more changes according to review

pyproject.toml

@@ -21,8 +21,7 @@ dependencies = [
     "polars~=1.10.0",
     "libpysal~=4.8.1",
     "spatialdata~=0.2.6",
-    "spatialdata_io~=0.1.6",
-    "alphashape~=1.3.1"
+    "spatialdata_io~=0.1.6"
 ]
 readme = "README.md"
 

src/celldega/pre/boundary_tile.py

@@ -59,7 +59,7 @@ def filter_and_save_fine_boundary(coarse_tile, fine_i, fine_j, fine_tile_x_min,
         filename = f"{path_output}/cell_tile_{fine_i}_{fine_j}.parquet"
         fine_tile_cells.to_parquet(filename)
 
-def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers):
+def process_fine_boundaries(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_output, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=1):
     with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
         futures = []
         for fine_i in range(n_fine_tiles_x):
@@ -134,7 +134,7 @@ def make_cell_boundary_tiles(
     tile_size=250,
     tile_bounds=None,
     image_scale=1,
-    max_workers=8
+    max_workers=1
 ):
 
 
@@ -163,7 +163,7 @@ def make_cell_boundary_tiles(
         Dictionary containing the minimum and maximum bounds for x and y coordinates.
     image_scale : float, optional, default=1
         Scale factor to apply to the geometry data.
-    max_workers : int, optional, default=8
+    max_workers : int, optional, default=1
         Maximum number of parallel workers for processing tiles.
 
     Returns

src/celldega/pre/trx_tile.py

@@ -5,7 +5,7 @@
 import concurrent.futures
 import pandas as pd
 
-def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers):
+def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=1):
     # Filter the entire dataset for the current coarse tile
     coarse_tile = trx.filter(
         (pl.col("transformed_x") >= coarse_tile_x_min) & (pl.col("transformed_x") < coarse_tile_x_max) &
@@ -16,7 +16,7 @@ def process_coarse_tile(trx, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_
         # Now process fine tiles using global fine tile indices
         process_fine_tiles(coarse_tile, i, j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers)   
 
-def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=8):
+def process_fine_tiles(coarse_tile, coarse_i, coarse_j, coarse_tile_x_min, coarse_tile_x_max, coarse_tile_y_min, coarse_tile_y_max, tile_size, path_trx_tiles, x_min, y_min, n_fine_tiles_x, n_fine_tiles_y, max_workers=1):
 
     # Use ThreadPoolExecutor for parallel processing of fine-grain tiles within the coarse tile
     with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
@@ -125,7 +125,7 @@ def make_trx_tiles(
     chunk_size=1000000,
     verbose=False,
     image_scale=1,
-    max_workers=8
+    max_workers=1
 ):
     """
     Processes transcript data by dividing it into coarse-grain and fine-grain tiles,
@@ -152,7 +152,7 @@ def make_trx_tiles(
     image_scale : float, optional
         Scale factor to apply to the transcript coordinates (default is 0.5).
     max_workers : int, optional
-        Maximum number of parallel workers for processing tiles (default is 8).
+        Maximum number of parallel workers for processing tiles (default is 1).
 
     Returns
     -------