function TetraColorSpace
%TETRACOLORSPACE VERSION 1a, 2008

%==========================================================================  
%==========================================================================
%==========================================================================

%Created by Mary Caswell Stoddard and Richard O. Prum.

% The visual systems of birds, many other reptiles, and many fishes include four color-sensitive retinal cone types. 
% As a consequence, their color vision is more complex than human color vision. We have developed a new computational 
% tool that will allow users to model the visual color stimuli for these tetrahedral visual systems.
% 
% TETRACOLORSPACE is a computer program developed for the tetrahedral analysis of colors measured from reflectance spectra 
% or four cone stimulus values, using MATLAB 7 software (MathWorks, Natick, MA).  TETRACOLORSPACE can analyze colors based 
% on ultraviolet or violet cone-type avian visual systems, or use any four cone-sensitivity functions input by the user.  
% TETRACOLORSPACE provides an assortment of quantitative analyses and graphical tools for describing color stimulus variation 
% and diversity.  Details are available in Stoddard and Prum (2008).  
% 
% TETRACOLORSPACE is provided for free at http://www.yale.edu/eeb/prum/. When using the program, please cite the original publication:
% 
% Stoddard, M. C. & Prum, R. O. 2008. Evolution of avian plumage color in a tetrahedral color space: A phylogenetic 
% analysis of new world buntings. American Naturalist, 171, 755-776.


%==========================================================================  
%==========================================================================
%==========================================================================

disp(' ')

disp('Welcome to TetraColorSpace.')
disp(' ')
disp('Created by Mary Caswell Stoddard and Richard O. Prum ')
disp(' ')
disp('Please refer to the User''s Manual for instructions and details of analyses performed here.')
disp('When using the program, please cite the original publication:')
disp(' ')
disp('Stoddard, M. C. & Prum, R. O. 2008. Evolution of avian plumage color in a tetrahedral color space: A phylogenetic') 
disp('analysis of new world buntings. American Naturalist, 171, 755-776.')

disp(' ')
disp(' ')

datapathname = 'C:\';
pathname='C:\';

fileformat= 'xls';

active = 0;

%==========================================================================  
%======================MAIN MENU===========================================
%==========================================================================

while (active == 0)
    qstring= 'TETRACOLORSPACE MAIN MENU: Select the type of data contained in Excel files.';
    choice = menu(qstring, ' 1)  Reflectance spectra.', '2)  Photon catch data (already calculated from digital photographs or reflectance spectra).', '3)  Quit.');

%===========REFLECTANCE SPECTRA============================================    
    if choice==1
        continu=1; 
        LightReply = 0; 
    end
    
%===========PHOTON CATCH DATA==============================================    
    if choice==2
        %Get user's Excel file containing u/v s m l values
        [filename,pathname]=uigetfile(strcat('*.',fileformat),'Choose Excel file containing photon catch data.');
            
        if (filename ~= 0)
            cd(pathname);
            [User, textdata] = xlsread(filename); 
           
            labeltxt = textdata(1,:);
            patnames = textdata(:, 1);
            patnames(1) = [];
            [patnum columns] = size(patnames); %patnum is the number of patches
            Mega = User;
        
            %Convert cone stimulation values to Cartesian coordinates, store in vectors
            %X, Y, Z
            for i = 1:patnum
            X(i) = (1-2*Mega(i,2) - Mega(i,3) - Mega(i,1))/2*sqrt(3/2);
            end
            for i = 1:patnum
            Y(i) = (-1 + 3*Mega(i,3)+Mega(i,1))/(2*sqrt(2));
            end
            for i = 1:patnum
            Z(i) = (Mega(i,1))-1/4;
            end

            %Put X, Y, Z elements into one matrix, M, with dimensions [patnum x 3]
            for i = 1:patnum
            M(i, 1) = X(i);
            M(i, 2) = Y(i);
            M(i, 3) = Z(i); 
            end
              
            continu = 2;
            UVreply = 0;
            
            photoncatch_loaded = 1;
            disp (['The following file has been loaded: ', char(filename)]); 
            disp(' ');
        end
    end
    
%===========QUIT===========================================================
    if choice==3
        clear all;
        close all;
        break;
    end
    
%==========================================================================
%=================SPECTRA MENU=============================================
%==========================================================================
while (continu==1)
    qstring= 'Spectra menu.';
    action = menu(qstring, '1)  Load spectra file.', '2)  Load additional spectra files (OPTIONAL).',  '3)  Load ambient light spectra (OPTIONAL).', '4)  Choose UVS-type vision or VS-type vision for color analyses.', '5)  Process spectra.', '6)  View analyses.', '7)  Return to main menu.');
    
   if (action==1)  
%==================LOAD SPECTRA FILES======================================
        close all;
        
        %Get user's Excel file: should be in format with nanometers in column 1 and
        %color patches in each subsequent column
        [filename,pathname]=uigetfile(strcat('*.',fileformat),'Choose spectra file.');
     
        if (filename ~= 0)
            cd(pathname);
            [User, rawpatnames] = xlsread(filename); 

            spectra_loaded = 1;
            disp (['The following spectra file has been loaded: ', char(filename)]);            
        end
   
    elseif ((action==2)&(spectra_loaded == 1))  
%================LOAD ADDITIONAL SPECTRA FILES (OPTIONAL)==================
    if spectra_loaded == 1;   
            [filename,pathname]=uigetfile(strcat('*.',fileformat),'Choose an additional spectra file.');
        
            if (filename ~= 0)
            cd(pathname);
    
                [User2, rawpatnames2] = xlsread(filename);
                User2(:, 1) = [];       %Removes first column from User2, leaving 401 x (number of patches)
                rawpatnames2(:, 1) = [];%Removes first column from rawpatnames2
                User = cat(2, User, User2);
                rawpatnames = cat(2, rawpatnames, rawpatnames2);
             
                disp (['The following additional spectra file has been loaded: ', char(filename)])
            end
    end
     
    elseif (action==3&(spectra_loaded == 1))     
%==================LOAD LIGHT SPECTRA FILE (OPTIONAL) =====================         
    if spectra_loaded == 1; 
    
        [filename,pathname]=uigetfile(strcat('*.',fileformat),'Choose ambient light spectra file.');
        
        if (filename ~= 0)
            cd(pathname);
            [Light] = xlsread(filename);  
         
           NormalizeLight = Light; 
    
           %Get size of Light matrix
           [rows cols] = size (Light);
    
                for c = 2: cols %Normalizes Light matrix so that sum of Light = 1
           
                sumLight = sum(Light(:, c));
                NormalizeLight(:,c) = Light(:, c)/sumLight;          
                end

            Light = NormalizeLight;

               for r = 1: rows %Puts Light into a 401 x 2 matrix, 300 - 700 nm

               uround = Light(r, 1);
               d = round(uround);
               Light(r, 1) = d;

               end
    
            LightZero = Light; 

               for c = 2: cols
               LightZero (:, c) = NaN;  
               end

            Light = cat(1, Light, LightZero);

                for i = 300:700
                LightMatrix((i-299),:) = nanmean(Light(find(Light(:,1)==i),:));
                end; 

            LightMatrix(:, 1) = [];%Removes first column from meanMatrix, leaving 401 x (number of patches)

            
            LightReply = 2; 
            disp (['The following light spectra file has been loaded: ', char(filename)]);            
        end
    end
    
   elseif (action==4&(spectra_loaded == 1))     
%==================SELECT TYPE OF VISION===================================
if spectra_loaded == 1; 

    qstring = 'Choose UVS type or VS color vision.';
    UVreply = menu(qstring, '1) UVS - Average bird', '2) UVS - Blue tit', '3) UVS - Starling', '4) VS - Average bird', '5) VS - Peafowl', '6) Input your own curves');
    %Mega is the [patnum x 4] numbers-only matrix containing the u/v s m l
    %proportions for all colors

    UserDefinedCurves = []; 
    
    if UVreply == 1
        visiontype = 'UVS Average bird';
        labeltxt = [' ' 'u' 's' 'm' 'l']; 
    end
    if UVreply ==2 
        visiontype = 'UVS Blue tit';
        labeltxt = [' ' 'u' 's' 'm' 'l']; 
    end
    if UVreply == 3
        visiontype = 'UVS Starling'; 
        labeltxt = [' ' 'u' 's' 'm' 'l']; 
    end
    if UVreply == 4
        visiontype = 'VS Average bird';
        labeltxt = [' ' 'v' 's' 'm' 'l'];
    end
    if UVreply == 5
        visiontype = 'VS Peafowl';
        labeltxt = [' ' 'v' 's' 'm' 'l'];
    end
    if UVreply == 6
        visiontype = 'User Defined'; 
        labeltxt = [' ' 'u/v' 's' 'm' 'l']; 
        [filename,pathname]=uigetfile(strcat('*.',fileformat),'Choose file containing spectral sensitivity curves.');
            if (filename ~= 0)
            cd(pathname);
            [UserDefinedCurves] = xlsread(filename); 
            
                    %Normalize user defined curves to sum 1
            
                    NormalizeUserDefinedCurves = UserDefinedCurves;

                    [rows cols] = size (UserDefinedCurves);

                    for c = 2: cols
           
                    sumUserDefinedCurves = sum(UserDefinedCurves(:, c));
                    NormalizeUserDefinedCurves(:,c) = UserDefinedCurves(:, c)/sumUserDefinedCurves;           
                    
                    end
            
            end
    end
                 
    if LightReply == 2
        Mega = LightProcessMatrix(User, LightMatrix, visiontype, UserDefinedCurves);
        disp(['This analysis is performed using light spectra and the vision type: ', char(visiontype)]);
        disp(' ');
        else
        Mega = processMatrix(User, visiontype, UserDefinedCurves);
        disp(['This analysis is performed using vision type: ', char(visiontype)]); 
        disp(' ');
    end
  
    visiontype_selected=4; 
end
    elseif (action==5&(spectra_loaded == 1)&(visiontype_selected == 4))  
%==================PROCESS SPECTRA=========================================     
      if spectra_loaded == 1;  
        
                %Normalize reflectance spectra to sum 1

                UserOriginal = User; %This will remain untouched for brilliance analysis.
                NormalizeU = User;

                [rows cols] = size (User);

                    for c = 2: cols
           
                    sumUser = sum(User(:, c));
                    NormalizeU(:,c) = User(:, c)/sumUser;           
                    
                    end

                User = NormalizeU; %User has now been normalized so that reflectance spectra sum to 1.
                disp('The spectra file(s) has been processed.')
                spectra_normalized = 5; 
    
                %Define patnames, patnum

                rawpatnames(1) = [];
                patnames = rawpatnames; %patnames is a vector containing patch names
                [row patnum] = size(patnames); %patnum is the number of patches

            %================(Brilliance analysis)=========================
            %Checks each cell of NormBril to determine whether normalized brilliance
            %for the patch is > or < 5%.  The NormBril matrix is not printed.
            %Patches with <5% normalized brilliance are placed at the
            %achromatic point.
            
            NormBril = brillianceNoPrint(UserOriginal, patnum, patnames);

            for i=1:patnum
                if NormBril(i) < .05
                    Mega(i, 1) = .25; 
                    Mega(i, 2) = .25; 
                    Mega(i, 3) = .25; 
                    Mega(i, 4) = .25;
                end
            end
       
            %Convert cone stimulation values to Cartesian coordinates, store in vectors
            %X, Y, Z
            for i = 1:patnum
            X(i) = (1-2*Mega(i,2) - Mega(i,3) - Mega(i,1))/2*sqrt(3/2);
            end
            for i = 1:patnum
            Y(i) = (-1 + 3*Mega(i,3)+Mega(i,1))/(2*sqrt(2));
            end
            for i = 1:patnum
            Z(i) = (Mega(i,1))-1/4;
            end
    
            %Put X, Y, Z elements into one matrix, M, with dimensions [patnum x 3]
            for i = 1:patnum
            M(i, 1) = X(i);
            M(i, 2) = Y(i);
            M(i, 3) = Z(i);
            end
      end

   elseif (action==6&(spectra_loaded == 1)&(visiontype_selected == 4)&(spectra_normalized == 5))     
%==================ANALYSES================================================
        if spectra_loaded == 1;
            continu = 2; 
        end
        
        
   elseif (action==7)     
%======================RETURN TO MAIN MENU=================================
    active = 0; 
    break;
   end
end

%==========================================================================
%=================SELECT ANALYSIS MENU=====================================
%==========================================================================

while (continu==2)
qstring = 'Select analysis.';
option = menu(qstring, '1)  Relative stimulation values of the UVS/VS, SWS, MWS, and LWS cones for color patches.', '2)  Tetrahedron plot.', '3)  Robinson projection.', '4)   Hue functions.', '5)  Summary of hue and chroma for all colors.', '6)  Brilliance analysis.', '7)  Span analysis and distance matrix.', '8)  Volume analysis.', '9)  Hue disparity analysis.', '10)  Summary of color measurements.', '11)  Return to main menu.');

%========STIMULATION VALUES================================================
if option==1
    %Display matrix by calling showmatrix function
    disp(' ')
    disp('RELATIVE STIMULATION VALUES')
    disp('Relative stimulation values of the UVS/VS, SWS, MWS, and LWS cones for color patches:')
    showmatrix(patnum, Mega, labeltxt, patnames)
    disp(' ')
    disp(' ')
end

%========TETRAHEDRON PLOT==================================================
if option==2
%Set size and color of color patch points

S = 45; 
C = [0 0 0]; 
col = [.5 .5 .5]; 

%Ask user to submit preferences for tetrahedron display
disp(' ')
disp('TETRAHEDRON PLOT - SEE FIGURE')
reply = menu('Tetrahedron options.','Press 1 to view the tetrahedron in color.', 'Press 2 to view the tetrahedron in color with patches labeled.', 'Press 3 to view the tetrahedron in white.', 'Press 4 to view the tetrahedron in white with patches labeled.',  'Press 5 to view the tetrahedron with no grid lines or labels.','Return.'); 
disp(' ')
disp(' ')

%Plot color patch points and tetrahedron according to user's preferences

if reply ==1
    figure;
    scatter3(X,Y,Z,S,col, '.','filled', 'MarkerEdgeColor', 'k');
    hold on
    tetraplotter(UVreply)
elseif reply==2
    figure;
    scatter3(X,Y,Z,S,col, '.', 'filled', 'MarkerEdgeColor', 'k');
    hold on 
    for i = 1:patnum
        word = patnames(i);
        text(X(i), Y(i), Z(i),[' ' word])
    end
    tetraplotter(UVreply)
elseif reply==3
    figure;
    scatter3(X,Y,Z,S,col, '.','filled', 'MarkerEdgeColor', 'k');
%     TO SEE THE ACHROMATIC POINT
%     originx = 0; originy = 0; originz =0; 
%     scatter3(originx, originy, originz, 30, 'k')
    hold on
    tetraplotterWhite(UVreply); 
elseif reply==4
    figure;
    scatter3(X,Y,Z,S,col, '.', 'filled', 'MarkerEdgeColor', 'k');
    hold on 
    for i = 1:patnum
        word = patnames(i);
        text(X(i), Y(i), Z(i),[' ' word])
    end
    tetraplotterWhite(UVreply); 
elseif reply==5
    figure;
    scatter3(X,Y,Z,S,col, '.','filled', 'MarkerEdgeColor', 'k');
    hold on
    tetraplotterNoGrid; 
end
end

%===========ROBINSON PROJECTION============================================
if option==3

%Robinson projections

disp(' ')
disp('ROBINSON PROJECTION - SEE FIGURE')
reply = menu('Robinson projection options.','Press 1 to view the labeled Robinson projection.','Press 2 to view the unlabeled Robinson projection.', 'Return.');
disp(' ')
disp(' ')

    if reply==1
    LabeledRobinson(X, Y, Z, patnum, patnames, UVreply);
    end
    if reply==2
    BasicRobinson(X, Y, Z, patnum, patnames)
    end
end

%============HUE FUNCTIONS=================================================
if option==4

%Display u(r), s(r), m(r), l(r), which provide, for any r, the increments
%by which u, s, m, and l stimulation can be changed and still preserve hue
disp(' ')
disp('HUE FUNCTIONS')
disp('A set of four functions describes how stimulation of the four color channels varies as a function of')
disp('chroma r for any given hue, theta and phi.')

disp(' ')

disp('Below is the matrix of color patch hues.')

disp(' ')

showmatrixnum(patnum, Mega, labeltxt, patnames);

disp(' ')

reply = menu('Hue functions options.', 'Press 1 to view the hue functions graphed in color.' , 'Press 2 to view the hue functions in black and white.');

disp(' ')

if reply==1
    reply = input('Press the appropriate number to view the u/v(r), s(r), m(r), and l(r) functions and their plots \nfor a specific color patch hue. Then press enter.');
    disp(' ')
    if isempty(reply)
        reply = 1;
    end
    for i = 1:patnum
        if reply == i
            huefunctions (M, reply, patnames, UVreply);  
        end
    end
elseif reply==2
      reply = input('Press the appropriate number to view the u/v(r), s(r), m(r), and l(r) functions and their plots \nfor a specific color patch hue. Then press enter.');
    disp(' ')
    if isempty(reply)
        reply = 1;
    end
    for i = 1:patnum
        if reply == i
            huefunctionsBlackLines (M, reply, patnames, UVreply);  
        end
    end
    disp(' ')
    disp(' ')
end
end

%=============SUMMARY OF HUE AND CHROMA FOR ALL COLORS=====================
if option==5
disp(' ')
disp('SUMMARY OF HUE AND CHROMA FOR ALL COLORS')
disp ('These are the spherical coordinates of all color patches along with the maximum and % achieved chromaticities of all hues.');
disp(' ')

avchroma = rmax(X, Y, Z, patnum, patnames);
disp(' ')
disp(' ')
end

%==============BRILLIANCE ANALYSIS=========================================
if option==6
disp(' ')
disp('BRILLIANCE')
if choice == 2
    disp('Brilliance analysis cannot be performed on photon catch data.')

else

disp('This is the normalized brilliance, wavelength of peak reflectance, and peak % reflectance of color patches.');
disp(' ')

[NormBril, avBril] = brilliance(UserOriginal, patnum, patnames);

disp(' ')
disp(' ')
end
end

%===============SPAN ANALYSIS==============================================
if option==7  
disp(' ')
disp('COLOR SPAN AND DISTANCE MATRIX')

%Calculate color span, variance of color span, and display the distance matrix
disp(' ')
reply = menu('Color span options.','Press 1 to view the average color span and span variance.', 'Press 2 to also view the distance matrix.'); 
disp(' ')

if reply==1
    [avcolorspan, varcolorspan, maxcolorspan] = distmatrix(M, patnum, patnames);
elseif reply==2
    [avcolorspan, varcolorspan, maxcolorspan] = distmatrixprint(M, patnum, patnames);
end
disp(' ')
disp(' ')
end

%=============VOLUME ANALYSIS==============================================
if option==8  
disp(' ')
disp('VOLUME')


if patnum <= 3 
    disp('The volume cannot be calculated for three or fewer points.')
    v = 0;
else
    K = convhulln(M);
    [K, v] = convhulln(M);
    E = 'The volume of color space occupied is: ';
    [s] = sprintf(['%s', repmat('%.4e', 1, 1)], E , v); 
    disp(s)

    disp(' ')
    disp(' ')
    
%Ask user to submit preferences for tetrahedron display with volume
    disp('VOLUME PLOT - SEE FIGURE')
    reply = menu('Volume options.','Press 1 to view the convex hull in color.', 'Press 2 to view the convex hull in color with patches labeled.', 'Press 3 to view the convex hull in white.','Press 4 to view the convex hull in white with patches labeled.', 'Return.'); 
  
    disp(' ')
   
     
%Plot convex hull according to user's preferences

S = 45; 
C = [0 0 0]; 
col = [.5 .5 .5]; 
    
    if reply == 1
        figure;
        scatter3(X,Y,Z,S, col, 'o', 'filled', 'MarkerEdgeColor', 'k');
        hold on
        tetraplotterhull (K, M, UVreply); 
    elseif reply == 2
        figure;
        scatter3(X,Y,Z,S, col, 'o', 'filled', 'MarkerEdgeColor', 'k');
        hold on 
        for i = 1:patnum
            word = patnames(i);
            text(X(i), Y(i), Z(i),[' ' word])
        end
        tetraplotterhull (K, M, UVreply);
    elseif reply == 3
        figure;
        scatter3(X,Y,Z,S, col, 'o', 'filled', 'MarkerEdgeColor', 'k');
        hold on
        tetraplotterhullWhite (K, M, UVreply); 
    elseif reply == 4
        figure;
        scatter3(X,Y,Z,S, col, 'o', 'filled', 'MarkerEdgeColor', 'k');
        hold on 
        for i = 1:patnum
            word = patnames(i);
            text(X(i), Y(i), Z(i),[' ' word])
        end
        tetraplotterhullWhite (K, M, UVreply); 
    end
    disp('You may manipulate the color space tetrahedron in the figure window.')
end 
end

%==============HUE DISPARITY ANALYSIS======================================
if option==9 
disp(' ')
disp('HUE DISPARITY')

reply = menu('Hue disparity options.', 'Press 1 to view the hue disparity and variance of hue disparity.' , 'Press 2 to also view the hue disparity matrix.');
disp(' ')

[theta, phi, r] = cart2sph(X, Y, Z);

for i = 1:patnum
Huemat(i, 1) = theta(i);
Huemat(i, 2) = phi(i);
end

if reply==1
   [huedisparity, varhuedisparity, maxhuedisparity] = huedispmatrix(Huemat, patnum, patnames);
elseif reply==2
   [huedisparity, varhuedisparity, maxhuedisparity] = huedispmatrixprint(Huemat, patnum, patnames);
end

disp(' ')
disp(' ')

end

%=============OVERALL COLOR SUMMARY========================================
if option==10  
disp(' ')
disp('SUMMARY OF COLOR MEASUREMENTS')

%=====BRILLIANCE=============================
if choice == 2
    %do nothing
else
    [rows cols] = size (UserOriginal);
        for r = 1: rows

                   uround = UserOriginal(r, 1);
                   d = round(uround);
                   UserOriginal(r, 1) = d;     
        end
               UserZero = UserOriginal;

               for c = 2: cols
                    UserZero (:, c) = NaN;  
               end

            UserOriginal = cat(1, UserOriginal, UserZero); %stacks vertically User and User-size matrix full of Nans for calculating average in next step
        
        for i = 300:700
               brilMatrix((i-299),:) = nanmean(UserOriginal(find(UserOriginal(:,1)==i),:));
        end; 

        brilMatrix = roundn(brilMatrix, -6);

        for i = 1: patnum
           NormBril(i) = sum(brilMatrix(:,i+1))/(401*100); %(i+1) is desired column / column 1 of brilMatrix is 300 - 700
        end
           avBril = mean(NormBril);
end

%=====SPAN=======================================
        for i = 1:patnum
             for j = 1:patnum
                Mdist(i, j)= sqrt(sum((M(j,:)-M(i,:)).^2));
             end
         end

        upperMdist = triu(Mdist);
        upperMdist = roundn(upperMdist, -6);
        nonZeroUpperMdist = nonzeros(upperMdist);
        avcolorspan = mean2(nonZeroUpperMdist);
        varcolorspan = std2(nonZeroUpperMdist) * std2(nonZeroUpperMdist);
        maxcolorspan = max(nonZeroUpperMdist);

%======CHROMA=======================================
        [theta, phi, r] = cart2sph(X, Y, Z); 
        avchroma = mean(r); 

%=====VOLUME========================================
       
        if patnum <= 3 
            v = 0;
        else
            K = convhulln(M);
            [K, v] = convhulln(M);
        end

%=====HUE DISPARITY==================================
[theta, phi, r] = cart2sph(X, Y, Z);

        for i = 1:patnum
        Huemat(i, 1) = theta(i);
        Huemat(i, 2) = phi(i);
        end

         for i = 1:patnum
             for j = 1:patnum
                Huedisp(i, j)= acos(cos(Huemat(j,2))*cos(Huemat(i,2))*cos(Huemat(j,1)-Huemat(i,1))+sin(Huemat(j,2))*sin(Huemat(i,2)));
             end
         end

        upperHuedisp = triu(Huedisp);
        upperHuedisp = roundn(upperHuedisp, -6);
        nonZeroUpperHuedisp = nonzeros(upperHuedisp);
        huedisparity = mean2(nonZeroUpperHuedisp);
        varhuedisparity = std2(nonZeroUpperHuedisp) * std2(nonZeroUpperHuedisp);
        maxhuedisparity = max(nonZeroUpperHuedisp);

%=====SUMMARY OUTPUT==================================
if choice == 1 
    
[s] = sprintf(['%s\t\t', '%s\t', '%s\t\t', '%s\t\t\t', '%s\t\t', '%s\t', '%s\t', '%s\t', '%s\t'], 'Color span', 'Span variance', 'Max span', 'Volume', 'Hue disp', 'Hue disp var', 'Max hue disp', 'Average bril', 'Average chroma' );
disp(s)

[s] = sprintf([repmat('%.4e\t\t', 1, 9)], avcolorspan , varcolorspan, maxcolorspan, v,  huedisparity, varhuedisparity, maxhuedisparity, avBril, avchroma);
disp(s)

elseif choice == 2
    
[s] = sprintf(['%s\t\t', '%s\t', '%s\t\t', '%s\t\t\t', '%s\t\t', '%s\t', '%s\t', '%s\t'], 'Color span', 'Span variance', 'Max span', 'Volume', 'Hue disp', 'Hue disp var', 'Max hue disp', 'Average chroma' );
disp(s)

[s] = sprintf([repmat('%.4e\t\t', 1, 8)], avcolorspan , varcolorspan, maxcolorspan, v,  huedisparity, varhuedisparity, maxhuedisparity, avchroma);
disp(s)

    
disp(' ')
disp(' ')
end
end

%==========RETURN TO MAIN MENU=============================================
if option==11  %All but the specified variables are cleared.
continu = 0; 
keep('continu','datapathname', 'pathname', 'active', 'fileformat');
end
end
end

%==========================================================================
%========================QUIT==============================================
%==========================================================================

reply = input('Press enter to close all figures.', 's');

if reply 
    close all;
else 
    close all;
end

disp(' ')

disp('This is the end of the program.  Type TetraColorSpace in the Command Window to begin again.')

