def param_num(spins=None):
"""Determine the number of parameters in the model.
@keyword spins: The list of spin data containers for the block.
@type spins: list of SpinContainer instances
@return: The number of model parameters.
@rtype: int
"""
# Initialise the number.
num = 0
# The R2eff model.
if cdp.model_type == 'R2eff':
# Count the selected spins.
spin_num = count_spins(spins)
# Exponential curves (with clustering).
if has_exponential_exp_type():
return 2 * spin_num
# Fixed time period experiments (with clustering).
return 1 * spin_num
# Check the spin cluster.
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
if len(spin.params) != len(spins[0].params):
raise RelaxError("The number of parameters for each spin in the cluster are not the same.")
# Count the number of R10 parameters.
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if spin.params[i] in ['r1']:
for exp_type, frq in loop_exp_frq():
num += 1
# Count the number of R20 parameters.
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if spin.params[i] in PARAMS_R20:
for exp_type, frq in loop_exp_frq():
num += 1
# Count the number of spin specific parameters for all spins.
spin_params = ['phi_ex', 'phi_ex_B', 'phi_ex_C', 'padw2', 'dw', 'dwH']
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if spin.params[i] in spin_params:
num += 1
# Count all other parameters, but only for a single spin.
all_params = ['r1'] + PARAMS_R20 + spin_params
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if not spin.params[i] in all_params:
num += 1
break
# Return the number.
return num
def param_num(spins=None):
"""Determine the number of parameters in the model.
@keyword spins: The list of spin data containers for the block.
@type spins: list of SpinContainer instances
@return: The number of model parameters.
@rtype: int
"""
# Initialise the number.
num = 0
# The R2eff model.
if cdp.model_type == 'R2eff':
# Count the selected spins.
spin_num = count_spins(spins)
# Exponential curves (with clustering).
if has_exponential_exp_type():
return 2 * spin_num
# Fixed time period experiments (with clustering).
return 1 * spin_num
# Check the spin cluster.
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
if len(spin.params) != len(spins[0].params):
raise RelaxError("The number of parameters for each spin in the cluster are not the same.")
# Count the number of R10 parameters.
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if spin.params[i] in ['r1']:
for exp_type, frq in loop_exp_frq():
num += 1
# Count the number of R20 parameters.
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if spin.params[i] in PARAMS_R20:
for exp_type, frq in loop_exp_frq():
num += 1
# Count the number of spin specific parameters for all spins.
spin_params = ['phi_ex', 'phi_ex_B', 'phi_ex_C', 'padw2', 'dw', 'dwH']
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if spin.params[i] in spin_params:
num += 1
# Count all other parameters, but only for a single spin.
all_params = ['r1'] + PARAMS_R20 + spin_params
for spin in spins:
# Skip deselected spins.
if not spin.select:
continue
for i in range(len(spin.params)):
if not spin.params[i] in all_params:
num += 1
break
# Return the number.
return num
def run(self, processor, completed):
"""Set up and perform the optimisation."""
# Print out.
if self.verbosity >= 1:
# Individual spin block section.
top = 2
if self.verbosity >= 2:
top += 2
subsection(file=sys.stdout, text="Fitting to the spin block %s"%self.spin_ids, prespace=top)
# Grid search printout.
if search('^[Gg]rid', self.min_algor):
result = 1
for x in self.inc:
result = mul(result, x)
print("Unconstrained grid search size: %s (constraints may decrease this size).\n" % result)
# Initialise the function to minimise.
model = Dispersion(model=self.spins[0].model, num_params=self.param_num, num_spins=count_spins(self.spins), num_frq=len(self.fields), exp_types=self.exp_types, values=self.values, errors=self.errors, missing=self.missing, frqs=self.frqs, frqs_H=self.frqs_H, cpmg_frqs=self.cpmg_frqs, spin_lock_nu1=self.spin_lock_nu1, chemical_shifts=self.chemical_shifts, offset=self.offsets, tilt_angles=self.tilt_angles, r1=self.r1, relax_times=self.relax_times, scaling_matrix=self.scaling_matrix, r1_fit=self.r1_fit)
