[Numpy-discussion] array_arguments decorator

[Zachary Pincus]

Hi Tim -

Thanks for posting your featureful decorator. I didn't know that some  
of that stuff was possible -- very cool.

Hopefully I'll find some time to put these up on the wiki.

Zach


On Mar 31, 2006, at 7:48 PM, Tim Hochberg wrote:

> Zachary Pincus wrote:
>
>> Hi folks -
>>
>> I had seen some talk on this list about the utility of a  
>> decorator  for functions that need to convert their arguments to  
>> numpy arrays.  This would help eliminate boilerplate calls to  
>> 'asarray' like:
>>
>> def distance_squared(a, b):
>>   a = numpy.asarray(a)
>>   b = numpy.asarray(b)
>>   return ((a - b)**2).sum()
>>
>> Here is a trivial decorator I was thinking of adding to the wiki  
>> --  does this cover enough cases to be useful? In a bigger sense,  
>> would  it be worthwhile to add some decorators like this to numpy  
>> itself?  (I'm not sure I'm in favor of this, since I kind of like  
>> smaller APIs  over bigger ones.)
>>
>> def array_arguments(f):
>>   """Wrap a function such that any positional arguments are
>>   converted into numpy arrays before the function is called."""
>>   def convert_arg_wrapper(*args, **kwargs):
>>     array_args = [numpy.asarray(a) for a in args]
>>     return f(*array_args, **kwargs)
>>   return convert_arg_wrapper
>>
>> now distance_squared can look like:
>> @array_arguments
>> def distance_squared(a, b):
>>   return ((a - b)**2).sum()
>>
>> if using python 2.4, or if not so using:
>> def distance_squared(a, b):
>>   return ((a - b)**2).sum()
>> distance_squared = array_arguments(distance_squared)
>
> Great minds think alike. Or at least our minds think alike ;) I  
> also wrote up a decorator for this same purpose. Then I got  
> distracted and forgot to post it. Mine has more features at the  
> expense of being more complicated. The main extra feature is that  
> it allows you to decide both which args get checked and what there  
> types should be. It also preserves the signature of the original  
> function. Some of this stuff is accomplished using the decorator  
> decorator, which you can find here:
>
>    http://www.phyast.pitt.edu/~micheles/python/
>
> The upshot of all of this is that you can do stuff like:
>
>        @array_function(a=float, b=None, c=complex)
>        def foo(a, b, c=1, d=None):
>            print repr(a), repr(b), repr(c), repr(d)
>
> And it will convert 'a' and 'c' to float and complex arrays  
> respectively and convert 'b' to some type of array. Arguments not  
> mentioned don't get touched, unless you specify no arguments, in  
> which case all of the positional arguments get converted (*args and  
> **kwargs are not touched in this case).
>
> I'm not certain that this is the world's best interface, nor am I  
> certain that the extra complexity is worth it -- yours is certainly  
> easier to understand. However, it was fun to write. I am unlikely  
> to find the time to do anything with it anytime soon, so should you  
> desire to revamp and place it on the wiki, feel free. Or, if you  
> want to ignore it, feel free to do that as well.
>
> The code is below:
>
> Regards,
>
> -tim
>
>
>
> import inspect, decorator, numpy
>
> def array_function(**signature):
>    def deco(func):
>        regargs, varargs, varkwargs, defaults = inspect.getargspec 
> (func)
>        if not signature:
>            signature.update((name, None) for x in regargs)
>        def caller(func, *args, **kwargs):
>            args = list(args)
>            for i, (name, value) in enumerate(zip(regargs, args)):
>                if name in signature:
>                    args[i] = numpy.asarray(value, signature[name])
>            for name, value in kwargs.items():
>                if name in signature:
>                    kwargs[name] = numpy.asarray(value, signature 
> [name])
>            return func(*args, **kwargs)
>        return decorator._decorate(func, caller)
>    return deco
>
>
>
>
>
>
>
>
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