%**************************************************************************

%**************************************************************************

%*****************************SUBFUNCTIONS*********************************

%**************************************************************************

%**************************************************************************







%**************************************************************************
%Multiplies the input matrix by the sensitivity curves to generate the u/v s m l
%proportions for each color patch, incorportates ambient light.
function Mega = LightProcessMatrix(User, LightMatrix, visiontype, UserDefinedCurves)

switch visiontype

case('UVS Average bird')
%USML sensitivity curves [401 x 5]
sensitivityCurves = [300	0.00066924	0	0	0
301	0.00088357	0	0	0
302	0.00109922	0	0	0
303	0.00131585	0	0	0
304	0.00153312	0	0	0
305	0.00175074	0	0	0
306	0.00196843	0	0	0
307	0.00218598	0	0	0
308	0.00240318	0	0	0
309	0.00261987	0	0	0
310	0.00283592	0	0	0
311	0.00305125	0	0	0
312	0.0032658	0	0	0
313	0.00347955	0	0	0
314	0.0036925	0	0	0
315	0.0039047	0	0	0
316	0.00411622	0	0	0
317	0.00432715	0	0	0
318	0.00453758	0	0	0
319	0.00474765	0	0	0
320	0.0049575	0	0	0
321	0.00516727	0	0	0
322	0.00537711	0	0	0
323	0.00558717	0	0	0
324	0.0057976	0	0	0
325	0.00600854	0	0	0
326	0.00622011	0	0	0
327	0.00643243	0	0	0
328	0.00664559	0	0	0
329	0.00685967	0	0	0
330	0.00707471	0	0	0
331	0.00729074	0	0	0
332	0.00750776	0	0	0
333	0.00772575	0	0	0
334	0.00794463	0	0	0
335	0.00816433	0	0	0
336	0.00838472	0	0	0
337	0.00860567	0	0	0
338	0.00882698	0	0	0
339	0.00904846	0	0	0
340	0.00926987	0	0	0
341	0.00949096	0	0	0
342	0.00971144	0	0	0
343	0.00993101	0	0	0
344	0.01014935	0	0	0
345	0.0103661	0	0	0
346	0.0105809	0	0	0
347	0.01079337	0	0	0
348	0.01100311	0	0	0
349	0.01120972	0	0	0
350	0.01141277	0	0	0
351	0.01161184	0	0	0
352	0.01180646	0	0	0
353	0.01199621	0	0	0
354	0.01218061	0	0	0
355	0.0123592	0	0	0
356	0.01253152	0	0	0
357	0.01269707	0	0	0
358	0.01285537	0	0	0
359	0.01300595	0	0	0
360	0.01314829	0	0	0
361	0.01328191	0	0	0
362	0.01340629	0	0	0
363	0.01352093	0	0	0
364	0.01362532	0	0	0
365	0.01371894	0	0	0
366	0.01380128	0	0	0
367	0.0138718	0	0	0
368	0.01392999	0	0	0
369	0.01397533	0	0	0
370	0.01400727	0	0	0
371	0.01402531	0	0	0
372	0.01402892	0	0	0
373	0.01401758	0	0	0
374	0.01399077	0	0	0
375	0.013948	0	0	0
376	0.01388878	0	0	0
377	0.01381263	0	0	0
378	0.0137191	0	0	0
379	0.01360776	0	0	0
380	0.01347822	0	0	0
381	0.01333013	0	0	0
382	0.01316317	0	0	0
383	0.0129771	0	0	0
384	0.01277174	0	0	0
385	0.01254698	0	0	0
386	0.01230282	0	0	0
387	0.01203937	0	0	0
388	0.01175685	0	0	0
389	0.01145563	0	0	0
390	0.01113625	0	0	0
391	0.01079941	0	0	0
392	0.010446	0	0	0
393	0.01007714	0	0	0
394	0.00969412	0	0	0
395	0.00929849	0.00000001	0	0
396	0.00889198	0.00000003	0	0
397	0.00847655	0.00000008	0	0
398	0.00805434	0.00000021	0	0
399	0.00762765	0.00000052	0	0
400	0.00719891	0.00000119	0	0
401	0.00677065	0.00000254	0	0
402	0.0063454	0.00000514	0	0
403	0.00592571	0.0000098	0	0
404	0.00551403	0.00001778	0	0
405	0.00511269	0.00003075	0	0
406	0.00472383	0.00005092	0	0
407	0.00434937	0.00008102	0	0
408	0.00399095	0.00012427	0	0
409	0.00364991	0.00018426	0	0
410	0.00332729	0.00026484	0	0
411	0.00302382	0.00036993	0	0
412	0.00273991	0.00050331	0	0
413	0.00247569	0.00066841	0	0
414	0.00223102	0.0008681	0	0
415	0.00200552	0.00110459	0	0
416	0.00179862	0.00137924	0	0
417	0.00160958	0.00169253	0	0
418	0.00143754	0.00204404	0	0
419	0.00128154	0.00243248	0	0
420	0.00114056	0.00285574	0	0
421	0.00101355	0.00331103	0	0
422	0.00089946	0.00379495	0	0
423	0.00079724	0.00430369	0	0
424	0.00070586	0.00483311	0	0
425	0.00062436	0.00537887	0	0
426	0.00055181	0.00593661	0	0
427	0.00048734	0.006502	0	0
428	0.00043013	0.00707085	0	0
429	0.00037944	0.00763918	0	0
430	0.00033458	0.00820328	0	0
431	0.00029493	0.00875973	0	0
432	0.0002599	0.00930546	0	0
433	0.00022898	0.00983773	0	0
434	0.00020172	0.01035414	0	0
435	0.00017769	0.01085262	0	0
436	0.00015652	0.01133143	0	0
437	0.00013787	0.01178911	0	0
438	0.00012146	0.01222448	0	0
439	0.00010701	0.01263663	0	0
440	0.00009429	0.01302485	0	0
441	0.0000831	0.01338865	0	0
442	0.00007326	0.01372771	0	0
443	0.00006459	0.01404186	0	0
444	0.00005697	0.01433105	0	0
445	0.00005026	0.01459537	0	0
446	0.00004435	0.01483497	0	0
447	0.00003915	0.0150501	0	0
448	0.00003458	0.01524106	0	0
449	0.00003054	0.01540822	0	0
450	0.00002699	0.01555196	0	0
451	0.00002386	0.01567271	0	0
452	0.00002111	0.01577092	0	0
453	0.00001867	0.01584708	0	0
454	0.00001653	0.01590165	0	0
455	0.00001464	0.01593514	0	0
456	0.00001297	0.01594805	0	0
457	0.00001149	0.0159409	0	0
458	0.00001019	0.0159142	0	0
459	0.00000904	0.01586848	0	0
460	0.00000802	0.01580426	0	0
461	0.00000712	0.01572209	0	0
462	0.00000632	0.01562251	0	0
463	0.00000562	0.01550607	0	0
464	0.000005	0.01537332	0	0
465	0.00000444	0.01522484	0	0
466	0.00000395	0.0150612	0	0
467	0.00000352	0.014883	0	0
468	0.00000313	0.01469085	0	0
469	0.00000279	0.01448534	0	0
470	0.00000249	0.01426711	0	0
471	0.00000222	0.01403681	0	0
472	0.00000198	0.01379507	0	0
473	0.00000177	0.01354257	0	0
474	0.00000158	0.01327998	0	0
475	0.00000141	0.01300798	0	0
476	0.00000126	0.01272727	0	0
477	0.00000113	0.01243855	0	0
478	0.00000101	0.01214253	0	0
479	0.0000009	0.01183992	0	0
480	0.00000081	0.01153144	0	0
481	0.00000072	0.01121781	0	0
482	0.00000065	0.01089975	0	0
483	0.00000058	0.01057798	0	0
484	0.00000052	0.01025321	0	0
485	0.00000047	0.00992616	0.00000001	0
486	0.00000042	0.00959753	0.00000002	0
487	0.00000038	0.00926802	0.00000006	0
488	0.00000034	0.00893832	0.00000014	0
489	0.00000031	0.00860911	0.00000029	0
490	0.00000027	0.00828105	0.0000006	0
491	0.00000025	0.00795479	0.00000119	0
492	0.00000022	0.00763098	0.00000225	0
493	0.0000002	0.00731022	0.00000409	0
494	0.00000018	0.00699314	0.00000719	0
495	0.00000016	0.00668029	0.00001222	0
496	0.00000015	0.00637225	0.0000201	0
497	0.00000013	0.00606956	0.00003207	0
498	0.00000012	0.00577271	0.00004975	0
499	0.00000011	0.0054822	0.00007514	0
500	0.0000001	0.00519847	0.00011068	0
501	0.00000009	0.00492195	0.00015923	0
502	0.00000008	0.00465302	0.00022402	0
503	0.00000007	0.00439204	0.00030866	0
504	0.00000006	0.00413931	0.00041697	0
505	0.00000006	0.0038951	0.00055294	0
506	0.00000005	0.00365966	0.00072054	0
507	0.00000005	0.00343316	0.00092362	0
508	0.00000004	0.00321576	0.00116572	0
509	0.00000004	0.00300754	0.00144995	0
510	0.00000004	0.00280858	0.00177882	0
511	0.00000003	0.00261889	0.00215416	0
512	0.00000003	0.00243843	0.00257697	0
513	0.00000003	0.00226714	0.00304741	0
514	0.00000002	0.00210491	0.00356473	0
515	0.00000002	0.00195159	0.00412726	0
516	0.00000002	0.00180701	0.00473247	0
517	0.00000002	0.00167095	0.005377	0
518	0.00000002	0.00154319	0.00605674	0
519	0.00000002	0.00142345	0.00676691	0
520	0.00000001	0.00131145	0.00750222	0
521	0.00000001	0.0012069	0.00825694	0
522	0.00000001	0.00110948	0.00902506	0
523	0.00000001	0.00101887	0.00980039	0
524	0.00000001	0.00093473	0.01057668	0
525	0.00000001	0.00085675	0.01134774	0
526	0.00000001	0.00078457	0.01210758	0
527	0.00000001	0.00071788	0.01285041	0
528	0.00000001	0.00065634	0.01357082	0
529	0.00000001	0.00059963	0.01426375	0
530	0.00000001	0.00054745	0.01492463	0
531	0	0.0004995	0.01554935	0
532	0	0.00045547	0.01613431	0
533	0	0.00041511	0.01667643	0
534	0	0.00037813	0.01717318	0
535	0	0.00034429	0.01762253	0
536	0	0.00031335	0.01802294	0
537	0	0.00028508	0.01837338	0
538	0	0.00025928	0.01867328	0
539	0	0.00023574	0.01892249	0
540	0	0.00021428	0.01912126	0
541	0	0.00019474	0.01927019	0
542	0	0.00017694	0.01937023	0
543	0	0.00016074	0.01942259	0
544	0	0.00014601	0.01942877	0.00000001
545	0	0.00013261	0.01939046	0.00000002
546	0	0.00012044	0.01930955	0.00000004
547	0	0.00010937	0.01918807	0.00000009
548	0	0.00009932	0.01902818	0.00000019
549	0	0.00009019	0.01883214	0.00000038
550	0	0.0000819	0.01860226	0.00000072
551	0	0.00007438	0.01834092	0.00000133
552	0	0.00006755	0.0180505	0.00000237
553	0	0.00006135	0.0177334	0.00000409
554	0	0.00005573	0.017392	0.00000684
555	0	0.00005062	0.01702865	0.0000111
556	0	0.00004599	0.01664567	0.00001755
557	0	0.00004179	0.01624532	0.00002703
558	0	0.00003797	0.01582979	0.00004062
559	0	0.00003451	0.01540123	0.00005965
560	0	0.00003137	0.01496169	0.0000857
561	0	0.00002853	0.01451315	0.00012061
562	0	0.00002594	0.01405751	0.00016648
563	0	0.00002359	0.01359658	0.00022559
564	0	0.00002146	0.01313209	0.00030042
565	0	0.00001953	0.01266566	0.00039355
566	0	0.00001777	0.01219884	0.00050761
567	0	0.00001618	0.01173309	0.00064517
568	0	0.00001473	0.01126976	0.00080871
569	0	0.00001341	0.01081013	0.00100049
570	0	0.00001222	0.01035538	0.00122249
571	0	0.00001113	0.00990661	0.00147632
572	0	0.00001014	0.00946483	0.00176318
573	0	0.00000925	0.00903095	0.00208379
574	0	0.00000843	0.00860581	0.00243834
575	0	0.00000769	0.00819018	0.00282652
576	0	0.00000701	0.00778471	0.00324747
577	0	0.0000064	0.00739	0.00369979
578	0	0.00000584	0.00700657	0.00418162
579	0	0.00000533	0.00663485	0.0046906
580	0	0.00000487	0.00627521	0.00522398
581	0	0.00000445	0.00592793	0.00577864
582	0	0.00000406	0.00559324	0.00635117
583	0	0.00000371	0.00527129	0.00693794
584	0	0.0000034	0.00496218	0.00753515
585	0	0.0000031	0.00466593	0.00813889
586	0	0.00000284	0.00438254	0.00874525
587	0	0.0000026	0.0041119	0.00935031
588	0	0.00000238	0.00385391	0.00995024
589	0	0.00000218	0.00360839	0.01054136
590	0	0.00000199	0.00337511	0.01112013
591	0	0.00000183	0.00315385	0.01168324
592	0	0.00000167	0.0029443	0.01222761
593	0	0.00000153	0.00274615	0.01275041
594	0	0.0000014	0.00255907	0.0132491
595	0	0.00000129	0.0023827	0.01372141
596	0	0.00000118	0.00221666	0.01416538
597	0	0.00000108	0.00206056	0.01457933
598	0	0.00000099	0.00191401	0.01496187
599	0	0.00000091	0.00177659	0.01531191
600	0	0.00000084	0.00164789	0.01562862
601	0	0.00000077	0.00152752	0.01591142
602	0	0.00000071	0.00141505	0.01616
603	0	0.00000065	0.00131008	0.01637427
604	0	0.0000006	0.00121222	0.01655434
605	0	0.00000055	0.00112108	0.01670055
606	0	0.0000005	0.00103627	0.01681338
607	0	0.00000046	0.00095743	0.0168935
608	0	0.00000043	0.00088421	0.01694171
609	0	0.00000039	0.00081625	0.01695892
610	0	0.00000036	0.00075323	0.01694619
611	0	0.00000033	0.00069483	0.01690463
612	0	0.00000031	0.00064076	0.01683545
613	0	0.00000028	0.00059072	0.01673991
614	0	0.00000026	0.00054444	0.01661934
615	0	0.00000024	0.00050166	0.01647507
616	0	0.00000022	0.00046214	0.0163085
617	0	0.0000002	0.00042565	0.01612101
618	0	0.00000019	0.00039197	0.01591401
619	0	0.00000017	0.0003609	0.01568889
620	0	0.00000016	0.00033225	0.01544703
621	0	0.00000015	0.00030583	0.0151898
622	0	0.00000014	0.00028149	0.01491855
623	0	0.00000013	0.00025907	0.0146346
624	0	0.00000012	0.00023842	0.01433923
625	0	0.00000011	0.0002194	0.01403369
626	0	0.0000001	0.0002019	0.0137192
627	0	0.00000009	0.00018579	0.01339693
628	0	0.00000009	0.00017096	0.013068
629	0	0.00000008	0.00015732	0.01273351
630	0	0.00000007	0.00014477	0.01239448
631	0	0.00000007	0.00013323	0.01205192
632	0	0.00000006	0.00012261	0.01170677
633	0	0.00000006	0.00011284	0.01135994
634	0	0.00000005	0.00010386	0.01101229
635	0	0.00000005	0.00009561	0.01066462
636	0	0.00000005	0.00008801	0.01031771
637	0	0.00000004	0.00008103	0.00997228
638	0	0.00000004	0.00007461	0.00962901
639	0	0.00000004	0.0000687	0.00928854
640	0	0.00000003	0.00006327	0.00895147
641	0	0.00000003	0.00005828	0.00861836
642	0	0.00000003	0.00005369	0.00828972
643	0	0.00000003	0.00004946	0.00796604
644	0	0.00000003	0.00004558	0.00764775
645	0	0.00000002	0.000042	0.00733526
646	0	0.00000002	0.00003872	0.00702895
647	0	0.00000002	0.00003569	0.00672913
648	0	0.00000002	0.00003291	0.00643612
649	0	0.00000002	0.00003035	0.00615018
650	0	0.00000002	0.00002799	0.00587154
651	0	0.00000002	0.00002582	0.00560041
652	0	0.00000001	0.00002382	0.00533695
653	0	0.00000001	0.00002198	0.0050813
654	0	0.00000001	0.00002029	0.00483359
655	0	0.00000001	0.00001873	0.00459388
656	0	0.00000001	0.0000173	0.00436224
657	0	0.00000001	0.00001597	0.00413869
658	0	0.00000001	0.00001475	0.00392324
659	0	0.00000001	0.00001363	0.00371587
660	0	0.00000001	0.00001259	0.00351653
661	0	0.00000001	0.00001164	0.00332516
662	0	0.00000001	0.00001076	0.00314168
663	0	0.00000001	0.00000995	0.00296599
664	0	0.00000001	0.0000092	0.00279795
665	0	0.00000001	0.00000851	0.00263743
666	0	0.00000001	0.00000787	0.00248428
667	0	0	0.00000728	0.00233833
668	0	0	0.00000674	0.00219941
669	0	0	0.00000624	0.00206733
670	0	0	0.00000578	0.00194189
671	0	0	0.00000535	0.00182288
672	0	0	0.00000496	0.0017101
673	0	0	0.00000459	0.00160332
674	0	0	0.00000425	0.00150234
675	0	0	0.00000394	0.00140693
676	0	0	0.00000366	0.00131687
677	0	0	0.00000339	0.00123194
678	0	0	0.00000314	0.00115191
679	0	0	0.00000292	0.00107657
680	0	0	0.00000271	0.00100571
681	0	0	0.00000251	0.00093911
682	0	0	0.00000233	0.00087655
683	0	0	0.00000216	0.00081785
684	0	0	0.00000201	0.0007628
685	0	0	0.00000187	0.00071121
686	0	0	0.00000173	0.00066289
687	0	0	0.00000161	0.00061767
688	0	0	0.0000015	0.00057537
689	0	0	0.00000139	0.00053582
690	0	0	0.00000129	0.00049887
691	0	0	0.0000012	0.00046436
692	0	0	0.00000112	0.00043214
693	0	0	0.00000104	0.00040208
694	0	0	0.00000096	0.00036964
695	0	0	0.00000089	0.00034367
696	0	0	0.00000083	0.00031948
697	0	0	0.00000077	0.00029695
698	0	0	0.00000072	0.00027597
699	0	0	0.00000067	0.00025645
700	0	0	0.00000062	0.00023828
];

case('UVS Blue tit')
sensitivityCurves =[300	2.6951E-05	0	0	0
301	3.33529E-05	0	0	0
302	4.31668E-05	0	0	0
303	5.8245E-05	0	0	0
304	8.18108E-05	0	0	0
305	0.000118077	0	0	0
306	0.000173334	0	0	0
307	0.000258939	0	0	0
308	0.000389511	0	0	0
309	0.000577604	0	0	0
310	0.000833806	0	0	0
311	0.00115832	0	0	0
312	0.001532552	0	0	0
313	0.001935476	0	0	0
314	0.002343808	0	0	0
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653	1.29149E-43	6.38774E-13	1.011E-05	0.003831921
654	7.86313E-44	4.89319E-13	8.91299E-06	0.0036337
655	4.78097E-44	3.7419E-13	7.84713E-06	0.003445236
656	2.90362E-44	2.8565E-13	6.89938E-06	0.003261152
657	1.76174E-44	2.1769E-13	6.059E-06	0.003085981
658	1.06761E-44	1.6561E-13	5.31541E-06	0.002916927
659	6.45944E-45	1.25776E-13	4.65677E-06	0.002756435
660	3.9048E-45	9.53677E-14	4.07599E-06	0.002600523
661	2.35737E-45	7.21802E-14	3.56204E-06	0.002453016
662	1.42206E-45	5.45349E-14	3.10993E-06	0.00231034
663	8.5672E-46	4.11357E-14	2.71172E-06	0.002175355
664	5.15715E-46	3.0977E-14	2.36268E-06	0.002045509
665	3.09984E-46	2.3287E-14	2.05496E-06	0.001922967
666	1.8619E-46	1.74771E-14	1.78613E-06	0.001805226
667	1.11695E-46	1.30959E-14	1.55039E-06	0.001694492
668	6.69341E-47	9.79694E-15	1.3449E-06	0.001588975
669	4.00559E-47	7.31636E-15	1.16471E-06	0.001488846
670	2.39503E-47	5.45444E-15	1.00795E-06	0.001393363
671	1.43036E-47	4.05938E-15	8.70734E-07	0.001303404
672	8.5384E-48	3.01597E-15	7.5158E-07	0.001217987
673	5.08931E-48	2.23706E-15	6.47746E-07	0.001137714
674	3.03101E-48	1.65664E-15	5.5787E-07	0.001061551
675	1.80212E-48	1.22463E-15	4.7969E-07	0.000989882
676	1.07108E-48	9.03674E-16	4.12075E-07	0.000921697
677	6.35591E-49	6.64492E-16	3.53456E-07	0.000858003
678	3.76834E-49	4.88755E-16	3.02921E-07	0.000797848
679	2.23161E-49	3.58756E-16	2.59152E-07	0.000741443
680	1.32039E-49	2.62974E-16	2.21588E-07	0.00068828
681	7.80486E-50	1.92375E-16	1.89137E-07	0.000638528
682	4.60701E-50	1.40502E-16	1.61318E-07	0.000591746
683	2.7156E-50	1.02399E-16	1.37364E-07	0.000547927
684	1.59926E-50	7.45212E-17	1.16889E-07	0.000506859
685	9.40939E-51	5.41559E-17	9.92861E-08	0.000468541
686	5.53142E-51	3.92752E-17	8.42756E-08	0.000432649
687	3.2455E-51	2.84333E-17	7.14165E-08	0.000399335
688	1.90329E-51	2.0551E-17	6.04675E-08	0.000368191
689	1.11495E-51	1.48288E-17	5.11158E-08	0.000339162
690	6.52344E-52	1.06821E-17	4.31826E-08	0.000312135
691	3.81156E-52	7.68117E-18	3.64139E-08	0.000287059
692	2.22612E-52	5.51405E-18	3.06869E-08	0.000263771
693	1.29876E-52	3.9515E-18	2.582E-08	0.000242216
694	7.56936E-53	2.8272E-18	2.17023E-08	0.000222148
695	4.40576E-53	2.01921E-18	1.82109E-08	0.000203607
696	2.56156E-53	1.43874E-18	1.52692E-08	0.000186384
697	1.48794E-53	1.02382E-18	1.27856E-08	0.000170561
698	8.63706E-54	7.27411E-19	1.0695E-08	0.000155889
699	5.00648E-54	5.15888E-19	8.93142E-09	0.000142373
700	2.8991E-54	3.65168E-19	7.45376E-09	0.000129872]; 

