[SciPy-User] Use of numpy functions instead builtins

[Warren Weckesser]

Anne Archibald wrote:
> On 3 April 2010 06:02, Florian Lindner <mailinglists at xgm.de> wrote:
>   
>> Hello,
>> I've code that have a lot longer formulas like this on:
>>
>> tan_theta = ( 2 * (1/tan(sa))*(Ma**2*sin(sa)**2-1)) / ( 2+Ma**2 * (self.f_prop.kappa+1 - 2 * sin(sa)**2) )
>>
>> all variables are scalars so it works fine with the stuff I get from the math package. Since I"m using scipy anyway in my app I wonder if there is any advantage using the numpy math functions (sin, cos, ..., exp, power). Is there any advantage using them over the python bultins?
>>     
>
> The short answer is yes, you should use the numpy versions.
>
> The chief advantage, for you, is that your functions will work on
> whole arrays of numbers:
> In [1]: np.exp([0,1,2,3])
> Out[1]: array([  1.        ,   2.71828183,   7.3890561 ,  20.08553692])
>
> Beyond the simple convenience of not having to write loops, this is
> usually much faster than looping over the elements, and for simple
> scalar formulas, you get it more or less for free.
>
> There may be other advantages to using the functions in scipy, in
> particular; for example, scipy's cosine function works on complex
> numbers, and scipy provides a square root that returns complex numbers
> when given negative inputs. The numpy functions may also be cleaner
> about handling infinities and not-a-number inputs (that is, failing
> gracefully when roundoff error becomes overwhelming). But really, the
> vectorization is the key advantage.
>
>   

On the other hand, the numpy versions are *much* slower, so if all the 
variables are scalars and performance is an issue, you might prefer the 
standard math functions.

Warren


> Anne
>
>   
>> Thanks,
>>
>> Florian
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>>     
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