Automatic pre-commit fixes

examples/clustering/sklearn_clustering_with_aeon_distances.ipynb

@@ -119,7 +119,9 @@
     "from sklearn.cluster import AgglomerativeClustering\n",
     "\n",
     "# Perform Agglomerative Clustering\n",
-    "agg_clustering = AgglomerativeClustering(n_clusters=2, metric=\"precomputed\", linkage=\"average\")\n",
+    "agg_clustering = AgglomerativeClustering(\n",
+    "    n_clusters=2, metric=\"precomputed\", linkage=\"average\"\n",
+    ")\n",
     "labels = agg_clustering.fit_predict(distance_matrix)\n",
     "\n",
     "# Visualize the clustering results\n",
@@ -181,7 +183,7 @@
     "plt.figure(figsize=(10, 6))\n",
     "for label in np.unique(dbscan_labels):\n",
     "    cluster_data = X[np.where(dbscan_labels == label)]  # Fix indexing\n",
-    "    \n",
+    "\n",
     "    if label == -1:\n",
     "        plt.plot(cluster_data.mean(axis=0), label=\"Noise\", linestyle=\"--\", linewidth=2)\n",
     "    else:\n",
@@ -192,7 +194,7 @@
     "plt.ylabel(\"Mean Value\")\n",
     "plt.legend(loc=\"upper right\", fontsize=\"small\")\n",
     "plt.grid(True)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -239,22 +241,27 @@
     "\n",
     "    # Ensure correct shape for plotting\n",
     "    cluster_data = np.squeeze(cluster_data)\n",
-    "    if cluster_data.ndim == 1:  \n",
+    "    if cluster_data.ndim == 1:\n",
     "        cluster_data = cluster_data[:, np.newaxis]  # Convert to 2D if needed\n",
     "\n",
     "    # Compute mean representation of each cluster\n",
-    "    cluster_mean = cluster_data.mean(axis=0)  \n",
+    "    cluster_mean = cluster_data.mean(axis=0)\n",
     "\n",
     "    # Plot noise separately\n",
     "    if label == -1:\n",
     "        plt.plot(cluster_mean, linestyle=\"--\", color=\"gray\", alpha=0.5, label=\"Noise\")\n",
     "    else:\n",
-    "        plt.plot(cluster_mean, color=colors(label % colors.N), alpha=0.7, label=f\"Cluster {label}\")\n",
+    "        plt.plot(\n",
+    "            cluster_mean,\n",
+    "            color=colors(label % colors.N),\n",
+    "            alpha=0.7,\n",
+    "            label=f\"Cluster {label}\",\n",
+    "        )\n",
     "\n",
     "plt.title(\"OPTICS Clustering with DTW Distance\")\n",
     "plt.legend()\n",
     "plt.grid(True, linestyle=\"--\", alpha=0.5)  # Light grid for better readability\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -287,15 +294,24 @@
     "from sklearn.cluster import SpectralClustering\n",
     "from sklearn.metrics import pairwise_distances\n",
     "\n",
-    "X = np.vstack((np.random.normal(loc=[2, 2], scale=0.5, size=(50, 2)), \n",
-    "               np.random.normal(loc=[5, 5], scale=0.5, size=(50, 2))))\n",
-    "distance_matrix = pairwise_distances(X, metric='euclidean')\n",
+    "X = np.vstack(\n",
+    "    (\n",
+    "        np.random.normal(loc=[2, 2], scale=0.5, size=(50, 2)),\n",
+    "        np.random.normal(loc=[5, 5], scale=0.5, size=(50, 2)),\n",
+    "    )\n",
+    ")\n",
+    "distance_matrix = pairwise_distances(X, metric=\"euclidean\")\n",
     "inverse_distance_matrix = 1 - (distance_matrix / distance_matrix.max())\n",
     "spectral = SpectralClustering(n_clusters=2, affinity=\"precomputed\", random_state=42)\n",
     "spectral_labels = spectral.fit_predict(inverse_distance_matrix)\n",
     "plt.figure(figsize=(10, 6))\n",
     "for label in np.unique(spectral_labels):\n",
-    "    plt.scatter(X[spectral_labels == label, 0], X[spectral_labels == label, 1], label=f\"Cluster {label}\", alpha=0.7)\n",
+    "    plt.scatter(\n",
+    "        X[spectral_labels == label, 0],\n",
+    "        X[spectral_labels == label, 1],\n",
+    "        label=f\"Cluster {label}\",\n",
+    "        alpha=0.7,\n",
+    "    )\n",
     "plt.title(\"Spectral Clustering with Normalized Similarity Matrix\")\n",
     "plt.legend()\n",
     "plt.show()"