由于从 numpy的提出的有关的索引可以使用的 片 内置功能和元组串联创建变量指标。

As suggested from numpy's documentation about indexing you can use the slice built-in function and tuple concatenation to create variable indexes.

在事实上：在标简直就是一个片的文字符号文字

In fact the : in the subscript is simply the literal notation for a slice literal.

在特定的：等同于片（无）（其中，本身就是等于片（无，无，无）这里的参数是启动，停止和步）。

In particular : is equivalent to slice(None) (which, itself, is equivalent to slice(None, None, None) where the arguments are start, stop and step).

例如：

a[(0,) * N + (slice(None),)]

相当于：

a[0, 0, ..., 0, :]   # with N zeros

的：对于片符号只能直接下标内部使用。例如，这将失败：

The : notation for slices can only be used directly inside a subscript. For example this fails:

In [10]: a[(0,0,:)]
  File "<ipython-input-10-f41b33bd742f>", line 1
    a[(0,0,:)]
           ^
SyntaxError: invalid syntax

要允许任意尺寸的阵列中提取切片你可以写一个简单的功能，如：

To allow extracting a slice from an array of arbitrary dimensions you can write a simple function such as:

def make_index(num_dimension, slice_pos):
    zeros = [0] * num_dimension
    zeros[slice_pos] = slice(None)
    return tuple(zeros)

和使用它作为：

In [3]: a = np.array(range(24)).reshape((2, 3, 4))

In [4]: a[make_index(3, 2)]
Out[4]: array([0, 1, 2, 3])

In [5]: a[make_index(3, 1)]
Out[5]: array([0, 4, 8])

In [6]: a[make_index(3, 0)]
Out[6]: array([ 0, 12])

您可以概括 make_index 做任何事情。要记住的重要一点是，它应该在年底，返回包含无论是整数或片个元组。

You can generalize make_index to do any kind of things. The important thing to remember is that it should, in the end, return a tuple containing either integers or slices.