[SciPy-User] determining types in dtype

[Ernest Adrogué]

21/03/10 @ 13:14 (-0400), thus spake Skipper Seabold:
> Is there an easy way to determine if the dtype of a (non-nested)
> structured-array is homogenous/similar?  Basically, I want to know if
> all elements are int and/or float so it can be safely cast to an
> ndarray or if it contains strings (or, more generally, other objects)
> so it needs some more processing.
> 
> I thought issctype might provide what I'm looking for but
> 
> import numpy as np
> X = np.array([('1', 1.0), ('1', 1.0), ('1', 1.0)],
>       dtype=[('foo', 'a1'), ('bar', '<f8')])
> np.issctype(X.dtype)
> # True
> 
> Similar result for nested structs of string type
> 
> Y = np.array([(('1','2'), 1.0), (('1','2'), 1.0), (('1','2'), 1.0)],
>       dtype=[('foo', 'a1', (1,2)), ('bar', '<f8')])
> np.issctype(Y.dtype)
> # True
> 
> In particular, I find this odd
> 
> np.issctype(str)
> # True
> 
> I would expect this to be like
> 
> np.issctype(object)
> # False
> 
> Unless there is something I don't understand about types, which is
> probably the case.
> 
> The only other way I can think of is to use X.dtype.descr and actually
> parse the list to determine the types.  Any thoughts?

Yes, this is what I do. From X.dtype.fields you get the dtype
of each field:

dtypes = set(i[0] for i in X.dtype.fields.values())
if len(dtypes) == 1:
	print 'homogenous'
	view = X.view(dtypes.pop())
else:
	print 'heterogenous'

Bye.

> Skipper
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