def back_calc_peak_intensities(spin=None, exp_type=None, frq=None, offset=None, point=None):
"""Back-calculation of peak intensity for the given relaxation time.
@keyword spin: The specific spin data container.
@type spin: SpinContainer instance
@keyword exp_type: The experiment type.
@type exp_type: str
@keyword frq: The spectrometer frequency.
@type frq: float
@keyword offset: For R1rho-type data, the spin-lock offset value in ppm.
@type offset: None or float
@keyword point: The dispersion point data (either the spin-lock field strength in Hz or the nu_CPMG frequency in Hz).
@type point: float
@return: The back-calculated peak intensities for the given exponential curve.
@rtype: numpy rank-1 float array
"""
# Check.
if not has_exponential_exp_type():
raise RelaxError("Back-calculation is not allowed for the fixed time experiment types.")
# The key.
param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
# Create the initial parameter vector.
param_vector = assemble_param_vector(spins=[spin], key=param_key)
# The peak intensities and times.
values = []
errors = []
times = []
for time in loop_time(exp_type=exp_type, frq=frq, offset=offset, point=point):
# The data.
values.append(average_intensity(spin=spin, exp_type=exp_type, frq=frq, offset=offset, point=point, time=time))
errors.append(average_intensity(spin=spin, exp_type=exp_type, frq=frq, offset=offset, point=point, time=time, error=True))
times.append(time)
# The scaling matrix in a diagonalised list form.
scaling_list = []
for i in range(len(param_vector)):
scaling_list.append(1.0)
# Initialise the relaxation fit functions.
model = Relax_fit_opt(model='exp', num_params=len(spin.params), values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
# Make a single function call. This will cause back calculation and the data will be stored in the C module.
model.func(param_vector)
# Get the data back.
results = model.back_calc_data()
# Return the correct peak height.
return results
def back_calc_r2eff(spin=None, spin_id=None, cpmg_frqs=None, spin_lock_nu1=None, store_chi2=False):
"""Back-calculation of R2eff/R1rho values for the given spin.
@keyword spin: The specific spin data container.
@type spin: SpinContainer instance
@keyword spin_id: The spin ID string for the spin container.
@type spin_id: str
@keyword cpmg_frqs: The CPMG frequencies to use instead of the user loaded values - to enable interpolation.
@type cpmg_frqs: list of lists of numpy rank-1 float arrays
@keyword spin_lock_nu1: The spin-lock field strengths to use instead of the user loaded values - to enable interpolation.
@type spin_lock_nu1: list of lists of numpy rank-1 float arrays
@keyword store_chi2: A flag which if True will cause the spin specific chi-squared value to be stored in the spin container.
@type store_chi2: bool
@return: The back-calculated R2eff/R1rho value for the given spin.
@rtype: numpy rank-1 float array
"""
# Skip protons for MMQ data.
if spin.model in MODEL_LIST_MMQ and spin.isotope == '1H':
return
# Create the initial parameter vector.
param_vector = assemble_param_vector(spins=[spin])
# Create a scaling matrix.
scaling_matrix = assemble_scaling_matrix(spins=[spin], scaling=False)
# Number of spectrometer fields.
fields = [None]
field_count = 1
if hasattr(cdp, 'spectrometer_frq_count'):
fields = cdp.spectrometer_frq_list
field_count = cdp.spectrometer_frq_count
# Initialise the data structures for the target function.
values, errors, missing, frqs, frqs_H, exp_types, relax_times = return_r2eff_arrays(spins=[spin], spin_ids=[spin_id], fields=fields, field_count=field_count)
# The offset and R1 data.
chemical_shifts, offsets, tilt_angles = return_offset_data(spins=[spin], spin_ids=[spin_id], field_count=field_count, fields=spin_lock_nu1)
r1 = return_r1_data(spins=[spin], spin_ids=[spin_id], field_count=field_count)
# The dispersion data.
recalc_tau = True
if cpmg_frqs == None and spin_lock_nu1 == None:
cpmg_frqs = return_cpmg_frqs(ref_flag=False)
spin_lock_nu1 = return_spin_lock_nu1(ref_flag=False)
# Reconstruct the structures for interpolation.
else:
recalc_tau = False
values = []
errors = []
missing = []
for exp_type, ei in loop_exp(return_indices=True):
values.append([])
errors.append([])
missing.append([])
for si in range(1):
values[ei].append([])
errors[ei].append([])
missing[ei].append([])
for frq, mi in loop_frq(return_indices=True):
values[ei][si].append([])
errors[ei][si].append([])
missing[ei][si].append([])
for offset, oi in loop_offset(exp_type=exp_type, frq=frq, return_indices=True):
if exp_type in EXP_TYPE_LIST_CPMG:
num = len(cpmg_frqs[ei][mi][oi])
else:
num = len(spin_lock_nu1[ei][mi][oi])
values[ei][si][mi].append(zeros(num, float64))
errors[ei][si][mi].append(ones(num, float64))
missing[ei][si][mi].append(zeros(num, int32))
