Osl-dynamics

PyPi release: https://pypi.org/project/osl-dynamics/1.4.0/

This release uses TensorFlow 2.11.1 and tensorflow-probability 0.19.

Changes:
- Updated installation instructions.
- Added M-DyNeMo config API wrapper.

PyPi release: https://pypi.org/project/osl-dynamics/1.3.2/

This is the last release using TensorFlow 2.9.1 and tensorflow-probability 0.17 (next release will use newer versions).

Changes:
- Models:
- Refactored M-DyNeMo.
- Enhanced HIVE/DIVE and fixed bugs.
- Added Simplified-DyNeMo.
- Improved robustness of `random_state_time_course_initialization`.
- Option to select session and/or channels in the Data object.
- Other features/enhancements
- Cleaned up messages printed to screen (suppressing external loggers).
- Option to combine power map/connectivity network plots.
- Added standalone HMM dual estimation function.
- New function to plot HMM summary stats.

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.