Difference between revisions of "Relax 3.3.0"

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| type    = Major feature
 
| type    = Major feature
 
| date    = 3 September 2014
 
| date    = 3 September 2014
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| manual  = yes
 
}}
 
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= Description =
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== Description ==
  
 
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= Download =
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== Download ==
  
 
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= CHANGES file =
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== CHANGES file ==
  
 
<section begin=metadata/>
 
<section begin=metadata/>
 
Version 3.3.0 <br/>
 
Version 3.3.0 <br/>
 
(3 September 2014, from /trunk) <br/>
 
(3 September 2014, from /trunk) <br/>
http://svn.gna.org/svn/relax/tags/3.3.0
+
{{relax url|tag=3.3.0}}
 
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<section end=metadata/>
  
== Features ==
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=== Features ===
  
 
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<section end=features/>
  
== Changes ==
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=== Changes ===
  
 
<section begin=changes/>
 
<section begin=changes/>
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* Updated the script for finding unused imports in the relax source code.  Now the file name is only printed for Python files which have unused imports.
 
* Updated the script for finding unused imports in the relax source code.  Now the file name is only printed for Python files which have unused imports.
 
* Completely removed all mentions of Freecode from the release document.  The [http://freecode.com/projects/nmr-relax old relax links are still there], but [http://freecode.com/about Freecode is dead].
 
* Completely removed all mentions of Freecode from the release document.  The [http://freecode.com/projects/nmr-relax old relax links are still there], but [http://freecode.com/about Freecode is dead].
* Updated the minfx version in the release checklist document to [https://gna.org/forum/forum.php?forum_id=2471 1.0.8].  This version has not been released yet, but it will include important fixes and additions for constrained parallelised grid searches.
+
* Updated the minfx version in the release checklist document to {{gna link|url=gna.org/forum/forum.php?forum_id=2471|text=1.0.8}}.  This version has not been released yet, but it will include important fixes and additions for constrained parallelised grid searches.
 
* Fix for a broken link in the development chapter of the relax manual.
 
* Fix for a broken link in the development chapter of the relax manual.
 
* Fixes for dead hyperlinks in the relaxation dispersion chapter of the relax manual.  The [[B14]] model links to http://www.nmr-relax.com/api/3.2/lib.dispersion.b14-module.html were broken as the B in [[B14]] was capitalised.
 
* Fixes for dead hyperlinks in the relaxation dispersion chapter of the relax manual.  The [[B14]] model links to http://www.nmr-relax.com/api/3.2/lib.dispersion.b14-module.html were broken as the B in [[B14]] was capitalised.
* Sent in the verbosity argument value to the [http://home.gna.org/minfx/minfx.grid-module.html#grid_split minfx.grid.grid_split() function].  The minfx function in the next release ([https://gna.org/forum/forum.php?forum_id=2471 1.0.8]) will now be more verbose, so this will help with user feedback when running the model-free analysis on a cluster or multi-core system using MPI.
+
* Sent in the verbosity argument value to the [http://home.gna.org/minfx/minfx.grid-module.html#grid_split minfx.grid.grid_split() function].  The minfx function in the next release ({{gna link|url=gna.org/forum/forum.php?forum_id=2471|text=1.0.8}}) will now be more verbose, so this will help with user feedback when running the model-free analysis on a cluster or multi-core system using MPI.
 
* The [http://www.nmr-relax.com/manual/time.html time user function] now uses the chronometer [https://en.wikipedia.org/wiki/Oxygen_Project Oxygen icon] in the GUI.
 
* The [http://www.nmr-relax.com/manual/time.html time user function] now uses the chronometer [https://en.wikipedia.org/wiki/Oxygen_Project Oxygen icon] in the GUI.
 
* Removed the line wrapping in the epydoc parameter section of the optimisation function docstrings.  This is for the pipe_control.minimise module.
 
* Removed the line wrapping in the epydoc parameter section of the optimisation function docstrings.  This is for the pipe_control.minimise module.
Line 361: Line 362:
 
* Bug fix for the parameter units descriptions.  This only affects a few rare parameters.  The [http://www.nmr-relax.com/api/3.3/specific_analyses.parameter_object.Param_list-class.html#units specific analysis API parameter object units() method] was incorrectly checking if the units value is a function - it was checking the parameter conversion factor instead.
 
* Bug fix for the parameter units descriptions.  This only affects a few rare parameters.  The [http://www.nmr-relax.com/api/3.3/specific_analyses.parameter_object.Param_list-class.html#units specific analysis API parameter object units() method] was incorrectly checking if the units value is a function - it was checking the parameter conversion factor instead.
 
* Modified the [http://www.nmr-relax.com/manual/align_tensor_init.html align_tensor.init user function] so that the parameters are now optional.  This allows alignment tensors to be initialised without specifying the parameter values for that tensor.
 
* Modified the [http://www.nmr-relax.com/manual/align_tensor_init.html align_tensor.init user function] so that the parameters are now optional.  This allows alignment tensors to be initialised without specifying the parameter values for that tensor.
* Modified profiling script to have different number of NCYC points per frequency.  This is to complicate the data, so any erroneous reshaping of data is discovered.  It is expected, that experiments can have different number of NCYC points per spectrometer frequency.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script to have different number of NCYC points per frequency.  This is to complicate the data, so any erroneous reshaping of data is discovered.  It is expected, that experiments can have different number of NCYC points per spectrometer frequency.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Initial try to alter the [http://www.nmr-relax.com/api/3.3/target_functions.relax_disp.Dispersion-class.html#calc_CR72_chi2 target function calc_CR72_chi2].  This is the first test to restructure the arrays, to allow for higher dimensional computation.  All numpy arrays have to have same shape to allow to multiply together.  The dimensions should be [ei][si][mi][oi][di]. [Experiment][spins][spec. frq][offset][disp points].  This is complicated with number of disp point can change per spectrometer frequency.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].  This implementation brings a high overhead.  The first test shows no winning of time.  The creation of arrays takes all the time.
+
* Initial try to alter the [http://www.nmr-relax.com/api/3.3/target_functions.relax_disp.Dispersion-class.html#calc_CR72_chi2 target function calc_CR72_chi2].  This is the first test to restructure the arrays, to allow for higher dimensional computation.  All numpy arrays have to have same shape to allow to multiply together.  The dimensions should be [ei][si][mi][oi][di]. [Experiment][spins][spec. frq][offset][disp points].  This is complicated with number of disp point can change per spectrometer frequency.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.  This implementation brings a high overhead.  The first test shows no winning of time.  The creation of arrays takes all the time.
 
* Temporary changed the [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/cr72.py] function to unsafe state.  This change turns-off all the safety measures, since they have to be re-implemented for higher dimensional structures.
 
* Temporary changed the [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/cr72.py] function to unsafe state.  This change turns-off all the safety measures, since they have to be re-implemented for higher dimensional structures.
* Altered profiling script to report cumulative timings and save to temporary files.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].  This indeed shows that the efficiency has gone down.
+
* Altered profiling script to report cumulative timings and save to temporary files.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.  This indeed shows that the efficiency has gone down.
* Added print out of &chi;<sup>2</sup> to profile script.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added print out of &chi;<sup>2</sup> to profile script.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the creation of special numpy structures outside target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the creation of special numpy structures outside target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script to calculate correct values when setting up R<sub>2eff</sub> values.  This is to test, that the return of &chi;<sup>2</sup> gets zero.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script to calculate correct values when setting up R<sub>2eff</sub> values.  This is to test, that the return of &chi;<sup>2</sup> gets zero.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removing looping over exp and offset indices in calc_chi2.  They are always 0 anyway.  This brings a little speed.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removing looping over exp and offset indices in calc_chi2.  They are always 0 anyway.  This brings a little speed.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* In profiling script, moved up the calculation of values one level.  This is to better see the output of the profiling iterations for [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html CR72.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* In profiling script, moved up the calculation of values one level.  This is to better see the output of the profiling iterations for [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html CR72.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for calculation of the Larmor frequency per spin in profiling script.  The frq loop should also be up-shifted.  It was now extracted as 0.0.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for calculation of the Larmor frequency per spin in profiling script.  The frq loop should also be up-shifted.  It was now extracted as 0.0.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Re-inserted safety checks in [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/CR72.py] file.  This is re-inserted for the rank_1 cases.  This makes the unit-tests pass again.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Re-inserted safety checks in [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/CR72.py] file.  This is re-inserted for the rank_1 cases.  This makes the unit-tests pass again.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Important fix for extracting the correct shape to create new arrays.  If using just one field, or having the same number of dispersion points, the shape would extend to the dispersion number.  It would report [ei][si][mi][oi][di] when calling ndarray.shape.  Shape always has to be reported as: [ei][si][mi][oi].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Important fix for extracting the correct shape to create new arrays.  If using just one field, or having the same number of dispersion points, the shape would extend to the dispersion number.  It would report [ei][si][mi][oi][di] when calling ndarray.shape.  Shape always has to be reported as: [ei][si][mi][oi].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made it easier to switch between single and cluster reporting in profiling script.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made it easier to switch between single and cluster reporting in profiling script.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Important fix for the creation of the multi dimensional p<sub>A</sub> numpy array.  It should be created as numpy.zeros([ei][si][mi][oi]) instead of numpy.ones([ei][si][mi][oi]).  This allows for rapid testing of all dimensions with np.allclose(pA, numpy.ones(dw.shape)).  p<sub>A</sub> can have missing filled out values, when the number of dispersion points are different per spectrometer frequency.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Important fix for the creation of the multi dimensional p<sub>A</sub> numpy array.  It should be created as numpy.zeros([ei][si][mi][oi]) instead of numpy.ones([ei][si][mi][oi]).  This allows for rapid testing of all dimensions with np.allclose(pA, numpy.ones(dw.shape)).  p<sub>A</sub> can have missing filled out values, when the number of dispersion points are different per spectrometer frequency.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added unit tests demonstrating edge cases 'no Rex' failures of the model [[CR72 full]], for a clustered multi dimensional calculation.  This is implemented for one field.  This is to implement catching of math domain errors, before they occur.  These tests cover all parameter value combinations which result in no exchange:  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added unit tests demonstrating edge cases 'no Rex' failures of the model [[CR72 full]], for a clustered multi dimensional calculation.  This is implemented for one field.  This is to implement catching of math domain errors, before they occur.  These tests cover all parameter value combinations which result in no exchange:  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Re-implemented safety checks in [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/cr72.py].  This is now implemented for both rank-1 float array and of higher dimensions.  This makes the unit tests pass for multi dimensional computing.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Re-implemented safety checks in [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/cr72.py].  This is now implemented for both rank-1 float array and of higher dimensions.  This makes the unit tests pass for multi dimensional computing.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added unit tests demonstrating edge cases 'no Rex' failures of the model [[CR72 full]], for a clustered multi dimensional calculation.  This is implemented for three fields.  This is to implement catching of math domain errors, before they occur.  These tests cover all parameter value combinations which result in no exchange:  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added unit tests demonstrating edge cases 'no Rex' failures of the model [[CR72 full]], for a clustered multi dimensional calculation.  This is implemented for three fields.  This is to implement catching of math domain errors, before they occur.  These tests cover all parameter value combinations which result in no exchange:  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed that special numpy structure is also created for [[CR72]].  This makes most system tests pass.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed that special numpy structure is also created for [[CR72]].  This makes most system tests pass.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Critical fix for the slicing of values in target function.  This makes system test: Relax_disp.test_sod1wt_t25_to_cr72 pass.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Critical fix for the slicing of values in target function.  This makes system test: Relax_disp.test_sod1wt_t25_to_cr72 pass.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added self.has_missing keyword in initialization of the Dispersion class.  This is to test once, per spin or cluster.  This saves a looping over the dispersion points, when collection the data.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added self.has_missing keyword in initialization of the Dispersion class.  This is to test once, per spin or cluster.  This saves a looping over the dispersion points, when collection the data.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Created multi dimensional error and value numpy arrays.  This is to calculate the &chi;<sup>2</sup> sum much faster.  Reordered the loop over missing data points, so it is only initiated if missing points is detected.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Created multi dimensional error and value numpy arrays.  This is to calculate the &chi;<sup>2</sup> sum much faster.  Reordered the loop over missing data points, so it is only initiated if missing points is detected.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Switch the looping from spin->frq to frq->spin.  Since the number of dispersion points are the same for all spins, this allows to move the calculation of p<sub>A</sub> and k<sub>ex</sub> array one level up.  This saves a lot of computation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Switch the looping from spin->frq to frq->spin.  Since the number of dispersion points are the same for all spins, this allows to move the calculation of p<sub>A</sub> and k<sub>ex</sub> array one level up.  This saves a lot of computation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed all the creation of special numpy arrays to be of float64 type.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed all the creation of special numpy arrays to be of float64 type.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the data filling of special numpy array errors and values, to initialization of Dispersion class.  These values does not change, and can safely be stored outside.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the data filling of special numpy array errors and values, to initialization of Dispersion class.  These values does not change, and can safely be stored outside.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Just a tiny little more speed, by removing temporary storage of &chi;<sup>2</sup> calculation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Just a tiny little more speed, by removing temporary storage of &chi;<sup>2</sup> calculation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made copies of numpy arrays instead of creating from new.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made copies of numpy arrays instead of creating from new.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added a self.frqs_a as a multidimensional numpy array.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added a self.frqs_a as a multidimensional numpy array.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Small fix for the indices to the errors and values numpy array.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Small fix for the indices to the errors and values numpy array.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Lowered the number of iterations to the profiling scripts.  This is to use the profiling script as bug finder.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Lowered the number of iterations to the profiling scripts.  This is to use the profiling script as bug finder.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the calculation of dw_frq out of spin and spectrometer loop.  This is done by having a special 1/0 spin numpy array, which turns on or off the values in the numpy array multiplication.  The multiplication needs to first axis expand &Delta;&omega;, and then tile the arrays according to the numpy structure.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the calculation of dw_frq out of spin and spectrometer loop.  This is done by having a special 1/0 spin numpy array, which turns on or off the values in the numpy array multiplication.  The multiplication needs to first axis expand &Delta;&omega;, and then tile the arrays according to the numpy structure.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the calculation of p<sub>A</sub> and k<sub>ex</sub> out off all loops.  This was done by having two special 1/0 spin structure arrays.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the calculation of p<sub>A</sub> and k<sub>ex</sub> out off all loops.  This was done by having two special 1/0 spin structure arrays.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed dw_frq_a numpy array, as it was not necessary.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed dw_frq_a numpy array, as it was not necessary.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed all looping over spin and spectrometer frequency.  This is the last loop! Wuhu.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed all looping over spin and spectrometer frequency.  This is the last loop! Wuhu.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Reordered arrays for beauty of code.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Reordered arrays for beauty of code.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the back_calc array be initiated as copy of the values array.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the back_calc array be initiated as copy of the values array.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Small edit to profiling script, to help bug finding.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Small edit to profiling script, to help bug finding.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fixed that arrays are correctly initiated with one or zero values.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fixed that arrays are correctly initiated with one or zero values.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Very important fix, for only replacing part of data array which have Nan values.  Before, all values were replaced, which was wrong.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Very important fix, for only replacing part of data array which have Nan values.  Before, all values were replaced, which was wrong.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Needed to increase the relative tolerance when testing if p<sub>A</sub> array is 1.  Now system test Relax_disp.test_hansen_cpmg_data_missing_auto_analysis passes.  Also added some comments lines, to prepare for mask replace of values.  For example if only some of etapos values should be replaced.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Needed to increase the relative tolerance when testing if p<sub>A</sub> array is 1.  Now system test Relax_disp.test_hansen_cpmg_data_missing_auto_analysis passes.  Also added some comments lines, to prepare for mask replace of values.  For example if only some of etapos values should be replaced.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Restored profiling script to normal.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Restored profiling script to normal.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the logic and comments much clearer about how to reshape, expand axis, and tile numpy arrays.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the logic and comments much clearer about how to reshape, expand axis, and tile numpy arrays.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented a masked array search for where "missing" array is equal 1.  This makes it possible to replace all values with this mask, from the value array.  This eliminates the last loops over the missing values.  It took over 4 hours to figure out, that the mask should be called with mask.mask, to return the same fulls structure,  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented a masked array search for where "missing" array is equal 1.  This makes it possible to replace all values with this mask, from the value array.  This eliminates the last loops over the missing values.  It took over 4 hours to figure out, that the mask should be called with mask.mask, to return the same fulls structure,  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Yet another small improvement for the profiling script.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Yet another small improvement for the profiling script.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed the multi dimensional structure of p<sub>A</sub>.  p<sub>A</sub> is not multi-dimensional, and can just be multiplied with numpy arrays.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the multi dimensional structure of p<sub>A</sub>.  p<sub>A</sub> is not multi-dimensional, and can just be multiplied with numpy arrays.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for testing of p<sub>A</sub> in lib function, when p<sub>A</sub> is just float.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for testing of p<sub>A</sub> in lib function, when p<sub>A</sub> is just float.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified unit tests, so p<sub>A</sub> is sent to target function as float instead of array.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified unit tests, so p<sub>A</sub> is sent to target function as float instead of array.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed the multi dimensional structure of k<sub>ex</sub>.  k<sub>ex</sub> is not multi-dimensional, and can just be multiplied with numpy arrays.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the multi dimensional structure of k<sub>ex</sub>.  k<sub>ex</sub> is not multi-dimensional, and can just be multiplied with numpy arrays.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for testing of k<sub>ex</sub> in lib function, when k<sub>ex</sub> is just float.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for testing of k<sub>ex</sub> in lib function, when k<sub>ex</sub> is just float.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified unit tests, so k<sub>ex</sub> is sent to target function as float instead of array.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified unit tests, so k<sub>ex</sub> is sent to target function as float instead of array.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Important fix for replacing values if eta_pos > 700 is violated.  This fixes system test: Relax_disp.test_sod1wt_t25_to_cr72, which failed after making k<sub>ex</sub> to a numpy float.  The trick is to make a numpy mask which stores the position where to replace the values.  Then replace the values just before last return.  This makes sure, that not all values are changed.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Important fix for replacing values if eta_pos > 700 is violated.  This fixes system test: Relax_disp.test_sod1wt_t25_to_cr72, which failed after making k<sub>ex</sub> to a numpy float.  The trick is to make a numpy mask which stores the position where to replace the values.  Then replace the values just before last return.  This makes sure, that not all values are changed.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Increased the k<sub>ex</sub> speed to 1e<sup>7</sup> in clustered unit tests cases.  This is to demonstrate where there will be no excange.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Increased the k<sub>ex</sub> speed to 1e<sup>7</sup> in clustered unit tests cases.  This is to demonstrate where there will be no excange.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added a multi-dimensional numpy array &chi;<sup>2</sup> value calculation function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added a multi-dimensional numpy array &chi;<sup>2</sup> value calculation function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Called the newly created &chi;<sup>2</sup> function to calculate for multi dimensional numpy arrays.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Called the newly created &chi;<sup>2</sup> function to calculate for multi dimensional numpy arrays.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Renamed chi2_ND to chi2_rankN.  This is a better name for representing multiple axis calculation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Renamed chi2_ND to chi2_rankN.  This is a better name for representing multiple axis calculation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made special ei, si, mi, and oi numpy structure array.  This is for rapid speed-up of numpy array creation in target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made special ei, si, mi, and oi numpy structure array.  This is for rapid speed-up of numpy array creation in target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Replaced self.spins_a with self.disp_struct.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replaced self.spins_a with self.disp_struct.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made initialisation structures for &Delta;&omega;.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made initialisation structures for &Delta;&omega;.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Initial try to reshape &Delta;&omega; faster.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Initial try to reshape &Delta;&omega; faster.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Switched to use self.ei, self.si, self.mi, self.oi, self.di.  This is for better reading of code.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Switched to use self.ei, self.si, self.mi, self.oi, self.di.  This is for better reading of code.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Comment out the sys.exit(), which would make the code fail for wrong calculation of &Delta;&omega;.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Comment out the sys.exit(), which would make the code fail for wrong calculation of &Delta;&omega;.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Copied profiling script for CPMG model [[CR72]] to R<sub>1&rho;</sub> [[DPL94]] model.  The framework of the script will be the same, but the data a little different.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Copied profiling script for CPMG model [[CR72]] to R<sub>1&rho;</sub> [[DPL94]] model.  The framework of the script will be the same, but the data a little different.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Started converting profiling script to [[DPL94]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Started converting profiling script to [[DPL94]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Replaced self.(ei,si,mi,oi,di) with self.(NE,NS,NM,NO,ND).  These numbers represents the maximum number of dimensions, instead of index.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replaced self.(ei,si,mi,oi,di) with self.(NE,NS,NM,NO,ND).  These numbers represents the maximum number of dimensions, instead of index.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added the ei index, when creating the first dw_mask.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added the ei index, when creating the first dw_mask.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Reordered how the structures &Delta;&omega; init structures are created.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Reordered how the structures &Delta;&omega; init structures are created.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Clearing the dw_struct before calculation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Clearing the dw_struct before calculation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Started using the new way of constructing &Delta;&omega;.  This is for running system tests.  Note, somewhere in the &Delta;&omega; array, the frequencies will be different between the two implementations.  But apparently, this does not matter.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Started using the new way of constructing &Delta;&omega;.  This is for running system tests.  Note, somewhere in the &Delta;&omega; array, the frequencies will be different between the two implementations.  But apparently, this does not matter.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Inserted temporary method to switch for profiling.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Inserted temporary method to switch for profiling.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* First try to speed-up the old &Delta;&omega; structure calculation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* First try to speed-up the old &Delta;&omega; structure calculation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Simplified calculation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Simplified calculation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Yet another try to implement a fast &Delta;&omega; structure method.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Yet another try to implement a fast &Delta;&omega; structure method.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented the fastest way to calculate the &Delta;&omega; structure.  This uses the numpy ufunc multiply.outer function to create the outer array, and then multiply with the frqs_structure.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented the fastest way to calculate the &Delta;&omega; structure.  This uses the numpy ufunc multiply.outer function to create the outer array, and then multiply with the frqs_structure.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Renamed &Delta;&omega; temporary structure to generic structure.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Renamed &Delta;&omega; temporary structure to generic structure.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Restructured the calculation of R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup> to the most efficient way.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Restructured the calculation of R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup> to the most efficient way.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/CR72.py] to a numpy multi dimensional numpy array calculation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/CR72.py] to a numpy multi dimensional numpy array calculation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed the catching when &Delta;&omega; is zero, to use masked array.  Implemented backwards compatibility with unit tests.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed the catching when &Delta;&omega; is zero, to use masked array.  Implemented backwards compatibility with unit tests.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Bugfix for testing if k<sub>ex</sub> is zero.  It was tested if k<sub>ex</sub> was equal 1.0.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Bugfix for testing if k<sub>ex</sub> is zero.  It was tested if k<sub>ex</sub> was equal 1.0.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented masked replacement if fact is less that 1.0.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented masked replacement if fact is less that 1.0.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Replaced isnan mask with function that catches all invalid values.
 