# Initialise the relaxation dispersion fit functions.
model = Dispersion(model=spin.model, num_params=param_num(spins=[spin]), num_spins=1, num_frq=field_count, exp_types=exp_types, values=values, errors=errors, missing=missing, frqs=frqs, frqs_H=frqs_H, cpmg_frqs=cpmg_frqs, spin_lock_nu1=spin_lock_nu1, chemical_shifts=chemical_shifts, offset=offsets, tilt_angles=tilt_angles, r1=r1, relax_times=relax_times, scaling_matrix=scaling_matrix, recalc_tau=recalc_tau)
# Make a single function call. This will cause back calculation and the data will be stored in the class instance.
chi2 = model.func(param_vector)
# Store the chi-squared value.
if store_chi2:
spin.chi2 = chi2
# Return the structure.
return model.back_calc
def __init__(self, spins=None, spin_ids=None, sim_index=None, scaling_matrix=None, min_algor=None, min_options=None, func_tol=None, grad_tol=None, max_iterations=None, constraints=False, verbosity=0, lower=None, upper=None, inc=None, fields=None, param_names=None):
"""Initialise the base class, storing all the master data to be sent to the slave processor.
This method is run on the master processor whereas the run() method is run on the slave processor.
@keyword spins: The list of spin data container for the cluster. If this argument is supplied, then the spin_id argument will be ignored.
@type spins: list of SpinContainer instances
@keyword spin_ids: The list of spin ID strings corresponding to the spins argument.
@type spin_ids: list of str
@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, square scaling matrix.
@type scaling_matrix: numpy diagonal matrix
@keyword min_algor: The minimisation algorithm to use.
@type min_algor: str
@keyword min_options: An array of options to be used by the minimisation algorithm.
@type min_options: array of str
@keyword func_tol: The function tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type func_tol: None or float
@keyword grad_tol: The gradient tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type grad_tol: None or float
@keyword max_iterations: The maximum number of iterations for the algorithm.
@type max_iterations: int
@keyword constraints: If True, constraints are used during optimisation.
@type constraints: bool
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword lower: The lower bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type lower: array of numbers
@keyword upper: The upper bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type upper: array of numbers
@keyword 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. This argument is only used when doing a grid search.
@type inc: array of int
@keyword fields: The list of unique of spectrometer field strengths.
@type fields: int
@keyword param_names: The list of parameter names to use in printouts.
@type param_names: str
"""
# Execute the base class __init__() method.
super(Disp_minimise_command, self).__init__()
# Store the arguments needed by the run() method.
self.spins = spins
self.spin_ids = spin_ids
self.sim_index = sim_index
self.scaling_matrix = scaling_matrix
self.verbosity = verbosity
self.min_algor = min_algor
self.min_options = min_options
self.func_tol = func_tol
self.grad_tol = grad_tol
self.max_iterations = max_iterations
self.fields = fields
self.param_names = param_names
# Create the initial parameter vector.
self.param_vector = assemble_param_vector(spins=self.spins)
if len(scaling_matrix):
self.param_vector = dot(inv(scaling_matrix), self.param_vector)
# Get the grid search minimisation options.
self.lower_new, self.upper_new = None, None
if search('^[Gg]rid', min_algor):
self.grid_size, self.inc_new, self.lower_new, self.upper_new = grid_search_setup(spins=spins, spin_ids=spin_ids, param_vector=self.param_vector, lower=lower, upper=upper, inc=inc, scaling_matrix=self.scaling_matrix)
# Linear constraints.
self.A, self.b = None, None
if constraints:
self.A, self.b = linear_constraints(spins=spins, scaling_matrix=scaling_matrix)
# Test if the spectrometer frequencies have been set.
if spins[0].model in [MODEL_LM63, MODEL_CR72, MODEL_CR72_FULL, MODEL_M61, MODEL_TP02, MODEL_TAP03, MODEL_MP05] and not hasattr(cdp, 'spectrometer_frq'):
raise RelaxError("The spectrometer frequency information has not been specified.")
# The R2eff/R1rho data.
self.values, self.errors, self.missing, self.frqs, self.frqs_H, self.exp_types, self.relax_times = return_r2eff_arrays(spins=spins, spin_ids=spin_ids, fields=fields, field_count=len(fields), sim_index=sim_index)
# The offset and R1 data.
self.chemical_shifts, self.offsets, self.tilt_angles = return_offset_data(spins=spins, spin_ids=spin_ids, field_count=len(fields))
self.r1 = return_r1_data(spins=spins, spin_ids=spin_ids, field_count=len(fields), sim_index=sim_index)
# Parameter number.
self.param_num = param_num(spins=spins)
# The dispersion data.
self.dispersion_points = cdp.dispersion_points
self.cpmg_frqs = return_cpmg_frqs(ref_flag=False)
self.spin_lock_nu1 = return_spin_lock_nu1(ref_flag=False)
def minimise_r2eff(spins=None, spin_ids=None, min_algor=None, min_options=None, func_tol=None, grad_tol=None, max_iterations=None, constraints=False, scaling_matrix=None, verbosity=0, sim_index=None, lower=None, upper=None, inc=None):
"""Optimise the R2eff model by fitting the 2-parameter exponential curves.
