WCOExperimentCutOff.m

%% REFERENCE
  % [1] Thomas Schreiber and Andreas Schmitz (2009)
  % "Surrogate time series Thomas"
function varargout = WCOExperimentCutOff(ts_x1, ts_x2, labels_vec, parameter_space)

  %% number of surrogates
  % approximation possible (eg., |2/(0.99-1)|  = 200), see [1] here larger number to increase discrimination power due to still increasing statistics after 100 surrogates

disp = false;  % display options
source = 'J';  % use Matlab for Matlab implementation of the CWT
  %% continious wavelet transform
  [cwt_1,scales,periods,coi,time] = CWT(ts_x1, ...
                                          'wave_normalization', char(parameter_space.wave_normalization),...
                                          'morse_space', char(parameter_space.morse_space),...
                                          'K', 1, ...
                                          'be', parameter_space.be,...
                                          'ga', parameter_space.ga,...
                                          'disp_cwt', false,...
                                          'wavelet_type', char(parameter_space.wavelet_type),...
                                          'source', source...
                                          );
  cwt_2 = CWT(ts_x2,...
                    'wave_normalization', char(parameter_space.wave_normalization),...
                    'morse_space', char(parameter_space.morse_space),...
                    'K', 1, ...
                    'be', parameter_space.be,...
                    'ga', parameter_space.ga,...
                    'disp_cwt', false,...
                    'wavelet_type', char(parameter_space.wavelet_type),...
                    'source', source...
                    );

  %% wavelet coherence
  inputWCO = WCO(cwt_1,cwt_2,scales,periods,...
              'smoothing', char(parameter_space.smoothing),...
              'ws_size', parameter_space.ws_size,...
              'wt_size', parameter_space.wt_size,...
              'disp_coherence', false,...
              'coi',cwt_1,...
              'time_series',[ts_x1 ts_x2],...
              'y_axis', 'scale',...
              'coherence', char(parameter_space.coherence)...
              );

  %% calculate significantly wavelet coherence
  [sigWCO, classifier] = SigniWCOCutOff(inputWCO, parameter_space.cut_off, scales, periods,...
                          'coi',coi,...
                          'time_series',[ts_x1 ts_x2],...
                          'disp_coherence', disp,...
                          'y_axis', 'period',...
                          'connectivity_vector', labels_vec...
                          );
  Taskperformance = PM(labels_vec,classifier, 'soi', [2.02 12.8], 'space', periods);
  Taskperformance.surrogate = string('Cut-Off');  % required because available in WCOExperiment
  Objective = 1-Taskperformance.f_score;
  varargout = {Objective, struct2table(Taskperformance)}
  end