Neal Becker wrote: > Charles R Harris wrote: > > >> On Fri, May 1, 2009 at 7:39 PM, Charles R Harris >> <charlesr.har...@gmail.com>wrote: >> >> >>> On Fri, May 1, 2009 at 7:24 PM, Neal Becker <ndbeck...@gmail.com> wrote: >>> >>> >>>> Charles R Harris wrote: >>>> >>>> >>>>> On Fri, May 1, 2009 at 1:02 PM, Neal Becker <ndbeck...@gmail.com> >>>>> >>>> wrote: >>>> >>>>>> In [16]: (np.linspace (0, len (x)-1, len(x)).astype >>>>>> >>>> (np.uint64)*2).dtype >>>> >>>>>> Out[16]: dtype('uint64') >>>>>> >>>>>> In [17]: (np.linspace (0, len (x)-1, len(x)).astype >>>>>> >>>> (np.uint64)*n).dtype >>>> >>>>>> Out[17]: dtype('float64') >>>>>> >>>>>> In [18]: type(n) >>>>>> Out[18]: <type 'int'> >>>>>> >>>>>> Now that's just strange. What's going on? >>>>>> >>>>>> >>>>>> >>>>> The n is signed, uint64 is unsigned. So a signed type that can hold >>>>> uint64 is needed. There ain't no such integer, so float64 is used. I >>>>> >>>> think >>>> >>>>> the logic here is a bit goofy myself since float64 doesn't have the >>>>> >>>> needed >>>> >>>>> 64 bit precision and the conversion from int kind to float kind is >>>>> confusing. I think it would be better to raise a NotAvailable error or >>>>> some such. Lest you think this is an isolated oddity, sometimes >>>>> numeric arrays can be converted to object arrays. >>>>> >>>>> Chuck >>>>> >>>> I don't think that any type of integer arithmetic should ever be >>>> automatically promoted to float. >>>> >>>> Besides that, what about the first example? There, I used '2' rather >>>> than >>>> 'n'. Is not '2' also an int? >>>> >>> What version of numpy are you using? >>> >>> >> And what is the value of n? >> >> > > >> Chuck >> > > np.version.version > Out[5]: '1.3.0' > (I think the previous test was on 1.2.0 and did the same thing) > > (np.linspace (0, 1023,1024).astype(np.uint64)*2).dtype > Out[2]: dtype('uint64') > > In [3]: n=-7 > > In [4]: (np.linspace (0, 1023,1024).astype(np.uint64)*n).dtype > Out[4]: dtype('float64') > > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > Hi, //I think this behavior has been raised before. IIRC, Numpy is trying to do the operation that is requested by converting the dtype into floats since this is a generic solution that will avoid overflow with any ints not just unsigned ints.
Note that you get a different result if you use subtraction than multiplication. >>> np.linspace (0, 1023,1024) array([ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00, ..., 1.02100000e+03, 1.02200000e+03, 1.02300000e+03]) >>> np.linspace (0, 1023,1024).astype(np.uint64)*-7 array([ -0.00000000e+00, -7.00000000e+00, -1.40000000e+01, ..., -7.14700000e+03, -7.15400000e+03, -7.16100000e+03]) >>> np.linspace (0, 1023,1024).astype(np.uint64)-7 array([18446744073709551609, 18446744073709551610, 18446744073709551611, ..., 1014, 1015, 1016], dtype=uint64) Bruce _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion