audit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I. Accounts Status at Block Height "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare account export files from [java-tron:account_export_6m](https://github.com/tronprotocol/java-tron/tree/account_export_6m). Dump into ./raw folder.\n",
    "\n",
    "\n",
    "## 1. Read accounts dump\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import copy\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Notice:__ Change following constants as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from constants import AIRDROP_AMOUNT, BLACK_LIST\n",
    "BLOCK_HEIGHT = 6600000\n",
    "TRX = 1000000\n",
    "MILLION = 1000000\n",
    "THREADHOLD = TRX  # trx in sun smaller than this value will be trim off\n",
    "AIRDROP_FILE_NAME = 'account.csv'\n",
    "TOKEN_NET_COST_MIN = 279    # 1 token\n",
    "TOKEN_NET_COST_MAX = 283    # 10 B tokens\n",
    "DYNAMIC_TOTAL_NET_WEIGHT = 6880167579      # 2/8/2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All:                                      address  balance\n",
      "1765102  TGJfesD476E8sB4EeMZukwyci2VMZRLHWq  1994000\n",
      "1765103  TFqWSaoVBhomTSoJL2fqw5p6PrvgNhSS6o      341\n",
      "1765104  TDnEYjB68dfbBCr7oqHePA5a2p7CGyaZpK        0\n",
      "1765105  TQU8Mdg69sgZXosM3epVHFAXYLibL1DnsL  1000000\n",
      "1765106  TFJv7MeBvSBsGWTmKJvskScoiUskMojVha        0\n",
      "Normal:                                      address  balance\n",
      "1760434  TGJfesD476E8sB4EeMZukwyci2VMZRLHWq  1994000\n",
      "1760435  TFqWSaoVBhomTSoJL2fqw5p6PrvgNhSS6o      341\n",
      "1760436  TDnEYjB68dfbBCr7oqHePA5a2p7CGyaZpK        0\n",
      "1760437  TQU8Mdg69sgZXosM3epVHFAXYLibL1DnsL  1000000\n",
      "1760438  TFJv7MeBvSBsGWTmKJvskScoiUskMojVha        0\n",
      "Contract:                                   address  balance\n",
      "4637  TY4JeDPGUhVod6A1x2RRDKjhYRCedcG6mn        0\n",
      "4638  TD6qYgnHkduZ1K1KCJmPtuDHDhxrM5kJEy        0\n",
      "4639  TMaXQvzpx7v6tB1B2Fox5dtcfQ8CJ8Mxpx        0\n",
      "4640  TBCXm3HsoSBLLR7HoiG5ZGwSZKabQKtfTu        0\n",
      "4641  TA1Kqpw81M32vqe2TCoJrMVrnvNjsqTQWx        0\n",
      "Asset Issue:                                 address  balance\n",
      "23  TBYsHxDmFaRmfCF3jZNmgeJE8sDnTNKHbz        0\n",
      "24  TAa14iLEKPAetX49mzaxZmH6saRxcX7dT5     1100\n",
      "25  TWKZN1JJPFydd5rMgMCV5aZTSiwmoksSZv     1100\n",
      "26  TCf5cqLffPccEY7hcsabiFnMfdipfyryvr      100\n",
      "27  TLCjmH6SqGK8twZ9XrBDWpBbfyvEXihhNS        0\n"
     ]
    }
   ],
   "source": [
    "all_account_dump_path = './raw/block_' + str(BLOCK_HEIGHT) + '_all_accounts.csv'\n",
    "normal_account_dump_path = './raw/block_' + str(BLOCK_HEIGHT) + '_normal_accounts.csv'\n",
    "contract_account_dump_path = './raw/block_' + str(BLOCK_HEIGHT) + '_contract_accounts.csv'\n",
    "asset_account_dump_path = './raw/block_' + str(BLOCK_HEIGHT) + '_assetissue_accounts.csv'\n",
    "\n",
    "all_acc_data = pd.read_csv(all_account_dump_path)\n",
    "normal_acc_data = pd.read_csv(normal_account_dump_path)\n",
    "contract_acc_data = pd.read_csv(contract_account_dump_path)\n",
    "asset_acc_data = pd.read_csv(asset_account_dump_path)\n",
    "\n",
    "print('All: ', all_acc_data.tail())\n",
    "print('Normal: ', normal_acc_data.tail())\n",
    "print('Contract: ', contract_acc_data.tail())\n",
