Hey r/ScientificComputing !

I'm a physics engineeering student, and I spend a lot of my time writing numerical simulations and analyzing data.

Programming in C++ is enjoyable, but most of numerical computing libs in are just unpleasant to use. So I started building my own solution in my free time.

GitHub: https://github.com/mslotwinski-dev/NumC

Some of the things I built into it:

• You can write mathematical expressions naturally, like sin(x) * exp(-x), and differentiate or integrate them in a single line thanks to lazy expression trees.
• It has a built-in plotting engine, so you can display graphs in a native Win32 window or export them as clean SVGs ready to drop into a LaTeX report.

Of course, the project won't surpass the quality of professional libraries. Its goal is to be convenient and accessible for users whose passions lie more in math, rather than programming.

If you're using C++ for simulations, numerical methods, physics, or data analysis, I'd really appreciate any feedback.

Hey r/developers!

As a Technical Physics student, I spend a lot of time doing numerical simulations and data analysis. While I love C++ for its performance, doing math in it usually sucks. You either have to install massive libraries like Eigen just to solve a system of equations, or you have to dump your data into text files and open Python just to see a simple plot.

So, I spent my free time building NumC – a header-only scientific computing framework for C++20.

GitHub: https://github.com/mslotwinski-dev/NumC

Why it's useful for scientific work:

1. It reads like a textbook: Thanks to lazy-evaluated expression trees, you can write formulas naturally (sin(x) * exp(-x)) and differentiate or integrate them in one line.
2. Instant Graphics: It has its own built-in plotting engine. One function call pops up a native Win32 window with your graph, or renders a sharp SVG file that you can drag-and-drop straight into a LaTeX lab report.
3. Built from scratch: To properly learn the math and the language, I coded everything from the ground up: from the Runge-Kutta ODE solvers and curve-fitting algorithms to MLP neural networks.

If you're a student, researcher, or developer working on simulations, physics engines, or data analysis and want a lightweight tool without dependency hell, give it a look. I'd love to hear what you think!

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