# TensorWaves
[](https://doi.org/10.5281/zenodo.5526650)
[](https://www.gnu.org/licenses/gpl-3.0-standalone.html)
[](https://pypi.org/project/tensorwaves)
[](https://anaconda.org/conda-forge/tensorwaves)
[](https://pypi.org/project/tensorwaves)
[](https://mybinder.org/v2/gh/ComPWA/tensorwaves/stable?filepath=docs/usage)
[](https://colab.research.google.com/github/ComPWA/tensorwaves/blob/stable)
[](https://open.vscode.dev/ComPWA/tensorwaves)
[](https://gitpod.io/#https://github.com/ComPWA/tensorwaves)
[](https://tensorwaves.readthedocs.io)
[](https://results.pre-commit.ci/latest/github/ComPWA/tensorwaves/main)
[](https://github.com/ComPWA/tensorwaves/actions?query=branch%3Amain+workflow%3Apytest)
[](https://mypy.readthedocs.io)
[](https://codecov.io/gh/ComPWA/tensorwaves)
[](https://www.codacy.com/gh/ComPWA/tensorwaves)
[](https://github.com/streetsidesoftware/cspell/tree/master/packages/cspell)
[](https://github.com/prettier/prettier)
[](https://github.com/astral-sh/ruff)
TensorWaves is a fitter package that optimizes mathematical models to data samples. The
models can be any _symbolic_ mathematical expression that is then converted to any
computational backend. In addition, TensorWaves provides functionality to generate toy
Monte Carlo data samples. The package is developed in parallel with
[AmpForm](https://github.com/ComPWA/ampform), which implements physics models, but its
'lambdifying' mechanisms can in principle be used independently.
All documentation can be found on
[tensorwaves.rtfd.io](https://tensorwaves.readthedocs.io).
For an overview of **upcoming releases and planned functionality**, see
[here](https://github.com/ComPWA/tensorwaves/milestones?direction=asc&sort=title&state=open).
## Contribute
See [`CONTRIBUTING.md`](./CONTRIBUTING.md)
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