Sorry, forgot to attach the script.
TP
-----Original Message-----
From: Tiago Pais [mailto:tpais@xxxxxxxxxxx]
Sent: terça-feira, 27 de Outubro de 2009 12:17
To: 'Edward d'Auvergne'
Cc: 'Sébastien Morin'; 'Boaz Shapira'; 'relax-users@xxxxxxx'
Subject: RE: Error when running full_analysis.py script
Dear Edward,
I will try to provid the most complete information since I have not managed
to solve the problem yet:
1- Relax version 1.3.4 running under Ubuntu 9.04 virtual machine
2- I have executed the script in the command line by typing "relax
full_analysis.py". The location is /home/tpais/ModelFree/PHS/teste/
3- I am in the initial steps of the script with the diffusion model set to
"local_tm".
4-the script still manages to run for a couple of minutes executing some
Newton minimizations and still writes 4 files (tm0, tm1,...) within the
subdirectory "/local_tm". These are the last lines showing up before the
error occurs:
"File "full_analysis.py", line 220, in __init__
self.multi_model(local_tm=True)
File "full_analysis.py", line 662, in multi_model
grid_search(inc=GRID_INC)
File "/usr/local/relax/prompt/minimisation.py", line 156, in grid_search
minimise.grid_search(lower=lower, upper=upper, inc=inc,
constraints=constraints, verbosity=verbosity)
File "/usr/local/relax/generic_fns/minimise.py", line 191, in grid_search
grid_search(lower=lower, upper=upper, inc=inc, constraints=constraints,
verbosity=verbosity)
File "/usr/local/relax/specific_fns/model_free/mf_minimise.py", line 479,
in grid_search
self.minimise(min_algor='grid', lower=lower, upper=upper, inc=inc,
constraints=constraints, verbosity=verbosity, sim_index=sim_index)
File "/usr/local/relax/specific_fns/model_free/mf_minimise.py", line 789,
in minimise
model_type = self.determine_model_type()
File "/usr/local/relax/specific_fns/model_free/main.py", line 1015, in
determine_model_type
if cdp.diff_tensor.fixed:
AttributeError: 'PipeContainer' object has no attribute 'diff_tensor'"
5- I also send attached the script as I am using it.
By the way, I tried to run the script with the new function and it gave an
indentation error ("IndentationError: expected an indented block") probably
because I copy/past it directly from the link you provided)
Regards,
TiagoP
-----Original Message-----
From: edward.dauvergne@xxxxxxxxx [mailto:edward.dauvergne@xxxxxxxxx] On
Behalf Of Edward d'Auvergne
Sent: terça-feira, 27 de Outubro de 2009 10:02
To: Tiago Pais
Cc: Sébastien Morin; Boaz Shapira; relax-users@xxxxxxx
Subject: Re: Error when running full_analysis.py script
Dear Tiago,
This is a hard one to catch. I have just added a function to this
script to check the validity of the user variables
(http://svn.gna.org/viewcvs/relax/1.3/sample_scripts/full_analysis.py?rev=97
94&view=markup).
This should give better error messages specifically identifying the
problem. For this second issue you are having, it is a little hard to
see what the problem is. Would you be able to supply a little more
information. For example the version of relax you are using, the full
error message, how you have executed the full_analysis.py script and
at what stage you are up to, etc. With more information, I should be
able to track down the issue.
Cheers,
Edward
2009/10/22 Tiago Pais <tpais@xxxxxxxxxxx>:
Hi Sebastien,
The thing is it was not exactly an error of the script. In the line 639
you
have to adjust the script so that it agrees with the format of the
function
that you have used in line 174 "Relax_Data=[...". If in line 174 you
describe the data sets with only three parameters (e.g.[Rilabel, freq,
file]) then, in line 639 you have to adapt the function so that the
program
reads only three sets of parameters (e.g. relax_data.read(data[0],
data[1],
data[2], data[3]) while in the original file was set to read 13.
This is beginners' stuff, but that's exactly what I am with respect to
RELAX
and PYTHON, so it may be usefull for other beginners.
However, I still have not managed to run the script to the end. At the
moment I am stuck with yet another error message: AttributeError:
'PipeContainer' object has no attribute 'diff_tensor'
If anyone can help me and avoid that I loose time with this probably easy
to
solve error, please do.
Thanks
Tiago P
-----Original Message-----
From: Sébastien Morin [mailto:sebastien.morin.1@xxxxxxxxx]
Sent: quarta-feira, 21 de Outubro de 2009 19:46
To: Tiago Pais
Cc: 'Edward d'Auvergne'; 'Boaz Shapira'; relax-users@xxxxxxx
Subject: Re: Error when running full_analysis.py script
Dear Tiago,
Just for the logs, could you tell us what was the problem ?
Regards,
Sébastien
Tiago Pais wrote:
Ok, managed to solve this already.
Cheers
Tiago
-----Original Message-----
From: relax-users-bounces@xxxxxxx [mailto:relax-users-bounces@xxxxxxx] On
Behalf Of Tiago Pais
Sent: quarta-feira, 21 de Outubro de 2009 15:53
To: 'Edward d'Auvergne'; 'Boaz Shapira'
Cc: relax-users@xxxxxxx
Subject: Error when running full_analysis.py script
Dear all,
I am using 15N relaxation data previously analyzed to test some of the
scripts available in Relax to make sure that I am using it correctly.
However, I get the following error message when running the sample script
full_analysis.py: "List Index out of Range"
Below is "print screen" where the error shows up at the bottom:
"None 149 Q None None
relax> spin.name(spin_id=None, name='N', force=False)
Traceback (most recent call last):
File "/usr/local/bin/relax", line 418, in <module>
Relax()
File "/usr/local/bin/relax", line 127, in __init__
self.interpreter.run(self.script_file)
File "/usr/local/relax/prompt/interpreter.py", line 276, in run
return run_script(intro=self.__intro_string, local=self.local,
script_file=script_file, quit=self.__quit_flag,
show_script=self.__show_script,
raise_relax_error=self.__raise_relax_error)
File "/usr/local/relax/prompt/interpreter.py", line 537, in run_script
return console.interact(intro, local, script_file, quit,
show_script=show_script, raise_relax_error=raise_relax_error)
File "/usr/local/relax/prompt/interpreter.py", line 433, in
interact_script
execfile(script_file, local)
File "full_analysis.py", line 671, in <module>
Main(self.relax)
File "full_analysis.py", line 220, in __init__
self.multi_model(local_tm=True)
File "full_analysis.py", line 639, in multi_model
relax_data.read(data[0], data[1], data[2], data[3], data[4], data[5],
data[6], data[7], data[8], data[9], data[10], data[11],
data[12])
IndexError: list index out of range"
I can't understand if the error relates to the last command listed,
actually
I can't understand the command given in this line (639) - what is it
suppose
to do?
Best regards
TiagoP
_______________________________________________
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_______________________________________________
relax (http://nmr-relax.com)
This is the relax-users mailing list
relax-users@xxxxxxx
To unsubscribe from this list, get a password
reminder, or change your subscription options,
visit the list information page at
https://mail.gna.org/listinfo/relax-users
--
Sébastien Morin
PhD Student
S. Gagné NMR Laboratory
Université Laval & PROTEO
Québec, Canada
###############################################################################
#
#
# Copyright (C) 2004-2008 Edward d'Auvergne
#
#
#
# This file is part of the program relax.
#
#
#
# relax is free software; you can redistribute it and/or modify
#
# it under the terms of the GNU General Public License as published by
#
# the Free Software Foundation; either version 2 of the License, or
#
# (at your option) any later version.
#
#
#
# relax is distributed in the hope that it will be useful,
#
# but WITHOUT ANY WARRANTY; without even the implied warranty of
#
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#
# GNU General Public License for more details.
#
#
#
# You should have received a copy of the GNU General Public License
#
# along with relax; if not, write to the Free Software
#
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
#
#
###############################################################################
"""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 float import floatAsByteArray
from generic_fns.mol_res_spin import generate_spin_id, spin_index_loop,
spin_loop
from generic_fns import pipes
from relax_errors import RelaxError
# User variables.
#################
# The diffusion model.
DIFF_MODEL = 'local_tm'
# The model-free models (do not change these unless absolutely necessary).
MF_MODELS = ['m0', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6', 'm7', 'm8', 'm9']
LOCAL_TM_MODELS = ['tm0', 'tm1', 'tm2', 'tm3', 'tm4', 'tm5', 'tm6', 'tm7',
'tm8', 'tm9']
# The PDB file (set this to None if no structure is available).
PDB_FILE = 'snPHScProt.pdb'
# The sequence data (file name, dir, mol_name_col, res_num_col, res_name_col,
spin_num_col, spin_name_col, sep). These are the arguments to the
sequence.read() user function, for more information please see the
documentation for that function.
SEQ_ARGS = ['snase.seq', None, None, 0, 1, None, None, None]
# The heteronucleus atom name corresponding to that of the PDB file (used if
the spin name is not in the sequence data).
HET_NAME = 'N'
# The relaxation data (data type, frequency label, frequency, file name, dir,
mol_name_col, res_num_col, res_name_col, spin_num_col, spin_name_col,
data_col, error_col, sep). These are the arguments to the relax_data.read()
user function, please see the documentation for that function for more
information.
RELAX_DATA = [['R1', '500', 500.333 * 1e6, 'R1.500.out.txt'], #, None,
None, 0, 1, None, None, 2, 3, None],
['R2', '500', 500.333 * 1e6, 'R2.500.out.txt'], # None, None,
0, 1, None, None, 2, 3, None],
['NOE', '500', 500.333 * 1e6, 'NOE.500.out.txt'] # None, None,
0, 1, None, None, 2, 3, None]
]
# The file containing the list of unresolved spins to exclude from the
analysis (set this to None if no spin is to be excluded).
UNRES = None
# A file containing a list of spins which can be dynamically excluded at any
point within the analysis (when set to None, this variable is not used).
EXCLUDE = None
# The bond length, CSA values, heteronucleus type, and proton type.
BOND_LENGTH = 1.02 * 1e-10
CSA = -172 * 1e-6
HETNUC = '15N'
PROTON = '1H'
# The grid search size (the number of increments per dimension).
GRID_INC = 11
# The optimisation technique.
MIN_ALGOR = 'newton'
# The number of Monte Carlo simulations to be used for error analysis at the
end of the analysis.
MC_NUM = 500
# Automatic looping over all rounds until convergence (must be a boolean
value of True or False).
CONV_LOOP = True
class Main:
def __init__(self, relax):
"""Execute the model-free analysis."""
# Setup.
self.relax = relax
# MI - Local tm.
################
if DIFF_MODEL == 'local_tm':
# Base directory to place files into.
self.base_dir = 'local_tm'+sep
# Sequential optimisation of all model-free models (function must
be modified to suit).
self.multi_model(local_tm=True)
# Model selection.
self.model_selection(modsel_pipe='aic', dir=self.base_dir + 'aic')
# Diffusion models MII to MV.
#############################
elif DIFF_MODEL == 'sphere' or DIFF_MODEL == 'prolate' or DIFF_MODEL
== 'oblate' or DIFF_MODEL == 'ellipsoid':
# Loop until convergence if CONV_LOOP is set, otherwise just loop
once.
# This looping could be made much cleaner by removing the
dependence on the determine_rnd() function.
while 1:
# Determine which round of optimisation to do (init, round_1,
round_2, etc).
self.round = self.determine_rnd(model=DIFF_MODEL)
# Inital round of optimisation for diffusion models MII to MV.
if self.round == 0:
# Base directory to place files into.
self.base_dir = DIFF_MODEL + sep+'init'+sep
# Run name.
name = DIFF_MODEL
# Create the data pipe.
pipe.create('teste', 'mf')
# Load the local tm diffusion model MI results.
results.read(file='results', dir='local_tm'+sep+'aic')
# Remove the tm parameter.
model_free.remove_tm()
# Deselect the spins in the EXCLUDE list.
if EXCLUDE:
deselect.read(file=EXCLUDE)
# Load the PDB file and calculate the unit vectors
parallel to the XH bond.
if PDB_FILE:
structure.read_pdb(PDB_FILE)
structure.vectors(attached='H')
# Add an arbitrary diffusion tensor which will be
optimised.
if DIFF_MODEL == 'sphere':
diffusion_tensor.init(10e-9, fixed=False)
inc = 11
elif DIFF_MODEL == 'prolate':
diffusion_tensor.init((10e-9, 0, 0, 0),
spheroid_type='prolate', fixed=False)
inc = 11
elif DIFF_MODEL == 'oblate':
diffusion_tensor.init((10e-9, 0, 0, 0),
spheroid_type='oblate', fixed=False)
inc = 11
elif DIFF_MODEL == 'ellipsoid':
diffusion_tensor.init((10e-09, 0, 0, 0, 0, 0),
fixed=False)
inc = 6
# Minimise just the diffusion tensor.
fix('all_spins')
grid_search(inc=inc)
minimise(MIN_ALGOR)
# Write the results.
results.write(file='results', dir=self.base_dir,
force=True)
# Normal round of optimisation for diffusion models MII to MV.
else:
# Base directory to place files into.
self.base_dir = DIFF_MODEL + sep+'round_'+`self.round`+sep
# Load the optimised diffusion tensor from either the
previous round.
self.load_tensor()
# Sequential optimisation of all model-free models
(function must be modified to suit).
self.multi_model()
# Model selection.
self.model_selection(modsel_pipe='aic', dir=self.base_dir
+ 'aic')
# Final optimisation of all diffusion and model-free
parameters.
fix('all', fixed=False)
# Minimise all parameters.
minimise(MIN_ALGOR)
# Write the results.
dir = self.base_dir + 'opt'
results.write(file='results', dir=dir, force=True)
# Test for convergence.
converged = self.convergence()
# Break out of the infinite while loop if automatic
looping is not activated or if convergence has occurred.
if converged or not CONV_LOOP:
break
# Final run.
############
elif DIFF_MODEL == 'final':
# Diffusion model selection.
############################
# All the global diffusion models to be used in the model
selection.
self.pipes = ['local_tm', 'sphere', 'prolate', 'oblate',
'ellipsoid']
# Create the local_tm data pipe.
pipe.create('local_tm', 'mf')
# Load the local tm diffusion model MI results.
results.read(file='results', dir='local_tm'+sep+'aic')
# Loop over models MII to MV.
for model in ['sphere', 'prolate', 'oblate', 'ellipsoid']:
# Determine which was the last round of optimisation for each
of the models.
self.round = self.determine_rnd(model=model) - 1
# If no directories begining with 'round_' exist, the script
has not been properly utilised!
if self.round < 1:
# Construct the name of the diffusion tensor.
name = model
if model == 'prolate' or model == 'oblate':
name = name + ' spheroid'
# Throw an error to prevent misuse of the script.
raise RelaxError, "Multiple rounds of optimisation of the
" + name + " (between 8 to 15) are required for the proper execution of this
script."
# Create the data pipe.
pipe.create(model, 'mf')
# Load the diffusion model results.
results.read(file='results', dir=model +
sep+'round_'+`self.round`+sep+'opt')
# Model selection between MI to MV.
self.model_selection(modsel_pipe='final', write_flag=False)
# Monte Carlo simulations.
##########################
# Fix the diffusion tensor, if it exists.
if hasattr(pipes.get_pipe('final'), 'diff_tensor'):
fix('diff')
# Simulations.
monte_carlo.setup(number=MC_NUM)
monte_carlo.create_data()
monte_carlo.initial_values()
minimise(MIN_ALGOR)
eliminate()
monte_carlo.error_analysis()
# Write the final results.
##########################
results.write(file='results', dir='final', force=True)
# Unknown script behaviour.
###########################
else:
raise RelaxError, "Unknown diffusion model, change the value of
'DIFF_MODEL'"
def convergence(self):
"""Test for the convergence of the global model."""
# Alias the data pipes.
cdp = pipes.get_pipe()
prev_pipe = pipes.get_pipe('previous')
# Print out.
print "\n\n\n"
print "#####################"
print "# Convergence tests #"
print "#####################\n\n"
# Convergence flags.
chi2_converged = True
models_converged = True
params_converged = True
# Chi-squared test.
###################
print "Chi-squared test:"
print " chi2 (k-1): " + `prev_pipe.chi2`
print " (as an IEEE-754 byte array: " +
`floatAsByteArray(prev_pipe.chi2)` + ')'
print " chi2 (k): " + `cdp.chi2`
print " (as an IEEE-754 byte array: " +
`floatAsByteArray(cdp.chi2)` + ')'
print " chi2 (difference): " + `prev_pipe.chi2 - cdp.chi2`
if prev_pipe.chi2 == cdp.chi2:
print " The chi-squared value has converged.\n"
else:
print " The chi-squared value has not converged.\n"
chi2_converged = False
# Identical model-free model test.
##################################
print "Identical model-free models test:"
# Create a string representation of the model-free models of the
previous data pipe.
prev_models = ''
for spin in spin_loop(pipe='previous'):
if hasattr(spin, 'model'):
if not spin.model == 'None':
prev_models = prev_models + spin.model
# Create a string representation of the model-free models of the
current data pipe.
curr_models = ''
for spin in spin_loop():
if hasattr(spin, 'model'):
if not spin.model == 'None':
curr_models = curr_models + spin.model
# The test.
if prev_models == curr_models:
print " The model-free models have converged.\n"
else:
print " The model-free models have not converged.\n"
models_converged = False
# Identical parameter value test.
#################################
print "Identical parameter test:"
# Only run the tests if the model-free models have converged.
if models_converged:
# Diffusion parameter array.
if DIFF_MODEL == 'sphere':
params = ['tm']
elif DIFF_MODEL == 'oblate' or DIFF_MODEL == 'prolate':
params = ['tm', 'Da', 'theta', 'phi']
elif DIFF_MODEL == 'ellipsoid':
params = ['tm', 'Da', 'Dr', 'alpha', 'beta', 'gamma']
# Tests.
for param in params:
# Get the parameter values.
prev_val = getattr(prev_pipe.diff_tensor, param)
curr_val = getattr(cdp.diff_tensor, param)
# Test if not identical.
if prev_val != curr_val:
print " Parameter: " + param
print " Value (k-1): " + `prev_val`
print " (as an IEEE-754 byte array: " +
`floatAsByteArray(prev_val)` + ')'
print " Value (k): " + `curr_val`
print " (as an IEEE-754 byte array: " +
`floatAsByteArray(curr_val)` + ')'
print " The diffusion parameters have not converged.\n"
params_converged = False
# Skip the rest if the diffusion tensor parameters have not
converged.
if params_converged:
# Loop over the spins.
for mol_index, res_index, spin_index in spin_index_loop():
# Alias the spin containers.
prev_spin =
prev_pipe.mol[mol_index].res[res_index].spin[spin_index]
curr_spin =
cdp.mol[mol_index].res[res_index].spin[spin_index]
# Skip if the parameters have not converged.
if not params_converged:
break
# Skip spin systems with no 'params' object.
if not hasattr(prev_spin, 'params') or not
hasattr(curr_spin, 'params'):
continue
# The spin ID string.
spin_id =
generate_spin_id(mol_name=cdp.mol[mol_index].name,
res_num=cdp.mol[mol_index].res[res_index].num,
res_name=cdp.mol[mol_index].res[res_index].name,
spin_num=cdp.mol[mol_index].res[res_index].spin[spin_index].num,
spin_name=cdp.mol[mol_index].res[res_index].spin[spin_index].name)
# Loop over the parameters.
for j in xrange(len(curr_spin.params)):
# Get the parameter values.
prev_val = getattr(prev_spin,
lower(prev_spin.params[j]))
curr_val = getattr(curr_spin,
lower(curr_spin.params[j]))
# Test if not identical.
if prev_val != curr_val:
print " Spin ID: " + `spin_id`
print " Parameter: " + curr_spin.params[j]
print " Value (k-1): " + `prev_val`
print " (as an IEEE-754 byte array: " +
`floatAsByteArray(prev_val)` + ')'
print " Value (k): " + `curr_val`
print " (as an IEEE-754 byte array: " +
`floatAsByteArray(prev_val)` + ')'
print " The model-free parameters have not
converged.\n"
params_converged = False
break
# The model-free models haven't converged hence the parameter values
haven't converged.
else:
print " The model-free models haven't converged hence the
parameters haven't converged.\n"
params_converged = False
# Print out.
if params_converged:
print " The diffusion tensor and model-free parameters have
converged.\n"
# Final print out.
##################
print "\nConvergence:"
if chi2_converged and models_converged and params_converged:
print " [ Yes ]"
return True
else:
print " [ No ]"
return False
def determine_rnd(self, model=None):
"""Function for returning the name of next round of optimisation."""
# Get a list of all files in the directory model. If no directory
exists, set the round to 'init' or 0.
try:
dir_list = listdir(getcwd()+sep+model)
except:
return 0
# Set the round to 'init' or 0 if there is no directory called 'init'.
if 'init' not in dir_list:
return 0
# Create a list of all files which begin with 'round_'.
rnd_dirs = []
for file in dir_list:
if search('^round_', file):
rnd_dirs.append(file)
# Create a sorted list of integer round numbers.
numbers = []
for dir in rnd_dirs:
try:
numbers.append(int(dir[6:]))
except:
pass
numbers.sort()
# No directories begining with 'round_' exist, set the round to 1.
if not len(numbers):
return 1
# Determine the number for the next round (add 1 to the highest
number).
return numbers[-1] + 1
def load_tensor(self):
"""Function for loading the optimised diffusion tensor."""
# Create the data pipe for the previous data (deleting the old data
pipe first if necessary).
if pipes.has_pipe('previous'):
pipe.delete('previous')
pipe.create('previous', 'mf')
# Load the optimised diffusion tensor from the initial round.
if self.round == 1:
results.read('results', DIFF_MODEL + sep+'init')
# Load the optimised diffusion tensor from the previous round.
else:
results.read('results', DIFF_MODEL +
sep+'round_'+`self.round-1`+sep+'opt')
def model_selection(self, modsel_pipe=None, dir=None, write_flag=True):
"""Model selection function."""
# Model elimination.
if modsel_pipe != 'final':
eliminate()
# Model selection (delete the model selection pipe if it already
exists).
if pipes.has_pipe(modsel_pipe):
pipe.delete(modsel_pipe)
model_selection(method='AIC', modsel_pipe=modsel_pipe,
pipes=self.pipes)
# Write the results.
if write_flag:
results.write(file='results', dir=dir, force=True)
def multi_model(self, local_tm=False):
"""Function for optimisation of all model-free models."""
# Set the data pipe names (also the names of preset model-free
models).
if local_tm:
self.pipes = LOCAL_TM_MODELS
else:
self.pipes = MF_MODELS
# Loop over the data pipes.
for name in self.pipes:
# Create the data pipe.
if pipes.has_pipe(name):
pipe.delete(name)
pipe.create(name, 'mf')
# Load the sequence.
sequence.read(SEQ_ARGS[0], SEQ_ARGS[1], SEQ_ARGS[2], SEQ_ARGS[3],
SEQ_ARGS[4], SEQ_ARGS[5], SEQ_ARGS[6], SEQ_ARGS[7])
# Name the spins if necessary.
if SEQ_ARGS[6] == None:
spin.name(name=HET_NAME)
# Load the PDB file and calculate the unit vectors parallel to
the XH bond.
if not local_tm and PDB_FILE:
structure.read_pdb(PDB_FILE)
structure.vectors(attached='H')
# Load the relaxation data.
for data in RELAX_DATA:
relax_data.read(data[0], data[1], data[2], data[3])
# Deselect spins to be excluded (including unresolved and
specifically excluded spins).
if UNRES:
deselect.read(file=UNRES)
if EXCLUDE:
deselect.read(file=EXCLUDE)
# Copy the diffusion tensor from the 'opt' data pipe and prevent
it from being minimised.
if not local_tm:
diffusion_tensor.copy('previous')
fix('diff')
# Set all the necessary values.
value.set(BOND_LENGTH, 'bond_length')
value.set(CSA, 'csa')
value.set(HETNUC, 'heteronucleus')
value.set(PROTON, 'proton')
# Select the model-free model.
model_free.select_model(model=name)
# Minimise.
grid_search(inc=GRID_INC)
minimise(MIN_ALGOR)
# Write the results.
dir = self.base_dir + name
results.write(file='results', dir=dir, force=True)
# The main class.
Main(self.relax)
RE: Error when running full_analysis.py script