On Thu, 2007-02-08 at 18:55 -0500, John Denker wrote: > On 02/07/2007 02:14 PM, Andy Ross wrote: > > > So if there's a design flaw here, it's at a higher level. Maybe > > what's really needed is a "digit extractor" animation instead. > > Good news: The /design/ is not as flawed as it looks. > > All evidence indicates that the primary problem with the > step-and-scroll function was not the design; the primary > problem had more to do with the implementation. The code > was practically begging to get bit by roundoff errors.
John, I stripped the apply_mod() function out into its own small program for analysis. In the first test, input to the program was a simulated property changing from 109.89899999999999 to 109.89999999999998. The '8' marches one place to the right each run. Both algorithms were tested with and without bias, the original algorithm was also ran with a scroll value of 1e-6*step. In the second test, property was held constant and step was varied between 0.001 and 100. The test was ran again with a slightly larger property. This test shows the most common usage of the apply_mod function. My conclusions: - With bias set to 0.5 * (smallest expected step) both algorithms return a correctly rounded result. - With scroll set to 1e-6*step the original algorithm returns the same result as your algorithm. - With a very small error in property your algorithm returns a result which is close to desired however the exact result varies with the input error. While your algorithm returns acceptable results in the instant case I can not be certain it will not fail in different cases. - Your algorithm's output can be achieved with the current code, however you algorithm can not always reproduce the current code's output. Therefore I recommend we stay with the current code and add either a bias or scroll value to the xml files of the instruments which require it. V/r Ron **Test One** Original algorithm with bias: 109.8989999999999867=109.9000000000000057 (delta=-0.0010000000000190) with step=0.0100, scroll=0.0000, bias=0.0050 109.8998999999999882=109.9000000000000057 (delta=-0.0001000000000175) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999899999999883=109.9000000000000057 (delta=-0.0000100000000174) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999989999999940=109.9000000000000057 (delta=-0.0000010000000117) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999998999999832=109.9000000000000057 (delta=-0.0000001000000225) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999899999835=109.9000000000000057 (delta=-0.0000000100000221) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999989999878=109.9000000000000057 (delta=-0.0000000010000178) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999998999897=109.9000000000000057 (delta=-0.0000000001000160) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999899870=109.9000000000000057 (delta=-0.0000000000100187) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999989967=109.9000000000000057 (delta=-0.0000000000010090) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999998920=109.9000000000000057 (delta=-0.0000000000001137) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999999773=109.9000000000000057 (delta=-0.0000000000000284) with step=0.0100, scroll=0.0000, bias=0.0050 Original algorithm without bias: 109.8989999999999867=109.8900000000000006 (delta=0.0089999999999861) with step=0.0100, scroll=0.0000, bias=0.0000 109.8998999999999882=109.8900000000000006 (delta=0.0098999999999876) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999899999999883=109.8900000000000006 (delta=0.0099899999999877) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999989999999940=109.8900000000000006 (delta=0.0099989999999934) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999998999999832=109.8900000000000006 (delta=0.0099998999999826) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999899999835=109.8900000000000006 (delta=0.0099999899999830) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999989999878=109.8900000000000006 (delta=0.0099999989999873) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999998999897=109.8900000000000006 (delta=0.0099999998999891) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999899870=109.8900000000000006 (delta=0.0099999999899865) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999989967=109.8900000000000006 (delta=0.0099999999989961) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999998920=109.8900000000000006 (delta=0.0099999999998914) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999999773=109.8900000000000006 (delta=0.0099999999999767) with step=0.0100, scroll=0.0000, bias=0.0000 Original algorithm without bias, with scroll=0.00000001: 109.8989999999999867=109.8900000000000006 (delta=0.0089999999999861) with step=0.0100, scroll=0.0000, bias=0.0000 109.8998999999999882=109.8900000000000006 (delta=0.0098999999999876) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999899999999883=109.8900000000000006 (delta=0.0099899999999877) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999989999999940=109.8900000000000006 (delta=0.0099989999999934) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999998999999832=109.8900000000000006 (delta=0.0099998999999826) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999899999835=109.8900000000000006 (delta=0.0099999899999830) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999989999878=109.8989999855502759 (delta=0.0010000134497119) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999998999897=109.8998999874010991 (delta=0.0001000124988906) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999899870=109.8999899847440105 (delta=0.0000100152459765) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999989967=109.8999989944258999 (delta=0.0000010055730968) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999998920=109.8999998897097470 (delta=0.0000001102901450) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999999773=109.8999999749748753 (delta=0.0000000250251020) with