On 14/10/06, Charles R Harris <[EMAIL PROTECTED]> wrote: > > > On 10/13/06, A. M. Archibald <[EMAIL PROTECTED]> wrote: > > On 13/10/06, Tim Hochberg <[EMAIL PROTECTED]> wrote: > > > Charles R Harris wrote: > > <snip> > > > > On the other hand if error handling is set to 'raise', then a > > > FloatingPointError is issued. Is a FloatingPointWarning in order to > > > mirror the FloatingPointError? And if so, would it be appropriate to use > > > for condition number? > > > > I submitted a patchto use warnings for several functions in scipy a > > while ago, and the approach I took was to create a ScipyWarning, from > > which more specific warnings were derived (IntegrationWarning, for > > example). That was perhaps a bit short-sighted. > > > > I'd suggest a FloatingPointWarning as a base class, with > > IllConditionedMatrix as a subclass (it should include the condition > > number, but probably not the matrix itself unless it's small, as > > debugging information). > > > > Let's pin this down a bit. Numpy seems to need its own warning classes so we > can control the printing of the warnings. For the polyfit function I think > it should *always* warn by default because it is likely to be used > interactively and one can fool around with the degree of the fit. For > instance, I am now scaling the the input x vector so norm_inf(x) == 1 and > this seems to work pretty well for lots of stuff with rcond=-1 (about 1e-16) > and a warning on rank reduction. As long as the rank stays the same things > seem to work ok, up to fits of degree 21 on the test data that started this > conversation.
Numerical Recipes (http://www.nrbook.com/a/bookcpdf/c15-4.pdf ) recommend setting rcond to the number of data points times machine epsilon (which of course is different for single/double). We should definitely warn the user if any singular value is below s[0]*rcond (as that means that there is effectively a useless basis function, up to roundoff). I'm not sure how to control the default warnings setting ("once" vs. "always"); it's definitely not possible using the standard API to save the warnings state and restore it later. One might be able to push such a change into the warnings module by including it in a ContextManager. ipython should probably reset all the "show once" warnings every time it shows an interactive prompt. I suppose more accurately, it should do that only for warnings the user hasn't given instructions about. That way you'll get a warning about bad polynomial fits every time you run a command that contains one, but if your function runs thousands of fits you don't drown in warnings. > BTW, how does one turn warnings back on? If I do a > > >>> warnings.simplefilter('always', mywarn) > > things work fine. Following this by > > >>> warnings.simplefilter('once', mywarn) > > does what is supposed to do. Once again issuing > > >>> warnings.simplefilter('always', mywarn) > > fails to have any effect. Resetwarnings doesn't help. Hmmm... I don't get the impression that the warnings module is much tested; I had similar headaches. A. M. Archibald ------------------------------------------------------------------------- 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
