# cupyx.scipy.signal.argrelmin#

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

Calculate the relative minima of data.

Parameters
• data (ndarray) – Array in which to find the relative minima.

• axis (int, optional) – Axis over which to select from data. Default is 0.

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

• mode (str, 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 cupy.take.

Returns

extrema – 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 one-dimensional.

Return type

tuple of ndarrays

Notes

This function uses argrelextrema with cupy.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 one-dimensional data find_peaks can be used to detect all local minima, including flat ones, by calling it with negated data.

Examples

```>>> from cupyx.scipy.signal import argrelmin
>>> import cupy
>>> x = cupy.array([2, 1, 2, 3, 2, 0, 1, 0])
>>> argrelmin(x)
(array([1, 5]),)
>>> y = cupy.array([[1, 2, 1, 2],
...               [2, 2, 0, 0],
...               [5, 3, 4, 4]])
...
>>> argrelmin(y, axis=1)
(array([0, 2]), array([2, 1]))
```