Thanks Stéfan, your script works well. There's a small typo on line 12. I also discovered the functions 'np.iinfo' and 'np.finfo' for machine limits on integer/float types (a note for myself, you might be already familiar with them). After having read the docstring, I was only curious why this feature is not provided by the function itself, as returning the input array's dtype seems not so useful (can't imagine such a use case). Gregorio 2013/9/2 Stéfan van der Walt :

On Mon, Sep 2, 2013 at 4:21 PM, Gregorio Bastardo wrote:

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np.min_scalar_type([-1,256]) # int16 expected dtype('int32')

Am I missing something? Anyone knows how to achieve the desired operation?

The docstring states explicitly that this use case is not supported.

Here's one way of doing it: https://gist.github.com/stefanv/6413742

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On Tue, Sep 3, 2013 at 2:47 PM, Robert Kern wrote:

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Here's one way of doing it: https://gist.github.com/stefanv/6413742

You can probably reduce the amount of work by only comparing a.min() and a.max() instead of the whole array.

Thanks, fixed. Stéfan

Hi Stéfan, I ran into a problem:

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min_typecode( (18446744073709551615L,) ) # ok

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min_typecode( (0, 18446744073709551615L,) ) # ? Traceback (most recent call last): ... ValueError: Can only handle integer arrays.

It seems that np.asarray converts the input sequence into a float64 array in the second case (same behaviour with np.array). Anyone knows the reason behind? python 2.7.4 win32 numpy 1.7.1 Gregorio 2013/9/4 Gregorio Bastardo :

@Stéfan: the 'np.all' calls are now unnecessary on line 26

@Stéfan, Robert: Is it worth to bring this solution into numpy? I mean it's probably not a rare problem, and now users have to bring this snippet into their codebase.

Gregorio

2013/9/3 Stéfan van der Walt :

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On Tue, Sep 3, 2013 at 2:47 PM, Robert Kern wrote:

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Here's one way of doing it: https://gist.github.com/stefanv/6413742

You can probably reduce the amount of work by only comparing a.min() and a.max() instead of the whole array.

Thanks, fixed.

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