Difference between revisions of "Relax 3.3.0"

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* Added the R<sub>1</sub> parameter fitting GUI element to the dispersion GUI tab.  This is a simple Boolean toggle element that allows the R<sub>1</sub> optimisation to be turned on.  The value is passed into the auto-analysis.
 
* Added the R<sub>1</sub> parameter fitting GUI element to the dispersion GUI tab.  This is a simple Boolean toggle element that allows the R<sub>1</sub> optimisation to be turned on.  The value is passed into the auto-analysis.
 
* Added the r1_fit argument to the relaxation dispersion auto-analysis.  When this is True, the [http://www.nmr-relax.com/manual/relax_disp_r1_fit.html relax_disp.r1_fit user function] will be called to turn R<sub>1</sub> parameter fitting on.
 
* Added the r1_fit argument to the relaxation dispersion auto-analysis.  When this is True, the [http://www.nmr-relax.com/manual/relax_disp_r1_fit.html relax_disp.r1_fit user function] will be called to turn R<sub>1</sub> parameter fitting on.
* Added the [http://www.nmr-relax.com/manual/relax_disp_spin_lock_offset.html relax_disp.spin_lock_offset user function] to the dispersion GUI.  This has been added to the pop up menu in the spectrum list GUI element, when the relax_disp_flag has been set.  It simply mimics the relax_disp.spin_lock_field functionality already present.  This follows from [https://gna.org/task/?7820 task #7820].
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* Added the [http://www.nmr-relax.com/manual/relax_disp_spin_lock_offset.html relax_disp.spin_lock_offset user function] to the dispersion GUI.  This has been added to the pop up menu in the spectrum list GUI element, when the relax_disp_flag has been set.  It simply mimics the relax_disp.spin_lock_field functionality already present.  This follows from {{gna task link|7820}}.
 
* Fix for the [http://www.nmr-relax.com/manual/relax_disp_spin_lock_offset.html relax_disp.spin_lock_offset user function] in the dispersion GUI tab.  This is in the spectrum list element popup menu.
 
* Fix for the [http://www.nmr-relax.com/manual/relax_disp_spin_lock_offset.html relax_disp.spin_lock_offset user function] in the dispersion GUI tab.  This is in the spectrum list element popup menu.
* Added the offset column to the spectrum list GUI element for the dispersion analysis.  This is to complete [https://gna.org/task/?7820 task #7820].  The spectrum list GUI element add_offset() method has been added to insert the offset column when the relax_disp_flag is set.  This is called by the update_data() method to fill and update the GUI element.
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* Added the offset column to the spectrum list GUI element for the dispersion analysis.  This is to complete {{gna task link|7820}}.  The spectrum list GUI element add_offset() method has been added to insert the offset column when the relax_disp_flag is set.  This is called by the update_data() method to fill and update the GUI element.
 
* Implemented the GUI test Relax_disp.test_bug_22501_close_all_analyse to catch [https://gna.org/bugs/?22501 bug #22501, 'Close all analyses' raises error].
 
* Implemented the GUI test Relax_disp.test_bug_22501_close_all_analyse to catch [https://gna.org/bugs/?22501 bug #22501, 'Close all analyses' raises error].
 
* Inserted intermediate system test, to profile R<sub>2eff</sub> calculation for R<sub>1&rho;</sub>.  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 R<sub>2eff</sub> values stored as dictionary, and pipe_control.minimise.grid_setup() will fail.  The function 'isNaN(values[i])' cannot handle dictionary.
 
* Inserted intermediate system test, to profile R<sub>2eff</sub> calculation for R<sub>1&rho;</sub>.  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 R<sub>2eff</sub> values stored as dictionary, and pipe_control.minimise.grid_setup() will fail.  The function 'isNaN(values[i])' cannot handle dictionary.
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* 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 R<sub>2eff</sub> 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.
 
* 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 R<sub>2eff</sub> 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<sub>1&rho;</sub>, where the errors of the R<sub>2eff</sub> 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 R<sub>2eff</sub> values for 15 residues.  It estimates the errors of R<sub>2eff</sub> 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 R<sub>2eff</sub> values and errors can by scipy.optimize.leastsq can instead be calculated in: 15 residues * 0.02 seconds = 0.3 seconds.
 
* 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<sub>1&rho;</sub>, where the errors of the R<sub>2eff</sub> 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 R<sub>2eff</sub> values for 15 residues.  It estimates the errors of R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Moved the target function for minimisation of exponential fit into the target functions folder.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and errors by scipy.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Implemented initial system test Relax_disp.test_estimate_r2eff for setting up the new user function to estimate R<sub>2eff</sub> and errors by scipy.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Added front end user function relax_disp.r2eff_estimate to estimate R<sub>2eff</sub> and errors by exponential curve fitting in scipy.optimize.leastsq.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Added front end user function relax_disp.r2eff_estimate to estimate R<sub>2eff</sub> and errors by exponential curve fitting in scipy.optimize.leastsq.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Modified check for model, to accept model as input, for error printing.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Implemented back end for estimating R<sub>2eff</sub> and errors by exponential curve fitting with scipy.optimize.leastsq.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Implemented back end for estimating R<sub>2eff</sub> and errors by exponential curve fitting with scipy.optimize.leastsq.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Documentation fix for new exponential target function.  Also added new function to estimate R<sub>2eff</sub> and I<sub>0</sub> parameters, before minimisation.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Documentation fix for new exponential target function.  Also added new function to estimate R<sub>2eff</sub> and I<sub>0</sub> parameters, before minimisation.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Small changes to verification scripts, to use &chi;<sup>2</sup> function and use the scaling matrix correct.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Small changes to verification scripts, to use &chi;<sup>2</sup> function and use the scaling matrix correct.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Split up of system test test_r1rho_kjaergaard_missing_r1, into a verification part.  This is to test the new R<sub>2eff</sub> estimation, which should get the parameter values, as a this 2000 Monto Carlo simulation.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Modified system test Relax_disp.test_estimate_r2eff.  This is to compare against errors simulated with 2000 MC.  The parameters are comparable, but not equal.  Mostly, it seems that the errors from scipy.optimize.leastsq, are twice as high than the Monte Carlo simulations.  This affect model fitting, and the calculated &chi;<sup>2</sup> value.
 
* Modified system test Relax_disp.test_estimate_r2eff.  This is to compare against errors simulated with 2000 MC.  The parameters are comparable, but not equal.  Mostly, it seems that the errors from scipy.optimize.leastsq, are twice as high than the Monte Carlo simulations.  This affect model fitting, and the calculated &chi;<sup>2</sup> value.
 
