Understanding_Transaction_Graphs.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "Understanding Transaction Graphs",
      "provenance": [],
      "collapsed_sections": [
        "OZ6mVjwOCQtd",
        "6Kj2j3X22HaO",
        "2E25xMyW6lqb",
        "5UxKi47_2uuh",
        "8PDOOdza7Sxy",
        "5U9WfOrf3WTL",
        "6f5k6ewB4gqC",
        "uw5Bvp2K5bwL",
        "w_NzMlVLcLF6",
        "eMCbwqr_7tgS",
        "ZEWhUBf-8cLc",
        "Ke4GKZDj9TDl",
        "dn_wyvfubEWm",
        "Ss2UivLLA-Hr",
        "PdBH4dbTBSUz",
        "TN5t__qBKQT6",
        "K2SWNCsmKQUG",
        "3HhbM2efxafo",
        "QRFxqoeDPgyP",
        "w_dJdg3SxOsr",
        "m5c6RHcdPg_t",
        "CYkdBIb45Sl8"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rkoprik/txs_graph/blob/main/Understanding_Transaction_Graphs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RO6MxZf1kPw"
      },
      "source": [
        "# Transaction data\n",
        "\n",
        "\n",
        "https://chain.so/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZ6mVjwOCQtd"
      },
      "source": [
        "# BITCOIN EXTRACTION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4wooXF9Mswh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "e74167a1-64d3-44d0-9e6f-0f420e4028b1"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "#### EXTRACTION #####\n",
        "#####################\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        " \n",
        "\n",
        "# 683093 - 683200: 11/05/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(683093, 683200)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 20\n",
        "size_block = range(r1, r2)\n",
        " \n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://chain.so/api/v2/get_block/BTC/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        " \n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly                                                                                                                   \n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "      \n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        " \n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://chain.so/api/v2/tx/BTC/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        " \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        " \n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        " \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        " \n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        " \n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        " \n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_bc_01 = pd.DataFrame({'transaction_id': transaction_id, 'from': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_bc = df_bc_01[df_bc_01.to != \"nulldata\"]\n",
        "df_bc.reset_index(drop=True, inplace=True)\n",
        " \n",
        " \n",
        " # fill NaN with previous data for all NaN, None\n",
        "df_bc.date = df_bc.date.fillna(method = 'ffill')\n",
        "df_bc = df_bc.fillna(value=np.nan)\n",
        "\n",
        "# convert epoch time\n",
        "for each in range(len(df_bc.date)):\n",
        "  date_form.append(datetime.datetime.fromtimestamp(df_bc.date[each]).strftime('%c'))\n",
        "\n",
        "# add date_form to df, drop old column and rename\n",
        "df_bc['date_form'] = date_form\n",
        "df_bc = df_bc.drop(columns = 'date')\n",
        "df_bc = df_bc.rename(columns = {'date_form': 'date'}, inplace = False)\n",
        "\n",
        "df_bc.to_csv(\"df_bc.csv\") \n",
        "df_bc \n",
        "\n",
        "  # NOTE! We need to be aware of the following different transaction types:\n",
        "  # Newly generated coins, identifiable via the participating address \"coinbase!"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5170: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>transaction_id</th>\n",
              "      <th>from</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>to</th>\n",
              "      <th>amount</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5904733a3b5c0762d00fa931358b07af4c7d27272f5307...</td>\n",
              "      <td>coinbase</td>\n",
              "      <td>7.29566629</td>\n",
              "      <td>bc1qx9t2l3pyny2spqpqlye8svce70nppwtaxwdrp4</td>\n",
              "      <td>7.29566629</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>46fde81ba436974ff04118d178889f3e1e1949e1225566...</td>\n",
              "      <td>bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar</td>\n",
              "      <td>0.94516086</td>\n",
              "      <td>132azVxirjmW5nQqrUHGB1jVzogHv8aWmL</td>\n",
              "      <td>0.00123653</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3MR6fT6GT34QPMMszKDi4ALAm8cyqy2xD3</td>\n",
              "      <td>0.00284063</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1D2Gb8EATEqS8YcMxTyi5RR5ismqQHzBsK</td>\n",
              "      <td>0.00422860</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3AN3E6jF439iBqeyBG7CkVVWZBR88wbdNR</td>\n",
              "      <td>0.02117593</td>\n",
              "      <td>Tue May 11 11:02:40 2021</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195956</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17HfWyNFBDrJgMigYrr67gKfYM2sRzN1K7</td>\n",
              "      <td>0.00003600</td>\n",
              "      <td>Tue May 11 13:25:43 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195957</th>\n",
              "      <td>90e89396be66bc2b68962946bb3e9e3954edecb3055330...</td>\n",
              "      <td>333DEeSUcpcPBXWjKkLdcxZr1XzhwZCquN</td>\n",
              "      <td>0.86345588</td>\n",
              "      <td>3NB1TnKzYFpiTdpt31rok7Hq4Lph9qpp6F</td>\n",
              "      <td>0.50000000</td>\n",
              "      <td>Tue May 11 13:25:43 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195958</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3MCabht94FxzpJ8rynmN7WrzRVrSeqTmjs</td>\n",
              "      <td>0.36343908</td>\n",
              "      <td>Tue May 11 13:25:43 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195959</th>\n",
              "      <td>de118712af59c4936fea73f73b2ce9734148555c7981ad...</td>\n",
              "      <td>3NB1TnKzYFpiTdpt31rok7Hq4Lph9qpp6F</td>\n",
              "      <td>0.50000000</td>\n",
              "      <td>33LBDkMJnkai9rhtJe8k5stay5aarf4Xew</td>\n",
              "      <td>0.49998000</td>\n",
              "      <td>Tue May 11 13:25:43 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195960</th>\n",
              "      <td>c2057a17b47bc23fd59057cf51dd054b7538012aeeb026...</td>\n",
              "      <td>3D81zY5puUBUKpamH71Xbx8oKD98AsctdU</td>\n",
              "      <td>0.01090738</td>\n",
              "      <td>bc1qt5jex00qvwx835zvcf46yf7nzn5rxf7jm69g4q</td>\n",
              "      <td>0.01089130</td>\n",
              "      <td>Tue May 11 13:25:43 2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>195961 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           transaction_id  ...                      date\n",
              "0       5904733a3b5c0762d00fa931358b07af4c7d27272f5307...  ...  Tue May 11 11:02:40 2021\n",
              "1       46fde81ba436974ff04118d178889f3e1e1949e1225566...  ...  Tue May 11 11:02:40 2021\n",
              "2                                                     NaN  ...  Tue May 11 11:02:40 2021\n",
              "3                                                     NaN  ...  Tue May 11 11:02:40 2021\n",
              "4                                                     NaN  ...  Tue May 11 11:02:40 2021\n",
              "...                                                   ...  ...                       ...\n",
              "195956                                                NaN  ...  Tue May 11 13:25:43 2021\n",
              "195957  90e89396be66bc2b68962946bb3e9e3954edecb3055330...  ...  Tue May 11 13:25:43 2021\n",
              "195958                                                NaN  ...  Tue May 11 13:25:43 2021\n",
              "195959  de118712af59c4936fea73f73b2ce9734148555c7981ad...  ...  Tue May 11 13:25:43 2021\n",
              "195960  c2057a17b47bc23fd59057cf51dd054b7538012aeeb026...  ...  Tue May 11 13:25:43 2021\n",
              "\n",
              "[195961 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Kj2j3X22HaO"
      },
      "source": [
        "# BITCOIN INDICATORS OVER TIME"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-gUBpmeRFVk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "outputId": "fff33b38-5eea-4d62-9827-e6fdae53814d"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "#### INDICATORS #####\n",
        "#####################\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# summarizes df\n",
        "df_bc_solution = df_bc.pivot_table(index=['date'], aggfunc='count')\n",
        "# creates new var with vertices\n",
        "df_bc_solution['vertices'] = df_bc_solution['to'] + df_bc_solution['from']\n",
        "# consolidated edges and vertices data \n",
        "df_bc_egd_vert = pd.DataFrame({'edges': df_bc_solution.transaction_id, 'vertices': df_bc_solution.vertices})\n",
        "# plot\n",
        "plot_bc = df_bc_egd_vert.plot()\n",
        "  # rename ticks  \n",
        "plt.xticks(np.arange(0, 20, 2), ['11:02', '11:18', '11:34', '11:40', '11:56', '12:12', '12:36', '12:52', '13:08', '13:24']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "  # save\n",
        "plt.savefig('bc_egdes_vertices.png', dpi = 300, transparent=True)\n",
        "df_bc_solution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>amount</th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>transaction_id</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>vertices</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>Tue May 11 11:02:40 2021</th>\n",
              "      <td>10310</td>\n",
              "      <td>5477</td>\n",
              "      <td>10310</td>\n",
              "      <td>3192</td>\n",
              "      <td>5477</td>\n",
              "      <td>15787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:10:57 2021</th>\n",
              "      <td>7903</td>\n",
              "      <td>5960</td>\n",
              "      <td>7903</td>\n",
              "      <td>2671</td>\n",
              "      <td>5960</td>\n",
              "      <td>13863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:14:41 2021</th>\n",
              "      <td>7230</td>\n",
              "      <td>6168</td>\n",
              "      <td>7230</td>\n",
              "      <td>2322</td>\n",
              "      <td>6168</td>\n",
              "      <td>13398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:46:04 2021</th>\n",
              "      <td>18766</td>\n",
              "      <td>11653</td>\n",
              "      <td>18766</td>\n",
              "      <td>5826</td>\n",
              "      <td>11653</td>\n",
              "      <td>30419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:51:12 2021</th>\n",
              "      <td>3833</td>\n",
              "      <td>6893</td>\n",
              "      <td>3833</td>\n",
              "      <td>1564</td>\n",
              "      <td>6893</td>\n",
              "      <td>10726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:51:52 2021</th>\n",
              "      <td>5984</td>\n",
              "      <td>5656</td>\n",
              "      <td>5984</td>\n",
              "      <td>2233</td>\n",
              "      <td>5656</td>\n",
              "      <td>11640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:52:52 2021</th>\n",
              "      <td>7518</td>\n",
              "      <td>4523</td>\n",
              "      <td>7518</td>\n",
              "      <td>2073</td>\n",
              "      <td>4523</td>\n",
              "      <td>12041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:53:29 2021</th>\n",
              "      <td>1975</td>\n",
              "      <td>6424</td>\n",
              "      <td>1975</td>\n",
              "      <td>718</td>\n",
              "      <td>6424</td>\n",
              "      <td>8399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:09:14 2021</th>\n",
              "      <td>6071</td>\n",
              "      <td>5993</td>\n",
              "      <td>6071</td>\n",
              "      <td>1885</td>\n",
              "      <td>5993</td>\n",
              "      <td>12064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:11:38 2021</th>\n",
              "      <td>4041</td>\n",
              "      <td>6499</td>\n",
              "      <td>4041</td>\n",
              "      <td>1590</td>\n",
              "      <td>6499</td>\n",
              "      <td>10540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:29:19 2021</th>\n",
              "      <td>8169</td>\n",
              "      <td>5915</td>\n",
              "      <td>8169</td>\n",
              "      <td>2598</td>\n",
              "      <td>5915</td>\n",
              "      <td>14084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:41:13 2021</th>\n",
              "      <td>9064</td>\n",
              "      <td>5620</td>\n",
              "      <td>9064</td>\n",
              "      <td>2676</td>\n",
              "      <td>5620</td>\n",
              "      <td>14684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:46:04 2021</th>\n",
              "      <td>5662</td>\n",
              "      <td>5893</td>\n",
              "      <td>5662</td>\n",
              "      <td>2070</td>\n",
              "      <td>5893</td>\n",
              "      <td>11555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:48:45 2021</th>\n",
              "      <td>4069</td>\n",
              "      <td>6198</td>\n",
              "      <td>4069</td>\n",
              "      <td>1790</td>\n",
              "      <td>6198</td>\n",
              "      <td>10267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:52:56 2021</th>\n",
              "      <td>3318</td>\n",
              "      <td>8273</td>\n",
              "      <td>3318</td>\n",
              "      <td>1052</td>\n",
              "      <td>8273</td>\n",
              "      <td>11591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:59:14 2021</th>\n",
              "      <td>4344</td>\n",
              "      <td>7860</td>\n",
              "      <td>4344</td>\n",
              "      <td>1602</td>\n",
              "      <td>7860</td>\n",
              "      <td>12204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 13:05:17 2021</th>\n",
              "      <td>4305</td>\n",
              "      <td>7839</td>\n",
              "      <td>4305</td>\n",
              "      <td>1537</td>\n",
              "      <td>7839</td>\n",
              "      <td>12144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 13:21:23 2021</th>\n",
              "      <td>6426</td>\n",
              "      <td>4265</td>\n",
              "      <td>6426</td>\n",
              "      <td>1967</td>\n",
              "      <td>4265</td>\n",
              "      <td>10691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 13:25:43 2021</th>\n",
              "      <td>5553</td>\n",
              "      <td>5304</td>\n",
              "      <td>5553</td>\n",
              "      <td>1876</td>\n",
              "      <td>5304</td>\n",
              "      <td>10857</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          amount   from  ...  tt_value  vertices\n",
              "date                                     ...                    \n",
              "Tue May 11 11:02:40 2021   10310   5477  ...      5477     15787\n",
              "Tue May 11 11:10:57 2021    7903   5960  ...      5960     13863\n",
              "Tue May 11 11:14:41 2021    7230   6168  ...      6168     13398\n",
              "Tue May 11 11:46:04 2021   18766  11653  ...     11653     30419\n",
              "Tue May 11 11:51:12 2021    3833   6893  ...      6893     10726\n",
              "Tue May 11 11:51:52 2021    5984   5656  ...      5656     11640\n",
              "Tue May 11 11:52:52 2021    7518   4523  ...      4523     12041\n",
              "Tue May 11 11:53:29 2021    1975   6424  ...      6424      8399\n",
              "Tue May 11 12:09:14 2021    6071   5993  ...      5993     12064\n",
              "Tue May 11 12:11:38 2021    4041   6499  ...      6499     10540\n",
              "Tue May 11 12:29:19 2021    8169   5915  ...      5915     14084\n",
              "Tue May 11 12:41:13 2021    9064   5620  ...      5620     14684\n",
              "Tue May 11 12:46:04 2021    5662   5893  ...      5893     11555\n",
              "Tue May 11 12:48:45 2021    4069   6198  ...      6198     10267\n",
              "Tue May 11 12:52:56 2021    3318   8273  ...      8273     11591\n",
              "Tue May 11 12:59:14 2021    4344   7860  ...      7860     12204\n",
              "Tue May 11 13:05:17 2021    4305   7839  ...      7839     12144\n",
              "Tue May 11 13:21:23 2021    6426   4265  ...      4265     10691\n",
              "Tue May 11 13:25:43 2021    5553   5304  ...      5304     10857\n",
              "\n",
              "[19 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7Y1jbvZ_AXh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "923be65b-eccc-4edb-e889-bf1e1dda2edd"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "### INDICATORS II ###\n",
        "#####################\n",
        "\n",
        "# creates new col with edge to vert ratio\n",
        "df_bc_solution['edge_vertices_ratio'] = df_bc_solution['vertices'] / df_bc_solution['transaction_id']\n",
        "# creates new col with to from ratio\n",
        "df_bc_solution['to_from_ratio'] = df_bc_solution['to'] / df_bc_solution['from']\n",
        "# creates dataframe\n",
        "df_bc_ratios = pd.DataFrame({'transaction_vertices_ratio': df_bc_solution.edge_vertices_ratio, 'to_from_ratio': df_bc_solution.to_from_ratio})\n",
        "# plot\n",
        "df_bc_ratios.plot()\n",
        "  # rename ticks\n",
        "plt.xticks(np.arange(0, 20, 2), ['11:02', '11:18', '11:34', '11:40', '11:56', '12:12', '12:36', '12:52', '13:08', '13:24']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "  # save\n",
        "plt.savefig('ratios_bc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PPNoWDXw4NJi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "e5e35573-4509-400d-f92c-cc08dfe610f1"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "### INDICATORS III ##\n",
        "#####################\n",
        "\n",
        "# density\n",
        "df_bc_solution['density'] = (2*df_bc_solution['transaction_id']) / (df_bc_solution['vertices']*(df_bc_solution['vertices']-1))\n",
        "# plot\n",
        "df_bc_solution.density.plot()\n",
        "  # rename ticks\n",
        "plt.xticks(np.arange(0, 20, 2), ['11:02', '11:18', '11:34', '11:40', '11:56', '12:12', '12:36', '12:52', '13:08', '13:24']) \n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "  # save\n",
        "plt.savefig('density_bc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "wgiWEWAWL5QC",
        "outputId": "952b438f-fdf1-4e6c-9f6c-acc1ae144bf9"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "### INDICATORS IV ###\n",
        "#####################\n",
        "\n",
        "# consolidated data with ratios\n",
        "df_bc_indicators = pd.DataFrame({'edges': df_bc_solution.transaction_id, 'vertices': df_bc_solution.vertices,\n",
        "                              'edge_vertices_ratio': df_bc_solution.edge_vertices_ratio, 'to_from_ratio': df_bc_solution.to_from_ratio,\n",
        "                              'density': df_bc_solution.density})\n",
        "\n",
        "df_bc_indicators.to_csv(\"df_bc_indicators.csv\") \n",
        "df_bc_indicators"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>edges</th>\n",
              "      <th>vertices</th>\n",
              "      <th>edge_vertices_ratio</th>\n",
              "      <th>to_from_ratio</th>\n",
              "      <th>density</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>Tue May 11 11:02:40 2021</th>\n",
              "      <td>3192</td>\n",
              "      <td>15787</td>\n",
              "      <td>4.945802</td>\n",
              "      <td>1.882417</td>\n",
              "      <td>0.000026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:10:57 2021</th>\n",
              "      <td>2671</td>\n",
              "      <td>13863</td>\n",
              "      <td>5.190191</td>\n",
              "      <td>1.326007</td>\n",
              "      <td>0.000028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:14:41 2021</th>\n",
              "      <td>2322</td>\n",
              "      <td>13398</td>\n",
              "      <td>5.770026</td>\n",
              "      <td>1.172179</td>\n",
              "      <td>0.000026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:46:04 2021</th>\n",
              "      <td>5826</td>\n",
              "      <td>30419</td>\n",
              "      <td>5.221250</td>\n",
              "      <td>1.610401</td>\n",
              "      <td>0.000013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:51:12 2021</th>\n",
              "      <td>1564</td>\n",
              "      <td>10726</td>\n",
              "      <td>6.858056</td>\n",
              "      <td>0.556071</td>\n",
              "      <td>0.000027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:51:52 2021</th>\n",
              "      <td>2233</td>\n",
              "      <td>11640</td>\n",
              "      <td>5.212718</td>\n",
              "      <td>1.057992</td>\n",
              "      <td>0.000033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:52:52 2021</th>\n",
              "      <td>2073</td>\n",
              "      <td>12041</td>\n",
              "      <td>5.808490</td>\n",
              "      <td>1.662171</td>\n",
              "      <td>0.000029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 11:53:29 2021</th>\n",
              "      <td>718</td>\n",
              "      <td>8399</td>\n",
              "      <td>11.697772</td>\n",
              "      <td>0.307441</td>\n",
              "      <td>0.000020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:09:14 2021</th>\n",
              "      <td>1885</td>\n",
              "      <td>12064</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>1.013015</td>\n",
              "      <td>0.000026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:11:38 2021</th>\n",
              "      <td>1590</td>\n",
              "      <td>10540</td>\n",
              "      <td>6.628931</td>\n",
              "      <td>0.621788</td>\n",
              "      <td>0.000029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:29:19 2021</th>\n",
              "      <td>2598</td>\n",
              "      <td>14084</td>\n",
              "      <td>5.421093</td>\n",
              "      <td>1.381065</td>\n",
              "      <td>0.000026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:41:13 2021</th>\n",
              "      <td>2676</td>\n",
              "      <td>14684</td>\n",
              "      <td>5.487294</td>\n",
              "      <td>1.612811</td>\n",
              "      <td>0.000025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:46:04 2021</th>\n",
              "      <td>2070</td>\n",
              "      <td>11555</td>\n",
              "      <td>5.582126</td>\n",
              "      <td>0.960801</td>\n",
              "      <td>0.000031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:48:45 2021</th>\n",
              "      <td>1790</td>\n",
              "      <td>10267</td>\n",
              "      <td>5.735754</td>\n",
              "      <td>0.656502</td>\n",
              "      <td>0.000034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:52:56 2021</th>\n",
              "      <td>1052</td>\n",
              "      <td>11591</td>\n",
              "      <td>11.018061</td>\n",
              "      <td>0.401064</td>\n",
              "      <td>0.000016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 12:59:14 2021</th>\n",
              "      <td>1602</td>\n",
              "      <td>12204</td>\n",
              "      <td>7.617978</td>\n",
              "      <td>0.552672</td>\n",
              "      <td>0.000022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 13:05:17 2021</th>\n",
              "      <td>1537</td>\n",
              "      <td>12144</td>\n",
              "      <td>7.901106</td>\n",
              "      <td>0.549177</td>\n",
              "      <td>0.000021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 13:21:23 2021</th>\n",
              "      <td>1967</td>\n",
              "      <td>10691</td>\n",
              "      <td>5.435180</td>\n",
              "      <td>1.506682</td>\n",
              "      <td>0.000034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue May 11 13:25:43 2021</th>\n",
              "      <td>1876</td>\n",
              "      <td>10857</td>\n",
              "      <td>5.787313</td>\n",
              "      <td>1.046946</td>\n",
              "      <td>0.000032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          edges  vertices  ...  to_from_ratio   density\n",
              "date                                       ...                         \n",
              "Tue May 11 11:02:40 2021   3192     15787  ...       1.882417  0.000026\n",
              "Tue May 11 11:10:57 2021   2671     13863  ...       1.326007  0.000028\n",
              "Tue May 11 11:14:41 2021   2322     13398  ...       1.172179  0.000026\n",
              "Tue May 11 11:46:04 2021   5826     30419  ...       1.610401  0.000013\n",
              "Tue May 11 11:51:12 2021   1564     10726  ...       0.556071  0.000027\n",
              "Tue May 11 11:51:52 2021   2233     11640  ...       1.057992  0.000033\n",
              "Tue May 11 11:52:52 2021   2073     12041  ...       1.662171  0.000029\n",
              "Tue May 11 11:53:29 2021    718      8399  ...       0.307441  0.000020\n",
              "Tue May 11 12:09:14 2021   1885     12064  ...       1.013015  0.000026\n",
              "Tue May 11 12:11:38 2021   1590     10540  ...       0.621788  0.000029\n",
              "Tue May 11 12:29:19 2021   2598     14084  ...       1.381065  0.000026\n",
              "Tue May 11 12:41:13 2021   2676     14684  ...       1.612811  0.000025\n",
              "Tue May 11 12:46:04 2021   2070     11555  ...       0.960801  0.000031\n",
              "Tue May 11 12:48:45 2021   1790     10267  ...       0.656502  0.000034\n",
              "Tue May 11 12:52:56 2021   1052     11591  ...       0.401064  0.000016\n",
              "Tue May 11 12:59:14 2021   1602     12204  ...       0.552672  0.000022\n",
              "Tue May 11 13:05:17 2021   1537     12144  ...       0.549177  0.000021\n",
              "Tue May 11 13:21:23 2021   1967     10691  ...       1.506682  0.000034\n",
              "Tue May 11 13:25:43 2021   1876     10857  ...       1.046946  0.000032\n",
              "\n",
              "[19 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2E25xMyW6lqb"
      },
      "source": [
        "# BTC EXTRACTION - NETWORKX"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1FxONYJXmJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "outputId": "f857e19f-782e-4908-e379-1f24186b1935"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        " \n",
        " \n",
        "# 683093 - 683200: 11/05/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(683093, 683200)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 10\n",
        "size_block = range(r1, r2)\n",
        " \n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://chain.so/api/v2/get_block/BTC/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        " \n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly                                                                                                                   \n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "      \n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        " \n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://chain.so/api/v2/tx/BTC/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        " \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        " \n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        " \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        " \n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        " \n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_bc_nx_01 = pd.DataFrame({'transaction_id': transaction_id, 'from_leg': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_bc_nx = df_bc_nx_01[df_bc_nx_01.to != \"nulldata\"]\n",
        "df_bc_nx.reset_index(drop=True, inplace=True)\n",
        " \n",
        " \n",
        " # fill NaN with previous data for all NaN, None\n",
        "df_bc_nx.date = df_bc_nx.date.fillna(method = 'ffill')\n",
        "df_bc_nx.from_leg = df_bc_nx.from_leg.fillna(method = 'ffill')\n",
        "df_bc_nx.transaction_id = df_bc_nx.transaction_id.fillna(method = 'ffill')\n",
        "df_bc_nx.tt_value = df_bc_nx.tt_value.fillna(method = 'ffill')\n",
        "df_bc_nx = df_bc_nx.fillna(value=np.nan)\n",
        "\n",
        "# convert epoch time\n",
        "for each in range(len(df_bc_nx.date)):\n",
        "  date_form.append(datetime.datetime.fromtimestamp(df_bc_nx.date[each]).strftime('%c'))\n",
        "\n",
        "# add date_form to df, drop old column and rename\n",
