preprocess_FEGS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://fcx-raw-data-temp/FEGS/data/goesr_plt_FEGS_20170321_Flash_v2.txt\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "bucket_src = \"fcx-raw-data-temp\"\n",
    "path_to_file=\"FEGS/data\"\n",
    "filename = \"goesr_plt_FEGS_20170321_Flash_v2.txt\"\n",
    "s3path=f\"s3://{bucket_src}/{path_to_file}/{filename}\"\n",
    "print(s3path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             peak\n",
      "FlashID          \n",
      "1        0.000742\n",
      "2        0.000486\n",
      "3        0.000389\n",
      "4        0.001074\n",
      "5        0.000370\n",
      "...           ...\n",
      "2285     0.000462\n",
      "2286     0.000495\n",
      "2287     0.000540\n",
      "2288     0.000720\n",
      "2289     0.000323\n",
      "\n",
      "[2289 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "subset = pd.read_csv(s3path, sep=\",\",index_col=False,usecols=\n",
    "                  ['FlashID', 'peak'])\n",
    "\n",
    "subset = pd.read_csv(s3path, sep=\",\", index_col='FlashID', usecols=['FlashID', 'peak'])\n",
    "print(subset)\n",
    "op = json.loads(subset.to_json(orient='split'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       " 0.003443,\n",
       " 0.019435,\n",
       " 0.001923,\n",
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       " 0.009854,\n",
       " 0.000493,\n",
       " 0.003669,\n",
       " 0.000657,\n",
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       " 0.000643,\n",
       " 0.006158,\n",
       " 0.000717,\n",
       " 0.003911,\n",
       " 0.016226,\n",
       " 0.006041,\n",
       " 0.008115,\n",
       " 0.001763,\n",
       " 0.007972,\n",
       " 0.006732,\n",
       " 0.000295,\n",
       " 0.003185,\n",
       " 0.002428,\n",
       " 0.001284,\n",
       " 0.002911,\n",
       " 0.000254,\n",
       " 0.000584,\n",
       " 0.004804,\n",
       " 0.007891,\n",
       " 0.000535,\n",
       " 0.001564,\n",
       " 0.003332,\n",
       " 0.009348,\n",
       " 0.00329,\n",
