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)
| File | Size | Uploaded | |
|---|---|---|---|
| sionna_rt_propasim-1.1.0.tar.gz | 33.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| 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 logRelease 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