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https://github.com/georgebv/pyextremes
Extreme Value Analysis (EVA) in Python
https://github.com/georgebv/pyextremes
block-maxima eva extreme-events extreme-value-analysis extreme-value-statistics extremes peaks-over-threshold python statistics
Last synced: about 2 months ago
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Extreme Value Analysis (EVA) in Python
- Host: GitHub
- URL: https://github.com/georgebv/pyextremes
- Owner: georgebv
- License: mit
- Created: 2020-04-11T16:15:01.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-30T21:42:26.000Z (about 2 months ago)
- Last Synced: 2024-07-30T21:44:53.150Z (about 2 months ago)
- Topics: block-maxima, eva, extreme-events, extreme-value-analysis, extreme-value-statistics, extremes, peaks-over-threshold, python, statistics
- Language: Python
- Homepage: https://georgebv.github.io/pyextremes/
- Size: 6.16 MB
- Stars: 234
- Watchers: 6
- Forks: 46
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-meteo - pyextremes
README
pyextremes
Extreme Value Analysis (EVA) in Python
# 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
Model diagnostic
Extreme value extraction
Trace plot
Corner plot
# 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.