This mimics the R1 and R2 relax_fit analysis.
@keyword spins: The list of spins for the cluster.
@type spins: list of SpinContainer instances
@keyword spin_ids: The list of spin IDs for the cluster.
@type spin_ids: list of str
@keyword min_algor: The minimisation algorithm to use.
@type min_algor: str
@keyword min_options: An array of options to be used by the minimisation algorithm.
@type min_options: array of str
@keyword func_tol: The function tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type func_tol: None or float
@keyword grad_tol: The gradient tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type grad_tol: None or float
@keyword max_iterations: The maximum number of iterations for the algorithm.
@type max_iterations: int
@keyword constraints: If True, constraints are used during optimisation.
@type constraints: bool
@keyword scaling_matrix: The diagonal and square scaling matrix.
@type scaling_matrix: numpy rank-2, float64 array or None
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@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 lower: The model specific lower bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type lower: list of numbers
@keyword upper: The model specific upper bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type upper: list of numbers
@keyword inc: The model specific 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. This argument is only used when doing a grid search.
@type inc: list of int
"""
# Check that the C modules have been compiled.
if not C_module_exp_fn:
raise RelaxError("Relaxation curve fitting is not available. Try compiling the C modules on your platform.")
# Loop over the spins.
for si in range(len(spins)):
# Skip deselected spins.
if not spins[si].select:
continue
# Loop over each spectrometer frequency and dispersion point.
for exp_type, frq, offset, point in loop_exp_frq_offset_point():
# The parameter key.
param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
# The initial parameter vector.
param_vector = assemble_param_vector(spins=[spins[si]], key=param_key, sim_index=sim_index)
# Diagonal scaling.
if scaling_matrix is not None:
param_vector = dot(inv(scaling_matrix), param_vector)
# Linear constraints.
A, b = None, None
if constraints:
A, b = linear_constraints(spins=[spins[si]], scaling_matrix=scaling_matrix)
# Print out.
if verbosity >= 1:
# Individual spin section.
top = 2
if verbosity >= 2:
top += 2
text = "Fitting to spin %s, frequency %s and dispersion point %s" % (spin_ids[si], frq, point)
subsection(file=sys.stdout, text=text, prespace=top)
# Grid search printout.
if match('^[Gg]rid', min_algor):
result = 1
for x in inc:
result = mul(result, x)
print("Unconstrained grid search size: %s (constraints may decrease this size).\n" % result)
# The peak intensities, errors and times.
values = []
errors = []
times = []
for time in loop_time(exp_type=exp_type, frq=frq, offset=offset, point=point):
values.append(average_intensity(spin=spins[si], exp_type=exp_type, frq=frq, offset=offset, point=point, time=time, sim_index=sim_index))
errors.append(average_intensity(spin=spins[si], exp_type=exp_type, frq=frq, offset=offset, point=point, time=time, error=True))
times.append(time)
# Raise errors if number of time points is less than 2.
if len(times) < 3:
subsection(file=sys.stdout, text="Exponential curve fitting error for point:", prespace=2)
point_info = "%s at %3.1f MHz, for offset=%3.3f ppm and dispersion point %-5.1f, with %i time points." % (exp_type, frq/1E6, offset, point, len(times))
print(point_info)
raise RelaxError("The data setup points to exponential curve fitting, but only %i time points was found, where 3 time points is minimum. If calculating R2eff values for fixed relaxation time period data, check that a reference intensity has been specified for each offset value."%(len(times)))
# The scaling matrix in a diagonalised list form.
scaling_list = []
if scaling_matrix is None:
for i in range(len(param_vector)):
scaling_list.append(1.0)
else:
for i in range(len(scaling_matrix)):
scaling_list.append(scaling_matrix[i, i])
# Initialise the function to minimise.
model = Relax_fit_opt(model='exp', num_params=len(param_vector), values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
# Grid search.
if search('^[Gg]rid', min_algor):
results = grid(func=model.func, args=(), num_incs=inc, lower=lower, upper=upper, A=A, b=b, verbosity=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, dfunc=model.dfunc, d2func=model.d2func, args=(), x0=param_vector, min_algor=min_algor, min_options=min_options, func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, A=A, b=b, full_output=True, print_flag=verbosity)
# Unpack the results.
if results == None:
return
param_vector, chi2, iter_count, f_count, g_count, h_count, warning = results
# Scaling.
if scaling_matrix is not None:
param_vector = dot(scaling_matrix, param_vector)
# Disassemble the parameter vector.
disassemble_param_vector(param_vector=param_vector, spins=[spins[si]], key=param_key, sim_index=sim_index)
# Monte Carlo minimisation statistics.
if sim_index != None:
