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Most scientific journals have clear expectations when it comes to figure styling, particularly for plots and graphs. They typically prefer clean, minimal designs with uniform font styles such as Arial, Times New Roman, etc and font sizes that remain clearly legible after reduction—usually no smaller than 8–10 pt. Axis labels and tick marks should be bold and distinct, with consistent sizing across all figures in a manuscript. Journals often discourage excessive grid lines, colorful or decorative fonts, and cluttered legends. Instead, they favor plots with simple tick marks, properly aligned units, and enough white space to make the data easy to interpret. This consistent visual clarity helps reviewers and readers focus on the results without distraction, and adhering to these standards from the beginning can reduce formatting revisions during the publication process.

If you're a researcher or engineer working with COMSOL® Multiphysics, you're probably familiar with the tedious process of turning raw simulation data into clean, well-formatted plots suitable for publication. Typically, COMSOL® exports CSV files that require a series of manual steps—cleaning the data, importing into tools like Origin or MATLAB, formatting fonts and axis labels, adjusting plot sizes, and dealing with journals' strict figure standards. All of this takes time, especially when you have to revise plots repeatedly. That’s exactly the problem this new Python-based tool was built to solve.

This standalone desktop application is designed specifically to handle COMSOL®-exported CSV files and convert them directly into Publication Ready Plots from COMSOL. Built using PyQt5 and Matplotlib, the app provides a graphical interface that gives users full control over the appearance and formatting of their plots—without writing a single line of code.

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Core Features and Customizations

Once you open a CSV file, the app skips over COMSOL®’s metadata lines (typically marked with %), detects columns automatically, and lets you assign custom legends to each curve. You can:

Select fonts such as Arial, Times New Roman, or even Comic Sans (if you're feeling rebellious)
Adjust font sizes independently for axis labels, tick labels, legends, and plot titles
Bold specific elements like axis labels, title, or tick marks
Choose from multiple color schemes—default, viridis, plasma, coolwarm, grayscale
Define exact plot sizes in inches to match journal submission requirements
Set manual axis limits to zoom into specific regions of interest
Add reference lines—both vertical and horizontal—to highlight key data thresholds
Label your axes with physical units or other context-specific annotations

The app doesn’t try to reinvent the plotting process—it simply makes it more efficient for researchers who already know what they need. Unlike general-purpose tools, this one is built with COMSOL®’s structure and academic formatting standards in mind.

For example, if you're working on optical simulations and want to mark a resonance peak at a specific wavelength, you can drop a vertical line exactly where needed and label the axes accordingly. Or if you're preparing comparative plots from multiple simulation sweeps, you can label them with precise custom legends, adjust their line colors, and generate figures that align perfectly across papers. This is completely free to download and use without limits. But it can do only specific tasks (that was the goal anyway) . If you need powerful analysis, etc. you better go with data visualization and data processing tools, either within the software or tools like originlab, etc.

No External Dependencies Required

Since it's built with PyQt5 and Matplotlib, it runs on any standard Python environment (for now in windows 11) and doesn't require any other tool to be installed. It’s lightweight and fast, and the interface is designed to be intuitive even for those who aren't familiar with programming.