diff --git a/.github/workflows/Dockerfile b/.github/workflows/Dockerfile
index 888afa27..bbdeae95 100644
--- a/.github/workflows/Dockerfile
+++ b/.github/workflows/Dockerfile
@@ -14,5 +14,5 @@ RUN pip install torch==2.4.1+cu121 --index-url https://download.pytorch.org/whl/
 RUN pip install torch_geometric>=2.5.0
 RUN pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.4.0+cu121.html
 
-# install pathpy
-RUN pip install -e '.[dev,test,doc,vis]'
\ No newline at end of file
+# install pathpyG
+RUN pip install -e .
\ No newline at end of file
diff --git a/docs/tutorial/netzschleuder.ipynb b/docs/tutorial/netzschleuder.ipynb
index 3ec56bd1..049d0f56 100644
--- a/docs/tutorial/netzschleuder.ipynb
+++ b/docs/tutorial/netzschleuder.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,14 +81,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Undirected graph with 105 nodes and 882 (directed) edges\n",
+      "Undirected graph with 105 nodes and 441 edges\n",
       "{   'Edge Attributes': {},\n",
       "    'Graph Attributes': {   'analyses_average_degree': \"<class 'float'>\",\n",
       "                            'analyses_degree_assortativity': \"<class 'float'>\",\n",
@@ -114,7 +114,7 @@
     }
    ],
    "source": [
-    "g = pp.io.read_netzschleuder_graph('polbooks')\n",
+    "g = pp.io.read_netzschleuder_graph(name='polbooks')\n",
     "print(g)"
    ]
   },
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -161,7 +161,7 @@
        "\n",
        "</style>\n",
        "\n",
-       "<div id = \"x4ab670539da945e99d27f79d705728b1\"> </div>\n",
+       "<div id = \"xa3d8f04591284bc7b617067c4fb6bc2c\"> </div>\n",
        "<script charset=\"utf-8\" src=\"https://d3js.org/d3.v5.min.js\"></script>\n",
        "<script charset=\"utf-8\">\n",
        "// Load via requireJS if available (jupyter notebook environment)\n",
@@ -193,8 +193,8 @@
        "    }\n",
        "};\n",
        "require(['d3'], function(d3){ //START\n",
-       "const data = {\"edges\": [{\"uid\": \"0-2\", \"source\": \"0\", \"target\": \"2\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"0-1\", \"source\": \"0\", \"target\": \"1\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"0-6\", \"source\": \"0\", \"target\": \"6\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"0-5\", \"source\": \"0\", \"target\": \"5\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"0-3\", \"source\": \"0\", \"target\": \"3\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"0-4\", \"source\": \"0\", \"target\": \"4\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"1-3\", \"source\": \"1\", \"target\": \"3\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"1-5\", \"source\": \"1\", \"target\": \"5\", \"size\": 5, \"color\": \"lightgray\", \"weight\": 1}, {\"uid\": \"1-6\", \"source\": \"1\", \"target\": \"6\", \"size\": 5, 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{\"uid\": \"91-75\", \"source\": \"91\", \"target\": \"75\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"91-74\", \"source\": \"91\", \"target\": \"74\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"91-72\", \"source\": \"91\", \"target\": \"72\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"91-31\", \"source\": \"91\", \"target\": \"31\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"92-72\", \"source\": \"92\", \"target\": \"72\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"92-73\", \"source\": \"92\", \"target\": \"73\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"92-75\", \"source\": \"92\", \"target\": \"75\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"93-66\", \"source\": \"93\", \"target\": \"66\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"93-102\", \"source\": \"93\", \"target\": \"102\", 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\"lightgray\", \"size\": 5}, {\"uid\": \"100-83\", \"source\": \"100\", \"target\": \"83\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"100-79\", \"source\": \"100\", \"target\": \"79\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"100-73\", \"source\": \"100\", \"target\": \"73\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"100-66\", \"source\": \"100\", \"target\": \"66\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"100-62\", \"source\": \"100\", \"target\": \"62\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"100-84\", \"source\": \"100\", \"target\": \"84\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"101-100\", \"source\": \"101\", \"target\": \"100\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"101-94\", \"source\": \"101\", \"target\": \"94\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"101-86\", \"source\": \"101\", \"target\": \"86\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"101-84\", \"source\": \"101\", \"target\": \"84\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"101-61\", \"source\": \"101\", \"target\": \"61\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"102-46\", \"source\": \"102\", \"target\": \"46\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"102-93\", \"source\": \"102\", \"target\": \"93\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"102-94\", \"source\": \"102\", \"target\": \"94\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"102-95\", \"source\": \"102\", \"target\": \"95\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"103-67\", \"source\": \"103\", \"target\": \"67\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"103-104\", \"source\": \"103\", \"target\": \"104\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"104-67\", \"source\": \"104\", \"target\": \"67\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"104-69\", \"source\": \"104\", \"target\": \"69\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}, {\"uid\": \"104-103\", \"source\": \"104\", \"target\": \"103\", \"weight\": 1, \"color\": \"lightgray\", \"size\": 5}], \"nodes\": [{\"uid\": \"0\", \"label\": \"1000 Years for Revenge\"}, {\"uid\": \"1\", \"label\": \"Bush vs. the Beltway\"}, {\"uid\": \"2\", \"label\": \"Charlie Wilson's War\"}, {\"uid\": \"3\", \"label\": \"Losing Bin Laden\"}, {\"uid\": \"4\", \"label\": \"Sleeping With the Devil\"}, {\"uid\": \"5\", \"label\": \"The Man Who Warned America\"}, {\"uid\": \"6\", \"label\": \"Why America Slept\"}, {\"uid\": \"7\", \"label\": \"Ghost Wars\"}, {\"uid\": \"8\", \"label\": \"A National Party No More\"}, {\"uid\": \"9\", \"label\": \"Bush Country\"}, {\"uid\": \"10\", \"label\": \"Dereliction of Duty\"}, {\"uid\": \"11\", \"label\": \"Legacy\"}, {\"uid\": \"12\", \"label\": \"Off with Their Heads\"}, {\"uid\": \"13\", \"label\": \"Persecution\"}, {\"uid\": \"14\", \"label\": \"Rumsfeld's War\"}, {\"uid\": \"15\", \"label\": \"Breakdown\"}, {\"uid\": \"16\", \"label\": \"Betrayal\"}, {\"uid\": \"17\", \"label\": \"Shut Up and Sing\"}, {\"uid\": \"18\", \"label\": \"Meant To Be\"}, {\"uid\": \"19\", \"label\": \"The Right Man\"}, {\"uid\": \"20\", \"label\": \"Ten Minutes from Normal\"}, {\"uid\": \"21\", \"label\": \"Hillary's Scheme\"}, {\"uid\": \"22\", \"label\": \"The French Betrayal of America\"}, {\"uid\": \"23\", \"label\": \"Tales from the Left Coast\"}, {\"uid\": \"24\", \"label\": \"Hating America\"}, {\"uid\": \"25\", \"label\": \"The Third Terrorist\"}, {\"uid\": \"26\", \"label\": \"Endgame\"}, {\"uid\": \"27\", \"label\": \"Spin Sisters\"}, {\"uid\": \"28\", \"label\": \"All the Shah's Men\"}, {\"uid\": \"29\", \"label\": \"Dangerous Dimplomacy\"}, {\"uid\": \"30\", \"label\": \"The Price of Loyalty\"}, {\"uid\": \"31\", \"label\": \"House of Bush, House of Saud\"}, {\"uid\": \"32\", \"label\": \"The Death of Right and Wrong\"}, {\"uid\": \"33\", \"label\": \"Useful Idiots\"}, {\"uid\": \"34\", \"label\": \"The O'Reilly Factor\"}, {\"uid\": \"35\", \"label\": \"Let Freedom Ring\"}, {\"uid\": \"36\", \"label\": \"Those Who Trespass\"}, {\"uid\": \"37\", \"label\": \"Bias\"}, {\"uid\": \"38\", \"label\": \"Slander\"}, {\"uid\": \"39\", \"label\": \"The Savage Nation\"}, {\"uid\": \"40\", \"label\": \"Deliver Us from Evil\"}, {\"uid\": \"41\", \"label\": \"Give Me a Break\"}, {\"uid\": \"42\", \"label\": \"The Enemy Within\"}, {\"uid\": \"43\", \"label\": \"The Real America\"}, {\"uid\": \"44\", \"label\": \"Who's Looking Out for You?\"}, {\"uid\": \"45\", \"label\": \"The Official Handbook Vast Right Wing Conspiracy\"}, {\"uid\": \"46\", \"label\": \"Power Plays\"}, {\"uid\": \"47\", \"label\": \"Arrogance\"}, {\"uid\": \"48\", \"label\": \"The Perfect Wife\"}, {\"uid\": \"49\", \"label\": \"The Bushes\"}, {\"uid\": \"50\", \"label\": \"Things Worth Fighting For\"}, {\"uid\": \"51\", \"label\": \"Surprise, Security, the American Experience\"}, {\"uid\": \"52\", \"label\": \"Allies\"}, {\"uid\": \"53\", \"label\": \"Why Courage Matters\"}, {\"uid\": \"54\", \"label\": \"Hollywood Interrupted\"}, {\"uid\": \"55\", \"label\": \"Fighting Back\"}, {\"uid\": \"56\", \"label\": \"We Will Prevail\"}, {\"uid\": \"57\", \"label\": \"The Faith of George W Bush\"}, {\"uid\": \"58\", \"label\": \"Rise of the Vulcans\"}, {\"uid\": \"59\", \"label\": \"Downsize This!