Osl-dynamics

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.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.3/

Changes:
- Analysis:
- Added cycles analysis (TINDA).
- New feature to hierarchically cluster state time courses from different runs.
- Models: subject embedding model for the HMM (SE-HMM).
- Examples: added toolbox paper scripts.
- Docs: updated the Fisher kernel write up.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.2/

**This version was used for the OSL workshop 2023.**

Changes (mainly patching bugs):
- The default learning rate of `config_api.wrappers.train_hmm` is higher (1e-3 -> 1e-2).
- `config_api.wrappers.train_dynemo` also saves a `free_energy` values to the `history` dict.
- Bug in the config API (finding custom functions) was fixed.
- New command line option for using the config API.
- Fixed issue re-initialising variables in Dense layers.

PyPi release: https://pypi.org/project/osl-dynamics/1.2.1/

Changes:
- Docs: 'Using BMRC' page removed from readthedocs, it is now a readme on the repo.
- Analysis:
- `analysis.statistics` module and examples for evoked response and comparing groups.
- Added Fisher kernel analysis code and example.
- Data: enforced `n_window` is odd; fixed bigs in `trim_time_series`.
- Simulation: new simulation class for soft mixtures with subject variability.
- Config API: more complete implementation for performing dynamic network analysis.