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 (http://www.nmr-relax.com/manual/minimise_grid_zoom.html)
algorithm for use in all analysis types, expanded plotting
capabilities for R1rho values in the relaxation dispersion analysis
(http://www.nmr-relax.com/manual/relax_disp_plot_disp_curves.html),
the ability to optimise the R1 parameter in all off-resonance
dispersion models
(http://www.nmr-relax.com/manual/relax_disp_r1_fit.html), 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
(http://www.nmr-relax.com/manual/time.html), the value.copy user
function accepting a force argument for overwriting values
(http://www.nmr-relax.com/manual/value_copy.html), 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
(http://www.nmr-relax.com/manual/relax_disp_r2eff_err_estimate.html),
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
(http://www.nmr-relax.com/manual/minimise_calculate.html,
http://www.nmr-relax.com/manual/minimise_grid_search.html,
http://www.nmr-relax.com/manual/minimise_execute.html). 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 (http://www.nmr-relax.com/manual/bruker_read.html)
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
(http://www.nmr-relax.com/manual/structure_create_diff_tensor_pdb.html),
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/R1rho 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 (http://wiki.nmr-relax.com/Relax_3.2.3) compared to the
new 3.3.0 version:
100 single spins analysis (times in seconds):
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):
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 R1rho 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.
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).
The full list of changes is:
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 2**zoom_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 R1rho 2D graph plotting in the
relax_disp.plot_disp_curves user function as the X-axis can now be the
nu1 value, the effective field omega_eff, or the rotating frame title
angle. 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/R1rho 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:
* Improved model handling for the internal structural object.
The set_model() method has been added to allow either a model number
to be set to the first unnumbered model (in preparation for adding new
models) or to allow models to be renumbered. The logic of the
add_model() has also been changed. Rather than looping over all atoms
of the first model and copying them, which does not work due to the
model validity checks, the entire MolList (molecule list) data
structure is copied using copy.deepcopy() to make a perfect copy of
the structural data. The ModelList.add_item() method has also been
modified to return the newly added or numbered model. This is used by
the add_model() structural object method to obtain the model object.
* Updated the Mac OS X framework setting up instruction
document. New sections have been added for the nose and matplotlib
Python packages, as nose is needed for the numpy and scipy testing
frameworks and matplotlib might be a useful optional dependency in the
future. The mpy4py section has been updated to avoid the
non-framework fink version of mpicc which cannot produce universal
binaries. A few other parts also have small edits.
* Removed the Freecode section from the release checklist as
Freecode has been permanently shut down. The old relax links are
still there (http://freecode.com/projects/nmr-relax), but Freecode is
dead (http://freecode.com/about).
* Fix for the internal structural object
MolContainer.last_residue() method. This can now operate when no
structural information is present, returning 0 instead of resulting in
an IndexError.
* 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 old relax links are still there
(http://freecode.com/projects/nmr-relax), but Freecode is dead
(http://freecode.com/about).
* Updated the minfx version in the release checklist document
to 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.
* 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
minfx.grid.grid_split() function. The minfx function in the next
release (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 time user function now uses the chronometer 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.
* More docstring line wrapping removal from pipe_control.minimise.
* Bug fix for the parameter units descriptions. This only
affects a few rare parameters. The 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 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. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Initial try to alter the 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. Task #7807 (https://gna.org/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 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. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis. This indeed
shows that the efficiency has gone down.
* Added print out of chi2 to profile script. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the creation of special numpy structures outside
target function. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Modified profiling script to calculate correct values when
setting up R2eff values. This is to test, that the return of chi2
gets zero. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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 CR72.py. Task #7807 (https://gna.org/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. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Re-inserted safety checks in lin/dispersion/CR72.py file.
This is re-inserted for the rank_1 cases. This makes the unit-tests
pass again. Task #7807 (https://gna.org/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]. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made it easier to switch between single and cluster
reporting in profiling script. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Important fix for the creation of the multi dimensional pA
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)).
pA can have missing filled out values, when the number of dispersion
points are different per spectrometer frequency. Task #7807
(https://gna.org/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: Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Re-implemented safety checks in 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. Task #7807 (https://gna.org/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: Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task
#7807 (https://gna.org/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.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Created multi dimensional error and value numpy arrays.
This is to calculate the chi2 sum much faster. Reordered the loop
over missing data points, so it is only initiated if missing points is
detected. Task #7807 (https://gna.org/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 pA and kex array one level up. This saves a
lot of computation. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Changed all the creation of special numpy arrays to be of
float64 type. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Just a tiny little more speed, by removing temporary storage
of chi2 calculation. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Made copies of numpy arrays instead of creating from new.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Added a self.frqs_a as a multidimensional numpy array. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Small fix for the indices to the errors and values numpy
array. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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 dw, and then tile the arrays
according to the numpy structure. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the calculation of pA and kex out off all loops. This
was done by having two special 1/0 spin structure arrays. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed dw_frq_a numpy array, as it was not necessary. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed all looping over spin and spectrometer frequency.
This is the last loop! Wuhu. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Reordered arrays for beauty of code. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made the back_calc array be initiated as copy of the values
array. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Small edit to profiling script, to help bug finding. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fixed that arrays are correctly initiated with one or zero
values. Task #7807 (https://gna.org/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. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Needed to increase the relative tolerance when testing if pA
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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Restored profiling script to normal. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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,
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Yet another small improvement for the profiling script.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed the multi dimensional structure of pA. pA is not
multi-dimensional, and can just be multiplied with numpy arrays. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for testing of pA in lib function, when pA is just
float. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Modified unit tests, so pA is sent to target function as
float instead of array. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Removed the multi dimensional structure of kex. kex is not
multi-dimensional, and can just be multiplied with numpy arrays. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for testing of kex in lib function, when kex is just
float. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Modified unit tests, so kex is sent to target function as
float instead of array. Task #7807 (https://gna.org/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 kex 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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Increased the kex speed to 1e7 in clustered unit tests
cases. This is to demonstrate where there will be no excange. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added a multi-dimensional numpy array chi2 value calculation
function. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Called the newly created chi2 function to calculate for
multi dimensional numpy arrays. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Replaced self.spins_a with self.disp_struct. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made initialisation structures for dw. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Initial try to reshape dw faster. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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 dw. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Copied profiling script for CPMG model CR72 to R1rho DPL94
model. The framework of the script will be the same, but the data a
little different. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Started converting profiling script to DPL94. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Added the ei index, when creating the first dw_mask. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Reordered how the structures dw init structures are created.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Clearing the dw_struct before calculation. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Started using the new way of constructing dw. This is for
running system tests. Note, somewhere in the dw array, the
frequencies will be different between the two implementations. But
apparently, this does not matter. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Inserted temporary method to switch for profiling. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* First try to speed-up the old dw structure calculation.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Simplified calculation. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Yet another try to implement a fast dw structure method.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Implemented the fastest way to calculate the dw structure.
This uses the numpy ufunc multiply.outer function to create the outer
array, and then multiply with the frqs_structure. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Renamed dw temporary structure to generic structure. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Restructured the calculation of R20A and R20B to the most
efficient way. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Made the lib/CR72.py to a numpy multi dimensional numpy
array calculation. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Changed the catching when dw is zero, to use masked array.
Implemented backwards compatibility with unit tests. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Bugfix for testing if kex is zero. It was tested if kex was
equal 1.0. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Implemented masked replacement if fact is less that 1.0.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* 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.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed the slow allclose() function to test if R20A and
R20B is equal. It is MUCH faster to just subtract and check sum is
not 0.0. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Replaced the temporary variable R2eff with back_calc, and
used numpy subtract to speed up. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made the lib function into a pure numpy array calculation.
This requires, that r20a, r20b and dw has same dimension as the
dispersion points. Task #7807 (https://gna.org/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. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Removed the creation of an unnecessary structure by using
numpy multiply. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Moved the mask which finds where to replace values into the
__init__ function. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Copied profiling script for CR72 to B14 model. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Modified profiling script for the B14 model. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed the target function to handle the B14 model for
faster numpy computation. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Changed unit test for B14 to match numpy input requirement.
Task #7807 (https://gna.org/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. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Comment fix for finding when E0 is above 700 in lib function
of B14. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Removed use of "asarray", since the variables are already
arrays. Task #7807 (https://gna.org/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 R20A, R20B and dw. dw is tested for
zero, to return flat lines. It is faster to search in the smaller
numpy array, than the 5 dimensional dw array. This is for speed-up.
R20A and R20B 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. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made the lib function of CR72 accept the R20A, R20B and dw
of the original array. This is for speed-up. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed unit-tests, to send in the original R20A, R20B and
dw_orig to the testing of the lib function CR72. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed profiling script to send R20A, R20B and dw, as
original parameters to the lib function. Task #7807
(https://gna.org/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 dw. dw is tested for zero, to
return flat lines. It is faster to search in the smaller numpy array,
than the 5 dimensional dw array. This is for speed-up. These small
tricks are expected to give 5-10 pct. speed-up. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made the lib function of B14 accept dw of the original
array. This is for speed-up. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed profiling script to send dw as original parameters
to the lib function B14. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Copied profiling script for CR72 model to TSMFK01 model.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Modified profiling script to be used for model TSMFK01.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Modified target function for model TSMFK01, to send in dw as
original parameter. Task #7807 (https://gna.org/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. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Made unit tests for model TSMFK01 send in R20A and dw as a
numpy array. Task #7807 (https://gna.org/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.
* Replacing math domain checking in model DPL94, with masked
array replacement. Task #7807 (https://gna.org/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.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Trying to move some of the structures into its own part.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Fix for forgetting to multiply frqs to power 2. This was
found by inspecting all print out before and after implementation.
New implementation of DPL94 now passes all system and unit tests.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Moved the expansion of the R1 structure out of the for
loops. This is to speed-up the __init__ of the class of the target
function. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Moved the packing of errors and values out of for loop in
the __init__ class of target function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the multi dimensional expansion of inv_relax_times out
of for loop. This can be done for all structures, which does not have
missing points. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* For inv_relax_times, expanded one axis, and tiled up to NR
spins, before reshaping and blowing up to full structure. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the expansion of frqs out of for loops. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Documentation fix for description of input arrays to lib
functions. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Converted TAP03 model to use multi dimensional numpy arrays.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Made dw in unit tests of TAP03 be of numpy array. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Replaced the loop structure in target function of TAP03 with
numpy arrays. This makes the model faster. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Reordered the initialization structure of the special numpy
arrays. This was done in the init part of the target function of
relaxation dispersion. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Added model MODEL_TSMFK01 also get self.tau_cpmg calculated
in init part. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Methods to replace math domain errors in model TP02, has
been replaced with numpy masks. Documentation is also fixed. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for sending in dw as numpy array in unit tests of model
TP02. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Replaced target function for model TP02, to use higher
dimensional numpy array structures. That makes the model much faster.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Fix for adding model TP02 to part of init class to
initialize preparation of higher dimension numpy structures. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made the NOREX model a faster numpy array calculation. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed an unnecessary frq_struct in init of target
function. frqs can just be expanded, and back_calc is cleaned
afterwards with disp_struct. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Methods to replace math domain errors in model M61, has been
replaced with numpy masks. Documentation is also fixed. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for sending in r1rho_prime and phi_ex_scaled as numpy
array in unit tests of model M61. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Replaced target function for model M61, to use higher
dimensional numpy array structures. That makes the model much faster.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Methods to replace math domain errors in model M61b, has
been replaced with numpy masks. Documentation is also fixed. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for sending in r1rho_prime and dw as numpy array in unit
tests of model M61b. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Replaced target function for model M61b, to use higher
dimensional numpy array structures. That makes the model much faster.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed number of points to be send to lib function of model
TSMFK01. These are not used anymore. Also removed in corresponding
unit tests. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Removed number of points and pB to be send to lib function
of model TP02. Number of points are not used anymore. pB is
calculated in lib function instead. Also removed in corresponding
unit tests. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Removed number of points and pB to send to lib function of
model TP02. pB is calculated in lib function instead. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed number of points, pB, k_AB, k_BA to be send to lib
function of model B14. Number of points are not used anymore. pB is
calculated in lib function instead. k_AB, and k_BA are calculated in
lib functions instead. Fixed in target function. Fixed in lib
function. Fixed in corresponding unit tests. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for sending number of points in target function of
TSMFK01. This was removed in lib function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed number of points, pB, to be send to lib function of
model TAP03. Number of points are not used anymore. pB is calculated
in lib function instead. Fixed in target function. Fixed in lib
function. Fixed in corresponding unit tests. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed number of points, to be send to lib function of
model CR72. Number of points are not used anymore. Fixed in target
function. Fixed in lib function. Fixed in corresponding unit tests.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed number of points, to be send to lib function of
model DPL94. Number of points are not used anymore. Fixed in target
function. Fixed in lib function. Fixed in corresponding unit tests.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed number of points, to be send to lib function of
model M61. Number of points are not used anymore. Fixed in target
function. Fixed in lib function. Fixed in corresponding unit tests.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed number of points, to be send to lib function of
model M61b. Number of points are not used anymore. Fixed in target
function. Fixed in lib function. Fixed in corresponding unit tests.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Methods to replace math domain errors in model MP05, has
been replaced with numpy masks. Number of points has been removed, as
the masks utility replaces this. Calculation of pB, has been moved to
lib function for simplicity. Documentation is also fixed. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for sending in dw as numpy array in unit tests of model
MP05. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Replaced target function for model MP05, to use higher
dimensional numpy array structures. That makes the model much faster.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Methods to replace math domain errors in model LM63, has
been replaced with numpy masks. Number of points has been removed, as
the masks utility replaces this. Documentation is also fixed. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for sending in number of points in unit tests of model
LM63. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Replaced target function for model LM63, to use higher
dimensional numpy array structures. That makes the model much faster.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Fix for replacement of values with mask, when phi_ex is
zero. This can be spin specific. System test:
Relax_disp.test_hansen_cpmg_data_to_lm63 starts to fail: Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for sending in r20 and phi_ex as numpy array in unit
tests of LM63. This is after using masks as replacement. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* 1 digit decrease in parameter check in system test:
Relax_disp.test_hansen_cpmg_data_to_lm63. It is unknown, why this has
occurred. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Methods to replace math domain errors in model IT99, has
been replaced with numpy masks. Number of points has been removed, as
the masks utility replaces this. pB is now moved to be calculated
inside. This makes the lib function simpler. Documentation is also
fixed. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Fix for sending in r20 and dw as numpy array in unit tests
of IT99. This is after using masks as replacement. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Replaced target function for model IT99, to use higher
dimensional numpy array structures. That makes the model much faster.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Methods to replace math domain errors in model
ns_cpmg_2site_expanded, has been replaced with numpy masks. Number of
points has been removed, as the masks utility replaces this. pB is
now moved to be calculated inside. This makes the lib function
simpler. k_AB and k_BA is also now calculated here. Documentation is
also fixed. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Fix for sending in r20 and dw as numpy array in unit tests
of ns_cpmg_2site_expanded. This is after using masks as replacement.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Replaced target function for model ns_cpmg_2site_expanded,
to use higher dimensional numpy array structures. That makes the
model much faster. I cannot get system test:
Relax_disp.test_cpmg_synthetic_dx_map_points to pass. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for system test
Relax_disp.test_cpmg_synthetic_dx_map_points. By just copying
self.back_calc_a to self.back_calc, problem was solved. In
specific_analysis.relax_disp.optimisation in function
back_calc_r2eff(), the function gets the last values stores in the
class function. This is in "class
Disp_result_command(Result_command)" with self.back_calc = back_calc.
And back_calc_r2eff() have return model.back_calc. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Methods to replace math domain errors in model
ns_cpmg_2site_3d, has been replaced with numpy masks. Number of
points has been removed, as the masks utility replaces this. pB is
now moved to be calculated inside. This makes the lib function
simpler. k_AB and k_BA is also now calculated here. Magnetization
vector is also now filled in lib function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for unit tests of model NS CPMG 2-site 3D to the reduced
input to the lib function. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Change to the target function to the model NS CPMG 2-site 3D
to use the reduced input to the lib function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed linked matrix/vector inner products into chained dot
expressions. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Wrote the essential dot matrix up to be initiated earlier.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Lowered the number of dot iterations, by pre-prepare the dot
matrix another round. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Turned Mint vector into a 7,1 matrix, so dimensions fit with
evolution matrix. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Lowered the number of dot operations, by pre-preparing the
evolution matrix another round. The power is in system tests always
even. The trick to removing this for loop, would be to make a general
multi dot function. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Moved the bulk operation of model NS CPMG 2-site 3D into the
lib file. This is to keep the API clean. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed the unit test of NS CPMG 2-site 3D, after the input
to the function has changed. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Changed the target function for NS CPMG 2-site 3D. This
reflects the new API layout. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Changed the lib function of NS CPMG 2-site star, to get
input of dw and r20a+r20b of higher dimensional type. This is to move
the main operations from the target function to the lib function, and
make the API code clean and consistent. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed the target function of NS CPMG 2-site star, to
reflect the input to the function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made the dot evolution structure faster for NS CPMG 2-site
3D. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Implemented the BLAS method of dot product, which should be
faster. I cannot get the "out" argument to work. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Small fix for the dot method. But the out argument does not
work. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Implemented the dot method via blas. This needs a array
with one more axis. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Last try to use the out argument. In the last dotting loop,
the out argument wont work, no matter what I do. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Inner product fix in model NS CPMG 2-site 3D. Fix for
system tests: Relax_disp.test_cpmg_synthetic_ns3d_to_b14,
Relax_disp.test_cpmg_synthetic_ns3d_to_CR72,
Relax_disp.test_cpmg_synthetic_ns3d_to_CR72_noise_cluster. The number
of dotting with Mint, should correspond to the power. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Replaced the temporary structure self.frqs_a to self.frqs,
which works for all target functions. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Replaced the temporary structure self.cpmg_frqs_a to
self.cpmg_frqs, which works for all target functions. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Restructured all data structures into higher dimension in
target function. Fix for the input to the different models.
