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More R2eff parameter formatting.
* Tiny fix for the Diffusion_tensor.test_create_diff_tensor_pdb_ellipsoid system test. The switch to using the lib.periodic_table module for atomic masses has caused the centre of mass of the ellipsoid to shift just enough that one ATOM coordinate in the PDB file has changed its last significant digit.
* Created the lib.periodic_table.process_symbol() function. This will take an atomic symbol and return a copy of it with an uppercase first letter and lowercase second letter. This is used by the Periodic_table methods atomic_mass() and atomic_weight() to allow for non-standard symbol input, for example if the element name comes directly from the all uppercase PDB file format without translation.
* Tried to scale the covariance matrix, as explained here: http://www.orbitals.com/self/least/least.htm. This does not work better. Also replaced "errors" to "weights" to the multifit_covar(), to better determine control calculations. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Added all gyromagnetic ratio information from lib.physical_constants to lib.periodic_table. The Periodic_table.gyromagnetic_ratio() method has been added to allow this value to be easily returned.
* Added to back-end of R2eff R<sub>2eff</sub> estimate module, to be able to switch between the function Jacobian or the χ<sup>2</sup> Jacobian. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Modified user function 'relax_disp.r2eff_err_estimate', to be able switch between the Jacobians. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Modified system test Relax_disp.verify_estimate_r2eff_err_compare_mc, to try the difference between the Jacobian. The results are: Printing the estimated R2eff R<sub>2eff</sub> error as function of estimation from covariance and number of Monte Carlo simulations. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Deleted the gyromagnetic ratio values and return_gyromagnetic_ratio() function from lib.physical_constants.
* Shifted all of relax to use the lib.periodic_table module for gyromagnetic ratios. The values and value returning function have been removed from lib.physical_constants and replaced by the Periodic_table.gyromagnetic_ratio() method in the lib.periodic_table module.
* Started making functions in R2eff R<sub>2eff</sub> estimate module, independent on the informations stored in the Class. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Cleaned up code in R2eff R<sub>2eff</sub> estimate module, by making each function independent of class. This is to give a better overview, how the different functions connect together. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Made the user function, which estimates the R2eff R<sub>2eff</sub> errors, use the Jacobian derived from χ<sup>2</sup> function. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Modified system test verify_estimate_r2eff_err_compare_mc() to first use the direct function Jacobian, and then the χ<sup>2</sup> derived Jacobian. This shows the result better. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Added digit to printout in R2eff R<sub>2eff</sub> estimate module. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Locked values for system test test_estimate_r2eff_err, to estimate how the R2eff R<sub>2eff</sub> error estimation reflects on fitted parameters. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* More locking of values, when trying to use different methods for estimating R2eff R<sub>2eff</sub> err values. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* More locking of values. This actually shows, that errors should be estimated from the direct Jacobian. Not, the χ<sup>2</sup> Jacobian. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Reverted the logic, that the χ<sup>2</sup> Jacobian should be used. Instead, the direct Jacobian exponential is used instead. When fitting with the estimated errors from the direct Jacobian, the results are MUCH better, and comparable to 2000 Monte Carlo simulations. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Various precision fixes for different machine precision. This is in: verify_r1rho_kjaergaard_missing_r1 [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* First attempt at properly implementing the target_functions.relax_fit.jacobian() function. This is now the Jacobian of the chi-squared function. A new jacobian_matrix data structure has been created for holding the matrix data prior to converting it into a Python list of lists. The equation used was simply the chi-squared gradient whereby the sum over i has been dropped and the i elements are stored in the second dimension of matrix.
* Speed up of the target_functions.relax_fit C module. The variances are now pre-calculated in the setup() function from the errors, so that the use of the square() function is minimised. The chi-squared equation, gradient, and Hessian functions now accept the variance rather than standard deviation argument and hence the squaring of errors has been removed. This avoids a lot of duplicated maths operations.
* Alphabetical ordering of global variable declarations in the target_functions.relax_fit header file.
* Added RelaxError, if less than 2 time points is used for exponential curve fitting in R2effR<sub>2eff</sub>. This follows: http://thread.gmane.org/gmane.science.nmr.relax.user/1718 http://thread.gmane.org/gmane.science.nmr.relax.user/1735 Specifically, data was attached here: http://thread.gmane.org/gmane.science.nmr.relax.user/1735/focus=1736.
