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3daa112 1fe0c4a 3daa112 1fe0c4a 3daa112 1fe0c4a 3daa112 69f842d 3daa112 69f842d 3daa112 69f842d 3daa112 69f842d 3daa112 69f842d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | from dataclasses import dataclass
from typing import Optional
from src.data_loader import load_carriers, load_vessels
from src.temporal_model import TemporalModel
from src.world_model import MaritimeWorldModel
SEVERITY_MULTIPLIERS = {"full": 1.0, "partial": 0.6, "temporary": 0.3}
DAILY_VESSEL_COST_USD = 60000
DAILY_RISK_BASE_USD = 500000
AVERAGE_SPEED_KNOTS = 14
@dataclass
class SimulationResult:
scenario: dict
affected_routes: list
rerouting: dict
carriers: list
port_congestion: list
cascade: dict
def __getitem__(self, key: str):
return getattr(self, key)
def to_dict(self) -> dict:
return {
"scenario": self.scenario,
"affected_routes": self.affected_routes,
"rerouting": self.rerouting,
"carriers": self.carriers,
"port_congestion": self.port_congestion,
"cascade": self.cascade,
}
class DisruptionSimulator:
def __init__(self, world_model: MaritimeWorldModel, temporal_model: TemporalModel):
self._world_model = world_model
self._temporal_model = temporal_model
self._routes = world_model.get_routes()
self._carriers_df = load_carriers()
self._vessels = world_model._vessels
self._graph = world_model.get_port_graph()
self._route_data = {route["id"]: route for _, route in self._routes.iterrows()}
self._carrier_data = {c["id"]: c for _, c in self._carriers_df.iterrows()}
def run_scenario(self, chokepoint_ids: list, severity: str) -> SimulationResult:
if severity not in SEVERITY_MULTIPLIERS:
raise ValueError(f"Unknown severity: {severity}. Must be one of {list(SEVERITY_MULTIPLIERS.keys())}")
blocked_set = set(chokepoint_ids)
affected_routes = self._find_affected_routes(chokepoint_ids)
affected_route_ids = {r["id"] for r in affected_routes}
affected_vessels = self._count_affected_vessels(affected_route_ids, severity)
rerouting = self._compute_rerouting(affected_routes, blocked_set, severity)
carriers = self._score_carriers(affected_route_ids, chokepoint_ids, severity)
port_congestion = self._forecast_port_congestion(affected_routes, chokepoint_ids, severity)
cascade = self._compute_cascade(affected_routes, severity)
return SimulationResult(
scenario={
"chokepoints": chokepoint_ids,
"severity": severity,
"affected_vessels": affected_vessels,
},
affected_routes=[{"id": r["id"], "name": r["name"], "origin_port_id": r["origin_port_id"], "destination_port_id": r["destination_port_id"]} for r in affected_routes],
rerouting=rerouting,
carriers=carriers,
port_congestion=port_congestion,
cascade=cascade,
)
def _find_affected_routes(self, chokepoint_ids: list) -> list[dict]:
if not chokepoint_ids:
return []
blocked_set = set(chokepoint_ids)
results = []
for _, route in self._routes.iterrows():
transit_set = set(route["chokepoints_transited"])
if transit_set & blocked_set:
results.append(route.to_dict())
return results
def _count_affected_vessels(self, affected_route_ids: set, severity: str) -> int:
if not affected_route_ids:
return 0
multiplier = SEVERITY_MULTIPLIERS[severity]
carrier_ids_with_affected_routes = set()
for carrier_id, carrier in self._carrier_data.items():
for route_id in carrier["typical_routes"]:
if route_id in affected_route_ids:
carrier_ids_with_affected_routes.add(carrier_id)
break
vessels_df = self._vessels[self._vessels["carrier_id"].isin(carrier_ids_with_affected_routes)]
return int(len(vessels_df) * multiplier)
def _compute_rerouting(self, affected_routes: list, blocked_set: set, severity: str) -> dict:
multiplier = SEVERITY_MULTIPLIERS[severity]
alternatives = []
seen_pairs = set()
for route in affected_routes:
origin = route["origin_port_id"]
dest = route["destination_port_id"]
pair_key = (origin, dest)
if pair_key in seen_pairs:
continue
seen_pairs.add(pair_key)
alt_route = self._find_alternative_route(origin, dest, blocked_set)
if alt_route is None:
continue
additional_days = alt_route["transit_days"] - route["transit_days"]
