Author: bugman
Date: Mon Dec 27 13:05:10 2010
New Revision: 11965
URL: http://svn.gna.org/viewcvs/relax?rev=11965&view=rev
Log:
The dauvergne_protocol module docstring is now being created by
doc_builder.to_docstring().
The original docstring has been converted into a list of lists data structure.
Modified:
branches/bieri_gui/auto_analyses/dauvergne_protocol.py
Modified: branches/bieri_gui/auto_analyses/dauvergne_protocol.py
URL:
http://svn.gna.org/viewcvs/relax/branches/bieri_gui/auto_analyses/dauvergne_protocol.py?rev=11965&r1=11964&r2=11965&view=diff
==============================================================================
--- branches/bieri_gui/auto_analyses/dauvergne_protocol.py (original)
+++ branches/bieri_gui/auto_analyses/dauvergne_protocol.py Mon Dec 27
13:05:10 2010
@@ -20,132 +20,15 @@
#
#
###############################################################################
-"""Script for black-box model-free analysis.
-
-This script is designed for those who appreciate black-boxes or those who
appreciate complex code. Importantly data at multiple magnetic field
strengths is essential for this analysis. The script will need to be heavily
tailored to the molecule in question by changing the variables just below
this documentation. If you would like to change how model-free analysis is
performed, the code in the class Main can be changed as needed. For a
description of object-oriented coding in python using classes,
functions/methods, self, etc., see the python tutorial.
-
-If you have obtained this script without the program relax, please visit
http://nmr-relax.com.
-
-
-References
-==========
-
-The model-free optimisation methodology herein is that of:
-
- - d'Auvergne, E. J. and Gooley, P. R. (2008b). Optimisation of NMR
dynamic models II. A new methodology for the dual optimisation of the
model-free parameters and the Brownian rotational diffusion tensor. J.
Biomol. NMR, 40(2), 121-133
-
-Other references for features of this script include model-free model
selection using Akaike's Information Criterion:
-
- - d'Auvergne, E. J. and Gooley, P. R. (2003). The use of model selection
in the model-free analysis of protein dynamics. J. Biomol. NMR, 25(1), 25-39.
-
-The elimination of failed model-free models and Monte Carlo simulations:
-
- - d'Auvergne, E. J. and Gooley, P. R. (2006). Model-free model
elimination: A new step in the model-free dynamic analysis of NMR relaxation
data. J. Biomol. NMR, 35(2), 117-135.
-
-Significant model-free optimisation improvements:
-
- - d'Auvergne, E. J. and Gooley, P. R. (2008a). Optimisation of NMR
dynamic models I. Minimisation algorithms and their performance within the
model-free and Brownian rotational diffusion spaces. J. Biomol. NMR, 40(2),
107-109.
-
-Rather than searching for the lowest chi-squared value, this script searches
for the model with the lowest AIC criterion. This complex multi-universe,
multi-dimensional search is formulated using set theory as the universal
solution:
-
- - d'Auvergne, E. J. and Gooley, P. R. (2007). Set theory formulation of
the model-free problem and the diffusion seeded model-free paradigm. 3(7),
483-494.
-
-The basic three references for the original and extended model-free theories
are:
-
- - Lipari, G. and Szabo, A. (1982a). Model-free approach to the
interpretation of nuclear magnetic-resonance relaxation in macromolecules I.
Theory and range of validity. J. Am. Chem. Soc., 104(17), 4546-4559.
-
- - Lipari, G. and Szabo, A. (1982b). Model-free approach to the
interpretation of nuclear magnetic-resonance relaxation in macromolecules II.
Analysis of experimental results. J. Am. Chem. Soc., 104(17), 4559-4570.
-
- - Clore, G. M., Szabo, A., Bax, A., Kay, L. E., Driscoll, P. C., and
Gronenborn, A.M. (1990). Deviations from the simple 2-parameter model-free
approach to the interpretation of N-15 nuclear magnetic-relaxation of
proteins. J. Am. Chem. Soc., 112(12), 4989-4991.
-
-
-How to use this script
-======================
-
-The value of the variable diff_model will determine the behaviour of this
script. The five diffusion models used in this script are:
-
- - Model I (MI) - Local tm.
- - Model II (MII) - Sphere.
- - Model III (MIII) - Prolate spheroid.
- - Model IV (MIV) - Oblate spheroid.
- - Model V (MV) - Ellipsoid.
-
-Model I must be optimised prior to any of the other diffusion models, while
the Models II to V can be optimised in any order. To select the various
models, set the variable diff_model to the following strings:
-
- - MI - 'local_tm'
- - MII - 'sphere'
- - MIII - 'prolate'
- - MIV - 'oblate'
- - MV - 'ellipsoid'
-
-This approach has the advantage of eliminating the need for an initial
estimate of a global diffusion tensor and removing all the problems
associated with the initial estimate.
-
-It is important that the number of parameters in a model does not exceed the
number of relaxation data sets for that spin. If this is the case, the list
of models in the mf_models and local_tm_models variables will need to be
trimmed.
