function plot_mckenzie2022(spectralDifference,testDirections)
%PLOT_MCKENZIE2022 calculate max and minimum values
%
%   Usage:
%     plot_mckenzie2022(spectralDifference,testDirections);
%
%   Input parameters:
%     spectralDifference   : vector of spectral differences
%     testDirections       : vector of test directions
%
%   This function generates a spherical heatmap.
%
%   Url: http://amtoolbox.org/amt-1.4.0/doc/plot/plot_mckenzie2022.php


%   #StatusDoc: Good
%   #StatusCode: Good
%   #Verification: Unknown
%   #Requirements: MATLAB M-Stats M-Curvefit
%   #Author: Thomas McKenzie (2022)
%   #Author: Cal Armstrong (2022)
%   #Author: Lauren Ward (2022)
%   #Author: Damian Murphy (2022)
%   #Author: Gavin Kearney (2022)

% This file is licensed unter the GNU General Public License (GPL) either 
% version 3 of the license, or any later version as published by the Free Software 
% Foundation. Details of the GPLv3 can be found in the AMT directory "licences" and 
% at <https://www.gnu.org/licenses/gpl-3.0.html>. 
% You can redistribute this file and/or modify it under the terms of the GPLv3. 
% This file is distributed without any warranty; without even the implied warranty 
% of merchantability or fitness for a particular purpose. 

maxValue = max(spectralDifference);
minValue = min(spectralDifference);

figure('position',[100,100,1200 400]);
for i = 1:length(spectralDifference)/length(testDirections)
    subplot(2,3,i)
    
    % plot values on spherical map
    local_heatmap_plot(testDirections(:,1),testDirections(:,2),spectralDifference((i-1)*length(testDirections)+1:i*(length(testDirections))));
    
    % title for each subplot, where OA denotes Order of Ambisonics, DFE
    % denotes diffuse-field equalisation and PBC denotes predicted binaural
    % colouration. 
    averagePBCvalue = mean(spectralDifference((i-1)*length(testDirections)+1:i*(length(testDirections))));
    switch i
        case 1
            title(strcat('1OA no DFE. Mean PBC=',num2str(averagePBCvalue),' sones'))
        case 2
            title(strcat('3OA no DFE. Mean PBC=',num2str(averagePBCvalue),' sones'))
        case 3
            title(strcat('5OA no DFE. Mean PBC=',num2str(averagePBCvalue),' sones'))
        case 4
            title(strcat('1OA with DFE. Mean PBC=',num2str(averagePBCvalue),' sones'))
        case 5
            title(strcat('3OA with DFE. Mean PBC=',num2str(averagePBCvalue),' sones'))
        case 6
            title(strcat('5OA with DFE. Mean PBC=',num2str(averagePBCvalue),' sones'))
    end
    c2 = colorbar; c2.Label.String = 'PBC (sones)';
    caxis([minValue,maxValue]);
    xlim([0 180]); % as this data is only in a hemisphere, constrain axis limits
end

end

function local_heatmap_plot(az,el,spectralDifference)
% Plot spectral difference of a large spherical set of points on a
% rectangular plot. Need input vectors of azimuth, elevation and the
% spectral difference of the points.

xlin = linspace(min(az),max(az),180*2);
ylin = linspace(min(el),max(el),90*2);
[X,Y] = meshgrid(xlin,ylin);

Z = griddata(az,el,spectralDifference,X,Y,'cubic');

surf(X,Y,Z,'EdgeColor','none')
xlabel('Azimuth (°)'); ylabel('Elevation (°)');
set(gca, 'XDir', 'reverse', 'YTick', -75:75:75, 'XTick', -150:75:150);
xlim([-180 180]); ylim([-90 90]); view ([0 90]);
colormap(flipud(parula)); set(gcf, 'Color', 'w');
axis tight; box on; pbaspect([2 1 1]);
end