\"}, {\"uid\": \"60\", \"label\": \"Stupid White Men\"}, {\"uid\": \"61\", \"label\": \"Rush Limbaugh Is a Big Fat Idiot\"}, {\"uid\": \"62\", \"label\": \"The Best Democracy Money Can Buy\"}, {\"uid\": \"63\", \"label\": \"The Culture of Fear\"}, {\"uid\": \"64\", \"label\": \"America Unbound\"}, {\"uid\": \"65\", \"label\": \"The Choice\"}, {\"uid\": \"66\", \"label\": \"The Great Unraveling\"}, {\"uid\": \"67\", \"label\": \"Rogue Nation\"}, {\"uid\": \"68\", \"label\": \"Soft Power\"}, {\"uid\": \"69\", \"label\": \"Colossus\"}, {\"uid\": \"70\", \"label\": \"The Sorrows of Empire\"}, {\"uid\": \"71\", \"label\": \"Against All Enemies\"}, {\"uid\": \"72\", \"label\": \"American Dynasty\"}, {\"uid\": \"73\", \"label\": \"Big Lies\"}, {\"uid\": \"74\", \"label\": \"The Lies of George W. Bush\"}, {\"uid\": \"75\", \"label\": \"Worse Than Watergate\"}, {\"uid\": \"76\", \"label\": \"Plan of Attack\"}, {\"uid\": \"77\", \"label\": \"Bush at War\"}, {\"uid\": \"78\", \"label\": \"The New Pearl Harbor\"}, {\"uid\": \"79\", \"label\": \"Bushwomen\"}, {\"uid\": \"80\", \"label\": \"The Bubble of American Supremacy\"}, {\"uid\": \"81\", \"label\": \"Living History\"}, {\"uid\": \"82\", \"label\": \"The Politics of Truth\"}, {\"uid\": \"83\", \"label\": \"Fanatics and Fools\"}, {\"uid\": \"84\", \"label\": \"Bushwhacked\"}, {\"uid\": \"85\", \"label\": \"Disarming Iraq\"}, {\"uid\": \"86\", \"label\": \"Lies and the Lying Liars Who Tell Them\"}, {\"uid\": \"87\", \"label\": \"MoveOn's 50 Ways to Love Your Country\"}, {\"uid\": \"88\", \"label\": \"The Buying of the President 2004\"}, {\"uid\": \"89\", \"label\": \"Perfectly Legal\"}, {\"uid\": \"90\", \"label\": \"Hegemony or Survival\"}, {\"uid\": \"91\", \"label\": \"The Exception to the Rulers\"}, {\"uid\": \"92\", \"label\": \"Freethinkers\"}, {\"uid\": \"93\", \"label\": \"Had Enough?\"}, {\"uid\": \"94\", \"label\": \"It's Still the Economy, Stupid!\"}, {\"uid\": \"95\", \"label\": \"We're Right They're Wrong\"}, {\"uid\": \"96\", \"label\": \"What Liberal Media?\"}, {\"uid\": \"97\", \"label\": \"The Clinton Wars\"}, {\"uid\": \"98\", \"label\": \"Weapons of Mass Deception\"}, {\"uid\": \"99\", \"label\": \"Dude, Where's My Country?\"}, {\"uid\": \"100\", \"label\": \"Thieves in High Places\"}, {\"uid\": \"101\", \"label\": \"Shrub\"}, {\"uid\": \"102\", \"label\": \"Buck Up Suck Up\"}, {\"uid\": \"103\", \"label\": \"The Future of Freedom\"}, {\"uid\": \"104\", \"label\": \"Empire\"}]}\n",
+       "const config = {\"edge_color\": \"lightgray\", \"edge_size\": 5, \"directed\": false, \"curved\": false, \"selector\": \"#xa3d8f04591284bc7b617067c4fb6bc2c\"}\n",
        "console.log(\"Static Network Template\");\n",
        "/* Resources\n",
        "   https://bl.ocks.org/mapio/53fed7d84cd1812d6a6639ed7aa83868\n",
@@ -423,14 +423,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Undirected graph with 34 nodes and 156 (directed) edges\n",
+      "Undirected graph with 34 nodes and 78 edges\n",
       "{   'Edge Attributes': {},\n",
       "    'Graph Attributes': {   'analyses_average_degree': \"<class 'float'>\",\n",
       "                            'analyses_degree_assortativity': \"<class 'float'>\",\n",
@@ -458,13 +458,13 @@
     }
    ],
    "source": [
-    "g = pp.io.read_netzschleuder_graph('karate', '78').to(device)\n",
+    "g = pp.io.read_netzschleuder_graph(name='karate', network='78').to(device)\n",
     "print(g)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -498,7 +498,7 @@
        "\n",
        "</style>\n",
        "\n",
-       "<div id = \"x9b3c830b2aa54f71910e0e990619990a\"> </div>\n",
+       "<div id = \"xbff4b38d0aae4040833c40c57b822b75\"> </div>\n",
        "<script charset=\"utf-8\" src=\"https://d3js.org/d3.v5.min.js\"></script>\n",
        "<script charset=\"utf-8\">\n",
        "// Load via requireJS if available (jupyter notebook environment)\n",
@@ -530,8 +530,8 @@
        "    }\n",
        "};\n",
        "require(['d3'], function(d3){ //START\n",
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{\"uid\": \"13-0\", \"source\": \"13\", \"target\": \"0\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"14-32\", \"source\": \"14\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"14-33\", \"source\": \"14\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"15-32\", \"source\": \"15\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"15-33\", \"source\": \"15\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"16-5\", \"source\": \"16\", \"target\": \"5\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"16-6\", \"source\": \"16\", \"target\": \"6\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"17-0\", \"source\": \"17\", \"target\": \"0\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"17-1\", \"source\": \"17\", \"target\": \"1\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"18-32\", \"source\": \"18\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"18-33\", \"source\": \"18\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"19-0\", \"source\": \"19\", \"target\": \"0\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"19-1\", \"source\": \"19\", \"target\": \"1\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"19-33\", \"source\": \"19\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"20-32\", \"source\": \"20\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"20-33\", \"source\": \"20\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"21-0\", \"source\": \"21\", \"target\": \"0\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"21-1\", \"source\": \"21\", \"target\": \"1\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"22-32\", \"source\": \"22\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"22-33\", \"source\": \"22\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"23-25\", \"source\": \"23\", \"target\": \"25\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"23-27\", \"source\": \"23\", \"target\": \"27\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"23-29\", \"source\": \"23\", \"target\": \"29\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"23-32\", \"source\": \"23\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"23-33\", \"source\": \"23\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"24-27\", \"source\": \"24\", \"target\": \"27\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"24-31\", \"source\": \"24\", \"target\": \"31\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"24-25\", \"source\": \"24\", \"target\": \"25\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"25-23\", \"source\": \"25\", \"target\": \"23\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"25-24\", \"source\": \"25\", \"target\": \"24\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"25-31\", \"source\": \"25\", \"target\": \"31\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"26-29\", \"source\": \"26\", \"target\": \"29\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"26-33\", \"source\": \"26\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"27-33\", \"source\": \"27\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"27-24\", \"source\": \"27\", \"target\": \"24\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"27-23\", \"source\": \"27\", \"target\": \"23\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"27-2\", \"source\": \"27\", \"target\": \"2\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"28-2\", \"source\": \"28\", \"target\": \"2\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"28-31\", \"source\": \"28\", \"target\": \"31\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"28-33\", \"source\": \"28\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"29-23\", \"source\": \"29\", \"target\": \"23\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"29-26\", \"source\": \"29\", \"target\": \"26\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"29-32\", \"source\": \"29\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"29-33\", \"source\": \"29\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"30-1\", \"source\": \"30\", \"target\": \"1\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"30-33\", \"source\": \"30\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"30-8\", \"source\": \"30\", \"target\": \"8\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"30-32\", \"source\": \"30\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"31-0\", \"source\": \"31\", \"target\": \"0\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"31-24\", \"source\": \"31\", \"target\": \"24\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"31-25\", \"source\": \"31\", \"target\": \"25\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"31-28\", \"source\": \"31\", \"target\": \"28\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"31-32\", \"source\": \"31\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"31-33\", \"source\": \"31\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-31\", \"source\": \"32\", \"target\": \"31\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-33\", \"source\": \"32\", \"target\": \"33\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-30\", \"source\": \"32\", \"target\": \"30\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-29\", \"source\": \"32\", \"target\": \"29\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-23\", \"source\": \"32\", \"target\": \"23\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-22\", \"source\": \"32\", \"target\": \"22\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-18\", \"source\": \"32\", \"target\": \"18\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-15\", \"source\": \"32\", \"target\": \"15\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-14\", \"source\": \"32\", \"target\": \"14\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-8\", \"source\": \"32\", \"target\": \"8\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-2\", \"source\": \"32\", \"target\": \"2\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"32-20\", \"source\": \"32\", \"target\