Écrire le sidecar qualité aussi quand le DTM primaire vient du cache

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

lidar_pipeline/tests/test_pipeline.py

@@ -287,6 +287,77 @@ class TestEffectiveGroundMethod:
             assert p._effective_ground_method() == "smrf"
 
 
+class TestQualityCacheHit:
+    """Sidecar qualité écrit même quand le DTM primaire est réutilisé depuis
+    le cache (classification sol sautée, pas de LAS sol disponible)."""
+
+    @staticmethod
+    def _write_las(path, x, y, cls, t):
+        import laspy
+        import numpy as np
+        header = laspy.LasHeader(point_format=6, version="1.4")
+        header.scales = [0.01, 0.01, 0.01]
+        header.offsets = [652000.0, 6861000.0, 0.0]
+        header.global_encoding.gps_time_type = laspy.header.GpsTimeType.STANDARD
+        las = laspy.LasData(header)
+        las.x = np.asarray(x, float); las.y = np.asarray(y, float)
+        las.z = np.zeros(len(x))
+        las.classification = np.asarray(cls, np.uint8)
+        las.return_number = np.ones(len(x), np.uint8)
+        las.number_of_returns = np.ones(len(x), np.uint8)
+        las.gps_time = np.asarray(t, float)
+        las.write(str(path))
+
+    def test_cache_hit_writes_quality_sidecar(self, tmp_path, monkeypatch):
+        import numpy as np
+        import rasterio
+        from rasterio.transform import from_bounds
+        from lidar_pipeline.pipeline import LidarArchaeoPipeline
+        import lidar_pipeline.pipeline as pipeline_mod
+        from lidar_pipeline.dtm import GAP_FILL_TAG, GAP_FILL_VERSION
+        from lidar_pipeline.quality import read_quality
+
+        input_dir = tmp_path / "input"
+        input_dir.mkdir()
+        base = "LHD_FXX_0652_6862_PTS_LAMB93_IGN69"
+        laz = input_dir / f"{base}.laz"
+        import datetime
+        epoch = datetime.datetime(1980, 1, 6, tzinfo=datetime.timezone.utc)
+        gps = (datetime.datetime(2022, 6, 1, 12, tzinfo=datetime.timezone.utc) - epoch).total_seconds() - 1e9
+        self._write_las(laz, [652100, 652200, 652300], [6861100, 6861200, 6861300],
+                         [2, 2, 6], [gps, gps, gps])
+
+        pipeline = LidarArchaeoPipeline(
+            str(input_dir), str(tmp_path / "output"), ground_method='ign',
+            ign_classes="sol", strip_align=False, edge_buffer=0.0)
+        pipeline.viz_steps = []  # aucune visualisation à calculer (hors périmètre)
+
+        # DTM déjà en cache, compatible avec la config du run (pas de calage,
+        # pas de raccord, comblement à la version courante) : le run doit
+        # emprunter la branche « DTM existant » sans reclassifier ni régénérer.
+        dtm_path = pipeline.dtm_dir / f"{base}_dtm.tif"
+        with rasterio.open(
+            dtm_path, "w", driver="GTiff", height=10, width=10, count=1,
+            dtype="float32", crs="EPSG:2154",
+            transform=from_bounds(652000.0, 6861995.0, 652005.0, 6862000.0, 10, 10),
+        ) as dst:
+            dst.write(np.zeros((10, 10), dtype="float32"), 1)
+            dst.update_tags(**{GAP_FILL_TAG: GAP_FILL_VERSION})
+
+        def boom(*a, **k):
+            raise AssertionError("cache hit attendu : ne doit pas reclassifier/régénérer le DTM")
+
+        monkeypatch.setattr(pipeline_mod, "classify_ground", boom)
+        monkeypatch.setattr(pipeline_mod, "create_dtm_fast", boom)
+
+        assert pipeline.process_file(laz) is True
+
+        data = read_quality(pipeline.output_dir, base)
+        assert data is not None
+        assert abs(data["ground_density"] - 2e-6) < 1e-9  # 2 points de classe 2 sur 1 km²
+        assert data["acq_start"] == "2022-06-01" and data["acq_source"] == "gps"
+
+
 class TestResolveWorkers:
     def test_auto_scales_with_cpus(self):
         from lidar_pipeline.pipeline import resolve_workers