Potential_paths_interactive_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# disable warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter out small nodes: keep those with at least 10 channels and at least 0.3 btc in capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pandas import json_normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the file\n",
    "file_name = '../Datos/graph_metrics_2022-07-11.json'\n",
    "graph_data = open(file_name, encoding='utf8')\n",
    "graph_json = json.load(graph_data)\n",
    "# converting data to pd dfs\n",
    "nodes_graph = json_normalize(graph_json['nodes'])\n",
    "channels_graph = json_normalize(graph_json['edges'])\n",
    "channels_graph.channel_id = channels_graph.capacity.astype(int)\n",
    "channels_graph.capacity = channels_graph.capacity.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_node_chan_info(df_nodes, df_channels):\n",
    "    df_nodes = pd.concat([\n",
    "        df_nodes,\n",
    "        pd.DataFrame(columns=[\n",
    "                'num_enabled_channels',\n",
    "                'num_channels',\n",
    "                'total_node_capacity'\n",
    "        ])\n",
    "    ], sort=False)\n",
    "    \n",
    "    for index, node in df_nodes.iterrows():\n",
    "\n",
    "        pub_key = node['pub_key']\n",
    "        # obtain those channels that were opened by this specific node\n",
    "        node1_channels = df_channels[df_channels.node1_pub == pub_key]\n",
    "        \n",
    "        # obtain those channels that were opened by other nodes\n",
    "        node2_channels = df_channels[df_channels.node2_pub == pub_key]\n",
    "        \n",
    "        total_capacity = (node1_channels['capacity'].sum() + node2_channels['capacity'].sum())\n",
    "        df_nodes.loc[index, 'total_node_capacity'] = total_capacity\n",
    "        \n",
    "        \n",
    "        # transform False to True and vice versa and make the sum -> this is the enabled channels of a node\n",
    "        enabled1_channels = (node1_channels['node1_policy.disabled'].transform(lambda x: not x)).sum()\n",
    "        enabled2_channels = (node2_channels['node2_policy.disabled'].transform(lambda x: not x)).sum()\n",
    "        \n",
    "        df_nodes.loc[index, 'num_enabled_channels'] = enabled1_channels + enabled2_channels\n",
    "        df_nodes.loc[index, 'num_channels'] = node1_channels.shape[0] + node2_channels.shape[0]\n",
    "\n",
    "    return df_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# network = add_node_chan_info(nodes_graph, channels_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# network.to_csv('../Datos/network.csv', index=False, encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = pd.read_csv('../Datos/network.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_update</th>\n",
       "      <th>pub_key</th>\n",
       "      <th>alias</th>\n",
       "      <th>addresses</th>\n",
       "      <th>color</th>\n",
       "      <th>features.0.name</th>\n",
       "      <th>features.0.is_required</th>\n",
       "      <th>features.0.is_known</th>\n",
       "      <th>features.5.name</th>\n",
       "      <th>features.5.is_required</th>\n",
       "      <th>...</th>\n",
       "      <th>features.3.is_known</th>\n",
       "      <th>features.105.name</th>\n",
       "      <th>features.105.is_required</th>\n",
       "      <th>features.105.is_known</th>\n",
       "      <th>features.1339.name</th>\n",
       "      <th>features.1339.is_required</th>\n",
       "      <th>features.1339.is_known</th>\n",
       "      <th>num_enabled_channels</th>\n",
       "      <th>num_channels</th>\n",
       "      <th>total_node_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.657364e+09</td>\n",
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       "      <td>#3399ff</td>\n",
       "      <td>data-loss-protect</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>upfront-shutdown-script</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>OutaSpace 🚀</td>\n",
       "      <td>[{'network': 'tcp', 'addr': '176.28.11.68:9760...</td>\n",
       "      <td>#123456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>upfront-shutdown-script</td>\n",
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       "<p>2 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    last_update                                            pub_key  \\\n",
       "0  1.657364e+09  020003b9499a97c8dfbbab6b196319db37ba9c37bccb60...   \n",
       "1  1.657199e+09  0200072fd301cb4a680f26d87c28b705ccd6a1d5b00f1b...   \n",
       "\n",
       "               alias                                          addresses  \\\n",
       "0  BJCR_BTCPayServer  [{'network': 'tcp', 'addr': '95.217.192.209:97...   \n",
       "1        OutaSpace 🚀  [{'network': 'tcp', 'addr': '176.28.11.68:9760...   \n",
       "\n",
       "     color    features.0.name features.0.is_required features.0.is_known  \\\n",
       "0  #3399ff  data-loss-protect                   True                True   \n",
       "1  #123456                NaN                    NaN                 NaN   \n",
       "\n",
       "           features.5.name features.5.is_required  ... features.3.is_known  \\\n",
       "0  upfront-shutdown-script                  False  ...                 NaN   \n",
       "1  upfront-shutdown-script                  False  ...                 NaN   \n",
       "\n",
       "  features.105.name features.105.is_required features.105.is_known  \\\n",
       "0               NaN                      NaN                   NaN   \n",
       "1               NaN                      NaN                   NaN   \n",
       "\n",
       "  features.1339.name features.1339.is_required features.1339.is_known  \\\n",
       "0                NaN                       NaN                    NaN   \n",
       "1                NaN                       NaN                    NaN   \n",
       "\n",
       "  num_enabled_channels num_channels total_node_capacity  \n",
       "0                   22           23           111713145  \n",
       "1                    3            7             1893366  \n",
       "\n",
       "[2 rows x 101 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "filters = (network['num_channels'] >= 10) & (network['total_node_capacity'] >= 30000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1809, 101)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network[filters].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = network[filters]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['pub_key', 'alias' , 'num_channels', 'total_node_capacity']\n",
    "nodes = nodes[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask1 = channels_graph['node1_pub'].isin(nodes['pub_key'])\n",
    "mask2 = channels_graph['node2_pub'].isin(nodes['pub_key'])\n",
    "channels_graph = channels_graph[mask1 & mask2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85915</th>\n",
       "      <td>50000000</td>\n",
       "      <td>57c3d89c943bd420bd967cb891a2399d243b8b0a072b62...</td>\n",
       "      <td>1657552141</td>\n",
       "      <td>02343c19a39dd11cfc6f7571f36213dd52fb70ee45ee40...</td>\n",
       "      <td>03864ef025fde8fb587d989186ce6a4a186895ee44a926...</td>\n",
       "      <td>50000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>20000000000</td>\n",
       "      <td>1.657552e+09</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>499</td>\n",
       "      <td>False</td>\n",
       "      <td>50000000000</td>\n",
       "      <td>1.657552e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85916</th>\n",
       "      <td>200000000</td>\n",
       "      <td>3e908ae3c6dc3946a904522642a3ffac5002d2e32380c6...</td>\n",
       "      <td>1657552352</td>\n",
       "      <td>033d8656219478701227199cbd6f670335c8d408a92ae8...</td>\n",
       "      <td>03aab7e9327716ee946b8fbfae039b0db85356549e72c5...</td>\n",
       "      <td>200000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>198000000000</td>\n",
       "      <td>1.657552e+09</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>False</td>\n",
       "      <td>198000000000</td>\n",
       "      <td>1.657552e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85917</th>\n",
       "      <td>100000000</td>\n",
       "      <td>5f57b3fcd39f9f3c0c4ce73445d7745d6dd77bbdd18c44...</td>\n",
       "      <td>1657553560</td>\n",
       "      <td>03037dc08e9ac63b82581f79b662a4d0ceca8a8ca162b1...</td>\n",
       "      <td>037659a0ac8eb3b8d0a720114efc861d3a940382dcfa14...</td>\n",
       "      <td>100000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>99000000000</td>\n",
       "      <td>1.657554e+09</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>99000000000</td>\n",
       "      <td>1.657552e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38706 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       channel_id                                         chan_point  \\\n",
       "1092        63874  69cb9da0c31b617a510b06ce6dcb951e862a04c434cbec...   \n",
       "1499      1831257  77d828e508f43ade02cb01190021644a9be5ffba5fad27...   \n",
       "2796       357883  0bb0411d186af60cea6070695e393606b8810a5edbfdcb...   \n",
       "2925      2000000  9f0358a76c9a0d06c135007a7d8d15c3397ae82d98decc...   \n",
       "2956      1600000  19746a8b45e23766e6cb923971922415c8c21dafa8ea52...   \n",
       "...           ...                                                ...   \n",
       "85911    15114842  ec976deb404e406fa5f97cd051167906a5ec3fc4c60670...   \n",
       "85912     3000000  ac076bd82418e8387e6bc5f86f177626c7c1b32aaf97bf...   \n",
       "85915    50000000  57c3d89c943bd420bd967cb891a2399d243b8b0a072b62...   \n",
       "85916   200000000  3e908ae3c6dc3946a904522642a3ffac5002d2e32380c6...   \n",
       "85917   100000000  5f57b3fcd39f9f3c0c4ce73445d7745d6dd77bbdd18c44...   \n",
       "\n",
       "       last_update                                          node1_pub  \\\n",
       "1092    1657478774  03434a39cd9a537c852fc8fb72454086d726f9111e9f73...   \n",
       "1499    1657526194  02445ff6a24e69242d6f6f38e1b7e8ae9c01eb9b660f3d...   \n",
       "2796    1657543838  037172d2110c4148d6ed0c2790ec8be948458022425dce...   \n",
       "2925    1657520173  02740c62f38896511eb98479036b06907fcac283f62d08...   \n",
       "2956    1657532551  02a45def9ae014fdd2603dd7033d157faa3a55a72b06a6...   \n",
       "...            ...                                                ...   \n",
       "85911   1657550054  0279f06eba0e1080f6a693201f090d0635a0e5dd2ef57d...   \n",
       "85912   1657551701  02c5dd45bd62dfd948ccb58490e54b8a652f8e8b08ed2f...   \n",
       "85915   1657552141  02343c19a39dd11cfc6f7571f36213dd52fb70ee45ee40...   \n",
       "85916   1657552352  033d8656219478701227199cbd6f670335c8d408a92ae8...   \n",
       "85917   1657553560  03037dc08e9ac63b82581f79b662a4d0ceca8a8ca162b1...   \n",
       "\n",
       "                                               node2_pub   capacity  \\\n",
       "1092   03d301eedc0949238bf919452ee7ef5c45bda4adbe17fa...      63874   \n",
