使用 idx 作为 (n,3) 索引数组，一种使用 linear 的方法-indexing 将与 np.ravel_multi_index -

With idx as the (n,3) indexing array, one approach using linear-indexing would be with np.ravel_multi_index -

np.take(newmesh,np.ravel_multi_index(idx.T,newmesh.shape))

元组形成的方法看起来像这样 -

An approach with tuple formation would look like this -

newmesh[tuple(idx.T)]

如果只有三个维度，你甚至可以只使用柱状切片来索引每个维度，就像这样 -

If there are just three dimensions, you can even just use columnar slices for indexing into each dimension, like so -

newmesh[idx[:,0],idx[:,1],idx[:,2]]

运行时测试如果有人有兴趣查看与所列方法相关的性能数据，这里有一个快速运行时测试 -

Runtime test If anyone's interested in seeing the performance numbers associated with the listed approaches, here's a quick runtime test -

In [18]: newmesh = np.random.rand(40,40,40)

In [19]: idx = np.random.randint(0,40,(1000,3))

In [20]: %timeit np.take(newmesh,np.ravel_multi_index(idx.T,newmesh.shape))
10000 loops, best of 3: 22.5 µs per loop

In [21]: %timeit newmesh[tuple(idx.T)]
10000 loops, best of 3: 20.9 µs per loop

In [22]: %timeit newmesh[idx[:,0],idx[:,1],idx[:,2]]
100000 loops, best of 3: 17.2 µs per loop