Numpy array, how to test if a value is the local minimum-CodePudding

I have a 2d Numpy array like:

array([[0.87, 0.13, 0.18, 0.04, 0.79],
       [0.07, 0.58, 0.84, 0.82, 0.76],
       [0.12, 0.77, 0.68, 0.58, 0.8 ],
       [0.43, 0.2 , 0.57, 0.91, 0.01],
       [0.43, 0.74, 0.56, 0.11, 0.58]])

I'd like to test each number to evaluate if it is the local minima within a window of x*y, for example, a 3x3 window would return this

array([[False, False, False,  True, False],
       [ True, False, False, False, False],
       [False, False, False, False, False],
       [False, False, False, False,  True],
       [False, False, False, False, False]])

I want to avoid using a native python loop, since my array is quite large

CodePudding user response：

You can use scipy.ndimage.minimum_filter to compute the 2D minima, then perform a comparison to the original:

from scipy.ndimage import minimum_filter

mins = minimum_filter(a, mode='constant', size=(3,3), cval=np.inf)

a==mins # or np.isclose(a, mins)

output:

array([[False, False, False,  True, False],
       [ True, False, False, False, False],
       [False, False, False, False, False],
       [False, False, False, False,  True],
       [False, False, False, False, False]])

intermediate mins:

array([[0.07, 0.07, 0.04, 0.04, 0.04],
       [0.07, 0.07, 0.04, 0.04, 0.04],
       [0.07, 0.07, 0.2 , 0.01, 0.01],
       [0.12, 0.12, 0.11, 0.01, 0.01],
       [0.2 , 0.2 , 0.11, 0.01, 0.01]])