* Added system test Relax_disp.test_estimate_r2eff_error().  This is to get insight in the error difference between 2000 Monto Carlo simulations and then scipy.optimize.leastsq.
 
* Added system test Relax_disp.test_estimate_r2eff_error().  This is to get insight in the error difference between 2000 Monto Carlo simulations and then scipy.optimize.leastsq.
* Add dependency check for scipy.optimize.leastsq.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Add dependency check for scipy.optimize.leastsq.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Lowering precision in system test Relax_disp.test_r1rho_kjaergaard_missing_r1.  This is R<sub>1</sub> estimation with MODEL_NS_R1RHO_2SITE.  The lowering of precision is due different system precision.
 
* Lowering precision in system test Relax_disp.test_r1rho_kjaergaard_missing_r1.  This is R<sub>1</sub> estimation with MODEL_NS_R1RHO_2SITE.  The lowering of precision is due different system precision.
 
* Reused the dependency check "scipy_module", since leastsq() has been part of Scipy since 2003.
 
* Reused the dependency check "scipy_module", since leastsq() has been part of Scipy since 2003.
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* Isolated all code related to [http://www.nmr-relax.com/manual/relax_disp_r2eff_estimate.html user function relax_disp.r2eff_estimate] into independent module file.  All has been isolated to: specific_analyses.relax_disp.estimate_r2eff.
 
* Isolated all code related to [http://www.nmr-relax.com/manual/relax_disp_r2eff_estimate.html user function relax_disp.r2eff_estimate] into independent module file.  All has been isolated to: specific_analyses.relax_disp.estimate_r2eff.
 
* Split function to minimise with scipy.optimize.leastsq out in estimate_r2eff module.  This is to prepare for implementing with minfx.
 
* Split function to minimise with scipy.optimize.leastsq out in estimate_r2eff module.  This is to prepare for implementing with minfx.
* Implemented first try to minimise with minfx in estimate_r2eff() function.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Implemented first try to minimise with minfx in estimate_r2eff() function.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Implementation of the target_functions.relax_fit.jacobian() function.  This follows from the discussions at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807.  The function will calculate the Jacobian matrix for the exponential curve-fitting module.  The Jacobian can be used to directly calculate the covariance matrix, for example as described at https://www.gnu.org/software/gsl/manual/html_node/Computing-the-covariance-matrix-of-best-fit-parameters.html.  The Jacobian is calculated using the help of the new exponential_dI() and exponential_dR() functions in the target_functions/exponential.c file.  These calculate the partial derivatives of the exponential curve with respect to each model parameter separately.  The implementation still needs testing and debugging.
 
* Implementation of the target_functions.relax_fit.jacobian() function.  This follows from the discussions at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807.  The function will calculate the Jacobian matrix for the exponential curve-fitting module.  The Jacobian can be used to directly calculate the covariance matrix, for example as described at https://www.gnu.org/software/gsl/manual/html_node/Computing-the-covariance-matrix-of-best-fit-parameters.html.  The Jacobian is calculated using the help of the new exponential_dI() and exponential_dR() functions in the target_functions/exponential.c file.  These calculate the partial derivatives of the exponential curve with respect to each model parameter separately.  The implementation still needs testing and debugging.
 
* Fixes for the new target_functions.relax_fit.jacobian() function.  The Python list of lists is now correctly created and returned.
 
* Fixes for the new target_functions.relax_fit.jacobian() function.  The Python list of lists is now correctly created and returned.
 
* 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.
 
* 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Intermediate step in estimate R<sub>2eff</sub> module.  It seems that minfx is minimising in a quadratic space because of the power of &chi;<sup>2</sup>, while the general input to scipy.optimize does not do this.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Intermediate step in estimate R<sub>2eff</sub> module.  It seems that minfx is minimising in a quadratic space because of the power of &chi;<sup>2</sup>, while the general input to scipy.optimize does not do this.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Cleaned up target function for leastsq, since arguments to function can be extracted from class.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Implemented the chi-squared gradient as a C module for the target functions.  This simply translates the Python code into C to allow any target function C modules to build its own gradient function.
 
* Implemented the chi-squared gradient as a C module for the target functions.  This simply translates the Python code into C to allow any target function C modules to build its own gradient function.
 
* Implemented the target_functions.relax_fit.dfunc() gradient function.  This is using the Python/C interface to provide a Python function for calculating and returned the chi-squared gradient for the exponential curves.
 
* Implemented the target_functions.relax_fit.dfunc() gradient function.  This is using the Python/C interface to provide a Python function for calculating and returned the chi-squared gradient for the exponential curves.
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* The parameter index is now passed into exponential_dI0() and exponential_dR().  This is for the relaxation curve-fitting C module so that the indices are not hardcoded.
 
* The parameter index is now passed into exponential_dI0() and exponential_dR().  This is for the relaxation curve-fitting C module so that the indices are not hardcoded.
 
* The I<sub>0</sub> and R parameter indices are now defined in the target_function/relax_fit.h header file.  This is to abstract the exponential curve parameter indices even more.
 
* The I<sub>0</sub> and R parameter indices are now defined in the target_function/relax_fit.h header file.  This is to abstract the exponential curve parameter indices even more.
* Big cleanup of estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Big cleanup of estimate R<sub>2eff</sub> module.  This is to make the documentation more easy to read and understand.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Created 2 unit tests for the target_functions.relax_fit relax C module.  These check the func() and dfunc() Python methods exposed by the module.
 
* Created 2 unit tests for the target_functions.relax_fit relax C module.  These check the func() and dfunc() Python methods exposed by the module.
 
* The relax_fit C module unit tests now check if the parameter scaling is functional.
 
* The relax_fit C module unit tests now check if the parameter scaling is functional.
* Added several comments to the R<sub>2eff</sub> estimate module.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
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* Added several comments to the R<sub>2eff</sub> estimate module.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Added a script and log file for calculating the numerical gradient for an exponential curve.  This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and calculates the gradient using the scipy.misc.derivative() function 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 file for calculating the numerical gradient for an exponential curve.  This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and calculates the gradient using the scipy.misc.derivative() function 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.
 
* Created a unit test to check the dfunc() function of the relax_fit C module off the minimum.
 
