MATLAB

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% Comments start with %

%% Section comments (creates code cells)

% Variable assignment
x = 5;              % Semicolon suppresses output
y = 10              % No semicolon shows result

% Arrays and matrices
row_vector = [1 2 3 4];           % Row vector
col_vector = [1; 2; 3; 4];        % Column vector
matrix = [1 2; 3 4; 5 6];         % 3x2 matrix

% Array indexing (1-based!)
first_element = row_vector(1);    % Returns 1 (not 0!)
subarray = row_vector(2:4);       % Returns [2 3 4]

% Element-wise operations
a = [1 2 3];
b = [4 5 6];
c = a + b;          % [5 7 9]
d = a .* b;         % [4 10 18] (element-wise multiplication)
e = a * b';         % 32 (dot product, b' is transpose)

% Common functions
length(a)           % 3
size(matrix)        % [3 2]
zeros(3, 3)         % 3x3 zero matrix
ones(2, 4)          % 2x4 ones matrix
eye(3)              % 3x3 identity matrix

% If statements
if x > 5
    disp('x is greater than 5');
elseif x == 5
    disp('x equals 5');
else
    disp('x is less than 5');
end

% For loops
for i = 1:10
    fprintf('Iteration %d\n', i);
end

% While loops
count = 1;
while count <= 5
    count = count + 1;
end

% Logical operators
% && (AND), || (OR), ~ (NOT), == (equal), ~= (not equal)

% Save variables to .mat file
save('simulation_results.mat', 'trajectory', 'wheel_speeds', 't');

% Load variables from .mat file
load('simulation_results.mat');

% Import CSV data
data = readmatrix('encoder_data.csv');
time = data(:, 1);
position = data(:, 2);

% Export to CSV
writematrix([time position], 'processed_data.csv');

% Figure 1: Trajectory
figure(1);
plot(trajectory(1,:), trajectory(2,:));
title('Robot Path');

% Figure 2: Velocities
figure(2);
subplot(2,1,1);
plot(t, vx);
title('X Velocity');

subplot(2,1,2);
plot(t, vy);
title('Y Velocity');

% Save figure
saveas(gcf, 'velocity_plot.png');

% Animated plot
figure;
for i = 1:length(t)
    clf;  % Clear figure
    plot(trajectory(1, 1:i), trajectory(2, 1:i), 'b-');
    hold on;
    plot(trajectory(1, i), trajectory(2, i), 'ro', 'MarkerSize', 10);
    axis equal;
    title(sprintf('Time: %.2f s', t(i)));
    drawnow;
    pause(0.01);
end

% Trigonometry
sin(pi/2)       % Sine (radians)
cosd(90)        % Sine (degrees)
atan2(y, x)     % 4-quadrant inverse tangent

% Linear algebra
A = [1 2; 3 4];
inv(A)          % Matrix inverse
det(A)          % Determinant
eig(A)          % Eigenvalues
A'              % Transpose

% Calculus
diff([1 2 4 7 11])      % Differences: [1 2 3 4]
cumsum([1 2 3 4])       % Cumulative sum: [1 3 6 10]
trapz(x, y)             % Numerical integration

% 2D plotting
plot(x, y, 'r--', 'LineWidth', 2);      % Dashed red line
scatter(x, y, 50, 'filled');            % Scatter plot
bar([1 2 3], [10 20 15]);               % Bar chart

% 3D plotting
plot3(x, y, z);                         % 3D line plot
surf(X, Y, Z);                          % Surface plot
mesh(X, Y, Z);                          % Mesh plot

% Annotations
xlabel('Time (s)');
ylabel('Velocity (m/s)');
title('Robot Velocity Profile');
legend('v_x', 'v_y');
grid on;

% Statistics
mean(data)          % Average
median(data)        % Median
std(data)           % Standard deviation
max(data)           % Maximum value
[val, idx] = min(data)  % Minimum value and index

% Filtering
filtered = movmean(noisy_data, 10);     % Moving average (10 samples)
filtered = smooth(noisy_data, 0.1, 'loess');  % LOESS smoothing

% Interpolation
yi = interp1(x, y, xi);                 % 1D interpolation
yi = interp1(x, y, xi, 'spline');       % Cubic spline