Hi, Some more information is needed, as it's not possible to tell where this stopped. A good idea would be to turn up the verbosity level to see what minfx is doing. Did you call the grid_search user function? Or the minimise user function? Did the grid search say something like "Searching through 1 grid nodes"?
Regards, Edward On 6 June 2014 14:48, Troels Emtekær Linnet <[email protected]> wrote: > Hi Edward. > > When I try to do a grid search, it does not initalize func_CR72_full in the > target function? > > If I make > import sys > sys.exit() > > It does not stop? > Only if I do a minimise, it stops? > > It is in > specific_analyses/relax_disp/optimisation.py > line 745 > > Inserting > print model.func.im_func.__name__ > gives func_CR72_full > > How does it know how to unpack and calculate? > > Best > Troels > > > 2014-06-06 12:32 GMT+02:00 Edward d'Auvergne <[email protected]>: > >> Hi, >> >> That sounds good. Maybe it's best to have the number of fields and >> number of spins set to something different and not to 2? That way the >> unpacking is stressed as much as possible and there cannot be a >> accidental swap of the field and spin dimensions being unnoticed by >> the test. This is not likely, but I've encountered enough weird and >> supposedly impossible situations in the development of relax that it >> would not surprise me. >> >> Cheers, >> >> Edward >> >> On 6 June 2014 12:27, Troels Emtekær Linnet <[email protected]> wrote: >> > Check. >> > >> > I am generating R2eff data for 3 fields, and 3 spins, for full model. >> > I will put the data in >> > test_suite/shared_data/dispersion/bug_22146_unpacking_r2a_r2b_cluster >> > >> > Best >> > Troels >> > >> > >> > 2014-06-06 12:11 GMT+02:00 Edward d'Auvergne <[email protected]>: >> > >> >> Hi, >> >> >> >> Right, you have the 2 parameters in the self.num_spins*2 part. And I >> >> forgot about the parameters being different for each field. It would >> >> be good to then have a multi-field and multi-spin cluster system test >> >> to really make sure that relax operates correctly, especially with the >> >> data going into the target function and the subsequently unpacking the >> >> results into the relax data store. For example someone might modify >> >> the loop_parameters() function - this concept could be migrated into >> >> the specific API and converted into a common mechanism for all the >> >> analysis types as it is quite powerful - and they may not know that >> >> the change they just made broke code in the >> >> target_functions.relax_disp module. >> >> >> >> Cheers, >> >> >> >> Edward >> >> >> >> >> >> On 6 June 2014 12:05, Troels Emtekær Linnet <[email protected]> >> >> wrote: >> >> > Hi Ed. >> >> > >> >> > The implementations needs: >> >> > R20 = params[:self.end_index[1]].reshape(self.num_spins*2, >> >> > self.num_frq) >> >> > R20A = R20[::2].flatten() >> >> > R20B = R20[1::2].flatten() >> >> > >> >> > >> >> > >> >> > >> >> > >> >> > 2014-06-06 11:55 GMT+02:00 Edward d'Auvergne <[email protected]>: >> >> > >> >> >> The different unpacking implementations can be tested with the >> >> >> timeit >> >> >> Python module to see which is fastest >> >> >> >> >> >> >> >> >> (http://thread.gmane.org/gmane.science.nmr.relax.devel/5937/focus=6010). >> >> >> >> >> >> Cheers, >> >> >> >> >> >> Edward >> >> >> >> >> >> >> >> >> >> >> >> On 6 June 2014 11:53, Edward d'Auvergne <[email protected]> >> >> >> wrote: >> >> >> > 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
