Osl-dynamics

PyPi release: https://pypi.org/project/osl-dynamics/1.2.9/

Changes:
- Fixed a bug calculating power when subject-specific PSDs were passed to `power.variance_from_spectra`.
- Default to using a progress bar when getting inferred parameters.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.8/

Changes:
- Major improvements to spectral estimation:
- General refactor of code and improved documentation.
- Added new function to calculate (HMM state/static) spectra with Welch's method.
- Benchmarked welch/multitaper against scipy/MNE. Note, PSDs are now a factor of 2 larger than in previous versions.
- Renamed `static.power_spectra` to `static.welch_spectra` for consistency with the `analysis.spectral` module.
- Removed the `glassbrain` argument from `connectivity.save` and added a new function for saving interactive connectivity plots (`connectivity.save_interactive`).
- Removed the `asymmetric_data` argument from `power.save`, the user should now pass `vmin`/`vmax` via `plot_kwargs` to the underlying nilearn plotting function.
- Added argument to allow the user to specify the method for calculating power from spectra: `power.variance_from_spectra(..., method="mean")`, where `method` can be `"mean"` or `"sum"`.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.7/

Changes:
- TensorFlow implementation of the Baum-Welch algorithm (using log probabilities rather than the probabilities directly).
- Config API:
- Added a `get_inf_params` wrapper.
- Added a `train_sehmm` wrapper.
- Improved the `asymmetric_data` argument in `analysis.power.save`. Can now specify the limits of the colorbar.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.6/

**This version of osl-dynamics was used to produce the results in: https://www.biorxiv.org/content/10.1101/2023.08.07.549346v1.**

Changes:
- Option to use TFRecords:
- This is the preferred approach for training on large datasets with TensorFlow.
- This feature was tested with TensorFlow 2.9.1. Newer versions of TensorFlow sometimes raise an error when using the TFRecords dataset. This will be fixed in a future release.
- Improved shuffling of TensorFlow datasets.
- Improved documentation.
- Refactored implementation of scaling 'static' quantities in the loss function.
- Tweaked the normalisation of power maps when using `power.variance_from_spectra`.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.5/

Changes:
- **API changes to the Data class**. See: https://github.com/OHBA-analysis/osl-dynamics/pull/161.
- Added methods for dual estimation and subject fine tuning.
- Option to do stats testing on time-frequency evoked response.
- Updated tutorials.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.4/

Changes:
- Renamed the command line interface: `osld-pipeline` -> `osl-dynamics`.
- Added option to predict overlapping alphas, which can be used to avoid discontinuities between sequences.
- Removed the `data.osl.HMM_MAR` (and `OSL_HMM`) class (which use to be used to load Matlab HMM runs - no longer needed).
- Added a method to get the inferred logits (theta).
- Handle failed initialisations (e.g. KL term errors) when using `random_subset_initialization`.
- Fixed bug reinitialising models (when you call `model.reset()`). This bug only affects users with TensorFlow 2.10 or newer.
- Added a method to calculate the Bayesian Information Criterion.
- New functions to calculate and plot a wavelet transform.
- Fixed bugs in examples: TINDA, SE-HMM.