Author: bugman
Date: Tue Jan 22 20:13:33 2013
New Revision: 18260
URL: http://svn.gna.org/viewcvs/relax?rev=18260&view=rev
Log:
Clean up of some of the logic in N-state model analysis specific code.
The following methods have been added:
_opt_tensor(),
_opt_uses_align_data(),
_opt_uses_pcs(),
_opt_uses_rdc()
These are used through the class to determine what is needed for or used
during optimisation,
making a lot of checking code more consistent (hence removing latent bugs).
Modified:
trunk/specific_fns/n_state_model.py
Modified: trunk/specific_fns/n_state_model.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/specific_fns/n_state_model.py?rev=18260&r1=18259&r2=18260&view=diff
==============================================================================
--- trunk/specific_fns/n_state_model.py (original)
+++ trunk/specific_fns/n_state_model.py Tue Jan 22 20:13:33 2013
@@ -99,15 +99,10 @@
param_vector = []
# A RDC or PCS data type requires the alignment tensors to be at the
start of the parameter vector (unless the tensors are fixed).
- if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
- # Loop over the alignments, adding the alignment tensor
parameters to the parameter vector.
+ if self._opt_uses_align_data():
for i in range(len(cdp.align_tensors)):
- # No alignment ID, so skip the tensor as it will not be
optimised.
- if cdp.align_tensors[i].name not in cdp.align_ids:
- continue
-
- # Fixed tensor.
- if cdp.align_tensors[i].fixed:
+ # Skip non-optimised tensors.
+ if not self._opt_tensor(cdp.align_tensors[i]):
continue
# Add the parameters.
@@ -194,23 +189,23 @@
# Starting point of the populations.
pop_start = 0
- if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
- # Loop over the alignments.
- tensor_num = 0
- for i in range(len(cdp.align_tensors)):
- # Fixed tensor.
- if cdp.align_tensors[i].fixed:
- continue
-
- # Add the 5 alignment parameters.
- pop_start = pop_start + 5
-
- # The alignment parameters.
- for j in range(5):
- scaling_matrix[5*tensor_num + j, 5*tensor_num + j] = 1.0
-
- # Increase the tensor number.
- tensor_num += 1
+
+ # Loop over the alignments.
+ tensor_num = 0
+ for i in range(len(cdp.align_tensors)):
+ # Skip non-optimised tensors.
+ if not self._opt_tensor(cdp.align_tensors[i]):
+ continue
+
+ # Add the 5 alignment parameters.
+ pop_start = pop_start + 5
+
+ # The alignment parameters.
+ for j in range(5):
+ scaling_matrix[5*tensor_num + j, 5*tensor_num + j] = 1.0
+
+ # Increase the tensor number.
+ tensor_num += 1
# Loop over the populations, and set the scaling factor.
if cdp.model in ['2-domain', 'population']:
@@ -559,12 +554,8 @@
# Loop over the alignments, adding the alignment tensor
parameters to the tensor data container.
tensor_num = 0
for i in range(len(cdp.align_tensors)):
- # No alignment ID, so skip the tensor as it will not be
optimised.
- if cdp.align_tensors[i].name not in cdp.align_ids:
- continue
-
- # Fixed tensor.
- if cdp.align_tensors[i].fixed:
+ # Skip non-optimised tensors.
+ if not self._opt_tensor(cdp.align_tensors[i]):
continue
# Normal tensors.
@@ -746,8 +737,8 @@
if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
# Loop over the alignments.
for i in range(len(cdp.align_tensors)):
- # Fixed tensor.
- if cdp.align_tensors[i].fixed:
+ # Skip non-optimised tensors.
+ if not self._opt_tensor(cdp.align_tensors[i]):
continue
# Add 5 parameters.
@@ -805,13 +796,14 @@
return
# Loop over each alignment.
- for align_index in range(len(cdp.align_ids)):
- # Fixed tensor.
