Dear Edward,
I have set sel.diff_model=local_tm, sphere, prolate, oblate and ellipsoid and
run full_analysis.py each time to generate directory and init in each model
and
then set sel.diff_model=final to run the script again. RelaxError: No
diffusion
tensor data is loaded for the run 'final' appears and please see the attached
file for the information appeared on the screen.
Best regards,
Hongyan
Quoting Edward d'Auvergne <edward.dauvergne@xxxxxxxxx>:
Hi,
Unfortunately I cannot determine what the problem is with this
information. Would you be able to include a few more details
including what you had run prior to this error occurring. Maybe a
number of lines printed from relax would help as well. Also if you
run relax with the '--debug' command line argument, more information
about the error will be printed and hopefully a save file which can be
attached to a bug report (https://gna.org/bugs/?group=relax) along
with the script. Any additional information would be appreciated.
Even a recursive file and directory listing could be useful in pinning
down the problem.
Cheers,
Edward
On 5/4/07, Hongyan Li <hylichem@xxxxxxxxxxxx> wrote:
Dear relax users,
when I run full-analysis.py script, I got the error message (no diffusion
tensor
data is loaded for the run 'tensor'). I am not sure how to set tensor,
does
the
program automatically generate tensor from pdb files?
Any suggestion will be highly appreciated!
best regards,
Hongyan
Dr. Hongyan Li
Department of Chemistry
The University of Hong Kong
Pokfulam Road
Hong Kong
_______________________________________________
relax (http://nmr-relax.com)
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Dr. Hongyan Li
Department of Chemistry
The University of Hong Kong
Pokfulam Road
Hong Kong
Script started on Tue 08 May 2007 12:36:05 PM HKT
[1mhsun@com371:~/SAMCx-CN/relax-1.2.9/full_analysis>[mrelax
full_analysis.py
relax 1.2.9
Protein dynamics by NMR relaxation data analysis
Copyright (C) 2001-2006 Edward d'Auvergne
This is free software which you are welcome to modify and redistribute under
the conditions of the
GNU General Public License (GPL). This program, including all modules, is
licensed under the GPL
and comes with absolutely no warranty. For details type 'GPL'. Assistance
in using this program
can be accessed by typing 'help'.
script = 'full_analysis.py'
----------------------------------------------------------------------------------------------------
# Script for complete model-free analysis.
#
# This script is designed for those who appreciate black-boxes, although it
will need to be
# heavily tailored to the protein in question, or those who appreciate
complex code. For a
# description of object-oriented coding in python using classes,
functions/methods, self, etc,
# see the python tutorial.
# Import functions from the python modules 'os' and 're'.
from os import getcwd, listdir
from re import search
class Main:
def __init__(self):
"""Script for black-box model-free analysis.
The value of the variable self.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
self.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
excede the number of
relaxation data sets for that residue. If this is the case, the list
of models in the
'multi_model' functions will need to be trimmed.
Model I - Local tm
~~~~~~~~~~~~~~~~~~
This will optimise the diffusion model whereby all residues of the
protein 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 residue 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 residue.
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 residue 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
residue.
AIC model selection is used to select the models for each residue.
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 self.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).
The final black-box model-free results will be placed in the file
'final/results'.
"""
# The diffusion model (this is the variable which should be changed).
self.diff_model = 'final'
# MI - Local tm.
################
if self.diff_model == 'local_tm':
# Base directory to place files into.
self.base_dir = 'local_tm/'
# Sequential optimisation of all model-free models (function must
be modified to suit).
self.multi_model(local_tm=1)
# Model selection run.
run.create('aic', 'mf')
# Model selection.
self.model_selection(run='aic', dir=self.base_dir + 'aic')
# Diffusion models MII to MV.
#############################
elif self.diff_model == 'sphere' or self.diff_model == 'prolate' or
self.diff_model == 'oblate' or self.diff_model == 'ellipsoid':
# Determine which round of optimisation to do (init, round_1,
round_2, etc).
