Osl-dynamics

PyPi release: https://pypi.org/project/osl-dynamics/1.3.0/

Changes:
- Models:
- Subject embedding models finalised: HIVE and DIVE.
- New HMM with a Poisson observation model.
- Data class:
- New method to select channels.
- No longer uses memory maps by default.
- Added decoding examples.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.11/

Changes:
- Multiple GPU training added.
- Fixed repeated calls to the same method in data preparation.
- Option to remove edge effects when getting HMM state probabilities.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.10/

Changes:
- Major update to examples:
- Update MEG examples.
- New fMRI examples.
- Switched to an analytical calculation for dual estimation with the HMM.
- Improvements to SE-HMM.

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.