function varargout = plotTuningCurveArrayFromTable(StimTableCell, figDir, varargin)
%% Input should be a cell array of tables for XOFF, YOFF, XON, YON.
switch nargin
    case 2
        alignToOff = false;
        alignToAll = false;
        thresholdResponseInd = 0;
        thresholdRSquare = -20;
        padWithNaNs = 0;
    case 3
        alignToOff = varargin{1};
        alignToAll = false;
        thresholdResponseInd = 0;
        thresholdRSquare = -20;
        padWithNaNs = 0;
    case 4
        alignToOff = varargin{1};
        alignToAll = varargin{2};
        thresholdResponseInd = 0;
        thresholdRSquare = -20;
        padWithNaNs = 0;
    case 5
        alignToOff = varargin{1};
        alignToAll = varargin{2};
        thresholdResponseInd = varargin{3};
        thresholdRSquare = -20;
        padWithNaNs = 0;
    case 6
        alignToOff = varargin{1};
        alignToAll = varargin{2};
        thresholdResponseInd = varargin{3};
        thresholdRSquare = varargin{4};
        padWithNaNs = 0;
    case 7
        alignToOff = varargin{1};
        alignToAll = varargin{2};
        thresholdResponseInd = varargin{3};
        thresholdRSquare = varargin{4};
        padWithNaNs = varargin{5};
    otherwise
        warning('Too many arguments, cannot process them meaningfully!');
end

% try
    [alignedRFsCell, ...
    alignedRFsCellNaN] = getAlignedTuningCurveArrayFromTable( StimTableCell, ...
                                                               thresholdResponseInd, ...
                                                               thresholdRSquare, ...
                                                               alignToOff, ...
                                                               alignToAll);
% catch
%     fprintf('Something went wrong! \n')
%     return
% end
%     

qualityMeasure = 'responseQuality';

hFig = createPrintFig(15 * 1 * [1 0.8]);
nStims = numel(StimTableCell);
data = cellfun(@vec, alignedRFsCell, 'uni', 0);
data = cat(1, data{:});
% colorBounds = ZeroCenteredBounds(data, [0 100]);
for iStim = 1: nStims
    StimTable = StimTableCell{iStim};
    tuningCurves = alignedRFsCell{iStim};
    tuningCurvesNaN = alignedRFsCellNaN{iStim};
    hSubAx(iStim) = subplot(2, 2, iStim);
    colorMap = setFavoriteColormap;
    barSpacing = [StimTable.stimParams{1}.spacing2];
    barPosition = arrayfun(@(x) x.amp, [StimTable.stimParams{1}.stimtrans]);
    % Center position.
    barPosition = barPosition - median(barPosition);
    % To ignore bar position just use the units of indices.
    barPosition = mean(barSpacing) * (1: size(tuningCurves, 2));
    imAlpha = ones(size(tuningCurvesNaN(:, :)));
    if padWithNaNs
        imAlpha(isnan(tuningCurvesNaN(:, :))) = 0;
    end
    imagesc(barPosition(2: end), 1: size(tuningCurves, 1), tuningCurves(:, :), ...
             'AlphaData', imAlpha);
    colorBounds = ZeroCenteredBounds(tuningCurves, [0 100]);
    set(gca, 'CLim', colorBounds);
    set(gca, 'color', 1 * [1 1 1]);

end

formatSubAxes(hSubAx, padWithNaNs);
[~, ~, genotype] = parseFlyPath(fileparts(StimTable.timeSeriesPath{1}));
figFileName = ['RF-TuningCurves-' genotype '-' ...
                qualityMeasure '-' num2str(thresholdResponseInd) '-'...
                'rSquare-' num2str(thresholdRSquare) '-', ...
                'All-' num2str(alignToAll) ...
                'Off-' num2str(alignToOff)];
% set(gcf,'renderer','painters')
% set(gcf, 'PaperPositionMode', 'auto');
print(hFig, [figDir figFileName '.pdf'], '-dpdf')
print(hFig, [figDir figFileName '.png'], '-dpng', '-r300')
% print(hFig, [figDir figFileName '.eps'], '-depsc2', '-r300')
varargout{1} = alignedRFsCell;


    function tryout
    %% Plot tuning curves.
    nStims = 4;
    for iStim = 2: 5
        StimTable = StimTableCell{iStim};
        % Retrieve table data in arrays.
        [tcArray, responseIndArray, rSquareArray, fitParams] = getTuningCurveArrayFromTable(StimTable);
        % We can threshold now.
        thresholdResponseInd = 0.0;
        isAboveThreshold = responseIndArray > thresholdResponseInd;
        tcArray = tcArray(isAboveThreshold, :);
        responseIndArray = responseIndArray(isAboveThreshold);
        rSquareArray = rSquareArray(isAboveThreshold);
        % Sort cells by response quality.
        [sortedResponseIndArray, sortInd] = sort(responseIndArray, 'descend');
        subplot(4, nStims, iStim - 1);
        imagesc(tcArray(sortInd, :), ZeroCenteredBounds(tcArray, [0 100]));
        % setFavoriteColormap;
        colormap(brewermap([], 'BrBG'))
        % Sort by fit quality.

