I can't really figure out if that's the case in your code, but if you need to repeat the mask along a new dimension (for instance, the first one), you can do:
numpy.tile(mask.mask, [number_of_repeats] + [1] * len(mask.mask.shape)) (not sure that's the most elegant way to do it, but it should work) -=- Olivier 2011/11/21 questions anon <[email protected]> > Excellent, thank you. > I just realised this does not work with my data because of the extra > dimension. > I have a mask that matches my 2-dimensional array but my data is for every > hour over a month so the arrays do not match. Is there a way to make them > match or mask each time? > thanks again > > This is some of my code: > > > for ncfile in files: > if ncfile[-3:]=='.nc': > print "dealing with ncfiles:", > path+ncfile > ncfile=os.path.join(path,ncfile) > ncfile=Dataset(ncfile, 'r+', 'NETCDF4') > TSFC=ncfile.variables['T_SFC'][:] > TIME=ncfile.variables['time'][:] > > fillvalue=ncfile.variables['T_SFC']._FillValue > TSFC=MA.masked_values(TSFC, fillvalue) > ncfile.close() > > TSFC=MA.masked_array(TSFC, > mask=newmask.mask) > > > > > > > On Tue, Nov 22, 2011 at 11:21 AM, Olivier Delalleau <[email protected]> wrote: > >> If your new array is x, you can use: >> >> numpy.ma.masked_array(x, mask=mask.mask) >> >> -=- Olivier >> >> 2011/11/21 questions anon <[email protected]> >> >>> I am trying to mask one array using another array. >>> >>> I have created a masked array using >>> mask=MA.masked_equal(myarray, >>> 0), >>> that looks something like: >>> [1 - - 1, >>> 1 1 - 1, >>> 1 1 1 1, >>> - 1 - 1] >>> >>> I have an array of values that I want to mask whereever my mask has a a >>> '-'. >>> how do I do this? >>> I have looked at >>> http://www.cawcr.gov.au/bmrc/climdyn/staff/lih/pubs/docs/masks.pdf but >>> the command: >>> >>> d = array(a, mask=c.mask() >>> >>> results in this error: >>> TypeError: 'numpy.ndarray' object is not callable >>> >>> I basically want to do exactly what that article does in that equation. >>> >>> Any feedback will be greatly appreciated. >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> [email protected] >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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