<p align="center" style="font-size:40px; margin:0px 10px 0px 10px">
<em>pyextremes</em>
</p>
<p align="center">
<em>Extreme Value Analysis (EVA) in Python</em>
</p>
<p align="center">
<a href="https://github.com/georgebv/pyextremes/actions/workflows/test.yml" target="_blank">
<img src="https://github.com/georgebv/pyextremes/actions/workflows/test.yml/badge.svg?event=pull_request" alt="Test">
</a>
<a href="https://codecov.io/gh/georgebv/pyextremes" target="_blank">
<img src="https://codecov.io/gh/georgebv/pyextremes/branch/master/graph/badge.svg" alt="Coverage">
</a>
<a href="https://pypi.org/project/pyextremes" target="_blank">
<img src="https://badge.fury.io/py/pyextremes.svg" alt="PyPI Package">
</a>
<a href="https://anaconda.org/conda-forge/pyextremes" target="_blank">
<img src="https://img.shields.io/conda/vn/conda-forge/pyextremes.svg" alt="Anaconda Package">
</a>
</p>
# About
**Documentation:** https://georgebv.github.io/pyextremes/
**License:** [MIT](https://opensource.org/licenses/MIT)
**Support:** [ask a question](https://github.com/georgebv/pyextremes/discussions)
or [create an issue](https://github.com/georgebv/pyextremes/issues/new/choose),
any input is appreciated and would help develop the project
**pyextremes** is a Python library aimed at performing univariate
[Extreme Value Analysis (EVA)](https://en.wikipedia.org/wiki/Extreme_value_theory).
It provides tools necessary to perform a wide range of tasks required to
perform EVA, such as:
- extraction of extreme events from time series using methods such as
Block Maxima (BM) or Peaks Over Threshold (POT)
- fitting continuous distributions, such as GEVD, GPD, or user-specified
continous distributions to the extracted extreme events
- visualization of model inputs, results, and goodness-of-fit statistics
- estimation of extreme events of given probability or return period
(e.g. 100-year event) and of corresponding confidence intervals
- tools assisting with model selection and tuning, such as selection of
block size in BM and threshold in POT
Check out [this repository](https://github.com/georgebv/pyextremes-notebooks)
with Jupyter notebooks used to produce figures for this readme
and for the official documentation.
# Installation
Get latest version from PyPI:
```shell
pip install pyextremes
```
Install with optional dependencies:
```shell
pip install pyextremes[full]
```
Get latest experimental build from GitHub:
```shell
pip install "git+https://github.com/georgebv/pyextremes.git#egg=pyextremes"
```
Get pyextremes for the Anaconda Python distribution:
```shell
conda install -c conda-forge pyextremes
```
# Illustrations
<p align="center" style="font-size:20px; margin:10px 10px 0px 10px">
<em>Model diagnostic</em>
</p>
<p align="center" style="font-size:20px; margin:10px 10px 40px 10px">
<img src="https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/diagnostic.png" alt="Diagnostic plot" width="600px">
</p>
<p align="center" style="font-size:20px; margin:10px 10px 0px 10px">
<em>Extreme value extraction</em>
</p>
<p align="center" style="font-size:20px; margin:10px 10px 40px 10px">
<img src="https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/extremes.png" alt="Diagnostic plot" width="600px">
</p>
<p align="center" style="font-size:20px; margin:10px 10px 0px 10px">
<em>Trace plot</em>
</p>
<p align="center" style="font-size:20px; margin:10px 10px 40px 10px">
<img src="https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/trace.png" alt="Diagnostic plot" width="600px">
</p>
<p align="center" style="font-size:20px; margin:10px 10px 0px 10px">
<em>Corner plot</em>
</p>
<p align="center" style="font-size:20px; margin:10px 10px 40px 10px">
<img src="https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/corner.png" alt="Diagnostic plot" width="600px">
</p>
# Acknowledgements
I wanted to give kudos to [Jean Toilliez](https://github.com/jtoilliez) who has inspired me to develop this open-source project and who taught me a lot about the extreme value theory. Also big thanks to Max Larson who has introduced me to software development and statistics.
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