On Wed, 11 Oct 2006, Travis Oliphant wrote: > >Interestingly, in worst cases numpy.sqrt is approximately ~3 times slower > >than scipy.sqrt on negative input but ~2 times faster on positive input: > > > >In [47]: pos_input = numpy.arange(1,100,0.001) > > > >In [48]: %timeit -n 1000 b=numpy.sqrt(pos_input) > >1000 loops, best of 3: 4.68 ms per loop > > > >In [49]: %timeit -n 1000 b=scipy.sqrt(pos_input) > >1000 loops, best of 3: 10 ms per loop > > > > > > This is the one that concerns me. Slowing everybody down who knows they > have positive values just for people that don't seems problematic.
I think the code in scipy.sqrt can be optimized from def _fix_real_lt_zero(x): x = asarray(x) if any(isreal(x) & (x<0)): x = _tocomplex(x) return x def sqrt(x): x = _fix_real_lt_zero(x) return nx.sqrt(x) to (untested) def _fix_real_lt_zero(x): x = asarray(x) if not isinstance(x,(nt.csingle,nt.cdouble)) and any(x<0): x = _tocomplex(x) return x def sqrt(x): x = _fix_real_lt_zero(x) return nx.sqrt(x) or def sqrt(x): old = nx.seterr(invalid='raises') try: r = nx.sqrt(x) except FloatingPointError: x = _tocomplex(x) r = nx.sqrt(x) nx.seterr(**old) return r I haven't timed these cases yet.. Pearu ------------------------------------------------------------------------- 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