[Numpy-discussion] Bug in nanmin called with unsigned integers
Charles R Harris
Sun Jun 6 22:44:19 CDT 2010
On Wed, May 26, 2010 at 7:59 AM, Tony S Yu <email@example.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 <firstname.lastname@example.org> 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>
>> 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.
> 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)
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