perf(setup): enable manupulate large-scale emptydroplets

scar/main/_setup.py

@@ -92,7 +92,6 @@ def setup_anndata(
     raw_adata._inplace_subset_obs(raw_adata.X.sum(axis=1) >= min_raw_counts)
 
     raw_adata.obs["total_counts"] = raw_adata.X.sum(axis=1)
-    raw_count = raw_adata.X.astype(int).A
 
     # initial estimation of ambient profile, will be update
     ambient_prof = raw_adata.X.sum(axis=0) / raw_adata.X.sum()
@@ -105,9 +104,16 @@ def setup_anndata(
         # calculate joint probability (log) of being cell-free droplets for each droplet
 
         log_prob = []
-        batches = np.array_split(raw_count, n_batch)
+        batch_idx = np.floor(
+            np.array(range(raw_adata.shape[0])) / raw_adata.shape[0] * n_batch
+        )
+
+        # batches = np.array_split(raw_count, n_batch)
         for b in range(n_batch):
-            count_batch = batches[b]
+            try:
+                count_batch = raw_adata[batch_idx == b].X.astype(int).A
+            except MemoryError:
+                raise MemoryError("use more batches by setting a higher n_batch")
             log_prob_batch = Multinomial(
                 probs=torch.tensor(ambient_prof), validate_args=False
             ).log_prob(torch.Tensor(count_batch))