    "print('Asset Issue: ', asset_acc_data.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Account Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total trx balance(SUN):  99209134924344441\n",
      "Total trx balance(TRX):  99209134924.34445\n",
      "Total trx balance(M-TRX):  99209.13492434444\n",
      "Sum of all accounts:  -9124122478850845715\n",
      "Blackhole amount:  -9223331613775190156\n"
     ]
    }
   ],
   "source": [
    "total_balance = all_acc_data['balance'][0]\n",
    "total_sum = all_acc_data['balance'].sum()-all_acc_data['balance'][0]\n",
    "print('Total trx balance(SUN): ', total_balance)\n",
    "print('Total trx balance(TRX): ', total_balance/TRX)\n",
    "print('Total trx balance(M-TRX): ', total_balance/(TRX*MILLION))\n",
    "print('Sum of all accounts: ', total_sum)\n",
    "print('Blackhole amount: ', total_sum-total_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total accounts:  1765106\n"
     ]
    }
   ],
   "source": [
    "total_accounts = len(all_acc_data['balance']) - 1\n",
    "print('Total accounts: ', total_accounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total None Zero Accounts:  800492\n"
     ]
    }
   ],
   "source": [
    "total_none_zero_accounts = 0\n",
    "i = 1\n",
    "total_none_zero_accounts = len(all_acc_data['balance'].nonzero()[0])\n",
    "print('Total None Zero Accounts: ', total_none_zero_accounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normal Account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal accounts:  1760438\n",
      "Volume(Million-TRX):  99141.60086831223\n",
      "---------\n",
      "Total:  1760438\n",
      ">=1 SUN:  799804\n",
      ">=1 TRX:  285988\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ukbZIffObzav9Nf5qoBSoindM8YH5nss9aap0QrbtjD6W8TVKchZQ4m3t4+OITiSQ+8dFADKyQiSSjupw2+a2tU1vNFVj8G/gP75KXyCeaxvu9uRGDP463qNutvVPl1ou8GXg1/3wWCJGArmPYrPyrs+YdE6GPcMz2up6xOAUPBysaXy9cVO4MbwGeNJX6Yt7lEPtfM/Xh7vU/S6n8vRzWd+hJOcPMi65/0gg991MINc19ULpOxb9LhqItrSsaFnTVtW2HXgCWOOr9MW1WlHtfE+hy6menJBtS9Son/HAZ4GFCXr8IUcCue+utWcNNxy542ZaXYgYPLShDX+Nv7JxSeNWHdKvAS/5Kn1x9dc2fS/bFYzo343NUl+025T99Ff0yV2U5DxKiVcWxekHEsh9EBvq9r7MmVdOVjZbor/xxRARbgrva1zcuD5YF6wCHvNV+nbFe+2B+Z5PDstQDw132QZqd5oZwEeBfw3Q8w1qEsh9cxnKZqSNnXae1YWI1GeEjHbfWt8a72rvduDvwFu+Sl8knmvr7/JMBp7My7ZdltAiu/c9JJD7hQRyL7kLitKBD7qmXJBpS3OduH2EEHHSWuvA3kBVw+sNmw2/8RbwnK/S1xTXxSU5jlqf8ePRmepOp11ZNeTyYkpyLqLEu9qi5x80JJB771wgI33CzDOtLkSkrkhr5FDT0qa1Hbs7tmKuU7w13jHF+7/j+WBOhnp0QrYtL6FFxucTgARyH0kg996HUMrnGDZOAln0mI7oYOum1rXNy5u3Y/ACsMhX6Ytr872W4uyRwYhemJet/iuJZul/HPiu1UWkOgnkXnAXFOUAUzMmnYPNkZZpdT0itQQPBqsbXmvYFGmJvAM87av0HYrrwpIcVesz5o90qx/nZtiSba3tydJt0XcSyL0zA1DpE86abnUhInVE/dGm5uXNq9u3tNdgTnleH2/3RN2dngtcDvXEhGzbjMRW2Se3IN0WfSKB3DsXA+3O4eMlkMVpaUNH27e1r29a2rRNR/R/gH/7Kn3t8VzrLc7O9If1Q2Oz1G02pWwJLrWvPg7cZXURqUwCuYfcBUUuoNA5YqLflp453Op6RHILNYR2Ny5q3BA6fGwbpb3xXntgvufzw13qgXEZttwEltif8inJuZAS7xqrC0lVEsg9Nw2wpeedPcXqQkTyMoJGq7fCu8a33nd0G6WV8W6jdPBOz5lOO0/kZdtScZPcWwAJ5F6SQO65QiDiHDlxqtWFiOSjtdYduzo2Ni5q3GIEjKXAP+LdRomSnLS6VmPB6Ez1zdNso5TMbgHutrqIVJWq/+mWcBcUKeAClK3F4RnZ1/VkxSAT8UbqGpc0rgvsC2zGnPJcHe+1B+Z7PpKTrv463mMbm8ASB0I+JTkXUOJda3UhqUgCuWdGA7npeWdFld3Zm50WxCBkhI1A64bWNS0rW7ajeR5YGu82Sg13e8YamtK8bNu1CS5zIN0CSCD3ggRyz0wFcI6YOM7qQkRyCBwIbGl4raEq2hpdCTzjq/TVx3WhuY3S90e61b3pDpWR2CoH3Icw17cQPSSB3DPnAh0Oz8h4ducVg1i0PdrQ9GbTav92/3bMKc+b4h1TXDvf8/6sNPX4hGzbYL0PUUhJTgYl3rh2MxHvkUDumWlAqz0zVwJ5iIpto7Su6c2maqLHtlHqiOfatnuys9tC+i/jPOqT/bSNUrJyALOBVVYXkmokkOMUG388DNhnk62ahqTgkeCOxtcbN4YbwuuAJ3yVvgPxXntgvufLI1zql2OzbNkJLDGZXIQEco9JIMdvDKCdw/Nyld0hN/SGkGgg6m1Z2bKmbdOxbZRWx7uN0sE7PWenO9STedm2WYmtMulcaHUBqUgCOX5jAOUcNVm6K4YIbWjDv8Nf2bi4casO6UXAi/Fuo9RwtycjHOWBUZnqS47Eb6OUjFJxUovlJJDjlw9EHDljJJCHgHBzeH/j4sb1wdpj2yjtjPfaA/M9H8/NUH8a6VajElhisptGSc4wSrzNVheSSiSQ4zcVaLdnDZdAHsSMkOH3rfOt9lZ4azC3UXoz3m2Ujtzlmajgibxs25WJrTIlmJOoYJHVhaQSCeQ4uAuKbMAkoMHuzpFAHoS01gT2BaoaFzVujrZFj26j1BjXxSU59lqfUTIqU92dZt02SsnoIiSQe0QCOT7DgDTlzECluWWFt0Em0hY53LS0aW3HrmPbKG2Jd0zxgfmeD3jS1MIJ2bbJCS0yNUk/cg9JIMdnDKAdOWOy1eAePzqk6IgOtVa1rm1+u7kagxeB1+PdRqnpe9nDw1G9cIJHfVS+JU5KRlr0kARyfMYCypaR5bK6ENE/ggeD1Q2vN1RFmiMVwFO+St/BeK+tne/59gi3+mmGyybbd53aOEpycijxxrfanZBAjtMoIGzLyJIfwBQX7Yg2tyxvWd22+diY4rU9mPJ8ntupnpyQbZuZ2CoHlTxAAjlOEsjxyQEitnS3tJBTlDZ0tL06to1SWL8ClMW7jVJLcba7I6wfHJul5tltSb+NUrKZCGy2uohUIYEcn2wgbEtzSSCnoFBjaE/josYNoUPHtlHaE++1B+Z7bhvuUr/PzbANS1yFg1qe1QWkEgnk+HiAiHJKIKcSI2i0eVd7V/vW+bYDzwLL491G6fB3PVPtNp7My7ZdnNgqB72JVheQSiSQ45MFhJQzXQI5BWitdcfujo2Nixq3Gh3HtlFqievikhxnXatx/yi3+pbTrpyJrXRImGB1AalEAjk+HuCQTQI56UV8kYONSxrXBvYGtgKP+ip92+K99sB8zw2xbZTGJ7DEoWYoTx/vMQnk03AXFDkBJ2AohwRysjIisW2UVrRsR/MCsNhX6QvFc239XZ4xGh7Ly7Z9OMFlDkUykaoHJJBPzwUYAMqRJoGchAK1ga2NrzdWRbyRo9soHYnrwpIcVeszvjfSrX6Y7lDyf5sYI6wuIJVIIJ+eCzDHqdrtsk5BEom2Rxub32pe3V7dXg2UAhvjHVNcd6fnYrdTPTEh2zYtsVUOeRLIPSCBfHrvtZwMI6479CKxdNRutL7bWtH8RnO1juij2yj547m29fvZnvaw/vPYLHXrIN9GKVnIcMEekEHup/deq9iIxrW1u0iswIHzVdPipjId0ff6Kn3/iDeMa+d7vuSwcWBslu3TEsYDxklJjuywEydpIZ/esbVwtQRyUnAMmznSfdYdvzz01G/jWqe47k7PWel2npiQbTsv0bWJbsnPTZwkkE/v2DeTNqJxBYBILGWzezImXnMJ8Papzjtylyc9YvCb0Znqyw6bku91a0Qp8ca1/6CQLot4vBfC0kJOJh851ReXfyGz0O1U+8Z7bF+XMLZUXEMPhUm+UU8vjLkdzYC1kA88/AVsaS6w2VA2O+Pm/e7Y17wVL9DyxkLyvvkUdnfOCddGfEdofOWPRHz1KKUYfUsJjpwx1P/7V4Tr9+KaeiHDrpwHQMuKZ0gbfQbugpScHTwH+F7Xg3OnOx3A5dnp3FZ6o2vkwJclupBA7gEJ5NN7L4SjkQFrIY+59ecnBG7EV09gTyX27JNPfmp4+bfkXPJJXGfMxgh1gFKEjuwGYPwXHuTQU3djBNsxwkFCB7eTe+mtCX0dCXR2fnF5/p4Fc/Z0OX4z8BFfkNqDbXpvXrY6w4LaxHskkHtAuixOr1MLeeACuTvNS/7KsKtvP1rOCUIN+8AwcJ0xGwBbmgubMwNlc6AjIbQ20NEIKBvet58k9/LPDmD1CdFdt0UD5v9ZYGu9sX2A6xEnkkDuAQnk03svkKORgbmppxRHnvshBx/7H1o3vAqAv6YCu2cEaaOnnPSySFMttoxMjrz4M+oe/RbNyxaijSjOkRNxeEZx8LH/IXPGZUSazc0x0sZMHZCXk0BzujlWTWwiz9LdEQlk60kg94B0WZzegHdZjP3ML3F4RhBtb+Hw3+/FOSIP76q/M+aTPznlddqIEti/mXG3/wFH9igaXvoFbVVL8Jx7LcM/+N/Hzjvyjx8x/Lpv4F35d0JHdpORPwvPrJRcxuHq/OLyzD0L5nReaL4WaAUyNtcbTd6AbszJUDJbzDoSyD0gLeTT8NdUGEAUsBnB9rh2mOgrh8fMD3tmLu4zLyGwr4qI9zB1C7/JgYe/QLS1gYOPfZtoW3OX60aSNmYKztyxKJsdV8HFhA7v7PJ63iFtbAE6HCDUsJdRNxbTvnkZRjgwEC+tv6UD13Q+UFYd1sA7xKbs7mgyaiyoS7xHArkHJJDjEwRskbam5tOe2UdGKIAR9B/7OLC7kvRxZzLxm0+R99WF5H11IXbPSMZ9/nfYs46flZo2rgAj0EbUb25hFti7ibSR760PrqMRfGvLyC66CR0JcqwvWmtI3SHW3fUjbyL2vV1RG5VuC2tJIPdAynVZKKU+Bvxvl8PnAHO01q8k6Gm9QFrUezi+Rc77IOpvof6Fn5qfGAaZZ12Ja8r5Jz0/eLCGtg2vMOL6b6FsdoZd/UUOP/sD0Jq0sdPIOve6Y+e2ri8n633XYHNm4Bx1BqCpe+TruKZegC0jK8GvLGFu6ObYTswV+uxLd0f23nGeM5hmVzJ91xodVheQSpTWcS2OlbSUUv8NfAa4Wmt9yhlByly/QJ3uvK7cBUXfAqZhs7eMuvGee5Wsg5BsztuzYE5l5wNzpzu/DUwFGn97XcYt04bbzrKmtCHvSUq8t1ldRKpI6S4LpdSZwA+B27TWhlLqLqXUGqXUJqXUj2Ln5Cultiql/gSsByYqpW5VSlUppd5VSv0ijqc6CGRgRA0dDvgS94pEL3XXbVEBZAJUHY5KP7J1dp7+FHFUygayUsoJPA18V2u9Tyl1LVAAXATMAs5XSl0RO3068LjWejbmMLZfAB+InXehUurG0zzdQWLdO0awvanfX4zoq+6Gv20n1km+aFekxkj1PwVT1w6rC0glKRvIwE+AzVrrZ2OfXxt7q8RsCc/ADGiAvVrrd2IfXwi8obWu11pHgKeAKzi1FmJjWw2/t77/XoLoJxfmF5eP7nygrDrcCNQBWQd8ur3Br+usKW3IkxZyD6RkICulrsKcIvuNzoeB+7XWs2Jv07TWj8S+1t7lvJ5qJhbIkbYmCeTkYwOu7+b4O8QWSN/WILP2