Cropping a NumPy array of an arbitrary dimension given two corners

Question

There are several posts for slicing 2D and 3D NumPy arrays, but in my program dimension of the array is not known. Consider a NumPy ndarray A of arbitrary dimension n (positive integer), and shape D=[d1,...,dn] (dis nonnegative integers), For example:

import numpy as np

D=np.array([2,3,4,5])

A=np.random.rand(*D)

Now I need to extract a block of A starting from D1=[d11,...,d1n] to D2=[d21,...,d2n], where for all 0<i<=n : 0<=d1i<=d2i<=di . Something like:

A[D1:D2]

If I new n then I could simple use A[d11:d21,...,d1i:d2i,...,d1n:d2n], but that's not the case for me. I would appreciate if you could help me know what is the most efficient way of cropping A given D1 and D2.

@Joe I'm not sure if I understand your question. `D1` and `D2` should be two corners of the cropped ndarray. — Foad S. Farimani, Aug 10 '18 at 14:25
You could dynamically put together a list or an array and use that for slicing. — Joe, Aug 10 '18 at 14:26
Google for "advanced indexing numpy", will add an example later if needed. — Joe, Aug 10 '18 at 14:27
@Joe Thanks. I had seen, that page, but I will read it again. An example would be appreciated any time possible. — Foad S. Farimani, Aug 10 '18 at 14:29
Does this work? `A[tuple(slice(D1[i], D2[i]) for i in range(D1.shape[0]))]` — Aaron, Aug 10 '18 at 14:37
@Aaron awesome. this seems to work perfectly. Do you like to write it in a post below? — Foad S. Farimani, Aug 10 '18 at 14:43

Aaron · Accepted Answer · 2018-08-10T14:51:48.310

The portion of the numpy indexing page that @Joe referred to is likely this:

Note
Remember that a slicing tuple can always be constructed as obj and used in the x[obj] notation. Slice objects can be used in the construction in place of the [start:stop:step] notation. For example, x[1:10:5,::-1] can also be implemented as:

obj = (slice(1,10,5), slice(None,None,-1))
x[obj]

This can be useful for constructing generic code that works on arrays of arbitrary dimension.

Using this concept you should be able to build your tuple of slices ahead of time then apply it to A.

obj = tuple(slice(D1[i], D2[i]) for i in range(D1.shape[0]))
A[obj]

Note* this is not actually using advanced indexing, as you are still providing a tuple of slice objects which is just the longhand / functional equivalent of using slices separated by colons and commas: A[d11:d21, ... Advanced indexing utilizes arrays of different datatypes rather than exclusively tuples of slice objects.

Cropping a NumPy array of an arbitrary dimension given two corners

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