agi-app-flight-telemetry

agi-app-flight-telemetry

agi-app-flight-telemetry publishes the flight_telemetry_project AGILAB app as a self-contained PyPI payload. It is the first app to try when you want to prove the AGILAB flow with a concrete dataset and visible analysis output.

Purpose

Use this package to run a compact flight-data ingestion demo: raw public sample files are converted into a reusable dataframe dataset, then inspected through AGILAB analysis pages such as maps and network-style trajectory views.

It also demonstrates AGILAB's worker-only Cython pattern on a real app. The Polars ingestion, output writing, and reducer artifact contract stay in Python, while the per-row haversine distance kernel can run as a typed compiled worker hot loop and reports runtime/checksum evidence.

Installed Project

The distribution name is agi-app-flight-telemetry; the AGILAB project name is flight_telemetry_project. The package exposes both flight_telemetry and flight_telemetry_project through the agilab.apps entry point group, so AgiEnv(app="flight_telemetry_project") works without a monorepo checkout.

Install

pip install agi-app-flight-telemetry

Most users get this package through agi-apps, agilab[ui], or agilab[examples]; direct installation is useful when validating one app package in isolation.

Run In AGILAB

From the UI, select flight_telemetry_project, open ORCHESTRATE, then run Deploy scheduler & workers and RUN. From Python, create an AGILAB environment with AgiEnv(app="flight_telemetry_project") and run the project through the normal AGI.run(..., request=RunRequest(...)) path.

Expected Inputs

The packaged project seeds a small flight dataset under shared storage on first run. No cloud account, private repository, external database, or API key is required for the default proof.

Expected Outputs

The run writes the processed flight dataframe, reducer summaries, and analysis-ready artifacts under the project output paths. Open ANALYSIS with view_maps or view_maps_network to inspect the generated evidence. The reducer summary includes speed_kernel_runtime, speed_dtype_contract, and speed_kernel_checksum_m fields for the worker-only Cython path.

Change One Thing

Limit the number of input files or switch the output format, then rerun the app. The reducer summary should still show which files were read, how many rows were produced, and which output artifacts were written.

Scope

This is a public reproducibility demo. It does not claim live aircraft telemetry, production search ingestion, or a full domain-specific flight-study platform.