LCIt5B5IuUBWSg0DHgU+p7Vu7fSl14AvKKWyYudNUEqN7uYhKoArlVIjlVJ24FbgzdM8bQPmv5WKeA839PlFiETorh9589EPVuyT4W8W8FEif1H2RMoFMvAVYDTwsFJqw9E3zJbQ08AqpVQV8A/A0/VirfVB4PvAMmAjsF5r/dKpntBfUxEEGoH0cMNe+QZLTtfmF5c7uxzbizmG3LnqQPSQP3zcL3CReNJd0UMpNw5Za30/cP8pTvl9N8fe1+UxnsYM757YB5wZbW1sNMLBNpszPWUH7g5S2cDlwNKjB8qqw9G5051rMW/0HtrZZNQUjrHLriEDRwK5h1KxhWyVXYAbINrasMfaUsRJdNdtsZ7YvojrD0q3xQCT/uMekkCOX+3RD8JNB/ZYWIc4ue6Gvx0NBbVoV2RXxNBJM0d8v9fg6tJ2Zj7Uxtl/auP37wQB2HgoyiWPtFP4cBv/9YwfX/DEEXsnuxbge4sCnPNwG5978b1Jck9sDB13zgCRFnIPSSDHby+xERrB2m27La5FdO/M/OLygs4HyqrDbUANkOMLEq5r1XssqawbDhv85toMtn49i3e+mMlDa8JsqY9yx787WHBNOlVfzeJjMxz8asWJQXqya70BzcoDUTZ9NYuo1lQdjtIR1jy2MczXLkzrpoqEkr9IekgCOU7+mopmoB5whxv2NhkhmbGXpLprJb+D2cfM5iPJM2tvnMfGeePsAHjSFTNH2aj1aaobDK6YbB7/0BQH/9x6YqP+ZNfaFISiGq01HWFw2uFXK0N866I0nPYBnfEfAtYM5BMOBhLIPbOe2LjWSGu9tJKTU3f9yNuOfrB0d3L2I+9pMag8GKUoz877RtspqzZD+PktYfb7Tr30SudrPemKm2c6mf2Xds7ItZGTrlhTF+WjM7oOQEm4dyjxysJCPSSB3DNbATtAuGG/BHJyuiK/uLzrcMdDmJN7XNWNRktzh06qoYttIc3Nz/n53YczyE5XLPxoBg+tCXH+/7WjZPBuAAAVlklEQVTRGoS0U7Rsu14LcPel6Wz4Sha/uS6D+5YF+fFV6fxtfYhPPO/np28NWD/ysoF6osFEArln9hz9IFi7Zc/JTxMWcmJOoT+m06L1wwFqmpKnlRyOmoH6mUInN800W7EzRtp5/bZM1v13FrcWOpg6rPtA7u7azioPRgE4c4SNxzeGee4WN+8eiVLTGE3cC3rP0tOfIrqSQO4Bf02FD3OhoaxIc53XCPoTvmC96JXuui2qOLpo/YHk6EfWWvPFsgAzR9qZf8l7yzUfaTe7KAyt+elbIb5ywYk34052bWf3LQvy46vTCRsQjQ3UsCnwJ3gjMq21H/MXoOghCeSeWw/kAER8R6TbIjldn19c3rVZuQtzKy77sj3RfcGItnzPqhX7ozyxKczS3RFm/bmNWX9u4z81YZ6pCnPmH9uY8WA74z2K22eZrd+6VoMbnvKf8tqj/rUtzIXj7Yz32MjNUFySZ6fw4TaUgnPH2hP6upRSKyjxyk4hvZDyC9QPNHdB0dnAncA+98wrzso666pbrK5JdKtoz4I5qzsfmDvd+Q1gJlD/62vTbz5zhP193V8q+uj7lHgXWF1EKpIWcs/tib1XHTtWb9fRyICPthdx6W742xrABbDxkKz+lkDSf9xLEsg95K+paMcM5WwdDkTCTbWbT3OJsEZ3/cjHFq1/fWdkhyxa3/+01l5gndV1pCoJ5N5ZRqwfObB3wwaLaxHdm51fXD6u84Gy6nAzsB/wHG7XHUfa9QFrShu8lFJvUeIdkGEcg5EEcu9sxFyw3hbYu3G/EfTLtk7JR9F9t8UqIBdga710WyTAYqsLSGUSyL0QG/62HhgJEDqya6O1FYmT6K7bYsvRD5bvi0gg9yOtdRR4zuo6UpkEcu+9RewGkb/mnY3SHZmUPphfXN51kO5+oANIW1NnHGkLaa8FdQ1WiynxHrK6iFQmgdx72zD36kuPNNd5o21NMiY5+WQCV3U+UFYdNjBHW4wA2NEk3Rb9RSn1hNU1pDoJ5F7y11SEgTeIdVsE67bJzb3k1F0/ciXmFGvW1SXHrL1UZ2jdDrxodR2pTgK5byqILTbk375yq45GZHZS8jnZovUasC3aFdkdMXSCJxMPfgpeoMTr75fHUuobSqkdSimtlBoZO3Z7pz00Q0qpqtjHC5RSn1dK1cc+36aU+k6nx/qDUuq+Tp//QCn1UH/UmQgSyH1zAKgDsnWoIxxu3L/J6oLECabkF5fP7HygrDrsB6qBXH+YyAGflu6mPlJK/a2P16cppTJjn64APoi5KQQAWutHtdaztNazMH/mro59Xhw75e+xr10K/EApNTF2/F7gdqXUFKXUGcAdwA/6UmsiSSD3gb+mQgOLiA2jant36XKtjVMvXius0N1oi3eALIB3jyTP6m+pKGLo7ZR43+rNtUqpmUqp32D+gjwTQGtdqXXvdnbRWjdi/gU0Lva5DzOAHwQeAn6otW7pzWMPBAnkvqvEXLTGEWmu9YYb9lVaXZA4QXeBXH30gyW7ItKP3Ad2RY+6AJRSmbEuiOXA3zDXGT9Ha93nnx2l1CQgAzj216rW+hnMjSWytdZJfeNRArmPYmOSFwFjAdqqFr8treSk8/784vLcLseOAA2Ae2ez9jX6jcMW1JXyDK0DSqnHe3jZQeCLwB1a60u11n/TWrf2sZRPKqU2Y67q93ut31vNTymVh/nzOV4pldXH50koCeT+sSj23hFprpNWcvJxAB/ufKDrovXbG2X4W29ozd8p8fa0C+DjmLu4v6iU+qFSanI/lPJ3rfXZwOXAb5RSYzt97fdACeaklf/th+dKGAnkfhDbAPU1Yv1WbVWL39aGIfP5k0t3oy3eJbbY0KoD0o/cU1prw25Tv+7Fda9rrT8JXAZ4gZeUUouVUvn9UNMq4AngfwCUUtcDo4HHgZ8AH1NKndXX50kUCeT+sxhzKFWslbxXxiUnl+vzi8u7fr/vBiKA46290dpARPfLsK2hIhDhX5R43+3t9VrrRq3172OjI+7BvBeDUupbSqkDQB6wqRcjOH6BObLCA/wO+Jo2tQN3Y97gS0oSyP2km1byW9JKTiojgIs7HyirDocxb8oONzR6d7Oxw5LKUpChteFyqrv76/G01qu11vtjH/9Ba52ntXZorcdrre/ocm6+1rqh0+ePaa2/0enzOq31WK11q9Z6uta6qtPXXtBaf6C/6u5vEsj9axFmK9kZaTnoCzfslb7k5NLdaIu1mHfl2SCL1setPcQzlHh3Wl3HYCOB3I/8NRUtwKt0HnEhreRk0l0gHx3ypmKL1ssImdOIGjrsSVffs7qOwUgCuf8tBgxireRg3bYVVhckjinMLy6f2PlAWXXYizkjzNPYoYOH2vQ+a0pLHf4wCynx1lpdx2AkgdzPYq3kY33Jrev