> There are two related hierarchies of datatypes: different kinds > (integer, > floating point, complex floating point) and different precisions > within a given > kind (int8, int16, int32, int64). The term "downcasting" should > probably be > reserved for the latter only. > > It seems to me that Zach and Scott are defending downcasting of > precisions > within a given kind. It does not necessarily follow that the > behavior we choose > for dtypes within a given kind should be the behavior when we are > dealing with > dtypes across different kinds. We can keep the precision > downcasting behavior > that you want while raising an error when one attempts to assign a > complex > number into a floating point array.
Actually, my points were explicitly about cross-kind conversions! A lot of image-processing code features heavy use of converting integer images to float for intermediate calculations, and then rescaling and down-converting back to the original type for display, etc. In fact, scipy.ndimage makes use of this, allowing for operations with output into user-specified arrays, or arrays of user-specified dtype, while (I believe) carrying out some of the intermediate calculations at higher precision. As such, pretty much any code that takes a user-specified array and assigns to it the result of a (potentially) mixed-mode calculation would need to be changed from: A[i] = calculate(B) to A[i] = calculate(B).astype(A.dtype) with the proliferation of temp arrays that that entails. I think, but am not sure, that there is a lot of code out there that does this, intentionally, which would be broken by this change. Explicit is indeed better than implicit, but in this case, finding all of the places where mixed-mode conversions happen and tracking them down could be a pain on the same scale as const chasing in C++, where fixing the error in one place makes it reappear elsewhere in the chain of usage, leading to long, painful, and often totally pointless debugging sessions. In the specific case of converting from complex to anything else, I can see the desire for a warning to prevent data loss. But converting from float to integer is a lot more routine and is used a lot in the kind of work that I do, at least. Zach _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