* Created a unit test to check the dfunc() function of the relax_fit C module off the minimum.
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* Fix for the test_dfunc_off_minimum() unit test.  This is the test class test_suite.unit_tests._target_functions.test_relax_fit.Test_relax_fit.  The wrong gradient was being scaled.
 
* Fix for the test_dfunc_off_minimum() unit test.  This is the test class test_suite.unit_tests._target_functions.test_relax_fit.Test_relax_fit.  The wrong gradient was being scaled.
 
* Switched the optimisation algorithm in test_suite/system_tests/scripts/relax_fit.py.  This script, used by the Relax_fit.test_curve_fitting_height and Relax_fit.test_curve_fitting_volume system tests, now uses the BFGS optimisation.  This is to demonstrate that the exponential curve gradient function dfunc() is implemented correctly and that more advanced optimisation algorithms can be used (excluding those that require the full Hessian d2func() function).
 
* Switched the optimisation algorithm in test_suite/system_tests/scripts/relax_fit.py.  This script, used by the Relax_fit.test_curve_fitting_height and Relax_fit.test_curve_fitting_volume system tests, now uses the BFGS optimisation.  This is to demonstrate that the exponential curve gradient function dfunc() is implemented correctly and that more advanced optimisation algorithms can be used (excluding those that require the full Hessian d2func() function).
* Got the method of 'Steepest descent' to work properly, by specifying the Jacobian correctly.  The Jacobian was derived according to the &chi;<sup>2</sup> 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)<sup>2</sup> ], and minimises this.  This is the &chi;<sup>2</sup>.  Minfx requires the function to minimise before hand.  So, the "func" should be &chi;<sup>2</sup>.  Then the dfunc, and d2func, should be derivative of &chi;<sup>2</sup>, but all elements in the array should still be summed together.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 &chi;<sup>2</sup> 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)<sup>2</sup> ], and minimises this.  This is the &chi;<sup>2</sup>.  Minfx requires the function to minimise before hand.  So, the "func" should be &chi;<sup>2</sup>.  Then the dfunc, and d2func, should be derivative of &chi;<sup>2</sup>, but all elements in the array should still be summed together.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added initial documentation for multifit_covar.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Modified profiling script to use the new estimate R<sub>2eff</sub> module.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Modified profiling script to use the new estimate R<sub>2eff</sub> module.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Modified verify error script, to use new estimate R<sub>2eff</sub> module.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Modified verify error script, to use new estimate R<sub>2eff</sub> module.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Removed all unnecessary code from estimate R<sub>2eff</sub> module.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Removed all unnecessary code from estimate R<sub>2eff</sub> module.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* More removal of code.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* More removal of code.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Changed the array declarations in the target_functions/exponential C file and header.  Instead of using the pointer format of *xyz, the array format of xyz[] is now being used.  These are equivalent and the later is more obvious that this is an array.
 
* Changed the array declarations in the target_functions/exponential C file and header.  Instead of using the pointer format of *xyz, the array format of xyz[] is now being used.  These are equivalent and the later is more obvious that this is an array.
 
* Changed the array declarations in the target_functions/c_chi2 C file and header.  Instead of using the pointer format of *xyz, the array format of xyz[] is now being used.  These are equivalent and the later is more obvious that this is an array.
 
* Changed the array declarations in the target_functions/c_chi2 C file and header.  Instead of using the pointer format of *xyz, the array format of xyz[] is now being used.  These are equivalent and the later is more obvious that this is an array.
Line 1,387: Line 1,387:
 
* Fixes for the Hessian.py script for numerical integrating the Hessian for an exponential curve.
 
* Fixes for the Hessian.py script for numerical integrating the Hessian for an exponential curve.
 
* Implemented two unit tests to check the Hessian of the target_functions.relax_fit.d2func() function.  This compares the calculated Hessian to the numerically integrated values from the test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.py script, showing that the d2func() function is implemented correctly.
 
* Implemented two unit tests to check the Hessian of the target_functions.relax_fit.d2func() function.  This compares the calculated Hessian to the numerically integrated values from the test_suite/shared_data/curve_fitting/numeric_gradient/Hessian.py script, showing that the d2func() function is implemented correctly.
* 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Modified profiling script, but it seems that the dfunc from target_functions.relax_fit does not work.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Modified estimate R<sub>2eff</sub> 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 estimate R<sub>2eff</sub> 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.
 
* Created an initial test suite data directory for a mixed R<sub>1&rho;</sub> + CPMG dispersion analysis.  The generate.py script will be extended in the future to generate both synthetic R<sub>1&rho;</sub> and CPMG data for a common exchange process.  Such a data combination should show some minor flaws in the current design of the dispersion analysis and will help to solve these.
 
* Created an initial test suite data directory for a mixed R<sub>1&rho;</sub> + CPMG dispersion analysis.  The generate.py script will be extended in the future to generate both synthetic R<sub>1&rho;</sub> and CPMG data for a common exchange process.  Such a data combination should show some minor flaws in the current design of the dispersion analysis and will help to solve these.
 
* Improvements to the pipe_control.minimise.reset_min_stats() function.  The minimise statistics resetting is now more elegantly implemented.  And the sim_index keyword argument is accepted by the function and individual Monte Carlo simulation elements can now be reset.
 
* Improvements to the pipe_control.minimise.reset_min_stats() function.  The minimise statistics resetting is now more elegantly implemented.  And the sim_index keyword argument is accepted by the function and individual Monte Carlo simulation elements can now be reset.
* 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Modified wrapper function for curve_fit, to only change to list type, if the type is a ndarray.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* The model-free reset_min_stats() function has been replaced with the pipe_control.minimise version.  The specific_analyses.model_free.optimisation.reset_min_stats() function has been deleted and instead the pipe_control.minimise version is being used.
 
* The model-free reset_min_stats() function has been replaced with the pipe_control.minimise version.  The specific_analyses.model_free.optimisation.reset_min_stats() function has been deleted and instead the pipe_control.minimise version is being used.
* Implemented the first try to compute the variance of R<sub>2eff</sub> and I<sub>0</sub>, 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Implemented the first try to compute the variance of R<sub>2eff</sub> and I<sub>0</sub>, by the covariance.  This uses the Jacobian matrix.  The errors calculated, are though way to small compared 2000 Monte Carlo simulations.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Modified profiling script to calculate timings.  The timings for C code are:  Simplex, with constraints = 2.192;  Simplex, without constraints = 0.216;  BFGS, without constraints = 0.079;  Newton, without constraints = 0.031;  This is pretty pretty fast.  To this profiling script, I would also now add some verification on calculations.
 
* Modified profiling script to calculate timings.  The timings for C code are:  Simplex, with constraints = 2.192;  Simplex, without constraints = 0.216;  BFGS, without constraints = 0.079;  Newton, without constraints = 0.031;  This is pretty pretty fast.  To this profiling script, I would also now add some verification on calculations.
* Tried to verify solution to profiling script.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Tried to verify solution to profiling script.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Set the constraints=False when doing Monte Carlo simulations for R<sub>2eff</sub>.  This is to speed up the Monte Carlo simulations by a factor X10, when estimating the error for R<sub>2eff</sub>.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Set the constraints=False when doing Monte Carlo simulations for R<sub>2eff</sub>.  This is to speed up the Monte Carlo simulations by a factor X10, when estimating the error for R<sub>2eff</sub>.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Implemented the use of "Newton" as minimisation algorithm for R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Implemented the use of "Newton" as minimisation algorithm for R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Changed the relax_fit.py sample script to use Newton rather than Simplex optimisation.  This can lead to significantly faster optimisation times, as shown in the commit message http://article.gmane.org/gmane.science.nmr.relax.scm/23081.
 
* Changed the relax_fit.py sample script to use Newton rather than Simplex optimisation.  This can lead to significantly faster optimisation times, as shown in the commit message http://article.gmane.org/gmane.science.nmr.relax.scm/23081.
 
* Changed the optimisation description in the relaxation curve-fitting chapter of the manual.  The script example has been converted to match the sample script, replacing the Nelder-Mead simplex algorithm with Newton optimisation, and removing the argument turning diagonal scaling off.  All the text about only the simplex algorithm being supported due to the missing gradients and Hessians in the C module have been deleted.  The text that linear constraints are not supported has also been removed - but this was fixed when the logarithmic barrier constraint algorithm was added to minfx.
 
* Changed the optimisation description in the relaxation curve-fitting chapter of the manual.  The script example has been converted to match the sample script, replacing the Nelder-Mead simplex algorithm with Newton optimisation, and removing the argument turning diagonal scaling off.  All the text about only the simplex algorithm being supported due to the missing gradients and Hessians in the C module have been deleted.  The text that linear constraints are not supported has also been removed - but this was fixed when the logarithmic barrier constraint algorithm was added to minfx.
* 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<sup>2</sup>.  There is though some unanswered questions left.  The Jacobian used, is the direct derivative of the function.  It is not the &chi;<sup>2</sup> derivative Jacobian.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> 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<sup>2</sup>.  There is though some unanswered questions left.  The Jacobian used, is the direct derivative of the function.  It is not the &chi;<sup>2</sup> derivative Jacobian.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Fixed naming of functions, to better represent what they do in module of estimating R<sub>2eff</sub>.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Fixed naming of functions, to better represent what they do in module of estimating R<sub>2eff</sub>.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Tried to implement a safety test for linearly-dependent columns in the covariance matrix.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Fixes for the [http://www.nmr-relax.com/manual/relax_disp_r2eff_estimate.html relax_disp.r2eff_estimate user function documentation].  This is to allow the relax manual to compile again as the original documentation was causing LaTeX failures.
 
* Fixes for the [http://www.nmr-relax.com/manual/relax_disp_r2eff_estimate.html relax_disp.r2eff_estimate user function documentation].  This is to allow the relax manual to compile again as the original documentation was causing LaTeX failures.
 
* Clean up of the declarations in the target_functions.relax_fit C module.  The Python list objects are now declared at the start of the functions, and then PyList_New() is called later on.  This allows the code to compile on certain Windows systems.
 
* Clean up of the declarations in the target_functions.relax_fit C module.  The Python list objects are now declared at the start of the functions, and then PyList_New() is called later on.  This allows the code to compile on certain Windows systems.
* Removed the user function to estimate the R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Removed the user function to estimate the R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Added front-end to the new [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html user function relax_disp.r2eff_err_estimate], which will estimate the R<sub>2eff</sub> errors from a pipe and spins with optimised values of R<sub>2eff</sub> and I<sub>0</sub>.  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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added front-end to the new [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html user function relax_disp.r2eff_err_estimate], which will estimate the R<sub>2eff</sub> errors from a pipe and spins with optimised values of R<sub>2eff</sub> and I<sub>0</sub>.  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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added method to automatically perform error analysis on peak heights.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and I<sub>0</sub>.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and I<sub>0</sub>.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Added back-end to estimate R<sub>2eff</sub> errors.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added back-end to estimate R<sub>2eff</sub> errors.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Fix to system test test_estimate_r2eff_error(), to first delete the old error estimations.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added several tests to: test_estimate_r2eff_error, to compare different output from algorithms.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Cleaned up code in R<sub>2eff</sub> error module.  Also removed a non working Hessian matrix.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Cleaned up code in R<sub>2eff</sub> error module.  Also removed a non working Hessian matrix.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Moved code around, and made function multifit_covar() independent of class object.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Inserted checks for C module is available in module for estimating R<sub>2eff</sub> error.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Inserted checks for C module is available in module for estimating R<sub>2eff</sub> error.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Removed unnecessary call to experimental Exp class.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Renamed system test, that test the user function for estimating the R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Renamed system test, that test the user function for estimating the R<sub>2eff</sub> error:  test_estimate_r2eff_err, test the user function. test_estimate_r2eff_err_methods, test different methods for getting the error.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added script, to be used in GUI test.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> error estimation.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> error estimation.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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'".  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added a warning to the auto-analyses about error estimation from the covariance.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Removed yet another comma from GUI tooltip.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Formatting changes for the lib.periodic_table module.  This is in preparation for extending the information content of this module.
 
* Formatting changes for the lib.periodic_table module.  This is in preparation for extending the information content of this module.
* 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 R<sub>2eff</sub> 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  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>1</sub>.  So this means R<sub>1</sub> fit button is not functioning properly.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>1</sub>.  So this means R<sub>1</sub> fit button is not functioning properly.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Fix for warning message in the auto-analyses in the GUI.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Tried to improve docstring for API documentation.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Added all of the IUPAC 2011 atomic weights to the lib.periodic_table module.  These will be useful for correctly calculating the centre of mass of a molecule.
 
* Added all of the IUPAC 2011 atomic weights to the lib.periodic_table module.  These will be useful for correctly calculating the centre of mass of a molecule.
 
* The lib.periodic_table method for adding elements is now private.
 
* The lib.periodic_table method for adding elements is now private.
 
