Metadata-Version: 2.1
Name: pyextremes
Version: 2.0.1
Summary: Extreme Value Analysis (EVA) in Python
Home-page: https://github.com/georgebv/pyextremes
Author: Georgii Bocharov
Author-email: bocharovgeorgii@gmail.com
License: MIT
Project-URL: GitHub, https://github.com/georgebv/pyextremes
Project-URL: PyPI, https://pypi.org/project/pyextremes/
Project-URL: conda-forge, https://anaconda.org/conda-forge/pyextremes
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           :language: bash
        
        .. role:: python(code)
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        |build status| |coverage status| |pypi package| |conda version|
        
        .. contents:: Table of Contents
        
        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) <https://en.wikipedia.org/wiki/Extreme_value_theory>`_. 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 :python:`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:
        
        - :python:`MLE` (default model) - Maximum Likelihood Estimate, uses `scipy <https://www.scipy.org/>`_
        - :python:`Emcee` - Markov Chain Monte Calro, uses `emcee <https://emcee.readthedocs.io/en/stable/>`_
        
        Installation
        ============
        Available via pip:
        
        .. code:: bash
        
            pip install pyextremes
        
        Via anaconda:
        
        .. code:: bash
        
            conda install -c conda-forge pyextremes
        
        Or from GitHub directly:
        
        .. code:: bash
           
           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
        =========
        - `Basic usage <https://nbviewer.jupyter.org/github/georgebv/pyextremes-notebooks/blob/master/notebooks/EVA%20basic.ipynb>`_
        - Fitting models
        - Statistical distributions
        - Block maxima
          
          - Selecting block size
        
        - Peaks over threshold
          
          - `Threshold selection <https://nbviewer.jupyter.org/github/georgebv/pyextremes-notebooks/blob/master/notebooks/tutorials/threshold%20selection.ipynb>`_
        
        - Extreme high and extreme low values
        
        Illustrations
        =============
        
        Model diagnostic
        ----------------
        
        |model diagnostic image|
        
        Extreme value extraction
        ------------------------
        
        |extremes image|
        
        Model fitting (MCMC)
        --------------------
        
        Trace plot
        
        |trace image|
        
        Corner plot
        
        |corner image|
        
        .. |build status| image:: https://github.com/georgebv/pyextremes/workflows/build/badge.svg
           :target: https://github.com/georgebv/pyextremes/actions?query=workflow%3Abuild
        
        .. |coverage status| image:: https://codecov.io/gh/georgebv/pyextremes/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/georgebv/pyextremes
        
        .. |pypi package| image:: https://badge.fury.io/py/pyextremes.svg
            :target: https://pypi.org/project/pyextremes/
        
        .. |conda version| image:: https://img.shields.io/conda/vn/conda-forge/pyextremes.svg
            :target: https://anaconda.org/conda-forge/pyextremes
        
        .. |model diagnostic image| image:: https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/diagnostic.png
        
        .. |extremes image| image:: https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/extremes.png
        
        .. |trace image| image:: https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/trace.png
        
        .. |corner image| image:: https://raw.githubusercontent.com/georgebv/pyextremes-notebooks/master/notebooks/documentation/readme%20figures/corner.png
        
Keywords: statistics,extreme,extreme value analysis,eva,coastal,ocean,marine,environmental,engineering
Platform: linux
Platform: windows
Platform: mac
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Hydrology
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
