Skip to main content

Sionna RT: The Ray Tracing Package of Sionna™

Sionna RT is the stand-alone ray tracing package of the Sionna™ Library for Research on Communication Systems. It is built on top of Mitsuba 3 and is interoperable with TensorFlow, PyTorch, and JAX.

The official documentation can be found on the Sionna website.

Installation

The recommended way to install Sionna RT is via pip:

pip install sionna-rt

Sionna RT has the same requirements as Mitsuba 3 and we refer to its installation guide for further information.

To run Sionna RT on CPU, LLVM is required by Dr.Jit. Please check the installation instructions for the LLVM backend.

Installation from source

After to cloning the repository, you can install sionna-rt by running the following command from within the repository's root directory:

pip install .

Testing

First, you need to install the test requirements by executing the following command from the repository's root directory:

pip install '.[test]'

The unit tests can then be executed by running pytest from within the test folder.

Building the Documentation

Install the requirements for building the documentation by running the following command from the repository's root directory:

pip install '.[doc]'

You might need to install pandoc manually.

You can then build the documentation by executing make html from within the doc folder.

The documentation can then be served by any web server, e.g.,

python -m http.server --dir build/html

For Developers

The documentation of Sionna RT includes developer guides explaining how to extend it with custom antenna patterns, radio materials, etc.

Development requirements can be installed by executing from the repository's root directory:

pip install '.[dev]'

Linting of the code can be achieved by running pylint src/ from the repository's root directory.

License and Citation

Sionna RT is Apache-2.0 licensed, as found in the LICENSE file.

If you use this software, please cite it as:

@software{sionna,
 title = {Sionna},
 author = {Hoydis, Jakob and Cammerer, Sebastian and {Ait Aoudia}, Fayçal and Nimier-David, Merlin and Maggi, Lorenzo and Marcus, Guillermo and Vem, Avinash and Keller, Alexander},
 note = {https://propagamap.github.io/sionna-rt/},
 year = {2022},
 version = {2.0.1}
}

Metadata

Release files for sionna-rt-propasim 1.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for sionna-rt-propasim 1.1.0
File Size Uploaded
sionna_rt_propasim-1.1.0.tar.gz 33.9 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for sionna-rt-propasim 1.1.0
File Interpreter ABI Platform
sionna_rt_propasim-1.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 45.7 MB

Release files / sionna_rt_propasim-1.1.0.tar.gz

Download URL sionna_rt_propasim-1.1.0.tar.gz
Size 33.9 MB
Tags Source
SHA-256 checksum
How to use checksums
ec7aee721518e01b3bd4a588efea25c9c4f75b86749ce19411c870802836c408
BLAKE2b-256 checksum
How to use checksums
4ec7b3fb52e129cd7c99fd01b45636df22153bc63f9e8c13d1fab743f731d7a4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 6, 2026.

Transparency log

Release files / sionna_rt_propasim-1.1.0-py3-none-any.whl

Download URL sionna_rt_propasim-1.1.0-py3-none-any.whl
Size 11.8 MB
Tags Python 3
SHA-256 checksum
How to use checksums
82d491a4af1b7d6ddcb77aafadb869118134b36a57503ed912ed3195fdcc940c
BLAKE2b-256 checksum
How to use checksums
3294a2ae1d44397f2b2a02fe7abce0df594942deb506befebf64ed5c2dd29fd2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 6, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

1.1.0 This release

2 release files

1.0.3

2 release files

1.0.2

2 release files

1.0.1

2 release files

1.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page