        [sortedRSquareArray, sortInd] = sort(rSquareArray, 'descend');
        subplot(4, nStims, iStim - 1 + nStims);
        imagesc(tcArray(sortInd, :), ZeroCenteredBounds(tcArray, [0 100]));
        setFavoriteColormap;
        colormap(brewermap([], 'BrBG'))
    %     colormap(brewermap([], 'RdBu'))
        % Check how it looks aligning the tuning curves.
        [alignedRFsCell, lagDiffsCell] = alignRFsToAbsMax(tcArray, 0);
        % alignedRFs = tcArray;
    %     [alignedRFs, lagDiffs] = alignRFsToAbsMax(alignedRFs, 0);
    %     [alignedRFs, lagDiffs] = alignRFsToAbsMax(alignedRFs, 0);
        % [alignedRFs, lagDiffs] = alignRFsToAbsMax(alignedRFs, 0);

        [sortedRSquareArray, sortInd] = sort(rSquareArray, 'descend');
        subplot(4, nStims,  iStim - 1 + 2 * nStims);
        imagesc(alignedRFsCell(:, :), ZeroCenteredBounds(alignedRFsCell, [0 100]));
    %     setFavoriteColormap;
        colormap(brewermap([], 'BrBG'))
        % colormap(brewermap([], 'RdBu'))

        [sortedRSquareArray, sortInd] = sort(rSquareArray, 'descend');
        subplot(4, nStims,  iStim - 1 + 3 * nStims);
        imagesc(tcArray(:, :), ZeroCenteredBounds(alignedRFsCell, [0 100]));
    %     setFavoriteColormap;
    %     colormap(brewermap([], 'RdBu'))
        % imagesc(sortedRSquareArray', ZeroCenteredBounds(sortedRSquareArray, [0 100]));
    end
    end
end


function polarity = getStimPolarity(StimTable)

% Get stim polarity.
tokens = regexp(StimTable(1, :).stimParamFileName{1}, '.*_([mp]).*Con_.*', 'tokens');
switch tokens{1}{1}
    case 'm'
        polarity = 'off';
    case 'p'
        polarity = 'on';
    otherwise
        polarity = 'other';
end

end

function formatSubAxes(hSubAx, varargin)

if nargin > 1
    padWithNaNs = varargin{1};
else
    padWithNaNs = 0;
end

% Move the plots closer together.
hSubAx(2).Position(1) = hSubAx(2).Position(1) - 0.1;
hSubAx(4).Position(1) = hSubAx(4).Position(1) - 0.1;

% Set colorbar formatting.
tempPos = hSubAx(2).Position;
hColorBar(1) = colorbar(hSubAx(2));
hSubAx(2).Position = tempPos;

tempPos = hSubAx(4).Position;
hColorBar(2) = colorbar(hSubAx(4));
hSubAx(4).Position = tempPos;

hColorBar(2).Label.String = '\DeltaF / F_0';

hFig = hSubAx(1).Parent;
fileStimSuffix = {'XOFF', 'YOFF', 'XON', 'YON'};

axis(hSubAx, 'square');
arrayfun(@(x) set(x, 'DataAspectRatio'), hSubAx);
for iStim = 1: numel(hSubAx)
    hSubTitle(iStim) = title(hSubAx(iStim), fileStimSuffix{iStim});
end
hYLabel(1) = ylabel(hSubAx(3), 'ROI No.');
hXLabel(1) = xlabel(hSubAx(3), 'Bar Position (\circ)');
arrayfun(@prettifyAxes, [hSubAx hColorBar]); 
% arrayfun(@offsetAxes, hSubAx); 
% Make the yaxis same so you can see the relative contribution of number of
% cells for each stimulus.
% linkaxes(hSubAx, 'y');
% Set color of axis to zero color to highlight lack of data. Maybe zero is
% misleading but its perfectly uniform so i will not look like the rest of
% the data.
colorMap = colormap(hSubAx(1));
if ~padWithNaNs
    set(hSubAx, 'Color', colorMap(end / 2, :));
end
setFontForThesis([hSubAx hColorBar], hFig)

end