Restructured how to detect the number of offset and dispersion points.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Various index fixes, after the data structures have been
reordered. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Fix for unit test, where the dimension of points has to be
one lower. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Fix for plotting, since the back_calc now can hold more data
points that cpmg frequencies. This is because the numpy array has
been expanded to the maximum number of points. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented a frqs_squared calculation in the init of target
function. This is to speed up the calculations. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Restructured frqs_H to higher dimension in target function.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Moved the calculation of dw and dwH out of for loops for
model MMQ CR72. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Removed looping over spin and frequencies for model MMQ
CR72. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Temporary removed check for dw = 0.0 in MMQ CR72. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed number of points to be parsed to model MMQ CR72.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed power to be parsed to MMQ CR72, since it is not
used. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Changed MMQ CR72 to use multi dimensional data. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed unit test of MMQ CR72 to pass. dw needs to be of
numpy structure. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Moved the calculation of dw out of for loops for model NS
MMQ 2-site. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Modified lib function for NS MMQ 2-site, to have looping
over spins and frequencies inside lib function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fixed the use of higher dimensional data in NS MMQ 2-site SQ
DQ ZQ. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Fix for documentation in NS MMQ 2-site/SQ/DQ/ZQ/MQ. Now
explains which dimension data should be in. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Changed the reshaping of dw and dwH, since it is not
dependent on experiment. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Changed the calculation of inner product in model NS CPMG
2-site 3D. The out argument of numpy.dot is buggy, and should not be
used. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Added missing instances of cleaning the data. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Bug fix for model LM63 3-site. The index si has to be used
to extract data to lib function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Temporary added system test:
test_korzhnev_2005_all_data_disp_speed_bug. This makes a minimisation
with 1 iteration, and so will give the chi2 value at the preset
parameter values. This is chi2 value should give 162.5, but gives
74.7104. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Updated documentation on dimensionality of numpy array
num_points. They are in dimension [NE][NS][[NM][NO], where oi gives
the number of dispersion points at that offset. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for system test: test_korzhnev_2005_all_data. The
masking for replacing values was wrong. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the cleaning of data points and replacing of values of
out loop for model NS MMQ 2-site. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for structure cleaning and value replacing for model MMQ
CR72. System test: test_korzhnev_2005_all_data revealed how this
should be done properly. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Fix for system test
test_korzhnev_2005_all_data_disp_speed_bug. The precision is lowered,
and now matches the original system test. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Replaced index to numpy array from example [0][si][mi][oi]
to [0, si, mi, oi]. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* More replacing of numpy index. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Documentation fix, where a double bracket "[[" has been
copied into all lib functions. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* More fixes for numpy index in lib functions. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Restructured target function for NS MMQ 3-site to the new
API structure of higher dimensional data. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Reordered the lib function for NS MMQ 3-site to use higher
dimensional data. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Documentation fix for which dimensionality number of points
have. Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion
models for clustered analysis.
* Documentation fix for the initial data structure of
cpmg_frqs, spin_lock_nu1, r1. They were incorrect. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* First attempt to implement target function for NS R1rho
2-site. But it does not work yet. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* First attempt to implement lib function for NS R1rho 2-site.
But it does not work yet. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Fatal fix for calling inv_relax_time from relax_time
variable. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Removal of the temporary offset argument. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Documentation fix for the dimensionality of the input
arrays. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Implemented the target function for NS R1rho 3-site. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented the lib function for NS R1rho 3-site. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented target function for LM63 3-site. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented the lib function for LM63 3-site, for higher
dimensional data. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Removed the number of disp points in target function for
LM63 3-site, since it is no longer used, but have been replaced with
mask replacements. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Implemented class function in target class, to return
back_calc values as list of lists. This is the back and forth
conversion between the data structures implemented when gathering the
data, and the data send to the library function of higher
dimensionality. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Used the new class function: get_back_calc(), to get the
data in the right structures when interpolating for graphs. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed superfluous check, after the returned data is now in
right structure. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Made changes to the dir argument of system test
Relax_disp.test_r1rho_kjaergaard. This is to prepare for: sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff and sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1]. This is
also to test an expected bug, if R1 is not loaded. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* 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.
* Split the system test test_r1rho_kjaergaard into a setup
function, and a test function. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Modified all of 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 R1 data is not loaded. The system test passes, so
target function is safe. Task #7807 (https://gna.org/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.
* Replaced double or triple hash-tags "##" with single
hash-tags "#". Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Copyright fixes for all the models, where Troels E. Linnet
have made changes to make them functional for higher dimensional data.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Copyright fix for model TSMFK01. Sebastien Morin did not
take part of implementing the TSMFK01 model. Task #7807
(https://gna.org/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
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.
* 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. Task #7807 (https://gna.org/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.
* 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 profiling script for the 'NS CPMG 2-site expanded'
dispersion model. This is the model at
http://wiki.nmr-relax.com/NS_CPMG_2-site_expanded. The script was
copied from that of the CR72 model, and it only needed to be changed
in a few places. This is the first numeric model profiling script.
* Updated the profiling super script and log for the 'NS CPMG
2-site expanded' model. This is the model at
http://wiki.nmr-relax.com/NS_CPMG_2-site_expanded. This shows that
the single spin calculation is 1.8 times faster, and the cluster of
100 11.7 times faster, when compared to relax 3.2.2.
* Modified all of the dispersion model profiling scripts. The
single() function for timing the single spin target function speed has
been modified to include a second outer loop over 100 'spins'. This
means that the timing numbers are equivalent to the cluster timings,
as both are then over 100 spins. This now allows not only relax
version differences and model differences to be compared, but also the
non-clustered and clustered analysis speeds.
* Added a script for profiling the 'NS CPMG 2-site 3D'
relaxation dispersion model. This is the model at
http://wiki.nmr-relax.com/NS_CPMG_2-site_3D. 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 DPL94 profiling script. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Modified profiling script for TSMFK01, to use correct
parameters k_AB and r20a. Or else, the lib functions is just
calculating with zero? Task #7807 (https://gna.org/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 r20a and r20b, but only r2. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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.
* 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.
* Added profiling script for NS R1rho 2-site. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* 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.
* Moved the parter conversion in LM63 3-site into the lib
function. This cleans up the target api function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Copied profiling script for DPL94 to TAP03.
* Copied profiling script for DPL94 to TP02.
* Copied profiling script for DPL94 to MP05.
* Copied profiling script for DPL94 to M61.
* Modified profiling script for TAP03 to be used. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Modified profiling script for TP02, to be used. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Modified profiling script for MP05. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Modified profiling script for M61. This is the last one.
Task #7807 (https://gna.org/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.
* Moved the parameter conversion of MMQ CR72 into lib
function. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Moved the parameter conversions of k_AB, k_BA and pB into
lib function of NS MMQ 2-site. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. 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.
* 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 R20 =
R20A = R20B. The log file has been updated to match.
* Moved the parameter conversion for NS MMQ 3-site into lib
function. Task #7807 (https://gna.org/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. 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. Task #7807 (https://gna.org/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 for B14 to normal and full model.
* Created a text file suitable for use as part of the relax
release notes. This contains the statistically averaged profiling
information of the speed of the dispersion models in the
disp_spin_speed branch vs. relax-3.2.2. This file has been created so
that it can be used as part of the release notes for the version of
relax that contains the insane speed ups of this branch. This file
will be updated as new models are profiled and if any more speed ups
magically appear.
* Copied profiling script for NS CPMG 2-site 3D.
* Copied profiling script for NS CPMG 2-site star.
* Copied profiling script for No Rex.
* Modified profiling script for B14, to R20A=R20B. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented profiling script for NS CPMG 2-site 3D. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented profiling script for NS CPMG 2-site star and
star full. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Copied profiling script to be used for LM63.
* Copied profiling script to model IT99.
* Added profiling script for IT99. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented profiling script for LM63. Task #7807
(https://gna.org/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. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix for spaces aroung "=" outside functions. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Critical fix for wrong space inserted in NS MMQ 3-site MQ.
Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Comment fixing, for explaining the masking and replacing
when dw is zero. Task #7807 (https://gna.org/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.
* Implemented the collection of the 3D exchange matrix, for
rank [NE][NS][NM][NO][ND][7][7]. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Shifted the computation of Rexpo two loops up. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added intermediate step with for loops. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added another intermediate step. Task #7807
(https://gna.org/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.
Task #7807 (https://gna.org/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.
Task #7807 (https://gna.org/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.
* Made notation consistent for variables, using "_i" to
clarify extracted data from matrix. Task #7807
(https://gna.org/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.
* Implemented system test:
test_cpmg_synthetic_b14_to_ns3d_cluster. This is to catch failures of
the model, when data is clustered. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed unused variables in NS CPMG 2-site 3D, to clean up
the code. Task #7807 (https://gna.org/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].
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Added a check in 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. Task #7807 (https://gna.org/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. Task
#7807 (https://gna.org/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-13. Task #7807 (https://gna.org/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. Task
#7807 (https://gna.org/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. Task #7807
(https://gna.org/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.
* Made the returned multidimensional rr1rho_3d_rankN, be of
float64 type. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/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.
Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/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.
* Changes of values to system test
test_hansen_cpmg_data_to_ns_cpmg_2site_star. The values are changed,
since chi2 is lower than before. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the costly finding of matrix exponential out of for
loops for eR_tcp. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Rearranged the code, to properly show the nested matrix
exponentials in dot functions. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the costly matrix_exponential of cR2 out of for loops.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Rearranged the dot code, for better view. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Cleaned up the code in model NS CPMG 2-site star. Task
#7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Further cleaned up the code in NS CPMG 2-site star. Task
#7807 (https://gna.org/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. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Started using the newly created multidimensional m1 and m2
matrices. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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.
* Reinserted old code. This fixes:
test_korzhnev_2005_15n_mq_data. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task
#7807 (https://gna.org/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. Task #7807
(https://gna.org/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.
Task #7807 (https://gna.org/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. Task #7807 (https://gna.org/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. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Moved the Bloch-McConnell matrix for 2-site exchange into
lib/dispersion/ns_matrices.py. This is for consistency with the other
code. Task #7807 (https://gna.org/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 ns_matrices.py. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved the Bloch-McConnell matrix for 3-site exchange, into
the lib/dispersion/ns_matrices.py. This is to standardize the code.
Task #7807 (https://gna.org/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. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented the Bloch-McConnell matrix for 3-site exchange,
for multidimensional data. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Started using the newly created higher dimensional
Bloch-McConnell matrix for 3-site exchange. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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.
Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Renamed some numerical matrices, to get consistency in
naming. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Implemented multidimensional NS R1rho 3-site exchange
matrix. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Started using the newly multidimensional 3D exchange matrix.
Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Moved parameter conversion for NS R1rho 3-site inside lib
function. Task #7807 (https://gna.org/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. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Removed pA and pB from the matrix population function
rcpmg_star_rankN, since they are not used. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed pA and pB from the matrix population function
rr1rho_3d_2site_rankN, since they are not used. Task #7807
(https://gna.org/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].
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Removed pA, pB and pC from the matrix population function
rr1rho_3d_3site_rankN, since they are not used. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Deleted the profiling of eig function profiling script.
This was never implemented. Task #7807 (https://gna.org/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 3.2.2,
that they were of list types. Also fixed IT99 to only have 1 spin,
since clustering is broken in 3.2.2. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added comment field to super profiling script. Task #7807
(https://gna.org/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 1e100. Lastly, if the elements
are not part of the "True" dispersion point structure, they are
cleaned out. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added log files for super profiling against tags 3.2.1 and
3.2.2. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Copied lib.linear_algebra.matrix_exponential to
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 lib.dispersion.matrix_exponential
module. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Renamed function to return data in unit test
_lib/_dispersion/test_matrix_exponential.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Fix to lib/dispersion/matrix_exponential.py, since the svn
copy command was used on non-updated version of the file. Task #7807
(https://gna.org/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 lib/dispersion/ns_mmq_2site.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added data for unit test for the testing of the
matrix_exponential_rankN. Task #7807 (https://gna.org/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
3.2.2 to 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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Moved numerical solution matrices into the corresponding lib
function. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Copied profiling scripts, to be used for 3-site models and MMQ
models.
* Implemented profiling script for LM63 3-site. Task #7807
(https://gna.org/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.
* 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 3.2.1 vs. 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 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.
* 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.
* Added a relaxation dispersion model profiling log file for
relax version 3.2.2 vs. 3.2.1. This is the output from the dispersion
model profiling master script. It shows that the relax 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 R1rho models,
there is a 1 to 2 times decrease in speed.
* Added a relaxation dispersion model profiling log file for
relax version 3.2.0 vs. 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 3.1.7 vs. 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. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Modified profiling script for NS R1rho 3-site linear to be
functional. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Added a relaxation dispersion model profiling log file for
relax version 3.1.3 vs. 3.1.2 vs. 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 profiling script for model MMQ CR72, Task #7807
(https://gna.org/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 r20, but should instead be 1e100.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Copied profiling script from MMQ CR72, to NS MMQ 2-site and 3-site.
* Copied profiling script to NS MMQ 3-site linear.
* Implemented profiling script for NS MMQ 2-site. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Implemented profiling script for NS MMQ 3-site and 3-site
linear. Task #7807 (https://gna.org/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. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added a relaxation dispersion model profiling log file for
relax version 3.1.4 vs. 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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added a relaxation dispersion model profiling log file for
relax version 3.1.5 vs. 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.
Task #7807 (https://gna.org/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.
Task #7807 (https://gna.org/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.
* Added a relaxation dispersion model profiling log file for
relax version 3.1.6 vs. 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. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Implemented a clustered version of system test
test_tp02_data_to_ns_r1rho_2site. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807
(https://gna.org/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. Task #7807 (https://gna.org/task/?7807):
Speed-up of dispersion models for clustered analysis.
* Speeded up NS CPMG 2-site 3D, by preforming the
magnetisation. Task #7807 (https://gna.org/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**l. 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). Task #7807
(https://gna.org/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.
Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/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. Task
#7807 (https://gna.org/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. Task
#7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Comment and spell fixing in NS CPMG 2-site 3D. Task #7807
(https://gna.org/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. Task
#7807 (https://gna.org/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. 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 3.2.2 vs. 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.
* 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. Task #7807 (https://gna.org/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. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Added matrix_power to the init file in lib/dispersion. Task
#7807 (https://gna.org/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(). Task #7807
(https://gna.org/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.
* Fix for unit test passing on old numpy systems. The error
was: ValueError: setting an array element with a sequence. Task #7807
(https://gna.org/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.
* 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.
* Initial try to write up a 2x2 matrix by closed form. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Made the validation check in profiling of matrix_power check
all values. Task #7807 (https://gna.org/task/?7807): Speed-up of
dispersion models for clustered analysis.
* Replaced all self.spins with self.NS in target function.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Replaced all self.num_exp with self.NE in target function.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Replaced all self.num_frq with self.NM in target function.
Task #7807 (https://gna.org/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_BA and k_AB 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".
* 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.
* 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. Task
#7807 (https://gna.org/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
http://wiki.nmr-relax.com/Category:Relaxation_dispersion 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.
* Code validation of lib/dispersion/b14.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/cr72.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/dpl94.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/lm63_3site.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/lm63.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/m61b.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/m61.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/matrix_exponential. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/mp05.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/ns_cpmg_2site_expanded.py.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Code validation of lib/dispersion/ns_cpmg_2site_star.py.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Code validation of lib/dispersion/ns_mmq_2site.py. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/ns_mmq_3site.py. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/ns_r1rho_2site.py. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/ns_r1rho_3site.py. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/tap03.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/tp02.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of lib/dispersion/two_point.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Code validation of target_functions/relax_disp.py. Task
#7807 (https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* For model NS MMQ 3-site, moved the parameter conversion of
dw_AB from target function to lib function. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed chi sum initialisation in func_ns_mmq_2site() as
this is not used. Task #7807 (https://gna.org/task/?7807): Speed-up
of dispersion models for clustered analysis.
* Documentation fix for the get_back_calc() function in
target_function/relax_disp.py. Task #7807
(https://gna.org/task/?7807): Speed-up of dispersion models for
clustered analysis.
* Removed unnecessary repetitive calculation of kex**2 in
model DPL94. Task #7807 (https://gna.org/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.
Task #7807 (https://gna.org/task/?7807): Speed-up of dispersion models
for clustered analysis.
* Updated the 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.
* 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.
* Redesign of the optimisation user functions calculate,
grid_search, and minimise. In preparation for expanding the number of
optimisation user functions, these three current user functions have
been shifted into the new minimise user function class. The calculate
user function is now accessed as minimise.calculate. The grid search
as minimise.grid_search. And minimisation is via the minimise.execute
user function. The icon used for the new user function class is the
Rosenbrock function or the banana optimisation problem. As this is
such a radical change, a huge number of changes in the relax source
code, the sample scripts, the user manual, and the test suite were
required.
* Created the new minimise.grid_zoom user function. This
allows the grid zoom level to be set. The value is stored in the
current data pipe and will be used later by the minimise.grid_search
user function.
* The minimise.grid_zoom user function now uses the zoom-in Oxygen
icon.
* Created the Relax_fit.test_zooming_grid_search system test.
This will be used to test the implementation of the zooming grid
search. The relaxation curve-fitting analysis should be one of the
fastest for testing this.
* Added the print_model_title() method to the specific
analysis base API class. This will be used to format and print out
the information returned by the model_info() API method.
* Implemented the print_model_title() specific analysis API
method for the dispersion analysis.
* Modified the specific analysis API _model_loop_spin() common
method. This now additionally returns the spin ID string to allow the
corresponding spin container to be identified.
* Implemented the specific analysis API common method
_print_model_title_spin(). This is for the corresponding
_model_loop_spin() method. It can be aliased in the specific analyses
to provide the print_model_title() API method.
* Aliased the _print_model_title_spin() specific analysis API
common method in a few analyses. This provides the
print_model_title() API method for the J(w) mapping, consistency
testing, and relaxation curve fitting analyses.
* Updated all the specific analysis methods affected by the
_model_info_spin() API method change. This is for the change whereby
the common API method now returns the spin ID string as well.