-
-
-Model I - Local tm
-------------------
-
-This will optimise the diffusion model whereby all spin of the molecule have
a local tm value, i.e. there is no global diffusion tensor. This model needs
to be optimised prior to optimising any of the other diffusion models. Each
spin is fitted to the multiple model-free models separately, where the
parameter tm is included in each model.
-
-AIC model selection is used to select the models for each spin.
-
-
-Model II - Sphere
------------------
-
-This will optimise the isotropic diffusion model. Multiple steps are
required, an initial optimisation of the diffusion tensor, followed by a
repetitive optimisation until convergence of the diffusion tensor. Each of
these steps requires this script to be rerun. For the initial optimisation,
which will be placed in the directory './sphere/init/', the following steps
are used:
-
-The model-free models and parameter values for each spin are set to those of
diffusion model MI.
-
-The local tm parameter is removed from the models.
-
-The model-free parameters are fixed and a global spherical diffusion tensor
is minimised.
-
-
-For the repetitive optimisation, each minimisation is named from 'round_1'
onwards. The initial 'round_1' optimisation will extract the diffusion
tensor from the results file in './sphere/init/', and the results will be
placed in the directory './sphere/round_1/'. Each successive round will take
the diffusion tensor from the previous round. The following steps are used:
-
-The global diffusion tensor is fixed and the multiple model-free models are
fitted to each spin.
-
-AIC model selection is used to select the models for each spin.
-
-All model-free and diffusion parameters are allowed to vary and a global
optimisation of all parameters is carried out.
-
-
-Model III - Prolate spheroid
-----------------------------
-
-The methods used are identical to those of diffusion model MII, except that
an axially symmetric diffusion tensor with Da >= 0 is used. The base
directory containing all the results is './prolate/'.
-
-
-Model IV - Oblate spheroid
---------------------------
-
-The methods used are identical to those of diffusion model MII, except that
an axially symmetric diffusion tensor with Da <= 0 is used. The base
directory containing all the results is './oblate/'.
-
-
-Model V - Ellipsoid
--------------------
-
-The methods used are identical to those of diffusion model MII, except that
a fully anisotropic diffusion tensor is used (also known as rhombic or
asymmetric diffusion). The base directory is './ellipsoid/'.
-
-
-
-Final run
----------
-
-Once all the diffusion models have converged, the final run can be executed.
This is done by setting the variable diff_model to 'final'. This consists
of two steps, diffusion tensor model selection, and Monte Carlo simulations.
Firstly AIC model selection is used to select between the diffusion tensor
models. Monte Carlo simulations are then run solely on this selected
diffusion model. Minimisation of the model is bypassed as it is assumed that
the model is already fully optimised (if this is not the case the final run
is not yet appropriate).
-
-The final black-box model-free results will be placed in the file
'final/results'.
-"""
-
# Python module imports.
from os import getcwd, listdir, sep
from re import search
from string import lower
# relax module imports.
+from doc_builder import LIST, PARAGRAPH, SECTION, SUBSECTION, TITLE,
to_docstring
from float import floatAsByteArray
+from info import Info_box; info = Info_box()
from generic_fns.mol_res_spin import generate_spin_id, spin_index_loop,
spin_loop
from generic_fns import pipes
from generic_fns import selection
@@ -153,6 +36,68 @@
from relax_errors import RelaxError
from relax_io import DummyFileObject
from status import Status
+
+
+doc = [
+ [TITLE, "Script for black-box model-free analysis."],
+ [PARAGRAPH, "This script is designed for those who appreciate
black-boxes or those who appreciate complex code. Importantly data at
multiple magnetic field strengths is essential for this analysis. The script
will need to be heavily tailored to the molecule in question by changing the
variables just below this documentation. If you would like to change how
model-free analysis is performed, the code in the class Main can be changed
as needed. For a description of object-oriented coding in python using
classes, functions/methods, self, etc., see the python tutorial."],
+ [PARAGRAPH, "If you have obtained this script without the program
relax, please visit http://nmr-relax.com.";],
+ [SECTION, "References"],
+ [PARAGRAPH, "The model-free optimisation methodology herein is that
of:"],
+ [LIST, info.bib['dAuvergneGooley08b'].cite_short()],
+ [PARAGRAPH, "Other references for features of this script include
model-free model selection using Akaike's Information Criterion:"],
+ [LIST, info.bib['dAuvergneGooley03'].cite_short()],
+ [PARAGRAPH, "The elimination of failed model-free models and Monte
Carlo simulations:"],
+ [LIST, info.bib['dAuvergneGooley06'].cite_short()],
+ [PARAGRAPH, "Significant model-free optimisation improvements:"],
+ [LIST, info.bib['dAuvergneGooley08a'].cite_short()],
+ [PARAGRAPH, "Rather than searching for the lowest chi-squared value,
this script searches for the model with the lowest AIC criterion. This
complex multi-universe, multi-dimensional search is formulated using set
theory as the universal solution:"],
+ [LIST, info.bib['dAuvergneGooley07'].cite_short()],
+ [PARAGRAPH, "The basic three references for the original and
extended model-free theories are:"],
+ [LIST, info.bib['LipariSzabo82a'].cite_short()],
+ [LIST, info.bib['LipariSzabo82b'].cite_short()],
+ [LIST, info.bib['Clore90'].cite_short()],
+ [SECTION, "How to use this script"],
+ [PARAGRAPH, "The value of the variable diff_model will determine the
behaviour of this script. The five diffusion models used in this script
are:"],
+ [LIST, "Model I (MI) - Local tm."],
+ [LIST, "Model II (MII) - Sphere."],
+ [LIST, "Model III (MIII) - Prolate spheroid."],
+ [LIST, "Model IV (MIV) - Oblate spheroid."],
+ [LIST, "Model V (MV) - Ellipsoid."],
+ [PARAGRAPH, "Model I must be optimised prior to any of the other
diffusion models, while the Models II to V can be optimised in any order. To
select the various models, set the variable diff_model to the following
strings:"],
+ [LIST, "MI - 'local_tm'"],
+ [LIST, "MII - 'sphere'"],
+ [LIST, "MIII - 'prolate'"],
+ [LIST, "MIV - 'oblate'"],
+ [LIST, "MV - 'ellipsoid'"],
+ [PARAGRAPH, "This approach has the advantage of eliminating the need
for an initial estimate of a global diffusion tensor and removing all the
problems associated with the initial estimate."],
+ [PARAGRAPH, "It is important that the number of parameters in a
model does not exceed the number of relaxation data sets for that spin. If
this is the case, the list of models in the mf_models and local_tm_models
variables will need to be trimmed."],
+ [SUBSECTION, "Model I - Local tm"],
+ [PARAGRAPH, "This will optimise the diffusion model whereby all spin
of the molecule have a local tm value, i.e. there is no global diffusion
tensor. This model needs to be optimised prior to optimising any of the
other diffusion models. Each spin is fitted to the multiple model-free
models separately, where the parameter tm is included in each model."],
+ [PARAGRAPH, "AIC model selection is used to select the models for
each spin."],
+ [SUBSECTION, "Model II - Sphere"],
+ [PARAGRAPH, "This will optimise the isotropic diffusion model.
Multiple steps are required, an initial optimisation of the diffusion tensor,
followed by a repetitive optimisation until convergence of the diffusion
tensor. Each of these steps requires this script to be rerun. For the
initial optimisation, which will be placed in the directory './sphere/init/',
the following steps are used:"],
+ [PARAGRAPH, "The model-free models and parameter values for each
spin are set to those of diffusion model MI."],
+ [PARAGRAPH, "The local tm parameter is removed from the models."],
+ [PARAGRAPH, "The model-free parameters are fixed and a global
spherical diffusion tensor is minimised."],
+ [PARAGRAPH, "For the repetitive optimisation, each minimisation is
named from 'round_1' onwards. The initial 'round_1' optimisation will
extract the diffusion tensor from the results file in './sphere/init/', and
the results will be placed in the directory './sphere/round_1/'. Each
successive round will take the diffusion tensor from the previous round. The
following steps are used:"],
+ [PARAGRAPH, "The global diffusion tensor is fixed and the multiple
model-free models are fitted to each spin."],
+ [PARAGRAPH, "AIC model selection is used to select the models for
each spin."],
+ [PARAGRAPH, "All model-free and diffusion parameters are allowed to
vary and a global optimisation of all parameters is carried out."],
+ [SUBSECTION, "Model III - Prolate spheroid"],
+ [PARAGRAPH, "The methods used are identical to those of diffusion
model MII, except that an axially symmetric diffusion tensor with Da >= 0 is
used. The base directory containing all the results is './prolate/'."],
+ [SUBSECTION, "Model IV - Oblate spheroid"],
+ [PARAGRAPH, "The methods used are identical to those of diffusion
model MII, except that an axially symmetric diffusion tensor with Da <= 0 is
used. The base directory containing all the results is './oblate/'."],
+ [SUBSECTION, "Model V - Ellipsoid"],
+ [PARAGRAPH, "The methods used are identical to those of diffusion
model MII, except that a fully anisotropic diffusion tensor is used (also
known as rhombic or asymmetric diffusion). The base directory is
'./ellipsoid/'."],
+ [SUBSECTION, "Final run"],
+ [PARAGRAPH, "Once all the diffusion models have converged, the final
run can be executed. This is done by setting the variable diff_model to
'final'. This consists of two steps, diffusion tensor model selection, and
Monte Carlo simulations. Firstly AIC model selection is used to select
between the diffusion tensor models. Monte Carlo simulations are then run
solely on this selected diffusion model. Minimisation of the model is
bypassed as it is assumed that the model is already fully optimised (if this
is not the case the final run is not yet appropriate)."],
+ [PARAGRAPH, "The final black-box model-free results will be placed
in the file 'final/results'."]
+]
+
+
+# Build the module docstring.
+__doc__ = to_docstring(doc)
r11965 - /branches/bieri_gui/auto_analyses/dauvergne_protocol.py