        "df_bc_nx['date_form'] = date_form\n",
        "df_bc_nx = df_bc_nx.drop(columns = 'date')\n",
        "df_bc_nx = df_bc_nx.rename(columns = {'date_form': 'date'}, inplace = False)\n",
        "\n",
        "df_bc_nx.to_csv(\"df_bc_nx.csv\") \n",
        "df_bc_nx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5170: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>transaction_id</th>\n",
              "      <th>from_leg</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>to</th>\n",
              "      <th>amount</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5904733a3b5c0762d00fa931358b07af4c7d27272f5307...</td>\n",
              "      <td>coinbase</td>\n",
              "      <td>7.29566629</td>\n",
              "      <td>bc1qx9t2l3pyny2spqpqlye8svce70nppwtaxwdrp4</td>\n",
              "      <td>7.29566629</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>46fde81ba436974ff04118d178889f3e1e1949e1225566...</td>\n",
              "      <td>bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar</td>\n",
              "      <td>0.94516086</td>\n",
              "      <td>132azVxirjmW5nQqrUHGB1jVzogHv8aWmL</td>\n",
              "      <td>0.00123653</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46fde81ba436974ff04118d178889f3e1e1949e1225566...</td>\n",
              "      <td>bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar</td>\n",
              "      <td>0.94516086</td>\n",
              "      <td>3MR6fT6GT34QPMMszKDi4ALAm8cyqy2xD3</td>\n",
              "      <td>0.00284063</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46fde81ba436974ff04118d178889f3e1e1949e1225566...</td>\n",
              "      <td>bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar</td>\n",
              "      <td>0.94516086</td>\n",
              "      <td>1D2Gb8EATEqS8YcMxTyi5RR5ismqQHzBsK</td>\n",
              "      <td>0.00422860</td>\n",
              "      <td>Tue May 11 11:02:40 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>46fde81ba436974ff04118d178889f3e1e1949e1225566...</td>\n",
              "      <td>bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar</td>\n",
              "      <td>0.94516086</td>\n",
              "      <td>3AN3E6jF439iBqeyBG7CkVVWZBR88wbdNR</td>\n",
              "      <td>0.02117593</td>\n",
              "      <td>Tue May 11 11:02:40 2021</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>ef7fc3af7c75ee4a3289577f4bb207632543ad9d1a569b...</td>\n",
              "      <td>3GmYvnC345CHPfJQ3w68mmkRWRw5VKxy14</td>\n",
              "      <td>20.57007792</td>\n",
              "      <td>1D4CgFY5fRBsNyMJaCPkD6Ev3RCCqe3cA1</td>\n",
              "      <td>0.00201600</td>\n",
              "      <td>Tue May 11 12:09:14 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>5ef9554ca0c5a227bb5c7f09e24683bf2ec417794774e3...</td>\n",
              "      <td>3DVyDYT4o12jRQaUEamnyPhhn7ZWhJ5qmm</td>\n",
              "      <td>20.56738992</td>\n",
              "      <td>38Vy9HHEmdNuruYB9itgaNKvPMrH9q7C1J</td>\n",
              "      <td>0.00140000</td>\n",
              "      <td>Tue May 11 12:09:14 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>5ef9554ca0c5a227bb5c7f09e24683bf2ec417794774e3...</td>\n",
              "      <td>3DVyDYT4o12jRQaUEamnyPhhn7ZWhJ5qmm</td>\n",
              "      <td>20.56738992</td>\n",
              "      <td>3BA1rdCWDnZnrZySJ7GVyprEd1x9wondw6</td>\n",
              "      <td>20.56532592</td>\n",
              "      <td>Tue May 11 12:09:14 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>c99a56f1625446f58caf4474f833249385e817dd1be4bc...</td>\n",
              "      <td>39VgGLvtWE8FnTYHYoUxDHFWyWZntJ5KGe</td>\n",
              "      <td>0.01137949</td>\n",
              "      <td>1A8fjgbBz5etP8gLi2FNg6ggXiFbMQRwct</td>\n",
              "      <td>0.00202145</td>\n",
              "      <td>Tue May 11 12:09:14 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>c99a56f1625446f58caf4474f833249385e817dd1be4bc...</td>\n",
              "      <td>39VgGLvtWE8FnTYHYoUxDHFWyWZntJ5KGe</td>\n",
              "      <td>0.01137949</td>\n",
              "      <td>37UQ8v9un3NT3mGMaiZpnBgpxYqnRFELAa</td>\n",
              "      <td>0.00868604</td>\n",
              "      <td>Tue May 11 12:09:14 2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>235 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        transaction_id  ...                      date\n",
              "0    5904733a3b5c0762d00fa931358b07af4c7d27272f5307...  ...  Tue May 11 11:02:40 2021\n",
              "1    46fde81ba436974ff04118d178889f3e1e1949e1225566...  ...  Tue May 11 11:02:40 2021\n",
              "2    46fde81ba436974ff04118d178889f3e1e1949e1225566...  ...  Tue May 11 11:02:40 2021\n",
              "3    46fde81ba436974ff04118d178889f3e1e1949e1225566...  ...  Tue May 11 11:02:40 2021\n",
              "4    46fde81ba436974ff04118d178889f3e1e1949e1225566...  ...  Tue May 11 11:02:40 2021\n",
              "..                                                 ...  ...                       ...\n",
              "230  ef7fc3af7c75ee4a3289577f4bb207632543ad9d1a569b...  ...  Tue May 11 12:09:14 2021\n",
              "231  5ef9554ca0c5a227bb5c7f09e24683bf2ec417794774e3...  ...  Tue May 11 12:09:14 2021\n",
              "232  5ef9554ca0c5a227bb5c7f09e24683bf2ec417794774e3...  ...  Tue May 11 12:09:14 2021\n",
              "233  c99a56f1625446f58caf4474f833249385e817dd1be4bc...  ...  Tue May 11 12:09:14 2021\n",
              "234  c99a56f1625446f58caf4474f833249385e817dd1be4bc...  ...  Tue May 11 12:09:14 2021\n",
              "\n",
              "[235 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UxKi47_2uuh"
      },
      "source": [
        "# BTC NETWORKX INDICATORS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9I31VMAB2Pw"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "\n",
        "# Prep for NetworkX\n",
        "\n",
        "# creates tuple list\n",
        "d_bc = list(df_bc_nx.from_leg)\n",
        "e_bc = list(df_bc_nx.to)\n",
        "lst_tuple_bc = list(zip(d_bc, e_bc))\n",
        "\n",
        "# create an empty graph\n",
        "g_bc = nx.Graph()\n",
        "\n",
        "# adds edges\n",
        "g_bc.add_edges_from(lst_tuple_bc)\n",
        "\n",
        "# plots\n",
        "nx.draw_kamada_kawai(g_bc, with_labels=False)    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8HUyKDGDJms",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "outputId": "eab96688-498d-4c4d-e8ed-07e57fffc3fb"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS I ####\n",
        "#####################\n",
        "\n",
        "# Get information on the graph level\n",
        "\n",
        "# Number of vertices\n",
        "g_bc.number_of_nodes()\n",
        "nodes_summary = nx.number_of_nodes(g_bc)\n",
        "\n",
        "# Number of edges\n",
        "g_bc.number_of_edges()\n",
        "edges_summary = nx.number_of_edges(g_bc)\n",
        "\n",
        "# Connectedness\n",
        "  # A graph is connected if every node can be reached from any other node\n",
        "connection_summary = nx.is_connected(g_bc)\n",
        "\n",
        "# Density\n",
        "density_summary = nx.density(g_bc)\n",
        "\n",
        "# Transitivity\n",
        "  # relating the number of triangles with the number of triples in the graph\n",
        "transitivity_summary = nx.transitivity(g_bc)\n",
        "\n",
        "# Clustering coefficient\n",
        "clustering_summary = nx.average_clustering(g_bc)\n",
        "\n",
        "# Average degree\n",
        "  # average of all vertex degrees in the graph\n",
        "avgdegree_summary = np.mean([i[1] for i in g_bc.degree()])\n",
        "\n",
        "# summary table\n",
        "summary_bc = pd.DataFrame({'Number of vertices': nodes_summary, \n",
        "                        'Number of edges': edges_summary, \n",
        "                        'Connectedness': connection_summary, \n",
        "                        'Density': density_summary, \n",
        "                        'Transitivity': transitivity_summary, \n",
        "                        'Clustering': clustering_summary,\n",
        "                        'Average degree': avgdegree_summary}, index=[0])\n",
        "summary_bc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Number of vertices</th>\n",
              "      <th>Number of edges</th>\n",
              "      <th>Connectedness</th>\n",
              "      <th>Density</th>\n",
              "      <th>Transitivity</th>\n",
              "      <th>Clustering</th>\n",
              "      <th>Average degree</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>261</td>\n",
              "      <td>215</td>\n",
              "      <td>False</td>\n",
              "      <td>0.006337</td>\n",
              "      <td>0.007264</td>\n",
              "      <td>0.007769</td>\n",
              "      <td>1.64751</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Number of vertices  Number of edges  ...  Clustering  Average degree\n",
              "0                 261              215  ...    0.007769         1.64751\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3UlzsQHAn6Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "65679b75-376b-455e-e1c5-051b51d79e5e"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS II ###\n",
        "#####################\n",
        "\n",
        "# Degree distribution\n",
        "  #  degree of a node in a network is the number of connections it has to other \n",
        "  # nodes and the degree distribution is the probability distribution of these \n",
        "  # degrees over the whole network.\n",
        "degs = [i[1] for i in g_bc.degree()]\n",
        "fig, ax = plt.subplots(figsize=(12, 9))\n",
        "ax.spines[\"top\"].set_visible(False) # Remove plot frame line on the top \n",
        "ax.spines[\"right\"].set_visible(False) # Remove plot frame line on the right\n",
        "ax.get_xaxis().tick_bottom()  # Remove ticks on the bottom\n",
        "ax.get_yaxis().tick_left()  # Remove the ticks on the left\n",
        "ax.hist(degs, color=\"#75846b\", bins='auto'); \n",
        "  # TRY IT OUT WITH 7!\n",
        "\n",
        "plt.savefig('degree_dist_bc.png', dpi = 300, transparent=True)\n",
        "  # only very few vertices with high degrees, and many with relatively small degrees \n",
        "  # -- a typical results for social networks. In such cases, we say that the degree \n",
        "  # distribution is heavy tailed."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHuKSVtdC-AB"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS III ##\n",
        "#####################\n",
        "\n",
        "# Betweeness Centrality\n",
        "  # a vertex is important when it connects two large communities, which would \n",
        "  # remain unconnected if the vertex was not there.\n",
        "  \n",
        "between_centralities = nx.betweenness_centrality(g_bc).values()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 9))\n",
        "ax.spines[\"top\"].set_visible(False) # Remove plot frame line on the top \n",
        "ax.spines[\"right\"].set_visible(False) # Remove plot frame line on the right\n",
        "ax.get_xaxis().tick_bottom()  # Remove ticks on the bottom\n",
        "ax.get_yaxis().tick_left()  # Remove the ticks on the left\n",
        "ax.hist(between_centralities, color=\"#75846b\", bins='auto');\n",
        "  # TRY IT OUT WITH BINS = 7\n",
        "\n",
        "plt.savefig('betweeness_bc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1F4-cMxqK1qj"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS IV ###\n",
        "#####################\n",
        "\n",
        "# Get information on the vertex level\n",
        "\n",
        "# Neighborhood\n",
        "  # neighborhood of vertex  vi  is the set of all vertices that are adjacent to  vi\n",
        "nx.all_neighbors(g_bc, \"bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar\")\n",
        "\n",
        "# Connectedness\n",
        "  # Two vertices are connected of there is a path between then\n",
        "nx.has_path(g_bc, \"bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar\", \"coinbase\")\n",
        "\n",
        "# Clustering coefficient\n",
        "  # The clustering coefficient of a single vertex informs us about how well the \n",
        "  # neighbors of the vertex are themselves connected. The maximum amount of \n",
        "  # clustering is achieved if all neighbors of the vertex are neighbores as well\n",
        "nx.clustering(g_bc, \"bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar\")\n",
        "\n",
        "# Degree \n",
        "  # The degree of  vi  is just the number of adjacent vertices.\n",
        "g_bc.degree(\"bc1qy5t8srull5qxahd8fxeek02jgufm7kl69pj9ar\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PDOOdza7Sxy"
      },
      "source": [
        "# DOGECOIN EXTRACTION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "NiBLQebHvmWn",
        "outputId": "7f52fd84-a3d0-400b-812c-fde0fb55cd0e"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "#### EXTRACTION #####\n",
        "#####################\n",
        "\n",
        "import requests\n",
        "\n",
        "# 3802040 - 3802147: 07/07/2021 \n",
        "# range block\n",
        "block_range = list(range(3802040, 3902147)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 10\n",
        "size_block = range(r1, r2)\n",
        " \n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://sochain.com/api/v2/get_block/DOGE/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        "\n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly\n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "\n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        "\n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://sochain.com/api/v2/tx/DOGE/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        "  \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        "\n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        "  \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        "\n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        "\n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_dc_01 = pd.DataFrame({'transaction_id': transaction_id, 'from': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_dc= df_dc_01[df_dc_01.to != \"nulldata\"]\n",
        "df_dc.reset_index(drop=True, inplace=True)\n",
        " \n",
        " \n",
        "# fill NaN with previous data for all NaN, None\n",
        "df_dc.date = df_dc.date.fillna(method = 'ffill')\n",
        "df_dc = df_dc.fillna(value=np.nan)\n",
        "\n",
        "# convert epoch time\n",
        "for each in range(len(df_dc.date)):\n",
        "  date_form.append(datetime.datetime.fromtimestamp(df_dc.date[each]).strftime('%c'))\n",
        "\n",
        "# add date_form to df, drop old column and rename\n",
        "df_dc['date_form'] = date_form\n",
        "df_dc = df_dc.drop(columns = 'date')\n",
        "df_dc = df_dc.rename(columns = {'date_form': 'date'}, inplace = False)\n",
        "\n",
        "df_dc.to_csv(\"df_dc.csv\") \n",
        "df_dc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>transaction_id</th>\n",
              "      <th>from</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>to</th>\n",
              "      <th>amount</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cd599031e9bb345da38055bfd311643a1c50642de6fec6...</td>\n",
              "      <td>coinbase</td>\n",
              "      <td>10026.15000000</td>\n",
              "      <td>DQvuJB3eHEUmdB2wi2K9B6Vdimq9DNJU7Z</td>\n",
              "      <td>10026.15000000</td>\n",
              "      <td>Wed Jul  7 05:05:25 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8dcbe3052ce153ec844dfaa7132e023678caa19dee65fb...</td>\n",
              "      <td>DN2GEaPpUyZS6GyuMf6ZkoJ9viAfAgwRwJ</td>\n",
              "      <td>44479.22000000</td>\n",
              "      <td>DTkNwRnWJgyZ3Puq9QwKVQLecrhn8C7CAP</td>\n",
              "      <td>44477.30000000</td>\n",
              "      <td>Wed Jul  7 05:05:25 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1006e2a267b3f5225c0791722ce7bd47098c9138ef003c...</td>\n",
              "      <td>DFAiZVrFhkFwVd8YiqxoiuQjrSdfM7W1KQ</td>\n",
              "      <td>45.59026418</td>\n",
              "      <td>DERZES1iRBsYUbeyhYXkSSDMFns3inkA9r</td>\n",
              "      <td>1.00200194</td>\n",
              "      <td>Wed Jul  7 05:05:25 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>DKMxE68RTwVhMsaFnvGfqoh7FoBkZfFqw1</td>\n",
              "      <td>214.75991146</td>\n",
              "      <td>DPUELGczRSs9rrksdjhStjyJ7JpBSj5pL9</td>\n",
              "      <td>6410.00000000</td>\n",
              "      <td>Wed Jul  7 05:05:25 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>DKMxE68RTwVhMsaFnvGfqoh7FoBkZfFqw1</td>\n",
              "      <td>228.32603189</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 05:05:25 2021</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>55d113ca06e75819af56c003c4d40f7fb703fdcf637662...</td>\n",
              "      <td>DQ3yLUYJ5UZx66fSmbctqdX5KvjSXVMHiA</td>\n",
              "      <td>11.10253600</td>\n",
              "      <td>DGwjJyfWMcCf5txd8gAYu4AtCPEjnsaoT9</td>\n",
              "      <td>5.00000000</td>\n",
              "      <td>Wed Jul  7 05:10:08 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DQ3yLUYJ5UZx66fSmbctqdX5KvjSXVMHiA</td>\n",
              "      <td>5.10253600</td>\n",
              "      <td>Wed Jul  7 05:10:08 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>8820326b2d93acaa0ba0b43c4bb015b0bddf6867bb1fb0...</td>\n",
              "      <td>coinbase</td>\n",
              "      <td>10002.98320000</td>\n",
              "      <td>D5gk59fVCVa6b4mmYGaVH7FLBCwxnFyqLj</td>\n",
              "      <td>10002.98320000</td>\n",
              "      <td>Wed Jul  7 05:10:17 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>f4a3395cd7a978663056e5e379cd88ad5efe5068b2ae85...</td>\n",
              "      <td>DLPaeuaJi2JLUcvYHD4ddLxadwnGaVSt4p</td>\n",
              "      <td>38726.90755936</td>\n",
              "      <td>DRTSLDSRRDWWrMxRoWUqfWN5crXgoDDc8F</td>\n",
              "      <td>950.00000000</td>\n",
              "      <td>Wed Jul  7 05:10:17 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DLPaeuaJi2JLUcvYHD4ddLxadwnGaVSt4p</td>\n",
              "      <td>37773.92435936</td>\n",
              "      <td>Wed Jul  7 05:10:17 2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        transaction_id  ...                      date\n",
              "0    cd599031e9bb345da38055bfd311643a1c50642de6fec6...  ...  Wed Jul  7 05:05:25 2021\n",
              "1    8dcbe3052ce153ec844dfaa7132e023678caa19dee65fb...  ...  Wed Jul  7 05:05:25 2021\n",
              "2    1006e2a267b3f5225c0791722ce7bd47098c9138ef003c...  ...  Wed Jul  7 05:05:25 2021\n",
              "3                                                  NaN  ...  Wed Jul  7 05:05:25 2021\n",
              "4                                                  NaN  ...  Wed Jul  7 05:05:25 2021\n",
              "..                                                 ...  ...                       ...\n",
              "239  55d113ca06e75819af56c003c4d40f7fb703fdcf637662...  ...  Wed Jul  7 05:10:08 2021\n",
              "240                                                NaN  ...  Wed Jul  7 05:10:08 2021\n",
              "241  8820326b2d93acaa0ba0b43c4bb015b0bddf6867bb1fb0...  ...  Wed Jul  7 05:10:17 2021\n",
              "242  f4a3395cd7a978663056e5e379cd88ad5efe5068b2ae85...  ...  Wed Jul  7 05:10:17 2021\n",
              "243                                                NaN  ...  Wed Jul  7 05:10:17 2021\n",
              "\n",
              "[244 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5U9WfOrf3WTL"
      },
      "source": [
        "# DOGECOIN INDICATORS OVER TIME"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YcX8f8SsAwJu",
        "outputId": "89a5ccd2-4856-4f75-dfbf-8837bc05166c"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "#### INDICATORS #####\n",
        "#####################\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# summarizes df\n",
        "df_dc_solution = df_dc.pivot_table(index=['date'], aggfunc='count')\n",
        "# creates new var with vertices\n",
        "df_dc_solution['vertices'] = df_dc_solution['to'] + df_dc_solution['from']\n",
        "# consolidated edges and vertices data \n",
        "df_dc_egd_vert = pd.DataFrame({'edges': df_dc_solution.transaction_id, 'vertices': df_dc_solution.vertices})\n",
        "# plot\n",
        "df_dc_egd_vert.plot()\n",
        "  # rename ticks  \n",
        "plt.xticks(np.arange(0, 50, 5), ['05:05', '05:10', '05:15', '05:20', '05:25', '05:30', '05:40', '05:45', '05:50', '05:55']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "plt.savefig('egdes_vertices_dc.png', dpi = 300, transparent=True)\n",
        "df_dc_solution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>amount</th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>transaction_id</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>vertices</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>Wed Jul  7 05:05:25 2021</th>\n",
              "      <td>38</td>\n",
              "      <td>66</td>\n",
              "      <td>38</td>\n",
              "      <td>16</td>\n",
              "      <td>66</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:05:52 2021</th>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:06:14 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:06:28 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:07:08 2021</th>\n",
              "      <td>29</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>12</td>\n",
              "      <td>23</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:07:56 2021</th>\n",
              "      <td>35</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>16</td>\n",
              "      <td>21</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:08:23 2021</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:08:48 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:10:08 2021</th>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
              "      <td>50</td>\n",
              "      <td>22</td>\n",
              "      <td>40</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:10:17 2021</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:11:21 2021</th>\n",
              "      <td>47</td>\n",
              "      <td>19</td>\n",
              "      <td>47</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:12:26 2021</th>\n",
              "      <td>30</td>\n",
              "      <td>19</td>\n",
              "      <td>30</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:13 2021</th>\n",
              "      <td>30</td>\n",
              "      <td>31</td>\n",
              "      <td>30</td>\n",
              "      <td>13</td>\n",
              "      <td>31</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:36 2021</th>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:43 2021</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:16:10 2021</th>\n",
              "      <td>40</td>\n",
              "      <td>59</td>\n",
              "      <td>40</td>\n",
              "      <td>19</td>\n",
              "      <td>59</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:17:32 2021</th>\n",
              "      <td>49</td>\n",
              "      <td>23</td>\n",
              "      <td>49</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:21:17 2021</th>\n",
              "      <td>112</td>\n",
              "      <td>56</td>\n",
              "      <td>112</td>\n",
              "      <td>45</td>\n",
              "      <td>56</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:22:14 2021</th>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:22:39 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:23:30 2021</th>\n",
              "      <td>38</td>\n",
              "      <td>33</td>\n",
              "      <td>38</td>\n",
              "      <td>18</td>\n",
              "      <td>33</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:24:00 2021</th>\n",
              "      <td>27</td>\n",
              "      <td>16</td>\n",
              "      <td>27</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:24:29 2021</th>\n",
              "      <td>13</td>\n",
              "      <td>27</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>27</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:27:09 2021</th>\n",
              "      <td>80</td>\n",
              "      <td>60</td>\n",
              "      <td>80</td>\n",
              "      <td>33</td>\n",
              "      <td>60</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:27:58 2021</th>\n",
              "      <td>33</td>\n",
              "      <td>16</td>\n",
              "      <td>33</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:28:06 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:28:52 2021</th>\n",
              "      <td>30</td>\n",
              "      <td>19</td>\n",
              "      <td>30</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:29:09 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:29:17 2021</th>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:29:42 2021</th>\n",
              "      <td>20</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:30:45 2021</th>\n",
              "      <td>45</td>\n",
              "      <td>26</td>\n",
              "      <td>45</td>\n",
              "      <td>17</td>\n",
              "      <td>26</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:32:51 2021</th>\n",
              "      <td>58</td>\n",
              "      <td>36</td>\n",
              "      <td>58</td>\n",
              "      <td>26</td>\n",
              "      <td>36</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:34:49 2021</th>\n",
              "      <td>62</td>\n",
              "      <td>49</td>\n",
              "      <td>62</td>\n",
              "      <td>29</td>\n",
              "      <td>49</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:34:54 2021</th>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:36:18 2021</th>\n",
              "      <td>57</td>\n",
              "      <td>48</td>\n",
              "      <td>57</td>\n",
              "      <td>21</td>\n",
              "      <td>48</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:37:04 2021</th>\n",
              "      <td>46</td>\n",
              "      <td>40</td>\n",
              "      <td>46</td>\n",
              "      <td>21</td>\n",
              "      <td>40</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:38:01 2021</th>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:38:09 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:38:38 2021</th>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:41:08 2021</th>\n",
              "      <td>69</td>\n",
              "      <td>56</td>\n",
              "      <td>69</td>\n",
              "      <td>30</td>\n",
              "      <td>56</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:41:59 2021</th>\n",
              "      <td>37</td>\n",
              "      <td>13</td>\n",
              "      <td>37</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:42:12 2021</th>\n",
              "      <td>9</td>\n",
              "      <td>21</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:43:01 2021</th>\n",
              "      <td>22</td>\n",
              "      <td>31</td>\n",
              "      <td>22</td>\n",
              "      <td>8</td>\n",
              "      <td>31</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:44:05 2021</th>\n",
              "      <td>35</td>\n",
              "      <td>19</td>\n",
              "      <td>35</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:44:17 2021</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:45:00 2021</th>\n",
              "      <td>20</td>\n",
              "      <td>85</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>85</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:46:00 2021</th>\n",
              "      <td>28</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>14</td>\n",
              "      <td>23</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:46:27 2021</th>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>22</td>\n",
              "      <td>9</td>\n",
              "      <td>23</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:46:59 2021</th>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:55:49 2021</th>\n",
              "      <td>250</td>\n",
              "      <td>138</td>\n",
              "      <td>250</td>\n",
              "      <td>101</td>\n",
              "      <td>138</td>\n",
              "      <td>388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          amount  from   to  transaction_id  tt_value  vertices\n",
              "date                                                                           \n",
              "Wed Jul  7 05:05:25 2021      38    66   38              16        66       104\n",
              "Wed Jul  7 05:05:52 2021      16    12   16               9        12        28\n",
              "Wed Jul  7 05:06:14 2021       1     1    1               1         1         2\n",
              "Wed Jul  7 05:06:28 2021       1     1    1               1         1         2\n",