				
case('UVS Starling')				
sensitivityCurves =[300	6.74644E-06	0	0	0
301	8.31802E-06	0	0	0
302	1.07718E-05	0	0	0
303	1.46004E-05	0	0	0
304	2.07222E-05	0	0	0
305	3.07207E-05	0	0	0
306	4.69256E-05	0	0	0
307	7.36043E-05	0	0	0
308	0.000115578	0	0	0
309	0.000184497	0	0	0
310	0.000293545	0	0	0
311	0.000457033	1.86218E-07	0	0
312	0.000684901	2.31144E-06	0	0
313	0.000978174	6.17787E-06	0	0
314	0.001332844	1.22896E-05	0	0
315	0.001725578	2.08429E-05	0	0
316	0.002129592	3.16856E-05	0	0
317	0.002520301	4.43786E-05	0	0
318	0.002885841	5.84566E-05	0	0
319	0.003215652	7.33532E-05	0	0
320	0.00351568	8.8817E-05	0	0
321	0.003788796	0.000104581	0	0
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384	0.014181119	0.002097503	0	0
385	0.013708946	0.002182241	0	0
386	0.013206312	0.002268904	0	0
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388	0.012147823	0.002452258	0	0
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436	9.90956E-05	0.009898621	0	0
437	8.87205E-05	0.010032117	0	0
438	7.94663E-05	0.010145649	0	0
439	7.11748E-05	0.010264488	0	0
440	6.3763E-05	0.010359149	0	0
441	5.71223E-05	0.010455446	0	0
442	5.11767E-05	0.010531136	0	0
443	4.58496E-05	0.010605188	0	0
444	4.10963E-05	0.010670649	0	0
445	3.68526E-05	0.010736269	0	0
446	3.3037E-05	0.010781568	0	0
447	2.96188E-05	0.010822115	0	0
448	2.6573E-05	0.010858591	0	0
449	2.38393E-05	0.010886724	0	0
450	2.14051E-05	0.010911512	0	0
451	1.92215E-05	0.010930145	0	0
452	1.72679E-05	0.010943682	0	0
453	1.55185E-05	0.010956329	0	0
454	1.39423E-05	0.010951058	0	0
455	1.25244E-05	0.010935164	0	0
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457	1.01123E-05	0.010879319	0	0
458	9.08937E-06	0.010838915	0	0
459	8.16949E-06	0.010789578	0	0
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461	6.60698E-06	0.010674176	0	0
462	5.94566E-06	0.010609791	0	0
463	5.35162E-06	0.010539795	0	0
464	4.81731E-06	0.01045734	0	0
465	4.33786E-06	0.010372224	0	0
466	3.90807E-06	0.010281192	0	0
467	3.52086E-06	0.010182449	0	0
468	3.17248E-06	0.010076959	0	0
469	2.85968E-06	0.009968623	0	0
470	2.57772E-06	0.009849392	0	0
471	2.32382E-06	0.00972162	0	0
472	2.09491E-06	0.009585385	0	0
473	1.88846E-06	0.009441835	0	0
474	1.70299E-06	0.009295205	0	0
475	1.53695E-06	0.009146702	0	0
476	1.38784E-06	0.008995592	0	0
477	1.25298E-06	0.008837369	0	0
478	1.13138E-06	0.008673862	0	0
479	1.02154E-06	0.008503469	0	0
480	9.22398E-07	0.0083277	0	0
481	8.33561E-07	0.008153305	0	0
482	7.53542E-07	0.007978315	0	0
483	6.81186E-07	0.007797845	0	0
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485	5.57413E-07	0.007431158	0	0
486	5.04189E-07	0.007242593	0	0
487	4.56041E-07	0.007049529	0	0
488	4.12703E-07	0.00685814	0	0
489	3.73519E-07	0.006663729	0	0
490	3.38225E-07	0.006472358	0	0
491	3.06295E-07	0.006275929	0	0
492	2.77326E-07	0.006079821	0	0
493	2.51105E-07	0.005881407	0	0
494	2.27423E-07	0.005684586	0	0
495	2.05944E-07	0.005485546	0	0
496	1.86505E-07	0.005287955	0	0
497	1.68957E-07	0.005090817	0	0
498	1.53115E-07	0.004897264	0	0
499	1.38753E-07	0.004704247	0	0
500	1.25774E-07	0.004515502	0	0
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503	9.38172E-08	0.00396494	0	0
504	8.5132E-08	0.003788537	0	0
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508	5.78374E-08	0.003123391	0.002636964	0
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510	4.77097E-08	0.002815707	0.004000906	0
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512	3.94075E-08	0.002526955	0.005347925	0
513	3.58192E-08	0.00238909	0.006011017	0
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517	2.44971E-08	0.001886636	0.008575063	0
518	2.22744E-08	0.001771757	0.00918338	0
519	2.02577E-08	0.001661797	0.009778398	0
520	1.84293E-08	0.001557276	0.010360258	0
521	1.67716E-08	0.00145754	0.010928571	0
522	1.52684E-08	0.00136263	0.011400065	0
523	1.39026E-08	0.001272466	0.011913618	0
524	1.26594E-08	0.001186687	0.012262501	0
525	1.15353E-08	0.001105737	0.012810393	0
526	1.05056E-08	0.001028357	0.01316468	0
527	9.57282E-09	0.000955305	0.013722267	0
528	8.72879E-09	0.000886674	0.014091259	0
529	7.96562E-09	0.000822838	0.014657283	0
530	7.27004E-09	0.00076244	0.015013883	0
531	6.63555E-09	0.000705305	0.015553029	0
532	6.05492E-09	0.000651488	0.015872972	0
533	5.5228E-09	0.000600742	0.016353999	0
534	5.03804E-09	0.000552939	0.016614806	0
535	4.59919E-09	0.000508641	0.016965562	0
536	4.19888E-09	0.000467298	0.017227988	0
537	3.83381E-09	0.000428706	0.017464172	0
538	3.50181E-09	0.000392968	0.017678587	0
539	3.19738E-09	0.000359561	0.017857847	0
540	2.91932E-09	0.000328391	0.018007662	0
541	2.66651E-09	0.000299571	0.018135771	0
542	2.43629E-09	0.000263775	0.018215054	0
543	2.22661E-09	0.000240345	0.018290519	0
544	2.03561E-09	0.000219089	0.018210553	0
545	1.86117E-09	0.000199719	0.01820221	0
546	1.70115E-09	0.000181995	0.018040467	0
547	1.55408E-09	0.000165743	0.017924078	0
548	1.42108E-09	0.000151137	0.017700505	0
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554	8.33485E-10	8.70293E-05	0.016024091	0
555	7.63227E-10	7.9437E-05	0.01569176	0
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557	6.40477E-10	6.62685E-05	0.014941028	0
558	5.87026E-10	6.0556E-05	0.014537326	0
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560	4.93067E-10	5.05399E-05	0.013729144	0
561	4.5202E-10	4.61862E-05	0.013326808	0
562	4.14394E-10	4.22225E-05	0.01289897	0
563	3.79934E-10	3.85823E-05	0.012486165	0
564	3.48389E-10	3.52669E-05	0.012055493	0
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566	2.93015E-10	2.94753E-05	0.011208172	0.001046316
567	2.68769E-10	2.69581E-05	0.010797002	0.001658861
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569	2.2615E-10	2.25384E-05	0.009967484	0.002875643
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573	1.60265E-10	1.5768E-05	0.008376278	0.005252575
574	1.47074E-10	1.44257E-05	0.007994763	0.005830068
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577	1.13923E-10	1.10718E-05	0.006924983	0.007522445
578	1.04658E-10	1.01396E-05	0.006584016	0.008068551
579	9.61985E-11	9.29085E-06	0.006259006	0.008607821
580	8.84237E-11	8.5165E-06	0.005940569	0.00897732
581	8.13251E-11	7.80949E-06	0.005635774	0.009498385
582	7.47863E-11	7.16031E-06	0.00533833	0.009863291
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584	6.3289E-11	6.0235E-06	0.004777021	0.010797985
585	5.82195E-11	5.5241E-06	0.00451089	0.011365847
586	5.35627E-11	5.06718E-06	0.004254551	0.011744157
587	4.92761E-11	4.64802E-06	0.004008783	0.012308081
588	4.53249E-11	4.26283E-06	0.003771277	0.01266892
589	4.17154E-11	3.91101E-06	0.003546942	0.013217092
590	3.83922E-11	3.58883E-06	0.003330704	0.013549329
591	3.533E-11	3.29242E-06	0.00312444	0.014053507
592	3.25174E-11	3.02141E-06	0.002927981	0.01435358
593	2.99404E-11	2.77366E-06	0.002742235	0.014730314
594	2.75653E-11	2.54596E-06	0.002564188	0.0150395
595	2.53935E-11	2.3389E-06	0.002396497	0.015336595
596	2.33994E-11	2.14893E-06	0.002237675	0.015616755
597	2.15632E-11	1.97446E-06	0.002086886	0.015876501
598	1.98705E-11	1.81401E-06	0.001943616	0.016113957
599	1.8319E-11	1.66727E-06	0.001809092	0.016336672
600	1.68831E-11	1.53223E-06	0.001680781	0.016486451
601	1.55634E-11	1.40841E-06	0.001560446	0.016680822
602	1.43564E-11	1.29534E-06	0.001447854	0.016699088
603	1.32389E-11	1.19079E-06	0.001341224	0.016817098
604	1.22088E-11	1.09511E-06	0.001240904	0.016769222
605	1.12614E-11	1.00701E-06	0.001147316	0.016821963
606	1.03814E-11	9.25512E-07	0.001058762	0.016707963
607	9.57518E-12	8.51118E-07	0.000976293	0.016691889
608	8.83359E-12	7.82861E-07	0.000868939	0.01654187
609	8.15088E-12	7.20222E-07	0.000800383	0.016467563
610	7.51985E-12	6.62575E-07	0.000736974	0.016265134
611	6.93723E-12	6.09322E-07	0.00067868	0.016134979
612	6.40173E-12	5.60507E-07	0.000625067	0.015897746
613	5.90786E-12	5.15728E-07	0.000575553	0.01572012
614	5.45256E-12	4.74685E-07	0.00053015	0.015451665
615	5.03386E-12	4.36852E-07	0.000488396	0.015236814
616	4.65108E-12	4.02488E-07	0.000450252	0.01495493
617	4.29757E-12	3.70793E-07	0.000415116	0.01471621
618	3.96995E-12	3.41525E-07	0.0003827	0.014405573
619	3.66992E-12	3.14825E-07	0.000353029	0.014142259
620	3.39263E-12	2.90197E-07	0.000325664	0.013825304
621	3.13737E-12	2.67588E-07	0.00030053	0.013546444
622	2.90203E-12	2.4688E-07	0.000277406	0.013218735
623	2.68429E-12	2.27681E-07	0.000256022	0.012918164
624	2.48303E-12	2.10003E-07	0.000236391	0.012579228
625	2.29713E-12	1.93688E-07	0.000218192	0.012266639
626	2.12474E-12	1.78666E-07	0.000201406	0.011919448
627	1.96394E-12	1.64644E-07	0.000185739	0.011581895
628	1.81657E-12	1.51867E-07	0.000171427	0.011229055
629	1.68058E-12	1.40069E-07	0.000158204	0.010896282
630	1.55454E-12	1.29196E-07	0.000146021	0.010543843
631	1.43786E-12	1.19138E-07	0.000134723	0.010204486
632	1.3299E-12	1.09885E-07	0.000124327	0.00985011
633	1.23043E-12	1.01358E-07	0.000114745	0.009514191
634	1.13834E-12	9.35231E-08	0.000105924	0.009169102
635	1.05323E-12	8.62666E-08	9.77461E-05	0.008838368
636	9.74753E-13	7.9617E-08	9.02578E-05	0.008503133
637	9.0273E-13	7.35265E-08	8.33815E-05	0.008186169
638	8.36425E-13	6.79379E-08	7.70914E-05	0.00786987
639	7.74763E-13	6.27449E-08	7.12435E-05	0.007562783
640	7.18027E-13	5.79971E-08	6.58674E-05	0.007258671
641	6.65432E-13	5.36031E-08	6.08959E-05	0.006965595
642	6.16942E-13	4.95706E-08	5.63377E-05	0.006674893
643	5.72017E-13	4.58262E-08	5.21098E-05	0.006396863
644	5.302E-13	4.23683E-08	4.81868E-05	0.006116835
645	4.91593E-13	3.91718E-08	4.45694E-05	0.005850506
646	4.5575E-13	3.62233E-08	4.12251E-05	0.005584066
647	4.22523E-13	3.34899E-08	3.8129E-05	0.005329129
648	3.91619E-13	3.0954E-08	3.52549E-05	0.005075311
649	3.62998E-13	2.86131E-08	3.25986E-05	0.00483425
650	3.36701E-13	2.64666E-08	3.01633E-05	0.004598657
651	3.12086E-13	2.44642E-08	2.78951E-05	0.004370318
652	2.89411E-13	2.26232E-08	2.58057E-05	0.004148004
653	2.68407E-13	2.09233E-08	2.38735E-05	0.003936516
654	2.4894E-13	1.93531E-08	2.20911E-05	0.003729558
655	2.31004E-13	1.79089E-08	2.04498E-05	0.003534103
656	2.14404E-13	1.65761E-08	1.89372E-05	0.003343795
657	1.99061E-13	1.53485E-08	1.75383E-05	0.003163567
658	1.8491E-13	1.42188E-08	1.62562E-05	0.002990825
659	1.71794E-13	1.31732E-08	1.50659E-05	0.002826586
660	1.5956E-13	1.22015E-08	1.39575E-05	0.002665775
661	1.48178E-13	1.13009E-08	1.29297E-05	0.002513473
662	1.37689E-13	1.04717E-08	1.19851E-05	0.002367125
663	1.27883E-13	9.69955E-09	1.1103E-05	0.002227348
664	1.18774E-13	8.98471E-09	1.02881E-05	0.00209274
665	1.1036E-13	8.32541E-09	9.53338E-06	0.00196621
666	1.02578E-13	7.71823E-09	8.84062E-06	0.001845285
667	9.5338E-14	7.15391E-09	8.19516E-06	0.001730791
668	8.86078E-14	6.63072E-09	7.59951E-06	0.001621418
669	8.23245E-14	6.14348E-09	7.04033E-06	0.001516997
670	7.64817E-14	5.69275E-09	6.52625E-06	0.001417605
671	7.10949E-14	5.27691E-09	6.05E-06	0.001324413
672	6.60309E-14	4.88902E-09	5.60833E-06	0.001235263
673	6.13618E-14	4.53068E-09	5.19768E-06	0.001151689
674	5.70367E-14	4.19997E-09	4.82109E-06	0.001072677
675	5.30417E-14	3.89524E-09	4.47121E-06	0.000998879
676	4.93153E-14	3.61278E-09	4.14841E-06	0.00092876
677	4.58617E-14	3.3499E-09	3.84631E-06	0.000833908
678	4.26322E-14	3.10675E-09	3.56835E-06	0.000773992
679	3.96641E-14	2.88196E-09	3.31008E-06	0.000718688
680	3.68936E-14	2.67352E-09	3.07154E-06	0.000667107
681	3.43373E-14	2.48139E-09	2.85057E-06	0.000619634
682	3.1965E-14	2.30485E-09	2.64873E-06	0.000575907
683	2.97731E-14	2.14129E-09	2.46024E-06	0.000535383
684	2.77399E-14	1.98947E-09	2.2866E-06	0.000497624
685	2.58398E-14	1.84796E-09	2.12345E-06	0.000462472
686	2.40602E-14	1.71634E-09	1.97285E-06	0.000429647
687	2.24053E-14	1.59413E-09	1.83225E-06	0.000399357
688	2.08698E-14	1.48156E-09	1.70351E-06	0.000371362
689	1.94566E-14	1.37685E-09	1.58277E-06	0.000345242
690	1.8124E-14	1.27903E-09	1.47075E-06	0.000320808
691	1.68823E-14	1.18826E-09	1.3663E-06	0.000298191
692	1.57065E-14	1.10358E-09	1.26936E-06	0.00027706
693	1.4635E-14	1.0246E-09	1.17834E-06	0.000257372
694	1.363E-14	9.52091E-10	1.09542E-06	0.000239199
695	1.27014E-14	8.85032E-10	1.01815E-06	0.000222467
696	1.18475E-14	8.22926E-10	9.47109E-07	0.000206896
697	1.10439E-14	7.64989E-10	8.80415E-07	0.000192443
698	1.02961E-14	7.11282E-10	8.18749E-07	0.000178965
699	9.57814E-15	6.60966E-10	7.60755E-07	0.000166354
700	8.93872E-15	6.14603E-10	7.07667E-07	0.000154679]; 

