Replay Tabs:

- Add stability detection and threshold classification features with corresponding tests

src/pygui/backend/frame.py

@@ -6,6 +6,6 @@ from dataclasses import dataclass
 class Frame:
     """One sample across all sensors ata a point in time"""
 
-    seq: int
+    seq: int            # monotonically increasing
-    t: float
+    t: float             # seconds (relative or epoch)
-    values: list[float]
+    values: list[float]  # one per sensor, in sensor-layout order

src/pygui/backend/frame_stats.py

@@ -0,0 +1,37 @@
+"""Per-frame descriptive statistics"""
+
+from __future__ import annotations
+import math
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class Stats:
+    min: float; min_index: int
+    max: float; max_index: int
+    diff: float; avg: float
+    sigma: float; three_sigma: float
+
+
+def compute_stats(values: list[float]) -> Stats:
+    clean = [(i, v) for i, v in enumerate(values) if not math.isnan(v)]
+    if not clean:
+        return Stats(0.0, -1, 0.0, -1, 0.0, 0.0, 0.0, 0.0)
+
+    min_index, min_v = min(clean, key=lambda iv: iv[1])
+    max_index, max_v = max(clean, key=lambda iv: iv[1])
+    nums = [v for _, v in clean]
+    avg = sum(nums) / len(nums)
+    variance = sum((v - avg) ** 2 for v in nums) / len(nums)
+    sigma = math.sqrt(variance)
+
+    return Stats(
+        min=min_v,
+        min_index=min_index,
+        max=max_v,
+        max_index=max_index,
+        diff=max_v - min_v,
+        avg=avg,
+        sigma=sigma,
+        three_sigma=3 * sigma,
+    )

src/pygui/backend/stability_detector.py

@@ -0,0 +1,34 @@
+"""Detect process state (Idle, Ramp, Set) from the running average temp"""
+from __future__ import annotations
+from typing import Optional
+
+STATE_IDLE = "idle"
+STATE_RAMP = "ramp"
+STATE_SET = "set"
+
+
+class StabilityDetector: 
+    def __init__(self, idle_below: float = 50.0, tolerance: float = 1.0, 
+                 settle_seconds: float = 10.0) -> None:
+        self._idle_below = idle_below
+        self._tolerance = tolerance
+        self._settle_seconds = settle_seconds
+        self._near_since: Optional[float] = None    # When avg entered the +- tolerance band
+        
+        
+    def reset(self) -> None:
+        self._near_since = None
+        
+    def update(self, avg: float, time: float, set_point: float) ->str:
+        if avg < self._idle_below:
+            self._near_since = None
+            return STATE_IDLE
+        if abs(avg - set_point) <= self._tolerance:
+            if self._near_since is None:
+                self._near_since = time 
+            if time - self._near_since >= self._settle_seconds:
+                return STATE_SET
+            return STATE_RAMP
+        self._near_since = None
+        return STATE_RAMP
+        

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 ]

tests/test_data_parser.py

@@ -1,7 +1,7 @@
 """Tests for serialcomm/data_parser.py binary parsing pipeline."""
 
 import pytest
-from serialcomm.data_parser import (
+from pygui.serialcomm.data_parser import (
     csv_column_count,
     parse_binary_data,
     convert_to_temperatures,
@@ -12,7 +12,6 @@ from serialcomm.data_parser import (
     MAXDUT_X,
 )
 
-
 # ── csv_column_count ──────────────────────────────────────────────────────────
 
 
@@ -203,6 +202,7 @@ class TestConvertToTemperatures:
     def test_p_family_single_block(self):
         data = _make_p_block(1, value=0x0100)
         hex_data = parse_binary_data(data, "P")
+        assert hex_data is not None
         result = convert_to_temperatures(hex_data, "P")
         assert len(result) == 1
         assert all(isinstance(v, str) for v in result[0])
@@ -270,9 +270,9 @@ class TestSaveToCsv:
             result = save_to_csv(data, family, f"{family}00001", str(tmp_path))
             assert result is not None, f"save_to_csv returned None for {family}"
             headers = open(result).readline().strip().split(",")
-            assert len(headers) == expected_cols, (
+            assert (
-                f"{family}: expected {expected_cols} headers, got {len(headers)}"
+                len(headers) == expected_cols
-            )
+            ), f"{family}: expected {expected_cols} headers, got {len(headers)}"
             assert headers[0] == "Sensor1"
             assert headers[-1] == f"Sensor{expected_cols}"
 

