Author: bugman
Date: Fri Aug 8 11:27:48 2008
New Revision: 7119
URL: http://svn.gna.org/viewcvs/relax?rev=7119&view=rev
Log:
Converted minimise_setup_rdcs() and minimise_setup_tensors() into private
methods.
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=7119&r1=7118&r2=7119&view=diff
==============================================================================
--- branches/rdc_analysis/specific_fns/n_state_model.py (original)
+++ branches/rdc_analysis/specific_fns/n_state_model.py Fri Aug 8 11:27:48
2008
@@ -345,6 +345,136 @@
# Return the contraint objects.
return A, b
+
+
+ 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.
+ These include:
+ - rdcs, the RDC values.
+ - xh_vectors, the heteronucleus to proton vectors.
+ - dj, the dipolar constants.
+ @rtype: tuple of (numpy rank-2 array, numpy rank-2 array, numpy
rank-2 array)
+ """
+
+ # Alias the current data pipe.
+ cdp = ds[ds.current_pipe]
+
+ # Initialise.
+ rdcs = []
+ xh_vectors = []
+ dj = []
+
+ # Spin loop.
+ for spin, spin_id in spin_loop(return_id=True):
+ # Skip deselected spins.
+ if not spin.select:
+ continue
+
+ # Skip spins without RDC data or unit XH bond vectors.
+ if not hasattr(spin, 'rdc'):
+ continue
+
+ # RDC data exists but the XH bond vectors are missing?
+ if not hasattr(spin, 'xh_vect'):
+ warn(RelaxWarning("RDC data exists but the XH bond vectors
are missing, skipping spin " + spin_id))
+ continue
+
+ # Append the RDC and XH vectors to the lists.
+ rdcs.append(spin.rdc)
+ xh_vectors.append(spin.xh_vect)
+
+ # Gyromagnetic ratios.
+ gx = return_gyromagnetic_ratio(spin.heteronuc_type)
+ gh = return_gyromagnetic_ratio(spin.proton_type)
+
+ # Calculate the RDC dipolar constant (in Hertz, and the 3 comes
from the alignment tensor), and append it to the list.
+ dj.append(3.0/(2.0*pi) * dipolar_constant(gx, gh, spin.r))
+
+ # Initialise the numpy objects (the rdc matrix is transposed!).
+ rdcs_numpy = zeros((len(rdcs[0]), len(rdcs)), float64)
+ xh_vect_numpy = zeros((len(xh_vectors), len(xh_vectors[0]), 3),
float64)
+
+ # Loop over the spins.
+ for spin_index in xrange(len(rdcs)):
+ # Loop over the alignments.
+ for align_index in xrange(len(rdcs[spin_index])):
+ # Transpose and store the RDC value.
+ rdcs_numpy[align_index, spin_index] =
rdcs[spin_index][align_index]
+
+ # Loop over the N states.
+ for state_index in xrange(len(xh_vectors[spin_index])):
+ # Store the unit vector.
+ xh_vect_numpy[spin_index, state_index] =
xh_vectors[spin_index][state_index]
+
+ # Return the data structures.
+ return rdcs_numpy, xh_vect_numpy, dj
+
+
+ def __minimise_setup_tensors(self):
+ """Set up the data structures for optimisation using alignment
tensors as base data sets.
+
+ @return: The assembled data structures for using alignment
tensors as the base data for
+ optimisation. These include:
+ - full_tensors, the data of the full alignment
tensors.
+ - red_tensor_elem, the tensors as concatenated
rank-1 5D arrays.
+ - red_tensor_err, the tensor errors as concatenated
rank-1 5D arrays.
+ - full_in_ref_frame, flags specifying if the tensor
in the reference frame
+ is the full or reduced tensor.
+ @rtype: tuple of (list, numpy rank-1 array, numpy rank-1 array,
numpy rank-1 array)
+ """
+
+ # Alias the current data pipe.
+ cdp = ds[ds.current_pipe]
+
+ # Initialise.
+ full_tensors = []
+ red_tensor_elem = []
+ red_tensor_err = []
+ full_in_ref_frame = []
+
+ # Loop over all tensors.
+ for tensor in cdp.align_tensors:
+ # The full tensor.
