On Tue, Oct 28, 2014 at 1:42 PM, Stephan Hoyer <sho...@gmail.com> wrote:
> .flat lets you iterate over all elements of a N-dimensional array as if it > was 1D, without ever needing to make a copy of the array. In contrast, > ravel() and reshape(-1) cannot always avoid a copy, because they need to > return another ndarray. In some cases ravel() returns a copy where a view can be easily constructed. For example, >>> x = np.arange(10) >>> y = x[::2] >>> y.ravel().flags['OWNDATA'] True Interestingly, in the same case reshape(-1) returns a view: >>> y.reshape(-1).flags['OWNDATA'] False (This suggests at least a documentation bug - numpy.ravel documentation says that it is equivalent to reshape(-1).) It is only in situations like this >>> a = np.arange(16).reshape((4,4)) >>> a[1::2,1::2].ravel() array([ 5, 7, 13, 15]) where flat view cannot be an ndarray, but .flat can still return something that is at least duck-typing compatible with ndarray (if not an ndarray subclass) and behaves as a view into original data. My preferred design would be for x.flat to return a flat view into x. This would be consistent with the way .T and .real attributes are defined and close enough to .imag. An obvious way to obtain a flat copy would be x.flat.copy(). Once we have this, ravel() and flatten() can be deprecated and reshape(-1) discouraged. I think this would be backward compatible except for rather questionable situations like this: >>> i = x.flat >>> list(i) [0, 1, 2, 3, 4, 0, 6, 7, 8, 9] >>> list(i) [] >>> np.array(i) array([0, 1, 2, 3, 4, 0, 6, 7, 8, 9])
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