%% txt file plotting
close all

% use calculate_scale to find the scale for the images, then input here
amscale = 210.2/ 0.0254; %pix/in / m/in 
% scale for the professional images
profscale = 224.9/ 0.0254; %pix/in / m/in 

%call datafromtxt and then convert from pixels to m by division
%make sure to divide by the scale that matches with the images you put in
%PIVlab to get the text files...
% datafromtext exists at the bottom of this file
% 5 different data sets
professional = datafromtxt('prof', 9,';',1,'B')/profscale;
amateur = datafromtxt('ours', 9,';',1, 'B')/amscale;
oursPIV = datafromtxt('PIVlabours', 9, ',',3, 'B')/amscale;
oursPIVwIP = datafromtxt('ourswIP', 9, ',',3, 'B')/amscale;
largeOurs = datafromtxt('largeredo', 59, ',',3, 'largeredo_')/amscale;

fps = 120; %define fps
%convert from frames to sec: only for u and v (3 and 4)
professional(1,:,3) = professional(1,:,3)*fps;
professional(1,:,4) = professional(1,:,4)*fps;
amateur(1,:,3) = amateur(1,:,3)*fps;
amateur(1,:,4) = amateur(1,:,4)*fps;
oursPIV(1,:,3) = oursPIV(1,:,3)*fps;
oursPIV(1,:,4) = oursPIV(1,:,4)*fps;
oursPIVwIP(1,:,3) = oursPIVwIP(1,:,3)*fps;
oursPIVwIP(1,:,4) = oursPIVwIP(1,:,4)*fps;
largeOurs(1,:,3) = largeOurs(1,:,3)*fps;
largeOurs(1,:,4) = largeOurs(1,:,4)*fps;

%adjust as needed for two datasets to line up
%especially if camera pos changes or if comparing diff softwares
professional(1,:,1) = professional(1,:,1)+.001;
professional(1,:,2) = professional(1,:,2)*2;
%professional(1,:,3) = professional(1,:,3)*2
amateur(1,:,1) = amateur(1,:,1)-.5*10^-4;
amateur(1,:,2) = amateur(1,:,2)+.5*10^-4;

%plot different datasets as desired
figure
quiver(professional(1,:, 1),professional(1,:, 2),professional(1,:, 3),professional(1,:, 4), 'r', 'LineWidth', 1.5, 'DisplayName', 'Professional')
xlabel('X Location, meters','fontweight','bold', 'fontsize', 14)
ylabel('Y Location, meters','fontweight','bold', 'fontsize', 14)
title('Professional Vector field, LaVision')

figure
quiver(amateur(1,:, 1),amateur(1,:, 2),amateur(1,:, 3),amateur(1,:, 4), 'b', 'LineWidth', 1.5, 'DisplayName', 'Amateur')
xlabel('X Location, meters','fontweight','bold', 'fontsize', 14)
ylabel('Y Location, meters','fontweight','bold', 'fontsize', 14)
title('Amateur Vector field, LaVision')

figure
quiver(amateur(1,:, 1),amateur(1,:, 2),amateur(1,:, 3),amateur(1,:, 4), 'b', 'LineWidth', 1.5, 'DisplayName', 'Amateur')
hold on
quiver(professional(1,:, 1),professional(1,:, 2),professional(1,:, 3),professional(1,:, 4), 'r', 'LineWidth', 1.5, 'DisplayName', 'Professional')
xlabel('X Location, meters','fontweight','bold', 'fontsize', 14)
ylabel('Y Location, meters','fontweight','bold', 'fontsize', 14)
title('Vector plot comparison: Professional vs Amateur, both LaVision')
legend('show', 'Location', 'SouthWest')

figure
quiver(oursPIV(1,:, 1),oursPIV(1,:, 2),oursPIV(1,:, 3),oursPIV(1,:, 4), 'c','LineWidth', 1.5)
xlabel('X Location, meters','fontweight','bold', 'fontsize', 14)
ylabel('Y Location, meters','fontweight','bold', 'fontsize', 14)
title('Amateur Vector field, PIVlab')

figure
quiver(amateur(1,:, 1),amateur(1,:, 2),amateur(1,:, 3),amateur(1,:, 4), 'b', 'LineWidth', 1.5, 'DisplayName', 'LaVision')
hold on
quiver(oursPIV(1,:, 1),oursPIV(1,:, 2),oursPIV(1,:, 3),oursPIV(1,:, 4), 'c', 'LineWidth', 1.5, 'DisplayName', 'PIVlab')
xlabel('X Location, meters','fontweight','bold', 'fontsize', 14)
ylabel('Y Location, meters','fontweight','bold', 'fontsize', 14)
title('Vector plot comparison: PIVlab vs LaVision')
legend('show', 'Location', 'SouthWest')

figure
quiver(oursPIVwIP(1,:, 1),oursPIVwIP(1,:, 2),oursPIVwIP(1,:, 3),oursPIVwIP(1,:, 4), 'b', 'LineWidth', 1.5, 'DisplayName', 'PIVlab with image processing')
hold on
quiver(oursPIV(1,:, 1),oursPIV(1,:, 2),oursPIV(1,:, 3),oursPIV(1,:, 4), 'c', 'LineWidth', 1, 'DisplayName', 'PIVlab')
xlabel('X Location, meters','fontweight','bold', 'fontsize', 14)
ylabel('Y Location, meters','fontweight','bold', 'fontsize', 14)
title('Vector plot comparison: PIVlab image processing vs none')
legend('show', 'Location', 'SouthWest')

