Add WeatherNext2 worker and prevent empty scan cache

tests/test_weathernext2_calibration.py

@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+from src.analysis.weathernext2_calibration import (
+    apply_quantile_calibration_to_payload,
+    train_lightgbm_quantile_calibrator,
+)
+from src.data_collection.weathernext2_sources import build_weathernext2_city_probability
+
+
+def _synthetic_training_rows(count: int = 170):
+    rows = []
+    for idx in range(count):
+        city = "houston" if idx % 2 == 0 else "shanghai"
+        median = 30.0 + (idx % 7) * 0.2
+        residual = 1.0 if city == "houston" else -0.5
+        rows.append(
+            {
+                "city": city,
+                "target_date": f"2026-05-{idx % 28 + 1:02d}",
+                "actual_high_c": median + residual,
+                "weathernext2": {
+                    "summary": {
+                        "mean": median,
+                        "median": median,
+                        "p10": median - 1.0,
+                        "p25": median - 0.5,
+                        "p75": median + 0.5,
+                        "p90": median + 1.0,
+                        "spread": 2.0,
+                    }
+                },
+                "deb_prediction_c": median + 0.2,
+                "model_median_c": median + 0.1,
+                "model_spread": 1.4,
+                "current_max_so_far_c": median - 2.0,
+                "local_hour": 12,
+            }
+        )
+    return rows
+
+
+def test_lightgbm_quantile_calibrator_trains_and_saves_ordered_quantiles(tmp_path):
+    result = train_lightgbm_quantile_calibrator(
+        _synthetic_training_rows(),
+        model_dir=tmp_path,
+        min_global_samples=150,
+        min_city_samples=5,
+    )
+
+    assert result["trained"] is True
+    assert result["samples"] == 170
+    assert (tmp_path / "metadata.json").is_file()
+    assert (tmp_path / "q10.pkl").is_file()
+    assert (tmp_path / "q50.pkl").is_file()
+    assert (tmp_path / "q90.pkl").is_file()
+    assert result["validation"]["ordered_quantiles"] is True
+
+
+def test_lightgbm_quantile_calibrator_skips_when_samples_are_insufficient(tmp_path):
+    result = train_lightgbm_quantile_calibrator(
+        _synthetic_training_rows(20),
+        model_dir=tmp_path,
+        min_global_samples=150,
+        min_city_samples=5,
+    )
+
+    assert result["trained"] is False
+    assert result["reason"] == "insufficient_global_samples"
+
+
+def test_calibrated_distribution_rebuilds_market_buckets_from_shifted_members(tmp_path):
+    train_lightgbm_quantile_calibrator(
+        _synthetic_training_rows(),
+        model_dir=tmp_path,
+        min_global_samples=150,
+        min_city_samples=5,
+    )
+    raw = build_weathernext2_city_probability(
+        city="houston",
+        member_highs=[30.0, 30.2, 30.4, 30.6],
+        temp_symbol="°C",
+        target_date="2026-06-29",
+    )
+
+    calibrated = apply_quantile_calibration_to_payload(raw, model_dir=tmp_path)
+
+    assert calibrated["calibration"]["engine"] == "lightgbm_quantile"
+    assert calibrated["calibration"]["samples"] == 170
+    assert calibrated["calibration"]["raw_summary"]["median"] == raw["summary"]["median"]
+    assert calibrated["calibration"]["calibrated_summary"]["median"] > raw["summary"]["median"]
+    assert calibrated["buckets"]
+    assert calibrated["top_bucket"]["label"].endswith("°C")