On Mon, Oct 19, 2015 at 1:27 AM, <josef.p...@gmail.com> wrote: > > > On Mon, Oct 19, 2015 at 1:10 AM, Stephan Hoyer <sho...@gmail.com> wrote: > >> Looking at the git logs, column_stack appears to have been that way >> (creating a new array with concatenate) since at least NumPy 0.9.2, way >> back in January 2006: >> https://github.com/numpy/numpy/blob/v0.9.2/numpy/lib/shape_base.py#L271 >> > > Then it must have been changed somewhere else between 1.6.1 amd 1.9.2rc1 > > I have my notebook and my desktop with different numpy and python versions > next to each other and I don't see a typo in my command. > > I assume python 2.7 versus python 3.4 doesn't make a difference. > > ------------------ > > >>> np.column_stack((np.ones(10), np.ones(10))).flags > C_CONTIGUOUS : False > F_CONTIGUOUS : True > OWNDATA : False > WRITEABLE : True > ALIGNED : True > UPDATEIFCOPY : False > > >>> np.__version__ > '1.6.1' > >>> import sys > >>> sys.version > '2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit (Intel)]' > > ---------------- > > >>> np.column_stack((np.ones(10), np.ones(10))).flags > C_CONTIGUOUS : True > F_CONTIGUOUS : False > OWNDATA : True > WRITEABLE : True > ALIGNED : True > UPDATEIFCOPY : False > > >>> np.__version__ > '1.9.2rc1' > >>> import sys > >>> sys.version > '3.4.3 (v3.4.3:9b73f1c3e601, Feb 24 2015, 22:44:40) [MSC v.1600 64 bit > (AMD64)]' > > --------------------------- > > comparing all flags, owndata also has changed, but I don't think that has > any effect >
qualification It looks like in 1.9 it depends on the order of the 2-d arrays, which it didn't do in 1.6 >>> np.column_stack((np.ones(10), np.ones((10, 2), order='F'))).flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False which means the default order looks more like "K" now, not "C", IIUC Josef > > Josef > > >> >> >> Stephan >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> >
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