Changes

* Added functionality to plot R_1ρ/R2 as function of effective field w_eff, for the [[R2eff ]] model. Also renamed a function, to better reflect is functionality. The hard-coding of which models to plot, has been removed. If the exp-type is R_1ρ, then the plotting will commence. [https://gna.org/support/?3124 Sr #3124]: Grace graphs production for R_1ρ analysis with R_2eff as function of Ω_eff. [https://gna.org/support/?3138 Sr #3138]: Interpolating theta through spin-lock offset [Omega], rather than spin-lock field strength [w1].

* Added system test Relax_disp.test_kteilum_fmpoulsen_makke_check_graphs to check all CPMG graph combinations of: y_axis_types = [Y_AXIS_R2_EFF, Y_AXIS_R2_R1RHO]; x_axis_types = [X_AXIS_DISP, X_AXIS_THETA, X_AXIS_W_EFF]; interpolate_types = [INTERPOLATE_DISP]. This is a total of 6 graphs. The graphs will in most cases be totally equal, since the theta angle is calculated to 90 degrees, and R1 is returned as 0.0, then R2=(R_1ρ - R1 cos^2(theta)) / sin^2(theta) = R_1ρ = R2eff R_2eff for CPMG models. [https://gna.org/support/?3124 Sr #3124]: Grace graphs production for R_1ρ analysis with R_2eff as function of Ω_eff. [https://gna.org/support/?3138 Sr #3138]: Interpolating theta through spin-lock offset [Omega], rather than spin-lock field strength [w1].

* Replaced in auto analysis all instances of [[No Rex]] and '[[R2eff' ]] with its equivalent defined variables. [https://gna.org/support/?3135 Sr #3135]: Optimisation of the R1 relaxation rate for the off-resonance R_1ρ relaxation dispersion models.* Re-arranged plotting and writing in auto-analyses of relax disp, when model is [[R2eff]]. [https://gna.org/support/?3135 Sr #3135]: Optimisation of the R1 relaxation rate for the off-resonance R_1ρ relaxation dispersion models.

* Fix for system test Relax_disp.test_r1rho_kjaergaard_auto_check_graphs(), where the special R_1ρ graphs is no longer produced for the [[R2eff ]] models. [https://gna.org/support/?3135 Sr #3135]: Optimisation of the R1 relaxation rate for the off-resonance R_1ρ relaxation dispersion models.

* Implemented partial reading of results file. Before reading a results file, it is determined if the file exists. This makes is possible to read a directory with partial results from a previous analysis. This can be handsome, if reading R2eff R_2eff values in R_1ρ experiments, and the error estimation has been prepared with a high number of Monte Carlo simulations. [https://gna.org/support/?3135 Sr #3135]: Optimisation of the R1 relaxation rate for the off-resonance R_1ρ relaxation dispersion models.* Added keyword to relax_disp auto analysis, if R2eff R_2eff values should be optimised. Here optimisation means minimisation and Monte Carlo simulations of the error. [https://gna.org/support/?3135 Sr #3135]: Optimisation of the R1 relaxation rate for the off-resonance R_1ρ relaxation dispersion models.* Modified system test Relax_disp.test_r1rho_kjaergaard_missing_r1 to load previous R2eff R_2eff values, and not optimise them. [https://gna.org/support/?3135 Sr #3135]: Optimisation of the R1 relaxation rate for the off-resonance R_1ρ relaxation dispersion models.

* Locked values in system test Relax_disp.test_r1rho_kjaergaard_missing_r1. This is possible after locking the R2eff R_2eff values and errors from a previous run. [https://gna.org/support/?3135 Sr #3135]: Optimisation of the R1 relaxation rate for the off-resonance R_1ρ relaxation dispersion models.

* Bugfix for Relax_disp.test_bug_21715_clustered_indexerror, where only '[[R2eff']], [[No Rex]] is analysed. This special case was not tested in the translating function.

* Rewrote the logic of the key-word 'optimise_r2eff' in the auto-analyses of relax disp. If [[R2eff ]] result file exist in the 'pre_run_dir', this is loaded. If the results contain both values, and errors, then no optimisation is performed on the [[R2eff ]] model. Unless the 'optimise_r2eff' flag is raised, which is not standard.

* Copying the R2eff R_2eff value from the reading of [[R2eff ]] results in the auto-analyses of relax_disp.