* Replaced isnan mask with function that catches all invalid values.
* Removed the masked replacement if fact is less than 1.0.  This is very strange, but otherwise system test: Relax_disp.test_hansen_cpmg_data_missing_auto_analysis would fail.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the masked replacement if fact is less than 1.0.  This is very strange, but otherwise system test: Relax_disp.test_hansen_cpmg_data_missing_auto_analysis would fail.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed the slow allclose() function to test if R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup> is equal.  It is MUCH faster to just subtract and check sum is not 0.0.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the slow allclose() function to test if R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup> is equal.  It is MUCH faster to just subtract and check sum is not 0.0.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Replaced the temporary variable R<sub>2eff</sub> with back_calc, and used numpy subtract to speed up.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replaced the temporary variable R<sub>2eff</sub> with back_calc, and used numpy subtract to speed up.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the lib function into a pure numpy array calculation.  This requires, that R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega; has same dimension as the dispersion points.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the lib function into a pure numpy array calculation.  This requires, that R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega; has same dimension as the dispersion points.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changes too unit tests, so data is sent to target function in numpy array format.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changes too unit tests, so data is sent to target function in numpy array format.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed the creation of an unnecessary structure by using numpy multiply.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the creation of an unnecessary structure by using numpy multiply.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the mask which finds where to replace values into the __init__ function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the mask which finds where to replace values into the __init__ function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Copied profiling script for [[CR72]] to [[B14]] model.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Copied profiling script for [[CR72]] to [[B14]] model.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script for the [[B14]] model.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for the [[B14]] model.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified model [[B14]] lib file to faster numpy multidimensional mode.  The implementations comes almost directly from the [[CR72]] model file.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified model [[B14]] lib file to faster numpy multidimensional mode.  The implementations comes almost directly from the [[CR72]] model file.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Reverted the use of the mask "mask_set_blank".  It did not work, and many system tests started failing.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Reverted the use of the mask "mask_set_blank".  It did not work, and many system tests started failing.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed the target function to handle the [[B14]] model for faster numpy computation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed the target function to handle the [[B14]] model for faster numpy computation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed unit test for [[B14]] to match numpy input requirement.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed unit test for [[B14]] to match numpy input requirement.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added additional tests in [[B14]], when math errors can occur.  This is very easy with a conditional masked search in arrays.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added additional tests in [[B14]], when math errors can occur.  This is very easy with a conditional masked search in arrays.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Comment fix for finding when E0 is above 700 in lib function of [[B14]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Comment fix for finding when E0 is above 700 in lib function of [[B14]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed use of "asarray", since the variables are already arrays.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed use of "asarray", since the variables are already arrays.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed target function for model [[CR72]].  To [[CR72]] is now also the input of the parameters of R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega;.  &Delta;&omega; is tested for zero, to return flat lines.  It is faster to search in the smaller numpy array, than the 5 dimensional &Delta;&omega; array.  This is for speed-up.  R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup> is also subtracted, to see if the full model should be used.  In the same way, it is faster to subtract the smaller array.  These small tricks are expected to give 5-10 pct. speeed-up.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed target function for model [[CR72]].  To [[CR72]] is now also the input of the parameters of R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega;.  &Delta;&omega; is tested for zero, to return flat lines.  It is faster to search in the smaller numpy array, than the 5 dimensional &Delta;&omega; array.  This is for speed-up.  R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup> is also subtracted, to see if the full model should be used.  In the same way, it is faster to subtract the smaller array.  These small tricks are expected to give 5-10 pct. speeed-up.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the lib function of [[CR72]] accept the R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega; of the original array.  This is for speed-up.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the lib function of [[CR72]] accept the R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega; of the original array.  This is for speed-up.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed unit-tests, to send in the original R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and dw_orig to the testing of the lib function [[CR72]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed unit-tests, to send in the original R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and dw_orig to the testing of the lib function [[CR72]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed profiling script to send R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega;, as original parameters to the lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed profiling script to send R<sub>2A</sub><sup>0</sup>, R<sub>2B</sub><sup>0</sup> and &Delta;&omega;, as original parameters to the lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed target function for model [[B14]].  To [[B14]] now also send the input of the original parameters &Delta;&omega;.  &Delta;&omega; is tested for zero, to return flat lines.  It is faster to search in the smaller numpy array, than the 5 dimensional &Delta;&omega; array.  This is for speed-up.  These small tricks are expected to give 5-10 pct. speed-up.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed target function for model [[B14]].  To [[B14]] now also send the input of the original parameters &Delta;&omega;.  &Delta;&omega; is tested for zero, to return flat lines.  It is faster to search in the smaller numpy array, than the 5 dimensional &Delta;&omega; array.  This is for speed-up.  These small tricks are expected to give 5-10 pct. speed-up.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the lib function of [[B14]] accept &Delta;&omega; of the original array.  This is for speed-up.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the lib function of [[B14]] accept &Delta;&omega; of the original array.  This is for speed-up.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed unit-tests, to send in the original dw_orig to the testing of the lib function [[B14]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed unit-tests, to send in the original dw_orig to the testing of the lib function [[B14]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changed profiling script to send &Delta;&omega; as original parameters to the lib function [[B14]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changed profiling script to send &Delta;&omega; as original parameters to the lib function [[B14]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Copied profiling script for [[CR72]] model to [[TSMFK01]] model.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Copied profiling script for [[CR72]] model to [[TSMFK01]] model.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script to be used for model [[TSMFK01]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script to be used for model [[TSMFK01]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified target function for model [[TSMFK01]], to send in &Delta;&omega; as original parameter.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified target function for model [[TSMFK01]], to send in &Delta;&omega; as original parameter.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified lib function for model [[TSMFK01]] to accept dw_orig as input and replaced functions to find math domain errors into maske replacements.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified lib function for model [[TSMFK01]] to accept dw_orig as input and replaced functions to find math domain errors into maske replacements.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made unit tests for model [[TSMFK01]] send in R<sub>2A</sub><sup>0</sup> and &Delta;&omega; as a numpy array.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made unit tests for model [[TSMFK01]] send in R<sub>2A</sub><sup>0</sup> and &Delta;&omega; as a numpy array.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Large increase in speed for model [[TSMFK01]] by changing target functions to use multidimensional numpy arrays in calculation.  This is done by restructuring data into multidimensional arrays of dimension [NE][NS][NM][NO][ND], which are number of spins, number of magnetic field strength, number of offsets, maximum number of dispersion point.  The speed comes from using numpy ufunc operations.  The new version is 2.4X as fast per spin calculation, and 54X as fast for clustered analysis.
 
* Large increase in speed for model [[TSMFK01]] by changing target functions to use multidimensional numpy arrays in calculation.  This is done by restructuring data into multidimensional arrays of dimension [NE][NS][NM][NO][ND], which are number of spins, number of magnetic field strength, number of offsets, maximum number of dispersion point.  The speed comes from using numpy ufunc operations.  The new version is 2.4X as fast per spin calculation, and 54X as fast for clustered analysis.
* Replacing math domain checking in model [[DPL94]], with masked array replacement.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replacing math domain checking in model [[DPL94]], with masked array replacement.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* First try to speed up model [[DPL94]].  This has not succeeded, since system test: Relax_disp.test_dpl94_data_to_dpl94 still fails.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* First try to speed up model [[DPL94]].  This has not succeeded, since system test: Relax_disp.test_dpl94_data_to_dpl94 still fails.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Trying to move some of the structures into its own part.  [https
+
* Trying to move some of the structures into its own part.  {{gna task link|
 
* The relaxation dispersion target function can now be set up when the optional frqs_H argument is None.  This allows the profiling scripts to run.
 
