Author: bugman
Date: Wed Jul 9 13:29:21 2008
New Revision: 6869
URL: http://svn.gna.org/viewcvs/relax?rev=6869&view=rev
Log:
Spun out some of the minimisation code into the minimise_setup_tensors()
method.
Modified:
branches/rdc_analysis/specific_fns/n_state_model.py
Modified: branches/rdc_analysis/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/specific_fns/n_state_model.py?rev=6869&r1=6868&r2=6869&view=diff
==============================================================================
--- branches/rdc_analysis/specific_fns/n_state_model.py (original)
+++ branches/rdc_analysis/specific_fns/n_state_model.py Wed Jul 9 13:29:21
2008
@@ -32,6 +32,7 @@
import generic_fns
import generic_fns.structure.geometric
import generic_fns.structure.mass
+from generic_fns.mol_res_spin import spin_loop
from generic_fns.structure.internal import Internal
from maths_fns.n_state_model import N_state_opt
from maths_fns.rotation_matrix import R_2vect, R_euler_zyz
@@ -559,6 +560,94 @@
if constraints:
A, b = self.linear_constraints()
+ # Determine if alignment tensors or RDCs are to be used.
+ tensor_flag = False
+ rdc_flag = False
+ if hasattr(cdp, 'align_tensors'):
+ tensor_flag = True
+ for spin in spin_loop():
+ if hasattr(spin, 'rdc'):
+ rdc_flag = True
+ break
+
+ # Ok, have no idea what to do, so complain.
+ if tensor_flag and rdc_flag:
+ raise RelaxError, "Both RDCs and alignment tensors are present.
Cannot determine which will be used for optimisation."
+
+ # Set up minimisation using alignment tensors.
+ if tensor_flag:
+ results = self.minimise_setup_tensors()
+
+ # Minimisation.
+ if constraints:
+ results = generic_minimise(func=model.func, args=(),
x0=param_vector, min_algor=min_algor, min_options=min_options,
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, A=A, b=b,
full_output=1, print_flag=verbosity)
+ else:
+ results = generic_minimise(func=model.func, args=(),
x0=param_vector, min_algor=min_algor, min_options=min_options,
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, full_output=1,
print_flag=verbosity)
+ if results == None:
+ return
+
+ # Disassemble the results.
+ param_vector, func, iter_count, f_count, g_count, h_count, warning =
results
+
+ # Catch infinite chi-squared values.
+ if isInf(func):
+ raise RelaxInfError, 'chi-squared'
+
+ # Catch chi-squared values of NaN.
+ if isNaN(func):
+ raise RelaxNaNError, 'chi-squared'
+
+ # Disassemble the parameter vector.
+ self.disassemble_param_vector(param_vector=param_vector,
sim_index=sim_index)
+
+ # Monte Carlo minimisation statistics.
+ if sim_index != None:
+ # Chi-squared statistic.
+ cdp.chi2_sim[sim_index] = func
+
+ # Iterations.
+ cdp.iter_sim[sim_index] = iter_count
+
+ # Function evaluations.
+ cdp.f_count_sim[sim_index] = f_count
+
+ # Gradient evaluations.
+ cdp.g_count_sim[sim_index] = g_count
+
+ # Hessian evaluations.
+ cdp.h_count_sim[sim_index] = h_count
+
+ # Warning.
+ cdp.warning_sim[sim_index] = warning
+
+ # Normal statistics.
+ else:
+ # Chi-squared statistic.
+ cdp.chi2 = func
+
+ # Iterations.
+ cdp.iter = iter_count
+
+ # Function evaluations.
+ cdp.f_count = f_count
+
+ # Gradient evaluations.
+ cdp.g_count = g_count
+
+ # Hessian evaluations.
+ cdp.h_count = h_count
+
+ # Warning.
+ cdp.warning = warning
+
+
+ def minimise_setup_tensors(self):
+ """Set up minimisation for the N-state model using alignment tensors.
+
+ @return: The initialised N_state_opt class for minimisation.
+ @rteyp: N_state_opt instance
+ """
+
# Initialise.
full_tensors = []
red_tensor_elem = []
@@ -607,65 +696,8 @@
# Set up the class instance containing the target function.
model = N_state_opt(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)
- # Minimisation.
- if constraints:
- results = generic_minimise(func=model.func, args=(),
x0=param_vector, min_algor=min_algor, min_options=min_options,
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, A=A, b=b,
full_output=1, print_flag=verbosity)
- else:
- results = generic_minimise(func=model.func, args=(),
x0=param_vector, min_algor=min_algor, min_options=min_options,
func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, full_output=1,
print_flag=verbosity)
- if results == None:
- return
- param_vector, func, iter_count, f_count, g_count, h_count, warning =
results
-
- # Catch infinite chi-squared values.
- if isInf(func):
- raise RelaxInfError, 'chi-squared'
-
- # Catch chi-squared values of NaN.
- if isNaN(func):
- raise RelaxNaNError, 'chi-squared'
-
- # Disassemble the parameter vector.
- self.disassemble_param_vector(param_vector=param_vector,
sim_index=sim_index)
-
- # Monte Carlo minimisation statistics.
- if sim_index != None:
- # Chi-squared statistic.
- cdp.chi2_sim[sim_index] = func
-
- # Iterations.
- cdp.iter_sim[sim_index] = iter_count
-
- # Function evaluations.
- cdp.f_count_sim[sim_index] = f_count
-
- # Gradient evaluations.
- cdp.g_count_sim[sim_index] = g_count
-
- # Hessian evaluations.
- cdp.h_count_sim[sim_index] = h_count
-
- # Warning.
- cdp.warning_sim[sim_index] = warning
-
- # Normal statistics.
- else:
- # Chi-squared statistic.
- cdp.chi2 = func
-
- # Iterations.
- cdp.iter = iter_count
-
- # Function evaluations.
- cdp.f_count = f_count
-
- # Gradient evaluations.
- cdp.g_count = g_count
-
- # Hessian evaluations.
- cdp.h_count = h_count
-
- # Warning.
- cdp.warning = warning
+ # Return the instantiated class.
+ return model
def number_of_states(self, N=None):
r6869 - /branches/rdc_analysis/specific_fns/n_state_model.py