       "1499   029a8741675c4c9078b577ddc4348d602d2fb45a12e608...    1831257   \n",
       "2796   03beddc8adbf7d56a7da15cdaf95d97b24d07088c3571b...     357883   \n",
       "2925   03beddc8adbf7d56a7da15cdaf95d97b24d07088c3571b...    2000000   \n",
       "2956   037172d2110c4148d6ed0c2790ec8be948458022425dce...    1600000   \n",
       "...                                                  ...        ...   \n",
       "85911  03dab87ff8635982815c4567eb58af48f9944d11c56beb...   15114842   \n",
       "85912  02f94c8c175b002c6421f7e97f11f3ce846d2fd7d88a2b...    3000000   \n",
       "85915  03864ef025fde8fb587d989186ce6a4a186895ee44a926...   50000000   \n",
       "85916  03aab7e9327716ee946b8fbfae039b0db85356549e72c5...  200000000   \n",
       "85917  037659a0ac8eb3b8d0a720114efc861d3a940382dcfa14...  100000000   \n",
       "\n",
       "       node1_policy  node2_policy  node1_policy.time_lock_delta  \\\n",
       "1092            NaN           NaN                          40.0   \n",
       "1499            NaN           NaN                         144.0   \n",
       "2796            NaN           NaN                          40.0   \n",
       "2925            NaN           NaN                          30.0   \n",
       "2956            NaN           NaN                          24.0   \n",
       "...             ...           ...                           ...   \n",
       "85911           NaN           NaN                          34.0   \n",
       "85912           NaN           NaN                          40.0   \n",
       "85915           NaN           NaN                          40.0   \n",
       "85916           NaN           NaN                          40.0   \n",
       "85917           NaN           NaN                          40.0   \n",
       "\n",
       "      node1_policy.min_htlc  ... node1_policy.disabled  \\\n",
       "1092                   1000  ...                  True   \n",
       "1499                   1000  ...                 False   \n",
       "2796                   1000  ...                 False   \n",
       "2925                   1000  ...                 False   \n",
       "2956                   1000  ...                  True   \n",
       "...                     ...  ...                   ...   \n",
       "85911                     1  ...                 False   \n",
       "85912                  1000  ...                 False   \n",
       "85915                  1000  ...                 False   \n",
       "85916                  1000  ...                 False   \n",
       "85917                  1000  ...                  True   \n",
       "\n",
       "      node1_policy.max_htlc_msat node1_policy.last_update  \\\n",
       "1092                    63236000             1.657479e+09   \n",
       "1499                  1812945000             1.657526e+09   \n",
       "2796                   354305000             1.648533e+09   \n",
       "2925                  1980000000             1.657520e+09   \n",
       "2956                  1584000000             1.657533e+09   \n",
       "...                          ...                      ...   \n",
       "85911                14963694000             1.657549e+09   \n",
       "85912                 2970000000             1.657552e+09   \n",
       "85915                20000000000             1.657552e+09   \n",
       "85916               198000000000             1.657552e+09   \n",
       "85917                99000000000             1.657554e+09   \n",
       "\n",
       "      node2_policy.time_lock_delta  node2_policy.min_htlc  \\\n",
       "1092                         144.0                   1000   \n",
       "1499                         144.0                   1000   \n",
       "2796                          18.0                1000000   \n",
       "2925                          18.0                1000000   \n",
       "2956                          40.0                   1000   \n",
       "...                            ...                    ...   \n",
       "85911                         40.0                   1000   \n",
       "85912                          NaN                    NaN   \n",
       "85915                        144.0                      1   \n",
       "85916                         40.0                   1000   \n",
       "85917                         40.0                   1000   \n",
       "\n",
       "       node2_policy.fee_base_msat node2_policy.fee_rate_milli_msat  \\\n",
       "1092                         1000                                1   \n",
       "1499                          888                                1   \n",
       "2796                         1000                              299   \n",
       "2925                         1000                              299   \n",
       "2956                          100                              120   \n",
       "...                           ...                              ...   \n",
       "85911                        2250                               55   \n",
       "85912                         NaN                              NaN   \n",
       "85915                        1000                              499   \n",
       "85916                           0                              500   \n",
       "85917                        1000                                1   \n",
       "\n",
       "      node2_policy.disabled node2_policy.max_htlc_msat  \\\n",
       "1092                  False                          0   \n",
       "1499                  False                 1812945000   \n",
       "2796                   True                  354305000   \n",
       "2925                  False                 1980000000   \n",
       "2956                  False                 1584000000   \n",
       "...                     ...                        ...   \n",
       "85911                 False                15114842000   \n",
       "85912                   NaN                        NaN   \n",
       "85915                 False                50000000000   \n",
       "85916                 False               198000000000   \n",
       "85917                 False                99000000000   \n",
       "\n",
       "      node2_policy.last_update  \n",
       "1092              1.532332e+09  \n",
       "1499              1.657526e+09  \n",
       "2796              1.657544e+09  \n",
       "2925              1.657515e+09  \n",
       "2956              1.648535e+09  \n",
       "...                        ...  \n",
       "85911             1.657550e+09  \n",
       "85912                      NaN  \n",
       "85915             1.657552e+09  \n",
       "85916             1.657552e+09  \n",
       "85917             1.657552e+09  \n",
       "\n",
       "[38706 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channels_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['channel_id', 'chan_point', 'last_update', 'node1_pub', 'node2_pub',\n",
       "       'capacity', 'node1_policy', 'node2_policy',\n",
       "       'node1_policy.time_lock_delta', 'node1_policy.min_htlc',\n",
       "       'node1_policy.fee_base_msat', 'node1_policy.fee_rate_milli_msat',\n",
       "       'node1_policy.disabled', 'node1_policy.max_htlc_msat',\n",
       "       'node1_policy.last_update', 'node2_policy.time_lock_delta',\n",
       "       'node2_policy.min_htlc', 'node2_policy.fee_base_msat',\n",
       "       'node2_policy.fee_rate_milli_msat', 'node2_policy.disabled',\n",
       "       'node2_policy.max_htlc_msat', 'node2_policy.last_update'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channels_graph.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data for network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of edges: each tuple is an edge (node1, node2)\n",
    "all_edges = list(zip(channels_graph['node1_pub'], channels_graph['node2_pub']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dictionary of sequences: key is attribute name, value is list of values for each edge\n",
    "all_edge_attrs = {\n",
    "    'capacity': channels_graph['capacity'].values,\n",
    "    'node1_fee_rate': channels_graph['node1_policy.fee_rate_milli_msat'].values,\n",
    "    'node2_fee_rate': channels_graph['node2_policy.fee_rate_milli_msat'].values,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_capital = '0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae35f0d857c54d3b52e4e45'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "okcoin = '036b53093df5a932deac828cca6d663472dbc88322b05eec1d42b26ab9b16caa1c'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "wallet = '035e4ff418fc8b5554c5d9eea66396c227bd429a3251c8cbc711002ba215bfc226'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build networkx graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add edges\n",
    "G.add_edges_from(all_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_atts = {(e[0], e[1]): {'capacity': c, 'node1_fee_rate': n1, 'node2_fee_rate': n2} for e, c, n1, n2 in zip(all_edges, all_edge_attrs['capacity'], all_edge_attrs['node1_fee_rate'], all_edge_attrs['node2_fee_rate'])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add edges attributes\n",
    "nx.set_edge_attributes(G, dict_atts)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dest_nodes = G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_nodes = G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = nx.all_simple_paths(G, source=ln_capital, target=okcoin, cutoff=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_paths = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for s_node in source_nodes:\n",
    "for d_node in dest_nodes:\n",
    "    if not nx.has_path(G, source=ln_capital, target=d_node):\n",
    "        continue\n",
    "    for i in range(9):\n",
    "        stop = 0\n",
    "        paths = nx.all_simple_paths(G, source=ln_capital, target=d_node, cutoff=i+1)\n",
    "        while stop < 10:\n",
    "            try:\n",
    "                some_paths.append(next(paths))\n",
    "                stop += 1\n",
    "            except StopIteration:\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paths from okcoin to ln_capital\n",
    "paths_okcoin_ln_capital = []\n",
    "for i in range(9):\n",
    "    stop = 0\n",
    "    paths = nx.all_simple_paths(G, source=okcoin, target=ln_capital, cutoff=i+1)\n",
    "    while stop < 10:\n",
    "        try:\n",
    "            paths_okcoin_ln_capital.append(next(paths))\n",
    "            stop += 1\n",
    "        except StopIteration:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################\n",
    "## testing\n",
    "############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.DataFrame(data={'testing':some_paths})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_test = test.explode('testing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>testing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                            testing\n",
       "0      0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...\n",
       "1      0  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17..."
