Channels_second_analysis_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd59db3a",
   "metadata": {},
   "source": [
    "# Continue channels analysis based on their activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6f5a9f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b75b19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "92faf4ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is just for style to the charts\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('darkgrid') # darkgrid, white grid, dark, white and ticks\n",
    "plt.rc('axes', titlesize=16)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=13)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=12)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=12)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=12)    # legend fontsize\n",
    "plt.rc('font', size=12)          # controls default text sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81924553",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_pubkey2(series, channels):\n",
    "    channels = channels.drop_duplicates(['chan_id'], keep='last')\n",
    "    channels['chan_id'] = channels['chan_id'].astype(np.int64)\n",
    "    series = series.reset_index().rename({'index':'outgoing_channel_id',\n",
    "                                      0:'balanced_metric'}, axis=1)\n",
    "    series['pubkey'] = ''\n",
    "    out_chan_ids = series['outgoing_channel_id'].unique()\n",
    "    \n",
    "    for i in out_chan_ids:\n",
    "        \n",
    "        for z, j in channels[['chan_id', 'pub_key']].iterrows():\n",
    "            \n",
    "            if i == j['chan_id']:\n",
    "                series.loc[series['outgoing_channel_id'] == i, 'pubkey'] = j['pub_key']\n",
    "                \n",
    "                break\n",
    "                \n",
    "    return series['pubkey']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381bc403",
   "metadata": {},
   "source": [
    "**In the first notebook we create a dataframe of the peer nodes connected to our channels. Let's bring that dataframe here and continue with the analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8783dbd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "active_routing_nodes = pd.read_csv('active_routing_nodes.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87463d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>success_rate</th>\n",
       "      <th>alias</th>\n",
       "      <th>balanced_rate</th>\n",
       "      <th>total_Forwards</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pubkey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0203f3e0fdaa52ca5c804d145b6a47e5a620c02123106578230061deb212e720bd</th>\n",
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       "      <td>0.292635</td>\n",
       "      <td>3104.0</td>\n",
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       "      <th>0205306aa071fc026a4884c210d99d886f199068a6ccc65ec1bde085f846be3641</th>\n",
       "      <td>0.003647</td>\n",
       "      <td>Magrathea</td>\n",
       "      <td>0.672870</td>\n",
       "      <td>4113.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                                    success_rate  \\\n",
       "pubkey                                                             \n",
       "0203f3e0fdaa52ca5c804d145b6a47e5a620c0212310657...      0.002255   \n",
       "0205306aa071fc026a4884c210d99d886f199068a6ccc65...      0.003647   \n",
       "\n",
       "                                                                   alias  \\\n",
       "pubkey                                                                     \n",
       "0203f3e0fdaa52ca5c804d145b6a47e5a620c0212310657...  0203f3e0fdaa52ca5c80   \n",
       "0205306aa071fc026a4884c210d99d886f199068a6ccc65...             Magrathea   \n",
       "\n",
       "                                                    balanced_rate  \\\n",
       "pubkey                                                              \n",
       "0203f3e0fdaa52ca5c804d145b6a47e5a620c0212310657...       0.292635   \n",
