[英]masking a numpy array using an existing mask of the same shape
Say I have a numpy array mask called m1 = [[False, True, False], [True, False, True]]
And I want to find a mask m2 such that its (i,j) entry is True iff j >= 0 and m1[i, j+1] == True
.假设我有一个名为m1 = [[False, True, False], [True, False, True]]
的 numpy 数组掩码m1 = [[False, True, False], [True, False, True]]
我想找到一个掩码 m2 ,使得它的 (i,j) 条目为 True iff j >= 0 and m1[i, j+1] == True
。 Any elegant and efficient ideas as to how to pull that off?关于如何实现这一目标的任何优雅而有效的想法? Thanks谢谢
Here's a way slicing and using binary operators:这是一种切片和使用二元运算符的方法:
m1 = np.array([[False, True, False], [True, False, True]])
m2 = np.full(m1.shape, False)
m2[:, :-1] = m1[:, 1:] | m2[:, :-1]
print(m2)
array([[ True, False, False],
[False, True, False]])
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