circvar#

astropy.stats.circvar(data: NDArray | Quantity, axis: int | None = None, weights: NDArray | None = None) → NDArray | Quantity[source]#

Computes the circular variance of an array of circular data.

There are some concepts for defining measures of dispersion for circular data. The variance implemented here is based on the definition given by [1], which is also the same used by the R package ‘CircStats’ [2].

Parameters:

datandarray or Quantity: Array of circular (directional) data, which is assumed to be in radians whenever data is numpy.ndarray. Dimensionless, if Quantity.
axispython:int, optional: Axis along which circular variances are computed. The default is to compute the variance of the flattened array.
weightsnumpy.ndarray, optional: In case of grouped data, the i-th element of weights represents a weighting factor for each group such that sum(weights, axis) equals the number of observations. See [1], remark 1.4, page 22, for detailed explanation.

Returns:

circvarndarray or Quantity [:ref: ‘dimensionless’]: Circular variance.

Notes

For Scipy < 1.9.0, scipy.stats.circvar uses a different definition based on an approximation using the limit of small angles that approaches the linear variance. For Scipy >= 1.9.0, scipy.stats.cirvar uses a definition consistent with this implementation.

References

[1] (1,2)

S. R. Jammalamadaka, A. SenGupta. “Topics in Circular Statistics”. Series on Multivariate Analysis, Vol. 5, 2001.

[2]

C. Agostinelli, U. Lund. “Circular Statistics from ‘Topics in Circular Statistics (2001)’”. 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>

Examples

>>> import numpy as np
>>> from astropy.stats import circvar
>>> from astropy import units as u
>>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
>>> circvar(data) 
<Quantity 0.16356352748437508>