[Numpy-discussion] Unpleasant behavior with poly1d and numpy scalar multiplication
Timothy Hochberg
tim.hochberg@ieee....
Tue Jul 31 16:43:09 CDT 2007
On 7/31/07, Fernando Perez <fperez.net@gmail.com> wrote:
>
> Hi all,
>
> consider this little script:
>
> from numpy import poly1d, float, float32
> p=poly1d([1.,2.])
> three=float(3)
> three32=float32(3)
>
> print 'three*p:',three*p
> print 'three32*p:',three32*p
> print 'p*three32:',p*three32
>
>
> which produces when run:
>
> In [3]: run pol1d.py
> three*p:
> 3 x + 6
> three32*p: [ 3. 6.]
> p*three32:
> 3 x + 6
>
>
> The fact that multiplication between poly1d objects and numbers is:
>
> - non-commutative when the numbers are numpy scalars
> - different for the same number if it is a python float vs a numpy scalar
>
> is rather unpleasant, and I can see this causing hard to find bugs,
> depending on whether your code gets a parameter that came as a python
> float or a numpy one.
>
> This was found today by a colleague on numpy 1.0.4.dev3937. It feels
> like a bug to me, do others agree? Or is it consistent with a part of
> the zen of numpy I've missed thus far?
It looks like a bug to me, but it also looks like it's going to be tricky to
fix. What looks like is going on is that float32.__mul__ is called first.
For some reason it calls poly1d.__array__. If one comments out __array__ it
ends up doing something odd with __iter__ and __len__ and spitting out a
different wrong answer. If both of those are removed, this script works OK.
My guess is that this is the scalar object being too clever, but it might
just be a bad interaction between the scalar object and poly1d. Poly1d has a
lot of, perhaps too much, trickiness.
--
. __
. |-\
.
. tim.hochberg@ieee.org
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