* Implemented get_param_names() and get_param_values() for the
relaxation curve-fitting analysis. These are part of the specific
analysis API.
* Created the specific analysis API return_parameter_object()
function. This is used by the non-specific analysis code to obtain
the parameter object (a singleton object). It will allow for more
direct access to the parameter information.
* Created the parameter object infrastructure for adding the
grid search lower and upper bounds. The _add() method now accepts the
grid_lower and grid_upper keyword arguments, which can be either
values or functions. These are then stored in the _grid_lower and
_grid_upper class dictionaries. The public methods grid_lower() and
grid_upper() have been added to return the value corresponding to the
given parameter.
* Modified the specific analysis parameter object grid_lower()
and grid_upper() methods. These now accept the model information from
the model_loop() API method and send that into an grid lower and upper
functions. These functions will require the information to pull out
the correct spin, spin cluster, or other information from the current
data pipe to determine what the bounds should be.
* Implemented infrastructure in the grid_search user function
in preparation for the zooming grid. The grid search backend now
calls the new grid_bounds() function. This takes the lower and upper
bounds as arguments, uses the specific API to determine the per-model
parameter grid search bounds, and then returns a per model list of
lower and upper bounds. The specific API get_param_names() and
get_param_values() are called to obtain the current model parameter
names and values, and then the parameter names and model info are used
in the new parameter object grid_lower() and grid_upper() methods to
obtain the bounds. This shifts all of the grid search bounds logic
out of the specific analyses and into the grid search backend, so it
should allow the specific analysis code to be simplified.
* More modifications of the minimise.grid_search user function
backend. The grid_bounds() function has been renamed to grid_setup(),
and it now accepts and processes the inc user function argument. The
error checking code of the relaxation curve-fitting
grid_search_setup() optimisation function has been shifted into this
analysis independent grid_setup() function to shift the
minimise.grid_search user function error checking out of the specific
analyses. The function now scales the parameter bounds, using the
yet-to-be implemented scaling() method of the parameter object. And
the grid search increments are converted into a per-model list of
lists.
* Created the parameter object infrastructure for registering
parameter scalings. The _add() method now accepts the scaling keyword
argument, which can be either a value or function. This is then
stored in the _scaling dictionary. The public method scaling() has
been added to return the scaling factor corresponding to the given
parameter.
* Modified the analysis specific API optimisation method. The
base calculate(), grid_search() and minimise() methods now all accept
the scaling_matrix argument, and the minimise() scaling argument has
been removed. This scaling_matrix argument should be a per-model list
of scaling matrices. To handle the change, the
pipe_control.minimise.assemble_scaling_matrix() function has been
created. This uses the new parameter object scaling values to create
the list of scaling matrices. This will in the end replace all of the
analysis specific assemble_scaling_matrix() functions and simplify
their optimisation code paths.
* Changed the order of operations in the minimisation user
function backends. The specific analysis API overfit_deselect()
method needs to be called before any grid bounds, increments, or the
scaling matrices are assembled. This is for the cases when the grid
bounds or scaling factors are functions rather than values.
* Converted the relaxation curve-fitting 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
curve-fitting parameters. This includes three functions i_scaling(),
i0() and iinf() in the specific_analyses.relax_fit.parameter_object
module for calculating some of these values. The
specific_analyses.relax_fit.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
specific_analyses.relax_fit.optimisation.grid_search_setup() function
was redundant and was deleted.
* Created the consistency testing specific API method
get_param_names(). This is now required for the minimise.calculate
user function, specifically for the analysis independent
assemble_scaling_matrix() function. The get_param_names() method
simply returns the fixed list of parameter names.
* Standardisation of the specific analysis API with respect to
the model_loop() and base_data_loop(). The model information
arguments for the data returned by model_loop(), and the data
arguments for the data arguments for the data returned by
base_data_loop() have been standardised throughout the API.
* Epydoc parameter order rearrangement in the specific
analysis API base class.
* Updated the specific analysis API common methods for the
recent model_info argument changes.
* Updated all of the specific API calculate() methods to
accept the scaling_matrix argument. The list of per-model scaling
matrices is now created independently of the analysis type by the
pipe_control.minimise methods for the minimise.calculate,
minimise.grid_search and minimise.execute user functions and sent into
the specific analysis backend.
* Updated all of the specific API grid_search() methods to
accept the scaling_matrix argument. The list of per-model scaling
matrices is now created independently of the analysis type by the
pipe_control.minimise methods for the minimise.calculate,
minimise.grid_search and minimise.execute user functions and sent into
the specific analysis backend. The argument is also passed into the
minimise() API method from the grid_search() method when that is used.
* Updated all of the specific API minimise() methods to accept
the scaling_matrix argument. The list of per-model scaling matrices
is now created independently of the analysis type by the
pipe_control.minimise methods for the minimise.calculate,
minimise.grid_search and minimise.execute user functions and sent into
the specific analysis backend.
* Fix for the Monte Carlo simulations for the model_info
argument changes in the specific API.
* Fixes for the consistency testing and J(w) mapping
calculate() methods. This is for the changes to the data_init()
specific analysis API method.
* More fixes for the Monte Carlo simulations for the
model_info argument changes in the specific API.
* Updated all of the data_init() specific API calls where the
spin ID is expected.
* Fixes for the _data_init_spin() specific API common method.
The data returned from _base_data_loop_spin() is just the spin ID, the
spin container is not included.
* Updated the eliminate user function backend to work with the
model_info argument changes in the specific API.
* The new pipe_control.minimise module functions can now
handle models with no parameters. The new assemble_scaling_matrix()
and grid_setup() functions will now handle models with no parameters,
as this is required for the relaxation dispersion analysis.
* More fixes for the eliminate user function backend. This is
for the model_info argument changes in the specific API.
* Fixes for the grid search backend for a recent breakage and
expansion of its capabilities. The user supplied lower and upper
bounds for the grid search were no longer being scaled via the scaling
matrix. In addition, the code has been refactored to be simpler and
more flexible. The user can now supply just the lower or just the
upper bounds and the grid search will work.
* The grid search setup function now prints out the grid
search bounds to be used. This is in the
pipe_control.minimise.grid_setup() function, hence it is analysis
independent. This is useful feedback for the user to know what the
grid search is doing. And it will be even more useful for the zooming
grid search to understand what is happening.
* The grid search setup printout now also included the number
of increments for each parameter.
* Modified the new print_model_title() specific analysis API
method. This now accepts the prefix argument for creating different
titles independently of the specific analysis.
* The grid search setup function now uses the prefix argument
to the print_model_title() API function. This is simply set to 'Grid
search setup:'.
* The relaxation dispersion API now uses the MODEL_R2EFF
variable for identifying the R2eff model.
* Changes to the minimise.grid_search user function frontend.
The Boolean constraints argument has been shifted to the end, and
empty lines have been removed.
* Epydoc docstring fixes for the keyword arguments of the
pipe_control.minimise module.
* Shifted the constraints Boolean argument to the end of the
grid_search() function argument list.
* Major change to the grid search user function. The
minimise.grid_search user function now accepts the skip_preset flag.
When True, the grid search will skip any parameters with a preset
value. This allows the user to set parameters via the value.set user
function and then have these parameters skipped in the grid search.
The new skip_preset argument is passed into the
pipe_control.minimise.grid_setup() function in the backend. This then
sets both the grid lower and upper bounds to the preset parameter
value and sets the number of increments to 1 for that parameter so
that it is essentially skipped in the grid search.
* Small change to the table printed out during the
minimise.grid_search setup.
* Fix for the skipping of preset parameters in the grid
search. Dictionary and list type parameters are now handled
correctly.
* Converted the relaxation dispersion 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 dispersion
parameters. This includes the functions dw_lower(), dwH_lower(),
pA_lower() and i0_upper() in the
specific_analyses.relax_disp.parameter_object module for calculating
some of these values. The
specific_analyses.relax_disp.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
specific_analyses.relax_disp.optimisation.grid_search_setup() function
was redundant and was deleted. The
specific_analyses.relax_disp.parameters.get_param_names() function was
also modified with the full argument added, to allow either the base
parameter names or an augmented form with the dictionary key for
presenting to the user to be returned. Importantly to allow the
changes to be operational, the model_loop() API method was redesigned
so that, for the R2eff base model, the individual spins rather than
spin clusters will be looped over. This allows the
specific_analyses.relax_disp.optimisation.minimise_r2eff() function to
continue to operate correctly.
* Implemented the J(w) mapping analysis get_param_names() API
method. This simply returns the hardcoded list of 3 parameters of the
model, and allows the minimise.calculate user function to operate.
* Updated the _print_model_title_spin() specific API common
method. This now accepts the prefix argument and adds this to the
title.
* The minimise.grid_search user function can now properly
handle preset values of NaN. This occurs when the parameter vector
contains values of None due to the parameter not being set and then
the Python list being converted to a numpy array. The value of NaN is
now caught and the parameter is no longer identified as being preset.
* Fixes for the relaxation curve-fitting grid search. The
parameters which are not set are no longer defaulting to 0.0. This
means that the parameter vector will sometimes contain NaN values, but
this is important for the correct operation of the new
minimise.grid_search user function backend.
* Updated the NOE analysis to handle the changes for the
minimise.calculate user function. This now requires the model_loop()
and get_param_names() API methods to be implemented. The first is
provided by the API common _model_loop_spin() method and the second
simply returns a list of the single 'noe' parameter.
* Created the _print_model_title_global() specific analysis
API method. This is to be paired with the _model_loop_single_global()
API method and it simply prints out the prefix as the title.
* Created the specific analysis parameter object
_add_align_tensor() method. When called by a specific analysis, this
will add the [Axx, Ayy, Axy, Axz, Ayz] parameters to the corresponding
parameter object.
* Deleted the lib.optimisation module. The checks in the
single test_grid_ops() function are implemented in the
pipe_control.minimise.grid_setup() function and are now redundant.
* Removed the import of the now deleted lib.optimisation
module from the model-free analysis.
* Converted the N-state model 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 N-state model
parameters. The
specific_analyses.n_state_model.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.
In addition, all of the lower bounds defined in the grid_search() API
method have been deleted as this is now in the parameter object. The
new API function print_model_title() has been aliased from
_print_model_title_global(). And the get_param_names() and
get_param_values() API methods have been implemented.
* The grid search upper and lower bound functions now must
accept the incs argument. For a few analyses, the number of grid
search increments are used to remove the end point of the grid, to
remove duplicate points due to the circular nature of angles.
Therefore the parameter object grid_lower() and grid_upper() methods
now send the grid increment number for each parameter into all grid
bound determining functions. The relaxation dispersion and
curve-fitting analyses have been updated for the change.
* Converted the frame order 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 dispersion parameters.
This includes the functions angle_upper_excluding_bound(),
axis_alpha_upper(), cone_angle_lower(), cone_angle_upper(),
pivot_grid_bound(), pivot_x_lower(), pivot_x_upper(), pivot_y_lower(),
pivot_y_upper(), pivot_z_lower(), and pivot_z_upper() in the
specific_analyses.frame_order.parameter_object module for calculating
some of these values. The
specific_analyses.frame_order.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 setup and error
checking code in the grid_search() API method was redundant and was
deleted.
* Modified all calls to the parameter object _add() base class
method. These are now all spread across multiple lines, with each
argument on a separate line. This is for easier maintenance of the
specific analysis parameters, as the code is now much cleaner and
argument changes will only have diffs for that argument. It is also
visually easier to see all the settings for each parameter.
* Fix for the grid search setup printout when parameters are preset.
* Changes to the diffusion tensor initialisation in the
model-free auto-analysis. The values of the tensor are now
initialised to None. This is to allow for the new grid search preset
flag which defaults to True, setting the values to None indicates that
a grid search should be performed.
* The diffusion_tensor.init user function can now set initial
tensor parameter values of None. This is to allow for the new grid
search preset flag. Therefore allowing the values to be None allows
for a grid search to be performed by default.
* 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.
* Modified the pipe_control.minimise.grid_setup() function for
when no parameters are present. For the current version of minfx to
function correctly (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 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.
* Modified the behaviour of the parameter object units()
method. If the unit is set to the default of None, this method will
now return an empty string instead of None.
* The rx parameter of the relaxation curve-fitting analysis
now has 'rad.s^-1' units defined.
* Implemented the zooming grid search. If the zoom level is
set to any value other than 0, then the grid width will be divided by
2**zoom_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.
* Modified the zooming grid search algorithm. If the zoom
level is negative, hence the grid will be larger than the original,
the checks that the grid is within the original are no longer active.
* Changed the minimise.grid_zoom user function. The zoom
level can now be any floating point number or integer, including
negative values. The user function docstring has been significantly
expanded to explain the entire zooming grid search concept.
* Alphabetical ordering of the minimisation user functions in
the user_functions.minimisation module.
* Large expansion of the minimise.grid_search user function
documentation. The previous documentation was essentially
non-existent.
* Expanded the minimise.grid_zoom user function documentation.
A few sentences about the limitations of the algorithm have been
added.
* Completed the Relax_fit.test_zooming_grid_search system
test. Now only a single spin is optimised. The zooming levels
increase in integer increments from 0 to 50 so that the final zoomed
grid is insanely small (as the curve-fitting C modules are incredibly
fast, this test is nevertheless relatively quick). The final zooming
grid search parameter values are checked to see if they are the same
as those optimised in the Relax_fit.test_curve_fitting_height system
test to demonstrate the success of the algorithm.
* Modified the grid search upper bounds functions for the
relaxation curve-fitting. This is for both the exponential relaxation
curve-fitting analysis and the same fitting in the dispersion
analysis. The intensity values are doubled and then rounded to the
next order. This ensures that I0 and Iinf will be within the grid
bounds. Hence the zooming grid search can be used for these curves.
* Expanded the documentation for the minimise.calculate user
function. This now explains the dual operations of the user function.
* Fixes for some relaxation dispersion system tests not
converted to the new optimisation user functions. Minimisation is now
via the minimise.execute user function, which used to be the minimise
user function.
* Added a 128x128 pixel version of the zoom-in Oxygen icon.
This icon size is not available in the repository located at
svn://anonsvn.kde.org/home/kde/trunk/kdesupport/oxygen-icons.
Therefore the scalable/actions/small/48x48/zoom-in.svgz file was
copied and then exported into a 128x128 PNG, and finally converted to
a Gzipped EPS file for the relax manual.
* The frame order grid search bound functions can now handle
increment values of None or 1. These cases are now caught and the
full lower or upper bound is now returned.
* More even spacing for the frame order grid search. This is
for the parameters which exclude end points in the grid search, as
these excluded points do not decrease the number of increments
searched over.
* Even more even spacing for the frame order grid search.
This is for the parameters which exclude end points in the grid
search, as these excluded points do not decrease the number of
increments searched over. This fixes the algorithm for all of the
bounds.
* Improved the logic for skipping parameters in the grid
search. The logic is also fully explained in the user function
documentation.
* Removal of all unused imports. These were identified using
the devel_scripts/find_unused_imports.py script.
* Reverted the deletion of the
Relax_disp.test_hansen_cpmg_data_to_lm63_3site system test which
occurred in relax 3.2.3. See the thread at
http://thread.gmane.org/gmane.science.nmr.relax.scm/21774/focus=6300
for the request that this deletion be reverted. This is the only
system test for the 'LM63 3-site' dispersion model using real data.
Having this test allows for better coverage of the code.
* Updated the Relax_disp.test_hansen_cpmg_data_to_lm63_3site
system test. This is for the changes to the optimisation user
functions.
* Updated the checks in the
Relax_disp.test_hansen_cpmg_data_to_lm63_3site system test. The
values were incorrect due to a bug in relax and a non-optimal minfx
setting (https://gna.org/bugs/?22210 and https://gna.org/bugs/?22211).
* Fix for a fatal bug for the prompt UI on MS Windows. The
improvements in the tab completion support for the prompt UI on Mac OS
X systems was fatal for certain Python readline modules on MS Windows,
as readline.__doc__ can be None. This is now correctly handled.
* Decreased the precision the
Relax_disp.test_hansen_cpmg_data_to_lm63_3site system test. This is
to allow the test to pass on Mac OS X systems.
* 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.
* 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 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.
* 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.
* Added Andy Baldwin's 2013 R1rho relaxation dispersion model
(BK13) to the manual. The model has been added to the table of
dispersion models and to the dispersion software comparison table of
the dispersion chapter of the manual. The citation has also been
added to the bibliography.
* The BK13 dispersion model is now properly added to the
software comparison table.
* Added the 'BK13' and 'BK13 full' dispersion models to the to
do section of the manual.
* Standardisation of the author names in the bibliography of
the relax manual.
* Added links for the BK13 model to
https://gna.org/support/?3155 in the manual.
* Expansion of the 'to do' section of the dispersion chapter
of the manual.
* Editing of the 'to do' section of the dispersion chapter of
the manual.
* Split out the interpolating in
specific_analyses.relax_disp.data.plot_disp_curves() into separate
function. This is to prepare for a interpolating function for
spin-lock offset rather than spin-lock field strength for R1rho
models. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Split out the looping over frequency and offset into its own
function, in function of
specific_analysis.relax_disp.data.plot_disp_curves(). Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Split out the writing of dispersion graph files in
specific_analyses.relax_disp.data.plot_disp_curves(). This is to
prepare for a stand-alone function to plot R1rho graphs, interpolating
theta through spin-lock offset rather than spin-lock field strength.
Sr #3124 (https://gna.org/support/?3124): Grace graphs production for
R1rho analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for function calling and default values of None in
sub-plotting functions. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Large extension of specific_analyses.relax_disp.data(), by
adding several helper plotting functions. This is to prepare for
plotting R1rho/R2 as function of effective field in rotating frame:
w_eff. R_2 = R1rho / sin^2(theta) - R_1 / tan^2(theta) = (R1rho - R_1
* cos^2(theta) ) / sin^2(theta). Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Extended
specific_analyses.relax_disp.optimisation.back_calc_r2eff() to handle
interpolated spin-lock offset values. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Removed a wrong adding of empty offset dimension in the
get_back_calc() function of target_functions.relax_disp(). Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added the back calculated R2 as function of effective field
in rotating frame: w_eff. R1rho R_2 is defined as: R_2 = R1rho /
sin^2(theta) - R_1 / tan^2(theta) = (R1rho - R_1 * cos^2(theta) ) /
sin^2(theta). This is described more at:
http://wiki.nmr-relax.com/DPL94#Equation_-_re-writed_forms. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added an intermediate attempt to show the back calculated
data in the graph for R1rho/R2 as function of the effective field in
rotating frame: w_eff. The graph is aiming for the representation of
Figure 2 in Kjaergaard et al 2013.