%VSML sensitivity curves [401 x 5]
case('VS Average bird')
sensitivityCurves =[300	0	0	0	0
301	0	0	0	0
302	0	0	0	0
303	0	0	0	0
304	0	0	0	0
305	0	0	0	0
306	0	0	0	0
307	0	0	0	0
308	0	0	0	0
309	0	0	0	0
310	0	0	0	0
311	0	0	0	0
312	0	0	0	0
313	0	0	0	0
314	0	0	0	0
315	0	0	0	0
316	0.00002333	0	0	0
317	0.00014856	0	0	0
318	0.0002716	0	0	0
319	0.00039242	0	0	0
320	0.00051098	0	0	0
321	0.00062724	0	0	0
322	0.00074121	0	0	0
323	0.00085288	0	0	0
324	0.00096228	0	0	0
325	0.00106943	0	0	0
326	0.00117438	0	0	0
327	0.0012772	0	0	0
328	0.00137797	0	0	0
329	0.00147678	0	0	0
330	0.00157373	0	0	0
331	0.00166897	0	0	0
332	0.00176263	0	0	0
333	0.00185487	0	0	0
334	0.00194585	0	0	0
335	0.00203576	0	0	0
336	0.00212478	0	0	0
337	0.00221312	0	0	0
338	0.00230099	0	0	0
339	0.0023886	0	0	0
340	0.00247618	0	0	0
341	0.00256396	0	0	0
342	0.00265215	0	0	0
343	0.00274099	0	0	0
344	0.0028307	0	0	0
345	0.00292151	0	0	0
346	0.00301363	0	0	0
347	0.00310728	0	0	0
348	0.00320265	0	0	0
349	0.00329994	0	0	0
350	0.00339933	0	0	0
351	0.00350098	0	0	0
352	0.00360505	0	0	0
353	0.00371168	0	0	0
354	0.00382098	0	0	0
355	0.00393306	0	0	0
356	0.004048	0	0	0
357	0.00416586	0	0	0
358	0.00428668	0	0	0
359	0.0044105	0	0	0
360	0.00453732	0	0	0
361	0.00466712	0	0	0
362	0.00479986	0	0	0
363	0.00493549	0	0	0
364	0.00507393	0	0	0
365	0.0052151	0	0	0
366	0.00535888	0	0	0
367	0.00550514	0	0	0
368	0.00565375	0	0	0
369	0.00580454	0	0	0
370	0.00595735	0	0	0
371	0.00611198	0	0	0
372	0.00626826	0	0	0
373	0.00642596	0	0	0
374	0.00658488	0	0	0
375	0.0067448	0	0	0
376	0.00690548	0	0	0
377	0.0070667	0	0	0
378	0.00722822	0	0	0
379	0.0073898	0	0	0
380	0.00755118	0	0	0
381	0.00771214	0	0	0
382	0.00787242	0	0	0
383	0.00803177	0	0	0
384	0.00818996	0	0	0
385	0.00834673	0	0	0
386	0.00850185	0	0	0
387	0.00865507	0	0	0
388	0.00880616	0	0	0
389	0.00895487	0	0	0
390	0.00910099	0	0	0
391	0.00924426	0	0	0
392	0.00938447	0	0	0
393	0.00952139	0	0	0
394	0.00965479	0	0	0
395	0.00978445	0	0	0
396	0.00991016	0	0	0
397	0.01003168	0	0	0
398	0.01014881	0	0	0
399	0.01026133	0	0	0
400	0.01036903	0	0	0
401	0.01047168	0	0	0
402	0.01056909	0	0	0
403	0.01066103	0	0	0
404	0.0107473	0	0	0
405	0.01082768	0	0	0
406	0.01090196	0	0	0
407	0.01096993	0	0	0
408	0.0110314	0	0	0
409	0.01108614	0	0	0
410	0.01113395	0	0	0
411	0.01117462	0	0	0
412	0.01120797	0	0	0
413	0.01123378	0	0	0
414	0.01125187	0	0	0
415	0.01126205	0	0	0
416	0.01126413	0	0	0
417	0.01125794	0	0	0
418	0.01124331	0	0	0
419	0.01122009	0	0	0
420	0.01118812	0	0	0
421	0.01114728	0	0	0
422	0.01109745	0	0	0
423	0.01103851	0	0	0
424	0.01097038	0	0	0
425	0.010893	0	0	0
426	0.01080632	0	0	0
427	0.01071031	0	0	0
428	0.01060497	0.00000001	0	0
429	0.01049031	0.00000002	0	0
430	0.0103664	0.00000007	0	0
431	0.01023331	0.0000002	0	0
432	0.01009114	0.00000055	0	0
433	0.00994003	0.00000137	0	0
434	0.00978015	0.00000317	0	0
435	0.0096117	0.00000684	0	0
436	0.00943489	0.00001386	0	0
437	0.00925001	0.00002644	0	0
438	0.00905735	0.0000478	0	0
439	0.00885722	0.00008223	0	0
440	0.00865001	0.00013515	0	0
441	0.0084361	0.00021307	0	0
442	0.00821592	0.0003233	0	0
443	0.00798993	0.00047366	0	0
444	0.00775863	0.00067199	0	0
445	0.00752252	0.00092566	0	0
446	0.00728217	0.00124107	0	0
447	0.00703815	0.00162315	0	0
448	0.00679107	0.00207507	0	0
449	0.00654154	0.00259793	0	0
450	0.00629023	0.00319072	0	0
451	0.00603779	0.00385031	0	0
452	0.00578491	0.00457163	0	0
453	0.00553228	0.00534791	0	0
454	0.00528061	0.006171	0	0
455	0.0050306	0.00703175	0	0
456	0.00478294	0.00792031	0	0
457	0.00453834	0.00882655	0	0
458	0.00429747	0.00974034	0	0
459	0.00406099	0.01065185	0	0
460	0.00382953	0.01155176	0	0
461	0.00360369	0.01243144	0	0
462	0.00338402	0.0132831	0	0
463	0.00317104	0.01409984	0	0
464	0.00296519	0.0148757	0	0
465	0.0027669	0.0156057	0	0
466	0.00257648	0.01628575	0	0
467	0.00239423	0.01691267	0	0
468	0.00222035	0.01748412	0	0
469	0.002055	0.01799849	0	0
470	0.00189825	0.0184549	0	0
471	0.00175011	0.01885304	0	0
472	0.00161055	0.01919317	0	0
473	0.00147946	0.01947601	0	0
474	0.0013567	0.01970266	0	0
475	0.00124205	0.01987457	0	0
476	0.00113528	0.01999345	0	0
477	0.00103612	0.02006126	0	0
478	0.00094426	0.02008009	0	0
479	0.00085936	0.02005222	0	0
480	0.0007811	0.01997997	0	0
481	0.0007091	0.01986578	0	0
482	0.00064301	0.0197121	0	0
483	0.00058246	0.01952141	0	0
484	0.0005271	0.01929619	0	0
485	0.00047658	0.01903889	0	0
486	0.00043053	0.01875195	0	0
487	0.00038864	0.01843775	0	0
488	0.00035059	0.01809865	0	0
489	0.00031606	0.01773691	0.00000001	0
490	0.00028477	0.01735478	0.00000003	0
491	0.00025645	0.01695439	0.00000009	0
492	0.00023084	0.01653784	0.00000023	0
493	0.00020771	0.01610716	0.00000056	0
494	0.00018684	0.01566426	0.00000124	0
495	0.00016801	0.01521104	0.0000026	0
496	0.00015104	0.01474928	0.00000519	0
497	0.00013576	0.01428071	0.00000984	0
498	0.00012201	0.01380697	0.00001781	0
499	0.00010963	0.01332964	0.00003088	0
500	0.00009851	0.01285022	0.00005146	0
501	0.00008851	0.01237016	0.00008263	0
502	0.00007952	0.0118908	0.00012818	0
503	0.00007144	0.01141345	0.00019259	0
504	0.00006419	0.01093934	0.00028089	0
505	0.00005768	0.01046963	0.00039852	0
506	0.00005183	0.0100054	0.00055111	0
507	0.00004658	0.0095477	0.00074418	0
508	0.00004187	0.00909747	0.00098289	0
509	0.00003764	0.00865563	0.00127175	0
510	0.00003384	0.00822298	0.00161431	0
511	0.00003043	0.0078003	0.00201305	0
512	0.00002737	0.00738827	0.00246909	0
513	0.00002463	0.00698753	0.00298223	0
514	0.00002216	0.00659863	0.00355081	0
515	0.00001994	0.00622205	0.00417186	0
516	0.00001796	0.00585821	0.00484112	0
517	0.00001617	0.00550745	0.00555323	0
518	0.00001456	0.00517005	0.00630192	0
519	0.00001312	0.00484622	0.00708018	0
520	0.00001183	0.00453608	0.00788054	0
521	0.00001066	0.00423971	0.0086952	0
522	0.00000961	0.00395711	0.00951631	0
523	0.00000867	0.00368822	0.01033611	0
524	0.00000782	0.00343292	0.01114711	0
525	0.00000706	0.00319103	0.01194222	0
526	0.00000637	0.00296232	0.01271488	0
527	0.00000576	0.00274652	0.01345911	0
528	0.0000052	0.00254331	0.01416957	0
529	0.0000047	0.00235233	0.01484163	0
530	0.00000425	0.00217318	0.01547136	0
531	0.00000384	0.00200545	0.0160555	0
532	0.00000348	0.00184869	0.01659151	0
533	0.00000314	0.00170245	0.01707745	0
534	0.00000285	0.00156625	0.01751203	0
535	0.00000258	0.0014396	0.01789449	0
536	0.00000233	0.00132203	0.01822462	0
537	0.00000211	0.00121305	0.01850263	0
538	0.00000192	0.00111217	0.01872918	0
539	0.00000174	0.00101893	0.01890526	0
540	0.00000158	0.00093286	0.0190322	0
541	0.00000143	0.0008535	0.01911157	0.00000001
542	0.0000013	0.00078043	0.01914515	0.00000002
543	0.00000118	0.0007132	0.01913493	0.00000004
544	0.00000107	0.00065144	0.01908302	0.00000008
545	0.00000097	0.00059473	0.01899162	0.00000017
546	0.00000088	0.00054273	0.01886303	0.00000033
547	0.0000008	0.00049508	0.01869958	0.00000063
548	0.00000073	0.00045145	0.01850362	0.00000117
549	0.00000066	0.00041153	0.01827753	0.00000209
550	0.0000006	0.00037504	0.01802363	0.0000036
551	0.00000055	0.00034169	0.01774424	0.00000603
552	0.0000005	0.00031124	0.01744162	0.00000981
553	0.00000046	0.00028345	0.01711798	0.00001552
554	0.00000042	0.0002581	0.01677546	0.00002394
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556	0.00000035	0.00021392	0.016042	0.00005295
557	0.00000032	0.00019472	0.01565496	0.00007617
558	0.00000029	0.00017724	0.01525686	0.00010734
559	0.00000026	0.00016132	0.01484944	0.00014834
560	0.00000024	0.00014682	0.01443436	0.00020127
561	0.00000022	0.00013363	0.0140132	0.00026838
562	0.0000002	0.00012163	0.01358743	0.00035204
563	0.00000018	0.00011071	0.01315848	0.00045467
564	0.00000017	0.00010078	0.01272765	0.00057866
565	0.00000015	0.00009174	0.01229621	0.00072633
566	0.00000014	0.00008352	0.01186531	0.00089982
567	0.00000013	0.00007605	0.01143605	0.00110103
568	0.00000012	0.00006925	0.01100944	0.00133154
569	0.00000011	0.00006307	0.01058644	0.00159258
570	0.0000001	0.00005745	0.01016792	0.00188493
571	0.00000009	0.00005233	0.0097547	0.00220896
572	0.00000008	0.00004768	0.00934753	0.00256453
573	0.00000008	0.00004345	0.0089471	0.00295103
574	0.00000007	0.0000396	0.00855405	0.00336737
575	0.00000006	0.0000361	0.00816894	0.00381201
576	0.00000006	0.00003292	0.0077923	0.00428298
577	0.00000005	0.00003002	0.0074246	0.00477792
578	0.00000005	0.00002738	0.00706625	0.00529413
579	0.00000004	0.00002498	0.0067176	0.00582864
580	0.00000004	0.00002279	0.00637898	0.00637823
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582	0.00000003	0.00001899	0.00573283	0.00750905
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584	0.00000003	0.00001584	0.00512937	0.00865856
585	0.00000003	0.00001447	0.00484394	0.00923149
586	0.00000002	0.00001322	0.00456947	0.00979864
587	0.00000002	0.00001209	0.00430594	0.01035673
588	0.00000002	0.00001105	0.00405333	0.01090263
589	0.00000002	0.0000101	0.00381158	0.01143344
590	0.00000002	0.00000924	0.00358057	0.01194645
591	0.00000002	0.00000845	0.00336019	0.01243919
592	0.00000002	0.00000774	0.00315025	0.01290943
593	0.00000001	0.00000708	0.00295058	0.0133552
594	0.00000001	0.00000648	0.00276095	0.01377477
595	0.00000001	0.00000594	0.00258112	0.0141667
596	0.00000001	0.00000544	0.00241085	0.01452977
597	0.00000001	0.00000498	0.00224984	0.014863
598	0.00000001	0.00000457	0.0020978	0.01516568
599	0.00000001	0.00000419	0.00195444	0.01543729
600	0.00000001	0.00000384	0.00181943	0.01567754
601	0.00000001	0.00000352	0.00169246	0.01588634
602	0.00000001	0.00000323	0.00157319	0.01606378
603	0.00000001	0.00000297	0.0014613	0.01621011
604	0.00000001	0.00000272	0.00135645	0.01632576
605	0.00000001	0.0000025	0.00125831	0.01641128
606	0	0.0000023	0.00116656	0.01646734
607	0	0.00000211	0.00108086	0.01649475
608	0	0.00000194	0.00100091	0.01649438
609	0	0.00000178	0.00092638	0.0164672
610	0	0.00000164	0.00085698	0.01641426
611	0	0.00000151	0.00079241	0.01633666
612	0	0.00000138	0.00073239	0.01623553
613	0	0.00000127	0.00067663	0.01611206
614	0	0.00000117	0.00062488	0.01596746
615	0	0.00000108	0.00057689	0.01580295
616	0	0.00000099	0.0005324	0.01561977
617	0	0.00000092	0.0004912	0.01541915
618	0	0.00000084	0.00045307	0.01520232
619	0	0.00000078	0.00041778	0.0149705
620	0	0.00000072	0.00038516	0.01472491
621	0	0.00000066	0.00035501	0.01446671
622	0	0.00000061	0.00032716	0.01419707
623	0	0.00000056	0.00030145	0.01391713
624	0	0.00000052	0.00027771	0.01362797
625	0	0.00000048	0.00025581	0.01333068
626	0	0.00000044	0.00023562	0.01302628
627	0	0.00000041	0.000217	0.01271576
628	0	0.00000038	0.00019983	0.01240009
629	0	0.00000035	0.00018402	0.01208019
630	0	0.00000032	0.00016945	0.01175694
631	0	0.0000003	0.00015603	0.01143117
632	0	0.00000027	0.00014367	0.0111037
633	0	0.00000025	0.00013229	0.01077529
634	0	0.00000023	0.00012181	0.01044666
635	0	0.00000022	0.00011216	0.0101185
636	0	0.0000002	0.00010329	0.00979146
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638	0	0.00000017	0.0000876	0.00914318
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640	0	0.00000015	0.00007431	0.0085063
641	0	0.00000014	0.00006845	0.00819338
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651	0	0.00000006	0.00003031	0.00535754
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653	0	0.00000006	0.00002579	0.00486841
654	0	0.00000005	0.0000238	0.00463472
655	0	0.00000005	0.00002196	0.00440841
656	0	0.00000004	0.00002027	0.00418951
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658	0	0.00000004	0.00001728	0.0037741
659	0	0.00000004	0.00001596	0.00357757
660	0	0.00000003	0.00001474	0.00338847
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662	0	0.00000003	0.00001259	0.0030323
663	0	0.00000003	0.00001163	0.00286508
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665	0	0.00000002	0.00000994	0.00255186
666	0	0.00000002	0.00000919	0.00240562
667	0	0.00000002	0.0000085	0.00226609
668	0	0.00000002	0.00000787	0.00213312
669	0	0.00000002	0.00000728	0.00200656
670	0	0.00000002	0.00000674	0.00188623
671	0	0.00000002	0.00000623	0.00177193
672	0	0.00000001	0.00000577	0.0016635
673	0	0.00000001	0.00000534	0.00156073
674	0	0.00000001	0.00000495	0.00146343
675	0	0.00000001	0.00000459	0.00137141
676	0	0.00000001	0.00000425	0.00128444
677	0	0.00000001	0.00000394	0.00120235
678	0	0.00000001	0.00000365	0.00112492
679	0	0.00000001	0.00000338	0.00105196
680	0	0.00000001	0.00000314	0.00098326
681	0	0.00000001	0.00000291	0.00091863
682	0	0.00000001	0.0000027	0.00085788
683	0	0.00000001	0.00000251	0.00080082
684	0	0.00000001	0.00000232	0.00074726
685	0	0.00000001	0.00000216	0.00069703
686	0	0.00000001	0.000002	0.00064994
687	0	0.00000001	0.00000186	0.00060584
688	0	0	0.00000173	0.00056456
689	0	0	0.00000161	0.00052594
690	0	0	0.00000149	0.00048982
691	0	0	0.00000139	0.00045608
692	0	0	0.00000129	0.00042455
693	0	0	0.0000012	0.00039513
694	0	0	0.00000111	0.00036766
695	0	0	0.00000104	0.00034205
696	0	0	0.00000096	0.00031817
697	0	0	0.0000009	0.0002959
698	0	0	0.00000084	0.00027516
699	0	0	0.00000078	0.00025585
700	0	0	0.00000072	0.00023786];

case ('VS Peafowl')		
sensitivityCurves =[300	1.8163E-08	0	0	0
301	2.41707E-08	0	0	0
302	3.05616E-08	0	0	0
303	4.05171E-08	0	0	0
304	5.64676E-08	0	0	0
305	7.72296E-08	0	0	0
306	1.03364E-07	0	0	0
307	1.42713E-07	0	0	0
308	2.09225E-07	0	0	0
309	3.01919E-07	0	0	0
310	4.32789E-07	0	0	0
311	6.1201E-07	0	0	0
312	8.61385E-07	0	0	0
313	1.22041E-06	0	0	0
314	1.64637E-06	0	0	0
315	2.25323E-06	0	0	0
316	3.05665E-06	0	0	0
317	3.84376E-06	0	0	0
318	4.43718E-06	0	0	0
319	4.90507E-06	0	0	0
320	5.44635E-06	0	0	0
321	6.31115E-06	0	0	0
322	7.68297E-06	0	0	0
323	9.39508E-06	0	0	0
324	1.11521E-05	0	0	0
325	1.25824E-05	0	0	0
326	1.40076E-05	0	0	0
327	1.5684E-05	0	0	0
328	1.87023E-05	0	0	0
329	2.40122E-05	0	0	0
330	3.14382E-05	0	0	0
331	4.10393E-05	0	0	0
332	5.23279E-05	0	0	0
333	6.58948E-05	0	0	0
334	8.31072E-05	0	0	0
335	0.00010683	0	0	0
336	0.000142945	0	0	0
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356	0.002285609	0	0	0
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380	0.005282624	0	0	0
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384	0.005896185	0	0	0
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386	0.006203413	0	0	0
387	0.006354216	0	0	0
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390	0.006799	0	0	0
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475	0.004555913	0.016127077	0	0
476	0.004398353	0.016187113	0	0
477	0.004243607	0.016253368	0	0
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479	0.003944457	0.016249352	0	0
480	0.003800855	0.016183653	0	0
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482	0.003526379	0.016010928	0	0
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509	0.001187363	0.009537349	0.000609041	0
510	0.001139398	0.009261744	0.00167898	0
511	0.00109318	0.008987288	0.002745355	0
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513	0.001006475	0.008447757	0.004862332	0
514	0.000965859	0.008185145	0.005910264	0
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517	0.000852914	0.007417236	0.008982758	0
518	0.000817966	0.007166382	0.009974681	0
519	0.00078454	0.006923239	0.010949862	0
520	0.000752694	0.006688003	0.011877589	0
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528	0.000540174	0.004994116	0.018525913	0
529	0.000518728	0.004812828	0.019189069	0
530	0.000498108	0.004635532	0.019662876	0
531	0.000478127	0.004463039	0.020158572	0
532	0.000458954	0.004295575	0.020478606	0
533	0.00044059	0.0041336	0.02080958	0
534	0.000423071	0.003978262	0.020988359	0
535	0.000406263	0.003827054	0.021165813	0
536	0.000390304	0.00368312	0.021220884	0
537	0.000375524	0.003549183	0.021296897	0
538	0.000361363	0.003418617	0.021262247	0
539	0.00034747	0.003290266	0.021197738	0
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541	0.000321323	0.003047485	0.020877505	0
542	0.000309047	0.00293212	0.020635788	0
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544	0.000286003	0.002714261	0.020069008	0
545	0.00027509	0.002609995	0.019738724	0
546	0.000264706	0.002511097	0.019367583	0
547	0.000254775	0.002416004	0.018985749	0
548	0.000245261	0.002324319	0.018570215	0
549	0.000236169	0.002236828	0.01814789	0
550	0.000227313	0.002151116	0.017686793	0
551	0.000218693	0.002067193	0.017207089	0
552	0.000210529	0.001987907	0.016717473	0
553	0.000202742	0.001911674	0.016225782	0
554	0.000195249	0.001838036	0.015720714	0
555	0.000188086	0.00176759	0.015211094	0
556	0.000181232	0.001700344	0.014693775	0
557	0.000174627	0.001635048	0.014174299	0
558	0.000168284	0.001572295	0.013650969	0
559	0.000162194	0.001512128	0.013128876	0
560	0.000156278	0.001453647	0.01260256	0
561	0.000150631	0.001397659	0.012083338	0
562	0.000145266	0.001344528	0.011571977	0
563	0.000140079	0.001292782	0.011062837	0
564	0.000135061	0.001242726	0.010556498	0
565	0.000130201	0.00119468	0.010055408	0
566	0.00012552	0.001148585	0.009565175	0.000482748
567	0.000121032	0.001104134	0.009084754	0.001343951
568	0.000116713	0.001061474	0.008616001	0.002204381
569	0.000112546	0.001020055	0.0081572	0.003062666
570	0.00010853	0.000980369	0.007711315	0.003917607
571	0.000104686	0.000942086	0.007279854	0.004769096
572	0.000101012	0.000905578	0.006865333	0.005616845
573	9.74648E-05	0.000870297	0.006463869	0.006457946
574	9.40218E-05	0.000836251	0.006075635	0.007289494
575	9.07329E-05	0.00080365	0.005704205	0.008114134
576	8.76064E-05	0.000772694	0.005350894	0.008932855
577	8.45987E-05	0.000742754	0.005011289	0.009660967
578	8.17039E-05	0.000713944	0.004689306	0.010279386
579	7.89287E-05	0.000686317	0.004382149	0.011021189
580	7.6269E-05	0.000659831	0.004092048	0.01169183
581	7.37155E-05	0.000634398	0.003816154	0.012478456
582	7.12539E-05	0.000609891	0.003554939	0.013177439
583	6.88759E-05	0.000586233	0.003306816	0.013982178
584	6.65851E-05	0.00056345	0.00307309	0.014681204
585	6.43941E-05	0.000541644	0.002853795	0.015480888
586	6.22769E-05	0.000520589	0.002648054	0.01616148
587	6.02322E-05	0.000500269	0.002454055	0.016920302
588	5.82649E-05	0.000480722	0.002273015	0.017551205
589	5.63702E-05	0.000461903	0.00210279	0.018188736
590	5.45414E-05	0.00044375	0.001944319	0.018737121
591	5.27563E-05	0.000426082	0.001795384	0.019245219
592	5.1035E-05	0.000409057	0.001657103	0.019718564
593	4.93981E-05	0.000392836	0.00152889	0.020164804
594	4.78187E-05	0.000377197	0.001410038	0.02057316
595	4.62958E-05	0.000362126	0.001299429	0.020942369
596	4.48181E-05	0.000347533	0.001196787	0.021266545
597	4.3386E-05	0.00033342	0.001101248	0.021544819
598	4.20019E-05	0.000319798	0.001013055	0.021687829
599	4.06654E-05	0.000306664	0.00093138	0.021801022
600	3.93661E-05	0.000293935	0.000855826	0.021775675
601	3.81101E-05	0.000281654	0.000785982	0.021750524
602	3.69151E-05	0.000269948	0.000721956	0.021612918
603	3.57492E-05	0.000258577	0.000662607	0.021465001
604	3.46086E-05	0.000247514	0.000607879	0.021200816
605	3.35106E-05	0.000236881	0.000557505	0.020934999
606	3.2454E-05	0.000226665	0.000511348	0.020571585
607	3.14382E-05	0.000216858	0.0004689	0.020208797
608	3.04586E-05	0.000207423	0.000429929	0.019757389
609	2.95163E-05	0.000198364	0.000394157	0.019307214
610	2.86048E-05	0.000189632	0.000361349	0.018784339
611	2.77284E-05	0.000181255	0.000331298	0.018265676
612	2.68787E-05	0.000173172	0.000303709	0.017688796
613	2.60437E-05	0.000165304	0.000278213	0.017103335
614	2.52411E-05	0.000157765	0.000254932	0.016477059
615	2.44697E-05	0.000150541	0.000233573	0.015855196
616	2.3724E-05	0.000143595	0.00021405	0.015205273
617	2.30053E-05	0.000136931	0.000196154	0.014567478
618	2.23087E-05	0.000130516	0.00017974	0.013906426
619	2.16295E-05	0.000124321	0.000164678	0.013259957
620	2.09718E-05	0.000118368	0.000150886	0.012604211
621	2.03429E-05	0.000112694	0.000138306	0.01196998
622	1.97327E-05	0.000107238	0.000126783	0.011333722
623	1.91421E-05	0.000102003	0.000116175	0.010717537
624	1.85758E-05	9.70092E-05	0.000106526	0.010111157
625	1.80299E-05	9.22333E-05	9.76917E-05	0.009529033
626	1.75015E-05	8.76552E-05	8.96083E-05	0.008961305
627	1.69912E-05	8.32753E-05	8.21968E-05	0.008416998
628	1.64943E-05	7.90679E-05	7.54068E-05	0.007887342
629	1.60122E-05	7.50362E-05	6.91733E-05	0.007383815
630	1.55527E-05	7.12136E-05	6.34928E-05	0.006902015
631	1.51033E-05	6.75378E-05	5.82748E-05	0.006443591
632	1.46662E-05	6.40172E-05	5.34879E-05	0.006004583
633	1.42462E-05	6.06691E-05	4.91014E-05	0.005591781
634	1.38365E-05	5.74607E-05	4.50912E-05	0.005198676
635	1.34403E-05	5.44021E-05	4.14032E-05	0.004828965
636	1.30581E-05	5.14921E-05	3.80376E-05	0.004481221
637	1.26863E-05	4.87129E-05	3.49386E-05	0.00415399
638	1.23238E-05	4.6057E-05	3.2094E-05	0.003845133
639	1.19733E-05	4.35314E-05	2.94835E-05	0.003558365
640	1.16346E-05	4.11322E-05	2.71003E-05	0.00328933
641	1.13072E-05	3.88537E-05	2.49153E-05	0.003039378
642	1.09929E-05	3.6698E-05	2.29138E-05	0.002806609
643	1.06843E-05	3.46371E-05	2.10674E-05	0.002589401
644	1.03823E-05	3.26713E-05	1.93731E-05	0.002386941
645	1.00931E-05	3.08175E-05	1.78209E-05	0.002200099
646	9.81198E-06	2.90569E-05	1.64006E-05	0.002026916
647	9.53691E-06	2.73809E-05	1.50865E-05	0.001865718
648	9.27172E-06	2.57977E-05	1.38877E-05	0.00171716
649	9.01665E-06	2.43042E-05	1.27876E-05	0.001580566
650	8.77125E-06	2.28957E-05	1.17805E-05	0.00145413
651	8.53239E-06	2.15608E-05	1.08525E-05	0.001337635
652	8.29987E-06	2.02963E-05	1.00013E-05	0.00122994
653	8.07355E-06	1.90992E-05	9.21702E-06	0.001130626
654	7.85262E-06	1.79651E-05	8.49516E-06	0.001038871
655	7.64169E-06	1.69019E-05	7.83319E-06	0.000954851
656	7.43981E-06	1.59039E-05	7.22881E-06	0.000877769
657	7.24237E-06	1.49587E-05	6.66983E-06	0.000806606
658	7.04913E-06	1.40637E-05	6.15399E-06	0.000741039
659	6.86214E-06	1.32207E-05	5.67975E-06	0.000680868
660	6.68172E-06	1.24281E-05	5.24408E-06	0.000625596
661	6.5047E-06	1.16777E-05	4.84151E-06	0.000574597
662	6.33225E-06	1.09698E-05	4.47037E-06	0.000527752
663	6.16328E-06	1.03006E-05	4.12753E-06	0.000484572
664	5.99697E-06	9.66724E-06	3.81038E-06	0.000444731
665	5.83828E-06	9.07583E-06	3.51997E-06	0.000408428
666	5.68325E-06	8.51811E-06	3.25187E-06	0.000375018
667	5.53225E-06	7.99306E-06	3.00454E-06	0.000344283
668	5.38947E-06	7.50491E-06	2.77861E-06	0.000316358
669	5.2528E-06	7.04866E-06	2.57122E-06	0.000290811
670	5.11983E-06	6.61944E-06	2.37976E-06	0.000267335
671	4.98895E-06	6.21384E-06	2.20231E-06	0.000245693
672	4.86063E-06	5.83135E-06	2.03805E-06	0.000225832
673	4.7363E-06	5.47251E-06	1.88659E-06	0.000207557
674	4.61618E-06	5.13626E-06	1.74703E-06	0.000190803
675	4.50146E-06	4.82263E-06	1.61887E-06	0.00017552
676	4.38883E-06	4.52694E-06	1.50006E-06	0.000161439
677	4.27787E-06	4.2478E-06	1.3898E-06	0.000148448
678	4.16952E-06	3.98534E-06	1.28777E-06	0.00013654
679	4.06274E-06	3.73774E-06	1.19306E-06	0.000125542
680	3.958E-06	3.50462E-06	1.10528E-06	0.000115422
681	3.85605E-06	3.28589E-06	1.02412E-06	0.000106114
682	3.75694E-06	3.08083E-06	9.49113E-07	9.75822E-05
683	3.66142E-06	2.88921E-06	8.79975E-07	8.97444E-05
684	3.57002E-06	2.71067E-06	8.16376E-07	8.26018E-05
685	3.4814E-06	2.54344E-06	7.5759E-07	7.60649E-05
686	3.39512E-06	2.38651E-06	7.03164E-07	7.00506E-05
687	3.3116E-06	2.23965E-06	6.52865E-07	6.45181E-05
688	3.23153E-06	2.10267E-06	6.06508E-07	5.94631E-05
689	3.15267E-06	1.97357E-06	5.63395E-07	5.47926E-05
690	3.07482E-06	1.85183E-06	5.23265E-07	5.04775E-05
691	2.99842E-06	1.73731E-06	4.85982E-07	4.65003E-05
692	2.92371E-06	1.62974E-06	4.5139E-07	4.28458E-05
693	2.85277E-06	1.52987E-06	4.19596E-07	3.94989E-05
694	2.78436E-06	1.43654E-06	3.9021E-07	3.64422E-05
695	2.71616E-06	1.3482E-06	3.62742E-07	3.36017E-05
696	2.6498E-06	1.26538E-06	3.37275E-07	3.09869E-05
697	2.58476E-06	1.18755E-06	3.13603E-07	2.85712E-05
698	2.52094E-06	1.11436E-06	2.91591E-07	2.63521E-05
699	2.45956E-06	1.04607E-06	2.71256E-07	2.43141E-05
700	2.40044E-06	9.823E-07	2.52453E-07	2.24418E-05];

case  ('User Defined')
sensitivityCurves = UserDefinedCurves;
end

%Get size of user matrix
[rows cols] = size (User);

for r = 1: rows
            
           uround = User(r, 1);
           d = round(uround);
           User(r, 1) = d;
           
end

       UserZero = User; 
       
       for c = 2: cols
            UserZero (:, c) = NaN;  
       end

       User = cat(1, User, UserZero);

for i = 300 : 700
    meanMatrix((i-299),:) = nanmean(User(find(User(:,1)==i),:));
end; 

meanMatrix(:, 1) = [];%Removes first column from meanMatrix, leaving 401 x (number of patches)

for i = 300 : 700
    meanMatrix(i-299, :) = meanMatrix(i - 299, :)* LightMatrix (i-299); 
end


