Calculate the minimum of the values of an array over labeled regions.
Parameters : | input: array_like :
labels: array_like, optional :
index: array_like, optional :
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Returns : | output : float or list of floats
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See also
label, maximum, minimum_position, extrema, sum, mean, variance, standard_deviation
Notes
The function returns a Python list and not a Numpy array, use np.array to convert the list to an array.
Examples
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> labels, labels_nb = ndimage.label(a)
>>> labels
array([[1, 1, 0, 0],
[1, 1, 0, 2],
[0, 0, 0, 2],
[3, 3, 0, 0]])
>>> ndimage.minimum(a, labels=labels, index=np.arange(1, labels_nb + 1))
[1.0, 4.0, 3.0]
>>> ndimage.minimum(a)
0.0
>>> ndimage.minimum(a, labels=labels)
1.0