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>
>
> _______________________________________________
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> http://gna.org/
>
>
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