(In this post, let np be shorthand for numpy.)

Suppose a is a (n + k)‑dimensional np.ndarray object, for some integers n > 1 and k > 1. (IOW, n + k > 3 is the value of a.ndim). I want to enumerate a over its first n dimensions; this means that, at each iteration, the enumerator/iterator produces a pair whose first element is a tuple ii of n indices, and second element is the k‑dimensional sub-ndarray at a[ii] .

Granted, it is not difficult to code a function to do this (in fact, I give an example of such a function below), but I want to know this:

does numpy provide any special syntax or functions for carrying out this type of "partial" enumeration?

(Normally, when I want to iterate over an multidimensional np.ndarray object, I use np.ndenumerate, but it wouldn't help here, because (as far as I can tell) np.ndenumerate would iterate over all n + k dimensions.)

Assuming that the answer to the question above is yes, then there's this follow-up:

what about the case where the n dimensions to iterate over are not contiguous?

(In this case, the first element of the pair returned at each iteration by the enumerator/iterator would be a tuple of r > n elements, some of which would be a special value denoting "all", e.g. slice(None); the second element of this pair would still be an ndarray of length k.)

Thanks!

The code below hopefully clarifies the problem specification. The function partial_enumerate does what I would like to do using any special numpy constructs available for the purpose. Following the definition of partial_enumerate is a simple example for the case n = k = 2.

import numpy as np
import itertools as it
def partial_enumerate(nda, n):"""Enumerate over the first N dimensions of the numpy.ndarray NDA.Returns an iterator of pairs.  The first element of each pair is a tuple of N integers, corresponding to a partial index I into NDA; the second elementis the subarray of NDA at I."""# ERROR CHECKING & HANDLING OMITTEDfor ii in it.product(*[range(d) for d in nda.shape[:n]]):yield ii, nda[ii]a = np.zeros((2, 3, 4, 5))
for ii, vv in partial_enumerate(a, 2):print ii, vv.shape

Each line of the output is a "pair of tuples", where the first tuple represents a partial set of n coordinates in a, and the second one represents the shape of the k‑dimensional subarray of a at those partial coordinates; (the value of this second pair is the same for all lines, as expected from the regularity of the array):

(0, 0) (4, 5)
(0, 1) (4, 5)
(0, 2) (4, 5)
(1, 0) (4, 5)
(1, 1) (4, 5)
(1, 2) (4, 5)

In contrast, iterating over np.ndenumerate(a) in this case would result in a.size iterations, each visiting an individual cell of a.

You can use the numpy broadcasting rules to generate a cartesian product. The numpy.ix_ function creates a list of the appropriate arrays. It's equivalent to the below:

>>> def pseudo_ix_gen(*arrays):
...     base_shape = [1 for arr in arrays]
...     for dim, arr in enumerate(arrays):
...         shape = base_shape[:]
...         shape[dim] = len(arr)
...         yield numpy.array(arr).reshape(shape)
... 
>>> def pseudo_ix_(*arrays):
...     return list(pseudo_ix_gen(*arrays))

Or, more concisely:

>>> def pseudo_ix_(*arrays):
...     shapes = numpy.diagflat([len(a) - 1 for a in arrays]) + 1
...     return [numpy.array(a).reshape(s) for a, s in zip(arrays, shapes)]

The result is a list of broadcastable arrays:

>>> numpy.ix_(*[[2, 4], [1, 3], [0, 2]])
[array([[[2]],[[4]]]), array([[[1],[3]]]), array([[[0, 2]]])]

Compare this to the result of numpy.ogrid:

>>> numpy.ogrid[0:2, 0:2, 0:2]
[array([[[0]],[[1]]]), array([[[0],[1]]]), array([[[0, 1]]])]

As you can see, it's the same, but numpy.ix_ allows you to use non-consecutive indices. Now when we apply the numpy broadcasting rules, we get a cartesian product:

>>> list(numpy.broadcast(*numpy.ix_(*[[2, 4], [1, 3], [0, 2]])))
[(2, 1, 0), (2, 1, 2), (2, 3, 0), (2, 3, 2), (4, 1, 0), (4, 1, 2), (4, 3, 0), (4, 3, 2)]

If, instead of passing the result of numpy.ix_ to numpy.broadcast, we use it to index an array, we get this:

>>> a = numpy.arange(6 ** 4).reshape((6, 6, 6, 6))
>>> a[numpy.ix_(*[[2, 4], [1, 3], [0, 2]])]
array([[[[468, 469, 470, 471, 472, 473],[480, 481, 482, 483, 484, 485]],[[540, 541, 542, 543, 544, 545],[552, 553, 554, 555, 556, 557]]],[[[900, 901, 902, 903, 904, 905],[912, 913, 914, 915, 916, 917]],[[972, 973, 974, 975, 976, 977],[984, 985, 986, 987, 988, 989]]]])

However, caveat emptor. Broadcastable arrays are useful for indexing, but if you literally want to enumerate the values, you might be better off using itertools.product:

>>> %timeit list(itertools.product(range(5), repeat=5))
10000 loops, best of 3: 196 us per loop
>>> %timeit list(numpy.broadcast(*numpy.ix_(*([range(5)] * 5))))
100 loops, best of 3: 2.74 ms per loop

So if you're incorporating a for loop anyway, then itertools.product will likely be faster. Still, you can use the above methods to get some similar data structures in pure numpy:

>> pgrid_idx = numpy.ix_(*[[2, 4], [1, 3], [0, 2]])
>>> sub_indices = numpy.rec.fromarrays(numpy.indices((6, 6, 6)))
>>> a[pgrid_idx].reshape((8, 6))
array([[468, 469, 470, 471, 472, 473],[480, 481, 482, 483, 484, 485],[540, 541, 542, 543, 544, 545],[552, 553, 554, 555, 556, 557],[900, 901, 902, 903, 904, 905],[912, 913, 914, 915, 916, 917],[972, 973, 974, 975, 976, 977],[984, 985, 986, 987, 988, 989]])
>>> sub_indices[pgrid_idx].reshape((8,))
rec.array([(2, 1, 0), (2, 1, 2), (2, 3, 0), (2, 3, 2), (4, 1, 0), (4, 1, 2), (4, 3, 0), (4, 3, 2)], dtype=[('f0', '<i8'), ('f1', '<i8'), ('f2', '<i8')])