On 01/24/2012 12:33 PM, K.-Michael Aye 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 You have a million 32-bit floating point numbers that are in the 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion