argrelmax(data, axis=0, order=1, mode='clip')
This function uses argrelextrema
with np.greater as comparator. Therefore, it requires a strict inequality on both sides of a value to consider it a maximum. This means flat maxima (more than one sample wide) are not detected. In case of 1-D data
find_peaks
can be used to detect all local maxima, including flat ones.
Array in which to find the relative maxima.
Axis over which to select from data
. Default is 0.
How many points on each side to use for the comparison to consider comparator(n, n+x)
to be True.
How the edges of the vector are treated. Available options are 'wrap' (wrap around) or 'clip' (treat overflow as the same as the last (or first) element). Default 'clip'. See numpy.take
.
Indices of the maxima in arrays of integers. extrema[k]
is the array of indices of axis :None:None:`k` of data
. Note that the return value is a tuple even when data
is 1-D.
Calculate the relative maxima of data
.
>>> from scipy.signal import argrelmax
... x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
... argrelmax(x) (array([3, 6]),)
>>> y = np.array([[1, 2, 1, 2],
... [2, 2, 0, 0],
... [5, 3, 4, 4]]) ...
>>> argrelmax(y, axis=1) (array([0]), array([1]))See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._peak_finding._boolrelextrema
scipy.signal._peak_finding_utils._local_maxima_1d
scipy.signal._peak_finding.argrelmax
scipy.signal._peak_finding.argrelmin
scipy.signal._peak_finding.argrelextrema
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