def calculate_background(self, fit_function: IFunction) -> None:
"""Calculates the background in the counts workspace."""
params = self._get_parameters_for_background_fit(fit_function,
create_output=False)
function, fit_status, chi_squared = run_Fit(
params, AlgorithmManager.create("Fit"))
self._handle_background_fit_output(function, fit_status, chi_squared)
def create_background_output_workspaces(self,
fit_function: IFunction) -> tuple:
"""Creates the output workspaces for the currently stored background data."""
params = self._get_parameters_for_background_fit(fit_function,
create_output=True,
max_iterations=0)
_, parameter_table_name, _, _, _, covariance_matrix_name = run_Fit(
params, AlgorithmManager.create("Fit"))
return parameter_table_name, covariance_matrix_name
def do_single_fit_and_return_workspace_parameters_and_fit_function(
self, parameters_dict):
alg = mantid.AlgorithmManager.create("Fit")
output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix = run_Fit(
parameters_dict, alg)
CopyLogs(InputWorkspace=parameters_dict['InputWorkspace'],
OutputWorkspace=output_workspace,
StoreInADS=False)
return output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix
def do_single_fit_and_return_workspace_parameters_and_fit_function(
self, parameters_dict):
if 'DoublePulseEnabled' in self.context.gui_context and self.context.gui_context[
'DoublePulseEnabled']:
alg = self._create_double_pulse_alg()
else:
alg = mantid.AlgorithmManager.create("Fit")
output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix = run_Fit(
parameters_dict, alg)
CopyLogs(InputWorkspace=parameters_dict['InputWorkspace'],
OutputWorkspace=output_workspace,
StoreInADS=False)
return output_workspace, output_parameters, function_object, output_status, output_chi, covariance_matrix
def do_single_fit_and_return_workspace_parameters_and_fit_function(self, parameters_dict):
alg = mantid.AlgorithmManager.create("Fit")
return run_Fit(parameters_dict, alg)
