Count_TX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# شمارش تعداد تراکنش ها"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://en.bitcoin.it/wiki/API_reference_(JSON-RPC)#JSON-RPC\n",
    "# https://bitcoin.org/en/developer-reference\n",
    "\n",
    "host_url = \"http://178.162.214.41:5000\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import time\n",
    "import json\n",
    "\n",
    "# block_511798 = \"000000000000000000558c3bf3c4695bde12386d1d75aa31dafe5107f900950e\"\n",
    "genesis = \"000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f\"\n",
    "\n",
    "def get_block(block_hash):\n",
    "    url = 'http://127.0.0.1:8332/rest/block/'+ block_hash +'.json'\n",
    "    block_response = requests.get(url)\n",
    "    result = json.loads(block_response.content)\n",
    "    return result\n",
    "\n",
    "# print (block_response.status_code)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1(1,1),1(1020,1001),1(2031,2001),1(3051,3001),1(4059,4001),1(5064,5001),1(6070,6001),1(7078,7001),1(8083,8001),1(9087,9001),"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "current_block_hash = genesis\n",
    "sum_tx = 0\n",
    "tx_list = []\n",
    "\n",
    "for i in range (10000):\n",
    "    current_block = get_block(current_block_hash)\n",
    "    sum_tx += len(current_block[\"tx\"])\n",
    "    tx_list.append([datetime.datetime.fromtimestamp(current_block[\"time\"]).strftime('%Y'),datetime.datetime.fromtimestamp(current_block[\"time\"]).strftime('%m'),len(current_block[\"tx\"])])\n",
    "    if (i%1000 == 0):\n",
    "        print (\"{}({},{}),\".format(len(current_block[\"tx\"]), sum_tx,len(tx_list)),end='')\n",
    "#         time.sleep(0.5)\n",
    "    current_block_hash = current_block[\"nextblockhash\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>513788</th>\n",
       "      <td>2018</td>\n",
       "      <td>03</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513789</th>\n",
       "      <td>2018</td>\n",
       "      <td>03</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513790</th>\n",
       "      <td>2018</td>\n",
       "      <td>03</td>\n",
       "      <td>1691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513791</th>\n",
       "      <td>2018</td>\n",
       "      <td>03</td>\n",
       "      <td>1735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513792</th>\n",
       "      <td>2018</td>\n",
       "      <td>03</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0   1     2\n",
       "513788  2018  03   540\n",
       "513789  2018  03   756\n",
       "513790  2018  03  1691\n",
       "513791  2018  03  1735\n",
       "513792  2018  03  1600"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(tx_list)\n",
