matlab

name matlab description MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter. category coding license For MATLAB (https://www.mathworks.com/pricing-licensing.html) and for Octave (GNU General Public License version 3) compatibility Requires either MATLAB or Octave to be installed for testing, but not required for just generating scripts. metadata {"skill-author":"Synthetic Sciences"} MATLAB/Octave Scientific Computing MATLAB is a numerical computing environment optimized for matrix operations and scientific computing. GNU Octave is a free, open-source alternative with high MATLAB compatibility. Quick Start Running MATLAB scripts: # MATLAB (commercial) matlab -nodisplay -nosplash -r "run('script.m'); exit;" # GNU Octave (free, open-source) octave script.m Install GNU Octave: # macOS brew install octave # Ubuntu/Debian sudo apt install octave # Windows - download from https://octave.org/download Core Capabilities 1. Matrix Operations MATLAB operates fundamentally on matrices and arrays: % Create matrices A = [1 2 3; 4 5 6; 7 8 9]; % 3x3 matrix v = 1:10; % Row vector 1 to 10 v = linspace(0, 1, 100); % 100 points from 0 to 1 % Special matrices I = eye(3); % Identity matrix Z = zeros(3, 4); % 3x4 zero matrix O = ones(2, 3); % 2x3 ones matrix R = rand(3, 3); % Random uniform N = randn(3, 3); % Random normal % Matrix operations B = A'; % Transpose C = A * B; % Matrix multiplication D = A .* B; % Element-wise multiplication E = A \ b; % Solve linear system Ax = b F = inv(A); % Matrix inverse For complete matrix operations, see references/matrices-arrays.md . 2. Linear Algebra % 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 decomposition R = chol(A); % Cholesky (symmetric positive definite) % Solve linear systems x = A \ b; % Preferred method x = linsolve(A, b); % With options x = inv(A) * b; % Less efficient For comprehensive linear algebra, see references/mathematics.md . 3. Plotting and Visualization % 2D Plots x = 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 plots hold 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 figures saveas(gcf, 'plot.png'); print('-dpdf', 'plot.pdf'); For complete visualization guide, see references/graphics-visualization.md . 4. Data Import/Export % Read tabular data T = readtable('data.csv'); M = readmatrix('data.csv'); % Write data writetable(T, 'output.csv'); writematrix(M, 'output.csv'); % MAT files (MATLAB native) save('data.mat', 'A', 'B', 'C'); % Save variables load('data.mat'); % Load all S = load('data.mat', 'A'); % Load specific % Images img = imread('image.png'); imwrite(img, 'output.jpg'); For complete I/O guide, see references/data-import-export.md . 5. Control Flow and Functions % Conditionals if x > 0 disp('positive'); elseif x < 0 disp('negative'); else disp('zero'); end % Loops for 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 functions f = @(x) x.^2 + 2*x + 1; result = f(5); % 36 For complete programming guide, see references/programming.md . 6. Statistics and Data Analysis % Descriptive statistics m = mean(data); s = std(data); v = var(data); med = median(data); [minVal, minIdx] = min(data); [maxVal, maxIdx] = max(data); % Correlation R = corrcoef(X, Y); C = cov(X, Y); % Linear regression p = polyfit(x, y, 1); % Linear fit y_fit = polyval(p, x); % Moving statistics y_smooth = movmean(y, 5); % 5-point moving average For statistics reference, see references/mathematics.md . 7. Differential Equations % ODE solving % dy/dt = -2y, y(0) = 1 f = @(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 - y1 f = @(t, y) [y(2); -2*y(2) - y(1)]; [t, y] = ode45(f, [0 10], [1; 0]); For ODE solvers guide, see references/mathematics.md . 8. Signal Processing % FFT Y = fft(signal); f = (0:length(Y)-1) * fs / length(Y); plot(f, abs(Y)); % Filtering b = fir1(50, 0.3); % FIR filter design y_filtered = filter(b, 1, signal); % Convolution y = conv(x, h, 'same'); For signal processing, see references/mathematics.md . Common Patterns Pattern 1: Data Analysis Pipeline % Load data data = readtable('experiment.csv'); % Clean data data = rmmissing(data); % Remove missing values % Analyze grouped = groupsummary(data, 'Category', 'mean', 'Value'); % Visualize figure; bar(grouped.Category, grouped.mean_Value); xlabel('Category'); ylabel('Mean Value'); title('Results by Category'); % Save writetable(grouped, 'results.csv'); saveas(gcf, 'results.png'); Pattern 2: Numerical Simulation % Parameters L = 1; N = 100; T = 10; dt = 0.01; x = linspace(0, L, N); dx = x(2) - x(1); % Initial condition u = 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); Pattern 3: Batch Processing % Process multiple files files = 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 function end % Combine results all_results = vertcat(results{:}); Reference Files matrices-arrays.md - Matrix creation, indexing, manipulation, and operations mathematics.md - Linear algebra, calculus, ODEs, optimization, statistics graphics-visualization.md - 2D/3D plotting, customization, export data-import-export.md - File I/O, tables, data formats programming.md - Functions, scripts, control flow, OOP python-integration.md - Calling Python from MATLAB and vice versa octave-compatibility.md - Differences between MATLAB and GNU Octave executing-scripts.md - Executing generated scripts and for testing GNU Octave Compatibility GNU Octave is highly compatible with MATLAB. Most scripts work without modification. Key differences: Use # or % for comments (MATLAB only % ) Octave allows ++ , -- , += operators Some toolbox functions unavailable in Octave Use pkg load for Octave packages For complete compatibility guide, see references/octave-compatibility.md . Best Practices Vectorize operations - Avoid loops when possible: % Slow for i = 1:1000 y(i) = sin(x(i)); end % Fast y = sin(x); Preallocate arrays - Avoid growing arrays in loops: % Slow for i = 1:1000 y(i) = i^2; end % Fast y = zeros(1, 1000); for i = 1:1000 y(i) = i^2; end Use appropriate data types - Tables for mixed data, matrices for numeric: % Numeric data M = readmatrix('numbers.csv'); % Mixed data with headers T = readtable('mixed.csv'); Comment and document - Use function help: function y = myfunction(x) %MYFUNCTION Brief description % Y = MYFUNCTION(X) detailed description % % Example: % y = myfunction(5); y = x.^2; end Additional Resources MATLAB Documentation: https://www.mathworks.com/help/matlab/ GNU Octave Manual: https://docs.octave.org/latest/ MATLAB Onramp (free course): https://www.mathworks.com/learn/tutorials/matlab-onramp.html File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/