Changelog

Version 0.2.0

New features
- Added support for Python 3.13 (#176)
Changes
- Replaced numpy.trapz with scipy.integrate.trapezoid for consistent behavior and compatibility with NumPy < 2 (#178)

Changes
- Replaced the Histogram feature with Histogram mode (#167)
Improvements
- The correlated_features method now supports returning a filtered feature vector (#163)
Documentation
- Set up a Slack community invite (#164)
- Enhanced documentation for the time_series_feature_extraction and get_features_by_domain functions
- Updated the main example notebook on Human Activity Recognition (#159)

New Features
- Added a new Datasets module with convenient methods to load single-problem datasets (#156)
- Improved the get_features_by_domain method, allowing easier selection of multiple feature domain combinations
Improvements
- System Message: WARNING/2 (../CHANGELOG.rst, line 3); backlink
  
  Duplicate explicit target name: “#156”.
  
  Significantly reduced the computational time for the LPCC feature (#156)
- Resolved deprecation issues with SciPy Wavelets by switching to PyWavelets for features that rely on wavelets (#147)
- Renamed the fft_mean_coefficient feature to spectrogram_mean_coefficient for descriptive correctness (#145)
Bugfixes
- Fixed a bug causing a circular import issue (#154)
- Fixed a ResourceWarning when loading the feature configuration file (#152)
- Removed the use of eval (#150)
Documentation
- Major documentation updates, including detailed explanations of the expected input and output data formats

New features
- Implemented the Lempel-Ziv-Complexity in the temporal domain (#146)
- Added the fractal domain with the following features (#144):
  
  Detrended fluctuation analysis (DFA)
  
  Higuchi fractal dimension
  
  Hurst exponent
  
  Maximum fractal length
  
  Multiscale entropy (MSE)
  
  Petrosian fractal dimension
Changes
- Changed the autocorrelation logic. It now measures the first lag below (1/e) from the ACF (#142).

Changes
- Feature total energy changed name to average power
- Features peak to peak, absolute energy and entropy are now classified as statistical
Bugfixes
- Fixed a bug on numpy bool usage (#133)
- Fixed a bug on features’ header names
Improvements
- Correlated features are now computed using absolute value
- Unit tests improvements
- Refactoring of some code sections and overall improved stability

Bugfixes - Fixed a bug on scipy function median_absolute_deviation to median_abs_deviation (#128) - Fixed on pandas function df.append to pd.concat (#120)

Bugfixes
- Fixed a bug on the progress bar not being displayed if the signal is passed already divided into windows (#49)
- Fixed a bug on the distance feature (#54)
- Fixed a bug raising zero division in the ECDF slope feature (#57)
- Fixed a bug when adding customised features using the JSON
- Fixed a bug on LPC was returning inconsistent values (#58)
- Fixed a bug on normalised autocorrelation (#64)
Improvements
- Refactoring of some code sections and overall improved stability
- The documentation has been improved and a FAQ section was created
- The window_splitter parameter is now deprecated. If the user selected a window_size it is assumed that the signal must be divided into windows.
- Unit tests improvements
New features
- Added to return the size of the feature vector from the configuration dictionary (#50)

Bugfixes
- Bug fixes on computational complexity calculation (#15)
- Fixed an error on lpcc feature (#38)
- Removed entropy warning (#38)
Improvements
- Code cleaning on (TSFEL_HAR_Example.ipynb)
- ecdf code cleaning and computational optimization
- Overlap value is now optional and set to default as 0
- Unit test improvements
- Nomenclature review of peak-related features
New features:
- Added new tutorials based on Jupyter notebooks (#19)
- Added progress bar during feature extraction (#16)
- Implemented multiprocessing. The n_jobs kwarg selects the number of CPUs to be scheduled (#30)
- Added the neighbourhood_peaks feature