": \"20\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-32\", \"source\": \"33\", \"target\": \"32\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-31\", \"source\": \"33\", \"target\": \"31\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-30\", \"source\": \"33\", \"target\": \"30\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-29\", \"source\": \"33\", \"target\": \"29\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-28\", \"source\": \"33\", \"target\": \"28\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-27\", \"source\": \"33\", \"target\": \"27\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-26\", \"source\": \"33\", \"target\": \"26\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-23\", \"source\": \"33\", \"target\": \"23\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-22\", \"source\": \"33\", \"target\": \"22\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-20\", \"source\": \"33\", \"target\": \"20\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-19\", \"source\": \"33\", \"target\": \"19\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-18\", \"source\": \"33\", \"target\": \"18\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-15\", \"source\": \"33\", \"target\": \"15\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-14\", \"source\": \"33\", \"target\": \"14\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-13\", \"source\": \"33\", \"target\": \"13\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-9\", \"source\": \"33\", \"target\": \"9\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"33-8\", \"source\": \"33\", \"target\": \"8\", \"weight\": 1, \"color\": \"gray\"}], \"nodes\": [{\"uid\": \"0\"}, {\"uid\": \"1\"}, {\"uid\": \"2\"}, {\"uid\": \"3\"}, {\"uid\": \"4\"}, {\"uid\": \"5\"}, {\"uid\": \"6\"}, {\"uid\": \"7\"}, {\"uid\": \"8\"}, {\"uid\": \"9\"}, {\"uid\": \"10\"}, {\"uid\": \"11\"}, {\"uid\": \"12\"}, {\"uid\": \"13\"}, {\"uid\": \"14\"}, {\"uid\": \"15\"}, {\"uid\": \"16\"}, {\"uid\": \"17\"}, {\"uid\": \"18\"}, {\"uid\": \"19\"}, {\"uid\": \"20\"}, {\"uid\": \"21\"}, {\"uid\": \"22\"}, {\"uid\": \"23\"}, {\"uid\": \"24\"}, {\"uid\": \"25\"}, {\"uid\": \"26\"}, {\"uid\": \"27\"}, {\"uid\": \"28\"}, {\"uid\": \"29\"}, {\"uid\": \"30\"}, {\"uid\": \"31\"}, {\"uid\": \"32\"}, {\"uid\": \"33\"}]}\n",
+       "const config = {\"edge_color\": \"gray\", \"directed\": false, \"curved\": false, \"selector\": \"#xbff4b38d0aae4040833c40c57b822b75\"}\n",
        "console.log(\"Static Network Template\");\n",
        "/* Resources\n",
        "   https://bl.ocks.org/mapio/53fed7d84cd1812d6a6639ed7aa83868\n",
@@ -762,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -770,12 +770,12 @@
      "output_type": "stream",
      "text": [
       "tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2,\n",
-      "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2], device='cuda:0')\n"
+      "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2])\n"
      ]
     }
    ],
    "source": [
-    "print(g['node_groups'])"
+    "print(g.data.node_groups)"
    ]
   },
   {
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -795,13 +795,13 @@
      "output_type": "stream",
      "text": [
       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
-      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1], device='cuda:0')\n"
+      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n"
      ]
     }
    ],
    "source": [
-    "g['node_groups'] -= 1\n",
-    "print(g['node_groups'])"
+    "g.data.node_groups -= 1\n",
+    "print(g.data.node_groups)"
    ]
   },
   {
@@ -813,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -847,7 +847,7 @@
        "\n",
        "</style>\n",
        "\n",
-       "<div id = \"x8a333917cdf44ec992a9e4e980709ae2\"> </div>\n",
+       "<div id = \"x8cec52da426c4429a7c82b2c72203201\"> </div>\n",
        "<script charset=\"utf-8\" src=\"https://d3js.org/d3.v5.min.js\"></script>\n",
        "<script charset=\"utf-8\">\n",
        "// Load via requireJS if available (jupyter notebook environment)\n",
@@ -880,7 +880,7 @@
        "};\n",
        "require(['d3'], function(d3){ //START\n",
        "const data = {\"edges\": [{\"uid\": \"0-2\", \"source\": \"0\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"0-1\", \"source\": \"0\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"0-31\", \"source\": \"0\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"0-21\", \"source\": \"0\", \"target\": \"21\", \"weight\": 1}, {\"uid\": \"0-19\", \"source\": \"0\", \"target\": \"19\", \"weight\": 1}, {\"uid\": \"0-17\", \"source\": \"0\", \"target\": \"17\", \"weight\": 1}, {\"uid\": \"0-13\", \"source\": \"0\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"0-12\", \"source\": \"0\", \"target\": \"12\", \"weight\": 1}, {\"uid\": \"0-11\", \"source\": \"0\", \"target\": \"11\", \"weight\": 1}, {\"uid\": \"0-8\", \"source\": \"0\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"0-7\", \"source\": \"0\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"0-6\", \"source\": \"0\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"0-5\", \"source\": \"0\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"0-4\", \"source\": \"0\", \"target\": \"4\", \"weight\": 1}, {\"uid\": \"0-3\", \"source\": \"0\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"0-10\", \"source\": \"0\", \"target\": \"10\", \"weight\": 1}, {\"uid\": \"1-7\", \"source\": \"1\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"1-30\", \"source\": \"1\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"1-21\", \"source\": \"1\", \"target\": \"21\", \"weight\": 1}, {\"uid\": \"1-19\", \"source\": \"1\", \"target\": \"19\", \"weight\": 1}, {\"uid\": \"1-17\", \"source\": \"1\", \"target\": \"17\", \"weight\": 1}, {\"uid\": \"1-13\", \"source\": \"1\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"1-3\", \"source\": \"1\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"1-2\", \"source\": \"1\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"1-0\", \"source\": \"1\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"2-3\", \"source\": \"2\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"2-32\", \"source\": \"2\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"2-28\", \"source\": \"2\", \"target\": \"28\", \"weight\": 1}, {\"uid\": \"2-27\", \"source\": \"2\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"2-13\", \"source\": \"2\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"2-9\", \"source\": \"2\", \"target\": \"9\", \"weight\": 1}, {\"uid\": \"2-8\", \"source\": \"2\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"2-7\", \"source\": \"2\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"2-1\", \"source\": \"2\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"2-0\", \"source\": \"2\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"3-0\", \"source\": \"3\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"3-1\", \"source\": \"3\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"3-2\", \"source\": \"3\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"3-7\", \"source\": \"3\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"3-12\", \"source\": \"3\", \"target\": \"12\", \"weight\": 1}, {\"uid\": \"3-13\", \"source\": \"3\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"4-0\", \"source\": \"4\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"4-6\", \"source\": \"4\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"4-10\", \"source\": \"4\", \"target\": \"10\", \"weight\": 1}, {\"uid\": \"5-0\", \"source\": \"5\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"5-16\", \"source\": \"5\", \"target\": \"16\", \"weight\": 1}, {\"uid\": \"5-6\", \"source\": \"5\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"5-10\", \"source\": \"5\", \"target\": \"10\", \"weight\": 1}, {\"uid\": \"6-0\", \"source\": \"6\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"6-4\", \"source\": \"6\", \"target\": \"4\", \"weight\": 1}, {\"uid\": \"6-5\", \"source\": \"6\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"6-16\", \"source\": \"6\", \"target\": \"16\", \"weight\": 1}, {\"uid\": \"7-0\", \"source\": \"7\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"7-1\", \"source\": \"7\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"7-2\", \"source\": \"7\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"7-3\", \"source\": \"7\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"8-0\", \"source\": \"8\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"8-2\", \"source\": \"8\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"8-30\", \"source\": \"8\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"8-32\", \"source\": \"8\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"8-33\", \"source\": \"8\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"9-33\", \"source\": \"9\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"9-2\", \"source\": \"9\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"10-0\", \"source\": \"10\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"10-4\", \"source\": \"10\", \"target\": \"4\", \"weight\": 