def back_calc():
"""Back calculate the peak intensities.
The simple two parameter exponential curve (Rx, I0) is assumed.
"""
# Loop over the spins.
for spin in spin_loop():
# Skip deselected spins.
if not spin.select:
continue
# The parameter vector.
param_vector = array([spin.rx, spin.i0], float64)
# Initialise the relaxation fit functions.
setup(num_params=len(spin.params),
num_times=len(cdp.relax_times),
values=spin.ave_intensities,
sd=cdp.sd,
relax_times=cdp.relax_times,
scaling_matrix=identity(2, float64))
# Make a single function call. This will cause back calculation and the data will be stored in the C module.
func(param_vector)
# Get the data and store it in the spin specific data structure.
spin.fit_int = back_calc_I()
def back_calc_data(self):
"""Return the back-calculated data from the C code.
@return: The back-calculated peak intensities.
@rtype: list of float
"""
# Return the data.
return back_calc_I()
def back_calc_data(self):
"""Return the back-calculated data from the C code.
@return: The back-calculated peak intensities.
@rtype: list of float
"""
# Return the data.
return back_calc_I()
def _back_calc(self, spin=None, relax_time_id=None):
"""Back-calculation of peak intensity for the given relaxation time.
@keyword spin: The spin container.
@type spin: SpinContainer instance
@keyword relax_time_id: The ID string for the desired relaxation time.
@type relax_time_id: str
@return: The peak intensity for the desired relaxation time.
@rtype: float
"""
# Create the initial parameter vector.
param_vector = self._assemble_param_vector(spin=spin)
# Create a scaling matrix.
scaling_matrix = self._assemble_scaling_matrix(spin=spin, scaling=False)
# The keys.
keys = list(spin.intensities.keys())
# The peak intensities and times.
values = []
errors = []
times = []
for key in keys:
values.append(spin.intensities[key])
errors.append(spin.intensity_err[key])
times.append(cdp.relax_times[key])
# The scaling matrix in a diagonalised list form.
scaling_list = []
for i in range(len(scaling_matrix)):
scaling_list.append(scaling_matrix[i, i])
# Initialise the relaxation fit functions.
setup(num_params=len(spin.params), num_times=len(cdp.relax_times), values=values, sd=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.
self._func(param_vector)
# Get the data back.
results = back_calc_I()
# Return the correct peak height.
return results[keys.index(relax_time_id)]
def back_calc():
"""Back calculate the peak intensities.
The simple two parameter exponential curve (Rx, I0) is assumed.
"""
# Loop over the spins.
for spin in spin_loop():
# Skip deselected spins.
if not spin.select:
continue
# The parameter vector.
param_vector = array([spin.rx, spin.i0], float64)
# Initialise the relaxation fit functions.
setup(num_params=len(spin.params), num_times=len(cdp.relax_times), values=spin.ave_intensities, sd=cdp.sd, relax_times=cdp.relax_times, scaling_matrix=identity(2, float64))
# Make a single function call. This will cause back calculation and the data will be stored in the C module.
func(param_vector)
# Get the data and store it in the spin specific data structure.
spin.fit_int = back_calc_I()