[Numpy-discussion] Why does np.nan{min, max} clobber my array mask?
Bruce Southey
bsouthey at gmail.com
Fri Mar 5 10:16:54 EST 2010
On Mon, Feb 15, 2010 at 9:24 PM, Bruce Southey <bsouthey at gmail.com> wrote:
> On Mon, Feb 15, 2010 at 8:35 PM, Pierre GM <pgmdevlist at gmail.com> wrote:
>> On Feb 15, 2010, at 8:51 PM, David Carmean wrote:
>>> On Sun, Feb 14, 2010 at 03:22:04PM -0500, Pierre GM wrote:
>>>
>>>>
>>>> I'm sorry, I can't follow you. Can you post a simpler self-contained example I can play with ?
>>>> Why using np.nanmin/max ? These functions are designed for ndarrays, to avoid using a masked array: can't you just use min/max on the masked array ?
>>>
>>> I was using np.nanmin/max because I did not yet understand how masked arrays worked; perhaps the
>>> docs for those methods need a note indicating that "If you can take the (small?) memory hit,
>>> use Masked Arrays instead". Now that I know different... I'm going to drop it unless you
>>> reall want to dig into it.
>>
>>
>> I'm curious. Can you post an excerpt of your array, so that I can check what goes wrong?
>>
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> Hi,
> David, please file a bug report.
>
> I think is occurs with np.nansum, np.nanmin and np.nanmax. Perhaps
> some thing with the C99 changes as I think it exists with numpy 1.3.
>
> I think this code shows the problem with Linux and recent numpy svn:
>
> import numpy as np
> uut = np.array([[2, 1, 3, np.nan], [5, 2, 3, np.nan]])
> msk = np.ma.masked_invalid(uut)
> msk
> np.nanmin(msk, axis=1)
> msk
>
> $ python
> Python 2.6 (r26:66714, Nov 3 2009, 17:33:18)
> [GCC 4.4.1 20090725 (Red Hat 4.4.1-2)] on linux2
> Type "help", "copyright", "credits" or "license" for more information.
>>>> import numpy as np
>>>> uut = np.array([[2, 1, 3, np.nan], [5, 2, 3, np.nan]])
>>>> msk = np.ma.masked_invalid(uut)
>>>> msk
> masked_array(data =
> [[2.0 1.0 3.0 --]
> [5.0 2.0 3.0 --]],
> mask =
> [[False False False True]
> [False False False True]],
> fill_value = 1e+20)
>
>>>> np.nanmin(msk, axis=1)
> masked_array(data = [1.0 2.0],
> mask = [False False],
> fill_value = 1e+20)
>
>>>> msk
> masked_array(data =
> [[2.0 1.0 3.0 nan]
> [5.0 2.0 3.0 nan]],
> mask =
> [[False False False False]
> [False False False False]],
> fill_value = 1e+20)
>
>
> Bruce
>
Hi,
I filed this ticket and hopefully the provided code is sufficient for a test:
http://projects.scipy.org/numpy/ticket/1421
The bug is with the _nanop function because nansum, nanmin, nanmax,
nanargmin and nanargmax have the same issue.
Bruce
Bruce
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