Author: tlinnet
Date: Wed Aug 20 18:09:29 2014
New Revision: 25103
URL: http://svn.gna.org/viewcvs/relax?rev=25103&view=rev
Log:
Rewrote the logic of the key-word 'optimise_r2eff' in the auto analyses of
relax disp.
If R2eff result file exist in the 'pre_run_dir', this is loaded.
If the results contain both values, and errors, then no optimisation is
performed on the R2eff model.
Unless the 'optimise_r2eff' flag is raised, which is not standard.
Modified:
trunk/auto_analyses/relax_disp.py
Modified: trunk/auto_analyses/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/auto_analyses/relax_disp.py?rev=25103&r1=25102&r2=25103&view=diff
==============================================================================
--- trunk/auto_analyses/relax_disp.py (original)
+++ trunk/auto_analyses/relax_disp.py Wed Aug 20 18:09:29 2014
@@ -51,7 +51,7 @@
opt_func_tol = 1e-25
opt_max_iterations = int(1e7)
- def __init__(self, pipe_name=None, pipe_bundle=None, results_dir=None,
models=[MODEL_R2EFF], grid_inc=11, mc_sim_num=500, exp_mc_sim_num=None,
modsel='AIC', pre_run_dir=None, optimise_pre_run_r2eff=True,
insignificance=0.0, numeric_only=False, mc_sim_all_models=False,
eliminate=True, set_grid_r20=False):
+ def __init__(self, pipe_name=None, pipe_bundle=None, results_dir=None,
models=[MODEL_R2EFF], grid_inc=11, mc_sim_num=500, exp_mc_sim_num=None,
modsel='AIC', pre_run_dir=None, optimise_r2eff=False, insignificance=0.0,
numeric_only=False, mc_sim_all_models=False, eliminate=True,
set_grid_r20=False):
"""Perform a full relaxation dispersion analysis for the given list
of models.
@keyword pipe_name: The name of the data pipe
containing all of the data for the analysis.
@@ -72,8 +72,8 @@
@type modsel: str
@keyword pre_run_dir: The optional directory
containing the dispersion auto-analysis results from a previous run. The
optimised parameters from these previous results will be used as the starting
point for optimisation rather than performing a grid search. This is
essential for when large spin clusters are specified, as a grid search
becomes prohibitively expensive with clusters of three or more spins. At
some point a RelaxError will occur because the grid search is impossibly
large. For the cluster specific parameters, i.e. the populations of the
states and the exchange parameters, an average value will be used as the
starting point. For all other parameters, the R20 values for each spin and
magnetic field, as well as the parameters related to the chemical shift
difference dw, the optimised values of the previous run will be directly
copied.
@type pre_run_dir: None or str
- @keyword optimise_pre_run_r2eff: Flag to specify if the read
previous R2eff results should be optimised. For R1rho models where the error
of R2eff values are determined by Monte-Carlo simulations, it can be valuable
to make an initial R2eff run with a high number of Monte-Carlo simulations.
Any subsequent model analysis can then be based on these R2eff values,
without optimising the R2eff values.
- @type optimise_pre_run_r2eff: bool
+ @keyword optimise_r2eff: Flag to specify if the read
previous R2eff results should be optimised. For R1rho models where the error
of R2eff values are determined by Monte-Carlo simulations, it can be valuable
to make an initial R2eff run with a high number of Monte-Carlo simulations.
Any subsequent model analysis can then be based on these R2eff values,
without optimising the R2eff values.
+ @type optimise_r2eff: bool
@keyword insignificance: The R2eff/R1rho value in rad/s
by which to judge insignificance. If the maximum difference between two
points on all dispersion curves for a spin is less than this value, that spin
will be deselected. This does not affect the 'No Rex' model. Set this value
to 0.0 to use all data. The value will be passed on to the
relax_disp.insignificance user function.
@type insignificance: float
@keyword numeric_only: The class of models to use in
the model selection. The default of False allows all dispersion models to be
used in the analysis (no exchange, the analytic models and the numeric
models). The value of True will activate a pure numeric solution - the
analytic models will be optimised, as they are very useful for replacing the
grid search for the numeric models, but the final model selection will not
include them.
@@ -105,7 +105,7 @@
self.exp_mc_sim_num = exp_mc_sim_num
self.modsel = modsel
self.pre_run_dir = pre_run_dir
- self.optimise_pre_run_r2eff = optimise_pre_run_r2eff
+ self.optimise_r2eff = optimise_r2eff
self.insignificance = insignificance
self.set_grid_r20 = set_grid_r20
self.numeric_only = numeric_only
@@ -431,21 +431,63 @@
self.interpreter.value.set(param=param, index=None)
# Minimise.
+ do_minimise = False
if model == MODEL_R2EFF:
- if self.optimise_pre_run_r2eff:
- self.interpreter.minimise.execute('simplex',
func_tol=self.opt_func_tol, max_iter=self.opt_max_iterations,
constraints=True)
- else:
+ # Check if all spins contains 'r2eff and it associated error.
+ has_r2eff = False
+
+ # Loop over all spins.
+ for cur_spin, spin_id in spin_loop(return_id=True,
skip_desel=True):
+ # Check 'r2eff'
+ if hasattr(cur_spin, 'r2eff') and hasattr(cur_spin,
'r2eff_err'):
+ has_r2eff = True
+ else:
+ has_r2eff = False
+ break
+
+ # Skip optimisation, if 'r2eff' + 'r2eff_err' is present and
flag for forcing optimisation is not raised.
+ if has_r2eff and not self.optimise_r2eff:
pass
- else:
+
+ # Do optimisation, if 'r2eff' + 'r2eff_err' is present and flag
for forcing optimisation is raised.
+ elif has_r2eff and self.optimise_r2eff:
+ do_minimise = True
+
+ # Optimise, if no R2eff and error is present.
+ elif not has_r2eff:
+ do_minimise = True
+
+ else:
+ do_minimise = True
+
+ # Do the minimisation.
+ if do_minimise:
self.interpreter.minimise.execute('simplex',
func_tol=self.opt_func_tol, max_iter=self.opt_max_iterations,
constraints=True)
-
# Model elimination.
if self.eliminate:
self.interpreter.eliminate()
# Monte Carlo simulations.
- if self.mc_sim_all_models or len(self.models) < 2 or (model ==
MODEL_R2EFF and self.optimise_pre_run_r2eff):
+ do_monte_carlo = False
+ if model == MODEL_R2EFF:
+ # Skip optimisation, if 'r2eff' + 'r2eff_err' is present and
flag for forcing optimisation is not raised.
+ if has_r2eff and not self.optimise_r2eff:
+ pass
+
+ # Do optimisation, if 'r2eff' + 'r2eff_err' is present and flag
for forcing optimisation is raised.
+ elif has_r2eff and self.optimise_r2eff:
+ do_monte_carlo = True
+
+ # Optimise, if no R2eff and error is present.
+ elif not has_r2eff:
+ do_monte_carlo = True
+
+ elif self.mc_sim_all_models or len(self.models) < 2:
+ do_monte_carlo = True
+
+ # Do Monte Carlo simulations.
+ if do_monte_carlo:
if model == MODEL_R2EFF and self.exp_mc_sim_num != None:
self.interpreter.monte_carlo.setup(number=self.exp_mc_sim_num)
else:
r25103 - /trunk/auto_analyses/relax_disp.py