[Numpy-discussion] numpy type mismatch

[Olivier Delalleau]

It's ok to have two different dtypes (in the sense that "d1 is not d2") such
that they represent the same kind of data (in the sense that d1 == d2).

However I think your very first test should have returned True (for what
it's worth, it returns true with 1.5.1 on Windows 32 bit).

-=- Olivier

2011/6/10 Benjamin Root <ben.root at ou.edu>

> 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.
>
> Weirdness...
> Ben Root
>
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