def minimise_setup_fixed_tensors():
"""Set up the data structures for the fixed alignment tensors.
@return: The assembled data structures for the fixed alignment tensors.
@rtype: numpy rank-1 array.
"""
# Initialise.
n = align_tensor.num_tensors(skip_fixed=False) - align_tensor.num_tensors(
skip_fixed=True)
tensors = zeros(n * 5, float64)
# Nothing to do.
if n == 0:
return None
# Loop over the tensors.
index = 0
for i in range(len(cdp.align_tensors)):
# Skip non-optimised data.
if not opt_uses_align_data(cdp.align_tensors[i].name):
continue
# No parameters have been set.
if not hasattr(cdp.align_tensors[i], 'Axx'):
continue
# The real tensors.
tensors[5 * index + 0] = cdp.align_tensors[i].Axx
tensors[5 * index + 1] = cdp.align_tensors[i].Ayy
tensors[5 * index + 2] = cdp.align_tensors[i].Axy
tensors[5 * index + 3] = cdp.align_tensors[i].Axz
tensors[5 * index + 4] = cdp.align_tensors[i].Ayz
# Increment the index.
index += 1
# Return the data structure.
return tensors
def minimise_setup_fixed_tensors():
"""Set up the data structures for the fixed alignment tensors.
@return: The assembled data structures for the fixed alignment tensors.
@rtype: numpy rank-1 array.
"""
# Initialise.
n = align_tensor.num_tensors(skip_fixed=False) - align_tensor.num_tensors(skip_fixed=True)
tensors = zeros(n*5, float64)
# Nothing to do.
if n == 0:
return None
# Loop over the tensors.
index = 0
for i in range(len(cdp.align_tensors)):
# Skip non-optimised data.
if not opt_uses_align_data(cdp.align_tensors[i].name):
continue
# No parameters have been set.
if not hasattr(cdp.align_tensors[i], 'Axx'):
continue
# The real tensors.
tensors[5*index + 0] = cdp.align_tensors[i].Axx
tensors[5*index + 1] = cdp.align_tensors[i].Ayy
tensors[5*index + 2] = cdp.align_tensors[i].Axy
tensors[5*index + 3] = cdp.align_tensors[i].Axz
tensors[5*index + 4] = cdp.align_tensors[i].Ayz
# Increment the index.
index += 1
# Return the data structure.
return tensors
def sim_return_param(self, index, model_info=None):
"""Return the array of simulation parameter values.
@param index: The index of the parameter to return the array of values for.
@type index: int
@keyword model_info: The model information from model_loop(). This is unused.
@type model_info: None
@return: The array of simulation parameter values.
@rtype: list of float
"""
# Align parameters.
names = ['Axx', 'Ayy', 'Axy', 'Axz', 'Ayz']
# Alignment tensor parameters.
if index < align_tensor.num_tensors(skip_fixed=True)*5:
# The tensor and parameter index.
param_index = index % 5
tensor_index = (index - index % 5) / 5
# Return the simulation parameter array.
tensor = align_tensor.return_tensor(index=tensor_index, skip_fixed=True)
return getattr(tensor, names[param_index]+'_sim')
def sim_return_param(self, index, model_info=None):
"""Return the array of simulation parameter values.
@param index: The index of the parameter to return the array of values for.
@type index: int
@keyword model_info: The model information from model_loop(). This is unused.
@type model_info: None
@return: The array of simulation parameter values.
@rtype: list of float
"""
# Align parameters.
names = ['Axx', 'Ayy', 'Axy', 'Axz', 'Ayz']
# Alignment tensor parameters.
if index < align_tensor.num_tensors(skip_fixed=True) * 5:
# The tensor and parameter index.
param_index = index % 5
tensor_index = (index - index % 5) / 5
# Return the simulation parameter array.
tensor = align_tensor.return_tensor(index=tensor_index,
skip_fixed=True)
return getattr(tensor, names[param_index] + '_sim')
def grid_search(self, lower=None, upper=None, inc=None, scaling_matrix=None, constraints=False, verbosity=0, sim_index=None):
"""The grid search function.
@param lower: The lower bounds of the grid search which must be equal to the number of parameters in the model.
@type lower: list of lists of floats
@param upper: The upper bounds of the grid search which must be equal to the number of parameters in the model.
@type upper: list of lists of floats
@param inc: The increments for each dimension of the space for the grid search. The number of elements in the array must equal to the number of parameters in the model.
@type inc: list of lists of int
@keyword scaling_matrix: The per-model list of diagonal and square scaling matrices.
@type scaling_matrix: list of numpy rank-2, float64 array or list of None
@param constraints: If True, constraints are applied during the grid search (elinating parts of the grid). If False, no constraints are used.
@type constraints: bool
@param verbosity: A flag specifying the amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
"""
# Test if the N-state model has been set up.
if not hasattr(cdp, 'model'):
raise RelaxNoModelError('N-state')
# The number of parameters.
n = param_num()
# Determine the data type.
data_types = base_data_types()
# The number of tensors to optimise.
tensor_num = align_tensor.num_tensors(skip_fixed=True)
# Custom sub-grid search for when only tensors are optimised (as each tensor is independent, the number of points collapses from inc**(5*N) to N*inc**5).
if cdp.model == 'fixed' and tensor_num > 1 and ('rdc' in data_types or 'pcs' in data_types) and not align_tensor.all_tensors_fixed() and hasattr(cdp, 'paramag_centre_fixed') and cdp.paramag_centre_fixed:
# Print out.
print("Optimising each alignment tensor separately.")
