opp-repl-result-analysis

Name: opp-repl-result-analysis
Availability: InStock
Author: tabgab

Consolidate OMNeT++ simulation results into pandas DataFrames.

Updated Apr 23, 2026

Search Tags:#simulation #pandas #vectors #results #scalars #opp_repl #omnetpp

Ready to run

npx skills add https://github.com/tabgab/opp_repl-skill --skill opp-repl-result-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: opp-repl-result-analysis
Source: https://github.com/tabgab/opp_repl-skill/tree/main/opp-repl-result-analysis
Command: npx skills add https://github.com/tabgab/opp_repl-skill --skill opp-repl-result-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Read and consolidate OMNeT++ simulation results produced by opp_repl into analytics-ready data frames for downstream analysis.

Core Features & Use Cases

Read per-run scalar, vector, and histogram data and convert them into Pandas DataFrames for quick exploration.
Supports multiple pathways: the current opp_repl result API (scalars/vectors/histograms), the opp_scavetool CLI for shell and CI workflows, and the OMNeT++ Python API for legacy setups.
Includes a bundled script to extract scalars from a results directory to enable automation in shells or CI.

Quick Start

Load a completed run's results and obtain a combined DataFrame of scalar values.

Frequently Asked Questions about opp-repl-result-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert OMNeT++ simulation results into Pandas DataFrames?▼

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.

Can I extract OMNeT++ scalars from a results directory in a CI pipeline?▼

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.

Does this approach support legacy OMNeT++ Python API setups?▼

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.

What is the best way to aggregate OMNeT++ scalars and vectors across replications?▼

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.

How do I validate OMNeT++ simulation results against reference values?▼

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.

Why use opp_scavetool instead of the opp_repl API for result analysis?▼

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.