Author: bugman
Date: Mon Jul 28 14:59:26 2008
New Revision: 6988
URL: http://svn.gna.org/viewcvs/relax?rev=6988&view=rev
Log:
Made the assemble_param_vector() and disassemble_param_vector() methods
private.
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=6988&r1=6987&r2=6988&view=diff
==============================================================================
--- branches/rdc_analysis/specific_fns/n_state_model.py (original)
+++ branches/rdc_analysis/specific_fns/n_state_model.py Mon Jul 28 14:59:26
2008
@@ -48,6 +48,75 @@
class N_state_model(Common_functions):
"""Class containing functions for the N-state model."""
+ def __assemble_param_vector(self, sim_index=None):
+ """Assemble all the parameters of the model into a single array.
+
+ @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
+ @return: The parameter vector used for optimisation.
+ @rtype: numpy array
+ """
+
+ # Alias the current data pipe.
+ cdp = ds[ds.current_pipe]
+
+ # Determine the data type.
+ data_type = self.__determine_data_type()
+
+ # Initialise the parameter vector.
+ param_vector = []
+
+ # A RDC data type requires the alignment tensors to be at the start
of the parameter vector.
+ if data_type == 'rdc':
+ # Loop over the alignments, adding the alignment tensor
parameters to the parameter vector.
+ for i in xrange(len(cdp.rdc_ids)):
+ param_vector = param_vector +
list(cdp.align_tensors[i].tensor_5D)
+
+ # Monte Carlo simulation data structures.
+ if sim_index != None:
+ # Populations.
+ if cdp.model in ['2-domain', 'population']:
+ probs = cdp.probs_sim[sim_index]
+
+ # Euler angles.
+ if cdp.model == '2-domain':
+ alpha = cdp.alpha_sim[sim_index]
+ beta = cdp.beta_sim[sim_index]
+ gamma = cdp.gamma_sim[sim_index]
+
+ # Normal data structures.
+ else:
+ # Populations.
+ if cdp.model in ['2-domain', 'population']:
+ probs = cdp.probs
+
+ # Euler angles.
+ if cdp.model == '2-domain':
+ alpha = cdp.alpha
+ beta = cdp.beta
+ gamma = cdp.gamma
+
+ # The probabilities (exclude that of state N).
+ if cdp.model in ['2-domain', 'population']:
+ param_vector = param_vector + probs[0:-1]
+
+ # The Euler angles.
+ if cdp.model == '2-domain':
+ for i in xrange(cdp.N):
+ param_vector.append(alpha[i])
+ param_vector.append(beta[i])
+ param_vector.append(gamma[i])
+
+ # Convert all None values to zero (to avoid conversion to NaN).
+ for i in xrange(len(param_vector)):
+ if param_vector[i] == None:
+ param_vector[i] = 0.0
+
+ # Return a numpy arrary.
+ return array(param_vector, float64)
+
+
def __assemble_scaling_matrix(self, data_type=None, scaling=True):
"""Create and return the scaling matrix.
@@ -81,6 +150,79 @@
# Return the matrix.
return scaling_matrix
+
+
+ def __disassemble_param_vector(self, param_vector=None, sim_index=None):
+ """Disassemble the parameter vector and place the values into the
relevant variables.
+
+ For the 2-domain N-state model, the parameters are stored in the
probability and Euler angle
+ data structures. For the population N-state model, only the
probabilities are stored. If
+ RDCs are present and alignment tensors are optimised, then these are
stored as well.
+
+ @keyword param_vector: The parameter vector returned from
optimisation.
+ @type param_vector: numpy array
+ @keyword 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 = ds[ds.current_pipe]
+
+ # Determine the data type.
+ data_type = self.__determine_data_type()
+
+ # Unpack and strip off the alignment tensor parameters.
+ if data_type == 'rdc':
+ # Loop over the alignments, adding the alignment tensor
parameters to the tensor data container.
+ for i in xrange(len(cdp.rdc_ids)):
+ cdp.align_tensors[i].Sxx = param_vector[5*i]
+ cdp.align_tensors[i].Syy = param_vector[5*i+1]
+ cdp.align_tensors[i].Sxy = param_vector[5*i+2]
+ cdp.align_tensors[i].Sxz = param_vector[5*i+3]
+ cdp.align_tensors[i].Syz = param_vector[5*i+4]
+
+ # Create a new parameter vector without the tensors.
+ param_vector = param_vector[len(cdp.rdc_ids):]
+
+ # Monte Carlo simulation data structures.
+ if sim_index != None:
+ # Populations.
+ if cdp.model in ['2-domain', 'population']:
+ probs = cdp.probs_sim[sim_index]
+
+ # Euler angles.
+ if cdp.model == '2-domain':
+ alpha = cdp.alpha_sim[sim_index]
+ beta = cdp.beta_sim[sim_index]
+ gamma = cdp.gamma_sim[sim_index]
+
+ # Normal data structures.
+ else:
+ # Populations.
+ if cdp.model in ['2-domain', 'population']:
+ probs = cdp.probs
+
+ # Euler angles.
+ if cdp.model == '2-domain':
+ alpha = cdp.alpha
+ beta = cdp.beta
+ gamma = cdp.gamma
+
+ # The probabilities for states 0 to N-1.
+ if cdp.model in ['2-domain', 'population']:
+ for i in xrange(cdp.N-1):
+ probs[i] = param_vector[i]
+
+ # The probability for state N.
+ probs[-1] = 1 - sum(probs[0:-1])
+
+ # The Euler angles.
+ if cdp.model == '2-domain':
+ for i in xrange(cdp.N):
+ alpha[i] = param_vector[cdp.N-1 + 3*i]
+ beta[i] = param_vector[cdp.N-1 + 3*i + 1]
+ gamma[i] = param_vector[cdp.N-1 + 3*i + 2]
def __update_model(self):
@@ -254,75 +396,6 @@
# Return the contraint objects.
return A, b
-
-
- def assemble_param_vector(self, sim_index=None):
- """Assemble all the parameters of the model into a single array.
-
- @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
- @return: The parameter vector used for optimisation.
- @rtype: numpy array
- """
-
- # Alias the current data pipe.
- cdp = ds[ds.current_pipe]
-
- # Determine the data type.
- data_type = self.__determine_data_type()
-
- # Initialise the parameter vector.
- param_vector = []
-
- # A RDC data type requires the alignment tensors to be at the start
of the parameter vector.
- if data_type == 'rdc':
- # Loop over the alignments, adding the alignment tensor
parameters to the parameter vector.
- for i in xrange(len(cdp.rdc_ids)):
- param_vector = param_vector +
list(cdp.align_tensors[i].tensor_5D)
-
- # Monte Carlo simulation data structures.
- if sim_index != None:
- # Populations.
- if cdp.model in ['2-domain', 'population']:
- probs = cdp.probs_sim[sim_index]
-
- # Euler angles.
- if cdp.model == '2-domain':
- alpha = cdp.alpha_sim[sim_index]
- beta = cdp.beta_sim[sim_index]
- gamma = cdp.gamma_sim[sim_index]
-
- # Normal data structures.
- else:
- # Populations.
- if cdp.model in ['2-domain', 'population']:
- probs = cdp.probs
-
- # Euler angles.
- if cdp.model == '2-domain':
- alpha = cdp.alpha
- beta = cdp.beta
- gamma = cdp.gamma
-
- # The probabilities (exclude that of state N).
- if cdp.model in ['2-domain', 'population']:
- param_vector = param_vector + probs[0:-1]
-
- # The Euler angles.
- if cdp.model == '2-domain':
- for i in xrange(cdp.N):
- param_vector.append(alpha[i])
- param_vector.append(beta[i])
- param_vector.append(gamma[i])
-
- # Convert all None values to zero (to avoid conversion to NaN).