# Chi-squared statistic.
spins[si].chi2_sim[sim_index] = chi2
# Iterations.
spins[si].iter_sim[sim_index] = iter_count
# Function evaluations.
spins[si].f_count_sim[sim_index] = f_count
# Gradient evaluations.
spins[si].g_count_sim[sim_index] = g_count
# Hessian evaluations.
spins[si].h_count_sim[sim_index] = h_count
# Warning.
spins[si].warning_sim[sim_index] = warning
# Normal statistics.
else:
# Chi-squared statistic.
spins[si].chi2 = chi2
# Iterations.
spins[si].iter = iter_count
# Function evaluations.
spins[si].f_count = f_count
# Gradient evaluations.
spins[si].g_count = g_count
# Hessian evaluations.
spins[si].h_count = h_count
# Warning.
spins[si].warning = warning
def back_calc_r2eff(spins=None, spin_ids=None, cpmg_frqs=None, spin_lock_offset=None, spin_lock_nu1=None, relax_times_new=None, store_chi2=False):
"""Back-calculation of R2eff/R1rho values for the given spin.
@keyword spins: The list of specific spin data container for cluster.
@type spins: List of SpinContainer instances
@keyword spin_ids: The list of spin ID strings for the spin containers in cluster.
@type spin_ids: list of str
@keyword cpmg_frqs: The CPMG frequencies to use instead of the user loaded values - to enable interpolation.
@type cpmg_frqs: list of lists of numpy rank-1 float arrays
@keyword spin_lock_offset: The spin-lock offsets to use instead of the user loaded values - to enable interpolation.
@type spin_lock_offset: list of lists of numpy rank-1 float arrays
@keyword spin_lock_nu1: The spin-lock field strengths to use instead of the user loaded values - to enable interpolation.
@type spin_lock_nu1: list of lists of numpy rank-1 float arrays
@keyword relax_times_new: The interpolated experiment specific fixed time period for relaxation (in seconds). The dimensions are {Ei, Mi, Oi, Di, Ti}.
@type relax_times_new: rank-4 list of floats
@keyword store_chi2: A flag which if True will cause the spin specific chi-squared value to be stored in the spin container.
@type store_chi2: bool
@return: The back-calculated R2eff/R1rho value for the given spin.
@rtype: numpy rank-1 float array
"""
# Create the initial parameter vector.
param_vector = assemble_param_vector(spins=spins)
# Number of spectrometer fields.
fields = [None]
field_count = 1
if hasattr(cdp, 'spectrometer_frq_count'):
fields = cdp.spectrometer_frq_list
field_count = cdp.spectrometer_frq_count
# Initialise the data structures for the target function.
values, errors, missing, frqs, frqs_H, exp_types, relax_times = return_r2eff_arrays(spins=spins, spin_ids=spin_ids, fields=fields, field_count=field_count)
# The offset and R1 data.
r1_setup()
offsets, spin_lock_fields_inter, chemical_shifts, tilt_angles, Delta_omega, w_eff = return_offset_data(spins=spins, spin_ids=spin_ids, field_count=field_count, spin_lock_offset=spin_lock_offset, fields=spin_lock_nu1)
r1 = return_r1_data(spins=spins, spin_ids=spin_ids, field_count=field_count)
r1_fit = is_r1_optimised(spins[0].model)
# The relaxation times.
if relax_times_new != None:
relax_times = relax_times_new
# The dispersion data.
recalc_tau = True
if cpmg_frqs == None and spin_lock_nu1 == None and spin_lock_offset == None:
cpmg_frqs = return_cpmg_frqs(ref_flag=False)
spin_lock_nu1 = return_spin_lock_nu1(ref_flag=False)
# Reset the cpmg_frqs if interpolating R1rho models.
elif cpmg_frqs == None and spin_lock_offset != None:
cpmg_frqs = None
spin_lock_nu1 = spin_lock_fields_inter
recalc_tau = False
values = []
errors = []
missing = []
for exp_type, ei in loop_exp(return_indices=True):
values.append([])
errors.append([])
missing.append([])
for si in range(len(spins)):
values[ei].append([])
errors[ei].append([])
missing[ei].append([])
for frq, mi in loop_frq(return_indices=True):
values[ei][si].append([])
errors[ei][si].append([])
missing[ei][si].append([])
for oi, offset in enumerate(offsets[ei][si][mi]):
num = len(spin_lock_nu1[ei][mi][oi])
values[ei][si][mi].append(zeros(num, float64))
errors[ei][si][mi].append(ones(num, float64))
missing[ei][si][mi].append(zeros(num, int32))
