Responsive Visualization Design

The Problem: A Static View in a Dynamic World

The issue with our current code lies in how we define the drawing canvas. We calculate the canvas corners, canvas_p0 and canvas_p1, once at the beginning of our plotting function. These values are then used for all coordinate transformations and drawing operations in that frame.

If the user resizes the native application window, the ImGui window containing our plot might also change size. However, our code is still using the old, now-incorrect canvas dimensions, leading to visual glitches and broken interactions.

The solution is to make our canvas definition dynamic, querying the available drawing space on every single frame.

Step 1: Making the Native Window Resizable

Before our ImGui code can adapt, the underlying OS window (created with a library like SDL, GLFW, or SFML) must be configured to be resizable in the first place. This is typically done by setting a flag during window creation.

Once the native window can be resized, the ImGui viewport within it will automatically adjust. Our next task is to make our custom plot aware of this change.

Step 2: Dynamically Sizing the Canvas

ImGui provides a simple way to determine the available space for drawing within a window. Instead of calculating a static canvas size, we can ask ImGui: "How much space is left in this window?"

The key functions are:

• ImGui::GetCursorScreenPos(): Gives the top-left corner of where the next widget or drawing command will be placed. This will be our canvas_p0.
• ImGui::GetContentRegionAvail(): Returns the width and height of the remaining drawable area in the window. We can add this to canvas_p0 to find our canvas_p1.

This approach ensures our canvas always fills the entire client area of the ImGui window it's in, no matter how it's resized.

To protect our drawing from spilling outside these dynamic boundaries, we must also use clipping.

• draw_list->PushClipRect(canvas_p0, canvas_p1, true): Tells the renderer to discard any pixels drawn outside the specified rectangle.
• draw_list->PopClipRect(): Restores the previous clipping rectangle.

All your custom drawing commands for the plot must be placed between these two calls.

Step 3: Updating Your Plotting Code

Now, let's apply this pattern to your PlotMyData function. You will replace your previous static calculation of canvas_p0 and canvas_p1 with this new dynamic approach.

Here's how your function's structure should look. Note how the canvas definition and clipping are now central to the implementation.

void PlotMyData(
    /* your parameters: pan_offset, zoom_level, etc. */
)
{
    ImGui::Begin("My Plot");

    // 1. Dynamically calculate canvas boundaries every frame
    ImVec2 canvas_p0 = ImGui::GetCursorScreenPos();      // Top-left corner of the drawing area
    ImVec2 canvas_size = ImGui::GetContentRegionAvail(); // Size of the drawing area
    if (canvas_size.x < 50.0f) canvas_size.x = 50.0f;    // Ensure canvas is not too small
    if (canvas_size.y < 50.0f) canvas_size.y = 50.0f;
    ImVec2 canvas_p1 = ImVec2(canvas_p0.x + canvas_size.x, canvas_p0.y + canvas_size.y); // Bottom-right corner

    ImGuiIO& io = ImGui::GetIO();
    ImDrawList* draw_list = ImGui::GetWindowDrawList();

    // Draw the plot background
    draw_list->AddRectFilled(canvas_p0, canvas_p1, IM_COL32(50, 50, 50, 255));
    
    // 2. Push a clipping rectangle to ensure all drawing stays within the canvas
    draw_list->PushClipRect(canvas_p0, canvas_p1, true);

    // --- All your drawing and interaction logic goes here ---
    // Your coordinate transformation functions (WorldToScreen, ScreenToWorld)
    // will now use these dynamic canvas_p0 and canvas_p1 values.
    
    // Example: Drawing grid lines
    // ... your grid line drawing code ...

    // Example: Drawing data
    // ... your data plotting code ...
    
    // Example: Mouse interaction handling (panning, zooming, tooltips)
    // This logic remains the same, but it will now operate with the correct,
    // up-to-date canvas boundaries.
    // ... your input handling code ...

    // --- End of drawing and interaction logic ---

    // 3. Pop the clipping rectangle
    draw_list->PopClipRect();

    // Draw the border on top of the clipped content
    draw_list->AddRect(canvas_p0, canvas_p1, IM_COL32(255, 255, 255, 255));

    ImGui::End();
}

By making this change, your WorldToScreen and ScreenToWorld functions will automatically use the correct canvas dimensions each frame, ensuring that all calculations for zooming, panning, and coordinate display remain accurate as the window is resized.

A Note on Viewports and High-DPI Scaling

Adapting to the "viewport" means more than just handling size changes; it also means handling pixel density. Modern displays, like Apple's Retina screens, have a high density of physical pixels for each "logical" pixel used for layout. If not handled, your application's UI can appear tiny.

Your windowing library (like SDL2) and ImGui can work together to handle this. The general idea is to calculate a scaling factor and have ImGui use it to render its fonts and widgets at the correct physical size.

This is an important concept for building professional-grade applications, ensuring a consistent user experience across different hardware.

Conclusion

Congratulations! You have now built a complete, robust, and interactive custom plotting widget. It can transform data coordinates, respond to user input for navigation, and correctly adapt to any window size. This is a foundational component for our telemetry engine's visualizer.

Key Takeaways:

• A resizable UI starts with a resizable native window (e.g., using SDL_WINDOW_RESIZABLE).
• To create a resizable canvas in ImGui, you must query for the available space every frame using ImGui::GetContentRegionAvail().
• All custom drawing should be wrapped in draw_list->PushClipRect() and draw_list->PopClipRect() to prevent rendering outside the canvas boundaries.
• Robust viewport handling also includes accounting for High-DPI scaling to ensure the UI is legible on all displays.

What's Next?

Our plotting widget is functionally complete, but it has a major performance limitation: it's not designed to render millions of data points. If we tried to plot a few minutes of high-frequency telemetry data, the application would grind to a halt.

In the next module, Building the High-Performance LOD Plotter, we will solve this exact problem. You will learn how to use data decimation and aggregation techniques (specifically, a min-max tree) to render enormous datasets at interactive frame rates, decoupling rendering performance from data volume.