def get_param_names(self, model_info=None):
"""Return a vector of parameter names.
@keyword model_info: The spin container and the spin ID string from the _model_loop_spin() method.
@type model_info: SpinContainer instance, str
@return: The vector of parameter names.
@rtype: list of str
"""
# Check that the model is setup.
check_model_setup()
# Unpack the data.
spin, spin_id = model_info
# Return the parameter names.
return spin.params
def get_param_names(self, model_info=None):
"""Return a vector of parameter names.
@keyword model_info: The spin container and the spin ID string from the _model_loop_spin() method.
@type model_info: SpinContainer instance, str
@return: The vector of parameter names.
@rtype: list of str
"""
# Check that the model is setup.
check_model_setup()
# Unpack the data.
spin, spin_id = model_info
# Return the parameter names.
return spin.params
def overfit_deselect(self, data_check=True, verbose=True):
"""Deselect spins which have insufficient data to support minimisation.
@keyword data_check: A flag to signal if the presence of base data is to be checked for.
@type data_check: bool
@keyword verbose: A flag which if True will allow printouts.
@type verbose: bool
"""
# Print out.
if verbose:
print("\nOver-fit spin deselection:")
# Checks.
check_mol_res_spin_data()
check_model_setup()
# Loop over spin data.
deselect_flag = False
for spin, spin_id in spin_loop(return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Check if data exists.
if not hasattr(spin, 'peak_intensity'):
warn(RelaxDeselectWarning(spin_id, 'missing intensity data'))
spin.select = False
deselect_flag = True
continue
# Require 3 or more data points.
elif len(spin.peak_intensity) < 3:
warn(RelaxDeselectWarning(spin_id, 'insufficient data, 3 or more data points are required'))
spin.select = False
deselect_flag = True
continue
# Check that the number of relaxation times is complete.
if len(spin.peak_intensity) != len(cdp.relax_times):
raise RelaxError("The %s peak intensity points of the spin '%s' does not match the expected number of %s (the IDs %s do not match %s)." % (len(spin.peak_intensity), spin_id, len(cdp.relax_times), sorted(spin.peak_intensity.keys()), sorted(cdp.relax_times.keys())))
# Final printout.
if verbose and not deselect_flag:
print("No spins have been deselected.")
def overfit_deselect(self, data_check=True, verbose=True):
"""Deselect spins which have insufficient data to support minimisation.
@keyword data_check: A flag to signal if the presence of base data is to be checked for.
@type data_check: bool
@keyword verbose: A flag which if True will allow printouts.
@type verbose: bool
"""
# Print out.
if verbose:
print("\nOver-fit spin deselection:")
# Checks.
check_mol_res_spin_data()
check_model_setup()
# Loop over spin data.
deselect_flag = False
for spin, spin_id in spin_loop(return_id=True):
# Skip deselected spins.
if not spin.select:
continue
# Check if data exists.
if not hasattr(spin, 'peak_intensity'):
warn(RelaxDeselectWarning(spin_id, 'missing intensity data'))
spin.select = False
deselect_flag = True
continue
# Require 3 or more data points.
elif len(spin.peak_intensity) < 3:
warn(RelaxDeselectWarning(spin_id, 'insufficient data, 3 or more data points are required'))
spin.select = False
deselect_flag = True
continue
# Check that the number of relaxation times is complete.
if len(spin.peak_intensity) != len(cdp.relax_times):
raise RelaxError("The %s peak intensity points of the spin '%s' does not match the expected number of %s (the IDs %s do not match %s)." % (len(spin.peak_intensity), spin_id, len(cdp.relax_times), sorted(spin.peak_intensity.keys()), sorted(cdp.relax_times.keys())))
# Final printout.
if verbose and not deselect_flag:
print("No spins have been deselected.")