tests/test_frame_stats.py

@@ -0,0 +1,22 @@
+import math
+import pytest
+from pygui.backend.frame_stats import compute_stats, Stats
+
+
+def test_basic_stat():
+    s = compute_stats([148.0, 150.0, 149.0])
+    assert s.min == 148.0 and s.min_index == 0
+    assert s.max == 150.0 and s.max_index == 1
+    assert s.diff == pytest.approx(2.0)
+    assert s.avg == pytest.approx(149.0)
+    assert s.sigma == pytest.approx(math.sqrt(2 / 3))
+    assert s.three_sigma == pytest.approx(3 * math.sqrt(2 / 3))
+
+
+def test_empty_values_returns_zeros():
+    s = compute_stats([])
+    assert s == Stats(0.0, -1, 0.0, -1, 0.0, 0.0, 0.0, 0.0)
+
+
+def test_ignores_nan():
+    s = compute_stats([149.0, float("nan"), 151.0])

tests/test_stability__detector.py

@@ -0,0 +1,35 @@
+from pygui.backend.stability_detector import (
+    StabilityDetector, STATE_IDLE, STATE_RAMP, STATE_SET,
+)
+
+
+SET_POINT = 149.0
+
+def make():
+    return StabilityDetector(idle_below = 50.0, tolerance=1.0, settle_seconds=2.0)
+
+def test_idle_when_cold():
+    d = make()
+    assert d.update(avg=25.0, time=0.0, set_point=SET_POINT) == STATE_IDLE
+    
+def test_ramp_while_far_from_setpoint():
+    d = make()
+    d.update(avg=100.0, time=0.0, set_point=SET_POINT)
+    
+def test_ramp_until_settle_time_elapses():
+    d = make()
+    assert d.update(avg=149.2, time=0.0, set_point=SET_POINT) == STATE_RAMP
+    assert d.update(avg=148.9, time=0.0, set_point=SET_POINT) == STATE_RAMP
+    
+def test_set_after_holding_near_setpoint():
+    d = make()
+    d.update(avg=149.2, time=0.0, set_point=SET_POINT)
+    d.update(avg=148.9, time=1.0, set_point=SET_POINT)
+    assert d.update(avg=149.0, time=2.5, set_point=SET_POINT) == STATE_SET
+
+
+def test_back_to_ramp_on_disturbance():
+    d = make()
+    for t in (0.0, 1.0, 2.5):
+        d.update(149.0, t, set_point=SET_POINT)
+    assert d.update(avg=160.0, time=3.0, set_point=SET_POINT) == STATE_RAMP

tests/test_threshold_classifier.py

@@ -0,0 +1,29 @@
+import math
+
+from pygui.backend.threshold_classifier import (
+    ThresholdConfig, classify, classify_all, resolve_bounds,
+    BAND_IN, BAND_HIGH, BAND_LOW
+)
+
+
+def test_classify_about_target_margin():
+    assert classify(149.0, target=149.0, margin=1.0) ==BAND_IN      # exactly
+    assert classify(149.9, target=149.0, margin=1.0) ==BAND_IN      # within
+    assert classify(150.5, target=149.0, margin=1.0) ==BAND_HIGH    # 1.5 above
+    assert classify(147.5, target=149.0, margin=1.0) ==BAND_LOW     # 1.5 below
+    
+    
+def test_manual_bounds_use_set_point_and_margin():
+    cfg = ThresholdConfig(set_point=149.0, margin=1.0, auto=False)
+    assert resolve_bounds([200.0, 0.0], cfg) == (149.0, 1.0)
+    
+def test_auto_bounds_use_mean_and_sigma():
+    cfg = ThresholdConfig(auto=True)
+    target, margin = resolve_bounds([148.0, 150.0, 149.0], cfg)
+    assert target == 149.0
+    assert margin == math.sqrt(2 / 3) 
+    
+def test_classify_all_manual():
+    cfg = ThresholdConfig(set_point=149.0, margin=1.0, auto=False)
+    assert classify_all([149.0, 151.0, 147.0], cfg) == [BAND_IN, BAND_HIGH, BAND_LOW]
+