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sa_peth.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% peth (peri-event time histograms)
% by Til Ole Bergmann 2013
% last modified 2016/11/22 by TOB
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% calculates peri-event time histograms
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sa_peth(gcfg)
display('Generating peri-event time histograms...');
SO_peth_already_plotted = 0; % reset
spindle_peth_already_plotted = 0; % reset
for tc = 1:numel(gcfg.tc) % loop over trial classes
%% define events for current trial class
switch gcfg.tc{tc}
% 'stage' 'startTime' 'midTime' 'endTime' 'duration' 'maxTime' 'minTime' 'minAmp' 'maxAmp' 'p2pAmp' 'p2pTime'
case {'SO_all','SO_w_spindle','SO_wo_spindle'}
%%% spindle center relative to SW down-state
% event 1 (reference event)
gcfg.event1.targetchannel = gcfg.event1channelname; % 'HC' or 'Cz'
gcfg.event1.chan = gcfg.event1channel;
gcfg.event1.spec = ['SO_' gcfg.SOspec '_' gcfg.SO_timelockevent];
gcfg.event1.eventdata = 'SO_eventdata';
gcfg.event1.tic = 7; % minTime (trialinfo column containing reference time point for event 1)
% event 2 (target event)
gcfg.event2.targetchannel = gcfg.event2channelname; % 'HC'or 'Cz'
gcfg.event2.chan = gcfg.event2channel;
gcfg.event2.spec = ['spindle_' gcfg.spindle_timelockevent];
gcfg.event2.eventdata = 'spindle_eventdata';
gcfg.event2.tic = 3; % midTime (trialinfo column containing reference time point for event 2)
gcfg.eow = [0.2 1.0]; % event occurence window in seconds
case {'spindle_all', 'spindle_w_SO', 'spindle_wo_SO'}
%%% SW down-state relative to spindle center
% event 1 (reference event)
gcfg.event1.targetchannel = gcfg.event1channelname; % 'HC' or 'Cz'
gcfg.event1.chan = gcfg.event1channel;
gcfg.event1.spec = ['spindle_' gcfg.spindle_timelockevent];
gcfg.event1.eventdata = 'spindle_eventdata';
gcfg.event1.tic = 3; % midTime (trialinfo column containing reference time point for event 1)
% event 2 (counted event)
gcfg.event2.targetchannel = gcfg.event2channelname; % 'HC' or 'Cz'
gcfg.event2.chan = gcfg.event2channel;
gcfg.event2.spec = ['SO_' gcfg.SOspec '_' gcfg.SO_timelockevent];
gcfg.event2.eventdata = 'SO_eventdata';
gcfg.event2.tic = 7; % minTime (trialinfo column containing reference time point for event 2)
gcfg.eow = [-1 -0.2]; % event occurence window in seconds
case {'SO_w_SO','SO_wo_SO'}
%%% SW down-state relative to SW down-state
% event 1 (reference event)
gcfg.event1.targetchannel = gcfg.event1channelname; % 'HC' or 'Cz'
gcfg.event1.chan = gcfg.event1channel;
gcfg.event1.spec = ['SO_' gcfg.SOspec '_' gcfg.SO_timelockevent];
gcfg.event1.eventdata = 'SO_eventdata';
gcfg.event1.tic = 7; % minTime (trialinfo column containing reference time point for event 1)
% event 2 (counted event)
gcfg.event2.targetchannel = gcfg.event2channelname; % 'HC' or 'Cz'
gcfg.event2.chan = gcfg.event2channel;
gcfg.event2.spec = ['SO_' gcfg.SOspec '_' gcfg.SO_timelockevent];
gcfg.event2.eventdata = 'SO_eventdata';
gcfg.event2.tic = 7; % minTime (trialinfo column containing reference time point for event 2)
gcfg.eow = [-2.5 2.5]; % event occurence window in seconds
end % of switch
%% load individual data
for i = 1:numel(gcfg.subjects) % loop over subjects
temp1 = load(fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_' gcfg.event1.spec '_' gcfg.event1.chan{gcfg.subjects(i)} '_eventdata.mat']), gcfg.event1.eventdata);
temp2 = load(fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_' gcfg.event2.spec '_' gcfg.event2.chan{gcfg.subjects(i)} '_eventdata.mat']), gcfg.event2.eventdata);
% 'stage' 'startTime' 'midTime' 'endTime' 'duration' 'maxTime' 'minTime' 'minAmp' 'maxAmp' 'p2pAmp' 'p2pTime'
fsample = temp1.(gcfg.event1.eventdata).fsample;
event1{i} = temp1.(gcfg.event1.eventdata).trialinfo;
event2{i} = temp2.(gcfg.event2.eventdata).trialinfo;
% apply esv to event1
if strcmp(gcfg.event1.eventdata, 'SO_eventdata') && isfield(gcfg,'SO_esv')
event1{i} = event1{i}(logical(gcfg.SO_esv{i}),:);
elseif strcmp(gcfg.event1.eventdata, 'spindle_eventdata') && isfield(gcfg,'spindle_esv')
event1{i} = event1{i}(logical(gcfg.spindle_esv{i}),:);
end
% apply esv to event2
if strcmp(gcfg.event2.eventdata, 'SO_eventdata') && isfield(gcfg,'SO_esv')
event2{i} = event2{i}(logical(gcfg.SO_esv{i}),:);
elseif strcmp(gcfg.event2.eventdata, 'spindle_eventdata') && isfield(gcfg,'spindle_esv')
event2{i} = event2{i}(logical(gcfg.spindle_esv{i}),:);
end
end
%% setup search window
edges = gcfg.edges * fsample; % gcfg.edges in datapoints
eow = gcfg.eow * fsample; % gcfg.eow in datapoints
eowidx = find(ismember(edges, eow));
eowidx(2) = eowidx(2)-1; % to ensure the right limit of eow does not cause inclusion of another bin starting at eow(2)!!!
zerobin = find(ismember(edges, 0));
%% calculate PETH, tsv and PEO
for i = 1:numel(event1) % loop over subjects
% count target events in peri-event neighbourhood around reference event
tsv{i} = ones(1,size(event1{i},1)); % preset tsv
AllTrial_peth{i} = zeros(size(edges,2),size(event1{i},1)); % preset PETH
for j = 1:size(event1{i},1) % loop over events of event type 1
[AllTrial_peth{i}(:,j),bin] = histc(event2{i}(:,round(gcfg.event2.tic)),[event1{i}(j,round(gcfg.event1.tic)) + edges]);
switch gcfg.tc{tc} % set tsv value for current trial
case 'SO_all'
tsv{i}(j) = tsv{i}(j);
case 'SO_w_spindle'
tsv{i}(j) = any(AllTrial_peth{i}(eowidx(1):eowidx(2),j)); % set tsv value for current trial
case 'SO_wo_spindle'
tsv{i}(j) = ~any(AllTrial_peth{i}(eowidx(1):eowidx(2),j)); % set tsv value for current trial
case 'SO_w_SO'
tsv{i}(j) = any(AllTrial_peth{i}([eowidx(1):zerobin-1, zerobin+1:eowidx(2)],j)); % set tsv value for current trial (auto-correlation removed)
case 'SO_wo_SO'
tsv{i}(j) = ~any(AllTrial_peth{i}([eowidx(1):zerobin-1, zerobin+1:eowidx(2)],j)); % set tsv value for current trial (auto-correlation removed)
case 'spindle_all'
tsv{i}(j) = tsv{i}(j);
case 'spindle_w_SO'
tsv{i}(j) = any(AllTrial_peth{i}(eowidx(1):eowidx(2),j)); % set tsv value for current trial