1}, {\"uid\": \"10-5\", \"source\": \"10\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"11-0\", \"source\": \"11\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"12-0\", \"source\": \"12\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"12-3\", \"source\": \"12\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"13-1\", \"source\": \"13\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"13-33\", \"source\": \"13\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"13-3\", \"source\": \"13\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"13-2\", \"source\": \"13\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"13-0\", \"source\": \"13\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"14-32\", \"source\": \"14\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"14-33\", \"source\": \"14\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"15-32\", \"source\": \"15\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"15-33\", \"source\": \"15\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"16-5\", \"source\": \"16\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"16-6\", \"source\": \"16\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"17-0\", \"source\": \"17\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"17-1\", \"source\": \"17\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"18-32\", \"source\": \"18\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"18-33\", \"source\": \"18\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"19-0\", \"source\": \"19\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"19-1\", \"source\": \"19\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"19-33\", \"source\": \"19\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"20-32\", \"source\": \"20\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"20-33\", \"source\": \"20\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"21-0\", \"source\": \"21\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"21-1\", \"source\": \"21\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"22-32\", \"source\": \"22\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"22-33\", \"source\": \"22\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"23-25\", \"source\": \"23\", \"target\": \"25\", \"weight\": 1}, {\"uid\": \"23-27\", \"source\": \"23\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"23-29\", \"source\": \"23\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"23-32\", \"source\": \"23\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"23-33\", \"source\": \"23\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"24-27\", \"source\": \"24\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"24-31\", \"source\": \"24\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"24-25\", \"source\": \"24\", \"target\": \"25\", \"weight\": 1}, {\"uid\": \"25-23\", \"source\": \"25\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"25-24\", \"source\": \"25\", \"target\": \"24\", \"weight\": 1}, {\"uid\": \"25-31\", \"source\": \"25\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"26-29\", \"source\": \"26\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"26-33\", \"source\": \"26\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"27-33\", \"source\": \"27\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"27-24\", \"source\": \"27\", \"target\": \"24\", \"weight\": 1}, {\"uid\": \"27-23\", \"source\": \"27\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"27-2\", \"source\": \"27\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"28-2\", \"source\": \"28\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"28-31\", \"source\": \"28\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"28-33\", \"source\": \"28\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"29-23\", \"source\": \"29\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"29-26\", \"source\": \"29\", \"target\": \"26\", \"weight\": 1}, {\"uid\": \"29-32\", \"source\": \"29\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"29-33\", \"source\": \"29\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"30-1\", \"source\": \"30\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"30-33\", \"source\": \"30\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"30-8\", \"source\": \"30\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"30-32\", \"source\": \"30\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"31-0\", \"source\": \"31\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"31-24\", \"source\": \"31\", \"target\": \"24\", \"weight\": 1}, {\"uid\": \"31-25\", \"source\": \"31\", \"target\": \"25\", \"weight\": 1}, {\"uid\": \"31-28\", \"source\": \"31\", \"target\": \"28\", \"weight\": 1}, {\"uid\": \"31-32\", \"source\": \"31\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"31-33\", \"source\": \"31\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"32-31\", \"source\": \"32\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"32-33\", \"source\": \"32\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"32-30\", \"source\": \"32\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"32-29\", \"source\": \"32\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"32-23\", \"source\": \"32\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"32-22\", \"source\": \"32\", \"target\": \"22\", \"weight\": 1}, {\"uid\": \"32-18\", \"source\": \"32\", \"target\": \"18\", \"weight\": 1}, {\"uid\": \"32-15\", \"source\": \"32\", \"target\": \"15\", \"weight\": 1}, {\"uid\": \"32-14\", \"source\": \"32\", \"target\": \"14\", \"weight\": 1}, {\"uid\": \"32-8\", \"source\": \"32\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"32-2\", \"source\": \"32\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"32-20\", \"source\": \"32\", \"target\": \"20\", \"weight\": 1}, {\"uid\": \"33-32\", \"source\": \"33\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"33-31\", \"source\": \"33\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"33-30\", \"source\": \"33\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"33-29\", \"source\": \"33\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"33-28\", \"source\": \"33\", \"target\": \"28\", \"weight\": 1}, {\"uid\": \"33-27\", \"source\": \"33\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"33-26\", \"source\": \"33\", \"target\": \"26\", \"weight\": 1}, {\"uid\": \"33-23\", \"source\": \"33\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"33-22\", \"source\": \"33\", \"target\": \"22\", \"weight\": 1}, {\"uid\": \"33-20\", \"source\": \"33\", \"target\": \"20\", \"weight\": 1}, {\"uid\": \"33-19\", \"source\": \"33\", \"target\": \"19\", \"weight\": 1}, {\"uid\": \"33-18\", \"source\": \"33\", \"target\": \"18\", \"weight\": 1}, {\"uid\": \"33-15\", \"source\": \"33\", \"target\": \"15\", \"weight\": 1}, {\"uid\": \"33-14\", \"source\": \"33\", \"target\": \"14\", \"weight\": 1}, {\"uid\": \"33-13\", \"source\": \"33\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"33-9\", \"source\": \"33\", \"target\": \"9\", \"weight\": 1}, {\"uid\": \"33-8\", \"source\": \"33\", \"target\": \"8\", \"weight\": 1}], \"nodes\": [{\"uid\": \"0\", \"color\": \"#00ff00\"}, {\"uid\": \"1\", \"color\": \"#00ff00\"}, {\"uid\": \"2\", \"color\": \"#00ff00\"}, {\"uid\": \"3\", \"color\": \"#00ff00\"}, {\"uid\": \"4\", \"color\": \"#00ff00\"}, {\"uid\": \"5\", \"color\": \"#00ff00\"}, {\"uid\": \"6\", \"color\": \"#00ff00\"}, {\"uid\": \"7\", \"color\": \"#00ff00\"}, {\"uid\": \"8\", \"color\": \"#00ff00\"}, {\"uid\": \"9\", \"color\": \"#ff0000\"}, {\"uid\": \"10\", \"color\": \"#00ff00\"}, {\"uid\": \"11\", \"color\": \"#00ff00\"}, {\"uid\": \"12\", \"color\": \"#00ff00\"}, {\"uid\": \"13\", \"color\": \"#00ff00\"}, {\"uid\": \"14\", \"color\": \"#ff0000\"}, {\"uid\": \"15\", \"color\": \"#ff0000\"}, {\"uid\": \"16\", \"color\": \"#00ff00\"}, {\"uid\": \"17\", \"color\": \"#00ff00\"}, {\"uid\": \"18\", \"color\": \"#ff0000\"}, {\"uid\": \"19\", \"color\": \"#00ff00\"}, {\"uid\": \"20\", \"color\": \"#ff0000\"}, {\"uid\": \"21\", \"color\": \"#00ff00\"}, {\"uid\": \"22\", \"color\": \"#ff0000\"}, {\"uid\": \"23\", \"color\": \"#ff0000\"}, {\"uid\": \"24\", \"color\": \"#ff0000\"}, {\"uid\": \"25\", \"color\": \"#ff0000\"}, {\"uid\": \"26\", \"color\": \"#ff0000\"}, {\"uid\": \"27\", \"color\": \"#ff0000\"}, {\"uid\": \"28\", \"color\": \"#ff0000\"}, {\"uid\": \"29\", \"color\": \"#ff0000\"}, {\"uid\": \"30\", \"color\": \"#ff0000\"}, {\"uid\": \"31\", \"color\": \"#ff0000\"}, {\"uid\": \"32\", \"color\": \"#ff0000\"}, {\"uid\": \"33\", \"color\": \"#ff0000\"}]}\n",
-       "const config = {\"node_color\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"directed\": false, \"curved\": false, \"selector\": \"#x8a333917cdf44ec992a9e4e980709ae2\"}\n",
+       "const config = {\"node_color\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"directed\": false, \"curved\": false, \"selector\": \"#x8cec52da426c4429a7c82b2c72203201\"}\n",
        "console.log(\"Static Network Template\");\n",
        "/* Resources\n",
        "   https://bl.ocks.org/mapio/53fed7d84cd1812d6a6639ed7aa83868\n",
@@ -1098,10 +1098,10 @@
     {
      "data": {
       "text/plain": [
-       "<pathpyG.visualisations.network_plots.StaticNetworkPlot at 0x7fe4c0a0bbd0>"
+       "<pathpyG.visualisations.network_plots.StaticNetworkPlot at 0x7f36e006f1d0>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1119,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1153,7 @@
        "\n",
        "</style>\n",
        "\n",
-       "<div id = \"xee29d51aa1df46a8993cebf0e6acf7b8\"> </div>\n",
+       "<div id = \"xafea9563787b4286b19ec64968d2a97e\"> </div>\n",
        "<script charset=\"utf-8\" src=\"https://d3js.org/d3.v5.min.js\"></script>\n",
        "<script charset=\"utf-8\">\n",
        "// Load via requireJS if available (jupyter notebook environment)\n",
@@ -1186,7 +1186,7 @@
        "};\n",
        "require(['d3'], function(d3){ //START\n",