# Reconstruct the structures for interpolation.
else:
recalc_tau = False
values = []
errors = []
missing = []
for exp_type, ei in loop_exp(return_indices=True):
values.append([])
errors.append([])
missing.append([])
for si in range(len(spins)):
values[ei].append([])
errors[ei].append([])
missing[ei].append([])
for frq, mi in loop_frq(return_indices=True):
values[ei][si].append([])
errors[ei][si].append([])
missing[ei][si].append([])
for offset, oi in loop_offset(exp_type=exp_type, frq=frq, return_indices=True):
if exp_type in EXP_TYPE_LIST_CPMG:
num = len(cpmg_frqs[ei][mi][oi])
else:
num = len(spin_lock_nu1[ei][mi][oi])
values[ei][si][mi].append(zeros(num, float64))
errors[ei][si][mi].append(ones(num, float64))
missing[ei][si][mi].append(zeros(num, int32))
# Initialise the relaxation dispersion fit functions.
model = Dispersion(model=spins[0].model, num_params=param_num(spins=spins), num_spins=len(spins), num_frq=field_count, exp_types=exp_types, values=values, errors=errors, missing=missing, frqs=frqs, frqs_H=frqs_H, cpmg_frqs=cpmg_frqs, spin_lock_nu1=spin_lock_nu1, chemical_shifts=chemical_shifts, offset=offsets, tilt_angles=tilt_angles, r1=r1, relax_times=relax_times, recalc_tau=recalc_tau, r1_fit=r1_fit)
# Make a single function call. This will cause back calculation and the data will be stored in the class instance.
chi2 = model.func(param_vector)
# Store the chi-squared value.
if store_chi2:
for spin in spins:
spin.chi2 = chi2
# Return the structure.
return model.get_back_calc()
def __init__(self, spins=None, spin_ids=None, sim_index=None, scaling_matrix=None, min_algor=None, min_options=None, func_tol=None, grad_tol=None, max_iterations=None, constraints=False, verbosity=0, lower=None, upper=None, inc=None, fields=None, param_names=None):
"""Initialise the base class, storing all the master data to be sent to the slave processor.
This method is run on the master processor whereas the run() method is run on the slave processor.
@keyword spins: The list of spin data container for the cluster. If this argument is supplied, then the spin_id argument will be ignored.
@type spins: list of SpinContainer instances
@keyword spin_ids: The list of spin ID strings corresponding to the spins argument.
@type spin_ids: list of str
@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, square scaling matrix.
@type scaling_matrix: numpy diagonal matrix
@keyword min_algor: The minimisation algorithm to use.
@type min_algor: str
@keyword min_options: An array of options to be used by the minimisation algorithm.
@type min_options: array of str
@keyword func_tol: The function tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type func_tol: None or float
@keyword grad_tol: The gradient tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type grad_tol: None or float
@keyword max_iterations: The maximum number of iterations for the algorithm.
@type max_iterations: int
@keyword constraints: If True, constraints are used during optimisation.
@type constraints: bool
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword lower: The lower bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type lower: array of numbers
@keyword upper: The upper bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type upper: array of numbers
@keyword 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. This argument is only used when doing a grid search.
@type inc: array of int
@keyword fields: The list of unique of spectrometer field strengths.
@type fields: int
@keyword param_names: The list of parameter names to use in printouts.
@type param_names: str
"""
# Execute the base class __init__() method.
super(Disp_minimise_command, self).__init__()
# Store the arguments needed by the run() method.
self.spins = spins
self.spin_ids = spin_ids
self.sim_index = sim_index
self.scaling_matrix = scaling_matrix
self.verbosity = verbosity
self.min_algor = min_algor
self.min_options = min_options
self.func_tol = func_tol
self.grad_tol = grad_tol
self.max_iterations = max_iterations
self.lower = lower
self.upper = upper
self.inc = inc
self.fields = fields
self.param_names = param_names
# Create the initial parameter vector.
self.param_vector = assemble_param_vector(spins=self.spins)
if len(scaling_matrix):
self.param_vector = dot(inv(scaling_matrix), self.param_vector)
# Linear constraints.
self.A, self.b = None, None
if constraints:
self.A, self.b = linear_constraints(spins=spins, scaling_matrix=scaling_matrix)
# Test if the spectrometer frequencies have been set.
if spins[0].model in [MODEL_LM63, MODEL_CR72, MODEL_CR72_FULL, MODEL_M61, MODEL_TP02, MODEL_TAP03, MODEL_MP05] and not hasattr(cdp, 'spectrometer_frq'):
raise RelaxError("The spectrometer frequency information has not been specified.")
# The R2eff/R1rho data.
self.values, self.errors, self.missing, self.frqs, self.frqs_H, self.exp_types, self.relax_times = return_r2eff_arrays(spins=spins, spin_ids=spin_ids, fields=fields, field_count=len(fields), sim_index=sim_index)
# The offset and R1 data.
r1_setup()
self.offsets, spin_lock_fields_inter, self.chemical_shifts, self.tilt_angles, self.Delta_omega, self.w_eff = return_offset_data(spins=spins, spin_ids=spin_ids, field_count=len(fields))
self.r1 = return_r1_data(spins=spins, spin_ids=spin_ids, field_count=len(fields), sim_index=sim_index)
self.r1_fit = is_r1_optimised(spins[0].model)
# Parameter number.
self.param_num = param_num(spins=spins)
# The dispersion data.
self.dispersion_points = cdp.dispersion_points
self.cpmg_frqs = return_cpmg_frqs(ref_flag=False)
self.spin_lock_nu1 = return_spin_lock_nu1(ref_flag=False)
def back_calc_peak_intensities(spin=None, spin_id=None, exp_type=None, frq=None, offset=None, point=None):
"""Back-calculation of peak intensity for the given relaxation time.