* Created the unit test infrastructure for the lib.periodic_table module.  This includes one unit test of the lib.periodic_table.periodic_table.atomic_weight() function which has not been implemented yet.
 
* Created the unit test infrastructure for the lib.periodic_table module.  This includes one unit test of the lib.periodic_table.periodic_table.atomic_weight() function which has not been implemented yet.
 
* Implemented the lib.periodic_table.periodic_table.atomic_weight() method.  This returns the standard atomic weight of the atom as a float.
 
* Implemented the lib.periodic_table.periodic_table.atomic_weight() method.  This returns the standard atomic weight of the atom as a float.
* Yet another try to make the API documentation working.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Yet another try to make the API documentation working.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Implemented system test Relax_disp.verify_estimate_r2eff_err_compare_mc for testing R<sub>2eff</sub> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Implemented system test Relax_disp.verify_estimate_r2eff_err_compare_mc for testing R<sub>2eff</sub> 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Converted the periodic table in lib.periodic_table into a dictionary type object.  The new Element container has been added for storing the information about each element in the table.  The Periodic_table object used the atomic symbol as a key for each Element instance.
 
* Converted the periodic table in lib.periodic_table into a dictionary type object.  The new Element container has been added for storing the information about each element in the table.  The Periodic_table object used the atomic symbol as a key for each Element instance.
 
* Modified system test test Relax_disp.test_estimate_r2eff_err_methods() to show the difference between using the direct function Jacobian, or the &chi;<sup>2</sup> function Jacobian.  Added also the functionality to the estimate R<sub>2eff</sub> module, to switch between using the different Jacobians.  The results show, that R<sub>2eff</sub> can be estimated better.
 
* Modified system test test Relax_disp.test_estimate_r2eff_err_methods() to show the difference between using the direct function Jacobian, or the &chi;<sup>2</sup> function Jacobian.  Added also the functionality to the estimate R<sub>2eff</sub> module, to switch between using the different Jacobians.  The results show, that R<sub>2eff</sub> can be estimated better.
Line 1,451: Line 1,451:
 
* 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.
 
* 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.
 
* 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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 R<sub>2eff</sub> estimate module, to be able to switch between the function Jacobian or the &chi;<sup>2</sup> Jacobian.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added to back-end of R<sub>2eff</sub> estimate module, to be able to switch between the function Jacobian or the &chi;<sup>2</sup> Jacobian.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Modified [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Modified [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html user function relax_disp.r2eff_err_estimate], to be able switch between the Jacobians.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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 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 R<sub>2eff</sub> error as function of estimation from covariance and number of Monte Carlo simulations.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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.
 
* 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.
 
* 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 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Started making functions in R<sub>2eff</sub> estimate module, independent on the informations stored in the Class.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Cleaned up code in 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Cleaned up code in 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Made the user function, which estimates the R<sub>2eff</sub> errors, use the Jacobian derived from &chi;<sup>2</sup> function.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Made the user function, which estimates the R<sub>2eff</sub> errors, use the Jacobian derived from &chi;<sup>2</sup> function.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> derived Jacobian.  This shows the result better.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> derived Jacobian.  This shows the result better.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Added digit to printout in R<sub>2eff</sub> estimate module.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added digit to printout in R<sub>2eff</sub> estimate module.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Locked values for system test test_estimate_r2eff_err, to estimate how the R<sub>2eff</sub> error estimation reflects on fitted parameters.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Locked values for system test test_estimate_r2eff_err, to estimate how the R<sub>2eff</sub> error estimation reflects on fitted parameters.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* More locking of values, when trying to use different methods for estimating R<sub>2eff</sub> err values.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* More locking of values, when trying to use different methods for estimating R<sub>2eff</sub> err values.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> Jacobian.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> Jacobian.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Reverted the logic, that the &chi;<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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Reverted the logic, that the &chi;<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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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.
 
* 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.
 
* 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.
Line 1,473: Line 1,473:
 
* Added RelaxError, if less than 2 time points is used for exponential curve fitting in R<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 RelaxError, if less than 2 time points is used for exponential curve fitting in R<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.
 
* 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Parameter precision lowered for Relax_disp.test_estimate_r2eff_err_auto().  This is due to change to C code.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Created the [http://www.nmr-relax.com/manual/select_display.html 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.
 
* Created the [http://www.nmr-relax.com/manual/select_display.html 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 &chi;<sup>2</sup> function.  This changes the R<sub>2eff</sub> error predictions, and hence parameter fitting.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* 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 &chi;<sup>2</sup> function.  This changes the R<sub>2eff</sub> error predictions, and hence parameter fitting.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Increased the number of time points for exponential curve fitting to 3.
 
* Increased the number of time points for exponential curve fitting to 3.
* Fix to weight properly according to if minimising with direct Jacobian or &chi;<sup>2</sup> Jacobian.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Fix to weight properly according to if minimising with direct Jacobian or &chi;<sup>2</sup> Jacobian.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Fix to system test test_estimate_r2eff_err_methods, after modification of weighting.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Switched in estimate_r2eff_err() to use the &chi;<sup>2</sup> Jacobian from C code, and Jacobian from Python code.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Switched in estimate_r2eff_err() to use the &chi;<sup>2</sup> Jacobian from C code, and Jacobian from Python code.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Removed all references to test values which was received by wrong weighting.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.
 
* 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 &chi;<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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Tried implementing getting the &chi;<sup>2</sup> gradient, using target_function.chi2.dchi2().  The output seem equal.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Replaced the way to calculate the &chi;<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 &chi;<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 &chi;<sup>2</sup> library, which turn it into the &chi;<sup>2</sup> gradient function which minfx use.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Replaced the way to calculate the &chi;<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 &chi;<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 &chi;<sup>2</sup> library, which turn it into the &chi;<sup>2</sup> gradient function which minfx use.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Moved unnecessary function in R<sub>2eff</sub> error estimate module into experimental class.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Moved unnecessary function in R<sub>2eff</sub> error estimate module into experimental class.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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 [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html relax_disp.r2eff_err_estimate user function] with the chi2_jacobian flag set to True performs extremely poorly.
 
* An attempt at documenting the Monte Carlo simulation verses covariance matrix error estimates.  This is for the 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 [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html 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.
 
* 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 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.
 
* Epydoc docstring fixes for many methods in the relaxation dispersion auto-analysis module.
* If math domain errors are found when calculating the two point R<sub>2eff</sub> values, the point is being skipped.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* If math domain errors are found when calculating the two point R<sub>2eff</sub> values, the point is being skipped.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Moved intensity negative value from reference to CPMG point.
 