%sensitivityCurves 401 x 5
sensitivityCurves = sensitivityCurves.';%Transposes sensitivityCurves to make a 5 X 401

sensitivityCurves(1, :) = [];%Removes the first row(300 - 700 nm) from sensitivityCurves, now 4 x 401

newMatrix = sensitivityCurves * meanMatrix; %[4 x 401] * [401 x numpatches] gives a [4 x numpatches]
newMatrix = newMatrix.';%Transposes to numpatches x 4 matrix

lightUSMLmatrix = sensitivityCurves * LightMatrix; %[4 x 401] x [401 x 1] = [4 x 1]
lightUSMLmatrix = lightUSMLmatrix.'; %Transposes to [1 x 4]

[newrows newcols] = size(newMatrix); 

for n = 1: newrows 
    newMatrix(n, :) = newMatrix(n, :)./lightUSMLmatrix(1, :);
end 

for n = 1:newrows
      rowSum = sum(newMatrix(n, :));
    for c = 1:newcols
      newMatrix(n, c) = newMatrix(n, c) / rowSum;
    end
end

Mega = newMatrix; %Mega contains the patches x 4 matrix containing u/v s m l relative stimulation values
Mega = roundn(Mega, -6);


%*************************************************************************%
%Multiplies the input matrix by the sensitivity curves to generate the u/v s m l
%proportions for each color patch. 

function Mega = processMatrix(User, visiontype, UserDefinedCurves);

switch visiontype

case('UVS Average bird')
%USML sensitivity curves [401 x 5]
sensitivityCurves = [300	0.00066924	0	0	0
301	0.00088357	0	0	0
302	0.00109922	0	0	0
303	0.00131585	0	0	0
304	0.00153312	0	0	0
305	0.00175074	0	0	0
306	0.00196843	0	0	0
307	0.00218598	0	0	0
308	0.00240318	0	0	0
309	0.00261987	0	0	0
310	0.00283592	0	0	0
311	0.00305125	0	0	0
312	0.0032658	0	0	0
313	0.00347955	0	0	0
314	0.0036925	0	0	0
315	0.0039047	0	0	0
316	0.00411622	0	0	0
317	0.00432715	0	0	0
318	0.00453758	0	0	0
319	0.00474765	0	0	0
320	0.0049575	0	0	0
321	0.00516727	0	0	0
322	0.00537711	0	0	0
323	0.00558717	0	0	0
324	0.0057976	0	0	0
325	0.00600854	0	0	0
326	0.00622011	0	0	0
327	0.00643243	0	0	0
328	0.00664559	0	0	0
329	0.00685967	0	0	0
330	0.00707471	0	0	0
331	0.00729074	0	0	0
332	0.00750776	0	0	0
333	0.00772575	0	0	0
334	0.00794463	0	0	0
335	0.00816433	0	0	0
336	0.00838472	0	0	0
337	0.00860567	0	0	0
338	0.00882698	0	0	0
339	0.00904846	0	0	0
340	0.00926987	0	0	0
341	0.00949096	0	0	0
342	0.00971144	0	0	0
343	0.00993101	0	0	0
344	0.01014935	0	0	0
345	0.0103661	0	0	0
346	0.0105809	0	0	0
347	0.01079337	0	0	0
348	0.01100311	0	0	0
349	0.01120972	0	0	0
350	0.01141277	0	0	0
351	0.01161184	0	0	0
352	0.01180646	0	0	0
353	0.01199621	0	0	0
354	0.01218061	0	0	0
355	0.0123592	0	0	0
356	0.01253152	0	0	0
357	0.01269707	0	0	0
358	0.01285537	0	0	0
359	0.01300595	0	0	0
360	0.01314829	0	0	0
361	0.01328191	0	0	0
362	0.01340629	0	0	0
363	0.01352093	0	0	0
364	0.01362532	0	0	0
365	0.01371894	0	0	0
366	0.01380128	0	0	0
367	0.0138718	0	0	0
368	0.01392999	0	0	0
369	0.01397533	0	0	0
370	0.01400727	0	0	0
371	0.01402531	0	0	0
372	0.01402892	0	0	0
373	0.01401758	0	0	0
374	0.01399077	0	0	0
375	0.013948	0	0	0
376	0.01388878	0	0	0
377	0.01381263	0	0	0
378	0.0137191	0	0	0
379	0.01360776	0	0	0
380	0.01347822	0	0	0
381	0.01333013	0	0	0
382	0.01316317	0	0	0
383	0.0129771	0	0	0
384	0.01277174	0	0	0
385	0.01254698	0	0	0
386	0.01230282	0	0	0
387	0.01203937	0	0	0
388	0.01175685	0	0	0
389	0.01145563	0	0	0
390	0.01113625	0	0	0
391	0.01079941	0	0	0
392	0.010446	0	0	0
393	0.01007714	0	0	0
394	0.00969412	0	0	0
395	0.00929849	0.00000001	0	0
396	0.00889198	0.00000003	0	0
397	0.00847655	0.00000008	0	0
398	0.00805434	0.00000021	0	0
399	0.00762765	0.00000052	0	0
400	0.00719891	0.00000119	0	0
401	0.00677065	0.00000254	0	0
402	0.0063454	0.00000514	0	0
403	0.00592571	0.0000098	0	0
404	0.00551403	0.00001778	0	0
405	0.00511269	0.00003075	0	0
406	0.00472383	0.00005092	0	0
407	0.00434937	0.00008102	0	0
408	0.00399095	0.00012427	0	0
409	0.00364991	0.00018426	0	0
410	0.00332729	0.00026484	0	0
411	0.00302382	0.00036993	0	0
412	0.00273991	0.00050331	0	0
413	0.00247569	0.00066841	0	0
414	0.00223102	0.0008681	0	0
415	0.00200552	0.00110459	0	0
416	0.00179862	0.00137924	0	0
417	0.00160958	0.00169253	0	0
418	0.00143754	0.00204404	0	0
419	0.00128154	0.00243248	0	0
420	0.00114056	0.00285574	0	0
421	0.00101355	0.00331103	0	0
422	0.00089946	0.00379495	0	0
423	0.00079724	0.00430369	0	0
424	0.00070586	0.00483311	0	0
425	0.00062436	0.00537887	0	0
426	0.00055181	0.00593661	0	0
427	0.00048734	0.006502	0	0
428	0.00043013	0.00707085	0	0
429	0.00037944	0.00763918	0	0
430	0.00033458	0.00820328	0	0
431	0.00029493	0.00875973	0	0
432	0.0002599	0.00930546	0	0
433	0.00022898	0.00983773	0	0
434	0.00020172	0.01035414	0	0
435	0.00017769	0.01085262	0	0
436	0.00015652	0.01133143	0	0
437	0.00013787	0.01178911	0	0
438	0.00012146	0.01222448	0	0
439	0.00010701	0.01263663	0	0
440	0.00009429	0.01302485	0	0
441	0.0000831	0.01338865	0	0
442	0.00007326	0.01372771	0	0
443	0.00006459	0.01404186	0	0
444	0.00005697	0.01433105	0	0
445	0.00005026	0.01459537	0	0
446	0.00004435	0.01483497	0	0
447	0.00003915	0.0150501	0	0
448	0.00003458	0.01524106	0	0
449	0.00003054	0.01540822	0	0
450	0.00002699	0.01555196	0	0
451	0.00002386	0.01567271	0	0
452	0.00002111	0.01577092	0	0
453	0.00001867	0.01584708	0	0
454	0.00001653	0.01590165	0	0
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case('UVS Blue tit')
sensitivityCurves =[300	2.6951E-05	0	0	0
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478	3.82003E-11	0.009718873	0	0
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515	6.93394E-19	0.002052127	0.008775917	0
516	4.98309E-19	0.001926218	0.009324572	0
517	3.58372E-19	0.001805856	0.009863326	0
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521	9.53968E-20	0.001378373	0.011806958	0
522	6.83872E-20	0.001284625	0.01216103	0
523	4.89747E-20	0.001195815	0.012694793	0
524	3.50304E-20	0.001111684	0.013056338	0
525	2.50149E-20	0.001031964	0.013585239	0
526	1.78332E-20	0.00095668	0.013926924	0
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528	9.03316E-21	0.000819696	0.014760948	0
529	6.41772E-21	0.000757307	0.015220124	0
530	4.55491E-21	0.000698986	0.015526601	0
531	3.22755E-21	0.00064433	0.015869463	0
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533	1.61381E-21	0.000545146	0.016426314	0
534	1.13867E-21	0.000500573	0.016671711	0
535	8.02243E-22	0.00045901	0.01689601	0
536	5.64545E-22	0.000420303	0.017097844	0
537	3.96828E-22	0.000384434	0.017280094	0
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539	1.95264E-22	0.000320301	0.017495435	0
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542	6.67116E-23	0.000241054	0.017407851	0
543	4.65085E-23	0.000218697	0.017368075	0
544	3.23881E-23	0.000198254	0.017215117	0
545	2.25244E-23	0.000179371	0.017107102	0
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548	7.52265E-24	0.000131966	0.016475735	0
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550	3.59734E-24	0.000106827	0.015959153	0
551	2.48153E-24	9.58901E-05	0.015690571	0
552	1.71041E-24	8.59826E-05	0.01536209	0
553	1.17699E-24	7.6969E-05	0.015059415	0
554	8.08805E-25	6.88274E-05	0.014708394	0
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556	3.80523E-25	5.47838E-05	0.014007967	0
557	2.60482E-25	4.87688E-05	0.013656775	0
558	1.78125E-25	4.33724E-05	0.013279429	0
559	1.2162E-25	3.84993E-05	0.012916342	0
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561	5.64703E-26	3.02162E-05	0.012157348	0
562	3.84163E-26	2.67201E-05	0.011769029	0
563	2.60903E-26	2.35859E-05	0.011397052	0
564	1.77057E-26	2.0799E-05	0.01101188	0
565	1.20005E-26	1.83038E-05	0.010639667	0
566	8.1225E-27	1.60934E-05	0.010261601	0.000536214
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569	2.49713E-27	1.08329E-05	0.009158742	0.002500456
570	1.68083E-27	9.46867E-06	0.008796382	0.003148061
571	1.13006E-27	8.2601E-06	0.008444564	0.003790736
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573	5.08723E-28	6.26294E-06	0.007758593	0.005057494
574	3.40708E-28	5.44175E-06	0.007423182	0.005680024
575	2.27848E-28	4.71987E-06	0.007098969	0.006293769
576	1.52188E-28	4.09028E-06	0.006780661	0.006898937
577	1.01536E-28	3.53918E-06	0.006472476	0.007495048
578	6.76715E-29	3.05869E-06	0.006171767	0.008081132
579	4.50574E-29	2.63871E-06	0.005881717	0.008656617
580	2.99642E-29	2.27449E-06	0.005598461	0.009220291
581	1.98999E-29	1.9567E-06	0.005325343	0.009771735
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583	8.74506E-30	1.44272E-06	0.004806922	0.010831105
584	5.78825E-30	1.23639E-06	0.004559892	0.011215926
585	3.82699E-30	1.05764E-06	0.004322911	0.011797485
586	2.52605E-30	9.03607E-07	0.004093605	0.012183777
587	1.66589E-30	7.70791E-07	0.003873817	0.01276364
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589	7.21333E-31	5.58037E-07	0.003458029	0.013652
590	4.73694E-31	4.73712E-07	0.003261583	0.014021877
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593	1.33222E-31	2.87102E-07	0.002720883	0.015146903
594	8.70171E-32	2.42171E-07	0.002555335	0.015465956
595	5.67854E-32	2.03978E-07	0.002398066	0.015765216
596	3.70114E-32	1.71562E-07	0.002247784	0.016044672
597	2.40954E-32	1.44057E-07	0.002105093	0.016300435
598	1.56636E-32	1.20769E-07	0.001969258	0.016533365
599	1.0172E-32	1.01097E-07	0.001840309	0.016745497
600	6.59919E-33	8.44954E-08	0.001717901	0.01684524
601	4.27535E-33	7.05024E-08	0.001602343	0.017016158
602	2.76541E-33	5.8739E-08	0.001492976	0.016998987
603	1.78683E-33	4.88631E-08	0.001389573	0.017090598
604	1.15287E-33	4.05829E-08	0.001292051	0.017014459
605	7.4323E-34	3.36537E-08	0.001200314	0.017039048
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609	1.26763E-34	1.56662E-08	0.000884487	0.01658993
610	8.1206E-35	1.28895E-08	0.00081746	0.016365425
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612	3.32018E-35	8.6844E-09	0.000695915	0.015959899
613	2.11984E-35	7.11191E-09	0.000641152	0.015764879
614	1.35156E-35	5.81536E-09	0.000590258	0.015481352
615	8.60913E-36	4.74824E-09	0.000542867	0.015251636
616	5.47721E-36	3.87043E-09	0.000498726	0.014944112
617	3.47973E-36	3.1493E-09	0.000457632	0.014679498
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619	1.39975E-36	2.07505E-09	0.00038408	0.014060166
620	8.86091E-37	1.68024E-09	0.000351413	0.013719578
621	5.60091E-37	1.35832E-09	0.000321035	0.013414955
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623	2.2311E-37	8.83641E-10	0.000267227	0.012742993
624	1.40553E-37	7.10928E-10	0.000243395	0.01238589
625	8.84504E-38	5.71035E-10	0.000221389	0.0120561
626	5.56046E-38	4.57942E-10	0.000201224	0.011698053
627	3.49053E-38	3.66676E-10	0.000182657	0.011360959
628	2.18928E-38	2.93155E-10	0.000165681	0.011004485
629	1.37151E-38	2.33995E-10	0.000150094	0.010668066
630	8.5806E-39	1.86463E-10	0.000135857	0.010315923
631	5.36191E-39	1.48355E-10	0.000122798	0.009980295
632	3.34822E-39	1.17843E-10	0.000110911	0.009631811
633	2.08791E-39	9.34455E-11	0.000100038	0.009299643
634	1.30033E-39	7.39824E-11	9.017E-05	0.008961187
635	8.09014E-40	5.84789E-11	8.11755E-05	0.008638005
636	5.02697E-40	4.61464E-11	7.29884E-05	0.008309036
637	3.11872E-40	3.63522E-11	6.55501E-05	0.007993381
638	1.93342E-40	2.85906E-11	5.88229E-05	0.007676976
639	1.19671E-40	2.24482E-11	5.27122E-05	0.00737316
640	7.39978E-41	1.75948E-11	4.71834E-05	0.007069378
641	4.57081E-41	1.37691E-11	4.21868E-05	0.006778783
642	2.82065E-41	1.07576E-11	3.76834E-05	0.006489484
643	1.73809E-41	8.39084E-12	3.36218E-05	0.0062138
644	1.06998E-41	6.53417E-12	2.99639E-05	0.005939816
645	6.57895E-42	5.07978E-12	2.6672E-05	0.005678172
646	4.04014E-42	3.94252E-12	2.37159E-05	0.005418187
647	2.47848E-42	3.05474E-12	2.1067E-05	0.005170356
648	1.51933E-42	2.36291E-12	1.86905E-05	0.004925636
649	9.30044E-43	1.82475E-12	1.65625E-05	0.004693541
650	5.68715E-43	1.4071E-12	1.46651E-05	0.004465346
651	3.47357E-43	1.08333E-12	1.29708E-05	0.004248185
652	2.11925E-43	8.32553E-13	1.14593E-05	0.004034783
653	1.29149E-43	6.38774E-13	1.011E-05	0.003831921
654	7.86313E-44	4.89319E-13	8.91299E-06	0.0036337
655	4.78097E-44	3.7419E-13	7.84713E-06	0.003445236
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657	1.76174E-44	2.1769E-13	6.059E-06	0.003085981
658	1.06761E-44	1.6561E-13	5.31541E-06	0.002916927
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661	2.35737E-45	7.21802E-14	3.56204E-06	0.002453016
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665	3.09984E-46	2.3287E-14	2.05496E-06	0.001922967
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668	6.69341E-47	9.79694E-15	1.3449E-06	0.001588975
669	4.00559E-47	7.31636E-15	1.16471E-06	0.001488846
670	2.39503E-47	5.45444E-15	1.00795E-06	0.001393363
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672	8.5384E-48	3.01597E-15	7.5158E-07	0.001217987
673	5.08931E-48	2.23706E-15	6.47746E-07	0.001137714
674	3.03101E-48	1.65664E-15	5.5787E-07	0.001061551
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676	1.07108E-48	9.03674E-16	4.12075E-07	0.000921697
677	6.35591E-49	6.64492E-16	3.53456E-07	0.000858003
678	3.76834E-49	4.88755E-16	3.02921E-07	0.000797848
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680	1.32039E-49	2.62974E-16	2.21588E-07	0.00068828
681	7.80486E-50	1.92375E-16	1.89137E-07	0.000638528
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683	2.7156E-50	1.02399E-16	1.37364E-07	0.000547927
684	1.59926E-50	7.45212E-17	1.16889E-07	0.000506859
685	9.40939E-51	5.41559E-17	9.92861E-08	0.000468541
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687	3.2455E-51	2.84333E-17	7.14165E-08	0.000399335
688	1.90329E-51	2.0551E-17	6.04675E-08	0.000368191
689	1.11495E-51	1.48288E-17	5.11158E-08	0.000339162
690	6.52344E-52	1.06821E-17	4.31826E-08	0.000312135
691	3.81156E-52	7.68117E-18	3.64139E-08	0.000287059
692	2.22612E-52	5.51405E-18	3.06869E-08	0.000263771
693	1.29876E-52	3.9515E-18	2.582E-08	0.000242216
694	7.56936E-53	2.8272E-18	2.17023E-08	0.000222148
695	4.40576E-53	2.01921E-18	1.82109E-08	0.000203607
696	2.56156E-53	1.43874E-18	1.52692E-08	0.000186384
697	1.48794E-53	1.02382E-18	1.27856E-08	0.000170561
698	8.63706E-54	7.27411E-19	1.0695E-08	0.000155889
699	5.00648E-54	5.15888E-19	8.93142E-09	0.000142373
700	2.8991E-54	3.65168E-19	7.45376E-09	0.000129872]; 

				
case('UVS Starling')				
sensitivityCurves =[300	6.74644E-06	0	0	0
301	8.31802E-06	0	0	0
302	1.07718E-05	0	0	0
303	1.46004E-05	0	0	0
304	2.07222E-05	0	0	0
305	3.07207E-05	0	0	0
306	4.69256E-05	0	0	0
307	7.36043E-05	0	0	0
308	0.000115578	0	0	0
309	0.000184497	0	0	0
310	0.000293545	0	0	0
311	0.000457033	1.86218E-07	0	0
312	0.000684901	2.31144E-06	0	0
313	0.000978174	6.17787E-06	0	0
314	0.001332844	1.22896E-05	0	0
315	0.001725578	2.08429E-05	0	0
316	0.002129592	3.16856E-05	0	0
317	0.002520301	4.43786E-05	0	0
318	0.002885841	5.84566E-05	0	0
319	0.003215652	7.33532E-05	0	0
320	0.00351568	8.8817E-05	0	0
321	0.003788796	0.000104581	0	0
322	0.004034243	0.000120309	0	0
323	0.004264013	0.000136086	0	0
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668	8.86078E-14	6.63072E-09	7.59951E-06	0.001621418
669	8.23245E-14	6.14348E-09	7.04033E-06	0.001516997
670	7.64817E-14	5.69275E-09	6.52625E-06	0.001417605
671	7.10949E-14	5.27691E-09	6.05E-06	0.001324413
672	6.60309E-14	4.88902E-09	5.60833E-06	0.001235263
673	6.13618E-14	4.53068E-09	5.19768E-06	0.001151689
674	5.70367E-14	4.19997E-09	4.82109E-06	0.001072677
675	5.30417E-14	3.89524E-09	4.47121E-06	0.000998879
676	4.93153E-14	3.61278E-09	4.14841E-06	0.00092876
677	4.58617E-14	3.3499E-09	3.84631E-06	0.000833908
678	4.26322E-14	3.10675E-09	3.56835E-06	0.000773992
679	3.96641E-14	2.88196E-09	3.31008E-06	0.000718688
680	3.68936E-14	2.67352E-09	3.07154E-06	0.000667107
681	3.43373E-14	2.48139E-09	2.85057E-06	0.000619634
682	3.1965E-14	2.30485E-09	2.64873E-06	0.000575907
683	2.97731E-14	2.14129E-09	2.46024E-06	0.000535383
684	2.77399E-14	1.98947E-09	2.2866E-06	0.000497624
685	2.58398E-14	1.84796E-09	2.12345E-06	0.000462472
686	2.40602E-14	1.71634E-09	1.97285E-06	0.000429647
687	2.24053E-14	1.59413E-09	1.83225E-06	0.000399357
688	2.08698E-14	1.48156E-09	1.70351E-06	0.000371362
689	1.94566E-14	1.37685E-09	1.58277E-06	0.000345242
690	1.8124E-14	1.27903E-09	1.47075E-06	0.000320808
691	1.68823E-14	1.18826E-09	1.3663E-06	0.000298191
692	1.57065E-14	1.10358E-09	1.26936E-06	0.00027706
693	1.4635E-14	1.0246E-09	1.17834E-06	0.000257372
694	1.363E-14	9.52091E-10	1.09542E-06	0.000239199
695	1.27014E-14	8.85032E-10	1.01815E-06	0.000222467
696	1.18475E-14	8.22926E-10	9.47109E-07	0.000206896
697	1.10439E-14	7.64989E-10	8.80415E-07	0.000192443
698	1.02961E-14	7.11282E-10	8.18749E-07	0.000178965
699	9.57814E-15	6.60966E-10	7.60755E-07	0.000166354
700	8.93872E-15	6.14603E-10	7.07667E-07	0.000154679]; 

