Does taking a slice of a flatiter always make a copy? That appears to be the behaviour in numpy 1.0.3. For example a.flat[1:3][0] = 5 does not modify the original array a, even when a is contiguous. Is this a bug?
import numpy as n n.version.version '1.0.3' a = n.zeros((2,3), n.int32) a array([[0, 0, 0], [0, 0, 0]]) b = a.flat[1:3] b[0] = 5 b array([5, 0]) a array([[0, 0, 0], [0, 0, 0]]) b = None a array([[0, 0, 0], [0, 0, 0]])
This behavior does not seem to match what is described in the Numpy book (Dec 7, 2006 version), section 3.1.3 "Other attributes", page 51: "flat Returns an iterator object (numpy.flatiter) that acts like a 1-d version of the array. 1-d indexing works on this array and it can be passed in to most routines as an array wherein a 1-d array will be constructed from it. The new 1-d array will reference this array's data if this array is C-style contiguous, otherwise, new memory will be allocated for the 1-d array, the UPDATEIFCOPY flag will be set for the new array, and this array will have its WRITEABLE flag set FALSE until the the last reference to the new array disappears. When the last reference to the new 1-d array disappears, the data will be copied over to this non-contiguous array. This is done so that a.flat effectively references the current array regardless of whether or not it is contiguous or non-contiguous." Tom Goddard UC San Francisco