I does not solve the slowness problem. I think I read on the list about an experimental code for fast vectorization.

Nadav.

-----הודעה מקורית----- מאת: numpy-discussion-bounces@scipy.org בשם Emmanuelle Gouillart נשלח: ב 01-דצמבר-08 12:28 אל: Discussion of Numerical Python נושא: Re: [Numpy-discussion] optimising single value functions for array calculations

Hello Timmie,

numpy.vectorize(myfunc) should do what you want.

Cheers,

Emmanuelle

Hello, I am developing a module which bases its calculations on another specialised module. My module uses numpy arrays a lot. The problem is that the other module I am building upon, does not work with (whole) arrays but with single values. Therefore, I am currently forces to loop over the array:

### a = numpy.arange(100) b = numpy.arange(100,200) for i in range(0,a.size): a[i] = myfunc(a[i])* b[i]

###

The results come out well. But the problem is that this way of calculation is very ineffiecent and takes time.

May anyone give me a hint on how I can improve my code without having to modify the package I am building upon. I do not want to change it a lot because I would always have to run behind the chnages in the other package.

To summarise: How to I make a calculation function array-aware?

Thanks in advance, Timmie

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Hi,

thanks for all your answers. I will certainly test it.

numpy.vectorize(myfunc) should do what you want.

Just to add a better example based on a recent discussion here on this list [1]:

myfunc(x): res = math.sin(x)

return res

a = numpy.arange(1,20)

=> myfunc(a) will not work. => myfunc need to have a possibility to pass single values to math.sin either through interation (see my inital email) or through other options.

(I know that numpy has a array aware sinus but wanted to use it as an example here.)

My orriginal problem evolves from here timeseries computing [2].

Well, I will test and report back further.

Thanks again and until soon, Timmie

[1]: http://thread.gmane.org/gmane.comp.python.numeric.general/26417/focus=26418 [2]: http://thread.gmane.org/gmane.comp.python.scientific.user/18253