              "Wed Jul  7 05:07:08 2021      29    23   29              12        23        52\n",
              "Wed Jul  7 05:07:56 2021      35    21   35              16        21        56\n",
              "Wed Jul  7 05:08:23 2021       5     3    5               3         3         8\n",
              "Wed Jul  7 05:08:48 2021       1     1    1               1         1         2\n",
              "Wed Jul  7 05:10:08 2021      50    40   50              22        40        90\n",
              "Wed Jul  7 05:10:17 2021       3     2    3               2         2         5\n",
              "Wed Jul  7 05:11:21 2021      47    19   47              14        19        66\n",
              "Wed Jul  7 05:12:26 2021      30    19   30              17        19        49\n",
              "Wed Jul  7 05:14:13 2021      30    31   30              13        31        61\n",
              "Wed Jul  7 05:14:36 2021       7     6    7               5         6        13\n",
              "Wed Jul  7 05:14:43 2021       3     2    3               2         2         5\n",
              "Wed Jul  7 05:16:10 2021      40    59   40              19        59        99\n",
              "Wed Jul  7 05:17:32 2021      49    23   49              17        23        72\n",
              "Wed Jul  7 05:21:17 2021     112    56  112              45        56       168\n",
              "Wed Jul  7 05:22:14 2021      22    18   22              12        18        40\n",
              "Wed Jul  7 05:22:39 2021       1     1    1               1         1         2\n",
              "Wed Jul  7 05:23:30 2021      38    33   38              18        33        71\n",
              "Wed Jul  7 05:24:00 2021      27    16   27               9        16        43\n",
              "Wed Jul  7 05:24:29 2021      13    27   13               8        27        40\n",
              "Wed Jul  7 05:27:09 2021      80    60   80              33        60       140\n",
              "Wed Jul  7 05:27:58 2021      33    16   33              15        16        49\n",
              "Wed Jul  7 05:28:06 2021       1     1    1               1         1         2\n",
              "Wed Jul  7 05:28:52 2021      30    19   30              14        19        49\n",
              "Wed Jul  7 05:29:09 2021       1     1    1               1         1         2\n",
              "Wed Jul  7 05:29:17 2021      12    12   12               7        12        24\n",
              "Wed Jul  7 05:29:42 2021      20    30   20              10        30        50\n",
              "Wed Jul  7 05:30:45 2021      45    26   45              17        26        71\n",
              "Wed Jul  7 05:32:51 2021      58    36   58              26        36        94\n",
              "Wed Jul  7 05:34:49 2021      62    49   62              29        49       111\n",
              "Wed Jul  7 05:34:54 2021       2    17    2               2        17        19\n",
              "Wed Jul  7 05:36:18 2021      57    48   57              21        48       105\n",
              "Wed Jul  7 05:37:04 2021      46    40   46              21        40        86\n",
              "Wed Jul  7 05:38:01 2021       7    10    7               4        10        17\n",
              "Wed Jul  7 05:38:09 2021       1     1    1               1         1         2\n",
              "Wed Jul  7 05:38:38 2021      10     7   10               4         7        17\n",
              "Wed Jul  7 05:41:08 2021      69    56   69              30        56       125\n",
              "Wed Jul  7 05:41:59 2021      37    13   37              11        13        50\n",
              "Wed Jul  7 05:42:12 2021       9    21    9               6        21        30\n",
              "Wed Jul  7 05:43:01 2021      22    31   22               8        31        53\n",
              "Wed Jul  7 05:44:05 2021      35    19   35              15        19        54\n",
              "Wed Jul  7 05:44:17 2021       9     6    9               5         6        15\n",
              "Wed Jul  7 05:45:00 2021      20    85   20              12        85       105\n",
              "Wed Jul  7 05:46:00 2021      28    23   28              14        23        51\n",
              "Wed Jul  7 05:46:27 2021      22    23   22               9        23        45\n",
              "Wed Jul  7 05:46:59 2021      20     4   20               4         4        24\n",
              "Wed Jul  7 05:55:49 2021     250   138  250             101       138       388"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "L6-iiYLJBGwN",
        "outputId": "4a5e50c8-d4f1-4c8f-9f08-0e445bc4614c"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "### INDICATORS II ###\n",
        "#####################\n",
        "\n",
        "# creates new col with edge to vert ratio\n",
        "df_dc_solution['edge_vertices_ratio'] = df_dc_solution['vertices'] / df_dc_solution['transaction_id']\n",
        "# creates new col with to from ratio\n",
        "df_dc_solution['to_from_ratio'] = df_dc_solution['to'] / df_dc_solution['from']\n",
        "# creates dataframe\n",
        "df_dc_ratios = pd.DataFrame({'edge_vertices_ratio': df_dc_solution.edge_vertices_ratio, 'to_from_ratio': df_dc_solution.to_from_ratio})\n",
        "# plot\n",
        "df_dc_ratios.plot()\n",
        "  # rename ticks  \n",
        "plt.xticks(np.arange(0, 50, 5), ['05:05', '05:10', '05:15', '05:20', '05:25', '05:30', '05:40', '05:45', '05:50', '05:55']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "plt.savefig('ratios_dc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "wFPqRiz1BqB3",
        "outputId": "94c4966c-f600-4d2c-8f05-7cd0c0356534"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "## INDICATORS III ###\n",
        "#####################\n",
        "\n",
        "# density\n",
        "df_dc_solution['density'] = (2*df_dc_solution['transaction_id']) / (df_dc_solution['vertices']*(df_dc_solution['vertices']-1))\n",
        "# plot\n",
        "df_dc_solution.density.plot()\n",
        "  # rename ticks  \n",
        "plt.xticks(np.arange(0, 50, 5), ['05:05', '05:10', '05:15', '05:20', '05:25', '05:30', '05:40', '05:45', '05:50', '05:55']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "plt.savefig('density_dc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9687DsWlBZPx",
        "outputId": "1900ce85-58cf-4d22-ef48-e1417d38531b"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "### INDICATORS IV ###\n",
        "#####################\n",
        "\n",
        "# consolidated data with ratios\n",
        "df_dc_indicators = pd.DataFrame({'edges': df_dc_solution.transaction_id, 'vertices': df_dc_solution.vertices,\n",
        "                              'edge_vertices_ratio': df_dc_solution.edge_vertices_ratio, 'to_from_ratio': df_dc_solution.to_from_ratio,\n",
        "                              'density': df_dc_solution.density})\n",
        "df_dc_indicators"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>edges</th>\n",
              "      <th>vertices</th>\n",
              "      <th>edge_vertices_ratio</th>\n",
              "      <th>to_from_ratio</th>\n",
              "      <th>density</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>Wed Jul  7 05:05:25 2021</th>\n",
              "      <td>16</td>\n",
              "      <td>104</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>0.575758</td>\n",
              "      <td>0.002987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:05:52 2021</th>\n",
              "      <td>9</td>\n",
              "      <td>28</td>\n",
              "      <td>3.111111</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>0.023810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:06:14 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:06:28 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:07:08 2021</th>\n",
              "      <td>12</td>\n",
              "      <td>52</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>1.260870</td>\n",
              "      <td>0.009050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:07:56 2021</th>\n",
              "      <td>16</td>\n",
              "      <td>56</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>0.010390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:08:23 2021</th>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>0.107143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:08:48 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:10:08 2021</th>\n",
              "      <td>22</td>\n",
              "      <td>90</td>\n",
              "      <td>4.090909</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>0.005493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:10:17 2021</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:11:21 2021</th>\n",
              "      <td>14</td>\n",
              "      <td>66</td>\n",
              "      <td>4.714286</td>\n",
              "      <td>2.473684</td>\n",
              "      <td>0.006527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:12:26 2021</th>\n",
              "      <td>17</td>\n",
              "      <td>49</td>\n",
              "      <td>2.882353</td>\n",
              "      <td>1.578947</td>\n",
              "      <td>0.014456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:13 2021</th>\n",
              "      <td>13</td>\n",
              "      <td>61</td>\n",
              "      <td>4.692308</td>\n",
              "      <td>0.967742</td>\n",
              "      <td>0.007104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:36 2021</th>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.064103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:43 2021</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:16:10 2021</th>\n",
              "      <td>19</td>\n",
              "      <td>99</td>\n",
              "      <td>5.210526</td>\n",
              "      <td>0.677966</td>\n",
              "      <td>0.003917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:17:32 2021</th>\n",
              "      <td>17</td>\n",
              "      <td>72</td>\n",
              "      <td>4.235294</td>\n",
              "      <td>2.130435</td>\n",
              "      <td>0.006651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:21:17 2021</th>\n",
              "      <td>45</td>\n",
              "      <td>168</td>\n",
              "      <td>3.733333</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.003208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:22:14 2021</th>\n",
              "      <td>12</td>\n",
              "      <td>40</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>1.222222</td>\n",
              "      <td>0.015385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:22:39 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:23:30 2021</th>\n",
              "      <td>18</td>\n",
              "      <td>71</td>\n",
              "      <td>3.944444</td>\n",
              "      <td>1.151515</td>\n",
              "      <td>0.007243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:24:00 2021</th>\n",
              "      <td>9</td>\n",
              "      <td>43</td>\n",
              "      <td>4.777778</td>\n",
              "      <td>1.687500</td>\n",
              "      <td>0.009967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:24:29 2021</th>\n",
              "      <td>8</td>\n",
              "      <td>40</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.481481</td>\n",
              "      <td>0.010256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:27:09 2021</th>\n",
              "      <td>33</td>\n",
              "      <td>140</td>\n",
              "      <td>4.242424</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>0.003392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:27:58 2021</th>\n",
              "      <td>15</td>\n",
              "      <td>49</td>\n",
              "      <td>3.266667</td>\n",
              "      <td>2.062500</td>\n",
              "      <td>0.012755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:28:06 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:28:52 2021</th>\n",
              "      <td>14</td>\n",
              "      <td>49</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.578947</td>\n",
              "      <td>0.011905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:29:09 2021</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:29:17 2021</th>\n",
              "      <td>7</td>\n",
              "      <td>24</td>\n",
              "      <td>3.428571</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.025362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:29:42 2021</th>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.008163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:30:45 2021</th>\n",
              "      <td>17</td>\n",
              "      <td>71</td>\n",
              "      <td>4.176471</td>\n",
              "      <td>1.730769</td>\n",
              "      <td>0.006841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:32:51 2021</th>\n",
              "      <td>26</td>\n",
              "      <td>94</td>\n",
              "      <td>3.615385</td>\n",
              "      <td>1.611111</td>\n",
              "      <td>0.005948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:34:49 2021</th>\n",
              "      <td>29</td>\n",
              "      <td>111</td>\n",
              "      <td>3.827586</td>\n",
              "      <td>1.265306</td>\n",
              "      <td>0.004750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:34:54 2021</th>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.011696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:36:18 2021</th>\n",
              "      <td>21</td>\n",
              "      <td>105</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.187500</td>\n",
              "      <td>0.003846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:37:04 2021</th>\n",
              "      <td>11</td>\n",
              "      <td>35</td>\n",
              "      <td>3.181818</td>\n",
              "      <td>1.692308</td>\n",
              "      <td>0.018487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:38:38 2021</th>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>1.428571</td>\n",
              "      <td>0.029412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:41:08 2021</th>\n",
              "      <td>30</td>\n",
              "      <td>125</td>\n",
              "      <td>4.166667</td>\n",
              "      <td>1.232143</td>\n",
              "      <td>0.003871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:41:59 2021</th>\n",
              "      <td>11</td>\n",
              "      <td>50</td>\n",
              "      <td>4.545455</td>\n",
              "      <td>2.846154</td>\n",
              "      <td>0.008980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:42:12 2021</th>\n",
              "      <td>6</td>\n",
              "      <td>30</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.013793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:43:01 2021</th>\n",
              "      <td>8</td>\n",
              "      <td>53</td>\n",
              "      <td>6.625000</td>\n",
              "      <td>0.709677</td>\n",
              "      <td>0.005806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:44:05 2021</th>\n",
              "      <td>15</td>\n",
              "      <td>54</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>1.842105</td>\n",
              "      <td>0.010482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:44:17 2021</th>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.047619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:45:00 2021</th>\n",
              "      <td>12</td>\n",
              "      <td>105</td>\n",
              "      <td>8.750000</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>0.002198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:46:00 2021</th>\n",
              "      <td>14</td>\n",
              "      <td>51</td>\n",
              "      <td>3.642857</td>\n",
              "      <td>1.217391</td>\n",
              "      <td>0.010980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:46:27 2021</th>\n",
              "      <td>9</td>\n",
              "      <td>45</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.956522</td>\n",
              "      <td>0.009091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:46:59 2021</th>\n",
              "      <td>4</td>\n",
              "      <td>24</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.014493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:55:49 2021</th>\n",
              "      <td>101</td>\n",
              "      <td>388</td>\n",
              "      <td>3.841584</td>\n",
              "      <td>1.811594</td>\n",
              "      <td>0.001345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          edges  vertices  ...  to_from_ratio   density\n",
              "date                                       ...                         \n",
              "Wed Jul  7 05:05:25 2021     16       104  ...       0.575758  0.002987\n",
              "Wed Jul  7 05:05:52 2021      9        28  ...       1.333333  0.023810\n",
              "Wed Jul  7 05:06:14 2021      1         2  ...       1.000000  1.000000\n",
              "Wed Jul  7 05:06:28 2021      1         2  ...       1.000000  1.000000\n",
              "Wed Jul  7 05:07:08 2021     12        52  ...       1.260870  0.009050\n",
              "Wed Jul  7 05:07:56 2021     16        56  ...       1.666667  0.010390\n",
              "Wed Jul  7 05:08:23 2021      3         8  ...       1.666667  0.107143\n",
              "Wed Jul  7 05:08:48 2021      1         2  ...       1.000000  1.000000\n",
              "Wed Jul  7 05:10:08 2021     22        90  ...       1.250000  0.005493\n",
              "Wed Jul  7 05:10:17 2021      2         5  ...       1.500000  0.200000\n",
              "Wed Jul  7 05:11:21 2021     14        66  ...       2.473684  0.006527\n",
              "Wed Jul  7 05:12:26 2021     17        49  ...       1.578947  0.014456\n",
              "Wed Jul  7 05:14:13 2021     13        61  ...       0.967742  0.007104\n",
              "Wed Jul  7 05:14:36 2021      5        13  ...       1.166667  0.064103\n",
              "Wed Jul  7 05:14:43 2021      2         5  ...       1.500000  0.200000\n",
              "Wed Jul  7 05:16:10 2021     19        99  ...       0.677966  0.003917\n",
              "Wed Jul  7 05:17:32 2021     17        72  ...       2.130435  0.006651\n",
              "Wed Jul  7 05:21:17 2021     45       168  ...       2.000000  0.003208\n",
              "Wed Jul  7 05:22:14 2021     12        40  ...       1.222222  0.015385\n",
              "Wed Jul  7 05:22:39 2021      1         2  ...       1.000000  1.000000\n",
              "Wed Jul  7 05:23:30 2021     18        71  ...       1.151515  0.007243\n",
              "Wed Jul  7 05:24:00 2021      9        43  ...       1.687500  0.009967\n",
              "Wed Jul  7 05:24:29 2021      8        40  ...       0.481481  0.010256\n",
              "Wed Jul  7 05:27:09 2021     33       140  ...       1.333333  0.003392\n",
              "Wed Jul  7 05:27:58 2021     15        49  ...       2.062500  0.012755\n",
              "Wed Jul  7 05:28:06 2021      1         2  ...       1.000000  1.000000\n",
              "Wed Jul  7 05:28:52 2021     14        49  ...       1.578947  0.011905\n",
              "Wed Jul  7 05:29:09 2021      1         2  ...       1.000000  1.000000\n",
              "Wed Jul  7 05:29:17 2021      7        24  ...       1.000000  0.025362\n",
              "Wed Jul  7 05:29:42 2021     10        50  ...       0.666667  0.008163\n",
              "Wed Jul  7 05:30:45 2021     17        71  ...       1.730769  0.006841\n",
              "Wed Jul  7 05:32:51 2021     26        94  ...       1.611111  0.005948\n",
              "Wed Jul  7 05:34:49 2021     29       111  ...       1.265306  0.004750\n",
              "Wed Jul  7 05:34:54 2021      2        19  ...       0.117647  0.011696\n",
              "Wed Jul  7 05:36:18 2021     21       105  ...       1.187500  0.003846\n",
              "Wed Jul  7 05:37:04 2021     11        35  ...       1.692308  0.018487\n",
              "Wed Jul  7 05:38:38 2021      4        17  ...       1.428571  0.029412\n",
              "Wed Jul  7 05:41:08 2021     30       125  ...       1.232143  0.003871\n",
              "Wed Jul  7 05:41:59 2021     11        50  ...       2.846154  0.008980\n",
              "Wed Jul  7 05:42:12 2021      6        30  ...       0.428571  0.013793\n",
              "Wed Jul  7 05:43:01 2021      8        53  ...       0.709677  0.005806\n",
              "Wed Jul  7 05:44:05 2021     15        54  ...       1.842105  0.010482\n",
              "Wed Jul  7 05:44:17 2021      5        15  ...       1.500000  0.047619\n",
              "Wed Jul  7 05:45:00 2021     12       105  ...       0.235294  0.002198\n",
              "Wed Jul  7 05:46:00 2021     14        51  ...       1.217391  0.010980\n",
              "Wed Jul  7 05:46:27 2021      9        45  ...       0.956522  0.009091\n",
              "Wed Jul  7 05:46:59 2021      4        24  ...       5.000000  0.014493\n",
              "Wed Jul  7 05:55:49 2021    101       388  ...       1.811594  0.001345\n",
              "\n",
              "[48 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6f5k6ewB4gqC"
      },
      "source": [
        "# DOGECOIN EXTRACTION - NETWORKX"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RE-EP5HgEJiS",
        "outputId": "a913f988-a15f-41af-ea2a-1f90c63e3883"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        " \n",
        " \n",
        "# 3802140 - 3802147: 07/07/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(3802140, 3803147)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 10\n",
        "size_block = range(r1, r2)\n",
        " \n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://chain.so/api/v2/get_block/DOGE/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        " \n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly                                                                                                                   \n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "      \n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        " \n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://sochain.com/api/v2/tx/DOGE/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        " \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        " \n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        " \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        " \n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        " \n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_dc_nx_01 = pd.DataFrame({'transaction_id': transaction_id, 'from_leg': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_dc_nx= df_dc_nx_01[df_dc_nx_01.to != \"nulldata\"]\n",
        "df_dc_nx.reset_index(drop=True, inplace=True)\n",
        " \n",
        " \n",
        " # fill NaN with previous data for all NaN, None\n",
        "df_dc_nx.date = df_dc_nx.date.fillna(method = 'ffill')\n",
        "df_dc_nx.from_leg = df_dc_nx.from_leg.fillna(method = 'ffill')\n",
        "df_dc_nx.transaction_id = df_dc_nx.transaction_id.fillna(method = 'ffill')\n",
        "df_dc_nx.tt_value = df_dc_nx.tt_value.fillna(method = 'ffill')\n",
        "df_dc_nx = df_dc_nx.fillna(value=np.nan)\n",
        "\n",
        "# convert epoch time\n",
        "for each in range(len(df_dc_nx.date)):\n",
        "  date_form.append(datetime.datetime.fromtimestamp(df_dc_nx.date[each]).strftime('%c'))\n",
        "\n",
        "# add date_form to df, drop old column and rename\n",
        "df_dc_nx['date_form'] = date_form\n",
        "df_dc_nx = df_dc_nx.drop(columns = 'date')\n",
        "df_dc_nx = df_dc_nx.rename(columns = {'date_form': 'date'}, inplace = False)\n",
        "\n",
        "df_dc_nx.to_csv(\"df_dc_nx.csv\") \n",
        "df_dc_nx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>transaction_id</th>\n",
              "      <th>from_leg</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>to</th>\n",
              "      <th>amount</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8ac00ecb8eb4093eca89ed5d5ff7d5278b7fc948b17e5f...</td>\n",
              "      <td>coinbase</td>\n",
              "      <td>10020.09818466</td>\n",
              "      <td>DL7PtcKycScxfBn34WgiRELGvR5FSuMD4t</td>\n",
              "      <td>10020.09818466</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>63762b983bca3c0d70fa6d1b6ad7047dbaffc3009fc74b...</td>\n",
              "      <td>DLCDJhnh6aGotar6b182jpzbNEyXb3C361</td>\n",
              "      <td>19131.74161126</td>\n",
              "      <td>DLXAXirdAtb7NNAu7xC9NhbzqhEbYkKp6t</td>\n",
              "      <td>1040.19141300</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63762b983bca3c0d70fa6d1b6ad7047dbaffc3009fc74b...</td>\n",
              "      <td>DLCDJhnh6aGotar6b182jpzbNEyXb3C361</td>\n",
              "      <td>19131.74161126</td>\n",
              "      <td>DLCDJhnh6aGotar6b182jpzbNEyXb3C361</td>\n",
              "      <td>18089.55019826</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>aab2df8e355ead48da571e22f6943a88bdd124f8403160...</td>\n",
              "      <td>DGkTm3kPnVGBbwbg5jCffyqrFDqZnGiz7f</td>\n",
              "      <td>12318.84684978</td>\n",
              "      <td>D6viz2kusDhFe4Zqxotr19iGpEohJyf6A4</td>\n",
              "      <td>1300.00000000</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>aab2df8e355ead48da571e22f6943a88bdd124f8403160...</td>\n",
              "      <td>DGkTm3kPnVGBbwbg5jCffyqrFDqZnGiz7f</td>\n",
              "      <td>12318.84684978</td>\n",
              "      <td>DGkTm3kPnVGBbwbg5jCffyqrFDqZnGiz7f</td>\n",
              "      <td>11016.58684978</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>b6031ba0e1200c8bb0c26e6dc588746ea571c75f0e6db5...</td>\n",
              "      <td>DRWFxvQZhkabRN6iW582g1T46k5EFyWqEn</td>\n",
              "      <td>1523.00000000</td>\n",
              "      <td>DEKcuD5qV4tUoifjXyr5xCTWBsVCg1EErb</td>\n",
              "      <td>1998.30999662</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>b6031ba0e1200c8bb0c26e6dc588746ea571c75f0e6db5...</td>\n",
              "      <td>DRWFxvQZhkabRN6iW582g1T46k5EFyWqEn</td>\n",
              "      <td>477.00000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8b0399d580a61a755bc63e46b8d4a5e89a47beb701d5d0...</td>\n",
              "      <td>D64G73v595vhHHR9b1L9KRHCYw4AbEEqjk</td>\n",
              "      <td>98.99992400</td>\n",
              "      <td>DPQcYynnuduPiYfYncD1iwTkb91mN9TKru</td>\n",
              "      <td>1.00000000</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8b0399d580a61a755bc63e46b8d4a5e89a47beb701d5d0...</td>\n",
              "      <td>D64G73v595vhHHR9b1L9KRHCYw4AbEEqjk</td>\n",
              "      <td>98.99992400</td>\n",
              "      <td>DNhNShoubaTeUjWJhJSD1UYMYdNqmaZ83X</td>\n",
              "      <td>96.99874400</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>d143f81a621796f4d1bcc0bf9160691bd054c3825676a9...</td>\n",
              "      <td>DCVsYMBSGRTQ8NV1ziwouukDADwUrJbgnT</td>\n",
              "      <td>1.00000000</td>\n",
              "      <td>D78MAG4jjsPY52cqC9NUCFomST8s5jb2At</td>\n",
              "      <td>434.43000000</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>d143f81a621796f4d1bcc0bf9160691bd054c3825676a9...</td>\n",
              "      <td>D8Zaxmh1wedXKNwSYDEUeVTnuuiB2YmtNV</td>\n",
              "      <td>435.52104654</td>\n",
              "      <td>DUEguJY5JCYLZiKDSJeag3gTZFnbp3GGj1</td>\n",
              "      <td>1.09104654</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7f7caf7e13fc3abc7e78dc451e2ac53d65d19805e8f803...</td>\n",
              "      <td>DKR2caya9SXBuiEShVQ4kMVuSxWpLedTaQ</td>\n",
              "      <td>102145.25178152</td>\n",
              "      <td>DCUrdaVWg71kBqNSrYWHV4AnXgd7XDmHK1</td>\n",
              "      <td>102141.25178152</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>af95b90f208fe578db08aca2b61e88f67a16aa1b253a77...</td>\n",
              "      <td>DDmD3eQM59T8fpz9mjkWHwVXjGmromjEGJ</td>\n",
              "      <td>352.00000000</td>\n",
              "      <td>DHCWGrHZLnGGPfeZTMKZ6TGvF2fFEco7Y9</td>\n",
              "      <td>350.00000000</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>f1a2c4120dc5a66818a4802df6a8b9366af92ec7fe5c1b...</td>\n",
              "      <td>DRuFWKRRR9mWh2VQMfhoZoaD88UNqUEMbL</td>\n",
              "      <td>1689.57032856</td>\n",
              "      <td>DF5uEo35b3oa22KAQZZ4ARvPUFYZYdCpzC</td>\n",
              "      <td>1688.57032728</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>cac4a3088bd69922f6305f3f36f538bc6aaa0b08fe3dc1...</td>\n",
              "      <td>DMjy9qKyVLU96bDx8DorZPjVJ7q717dSRV</td>\n",
              "      <td>438.00213730</td>\n",
              "      <td>DKvhSGonop1hW77sY4ovcgPTuoBEYuvLsv</td>\n",
              "      <td>70.00000000</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>cac4a3088bd69922f6305f3f36f538bc6aaa0b08fe3dc1...</td>\n",
              "      <td>DMjy9qKyVLU96bDx8DorZPjVJ7q717dSRV</td>\n",
              "      <td>438.00213730</td>\n",
              "      <td>DMjy9qKyVLU96bDx8DorZPjVJ7q717dSRV</td>\n",
