2011/6/10 Charles R Harris <charlesr.har...@gmail.com> > > > On Fri, Jun 10, 2011 at 5:19 PM, Olivier Delalleau <sh...@keba.be> wrote: > >> 2011/6/10 Charles R Harris <charlesr.har...@gmail.com> >> >>> >>> >>> On Fri, Jun 10, 2011 at 3:43 PM, Benjamin Root <ben.r...@ou.edu> wrote: >>> >>>> >>>> >>>> On Fri, Jun 10, 2011 at 3:24 PM, Charles R Harris < >>>> charlesr.har...@gmail.com> wrote: >>>> >>>>> >>>>> >>>>> On Fri, Jun 10, 2011 at 2:17 PM, Benjamin Root <ben.r...@ou.edu>wrote: >>>>> >>>>>> >>>>>> >>>>>> On Fri, Jun 10, 2011 at 3:02 PM, Charles R Harris < >>>>>> charlesr.har...@gmail.com> wrote: >>>>>> >>>>>>> >>>>>>> >>>>>>> On Fri, Jun 10, 2011 at 1:50 PM, Benjamin Root <ben.r...@ou.edu>wrote: >>>>>>> >>>>>>>> Came across an odd error while using numpy master. Note, my system >>>>>>>> is 32-bits. >>>>>>>> >>>>>>>> >>> import numpy as np >>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.int32)) == np.int32 >>>>>>>> False >>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.int64)) == np.int64 >>>>>>>> True >>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.float32)) == np.float32 >>>>>>>> True >>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.float64)) == np.float64 >>>>>>>> True >>>>>>>> >>>>>>>> So, only the summation performed with a np.int32 accumulator results >>>>>>>> in a type that doesn't match the expected type. Now, for even more >>>>>>>> strangeness: >>>>>>>> >>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.int32)) >>>>>>>> <type 'numpy.int32'> >>>>>>>> >>> hex(id(type(np.sum([1, 2, 3], dtype=np.int32)))) >>>>>>>> '0x9599a0' >>>>>>>> >>> hex(id(np.int32)) >>>>>>>> '0x959a80' >>>>>>>> >>>>>>>> So, the type from the sum() reports itself as a numpy int, but its >>>>>>>> memory address is different from the memory address for np.int32. >>>>>>>> >>>>>>>> >>>>>>> One of them is probably a long, print out the typecode, dtype.char. >>>>>>> >>>>>>> Chuck >>>>>>> >>>>>>> >>>>>>> >>>>>> Good intuition, but odd result... >>>>>> >>>>>> >>> import numpy as np >>>>>> >>> a = np.sum([1, 2, 3], dtype=np.int32) >>>>>> >>> b = np.int32(6) >>>>>> >>> type(a) >>>>>> <type 'numpy.int32'> >>>>>> >>> type(b) >>>>>> <type 'numpy.int32'> >>>>>> >>> a.dtype.char >>>>>> 'i' >>>>>> >>> b.dtype.char >>>>>> 'l' >>>>>> >>>>>> So, the standard np.int32 is getting listed as a long somehow? To >>>>>> further investigate: >>>>>> >>>>>> >>>>> Yes, long shifts around from int32 to int64 depending on the OS. For >>>>> instance, in 64 bit Windows it's 32 bits while in 64 bit Linux it's 64 >>>>> bits. >>>>> On 32 bit systems it is 32 bits. >>>>> >>>>> Chuck >>>>> >>>>> >>>> Right, that makes sense. But, the question is why does sum() put out a >>>> result dtype that is not identical to the dtype that I requested, or even >>>> the dtype of the input array? Could this be an indication of a bug >>>> somewhere? Even if the bug is harmless (it was only noticed within the >>>> test >>>> suite of larry), is this unexpected? >>>> >>>> >>> I expect sum is using a ufunc and it acts differently on account of the >>> cleanup of the ufunc casting rules. And yes, a long *is* int32 on your >>> machine. On mine >>> >>> In [4]: dtype('q') # long long >>> Out[4]: dtype('int64') >>> >>> In [5]: dtype('l') # long >>> Out[5]: dtype('int64') >>> >>> The mapping from C types to numpy width types isn't 1-1. Personally, I >>> think we should drop long ;) But it used to be the standard Python type in >>> the C API. Mark has also pointed out the problems/confusion this ambiguity >>> causes and someday we should probably think it out and fix it. But I don't >>> think it is the most pressing problem. >>> >>> Chuck >>> >>> >> But isn't it a bug if numpy.dtype('i') != numpy.dtype('l') on a 32 bit >> computer where both are int32? >> >> > Maybe yes, maybe no ;) They have different descriptors, so from numpy's > perspective they are different, but at the hardware/precision level they are > the same. It's more of a decision as to what != means in this case. Since > numpy started as Numeric with only the c types the current behavior is > consistent, but that doesn't mean it shouldn't change at some point. > > Chuck >
Well apparently it was actually changed recently, since in Numpy 1.5.1 on a Windows 32 bit machine, they are considered equal with '=='. Personally I think if the string representation of two dtypes is "int32", then they should be ==, otherwise it wouldn't make much sense given that you can directly test the equality of a dtype with a string like "int32" (like dtype('i') == "int32" and dtype('l') == "int32"). -=- Olivier
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