def sobol_setup(max_num=200, oversample=100):
"""Oversampling setup for the quasi-random Sobol' sequence used for numerical PCS integration.
@keyword max_num: The maximum number of integration points N.
@type max_num: int
@keyword oversample: The oversampling factor Ov used for the N * Ov * 10**M, where M is the number of dimensions or torsion-tilt angles for the system.
@type oversample: int
"""
# Test if the current data pipe exists.
check_pipe()
# Throw a warning to the user if not enough points are being used.
if max_num < 200:
warn(
RelaxWarning(
"To obtain reliable results in a frame order analysis, the maximum number of integration points should be greater than 200."
))
# Store the values.
cdp.sobol_max_points = max_num
cdp.sobol_oversample = oversample
# Count the number of Sobol' points for the current model.
count_sobol_points()
def sobol_setup(max_num=200, oversample=100):
"""Oversampling setup for the quasi-random Sobol' sequence used for numerical PCS integration.
@keyword max_num: The maximum number of integration points N.
@type max_num: int
@keyword oversample: The oversampling factor Ov used for the N * Ov * 10**M, where M is the number of dimensions or torsion-tilt angles for the system.
@type oversample: int
"""
# Test if the current data pipe exists.
check_pipe()
# Throw a warning to the user if not enough points are being used.
if max_num < 200:
warn(
RelaxWarning(
"To obtain reliable results in a frame order analysis, the maximum number of integration points should be greater than 200."
)
)
# Store the values.
cdp.sobol_max_points = max_num
cdp.sobol_oversample = oversample
# Count the number of Sobol' points for the current model.
count_sobol_points()
def calculate(self, spin_id=None, scaling_matrix=None, verbosity=1, sim_index=None):
"""Calculate the chi-squared value for the current parameter values.
@keyword spin_id: The spin identification string (unused).
@type spin_id: None
@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
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword sim_index: The optional MC simulation index (unused).
@type sim_index: None or int
"""
# Set up the data structures for the target function.
param_vector, full_tensors, full_in_ref_frame, rdcs, rdc_err, rdc_weight, rdc_vect, rdc_const, pcs, pcs_err, pcs_weight, atomic_pos, temp, frq, paramag_centre, com, ave_pos_pivot, pivot, pivot_opt = target_fn_data_setup(sim_index=sim_index, verbosity=verbosity, unset_fail=True)
# The numeric integration information.
if not hasattr(cdp, 'quad_int'):
cdp.quad_int = False
sobol_max_points, sobol_oversample = None, None
if hasattr(cdp, 'sobol_max_points'):
sobol_max_points = cdp.sobol_max_points
sobol_oversample = cdp.sobol_oversample
# Set up the optimisation target function class.
target_fn = frame_order.Frame_order(model=cdp.model, init_params=param_vector, full_tensors=full_tensors, full_in_ref_frame=full_in_ref_frame, rdcs=rdcs, rdc_errors=rdc_err, rdc_weights=rdc_weight, rdc_vect=rdc_vect, dip_const=rdc_const, pcs=pcs, pcs_errors=pcs_err, pcs_weights=pcs_weight, atomic_pos=atomic_pos, temp=temp, frq=frq, paramag_centre=paramag_centre, com=com, ave_pos_pivot=ave_pos_pivot, pivot=pivot, pivot_opt=pivot_opt, sobol_max_points=sobol_max_points, sobol_oversample=sobol_oversample, quad_int=cdp.quad_int)
# Make a single function call. This will cause back calculation and the data will be stored in the class instance.
chi2 = target_fn.func(param_vector)
# Set the chi2.
cdp.chi2 = chi2
# Store the back-calculated data.
store_bc_data(A_5D_bc=target_fn.A_5D_bc, pcs_theta=target_fn.pcs_theta, rdc_theta=target_fn.rdc_theta)
# Feedback on the number of integration points used.
if not cdp.quad_int:
count_sobol_points(target_fn=target_fn, verbosity=verbosity)
# Printout.
if verbosity:
print("Chi2: %s" % chi2)
def calculate(self,
spin_id=None,
scaling_matrix=None,
verbosity=1,
sim_index=None):
"""Calculate the chi-squared value for the current parameter values.
@keyword spin_id: The spin identification string (unused).
@type spin_id: None
@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
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword sim_index: The optional MC simulation index (unused).
@type sim_index: None or int
"""
# Set up the data structures for the target function.
param_vector, full_tensors, full_in_ref_frame, rdcs, rdc_err, rdc_weight, rdc_vect, rdc_const, pcs, pcs_err, pcs_weight, atomic_pos, temp, frq, paramag_centre, com, ave_pos_pivot, pivot, pivot_opt = target_fn_data_setup(
sim_index=sim_index, verbosity=verbosity, unset_fail=True)
# The numeric integration information.
if not hasattr(cdp, 'quad_int'):
cdp.quad_int = False
sobol_max_points, sobol_oversample = None, None
if hasattr(cdp, 'sobol_max_points'):
sobol_max_points = cdp.sobol_max_points
sobol_oversample = cdp.sobol_oversample
# Set up the optimisation target function class.
target_fn = frame_order.Frame_order(
model=cdp.model,
init_params=param_vector,
full_tensors=full_tensors,
full_in_ref_frame=full_in_ref_frame,
rdcs=rdcs,
rdc_errors=rdc_err,
rdc_weights=rdc_weight,
rdc_vect=rdc_vect,
dip_const=rdc_const,
pcs=pcs,
pcs_errors=pcs_err,
pcs_weights=pcs_weight,
atomic_pos=atomic_pos,
temp=temp,
frq=frq,
paramag_centre=paramag_centre,
com=com,
ave_pos_pivot=ave_pos_pivot,
pivot=pivot,
pivot_opt=pivot_opt,
sobol_max_points=sobol_max_points,
sobol_oversample=sobol_oversample,
quad_int=cdp.quad_int)
# Make a single function call. This will cause back calculation and the data will be stored in the class instance.
chi2 = target_fn.func(param_vector)
# Set the chi2.
cdp.chi2 = chi2
# Store the back-calculated data.
store_bc_data(A_5D_bc=target_fn.A_5D_bc,
pcs_theta=target_fn.pcs_theta,
rdc_theta=target_fn.rdc_theta)
# Feedback on the number of integration points used.
if not cdp.quad_int:
count_sobol_points(target_fn=target_fn, verbosity=verbosity)
# Printout.
if verbosity:
print("Chi2: %s" % chi2)