* Inserted intermediate system test, to profile R2eff R_2eff calculation for R_1ρ. System test is: Relax_disp.test_bug_9999_slow_r1rho_r2eff_error_with_mc. This system test actually fails, if one tries to do a grid search. This is related to the R2eff R_2eff values stored as dictionary, and pipe_control.minimise.grid_setup() will fail. The function 'isNaN(values[i])' cannot handle dictionary.* Modified intermediate system test Relax_disp.test_bug_9999_slow_r1rho_r2eff_error_with_mc to see if the initial grid search for 'i0' I₀ and 'R2eff' R_2eff estimation can be skipped. This is done by converting the exponential curve, to a linear curve, and calculate the best parameters by a line of best fit by least squares. This seems like a promising method as an initial estimator of 'i0' I₀ and 'r2eff'R_2eff. For 500 to 2000 Monte Carlo simulations, this could have an influence on the timings, as all grid searchs could be skipped.

* Modified following functions (time points are now saved at the [ei][mi][oi][di] index level, at this index level all time points are saved for the R2eff R_2eff point): interpolate_disp(), to interpolate time points, all time points through the original dispersion points di, are collected and then made unique - this time list can potentially be the largest of all time lists; interpolate_offset(), to interpolate time points, all time points through the original offset points, and then dispersion points di, are collected and then made unique - this time list can potentially be the largest of all time lists; plot_disp_curves_to_file(), to acquire the original relax_times points; return_r2eff_arrays(), to save all time points on the level of [ei][mi][oi][di]. At this index level, it will be a numpy array list with all time values used for fitting. [https://gna.org/bugs/?22461 Bug #22461]: NS R1rho 2-site_fit_r1 has extremely high χ² value in system test Relax_disp.test_r1rho_kjaergaard_missing_r1.

* Further improved the profiling of relax curve fit. This profiling shows, that Python code is about twice as slow as the C code implemented. But it also shows that optimising with scipy.optimize.leastsq is 20 X faster. It also gives reasonable error values. Combining a function for a linear fit to guess the initial values, together with scipy optimise, will be an extreme time win for estimating R2eff R_2eff values fast. A further test would be to use relax Monte Carlo simulations for say 1000-2000 iterations, and compare to the errors extracted from estimated covariance.* Added verification script, that shows that using scipy.optimize.leastsq reaches the exact same parameters as minfx for exponential curve fitting. The profiling shows that scipy.optimize.leastsq is 10X as fast as using minfx (with no linear constraints). scipy.optimize.leastsq is a wrapper around wrapper around MINPACK's lmdif and lmder algorithms. MINPACK is a FORTRAN90 library which solves systems of nonlinear equations, or carries out the least squares minimization of the residual of a set of linear or nonlinear equations. The verification script also shows, that a very heavy and time consuming Monte Carlo simulation of 2000 steps, reaches the same errors as the errors reported by scipy.optimize.leastsq. The return from scipy.optimize.leastsq, gives the estimated covariance. Taking the square root of the covariance corresponds with 2X error reported by minfx. This could be an extremely time saving step, when performing model fitting in R_1ρ, where the errors of the R2eff R_2eff values, are estimated by Monte Carlo simulations. The following setup illustrates the problem. This was analysed on a MacBook Pro, 13-inch, Late 2011 with no multi-core setup. Script running is: test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/2_pre_run_r2eff.py. This script analyses just the R2eff R_2eff values for 15 residues. It estimates the errors of R2eff R_2eff based on 2000 Monte Carlo simulations. For each residues, there is 14 exponential graphs. The script was broken after 35 simulations. This was measured to 20 minutes. So 500 simulations would take about 4.8 Hours. The R2eff R_2eff values and errors can by scipy.optimize.leastsq can instead be calculated in: 15 residues * 0.02 seconds = 0.3 seconds.* Moved the target function for minimisation of exponential fit into the target functions folder. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Implemented initial system test Relax_disp.test_estimate_r2eff for setting up the new user function to estimate R2eff R_2eff and errors by scipy. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added front end user function relax_disp.r2eff_estimate to estimate R2eff R_2eff and errors by exponential curve fitting in scipy.optimize.leastsq. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Modified check for model, to accept model as input, for error printing. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Implemented back end for estimating R2eff R_2eff and errors by exponential curve fitting with scipy.optimize.leastsq. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Documentation fix for new exponential target function. Also added new function to estimate R2eff R_2eff and I₀ parameters, before minimisation. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Small changes to verification scripts, to use χ² function and use the scaling matrix correct. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Split up of system test test_r1rho_kjaergaard_missing_r1, into a verification part. This is to test the new r2eff R_2eff estimation, which should get the parameter values, as a this 2000 Monto Carlo simulation. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Add dependency check for scipy.optimize.leastsq. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Implemented first try to minimise with minfx in estimate_r2eff() function. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Turned off the optimisation constraints for the '[[R2eff' ]] model in the dispersion auto-analysis. This follows from http://thread.gmane.org/gmane.science.nmr.relax.scm/22977/focus=6829. This model does not require constraints at all, and the constraints only cause the optimisation to take 10x longer to complete. Therefore the constraint flag has been set to False for the model.* Initial try to form the Jacobian and Hessian matrix for exponential decay. This can be tried with system test: relax -s Relax_disp.test_estimate_r2eff_error. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Intermediate step in estimate R2eff R_2eff module. It seems that minfx is minimising in a quadratic space because of the power of χ², while the general input to scipy.optimize does not do this. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Cleaned up target function for leastsq, since arguments to function can be extracted from class. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Tried to implement with scipy.optimize.fmin_ncg and scipy.optimize.fmin_cg, but cannot get it to work. The matrices are not aligned well. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Big cleanup of estimate R2eff R_2eff module. This is to make the documentation more easy to read and understand. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Added several comments to the R2eff R_2eff estimate module. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Got the method of 'Steepest descent' to work properly, by specifying the Jacobian correctly. The Jacobian was derived according to the χ² function. The key point was to evaluate the two derivative arrays for all times points, and then sum each of the two arrays together, before constructing the Jacobian. This clearly shows the difference between minfx and scipy.optimize.leastsq. scipy.optimize.leastsq takes as input a function F(x0), which should return the array of weighted differences between function value and measured values: "1. / self.errors * (self.calc_exp(self.times, *params) - self.values)". This will be an array with number of elements 'i' corresponding to number of elements. scipy.optimize.leastsq then internally evaluates the sum of squares -> sum[ (O - E)**2 ], and minimises this. This is the χ². Minfx requires the function to minimise before hand. So, the "func" should be χ². Then the dfunc, and d2func, should be derivative of χ², but all elements in the array should still be summed together. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Got the Quasi-Newton BFGS to work. This uses only the gradient, this gets the same results as 2000 Monte Carlo with simplex and scipy.optimize.leastsq. Error estimation still not provided. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Removed all code regarding scipy.optimize fmin_cg and fmin_ncg. This problem should soon be able to be solved with minfx. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added initial documentation for multifit_covar. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Modified profiling script to use the new estimate R2eff R_2eff module. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Modified verify error script, to use new estimate R2eff R_2eff module. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Removed all unnecessary code from estimate R2eff R_2eff module. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* More removal of code. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Modified profiling script, but it seems that the dfunc from target_functions.relax_fit does not work. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Modified estimate R2eff R_2eff module, to use C code. But system test Relax_disp.test_estimate_r2eff_error shows that the Jacobian is not correctly implemented to be called in minfx.