# Grid search.
if search('^[Gg]rid', self.min_algor):
results = grid(func=model.func, args=(), num_incs=self.inc, lower=self.lower, upper=self.upper, A=self.A, b=self.b, verbosity=self.verbosity)
# Unpack the results.
param_vector, chi2, iter_count, warning = results
f_count = iter_count
g_count = 0.0
h_count = 0.0
# Minimisation.
else:
results = generic_minimise(func=model.func, args=(), x0=self.param_vector, min_algor=self.min_algor, min_options=self.min_options, func_tol=self.func_tol, grad_tol=self.grad_tol, maxiter=self.max_iterations, A=self.A, b=self.b, full_output=True, print_flag=self.verbosity)
# Unpack the results.
if results == None:
return
param_vector, chi2, iter_count, f_count, g_count, h_count, warning = results
# Optimisation printout.
if self.verbosity:
print("\nOptimised parameter values:")
for i in range(len(param_vector)):
print("%-20s %25.15f" % (self.param_names[i], param_vector[i]*self.scaling_matrix[i, i]))
# Create the result command object to send back to the master.
processor.return_object(Disp_result_command(processor=processor, memo_id=self.memo_id, param_vector=param_vector, chi2=chi2, iter_count=iter_count, f_count=f_count, g_count=g_count, h_count=h_count, warning=warning, missing=self.missing, back_calc=model.get_back_calc(), completed=False))
def run(self, processor, completed):
"""Set up and perform the optimisation."""
# Print out.
if self.verbosity >= 1:
# Individual spin block section.
top = 2
if self.verbosity >= 2:
top += 2
subsection(file=sys.stdout, text="Fitting to the spin block %s"%self.spin_ids, prespace=top)
# Grid search printout.
if search('^[Gg]rid', self.min_algor):
result = 1
for x in self.inc:
result = mul(result, x)
print("Unconstrained grid search size: %s (constraints may decrease this size).\n" % result)
# Initialise the function to minimise.
model = Dispersion(model=self.spins[0].model, num_params=self.param_num, num_spins=count_spins(self.spins), num_frq=len(self.fields), exp_types=self.exp_types, values=self.values, errors=self.errors, missing=self.missing, frqs=self.frqs, frqs_H=self.frqs_H, cpmg_frqs=self.cpmg_frqs, spin_lock_nu1=self.spin_lock_nu1, chemical_shifts=self.chemical_shifts, offset=self.offsets, tilt_angles=self.tilt_angles, r1=self.r1, relax_times=self.relax_times, scaling_matrix=self.scaling_matrix, r1_fit=self.r1_fit)
# Grid search.
if search('^[Gg]rid', self.min_algor):
results = grid(func=model.func, args=(), num_incs=self.inc, lower=self.lower, upper=self.upper, A=self.A, b=self.b, verbosity=self.verbosity)
# Unpack the results.
param_vector, chi2, iter_count, warning = results
f_count = iter_count
g_count = 0.0
h_count = 0.0
# Minimisation.
else:
results = generic_minimise(func=model.func, args=(), x0=self.param_vector, min_algor=self.min_algor, min_options=self.min_options, func_tol=self.func_tol, grad_tol=self.grad_tol, maxiter=self.max_iterations, A=self.A, b=self.b, full_output=True, print_flag=self.verbosity)
# Unpack the results.
if results == None:
return
param_vector, chi2, iter_count, f_count, g_count, h_count, warning = results
# Optimisation printout.
if self.verbosity:
print("\nOptimised parameter values:")
for i in range(len(param_vector)):
print("%-20s %25.15f" % (self.param_names[i], param_vector[i]*self.scaling_matrix[i, i]))
# Create the result command object to send back to the master.
processor.return_object(Disp_result_command(processor=processor, memo_id=self.memo_id, param_vector=param_vector, chi2=chi2, iter_count=iter_count, f_count=f_count, g_count=g_count, h_count=h_count, warning=warning, missing=self.missing, back_calc=model.get_back_calc(), completed=False))
