[Numpy-discussion] Bug in nanmin called with unsigned integers

[Charles R Harris]

On Wed, May 26, 2010 at 7:59 AM, Tony S Yu <tsyu80@gmail.com> wrote:

>
> On May 25, 2010, at 10:57 PM, Charles R Harris wrote:
>
>
>
> On Tue, May 25, 2010 at 8:21 PM, Tony S Yu <tsyu80@gmail.com> wrote:
>
>> I got bit again by this bug with unsigned integers<http://projects.scipy.org/numpy/ticket/1300>.
>> (My original changes got overwritten when I updated from svn and,
>> unfortunately, merged conflicts without actually looking over the changes.)
>>
>> In any case, I thought it'd be a good time to bump the issue (with patch<http://projects.scipy.org/numpy/ticket/1300#comment:2>
>> ).
>>
>> Cheers,
>> -Tony
>>
>> PS: Just for context, this issue comes up when displaying images with
>> Chaco (which converts images to unsigned integer arrays and calls nanmin).
>>
>>
> Fixed in r8445. Please add some tests.
>
>
>
> I'm not totally sure what's appropriate to test, so I just added a simple
> test to the comments for the ticket<http://projects.scipy.org/numpy/ticket/1300#comment:4>
> .
>
> On a side note, I noticed that all the nan-ops degenerate to their
> non-nan-ops counterparts (i.e. nanmin --> min) when called with integer
> dtypes. Below is a diff where that's made a little more obvious by returning
> early for integer dtypes.
>
> Cheers,
> -Tony
>
>
> Index: numpy/lib/function_base.py
> ===================================================================
> --- numpy/lib/function_base.py (revision 8445)
> +++ numpy/lib/function_base.py (working copy)
> @@ -1295,15 +1295,15 @@
>
>      """
>      y = array(a, subok=True)
> -    mask = isnan(a)
>
>      # We only need to take care of NaN's in floating point arrays
> -    if not np.issubdtype(y.dtype, np.integer):
> -        # y[mask] = fill
> -        # We can't use fancy indexing here as it'll mess w/ MaskedArrays
> -        # Instead, let's fill the array directly...
> -        np.putmask(y, mask, fill)
> -
> +    if np.issubdtype(y.dtype, np.integer):
> +        return op(y, axis=axis)
> +    mask = isnan(a)
> +    # y[mask] = fill
> +    # We can't use fancy indexing here as it'll mess w/ MaskedArrays
> +    # Instead, let's fill the array directly...
> +    np.putmask(y, mask, fill)
>      res = op(y, axis=axis)
>      mask_all_along_axis = mask.all(axis=axis)
>
>
>
Applied. Thanks.
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