       " ...]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op['data'] = np.array(op['data']).flatten().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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mvbLZ7KIACDCEEQCdYk16viSpoLzW5JIACDSEEQAAYCrCCAAAMBVhBIBLDO0F4GuEEQAAYCrCCACXuGsvAF8jjAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCACXGNoLwNcIIwBcYjQNAF8jjAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCACXGE0DwNcII+iRvjqYpyWbMswuBgBAUi+zCwCY4X//nixJunzMQI0f1d/cwvg5hvYC8DVqRtCjnSmvNbsIANDjEUYAAICpCCMAOgXNOQC8RRgBAACmIowAcMndob0MAQbgLcIIAJdofgHga4QRAABgKsIIgE5BDQoAbxFGAACAqQgjADoFHVgBeIswAgAATEUYQY9GN4f2UeMBwNcIIwBcomMqAF8jjAAAAFMRRgAAgKkII+jR6A4BAOYjjAAAAFMRRgC4xGgaAL5GGAHgEqNpAPgaYQQAAJiKMAIAAExFGPETJZV1mr0sRVuOFZpdFAAAuhRhxE/8cWW6Pk3N0Z1LdphdFAAAuhRhxE+cLqkyuwgAAJiCMALAJYb2AvA1wggAlxjaC8DXCCMAAMBUhBEAAGAqwgh6NFogAMB8XoWRRYsWKT4+XhEREUpISNCmTZucrrtixQrdcMMNGjJkiKKiojRp0iStXr3a6wIDAIDuxeMwsnz5cs2ePVtPPvmkUlJSNGXKFE2dOlWZmZkO19+4caNuuOEGrVy5UsnJyfre976nm2++WSkpKR0uPAAACHweh5EFCxZo5syZuu+++3TBBRdo4cKFGjVqlBYvXuxw/YULF+o3v/mNLr/8cp133nl67rnndN555+nf//53hwsPwPcY2gvA1zwKI3V1dUpOTlZiYqLd8sTERG3dutWt17BarSovL9fAgQOdrlNbW6uysjK7PwDmYGgvAF/zKIwUFhaqsbFRMTExdstjYmKUl5fn1mu89NJLqqys1O233+50nfnz5ys6Otr2N2rUKE+KCQAAAohXHViDWtXbGobRZpkjH374oZ566iktX75cQ4cOdbre3LlzZbFYbH9ZWVneFBMAAASAXp6sPHjwYIWEhLSpBSkoKGhTW9La8uXLNXPmTH388ce6/vrrXa4bHh6u8PBwT4oGAEBAyimt1teHCvTfCSMVERpidnFM4VHNSFhYmBISEpSUlGS3PCkpSZMnT3b6vA8//FD33HOP/vGPf+imm27yrqTdHO3yANAz3fjKJv3u0wN6cdVhs4tiGo9qRiRpzpw5uuuuuzRhwgRNmjRJb731ljIzMzVr1ixJTU0s2dnZeu+99yQ1BZEZM2bo5Zdf1pVXXmmrVendu7eio6M78aMA7jFIfh5hNA3gW6VV9ZKkTUfPmFwS83gcRqZPn66ioiI988wzys3N1bhx47Ry5UrFxcVJknJzc+3mHHnzzTfV0NCgBx98UA8++KBt+d133613332345+gm+CED39FdgPgax6HEUl64IEH9MADDzh8rHXAWL9+vTdvAfgMF1cA8C/cm6YbsVTV0wQBAAg4hBE/0dEMsfZQvsY/85We+vxg5xSoGyOuAYB/IYx0Ey982dQL+2/bTplcksBCVx0AMB9hxE/QgbXr0JQFAP6FMALAJYIyAF8jjMAtb2w4rlUHcs0uRqegXsQzVCQB8DWvhvai8/nzCT/5VIme//KQJOnk88ygCwDoXNSMoF1nymvNLoLP+HEGBIAegzDiJ2iX7zr+XAsFAD0RYcRPcIEEgO6LUXyuEUbQ4xg0zgDoQklp+br0mSStO1RgdlH8FmEEAAAfuv+93bJU1+ved3eZXRS/RRhBj0Ntqe/lWqpVU99odjEABAjCiJ+gAyu6k0nz1+r7L20wuxgAAgRhxE8Eyq/197dz7xu4J7u02uwiAAgQhBF45LefHtCJwkqziwEA6EYII/BYcWWd2UXoEH+phTIMg34VACDCCGCahz5M0fm/W6XTJVVmFwUATEUY8ROB1IE1kMrqiL/MM/LFvqYbD364M9Pkkniuqq6BSZwAdBrCiJ/gvI5AcTivXBf+frXmfLTX7KIA6CYII/BYoAenQC+/2d7ceFyS9ElKtsklAdBdEEYAeCRIAd5OB8DvEEZ6qM9Ss3Xf33arvKbe7KJ0OX+rGAm0i3ug9xkC4H8IIx6ob7RqzkepWrHndKe/dlef4B9dlqo16flavP64x8/lYtSzBbP/AXQywogH/pl8Wiv2ZPuk455Z/RhKqnpezQg6xtuanEarv9VJoadiJJj/IYx4INAn+0ITTkQd403N2PrDBbpo3ip9lkqnV5hryaYMTXh2jY4VVJhdFLRAGAFMFmjNXt6U9553dqmm3qpHl6V2enkATzz7RbqKKuv01OcHzS4KWiCMdBNdeUELsGtnG9SLdExQoKUnAH6PMNJN0PKArkIUAXyjJ+d8wgh6HIJbx/TkEybgSz353EQYAQAApiKMdBPu/lpNzSrVHX/d7tvC+Lse/OujMwTaJG1AoDhaUKGqugazi2GKXmYXAF3rtkVb5Pl0D1y9cZYnk549/e+DiggN8V1hgG7mT6sPa97NF5ldjC5HGOkm3G1rbBtEel7QMPzsMwdaPYMno2ne2XLSdwUBuqEdGcVmF8EUNNPADYF2uQSAwORfP5W6DmGkx+t5QaMn91jvDIymAXynp84QTRjpJrhAoKsE82UDfKaHZhHCCDwX6DNw+t2xHmDbM7BKCwQWaw9NI4SRbsL772/P/OLDewGWnQCfOZBt0cMfpiizqKrTXpMwArgp0Ns0W5a/xB/uxBxg2zPQa8aAzjLt1c36994czXo/WYZh6O/bTmpvVmmHXjPATgedhjDS43l+YQm0Y+Wx5al6/OO9Dh/7zb/2dXFpmsLQ+9tPdfn7dhaiCGDv+JkKrdyfp999dlA/en1Lh14r0M6vnYUw4gF3agT2ZpUqq7jzquzc1ZU/VgMpueeX1eiTlGx9nHxa5TX1krr+YP/7tpO67s/rdbqk6Xux7XiRfvvpgbMrBFpNQ4AVF/C1oCDpcH55p7wWzTRwqq7Bquq6xnbXyyyq0o9e36IpL67rglKZKXAOlsYWs7x1dal3nSzWNX9ap999dlAZhZX64xfpkqSMwsouLknnYjp4wHe6Mov84T9penPD8a57QxcIIw4UlNfoWEGF7f9Xzv9aF85bpboGq8vnHcor83XRnOrKDqw9NLh77M6/7tCpFh3bvjyQpx+9vkUNja6/R/4u0Cpy/EFOabVySqvNLgZa2HyssNPuA1NTb1Vt/dkfrH9afUhl39TEeqqrakYO55Xr7c0nNP/LQ13yfu0hjDhwxR+/1vULNijXUi3DMFRcWSfDCMxftPU+uPAFehbpqjBV52Db780q1a6TJXbLAuXabhiGjuSXE0Y9VNvQqMnPr9Xk59e2+4MGbZVW1Wn+l+k64kYziGEYHgWMxz/uvD5jf9t20vbv19cd17P/SfPqdfIsNbr6xXU+r7HwtxvyEUa+kZZTphdXHbL1K5Ck9NwyuxOvP5+DHf1aPV1SpYvmrdZvP93v6pkev5fV8zvtec0wDFtfiw6/lp9cB77Yn2t2EbzyxoYMJf5lo97wk2rdQFFWffakX1nrXxcAM+07XepwNNtnqdnaeeLs/VnmfX5Qb37z3WvP7OWpuvD3q3WsoG1wOZJfrpOtflB25rFYU29/gtl32qIThZWqqW+/ib+lBquhzOIqv6mx6CrcKO8bN76ySZLaVK01tkgj7V22/W3I4183Zqiuwar3t2fq2Vsv7rTX9TSKGIbh9bZ5+t9penfrST3zo4s0Y9IYr16jWXP1p7/dKK+jsoqrtPZQgaZfPsqnd8hduOZIm2Ud2bfoeU4VVepwXrn69wnT7W9ukyRdFBulyIheunrsEF07dqgeXZYqSTr5/E2Smi7qrTn73n2WmiNJenvzSc2/7ew5z1Jd71aY6UyH8sr1vT+v1/nDIjv8Ws2DJzpyrB3Msej97Zl67IbzNDQyos3rm30cE0ZaOZhj3++jZfud2TvL15oTfHsXtOZNUl3XqJl/26XvXxCjmd+Nd7juAx8kK7O4Sp8+cJV6hXheEffu1pOSpBe+PNRpYaS15FMlKqup1/e+PbRDr99Rh/LKFBYSrHOG9PPoed9/aYPqGq3KtdToiann+6h0jlkNKaR7HxboJFV1DbrmT+slye4C3XzO3Z5RrKKKtjUlrb9eBWU1mvbqZv33hJF6/AeOv++tT9X5ZTVel7ujDuV1fJTN/e/tVq6lRp896N15VJJuemWzJCnXUq13773C7