opp-repl-result-analysis

You can convert OMNeT++ simulation results into Pandas DataFrames by reading per-run scalar, vector, and histogram data using the opp_repl result API, which structures the output specifically for quick data exploration.

Yes, you can extract OMNeT++ scalars in CI pipelines using a bundled script or by leveraging the opp_scavetool CLI, which supports shell workflows and automation for processing simulation results.

Yes, the approach supports legacy OMNeT++ setups by providing fallbacks to the OMNeT++ Python API, ensuring you can still consolidate scalars, vectors, and histograms into data frames if newer APIs are unavailable.

The best way to aggregate OMNeT++ scalars and vectors across replications is to load the completed run results through the opp_repl APIs, which consolidate the values into analytics-ready data frames for validation.

To validate OMNeT++ simulation results against reference values, load the completed run's output into combined data frames using the opp_repl result API, allowing you to compare the extracted scalars and vectors directly.

You would use opp_scavetool instead of the opp_repl API when you need to extract OMNeT++ simulation results directly within shell scripts or CI pipelines, where command-line automation is preferred over Python REPL environments.