# Store the alignment tensor fixed flags.
fixed_flags = []
for i in range(len(cdp.align_ids)):
# Get the tensor object.
tensor = align_tensor.return_tensor(index=i, skip_fixed=False)
# Store the flag.
fixed_flags.append(tensor.fixed)
# Fix the tensor.
tensor.set('fixed', True)
# Loop over each sub-grid.
for i in range(len(cdp.align_ids)):
# Skip the tensor if originally fixed.
if fixed_flags[i]:
continue
# Get the tensor object.
tensor = align_tensor.return_tensor(index=i, skip_fixed=False)
# Unfix the current tensor.
tensor.set('fixed', False)
# Grid search parameter subsets.
lower_sub = lower[0][i*5:i*5+5]
upper_sub = upper[0][i*5:i*5+5]
inc_sub = inc[0][i*5:i*5+5]
# Minimisation of the sub-grid.
self.minimise(min_algor='grid', lower=[lower_sub], upper=[upper_sub], inc=[inc_sub], scaling_matrix=[None], constraints=constraints, verbosity=verbosity, sim_index=sim_index)
# Fix the tensor again.
tensor.set('fixed', True)
# Reset the state of the tensors.
for i in range(len(cdp.align_ids)):
# Get the tensor object.
tensor = align_tensor.return_tensor(index=i, skip_fixed=False)
# Fix the tensor.
tensor.set('fixed', fixed_flags[i])
# All other minimisation.
else:
self.minimise(min_algor='grid', lower=lower, upper=upper, inc=inc, scaling_matrix=scaling_matrix, constraints=constraints, verbosity=verbosity, sim_index=sim_index)
def grid_search(self,
lower=None,
upper=None,
inc=None,
scaling_matrix=None,
constraints=False,
verbosity=0,
sim_index=None):
"""The grid search function.
@param lower: The lower bounds of the grid search which must be equal to the number of parameters in the model.
@type lower: list of lists of floats
@param upper: The upper bounds of the grid search which must be equal to the number of parameters in the model.
@type upper: list of lists of floats
@param inc: The increments for each dimension of the space for the grid search. The number of elements in the array must equal to the number of parameters in the model.
@type inc: list of lists of int
@keyword scaling_matrix: The per-model list of diagonal and square scaling matrices.
@type scaling_matrix: list of numpy rank-2, float64 array or list of None
@param constraints: If True, constraints are applied during the grid search (elinating parts of the grid). If False, no constraints are used.
@type constraints: bool
@param verbosity: A flag specifying the amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
"""
# Test if the N-state model has been set up.
if not hasattr(cdp, 'model'):
raise RelaxNoModelError('N-state')
# The number of parameters.
n = param_num()
# Determine the data type.
data_types = base_data_types()
# The number of tensors to optimise.
tensor_num = align_tensor.num_tensors(skip_fixed=True)
# Custom sub-grid search for when only tensors are optimised (as each tensor is independent, the number of points collapses from inc**(5*N) to N*inc**5).
if cdp.model == 'fixed' and tensor_num > 1 and (
'rdc' in data_types or 'pcs' in data_types
) and not align_tensor.all_tensors_fixed() and hasattr(
cdp, 'paramag_centre_fixed') and cdp.paramag_centre_fixed:
# Print out.
print("Optimising each alignment tensor separately.")
# Store the alignment tensor fixed flags.
fixed_flags = []
for i in range(len(cdp.align_ids)):
# Get the tensor object.
tensor = align_tensor.return_tensor(index=i, skip_fixed=False)
# Store the flag.
fixed_flags.append(tensor.fixed)
# Fix the tensor.
tensor.set('fixed', True)
# Loop over each sub-grid.
for i in range(len(cdp.align_ids)):
# Skip the tensor if originally fixed.
if fixed_flags[i]:
continue
# Get the tensor object.
tensor = align_tensor.return_tensor(index=i, skip_fixed=False)
# Unfix the current tensor.
tensor.set('fixed', False)
# Grid search parameter subsets.
lower_sub = lower[0][i * 5:i * 5 + 5]
upper_sub = upper[0][i * 5:i * 5 + 5]
inc_sub = inc[0][i * 5:i * 5 + 5]
# Minimisation of the sub-grid.
self.minimise(min_algor='grid',
lower=[lower_sub],
upper=[upper_sub],
inc=[inc_sub],
scaling_matrix=[None],
constraints=constraints,
verbosity=verbosity,
sim_index=sim_index)
# Fix the tensor again.
tensor.set('fixed', True)
# Reset the state of the tensors.
for i in range(len(cdp.align_ids)):
# Get the tensor object.
tensor = align_tensor.return_tensor(index=i, skip_fixed=False)
# Fix the tensor.
tensor.set('fixed', fixed_flags[i])
# All other minimisation.
else:
self.minimise(min_algor='grid',
lower=lower,
upper=upper,
inc=inc,
scaling_matrix=scaling_matrix,
constraints=constraints,
verbosity=verbosity,
sim_index=sim_index)