Fixed efast more.

call_efast.py

@@ -9,10 +9,13 @@ from rasterio.warp import Resampling
 from rasterio.vrt import WarpedVRT
 from rasterio import shutil as rio_shutil
 
+def _import_efast():
+    """Lazy import of efast to avoid import errors when not using efast functions."""
     try:
         import efast
         from efast.s2_processing import distance_to_clouds
         from efast.s3_processing import reproject_and_crop_s3
+        return efast, distance_to_clouds, reproject_and_crop_s3
     except ImportError:
         raise ImportError(
             "efast package not found. Install with: pip install git+https://github.com/DHI-GRAS/efast.git"
@@ -46,8 +49,8 @@ def _reproject_raster_to_target(src_path, dst_path, target_bounds, target_crs, w
         }
         with WarpedVRT(src, **vrt_options) as vrt:
             profile = vrt.profile.copy()
-            profile.update({"dtype": "float32", "nodata": 0})
-            rio_shutil.copy(vrt, dst_path, driver="GTiff", **profile)
+            profile.update({"dtype": "float32", "nodata": 0, "driver": "GTiff"})
+            rio_shutil.copy(vrt, dst_path, **profile)
 
 
 def prepare_s2(season, site_position, site_name, date_range=None):
@@ -88,6 +91,7 @@ def prepare_s2(season, site_position, site_name, date_range=None):
         _reproject_raster_to_target(temp_normalized, refl_dst, target_bounds, target_crs, s2_width, s2_height)
         temp_normalized.unlink()
 
+    _, distance_to_clouds, _ = _import_efast()
     distance_to_clouds(s2_output_dir, ratio=RESOLUTION_RATIO)
 
 
@@ -128,6 +132,7 @@ def prepare_s3(season, site_position, site_name, date_range=None):
             with rasterio.open(composite_path, "w", **profile) as dst:
                 dst.write(composite)
 
+    _, _, reproject_and_crop_s3 = _import_efast()
     reproject_and_crop_s3(temp_composite_dir, s2_prepared_dir, s3_preprocessed_dir)
     shutil.rmtree(temp_composite_dir)
 

ndvi.py

@@ -94,6 +94,16 @@ def _process_ndvi_files(
         return
 
     for geotiff_file in geotiff_files:
+        # Check if file has enough bands (need at least 4 for RED and NIR)
+        try:
+            with rasterio.open(geotiff_file) as src:
+                if src.count < 4:
+                    print(f"[NDVI-{source_name}] Skipping {geotiff_file.name} (only {src.count} band(s), need 4+)")
+                    continue
+        except Exception as e:
+            print(f"[NDVI-{source_name}] Skipping {geotiff_file.name} (error reading: {e})")
+            continue
+
         output_file = output_dir / (
             output_namer(geotiff_file) if output_namer else geotiff_file.name
         )

run.py

@@ -1,4 +1,4 @@
-from efast import run_efast, prepare_s2, prepare_s3
+from call_efast import run_efast, prepare_s2, prepare_s3
 from ndvi import (
     generate_ndvi_raw,
     create_ndvi_timeseries_raw,
@@ -12,8 +12,8 @@ from clouds import detect_clouds
 
 def run_pipeline(season, site_position, site_name):
     try:
-        print(f"Downloading data for {site_name}, {season}")
-        download_s2(season, site_position, site_name)
+        # print(f"Downloading data for {site_name}, {season}")
+        # download_s2(season, site_position, site_name)
         # download_s3(season, site_position, site_name)
 
         # print(f"Generating NDVI for raw data: {site_name}, {season}")
@@ -23,7 +23,7 @@ def run_pipeline(season, site_position, site_name):
         # print(f"Detecting clouds for {site_name}, {season}")
         # detect_clouds(season, site_name)
 
-        # print(f"Preparing data for EFAST fusion for {site_name}, {season}")
+        print(f"Preparing data for EFAST fusion for {site_name}, {season}")
         # prepare_s2(season, site_position, site_name)
         # prepare_s3(season, site_position, site_name)
 
@@ -31,8 +31,8 @@ def run_pipeline(season, site_position, site_name):
         # run_efast(season, site_position, site_name)
 
         # print(f"Generating NDVI for prepared outputs: {site_name}, {season}")
-        # generate_ndvi_prepared(season, site_position, site_name)
-        # create_ndvi_timeseries_prepared(season, site_position, site_name)
+        generate_ndvi_prepared(season, site_position, site_name)
+        create_ndvi_timeseries_prepared(season, site_position, site_name)
 
     except Exception as e:
         print(f"Error: {e}")