case 'spindle_wo_SO'
tsv{i}(j) = ~any(AllTrial_peth{i}(eowidx(1):eowidx(2),j)); % set tsv value for current trial
end
end % of loop over events of event type 1
switch gcfg.tc{tc} % calculate PEO
case 'SO_all'
All_PEO{i} = 1;
case 'SO_w_spindle'
All_PEO{i} = mean(any(AllTrial_peth{i}(eowidx(1):eowidx(2),:),1));
case 'SO_wo_spindle'
All_PEO{i} = 1-mean(any(AllTrial_peth{i}(eowidx(1):eowidx(2),:),1));
case 'SO_w_SO'
All_PEO{i} = mean(any(AllTrial_peth{i}([eowidx(1):zerobin-1, zerobin+1:eowidx(2)],:),1)); % probability of target event to occur in specified event occurence window (eow) around reference event
case 'SO_wo_SO'
All_PEO{i} = 1-mean(any(AllTrial_peth{i}([eowidx(1):zerobin-1, zerobin+1:eowidx(2)],:),1)); % probability of target event to occur in specified event occurence window (eow) around reference event
case 'spindle_all'
All_PEO{i} = 1;
case 'spindle_w_SO'
All_PEO{i} = mean(any(AllTrial_peth{i}(eowidx(1):eowidx(2),:),1));
case 'spindle_wo_SO'
All_PEO{i} = 1 - mean(any(AllTrial_peth{i}(eowidx(1):eowidx(2),:),1));
end
All_peth{i} = mean(AllTrial_peth{i},2);
%% save tsv (trial selection vector)
peo = All_PEO{i};
peth = All_peth{i};
switch gcfg.tc{tc}
case 'SO_all'
tsv_SO_all = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_all.mat']), 'tsv_SO_all', 'peo', 'peth');
case 'SO_w_spindle'
tsv_SO_w_spindle = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_w_spindle.mat']), 'tsv_SO_w_spindle', 'peo', 'peth');
case 'SO_wo_spindle'
tsv_SO_wo_spindle = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_wo_spindle.mat']), 'tsv_SO_wo_spindle', 'peo', 'peth');
case 'SO_w_SO'
tsv_SO_w_SO = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_w_SO.mat']), 'tsv_SO_w_SO', 'peo', 'peth');
case 'SO_wo_SO'
tsv_SO_wo_SO = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_wo_SO.mat']), 'tsv_SO_wo_SO', 'peo', 'peth');
case 'spindle_all'
tsv_spindle_all = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_spindle_' gcfg.spindle_timelockevent '_' gcfg.spindle_targetchannel{gcfg.subjects(i)} '_tsv_spindle_all.mat']), 'tsv_spindle_all', 'peo', 'peth');
case 'spindle_w_SO'
tsv_spindle_w_SO = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_spindle_' gcfg.spindle_timelockevent '_' gcfg.spindle_targetchannel{gcfg.subjects(i)} '_tsv_spindle_w_SO.mat']), 'tsv_spindle_w_SO', 'peo', 'peth');
case 'spindle_wo_SO'
tsv_spindle_wo_SO = tsv{i};
save('-v7.3', fullfile(gcfg.resultsPath,gcfg.subjectNames{i},[gcfg.subjectNames{i} '_spindle_' gcfg.spindle_timelockevent '_' gcfg.spindle_targetchannel{gcfg.subjects(i)} '_tsv_spindle_wo_SO.mat']), 'tsv_spindle_wo_SO', 'peo', 'peth');
end
end % of loop over subjects
%% calculate group results
% PETH (peri-event time histograms)
GA_peth = mean([All_peth{:}],2); % average over trials AND subjects...
% PEO (probability of event occurence
GA_PEO = mean([All_PEO{:}]); % average over trials AND subjects...
GSD_PEO = std([All_PEO{:}]); % sd over trials AND subjects...