* Moved intensity negative value from reference to CPMG point.
* Modified system test test_bug_negative_intensities_cpmg, to prepare for testing number of R<sub>2eff</sub> points.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Modified system test test_bug_negative_intensities_cpmg, to prepare for testing number of R<sub>2eff</sub> points.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html 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 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.
 
* 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 [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html 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 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.
 
* The target_functions.relax_fit C module Python function jacobian_chi2() is now exposed.  This was previously not visible from within Python.
Line 1,507: Line 1,507:
 
* 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 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.
 
* 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 R<sub>2eff</sub> errors, to use the C code Jacobian.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Modified module to estimate R<sub>2eff</sub> errors, to use the C code Jacobian.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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.
 
* 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 R<sub>2eff</sub> points.  Spin 4, which has one negative intensity, is expected to have one less 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.
 
* Improved system test test_bug_negative_intensities_cpmg, by counting number of R<sub>2eff</sub> points.  Spin 4, which has one negative intensity, is expected to have one less 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.
Line 1,516: Line 1,516:
 
* 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 R<sub>1</sub> 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 R<sub>1</sub> fit flag does not work in the GUI].
 
* API documentation fixes.
 
* 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Moved multifit_covar into lib.statistics, since it is an independent module.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Moved "func_exp_grad" into experimental class for different minimisation methods.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Improved [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html documentation to user function relax_disp.r2eff_err_estimate], and removed the possibility to use the &chi;<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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Improved [http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html documentation to user function relax_disp.r2eff_err_estimate], and removed the possibility to use the &chi;<sup>2</sup> Jacobian, as this is rubbish.  But the back-end still have this possibility, should one desire to try this.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> Jacobian.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> Jacobian.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> distribution.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate 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 &chi;<sup>2</sup> distribution.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* First try to make a test script for estimating efficiency on R<sub>2eff</sub> error calculations.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* First try to make a test script for estimating efficiency on R<sub>2eff</sub> error calculations.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* In module for estimating 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* In module for estimating R<sub>2eff</sub> errors, removed "values, errors" to be send to function for gradient, since they are not used.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Added Jacobian to test script, and now correctly do simulations, per R<sub>2eff</sub> points.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added Jacobian to test script, and now correctly do simulations, per R<sub>2eff</sub> points.  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
* Improved analysing test script, with plotting.  It seems that R<sub>2eff</sub> error estimation always get the same result.  [https://gna.org/task/?7822 Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Improved analysing test script, with plotting.  It seems that R<sub>2eff</sub> error estimation always get the same result.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added initial dataset for test analysis.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Deleted test data set.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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: &chi;<sup>2</sup> value returned from C code curve-fitting return 0.0 for wrong parameters -> Expected influence on Monte Carlo sim].
 
* 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: &chi;<sup>2</sup> 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 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.
 
* 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 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* 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":  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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 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 R<sub>2eff</sub> values made by bootstrapping and Monte Carlo simulations.  Rest of the analysis will be performed in relax.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Added initial peak lists to be analysed in relax for test purposes.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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].
+
* Comment fix to system test Relax_disp.test_estimate_r2eff_err_methods, after the found of bug in relax.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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 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 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.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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 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 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 R<sub>2eff</sub> error estimation.  The module runs perfect as it does now.  {{gna task link|7822|text=Task #7822: Implement user function to estimate 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: &chi;<sup>2</sup> 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.
 
* 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: &chi;<sup>2</sup> 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.
 
* 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.
Line 1,591: Line 1,591:
 
* Fix for time not extracted for CPMG experiments in target_function.  [https://gna.org/bugs/?22461 Bug #22461: NS R1rho 2-site_fit_r1 has extremely high &chi;<sup>2</sup> value in system test Relax_disp.test_r1rho_kjaergaard_missing_r1].
 
* Fix for time not extracted for CPMG experiments in target_function.  [https://gna.org/bugs/?22461 Bug #22461: NS R1rho 2-site_fit_r1 has extremely high &chi;<sup>2</sup> value in system test Relax_disp.test_r1rho_kjaergaard_missing_r1].
 
* Fix for interpolating time points, when producing xmgrace files for CPMG experiments.  [https://gna.org/bugs/?22461 Bug #22461: NS R1rho 2-site_fit_r1 has extremely high &chi;<sup>2</sup> value in system test Relax_disp.test_r1rho_kjaergaard_missing_r1].
 
* Fix for interpolating time points, when producing xmgrace files for CPMG experiments.  [https://gna.org/bugs/?22461 Bug #22461: NS R1rho 2-site_fit_r1 has extremely high &chi;<sup>2</sup> value in system test Relax_disp.test_r1rho_kjaergaard_missing_r1].
* Correction for catastrophic implementation of Monte Carlo simulations for exponential curve-fitting R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 &chi;<sup>2</sup> 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 R<sub>2eff</sub> and associated errors for exponential curve fitting].
+
* Correction for catastrophic implementation of Monte Carlo simulations for exponential curve-fitting R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 R<sub>2eff</sub> 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 &chi;<sup>2</sup> 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].  {{gna task link|7822|text=Task #7822: Implement user function to estimate R<sub>2eff</sub> and associated errors for exponential curve fitting}}.
 
* Added a minfx minimum version check to the dep_check module.  This is to avoid problems such as that reported at [http://gna.org/bugs/?22408 bug #22408].
 
* Added a minfx minimum version check to the dep_check module.  This is to avoid problems such as that reported at [http://gna.org/bugs/?22408 bug #22408].
 
<section end=bugfixes/>
 
<section end=bugfixes/>

Latest revision as of 20:34, 16 October 2020


Official relax releases
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relax version 3.3.0
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← relax 3.2.3 relax 3.3.1 →

Keywords Relaxation dispersion, speed
Release type Major feature
Release date 3 September 2014

The PDF version of the relax 3.3.0 user manual The relax 3.3.0 user manual

Description

This is a major feature release which includes a huge number of changes, as can be seen below. The most important change is an incredible speed up of all relaxation dispersion models. See the table below for a comparison to the previous relax 3.2.3 release. The maximum possible advantage of linear algebra operations are used to eliminate all of the slow Python looping and to obtain the ultimate algorithms for speed. As this is using NumPy, conversion to C or FORTRAN will not result in any significant speed advantage. With these huge speed ups, relax should now be one of the fastest software packages for analysing relaxation dispersion phenomena.

