incorporating Alex and Jay code

% [Aaux, rcMap] = hmrR_tCCA(data, aux, probe, runIdx, flagtCCA, flagICRegressors, tCCAparams, tCCAaux_inx, rhoSD_ssThresh, runIdxResting)

%

% UI NAME:

% User_Friendly_Name_For_hmrR_tCCA

% tCCA regressor generator

%

% DESCRIPTION:

% This script generates regressors using the regularized temporally embedded

        % zscore AUX signals

        AUX = zscore(AUX);

        param.NumOfEmb = ceil(timelag*fq / tCCAparams(2));

        %% DO THE TCCA WORK

        filterFilename = sprintf('./tCCAfilter_%d.txt', iBlk);

        tCCAexists = isfile(sprintf('./tCCAfilter_%d.txt', iBlk));

        isTrainingRun = runIdxResting == runIdx;

        if ~tCCAexists && isTrainingRun

            dotCCA = 'train';

        elseif tCCAexists && ~isTrainingRun

            dotCCA = 'apply';

        else

            dotCCA = 'skip';

        end

        switch dotCCA

            case 'train'

                %% if the tCCAfilter variable is not existing, learn and save it (this is the training/resting run)

                %       If flagICRegressors = 1,

                %       then each column corresponds to an indiviudal channel

                %       regressor. Otherwise, columns correspond to common regressors

                %       for all channels in descending order ranked by canonical correlation coefficient

                %       number of embeddings

        param.NumOfEmb = ceil(timelag*fq / param.tau);

        if runIdx == runIdxResting

            %% if the tCCAfilter variable is empty, learn and put it out (this is the training/resting run)

                if flagICRegressors %% learn channel specific filters and regressors

                    for cc = 1:size(d_long,2)

                        %% perform tCCA with shrinkage for each fNIRS channel

                    [REG,  ADD] = rtcca_dummy(d_long(:,cc),AUX,param,flags);    %%%%%%% JD: rtcca generates error, "Undefined function or variable 'cshrink'". %%%%%%% 

                        [REG,  ADD] = rtcca_dummy(d_long(:,cc),AUX,param,flags);

                        %% save individual regressor and channel map

                        % return the reduced number of available regressors

                        Aaux{iBlk}(:,cc) = REG(:,1);

                        % save channel-regressor map. First half HbO, second half HbR

                        rcMap{iBlk}{cc} = cc;

                    end

                    % reshape (HbO first row, HbR second row)

                    rcMap = reshape(rcMap,[2,numel(rcMap)/2]);

                else %% learn common set of regressors for all channels (default)

                    %% perform tCCA with shrinkage for all fNIRS channels

                [REG,  ADD] = rtcca_dummy(d_long,AUX,param,flags);    %%%%%%% JD: rtcca generates error, "Undefined function or variable 'cshrink'". %%%%%%% 

                %% save and output tCCA filter.

                    [REG,  ADD] = rtcca_dummy(d_long,AUX,param,flags);                 %% save and output tCCA filter.

                    %reduce filter matrix with the help of correlation threshold or max number of regressors

                %%%%%%% JD: Undefined variable "ADD_trn" or class "ADD_trn.ccac". %%%%%%% 

                    if ctr < 1

                        % use only auxiliary tcca components that have correlation > ct

                    % compindex=find(ADD_trn.ccac>param.ct);

                    compindex=1:10;

                        compindex=find(ADD_trn.ccac>param.ct);

                        %% JD had a comment here as Undefined variable "ADD_trn" or class "ADD_trn.ccac"

                    else

                        % use only the first ctr auxiliary tcca components (fixed number of

                        % regressors = ctr)

                    % if numel(ADD_trn.ccac) >= ctr

                    %     compindex = 1:ctr;

                    % else % not as many components available as regressors requested, provide all

                    %     compindex = 1:numel(ADD_trn.ccac);

                    % end                    

                    compindex=1:10;

                        if numel(ADD_trn.ccac) >= ctr

                            compindex = 1:ctr;

                        else % not as many components available as regressors requested, provide all

                            compindex = 1:numel(ADD_trn.ccac);

                        end

                    end

                    % return the reduced number of available regressors

                    Aaux{iBlk} = REG(:,compindex);

                    % return reduced mapping matrix Av, this is the tCCA filter

                    tCCAfilter = ADD.Av(:,compindex);

                    % set channel-regressor map to empty (GLM will use all available regressors for all channels)

                rcMap{iBlk} = [];

                    rcMap{iBlk} = 'all';

                end

                %% save tCCAfilter matrix that was learned from resting state data.

                fprintf('hmrR_tCCA: run idx = %d. Generated and Saved tCCAfilter\n', runIdx)

                print_filter(tCCAfilter);

                save(filterFilename, '-ascii', 'tCCAfilter');

        elseif exist(filterFilename,'file')

            %% if the tCCAfilter variable exists, apply the filtering and generate the tCCA regressors

            fprintf('hmrR_tCCA: run idx = %d. Loading and Using tCCAfilter\n', runIdx)

            case 'apply'

                %% if the tCCAfilter variable exists, load it, apply the filtering and generate the tCCA regressors

                % Load the filter for the iBlk data block

                fprintf('hmrR_tCCA: run idx = %d. Loading and Using tCCAfilter\n', runIdx)

                tCCAfilter = load(filterFilename,'-ascii');

                print_filter(tCCAfilter);

                % Temporal embedding and zscoring of auxiliary data

                aux_sigs = AUX;

                aux_emb = aux_sigs;

                    aux_emb = [aux_emb aux];

                end

                %zscore

            % aux_emb = zscore(aux_emb);

            aux_emb = 1;     % JD: Dummy value 

                aux_emb = zscore(aux_emb);

                % Apply tCCA filter (generate regressors)

            Aaux{iBlk} = aux_emb * tCCAfilter;

                Aaux = aux_emb * tCCAfilter;

                % if the individual channel regressor flag was set, indicate by providing rc mapping

                if flagICRegressors

                    for cc = 1:size(d_long,2)

                        % save channel-regressor map. First half HbO, second half HbR

                        rcMap{iBlk}{cc} = cc;

                    end

                    % reshape (HbO first row, HbR second row)

                    rcMap = reshape(rcMap,[2,numel(rcMap)/2]);

                else

                rcMap{iBlk} = [];

                    rcMap{iBlk} = 'all';

                end

        else

            Aaux{iBlk} = [];

            rcMap{iBlk} = [];

            % tCCAfilter = []; if uncommented, retraining necessary whenever tcca is temporarily flagged out of processing stream

            case 'skip'

                Aaux = [];

                rcMap = [];

        end

    end

else

    Aaux = [];

    rcMap = [];

    % tCCAfilter = []; if uncommented, retraining necessary whenever tcca is temporarily flagged out of processing stream

end