On Wed, Apr 23, 2014 at 5:32 AM, Sebastian Berg <sebast...@sipsolutions.net> wrote: > On Di, 2014-04-22 at 15:35 -0600, Charles R Harris wrote: >> Hi All, >> >> >> I'd like to branch 1.9.x at the end of the month. There are a couple >> of reasons for the timing. First, we have a lot of new stuff in the >> development branch. Second, there is work ongoing in masked arrays >> that I'd like to keep out of the release so that it has more time to >> settle. Third, it's past time ;) > > Sounds good. > >> There are currently a number of 1.9.0 blockers, which can be seen >> here. >> >> Datetime timezone handling broken in 1.7.x >> >> I don't think there is time to get this done for 1.9.0 and it needs to >> be pushed off to 1.10.0. >> >> Return multiple field selection as ro view >> >> I have a branch for this, but because the returned type differs from a >> copy by alignment spacing there was a test failure. Merging that >> branch might cause some incompatibilities. >> > > I am a bit worried here that comparisons might make trouble. > >> Object array creation new conversion to int >> >> >> This one needs a decision. Julian, Sebastian, thoughts? >> > > Maybe for all to consider this is about what happens for object arrays > if you do things like: > > # Array cast to object array (np.array(arr) would be identical): > a = np.arange(10).astype(object) > # Array passed into new array creation (not just *one* array): > b = np.array([np.arange(10)], dtype=object) > # Numerical array is assigned to object array: > c = np.empty(10, dtype=object) > c[...] = np.arange(10) > > Before this change, the result was: > type(a[0]) is int > type(b[0,0]) is np.int_ # Note the numpy type > type(c[0]) is int > > After this change, they are all `int`. Though note that the numpy type > is preserved for example for long double. On the one hand preserving the > numpy type might be nice, but on the other hand we don't care much about > the dtypes of scalars and in practice the python types are probably more > often wanted.
what if I don't like python? >>> np.int_(0)**(-1) inf >>> 0**-1 Traceback (most recent call last): File "<pyshell#28>", line 1, in <module> 0**-1 ZeroDivisionError: 0.0 cannot be raised to a negative power >>> type(np.arange(5)[0]) <type 'numpy.int32'> >>> np.arange(5)[0]**-1 inf >>> type(np.arange(5)[0].item()) <type 'int'> >>> np.arange(5)[0].item()**-1 Traceback (most recent call last): File "<pyshell#40>", line 1, in <module> np.arange(5)[0].item()**-1 ZeroDivisionError: 0.0 cannot be raised to a negative power >>> np.__version__ '1.6.1' I remember struggling through this (avoiding python operations) quite a bit in my early bugfixes to scipy.stats.distributions. (IIRC I ended up avoiding most ints.) Josef > > Since I just realized that things are safe (float128 does not cast to > float after all), I changed my mind and am tempted to keep the new > behaviour. That is, if it does not create any problems (there was some > issue in scipy, not sure how bad). > > - Sebastian > >> Median of np.matrix is broken( >> >> >> Not sure what the status of this one is. >> >> 1.8 deprecations: Follow-up ticket >> >> >> Things that might should be removed. >> >> ERROR: test_big_arrays (test_io.TestSavezLoad) on OS X + Python 3.3 >> >> >> I believe this one was fixed. For general problems reading/writing big >> files on OS X, I believe they were fixed in Maverick and I'm inclined >> to recommend an OS upgrade rather than work to chunk all the io. >> >> Deprecate NPY_CHAR >> This one is waiting on a fix from Pearu to make f2py use numpy >> strings. I think we will need to do this ourselves if we want to carry >> through the deprecation. In any case it probably needs to be pushed >> off to 1.10. >> >> 1.7 deprecations: Follow-up ticket >> Some of these changes have been made, ro diagonal view for instance, >> some still remain. >> >> >> >> Additions, updates, and thoughts welcome. >> >> >> Chuck >> >> >> >> >> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion