Author: bugman
Date: Fri Jul 2 17:50:23 2010
New Revision: 11284
URL: http://svn.gna.org/viewcvs/relax?rev=11284&view=rev
Log:
The paramagnetic centre optimisation has been merged into the
func_population() target function.
Modified:
1.3/maths_fns/n_state_model.py
1.3/specific_fns/n_state_model.py
Modified: 1.3/maths_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/1.3/maths_fns/n_state_model.py?rev=11284&r1=11283&r2=11284&view=diff
==============================================================================
--- 1.3/maths_fns/n_state_model.py (original)
+++ 1.3/maths_fns/n_state_model.py Fri Jul 2 17:50:23 2010
@@ -39,7 +39,7 @@
class N_state_opt:
"""Class containing the target function of the optimisation of the
N-state model."""
- def __init__(self, model=None, N=None, init_params=None,
full_tensors=None, red_data=None, red_errors=None, full_in_ref_frame=None,
pcs=None, pcs_errors=None, pcs_weights=None, rdcs=None, rdc_errors=None,
rdc_weights=None, xh_vect=None, temp=None, frq=None, dip_const=None,
atomic_pos=None, paramag_centre=None, scaling_matrix=None, centre_fix=True):
+ def __init__(self, model=None, N=None, init_params=None,
full_tensors=None, red_data=None, red_errors=None, full_in_ref_frame=None,
pcs=None, pcs_errors=None, pcs_weights=None, rdcs=None, rdc_errors=None,
rdc_weights=None, xh_vect=None, temp=None, frq=None, dip_const=None,
atomic_pos=None, paramag_centre=None, scaling_matrix=None, centre_fixed=True):
"""Set up the class instance for optimisation.
The N-state models
@@ -141,8 +141,8 @@
@type paramag_centre: numpy rank-1 array
@keyword scaling_matrix: The square and diagonal scaling matrix.
@type scaling_matrix: numpy rank-2 array
- @keyword centre_fix: A flag which if False will cause the
paramagnetic centre to be optimised.
- @type centre_fix: bool
+ @keyword centre_fixed: A flag which if False will cause the
paramagnetic centre to be optimised.
+ @type centre_fixed: bool
"""
# Store the data inside the class instance namespace.
@@ -154,6 +154,9 @@
self.dip_const = dip_const
self.temp = temp
self.frq = frq
+ self.atomic_pos = atomic_pos
+ self.paramag_centre = paramag_centre
+ self.centre_fixed = centre_fixed
self.total_num_params = len(init_params)
# Initialise the function value, gradient, and Hessian.
@@ -352,15 +355,11 @@
self.paramag_unit_vect = zeros(atomic_pos.shape, float64)
self.paramag_dist = zeros((self.num_spins, self.N), float64)
self.pcs_const = zeros((self.num_align, self.num_spins,
self.N), float64)
-
- # Get the vectors and distances.
- paramag_data(atomic_pos, paramag_centre,
self.paramag_unit_vect, self.paramag_dist)
-
- # The PCS constants.
- for i in range(self.num_align):
- for j in range(self.num_spins):
- for c in range(self.N):
- self.pcs_const[i, j, c] =
pcs_constant(self.temp[i], self.frq[i], self.paramag_dist[j, c])
+ if self.paramag_centre == None:
+ self.paramag_centre = zeros(3, float64)
+
+ # Set up the paramagnetic info.
+ self.paramag_info()
# The probability array (all structures have initial equal
probability).
self.probs = ones(self.N, float64) / self.N
@@ -376,8 +375,8 @@
self.d2Dij_theta = zeros((self.total_num_params,
self.total_num_params, self.num_align, self.num_spins), float64)
# Set the target function, gradient, and Hessian (paramagnetic
centre optimisation).
- if not centre_fix:
- self.func = self.func_paramag_centre
+ if not centre_fixed:
+ self.func = self.func_population
self.dfunc = None
self.d2func = None
@@ -614,6 +613,14 @@
# Unpack the probabilities (located at the end of the parameter
array).
if self.N > 1:
self.probs = params[-(self.N-1):]
+
+ # Unpack the paramagnetic centre.
+ if not self.centre_fixed:
+ # The position.
+ self.paramag_centre = params[-3:]
+
+ # Update the paramagnetic info.
+ self.paramag_info()
# Loop over each alignment.
for i in xrange(self.num_align):
@@ -1552,3 +1559,16 @@
# The gradient.
return self.d2chi2
+
+
+ def paramag_info(self):
+ """Calculate the paramagnetic centre to spin vectors, distances and
constants."""
+
+ # Get the vectors and distances.
+ paramag_data(self.atomic_pos, self.paramag_centre,
self.paramag_unit_vect, self.paramag_dist)
+
+ # The PCS constants.
+ for i in range(self.num_align):
+ for j in range(self.num_spins):
+ for c in range(self.N):
+ self.pcs_const[i, j, c] = pcs_constant(self.temp[i],
self.frq[i], self.paramag_dist[j, c])
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=11284&r1=11283&r2=11284&view=diff
==============================================================================
--- 1.3/specific_fns/n_state_model.py (original)
+++ 1.3/specific_fns/n_state_model.py Fri Jul 2 17:50:23 2010
@@ -1354,16 +1354,16 @@
full_tensors =
self._minimise_setup_fixed_tensors(sim_index=sim_index)
# Get the atomic_positions.
- atomic_pos, paramag_centre, centre_fix = None, None, True
+ atomic_pos, paramag_centre, centre_fixed = None, None, True
if 'pcs' in data_types or 'pre' in data_types:
atomic_pos, paramag_centre = self._minimise_setup_atomic_pos()
# Optimisation of the centre.
if hasattr(cdp, 'paramag_centre_fixed'):
- centre_fix = cdp.paramag_centre_fixed
+ centre_fixed = cdp.paramag_centre_fixed
# 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_weights=pcs_weight, rdc_weights=rdc_weight,
xh_vect=xh_vect, temp=temp, frq=frq, dip_const=rdc_dj, atomic_pos=atomic_pos,
paramag_centre=paramag_centre, scaling_matrix=scaling_matrix,
centre_fix=centre_fix)
+ 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_weights=pcs_weight, rdc_weights=rdc_weight,
xh_vect=xh_vect, temp=temp, frq=frq, dip_const=rdc_dj, atomic_pos=atomic_pos,
paramag_centre=paramag_centre, scaling_matrix=scaling_matrix,
centre_fixed=centre_fixed)
# Return the data.
return model, param_vector, data_types, scaling_matrix
r11284 - in /1.3: maths_fns/n_state_model.py specific_fns/n_state_model.py