        "const data = {\"edges\": [{\"uid\": \"0-2\", \"source\": \"0\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"0-1\", \"source\": \"0\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"0-31\", \"source\": \"0\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"0-21\", \"source\": \"0\", \"target\": \"21\", \"weight\": 1}, {\"uid\": \"0-19\", \"source\": \"0\", \"target\": \"19\", \"weight\": 1}, {\"uid\": \"0-17\", \"source\": \"0\", \"target\": \"17\", \"weight\": 1}, {\"uid\": \"0-13\", \"source\": \"0\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"0-12\", \"source\": \"0\", \"target\": \"12\", \"weight\": 1}, {\"uid\": \"0-11\", \"source\": \"0\", \"target\": \"11\", \"weight\": 1}, {\"uid\": \"0-8\", \"source\": \"0\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"0-7\", \"source\": \"0\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"0-6\", \"source\": \"0\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"0-5\", \"source\": \"0\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"0-4\", \"source\": \"0\", \"target\": \"4\", \"weight\": 1}, {\"uid\": \"0-3\", \"source\": \"0\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"0-10\", \"source\": \"0\", \"target\": \"10\", \"weight\": 1}, {\"uid\": \"1-7\", \"source\": \"1\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"1-30\", \"source\": \"1\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"1-21\", \"source\": \"1\", \"target\": \"21\", \"weight\": 1}, {\"uid\": \"1-19\", \"source\": \"1\", \"target\": \"19\", \"weight\": 1}, {\"uid\": \"1-17\", \"source\": \"1\", \"target\": \"17\", \"weight\": 1}, {\"uid\": \"1-13\", \"source\": \"1\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"1-3\", \"source\": \"1\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"1-2\", \"source\": \"1\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"1-0\", \"source\": \"1\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"2-3\", \"source\": \"2\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"2-32\", \"source\": \"2\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"2-28\", \"source\": \"2\", \"target\": \"28\", \"weight\": 1}, {\"uid\": \"2-27\", \"source\": \"2\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"2-13\", \"source\": \"2\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"2-9\", \"source\": \"2\", \"target\": \"9\", \"weight\": 1}, {\"uid\": \"2-8\", \"source\": \"2\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"2-7\", \"source\": \"2\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"2-1\", \"source\": \"2\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"2-0\", \"source\": \"2\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"3-0\", \"source\": \"3\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"3-1\", \"source\": \"3\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"3-2\", \"source\": \"3\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"3-7\", \"source\": \"3\", \"target\": \"7\", \"weight\": 1}, {\"uid\": \"3-12\", \"source\": \"3\", \"target\": \"12\", \"weight\": 1}, {\"uid\": \"3-13\", \"source\": \"3\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"4-0\", \"source\": \"4\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"4-6\", \"source\": \"4\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"4-10\", \"source\": \"4\", \"target\": \"10\", \"weight\": 1}, {\"uid\": \"5-0\", \"source\": \"5\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"5-16\", \"source\": \"5\", \"target\": \"16\", \"weight\": 1}, {\"uid\": \"5-6\", \"source\": \"5\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"5-10\", \"source\": \"5\", \"target\": \"10\", \"weight\": 1}, {\"uid\": \"6-0\", \"source\": \"6\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"6-4\", \"source\": \"6\", \"target\": \"4\", \"weight\": 1}, {\"uid\": \"6-5\", \"source\": \"6\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"6-16\", \"source\": \"6\", \"target\": \"16\", \"weight\": 1}, {\"uid\": \"7-0\", \"source\": \"7\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"7-1\", \"source\": \"7\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"7-2\", \"source\": \"7\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"7-3\", \"source\": \"7\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"8-0\", \"source\": \"8\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"8-2\", \"source\": \"8\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"8-30\", \"source\": \"8\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"8-32\", \"source\": \"8\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"8-33\", \"source\": \"8\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"9-33\", \"source\": \"9\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"9-2\", \"source\": \"9\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"10-0\", \"source\": \"10\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"10-4\", \"source\": \"10\", \"target\": \"4\", \"weight\": 1}, {\"uid\": \"10-5\", \"source\": \"10\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"11-0\", \"source\": \"11\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"12-0\", \"source\": \"12\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"12-3\", \"source\": \"12\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"13-1\", \"source\": \"13\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"13-33\", \"source\": \"13\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"13-3\", \"source\": \"13\", \"target\": \"3\", \"weight\": 1}, {\"uid\": \"13-2\", \"source\": \"13\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"13-0\", \"source\": \"13\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"14-32\", \"source\": \"14\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"14-33\", \"source\": \"14\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"15-32\", \"source\": \"15\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"15-33\", \"source\": \"15\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"16-5\", \"source\": \"16\", \"target\": \"5\", \"weight\": 1}, {\"uid\": \"16-6\", \"source\": \"16\", \"target\": \"6\", \"weight\": 1}, {\"uid\": \"17-0\", \"source\": \"17\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"17-1\", \"source\": \"17\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"18-32\", \"source\": \"18\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"18-33\", \"source\": \"18\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"19-0\", \"source\": \"19\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"19-1\", \"source\": \"19\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"19-33\", \"source\": \"19\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"20-32\", \"source\": \"20\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"20-33\", \"source\": \"20\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"21-0\", \"source\": \"21\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"21-1\", \"source\": \"21\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"22-32\", \"source\": \"22\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"22-33\", \"source\": \"22\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"23-25\", \"source\": \"23\", \"target\": \"25\", \"weight\": 1}, {\"uid\": \"23-27\", \"source\": \"23\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"23-29\", \"source\": \"23\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"23-32\", \"source\": \"23\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"23-33\", \"source\": \"23\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"24-27\", \"source\": \"24\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"24-31\", \"source\": \"24\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"24-25\", \"source\": \"24\", \"target\": \"25\", \"weight\": 1}, {\"uid\": \"25-23\", \"source\": \"25\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"25-24\", \"source\": \"25\", \"target\": \"24\", \"weight\": 1}, {\"uid\": \"25-31\", \"source\": \"25\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"26-29\", \"source\": \"26\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"26-33\", \"source\": \"26\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"27-33\", \"source\": \"27\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"27-24\", \"source\": \"27\", \"target\": \"24\", \"weight\": 1}, {\"uid\": \"27-23\", \"source\": \"27\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"27-2\", \"source\": \"27\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"28-2\", \"source\": \"28\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"28-31\", \"source\": \"28\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"28-33\", \"source\": \"28\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"29-23\", \"source\": \"29\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"29-26\", \"source\": \"29\", \"target\": \"26\", \"weight\": 1}, {\"uid\": \"29-32\", \"source\": \"29\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"29-33\", \"source\": \"29\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"30-1\", \"source\": \"30\", \"target\": \"1\", \"weight\": 1}, {\"uid\": \"30-33\", \"source\": \"30\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"30-8\", \"source\": \"30\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"30-32\", \"source\": \"30\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"31-0\", \"source\": \"31\", \"target\": \"0\", \"weight\": 1}, {\"uid\": \"31-24\", \"source\": \"31\", \"target\": \"24\", \"weight\": 1}, {\"uid\": \"31-25\", \"source\": \"31\", \"target\": \"25\", \"weight\": 