Stan Schymanski wrote:
> Hi Mike and Jason,
>
> Thanks a lot for the quick response. My problem becomes a bit more
> obvious if I have a function of several variables. Then the map
> function becomes somehow impractical because I can't define which list
> is used for which variable. List comprehensions also get a lot more
> difficult. Example:
>
> sage: var('a b c')
> sage: f = a*x^2+b
> sage: w = [4,5,6]
> sage: z = [7,8,9]
> sage: map(f,v,w,z)
> [53, 133, 249]
> sage: map(f,w,v,z)
> [197, 322, 489]
>
> I wouldn't even know how to do this with a list comprehension in one
> step. Since the functions I work with have to be applied to time
> series of quite a few variables, I am really desperate for a practical
> way of doing this. So far, all mathematical programs I worked with
> were able to do this and I bet that many people are used to applying
> functions to lists and arrays. Isn't this a lot faster than defining
> loops?
I would generally use list comprehensions over map. So, you could do:
sage: [f(a=i,x=j,b=k) for i,j,k in zip(v,w,z)]
Note that numpy has the concept of "universal functions" which
automatically thread over a list, much like Matlab would.
sage: np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180. )
Also, if you want threadable functions, you can do it easily with pure
python functions and a decorator:
def threadable(f):
def threadable_f(*args,**kwds):
return [f(*tuple, **kwds) for tuple in zip(*args)]
return threadable_f
@threadable
def f(x,y,z):
return x*y^2+z
f([1,2,3],[4,5,6],[7,8,9])
This last call produces:
[23, 58, 117]
Jason
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