self.round = self.determine_rnd(model=self.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 = self.diff_model + '/init/'
# Run name.
name = self.diff_model
# Create the run.
run.create(name, 'mf')
# Load the local tm diffusion model MI results.
results.read(run=name, file='results', dir='local_tm/aic')
# Remove the tm parameter.
model_free.remove_tm(run=name)
# Load the PDB file.
pdb(name, 'sam-rdc.pdb')
# Add an arbitrary diffusion tensor which will be optimised.
if self.diff_model == 'sphere':
diffusion_tensor.init(name, 10e-9, fixed=0)
inc = 11
elif self.diff_model == 'prolate':
diffusion_tensor.init(name, (10e-9, 0, 0, 0),
spheroid_type='prolate', fixed=0)
inc = 11
elif self.diff_model == 'oblate':
diffusion_tensor.init(name, (10e-9, 0, 0, 0),
spheroid_type='oblate', fixed=0)
inc = 11
elif self.diff_model == 'ellipsoid':
diffusion_tensor.init(name, (10e-09, 0, 0, 0, 0, 0),
fixed=0)
inc = 6
# Minimise just the diffusion tensor.
fix(name, 'all_res')
grid_search(name, inc=inc)
minimise('newton', run=name)
# Write the results.
results.write(run=name, file='results', dir=self.base_dir,
force=1)
# Normal round of optimisation for diffusion models MII to MV.
else:
# Base directory to place files into.
self.base_dir = self.diff_model + '/round_' + `self.round` +
'/'
# 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()
# Delete the run containing the optimised diffusion tensor.
run.delete('tensor')
# Create the final run (for model selection and final
optimisation).
name = 'final'
run.create(name, 'mf')
# Model selection.
self.model_selection(run=name, dir=self.base_dir + 'aic')
# Final optimisation of all diffusion and model-free
parameters.
fix(name, 'all', fixed=0)
# Minimise all parameters.
minimise('newton', run=name)
# Write the results.
dir = self.base_dir + 'opt'
results.write(run=name, file='results', dir=dir, force=1)
# Final run.
############
elif self.diff_model == 'final':
# Diffusion model selection.
############################
# Create the local_tm run.
run.create('local_tm', 'mf')
# Load the local tm diffusion model MI results.
results.read(run='local_tm', file='results', dir='local_tm/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
# Skip the diffusion model if no directories begining with
'round_' exist.
if self.round < 1:
continue
# Create the run.
run.create(model, 'mf')
# Load the diffusion model results.
results.read(run=model, file='results', dir=model + '/round_'
+ `self.round` + '/opt')
# Create the run for model selection (which will be a copy of the
selected diffusion model or run).
run.create('final', 'mf')
# Model selection between MI to MV.
self.model_selection(run='final', write_flag=0)
# Monte Carlo simulations.
##########################
# Fix the diffusion tensor.
fix('final', 'diff')
# Simulations.
monte_carlo.setup('final', number=200)
monte_carlo.create_data('final')
monte_carlo.initial_values('final')
minimise('newton', run='final')
eliminate('final')
monte_carlo.error_analysis('final')
# Write the final results.
##########################
results.write(run='final', file='results', dir='final', force=1)
# Unknown script behaviour.
###########################
else:
raise RelaxError, "Unknown diffusion model, change the value of
'self.diff_model'"
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() + '/' + 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 run for the previous data.
run.create('tensor', 'mf')
# Load the optimised diffusion tensor from the initial round.
if self.round == 1:
results.read('tensor', 'results', self.diff_model + '/init')
# Load the optimised diffusion tensor from the previous round.
else:
results.read('tensor', 'results', self.diff_model + '/round_' +
`self.round - 1` + '/opt')
def model_selection(self, run=None, dir=None, write_flag=1):
"""Model selection function."""
# Model elimination.
eliminate()
# Model selection.
model_selection('AIC', run)
# Write the results.
if write_flag:
results.write(run=run, file='results', dir=dir, force=1)
def multi_model(self, local_tm=0):
"""Function for optimisation of all model-free models."""
# Set the run names (also the names of preset model-free models).
if local_tm:
runs = ['tm0', 'tm1', 'tm2', 'tm3', 'tm4', 'tm5', 'tm6', 'tm7',
'tm8', 'tm9']
else:
runs = ['m0', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6', 'm7', 'm8',
'm9']
# Nuclei type
nuclei('N')
for name in runs:
# Create the run.
run.create(name, 'mf')
# Load the sequence.
sequence.read(name, 'noe.600.out')
# Load the PDB file.
if not local_tm:
pdb(name, 'sam-rdc.pdb')
# Load the relaxation data.
relax_data.read(name, 'R1', '600', 600.13 * 1e6, 'r1.600.out')
relax_data.read(name, 'R2', '600', 600.13 * 1e6, 'r2.600.out')
relax_data.read(name, 'NOE', '600', 600.13 * 1e6, 'noe.600.out')
# Unselect unresolved residues.
#unselect.read(name, file='unresolved')
# Copy the diffusion tensor from the run 'opt' and prevent it
from being minimised.
if not local_tm:
diffusion_tensor.copy('tensor', name)
fix(name, 'diff')
# Set the bond length and CSA values.
value.set(name, 1.02 * 1e-10, 'bond_length')
value.set(name, -172 * 1e-6, 'csa')
# Select the model-free model.
model_free.select_model(run=name, model=name)
# Minimise.
grid_search(name, inc=11)
minimise('newton', run=name)
# Write the results.
dir = self.base_dir + name
results.write(run=name, file='results', dir=dir, force=1)
# The main class.
Main()
----------------------------------------------------------------------------------------------------
relax> run.create(run='local_tm', run_type='mf')
relax> results.read(run='local_tm', file='results', dir='local_tm/aic',
format='columnar')
Opening the file 'local_tm/aic/results.bz2' for reading.
relax> run.create(run='final', run_type='mf')
relax> eliminate(run=None, function=None, args=None)
relax> model_selection(method='AIC', modsel_run='final', runs=None)
AIC model selection.
Instance 0.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 1.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 2.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 3.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 4.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 5.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.77288
4.77288
The model from the run 'local_tm' has been selected.
Instance 6.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 1 3 1.41399
3.41399
The model from the run 'local_tm' has been selected.
Instance 7.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 8.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 9.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 1.15000
5.15000
The model from the run 'local_tm' has been selected.
Instance 10.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.12856
4.12856
The model from the run 'local_tm' has been selected.
Instance 11.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.50239
4.50239
The model from the run 'local_tm' has been selected.
Instance 12.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.00226
4.00226
The model from the run 'local_tm' has been selected.
Instance 13.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.30244
4.30244
The model from the run 'local_tm' has been selected.
Instance 14.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 1.95113
5.95113
The model from the run 'local_tm' has been selected.
Instance 15.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.18734
4.18734
The model from the run 'local_tm' has been selected.
Instance 16.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.30022
4.30022
The model from the run 'local_tm' has been selected.
Instance 17.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.19671
4.19671
The model from the run 'local_tm' has been selected.
Instance 18.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 19.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.00919
4.00919
The model from the run 'local_tm' has been selected.
Instance 20.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 1.32035
5.32035
The model from the run 'local_tm' has been selected.
Instance 21.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 22.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.95633
4.95633
The model from the run 'local_tm' has been selected.
Instance 23.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.10473
4.10473
The model from the run 'local_tm' has been selected.
Instance 24.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.11792
4.11792
The model from the run 'local_tm' has been selected.
Instance 25.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 1.58317
5.58317
The model from the run 'local_tm' has been selected.
Instance 26.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.05478
4.05478
The model from the run 'local_tm' has been selected.
Instance 27.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.00029
4.00029
The model from the run 'local_tm' has been selected.
Instance 28.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.28605
4.28605
The model from the run 'local_tm' has been selected.
Instance 29.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.31213
4.31213
The model from the run 'local_tm' has been selected.
Instance 30.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 31.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.01781
4.01781
The model from the run 'local_tm' has been selected.
Instance 32.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.92658
4.92658
The model from the run 'local_tm' has been selected.
Instance 33.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 1.39575
5.39575
The model from the run 'local_tm' has been selected.
Instance 34.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.00102
4.00102
The model from the run 'local_tm' has been selected.
Instance 35.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 1 3 2.48640
4.48640
The model from the run 'local_tm' has been selected.
Instance 36.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 1 3 1.63000
3.63000
The model from the run 'local_tm' has been selected.
Instance 37.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.41412
4.41412
The model from the run 'local_tm' has been selected.
Instance 38.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 1.81545
5.81545
The model from the run 'local_tm' has been selected.
Instance 39.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 40.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 1 3 3.77726
5.77726
The model from the run 'local_tm' has been selected.
Instance 41.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 42.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 43.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 44.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 45.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 46.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 47.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 48.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 49.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 50.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 51.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 52.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 53.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 3 3 0.00000
6.00000
The model from the run 'local_tm' has been selected.
Instance 54.
Run Num_params_(k) Num_data_sets_(n) Chi2
Criterion
local_tm 2 3 0.44453
4.44453
The model from the run 'local_tm' has been sele
relax> fix(run='final', element='diff', fixed=1)
RelaxError: No diffusion tensor data is loaded for the run 'final'.
Re: full-analysis.py