[Numpy-discussion] wrong casting of augmented assignment statements

[Christopher Barker]

Sebastian Walter wrote:
>>> However, this particular problem occurs when you try to automatically
>>> differentiate an algorithm by using an Algorithmic Differentiation
>>> (AD) tool.
>>> E.g. given a function
>>>
>>> x = numpy.ones(2)
>>> def f(x):
>>>   a = numpy.ones(2)
>>>   a *= x
>>>   return numpy.sum(a)

I don't know anything about AD, but in general, when I write a function 
that requires a given numpy array type as input, I'll do something like:

def f(x):
   x = np.asarray(a, dtype=np.float)
   a = np.ones(2)
   a *= x
   return np.sum(a)


That makes the casting explicit, and forces it to happen at the top of 
the function, where the error will be more obvious. asarray will just 
pass through a conforming array, so little performance penalty when you 
do give it the right type.

-Chris


-- 
Christopher Barker, Ph.D.
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