Caution with numpy float32 images

There is an interesting thread on the numpy discussion group about an unexpected result of taking the mean (or sum) of a large array of 32-bit floating point numbers.  If you exceed the 32-big float precision then you will get the wrong answer on some platforms.  Takeaway: use float64 or specify the precision of the accumulator.Here are some examples:In [1]: d = np.ones((1000, 1000), dtype=np.float32) * 10

In [2]: d.mean()
Out[2]: 10.0

In [3]: d = np.ones((1000, 1000), dtype=np.float32) * 100

In [4]: d.mean()
Out[4]: 98.684352000000004

In [5]: d.mean(dtype=np.float64)
Out[5]: 100.0