from mskit import Simu, DEMLoader, TrafficRouter

loader = DEMLoader()
router = TrafficRouter(loader)

# Interactive startup (brain + sim mode prompts)
simu = Simu(dem_loader=loader, traffic_router=router)

# Skip prompts programmatically
simu = Simu(
    dem_loader=loader,
    traffic_router=router,
    auto_select="huggingface",   # or "untrained" / "custom"
    auto_simmode="custom",       # or "random" / "everything"
)

# Chat in plain English
result = simu.chat("Run a random walk in Tokyo for 1000 steps")
result = simu.chat("Shoot a projectile from Mount Fuji east at 45° 80 m/s")
result = simu.chat("What's the traffic like in London?")
result = simu.chat("Simulate water flow in Zurich")
result = simu.chat("Navigate an agent from 35.6,139.7 to 35.65,139.75")

# Run all 6 simulations at once
results = simu.run_all("Tokyo")

# Switch brain/mode mid-session
simu.switch_brain()
simu.switch_sim_mode()

# HuggingFace repo
simu = Simu(auto_select="custom",
            custom_model="mistralai/Mistral-7B-Instruct-v0.3")

# Local transformers folder
simu = Simu(auto_select="custom",
            custom_model="/home/user/my-finetuned-model/")

# GGUF file via llama.cpp  (pip install mskit[gguf])
simu = Simu(auto_select="custom",
            custom_model="/home/user/mistral-7b-q4.gguf")

from mskit import OpenCTV

ctv = OpenCTV()

# Sources available — all free, no API key needed
print(ctv.sources)
# ['singapore', 'london_tfl', 'sydney_rms', 'portland_or']

# Find cameras near a location
cams = ctv.cameras_near(1.3521, 103.8198, radius_km=2)   # Singapore
cams = ctv.cameras_near(51.5074, -0.1278, radius_km=1)    # London

# Get snapshot URL
cam = cams[0]
print(cam.image_url)   # live JPEG URL
fresh = ctv.snapshot(cam)  # refresh URL

# Structured reading near a point
reading = ctv.reading_at(1.3521, 103.8198)
print(reading.cameras_used)    # 4
print(reading.image_url)       # nearest camera snapshot

from mskit import DEMLoader, TrafficRouter

router = TrafficRouter(DEMLoader())
info = router.traffic_at(51.5074, -0.1278)   # London
print(info.speed_kmh)           # 42.3
print(info.congestion_level)    # "moderate"
print(info.source)              # "utd19" or "osrm" or "synthetic"

from mskit import DEMLoader, RandomWalk, Projectile, WaterFlow, TerrainAgent

loader = DEMLoader()

# Slope-biased random walk
rw = RandomWalk(loader, lat=35.68, lon=139.69, slope_bias=0.6)
path = rw.run(steps=500)

# Ballistic trajectory over real terrain
proj = Projectile(loader, lat=35.36, lon=138.73,
                  elevation_deg=45, azimuth_deg=90, speed_ms=80)
traj = proj.run()

# D8 water runoff routing
wf = WaterFlow(loader, patch_km=10)
flow = wf.run(lat=47.38, lon=8.54)

# RL agent navigation episode
agent = TerrainAgent(loader, 35.68, 139.69, 35.73, 139.74)
episode = agent.generate_episode(max_steps=300)