       "0205306aa071fc026a4884c210d99d886f199068a6ccc65...       0.672870   \n",
       "\n",
       "                                                    total_Forwards  \n",
       "pubkey                                                              \n",
       "0203f3e0fdaa52ca5c804d145b6a47e5a620c0212310657...          3104.0  \n",
       "0205306aa071fc026a4884c210d99d886f199068a6ccc65...          4113.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_routing_nodes.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c243fb4",
   "metadata": {},
   "source": [
    "## Add total revenue to the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87f665b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open forwards dataset\n",
    "forwards_path = '../Datos/forwards_2022-06-29.csv'\n",
    "df_forwards = pd.read_csv(forwards_path)\n",
    "\n",
    "# changing the type of the channel ids to be displayed correctly\n",
    "df_forwards[['outgoing_channel_id', 'incoming_channel_id']] = df_forwards[['outgoing_channel_id', \n",
    "                                                                           'incoming_channel_id']].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "23d16212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open channel events dataset\n",
    "channels_path = '../Datos/channel_events_2022-06-29.csv'\n",
    "df_channels = pd.read_csv(channels_path)\n",
    "df_channels['chan_id'] = df_channels['chan_id'].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5bc4c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# total revenue\n",
    "total_revenue_df = df_forwards.groupby('outgoing_channel_id')['fee_msat'].aggregate('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10403ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outgoing_channel_id</th>\n",
       "      <th>fee_msat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pubkey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>027578e823ce91a00ef9576d3d24e0a21abfa3332ba186654730a0c8804cbb844b</th>\n",
       "      <td>767907716957798400</td>\n",
       "      <td>212160.0</td>\n",
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       "    <tr>\n",
       "      <th>02b37a7de2b3a6b4b04485a2a6eba03feff0d5d34813a0401d09fbcbfedfbb1a1f</th>\n",
       "      <td>767913214610702336</td>\n",
       "      <td>16814.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                    outgoing_channel_id  \\\n",
       "pubkey                                                                    \n",
       "027578e823ce91a00ef9576d3d24e0a21abfa3332ba1866...   767907716957798400   \n",
       "02b37a7de2b3a6b4b04485a2a6eba03feff0d5d34813a04...   767913214610702336   \n",
       "\n",
       "                                                    fee_msat  \n",
       "pubkey                                                        \n",
       "027578e823ce91a00ef9576d3d24e0a21abfa3332ba1866...  212160.0  \n",
       "02b37a7de2b3a6b4b04485a2a6eba03feff0d5d34813a04...   16814.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_revenue = total_revenue_df.copy()\n",
    "total_revenue = total_revenue.reset_index()\n",
    "# Add pubkey of the peers the channels are linked to\n",
    "total_revenue['pubkey'] = add_pubkey2(total_revenue_df, df_channels)\n",
    "\n",
    "total_revenue.set_index('pubkey', inplace=True)\n",
    "total_revenue.rename({0:'normalized_revenue'}, axis=1, inplace=True)\n",
    "total_revenue.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe4d23d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop duplicates\n",