%VSML sensitivity curves [401 x 5]
case('VS Average bird')
sensitivityCurves =[300	0	0	0	0
301	0	0	0	0
302	0	0	0	0
303	0	0	0	0
304	0	0	0	0
305	0	0	0	0
306	0	0	0	0
307	0	0	0	0
308	0	0	0	0
309	0	0	0	0
310	0	0	0	0
311	0	0	0	0
312	0	0	0	0
313	0	0	0	0
314	0	0	0	0
315	0	0	0	0
316	0.00002333	0	0	0
317	0.00014856	0	0	0
318	0.0002716	0	0	0
319	0.00039242	0	0	0
320	0.00051098	0	0	0
321	0.00062724	0	0	0
322	0.00074121	0	0	0
323	0.00085288	0	0	0
324	0.00096228	0	0	0
325	0.00106943	0	0	0
326	0.00117438	0	0	0
327	0.0012772	0	0	0
328	0.00137797	0	0	0
329	0.00147678	0	0	0
330	0.00157373	0	0	0
331	0.00166897	0	0	0
332	0.00176263	0	0	0
333	0.00185487	0	0	0
334	0.00194585	0	0	0
335	0.00203576	0	0	0
336	0.00212478	0	0	0
337	0.00221312	0	0	0
338	0.00230099	0	0	0
339	0.0023886	0	0	0
340	0.00247618	0	0	0
341	0.00256396	0	0	0
342	0.00265215	0	0	0
343	0.00274099	0	0	0
344	0.0028307	0	0	0
345	0.00292151	0	0	0
346	0.00301363	0	0	0
347	0.00310728	0	0	0
348	0.00320265	0	0	0
349	0.00329994	0	0	0
350	0.00339933	0	0	0
351	0.00350098	0	0	0
352	0.00360505	0	0	0
353	0.00371168	0	0	0
354	0.00382098	0	0	0
355	0.00393306	0	0	0
356	0.004048	0	0	0
357	0.00416586	0	0	0
358	0.00428668	0	0	0
359	0.0044105	0	0	0
360	0.00453732	0	0	0
361	0.00466712	0	0	0
362	0.00479986	0	0	0
363	0.00493549	0	0	0
364	0.00507393	0	0	0
365	0.0052151	0	0	0
366	0.00535888	0	0	0
367	0.00550514	0	0	0
368	0.00565375	0	0	0
369	0.00580454	0	0	0
370	0.00595735	0	0	0
371	0.00611198	0	0	0
372	0.00626826	0	0	0
373	0.00642596	0	0	0
374	0.00658488	0	0	0
375	0.0067448	0	0	0
376	0.00690548	0	0	0
377	0.0070667	0	0	0
378	0.00722822	0	0	0
379	0.0073898	0	0	0
380	0.00755118	0	0	0
381	0.00771214	0	0	0
382	0.00787242	0	0	0
383	0.00803177	0	0	0
384	0.00818996	0	0	0
385	0.00834673	0	0	0
386	0.00850185	0	0	0
387	0.00865507	0	0	0
388	0.00880616	0	0	0
389	0.00895487	0	0	0
390	0.00910099	0	0	0
391	0.00924426	0	0	0
392	0.00938447	0	0	0
393	0.00952139	0	0	0
394	0.00965479	0	0	0
395	0.00978445	0	0	0
396	0.00991016	0	0	0
397	0.01003168	0	0	0
398	0.01014881	0	0	0
399	0.01026133	0	0	0
400	0.01036903	0	0	0
401	0.01047168	0	0	0
402	0.01056909	0	0	0
403	0.01066103	0	0	0
404	0.0107473	0	0	0
405	0.01082768	0	0	0
406	0.01090196	0	0	0
407	0.01096993	0	0	0
408	0.0110314	0	0	0
409	0.01108614	0	0	0
410	0.01113395	0	0	0
411	0.01117462	0	0	0
412	0.01120797	0	0	0
413	0.01123378	0	0	0
414	0.01125187	0	0	0
415	0.01126205	0	0	0
416	0.01126413	0	0	0
417	0.01125794	0	0	0
418	0.01124331	0	0	0
419	0.01122009	0	0	0
420	0.01118812	0	0	0
421	0.01114728	0	0	0
422	0.01109745	0	0	0
423	0.01103851	0	0	0
424	0.01097038	0	0	0
425	0.010893	0	0	0
426	0.01080632	0	0	0
427	0.01071031	0	0	0
428	0.01060497	0.00000001	0	0
429	0.01049031	0.00000002	0	0
430	0.0103664	0.00000007	0	0
431	0.01023331	0.0000002	0	0
432	0.01009114	0.00000055	0	0
433	0.00994003	0.00000137	0	0
434	0.00978015	0.00000317	0	0
435	0.0096117	0.00000684	0	0
436	0.00943489	0.00001386	0	0
437	0.00925001	0.00002644	0	0
438	0.00905735	0.0000478	0	0
439	0.00885722	0.00008223	0	0
440	0.00865001	0.00013515	0	0
441	0.0084361	0.00021307	0	0
442	0.00821592	0.0003233	0	0
443	0.00798993	0.00047366	0	0
444	0.00775863	0.00067199	0	0
445	0.00752252	0.00092566	0	0
446	0.00728217	0.00124107	0	0
447	0.00703815	0.00162315	0	0
448	0.00679107	0.00207507	0	0
449	0.00654154	0.00259793	0	0
450	0.00629023	0.00319072	0	0
451	0.00603779	0.00385031	0	0
452	0.00578491	0.00457163	0	0
453	0.00553228	0.00534791	0	0
454	0.00528061	0.006171	0	0
455	0.0050306	0.00703175	0	0
456	0.00478294	0.00792031	0	0
457	0.00453834	0.00882655	0	0
458	0.00429747	0.00974034	0	0
459	0.00406099	0.01065185	0	0
460	0.00382953	0.01155176	0	0
461	0.00360369	0.01243144	0	0
462	0.00338402	0.0132831	0	0
463	0.00317104	0.01409984	0	0
464	0.00296519	0.0148757	0	0
465	0.0027669	0.0156057	0	0
466	0.00257648	0.01628575	0	0
467	0.00239423	0.01691267	0	0
468	0.00222035	0.01748412	0	0
469	0.002055	0.01799849	0	0
470	0.00189825	0.0184549	0	0
471	0.00175011	0.01885304	0	0
472	0.00161055	0.01919317	0	0
473	0.00147946	0.01947601	0	0
474	0.0013567	0.01970266	0	0
475	0.00124205	0.01987457	0	0
476	0.00113528	0.01999345	0	0
477	0.00103612	0.02006126	0	0
478	0.00094426	0.02008009	0	0
479	0.00085936	0.02005222	0	0
480	0.0007811	0.01997997	0	0
481	0.0007091	0.01986578	0	0
482	0.00064301	0.0197121	0	0
483	0.00058246	0.01952141	0	0
484	0.0005271	0.01929619	0	0
485	0.00047658	0.01903889	0	0
486	0.00043053	0.01875195	0	0
487	0.00038864	0.01843775	0	0
488	0.00035059	0.01809865	0	0
489	0.00031606	0.01773691	0.00000001	0
490	0.00028477	0.01735478	0.00000003	0
491	0.00025645	0.01695439	0.00000009	0
492	0.00023084	0.01653784	0.00000023	0
493	0.00020771	0.01610716	0.00000056	0
494	0.00018684	0.01566426	0.00000124	0
495	0.00016801	0.01521104	0.0000026	0
496	0.00015104	0.01474928	0.00000519	0
497	0.00013576	0.01428071	0.00000984	0
498	0.00012201	0.01380697	0.00001781	0
499	0.00010963	0.01332964	0.00003088	0
500	0.00009851	0.01285022	0.00005146	0
501	0.00008851	0.01237016	0.00008263	0
502	0.00007952	0.0118908	0.00012818	0
503	0.00007144	0.01141345	0.00019259	0
504	0.00006419	0.01093934	0.00028089	0
505	0.00005768	0.01046963	0.00039852	0
506	0.00005183	0.0100054	0.00055111	0
507	0.00004658	0.0095477	0.00074418	0
508	0.00004187	0.00909747	0.00098289	0
509	0.00003764	0.00865563	0.00127175	0
510	0.00003384	0.00822298	0.00161431	0
511	0.00003043	0.0078003	0.00201305	0
512	0.00002737	0.00738827	0.00246909	0
513	0.00002463	0.00698753	0.00298223	0
514	0.00002216	0.00659863	0.00355081	0
515	0.00001994	0.00622205	0.00417186	0
516	0.00001796	0.00585821	0.00484112	0
517	0.00001617	0.00550745	0.00555323	0
518	0.00001456	0.00517005	0.00630192	0
519	0.00001312	0.00484622	0.00708018	0
520	0.00001183	0.00453608	0.00788054	0
521	0.00001066	0.00423971	0.0086952	0
522	0.00000961	0.00395711	0.00951631	0
523	0.00000867	0.00368822	0.01033611	0
524	0.00000782	0.00343292	0.01114711	0
525	0.00000706	0.00319103	0.01194222	0
526	0.00000637	0.00296232	0.01271488	0
527	0.00000576	0.00274652	0.01345911	0
528	0.0000052	0.00254331	0.01416957	0
529	0.0000047	0.00235233	0.01484163	0
530	0.00000425	0.00217318	0.01547136	0
531	0.00000384	0.00200545	0.0160555	0
532	0.00000348	0.00184869	0.01659151	0
533	0.00000314	0.00170245	0.01707745	0
534	0.00000285	0.00156625	0.01751203	0
535	0.00000258	0.0014396	0.01789449	0
536	0.00000233	0.00132203	0.01822462	0
537	0.00000211	0.00121305	0.01850263	0
538	0.00000192	0.00111217	0.01872918	0
539	0.00000174	0.00101893	0.01890526	0
540	0.00000158	0.00093286	0.0190322	0
541	0.00000143	0.0008535	0.01911157	0.00000001
542	0.0000013	0.00078043	0.01914515	0.00000002
543	0.00000118	0.0007132	0.01913493	0.00000004
544	0.00000107	0.00065144	0.01908302	0.00000008
545	0.00000097	0.00059473	0.01899162	0.00000017
546	0.00000088	0.00054273	0.01886303	0.00000033
547	0.0000008	0.00049508	0.01869958	0.00000063
548	0.00000073	0.00045145	0.01850362	0.00000117
549	0.00000066	0.00041153	0.01827753	0.00000209
550	0.0000006	0.00037504	0.01802363	0.0000036
551	0.00000055	0.00034169	0.01774424	0.00000603
552	0.0000005	0.00031124	0.01744162	0.00000981
553	0.00000046	0.00028345	0.01711798	0.00001552
554	0.00000042	0.0002581	0.01677546	0.00002394
555	0.00000038	0.00023498	0.01641613	0.00003601
556	0.00000035	0.00021392	0.016042	0.00005295
557	0.00000032	0.00019472	0.01565496	0.00007617
558	0.00000029	0.00017724	0.01525686	0.00010734
559	0.00000026	0.00016132	0.01484944	0.00014834
560	0.00000024	0.00014682	0.01443436	0.00020127
561	0.00000022	0.00013363	0.0140132	0.00026838
562	0.0000002	0.00012163	0.01358743	0.00035204
563	0.00000018	0.00011071	0.01315848	0.00045467
564	0.00000017	0.00010078	0.01272765	0.00057866
565	0.00000015	0.00009174	0.01229621	0.00072633
566	0.00000014	0.00008352	0.01186531	0.00089982
567	0.00000013	0.00007605	0.01143605	0.00110103
568	0.00000012	0.00006925	0.01100944	0.00133154
569	0.00000011	0.00006307	0.01058644	0.00159258
570	0.0000001	0.00005745	0.01016792	0.00188493
571	0.00000009	0.00005233	0.0097547	0.00220896
572	0.00000008	0.00004768	0.00934753	0.00256453
573	0.00000008	0.00004345	0.0089471	0.00295103
574	0.00000007	0.0000396	0.00855405	0.00336737
575	0.00000006	0.0000361	0.00816894	0.00381201
576	0.00000006	0.00003292	0.0077923	0.00428298
577	0.00000005	0.00003002	0.0074246	0.00477792
578	0.00000005	0.00002738	0.00706625	0.00529413
579	0.00000004	0.00002498	0.0067176	0.00582864
580	0.00000004	0.00002279	0.00637898	0.00637823
581	0.00000004	0.0000208	0.00605065	0.00693953
582	0.00000003	0.00001899	0.00573283	0.00750905
583	0.00000003	0.00001734	0.00542569	0.00808324
584	0.00000003	0.00001584	0.00512937	0.00865856
585	0.00000003	0.00001447	0.00484394	0.00923149
586	0.00000002	0.00001322	0.00456947	0.00979864
587	0.00000002	0.00001209	0.00430594	0.01035673
588	0.00000002	0.00001105	0.00405333	0.01090263
589	0.00000002	0.0000101	0.00381158	0.01143344
590	0.00000002	0.00000924	0.00358057	0.01194645
591	0.00000002	0.00000845	0.00336019	0.01243919
592	0.00000002	0.00000774	0.00315025	0.01290943
593	0.00000001	0.00000708	0.00295058	0.0133552
594	0.00000001	0.00000648	0.00276095	0.01377477
595	0.00000001	0.00000594	0.00258112	0.0141667
596	0.00000001	0.00000544	0.00241085	0.01452977
597	0.00000001	0.00000498	0.00224984	0.014863
598	0.00000001	0.00000457	0.0020978	0.01516568
599	0.00000001	0.00000419	0.00195444	0.01543729
600	0.00000001	0.00000384	0.00181943	0.01567754
601	0.00000001	0.00000352	0.00169246	0.01588634
602	0.00000001	0.00000323	0.00157319	0.01606378
603	0.00000001	0.00000297	0.0014613	0.01621011
604	0.00000001	0.00000272	0.00135645	0.01632576
605	0.00000001	0.0000025	0.00125831	0.01641128
606	0	0.0000023	0.00116656	0.01646734
607	0	0.00000211	0.00108086	0.01649475
608	0	0.00000194	0.00100091	0.01649438
609	0	0.00000178	0.00092638	0.0164672
610	0	0.00000164	0.00085698	0.01641426
611	0	0.00000151	0.00079241	0.01633666
612	0	0.00000138	0.00073239	0.01623553
613	0	0.00000127	0.00067663	0.01611206
614	0	0.00000117	0.00062488	0.01596746
615	0	0.00000108	0.00057689	0.01580295
616	0	0.00000099	0.0005324	0.01561977
617	0	0.00000092	0.0004912	0.01541915
618	0	0.00000084	0.00045307	0.01520232
619	0	0.00000078	0.00041778	0.0149705
620	0	0.00000072	0.00038516	0.01472491
621	0	0.00000066	0.00035501	0.01446671
622	0	0.00000061	0.00032716	0.01419707
623	0	0.00000056	0.00030145	0.01391713
624	0	0.00000052	0.00027771	0.01362797
625	0	0.00000048	0.00025581	0.01333068
626	0	0.00000044	0.00023562	0.01302628
627	0	0.00000041	0.000217	0.01271576
628	0	0.00000038	0.00019983	0.01240009
629	0	0.00000035	0.00018402	0.01208019
630	0	0.00000032	0.00016945	0.01175694
631	0	0.0000003	0.00015603	0.01143117
632	0	0.00000027	0.00014367	0.0111037
633	0	0.00000025	0.00013229	0.01077529
634	0	0.00000023	0.00012181	0.01044666
635	0	0.00000022	0.00011216	0.0101185
636	0	0.0000002	0.00010329	0.00979146
637	0	0.00000019	0.00009512	0.00946617
638	0	0.00000017	0.0000876	0.00914318
639	0	0.00000016	0.00008068	0.00882305
640	0	0.00000015	0.00007431	0.0085063
641	0	0.00000014	0.00006845	0.00819338
642	0	0.00000013	0.00006306	0.00788475
643	0	0.00000012	0.0000581	0.00758081
644	0	0.00000011	0.00005354	0.00728194
645	0	0.0000001	0.00004934	0.0069885
646	0	0.00000009	0.00004547	0.0067008
647	0	0.00000009	0.00004192	0.00641913
648	0	0.00000008	0.00003864	0.00614375
649	0	0.00000007	0.00003563	0.00587489
650	0	0.00000007	0.00003286	0.00561276
651	0	0.00000006	0.00003031	0.00535754
652	0	0.00000006	0.00002796	0.00510938
653	0	0.00000006	0.00002579	0.00486841
654	0	0.00000005	0.0000238	0.00463472
655	0	0.00000005	0.00002196	0.00440841
656	0	0.00000004	0.00002027	0.00418951
657	0	0.00000004	0.00001872	0.00397807
658	0	0.00000004	0.00001728	0.0037741
659	0	0.00000004	0.00001596	0.00357757
660	0	0.00000003	0.00001474	0.00338847
661	0	0.00000003	0.00001362	0.00320673
662	0	0.00000003	0.00001259	0.0030323
663	0	0.00000003	0.00001163	0.00286508
664	0	0.00000003	0.00001075	0.00270498
665	0	0.00000002	0.00000994	0.00255186
666	0	0.00000002	0.00000919	0.00240562
667	0	0.00000002	0.0000085	0.00226609
668	0	0.00000002	0.00000787	0.00213312
669	0	0.00000002	0.00000728	0.00200656
670	0	0.00000002	0.00000674	0.00188623
671	0	0.00000002	0.00000623	0.00177193
672	0	0.00000001	0.00000577	0.0016635
673	0	0.00000001	0.00000534	0.00156073
674	0	0.00000001	0.00000495	0.00146343
675	0	0.00000001	0.00000459	0.00137141
676	0	0.00000001	0.00000425	0.00128444
677	0	0.00000001	0.00000394	0.00120235
678	0	0.00000001	0.00000365	0.00112492
679	0	0.00000001	0.00000338	0.00105196
680	0	0.00000001	0.00000314	0.00098326
681	0	0.00000001	0.00000291	0.00091863
682	0	0.00000001	0.0000027	0.00085788
683	0	0.00000001	0.00000251	0.00080082
684	0	0.00000001	0.00000232	0.00074726
685	0	0.00000001	0.00000216	0.00069703
686	0	0.00000001	0.000002	0.00064994
687	0	0.00000001	0.00000186	0.00060584
688	0	0	0.00000173	0.00056456
689	0	0	0.00000161	0.00052594
690	0	0	0.00000149	0.00048982
691	0	0	0.00000139	0.00045608
692	0	0	0.00000129	0.00042455
693	0	0	0.0000012	0.00039513
694	0	0	0.00000111	0.00036766
695	0	0	0.00000104	0.00034205
696	0	0	0.00000096	0.00031817
697	0	0	0.0000009	0.0002959
698	0	0	0.00000084	0.00027516
699	0	0	0.00000078	0.00025585
700	0	0	0.00000072	0.00023786];

