Author: bugman
Date: Fri Aug 5 09:15:48 2011
New Revision: 14159
URL: http://svn.gna.org/viewcvs/relax?rev=14159&view=rev
Log:
The opt precision can be changed via class vars and the diff tensor grid with
args.
The dauvergne_protocol class variables for the function tolerance and maximum
number of iterations
allows the test suite to change these without the user having easy access,
preventing any silliness.
The diffusion tensor grid search increments can be changed easily by the user
though through a new
keyword argument.
Modified:
branches/gui_testing/auto_analyses/dauvergne_protocol.py
Modified: branches/gui_testing/auto_analyses/dauvergne_protocol.py
URL:
http://svn.gna.org/viewcvs/relax/branches/gui_testing/auto_analyses/dauvergne_protocol.py?rev=14159&r1=14158&r2=14159&view=diff
==============================================================================
--- branches/gui_testing/auto_analyses/dauvergne_protocol.py (original)
+++ branches/gui_testing/auto_analyses/dauvergne_protocol.py Fri Aug 5
09:15:48 2011
@@ -113,30 +113,38 @@
class dAuvergne_protocol:
- def __init__(self, pipe_name=None, results_dir=None, diff_model=None,
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'], grid_inc=11, min_algor='newton', mc_sim_num=500,
max_iter=None, user_fns=None, conv_loop=True):
+ """The model-free auto-analysis."""
+
+ # Some class variables.
+ opt_func_tol = 1e-25
+ opt_max_iterations = 1e7
+
+ def __init__(self, pipe_name=None, results_dir=None, diff_model=None,
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'], grid_inc=11, diff_tensor_grid_inc={'sphere': 11, 'prolate':
11, 'oblate': 11, 'ellipsoid': 6}, min_algor='newton', mc_sim_num=500,
max_iter=None, user_fns=None, conv_loop=True):
"""Perform the full model-free analysis protocol of d'Auvergne and
Gooley, 2008b.
- @keyword pipe_name: The name of the data pipe containing the
sequence info. This data pipe should have all values set including the CSA
value, the bond length, the heteronucleus name and proton name. It should
also have all relaxation data loaded.
- @keyword results_dir: The directory, where files are saved in.
- @type results_dir: str
- @keyword diff_model: The global diffusion model to optimise.
This can be one of 'local_tm', 'sphere', 'oblate', 'prolate', 'ellipsoid', or
'final'.
- @type diff_model: str
- @keyword mf_models: The model-free models.
- @type mf_models: list of str
- @keyword local_tm_models: The model-free models.
- @type local_tm_models: list of str
- @keyword grid_inc: The grid search size (the number of
increments per dimension).
- @type grid_inc: int
- @keyword min_algor: The minimisation algorithm (in most
cases this should not be changed).
- @type min_algor: str
- @keyword mc_sim_num: The number of Monte Carlo simulations to
be used for error analysis at the end of the analysis.
- @type mc_sim_num: int
- @keyword max_iter: The maximum number of iterations for the
global iteration. Set to None, then the algorithm iterates until convergence.
- @type max_iter: int or None.
- @keyword user_fns: A dictionary of replacement user
functions. These will overwrite the standard user functions. The key should
be the name of the user function or user function class and the value should
be the function or class instance.
- @type user_fns: dict
- @keyword conv_loop: Automatic looping over all rounds until
convergence.
- @type conv_loop: bool
+ @keyword pipe_name: The name of the data pipe containing
the sequence info. This data pipe should have all values set including the
CSA value, the bond length, the heteronucleus name and proton name. It
should also have all relaxation data loaded.
+ @keyword results_dir: The directory, where files are saved
in.
+ @type results_dir: str
+ @keyword diff_model: The global diffusion model to
optimise. This can be one of 'local_tm', 'sphere', 'oblate', 'prolate',
'ellipsoid', or 'final'.
+ @type diff_model: str
+ @keyword mf_models: The model-free models.
+ @type mf_models: list of str
+ @keyword local_tm_models: The model-free models.
+ @type local_tm_models: list of str
+ @keyword grid_inc: The grid search size (the number of
increments per dimension).
+ @type grid_inc: int
+ @keyword diff_tensor_grid_inc: A list of grid search sizes for the
optimisation of the sphere, prolate spheroid, oblate spheroid, and ellipsoid.
+ @type diff_tensor_grid_inc: list of int
+ @keyword min_algor: The minimisation algorithm (in most
cases this should not be changed).
+ @type min_algor: str
+ @keyword mc_sim_num: The number of Monte Carlo
simulations to be used for error analysis at the end of the analysis.
+ @type mc_sim_num: int
+ @keyword max_iter: The maximum number of iterations for
the global iteration. Set to None, then the algorithm iterates until
convergence.
+ @type max_iter: int or None.
+ @keyword user_fns: A dictionary of replacement user
functions. These will overwrite the standard user functions. The key should
be the name of the user function or user function class and the value should
be the function or class instance.
+ @type user_fns: dict
+ @keyword conv_loop: Automatic looping over all rounds
until convergence.
+ @type conv_loop: bool
"""
# Execution lock.
@@ -148,6 +156,7 @@
self.mf_models = mf_models
self.local_tm_models = local_tm_models
self.grid_inc = grid_inc
+ self.diff_tensor_grid_inc = diff_tensor_grid_inc
self.min_algor = min_algor
self.mc_sim_num = mc_sim_num
self.max_iter = max_iter
@@ -266,6 +275,18 @@
# Min vars.
if not isinstance(self.grid_inc, int):
raise RelaxError("The grid_inc user variable '%s' is incorrectly
set. It should be an integer." % self.grid_inc)
+ if not isinstance(self.diff_tensor_grid_inc, dict):
+ raise RelaxError("The diff_tensor_grid_inc user variable '%s' is
incorrectly set. It should be a dictionary." % self.diff_tensor_grid_inc)
+ if not self.diff_tensor_grid_inc.has_key('sphere'):
+ raise RelaxError("The diff_tensor_grid_inc user variable '%s' is
incorrectly set. It should contain the 'sphere' key." %
self.diff_tensor_grid_inc)
+ if not isinstance(self.diff_tensor_grid_inc, dict):
+ raise RelaxError("The diff_tensor_grid_inc user variable '%s' is
incorrectly set. It should contain the 'sphere' key." %
self.diff_tensor_grid_inc)
+ if not self.diff_tensor_grid_inc.has_key('prolate'):
+ raise RelaxError("The diff_tensor_grid_inc user variable '%s' is
incorrectly set. It should contain the 'prolate' key." %
self.diff_tensor_grid_inc)
+ if not self.diff_tensor_grid_inc.has_key('oblate'):
+ raise RelaxError("The diff_tensor_grid_inc user variable '%s' is
incorrectly set. It should contain the 'oblate' key." %
self.diff_tensor_grid_inc)
+ if not self.diff_tensor_grid_inc.has_key('ellipsoid'):
+ raise RelaxError("The diff_tensor_grid_inc user variable '%s' is
incorrectly set. It should contain the 'ellipsoid' key." %
self.diff_tensor_grid_inc)
if not isinstance(self.min_algor, str):
raise RelaxError("The min_algor user variable '%s' is
incorrectly set. It should be a string." % self.min_algor)
if not isinstance(self.mc_sim_num, int):
@@ -529,21 +550,21 @@
# Add an arbitrary diffusion tensor which will be
optimised.
if self.diff_model == 'sphere':
self.interpreter.diffusion_tensor.init(10e-9,
fixed=False)
- inc = 11
+ inc = self.diff_tensor_grid_inc['sphere']
elif self.diff_model == 'prolate':
self.interpreter.diffusion_tensor.init((10e-9, 0, 0,
0), spheroid_type='prolate', fixed=False)
- inc = 11
+ inc = self.diff_tensor_grid_inc['prolate']
elif self.diff_model == 'oblate':
self.interpreter.diffusion_tensor.init((10e-9, 0, 0,
0), spheroid_type='oblate', fixed=False)
- inc = 11
+ inc = self.diff_tensor_grid_inc['oblate']
elif self.diff_model == 'ellipsoid':
self.interpreter.diffusion_tensor.init((10e-09, 0,
0, 0, 0, 0), fixed=False)
- inc = 6
+ inc = self.diff_tensor_grid_inc['ellipsoid']
# Minimise just the diffusion tensor.
self.interpreter.fix('all_spins')
self.interpreter.grid_search(inc=inc)
- self.interpreter.minimise(self.min_algor)
+ self.interpreter.minimise(self.min_algor,
func_tol=self.opt_func_tol, max_iterations=self.opt_max_iterations)
# Write the results.
self.interpreter.results.write(file='results',
dir=self.base_dir, force=True)
@@ -567,7 +588,7 @@
self.interpreter.fix('all', fixed=False)
# Minimise all parameters.
- self.interpreter.minimise(self.min_algor)
+ self.interpreter.minimise(self.min_algor,
func_tol=self.opt_func_tol, max_iterations=self.opt_max_iterations)
# Write the results.
dir = self.base_dir + 'opt'
@@ -650,7 +671,7 @@
self.interpreter.monte_carlo.setup(number=self.mc_sim_num)
self.interpreter.monte_carlo.create_data()
self.interpreter.monte_carlo.initial_values()
- self.interpreter.minimise(self.min_algor)
+ self.interpreter.minimise(self.min_algor,
func_tol=self.opt_func_tol, max_iterations=self.opt_max_iterations)
self.interpreter.eliminate()
self.interpreter.monte_carlo.error_analysis()
@@ -731,7 +752,7 @@
# Minimise.
self.interpreter.grid_search(inc=self.grid_inc)
- self.interpreter.minimise(self.min_algor)
+ self.interpreter.minimise(self.min_algor,
func_tol=self.opt_func_tol, max_iterations=self.opt_max_iterations)
# Model elimination.
self.interpreter.eliminate()
r14159 - /branches/gui_testing/auto_analyses/dauvergne_protocol.py