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# circstd

astropy.stats.circstats.circstd(data, axis=None, weights=None, method='angular')

  Computes the circular standard deviation of an array of circular data.

  The standard deviation implemented here is based on the definitions given by [1], which is also the same used by the R package ‘CirStat’ [2].

  Two methods are implemented: ‘angular’ and ‘circular’. The former is defined as sqrt(2 * (1 - R)) and it is bounded in [0, 2*Pi]. The latter is defined as sqrt(-2 * ln(R)) and it is bounded in [0, inf].

  Following ‘CircStat’ the default method used to obtain the standard deviation is ‘angular’.

  Parameters:
    data `ndarray` or `Quantity`

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

    axis `python:int`, optional

      Axis along which circular variances are computed. The default is to compute the variance of the flattened array.

    weights `numpy.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 [3], remark 1.4, page 22, for detailed explanation.

    method `python:str`, optional

      The method used to estimate the standard deviation:

      - ‘angular’ : obtains the angular deviation

      - ‘circular’ : obtains the circular deviation

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

      Angular or circular standard deviation.

  References

  [1]

  P. Berens. “CircStat: A MATLAB Toolbox for Circular Statistics”. Journal of Statistical Software, vol 31, issue 10, 2009.

  [2]

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

  [3]

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

  Examples

      >>> import numpy as np
      >>> from astropy.stats import circstd
      >>> from astropy import units as u
      >>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
      >>> circstd(data)
      <Quantity 0.57195022>

  Alternatively, using the ‘circular’ method:

      >>> import numpy as np
      >>> from astropy.stats import circstd
      >>> from astropy import units as u
      >>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
      >>> circstd(data, method='circular')
      <Quantity 0.59766999>