On Wed, Oct 20, 2010 at 9:46 AM, Zachary Pincus <zachary.pincus@yale.edu> wrote:
I'm trying to write an implementation of the amoeba function from numerical recipes and need to be able to pass a function name and parameter list to be called from within the amoeba function. Simply passing the name as a string doesn't work since python doesn't know it is a function and throws a typeerror. Is there something similar to IDL's 'call_function' routine in python/numpy or a pythonic/numpy means of passing function names?
Just pass the function itself! For example:
def foo(): print 6
def call_function_repeatedly(func, count): for i in range(count): func()
call_function_repeatedly(foo, 2) # calls foo twice
bar = foo bar() # still calls foo... we've just assigned the function to a different name
In python, functions (and classes, and everything else) are first- class objects and can be assigned to variables, passed around, etc, etc, just as anything else.
This is the best way, but if you want to pass the function name as string, then you need to get the function with getattr for example, scipy has code like this def func(distname, args): distfn = getattr(scipy.stats, distname) distfn.rvs(args) func('norm') Josef
However, note that scipy.optimize.fmin implements the Nelder-Mead simplex algorithm, which is (I think) the same as the "amoeba" optimizer. Also you might be interested in the openopt package, which implements more optimizers a bit more consistently than scipy.optimize.
Zach _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion