relax SVN archive

Started using the newly cR2 variable, extracted from higher dimensional data.

This should be okay, but systemtest test_hansen_cpmg_data_to_ns_cpmg_2site_star, now fails:

Optimised parameter values:
r2 (SQ CPMG - 500.00000000 MHz) 4.998935658499767
r2 (SQ CPMG - 800.00000000 MHz) 6.898256259440341
dw 2.092924953509931
pA 0.986716058519642
kex 2438.044235414625746

Optimised parameters:

Parameter Value (:70) Value (:71)
R2 (500 MHz) 6.95812598916492 4.99893565849977
R2 (800 MHz) 9.39656319607375 6.89825625944034
pA 0.989701272773474 0.986716058519642
dw 5.67316761415756 2.09292495350993
kex 1713.67781672744 2438.04423541463
chi2 52.510693756237 15.1644902423334

F
======================================================================
FAIL: test_hansen_cpmg_data_to_ns_cpmg_2site_star (test_suite.system_tests.relax_disp.Relax_disp)
Optimisation of Dr. Flemming Hansen's CPMG data to the 'NS CPMG 2-site star' dispersion model.
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/tlinnet/software/disp_spin_speed/test_suite/system_tests/relax_disp.py", line 3211, in test_hansen_cpmg_data_to_ns_cpmg_2site_star
self.assertAlmostEqual(spin71.pA, 0.992258541625787, 2)
AssertionError: 0.98671605851964195 != 0.992258541625787 within 2 places

---------------
Compared to systemtest:
It seems that there has been "problems" with residue 71 before.

# Checks for residue :70.
self.assertAlmostEqual(spin70.r2[r20_key1], 6.95543947938561, 1)
self.assertAlmostEqual(spin70.r2[r20_key2], 9.38991914134929, 1)
self.assertAlmostEqual(spin70.pA, 0.989702750971153, 3)
self.assertAlmostEqual(spin70.dw, 5.67527122494516, 1)
self.assertAlmostEqual(spin70.kex/1000, 1715.72032391817/1000, 1)
self.assertAlmostEqual(spin70.chi2, 52.5011991483842, 1)

# Checks for residue :71.
self.assertAlmostEqual(spin71.r2[r20_key1], 4.992594256544, 1)
self.assertAlmostEqual(spin71.pA, 0.992258541625787, 2)
self.assertAlmostEqual(spin71.dw/100, 2.75140650899058/100, 2)
self.assertAlmostEqual(spin71.kex/100000, 2106.60885247431/100000, 2)
self.assertAlmostEqual(spin71.chi2/100, 17.3293856656588/100, 1)

# Test the conversion to k_AB from kex and pA.
self.assertEqual(spin70.k_AB, spin70.kex * (1.0 - spin70.pA))
self.assertEqual(spin71.k_AB, spin71.kex * (1.0 - spin71.pA))

# Test the conversion to k_BA from kex and pA.
self.assertEqual(spin70.k_BA, spin70.kex * spin70.pA)
self.assertEqual(spin71.k_BA, spin71.kex * spin71.pA)

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Added the systemtest: test_cpmg_synthetic_b14_to_ns_star_cluster, to check
for the model is still working after change.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Started using the newly created multidimensional matrix.

test_hansen_cpmg_data_to_ns_cpmg_2site_star

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Insert check, that the newly created multidimensional matrix is the same.

They are, but only to the fifth digit.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Added the function "rcpmg_star_rankN" for the collection of the multidimensional relaxation matrix for model NS CPMG 2site star.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Removed the parsing of a matrix to the lib function of NS R1rho 2site.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Cleaned up the code of NS R1rho 2site, and removed the matrix argument to the function.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Made the returned multidimensional rr1rho_3d_rankN, be of float64 type.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Moved the costly calculation of the matrix exponential out of for loops.

It was the numpy.eig and numpy.inv which was draining power.

This speeds up model NS R1rho 2site, by a factor 4X:
BEFORE:
Single:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 32.552 32.552 <string>:1(<module>)
1 0.002 0.002 32.552 32.552 pf_nsr1rho2site:530(single)
Cluster:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 33.307 33.307 <string>:1(<module>)
1 0.008 0.008 33.307 33.307 pf_nsr1rho2site:554(cluster)

AFTER:
Single:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 8.286 8.286 <string>:1(<module>)
1 0.002 0.002 8.286 8.286 pf_nsr1rho2site:530(single)
Cluster:
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 8.223 8.223 <string>:1(<module>)
1 0.007 0.007 8.223 8.223 pf_nsr1rho2site:554(cluster)

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.

Fix to the matrix_exponential_rankN, to return the exact exponential for any higher dimensional square matrix
of shape [NE][NS][NM][NO][ND][X][X].

The fix was to the eye(X), to make the shape the same as the input shape.

Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion models for Clustered analysis.