traffic is a Python library and CLI tool for processing, analyzing, and visualizing air traffic data. It ingests trajectory data from multiple sources including OpenSky Network (via REST/Trino), FlightRadar24, Eurocontrol (DDR, AIXM), FAA, and local ADS-B decoders. The core abstractions are `Flight` (single trajectory) and `Traffic` (collection) built on pandas DataFrames, with extensive methods for cleaning, resampling, filtering, geometric operations, navigation event detection (landing, go-around, holding patterns), performance estimation (fuel flow, emissions), and clustering. The library provides a declarative lazy evaluation pipeline (`LazyTraffic`) that chains operations before a single `.eval()` call, enabling efficient parallel processing. It also offers visualization backends for Matplotlib, Altair, Leaflet, and Plotly, and includes a Vue.js/Deck.gl plugin (Tangram) for real-time landing alignment in a web dashboard.
ClawBench is a rigorous execution-first benchmarking framework for evaluating AI agents (particularly OpenClaw) on multi-step tasks. The system orchestrates agent runs through a gateway or containerized native evaluation, scores performance across four axes (completion, trajectory, behavior, advisory judge), and generates detailed diagnostic reports. Its architecture includes a job queue with HuggingFace Dataset persistence, a Gradio web app for interactive leaderboards, and a comprehensive CLI for running evaluations and analyzing results. The framework supports multiple agent runtimes via a pluggable adapter pattern (OpenClaw, Hermes, Claude Code, Codex) and provides a canonical task schema for agent-agnostic task definitions. Beyond basic scoring, ClawBench applies dynamical-systems and statistical methods to agent behavior: trajectory regime classification (trapped, limit-cycle, diffusive), Kaplan-Meier survival analysis, constraint index computation, and variance decomposition. It includes a configuration-space benchmarking system (v0.5) that fingerprints plugin profiles for cold-start prediction and fANOVA factor analysis. The codebase is well-tested with over 60 test files, uses Docker for reproducibility, and includes CI/CD pipelines for automated evaluation campaigns. However, the system is complex and has some hardcoded paths and environment variables tied to specific deployment contexts (Crabbox, OpenClaw gateway), and its heavy dependency on external services introduces maintenance risk. Overall, it is a production-grade benchmark with sophisticated analysis capabilities.