Travis-CI:

# 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: Returns: 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. (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). 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’.