Replay Tabs:

- Add stability detection and threshold classification features with corresponding tests

src/pygui/backend/threshold_classifier.py

@@ -0,0 +1,42 @@
+"""Classify sensor values into three bands around (target, margin)
+
+Auto mode derives target=mean, margin=1
+"""
+
+from __future__ import annotations
+import math
+from dataclasses import dataclass
+
+BAND_IN = "in_range"
+BAND_HIGH = "high" 
+BAND_LOW = "low"
+
+
+@dataclass(frozen=True)
+class ThresholdConfig:
+    set_point: float = 149.0   # process target: used as band TARGET when auto=False
+    margin: float = 1.0        # used as band MARGIN when auto=False
+    auto: bool = True         # auto=True: target=frame mean, margin=frame 1σ
+
+def resolve_bounds(values: list[float], cfg: ThresholdConfig) -> tuple[float, float]:
+    if not cfg.auto:
+        return cfg.set_point, cfg.margin
+    clean = [v for v in values if not math.isnan(v)]
+    if not clean:
+        return cfg.set_point, cfg.margin
+    mean = sum(clean) / len(clean)
+    variance = sum((v - mean) ** 2 for v in clean) / len(clean)
+    return mean, math.sqrt(variance)
+
+def classify(value: float, target: float, margin: float) -> str:
+    if math.isnan(value):
+        return BAND_IN
+    if value > target + margin:
+        return BAND_HIGH
+    if value < target - margin:
+        return BAND_LOW
+    return BAND_IN
+
+def classify_all(values: list[float], cfg: ThresholdConfig) -> list[str]:
+    target, margin = resolve_bounds(values, cfg)
+    return [classify(v, target, margin)for v in values ]