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
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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
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Visualisation of EWS with plots of time-series and power spectra.
Dependencies include
- numpy, pandas, seaborn
- lmfit, arch
ewstools requires Python version 3.7 or later to be installed on your system. You can than install ewstools by entering
pip install ewstoolsinto the command line.
Installation with conda will be available soon, for those with an Anaconda distribution.
Full documentation is available here.
iPython notebooks demonstrating how to use the software are available here.