[英]Filter numpy array base 1-0 mask
Say I have a numpy ndarray of shape (172,40,20) and a 1-0 mask of shape (172, 40).假设我有一个形状为 (172,40,20) 的 numpy ndarray 和一个形状为 (172, 40) 的 1-0 掩码。 I would like to do something similar to bitwise_and: to keep those values where the mask value is 1 while other values set to 0 where the mask value is 0.我想做一些类似于 bitwise_and 的事情:在掩码值为 1 的地方保留这些值,而在掩码值为 0 的地方将其他值设置为 0。
Is there a way that I can do without looping?有没有一种方法可以不用循环? for example例如
a3D = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(a3D.shape)
mask = np.array([[1, 0], [0, 1]])
print(mask.shape)
# (2, 2, 2)
# (2, 2)
# my func
result = bitwise_and(a3D, mask)
# result = np.array([[[1, 2], [0, 0]], [[0, 0], [7, 8]]])
Simply index the array using the mask as boolean indices :只需使用掩码作为boolean 索引对数组进行索引:
result = a3D.copy()
result[mask == 0, :] = 0
print(result)
outputs产出
[[[1 2]
[0 0]]
[[0 0]
[7 8]]]
You can use np.where()
:您可以使用np.where()
:
np.where(mask[:, :, np.newaxis], a3D, 0)
[[[1 2]
[0 0]]
[[0 0]
[7 8]]]
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