relax SVN archive

Re-used the dependency check "scipy_module", since leastsq() has been part of Scipy since 2003.

Lowering precision in systemtest Relax_disp.test_r1rho_kjaergaard_missing_r1.

This is R1 estimation with: MODEL_NS_R1RHO_2SITE.

The lowering of precision is due different system precision.

Add dependency check for scipy.optimize.leastsq.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Added systemtest Relax_disp.test_estimate_r2eff_error().

This is to get insight in the error difference between 2000 monto carlo simulations and then scipy.optimize.leastsq.

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 431.0.
r2eff=8.646/8.646 r2eff_err=0.0348/0.0692 i0=202664.191/202664.191 i0_err=699.6443/712.4201

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 651.2.
r2eff=10.377/10.377 r2eff_err=0.0403/0.0810 i0=206049.558/206049.558 i0_err=776.4215/782.1833

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 800.5.
r2eff=10.506/10.506 r2eff_err=0.0440/0.0853 i0=202586.332/202586.332 i0_err=763.9678/758.7052

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 984.0.
r2eff=10.903/10.903 r2eff_err=0.0476/0.0922 i0=203455.021/203455.021 i0_err=837.8779/828.7280

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 1341.1.
r2eff=10.684/10.684 r2eff_err=0.0446/0.0853 i0=218670.412/218670.411 i0_err=850.0210/830.9558

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 1648.5.
r2eff=10.501/10.501 r2eff_err=0.0371/0.0742 i0=206502.512/206502.512 i0_err=794.0523/772.9843

R1rho at 799.8 MHz, for offset=124.247 ppm and dispersion point 1341.1.
r2eff=11.118/11.118 r2eff_err=0.0413/0.0827 i0=216447.241/216447.241 i0_err=784.6562/788.0384

R1rho at 799.8 MHz, for offset=130.416 ppm and dispersion point 800.5.
r2eff=7.866/7.866 r2eff_err=0.0347/0.0695 i0=211869.715/211869.715 i0_err=749.2776/763.6930

R1rho at 799.8 MHz, for offset=130.416 ppm and dispersion point 1341.1.
r2eff=9.259/9.259 r2eff_err=0.0331/0.0661 i0=217703.151/217703.151 i0_err=682.2137/685.5838

R1rho at 799.8 MHz, for offset=130.416 ppm and dispersion point 1648.5.
r2eff=9.565/9.565 r2eff_err=0.0373/0.0745 i0=211988.939/211988.939 i0_err=839.0313/827.0373

R1rho at 799.8 MHz, for offset=142.754 ppm and dispersion point 800.5.
r2eff=3.240/3.240 r2eff_err=0.0127/0.0253 i0=214417.382/214417.382 i0_err=595.8865/613.4378

R1rho at 799.8 MHz, for offset=142.754 ppm and dispersion point 1341.1.
r2eff=5.084/5.084 r2eff_err=0.0177/0.0352 i0=226358.691/226358.691 i0_err=660.5314/655.7670

R1rho at 799.8 MHz, for offset=179.768 ppm and dispersion point 1341.1.
r2eff=2.208/2.208 r2eff_err=0.0091/0.0178 i0=228620.553/228620.553 i0_err=564.8353/560.0873

R1rho at 799.8 MHz, for offset=241.459 ppm and dispersion point 1341.1.
r2eff=1.711/1.711 r2eff_err=0.0077/0.0155 i0=224087.486/224087.486 i0_err=539.4300/546.4217

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Modified systemtest Relax_disp.test_estimate_r2eff.

This is to compare against errors simulated with 2000 MC.

The paramaters are comparable, but not equal.
Mostly, it seems that the errors from scipy.optimize.leastsq, are twice as high than the Monte Carlo simulations.
This affect model fitting, and the calculated chi2 value.

Left column is 2000 Monte Carlo, right column is scipy.optimize.leastsq.

Optimised parameters for spin: 52V @N
Model: No Rex
Parameter: r1 Value: 1.46138806 - 1.46328102
Parameter: r2 Value: 11.48392438 - 11.48040934
Parameter: chi2 Value: 848.42015672 - 3363.95829122

Model: DPL94
Parameter: r1 Value: 1.44845743 - 1.45019848
Parameter: r2 Value: 10.15688373 - 10.16304892
Parameter: phi_ex Value: 0.07599563 - 0.07561937
Parameter: kex Value: 4460.43707304 - 4419.03906628
Parameter: chi2 Value: 179.47041255 - 710.24767560

Model: TP02
Parameter: r1 Value: 1.54354392 - 1.54352369
Parameter: r2 Value: 9.72654895 - 9.72772727
Parameter: pA Value: 0.88827039 - 0.88807488
Parameter: dw Value: 1.08875836 - 1.08765645
Parameter: kex Value: 4921.28597928 - 4904.70134941
Parameter: chi2 Value: 29.33882481 - 114.47142772

Model: TAP03
Parameter: r1 Value: 1.54356410 - 1.54354368
Parameter: r2 Value: 9.72641885 - 9.72759371
Parameter: pA Value: 0.88828925 - 0.88809317
Parameter: dw Value: 1.08837248 - 1.08726695
Parameter: kex Value: 4926.42974479 - 4909.86896567
Parameter: chi2 Value: 29.29050624 - 114.27987534

Model: MP05
Parameter: r1 Value: 1.54356415 - 1.54354372
Parameter: r2 Value: 9.72641730 - 9.72759220
Parameter: pA Value: 0.88828927 - 0.88809322
Parameter: dw Value: 1.08837250 - 1.08726707
Parameter: kex Value: 4926.44228958 - 4909.88128236
Parameter: chi2 Value: 29.29054252 - 114.28002272

Model: NS R1rho 2-site
Parameter: r1 Value: 1.41359226 - 1.41321968
Parameter: r2 Value: 9.34531364 - 9.34602793
Parameter: pA Value: 0.94504369 - 0.94496541
Parameter: dw Value: 1.56001843 - 1.55833321
Parameter: kex Value: 5628.66529504 - 5610.20221435
Parameter: chi2 Value: 34.44010458 - 134.14368365

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Split up of systemtest test_r1rho_kjaergaard_missing_r1, into a verification part.

This is to test the new r2eff estimation, which should get the parameter values, as a
this 2000 monto carlo simulation.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Small changes to verification scripts, to use chi2 function and use the scaling matrix correct.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Documentation fix for new exponential target function.

Also added new function to estimate R2eff and i0 parameters, before minimisation.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Implemented back end for estimating r2eff and errors by exponential curve fitting with scipy.optimize.leastsq.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Modified check for model, to accept model as input, for error printing.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.