Sonar Simulation Pipeline

Production-grade marine physics simulation: FLUX opcode engine, geometric ray tracing, multi-agent AUV fleet simulation, mission planning, digital twin, and live dashboard.

pip install sonar-sim-pipeline

Quick Start

from sonar_sim_pipeline import *

# 1. Physics at a single depth
env = compute_physics(25.0, chl=4.0, season="summer", sediment="sand")
print(f"{env['temperature']:.1f}C, {env['sound_speed']:.0f} m/s")

# 2. Dive profile
prof = dive_profile(0, 50, 5)
for p in prof:
    print(f"{p['depth']:5.0f}m | {p['temperature']:5.1f}C | {p['sound_speed']:6.0f}m/s | {p['visibility']:5.1f}m")

# 3. Ray tracing
rt = SonarRayTracer()
ret = rt.compute_return(source_depth=10.0, target_depth=50.0, range=100.0)
print(f"Travel time: {ret['total_travel_time_s']:.4f}s, Loss: {ret['total_loss_db']:.1f}dB")

# 4. Survey mission
planner = MissionPlanner()
mission = planner.lawnmower("kachemak-bay", width=500, height=200, depth=25)
print(f"{len(mission.waypoints)} waypoints, {mission.total_distance():.0f}m total")

# 5. Fleet simulation
from sonar_sim_pipeline import AUVFleetSimulator, FluxPhysics, Formation
sim = AUVFleetSimulator(FluxPhysics())
sim.spawn_fleet(5, depth=20)
sim.formation(Formation.V, spacing=40)
sim.run_for(60)
print(f"Fleet: {sim.fleet_summary()['avg_battery']:.1f}% avg battery")

# 6. Digital twin
from sonar_sim_pipeline import MarineDigitalTwin
twin = MarineDigitalTwin(max_depth=100, chl=4.0)
twin.spawn_fleet(3)
twin.run_fleet(30)
print(twin.summary())

CLI

# Dive profile (CSV)
sonar-env --start 0 --end 100 --step 5

# Survey mission
sonar-survey --type lawnmower --width 500 --height 200 --depth 25

# Ray trace
sonar-ray --source-depth 10 --angle 15 --range 100

# Mission plan
sonar-plan --type spiral --radius 100 --depth 20

API Server

python api_server.py --port 8080
curl localhost:8080/health
curl "localhost:8080/v1/physics?depth=25&season=summer"

Dashboard

Open dashboard.html in any browser — offline FLUX physics, live waterfall, depth profile chart, no dependencies.

Modules

Verified Values

• 22.0°C @15m (summer thermocline, chl=4.0, sand sediment)
• 1527 m/s @15m (Mackenzie sound speed model)
• 0.1386s travel time @100m range (geometric ray tracer, Snell's law)
• 48m optimal survey depth (constraint snapping)

Registry

• PyPI: sonar-sim-pipeline
• Source: github.com/SuperInstance/sonar-vision
• crates.io: sonar-vision-physics (Rust reference impl)
• npm: @superinstance/sonar-vision-tool

License

MIT — SuperInstance