case ('VS Peafowl')		
sensitivityCurves =[300	1.8163E-08	0	0	0
301	2.41707E-08	0	0	0
302	3.05616E-08	0	0	0
303	4.05171E-08	0	0	0
304	5.64676E-08	0	0	0
305	7.72296E-08	0	0	0
306	1.03364E-07	0	0	0
307	1.42713E-07	0	0	0
308	2.09225E-07	0	0	0
309	3.01919E-07	0	0	0
310	4.32789E-07	0	0	0
311	6.1201E-07	0	0	0
312	8.61385E-07	0	0	0
313	1.22041E-06	0	0	0
314	1.64637E-06	0	0	0
315	2.25323E-06	0	0	0
316	3.05665E-06	0	0	0
317	3.84376E-06	0	0	0
318	4.43718E-06	0	0	0
319	4.90507E-06	0	0	0
320	5.44635E-06	0	0	0
321	6.31115E-06	0	0	0
322	7.68297E-06	0	0	0
323	9.39508E-06	0	0	0
324	1.11521E-05	0	0	0
325	1.25824E-05	0	0	0
326	1.40076E-05	0	0	0
327	1.5684E-05	0	0	0
328	1.87023E-05	0	0	0
329	2.40122E-05	0	0	0
330	3.14382E-05	0	0	0
331	4.10393E-05	0	0	0
332	5.23279E-05	0	0	0
333	6.58948E-05	0	0	0
334	8.31072E-05	0	0	0
335	0.00010683	0	0	0
336	0.000142945	0	0	0
337	0.000188095	0	0	0
338	0.000239147	0	0	0
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340	0.000377321	0	0	0
341	0.000462489	0	0	0
342	0.000558393	0	0	0
343	0.000663382	0	0	0
344	0.000777702	0	0	0
345	0.000900818	0	0	0
346	0.001031083	0	0	0
347	0.001165819	0	0	0
348	0.001301796	0	0	0
349	0.001437218	0	0	0
350	0.001570233	0	0	0
351	0.001698063	0	0	0
352	0.0018225	0	0	0
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354	0.002056345	0	0	0
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356	0.002285609	0	0	0
357	0.002391921	0	0	0
358	0.002494593	0	0	0
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360	0.002702509	0	0	0
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362	0.002912475	0	0	0
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364	0.003129377	0	0	0
365	0.003249592	0	0	0
366	0.00336257	0	0	0
367	0.003474425	0	0	0
368	0.003589339	0	0	0
369	0.003708148	0	0	0
370	0.003833532	0	0	0
371	0.003965802	0	0	0
372	0.004103663	0	0	0
373	0.004243383	0	0	0
374	0.004385903	0	0	0
375	0.004529815	0	0	0
376	0.004673131	0	0	0
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378	0.004969822	0	0	0
379	0.005125159	0	0	0
380	0.005282624	0	0	0
381	0.005438414	0	0	0
382	0.005592172	0	0	0
383	0.005742843	0	0	0
384	0.005896185	0	0	0
385	0.006051193	0	0	0
386	0.006203413	0	0	0
387	0.006354216	0	0	0
388	0.006505905	0	0	0
389	0.006656732	0	0	0
390	0.006799	0	0	0
391	0.006937813	0	0	0
392	0.007074409	0	0	0
393	0.00720644	0	0	0
394	0.00734117	0	0	0
395	0.007472234	0	0	0
396	0.00759718	0	0	0
397	0.007720348	0	0	0
398	0.007837289	0	0	0
399	0.007947494	0	0	0
400	0.008055293	0	0	0
401	0.008158428	0	0	0
402	0.008253623	0	0	0
403	0.008346042	0	0	0
404	0.008435616	0	0	0
405	0.008515865	0	0	0
406	0.008593582	0	0	0
407	0.008670412	0	0	0
408	0.008742806	0	0	0
409	0.008812752	0	0	0
410	0.008881192	0	0	0
411	0.008949576	0	0	0
412	0.00901741	0	0	0
413	0.009086141	0	0	0
414	0.009154527	0	0	0
415	0.009222219	0	0	0
416	0.009291723	0	0	0
417	0.009358172	0	0	0
418	0.009420387	0	0	0
419	0.009481976	0	0	0
420	0.00955001	0	0	0
421	0.009620381	0	0	0
422	0.009689423	0	0	0
423	0.009758652	0	0	0
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426	0.009934959	0	0	0
427	0.009986223	0	0	0
428	0.010032306	0	0	0
429	0.010072298	0	0	0
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431	0.010125075	0	0	0
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436	0.010086616	0	0	0
437	0.010052564	0	0	0
438	0.010003534	0	0	0
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440	0.009881516	0	0	0
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445	0.009431511	0	0	0
446	0.009316236	0.000103995	0	0
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448	0.009066952	0.001381148	0	0
449	0.008934968	0.002032811	0	0
450	0.008797047	0.002692011	0	0
451	0.008651592	0.003357082	0	0
452	0.008501062	0.004027578	0	0
453	0.008346538	0.004702946	0	0
454	0.008185091	0.005380225	0	0
455	0.008017235	0.006057734	0	0
456	0.007849204	0.006738641	0	0
457	0.007679368	0.007420918	0	0
458	0.007505132	0.008100636	0	0
459	0.007329074	0.008778647	0	0
460	0.007153451	0.009456366	0	0
461	0.006976505	0.010130327	0	0
462	0.006796924	0.010775554	0	0
463	0.006615257	0.011453724	0	0
464	0.006434579	0.012020702	0	0
465	0.006256767	0.012661278	0	0
466	0.006080621	0.013174341	0	0
467	0.005904545	0.013736065	0	0
468	0.005730001	0.014166902	0	0
469	0.005557381	0.014635098	0	0
470	0.005384348	0.014969165	0	0
471	0.005212697	0.01532886	0	0
472	0.005044166	0.015565485	0	0
473	0.004878346	0.015818251	0	0
474	0.004715923	0.015966718	0	0
475	0.004555913	0.016127077	0	0
476	0.004398353	0.016187113	0	0
477	0.004243607	0.016253368	0	0
478	0.004092313	0.016251622	0	0
479	0.003944457	0.016249352	0	0
480	0.003800855	0.016183653	0	0
481	0.003662055	0.016124105	0	0
482	0.003526379	0.016010928	0	0
483	0.003393759	0.01589542	0	0
484	0.003265313	0.015754013	0	0
485	0.003140442	0.015605253	0	0
486	0.00301952	0.015423394	0	0
487	0.002903252	0.015249552	0	0
488	0.0027913	0.01504936	0	0
489	0.002682431	0.014843438	0	0
490	0.002576683	0.014618127	0	0
491	0.002475066	0.014398753	0	0
492	0.002377581	0.014162731	0	0
493	0.00228385	0.013929474	0	0
494	0.002193731	0.013679034	0	0
495	0.002107069	0.013426358	0	0
496	0.002023751	0.013165331	0	0
497	0.001943602	0.012907478	0	0
498	0.001865756	0.012633029	0	0
499	0.001790772	0.012358269	0	0
500	0.001719363	0.012081342	0	0
501	0.001651186	0.011807563	0	0
502	0.001584959	0.011522122	0	0
503	0.001520663	0.011234937	0	0
504	0.00145916	0.010945444	0	0
505	0.001400108	0.01066536	0	0
506	0.001343304	0.010377852	0	0
507	0.001288936	0.010096626	0	0
508	0.001237032	0.009815193	0	0
509	0.001187363	0.009537349	0.000609041	0
510	0.001139398	0.009261744	0.00167898	0
511	0.00109318	0.008987288	0.002745355	0
512	0.001048848	0.008713068	0.003806756	0
513	0.001006475	0.008447757	0.004862332	0
514	0.000965859	0.008185145	0.005910264	0
515	0.000926733	0.007926529	0.00694754	0
516	0.000889122	0.0076684	0.007972577	0
517	0.000852914	0.007417236	0.008982758	0
518	0.000817966	0.007166382	0.009974681	0
519	0.00078454	0.006923239	0.010949862	0
520	0.000752694	0.006688003	0.011877589	0
521	0.00072232	0.006458377	0.012850164	0
522	0.000693311	0.006235064	0.013720882	0
523	0.000665341	0.006017344	0.014667163	0
524	0.000638506	0.005804379	0.015519074	0
525	0.000612678	0.005595553	0.016376087	0
526	0.000587173	0.005385846	0.017138824	0
527	0.000562835	0.00518408	0.017918029	0
528	0.000540174	0.004994116	0.018525913	0
529	0.000518728	0.004812828	0.019189069	0
530	0.000498108	0.004635532	0.019662876	0
531	0.000478127	0.004463039	0.020158572	0
532	0.000458954	0.004295575	0.020478606	0
533	0.00044059	0.0041336	0.02080958	0
534	0.000423071	0.003978262	0.020988359	0
535	0.000406263	0.003827054	0.021165813	0
536	0.000390304	0.00368312	0.021220884	0
537	0.000375524	0.003549183	0.021296897	0
538	0.000361363	0.003418617	0.021262247	0
539	0.00034747	0.003290266	0.021197738	0
540	0.000334143	0.003166999	0.021046575	0
541	0.000321323	0.003047485	0.020877505	0
542	0.000309047	0.00293212	0.020635788	0
543	0.000297321	0.002821673	0.020383377	0
544	0.000286003	0.002714261	0.020069008	0
545	0.00027509	0.002609995	0.019738724	0
546	0.000264706	0.002511097	0.019367583	0
547	0.000254775	0.002416004	0.018985749	0
548	0.000245261	0.002324319	0.018570215	0
549	0.000236169	0.002236828	0.01814789	0
550	0.000227313	0.002151116	0.017686793	0
551	0.000218693	0.002067193	0.017207089	0
552	0.000210529	0.001987907	0.016717473	0
553	0.000202742	0.001911674	0.016225782	0
554	0.000195249	0.001838036	0.015720714	0
555	0.000188086	0.00176759	0.015211094	0
556	0.000181232	0.001700344	0.014693775	0
557	0.000174627	0.001635048	0.014174299	0
558	0.000168284	0.001572295	0.013650969	0
559	0.000162194	0.001512128	0.013128876	0
560	0.000156278	0.001453647	0.01260256	0
561	0.000150631	0.001397659	0.012083338	0
562	0.000145266	0.001344528	0.011571977	0
563	0.000140079	0.001292782	0.011062837	0
564	0.000135061	0.001242726	0.010556498	0
565	0.000130201	0.00119468	0.010055408	0
566	0.00012552	0.001148585	0.009565175	0.000482748
567	0.000121032	0.001104134	0.009084754	0.001343951
568	0.000116713	0.001061474	0.008616001	0.002204381
569	0.000112546	0.001020055	0.0081572	0.003062666
570	0.00010853	0.000980369	0.007711315	0.003917607
571	0.000104686	0.000942086	0.007279854	0.004769096
572	0.000101012	0.000905578	0.006865333	0.005616845
573	9.74648E-05	0.000870297	0.006463869	0.006457946
574	9.40218E-05	0.000836251	0.006075635	0.007289494
575	9.07329E-05	0.00080365	0.005704205	0.008114134
576	8.76064E-05	0.000772694	0.005350894	0.008932855
577	8.45987E-05	0.000742754	0.005011289	0.009660967
578	8.17039E-05	0.000713944	0.004689306	0.010279386
579	7.89287E-05	0.000686317	0.004382149	0.011021189
580	7.6269E-05	0.000659831	0.004092048	0.01169183
581	7.37155E-05	0.000634398	0.003816154	0.012478456
582	7.12539E-05	0.000609891	0.003554939	0.013177439
583	6.88759E-05	0.000586233	0.003306816	0.013982178
584	6.65851E-05	0.00056345	0.00307309	0.014681204
585	6.43941E-05	0.000541644	0.002853795	0.015480888
586	6.22769E-05	0.000520589	0.002648054	0.01616148
587	6.02322E-05	0.000500269	0.002454055	0.016920302
588	5.82649E-05	0.000480722	0.002273015	0.017551205
589	5.63702E-05	0.000461903	0.00210279	0.018188736
590	5.45414E-05	0.00044375	0.001944319	0.018737121
591	5.27563E-05	0.000426082	0.001795384	0.019245219
592	5.1035E-05	0.000409057	0.001657103	0.019718564
593	4.93981E-05	0.000392836	0.00152889	0.020164804
594	4.78187E-05	0.000377197	0.001410038	0.02057316
595	4.62958E-05	0.000362126	0.001299429	0.020942369
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597	4.3386E-05	0.00033342	0.001101248	0.021544819
598	4.20019E-05	0.000319798	0.001013055	0.021687829
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600	3.93661E-05	0.000293935	0.000855826	0.021775675
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605	3.35106E-05	0.000236881	0.000557505	0.020934999
606	3.2454E-05	0.000226665	0.000511348	0.020571585
607	3.14382E-05	0.000216858	0.0004689	0.020208797
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609	2.95163E-05	0.000198364	0.000394157	0.019307214
610	2.86048E-05	0.000189632	0.000361349	0.018784339
611	2.77284E-05	0.000181255	0.000331298	0.018265676
612	2.68787E-05	0.000173172	0.000303709	0.017688796
613	2.60437E-05	0.000165304	0.000278213	0.017103335
614	2.52411E-05	0.000157765	0.000254932	0.016477059
615	2.44697E-05	0.000150541	0.000233573	0.015855196
616	2.3724E-05	0.000143595	0.00021405	0.015205273
617	2.30053E-05	0.000136931	0.000196154	0.014567478
618	2.23087E-05	0.000130516	0.00017974	0.013906426
619	2.16295E-05	0.000124321	0.000164678	0.013259957
620	2.09718E-05	0.000118368	0.000150886	0.012604211
621	2.03429E-05	0.000112694	0.000138306	0.01196998
622	1.97327E-05	0.000107238	0.000126783	0.011333722
623	1.91421E-05	0.000102003	0.000116175	0.010717537
624	1.85758E-05	9.70092E-05	0.000106526	0.010111157
625	1.80299E-05	9.22333E-05	9.76917E-05	0.009529033
626	1.75015E-05	8.76552E-05	8.96083E-05	0.008961305
627	1.69912E-05	8.32753E-05	8.21968E-05	0.008416998
628	1.64943E-05	7.90679E-05	7.54068E-05	0.007887342
629	1.60122E-05	7.50362E-05	6.91733E-05	0.007383815
630	1.55527E-05	7.12136E-05	6.34928E-05	0.006902015
631	1.51033E-05	6.75378E-05	5.82748E-05	0.006443591
632	1.46662E-05	6.40172E-05	5.34879E-05	0.006004583
633	1.42462E-05	6.06691E-05	4.91014E-05	0.005591781
634	1.38365E-05	5.74607E-05	4.50912E-05	0.005198676
635	1.34403E-05	5.44021E-05	4.14032E-05	0.004828965
636	1.30581E-05	5.14921E-05	3.80376E-05	0.004481221
637	1.26863E-05	4.87129E-05	3.49386E-05	0.00415399
638	1.23238E-05	4.6057E-05	3.2094E-05	0.003845133
639	1.19733E-05	4.35314E-05	2.94835E-05	0.003558365
640	1.16346E-05	4.11322E-05	2.71003E-05	0.00328933
641	1.13072E-05	3.88537E-05	2.49153E-05	0.003039378
642	1.09929E-05	3.6698E-05	2.29138E-05	0.002806609
643	1.06843E-05	3.46371E-05	2.10674E-05	0.002589401
644	1.03823E-05	3.26713E-05	1.93731E-05	0.002386941
645	1.00931E-05	3.08175E-05	1.78209E-05	0.002200099
646	9.81198E-06	2.90569E-05	1.64006E-05	0.002026916
647	9.53691E-06	2.73809E-05	1.50865E-05	0.001865718
648	9.27172E-06	2.57977E-05	1.38877E-05	0.00171716
649	9.01665E-06	2.43042E-05	1.27876E-05	0.001580566
650	8.77125E-06	2.28957E-05	1.17805E-05	0.00145413
651	8.53239E-06	2.15608E-05	1.08525E-05	0.001337635
652	8.29987E-06	2.02963E-05	1.00013E-05	0.00122994
653	8.07355E-06	1.90992E-05	9.21702E-06	0.001130626
654	7.85262E-06	1.79651E-05	8.49516E-06	0.001038871
655	7.64169E-06	1.69019E-05	7.83319E-06	0.000954851
656	7.43981E-06	1.59039E-05	7.22881E-06	0.000877769
657	7.24237E-06	1.49587E-05	6.66983E-06	0.000806606
658	7.04913E-06	1.40637E-05	6.15399E-06	0.000741039
659	6.86214E-06	1.32207E-05	5.67975E-06	0.000680868
660	6.68172E-06	1.24281E-05	5.24408E-06	0.000625596
661	6.5047E-06	1.16777E-05	4.84151E-06	0.000574597
662	6.33225E-06	1.09698E-05	4.47037E-06	0.000527752
663	6.16328E-06	1.03006E-05	4.12753E-06	0.000484572
664	5.99697E-06	9.66724E-06	3.81038E-06	0.000444731
665	5.83828E-06	9.07583E-06	3.51997E-06	0.000408428
666	5.68325E-06	8.51811E-06	3.25187E-06	0.000375018
667	5.53225E-06	7.99306E-06	3.00454E-06	0.000344283
668	5.38947E-06	7.50491E-06	2.77861E-06	0.000316358
669	5.2528E-06	7.04866E-06	2.57122E-06	0.000290811
670	5.11983E-06	6.61944E-06	2.37976E-06	0.000267335
671	4.98895E-06	6.21384E-06	2.20231E-06	0.000245693
672	4.86063E-06	5.83135E-06	2.03805E-06	0.000225832
673	4.7363E-06	5.47251E-06	1.88659E-06	0.000207557
674	4.61618E-06	5.13626E-06	1.74703E-06	0.000190803
675	4.50146E-06	4.82263E-06	1.61887E-06	0.00017552
676	4.38883E-06	4.52694E-06	1.50006E-06	0.000161439
677	4.27787E-06	4.2478E-06	1.3898E-06	0.000148448
678	4.16952E-06	3.98534E-06	1.28777E-06	0.00013654
679	4.06274E-06	3.73774E-06	1.19306E-06	0.000125542
680	3.958E-06	3.50462E-06	1.10528E-06	0.000115422
681	3.85605E-06	3.28589E-06	1.02412E-06	0.000106114
682	3.75694E-06	3.08083E-06	9.49113E-07	9.75822E-05
683	3.66142E-06	2.88921E-06	8.79975E-07	8.97444E-05
684	3.57002E-06	2.71067E-06	8.16376E-07	8.26018E-05
685	3.4814E-06	2.54344E-06	7.5759E-07	7.60649E-05
686	3.39512E-06	2.38651E-06	7.03164E-07	7.00506E-05
687	3.3116E-06	2.23965E-06	6.52865E-07	6.45181E-05
688	3.23153E-06	2.10267E-06	6.06508E-07	5.94631E-05
689	3.15267E-06	1.97357E-06	5.63395E-07	5.47926E-05
690	3.07482E-06	1.85183E-06	5.23265E-07	5.04775E-05
691	2.99842E-06	1.73731E-06	4.85982E-07	4.65003E-05
692	2.92371E-06	1.62974E-06	4.5139E-07	4.28458E-05
693	2.85277E-06	1.52987E-06	4.19596E-07	3.94989E-05
694	2.78436E-06	1.43654E-06	3.9021E-07	3.64422E-05
695	2.71616E-06	1.3482E-06	3.62742E-07	3.36017E-05
696	2.6498E-06	1.26538E-06	3.37275E-07	3.09869E-05
697	2.58476E-06	1.18755E-06	3.13603E-07	2.85712E-05
698	2.52094E-06	1.11436E-06	2.91591E-07	2.63521E-05
699	2.45956E-06	1.04607E-06	2.71256E-07	2.43141E-05
700	2.40044E-06	9.823E-07	2.52453E-07	2.24418E-05];

case('User Defined')
sensitivityCurves = UserDefinedCurves;
end

%Get size of user matrix
[rows cols] = size (User);

%Averages the reflectance spectrum for each patch to give one number per
%wavelength
for r = 1: rows
            
           uround = User(r, 1);
           d = round(uround);
           User(r, 1) = d;
           
end

       UserZero = User; 
       for c = 2: cols
            UserZero (:, c) = NaN;  
       end
       
       User = cat(1, User, UserZero);

for i = 300:700
    
       meanMatrix((i-299),:) = nanmean(User(find(User(:,1)==i),:));
end; 

meanMatrix(:, 1) = [];%Removes first column from meanMatrix, leaving 401 x (number of patches).

%sensitivityCurves 401 x 5
sensitivityCurves = sensitivityCurves.';%Transforms sensitivityCurves to make a 5 X 401
sensitivityCurves(1, :) = [];%Removes the first row(300 - 700 nm) from sensitivityCurves, now 4 x 401

newMatrix = sensitivityCurves * meanMatrix; %[4 x 401] * [401 x numpatches] gives a [4 x numpatches]
newMatrix = newMatrix.';%Transforms to numpatches x 4 matrix

[newrows newcols] = size(newMatrix); 

%The idealized stimulation values of the four color cones are normalized to
%sum to 1.  
for n = 1:newrows
      rowSum = sum(newMatrix(n, :));
    for c = 1:newcols
      newMatrix(n, c) = newMatrix(n, c) / rowSum;
    end
end

Mega = newMatrix;
Mega = roundn(Mega, -6);


%*************************************************************************%
%Gets brilliance Matrix
function NormBril = brillianceNoPrint(UserOriginal, patnum, patnames)
[rows cols] = size (UserOriginal);
for r = 1: rows
    
           uround = UserOriginal(r, 1);
           d = round(uround);
           UserOriginal(r, 1) = d;     
end
       UserZero = UserOriginal;
      
       for c = 2: cols
            UserZero (:, c) = NaN;  
       end

    UserOriginal = cat(1, UserOriginal, UserZero); %stacks vertically User and User-size matrix full of Nans for calculating average in next step

for i = 300:700
       brilMatrix((i-299),:) = nanmean(UserOriginal(find(UserOriginal(:,1)==i),:));
end; 

brilMatrix = roundn(brilMatrix, -6);

for i = 1: patnum
   NormBril(i) = sum(brilMatrix(:,i+1))/(401*100); %(i+1) is desired column / column 1 of brilMatrix is 300 - 700
   ReflectanceMax(i) = max(brilMatrix(:,i+1));
   [a b] = find(brilMatrix(:, i+1) == ReflectanceMax(i));
   [j k] = size(a); 
   if j > 1
       E = 'Multiple occurences';
       break
       break
   else  
    Wavelength(i) = brilMatrix(a, 1);
   end
end


%*************************************************************************%
%Displays selected matrix
function showmatrix(patnum, Mega, labeltxt, patnames)
D = ['                  '];
for i = 1:5
    cone = labeltxt(1,i);
    ncone = char(cone);
    ncone(end+1:12) = ' '; 
    D = [D ncone];
end
disp(D)
for i = 1:patnum
   C = Mega(i, :);
   word = patnames(i);
   S = char(word);
   S(end+1:30) = ' ';
   [s, errmsg] = sprintf(['%s', repmat('%.4f      ',1, 4)], S, C);
   disp(s)
end


%*************************************************************************%
%Plots color tetrahedron
function tetraplotter (UVreply)
vertex_matrix = [0 0 .75; -.61237 -.35355 -.25; 0 .70711 -.25;
.61237 -.35355 -.25];
faces_matrix = [1 2 3; 1 3 4; 1 4 2; 2 3 4];
FaceVertexCData = [1 1 1; 0 0 1; 0 1 0; 1 0 0];
patch ('Vertices',vertex_matrix,'Faces',faces_matrix,'FaceVertexCData', FaceVertexCData, 'FaceColor', 'interp')
if UVreply == 1 || UVreply == 2 || UVreply == 3
text(0, 0, .75, ' u')
elseif UVreply == 4 || UVreply == 5
text(0, 0, .75, ' v')   
else
text(0, 0, .75, ' u/v')
end
text(-.61237, -.35355, -.25,' s')
text(0, .70711, -.25, ' m')
text(.61237, -.35355, -.25, ' l')
alpha(.6)
view(3)
drawnow;
axis image;
view(170, 20); 


%*************************************************************************%
%Plots white tetrahedron
function tetraplotterWhite(UVreply)
vertex_matrix = [0 0 .75; -.61237 -.35355 -.25; 0 .70711 -.25;
.61237 -.35355 -.25];
faces_matrix = [1 2 3; 1 3 4; 1 4 2; 2 3 4];
FaceVertexCData = [1 1 1; 1 1 1; 1 1 1; 1 1 1];
patch ('Vertices',vertex_matrix,'Faces',faces_matrix,'FaceVertexCData', FaceVertexCData, 'Facecolor', 'white', 'EdgeColor', 'black')
if UVreply == 1 || UVreply == 2 || UVreply == 3
text(0, 0, .75, ' u')
elseif UVreply == 4 || UVreply == 5
text(0, 0, .75, ' v')   
else
text(0, 0, .75, ' u/v')
end
text(-.61237, -.35355, -.25,' s')
text(0, .70711, -.25, ' m')
text(.61237, -.35355, -.25, ' l')
alpha(0)
view(3)
drawnow;
axis image;
view(170, 20); 


%*************************************************************************%
%Plots white tetrahedron with no grid or u,s,m,l labels
function tetraplotterNoGrid
grid off; 
vertex_matrix = [0 0 .75; -.61237 -.35355 -.25; 0 .70711 -.25;
.61237 -.35355 -.25];
faces_matrix = [1 2 3; 1 3 4; 1 4 2; 2 3 4];
FaceVertexCData = [1 1 1; 1 1 1; 1 1 1; 1 1 1];
patch ('Vertices',vertex_matrix,'Faces',faces_matrix,'FaceVertexCData', FaceVertexCData, 'Facecolor', 'white', 'EdgeColor', 'black')
set(gca,'XTickLabel',[' '])
set(gca,'YTickLabel',[' '])
set(gca,'ZTickLabel',[' '])
alpha(0)
view(3)
drawnow;
axis image;
view(170, 20); 


%*************************************************************************%
%Converts Cartesian coordinates to spherical coordinates
function spherical(X, Y, Z, patnum, patnames)
[theta, phi, r] = cart2sph(X, Y, Z); 
[s] = sprintf(['%s\t\t\t\t\t\t\t   ', '%s\t\t\t   ', '%s\t\t\t\t', '%s\t\t\t'], ' ', 'theta', 'phi', 'r');
disp(s)
f = 1; 
for i = 1:patnum
   T = theta(i);
   P = phi(i);
   R = r(i); 
   word = patnames(i);
   S = char(word);
   S(end+1:30) = ' '; 
   [s] = sprintf(['%s', repmat('%.4f\t\t\t', 1, 3)], S, T, P, R);
   disp(s)
end 


%*************************************************************************%
%Displays Robinson projection with labels
function LabeledRobinson(X, Y, Z, patnum, patnames, UVreply)
[theta, phi, r] = cart2sph(X, Y, Z); 
f = 1; 
for i = 1:patnum
   T = theta(i);
   P = phi(i);
   lon(f) = T;
   lat(f) = P;
   f = f + 1; 
end
h = figure('Color','white');
axesm('robinson', 'Frame', 'on', 'Grid', 'on', 'AngleUnits', 'radians');
geoshow(lat, lon, 'DisplayType', 'point', 'Marker', '.','MarkerSize', 6, 'MarkerEdgeColor','k',...
                'MarkerFaceColor','k');
f=1;
for i = 1:patnum
    word = patnames(i); 
    S = char(word);
    textm(lat(f), lon(f), S)
    f = f+1; 
end
vertex_matrix = [0 0 .75; -.61237 -.35355 -.25; 0 .70711 -.25;
.61237 -.35355 -.25];
faces_matrix = [1 2 3; 1 3 4; 1 4 2; 2 3 4];
FaceVertexCData = [1 1 1; 0 0 1; 0 1 0; 1 0 0];
X = [0, -.61237, 0, .61237];
Y = [0, -.35355, .70711, -.35355];
Z = [.75, -.25, -.25, -.25];
[theta, phi, r] = cart2sph(X,Y,Z);
for i = 1:4
tetralon(i) = theta(i);  
tetralat(i) = phi(i);
end
geoshow(tetralat, tetralon, 'DisplayType', 'point', 'Marker', '.', 'MarkerSize', 6, 'MarkerEdgeColor', 'k',...
       'MarkerFaceColor', 'k'); 
if UVreply == 1 || UVreply == 2 || UVreply == 3
   v = char('u', 's', 'm', 'l');
elseif UVreply == 4 || UVreply ==5 
   v = char('v', 's', 'm', 'l');
else
  v = char('u/v', 's', 'm', 'l');
end
f=1; 
for i = 1:4
    word = v(i);
    textm(tetralat(f), tetralon(f), word)
    f = f+1; 
end


%*************************************************************************%
%Displays Robinson projection with no labels, big dots, and triangle labels at u/v, s, m, l
function BasicRobinson(X, Y, Z, patnum, patnames)
[theta, phi, r] = cart2sph(X, Y, Z);
f = 1; 
for i = 1:patnum
   T = theta(i);
   P = phi(i);
   lon(f) = T;
   lat(f) = P;
   f = f + 1; 
end
h = figure('Color','white');
axesm('robinson', 'Frame', 'on', 'Grid', 'on', 'AngleUnits', 'radians');
geoshow(lat, lon, 'DisplayType', 'point', 'Marker', 'o','MarkerSize', 4, 'MarkerEdgeColor','k',...
                'MarkerFaceColor','k');
vertex_matrix = [0 0 .75; -.61237 -.35355 -.25; 0 .70711 -.25;
.61237 -.35355 -.25];
faces_matrix = [1 2 3; 1 3 4; 1 4 2; 2 3 4];
FaceVertexCData = [1 1 1; 0 0 1; 0 1 0; 1 0 0];
X = [0, -.61237, 0, .61237];
Y = [0, -.35355, .70711, -.35355];
Z = [.75, -.25, -.25, -.25];
[theta, phi, r] = cart2sph(X,Y,Z);
for i = 1:4
tetralon(i) = theta(i);  
tetralat(i) = phi(i);
end
geoshow(tetralat, tetralon, 'DisplayType', 'point', 'Marker', 'v', 'MarkerSize', 10, 'MarkerEdgeColor', 'k',...
       'MarkerFaceColor', 'k'); 

