On Tue, Feb 16, 2016 at 12:09 AM, <josef.p...@gmail.com> wrote: > > > On Mon, Feb 15, 2016 at 11:31 PM, Charles R Harris < > charlesr.har...@gmail.com> wrote: > >> >> >> On Mon, Feb 15, 2016 at 9:15 PM, <josef.p...@gmail.com> wrote: >> >>> >>> >>> On Mon, Feb 15, 2016 at 11:05 PM, Charles R Harris < >>> charlesr.har...@gmail.com> wrote: >>> >>>> >>>> >>>> On Mon, Feb 15, 2016 at 8:50 PM, <josef.p...@gmail.com> wrote: >>>> >>>>> >>>>> >>>>> On Mon, Feb 15, 2016 at 10:46 PM, <josef.p...@gmail.com> wrote: >>>>> >>>>> >>>>>> >>>>>> On Fri, Feb 12, 2016 at 4:19 PM, Nathan Goldbaum < >>>>>> nathan12...@gmail.com> wrote: >>>>>> >>>>>>> >>>>>>> https://github.com/numpy/numpy/blob/master/doc/release/1.11.0-notes.rst >>>>>>> >>>>>>> On Fri, Feb 12, 2016 at 3:17 PM, Andreas Mueller <t3k...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi. >>>>>>>> Where can I find the changelog? >>>>>>>> It would be good for us to know which changes are done one purpos >>>>>>>> without hunting through the issue tracker. >>>>>>>> >>>>>>>> Thanks, >>>>>>>> Andy >>>>>>>> >>>>>>>> >>>>>>>> On 02/09/2016 09:09 PM, Charles R Harris wrote: >>>>>>>> >>>>>>>> Hi All, >>>>>>>> >>>>>>>> I'm pleased to announce the release of NumPy 1.11.0b3. This beta >>>>>>>> contains additional bug fixes as well as limiting the number of >>>>>>>> FutureWarnings raised by assignment to masked array slices. One issue >>>>>>>> that >>>>>>>> remains to be decided is whether or not to postpone raising an error >>>>>>>> for >>>>>>>> floats used as indexes. Sources may be found on Sourceforge >>>>>>>> <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0b3/> and >>>>>>>> both sources and OS X wheels are availble on pypi. Please test, >>>>>>>> hopefully >>>>>>>> this will be that last beta needed. >>>>>>>> >>>>>>>> As a note on problems encountered, twine uploads continue to fail >>>>>>>> for me, but there are still variations to try. The wheeluploader >>>>>>>> downloaded >>>>>>>> wheels as it should, but could not upload them, giving the error >>>>>>>> message >>>>>>>> "HTTPError: 413 Client Error: Request Entity Too Large for url: >>>>>>>> <https://www.python.org/pypi>https://www.python.org/pypi". Firefox >>>>>>>> also complains that http://wheels.scipy.org is incorrectly >>>>>>>> configured with an invalid certificate. >>>>>>>> >>>>>>>> Enjoy, >>>>>>>> >>>>>>>> Chuck >>>>>>>> >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> NumPy-Discussion mailing >>>>>>>> listNumPy-Discussion@scipy.orghttps://mail.scipy.org/mailman/listinfo/numpy-discussion >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> NumPy-Discussion mailing list >>>>>>>> NumPy-Discussion@scipy.org >>>>>>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> _______________________________________________ >>>>>>> NumPy-Discussion mailing list >>>>>>> NumPy-Discussion@scipy.org >>>>>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion >>>>>>> >>>>>>> >>>>>> >>>>> (try to send again) >>>>> >>>>> >>>>>> >>>>>> another indexing question: (not covered by unit test but showed up >>>>>> in examples in statsmodels) >>>>>> >>>>>> >>>>>> This works in numpy at least 1.9.2 and 1.6.1 (python 2.7, and >>>>>> python 3.4) >>>>>> >>>>>> >>> list(range(5))[np.array([0])] >>>>>> 0 >>>>>> >>>>>> >>>>>> >>>>>> on numpy 0.11.0b2 (I'm not yet at b3) (python 3.4) >>>>>> >>>>>> I get the same exception as here but even if there is just one element >>>>>> >>>>>> >>>>>> >>> list(range(5))[np.array([0, 1])] >>>>>> Traceback (most recent call last): >>>>>> File "<pyshell#7>", line 1, in <module> >>>>>> list(range(5))[np.array([0, 1])] >>>>>> TypeError: only integer arrays with one element can be converted to >>>>>> an index >>>>>> >>>>> >>>> Looks like a misleading error message. Apparently it requires scalar >>>> arrays (ndim == 0) >>>> >>>> In [3]: list(range(5))[np.array(0)] >>>> Out[3]: 0 >>>> >>> >>> >>> We have a newer version of essentially same function a second time that >>> uses squeeze and that seems to work fine. >>> >>> Just to understand >>> >>> Why does this depend on the numpy version? I would have understood that >>> this always failed, but this code worked for several years. >>> https://github.com/statsmodels/statsmodels/issues/2817 >>> >> >> It's part of the indexing cleanup. >> >> In [2]: list(range(5))[np.array([0])] >> /home/charris/.local/bin/ipython:1: VisibleDeprecationWarning: converting >> an array with ndim > 0 to an index will result in an error in the future >> #!/usr/bin/python >> Out[2]: 0 >> >> The use of multidimensional arrays as indexes is likely a coding error. >> Or so we hope... >> > > Thanks for the explanation > > > Or, it forces everyone to watch out for the color of the ducks :) > > It's just a number, whether it's python scalar, numpy scalar, 1D or 2D. > And once we squeeze, we cannot iterate over it anymore. > > > This looks like the last problem with have in statsmodels master. > Part of the reason that 0.10 hurt quite a bit is that we are using in > statsmodels some of the grey zones so we don't have to commit to a specific > usage. Even if a user or developer tries a "weird" case, it works for most > of the results, but breaks in some unknown places. > > I meant 1.11 here.
> (In the current case a cryptic exception would be raised if the user has > two constant columns in the regression. Which is fine for some usecases but > not for every result.) > > Josef > > >> >> Chuck >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> >
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