argrelmin(data, axis=0, order=1, mode='clip')
This function uses argrelextrema
with np.less as comparator. Therefore, it requires a strict inequality on both sides of a value to consider it a minimum. This means flat minima (more than one sample wide) are not detected. In case of 1-D data
find_peaks
can be used to detect all local minima, including flat ones, by calling it with negated data
.
Array in which to find the relative minima.
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 minima 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 minima of data
.
>>> from scipy.signal import argrelmin
... x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
... argrelmin(x) (array([1, 5]),)
>>> y = np.array([[1, 2, 1, 2],
... [2, 2, 0, 0],
... [5, 3, 4, 4]]) ...
>>> argrelmin(y, axis=1) (array([0, 2]), array([2, 1]))See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._peak_finding.argrelextrema
scipy.signal._peak_finding._boolrelextrema
scipy.signal._peak_finding.argrelmax
scipy.signal._peak_finding.argrelmin
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