    "total_revenue = total_revenue.reset_index().sort_values(by='fee_msat').drop_duplicates('pubkey', keep='last').set_index('pubkey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d8438d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "active_routing_nodes['total_revenue'] = total_revenue['fee_msat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "46dcf47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# active_routing_nodes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "802b849b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qbXWFMWufocULGF7MhhYvYHgxFxevvb1Nkcs0YdUQEZGRYyIgIjJyTAREREaOiYCIyMgxERARGTm97DVEmsVcTcaaE3eQnJYDBxsLjO/gBL8mnNSDiEqHicDAxFxNxuJDN5GteD4JSFJaDhYfej72PJMBEZUGq4YMzJoTd1RJoEC2Ih9rTtzRTUBEZPCYCAxMclpOib4nIioOE4GBcbCxKNH3RETFYSIwMOM7OMHSVP2fzdJUjvEdnHQTEBEZPDYWG5iCBmH2GiKi8lIhieDw4cNYtWoV5HI5bG1tsWjRItStW7ciDl0p+TVx4IWfiMqN1quGsrOzERwcjLCwMOzduxedO3fGokWLtH1YIiKSSOuJQKlUQgiBtLQ0AM/nE7awYMMmEZG+qJCpKr///nvMnj0bdnZ2yM/Px7Zt21CvXr0i1y/bxDRyKJX5xa+oRxiz9hlavIDhxWxo8QKGF3Nx8ertxDTXr19HeHg4Dhw4gLp16+Lbb7/FxIkTsXfvXshkskK34cQ0+s/QYja0eAHDi9nQ4gUML2aDnZjm5MmTaNGihapxeOjQobh58yZSUlK0fWgiIpJA64ng7bffxtmzZ/H48WMAwJEjR1CnTh1Ur15d24cmIiIJtF415O3tjaCgIAwfPhxmZmawtbXFmjVrtH1YIiKSqELeIxg6dCiGDh1aEYciIqIS4hATRERGjomAiMjIMREQERk5JgIiIiPHREBEZOSYCIiIjBwTARGRkWMiICIyckwERERGjomAiMjIMREQERk5JgIiIiPHREBEZOSYCIiIjBwTARGRkWMiICIyckwERERGjomAiMjIMREQERk5JgIiIiPHREBEZOSYCIiIjBwTARGRkWMiICIyckwERERGjomAiMjIMREQERk5JgIiIiPHREBEZOSYCIiIjBwTARGRkWMiICIyckwERERGjomAiMjIMREQERk5JgIiIiPHREBEZOSYCIiIjJyp1BXv3r2L3377DcnJyZDL5XB0dIS3tzdq1aqlzfiIiEjLik0EZ86cQVhYGG7evAkXFxfUrFkTCoUCJ0+exMKFC+Hq6orx48fD29u7yH1cv34dixYtQlpaGuRyOT7//HO4urqW6w8hIqLS0ZgIPvvsM2RkZGDs2LFo27YtTExM1JYrFAqcOHECGzduxN69e/HVV1+9so+srCwEBQXhiy++gK+vL44cOYJPP/0UBw8eLN9fQkREpaIxEfTv3x8tW7YsemNTU3Tq1AmdOnXCuXPnCl3n1KlTePPNN+Hr6wsA6NKlC+rUqVOGkImIqDzJhBCipBvFx8fD1tYW9erVK3bddevW4dKlS7C2tsa1a9dQtWpVBAcHo2nTpkVuk5+fD6WyxGEBAExM5FAq80u1ra4wZu0ztHgBw4vZ0OIFDC/m4uI1MzMpcpkmkhqLT506hTlz5uCnn35CZGQkwsLCIJfLMW/ePPTp00fjtgqFAsePH8e3336LZs2a4ciRIxg9ejSOHTsGc3PzQrdRKgVSUzNL/msA2NlZlXpbXWHM2mdo8QKGF7OhxQsYXszFxWtvb1Oq/UrqPrpixQpMnDgRSqUSGzduRHh4OLZv347w8PBit61ZsyYaNmyIZs2aAQC6du0KpVKJ+/fvlypgIiIqX5ISwb1799CnTx9cunQJCoUC7dq1Q+PGjfHkyZNit/Xx8cGDBw9w+fJlAMDZs2chk8nYTkBEpCckVQ3Z2dnh6tWr2LNnD7y9vSGXy3Hy5Ek4OjoWu629vT3Cw8OxYMECZGVlwdzcHKtXr4aFhUWZgyciorKTlAgmTJiAgQMHwsrKCps2bcLZs2cxfvx4hISESDpIq1at8N1335UpUCIi0g5JiSAwMBDdunWDqakpTE1NkZ6ejqNHj8LW1lbb8RERkZZJaiNo3749LC0tYWr6PG9YW1vD3t4e7du312pwRESkfUU+ETx48ABTp06FEAJPnjxB//791ZZnZGSgevXqWg+QiIi0q8hEUKdOHYwbNw6pqamYP38+hg4dqrbc3NwcrVq10nqARESkXRrbCDp37gwAaNSoEdzc3CokICIiqliSGotr166NVatWITk5Gfn5z19vVigUuHXrFqKjo7UaIBERaZekRBAcHIy0tDRUq1YNKSkpeOutt3D06FEMGjRI2/EREZGWSUoE58+fR1xcHBITExESEoLFixfjnXfewdq1a7UdHxERaZmk7qNVqlRRjTZ648YNAICvry9u3bql1eCIiEj7JCWCRo0aISoqCpaWlrCyssKlS5dw8+ZNyOWc8piIyNBJbiOYMmUKfHx8MGHCBAwePBgAMH78eK0GR0RE2icpEbz99ts4dOgQAODNN99Eq1atkJGRgQYNGmg1OCIi0j5JdTu5ubnYvXs3AODu3buYPXs2vvnmG0nDUBMRkX6TlAg+//xzREVFAQBmz56NKlWqQC6XY/bs2VoNjoiItE9S1dDp06exd+9e/PPPP/j9999x/Phx2Nraom3bttqOj4iItEzSE0FGRgasrKxw4sQJvPXWW7C3t0dubi5MTEo3UTIREekPSU8Enp6emDp1Kv744w8MHDgQiYmJmDdvHtq1a6ft+IiISMskPRF89dVXaNiwIYYOHYpRo0bh6dOnePPNN/H5559rOz4iItIymRBC6DqIl+XlKZGamlmqbe3srEq9ra4wZu0ztHgBw4vZ0OIFDC/m4uK1t7cp1X4lVQ398ssvCA0NRUJCgmr00QJnzpwp1YGJiEg/SEoEs2bNQkBAANq2bcthJYiIKhlJieDZs2eYPHkyewkREVVCkm7v+/Tpo3qhjIiIKhdJTwTe3t6YMGECQkJCUKVKFbVlbCMgIjJskhLBggULMGrUKLRu3ZrVQ0RElYykRJCZmYkpU6ZoORQiItIFSW0EAwYMwKZNm17pOkpERIZP0hPBmTNncOXKFYSGhqJKlSqQyWRqy4iIyHBJSgQzZszQdhxERKQjGhPBzZs30ahRI3h5eRW7o+vXr8PFxaXcAiMiooqhMREsX74c1apVw+DBg+Hq6lroOvHx8di6dSvS0tIQERGhlSCJiEh7NCaCNWvWYN++fZg+fTqysrLg5uYGe3t7CCHw6NEj/Oc//0HVqlUxfvx4+Pv7V1TMRERUjoptI+jZsyd69uyJ8+fP49y5c0hKSoJMJoO7uzvGjBmDpk2bVkScRESkJZIaiwGgRYsWaNGihTZjISIiHeBQokRERo6JgIjIyDEREBEZOcmJQKlU4vTp09i1axdycnJw584dLYZFREQVRVJj8Z07dzBmzBgoFAo8efIErVq1QmBgIFauXInOnTtrO0YiItIiSU8ECxYswNChQ3H06FGYmpqiXr16WLZsGZYvX16igx05cgQeHh6lCpSIiLRDUiL4448/MHToUABQDTjXrVs3JCQkSD7QnTt3sGTJklKESERE2iQpETg4OODSpUtq3125cgW1atWSdJCsrCwEBwdz8DoiIj0kqY1g4sSJGDVqFPr06YPc3FysXr0aO3fuxGeffSbpIHPnzsV7770neVA6ExMZ7OysJK376rbyUm+rK4xZ+wwtXsDwYja0eAHDi1lb8UpKBN27d4ejoyN2794NLy8vJCUlYfny5fD09Cx226ioKJiamqJ///548OCBpKCUSoHU1ExJ677Mzs6q1NvqCmPWPkOLFzC8mA0tXsDwYi4uXnt7m1LtV/IQE+7u7nB3dy/xAfbs2YPs7Gz07t0beXl5qr9HRkbCwcGhxPsjIqLyJSkRdO7cWW1WshcdPXpU47a7du1S/f3BgwcIDAzE3r17SxAiERFpk6REMHfuXLXPKSkp2LFjB4eeJiKqBCQlgo4dO77ynY+PD4YNG4YRI0ZIPlidOnVw4cIFyesTEZH2lXqsIZlMhsePH5dnLEREpAOSngiWLl2q9jkvLw8///yzpLmMiYhIv0lKBCkpKWqf5XI5+vfvj/fee08rQRERUcWRlAi+/PJLbcdBREQ6IikRpKWlYdu2bbh79y7y8/PVljFJEBEZNkmJIDg4GHfu3EGHDh1gair5HTSS6IeLCfg69jqS03LgYGOB8R2c4NeEL9sRUcWQdFU/d+4cYmNjUaNGDW3HYzBiriZjzYk7Zb54x1xNxuLDN5Gd9/xJKyktB4sP3QQAJgMiqhCSuo/WqFEDcjlntSwQczUZiw/dRFJaDgT+d/GOuZpc4n2tOXFHlQQKZCvysebEnfIJloioGJKeCPr164dx48bhvffeQ/Xq1dWW+fr6aiUwfbbmxB1kKwq/eJf0Lj45LadE3xMRlTdJiWD79u0AgNWrV6t9L5PJih1rqDIqz4u3g40FkgrZzsHGosT7IiIqDUmJ4KefftJ2HAalPC/e4zs4qbURAIClqRzjOziVJUQiIskkV/zfvXsXy5cvx2effYanT59i9+7d2oxLr43v4ARLU/VTV9qLt18TB3zR2xWONhaQAXC0scDM7o3YUExEFUbSE8Hx48cRHByMLl26IDY2FlOmTMHKlSvx+PFjjBkzRtsx6p2Ci3R59BoCgF7N3oBPPbtyjJCISDpJiSA0NBTh4eFo1aoVjhw5AgcHB2zcuBFBQUFGmQiA58mAd+1EVBlIqhpKTExUTUtZMEFN/fr1kZGRob3IiIioQkhKBI0bN8aOHTvUvouJiZE8GT0REekvSVVDs2fPRlBQELZv347MzEwMHz4ct2/fxvr167UdHxERaZmkRODi4oLY2FjExcUhMTER9vb26NixI2xtbbUdHxERaZmkRDBt2jT07t0bfn5+lXqoifIaP4hIH7A8k1SSEkHdunXxxRdfID09HQEBAejduzeaNm2q7dgqVMH4QQVDR3DwNzJkLM9UEpJu7ydPnozY2FhEREQAAMaNG4eAgABERkZqNbiKpGn8ICJDw/JMJVGiyQXc3d1Rp04d1K9fH5GRkdi4cSNGjx6trdgqVEUP/vbiY3stW0uMbVePd2pUbjiYIZWE5BnKDh06hP379+PChQto3749Zs2aValGHq3Iwd9efmxPeJrNx3YqVxzMkEpCUtVQ27Zt8d1336Fr166Ii4vD6tWr0bVrV5iZmWk7vgpTnuMHFYeP7aRtFVmeyfBJeiL48ccf4eTkpOVQdKu8xw/ShI/tpG0VWZ7J8ElKBE5OTtizZw927NiBx48fY+vWrVi+fDnmzp2LKlWqaDvGClNR4wfxsZ0qAsfDIqkkVQ1FRkbi3//+NwYNGoTU1FRUqVIFSUlJWLhwobbjq5T42E5E+kRSItixYwfWrl2Ld999F3K5HDY2Nli5ciXi4uK0HF7l5NfEATO7N1LNQfCGrSXnICAinZFUNZSVlYUaNWoAAIQQAIDXXnsNJiYm2ousAkh981Ibb2i++NhuZ2eF1NTMMu2PiKi0JD0RtGvXDvPnz8fTp08hk8mgUCgQGhqKNm3aaDs+rSnowpmUlgOB/715GXM1uVTrEREZKkmJYPbs2fjnn3/Qpk0bPHv2DM2bN8eNGzcwc+ZMbcenNVK7cLKrJxFVdpKqhmxtbREZGYnHjx8jISEBNWvWhKOjo6qayBBJ7cKpyzeO2eWPiCpCsU8Et2/fxqFDh5CcnIzXX38d7u7ucHR0xI0bNzBgwICKiFEriuqq+fL3UtcrD6yGIiJd0JgIdu3ahZ49e2L27Nno0aMHLl26BABYu3Yt+vbta9DzEUjtwsk3jl8VczUZviFx8Ar9GYGRvzJRERk4jVVD69atw5IlSxAYGIhNmzZh7dq1qFatGmJjY7Fw4UL06dOnouIsd1LfvOQbx+o4vDFR5aMxETx8+BA9e/YEAAwdOhTLli1Dw4YN8f333+ONN96okAC1Seqbl3zj+H80PbUwERAZJo2JQCaTQSaTAYBqgLmwsLBKkQR0obiG4PEdnNTutgH9e+PYEJ5ajBk7G1BplGg+AgsLC9SuXVtbsVRqmqpUBnvXB2AYA4UZwlOLsWK1HZWWxkQghMCtW7dU3URf/gwAb731VrEH2bt3LzZs2ACZTIbXXnsNs2bNgpubWxlDNyyaqlQKEgGg/wOFGcJTi7FitR2VlsZEkJWVhZ49e6pd+AMCAlR/l8lkuHr1qsYD3L59G19//TWio6NRs2ZNHD9+HBMnTjS6cYoqS5VKwQXlm1N3kfg0Wy+fWoxVZSljVPE0JoJr166V+QDm5uZYtGgRatasCQBwdXXF48ePkZubC3Nz8zLv31BUpioVvyYOGOxdn+Mj6ZnKVMaoYslEBb4eLIRAcHAwcnNzsWrVqiLXy8/Ph1JZurBMTORQKvOLX7GC/XAxAbP2XkZ23gtVKmZyfNHbFX1a1NHLmDXR1/NcFEOLFyh5zJrKWK9m2u/gYQznWNeKi9fMrHQDgVZYIsjMzMSMGTOQlJSE9evXo2rVqkWum5enLPXdpj6P5FlUjw59jrkohhazocULlC5mXfYaMpZzrEvFxWtvb1Oq/Zao11BpJSQkYOzYsWjYsCG+/fZbWFpaVsRh9Y6+NwST4WMZo9LQeiJIT0/H8OHD0adPH0yYMEHbhyMiohLSeiKIiopCQkICDh8+jMOHD6u+37RpE6pVq6btwxMZFb5QRqWh9UQwZswYjBkzRtuHITJ6fKGMSqtC2giIKoox3xHzhTIqLUkzlBEZAmOfz4EvlFFpMRFQpWEo8zloS0VOokSVCxOBjnGSl/Jj7HfEFTmJElUubCPQITbulS9jH2LBEEavpaLpsn2LiUCH2LhXvjgyKl8oM1S6vilkItChklZlGHOPGCl4R0yGStc3hUwEOlSSqgxd3zEYCt4RkyHSdfsWG4tfEnM1GYGRv1ZI421JGveMvUcMUWWm6x5fTAQvqOh+6H5NHDCzeyO8YWsJGQBHGwvM7N6o0DtaXd8xEJH26LrHF6uGXqCLejqpk7wYe48YospM1+1bTAQvKOruurALcEUrS48YNjIT6T9dtm8xEbygqqUpnmYrCl0WczW5VP9I5XURLu0dgyE2MjNxEVUso04EL19wcvKURa5bmuqh8r4Il+aOQdfd0krKEBMXkaEz2kRQ2AVHE6mNsi8mF5kMyH9pItBsRT5Cf7qlloCC33GBTz270vyMYhlaI7OhJS6iysBoew0VdsHRREqj7Mu9jl5OAgWeZivUeibN2nu5zD2Tiur2qutuaSVlaImLqDIw2kRQkguLmVwmqVG2pMmlQHZe2d4H0NTtVdfd0krK0BIXUWVgdImg4M65iJt12FqaoqqFidrnOT2cJVVLlKV3UVnueIurTpnZvREcbSyKfVdBHxha4iKqDIyqjeDldoGXWZrKMa1zw1JdJL86cqPIZXIZIMTzu9rMXAWe5bzaKF2WO97iqlMMadgFXfenJjJGRpUINFXd2FqaljoJxFxNxu6LSUUun+/notpvYcnI0qxsd7yV7WUzQ0pcRJWBUVUNaap+ec3MpNQXn+Lq99ecuKNqxAXwSlXNF71dy3ThY3UKEZWFUT0RFHXnDJStjr64bQuOWdCIO7N7I/w4urVquZ2dVbFDTGjC6hQiKgujSgTjOzhh7oHrhS57uRqlJG+3akowL9NWn3hWp2gP33Smys6oqob8mjig1ZtVC13WrkE11d9LOgppYVUzmrBPvOGo6BFpiXTBqBIBANxPLfwifOp2CoDn//Hnx1wvtDvm3APXC52joLAumv2aOUIuKzwGQ23ENUacB4KMgdElAk1dLQvu/op6Ixgo/I7w5aqDdg2qYf8fDwvdDxtxDQvfdCZjYHSJQNObq1LfDH7xjrCwqoPdF5OK3E9A05qsXzYgfNOZjIHRJYKi6vOz8pQlejO44I6wpMNK7P/jIeuXDQi75pIxMKpeQ8D/ulqGHP1T7Q3fouYhKErBHWFJqwiyFfmYH3Md8w5cV/VAGexdX7WcPVT0C7vmkjEwqkTw8hDRpfXiHWFJuo4WKGg7KGhvqGJlAZ96dhyLX0+xay5VdkZTNSR1iOjivDxoW1FVB/2aOUJKrslW5CP08PNxithDhYh0wWieCEo7RPSLZIDaG8GA5qqDw9ceFTrA3MsSn2YDYA8VItKNSpkIXqwCqmppCiGEpAtyARlQ6DDVMlnhcxcXVXUg9Zi1bC0BaHfwOH1oe9CHGIjoVZUuEfxwMUGtnr2kjcAFzOQy5L1Uf5QvUGidfWEXuJJIeJqNwMhfVe8fqI1MWg49VApre5h74DrmHrgOxwq6ILP9g0h/Vbo2gtDDN8pcBSQAFDVixMt19oW9R1BwkS3KixPfFEhKy8H+Px4ioGnNcp9ERlO1WEUNmcD2DyL9VemeCArq28sqS1F0a/KLdfalaXs4OqEdAiN/faUaKFuRj1O3U15phyir4toYKmJyeLZ/EOmvSvdEUFDfrk0FbQVAyS9kjsW8f6CNC6OUNgZtX5D5hi6R/qpUiSDmajIyc0vXJlASBW0FMVeTS3Qhe/n9g8Jo48IoZXRUbV+Q+YYukf6qNFVDxc1HXN4K3hDu4+74SgPvi16cr/jFRtnxHZxenbJSwoWxND1vXuziWlivpJJckEvb84dv6BLpL5kQopSvVkkXFxeH0NBQ5ObmwsXFBYsXL4a1tXWR6+flKUs8Y1fX8NOl7iFUFpamcgQ0rYkj1x+/cnxLU7nGxt6SXlQLne+4mGOUx3HL+/i6UNZZ4HTB0GI2tHgBw4u5uHjt7W1KtV+tJ4InT54gICAA27Ztg5OTE77++mtkZGRg/vz5RW5T0kQQczVZYy+dsrA0lcPCVK4xyTjaWODH0a1LfYGVWhgLa2B+8fjapuvjl4Wh/YcHDC9mQ4sXMLyYtZUItF41dPLkSbi5ucHJyQkAMHjwYPTu3Rvz5s2DrCwD/rxAW10QHV94J0BTtVNBQ6u2x6TRdc8bXR+fiLRD64kgKSkJjo6Oqs+Ojo5IT09HRkZGkdVDJiYy2NlZST6GNi5Eb9ha4vinHVWfq1hZ4F+746Es5Pmplq1lieJ9mYmJXNL2tWwtkVBI99iyHl8qXR+/LKSeY31iaDEbWryA4cWsrXi1ngjy8/MLvfOXy4vuxaJUihI9rpVmBFBNLE3lGNuunloMPvXsMM/PpdA68pfXLSmpj6dj29XTyvGl0vXxy8LQqgAAw4vZ0OIFDC9mbVUNab37aK1atfDw4UPV5+TkZNja2sLKqvyyWkkmj7c0kWkcFbSqhUmRjZ+FzU1ckQ2lxn58ItIOrT8RtG/fHkuWLMGdO3fg5OSE7du3o0uXLuV6jJe7Jla1NEVmjgJ5/63GkQHo28wRM7o6q21X2q6Yurzw6cvxDe1OioiKViHdR48fP47Q0FDk5eWhbt26WLJkCezs7IpcvzTdRwsY4gWKMWufocULGF7MhhYvYHgxG2yvIQDw9fWFr69vRRyKiIhKqFINMUFERCXHREBEZOSYCIiIjBwTARGRkauQXkNERKS/+ERARGTkmAiIiIwcEwERkZFjIiAiMnJMBERERo6JgIjIyDEREBEZuQoZdK60pEx6X9Q6SqUSX331FU6cOAGlUomPPvoIgwcPBgDcuXMHs2bNQkpKCqysrLBkyRI0bNhQ5zFnZ2djwYIFuHTpEoQQcHd3x7x582BpaYmffvoJM2bMQK1atVT7iYqKKnKWt4qIFwBat26tNgNdUFAQevXqpbfneNKkSbh7965qvQcPHqBVq1b45ptvdHqOAUAIgRkzZsDZ2RlBQUEAoNfluKiY9bUcFxUvoL/luKiYy70cCz31zz//iDZt2oi//vpLCCHE0qVLxbx58ySvs3XrVjFy5EiRl5cnUlNTxTvvvCMuXrwohBCiX79+4ocffhBCCBEXFycCAgJEfn6+zmNetmyZCA4OFkqlUigUCjF16lSxYsUKIYQQISEhIiIioswxlme8t27dEt27dy903/p6jl908eJF0bFjR5GQkCCE0N05FkKIP//8UwwfPlw0a9ZMrF+/XvW9vpZjTTHrYznWFK++lmNNMb+oPMqx3lYNFTbp/Y8//gjxwovQmtY5cuQI+vbtC1NTU9ja2iIgIAA//PADkpOTcfv2bQQEBAB4PkR2ZmYmrly5ovOYW7VqhXHjxkEul8PExARNmjRBQkICAODChQv45Zdf0KtXLwwZMgRnz57VebwXLlyAXC7HkCFDEBgYiLCwMCiVSr0+xwVyc3MxY8YMzJw5U3XnpKtzDDy/YxswYAB69Oih9r2+lmNNMetjOdYUr76WY00xFyivcqy3iUDTpPdS1klMTFR7NHJ0dERSUhISExNRs2ZNtTmTHRwckJSUpPOY27dvj/r16wMA/v77b2zevFlVAOzs7DBo0CDs3bsXn3zyCSZMmFDmmMsar1KpRNu2bbF+/XpERUXh5MmT2LJli16f4wK7du1CzZo10a1bN9V3ujrHADB37lwEBga+sr2+lmNNMetjOdYUr76WY00xFyivcqy3bQRSJr3XtI4QQm2ZEAJyubzQbYQQMDEx0XnMBS5fvowJEyZg2LBh6NSpEwAgLCxMtdzT0xMeHh44deoU+vXrp7N4Bw4cqPbdhx9+iC1btsDd3V3vz/HmzZvx+eefqy3X1TnWRF/LsRT6VI410ddyLEV5lWO9fSKQMum9pnVeXvbw4UM4OjrijTfewKNHj9QewQqW6TpmANi/fz8++ugjTJs2DWPHjgUAPHv2DN98841azEIImJqWLY+XNd7vv/8e165deyUmfT/HV65cgUKhgJeXl2odXZ7jkmyvL+W4OPpWjjXR13JcnPIsx3qbCNq3b4+LFy/izp07AFDopPea1unSpQt2794NhUKBZ8+eYf/+/ejatSscHR1Rt25dHDhwAABw4sQJyOVyODurT2yvi5h/+uknLFq0CBs2bFB7HKxSpQqioqJw6NAhAM8LQHx8PDp06KDTeG/evIlVq1ZBqVQiOzsbUVFR8Pf31+tzDAC//fYb2rRpo3ZHpstzrIm+lmNN9LEca6Kv5bg45VqOS9S0XMHi4uJEYGCg6NGjhxg9erRISUkR8fHxolevXhrXEUKIvLw8sWjRIuHv7y+6deum1uL+119/iWHDhomAgADRp08fcfnyZb2IuXv37qJ169aiV69eqj/z588XQggRHx8vBg4cKAICAkSvXr3EmTNndB5vZmammDFjhvDz8xPdunUToaGhqh4V+nqOhRBi/vz5Ijw8/JX96vIcF5g+fbpaWdXnclxUzPpajouKV5/LcVExC1G+5ZjzERARGTm9rRoiIqKKwURARGTkmAiIiIwcEwERkZFjIiDSc/fv39d1CFTJMRGQXvvrr78wbtw4tGrVCh4eHujVqxe+++47XYdVYbZu3Yqvv/5a12FQJae3Q0wQ5efnY+TIkejbty+WL18Oc3NznDt3DhMmTEDVqlXxzjvv6DpErUtJSdF1CGQE+ERAeislJQUPHjxAr169YGlpCblcDi8vLwQHByMvLw8AsHr1akyaNEm1zY0bN+Di4qL6HBsbi4CAAHh4eKB///64fPkygOev4gcHB8PT0xPe3t5YunSp6rX8s2fPol+/fvD09MSAAQMQHx+v2t+mTZvQsWNHtG7dGkOHDlXtLyEhASNGjICnpye6du2qtr8XRUdHY8iQIRgwYABat26Nu3fv4syZMxg0aBDatGmDFi1aYNKkScjKykJsbCzWrl2LI0eOoH///qrjjB07Fq1bt0b37t2xe/fucj7rZJRK904cUcUYNmyY6NKli1i5cqU4c+aMyMjIUFu+atUqMXHiRNXn69evC2dnZyGEEDdu3BBubm7i+PHjQqlUiq1btwpfX1+hUCjElClTxJgxY0Rqaqp4/Pix8Pf3F9u3bxd///238PDwEIcPHxZ5eXniwIEDwsvLS6SkpIi7d++K5s2biwcPHoj8/HyxatUqMWTIECGEEJ9++qlYuHChUCqVIjExUfj4+IhTp0698nt2794tXFxcxOnTp8WzZ89ERkaGaNGihThy5IgQQojExETRqVMnsXPnzld+n0KhEIGBgSIkJETk5OSIq1evinbt2pXb27lkvPhEQHpt/fr1GDZsGH755ReMHDkSXl5e+OSTTyRVmcTExKBDhw7w8fGBXC7H4MGDsXz5ciiVShw+fBhTpkyBra0tatSogYiICPj6+mLfvn1o3bo1unbtClNTU/j5+cHZ2RmxsbF47bXXkJeXh507d+LatWv4+OOPERUVBQCwsbHB2bNnERsbCysrKxw7dgxt27YtNC57e3t4e3vDxsYGFhYW2LNnD7p06YK0tDQ8fPgQdnZ2SE5OfmW7S5cuITExEVOnToW5uTkaN26MQYMGGVWbCWkH2whIr1lYWOCDDz7ABx98gJycHPz+++/4+uuvMXPmTERERGjc9vHjx2ojRcrlcnh4eODRo0fIy8uDg4ODalndunUBPK96OXHiBDw9PVXLFAoFWrZsCXt7e6xbtw7r16/Hpk2bYGtri8mTJ6Nfv34IDg7GqlWrsGzZMkybNg0+Pj5YtGgRXn/99Vfisre3V/3dxMQEP/30EzZv3gwAcHFxQVZWVqHVSgkJCUhPT1cbbVKpVKJp06bFnUYijZgISG8dOHAAISEhOHr0KGQyGSwsLNC2bVuMGzcOX375JYDnF/fc3FzVNqmpqaq/Ozg44OrVq6rPQgh8/fXX+Oijj2BmZobk5GRUq1YNwPORJVNTU2Fvbw9/f38sXbpUtd39+/dRrVo1PHnyBFZWVtiwYQNycnJw8OBBTJ8+He3bt0diYiJGjRqF6dOn4969e5g1axZWrVr1yljxLzt//jzCw8Px3XffqWarGjFiRKHr1qxZEw4ODoiLi1N99/jx40KTBlFJsGqI9Ja3tzcyMzPxxRdf4J9//oEQAnfv3sX27dtVE53Ur18f58+fx71795Ceno5Nmzaptvfz88OpU6dw5swZ5Ofn4//+7/9w8OBBVKtWDf7+/li1ahXS09Px6NEjLF26FNnZ2QgICMCxY8dw5swZCCHw+++/o1evXrh06RL+/vtvfPjhh/jjjz9gYWGBatWqwcLCAlZWVoiIiEBISAhycnJQo0YNmJiYqJKMJunp6ZDL5bC0tIRSqcT333+Pc+fOQaFQAADMzc2Rnp4OAGjWrBksLS2xfv165OXlISkpCR9++KGqeoqotDj6KOm127dvY8WKFTh79iwyMzNRo0YNBAYG4uOPP4a5uTlyc3Mxd+5cHDt2DFWqVMGYMWMwd+5cXL9+HQBw7NgxLFu2DH///TdcXFywYMECODs7Iy0tDYsXL8bx48chk8nw3nvvqXofnTp1CqGhobhz5w6qV6+O0aNHq2axioqKwoYNG/DkyRO88cYbCA4ORqdOnZCYmIg5c+bg4sWLAIBOnTph/vz5r0w0Eh0dja1btyI6OhrA8y6yCxYswIEDByCXy+Hq6gpHR0c8ffoUYWFhuH79OoKCglClShXExsbi3r17WLRoES5evAgTExP4+/tj+vTpMDMzq6h/EqqEmAiIiIwcq4aIiIwcEwERkZFjIiAiMnJMBERERo6JgIjIyDEREBEZOSYCIiIjx0RARGTk/h++Z8ujwRDSRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(active_routing_nodes['success_rate'],active_routing_nodes['total_revenue'])\n",
    "plt.title('success rate vs revenue')\n",
    "plt.ylabel('Revenue (msats)')\n",
    "_ = plt.xlabel('Success rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65afffbe",
   "metadata": {},
   "source": [
    "We see an increasing patter where if the success rate increases, the revenue does too. It makes total sense, since if a channel forwards more payments successfully, then it will charge more fees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e780c86b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def compute_corr_mat(df):\n",
    "    \"\"\"Creates the correlation matrix\"\"\"\n",
    "    \n",
    "    corr = df.corr()\n",
    "\n",
    "    # Generate a mask for the upper triangle\n",
    "    mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "    # Set up the matplotlib figure\n",
    "    f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "    # Generate a custom diverging colormap\n",
    "    cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "    # Draw the heatmap with the mask and correct aspect ratio\n",
    "    sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "                square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "    _ = plt.title('Correlation Matrix')\n",
    "    return corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a7073b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = compute_corr_mat(active_routing_nodes[['success_rate','balanced_rate','total_Forwards','total_revenue']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "69cdf77a",
   "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>success_rate</th>\n",
       "      <th>balanced_rate</th>\n",
       "      <th>total_Forwards</th>\n",
       "      <th>total_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>success_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.276779</td>\n",
       "      <td>-0.131584</td>\n",
       "      <td>0.293115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balanced_rate</th>\n",
       "      <td>0.276779</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213419</td>\n",
       "      <td>0.313988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_Forwards</th>\n",
       "      <td>-0.131584</td>\n",
       "      <td>0.213419</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.219504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_revenue</th>\n",
       "      <td>0.293115</td>\n",
       "      <td>0.313988</td>\n",
       "      <td>0.219504</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                success_rate  balanced_rate  total_Forwards  total_revenue\n",
