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