(http://dx.doi.org/10.1021/bi4001062). The figure can be seen at
https://gna.org/support/download.php?file_id=20208. It becomes clear,
that it is not necessary interpolate through the spin-lock offset, but
it is sufficient to interpolate through the spin-lock field strengths.
The necessary step was the extraction of the effective field in
rotating frame, w_eff. In earlier attempt is shown at:
http://wiki.nmr-relax.com/File:Matplotlib_52_N_R1_rho_R2eff_w_eff.png
This though show lines for 6 offset values. The question is how to
show the single line of interpolation. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added NMR library function to convert the given frequency
from rad/s to ppm units. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Hard-coded restriction of R1rho extra plotting to model
DPL94, TP02, TAP03, MP05, NS_R1RHO_2SITE. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for sending the correct data structures to target
function, and fix for the spin index which is always zero in graph
production. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Moved the file_name creation out of the interpolate
function, to make it a general function for interpolating. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Made both interpolation functions of the spin-lock field
strength and spin-lock offset return the offset data. This is make it
possible to switch between the interpolating functions, when plotting
R1rho graphs. This is necessary to produce the R2 as function of
effective field in rotating frame w_eff, and to produce R1rho as
function of theta, when ramping the spin-lock offset. These graphs
can be seen at:
http://wiki.nmr-relax.com/Matplotlib_DPL94_R1rho_R2eff. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Changed interpolation function from offset to spin lock
field strength, to plot R1rho/R2 as function of effective field. Sr
#3124 (https://gna.org/support/?3124): Grace graphs production for
R1rho analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Improved axis label for plotting R1rho/R2 as function of
effective field w_eff. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Added sub-title to the plot of R1rho/R2 as function of
effective field. This is to add information about how the effective
field has been interpolated. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added functionality to plot R1rho/R2 as function of
effective field w_eff, for the R2eff model. Also renamed a function,
to better reflect is functionality. The hard-coding of which models
to plot, has been removed. If the exp-type is R1rho, then the
plotting will commence. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Added the spin specific residue name and spin_id to the
title of the dispersion plots. This is handy, since it is often of
interest to have this information at hand, when looking through many
graphs. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Renamed and improved epydoc information for interpolating
function for dispersion values. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Renamed the interpolating function for offset, and improved
the epydoc information. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Corrected the epydoc information for the
return_offset_data() function in specific_analyses.relax_disp.data.
The function has been extended to return more data. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Renamed function to represent the functionality of returning
data in correct xmgrace form. Also improved the epydoc information,
for the return of values. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Renamed the other function to represent the functionality of
returning data in correct xmgrace form. Also improved the epydoc
information, for the return of values. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added function to calculate rotating frame parameters for
lib/nmr.py. This function is called several times in
specific_analyses/relax_disp/data.py by plotting functions. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Replaced repeated calculation of rotating frame parameters
to use function in lib/nmr.py. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Skip production of plotting R1rho/R2 as function of
effective field w_eff, when spin.isotope is not present. This can
happen when it is 'exp_fit' model curve fitting. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added functionality to function to accept how the first part
of the filename is formed. This is to prepare to reuse the same
plotting function. Sr #3124 (https://gna.org/support/?3124): Grace
graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Renamed plotting sub function, to accept different file name
arguments, and X-axis arguments. This is to reuse the sub-plot
function to plot against different X-axis. Added plot of R1rho as
function of theta, where interpolated against spin-lock field
strength. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Prepared flag, to tell which data type to interpolate
through. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Preparation to interpolate through the offset to plot R1rho
as function of theta, interpolated through spin-lock offset. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added graph functionality, to plot R1rho as function of
theta, when spin-lock offset is interpolated. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Ensured production of plotting R1rho/R2 as function of
effective field w_eff, when spin.isotope is not present. The offset
in radians would be set to 0.0 instead. This can happen when it is
'exp_fit' model curve fitting. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added function to return spin info, and a function to return
a spin string for graphs. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Replaced the spin info string in the title of graphs, with
the new function method. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Added keywords to be used to backend function of
plot_disp_curves. The keyword 'y_axis', determine which y data to be
plotted on this axis. The keyword 'x_axis', determine which x data to
be plotted on this axis. The keyword 'extend_hz' to determine how far
to extend interpolated CPMG frequency or spin-lock field strenght.
The keyword 'extend_ppm' to determine how far to extend interpolated
spin-lock offsets. The keyword 'interpolate' to determine to
interpolate dispersion points, or offset. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Made the plotting function uniform into accepting both CPMG
and R1rho data. Also made a function to return data, depending if it
is data, back calculated, interpolated or residual. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Renamed return grace data function, to a better shorter
name. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Renamed the other return grace data function, to a better
shorter name. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Hardcoded xmgrace colour_order, and made function to return
data label, and data plot settings, depending on data type. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Renamed plotting function to represent the function of
writing to file. Sr #3124 (https://gna.org/support/?3124): Grace
graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Removed unused plotting function. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Streamlined plotting functions, to have similar input.
Reordered the output from return_offset_data(), interpolate_disp() and
interpolate_offset(), to reflect the order of data type. Made the
input to return_grace_data_vs_disp(), return_grace_data_vs_offset the
same(). Added the interpolate flag to return_grace_data_vs_disp() and
return_grace_data_vs_offset(), to help return correct X-value. Added
the interpolate flag to return_x_y_point(), to help determine if "disp
point" or "offset point" should be returned. Added the "offset point"
to return_x_y_point() function, to make it possible to plot against
offset. Cleaned up the return_grace_data_vs_offset() function, to use
newly created return_x_y_labels() function. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for output catching after reorder or
return_offset_data() function. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Removed unused return_grace_data() function. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Made uniform function for returning x_axis and y_axis labels
for xmgrace plotting. Sr #3124 (https://gna.org/support/?3124): Grace
graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Renamed return functions, to reflect they are specific for
xmgrace plotting. Sr #3124 (https://gna.org/support/?3124): Grace
graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Extended relax_disp auto_analyses to plot special R1rho
graphs. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Modified front-end user function relax_disp.plot_disp_curves
to send new arguments to back-end function. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added new Unicode symbols to be used by the GUI drop-down
menu. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Modified back-end of plot_disp_curves() to reflect changes
to the front-end function. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Extended system test
Relax_disp.test_r1rho_kjaergaard_auto(), to check that the expected
graphs exist. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Added model 'No Rex' to system test
Relax_disp.test_r1rho_kjaergaard_auto(), to check all graphs are
produced. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Added function to return the initial part of the file name
for grace plotting. Sr #3124 (https://gna.org/support/?3124): Grace
graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Modified system test to use the new function to return
initial part of file name for grace plotting. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Started testing all possible combinations of graphs for
R1rho analysis. Sr #3124 (https://gna.org/support/?3124): Grace
graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Added more printout, to detect which graphs are not working.
Sr #3124 (https://gna.org/support/?3124): Grace graphs production for
R1rho analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added forgotten "interpolate" type to function which return
X,Y point to xmgrace graphs. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for interpolation graph when plotting R1rho/R2 as
function of offset (ppm). Missed to extract the offset value from
list. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Forcing overwrite of special R1rho graphs in auto analyses
in relax_disp. The other graphs are also auto forced. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added system test
Relax_disp.test_r1rho_kjaergaard_auto_check_graphs, to check that the
contents of all combinations of graphs are consistent. The system
test actual show that the error is changing per run-through. This is
a bug, which should be corrected. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added graphs to check against in system test
test_r1rho_kjaergaard_auto_check_graphs. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Code validation of system test file for Relax_disp. Sr
#3124 (https://gna.org/support/?3124): Grace graphs production for
R1rho analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix to system test
Relax_disp.test_r1rho_kjaergaard_auto_check_graphs by only comparing
X,Y values, and skipping the error. This is a hack until the error
difference bug gets corrected. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added system test
Relax_disp.test_kteilum_fmpoulsen_makke_check_graphs() to check all
possible combinations of dispersion plotting. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for GUI text in MS Windows, since subscript 1 and Greek
theta symbol is not working in this Unicode system. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for desc_short, in user function
relax_disp.plot_disp_curves. The text "The " is preformed in the
formatting. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Fix for system test, after moving graphs to check against.
Sr #3124 (https://gna.org/support/?3124): Grace graphs production for
R1rho analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Moved graph files up one level in system test. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for forgotten removal of counter. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Added check function for relax_disp. This function check if
interpolating against offset for non-R1rho exp types, and will raise
an Error. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Added check function to plot_disp_curves, to check that CPMG
exp types are not interpolated against offset, which is not
implemented. Sr #3124 (https://gna.org/support/?3124): Grace graphs
production for R1rho analysis with R2eff as function of Omega_eff. Sr
#3138 (https://gna.org/support/?3138): Interpolating theta through
spin-lock offset [Omega], rather than spin-lock field strength [w1].
* Fix for forgotten "1" in lib text GUI. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Improved description in GUI text for user function
relax_disp.plot_disp_curves(). The improved description now explains
the new features. Sr #3124 (https://gna.org/support/?3124): Grace
graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Extended graph labelling, file naming and return of data for
multiple CPMG graphs types. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Added system test
Relax_disp.test_kteilum_fmpoulsen_makke_check_graphs to check all CPMG
graph combinations of: y_axis_types = [Y_AXIS_R2_EFF,
Y_AXIS_R2_R1RHO]; x_axis_types = [X_AXIS_DISP, X_AXIS_THETA,
X_AXIS_W_EFF]; interpolate_types = [INTERPOLATE_DISP]. This is a
total of 6 graphs. The graphs will in most cases be totally equal,
since the theta angle is calculated to 90 degrees, and R1 is returned
as 0.0, then R2=(R1rho - R1 cos^2(theta)) / sin^2(theta) = R1rho =
R2eff for CPMG models. Sr #3124 (https://gna.org/support/?3124):
Grace graphs production for R1rho analysis with R2eff as function of
Omega_eff. Sr #3138 (https://gna.org/support/?3138): Interpolating
theta through spin-lock offset [Omega], rather than spin-lock field
strength [w1].
* Added graphs to check against for system test:
Relax_disp.test_kteilum_fmpoulsen_makke_check_graphs. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Expanded ex. to example in help text for function. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Fix for unit test, where return_offset() function return has
been expanded and reordered. Sr #3124
(https://gna.org/support/?3124): Grace graphs production for R1rho
analysis with R2eff as function of Omega_eff. Sr #3138
(https://gna.org/support/?3138): Interpolating theta through spin-lock
offset [Omega], rather than spin-lock field strength [w1].
* Created the Bruker.test_bug_22411_T1_read_fail system test.
This is to catch bug #22411 (https://gna.org/bugs/?22411) as reported
by Olena Dobrovolska.
* Fix for system test
Relax_disp.test_kteilum_fmpoulsen_makke_check_graphs where minimise
has been extended with execute.
* Changed graphs after new minimisation algorithm has been
implemented. The values are now slightly different.
* Implemented first try to stride through data, when computing
the eig() of higher dimensional data. System test
test_cpmg_synthetic_b14_to_ns3d_cluster survived this transformation.
The system test will go from about 11 seconds to 22 seconds.
* Implemented second try to stride through data, when
computing the eig() of higher dimensional data. This of data of form:
NS, NM, NO, ND, Row, Col. System test
test_sprangers_data_to_ns_mmq_2site survived this transformation. The
system test will go from about 2 seconds to 4 seconds.
* Created function to create the helper index numpy array, to
help figuring out the indices to store in the exchange data matrix.
This is for striding through data and store the data correct in the
data matrix. This is for a special situation where numpy version is <
1.8, and where the numpy.linalg.eig() function can only be performed
on square matrices, and not on higher dimensional data. For this
situation, it is necessary to stride through the data.
* Created the numpy array self.index target function which
contains index to store the data. This is for situations where numpy
version is under 1.8.
* Added function to get the data view via striding through a
higher dimensional column numpy array.
* Extracted the data view of the index indices numpy array in
the target_function.
* A profiling showed, that it was not faster to preform the index
view.
* Made new general stride helper function and
matrix_exponential function.
* Changed to the matrix_exponential function for NS R1rho 2site.
* Removed all unused helper functions, and matrix exponential
functions. They are now condensed to the fewest possible functions.
* Fix for eye matrix being formed incorrectly.
* Replaced all matrix_exponential functions in numerical
models to use the new general matrix_exponential function.
* Added warning message to auto analysis in relax disp, if
numpy is below 1.8 and using numerical model. This will make the
analysis 5-6 times slower.
* Fixes for numpy version under 1.8, when striding through data.
* Fix to unit tests, after changing the name of
matrix_exponential function.
* Added graphs and results for run with MC=2000, for system
test Relax_disp.test_r1rho_kjaergaard_auto(). This is to be able to
extend graph testing for interpolated R1rho graphs, and to add figures
to the latex manual.
* Added list of R1rho models, which use R1 in their equations.
Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models. Bug #21788 (https://gna.org/bugs/?21788): Only Warning is
raised for missing loading R1 relaxation rate for the off-resonance
R1rho relaxation dispersion models.
* Modified the warning and error messages being raised when
calling return_r1_data(). Now warnings is raised if no R1 data is
available. An error is raised if the R1rho model is expected to have
R1 data, and it is not available. That makes system test
Relax_disp.test_r1rho_kjaergaard_missing_r1() fail, which is the
expected behaviour. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models. Bug #21788
(https://gna.org/bugs/?21788): Only Warning is raised for missing
loading R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Added the model "MODEL_DPL94_FIT_R1", to the full list of
models. Sr #3135 (https://gna.org/support/?3135): Optimisation of the
R1 relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* In system test
Relax_disp.test_r1rho_kjaergaard_missing_r1(), started using the new
model MODEL_DPL94_FIT_R1. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added "r1_fit" as a parameter object. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added functionality to return r1_fit parameter in
loop_parameters() function. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added model variable MODEL_DPL94_FIT_R1, to relax_disp
target function. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Split the target function of model DPL94 into a func_DPL94
and calc_DPL94. This is to prepare for a target function
func_DPL94_fit_r1. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added initial target function for model DPL94_fit_r1. Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added list of R1rho models, which can fit R1 in their
equations. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Modified return_r1_data() function, to return numpy array of
"None", if model is in list of "MODEL_LIST_R1RHO_FIT_R1". Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Modified target function func_DPL94_fit_r1(), to unpack
fitted parameters correct. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added linear linear_constraints for parameter "r1_fit". Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added warning message when using function return_r1_data(),
and model is in list MODEL_LIST_R1RHO_FIT_R1. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added model DPL94_FIT_R1 to the list of MODEL_LIST_R1RHO and
MODEL_LIST_R1RHO_FULL. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Modified return_r1_data(), to be dependent on fitting model.
Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Made function return_r1_err_data() be dependent on model
type. Sr #3135 (https://gna.org/support/?3135): Optimisation of the
R1 relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Replaced instances of "['r2', 'r2a', 'r2b']" with variable
PARAMS_R20. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Implemented model list, which uses parameter of inverted
relax delay times. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Implemented model list, which uses parameter of R20B. Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Fix in target function for relax_disp, where model IT99 does
not belong to model list with several chemical shift correlated
parameters. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Added new variable, for models which has parameters with
mixed dw, and has two variables. For example with both dw and dwH or
dw_AB and dw_BC or phi_ex_B and phi_ex_C. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added model MODEL_NS_R1RHO_3SITE,
MODEL_NS_R1RHO_3SITE_LINEAR, to list of models who has who dw
parameters. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Added new variable, for models which has parameters with
mixed dw, and has four variables Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added to relax_disp auto_analyses, that R1_fit should be
plotted and written out. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added 2 new models, MODEL_NOREX_R1RHO and
MODEL_NOREX_R1RHO_FIT_R1. The "NOREX" model is not covering R1rho
models. The target function for "NOREX" is calculated as: back_calc =
R20 R20 is for R1hro models equivalent to R1rho prime (R1rho'), which
for example in the model of DPL94 would mean: R1rho = R1rho' But for
the "NOREX" case, the return should be R1rho = R1 * cos^2(theta) +
(R1rho' + 0) * sin^2(theta). This affects all off-resonance model
calculations. These two target functions will be implemented. Bug
#22440 (https://gna.org/bugs/?22440): The "NOREX" model is not
covering R1rho models. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Made the model MODEL_NOREX_R1RHO_FIT_R1, be tested in system
test Relax_disp.test_r1rho_kjaergaard_missing_r1(). Bug #22440
(https://gna.org/bugs/?22440): The "NOREX" model is not covering R1rho
models. Sr #3135 (https://gna.org/support/?3135): Optimisation of the
R1 relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added models MODEL_NOREX_R1RHO and MODEL_NOREX_R1RHO_FIT_R1
to MODEL_LIST_FULL. Bug #22440 (https://gna.org/bugs/?22440): The
"NOREX" model is not covering R1rho models. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Implemented target and calculation function for
MODEL_NOREX_R1RHO, MODEL_NOREX_R1RHO_FIT_R1. Bug #22440
(https://gna.org/bugs/?22440): The "NOREX" model is not covering R1rho
models. Sr #3135 (https://gna.org/support/?3135): Optimisation of the
R1 relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Made the model "MODEL_NOREX_R1RHO", be testes in system test
Relax_disp.test_r1rho_kjaergaard_auto. This is for system test where
R1 has been loaded from earlier results, which was not analysed in
relax. Bug #22440 (https://gna.org/bugs/?22440): The "NOREX" model is
not covering R1rho models. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Made list of models which fit pA or pA and pB. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added function to auto_analysis, to test if it give meaning
to write and plot out the parameter. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Made the writing out of parameter pC, be tested with the new
function. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Implemented models list with phi_ex, phi_ex_B, and phi_ex_C,
and added to test in auto_analyses of relax_disp. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Made use of the dictionary MODEL_PARAMS, to determine if
parameter is present. This makes the list of models belonging to
parameter lists superfluous. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Removed unnecessary list of models which support a
parameter. This functionality already exists with the dictionary
MODEL_PARAMS. Sr #3135 (https://gna.org/support/?3135): Optimisation
of the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Rearranged the writing out of parameters in auto_analysis of
relax_disp. This is to prevent writing out all possible parameters
in the final round, if any of those parameters have not been tested.
Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Extended the writing and checking of parameters, to use
different file name, than the parameter name. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Moved the auto-analyses writing out of w_eff and theta into
check for has_r1rho_exp_type(). Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Re-arranged all model variable lists, to be able to re-use
earlier lists. This is to prevent user errors, when setting up the
lists, and re-use the lists through all code. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Fix for MODEL_NOREX_R1RHO_FIT_R1 not being part of list:
MODEL_LIST_DISP. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Moved the auto_analyses plot of special R1rho graphs into
the check of has_r1rho_exp_type(). Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Moved the auto-analyses plotting and writing of r2, r2a and
r2b for CPMG models into test of has_cpmg_exp_type(). Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Replaced in auto analysis all instances of 'No Rex' and
'R2eff' with its equivalent defined variables. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Re-arranged plotting and writing in auto-analyses of relax
disp, when model is R2eff. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Removed unused variables in auto-analyses of relax_disp. Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Implemented the list "MODEL_LIST_NEST", which define which
model are used for nesting. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Fix for the nested copying of r2, if using a nested list.
Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added the equivalent R1 fit models for: TP02, TAP03, MP05
and NS_R1RHO_2SITE. The R1 fit models will no be implemented for
3-site models, because there will be to many variables. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added the new R1 fit models to system test
Relax_disp.test_r1rho_kjaergaard_missing_r1(). Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Split target function of model TP02, into a calc and two
func_TP02* variants. One target function will use measured R1 values,
while one target function will use the fitted R1 values. They will
use the same calculation function. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Fix for error checking covering R1rho off resonance models
in target function. This is for checking presence of chemical shifts
and r1. Sr #3135 (https://gna.org/support/?3135): Optimisation of the
R1 relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Split target function of model TAP03, into a calc and two
func_TAP03* variants. One target function will use measured R1
values, while one target function will use the fitted R1 values. They
will use the same calculation function. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Split target function of model MP05, into a calc and two
func_MP05* variants. One target function will use measured R1 values,
while one target function will use the fitted R1 values. They will
use the same calculation function. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Fix for system test
Relax_disp.test_r1rho_kjaergaard_auto_check_graphs(), where the
special R1rho graphs is no longer produced for the R2eff models. Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Split target function of model ns_r1rho_2site, into a calc
and two func_ns_r1rho_2site* variants. One target function will use
measured R1 values, while one target function will use the fitted R1
values. They will use the same calculation function. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Removed num_points to be used in target and lib function of
model ns_r1rho_2site. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added a variable describing the model year, for all
relaxation dispersion models. This could be used, when trying to
write up an intelligent detect+select nesting function. This function
need some meta-data describing the models, in order to sort the
self.models before calculations, and to select a proper nested model
pipe. Other meta data could be: Accept of Exp_type, full or normal
model for CPMG, fitted or loaded R1 for R1rho, is analytic, silico or
numeric type. Sr #3135 (https://gna.org/support/?3135): Optimisation
of the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Added initial unit test class for testing
specific_analysis.relax_disp.variables. There will be more tests
added, when a nesting selection function has been written here. Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added dictionary for returning year, when using model as
key. Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added unit test for the dictionary of model years. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Reused the EXP_TYPE_LIST_CPMG and EXP_TYPE_LIST_R1RHO, to
combine for the list EXP_TYPE_LIST, the list of all dispersion
experiment types. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added a common EXP_TYPE_CPMG MMQ description for models
which handle MMQ. This is part of adding meta data for each model,
making it possible to device a sensible nesting selection function.
Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added meta data about EXP_TYPE per model, and made a
dictionary for it. Added unit test for the new dictionary. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added three new EXP_TYPE variables: EXP_TYPE_R2EFF =
'R2eff/R1rho', EXP_TYPE_NOREX = 'No Rex', EXP_TYPE_NOREX_R1RHO = 'No
Rex: R1rho off res'. These are used to add meta-data information to
each model, making it possible to make a nesting function, determining
which model to nest from. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added model meta information about number of chemical
exchange sites. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added meta information about equation type. The models are
divided into: analytic, silico or numeric. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Implemented nesting function, which will determine which
model to nest from. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* In auto analysis of relax_disp, started implementing the new
nesting function. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Modified nesting function to return all model info for the
current model, and the comparable model. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Fix for nesting kex, when model is CR72, and analysed models
is LM63. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Fix for nesting kex, when model is CR72, and analysed models
is IT99. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Added a return from the nesting model, if all fails. Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Replaced the test, if acquiring the model info to a
numerical model from a analytical model. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Improved the printing when nesting parameters from
equivalent models. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Removed unused import of models in auto-analyses of
relax_disp. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Improved the printing of system test
Relax_disp.test_r1rho_kjaergaard_missing_r1. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Improved the printing of system test
Relax_disp.test_r1rho_kjaergaard_missing_r1. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added function to sort models before auto-analyses. They
are sorted in order: exp_type: EXP_TYPE_R2EFF, EXP_TYPE_NOREX,
EXP_TYPE_NOREX_R1RHO, EXP_TYPE_CPMG_SQ, EXP_TYPE_CPMG_MMQ,
EXP_TYPE_R1RHO; equation: EQ_SILICO, EQ_ANALYTIC, EQ_NUMERIC; Nr of
chemical sites: 2 or 3; Year: Newest models first; Nr of parameters.
Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added unit test, to test the expected sorting of models for
auto-analyses. Sr #3135 (https://gna.org/support/?3135): Optimisation
of the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Added more models to be tested in system test
Relax_disp.test_r1rho_kjaergaard_auto. Bug #22461
(https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has extremely
high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Implemented the sorting of models, for auto-analyses. Sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Implemented partial reading of results file. Before reading
a results file, it is determined if the file exists. This makes is
possible to read a directory with partial results from a previous
analysis. This can be handsome, if reading R2eff values in R1rho
experiments, and the error estimation has been prepared with a high
number of Monte Carlo simulations. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added keyword to relax_disp auto analysis, if R2eff values
should be optimised. Here optimisation means minimisation and Monte
Carlo simulations of the error. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Modified system test
Relax_disp.test_r1rho_kjaergaard_missing_r1 to load previous R2eff
values, and not optimise them. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Fix in back end for relax_disp.parameter_copy, where r2a and
r2b should be skipped, since it has already been copied. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Locked values in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1. This is possible after
locking the R2eff values and errors from a previous run. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Removed MODEL_NS_CPMG_2SITE_EXPANDED to be analysed in
system test test_hansen_cpmg_data_missing_auto_analysis. The new
ordering of models, will make MODEL_NS_CPMG_2SITE_EXPANDED be analysed
first, and results copied to model CR72. This will interfere with the
old results. Sr #3135 (https://gna.org/support/?3135): Optimisation
of the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Lowered the precision of dw for model NS CPMG 2-site
expanded, in system test est_hansen_cpmg_data_auto_analysis_numeric.
Model NS CPMG 2-site expanded is now analysed before MODEL_CR72, which
alter the values a bit. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Added GUI text for parameter r1_fit. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added front-end description of the 6 new R1 fit R1rho models
for relax_disp.select_model. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added a paragraph for the no chemical exchange model in help
text description for selecting models. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Added the new R1rho models where R1 is fitted, to the GUI
model selection. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Deleted system test test_r1rho_kjaergaard_man, since it was
no necessary. Sr #3135 (https://gna.org/support/?3135): Optimisation
of the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Deleted unused script files in data folder for
Kjaergaard_et_al_2013. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Fix for the linear constraints where parameter r1_fit was
written as R1_fit. Sr #3135 (https://gna.org/support/?3135):
Optimisation of the R1 relaxation rate for the off-resonance R1rho
relaxation dispersion models.
* Fix for the numbers of parameters not getting counted
correct. Sr #3135 (https://gna.org/support/?3135): Optimisation of
the R1 relaxation rate for the off-resonance R1rho relaxation
dispersion models.
* Triggering an error in test_r1rho_kjaergaard_missing_r1.
There is a bug fetching the standard value of parameter 'r1_fit'.
AttributeError: 'float' object has no attribute 'keys'. Sr #3135
(https://gna.org/support/?3135): Optimisation of the R1 relaxation
rate for the off-resonance R1rho relaxation dispersion models.
* Removed unused scripts in folder of Kjaergaard et al., 2013.
Sr #3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Added new analysis scripts in folder of Kjaergaard et al.,
2013. Sr #3135 (https://gna.org/support/?3135): Optimisation of the
R1 relaxation rate for the off-resonance R1rho relaxation dispersion
models.
* Removed triggering an error in
test_r1rho_kjaergaard_missing_r1. There is a bug fetching the
standard value of parameter 'r1_fit'.
* Fix for system test
Relax_disp.test_r1rho_kjaergaard_missing_r1, running on 64 bit system.
* Inserted LaTeX bibliography for reference to linear
constraints of the exchange rate.
* Equation fix for $kex$ in manual.
* Added system test
Relax_disp.test_bug_22477_grace_write_k_AB_mixed_analysis and data.
This is for bug #22477 (https://gna.org/bugs/?22477): Not possible to
perform grace.write() on k_AB parameter for mixed CPMG analysis.
* Added more printout to system test
Relax_disp.test_bug_22477_grace_write_k_AB_mixed_analysis and data.
* Fix for system test
Relax_disp.test_bug_22477_grace_write_k_AB_mixed_analysis. Bug #22477
(https://gna.org/bugs/?22477): Not possible to perform grace.write()
on k_AB parameter for mixed CPMG analysis.
* Set the default value of r1_fit to 5.0.
* Added the relax_disp pipe type to be setup for unit tests of
value function.
* Setup a unit test for the value.set functionality for param
r1_fit. Bug #22470 (https://gna.org/bugs/?22470): value.set does not
work for parameter r1_fit.
* Fix in relax_disp API, how to handle the r1_fit parameter
type. Bug #22470 (https://gna.org/bugs/?22470): value.set does not
work for parameter r1_fit.
* Modified system test
Relax_disp.test_r1rho_kjaergaard_missing_r1 to use GRID_INC=None, and
thereby speeding up the analysis. Bug #22470
(https://gna.org/bugs/?22470): value.set does not work for parameter
r1_fit.
* Added to system test a count of number of headers and
values, when issuing a value.write(). Sr #3121
(https://gna.org/support/?3121): Support request for replacing space
in header files for the value.write functions.
* Fix for replacing spaces " " with "_" in header files. Sr
#3121 (https://gna.org/support/?3121): Support request for replacing
space in header files for the value.write functions.
* Fix for comment, which mentions R2 parameter, when it
relates to R1 fit.
* Replaced variable name: MODEL_PARAM_INV_RELAX_TIMES with
MODEL_LIST_INV_RELAX_TIMES, to match all of the other MODEL_LIST_*
variables. Also added a newline to end of file.
* Replaced remaining variable names: MODEL_PARAM_* with
MODEL_LIST_*, to match all of the other MODEL_LIST_* variables.
* Renamed R1rho off resonance models where R1 is fitted. This
removes the "underscore". This is a better representation presented
to the user, for example in the GUI model selection list or the
relax_disp.select_model user function in all UI.
* Renamed the parameter "r1_fit" to "r1". This naming fits
better to all other parameters.
* Split the unit test of
specific_analyses.relax_disp.checks.get_times() into its own unit test
file.
* Added a "check" function, what will determine if R1 data is
missing for a model to analyse. Also added corresponding unit tests,
to test the functionality.
* Modified in documentation, that the "No Rex" model have one
chemical exchange site, namely itself.
* Copied variables.py to model.py. There should not exist any
functions in variables.py. It should only consist of hardcoded
variables, and the functions related to model sorting and nesting is
split into its own file.
* Parted the file of variables.py into model.py.
* Removed the unit test regarding model.py in test_variables.py.
* Added unit tests regarding model.py and its functions.
* Removed the auto-sorting of models, when performing auto
analysis of Relaxation dispersion. This was discussed in:
http://thread.gmane.org/gmane.science.nmr.relax.scm/22733,
http://thread.gmane.org/gmane.science.nmr.relax.scm/22734,
http://thread.gmane.org/gmane.science.nmr.relax.scm/22737. Through
this discussion, it has appeared that the order of how models are
sorted for analysis, and hence the possibility for nesting, is a
complicated case. The order of analysis should be possible to
manually put into the auto analysis. This was not the scope of sr
#3135 (https://gna.org/support/?3135): Optimisation of the R1
relaxation rate for the off-resonance R1rho relaxation dispersion
models to implement such a feature. Such a feature could be
implemented for the next version of relax. It could be designed as
function to "suggest" an order in the GUI. But this functionality
would have to wait.
* Removing the 'tex'->'kex' conversion, and 'phi_ex' from dw
and pA. This solution is not a proper implementation, but these
parameters should rather be found by grid search.
* Re-inserted "MODEL_NS_CPMG_2SITE_EXPANDED" to be tested in
system test test_hansen_cpmg_data_missing_auto_analysis.
* Removed special cases for nesting. Now following order is
determined. First sort completed models into: EQ_NUMERIC, EQ_SILICO,
EQ_ANALYTIC. Then into year, with newest first. Then number of
chemical sites, which reflects number of parameters. Go through the
completed models. If the experiment types are the same, then look
for: If a completed model has same parameters, then nest from this.
If a completed model has all other parameters than R20 parameters,
then nest from this. Special cases are taken care of by:
MODEL_LM63_3SITE from MODEL_LM63, MODEL_NS_MMQ_3SITE,
MODEL_NS_MMQ_3SITE_LINEAR from MODEL_NS_MMQ_2SITE,
MODEL_NS_R1RHO_3SITE, MODEL_NS_R1RHO_3SITE_LINEAR from
MODEL_NS_R1RHO_2SITE, MODEL_MMQ_CR72 from MODEL_CR72. This
functionality represents the hard-coding from previous implementation.
* Moved the nesting lists down in variables file.
* Small verb tense fix for the descriptions of the '* R1 fit'
relaxation dispersion models.
* Added definition and dictionary, for each model, to
determine which model they nest from. This is better to hardcode,
since it makes it possible to produce a table with an overview, and
accurately determine which model is nested from. This is discussed in
thread http://thread.gmane.org/gmane.science.nmr.relax.devel/6684.
* Moved the lookup in dictionaries for model information, into
the class of model info.
* Division of unit tests of
specific_analyses/relax_disp/model.py into different functions. Also
added more print information to each tests.
* Added the ordered list of nest models to the class of model
information.
* Modified the defined list of nesting, according to thread:
http://thread.gmane.org/gmane.science.nmr.relax.devel/6684. More
specific, thread:
http://thread.gmane.org/gmane.science.nmr.relax.devel/6694.
* Modified unit test regarding finding correct nested model.
This was discussed in:
http://thread.gmane.org/gmane.science.nmr.relax.devel/6684. More
specific, thread:
http://thread.gmane.org/gmane.science.nmr.relax.devel/6694.
* Modified nesting function, to pull list of possible models
from dictionary, and check if these models are available in the
completed models.
* Added initial Python script, to help print each model and
its corresponding nested models. It can be executed by: relax
test_suite/shared_data/dispersion/print_model_info/print_model_info.py.
* Added a relaxation dispersion example to show how certain
literature statements are just utter crap. This follows from
http://thread.gmane.org/gmane.science.nmr.relax.scm/22774/focus=6693,
and the change http://thread.gmane.org/gmane.science.nmr.relax.scm/22774
which implement such dangerous literature conjectures. To see how a
real minimum is excluded from the optimisation space, here for residue
:2, execute the R1rho_analysis.py script in relax. This is synthetic
data generated with kex = 1e5 assuming the model TP02. For the case
of residue :2, this still produces an optimisable minimum in the space
and dispersion curves. However the change blocks optimisation to the
minimum.
* Added a function to determine how to nest/copy the
parameters, when nesting from another model. It takes the list of
parameters from the current model, and the list of parameters
available in the nested model, and return a dictionary of parameter
conversion for the current model params.
* Added unit test for the new function which determine how
parameters are copied for a nested model parameters.
* Added a table for dispersion model nesting in the
auto-analysis to the manual. This adds the ideas discussed in the
thread http://thread.gmane.org/gmane.science.nmr.relax.devel/6684.
* Added the string 'me' for Methods in Enzymology to the
bibtex file for the manual.
* Set the average value of R1 to 2.0 instead of 5.0. This is
"normally" a better guess for R1.
* Implemented the function, which translates how parameters
are copied from a nested model in the auto_analyses for relax_disp.
This makes it possible to test the translating code, and makes logic
clearer in the auto_analyses.
* Modified the r1rho_off_res_tp02_high_kex dispersion test
data. The kex value is now set to 2e5.
* Removed the 'DPL94 R1 fit' model nesting from the table in
the dispersion chapter of the manual. This was identified using the
dispersion test suite data script print_model_info/print_model_info.py
(http://thread.gmane.org/gmane.science.nmr.relax.scm/22823).
* Added to system test
Relax_disp.test_r1rho_kjaergaard_missing_r1 to check, that values are
not None when writing .out files. This is related to:
https://gna.org/support/?3121 sr #3121: Support request for replacing
space in header files for the value.write functions. The fix for this
bug, destroyed getting the values.
* Fix for earlier bug fix destroyed functionality. Altering
the data-keys to early, meant that data was not fetched correctly.
This is related to: https://gna.org/support/?3121 sr #3121: Support
request for replacing space in header files for the value.write
functions.
* Moved the unit test of
specific_analyses.relax_disp.checks.check_missing_r1() from a unit
test to a system test. This is because the unit test involved several
functions of relax.