+ if not tensor.red:
+ # The full tensor corresponds to the frame of reference.
+ if cdp.ref_domain == tensor.domain:
+ full_in_ref_frame.append(1)
+ else:
+ full_in_ref_frame.append(0)
+
+ # Create a list of matrices consisting of all the full
alignment tensors.
+ full_tensors.append(tensor.tensor)
+
+ # Create a list of all the reduced alignment tensor elements and
their errors (for the chi-squared function).
+ elif tensor.red:
+ # Append the 5 unique elements.
+ red_tensor_elem.append(tensor.Sxx)
+ red_tensor_elem.append(tensor.Syy)
+ red_tensor_elem.append(tensor.Sxy)
+ red_tensor_elem.append(tensor.Sxz)
+ red_tensor_elem.append(tensor.Syz)
+
+ # Append the 5 unique error elements (if they exist).
+ if hasattr(tensor, 'Sxx_err'):
+ red_tensor_err.append(tensor.Sxx_err)
+ red_tensor_err.append(tensor.Syy_err)
+ red_tensor_err.append(tensor.Sxy_err)
+ red_tensor_err.append(tensor.Sxz_err)
+ red_tensor_err.append(tensor.Syz_err)
+
+ # Otherwise append errors of 1.0 to convert the chi-squared
equation to the SSE equation (for the tensors without errors).
+ else:
+ red_tensor_err = red_tensor_err + [1.0, 1.0, 1.0, 1.0,
1.0]
+
+ # Convert the reduced alignment tensor element lists into numpy
arrays (for the chi-squared function maths).
+ red_tensor_elem = array(red_tensor_elem, float64)
+ red_tensor_err = array(red_tensor_err, float64)
+ full_in_ref_frame = array(full_in_ref_frame, float64)
+
+ # Return the data structures.
+ return full_tensors, red_tensor_elem, red_tensor_err,
full_in_ref_frame
def __update_model(self):
@@ -785,17 +915,17 @@
# 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()
+ full_tensors, red_tensor_elem, red_tensor_err, full_in_ref_frame
= self.__minimise_setup_tensors()
# Get the data structures for optimisation using PCSs as base data
sets.
pcs, pcs_vect, pcs_dj, temp, frq = None, None, None, None, None
if 'pcs' in data_types:
- pcs, pcs_vect, pcs_dj, temp, frq = self.minimise_setup_pcs()
+ pcs, pcs_vect, pcs_dj, temp, frq = self.__minimise_setup_pcs()
# Get the data structures for optimisation using RDCs as base data
sets.
rdcs, xh_vect, rdc_dj = None, None, None
if 'rdc' in data_types:
- rdcs, xh_vect, rdc_dj = self.minimise_setup_rdcs()
+ rdcs, 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_vect=pcs_vect,
xh_vect=xh_vect, pcs_const=pcs_dj, dip_const=rdc_dj,
scaling_matrix=scaling_matrix)
@@ -865,136 +995,6 @@
# Warning.
cdp.warning = warning
-
-
- 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.
- These include:
- - rdcs, the RDC values.
- - xh_vectors, the heteronucleus to proton vectors.
- - dj, the dipolar constants.
- @rtype: tuple of (numpy rank-2 array, numpy rank-2 array, numpy
rank-2 array)
- """
-
- # Alias the current data pipe.
- cdp = ds[ds.current_pipe]
-
- # Initialise.
- rdcs = []
- xh_vectors = []
- dj = []
-
- # Spin loop.
- for spin, spin_id in spin_loop(return_id=True):
- # Skip deselected spins.
- if not spin.select:
- continue
-
- # Skip spins without RDC data or unit XH bond vectors.
- if not hasattr(spin, 'rdc'):
- continue
-
- # RDC data exists but the XH bond vectors are missing?
- if not hasattr(spin, 'xh_vect'):
- warn(RelaxWarning("RDC data exists but the XH bond vectors
are missing, skipping spin " + spin_id))
- continue
-
- # Append the RDC and XH vectors to the lists.
- rdcs.append(spin.rdc)
- xh_vectors.append(spin.xh_vect)
-
- # Gyromagnetic ratios.
