diff --git a/concha/utils.ipynb b/concha/utils.ipynb
index 2b9aa683d46b29e3935a00d2d43c38ba771d6c5e..c672672bf682d2af877f647b4aa5da01b4fe9542 100644
--- a/concha/utils.ipynb
+++ b/concha/utils.ipynb
@@ -123,7 +123,7 @@
    "source": [
     "def plot_2umap(embedding_2d, clustering_partition, ensemble_name, results_path):\n",
     "    \n",
-    "    classified = np.where(labels_umap >= 0)[0]\n",
+    "    classified = np.where(clustering_partition >= 0)[0]\n",
     "    \n",
     "    output1 = widgets.Output()\n",
     "    with output1:\n",
@@ -135,7 +135,7 @@
     "                alpha=0.5)\n",
     "        scatter = ax.scatter(embedding_2d[classified, 0],\n",
     "                embedding_2d[classified, 1],\n",
-    "                c=labels_umap[classified],\n",
+    "                c=clustering_partition[classified],\n",
     "                s=0.5,\n",
     "                alpha = 1,\n",
     "                cmap='Spectral')\n",
@@ -147,9 +147,9 @@
     "\n",
     "    output2 = widgets.Output()\n",
     "    with output2:\n",
-    "        repartition = pd.Series(labels_umap).value_counts()\n",
+    "        repartition = pd.Series(clustering_partition).value_counts()\n",
     "        repartition.index = [\"Unclassified\" if i == -1 else i for i in repartition.index]\n",
-    "        display(pd.DataFrame({\"Cluster\" : np.array(repartition.index), \"Occupancy (%)\" : 100*np.array(repartition.values)/len(labels_umap)}))\n",
+    "        display(pd.DataFrame({\"Cluster\" : np.array(repartition.index), \"Occupancy (%)\" : 100*np.array(repartition.values)/len(clustering_partition)}))\n",
     "    two_columns = widgets.HBox([output1, output2])\n",
     "    display(two_columns)"
    ]