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CHANGELOG.rst

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Changelog

Version 0.1.9

  • 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)

Version 0.1.8

  • 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
    • 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

Version 0.1.7

  • 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).

Version 0.1.6

  • 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

Version 0.1.5

  • 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)

Version 0.1.4

  • 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)

Version 0.1.3

  • 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

Version 0.1.1

  • Added new features
    • Empirical cumulative distribution function
    • Empirical cumulative distribution function percentile
    • Empirical cumulative distribution function slope
    • Empirical cumulative distribution function percentile count
    • Spectral entropy
    • Wavelet entropy
    • Wavelet absolute mean
    • Wavelet standard deviation
    • Wavelet variance
    • Wavelet energy
  • Minor fixes for Google Colab

Version 0.1.0

  • Release of TSFEL with documentation