circcorrcoef¶

astropy.stats.
circcorrcoef
(alpha, beta, axis=None, weights_alpha=None, weights_beta=None)[source]¶ 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
isnumpy.ndarray
. beta : numpy.ndarray or Quantity
Array of circular (directional) data, which is assumed to be in radians whenever
data
isnumpy.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 ith element of
weights_alpha
represents a weighting factor for each group such thatsum(weights_alpha, axis)
equals the number of observations. See [1], 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
[1] 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.rproject.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) # doctest: +FLOAT_CMP <Quantity 0.2704648826748831>