diff --git a/src/sparcstools/utils/dataset_manipulation.py b/src/sparcstools/utils/dataset_manipulation.py
index 6739005..0cdecc7 100644
--- a/src/sparcstools/utils/dataset_manipulation.py
+++ b/src/sparcstools/utils/dataset_manipulation.py
@@ -6,6 +6,7 @@
 from rasterio.features import rasterize
 import numpy.ma as ma
 from tqdm.auto import tqdm
+import shutil
 
 def get_indexes(project_location, cell_ids, return_annotation = False):
     
@@ -20,16 +21,22 @@ def get_indexes(project_location, cell_ids, return_annotation = False):
 
     index_locs = []
     for cell_id in tqdm(cell_ids, desc = "getting indexes"):
-        index_locs.append(lookup[cell_id])
+        try:
+            index_locs.append(lookup[cell_id])
+        except KeyError:
+            index_locs.append(np.nan)
+            print(f"cell_id {cell_id} not found in dataset. Skipping...")
+            continue
     
     if return_annotation:
         annotation = pd.DataFrame({"index_hdf5": index_locs, "cell_id":cell_ids})
         annotation = annotation.sort_values("index_hdf5")
+        annotation = annotation.dropna() #remove nan values
         return(np.array(annotation.index_hdf5.tolist()), np.array(annotation.cell_id.tolist()))
     else:
         return(np.sort(index_locs))    
     
-def save_cells_to_new_hdf5(project_location, name, cell_ids, annotation = "selected_cells", append = False):
+def save_cells_to_new_hdf5(project_location, name, cell_ids, annotation = "selected_cells", append = False, temp_dir = "/tmp"):
     
     #get output directory
     outdir = f"{project_location}/extraction/filtered_data/{name}/"
@@ -49,9 +56,21 @@ def save_cells_to_new_hdf5(project_location, name, cell_ids, annotation = "selec
     
     #get cell images we want to write to new location
     print("getting cell images for selected cells...")
+
     with h5py.File(f"{project_location}/extraction/data/single_cells.h5", "r") as hf_in:
-        cell_images = hf_in.get("single_cell_data")[indexes]
-    
+        
+        #generate container for single_cell_data
+        from alphabase.io import tempmmap
+        TEMP_DIR_NAME = tempmmap.redefine_temp_location(temp_dir)
+        _, c, x, y = hf_in["single_cell_data"].shape
+        cell_images = tempmmap.array(shape = (len(indexes), c, x, y), dtype = np.float16)
+        
+        #actually add the single_cell_data to the container
+        i = 0
+        for index in tqdm(indexes):
+            cell_images[i] = hf_in.get("single_cell_data")[int(index)]
+            i += 1
+
     #delete file if append is False and it already exists so that we generate a new one
     if not append:
         if os.path.isfile(outfile):
@@ -88,7 +107,6 @@ def save_cells_to_new_hdf5(project_location, name, cell_ids, annotation = "selec
             
             #create index file
             hf_out.create_dataset('single_cell_index', (n_cells, 2), maxshape = (None, 2), dtype="uint64")
-            
 
             #create dataset
             hf_out.create_dataset('single_cell_data', (n_cells, n_channels, x, y), 
@@ -105,6 +123,10 @@ def save_cells_to_new_hdf5(project_location, name, cell_ids, annotation = "selec
             hf_out.get("annotation")[:] = annotation_df
         print("results saved.")
 
+    #cleanup temp directories
+    del cell_images
+    shutil.rmtree(TEMP_DIR_NAME, ignore_errors=True)
+
 def _read_napari_csv(path):
     # read csv table
     shapes = pd.read_csv(path, sep = ",")
@@ -117,8 +139,15 @@ def _read_napari_csv(path):
 
     for shape_id in shape_ids:
         _shapes = shapes.loc[shapes.index_shape == shape_id]
-        x = _shapes["axis-0"].tolist()
-        y = _shapes["axis-1"].tolist()
+        x = _shapes["axis-1"].tolist()
+        y = _shapes["axis-0"].tolist()
+
+        if len(x) < 3:
+            print("shape with less than 3 points found. Skipping...")
+            continue
+        if len(y) < 3:
+            print("shape with less than 3 points found. Skipping...")
+            continue
 
         polygon = Polygon(zip(x, y))
         polygons.append(polygon)
@@ -130,7 +159,7 @@ def _generate_mask_polygon(poly, outshape):
     img = rasterize(poly, out_shape = (x, y))
     return(img.astype("bool"))
 
-def extract_single_cells_napari_area(napari_path, project_location):
+def extract_single_cells_napari_area(napari_path, project_location, temp_dir = "/tmp"):
 
     #get name from naparipath
     name = os.path.basename(napari_path).split(".")[0]
@@ -171,4 +200,4 @@ def extract_single_cells_napari_area(napari_path, project_location):
     cell_ids = list(cell_ids) #need list type for lookup of indexes
 
     print(f"found {len(cell_ids)} unique cell_ids in selected area. Will now export to new HDF5 single cell dataset.")
-    save_cells_to_new_hdf5(project_location, cell_ids = cell_ids, name = name, annotation = name, append = False)
+    save_cells_to_new_hdf5(project_location, cell_ids = cell_ids, name = name, annotation = name, append = False, temp_dir=temp_dir)