//bYsYJ9UumslryK2A8wWWbT+lCKGDnjS1b1W1zFYSSAnxmuYa+66dTgYad/6ZrnVBYljuhv+tpXY8Le39sqsvVMJRHiQEm/D6c8UvSGBnAD+mopWzLGQYwA6dq7ZFW6q7fXwINGvPpBfXO7qcqwWaAPSNxwyGnxB3WxBXUkvHNVtWWnqJ1bXMZhJICfOasztgsYA+Na+9KqOhDusLUlg7vJy3LCn2KL1q5FF608pGOVXlHh9VtcxmEkgJ4i/psLAbCWnA85oa0O7f0fFKxaXJUzd9SNvwJxizZpambXXlT+sD2SlqV9aXcdgJ4GcQP6aijrgX8AEgPbNS6vCLYe2WVuV4OSL1huAbcnuyN5wVMtmAzGG1rq5Q3+WEm/g9GeLvpBATrxXMTfWHAHgW/3CyzoSkq4La03MLy4/p/OBsupwALOLaVggQnS/T++yprTks6dFPz3ht61vWl3HUCCBnGD+mooQ5pqvWRztuti+SkZdWK+7bosKzI1R2XRYui0Amjr0wYOtxh2nP1P0BwnkAeCvqdgLvMDRroutb24OHtpRYW1VQ1533RbHQnjxrkiN1noAy0k+UUMbe1qMT1+6sF26KgaIBPLAOdp1MQrAu+rvr0daG2UvN+tcnF9cPqLLsQbgMJC5z6vbGvy6zoK6ksbuFv3IeX9pe8PqOoYSCeQB4q+pCGPu6eUAsjCihnfF088boY6k3d9rkLMB13c+EFu0fhXmdj9UNxpDdpJIg9/Yt7PJ+JrVdQw1EsgDyF9TcQj4I+aC2c5oe3OHb23Zs9qIyjb01uiuH3kzsVl7K/cPzX7kiKGj+736E9c92R6xupahRgJ5gPlrKt4FngUmAip0sPqwf9vylywua6i6Lr+43NHl2F4gDDhX7IvW+cO6zYK6LLWr2Xhw9l/a5B6HBSSQrfEqsBIzlM2bfHXblltb0pCUC1za+UBZdTgCrAOGa2B389DqtjjYamw70q7nW13HUCWBbIHYLL5S4ACxqdXed55fGvEeGVI//Emiu26LdZgzLFl/cOh0WzT4jaZleyLXXbawXdaEtogEskX8NRUdmP3JGvCgtW5Z8dQ/jUB7o8WlDTXdDX/rvGj9rqihB/0mA+0hHfjnlshnP/3PDlkP2kISyBby11TUA3/AnMWXZnS0BltWPvOEEfTLamMDZ2Z+cfmUzgfKqsOtwC4gxxskVNeq91pT2sAIR3XkH1vC93z55Q5Za8ViEsgW89dUbMNchCgPsEea67wtK54ulVAeUN21klcB2TC4F603tNb/3h758z+3Rn5vdS1CAjlZLAVeBiZzNJSXP/WYhPKA6a4f+dgiUMv2RAZtIL+5J1r+2IbwnbElSIXFJJCTgL+mQgPP0zmUWw76JJQHzJX5xeVZXY7VAT4gY0u90dwS0IOub7/yYHT9A++EPl1WHZaV7ZKEBHKSkFC2VDrwwc4HOs3aGw5Q0zi4ui12NRt7H1wdmhPrLxdJQgI5iUgoW6q7bosqYovWrx5Ei9YfaTeaHt8YmvtIZeiQ1bWI40kgJxkJZcvckF9crroc24m5aL196e7IvlBUBy2oq18dbjOa/7Y+/KmSN4KbrK5FnEgCOQmdPJSffCza4ZNWTWKMA87rfKCsOhzEbCUPCxsYe1uMHZZU1k/qWo3GX64IffmeJYFFVtciuieBnKS6D+VDvqZFf34k3HSgytrqBq3uhr9VAG6ATYdTdxr1Pq9x5MdvBu+saTL+YXUt4uQkkJNYp1B+CTOUM3Q4EGletvCFjj0bFumhvoJ6/+uuH7mG2Opvr++M1Bgp+G++q9k4VPJG8Jt1rfrx2M1KkaQkkJNcLJRfAB7GXNx+GEDrurKVbRtffVJHwrI/X/+5IL+4fEznA2XV4UbMIXCeg23aX9+ua60prXeqDkf33Lc08JUGv35ewjj5SSCnAH9NhfbXVKwCfoJ5k2k8QMfONbtalj/512hH6xFLCxw8FHBDN8dXYa4Mx7aG1Bn+9tbeyJb7lgXvaA1RJmGcGiSQU4i/pmIP8CPMdRbyAVu4cX9z0+K//C3cXLfFytoGkZMtWg/A8n3JP/zN0Fq/uDW8+tcrQ1/617bwEgnj1CGBnGL8NRUtwG+ARZihnK5D/nDz0r89H9i3aYn0K/fZh/KLy9O6HNsHBIC0itro4faQ9llQV1zCUR1+bEN46aMbwl8oqw6vtLoe0TMSyCkotj/f08D/Ya6nnAvgW/Ov5a3ryh41grKEZx94gCs6HyirDkeBtcRm7e1M0kXrj7QbDfctCz77r22RO8qqw5tPf4VINhLIKSrWr7wc+Blm3+d4QAX2btzf+NpDfw4erFklreVe6274WyWQBsm5aP2q/ZEt3/hP4LEt9cZ3y6rDe6yuR/SOBHKK89dU7ARKgC3AGYBLhwMR78pnXm9d+5K0lnvnZMPfNKAW7Yzsjhg6KTYA7QjrjodWh5bevzz0x0CEkrLqsNzgTWFKGlGDg7ugSAGXAJ/DbDEfBFCONLvn/LmXp4+fcZmy2exW1phiZuxZMKe684G5053FmDP6mv94fcZnJufapllTmmmf16hdsDz45gGffhhYITfvUp+0kAeJWBfGSuAezNbyFMCtI6Gor+Ifb7S8/cSfIq0Nu6ytMqWcbNaeB2BzvXXdFobW+vWdkfXfeiXw6AGfvqusOrxcwnhwkEAeZPw1FU2Y20I9hDnldyJgCzfsbWp6/U9PtG99+59GqMNraZGp4WSL1iuAJbusCeTWoPb9YnnotQdXh35jaH5aVh2us6IOkRjSZTGIuQuKPMCNwDVAK2D2J9sdtqz3XTM7Y9I5l9vSXDkWlpjMwsDIPQvmHBviNne6UwG/BiJAx2M3ur463KVGD0QxhtZ6bZ2x5eE1oYrGDv1HYKO0igcfCeQhwF1QNBX4PDAJM5TNkJFgPp1P7Fkw5/nOB+ZOd34cuA6ovefytGsuznNclugi9rYYux5eG9q0pd6oAP4am84tBiHpshgCYiMxfgQ8iNm6mwxkE40YbRtfW9fwnwf+4N9R8bJ0ZZygu37kKmI/NxUHogkdj9zcoRseXB189ZuvBJZtqTceAX4tYTy4SQt5iHEXFDmA2cAtwGigic4t5rOvmZUx+ZzLbWmuXOuqTBpHgHF7Fsw5tgHo3OnONMxfbEccNqLP3Oy6K92hXP35pIGI7nhtR2R16cbw3ojBSuCFsupwfX8+h0hOEshDVCyYZ2EG8xi6BHPmzCsL08fPmGXPGp6vVNeNNIaUS/YsmPNO5wNzpzu/AcwE6n/1ofSbpo+0F/bHE0UNbayti67/05pQTXOAauCpsupwSi+KL3rGYXUBwhr+mooIsNZdULSB94I5H2gmGvG2v7tkY/u7SzY6hufluqdddE7a6CmzbOnuYVbWbJE5wDtdjq0GzgfYcMjY3tdA1lqzq1lvf3htqGp7o1ELPAWsLasOG6e7Vgwu0kIWwHEt5o9hTsOOAPXAsS3iMyafOzFj8rmznMMnnK3sznRrKh1wG/YsmDO784G5053DMBd42jc6U2X8339l3G3rxZ8RoagOvnvE2PhMVXhPdaPhw9yIYHFZdTjQP6WLVCOBLI4Tm/E3CSgCrgJcQAfQgLkWM8qZ4XAXXDIjfcKMWXbPyClq8Pdp5O1ZMOe4hennTnf+GMgCfH/5SMbt4zy2SfE+WEtAN6zYF1n7zLvhQ74gCngb+JfcsBPSZSGOE9uhZC+w111Q9CIwA7gS80agArw6HPC2b1n2bvuWZe/a3DkZGRML852jJuc7skefYcvIGj1Y8lkb0Xpls5cDGd18eRVmN49va4Ox/XSBbGit97bo7f+piWx4bWfEj/nLbQWwpKw6vK/fixcpSVrIIi7ugqJs4FzgQ5iz/zTgxZxwcqyv05413J0+8X35aSMnHQ3okZYU3As6Eg5E/S214abanYF9Vd5w/e5X/TUVC7s7d+5052Tgh8D+88fZRv3vVRlf6+68YEQHNh021j/zbnjHjibDwPw3+w9QUVYdlmGG4jjSQhZx8ddU+IC33QVFyzH7mAuB84CpnU7zRduafP6tb23xm+tpYM8elZWRd3a+c8TEyTZ3zih7RtYo5UhzD/wreI/WWuugvzHqbzkcbWs6HG45dChcv+dwpOVgAHPPwqP946dqrewH/ED6uoNGfWtQt3jSVS5AOKpDe1qMmrV1xvay6nBzexgbsB0ziDeXVYeTYqU4kXykhSz6xF1QlIE50aQAs1sjv9OXj7agj/sms2cOczlHThrlyBk70p6ZO8yWkZVrS8/MVWmuXOVIy+prl4fWWhMNB3Q07NeRUIeOhDuigdamqPfI4XBz7eHQ4V1HtHnfLBOzH9geqzEArAM2Ajtju7Oc1NzpztuAy4CD37s07aqcDJW9uja69dUdkfpAhGwgCrwFLANqZaqzOB0JZNGv3AVFLsxQLsBsQU8ito5w7H0H0I4Zfid88ylnusPmys6wOdIdypnuVI40h3KkOZTd6Tz6HrvDoexOB8pm0+FAwAi2dxiBtg6jo9Uf7fB2GH5v51EKCvPGZGbs/dFagsAeYAfmFk0HgTp/TUXcQ83mTneeA3wbs7WcA2THvlQHLAVWl1WH2+J9PCEkkEVCxQJ6JDAKGIu5iP6k2DF4LyBtmC1KI/bW+eOuxzRmd5uz03tnp8c7+k2tYm/1wE7M8D0IHAaaYzcwe23udKcbeCD23Dsxb9JtBhqkNSx6QwJZWMJdUGTDbFHmxt6GYXYfuDC3SkqPvaVhjnJI63TcjtnK9sXevEAL0IbZAvfH3ncALf6aimCiXsfc6c4JQHtZdfiU3RtCxEMCWQghkoSs9iaEEElCAlkIIZKEBLIQQiQJCWQhhEgSEshCCJEkJJCFECJJSCALIUSSkEAWQogkIYEshBBJQgJZCCGShASyEEIkCQlkIYRIEhLIQgiRJCSQhRAiSUggCyFEkpBAFkKIJCGBLIQQSUICWQghksT/A5kVPDq8vargAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------\n",
      "Zero:  960634\n",
      "<1TRX:  513816\n",
      ">=1TRX:  285988\n"
     ]
    }
   ],
   "source": [
    "normal_accounts = len(normal_acc_data['balance']) - 1\n",
    "normal_analysis = copy.deepcopy(normal_acc_data)\n",
    "print('Normal accounts: ', normal_accounts)\n",
    "print('Volume(Million-TRX): ', normal_acc_data['balance'][0]/(TRX*MILLION))\n",
    "\n",
    "\n",
    "# all\n",
    "acc_0 = len(normal_analysis['balance']) - 1\n",
    "# 0\n",
    "# normal_analysis = normal_analysis.drop(normal_acc_data[normal_acc_data['balance'] <= 0].index)\n",
    "# acc_1 = acc_0 - len(normal_analysis['balance'])\n",
    "acc_1 = len(normal_analysis.drop(normal_analysis[normal_analysis['balance'] < 1].index)) - 1\n",
    "# 1 ~ 1,000,000\n",
    "# normal_analysis = normal_analysis.drop(normal_acc_data[normal_acc_data['balance'] <= TRX].index)\n",
    "# acc_2 = acc_1 - len(normal_analysis['balance'])\n",
    "acc_2 = len(normal_analysis.drop(normal_analysis[normal_analysis['balance'] <= TRX].index)) - 1\n",
    "\n",
    "\"\"\"\n",
    "Draw Bar\n",
    "\"\"\"\n",
    "acc_draw = pd.Series([acc_0, acc_1, acc_2], index=['Total','>=1 SUN','>=1 TRX'])\n",
    "\n",
    "acc_draw.plot.bar(figsize=(20,10))\n",
    "plt.xlabel('TRX Holding')\n",
    "plt.ylabel('Account#')\n",
    "plt.title(\"Normal Account Summary\");\n",
    "\n",
    "print('---------')\n",
    "print('Total: ', acc_0)\n",
    "print('>=1 SUN: ', acc_1)\n",
    "print('>=1 TRX: ', acc_2)\n",
    "\n",
    "\"\"\"\n",
    "Draw Pie\n",
    "\"\"\"\n",
    "num1 = acc_0 - acc_1\n",
    "num2 = acc_1 - acc_2\n",
    "num3 = acc_2\n",
    "labels = 'Zero', '<1TRX', '>=1TRX'\n",
    "sizes = [num1, num2, num3]\n",
    "explode = (0, 0.1, 0)\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('---------')\n",