1}, {\"uid\": \"31-28\", \"source\": \"31\", \"target\": \"28\", \"weight\": 1}, {\"uid\": \"31-32\", \"source\": \"31\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"31-33\", \"source\": \"31\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"32-31\", \"source\": \"32\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"32-33\", \"source\": \"32\", \"target\": \"33\", \"weight\": 1}, {\"uid\": \"32-30\", \"source\": \"32\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"32-29\", \"source\": \"32\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"32-23\", \"source\": \"32\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"32-22\", \"source\": \"32\", \"target\": \"22\", \"weight\": 1}, {\"uid\": \"32-18\", \"source\": \"32\", \"target\": \"18\", \"weight\": 1}, {\"uid\": \"32-15\", \"source\": \"32\", \"target\": \"15\", \"weight\": 1}, {\"uid\": \"32-14\", \"source\": \"32\", \"target\": \"14\", \"weight\": 1}, {\"uid\": \"32-8\", \"source\": \"32\", \"target\": \"8\", \"weight\": 1}, {\"uid\": \"32-2\", \"source\": \"32\", \"target\": \"2\", \"weight\": 1}, {\"uid\": \"32-20\", \"source\": \"32\", \"target\": \"20\", \"weight\": 1}, {\"uid\": \"33-32\", \"source\": \"33\", \"target\": \"32\", \"weight\": 1}, {\"uid\": \"33-31\", \"source\": \"33\", \"target\": \"31\", \"weight\": 1}, {\"uid\": \"33-30\", \"source\": \"33\", \"target\": \"30\", \"weight\": 1}, {\"uid\": \"33-29\", \"source\": \"33\", \"target\": \"29\", \"weight\": 1}, {\"uid\": \"33-28\", \"source\": \"33\", \"target\": \"28\", \"weight\": 1}, {\"uid\": \"33-27\", \"source\": \"33\", \"target\": \"27\", \"weight\": 1}, {\"uid\": \"33-26\", \"source\": \"33\", \"target\": \"26\", \"weight\": 1}, {\"uid\": \"33-23\", \"source\": \"33\", \"target\": \"23\", \"weight\": 1}, {\"uid\": \"33-22\", \"source\": \"33\", \"target\": \"22\", \"weight\": 1}, {\"uid\": \"33-20\", \"source\": \"33\", \"target\": \"20\", \"weight\": 1}, {\"uid\": \"33-19\", \"source\": \"33\", \"target\": \"19\", \"weight\": 1}, {\"uid\": \"33-18\", \"source\": \"33\", \"target\": \"18\", \"weight\": 1}, {\"uid\": \"33-15\", \"source\": \"33\", \"target\": \"15\", \"weight\": 1}, {\"uid\": \"33-14\", \"source\": \"33\", \"target\": \"14\", \"weight\": 1}, {\"uid\": \"33-13\", \"source\": \"33\", \"target\": \"13\", \"weight\": 1}, {\"uid\": \"33-9\", \"source\": \"33\", \"target\": \"9\", \"weight\": 1}, {\"uid\": \"33-8\", \"source\": \"33\", \"target\": \"8\", \"weight\": 1}], \"nodes\": [{\"uid\": \"0\", \"color\": \"red\"}, {\"uid\": \"1\", \"color\": \"red\"}, {\"uid\": \"2\", \"color\": \"red\"}, {\"uid\": \"3\", \"color\": \"red\"}, {\"uid\": \"4\", \"color\": \"red\"}, {\"uid\": \"5\", \"color\": \"red\"}, {\"uid\": \"6\", \"color\": \"red\"}, {\"uid\": \"7\", \"color\": \"red\"}, {\"uid\": \"8\", \"color\": \"red\"}, {\"uid\": \"9\", \"color\": \"blue\"}, {\"uid\": \"10\", \"color\": \"red\"}, {\"uid\": \"11\", \"color\": \"red\"}, {\"uid\": \"12\", \"color\": \"red\"}, {\"uid\": \"13\", \"color\": \"red\"}, {\"uid\": \"14\", \"color\": \"blue\"}, {\"uid\": \"15\", \"color\": \"blue\"}, {\"uid\": \"16\", \"color\": \"red\"}, {\"uid\": \"17\", \"color\": \"red\"}, {\"uid\": \"18\", \"color\": \"blue\"}, {\"uid\": \"19\", \"color\": \"red\"}, {\"uid\": \"20\", \"color\": \"blue\"}, {\"uid\": \"21\", \"color\": \"red\"}, {\"uid\": \"22\", \"color\": \"blue\"}, {\"uid\": \"23\", \"color\": \"blue\"}, {\"uid\": \"24\", \"color\": \"blue\"}, {\"uid\": \"25\", \"color\": \"blue\"}, {\"uid\": \"26\", \"color\": \"blue\"}, {\"uid\": \"27\", \"color\": \"blue\"}, {\"uid\": \"28\", \"color\": \"blue\"}, {\"uid\": \"29\", \"color\": \"blue\"}, {\"uid\": \"30\", \"color\": \"blue\"}, {\"uid\": \"31\", \"color\": \"blue\"}, {\"uid\": \"32\", \"color\": \"blue\"}, {\"uid\": \"33\", \"color\": \"blue\"}]}\n",
-       "const config = {\"node_color\": [\"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"blue\", \"red\", \"red\", \"red\", \"red\", \"blue\", \"blue\", \"red\", \"red\", \"blue\", \"red\", \"blue\", \"red\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\"], \"directed\": false, \"curved\": false, \"selector\": \"#xee29d51aa1df46a8993cebf0e6acf7b8\"}\n",
+       "const config = {\"node_color\": [\"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"red\", \"blue\", \"red\", \"red\", \"red\", \"red\", \"blue\", \"blue\", \"red\", \"red\", \"blue\", \"red\", \"blue\", \"red\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\", \"blue\"], \"directed\": false, \"curved\": false, \"selector\": \"#xafea9563787b4286b19ec64968d2a97e\"}\n",
        "console.log(\"Static Network Template\");\n",
        "/* Resources\n",
        "   https://bl.ocks.org/mapio/53fed7d84cd1812d6a6639ed7aa83868\n",
@@ -1424,7 +1424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1448,7 +1448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1467,7 +1467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1481,7 +1481,7 @@
        ")"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1505,7 +1505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1522,12 +1522,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1566,7 +1566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1575,7 +1575,7 @@
        "1.0"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1595,12 +1595,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/src/pathpyG/io/__init__.py b/src/pathpyG/io/__init__.py
index b11c8a25..30a9bc7a 100644
--- a/src/pathpyG/io/__init__.py
+++ b/src/pathpyG/io/__init__.py
@@ -1,5 +1,4 @@
 from pathpyG.io.netzschleuder import read_netzschleuder_graph, read_netzschleuder_record, list_netzschleuder_records
-from pathpyG.io.graphtool import read_graphtool
 from pathpyG.io.pandas import (
     df_to_graph,
     df_to_temporal_graph,
diff --git a/src/pathpyG/io/graphtool.py b/src/pathpyG/io/graphtool.py
deleted file mode 100644
index 0cc410af..00000000
--- a/src/pathpyG/io/graphtool.py
+++ /dev/null
@@ -1,298 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
-
-import pickle
-import struct
-
-from numpy import array
-import torch
-
-from pathpyG.core.graph import Graph
-from pathpyG.utils.config import config
-
-
-def _parse_property_value(data: bytes, ptr: int, type_index: int, endianness: str) -> Tuple[Optional[Any], int]:
-    """
-    Parse a property value as well as the number of processed bytes.
-
-    Args:
-        data: byte array containing the data to be decoded
-        ptr: index of the first byte to be parsed
-        type_index: integer representing the type of the property value to be parsed
-        endianness: string representation of endianness, where `>` represents Big Endian
-        and `<` represents Little Endian
-
-    Returns:
-        Tuple $(v, n)$ consisting of the property value $v$ and the number of bytes $n$ processed
-    """
-    if type_index == 0:
-        return (bool(data[ptr]), 1)
-    elif type_index == 1:
-        return (struct.unpack(endianness + "h", data[ptr : ptr + 2])[0], 2)
-    elif type_index == 2:
-        return (struct.unpack(endianness + "i", data[ptr : ptr + 4])[0], 4)
-    elif type_index == 3:
-        return (struct.unpack(endianness + "q", data[ptr : ptr + 8])[0], 8)
-    elif type_index == 4:
-        return (struct.unpack(endianness + "d", data[ptr : ptr + 8])[0], 8)
-    elif type_index == 5:
-        print("pathpy does not support properties with type long double. Properties have been dropped.")
-        return (None, 16)
-    elif type_index == 6:
-        str_len = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        str = data[ptr + 8 : ptr + 8 + str_len].decode("utf-8")
-        return (str, 8 + str_len)
-    elif type_index == 7:
-        num_values = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        offset = 8
-        vals = []
-        for i in range(num_values):
-            vals.append(bool(data[ptr + offset : ptr + offset + 1]))
-            offset += 1
-        return (array(vals), 8 + num_values)
-    elif type_index == 8:
-        num_values = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        offset = 8
-        vals = []
-        for i in range(num_values):
-            vals.append(struct.unpack(endianness + "h", data[ptr + offset : ptr + offset + 2])[0])
-            offset += 4
-        return (array(vals), 8 + 2 * num_values)
-    elif type_index == 9:
-        num_values = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        offset = 8
-        vals = []
-        for i in range(num_values):
-            vals.append(struct.unpack(endianness + "i", data[ptr + offset : ptr + offset + 4])[0])
-            offset += 4
-        return (array(vals), 8 + 4 * num_values)
-    elif type_index == 10:
-        num_values = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        offset = 8
-        vals = []
-        for i in range(num_values):
-            vals.append(struct.unpack(endianness + "Q", data[ptr + offset : ptr + offset + 8])[0])
-            offset += 8
-        return (None, 8 + 8 * num_values)
-    elif type_index == 11:
-        num_values = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        offset = 8
-        vals = []
-        for i in range(num_values):
-            vals.append(struct.unpack(endianness + "d", data[ptr + offset : ptr + offset + 8])[0])
-            offset += 8
-        return (array(vals), 8 + 8 * num_values)
-    elif type_index == 12:
-        val_len = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        print("pathpyG does not support properties with type vector<long double>. Properties have been dropped.")
