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matplotlib.pyplot.xcorr(x, y, normed=True, detrend=<function detrend_none>, usevlines=True, maxlags=10, *, data=None, **kwargs)[source]

Plot the cross correlation between x and y.

The correlation with lag k is defined as \(\sum_n x[n+k] \cdot y^*[n]\), where \(y^*\) is the complex conjugate of \(y\).

x : sequence of scalars of length n
y : sequence of scalars of length n
detrend : callable, optional, default: mlab.detrend_none

x is detrended by the detrend callable. Default is no normalization.

normed : bool, optional, default: True

If True, input vectors are normalised to unit length.

usevlines : bool, 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 : int, optional

Number of lags to show. If None, will return all 2 * len(x) - 1 lags. Default is 10.

lags : array (length 2*maxlags+1)

lag vector.

c : array (length 2*maxlags+1)

auto correlation vector.

line : LineCollection or Line2D

Artist added to the axes of the correlation

LineCollection if usevlines is True Line2D if usevlines is False

b : Line2D or None

Horizontal line at 0 if usevlines is True None usevlines is False

Other Parameters:
linestyle : Line2D property, optional

Only used if usevlines is False.

marker : string, optional

Default is 'o'.


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


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'.

Objects passed as data must support item access (data[<arg>]) and membership test (<arg> in data).