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https://github.com/BigBodyCobain/Shadowbroker.git
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BigBodyCobain
142 lines
4.8 KiB
Python
142 lines
4.8 KiB
Python
"""Unified SAR scene + anomaly schema.
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Every provider response (ASF, OPERA, EGMS, GFM, EMS, UNOSAT) lands in
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one of these two shapes before it touches anything else in the system.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import time
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from dataclasses import asdict, dataclass, field
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from typing import Any
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# All anomaly kinds the SAR layer can emit. Frontend and OpenClaw skill
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# must use exactly these strings.
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ANOMALY_KINDS = (
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"ground_deformation", # mm or mm/yr — InSAR / EGMS / OPERA-DISP
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"surface_water_change", # OPERA DSWx — water mask delta
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"vegetation_disturbance", # OPERA DIST-ALERT — canopy loss
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"flood_extent", # GFM Sentinel-1 flood polygons
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"damage_assessment", # Copernicus EMS / UNOSAT damage maps
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"coherence_change", # CCD — something physically changed
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"scene_pass", # Mode A only — informational, not an anomaly
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)
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@dataclass
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class SarScene:
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"""A single SAR acquisition (Mode A — catalog only).
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No pixels — this is just metadata that says "Sentinel-1 flew over
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this AOI at this time, here is the download URL if you ever want it".
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"""
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scene_id: str
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platform: str
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mode: str # IW / EW / SM / WV
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level: str # SLC / GRD / RAW
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time: str # ISO-8601 UTC
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aoi_id: str
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relative_orbit: int
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flight_direction: str # ASCENDING / DESCENDING
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bbox: list[float] # [min_lon, min_lat, max_lon, max_lat]
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download_url: str
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provider: str # ASF / Copernicus / Earthdata
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raw_provider_id: str = ""
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def to_dict(self) -> dict[str, Any]:
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return asdict(self)
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@dataclass
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class SarAnomaly:
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"""A pre-processed SAR finding (Mode B).
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Confidence is 0..1. ``magnitude`` + ``magnitude_unit`` interpretation
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depends on ``kind`` — see ANOMALY_KINDS for the canonical list.
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"""
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anomaly_id: str
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kind: str
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lat: float
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lon: float
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magnitude: float
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magnitude_unit: str
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confidence: float
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first_seen: int # epoch seconds
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last_seen: int # epoch seconds
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aoi_id: str
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scene_count: int
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solver: str # OPERA-DISP, EGMS, GFM, EMS, UNOSAT, ...
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source_constellation: str # Sentinel-1, ALOS, ...
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provenance_url: str
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category: str # infrastructure / conflict / geohazard / watchlist
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title: str
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summary: str
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evidence_hash: str = ""
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extras: dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> dict[str, Any]:
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return asdict(self)
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def to_pin_dict(self) -> dict[str, Any]:
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"""Convert to the AI Intel pin shape used by ai_pin_store."""
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return {
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"lat": self.lat,
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"lng": self.lon,
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"label": self.title or f"SAR {self.kind}",
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"category": _kind_to_pin_category(self.kind, self.category),
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"description": (
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f"{self.summary}\n\n"
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f"Solver: {self.solver}\n"
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f"Constellation: {self.source_constellation}\n"
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f"Magnitude: {self.magnitude} {self.magnitude_unit}\n"
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f"Confidence: {self.confidence:.2f}\n"
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f"Scenes: {self.scene_count}\n"
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f"Evidence: {self.evidence_hash[:16] or 'n/a'}"
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),
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"source": f"SAR · {self.solver}",
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"source_url": self.provenance_url,
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"confidence": self.confidence,
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}
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def _kind_to_pin_category(kind: str, default: str) -> str:
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"""Map SAR anomaly kind to ShadowBroker pin category color."""
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return {
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"ground_deformation": "infrastructure",
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"surface_water_change": "weather",
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"vegetation_disturbance": "research",
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"flood_extent": "weather",
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"damage_assessment": "threat",
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"coherence_change": "anomaly",
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}.get(kind, "satellite")
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def canonical_anomaly_json(payload: dict[str, Any]) -> str:
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"""Stable JSON encoding for evidence_hash + signature payloads."""
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return json.dumps(payload, sort_keys=True, separators=(",", ":"))
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def evidence_hash_for_payload(payload: dict[str, Any]) -> str:
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"""SHA-256 hex digest used to bind anomaly events to their source data.
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Mirrors the gate_envelope ``envelope_hash`` pattern from the audit:
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the raw provider response is hashed and the digest is bound into the
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signed event so downstream consumers can re-verify the lineage.
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"""
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return hashlib.sha256(canonical_anomaly_json(payload).encode("utf-8")).hexdigest()
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def make_anomaly_id(solver: str, raw_id: str, lat: float, lon: float) -> str:
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"""Stable, dedup-friendly anomaly id."""
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base = f"{solver}|{raw_id}|{round(lat, 4)}|{round(lon, 4)}"
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digest = hashlib.sha256(base.encode("utf-8")).hexdigest()[:16]
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return f"sar_{solver.lower().replace('-', '_')}_{digest}"
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def now_epoch() -> int:
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return int(time.time())
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