Osl-dynamics

PyPi release: https://pypi.org/project/osl-dynamics/1.4.3/

Changes:
- Tested installation against TensorFlow versions 2.11-2.15.
- Added functions to calculate partial coherence and partial directed coherence.
- Benchmarked the multitaper calculation against the HMM-MAR.
- Update docs.

PyPi release: https://pypi.org/project/osl-dynamics/1.4.2/

Changes:
- **Cost function calculation now takes the average over the time dimension by default.**
- Option to calcalate Viterbi path with the HMM.
- Option to calculate cross multitaper spectra.
- Tweaked the scaling of subject/state-specific multitaper/welch spectra.
- Allow 3D parcellations in plotting.
- Removed source files for S/MAGE (haven't been supported for a while).

PyPi release: https://pypi.org/project/osl-dynamics/1.4.1/

Changes:
- Update tutorials.
- Reduced default buffer size (for shuffling) from 100000 to 4000.
- Updated TINDA functions.

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).