- for i in xrange(len(param_vector)):
- if param_vector[i] == None:
- param_vector[i] = 0.0
-
- # Return a numpy arrary.
- return array(param_vector, float64)
def CoM(self, pivot_point=None, centre=None):
@@ -530,79 +603,6 @@
# Euler angles.
elif name == 'alpha' or name == 'beta' or name == 'gamma':
return (float(index)+1) * pi / (N+1.0)
-
-
- def disassemble_param_vector(self, param_vector=None, sim_index=None):
- """Disassemble the parameter vector and place the values into the
relevant variables.
-
- For the 2-domain N-state model, the parameters are stored in the
probability and Euler angle
- data structures. For the population N-state model, only the
probabilities are stored. If
- RDCs are present and alignment tensors are optimised, then these are
stored as well.
-
- @keyword param_vector: The parameter vector returned from
optimisation.
- @type param_vector: numpy array
- @keyword 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 = ds[ds.current_pipe]
-
- # Determine the data type.
- data_type = self.__determine_data_type()
-
- # Unpack and strip off the alignment tensor parameters.
- if data_type == 'rdc':
- # Loop over the alignments, adding the alignment tensor
parameters to the tensor data container.
- for i in xrange(len(cdp.rdc_ids)):
- cdp.align_tensors[i].Sxx = param_vector[5*i]
- cdp.align_tensors[i].Syy = param_vector[5*i+1]
- cdp.align_tensors[i].Sxy = param_vector[5*i+2]
- cdp.align_tensors[i].Sxz = param_vector[5*i+3]
- cdp.align_tensors[i].Syz = param_vector[5*i+4]
-
- # Create a new parameter vector without the tensors.
- param_vector = param_vector[len(cdp.rdc_ids):]
-
- # Monte Carlo simulation data structures.
- if sim_index != None:
- # Populations.
- if cdp.model in ['2-domain', 'population']:
- probs = cdp.probs_sim[sim_index]
-
- # Euler angles.
- if cdp.model == '2-domain':
- alpha = cdp.alpha_sim[sim_index]
- beta = cdp.beta_sim[sim_index]
- gamma = cdp.gamma_sim[sim_index]
-
- # Normal data structures.
- else:
- # Populations.
- if cdp.model in ['2-domain', 'population']:
- probs = cdp.probs
-
- # Euler angles.
- if cdp.model == '2-domain':
- alpha = cdp.alpha
- beta = cdp.beta
- gamma = cdp.gamma
-
- # The probabilities for states 0 to N-1.
- if cdp.model in ['2-domain', 'population']:
- for i in xrange(cdp.N-1):
- probs[i] = param_vector[i]
-
- # The probability for state N.
- probs[-1] = 1 - sum(probs[0:-1])
-
- # The Euler angles.
- if cdp.model == '2-domain':
- for i in xrange(cdp.N):
- alpha[i] = param_vector[cdp.N-1 + 3*i]
- beta[i] = param_vector[cdp.N-1 + 3*i + 1]
- gamma[i] = param_vector[cdp.N-1 + 3*i + 2]
def grid_search(self, lower, upper, inc, constraints=False, verbosity=0,
sim_index=None):
@@ -747,7 +747,7 @@
self.__update_model()
# Create the initial parameter vector.
- param_vector = self.assemble_param_vector(sim_index=sim_index)
+ param_vector = self.__assemble_param_vector(sim_index=sim_index)
# Determine if alignment tensors or RDCs are to be used.
data_type = self.__determine_data_type()
@@ -792,7 +792,7 @@
param_vector = dot(scaling_matrix, param_vector)
# Disassemble the parameter vector.
- self.disassemble_param_vector(param_vector=param_vector,
sim_index=sim_index)
+ self.__disassemble_param_vector(param_vector=param_vector,
sim_index=sim_index)
# Monte Carlo minimisation statistics.
if sim_index != None:
r6988 - /branches/rdc_analysis/specific_fns/n_state_model.py