In [125]: d = np.load('data.npy')

In [126]: d.mean()

Out[126]: 3067.0243839999998

In [127]: d64 = d.astype('float64')

In [128]: d64.mean()

Out[128]: 3045.747251076416

In [129]: d.mean(axis=0).mean()

Out[129]: 3045.7487500000002

In [130]: d.mean(axis=1).mean()

Out[130]: 3045.7444999999998

In [131]: np.version.full_version

Out[131]: '2.0.0.dev-55472ca'

--

On Tue, 2012-01-24 at 12:33 -0600, K.-MichaelA 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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