       "success_rate        1.000000       0.276779       -0.131584       0.293115\n",
       "balanced_rate       0.276779       1.000000        0.213419       0.313988\n",
       "total_Forwards     -0.131584       0.213419        1.000000       0.219504\n",
       "total_revenue       0.293115       0.313988        0.219504       1.000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ef5c1a",
   "metadata": {},
   "source": [
    "- *Total forwards* and *total revenue* are positively correalted. This makes sense, since the more successful payments forwarded, the more fees you would charge. \n",
    "- *Success rate* and *total_revenue* are correlated, just like *balanced rate* and *total revenue*. Remember in the first notebook we stated why when a channel is balanced it might present more traffic. It it does, then it's success rate might increase as well, hence increasing the total revenue. \n",
    "\n",
    "Se how everything **connects** with everything."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9a7a80",
   "metadata": {},
   "source": [
    "## Does channels linked to nodes with more channels have more forwardattempts?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b4feec",
   "metadata": {},
   "source": [
    "**ADD THE TOTAL NUMBER OF CHANNELS A NODE HAS TO THE ANALYSIS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "56b15a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pandas import json_normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "aafb110c",
   "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_channels',\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",
    "        df_nodes.loc[index, 'num_channels'] = node1_channels.shape[0] + node2_channels.shape[0]\n",
    "\n",
    "    return df_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddc09f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the whole network graph dataset\n",
    "file_name = '../Datos/graph_metrics_2022-06-29.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": null,
   "id": "49393f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_graph = add_node_chan_info(nodes_graph, channels_graph)\n",
    "# You can comment this cell and the one above when they finish runing.\n",
    "# This cell takes its time to process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "752f2b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You may comment this cell after running it.\n",
    "ln_graph.to_csv('ln_graph.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0ebb6e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the cells above have been run once, then you can comment them and load\n",
    "# the network graph directly from the dataset saved in the above cell.\n",
    "ln_graph = pd.read_csv('ln_graph.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "01c50efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Appending peers number of channels to the analysis\n",
    "active_routing_nodes['num_channels'] = ln_graph.set_index('pub_key')['num_channels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5f73428a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_routing_nodes['num_channels'].isna().sum()\n",
    "# If there are nan values, it means some nodes in the network are dead already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7ab3494c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set nan values to 0\n",
    "active_routing_nodes['num_channels'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "45635229",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering out channels connected to dead nodes.\n",
    "active_routing_nodes = active_routing_nodes[active_routing_nodes['num_channels'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "98379164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(active_routing_nodes['num_channels'], active_routing_nodes['total_Forwards'], label='channels')\n",
    "plt.ylabel('Total forwards')\n",
    "plt.xlabel('Number of channels')\n",
    "plt.legend()\n",
