NumPy：在3D切片中使用argmin的2D索引数组

我正在尝试使用来自argmin（或相关的argmax等函数）的2D数组索引来索引大型3D数组。这是我的示例数据：

I'm trying to index large 3D arrays using a 2D array of indicies from argmin (or related argmax, etc. functions). Here is my example data:

import numpy as np
shape3d = (16, 500, 335)
shapelen = reduce(lambda x, y: x*y, shape3d)

# 3D array of [random] source integers
intcube = np.random.uniform(2, 50, shapelen).astype('i').reshape(shape3d)

# 2D array of indices of minimum value along first axis
minax0 = intcube.argmin(axis=0)

# Another 3D array where I'd like to use the indices from minax0
othercube = np.zeros(shape3d)

# A 2D array of [random] values I'd like to assign in othercube
some2d = np.empty(shape3d[1:])

此时，两个3D阵列具有相同的形状，而 minax0 阵列具有形状（500,335）。现在，我想使用将2D数组 some2d 中的值分配给3D数组 othercube minax0 表示第一维的索引位置。这是我正在尝试但不起作用：

At this point, both 3D arrays have the same shape, while the minax0 array has the shape (500, 335). Now I'd like assign values from the 2D array some2d to the 3D array othercube using minax0 for the index position of the first dimension. This is what I'm trying, but doesn't work:

othercube[minax0] = some2d    # or
othercube[minax0,:] = some2d

抛出错误：

ValueError：花式索引中的维度太大

ValueError: dimensions too large in fancy indexing

注意：我是什么目前使用，但不是非常NumPythonic：

Note: What I'm currently using, but is not very NumPythonic:

for r in range(shape3d[1]):
    for c in range(shape3d[2]):
        othercube[minax0[r, c], r, c] = some2d[r, c]

我一直在网上挖掘找到可以索引 othercube 的类似例子，但我找不到任何优雅的东西。这需要高级索引吗？任何提示？

I've been digging around the web to find similar examples that can index othercube, but I'm not finding anything elegant. Would this require an advanced index? Any tips?