def get_param_values(self, model_info=None, sim_index=None):
"""Return a vector of parameter values.
@keyword model_info: The model information from model_loop(). This is unused.
@type model_info: None
@keyword sim_index: The optional Monte Carlo simulation index.
@type sim_index: int
@return: The vector of parameter values.
@rtype: list of str
"""
# Return the vector.
return assemble_param_vector(sim_index=sim_index)
def get_param_values(self, model_info=None, sim_index=None):
"""Return a vector of parameter values.
@keyword model_info: The model information from model_loop(). This is unused.
@type model_info: None
@keyword sim_index: The optional Monte Carlo simulation index.
@type sim_index: int
@return: The vector of parameter values.
@rtype: list of str
"""
# Return the vector.
return assemble_param_vector(sim_index=sim_index)
def target_fn_setup(sim_index=None, scaling_matrix=None, verbosity=0):
"""Initialise the target function for optimisation or direct calculation.
@keyword sim_index: The index of the simulation to optimise. This should be None if normal optimisation is desired.
@type sim_index: None or int
@keyword scaling_matrix: The diagonal and square scaling matrix.
@type scaling_matrix: numpy rank-2, float64 array or None
@keyword 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')
# '2-domain' model setup tests.
if cdp.model == '2-domain':
# The number of states.
if not hasattr(cdp, 'N'):
raise RelaxError("The number of states has not been set.")
# The reference domain.
if not hasattr(cdp, 'ref_domain'):
raise RelaxError("The reference domain has not been set.")
# Update the model parameters if necessary.
update_model()
# Create the initial parameter vector.
param_vector = assemble_param_vector(sim_index=sim_index)
# Replace all NaNs with 0.0.
fix_invalid(param_vector, copy=False, fill_value=0.0)
# Determine if alignment tensors or RDCs are to be used.
data_types = base_data_types()
# The probabilities.
probs = None
if hasattr(cdp, 'probs') and len(cdp.probs) and cdp.probs[0] != None:
probs = cdp.probs
# Diagonal scaling.
if len(param_vector) and scaling_matrix is not None:
param_vector = dot(inv(scaling_matrix), param_vector)
# 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 = minimise_setup_tensors(sim_index=sim_index)
# Get the data structures for optimisation using PCSs as base data sets.
pcs, pcs_err, pcs_weight, temp, frq, pcs_pseudo_flags = None, None, None, None, None, None
if 'pcs' in data_types:
pcs, pcs_err, pcs_weight, temp, frq, pcs_pseudo_flags = return_pcs_data(sim_index=sim_index, verbosity=verbosity)
# Get the data structures for optimisation using RDCs as base data sets.
rdcs, rdc_err, rdc_weight, rdc_vector, rdc_dj, absolute_rdc, T_flags, j_couplings, rdc_pseudo_flags = None, None, None, None, None, None, None, None, None
if 'rdc' in data_types:
# The data.
rdcs, rdc_err, rdc_weight, rdc_vector, rdc_dj, absolute_rdc, T_flags, j_couplings, rdc_pseudo_flags = return_rdc_data(sim_index=sim_index, verbosity=verbosity)
# Get the fixed tensors.
fixed_tensors = None
if 'rdc' in data_types or 'pcs' in data_types:
full_tensors = minimise_setup_fixed_tensors()
# The flag list.
fixed_tensors = []
for i in range(len(cdp.align_tensors)):
# Skip non-optimised data.
if not opt_uses_align_data(cdp.align_tensors[i].name):
continue
if cdp.align_tensors[i].fixed:
fixed_tensors.append(True)
else:
fixed_tensors.append(False)
# Get the atomic_positions.
atomic_pos, paramag_centre, centre_fixed = None, None, True
if 'pcs' in data_types or 'pre' in data_types:
atomic_pos, paramag_centre = minimise_setup_atomic_pos(sim_index=sim_index)
# Optimisation of the centre.
if hasattr(cdp, 'paramag_centre_fixed'):
centre_fixed = cdp.paramag_centre_fixed
# Set up the class instance containing the target function.
model = N_state_opt(model=cdp.model, N=cdp.N, init_params=param_vector, probs=probs, full_tensors=full_tensors, red_data=red_tensor_elem, red_errors=red_tensor_err, full_in_ref_frame=full_in_ref_frame, fixed_tensors=fixed_tensors, pcs=pcs, rdcs=rdcs, pcs_errors=pcs_err, rdc_errors=rdc_err, T_flags=T_flags, j_couplings=j_couplings, rdc_pseudo_flags=rdc_pseudo_flags, pcs_pseudo_flags=pcs_pseudo_flags, pcs_weights=pcs_weight, rdc_weights=rdc_weight, rdc_vect=rdc_vector, temp=temp, frq=frq, dip_const=rdc_dj, absolute_rdc=absolute_rdc, atomic_pos=atomic_pos, paramag_centre=paramag_centre, scaling_matrix=scaling_matrix, centre_fixed=centre_fixed)
# Return the data.
return model, param_vector, data_types
def target_fn_setup(sim_index=None, scaling_matrix=None, verbosity=0):
"""Initialise the target function for optimisation or direct calculation.