* The relaxation dispersion target function can now be set up when the optional frqs_H argument is None.  This allows the profiling scripts to run.
 
* More stability fixes for the relaxation dispersion target function initialisation.  The target function can now be initialised when the r1 and chemical_shift arguments are None.
 
* More stability fixes for the relaxation dispersion target function initialisation.  The target function can now be initialised when the r1 and chemical_shift arguments are None.
* Split the system test test_r1rho_kjaergaard into a setup function, and a test function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Split the system test test_r1rho_kjaergaard into a setup function, and a test function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Renamed system test test_r1rho_kjaergaard to test_r1rho_kjaergaard_auto.  This corresponds to the use of the automatic analysis method.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Renamed system test test_r1rho_kjaergaard to test_r1rho_kjaergaard_auto.  This corresponds to the use of the automatic analysis method.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Split system test test_r1rho_kjaergaard into test_r1rho_kjaergaard_auto and test_r1rho_kjaergaard_man.  This is to test use of the manual way to analyse.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Split system test test_r1rho_kjaergaard into test_r1rho_kjaergaard_auto and test_r1rho_kjaergaard_man.  This is to test use of the manual way to analyse.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified all of [https://gna.org/users/tlinnet Troels'] dispersion profiling scripts to work with older relax versions.  This is in preparation for obtaining some powerful timing statistics.  The calls to the r2eff_*() functions are unnecessary and are the only failure point in the scripts between the current code in the disp_spin_speed branch and trunk or older versions of relax.  So these function calls have been eliminated.
+
* Modified all of {{relax developer link|username=tlinnet|text=Troels'}} dispersion profiling scripts to work with older relax versions.  This is in preparation for obtaining some powerful timing statistics.  The calls to the r2eff_*() functions are unnecessary and are the only failure point in the scripts between the current code in the disp_spin_speed branch and trunk or older versions of relax.  So these function calls have been eliminated.
* Implemented system test test_r1rho_kjaergaard_missing_r1, for safety check if R<sub>1</sub> data is not loaded.  The system test passes, so target function is safe.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented system test test_r1rho_kjaergaard_missing_r1, for safety check if R<sub>1</sub> data is not loaded.  The system test passes, so target function is safe.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Python 3 support for the dispersion profiling scripts.  The xrange() builtin function does not exist in Python 3, so this is now aliased to range() which is the same thing.
 
* Python 3 support for the dispersion profiling scripts.  The xrange() builtin function does not exist in Python 3, so this is now aliased to range() which is the same thing.
* Replaced double or triple hash-tags "##" with single hash-tags "#".  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replaced double or triple hash-tags "##" with single hash-tags "#".  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Copyright fixes for all the models, where [https://gna.org/users/tlinnet Troels E. Linnet] have made changes to make them functional for higher dimensional data.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Copyright fixes for all the models, where {{relax developer link|username=tlinnet|text=Troels E. Linnet}} have made changes to make them functional for higher dimensional data.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Copyright fix for model [[TSMFK01]].  [https://gna.org/users/semor Sebastien Morin] did not take part of implementing the [[TSMFK01]] model.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Copyright fix for model [[TSMFK01]].  {{relax developer link|username=semor|text=Sebastien Morin}} did not take part of implementing the [[TSMFK01]] model.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Created a super script for profiling the relaxation dispersion models.  This script will execute all of the current profiling scripts in the directory test_suite/shared_data/dispersion/profiling for both the current version of relax and any other specified version (current set to the [[relax 3.2.2|3.2.2 relax]] tag).  It will run the scripts and relax versions interleaved N=10 times and extract the func_*() target function call profile timings.  This interleaving makes the numbers much more consistent.  Averages and standard deviations are then calculated, as well as the speed up between the two relax versions.  The results are printed out in a format suitable for the relax release messages.
 
* Created a super script for profiling the relaxation dispersion models.  This script will execute all of the current profiling scripts in the directory test_suite/shared_data/dispersion/profiling for both the current version of relax and any other specified version (current set to the [[relax 3.2.2|3.2.2 relax]] tag).  It will run the scripts and relax versions interleaved N=10 times and extract the func_*() target function call profile timings.  This interleaving makes the numbers much more consistent.  Averages and standard deviations are then calculated, as well as the speed up between the two relax versions.  The results are printed out in a format suitable for the relax release messages.
 
* Increased the number of iterations to 1000 in all of the profiling scripts.  This is for better statistics in the disp_profile_all.py script, and makes the number consistent between the different models.
 
* Increased the number of iterations to 1000 in all of the profiling scripts.  This is for better statistics in the disp_profile_all.py script, and makes the number consistent between the different models.
 
* Added a log file for comparing the speed of the disp_speed_branch to [[relax 3.2.2]].  This is from the disp_profile_all.py statistics generating script.
 
* Added a log file for comparing the speed of the disp_speed_branch to [[relax 3.2.2]].  This is from the disp_profile_all.py statistics generating script.
* Made the processor.return_object get the back_calc structure in the expected order.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the processor.return_object get the back_calc structure in the expected order.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Fixed the ordering of the relax versions in the dispersion super profiling script disp_profile_all.py.  This has also been fixed in the disp_spin_speed branch to [[relax 3.2.2]] comparison log.
 
* Fixed the ordering of the relax versions in the dispersion super profiling script disp_profile_all.py.  This has also been fixed in the disp_spin_speed branch to [[relax 3.2.2]] comparison log.
 
* Added a log file for comparing the speed of the disp_speed_branch to [[relax 3.2.1]].  This is from the disp_profile_all.py statistics generating script.
 
* Added a log file for comparing the speed of the disp_speed_branch to [[relax 3.2.1]].  This is from the disp_profile_all.py statistics generating script.
Line 615: Line 616:
 
* Added a script for profiling the [[NS CPMG 2-site 3D]] relaxation dispersion model.  Again this only involved copying one of the other scripts and modifying a few variable and function names.
 
* Added a script for profiling the [[NS CPMG 2-site 3D]] relaxation dispersion model.  Again this only involved copying one of the other scripts and modifying a few variable and function names.
 
* Added the [[NS CPMG 2-site 3D]] model to the dispersion super profiling script.  To handle the fact that this script has nr_iter set to 100 rather than 1000 (as otherwise it is too slow), a list of scaling factors has been created to scale all timing numbers to equivalent values.
 
* Added the [[NS CPMG 2-site 3D]] model to the dispersion super profiling script.  To handle the fact that this script has nr_iter set to 100 rather than 1000 (as otherwise it is too slow), a list of scaling factors has been created to scale all timing numbers to equivalent values.
* Added [[DPL94]] profiling script.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added [[DPL94]] profiling script.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script for [[TSMFK01]], to use correct parameters k<sub>AB</sub> and R<sub>2A</sub><sup>0</sup>.  Or else, the lib functions is just calculating with zero?  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[TSMFK01]], to use correct parameters k<sub>AB</sub> and R<sub>2A</sub><sup>0</sup>.  Or else, the lib functions is just calculating with zero?  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changes to profiling script of [[NS CPMG 2-site expanded]].  The model does not have R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup>, but only R<sub>2</sub>.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changes to profiling script of [[NS CPMG 2-site expanded]].  The model does not have R<sub>2A</sub><sup>0</sup> and R<sub>2B</sub><sup>0</sup>, but only R<sub>2</sub>.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made changes to the profiling script of [[NS CPMG 2-site 3D]].  Need to use the full model, when r2a and r2b is specified.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made changes to the profiling script of [[NS CPMG 2-site 3D]].  Need to use the full model, when r2a and r2b is specified.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changes to profiling script of [[NS CPMG 2-site expanded]].  The unpacking can be removed.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changes to profiling script of [[NS CPMG 2-site expanded]].  The unpacking can be removed.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for the profiling script of [[NS CPMG 2-site 3D]].  The model should also be specified to full.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for the profiling script of [[NS CPMG 2-site 3D]].  The model should also be specified to full.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* The disp_profile_all.py super script now prints out the current relax version information.  This is so that the log files contain information about the repository revision and path.
 
* The disp_profile_all.py super script now prints out the current relax version information.  This is so that the log files contain information about the repository revision and path.
 
* Copied profiling script of [[DPL94]] to [[NS R1rho 2-site]].
 
* Copied profiling script of [[DPL94]] to [[NS R1rho 2-site]].
 
* Improved the final printout from the disp_profile_all.py dispersion model super profiling script.
 
* Improved the final printout from the disp_profile_all.py dispersion model super profiling script.
* Added profiling script for [[NS R1rho 2-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added profiling script for [[NS R1rho 2-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* The disp_profile_all.py dispersion model super profiling script is now executable.
 
* The disp_profile_all.py dispersion model super profiling script is now executable.
 
* Decreased all nr_iter values by 10 and added more dispersion models to the super profiling script.  This is for the dispersion model profiling scripts in test_suite/shared_data/dispersion/profiling/, all controlled by the disp_profile_all.py super profiling script for generating statistics using all of the other profiling scripts.  The number of iterations needed to be decreased as otherwise it would now take almost 1 day to generate the statistics table.
 
* Decreased all nr_iter values by 10 and added more dispersion models to the super profiling script.  This is for the dispersion model profiling scripts in test_suite/shared_data/dispersion/profiling/, all controlled by the disp_profile_all.py super profiling script for generating statistics using all of the other profiling scripts.  The number of iterations needed to be decreased as otherwise it would now take almost 1 day to generate the statistics table.
* Moved the parter conversion in [[LM63 3-site]] into the lib function.  This cleans up the target api function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the parter conversion in [[LM63 3-site]] into the lib function.  This cleans up the target api function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Copied profiling script for [[DPL94]] to [[TAP03]].
 
* Copied profiling script for [[DPL94]] to [[TAP03]].
 
* Copied profiling script for [[DPL94]] to [[TP02]].
 
* Copied profiling script for [[DPL94]] to [[TP02]].
 
* Copied profiling script for [[DPL94]] to [[MP05]].
 
* Copied profiling script for [[DPL94]] to [[MP05]].
 
* Copied profiling script for [[DPL94]] to [[M61]].
 
* Copied profiling script for [[DPL94]] to [[M61]].
* Modified profiling script for [[TAP03]] to be used.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[TAP03]] to be used.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script for [[TP02]], to be used.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[TP02]], to be used.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script for [[MP05]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[MP05]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script for [[M61]].  This is the last one.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[M61]].  This is the last one.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Expansion of the disp_profile_all.py dispersion model super profiling scripts.  The newly added profiling scripts for models [[M61]], [[TP02]], [[TAP03]], and [[MP05]] are now included in the super script to generate statistics for all of these as well.  The nr_iter variable has also been changed to match the other analytic models, so that the standard deviations are lowered and the statistics are better.
 
* Expansion of the disp_profile_all.py dispersion model super profiling scripts.  The newly added profiling scripts for models [[M61]], [[TP02]], [[TAP03]], and [[MP05]] are now included in the super script to generate statistics for all of these as well.  The nr_iter variable has also been changed to match the other analytic models, so that the standard deviations are lowered and the statistics are better.
* Moved the parameter conversion of [[MMQ CR72]] into lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the parameter conversion of [[MMQ CR72]] into lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the parameter conversions of k<sub>AB</sub>, k<sub>BA</sub> and p<sub>B</sub> into lib function of [[NS MMQ 2-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the parameter conversions of k<sub>AB</sub>, k<sub>BA</sub> and p<sub>B</sub> into lib function of [[NS MMQ 2-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the parameter conversion from target function to lib function for [[NS R1rho 2-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the parameter conversion from target function to lib function for [[NS R1rho 2-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Updated the dispersion model speed statistics for the disp_spin_speed branch vs. [[relax 3.2.2|relax-3.2.2]].  This now includes the [[NS CPMG 2-site 3D]], [[DPL94]], and [[NS R1rho 2-site]] dispersion models.  The timings for the single spin analyses are now comparable to the clustered analysis, as the equivalent of 100 single spins is being used.  The final printout is also in a better format to present for the relax release messages.  These new results show the insane 160x speed up of the [[DPL94]] model.
 
* Updated the dispersion model speed statistics for the disp_spin_speed branch vs. [[relax 3.2.2|relax-3.2.2]].  This now includes the [[NS CPMG 2-site 3D]], [[DPL94]], and [[NS R1rho 2-site]] dispersion models.  The timings for the single spin analyses are now comparable to the clustered analysis, as the equivalent of 100 single spins is being used.  The final printout is also in a better format to present for the relax release messages.  These new results show the insane 160x speed up of the [[DPL94]] model.
 
* Alignment improvements for the final printout from the dispersion model super profiling script.  The log file has been updated with what the new formatting will look like.
 
* Alignment improvements for the final printout from the dispersion model super profiling script.  The log file has been updated with what the new formatting will look like.
 
* Updated the model names in the dispersion model super profiling script.  The [[CR72]], [[B14]] and [[NS CPMG 2-site 3D]] models are the full, slower versions rather than the faster models with R<sub>2</sub><sup>0</sup> = R<sub>2A</sub><sup>0</sup> = R<sub>2B</sub><sup>0</sup>.  The log file has been updated to match.
 
* Updated the model names in the dispersion model super profiling script.  The [[CR72]], [[B14]] and [[NS CPMG 2-site 3D]] models are the full, slower versions rather than the faster models with R<sub>2</sub><sup>0</sup> = R<sub>2A</sub><sup>0</sup> = R<sub>2B</sub><sup>0</sup>.  The log file has been updated to match.
* Moved the parameter conversion for [[NS MMQ 3-site]] into lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the parameter conversion for [[NS MMQ 3-site]] into lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Updated the dispersion model profiling comparison of the disp_spin_speed branch vs. [[relax 3.2.2|relax-3.2.2]].  The [[M61]], [[TP02]], [[TAP03]], and [[MP05]] models are now included.  The final printout has been manually updated to reflect the newest version of the disp_profile_all.py super profiling script.
 
* Updated the dispersion model profiling comparison of the disp_spin_speed branch vs. [[relax 3.2.2|relax-3.2.2]].  The [[M61]], [[TP02]], [[TAP03]], and [[MP05]] models are now included.  The final printout has been manually updated to reflect the newest version of the disp_profile_all.py super profiling script.
* Moved the parameter conversion for [[NS R1rho 3-site]] into lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the parameter conversion for [[NS R1rho 3-site]] into lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Copied profiling script for [[CR72]], so there is now a normal and a full version.
 
* Copied profiling script for [[CR72]], so there is now a normal and a full version.
 
* Copied profiling for [[B14]] to normal and full model.
 
* Copied profiling for [[B14]] to normal and full model.
Line 652: Line 653:
 
* Copied profiling script for [[NS CPMG 2-site star]].
 
* Copied profiling script for [[NS CPMG 2-site star]].
 
* Copied profiling script for [[No Rex]].
 