    "print('Zero: ', num1)\n",
    "print('<1TRX: ', num2)\n",
    "print('>=1TRX: ', num3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contract Account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contract accounts:  4641\n",
      "Volume(Million-TRX):  67.534056022096\n"
     ]
    }
   ],
   "source": [
    "contract_accounts = len(contract_acc_data['balance']) - 1\n",
    "print('Contract accounts: ', contract_accounts)\n",
    "print('Volume(Million-TRX): ', contract_acc_data['balance'][0]/(TRX*MILLION))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Asset Issue Account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asset Issue accounts:  27\n",
      "Volume(TRX):  0.0101\n"
     ]
    }
   ],
   "source": [
    "asset_accounts = len(asset_acc_data['balance']) - 1\n",
    "print('Asset Issue accounts: ', asset_accounts)\n",
    "print('Volume(TRX): ', asset_acc_data['balance'][0]/(TRX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify Total Account Num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of accounts from 3 different account sheets: 1765106\n",
      "Total accounts count in all accounts sheet: 1765106\n",
      "---------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------\n",
      "Normal Accounts:  1760438\n",
      "Contract Accounts:  4641\n",
      "Asset Accounts(Ignored):  27\n"
     ]
    }
   ],
   "source": [
    "print('Sum of accounts from 3 different account sheets:', normal_accounts + contract_accounts + asset_accounts)\n",
    "print('Total accounts count in all accounts sheet:', total_accounts)\n",
    "\n",
    "\"\"\"\n",
    "Draw Pie\n",
    "\"\"\"\n",
    "print('---------')\n",
    "labels = 'Normal Accounts', 'Contract Accounts'\n",
    "sizes = [normal_accounts, contract_accounts]\n",
    "explode = (0, 0.1)\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('---------')\n",
    "print('Normal Accounts: ', normal_accounts)\n",
    "print('Contract Accounts: ', contract_accounts)\n",
    "print('Asset Accounts(Ignored): ', asset_accounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Calculate Airdrop Ratio\n",
    "__[airdrop amount]__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airdrop Amount(Million): 10890.0\n"
     ]
    }
   ],
   "source": [
    "AIRDROP_AMOUNT = 10890000000\n",
    "print('Airdrop Amount(Million):', AIRDROP_AMOUNT/1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airdrop Ratio SUN/BTT 9110113.399847975\n",
      "Airdrop Ratio TRX/BTT 9.110113399847975\n"
     ]
    }
   ],
   "source": [
    "print('Airdrop Ratio SUN/BTT', total_balance/AIRDROP_AMOUNT)\n",
    "print('Airdrop Ratio TRX/BTT', (total_balance/1000000)/AIRDROP_AMOUNT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Trim Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black list trimed off accounts amount:  23\n",
      "Accounts left amount:  1760415\n",
      "Threadhold trimed off accounts amount:  1420470\n",
      "Accounts left amount:  339945\n"
     ]
    }
   ],
   "source": [
    "black_list = copy.deepcopy(BLACK_LIST)\n",
    "# normal_acc_data = normal_acc_data.drop(normal_acc_data[normal_acc_data['address'] in black_list].index)\n",
    "while len(black_list):\n",
    "    acc = black_list.pop()\n",
    "    normal_acc_data = normal_acc_data.drop(normal_acc_data[normal_acc_data['address'] == acc].index)\n",
    "    \n",
