diff --git a/examples/notebooks/rVAE_graphene.ipynb b/examples/notebooks/rVAE_graphene.ipynb
index dd82cf2c..5f52e000 100644
--- a/examples/notebooks/rVAE_graphene.ipynb
+++ b/examples/notebooks/rVAE_graphene.ipynb
@@ -8,7 +8,7 @@
       "collapsed_sections": [],
       "machine_shape": "hm",
       "mount_file_id": "1p7Yp7xNyVFOqNbl6a-1X5emBGVnvYcv7",
-      "authorship_tag": "ABX9TyP7wgB62ZfkJj8lxAPN4euq",
+      "authorship_tag": "ABX9TyNdCVwr+0xLmTygrUrAQ2IX",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -65,7 +65,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "e19866da-5282-400a-9a3a-a0f4d87c6c31"
+        "outputId": "615b0cf8-3aaa-4a29-f373-227a8c144911"
       },
       "source": [
         "#@title Download dataset\n",
@@ -73,7 +73,7 @@
         "!gdown https://drive.google.com/uc?id=1-84vLdGFsimD1jaTcGcMzNRCSvjId7-Y\n",
         "!gdown https://drive.google.com/uc?id=1-Lowglj7fwEFaJoC9EBKDyfCIsMgOnyu"
       ],
-      "execution_count": null,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
@@ -81,15 +81,15 @@
             "Downloading...\n",
             "From: https://drive.google.com/uc?id=1-JZSRjIjNjkR0ZQ8ffRDAZ2FID53Yhon\n",
             "To: /content/3DStack13-1-dec.npy\n",
-            "157MB [00:01, 129MB/s]\n",
+            "157MB [00:00, 278MB/s]\n",
             "Downloading...\n",
             "From: https://drive.google.com/uc?id=1-84vLdGFsimD1jaTcGcMzNRCSvjId7-Y\n",
             "To: /content/3DStack13-1-exp.npy\n",
-            "52.4MB [00:00, 85.2MB/s]\n",
+            "52.4MB [00:00, 60.6MB/s]\n",
             "Downloading...\n",
             "From: https://drive.google.com/uc?id=1-Lowglj7fwEFaJoC9EBKDyfCIsMgOnyu\n",
             "To: /content/3DStack13-1-coord.npy\n",
-            "2.45MB [00:00, 79.7MB/s]\n"
+            "2.45MB [00:00, 158MB/s]\n"
           ],
           "name": "stdout"
         }
@@ -110,7 +110,7 @@
         "id": "jjjDRjOKiSY6"
       },
       "source": [
-        "!pip install -q atomai"
+        "!pip install atomai"
       ],
       "execution_count": null,
       "outputs": []
@@ -136,7 +136,7 @@
         "import cv2\n",
         "import atomai as aoi"
       ],
-      "execution_count": null,
+      "execution_count": 3,
       "outputs": []
     },
     {
@@ -167,7 +167,7 @@
         "decoded_imgs = np.load('3DStack13-1-dec.npy')\n",
         "lattice_coord = np.load('3DStack13-1-coord.npy', allow_pickle=True)[()]"
       ],
-      "execution_count": null,
+      "execution_count": 4,
       "outputs": []
     },
     {
@@ -185,9 +185,9 @@
         "id": "obAUWr4Dk9ff",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 411
+          "height": 420
         },
-        "outputId": "3bf8c7fe-7e6e-41d6-b0d4-69efa2c41377"
+        "outputId": "419614ca-a4a4-4bbe-b5af-37441e26afca"
       },
       "source": [
         "i = 10 # Choose movie frame\n",
@@ -216,7 +216,7 @@
         "ax3.axis('off')\n",
         "ax3.set_title('Atomic coordinates', fontsize = 14)"
       ],
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -228,7 +228,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 17
+          "execution_count": 5
         },
         {
           "output_type": "display_data",
@@ -257,11 +257,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "45FCqpIcuc3D",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "90d81b5b-d026-4a8a-deb8-fc9a47524a33"
+        "id": "45FCqpIcuc3D"
       },
       "source": [
         "window_size = 24\n",
@@ -269,17 +265,8 @@
         "    np.sum(decoded_imgs[..., :-1], -1)[..., None], # convert to a single channel (no background)\n",
         "    lattice_coord, window_size, 0)"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/atomai/stat/multivar.py:94: UserWarning: The crop_size argument is deprecated. Use window_size to specify size of subimages\n",
