[Python] 0-dimensional array problems with `numpy.vectorize`

numpy.vectorize conveniently converts a scalar function to vectorized functions that can be applied directly to arrays. However, when inputting a single value into the vectorized function, the output is a 0-dimentional array instead of the corresponding value type, which can cause errors when using the result elsewhere due to typing issues. My question is: is there a mechanism in numpy that can resolve this problem by automatically convert the 0-dimensional array return value to the corresponding data type?

For explanation I'd give an example:

@np.vectorize ( excluded = ( 1, 2 ) )
def rescale (
value: float,
srcRange: tuple [ float, float ],
dstRange: tuple [ float, float ] = ( 0, 1 ),
) -> float:
srcMin, srcMax = srcRange
dstMin, dstMax = dstRange
t = ( value - srcMin ) / ( srcMax - srcMin )
return dstMin + t * ( dstMax - dstMin )


When calling the function above with rescale ( 5, ( 0, 10 ) ) the return value is numpy.array(0.5) instead of just the value 0.5.

Currently I resolve this problem by a self-defined decorator:

def vectorize0dFix ( func ):
def _func ( *args, **kwargs ):
result = func ( *args, **kwargs )
if isinstance ( result, np.ndarray ) and result.shape == ( ):
return result.item ( )
else:
return result
return _func


But if this problem do causes trouble there should be a mechanism in numpy which properly deals with the problem. I wonder whether there is one or why there isn't.

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