              "      <td>367.00213730</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DCSGNmBBepFirvn6656WL45YZVPk2vQWsf</td>\n",
              "      <td>196.71082663</td>\n",
              "      <td>DSwBEZCyFzt9avwVc29dhrrATUkY9gBoSr</td>\n",
              "      <td>405.02385743</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DG5gc9HDJYBGrm3umRoedQPvsctS1s86Kf</td>\n",
              "      <td>49.99258051</td>\n",
              "      <td>DDUoTGov76gcqAEBXXpUHzSuSQkPYKze9N</td>\n",
              "      <td>164.26591645</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DLeB87PsFBqSoDsJ7vv9QN5WTUfpk3o1Ev</td>\n",
              "      <td>31.14318988</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DCaZoCtJZXPRFRznNc6yEtiecKEUX6871Y</td>\n",
              "      <td>27.83268279</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>D6mFHoJxRV699XWPXBoNAZgE5ARhk4zNxk</td>\n",
              "      <td>27.81373436</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DP6rYnaVgBQoPubfzXLTCU5HX9NFgn7P7R</td>\n",
              "      <td>26.52861753</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DD8xSFvytJCjsZbCKzrNYBZy5xWQ6CwU1f</td>\n",
              "      <td>25.39621709</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DPVjtHkGPjNqYh2GRtJXR3G5fboeK5twNA</td>\n",
              "      <td>23.96727498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...</td>\n",
              "      <td>DDUoTGov76gcqAEBXXpUHzSuSQkPYKze9N</td>\n",
              "      <td>162.61165011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8532b94bd2804bdd76350ce87837cdb5fbba2b9b0817b8...</td>\n",
              "      <td>D8MhHYhWndTYEkwg7KH6ERg8cmNrEgobQx</td>\n",
              "      <td>29.71405273</td>\n",
              "      <td>D9qQAqHQeuofyNpvyNS4Q2595gmzz1qcU4</td>\n",
              "      <td>15.51728751</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>8532b94bd2804bdd76350ce87837cdb5fbba2b9b0817b8...</td>\n",
              "      <td>D8zo5hhKRqxSzasbyvN4MVqWvsmCM7xgKa</td>\n",
              "      <td>28.58616458</td>\n",
              "      <td>DDUoTGov76gcqAEBXXpUHzSuSQkPYKze9N</td>\n",
              "      <td>41.34292980</td>\n",
              "      <td>Wed Jul  7 06:47:56 2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       transaction_id  ...                      date\n",
              "0   8ac00ecb8eb4093eca89ed5d5ff7d5278b7fc948b17e5f...  ...  Wed Jul  7 06:47:56 2021\n",
              "1   63762b983bca3c0d70fa6d1b6ad7047dbaffc3009fc74b...  ...  Wed Jul  7 06:47:56 2021\n",
              "2   63762b983bca3c0d70fa6d1b6ad7047dbaffc3009fc74b...  ...  Wed Jul  7 06:47:56 2021\n",
              "3   aab2df8e355ead48da571e22f6943a88bdd124f8403160...  ...  Wed Jul  7 06:47:56 2021\n",
              "4   aab2df8e355ead48da571e22f6943a88bdd124f8403160...  ...  Wed Jul  7 06:47:56 2021\n",
              "5   b6031ba0e1200c8bb0c26e6dc588746ea571c75f0e6db5...  ...  Wed Jul  7 06:47:56 2021\n",
              "6   b6031ba0e1200c8bb0c26e6dc588746ea571c75f0e6db5...  ...  Wed Jul  7 06:47:56 2021\n",
              "7   8b0399d580a61a755bc63e46b8d4a5e89a47beb701d5d0...  ...  Wed Jul  7 06:47:56 2021\n",
              "8   8b0399d580a61a755bc63e46b8d4a5e89a47beb701d5d0...  ...  Wed Jul  7 06:47:56 2021\n",
              "9   d143f81a621796f4d1bcc0bf9160691bd054c3825676a9...  ...  Wed Jul  7 06:47:56 2021\n",
              "10  d143f81a621796f4d1bcc0bf9160691bd054c3825676a9...  ...  Wed Jul  7 06:47:56 2021\n",
              "11  7f7caf7e13fc3abc7e78dc451e2ac53d65d19805e8f803...  ...  Wed Jul  7 06:47:56 2021\n",
              "12  af95b90f208fe578db08aca2b61e88f67a16aa1b253a77...  ...  Wed Jul  7 06:47:56 2021\n",
              "13  f1a2c4120dc5a66818a4802df6a8b9366af92ec7fe5c1b...  ...  Wed Jul  7 06:47:56 2021\n",
              "14  cac4a3088bd69922f6305f3f36f538bc6aaa0b08fe3dc1...  ...  Wed Jul  7 06:47:56 2021\n",
              "15  cac4a3088bd69922f6305f3f36f538bc6aaa0b08fe3dc1...  ...  Wed Jul  7 06:47:56 2021\n",
              "16  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "17  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "18  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "19  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "20  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "21  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "22  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "23  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "24  c86df9b1bba91859bca416a1ee30b7a54fd8c2822021a8...  ...  Wed Jul  7 06:47:56 2021\n",
              "25  8532b94bd2804bdd76350ce87837cdb5fbba2b9b0817b8...  ...  Wed Jul  7 06:47:56 2021\n",
              "26  8532b94bd2804bdd76350ce87837cdb5fbba2b9b0817b8...  ...  Wed Jul  7 06:47:56 2021\n",
              "\n",
              "[27 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uw5Bvp2K5bwL"
      },
      "source": [
        "# DOGECOIN NETWORKX INDICATORS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpkhJPVdFC1B"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "\n",
        "# Prep for NetworkX\n",
        "\n",
        "# creates tuple list\n",
        "d_dc = list(df_dc_nx.from_leg)\n",
        "e_dc = list(df_dc_nx.to)\n",
        "# tuple\n",
        "lst_tuple_dc = list(zip(d_dc, e_dc))\n",
        "\n",
        "# create an empty graph\n",
        "g_dc = nx.Graph()\n",
        "\n",
        "# adds edges\n",
        "g_dc.add_edges_from(lst_tuple_dc)\n",
        "\n",
        "# plots\n",
        "nx.draw_kamada_kawai(g_dc, with_labels=False)    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "id": "ECOGSCyMFzQY",
        "outputId": "7e16c6bf-0d31-4c62-8d1a-c4948969758c"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS I ####\n",
        "#####################\n",
        "\n",
        "# Get information on the graph level\n",
        "\n",
        "# Number of vertices\n",
        "g_dc.number_of_nodes()\n",
        "nodes_summary = nx.number_of_nodes(g_dc)\n",
        "\n",
        "# Number of edges\n",
        "g_dc.number_of_edges()\n",
        "edges_summary = nx.number_of_edges(g_dc)\n",
        "\n",
        "# Connectedness\n",
        "  # A graph is connected if every node can be reached from any other node\n",
        "connection_summary = nx.is_connected(g_dc)\n",
        "\n",
        "# Density\n",
        "density_summary = nx.density(g_dc)\n",
        "\n",
        "# Transitivity\n",
        "  # relating the number of triangles with the number of triples in the graph\n",
        "transitivity_summary = nx.transitivity(g_dc)\n",
        "\n",
        "# Clustering coefficient\n",
        "clustering_summary = nx.average_clustering(g_dc)\n",
        "\n",
        "# Average degree\n",
        "  # average of all vertex degrees in the graph\n",
        "avgdegree_summary = np.mean([i[1] for i in g_dc.degree()])\n",
        "\n",
        "# summary table\n",
        "summary_dc = pd.DataFrame({'Number of vertices': nodes_summary, \n",
        "                        'Number of edges': edges_summary, \n",
        "                        'Connectedness': connection_summary, \n",
        "                        'Density': density_summary, \n",
        "                        'Transitivity': transitivity_summary, \n",
        "                        'Clustering': clustering_summary,\n",
        "                        'Average degree': avgdegree_summary}, index=[0])\n",
        "summary_dc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Number of vertices</th>\n",
              "      <th>Number of edges</th>\n",
              "      <th>Connectedness</th>\n",
              "      <th>Density</th>\n",
              "      <th>Transitivity</th>\n",
              "      <th>Clustering</th>\n",
              "      <th>Average degree</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37</td>\n",
              "      <td>27</td>\n",
              "      <td>False</td>\n",
              "      <td>0.040541</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.459459</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Number of vertices  Number of edges  ...  Clustering  Average degree\n",
              "0                  37               27  ...         0.0        1.459459\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "ti0ZG3OEF6RD",
        "outputId": "5c09218f-21b0-45ca-9246-9d9961ad22aa"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS II ###\n",
        "#####################\n",
        "\n",
        "# Degree distribution\n",
        "  #  degree of a node in a network is the number of connections it has to other \n",
        "  # nodes and the degree distribution is the probability distribution of these \n",
        "  # degrees over the whole network.\n",
        "degs = [i[1] for i in g_dc.degree()]\n",
        "fig, ax = plt.subplots(figsize=(12, 9))\n",
        "ax.spines[\"top\"].set_visible(False) # Remove plot frame line on the top \n",
        "ax.spines[\"right\"].set_visible(False) # Remove plot frame line on the right\n",
        "ax.get_xaxis().tick_bottom()  # Remove ticks on the bottom\n",
        "ax.get_yaxis().tick_left()  # Remove the ticks on the left\n",
        "ax.hist(degs, color=\"#75846b\", bins='auto');\n",
        "# plt.xlim([0, 10])\n",
        "plt.savefig('degree_dist_dc.png', dpi = 300, transparent=True)\n",
        "  # only very few vertices with high degrees, and many with relatively small degrees \n",
        "  # -- a typical results for social networks. In such cases, we say that the degree \n",
        "  # distribution is heavy tailed."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "4MN2s0wUF_Sy",
        "outputId": "842f3e30-1ab6-4f3a-b5cd-695a2a742241"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS III ##\n",
        "#####################\n",
        "\n",
        "# Betweeness Centrality\n",
        "  # a vertex is important when it connects two large communities, which would \n",
        "  # remain unconnected if the vertex was not there.\n",
        "  \n",
        "between_centralities = nx.betweenness_centrality(g_dc).values()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 9))\n",
        "ax.spines[\"top\"].set_visible(False) # Remove plot frame line on the top \n",
        "ax.spines[\"right\"].set_visible(False) # Remove plot frame line on the right\n",
        "ax.get_xaxis().tick_bottom()  # Remove ticks on the bottom\n",
        "ax.get_yaxis().tick_left()  # Remove the ticks on the left\n",
        "ax.hist(between_centralities, color=\"#75846b\", bins='auto');\n",
        "\n",
        "plt.savefig('betweeness_dc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "yqnQyVzRGDGf"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INDICATORS IV ###\n",
        "#####################\n",
        "\n",
        "# Get information on the vertex level\n",
        "\n",
        "# Neighborhood\n",
        "  # neighborhood of vertex  vi  is the set of all vertices that are adjacent to  vi\n",
        "nx.all_neighbors(g_dc, \"DLCDJhnh6aGotar6b182jpzbNEyXb3C361\")\n",
        "\n",
        "# Connectedness\n",
        "  # Two vertices are connected of there is a path between then\n",
        "nx.has_path(g_dc, \"DLCDJhnh6aGotar6b182jpzbNEyXb3C361\", \"coinbase\")\n",
        "\n",
        "# Clustering coefficient\n",
        "  # The clustering coefficient of a single vertex informs us about how well the \n",
        "  # neighbors of the vertex are themselves connected. The maximum amount of \n",
        "  # clustering is achieved if all neighbors of the vertex are neighbores as well\n",
        "nx.clustering(g_dc, \"DLCDJhnh6aGotar6b182jpzbNEyXb3C361\")\n",
        "\n",
        "# Degree \n",
        "  # The degree of  vi  is just the number of adjacent vertices.\n",
        "g_dc.degree(\"DLCDJhnh6aGotar6b182jpzbNEyXb3C361\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_NzMlVLcLF6"
      },
      "source": [
        "# LITECOIN EXTRACTION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNmI6JeXbEWY"
      },
      "source": [
        "######################\n",
        "###### LITECOIN ######\n",
        "######################\n",
        "##### EXTRACTION #####\n",
        "######################\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        "\n",
        "# 2082158 - 2082265: 07/07/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(2082158, 2083265)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 20\n",
        "size_block = range(r1, r2)\n",
        " \n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://sochain.com/api/v2/get_block/LTC/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        "\n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly\n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "\n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        "\n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://sochain.com/api/v2/tx/LTC/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        "  \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        "\n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        "  \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        "\n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        "\n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_lc_01 = pd.DataFrame({'transaction_id': transaction_id, 'from': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_lc= df_lc_01[df_lc_01.to != \"nulldata\"]\n",
        "df_lc.reset_index(drop=True, inplace=True)\n",
        " \n",
        " \n",
        "# fill NaN with previous data for all NaN, None\n",
        "df_lc.date = df_lc.date.fillna(method = 'ffill')\n",
        "df_lc = df_lc.fillna(value=np.nan)\n",
        "\n",
        "# convert epoch time\n",
        "for each in range(len(df_lc.date)):\n",
        "  date_form.append(datetime.datetime.fromtimestamp(df_lc.date[each]).strftime('%c'))\n",
        "\n",
        "# add date_form to df, drop old column and rename\n",
        "df_lc['date_form'] = date_form\n",
        "df_lc = df_lc.drop(columns = 'date')\n",
        "df_lc = df_lc.rename(columns = {'date_form': 'date'}, inplace = False)\n",
        "\n",
        "df_lc.to_csv(\"df_lc.csv\") \n",
        "df_lc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eMCbwqr_7tgS"
      },
      "source": [
        "# LITECOIN INDICATORS OVER TIME"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "OEV3GFbRbEWa",
        "outputId": "d429f544-a505-4287-e338-f5ff1682c7ed"
      },
      "source": [
        "#####################\n",
        "###### LITECOIN #####\n",
        "#####################\n",
        "#### INDICATORS #####\n",
        "#####################\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# summarizes df\n",
        "df_lc_solution = df_lc.pivot_table(index=['date'], aggfunc='count')\n",
        "# creates new var with vertices\n",
        "df_lc_solution['vertices'] = df_lc_solution['to'] + df_lc_solution['from']\n",
        "# consolidated edges and vertices data \n",
        "df_lc_egd_vert = pd.DataFrame({'edges': df_lc_solution.transaction_id, 'vertices': df_lc_solution.vertices})\n",
        "# plot\n",
        "df_lc_egd_vert.plot()\n",
        "  # rename ticks  \n",
        "plt.xticks(np.arange(0, 10, 1), ['04:52', '04:54', '04:55', '04:58', '05:08', '05:10', '05:11', '05:14', '05:16']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "plt.savefig('egdes_vertices_lc.png', dpi = 300, transparent=True)\n",
        "df_lc_solution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>amount</th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>transaction_id</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>vertices</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>Wed Jul  7 04:52:02 2021</th>\n",
              "      <td>392</td>\n",
              "      <td>203</td>\n",
              "      <td>392</td>\n",
              "      <td>99</td>\n",
              "      <td>203</td>\n",
              "      <td>595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 04:54:10 2021</th>\n",
              "      <td>485</td>\n",
              "      <td>122</td>\n",
              "      <td>485</td>\n",
              "      <td>84</td>\n",
              "      <td>122</td>\n",
              "      <td>607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 04:55:15 2021</th>\n",
              "      <td>233</td>\n",
              "      <td>85</td>\n",
              "      <td>233</td>\n",
              "      <td>48</td>\n",
              "      <td>85</td>\n",
              "      <td>318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 04:58:26 2021</th>\n",
              "      <td>1734</td>\n",
              "      <td>521</td>\n",
              "      <td>1734</td>\n",
              "      <td>257</td>\n",
              "      <td>521</td>\n",
              "      <td>2255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:08:24 2021</th>\n",
              "      <td>2338</td>\n",
              "      <td>1075</td>\n",
              "      <td>2338</td>\n",
              "      <td>358</td>\n",
              "      <td>1075</td>\n",
              "      <td>3413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:10:05 2021</th>\n",
              "      <td>492</td>\n",
              "      <td>229</td>\n",
              "      <td>492</td>\n",
              "      <td>79</td>\n",
              "      <td>229</td>\n",
              "      <td>721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:11:01 2021</th>\n",
              "      <td>135</td>\n",
              "      <td>177</td>\n",
              "      <td>135</td>\n",
              "      <td>30</td>\n",
              "      <td>177</td>\n",
              "      <td>312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:11 2021</th>\n",
              "      <td>755</td>\n",
              "      <td>276</td>\n",
              "      <td>755</td>\n",
              "      <td>129</td>\n",
              "      <td>276</td>\n",
              "      <td>1031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:16:08 2021</th>\n",
              "      <td>499</td>\n",
              "      <td>423</td>\n",
              "      <td>499</td>\n",
              "      <td>93</td>\n",
              "      <td>423</td>\n",
              "      <td>922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          amount  from  ...  tt_value  vertices\n",
              "date                                    ...                    \n",
              "Wed Jul  7 04:52:02 2021     392   203  ...       203       595\n",
              "Wed Jul  7 04:54:10 2021     485   122  ...       122       607\n",
              "Wed Jul  7 04:55:15 2021     233    85  ...        85       318\n",
              "Wed Jul  7 04:58:26 2021    1734   521  ...       521      2255\n",
              "Wed Jul  7 05:08:24 2021    2338  1075  ...      1075      3413\n",
              "Wed Jul  7 05:10:05 2021     492   229  ...       229       721\n",
              "Wed Jul  7 05:11:01 2021     135   177  ...       177       312\n",
              "Wed Jul  7 05:14:11 2021     755   276  ...       276      1031\n",
              "Wed Jul  7 05:16:08 2021     499   423  ...       423       922\n",
              "\n",
              "[9 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "NEdv7VEMbEWb",
        "outputId": "3e3c06e1-27cf-43b5-a825-3d002d803d06"
      },
      "source": [
        "#####################\n",
        "###### LITECOIN #####\n",
        "#####################\n",
        "### INDICATORS II ###\n",
        "#####################\n",
        "\n",
        "# creates new col with edge to vert ratio\n",
        "df_lc_solution['edge_vertices_ratio'] = df_lc_solution['vertices'] / df_lc_solution['transaction_id']\n",
        "# creates new col with to from ratio\n",
        "df_lc_solution['to_from_ratio'] = df_lc_solution['to'] / df_lc_solution['from']\n",
        "# creates dataframe\n",
        "df_lc_ratios = pd.DataFrame({'edge_vertices_ratio': df_lc_solution.edge_vertices_ratio, 'to_from_ratio': df_lc_solution.to_from_ratio})\n",
        "# plot\n",
        "df_lc_ratios.plot()\n",
        "  # rename ticks  \n",
        "plt.xticks(np.arange(0, 10, 1), ['04:52', '04:54', '04:55', '04:58', '05:08', '05:10', '05:11', '05:14', '05:16']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "plt.savefig('ratios_lc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "K81dT0O9bEWc",
        "outputId": "d99d70d7-0061-46f5-dbd2-26bce70127e6"
      },
      "source": [
        "#####################\n",
        "###### LITECOIN #####\n",
        "#####################\n",
        "## INDICATORS III ###\n",
        "#####################\n",
        "\n",
        "# density\n",
        "df_lc_solution['density'] = (2*df_lc_solution['transaction_id']) / (df_lc_solution['vertices']*(df_lc_solution['vertices']-1))\n",
        "# plot\n",
        "df_lc_solution.density.plot()\n",
        "  # rename ticks  \n",
        "plt.xticks(np.arange(0, 10, 1), ['04:52', '04:54', '04:55', '04:58', '05:08', '05:10', '05:11', '05:14', '05:16']) \n",
        "  # legend loc\n",
        "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol= 2)\n",
        "plt.savefig('density_lc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "6ku9s-qabEWb",
        "outputId": "b0ac8726-c7d2-40b7-f468-9e86f07086ac"
      },
      "source": [
        "#####################\n",
        "###### LITECOIN #####\n",
        "#####################\n",
        "### INDICATORS IV ###\n",
        "#####################\n",
        "\n",
        "# consolidated data with ratios\n",
        "df_lc_indicators = pd.DataFrame({'edges': df_lc_solution.transaction_id, 'vertices': df_lc_solution.vertices,\n",
        "                              'edge_vertices_ratio': df_lc_solution.edge_vertices_ratio, 'to_from_ratio': df_lc_solution.to_from_ratio,\n",
        "                              'density': df_lc_solution.density})\n",
        "df_lc_indicators"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>edges</th>\n",
              "      <th>vertices</th>\n",
              "      <th>edge_vertices_ratio</th>\n",
              "      <th>to_from_ratio</th>\n",
              "      <th>density</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>Wed Jul  7 04:52:02 2021</th>\n",
              "      <td>99</td>\n",
              "      <td>595</td>\n",
              "      <td>6.010101</td>\n",
              "      <td>1.931034</td>\n",
              "      <td>0.000560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 04:54:10 2021</th>\n",
              "      <td>84</td>\n",
              "      <td>607</td>\n",
              "      <td>7.226190</td>\n",
              "      <td>3.975410</td>\n",
              "      <td>0.000457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 04:55:15 2021</th>\n",
              "      <td>48</td>\n",
              "      <td>318</td>\n",
              "      <td>6.625000</td>\n",
              "      <td>2.741176</td>\n",
              "      <td>0.000952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 04:58:26 2021</th>\n",
              "      <td>257</td>\n",
              "      <td>2255</td>\n",
              "      <td>8.774319</td>\n",
              "      <td>3.328215</td>\n",
              "      <td>0.000101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:08:24 2021</th>\n",
              "      <td>358</td>\n",
              "      <td>3413</td>\n",
              "      <td>9.533520</td>\n",
              "      <td>2.174884</td>\n",
              "      <td>0.000061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:10:05 2021</th>\n",
              "      <td>79</td>\n",
              "      <td>721</td>\n",
              "      <td>9.126582</td>\n",
              "      <td>2.148472</td>\n",
              "      <td>0.000304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:11:01 2021</th>\n",
              "      <td>30</td>\n",
              "      <td>312</td>\n",
              "      <td>10.400000</td>\n",
              "      <td>0.762712</td>\n",
              "      <td>0.000618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:14:11 2021</th>\n",
              "      <td>129</td>\n",
              "      <td>1031</td>\n",
              "      <td>7.992248</td>\n",
              "      <td>2.735507</td>\n",
              "      <td>0.000243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed Jul  7 05:16:08 2021</th>\n",
              "      <td>93</td>\n",
              "      <td>922</td>\n",
              "      <td>9.913978</td>\n",
              "      <td>1.179669</td>\n",
              "      <td>0.000219</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          edges  vertices  ...  to_from_ratio   density\n",
              "date                                       ...                         \n",
              "Wed Jul  7 04:52:02 2021     99       595  ...       1.931034  0.000560\n",
              "Wed Jul  7 04:54:10 2021     84       607  ...       3.975410  0.000457\n",
              "Wed Jul  7 04:55:15 2021     48       318  ...       2.741176  0.000952\n",
              "Wed Jul  7 04:58:26 2021    257      2255  ...       3.328215  0.000101\n",
              "Wed Jul  7 05:08:24 2021    358      3413  ...       2.174884  0.000061\n",
              "Wed Jul  7 05:10:05 2021     79       721  ...       2.148472  0.000304\n",
              "Wed Jul  7 05:11:01 2021     30       312  ...       0.762712  0.000618\n",
              "Wed Jul  7 05:14:11 2021    129      1031  ...       2.735507  0.000243\n",
              "Wed Jul  7 05:16:08 2021     93       922  ...       1.179669  0.000219\n",
              "\n",
              "[9 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEWhUBf-8cLc"
      },
      "source": [
        "# LITECOIN EXTRACTION - NETWORKX"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYyMT1qGbEWg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 813
        },
        "outputId": "4ce27a0d-2bb2-44a7-d012-3fae67d0fd79"
      },
      "source": [
        "#####################\n",
        "##### LITECOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        " \n",
        " \n",
        "# 2082258 - 2082265: 07/07/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(2082258, 2082365)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 10\n",
        "size_block = range(r1, r2)\n",
        " \n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://chain.so/api/v2/get_block/LTC/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        " \n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly                                                                                                                   \n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "      \n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        " \n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://sochain.com/api/v2/tx/LTC/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        " \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        " \n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        " \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        " \n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        " \n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_lc_nx_01 = pd.DataFrame({'transaction_id': transaction_id, 'from_leg': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_lc_nx = df_lc_nx_01[df_lc_nx_01.to != \"nulldata\"]\n",
        "df_lc_nx.reset_index(drop=True, inplace=True)\n",
        " \n",
        " \n",
        " # fill NaN with previous data for all NaN, None\n",
        "df_lc_nx.date = df_lc_nx.date.fillna(method = 'ffill')\n",
        "df_lc_nx.from_leg = df_lc_nx.from_leg.fillna(method = 'ffill')\n",
        "df_lc_nx.transaction_id = df_lc_nx.transaction_id.fillna(method = 'ffill')\n",
        "df_lc_nx.tt_value = df_lc_nx.tt_value.fillna(method = 'ffill')\n",
        "df_lc_nx = df_lc_nx.fillna(value=np.nan)\n",
        "\n",
        "# convert epoch time\n",
        "for each in range(len(df_lc_nx.date)):\n",
        "  date_form.append(datetime.datetime.fromtimestamp(df_lc_nx.date[each]).strftime('%c'))\n",
        "\n",
        "# add date_form to df, drop old column and rename\n",
        "df_lc_nx['date_form'] = date_form\n",
        "df_lc_nx = df_lc_nx.drop(columns = 'date')\n",
        "df_lc_nx = df_lc_nx.rename(columns = {'date_form': 'date'}, inplace = False)\n",
        "\n",
        "df_lc_nx.to_csv(\"df_lc_nx.csv\") \n",
        "df_lc_nx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5170: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>transaction_id</th>\n",
              "      <th>from_leg</th>\n",
              "      <th>tt_value</th>\n",
              "      <th>to</th>\n",
              "      <th>amount</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>c7b57b498aebfe0e106685428f457de793d87cbccdbe22...</td>\n",
              "      <td>coinbase</td>\n",
              "      <td>12.56826642</td>\n",
              "      <td>LajyQBeZaBA1NkZDeY8YT5RYYVRkXMvb2T</td>\n",
              "      <td>12.56826642</td>\n",
              "      <td>Wed Jul  7 09:31:24 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1380fa7fd8433dbadafb321092becb3b47680d43adbcf2...</td>\n",
              "      <td>LPFRDzav62oG91b5mHKj9k3g4tjtn5Vjtp</td>\n",
              "      <td>1.43999580</td>\n",
              "      <td>LUqSZcT8M9br5MggekV6LC2Vphf4wSbCsC</td>\n",
              "      <td>0.17980000</td>\n",
              "      <td>Wed Jul  7 09:31:24 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1380fa7fd8433dbadafb321092becb3b47680d43adbcf2...</td>\n",
