scipy.stats.circmean

scipy.stats.circmean(samples, high=6.283185307179586, low=0, axis=None, nan_policy='propagate')

Compute the circular mean for samples in a range.

Parameters:

samplesarray_like

Input array.

highfloat or int, optional

High boundary for the sample range. Default is 2*pi.

lowfloat or int, optional

Low boundary for the sample range. Default is 0.

axisint, optional

Axis along which means are computed. The default is to compute the mean of the flattened array.

nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional

Defines how to handle when input contains nan. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default is ‘propagate’.

Returns:

circmeanfloat

Circular mean.

See also

circstd

Circular standard deviation.

circvar

Circular variance.

Examples

For simplicity, all angles are printed out in degrees.

>>> import numpy as np
>>> from scipy.stats import circmean
>>> import matplotlib.pyplot as plt
>>> angles = np.deg2rad(np.array([20, 30, 330]))
>>> circmean = circmean(angles)
>>> np.rad2deg(circmean)
7.294976657784009

>>> mean = angles.mean()
>>> np.rad2deg(mean)
126.66666666666666

Plot and compare the circular mean against the arithmetic mean.

>>> plt.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
...          np.sin(np.linspace(0, 2*np.pi, 500)),
...          c='k')
>>> plt.scatter(np.cos(angles), np.sin(angles), c='k')
>>> plt.scatter(np.cos(circmean), np.sin(circmean), c='b',
...             label='circmean')
>>> plt.scatter(np.cos(mean), np.sin(mean), c='r', label='mean')
>>> plt.legend()
>>> plt.axis('equal')
>>> plt.show()