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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased

2.0.0 - 2026-01-22

Breaking Changes

  • Transform System: Complete refactor to PyTorch named tensors for GPU acceleration. Transforms now use ('channel', 'time') or ('batch', 'channel', 'time') tensor conventions
  • Dataset Architecture: Refactored datasets module with layered architecture (WindowedDataset, DataModule, DatasetCreator)
  • Models Restructure: Flattened model hierarchy from 4 levels to 2 (models/raul_net/v16.py). Removed deprecated models (V1, V4, V9 variants, classification), removed online/offline distinction
  • Datatypes Package: Split datatypes.py into organized package structure
  • Storage Format: Enforced .zip format for datasets, removed GPU Direct Storage support

Added

  • SlidingWindowTransform base class for consistent transform implementations
  • MLflow experiment tracking and visualization (myoverse/tracking.py)
  • RAM caching and multiprocessing support in ContinuousDataset
  • Lazy dataloader initialization for faster startup
  • Cache pre-loading in main process before spawning workers
  • Nested dataset storage structure (split/modality/task)
  • ZipStore for faster dataset I/O on Windows
  • py.typed marker for PEP 561 compliance

Changed

  • Lazy import of heavy dependencies in datasets module for faster imports
  • Modernized codebase to Python 3.12+ type hints and modern super() calls
  • Ran ruff format across entire codebase
  • Removed graph visualization from _Data class
  • Removed Rich auto-coloring of numbers and text for cleaner output
  • Moved emg_xarray and emg_tensor to datatypes module

Removed

  • Unused utils module
  • tests/workflow.py
  • icecream dependency
  • Deprecated model variants (V1, V4, V9, classification models)
  • GPU Direct Storage (GDS) support

Fixed

  • MedianFilter padding
  • Tests updated to match simplified _Data API

1.1.6 - 2025-12-23

Added

  • GitHub Actions workflow for automated PyPI publishing using trusted publishing

Changed

  • Fixed numcodecs dependency specification to >=0.13.0,<0.16 to avoid cbuffer_sizes import errors
  • Renamed __add_data_to_dataset to _add_data_to_dataset in EMGDataset to fix Sphinx documentation generation

Fixed

  • Resolved Sphinx autosummary import error caused by Python name mangling of double-underscore methods

1.1.5 - 2025-12-23

Changed

  • Fixed numba dependency specification from <=0.61.0 to >=0.61.0 for improved compatibility
  • Updated documentation structure with enhanced README in docs/source
  • Updated dependency lock file (uv.lock) to reflect current dependencies

Added

  • .gitignore entry for version_* files to prevent tracking temporary version files

1.1.4 - 2025-05-01

Added

  • Added description to pyproject.toml

Changed

  • Version bump from 1.1.3 to 1.1.4

1.1.3 - 2025-05-01

Added

  • Package __version__ attribute in myoverse/__init__.py that dynamically reads from pyproject.toml
  • Version number included in logging timestamp prefix

Changed

  • PyPI compatible dependency specifications for PyTorch with separate configurations for Linux and Windows
  • Updated installation instructions in README for clarity on platform-specific PyTorch installations
  • Enhanced GitHub links in project.urls section

1.1.2 - 2025-05-01

Changed

  • Updated README logo URL to use raw GitHub link for better cross-platform compatibility
  • Revised Lightning to version 2.5.0.post0
  • Removed PyQt5 and related packages from development dependencies
  • Streamlined dependency list while maintaining compatibility

1.1.1 - 2025-04-05

Changed

  • Removed unnecessary string handling in EMGDataset class
  • Mean and standard deviation are no longer nn.Parameter objects in RaulNetV17 model. They should have been regular tensors
  • Fix version of lightning to 2.5.0.post0 in pyproject.toml to avoid bug with mlflow integration

1.1.0 - 2025-04-03

Added

  • RaulNetV17 model for decoding kinematics from EMG data
  • Workflow module for EMG data processing, model training, and result visualization
  • New reStructuredText files for data types and functions documentation
  • Unit tests for EMG data augmentation filters and default datasets

Changed

  • Refactored code for improved readability and consistency in optimizer configuration, visualization functions, and data class initializations
  • Updated test data class to include dimensions when unchunked
  • Downgraded zarr version from 3 to 2 in loader and supervised modules
  • Refactored optimizer configuration and updated ground truth handling in training steps
  • Refactored EMG and kinematics data classes to include dimensions when unchunked and streamlined chunk checking logic
  • Enhanced kinematics visualization with interactive features and improved styling
  • Updated zarr version and added mlflow and pyqt5 dependencies
  • Renamed AveragingSpatialFilter to ApplyFunctionSpatialFilter in documentation (filters.rst).
  • Refactored spatial filters (spatial.py) simplifying parameters and type hints.
  • Refactored SOSFrequencyFilter for easier usability.
  • Enhanced grid layout documentation and validation in EMGData class.

Removed

  • ElectrodeSelector and GridReshaper filters mentioned in documentation (filters.rst).
  • Unnecessary spatial filters.

1.0.0 - 2024-05-20

Added

  • Examples for documentation
  • Sections for Filters and Tutorials in README
  • Example scripts for training models and dataset creation
  • Enhanced documentation structure with autosummary for datasets and filters
  • Unit tests for core functionality

Changed

  • Bump version to 1.0.0 and add pydata-sphinx-theme dependency
  • Updated model definitions to indicate archival status
  • Replaced pytorch_lightning with lightning
  • Enhanced documentation for VirtualHandKinematics class by adding reference to MyoGestic
  • Refactored activation function documentation and improved loss calculation formatting
  • Enhanced dataset creation and loading by improving EMG dataset handling
  • Added empty data checks and refined augmentation pipelines
  • Refactored dataset creation with new CustomDataClass
  • Enhanced EMG dataset loading to support Zarr files with improved validation
  • Enhanced filter configurations by adding input_is_chunked parameter
  • Improved EMGAugmentation class with additional parameters and better documentation
  • Improved spatial filter API
  • Enhanced filter methods to accept additional keyword arguments
  • Enhanced layout algorithm for EMG visualizations with topological sorting and improved node positioning

0.1.0 - 2023-10-01

Added

  • Core EMG signal processing functionality with preprocessing filters
  • Data loaders for biomechanical data
  • PyTorch and PyTorch Lightning integration
  • AI models for EMG analysis and movement prediction
  • Kinematics and kinetics computation tools
  • Visualization utilities
  • Example notebooks for common use cases
  • Initial documentation and API reference

Publications

The following research papers have utilized MyoVerse: