Happy new year everybody! I've been upgrading my code to start to support array indexing and in my tests I found something that was well documented, but surprising to me.
I've tried to read through https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing and even after multiple passes, I still find it very terse... Consider a mutli dimensional dataset: import numpy as np shape = (10, 20, 30) original = np.arange(np.prod(shape)).reshape(shape) Let's consider we want to collapse dim 0 to a single entry Let's consider we want a subset from dim 1, with a slice Let's consider that we want want 3 elements from dim 2 i = 2 j = slice(1, 6) k = slice(7, 10) out_basic = original[i, j, k] assert out_basic.shape == (5, 3) Now consider we want to provide freedom to have instead of a slice for k, an arbitrary "array" k = [7, 11, 13] out_array = original[i, j, k] assert out_array.shape == (5, 3), f"shape is actually {out_array.shape}" AssertionError: shape is actually (3, 5) To get the result "Mark expects", one has to do it in two steps integer_types = (int, np.integer) integer_indexes = ( i if isinstance(i, integer_types) else slice(None), j if isinstance(j, integer_types) else slice(None), k if isinstance(k, integer_types) else slice(None), ) non_integer_indexes = ( ((i,) if not isinstance(i, integer_types) else ()) + ((j,) if not isinstance(j, integer_types) else ()) + ((k,) if not isinstance(k, integer_types) else ()) ) out_double_indexed = original [integer_indexes][non_integer_indexes] assert out_double_indexed.shape == (5, 3), f"shape is actually {out_double_indexed.shape}" This is somewhat very surprising to me. I totally understand that things won't change in terms of this kind of indexing in numpy, but is there a way I can adjust my indexing strategy to regain the ability to slice into my array in a "single shot". The main usecase is for arrays that are truly huge, but chucked in ways where slicing into them can be quite efficient. This multi-dimensional imaging data. Each chunk is quite "huge" so this kind of metadata manipulation is worthwhile to avoid unecessary IO. Perhaps there is a "simple" distinction I am missing, for example using a tuple for k instead of a list???? Thanks for your input! Mark (I tried to keep my code copy pastable)
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