%% save group results
switch gcfg.tc{tc}
case 'SO_all'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_all.mat']), 'tsv_SO_all', 'GA_PEO', 'GSD_PEO', 'GA_peth');
case 'SO_w_spindle'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_w_spindle.mat']), 'tsv_SO_w_spindle', 'GA_PEO', 'GSD_PEO', 'GA_peth');
case 'SO_wo_spindle'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_wo_spindle.mat']), 'tsv_SO_wo_spindle', 'GA_PEO', 'GSD_PEO', 'GA_peth');
case 'SO_w_SO'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_w_SO.mat']), 'tsv_SO_w_SO', 'GA_PEO', 'GSD_PEO', 'GA_peth');
case 'SO_wo_SO'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.SO_targetchannel{gcfg.subjects(i)} '_tsv_SO_wo_SO.mat']), 'tsv_SO_wo_SO', 'GA_PEO', 'GSD_PEO', 'GA_peth');
case 'spindle_all'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_spindle_' gcfg.spindle_timelockevent '_' gcfg.spindle_targetchannel{gcfg.subjects(i)} '_tsv_spindle_all.mat']), 'tsv_spindle_all', 'GA_PEO', 'GSD_PEO', 'GA_peth');
case 'spindle_w_SO'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_spindle_' gcfg.spindle_timelockevent '_' gcfg.spindle_targetchannel{gcfg.subjects(i)} '_tsv_spindle_w_SO.mat']), 'tsv_spindle_w_SO', 'GA_PEO', 'GSD_PEO', 'GA_peth');
case 'spindle_wo_SO'
save('-v7.3', fullfile(gcfg.resultsPath,'group',['GA_spindle_' gcfg.spindle_timelockevent '_' gcfg.spindle_targetchannel{gcfg.subjects(i)} '_tsv_spindle_wo_SO.mat']), 'tsv_spindle_wo_SO', 'GA_PEO', 'GSD_PEO', 'GA_peth');
end
%% display results
filename = fullfile(gcfg.resultsPath, 'group', ['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.event1.targetchannel '_PETH_' gcfg.timestamp '.txt']);
diary(filename);
display([gcfg.tc{tc} ':']);
display(['Probability of counting event2 occuring (w) / not occuring (wo) within [' num2str(gcfg.eow(1)) ' ' num2str(gcfg.eow(2)) '] ms releative to reference event1:']);
display('Subject-wise:');
display(num2str([All_PEO{:}]));
display(' ');
display('Group:');
display(['Mean: ' num2str(GA_PEO) ' and SD: ' num2str(GSD_PEO)]);
display(' ');
display(' ');
diary off;
%% plot PETH
if gcfg.plot
% apply tsv to PETH
for i = 1:numel(event1) % loop over subjects
AllTrial_peth_plot{i} = AllTrial_peth{i}(:,logical(tsv{i})); % REcalculate Trial results for condition-wise plotting
All_peth_plot{i} = mean(AllTrial_peth_plot{i},2); % REcalculate subject results for condition-wise plotting
end
GA_peth_plot = mean([All_peth_plot{:}],2); % REcalculate group results for condition-wise plotting
if any(ismember(gcfg.tc{tc},{'SO_all','SO_w_spindle','SO_wo_spindle','SO_w_SO','SO_wo_SO'})) % && ~SO_peth_already_plotted
% remove counts for event itself
if any(ismember(gcfg.tc{tc},{'SO_w_SO','SO_wo_SO'}))
GA_peth_plot(ceil(size(GA_peth_plot,1)/2)) = 0;
end
% plot group peth
x = edges/fsample;
figure;
bar(x,GA_peth_plot); % relative frequency
ylabel(['probability of ' gcfg.event2.spec ' in ' gcfg.event2.targetchannel], 'interpreter', 'none');
xlabel(['time (s) relative to ' gcfg.event1.spec ' in ' gcfg.event1.targetchannel], 'interpreter', 'none');
title(['Peri-Event Time Histogram (PETH) for group of ' gcfg.tc{tc} ], 'interpreter', 'none');
% save group figure
set(gcf, 'Name',['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.event1.targetchannel '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp],'NumberTitle','off','units','normalized','outerposition',[0 0 1 1]);
filename = fullfile(gcfg.resultsPath, 'group', ['GA_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.event1.targetchannel '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp]);
saveas(gcf,[filename '.fig']);
set(gcf, 'PaperUnits', 'centimeters', 'PaperSize', [25.4 19.05], 'PaperPosition', [-3 -1 30 19.5]);
print(gcf, '-dpdf', '-r300', [filename '.pdf']);
% plot individual PETH
for i = 1:numel(All_peth) % loop over subjects
% remove counts for event itself
if any(ismember(gcfg.tc{tc},{'SO_w_SO','SO_wo_SO'}))
All_peth_plot{i}(ceil(size(All_peth_plot{i},1)/2)) = 0;
end
x = edges/fsample;
figure;
bar(x,All_peth_plot{i}); % relative frequency
ylabel(['probability of ' gcfg.event2.spec ' in ' gcfg.event2.targetchannel], 'interpreter', 'none');
xlabel(['time (s) relative to ' gcfg.event1.spec ' in ' gcfg.event1.targetchannel], 'interpreter', 'none');
title(['Peri-Event Time Histogram (PETH) for ' gcfg.subjectNames{i} ' of ' gcfg.tc{tc} ], 'interpreter', 'none');
% save individual figure
set(gcf, 'Name',[gcfg.subjectNames{i} '_SO_' gcfg.SOspec '_' gcfg.SO_timelockevent '_' gcfg.event1.chan{gcfg.subjects(i)} '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp],'NumberTitle','off','units','normalized','outerposition',[0 0 1 1]);
filename = fullfile(gcfg.resultsPath, gcfg.subjectNames{i}, [gcfg.subjectNames{i} '_SO_' gcfg.SOspec '_' gcfg.spindle_timelockevent '_' gcfg.event1.chan{gcfg.subjects(i)} '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp]);
saveas(gcf,[filename '.fig']);
set(gcf, 'PaperUnits', 'centimeters', 'PaperSize', [25.4 19.05], 'PaperPosition', [-3 -1 30 19.5]);
print(gcf, '-dpdf', '-r300', [filename '.pdf']);
close(gcf); % close current individual figures (would be way too many open figures!)
