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PyCircStat2: Circular statistics with Python

PyPI version

A rework of pycircstat.

Key Features | Installlation | API Reference | Examples ( Books | Topics )

Key Features

  • One-Stop Circular Data Analysis Pipeline with Circular Class

    The Circular class simplifies circular data analysis by providing automatic data transformation, descriptive statistics, hypothesis testing, and visualization tools—all in one place.

  • Compatibility with Legacy APIs

    APIs for descriptive statistics and hypothesis testing follow the conventions established by the original circstat-matlab and pycircstat, ensuring ease of use for existing users.

  • Wide-Ranging Circular Distributions

    The package supports a variety of circular distributions, including:

    • Symmetric distributions: Circular Uniform, Cardioid, Cartwright, Wrapped Normal, Wrapped Cauchy, von Mises (and its flat-top extension), and Jones-Pewsey.
    • Asymmetric distributions: Sine-skewed Jones-Pewsey, Asymmetric Extended Jones-Pewsey, Inverse Batschelet.

Also see the full feature checklist here.

Installation

To install the latest tagged version:

pip install pycircstat2

Or to install the development version, clone the repository and install it with pip install -e:

git clone https://github.com/circstat/pycircstat2
pip install -e pycircstat2

API Reference

The API reference is available here.

Example notebooks

In the notebooks below, we reproduce examples and figures from a few textbooks on circular statistics.

Books

And a few more examples on selective topics:

Topics