On Wed, Oct 7, 2015 at 12:59 PM, Matti Picus <matti.picus@gmail.com> wrote:
I am trying to understand how nditer(ops, order='K') handles C and F order. In the documentation it states "‘K’ means as close to the order the array elements appear in memory as possible" but I seem to be getting inconsistent results (numpy 1.9):
>>> a = np.array([[1, 2], [3, 4]], order="C") >>> b = np.array([[1, 2], [3, 4]], order="F") >>> [v for v in np.nditer([a], order='K')]
[array(1), array(2), array(3), array(4)]
>>> [v for v in np.nditer([b], order='K')] [array(1), array(3), array(2), array(4)] >>> [v for v in np.nditer([a,b], order='K')] [(array(1), array(1)), (array(2), array(2)), (array(3), array(3)), (array(4), array(4))]
The result for np.nditer([b], order='K') seems to be wrong. Could someone confirm this is an issue or explain what is going on?
In this example, elements of a and b are being matched up according to their array indices, and then the iteration order is chosen according to the 'K' rule. The array a suggests to go in 'C' order, while the array b suggests to go in 'F' order. When there's a conflict/ambiguity such as this, it's resolved in the direction of 'C' order. If it were to go through a and b in each individual 'K' order, the elements wouldn't be paired up/broadcast together, which is the whole point of iterating over multiple arrays via the nditer. -Mark
Matti
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