Extreme Value Analysis (EVA) in Python
Project description
About
Version: 2.0.0
License: MIT
E-Mail: bocharovgeorgii@gmail.com
Documentation: see the Tutorials section
pyextremes is a Python library implementing an easy-to-use extensible framework used to perform Extreme Value Analysis (EVA). It provides tools necessary to perform typical tasks constituting EVA, such as:
extraction of extreme events from time series using Block Maxima or Peaks Over Threshold methods
fitting continuous distributions, such as GEV, GPD, or user specified continous distribution, to the extracted extreme events
visualization of model performance and goodness-of-fit statistics
estimation of extreme events of given probability (e.g. 100-year event) and corresponding confidence intervals
tools assisting with model selection and tuning (block size in BM, threshold in POT)
(work-in-progress) multivariate extreme value analysis
Framework provided by the pyextremes library is easy to use and requires minimum user input to get good results. Its default parameters are configured in compliance with best industry standards (many concepts are based on the “An Introduction to Statistical Modeling of Extreme Values” book by Stuard Coles).
The framework also supports more in-depth configuration for specific cases. It supports all scipy continous distributions and also custom user-made distributions, which are subclasses of scipy.stats.rv_continuous
. Any parameter of a distribution may be frozen to investigate degenerate models (e.g. GEV->Gumbel). Distributions are fitted to the data using one of the following models:
Installation
Available via pip:
pip install pyextremes
Via anaconda:
conda install -c conda-forge pyextremes
Or from GitHub directly:
pip install git+https://github.com/georgebv/pyextremes
Dependencies
Python version: 3.7 or later
Required packages:
emcee >= 3.0
matplotlib
numpy
pandas
scipy
Tutorials
Models
Statistical distributions
Illustrations
Model diagnostic
Extreme value extraction
Model fitting (MCMC)
Trace plot
Corner plot
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