* Copied profiling script for [[No Rex]].
* Modified profiling script for [[B14]], to R<sub>2A</sub><sup>0</sup>=R<sub>2B</sub><sup>0</sup>.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[B14]], to R<sub>2A</sub><sup>0</sup>=R<sub>2B</sub><sup>0</sup>.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented profiling script for [[NS CPMG 2-site 3D]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented profiling script for [[NS CPMG 2-site 3D]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented profiling script for [[NS CPMG 2-site star]] and [[NS CPMG 2-site star full|star full]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented profiling script for [[NS CPMG 2-site star]] and [[NS CPMG 2-site star full|star full]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Copied profiling script to be used for [[LM63]].
 
* Copied profiling script to be used for [[LM63]].
 
* Copied profiling script to model [[IT99]].
 
* Copied profiling script to model [[IT99]].
* Added profiling script for [[IT99]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added profiling script for [[IT99]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented profiling script for [[LM63]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented profiling script for [[LM63]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the "eta_scale = 2.0**(-3.0/2.0)" out of lib function for [[MMQ CR72]], since this is only needs to be computed once.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the "eta_scale = 2.0**(-3.0/2.0)" out of lib function for [[MMQ CR72]], since this is only needs to be computed once.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for spaces aroung "=" outside functions.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for spaces aroung "=" outside functions.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Critical fix for wrong space inserted in [[NS MMQ 3-site]] MQ.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Critical fix for wrong space inserted in [[NS MMQ 3-site]] MQ.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fixed the input for unit test of [[MMQ CR72]].  The number of input parameters has been lowered.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fixed the input for unit test of [[MMQ CR72]].  The number of input parameters has been lowered.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added additional math domain checking in [[B14]].  This is when v1c is less than 1.0.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added additional math domain checking in [[B14]].  This is when v1c is less than 1.0.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Comment fixing, for explaining the masking and replacing when &Delta;&omega; is zero.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Comment fixing, for explaining the masking and replacing when &Delta;&omega; is zero.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Copied profiling script to be used for profiling the use of higher dimensional data for the numpy eig function.
 
* Copied profiling script to be used for profiling the use of higher dimensional data for the numpy eig function.
* Implemented the collection of the 3D exchange matrix, for rank [NE][NS][NM][NO][ND][7][7].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented the collection of the 3D exchange matrix, for rank [NE][NS][NM][NO][ND][7][7].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented test, to see if 3D exchange matrices are the same.  This can be tested while running system test test_hansen_cpmg_data_to_ns_cpmg_2site_3D.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented test, to see if 3D exchange matrices are the same.  This can be tested while running system test test_hansen_cpmg_data_to_ns_cpmg_2site_3D.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Shifted the computation of Rexpo two loops up.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Shifted the computation of Rexpo two loops up.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added intermediate step with for loops.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added intermediate step with for loops.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added another intermediate step.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added another intermediate step.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added function to compute the matrix exponential for higher dimensional data of shape [NE][NS][NM][NO][ND][7][7].  This is done by using numpy.einsum, to make the dot product of the last two axis.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added function to compute the matrix exponential for higher dimensional data of shape [NE][NS][NM][NO][ND][7][7].  This is done by using numpy.einsum, to make the dot product of the last two axis.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Inserted intermediate step, to check if the matrix propagator to evolve the magnetization is equal when done for lower dimensional data of shape [7][7] and higher dimensional data of shape [NE][NS][NM][NO][ND][7][7].  A short example is shown at the wiki: http://wiki.nmr-relax.com/Numpy_linalg#Ellipsis_broadcasting_in_numpy.einsum.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Inserted intermediate step, to check if the matrix propagator to evolve the magnetization is equal when done for lower dimensional data of shape [7][7] and higher dimensional data of shape [NE][NS][NM][NO][ND][7][7].  A short example is shown at the wiki: http://wiki.nmr-relax.com/Numpy_linalg#Ellipsis_broadcasting_in_numpy.einsum.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Implemented double speed of model [[NS CPMG 2-site 3D]].  This is done by moving the costly calculation of the matrix exponential out of the for loops.  The trick was to find a method to do dot product of higher dimensions.  This was done with numpy.einsum, example at: http://wiki.nmr-relax.com/Numpy_linalg#Ellipsis_broadcasting_in_numpy.einsum.  Example: dot_V_W = einsum('...ij,...jk', V, W_exp_diag) where V, and W_exp_diag has shape [NE][NS][NM][NO][ND][7][7].  The profiling script shows a 2X speed up.
 
* Implemented double speed of model [[NS CPMG 2-site 3D]].  This is done by moving the costly calculation of the matrix exponential out of the for loops.  The trick was to find a method to do dot product of higher dimensions.  This was done with numpy.einsum, example at: http://wiki.nmr-relax.com/Numpy_linalg#Ellipsis_broadcasting_in_numpy.einsum.  Example: dot_V_W = einsum('...ij,...jk', V, W_exp_diag) where V, and W_exp_diag has shape [NE][NS][NM][NO][ND][7][7].  The profiling script shows a 2X speed up.
* Made notation consistent for variables, using "_i" to clarify extracted data from matrix.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made notation consistent for variables, using "_i" to clarify extracted data from matrix.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Moved the calculation the evolution matrix out of for loops.  The trick is that numpy.einsum allows for dot product of higher dimension:  The essential evolution matrix; This is a dot product of the outer [7][7] matrix of the Rexpo_mat and r180x_mat matrices, which have the shape [NE][NS][NM][NO][ND][7][7]; This can be achieved by using numpy einsum, and where ellipsis notation will use the last axis.
 
* Moved the calculation the evolution matrix out of for loops.  The trick is that numpy.einsum allows for dot product of higher dimension:  The essential evolution matrix; This is a dot product of the outer [7][7] matrix of the Rexpo_mat and r180x_mat matrices, which have the shape [NE][NS][NM][NO][ND][7][7]; This can be achieved by using numpy einsum, and where ellipsis notation will use the last axis.
* Implemented system test: test_cpmg_synthetic_b14_to_ns3d_cluster.  This is to catch failures of the model, when data is clustered.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented system test: test_cpmg_synthetic_b14_to_ns3d_cluster.  This is to catch failures of the model, when data is clustered.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed unused variables in [[NS CPMG 2-site 3D]], to clean up the code.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed unused variables in [[NS CPMG 2-site 3D]], to clean up the code.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added the NS matrices, rr1rho_3d_rankN, to collect the multi dimensional 3D exchange matrix, of rank [NE][NS][NM][NO][ND][6][6].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added the NS matrices, rr1rho_3d_rankN, to collect the multi dimensional 3D exchange matrix, of rank [NE][NS][NM][NO][ND][6][6].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added a check in [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_r1rho_2site-module.html lib/dispersion/ns_r1hro_2site.py], to see if the newly created multidimensional ns matrix of rank NE][NS][NM][NO][ND][6][6], is equal to the previous [6][6] matrix.  It is.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added a check in [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_r1rho_2site-module.html lib/dispersion/ns_r1hro_2site.py], to see if the newly created multidimensional ns matrix of rank NE][NS][NM][NO][ND][6][6], is equal to the previous [6][6] matrix.  It is.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added the relax_time to collection of rr1rho_3d_rankN matrix collection.  This is to pre-multiply all elements with the time.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added the relax_time to collection of rr1rho_3d_rankN matrix collection.  This is to pre-multiply all elements with the time.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added a check, that the pre- relax_time multiplied multidimensional array, equal the previous.  It does, to the sum of 1.0e<sup>-13</sup>.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added a check, that the pre- relax_time multiplied multidimensional array, equal the previous.  It does, to the sum of 1.0e<sup>-13</sup>.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the function use the new multidimensional R_mat matrix.  System test: test_tp02_data_to_ns_r1rho_2site still passes.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the function use the new multidimensional R_mat matrix.  System test: test_tp02_data_to_ns_r1rho_2site still passes.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix to the matrix_exponential_rankN, to return the exact exponential for any higher dimensional square matrix of shape [NE][NS][NM][NO][ND][X][X].  The fix was to the eye(X), to make the shape the same as the input shape.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix to the matrix_exponential_rankN, to return the exact exponential for any higher dimensional square matrix of shape [NE][NS][NM][NO][ND][X][X].  The fix was to the eye(X), to make the shape the same as the input shape.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Moved the costly calculation of the matrix exponential out of for loops.  It was the numpy.eig and numpy.inv which was draining power.  This speeds up model [[NS R1rho 2-site]], by a factor 4X.
 
* Moved the costly calculation of the matrix exponential out of for loops.  It was the numpy.eig and numpy.inv which was draining power.  This speeds up model [[NS R1rho 2-site]], by a factor 4X.
* Made the returned multidimensional rr1rho_3d_rankN, be of float64 type.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the returned multidimensional rr1rho_3d_rankN, be of float64 type.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Cleaned up the code of [[NS R1rho 2-site]], and removed the matrix argument to the function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Cleaned up the code of [[NS R1rho 2-site]], and removed the matrix argument to the function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed the parsing of a matrix to the lib function of [[NS R1rho 2-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the parsing of a matrix to the lib function of [[NS R1rho 2-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added the function "rcpmg_star_rankN" for the collection of the multidimensional relaxation matrix for model [[NS CPMG 2-site star]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added the function "rcpmg_star_rankN" for the collection of the multidimensional relaxation matrix for model [[NS CPMG 2-site star]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Insert check, that the newly created multidimensional matrix is the same.  They are, but only to the fifth digit.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Insert check, that the newly created multidimensional matrix is the same.  They are, but only to the fifth digit.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Started using the newly created multidimensional matrix.  test_hansen_cpmg_data_to_ns_cpmg_2site_star.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Started using the newly created multidimensional matrix.  test_hansen_cpmg_data_to_ns_cpmg_2site_star.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added the system test: test_cpmg_synthetic_b14_to_ns_star_cluster, to check for the model is still working after change.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added the system test: test_cpmg_synthetic_b14_to_ns_star_cluster, to check for the model is still working after change.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Started using the newly cR2 variable, extracted from higher dimensional data.  This should be okay, but system test test_hansen_cpmg_data_to_ns_cpmg_2site_star, now fails.
 
* Started using the newly cR2 variable, extracted from higher dimensional data.  This should be okay, but system test test_hansen_cpmg_data_to_ns_cpmg_2site_star, now fails.
* Changes of values to system test test_hansen_cpmg_data_to_ns_cpmg_2site_star.  The values are changed, since &chi;<sup>2</sup> is lower than before.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changes of values to system test test_hansen_cpmg_data_to_ns_cpmg_2site_star.  The values are changed, since &chi;<sup>2</sup> is lower than before.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the costly finding of matrix exponential out of for loops for eR_tcp.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the costly finding of matrix exponential out of for loops for eR_tcp.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Rearranged the code, to properly show the nested matrix exponentials in dot functions.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Rearranged the code, to properly show the nested matrix exponentials in dot functions.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the costly matrix_exponential of cR2 out of for loops.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the costly matrix_exponential of cR2 out of for loops.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Rearranged the dot code, for better view.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Rearranged the dot code, for better view.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Cleaned up the code in model [[NS CPMG 2-site star]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Cleaned up the code in model [[NS CPMG 2-site star]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Simplified model [[NS CPMG 2-site 3D]].  The expansion of matrices to higher dimensionality is not necessary.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Simplified model [[NS CPMG 2-site 3D]].  The expansion of matrices to higher dimensionality is not necessary.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Further cleaned up the code in [[NS CPMG 2-site star]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Further cleaned up the code in [[NS CPMG 2-site star]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed input of matrix, Rr, Rex, RCS and R to model [[NS CPMG 2-site star]].  These matrices is now extracted from NS matrix function rcpmg_star_rankN.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed input of matrix, Rr, Rex, RCS and R to model [[NS CPMG 2-site star]].  These matrices is now extracted from NS matrix function rcpmg_star_rankN.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented the collection of the multidimensional matrix m1 and m2 in model [[NS MMQ 2-site]].  Inserted also a check, that the newly computed matrix is equal.  They are, to the 6 digit.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented the collection of the multidimensional matrix m1 and m2 in model [[NS MMQ 2-site]].  Inserted also a check, that the newly computed matrix is equal.  They are, to the 6 digit.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Started using the newly created multidimensional m1 and m2 matrices.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Started using the newly created multidimensional m1 and m2 matrices.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the costly calculation of matrix_exponential of M1 and M2 out of for loop, in model ns_mmq_2site_mq.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the costly calculation of matrix_exponential of M1 and M2 out of for loop, in model ns_mmq_2site_mq.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the function matrix_exponential_rankN also find the exponential if the experiments indices are missing.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the function matrix_exponential_rankN also find the exponential if the experiments indices are missing.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for an extra axis inserted in eye function, when dimensionality is only [NS][NM][NO][ND].  This also fixes the index in the lib function of ns_mmq_2site_mq.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for an extra axis inserted in eye function, when dimensionality is only [NS][NM][NO][ND].  This also fixes the index in the lib function of ns_mmq_2site_mq.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented same functionality in mmq_2site_sq_dq_zq.  Problem, following system test fails: test_korzhnev_2005_15n_dq_data, test_korzhnev_2005_15n_mq_data, test_korzhnev_2005_15n_sq_data, test_korzhnev_2005_1h_mq_data, test_korzhnev_2005_1h_sq_data, test_korzhnev_2005_all_data, test_korzhnev_2005_all_data_disp_speed_bug.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented same functionality in mmq_2site_sq_dq_zq.  Problem, following system test fails: test_korzhnev_2005_15n_dq_data, test_korzhnev_2005_15n_mq_data, test_korzhnev_2005_15n_sq_data, test_korzhnev_2005_1h_mq_data, test_korzhnev_2005_1h_sq_data, test_korzhnev_2005_all_data, test_korzhnev_2005_all_data_disp_speed_bug.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed grid search, and lowered number of iterations for system test: test_cpmg_synthetic_b14_to_ns3d_cluster, test_cpmg_synthetic_b14_to_ns_star_cluster.  This is to speed them up, since they before took 30 seconds.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed grid search, and lowered number of iterations for system test: test_cpmg_synthetic_b14_to_ns3d_cluster, test_cpmg_synthetic_b14_to_ns_star_cluster.  This is to speed them up, since they before took 30 seconds.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Fix for ns_mmq_2site_mq.  Variable was wrong called.  There seems to be a serious problem more with MQ.
 
