Coverage.py vs. Python vs. RunMat vs. scikit-image Comparison Chart

Coverage.py is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not. Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not. Use coverage run to run your test suite and gather data. However you normally run your test suite, and you can run your test runner under coverage. If your test runner command starts with “python”, just replace the initial “python” with “coverage run”. To limit coverage measurement to code in the current directory, and also find files that weren’t executed at all, add the source argument to your coverage command line. By default, it will measure line (statement) coverage. It can also measure branch coverage. It can tell you what tests ran which lines.

RunMat (by Dystr) is a fast, free, open-source alternative for running MATLAB code. Users can run their existing MATLAB code with complete language grammar and core semantics. No license fees, no lock-in. RunMat is built with a modern compiler, which enables blazing-fast calculations. It boots in 5 milliseconds, GPU optimization is enabled by default, and it's a single, compact, cross-platform binary. Typical engineering use cases - Controls/signal processing & numerics: accelerate MATLAB-style loops plus heavy linear algebra; enjoy faster iteration due to instant startup and tiered JIT. - Batch/CI & serverless jobs: snapshots + compact binaries make it easy to run .m workloads in containers or ephemeral runners at scale. - Plot-heavy workflows: interactive GPU plots for exploratory analysis and reportable exports for stakeholders. - Education: remove license friction and start labs instantly; Jupyter kernel supports reproducible worksheets.