Re: [bug #22146] Unpacking of R2A and R2B is performed wrong for clustered "full" dispersion models -- June 06, 2014

That would work.  I usually bypass the target functions and have a
small function or block of code in the generation script which
back-calculates the data - specifically to avoid a bug in the code
causing the base data to be buggy and hence the test passes.  As long
as you catch the problem though, it doesn't matter how it's done.  And
we know exactly what it is, so it should be easy to catch now.

Cheers,

Edward



On 6 June 2014 17:01, Troels Emtekær Linnet <tlinnet@xxxxxxxxxxxxx> wrote:

Clever!

So, I create the data with the new unpack method.

Best
Troels


2014-06-06 16:31 GMT+02:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:


Hi,

That's true, this will only affect the target function value.  But at
this point the chi-squared value at the solution should be zero, as
the relaxation data was created with the same parameter values.
Whereas if the parameters are unpacked incorrectly, you will see a
large chi-squared value.  You could probably use this instead to
construct the system test.

Regards,

Edward



On 6 June 2014 16:26, Troels Emtekær Linnet <tlinnet@xxxxxxxxxxxxx> wrote:

Hm.

There is no way to catch these parameters.

You send in alterations of
1) parameters in list
2) they are unpacked wrong and sent to chi func
3) return of chi func

But the parameters are not swapped/altered in the middle.

So it is not possible to catch with a system test.

Or is it possible to call the Dispersion class,
and get the last call to calc_CR72_chi2?

Best
Troels



2014-06-06 16:00 GMT+02:00 Troels Emtekær Linnet
<tlinnet@xxxxxxxxxxxxx>:


Ah ja.

Okay, the "Constraint violated, skipping grid point." was stopping it,

Best
Troels


2014-06-06 15:48 GMT+02:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:


The grid search in minfx will make target function calls to obtain the
chi-squared value.  See line 223 of the minfx.grid module
(http://home.gna.org/minfx/minfx.grid-pysrc.html#grid).  Once
complete, the Disp_result_command.run() method in
specific_analyses.relax_disp.optimisation will call
disassemble_param_vector() to unpack the results.

Regards,

Edward



On 6 June 2014 15:42, Troels Emtekær Linnet <tlinnet@xxxxxxxxxxxxx>
wrote:

Allright.

That is changed.

But, does the grid_search not call the target function?

Best
Troels


2014-06-06 15:36 GMT+02:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:


Ok, I just checked for the problem and it is the parameter values
of:

R20 = [10.1, 10.2, 10.3, 100.1, 100.2, 100.3, 20.1, 20.2, 20.3,
200.1,
200.2, 200.3, 30.1, 30.2, 30.3, 300.1, 300.2, 300.3, 40.1, 40.2,
40.3,
400.1, 400.2, 400.3]

If you look at the
specific_analyses.relax_disp.parameters.linear_constraints()
function,
you will see that the R20 constraints are:

        0 <= R2 <= 200
        0 <= R2A <= 200
        0 <= R2B <= 200

You have quite a few R20 values above 200, and that is the
constraint
violation.  When there is a violation, the point is skipped and, as
the grid search only has one point, then the grid search will do
nothing.  The value of 200 is reasonable as with such a relaxation
rate, you will no longer observe any peaks in your spectra.  That
is
the reason for this value, and it prevents optimisation from
sending
the R20 values to unreasonably high values.  Maybe you should
divide
all your R20 values by 10?

Regards,

Edward








On 6 June 2014 15:28, Troels Emtekær Linnet <tlinnet@xxxxxxxxxxxxx>
wrote:

Hi Ed.

All r2, r2a and r2b values should be between 5 - 20.

Thanks.

But, how does the grid_search skip the target function?

Best
Troels



2014-06-06 15:25 GMT+02:00 Edward d'Auvergne
<edward@xxxxxxxxxxxxx>:

Note that the indices of ci match the b vector, so you can look
at
the
assemble_linear_constraints() function and work out which
constraint
is being violated.

Regards,

Edward

On 6 June 2014 15:23, Edward d'Auvergne <edward@xxxxxxxxxxxxx>
wrote:

Here, all negative ci values indicate a constraint violation,
as:

ci = A.x - b,

where A and b are the constraint matrices and x is the
parameter
vector.  The linear constraints used by minfx are defined as:

A.x - b >= 0.

So you will need to determine which parameters are violating
the
constraints and change the input.

Regards,

Edward



On 6 June 2014 15:20, Troels Emtekær Linnet
<tlinnet@xxxxxxxxxxxxx>
wrote:

Ah

"Constraint violated, skipping grid point."


Fitting to the spin block [':1@N', ':2@N', ':3@N', ':4@N']
----------------------------------------------------------

Unconstrained grid search size: 1 (constraints may decrease
this
size).



Grid search
~~~~~~~~~~~
Linear constraint matrices.
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b: array([   0. ,  -20. ,    0. ,  -20. ,    0. ,  -20. ,
0.
,
-20.
,
          0. ,  -20. ,    0. ,  -20. ,    0. ,  -20. ,    0.
,
-20.
,
          0. ,  -20. ,    0. ,  -20. ,    0. ,  -20. ,    0.
,
-20.
,
          0. ,  -20. ,    0. ,  -20. ,    0. ,  -20. ,    0.
,
-20.
,
          0. ,  -20. ,    0. ,  -20. ,    0. ,  -20. ,    0.
,
-20.
,
          0. ,  -20. ,    0. ,  -20. ,    0. ,  -20. ,    0.
,
-20.
,
          0. ,    0. ,    0. ,    0. ,   -1. ,    0.5,    0.
,
-200.
])

Searching through 1 grid nodes.
k: 0        xk: [           1.01,            1.02,
1.03,
10.01,           10.02,           10.03,            2.01,
2.02,
2.03,           20.01,           20.02,           20.03,
3.01,
3.02,            3.03,           30.01,           30.02,
30.03,
4.01,            4.02,            4.03,           40.01,
40.02,
40.03,               1,               2,               3,
4,
0.9,             0.1]
Constraint violated, skipping grid point.
ci: array([  1.010000000000000e+00,   1.899000000000000e+01,
         1.020000000000000e+00,   1.898000000000000e+01,
         1.030000000000000e+00,   1.897000000000000e+01,
         1.001000000000000e+01,   9.990000000000000e+00,
         1.002000000000000e+01,   9.980000000000000e+00,
         1.003000000000000e+01,   9.970000000000001e+00,
         2.010000000000000e+00,   1.799000000000000e+01,
         2.020000000000000e+00,   1.798000000000000e+01,
         2.030000000000000e+00,   1.797000000000000e+01,
         2.001000000000000e+01,  -9.999999999998010e-03,
         2.002000000000000e+01,  -1.999999999999957e-02,
         2.003000000000000e+01,  -3.000000000000114e-02,
         3.010000000000000e+00,   1.699000000000000e+01,
         3.020000000000000e+00,   1.698000000000000e+01,
         3.030000000000000e+00,   1.697000000000000e+01,
         3.001000000000000e+01,  -1.001000000000000e+01,
         3.002000000000000e+01,  -1.002000000000000e+01,
         3.003000000000000e+01,  -1.003000000000000e+01,
         4.010000000000000e+00,   1.599000000000000e+01,
         4.020000000000000e+00,   1.598000000000000e+01,
         4.029999999999999e+00,   1.597000000000000e+01,
         4.001000000000001e+01,  -2.001000000000001e+01,
         4.002000000000000e+01,  -2.002000000000000e+01,
         4.003000000000000e+01,  -2.003000000000000e+01,
         1.000000000000000e+00,   2.000000000000000e+00,
         3.000000000000000e+00,   4.000000000000000e+00,
         9.999999999999998e-02,   4.000000000000000e-01,
         1.000000000000000e-01,   1.999000000000000e+02])


Optimised parameter values:
r2a (SQ CPMG - 500.00000000 MHz)        10.100000000000000
r2a (SQ CPMG - 600.00000000 MHz)        10.199999999999999
r2a (SQ CPMG - 700.00000000 MHz)        10.300000000000001
r2b (SQ CPMG - 500.00000000 MHz)       100.099999999999994
r2b (SQ CPMG - 600.00000000 MHz)       100.199999999999989
r2b (SQ CPMG - 700.00000000 MHz)       100.299999999999997
r2a (SQ CPMG - 500.00000000 MHz)        20.100000000000001
r2a (SQ CPMG - 600.00000000 MHz)        20.199999999999999
r2a (SQ CPMG - 700.00000000 MHz)        20.300000000000004
r2b (SQ CPMG - 500.00000000 MHz)       200.099999999999966
r2b (SQ CPMG - 600.00000000 MHz)       200.199999999999989
r2b (SQ CPMG - 700.00000000 MHz)       200.300000000000011
r2a (SQ CPMG - 500.00000000 MHz)        30.100000000000001
r2a (SQ CPMG - 600.00000000 MHz)        30.199999999999999
r2a (SQ CPMG - 700.00000000 MHz)        30.300000000000004
r2b (SQ CPMG - 500.00000000 MHz)       300.100000000000023
r2b (SQ CPMG - 600.00000000 MHz)       300.199999999999989
r2b (SQ CPMG - 700.00000000 MHz)       300.300000000000011
r2a (SQ CPMG - 500.00000000 MHz)        40.099999999999994
r2a (SQ CPMG - 600.00000000 MHz)        40.200000000000003
r2a (SQ CPMG - 700.00000000 MHz)        40.299999999999997
r2b (SQ CPMG - 500.00000000 MHz)       400.100000000000023
r2b (SQ CPMG - 600.00000000 MHz)       400.199999999999932
r2b (SQ CPMG - 700.00000000 MHz)       400.300000000000011
dw                           1.000000000000000
dw                           2.000000000000000
dw                           3.000000000000000
dw                           4.000000000000000
pA                           0.900000000000000
kex                       1000.000000000000000
('CR72 full', 'Ala', ':1@N', 'r2a', 'SQ CPMG - 600.00000000
MHz',
10.2,
10.2)
('CR72 full', 'Ala', ':1@N', 'r2a', 'SQ CPMG - 500.00000000
MHz',
10.1,
10.1)
('CR72 full', 'Ala', ':1@N', 'r2a', 'SQ CPMG - 700.00000000
MHz',
10.3,
10.3)
('CR72 full', 'Ala', ':1@N', 'r2b', 'SQ CPMG - 600.00000000
MHz',
100.19999999999999, 100.2)
('CR72 full', 'Ala', ':1@N', 'r2b', 'SQ CPMG - 500.00000000
MHz',
100.1,
100.1)
('CR72 full', 'Ala', ':1@N', 'r2b', 'SQ CPMG - 700.00000000
MHz',
100.3,
100.3)
('CR72 full', 'Ala', ':2@N', 'r2a', 'SQ CPMG - 600.00000000
MHz',
20.2,
20.2)
('CR72 full', 'Ala', ':2@N', 'r2a', 'SQ CPMG - 500.00000000
MHz',
20.1,
20.1)
('CR72 full', 'Ala', ':2@N', 'r2a', 'SQ CPMG - 700.00000000
MHz',
20.300000000000004, 20.3)
('CR72 full', 'Ala', ':2@N', 'r2b', 'SQ CPMG - 600.00000000
MHz',
200.2,
200.2)
('CR72 full', 'Ala', ':2@N', 'r2b', 'SQ CPMG - 500.00000000
MHz',
200.09999999999997, 200.1)
('CR72 full', 'Ala', ':2@N', 'r2b', 'SQ CPMG - 700.00000000
MHz',
200.3,
200.3)
('CR72 full', 'Ala', ':3@N', 'r2a', 'SQ CPMG - 600.00000000
MHz',
30.2,
30.2)
('CR72 full', 'Ala', ':3@N', 'r2a', 'SQ CPMG - 500.00000000
MHz',
30.1,
30.1)
('CR72 full', 'Ala', ':3@N', 'r2a', 'SQ CPMG - 700.00000000
MHz',
30.300000000000004, 30.3)
('CR72 full', 'Ala', ':3@N', 'r2b', 'SQ CPMG - 600.00000000
MHz',
300.2,
300.2)
('CR72 full', 'Ala', ':3@N', 'r2b', 'SQ CPMG - 500.00000000
MHz',
300.1,
300.1)
('CR72 full', 'Ala', ':3@N', 'r2b', 'SQ CPMG - 700.00000000
MHz',
300.3,
300.3)
('CR72 full', 'Ala', ':4@N', 'r2a', 'SQ CPMG - 600.00000000
MHz',
40.2,
40.2)
('CR72 full', 'Ala', ':4@N', 'r2a', 'SQ CPMG - 500.00000000
MHz',
40.099999999999994, 40.1)
('CR72 full', 'Ala', ':4@N', 'r2a', 'SQ CPMG - 700.00000000
MHz',
40.3,
40.3)
('CR72 full', 'Ala', ':4@N', 'r2b', 'SQ CPMG - 600.00000000
MHz',
400.19999999999993, 400.2)
('CR72 full', 'Ala', ':4@N', 'r2b', 'SQ CPMG - 500.00000000
MHz',
400.1,
400.1)
('CR72 full', 'Ala', ':4@N', 'r2b', 'SQ CPMG - 700.00000000
MHz',
400.3,
400.3)



2014-06-06 15:14 GMT+02:00 Edward d'Auvergne
<edward@xxxxxxxxxxxxx>:

Try setting "verbosity=10" in the grid_search user function
call.
This might reveal the problem.

Regards,

Edward


On 6 June 2014 15:12, Troels Emtekær Linnet
<tlinnet@xxxxxxxxxxxxx>
wrote:

I put in a print command in the target function to see
R20A
and
R20B.

This is not showing when doing grid search, but only when
doing
minimisation?

Best
Troels


2014-06-06 15:10 GMT+02:00 Troels Emtekær Linnet
<tlinnet@xxxxxxxxxxxxx>:

I tried:

        ## Experiments
        # Exp 1
        sfrq_1 = 500.0*1E6
        r20_key_1 =
generate_r20_key(exp_type=EXP_TYPE_CPMG_SQ,
frq=sfrq_1)
        time_T2_1 = 0.05
        ncycs_1 = range(2,22,2)
        # Here you define the direct R2eff errors
(rad/s),
as
being
added
or subtracted for the created R2eff point in the
corresponding
ncyc
cpmg
frequence.
        #r2eff_errs_1 = [0.05, -0.05, 0.05, -0.05, 0.05,
-0.05,
0.05,
-0.05, 0.05, -0.05, 0.05, -0.05, 0.05, -0.05, 0.05]
        r2eff_errs_1 = [0.0] * len(ncycs_1)
        exp_1 = [sfrq_1, time_T2_1, ncycs_1,
r2eff_errs_1]

        sfrq_2 = 600.0*1E6
        r20_key_2 =
generate_r20_key(exp_type=EXP_TYPE_CPMG_SQ,
frq=sfrq_2)
        time_T2_2 = 0.06
        ncycs_2 = range(2,22,2)
        # Here you define the direct R2eff errors
(rad/s),
as
being
added
or subtracted for the created R2eff point in the
corresponding
ncyc
cpmg
frequence.
        #r2eff_errs_2 = [0.05, -0.05, 0.05, -0.05, 0.05,
-0.05,
0.05,
-0.05, 0.05, -0.05, 0.05, -0.05, 0.05, -0.05, 0.05,
