[Numpy-discussion] bug in numpy.mean() ?

Val Kalatsky kalatsky@gmail....
Tue Jan 24 13:01:40 CST 2012


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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