from datetime import datetime, timedelta
from typing import List, Dict, Any

class Chronos:
    """
    Meta-Agent II: Chronos.
    Manages Temporal Memory (HTM) and Bitemporal Knowledge Graph.
    """

    def __init__(self):
        self.knowledge_graph = [] # Mock Bitemporal Store
        # self.htm_network = Network() # htm.core placeholder

    def ingest_fact(self, subject: str, predicate: str, object_: str, valid_start: datetime):
        """
        Ingests a fact with bitemporal coordinates.
        """
        transaction_time = datetime.now()
        fact = {
            "subject": subject,
            "predicate": predicate,
            "object": object_,
            "vt_start": valid_start,
            "vt_end": None, # Open-ended
            "tx_start": transaction_time,
            "tx_end": None
        }
        self.knowledge_graph.append(fact)
        print(f"Chronos: Ingested fact '{subject} {predicate} {object_}' at tx={transaction_time}")

    def time_travel_query(self, query_time: datetime, perspective_time: datetime) -> List[Dict]:
        """
        "What did we know at `perspective_time` about the state of the world at `query_time`?"
        """
        results = []
        for fact in self.knowledge_graph:
            # 1. Check Transaction Time (What the system knew)
            tx_valid = fact["tx_start"] <= perspective_time and (fact["tx_end"] is None or fact["tx_end"] > perspective_time)

            # 2. Check Valid Time (What was true in the world)
            vt_valid = fact["vt_start"] <= query_time and (fact["vt_end"] is None or fact["vt_end"] > query_time)

            if tx_valid and vt_valid:
                results.append(fact)

        return results

    def detect_anomaly(self, sequence: List[float]) -> bool:
        """
        Mock HTM Anomaly Detection.
        """
        # In real impl, this would feed data to HTM Spatial/Temporal Poolers
        if len(sequence) < 2:
            return False

        # Simple heuristic: Spike detection
        last_val = sequence[-1]
        avg_val = sum(sequence[:-1]) / len(sequence[:-1])
        if last_val > avg_val * 1.5:
            return True
        return False