如何获取"numpy.array"的边界? [英] How to get Boundaries of an 'numpy.array'?

问题描述

如果我有d维np.array，如何获取边界的标记?

If I have an d dimensional np.array, how can I get the indicies of the boundary?

例如，在2d中，

test = np.arange(16).reshape((4, 4))
test
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15]])

现在我想了解边界

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

如果有效并且在任意数量的维度上都有效，则非常好，但必须至少工作3个.数组不一定是超立方体，而可能是超矩形:在所有维度上的网格点数不一定相同，与示例不同.

Great if efficient and works for arbitrary number of dimensions, but it has to work at least 3. The array is not a necessarily a hypercube, but potentially a hyperrectangle: the number of grid points in all dimension are not necessarily the same, unlike in the example.

对于形状为(4, 5, 6)的数组，预期输出为

For an array of shape (4, 5, 6), the expected output is

array([[[ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True]],
       [[ True,  True,  True,  True,  True,  True],
        [ True, False, False, False, False,  True],
        [ True, False, False, False, False,  True],
        [ True, False, False, False, False,  True],
        [ True,  True,  True,  True,  True,  True]],
       [[ True,  True,  True,  True,  True,  True],
        [ True, False, False, False, False,  True],
        [ True, False, False, False, False,  True],
        [ True, False, False, False, False,  True],
        [ True,  True,  True,  True,  True,  True]],
       [[ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True,  True]]], dtype=bool)

推荐答案

您可以通过构建切片的元组来实现此目的，例如

You could do this by constructing a tuple of slices, e.g.

import numpy as np

def edge_mask(x):
    mask = np.ones(x.shape, dtype=bool)
    mask[x.ndim * (slice(1, -1),)] = False
    return mask

x = np.random.rand(4, 5)
edge_mask(x)

# array([[ True,  True,  True,  True,  True],
#        [ True, False, False, False,  True],
#        [ True, False, False, False,  True],
#        [ True,  True,  True,  True,  True]], dtype=bool)

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