@keyword spin: The specific spin data container.
@type spin: SpinContainer instance
@keyword spin_id: The optional spin ID string for use in warning messages.
@type spin_id: str or None
@keyword exp_type: The experiment type.
@type exp_type: str
@keyword frq: The spectrometer frequency.
@type frq: float
@keyword offset: For R1rho-type data, the spin-lock offset value in ppm.
@type offset: None or float
@keyword point: The dispersion point data (either the spin-lock field strength in Hz or the nu_CPMG frequency in Hz).
@type point: float
@return: The back-calculated peak intensities for the given exponential curve.
@rtype: numpy rank-1 float array
"""
# Check.
if not has_exponential_exp_type():
raise RelaxError("Back-calculation is not allowed for the fixed time experiment types.")
# The key.
param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
# Create the initial parameter vector.
param_vector = assemble_param_vector(spins=[spin], key=param_key)
# The peak intensities and times.
values = []
errors = []
times = []
for time in loop_time(exp_type=exp_type, frq=frq, offset=offset, point=point):
# Check the peak intensity keys.
int_keys = find_intensity_keys(exp_type=exp_type, frq=frq, offset=offset, point=point, time=time)
for i in range(len(int_keys)):
if int_keys[i] not in spin.peak_intensity:
if spin_id:
warn(RelaxWarning("The spin %s peak intensity key '%s' is not present, skipping the back-calculation." % (spin_id, int_keys[i])))
else:
warn(RelaxWarning("The peak intensity key '%s' is not present, skipping the back-calculation." % int_keys[i]))
return
# The data.
values.append(average_intensity(spin=spin, exp_type=exp_type, frq=frq, offset=offset, point=point, time=time))
errors.append(average_intensity(spin=spin, exp_type=exp_type, frq=frq, offset=offset, point=point, time=time, error=True))
times.append(time)
# The scaling matrix in a diagonalised list form.
scaling_list = []
for i in range(len(param_vector)):
scaling_list.append(1.0)
# Initialise the relaxation fit functions.
model = Relax_fit_opt(model='exp', num_params=len(spin.params), values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
# Make a single function call. This will cause back calculation and the data will be stored in the C module.
model.func(param_vector)
# Get the data back.
results = model.back_calc_data()
# Return the correct peak height.
return results
def minimise_r2eff(spins=None, spin_ids=None, min_algor=None, min_options=None, func_tol=None, grad_tol=None, max_iterations=None, constraints=False, scaling_matrix=None, verbosity=0, sim_index=None, lower=None, upper=None, inc=None):
"""Optimise the R2eff model by fitting the 2-parameter exponential curves.
This mimics the R1 and R2 relax_fit analysis.
@keyword spins: The list of spins for the cluster.
@type spins: list of SpinContainer instances
@keyword spin_ids: The list of spin IDs for the cluster.
@type spin_ids: list of str
@keyword min_algor: The minimisation algorithm to use.
@type min_algor: str
@keyword min_options: An array of options to be used by the minimisation algorithm.
@type min_options: array of str
@keyword func_tol: The function tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type func_tol: None or float
@keyword grad_tol: The gradient tolerance which, when reached, terminates optimisation. Setting this to None turns of the check.
@type grad_tol: None or float
@keyword max_iterations: The maximum number of iterations for the algorithm.
@type max_iterations: int
@keyword constraints: If True, constraints are used during optimisation.
@type constraints: bool
@keyword scaling_matrix: The diagonal and square scaling matrix.
@type scaling_matrix: numpy rank-2, float64 array or None
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@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 lower: The model specific lower bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type lower: list of numbers
@keyword upper: The model specific upper bounds of the grid search which must be equal to the number of parameters in the model. This optional argument is only used when doing a grid search.
@type upper: list of numbers
@keyword inc: The model specific 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. This argument is only used when doing a grid search.
@type inc: list of int
"""
# Check that the C modules have been compiled.
if not C_module_exp_fn:
raise RelaxError("Relaxation curve fitting is not available. Try compiling the C modules on your platform.")
