[Numpy-discussion] Nasty bug using pre-initialized arrays

[Zachary Pincus]

Hello all,

> 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)
vs.
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.)

Zach