Hi, In this case, I think 'num_frq' should be fixed to 2. This dimension corresponds to the parameters R20A and R20B so it is always fixed to 2.
Regards, Edward On 6 June 2014 11:51, Troels Emtekær Linnet <[email protected]> wrote: > Hi. > > Another way is: > > ml = params[:end_index[1]].reshape(num_spins*2, num_frq) > R20A = ml[::2].flatten() > R20B = ml[1::2].flatten() > > > Best > Troels > > > > 2014-06-06 11:39 GMT+02:00 Troels Emtekær Linnet <[email protected]>: > >> There is no doubt that it is the unpacking of the R20A and R20B in the >> target function. >> >> I was thinking of creating a function, which do the the unpacking. >> >> This unpacking function could then be tested with a unit test? >> >> What do you think? >> Where should I position such a function? >> >> Best >> Troels >> >> >> >> 2014-06-06 11:26 GMT+02:00 Edward d'Auvergne <[email protected]>: >>> >>> Hi Troels, >>> >>> >>> The best way to handle this is to first create a unit test of the >>> specific_analyses.relax_disp.parameters.disassemble_param_vector() >>> where the problem is likely to be most easily found. I don't >>> understand how this could be a problem as the assemble_param_vector() >>> and disassemble_param_vector() functions both call the same >>> loop_parameters() function for the ordering of the parameter values! >>> Maybe the problem is in the unpacking of the parameter vector in the >>> target functions themselves, for example in the full B14 model: >>> >>> >>> def func_B14_full(self, params): >>> """Target function for the Baldwin (2014) 2-site exact >>> solution model for all time scales. >>> >>> This assumes that pA > pB, and hence this must be implemented >>> as a constraint. >>> >>> >>> @param params: The vector of parameter values. >>> @type params: numpy rank-1 float array >>> @return: The chi-squared value. >>> @rtype: float >>> """ >>> >>> # Scaling. >>> if self.scaling_flag: >>> params = dot(params, self.scaling_matrix) >>> >>> # Unpack the parameter values. >>> R20A = params[:self.end_index[0]] >>> R20B = params[self.end_index[0]:self.end_index[1]] >>> dw = params[self.end_index[1]:self.end_index[2]] >>> pA = params[self.end_index[2]] >>> kex = params[self.end_index[2]+1] >>> >>> # Calculate and return the chi-squared value. >>> return self.calc_B14_chi2(R20A=R20A, R20B=R20B, dw=dw, pA=pA, >>> kex=kex) >>> >>> >>> This R20A and R20B unpacking might be the failure point as this may >>> not match the loop_parameters() function - which it must! In any >>> case, having a unit or system test catch the problem would be very >>> useful for the stability of the dispersion analysis in relax. >>> >>> A code example might be useful: >>> >>> R20_params = array([1, 2, 3, 4]) >>> R20A, R20B = transpose(R20_params.reshape(2, 2) >>> print(R20A) >>> print(R20B) >>> >>> You should see that R20A is [1, 3], and R20B is [2, 4]. This is how >>> the parameters are handled in the loop_parameters() function which >>> defines the parameter vector in all parts of relax. There might be a >>> quicker way to unpack the parameters, but such an idea could be used >>> for the target functions. >>> >>> Cheers, >>> >>> Edward >>> >>> On 6 June 2014 11:08, Troels E. Linnet <[email protected]> >>> wrote: >>> > URL: >>> > <http://gna.org/bugs/?22146> >>> > >>> > Summary: Unpacking of R2A and R2B is performed wrong >>> > for >>> > clustered "full" dispersion models >>> > Project: relax >>> > Submitted by: tlinnet >>> > Submitted on: Fri 06 Jun 2014 09:08:58 AM UTC >>> > Category: relax's source code >>> > Specific analysis category: Relaxation dispersion >>> > Priority: 9 - Immediate >>> > Severity: 4 - Important >>> > Status: None >>> > Assigned to: None >>> > Originator Name: >>> > Originator Email: >>> > Open/Closed: Open >>> > Release: Repository: trunk >>> > Discussion Lock: Any >>> > Operating System: All systems >>> > >>> > _______________________________________________________ >>> > >>> > Details: >>> > >>> > The unpacking of the R2A and R2B parameters in the "full" model is >>> > performed >>> > wrong. >>> > This will happen performing a clustered analysis, using one of the >>> > "full" >>> > models. >>> > >>> > This bug affect all analysis performed running with a "full" model, >>> > with >>> > clustered residues. >>> > >>> > The bug is located in the target function: >>> > ./target_functions/relax_disp.py >>> > >>> > For all the "func_MODEL_full", the unpacking of: >>> > R20A = params[:self.end_index[0]] >>> > R20B = params[self.end_index[0]:self.end_index[1]] >>> > >>> > This is wrong, since the "params" list, is ordered: >>> > [spin, spin, spin, [dw], pA, kex], where spin = [nr_frq*r2a, >>> > nr_frq*r2b] >>> > >>> > This ordering happens in: >>> > ./specific_analysis/relax_disp/parameters.py >>> > in the loop_parameters.py >>> > >>> > A possible solutions i shown below. >>> > This alter the unpacking of the parameters. >>> > >>> > An example of profiling_cr72.py is attached. >>> > This can be downloaded, and run in base folder of relax: >>> > ./profiling_cr72.py . >>> > >>> > This is with 3 frq, and 3 spins. >>> > >>> > The current implementations would unpack: >>> > ('R20A', array([ 2., 2., 2., 4., 4., 4., 12., 12., 12.]), >>> > 9) >>> > ('R20B', array([ 14., 14., 14., 22., 22., 22., 24., 24., 24.]), >>> > 9) >>> > >>> > R2A is 2, 12, 22 for the spins 0-3 >>> > R2B is, 4, 14, 24 for the spins 0-3 >>> > >>> > The suggested unpacking loop, unpacks to: >>> > ('R20A', array([ 2., 2., 2., 12., 12., 12., 22., 22., 22.]), >>> > 9) >>> > ('R20B', array([ 4., 4., 4., 14., 14., 14., 24., 24., 24.]), >>> > 9) >>> > >>> > >>> > ------- >>> > from numpy import array, concatenate, delete, index_exp >>> > import numpy >>> > >>> > p = array([ 1.000000000000000e+01, 1.000000000000000e+01, >>> > 1.100000000000000e+01 >>> > , 1.100000000000000e+01, 1.000000000000000e+01, 1.000000000000000e+01 >>> > , 1.100000000000000e+01, 1.100000000000000e+01, 1.000000000000000e+00 >>> > , 1.000000000000000e+00, 9.000000000000000e-01, 1.000000000000000e+03]) >>> > >>> > e = [4, 8, 10] >>> > >>> > # Now >>> > r2a = p[:e[0]] >>> > print r2a >>> > r2b = p[e[0]:e[1]] >>> > print r2b >>> > dw = p[e[1]:e[2]] >>> > print dw >>> > pA = p[e[2]] >>> > print pA >>> > kex = p[e[2]+1] >>> > print kex >>> > >>> > print "new" >>> > ns = 2 >>> > nf = 2 >>> > >>> > ml = p[:e[1]] >>> > >>> > R20A = array([]) >>> > R20B = array([]) >>> > for i in range(0, ns): >>> > # Array sorted per [spin, spin, spin], where spin = [nr_frq*r2a, >>> > nr_frq*r2b] >>> > spin_AB = ml[:nf*2] >>> > ml = delete(ml, numpy.s_[:nf*2]) >>> > R20A = concatenate([R20A, spin_AB[:nf] ]) >>> > R20B = concatenate([R20B, spin_AB[nf:] ]) >>> > >>> > print R20A >>> > print R20B >>> > print dw >>> > print pA >>> > print kex >>> > >>> > >>> > >>> > _______________________________________________________ >>> > >>> > File Attachments: >>> > >>> > >>> > ------------------------------------------------------- >>> > Date: Fri 06 Jun 2014 09:08:58 AM UTC Name: profiling_cr72.py Size: >>> > 17kB >>> > By: tlinnet >>> > >>> > <http://gna.org/bugs/download.php?file_id=20938> >>> > >>> > _______________________________________________________ >>> > >>> > Reply to this item at: >>> > >>> > <http://gna.org/bugs/?22146> >>> > >>> > _______________________________________________ >>> > Message sent via/by Gna! >>> > http://gna.org/ >>> > >>> > >>> > _______________________________________________ >>> > relax (http://www.nmr-relax.com) >>> > >>> > This is the relax-devel mailing list >>> > [email protected] >>> > >>> > To unsubscribe from this list, get a password >>> > reminder, or change your subscription options, >>> > visit the list information page at >>> > https://mail.gna.org/listinfo/relax-devel >> >> > _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
