Author: bugman
Date: Thu Nov 23 06:44:10 2006
New Revision: 2875
URL: http://svn.gna.org/viewcvs/relax?rev=2875&view=rev
Log:
Ported r2864 and r2865 from the 1.3 line.
The command used was:
svn merge -r2863:2865 svn+ssh://bugman@xxxxxxxxxxx/svn/relax/1.3
This adds convergence tests to the 'full_analysis.py' script so that the user
is told whether
convergence has occured.
Modified:
1.2/sample_scripts/full_analysis.py
Modified: 1.2/sample_scripts/full_analysis.py
URL:
http://svn.gna.org/viewcvs/relax/1.2/sample_scripts/full_analysis.py?rev=2875&r1=2874&r2=2875&view=diff
==============================================================================
--- 1.2/sample_scripts/full_analysis.py (original)
+++ 1.2/sample_scripts/full_analysis.py Thu Nov 23 06:44:10 2006
@@ -9,6 +9,7 @@
# Import functions from the python modules 'os' and 're'.
from os import getcwd, listdir
from re import search
+from string import lower
class Main:
@@ -211,7 +212,7 @@
self.multi_model()
# Delete the run containing the optimised diffusion tensor.
- run.delete('tensor')
+ run.delete('previous')
# Create the final run (for model selection and final
optimisation).
name = 'final'
@@ -229,6 +230,10 @@
# 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)
+
# Final run.
@@ -299,6 +304,128 @@
else:
raise RelaxError, "Unknown diffusion model, change the value of
'self.diff_model'"
+
+
+ def convergence(self, run=None):
+ """Test for the convergence of the global model."""
+
+ # Print out.
+ print "\n\n\n"
+ print "#####################"
+ print "# Convergence tests #"
+ print "#####################\n\n"
+
+ # Convergence flags.
+ chi2_converged = 1
+ models_converged = 1
+ params_converged = 1
+
+
+ # Chi-squared test.
+ ###################
+
+ print "# Chi-squared test.\n"
+ print "chi2 (k-1): %s" + self.relax.data.chi2['previous']
+ print "chi2 (k): %s" + self.relax.data.chi2[run]
+ if self.relax.data.chi2['previous'] == self.relax.data.chi2[run]:
+ print "The chi-squared value has converged.\n"
+ else:
+ print "The chi-squared value has not converged.\n"
+ chi2_converged = 0
+
+
+ # Identical model-free model test.
+ ##################################
+
+ print "# Identical model-free models test."
+
+ # Create a string representation of the model-free models of the
previous run.
+ prev_models = ''
+ for i in xrange(len(self.relax.data.res['previous'])):
+ prev_models = prev_models +
self.relax.data.res['previous'][i].model
+
+ # Create a string representation of the model-free models of the
current run.
+ curr_models = ''
+ for i in xrange(len(self.relax.data.res[run])):
+ curr_models = curr_models + self.relax.data.res[run][i].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 = 0
+
+
+ # 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 self.diff_model == 'sphere':
+ params = ['tm']
+ elif self.diff_model == 'oblate' or self.diff_model == 'prolate':
+ params = ['tm', 'Da', 'theta', 'phi']
+ elif self.diff_model == 'ellipsoid':
+ params = ['tm', 'Da', 'Dr', 'alpha', 'beta', 'gamma']
+
+ # Tests.
+ for param in params:
+ # Get the parameter values.
+ prev_val = getattr(self.relax.data.diff['previous'], param)
+ curr_val = getattr(self.relax.data.diff[run], param)
+
+ # Test if not identical.
+ if prev_val != curr_val:
+ print "Parameter: " + param
+ print "Value (k-1): " + `prev_val`
+ print "Value (k): " + `curr_val`
+ print "The diffusion parameters have not converged.\n"
+ params_converged = 0
+
+ # Skip the rest if the diffusion tensor parameters have not
converged.
+ if not param_converged:
+ break
+
+ # Loop over the spin systems.
+ for i in xrange(len(self.relax.data.res[run])):
+ # Skip if the parameters have not converged.
+ if not params_converged:
+ break
+
+ # Loop over the parameters.
+ for j in xrange(len(self.relax.data.res[run][i].params)):
+ # Get the parameter values.
+ prev_val = getattr(self.relax.data.res['previous'][i],
lower(self.relax.data.res['previous'][i].params[j]))
+ curr_val = getattr(self.relax.data.res[run][i],
lower(self.relax.data.res[run][i].params[j]))
+
+ # Test if not identical.
+ if prev_val != curr_val:
+ print "Spin system: " +
`self.relax.data.res[run][i].num` + ' ' + self.relax.data.res[run][i].name
+ print "Parameter: " +
self.relax.data.res[run][i].params[j]
+ print "Value (k-1): " + `prev_val`
+ print "Value (k): " + `curr_val`
+ print "The model-free parameters have not
converged.\n"
+ params_converged = 0
+ 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."
+ params_converged = 0
+
+
+ # Final print out.
+ ##################
+
+ print "\n# Convergence:"
+ if chi2_converged and models_converged and paras_converged:
+ print " [ Yes ]"
+ else:
+ print " [ No ]"
def determine_rnd(self, model=None):
@@ -341,15 +468,15 @@
"""Function for loading the optimised diffusion tensor."""
# Create the run for the previous data.
- run.create('tensor', 'mf')
+ run.create('previous', 'mf')
# Load the optimised diffusion tensor from the initial round.
if self.round == 1:
- results.read('tensor', 'results', self.diff_model + '/init')
+ results.read('previous', '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')
+ results.read('previous', 'results', self.diff_model + '/round_'
+ `self.round - 1` + '/opt')
def model_selection(self, run=None, dir=None, write_flag=1):
@@ -402,7 +529,7 @@
# Copy the diffusion tensor from the run 'opt' and prevent it
from being minimised.
if not local_tm:
- diffusion_tensor.copy('tensor', name)
+ diffusion_tensor.copy('previous', name)
fix(name, 'diff')
# Set the bond length and CSA values.
r2875 - /1.2/sample_scripts/full_analysis.py