step=0.0100, scroll=0.0000, bias=0.0000 Your algorithm with bias: 109.8989999999999867=109.9000000000000057 (delta=-0.0010000000000190) with step=0.0100, scroll=0.0000, bias=0.0050 109.8998999999999882=109.9000000000000057 (delta=-0.0001000000000175) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999899999999883=109.9000000000000057 (delta=-0.0000100000000174) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999989999999940=109.9000000000000057 (delta=-0.0000010000000117) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999998999999832=109.9000000000000057 (delta=-0.0000001000000225) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999899999835=109.9000000000000057 (delta=-0.0000000100000221) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999989999878=109.9000000000000057 (delta=-0.0000000010000178) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999998999897=109.9000000000000057 (delta=-0.0000000001000160) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999899870=109.9000000000000057 (delta=-0.0000000000100187) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999989967=109.9000000000000057 (delta=-0.0000000000010090) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999998920=109.9000000000000057 (delta=-0.0000000000001137) with step=0.0100, scroll=0.0000, bias=0.0050 109.8999999999999773=109.9000000000000057 (delta=-0.0000000000000284) with step=0.0100, scroll=0.0000, bias=0.0050 Your algorithm without bias: 109.8989999999999867=109.8900000000000006 (delta=0.0089999999999861) with step=0.0100, scroll=0.0000, bias=0.0000 109.8998999999999882=109.8900000000000006 (delta=0.0098999999999876) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999899999999883=109.8900000000000006 (delta=0.0099899999999877) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999989999999940=109.8900000000000006 (delta=0.0099989999999934) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999998999999832=109.8900000000000006 (delta=0.0099998999999826) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999899999835=109.8900000000000006 (delta=0.0099999899999830) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999989999878=109.8989999872676577 (delta=0.0010000117323301) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999998999897=109.8998999891184809 (delta=0.0001000107815088) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999899870=109.8999899864613923 (delta=0.0000100135285948) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999989967=109.8999989961432817 (delta=0.0000010038557150) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999998920=109.8999998914271288 (delta=0.0000001085727632) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999999773=109.8999999766922571 (delta=0.0000000233077202) with step=0.0100, scroll=0.0000, bias=0.0000 **Test Two** Original algorithm with property=109.89998999999999 step varing between 0.001 and 100: 109.8999899999999883=109.8990000000000009 (delta=0.0009899999999874) with step=0.0010, scroll=0.0000, bias=0.0000 109.8999899999999883=109.8900000000000006 (delta=0.0099899999999877) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999899999999883=109.8000000000000114 (delta=0.0999899999999769) with step=0.1000, scroll=0.0000, bias=0.0000 109.8999899999999883=109.0000000000000000 (delta=0.8999899999999883) with step=1.0000, scroll=0.0000, bias=0.0000 109.8999899999999883=100.0000000000000000 (delta=9.8999899999999883) with step=10.0000, scroll=0.0000, bias=0.0000 109.8999899999999883=100.0000000000000000 (delta=9.8999899999999883) with step=100.0000, scroll=0.0000, bias=0.0000 Your algorithm with property=109.89998999999999 step varing between 0.001 and 100: 109.8999899999999883=109.8990000000000009 (delta=0.0009899999999874) with step=0.0010, scroll=0.0000, bias=0.0000 109.8999899999999883=109.8900000000000006 (delta=0.0099899999999877) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999899999999883=109.8000000000000114 (delta=0.0999899999999769) with step=0.1000, scroll=0.0000, bias=0.0000 109.8999899999999883=109.0000000000000000 (delta=0.8999899999999883) with step=1.0000, scroll=0.0000, bias=0.0000 109.8999899999999883=100.0000000000000000 (delta=9.8999899999999883) with step=10.0000, scroll=0.0000, bias=0.0000 109.8999899999999883=100.0000000000000000 (delta=9.8999899999999883) with step=100.0000, scroll=0.0000, bias=0.0000 Original algorithm with property=109.89999999999999 step varing between 0.001 and 100: 109.8999999999999915=109.8990000000000009 (delta=0.0009999999999906) with step=0.0010, scroll=0.0000, bias=0.0000 109.8999999999999915=109.8900000000000006 (delta=0.0099999999999909) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999999915=109.8000000000000114 (delta=0.0999999999999801) with step=0.1000, scroll=0.0000, bias=0.0000 109.8999999999999915=109.0000000000000000 (delta=0.8999999999999915) with step=1.0000, scroll=0.0000, bias=0.0000 109.8999999999999915=100.0000000000000000 (delta=9.8999999999999915) with step=10.0000, scroll=0.0000, bias=0.0000 109.8999999999999915=100.0000000000000000 (delta=9.8999999999999915) with step=100.0000, scroll=0.0000, bias=0.0000 Your algorithm with property=109.89999999999999 step varing between 0.001 and 100: 109.8999999999999915=109.8999999905631029 (delta=0.0000000094368886) with step=0.0010, scroll=0.0000, bias=0.0000 109.8999999999999915=109.8999999909031118 (delta=0.0000000090968797) with step=0.0100, scroll=0.0000, bias=0.0000 109.8999999999999915=109.8999999800992668 (delta=0.0000000199007246) with step=0.1000, scroll=0.0000, bias=0.0000 109.8999999999999915=109.0000000000000000 (delta=0.8999999999999915) with step=1.0000, scroll=0.0000, bias=0.0000 109.8999999999999915=100.0000000000000000 (delta=9.8999999999999915) with step=10.0000, scroll=0.0000, bias=0.0000 109.8999999999999915=100.0000000000000000 (delta=9.8999999999999915) with step=100.0000, scroll=0.0000, bias=0.0000 ------------------------------------------------------------------------- Using Tomcat but need to do more? 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