circstd#
- astropy.stats.circstd(data: NDArray | Quantity, axis: int | None = None, weights: NDArray | None = None, method: str | None = 'angular') NDArray | Quantity [source]#
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
orQuantity
Array of circular (directional) data, which is assumed to be in radians whenever
data
isnumpy.ndarray
. If quantity, must be dimensionless.- axis
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 thatsum(weights, axis)
equals the number of observations. See [3], remark 1.4, page 22, for detailed explanation.- method
str
, optional The method used to estimate the standard deviation:
‘angular’ : obtains the angular deviation
‘circular’ : obtains the circular deviation
- data
- Returns:
- circstd
ndarray
orQuantity
[:ref: ‘dimensionless’] Angular or circular standard deviation.
- circstd
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>