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cum_test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_test = cum_test.rename(columns={'testing':'source'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_test['destination'] = cum_test.source.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>destination</th>\n",
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       "   index                                             source  \\\n",
       "0      0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...   \n",
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       "3      1  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...   \n",
       "\n",
       "                                         destination  \n",
       "0  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...  \n",
       "1  03434a39cd9a537c852fc8fb72454086d726f9111e9f73...  \n",
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       "3  0260fab633066ed7b1d9b9b8a0fac87e1579d1709e874d...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cum_test.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = cum_test.groupby('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_channels = cum_test[grouped.cumcount(ascending=False) > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "<p>775026 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "         index                                             source  \\\n",
       "0            0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...   \n",
       "1            0  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...   \n",
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       "6            2  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...   \n",
       "...        ...                                                ...   \n",
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       "910342  135320  03935a378993d0b55056801b11957aaecb9f85f34b6424...   \n",
       "910343  135320  02b0172bb38617fa3afdb69664468b492d5a21062a4fa8...   \n",
       "910344  135320  0390b5d4492dc2f5318e5233ab2cebf6d48914881a33ef...   \n",
       "910345  135320  027ce055380348d7812d2ae7745701c9f93e70c1adeb26...   \n",
       "\n",
       "                                              destination  \n",
       "0       0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...  \n",
       "1       03434a39cd9a537c852fc8fb72454086d726f9111e9f73...  \n",
       "3       0260fab633066ed7b1d9b9b8a0fac87e1579d1709e874d...  \n",
       "4       03434a39cd9a537c852fc8fb72454086d726f9111e9f73...  \n",
       "6       03a503d8e30f2ff407096d235b5db63b4fcf3f89a653ac...  \n",
       "...                                                   ...  \n",
       "910341  03935a378993d0b55056801b11957aaecb9f85f34b6424...  \n",
       "910342  02b0172bb38617fa3afdb69664468b492d5a21062a4fa8...  \n",
       "910343  0390b5d4492dc2f5318e5233ab2cebf6d48914881a33ef...  \n",
       "910344  027ce055380348d7812d2ae7745701c9f93e70c1adeb26...  \n",
       "910345  024271a1be2d7a3e2a276b241257be734d843885d252f5...  \n",
       "\n",
       "[775026 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to add capacity and node fee rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make capacity and node fee rates columns\n",
    "all_channels[['capacity', 'node1_fee_rate', 'node2_fee_rate']] = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iterate over all_channels rows and add capacity and node fee rates from G edges\n",
    "for index, row in all_channels.iterrows():\n",
    "    all_channels.loc[index, 'capacity'] = G.get_edge_data(row['source'], row['destination'])['capacity']\n",
    "    all_channels.loc[index, 'node1_fee_rate'] = G.get_edge_data(row['source'], row['destination'])['node1_fee_rate']\n",
    "    all_channels.loc[index, 'node2_fee_rate'] = G.get_edge_data(row['source'], row['destination'])['node2_fee_rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   index                                             source  \\\n",
       "0      0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...   \n",
       "\n",
       "                                         destination capacity node1_fee_rate  \\\n",
       "0  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...  5000000            249   \n",
       "\n",
       "  node2_fee_rate  \n",
       "0            100  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_channels.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_channels = all_channels.rename(columns={'index':'path_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>path_id</th>\n",
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       "      <td>5000000</td>\n",
       "      <td>249</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   path_id                                             source  \\\n",
       "0        0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...   \n",
       "\n",
       "                                         destination capacity node1_fee_rate  \\\n",
       "0  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...  5000000            249   \n",
       "\n",
       "  node2_fee_rate  \n",
       "0            100  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_channels.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "aliases = nodes.set_index('pub_key')['alias']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_channels.set_index('source', inplace=True)\n",
    "all_channels['s_node'] = aliases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_channels.reset_index(inplace=True)\n",
    "all_channels.set_index('destination',inplace=True)\n",
    "all_channels['d_node'] = aliases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_channels.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>path_id</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>s_node</th>\n",
       "      <th>d_node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...</td>\n",
       "      <td>0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...</td>\n",
       "      <td>0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>249</td>\n",
       "      <td>100</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>BCash_Is_Trash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03434a39cd9a537c852fc8fb72454086d726f9111e9f73...</td>\n",
       "      <td>0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...</td>\n",
       "      <td>0</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>BCash_Is_Trash</td>\n",
       "      <td>***ROUTE 66***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         destination  \\\n",
       "0  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...   \n",
       "1  03434a39cd9a537c852fc8fb72454086d726f9111e9f73...   \n",
       "\n",
       "                                              source  path_id capacity  \\\n",
       "0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...        0  5000000   \n",
       "1  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...        0  1000000   \n",
       "\n",
       "  node1_fee_rate node2_fee_rate          s_node          d_node  \n",
       "0            249            100      LN.capital  BCash_Is_Trash  \n",
       "1              2             10  BCash_Is_Trash  ***ROUTE 66***  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_channels.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = all_channels.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['s_node', 'd_node']] = df[['s_node', 'd_node']].fillna('NO_NODE_ALIAS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DASH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jupyter_dash import JupyterDash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, dash_table, dcc, html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash.dependencies import Input, Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = JupyterDash(__name__)\n",
    "server = app.server\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='potential-paths-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in df.columns\n",
    "        ],\n",
    "        data=df.to_dict('records'),\n",
    "        editable=False, # cells are not editable \n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        sort_action='native', # sort by click\n",
    "        sort_mode='multi', # multi-column sorting\n",
    "        row_selectable='multi', # multiple rows can be selected\n",
    "        row_deletable=False,\n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=15,\n",
    "    ),\n",
    "    html.Div(id='datatable-container'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2baabfb93c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.run_server(mode=\"inline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Idea: table with paths packed in lists.\n",
    "\n",
    "Make a dataframe where each row is a path contained in a list. \n",
    "\n",
    "The fees, and channel capacities are the aggregate of the fees and capacities of the channels in the path.\n",
    "\n",
    "This way, we can easily compare the fees and capacities of different paths. We can make an interactive table to filter, or sort the paths. Then, when selecting one path, another table will show the different channels in the path with their fees and capacities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>path_id</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>s_node</th>\n",
       "      <th>d_node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...</td>\n",
       "      <td>0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...</td>\n",
       "      <td>0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>249</td>\n",
       "      <td>100</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>BCash_Is_Trash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03434a39cd9a537c852fc8fb72454086d726f9111e9f73...</td>\n",
       "      <td>0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...</td>\n",
       "      <td>0</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>BCash_Is_Trash</td>\n",
       "      <td>***ROUTE 66***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         destination  \\\n",
       "0  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...   \n",
       "1  03434a39cd9a537c852fc8fb72454086d726f9111e9f73...   \n",
       "\n",
       "                                              source  path_id  capacity  \\\n",
       "0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...        0   5000000   \n",
       "1  0298f6074a454a1f5345cb2a7c6f9fce206cd0bf675d17...        0   1000000   \n",
       "\n",
       "  node1_fee_rate node2_fee_rate          s_node          d_node  \n",
       "0            249            100      LN.capital  BCash_Is_Trash  \n",
