Hello all On Wed, 23 May 2007, Anne Archibald wrote:
On 23/05/07, Albert Strasheim <fullung@gmail.com> wrote:
Consider the following example:
First a comment: almost nobody needs to care how the data is stored internally. Try to avoid looking at the flags unless you're interfacing with a C library. The nice feature of numpy is that it hides all that junk - strides, contiguous storage, iteration, what have you - so that you don't have to deal with it.
As luck would have it, I am interfacing with a C library.
Is it correct that the F_CONTIGUOUS flag is set in the case of the fancy indexed x? I'm running NumPy 1.0.3.dev3792 here.
Numpy arrays are always stored in contiguous blocks of memory with uniform strides. The "CONTIGUOUS" flag actually means something totally different, which is unfortunate, but in any case, "fancy indexing" can't be done as a simple reindexing operation. It must make a copy of the array. So what you're seeing is the flags of a fresh new array, created from scratch (and numpy always creates arrays in C order internally, though that is an implementation detail you should not rely on).
If you are correct that this is in fact a fresh new array, I really don't understand where the values of these flags. To recap: In [19]: x = N.zeros((3,2)) In [20]: x.flags Out[20]: C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False In [21]: x[:,[1,0]].flags Out[21]: C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False So since x and x[:,[1,0]] are both new arrays, shouldn't their flags be identical? I'd expect at least C_CONTIGUOUS and OWNDATA to be True. Thanks. Cheers, Albert