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 You have a million 32-bit floating point numbers that are in the

On 01/24/2012 12:33 PM, K.-Michael Aye wrote: thousands. Thus you are exceeding the 32-bitfloat precision and, if you can, you need to increase precision of the accumulator in np.mean() or change the input dtype:

a.mean(dtype=np.float32) # default and lacks precision 3067.0243839999998 a.mean(dtype=np.float64) 3045.747251076416 a.mean(dtype=np.float128) 3045.7472510764160156 b=a.astype(np.float128) b.mean() 3045.7472510764160156

Otherwise you are left to using some alternative approach to calculate the mean. Bruce