* Inserted dictionary, that will convert a R1rho off-resonance
without R1, to the corresponding model which fit R1.
* Inserted to the check of missing R1, that MODEL_NOREX_R1RHO
also depends on R1.
* Implemented function that determine if any model in the list
of all models should be replaced or inserted as the correct 'No Rex'
model. It also translate the R1rho off-resonance model to the
corresponding 'R1 fit' models, if R1 is not loaded.
* Inserted system test Relax_disp.test_convert_no_rex_fit_r1,
which test the return for the function that will determine if models
self.models in the relax_disp should be translated/corrected.
* Fix for unit test, where the standard value of R1 was
lowered from 5.0 to 2.0. Also fixed an import error in another unit
test.
* Minimised the dependencies of the version module. This no
longer relies on the dep_check module.
* Inserted return of True/False flags from function which
convert models. The flag tells if: flag if 'No Rex' model for R1rho
off-resonance was translated; flag if 'No Rex' model for R1rho
off-resonance was inserted; flag if R1rho off-resonance was translated
to 'R1 fit' models if no R1 data was found.
* Changes to system test, after the number of returns from
function has been altered.
* Inserted into relax_disp auto_analyses, to convert the input
models. This will convert/insert the correct 'No Rex' to the
corresponding 'No Rex' model for R1rho off-resonance models. It will
also translate to the corresponding 'R1 fit' model, if no 'R1 data'
has been loaded with relax_data.read() function.
* Lowering of precision in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1(). This is due to
64/32-bit issues, between analysing on Linux computer, and testing on
Mac computer.
* Made the GUI selection of models for relaxation dispersion
more simple. After the implementation of a function which will
translate the models, the 'No Rex' model will be converted to the 'No
Rex' model for R1rho off-resonance. Also the corresponding 'R1 fit'
model will be chosen instead, if R1 data has not been loaded. This
makes the model selection easier in the GUI interface.
* Bugfix for Relax_disp.test_bug_21715_clustered_indexerror,
where only 'R2eff', 'No Rex' is analysed. This special case was not
tested in the translating function.
* Shortening the text in the auto_analysis, and raises a
warning if R1 data has not been loaded.
* Rewrote the logic of the key-word 'optimise_r2eff' in the
auto-analyses of relax disp. If R2eff result file exist in the
'pre_run_dir', this is loaded. If the results contain both values,
and errors, then no optimisation is performed on the R2eff model.
Unless the 'optimise_r2eff' flag is raised, which is not standard.
* Fixes for the
Relax_disp.test_hansen_cpmg_data_missing_auto_analysis system test on
MS Windows. This is for 32-bit MS Windows systems where the parameter
checks need to be loosened.
* Fixes for the Relax_disp.test_r1rho_kjaergaard_missing_r1
system test on MS Windows. This is for 32-bit MS Windows systems
where the parameter checks need to be loosened.
* Created the
specific_analyses.relax_disp.data.is_r1_optimsed() function. This
follows from an idea for handling R1 optimisation in the tread at
http://thread.gmane.org/gmane.science.nmr.relax.scm/22850/focus=6736.
This relaxation dispersion function can be used anywhere that requires
the knowledge that R1 values should be fitted during optimisation or
if loaded values should be used.
* Implemented the very basic relax_disp.r1_fit user function.
This is as discussed at
http://thread.gmane.org/gmane.science.nmr.relax.scm/22850/focus=6737.
* Implemented the
specific_analyses.relax_disp.parameters.r1_setup() function. This
matches the description at
http://thread.gmane.org/gmane.science.nmr.relax.scm/22850/focus=6739
and http://thread.gmane.org/gmane.science.nmr.relax.scm/22850/focus=6736.
* Spelling fix for the is_r1_optimised() function name.
* Implemented to the back_end of the value.copy, a "force"
flag to overwrite destination value.
* Implemented to the front_end of the value.copy, a "force"
flag to overwrite destination value.
* Copying the R2eff value from the reading of R2eff results in
the auto-analyses of relax_disp.
* Fix for the misspelled is_r1_optimised() function.
* The r1_fit flag is stored in the Disp_minimise_command class
and passed into the target function. This matches the details at
http://thread.gmane.org/gmane.science.nmr.relax.scm/22850/focus=6736.
The specific_analyses.relax_disp.optimisation.Disp_minimise_command
class calls is_r1_optimised() and stores the result. This is then
passed into the relaxation dispersion target function class.
* Modified that 'NS CPMG 2-site expanded' will be preferred
before 'NS CPMG 2-site 3D' and 'NS CPMG 2-site star'.
* Modified the nesting for 'NS MMQ 3-site linear'. 'NS MMQ
3-site linear' should be able to nest from 'NS MMQ 3-site'.
* Fix to unit test, after MODEL_NS_CPMG_2SITE_EXPANDED has
been preferred over other numerical CPMG models.
* Modified to unit test, that when model:
MODEL_PARAMS_NS_R1RHO_3SITE nest from: MODEL_PARAMS_NS_R1RHO_2SITE
the conversion should be: 'r2', 'r2' 'pA', 'pA' 'dw_AB', 'dw'
'kex_AB', 'kex' 'pB', '1 - pA' 'dw_BC', 'dw' 'kex_BC', 'kex' 'kex_AC',
'kex'. Here '1 - pA' is a special conversion flag.
* Implemented the special flag '1 - pA', when nesting
parameters from models with fewer chemical sites.
* Implemented the nesting of parameters from a model with
fewer chemical sites when nesting for NS R1rho 3-site.
* Inserted system test
Relax_disp.test_model_nesting_and_param() which will go through all
models, and then through all it nested models, testing that all
parameters have a conversion.
* Modified system test
Relax_disp.test_model_nesting_and_param() to only print, when the
converted parameter is different from the original parameter.
* Fix for parameter conversion when model is:
MODEL_PARAMS_NS_R1RHO_3SITE or MODEL_PARAMS_NS_R1RHO_3SITE_LINEAR.
* Fix for parameter conversion for: MODEL_PARAMS_NS_MMQ_3SITE
and MODEL_PARAMS_NS_MMQ_3SITE_LINEAR
* Fixes to unit tests, after parameter conversion have been corrected.
* Replaced that folder names for writing out results should be
with replaced with underscores "_". This is for the dispersion
auto-analysis.
* Fixes for the relaxation dispersion loop_parameters()
function. The R1, R20, R20A, R20B (and R1rho_prime, R1rho_primeA,
R1rho_primeB) parameters are now checked for in each spin container
rather than just the first of the cluster. This should make no
difference as all spins should have the same model and parameters, but
it might be a source of bugs in the future.
* The r1_fit flag is now used to switch between dispersion
target functions. This is as described in
http://thread.gmane.org/gmane.science.nmr.relax.scm/22850/focus=6736.
The change makes the '* R1 fit' models now redundant.
* Removed all of the '* R1 fit' models out of the
relax_disp.select_model user function frontend. These models are now
redundant as the question of R1 fitting is now determined internally
in relax.
* Removed all of the MODEL_*_FIT_R1 dependencies from the
specific_analyses.relax_disp package. These models are now redundant
as the question of R1 fitting is now determined internally in relax.
* Fix for the
specific_analyses.relax_disp.data.is_r1_optimised() function for
on-resonance R1rho data. This function needs to specifically catch
these models.
* Fix for the MODEL_LIST_R1RHO variable. Recent changes
causes this to not include the on-resonance R1rho dispersion models.
* Import fix for the Relax_disp.test_model_nesting_and_param
system test. Somehow the import of the convert_no_rex() function was
lost.
* Modified the MODEL_LIST_R1RHO_OFF_RES list to include
MODEL_NOREX_R1RHO.
* The specific_analyses.relax_disp.parameters.r1_setup()
function is now being called. This happens before the R1 data is
returned in the Disp_minimise_command class.
* The dispersion auto-analysis now handles the optional R1
parameter correctly. The value.set user function was no longer
setting the R1 parameter to the default value when the grid search was
deactivated, as it is no longer in MODEL_PARAMS. So instead the new
is_r1_optimised() function is being used to decide if the value.set
user function should set the 'r1' parameter value.
* The dispersion loop_parameter() function now calls
r1_setup() to handle R1 parameters correctly. This allows the R1
parameter to be removed or added to the parameter list prior to
looping over the parameters of the model. The change is required to
allow for the dynamic handling of R1 parameters.
* The dispersion back_calc_r2eff() function can now handle the
dynamic R1 parameter. This required a call to r1_setup() to add or
remove the parameter, and is_r1_optimised() to obtain the r1_fit flag
to be sent into the target function class.
* Updated the specific_analyses.relax_disp.model.Model_class
class to handle the dynamic R1 parameter. The class variable
self.params now has the 'r1' parameter prepended to the list if
is_r1_optimised() returns True.
* More changes for
specific_analyses.relax_disp.model.Model_class for the dynamic R1
parameter. The 'r1' parameter is only prepended to self.params if it
is not already in the list.
* Created the MODEL_LIST_FIT_R1 variable to keep track of
dispersion models with R1 fitting support.
* The is_r1_optimised() function now checks MODEL_LIST_FIT_R1.
If the model is not in MODEL_LIST_FIT_R1, i.e. R1 optimisation is not
supported, then the function will return False.
* Fix for the test_nesting_param_5 unit test. The 'r1'
parameter is now dynamic and hence will not be present in the initial
list.
* One final fix for the Model_class.params list with 'r1'.
The is_r1_optimised() function is now called with the model name
argument, as required.
* Updated the relax_disp.r1_fit user function docstring. This
now includes information about which models support R1 parameter
optimisation.
* Removed results files to allow the
Relax_disp.test_r1rho_kjaergaard_missing_r1 system test to pass.
These are the
test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/check_graphs/mc_2000/
results files for the 'No Rex' and 'DPL94' models, as well as the
final run. This commit is to allow the test to temporarily pass. It
can be reverted once a better solution is discussed and decided upon.
* Altered the number of Monte Carlo simulations in test script to
2000.
* Merger of the 'No Rex' and 'No Rex R1rho off res' models in
the specific_analyses.relax_disp package. In the 'variables' module,
all *_NOREX_R1RHO variables have simply been deleted and the
MODEL_LIST_* structures updated. For the 'data' module, the
is_r1_optimised() function was modified to catch the 'No Rex' model
and to then use the cdp.exp_type_list structure to determine if the
experiment type is EXP_TYPE_R1RHO. This will be modified in the
future by using a function for determining if the current experiment
is on or off-resonance. The return_r1_data() and return_r1_err_data()
functions have also been modified to check if R1 values are fit rather
than if the model is in MODEL_LIST_R1RHO_OFF_RES. In the 'model'
module, in addition to deleting all *_NOREX_R1RHO variables, the
convert_no_rex() function has also been deleted as it no longer serves
a purpose. In the 'checks' module, all 'No Rex R1rho off res' model
references have been replaced with 'No Rex'.
* Updated the dispersion auto-analysis for the universal 'No
Rex' model. The 'No Rex R1rho off res' references have all been
deleted. The model conversion logic is also no longer needed and has
been deleted.
* Converted the relaxation dispersion GUI interface to the
unified 'No Rex' model. All of the MODEL_NOREX_R1RHO references have
simply been deleted.
* Converted the relaxation dispersion target function class to
the unified 'No Rex' model design. On top of removing all references
to MODEL_NOREX_R1RHO, the aliasing of self.func now checks the
experiment type list to determine which target function to use. This
is not an ideal solution and will not handle mixed CPMG and R1rho
experiment, however neither will the target functions yet. The
creation of the off-resonance data structures has also been modified
so that they are now R1rho independent. This allows the structures to
be properly created while at the same time enabling this code to be
compatible with off-resonance CPMG data in the future.
* Removed the 'No Rex R1rho off res' model from the
relax_disp.select_model user function frontend.
* Removed all references to the 'No Rex R1rho off res' model
in the system tests. In addition, the Relax_disp.test_convert_no_rex
system test has been deleted as it no longer has a purpose. For the
Relax_disp.test_model_nesting_and_param system test, to allow this to
work the cdp.exp_type_list list is set to EXP_TYPE_LIST.
* Removed all references to the 'No Rex R1rho off res' model
in the unit tests.
* Updated the 'No Rex' dispersion model description in the
relax manual. The universal nature of the model is now described,
including the addition of the off-resonance CPMG and R1rho equations
for the absence of chemical exchange. The R1 parameter optimisation
is also shortly covered.
* Added a subsection to the dispersion chapter of the manual
about R1 parameter optimisation.
* Added the R1 parameter fitting GUI element to the dispersion
GUI tab. This is a simple Boolean toggle element that allows the R1
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 relax_disp.r1_fit user function
will be called to turn R1 parameter fitting on.
* Added the 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 task #7820 (https://gna.org/task/?7820).
* Fix for the 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 task #7820
(https://gna.org/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.
* Implemented the GUI test
Relax_disp.test_bug_22501_close_all_analyse to catch bug #22501
(https://gna.org/bugs/?22501), 'Close all analyses' raises error.
* Inserted intermediate system test, to profile R2eff
calculation for R1rho. System test is:
Relax_disp.test_bug_9999_slow_r1rho_r2eff_error_with_mc. This system
test actually fails, if one tries to do a grid search. This is
related to the R2eff values stored as dictionary, and
pipe_control.minimise.grid_setup() will fail. The function
'isNaN(values[i])' cannot handle dictionary.
* Modified intermediate system test
Relax_disp.test_bug_9999_slow_r1rho_r2eff_error_with_mc to see if the
initial grid search for 'i0' and 'R2eff' estimation can be skipped.
This is done by converting the exponential curve, to a linear curve,
and calculate the best parameters by a line of best fit by least
squares. This seems like a promising method as an initial estimator
of 'i0' and 'r2eff'. For 500 to 2000 Monte Carlo simulations, this
could have an influence on the timings, as all grid searchs could be
skipped.
* Modified system test
test_bug_9999_slow_r1rho_r2eff_error_with_mc to save data arrays.
This is to prepare a profiling script.
* Added start script with basic data for profiling the relax curve
fit.
* Created the Structure.test_create_diff_tensor_pdb system
test. This is to show the failure of the
structure.create_diff_tensor_pdb user function when no structural data
is present.
* Created the Structure.test_create_diff_tensor_pdb2 system
test. This is to catch another situation leading to bug #22505
(https://gna.org/bugs/?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 bug #22502 (https://gna.org/bugs/?22502), the
geometric prolate diffusion representation does not align with axis in
PDB, as reported by Martin Ballaschk (https://gna.org/users/mab). 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.
* Deleted the duplicated Structure.test_create_diff_tensor_pdb
system test.
* Created a number of system tests to check the diffusion
tensor PDB representation. This is to prevent bugs such as #22502
(https://gna.org/bugs/?22502) from ever reappearing. The PDB file
contents are hardcoded into the tests and checked. The tests include
Structure.test_create_diff_tensor_pdb_ellipsoid,
Structure.test_create_diff_tensor_pdb_oblate,
Structure.test_create_diff_tensor_pdb_prolate, and
Structure.test_create_diff_tensor_pdb_sphere.
* Improved data checking for all of the Structure system
tests. Before looping over the structural data, the number of lines
in the real file and the newly generated file are compared. This
avoids the situation whereby an empty file is produced, accidentally
allowing the test to pass.
* Modified following functions (time points are now saved at
the [ei][mi][oi][di] index level, at this index level all time points
are saved for the R2eff point): interpolate_disp(), to interpolate
time points, all time points through the original dispersion points
di, are collected and then made unique - this time list can
potentially be the largest of all time lists; interpolate_offset(),
to interpolate time points, all time points through the original
offset points, and then dispersion points di, are collected and then
made unique - this time list can potentially be the largest of all
time lists; plot_disp_curves_to_file(), to acquire the original
relax_times points; return_r2eff_arrays(), to save all time points on
the level of [ei][mi][oi][di]. At this index level, it will be a
numpy array list with all time values used for fitting. Bug #22461
(https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has extremely
high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Modified back_calc_r2eff() to accept interpolated time
points. Bug #22461 (https://gna.org/bugs/?22461): NS R1rho
2-site_fit_r1 has extremely high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Modified target function of relax dispersion, to use the new
list of time points, which are of higher dimension. Bug #22461
(https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has extremely
high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Fix to system test
Relax_disp.test_r1rho_kjaergaard_missing_r1(). After the relaxation
times have been fixed, this model now return reasonable chi2 values.
The reported parameters are though quite different from all other
models, and it seems something may still be wrong. Bug #22461
(https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has extremely
high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Fix for system test Relax_disp.test_exp_fit(), where the
spin.isotope was not set. The new call to return_r2eff_arrays(), when
producing graphs, raise RelaxSpinTypeError() if no isotope is set.
Bug #22461 (https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has
extremely high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Modified the Relax_disp.test_r1rho_kjaergaard_missing_r1
system test to pass on 64-bit Linux systems. The accuracy of the
checks of the optimised values have been decreased.
* Moved the storing of relax time up before check of missing
data in return_r2eff_arrays(). Bug #22461
(https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has extremely
high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Fix for system test not adding spin.isotope to setup
information. Bug #22461 (https://gna.org/bugs/?22461): NS R1rho
2-site_fit_r1 has extremely high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Fix for looping over data indices, where tilt_angles has the
si index. Bug #22461 (https://gna.org/bugs/?22461): NS R1rho
2-site_fit_r1 has extremely high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Added Nikolai's original Matlab code to the
lib.dispersion.ns_r1rho_2site module docstring. This is the code
taken directly form the original funNumrho.m file, which was the
origin of the code in this module.
* Further extended the profiling script for curve fitting.
Now profiling is in place for the implemented C code method in relax.
A similar code should now be devised for numpy array for comparing.
But this profiling shows that when contraints=True, is slowing down
this procedure by a factor 10 X.
* Further improved the profiling of relax curve fit. This
profiling shows, that Python code is about twice as slow as the C code
implemented. But it also shows that optimising with
scipy.optimize.leastsq is 20 X faster. It also gives reasonable error
values. Combining a function for a linear fit to guess the initial
values, together with scipy optimise, will be an extreme time win for
estimating R2eff 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 R1rho, where the errors of the R2eff
values, are estimated by Monte Carlo simulations. The following setup
illustrates the problem. This was analysed on a MacBook Pro, 13-inch,
Late 2011 with no multi-core setup. Script running is:
test_suite/shared_data/dispersion/Kjaergaard_et_al_2013/2_pre_run_r2eff.py.
