[Numpy-discussion] 1.9.x branch
sebastian at sipsolutions.net
Wed Apr 23 05:32:03 EDT 2014
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 ;)
> There are currently a number of 1.9.0 blockers, which can be seen
> 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) is int
type(b[0,0]) is np.int_ # Note the numpy type
type(c) 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
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).
> 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.
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