style(#3): small coderabbit fixes

README.MD

@@ -13,7 +13,7 @@ The main steps are:
 
 Before running the script, make sure your project folder has the following structure:
 
-```
+```text
 lasConverter.py
 surveys/
     ├── survey_name_1/
@@ -32,7 +32,8 @@ metadata/
 - Each survey folder contains the CSV files for that survey (arbitrarily named).
 
 ### `metadata/metadata.csv`
-- Must include the columns: `fid`, `survey_name`, `survey_area`, `geom`, `epsg`.  
+- Must include the columns: `fid`, `survey_name`, `survey_area`, `geom`, `epsg`.
+- Script assumes bounding polygons in `geom` use EPSG:4258.
 - Each row corresponds to a survey.
 
 ### `metadata/level1_h3_cells.csv`

lasConverter.py

@@ -44,7 +44,7 @@ def prepare_cells(metadata: pd.DataFrame, level1_cells: List[str]) -> Tuple[Defa
             poly = Polygon([(lng, lat) for lat, lng in coords])
             cell_polygons[c2] = transform(transformer_4326_to_4978.transform, poly)
 
-    # Transfrom survey polygons from EPSG:4258 to EPSG:4978
+    # Transform survey polygons from EPSG:4258 to EPSG:4978
     metadata["geom"] = metadata["geom"].apply(wkt.loads)
     transform_4258_to_4978 = Transformer.from_crs("EPSG:4258", "EPSG:4978", always_xy=True)
     metadata["geom_4978"] = metadata["geom"].apply(lambda g: transform(transform_4258_to_4978.transform, g))
@@ -143,7 +143,7 @@ def CSV_2_LAS(metadata: pd.DataFrame, chunk_size_bytes: str ="64MB") -> None:
                 header = laspy.LasHeader(point_format=6, version="1.4")
 
                 # Add extra dimensions
-                header.add_extra_dim(laspy.ExtraBytesParams(name="accepted", type=np.uint8))
+                header.add_extra_dim(laspy.ExtraBytesParams(name="Accepted", type=np.uint8))
                 header.add_extra_dim(laspy.ExtraBytesParams(name="TVU", type=np.float32))
                 header.add_extra_dim(laspy.ExtraBytesParams(name="THU", type=np.float32))
 
@@ -153,7 +153,7 @@ def CSV_2_LAS(metadata: pd.DataFrame, chunk_size_bytes: str ="64MB") -> None:
                 las.z = z
 
                 # Set extra dimensions
-                las["accepted"] = accepted
+                las["Accepted"] = accepted
                 las["TVU"] = tvu
                 las["THU"] = thu
                 las.point_source_id = ids
@@ -166,6 +166,9 @@ def CSV_2_LAS(metadata: pd.DataFrame, chunk_size_bytes: str ="64MB") -> None:
         # Merging chunked LAS files into single LAS
         print("Merging LAS chunks into final LAS...")
         las_files = sorted(temp_folder.glob(f"{survey_name}_chunk_*.las"))
+        if not las_files:
+            print(f"⚠️ No LAS chunks produced for {survey_name}, skipping merge.")
+            continue
         first_las = laspy.read(str(las_files[0]))
         merged_header = first_las.header
 
@@ -182,7 +185,7 @@ def CSV_2_LAS(metadata: pd.DataFrame, chunk_size_bytes: str ="64MB") -> None:
     temp_folder.rmdir()
 
 
-def group_by_h3_cell(cell_to_surveys: DefaultDict[str, List[int]], cell_polygons: Dict[str, Polygon]):
+def group_by_h3_cell(metadata: pd.DataFrame, cell_to_surveys: DefaultDict[str, List[str]], cell_polygons: Dict[str, Polygon]):
     """
     Groups survey LAS files by H3 level-2 cells and crops them using PDAL.
 
@@ -205,7 +208,7 @@ def group_by_h3_cell(cell_to_surveys: DefaultDict[str, List[int]], cell_polygons
         for fid in survey_fids:
             survey_name = metadata.loc[metadata['fid'] == fid, 'survey_name'].item()
 
-            src = Path("converted_surveys") / f"{survey_name}.las"
+            src = CONVERTED_PATH / f"{survey_name}.las"
             if not src.exists():
                 continue
 
@@ -267,4 +270,4 @@ def group_by_h3_cell(cell_to_surveys: DefaultDict[str, List[int]], cell_polygons
 
     CSV_2_LAS(metadata)
 
-    group_by_h3_cell(cell_to_surveys, cell_polygons)
+    group_by_h3_cell(metadata, cell_to_surveys, cell_polygons)