matplotlib.axes.Axes.xcorr

Axes.xcorr(x, y, normed=True, detrend=<function detrend_none>, usevlines=True, maxlags=10, *, data=None, **kwargs)

Plot the cross correlation between x and y.

The correlation with lag k is defined as sum_n x[n+k] * conj(y[n]).

Parameters:	x : sequence of scalars of length n y : sequence of scalars of length n hold : boolean, optional, deprecated, default: True detrend : callable, optional, default: mlab.detrend_none x is detrended by the detrend callable. Default is no normalization. normed : boolean, optional, default: True if True, input vectors are normalised to unit length. usevlines : boolean, optional, default: True if True, Axes.vlines is used to plot the vertical lines from the origin to the acorr. Otherwise, Axes.plot is used. maxlags : integer, optional, default: 10 number of lags to show. If None, will return all 2 * len(x) - 1 lags.
Returns:	(lags, c, line, b) : where: lags are a length 2`maxlags+1 lag vector. c is the 2`maxlags+1 auto correlation vectorI line is a Line2D instance returned by plot. b is the x-axis (none, if plot is used).
Other Parameters:
	linestyle : Line2D prop, optional, default: None Only used if usevlines is False. marker : string, optional, default: ‘o’

Notes

The cross correlation is performed with numpy.correlate() with mode = 2.

Note

In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:

• All arguments with the following names: ‘x’, ‘y’.