Other important features include the implementation of a zooming grid search algorithm for use in all analysis types, expanded plotting capabilities for R values in the relaxation dispersion analysis, the ability to optimise the R1 parameter in all off-resonance dispersion models, proper minimisation statistics resetting by the minimisation user functions, and a large expansion of the periodic table information for all elements in the relax library for correctly estimating molecular masses. Additional features are that there is better tab completion support in the prompt UI for Mac OS X, the addition of the time user function for printing the current date and time, the value.copy user function accepting a force argument for overwriting values, model nesting in the dispersion auto-analysis has been extended, the spin-lock offset is now shown in the dispersion analysis in the GUI, the relax_disp.r2eff_estimate user function has been added for fast R2eff and I0 parameter value and error estimation, and gradient and Hessian functions have been added to the exponential curve-fitting C module allowing for more advanced optimisation in the relaxation curve-fitting and dispersion analyses.

Note that this new 3.3 relax series breaks compatibility with old relax scripts. The important change, which is the main reason for starting the relax 3.3.x line, is the renaming of the calc, grid_search and minimise user functions to minimise.calculate, minimise.grid_search and minimise.execute respectively. Please update your scripts appropriately. A new relax feature is that old user function calls are detected in the prompt and script UIs and a RelaxError raised explaining what to rename the user function to.

Important bugfixes in this release include that relax can run on MS Windows systems again, numerous Python 3 fixes, the ability to load Bruker DC files when the file format has corrupted whitespace, the GUI "close all analyses" feature works and no longer raises an error, structure.create_diff_tensor_pdb user function now works when no structural data is present, the geometric prolate diffusion 3D PDB representation in a model-free analysis now aligns with the axis in the PDB as it was previously rotated by 90 degrees, and the Monte Carlo simulations in the relaxation dispersion analysis for exponential curve-fitting for R2eff/R parameter errors is now correct and no longer underestimating the errors by half. For more details about the new features and the bug fixes, please see below. For fully formatted and easy to navigate release notes, please see http://wiki.nmr-relax.com/Relax_3.3.0.

To demonstrate the huge speeds ups in the relaxation dispersion analysis, the following table compares the speed of dispersion models in relax 3.2.3 compared to the new 3.3.0 version:

100 single spins analysis (times in seconds):
Dispersion model relax 3.2.3 timings relax 3.3.0 timings Speed change
No Rex 0.824±0.017 0.269±0.016 3.068x faster.
LM63 1.616±0.017 0.749±0.008 2.157x faster.
LM63 3-site 3.218±0.039 0.996±0.013 3.230x faster.
CR72 2.639±0.042 1.536±0.019 1.718x faster.
CR72 full 2.808±0.027 1.689±0.075 1.663x faster.
IT99 1.838±0.032 0.868±0.011 2.118x faster.
TSMFK01 1.643±0.033 0.718±0.011 2.289x faster.
B14 5.841±0.050 3.747±0.044 1.559x faster.
B14 full 5.942±0.053 3.841±0.044 1.547x faster.
NS CPMG 2-site expanded 8.309±0.066 4.070±0.073 2.041x faster.
NS CPMG 2-site 3D 245.180±2.162 45.410±0.399 5.399x faster.
NS CPMG 2-site 3D full 237.217±2.582 45.177±0.415 5.251x faster.
NS CPMG 2-site star 183.423±1.966 36.542±0.451 5.020x faster.
NS CPMG 2-site star full 183.622±1.326 36.788±0.343 4.991x faster.
MMQ CR72 5.920±0.105 4.078±0.105 1.452x faster.
NS MMQ 2-site 363.659±2.610 82.588±1.197 4.403x faster.
NS MMQ 3-site linear 386.798±4.480 92.060±0.754 4.202x faster.
NS MMQ 3-site 391.195±3.442 93.025±0.829 4.205x faster.
M61 1.576±0.022 0.862±0.009 1.828x faster.
DPL94 22.794±0.517 1.101±0.008 20.705x faster.
TP02 19.892±0.363 1.232±0.007 16.152x faster.
TAP03 31.701±0.378 1.936±0.017 16.377x faster.
MP05 24.918±0.572 1.428±0.015 17.454x faster.
NS R1rho 2-site 244.604±2.493 35.125±0.202 6.964x faster.
NS R1rho 3-site linear 287.181±2.939 68.245±0.536 4.208x faster.
NS R1rho 3-site 290.486±3.614 70.449±0.686 4.123x faster.
Cluster of 100 spins analysis (times in seconds):
Dispersion model relax 3.2.3 timings relax 3.3.0 timings Speed change
No Rex 0.818±0.016 0.008±0.001 97.333x faster.
LM63 1.593±0.018 0.037±0.000 43.401x faster.
LM63 3-site 3.134±0.039 0.067±0.001 47.128x faster.
CR72 2.610±0.047 0.115±0.001 22.732x faster.
CR72 full 2.679±0.034 0.122±0.005 22.017x faster.
IT99 1.807±0.025 0.063±0.001 28.687x faster.
TSMFK01 1.636±0.036 0.039±0.001 42.170x faster.
B14 5.799±0.054 0.488±0.010 11.879x faster.
B14 full 5.803±0.043 0.484±0.006 11.990x faster.
NS CPMG 2-site expanded 8.326±0.081 0.685±0.012 12.160x faster.
NS CPMG 2-site 3D 244.869±2.382 41.217±0.467 5.941x faster.
NS CPMG 2-site 3D full 236.760±2.575 41.001±0.466 5.775x faster.
NS CPMG 2-site star 183.786±2.089 30.896±0.417 5.948x faster.
NS CPMG 2-site star full 183.243±1.615 30.898±0.343 5.931x faster.
MMQ CR72 5.978±0.094 0.847±0.007 7.061x faster.
NS MMQ 2-site 363.138±3.041 75.906±0.845 4.784x faster.
NS MMQ 3-site linear 384.978±5.402 83.703±0.773 4.599x faster.
NS MMQ 3-site 388.557±3.261 84.702±0.762 4.587x faster.
M61 1.555±0.021 0.034±0.001 45.335x faster.
DPL94 22.837±0.494 0.140±0.002 163.004x faster.
TP02 19.958±0.407 0.167±0.002 119.222x faster.
TAP03 31.698±0.424 0.287±0.003 110.484x faster.
MP05 25.009±0.683 0.187±0.007 133.953x faster.
NS R1rho 2-site 242.096±1.483 32.043±0.157 7.555x faster.
NS R1rho 3-site linear 280.778±2.589 62.866±0.616 4.466x faster.
NS R1rho 3-site 282.192±5.195 63.174±0.816 4.467x faster.

