v1.3.2

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.

v1.3.1

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).

v1.3.0

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.

v1.2.11

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.

v1.2.10

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.

v1.2.9

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.

v1.2.8

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".

v1.2.7

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.

v1.2.6

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.

v1.2.5

PyPi release: https://pypi.org/project/osl-dynamics/1.2.5/

Changes:

• API changes to the Data class. See: #161.
• Added methods for dual estimation and subject fine tuning.
• Option to do stats testing on time-frequency evoked response.
• Updated tutorials.