Matlab

% Create matricesA = [1 2 3; 4 5 6; 7 8 9];  % 3x3 matrixv = 1:10;                     % Row vector 1 to 10v = linspace(0, 1, 100);      % 100 points from 0 to 1
% Special matricesI = eye(3);          % Identity matrixZ = zeros(3, 4);     % 3x4 zero matrixO = ones(2, 3);      % 2x3 ones matrixR = rand(3, 3);      % Random uniformN = randn(3, 3);     % Random normal
% Matrix operationsB = A';              % TransposeC = A * B;           % Matrix multiplicationD = A .* B;          % Element-wise multiplicationE = A \ b;           % Solve linear system Ax = bF = inv(A);          % Matrix inverse

% Eigenvalues and eigenvectors[V, D] = eig(A);     % V: eigenvectors, D: diagonal eigenvalues
% Singular value decomposition[U, S, V] = svd(A);
% Matrix decompositions[L, U] = lu(A);      % LU decomposition[Q, R] = qr(A);      % QR decompositionR = chol(A);         % Cholesky (symmetric positive definite)
% Solve linear systemsx = A \ b;           % Preferred methodx = linsolve(A, b);  % With optionsx = inv(A) * b;      % Less efficient

% 2D Plotsx = 0:0.1:2*pi;y = sin(x);plot(x, y, 'b-', 'LineWidth', 2);xlabel('x'); ylabel('sin(x)');title('Sine Wave');grid on;
% Multiple plotshold on;plot(x, cos(x), 'r--');legend('sin', 'cos');hold off;
% 3D Surface[X, Y] = meshgrid(-2:0.1:2, -2:0.1:2);Z = X.^2 + Y.^2;surf(X, Y, Z);colorbar;
% Save figuressaveas(gcf, 'plot.png');print('-dpdf', 'plot.pdf');

% Read tabular dataT = readtable('data.csv');M = readmatrix('data.csv');
% Write datawritetable(T, 'output.csv');writematrix(M, 'output.csv');
% MAT files (MATLAB native)save('data.mat', 'A', 'B', 'C');  % Save variablesload('data.mat');                   % Load allS = load('data.mat', 'A');         % Load specific
% Imagesimg = imread('image.png');imwrite(img, 'output.jpg');

% Conditionalsif x > 0    disp('positive');elseif x < 0    disp('negative');else    disp('zero');end
% Loopsfor i = 1:10    disp(i);end
while x > 0    x = x - 1;end
% Functions (in separate .m file or same file)function y = myfunction(x, n)    y = x.^n;end
% Anonymous functionsf = @(x) x.^2 + 2*x + 1;result = f(5);  % 36

% Descriptive statisticsm = mean(data);s = std(data);v = var(data);med = median(data);[minVal, minIdx] = min(data);[maxVal, maxIdx] = max(data);
% CorrelationR = corrcoef(X, Y);C = cov(X, Y);
% Linear regressionp = polyfit(x, y, 1);  % Linear fity_fit = polyval(p, x);
% Moving statisticsy_smooth = movmean(y, 5);  % 5-point moving average

% ODE solving% dy/dt = -2y, y(0) = 1f = @(t, y) -2*y;[t, y] = ode45(f, [0 5], 1);plot(t, y);
% Higher-order: y'' + 2y' + y = 0% Convert to system: y1' = y2, y2' = -2*y2 - y1f = @(t, y) [y(2); -2*y(2) - y(1)];[t, y] = ode45(f, [0 10], [1; 0]);

% FFTY = fft(signal);f = (0:length(Y)-1) * fs / length(Y);plot(f, abs(Y));
% Filteringb = fir1(50, 0.3);           % FIR filter designy_filtered = filter(b, 1, signal);
% Convolutiony = conv(x, h, 'same');

% Load datadata = readtable('experiment.csv');
% Clean datadata = rmmissing(data);  % Remove missing values
% Analyzegrouped = groupsummary(data, 'Category', 'mean', 'Value');
% Visualizefigure;bar(grouped.Category, grouped.mean_Value);xlabel('Category'); ylabel('Mean Value');title('Results by Category');
% Savewritetable(grouped, 'results.csv');saveas(gcf, 'results.png');

% ParametersL = 1; N = 100; T = 10; dt = 0.01;x = linspace(0, L, N);dx = x(2) - x(1);
% Initial conditionu = sin(pi * x);
% Time stepping (heat equation)for t = 0:dt:T    u_new = u;    for i = 2:N-1        u_new(i) = u(i) + dt/(dx^2) * (u(i+1) - 2*u(i) + u(i-1));    end    u = u_new;end
plot(x, u);

% Process multiple filesfiles = dir('data/*.csv');results = cell(length(files), 1);
for i = 1:length(files)    data = readtable(fullfile(files(i).folder, files(i).name));    results{i} = analyze(data);  % Custom analysis functionend
% Combine resultsall_results = vertcat(results{:});