Now tile dataframe is created using a dict not pd.concat, reducing RA…

…M usage

src/landcoverpy/utilities/raster.py

@@ -1,5 +1,4 @@
 import json
-import math
 
 from itertools import compress
 from pathlib import Path
@@ -53,12 +52,13 @@ def _read_raster(
     """
 
     if window is not None and (mask_geometry is not None or path_to_disk is not None):
-        print("If a window is provided, the raster can't be saved to disk or cropped")
+        raise ValueError("If a window is provided, mask_geometry and path_to_disk must be None")
 
     band_name = _get_raster_name_from_path(str(band_path))
-    print(f"Reading raster {band_name}")
+
     with rasterio.open(band_path) as band_file:
 
+
         spatial_resolution = _get_spatial_resolution_raster(band_path)
         if window is not None and spatial_resolution != 10:
             # Transform the window to the actual resolution of the raster
@@ -100,7 +100,6 @@ def _read_raster(
                 path_to_disk = path_to_disk[:-3] + "tif"
 
     if normalize_range is not None:
-        print(f"Normalizing band {band_name}")
         value1, value2 = normalize_range
         band = _normalize(band, value1, value2)
 
@@ -111,7 +110,6 @@ def _read_raster(
     # Create a temporal memory file to mask the band
     # This is necessary because the band is previously read to scale its resolution
     if mask_geometry:
-        print(f"Cropping raster {band_name}")
         projected_geometry = _project_shape(mask_geometry, dcs=destination_crs)
         with rasterio.io.MemoryFile() as memfile:
             with memfile.open(**kwargs) as memfile_band:
@@ -430,7 +428,6 @@ def _rescale_band(
         rescaled_raster = np.ndarray(
             shape=(kwargs["count"], new_kwargs["height"], new_kwargs["width"]), dtype=np.float32)
 
-        print(f"Rescaling raster {band_name}, from: {img_resolution}m to {str(spatial_resol)}.0m")
         reproject(
             source=band,
             destination=rescaled_raster,

src/landcoverpy/workflow_predict.py

@@ -173,7 +173,6 @@ def _process_tile_predict(tile, execution_mode, used_columns=None, use_block_win
     rasters_by_season = defaultdict(dict)
 
     for season, products_metadata in product_per_season.items():
-        print(season)
         bucket_products = settings.MINIO_BUCKET_NAME_PRODUCTS
         bucket_composites = settings.MINIO_BUCKET_NAME_COMPOSITES
         current_bucket = None
@@ -218,7 +217,6 @@ def _process_tile_predict(tile, execution_mode, used_columns=None, use_block_win
         temp_product_folder = Path(settings.TMP_DIR, product_name + ".SAFE")
         if not temp_product_folder.exists():
             Path.mkdir(temp_product_folder)
-        print(f"Processing product {product_name}")
 
         rasters_by_season[season]["raster_paths"] = rasters_paths
         rasters_by_season[season]["is_band"] = is_band
@@ -249,8 +247,8 @@ def _process_tile_predict(tile, execution_mode, used_columns=None, use_block_win
         window_kwargs["height"] = window.height
         window_kwargs["transform"] = rasterio.windows.transform(window, kwargs_s2["transform"])
 
-        # Dataframe for storing data of a window
-        window_tile_df = None
+        # Dict for storing data of a window (all rasters and indexes)
+        window_tile_dict = {}
 
         crop_mask = np.zeros(shape=(int(window_kwargs["height"]), int(window_kwargs["width"])), dtype=np.uint8)
 
@@ -306,9 +304,7 @@ def _process_tile_predict(tile, execution_mode, used_columns=None, use_block_win
                 raster_masked = np.ma.masked_array(raster[0], mask=crop_mask)
                 raster_masked = np.ma.compressed(raster_masked)
 
-                window_raster_df = pd.DataFrame({f"{season}_{raster_name}": raster_masked})
-
-                window_tile_df = pd.concat([window_tile_df, window_raster_df], axis=1)
+                window_tile_dict.update({f"{season}_{raster_name}": raster_masked})
 
         for dem_name in dems_raster_names:
             # Add dem and aspect data
@@ -333,13 +329,9 @@ def _process_tile_predict(tile, execution_mode, used_columns=None, use_block_win
                 )
                 raster_masked = np.ma.masked_array(raster, mask=crop_mask)
                 raster_masked = np.ma.compressed(raster_masked).flatten()
-                window_raster_df = pd.DataFrame({dem_name: raster_masked})
-                window_tile_df = pd.concat([window_tile_df, window_raster_df], axis=1)
-
-
+                window_tile_dict.update({dem_name: raster_masked})
 
-        print("Dataframe information:")
-        print(window_tile_df.info())
+        window_tile_df = pd.DataFrame(window_tile_dict)
 
         if execution_mode == ExecutionMode.LAND_COVER_PREDICTION:
 
@@ -404,13 +396,19 @@ def _process_tile_predict(tile, execution_mode, used_columns=None, use_block_win
             ) as classification_file:
                 classification_file.write(encoded_sl_predictions, window=window)
 
+        print(f"Window {window} processed, output raster {classification_path} updated")
+
     minio_client.fput_object(
         bucket_name=settings.MINIO_BUCKET_CLASSIFICATIONS,
         object_name=f"{settings.MINIO_DATA_FOLDER_NAME}/{classification_name}",
         file_path=classification_path,
         content_type="image/tif",
     )
 
+    print(f"Classification raster {classification_name} uploaded to Minio")
+    print(f"Tile {tile} processed")
+    print("Cleaning up temporary files")
+
     for path in Path(settings.TMP_DIR).glob("**/*"):
         if path.is_file():
             path.unlink()