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Calcul scientifique
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Classe « Axes »
Signature de la méthode xcorr
def xcorr(self, x, y, *, normed=True, detrend=<function detrend_none at 0x0000020D9ABF6660>, usevlines=True, maxlags=10, data=None, **kwargs)
Description
help(Axes.xcorr)
Plot the cross correlation between *x* and *y*.
The correlation with lag k is defined as
:math:`\sum_n x[n+k] \cdot y^*[n]`, where :math:`y^*` is the complex
conjugate of :math:`y`.
Parameters
----------
x, y : array-like of length n
Neither *x* nor *y* are run through Matplotlib's unit conversion, so
these should be unit-less arrays.
detrend : callable, 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
normed : bool, default: True
If ``True``, input vectors are normalised to unit length.
usevlines : bool, 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`.
maxlags : int, default: 10
Number of lags to show. If None, will return all ``2 * len(x) - 1``
lags.
Returns
-------
lags : array (length ``2*maxlags+1``)
The lag vector.
c : array (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``.
marker : str, default: 'o'
The marker for plotting the data points.
Only used if *usevlines* is ``False``.
data : indexable object, optional
If given, the following parameters also accept a string ``s``, which is
interpreted as ``data[s]`` if ``s`` is a key in ``data``:
*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"``.
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Machine Learning
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