relax SVN archive

The dispersion get_param_values() API method now calls the r1_setup() function.

This relates to bug #22541 (https://gna.org/bugs/?22541), the R1 fit flag does not work in the GUI.

This is to make sure that the parameters are correctly set up prior to obtaining all parameter
values. The R1 parameter is dynamic hence r1_setup() needs to be called at any point model
parameters are accessed, as the R1 parameter can be turned on or off at any time with the
relax_disp.r1_fit user function.

Modified specific_analyses.relax_disp.parameters.r1_setup() to initialise the 'r1' variable.

This relates to bug 22541 (https://gna.org/bugs/?22541), the R1 fit flag does not work in the GUI.

This is a hack, as all of the dispersion analysis code assumes that all parameters are initialised.
This is a dangerous assumption that will have to be eliminated in the future.

Allow R2eff model to reach constrained methods in minfx through relax.

This is in systemtest Relax_disp.verify_estimate_r2eff_err_compare_mc()

This though not seem to be supported.

Initial try to reach constrained methods in minfx through relax.

This is in systemtest Relax_disp.verify_estimate_r2eff_err_compare_mc()

This though not seem to be supported.

Reverted r25463, related setting fit of R1 in GUI

The command used was:
svn merge -r25463:r25462 .

........
r25463 | tlinnet | 2014-08-30 00:00:45 +0200 (Sat, 30 Aug 2014) | 3 lines

Fix for 'r1' not being added to parameters, when looking up model in dictionary.

bug #22541(https://gna.org/bugs/?22541): The R1 fit flag does not work in the GUI.
........

Fix for inf values beeing returned from C-code exponential function. Values are now converted to high values.

This fixes systemtest Relax_disp.test_finite_value.

Example:
x = np.array([np.inf, -np.inf, np.nan, -128, 128])
np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])

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.
Ref: http://docs.scipy.org/doc/numpy/reference/generated/numpy.nan_to_num.html

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.