feat(burst-detection): update scada analysis flow

tests/unit/test_burst_detection_service.py

@@ -0,0 +1,160 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+
+import numpy as np
+import pandas as pd
+
+from app.services import burst_detection
+
+
+TARGET = datetime(2026, 6, 20, 5, 30, tzinfo=timezone.utc)
+
+
+def _complete_records(*, target: datetime, offset: float = 0.0) -> list[dict]:
+    start = target - timedelta(days=15) + timedelta(minutes=15)
+    return [
+        {
+            "time": (start + timedelta(minutes=15 * index)).isoformat(),
+            "value": float(index % 96) + offset,
+        }
+        for index in range(15 * 96)
+    ]
+
+
+def test_build_target_pressure_aligns_timestamps_and_excludes_incomplete_sensor(
+    monkeypatch,
+):
+    nodes = [f"J{index}" for index in range(6)]
+    mapping = {node: f"D{index}" for index, node in enumerate(nodes)}
+    scada_data = {
+        query_id: _complete_records(target=TARGET, offset=float(index))
+        for index, query_id in enumerate(mapping.values())
+    }
+    scada_data["D5"] = scada_data["D5"][:-1]
+
+    monkeypatch.setattr(
+        burst_detection,
+        "_get_pressure_sensor_mapping",
+        lambda _network: mapping,
+    )
+    monkeypatch.setattr(
+        burst_detection.InternalQueries,
+        "query_latest_scada_time",
+        lambda **_kwargs: TARGET,
+    )
+    monkeypatch.setattr(
+        burst_detection.InternalQueries,
+        "query_scada_by_ids_timerange",
+        lambda **_kwargs: scada_data,
+    )
+
+    frame, resolved_target, excluded = (
+        burst_detection._build_target_pressure_from_scada(
+            network="test",
+            sensor_nodes=nodes,
+            requested_target_time=TARGET,
+            sampling_interval_minutes=15,
+            points_per_day=96,
+        )
+    )
+
+    assert resolved_target == TARGET
+    assert frame.shape == (1440, 5)
+    assert frame.index[-1].to_pydatetime() == TARGET
+    assert excluded == [
+        {"sensor_node": "J5", "reason": "missing_or_invalid_samples"}
+    ]
+
+
+def test_target_mode_uses_fixed_parameters_and_only_classifies_target(monkeypatch):
+    index = pd.date_range(
+        start=TARGET - timedelta(days=15) + timedelta(minutes=15),
+        end=TARGET,
+        freq="15min",
+    )
+    values = np.tile(np.arange(96, dtype=float), 15)
+    frame = pd.DataFrame(
+        {f"J{sensor}": values + sensor for sensor in range(5)},
+        index=index,
+    )
+    monkeypatch.setattr(
+        burst_detection,
+        "_get_pressure_sensor_nodes",
+        lambda _network: list(frame.columns),
+    )
+    monkeypatch.setattr(
+        burst_detection,
+        "_build_target_pressure_from_scada",
+        lambda **_kwargs: (frame, TARGET, []),
+    )
+
+    payload = burst_detection.run_burst_detection(
+        network="test",
+        username="tester",
+        sampling_interval_minutes=15,
+    )
+
+    assert payload["target_time"] == TARGET.isoformat()
+    assert payload["sample_count"] == 1440
+    assert payload["points_per_day"] == 96
+    assert payload["algorithm_params"]["mu"] == 1
+    assert payload["summary"]["score_threshold"] == -0.04
+    assert [row["Role"] for row in payload["rows"]].count("target") == 1
+    assert all(not row["IsBurst"] for row in payload["rows"][:-1])
+    assert payload["reference_window"] == {
+        "start": (TARGET - timedelta(days=14)).isoformat(),
+        "end": (TARGET - timedelta(days=1)).isoformat(),
+        "day_count": 14,
+    }
+
+
+def test_sampling_interval_uses_scada_frequency_and_can_be_overridden(monkeypatch):
+    monkeypatch.setattr(
+        burst_detection,
+        "get_all_scada_info",
+        lambda _network: [
+            {
+                "type": "pressure",
+                "associated_element_id": "J1",
+                "transmission_frequency": "0:15:00",
+            },
+            {
+                "type": "pressure",
+                "associated_element_id": "J2",
+                "transmission_frequency": "0:15:00",
+            },
+        ],
+    )
+
+    assert (
+        burst_detection._resolve_sampling_interval_minutes(
+            network="test",
+            sensor_nodes=["J1", "J2"],
+            requested_interval=None,
+        )
+        == 15
+    )
+    assert (
+        burst_detection._resolve_sampling_interval_minutes(
+            network="test",
+            sensor_nodes=["J1", "J2"],
+            requested_interval=30,
+        )
+        == 30
+    )
+
+
+def test_target_threshold_is_applied_only_to_latest_row():
+    result = pd.DataFrame(
+        {
+            "Day": [1, 2, 3],
+            "Score": [-0.3, -0.2, -0.04],
+            "Prediction": [-1, -1, 1],
+            "IsBurst": [True, True, False],
+        }
+    )
+
+    rows = burst_detection._serialize_result_rows(result, target_only=True)
+
+    assert [row["IsBurst"] for row in rows] == [False, False, True]