Re: [bug #13259] full analysis-high Te values -- August 24, 2009 - 22:04

Hi Ed,

I just tested if the problem was still present using the same machine
and the same script, however with an updated version of the 1.3 line
(r9387).

The problem is still present...

============================
<ts desc="Slower motion effective internal correlation time (seconds)"
ieee_754_byte_array="[177, 90, 65, 161, 240, 180, 85, 62]">
2.0216029808800001e-08
</ts>
============================

Regards,


Séb  :)




Edward d'Auvergne wrote:
> Hi,
>
> I was just wondering if this is still a problem?  From memory this was
> found to be a problem in the full_analysis.py script which is now
> fixed.  If you could confirm this, it would be much appreciated.
>
> Cheers,
>
> Edward
>
>
> On Thu, Mar 26, 2009 at 7:42 PM, Sébastien
> Morin<sebastien.morin.1@xxxxxxxxx> wrote:
>   
> > Hi Ed,
> >
> > Tests #2 and #3 are now completed.
> >
> > Both tests show the bug is still present... (as seen in the 'aic' 
> > directory,
> > 'm8' is selected for residue A44).
> >
> > In the log, there is no place where the eliminate() function is called...
> >
> > Moreover, and this could have saved time, the test case (submitted with the
> > bug report) also fails to eliminate model 'm8' for A44... I was sure we had
> > tested that before doing the much longer tests (with datasets for all
> > residues) after r9003...
> >
> > Hence, this problem is due to r9003.
> >
> > Any idea..?
> >
> >
> > Séb  :)
> >
> >
> >
> >
> >
> >
> > Edward d'Auvergne wrote:
> >     
> > > You're welcome.  The test case files attached to the bug made the work
> > > much less for me, so thanks for that Pierre-Yves.
> > >
> > > Bye,
> > >
> > > Edward
> > >
> > >
> > > On Thu, Mar 26, 2009 at 4:47 PM, Sébastien Morin
> > > <sebastien.morin.1@xxxxxxxxx> wrote:
> > >
> > >       
> > > > Hi Ed,
> > > >
> > > > It is indeed fixed !!! Now, we'll have to wait for the rounds to complete
> > > > (for all three tests) so we are certain that no other problem was
> > > > introduced...
> > > >
> > > > Edward, sorry for taking so much of your time right before the ENC ! PY
> > > > and
> > > > I really appreciate your help ! Thanks a lot for your devotion !!!
> > > >
> > > >
> > > > Séb  :)
> > > >
> > > >
> > > > Edward d'Auvergne wrote:
> > > >
> > > >         
> > > > > Right, it might be fixed now.
> > > > >
> > > > > Edward
> > > > >
> > > > >
> > > > > On Thu, Mar 26, 2009 at 4:36 PM, Edward d'Auvergne
> > > > > <edward@xxxxxxxxxxxxx>
> > > > > wrote:
> > > > >
> > > > >
> > > > >           
> > > > > > Hi,
> > > > > >
> > > > > > The bug you see there is the regression.  I'm working on the fix right
> > > > > > now.  Should have some code to test soon.
> > > > > >
> > > > > > Cheers,
> > > > > >
> > > > > > Edward
> > > > > >
> > > > > >
> > > > > > On Thu, Mar 26, 2009 at 4:26 PM, Sébastien Morin
> > > > > > <sebastien.morin.1@xxxxxxxxx> wrote:
> > > > > >
> > > > > >
> > > > > >             
> > > > > > > Hi Ed,
> > > > > > >
> > > > > > > Pierre-Yves and I are now testing the fix...
> > > > > > >
> > > > > > > We set-up three different tests including all datasets (for all ~150
> > > > > > > residues). After the first round of prolate completed, we will know if
> > > > > > > residue 44 is still associated with a ts of ~20 ns in model 'm8' or if
> > > > > > > this
> > > > > > > model is eliminated...
> > > > > > >
> > > > > > > 1.
> > > > > > > Start from scratch with full_analysis.py. The local_tm run will be
> > > > > > > done
> > > > > > > and,
> > > > > > > after, the prolate run.
> > > > > > >
> > > > > > > 2.
> > > > > > > Use the old local_tm directory (from analysis before the bug report of
> > > > > > > yesterday) and only make the prolate run...
> > > > > > >
> > > > > > > 3.
> > > > > > > Use the old prolate directory (from analysis before the bug report of
> > > > > > > yesterday) including the init and round_1 sub-directories and only
> > > > > > > make
> > > > > > > round 2.
> > > > > > >
> > > > > > > We will have to wait to see if everything looks normal.
> > > > > > >
> > > > > > > However, there seems to be a problem, already. For test #2, we get the
> > > > > > > following error in the inital round of optimisation for the diffusion
> > > > > > > tensor
> > > > > > > (init round):
> > > > > > >
> > > > > > > ==============
> > > > > > > relax> diffusion_tensor.init(params=(1e-08, 0, 0, 0), time_scale=1.0,
> > > > > > > d_scale=1.0, angle_units='deg', param_types=0,
