# scipy.signal.argrelmin#

scipy.signal.argrelmin(data, axis=0, order=1, mode='clip')[source]#

Calculate the relative minima of data.

Parameters:
datandarray

Array in which to find the relative minima.

axisint, optional

Axis over which to select from data. Default is 0.

orderint, optional

How many points on each side to use for the comparison to consider `comparator(n, n+x)` to be True.

modestr, optional

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.

Returns:
extrematuple of ndarrays

Indices of the minima in arrays of integers. `extrema[k]` is the array of indices of axis k of data. Note that the return value is a tuple even when data is 1-D.

Notes

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.

New in version 0.11.0.

Examples

```>>> import numpy as np
>>> 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]))
```