Author: bugman
Date: Fri Jun 19 11:44:57 2009
New Revision: 9094
URL: http://svn.gna.org/viewcvs/relax?rev=9094&view=rev
Log:
Wrote the minimise() and __unpack_opt_results() methods.
The minimise() method is rudimentary and will not work.
Modified:
branches/frame_order/specific_fns/frame_order.py
Modified: branches/frame_order/specific_fns/frame_order.py
URL:
http://svn.gna.org/viewcvs/relax/branches/frame_order/specific_fns/frame_order.py?rev=9094&r1=9093&r2=9094&view=diff
==============================================================================
--- branches/frame_order/specific_fns/frame_order.py (original)
+++ branches/frame_order/specific_fns/frame_order.py Fri Jun 19 11:44:57 2009
@@ -25,10 +25,14 @@
# Python module imports.
from math import pi
+from minfx.generic import generic_minimise
+from numpy import array, float64
# relax module imports.
+from float import isNaN, isInf
from generic_fns import pipes
-from relax_errors import RelaxNoModelError
+from maths_fns import frame_order_models
+from relax_errors import RelaxInfError, RelaxNaNError, RelaxNoModelError
from specific_fns.base_class import Common_functions
@@ -59,6 +63,71 @@
cdp.beta = None
cdp.gamma = None
cdp.theta = None
+
+
+ def __unpack_opt_results(self, results):
+ """Unpack and store the Frame Order optimisation results.
+
+ @param results: The results tuple returned by the minfx
generic_minimise() function.
+ @type results: tuple
+ """
+
+ # Alias the current data pipe.
+ cdp = pipes.get_pipe()
+
+ # Disassemble the results.
+ param_vector, chi2[i], 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'
+
+ # Isotropic cone model.
+ if cdp.model == 'iso cone':
+ # Disassemble the parameter vector.
+ alpha, beta, gamma, theta = param_vector
+
+ # Wrap the cone angle to be between 0 and pi.
+ if theta < 0.0:
+ theta = -theta
+ if theta > pi:
+ theta = 2.0*pi - theta
+
+ # Monte Carlo simulation data structures.
+ if sim_index != None:
+ # Model parameters.
+ cdp.alpha_sim[sim_index] = alpha
+ cdp.beta_sim[sim_index] = beta
+ cdp.gamma_sim[sim_index] = gamma
+ cdp.theta_sim[sim_index] = theta
+
+ # Optimisation info.
+ cdp.chi2_sim[sim_index] = func
+ cdp.iter_sim[sim_index] = iter_count
+ cdp.f_count_sim[sim_index] = f_count
+ cdp.g_count_sim[sim_index] = g_count
+ cdp.h_count_sim[sim_index] = h_count
+ cdp.warning_sim[sim_index] = warning
+
+ # Normal data structures.
+ else:
+ # Model parameters.
+ cdp.alpha = alpha
+ cdp.beta = beta
+ cdp.gamma = gamma
+ cdp.theta = theta
+
+ # Optimisation info.
+ cdp.chi2 = func
+ cdp.iter = iter_count
+ cdp.f_count = f_count
+ cdp.g_count = g_count
+ cdp.h_count = h_count
+ cdp.warning = warning
def grid_search(self, lower, upper, inc, constraints=False, verbosity=0,
sim_index=None):
@@ -126,6 +195,52 @@
self.minimise(min_algor='grid', min_options=grid_ops,
constraints=constraints, verbosity=verbosity, sim_index=sim_index)
+ def minimise(self, min_algor=None, min_options=None, func_tol=None,
grad_tol=None, max_iterations=None, constraints=False, scaling=True,
verbosity=0, sim_index=None):
+ """Minimisation function.
+
+ @param min_algor: The minimisation algorithm to use.
+ @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.
+ @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.
+ @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.
+ @type scaling: bool
+ @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.
+ @type sim_index: None or int
+ """
+
+ # Alias the current data pipe.
+ cdp = pipes.get_pipe()
+
+ # Isotropic cone model.
+ if cdp.model == 'iso cone':
+ # The initial parameter vector (the Euler angles and the cone
angle).
+ param_vector = array([cdp.alpha, cdp.beta, cdp.gamma,
cdp.theta], float64)
+
+ # Set up the optimisation function.
+ model = frame_order_models.Frame_order()
+
+ # Minimisation.
+ results = generic_minimise(func=model.func, args=(),
x0=param_vector, min_algor=MIN_ALGOR, full_output=1, print_flag=1)
+
+ # Unpack the results.
+ self.__unpack_opt_results(results)
+
+
def select_model(self, model=None):
"""Select the Frame Order model.
r9094 - /branches/frame_order/specific_fns/frame_order.py