* Modified wrapper function for curve_fit, to only change to list type, if the type is a ndarray. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Implemented the first try to compute the variance of R2eff R_2eff and I₀, by the covariance. This uses the Jacobian matrix. The errors calculated, are though way to small compared 2000 Monte Carlo simulations. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Tried to implement the Jacobian from C code. This though also report errors which are to small. Maybe some scaling is wrong. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Tried to verify solution to profiling script. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Set the constraints=False when doing Monte Carlo simulations for R2effR_2eff. This is to speed up the Monte Carlo simulations by a factor X10, when estimating the error for R2effR_2eff. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Implemented the use of "Newton" as minimisation algorithm for R2eff R_2eff curve fitting instead of simplex. Running the test script: test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/2_pre_run_r2eff.py. For 50 Monte Carlo simulations, the time drop from: 3 minutes and 13 s, to 1 min an 5 seconds. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* By using minfx, and the reported Jacobian, it is now possible to get the exact same error estimation as scipy.optimize.leastsq. The fatal error was to set the weighting matrix with diagonal elements as the error. There weights are 1/errors**2. There is though some unanswered questions left. The Jacobian used, is the direct derivative of the function. It is not the χ² derivative Jacobian. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Fixed naming of functions, to better represent what they do in module of estimating R2effR_2eff. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Implemented the Jacobian of exponential function in Python code. This now also gets the same error as leastsq and C code. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Tried to implement a safety test for linearly-dependent columns in the covariance matrix. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Removed the user function to estimate the R2eff R_2eff values and errors with scipy.optimize.leastsq. With the newly implemented Jacobian and Hessian of the exponential decay function, the front-end to scipy.optimize.leastsq does not serve a purpose. This is because minfx is now as fast as scipy.optimize.leastsq, and can estimate the errors from the Jacobian to the exact same numbers as scipy.optimize.leastsq. In addition to that, the covariance can be calculated by QR decomposition. This adds additional feature for checking for a singular matrix. The back-end will still be kept in place for the coming tim, but could be removed later. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added front-end to the new user function relax_disp.r2eff_err_estimate(), which will estimate the R2eff R_2eff errors from a pipe and spins with optimised values of R2eff and IR_02eff. and I₀. The covariance matrix can be calculated from the optimised parameters, and the Jacobian. Big care should be taken not to directly trust these results, since the errors are quite different compared to the Monte Carlo simulations. This implementation, will reach the exact same error estimation as scipy.optimize.leastsq. But with much better control over the data, and insight into the calculations. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added method to automatically perform error analysis on peak heights. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Modified system test Relax_disp.test_estimate_r2eff() to first do a grid search, then minimise and then estimate the errors for R2eff and IR_2eff and I₀. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added back-end to estimate R2eff R_2eff errors. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff and R_2eff and associated errors for exponential curve fitting.* Fix to system test test_estimate_r2eff_error(), to first delete the old error estimations. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added several tests to: test_estimate_r2eff_error, to compare different output from algorithms. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Cleaned up code in R2eff error module. R_2eff error module. Also removed a non working Hessian matrix. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Moved code around, and made function multifit_covar() independent of class object. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Inserted checks for C module is available in module for estimating R2eff error. R_2eff error. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Removed unnecessary call to experimental Exp class. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Renamed system test, that test the user function for estimating the R2eff error: R_2eff error: test_estimate_r2eff_err, test the user function. test_estimate_r2eff_err_methods, test different methods for getting the error. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added system test, Relax_disp.test_estimate_r2eff_err_auto and extended functionality to the auto-analyses protocol. If "exp_mc_sim_num" is set to "-1" and sent to the auto-analyses, the errors of R2eff R_2eff will be estimated from the covariance matrix. These errors is HIGHLY likely to be wrong, but can be used in an initial test fase, to rapidly produce data for plotting data. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff and associated R_2eff and associated errors for exponential curve fitting.* Added script, to be used in GUI test. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added GUI test Relax_disp.test_r2eff_err_estimate, to test the setting of MC sim to -1 for exponential R2eff R_2eff error estimation. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff and associated errors R_2eff and associated errors for exponential curve fitting.* Added keyword "exp_mc_sim_num", to the auto-analyses in the GUI. This sets the number of Monte Carlo simulations for R2eff R_2eff error estimation in exponential curve fitting. When setting to -1, the errors are estimated from the covariance matrix. These errors are highly likely to be wrong, but can be used in Rapid testing of data and plotting. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Tried to click the "fit_r1" button in the GUI test, but receives an error: relax --gui-tests Relax_disp.test_r2eff_err_estimate, "AttributeError: 'SpinContainer' object has no attribute 'r1'". [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Moved the mc_sim_num GUI element in the analysis tab ip, as it is executed first. Also modified the tooltip. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Added a warning to the auto-analyses about error estimation from the covariance. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Removed yet another comma from GUI tooltip. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Modified system test 'test_estimate_r2eff_err_auto', to use the GUI script. It seems to work perfect. This is to test against GUI script: test_r2eff_err_estimate [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Modified test_estimate_r2eff_err_auto, to set r1_fit to False. This still make the system test pass, and fit R1. So this means R1 fit button is not functioning properly. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Fix for warning message in the auto-analyses in the GUI. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Tried to improve docstring for API documentation. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.

* Yet another try to make the API documentation working. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.* Implemented system test Relax_disp.verify_estimate_r2eff_err_compare_mc for testing R2eff R_2eff error as function of Monte Carlo simulation. Note, since the name does not start with "test", but with "verify", this test will not be issued in the system test suite. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff and associated errors for exponential curve fitting.

* Correction for catastrophic implementation of Monte Carlo simulations for exponential curve-fitting R2eff R_2eff values in the dispersion analysis. A wrong implemented "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 were not replicated). This was discovered for Monte Carlo simulations of R2eff R_2eff errors in exponential fit. This will affect all analyses using full relaxation exponential curves until now. By pure luck, it seems that the effect of this would be that R2eff R_2eff 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. [https://gna.org/bugs/?22554 Bug #22554]: The distribution of intensity with errors in Monte Carlo simulations are markedly more narrow than expected. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R_2eff and associated errors for exponential curve fitting.