       "1              2             10  BCash_Is_Trash  ***ROUTE 66***  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fee rates should be int\n",
    "dff[['node1_fee_rate', 'node2_fee_rate']] = dff[['node1_fee_rate', 'node2_fee_rate']].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a dataframe where each row is a path contained in a list\n",
    "\n",
    "grouped_routes = dff.groupby('path_id').apply(lambda x: x.s_node.values.tolist() + [x.d_node.values.tolist()[-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "path_id\n",
       "135319    [LN.capital, ln2me.com / LightningTo.Me, Korti...\n",
       "135320    [LN.capital, ln2me.com / LightningTo.Me, Korti...\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_routes = grouped_routes.to_frame().rename(columns={0: 'route'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# join aggregated capacity and fee rates to each path\n",
    "grouped_routes = grouped_routes.join(dff.groupby('path_id').aggregate(sum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add peers column ('first_node - last_node')\n",
    "grouped_routes['peers'] = grouped_routes.route.apply(lambda x: f'{x[0]} - {x[-1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>route</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>peers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[LN.capital, BCash_Is_Trash, ***ROUTE 66***]</td>\n",
       "      <td>6000000</td>\n",
       "      <td>251</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[LN.capital, southxchange.com, ***ROUTE 66***]</td>\n",
       "      <td>10550000</td>\n",
       "      <td>200</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  route  capacity  \\\n",
       "path_id                                                             \n",
       "0          [LN.capital, BCash_Is_Trash, ***ROUTE 66***]   6000000   \n",
       "1        [LN.capital, southxchange.com, ***ROUTE 66***]  10550000   \n",
       "\n",
       "         node1_fee_rate  node2_fee_rate                        peers  \n",
       "path_id                                                               \n",
       "0                   251             110  LN.capital - ***ROUTE 66***  \n",
       "1                   200             110  LN.capital - ***ROUTE 66***  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a 'hops' column that represents the number of hops in each path\n",
    "grouped_routes['hops'] = grouped_routes['route'].apply(lambda x: len(x) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "path_id\n",
       "0              [LN.capital, BCash_Is_Trash, ***ROUTE 66***]\n",
       "1            [LN.capital, southxchange.com, ***ROUTE 66***]\n",
       "2                   [LN.capital, Milky Way, ***ROUTE 66***]\n",
       "3         [LN.capital, ln2me.com / LightningTo.Me, south...\n",
       "4         [LN.capital, ln2me.com / LightningTo.Me, ACINQ...\n",
       "                                ...                        \n",
       "135316    [LN.capital, ln2me.com / LightningTo.Me, Korti...\n",
       "135317    [LN.capital, ln2me.com / LightningTo.Me, Korti...\n",
       "135318    [LN.capital, ln2me.com / LightningTo.Me, Korti...\n",
       "135319    [LN.capital, ln2me.com / LightningTo.Me, Korti...\n",
       "135320    [LN.capital, ln2me.com / LightningTo.Me, Korti...\n",
       "Name: route, Length: 135321, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes.route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change route column to be str instead of list\n",
    "grouped_routes['route'] = grouped_routes['route'].apply(lambda x: ' -> '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>route</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>peers</th>\n",
       "      <th>hops</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LN.capital -&gt; BCash_Is_Trash -&gt; ***ROUTE 66***</td>\n",
       "      <td>6000000</td>\n",
       "      <td>251</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LN.capital -&gt; southxchange.com -&gt; ***ROUTE 66***</td>\n",
       "      <td>10550000</td>\n",
       "      <td>200</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    route  capacity  \\\n",
       "path_id                                                               \n",
       "0          LN.capital -> BCash_Is_Trash -> ***ROUTE 66***   6000000   \n",
       "1        LN.capital -> southxchange.com -> ***ROUTE 66***  10550000   \n",
       "\n",
       "         node1_fee_rate  node2_fee_rate                        peers  hops  \n",
       "path_id                                                                     \n",
       "0                   251             110  LN.capital - ***ROUTE 66***     2  \n",
       "1                   200             110  LN.capital - ***ROUTE 66***     2  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_routes.insert(loc=0, column=\"id\", value=grouped_routes.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>route</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>peers</th>\n",
       "      <th>hops</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>LN.capital -&gt; BCash_Is_Trash -&gt; ***ROUTE 66***</td>\n",
       "      <td>6000000</td>\n",
       "      <td>251</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>LN.capital -&gt; southxchange.com -&gt; ***ROUTE 66***</td>\n",
       "      <td>10550000</td>\n",
       "      <td>200</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                             route  capacity  \\\n",
       "path_id                                                                   \n",
       "0         0    LN.capital -> BCash_Is_Trash -> ***ROUTE 66***   6000000   \n",
       "1         1  LN.capital -> southxchange.com -> ***ROUTE 66***  10550000   \n",
       "\n",
       "         node1_fee_rate  node2_fee_rate                        peers  hops  \n",
       "path_id                                                                     \n",
       "0                   251             110  LN.capital - ***ROUTE 66***     2  \n",
       "1                   200             110  LN.capital - ***ROUTE 66***     2  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DATASET FOR THE FIRST TABLE\n",
    "# Should contain the following columns:\n",
    "# source_node, destination_node, peers\n",
    "# the index should be same as peers\n",
    "# unique_sources, unique_destinations = [], []\n",
    "\n",
    "# unique sources\n",
    "unique_sources = [x.split(' - ')[0] for x in grouped_routes.peers.unique().tolist()]\n",
    "\n",
    "\n",
    "# unique destinations\n",
    "unique_destinations = [x.split(' - ')[-1] for x in grouped_routes.peers.unique().tolist() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "options_df = pd.DataFrame(\n",
    "    data={'source':unique_sources, 'destination':unique_destinations}, \n",
    "    index=grouped_routes.peers.unique()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LN.capital - ***ROUTE 66***</th>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>***ROUTE 66***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - cybertide.com</th>\n",
       "      <td>LN.capital - cybertide.com</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>cybertide.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - hmmmstrange</th>\n",
       "      <td>LN.capital - hmmmstrange</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>hmmmstrange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - ln.satjar.com</th>\n",
       "      <td>LN.capital - ln.satjar.com</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>ln.satjar.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - AQUARIOUS</th>\n",
       "      <td>LN.capital - AQUARIOUS</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>AQUARIOUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - Holtzman</th>\n",
       "      <td>LN.capital - Holtzman</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>Holtzman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - Libertas</th>\n",
       "      <td>LN.capital - Libertas</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>Libertas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - C-Nugget</th>\n",
       "      <td>LN.capital - C-Nugget</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>C-Nugget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - Greasedlightning</th>\n",
       "      <td>LN.capital - Greasedlightning</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>Greasedlightning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LN.capital - 🧙🏻‍♂️oberon | CLN</th>\n",
       "      <td>LN.capital - 🧙🏻‍♂️oberon | CLN</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>🧙🏻‍♂️oberon | CLN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            id      source  \\\n",
       "LN.capital - ***ROUTE 66***        LN.capital - ***ROUTE 66***  LN.capital   \n",
       "LN.capital - cybertide.com          LN.capital - cybertide.com  LN.capital   \n",
       "LN.capital - hmmmstrange              LN.capital - hmmmstrange  LN.capital   \n",
       "LN.capital - ln.satjar.com          LN.capital - ln.satjar.com  LN.capital   \n",
       "LN.capital - AQUARIOUS                  LN.capital - AQUARIOUS  LN.capital   \n",
       "...                                                        ...         ...   \n",
       "LN.capital - Holtzman                    LN.capital - Holtzman  LN.capital   \n",
       "LN.capital - Libertas                    LN.capital - Libertas  LN.capital   \n",
       "LN.capital - C-Nugget                    LN.capital - C-Nugget  LN.capital   \n",
       "LN.capital - Greasedlightning    LN.capital - Greasedlightning  LN.capital   \n",
       "LN.capital - 🧙🏻‍♂️oberon | CLN  LN.capital - 🧙🏻‍♂️oberon | CLN  LN.capital   \n",
       "\n",
       "                                      destination  \n",
       "LN.capital - ***ROUTE 66***        ***ROUTE 66***  \n",
       "LN.capital - cybertide.com          cybertide.com  \n",
       "LN.capital - hmmmstrange              hmmmstrange  \n",
       "LN.capital - ln.satjar.com          ln.satjar.com  \n",
       "LN.capital - AQUARIOUS                  AQUARIOUS  \n",
       "...                                           ...  \n",
       "LN.capital - Holtzman                    Holtzman  \n",
       "LN.capital - Libertas                    Libertas  \n",
       "LN.capital - C-Nugget                    C-Nugget  \n",
       "LN.capital - Greasedlightning    Greasedlightning  \n",
       "LN.capital - 🧙🏻‍♂️oberon | CLN  🧙🏻‍♂️oberon | CLN  \n",
       "\n",
       "[1797 rows x 3 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_df.insert(loc=0, column=\"id\", value=options_df.index)\n",
    "options_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = JupyterDash(__name__)\n",
    "server = app.server\n",
    "\n",
    "###############################################\n",
    "#               FIRST TABLE                   #\n",
    "###############################################\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='sourceDest-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in options_df.columns\n",
    "            if i != 'id'\n",
    "        ],\n",
    "        data=options_df.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        row_selectable='single',  \n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=6,\n",
    "    ),\n",
    "    html.Div(id='secondtable-container'),  \n",
    "    html.Div(id=\"output\") \n",
    "])\n",
    "\n",
    "\n",
    "###############################################\n",
    "#               Second table                  #\n",
    "###############################################\n",
    "\n",
    "@app.callback(\n",
    "    Output('secondtable-container', 'children'),\n",
    "    Output('output', 'children'),\n",
    "    Input('sourceDest-table', 'selected_row_ids')\n",
    ")\n",
    "def update_secondtable(selected_rows):\n",
    "    # app.layout = html.Div([\n",
    "    if selected_rows is None:\n",
    "        rows = []\n",
    "    \n",
    "    second_dff = grouped_routes[grouped_routes.peers.isin(selected_rows)]\n",
    "    return html.Div([\n",
    "        dash_table.DataTable(\n",
    "            id='potential-paths-table',\n",
    "            columns=[\n",
    "                {'name': i, 'id': i, 'deletable': True} for i in second_dff.columns\n",
    "                if i not in ['id', 's_node', 'd_node']\n",
    "            ],\n",
    "            style_cell={\n",
    "                'overflow': 'hidden',\n",
    "                'textOverflow': 'ellipsis',\n",
    "                'maxWidth': 0,\n",
    "            },\n",
    "\n",
    "            tooltip_data=[\n",
    "            {\n",
    "                column: {'value': str(value), 'type': 'markdown'}\n",
    "                for column, value in row.items()\n",
    "            } for row in second_dff.to_dict('records')\n",
    "            ],\n",
    "            tooltip_duration=None,\n",
    "\n",
    "            data=second_dff.to_dict('records'),\n",
    "            editable=False, # cells are not editable \n",
    "            filter_action='native', # filter by entering text in the input box\n",
    "            sort_action='native', # sort by click\n",
    "            sort_mode='multi', # multi-column sorting\n",
    "            row_selectable='single', # multiple rows can be selected\n",
    "            row_deletable=False,\n",
    "            page_action='native',\n",
    "            page_current=0,\n",
    "            page_size=6,\n",
    "        ),\n",
    "        html.Div(id='datatable-container'),\n",
    "        html.Div(id='output2')\n",
    "    ]), f'Row ids: {selected_rows}'\n",
    "    \n",
    "\n",
    "###############################################\n",
    "#               Third table                   #\n",
    "###############################################\n",
    "\n",
    "@app.callback(\n",
    "    Output('datatable-container', 'children'),\n",
    "    Output('output2', 'children'),\n",
    "    Input('potential-paths-table', 'selected_row_ids'),\n",
    ")\n",
    "def make_specific_path_table(row_ids):\n",
    "    if row_ids is None:\n",
    "        row_ids = []\n",
    "    \n",
    "    dff = df[df.path_id.isin(row_ids)]\n",
    "\n",
    "    return dash_table.DataTable(\n",
    "        id='specific-paths-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in dff.columns\n",
    "            # Omit path_id column\n",
    "            if i != 'path_id'\n",
    "        ],\n",
    "\n",
    "        style_table={'overflowX': 'auto'},\n",
    "        style_cell={\n",
    "            # all three widths are needed\n",
    "            'minWidth': '180px', 'width': '180px', 'maxWidth': '180px',\n",
    "            'overflow': 'hidden',\n",
    "            'textOverflow': 'ellipsis',\n",
    "        },\n",
    "\n",
    "        tooltip_data=[\n",
    "        {\n",
    "            column: {'value': str(value), 'type': 'markdown'}\n",
    "            for column, value in row.items()\n",
    "        } for row in df.to_dict('records')\n",
    "        ],\n",
    "        tooltip_duration=None,\n",
    "\n",
    "        data=dff.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        sort_action='native', # sort by click\n",
    "        sort_mode='multi', # multi-column sorting\n",
    "        row_deletable=False,\n",
    "\n",
    "    ), f'Row ids: {row_ids}'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8050/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2baea9b1b40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.run_server(mode=\"inline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Refactor the above code to adapt it for rebalancing purposes.\n",
    "\n",
    "**Idea**\n",
    "\n",
    "4 tables:\n",
    "\n",
    "1. Table to select source node (our node), central node, and the channel (channel out) that helps rebalance another channel (channel in).\n",
    "2. Table to select a path from the possible paths to get to the central node.\n",
    "3. Table to select channel in.\n",
    "4. Table to select a path from the possible paths to complete the rebalance (circular path).\n",
    "\n",
    "NOTE: At the end, another table could show the entire ciruclar path, with all the channels information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jupyter_dash import JupyterDash\n",
    "from dash import Dash, dash_table, dcc, html\n",
    "from dash.dependencies import Input, Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1st table (source node, central node, channel out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OUR NODE | CENTRAL NODE | CHANNEL OUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>route</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>peers</th>\n",
       "      <th>hops</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>LN.capital -&gt; BCash_Is_Trash -&gt; ***ROUTE 66***</td>\n",
       "      <td>6000000</td>\n",
       "      <td>251</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>LN.capital -&gt; southxchange.com -&gt; ***ROUTE 66***</td>\n",
       "      <td>10550000</td>\n",
       "      <td>200</td>\n",
       "      <td>110</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>LN.capital -&gt; Milky Way -&gt; ***ROUTE 66***</td>\n",
       "      <td>11000000</td>\n",
       "      <td>110</td>\n",
       "      <td>10050</td>\n",
       "      <td>LN.capital - ***ROUTE 66***</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                             route  capacity  \\\n",
       "path_id                                                                   \n",
       "0         0    LN.capital -> BCash_Is_Trash -> ***ROUTE 66***   6000000   \n",
       "1         1  LN.capital -> southxchange.com -> ***ROUTE 66***  10550000   \n",
       "2         2         LN.capital -> Milky Way -> ***ROUTE 66***  11000000   \n",
       "\n",
       "         node1_fee_rate  node2_fee_rate                        peers  hops  \n",
       "path_id                                                                     \n",
       "0                   251             110  LN.capital - ***ROUTE 66***     2  \n",
       "1                   200             110  LN.capital - ***ROUTE 66***     2  \n",
       "2                   110           10050  LN.capital - ***ROUTE 66***     2  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(135321, 7)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new dataframe where for each route retrieve the source and destination nodes, and the channel out (the second node)\n",
    "select_central_node_df = pd.DataFrame(columns=['source', 'central_node', 'channel_out'], index=grouped_routes.index)\n",
    "\n",
    "select_central_node_df['source'] = grouped_routes.peers.apply(lambda x: x.split(' - ')[0])\n",
    "select_central_node_df['central_node'] = grouped_routes.peers.apply(lambda x: x.split(' - ')[-1])\n",
    "select_central_node_df['channel_out'] = grouped_routes.route.apply(lambda x: x.split(' -> ')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# thirds column\n",
    "select_central_node_df['important_nodes'] = select_central_node_df.apply(lambda x: x.source + ' - ' + x.central_node + ' -> ' + x.channel_out, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "select_central_node_df['channel_out'] = select_central_node_df.source + ' - ' + select_central_node_df.channel_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop duplicates from the three columns\n",
    "select_central_node_df.drop_duplicates(subset='important_nodes', keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>central_node</th>\n",
       "      <th>channel_out</th>\n",
       "      <th>important_nodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LN.capital</td>\n",
       "      <td>***ROUTE 66***</td>\n",
       "      <td>LN.capital - BCash_Is_Trash</td>\n",
       "      <td>LN.capital - ***ROUTE 66*** -&gt; BCash_Is_Trash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LN.capital</td>\n",
       "      <td>***ROUTE 66***</td>\n",
       "      <td>LN.capital - southxchange.com</td>\n",
       "      <td>LN.capital - ***ROUTE 66*** -&gt; southxchange.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             source    central_node                    channel_out  \\\n",
       "path_id                                                              \n",
       "0        LN.capital  ***ROUTE 66***    LN.capital - BCash_Is_Trash   \n",
       "1        LN.capital  ***ROUTE 66***  LN.capital - southxchange.com   \n",
       "\n",
       "                                         important_nodes  \n",
       "path_id                                                   \n",
       "0          LN.capital - ***ROUTE 66*** -> BCash_Is_Trash  \n",
       "1        LN.capital - ***ROUTE 66*** -> southxchange.com  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_central_node_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "select_central_node_df['id'] = select_central_node_df.important_nodes.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = JupyterDash(__name__)\n",
    "server = app.server\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='first-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in select_central_node_df.columns\n",
    "            if i not in ['id', 'important_nodes']\n",
    "        ],\n",
    "        data=select_central_node_df.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        row_selectable='single',  \n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=6,\n",
    "    ),\n",
    "    html.Div(id='secondtable-container'),  \n",
