Osl-dynamics

PyPi release: https://pypi.org/project/osl-dynamics/1.3.1/

Changes:
- Models:
- The efficiency of the model initialisation methods (`random_subset_initialization`, `random_state_time_course_initialization`) was improved (minimised the number of shuffles).
- Methods was updated to ensure a TensorFlow (TFRecord) Dataset can be passed.
- Improvements to H/DIVE:
- Modification to the calculation the KL term in the loss.
- Ability to pass multiple embeddings.
- Data object:
- Option to pass arbitrary auxiliary inputs to models when creating datasets with the Data object.
- Option to save/load TFRecord datasets (useful for training on very large datasets).
- Simulation classes:
- **`random_seed` argument was removed** - this may cause old scripts to error due to the unexpected argument (can just be deleted in the script). The user can use `osl_dynamics.utils.misc.set_random_seed` to ensure scripts are deterministic now.
- Plotting:
- Improved spatial map plotting to work with fMRI data (can now handle cifti files).

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"`.