You can create a mask over the elements in a that are within the given ranges and use this mask whenever you need to operate on a. If you want the encapsulate the mask and array in one entity, have a look at the numpy.ma module.

a = np.array([[[0,1,2],[3,4,5]],
             [[6,7,8],[9,10,11]]])

b = np.array([[1,2],
              [0,0]])

mask = np.arange(3)[None,None,:] <= b[:,:,None]

a[mask]

Output:

array([0, 1, 3, 4, 5, 6, 9])

But if you want the output as a non-homogeneous array, I cannot think of a better way to do that than using loops.

First of all, it will be hard to proceed with certainty without "actually running baseline tests". Because your dilemma can be summarized, as follows:

readability and convenience vs. performance and efficiency

While readability, convenience, and maintainability of the code are very important, the heavy computations still have to be done efficiently and use as much of the computational power as possible. Arrays are very special in that regard because they are stored contiguously in memory (column- or row-wise); thus, if one accesses them in the order they are stored, it minimizes the amount of cache misses, improving the usage of the CPU. Moreover, the vectorization instructions can be applied easier (depends on the compiler and its settings).

Now, with the introduction of slicing the array, excessive using strides, one will definitely sacrifice some efficiency. How much? Depends on the heaviness of the array computations, surrounding logic, basic quality of the other code, compiler, CPU, RAM etc. So, actual benchmarking is important to make a decision on how to organize the data-flow.

The picture below (IT Hare) has much more information, but you might look into the difference between the time required for L1-, L2-, L3-, RAM-read. By introducing non-contiguous access to your array, you will increase the amount of time your code will have to do L2-read instead of L1 and L3 instead of L2. Or if your arrays are very large and access is completely random, most reads will be performed from RAM every time, which is horrendous.