* Fix for ns_mmq_2site_mq.  Variable was wrong called.  There seems to be a serious problem more with MQ.
* Reinserted old code.  This fixes: test_korzhnev_2005_15n_mq_data.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Reinserted old code.  This fixes: test_korzhnev_2005_15n_mq_data.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Forcing the dtype to be complex64, instead of complex128.  This solves a range of system tests.  The one who fails now is: test_korzhnev_2005_15n_zq_data, test_korzhnev_2005_1h_mq_data, test_korzhnev_2005_1h_sq_data.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Forcing the dtype to be complex64, instead of complex128.  This solves a range of system tests.  The one who fails now is: test_korzhnev_2005_15n_zq_data, test_korzhnev_2005_1h_mq_data, test_korzhnev_2005_1h_sq_data.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Forces complex64 in ns_mmq_2site_sq_dq_zq instead complex128.  This fixes system tests: test_korzhnev_2005_15n_zq_data,test_korzhnev_2005_1h_sq_data.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Forces complex64 in ns_mmq_2site_sq_dq_zq instead complex128.  This fixes system tests: test_korzhnev_2005_15n_zq_data,test_korzhnev_2005_1h_sq_data.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Force complex64 in ns_mmq_2site_mq.  This solves all system tests.  Forcing to be complex64, does not seems like a long standing solution, since complex128 is possible.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Force complex64 in ns_mmq_2site_mq.  This solves all system tests.  Forcing to be complex64, does not seems like a long standing solution, since complex128 is possible.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for using the old matrix_exponential of m1.  One: test_korzhnev_2005_15n_sq_data is still failing.  That still uses the matrix_exponential_rankN.  There seems to be a problem with matrix_exponential_rankN, when doing complex numbers.  Maybe the dtype has to get fixed?  Use it as a input argument?  It must be the einsum.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for using the old matrix_exponential of m1.  One: test_korzhnev_2005_15n_sq_data is still failing.  That still uses the matrix_exponential_rankN.  There seems to be a problem with matrix_exponential_rankN, when doing complex numbers.  Maybe the dtype has to get fixed?  Use it as a input argument?  It must be the einsum.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added the "dtype" argument to function matrix_exponential_rankN.  This is to force the conversion of dtype, if they are of other type.  This can be conversion from complex128 to complex64.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added the "dtype" argument to function matrix_exponential_rankN.  This is to force the conversion of dtype, if they are of other type.  This can be conversion from complex128 to complex64.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix the bug: "M2_i = M1_mat", which was causing the problems getting system tests to pass.  Removed the specifications of which dtype, the initial matrices are created.  They can be converted later, with the specification of dtype to matrix_exponential_rankN().  All system tests now pass.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix the bug: "M2_i = M1_mat", which was causing the problems getting system tests to pass.  Removed the specifications of which dtype, the initial matrices are created.  They can be converted later, with the specification of dtype to matrix_exponential_rankN().  All system tests now pass.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the Bloch-McConnell matrix for 2-site exchange into [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_matrices-module.html lib/dispersion/ns_matrices.py].  This is for consistency with the other code.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the Bloch-McConnell matrix for 2-site exchange into [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_matrices-module.html lib/dispersion/ns_matrices.py].  This is for consistency with the other code.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the matrices for Bloch-McConnell from lib ns_mmq_2site, since they are now defined in [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_matrices-module.html ns_matrices.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the matrices for Bloch-McConnell from lib ns_mmq_2site, since they are now defined in [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_matrices-module.html ns_matrices.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the Bloch-McConnell matrix for 3-site exchange, into the [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_matrices-module.html lib/dispersion/ns_matrices.py].  This is to standardize the code.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the Bloch-McConnell matrix for 3-site exchange, into the [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_matrices-module.html lib/dispersion/ns_matrices.py].  This is to standardize the code.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed m1 and m2 to be sent to lib function of [[NS MMQ 2-site]], since they are now populated inside the lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed m1 and m2 to be sent to lib function of [[NS MMQ 2-site]], since they are now populated inside the lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented the Bloch-McConnell matrix for 3-site exchange, for multidimensional data.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented the Bloch-McConnell matrix for 3-site exchange, for multidimensional data.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Inserted a check, that the new higher dimensional m1 and m2 matrices are equal to before.  They are, to the 5 digit.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Inserted a check, that the new higher dimensional m1 and m2 matrices are equal to before.  They are, to the 5 digit.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Started using the newly created higher dimensional Bloch-McConnell matrix for 3-site exchange.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Started using the newly created higher dimensional Bloch-McConnell matrix for 3-site exchange.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the calculation of the matrix exponential out of for loops for [[NS MMQ 3-site]] MQ.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the calculation of the matrix exponential out of for loops for [[NS MMQ 3-site]] MQ.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Converted [[NS MMQ 3-site]]/SQ/DQ/ZQ to calculate the matrix exponential out of the for loops.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Converted [[NS MMQ 3-site]]/SQ/DQ/ZQ to calculate the matrix exponential out of the for loops.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed the complex64 to be used as dtype in matrix exponential.  Fix for missing "_i" in variable.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the complex64 to be used as dtype in matrix exponential.  Fix for missing "_i" in variable.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed m1 and m2 to be sent to target function of ns_mmq_3site_chi2.  They are now populated inside the lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed m1 and m2 to be sent to target function of ns_mmq_3site_chi2.  They are now populated inside the lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Documentation and input fix for [[NS MMQ 2-site]].  The m1 and m2 matrices are populated inside the lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Documentation and input fix for [[NS MMQ 2-site]].  The m1 and m2 matrices are populated inside the lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Renamed some numerical matrices, to get consistency in naming.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Renamed some numerical matrices, to get consistency in naming.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented multidimensional [[NS R1rho 3-site]] exchange matrix.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented multidimensional [[NS R1rho 3-site]] exchange matrix.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Inserted check, that newly multi dimensional matrix is equal the old.  It is, to the 13 digit.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Inserted check, that newly multi dimensional matrix is equal the old.  It is, to the 13 digit.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Started using the newly multidimensional 3D exchange matrix.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Started using the newly multidimensional 3D exchange matrix.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved the calculation of the matrix exponential out of the for loops for [[NS R1rho 3-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved the calculation of the matrix exponential out of the for loops for [[NS R1rho 3-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed the parameter "matrix" to be send to lib function of [[NS R1rho 3-site]], since it is now populated inside the lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed the parameter "matrix" to be send to lib function of [[NS R1rho 3-site]], since it is now populated inside the lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved parameter conversion for [[NS R1rho 3-site]] inside lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved parameter conversion for [[NS R1rho 3-site]] inside lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Cleaned up the Dispersion class target function, for creation of matrices, which is now populated inside the lib functions instead.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Cleaned up the Dispersion class target function, for creation of matrices, which is now populated inside the lib functions instead.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed p<sub>A</sub> and p<sub>B</sub> from the matrix population function rcpmg_star_rankN, since they are not used.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed p<sub>A</sub> and p<sub>B</sub> from the matrix population function rcpmg_star_rankN, since they are not used.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed p<sub>A</sub> and p<sub>B</sub> from the matrix population function rr1rho_3d_2site_rankN, since they are not used.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed p<sub>A</sub> and p<sub>B</sub> from the matrix population function rr1rho_3d_2site_rankN, since they are not used.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Documentation fix for the dimensionality for model [[NS R1rho 2-site]].  The data is lined up to be of form [NE][NS][NM][NO][ND].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Documentation fix for the dimensionality for model [[NS R1rho 2-site]].  The data is lined up to be of form [NE][NS][NM][NO][ND].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed p<sub>A</sub>, p<sub>B</sub> and p<sub>C</sub> from the matrix population function rr1rho_3d_3site_rankN, since they are not used.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed p<sub>A</sub>, p<sub>B</sub> and p<sub>C</sub> from the matrix population function rr1rho_3d_3site_rankN, since they are not used.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Deleted the profiling of eig function profiling script.  This was never implemented.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Deleted the profiling of eig function profiling script.  This was never implemented.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* For all profiling scripts, added conversion to numpy array for CPMG frqs and spin_lock, since some models complained in [[relax 3.2.2|3.2.2]], that they were of list types.  Also fixed [[IT99]] to only have 1 spin, since clustering is broken in [[relax 3.2.2|3.2.2]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* For all profiling scripts, added conversion to numpy array for CPMG frqs and spin_lock, since some models complained in [[relax 3.2.2|3.2.2]], that they were of list types.  Also fixed [[IT99]] to only have 1 spin, since clustering is broken in [[relax 3.2.2|3.2.2]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified super profiling script, to allow input to script, where alternative version of relax is positioned.  Collected the variables in a list of lists, for better overview.  Added a print out comment to [[IT99]], to remember the bug.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified super profiling script, to allow input to script, where alternative version of relax is positioned.  Collected the variables in a list of lists, for better overview.  Added a print out comment to [[IT99]], to remember the bug.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added comment field to super profiling script.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added comment field to super profiling script.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Math domain fix for [[NS CPMG 2-site expanded]].  This is when t108 or t112 is zero, in the multidimensional array, a division error occurs.  The elements are first set to 1.0, to allow for computation.  Then elements are later replaced with 1e<sup>100</sup>.  Lastly, if the elements are not part of the "True" dispersion point structure, they are cleaned out.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Math domain fix for [[NS CPMG 2-site expanded]].  This is when t108 or t112 is zero, in the multidimensional array, a division error occurs.  The elements are first set to 1.0, to allow for computation.  Then elements are later replaced with 1e<sup>100</sup>.  Lastly, if the elements are not part of the "True" dispersion point structure, they are cleaned out.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Precision lowering of system test, test_korzhnev_2005_15n_sq_data and test_korzhnev_2005_1h_sq_data.  The system tests does not fail on Linux 64-bit system, but only on Mac 32-bit system.  This is due to floating error deviations.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Precision lowering of system test, test_korzhnev_2005_15n_sq_data and test_korzhnev_2005_1h_sq_data.  The system tests does not fail on Linux 64-bit system, but only on Mac 32-bit system.  This is due to floating error deviations.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added log files for super profiling against tags [[relax 3.2.1|3.2.1]] and [[relax 3.2.2|3.2.2]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added log files for super profiling against tags [[relax 3.2.1|3.2.1]] and [[relax 3.2.2|3.2.2]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Copied lib.linear_algebra.matrix_exponential to [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib.dispersion.matrix_exponential].  The matrix exponential of higher dimensional data is only used in the dispersion part of relax.
 
* Copied lib.linear_algebra.matrix_exponential to [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib.dispersion.matrix_exponential].  The matrix exponential of higher dimensional data is only used in the dispersion part of relax.
* Added to __init__, the new [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib.dispersion.matrix_exponential module].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added to __init__, the new [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib.dispersion.matrix_exponential module].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added to unit_tests/_lib/_dispersion/__init__.py, the new unit test file: test_matrix_exponential.py.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added to unit_tests/_lib/_dispersion/__init__.py, the new unit test file: test_matrix_exponential.py.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added numpy array save files.  They are the numpy array structures, which are send in from system test: Relax_disp.test_hansen_cpmg_data_to_ns_cpmg_2site_3D.  These numpy array structures, are used in unit tests.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added numpy array save files.  They are the numpy array structures, which are send in from system test: Relax_disp.test_hansen_cpmg_data_to_ns_cpmg_2site_3D.  These numpy array structures, are used in unit tests.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added unit test unit_tests/_lib/_dispersion/test_matrix_exponential.py to test the matrix exponential from higher dimensional data.  lib.dispersion.matrix_exponential.matrix_exponential_rankN will match against lib.linear_algebra.matrix_exponential.  Data which is used for comparison, comes from system test: Relax_disp.test_hansen_cpmg_data_to_ns_cpmg_2site_3D.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added unit test unit_tests/_lib/_dispersion/test_matrix_exponential.py to test the matrix exponential from higher dimensional data.  lib.dispersion.matrix_exponential.matrix_exponential_rankN will match against lib.linear_algebra.matrix_exponential.  Data which is used for comparison, comes from system test: Relax_disp.test_hansen_cpmg_data_to_ns_cpmg_2site_3D.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Renamed function to return data in unit test _lib/_dispersion/test_matrix_exponential.py.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Renamed function to return data in unit test _lib/_dispersion/test_matrix_exponential.py.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix to [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib/dispersion/matrix_exponential.py], since the svn copy command was used on non-updated version of the file.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix to [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib/dispersion/matrix_exponential.py], since the svn copy command was used on non-updated version of the file.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added unit test for doing the matrix exponential for complex data.  This test shows, that the dtype=complex64, should be removed from [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_mmq_2site-module.html lib/dispersion/ns_mmq_2site.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added unit test for doing the matrix exponential for complex data.  This test shows, that the dtype=complex64, should be removed from [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_mmq_2site-module.html lib/dispersion/ns_mmq_2site.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added data for unit test for the testing of the matrix_exponential_rankN.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added data for unit test for the testing of the matrix_exponential_rankN.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Expanded the dispersion profiling master script to handle any two relax versions.  To compare two relax versions, for example [[relax 3.2.2|3.2.2]] to [[relax 3.2.1|3.2.1]], either the path1 and path2 variables or two command line arguments can be supplied.  The first path should be for the newest version.  This will allow for comparing the speed differences between multiple relax versions in the future.
 
* Expanded the dispersion profiling master script to handle any two relax versions.  To compare two relax versions, for example [[relax 3.2.2|3.2.2]] to [[relax 3.2.1|3.2.1]], either the path1 and path2 variables or two command line arguments can be supplied.  The first path should be for the newest version.  This will allow for comparing the speed differences between multiple relax versions in the future.
* Split matrix_exponential_rankN into matrix_exponential_rank_NE_NS_NM_NO_ND_x_x and matrix_exponential_rank_NS_NM_NO_ND_x_x.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Split matrix_exponential_rankN into matrix_exponential_rank_NE_NS_NM_NO_ND_x_x and matrix_exponential_rank_NS_NM_NO_ND_x_x.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Moved numerical solution matrices into the corresponding lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Moved numerical solution matrices into the corresponding lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Copied profiling scripts, to be used for 3-site models and MMQ models.
 
* Copied profiling scripts, to be used for 3-site models and MMQ models.
* Implemented profiling script for [[LM63 3-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented profiling script for [[LM63 3-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Improved the relax version printouts for the dispersion model master profiling script.  This now reports both relax versions.
 
* Improved the relax version printouts for the dispersion model master profiling script.  This now reports both relax versions.
 
* Removed a tonne of unused imports from the dispersion model profiling scripts.  This is to allow most of the scripts to run on the relax 3.1.x versions, as well as to clean up the scripts.  The unused imports were found using the command:  pylint test_suite/shared_data/dispersion/profiling/*.py --disable=all --enable=unused-import.
 
* Removed a tonne of unused imports from the dispersion model profiling scripts.  This is to allow most of the scripts to run on the relax 3.1.x versions, as well as to clean up the scripts.  The unused imports were found using the command:  pylint test_suite/shared_data/dispersion/profiling/*.py --disable=all --enable=unused-import.
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.2.1|3.2.1]] vs. [[relax 3.2.0|3.2.0]].  This is the output from the dispersion model profiling master script.  It shows a 2.2 times increase in speed for the [[B14]] and [[B14 full]] models, with all other models remaining at the same speed.  This matches the changes for relax [[relax 3.2.1|3.2.1]] (https://gna.org/forum/forum.php?forum_id=2462), the main feature of which is a major bugfix for the [[B14]] models.
+
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.2.1|3.2.1]] vs. [[relax 3.2.0|3.2.0]].  This is the output from the dispersion model profiling master script.  It shows a 2.2 times increase in speed for the [[B14]] and [[B14 full]] models, with all other models remaining at the same speed.  This matches the changes for relax [[relax 3.2.1|3.2.1]], the main feature of which is a major bugfix for the [[B14]] models.
 
* The 'relax -v' command is now used for the dispersion model profiling script initial printout.  This is to show the two different relax versions being compared.
 
* The 'relax -v' command is now used for the dispersion model profiling script initial printout.  This is to show the two different relax versions being compared.
 