# Loop over the spins.
for si in range(len(spins)):
# Skip deselected spins.
if not spins[si].select:
continue
# Loop over each spectrometer frequency and dispersion point.
for exp_type, frq, offset, point in loop_exp_frq_offset_point():
# The parameter key.
param_key = return_param_key_from_data(exp_type=exp_type, frq=frq, offset=offset, point=point)
# The initial parameter vector.
param_vector = assemble_param_vector(spins=[spins[si]], key=param_key, sim_index=sim_index)
# Diagonal scaling.
if scaling_matrix is not None:
param_vector = dot(inv(scaling_matrix), param_vector)
# Linear constraints.
A, b = None, None
if constraints:
A, b = linear_constraints(spins=[spins[si]], scaling_matrix=scaling_matrix)
# Print out.
if verbosity >= 1:
# Individual spin section.
top = 2
if verbosity >= 2:
top += 2
text = "Fitting to spin %s, frequency %s and dispersion point %s" % (spin_ids[si], frq, point)
subsection(file=sys.stdout, text=text, prespace=top)
# Grid search printout.
if match('^[Gg]rid', min_algor):
result = 1
for x in inc:
result = mul(result, x)
print("Unconstrained grid search size: %s (constraints may decrease this size).\n" % result)
# The peak intensities, errors and times.
values = []
errors = []
times = []
data_flag = True
for time in loop_time(exp_type=exp_type, frq=frq, offset=offset, point=point):
# Check the peak intensity keys.
int_keys = find_intensity_keys(exp_type=exp_type, frq=frq, offset=offset, point=point, time=time)
peak_intensities = spins[si].peak_intensity
if sim_index != None:
peak_intensities = spins[si].peak_intensity_sim
for i in range(len(int_keys)):
if int_keys[i] not in peak_intensities:
if verbosity:
warn(RelaxWarning("The spin %s peak intensity key '%s' is not present, skipping the optimisation." % (spin_ids[si], int_keys[i])))
data_flag = False
break
if data_flag:
values.append(average_intensity(spin=spins[si], exp_type=exp_type, frq=frq, offset=offset, point=point, time=time, sim_index=sim_index))
errors.append(average_intensity(spin=spins[si], exp_type=exp_type, frq=frq, offset=offset, point=point, time=time, error=True))
times.append(time)
if not data_flag:
continue
# Raise errors if number of time points is less than 2.
if len(times) < 3:
subsection(file=sys.stdout, text="Exponential curve fitting error for point:", prespace=2)
point_info = "%s at %3.1f MHz, for offset=%3.3f ppm and dispersion point %-5.1f, with %i time points." % (exp_type, frq/1E6, offset, point, len(times))
raise RelaxError("The data setup points to exponential curve fitting, but only %i time points was found, where 3 time points is minimum. If calculating R2eff values for fixed relaxation time period data, check that a reference intensity has been specified for each offset value."%(len(times)))
# The scaling matrix in a diagonalised list form.
scaling_list = []
if scaling_matrix is None:
for i in range(len(param_vector)):
scaling_list.append(1.0)
else:
for i in range(len(scaling_matrix)):
scaling_list.append(scaling_matrix[i, i])
# Initialise the function to minimise.
model = Relax_fit_opt(model='exp', num_params=len(param_vector), values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
# Grid search.
if search('^[Gg]rid', min_algor):
results = grid(func=model.func, args=(), num_incs=inc, lower=lower, upper=upper, A=A, b=b, verbosity=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, dfunc=model.dfunc, d2func=model.d2func, args=(), x0=param_vector, min_algor=min_algor, min_options=min_options, func_tol=func_tol, grad_tol=grad_tol, maxiter=max_iterations, A=A, b=b, full_output=True, print_flag=verbosity)
# Unpack the results.
if results == None:
return
param_vector, chi2, iter_count, f_count, g_count, h_count, warning = results
# Scaling.
if scaling_matrix is not None:
param_vector = dot(scaling_matrix, param_vector)
# Disassemble the parameter vector.
disassemble_param_vector(param_vector=param_vector, spins=[spins[si]], key=param_key, sim_index=sim_index)
# Monte Carlo minimisation statistics.
if sim_index != None:
# Chi-squared statistic.
spins[si].chi2_sim[sim_index] = chi2
# Iterations.
spins[si].iter_sim[sim_index] = iter_count
# Function evaluations.
spins[si].f_count_sim[sim_index] = f_count
# Gradient evaluations.
spins[si].g_count_sim[sim_index] = g_count
# Hessian evaluations.
spins[si].h_count_sim[sim_index] = h_count
# Warning.
spins[si].warning_sim[sim_index] = warning
# Normal statistics.
else:
# Chi-squared statistic.
spins[si].chi2 = chi2
# Iterations.
spins[si].iter = iter_count
# Function evaluations.
spins[si].f_count = f_count
# Gradient evaluations.
spins[si].g_count = g_count
# Hessian evaluations.
spins[si].h_count = h_count
# Warning.
spins[si].warning = warning
def back_calc_r2eff(spins=None, spin_ids=None, cpmg_frqs=None, spin_lock_offset=None, spin_lock_nu1=None, relax_times_new=None, store_chi2=False):
"""Back-calculation of R2eff/R1rho values for the given spin.
@keyword spins: The list of specific spin data container for cluster.
@type spins: List of SpinContainer instances
@keyword spin_ids: The list of spin ID strings for the spin containers in cluster.
@type spin_ids: list of str
@keyword cpmg_frqs: The CPMG frequencies to use instead of the user loaded values - to enable interpolation.