@keyword sim_index: The index of the simulation to optimise. This should be None if normal optimisation is desired.
@type sim_index: None or int
@keyword scaling_matrix: The diagonal and square scaling matrix.
@type scaling_matrix: numpy rank-2, float64 array or None
@keyword 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')
# '2-domain' model setup tests.
if cdp.model == '2-domain':
# The number of states.
if not hasattr(cdp, 'N'):
raise RelaxError("The number of states has not been set.")
# The reference domain.
if not hasattr(cdp, 'ref_domain'):
raise RelaxError("The reference domain has not been set.")
# Update the model parameters if necessary.
update_model()
# Create the initial parameter vector.
param_vector = assemble_param_vector(sim_index=sim_index)
# Replace all NaNs with 0.0.
fix_invalid(param_vector, copy=False, fill_value=0.0)
# Determine if alignment tensors or RDCs are to be used.
data_types = base_data_types()
# The probabilities.
probs = None
if hasattr(cdp, 'probs') and len(cdp.probs) and cdp.probs[0] != None:
probs = cdp.probs
# Diagonal scaling.
if len(param_vector) and scaling_matrix is not None:
param_vector = dot(inv(scaling_matrix), param_vector)
# 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 = minimise_setup_tensors(
sim_index=sim_index)
# Get the data structures for optimisation using PCSs as base data sets.
pcs, pcs_err, pcs_weight, temp, frq, pcs_pseudo_flags = None, None, None, None, None, None
if 'pcs' in data_types:
pcs, pcs_err, pcs_weight, temp, frq, pcs_pseudo_flags = return_pcs_data(
sim_index=sim_index, verbosity=verbosity)
# Get the data structures for optimisation using RDCs as base data sets.
rdcs, rdc_err, rdc_weight, rdc_vector, rdc_dj, absolute_rdc, T_flags, j_couplings, rdc_pseudo_flags = None, None, None, None, None, None, None, None, None
if 'rdc' in data_types:
# The data.
rdcs, rdc_err, rdc_weight, rdc_vector, rdc_dj, absolute_rdc, T_flags, j_couplings, rdc_pseudo_flags = return_rdc_data(
sim_index=sim_index, verbosity=verbosity)
# Get the fixed tensors.
fixed_tensors = None
if 'rdc' in data_types or 'pcs' in data_types:
full_tensors = minimise_setup_fixed_tensors()
# The flag list.
fixed_tensors = []
for i in range(len(cdp.align_tensors)):
# Skip non-optimised data.
if not opt_uses_align_data(cdp.align_tensors[i].name):
continue
if cdp.align_tensors[i].fixed:
fixed_tensors.append(True)
else:
fixed_tensors.append(False)
# Get the atomic_positions.
atomic_pos, paramag_centre, centre_fixed = None, None, True
if 'pcs' in data_types or 'pre' in data_types:
atomic_pos, paramag_centre = minimise_setup_atomic_pos(
sim_index=sim_index)
# Optimisation of the centre.
if hasattr(cdp, 'paramag_centre_fixed'):
centre_fixed = cdp.paramag_centre_fixed
# Set up the class instance containing the target function.
model = N_state_opt(model=cdp.model,
N=cdp.N,
init_params=param_vector,
probs=probs,
full_tensors=full_tensors,
red_data=red_tensor_elem,
red_errors=red_tensor_err,
full_in_ref_frame=full_in_ref_frame,
fixed_tensors=fixed_tensors,
pcs=pcs,
rdcs=rdcs,
pcs_errors=pcs_err,
rdc_errors=rdc_err,
T_flags=T_flags,
j_couplings=j_couplings,
rdc_pseudo_flags=rdc_pseudo_flags,
pcs_pseudo_flags=pcs_pseudo_flags,
pcs_weights=pcs_weight,
rdc_weights=rdc_weight,
rdc_vect=rdc_vector,
temp=temp,
frq=frq,
dip_const=rdc_dj,
absolute_rdc=absolute_rdc,
atomic_pos=atomic_pos,
paramag_centre=paramag_centre,
scaling_matrix=scaling_matrix,
centre_fixed=centre_fixed)
# Return the data.
return model, param_vector, data_types
