Metadata-Version: 2.1
Name: ewstools
Version: 0.0.4
Summary: A package with tools to compute early warning signals (EWS)
    from time-series data
Home-page: https://github.com/ThomasMBury/ewstools
Author: Thomas M Bury
Author-email: tombury182@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: lmfit (>=0.9)
Requires-Dist: arch (>=4.7)

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# ewstools
**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.


## Install:

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.
```python
pip install ewstools
```

## Demos

For demonstrations/tutorials on using *ewstools*, please refer to these [iPython notebooks](https://github.com/ThomasMBury/ewstools/tree/master/demos).

## Documentation

Full documentation is available on [ReadTheDocs](https://ewstools.readthedocs.io/en/latest/).

## Contribution

If you are interested in being a contributer, or run into trouble with the package, please post on the [issue tracker](https://github.com/ThomasMBury/ewstools/issues).



