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# circcorrcoef

astropy.stats.circcorrcoef(alpha, beta, axis=None, weights_alpha=None, weights_beta=None)

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

  Parameters:
    alpha `ndarray` or `Quantity`

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

    beta `ndarray` or `Quantity`

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

    axis `python: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 [1], remark 1.4, page 22, for detailed explanation.

    weights_beta `numpy.ndarray`, optional

      See description of `weights_alpha`.

  Returns:
    rho `ndarray` or `Quantity` [:ref: ‘dimensionless’]

      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.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>