Full details of this comparison can be seen in the test_suite/shared_data/dispersion/profiling directory. For information about each of these models, please see the links: http://wiki.nmr-relax.com/No_Rex, http://wiki.nmr-relax.com/LM63, http://wiki.nmr-relax.com/LM63_3-site, http://wiki.nmr-relax.com/CR72, http://wiki.nmr-relax.com/CR72_full, http://wiki.nmr-relax.com/IT99, http://wiki.nmr-relax.com/TSMFK01, http://wiki.nmr-relax.com/B14, http://wiki.nmr-relax.com/B14_full, http://wiki.nmr-relax.com/NS_CPMG_2-site_expanded, http://wiki.nmr-relax.com/NS_CPMG_2-site_3D, http://wiki.nmr-relax.com/NS_CPMG_2-site_3D_full, http://wiki.nmr-relax.com/NS_CPMG_2-site_star, http://wiki.nmr-relax.com/NS_CPMG_2-site_star_full, http://wiki.nmr-relax.com/MMQ_CR72, http://wiki.nmr-relax.com/NS_MMQ_2-site, http://wiki.nmr-relax.com/NS_MMQ_3-site_linear, http://wiki.nmr-relax.com/NS_MMQ_3-site, http://wiki.nmr-relax.com/M61, http://wiki.nmr-relax.com/DPL94, http://wiki.nmr-relax.com/TP02, http://wiki.nmr-relax.com/TAP03, http://wiki.nmr-relax.com/MP05, http://wiki.nmr-relax.com/NS_R1rho_2-site, http://wiki.nmr-relax.com/NS_R1rho_3-site_linear, http://wiki.nmr-relax.com/NS_R1rho_3-site.

For CPMG statistics: 3 fields, each with 20 CPMG points. Total number of dispersion points per spin is 60.

For R experiments: 3 fields, each with 10 spin lock offsets, and each offset has been measured at 5 different spin lock fields. Per field there is 50 dispersion points. Total number of dispersion points per spin is 150.


Download

The new relax versions can be downloaded from http://www.nmr-relax.com/download.html. If binary distributions are not yet available for your platform and you manage to compile the binary modules, please consider contributing these to the relax project (described in section 3.6 of the relax manual, http://www.nmr-relax.com/manual/relax_distribution_archives.html).


CHANGES file

Version 3.3.0
(3 September 2014, from /trunk)
https://sourceforge.net/p/nmr-relax/code/ci/3.3.0/tree/


Features

  • Huge speed ups for all of the relaxation dispersion models ranging from 1.452x to 163.004x times faster. The speed ups for the clustered spin analysis are far greater than for the single spin analysis.
  • Implementation of a zooming grid search algorithm for optimisation in all analyses. This includes the addition of the minimise.grid_zoom user function to set the zoom level. The grid width will be divided by 2zoom_level and centred at the current parameter values. If the new grid is outside of the bounds of the original grid, the entire grid will be translated so that it lies entirely within the original.
  • Increased the amount of user feedback for the minimise.grid_search user function. Now a comment for each parameter is included in the printed grid search setup table. This includes if the lower or upper bounds, or both, have been supplied and if a preset value has been used instead.
  • Expanded support for R 2D graph plotting in the relax_disp.plot_disp_curves user function as the X-axis can now be the ν1 value, the effective field ωeff, or the rotating frame title angle θ. And the plots are interpolation over the spin-lock offset.
  • Ability to optimise the R1 relaxation rate parameter in the off-resonance relaxation dispersion models.
  • Creation of the relax_disp.r1_fit user function for activating and deactivating R1 fitting in the dispersion analysis.
  • Better tab completion support in the prompt UI for Mac OS X users. For some Python versions, the Mac supplied libedit library is used rather than GNU readline. But this library uses a completely different language and hence tab completion was non-functional on these systems. The library difference is now detected and the correct language sent into libedit to activate tab completion.
  • Created the time user function. This is just a shortcut for printing out the output of the time.asctime() function.
  • The value.copy user function now accepts the force flag to allow destination values to be overwritten.
  • Expanded model nesting capabilities in the relaxation dispersion auto-analysis to speed up the protocol.
  • The spin-lock offset is now included in the spectra list GUI element for the relaxation dispersion analysis.
  • Creation of the relax_disp.r2eff_estimate user function for the fast estimation of R2eff/R values and errors when full exponential curves have been collected. This experimental feature uses linearisation to estimate the R2eff and I0 parameters and the covariance matrix to estimate parameter errors.
  • Gradients and Hessians are implemented for the exponential curve-fitting, hence all optimisation algorithms and constraint algorithms are now available for this analysis type. Using Newton optimisation instead of Nelder-Mead Simplex can save over an order of magnitude in computation time. This is also available in the relaxation dispersion analysis.
  • The minimisation statistics are now being reset for all analysis types. The minimise.calculate, minimise.grid_search, and minimise.execute user functions now all reset the minimisation statistics for either the model or the Monte Carlo simulations prior to performing any optimisation. This is required for both parallelised grid searches and repetitive optimisation schemes to allow the result to overwrite an old result in all situations, as sometimes the original chi-squared value is lower and the new result hence is rejected.
  • Large expansion of the periodic table information in the relax library to include all elements, the IUPAC 2011 standard atomic weights for all elements, mass numbers and atomic masses for all stable isotopes, and gyromagnetic ratios.
  • Significant improvements to the structure centre of mass calculations by using the new periodic table information - all elements are now supported and exact masses are now used.
  • Added a button to the spectra list GUI element for the spectrum.error_analysis user function. This is placed after the 'Add' and 'Delete' buttons and is used in the NOE, R1 and R2 curve-fitting and relaxation dispersion analyses.
  • RelaxErrors are now raised in the prompt or script UI if an old user function is called, printing out the names of the old and new user functions. This is for help in upgrading old scripts and is currently for the calc(), grid_search(), and minimise() user function calls.


Changes


Bugfixes


Links

For reference, the announcement for this release can also be found at following links:

Softpedia also has information about the newest relax releases:


Announcements

If you would like to receive announcements about new relax versions, please subscribe to the relax announcement mailing list. This list only receives ~10 emails per year. It is archived at the SourceForge archives and in The Mail Archive.

See also