* Modifications to the dispersion model profiling master script.  The info.print_sys_info() function of the current relax version is being called at the start to show all information about the current system.  This is useful to know the speed of the machine, the OS, the Python version and numpy version.  The numpy version is important as future versions might optimise certain functions that are currently very slow, hence that could be a cause of model speed differences.  In addition, the path variables path1 and path2 have been renamed to path_new and path_old to make it clearer which is which.  And the individual profiling scripts are no longer copied to the base directory of the relax versions being compared, and are run in place.
 
* Modifications to the dispersion model profiling master script.  The info.print_sys_info() function of the current relax version is being called at the start to show all information about the current system.  This is useful to know the speed of the machine, the OS, the Python version and numpy version.  The numpy version is important as future versions might optimise certain functions that are currently very slow, hence that could be a cause of model speed differences.  In addition, the path variables path1 and path2 have been renamed to path_new and path_old to make it clearer which is which.  And the individual profiling scripts are no longer copied to the base directory of the relax versions being compared, and are run in place.
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.2.2|3.2.2]] vs. [[relax 3.2.1|3.2.1]].  This is the output from the dispersion model profiling master script.  It shows that the relax [[relax 3.2.2|3.2.2]] release did not in fact feature "a large speed up of all analytic relaxation dispersion models" as described in the release notes at https://gna.org/forum/forum.php?forum_id=2465.  For the CPMG models there is a 1 to 2 times increase in speed.  But for the R<sub>1&rho;</sub> models, there is a 1 to 2 times decrease in speed.
+
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.2.2|3.2.2]] vs. [[relax 3.2.1|3.2.1]].  This is the output from the dispersion model profiling master script.  It shows that the relax [[relax 3.2.2|3.2.2]] release did not in fact feature "a large speed up of all analytic relaxation dispersion models" as described in the release notes at {{gna url|gna.org/forum/forum.php?forum_id=2465}}.  For the CPMG models there is a 1 to 2 times increase in speed.  But for the R<sub>1&rho;</sub> models, there is a 1 to 2 times decrease in speed.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.2.0|3.2.0]] vs. [[relax 3.1.7|3.1.7]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.2.0|3.2.0]] vs. [[relax 3.1.7|3.1.7]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.7|3.1.7]] vs. [[relax 3.1.6|3.1.6]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.7|3.1.7]] vs. [[relax 3.1.6|3.1.6]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
* Modified profiling script for [[NS R1rho 3-site]], to be functional.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[NS R1rho 3-site]], to be functional.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Modified profiling script for [[NS R1rho 3-site linear]] to be functional.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Modified profiling script for [[NS R1rho 3-site linear]] to be functional.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.3|3.1.3]] vs. [[relax 3.1.2|3.1.2]] vs. [[relax 3.1.1|3.1.1]].  This is the output from the dispersion model profiling master script.  It shows that there are no major speed differences between these relax versions.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.3|3.1.3]] vs. [[relax 3.1.2|3.1.2]] vs. [[relax 3.1.1|3.1.1]].  This is the output from the dispersion model profiling master script.  It shows that there are no major speed differences between these relax versions.
 
* Added the system information printout to the dispersion model profiling master script output.  This is for the log files comparing one version of relax to the previous version.
 
* Added the system information printout to the dispersion model profiling master script output.  This is for the log files comparing one version of relax to the previous version.
* Added profiling script for model [[MMQ CR72]],  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added profiling script for model [[MMQ CR72]],  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Fix for the replacement value for invalid values in model [[MMQ CR72]].  The value was set to use R<sub>2</sub><sup>0</sup>, but should instead be 1e<sup>100</sup>.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for the replacement value for invalid values in model [[MMQ CR72]].  The value was set to use R<sub>2</sub><sup>0</sup>, but should instead be 1e<sup>100</sup>.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Copied profiling script from [[MMQ CR72]], to [[NS MMQ 2-site]] and [[NS MMQ 3-site|3-site]].
 
* Copied profiling script from [[MMQ CR72]], to [[NS MMQ 2-site]] and [[NS MMQ 3-site|3-site]].
 
* Copied profiling script to [[NS MMQ 3-site linear]].
 
* Copied profiling script to [[NS MMQ 3-site linear]].
* Implemented profiling script for [[NS MMQ 2-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented profiling script for [[NS MMQ 2-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented profiling script for [[NS MMQ 3-site]] and [[NS MMQ 3-site linear|3-site linear]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented profiling script for [[NS MMQ 3-site]] and [[NS MMQ 3-site linear|3-site linear]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Speeded up model [[NS CPMG 2-site star]], by moving the forming of the propagator matrix out of the for loops, and preform it.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up model [[NS CPMG 2-site star]], by moving the forming of the propagator matrix out of the for loops, and preform it.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.4|3.1.4]] vs. [[relax 3.1.3|3.1.3]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.4|3.1.4]] vs. [[relax 3.1.3|3.1.3]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
* Speeded up [[NS MMQ 2-site]], by moving the forming of evolution matrix out of the for loops, and preform it.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up [[NS MMQ 2-site]], by moving the forming of evolution matrix out of the for loops, and preform it.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Speeded up [[NS MMQ 3-site]], by moving the forming of evolution matrix out of the for loops, and preform it.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up [[NS MMQ 3-site]], by moving the forming of evolution matrix out of the for loops, and preform it.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.5|3.1.5]] vs. [[relax 3.1.4|3.1.4]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.5|3.1.5]] vs. [[relax 3.1.4|3.1.4]].  This is the output from the dispersion model profiling master script.  It shows that there are no speed differences.
* Speeded up [[NS R1rho 2-site]], by preforming the evolution matrices, and the M0 matrix in the init part of the target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up [[NS R1rho 2-site]], by preforming the evolution matrices, and the M0 matrix in the init part of the target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Speeded up [[NS R1rho 3-site]], by preforming the evolution matrices, and the M0 matrix in the init part of the target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up [[NS R1rho 3-site]], by preforming the evolution matrices, and the M0 matrix in the init part of the target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Expanded the dispersion model profiling master script to cover all the new profiling scripts.  This includes all 3-site and MMQ models.  The list is now complete and covers all models.  The only model not included in [[M61 skew]] which has redundant parameters and is not optimisable anyway.
 
* Expanded the dispersion model profiling master script to cover all the new profiling scripts.  This includes all 3-site and MMQ models.  The list is now complete and covers all models.  The only model not included in [[M61 skew]] which has redundant parameters and is not optimisable anyway.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.6|3.1.6]] vs. [[relax 3.1.5|3.1.5]].  This is the output from the dispersion model profiling master script.  It shows that there are almost no speed differences, except for a slight decrease in speed in the [[CR72 full]] model for single spins.
 
* Added a relaxation dispersion model profiling log file for relax version [[relax 3.1.6|3.1.6]] vs. [[relax 3.1.5|3.1.5]].  This is the output from the dispersion model profiling master script.  It shows that there are almost no speed differences, except for a slight decrease in speed in the [[CR72 full]] model for single spins.
* Split system test test_tp02_data_to_ns_r1rho_2site into a setup and test part.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Split system test test_tp02_data_to_ns_r1rho_2site into a setup and test part.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Implemented a clustered version of system test test_tp02_data_to_ns_r1rho_2site.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Implemented a clustered version of system test test_tp02_data_to_ns_r1rho_2site.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Inserted an extremely interesting development in [[NS R1rho 2-site]].  If one do a transpose of M0, one can calculate all the matrix evolutions in the start via numpy einsum.  Since M0 is in higher a dimensions, one should not do a numpy transpose, but swap/roll the outer M0 6x1 axis.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Inserted an extremely interesting development in [[NS R1rho 2-site]].  If one do a transpose of M0, one can calculate all the matrix evolutions in the start via numpy einsum.  Since M0 is in higher a dimensions, one should not do a numpy transpose, but swap/roll the outer M0 6x1 axis.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Shortened the code dramatically for [[NS R1rho 2-site]].  It is possible to calculate all in "one" go, after having the transposed/rolled-back M0 magnetization.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Shortened the code dramatically for [[NS R1rho 2-site]].  It is possible to calculate all in "one" go, after having the transposed/rolled-back M0 magnetization.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Speeded up the code of [[NS R1rho 2-site]].  This was essential done to numpy einsum, and doing the dot operations in multiple dimensions.  It was though necessary to realize, that to do the proper dot product operations, the outer two axis if M0 should be swapped, by rolling the outer axis one back.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up the code of [[NS R1rho 2-site]].  This was essential done to numpy einsum, and doing the dot operations in multiple dimensions.  It was though necessary to realize, that to do the proper dot product operations, the outer two axis if M0 should be swapped, by rolling the outer axis one back.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Speeded up the code of [[NS R1rho 3-site]].  This was essential done to numpy einsum, and doing the dot operations in multiple dimensions.  It was though necessary to realize, that to do the proper dot product operations, the outer two axis if M0 should be swapped, by rolling the outer axis one back.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up the code of [[NS R1rho 3-site]].  This was essential done to numpy einsum, and doing the dot operations in multiple dimensions.  It was though necessary to realize, that to do the proper dot product operations, the outer two axis if M0 should be swapped, by rolling the outer axis one back.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* For model [[NS CPMG 2-site 3D]], the M0 matrix was preformed for higher dimensionality in init of target function.  The transposes/rolled axis versions was also initiated.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* For model [[NS CPMG 2-site 3D]], the M0 matrix was preformed for higher dimensionality in init of target function.  The transposes/rolled axis versions was also initiated.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Swapped the dot product position, when propagating the magnetisation in model [[NS CPMG 2-site 3D]].  This it to try to align to same method as in [[NS R1rho 2-site]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Swapped the dot product position, when propagating the magnetisation in model [[NS CPMG 2-site 3D]].  This it to try to align to same method as in [[NS R1rho 2-site]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Lowered the looping in [[NS CPMG 2-site 3D]], by preforming the initial dot product.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Lowered the looping in [[NS CPMG 2-site 3D]], by preforming the initial dot product.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Speeded up [[NS CPMG 2-site 3D]], by preforming the magnetisation.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Speeded up [[NS CPMG 2-site 3D]], by preforming the magnetisation.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Got rid of the inner evolution of the magnetization.  If the looping over the number of CPMG elements is given by the index l, and the initial magnetization has been formed, then the number of times for propagation of magnetization is l = power_si_mi_di-1.  If the magnetization matrix "Mint" has the index Mint_(i,k) and the evolution matrix has the index Evol_(k,j), i=1, k=7, j=7 then the dot product is given by: Sum_{k=1}^{k} Mint_(1,k) * Evol_(k,j) = D_(1, j).  The numpy einsum formula for this would be: einsum('ik,kj -> ij', Mint, Evol).  Following evolution will be: Sum_{k=1}^{k} D_(1, j) * Evol_(k,j) = Mint_(1,k) * Evol_(k,j) * Evol_(k,j).  We can then realize, that the evolution matrix can be raised to the power l.  Evol_P = Evol<sup>l</sup>.  It will then be: einsum('ik,kj -> ij', Mint, Evol_P).  Get which power to raise the matrix to.  l = power_si_mi_di-1.  Raise the square evolution matrix to the power l.  evolution_matrix_T_pwer_i = matrix_power(evolution_matrix_T_i, l),  Mint_T_i = dot(Mint_T_i, evolution_matrix_T_pwer_i) or Mint_T_i = einsum('ik,kj -> ij', Mint_T_i, evolution_matrix_T_pwer_i).  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Got rid of the inner evolution of the magnetization.  If the looping over the number of CPMG elements is given by the index l, and the initial magnetization has been formed, then the number of times for propagation of magnetization is l = power_si_mi_di-1.  If the magnetization matrix "Mint" has the index Mint_(i,k) and the evolution matrix has the index Evol_(k,j), i=1, k=7, j=7 then the dot product is given by: Sum_{k=1}^{k} Mint_(1,k) * Evol_(k,j) = D_(1, j).  The numpy einsum formula for this would be: einsum('ik,kj -> ij', Mint, Evol).  Following evolution will be: Sum_{k=1}^{k} D_(1, j) * Evol_(k,j) = Mint_(1,k) * Evol_(k,j) * Evol_(k,j).  We can then realize, that the evolution matrix can be raised to the power l.  Evol_P = Evol<sup>l</sup>.  It will then be: einsum('ik,kj -> ij', Mint, Evol_P).  Get which power to raise the matrix to.  l = power_si_mi_di-1.  Raise the square evolution matrix to the power l.  evolution_matrix_T_pwer_i = matrix_power(evolution_matrix_T_i, l),  Mint_T_i = dot(Mint_T_i, evolution_matrix_T_pwer_i) or Mint_T_i = einsum('ik,kj -> ij', Mint_T_i, evolution_matrix_T_pwer_i).  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Tried to implement using lib.linear_algebra.matrix_power.square_matrix_power instead of matrix_power from numpy in [[NS CPMG 2-site 3D]].  Strangely, then system test: test_hansen_cpmg_data_to_ns_cpmg_2site_3D_full starts to fail.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Tried to implement using lib.linear_algebra.matrix_power.square_matrix_power instead of matrix_power from numpy in [[NS CPMG 2-site 3D]].  Strangely, then system test: test_hansen_cpmg_data_to_ns_cpmg_2site_3D_full starts to fail.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Changes to unit test of [[NS CPMG 2-site 3D]].  This is after the new initiated M0 matrix in init of target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Changes to unit test of [[NS CPMG 2-site 3D]].  This is after the new initiated M0 matrix in init of target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Double speed in [[NS CPMG 2-site star]], after using numpy.linalg.matrix_power instead of the lib version in relax.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Double speed in [[NS CPMG 2-site star]], after using numpy.linalg.matrix_power instead of the lib version in relax.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Triple speed in [[NS MMQ 2-site]], after using numpy.linalg.matrix_power instead of the lib version in relax.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Triple speed in [[NS MMQ 2-site]], after using numpy.linalg.matrix_power instead of the lib version in relax.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Small fix for making sure that power is a integer in [[NS MMQ 2-site]].  Following system tests was failing:  Relax_disp.test_korzhnev_2005_15n_dq_data, Relax_disp.test_korzhnev_2005_15n_sq_data, Relax_disp.test_korzhnev_2005_15n_zq_data, Relax_disp.test_korzhnev_2005_1h_sq_data, Relax_disp.test_korzhnev_2005_all_data, Relax_disp.test_korzhnev_2005_all_data_disp_speed_bug.  They should already be integers, but is now solved.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Small fix for making sure that power is a integer in [[NS MMQ 2-site]].  Following system tests was failing:  Relax_disp.test_korzhnev_2005_15n_dq_data, Relax_disp.test_korzhnev_2005_15n_sq_data, Relax_disp.test_korzhnev_2005_15n_zq_data, Relax_disp.test_korzhnev_2005_1h_sq_data, Relax_disp.test_korzhnev_2005_all_data, Relax_disp.test_korzhnev_2005_all_data_disp_speed_bug.  They should already be integers, but is now solved.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Comment and spell fixing in [[NS CPMG 2-site 3D]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Comment and spell fixing in [[NS CPMG 2-site 3D]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Triple speed in [[NS MMQ 3-site]], after using numpy.linalg.matrix_power instead of the lib version in relax.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Triple speed in [[NS MMQ 3-site]], after using numpy.linalg.matrix_power instead of the lib version in relax.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Updated the dispersion model profiling comparison of the disp_spin_speed branch vs. [[relax 3.2.2|relax-3.2.2]].  This now includes all dispersion models.  This shows the large speed increases in the numeric and MMQ models recently obtained.  Note that something went wrong with the [[NS CPMG 2-site 3D full]] model for the clustered analysis, most times were around 24 seconds except for the first which was strangely 292 seconds.
 
