Hi Stéfan,
I ran into a problem:
min_typecode( (18446744073709551615L,) ) # ok
type 'numpy.uint64'
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
@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 ste...@sun.ac.za:
On Tue, Sep
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
On Mon, Sep 2, 2013 at 3:55 PM, Stéfan van der Walt ste...@sun.ac.za
wrote:
On Mon, Sep 2, 2013 at 4:21 PM, Gregorio Bastardo
gregorio.basta...@gmail.com wrote:
np.min_scalar_type([-1,256]) # int16 expected
dtype('int32')
Am I missing something? Anyone knows how to achieve the desired
On Tue, Sep 3, 2013 at 2:47 PM, Robert Kern robert.k...@gmail.com wrote:
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
On Mon, Sep 2, 2013 at 4:21 PM, Gregorio Bastardo
gregorio.basta...@gmail.com wrote:
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.