@type cpmg_frqs: list of lists of numpy rank-1 float arrays
@keyword spin_lock_offset: The spin-lock offsets to use instead of the user loaded values - to enable interpolation.
@type spin_lock_offset: list of lists of numpy rank-1 float arrays
@keyword spin_lock_nu1: The spin-lock field strengths to use instead of the user loaded values - to enable interpolation.
@type spin_lock_nu1: list of lists of numpy rank-1 float arrays
@keyword relax_times_new: The interpolated experiment specific fixed time period for relaxation (in seconds). The dimensions are {Ei, Mi, Oi, Di, Ti}.
@type relax_times_new: rank-4 list of floats
@keyword store_chi2: A flag which if True will cause the spin specific chi-squared value to be stored in the spin container.
@type store_chi2: bool
@return: The back-calculated R2eff/R1rho value for the given spin.
@rtype: numpy rank-1 float array
"""
# Create the initial parameter vector.
param_vector = assemble_param_vector(spins=spins)
# Number of spectrometer fields.
fields = [None]
field_count = 1
if hasattr(cdp, 'spectrometer_frq_count'):
fields = cdp.spectrometer_frq_list
field_count = cdp.spectrometer_frq_count
# Initialise the data structures for the target function.
values, errors, missing, frqs, frqs_H, exp_types, relax_times = return_r2eff_arrays(spins=spins, spin_ids=spin_ids, fields=fields, field_count=field_count)
# The offset and R1 data.
r1_setup()
offsets, spin_lock_fields_inter, chemical_shifts, tilt_angles, Delta_omega, w_eff = return_offset_data(spins=spins, spin_ids=spin_ids, field_count=field_count, spin_lock_offset=spin_lock_offset, fields=spin_lock_nu1)
r1 = return_r1_data(spins=spins, spin_ids=spin_ids, field_count=field_count)
r1_fit = is_r1_optimised(spins[0].model)
# The relaxation times.
if relax_times_new != None:
relax_times = relax_times_new
# The dispersion data.
recalc_tau = True
if cpmg_frqs == None and spin_lock_nu1 == None and spin_lock_offset == None:
cpmg_frqs = return_cpmg_frqs(ref_flag=False)
spin_lock_nu1 = return_spin_lock_nu1(ref_flag=False)
# Reset the cpmg_frqs if interpolating R1rho models.
elif cpmg_frqs == None and spin_lock_offset != None:
cpmg_frqs = None
spin_lock_nu1 = spin_lock_fields_inter
recalc_tau = False
values = []
errors = []
missing = []
for exp_type, ei in loop_exp(return_indices=True):
values.append([])
errors.append([])
missing.append([])
for si in range(len(spins)):
values[ei].append([])
errors[ei].append([])
missing[ei].append([])
for frq, mi in loop_frq(return_indices=True):
values[ei][si].append([])
errors[ei][si].append([])
missing[ei][si].append([])
for oi, offset in enumerate(offsets[ei][si][mi]):
num = len(spin_lock_nu1[ei][mi][oi])
values[ei][si][mi].append(zeros(num, float64))
errors[ei][si][mi].append(ones(num, float64))
missing[ei][si][mi].append(zeros(num, int32))
# Reconstruct the structures for interpolation.
else:
recalc_tau = False
values = []
errors = []
missing = []
for exp_type, ei in loop_exp(return_indices=True):
values.append([])
errors.append([])
missing.append([])
for si in range(len(spins)):
values[ei].append([])
errors[ei].append([])
missing[ei].append([])
for frq, mi in loop_frq(return_indices=True):
values[ei][si].append([])
errors[ei][si].append([])
missing[ei][si].append([])
for offset, oi in loop_offset(exp_type=exp_type, frq=frq, return_indices=True):
if exp_type in EXP_TYPE_LIST_CPMG:
num = len(cpmg_frqs[ei][mi][oi])
else:
num = len(spin_lock_nu1[ei][mi][oi])
values[ei][si][mi].append(zeros(num, float64))
errors[ei][si][mi].append(ones(num, float64))
missing[ei][si][mi].append(zeros(num, int32))
# Initialise the relaxation dispersion fit functions.
model = Dispersion(model=spins[0].model, num_params=param_num(spins=spins), num_spins=len(spins), num_frq=field_count, exp_types=exp_types, values=values, errors=errors, missing=missing, frqs=frqs, frqs_H=frqs_H, cpmg_frqs=cpmg_frqs, spin_lock_nu1=spin_lock_nu1, chemical_shifts=chemical_shifts, offset=offsets, tilt_angles=tilt_angles, r1=r1, relax_times=relax_times, recalc_tau=recalc_tau, r1_fit=r1_fit)
# Make a single function call. This will cause back calculation and the data will be stored in the class instance.
chi2 = model.func(param_vector)
# Store the chi-squared value.
if store_chi2:
for spin in spins:
spin.chi2 = chi2
# Return the structure.
return model.get_back_calc()