Mimic axis equal in secondary y-axis

Hi @Chris Nemecek ,

To address the concerns raised above and enhance the robustness of my MATLAB plotting code, I have implemented a more dynamic approach that automatically adjusts axis limits and tick marks based on the data. Here’s a refined version of my code that includes these improvements:

% Sample Data
x = linspace(0, 10, 100);
y1 = sin(x);
y2 = cos(x) * 10; % Scale to demonstrate secondary axis

% Create a figure
figure;

% Create the first axis
yyaxis left;
plot(x, y1, 'b-', 'LineWidth', 2);
ylabel('Sine Values');
xlabel('X Values');
grid on;

% Create the second axis
yyaxis right;
plot(x, y2, 'r-', 'LineWidth', 2);
ylabel('Cosine Values (scaled)');

% Dynamically get limits for both axes
xLimits = xlim;
y1Limits = ylim; % Left axis limits
yyaxis right; % Activate right axis to get its limits
y2Limits = ylim; % Right axis limits

% Calculate aspect ratio dynamically
aspectRatio = (y1Limits(2) - y1Limits(1)) / (y2Limits(2) - 
y2Limits(1));

% Set equal aspect ratio dynamically
if aspectRatio > 1
  % Adjust x limits based on aspect ratio
  xlim([xLimits(1), xLimits(1) + (y1Limits(2) - y1Limits(1)) / 
  aspectRatio]);
else
  % Adjust y limits based on aspect ratio
  ylim([y2Limits(1), y2Limits(1) + (xLimits(2) - xLimits(1)) * 
  aspectRatio]);
end

% Final adjustments
axis tight; % Tighten the axes to fit the data

% Dynamically set equidistant ticks for both axes based on limits
set(gca, 'XTick', linspace(xLimits(1), xLimits(2), 5), ...
        'YTick', linspace(min([y1Limits(1), y2Limits(1)]), ...
                         max([y1Limits(2), y2Limits(2)]), 5));

grid on; % Ensure grid is displayed

So, this revised code uses linspace to automatically generate tick marks for both axes based on their current limits which eliminates the need for manual input and increases flexibility when working with different datasets. The aspect ratio calculation remains intact but is now tied directly to the dynamic limits retrieved from the plot which makes sure that any changes in data are automatically reflected in how axes are adjusted. Also, relying on calculated limits rather than hard-coded values, this code is more adaptable to varying datasets, enhancing its robustness and the inclusion of grid on makes sure that users can visually confirm that tick marks are equidistant and aligned properly across both axes.

I will advise to test this code with various datasets to ensure that it behaves as expected under different conditions and helps verify that plot remains clear and informative regardless of changes in data ranges. I will suggest testing this code across various datasets such as

Use sine and cosine functions with different frequencies or amplitudes (e.g., y1 = sin(2*x) or y2 = cos(3*x) * 20 to observe how well the dual-axis handles varying scales. Secondly, test with datasets that have different ranges (e.g., negative values or larger magnitudes) to verify that axis limits adjust correctly. Also, include edge cases such as all zero values or constant functions to see how well the plot adapts.

Please let me know if you have any further questions or need additional modifications!