This script analyses just the R2eff values for 15 residues. It
estimates the errors of R2eff based on 2000 Monte Carlo simulations.
For each residues, there is 14 exponential graphs. The script was
broken after 35 simulations. This was measured to 20 minutes. So 500
simulations would take about 4.8 Hours. The R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Implemented initial system test
Relax_disp.test_estimate_r2eff for setting up the new user function to
estimate R2eff and errors by scipy. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added front end user function relax_disp.r2eff_estimate to
estimate R2eff and errors by exponential curve fitting in
scipy.optimize.leastsq. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Modified check for model, to accept model as input, for
error printing. Task #7822 (https://gna.org/task/?7822): Implement
user function to estimate R2eff and associated errors for exponential
curve fitting.
* Implemented back end for estimating R2eff and errors by
exponential curve fitting with scipy.optimize.leastsq. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Documentation fix for new exponential target function. Also
added new function to estimate R2eff and i0 parameters, before
minimisation. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Small changes to verification scripts, to use chi2 function
and use the scaling matrix correct. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Split up of system test test_r1rho_kjaergaard_missing_r1,
into a verification part. This is to test the new r2eff estimation,
which should get the parameter values, as a this 2000 Monto Carlo
simulation. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff 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 chi2
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.
* Add dependency check for scipy.optimize.leastsq. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Lowering precision in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1. This is R1 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.
* Moved target function for curve fitting with scipy into
specific_analyses.relax_disp.estimate_r2eff. This will later include
the backend specific_analyses.relax_disp.optimisation.estimate_r2eff()
function and the code in the target_functions package. The code in
target_functions.relax_disp_curve_fit is a lot more than just a target
function, so it doesn't really belong in this package. This is also
to isolate this experimental feature.
* Isolated all code related to 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.
* Implemented first try to minimise with minfx in
estimate_r2eff() function. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff 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.
* 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.
* 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Intermediate step in estimate R2eff module. It seems that
minfx is minimising in a quadratic space because of the power of chi2,
while the general input to scipy.optimize does not do this. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Cleaned up target function for leastsq, since arguments to
function can be extracted from class. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task #7822 (https://gna.org/task/?7822): Implement
user function to estimate R2eff 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 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.
* Implementation of the
specific_analyses.relax_fit.optimisation.dfunc_wrapper() function.
This interfaces with the target_functions.relax_fit C module and
converts the gradient from a Python list to a numpy array.
* The exponential curve-fitting gradient is now scaled by the
scaling matrix.
* Clean up of the end of the
target_functions.relax_fit.dfunc() function.
* Fixes for the target function chi-squared gradient C function.
* Fixes for all of the exponential functions in
target_functions/exponential.c. The condition whereby Rx is zero is
now setting the value correctly - the exponential will be 1, not zero,
hence the intensity and gradient values should not be zero.
* Clean up of the target function C files (spacing fixes and
removal of unused code).
* Changed the argument and variable names in the C code
chi-squared gradient function.
* Modified all of the exponential curve functions and
gradients in the C target function module. Instead of passing in the
parameter vector, now the I0 and R values are passed in separately.
This is for greater code flexibility as the parameter order does not
need to be known.
* 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 I0 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 R2eff module. This is to make the
documentation more easy to read and understand. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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.
* The relax_fit C module unit tests now check if the parameter
scaling is functional.
* Added several comments to the R2eff estimate module. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff 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.
* Created a unit test to check the dfunc() function of the
relax_fit C module off the minimum.
* Fixes for the target_functions.relax_fit C module unit
tests. All values are now set to floats to avoid integer division
issues.
* Activated parameter scaling of the gradient in 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 exponential curve numeric gradient script now uses only
floating point numbers. This is to avoid integer truncation problems.
* Fix for the script for calculating the numerical gradient
for an exponential curve. The off-minimum derivative was not
correctly calculated.
* Increased the printouts for the script for calculating the
numerical gradient for an exponential curve.
* Bug fix for the chi-squared gradient calculation in the C
module. The definition of the square() function needed extra brackets
so that the 1/error**2 calculation would be 1/(error*error) rather
than the incorrect 1/error*error form.
* 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).
* Got the method of 'Steepest descent' to work properly, by
specifying the Jacobian correctly. The Jacobian was derived according
to the chi2 function. The key point was to evaluate the two
derivative arrays for all times points, and then sum each of the two
arrays together, before constructing the Jacobian. This clearly shows
the difference between minfx and scipy.optimize.leastsq.
scipy.optimize.leastsq takes as input a function F(x0), which should
return the array of weighted differences between function value and
measured values: "1. / self.errors * (self.calc_exp(self.times,
*params) - self.values)". This will be an array with number of
elements 'i' corresponding to number of elements.
scipy.optimize.leastsq then internally evaluates the sum of squares ->
sum[ (O - E)**2 ], and minimises this. This is the chi2. Minfx
requires the function to minimise before hand. So, the "func" should
be chi2. Then the dfunc, and d2func, should be derivative of chi2,
but all elements in the array should still be summed together. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff 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.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff 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.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Added initial documentation for multifit_covar. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Modified profiling script to use the new estimate R2eff
module. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Modified verify error script, to use new estimate R2eff
module. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Removed all unnecessary code from estimate R2eff module.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* More removal of code. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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/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.
* Shifted all of the parameter Python lists to C arrays into
the new param_to_c() function. This is for the
target_functions.relax_fit C module to avoid much duplicated code.
* Removed the comment and docstring saying that the
exponential curve-fitting gradient is unimplemented.
* Updated the copyright notices in the C files of the
target_functions directory.
* Implemented the C version of the chi-squared Hessian. This
is a direct translation of the Python code.
* Changed the internal variables of the chi-squared gradient C
code to match the Python code.
* Standardisation of the array dimensionality in the target
function C code. The new target_functions/dimensions.h header file
defines MAX_PARAMS and MAX_DATA which is then included in the header
files of all the other C files. All array declarations now explicitly
specify the length of each dimension. The values of MAX_PARAMS and
MAX_DATA have increased from 3 and 50 to 20 and 5000. This is to
allow for models with more parameters and to allow a much larger
number of input data points to be supported before memory corruptions
happen. The data structures now take up more memory, but as the
functions do not loop up to maximum but only the number of parameters
and points specified, this will not make the code slower.
* All of the C code chi-squared functions now have the array
argument dimensions explicitly declared.
* The target_functions/exponential.c file no longer includes
exponential.h. This is not needed as exponential.h only contains the
function definitions of the exponential.c file.
* Clean up of the header and includes of the
target_functions/c_chi2.c file. The square() function macro has been
shifted to the header file and the stdio.h and math.h standard library
headers are no longer included as they are not used.
* Partly implemented the front end
target_functions.relax_fit.d2func() C module Python function. This is
not fully implemented as the exponential curve double partial
derivatives are not implemented.
* Implemented the exponential curve second partial derivative
C functions. These are declared in the exponential.h header file and
are now used by the Python function
target_functions.relax_fit.d2func().
* The square() function macro is now defined for the
target_functions/exponential.c file. This is defined in the header,
and now the exponential.h is included in the C file to access it.
* Implemented the
specific_analyses.relax_fit.optimisation.d2func_wrapper() function.
This converts the numpy parameter array into a Python list, calls the
target_functions.relax_fit.d2func() function with this list, converts
the Hessian output list of lists into a numpy rank-2 array, and
returns it. This will allow Newton optimisation to be used for the
relaxation curve-fitting analysis.
* 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 Newton
optimisation. This is to demonstrate that the exponential curve
gradient function dfunc() and Hessian function d2func() are
implemented correctly.
* 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 Hessian using the numdifftools.Hessian object
construct and obtain the matrix, both at the minimum and at a point
away from the minimum. The values will be used to construct a unit
test to check the C module implementation.
* 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.
* Modified profiling script, but it seems that the dfunc from
target_functions.relax_fit does not work. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Modified estimate R2eff 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
R1rho + CPMG dispersion analysis. The generate.py script will be
extended in the future to generate both synthetic R1rho 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.
* Modified wrapper function for curve_fit, to only change to
list type, if the type is a ndarray. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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.
* Implemented the first try to compute the variance of R2eff
and i0, by the covariance. This uses the Jacobian matrix. The errors
calculated, are though way to small compared 2000 Monte Carlo
simulations. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff 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.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff 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.
* Tried to verify solution to profiling script. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Set the constraints=False when doing Monte Carlo simulations
for R2eff. This is to speed up the Monte Carlo simulations by a
factor X10, when estimating the error for R2eff. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Implemented the use of "Newton" as minimisation algorithm
for R2eff 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. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff 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 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**2. There is though some unanswered questions left. The
Jacobian used, is the direct derivative of the function. It is not
the chi2 derivative Jacobian. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Fixed naming of functions, to better represent what they do
in module of estimating R2eff. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Tried to implement a safety test for linearly-dependent
columns in the covariance matrix. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Fixes for the 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.
* Removed the user function to estimate the R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added front-end to the new user function
relax_disp.r2eff_err_estimate(), which will estimate the R2eff errors
from a pipe and spins with optimised values of R2eff and i0. The
covariance matrix can be calculated from the optimised parameters, and
the Jacobian. Big care should be taken not to directly trust these
results, since the errors are quite different compared to the Monte
Carlo simulations. This implementation, will reach the exact same
error estimation as scipy.optimize.leastsq. But with much better
control over the data, and insight into the calculations. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added method to automatically perform error analysis on peak
heights. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Modified system test Relax_disp.test_estimate_r2eff() to
first do a grid search, then minimise and then estimate the errors for
R2eff and i0. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Added back-end to estimate R2eff errors. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Fix to system test test_estimate_r2eff_error(), to first
delete the old error estimations. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added several tests to: test_estimate_r2eff_error, to
compare different output from algorithms. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Cleaned up code in R2eff error module. Also removed a non
working Hessian matrix. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Moved code around, and made function multifit_covar()
independent of class object. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Inserted checks for C module is available in module for
estimating R2eff error. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Removed unnecessary call to experimental Exp class. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Renamed system test, that test the user function for
estimating the R2eff error: test_estimate_r2eff_err, test the user
function. test_estimate_r2eff_err_methods, test different methods for
getting the error. Task #7822 (https://gna.org/task/?7822): Implement
user function to estimate R2eff and associated errors for exponential
curve fitting.
* Added system test, Relax_disp.test_estimate_r2eff_err_auto
and extended functionality to the auto-analyses protocol. If
"exp_mc_sim_num" is set to "-1" and sent to the auto-analyses, the
errors of R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added script, to be used in GUI test. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added GUI test Relax_disp.test_r2eff_err_estimate, to test
the setting of MC sim to -1 for exponential R2eff error estimation.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Added keyword "exp_mc_sim_num", to the auto-analyses in the
GUI. This sets the number of Monte Carlo simulations for R2eff 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. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff 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'". Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added a warning to the auto-analyses about error estimation
from the covariance. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Removed yet another comma from GUI tooltip. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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.
* 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 Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Modified test_estimate_r2eff_err_auto, to set r1_fit to
False. This still make the system test pass, and fit R1. So this
means R1 fit button is not functioning properly. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Fix for warning message in the auto-analyses in the GUI.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Tried to improve docstring for API documentation. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff 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.
* 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.
* 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. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Implemented system test
Relax_disp.verify_estimate_r2eff_err_compare_mc for testing R2eff
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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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.
* Modified system test test
Relax_disp.test_estimate_r2eff_err_methods() to show the difference
between using the direct function Jacobian, or the chi2 function
Jacobian. Added also the functionality to the estimate R2eff module,
to switch between using the different Jacobians. The results show,
that R2eff can be estimated better.
* Added isotope information to the lib.periodic_table module
including mass number and atomic mass. A new Isotope data container
has been added to store this information. The Periodic_table._add()
method now returns the initialised Element container. This container
has the _add_isotope() method which is used to initialise Isotope data
containers with the mass number and atomic mass and append it to the
list.
* Created a unit test for the Periodic_table.atomic_mass()
method. This method is not implemented yet.
* Changed the method call in the new
Test_periodic_table.test_get_atomic_mass unit test.
* Fix for the Test_periodic_table.test_get_atomic_mass unit
test - the method calls were incorrect.
* Implemented the lib.periodic_table module
Periodic_table.atomic_mass() method. This method will return either
the atomic mass of an isotope or the standard atomic weight.
* Changed the operation of the
lib.structure.mass.centre_of_mass() function. Instead of using the
lib.physical_constants.return_atomic_mass() function, the
centre_of_mass() function instead uses the
lib.periodic_table.periodic_table.atomic_mass() method. This is a
huge improvement in that the exact mass of absolutely all elements are
taken into account.
* Deletion of the lib.physical_constants.return_atomic_mass()
function and all relative atomic masses. These were inaccurate and
only included a tiny subset of all standard atomic weight and isotope
masses. The functionality has been replaced by the complete and 100%
accurate complete Periodic_table object in the lib.periodic_table
module.
* Fix for the MolContainer.fill_object_from_gaussian() method.
This is in the lib.structure.internal.molecules module. The
Periodic_table.lookup_z_to_symbol() method in the lib.periodic_table
module has been renamed to lookup_symbol().
* Fix for the Periodic_table.lookup_symbol() method. The
__init__() method of the Periodic_table has been reintroduced to
initialise a fast atomic symbol lookup table. The _add() method then
updates this table. And the lookup_symbol() method now uses this
lookup table to correctly return the symbol.
* Tiny fix for the
Diffusion_tensor.test_create_diff_tensor_pdb_ellipsoid system test.
The switch to using the lib.periodic_table module for atomic masses
has caused the centre of mass of the ellipsoid to shift just enough
that one ATOM coordinate in the PDB file has changed its last
significant digit.
* Created the lib.periodic_table.process_symbol() function.
This will take an atomic symbol and return a copy of it with an
uppercase first letter and lowercase second letter. This is used by
the Periodic_table methods atomic_mass() and atomic_weight() to allow
for non-standard symbol input, for example if the element name comes
directly from the all uppercase PDB file format without translation.
* Tried to scale the covariance matrix, as explained here:
http://www.orbitals.com/self/least/least.htm. This does not work
better. Also replaced "errors" to "weights" to the multifit_covar(),
to better determine control calculations. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added all gyromagnetic ratio information from
lib.physical_constants to lib.periodic_table. The
Periodic_table.gyromagnetic_ratio() method has been added to allow
this value to be easily returned.
* Added to back-end of R2eff estimate module, to be able to
switch between the function Jacobian or the chi2 Jacobian. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Modified user function 'relax_disp.r2eff_err_estimate', to
be able switch between the Jacobians. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Modified system test
Relax_disp.verify_estimate_r2eff_err_compare_mc, to try the difference
between the Jacobian. The results are: Printing the estimated R2eff
error as function of estimation from covariance and number of Monte
Carlo simulations. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Deleted the gyromagnetic ratio values and
return_gyromagnetic_ratio() function from lib.physical_constants.
* Shifted all of relax to use the lib.periodic_table module
for gyromagnetic ratios. The values and value returning function have
been removed from lib.physical_constants and replaced by the
Periodic_table.gyromagnetic_ratio() method in the lib.periodic_table
module.
* Started making functions in R2eff estimate module,
independent on the informations stored in the Class. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Cleaned up code in R2eff estimate module, by making each
function independent of class. This is to give a better overview, how
the different functions connect together. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Made the user function, which estimates the R2eff errors,
use the Jacobian derived from chi2 function. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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 chi2 derived
Jacobian. This shows the result better. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added digit to printout in R2eff estimate module. Task
#7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Locked values for system test test_estimate_r2eff_err, to
estimate how the R2eff error estimation reflects on fitted parameters.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* More locking of values, when trying to use different methods
for estimating R2eff err values. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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 chi2 Jacobian.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Reverted the logic, that the chi2 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. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Various precision fixes for different machine precision.
This is in: verify_r1rho_kjaergaard_missing_r1 Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* First attempt at properly implementing the
target_functions.relax_fit.jacobian() function. This is now the
Jacobian of the chi-squared function. A new jacobian_matrix data
structure has been created for holding the matrix data prior to
converting it into a Python list of lists. The equation used was
simply the chi-squared gradient whereby the sum over i has been
dropped and the i elements are stored in the second dimension of
matrix.
* Speed up of the target_functions.relax_fit C module. The
variances are now pre-calculated in the setup() function from the
errors, so that the use of the square() function is minimised. The
chi-squared equation, gradient, and Hessian functions now accept the
variance rather than standard deviation argument and hence the
squaring of errors has been removed. This avoids a lot of duplicated
maths operations.
* Alphabetical ordering of global variable declarations in the
target_functions.relax_fit header file.
* Added RelaxError, if less than 2 time points is used for
exponential curve fitting in R2eff. 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. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Created the select.display user function. This simply
displays the current spin selections of all spins. In the future it
can be extended to display the interatomic data container selections,
domain selections, etc.
* Fix for system test: test_estimate_r2eff_err_auto(). The
Jacobian to estimate the errors has been changed from the direct
function Jacobian, to the Jacobian of the chi2 function. This changes
the R2eff error predictions, and hence parameter fitting. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Implemented the direct Jacobian in Python, to be independent
of C code in development phase. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Increased the number of time points for exponential curve
fitting to 3.
* Fix to weight properly according to if minimising with
direct Jacobian or chi2 Jacobian. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Fix to system test test_estimate_r2eff_err_methods, after
modification of weighting. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Switched in estimate_r2eff_err() to use the chi2 Jacobian
from C code, and Jacobian from Python code. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Removed all references to test values which was received by
wrong weighting. Task #7822 (https://gna.org/task/?7822): Implement
user function to estimate R2eff and associated errors for exponential
curve fitting.
* Better error checking in the relaxation dispersion
overfit_deselect() API method. The model must be set for this
procedure to work, and the method now checks that this is the case.
* Better error checking for the
specific_analyses.relax_disp.average_intensity() function. This
function would fail with a traceback if a peak intensity error
analysis had not yet been performed. Now it fails instead with a
clean RelaxError so that the user knows what is wrong.
* Tried implementing getting the chi2 gradient, using
target_function.chi2.dchi2(). The output seem equal. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Replaced the way to calculate the chi2 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 chi2 gradient functions to minfx, since it is only necessary
to implement the direct gradient of the function, and then pass the
direct gradient to chi2 library, which turn it into the chi2 gradient
function which minfx use. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Moved unnecessary function in R2eff error estimate module
into experimental class. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* An attempt at documenting the Monte Carlo simulation verses
covariance matrix error estimates. This is for the R2eff and I0
parameters of the exponential curves. For the Monte Carlo errors,
10000 simulations were preformed. This means that these errors can
perform as a gold standard by which to judge the covariance matrix
technique. Currently it can be seen that the
relax_disp.r2eff_err_estimate user function with the chi2_jacobian
flag set to True performs extremely poorly.
* Reintroduced the original
target_functions.relax_fit.jacobian() function. The new function for
the Jacobian of the chi-squared function has been renamed to
target_functions.relax_fit.jacobian_chi2() so that both Python
functions are accessible within the C module.
* Epydoc fixes for the
pipe_control.mol_res_spin.format_info_full() function.
* Epydoc docstring fixes for many methods in the relaxation
dispersion auto-analysis module.
* If math domain errors are found when calculating the two
point R2eff values, the point is being skipped. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Moved intensity negative value from reference to CPMG point.
* Modified system test test_bug_negative_intensities_cpmg, to
prepare for testing number of R2eff points. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Comparison of 10,000 Monte Carlo simulations to a different
covariance matrix error estimate. The covariance_matrix.py script has
been duplicated and the chi2_jacobian argument of the
relax_disp.r2eff_err_estimate user function has been changed from True
to False. As can be seen in the 2D Grace plots, this error estimate
is incredibly different. The R2eff errors are overestimated by a
factor of 1.9555, which indicates that the Jacobian or covariance
matrix formula are not yet correct.
* The target_functions.relax_fit C module Python function
jacobian_chi2() is now exposed. This was previously not visible from
within Python.
* Added a script and log file for calculating the numerical
Jacobian for an exponential curve. This uses the data at
http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840
and calculates the Jacobian using the numdifftools.Jacobian object
construct and obtain the matrix, both at the minimum and at a point
away from the minimum. The values will be used to construct a unit
test to check the C module implementation.
* Created two unit tests showing the
target_functions.relax_fit.jacobian() function is correct. This
compares the calculated Jacobian to the numerically integrated values
from the test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py
script.
* Renamed the
test_data/shared_data/curve_fitting/numeric_gradient/ directory to
numeric_topology. This is to better reflect that it contains numeric
approximations to the gradient, Hessian, and Jacobian.
* Added a script and log for calculating the numerical
chi-squared Jacobian for an exponential curve. This uses the data at
http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840
and calculates the chi-squared Jacobian using the
numdifftools.Jacobian object construct and obtain the matrix, both at
the minimum and at a point away from the minimum. The values will be
used to construct a unit test to check the C module implementation.
* Fix for the chi-squared Jacobian numerical approximation
script. The function was modified to that a list of chi-squared
elements are returned, i.e. the sum part of the chi-squared equation
has been removed.
* Created two unit tests showing the
target_functions.relax_fit.jacobian_chi2() function is correct. This
compares the calculated chi-squared Jacobian to the numerically
integrated values from the
test_suite/shared_data/curve_fitting/numeric_topology/jacobian_chi2.py
script.
* Added a script and log for calculating the numerical
covariance matrix for an exponential curve. This uses the data at
http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840
and calculates the covariance matrix via the Jacobian calculated using
the numdifftools.Jacobian object construct and obtain the matrix, both
at the minimum and at a point away from the minimum. The covariance
is calculated as inv(J^T.W.J).
* Added a script and log for calculating the exponential curve
parameter errors via bootstrapping. This uses the data at
http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840
and calculates the parameter errors via bootstrapping. As the
parameters at the minimum are the exact parameter values,
bootstrapping and Monte Carlo simulation converge and hence this is a
true error estimate. 200,000 simulations where used, so the parameter
errors are extremely accurate.
* Modified module to estimate R2eff errors, to use the C code
Jacobian. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Modified system test test_estimate_r2eff_err_methods, to
check all Jacobian methods are correctly implemented. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added more print out information, when log(I / I_ref) is
negative, and raising errors. This can help the user track back
information to the error more easily.
* Improved system test test_bug_negative_intensities_cpmg, by
counting number of R2eff points. Spin 4, which has one negative
intensity, is expected to have one less R2eff point. This makes sure,
that all CPMG data set can be loaded and analysed, even if some peaks
are very weak are fluctuating with error level.
* Fix for also storing 'r1_fit' to cdp even though it is set
to False. Bug #22541 (https://gna.org/bugs/?22541): The R1 fit flag
does not work in the GUI.
* Cleanup in GUI test Relax_disp.test_r2eff_err_estimate.
This now passes after previous commit. Bug #22541
(https://gna.org/bugs/?22541): The R1 fit flag does not work in the
GUI.
* Added model DPL94, to be tested in GUI test
Relax_disp.test_r2eff_err_estimate. This shows that the bug is still
there. Bug #22541 (https://gna.org/bugs/?22541): The R1 fit flag does
not work in the GUI.
* Fix for system test test_estimate_r2eff_err and
test_r1rho_kjaergaard_missing_r1, where r1_fit=True, needed to be send
to Auto_analyses. Bug #22541 (https://gna.org/bugs/?22541): The R1
fit flag does not work in the GUI.
* API documentation fixes.
* Moved multifit_covar into lib.statistics, since it is an
independent module. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Moved "func_exp_grad" into experimental class for different
minimisation methods. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Improved documentation to user function
relax_disp.r2eff_err_estimate, and removed the possibility to use the
chi2 Jacobian, as this is rubbish. But the back-end still have this
possibility, should one desire to try this. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task #7822 (https://gna.org/task/?7822): Implement
user function to estimate R2eff and associated errors for exponential
curve fitting.
* Fix to system test test_estimate_r2eff_err, after removing
the possibility to use the chi2 Jacobian. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Fix for system test test_estimate_r2eff_err_methods. The
function was called wrong in experimental class. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Initial try write comments how to generalize the scaling of
the covariance according to the reduced chi2 distribution. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* First try to make a test script for estimating efficiency on
R2eff error calculations. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff 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. Task #7822 (https://gna.org/task/?7822): Implement user function
to estimate R2eff and associated errors for exponential curve fitting.
* In module for estimating R2eff errors, removed "values,
errors" to be send to function for gradient, since they are not used.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff and associated errors for exponential curve fitting.
* Added Jacobian to test script, and now correctly do
simulations, per R2eff points. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Improved analysing test script, with plotting. It seems
that R2eff error estimation always get the same result. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added initial dataset for test analysis. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Deleted test data set. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting.
* Added simulations that show, there is perfect agreement
between Monte Carlo simulations and covariance estimation. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff 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 i0 are positive, and the standard deviation from Monte Carlo
simulations are equal. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff 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. Bug #22552
(https://gna.org/bugs/?22552): Chi2 value returned from C code
curve-fitting return 0.0 for wrong parameters -> Expected influence on
Monte Carlo sim.
* Inserted possibility for bootstrapping in system test
Relax_disp.test_estimate_r2eff_err_methods. This shows, that the
bootstrapping method get the SAME estimation for R2eff 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": Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting.
* Added functionality to create peak lists, for virtual data.
This is to compare the distribution of R2eff values made by
bootstrapping and Monte Carlo simulations. Rest of the analysis will
be performed in relax. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff and associated errors for
exponential curve fitting.
* Added initial peak lists to be analysed in relax for test
purposes. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff 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.
Task #7822 (https://gna.org/task/?7822): Implement user function to
estimate R2eff 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. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff 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. Task #7822 (https://gna.org/task/?7822):
Implement user function to estimate R2eff 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. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff and associated errors for exponential curve
fitting. Bug #22554 (https://gna.org/bugs/?22554): The distribution
of intensity with errors in Monte Carlo simulations are markedly more
narrow than expected.
* Cleaned up user function for estimating R2eff errors.
Extensive tests have shown, there is a very good agreement between the
covariance estimation, and Monte Carlo simulations. This is indeed a
very positive implementation. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting. Bug #22554
(https://gna.org/bugs/?22554): The distribution of intensity with
errors in Monte Carlo simulations are markedly more narrow than
expected.
* Removed all junk comments from module for R2eff error
estimation. The module runs perfect as it does now. Task #7822
(https://gna.org/task/?7822): Implement user function to estimate
R2eff and associated errors for exponential curve fitting. Bug #22554
(https://gna.org/bugs/?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]). Bug
#22552(https://gna.org/bugs/?22552): Chi2 value returned from C code
curve-fitting return 0.0 for wrong parameters -> Expected influence on
Monte Carlo sim. Ref:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.nan_to_num.html.
* Initial try to reach constrained methods in minfx through
relax. This is in system test
Relax_disp.verify_estimate_r2eff_err_compare_mc() This though not
seem to be supported.
* Allow R2eff model to reach constrained methods in minfx
through relax. This is in system test
Relax_disp.verify_estimate_r2eff_err_compare_mc() This though not
seem to be supported.
* Modified specific_analyses.relax_disp.parameters.r1_setup()
to initialise the 'r1' variable. This relates to bug #22541
(https://gna.org/bugs/?22541), the R1 fit flag does not work in the
GUI. This is a hack, as all of the dispersion analysis code assumes
that all parameters are initialised. This is a dangerous assumption
that will have to be eliminated in the future.
* The dispersion get_param_values() API method now calls the
r1_setup() function. This relates to bug #22541
(https://gna.org/bugs/?22541), the R1 fit flag does not work in the
GUI. This is to make sure that the parameters are correctly set up
prior to obtaining all parameter values. The R1 parameter is dynamic
hence r1_setup() needs to be called at any point model parameters are
accessed, as the R1 parameter can be turned on or off at any time with
the relax_disp.r1_fit user function.
* Yet another try to implement constrained method in
verify_estimate_r2eff_err_compare_mc.
* Another attempt to reach constrained method in minfx through
relax. I would need to specify: l, lower bound constraint vector (l
<= x <= u); u, upper bound constraint vector (l <= x <= u); c: user
supplied constraint function; dc: user supplied constraint gradient
function.
* Added a derivation of the R2eff/R1rho error estimate for the
two-point measurement to the manual. This is from
http://thread.gmane.org/gmane.science.nmr.relax.devel/6929/focus=6993
and is for the rate uncertainty of a 2-parameter exponential curve
when only two data points have been collected. The derivation has
been added to the dispersion chapter of the manual.
* Equation fixes for the two-point exponential error
derivation in the dispersion chapter of the manual.
* Updated the minfx version numbers in the release checklist
document. The version is now 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.
* Fixes for the Relax_disp.test_estimate_r2eff_err system
test. The kex parameter value checks have all been scaled by 1e-5 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.
* Another fix for the minfx version checking in the dep_check
module. The version_comparison() function has been created to perform
a proper version number comparison by stripping trailing zeros,
converting the two version numbers to lists of int and comparing the
lists using the Python cmp() function. This will return -1 when the
version number is too low, 0 when the versions are equal, and 1 when
the version is higher than the minimum.
* Added a button for the spectrum.error_analysis user function
to the spectra list GUI element. This is placed after the 'Add' and
'Delete' buttons. The functionality could be improved by presetting
the spectrum ID argument to anything the user has selected in the
spectrum list.
* Modified the behaviour of the spectrum.error_analysis button
in the spectrum list GUI element. Now the subset argument of this
user function will be pre-set to any spectra selected in the list.
* Improvements for the spectrum.error_analysis button in the
spectrum list GUI element. The user function is now launched as being
modal so that the rest of the GUI freezes, and after the user function
is executed the relax controller window is show and scrolled to the
bottom.
* Added Relax_disp system tests to black-list, if they depend
on C code module.
* Improvements for the spectrum.error_analysis button in the
spectrum list GUI element. The subset argument is set to None if no
spectra are selected.
* Loosened a value check to allow the
Relax_disp.test_r1rho_kjaergaard_missing_r1 system test to pass. This
test fails on MS Windows systems.
* Fix for the Relax_disp.test_estimate_r2eff_err_auto system
test on MS Windows systems. One of the value checks has been
loosened.
* Python 2 vs. 3 compatibility fix for the pickle module.
This is for the estimate_errors*.py scripts in the directory
test_suite/shared_data/curve_fitting/numeric_topology/. The
lib.compat.pickle module is now used to allow both Python versions to
run relax.
* Python 3 fix, the cmp(v1, v2) notation in the
dep_check.version_comparison() function has been replaced with (v1 >
v2) - (v1 < v2). This allows relax to run on Python 3.
* Python 3 fix for the lib.periodic_table module, the Python
string module does not exist in Python 3.
* Created the user_functions.uf_translation_table list. The
elements of this list are the names of user functions before and after
a renaming. The list is provided for backwards compatibility for
relax scripts, though it is not used yet.
* Converted the user_functions.uf_translation_table object to
a dictionary. This is for faster access which does not require
looping.
* The prompt UI now uses the
user_functions.uf_translation_table dictionary. The modified
runcode() function will now check if the command typed by the user is
a function or method call and then will raise a RelaxError if the
command name is in the user_functions.uf_translation_table dictionary,
telling the user that the user function has been renamed to the new
name in the translation table. This appears to have no effect in the
script UI however.
* Hack in the script UI for handling missing user functions
due to it being renamed. This script UI requires a different solution
as the prompt UI. The script is executed via the runpy Python module
and there appears to be no clean way of catching each command before
it is executed. So instead, prior to executing the script, the
contents of the script are read and old user functions are searched
for using re.search(). The old user function name has "(" appended to
it in the search so that it is sure that it is a user function call.
And the old function must have a space or newline character preceding
it.
Bugfixes:
* MS Windows fixes to allow relax to run again. The code for
eliminating the GNU readline ^[[?1034h escape code emission on Linux
systems fails on Windows as the 'TERM' environmental variable does not
exist in os.environ.
* Fix for the relaxation dispersion analysis Monte Carlo
simulation printouts on clusters. The multi-processor code was
calling the print() function from the Slave_command.run() method,
however this runs on the slave processor. This has been shifted to
the Results_command.run() method which runs on the master once the
results have been returned via the Results_command. Now the printout
of the simulation number and cluster ID will be visible when running
via OpenMPI on a cluster.
* Bug fix for the lib.arg_check.is_num_tuple() function.
There was a typo in two of the RelaxError objects so that non-existent
errors were being raised.
* Grace string fixes for the alignment tensor parameters
defined in the base parameter_object module. This is essentially for
allowing relax to run using Python 3. All Grace '\' characters need
to be escaped as '\\' in Python strings.
* Another Python 3 fix - the string.split function no longer
exists, it is now only a string method.
* Fix for replacing reduce function. This is a Python 3 fix,
where this function has been removed. This was reported as a
necessity in thread:
http://thread.gmane.org/gmane.science.nmr.relax.devel/6544
* Fix for bug #22411 (https://gna.org/bugs/?22411), the
failure in loading a Bruker DC T1 data file. The problem was that
there was an empty line with spaces. The logic for skipping empty
lines could not handle lines with just whitespace. This has now been
fixed.
* Fix for bug #22501 (https://gna.org/bugs/?22501), "Close all
analyses" raises error in the GUI. The problem was general for all
analysis types. This used to work, but as it was not tested in the
test suite, a regression occurred.
* Fix for the return_r2eff_arrays() dispersion function for
exponential curves. This is a partial solution for bug #22461
(https://gna.org/bugs/?22461). For the
Relax_disp.test_r1rho_kjaergaard_missing_r1 system test, there are
multiple relaxation times for each data set. For example, printing
out the exp_type, frq, offset, point, ei, mi, oi, di, and relax_times
data gives: R1rho 799777399.1 118.078 431.0 0 0 0 0 [0.0, 0.04, 0.1,
0.2]; R1rho 799777399.1 118.078 651.2 0 0 0 1 [0.0, 0.04, 0.1, 0.2,
0.4]. Instead of taking the first relaxation time of 0.0, now the
maximum time is taken.
* Fix for bug #22505 (https://gna.org/bugs/?22505). This is
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 bug #22505 (https://gna.org/bugs/?22505).
This is 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 bug #22502 (https://gna.org/bugs/?22502). This is
the problem whereby the geometric prolate diffusion representation
does not align with axis in PDB, as reported by Martin Ballaschk
(https://gna.org/users/mab). 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. Bug #22461 (https://gna.org/bugs/?22461): NS R1rho
2-site_fit_r1 has extremely high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Fix for interpolating time points, when producing xmgrace
files for CPMG experiments. Bug #22461 (https://gna.org/bugs/?22461):
NS R1rho 2-site_fit_r1 has extremely high chi2 value in system test
Relax_disp.test_r1rho_kjaergaard_missing_r1.
* Correction for catastrophic implementation of Monte Carlo
simulations for exponential curve-fitting R2eff values in the
dispersion analysis. A wrong implemented "else if" statement, would
add the intensity for the simulated intensity together with the
original intensity. This means that all intensity values send to
minimisation would be twice as high than usually (if spectra were not
replicated). This was discovered for Monte Carlo simulations of R2eff
errors in exponential fit. This will affect all analyses using full
relaxation exponential curves until now. By pure luck, it seems that
the effect of this would be that R2eff errors are half the value they
should be. A further investigation shows, that for the selected data
set, this had a minimum on influence on the fitted parameters, because
the chi2 value would be scaled up by a factor 4. Bug #22554
(https://gna.org/bugs/?22554): The distribution of intensity with
errors in Monte Carlo simulations are markedly more narrow than
expected. Task #7822 (https://gna.org/task/?7822): Implement user
function to estimate R2eff 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 bug #22408
(http://gna.org/bugs/?22408).
relax version 3.3.0.
