> > Personally I think that the default error mode should be tightened > up. > Then people would only see these sort of things if they really care > about them. Using Python 2.5 and the errstate class I posted earlier: > > # This is what I like for the default error state > numpy.seterr (invalid='raise', divide='raise', over='raise', > under='ignore') > > > I like these choices too. Overflow, division by zero, and sqrt(-x) are > usually errors, indicating bad data or programming bugs. Underflowed > floats, OTOH, are just really, really small numbers and can be treated > as zero. An exception might be if the result is used in division and > no error is raised, resulting in a loss of accuracy. >
I'm fine with this. I've hesitated because error checking is by default slower. But, I can agree that it is "less surprising" to new-comers. People that don't mind no-checking can simply set their defaults back to 'ignore' -Travis ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
