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A rework ofpycircstat.

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

• One-Stop Circular Data Analysis Pipeline withCircular Class

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

  frompycircstat2importCirculardata= [30,60,90,120,150]circ=Circular(data,unit="degree")print(circ.summary())circ.plot(config={"rose":True,"mean":True})

• Compatibility with Legacy APIs

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

• Wide-Ranging Circular Distributions

  The package supports a variety of circular distributions, including but not limited to:

  • 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 checklisthere.

To install the latest tagged version:

pip install pycircstat2

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

git clone https://github.com/circstat/pycircstat2pip install -e pycircstat2

The API reference is availablehere.

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

• Statistical Analysis of Circular Data (Fisher, 1993)
• Chapter 26 and 27 from Biostatistical Analysis (Zar, 2010).
• Circular Statistics in R (Pewsey, et al., 2014)

And a few more examples on selective topics:

1. Utils
2. Descriptive Statistics
3. Hypothesis Testing
4. Circular Models
5. Regression
6. Clustering