feat: add 5 deterministic rules (IMU outliers/watchdog, USBL SNR/spike, battery_low)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/cosma_log_analyzer/rules/usbl_snr_low.py

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+from __future__ import annotations
+
+import os
+
+import pandas as pd
+
+from ..ingest import TOPIC_USBL
+from ..models import Anomaly
+from .base import Rule
+
+
+class UsblSnrLowRule(Rule):
+    """Fire when SNR stays below threshold for `consec` consecutive samples."""
+
+    name = "usbl_snr_low"
+    topic = TOPIC_USBL
+    severity = "warn"
+
+    def __init__(
+        self,
+        subject: str = "AUV000",
+        min_snr_db: float | None = None,
+        consec: int = 3,
+    ) -> None:
+        super().__init__(subject)
+        if min_snr_db is None:
+            min_snr_db = float(os.environ.get("USBL_SNR_LOW", 5.0))
+        self.min_snr_db = min_snr_db
+        self.consec = consec
+
+    def detect(self, df: pd.DataFrame) -> list[Anomaly]:
+        if df.empty or len(df) < self.consec:
+            return []
+        low = df["snr_db"] < self.min_snr_db
+        run = low.astype(int).groupby((~low).cumsum()).cumsum()
+        anomalies: list[Anomaly] = []
+        fired_run = -1
+        runs_id = (~low).cumsum()
+        for idx in df.index:
+            if run.iloc[idx] >= self.consec and runs_id.iloc[idx] != fired_run:
+                fired_run = int(runs_id.iloc[idx])
+                anomalies.append(
+                    self._make(
+                        ts=float(df.at[idx, "ts"]),
+                        value=float(df.at[idx, "snr_db"]),
+                        context={
+                            "min_snr_db": self.min_snr_db,
+                            "consec": self.consec,
+                        },
+                    )
+                )
+        return anomalies