[Numpy-discussion] Vectorizing a function
Charles R Harris
Wed Jan 30 11:35:04 CST 2008
On Jan 30, 2008 10:10 AM, Charles R Harris <email@example.com>
> On Jan 30, 2008 2:22 AM, Gael Varoquaux <firstname.lastname@example.org>
> > 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
> > arguments.
> IIRC, the way to do closures in Python is something like
> In : def factory(x) :
> ...: def f() :
> ...: print x
> ...: f.x = x
> ...: return f
Oops, looks like that needs to be:
In : def factory(x) :
...: def f() :
...: print f.x
...: f.x = x
...: return f
You can also do something simpler:
In : def f() : print f.x
In : f.x = "Hello World!"
In : f()
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