Author: bugman
Date: Tue Nov 24 15:32:47 2009
New Revision: 9926
URL: http://svn.gna.org/viewcvs/relax?rev=9926&view=rev
Log:
Converted all self.__*() private methods to self._*() non-API methods.
Modified:
1.3/specific_fns/n_state_model.py
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=9926&r1=9925&r2=9926&view=diff
==============================================================================
--- 1.3/specific_fns/n_state_model.py (original)
+++ 1.3/specific_fns/n_state_model.py Tue Nov 24 15:32:47 2009
@@ -68,7 +68,7 @@
raise RelaxNoModelError
# Determine the data type.
- data_types = self.__base_data_types()
+ data_types = self._base_data_types()
# Initialise the parameter vector.
param_vector = []
@@ -157,7 +157,7 @@
return scaling_matrix
- def __base_data_types(self):
+ def _base_data_types(self):
"""Determine all the base data types.
The base data types can include::
@@ -275,7 +275,7 @@
gamma[i] = param_vector[cdp.N-1 + 3*i + 2]
- def __linear_constraints(self, data_types=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
@@ -387,7 +387,7 @@
return A, b
- def __minimise_bc_data(self, model):
+ def _minimise_bc_data(self, model):
"""Extract and unpack the back calculated data.
@param model: The instantiated class containing the target
function.
@@ -426,7 +426,7 @@
data_index = data_index + 1
- def __minimise_setup_pcs(self):
+ def _minimise_setup_pcs(self):
"""Set up the data structures for optimisation using PCSs as base
data sets.
@return: The assembled data structures for using PCSs as the base
data for optimisation.
@@ -557,7 +557,7 @@
return pcs_numpy, pcs_err_numpy, unit_vect_numpy, array(pcs_const)
- def __minimise_setup_rdcs(self, param_vector=None, scaling_matrix=None):
+ def _minimise_setup_rdcs(self, param_vector=None, scaling_matrix=None):
"""Set up the data structures for optimisation using RDCs as base
data sets.
@return: The assembled data structures for using RDCs as the base
data for optimisation.
@@ -653,7 +653,7 @@
return rdcs_numpy, rdc_err_numpy, vect_numpy, array(dj, float64)
- def __minimise_setup_tensors(self, sim_index=None):
+ def _minimise_setup_tensors(self, sim_index=None):
"""Set up the data structures for optimisation using alignment
tensors as base data sets.
@keyword sim_index: The index of the simulation to optimise. This
should be None if
@@ -679,7 +679,7 @@
full_in_ref_frame = zeros(n, float64)
# Loop over the full tensors.
- for i, tensor in self.__tensor_loop(red=False):
+ for i, tensor in self._tensor_loop(red=False):
# The full tensor.
full_tensors[5*i + 0] = tensor.Axx
full_tensors[5*i + 1] = tensor.Ayy
@@ -692,7 +692,7 @@
full_in_ref_frame[i] = 1
# Loop over the reduced tensors.
- for i, tensor in self.__tensor_loop(red=True):
+ for i, tensor in self._tensor_loop(red=True):
# The reduced tensor (simulation data).
if sim_index != None:
red_tensors[5*i + 0] = tensor.Axx_sim[sim_index]
@@ -721,7 +721,7 @@
return full_tensors, red_tensors, red_err, full_in_ref_frame
- def __tensor_loop(self, red=False):
+ def _tensor_loop(self, red=False):
"""Generator method for looping over the full or reduced tensors.
@keyword red: A flag which if True causes the reduced tensors to
be returned, and if False
@@ -749,7 +749,7 @@
yield i, data[list[i][index]]
- def __q_factors_rdc(self):
+ def _q_factors_rdc(self):
"""Calculate the Q-factors for the RDC data."""
# Q-factor list.
@@ -819,7 +819,7 @@
cdp.q_rdc_norm2 = sqrt(cdp.q_rdc_norm2 /
len(cdp.q_factors_rdc_norm2))
- def __q_factors_pcs(self):
+ def _q_factors_pcs(self):
"""Calculate the Q-factors for the PCS data."""
# Q-factor list.
@@ -859,7 +859,7 @@
cdp.q_pcs = sqrt(cdp.q_pcs)
- def __update_model(self):
+ def _update_model(self):
"""Update the model parameters as necessary."""
# Initialise the list of model parameters.
@@ -903,7 +903,7 @@
cdp.gamma = [None] * cdp.N
# Determine the data type.
- data_types = self.__base_data_types()
+ data_types = self._base_data_types()
# Set up alignment tensors for each alignment.
ids = []
@@ -1386,13 +1386,13 @@
min_options = min_options[1:]
# Update the model parameters if necessary.
- self.__update_model()
+ self._update_model()
# Create the initial parameter vector.
param_vector = self._assemble_param_vector(sim_index=sim_index)
# Determine if alignment tensors or RDCs are to be used.
- data_types = self.__base_data_types()
+ data_types = self._base_data_types()
# Diagonal scaling.
scaling_matrix =
self._assemble_scaling_matrix(data_types=data_types, scaling=scaling)
@@ -1400,24 +1400,24 @@
# Linear constraints.
if constraints:
- A, b = self.__linear_constraints(data_types=data_types,
scaling_matrix=scaling_matrix)
+ A, b = self._linear_constraints(data_types=data_types,
scaling_matrix=scaling_matrix)
else:
A, b = None, None
# Get the data structures for optimisation using the tensors as base
data sets.
full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame =
None, None, None, None
if 'tensor' in data_types:
- full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame
= self.__minimise_setup_tensors(sim_index=sim_index)
+ full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame
= self._minimise_setup_tensors(sim_index=sim_index)
# Get the data structures for optimisation using PCSs as base data
sets.
pcs, pcs_err, pcs_vect, pcs_dj = None, None, None, None
if 'pcs' in data_types:
- pcs, pcs_err, pcs_vect, pcs_dj = self.__minimise_setup_pcs()
+ pcs, pcs_err, pcs_vect, pcs_dj = self._minimise_setup_pcs()
# Get the data structures for optimisation using RDCs as base data
sets.
rdcs, rdc_err, xh_vect, rdc_dj = None, None, None, None
if 'rdc' in data_types:
- rdcs, rdc_err, xh_vect, rdc_dj = self.__minimise_setup_rdcs()
+ rdcs, rdc_err, xh_vect, rdc_dj = self._minimise_setup_rdcs()
# 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_vect=pcs_vect, xh_vect=xh_vect, pcs_const=pcs_dj,
dip_const=rdc_dj, scaling_matrix=scaling_matrix)
@@ -1499,15 +1499,15 @@
# Statistical analysis.
if 'rdc' in data_types or 'pcs' in data_types:
# Get the final back calculated data (for the Q-factor and
- self.__minimise_bc_data(model)
+ self._minimise_bc_data(model)
# Calculate the RDC Q-factors.
if 'rdc' in data_types:
- self.__q_factors_rdc()
+ self._q_factors_rdc()
# Calculate the PCS Q-factors.
if 'pcs' in data_types:
- self.__q_factors_pcs()
+ self._q_factors_pcs()
def model_statistics(self, instance=None, spin_id=None,
global_stats=None):
@@ -1540,7 +1540,7 @@
"""
# Determine the data type.
- data_types = self.__base_data_types()
+ data_types = self._base_data_types()
# Init.
n = 0
@@ -1591,7 +1591,7 @@
cdp.N = N
# Update the model.
- self.__update_model()
+ self._update_model()
def _ref_domain(self, ref=None):
@@ -1624,7 +1624,7 @@
cdp.ref_domain = ref
# Update the model.
- self.__update_model()
+ self._update_model()
def _param_model_index(self, param):
@@ -1664,7 +1664,7 @@
"""
# Determine the data type.
- data_types = self.__base_data_types()
+ data_types = self._base_data_types()
# Init.
num = 0
@@ -1777,7 +1777,7 @@
cdp.params = []
# Update the model.
- self.__update_model()
+ self._update_model()
set_doc = """
r9926 - /1.3/specific_fns/n_state_model.py