On 9/14/07, Timothy Hochberg <[EMAIL PROTECTED]> wrote: > > > > On 9/14/07, Joris De Ridder <[EMAIL PROTECTED]> wrote: > > > > > > > > > the question is how to reduce user astonishment. > > > > IMHO this is exactly the point. There seems to be two questions here: > > 1) do we want to reduce user astonishment, and 2) if yes, how could > > we do this? Not everyone seems to be convinced of the first question, > > replying that in many cases linspace() could well replace arange(). > > In many cases, yes, but not all. For some cases arange() has its > > legitimate use, even for floating point, and in these cases you may > > get bitten by the inexact number representation. If Matlab seems to > > be able to avoid surprises, why not numpy? > > > Perhaps because it's a bad idea? This case may be different, but in > general in cases where you try to sweep the surprising nature of floating > point under the rug, you are never entirely successful. The end result is > that, although surprises crop up with less regularity, they are much, much > harder to diagnose and understand when they do crop up. >
Exactly. The problem becomes even more dependent on particular circumstance. For instance, if (.2 + .2 + .1) is used instead of (.2 + .4). If arange can be "fixed" in a way that's easy to understand, then great. > However, if the algorithm for deciding the points is anything but dirt > simple, leave it alone. Or, perhaps, deprecate floating point values as > arguments. I'm not very convinced by the arguments advanced thus far that > arange with floating point has legitimate uses. I've certainly used it this > way myself, but I believe that all of my uses could easily be replaced with > either linspace or arange with integer arguments. I suspect that cases > where the exact properties of arange are required are far between and it's > easy enough to simulate the current behaviour if needed. An advantage to > that is that the potential pitfalls become obvious when you roll your own > version. > In the case of arange it should be possible to determine when the result is potentially ambiguous and issue a warning. For instance, if the argument of the ceil function is close to its rounded value. Chuck
_______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