-            "  UserWarning)\n"
-          ],
-          "name": "stderr"
-        }
-      ]
+      "execution_count": 6,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -300,14 +287,14 @@
         "id": "SNjE9GKyql9v",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 749
+          "height": 750
         },
-        "outputId": "1c3f5b4e-a9e9-41da-ea8f-49f1d2778a14"
+        "outputId": "9a21e415-19f0-4a28-b029-f55608637e13"
       },
       "source": [
         "s.gmm(n_components=16, plot_results=True, random_state=42);"
       ],
-      "execution_count": null,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
@@ -379,7 +366,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "5f299a73-a52c-426a-aaec-b98bcf9b83c0"
+        "outputId": "628697bf-e2d2-4fe0-e967-a3ae8b0c8c3e"
       },
       "source": [
         "imstack = s.imgstack[..., 0] # will be used as training/test data\n",
@@ -391,7 +378,7 @@
         "    imstack, test_size=0.15, shuffle=True, random_state=0)\n",
         "print(imstack_train.shape, imstack_test.shape)"
       ],
-      "execution_count": null,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
@@ -418,7 +405,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "YdJy3ZcexbZI",
-        "outputId": "6963186b-1d46-42d1-b06c-55e2ed2d3ab5"
+        "outputId": "9ced0e0e-6e0e-4736-ae21-819207d22bcf"
       },
       "source": [
         "# Ititialize model\n",
@@ -427,7 +414,7 @@
         "# Train\n",
         "vae.fit(X_train=imstack_train, X_test=imstack_test, training_cycles=200, batch_size=200)"
       ],
-      "execution_count": null,
+      "execution_count": 9,
       "outputs": [
         {
           "output_type": "stream",
@@ -654,12 +641,12 @@
           "height": 622
         },
         "id": "rDNOor290pcp",
-        "outputId": "fb5e2e1b-99db-4810-de2d-c7c1c1097226"
+        "outputId": "711af311-b83e-4321-c14d-ee6f8b815428"
       },
       "source": [
-        "vae.manifold2d(cmap=\"jet\")"
+        "vae.manifold2d(cmap=\"jet\");"
       ],
-      "execution_count": null,
+      "execution_count": 55,
       "outputs": [
         {
           "output_type": "display_data",
@@ -716,7 +703,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "IcUldcN_0oe4",
-        "outputId": "2a4e8a55-4281-427a-f8b1-ac4cef82ef94"
+        "outputId": "c4014b1e-bda9-41a1-c1a4-03f9c2c1724f"
       },
       "source": [
         "# Ititialize model\n",
@@ -725,7 +712,7 @@
         "# Train\n",
         "rvae.fit(X_train=imstack_train, X_test=imstack_test, training_cycles=200, batch_size=200)"
       ],
-      "execution_count": null,
+      "execution_count": 11,
       "outputs": [
         {
           "output_type": "stream",
@@ -943,12 +930,12 @@
           "base_uri": "https://localhost:8080/",
           "height": 622
         },
-        "outputId": "f0991daa-d0c5-4439-ea87-0d2cb5ae7aaa"
+        "outputId": "6fd4ffa1-471d-4b87-e4eb-013a74e612c8"
       },
       "source": [
-        "rvae.manifold2d(cmap=\"jet\")"
+        "rvae.manifold2d(cmap=\"jet\");"
       ],
-      "execution_count": null,
+      "execution_count": 56,
       "outputs": [
         {
           "output_type": "display_data",
@@ -992,7 +979,7 @@
         "z_mean, z_sd = rvae.encode(imstack)\n",
         "z1, z2, z3 = z_mean[:,0], z_mean[:, 1:3], z_mean[:, 3:]"
       ],
-      "execution_count": null,
+      "execution_count": 13,
       "outputs": []
     },
     {
@@ -1010,9 +997,9 @@
         "id": "lK67FERxryOs",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 277
+          "height": 313
         },
-        "outputId": "cbf26d78-1fe6-43fa-e7d6-1dfcad8df56c"
+        "outputId": "1971eb76-a020-4d76-9b0e-51909790e750"
       },
       "source": [
         "fig, ax = plt.subplots(1, 3, figsize=(25, 5))\n",