    "    html.Div(id=\"output\") \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "app.run_server()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2nd table (paths to central node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_routes['channel_out'] = grouped_routes.route.apply(lambda x: x.split(' -> ')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = JupyterDash(__name__)\n",
    "server = app.server\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='first-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in select_central_node_df.columns\n",
    "            if i != 'id'\n",
    "        ],\n",
    "        data=select_central_node_df.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        row_selectable='single',  \n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=6,\n",
    "    ),\n",
    "    html.Div(id='secondtable-container'),  \n",
    "    html.Div(id=\"output\") \n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output('secondtable-container', 'children'),\n",
    "    Input('first-table', 'selected_row_ids'),\n",
    ")\n",
    "def make_second_table(selected_rows):\n",
    "    if selected_rows is None:\n",
    "        rows = []\n",
    "    else:\n",
    "        rows = [row.split(' -> ') for row in selected_rows]\n",
    "        source_central = [row[0] for row in rows]\n",
    "        channel_out = [row[1] for row in rows]\n",
    "\n",
    "    masks = (grouped_routes.peers.isin(source_central) & grouped_routes.channel_out.isin(channel_out))\n",
    "    second_dff = grouped_routes[masks]\n",
    "\n",
    "    return html.Div([\n",
    "        dash_table.DataTable(\n",
    "            id='second-table',\n",
    "            columns=[\n",
    "                {'name': i, 'id': i, 'deletable': True} for i in second_dff.columns\n",
    "                if i != 'id'\n",
    "            ],\n",
    "            data=second_dff.to_dict('records'),\n",
    "            editable=False, # cells are not editable\n",
    "            filter_action='native', # filter by entering text in the input box\n",
    "            row_selectable='single',  \n",
    "            page_action='native',\n",
    "            page_current=0,\n",
    "            page_size=10,\n",
    "        ),\n",
    "        html.Div(id='thirdtable-container'),  \n",
    "        html.Div(id=\"output2\") \n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "app.run_server()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Third table (to select channel in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paths from okcoin to ln_capital\n",
    "paths_okcoin_ln_capital = []\n",
    "for i in range(9):\n",
    "    stop = 0\n",
    "    paths = nx.all_simple_paths(G, source=okcoin, target=ln_capital, cutoff=i+1)\n",
    "    while stop < 100:\n",
    "        try:\n",
    "            paths_okcoin_ln_capital.append(next(paths))\n",
    "            stop += 1\n",
    "        except StopIteration:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2 = pd.DataFrame(data={'testing':paths_okcoin_ln_capital})\n",
    "testing = test2.explode('testing')\n",
    "testing.reset_index(inplace=True)\n",
    "\n",
    "testing = testing.rename(columns={'testing':'source'})\n",
    "testing['destination'] = testing.source.shift(-1)\n",
    "\n",
    "grouped2 = testing.groupby('index')\n",
    "all_channels2 = testing[grouped2.cumcount(ascending=False) > 0]\n",
    "\n",
    "# Need to add capacity and node fee rates\n",
    "# make capacity and node fee rates columns\n",
    "all_channels2[['capacity', 'node1_fee_rate', 'node2_fee_rate']] = None \n",
    "# iterate over all_channels2 rows and add capacity and node fee rates from G edges\n",
    "for index, row in all_channels2.iterrows():\n",
    "    all_channels2.loc[index, 'capacity'] = G.get_edge_data(row['source'], row['destination'])['capacity']\n",
    "    all_channels2.loc[index, 'node1_fee_rate'] = G.get_edge_data(row['source'], row['destination'])['node1_fee_rate']\n",
    "    all_channels2.loc[index, 'node2_fee_rate'] = G.get_edge_data(row['source'], row['destination'])['node2_fee_rate']\n",
    "all_channels2 = all_channels2.rename(columns={'index':'path_id'})\n",
    "aliases = nodes.set_index('pub_key')['alias']\n",
    "all_channels2.set_index('source', inplace=True)\n",
    "all_channels2['s_node'] = aliases\n",
    "all_channels2.reset_index(inplace=True)\n",
    "all_channels2.set_index('destination',inplace=True)\n",
    "all_channels2['d_node'] = aliases\n",
    "all_channels2.reset_index(inplace=True)\n",
    "central_to_source_df = all_channels2.copy()\n",
    "central_to_source_df[['s_node', 'd_node']] = central_to_source_df[['s_node', 'd_node']].fillna('NO_NODE_ALIAS')\n",
    "central_to_source_df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destination</th>\n",
       "      <th>source</th>\n",
       "      <th>path_id</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>s_node</th>\n",
       "      <th>d_node</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...</td>\n",
       "      <td>036b53093df5a932deac828cca6d663472dbc88322b05e...</td>\n",
       "      <td>0</td>\n",
       "      <td>10000000</td>\n",
       "      <td>790</td>\n",
       "      <td>500</td>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03cde60a6323f7122d5178255766e38114b4722ede08f7...</td>\n",
       "      <td>036b53093df5a932deac828cca6d663472dbc88322b05e...</td>\n",
       "      <td>1</td>\n",
       "      <td>500000000</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>okcoin</td>\n",
       "      <td>bfx-lnd1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         destination  \\\n",
       "0  0305f5f4013f6c6eeb097bd8607204ec1f31577a05fae3...   \n",
       "1  03cde60a6323f7122d5178255766e38114b4722ede08f7...   \n",
       "\n",
       "                                              source  path_id   capacity  \\\n",
       "0  036b53093df5a932deac828cca6d663472dbc88322b05e...        0   10000000   \n",
       "1  036b53093df5a932deac828cca6d663472dbc88322b05e...        1  500000000   \n",
       "\n",
       "  node1_fee_rate node2_fee_rate  s_node      d_node  \n",
       "0            790            500  okcoin  LN.capital  \n",
       "1            500           1000  okcoin    bfx-lnd1  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "central_to_source_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fee rates to int\n",
    "central_to_source_df['node1_fee_rate'] = central_to_source_df['node1_fee_rate'].astype(int)\n",
    "central_to_source_df['node2_fee_rate'] = central_to_source_df['node2_fee_rate'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_routes2 = central_to_source_df.groupby('path_id').apply(lambda x: x.s_node.values.tolist() + [x.d_node.values.tolist()[-1]])\n",
    "grouped_routes2 = grouped_routes2.to_frame().rename(columns={0: 'route'})\n",
    "# join aggregated capacity and fee rates to each path\n",
    "grouped_routes2 = grouped_routes2.join(central_to_source_df.groupby('path_id').aggregate(sum))\n",
    "# add peers column ('first_node - last_node')\n",
    "grouped_routes2['peers'] = grouped_routes2.route.apply(lambda x: f'{x[0]} - {x[-1]}')\n",
    "# add a 'hops' column that represents the number of hops in each path\n",
    "grouped_routes2['hops'] = grouped_routes2['route'].apply(lambda x: len(x) - 1)\n",
    "# change route column to be str instead of list\n",
    "grouped_routes2['route'] = grouped_routes2['route'].apply(lambda x: ' -> '.join(x))\n",
    "grouped_routes2.insert(loc=0, column=\"id\", value=grouped_routes2.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>route</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>peers</th>\n",
       "      <th>hops</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>okcoin -&gt; LN.capital</td>\n",
       "      <td>10000000</td>\n",
       "      <td>790</td>\n",
       "      <td>500</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; LN.capital</td>\n",
       "      <td>507000000</td>\n",
       "      <td>1290</td>\n",
       "      <td>2000</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                             route   capacity  node1_fee_rate  \\\n",
       "path_id                                                                    \n",
       "0         0              okcoin -> LN.capital   10000000             790   \n",
       "1         1  okcoin -> bfx-lnd1 -> LN.capital  507000000            1290   \n",
       "\n",
       "         node2_fee_rate                peers  hops  \n",
       "path_id                                             \n",
       "0                   500  okcoin - LN.capital     1  \n",
       "1                  2000  okcoin - LN.capital     2  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create a new dataframe where for each route retrieve the source and destination nodes, and the channel out (the second node)\n",
    "select_channel_in_df = pd.DataFrame(columns=['central_node', 'source', 'channel_in'], index=grouped_routes2.index)\n",
    "\n",
    "select_channel_in_df['central_node'] = grouped_routes2.peers.apply(lambda x: x.split(' - ')[0])\n",
    "select_channel_in_df['source'] = grouped_routes2.peers.apply(lambda x: x.split(' - ')[-1])\n",
    "select_channel_in_df['channel_in'] = grouped_routes2.route.apply(lambda x: x.split(' -> ')[-2])\n",
    "# thirds column\n",
    "select_channel_in_df['important_nodes'] = select_channel_in_df.apply(lambda x: x.central_node + ' - ' +  x.channel_in + ' - ' + x.source, axis=1)\n",
    "select_channel_in_df['channel_in'] = select_channel_in_df.channel_in + ' - ' + select_channel_in_df.source\n",
    "# drop duplicates from the three columns\n",
    "select_channel_in_df.drop_duplicates(subset='important_nodes', keep='first', inplace=True)\n",
    "select_channel_in_df['id'] = select_channel_in_df.important_nodes.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>central_node</th>\n",
       "      <th>source</th>\n",
       "      <th>channel_in</th>\n",
       "      <th>important_nodes</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>okcoin - okcoin - LN.capital</td>\n",
       "      <td>okcoin - okcoin - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>bfx-lnd1 - LN.capital</td>\n",
       "      <td>okcoin - bfx-lnd1 - LN.capital</td>\n",
       "      <td>okcoin - bfx-lnd1 - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>bfx-lnd0 - LN.capital</td>\n",
       "      <td>okcoin - bfx-lnd0 - LN.capital</td>\n",
       "      <td>okcoin - bfx-lnd0 - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>okex - LN.capital</td>\n",
       "      <td>okcoin - okex - LN.capital</td>\n",
       "      <td>okcoin - okex - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>BCash_Is_Trash - LN.capital</td>\n",
       "      <td>okcoin - BCash_Is_Trash - LN.capital</td>\n",
