feat: Add cross-mode analytics dashboard with geofencing, correlations, and data export

Adds a unified analytics mode under the Security nav group that aggregates
data across all signal modes. Includes emergency squawk alerting (7700/7600/7500),
vertical rate anomaly detection, ACARS/VDL2-to-ADS-B flight correlation,
geofence zones with enter/exit detection for aircraft/vessels/APRS stations,
temporal pattern detection, RSSI history tracking, Meshtastic topology mapping,
and JSON/CSV data export.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

utils/temporal_patterns.py

@@ -0,0 +1,93 @@
+"""Periodic pattern detection via interval analysis."""
+
+from __future__ import annotations
+
+import time
+from collections import defaultdict
+
+
+class TemporalPatternDetector:
+    """Detect periodic patterns from event timestamps per device."""
+
+    def __init__(self, max_timestamps: int = 200):
+        self._timestamps: dict[str, list[float]] = defaultdict(list)
+        self._max_timestamps = max_timestamps
+
+    def record_event(self, device_id: str, mode: str, timestamp: float | None = None) -> None:
+        key = f"{mode}:{device_id}"
+        ts = timestamp or time.time()
+        buf = self._timestamps[key]
+        buf.append(ts)
+        if len(buf) > self._max_timestamps:
+            del buf[: len(buf) - self._max_timestamps]
+
+    def detect_patterns(self, device_id: str, mode: str | None = None) -> dict | None:
+        """Detect periodic patterns for a device.
+
+        Returns dict with period_seconds, confidence, occurrences or None.
+        """
+        keys = []
+        if mode:
+            keys.append(f"{mode}:{device_id}")
+        else:
+            keys = [k for k in self._timestamps if k.endswith(f":{device_id}")]
+
+        for key in keys:
+            result = self._analyze_intervals(self._timestamps.get(key, []))
+            if result:
+                result['device_id'] = device_id
+                result['mode'] = key.split(':')[0]
+                return result
+        return None
+
+    def _analyze_intervals(self, timestamps: list[float]) -> dict | None:
+        if len(timestamps) < 4:
+            return None
+
+        intervals = [timestamps[i + 1] - timestamps[i] for i in range(len(timestamps) - 1)]
+
+        # Find the median interval
+        sorted_intervals = sorted(intervals)
+        median = sorted_intervals[len(sorted_intervals) // 2]
+
+        if median < 1.0:
+            return None
+
+        # Count how many intervals are within 20% of the median
+        tolerance = median * 0.2
+        matching = sum(1 for iv in intervals if abs(iv - median) <= tolerance)
+        confidence = matching / len(intervals)
+
+        if confidence < 0.5:
+            return None
+
+        return {
+            'period_seconds': round(median, 1),
+            'confidence': round(confidence, 3),
+            'occurrences': len(timestamps),
+        }
+
+    def get_all_patterns(self) -> list[dict]:
+        """Return all detected patterns across all devices."""
+        results = []
+        seen = set()
+        for key in self._timestamps:
+            mode, device_id = key.split(':', 1)
+            if device_id in seen:
+                continue
+            pattern = self.detect_patterns(device_id, mode)
+            if pattern:
+                results.append(pattern)
+                seen.add(device_id)
+        return results
+
+
+# Singleton
+_detector: TemporalPatternDetector | None = None
+
+
+def get_pattern_detector() -> TemporalPatternDetector:
+    global _detector
+    if _detector is None:
+        _detector = TemporalPatternDetector()
+    return _detector