Anni Matlab Fitted Curve - 1

%M=[44.71756,2554.679,2265.398,2576.034,2867.556,2667.752,2632.009,2600.986,2599.868;
%    44.14105,4206.833,4681.949,5677.823,6185.949,5101.299,6596.67,6414.134,1;
%    43.75766,4298.244,5801.197,2930.065,5323.585,2079.171,2078.888,2747.259,1;
%    43.18222,81941.96,89244.75,78504.41,63392.84,63388.52,6319.933,4119.334,1;
%    42.60572,12237.45,11236.08,12505.07,13619.54,13054.42,16876.52,12511.04,1;
 %   41.45342,149102.6,155620.9,101475.3,110534,192717.7,233582.2,228677,1;
 %   40.49352,21808.74,20459.26,17619.12,22288.7,348237.8,14841.43,12242.46,1];
%S=[44.71756,44.14105,43.75766,43.18222,42.60572,41.45342,40.49352]; % stress array
N=26; %number of stress
arr=[2262,2320,2321,2365,2400,2450,2451,2452,2465,2500,2501,2511,2515,2555,2556,2566,2567,2568,2615,2616,2617,2666,2667,2715,2715,2765,2820];
length(arr)
for i=1:1  % for different stress
    %arr=M(i,:);
    arr=sort(arr);
    %int p=0;
    x=(arr(2)-10):(arr(N+1)+10);
    %int s1=int32(arr(2));
    %int s2=mod(s1,10);
    s=2260;
    %int e1=int32(arr(N+1));
    %int e2=mod(e1,10);
    e=2870;
    %p=(e-s)/10;
    p=0;
    s_temp=s;
    while s_temp<e
        p=p+1;
        s_temp=s_temp+50;
    end
    x_co=zeros(1,p);
    y_co=zeros(1,p);
    ct=1;
    w=0;
    while s<=e
        mn=s;
        mx=s+50;
        count=0;
        for j=1:27
            ele=arr(j);
            if ele>=mn && ele<=mx
                count=count+1;
            %else
            %    break;
            end
        end
        x_co(ct)=(mn+mx)/2;
        y_co(ct)=count;
        if count>0
            w=w+1;
        end
        ct=ct+1;
        s=s+50;
       % if s>=e
       %     break;
        %end
    end
    x_coo=zeros(1,w);
    y_coo=zeros(1,w);
    l=1;
    for k=1:p
        if y_co(k)>0
            y_coo(l)=y_co(k);
            x_coo(l)=x_co(k);
            l=l+1;
        end
    end
    %plot(x_coo,y_coo);
    % ... (previous code)

% Fit a log-normal distribution to your data
% Define the distribution object using the 'lognormal' distribution type
   % ... (previous code)

% Fit a normal distribution to your data
% Define the distribution object using the 'Normal' distribution type
    % ... (previous code)

% Fit a normal distribution to your data
% Define the distribution object using the 'Normal' distribution type
    % ... (previous code)

% Fit a normal distribution to your data
% Define the distribution object using the 'Normal' distribution type
    % ... (previous code)

% Fit a normal distribution to your data
% Define the distribution object using the 'Normal' distribution type
    % ... (previous code)

% Fit a normal distribution to your data
% Define the distribution object using the 'Normal' distribution type
    % ... (previous code)

% Fit a normal distribution to your data
% Define the distribution object using the 'Normal' distribution type
    % ... (previous code)

% Create a bar plot to connect the data points
    pd = fitdist(x_coo', 'Normal');  % Note the transpose of x_coo

    % Extract the parameters of the fitted distribution
    mu = pd.mu;
    sigma = pd.sigma;

    % Generate x-values for the curve with smaller increments
    x_values = min(x_coo):10:max(x_coo);  % Use smaller increments for a smoother curve

    % Calculate the corresponding y-values for the normal distribution using the fitted parameters
    y_fit = pdf(pd, x_values);

    % Create a bar plot to connect the data points
    bar(x_coo, y_coo, 'yellow', 'BarWidth', 1);
    hold on;

    % Overlay the fitted Normal (Gaussian) curve
    plot(x_values, y_fit, 'r-', 'LineWidth', 2);
    legend('Data Points', 'LogNormal');

    % ... (rest of your code)




    %%%%%%%%%%%%%%%%%%%%%%%%%%%% Filtering Data and Plotting Smooth Curve %%%%%%%%%%%%%%%%%%%%%%%%%%%%
    filterSize = 2;
    x_mov_fil = zeros(length(x_coo)-filterSize+2, 1);
    y_mov_fil = zeros(length(x_coo)-filterSize+2, 1);

    x_mov_fil(1) = x_coo(1);
    y_mov_fil(1) = y_coo(1);

    for ii=1:length(x_coo) - filterSize+1
        x_mov_fil(ii+1) = (x_coo(ii) + x_coo(ii+1))/2;
        y_mov_fil(ii+1) = (y_coo(ii) + y_coo(ii+1))/2;
    end


    % Plotting approx plot
    [xx,is] = sort(x_mov_fil(:));
    yy = y_mov_fil(is);

    xx = smoothdata(xx, "sgolay");
    yy = smoothdata(yy, "sgolay");

    yy = yy(:);
    dx = diff(xx);
    dy = diff(yy);
    y0 = yy(1:end-1);
    n = numel(xx)-1;
    coefs = [-2*dy./(dx.^3), 3*dy./(dx.^2), 0*dy, y0];
    pp = struct('form', 'pp',...
        'breaks', xx(:)',...
        'coefs', coefs,...
        'pieces', n, ...
        'order', 4,...
        'dim', 1);
    % figure
    xi = linspace(min(x_mov_fil),max(x_mov_fil));
    yi = ppval(pp,xi);

    plot(xi,yi,'r');
    xlim([min(x),max(x)])
    grid on


    % Max curve value x and y point
    [~, maxIdx] = max(yi);
    maxiX = xi(maxIdx);
    maxiY = yi(maxIdx);
    disp("Maximum value y= "+ maxiY+ " at x="+ maxiX)

end