-        return (None, 8 + 16 * val_len)
-    elif type_index == 13:
-        num_strings = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        offset = 8
-        strs = []
-        for i in range(num_strings):
-            str_len = struct.unpack(endianness + "Q", data[ptr + offset : ptr + offset + 8])[0]
-            offset += 8
-            strs.append(data[ptr + offset : ptr + offset + str_len].decode("utf-8"))
-            offset += str_len
-
-        return (strs, offset)
-    elif type_index == 14:
-        val_len = struct.unpack(endianness + "Q", data[ptr : ptr + 8])[0]
-        return (pickle.loads(data[ptr + 8 : ptr + 8 + val_len]), 8 + val_len)
-    else:
-        msg = "Unknown type index {0} while parsing graphtool file".format(type_index)
-        print(msg)
-        raise Exception(msg)
-
-
-def parse_graphtool_format(data: bytes, id_node_attr=None) -> Graph:
-    """
-    Decodes data in graphtool binary format and returns a [`Graph`][pathpyG.Graph]. For a documentation of
-    the graphtool binary format, see see doc at https://graph-tool.skewed.de/static/doc/gt_format.html
-
-    Args:
-        data: Array of bytes to be decoded
-
-    Returns:
-        Graph: a static graph
-    """
-
-    # check magic bytes
-    if data[0:6] != b"\xe2\x9b\xbe\x20\x67\x74":
-        print("Invalid graphtool file. Wrong magic bytes.")
-        raise Exception("Invalid graphtool file. Wrong magic bytes.")
-    ptr = 6
-
-    # read graphtool version byte
-    graphtool_version = int(data[ptr])
-    ptr += 1
-
-    # read endianness
-    if bool(data[ptr]):
-        graphtool_endianness = ">"
-    else:
-        graphtool_endianness = "<"
-    ptr += 1
-
-    # read length of comment
-    str_len = struct.unpack(graphtool_endianness + "Q", data[ptr : ptr + 8])[0]
-    ptr += 8
-
-    # read string comment
-    comment = data[ptr : ptr + str_len].decode("ascii")
-    ptr += str_len
-
-    # read network directedness
-    directed = bool(data[ptr])
-    ptr += 1
-
-    # read number of nodes
-    n_nodes = struct.unpack(graphtool_endianness + "Q", data[ptr : ptr + 8])[0]
-    ptr += 8
-
-    # create pandas dataframe
-    network_dict = {}
-    # n = Network(directed = directed, multiedges=True)
-
-    # determine binary representation of neighbour lists
-    if n_nodes < 2**8:
-        fmt = "B"
-        d = 1
-    elif n_nodes < 2**16:
-        fmt = "H"
-        d = 2
-    elif n_nodes < 2**32:
-        fmt = "I"
-        d = 4
-    else:
-        fmt = "Q"
-        d = 8
-
-    sources = []
-    targets = []
-    # parse lists of out-neighbors for all n nodes
-    n_edges = 0
-    for v in range(n_nodes):
-        # read number of neighbors
-        num_neighbors = struct.unpack(graphtool_endianness + "Q", data[ptr : ptr + 8])[0]
-        ptr += 8
-
-        # add edges to record
-        for _ in range(num_neighbors):
-            w = struct.unpack(graphtool_endianness + fmt, data[ptr : ptr + d])[0]
-            ptr += d
-            sources.append(v)
-            targets.append(w)
-            n_edges += 1
-
-    # collect attributes from property maps
-    graph_attr = dict()
-    node_attr = dict()
-    edge_attr = dict()
-
-    # parse property maps
-    property_maps = struct.unpack(graphtool_endianness + "Q", data[ptr : ptr + 8])[0]
-    ptr += 8
-
-    for _ in range(property_maps):
-        key_type = struct.unpack(graphtool_endianness + "B", data[ptr : ptr + 1])[0]
-        ptr += 1
-
-        property_len = struct.unpack(graphtool_endianness + "Q", data[ptr : ptr + 8])[0]
-        ptr += 8
-
-        property_name = data[ptr : ptr + property_len].decode("ascii")
-        ptr += property_len
-
-        property_type = struct.unpack(graphtool_endianness + "B", data[ptr : ptr + 1])[0]
-        ptr += 1
-
-        if key_type == 0:  # graph-level property
-            res = _parse_property_value(data, ptr, property_type, graphtool_endianness)
-            graph_attr[property_name] = res[0]
-            ptr += res[1]
-        elif key_type == 1:  # node-level property
-            if property_name not in node_attr:
-                node_attr[property_name] = []
-            for v in range(n_nodes):
-                res = _parse_property_value(data, ptr, property_type, graphtool_endianness)
-                node_attr[property_name].append([res[0]])
-                ptr += res[1]
-        elif key_type == 2:  # edge-level property
-            if property_name not in edge_attr:
-                edge_attr[property_name] = []
-            for e in range(n_edges):
-                res = _parse_property_value(data, ptr, property_type, graphtool_endianness)
-                edge_attr[property_name].append(res[0])
-                ptr += res[1]
-        else:
-            print("Unknown key type {0}".format(key_type))
-
-    # LOG.info('Version \t= {0}'.format(graphtool_version))
-    # LOG.info('Endianness \t= {0}'.format(graphtool_endianness))
-    # LOG.info('comment size \t= {0}'.format(str_len))
-    # LOG.info('comment \t= {0}'.format(comment))
-    # LOG.info('directed \t= {0}'.format(directed))
-    # LOG.info('nodes \t\t= {0}'.format(n_nodes))
-
-    # add edge properties to data frame
-    # for p in edge_attribute_names:
-    #     # due to use of default_dict, this will add NA values to edges which have missing properties
-    #     network_data[p] = [ edge_attributes[e][p] for e in range(n_edges) ]
-
-    # create graph from pandas dataframe
-
-    # if 'time' in edge_attribute_names and not ignore_temporal:
-    #     raise Exception('')
-    #     n = to_temporal_network(network_data, directed=directed, **network_attributes)
-    # else:
-
-    if id_node_attr:
-        mapping = pp.IndexMap(node_attr[id_node_attr])
-    else:
-        mapping = None
-
-    g = Graph.from_edge_index(torch.LongTensor([sources, targets]).to(config["torch"]["device"]), mapping=mapping)
-    for a in node_attr:
-        if not a.startswith("node_"):
-            # print(node_attr[a])
-            g.data["node_{0}".format(a)] = node_attr[a]
-    for a in edge_attr:
-        if not a.startswith("edge_"):
-            g.data["edge_{0}".format(a)] = torch.tensor(edge_attr[a], dtype=torch.float).to(config["torch"]["device"])
-    for a in graph_attr:
-        g.data[a] = graph_attr[a]
-
-    if not directed:
-        return g.to_undirected()
-    return g
-
-    # for v in node_attributes:
-    #     for p in node_attributes[v]:
-    #         # for now we remove _pos for temporal networks due to type being incompatible with plotting
-    #         if p != '_pos' or ('time' not in edge_attribute_names or ignore_temporal):
-    #             n.nodes[v][p] = node_attributes[v][p]
-
-
-def read_graphtool(file: str, multiedges: bool = False) -> Graph:
-    """
-    Read a file in graphtool binary format.
-
-    Args:
-        file: Path to graphtool file to be read
-    """
-    with open(file, "rb") as f:
-        if ".zst" in file:
-            try:
-                import zstandard as zstd
-
-                dctx = zstd.ZstdDecompressor()
-                data = f.read()
-                return parse_graphtool_format(dctx.decompress(data, max_output_size=len(data)))
-            except ModuleNotFoundError:
-                msg = 'Package zstandard is required to decompress graphtool files. Please install module, e.g., using "pip install zstandard".'
