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 \(y\).

Parameters¶

x, yarray-like of length n

Neither x nor y are run through Matplotlib’s unit conversion, so these should be unit-less arrays.

detrendcallable, default: .mlab.detrend_none (no detrending)

A detrending function applied to x and y. It must have the signature

detrend(x: np.ndarray) -> np.ndarray

normedbool, default: True

If True, input vectors are normalised to unit length.

usevlinesbool, default: True

Determines the plot style.

If True, vertical lines are plotted from 0 to the xcorr value using .Axes.vlines. Additionally, a horizontal line is plotted at y=0 using .Axes.axhline.

If False, markers are plotted at the xcorr values using .Axes.plot.

maxlagsint, default: 10

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

Returns¶

lagsarray (length 2*maxlags+1)

The lag vector.

carray (length 2*maxlags+1)

The 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~matplotlib.lines.Line2D or None

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

Other Parameters¶

linestyle~matplotlib.lines.Line2D property, optional

The linestyle for plotting the data points. Only used if usevlines is False.

markerstr, default: ‘o’

The marker for plotting the data points. Only used if usevlines is False.

dataindexable object, optional

If given, the following parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception):

x, y

**kwargs

Additional parameters are passed to .Axes.vlines and .Axes.axhline if usevlines is True; otherwise they are passed to .Axes.plot.

Notes¶

The cross correlation is performed with numpy.correlate with mode = "full".