`State.cell_*()` functions brought to the same result form

dycpm/__init__.py

@@ -195,17 +195,15 @@ class State:
 
     def cell_positions(self):
         """Return cell positions"""
-        return np.asarray([
-            np.mean([
+        return {
+            cell_id: np.mean([
                 self.voxel_coordinates(voxel)
                 for voxel in cell.voxel_ids
-            ], axis = 0) for cell in self.cells.values()
-        ])
+            ], axis = 0) for cell_id, cell in self.cells.items()
+        }
     def cell_fates(self):
         """Return cell fates"""
-        return np.asarray(
-            [cell.type_id for cell in self.cells.values()], dtype=int
-        )        
+        return {cell_id: cell.type_id for cell_id, cell in self.cells.items()}
     def cell_gyration_tensors(self):
         """Return gyration tensors for each cell"""
         return {

dycpm/analysis.py

@@ -7,11 +7,11 @@ from . import ECM
 def sorting_score(state):
     """Calculate the sorting score of a state"""
     if len(state.cells) == 0: return np.nan
-    fates = state.cell_fates()
+    fates = np.asarray(list(state.cell_fates().values()), dtype = int)
     epi = fates == 1
     pre = fates == 2
 
-    pos = state.cell_positions()
+    pos = np.asarray(list(state.cell_positions().values()))
     pos -= np.mean(pos, axis = 0)
     rcom = np.linalg.norm(pos, axis = 1)
     scores = np.sign([pair[0] - pair[1] for pair in itertools.product(rcom[pre], rcom[epi])])