   
%*************************************************************************%
%Displays selected matrix with numbers next to patches
function showmatrixnum(patnum, Mega, labeltxt, patnames)
D = [];
for i = 1
    spaces = labeltxt(i);
    nspaces = char(spaces); 
    nspaces(end+1:40) = ' '; 
    D = nspaces; 
end
for i = 2:5
    cone = labeltxt(i);
    ncone = char(cone);
    ncone(end+1:12) = ' '; 
    D = [D ncone];
end  
disp(D)
for i = 1:patnum
   C = Mega(i, :);
   word = patnames(i);
   S = char(word);
   S(end+1:30) = ' ';
   x = num2str(i); 
   [s, errmsg] = sprintf(['%s      ', '%s', repmat('%.4f      ',1, 4)], x, S, C);
   disp(s)
end


%*************************************************************************%
%Displays graph showing u/v(r), s(r), m(r), l(r) for a given hue
function huefunctions (M, reply, patnames, UVreply)
H(1,:) = M(reply,:);
x = H(1, 1);
y = H(1, 2);
z = H(1, 3);
[theta, phi, r] = cart2sph(x, y, z);
if r == 0
    disp('This hue is achromatic.  It does not vary with chromaticity.  The maximum chromaticity is 0.')
    disp(' ')
else
utheta = 0; 
uphi = 1.570796; 
alphau = acos(cos(phi)*cos(uphi)*cos(theta - utheta)+sin(phi)*sin(uphi));
uhue = cos(alphau);
if UVreply == 1 || UVreply == 2 || UVreply == 3
[s] = sprintf(['%s', '%.4f'], 'u(r) = r * ', uhue); 
elseif UVreply == 4 || UVreply == 5
[s] = sprintf(['%s', '%.4f'], 'v(r) = r * ', uhue); 
else
[s] = sprintf(['%s', '%.4f'], 'u/v(r) = r * ', uhue);
end
disp(s)
stheta = -2.617994; 
sphi = -.339837; 
alphas = acos(cos(phi)*cos(sphi)*cos(theta - stheta)+sin(phi)*sin(sphi));
shue = cos(alphas);
[s] = sprintf(['%s', '%.4f'], 's(r) = r * ', shue); 
disp(s)
mtheta = 1.570796; 
mphi = -.339837; 
alpham = acos(cos(phi)*cos(mphi)*cos(theta - mtheta)+sin(phi)*sin(mphi));
mhue = cos(alpham);
[s] = sprintf(['%s', '%.4f'], 'm(r) = r * ', mhue); 
disp(s)
ltheta = -.523599; 
lphi = -.339837; 
alphal = acos(cos(phi)*cos(lphi)*cos(theta - ltheta)+sin(phi)*sin(lphi));
lhue = cos(alphal);
[s] = sprintf(['%s', '%.4f'], 'l(r) = r * ', lhue); 
disp(s)
A = [alphau alphas alpham alphal];
alphamax = max(A);
rmax = .25/(cos(pi-alphamax));
disp(' ')
disp('The maximum chromaticity, r, for this hue is:')
disp(' ')
[s, errmsg] = sprintf('%0.4f', rmax);
disp(s);
disp(' '); 
xdom = 0:.02:rmax;
y1 = uhue * xdom;
y2 = shue * xdom;
y3 = mhue * xdom;
y4 = lhue * xdom;
figure1 = figure;
set(gcf,'DefaultAxesColorOrder',[.45 .1 .45;0 0 1;0 1 0; 1 0 0])
plot(xdom, y1, xdom, y2, xdom, y3, xdom, y4)
axis([0 .75 -.25 .75]);
set(gca,'Xtick',[[0.00:0.15:0.75]])
set(gca,'Ytick',[-.25  0 .25 .50 .75])
ax1 = gca; 
location = 'northeastoutside'; 
if UVreply == 1 || UVreply == 2 || UVreply == 3
legend(['u(r) = ', num2str(uhue) , 'r'],['s(r) = ', num2str(shue) , 'r'],['m(r) = ', num2str(mhue) , 'r'],['l(r) = ', num2str(lhue) , 'r'], 'Location', location)
mytitle = patnames(reply);
title(mytitle)
xlabel('r')
ylabel('u(r), s(r), m(r), l(r)')
elseif UVreply == 4 || UVreply == 5
legend(['v(r) = ', num2str(uhue) , 'r'],['s(r) = ', num2str(shue) , 'r'],['m(r) = ', num2str(mhue) , 'r'],['l(r) = ', num2str(lhue) , 'r'], 'Location', location)
mytitle = patnames(reply);
title(mytitle)
xlabel('r')
ylabel('v(r), s(r), m(r), l(r)')
else
legend(['u/v(r) = ', num2str(uhue) , 'r'],['s(r) = ', num2str(shue) , 'r'],['m(r) = ', num2str(mhue) , 'r'],['l(r) = ', num2str(lhue) , 'r'], 'Location', location)
mytitle = patnames(reply);
title(mytitle)
xlabel('r')
ylabel('u/v(r), s(r), m(r), l(r)')   
end
ax2 = axes('Position',get(ax1,'Position'),...
           'XAxisLocation','top',...
           'YAxisLocation','right',...
           'YTick',[0 0.25 0.5 0.75 1],...
           'YTickLabel',{'0 %','25%','50%','75%','100%'},...
           'XTick', [[0.00:0.0:0.75]],...
           'XTickLabel',{' ', ' ', ' ', ' ', ' ', ' '},...
           'Color','none',...
           'Parent', figure1);
end


%*************************************************************************%
%Displays graph in black showing u/v(r), s(r), m(r), l(r) for a given hue 
function huefunctionsBlackLines (M, reply, patnames, UVreply)
H(1,:) = M(reply,:);
x = H(1, 1);
y = H(1, 2);
z = H(1, 3);
[theta, phi, r] = cart2sph(x, y, z);
if r == 0
    disp('This hue is achromatic.  It does not vary with chromaticity.  The maximum chromaticity is 0.')
    disp(' ')
else
utheta = 0; 
uphi = 1.570796; 
alphau = acos(cos(phi)*cos(uphi)*cos(theta - utheta)+sin(phi)*sin(uphi));
uhue = cos(alphau);
if UVreply == 1 || UVreply == 2 || UVreply == 3
[s] = sprintf(['%s', '%.4f'], 'u(r) = r * ', uhue); 
elseif UVreply == 4 || UVreply == 5
[s] = sprintf(['%s', '%.4f'], 'v(r) = r * ', uhue); 
else
[s] = sprintf(['%s', '%.4f'], 'u/v(r) = r * ', uhue);
end
stheta = -2.617994; 
sphi = -.339837; 
alphas = acos(cos(phi)*cos(sphi)*cos(theta - stheta)+sin(phi)*sin(sphi));
shue = cos(alphas);
[s] = sprintf(['%s', '%.4f'], 's(r) = r * ', shue); 
disp(s)
mtheta = 1.570796; 
mphi = -.339837; 
alpham = acos(cos(phi)*cos(mphi)*cos(theta - mtheta)+sin(phi)*sin(mphi));
mhue = cos(alpham);
[s] = sprintf(['%s', '%.4f'], 'm(r) = r * ', mhue); 
disp(s)
ltheta = -.523599; 
lphi = -.339837; 
alphal = acos(cos(phi)*cos(lphi)*cos(theta - ltheta)+sin(phi)*sin(lphi));
lhue = cos(alphal);
[s] = sprintf(['%s', '%.4f'], 'l(r) = r * ', lhue); 
disp(s)
A = [alphau alphas alpham alphal];
alphamax = max(A);
rmax = .25/(cos(pi-alphamax));
disp(' ')
disp('The maximum chromaticity, r, for this hue is:')
disp(' ')
[s, errmsg] = sprintf('%0.4f', rmax);
disp(s);
disp(' '); 
xdom = 0:.02:rmax;
y1 = uhue * xdom;
y2 = shue * xdom;
y3 = mhue * xdom;
y4 = lhue * xdom;
figure1 = figure;
set(0,'DefaultAxesColorOrder',[0 0 0;0 0 0;0 0 0;0 0 0])
plot(xdom, y1, '-.', xdom, y2, '-d', xdom, y3,'-o', xdom, y4, '-*')
axis([0 .75 -.25 .75])
set(gca,'Xtick',[0.00:0.15:0.75])
set(gca,'Ytick',[-.25  0 .25 .50 .75])
ax1 = gca; 
location = 'northeastoutside'; 
if UVreply == 1 || UVreply == 2 || UVreply == 3
legend(['u(r) = ', num2str(uhue) , 'r'],['s(r) = ', num2str(shue) , 'r'],['m(r) = ', num2str(mhue) , 'r'],['l(r) = ', num2str(lhue) , 'r'], 'Location', location)
mytitle = patnames(reply);
title(mytitle)
xlabel('r')
ylabel('u(r), s(r), m(r), l(r)')
elseif UVreply == 4 || UVreply == 5
legend(['v(r) = ', num2str(uhue) , 'r'],['s(r) = ', num2str(shue) , 'r'],['m(r) = ', num2str(mhue) , 'r'],['l(r) = ', num2str(lhue) , 'r'], 'Location', location)
mytitle = patnames(reply);
title(mytitle)
xlabel('r')
ylabel('v(r), s(r), m(r), l(r)')
else
legend(['u/v(r) = ', num2str(uhue) , 'r'],['s(r) = ', num2str(shue) , 'r'],['m(r) = ', num2str(mhue) , 'r'],['l(r) = ', num2str(lhue) , 'r'], 'Location', location)
mytitle = patnames(reply);
title(mytitle)
xlabel('r')
ylabel('u/v(r), s(r), m(r), l(r)')   
end
ax2 = axes('Position',get(ax1,'Position'),...
           'XAxisLocation','top',...
           'YAxisLocation','right',...
           'YTick',[0 0.25 0.5 0.75 1],...
           'YTickLabel',{'0 %','25%','50%','75%','100%'},...
           'XTick', [0.00:0:0.75],...    
           'XTickLabel',{' ', ' ', ' ', ' ', ' ', ' '},...
           'Color','none',...
           'Parent', figure1);
end


%*************************************************************************%
%Computes the achieved chroma and maximum chroma
function [avchroma] = rmax(X, Y, Z, patnum, patnames) 
[theta, phi, r] = cart2sph(X, Y, Z); 
[s] = sprintf(['%s\t\t\t\t\t\t\t\t\t   ', '%s\t\t\t', '%s\t\t\t\t', '%s\t\t\t\t', '%s\t\t\t', '%s\t\t\t'], ' ', 'theta', 'phi', 'r', 'rmax', 'achieved r');
disp(s)
for i = 1:patnum
   T (i) = theta(i);
   P (i)= phi(i);
   R (i)= r(i); 
   if R(i) == 0
       Rmax(i) = 0;
       AchievedR(i) = 0; 
   else
        utheta = 0; 
        uphi = 1.570796; 
        alphau = acos(cos(phi(i))*cos(uphi)*cos(theta(i) - utheta)+sin(phi(i))*sin(uphi));
        uhue = cos(alphau);
        stheta = -2.617994; 
        sphi = -.339837; 
        alphas = acos(cos(phi(i))*cos(sphi)*cos(theta(i) - stheta)+sin(phi(i))*sin(sphi));
        shue = cos(alphas);
        mtheta = 1.570796; 
        mphi = -.339837; 
        alpham = acos(cos(phi(i))*cos(mphi)*cos(theta(i) - mtheta)+sin(phi(i))*sin(mphi));
        mhue = cos(alpham);
        ltheta = -.523599; 
        lphi = -.339837; 
        alphal = acos(cos(phi(i))*cos(lphi)*cos(theta(i) - ltheta)+sin(phi(i))*sin(lphi));
        lhue = cos(alphal);
        A = [alphau alphas alpham alphal];
        alphamax = max(A);
   Rmax(i) = .25/(cos(pi-alphamax));
   AchievedR(i) = R(i)/Rmax(i); 
   end
   word = patnames(i);
   S = char(word);
   S(end+1:39) = ' '; 
   [s] = sprintf(['%s', repmat('%.4f\t\t\t', 1, 5)], S, T(i), P(i), R(i), Rmax(i), AchievedR(i));
   disp(s)
end
   E = 'AVERAGE';
   E(end+1:39) = ' '; 
   avchroma = mean(r); 
   [s] = sprintf(['%s', repmat('%.4f\t\t\t', 1, 5)], E , mean(theta), mean(phi), avchroma,  mean(Rmax), mean(AchievedR));
   disp(s)


%*************************************************************************%
%Calculates brilliance matrix
function [NormBril, avBril] = brilliance(UserOriginal, patnum, patnames)
[rows cols] = size (UserOriginal);
for r = 1: rows
    
           uround = UserOriginal(r, 1);
           d = round(uround);
           UserOriginal(r, 1) = d;     
end
       UserZero = UserOriginal;
      
       for c = 2: cols
            UserZero (:, c) = NaN;  
       end

    UserOriginal = cat(1, UserOriginal, UserZero); %stacks vertically User and User-size matrix full of Nans for calculating average in next step

for i = 300:700
       brilMatrix((i-299),:) = nanmean(UserOriginal(find(UserOriginal(:,1)==i),:));
end; 

[s] = sprintf(['%s\t\t\t   ', '%s\t\t\t\t\t', '%s\t\t\t\t\t\t\t', '%s\t\t\t\t\t'], ' ', 'normalized brilliance', 'wavelength max (nm)', '% reflectance at wavelength max');
disp(s)
brilMatrix = roundn(brilMatrix, -6);

for i = 1: patnum
   NormBril(i) = sum(brilMatrix(:,i+1))/(401*100); %(i+1) is desired column / column 1 of brilMatrix is 300 - 700
   ReflectanceMax(i) = max(brilMatrix(:,i+1));
   [a b] = find(brilMatrix(:, i+1) == ReflectanceMax(i));
   [j k] = size(a); 
   if j > 1
       E = 'Multiple occurences';
        word = patnames(i);
        S = char(word);
        S(end+1:30) = ' '; 
        [s] = sprintf(['%s', repmat('%.4f\t\t\t\t\t\t\t', 1, 1), '%s\t\t\t\t\t', repmat('%.4f\t\t\t\t', 1, 1)], S, NormBril(i), E, ReflectanceMax(i));
        disp(s)
       break
       break
   else  
    Wavelength(i) = brilMatrix(a, 1);
   end
   word = patnames(i);
   S = char(word);
   S(end+1:30) = ' '; 
   [s] = sprintf(['%s', repmat('%.4f\t\t\t\t\t\t\t', 1, 5)], S, NormBril(i), Wavelength(i), ReflectanceMax(i));
   disp(s)
end
   E = 'AVERAGE';
   E(end+1:30) = ' '; 
   avBril = mean(NormBril);
   [ss] = sprintf(['%s', repmat('%.4f\t\t\t', 1, 2)], E , avBril);
   disp(ss)
   
   
%*************************************************************************%
%Computes average color span, variance, and matrix with the distances between points in the tetrahedron
function [avcolorspan, varcolorspan, maxcolorspan] = distmatrix(M, patnum, patnames)
 for i = 1:patnum
     for j = 1:patnum
        Mdist(i, j)= sqrt(sum((M(j,:)-M(i,:)).^2));
     end
 end
 
upperMdist = triu(Mdist);
upperMdist = roundn(upperMdist, -6);
nonZeroUpperMdist = nonzeros(upperMdist);
avcolorspan = mean2(nonZeroUpperMdist);
varcolorspan = std2(nonZeroUpperMdist) * std2(nonZeroUpperMdist);
maxcolorspan = max(nonZeroUpperMdist);

N = 'The average color span is: ';
M = 'The color span variance is: ';
O = 'The max color span is: '; 

[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], N, avcolorspan);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], M, varcolorspan);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], O, maxcolorspan);
disp(s)
disp(' ')
L = []; 


%*************************************************************************%
%Computes average color span, variance, and matrix with the distances between points in the tetrahedron
function [avcolorspan, varcolorspan, maxcolorspan] = distmatrixprint(M, patnum, patnames)
 for i = 1:patnum
     for j = 1:patnum
        Mdist(i, j)= sqrt(sum((M(j,:)-M(i,:)).^2));
     end
 end
 
upperMdist = triu(Mdist);
upperMdist = roundn(upperMdist, -6);
nonZeroUpperMdist = nonzeros(upperMdist);
avcolorspan = mean2(nonZeroUpperMdist);
varcolorspan = std2(nonZeroUpperMdist) * std2(nonZeroUpperMdist);
maxcolorspan = max(nonZeroUpperMdist);

N = 'The average color span is: ';
M = 'The color span variance is: ';
O = 'The max color span is: '; 

[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], N, avcolorspan);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], M, varcolorspan);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], O, maxcolorspan);
disp(s)
disp(' ')
L = []; 

for i = 1:(patnum)
   B = Mdist(i, :);
   word = patnames(i);
   S = char(word);
   S(end+1:15) = ' ';
   [s, errmsg] = sprintf(['%s', repmat('%.4f         ',1,patnum)], S, B);
   disp(s)
   L = [L,S]; 
end

[t]=sprintf(['%s', repmat('%s', 1, patnum)], '               ', L);
disp(t)


%*************************************************************************%
%Plots color tetrahedron with convex hull 
function tetraplotterhull (K, M, UVreply)
vertex_matrix = [0 0 .75; -.61237 -.35355 -.25; 0 .70711 -.25;
.61237 -.35355 -.25];
trisurf(K, M(:,1), M(:,2), M(:,3));
faces_matrix = [1 2 3; 1 3 4; 1 4 2; 2 3 4];
FaceVertexCData = [1 1 1; 0 0 1; 0 1 0; 1 0 0];
patch ('Vertices',vertex_matrix,'Faces',faces_matrix,'FaceVertexCData', FaceVertexCData, 'FaceColor', 'interp')
if UVreply == 1 || UVreply == 2 || UVreply == 3
text(0, 0, .75, ' u')
elseif UVreply == 4 || UVreply == 5
text(0, 0, .75, ' v')   
else
text(0, 0, .75, ' u/v')
end
text(-.61237, -.35355, -.25,' s')
text(0, .70711, -.25, ' m')
text(.61237, -.35355, -.25, ' l')
alpha(.6)
view(3)
drawnow;
axis image;
view(170, 20); 


%*************************************************************************%
%Plots tetrahedron and convex hull in white
function tetraplotterhullWhite (K, M, UVreply) 
vertex_matrix = [0 0 .75; -.61237 -.35355 -.25; 0 .70711 -.25;
.61237 -.35355 -.25];
trisurf(K, M(:,1), M(:,2), M(:,3))
faces_matrix = [1 2 3; 1 3 4; 1 4 2; 2 3 4];
FaceVertexCData = [1 1 1; 1 1 1; 1 1 1; 1 1 1];
patch ('Vertices',vertex_matrix,'Faces',faces_matrix,'FaceVertexCData', FaceVertexCData, 'Facecolor', 'white', 'EdgeColor', 'black')
if UVreply == 1 || UVreply == 2 || UVreply == 3
text(0, 0, .75, ' u')
elseif UVreply == 4 || UVreply == 5
text(0, 0, .75, ' v')   
else
text(0, 0, .75, ' u/v')
end
text(-.61237, -.35355, -.25,' s')
text(0, .70711, -.25, ' m')
text(.61237, -.35355, -.25, ' l')
alpha(.6)
view(3)
drawnow;
axis image;
view(170, 20); 


%*************************************************************************%
%Computes average hue disparity and variance of hue
%disparity, outputs hue disparity matrix
function [huedisparity, varhuedisparity, maxhuedisparity] = huedispmatrix(Huemat, patnum, patnames)
 for i = 1:patnum
     for j = 1:patnum
        Huedisp(i, j)= acos(cos(Huemat(j,2))*cos(Huemat(i,2))*cos(Huemat(j,1)-Huemat(i,1))+sin(Huemat(j,2))*sin(Huemat(i,2)));
     end
 end
 
upperHuedisp = triu(Huedisp);
upperHuedisp = roundn(upperHuedisp, -6);
nonZeroUpperHuedisp = nonzeros(upperHuedisp);
huedisparity = mean2(nonZeroUpperHuedisp);
varhuedisparity = std2(nonZeroUpperHuedisp) * std2(nonZeroUpperHuedisp);
maxhuedisparity = max(nonZeroUpperHuedisp);

N = 'The hue disparity is: ';
M = 'The variance of hue disparity is: ';
O = 'The maximum hue disparity is: ';
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], N, huedisparity);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], M, varhuedisparity);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], O, maxhuedisparity);
disp(s)


%*************************************************************************%
%Computes average hue disparity and variance of hue
%disparity, outputs hue disparity matrix
function [huedisparity, varhuedisparity, maxhuedisparity] = huedispmatrixprint(Huemat, patnum, patnames)
 for i = 1:patnum
     for j = 1:patnum
        Huedisp(i, j)= acos(cos(Huemat(j,2))*cos(Huemat(i,2))*cos(Huemat(j,1)-Huemat(i,1))+sin(Huemat(j,2))*sin(Huemat(i,2)));
     end
 end
 
upperHuedisp = triu(Huedisp);
upperHuedisp = roundn(upperHuedisp, -6);
nonZeroUpperHuedisp = nonzeros(upperHuedisp);
huedisparity = mean2(nonZeroUpperHuedisp);
varhuedisparity = std2(nonZeroUpperHuedisp) * std2(nonZeroUpperHuedisp);
maxhuedisparity = max(nonZeroUpperHuedisp);

N = 'The hue disparity is: ';
M = 'The variance of hue disparity is: ';
O = 'The maximum hue disparity is: ';
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], N, huedisparity);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], M, varhuedisparity);
disp(s)
[s, errmsg] = sprintf(['%s', repmat('%.4e',1,1)], O, maxhuedisparity);
disp(s)
L = []; 
disp(' ')
for i = 1:patnum
   B = Huedisp(i, :);
   word = patnames(i);
   S = char(word);
   S(end+1:15) = ' ';
   [s, errmsg] = sprintf(['%s', repmat('%.4f         ',1,patnum)], S, B);
   disp(s)
   L = [L,S]; 
end
[t]=sprintf(['%s', repmat('%s', 1, patnum)], '               ', L);
disp(t)


%*************************************************************************%
%Rounds numbers to a given power of 10.
function y = roundn(x,n)

y = round(x/10^n)*10^n;

%*************************************************************************%
function keep(varargin)
%MATLAB library download
%KEEP keeps the caller workspace variables of your choice and clear the rest.
%       Its usage is just like "clear" but only for variables.
%
%       Xiaoning (David) Yang   xyang@lanl.gov 1998
%       Revision based on comments from Michael McPartland,
%       michael@gaitalf.mgh.harvard.edu, 1999
 
%       Keep all
if isempty(varargin)
        return
end


%       See what are in caller workspace
wh = evalin('caller','who');


%       Check workspace variables
if isempty(wh)
        error('  There is nothing to keep!')
end


%       Construct a string containing workspace variables delimited by ":"
variable = [];
for i = 1:length(wh)
        variable = [variable,':',wh{i}];
end
variable = [variable,':'];


%       Extract desired variables from string
flag = 0;
for i = 1:length(varargin)
        I = findstr(variable,[':',varargin{i},':']);
        if isempty(I)
                disp(['       ',varargin{i}, ' does not exist!'])
                flag = 1;
        elseif I == 1
                variable = variable(1+length(varargin{i})+1:length(variable));
        elseif I+length(varargin{i})+1 == length(variable)
                variable = variable(1:I);
        else
                variable = [variable(1:I),variable(I+length(varargin{i})+2:length(variable))];
        end
end


%       No delete if some input variables do not exist
if flag == 1
        disp('       No variables are deleted!')
        return
end


%       Convert string back to cell and delete the rest
I = findstr(variable,':');
if length(I) ~= 1
        for i = 1:length(I)-1
                if i ~= length(I)-1
                        del(i) = {[variable(I(i)+1:I(i+1)-1),' ']};
                else
                        del(i) = {variable(I(i)+1:length(variable)-1)};
                end
        end
        evalin('caller',['clear ',del{:}])
end