       "      <td>okcoin - BCash_Is_Trash - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>MJKC31 - LN.capital</td>\n",
       "      <td>okcoin - MJKC31 - LN.capital</td>\n",
       "      <td>okcoin - MJKC31 - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>02415d7ec37a7bb22b27 - LN.capital</td>\n",
       "      <td>okcoin - 02415d7ec37a7bb22b27 - LN.capital</td>\n",
       "      <td>okcoin - 02415d7ec37a7bb22b27 - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>AVATAR - LN.capital</td>\n",
       "      <td>okcoin - AVATAR - LN.capital</td>\n",
       "      <td>okcoin - AVATAR - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>CommonLawIsTheAnswer - LN.capital</td>\n",
       "      <td>okcoin - CommonLawIsTheAnswer - LN.capital</td>\n",
       "      <td>okcoin - CommonLawIsTheAnswer - LN.capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>okcoin</td>\n",
       "      <td>LN.capital</td>\n",
       "      <td>FritzAusputzer - LN.capital</td>\n",
       "      <td>okcoin - FritzAusputzer - LN.capital</td>\n",
       "      <td>okcoin - FritzAusputzer - LN.capital</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        central_node      source                         channel_in  \\\n",
       "path_id                                                               \n",
       "0             okcoin  LN.capital                okcoin - LN.capital   \n",
       "1             okcoin  LN.capital              bfx-lnd1 - LN.capital   \n",
       "2             okcoin  LN.capital              bfx-lnd0 - LN.capital   \n",
       "3             okcoin  LN.capital                  okex - LN.capital   \n",
       "4             okcoin  LN.capital        BCash_Is_Trash - LN.capital   \n",
       "...              ...         ...                                ...   \n",
       "261           okcoin  LN.capital                MJKC31 - LN.capital   \n",
       "331           okcoin  LN.capital  02415d7ec37a7bb22b27 - LN.capital   \n",
       "546           okcoin  LN.capital                AVATAR - LN.capital   \n",
       "554           okcoin  LN.capital  CommonLawIsTheAnswer - LN.capital   \n",
       "714           okcoin  LN.capital        FritzAusputzer - LN.capital   \n",
       "\n",
       "                                    important_nodes  \\\n",
       "path_id                                               \n",
       "0                      okcoin - okcoin - LN.capital   \n",
       "1                    okcoin - bfx-lnd1 - LN.capital   \n",
       "2                    okcoin - bfx-lnd0 - LN.capital   \n",
       "3                        okcoin - okex - LN.capital   \n",
       "4              okcoin - BCash_Is_Trash - LN.capital   \n",
       "...                                             ...   \n",
       "261                    okcoin - MJKC31 - LN.capital   \n",
       "331      okcoin - 02415d7ec37a7bb22b27 - LN.capital   \n",
       "546                    okcoin - AVATAR - LN.capital   \n",
       "554      okcoin - CommonLawIsTheAnswer - LN.capital   \n",
       "714            okcoin - FritzAusputzer - LN.capital   \n",
       "\n",
       "                                                 id  \n",
       "path_id                                              \n",
       "0                      okcoin - okcoin - LN.capital  \n",
       "1                    okcoin - bfx-lnd1 - LN.capital  \n",
       "2                    okcoin - bfx-lnd0 - LN.capital  \n",
       "3                        okcoin - okex - LN.capital  \n",
       "4              okcoin - BCash_Is_Trash - LN.capital  \n",
       "...                                             ...  \n",
       "261                    okcoin - MJKC31 - LN.capital  \n",
       "331      okcoin - 02415d7ec37a7bb22b27 - LN.capital  \n",
       "546                    okcoin - AVATAR - LN.capital  \n",
       "554      okcoin - CommonLawIsTheAnswer - LN.capital  \n",
       "714            okcoin - FritzAusputzer - LN.capital  \n",
       "\n",
       "[67 rows x 5 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_channel_in_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "app.layout = html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='third-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in select_channel_in_df.columns\n",
    "            if i != 'id'\n",
    "        ],\n",
    "        data=select_channel_in_df.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        row_selectable='single',  \n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=6,\n",
    "    ),\n",
    "    html.Div(id='fourthtable-container'),  \n",
    "    html.Div(id=\"output3\") \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "app.run_server()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fourth table (path to complete the rebalance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_routes2['channel_in'] = grouped_routes2.route.apply(lambda x: x.split(' -> ')[-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>route</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>peers</th>\n",
       "      <th>hops</th>\n",
       "      <th>channel_in</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>okcoin -&gt; LN.capital</td>\n",
       "      <td>10000000</td>\n",
       "      <td>790</td>\n",
       "      <td>500</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>1</td>\n",
       "      <td>okcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; LN.capital</td>\n",
       "      <td>507000000</td>\n",
       "      <td>1290</td>\n",
       "      <td>2000</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>2</td>\n",
       "      <td>bfx-lnd1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                             route   capacity  node1_fee_rate  \\\n",
       "path_id                                                                    \n",
       "0         0              okcoin -> LN.capital   10000000             790   \n",
       "1         1  okcoin -> bfx-lnd1 -> LN.capital  507000000            1290   \n",
       "\n",
       "         node2_fee_rate                peers  hops channel_in  \n",
       "path_id                                                        \n",
       "0                   500  okcoin - LN.capital     1     okcoin  \n",
       "1                  2000  okcoin - LN.capital     2   bfx-lnd1  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = JupyterDash(__name__)\n",
    "server = app.server\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='third-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in select_channel_in_df.columns\n",
    "            if i != 'id'\n",
    "        ],\n",
    "        data=select_channel_in_df.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        row_selectable='single',  \n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=6,\n",
    "    ),\n",
    "    html.Div(id='fourthtable-container'),  \n",
    "    html.Div(id=\"output3\") \n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output('fourthtable-container', 'children'),\n",
    "    Input('third-table', 'selected_row_ids')\n",
    ")\n",
    "def make_fourth_table(selected_rows):\n",
    "    if selected_rows is None:\n",
    "        central_source, chan_in = [], []\n",
    "    else:\n",
    "        rows = [row.split(' - ') for row in selected_rows]\n",
    "        central = [row[0] for row in rows]\n",
    "        chan_in = [row[1] for row in rows]\n",
    "        source = [row[-1] for row in rows]\n",
    "        central_source = [central[i] + ' - ' + source[i] for i in range(len(central))]\n",
    "\n",
    "    masks = (grouped_routes2.peers.isin(central_source) & grouped_routes2.channel_in.isin(chan_in))\n",
    "    second_dff = grouped_routes2[masks]\n",
    "\n",
    "    return html.Div([\n",
    "        dash_table.DataTable(\n",
    "            id='second-table',\n",
    "            columns=[\n",
    "                {'name': i, 'id': i, 'deletable': True} for i in second_dff.columns\n",
    "                if i != 'id'\n",
    "            ],\n",
    "            data=second_dff.to_dict('records'),\n",
    "            editable=False, # cells are not editable\n",
    "            filter_action='native', # filter by entering text in the input box\n",
    "            row_selectable='single',  \n",
    "            page_action='native',\n",
    "            page_current=0,\n",
    "            page_size=10,\n",
    "        ),\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "app.run_server()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join all tables together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "app = JupyterDash(__name__)\n",
    "server = app.server\n",
    "\n",
    "### FIRST TABLE ###\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='first-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in select_central_node_df.columns\n",
    "            if i != 'id' and i != 'important_nodes'\n",
    "        ],\n",
    "        data=select_central_node_df.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        row_selectable='single',  \n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=6,\n",
    "    ),\n",
    "    html.Div(id='secondtable-container'),  \n",
    "])\n",
    "\n",
    "### SECOND TABLE ###\n",
    "\n",
    "@app.callback(\n",
    "    Output('secondtable-container', 'children'),\n",
    "    Input('first-table', 'selected_row_ids'),\n",
    ")\n",
    "def make_second_table(selected_rows):\n",
    "    if selected_rows is None:\n",
    "        source_central, channel_out = [], []\n",
    "    else:\n",
    "        rows = [row.split(' -> ') for row in selected_rows]\n",
    "        source_central = [row[0] for row in rows]\n",
    "        channel_out = [row[1] for row in rows]\n",
    "\n",
    "    masks = (grouped_routes.peers.isin(source_central) & grouped_routes.channel_out.isin(channel_out))\n",
    "    second_dff = grouped_routes[masks]\n",
    "\n",
    "    return html.Div([\n",
    "        dash_table.DataTable(\n",
    "            id='second-table',\n",
    "            columns=[\n",
    "                {'name': i, 'id': i, 'deletable': True} for i in second_dff.columns\n",
    "                if i != 'id' and i != 'channel_out'\n",
    "            ],\n",
    "            data=second_dff.to_dict('records'),\n",
    "            editable=False, # cells are not editable\n",
    "            filter_action='native', # filter by entering text in the input box\n",
    "            sort_action='native',\n",
    "            sort_mode='multi',\n",
    "            row_selectable='single',  \n",
    "            page_action='native',\n",
    "            page_current=0,\n",
    "            page_size=10,\n",
    "        ),\n",
    "        html.Div(id='thirdtable-container'),  \n",
    "    ])\n",
    "\n",
    "### THIRD TABLE ###\n",
    "\n",
    "@app.callback(\n",
    "    Output('thirdtable-container', 'children'),\n",
