matplotlib.pyplot.xcorr

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 \(y\).

Parameters:	x, yarray-like of length n 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. lineLineCollection or Line2D Artist added to the Axes of the correlation: LineCollection if usevlines is True. Line2D if usevlines is False. bLine2D or None Horizontal line at 0 if usevlines is True None usevlines is False.
Other Parameters:	linestyleLine2D 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. **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".

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 can also be string s, which is interpreted as data[s] (unless this raises an exception): x, y.

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

Examples using matplotlib.pyplot.xcorr