additional_cost = additional_days * DAILY_VESSEL_COST_USD
vessels_affected = int(self._count_vessels_on_route(route["id"]) * multiplier)
alternatives.append({
"route_id": alt_route["id"],
"original_route_id": route["id"],
"additional_days": additional_days,
"additional_cost_usd": additional_cost,
"vessels_affected": vessels_affected,
})
return {"alternatives": alternatives}
def _find_alternative_route(self, origin: str, dest: str, blocked_set: set) -> Optional[dict]:
for _, candidate in self._routes.iterrows():
if candidate["origin_port_id"] == origin and candidate["destination_port_id"] == dest:
if not set(candidate["chokepoints_transited"]) & blocked_set:
return candidate.to_dict()
return None
def _count_vessels_on_route(self, route_id: str) -> int:
route = self._route_data.get(route_id)
if route is None:
return 0
exposed_carriers = [
cid for cid, c in self._carrier_data.items()
if route_id in c["typical_routes"]
]
return len(self._vessels[self._vessels["carrier_id"].isin(exposed_carriers)])
def _score_carriers(self, affected_route_ids: set, chokepoint_ids: list, severity: str) -> list:
if not affected_route_ids:
return []
multiplier = SEVERITY_MULTIPLIERS[severity]
results = []
for carrier_id, carrier in self._carrier_data.items():
routes_exposed = sum(1 for rid in carrier["typical_routes"] if rid in affected_route_ids)
if routes_exposed == 0:
continue
exposure_values = [
carrier["chokepoint_exposure"].get(cp, 0.0)
for cp in chokepoint_ids
]
exposure_score = sum(exposure_values) / len(exposure_values) if exposure_values else 0.0
daily_risk = routes_exposed * exposure_score * multiplier * DAILY_RISK_BASE_USD
results.append({
"name": carrier["name"],
"carrier_id": carrier_id,
"exposure_score": round(exposure_score, 4),
"routes_exposed": routes_exposed,
"estimated_daily_risk_usd": int(daily_risk),
})
results.sort(key=lambda x: x["exposure_score"], reverse=True)
return results
def _forecast_port_congestion(self, affected_routes: list, chokepoint_ids: list, severity: str) -> list:
multiplier = SEVERITY_MULTIPLIERS[severity]
port_vessel_counts = {}
for route in affected_routes:
for port_id in [route["origin_port_id"], route["destination_port_id"]]:
port_vessel_counts[port_id] = port_vessel_counts.get(port_id, 0) + 1
results = []
for port_id, vessel_count in port_vessel_counts.items():
try:
baseline = self._temporal_model.get_baseline(port_id)["congestion_baseline"]
except KeyError:
baseline = 0.5
delta = min(0.95 - baseline, (vessel_count / 20.0) * multiplier)
forecast = min(0.95, baseline + delta)
dwell_increase = (forecast - baseline) * 72
results.append({
"port_id": port_id,
"baseline_congestion": round(baseline, 4),
"forecast_congestion": round(forecast, 4),
"dwell_increase_hours": round(dwell_increase, 2),
})
return results
def _compute_cascade(self, affected_routes: list, severity: str) -> dict:
impacted_ports = {}
for route in affected_routes:
impacted_ports[route["origin_port_id"]] = 0
impacted_ports[route["destination_port_id"]] = 0
frontier = set(impacted_ports.keys())
visited = set(impacted_ports.keys())
propagation_edges = []
while frontier:
next_frontier = set()
for port_id in frontier:
for neighbor in self._world_model.get_connectivity(port_id):
if neighbor in visited:
continue
route_id = None
try:
edge_data = self._graph.edges[port_id, neighbor]
distance_nm = edge_data.get("weight", 1000)
route_id = edge_data.get("route_id")
transit_hours = distance_nm / AVERAGE_SPEED_KNOTS
except Exception:
transit_hours = 100
new_impact = impacted_ports[port_id] + transit_hours
if new_impact <= 720:
impacted_ports[neighbor] = new_impact
visited.add(neighbor)
next_frontier.add(neighbor)
if route_id:
propagation_edges.append({
"from_port": port_id,
"to_port": neighbor,
"route_id": route_id,
"hours_to_impact": int(new_impact),
})
frontier = next_frontier
timeline = sorted(
[{"port": pid, "hours_to_impact": int(hours)} for pid, hours in impacted_ports.items()],
key=lambda x: x["hours_to_impact"],
)
return {"impact_timeline": timeline, "propagation_edges": propagation_edges}
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