KP_2PVR_generate_outliers_mask_v3_singleTrialRes.m

function [sMask, sOrg1, sOrg0, sMaskInputStruct] = KP_2PVR_generate_outliers_mask_v3_singleTrialRes(neuHistInputStruct, in_sOrg, dataCat, maskID, threshold, direction, fieldHistInputStruct, inputStruct, postProcess)
if postProcess == "none"
    sMaskInputStruct.outpath = sprintf('%strialSuperMasks\\%s\\%s\\', inputStruct.outpath,dataCat,maskID);
elseif postProcess == "SensitiveIDs"
    sMaskInputStruct.outpath = sprintf('%strialSuperMasks_projectSensitiveIDs\\%s\\%s\\', inputStruct.outpath,dataCat,maskID);
end
sMaskInputStruct.expID = inputStruct.expID;
sMaskInputStruct.neuHistID = inputStruct.neuHistID;
sMaskInputStruct.fineQuantID = inputStruct.fineQuantID;
sMaskInputStruct.fieldHistID = inputStruct.fieldHistID;
sMaskInputStruct.trialResID = inputStruct.trialResID;
if postProcess == "SensitiveIDs"
    sMaskInputStruct.interpretID = inputStruct.interpretID;
end
sMaskInputStruct.fieldProperty = dataCat;
sMaskInputStruct.maskID = maskID;
sMaskInputStruct.maskFun = 'KP_2PVR_generate_outliers_mask_v3_singleTrialRes';
sMaskInputStruct.direction = direction;
sMaskInputStruct.theshold = threshold;
KP_2PVR_save_maskInputStruct(sMaskInputStruct);

transfGroups = fieldnames(neuHistInputStruct.Transformations);
for g = 1:length(transfGroups)
    group = transfGroups{g};
    fprintf('Processing %s\n', group)
    fprintf('Processing %s\n', group)
    dataVec = [];
    neurons = fieldnames(in_sOrg.(group));
    numNeurons = length(neurons);
    for n = 1:numNeurons
        neuron = neurons{n};
        sMask.(group).(neuron) = zeros(size(in_sOrg.(group).(neuron),1), size(in_sOrg.(group).(neuron),2), size(in_sOrg.(group).(neuron),3));
        sOrg1.(group).(neuron) = NaN(size(in_sOrg.(group).(neuron),1), size(in_sOrg.(group).(neuron),2), size(in_sOrg.(group).(neuron),3));
        sOrg0.(group).(neuron) = in_sOrg.(group).(neuron);
        dataVec = [dataVec, transpose(in_sOrg.(group).(neuron)(:))];
        if ~(all(all(all(isnan(in_sOrg.(group).(neuron))))))
            fprintf('Processing %s\n', neuron)
            for t = 1:size(in_sOrg.(group).(neuron),3)
                dataMat = in_sOrg.(group).(neuron)(:,:,t);
                if ~all(all(isnan(dataMat)))
                    if direction == "smallerThan"
                        [row, col] = find(dataMat < threshold);
                    elseif direction == "smallerOrEqualTo"
                        [row, col] = find(dataMat <= threshold);
                    elseif direction == "greaterThan"
                        [row, col] = find(dataMat > threshold);
                    end
                    for found = 1:length(row)
                        sMask.(group).(neuron)(row(found),col(found),t) = 1;
                        sOrg1.(group).(neuron)(row(found),col(found),t) = in_sOrg.(group).(neuron)(row(found),col(found),t);
                        sOrg0.(group).(neuron)(row(found),col(found),t) = NaN;
                    end
                end
            end
        end
    end
    if ~all(isnan(dataVec))
        fig_filepath = sprintf('%s%s\\',sMaskInputStruct.outpath,group);
        mkdir(fig_filepath);
        minDataVec = min(dataVec);
        maxDataVec = max(dataVec);
        edges = minDataVec:fieldHistInputStruct.bin_height:maxDataVec + fieldHistInputStruct.bin_height;
        fig = figure;
        histogram(dataVec, edges)
        xline(threshold,'Color','red','LineStyle','--','LineWidth',1)
        title(sprintf('All neurons %s', dataCat))
        fig_filename = sprintf('thresh_%s.pdf',string(threshold));
        saveas(fig,[fig_filepath,fig_filename])
    end
end
filenameMask = 'sMasked';
save([sMaskInputStruct.outpath,filenameMask],'sMask', 'sOrg1', 'sOrg0')
end