Sebastian Haase wrote:
> Hi!
> I just finished maybe a total of 5 hours tracking down a nasty bug.
>
> Finally I traced the problem down to a utility function:
> "is_number" - it is simply implemented as
> def is_number(val):
>      return (type(val) in [type(0.0),type(0)])
>
> As I said - now I finally saw that I always got
> False since the type of my number (0.025) is
>   <type 'float64scalar'>
> and that's neither <type 'float'> nor <type 'int'>
>
> OK - how should this have been done right ?
>
>   

Code that depends on specific types like this is going to be hard to 
maintain in Python because many types could reasonably act like a 
number.   I do see code like this pop up from time to time and it will 
bite you more with NumPy (which has a whole slew of scalar types).

The scalar-types are in a hierarchy and so you could replace the code with

def is_number(val):
      return isinstance(val, (int, float, numpy.number))

But, this will break with other "scalar-types" that it really should 
work with.   It's best to look at what is calling is_number and think 
about what it wants to do with the object and just try it and catch the 
exception.

-Travis



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