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.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
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