* Added system test Relax_disp.test_bug_atul_srivastava(), to catch a bug missing raising a RelaxError, since the setup points to a situation where the data shows it is exponential fitting, but only one time point is added per file. This follows: http://thread.gmane.org/gmane.science.nmr.relax.user/1718 http://thread.gmane.org/gmane.science.nmr.relax.user/1735 Specifically, data was attached here: http://thread.gmane.org/gmane.science.nmr.relax.user/1735/focus=1736.
* Parameter precision lowered for Relax_disp.test_estimate_r2eff_err_auto(). This is due to change to C code. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Created the select.display user function. This simply displays the current spin selections of all spins. In the future it can be extended to display the interatomic data container selections, domain selections, etc.
* Fix for system test: test_estimate_r2eff_err_auto(). The Jacobian to estimate the errors has been changed from the direct function Jacobian, to the Jacobian of the χ<sup>2</sup> function. This changes the R2eff R<sub>2eff</sub> error predictions, and hence parameter fitting. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Implemented the direct Jacobian in Python, to be independent of C code in development phase. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Activated all method try in: system test Relax_disp.test_estimate_r2eff_err_methods. This is to quickly estimate errors from all different methods. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Fix to system test: test_estimate_r2eff_err_auto, which now checks the values for the direct Jacobian. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Increased the number of time points for exponential curve fitting to 3.
* Fix to weight properly according to if minimising with direct Jacobian or χ<sup>2</sup> Jacobian. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Fix to system test test_estimate_r2eff_err_methods, after modification of weighting. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Switched in estimate_r2eff_err() to use the χ<sup>2</sup> Jacobian from C code, and Jacobian from Python code. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Removed all references to test values which was received by wrong weighting. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Better error checking in the relaxation dispersion overfit_deselect() API method. The model must be set for this procedure to work, and the method now checks that this is the case.
* Better error checking for the specific_analyses.relax_disp.average_intensity() function. This function would fail with a traceback if a peak intensity error analysis had not yet been performed. Now it fails instead with a clean RelaxError so that the user knows what is wrong.
* Tried implementing getting the χ<sup>2</sup> gradient, using target_function.chi2.dchi2(). The output seem equal. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Replaced the way to calculate the χ<sup>2</sup> Jacobian, for exponential fit in minfx. This is only for the test class, but reuses library code. This should make it much easier in the future to implement χ<sup>2</sup> gradient functions to minfx, since it is only necessary to implement the direct gradient of the function, and then pass the direct gradient to χ<sup>2</sup> library, which turn it into the χ<sup>2</sup> gradient function which minfx use. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Moved unnecessary function in R2eff R<sub>2eff</sub> error estimate module into experimental class. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Implemented system test: test_bug_negative_intensities_cpmg, to show lack of error message to user. Maybe these spins should be de-selected, or at least show a better warning. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* An attempt at documenting the Monte Carlo simulation verses covariance matrix error estimates. This is for the R2eff R<sub>2eff</sub> and I<sub>0</sub> parameters of the exponential curves. For the Monte Carlo errors, 10000 simulations were preformed. This means that these errors can perform as a gold standard by which to judge the covariance matrix technique. Currently it can be seen that the relax_disp.r2eff_err_estimate user function with the chi2_jacobian flag set to True performs extremely poorly.
* Reintroduced the original target_functions.relax_fit.jacobian() function. The new function for the Jacobian of the chi-squared function has been renamed to target_functions.relax_fit.jacobian_chi2() so that both Python functions are accessible within the C module.
* Epydoc fixes for the pipe_control.mol_res_spin.format_info_full() function.
* Epydoc docstring fixes for many methods in the relaxation dispersion auto-analysis module.
* If math domain errors are found when calculating the two point R2eff R<sub>2eff</sub> values, the point is being skipped. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Moved intensity negative value from reference to CPMG point.