    "# print(normal_acc_data.loc[normal_acc_data['address'] == 'TLsV52sRDL79HXGGm9yzwKibb6BeruhUzy'])\n",
    "blacklist_trimed_accounts = len(normal_acc_data['balance']) - 1\n",
    "print('Black list trimed off accounts amount: ', normal_accounts - blacklist_trimed_accounts)\n",
    "print('Accounts left amount: ', blacklist_trimed_accounts)\n",
    "\n",
    "normal_acc_data = normal_acc_data.drop(normal_acc_data[normal_acc_data['balance'] < THREADHOLD].index)\n",
    "ready2airdrop_accounts = len(normal_acc_data['balance']) - 1\n",
    "print('Threadhold trimed off accounts amount: ', blacklist_trimed_accounts - ready2airdrop_accounts)\n",
    "print('Accounts left amount: ', ready2airdrop_accounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Generate Airdrop List\n",
    "```\n",
    "scp account.csv congfullnode@40.83.218.12:~/AD\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_acc_data = normal_acc_data.drop(normal_acc_data[normal_acc_data['address'] == 'total'].index)\n",
    "normal_acc_data = normal_acc_data.sort_values(by=['balance'], ascending=False)\n",
    "normal_acc_data.to_csv(AIRDROP_FILE_NAME, encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data:                                address            balance\n",
      "0  TMuA6YqfCeX8EhbfYEg5y7S4DqzSJireY9  15169933530752418\n",
      "1  TAahLbGTZk6YuCycii72datPQEtyC5x231   4541621455952580\n",
      "2  TTiRNa312qQbvkjRGU3sHWVK3jZKSnNEXx   2624966593203613\n",
      "3  TWd4WrZ9wn84f5x1hZhL4DHvk738ns5jwb   1860000000000000\n",
      "4  TNaRAoLUyYEV2uF7GUrzSjRQTU8v5ZJ5VR   1498678648922243\n",
      "339945\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(AIRDROP_FILE_NAME)\n",
    "print('data: ', data.head())\n",
    "print(len(data['balance']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Transaction Cost\n",
    "\n",
    "Only for reference.\n",
    "\n",
    "### a. Bandwith Cost: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bandwith Cost Range:  94844655  -  96204435\n"
     ]
    }
   ],
   "source": [
    "bandwith_cost_min = ready2airdrop_accounts * TOKEN_NET_COST_MIN\n",
    "bandwith_cost_max = ready2airdrop_accounts * TOKEN_NET_COST_MAX\n",
    "\n",
    "print('Bandwith Cost Range: ', bandwith_cost_min, ' - ', bandwith_cost_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b. TRX Burn Cost:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRX Cost Range(sun):  948446550  -  962044350\n",
      "TRX Cost Range(trx):  948.44655  -  962.04435\n"
     ]
    }
   ],
   "source": [
    "print('TRX Cost Range(sun): ', bandwith_cost_min*10, ' - ', bandwith_cost_max*10)\n",
    "print('TRX Cost Range(trx): ', bandwith_cost_min*10/TRX, ' - ', bandwith_cost_max*10/TRX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c. Freeze Prepare:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bandwidth Freeze Ratio:  6.278916829272772\n",
      "TRX To Freeze For Bandwidth:  15105257.416028708  -  15321820.246366037\n"
     ]
    }
   ],
   "source": [
    "bandwidth_freeze_ratio = 1 / DYNAMIC_TOTAL_NET_WEIGHT * 43200000000\n",
    "print('Bandwidth Freeze Ratio: ', bandwidth_freeze_ratio)\n",
    "print('TRX To Freeze For Bandwidth: ', bandwith_cost_min/bandwidth_freeze_ratio, ' - ', bandwith_cost_max/bandwidth_freeze_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is dynamic, please double the higher range."
   ]
  }
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