- if cdp.align_tensors[align_index].fixed:
+ align_index = 0
+ for i in range(len(cdp.align_ids)):
+ # Skip non-optimised tensors.
+ if not self._opt_tensor(cdp.align_tensors[i]):
continue
# The alignment ID.
- align_id = cdp.align_ids[align_index]
+ align_id = cdp.align_ids[i]
# Data flags
rdc_flag = False
@@ -862,6 +854,9 @@
# Increment the data index if the interatom container
has data.
rdc_index = rdc_index + 1
+
+ # Increment the alignment index (for the optimised tensors).
+ align_index += 1
def _minimise_setup_atomic_pos(self, sim_index=None):
@@ -940,9 +935,12 @@
frq = []
# The PCS data.
- for align_id in cdp.align_ids:
- # No RDC or PCS data, so jump to the next alignment.
- if (hasattr(cdp, 'rdc_ids') and not align_id in cdp.rdc_ids) and
(hasattr(cdp, 'pcs_ids') and not align_id in cdp.pcs_ids):
+ for i in range(len(cdp.align_ids)):
+ # Alias the ID.
+ align_id = cdp.align_ids[i]
+
+ # Skip non-optimised data.
+ if not self._opt_uses_align_data(align_id):
continue
# Append empty arrays to the PCS structures.
@@ -1083,9 +1081,12 @@
unit_vect[i] = [[None, None, None]]*num
# The RDC data.
- for align_id in cdp.align_ids:
- # No RDC or PCS data, so jump to the next alignment.
- if (hasattr(cdp, 'rdc_ids') and not align_id in cdp.rdc_ids) and
(hasattr(cdp, 'pcs_ids') and not align_id in cdp.pcs_ids):
+ for i in range(len(cdp.align_ids)):
+ # Alias the ID.
+ align_id = cdp.align_ids[i]
+
+ # Skip non-optimised data.
+ if not self._opt_uses_align_data(align_id):
continue
# Append empty arrays to the RDC structures.
@@ -1258,8 +1259,8 @@
# Loop over the tensors.
index = 0
for i in range(len(cdp.align_tensors)):
- # Skip unfixed tensors.
- if not cdp.align_tensors[i].fixed:
+ # Skip non-optimised data.
+ if not self._opt_uses_align_data(cdp.align_tensors[i].name):
continue
# The real tensors.
@@ -1340,6 +1341,129 @@
self._update_model()
+ def _opt_tensor(self, tensor):
+ """Determine if the given tensor is to be optimised.
+
+ @param tensor: The alignment tensor to check.
+ @type tensor: AlignmentTensor object.
+ @return: True if the tensor is to be optimised, False
otherwise.
+ @rtype: bool
+ """
+
+ # Combine all RDC and PCS IDs.
+ ids = []
+ if hasattr(cdp, 'rdc_ids'):
+ ids += cdp.rdc_ids
+ if hasattr(cdp, 'pcs_ids'):
+ ids += cdp.pcs_ids
+
+ # No RDC or PCS data for the alignment, so skip the tensor as it
will not be optimised.
+ # No RDC or PCS data for the alignment, so skip the tensor as it
will not be optimised.
+ if tensor.name not in ids:
+ return False
+
+ # Fixed tensor.
+ if tensor.fixed:
+ return False
+
+ # The tensor is to be optimised.
+ return True
+
+
+ def _opt_uses_align_data(self, align_id=None):
+ """Determine if the PCS or RDC data for the given alignment ID is
needed for optimisation.
+
+ @keyword align_id: The optional alignment ID string.
+ @type align_id: str
+ @return: True if alignment data is to be used for
optimisation, False otherwise.
+ @rtype: bool
+ """
+
+ # No alignment IDs.
+ if not hasattr(cdp, 'align_ids'):
+ return False
+
+ # Convert the align IDs to an array, or take all IDs.