* Modified system test test_bug_negative_intensities_cpmg, to prepare for testing number of R2eff R<sub>2eff</sub> points. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Comparison of 10,000 Monte Carlo simulations to a different covariance matrix error estimate. The covariance_matrix.py script has been duplicated and the chi2_jacobian argument of the relax_disp.r2eff_err_estimate user function has been changed from True to False. As can be seen in the 2D Grace plots, this error estimate is incredibly different. The R2eff R<sub>2eff</sub> errors are overestimated by a factor of 1.9555, which indicates that the Jacobian or covariance matrix formula are not yet correct.
* The target_functions.relax_fit C module Python function jacobian_chi2() is now exposed. This was previously not visible from within Python.
* Added a script and log file for calculating the numerical Jacobian for an exponential curve. This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and calculates the Jacobian using the numdifftools.Jacobian object construct and obtain the matrix, both at the minimum and at a point away from the minimum. The values will be used to construct a unit test to check the C module implementation.
* Added a script and log for calculating the numerical covariance matrix for an exponential curve. This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and calculates the covariance matrix via the Jacobian calculated using the numdifftools.Jacobian object construct and obtain the matrix, both at the minimum and at a point away from the minimum. The covariance is calculated as inv(J^T.W.J).
* Added a script and log for calculating the exponential curve parameter errors via bootstrapping. This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and calculates the parameter errors via bootstrapping. As the parameters at the minimum are the exact parameter values, bootstrapping and Monte Carlo simulation converge and hence this is a true error estimate. 200,000 simulations where used, so the parameter errors are extremely accurate.
* Modified module to estimate R2eff R<sub>2eff</sub> errors, to use the C code Jacobian. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Modified system test test_estimate_r2eff_err_methods, to check all Jacobian methods are correctly implemented. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Added more print out information, when log(I / I_ref) is negative, and raising errors. This can help the user track back information to the error more easily.
* Improved system test test_bug_negative_intensities_cpmg, by counting number of R2eff R<sub>2eff</sub> points. Spin 4, which has one negative intensity, is expected to have one less R2eff R<sub>2eff</sub> point. This makes sure, that all CPMG data set can be loaded and analysed, even if some peaks are very weak are fluctuating with error level.
* Fix for also storing 'r1_fit' to cdp even though it is set to False. [https://gna.org/bugs/?22541 Bug #22541]: The R1 fit flag does not work in the GUI.
* Cleanup in GUI test Relax_disp.test_r2eff_err_estimate. This now passes after previous commit. [https://gna.org/bugs/?22541 Bug #22541]: The R1 fit flag does not work in the GUI.
* Fix for system test test_estimate_r2eff_err and test_r1rho_kjaergaard_missing_r1, where r1_fit=True, needed to be send to Auto_analyses. [https://gna.org/bugs/?22541 Bug #22541]: The R1 fit flag does not work in the GUI.
* API documentation fixes.
* Moved multifit_covar into lib.statistics, since it is an independent module. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Moved "func_exp_grad" into experimental class for different minimisation methods. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Improved documentation to user function relax_disp.r2eff_err_estimate, and removed the possibility to use the χ<sup>2</sup> Jacobian, as this is rubbish. But the back-end still have this possibility, should one desire to try this. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Moved the argument 'chi2_jacobian' as the last argument in estimate_r2eff_err. This argument is highly likely not to be used, but is kept for future testing purposes. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Fix to experimental class for fitting with different methods. After moving the function into class, 'self' should be added to the function. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Fix to system test test_estimate_r2eff_err, after removing the possibility to use the χ<sup>2</sup> Jacobian. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Fix for system test test_estimate_r2eff_err_methods. The function was called wrong in experimental class. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Initial try write comments how to generalize the scaling of the covariance according to the reduced χ<sup>2</sup> distribution. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* First try to make a test script for estimating efficiency on R2eff R<sub>2eff</sub> error calculations. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Added number of simulations to 10,000 in test script, and varied the random number of time point per simulation between 3 and 10. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* In module for estimating R2eff R<sub>2eff</sub> errors, removed "values, errors" to be send to function for gradient, since they are not used. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Added Jacobian to test script, and now correctly do simulations, per R2eff R<sub>2eff</sub> points. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Improved analysing test script, with plotting. It seems that R2eff R<sub>2eff</sub> error estimation always get the same result. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Added initial dataset for test analysis. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Deleted test data set. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Modified data script generator, to handle a situation with fixed 5 time points, and a situations with variable number of time points. Also modified analysis script. It seems, this has an influence how the error estimation is performing. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Added simulations that show, there is perfect agreement between Monte Carlo simulations and covariance estimation. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Inserted extra tests in system test Relax_disp.test_estimate_r2eff_err_methods to test that all values of R and I<sub>0</sub> are positive, and the standard deviation from Monte Carlo simulations are equal. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.
