few adjustments to Tile.remove_l2a

sen2chain/tiles.py

@@ -989,3 +989,8 @@ class Tile:
                 shutil.rmtree(l2a_path)
                 logger.info("Removing symlink: {}".format(l2a.path))
                 l2a.path.unlink()
+            else:
+                #~ l2a_path = os.readlink(str(l2a.path))
+                logger.info("Removing: {}".format(l2a.path))
+                shutil.rmtree(str(l2a.path))
+

sen2chain/time_series.py

@@ -297,6 +297,12 @@ class TimeSeries:
         result_dict["tile"] = proc_item[4]
         result_dict["filename"] = proc_item[0].identifier
         for prop in features[fid]["properties"]:
+            #~ if type(features[fid]["properties"][prop]) == float:
+                #~ result_dict[prop] = "{:.6f}".format(features[fid]["properties"][prop])
+                #~ logger.info("toto {}".format(result_dict[prop]))
+            #~ else:
+                #~ result_dict[prop] = features[fid]["properties"][prop]
+                #~ logger.info("tata {}".format(result_dict[prop]))
             result_dict[prop] = features[fid]["properties"][prop]
         shared_list.append(result_dict)
         
@@ -336,6 +342,7 @@ class TimeSeries:
                         #~ logger.info("{}".format(shared_list))
                         #~ for index1, prod in enumerate(products):
                         rows_list = list(shared_list)
+                        #~ logger.info("rows_list {}".format(rows_list))
                         if rows_list:
                             self._df_dicts[indice] = TimeSeries._list_to_df(rows_list)
                             
@@ -568,7 +575,9 @@ class TimeSeries:
                 for (index, row), ax, bx in zip((df.loc[df['fid'] == fid]).sort_values(by=['date']).iterrows(), 
                                                  np.array(axs).flatten(),
                                                  np.array(bxs).flatten()):
-                    prod_id = row['filename'][:(-12 - len(df_name))]
+                    #~ logger.info("row[filename]: {}".format(row['filename']))
+                    prod_id = IndiceProduct(row['filename']).l2a
+                    #~ prod_id = row['filename'][:(-12 - len(df_name))]
                     indice_png_tile_path = out_path_fid_folder / "{0}_QL_{1}_{2}_{3}_{4}_{5}.jpg".format(
                                             self._vectors_file.stem,
                                             fidname,
@@ -587,16 +596,17 @@ class TimeSeries:
                     tile_indice_path = tile_obj.paths["indices"][df_name.lower()]
                     tile_l2a_path = tile_obj.paths["l2a"]
                     prod_path = tile_indice_path / prod_id / row['filename']
-                    tci_path = L2aProduct(IndiceProduct(row['filename']).l2a).tci_10m
+                    tci_path = L2aProduct(prod_id).tci_10m
                     #~ tci_path = list(Path(str(tile_l2a_path / row['filename'][:(-12 - len(df_name))])+ '.SAFE/')\
                                              #~ .glob('GRANULE/*/IMG_DATA/R10m/*_TCI_10m.jp2'))
                                              
                     crop_extent = gpd.read_file(str(self._vectors_file))
-                    raster_tci = rasterio.open(tci_path[0])
+                    logger.info(tci_path)
+                    raster_tci = rasterio.open(tci_path)
                     crop_extent_new_proj = crop_extent.to_crs(raster_tci.crs)
                     extent_geojson = mapping(crop_extent_new_proj['geometry'][int(fid)].buffer(1000))
 
-                    with rasterio.open(tci_path[0]) as tci_data:
+                    with rasterio.open(tci_path) as tci_data:
                         tci_data_crop, tci_data_crop_affine = mask(tci_data,
                                                  [extent_geojson],
                                                  crop=True)