@@ -1027,7 +1014,7 @@
         "    _ax.tick_params(labelsize=15)\n",
         "plt.subplots_adjust(wspace=0.3)"
       ],
-      "execution_count": null,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1050,7 +1037,7 @@
         "id": "ARzaxWKz33NM"
       },
       "source": [
-        "Plot evolution of the encoded variables:"
+        "Plot evolution of the encoded latent variables:"
       ]
     },
     {
@@ -1058,10 +1045,10 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 504
+          "height": 312
         },
         "id": "VnkjCWZd3nKK",
-        "outputId": "6e1d5e07-488d-48d4-d09b-dd74c3576558"
+        "outputId": "5ac2c64e-7bba-49fd-deaf-742840fe8e9e"
       },
       "source": [
         "import seaborn as sns\n",
@@ -1075,42 +1062,24 @@
         "for _ax in fig.axes:\n",
         "    _ax.set_xlabel(\"Time\", fontsize=18)\n",
         "    _ax.tick_params(labelsize=15)\n",
-        "sns.kdeplot(frames_all, z1,\n",
+        "sns.kdeplot(frames_all, y=z1,\n",
         "            cmap=\"Reds\", shade=True, ax=ax[0],\n",
-        "            shade_lowest=None,  cut=0, bw=\"scott\")\n",
-        "sns.kdeplot(frames_all, z3[:, 0],\n",
+        "            thresh=0.15,  cut=0, bw_method=.075)\n",
+        "sns.kdeplot(frames_all, y=z3[:, 0],\n",
         "            cmap=\"Reds\", shade=True, ax=ax[1],\n",
-        "            shade_lowest=None, cut=0, bw=\"scott\")\n",
-        "sns.kdeplot(frames_all, z3[:, 1],\n",
+        "            thresh=0.15,  cut=0, bw_method=.075)\n",
+        "sns.kdeplot(frames_all, y=z3[:, 1],\n",
         "            cmap=\"Reds\", shade=True, ax=ax[2],\n",
-        "            shade_lowest=None, cut=0, bw=\"scott\")\n",
-        "plt.subplots_adjust(wspace=0.3)\n",
+        "            thresh=0.15,  cut=0, bw_method=.075)\n",
+        "plt.subplots_adjust(wspace=.3)\n",
         "plt.show()"
       ],
-      "execution_count": null,
+      "execution_count": 54,
       "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
-            "  FutureWarning\n",
-            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using scott for `bw_method`, but please see the docs for the new parameters and update your code.\n",
-            "  warnings.warn(msg, FutureWarning)\n",
-            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
-            "  FutureWarning\n",
-            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using scott for `bw_method`, but please see the docs for the new parameters and update your code.\n",
-            "  warnings.warn(msg, FutureWarning)\n",
-            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
-            "  FutureWarning\n",
-            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using scott for `bw_method`, but please see the docs for the new parameters and update your code.\n",
-            "  warnings.warn(msg, FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
+            "image/png": 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\n",
             "text/plain": [
               "<Figure size 1800x360 with 3 Axes>"
             ]
@@ -1137,9 +1106,9 @@
         "id": "OvQuGckrIsAo",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 378
+          "height": 428
         },
-        "outputId": "9b87aa60-e1f0-4a18-cc15-e051577cc46e"
+        "outputId": "db4174b2-0a5a-4b91-b5c0-bd31c2c69a4d"
       },
       "source": [
         "frame = 49 # select frame\n",
@@ -1175,7 +1144,7 @@
         "    _ax.axis(False)\n",
         "plt.show()"
       ],
-      "execution_count": null,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1210,7 +1179,7 @@
       "source": [
         ""
       ],
-      "execution_count": null,
+      "execution_count": 16,
       "outputs": []
     }
   ]