end
% SO_peth_already_plotted = 1;
end
if any(ismember(gcfg.tc{tc},{'spindle_all', 'spindle_w_SO', 'spindle_wo_SO'})) % && ~spindle_peth_already_plotted
x = edges/fsample;
figure;
bar(x,GA_peth_plot); % relative frequency
ylabel(['probability of ' gcfg.event2.spec ' in ' gcfg.event2.targetchannel], 'interpreter', 'none');
xlabel(['time (s) relative to ' gcfg.event1.spec ' in ' gcfg.event1.targetchannel], 'interpreter', 'none');
title(['Peri-Event Time Histogram (PETH) for group of ' gcfg.tc{tc} ], 'interpreter', 'none');
% save group figure
set(gcf, 'Name',['GA_spindle_' gcfg.spindle_timelockevent '_' gcfg.event1.targetchannel '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp],'NumberTitle','off','units','normalized','outerposition',[0 0 1 1]);
filename = fullfile(gcfg.resultsPath, 'group', ['GA_spindle_' gcfg.spindle_timelockevent '_' gcfg.event1.targetchannel '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp]);
saveas(gcf,[filename '.fig']);
set(gcf, 'PaperUnits', 'centimeters', 'PaperSize', [25.4 19.05], 'PaperPosition', [-3 -1 30 19.5]);
print(gcf, '-dpdf', '-r300', [filename '.pdf']);
% plot individual PETH
for i = 1:numel(All_peth_plot) % loop over subjects
x = edges/fsample;
figure;
bar(x,All_peth_plot{i}); % relative frequency
ylabel(['probability of ' gcfg.event2.spec ' in ' gcfg.event2.targetchannel], 'interpreter', 'none');
xlabel(['time (s) relative to ' gcfg.event1.spec ' in ' gcfg.event1.targetchannel], 'interpreter', 'none');
title(['Peri-Event Time Histogram (PETH) for ' gcfg.subjectNames{i} ' of ' gcfg.tc{tc} ], 'interpreter', 'none');
% save individual figure
set(gcf, 'Name',[gcfg.subjectNames{i} '_spindle_' gcfg.spindle_timelockevent '_' gcfg.event1.chan{gcfg.subjects(i)} '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp],'NumberTitle','off','units','normalized','outerposition',[0 0 1 1]);
filename = fullfile(gcfg.resultsPath, gcfg.subjectNames{i}, [gcfg.subjectNames{i} '_spindle_' gcfg.spindle_timelockevent '_' gcfg.event1.chan{gcfg.subjects(i)} '_PETH_' gcfg.tc{tc} '_' gcfg.timestamp]);
saveas(gcf,[filename '.fig']);
set(gcf, 'PaperUnits', 'centimeters', 'PaperSize', [25.4 19.05], 'PaperPosition', [-3 -1 30 19.5]);
print(gcf, '-dpdf', '-r300', [filename '.pdf']);
close(gcf); % close current individual figures (would be way too many open figures!)
end
% spindle_peth_already_plotted = 1;
end
end
end % of loop over trial classes
end % of function