* Inserted system test 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. [https://gna.org/bugs/?22552 Bug #22552]: Chi2 value returned from C code curve-fitting return 0.0 for wrong parameters -> Expected influence on Monte Carlo sim.
* Inserted possibility for bootstrapping in system test Relax_disp.test_estimate_r2eff_err_methods. This shows, that the bootstrapping method get the SAME estimation for R2eff R<sub>2eff</sub> 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.* Modified system test Relax_disp.verify_estimate_r2eff_err_compare_mc to include bootstrapping method. This shows there is excellent agreement between bootstrapping and estimation of errors from covariance, while relax Monte Carlo simulations are very much different. Boot strapping is the "-2": [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Added functionality to create peak lists, for virtual data. This is to compare the distribution of R2eff R<sub>2eff</sub> values made by bootstrapping and Monte Carlo simulations. Rest of the analysis will be performed in relax. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Added initial peak lists to be analysed in relax for test purposes. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> 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. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> 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. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> 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. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting.* Comment fix to system test Relax_disp.test_estimate_r2eff_err_methods, after the found of bug in relax. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting. [https://gna.org/bugs/?22554 Bug #22554]: The distribution of intensity with errors in Monte Carlo simulations are markedly more narrow than expected.* Cleaned up user function for estimating R2eff R<sub>2eff</sub> errors. Extensive tests have shown, there is a very good agreement between the covariance estimation, and Monte Carlo simulations. This is indeed a very positive implementation. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting. [https://gna.org/bugs/?22554 Bug #22554]: The distribution of intensity with errors in Monte Carlo simulations are markedly more narrow than expected.* Removed all junk comments from module for R2eff R<sub>2eff</sub> error estimation. The module runs perfect as it does now. [https://gna.org/task/?7822 Task #7822]: Implement user function to estimate R2eff R<sub>2eff</sub> and associated errors for exponential curve fitting. [https://gna.org/bugs/?22554 Bug #22554]: The distribution of intensity with errors in Monte Carlo simulations are markedly more narrow than expected.
* Fix for inf values being returned from C code exponential function. Values are now converted to high values. This fixes system test 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]). [https://gna.org/bugs/?22552 Bug #2255]: 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.
* Initial try to reach constrained methods in minfx through relax. This is in system test Relax_disp.verify_estimate_r2eff_err_compare_mc() This though not seem to be supported.
* Allow R2eff R<sub>2eff</sub> model to reach constrained methods in minfx through relax. This is in system test Relax_disp.verify_estimate_r2eff_err_compare_mc() This though not seem to be supported.
* Modified specific_analyses.relax_disp.parameters.r1_setup() to initialise the 'r1' variable. This relates to [https://gna.org/bugs/?22541 bug #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.
* The dispersion get_param_values() API method now calls the r1_setup() function. This relates to [https://gna.org/bugs/?22541 bug #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.
* Yet another try to implement constrained method in verify_estimate_r2eff_err_compare_mc.
* Another attempt to reach constrained method in minfx through relax. I would need to specify: l, lower bound constraint vector (l <= x <= u); u, upper bound constraint vector (l <= x <= u); c: user supplied constraint function; dc: user supplied constraint gradient function.
* Added a derivation of the R2effR<sub>2eff</sub>/R<sub>1ρ</sub> error estimate for the two-point measurement to the manual. This is from http://thread.gmane.org/gmane.science.nmr.relax.devel/6929/focus=6993 and is for the rate uncertainty of a 2-parameter exponential curve when only two data points have been collected. The derivation has been added to the dispersion chapter of the manual.
* Equation fixes for the two-point exponential error derivation in the dispersion chapter of the manual.
* Updated the minfx version numbers in the release checklist document. The version is now [https://gna.org/forum/forum.php?forum_id=2475 1.0.10], which has not been released yet but will contain the implementation of the log-barrier constraint algorithm gradient and Hessian.