-0.05,
0.05]
        r2eff_errs_2 = [0.0] * len(ncycs_2)
        exp_2 = [sfrq_2, time_T2_2, ncycs_2,
r2eff_errs_2]

        sfrq_3 = 700.0*1E6
        r20_key_3 =
generate_r20_key(exp_type=EXP_TYPE_CPMG_SQ,
frq=sfrq_3)
        time_T2_3 = 0.07
        ncycs_3 = range(2,22,2)
        # Here you define the direct R2eff errors
(rad/s),
as
being
added
or subtracted for the created R2eff point in the
corresponding
ncyc
cpmg
frequence.
        #r2eff_errs_2 = [0.05, -0.05, 0.05, -0.05, 0.05,
-0.05,
0.05,
-0.05, 0.05, -0.05, 0.05, -0.05, 0.05, -0.05, 0.05,
-0.05,
0.05]
        r2eff_errs_3 = [0.0] * len(ncycs_3)
        exp_3 = [sfrq_3, time_T2_3, ncycs_3,
r2eff_errs_3]

        # Collect all exps
        exps = [exp_1, exp_2, exp_3]

        R20 = [10.1, 10.2, 10.3, 100.1, 100.2, 100.3,
20.1,
20.2,
20.3,
200.1, 200.2, 200.3, 30.1, 30.2, 30.3, 300.1, 300.2,
300.3,
40.1,
40.2,
40.3, 400.1, 400.2, 400.3]
        #R20 = [10.1, 10.2, 10.3, 100.1, 100.2, 100.3,
20.1,
20.2,
20.3,
200.1, 200.2, 200.3]
        dw_arr = [1.0, 2.0, 3.0, 4.0]
        #dw_arr = [1.0, 2.0]
        pA_arr = [0.9]
        kex_arr = [1000.]

        spins = [
                ['Ala', 1, 'N', {'r2a': {r20_key_1:
R20[0],
r20_key_2:
R20[1], r20_key_3: R20[2]}, 'r2b': {r20_key_1: R20[3],
r20_key_2:
R20[4],
r20_key_3: R20[5]}, 'kex': kex_arr[0], 'pA': pA_arr[0],
'dw':
dw_arr[0]}],
                ['Ala', 2, 'N', {'r2a': {r20_key_1:
R20[6],
r20_key_2:
R20[7], r20_key_3: R20[8]}, 'r2b': {r20_key_1: R20[9],
r20_key_2:
R20[10],
r20_key_3: R20[11]}, 'kex': kex_arr[0], 'pA': pA_arr[0],
'dw':
dw_arr[1]}],
                ['Ala', 3, 'N', {'r2a': {r20_key_1:
R20[12],
r20_key_2:
R20[13], r20_key_3: R20[14]}, 'r2b': {r20_key_1: R20[15],
r20_key_2:
R20[16], r20_key_3: R20[17]}, 'kex': kex_arr[0], 'pA':
pA_arr[0],
'dw':
dw_arr[2]}],
                ['Ala', 4, 'N', {'r2a': {r20_key_1:
R20[18],
r20_key_2:
R20[19], r20_key_3: R20[20]}, 'r2b': {r20_key_1: R20[21],
r20_key_2:
R20[22], r20_key_3: R20[23]}, 'kex': kex_arr[0], 'pA':
pA_arr[0],
'dw':
dw_arr[3]}],
                ]

------

------------
relax> grid_search(lower=[10.1, 10.2, 10.3, 100.1, 100.2,
100.3,
20.1,
20.2, 20.3, 200.1, 200.2, 200.3, 30.1, 30.2, 30.3, 300.1,
300.2,
300.3,
40.1, 40.2, 40.3, 400.1, 400.2, 400.3, 1.0, 2.0, 3.0,
4.0,
0.9,
1000.0],
upper=[10.1, 10.2, 10.3, 100.1, 100.2, 100.3, 20.1, 20.2,
20.3,
200.1,
200.2, 200.3, 30.1, 30.2, 30.3, 300.1, 300.2, 300.3,
40.1,
40.2,
40.3,
400.1, 400.2, 400.3, 1.0, 2.0, 3.0, 4.0, 0.9, 1000.0],
inc=1,
constraints=True, verbosity=1)


Fitting to the spin block [':1@N', ':2@N', ':3@N',
':4@N']

----------------------------------------------------------

Unconstrained grid search size: 1 (constraints may
decrease
this
size).


Grid search
~~~~~~~~~~~

