On Sat, Feb 13, 2010 at 3:11 AM, Fernando Perez email@example.com wrote:
Mmh, today I got bitten by this again. It took me a while to figure out what was going on while trying to construct a pedagogical example manipulating numpy poly1d objects, and after searching for 'poly1d multiplication float' in my gmail inbox, the *only* post I found was this old one of mine, so I guess I'll just resuscitate it:
On Tue, Jul 31, 2007 at 2:54 PM, Fernando Perez firstname.lastname@example.org wrote:
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 : 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?
Tim H. mentioned how it might be tricky to fix. I'm wondering if there are any new ideas since on this front, because it's really awkward to explain to new students that poly1d objects have this kind of odd behavior regarding operations with scalars.
The same underlying problem happens for addition, but in this case the answer (depending on the order of operations) changes even more:
In : p Out: poly1d([ 1., 2.])
In : print(p)
1 x + 2
In : p+3 Out: poly1d([ 1., 5.])
In : p+three32 Out: poly1d([ 1., 5.])
In : three32+p Out: array([ 4., 5.]) # !!!
I'm ok with teaching students that in floating point, basic algebraic operations may not be exactly associative and that ignoring this fact can lead to nasty surprises. But explaining that a+b and b+a give completely different *types* of answer is kind of defeating my 'python is the simple language you want to learn' :)
Is this really unfixable, or does one of our resident gurus have some ideas on how to approach the problem?
From several recent discussion about selecting which method is called,
it looks like multiplication and addition could easily be fixed by adding a higher __array_priority__ to poly1d. I didn't see any __array_priority__ specified in class poly1d(object)
For the discussion about fixing equal, notequal or whichever other methods cannot be changed by __array_priority__ , I haven't seen any solution.
(but maybe I'm wrong)
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