fix

flox/core.py

@@ -397,7 +397,7 @@ def invert(x) -> tuple[np.ndarray, ...]:
         preferred_method = "cohorts"
 
     # Now normalize the dotproduct to get containment
-    containment = dotproduct / chunks_per_label
+    containment = csr_array(dotproduct / chunks_per_label)
 
     # Use a threshold to force some merging. We do not use the filtered
     # containment matrix for estimating "sparsity" because it is a bit
@@ -407,8 +407,6 @@ def invert(x) -> tuple[np.ndarray, ...]:
     containment.data[mask] = 0
     containment.eliminate_zeros()
 
-    containment = csr_array(containment)
-
     # Iterate over labels, beginning with those with most chunks
     logger.info("find_group_cohorts: merging cohorts")
     order = np.argsort(containment.sum(axis=LABEL_AXIS))[::-1]