Searching through 1 grid nodes.

Optimised parameter values:
r2a (SQ CPMG - 500.00000000 MHz)
10.100000000000000
r2a (SQ CPMG - 600.00000000 MHz)
10.199999999999999
r2a (SQ CPMG - 700.00000000 MHz)
10.300000000000001
r2b (SQ CPMG - 500.00000000 MHz)
100.099999999999994
r2b (SQ CPMG - 600.00000000 MHz)
100.199999999999989
r2b (SQ CPMG - 700.00000000 MHz)
100.299999999999997
r2a (SQ CPMG - 500.00000000 MHz)
20.100000000000001
r2a (SQ CPMG - 600.00000000 MHz)
20.199999999999999
r2a (SQ CPMG - 700.00000000 MHz)
20.300000000000004
r2b (SQ CPMG - 500.00000000 MHz)
200.099999999999966
r2b (SQ CPMG - 600.00000000 MHz)
200.199999999999989
r2b (SQ CPMG - 700.00000000 MHz)
200.300000000000011
r2a (SQ CPMG - 500.00000000 MHz)
30.100000000000001
r2a (SQ CPMG - 600.00000000 MHz)
30.199999999999999
r2a (SQ CPMG - 700.00000000 MHz)
30.300000000000004
r2b (SQ CPMG - 500.00000000 MHz)
300.100000000000023
r2b (SQ CPMG - 600.00000000 MHz)
300.199999999999989
r2b (SQ CPMG - 700.00000000 MHz)
300.300000000000011
r2a (SQ CPMG - 500.00000000 MHz)
40.099999999999994
r2a (SQ CPMG - 600.00000000 MHz)
40.200000000000003
r2a (SQ CPMG - 700.00000000 MHz)
40.299999999999997
r2b (SQ CPMG - 500.00000000 MHz)
400.100000000000023
r2b (SQ CPMG - 600.00000000 MHz)
400.199999999999932
r2b (SQ CPMG - 700.00000000 MHz)
400.300000000000011
dw                           1.000000000000000
dw                           2.000000000000000
dw                           3.000000000000000
dw                           4.000000000000000
pA                           0.900000000000000
kex                       1000.000000000000000
('CR72 full', 'Ala', ':1@N', 'r2a', 'SQ CPMG -
600.00000000
MHz',
10.2,
10.2)
('CR72 full', 'Ala', ':1@N', 'r2a', 'SQ CPMG -
500.00000000
MHz',
10.1,
10.1)
('CR72 full', 'Ala', ':1@N', 'r2a', 'SQ CPMG -
700.00000000
MHz',
10.3,
10.3)
('CR72 full', 'Ala', ':1@N', 'r2b', 'SQ CPMG -
600.00000000
MHz',
100.19999999999999, 100.2)
('CR72 full', 'Ala', ':1@N', 'r2b', 'SQ CPMG -
500.00000000
MHz',
100.1,
100.1)
('CR72 full', 'Ala', ':1@N', 'r2b', 'SQ CPMG -
700.00000000
MHz',
100.3,
100.3)
('CR72 full', 'Ala', ':2@N', 'r2a', 'SQ CPMG -
600.00000000
MHz',
20.2,
20.2)
('CR72 full', 'Ala', ':2@N', 'r2a', 'SQ CPMG -
500.00000000
MHz',
20.1,
20.1)
('CR72 full', 'Ala', ':2@N', 'r2a', 'SQ CPMG -
700.00000000
MHz',
20.300000000000004, 20.3)
('CR72 full', 'Ala', ':2@N', 'r2b', 'SQ CPMG -
600.00000000
MHz',
200.2,
200.2)
('CR72 full', 'Ala', ':2@N', 'r2b', 'SQ CPMG -
500.00000000
MHz',
200.09999999999997, 200.1)
('CR72 full', 'Ala', ':2@N', 'r2b', 'SQ CPMG -
700.00000000
MHz',
200.3,
200.3)
('CR72 full', 'Ala', ':3@N', 'r2a', 'SQ CPMG -
600.00000000
MHz',
30.2,
30.2)
('CR72 full', 'Ala', ':3@N', 'r2a', 'SQ CPMG -
500.00000000
MHz',
30.1,
30.1)
('CR72 full', 'Ala', ':3@N', 'r2a', 'SQ CPMG -
700.00000000
MHz',
30.300000000000004, 30.3)
('CR72 full', 'Ala', ':3@N', 'r2b', 'SQ CPMG -
600.00000000
MHz',
300.2,
300.2)
('CR72 full', 'Ala', ':3@N', 'r2b', 'SQ CPMG -
500.00000000
MHz',
300.1,
300.1)
('CR72 full', 'Ala', ':3@N', 'r2b', 'SQ CPMG -
700.00000000
MHz',
300.3,
300.3)
('CR72 full', 'Ala', ':4@N', 'r2a', 'SQ CPMG -
600.00000000
MHz',
40.2,
40.2)
('CR72 full', 'Ala', ':4@N', 'r2a', 'SQ CPMG -
500.00000000
MHz',
40.099999999999994, 40.1)
('CR72 full', 'Ala', ':4@N', 'r2a', 'SQ CPMG -
700.00000000
MHz',
40.3,
40.3)
('CR72 full', 'Ala', ':4@N', 'r2b', 'SQ CPMG -
600.00000000
MHz',
400.19999999999993, 400.2)
('CR72 full', 'Ala', ':4@N', 'r2b', 'SQ CPMG -
500.00000000
MHz',
400.1,
400.1)
('CR72 full', 'Ala', ':4@N', 'r2b', 'SQ CPMG -
700.00000000
MHz',
400.3,
400.3)

--------

It jumps over the target function?

This is a little weird?

Best
Troels



2014-06-06 15:02 GMT+02:00 Edward d'Auvergne
<edward@xxxxxxxxxxxxx>:

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
<tlinnet@xxxxxxxxxxxxx>
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
<edward@xxxxxxxxxxxxx>:

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
<tlinnet@xxxxxxxxxxxxx>
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
<edward@xxxxxxxxxxxxx>:

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
<tlinnet@xxxxxxxxxxxxx>
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
<edward@xxxxxxxxxxxxx>:

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
<edward@xxxxxxxxxxxxx>
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
<tlinnet@xxxxxxxxxxxxx>
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
<tlinnet@xxxxxxxxxxxxx>:

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
<edward@xxxxxxxxxxxxx>:


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
<NO-REPLY.INVALID-ADDRESS@xxxxxxx>
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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Re: [bug #22146] Unpacking of R2A and R2B is performed wrong for clustered "full" dispersion models

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