              "      <td>LPFRDzav62oG91b5mHKj9k3g4tjtn5Vjtp</td>\n",
              "      <td>1.43999580</td>\n",
              "      <td>LPFRDzav62oG91b5mHKj9k3g4tjtn5Vjtp</td>\n",
              "      <td>1.25843300</td>\n",
              "      <td>Wed Jul  7 09:31:24 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ca6638fbf422a5914602067cb7d48dd8aff28fa2bf8afe...</td>\n",
              "      <td>35Jd3RFpfNun8A2ah2n5Vx8wrNoadM4VqV</td>\n",
              "      <td>107.33377450</td>\n",
              "      <td>3G1SnisdmLxdLa9hzp8YUhgB42eEMPbKTk</td>\n",
              "      <td>107.20077450</td>\n",
              "      <td>Wed Jul  7 09:31:24 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ca6638fbf422a5914602067cb7d48dd8aff28fa2bf8afe...</td>\n",
              "      <td>35Jd3RFpfNun8A2ah2n5Vx8wrNoadM4VqV</td>\n",
              "      <td>107.33377450</td>\n",
              "      <td>35vsSgH4b387meBCtzXJwfBcxZQNJsUWAy</td>\n",
              "      <td>0.13200000</td>\n",
              "      <td>Wed Jul  7 09:31:24 2021</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16942</th>\n",
              "      <td>db8d527183eee2c8feb4c2ae296d1182371a156cbc5805...</td>\n",
              "      <td>ltc1qk8gvhhfj587m37lu4melpaakqx4p3dextzmtty</td>\n",
              "      <td>0.01666318</td>\n",
              "      <td>ltc1q43q5wrrhtvp60rqllz7fcy0tfy36xpq2fw42jm</td>\n",
              "      <td>0.10611481</td>\n",
              "      <td>Wed Jul  7 10:07:30 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16943</th>\n",
              "      <td>db8d527183eee2c8feb4c2ae296d1182371a156cbc5805...</td>\n",
              "      <td>ltc1qauelsfavem6hnw5u5ruexwdacm32v3h9cjz9s2</td>\n",
              "      <td>0.11294656</td>\n",
              "      <td>ltc1qglylyudhn9qyt9lnrzfh0nywm2lkqc63tyjuez</td>\n",
              "      <td>0.02349283</td>\n",
              "      <td>Wed Jul  7 10:07:30 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16944</th>\n",
              "      <td>596da855a0dc699ce1d933694037ddff162bd6bfc64360...</td>\n",
              "      <td>ltc1q06a8ypzlmt3gtssfm2a5rsfmjvlcrwfayaffjd</td>\n",
              "      <td>0.04100000</td>\n",
              "      <td>3HfrQb8612M7YzaJJQAaUKAbZnxTCNamEY</td>\n",
              "      <td>0.04099889</td>\n",
              "      <td>Wed Jul  7 10:07:30 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16945</th>\n",
              "      <td>61873cb32f84b54a5f2773b7d8bfffb5d483da0909ada6...</td>\n",
              "      <td>3N8XxWP77XiYFNsP6zJBxdZHvyWLybeF29</td>\n",
              "      <td>0.12022000</td>\n",
              "      <td>ltc1qdv4pkr6c6f96xka7wswp3c4hc9fl2splprlav7</td>\n",
              "      <td>0.10609518</td>\n",
              "      <td>Wed Jul  7 10:07:30 2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16946</th>\n",
              "      <td>61873cb32f84b54a5f2773b7d8bfffb5d483da0909ada6...</td>\n",
              "      <td>3N8XxWP77XiYFNsP6zJBxdZHvyWLybeF29</td>\n",
              "      <td>0.12022000</td>\n",
              "      <td>ltc1qnva5fgqrt0uu5mutqytahln0v2wdcqzqtlzfdq</td>\n",
              "      <td>0.01412318</td>\n",
              "      <td>Wed Jul  7 10:07:30 2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>16947 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          transaction_id  ...                      date\n",
              "0      c7b57b498aebfe0e106685428f457de793d87cbccdbe22...  ...  Wed Jul  7 09:31:24 2021\n",
              "1      1380fa7fd8433dbadafb321092becb3b47680d43adbcf2...  ...  Wed Jul  7 09:31:24 2021\n",
              "2      1380fa7fd8433dbadafb321092becb3b47680d43adbcf2...  ...  Wed Jul  7 09:31:24 2021\n",
              "3      ca6638fbf422a5914602067cb7d48dd8aff28fa2bf8afe...  ...  Wed Jul  7 09:31:24 2021\n",
              "4      ca6638fbf422a5914602067cb7d48dd8aff28fa2bf8afe...  ...  Wed Jul  7 09:31:24 2021\n",
              "...                                                  ...  ...                       ...\n",
              "16942  db8d527183eee2c8feb4c2ae296d1182371a156cbc5805...  ...  Wed Jul  7 10:07:30 2021\n",
              "16943  db8d527183eee2c8feb4c2ae296d1182371a156cbc5805...  ...  Wed Jul  7 10:07:30 2021\n",
              "16944  596da855a0dc699ce1d933694037ddff162bd6bfc64360...  ...  Wed Jul  7 10:07:30 2021\n",
              "16945  61873cb32f84b54a5f2773b7d8bfffb5d483da0909ada6...  ...  Wed Jul  7 10:07:30 2021\n",
              "16946  61873cb32f84b54a5f2773b7d8bfffb5d483da0909ada6...  ...  Wed Jul  7 10:07:30 2021\n",
              "\n",
              "[16947 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ke4GKZDj9TDl"
      },
      "source": [
        "# LITECOIN NETWORKX INDICATORS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-sY8RdMbEWh"
      },
      "source": [
        "#####################\n",
        "##### LITECOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "\n",
        "# creates tuple list\n",
        "d_lc = list(df_lc_nx.from_leg)\n",
        "e_lc = list(df_lc_nx.to)\n",
        "# tuple\n",
        "lst_tuple_lc = list(zip(d_lc,e_lc))\n",
        "\n",
        "# create an empty graph\n",
        "g_lc = nx.Graph()\n",
        "\n",
        "# adds edges\n",
        "g_lc.add_edges_from(lst_tuple_lc)\n",
        "\n",
        "# plots\n",
        "nx.draw_kamada_kawai(g_lc, with_labels=False)    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "id": "ZJnTGrFzbEWi",
        "outputId": "d1c04ca9-5edb-4740-ac9c-3ed8b3d72080"
      },
      "source": [
        "#####################\n",
        "##### LITECOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "### INIDICATORS I ###\n",
        "#####################\n",
        "\n",
        "# Get information on the graph level\n",
        "\n",
        "# Number of vertices\n",
        "g_lc.number_of_nodes()\n",
        "nodes_summary = nx.number_of_nodes(g_lc)\n",
        "\n",
        "# Number of edges\n",
        "g.number_of_edges()\n",
        "edges_summary = nx.number_of_edges(g_lc)\n",
        "\n",
        "# Connectedness\n",
        "  # A graph is connected if every node can be reached from any other node\n",
        "connection_summary = nx.is_connected(g_lc)\n",
        "\n",
        "# Density\n",
        "density_summary = nx.density(g_lc)\n",
        "\n",
        "# Transitivity\n",
        "  # relating the number of triangles with the number of triples in the graph\n",
        "transitivity_summary = nx.transitivity(g_lc)\n",
        "\n",
        "# Clustering coefficient\n",
        "clustering_summary = nx.average_clustering(g_lc)\n",
        "\n",
        "# Average degree\n",
        "  # average of all vertex degrees in the graph\n",
        "avgdegree_summary = np.mean([i[1] for i in g_lc.degree()])\n",
        "\n",
        "# summary table\n",
        "summary_lc = pd.DataFrame({'Number of vertices': nodes_summary, \n",
        "                        'Number of edges': edges_summary, \n",
        "                        'Connectedness': connection_summary, \n",
        "                        'Density': density_summary, \n",
        "                        'Transitivity': transitivity_summary, \n",
        "                        'Clustering': clustering_summary,\n",
        "                        'Average degree': avgdegree_summary}, index=[0])\n",
        "summary_lc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Number of vertices</th>\n",
              "      <th>Number of edges</th>\n",
              "      <th>Connectedness</th>\n",
              "      <th>Density</th>\n",
              "      <th>Transitivity</th>\n",
              "      <th>Clustering</th>\n",
              "      <th>Average degree</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14186</td>\n",
              "      <td>13992</td>\n",
              "      <td>False</td>\n",
              "      <td>0.000139</td>\n",
              "      <td>0.000028</td>\n",
              "      <td>0.003058</td>\n",
              "      <td>1.972649</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Number of vertices  Number of edges  ...  Clustering  Average degree\n",
              "0               14186            13992  ...    0.003058        1.972649\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "jELjYorTbEWj"
      },
      "source": [
        "#####################\n",
        "##### LITECOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "## INIDICATORS II ###\n",
        "#####################\n",
        "\n",
        "# Degree distribution\n",
        "  #  degree of a node in a network is the number of connections it has to other \n",
        "  # nodes and the degree distribution is the probability distribution of these \n",
        "  # degrees over the whole network.\n",
        "degs = [i[1] for i in g_lc.degree()]\n",
        "fig, ax = plt.subplots(figsize=(12, 9))\n",
        "ax.spines[\"top\"].set_visible(False) # Remove plot frame line on the top \n",
        "ax.spines[\"right\"].set_visible(False) # Remove plot frame line on the right\n",
        "ax.get_xaxis().tick_bottom()  # Remove ticks on the bottom\n",
        "ax.get_yaxis().tick_left()  # Remove the ticks on the left\n",
        "ax.hist(degs, color=\"#75846b\", bins='auto');\n",
        "\n",
        "plt.savefig('degree_dist_lc.png', dpi = 300, transparent=True)\n",
        "  # only very few vertices with high degrees, and many with relatively small degrees \n",
        "  # -- a typical results for social networks. In such cases, we say that the degree \n",
        "  # distribution is heavy tailed."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "yWkGZt8FbEWk"
      },
      "source": [
        "#####################\n",
        "##### LITECOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "## INIDICATORS III ##\n",
        "#####################\n",
        "\n",
        "# Betweeness Centrality\n",
        "  # a vertex is important when it connects two large communities, which would \n",
        "  # remain unconnected if the vertex was not there.\n",
        "  \n",
        "between_centralities = nx.betweenness_centrality(g_lc).values()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 9))\n",
        "ax.spines[\"top\"].set_visible(False) # Remove plot frame line on the top \n",
        "ax.spines[\"right\"].set_visible(False) # Remove plot frame line on the right\n",
        "ax.get_xaxis().tick_bottom()  # Remove ticks on the bottom\n",
        "ax.get_yaxis().tick_left()  # Remove the ticks on the left\n",
        "ax.hist(between_centralities, color=\"#75846b\", bins='auto');\n",
        "\n",
        "plt.savefig('betweeness_lc.png', dpi = 300, transparent=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "bd-hPLeFbEWk"
      },
      "source": [
        "#####################\n",
        "##### LITECOIN ######\n",
        "#####################\n",
        "##### NETWORKX ######\n",
        "#####################\n",
        "## INIDICATORS IV ###\n",
        "#####################\n",
        "\n",
        "# Get information on the vertex level\n",
        "\n",
        "# Neighborhood\n",
        "  # neighborhood of vertex  vi  is the set of all vertices that are adjacent to  vi\n",
        "nx.all_neighbors(g_lc, \"LPFRDzav62oG91b5mHKj9k3g4tjtn5Vjtp\")\n",
        "\n",
        "# Connectedness\n",
        "  # Two vertices are connected of there is a path between then\n",
        "nx.has_path(g_lc, \"LPFRDzav62oG91b5mHKj9k3g4tjtn5Vjtp\", \"coinbase\")\n",
        "\n",
        "# Clustering coefficient\n",
        "  # The clustering coefficient of a single vertex informs us about how well the \n",
        "  # neighbors of the vertex are themselves connected. The maximum amount of \n",
        "  # clustering is achieved if all neighbors of the vertex are neighbores as well\n",
        "nx.clustering(g_lc, \"LPFRDzav62oG91b5mHKj9k3g4tjtn5Vjtp\")\n",
        "\n",
        "# Degree \n",
        "  # The degree of  vi  is just the number of adjacent vertices.\n",
        "g_lc.degree(\"LPFRDzav62oG91b5mHKj9k3g4tjtn5Vjtp\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dn_wyvfubEWm"
      },
      "source": [
        "# BITCOIN EXTRACTION - TEMPORAL COMMUNITY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hubp7CLoygb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "e455070e-cc9e-4f31-ec5a-6b8cb0ed416e"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "#### EXTRACTION #####\n",
        "#####################\n",
        "#### COMM FORMAT ####\n",
        "#####################\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        " \n",
        " \n",
        "# 682950 - 683238: 11/05/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(683093, 683700))\n",
        " \n",
        "r1 = 0\n",
        "r2 = 10\n",
        "size_block = range(r1, r2)\n",
        " \n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://chain.so/api/v2/get_block/BTC/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        " \n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly                                                                                                                   \n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "      \n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        " \n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://chain.so/api/v2/tx/BTC/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        " \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        " \n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        " \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        " \n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        " \n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_bc_cm_01 = pd.DataFrame({'transaction_id': transaction_id, 'from_leg': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_bc_cm = df_bc_cm_01[df_bc_cm_01.to != \"nulldata\"]\n",
        "df_bc_cm.reset_index(drop=True, inplace=True)\n",
        " \n",
        " # fill NaN with previous data for all NaN, None\n",
        "df_bc_cm.date = df_bc_cm.date.fillna(method = 'ffill')\n",
        "df_bc_cm.from_leg = df_bc_cm.from_leg.fillna(method = 'ffill')\n",
        "df_bc_cm.transaction_id = df_bc_cm.transaction_id.fillna(method = 'ffill')\n",
        "df_bc_cm.tt_value = df_bc_cm.tt_value.fillna(method = 'ffill')\n",
        "df_bc_cm = df_bc_cm.fillna(value=np.nan)\n",
        "\n",
        "\n",
        "# rename columns to keep\n",
        "df_bc_cm = df_bc_cm.rename(columns = {'from_leg': 'from_id'}, inplace = False)\n",
        "df_bc_cm = df_bc_cm.rename(columns = {'to': 'to_id'}, inplace = False)\n",
        "df_bc_cm = df_bc_cm.rename(columns = {'date': 'timestamps'}, inplace = False)\n",
        "\n",
        "# drop unecessary columns\n",
        "df_bc_cm = df_bc_cm.drop(columns = 'transaction_id')\n",
        "df_bc_cm = df_bc_cm.drop(columns = 'tt_value')\n",
        "df_bc_cm = df_bc_cm.drop(columns = 'amount')\n",
        "df_bc_cm = df_bc_cm.dropna()\n",
        "\n",
        "\n",
        "# dictionary for convertion to int\n",
        "\n",
        "# to_id\n",
        "from_id_no_dups_bc = set(df_bc_cm.from_id) \n",
        "from_id_no_dups_bc.update(set(df_bc_cm.to_id))\n",
        "no_dupes_bc = list(from_id_no_dups_bc)\n",
        "k1 = 0 #range\n",
        "k2 = len(no_dupes_bc) #range\n",
        "key_bc = list(range(k1, k2)) # creates key id\n",
        "df_bc_to = pd.DataFrame({'to_id': no_dupes_bc, 'factor_to': key_bc}) # as dataframe\n",
        "\n",
        "# from_id\n",
        "from_id_no_dupes_bc = set(df_bc_cm.from_id) \n",
        "from_id_no_dupes_bc.update(set(df_bc_cm.to_id))\n",
        "no_dupes_bc = list(from_id_no_dupes_bc)\n",
        "k1 = 0 #range\n",
        "k2 = len(no_dupes_bc) #range\n",
        "key_bc = list(range(k1, k2)) # creates key id\n",
        "df_bc_from = pd.DataFrame({'from_id': no_dupes_bc, 'factor_to': key_bc}) # as dataframe\n",
        "\n",
        "# merges dfs\n",
        "f1_bc_df = pd.merge(df_bc_cm, df_bc_from, on = 'from_id', how ='left')\n",
        "full_bc_df = pd.merge(f1_bc_df, df_bc_to, on = 'to_id', how ='left')\n",
        "\n",
        "# drop columns and rename to keep\n",
        "full_bc_df = full_bc_df.drop(columns= 'from_id')\n",
        "full_bc_df = full_bc_df.drop(columns= 'to_id')\n",
        "full_bc_df = full_bc_df.rename(columns = {'factor_to_x': 'from_id'}, inplace = False)\n",
        "full_bc_df = full_bc_df.rename(columns = {'factor_to_y': 'to_id'}, inplace = False)\n",
        "\n",
        "# factorise timestamps\n",
        "full_bc_df['timestamps'] = pd.factorize(full_bc_df.timestamps)[0]\n",
        "\n",
        "# as int\n",
        "full_bc_df = full_bc_df.astype(int)\n",
        "\n",
        "# re order\n",
        "full_bc_df = full_bc_df[['from_id','to_id','timestamps']]\n",
        "\n",
        "# save\n",
        "full_bc_df.to_csv(\"full_bc_df.csv\", sep='\\t', header=False)  \n",
        "full_bc_df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5170: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>from_id</th>\n",
              "      <th>to_id</th>\n",
              "      <th>timestamps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>107</td>\n",
              "      <td>230</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>67</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>67</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>135</td>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>119</td>\n",
              "      <td>131</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>119</td>\n",
              "      <td>163</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>122</td>\n",
              "      <td>63</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>122</td>\n",
              "      <td>212</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>218 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     from_id  to_id  timestamps\n",
              "0        107    230           0\n",
              "1         67    141           0\n",
              "2         67     24           0\n",
              "3         67     17           0\n",
              "4         67     99           0\n",
              "..       ...    ...         ...\n",
              "213      135     70           9\n",
              "214      119    131           9\n",
              "215      119    163           9\n",
              "216      122     63           9\n",
              "217      122    212           9\n",
              "\n",
              "[218 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ss2UivLLA-Hr"
      },
      "source": [
        "# BITCOIN STABLE COMMUNITY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3jw4bbH4Ly9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80805ac3-33b5-43dc-fd4f-68d58bb6c08f"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "#### STABLE COM #####\n",
        "#####################\n",
        "\n",
        "# mounts drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BtILbNi5iCx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e872ccb0-23f1-46a3-ce50-11a4f63d3bae"
      },
      "source": [
        "# https://github.com/VeryLargeGraph/TSCAN\n",
        "# runs run.py - TSCAN\n",
        "!python3 \"/content/drive/My Drive/Colab Notebooks/run.py\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset name(str): full_bc_df.csv\n",
            "Epsilon(float): 0.5\n",
            "Tau(int): 1\n",
            "Miu(int): 1\n",
            "Type one number to chose the algorithm: [1]TSCANB; [2]TSACNS; [3]TSCANA. (int): 1\n",
            "227\n",
            "218\n",
            "temporal edges:218.0\n",
            "Runing time of SCANB:0.02752\n",
            "Cores output at: full_bc_df.csv.output-0.5-1-1_SCANB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "6IwXUnLSWQiK",
        "outputId": "7eb26223-4b3d-46fa-edb0-3fde4a92566c"
      },
      "source": [
        "# prints stable communities - STATIC ONES\n",
        "stable_bc_01 = pd.DataFrame(data=open('/content/full_bc_df_output_05_1_1_SCANB'))\n",
        "stable_bc_01"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[61, 237, 6, 7, 8, 9, 201, 202, 203, 62, 159, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[129, 10, 11, 231, 206, 207, 130]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[122, 22, 23, 107, 0, 24, 42, 63, 89, 19, 111,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[68, 229, 208, 209, 69, 102]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[29, 228, 77, 78, 79, 80, 81, 82, 194, 195, 19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>[41, 171, 138, 90, 91, 158, 47, 48, 46, 12, 13...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>[152, 103, 239, 153]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>[115, 104, 14, 114, 57, 58, 15, 238, 149, 150]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>[105, 240]\\n</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   0\n",
              "0  [61, 237, 6, 7, 8, 9, 201, 202, 203, 62, 159, ...\n",
              "1                [129, 10, 11, 231, 206, 207, 130]\\n\n",
              "2  [122, 22, 23, 107, 0, 24, 42, 63, 89, 19, 111,...\n",
              "3                     [68, 229, 208, 209, 69, 102]\\n\n",
              "4  [29, 228, 77, 78, 79, 80, 81, 82, 194, 195, 19...\n",
              "5  [41, 171, 138, 90, 91, 158, 47, 48, 46, 12, 13...\n",
              "6                             [152, 103, 239, 153]\\n\n",
              "7   [115, 104, 14, 114, 57, 58, 15, 238, 149, 150]\\n\n",
              "8                                       [105, 240]\\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "ttBgD4KWXMB5",
        "outputId": "695a26e1-99b0-4c02-f65e-cefba77c7a41"
      },
      "source": [
        "# Represent Stable Community\n",
        "\n",
        "import networkx as nx\n",
        "# importing matplotlib.pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "  \n",
        "g = nx.Graph()\n",
        "\n",
        "# [46, 1086, 1600, 1601, 47]\\n \n",
        "# [2257, 2530, 2531, 2551, 2553]\\n\n",
        "#[68, 229, 208, 209, 69, 102]\\n\n",
        "g.add_node(68)\n",
        "g.add_node(229)\n",
        "g.add_node(208)\n",
        "g.add_node(209)\n",
        "g.add_node(69)\n",
        "g.add_node(102)\n",
        "\n",
        "g.add_edge(68, 229)\n",
        "g.add_edge(68, 208)\n",
        "g.add_edge(68, 209)\n",
        "g.add_edge(68, 69)\n",
        "g.add_edge(68, 102)\n",
        "\n",
        "g.add_edge(229, 68)\n",
        "g.add_edge(229, 208)\n",
        "g.add_edge(229, 209)\n",
        "g.add_edge(229, 69)\n",
        "g.add_edge(229, 102)\n",
        "\n",
        "\n",
        "g.add_edge(208, 68)\n",
        "g.add_edge(208, 229)\n",
        "g.add_edge(208, 209)\n",
        "g.add_edge(208, 69)\n",
        "g.add_edge(208, 102)\n",
        "\n",
        "g.add_edge(209, 68)\n",
        "g.add_edge(209, 229)\n",
        "g.add_edge(209, 208)\n",
        "g.add_edge(209, 69)\n",
        "g.add_edge(209, 102)\n",
        "\n",
        "g.add_edge(69, 68)\n",
        "g.add_edge(69, 229)\n",
        "g.add_edge(69, 208)\n",
        "g.add_edge(69, 209)\n",
        "g.add_edge(69, 102)\n",
        "\n",
        "# drawing in spring layout\n",
        "nx.draw_spring(g, with_labels = True, node_color=\"#75846b\")\n",
        "plt.savefig(\"stable_bc.png\", transparent = True, dpi = 300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLUyYuji8SbC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdBH4dbTBSUz"
      },
      "source": [
        "# BITCOIN TEMPORAL LOUVAIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zWsvbtNkj6L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0f35ff18-71e0-4fd6-e21d-ad40604e2fed"
      },
      "source": [
        "#####################\n",
        "###### BITCOIN ######\n",
        "#####################\n",
        "#### TEMP MEAS ######\n",
        "#####################\n",
        "\n",
        "# imports teneto\n",
        "!pip install teneto\n",
        "import teneto\n",
        "from teneto import TemporalNetwork\n",
        "\n",
        "# define tnet\n",
        "df_bc_tnet = full_bc_df\n",
        "\n",
        "# rename columns to keep\n",
        "df_bc_tnet = df_bc_tnet.rename(columns = {'from_id': 'i'}, inplace = False)\n",
        "df_bc_tnet = df_bc_tnet.rename(columns = {'to_id': 'j'}, inplace = False)\n",
        "df_bc_tnet = df_bc_tnet.rename(columns = {'timestamps': 't'}, inplace = False)\n",
        "\n",
        "tnet_bc = TemporalNetwork(from_df=df_bc_tnet)\n",
        "tnet_bc.network\n",
        "\n",
        "# temporal louvain\n",
        "temporal_louv_bc = teneto.communitydetection.temporal_louvain(tnet_bc)\n",
        "\n",
        "# as dataframe\n",
        "temp_louv_bc = pd.DataFrame(temporal_louv_bc)\n",
        "temp_louv_bc.to_csv(\"temp_louv_dc.csv\")\n",
        "temp_louv_bc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: teneto in /usr/local/lib/python3.7/dist-packages (0.5.3)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (2.5.1)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.19.5)\n",
            "Requirement already satisfied: statsmodels>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.10.2)\n",
            "Requirement already satisfied: pandas>=0.21 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.1.5)\n",
            "Requirement already satisfied: templateflow>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.7.1)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.4.1)\n",
            "Requirement already satisfied: nilearn>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.8.0)\n",
            "Requirement already satisfied: pybids>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.13.1)\n",
            "Requirement already satisfied: python-louvain>=0.13 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.15)\n",
            "Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->teneto) (4.4.2)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.8.0->teneto) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->teneto) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->teneto) (2018.9)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from templateflow>=0.6.2->teneto) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from templateflow>=0.6.2->teneto) (4.41.1)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.7/dist-packages (from nilearn>=0.6.0->teneto) (1.0.1)\n",
            "Requirement already satisfied: nibabel>=2.5 in /usr/local/lib/python3.7/dist-packages (from nilearn>=0.6.0->teneto) (3.0.2)\n",
            "Requirement already satisfied: scikit-learn>=0.21 in /usr/local/lib/python3.7/dist-packages (from nilearn>=0.6.0->teneto) (0.22.2.post1)\n",
            "Requirement already satisfied: num2words in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (0.5.10)\n",
            "Requirement already satisfied: sqlalchemy<1.4.0.dev0 in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (1.3.24)\n",
            "Requirement already satisfied: bids-validator in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (1.7.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.4.0->statsmodels>=0.8.0->teneto) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (2.10)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from num2words->pybids>=0.11.1->teneto) (0.6.2)\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "         i     j  weight\n",
            "0        0     3       1\n",
            "1      252   255       1\n",
            "2      504   507       1\n",
            "3      756   759       1\n",
            "4     1008  1011       1\n",
            "...    ...   ...     ...\n",
            "6425  2263  2515       0\n",
            "6426  2264  2516       0\n",
            "6427  2265  2517       0\n",
            "6428  2266  2518       0\n",
            "6429  2267  2519       0\n",
            "\n",
            "[6430 rows x 3 columns]\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "         i     j  weight\n",
            "0        0     3       1\n",
            "1      252   255       1\n",
            "2      504   507       1\n",
            "3      756   759       1\n",
            "4     1008  1011       1\n",
            "...    ...   ...     ...\n",
            "6425  2263  2515       0\n",
            "6426  2264  2516       0\n",
            "6427  2265  2517       0\n",
            "6428  2266  2518       0\n",
            "6429  2267  2519       0\n",
            "\n",
            "[6430 rows x 3 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        0     1     2     3     4     5     6     7     8     9\n",
              "0     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "1     5.0   5.0   5.0   5.0   5.0   5.0   5.0   5.0   5.0   5.0\n",
              "2     9.0   9.0   9.0   9.0   9.0  32.0  32.0  32.0  15.0  15.0\n",
              "3     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "4    14.0  14.0  14.0  14.0  14.0  14.0  14.0  14.0  14.0  14.0\n",
              "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...\n",
              "247  66.0  66.0  66.0  66.0  66.0  66.0  66.0  66.0  66.0  66.0\n",
              "248  35.0  35.0  35.0  35.0  35.0  35.0  35.0  35.0  35.0  35.0\n",
              "249   4.0   4.0   4.0   4.0   4.0   4.0   4.0   4.0   4.0   4.0\n",
              "250  18.0  18.0  18.0  18.0  18.0  18.0  18.0  18.0  18.0  18.0\n",
              "251  13.0  13.0  13.0  13.0  13.0  13.0  13.0  13.0  13.0  13.0\n",
              "\n",
              "[252 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEZ3p5ieZsjj",
