relax SVN archive

Added png image that show that the distribution which relax makes are to narrow.

This is a potential huge flaw in implementation of Monte-Carlo simulations.

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

Added relax analysis script, to profile distribution of errors drawn in relax, and from python module "random".

It seems that relax draw a lot more narrow distribution of Intensity with errors, than python module "random".
This has an influence on estimated parameter error.

This is a potential huge error in relax.
A possible example of a catastrophic implementation of Monte-Carlo simulations.

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

Added initial peak lists to be analysed in relax for test purposes.

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

Added functionality to create peak lists, for virtual data.

This is to compare the distribution of R2eff values made by boot strapping and Monte-Carlo simulations.

Rest of the analysis will be performed in relax.

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.verify_estimate_r2eff_err_compare_mc to include boot strapping method.

This shows there is excellent agreement between boot-strapping and estimation of errors from Co-variance, while
relax Monte-Carlo simulations are very much different.

Boot strapping is the "-2":

-2 0.070 0.085 0.087 0.095 0.086 0.076 0.087 0.072 0.069 0.077 0.025 0.035 0.018 0.015 sum= 0.897
-1 0.069 0.081 0.085 0.092 0.085 0.074 0.083 0.069 0.066 0.074 0.025 0.035 0.018 0.016 sum= 0.874
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 sum= 0.000
400 0.034 0.043 0.044 0.049 0.046 0.037 0.042 0.035 0.031 0.039 0.014 0.018 0.009 0.008 sum= 0.450
800 0.032 0.040 0.041 0.046 0.042 0.036 0.040 0.034 0.034 0.037 0.013 0.018 0.009 0.008 sum= 0.431
1200 0.033 0.041 0.042 0.046 0.043 0.037 0.042 0.036 0.034 0.038 0.012 0.018 0.009 0.008 sum= 0.439
1600 0.036 0.041 0.042 0.047 0.043 0.038 0.042 0.035 0.035 0.037 0.013 0.018 0.009 0.008 sum= 0.443
2000 0.034 0.042 0.042 0.046 0.042 0.036 0.043 0.035 0.034 0.037 0.013 0.017 0.009 0.008 sum= 0.438

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

Inserted possibility for boot-strapping in systemtest Relax_disp.test_estimate_r2eff_err_methods.

This shows, that the boot strapping method get the SAME estimation for R2eff errors, as the estimate_r2eff_err() function!

This must either mean, that the OLD Monte-Carlo simulation was corrupted, or the creation of data in Monte-Carlo simulations is corrupted.

For the r2eff columns.

0.0348/0.0692/0.0348/0.0691

Old MC 2000/Estimated from Co-var/Manual re-calc of old MC/ Boot strapping 2000 simulations.

-------------
R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 431.0.
r2eff=8.646/8.646 r2eff_err=0.0348/0.0692/0.0348/0.0691 i0=202664.191/202664.191 i0_err=699.6443/712.4201/699.6443/693.1613

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 651.2.
r2eff=10.377/10.377 r2eff_err=0.0403/0.0810/0.0403/0.0823 i0=206049.558/206049.558 i0_err=776.4215/782.1833/776.4215/763.6342

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 800.5.
r2eff=10.506/10.506 r2eff_err=0.0440/0.0853/0.0440/0.0887 i0=202586.332/202586.332 i0_err=763.9678/758.7052/763.9678/776.2788

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 984.0.
r2eff=10.903/10.903 r2eff_err=0.0476/0.0922/0.0476/0.0968 i0=203455.021/203455.021 i0_err=837.8779/828.7280/837.8779/834.3398

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 1341.1.
r2eff=10.684/10.684 r2eff_err=0.0446/0.0853/0.0446/0.0889 i0=218670.412/218670.412 i0_err=850.0210/830.9558/850.0210/825.7990

R1rho at 799.8 MHz, for offset=118.078 ppm and dispersion point 1648.5.
r2eff=10.501/10.501 r2eff_err=0.0371/0.0742/0.0371/0.0754 i0=206502.512/206502.512 i0_err=794.0523/772.9843/794.0523/776.3687

R1rho at 799.8 MHz, for offset=124.247 ppm and dispersion point 1341.1.
r2eff=11.118/11.118 r2eff_err=0.0413/0.0827/0.0413/0.0870 i0=216447.241/216447.241 i0_err=784.6562/788.0384/784.6562/810.1911

R1rho at 799.8 MHz, for offset=130.416 ppm and dispersion point 800.5.
r2eff=7.866/7.866 r2eff_err=0.0347/0.0695/0.0347/0.0712 i0=211869.715/211869.715 i0_err=749.2776/763.6930/749.2776/747.9741

R1rho at 799.8 MHz, for offset=130.416 ppm and dispersion point 1341.1.
r2eff=9.259/9.259 r2eff_err=0.0331/0.0661/0.0331/0.0680 i0=217703.151/217703.151 i0_err=682.2137/685.5838/682.2137/700.1831

R1rho at 799.8 MHz, for offset=130.416 ppm and dispersion point 1648.5.
r2eff=9.565/9.565 r2eff_err=0.0373/0.0745/0.0373/0.0743 i0=211988.939/211988.939 i0_err=839.0313/827.0373/839.0313/815.4495

R1rho at 799.8 MHz, for offset=142.754 ppm and dispersion point 800.5.
r2eff=3.240/3.240 r2eff_err=0.0127/0.0253/0.0127/0.0254 i0=214417.382/214417.382 i0_err=595.8865/613.4378/595.8865/606.4650

R1rho at 799.8 MHz, for offset=142.754 ppm and dispersion point 1341.1.
r2eff=5.084/5.084 r2eff_err=0.0177/0.0352/0.0177/0.0353 i0=226358.691/226358.691 i0_err=660.5314/655.7670/660.5314/666.9979

R1rho at 799.8 MHz, for offset=179.768 ppm and dispersion point 1341.1.
r2eff=2.208/2.208 r2eff_err=0.0091/0.0178/0.0091/0.0176 i0=228620.553/228620.553 i0_err=564.8353/560.0873/564.8353/558.6988

R1rho at 799.8 MHz, for offset=241.459 ppm and dispersion point 1341.1.
r2eff=1.711/1.711 r2eff_err=0.0077/0.0155/0.0077/0.0158 i0=224087.486/224087.486 i0_err=539.4300/546.4217/539.4300/556.5093

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

Inserted systemtest Relax_disp.test_finite_value, to illustrate the return of inf from C-code exponential, when R is negative.

This can be an issue, if minfx takes a wrong step when no constraints are implemented.

bug #22552(https://gna.org/bugs/index.php?22552): Chi2 value returned from C-code Curve-fitting return 0.0 for wrong parameters -> Expected influence on Monte-Carlo sim.

Inserted extra tests in systemtest Relax_disp.test_estimate_r2eff_err_methods to test that all values of R and i0 are positive, and the standard deviation from Monte-Carlo simulations
are equal.

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

Added simulations that show, there is perfect agreement between Monte-Carlo simulations and Co-Variance estimation.

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

Modified data script generator, to handle a situation with fixed 5 timepoints, and a situations with variable number of timepoints.
Also modified analysis script.

It seems, this has an influence how the error estimation is performing.

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