circcorrcoef

astropy.stats.circcorrcoef(alpha, beta, axis=None, weights_alpha=None, weights_beta=None)[source] [edit on github]

Computes the circular correlation coefficient between two array of circular data.

Parameters:

alpha : numpy.ndarray or Quantity

Array of circular (directional) data, which is assumed to be in radians whenever data is numpy.ndarray.

beta : numpy.ndarray or Quantity

Array of circular (directional) data, which is assumed to be in radians whenever data is numpy.ndarray.

axis : int, optional

Axis along which circular correlation coefficients are computed. The default is the compute the circular correlation coefficient of the flattened array.

weights_alpha : numpy.ndarray, optional

In case of grouped data, the i-th element of weights_alpha represents a weighting factor for each group such that sum(weights_alpha, axis) equals the number of observations. See [R65], remark 1.4, page 22, for detailed explanation.

weights_beta : numpy.ndarray, optional

See description of weights_alpha.

Returns:

rho : numpy.ndarray or dimensionless Quantity

Circular correlation coefficient.

References

[R65](1, 2) S. R. Jammalamadaka, A. SenGupta. “Topics in Circular Statistics”. Series on Multivariate Analysis, Vol. 5, 2001.
[R66]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 circcorrcoef
>>> from astropy import units as u
>>> alpha = np.array([356, 97, 211, 232, 343, 292, 157, 302, 335, 302,
...                   324, 85, 324, 340, 157, 238, 254, 146, 232, 122,
...                   329])*u.deg
>>> beta = np.array([119, 162, 221, 259, 270, 29, 97, 292, 40, 313, 94,
...                  45, 47, 108, 221, 270, 119, 248, 270, 45, 23])*u.deg
>>> circcorrcoef(alpha, beta) 
<Quantity 0.2704648826748831>