    "df.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "513793"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GS6gFAADmq2ptdZgi1AIAwMUsLycLC8kVV0yGy8vzbtH20722Out3++3J7t2TPxjs3j2Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8affde5978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline  \n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 16, 5\n",
    "\n",
    "fig = plt.plot(df[0],df[2],'ro')\n",
    "# fig.set_size_inches(18.5, 10.5, forward=True)\n",
    "\n",
    "len(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8afc3999e8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eqk5jBgAAgPVM7AIAADAcsQsAAMBwxC4AAADDEbsAAAAMR+wCAAAwHLELAADAcMQuAAAA\nwxG7AAAADEfsAgAAMByxCwAAwHDELgAAAMMRuwAAAAxH7AIAADAcsQsAAMBwxC4AAADDEbsAAAAM\nR+wCAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAAMByxCwAAwHDELgAAAMMRuwAAAAxH7AIAADAc\nsQsAAMBwxC4AAADDEbsAAAAMR+wCAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAAMByxCwAAwHDE\nLgAAAMMRuwAAAAxH7AIAADAcsQsAAMBwxC4AAADDEbsAAAAMR+wCAAAwHLELAADAcMQuAAAAwxG7\nAAAADEfsAgAAMByxCwAAwHDELgAAAMMRuwAAAAxH7AIAADAcsQsAAMBwxC4AAADDEbsAAAAMR+wC\nAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAAMByxCwAAwHDELgAAAMMRuwAAAAxH7AIAADAcsQsA\nAMBwxC4AAADDEbsAAAAMR+wCAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAAMJyDxm5V3VZVz1bV\nV+fWTquq7VX1xPT51Ln7rqmqnVX1eFVdNLf+tqraMd330aqqaf1VVfXpaf3Bqto8t81l05/xRFVd\ndrSeNAAAAGM7lFd2b09y8cLa1Unu7+5zktw/fZ2qOjfJtiRvnLb5eFWdNG1zc5IPJDln+ti3z8uT\nPN/db0jykSQ3Tvs6Lcm1Sd6e5Lwk185HNQAAAOzPQWO3u/8wye6F5UuS3DHdviPJu+bW7+ru73b3\n15PsTHJeVZ2e5LXd/UB3d5JPLGyzb1/3JLlgetX3oiTbu3t3dz+fZHu+P7oBAADg+2xa5XZL3f3M\ndPubSZam22ckeWDucU9Pa9+bbi+u79vmqSTp7r1V9UKS182vr7DNK1TVFUmuSJKlpaUsLy+v6kkd\nL0snJ1e9ee9hb7fenxert2fPHt9fXmYeWGQmWGQmmGceWGQmZlYbuy/r7q6qPhoHcwTHcEuSW5Jk\ny5YtvXXr1rU8nIP62J335qYdh/9X/+T7tx79g2FdWF5eznqfW44f88AiM8EiM8E888AiMzGz2qsx\nf2s6NTnT52en9V1Jzpp73JnT2q7p9uL6K7apqk1JTkny3AH2BQAAAAe02ti9L8m+qyNfluTeufVt\n0xWWz87sQlRfnE55frGqzp/ej3vpwjb79vXuJF+Y3tf7+SQXVtWp04WpLpzWAAAA4IAOei5tVX0q\nydYkr6+qpzO7QvINSe6uqsuTfCPJe5Okux+pqruTPJpkb5Iru/ulaVcfzOzKzicn+dz0kSS3Jvlk\nVe3M7EJY26Z97a6q65I8ND3uw929eKEsAAAA+D4Hjd3uft9+7rpgP4+/Psn1K6w/nORNK6x/J8l7\n9rOv25LcdrBjBAAAgHmrPY0ZAAAA1i2xCwAAwHDELgAAAMMRuwAAAAxH7AIAADAcsQsAAMBwxC4A\nAADDEbsAAAAMR+wCAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAAMByxCwAAwHDELgAAAMMRuwAA\nAAxH7AIAADAcsQsAAMBwxC4AAADDEbsAAAAMR+wCAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAA\nMByxCwAAwHDELgAAAMMRuwAAAAxH7AIAADAcsQsAAMBwxC4AAADDEbsAAAAMR+wCAAAwHLELAADA\ncMQuAAAAwxG7AAAADEfsAgAAMByxCwAAwHDELgAAAMMRuwAAAAxH7AIAADAcsQsAAMBwxC4AAADD\nEbsAAAAMR+wCAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAAMByxCwAAwHDELgAAAMMRuwAAAAxH\n7AIAADAcsQsAAMBwxC4AAADDEbsAAAAMR+wCAAAwHLELAADAcMQuAAAAwxG7AAAADEfsAgAAMByx\nCwAAwHDELgAAAMMRuwAAAAxH7AIAADAcsQsAAMBwxC4AAADD2bTWB8Ch23z1Zw97mydveOcxOBIA\nAID1zSu7AAAADEfsAgAAMJwjit2qerKqdlTVV6rq4WnttKraXlVPTJ9PnXv8NVW1s6oer6qL5tbf\nNu1nZ1V9tKpqWn9VVX16Wn+wqjYfyfECAACwMRyNV3b/eXe/pbu3TF9fneT+7j4nyf3T16mqc5Ns\nS/LGJBcn+XhVnTRtc3OSDyQ5Z/q4eFq/PMnz3f2GJB9JcuNROF4AAAAGdyxOY74kyR3T7TuSvGtu\n/a7u/m53fz3JziTnVdXpSV7b3Q90dyf5xMI2+/Z1T5IL9r3qCwAAAPtzpFdj7iS/X1UvJfmv3X1L\nkqXufma6/5tJlqbbZyR5YG7bp6e17023F9f3bfNUknT33qp6Icnrkvz5/EFU1RVJrkiSpaWlLC8v\nH+HTOraWTk6uevPe4/Jnrfe/C2b27Nnje8XLzAOLzASLzATzzAOLzMTMkcbuP+3uXVX1d5Jsr6o/\nmb+zu7uq+gj/jIOaIvuWJNmyZUtv3br1WP+RR+Rjd96bm3Ycn9/69OT7tx6XP4cjs7y8nPU+txw/\n5oFFZoJFZoJ55oFFZmLmiE5j7u5d0+dnk/x2kvOSfGs6NTnT52enh+9Kctbc5mdOa7um24vrr9im\nqjYlOSXJc0dyzAAAAIxv1bFbVa+uqh/adzvJhUm+muS+JJdND7ssyb3T7fuSbJuusHx2Zhei+uJ0\nyvOLVXX+9H7cSxe22bevdyf5wvS+XgAAANivIzmXdinJb0/Xi9qU5H909+9W1UNJ7q6qy5N8I8l7\nk6S7H6mqu5M8mmRvkiu7+6VpXx9McnuSk5N8bvpIkluTfLKqdibZndnVnAEAAOCAVh273f21JD+6\nwvpzSS7YzzbXJ7l+hfWHk7xphfXvJHnPao8RAACAjelY/OohAAAAWFNiFwAAgOGIXQAAAIYjdgEA\nABiO2AUAAGA4YhcAAIDhiF0AAACGI3YBAAAYjtgFAABgOGIXAACA4YhdAAAAhrNprQ+AY2vz1Z9d\n1XZP3vDOo3wkAAAAx49XdgEAABiO2AUAAGA4YhcAAIDhiF0AAACGI3YBAAAYjtgFAABgOGIXAACA\n4YhdAAAAhiN2AQAAGI7YBQAAYDhiFwAAgOGIXQAAAIYjdgEAABiO2AUAAGA4YhcAAIDhiF0AAACG\nI3YBAAAYjtgFAABgOGIXAACA4YhdAAAAhrNprQ+A9Wnz1Z897G2evOGdx+BIAAAADp9XdgEAABiO\n2AUAAGA4YhcAAIDhiF0AAACGI3YBAAAYjtgFAABgOGIXAACA4YhdAAAAhiN2AQAAGI7YBQAAYDhi\nFwAAgOGIXQAAAIYjdgEAABiO2AUAAGA4m9b6ABjH5qs/u6rtnrzhnUf5SAAAgI3OK7sAAAAMxyu7\nsI54dRwAAI4OsQvHyOGE61Vv3pufWWXoAgAA30/ssuZW82qmVzJfyd8hAAC8kvfsAgAAMByv7MJB\nrPZ9tAAAwNoRu5yQBCgAAHAgTmMGAABgOF7ZhQ3KrzkCAGBkXtkFAABgOGIXAACA4YhdAAAAhiN2\nAQAAGI7YBQAAYDhiFwAAgOH41UPAYVnNryzy64oAADjevLILAADAcMQuAAAAwxG7AAAADEfsAgAA\nMBwXqAKOudVc1CpxYSsAAFbPK7sAAAAM54SI3aq6uKoer6qdVXX1Wh8PAAAA69u6P425qk5K8utJ\n/lWSp5M8VFX3dfeja3tkwLHmd/oCALBa6z52k5yXZGd3fy1JququJJckEbvA9/H+YAAAkhMjds9I\n8tTc108nefv8A6rqiiRXTF/uqarHj9Oxrdbrk/z5Wh8E68cvmok1Vzeu9RG8gnlgkZlgkZlgnnlg\n0egz8XcP5UEnQuweVHffkuSWtT6OQ1VVD3f3lrU+DtYPM8E888AiM8EiM8E888AiMzFzIlygaleS\ns+a+PnNaAwAAgBWdCLH7UJJzqursqvqbSbYluW+NjwkAAIB1bN2fxtzde6vq55N8PslJSW7r7kfW\n+LCO1AlzyjXHjZlgnnlgkZlgkZlgnnlgkZlIUt291scAAAAAR9WJcBozAAAAHBaxCwAAwHDE7nFU\nVRdX1eNVtbOqrl7r4+HYqaqzquoPqurRqnqkqj40rZ9WVdur6onp86lz21wzzcbjVXXR3PrbqmrH\ndN9Hq6rW4jlx5KrqpKr646r6n9PX5mEDq6ofrqp7qupPquqxqvrHZmLjqqp/P/178dWq+lRV/S3z\nsLFU1W1V9WxVfXVu7ajNQFW9qqo+Pa0/WFWbj+fz4/DtZyb+0/Tvxv+qqt+uqh+eu89MLBC7x0lV\nnZTk15P86yTnJnlfVZ27tkfFMbQ3yVXdfW6S85NcOX2/r05yf3efk+T+6etM921L8sYkFyf5+DQz\nSXJzkg8kOWf6uPh4PhGOqg8leWzua/Owsf2XJL/b3f8gyY9mNhtmYgOqqjOS/GKSLd39pswuyLkt\n5mGjuT3f//06mjNweZLnu/sNST6S5MZj9kw4Wm7P98/E9iRv6u5/mOR/J7kmMRP7I3aPn/OS7Ozu\nr3X3Xya5K8kla3xMHCPd/Ux3f3m6/ReZ/U/sGZl9z++YHnZHkndNty9Jcld3f7e7v55kZ5Lzqur0\nJK/t7gd6djW5T8xtwwmkqs5M8s4kvzm3bB42qKo6Jck/S3JrknT3X3b3/42Z2Mg2JTm5qjYl+cEk\n/yfmYUPp7j9Msnth+WjOwPy+7klygVf+17eVZqK7f6+7905fPpDkzOm2mViB2D1+zkjy1NzXT09r\nDG46JeStSR5MstTdz0x3fTPJ0nR7f/NxxnR7cZ0Tz39O8h+S/NXcmnnYuM5O8mdJ/lvNTm3/zap6\ndczEhtTdu5L8apI/TfJMkhe6+/diHji6M/DyNlMsvZDkdcfmsDlOfjbJ56bbZmIFYheOoap6TZLf\nSvJL3f3i/H3TT9f87q8NoKp+PMmz3f2l/T3GPGw4m5L8oyQ3d/dbk3w70+mJ+5iJjWN6H+Ylmf0Q\n5EeSvLqqfmr+MeYBM8C8qvqPmb1t7s61Ppb1TOweP7uSnDX39ZnTGoOqqh/ILHTv7O7PTMvfmk4n\nyfT52Wl9f/OxK399esr8OieWdyT5iap6MrO3MPyLqvrvMQ8b2dNJnu7uB6ev78ksfs3ExvQvk3y9\nu/+su7+X5DNJ/knMA0d3Bl7eZjpd/pQkzx2zI+eYqaqfSfLjSd4//RAkMRMrErvHz0NJzqmqs6vq\nb2b2BvL71viYOEam9zvcmuSx7v61ubvuS3LZdPuyJPfOrW+brop3dmYXD/jidOrSi1V1/rTPS+e2\n4QTR3dd095ndvTmz//a/0N0/FfOwYXX3N5M8VVV/f1q6IMmjMRMb1Z8mOb+qfnD6Pl6Q2bUezANH\ncwbm9/XuzP4t8krxCaaqLs7sbVE/0d3/b+4uM7GCTWt9ABtFd++tqp9P8vnMrrJ4W3c/ssaHxbHz\njiQ/nWRHVX1lWvvlJDckubuqLk/yjSTvTZLufqSq7s7sf3b3Jrmyu1+atvtgZlfjOzmz92Xse28G\nJz7zsLH9QpI7px+Afi3Jv83sh9BmYoPp7ger6p4kX87s+/vHSW5J8pqYhw2jqj6VZGuS11fV00mu\nzdH9d+LWJJ+sqp2ZXfRo23F4WhyB/czENUlelWT7dC2pB7r735mJldVg8Q4AAABOYwYAAGA8YhcA\nAIDhiF0AAACGI3YBAAAYjtgFAABgOGIXAACA4YhdAAAAhvP/AcRBxM91/xTPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8afc3b0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline  \n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "\n",
    "df[2].hist(bins=60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017(290139154,503001),1395(292093951,504001),1576(293871798,505001),561(295231027,506001),1108(296682224,507001),2088(297987702,508001),1641(299221104,509001),580(300403744,510001),205(301561672,511001),1218(302796634,512001),1022(304066307,513001),"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'nextblockhash'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-caa604772b58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"{}({},{}),\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_block\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tx\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msum_tx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtx_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mend\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      9\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0mcurrent_block_hash\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrent_block\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"nextblockhash\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'nextblockhash'"
     ]
    }
   ],
   "source": [
    "# For the Next Runs !\n",
    "\n",
    "for i in range (500000):\n",
    "    current_block = get_block(current_block_hash)\n",
    "    sum_tx += len(current_block[\"tx\"])\n",
    "    tx_list.append([datetime.datetime.fromtimestamp(current_block[\"time\"]).strftime('%Y'),datetime.datetime.fromtimestamp(current_block[\"time\"]).strftime('%m'),len(current_block[\"tx\"])])\n",
    "    if (i%1000 == 0):\n",
    "        print (\"{}({},{}),\".format(len(current_block[\"tx\"]), sum_tx,len(tx_list)),end='')\n",
    "        time.sleep(2)\n",
    "    current_block_hash = current_block[\"nextblockhash\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Year, Month, Count\n",
    "\n",
    "df.to_csv(\"tx_stats\",index=False)"
   ]
  }
 ],
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