+ if align_id:
+ align_ids = [align_id]
+ else:
+ align_ids = cdp.align_ids
+
+ # Check the PCS and RDC.
+ for align_id in align_ids:
+ if self._opt_uses_pcs(align_id) or self._opt_uses_rdc(align_id):
+ return True
+
+ # No alignment data is used for optimisation.
+ return False
+
+
+ def _opt_uses_pcs(self, align_id):
+ """Determine if the PCS data for the given alignment ID is needed
for optimisation.
+
+ @param align_id: The alignment ID string.
+ @type align_id: str
+ @return: True if the PCS data is to be used for
optimisation, False otherwise.
+ @rtype: bool
+ """
+
+ # No alignment IDs.
+ if not hasattr(cdp, 'pcs_ids'):
+ return False
+
+ # No PCS data for the alignment.
+ if align_id not in cdp.pcs_ids:
+ return False
+
+ # Is the tensor optimised?
+ tensor_flag =
self._opt_tensor(align_tensor.get_tensor_object(align_id))
+
+ # Is the paramagnetic position optimised?
+ pos_flag = False
+ if hasattr(cdp, 'paramag_centre_fixed') and not
cdp.paramag_centre_fixed:
+ pos_flag = True
+
+ # Not used.
+ if not tensor_flag and not pos_flag:
+ return False
+
+ # The PCS data is to be used for optimisation.
+ return True
+
+
+ def _opt_uses_rdc(self, align_id):
+ """Determine if the RDC data for the given alignment ID is needed
for optimisation.
+
+ @param align_id: The alignment ID string.
+ @type align_id: str
+ @return: True if the RDC data is to be used for
optimisation, False otherwise.
+ @rtype: bool
+ """
+
+ # No alignment IDs.
+ if not hasattr(cdp, 'rdc_ids'):
+ return False
+
+ # No RDC data for the alignment.
+ if align_id not in cdp.rdc_ids:
+ return False
+
+ # Is the tensor optimised?
+ tensor_flag =
self._opt_tensor(align_tensor.get_tensor_object(align_id))
+
+ # Is the paramagnetic position optimised?
+ pos_flag = False
+ if hasattr(cdp, 'paramag_centre_fixed') and not
cdp.paramag_centre_fixed:
+ pos_flag = True
+
+ # Not used.
+ if not tensor_flag and not pos_flag:
+ return False
+
+ # The RDC data is to be used for optimisation.
+ return True
+
+
def _param_model_index(self, param):
"""Return the N-state model index for the given parameter.
@@ -1386,12 +1510,8 @@
if ('rdc' in data_types or 'pcs' in data_types) and not
align_tensor.all_tensors_fixed():
# Loop over the alignments.
for i in range(len(cdp.align_tensors)):
- # No alignment ID, so skip the tensor as it is not part of
the parameter set.
- if cdp.align_tensors[i].name not in cdp.align_ids:
- continue
-
- # Fixed tensor.
- if cdp.align_tensors[i].fixed:
+ # Skip non-optimised tensors.
+ if not self._opt_tensor(cdp.align_tensors[i]):
continue
# Add 5 tensor parameters.
@@ -1540,6 +1660,10 @@
# The flag list.
fixed_tensors = []
for i in range(len(cdp.align_tensors)):
+ # Skip non-optimised data.
+ if not self._opt_uses_align_data(cdp.align_tensors[i].name):
+ continue
+
if cdp.align_tensors[i].fixed:
fixed_tensors.append(True)
else:
@@ -2486,8 +2610,8 @@
# Loop over the alignments, adding the alignment tensor
parameters to the tensor data container.
for i in range(len(cdp.align_tensors)):
- # Fixed tensor.
- if cdp.align_tensors[i].fixed:
+ # Skip non-optimised tensors.
+ if not self._opt_tensor(cdp.align_tensors[i]):
continue
# Set up the number of simulations.
r18260 - /trunk/specific_fns/n_state_model.py