rFGq6FeJh/INNO0w66Zxo8byzvagbWh0arxT3+lhD8k2Y1AcfyMpsc/2HFKW48X6Q8u2kZX7s/TgewypXZwIiBvtfwkto/V6uP9ZPFW3fvOLtM6GAYFNdXI/XDhJl330gaPm8Ga+6ZsyyjyRfFc6qnDEL3VU7eWYRi68Perbf93doF+e/OJtgtbXSMXrT+ugvJavb7OeXOhp31zymvq/bpj+Zr0Ah3MaTqPfrQ7q0Ph6lBu07Zv+eO6C1venSKMtNLye28YXTM09Gh+uXafKml/RR8qrKhTbYNVlXWNqmyvY9M3G6LKjeHOZmt5YW8vTHb011NlbYP+vu2kV69TUFZr+3djAF3g2wuusG8W9LfwZhiG7vvbLv3Pkh0+/bHV0IHvSevf6+6U85/Jp91+/cKKWl381Fea9upm7TpZrNfWHu2y77Wlul6WKtejblp+3r+sOaLf/HOfbu3AxGp535yfWr6uP3wvaaZph9VJn5EbFmzQzeNj9cj3z5PU1AO6ZefX42cqNHpgH4W6UaV2Qye0ZTpqQXLv6+X9DKztfX/9oSapZRGazy++KtWzX6Tpw51ZemNDhrY8cZ3Xr+PuicEwDP1nn7mdYf1gF0uSVuw5reCgIN36nRFmF6UNq7Xlv/1kg32jqq5Ra9ILJEk5lhqN6N/bJ+/TkYu7r5vHm4PLobxy/fcbTf1YhkX31n8ljPTp+zbXRren5abbcqyp9jPX4t4PnpX7c/XlgTy98BPXfQYJI34uKMh59dXRggotSDqiR75/nkoq63Tl/K/tHv/+Sxs05bzB+vvMiV1Q0s7Vfshw73X84VeztQvTf/NJPbuDzT1WF7XFTUMXG9U3vJdWH8zXwx+mdOi9OsofTmKW6nrN+ahpuv8fXDRMvcN814lXkl5YdUhfp+drxQNXqV94+6fQljVd/lbr5UkH/Y7w5nuy73SpXlt7TKeKfDulwvMORq20HnXTmQzD0IylO7XpaKFb63t6Hj1ZWKklmzP0i6vP1QMf7JEkfctBP7Sc0rOBxg9O1TTTtNZ6p1jdaKZJdzLZmbtfNl9peXJZd7jAyZe67bID2RbNX5nudNIed0ej+MOJ11EY8YNiueRqu/3mn/t0xR/X6EC2RSlZ5jbtSf4RRloOl613leQ6yeL1x3Ukv0LLd2W5tX7Lc4g/nPRbamw8W6CQ4KYzRlZxlR5dlqKDOW1HsTQ/viDpiIo9uMmkNz9Mbnlti75Ky1d9Y9dvtOZt4QubjhZ6dG3w9Bi7c8kOvb89U3cv3WlbVlhRa7dOSmaJHvzHHq/fwxcII6203im+3knOmjIMw9AnKaeVluPerK7tFfPed3bpxVXOx623DBh3LtmhNzdmtPjFYP/ize+15XjbA8pqNWyfyZ0T0J7MEl335/Vaeyi/3XW90bII7db4dOL7dmQ+CVfb7ePk06qsa9SfVh/2i96QXXDtb5ddv64uLI+7TS52gdjP0kjL8NZczP/9e7I+S83RtFc3O5wj47bFW/XK10ed3nzSEX+oJfVELx+GkSwP503ydNs118y6mqRzxZ5su//7w/eSMKKm6d+btQ0jZ//tiz4Qzr4DG46c0WPL99rmP3HFMAzlWtpvGnhzY4bTxxx94ZuH3LX+2M3/bTkxkdTUg/0HCzdqxjeJ3J2D6J6lO5VRWKmfv7tbaTllWn+4oN3neMJRJy1fzTPS8vR10bzVTtdrjzsnhvZC8rGCCp0pr3W5jivuPrejYT3jTIVW7Dnt8jOv2HNa65x8L2obGu2axRo6kI4OZFv04qpDqnAzSLrqylBT36jbFm3Ri6sO2R3jXf0LtLCiVqsO5DkdKdKy1qF52zXPdGoY0iVPfSVLtX0NafN3Y7sHo7c6K4y0vE2H1HR8X/3iOo154gvtaFWelse+p5s92IdhxFPu1jAbhqGiCu+OeT/IIoQRqWn692atz2X2v2o8f+3luzJdPu7s5JmW6/59bl5YdVhlNd7+Em866Eod9OhuPh5bj/RxFsr2ZJboaEGFNh0tlGEYbm2vlif+G1/ZpHve2dXmhNMR9heCTnvZTvXetlOa+bddtv+7e/LZcOSMw+XZpdW6fsEGXf7HNV6VJ6e02u3nenNxff7LQ6qsbdCxggpd99IGzflorz7fm+Nw3ZOFlZrz0V7d+87Z7WMYhu17c+vrW/XTt7bbHvsqLd+jIZqvrzum29/Yppr6Rk