[SciPy-User] Order of numpy orperations is not equal to logic (and also octave)?

[Oz Nahum Tiram]

Continuing this subject, I have a feeling that numpy behaves in very
un-intuitive way for me.
Here is an example that someone gave me in Stackoverflow.com:

>>> import numpy as np
>>> a=np.array([1,0])
>>> b=np.array([-1,1])

>>> np.sum(a)/np.sum(b)
0
>>> np.sum(a)/b
array([-1,  1])
>>> np.sum(a)/0
0


Octave warns when dividing by zero:
octave:1> a=[1,0]
a =

   1   0

octave:2> b=[-1,1]
b =

  -1   1

octave:3> sum(a)/sum(b)
warning: division by zero
ans = Inf
octave:4> sum(a)./sum(b)
warning: division by zero
ans = Inf
octave:5> sum(a)./b
ans =

  -1   1


I think this is very important that these differences are broad-casted to
new comers from matlab/octave to python and numpy.
I work interchangeably with both, and this is quite tricky...


-- 
Oz Nahum
Graduate Student
Zentrum für Angewandte Geologie
Universität Tübingen

---

Imagine there's no countries
it isn't hard to do
Nothing to kill or die for
And no religion too
Imagine all the people
Living life in peace
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