> > > > > > > spheroid_type='prolate',
> > > > > > > fixed=False)
> > > > > > >
> > > > > > > relax> fix(element='all_spins', fixed=True)
> > > > > > >
> > > > > > > relax> grid_search(lower=None, upper=None, inc=11, constraints=True,
> > > > > > > verbosity=1)
> > > > > > > The diffusion tensor parameters together with the model-free
> > > > > > > parameters
> > > > > > > for
> > > > > > > all spins will be used.
> > > > > > > Unconstrained grid search size:
> > > > > > >
> > > > > > >
> > > > > > > 3483241635926760276239670296061783033856385373964443346380512727207030807448244020315627244532223540371618455651157976598330893796730564039193972812374440994792715667315064916696072753116546554132476003724628552320667084135787252303417928004684992625923335554734448200360162656232011377981776056433588143239686879331L
> > > > > > > (constraints may decrease this size).
> > > > > > >
> > > > > > > RelaxError: A grid search of size
> > > > > > >
> > > > > > >
> > > > > > > 3483241635926760276239670296061783033856385373964443346380512727207030807448244020315627244532223540371618455651157976598330893796730564039193972812374440994792715667315064916696072753116546554132476003724628552320667084135787252303417928004684992625923335554734448200360162656232011377981776056433588143239686879331L
> > > > > > > is too large.
> > > > > > > ==============
> > > > > > >
> > > > > > > This problem also arises when not making the change at line 303
> > > > > > > (self.model_selection(modsel_pipe='final', dir=self.base_dir +
> > > > > > > 'aic')).
> > > > > > >
> > > > > > > So, my guess is that this problem might be due to changes in r8999.
> > > > > > >
> > > > > > > Regards,
> > > > > > >
> > > > > > >
> > > > > > > Séb  :)
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > Edward d'Auvergne wrote:
> > > > > > >
> > > > > > >
> > > > > > >               
> > > > > > > > Actually, this will cause a problem.  I've closely looked at the
> > > > > > > > script, and it appears that your suggestion is correct:
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > > > >                 
> > > > > > > > > > > > > I just tried something using the files in the bug report, as
> > > > > > > > > > > > > well
> > > > > > > > > > > > > as
> > > > > > > > > > > > > with
> > > > > > > > > > > > > the up-to-date 1.3 repository (r9001)...
> > > > > > > > > > > > >
> > > > > > > > > > > > > I modified line 303 of the script from:
> > > > > > > > > > > > >
> > > > > > > > > > > > >                 self.model_selection(modsel_pipe='final',
> > > > > > > > > > > > > dir=self.base_dir + 'aic')
> > > > > > > > > > > > >
> > > > > > > > > > > > > to:
> > > > > > > > > > > > >
> > > > > > > > > > > > >                 self.model_selection(modsel_pipe='aic',
> > > > > > > > > > > > > dir=self.base_dir + 'aic')
> > > > > > > > > > > > >
> > > > > > > > > > > > > With this change, model 'm8' is eliminated and model 'm1' is
> > > > > > > > > > > > > selected
> > > > > > > > > > > > > instead.
> > > > > > > > > > > > >
> > > > > > > > > > > > >
> > > > > > > > > > > > >
> > > > > > > > > > > > >                           
> > > > > > > > This data pipe is for AIC model selection and the results go into the
> > > > > > > > 'aic' directory.  This is followed by the 'opt' data pipe for
> > > > > > > > diffusion tensor optimisation.  So I'll make the change to the
> > > > > > > > repository.  But could you check if all works with the changes that
> > > > > > > > were made to the 1.3 line?  I think these changes were logical, but
> > > > > > > > there is a regression causing a system test to fail.
> > > > > > > >
> > > > > > > > Cheers,
> > > > > > > >
> > > > > > > > Edward
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > > > >                 
> > > > > > > --
> > > > > > > Sébastien Morin
> > > > > > > PhD Student
> > > > > > > S. Gagné NMR Laboratory
> > > > > > > Université Laval & PROTEO
> > > > > > > Québec, Canada
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > >               
> > > > >           
> > > > --
> > > > Sébastien Morin
> > > > PhD Student
> > > > S. Gagné NMR Laboratory
> > > > Université Laval & PROTEO
> > > > Québec, Canada
> > > >
> > > >
> > > >
> > > >         
> > >
> > >       
> > --
> > Sébastien Morin
> > PhD Student
> > S. Gagné NMR Laboratory
> > Université Laval & PROTEO
> > Québec, Canada
> >
> >
> >     
>
>   


-- 
Sébastien Morin
PhD Student
S. Gagné NMR Laboratory
Université Laval & PROTEO
Québec, Canada