Just what Bruce said. You can run the following to confirm: np.mean(data - data.mean())
If for some reason you do not want to convert to float64 you can add the result of the previous line to the "bad" mean: bad_mean = data.mean() good_mean = bad_mean + np.mean(data - bad_mean) Val On Tue, Jan 24, 2012 at 12:33 PM, K.-Michael Aye <kmichael....@gmail.com>wrote: > I know I know, that's pretty outrageous to even suggest, but please > bear with me, I am stumped as you may be: > > 2-D data file here: > http://dl.dropbox.com/u/139035/data.npy > > Then: > In [3]: data.mean() > Out[3]: 3067.0243839999998 > > In [4]: data.max() > Out[4]: 3052.4343 > > In [5]: data.shape > Out[5]: (1000, 1000) > > In [6]: data.min() > Out[6]: 3040.498 > > In [7]: data.dtype > Out[7]: dtype('float32') > > > A mean value calculated per loop over the data gives me 3045.747251076416 > I first thought I still misunderstand how data.mean() works, per axis > and so on, but did the same with a flattenend version with the same > results. > > Am I really soo tired that I can't see what I am doing wrong here? > For completion, the data was read by a osgeo.gdal dataset method called > ReadAsArray() > My numpy.__version__ gives me 1.6.1 and my whole setup is based on > Enthought's EPD. > > Best regards, > Michael > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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