relax SVN archive

Removed all Junk comments from module for R2eff error estimation. The module runs perfect as it does now.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.
bug #22554(https://gna.org/bugs/index.php?22554): The distribution of intensity with errors in Monte-Carlo simulations are markedly more narrow than expected.

Cleaned up user function for estimating R2eff errors.

Extensive tests have shown, there is a very good agreement between the Co-variance estimation, and Monte-Carlo simulations.

This is indeed a very positive implementation.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.
bug #22554(https://gna.org/bugs/index.php?22554): The distribution of intensity with errors in Monte-Carlo simulations are markedly more narrow than expected.

Comment fix to systemtest Relax_disp.test_estimate_r2eff_err_methods, after the found of bug in relax.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.
bug #22554(https://gna.org/bugs/index.php?22554): The distribution of intensity with errors in Monte-Carlo simulations are markedly more narrow than expected.

Correction for catastrophic implementation of Monte-Carlo simulations.

And wrong implemetented "else if" statement, would add the intensity for the simulated intensity together with
the original intensity.

This means that all intensity values send to minimisation would be twice as high than usually.
(If spectra was not replicated.)

This was discovered for Monte-Carlo simulations of R2eff errors in exponential fit.

The function is restricted to the analysis of errors for exponential fit in Relax Dispersion.
Such data are normally restricted to R1rho analysis.

This will affect all analysis of R1rho data performed until now.
By pure luck, it seems that the effect of this would be that R2eff errors are half the value they should be.
A further investigation shows, that for the selected data set, this had a minimum on influence on the fitted parameters,
because the chi2 value would be scaled up by a factor 4.

bug #22554(https://gna.org/bugs/index.php?22554): The distribution of intensity with errors in Monte-Carlo simulations are markedly more narrow than expected.
task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Modified analysis script, to also make histogram of Intensities.

This shows that the created intensities are totally off the true intensity.

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

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.