()
>>> data = session.render(1920, 1080, aa=4)
>>> with open("image.png", "wb") as f:
... f.write(data)Optionally, EnSight can work with an EnSight Docker container using code like this:
>>> from ansys.pyensight.core import DockerLauncher
>>> launcher = DockerLauncher(data_directory="d:/data", use_dev=True)
>>> launcher.pull()
>>> session = launcher.start()
>>> data = session.render(1920, 1080, aa=4)
>>> with open("image.png", "wb") as f:
... f.write(data)In the preceding code, the data_directory argument specifies the host directory
to map into the container at the mount point, providing access to the data within
the container. This provides a method for EnSight running in the container to access
the host's file system to read or write data. The optional use_dev=True argument
specifies that the latest development version of EnSight should be used.
Also, PyEnSight can be launched as other PyAnsys products with the launch_ensight method:
>>> from ansys.pyensight.core import launch_ensight
>>> session = launch_ensight(use_sos=3)
>>> data = session.render(1920, 1080, aa=4)
>>> with open("image.png", "wb") as f:
... f.write(data)You will need a locally installed and licensed copy of Ansys to run EnSight, with the first supported version being Ansys 2022 R2.
Please see the latest release documentation page for more details.
Please feel free to post issues and other questions at PyEnSight Issues. This is the best place to post questions and code.
PyEnSight is licensed under the MIT license.
PyEnsight makes no commercial claim over Ansys whatsoever. This library extends the functionality of Ansys EnSight by adding a remote Python interface to EnSight without changing the core behavior or license of the original software. The use of interactive control of PyEnSight requires a legally licensed local copy of Ansys.
For more information on EnSight, see the Ansys Ensight page on the Ansys website.