speedhive-tools 0.9.0

Utilities for MyLaps Event Results API.

speedhive-tools

CLI toolkit and Python library for the MyLaps Speedhive Event Results API.

Install

pip install speedhive-tools

Related Projects

• Web UI: speedhive-tools-ui at https://github.com/ncrosty58/speedhive-tools-ui

For local development:

git clone https://github.com/ncrosty58/speedhive-tools.git
cd speedhive-tools
python -m venv .venv && source .venv/bin/activate
pip install -e .

Quick CLI Usage

# Sync organization data into the primary SQLite cache (default database is ./web_data/speedhive.db)
speedhive sync-org --org 30476

# Run analysis directly from the SQLite cache
speedhive report-consistency --org 30476 --top 10
speedhive extract-driver-laps --org 30476 --driver "Firstname Lastname"
speedhive extract-track-records --org 30476
speedhive scan-track-records --org 30476
speedhive refresh-track-records --org 30476

# Offline utility commands (exporting raw dumps, then importing into cache)
speedhive export-dump --org 30476 --output ./output
speedhive import-dump --org 30476 --dump-dir ./output

Run speedhive --help for the full command list.

Python Usage

from speedhive.wrapper import SpeedhiveClient

client = SpeedhiveClient.create(token="your-api-token")
events = client.get_events(org_id=30476, limit=5)

Standard CLI Workflow

All analysis commands query the central SQLite cache (./web_data/speedhive.db by default). There are two standard ways to populate this cache:

Option A: Direct Sync (Recommended)

Query the remote Mylaps Speedhive API directly to populate the cache:

speedhive sync-org --org 30476

Option B: Offline Export / Import Ingest

If you want to migrate data or run analysis offline:

1. Export raw data dump:

   speedhive export-dump --org 30476 --output ./output
   

2. Import raw dumps into the SQLite cache:

   speedhive import-dump --org 30476 --dump-dir ./output
   

Running Analysis

Once data is in the SQLite cache, run reports against the database:

speedhive report-consistency --org 30476
speedhive extract-driver-laps --org 30476 --driver "Firstname/Lastname"
speedhive extract-track-records --org 30476
speedhive scan-track-records --org 30476
speedhive refresh-track-records --org 30476

Output Format

export-dump creates raw NDJSON snapshots in output/<org_id>/:

output/30476/
├── events.ndjson.gz
├── sessions.ndjson.gz
├── laps.ndjson.gz
├── announcements.ndjson.gz
├── results.ndjson.gz
└── .checkpoint.json

import-dump imports those files into the primary cache database, for example:

web_data/
└── speedhive.db

extract-track-records, export-lap-records, and export-db-dump emit NDJSON as well. extract-track-records writes a {"_meta": {...}} first line (org id, classification filter, generated-at timestamp) followed by one record per line.

Track-record curation lives in speedhive.curation:

• run_sync_and_diff(...) assumes the SQLite cache is already populated and only performs extract/normalize/diff against curated and rejected records.
• refresh_and_scan(...) is the orchestration helper used by the UI and CLI when they want to refresh the org cache first and then run the curation scan.
• load_curated(...), save_curated(...), load_candidates(...), save_candidates(...), load_rejected(...), and save_rejected(...) all use the shared NDJSON storage helpers.

Project Structure

Canonical implementation lives in src/speedhive/:

src/speedhive/
├── client.py
├── wrapper.py
├── generated/           # Auto-generated API client bindings
├── cli/                 # CLI entry point and dynamic discovery
│   ├── discovery.py
│   └── main.py
├── exporters/           # Scrapers and cache sync modules
│   ├── export_org_cache.py
│   ├── export_full_dump.py
│   └── ...
├── analyzers/           # Performance and lap analysis
│   ├── analyze_consistency.py
│   └── analyze_driver_laps.py
└── processing/          # SQLite ETL and track record compilation
    ├── process_sqlite_import.py
    ├── process_track_records.py
    ├── process_lap_analysis.py
    └── ndjson.py
└── curation.py           # Track-record curation and review-state orchestration

Notes

• SQLite Backend: All CSV storage workflows have been deprecated. Relational querying is fully powered by a local, indexed SQLite database file.
• Packaging is configured via pyproject.toml (PEP 621 + setuptools backend).
• The generated API client uses attrs; no Pydantic dependency.

License

MIT © Nathan Crosty