Thanks Pierre, good to know there are so many tricks available. Chao
On Tue, Jul 9, 2013 at 4:55 PM, Pierre Gerard-Marchant <[email protected] > wrote: > > On Jul 9, 2013, at 16:38 , Chao YUE <[email protected]> wrote: > > > Sorry I didn't the docs very carefully. there is no doc for np.ma.argmax > for indeed there is for np.ma.argmin > > Yeah, the doc of the function asks you to go check the doc of the method… > Not the best. > > > > so it's an expected behavior rather than a bug. Let some heavy users to > say their ideas. > > > > Practicaly, the returned value of 0 will be always confused with the > values which are not masked > > but do have the minimum or maximum values at the 0 position over the > specified axis. > > Well, it's just an index: if you take the corresponding value from the > input array, it'll be masked... > > > One way to walk around is: > > > > > > data_mask = np.ma.mean(axis=0).mask > > > > np.ma.masked_array(np.ma.argmax(data,axis=0), mask=data_mask) > > I find easier to use `mask=x.mask.prod(axis)` to get the combined mask > along the desired axis (you could also use a `reduce(np.logical_and, > x.mask)` for axis=0, but it's less convenient I think). > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- *********************************************************************************** Chao YUE Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL) UMR 1572 CEA-CNRS-UVSQ Batiment 712 - Pe 119 91191 GIF Sur YVETTE Cedex Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16 ************************************************************************************
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