Mapping function to numpy array, varying a parameter

First, let me show you the codez:

a = array([...])
for n in range(10000):
    func_curry = functools.partial(func, y=n)
    result = array(map(func_curry, a))
    do_something_else(result)
    ...

What I'm doing here is trying to apply func to an array, changing every time the value of the func's second parameter. This is SLOOOOW (creating a new function every iteration surely does not help), and I also feel I missed the pythonic wa开发者_如何学Cy of doing it. Any suggestion?

Could a solution that gives me a 2D array be a good idea? I don't know, but maybe it is.

Answers to possible questions:

• Yes, this is (using a broad definition), an optimization problem (do_something_else() hides this)
• No, scipy.optimize hasn't worked because I'm dealing with boolean values and it never seems to converge.

Did you try numpy.vectorize?

...
    vfunc_curry = vectorize(functools.partial(func, y=n))
    result = vfunc_curry(a)
...

If a is of significant size the bottleneck should not be the creation of the function, but the duplication of the array.

Can you rewrite the function? If possible, you should write the function to take two numpy arrays a and numpy.arange(n). You may need to reshape to get the arrays to line up for broadcasting.