Source code for astropy.stats.circstats

# Licensed under a 3-clause BSD style license - see LICENSE.rst

"""
This module contains simple functions for dealing with circular statistics, for
instance, mean, variance, standard deviation, correlation coefficient, and so
on. This module also cover tests of uniformity, e.g., the Rayleigh and V tests.
The Maximum Likelihood Estimator for the Von Mises distribution along with the
Cramer-Rao Lower Bounds are also implemented. Almost all of the implementations
are based on reference _, which is also the basis for the R package
'CircStats' _.
"""

import numpy as np
from astropy.units import Quantity

__all__ = ['circmean', 'circvar', 'circmoment', 'circcorrcoef', 'rayleightest',
'vtest', 'vonmisesmle']
__doctest_requires__ = {'vtest': ['scipy']}

def _components(data, p=1, phi=0.0, axis=None, weights=None):
# Utility function for computing the generalized rectangular components
# of the circular data.
if weights is None:
weights = np.ones((1,))
try:
weights = np.broadcast_to(weights, data.shape)
except ValueError:
raise ValueError('Weights and data have inconsistent shape.')

C = np.sum(weights * np.cos(p * (data - phi)), axis)/np.sum(weights, axis)
S = np.sum(weights * np.sin(p * (data - phi)), axis)/np.sum(weights, axis)

return C, S

def _angle(data, p=1, phi=0.0, axis=None, weights=None):
# Utility function for computing the generalized sample mean angle
C, S = _components(data, p, phi, axis, weights)

# theta will be an angle in the interval [-np.pi, np.pi)
# [-180, 180)*u.deg in case data is a Quantity
theta = np.arctan2(S, C)

if isinstance(data, Quantity):
theta = theta.to(data.unit)

return theta

def _length(data, p=1, phi=0.0, axis=None, weights=None):
# Utility function for computing the generalized sample length
C, S = _components(data, p, phi, axis, weights)
return np.hypot(S, C)

[docs]def circmean(data, axis=None, weights=None): """ Computes the circular mean angle of an array of circular data. Parameters ---------- data : 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 means are computed. The default is to compute the mean 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 _, remark 1.4, page 22, for detailed explanation. Returns ------- circmean : numpy.ndarray or Quantity Circular mean. Examples -------- >>> import numpy as np >>> from astropy.stats import circmean >>> from astropy import units as u >>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg >>> circmean(data) # doctest: +FLOAT_CMP <Quantity 48.62718088722989 deg> References ---------- ..  S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics". Series on Multivariate Analysis, Vol. 5, 2001. ..  C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in Circular Statistics (2001)'". 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> """ return _angle(data, 1, 0.0, axis, weights)
[docs]def circvar(data, axis=None, weights=None): """ Computes the circular variance of an array of circular data. There are some concepts for defining measures of dispersion for circular data. The variance implemented here is based on the definition given by _, which is also the same used by the R package 'CircStats' _. Parameters ---------- data : numpy.ndarray or dimensionless 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 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 _, remark 1.4, page 22, for detailed explanation. Returns ------- circvar : numpy.ndarray or dimensionless Quantity Circular variance. Examples -------- >>> import numpy as np >>> from astropy.stats import circvar >>> from astropy import units as u >>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg >>> circvar(data) # doctest: +FLOAT_CMP <Quantity 0.16356352748437508> References ---------- ..  S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics". Series on Multivariate Analysis, Vol. 5, 2001. ..  C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in Circular Statistics (2001)'". 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> Notes ----- The definition used here differs from the one in scipy.stats.circvar. Precisely, Scipy circvar uses an approximation based on the limit of small angles which approaches the linear variance. """ return 1.0 - _length(data, 1, 0.0, axis, weights)
[docs]def circmoment(data, p=1.0, centered=False, axis=None, weights=None): """ Computes the p-th trigonometric circular moment for an array of circular data. Parameters ---------- data : numpy.ndarray or Quantity Array of circular (directional) data, which is assumed to be in radians whenever data is numpy.ndarray. p : float, optional Order of the circular moment. centered : Boolean, optional If True, central circular moments are computed. Default value is False. axis : int, optional Axis along which circular moments are computed. The default is to compute the circular moment 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 _, remark 1.4, page 22, for detailed explanation. Returns ------- circmoment : numpy.ndarray or Quantity The first and second elements correspond to the direction and length of the p-th circular moment, respectively. Examples -------- >>> import numpy as np >>> from astropy.stats import circmoment >>> from astropy import units as u >>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg >>> circmoment(data, p=2) # doctest: +FLOAT_CMP (<Quantity 90.99263082432564 deg>, <Quantity 0.48004283892950717>) References ---------- ..  S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics". Series on Multivariate Analysis, Vol. 5, 2001. ..  C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in Circular Statistics (2001)'". 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> """ if centered: phi = circmean(data, axis, weights) else: phi = 0.0 return _angle(data, p, phi, axis, weights), _length(data, p, phi, axis, weights)
[docs]def circcorrcoef(alpha, beta, axis=None, weights_alpha=None, weights_beta=None): """ 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 _, 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. 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> References ---------- ..  S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics". Series on Multivariate Analysis, Vol. 5, 2001. ..  C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in Circular Statistics (2001)'". 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> """ if(np.size(alpha, axis) != np.size(beta, axis)): raise ValueError("alpha and beta must be arrays of the same size") mu_a = circmean(alpha, axis, weights_alpha) mu_b = circmean(beta, axis, weights_beta) sin_a = np.sin(alpha - mu_a) sin_b = np.sin(beta - mu_b) rho = np.sum(sin_a*sin_b)/np.sqrt(np.sum(sin_a*sin_a)*np.sum(sin_b*sin_b)) return rho
[docs]def rayleightest(data, axis=None, weights=None): """ Performs the Rayleigh test of uniformity. This test is used to identify a non-uniform distribution, i.e. it is designed for detecting an unimodal deviation from uniformity. More precisely, it assumes the following hypotheses: - H0 (null hypothesis): The population is distributed uniformly around the circle. - H1 (alternative hypothesis): The population is not distributed uniformly around the circle. Small p-values suggest to reject the null hypothesis. Parameters ---------- data : 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 the Rayleigh test will be performed. weights : numpy.ndarray, optional In case of grouped data, the i-th element of weights represents a weighting factor for each group such that np.sum(weights, axis) equals the number of observations. See _, remark 1.4, page 22, for detailed explanation. Returns ------- p-value : float or dimensionless Quantity p-value. Examples -------- >>> import numpy as np >>> from astropy.stats import rayleightest >>> from astropy import units as u >>> data = np.array([130, 90, 0, 145])*u.deg >>> rayleightest(data) # doctest: +FLOAT_CMP <Quantity 0.2563487733797317> References ---------- ..  S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics". Series on Multivariate Analysis, Vol. 5, 2001. ..  C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in Circular Statistics (2001)'". 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> ..  M. Chirstman., C. Miller. "Testing a Sample of Directions for Uniformity." Lecture Notes, STA 6934/5805. University of Florida, 2007. ..  D. Wilkie. "Rayleigh Test for Randomness of Circular Data". Applied Statistics. 1983. <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.211.4762> """ n = np.size(data, axis=axis) Rbar = _length(data, 1, 0.0, axis, weights) z = n*Rbar*Rbar # see  and  for the formulae below tmp = 1.0 if(n < 50): tmp = 1.0 + (2.0*z - z*z)/(4.0*n) - (24.0*z - 132.0*z**2.0 + 76.0*z**3.0 - 9.0*z**4.0)/(288.0 * n * n) p_value = np.exp(-z)*tmp return p_value
[docs]def vtest(data, mu=0.0, axis=None, weights=None): """ Performs the Rayleigh test of uniformity where the alternative hypothesis H1 is assumed to have a known mean angle mu. Parameters ---------- data : numpy.ndarray or Quantity Array of circular (directional) data, which is assumed to be in radians whenever data is numpy.ndarray. mu : float or Quantity, optional Mean angle. Assumed to be known. axis : int, optional Axis along which the V test will be performed. 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 _, remark 1.4, page 22, for detailed explanation. Returns ------- p-value : float or dimensionless Quantity p-value. Examples -------- >>> import numpy as np >>> from astropy.stats import vtest >>> from astropy import units as u >>> data = np.array([130, 90, 0, 145])*u.deg >>> vtest(data) # doctest: +FLOAT_CMP <Quantity 0.6223678199713766> References ---------- ..  S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics". Series on Multivariate Analysis, Vol. 5, 2001. ..  C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in Circular Statistics (2001)'". 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> ..  M. Chirstman., C. Miller. "Testing a Sample of Directions for Uniformity." Lecture Notes, STA 6934/5805. University of Florida, 2007. """ from scipy.stats import norm if weights is None: weights = np.ones((1,)) try: weights = np.broadcast_to(weights, data.shape) except ValueError: raise ValueError('Weights and data have inconsistent shape.') n = np.size(data, axis=axis) R0bar = np.sum(weights * np.cos(data - mu), axis)/np.sum(weights, axis) z = np.sqrt(2.0 * n) * R0bar pz = norm.cdf(z) fz = norm.pdf(z) # see reference  p_value = 1 - pz + fz*((3*z - z**3)/(16.0*n) + (15*z + 305*z**3 - 125*z**5 + 9*z**7)/(4608.0*n*n)) return p_value
def _A1inv(x): # Approximation for _A1inv(x) according R Package 'CircStats' # See http://www.scienceasia.org/2012.38.n1/scias38_118.pdf, equation (4) if 0 <= x < 0.53: return 2.0*x + x*x*x + (5.0*x**5)/6.0 elif x < 0.85: return -0.4 + 1.39*x + 0.43/(1.0 - x) else: return 1.0/(x*x*x - 4.0*x*x + 3.0*x)
[docs]def vonmisesmle(data, axis=None): """ Computes the Maximum Likelihood Estimator (MLE) for the parameters of the von Mises distribution. Parameters ---------- data : 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 the mle will be computed. Returns ------- mu : float or Quantity the mean (aka location parameter). kappa : float or dimensionless Quantity the concentration parameter. Examples -------- >>> import numpy as np >>> from astropy.stats import vonmisesmle >>> from astropy import units as u >>> data = np.array([130, 90, 0, 145])*u.deg >>> vonmisesmle(data) # doctest: +FLOAT_CMP (<Quantity 101.16894320013179 deg>, <Quantity 1.49358958737054>) References ---------- ..  S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics". Series on Multivariate Analysis, Vol. 5, 2001. ..  C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in Circular Statistics (2001)'". 2015. <https://cran.r-project.org/web/packages/CircStats/CircStats.pdf> """ mu = circmean(data, axis=None) kappa = _A1inv(np.mean(np.cos(data - mu), axis)) return mu, kappa