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.aye@gmail.comwrote:

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

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