Hi,
Oddly, but numpy 1.6 seems to behave more consistent manner:
In []: sys.version
Out[]: '2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)]'
In []: np.version.version
Out[]: '1.6.0'
In []: d= np.load('data.npy')
In []: d.dtype
Out[]: dtype('float32')
In []: d.mean()
Out[]: 3045.7471999999998
In []: d.mean(dtype= np.float32)
Out[]: 3045.7471999999998
In []: d.mean(dtype= np.float64)
Out[]: 3045.747251076416
In []: (d- d.min()).mean()+ d.min()
Out[]: 3045.7472508750002
In []: d.mean(axis= 0).mean()
Out[]: 3045.7472499999999
In []: d.mean(axis= 1).mean()
Out[]: 3045.7472499999999
Or does the results of calculations depend more on the platform?
My 2 cents,
eat
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