    "    Input('second-table', 'selected_row_ids'),\n",
    ")\n",
    "def make_third_table(selected_rows):\n",
    "    if selected_rows is None:\n",
    "        rows = []\n",
    "    else:\n",
    "        rows = select_channel_in_df\n",
    "    return html.Div([\n",
    "    dash_table.DataTable(\n",
    "        id='third-table',\n",
    "        columns=[\n",
    "            {'name': i, 'id': i, 'deletable': True} for i in rows.columns\n",
    "            if i != 'id' and i != 'important_nodes'\n",
    "        ],\n",
    "        data=rows.to_dict('records'),\n",
    "        editable=False, # cells are not editable\n",
    "        filter_action='native', # filter by entering text in the input box\n",
    "        row_selectable='single',  \n",
    "        page_action='native',\n",
    "        page_current=0,\n",
    "        page_size=6,\n",
    "    ),\n",
    "    html.Div(id='fourthtable-container'),  \n",
    "])\n",
    "\n",
    "### FOURTH TABLE ###\n",
    "\n",
    "@app.callback(\n",
    "    Output('fourthtable-container', 'children'),\n",
    "    Input('third-table', 'selected_row_ids')\n",
    ")\n",
    "def make_fourth_table(selected_rows):\n",
    "    if selected_rows is None:\n",
    "        rows = []\n",
    "    else:\n",
    "        rows = [row.split(' - ') for row in selected_rows]\n",
    "        central = [row[0] for row in rows]\n",
    "        chan_in = [row[1] for row in rows]\n",
    "        source = [row[-1] for row in rows]\n",
    "        central_source = [central[i] + ' - ' + source[i] for i in range(len(central))]\n",
    "\n",
    "    masks = (grouped_routes2.peers.isin(central_source) & grouped_routes2.channel_in.isin(chan_in))\n",
    "    second_dff = grouped_routes2[masks]\n",
    "\n",
    "    return html.Div([\n",
    "    dash_table.DataTable(\n",
    "            id='fourth-table',\n",
    "            columns=[\n",
    "                {'name': i, 'id': i, 'deletable': True} for i in second_dff.columns\n",
    "                if i != 'id' and i != 'channel_in'\n",
    "            ],\n",
    "            data=second_dff.to_dict('records'),\n",
    "            editable=False, # cells are not editable\n",
    "            filter_action='native', # filter by entering text in the input box\n",
    "            sort_action='native',\n",
    "            sort_mode='multi',\n",
    "            row_selectable='single',  \n",
    "            page_action='native',\n",
    "            page_current=0,\n",
    "            page_size=10,\n",
    "        ),\n",
    "        html.Div(id='final-container'),\n",
    "    ])\n",
    "\n",
    "### FINAL CONTAINER ###\n",
    "# this is for displaying all the circular path of the rebalancing, with all its data #\n",
    "\n",
    "@app.callback(\n",
    "    Output('final-container', 'children'),\n",
    "   \n",
    "    Input('second-table', 'selected_row_ids'),\n",
    "    Input('fourth-table', 'selected_row_ids'),\n",
    ")\n",
    "def fill_final_container(second_rows, fourth_rows):\n",
    "    if second_rows is None:\n",
    "        rows = []\n",
    "    else:\n",
    "        rows = second_rows\n",
    "\n",
    "    dff = grouped_routes.loc[rows]\n",
    "\n",
    "    if fourth_rows is None:\n",
    "        f_rows = []\n",
    "    else:\n",
    "        f_rows = fourth_rows\n",
    "    \n",
    "    f_dff = grouped_routes2.loc[f_rows]\n",
    "\n",
    "    circular_path = dff.route.values[0]\n",
    "\n",
    "    chan_in_path = f_dff.route.values[0]\n",
    "    \n",
    "    circular_path = circular_path + chan_in_path[chan_in_path.find(' -> '):]\n",
    "\n",
    "    aggregated_capacities = dff.capacity.values[0] + f_dff.capacity.values[0]\n",
    "    aggregated_fees = dff.node2_fee_rate.values[0] + f_dff.node2_fee_rate.values[0]\n",
    "    total_hops = dff.hops.values[0] + f_dff.hops.values[0]\n",
    "\n",
    "\n",
    "    return html.Div([\n",
    "        html.B(circular_path),\n",
    "        html.Br(),\n",
    "        html.B(f'Channel agg capacities: {aggregated_capacities}'),\n",
    "        html.Br(),\n",
    "        html.B(f'Channel agg fees: {aggregated_fees}'),\n",
    "        html.Br(),\n",
    "        html.B(f'Total hops in path: {total_hops}'),\n",
    "    ])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "app.run_server()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>route</th>\n",
       "      <th>capacity</th>\n",
       "      <th>node1_fee_rate</th>\n",
       "      <th>node2_fee_rate</th>\n",
       "      <th>peers</th>\n",
       "      <th>hops</th>\n",
       "      <th>channel_in</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>okcoin -&gt; LN.capital</td>\n",
       "      <td>10000000</td>\n",
       "      <td>790</td>\n",
       "      <td>500</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>1</td>\n",
       "      <td>okcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; LN.capital</td>\n",
       "      <td>507000000</td>\n",
       "      <td>1290</td>\n",
       "      <td>2000</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>2</td>\n",
       "      <td>bfx-lnd1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>okcoin -&gt; bfx-lnd0 -&gt; LN.capital</td>\n",
       "      <td>204000000</td>\n",
       "      <td>1100</td>\n",
       "      <td>1500</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>2</td>\n",
       "      <td>bfx-lnd0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>okcoin -&gt; okex -&gt; LN.capital</td>\n",
       "      <td>107000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>100</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>2</td>\n",
       "      <td>okex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>okcoin -&gt; BCash_Is_Trash -&gt; LN.capital</td>\n",
       "      <td>21777215</td>\n",
       "      <td>251</td>\n",
       "      <td>600</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>2</td>\n",
       "      <td>BCash_Is_Trash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>711</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; BCash_Is_Trash -&gt; tippin...</td>\n",
       "      <td>649302845</td>\n",
       "      <td>38625</td>\n",
       "      <td>4521</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>9</td>\n",
       "      <td>Kraken 🐙⚡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>712</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; BCash_Is_Trash -&gt; tippin...</td>\n",
       "      <td>546302845</td>\n",
       "      <td>72885</td>\n",
       "      <td>2176</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>9</td>\n",
       "      <td>Mintter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>713</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; BCash_Is_Trash -&gt; tippin...</td>\n",
       "      <td>557080060</td>\n",
       "      <td>17541</td>\n",
       "      <td>2186</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>9</td>\n",
       "      <td>ln.bitstamp.net [Bitstamp]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>714</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; BCash_Is_Trash -&gt; tippin...</td>\n",
       "      <td>547580060</td>\n",
       "      <td>17190</td>\n",
       "      <td>2078</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>9</td>\n",
       "      <td>FritzAusputzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>715</td>\n",
       "      <td>okcoin -&gt; bfx-lnd1 -&gt; BCash_Is_Trash -&gt; tippin...</td>\n",
       "      <td>546427845</td>\n",
       "      <td>17495</td>\n",
       "      <td>12219</td>\n",
       "      <td>okcoin - LN.capital</td>\n",
       "      <td>9</td>\n",
       "      <td>Milky Way</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>716 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                              route   capacity  \\\n",
       "path_id                                                                      \n",
       "0          0                               okcoin -> LN.capital   10000000   \n",
       "1          1                   okcoin -> bfx-lnd1 -> LN.capital  507000000   \n",
       "2          2                   okcoin -> bfx-lnd0 -> LN.capital  204000000   \n",
       "3          3                       okcoin -> okex -> LN.capital  107000000   \n",
       "4          4             okcoin -> BCash_Is_Trash -> LN.capital   21777215   \n",
       "...      ...                                                ...        ...   \n",
       "711      711  okcoin -> bfx-lnd1 -> BCash_Is_Trash -> tippin...  649302845   \n",
       "712      712  okcoin -> bfx-lnd1 -> BCash_Is_Trash -> tippin...  546302845   \n",
       "713      713  okcoin -> bfx-lnd1 -> BCash_Is_Trash -> tippin...  557080060   \n",
       "714      714  okcoin -> bfx-lnd1 -> BCash_Is_Trash -> tippin...  547580060   \n",
       "715      715  okcoin -> bfx-lnd1 -> BCash_Is_Trash -> tippin...  546427845   \n",
       "\n",
       "         node1_fee_rate  node2_fee_rate                peers  hops  \\\n",
       "path_id                                                              \n",
       "0                   790             500  okcoin - LN.capital     1   \n",
       "1                  1290            2000  okcoin - LN.capital     2   \n",
       "2                  1100            1500  okcoin - LN.capital     2   \n",
       "3                 10000             100  okcoin - LN.capital     2   \n",
       "4                   251             600  okcoin - LN.capital     2   \n",
       "...                 ...             ...                  ...   ...   \n",
       "711               38625            4521  okcoin - LN.capital     9   \n",
       "712               72885            2176  okcoin - LN.capital     9   \n",
       "713               17541            2186  okcoin - LN.capital     9   \n",
       "714               17190            2078  okcoin - LN.capital     9   \n",
       "715               17495           12219  okcoin - LN.capital     9   \n",
       "\n",
       "                         channel_in  \n",
       "path_id                              \n",
       "0                            okcoin  \n",
       "1                          bfx-lnd1  \n",
       "2                          bfx-lnd0  \n",
       "3                              okex  \n",
       "4                    BCash_Is_Trash  \n",
       "...                             ...  \n",
       "711                       Kraken 🐙⚡  \n",
       "712                         Mintter  \n",
       "713      ln.bitstamp.net [Bitstamp]  \n",
       "714                  FritzAusputzer  \n",
       "715                       Milky Way  \n",
       "\n",
       "[716 rows x 8 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_routes2"
   ]
  }
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