Author: bugman
Date: Mon Jan 18 17:15:33 2010
New Revision: 10242
URL: http://svn.gna.org/viewcvs/relax?rev=10242&view=rev
Log:
Shifted the target function initialisation into _target_fn_setup().
This will be shared by the calculate() method.
Modified:
1.3/specific_fns/n_state_model.py
Modified: 1.3/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/specific_fns/n_state_model.py?rev=10242&r1=10241&r2=10242&view=diff
==============================================================================
--- 1.3/specific_fns/n_state_model.py (original)
+++ 1.3/specific_fns/n_state_model.py Mon Jan 18 17:15:33 2010
@@ -1,6 +1,6 @@
###############################################################################
#
#
-# Copyright (C) 2007-2009 Edward d'Auvergne
#
+# Copyright (C) 2007-2010 Edward d'Auvergne
#
#
#
# This file is part of the program relax.
#
#
#
@@ -1235,6 +1235,64 @@
self._update_model()
+ def _target_fn_setup(self, sim_index=None, scaling=True):
+ """Initialise the target function for optimisation or direct
calculation.
+
+ @param sim_index: The index of the simulation to optimise.
This should be None if normal optimisation is desired.
+ @type sim_index: None or int
+ @param scaling: If True, diagonal scaling is enabled during
optimisation to allow the problem to be better conditioned.
+ @type scaling: bool
+ """
+
+ # Test if the N-state model has been set up.
+ if not hasattr(cdp, 'model'):
+ raise RelaxNoModelError('N-state')
+
+ # '2-domain' model setup tests.
+ if cdp.model == '2-domain':
+ # The number of states.
+ if not hasattr(cdp, 'N'):
+ raise RelaxError("The number of states has not been set.")
+
+ # The reference domain.
+ if not hasattr(cdp, 'ref_domain'):
+ raise RelaxError("The reference domain has not been set.")
+
+ # Update the model parameters if necessary.
+ self._update_model()
+
+ # Create the initial parameter vector.
+ param_vector = self._assemble_param_vector(sim_index=sim_index)
+
+ # Determine if alignment tensors or RDCs are to be used.
+ data_types = self._base_data_types()
+
+ # Diagonal scaling.
+ scaling_matrix =
self._assemble_scaling_matrix(data_types=data_types, scaling=scaling)
+ param_vector = dot(inv(scaling_matrix), param_vector)
+
+ # Get the data structures for optimisation using the tensors as base
data sets.
+ full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame =
None, None, None, None
+ if 'tensor' in data_types:
+ full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame
= self._minimise_setup_tensors(sim_index=sim_index)
+
+ # Get the data structures for optimisation using PCSs as base data
sets.
+ pcs, pcs_err, pcs_vect, pcs_dj = None, None, None, None
+ if 'pcs' in data_types:
+ pcs, pcs_err, pcs_vect, pcs_dj = self._minimise_setup_pcs()
+
+ # Get the data structures for optimisation using RDCs as base data
sets.
+ rdcs, rdc_err, xh_vect, rdc_dj = None, None, None, None
+ if 'rdc' in data_types:
+ rdcs, rdc_err, xh_vect, rdc_dj = self._minimise_setup_rdcs()
+
+ # Set up the class instance containing the target function.
+ model = N_state_opt(model=cdp.model, N=cdp.N,
init_params=param_vector, full_tensors=full_tensors,
red_data=red_tensor_elem, red_errors=red_tensor_err,
full_in_ref_frame=full_in_ref_frame, pcs=pcs, rdcs=rdcs, pcs_errors=pcs_err,
rdc_errors=rdc_err, pcs_vect=pcs_vect, xh_vect=xh_vect, pcs_const=pcs_dj,
dip_const=rdc_dj, scaling_matrix=scaling_matrix)
+
+ # Return the data.
+ return model, param_vector, data_types, scaling_matrix
+
+
def _tensor_loop(self, red=False):
"""Generator method for looping over the full or reduced tensors.
@@ -1347,9 +1405,9 @@
@type sim_index: None
"""
- # Test if the N-state model has been set up.
- if not hasattr(cdp, 'model'):
- raise RelaxNoModelError('N-state')
+ # Test if the N-state model has been set up.
+ if not hasattr(cdp, 'model'):
+ raise RelaxNoModelError('N-state')
# Init some numpy arrays.
num_restraints = len(cdp.noe_restraints)
@@ -1368,7 +1426,7 @@
dist[i] = self._calc_ave_dist(cdp.noe_restraints[i][0],
cdp.noe_restraints[i][1], exp=-6)
# Calculate the quadratic potential.
- quad_pot(dist, pot, lower, upper)
+ quad_pot(dist, pot, lower, upper)
# Store the distance and potential information.
cdp.ave_dist = []
@@ -1491,7 +1549,7 @@
elif search('^beta', cdp.params[i]):
lower.append(0.0)
upper.append(pi)
-
+
# Otherwise this must be an alignment tensor component.
else:
lower.append(-1e-3)
@@ -1525,24 +1583,19 @@
@type min_algor: str
@param min_options: An array of options to be used by the
minimisation algorithm.
@type min_options: array of str
- @param func_tol: The function tolerance which, when reached,
terminates optimisation.
- Setting this to None turns of the check.
+ @param func_tol: The function tolerance which, when reached,
terminates optimisation. Setting this to None turns of the check.
@type func_tol: None or float
- @param grad_tol: The gradient tolerance which, when reached,
terminates optimisation.
- Setting this to None turns of the check.
+ @param grad_tol: The gradient tolerance which, when reached,
terminates optimisation. Setting this to None turns of the check.
@type grad_tol: None or float
@param max_iterations: The maximum number of iterations for the
algorithm.
@type max_iterations: int
@param constraints: If True, constraints are used during
optimisation.
@type constraints: bool
- @param scaling: If True, diagonal scaling is enabled during
optimisation to allow
- the problem to be better conditioned.
+ @param scaling: If True, diagonal scaling is enabled during
optimisation to allow the problem to be better conditioned.
@type scaling: bool
- @param verbosity: A flag specifying the amount of information
to print. The higher
- the value, the greater the verbosity.
+ @param verbosity: A flag specifying the amount of information
to print. The higher the value, the greater the verbosity.
@type verbosity: int
- @param sim_index: The index of the simulation to optimise.
This should be None if
- normal optimisation is desired.
+ @param sim_index: The index of the simulation to optimise.
This should be None if normal optimisation is desired.
@type sim_index: None or int
@keyword lower: The lower bounds of the grid search which
must be equal to the number of parameters in the model. This optional
argument is only used when doing a grid search.
@type lower: array of numbers
@@ -1552,19 +1605,8 @@
@type inc: array of int
"""
- # Test if the N-state model has been set up.
- if not hasattr(cdp, 'model'):
- raise RelaxNoModelError('N-state')
-
- # '2-domain' model setup tests.
- if cdp.model == '2-domain':
- # The number of states.
- if not hasattr(cdp, 'N'):
- raise RelaxError("The number of states has not been set.")
-
- # The reference domain.
- if not hasattr(cdp, 'ref_domain'):
- raise RelaxError("The reference domain has not been set.")
+ # Set up the target function for direct calculation.
+ model, param_vector, data_types, scaling_matrix =
self._target_fn_setup(sim_index=sim_index, scaling=scaling)
# Right, constraints cannot be used for the 'fixed' model.
if constraints and cdp.model == 'fixed':
@@ -1575,43 +1617,12 @@
if min_algor == 'Method of Multipliers':
min_algor = min_options[0]
min_options = min_options[1:]
-
- # Update the model parameters if necessary.
- self._update_model()
-
- # Create the initial parameter vector.
- param_vector = self._assemble_param_vector(sim_index=sim_index)
-
- # Determine if alignment tensors or RDCs are to be used.
- data_types = self._base_data_types()
-
- # Diagonal scaling.
- scaling_matrix =
self._assemble_scaling_matrix(data_types=data_types, scaling=scaling)
- param_vector = dot(inv(scaling_matrix), param_vector)
# Linear constraints.
if constraints:
A, b = self._linear_constraints(data_types=data_types,
scaling_matrix=scaling_matrix)
else:
A, b = None, None
-
- # Get the data structures for optimisation using the tensors as base
data sets.
- full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame =
None, None, None, None
- if 'tensor' in data_types:
- full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame
= self._minimise_setup_tensors(sim_index=sim_index)
-
- # Get the data structures for optimisation using PCSs as base data
sets.
- pcs, pcs_err, pcs_vect, pcs_dj = None, None, None, None
- if 'pcs' in data_types:
- pcs, pcs_err, pcs_vect, pcs_dj = self._minimise_setup_pcs()
-
- # Get the data structures for optimisation using RDCs as base data
sets.
- rdcs, rdc_err, xh_vect, rdc_dj = None, None, None, None
- if 'rdc' in data_types:
- rdcs, rdc_err, xh_vect, rdc_dj = self._minimise_setup_rdcs()
-
- # Set up the class instance containing the target function.
- model = N_state_opt(model=cdp.model, N=cdp.N,
init_params=param_vector, full_tensors=full_tensors,
red_data=red_tensor_elem, red_errors=red_tensor_err,
full_in_ref_frame=full_in_ref_frame, pcs=pcs, rdcs=rdcs, pcs_errors=pcs_err,
rdc_errors=rdc_err, pcs_vect=pcs_vect, xh_vect=xh_vect, pcs_const=pcs_dj,
dip_const=rdc_dj, scaling_matrix=scaling_matrix)
# Grid search.
if search('^[Gg]rid', min_algor):
r10242 - /1.3/specific_fns/n_state_model.py