Author: bugman
Date: Mon Jun 2 18:56:20 2008
New Revision: 6309
URL: http://svn.gna.org/viewcvs/relax?rev=6309&view=rev
Log:
Merged revisions 6308 via svnmerge from
svn+ssh://bugman@xxxxxxxxxxx/svn/relax/1.3
........
r6308 | bugman | 2008-06-02 18:51:04 +0200 (Mon, 02 Jun 2008) | 3 lines
Grammar fixes in the full_analysis.py description.
........
Modified:
1.2/ (props changed)
1.2/sample_scripts/full_analysis.py
Propchange: 1.2/
------------------------------------------------------------------------------
--- svnmerge-integrated (original)
+++ svnmerge-integrated Mon Jun 2 18:56:20 2008
@@ -1,1 +1,1 @@
-/1.3:1-2505,2941,2947,2950,2974,2976,2979,2984,2988,3076,3083-3084,3087,3117,3299,3309,3312,3314,3318,3345,3372,4145,4473,4476,4939,5117,5255,5396-5398,5462-5465,5611-5612,5622,5663,5971,6020-6023,6025,6033,6041,6044,6104,6106,6306
+/1.3:1-2505,2941,2947,2950,2974,2976,2979,2984,2988,3076,3083-3084,3087,3117,3299,3309,3312,3314,3318,3345,3372,4145,4473,4476,4939,5117,5255,5396-5398,5462-5465,5611-5612,5622,5663,5971,6020-6023,6025,6033,6041,6044,6104,6106,6306,6308
Modified: 1.2/sample_scripts/full_analysis.py
URL:
http://svn.gna.org/viewcvs/relax/1.2/sample_scripts/full_analysis.py?rev=6309&r1=6308&r2=6309&view=diff
==============================================================================
--- 1.2/sample_scripts/full_analysis.py (original)
+++ 1.2/sample_scripts/full_analysis.py Mon Jun 2 18:56:20 2008
@@ -36,7 +36,7 @@
d'Auvergne, E. J. and Gooley, P. R. (2008). 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
-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 protein 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.
+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 protein 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.
The value of the variable DIFF_MODEL will determine the behaviour of this
script. The five diffusion models used in this script are:
@@ -56,7 +56,7 @@
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 excede the
number of relaxation data sets for that residue. If this is the case, the
list of models in the MF_MODELS and LOCAL_TM_MODELS variables will need to be
trimmed.
+It is important that the number of parameters in a model does not exceed the
number of relaxation data sets for that residue. 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
@@ -110,7 +110,7 @@
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 soley 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).
+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'.
"""
r6309 - in /1.2: ./ sample_scripts/full_analysis.py