Stairs.
corr
(other, lower=-inf, upper=inf, lag=0, clip='pre')¶Calculates either correlation, autocorrelation or cross-correlation.
All calculations are based off the Pearson correlation coefficient.
The calculation is between two step functions described by self and other. If lag is None or 0 then correlation is calculated, otherwise cross-correlation is calculated. Autocorrelation is a special case of cross-correlation when other is equal to self.
Parameters: |
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Returns: | The correlation (or cross-correlation) between self and other |
Return type: | float |
See also
Examples
>>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,5), sharey=True, sharex=True)
>>> for ax, title, stair_instance in zip(axes, ("s1", "s2"), (s1, s2)):
... stair_instance.plot(ax, label=title)
... ax.set_title(title)
>>> s1.corr(s2)
0.24687803791136045
>>> s2.corr(s1)
0.24687803791136045
>>> s1.corr(s2, lower=0, upper=6)
0.27500954910846337
>>> # autocorrelation with lag 1
>>> s1.corr(s1, lower=1, upper=5, lag=1)
-0.8660254037844386
>>> # cross-correlation with lag 1
>>> s1.corr(s2, lower=1, upper=5.5, lag=1)
0.4961389383568339
>>> # cross-correlation with lag 1
>>> s1.corr(s2, lower=1, upper=4.5, lag=1, clip='post')
0.4961389383568339