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Conversión de operations.db a triples RDF y formato sigma.js, más renderizado HTML standalone con dark theme y ForceAtlas2 layout. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
"""Convierte operations.db al formato JSON de sigma.js para visualizacion de grafos."""
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import json
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import sqlite3
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COLOR_MAP = {
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"person": "#e74c3c",
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"organization": "#3498db",
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"ip_address": "#2ecc71",
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"domain": "#f39c12",
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"crypto_wallet": "#f1c40f",
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"trading_signal": "#9b59b6",
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"vulnerability": "#e67e22",
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"malware": "#c0392b",
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"email": "#1abc9c",
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}
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DEFAULT_COLOR = "#95a5a6"
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SIZE_MIN = 5.0
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SIZE_MAX = 20.0
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def _calculate_degree(entity_id: str, relations: list[dict]) -> int:
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"""Cuenta cuantas relaciones involucran a esta entidad."""
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return sum(
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1 for r in relations
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if r["from_entity"] == entity_id or r["to_entity"] == entity_id
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)
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def _calculate_size(entity_id: str, metadata: dict, relations: list[dict]) -> float:
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"""Calcula el tamanio del nodo basado en degree y risk_score opcional."""
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degree = _calculate_degree(entity_id, relations)
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# Normalizar degree a rango [0, 1] asumiendo maximo razonable de 50
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degree_norm = min(degree / 50.0, 1.0)
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if "risk_score" in metadata:
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try:
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risk_norm = float(metadata["risk_score"]) / 100.0
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risk_norm = max(0.0, min(risk_norm, 1.0))
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score = (degree_norm + risk_norm) / 2.0
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except (ValueError, TypeError):
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score = degree_norm
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else:
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score = degree_norm
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return SIZE_MIN + score * (SIZE_MAX - SIZE_MIN)
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def ops_to_sigma_json(db_path: str) -> dict:
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"""Convierte operations.db al formato JSON esperado por sigma.js.
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Lee entities y relations de la base de datos de operaciones y construye
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el dict con nodos y aristas en el formato de graphology/sigma.js.
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El tamanio de cada nodo se calcula a partir de su degree en el grafo
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y, si esta disponible, de su risk_score en metadata (media 50/50).
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Args:
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db_path: Ruta al archivo operations.db.
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Returns:
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Dict con claves 'nodes' y 'edges' compatible con sigma.js / graphology.
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"""
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conn = sqlite3.connect(db_path)
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conn.row_factory = sqlite3.Row
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try:
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cur = conn.cursor()
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cur.execute(
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"SELECT id, name, type_ref, status, domain, metadata FROM entities"
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)
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raw_entities = [dict(row) for row in cur.fetchall()]
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cur.execute(
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"SELECT id, name, from_entity, to_entity, weight FROM relations"
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)
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raw_relations = [dict(row) for row in cur.fetchall()]
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finally:
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conn.close()
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nodes = []
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for entity in raw_entities:
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try:
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meta = json.loads(entity["metadata"]) if entity["metadata"] else {}
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except (json.JSONDecodeError, TypeError):
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meta = {}
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type_ref = entity["type_ref"] or "unknown"
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color = COLOR_MAP.get(type_ref, DEFAULT_COLOR)
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size = _calculate_size(entity["id"], meta, raw_relations)
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attributes = {
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"label": entity["name"] or entity["id"],
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"entity_type": type_ref,
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"color": color,
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"size": size,
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"domain": entity["domain"] or "",
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"status": entity["status"] or "",
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}
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# Aplana metadata como atributos adicionales (sin sobrescribir campos reservados)
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reserved = {"label", "entity_type", "color", "size", "domain", "status", "type", "x", "y", "hidden", "zIndex"}
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for k, v in meta.items():
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if k not in reserved:
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attributes[k] = v
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nodes.append({"key": entity["id"], "attributes": attributes})
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edges = []
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for rel in raw_relations:
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edges.append({
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"key": rel["id"],
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"source": rel["from_entity"],
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"target": rel["to_entity"],
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"attributes": {
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"label": rel["name"] or "",
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"weight": rel["weight"] if rel["weight"] is not None else 1.0,
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"type": "arrow",
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},
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})
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return {"nodes": nodes, "edges": edges}
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