figure
quiver(largeOurs(1,:, 1),largeOurs(1,:, 2),largeOurs(1,:, 3),largeOurs(1,:, 4), 'b', 'LineWidth', 1.5, 'DisplayName', 'PIVlab 59 frames')
hold on
%quiver(oursPIV(1,:, 1),oursPIV(1,:, 2),oursPIV(1,:, 3),oursPIV(1,:, 4), 'c', 'LineWidth', 1.5, 'DisplayName', 'PIVlab')
xlabel('X Location, meters','fontweight','bold', 'fontsize', 14)
ylabel('Y Location, meters','fontweight','bold', 'fontsize', 14)
title('Vector plot comparison: PIVlab large average vs small')
legend('show', 'Location', 'SouthWest')

%% do some matrix conversion--> save so mat represents plot

%call reorganize function to save data in a matrix where the row and column
%indices correspond to the x and y location of the vector
% makes it so you can do a percent error calc between vectors at each
% row,col location
newUsPIV = reorganize(oursPIV);
newUsLaVis = reorganize(amateur);

%pass these 2 into percent error function
errorMatrix = calcError(newUsPIV, newUsLaVis);
%take the mean percent error
xs = nanmean(errorMatrix,1);
ys = nanmean(xs,2);
ERROR_U = ys(1,1,1)
ERROR_V = ys(1,1,2)


function matByLoc = reorganize(A)
    sizeA = size(A);
    numpoints = sizeA(2);
    
    %get the min and max x locs of A
    minX = 4.2*10;
    maxX = 10*10;
    Xrange = (maxX-minX); %--> make it so tenths dig is int
    
    %get the min and max y locs of A
    minY = -4*10;
    maxY = 0;
    Yrange = (minY)*-1;
    
    newA = zeros(Xrange, Yrange, 2);

    %loop thru A and save in newA indices using a radix sort-type deal
    for i = 1:numpoints
       %get the first two digits of the x coord
       xcoord = A(1,i,1);
       xind = floor(xcoord*10^4); %floor will truncate!
       %get the first two digits of the y coord
       ycoord = A(1,i,2);
       yind = floor(ycoord*10^3); %floor will truncate!
       if xind>minX && xind<maxX && yind>minY && yind<maxY
            %save u in the matrix index x,y,1
            newA(xind-minX,yind-minY,1) = A(1,i,3);
            %save v in the matrix index x,y,2
            
            newA(xind-minX,yind-minY,2) = A(1,i,4);
       end
    end
    matByLoc = newA;
end
function percError = calcError(real, expected)

    sizeA = size(real);
    dim1 = sizeA(1);
    dim2 = sizeA(2);
    
    percentError = zeros(dim1, dim2,2);
    
    for x = 1:dim1
        for y = 1:dim2
            erroru = (real(x,y,1)-expected(x,y,1))/(expected(x,y,1));%error for u
            if abs(erroru) ~= Inf
                percentError(x,y,1) = erroru;
            end
            errorv = (real(x,y,2)-expected(x,y,2))/(expected(x,y,2));%error for v
            if abs(errorv) ~= Inf
                percentError(x,y,2) = errorv;
            end
        end
    end
    percError = percentError;
end

%% DATAFROMTXT
% datafromtxt takes in...
% folder: string: a folder name (must exist inside same
% directory/matlab path as the txtprocessing file (or edit filepath)
% nsize: integer: the number of frames analyzed in PIVlab, number of txt
% files in your folder
% delim: string: delimeter for text file (PIVlab default is comma ',')
% headerlines: integer: number of headerlines before data in txt file
% mainname: repeating name for txt files (PIVlab default is PIVlab_,
% followed by file number)
function meanData = datafromtxt(folder, nsize, delim, headerlines, mainname)

for n = 1:nsize %for all of the text files we have...
    % get the filename and path using the folder, the frame number, etc.
    if n<10 % this makes it so that we add an extra zero when we access the txt file 
            % (because PIV saves as 0001 but saves 10 as 0010)
        filename = sprintf('%s/%s000%d.txt',folder, mainname, n); %filename in format B000'n'
    else
        filename = sprintf('%s/%s00%d.txt',folder, mainname, n); %filename in format B000'n'
    end
    %open the file using the filename we just created, read mode (do not
    %want to edit!
    fileID = fopen(filename,'r'); %filename is basically filepath with n appended
    %write txt file into a cell table C using the textscan function
    C = textscan(fileID, '%f%f%f%f','Delimiter',delim,'headerLines',headerlines); %text file to cell array
    
    %these two lines make it so that we can know how many rows (datapoints)
    %there are in the text files.  This gets stored in sizeC
    Len = cellfun(@size, C, 'uni', false);
    sizeC = Len{:}
    
    data = zeros(nsize,sizeC(1),4); %define matrix size, matrix called data
    for col = 1:4 % loop thru four columns (four different types of data)
        % and save in columns of a matrix instead of cell table
        % 1: x pos, 2: y pos, 3: u, 4: v
        data(n,:, col) = C{:,col}; %cell array to matrix
        if col == 4 || col == 2
            %negate y pos and v so the plot does not invert later
            data(n,:, col) = data(n,:, col)*-1;
        end
    end
end

% now average each row and col across depth
meanData = mean(data, 1); %mean across diff files

end