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

[Christoph Deil]

On Jun 21, 2011, at 3:28 PM, nicky van foreest wrote:

>> 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?
> 
> You might try partial functions, see functools.partial in the functools module.
> 
> Nicky

I tried using functools.partial but couldn't make it work.
Can you give an example of how to use it to make a vectorized function accept keyword arguments?

E.g. the following lines still give the same TypeError as before:
a = np.array([10, 100, 1000])
cube_find_x = ft.partial(find_x, scale='cube')
print cube_find_x(a, cost)

Also this is not really what I want, which is to make find_x accept keyword arguments without writing the wrapper myself,
maybe there is a way to write a decorator handle_kwargs so that I can simply write something like the following code?

@handle_kwargs
@np.vectorize
def find_x(...)
    ....

> 
> 
>> 
>> 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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>> 
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