[SciPy-User] How to numpy.vectorize functions with keyword arguments?

[Christoph Deil]

Hi,

I have some functions that use if and for statements such as the simplified
example find_x below and would like to use them on numpy arrays.

Question 1: Is there a way to write an iterative algorithm with a stopping
condition (such as find_x) without if and for, only using numpy methods (I
need speed!) ?

Question 2: numpy.vectorized functions don't like being called with keyword
arguments, the first line in __main__ raises a TypeError.
Why does this happen? What is the standard method to make vectorized
functions callable with keyword arguments?

I found that writing a wrapper (wrapped_find_x) works, but I'd rather not
litter my code with many such wrapper functions.
In the example below it would be ok just using positional arguments, but I
have many functions, each with ~10 keyword arguments.

Christoph


import numpy as np
@np.vectorize
def cost(x, scale='square'):
    """Some complicated function that is supplied by the user"""
    if scale == 'square':
        return x ** 2
    elif scale == 'cube':
        return x ** 3
    else:
        return 0
@np.vectorize
def find_x(a, f, scale='square', maxiter=100):
    """Uses an iterative algorithm to determine a result"""
    x = 1
    # just to avoid possibly infinite loop, maxiter should never be reached
    for _ in range(maxiter):
        if f(x, scale) > a:
            break
        x *= 2
    return x
def wrapped_find_x(a, f, scale='square', maxiter=100):
    return find_x(a, f, scale, maxiter)
if __name__ == '__main__':
    print find_x(np.array([10, 100, 1000]), cost, scale='cube') # TypeError
    print wrapped_find_x(np.array([10, 100, 1000]), cost, scale='cube') # OK
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