PyCircStat2: Circular statistics with Python

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.
```
from pycircstat2 import Circular
data = [30, 60, 90, 120, 150]
circ = Circular(data, unit="degree")
print(circ.summary())
circ.plot(plot_rose=True, plot_mean=True)
```
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 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.

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

The API reference is available here.

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

And a few more examples on selective topics: