Author: bugman
Date: Sun Jun 24 17:37:48 2007
New Revision: 3311
URL: http://svn.gna.org/viewcvs/relax?rev=3311&view=rev
Log:
Merged revisions 3309 via svnmerge from
svn+ssh://bugman@xxxxxxxxxxx/svn/relax/1.3
........
r3309 | bugman | 2007-06-24 17:28:28 +0200 (Sun, 24 Jun 2007) | 8 lines
Automatic looping in the full_analysis.py script.
In response to the post by Douglas Kojetin <douglas dot kojetin att gmail
dot com> at
https://mail.gna.org/public/relax-users/2007-06/msg00002.html, I have added
the ability to
automatically loop over the optimisation rounds of the full_analysis.py
script until convergence.
This is not turned on automatically by default as it is likely to require a
bit of debugging.
........
Modified:
1.2/ (props changed)
1.2/sample_scripts/full_analysis.py
Propchange: 1.2/
------------------------------------------------------------------------------
--- svnmerge-integrated (original)
+++ svnmerge-integrated Sun Jun 24 17:37:48 2007
@@ -1,1 +1,1 @@
-/1.3:1-2505,2941,2947,2950,2974,2976,2979,2984,2988,3076,3083-3084,3087,3117,3299
+/1.3:1-2505,2941,2947,2950,2974,2976,2979,2984,2988,3076,3083-3084,3087,3117,3299,3309
Modified: 1.2/sample_scripts/full_analysis.py
URL:
http://svn.gna.org/viewcvs/relax/1.2/sample_scripts/full_analysis.py?rev=3311&r1=3310&r2=3311&view=diff
==============================================================================
--- 1.2/sample_scripts/full_analysis.py (original)
+++ 1.2/sample_scripts/full_analysis.py Sun Jun 24 17:37:48 2007
@@ -132,6 +132,8 @@
# The number of Monte Carlo simulations to be used for error analysis at the
end of the analysis.
MC_NUM = 200
+# Automatic looping over all rounds until convergence.
+CONV_LOOP = 0
class Main:
@@ -163,84 +165,89 @@
#############################
elif DIFF_MODEL == 'sphere' or DIFF_MODEL == 'prolate' or DIFF_MODEL
== 'oblate' or DIFF_MODEL == 'ellipsoid':
- # 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 + '/init/'
-
- # Run name.
- name = 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, PDB_FILE)
-
- # Add an arbitrary diffusion tensor which will be optimised.
- if DIFF_MODEL == 'sphere':
- diffusion_tensor.init(name, 10e-9, fixed=0)
- inc = 11
- elif DIFF_MODEL == 'prolate':
- diffusion_tensor.init(name, (10e-9, 0, 0, 0),
spheroid_type='prolate', fixed=0)
- inc = 11
- elif DIFF_MODEL == 'oblate':
- diffusion_tensor.init(name, (10e-9, 0, 0, 0),
spheroid_type='oblate', fixed=0)
- inc = 11
- elif 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(MIN_ALGOR, 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 = 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()
-
- # 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(MIN_ALGOR, run=name)
-
- # Write the results.
- dir = self.base_dir + 'opt'
- results.write(run=name, file='results', dir=dir, force=1)
-
- # Test for convergence.
- self.convergence(run=name)
-
+ # 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 + '/init/'
+
+ # Run name.
+ name = 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, PDB_FILE)
+
+ # Add an arbitrary diffusion tensor which will be
optimised.
+ if DIFF_MODEL == 'sphere':
+ diffusion_tensor.init(name, 10e-9, fixed=0)
+ inc = 11
+ elif DIFF_MODEL == 'prolate':
+ diffusion_tensor.init(name, (10e-9, 0, 0, 0),
spheroid_type='prolate', fixed=0)
+ inc = 11
+ elif DIFF_MODEL == 'oblate':
+ diffusion_tensor.init(name, (10e-9, 0, 0, 0),
spheroid_type='oblate', fixed=0)
+ inc = 11
+ elif 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(MIN_ALGOR, 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 = 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()
+
+ # 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(MIN_ALGOR, run=name)
+
+ # Write the results.
+ dir = self.base_dir + 'opt'
+ results.write(run=name, file='results', dir=dir, force=1)
+
+ # Test for convergence.
+ converged = self.convergence(run=name)
+
+ # 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.
@@ -436,8 +443,10 @@
print "\nConvergence:"
if chi2_converged and models_converged and params_converged:
print " [ Yes ]"
+ return 1
else:
print " [ No ]"
+ return 0
def determine_rnd(self, model=None):
r3311 - in /1.2: ./ sample_scripts/full_analysis.py