-                # LOG.error(msg)
-                raise Exception(msg)
-        else:
-            return parse_graphtool_format(f.read(), multiedges)
diff --git a/src/pathpyG/io/netzschleuder.py b/src/pathpyG/io/netzschleuder.py
index 6024b391..658d440b 100644
--- a/src/pathpyG/io/netzschleuder.py
+++ b/src/pathpyG/io/netzschleuder.py
@@ -1,5 +1,5 @@
 from __future__ import annotations
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Optional, Union
 
 import json
 from urllib import request
@@ -11,8 +11,8 @@
 import pandas as pd
 
 from pathpyG.core.graph import Graph
+from pathpyG.core.temporal_graph import TemporalGraph
 from pathpyG.io.pandas import df_to_graph, df_to_temporal_graph
-from pathpyG.io.graphtool import parse_graphtool_format
 from pathpyG.io.pandas import add_node_attributes
 
 
@@ -81,138 +81,113 @@ def read_netzschleuder_record(name: str, base_url: str = "https://networks.skewe
     Returns:
         Dictionary containing key-value pairs of metadata
     """
-    url = "/api/net/{0}".format(name)
+    url = f"/api/net/{name}"
     try:
         return json.loads(request.urlopen(base_url + url).read())
-    except HTTPError:
-        msg = "Could not connect to netzschleuder repository at {0}".format(base_url)
+    except HTTPError as exc:
+        msg = f"Could not connect to netzschleuder repository at {base_url}"
         # LOG.error(msg)
-        raise Exception(msg)
+        raise Exception(msg) from exc
 
 
 def read_netzschleuder_graph(
     name: str,
-    net: Optional[str] = None,
+    network: Optional[str] = None,
     multiedges: bool = False,
     time_attr: Optional[str] = None,
-    base_url: str = "https://networks.skewed.de",
-    format="csv",
-) -> Graph:
-    """Read a pathpyG graph or temporal graph from the netzschleuder repository.
+    base_url: str = "https://networks.skewed.de"
+) -> Union[Graph, TemporalGraph]:
+    """Read a graph or temporal graph from the netzschleuder repository.
 
     Args:
         name: Name of the network data set to read from
-        net: Identifier of the network within the data set to read. For data sets
+        network: Identifier of the network within the data set to read. For data sets
             containing a single network only, this can be set to None.
         ignore_temporal: If False, this function will return a static or temporal network depending
             on whether edges contain a time attribute. If True, pathpy will not interpret
             time attributes and thus always return a static network.
         base_url: Base URL of netzschleuder repository
-        format: for 'csv' a zipped csv file will be downloaded, for 'gt' the binary graphtool format will be retrieved via the API
 
     Examples:
         Read network '77' from karate club data set
 
         >>> import pathpyG as pp
-        >>> n = pp.io.read_netzschleuder_network('karate', '77')
+        >>> n = pp.io.read_netzschleuder_network(name='karate', network='77')
         >>> print(type(n))
         >>> pp.plot(n)
         pp.Graph
 
-
     Returns:
-        an instance of Graph
-
+        Graph or TemporalGraph object
     """
     # build URL
-
     try:
         # retrieve properties of data record via API
         properties = json.loads(request.urlopen(f"{base_url}/api/net/{name}").read())
-        # print(properties)
 
         timestamps = not (time_attr is None)
 
-        if not net:
+        if not network:
             analyses = properties["analyses"]
-            net = name
+            network = name
         else:
-            analyses = properties["analyses"][net]
+            analyses = properties["analyses"][network]
 
         try:
             is_directed = analyses["is_directed"]
             num_nodes = analyses["num_vertices"]
-        except KeyError:
-            raise Exception(f"Record {name} contains multiple networks, please specify network name.")
-
-        if format == "csv":
-            url = f"{base_url}/net/{name}/files/{net}.csv.zip"
-            try:
-                response = request.urlopen(url)
-
-                # decompress zip into temporary folder
-                data = BytesIO(response.read())
-
-                with zipfile.ZipFile(data, "r") as zip_ref:
-                    with tempfile.TemporaryDirectory() as temp_dir:
-                        zip_ref.extractall(path=temp_dir)
-
-                        # the gprop file contains lines with property name/value pairs
-                        # gprops = pd.read_csv(f'{temp_dir}/gprops.csv', header=0, sep=',', skip_blank_lines=True, skipinitialspace=True)
-
-                        # nodes.csv contains node indices with node properties (like name)
-                        edges = pd.read_csv(
-                            f"{temp_dir}/edges.csv", header=0, sep=",", skip_blank_lines=True, skipinitialspace=True
-                        )
-
-                        # rename columns
-                        edges.rename(columns={"# source": "v", "target": "w"}, inplace=True)
-                        if timestamps and time_attr:
-                            edges.rename(columns={time_attr: "t"}, inplace=True)
-
-                        # construct graph and assign edge attributes
-                        if timestamps:
-                            g = df_to_temporal_graph(df=edges, multiedges=multiedges, num_nodes=num_nodes)
-                        else:
-                            g = df_to_graph(df=edges, multiedges=multiedges, is_undirected=not is_directed, num_nodes=num_nodes)
-
-                        node_attrs = pd.read_csv(
-                            f"{temp_dir}/nodes.csv", header=0, sep=",", skip_blank_lines=True, skipinitialspace=True
-                        )
-                        node_attrs.rename(columns={"# index": "index"}, inplace=True)
-
-                        add_node_attributes(node_attrs, g)
-
-                        # add graph-level attributes
-                        for x in analyses:
-                            g.data["analyses_" + x] = analyses[x]
-
-                        return g
-            except HTTPError:
-                msg = f"Could not retrieve netzschleuder record at {url}"
-                raise Exception(msg)
-
-        elif format == "gt":
-            try:
-                import zstandard as zstd
-
-                url = f"/net/{name}/files/{net}.gt.zst"
-                try:
-                    f = request.urlopen(base_url + url)
-                    # decompress data
-                    dctx = zstd.ZstdDecompressor()
-                    reader = dctx.stream_reader(f)
-                    decompressed = reader.readall()
-
-                    # parse graphtool binary format
-                    return parse_graphtool_format(bytes(decompressed))
-                except HTTPError:
-                    msg = f"Could not retrieve netzschleuder record at {url}"
-                    raise Exception(msg)
-            except ModuleNotFoundError:
-                msg = 'Package zstandard is required to decompress graphtool files. Please install module, e.g., using "pip install zstandard.'
-                # LOG.error(msg)
-                raise Exception(msg)
-    except HTTPError:
+        except KeyError as exc:
+            raise Exception(f"Record {name} contains multiple networks, please specify network name.") from exc
+        
+        # Retrieve CSV data
+        url = f"{base_url}/net/{name}/files/{network}.csv.zip"
+        try:
+            response = request.urlopen(url)
+
+            # decompress zip into temporary folder
+            data = BytesIO(response.read())
+
+            with zipfile.ZipFile(data, "r") as zip_ref:
+                with tempfile.TemporaryDirectory() as temp_dir:
+                    zip_ref.extractall(path=temp_dir)
+
+                    # the gprop file contains lines with property name/value pairs
+                    # gprops = pd.read_csv(f'{temp_dir}/gprops.csv', header=0, sep=',', 
+                    #           skip_blank_lines=True, skipinitialspace=True)
+
+                    # nodes.csv contains node indices with node properties (like name)
+                    edges = pd.read_csv(
+                        f"{temp_dir}/edges.csv", header=0, sep=",", skip_blank_lines=True, skipinitialspace=True
+                    )
+
+                    # rename columns
+                    edges.rename(columns={"# source": "v", "target": "w"}, inplace=True)
+                    if timestamps and time_attr:
+                        edges.rename(columns={time_attr: "t"}, inplace=True)
+
+                    # construct graph and assign edge attributes
+                    if timestamps:
+                        g = df_to_temporal_graph(df=edges, multiedges=multiedges, num_nodes=num_nodes)
+                    else:
+                        g = df_to_graph(df=edges, multiedges=multiedges,
+                                        is_undirected=not is_directed, num_nodes=num_nodes)
+
+                    node_attrs = pd.read_csv(
+                        f"{temp_dir}/nodes.csv", header=0, sep=",", skip_blank_lines=True, skipinitialspace=True
+                    )
+                    node_attrs.rename(columns={"# index": "index"}, inplace=True)
+
+                    add_node_attributes(node_attrs, g)
+
+                    # add graph-level attributes
+                    for x in analyses:
+                        g.data["analyses_" + x] = analyses[x]
+
+                    return g
+        except HTTPError as exc:
+            msg = f"Could not retrieve netzschleuder record at {url}"
+            raise Exception(msg) from exc
+    except HTTPError as exc:
         msg = f"Could not retrieve netzschleuder record at {base_url}/api/net/{name}"
-        raise Exception(msg)
+        raise Exception(msg) from exc
+    return None