matplotlib.axes.Axes.acorr
-
Axes.acorr(self, x, *, data=None, **kwargs)
[source] -
Plot the autocorrelation of x.
Parameters: -
xarray-like
-
detrendcallable, default: mlab.detrend_none (no detrending)
-
A detrending function applied to x. 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 acorr value usingAxes.vlines
. Additionally, a horizontal line is plotted at y=0 usingAxes.axhline
.If
False
, markers are plotted at the acorr values usingAxes.plot
. -
maxlagsint, default: 10
-
Number of lags to show. If
None
, will return all2 * 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
andAxes.axhline
if usevlines isTrue
; otherwise they are passed toAxes.plot
.
Notes
The cross correlation is performed with
numpy.correlate
withmode = "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 asdata[s]
(unless this raises an exception): x.Objects passed as data must support item access (
data[s]
) and membership test (s in data
). -
Examples using matplotlib.axes.Axes.acorr
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Licensed under the Matplotlib License Agreement.
https://matplotlib.org/3.4.1/api/_as_gen/matplotlib.axes.Axes.acorr.html