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A package with tools to compute early warning signals (EWS) from time-series data

Project description

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Python package for computing, analysing and visualising early warning signals (EWS) in time-series data. Includes a novel approach to characterise bifurcations using EWS.

Functionality includes

  • Computing the following EWS

    • Variance metrics (variance, standard deviation, coefficient of variation)
    • Autocorrelation (at specified lag times)
    • Higher moments (skewness, kurtosis)
    • Power spectrum (including maximum frequency, coherence factor and AIC weights csp. to different canonical forms)
  • Block-bootstrapping time-series to obtain confidence bounds on EWS estimates

  • Visualisation of EWS with plots of time-series and power spectra.


The package ewstools requires Python version 3.7 or later to be installed on your system. It may then be installed using pip, by entering the following into your command line.

pip install ewstools


For demonstrations/tutorials on using ewstools, please refer to these iPython notebooks.


Full documentation is available on ReadTheDocs.


If you are interested in being a contributer, or run into trouble with the package, please post on the issue tracker.

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Files for ewstools, version 0.0.4
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