- gx = return_gyromagnetic_ratio(spin.heteronuc_type)
- gh = return_gyromagnetic_ratio(spin.proton_type)
-
- # Calculate the RDC dipolar constant (in Hertz, and the 3 comes
from the alignment tensor), and append it to the list.
- dj.append(3.0/(2.0*pi) * dipolar_constant(gx, gh, spin.r))
-
- # Initialise the numpy objects (the rdc matrix is transposed!).
- rdcs_numpy = zeros((len(rdcs[0]), len(rdcs)), float64)
- xh_vect_numpy = zeros((len(xh_vectors), len(xh_vectors[0]), 3),
float64)
-
- # Loop over the spins.
- for spin_index in xrange(len(rdcs)):
- # Loop over the alignments.
- for align_index in xrange(len(rdcs[spin_index])):
- # Transpose and store the RDC value.
- rdcs_numpy[align_index, spin_index] =
rdcs[spin_index][align_index]
-
- # Loop over the N states.
- for state_index in xrange(len(xh_vectors[spin_index])):
- # Store the unit vector.
- xh_vect_numpy[spin_index, state_index] =
xh_vectors[spin_index][state_index]
-
- # Return the data structures.
- return rdcs_numpy, xh_vect_numpy, dj
-
-
- def minimise_setup_tensors(self):
- """Set up the data structures for optimisation using alignment
tensors as base data sets.
-
- @return: The assembled data structures for using alignment
tensors as the base data for
- optimisation. These include:
- - full_tensors, the data of the full alignment
tensors.
- - red_tensor_elem, the tensors as concatenated
rank-1 5D arrays.
- - red_tensor_err, the tensor errors as concatenated
rank-1 5D arrays.
- - full_in_ref_frame, flags specifying if the tensor
in the reference frame
- is the full or reduced tensor.
- @rtype: tuple of (list, numpy rank-1 array, numpy rank-1 array,
numpy rank-1 array)
- """
-
- # Alias the current data pipe.
- cdp = ds[ds.current_pipe]
-
- # Initialise.
- full_tensors = []
- red_tensor_elem = []
- red_tensor_err = []
- full_in_ref_frame = []
-
- # Loop over all tensors.
- for tensor in cdp.align_tensors:
- # The full tensor.
- if not tensor.red:
- # The full tensor corresponds to the frame of reference.
- if cdp.ref_domain == tensor.domain:
- full_in_ref_frame.append(1)
- else:
- full_in_ref_frame.append(0)
-
- # Create a list of matrices consisting of all the full
alignment tensors.
- full_tensors.append(tensor.tensor)
-
- # Create a list of all the reduced alignment tensor elements and
their errors (for the chi-squared function).
- elif tensor.red:
- # Append the 5 unique elements.
- red_tensor_elem.append(tensor.Sxx)
- red_tensor_elem.append(tensor.Syy)
- red_tensor_elem.append(tensor.Sxy)
- red_tensor_elem.append(tensor.Sxz)
- red_tensor_elem.append(tensor.Syz)
-
- # Append the 5 unique error elements (if they exist).
- if hasattr(tensor, 'Sxx_err'):
- red_tensor_err.append(tensor.Sxx_err)
- red_tensor_err.append(tensor.Syy_err)
- red_tensor_err.append(tensor.Sxy_err)
- red_tensor_err.append(tensor.Sxz_err)
- red_tensor_err.append(tensor.Syz_err)
-
- # Otherwise append errors of 1.0 to convert the chi-squared
equation to the SSE equation (for the tensors without errors).
- else:
- red_tensor_err = red_tensor_err + [1.0, 1.0, 1.0, 1.0,
1.0]
-
- # Convert the reduced alignment tensor element lists into numpy
arrays (for the chi-squared function maths).
- red_tensor_elem = array(red_tensor_elem, float64)
- red_tensor_err = array(red_tensor_err, float64)
- full_in_ref_frame = array(full_in_ref_frame, float64)
-
- # Return the data structures.
- return full_tensors, red_tensor_elem, red_tensor_err,
full_in_ref_frame
def number_of_states(self, N=None):
r7119 - /branches/rdc_analysis/specific_fns/n_state_model.py