Author: bugman
Date: Fri Aug 8 10:46:34 2008
New Revision: 7113
URL: http://svn.gna.org/viewcvs/relax?rev=7113&view=rev
Log:
Fixed all the calls to __base_data_types() and subsequent use of the returned
list.
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=7113&r1=7112&r2=7113&view=diff
==============================================================================
--- branches/rdc_analysis/specific_fns/n_state_model.py (original)
+++ branches/rdc_analysis/specific_fns/n_state_model.py Fri Aug 8 10:46:34
2008
@@ -62,13 +62,13 @@
cdp = ds[ds.current_pipe]
# Determine the data type.
- data_type = self.__base_data_types()
+ data_types = self.__base_data_types()
# 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':
+ # A RDC or PCS data type requires the alignment tensors to be at the
start of the parameter vector.
+ if 'rdc' in data_types or 'pcs' in data_types:
# Loop over the alignments, adding the alignment tensor
parameters to the parameter vector.
for i in xrange(len(cdp.align_tensors)):
param_vector = param_vector +
list(cdp.align_tensors[i].tensor_5D)
@@ -117,15 +117,16 @@
return array(param_vector, float64)
- def __assemble_scaling_matrix(self, data_type=None, scaling=True):
+ def __assemble_scaling_matrix(self, data_types=None, scaling=True):
"""Create and return the scaling matrix.
- @keyword data_type: The type of data used in the optimisation -
either 'rdc' or 'tensor'.
- @type data_type: str
- @keyword scaling: If False, then the identity matrix will be
returned.
- @type scaling: bool
- @return: The square and diagonal scaling matrix.
- @rtype: numpy rank-2 array
+ @keyword data_types: The base data types used in the
optimisation. This list can contain
+ the elements 'rdc', 'pcs' or 'tensor'.
+ @type data_types: list of str
+ @keyword scaling: If False, then the identity matrix will be
returned.
+ @type scaling: bool
+ @return: The square and diagonal scaling matrix.
+ @rtype: numpy rank-2 array
"""
# Alias the current data pipe.
@@ -140,7 +141,7 @@
# Starting point of the populations.
pop_start = 0
- if data_type == 'rdc':
+ if 'rdc' in data_types or 'pcs' in data_types:
pop_start = pop_start + 5*len(cdp.align_tensors)
# Loop over the populations, and set the scaling factor.
@@ -186,15 +187,16 @@
return list
- def __disassemble_param_vector(self, param_vector=None, data_type=None,
sim_index=None):
+ def __disassemble_param_vector(self, param_vector=None, data_types=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 data_type: The type of data used in the optimisation -
either 'rdc' or 'tensor'.
- @type data_type: str
+ @keyword data_types: The base data types used in the
optimisation. This list can contain
+ the elements 'rdc', 'pcs' or 'tensor'.
+ @type data_types: list of str
@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
@@ -206,7 +208,7 @@
cdp = ds[ds.current_pipe]
# Unpack and strip off the alignment tensor parameters.
- if data_type == 'rdc':
+ if 'rdc' in data_types or 'pcs' in data_types:
# Loop over the alignments, adding the alignment tensor
parameters to the tensor data container.
for i in xrange(len(cdp.align_tensors)):
cdp.align_tensors[i].Sxx = param_vector[5*i]
@@ -326,7 +328,7 @@
generic_fns.align_tensor.init(tensor=id, params=[0.0,
0.0, 0.0, 0.0, 0.0])
- def __linear_constraints(self, data_type=None, scaling_matrix=None):
+ def __linear_constraints(self, data_types=None, scaling_matrix=None):
"""Function for setting up the linear constraint matrices A and b.
Standard notation
@@ -363,9 +365,9 @@
parameters simply add columns of zero to the A matrix and have no
effect on b.
- @keyword data_type: The type of data used in the
optimisation - either 'rdc' or
- 'tensor'.
- @type data_type: str
+ @keyword data_types: The base data types used in the
optimisation. This list can
+ contain the elements 'rdc', 'pcs' or
'tensor'.
+ @type data_types: list of str
@keyword scaling_matrix: The diagonal scaling matrix.
@type scaling_matrx: numpy rank-2 square matrix
@return: The matrices A and b.
@@ -378,7 +380,7 @@
# Starting point of the populations.
pop_start = 0
- if data_type == 'rdc':
+ if 'rdc' in data_types or 'pcs' in data_types:
pop_start = pop_start + 5*len(cdp.align_tensors)
# Initialisation (0..j..m).
@@ -762,22 +764,22 @@
param_vector = self.__assemble_param_vector(sim_index=sim_index)
# Determine if alignment tensors or RDCs are to be used.
- data_type = self.__base_data_types()
+ data_types = self.__base_data_types()
# Diagonal scaling.
- scaling_matrix = self.__assemble_scaling_matrix(data_type=data_type,
scaling=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_type=data_type,
scaling_matrix=scaling_matrix)
+ A, b = self.__linear_constraints(data_types=data_types,
scaling_matrix=scaling_matrix)
# Set up minimisation using alignment tensors.
- if data_type == 'tensor':
+ if 'tensor' in data_types:
model = self.minimise_setup_tensors(param_vector=param_vector,
scaling_matrix=scaling_matrix)
- # Set up minimisation using RDCs.
- elif data_type == 'rdc':
+ # Set up minimisation using RDCs or PCSs.
+ elif 'rdc' in data_types or 'pcs' in data_types:
model = self.minimise_setup_rdcs(param_vector=param_vector,
scaling_matrix=scaling_matrix)
# Minimisation.
@@ -804,7 +806,7 @@
param_vector = dot(scaling_matrix, param_vector)
# Disassemble the parameter vector.
- self.__disassemble_param_vector(param_vector=param_vector,
data_type=data_type, sim_index=sim_index)
+ self.__disassemble_param_vector(param_vector=param_vector,
data_types=data_types, sim_index=sim_index)
# Monte Carlo minimisation statistics.
if sim_index != None:
@@ -1054,13 +1056,13 @@
cdp = ds[ds.current_pipe]
# Determine the data type.
- data_type = self.__base_data_types()
+ data_types = self.__base_data_types()
# Init.
num = 0
# Alignment tensor params.
- if data_type == 'rdc':
+ if 'rdc' in data_types or 'pcs' in data_types:
num = num + 5*len(cdp.align_tensors)
# Populations.
r7113 - /branches/rdc_analysis/specific_fns/n_state_model.py