* Updated the dispersion model profiling comparison of the disp_spin_speed branch vs. [[relax 3.2.2|relax-3.2.2]].  This now includes all dispersion models.  This shows the large speed increases in the numeric and MMQ models recently obtained.  Note that something went wrong with the [[NS CPMG 2-site 3D full]] model for the clustered analysis, most times were around 24 seconds except for the first which was strangely 292 seconds.
 
* Updated the relaxation dispersion model profiling log file for relax version [[relax 3.2.2|3.2.2]] vs. [[relax 3.2.1|3.2.1]].  This adds the MMQ and 3-site models to the log file.  The new information shows that there was a 4.2 times speed up for the [[MMQ CR72]] model between these two relax versions, both for single spins and clustered spins, a much greater improvement than any other of the models.
 
* Updated the relaxation dispersion model profiling log file for relax version [[relax 3.2.2|3.2.2]] vs. [[relax 3.2.1|3.2.1]].  This adds the MMQ and 3-site models to the log file.  The new information shows that there was a 4.2 times speed up for the [[MMQ CR72]] model between these two relax versions, both for single spins and clustered spins, a much greater improvement than any other of the models.
 
* Removed the now redundant disp_profile_all_3.2.2.table.txt dispersion model profiling table.  As the dispersion model profiling master script now covers all dispersion models, the output from this script produces this table exactly.  Therefore the end of the log files saved from running this script contains this table.
 
* Removed the now redundant disp_profile_all_3.2.2.table.txt dispersion model profiling table.  As the dispersion model profiling master script now covers all dispersion models, the output from this script produces this table exactly.  Therefore the end of the log files saved from running this script contains this table.
* Initiated lengthy profiling script, that shows that doing square numpy matrix_power on strided data, can speed up the calculation by factor 1.5.  The profiling script can quickly be turned into a unit test, and includes small helper functions to calculate how to stride through the data.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Initiated lengthy profiling script, that shows that doing square numpy matrix_power on strided data, can speed up the calculation by factor 1.5.  The profiling script can quickly be turned into a unit test, and includes small helper functions to calculate how to stride through the data.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* First try to implement function that will calculate the matrix exponential by striding through data.  Interestingly, it does not work.  These system tests will fail: test_hansen_cpmg_data_to_ns_cpmg_2site_3D, test_hansen_cpmg_data_to_ns_cpmg_2site_3D_full.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* First try to implement function that will calculate the matrix exponential by striding through data.  Interestingly, it does not work.  These system tests will fail: test_hansen_cpmg_data_to_ns_cpmg_2site_3D, test_hansen_cpmg_data_to_ns_cpmg_2site_3D_full.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Added matrix_power to the init file in [http://www.nmr-relax.com/api/3.3/lib.dispersion-module.html lib/dispersion].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Added matrix_power to the init file in [http://www.nmr-relax.com/api/3.3/lib.dispersion-module.html lib/dispersion].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Deleted the printout in dep_check.  The printouts are only used for the essential packages before calling sys.exit().  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Deleted the printout in dep_check.  The printouts are only used for the essential packages before calling sys.exit().  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Added the missing "self.num_exp" to target function.  Testing on older system, this was failing the system test.  It is a wonder how these lines in __init__ could be performed without this.
 
* Added the missing "self.num_exp" to target function.  Testing on older system, this was failing the system test.  It is a wonder how these lines in __init__ could be performed without this.
* Fix for unit test passing on old numpy systems.  The error was: ValueError: setting an array element with a sequence.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Fix for unit test passing on old numpy systems.  The error was: ValueError: setting an array element with a sequence.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* Expanded the dispersion target function class documentation.  The NE, NS, NM, NO, and ND notation is now explained.
 
* Expanded the dispersion target function class documentation.  The NE, NS, NM, NO, and ND notation is now explained.
 
* Added Ti and NT to the dispersion target function class documentation.
 
* Added Ti and NT to the dispersion target function class documentation.
 
* Slight speed up of the [[B14]] and [[B14 full]] dispersion models by minimising repetitive maths.
 
* Slight speed up of the [[B14]] and [[B14 full]] dispersion models by minimising repetitive maths.
* Initial try to write up a 2x2 matrix by closed form.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Initial try to write up a 2x2 matrix by closed form.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Made the validation check in profiling of matrix_power check all values.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Made the validation check in profiling of matrix_power check all values.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Replaced all self.spins with self.NS in target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replaced all self.spins with self.NS in target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Replaced all self.num_exp with self.NE in target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replaced all self.num_exp with self.NE in target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Replaced all self.num_frq with self.NM in target function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Replaced all self.num_frq with self.NM in target function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* A very small speed up to the [[CR72]] dispersion models by minimising repetitive maths operations.  The k<sub>BA</sub> and k<sub>AB</sub> rates are used to simplify the Psi calculation, dropping from 3 to 2 multiplications and removing a squaring operation.  The Dpos and Dneg value calculations have been simplified to drop one multiplication operation.  And the calculation of eta_scale / cpmg_frqs now only occurs once rather than twice.
 
* A very small speed up to the [[CR72]] dispersion models by minimising repetitive maths operations.  The k<sub>BA</sub> and k<sub>AB</sub> rates are used to simplify the Psi calculation, dropping from 3 to 2 multiplications and removing a squaring operation.  The Dpos and Dneg value calculations have been simplified to drop one multiplication operation.  And the calculation of eta_scale / cpmg_frqs now only occurs once rather than twice.
 
* Removal of a tonne of unused imports in the lib.dispersion package.  These were identified using the command "pylint * --disable=all --enable=unused-import".
 
* Removal of a tonne of unused imports in the lib.dispersion package.  These were identified using the command "pylint * --disable=all --enable=unused-import".
 
* A very small speed up to the [[MMQ CR72]] dispersion model by minimising repetitive maths operations.  This matches the recent change for the [[CR72]] model, though the Psi calculation was already using the fast form.
 
* A very small speed up to the [[MMQ CR72]] dispersion model by minimising repetitive maths operations.  This matches the recent change for the [[CR72]] model, though the Psi calculation was already using the fast form.
 
* Created a master profiling script for comparing the speed between different dispersion models.  This is similar to the disp_profile_all.py script except it only operates on a single relax version.  The output is then simply the timings, with statistics, of the calculation time for 100 function calls for 100 spins (either 10,000 function calls for single spins or 100 function calls for the cluster of 100 spins).  The output of the script for the current disp_spin_speed branch code has also been added.
 
* Created a master profiling script for comparing the speed between different dispersion models.  This is similar to the disp_profile_all.py script except it only operates on a single relax version.  The output is then simply the timings, with statistics, of the calculation time for 100 function calls for 100 spins (either 10,000 function calls for single spins or 100 function calls for the cluster of 100 spins).  The output of the script for the current disp_spin_speed branch code has also been added.
* Critical fix for the recalculation of tau cpmg, when plotting for numerical models.  The interpolated dispersion points with tau_cpmg was calculated with frq, instead of cpmg_frq.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Critical fix for the recalculation of tau cpmg, when plotting for numerical models.  The interpolated dispersion points with tau_cpmg was calculated with frq, instead of cpmg_frq.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
 
* The new dispersion model profiling master script now includes links to the relax wiki.  The models are no longer presented by name but rather by the relax wiki links for each model (see [[:Category:Relaxation dispersion analysis]] for all these links).  This is to improve the Google rank of the relax wiki, as these links may appear in a number of locations.
 
* The new dispersion model profiling master script now includes links to the relax wiki.  The models are no longer presented by name but rather by the relax wiki links for each model (see [[:Category:Relaxation dispersion analysis]] for all these links).  This is to improve the Google rank of the relax wiki, as these links may appear in a number of locations.
 
* Removal of many unused imports in the disp_spin_speed branch.  These were detected using the devel_scripts/find_unused_imports.py script which uses pylint to find all unused imports.  The false positives also present in the trunk were ignored.
 
* Removal of many unused imports in the disp_spin_speed branch.  These were detected using the devel_scripts/find_unused_imports.py script which uses pylint to find all unused imports.  The false positives also present in the trunk were ignored.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.b14-module.html lib/dispersion/b14.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.b14-module.html lib/dispersion/b14.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/cr72.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.cr72-module.html lib/dispersion/cr72.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.dpl94-module.html lib/dispersion/dpl94.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.dpl94-module.html lib/dispersion/dpl94.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.lm63_3site-module.html lib/dispersion/lm63_3site.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.lm63_3site-module.html lib/dispersion/lm63_3site.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.lm63-module.html lib/dispersion/lm63.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.lm63-module.html lib/dispersion/lm63.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.m61b-module.html lib/dispersion/m61b.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.m61b-module.html lib/dispersion/m61b.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.m61-module.html lib/dispersion/m61.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.m61-module.html lib/dispersion/m61.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib/dispersion/matrix_exponential].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.matrix_exponential-module.html lib/dispersion/matrix_exponential].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.mp05-module.html lib/dispersion/mp05.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.mp05-module.html lib/dispersion/mp05.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_cpmg_2site_expanded-module.html lib/dispersion/ns_cpmg_2site_expanded.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_cpmg_2site_expanded-module.html lib/dispersion/ns_cpmg_2site_expanded.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_cpmg_2site_star-module.html lib/dispersion/ns_cpmg_2site_star.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_cpmg_2site_star-module.html lib/dispersion/ns_cpmg_2site_star.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_mmq_2site-module.html lib/dispersion/ns_mmq_2site.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_mmq_2site-module.html lib/dispersion/ns_mmq_2site.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_mmq_3site-module.html lib/dispersion/ns_mmq_3site.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_mmq_3site-module.html lib/dispersion/ns_mmq_3site.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_r1rho_2site-module.html lib/dispersion/ns_r1rho_2site.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_r1rho_2site-module.html lib/dispersion/ns_r1rho_2site.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_r1rho_3site-module.html lib/dispersion/ns_r1rho_3site.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.ns_r1rho_3site-module.html lib/dispersion/ns_r1rho_3site.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.tap03-module.html lib/dispersion/tap03.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.tap03-module.html lib/dispersion/tap03.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.tp02-module.html lib/dispersion/tp02.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.tp02-module.html lib/dispersion/tp02.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.two_point-module.html lib/dispersion/two_point.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/lib.dispersion.two_point-module.html lib/dispersion/two_point.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Code validation of [http://www.nmr-relax.com/api/3.3/target_functions.relax_disp-module.html target_functions/relax_disp.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Code validation of [http://www.nmr-relax.com/api/3.3/target_functions.relax_disp-module.html target_functions/relax_disp.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* For model [[NS MMQ 3-site]], moved the parameter conversion of &Delta;&omega;<sub>AB</sub> from target function to lib function.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* For model [[NS MMQ 3-site]], moved the parameter conversion of &Delta;&omega;<sub>AB</sub> from target function to lib function.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed chi sum initialisation in func_ns_mmq_2site() as this is not used.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed chi sum initialisation in func_ns_mmq_2site() as this is not used.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Documentation fix for the [http://www.nmr-relax.com/api/3.3/target_functions.relax_disp.Dispersion-class.html#get_back_calc get_back_calc() function in target_function/relax_disp.py].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Documentation fix for the [http://www.nmr-relax.com/api/3.3/target_functions.relax_disp.Dispersion-class.html#get_back_calc get_back_calc() function in target_function/relax_disp.py].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Removed unnecessary repetitive calculation of k<sub>ex</sub><sup>2</sup> in model [[DPL94]].  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* Removed unnecessary repetitive calculation of k<sub>ex</sub><sup>2</sup> in model [[DPL94]].  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* API documentation fixes, where a "\" is the last character on the line.  There should be a space " ", ending this character.  [https://gna.org/task/?7807 Task #7807: Speed-up of dispersion models for clustered analysis].
+
* API documentation fixes, where a "\" is the last character on the line.  There should be a space " ", ending this character.  {{gna task link|7807|text=Task #7807: Speed-up of dispersion models for clustered analysis}}.
* Updated the [https://gna.org/forum/forum.php?forum_id=2472 minfx version number to 1.0.9] in the release checklist document.  This as of yet unreleased version contains an important fix for parallelised grid searches when the number of increments is set to one (i.e. a preset parameter).
+
* Updated the {{gna link|url=gna.org/forum/forum.php?forum_id=2472|text=minfx version number to 1.0.9}} in the release checklist document.  This as of yet unreleased version contains an important fix for parallelised grid searches when the number of increments is set to one (i.e. a preset parameter).
 
* Fix for the _prompt.test_align_tensor.Test_align_tensor.test_init_argfail_params unit test.  As the alignment tensor can now be initialised as None, the None value can be accepted and a different RelaxError is raised when the params argument is incorrectly supplied.
 
* Fix for the _prompt.test_align_tensor.Test_align_tensor.test_init_argfail_params unit test.  As the alignment tensor can now be initialised as None, the None value can be accepted and a different RelaxError is raised when the params argument is incorrectly supplied.
 
* Added a new set of icons for use with the minimisation user functions.  These are of the Rosenbrock function and are much better suited for small icons than the current OpenDX 3D isosurface plots.  The matplotlib figure originates from public domain code at http://commons.wikimedia.org/wiki/File:Rosenbrock_function.svg.
 
* Added a new set of icons for use with the minimisation user functions.  These are of the Rosenbrock function and are much better suited for small icons than the current OpenDX 3D isosurface plots.  The matplotlib figure originates from public domain code at http://commons.wikimedia.org/wiki/File:Rosenbrock_function.svg.
Line 924: Line 925:
 
* Created two new model-free system tests.  These are Mf.test_m0_grid_with_grid_search and Mf.test_m0_grid_vs_m1_with_grid_search.  Their aim is to better test the grid search in a model-free analysis when parameters are preset.
 
* Created two new model-free system tests.  These are Mf.test_m0_grid_with_grid_search and Mf.test_m0_grid_vs_m1_with_grid_search.  Their aim is to better test the grid search in a model-free analysis when parameters are preset.
 
* Converted the model-free analysis to the new grid bounds and scaling factor design.  The parameter object now registers the grid bounds and scaling factors for all of the model-free parameters.  This includes the functions rex_scaling() and rex_upper() in the specific_analyses.model_free.parameter_object module for calculating some of these values.  The base parameter object has also been updated as that is where the diffusion parameters are defined.  Here the da_lower() and da_upper() have been defined to handle the different Da value constraints.  The specific_analyses.model_free.parameters.assemble_scaling_matrix() function has been deleted as this is now provided by the upstream code in pipe_control.minimise.  And the API methods grid_search() and minimise() has been modified to accept the list of scaling matrices.  As the grid bounds and increments are now handled by the upstream pipe_control.minimise.grid_setup() function, the grid_search_config(), grid_search_diff_bounds() and grid_search_spin_bounds() functions in the specific_analyses.model_free.optimisation module were redundant and were deleted.  The new API function print_model_title() has been implemented to handle the grid search setup printouts.
 