    "\n",
    "_ = plt.title('Relation between peer number of channels and forward attempts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9d780ce0",
   "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>success_rate</th>\n",
       "      <th>balanced_rate</th>\n",
       "      <th>total_Forwards</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>num_channels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>success_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.251656</td>\n",
       "      <td>-0.147589</td>\n",
       "      <td>0.288645</td>\n",
       "      <td>-0.089388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balanced_rate</th>\n",
       "      <td>0.251656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199773</td>\n",
       "      <td>0.300264</td>\n",
       "      <td>-0.010831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_Forwards</th>\n",
       "      <td>-0.147589</td>\n",
       "      <td>0.199773</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.185880</td>\n",
       "      <td>0.206207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_revenue</th>\n",
       "      <td>0.288645</td>\n",
       "      <td>0.300264</td>\n",
       "      <td>0.185880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.118454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_channels</th>\n",
       "      <td>-0.089388</td>\n",
       "      <td>-0.010831</td>\n",
       "      <td>0.206207</td>\n",
       "      <td>0.118454</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                success_rate  balanced_rate  total_Forwards  total_revenue  \\\n",
       "success_rate        1.000000       0.251656       -0.147589       0.288645   \n",
       "balanced_rate       0.251656       1.000000        0.199773       0.300264   \n",
       "total_Forwards     -0.147589       0.199773        1.000000       0.185880   \n",
       "total_revenue       0.288645       0.300264        0.185880       1.000000   \n",
       "num_channels       -0.089388      -0.010831        0.206207       0.118454   \n",
       "\n",
       "                num_channels  \n",
       "success_rate       -0.089388  \n",
       "balanced_rate      -0.010831  \n",
       "total_Forwards      0.206207  \n",
       "total_revenue       0.118454  \n",
       "num_channels        1.000000  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = compute_corr_mat(active_routing_nodes[['success_rate','balanced_rate','total_Forwards','total_revenue', 'num_channels']])\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1b635e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-6.788475508446619, pvalue=2.670410890792175e-09)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "# compute p value\n",
    "stats.ttest_ind(a=active_routing_nodes['num_channels'],b=active_routing_nodes['total_Forwards'],equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86cc5508",
   "metadata": {},
   "source": [
    "Based on this data, the answer to our question is yes, channels linked to nodes with more channels have more forward attempts. However, it seems like there's a limit to the number of channels a node has that increases significantly the forward attempts of our channel linked to that node. We saw this in the chart relating these two features, where an increasing pattern breaks after a peer node has 250+ channels.\n",
    "\n",
    "The correlation matrix confirms there's a weak correlation between these features.\n",
    "\n",
    "So, if we're looking to increase our node activity, **with who we may create channels?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7bf19df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(active_routing_nodes['balanced_rate'], active_routing_nodes['num_channels'], label='channels')\n",
    "plt.ylabel('Total forwards')\n",
    "plt.xlabel('Number of channels')\n",
    "plt.legend()\n",
    "\n",
    "_ = plt.title('Relation between peer number of channels and forward attempts')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}