Tensorwaves

_See all documentation for this version [here](https://tensorwaves.rtfd.io/en/0.1.4)._

💡 New features

- Upgraded from `expertsystem` 0.6.5 to `expertsystem` 0.6.8 (199), see release notes [0.6.6](https://github.com/ComPWA/expertsystem/releases/tag/0.6.6), [0.6.7](https://github.com/ComPWA/expertsystem/releases/tag/0.6.7) and [0.6.8](https://github.com/ComPWA/expertsystem/releases/tag/0.6.8)

⚠️ Interface

- The `Estimator` now requires a phase-space sample for normalization and the constructor of the `IntensityBuilder` does not require one (192)

🐛 Bug fixes

- Correctly import form factor in relativistic Breit-Wigner (187)
- Added _mass_*_width_ factor to relativistic Breit-Wigner without form factor (189)
- Correctly throw invalid _L_ error (188)

🖱️ Developer Experience

- Issue templates have been improved (190)
- Added new issue label for interface changes (193)
- Added tests for the shape of a kinematics dataset (194)
- Removed the Python example workflow script (197)

_See all documentation for this version [here](https://tensorwaves.rtfd.io/en/0.1.3)._

💡 New features

- It's now possible to generate **deterministic data samples**. By default, generated samples are undeterministic (168)
- Fit results can be exported through `Callback`s (see `tensorwaves.optimizer.callbacks`). This also allows the user to break off a fit and pick it up anytime from the dumped fit result. `Callbacks` also offer different ways of logging and monitoring the optimization process (159)

🔨 Interface changes

- Dynamics have to be configured through the `expertsystem` ― `tensorwaves` does not provide default dynamics anymore (180)

📝 Documentation

- The develop/contribute page has been outsourced to the PWA pages (175)

🖱️ Internals

- `markdownlint` is run as pre-commit hook, not only as a separate `npm` package (178)
- Angular distribution are now tested with a test structure similar to that of `pycompwa` (182)
- CUDA GPU devices has been disable on GitHub Actions (183)
- Developer requirements are pinned all the way to indirect dependencies and for each Python version. The corresponding `requirements*.txt` files are updated automatically for each PR and through a cron job (185)

_See all documentation for this version [here](https://tensorwaves.rtfd.io/en/0.1.2)._

🐛 Bug fixes

- Upgrade to [`expertsystem` 0.6.3](https://github.com/ComPWA/expertsystem/releases/tag/0.6.3) to fix problems with canonical formalism (#150)
- Upload wheel as well as source ditribution to PyPI (149). This enables running `tensorwaves` on Google Colab again.

📝 Documentation

- Fix links to `tensorwaves` in documentation (145). Was previously pointing to the `expertsystem`.
- Upgrade to Sphinx Book Theme 0.39 (145)

🖱️ Internals

- Limit to Python < 3.9 in Conda environment definition (145)

_See all documentation for this version [here](https://tensorwaves.rtfd.io/en/0.1.1)._

🐛 Bug fixes

- Prevent multiple entries of the same invariant mass (123)
- Switched to `tf.random.Generator` instead of `tf.random.uniform` (141)

📝 Documentation

- Documentation is now built with MyST-NB and related packages (132)
- Example notebooks are hosted on Binder and Google Colaboratory (132)
- Added links to related projects in the sidebar (136)
- Added plots of the fit results (142)

🖱️ Internals

- Upgrade to TF2.3 for Python 3.8 support (128)
- Upgraded to `expertsystem` 0.6.2 (134)
- Migrated from Travis to GitHub Actions (124)
- Release notes are drafted automatically from PR titles since last release (129)
- Remove conda-forge from channels (133)

_See all documentation for this version [here](https://tensorwaves.rtfd.io/en/0.1.0-alpha0)._

New features
* Upgrade to [expertsystem 0.2.0](https://github.com/ComPWA/expertsystem/releases/tag/0.2.0-alpha)

Interface changes
* Now the decay dynamics are by default `RelativisticBreitWigner`. This change is triggered by the expertsystem 0.2.0 update, which does not specify decay dynamics anymore. One exception is the top node in a decay, which is still set exported as `NonDynamic` by the expertsystem.

* Helicity formalism and canonical formalism with TensorFlow as backend
* Up to fit procedure, no data or visualization model yet