diff --git a/navis/nbl/nblast_funcs.py b/navis/nbl/nblast_funcs.py
index 2f92c220..5d57661d 100644
--- a/navis/nbl/nblast_funcs.py
+++ b/navis/nbl/nblast_funcs.py
@@ -582,7 +582,6 @@ def nblast_smart(query: Union[Dotprops, NeuronList],
                                         approx_nn=approx_nn,
                                         progress=progress,
                                         smat_kwargs=smat_kwargs)
-
                         # Add queries and targets
                         for i, ix in enumerate(qix):
                             this.append(query_dps[ix], query_self_hits[ix])
@@ -1141,7 +1140,7 @@ def nblast_allbyall(x: NeuronList,
     return scores
 
 
-def test_single_query_time(q, t, it=20):
+def test_single_query_time(q, t, it=100):
     """Test average time of a single NBLAST query."""
     # Get a median-sized query and target
     q_ix = np.argsort(q.n_points)[len(q)//2]
@@ -1156,7 +1155,7 @@ def test_single_query_time(q, t, it=20):
     return np.mean(timings)  # seconds per medium sized query
 
 
-def find_batch_partition(q, t, T=10, N_cores=None):
+def find_batch_partition(q, t, T=10, n_cores=None):
     """Find partitions such that each batch takes about `T` seconds.
 
     Parameters
@@ -1165,7 +1164,7 @@ def find_batch_partition(q, t, T=10, N_cores=None):
                 Query and targets, respectively.
     T :         int
                 Time (in seconds) to aim for.
-    N_cores :   int, optional
+    n_cores :   int, optional
                 Number of cores that will be used. If provided, will try to
                 make sure that (n_rows * n_cols) is a multiple of n_cores by
                 increasing the number of rows (thereby decreasing the time
@@ -1176,6 +1175,7 @@ def find_batch_partition(q, t, T=10, N_cores=None):
     n_rows, n_cols
 
     """
+    # Test a single query
     time_per_query = test_single_query_time(q, t)
 
     # Number of queries per job such that each job runs in `T` second
@@ -1184,14 +1184,11 @@ def find_batch_partition(q, t, T=10, N_cores=None):
     # Number of neurons per batch
     neurons_per_batch  = max(1, int(np.sqrt(queries_per_batch)))
 
-    #n_rows = max(1, len(q) // neurons_per_batch)
-    #n_cols = max(1, len(t) // neurons_per_batch)
-    n_rows = max(1, 1000 // neurons_per_batch)
-    n_cols = max(1, 1000 // neurons_per_batch)
+    n_rows = max(1, len(q) // neurons_per_batch)
+    n_cols = max(1, len(t) // neurons_per_batch)
 
-    if N_cores and ((n_rows * n_cols) > N_cores):
-        while (n_rows * n_cols) % N_cores:
-            print(n_rows, n_cols, (n_rows * n_cols) % N_cores)
+    if n_cores and ((n_rows * n_cols) > n_cores):
+        while (n_rows * n_cols) % n_cores:
             n_rows += 1
 
     return n_rows, n_cols