Add ops dashboards for training data and model coverage

tests/test_probability_snapshot_archive.py

@@ -2,7 +2,8 @@ import json
 from pathlib import Path
 import pytest
 
-from src.analysis.probability_snapshot_archive import append_probability_snapshot
+import src.analysis.probability_snapshot_archive as snapshot_archive
+from src.database.runtime_state import RuntimeStateDB, TrainingFeatureRecordRepository
 
 
 @pytest.fixture(autouse=True)
@@ -10,10 +11,22 @@ def _force_file_mode(monkeypatch):
     monkeypatch.setenv("POLYWEATHER_STATE_STORAGE_MODE", "file")
 
 
-def test_append_probability_snapshot_writes_jsonl(tmp_path: Path):
+def test_append_probability_snapshot_writes_jsonl(tmp_path: Path, monkeypatch):
     archive_path = tmp_path / "probability_training_snapshots.jsonl"
 
-    append_probability_snapshot(
+    db = RuntimeStateDB(str(tmp_path / "polyweather.db"))
+    monkeypatch.setattr(
+        snapshot_archive,
+        "_training_feature_repo",
+        TrainingFeatureRecordRepository(db),
+    )
+    monkeypatch.setattr(
+        snapshot_archive,
+        "_snapshot_repo",
+        snapshot_archive.ProbabilitySnapshotRepository(db),
+    )
+
+    snapshot_archive.append_probability_snapshot(
         city_name="ankara",
         local_date="2026-03-20",
         observation_time="2026-03-20T12:00:00+03:00",
@@ -77,8 +90,8 @@ def test_append_probability_snapshot_skips_near_duplicate(tmp_path: Path):
         archive_path=str(archive_path),
     )
 
-    append_probability_snapshot(**kwargs)
-    append_probability_snapshot(**kwargs)
+    snapshot_archive.append_probability_snapshot(**kwargs)
+    snapshot_archive.append_probability_snapshot(**kwargs)
 
     lines = archive_path.read_text(encoding="utf-8").strip().splitlines()
     assert len(lines) == 1
@@ -110,14 +123,61 @@ def test_append_probability_snapshot_writes_on_bucket_change(tmp_path: Path):
         archive_path=str(archive_path),
     )
 
-    append_probability_snapshot(
+    snapshot_archive.append_probability_snapshot(
         probabilities=[{"value": 15, "probability": 0.552}],
         **base_kwargs,
     )
-    append_probability_snapshot(
+    snapshot_archive.append_probability_snapshot(
         probabilities=[{"value": 16, "probability": 0.552}],
         **base_kwargs,
     )
 
     lines = archive_path.read_text(encoding="utf-8").strip().splitlines()
     assert len(lines) == 2
+
+
+def test_append_probability_snapshot_dual_writes_training_feature_store(tmp_path: Path, monkeypatch):
+    monkeypatch.setenv("POLYWEATHER_STATE_STORAGE_MODE", "sqlite")
+    monkeypatch.setenv("POLYWEATHER_DB_PATH", str(tmp_path / "polyweather.db"))
+    db = RuntimeStateDB(str(tmp_path / "polyweather.db"))
+
+    monkeypatch.setattr(
+        snapshot_archive,
+        "_training_feature_repo",
+        TrainingFeatureRecordRepository(db),
+    )
+    monkeypatch.setattr(
+        snapshot_archive,
+        "_snapshot_repo",
+        snapshot_archive.ProbabilitySnapshotRepository(db),
+    )
+
+    snapshot_archive.append_probability_snapshot(
+        city_name="ankara",
+        local_date="2026-03-20",
+        observation_time="2026-03-20T12:00:00+03:00",
+        temp_symbol="°C",
+        raw_mu=15.2,
+        raw_sigma=1.2,
+        deb_prediction=15.4,
+        ens_data={"p10": 14.8, "median": 15.8, "p90": 17.9},
+        current_forecasts={"ECMWF": 15.8, "GFS": 14.1},
+        max_so_far=15.0,
+        peak_status="before",
+        probabilities=[{"value": 15, "probability": 0.552}],
+        shadow_probabilities=[{"value": 15, "probability": 0.324}],
+        calibration_summary={
+            "engine": "legacy",
+            "mode": "emos_shadow",
+            "calibration_version": "emos-test",
+            "calibration_source": "artifacts/probability_calibration/default.json",
+            "calibrated_mu": 15.1,
+            "calibrated_sigma": 1.25,
+        },
+    )
+
+    payload = TrainingFeatureRecordRepository(db).get_record("ankara", "2026-03-20")
+    assert payload is not None
+    assert payload["mu"] == 15.2
+    assert payload["forecasts"]["ECMWF"] == 15.8
+    assert payload["probability_features"]["ens_median"] == 15.8