        "outputId": "4e3dda40-8b8e-4c2e-ba35-b2c5bdd8ed6b"
      },
      "source": [
        "# score for stable community\n",
        "temp_louv_bc['same_0'] = np.where( temp_louv_bc[0] == temp_louv_bc[1], 1, 0)\n",
        "temp_louv_bc['same_1'] = np.where( temp_louv_bc[1] == temp_louv_bc[2], 1, 0)\n",
        "temp_louv_bc['same_2'] = np.where( temp_louv_bc[2] == temp_louv_bc[3], 1, 0)\n",
        "temp_louv_bc['same_3'] = np.where( temp_louv_bc[3] == temp_louv_bc[4], 1, 0)\n",
        "temp_louv_bc['same_4'] = np.where( temp_louv_bc[4] == temp_louv_bc[5], 1, 0)\n",
        "temp_louv_bc['same_5'] = np.where( temp_louv_bc[5] == temp_louv_bc[6], 1, 0)\n",
        "temp_louv_bc['same_6'] = np.where( temp_louv_bc[6] == temp_louv_bc[7], 1, 0)\n",
        "temp_louv_bc['same_7'] = np.where( temp_louv_bc[7] == temp_louv_bc[8], 1, 0)\n",
        "temp_louv_bc['same_8'] = np.where( temp_louv_bc[8] == temp_louv_bc[9], 1, 0)\n",
        "temp_louv_bc['score']=temp_louv_bc['same_0']+temp_louv_bc['same_1']+temp_louv_bc['same_2']+temp_louv_bc['same_3']+temp_louv_bc['same_4']+temp_louv_bc['same_5']+temp_louv_bc['same_6']+temp_louv_bc['same_7']+temp_louv_bc['same_8']\n",
        "temp_louv_bc['stable'] = np.where( temp_louv_bc['score'] == 9, 1, 0)\n",
        "\n",
        "# as int\n",
        "temp_louv_bc = temp_louv_bc.astype(int)\n",
        "summary_temp_louv_bc = temp_louv_bc\n",
        "\n",
        "# summarizes df\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_0')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_1')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_2')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_3')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_4')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_5')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_6')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_7')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same_8')\n",
        "summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'score')\n",
        "# summary_temp_louv_bc = summary_temp_louv_bc.drop(columns = 'same')\n",
        "\n",
        "# number of stable communities\n",
        "summary_temp_louv_bc\n",
        "summary_temp_louv_bc.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         71\n",
              "1         71\n",
              "2         71\n",
              "3         71\n",
              "4         71\n",
              "5         71\n",
              "6         71\n",
              "7         71\n",
              "8         71\n",
              "9         71\n",
              "stable     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TN5t__qBKQT6"
      },
      "source": [
        "# DOGECOIN EXTRACTION - TEMPORAL COMMUNITY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "-mGcFSRPKQT9",
        "outputId": "4a13c002-9ffd-4481-f0be-4bf1b3ded02c"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "#### EXTRACTION #####\n",
        "#####################\n",
        "#### COMM FORMAT ####\n",
        "#####################\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        " \n",
        " \n",
        "# 3802140 - 3802147: 07/07/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(3802140, 3803147)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 10\n",
        "size_block = range(r1, r2)\n",
        "\n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://chain.so/api/v2/get_block/DOGE/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        " \n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly                                                                                                                   \n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "      \n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        " \n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://chain.so/api/v2/tx/DOGE/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        " \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        " \n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        " \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        " \n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        " \n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_dc_cm_01 = pd.DataFrame({'transaction_id': transaction_id, 'from_leg': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_dc_cm = df_dc_cm_01[df_dc_cm_01.to != \"nulldata\"]\n",
        "df_dc_cm.reset_index(drop=True, inplace=True)\n",
        " \n",
        " # fill NaN with previous data for all NaN, None\n",
        "df_dc_cm.date = df_dc_cm.date.fillna(method = 'ffill')\n",
        "df_dc_cm.from_leg = df_dc_cm.from_leg.fillna(method = 'ffill')\n",
        "df_dc_cm.transaction_id = df_dc_cm.transaction_id.fillna(method = 'ffill')\n",
        "df_dc_cm.tt_value = df_dc_cm.tt_value.fillna(method = 'ffill')\n",
        "df_dc_cm = df_dc_cm.fillna(value=np.nan)\n",
        "\n",
        "\n",
        "# rename columns to keep\n",
        "df_dc_cm = df_dc_cm.rename(columns = {'from_leg': 'from_id'}, inplace = False)\n",
        "df_dc_cm = df_dc_cm.rename(columns = {'to': 'to_id'}, inplace = False)\n",
        "df_dc_cm = df_dc_cm.rename(columns = {'date': 'timestamps'}, inplace = False)\n",
        "\n",
        "# drop unecessary columns\n",
        "df_dc_cm = df_dc_cm.drop(columns = 'transaction_id')\n",
        "df_dc_cm = df_dc_cm.drop(columns = 'tt_value')\n",
        "df_dc_cm = df_dc_cm.drop(columns = 'amount')\n",
        "df_dc_cm = df_dc_cm.dropna()\n",
        "\n",
        "\n",
        "# dictionary for convertion to int\n",
        "\n",
        "# to_id\n",
        "from_id_no_dups_dc = set(df_dc_cm.from_id) \n",
        "from_id_no_dups_dc.update(set(df_dc_cm.to_id))\n",
        "no_dupes_dc = list(from_id_no_dups_dc)\n",
        "k1 = 0 #range\n",
        "k2 = len(no_dupes_dc) #range\n",
        "key_dc = list(range(k1, k2)) # creates key id\n",
        "df_dc_to = pd.DataFrame({'to_id': no_dupes_dc, 'factor_to': key_dc}) # as dataframe\n",
        "\n",
        "# from_id\n",
        "from_id_no_dups_dc = set(df_dc_cm.from_id) \n",
        "from_id_no_dups_dc.update(set(df_dc_cm.to_id))\n",
        "no_dupes_dc = list(from_id_no_dups_dc)\n",
        "k1 = 0 #range\n",
        "k2 = len(no_dupes_dc) #range\n",
        "key_dc = list(range(k1, k2)) # creates key id\n",
        "df_dc_from = pd.DataFrame({'from_id': no_dupes_dc, 'factor_to': key_dc}) # as dataframe\n",
        "\n",
        "# merges dfs\n",
        "f1_dc_df = pd.merge(df_dc_cm, df_dc_from, on = 'from_id', how ='left')\n",
        "full_dc_df = pd.merge(f1_dc_df, df_dc_to, on = 'to_id', how ='left')\n",
        "\n",
        "# drop columns and rename to keep\n",
        "full_dc_df = full_dc_df.drop(columns= 'from_id')\n",
        "full_dc_df = full_dc_df.drop(columns= 'to_id')\n",
        "full_dc_df = full_dc_df.rename(columns = {'factor_to_x': 'from_id'}, inplace = False)\n",
        "full_dc_df = full_dc_df.rename(columns = {'factor_to_y': 'to_id'}, inplace = False)\n",
        "\n",
        "# factorise timestamps\n",
        "full_dc_df['timestamps'] = pd.factorize(full_dc_df.timestamps)[0]\n",
        "\n",
        "# as int\n",
        "full_dc_df = full_dc_df.astype(int)\n",
        "\n",
        "# re order\n",
        "full_dc_df = full_dc_df[['from_id','to_id','timestamps']]\n",
        "\n",
        "# save\n",
        "full_dc_df.to_csv(\"full_dc_df.csv\", sep='\\t', header=False)  \n",
        "full_dc_df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>from_id</th>\n",
              "      <th>to_id</th>\n",
              "      <th>timestamps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>345</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>363</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>363</td>\n",
              "      <td>363</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>198</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>432</th>\n",
              "      <td>330</td>\n",
              "      <td>488</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>433</th>\n",
              "      <td>330</td>\n",
              "      <td>330</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>434</th>\n",
              "      <td>418</td>\n",
              "      <td>179</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>418</td>\n",
              "      <td>418</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>436</th>\n",
              "      <td>345</td>\n",
              "      <td>50</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>437 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     from_id  to_id  timestamps\n",
              "0        345    298           0\n",
              "1        363     70           0\n",
              "2        363    363           0\n",
              "3        198    130           0\n",
              "4        198    198           0\n",
              "..       ...    ...         ...\n",
              "432      330    488           8\n",
              "433      330    330           8\n",
              "434      418    179           8\n",
              "435      418    418           8\n",
              "436      345     50           9\n",
              "\n",
              "[437 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2SWNCsmKQUG"
      },
      "source": [
        "# DOGECOIN TEMPORAL LOUVAIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5R2QdLsHKQUH",
        "outputId": "3dfb2b28-85ee-4f0d-f26f-830d3ff00e2d"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "#### TEMP MEAS ######\n",
        "#####################\n",
        "\n",
        "# imports teneto\n",
        "!pip install teneto\n",
        "import teneto\n",
        "from teneto import TemporalNetwork\n",
        "\n",
        "# define tnet\n",
        "df_dc_tnet = full_dc_df\n",
        "\n",
        "# rename columns to keep\n",
        "df_dc_tnet = df_dc_tnet.rename(columns = {'from_id': 'i'}, inplace = False)\n",
        "df_dc_tnet = df_dc_tnet.rename(columns = {'to_id': 'j'}, inplace = False)\n",
        "df_dc_tnet = df_dc_tnet.rename(columns = {'timestamps': 't'}, inplace = False)\n",
        "\n",
        "tnet_dc = TemporalNetwork(from_df=df_dc_tnet)\n",
        "tnet_dc.network\n",
        "\n",
        "# temporal louvain\n",
        "temporal_louv_dc = teneto.communitydetection.temporal_louvain(tnet_dc)\n",
        "\n",
        "# as dataframe\n",
        "temp_louv_dc = pd.DataFrame(temporal_louv_dc)\n",
        "temp_louv_dc.to_csv(\"temp_louv_dc.csv\")\n",
        "temp_louv_dc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: teneto in /usr/local/lib/python3.7/dist-packages (0.5.3)\n",
            "Requirement already satisfied: pandas>=0.21 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.1.5)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (2.5.1)\n",
            "Requirement already satisfied: python-louvain>=0.13 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.15)\n",
            "Requirement already satisfied: statsmodels>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.10.2)\n",
            "Requirement already satisfied: pybids>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.13.1)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.19.5)\n",
            "Requirement already satisfied: templateflow>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.7.1)\n",
            "Requirement already satisfied: nilearn>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.8.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->teneto) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->teneto) (2018.9)\n",
            "Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->teneto) (4.4.2)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.8.0->teneto) (0.5.1)\n",
            "Requirement already satisfied: bids-validator in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (1.7.2)\n",
            "Requirement already satisfied: sqlalchemy<1.4.0.dev0 in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (1.3.24)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (7.1.2)\n",
            "Requirement already satisfied: num2words in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (0.5.10)\n",
            "Requirement already satisfied: nibabel>=2.1 in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (3.0.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from templateflow>=0.6.2->teneto) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from templateflow>=0.6.2->teneto) (2.23.0)\n",
            "Requirement already satisfied: scikit-learn>=0.21 in /usr/local/lib/python3.7/dist-packages (from nilearn>=0.6.0->teneto) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.7/dist-packages (from nilearn>=0.6.0->teneto) (1.0.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.21->teneto) (1.15.0)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from num2words->pybids>=0.11.1->teneto) (0.6.2)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (3.0.4)\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          i     j  weight\n",
            "0         0    25       1\n",
            "1       503   528       1\n",
            "2      1006  1031       1\n",
            "3      1509  1534       1\n",
            "4      2012  2037       1\n",
            "...     ...   ...     ...\n",
            "13761  4522  5025       0\n",
            "13762  4523  5026       0\n",
            "13763  4524  5027       0\n",
            "13764  4525  5028       0\n",
            "13765  4526  5029       0\n",
            "\n",
            "[13766 rows x 3 columns]\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          i     j  weight\n",
            "0         0    25       1\n",
            "1       503   528       1\n",
            "2      1006  1031       1\n",
            "3      1509  1534       1\n",
            "4      2012  2037       1\n",
            "...     ...   ...     ...\n",
            "14053  4522  5025       0\n",
            "14054  4523  5026       0\n",
            "14055  4524  5027       0\n",
            "14056  4525  5028       0\n",
            "14057  4526  5029       0\n",
            "\n",
            "[14058 rows x 3 columns]\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "          i     j  weight\n",
            "0         0    25       1\n",
            "1       503   528       1\n",
            "2      1006  1031       1\n",
            "3      1509  1534       1\n",
            "4      2012  2037       1\n",
            "...     ...   ...     ...\n",
            "14053  4522  5025       0\n",
            "14054  4523  5026       0\n",
            "14055  4524  5027       0\n",
            "14056  4525  5028       0\n",
            "14057  4526  5029       0\n",
            "\n",
            "[14058 rows x 3 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  5.,   5.,   5., ...,   5.,   5.,   5.],\n",
              "       [ 11.,  11.,  11., ...,  11.,  11.,  11.],\n",
              "       [ 21.,  21.,  21., ...,  21.,  21.,  21.],\n",
              "       ...,\n",
              "       [ 33.,  33.,  33., ...,  33.,  33.,  33.],\n",
              "       [135., 135., 135., ..., 135., 135., 135.],\n",
              "       [ 86.,  86.,  86., ...,  86.,  86.,  86.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQyjNXblW8sV",
        "outputId": "4a25d4d0-1f7c-49b5-9a76-4e202307ed87"
      },
      "source": [
        "# score for stable community\n",
        "temp_louv_dc['same_0'] = np.where( temp_louv_dc[0] == temp_louv_dc[1], 1, 0)\n",
        "temp_louv_dc['same_1'] = np.where( temp_louv_dc[1] == temp_louv_dc[2], 1, 0)\n",
        "temp_louv_dc['same_2'] = np.where( temp_louv_dc[2] == temp_louv_dc[3], 1, 0)\n",
        "temp_louv_dc['same_3'] = np.where( temp_louv_dc[3] == temp_louv_dc[4], 1, 0)\n",
        "temp_louv_dc['same_4'] = np.where( temp_louv_dc[4] == temp_louv_dc[5], 1, 0)\n",
        "temp_louv_dc['same_5'] = np.where( temp_louv_dc[5] == temp_louv_dc[6], 1, 0)\n",
        "temp_louv_dc['same_6'] = np.where( temp_louv_dc[6] == temp_louv_dc[7], 1, 0)\n",
        "temp_louv_dc['same_7'] = np.where( temp_louv_dc[7] == temp_louv_dc[8], 1, 0)\n",
        "temp_louv_dc['same_8'] = np.where( temp_louv_dc[8] == temp_louv_dc[9], 1, 0)\n",
        "temp_louv_dc['score']=temp_louv_dc['same_0']+temp_louv_dc['same_1']+temp_louv_dc['same_2']+temp_louv_dc['same_3']+temp_louv_dc['same_4']+temp_louv_dc['same_5']+temp_louv_dc['same_6']+temp_louv_dc['same_7']+temp_louv_dc['same_8']\n",
        "temp_louv_dc['stable'] = np.where( temp_louv_dc['score'] == 9, 1, 0)\n",
        "\n",
        "# as int\n",
        "temp_louv_dc = temp_louv_dc.astype(int)\n",
        "summary_temp_louv_dc = temp_louv_dc\n",
        "\n",
        "# summarizes df\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_0')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_1')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_2')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_3')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_4')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_5')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_6')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_7')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same_8')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'score')\n",
        "summary_temp_louv_dc = summary_temp_louv_dc.drop(columns = 'same')\n",
        "\n",
        "# number of stable communities\n",
        "summary_temp_louv_dc\n",
        "summary_temp_louv_dc.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         160\n",
              "1         160\n",
              "2         160\n",
              "3         160\n",
              "4         160\n",
              "5         160\n",
              "6         160\n",
              "7         160\n",
              "8         160\n",
              "9         160\n",
              "stable      1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3HhbM2efxafo"
      },
      "source": [
        "# DOGECOIN STABLE COMMUNITY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1g9SduVKQUC",
        "outputId": "2e0c6771-8dab-4799-aeed-8069dd158646"
      },
      "source": [
        "#####################\n",
        "###### DOGECOIN #####\n",
        "#####################\n",
        "#### STABLE COM #####\n",
        "#####################\n",
        "\n",
        "# mounts drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19N5tTXAKQUE",
        "outputId": "928a9970-b1d7-4744-9162-ee34698b2951"
      },
      "source": [
        "# https://github.com/VeryLargeGraph/TSCAN\n",
        "# runs run.py - TSCAN\n",
        "!python3 \"/content/drive/My Drive/Colab Notebooks/run.py\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset name(str): full_dc_df.csv\n",
            "Epsilon(float): 0.5\n",
            "Tau(int): 1\n",
            "Miu(int): 2\n",
            "Type one number to chose the algorithm: [1]TSCANB; [2]TSACNS; [3]TSCANA. (int): 1\n",
            "2108\n",
            "2089\n",
            "temporal edges:2089.0\n",
            "Runing time of SCANB:0.03415\n",
            "Cores output at: full_dc_df.csv.output-0.5-1-2_SCANB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "IzUSRyP8aOlY",
        "outputId": "e638cf4e-a68e-4020-a3c4-d2309a4e09d5"
      },
      "source": [
        "# prints stable communities - STATIC ONES\n",
        "stable_dc_01 = pd.DataFrame(data=open('/content/full_dc_df_output_05_1_1_SCANB'))\n",
        "stable_dc_01 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[2141, 199, 200, 201, 202, 203, 204, 205, 1956...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[1160, 21, 1883, 1884, 1885, 2147, 1445, 1446,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[46, 1086, 1600, 1601, 47]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[626, 159, 160]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[980, 217, 218, 451, 2102, 981]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>[2045]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>[2046]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>[2047]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>[2048]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>[2049]\\n</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    0\n",
              "0   [2141, 199, 200, 201, 202, 203, 204, 205, 1956...\n",
              "1   [1160, 21, 1883, 1884, 1885, 2147, 1445, 1446,...\n",
              "2                        [46, 1086, 1600, 1601, 47]\\n\n",
              "3                                   [626, 159, 160]\\n\n",
              "4                   [980, 217, 218, 451, 2102, 981]\\n\n",
              "..                                                ...\n",
              "80                                           [2045]\\n\n",
              "81                                           [2046]\\n\n",
              "82                                           [2047]\\n\n",
              "83                                           [2048]\\n\n",
              "84                                           [2049]\\n\n",
              "\n",
              "[85 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "TAvTTFs6j1da",
        "outputId": "2a9c10e3-20c8-4b9e-ba8c-e889eba221da"
      },
      "source": [
        "# Represent Stable Community\n",
        "\n",
        "import networkx as nx\n",
        "# importing matplotlib.pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "  \n",
        "g = nx.Graph()\n",
        "\n",
        "# [46, 1086, 1600, 1601, 47]\\n \n",
        "# [2257, 2530, 2531, 2551, 2553]\\n\n",
        "g.add_node(46)\n",
        "g.add_node(1086)\n",
        "g.add_node(1600)\n",
        "g.add_node(1601)\n",
        "g.add_node(47)\n",
        "\n",
        "g.add_edge(46, 1086)\n",
        "g.add_edge(46, 1600)\n",
        "g.add_edge(46, 1601)\n",
        "g.add_edge(46, 47)\n",
        "\n",
        "g.add_edge(1086, 46)\n",
        "g.add_edge(1086, 1600)\n",
        "g.add_edge(1086, 1601)\n",
        "g.add_edge(1086, 47)\n",
        "\n",
        "g.add_edge(1601, 46)\n",
        "g.add_edge(1601, 1086)\n",
        "g.add_edge(1601, 1600)\n",
        "g.add_edge(1601, 47)\n",
        "\n",
        "g.add_edge(1600, 46)\n",
        "g.add_edge(1600, 1086)\n",
        "g.add_edge(1600, 1601)\n",
        "g.add_edge(1600, 47)\n",
        "\n",
        "# drawing in spring layout\n",
        "nx.draw_spring(g, with_labels = True, node_color=\"#75846b\")\n",
        "plt.savefig(\"stable_dc.png\", transparent = True, dpi = 300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRFxqoeDPgyP"
      },
      "source": [
        "# LITECOIN EXTRACTION - TEMPORAL COMMUNITY\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjIsQXBNPg_o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "7b38776d-af71-4976-d2bd-2a64bfc799bb"
      },
      "source": [
        "#####################\n",
        "###### LITECOIN #####\n",
        "#####################\n",
        "#### EXTRACTION #####\n",
        "#####################\n",
        "#### COMM FORMAT ####\n",
        "#####################\n",
        "\n",
        "# import packages\n",
        "import requests\n",
        "import tabulate\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import datetime\n",
        " \n",
        "\n",
        "\n",
        "# 2082258 - 2082265: 07/07/2021 \n",
        " \n",
        "# range block\n",
        "block_range = list(range(2082258, 2082365)) # replace as needed\n",
        " \n",
        "r1 = 0\n",
        "r2 = 10\n",
        "size_block = range(r1, r2)\n",
        "\n",
        "# transaction info\n",
        "transaction_id = []\n",
        "input_address = []\n",
        "input_value = []\n",
        "output_address = []\n",
        "output_value = []\n",
        "date = []\n",
        "date_form = []\n",
        " \n",
        "# range transaction\n",
        "r3 = 0\n",
        " \n",
        "for y in size_block:\n",
        " \n",
        "  # transaction link\n",
        "  link_block = 'https://chain.so/api/v2/get_block/LTC/' + str(block_range[y])\n",
        " \n",
        "  response = requests.get(link_block)\n",
        " \n",
        "  if response.status_code == 200:\n",
        "      # everything went swimmingly                                                                                                                   \n",
        "      # parse the response as JSON\n",
        "      content = response.json()\n",
        "      \n",
        "      # transaction id\n",
        "      txs = content['data']['txs']\n",
        "\n",
        "      # range\n",
        "      r4 = len(txs)\n",
        "      size = range(r3, r4)\n",
        " \n",
        "  for x in size:\n",
        " \n",
        "    # transaction link\n",
        "    link_txs = 'https://chain.so/api/v2/tx/LTC/' + str(txs[x])\n",
        " \n",
        "    response = requests.get(link_txs) \n",
        "      \n",
        "    if response.status_code == 200:\n",
        "          # everything went swimmingly parse the response as JSON\n",
        "          content = response.json()\n",
        " \n",
        "          # transaction_id\n",
        "          transaction_id.append(content['data']['txid'])\n",
        "          \n",
        "          # input leg\n",
        "          for each in content['data']['inputs']:\n",
        "            input_address.append(each['address'])\n",
        "            input_value.append(each['value'])\n",
        " \n",
        "          # output leg\n",
        "          for each in content['data']['outputs']:\n",
        "            output_address.append(each['address'])\n",
        "            output_value.append(each['value'])\n",
        " \n",
        "          # date\n",
        "          date.append(content['data']['time'])\n",
        " \n",
        "          largest = max(len(transaction_id), len(input_address), len(input_value), len(output_address), len(output_value))\n",
        "          \n",
        "    # filling for different len\n",
        "    len_transaction_id = largest - len(transaction_id)\n",
        "    len_input_address = largest - len(input_address)\n",
        "    len_output_address = largest - len(output_address)\n",
        "    len_date = largest - len(date)\n",
        "\n",
        "    corr_transaction_id = [None]*len_transaction_id\n",
        "    corr_input_address = [None]*len_input_address\n",
        "    corr_output_address = [None]*len_output_address\n",
        "    corr_date = [None]*len_date\n",
        " \n",
        "    transaction_id.extend(corr_transaction_id)\n",
        "    input_address.extend(corr_input_address)\n",
        "    input_value.extend(corr_input_address)\n",
        "    output_address.extend(corr_output_address)\n",
        "    output_value.extend(corr_output_address)\n",
        "    date.extend(corr_date)\n",
        "    \n",
        "# as data frame \n",
        "df_lc_cm_01 = pd.DataFrame({'transaction_id': transaction_id, 'from_leg': input_address, 'tt_value': input_value, 'to': output_address, 'amount': output_value, 'date': date})\n",
        " \n",
        " \n",
        "#clean data\n",
        "  # obmit nulldata & reindex\n",
        "df_lc_cm = df_lc_cm_01[df_lc_cm_01.to != \"nulldata\"]\n",
        "df_lc_cm.reset_index(drop=True, inplace=True)\n",
        " \n",
        " # fill NaN with previous data for all NaN, None\n",
        "df_lc_cm.date = df_lc_cm.date.fillna(method = 'ffill')\n",
        "df_lc_cm.from_leg = df_lc_cm.from_leg.fillna(method = 'ffill')\n",
        "df_lc_cm.transaction_id = df_lc_cm.transaction_id.fillna(method = 'ffill')\n",
        "df_lc_cm.tt_value = df_lc_cm.tt_value.fillna(method = 'ffill')\n",
        "df_lc_cm = df_lc_cm.fillna(value=np.nan)\n",
        "\n",
        "\n",
        "# rename columns to keep\n",
        "df_lc_cm = df_lc_cm.rename(columns = {'from_leg': 'from_id'}, inplace = False)\n",
        "df_lc_cm = df_lc_cm.rename(columns = {'to': 'to_id'}, inplace = False)\n",
        "df_lc_cm = df_lc_cm.rename(columns = {'date': 'timestamps'}, inplace = False)\n",
        "\n",
        "# drop unecessary columns\n",
        "df_lc_cm = df_lc_cm.drop(columns = 'transaction_id')\n",
        "df_lc_cm = df_lc_cm.drop(columns = 'tt_value')\n",
        "df_lc_cm = df_lc_cm.drop(columns = 'amount')\n",
        "df_lc_cm = df_lc_cm.dropna()\n",
        "\n",
        "\n",
        "# dictionary for convertion to int\n",
        "\n",
        "# to_id\n",
        "from_id_no_dupes_lc = set(df_lc_cm.from_id) \n",
        "from_id_no_dupes_lc.update(set(df_lc_cm.to_id))\n",
        "no_dupes_lc = list(from_id_no_dupes_lc)\n",
        "k1 = 0 #range\n",
        "k2 = len(no_dupes_lc) #range\n",
        "key_lc = list(range(k1, k2)) # creates key id\n",
        "df_lc_to = pd.DataFrame({'to_id': no_dupes_lc, 'factor_to': key_lc}) # as dataframe\n",
        "\n",
        "# from_id\n",
        "from_id_no_dupes_lc = set(df_lc_cm.from_id) \n",
        "from_id_no_dupes_lc.update(set(df_lc_cm.to_id))\n",
        "no_dupes_lc = list(from_id_no_dupes_lc)\n",
        "k1 = 0 #range\n",
        "k2 = len(no_dupes_lc) #range\n",
        "key_lc = list(range(k1, k2)) # creates key id\n",
        "df_lc_from = pd.DataFrame({'from_id': no_dupes_lc, 'factor_to': key_lc}) # as dataframe\n",
        "\n",
        "# merges dfs\n",
        "f1_lc_df = pd.merge(df_lc_cm, df_lc_from, on = 'from_id', how ='left')\n",
        "full_lc_df = pd.merge(f1_lc_df, df_lc_to, on = 'to_id', how ='left')\n",
        "\n",
        "# drop columns and rename to keep\n",
        "full_lc_df = full_lc_df.drop(columns= 'from_id')\n",
        "full_lc_df = full_lc_df.drop(columns= 'to_id')\n",
        "full_lc_df = full_lc_df.rename(columns = {'factor_to_x': 'from_id'}, inplace = False)\n",
        "full_lc_df = full_lc_df.rename(columns = {'factor_to_y': 'to_id'}, inplace = False)\n",
        "\n",
        "# factorise timestamps\n",
        "full_lc_df['timestamps'] = pd.factorize(full_lc_df.timestamps)[0]\n",
        "\n",
        "# as int\n",
        "full_lc_df = full_lc_df.astype(int)\n",
        "\n",
        "# re order\n",
        "full_lc_df = full_lc_df[['from_id','to_id','timestamps']]\n",
        "\n",
        "# save\n",
        "full_lc_df.to_csv(\"full_lc_df.csv\", sep='\\t', header=False)  \n",
        "full_lc_df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5170: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>from_id</th>\n",
              "      <th>to_id</th>\n",
              "      <th>timestamps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>251</td>\n",
              "      <td>265</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>351</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>95</td>\n",
              "      <td>292</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95</td>\n",
              "      <td>350</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>254</td>\n",
              "      <td>205</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>254</td>\n",
              "      <td>254</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>59</td>\n",
              "      <td>115</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>59</td>\n",
              "      <td>59</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>52</td>\n",
              "      <td>227</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>342 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     from_id  to_id  timestamps\n",
              "0        251    265           0\n",
              "1          4    351           0\n",
              "2          4      4           0\n",
              "3         95    292           0\n",
              "4         95    350           0\n",
              "..       ...    ...         ...\n",
              "337      254    205           9\n",
              "338      254    254           9\n",
              "339       59    115           9\n",
              "340       59     59           9\n",
              "341       52    227           9\n",
              "\n",
              "[342 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_dJdg3SxOsr"
      },
      "source": [
        "# LITECOIN STABLE COMMUNITY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P08WQlVsPg_q",
        "outputId": "3d4a036a-7247-475c-a52a-4a4f709d001a"
      },
      "source": [
        "#####################\n",
        "###### LITECOIN #####\n",
        "#####################\n",
        "#### STABLE COM #####\n",
        "#####################\n",
        "\n",
        "# mounts drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6Tj78MfPg_s",
        "outputId": "db23ca1a-6a5d-4f23-b653-fe4caf64a6b8"
      },
      "source": [
        "# https://github.com/VeryLargeGraph/TSCAN\n",
        "# runs run.py - TSCAN\n",
        "!python3 \"/content/drive/My Drive/Colab Notebooks/run.py\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset name(str): full_lc_df.csv\n",
            "Epsilon(float): 0.5\n",
            "Tau(int): 1\n",
            "Miu(int): 1\n",
            "Type one number to chose the algorithm: [1]TSCANB; [2]TSACNS; [3]TSCANA. (int): 1\n",
            "3018\n",
            "2931\n",
            "temporal edges:2931.0\n",
            "Runing time of SCANB:0.947159\n",
            "Cores output at: full_lc_df.csv.output-0.5-1-1_SCANB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onEfkXs2WOZp",
        "outputId": "5fccce0b-d8cf-4e58-92c2-f77c7a33c1f2"
      },
      "source": [
        "# prints stable communities - STATIC ONES\n",
        "stable_lc_01 = pd.DataFrame(data=open('/content/full_lc_df_output_05_1_1_SCANB'))\n",
        "stable_lc_01 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[362, 3064, 2724, 2725, 2726, 2727, 2728, 2729...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[3087, 2812, 2813, 2814, 2815, 2816, 2817, 271...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[2257, 2530, 2531, 2551, 2553]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[134, 3248, 2736, 2737, 2738, 1234, 1235, 133]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[2970, 2708, 2709, 2710, 2711]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>[2857, 3237]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>[2889, 3281]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>[2923]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>[2924]\\n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>[2925]\\n</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>98 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    0\n",
              "0   [362, 3064, 2724, 2725, 2726, 2727, 2728, 2729...\n",
              "1   [3087, 2812, 2813, 2814, 2815, 2816, 2817, 271...\n",
              "2                    [2257, 2530, 2531, 2551, 2553]\\n\n",
              "3    [134, 3248, 2736, 2737, 2738, 1234, 1235, 133]\\n\n",
              "4                    [2970, 2708, 2709, 2710, 2711]\\n\n",
              "..                                                ...\n",
              "93                                     [2857, 3237]\\n\n",
              "94                                     [2889, 3281]\\n\n",
              "95                                           [2923]\\n\n",
              "96                                           [2924]\\n\n",
              "97                                           [2925]\\n\n",
              "\n",
              "[98 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAnsjL_Mauvx",
        "outputId": "fd17482a-1961-4034-8810-508687c44782"
      },
      "source": [
        "# Represent Stable Community\n",
        "\n",
        "import networkx as nx\n",
        "# importing matplotlib.pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "  \n",
        "g = nx.Graph()\n",
        "  \n",
        "# [2257, 2530, 2531, 2551, 2553]\\n\n",
        "g.add_node(2257)\n",
        "g.add_node(2530)\n",
        "g.add_node(2531)\n",
        "g.add_node(2551)\n",
        "g.add_node(2553)\n",
        "\n",
        "g.add_edge(2257, 2530)\n",
        "g.add_edge(2257, 2531)\n",
        "g.add_edge(2257, 2551)\n",
        "g.add_edge(2257, 2553)\n",
        "\n",
        "g.add_edge(2530, 2257)\n",
        "g.add_edge(2530, 2531)\n",
        "g.add_edge(2530, 2551)\n",
        "g.add_edge(2530, 2553)\n",
        "\n",
        "g.add_edge(2531, 2257)\n",
        "g.add_edge(2531, 2530)\n",
        "g.add_edge(2531, 2551)\n",
        "g.add_edge(2531, 2553)\n",
        "\n",
        "g.add_edge(2551, 2257)\n",
        "g.add_edge(2551, 2530)\n",
        "g.add_edge(2551, 2531)\n",
        "g.add_edge(2551, 2553)\n",
        "\n",
        "# drawing in spring layout\n",
        "nx.draw_spring(g, with_labels = True, node_color=\"#75846b\")\n",
        "plt.savefig(\"stable_lc.png\", transparent = True, dpi = 300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAEuCAYAAADx63eqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd1RU19oG8GfoCthBVJrAUERQQOkIKgozYCxREAnYQuxdYzSaGL12bNgjFqwUWxTpVYoVO0hVUQHBhgLS53x/+DHRiEoZ5kzZv7VcN1eYOc8QMu/sffa7N4OiKAoEQRAEISYk6A5AEARBEPxECh9BEAQhVkjhIwiCIMQKKXwEQRCEWCGFjyAIghArpPARBEEQYoUUPoIgCEKskMJHEARBiBVS+AiCIAixQgofQRAEIVak6A5AiKbyijLcvHcVRSWFqKqqhJxcO/RQ7okBxpZQkFekOx5BEGKMQfbqJHjpWWE+4lIjkZWbATAYqKur5X5NSkoaoCjo6fTBEGsnqPXUoDEpQRDiihQ+gmeupCUhNOYs6urqQOHrv1YMMCAlJQVXxzGwMrPjY0KCIAgy1UnwSEPRq/1khPc1FCjU1tUiNPYsAJDiRxAEX5ERH9Ek1dXVmDlzJmJiYvDmzRtoa2tj/fr1YLFYSL2WDBtLO0jL/Ps5ytzOFNaDBwIAws7E4uG9bEhK/ruWau4KH0hISEACkriXmoOM9Azk5+cjPj4eDg4O/H55BEGIETLiI5qkrq4OampqSExMhLq6OsLCwuDm5ob79+8jNe0yAGDu7z6QkGx8obC5rQnshlk28ry16KysiOPLjmPcuHFt+hoIgiAAUviIJpKXl8eqVau4/9/V1RW9e/dGSkoyHuXntvh5JaQkoKHXA/1N+kFSUpIHSQmCIL6NFD6iRYqLi5GdnY16iWru3+33PQowAE1tNdg7W6O9fDvu1+5cf4A71x+gY+cOsLA3g56h9r9PxmDg5r1r/IxPEIQYI4WPaLba2lp4enpi4sSJkG4vBRlZKXjNGAdllW6orKxCzMXLuBQSjXGTfgAAmFkZYzDLBrKyMniS+xQXgqIgr9Aeqho9AHyc7nxRUkDnSyIIQoyQnVuIZuFwOPDy8oKMjAx27dqFqqpKyMjKQKWXMiQkJSCv0B5DXe3wJPcZaqprAADdeyqhXXs5SEhKQEtPE3366SInI++z531f9o6Ol0MQhBgiIz6iySiKwtSpU1FcXIywsDBIS0tDTq7dF9/HYDC4398oBvDfL126FI6ioiLMnTsXlpaW0NPTg76+PvT19aGpqUnu/xEEwTOk8BFNNmPGDDx8+BAxMTFo1+5jweuh3BMRha8gLS2Jzl07oaqqGrGhSVDr3QuycrIAgKwHuejNVIe0tDSe5D1Dxp1sjPnJ5ZNnZmDWL7NxPeEOfvrpJ8jJySE3NxfR0dHIzMxEcXExtLW1oa+v/1lB1NPTQ4cOHWj4SRAEIcxIHx/RJPn5+dDU1ISsrCykpP79vLTDbzsuRJ1BYmQKPlR8nPbU0FGDvZMVFBTlAQAnD5zFyxevAVAfF7cMMoOBMZP7HH/7HsW70rLPrvf48WNoamoCAD58+ICcnBxkZmYiMzMTWVlZ3P/t2LHjZ4Ww4Z/V1NQgIUFm8gmC+BIpfESrBZz+GxlZ97+5TdnXMMCAoZ4xvMf6NPuxHA4Hz58/5xbCT4vi27dvwWQyvyiKurq6kJeXb/a1CIIQHaTwEa32rDAfe45uRX19fbMfW1/HwYSRk2HWfyBPM5WVlSErK+uLopiTkwNlZeUvpkz19fXRs2dP7v1JgiBEFyl8RKvl5+djoo8nLB1MwaE4TX6ctJQ0pOraY//Ogzh06BDYbHYbpvyovr4e+fn5nxXEhqJYUVHRaEFkMpmQk5Nr82wEQfAHKXxEq7x58wY2NjaYPn06zG1Nm3E6gzRcHUfDyswOycnJmDBhAsaPH4+1a9dCWlqaj6/gX2/fvv1ilJiZmYnHjx+jV69ejRZFZWVlMkokCCFDCh/RYpWVlRg2bBisrKywefNmAB+nPeNTo5CZm/7V8/j0dQwx2Hr4Z+fxvXr1ChMnTsTbt28RGBgIdXV1vr+er6mtrcXjx48/u4fY8IfD4TS62lRbWxsyMjJ0RycIohGk8BEtUl9fDzc3N8jIyODEiRNfrKD8eAL7NbwoKcC169fQqVNn2NsOxgBji6+ewM7hcODr64stW7bA398fI0aM4MdLaZVXr159sbAmMzMTz549g4aGxmcFsaEodu3ale7YBCHWSOEjmo2iKMydOxfp6ekIDw+HrKzsN7/f19cXBQUF2LZtW5OePzU1FR4eHhg7dizWr18vlCOn6upq5OXlNVoUZWRkvhglNjTqf9oqQhBE2yCFj2i2TZs24fjx40hKSkLHjh2/+/3//PMPDhw4gNDQ0CZf4/Xr15g8eTKKi4sRFBTE7ekTdhRFobi4uNGCWFRU9NVG/ab8nAmCaBpS+IhmOXHiBJYvX47U1FT06tWrSY9JT0/HmDFjkJWV1axrURSFbdu2YePGjdi/fz9GjRrVkshCo7Ky8quN+oqKio026qurq5NGfYJoJlL4iCaLjY3FhAkTEBcXB0NDwyY/rqqqCp06dUJ5eXmLpvKuXbsGd3d3jBo1Cps2bRLKqc/WoCgKBQUFX7RfZGZm4vXr19xG/U8Loq6uLhQUFOiOThACiRQ+oknu3r2LYcOGISQkBPb29s1+vIaGBuLj46GlpdWi6799+xaTJ09GQUEBgoKCWvw8oqasrAzZ2dmNNup37dq10VFir169SAsGIdZI4SO+Kz8/HzY2Nti6dSvc3Nxa9ByOjo5YsmQJnJycWpyDoij4+flh7dq12Lt3L3788ccWP5eoq6+vx9OnTxvdzq2srOyrjfoNm48ThCgjhY/4pjdv3sDW1ha//PIL5s+f3+LnmTFjBgwNDTF79uxWZ7px4wbc3d3h4uICX1/f764qJT5XWlra6HZueXl56NGjxxftF/r6+ujevTsZJRIigxQ+4quqqqowbNgwWFhYwNfXt1XPtXXrVuTn52PHjh08yVZaWoqff/4Zjx8/RlBQEHR0dHjyvOKsrq6u0Ub9rKws1NbWNtqTqKOjI3b3XAnhRwof0aj6+nq4u7tDWlq60Qb15rp48SL27t2LsLAwHiX8OPW5Z88e/PXXX9i1a1eLp2GJ73v16lWj27k9ffoU6urqjU6dduvWje7YBNEoUviIL1AUhXnz5uH+/fuIiIjgyVRiZmYmRowYgZycHB4k/NytW7fg5uaGYcOGYdu2bWRDaT6qqanhNur/tyhKSUk12pOopaVFGvUJWpHCR3xh8+bNOHr0KJKSktCpUyeePGd1dTU6dOiA8vLyNtmE+t27d/jll1+QlZWF4OBg6Orq8vwaRNNRFIWSkpJGG/ULCwuhpaXV6NQpr37fCOJbSOEjPnPy5En89ttvSE1NhaqqKk+fu3fv3oiOjm6z+3EURWH//v1YuXIlduzYgQkTJrTJdYjWqaysRG5ubqON+vLy8o2OEjU0NCApKUl3dEJEkMJHcMXGxsLDwwNxcXHo27cvz59/+PDhWLBgAVgsFs+f+1N37tyBm5sbHBwcsGPHDrJEX0hQFIXCwsJGR4kvX75stFFfT0+PNOoTzUYKHwGg9Q3qTTFr1izo6elh7ty5bfL8nyorK8O0adPw4MEDBAcHQ19fv82vSbSdiooKZGdnf1EUs7Oz0aVLl6826pPt3IjGkDvMBJ4+fQpXV1fs2rWrzYoeADCZzDZZ3NIYRUVFnDhxAv7+/rCzs8PWrVvh5eXFl2sTvCcvLw8TExOYmJh89vccDuezRv2MjAycO3cOmZmZeP/+PXR1db8oiEwmE+3bt6fplRCCgIz4xNzbt29hY2MDHx8fLFiwoE2vdenSJezcuRMRERFtep3/unfvHtzc3GBtbY1du3aRNz0x8e7du89GiZ826nfv3r3RUaKKigpp1BcDpPCJsaqqKgwfPhwDBw7Eli1b2vx62dnZYLFYyMvLa/Nr/Vd5eTlmzJiBW7duISQkBH369OF7BkIw1NXV4cmTJ1+0X2RlZaG6urrRnkQdHR2yQ5AIIYVPTHE4HLi7u0NSUhInT57ky72QmpoaKCoqoqysjJbdPiiKwuHDh7F06VJs3rwZkyZN4nsGQrC9fv260e3cnjx5AlVV1UZHid26dSOjRCFDCp8YoigK8+fPx71793jWoN5U2traCAsLg56eHt+u+V/p6ekYN24cBg4ciD179kBeXp62LIRwqKmpwaNHjxpt1JeQkGh0lKilpdUmPattqbyiDDfvXUVRSSGqqiohJ9cOPZR7YoCxJRTkFemOxzOk8IkhX19fBAQE8LRBvamcnZ0xZ84cuLi48PW6/1VRUYHZs2fj6tWrCAkJaZP2DUL0URSFly9fNtqCUVBQgN69ezdaFDt37kx39M88K8xHXGoksnIzAAYDdXW13K9JSUkDFAU9nT4YYu0EtZ4aNCblDVL4xMypU6ewdOlSpKSkQE1Nje/XnzNnDrS1tVt10gMvBQQEYPHixdiwYQOmTJlCpqwInqmqquI26v93lNi+fftGexI1NTX53qh/JS0JoTFnUVdXBwpfLwcMMCAlJQVXxzGwMrPjY0LeI4VPjMTFxWH8+PGIjY2FkZERLRn8/PyQlZWF3bt303L9xmRkZMDNzQ39+/fH3r17oagoOlM6hOChKApFRUWNjhJLSkqgo6PT6HZubfF72VD0aj8Z4X2PtLQ0XIcKd/EjhU9M3Lt3D46OjggODoaDgwNtOcLDw7Ft2zZERUXRlqExHz58wNy5c5GUlISQkBAYGxvTHYkQQxUVFcjJyWm0Ub9Tp06NjhLV1NS+uTituroaM2fORExMDN68eQNtbW2sX78effv1wYYdf2HPpsOQlvm3pdvczhTWgwcCABIiUpF5LwfV1dWQk5NFv4GGsHQYAGkpGUz3moc1q9YiMTEROTk5OHTokNAsGCMN7GLg6dOncHFxwc6dO2ktegB/m9ibo3379vD398eJEycwdOhQrF27Fj4+PmTqk+AreXl59O/fH/379//s7zkcDp49e/bZ6PCff/5BZmYmSktLG23U19XVRfv27VFXVwc1NTUkJiZCXV0dYWFhcHNzw58bl6G+vh4AMPd3H0hIflk8jc0MYD1kIGRkpFH2vhwhRy6gi1Jn6BnqID41Cv369YO7uzuWLl3Kl58Pr5DCJ+Levn0LFouFBQsWwN3dne440NTURFFREaqrqwWyL8rT0xMDBgyAm5sb4uPjsX//fnTo0IHuWISYk5CQgIaGBjQ0NDB8+PDPvvb+/fvPGvXPnDmDzMxM5ObmQllZmVsIw8LCoK+vD1NTU2hoaCA1NQVKKl2/ed0uSp8vwmEwGHj75h0oUMjMTcfyOWugIK8odEeBkcInwqqqqjBq1Cg4OTlh4cKFdMcBAEhJSUFNTQ2PHj2CgYEB3XEapaenh6tXr2LBggUwMzNDcHDwF1tlEYSg6NChAwYMGIABAwZ89vf19fWfNerfuXMHQUFByMjIwOs3rzHQ4d+VzPt9jwIMQFNbDfbO1mgv/+/G7tcS03Al8SZqa+rQsXMH9DH+/yO/GAzcvHcNDlaOfHmdvEQKn4jicDjw9vaGiooKfH196Y7zmYbpTkEtfADQrl077Nu3D4GBgRg+fDj++usvzJgxg0x9EkJDUlIS2tra0NbWBpvNBgDU1taCxWKhDjVQ6tENNdU18JoxDsoq3VBZWYWYi5dxKSQa4yb9wH0eC3szmA8yRUnRK+Q8fARZuY+bT9TV1eJFSQEtr621yNblIoiiKCxcuBDFxcUICAgQuB3qmUwmcnNz6Y7RJOPHj0dqaioOHDgANzc3vHv3ju5IBNEiHA4HXl5ekJGRgefkj7c9ZGRloNJLGRKSEpBXaI+hrnZ4kvsMNdU1nz2WwWCge08lSEtJITn2OvfvK6sq+foaeEWw3hEJnti6dStiYmJw/vx5gZx7F9QFLl/DZDJx5coVKCsrw9TUFGlpaXRHIohmoSgKU6dORXFxMU6dOoWK8vJGv69hRuNri/05HArv3vz74a+dnHCedUkKn4gJDAzE9u3bER4eLnC7QzQQtsIHAHJycti9ezc2bNgAFouFnTt3fvXNgSAEjbe3NxITE6GoqAgNDQ0kJaQAFFD47AXevHwLikOh8kMVYkOToNa7F2TlZEFxKNy5/gBVlVUfew+fF+P2tftQ11YF8HFHl66dlVFV9fHrtbW1qKqqAofDofnVfh/p4xMh8fHxcHd3p7VBvSkePXqEwYMHIz8/n+4oLZKXlwc3Nzdoamri4MGDfN/2jSC+p7a2FleuXEFYWBi37UFCQgLS0tKQlJQEg8HAEBdbUBQHSdFX8aGiEjKyMtDQUYO9kxUUFOVBcSicPhaKF8+LUV9fDwVFefQ10YeFvRkYDAakJKWQdCkNycnJn107Pj6e9rap7yGFT0Tcv38fQ4cORVBQEAYPHkx3nG+qq6uDgoICSktLBXIqtimqq6uxZMkShIaGIjAwEObm5nRHIsRcYWEhIiIiEB4ejpiYGGhra4PFYoHNZsPc3PyLrdACTv+NjKz739ym7GsYYMBQzxjeY314FZ+vyKpOEfDs2TOw2Wz4+fkJfNEDPrY0aGpqIi8vD4aGhnTHaRFZWVn4+fnBwcEBrq6uWLZsGebPn09WfRJ8U1dXh6tXryI8PBxhYWHIz8/HsGHD4Orqip07d0JFReWbjx9i7YTsvIfN2q6sgZSUNAZbD//+NwooUviEXGlpKVgsFubPn4/x48fTHafJdHR0kJOTI7SFr8GYMWNgYmICd3d3JCQk4PDhw+jSpQvdsQgRVVxcjIiICISFhSE6OhoaGhpgs9nYuXMnLC0tISXV9Lf0Hsq98DT7BVQ0u4Ah0fQPbB/36hwt1Kc0kMUtQqyhQd3R0VFgGtSbShgXuHxN7969kZycDC0tLZiamuLq1at0RyJERH19PVJTU7Fy5UqYmZlBX18foaGhcHJywoMHD3D79m2sXbsWtra2zSp6FEVh5syZKHn+BiOdxkFaShoMfLv4McCAtJSM0G9QDZARn9DicDiYOHEilJWVsXXrVqGbYmMymbh79y7dMXhGRkYG27Ztg4ODA0aOHIklS5Zg4cKFAtdDSQi+kpISREZGIiwsDFFRUVBVVQWLxcK2bdtgZWXFk8Nt16xZg7S0NCQkJHxc6anaG/GpUcjMTf/qeXz6OoYYbD1cqEd6DcjiFiG1cOFCpKWlITIyUigXiERHR2P9+vWIi4ujOwrP5efnw93dHd26dUNAQAC6dv32foiEeKuvr8fNmze59+qys7MxZMgQsNlsODs7Q1VVlafXO3jwINauXYvU1NQv7gN+PIH9Gl6UFKCyqhIXLlzElIk/Y7CNo0idwE5GfEJo69atiIyMRHJyslAWPUC0pjr/S0NDA5cvX8by5cthYmKCU6dOwcbGhu5YhAB59eoVIiMjER4ejsjISHTv3h1sNhsbN26EjY0NZGRk2uS6ly5dwooVK5CYmNjo4hcFecXP9t48ui8IcgwFkSp6ABnxCZ3AwEAsWbIEqamptJygziv19fVQUFDA69ev0b59e7rjtJnQ0FBMnToVCxcuxJIlS8jUp5jicDhIS0vjjuoePnyIwYMHg8VigcViQV1dvc0zXLt2Da6urggNDYWFhUWTHjN37lxoaGhg0aJFbZyOv8iIT4gkJCRg7ty5iImJEeqiB3zcQLd3797Iy8sT6Gb71nJ1dcXNmzcxfvx4JCYmIiAgAEpKSnTHIvjgzZs3iIqKQlhYGCIjI9G1a1ew2WzuYhR+HsuVnZ2NUaNG4fDhw00uegBgZGSElJSUNkxGD/LxU0jcv38fbm5uCAoKEpnTwUV5uvNTampqSEhIgLGxMUxNTZGUlER3JKINcDgc3Lp1C//73/9gY2MDTU1NnDx5ElZWVrh27RoyMjLg6+uLoUOH8rXoFRcXg8ViYc2aNXB1dW3WY42MjHD//v02SkYfMuITAs+ePYOLiwt27NghFA3qTdXQyycOpKWlsWHDBtjb22PcuHGYM2cOli1bRqY+hdzbt28RHR2N8PBwhIeHo1OnTmCxWFi1ahXs7OxovwdfVlYGNpsNb29v/Pzzz81+fN++fZGZmYn6+vovdn4RZuQen4ArLS2Fra0tJk2ahMWLF9Mdh6f27duHmzdvwt/fn+4ofPX8+XN4eHigXbt2OH78OJSVlemORDQRRVG4e/cu917d3bt3YWdnBzabDRaLBS0tLbojctXW1mLEiBFQV1fH/v37W9zypK2tjbCwMOjp6fE4IX3Ix00BVl1djVGjRmHo0KEid3MZEK5z+XhJVVUV8fHxGDhwIExNTZGQkEB3JOIb3r17hzNnzmDq1KlQVVXFuHHj8OLFC/z+++8oLi7GpUuXMGvWLIEqehRF4eeff4aMjAz27NnTqj5fIyMj3Lt3j4fp6EdGfAKKw+HAw8MDHA4HgYGBIjXN0ODp06ewsrJCQYFwnuLMC1FRUZg4cSJmzJiB33//XST/PQsbiqJw//597vRlWloabG1tuSswmUwm3RG/a/ny5YiLi0NcXFyrV02vXLkSDAYDq1ev5lE6+pHCJ6AWLVqEGzduICoqivb7BG2Fw+FAQUEBL1++hLy8PN1xaFNYWAhPT09ISkri+PHj391cmOC99+/fIzY2FmFhYQgPD4esrCz3ZAMHBweharnZvXs3/Pz8kJKSgm7durX6+YKDg3Hq1CmcO3eOB+kEA5nqFEDbtm1DREQE/vnnH5EtegAgISEBLS0tsZzu/FTPnj0RExMDGxsbmJmZITY2lu5IIo+iKKSnp2Pz5s0YMmQIevXqhX379sHQ0BCxsbHIzc3Frl27wGazharonT17FuvWrUNERARPih4AGBsbi9zKTjLiEzBBQUFYvHgxUlJS+NLUSrfRo0fD09MTY8eOpTuKQIiJiYG3tzd8fHzwxx9/kKlPHiovL0dsbCx3YYqEhATYbDbYbDYGDx4s9LMOycnJGDNmDCIiImBqasqz562rq0OHDh1QUlICBQUFnj0vnciIT4AkJCRgzpw5uHTpklgUPUB8evmaytHREbdu3UJycjIcHR1RWFhIdyShRVEUHj58iK1bt8LR0RE9evTAzp07oauri8jISDx+/Bh79uyBq6ur0Be9jIwM/Pjjjzhx4gRPix7w8fxMfX19pKen8/R56UQKn4B48OAB3N3dERgYKDIN6k0hTr18TaWiooKoqCgMHjwYZmZmiIqKojuS0KioqEBoaChmzpwJLS0tODk5ITs7G3PmzEFhYSFiYmKwcOFCGBgYCN2JJl9TUFAAFosFX19fDBs2rE2uIWqN7KSBXQA8f/4cbDYb27Ztw5AhQ+iOw1dMJhPHjh2jO4bAkZSUxB9//IFBgwbhp59+wqRJk7Bq1apmnbkmDiiKQk5ODndRSmpqKgYOHAgWi4XQ0FD06dNHZApcYxoOop45cya8vLza7DqiVvjIPT6alZaWws7ODt7e3liyZAndcfju+fPnGDhwIIqKiuiOIrBKSkrw008/oaqqCqdOnUKvXr3ojkSrDx8+ICEhgXuvrrq6mrsCc+jQoejQoQPdEfmiuroazs7OMDIywo4dO9q0wEdFRWHDhg0ic4wYKXw04ucvrqDicDhQVFTEixcvoKgoWkef8BKHw8GGDRvg5+eHI0eOwNnZme5IfJWbm8sd1aWkpMDExIS7W4qRkZHY/bfD4XAwYcIE1NfX86XPt6ioCEZGRnj58qVI/KxJ4aNJwy9uXV0dgoKCxHr1nrGxMQICAmBiYkJ3FIF3+fJleHp6wtPTE2vWrOHJadyCqLKyEomJidxRXUVFBbeB3NHREZ06daI7Iq0WLlyImzdv8q3Pl6IoKCkp4f79++jRo0ebX6+tkRsGNPn1119RUFCA6OhosS56wL8rO0nh+75Bgwbh1q1b8Pb2hoODAwIDA4X+iKoGjx494ha6pKQk9OvXDywWCyEhIejXr59IjDR4YevWrYiKikJSUhLf+nwZDAb3Ph8pfESLbN++HWFhYUJ9gjovkZaG5lFSUsKlS5ewefNmDBgwAAcPHmz2cTOCoLq6GpcvX+ZOYZaWlsLZ2RkTJ07E8ePH0blzZ7ojCpxTp05h+/btSElJ4fvPp6HwDR8+nK/XbQuk8PFZcHAwfH19kZqaii5dutAdRyAwmUxyRl0zSUhIYOnSpbC1tYWHhwcSExOxbt06gZ/6fPLkCXcPzISEBPTt2xdsNhsnTpyAiYkJOabpG+Li4jBv3jzExsbSMso3NjZGcnIy36/bFshvGR8lJiZi9uzZYtWg3hSkl6/lbGxscPv2bTx8+BCDBg1Cfn4+3ZE+U1NTg9jYWCxevBh9+vSBubk5rly5ggkTJuDx48dITU3FihUrYGZmRoreN9y9exfjx49HcHAwjIyMaMkgSi0NZHELn6Snp2PIkCE4efIkhg4dSnccgVJYWIj+/fujpKSE7ihCi8PhYOvWrdi8eTP+/vtvjBw5krYsz549496ri4+Ph4GBAXcFJilwzZefnw8bGxts3boVbm5utOUoLy+HsrIy3r9/L/T9pKTw8cHz589hY2ODdevWwdPTk+44AoeiKCgqKqKgoAAdO3akO45Qu3LlCsaPH48xY8Zg48aNkJGRafNr1tbWIiUlhXuvrqioCM7OzmCxWHBycuLZZsni6PXr17C1tcX06dMxb948uuNAW1sbly5dgr6+Pt1RWoV89Gpj7969A5vNxqxZs0jR+woGgwEdHR2xP6WBF6ysrHD79m08evQItra2ePz4cZtcp6CgAP7+/vjxxx+hpKSEX3/9Fe3atYO/vz+Ki4tx/PhxeHp6kqLXCpWVlfjhhx8wYsQIgSh6gOhMd5LC14aqq6sxevRo2Nvbi+WuLM1BVnbyTpcuXXD+/Hl4eHjAwsKCJ+eo1dbW4vLly1i2bBn69esHY2NjxMbGYtSoUcjOzsb169fx119/wcLCQuzbc3ihvr4eEyZMQO/evbFhwwa643CJSuET7olaAcbhcDBp0iR07twZ27dvJz1I30EKH28xGAwsWLAANjY2cHd3R4dTcF8AACAASURBVHx8PDZv3gxZWdkmP0dRUREiIiIQFhaGmJgYaGlpgc1mY+/evTA3Nxf6+zyCiqIozJkzB2VlZQgKChKoe6LGxsY4efIk3TFajfzmtpGlS5fi2bNnpEG9iZhMJuLj4+mOIXLMzc1x+/ZtTJkyBTY2NggKCoK2tnaj31tXV4erV69yF6bk5+dj2LBhcHFxwc6dO8nJ8Hyyfv16XLlyBYmJiXy5R9scojLiE5yPEiJkx44dCA0NxYULF9CuXTu64wgFMuJrO506dcKZM2fg7e0NKysrhISEcL9WXFyMgIAAuLu7Q1lZGXPmzAFFUdi5cydKSkoQFBSESZMmkaLHJ0eOHMGBAwcQFhYmkJtt6+jooLCwEOXl5XRHaRWyqpPHQkJCsGDBAqSkpEBDQ4PuOELjxYsX6Nu3L169ekV3FJF27do1jBkzBj169ACHw8Hjx4/h6OgIFosFZ2dn9OzZk+6IYis8PByTJ09GYmIi9PT06I7zVaampti7dy8sLCzojtJiZKqThy5fvoxZs2YhKiqKFL1m6t69O6qrq/H27VuyVRWPvXz5EhEREQgPD0dUVBRUVFRQXl4ODoeDlJQU9OnTh+6IYu/GjRuYOHEi/vnnH4EuesC/053CXPjIVCePpKenY9y4cTh58iT69+9Pdxyhw2AwyHQnj9TX1+PatWtYtWoVzM3NwWQyce7cOQwePBh37tzBgwcP8PDhQ8yfPx/29vYIDAykO7JYy8vLw8iRI3HgwAFYWVnRHee7jI2Nhf4+Hxnx8UBBQQHYbDa2bNkCR0dHuuMILSaTidzcXJibm9MdRei8evUKUVFRCAsLQ2RkJLp37w42m42NGzfCxsbmi0USDAYDM2fOhJWVFdzc3JCQkIBt27aRe9J8VlJSAmdnZ/z555+07rbTHEZGRrh06RLdMVqFjPha6d27d2CxWJg5cyZ++uknuuMINTLiazoOh4ObN29i9erVsLKygra2NoKDg2FnZ4e0tDQ8ePAAmzZtwuDBg7+5MtDExARpaWkoLS2FpaUlsrKy+PgqxFt5eTlcXFzg4eGBadOm0R2nyYyMjHDv3j0I8/IQsrilFaqrq8FisdCnTx/s3LmT9Oq1UkBAAKKjo3H8+HG6owikN2/eICoqCuHh4YiIiEDXrl3BYrHAZrNha2vbrB69/6IoCn///TdWrFiB7du3k12G2lhtbS1GjhyJHj16wN/fX6jeO0ThUFoy1dlCHA4HkydPRqdOnbBjxw6h+sUVVEwmE3v27KE7hsDgcDi4c+cOdw/M+/fvw97eHmw2G3/99Rc0NTV5di0Gg4Fp06bB0tISbm5uiI+Ph5+fH9q3b8+zaxAfURSFadOmgcFgYN++fUL33iEKh9KSqc4W+u233/D06VOcOHGCNKjzCDmeCCgtLUVISAgmT56Mnj17wsPDA69fv8aff/6JkpISXLx4ETNmzOBp0ftUv379cPPmTVRVVcHCwgIPHz5sk+uIsz/++AMPHjxAcHCwwJ+f+DXCvsCFjPhawM/PDxcuXEBKSgpZDMBDSkpKqK+vx+vXr9G1a1e64/AFRVG4e/cu93DWO3fuwM7ODiwWCytWrPjqLittSVFREceOHcOhQ4cwaNAgbNmyBd7e3nzPIYr27duHwMBApKSkQF5enu44LWZkZCTch9JSRLOEhIRQvXr1oh4/fkx3FJFkZmZGXblyhe4Ybaq0tJQ6ffo0NWXKFKpnz56Ujo4ONWfOHCo8PJz68OED3fE+c+/ePUpfX5+aNGkSVV5eTnccoXb+/HmqR48eVG5uLt1RWu3q1auUiYkJ3TFajEx1NkNSUhJmzJiB0NDQNptqEneiuLKToijcv38fGzduhIODA1RVVeHv749+/fohISEBOTk58PPzg7Ozs8DNIBgZGeHGjRvgcDgwNzdHeno63ZGEUmpqKnx8fHDhwgVaRvG8ZmhoiMzMTNTV1dEdpUXIVGcTZWRkYOzYsaRBvY019PIJu7KyMsTExHCnMKWlpcFms/Hrr7/CwcFBqBaNKCgoICAgAEeOHIGDgwM2bdqESZMmCd2iDLpkZmZizJgxOHr0KAYMGEB3HJ5QUFBAjx49kJubK5SH0pLC1wQFBQVgsVjw9fXFsGHD6I4j0phMJsLDw+mO0WwURSEjI4N7ssGNGzdgZWUFNpuNRYsWQVdXV+gLxaRJk2Bubo5x48YhPj4ee/bsgYKCAt2xBFphYSFYLBY2btwIZ2dnuuPwVMMCF2EsfGSq8zsaTlCfMWMGvLy86I4j8oRpqrO8vBwXLlzA9OnToampCRcXFzx69AgLFizAixcvEBUVhfnz50NPT0/oi16DPn364MaNG5CWlsbAgQOFemVfW3v//j3YbDZ8fHwwceJEuuPwnDAfUUQa2L+hpqYGLBYL+vr62LVrl8i8eQmy169fQ1tbG2/fvhW4nzdFUcjKyuL21V29ehUWFhbcJnJ9fX2By9yWjh07hoULF2LdunX4+eefxeq1f09NTQ3YbDb09PRE9r0jJCQEJ06cwPnz5+mO0myk8H0Fh8OBl5cXPnz4gNOnT5NePT6hKApdunRBdnY2lJSU6I6DiooKxMfHc6cw6+vruYVuyJAhUFRUpDsirTIzMzFu3DgYGRlh//79Yv/zAMTnvSMzMxMuLi7Iy8ujO0qzkXt8X7Fs2TI8efIEMTExIvuLK4g+PaWBjsJHURRycnK4o7rU1FQMGDAAbDYbFy9ehKGhoUh+em8pfX19XL9+HfPmzYOZmRmCg4PFfvHX0qVLkZ+fj+joaJF+79DR0UFRURHKy8uF7l4vKXyN2LlzJ/755x/SoE6ThsJnbW3Nl+t9+PABCQkJ3FFdVVUV2Gw2pk2bhpCQEIE8CVuQtGvXDn///TdOnjyJYcOGYc2aNdwtucTN9u3bERoaKhbvHVJSUtDX10d6errQnc1HCt9/nDlzBhs2bEBKSorY7B4iaPixwCU3N5db6FJSUmBiYgI2m41z587ByMhILN+0W2vChAkYMGAAd6/PAwcOiNWHhuDgYGzZsgXJycno0qUL3XH4omFlp7AVPrKq8xNJSUmYPn06aVCnWVv08lVVVSEyMhLz5s2Drq4u7OzscOfOHUydOhVPnz5FYmIili5dCmNjY1L0WkFXVxdXr15Fly5dYGpqilu3btEdiS8SEhIwe/ZsXLp0CRoaGnTH4RthXdlJRnz/7+HDhxg7dixOnDgBExMTuuOINV6N+B4/fsy9V3f58mX069cPLBYLQUFB6N+/PylwbUROTg579+5FUFAQnJyc8Oeff2LWrFki+/O+f/8+3N3dERgYCGNjY7rj8JWRkRFCQ0PpjtFsZFUnPjaZWltbY/Xq1WQzXgHw9u1baGho4N27d816s6yursbly5e5U5ilpaVwdnYGm83GsGHD0Llz5zZMTTQmNzcXbm5u0NLSgr+/Pzp16kR3JJ56+vQpbGxssHnzZowfP57uOHxXVFQEIyMjvHz5Uqg+2Ih94Xv//j3s7Owwfvx4LFu2jO44xP/r1q0b0tPT0b17929+X35+PrfQJSYmwtDQEGw2GywWCyYmJpCQILP5dKuqqsLixYsRHh6OoKAgkdm26+3bt7C1tcXUqVOxcOFCuuPQghLSQ2nFeqqzpqYGY8aMgY2NDX777Te64xCfaDib77+Fr6amBsnJydwpzJcvX8LZ2RkeHh44fPgwWZAkgOTk5LBr1y6cPn0abDYbv//+O+bOnStUI4T/qqqqwsiRI+Hk5CS2RQ/42H7UsMBFmAqf2I74OBwOvL29UV5ejjNnzoh0v40w8vLywpAhQzB58mQ8e/aMO6qLj4+HgYEBt4nczMyMjOqEyKNHj+Du7g5VVVUcOnRIKKef6+vr4e7uDikpKZw8eVLsf//mzZsHdXV1LFq0iO4oTSa2/8aWL1+OR48e4eTJk6ToCZja2lpISUlh//79MDIygomJCRITEzFu3Djk5ubi6tWr+PPPPzFw4ECxf9MRNlpaWkhOToaGhgZMTU1x7do1uiM1C0VRmD9/Pt68eYOAgADy+4ePC1zu3btHd4xmEcupzl27duHcuXNITU0VquNhRFlBQQH3CJ/Y2Fh07doVsrKyOHz4MAYMGEA+nIgQWVlZbN++Hfb29hgxYgR+++03LFiwQCimPjdt2oTExEQkJSVBVlaW7jgCwcjICHv27KE7RrOI3VTn2bNnMWfOHCQnJ6N37950xxFbtbW1uHLlCncK8/nz5xg+fDhYLBacnJzw7Nkz+Pj44Pbt23RHJdrQkydP4O7uju7du+PIkSMC3fh97NgxrFy5EikpKejVqxfdcQRGeXk5lJWV8f79e0hJCcdYSqwKX3JyMkaPHo3IyEiYmprSHUfsFBUVISIiAmFhYYiJiYGWlhZ3Baa5ufln/9G8e/cOqqqqeP/+vVCMBIiWq6mpwbJly3D69GkEBgbCysqK7khfiIqKgpeXFxISEmBgYEB3HIGjo6OD0NBQ4TmbjxITGRkZlLKyMhUZGUl3FLFRW1tLJScnU8uXL6dMTEyozp07U25ubtThw4epoqKi7z5eSUmJKiws5ENSQhBcuHCBUlZWpjZt2kTV19fTHYcrLS2NUlJSopKSkuiOIrBGjRpFBQcH0x2jycTizmzDKcibNm3C8OHD6Y4j0oqLixEQEIDx48dDWVkZs2fPBkVR8PPzQ0lJCYKCgjBp0iSoqKh897mE6VBaovVGjBiBGzdu4OzZsxgxYgRevXpFdyQ8evQIrq6u2L9/P2xtbemOI7CEbYGLyBe+hlOQf/nlF5E8BZlu9fX1uHLlCv744w8MGDAA+vr6uHjxIoYPH44HDx7g9u3bWLduHWxtbZs9/9/Qy0eID3V1dVy+fBmGhoYwNTVFcnIybVkaekRXrFiB0aNH05ZDGAjbnp3CcSeyhWpqavDjjz/CysqK7MrCQy9fvkRkZCTCwsIQFRWFnj17gs1mY8uWLbC2toa0tDRPrkNGfOJJWloamzZtgr29PcaOHYt58+Zh6dKlfG0dqKiogKurK8aNG4eZM2fy7brCStgKn8gubqEoCt7e3nj//j3Onj1LlsO3AofDwc2bN7m7pWRmZmLo0KFgsVhgsVhQVVVtk+sGBQUhODgYZ86caZPnJwTf8+fPMX78eCgoKODYsWN8OZy4rq4Oo0ePRpcuXXDkyBGyuKoJ6urq0LFjRxQXFwvFobQiO9W5fPly5Obm4tSpU6TotcDr169x8uRJeHl5oXv37pgyZQoqKiqwYcMGvHz5EmfPnoWPj0+bFT2AjPgIQFVVFQkJCTA1NYWpqSkuX77cptejKAozZsxAbW0t/P39SdFrok8PpRUGIjnVuXv3bpw9exYpKSmkQb2JOBwObt26xR3VZWRkwMHBAWw2G2vXroW6ujrfMzGZTOTl5YGiKPIGJMakpKSwbt06DBo0CO7u7pg1axaWLVvWJh9oV69ejdu3byMhIYFnU/biomGBizAcSityhe/cuXNYt24dkpKS0K1bN7rjCLQ3b94gKioK4eHhiIiIQNeuXcFisfC///0Ptra2tO9MoaioCEVFRRQWFpKGYQLOzs64efMmJkyYgMTERBw/fvy7p3c0x4EDB3D06FGkpqYKxXSdoBGm+3wiNdWZkpKCX375BRcuXICWlhbdcQROw6hu7dq1sLGxgaamJk6cOAFLS0tcu3YNGRkZ2LJlC4YOHUp70WtApjuJT/Xq1QuxsbGwtLSEqakp4uLiePK8oaGh+OOPPxAREcHTYipOhKnwiczilszMTNjb2yMgIADOzs50xxEYpaWliI6ORlhYGCIiItChQwfubimDBg2CnJwc3RG/acqUKbCysoKPjw/dUQgBEx0djYkTJ2LatGlYsWJFi6c+r169ih9++AGhoaEwNzfncUrxIUyH0orEVGdRURFYLBY2btwo9kWPoijcu3ePe6/uzp07sLOzA4vFwooVK6CtrU13xGYhvXzE1wwbNgxpaWnw9PTE5cuXceLEiSZtjPCp7OxsjBo1CocPHyZFr5VUVFTAYDDw4sULgT+bT+inOhsa1KdOnYpJkybRHYcW7969w5kzZ/Dzzz9DVVUVP/74I4qKirB8+XIUFxfj0qVLmD17ttAVPYBMdRLf1qNHD0RHR8POzg6mpqaIiYlp8mNfvHgBZ2dnrFu3Di4uLm2YUjwwGAyh2cFFqKc6a2pq4OLiAm1tbezdu1fgh9e8QlEUHjx4wD3Z4NatW7C2tuZOYTKZTLoj8sydO3fw008/4cGDB3RHIQRcXFwcvLy8MGXKFPz555/f3CmorKwM9vb2GD16NFauXMnHlKJt3rx5UFNTw+LFi+mO8k1CW/goisLEiRNRWlqKs2fPCs1xGC1VVlaGmJgY7pl10tLS3EI3ePBgkW3bKC8vh5KSEioqKsihn8R3vXjxAj/99BNqa2tx6tQp9OzZ84vvqampgaurK3r37o19+/aJzQdmfvD390dSUhICAgLojvJNQvtO8vvvvyM7OxuBgYEiWfQoikJ6ejp8fX0xZMgQ9OzZE3v37oWBgQFiYmKQl5eHXbt2wcXFRWSLHgAoKCigc+fOKCgooDsKIQRUVFQQGRkJR0dHmJmZITIy8rOvUxSFqVOnol27dti9ezcpejwmLCs7hbJi7NmzB6dPnxa5E9TLy8sRFxfHXZjCYDDAYrGwYMECDB48WGx7ixru86mpqdEdhRACkpKSWLlyJQYNGgRPT094e3tj9erVkJKS4u7oFBsbK5IfmOlmaGiIzMxM1NXVCfTPV+imOs+dO4dZs2YhOTlZ6Hv1KIpCVlYWt9BdvXoV5ubm3ClMAwMD8okUwM8//4yBAwdi2rRpdEchhExJSQm8vLzw4cMHDB8+HMePH0dKSgrZ3KINCcOhtIJbkhuRmpqKX375BREREUJb9CoqKhAfH89dmFJfXw8Wi4VZs2bh7NmzUFRUpDuiwCErO4mWUlZWRnh4OLy8vLBq1Sr4+/uTotfGGlZ2ksLHA1lZWRgzZgyOHTsGMzMzuuM0GUVRyMnJ4Ra61NRUDBgwAGw2GxcvXoShoSEZ1X2Hjo4OUlNT6Y5BCKmUlBRER0fj77//xh9//IGHDx9i7dq1ZC/ONtJwn8/NzY3uKF8lFIWvqKgIzs7O2LBhg1A0qFdWViIhIYE7hVlZWQk2m41p06YhJCQEHTp0oDuiUCEjPqKl0tPTMXbsWJw8eRKOjo4YOXIkvL29YW9vj8DAQFo2Xxd1RkZGOHHiBN0xvkng7/EJS79Nbm4ut9UgOTkZJiYmYLFYYLPZMDIyIqO6Vvjw4QO6du2K8vJycsQU0WTPnz+HjY0N1q1bB09PT+7fczgc+Pr6YsuWLfD398eIESNoTCl6srKywGazkZeXR3eUrxLowldTU4MRI0ZAU1NT4PptqqqqkJiYyJ3CLCsr4xY6R0dHdOrUie6IIkVVVRXJycnQ1NSkOwohBEpLS2FnZwdvb28sWbKk0e9JTU2Fh4cHxo4di/Xr10NGRobPKUVTfX09OnToINCH0gpsHx9FUfDx8YGsrKzA9Ns8fvwYe/bsgaurK5SVlbFmzRooKysjKCgIBQUFOHToEMaOHUuKXhsg051EU1VVVWHUqFEYMmTIN3cQsba2xq1bt5CdnY1BgwbhyZMn/AspwiQlJaGvry/Quy0JbOFbsWIFsrKycOrUKdr6QaqrqxETE4OFCxfCwMAAlpaWuH79Ory8vPDkyRMkJydj+fLlMDExIbuKtDEmk4nc3Fy6YxACjsPhwNvbG8rKyti2bdt3PzB37doVFy5cwLhx42BhYYHz58/zKaloE/RGdoFc3LJ3716EhIQgJSUF8vLyfL12fn4+d/oyMTERhoaGYLFYOH78OClwNCIjPuJ7KIrCwoULUVJSgoiIiCb/t8pgMLBo0SLY2Nhg/PjxSEhIwKZNm8jUZyuQwtdM58+fx5o1a5CUlAQlJaU2v15NTQ2Sk5O5KzBfvnwJZ2dneHh44PDhw+jatWubZyC+j8lk4vLly3THIATYli1bEBMTg+Tk5BadM2lpaYnbt29j8uTJsLGxQVBQkND2C9PN2NgYoaGhdMf4KoEqfKmpqfDx8UF4eHibHqHz7Nkz7grMuLg4GBgYgMVi4ciRIzAzMyOjOgFEzuUjvuXkyZPw8/NDampqq+6xd+7cGefOnYOfnx8sLS2xb98+jBkzhodJxUPDiI+iKIFYn/FfArOqMysrC/b29jh8+DBYLBZPn7u2thYpKSncKcyioiI4OTmBzWZj+PDhfBlZEq1TWVmJzp07o7y8XKD3ACT4LzY2FhMmTEBsbCz69u3Ls+e9ceMG3N3d4eLiAl9fX8jKyvLsuUUdRVFQVlbG3bt3Gz0hg25t/g5SXlGGm/euoqikEFVVlZCTa4ceyj0xwNgSCvIft+d68eIFWCwW1q1bx7OiV1BQgIiICISFhSEuLg46Ojpgs9k4cOAABg4cSPrBhEy7du2grKyMp0+fkuknguvOnTvw8PDA6dOneVr0AGDgwIG4desWpk6dCmtrawQFBUFHR4en1xBVDYfS3r9/X7wK37PCfMSlRiIrNwNgMFBXV8v92n0paUQlXoKeTh9Y9reFh9tPmDRpEqZMmdLi69XV1eHKlSvce3XPnj3D8OHDMXLkSOzZswfdu3fnxcsiaNSwwIUUPgIAnjx5AldXV+zZsweDBg1qk2t06tQJp0+fxu7du2FtbY1du3YJ9FZcgqSh8Dk5OdEd5QttMtV5JS0JoTFnUVdXBwpff3oGGKivr0dVKQc7tzT/BPWioiJEREQgPDwc0dHR0NLS4jaRm5ubkykxETN9+nT07dsXs2fPpjsKQbPXr1/DxsYGs2bNwpw5c/hyzbS0NLi7u2PYsGHYtm1bixbQiBNBPpSW56s4GopebV3tN4seAFCgICEpgY7d2+HqreTvPnddXR1SUlKwYsUKmJqawtDQEOHh4WCz2Xj48CHS0tLwv//9D9bW1qToiSDSy0cAH7ewGzFiBEaOHMm3ogcAZmZmSEtLw5s3b2BpaYns7Gy+XVsYGRsbC2xLQ4uqQ3V1NWbOnImYmBi8efMG2traWL9+Pfr264PjwYexZ9NhSMv8+9TmdqawHjwQAJAQkYrMezmorq6GnJws+g00hKXDAITGnINqD3W8LnmLqVOn4uHDhzAwMMCmTZvw/PlzhIeHIyoqCurq6mCz2dixYwesrKxIgRMjTCYTcXFxdMcgaFRXVwcPDw/uew6/dezYEYGBgdi/fz9sbGzg5+cHDw8PvucQBoJ8KG2L0tTV1UFNTQ2JiYlQV1dHWFgY3Nzc8OfGZaivrwcAzP3dBxKSXw4ojc0MYD1kIGRkpFH2vhwhRy6gi1Jn6BnqIDoxDH/9tgGjRo2Ck5MTjh8/jmHDhmHUqFFwdXXF1q1bBfJGKcEfpKVBvFEUhdmzZ6OyshIhISG0tR0xGAxMnz4dlpaWcHNzQ3x8PHbs2IF27drRkkdQycvLo2fPnsjJyYGBgQHdcT7Tot8ceXl5rFq1CpqampCQkICrqys0NDSQmpry3enNLkqdISPz7zlYDAYDb9+8AwUK4RFheP78OeLi4sDhcHDs2DGoqqril19+wZQpU0jRE3NaWlp4+vQp6urq6I5C0GDt2rW4fv06zpw5IxC7qvTv3x9paWkoLy+HhYUFMjMz6Y4kcAR1BxeejD+Li4uRnZMNGydT7t/t9z0KMABNbTXYO1ujvfy/n4auJabhSuJN1NbUoWPnDuhjrAsAeP2yFP1MjHHr5m3u9xobGyM9PV0ozuEj2pacnBxUVFTw5MkTsqxczBw6dAiHDh1CamoqFBUV6Y7DpaioiBMnTsDf3x92dnbYunUrvLy86I4lMAT1UNpWF77a2lp4enrCzsEGHbt0QE11DbxmjIOySjdUVlYh5uJlXAqJxrhJP3AfY2FvBvNBpigpeoWch48gK/fx01tNTQ3k2n3+Sa5jx44oKytrbUxCRDS0NJDCJz7CwsKwfPlyJCYmQkVFhe44X2AwGPDx8YGFhQXc3NyQkJCAnTt3on379nRHo52xsTGOHz9Od4wvtGqSnMPhwMvLCzIyMhjvPRYAICMrA5VeypCQlIC8QnsMdbXDk9xnqKmu+eyxDAYD3XsqQVpKCsmx1z8+VkYa5eUVn33f+/fvBeoTHkEvslm1eLl+/TomTpyI8+fPQ09Pj+4432RsbIybN2+ipqYG5ubmyMjIoDsS7QR1qrPFhY+iKEydOhXFxcU4c+bMVw8cbOjN+1q7IIdD4d2bdwCArspdkJfzGLNnz8bOnTsRGRmJW7duQV9fv6UxCRFDCp/4yM3NxciRI3Hw4EFYWlrSHadJFBQUcPToUSxcuBD29vY4cuQI3ZFopaOjg6KiIoGbtWvxVOeMGTPw8OFDxMTEoF27j9uQ3ZeSwtPHzyEnJ4vOXTuhqqoasaFJUOvdC7JysqA4FO7eTIe+kQ5k5WTxoqAEt6/dh4X9x3uDvXXUIScri4KCAtTW1mLXrl0oLi7Gjz/+CC0tLejq6nL/MJlM6OrqQkVFRSA3QSXaBpPJRFRUFN0xiDZWXFwMZ2dn/PXXX/jhhx++/wABwmAwMGXKFFhYWGDcuHGIj4/Hnj17+H7EmiCQlJSEgYEB0tPTBerDS4t2bsnPz4empiZkZWU/688Y4mILiuIgKfoqPlRUQkZWBho6arB3soKCojwoDoXTx0Lx4nkx6uvroaAoj74m+rCwNwODwYCUpBRcBo3F3DnzkJGRAQMDAxw8eBB6enrIy8tDdnY2909OTg6ys7NRWVn5RTFs+OfOnTvz9IdF0C8rKwsuLi6kkV2ElZeXY/DgwWCz2fjrr7/ojtMqFRUVmDVrFq5du4aQkBCe7ycqDCZNmgQbGxv4+PjQHYWLp1uWBZz+GxlZ97/b0tBoEDBgqGcM77HN++G8ffsWOTk53EL4aWGUlZVtdJSoDnaI3gAAIABJREFUo6NDbjwLqZqaGigqKqK8vBzS0tLffwAhVGpra/HDDz+gV69eOHDggMjM5gQEBGDx4sXYsGEDpkyZIjKvqym2bt2KJ0+ewM/Pj+4oXDwtfM8K87Hv2HbUfrIhdVNRHAq/eM4FU4s3N7ApivrYZvGfEWJ2djYePXoEJSWlL0aJurq60NTUJG+oAk5LSwsRERHQ1dWlOwrBQxRFYfLkyXj16hXOnz8vcLt9tFZGRgbc3NzQv39/7Nu376vrIkRNdHQ01q1bh/j4eLqjcPF8k+oraUkIjT2L2tqmFz9pKWm8ev4eSbFXEBQUBCMjI15G+kJ9fT2ePn3a6CixoKAA6urqjY4Ue/XqRQ6pFQBOTk6YO3cuXFxc6I5C8NCKFSsQHR2NuLg4kb0f9uHDB8ydOxdJSUkICQmBsbEx3ZHa3IsXL2BoaIhXr14JzEiX5x+prMzsAKDJpzNISUnD1XE0rMzscPToUQwZMgRr1qzBtGnT2uyHJCkpid69e6N3794YPnz4Z1+rrq7Go0ePuIXw1q1bCAwMRHZ2Nt69ewcdHZ0vRolMJhPdunUTmH+poo6s7BQ9e/fuRXBwMFJSUkS26AFA+/bt4e/vj+PHj2Po0KFYu3YtfHx8RPq9o3v37pCQkEBRUZHA7L7VZiewPyvMR3xqFDJz0784j09KShqgKOjrGGKw9XCo9dTgfi0rKwvu7u5gMpk4cOAAOnXq1BbxWqSsrAy5ublfjBKzsrIAoNFRIpPJJH2IPLZjxw5kZ2dj9+7ddEcheODcuXOYNWsWkpOTxeqsxczMTLi5ucHQ0BD79+9Hhw4d6I7UZoYMGYKlS5cKzNl8bVb4Gnw8gf0aXpQUoLKqEu3k2kFFuRcGGFtwT2D/r6qqKixZsgSXLl3CqVOnYGFh0ZYRW42iKLx+/brR+4m5ubno2LHjF6NEXV1daGlpQVZWlu74QicsLAzbt28nbQ0iICUlBaNGjUJERATMzMzojsN3lZWVmD9/PuLi4hAcHAwTExO6I7WJ+fPnQ1VVFYsXL6Y7CgA+FL7WOHfuHKZPn45FixZh8eLFQnl/jcPhoLCwsNFWjPz8fPTs2bPRdgx1dXVISkrSHV8g5eTkwMnJCY8ePaI7CtEKDx8+hIODA44ePSowIwG6BAYGYs6cOVi9ejWmT58uclOfBw8exOXLlwXmUFqBLnzAx57BCRMmQFFREUePHoWysjLdkXimtrYWT548+WKUmJ2djZcvX6J3796kab8RtbW1UFBQQFlZmUDs0k80X2FhIaytrbF69Wp4e3vTHUcg5OTkwM3NjXubp2PHjnRH4pnr169j2rRpuH379ve/mQ8EvvABH9/o/vzzTwQEBODo0aMYOnQo3ZHa3IcPH0jT/jfo6OggNDSUbGcnhN69e4dBgwZh/PjxWLZsGd1xBEpVVRUWLVqEyMhIBAUFicz0b0VFBZSUlPD+/XuBaFMRisLXIDo6GhMnTsSUKVOwatUqgfgB0oE07QMsFgszZ87EiBEj6I5CNEN1dTVYLBb69OmDnTt3ivXMxbcEBwdj9uzZWLlyJWbPni0SPycdHR1cvHhRIA6lFarCB3zcw8/LywuVlZU4efIk1NTU6I4kMP7btP/pKFHUmvbnzp0LTU1NLFy4kO4oRBNxOBx4enqipqYGwcHB5B72d+Tl5cHNzQ2ampo4ePCgQK1wb4nRo0fDw8NDIM7mE7ohU/fu3REREYFNmzZhwIAB+PvvvzFy5Ei6YwkEBoMBFRUVqKioYNCgQZ99rbGm/cjISGRnZ6OwsFDomvaZTCY59kXI/Prrr3j+/DmioqJI0WsCbW1tpKamYsmSJTA1NUVgYCDMzc3pjtVixsbGAnMordCN+D515coVeHh44IcffsDmzZtJa0ALfdq0/9+FNoLatB8REQFfX1/ExMTQcn2iebZt24YDBw4gOTkZXbp0oTuO0Dl79iymT5+O5cuXY968eUI59Xn69GkcO3YM//zzD91RhLvwAR/vd02dOhVPnjxBUFAQmEwm3ZFESmNN+w1/APqa9vPy8vB/7d17XM73+wfwV6lUwigREiqmEhVFJ51lhWnsvpVSOY0x1r45zNlMjGERGklqHUyG9W2WjjqYHCKWQ/VtaSGHpHRQd/fn90e/bmtC0d3nPlzPx8M/bvd9v9qq634frvfbzs4OxcXFQn0f8v6io6Ph7++PzMxMDBo0iO04YquoqAgcDgf9+/fH4cOHxe4DxO3btzFp0iSRaEMS+8IHNK1t7d+/H+vXr8euXbswa9YstiNJvDc17efn5+ODDz4QatM+j8eDiooKKioqoKio2AFfERGGlJQUcDgcJCUlCf0MXmlQX1+PFStW4JdffkF0dLRI3XH3No2NjejevTvKyspYP81KIgpfs6tXr4LL5WL8+PHYu3evRJ/5J8r4fD5KS0tb3XXakU37w4cPxy+//AI9PT0hfjXkXeXm5sLBwQExMTGwtbVlO45EOXXqFObPnw9/f3/4+fmJ5Dp8a0xMTBAUFMR6wZaowgc0XWK5ZMkSnD/fdNPDqFGj2I5E/qG9TfvNxbG1pn0XFxfMnz+fNjeJoLt378LCwgI7duwAh8NhO45EKi4uBofDgZqaGsLCwqCqqsp2pLfy8fGBubk565fSSlzhaxYREYEvv/wSGzduxMKFC8VyMVjatLdpPy4uDvr6+li7di3b0ck/lJeXw9LSEvPmzcOXX37JdhyJVl9fj6+//ho///wzoqKiYG5uznakN9q5cyeKioqwZ88eVnNIbOEDgDt37oDD4UBbWxsHDx6UmlNNJFFrTfuZmZl48OBBq+uJurq6Ete0Lw5qa2vh6OiIcePGYceOHWzHkRpxcXGYM2cO/Pz84O/vL7JTn2fPnsW3336L1NRUVnNIdOEDmrbq+/v74/Tp04iKisL48ePZjkQ6SEJCAgICAhAVFSUVTfuirrGxETNmzICioiIiIiJE9pevpCopKQGXy0XPnj0RFhaGPn36sB3pFaJyKa3EF75mzYvBov6JiLRdUVERrK2tUVJS0urjrTXtN/8Rx6Z9UcYwDBYvXoxbt24hPj6eempZ0tDQgDVr1iAyMhKRkZGwsrJiO1ILDMNAXV0d165dY/VSWqkpfEDTgru7uzu6deuGo0ePom/fvmxHIu+hsbER3bp1Q3l5ebunNNvStN/aSFFVVZXWi1sREBCA6OhonDt3TqJuFRBX8fHx8PX1xZIlS7Bq1SqR+iBnb2+P5cuXs3oVlVQVPqCp/2vDhg04fPgwjh49CgcHB7YjkfcwYsQI/PzzzzAwMOiw1xTVpn1RFRYWhvXr1yMrK4vVT/Gkpb///hszZ86EsrIywsPDReZKN1G4lFbqCl+zpKQkeHl5wdvbGxs3bpTamx7E3ZQpU+Dj44Np06YJ/b3a0rTf2iixo5r2RdGZM2fg7e2NlJQUkTh1n7TE4/EEV7pFRETAxsaG7UgICQlBWloajh49yloGqS18QNNND15eXnj+/DmioqLoOCUx5Ofnh379+mH58uWs5mhP0/4/i2N7m/ZFyeXLl+Hs7IyTJ0/CwsKC7TjkDRISEjB79mwsXLgQq1evZvV7ThQupZXqwgc0/cLasWMHvv/+ewQHB+Pjjz9mOxJph/379+PKlSs4ePAg21Fe621N+0OHDm21HaO1pn1RUVhYCCsrK+zbt49+ZsTEvXv34O7uDjk5OURERKBfv36s5BCFS2mlvvA1++OPPzBz5ky4urpi+/btdP6jmEhMTMTmzZtZ7wt6V21t2v/3FCqbd7M9evQI5ubm+Oqrr/DZZ5+xloO0H4/Hw6ZNmxASEoKjR4/C3t6elRy6uro4ffo0a9PjVPj+oaKiAnPnzkVhYSFiYmIwbNgwtiORtyguLoa5uTlKS0vZjtLhmpv2WxspKikpsdK0X11dDVtbWzg5OWHz5s1Cex8iXImJifDy8sK8efOwbt26Tp/6dHNzA5fLZe1uPip8/8IwDIKDg7F27Vrs3LkTnp6ebEcib8Dn89GtWzc8fvxYag4lZxgGZWVlnd60z+PxMHXqVKirq+Pw4cMiOw1L2ubBgwfw8PAAn89HZGQkNDQ0Ou29169fj8bGRtY+PFHhe43c3FxwOByYmpoiKCgIKioqbEcir6Gvr4/IyEg6kBwvm/ZbGyW+T9M+wzCYO3cu7t27h9OnT9OpNxKisbER3377LQ4cOICwsDA4Ojp2yvuyfSktFb43qK6uxpIlS5CZmYmYmBiMHj2a7UikFR9//DE8PT3xySefsB1FpL1P0/6GDRsQHx+PlJQU+hAogVJSUjBr1iz4+Phgw4YNQt90wvaltFT42uCnn37CsmXLsGHDBixatIimeESMv78/VFVVsXLlSrajiK03Ne3X19ejsbERkydPhqGhITXtS6iysjJ4enqirq4OUVFRGDBggNDei+1LaanwtVF+fj64XC60tLQQEhJCNz2IkODgYGRnZyMkJITtKBKn+YzbPXv2oKamhpr2JRyfz0dAQAD27NmDI0eOwNnZWWjvNWbMGOzdu5eVS2npuJI20tXVRVZWFlasWAEjIyNERkaK/N1X0kJXVxc//fQT2zEkzvnz5zF37lzEx8dj7NixrzzeWtN+WlqaRDftSzpZWVmsXr0aVlZW8PDwwKxZs/DNN98IZepz5MiRyM3NZaXw0YjvHZw+fRrz5s3DsmXLsGLFCpE6AFYalZSUwNTUFPfv32c7isS4ffs2JkyYgNDQUEyaNKndz5fEpn1p8+jRI3h5eaGqqgpRUVHQ1NTs0Ndn81JaKnzvqKSkBO7u7lBUVER4eDhrpyCQppGHiooKa+sFkub+/fswNzfHunXr4OPj0+GvL45N+9KKz+dj+/bt2LVrF0JCQuDi4tJhr83mpbRU+N4Dj8fDxo0bERISgiNHjsDJyYntSFJr5MiROHr0KIyMjNiOItYqKysxYcIEfPLJJ1izZk2nv397m/aHDRsGHR0dKCkpdXpWaZKRkQF3d3dwOBxs2bKlQ9pZ2LyUlgpfB0hOToanpye8vLywadMm6nFiAdsnQUiC+vp6uLi4QFtbG/v37xepKce2Nu3/e5T4vk375KXHjx/D29sbT548QXR0NLS0tN7r9RiGQd++fXH16tVOv86KCl8HefjwIWbPno1nz54hKirqvb8pSPusWLECPXv2xNdff812FLHE5/MFN5XExsaK1eaT9jbtNxfHtzXtk1fx+Xzs3LkT27dvx8GDBzFlypT3ej17e3v4+/sLdfdoa6jwdaDmb4rvvvsOBw4cgJubG9uRpMahQ4eQmZmJ0NBQtqOIpRUrViA9PR2JiYlCPeuzs/27af+fxfFtTfuiNOIVNefPn8fMmTPh5uaGrVu3QkFB4Z1eZ9myZRgwYAD8/f07OOGbUeETggsXLmDmzJmYNGkSvv/+e7rpoROkpqZizZo1yMjIYDuK2AkMDMS+ffuQmZkJVVVVtuN0mqqqKuTn578ySrxz5w5kZGRa3XVKTfsvlZeXw8fHBw8ePEB0dDSGDBnS7tdg61JaKnxCUlFRgfnz5+POnTuIiYnB8OHD2Y4k0UpLS2FsbIyysjK2o4iVn3/+GV9++SUyMjIwePBgtuOIBIZh8OTJk1ZHidS03xLDMNi9ezcCAgIQHByMadOmtev5bF1KS4VPiBiGwY8//og1a9Zgx44dmD17NtuRJBbDMFBRUcH9+/fRo0cPtuOIhbS0NMyYMQMJCQl0Dm0btda03/zn7t27Utu0f+HCBXC5XEyePBnbt29v8wcAti6lpcLXCa5fvw4Oh4MxY8YgKCiIpkqEZNSoUTh8+DBMTEzYjiLybty4ATs7O0RFRbF2Gamk+WfT/r832jQ37bc2Uuzbt69ErCc+ffoUvr6+KCkpwbFjxzB06NA2PU9XVxenTp2Cnp6ekBO+RIWvk1RXV2Pp0qU4d+4cYmJiqN9MCKZPn47p06eDy+WyHUWklZSUwMLCAlu3boW7uzvbcaRCTU0NCgoKXhkp5ufno66urkUxFOemfYZhsGfPHmzevBn79u3D9OnT3/ocNzc3cDgccDicTkjYhApfJ4uKisIXX3yBdevWYfHixRLxSU9UrFq1CsrKyli7di3bUUTW06dPYWVlBW9vb/znP/9hOw7B25v2WxslinrT/qVLl8DhcODs7PzWDX7r1q9FvUwtRhmNRF1dLRQVlaCh3h9jDMdBpZtwZseo8LGgoKAAXC4XAwcOxOHDh9G7d2+2I0mEw4cPIzU1tdN3iImLuro6TJw4EcbGxti5cyd96BJx7Wna/2dxFJWm/WfPnmHu3LkoLCxETEwMdHV1Wzxecq8YyVm/I+/OdTTyGiHb5WVPpZycPMAwGK6jBzvzidDs37F90VT4WPLixQusWrUKsbGxiIyMhIWFBduRxN65c+ewcuVKZGVlsR1F5DQ2NoLL5UJWVhZRUVHUuC3m3ta0r6Wl1Wo7Rmc37TMMg3379mHDhg3Ys2ePYBni/OV0xCWeAI/HA4PXlyAZyEBOTg6uDm4Yb2LVYbmo8LEsLi4Oc+bMwRdffIGVK1dK9M4vYbt//z4MDQ3x6NEjtqOIFIZhsHTpUuTm5uLMmTPUVyrhRLFp/8qVK+BwOLC3t8dM7xlISItDA6+hzc+Xl5eHq33HFT8qfCLg77//hru7OxQUFBAeHg4NDQ22I4klhmHQo0cPlJSUiN2mAGH67rvvEB4ejvT0dPrvIuU6o2n/xYsXWLRoERITE1FeXg5tbW0EBATAwsIC3r5e+CX2FOQVXrYumFoZw9y26b7H+Ngk3My9gy7/mPb8Ys08yMrKouJJFf5IykHJ3RIAgImJCQIDA99pNyhdRCsCBg4ciOTkZGzevBkmJiYIDQ3FxIkT2Y4ldmRkZKCjo4P8/PxWL06VRhEREQgKCkJmZiYVPYLu3bvD2NgYxsbGLf6+tab948ePv1PTPo/Hg6amJtLS0jBo0CDEx8fj008/xfXr12Fua4pfYk/hi9XzWqzp/ZOppRGsHF+9nFZJuStmL5gJv4UrwefzERQUBC6Xi9zc3Hb/d6DCJyLk5OSwYcMG2NjYYNasWfDw8MDmzZtFYpFanOjq6lLh+39nz57FV199heTkZAwcOJDtOESEycjIQE1NDWpqajA3N2/xWGtN+2lpaW9s2p89ezY0NTUhKysLV1dXDBkyBJmZGfjf3YJ3zthVSQFPKstQXfMcil2V0KVLFxQUvNvrUeETMTY2NsjJycHs2bNhbW2NqKgoOkqqHZoLn7TLycmBh4cHYmNjoa+vz3YcIsZkZWWhqakJTU1N2NnZtXjs3037eXl5OHnyZIum/UGDBiEvLw85f14Cw29aWQvecRSQAQZra2KCszmUu71szbiafQNXs2+gZ68eMJtgguH62i/fUEYG/fppoK62Dnw+H5s2bXqnr4nW+EQUn8/Hrl27sG3bNuzfvx+ffPIJ25HEwpEjR5CYmIiIiAi2o7CmqKgIlpaWCAwMpO8bwpqamhrcvHkTPj4+UFRUxDhbY8h25aP8cQXU+6mhtrYOib+eQ/2LeszwbrreqOzeI/T4oDu6dlXAXwV3cTomAdNnT8ZArZf7HowNxmKyw3SEhYVBS0vrnW6Fpz3NIkpWVhZfffUV4uLisHz5cixcuBC1tbVsxxJ50j7ie/z4MZydnbFq1SoqeoRVioqK2L59OwYOHIjMzEwYGY+GQlcF9BugDtkusuimogx7Vyv8VVCC+hf1AIC+/ftASVkRsl1kMXT4YOiNGob8vMIWr1tbV4tu3brhs88+g5eXFx4+fNjubFT4RJypqSmuXLmC8vJyjBs3Djdv3mQ7kkjT0dF553l/cVdTU4PJkyfDzc0NixcvZjsOkWIMw2DOnDkoKytDbGws5OXlIdfl1ZW15vaJ1048ygD/fkhJsWlalM/no6amBqWlpe3OR4VPDPTs2RPR0dFYvHgxrK2tERoa+vpvFCmnrq6OhoYGlJeXsx2lU/F4PHC5XOjq6mLLli1sxyFSbuHChcjLy8PGjRuxa9cu2Nra4scDh1BafB/lj56C4TOoralDUlw6NIcMQFfFpp2ht28UoP5FPRg+g6L8u8i7egc6Hzbd8/dXQQkelz1FH9V+qKyshJ+fH3r16oURI0a0Ox+t8YmZGzdugMPhwMjICPv376ebHlphYmKCffv2wczMjO0onYJhGCxYsADFxcWIi4ujncCENXfv3kV0dDRWrFgBoGlEJycnhy5duuD773fg93NxSPs9EzXVtVDoqgAtHU1MmDgeKt27AQAiD57AowdPADBNm1usTTDCsOmos9s3CpCRlI0XNfVQUlKCqakpAgICYGho2O6cVPjEUE1NDZYuXYrU1FTExMS80pMj7bhcLlxdXTFr1iy2o3SKTZs24eTJk0hLS6MPQqRTVVVVITU1FWfPnkVCQgKePHkCR0dHwZ9/t9GEHf8Rebevv/GYsteRgQz0hxvCa/q8985N7QxiSFlZGQcPHkR0dDQmTpyINWvW4IsvvqBDh/+fNG1wCQkJwZEjR5CVlUVFjwhdY2MjLl++jISEBJw9exZXrlyBqakpnJycEBkZidGjR7/xLFA784m4U3izXceVNZOTk4etudP7xBegEZ+YKywsBJfLhYaGBkJDQ6Gqqsp2JNYdPXoUZ86cQWRkJNtRhOq///0v5syZg3PnzmHYsGFsxyESqri4GAkJCUhISEBycjI0NDTg5OQEJycnWFlZoVu3bu16vfOX0xGXdAINDeyd1UmbW8SctrY2MjMzoaurCyMjI6Snp7MdiXXSMOK7cOECvL29cerUKSp6pENVVlbi9OnTWLx4MYYNGwZTU1OkpqbC1dUVubm5uHHjBnbu3AlnZ+d2Fz0AGG9iBVd7N8jLyUMGb56lkoEM5OUUOrToATTikyjNI4DFixdj1apVUnvTw+PHj6Gjo4OnT59K5PTvnTt3MGHCBBw8eBCurq5sxyFijsfj4dKlS4J1uqtXr2LcuHFwdHSEk5MTDA0NhXKVUcm9YqRkJeBWwZ+AjAx4/5j+bL6P70MdfdiaO9F9fOTNSktL4eHhAVlZWURERKB///5sR+p0DMOgV69eKCgogJqaGttxOlRZWRnMzc2xatUqzJ07l+04REwVFRUJ1umaz3J1cnKCo6MjrKysoKys3GlZnldX4VLuBTx4WIraulooKSqhn/oAjDE0oxvYSds1NjZi8+bNOHDgAEJDQ+Hs7Mx2pE43duxYBAYGYvz48WxH6TBVVVWwsbHBlClTsH79erbjEDHy7NkzpKSkCIpdVVWVYETn4OAgdVehUeGTYGlpaZg1axZmzpyJzZs3Q0FBge1Incbd3R3Ozs7w8vJiO0qHaGhogKurK7S0tBAcHCyRU7ik4/B4PFy8eFGwKSU3Nxfm5uaCYjdy5Eip/h6iwifhHj9+DG9vbzx69AjR0dEYMmQI25E6xfr168Hn8/HNN9+wHeW9MQwDb29vlJeX45dffoGcHHUhkVcVFhYK1ulSUlKgpaUlKHSWlpZQUlJ6+4tICfoJknBqamr49ddfsWvXLpiZmSEoKAgzZsxgO5bQ6erqIi4uju0YHWL16tW4ffs2kpKSqOgRgYqKCiQnJwuKXU1NDZycnODm5oZ9+/ahX79+bEcUWfRTJAVkZGTg5+cHKysrcLlcJCUlYdeuXRL9CVBSWhqCgoIQGxuLzMzMd9o6TiRHQ0MDsrOzBet0169fh4WFBZycnLBo0SIYGBhI9fRle9BUp5SprKzEggULcOPGDcTExEBPT4/tSEJRXl6OwYMH49mzZ2L7y+DEiRNYsmQJ0tPTMXToULbjkE7GMAwKCgoEI7rU1FQMHTpUMH1pYWEBRUVFtmOKJSp8UohhGISEhGDVqlXYtm0bfHx8xLY4vImqqiry8vLQt29ftqO0W0ZGBtzc3HDmzBk6i1WKPH36FMnJyYJNKfX19S12X6qrq7MdUSJQ4ZNif/75JzgcDgwNDXHgwAH06NGD7UgdyszMDDt37oSFhQXbUdolLy8Ptra2CA8Ph5NTx5xNSERTQ0MD/vjjD8GoLi8vD5aWloJip6enJ5EfStlGR5ZJMX19fWRnZ6N79+4wNjbGpUuX2I7UocRxna+0tBSTJk3Cjh07qOhJIIZhcOfOHezduxdTpkyBmpoali1bhoaGBmzZsgWPHj1CfHw8vvzyS+jr61PRExLa3CLllJWVERwcjJiYGEyaNAmrV6/G0qVLJeIHTtwKX0VFBSZNmoRFixbB09OT7Tikg5SXlyMpKUkwfcnn8+Ho6Ah3d3eEhISgT58+bEeUOjTVSQT+97//gcvlom/fvggNDRX7474iIyNx8uRJHDt2jO0ob/XixQs4OzvDwMAAgYGBEvHBQ1rV19fj/PnzgunLW7duwdraWjB9+eGHH9L/X5ZR4SMt1NfXY/Xq1YiOjsZPP/0Ea2trtiO9s4sXL2L+/PnIyclhO8ob8fl8zJw5E42NjYiJiZHaw8XFFcMwuH37tqDNoPmaqOazL8ePH4+uXbuyHZP8AxU+0qr4+Hj4+vpi0aJFWL16tVj+Mq6oqICmpiYqKytF+hO2n58fLl26hISEBNqeLiYeP34smL48e/YsAAjuqLOzsxP72RJJR4WPvNa9e/fg4eEBAPjpp5/E8qaHPn36IDc3V2QP4d25cydCQkKQkZGBXr16sR2HvMaLFy9w/vx5wTpdfn4+rK2tBcVu2LBhIv3hirREm1vIa/Xv3x+JiYnYsmULjI2NcfjwYXz00Udsx2qX5g0uolj4oqKisGvXLmRlZVHREzEMw+DmzZuCdbr09HSMGDECjo6O2LlzJ8aNGydVh75LGhrxkTY5d+4cPDw8wOFwsGXLFrH5offy8oKNjQ18fX3ZjtJCcnKy4Pi4kSNHsh2HAHj06BESExMFxa5Lly6YOHEiHB0dYW9vj969e7MdkXQQGvGRNrG2tkZOTg4yl4rUAAAN+klEQVR8fHxgaWmJ6OhosThGSxRbGq5duwYul4tjx45R0WPRixcvkJmZKVinKygogI2NDZycnLBy5Uro6urS9KWEosJH2kxNTQ2nT59GYGAgzMzMsHfvXnA4HLZjvZGuri6OHz/OdgyB4uJiuLi4YO/evbCxsWE7jlRhGAZ5eXmCdbrMzEzo6enByckJP/zwA8zMzCAvL892TNIJaKqTvJPLly+Dw+HAzs4Ou3fvhrKyMtuRWnX58mX4+vri2rVrbEfBkydPYGlpic8++wxLly5lO45UePjwIRITEwWjOgUFhRa7L2ltVTpR4SPvrLKyEp999hlyc3MRExMDfX19tiO9orKyEhoaGnj+/Dmr01a1tbVwcHCAubk5tm/fzloOSVdXV4eMjAxBoSsqKoKtra2geVxbW5umLwkVPvJ+GIZBaGgoli9fjoCAAMydO1fkfrH07dsXV65cwYABA1h5/8bGRkyfPh3KysoIDw+HrCwdkdtRGIbBjRs3BNOXWVlZGDlypGBUZ2pqSpf3kldQ4SMdIi8vDxwOB/r6+ggODkbPnj3ZjiRgaWmJzZs3s7KmxjAMPv/8c9y5cwfx8fFisxtWlD148KDF9KWysrKg0Nna2uKDDz5gOyIRcfTRk3QIPT09ZGdno1evXjA2NsbFixfZjiTA5s7OLVu2ICsrCydOnKCi945qa2uRkJAAf39/jBo1CiNGjMCJEydgbm6OjIwMFBYWYv/+/Zg2bRoVPdImNAdAOoySkhL279+Pn3/+GS4uLli5ciWWLVvG+tSejo4OCgoKOv19jxw5gkOHDiErK0vi7joUJoZhkJubKxjRnT9/HqNGjYKTkxMOHDiAsWPH0vQleS801UmEoqioCFwuF2pqajhy5AirV68cO3YM0dHROHHiRKe952+//QYfHx+kpqbiww8/7LT3FVf3798XNI4nJiaie/fugulLGxsbkZo6J+KPPjYRoRgyZAgyMjKwevVqGBkZISIigrW+tc6e6rx48SK8vLxw6tQpKnqvUVNTg/T0dMGmlNLSUtjZ2cHJyQnffPMNhgwZwnZEIsFoxEeE7syZM/Dx8cGCBQuwdu3aTr/poaqqCn379sXz58+FPu1aWFgIKysr7N+/H1OnThXqe4kTPp+Pa9euCUZ1Fy5cgJGRkaDNYMyYMWJ5AwgRT1T4SKe4d+8ePD09wePxEBkZ2emtBRoaGsjOzoampqbQ3uPhw4ewsLDAf/7zHyxYsEBo7yMuSktLcfbsWcGfXr16Ce6os7GxoXVPwhqa6iSdon///khISEBAQABMTEwQEhICFxeXTnv/5ulOYRW+58+fw8XFBVwuV2qLXnV1Nc6dOyfYlHL//n3Y29vDyckJW7ZsgZaWFtsRCQFAIz7CgvT0dHh4eGD69OnYunVrp2zznzNnDkxNTYVSlBoaGjB16lT069cPISEhItfALyx8Ph9Xr14VrNNdvHgRxsbGgk0pxsbGNH1JRBKN+Eins7KyQk5ODnx9fWFhYYHo6Ghoa2sL9T11dHSEssGFYRhBMQ0ODpb4oldSUiKYukxMTISamhocHR3h5+eHCRMmoHv37mxHJOStqPARVqiqquLkyZPYs2cPxo0bhz179oDL5Qrt/XR1dXHhwoUOf91169bhxo0bSElJkciT/Z8/f460tDTBppSHDx/CwcEBjo6O2LZtGwYNGsR2RELajaY6CeuuXLkCDoeDCRMmIDAwUCg3PVy7dg3u7u74888/O+w1Dxw4gO+//x6ZmZlQV1fvsNdlU2NjI3JycgTrdJcuXcKYMWMEm1KMjIxo+pKIPSp8RCRUVVVh4cKFyMnJQUxMDAwMDDr09aurq6Gmpobq6uoOaWk4efIkFi1ahPT0dKFP0wrb3bt3BSO6pKQkqKurC9bprK2toaKiwnZEQjoUFT4iMhiGQVhYGPz9/fHtt99i3rx5HbpmNmDAAGRlZb337sKsrCxMnToVv/32G8aMGdNB6TpPVVUVUlNTBcXuyZMncHBwEIzqBg4cyHZEQoSK1viIyJCRkYG3tzfMzMzA4XCQlJSEH3/8scOOq2puaXifwnfr1i24ubkhPDxcbIpeY2MjLl++LJi+vHz5MszMzODo6IjIyEiMHj2a9fNUCelM9N1ORM6IESNw4cIFqKqqwsjICNnZ2R3yuu97dNm9e/fg7OyMrVu3wtnZuUMyCUtxcTEOHjyIGTNmQF1dHb6+vigvL8fKlStRVlaGpKQkrFy5EsbGxlT0iNShER8RSUpKSti3bx9iY2Ph6uqK5cuXw8/P771+Sb9P4ausrMRHH32EefPmwdvb+50zCEtlZSVSU1MFPXXPnj2Dg4MDXF1dsXv3btYu4SVEFNEaHxF5f/31F2bOnIlevXohLCzsnW96OHHiBEJDQ/Hrr7+263n19fX46KOPMGzYMAQFBYlErx6Px8OlS5cE63RXr16FmZmZYFOKoaEhjeQIeQ0qfEQsNDQ0YO3atYiIiEB4eDhsbW3b/RrXr1/Hp59+ips3b7b5OXw+H56enqipqcHx48dZ3cpfVFQkWKdLTk7GwIEDBYc8W1lZCaUNhBBJRIWPiJXff/8d3t7emD9/PtauXduuC0lramrQu3dvVFdXt7mA+fv7IysrC4mJiVBSUnrX2O/k2bNnSElJERS7qqoqODo6wtHREQ4ODujfv3+n5iFEUlDhI2Ln/v378PT0RH19PSIjI9u1/V5TUxPnzp1r031vu3fvRnBwMDIzM9G7d+/3idwmPB4PFy9eFKzT5ebmYvz48YLpy5EjR4rENCsh4o42txCxo6Ghgd9//x3btm2DiYkJDh06hMmTJ7fpuc0bXN5W+I4dO4YdO3YIvegVFhYK1ulSUlKgpaUFR0dHbNiwAZaWlp0+yiREGtCIj4i1jIwMeHh4wM3NDVu3bkXXrl3f+O8XLFgAQ0NDfP7556/9N6mpqfj0009x9uxZjBo1qkPzVlRUIDk5WVDsampqBI3jDg4O6NevX4e+HyHkVVT4iNgrLy+Hr68vSkpKEBMTAx0dnVb/3fPqKny/Zxsqq5/BwEAfiopK0FDvjzGG46DSrelWgevXr8Pe3h7R0dGws7N772wNDQ3Izs4WrNNdv34dFhYWgmJnYGBA05eEdDIqfEQiMAyDvXv3YtOmTfjhhx/g7u4ueKzkXjGSs37H7YI8NPL5YBi+4DE5OXmAYTBcRw96Q0fBbep0bN++/Z1vimAYBgUFBYIRXWpqKoYMGSJYp7OwsICiouJ7f72EkHdHhY9IlJycHHA4HFhZWSEwMBC5t64gLvEEeDweGLzpW10GPB4PvRTVsXbFxna959OnT5GcnCzYlFJfXy9oM3BwcJCYmxsIkRRU+IjEqaqqwueff46HFfdgZG6AxkZem58rLy8PV3s3jDexeu2/aWhowB9//CEY1eXl5cHS0lJQ7PT09Gj6khARRoWPiLUXL15g0aJFSExMRHl5ObS1tREQEACDUXr4epM/0hPPo+zeI8jIyEBzyADYu1pBpXs3AEBmUjb+SLuMLnIvTzjxXsxFH/U+MBo2Ht6ePoK/ZxgGNTU1MDExQX5+PnR0dATrdBYWFm/dVEMIER3UzkDEGo/Hg6amJtLS0jBo0CDEx8fj008/xfptq1BXW4dRY/UxWGcQZGVlkBh3Dr+dSMaM2S9bH4aP1IHrDMd/vWYDKmofIjQ0VDB9WVNTg/r6eixevBguLi7vfGwaIYR9NOIjEsfAwADDRmtBZ0TLXr2ye48QdegXLFs3H0DTiO9p+bNXCh/QVFALrpTC3q7pnrrvvvsOABAaGir8L4AQIlQ04iMSpaysDHfy78BiovErj5X8dQ9q6i2b0Qtv/YU93x5Ct+7KMDIzhJFZ083vioqK8Fu5FDbjHVBdXY3jx4+3+3BrQohoosJHJEZDQwM8PDxgZWOBnr17tHjs4YPHOJ9yEdM8PhL83fCROhg1Vh/KKkq4/3cZTkWegaKiAkaMGgYej4cHD0sBNN3qoKamhgkTJnTq10MIEQ66t4RIhOZbFBQUFMD1mt7isadPKhAbFge7j6wwcPDLg53V1HtDpUc3yMrKYsAgDZiMN8TtPwsFj9fW1QIAwsLC4OXlRTs1CZEQVPiI2GMYBnPmzEFZWRliY2OhoqIieOzZ00ocCz2N8bZjoG80/M0v9K/CpqSohJKSEqSmpsLLy0sY0QkhLKDCR8TewoULcfPmTfz6669QUmo6hkxOTg5Vlc8Rc/gUjMaNxGhTg1eel3/zf6irrQPDMLj/dxmunM+FzodNG2Lk5OTRT30AwsPDYW5uDm1t7c7+sgghQkK7OolYKy4uxuDBg9G1a9cWd/PZuVjiyeNyZCVfhLxCy6XsZesWAAB+jUnAXwV30djYiO49VDDazAAm45sOpZbrIoevl3yDMSZj4e/vjzlz5nTeF0UIESoqfEQihR3/EXm3r7/lmLLWyUAG+sMN4TV9nhCSEULYRlOdRCLZmU9s1+3s/yQnJw9bc6cOTkQIERVU+IhE0uyvBVcHN8jLy7frefLy8nB1mAbN/lpCSkYIYRv18RGJ1XzQdFtuZ5CBDOTk5OFqP+2NB1QTQsQfrfERiVdyrxgpWQm4VfAnICMDHq9B8FjzfXwf6ujD1tyJRnqESAEqfERqPK+uwqXcC3jwsBS1dbVQUlRCP/UBGGNoJriBnRAi+ajwEUIIkSq0uYUQQohUocJHCCFEqlDhI4QQIlWo8BFCCJEqVPgIIYRIFSp8hBBCpAoVPkIIIVKFCh8hhBCpQoWPEEKIVPk/uvggbvSoK2AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5c6RHcdPg_t"
      },
      "source": [
        "# LITECOIN TEMPORAL LOUVAIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bmFQ1EfPg_u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "75f64650-231c-43cd-affd-b318883fd6cc"
      },
      "source": [
        "#####################\n",
        "###### LITECOIN #####\n",
        "#####################\n",
        "#### TEMP MEAS ######\n",
        "#####################\n",
        "\n",
        "# imports teneto\n",
        "!pip install teneto\n",
        "import teneto\n",
        "from teneto import TemporalNetwork\n",
        "\n",
        "# define tnet\n",
        "df_lc_tnet = full_lc_df\n",
        "\n",
        "# rename columns to keep\n",
        "df_lc_tnet = df_lc_tnet.rename(columns = {'from_id': 'i'}, inplace = False)\n",
        "df_lc_tnet = df_lc_tnet.rename(columns = {'to_id': 'j'}, inplace = False)\n",
        "df_lc_tnet = df_lc_tnet.rename(columns = {'timestamps': 't'}, inplace = False)\n",
        "\n",
        "tnet_lc = TemporalNetwork(from_df=df_lc_tnet)\n",
        "tnet_lc.network\n",
        "\n",
        "# temporal louvain\n",
        "temporal_louv_lc = teneto.communitydetection.temporal_louvain(tnet_lc)\n",
        "  # IndexError: index 408424201934 is out of bounds for axis 0 with size 1006740\n",
        "\n",
        "# as dataframe\n",
        "temp_louv_lc = pd.DataFrame(temporal_louv_lc)\n",
        "temp_louv_lc.to_csv(\"temp_louv_lc.csv\")\n",
        "temp_louv_lc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: teneto in /usr/local/lib/python3.7/dist-packages (0.5.3)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.19.5)\n",
            "Requirement already satisfied: pybids>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.13.1)\n",
            "Requirement already satisfied: pandas>=0.21 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.1.5)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (2.5.1)\n",
            "Requirement already satisfied: templateflow>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.7.1)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from teneto) (1.4.1)\n",
            "Requirement already satisfied: statsmodels>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.10.2)\n",
            "Requirement already satisfied: python-louvain>=0.13 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.15)\n",
            "Requirement already satisfied: nilearn>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from teneto) (0.8.0)\n",
            "Requirement already satisfied: sqlalchemy<1.4.0.dev0 in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (1.3.24)\n",
            "Requirement already satisfied: bids-validator in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (1.7.2)\n",
            "Requirement already satisfied: num2words in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (0.5.10)\n",
            "Requirement already satisfied: nibabel>=2.1 in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (3.0.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (7.1.2)\n",
            "Requirement already satisfied: patsy in /usr/local/lib/python3.7/dist-packages (from pybids>=0.11.1->teneto) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->teneto) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->teneto) (2018.9)\n",
            "Requirement already satisfied: decorator<5,>=4.3 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.0->teneto) (4.4.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from templateflow>=0.6.2->teneto) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from templateflow>=0.6.2->teneto) (2.23.0)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.7/dist-packages (from nilearn>=0.6.0->teneto) (1.0.1)\n",
            "Requirement already satisfied: scikit-learn>=0.21 in /usr/local/lib/python3.7/dist-packages (from nilearn>=0.6.0->teneto) (0.22.2.post1)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from num2words->pybids>=0.11.1->teneto) (0.6.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy->pybids>=0.11.1->teneto) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->templateflow>=0.6.2->teneto) (2021.5.30)\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "         i     j  weight\n",
            "0        1    76       1\n",
            "1      368   443       1\n",
            "2      735   810       1\n",
            "3     1102  1177       1\n",
            "4     1469  1544       1\n",
            "...    ...   ...     ...\n",
            "6680  3298  3665       0\n",
            "6681  3299  3666       0\n",
            "6682  3300  3667       0\n",
            "6683  3301  3668       0\n",
            "6684  3302  3669       0\n",
            "\n",
            "[6685 rows x 3 columns]\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "         i     j  weight\n",
            "0        1    76       1\n",
            "1      368   443       1\n",
            "2      735   810       1\n",
            "3     1102  1177       1\n",
            "4     1469  1544       1\n",
            "...    ...   ...     ...\n",
            "9830  3298  3665       0\n",
            "9831  3299  3666       0\n",
            "9832  3300  3667       0\n",
            "9833  3301  3668       0\n",
            "9834  3302  3669       0\n",
            "\n",
            "[9835 rows x 3 columns]\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:242: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['i'] = self.network['i'].astype(int)\n",
            "/usr/local/lib/python3.7/dist-packages/teneto/classes/network.py:243: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.network['j'] = self.network['j'].astype(int)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "         i     j  weight\n",
            "0        1    76       1\n",
            "1      368   443       1\n",
            "2      735   810       1\n",
            "3     1102  1177       1\n",
            "4     1469  1544       1\n",
            "...    ...   ...     ...\n",
            "9830  3298  3665       0\n",
            "9831  3299  3666       0\n",
            "9832  3300  3667       0\n",
            "9833  3301  3668       0\n",
            "9834  3302  3669       0\n",
            "\n",
            "[9835 rows x 3 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>152.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>364</th>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "      <td>149.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365</th>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>367 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         0      1      2      3      4      5      6      7      8      9\n",
              "0     26.0   26.0   26.0   26.0   26.0   26.0   26.0   26.0   26.0   26.0\n",
              "1      4.0    4.0    4.0    4.0    4.0    4.0    4.0    4.0    4.0    4.0\n",
              "2      9.0    9.0    9.0    9.0    9.0    9.0    9.0    9.0    9.0    9.0\n",
              "3     27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0   27.0\n",
              "4     12.0   12.0   12.0   12.0   12.0   12.0   12.0   12.0   12.0   12.0\n",
              "..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...\n",
              "362  176.0  176.0  176.0  176.0  176.0  176.0  176.0  176.0  176.0  176.0\n",
              "363  152.0  152.0  152.0  152.0  152.0  152.0  152.0  152.0  152.0  152.0\n",
              "364  149.0  149.0  149.0  149.0  149.0  149.0  149.0  149.0  149.0  149.0\n",
              "365   14.0   14.0   14.0   14.0   14.0   14.0   14.0   14.0   14.0   14.0\n",
              "366   20.0   20.0   20.0   20.0   20.0   20.0   20.0   20.0   20.0   20.0\n",
              "\n",
              "[367 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvHSD4kI4no2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a61ed3d-7c61-4e81-cb9a-3827410bfbef"
      },
      "source": [
        "# score for stable community\n",
        "temp_louv_lc['same_0'] = np.where( temp_louv_lc[0] == temp_louv_lc[1], 1, 0)\n",
        "temp_louv_lc['same_1'] = np.where( temp_louv_lc[1] == temp_louv_lc[2], 1, 0)\n",
        "temp_louv_lc['same_2'] = np.where( temp_louv_lc[2] == temp_louv_lc[3], 1, 0)\n",
        "temp_louv_lc['same_3'] = np.where( temp_louv_lc[3] == temp_louv_lc[4], 1, 0)\n",
        "temp_louv_lc['same_4'] = np.where( temp_louv_lc[4] == temp_louv_lc[5], 1, 0)\n",
        "temp_louv_lc['same_5'] = np.where( temp_louv_lc[5] == temp_louv_lc[6], 1, 0)\n",
        "temp_louv_lc['same_6'] = np.where( temp_louv_lc[6] == temp_louv_lc[7], 1, 0)\n",
        "temp_louv_lc['same_7'] = np.where( temp_louv_lc[7] == temp_louv_lc[8], 1, 0)\n",
        "temp_louv_lc['same_8'] = np.where( temp_louv_lc[8] == temp_louv_lc[9], 1, 0)\n",
        "temp_louv_lc['score']=temp_louv_lc['same_0']+temp_louv_lc['same_1']+temp_louv_lc['same_2']+temp_louv_lc['same_3']+temp_louv_lc['same_4']+temp_louv_lc['same_5']+temp_louv_lc['same_6']+temp_louv_lc['same_7']+temp_louv_lc['same_8']\n",
        "temp_louv_lc['stable'] = np.where( temp_louv_lc['score'] == 9, 1, 0)\n",
        "\n",
        "# as int\n",
        "temp_louv_lc = temp_louv_lc.astype(int)\n",
        "summary_temp_louv_lc = temp_louv_lc\n",
        "\n",
        "# summarizes df\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_0')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_1')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_2')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_3')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_4')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_5')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_6')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_7')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'same_8')\n",
        "summary_temp_louv_lc = summary_temp_louv_lc.drop(columns = 'score')\n",
        "\n",
        "# number of stable communities\n",
        "summary_temp_louv_lc\n",
        "summary_temp_louv_lc.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         194\n",
              "1         194\n",
              "2         194\n",
              "3         194\n",
              "4         194\n",
              "5         194\n",
              "6         194\n",
              "7         194\n",
              "8         194\n",
              "9         194\n",
              "stable      1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYkdBIb45Sl8"
      },
      "source": [
        "# NETWORK GRAPH - REPRESENTATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "rjj-Xb0Cq-zN",
        "outputId": "748f9a8b-e9cf-4734-f4da-81b8702246ab"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "from matplotlib.animation import FuncAnimation\n",
        "\n",
        "# number of nodes\n",
        "size = 10\n",
        "\n",
        "# generate graph\n",
        "G=nx.complete_graph(size)\n",
        "\n",
        "# generating input frames here, since my data is too big\n",
        "# its important that the frames go as input and is not generated\n",
        "# on the fly\n",
        "frame = np.random.random_integers(0, 5, (size, size)) # random ndarray between 0 and 5, length and number of frames = number of nodes in the graph\n",
        "\n",
        "# draw the topology of the graph, what changes during animation\n",
        "# is just the color\n",
        "pos = nx.spring_layout(G)\n",
        "nodes = nx.draw_networkx_nodes(G,pos)\n",
        "edges = nx.draw_networkx_edges(G,pos)\n",
        "plt.axis('off')\n",
        "\n",
        "# pass frames to funcanimation via update function\n",
        "# this is where I get stuck, since I cannot break\n",
        "# out of the loop, neither can I read every array of\n",
        "# the ndarray without looping over it explicitly\n",
        "def update(i):\n",
        "    # for i in range(len(frame)):\n",
        "    # instead of giving frame as input, if I randomly generate it, then it works\n",
        "    nc = frame[i] # np.random.randint(2, size=200)\n",
        "    nodes.set_array(nc)\n",
        "    return nodes,\n",
        "\n",
        "# output animation; its important I save it\n",
        "fig = plt.gcf()\n",
        "ani = FuncAnimation(fig, update, interval=50, frames=range(size), blit=True)\n",
        "ani.save('crap.gif', writer='imagemagick', savefig_kwargs={'facecolor':'white'}, fps=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: This function is deprecated. Please call randint(0, 5 + 1) instead\n",
            "  from ipykernel import kernelapp as app\n",
            "MovieWriter imagemagick unavailable; trying to use pillow instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-91-f535587e22f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mani\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFuncAnimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'crap.gif'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagemagick'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msavefig_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'facecolor'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1109\u001b[0m                 writer = alt_writer(\n\u001b[1;32m   1110\u001b[0m                     \u001b[0mfps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcodec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbitrate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1111\u001b[0;31m                     extra_args=extra_args, metadata=metadata)\n\u001b[0m\u001b[1;32m   1112\u001b[0m         \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Animation.save using %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'str' object is not callable"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "E-eJIAcCJKNJ",
        "outputId": "b2cd6b25-4db7-4974-8ceb-39b13d4fa588"
      },
      "source": [
        "# Network graph\n",
        "\n",
        "import networkx as nx\n",
        "# importing matplotlib.pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "  \n",
        "g = nx.Graph()\n",
        "  \n",
        "g.add_edge(1, 2)\n",
        "g.add_edge(2, 3)\n",
        "g.add_edge(3, 4)\n",
        "g.add_edge(1, 4)\n",
        "g.add_edge(1, 5)\n",
        "g.add_edge(5, 6)\n",
        "g.add_edge(5, 7)\n",
        "g.add_edge(4, 8)\n",
        "g.add_edge(3, 8)\n",
        "  \n",
        "# drawing in spring layout\n",
        "nx.draw_spring(g, with_labels = True, node_color=\"#75846b\")\n",
        "plt.savefig(\"tg.png\", transparent = True, dpi = 300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}