[Numpy-discussion] Vectorizing a function
Charles R Harris
Wed Jan 30 11:10:29 CST 2008
On Jan 30, 2008 2:22 AM, Gael Varoquaux <email@example.com>
> On Wed, Jan 30, 2008 at 12:49:44AM -0800, LB wrote:
> > My problem is that the complexe calculations made in calc_0d use some
> > parameters, which are currently defined at the head of my python file.
> > This is not very nice and I can't define a module containing theses
> > two functions and call them with different parameters.
> > I would like to make this cleaner and pass theses parameter as
> > keyword argument, but this don't seems to be possible with vectorize.
> > Indeed, some of theses parameters are array parameters and only the x
> > and y arguments should be interpreted with the broadcasting rules....
> > What is the "good way" for doing this ?
> I don't know what the "good way" is, but you can always use functional
> programming style (Oh, no, CaML is getting on me !):
> def calc_0d_params(param1, param2, param3):
> def calc_0d(x, y):
> # Here your code making use of param1, param2, param3)
> return calc_0d(x, y)
> you call the function like this:
> calc_0d_params(param1, param2, param3)(x, y)
> To vectorize it you can do:
> calc_0d_vect = lambda *params: vectorize(calc_0d_params(*params))
> This is untested code, but I hope you get the idea. It all about partial
> evaluation of arguments. By the way, the parameters can now be keyword
IIRC, the way to do closures in Python is something like
In : def factory(x) :
...: def f() :
...: print x
...: f.x = x
...: return f
In : f = factory("Hello world.")
In : f()
There is a reason to do it that way, but I don't recall what it is. Nor do I
know if the result can be vectorized, I've never used vectorize.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the Numpy-discussion