v1.7.0

[1.7.0] - 2025-10-17

Added

• Flexible Signal Normalization: New normalize_signal() function supporting both z-score (mean/std) and median/MAD normalization methods

  • Median/MAD normalization provides robust statistics less sensitive to outliers
  • Configurable normalization baseline via normalization_mask or normalization_time_range parameters
  • All detectors (Kay_ripple_detector, Karlsson_ripple_detector, Roumis_ripple_detector, multiunit_HSE_detector) now support these parameters
  • Enables advanced use cases: normalize during immobility only, use baseline period, exclude artifacts
  • Comprehensive test coverage including 23 new tests for normalization functionality

• Tutorial Notebook: Added examples/ripple_detection_tutorial.ipynb with step-by-step guide

  • Demonstrates basic ripple detection workflow
  • Shows how to use different normalization methods
  • Includes visualization examples

Changed

• Parameter Enhancement: Added normalization_method, normalization_mask, and normalization_time_range parameters to all detector functions
  • Default behavior unchanged (z-score normalization on full signal)
  • New parameters provide fine-grained control over normalization baseline

Deprecated

• use_speed_threshold_for_zscore parameter in multiunit_HSE_detector
  • Use normalization_mask=speed < speed_threshold instead for equivalent functionality
  • Deprecation warning added with migration guidance

Fixed

• Updated README badges to reference release workflow and codecov
• Reformatted pyproject.toml for better readability

Removed

• PR test GitHub Actions workflow (consolidated with main test workflow)

Closed Issues

• Issue #8: Add support for using Median / MAD for ripple detection
• Issue #9: Allow users to customize where the mean and std come from