[Numpy-discussion] Nasty bug using pre-initialized arrays
Fri Jan 4 19:15:53 CST 2008
> That's well and good. But NumPy should *never* automatically -- and
> silently -- chop the imaginary part off your complex array elements,
> particularly if you are just doing an innocent assignment!
> Doing something drastic like silently throwing half your data away can
> lead to all kinds of bugs in code written by somebody who is unaware
> of this behavior (i.e. most people)!
> It sounds to me like the right thing is to throw an exception instead
> of "downcasting" a data object.
I'm not sure that I agree! I'd rather not have to litter my code with
"casting" operations every time I wanted to down-convert data types
(creating yet more temporary arrays...) via assignment. e.g.:
A[i] = calculate(B).astype(A.dtype)
A[i] = calculate(B)
Further, writing functions to operate on generic user-provided output
arrays (or arrays of user-provided dtype; both of which are common
e.g. in scipy.ndimage) becomes more bug-prone, as every assignment
would need to be modified as above.
This change would break a lot of the image-processing code I've
written (where one often does computations in float or even double,
and then re-scales and down-converts the result to integer for
display), for example.
I guess that this could be rolled in via the geterr/seterr mechanism,
and could by default just print warnings. I agree that silent
truncation can be troublesome, but not having to spell every
conversion out in explicit ( and error-prone) detail is also pretty
useful. (And arguably more pythonic.)
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