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librosa.util.localmin

librosa.util.localmin(x, axis=0)[source]

Find local minima in an array

An element x[i] is considered a local minimum if the following conditions are met:

x[i] < x[i-1]
x[i] <= x[i+1]

Note that the first condition is strict, and that the first element x[0] will never be considered as a local minimum.

Parameters:

xnp.ndarray [shape=(d1,d2,…)]: input vector or array
axisint: axis along which to compute local minimality

Returns:

mnp.ndarray [shape=x.shape, dtype=bool]: indicator array of local minimality along axis

See also

localmax

Examples

>>> x = np.array([1, 0, 1, 2, -1, 0, -2, 1])
>>> librosa.util.localmin(x)
array([False,  True, False, False,  True, False,  True, False])

>>> # Two-dimensional example
>>> x = np.array([[1,0,1], [2, -1, 0], [2, 1, 3]])
>>> librosa.util.localmin(x, axis=0)
array([[False, False, False],
       [False,  True,  True],
       [False, False, False]])

>>> librosa.util.localmin(x, axis=1)
array([[False,  True, False],
       [False,  True, False],
       [False,  True, False]])