Thank you Bruce and all,
I knew I was doing something wrong (should have read the mean method doc more closely). Am of course glad that's so easy understandable.
But: If the error can get so big, wouldn't it be a better idea for the accumulator to always be of type 'float64' and then convert later to the type of the original array?
As one can see in this case, the result would be much closer to the true value.
Michael
On 2012-01-24 19:01:40 +0000, Val Kalatsky said:
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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