[Numpy-discussion] type-casting differences for comparisons

[Benjamin Root]

I just came across a real-head scratcher that took me a bit to figure out. I
don't know if it counts as a bug or not.

I have an array with dtype "f4" and a separate python float. Some elements
of this array gets assigned this numpy float64 scalar value. (I know, I
should be better off with a mask, but bear with me, this is just a
demonstration code to isolate the core problem from a much more complicated
program...)

import numpy as np

a = np.empty((500,500), dtype='f4')
a[:] = np.random.random(a.shape)
bad_val = 10*a.max()

b = np.where(a > 0.8, bad_val, a)

Now, the following seems to always evaluate to False, as expected:

>>> np.any(b > bad_val)

but, if I am (un-)lucky enough, this will sometimes evaluate to True:

>>> any([(c > bad_val) for c in b.flat])

What it seems to me is that for the first comparison test, bad_val is casted
down to float32 (or maybe b is casted up to float64?), but for the second
example, the opposite is true.  This can lead to some unexpected behaviors.
Is there some sort of difference between type-casting of numpy scalars and
numpy arrays?  I would expect both to be the same.

Ben Root
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