* Converted the model-free analysis to the new grid bounds and scaling factor design.  The parameter object now registers the grid bounds and scaling factors for all of the model-free parameters.  This includes the functions rex_scaling() and rex_upper() in the specific_analyses.model_free.parameter_object module for calculating some of these values.  The base parameter object has also been updated as that is where the diffusion parameters are defined.  Here the da_lower() and da_upper() have been defined to handle the different Da value constraints.  The specific_analyses.model_free.parameters.assemble_scaling_matrix() function has been deleted as this is now provided by the upstream code in pipe_control.minimise.  And the API methods grid_search() and minimise() has been modified to accept the list of scaling matrices.  As the grid bounds and increments are now handled by the upstream pipe_control.minimise.grid_setup() function, the grid_search_config(), grid_search_diff_bounds() and grid_search_spin_bounds() functions in the specific_analyses.model_free.optimisation module were redundant and were deleted.  The new API function print_model_title() has been implemented to handle the grid search setup printouts.
* Modified the pipe_control.minimise.grid_setup() function for when no parameters are present.  For the current version of minfx to function correctly ([https://gna.org/forum/forum.php?forum_id=2471 1.0.8]), the lower, upper and inc values should be set to [] rather than None.
+
* Modified the pipe_control.minimise.grid_setup() function for when no parameters are present.  For the current version of minfx to function correctly ({{gna link|url=gna.org/forum/forum.php?forum_id=2471|text=1.0.8}}), the lower, upper and inc values should be set to [] rather than None.
 
* Fix for the lib.arg_check.is_num_or_num_tuple().  When the can_be_none flag is set to True, the tuple of None values is now considered valid.  This enable the [http://www.nmr-relax.com/manual/diffusion_tensor_init.html diffusion_tensor.init user function] to accept the spheroid tensor values of (None, None, None, None), and the ellipsoid tensor values as a tuple of 6 None.
 
* Fix for the lib.arg_check.is_num_or_num_tuple().  When the can_be_none flag is set to True, the tuple of None values is now considered valid.  This enable the [http://www.nmr-relax.com/manual/diffusion_tensor_init.html diffusion_tensor.init user function] to accept the spheroid tensor values of (None, None, None, None), and the ellipsoid tensor values as a tuple of 6 None.
 
* Fix for the _prompt.test_diffusion_tensor.Test_diffusion_tensor.test_init_argfail_params unit test.  As the diffusion tensor can now be initialised as None, the None value can be accepted and a different RelaxError is raised when the params argument is incorrectly supplied.
 
* Fix for the _prompt.test_diffusion_tensor.Test_diffusion_tensor.test_init_argfail_params unit test.  As the diffusion tensor can now be initialised as None, the None value can be accepted and a different RelaxError is raised when the params argument is incorrectly supplied.
Line 952: Line 953:
 
* Unit test fix for Mac OS X.  This is for the test_ns_mmq_2site_korzhnev_2005_15n_dq_data_complex128 test of test_suite.unit_tests._lib._dispersion.test_matrix_exponential.Test_matrix_exponential.  The tests no longer check for exact values, but use the assertAlmostEqual() calls instead.
 
* Unit test fix for Mac OS X.  This is for the test_ns_mmq_2site_korzhnev_2005_15n_dq_data_complex128 test of test_suite.unit_tests._lib._dispersion.test_matrix_exponential.Test_matrix_exponential.  The tests no longer check for exact values, but use the assertAlmostEqual() calls instead.
 
* Deleted the ancient optimisation_testing.py development script, as this no longer works and is of no use.
 
* Deleted the ancient optimisation_testing.py development script, as this no longer works and is of no use.
* Implemented the pipe_control.mol_res_spin.format_info_full() function.  This follows from http://thread.gmane.org/gmane.science.nmr.relax.scm/22522/focus=6534.  This is a verbose representation of the spin information which can be used for presenting to the user.  Functions for shorter string versions will also be of great use, for example as described by [https://gna.org/users/tlinnet Troels] at http://thread.gmane.org/gmane.science.nmr.relax.scm/22522/focus=6535.
+
* Implemented the pipe_control.mol_res_spin.format_info_full() function.  This follows from http://thread.gmane.org/gmane.science.nmr.relax.scm/22522/focus=6534.  This is a verbose representation of the spin information which can be used for presenting to the user.  Functions for shorter string versions will also be of great use, for example as described by {{relax developer link|username=tlinnet|text=Troels}} at http://thread.gmane.org/gmane.science.nmr.relax.scm/22522/focus=6535.
 
* Created a unit test for the pipe_control.mol_res_spin.format_info_full() function.  This comprehensive test covers all input argument combinations.
 
* Created a unit test for the pipe_control.mol_res_spin.format_info_full() function.  This comprehensive test covers all input argument combinations.
 
* Changed the behaviour of the pipe_control.structure.mass.pipe_centre_of_mass() function.  This function returns the CoM and optionally the mass of the structural data loaded into the current data pipe.  However it was matching the structural data to the molecule-residue-spin data structure and skipping spins that were deselected.  This illogical deselection part has been eliminated, as spins can be deselected for various analysis purposes and this should not change the CoM.  The deletion also significantly speeds up the function.
 
* Changed the behaviour of the pipe_control.structure.mass.pipe_centre_of_mass() function.  This function returns the CoM and optionally the mass of the structural data loaded into the current data pipe.  However it was matching the structural data to the molecule-residue-spin data structure and skipping spins that were deselected.  This illogical deselection part has been eliminated, as spins can be deselected for various analysis purposes and this should not change the CoM.  The deletion also significantly speeds up the function.
Line 1,281: Line 1,282:
 
* 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].
+
* 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.
+
* 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.
Line 1,291: Line 1,292:
 
* Created the [http://www.nmr-relax.com/api/3.3/test_suite.system_tests.structure-pysrc.html#Structure.test_create_diff_tensor_pdb Structure.test_create_diff_tensor_pdb system test].  This is to show the failure of the [http://www.nmr-relax.com/manual/structure_create_diff_tensor_pdb.html structure.create_diff_tensor_pdb user function] when no structural data is present.
 
* Created the [http://www.nmr-relax.com/api/3.3/test_suite.system_tests.structure-pysrc.html#Structure.test_create_diff_tensor_pdb Structure.test_create_diff_tensor_pdb system test].  This is to show the failure of the [http://www.nmr-relax.com/manual/structure_create_diff_tensor_pdb.html structure.create_diff_tensor_pdb user function] when no structural data is present.
 
* Created the [http://www.nmr-relax.com/api/3.3/test_suite.system_tests.structure-pysrc.html#Structure.test_create_diff_tensor_pdb2 Structure.test_create_diff_tensor_pdb2 system test].  This is to catch another situation leading to [https://gna.org/bugs/?22505 bug #22505, the failure of the structure.create_diff_tensor_pdb user function when no structural data is present].
 
* Created the [http://www.nmr-relax.com/api/3.3/test_suite.system_tests.structure-pysrc.html#Structure.test_create_diff_tensor_pdb2 Structure.test_create_diff_tensor_pdb2 system test].  This is to catch another situation leading to [https://gna.org/bugs/?22505 bug #22505, the failure of the structure.create_diff_tensor_pdb user function when no structural data is present].
* Added an optimisation script for the test_suite/shared_data/diffusion_tensor/ellipsoid relaxation data.  This is to help catch [https://gna.org/bugs/?22502 bug #22502, the geometric prolate diffusion representation does not align with axis in PDB], as reported by [https://gna.org/users/mab Martin Ballaschk].  The PDB files of the optimised tensor demonstrate exactly the same problem as seen in the files attached to the bug report.  The oblate and spherical diffusion tensor representations match that of the ellipsoid.  But the prolate axis and tensor orientation are both different from the ellipsoid as well as themselves.
+
* Added an optimisation script for the test_suite/shared_data/diffusion_tensor/ellipsoid relaxation data.  This is to help catch [https://gna.org/bugs/?22502 bug #22502, the geometric prolate diffusion representation does not align with axis in PDB], as reported by {{gna link|url=gna.org/users/mab|text=Martin Ballaschk}}.  The PDB files of the optimised tensor demonstrate exactly the same problem as seen in the files attached to the bug report.  The oblate and spherical diffusion tensor representations match that of the ellipsoid.  But the prolate axis and tensor orientation are both different from the ellipsoid as well as themselves.
 
* Updated the diffusion tensor PDB representation files.  This replaces the broken prolate representation with the corrected representation.
 
* Updated the diffusion tensor PDB representation files.  This replaces the broken prolate representation with the corrected representation.
 
* Deleted the duplicated [http://www.nmr-relax.com/api/3.3/test_suite.system_tests.structure-pysrc.html#Structure.test_create_diff_tensor_pdb Structure.test_create_diff_tensor_pdb system test].
 
* Deleted the duplicated [http://www.nmr-relax.com/api/3.3/test_suite.system_tests.structure-pysrc.html#Structure.test_create_diff_tensor_pdb Structure.test_create_diff_tensor_pdb system test].
Line 1,309: Line 1,310:
 
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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.
Line 1,325: Line 1,326:
 
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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].
+
* 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.
Line 1,345: Line 1,346:
 
* 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].
+
* 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].
+
* 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.
Line 1,359: Line 1,360:
 
* 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 1,386: 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 1,450: 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 1,472: 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 1,506: 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 1,515: 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 1,553: Line 1,554:
 
* Added a derivation of the R<sub>2eff</sub>/R<sub>1&rho;</sub> error estimate for the two-point measurement to the manual.  This is from http://thread.gmane.org/gmane.science.nmr.relax.devel/6929/focus=6993 and is for the rate uncertainty of a 2-parameter exponential curve when only two data points have been collected.  The derivation has been added to the dispersion chapter of the manual.
 
* Added a derivation of the R<sub>2eff</sub>/R<sub>1&rho;</sub> error estimate for the two-point measurement to the manual.  This is from http://thread.gmane.org/gmane.science.nmr.relax.devel/6929/focus=6993 and is for the rate uncertainty of a 2-parameter exponential curve when only two data points have been collected.  The derivation has been added to the dispersion chapter of the manual.
 
* Equation fixes for the two-point exponential error derivation in the dispersion chapter of the manual.
 
* Equation fixes for the two-point exponential error derivation in the dispersion chapter of the manual.
* Updated the minfx version numbers in the release checklist document.  The version is now [https://gna.org/forum/forum.php?forum_id=2475 1.0.10], which has not been released yet but will contain the implementation of the log-barrier constraint algorithm gradient and Hessian.
+
* Updated the minfx version numbers in the release checklist document.  The version is now {{gna link|url=gna.org/forum/forum.php?forum_id=2475|text=1.0.10}}, which has not been released yet but will contain the implementation of the log-barrier constraint algorithm gradient and Hessian.
 
* Fix for the minfx version checking logic in the dep_check module.  Now newer versions of minfx will be handled.
 
* Fix for the minfx version checking logic in the dep_check module.  Now newer versions of minfx will be handled.
 
* Fixes for the Relax_disp.test_estimate_r2eff_err system test.  The k<sub>ex</sub> parameter value checks have all been scaled by 1e<sup>-5</sup> to allow for a meaningful floating point number comparison.  The number of significant figures have also been scaled.  This allows the test to now pass on one 64-bit GNU/Linux system.
 
* Fixes for the Relax_disp.test_estimate_r2eff_err system test.  The k<sub>ex</sub> parameter value checks have all been scaled by 1e<sup>-5</sup> to allow for a meaningful floating point number comparison.  The number of significant figures have also been scaled.  This allows the test to now pass on one 64-bit GNU/Linux system.
Line 1,573: Line 1,574:
 
<section end=changes/>
 
<section end=changes/>
  
== Bugfixes ==
+
=== Bugfixes ===
  
 
<section begin=bugfixes/>
 
<section begin=bugfixes/>
Line 1,587: Line 1,588:
 
* Fix for [https://gna.org/bugs/?22505 bug #22505, the failure of the structure.create_diff_tensor_pdb user function when no structural data is present].  The solution was simple - the CoM of the representation is set to the origin when no structural data is present, and the check for the presence of data removed.
 
* Fix for [https://gna.org/bugs/?22505 bug #22505, the failure of the structure.create_diff_tensor_pdb user function when no structural data is present].  The solution was simple - the CoM of the representation is set to the origin when no structural data is present, and the check for the presence of data removed.
 
* Another fix for [https://gna.org/bugs/?22505 bug #22505, the failure of the structure.create_diff_tensor_pdb user function when no structural data is present].  Now the cdp.structure data structure is checked, when present, if it contains any data using its own empty() method.
 
* Another fix for [https://gna.org/bugs/?22505 bug #22505, the failure of the structure.create_diff_tensor_pdb user function when no structural data is present].  Now the cdp.structure data structure is checked, when present, if it contains any data using its own empty() method.
* Fix for [https://gna.org/bugs/?22502 bug #22502, the problem whereby the geometric prolate diffusion representation does not align with axis in PDB], as reported by [https://gna.org/users/mab Martin Ballaschk].  This problem was not the main prolate tensor axis, but that the geometric object needed to be rotated 90 degrees about the z-axis to bring the object and axis into the same frame.
+
* Fix for [https://gna.org/bugs/?22502 bug #22502, the problem whereby the geometric prolate diffusion representation does not align with axis in PDB], as reported by {{gna link|url=gna.org/users/mab|text=Martin Ballaschk}}.  This problem was not the main prolate tensor axis, but that the geometric object needed to be rotated 90 degrees about the z-axis to bring the object and axis into the same frame.
 
* 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/>
  
= Links =
+
== Links ==
  
 
<section begin=links/>
 
<section begin=links/>
Line 1,600: Line 1,601:
  
 
* [http://wiki.nmr-relax.com/Relax_3.3.0 Official release notes on the relax wiki].
 
* [http://wiki.nmr-relax.com/Relax_3.3.0 Official release notes on the relax wiki].
* [https://gna.org/forum/forum.php?forum_id=2476 Gna! news item].
+
* {{gna link|url=gna.org/forum/forum.php?forum_id=2476|text=Gna! news item}}.
 
* [http://article.gmane.org/gmane.science.nmr.relax.announce/58 Gmane mailing list archive].
 
* [http://article.gmane.org/gmane.science.nmr.relax.announce/58 Gmane mailing list archive].
* [https://mail.gna.org/public/relax-announce/2014-09/msg00000.html Local archives].
+
* [http://www.nmr-relax.com/mail.gna.org/public/relax-announce/2014-09/msg00000.html Local archives].
 
* [http://marc.info/?l=relax-announce&m=141013209125235&w=2 Mailing list ARChives (MARC)].
 
* [http://marc.info/?l=relax-announce&m=141013209125235&w=2 Mailing list ARChives (MARC)].
  
Line 1,608: Line 1,609:
 
<section end=links/>
 
<section end=links/>
  
= Announcements =
+
== Announcements ==
  
 
{{:relax release announcements}}
 
{{:relax release announcements}}
  
 
+
== See also ==
= See also =
 
  
 
* [http://www.nmr-relax.com/api/3.3/ The relax 3.3 API documentation]
 
* [http://www.nmr-relax.com/api/3.3/ The relax 3.3 API documentation]
 
{{:relax release see also}}
 
{{:relax release see also}}
 
[[Category:Relaxation dispersion analysis]]
 
[[Category:Relaxation dispersion analysis]]

Latest revision as of 20:34, 16 October 2020


Official relax releases
relax logo
relax version 3.3.0
Previous version Next version
← 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