17dbMWrT+uhUltJ3dz5PiZCv3sre1tQrnU1HFwT2apFq0/bnchbvnv8pp6PbospU2tYLmX9zRpyVJdrx+9tlkTnl2jWe8na+kWB0NmJbtt1Xy8trwM1zVatemo4++ZJzqryXbqy/ad/ZNPlSjzm7ukT2/xPZCkjUcLbXdQ9/R76stmGk9uNFlT3+h2rcWcj/Yq4VnHx+3qg3kun0szjR8qqjybLFMzS+1udObNAfV//3I1FbvzXwzBHvQgbz09d0F5jZ78ZL/SPZhsp7mjU0vNJWg99t/ZZihvEYgarYZd0PrPvlwdyHbcBt2ao85jrXvUrz2Ur3ve2dnuMNrO6DOSVVzl00mTiivr7L5njk4+FbUNdheOzccKnc7VsP90abvvWVBeo/lfpjvc1q0vPpbqeqf9A7wJeG9sOK6L5q3W9Qs22JalZDru/9Jyu1uthg7nlWv28lSNm7daR/LLld4qtM9dsV9vbXJeA9jan1Yf1s6TxTr/d6tsy9ydpOrDnVnallFkm0m0pYIWNUvOOlG/vOaoPkvN0c/f3d30mNXQwjVHdPFTX3l9X5Nmb28+ob0tZi5dfTDf4Y+IljUjzs5vzs5FlXWN+oubwa2zmvPqGw2tbVFL5mqOo7uX7tSUF9epuq7R8zDi4vy7ZFOGrvnTOq/nJfKkZvb8363SgWz3fph+kpLt9LGCdmo6aabxQ/kt5nt4Ze0xuxONo4O5pLJOGWe873ntPIx4/ZJ6/ON9+mBHpsNfbG01vX/zrwt3LF5/XPe/t7vN8pY3XqpvNOxObu9uPalpr25u8xxHw/aCg5te6+U1R23LWled//zd3Vp/+Iye+vygy7Ja7apqXR9wjh4uq6nXlBfXaeJzX7d9sIVjBeXtHvDuan1R+MN/0jRu3mrdtniry7I2c3VeaQ4017+0QW9uyNC1f17fZruEBLc9Lfy/FY5DdWf9onI2BL7lZ/njynT9YOFG25TfS5yEjs+/edzbZtVQL2eiTD5VrC+/uddJyxEa1lbHQX5Zjd7ZckJ7W4XGZbuytPCb7/wSR5N/eaC61VxByadKFD93pcY88YXSc8tktRq6/73ddoFwy7GmGk1Hc4LM+nuyw9sBvPz10TadIyVp18li/XDhRts5qDM7s2cVnw0Bex1MFd9admm17np7Z7vrteSqZuTZL9J1qqhKL33lXhBrbdMRzwY2vJR02OFyq9XQgWxLp9wM1Q+yCEN7PeFoh02c/3W7s/0dyS/X2BjH9ydwFkbWHTr767Sksk4D+oa5XU5nveA95Wx8/86T7Yecl7467FGZ7d5XQfpL0hH9dZP9CdnRdkjNKnV5XwVP5hkxDMP2WjX1jXr4wxQlpZ2tQm+0Gk5PUtcv6Lz7Xth1yDQMW82Uo3t0ONL6vH+isFIHsi3acqxQXx7I05o519g1693+5jb9feZE220AHHXeczZyp7PCyJLNJ/TbaRe6fP3WNXTOTqAZZyr12PJU7cks0ZePTlGfsLanuQPZFqfb09V0286O18c/3quPv5mvYs2cq+0eazknxPvbM/X+dsdNtx8nuzc6x1GZSqvqNKhfuG2Zq91y55IdmnPDWLvvtiT99tMDGhYV0Wb93392QIUVdVp1ME+zrx/b5vGKmgYNbvHekjT9zW2yGk3frWN/nKpqEydIfHfrCY9G/kj65u7Uru/X4uiu3O5Y1U6TSWvOvnNLNmfouZWHdMv4WL3ys+949JqtPxbNNAHG0ZfCnWmHnd2gyVJdr/99L9nhYy2n9v7OH5Kc/gp0pNDB/R28ESTPOp61/EIv2XyiacRHKy+sOqRjBeUu03xQkBxOH1/T0Kj/WbJD97XoX5FrqdFfWtSgtHlOi9EA7R1wJVX1mvz8Ws1dsU/vbz/V5mTtaF+fLKx0OE9BR/xk8VbbsOrWdwJ1R8vPueHIGX3vz+v18IcpWrYrS5bqev1jh/3FcNfJEv3fv87eSt1R5z1n7dz7Tlv0+Md7td/NoOQpV/vM2WN1jVZ9kpKtU0VVbfah1BTwpr26Wf/vE8e1Pa5qRpx9bz9uMeNny1/ukuz6u7jibWXoPe/sVMKza7Qns0Q/f3eXlrZTq1JcWWe751NrjkbItTyfOKptuumbTvb1jVZZquu1aP0xu6D4g4Ub7WpgulrL2Y3dtWxXll7+2vl5RZJ2nuia2y80OBna/NzKpvPO53tzOtzPyB9GO/X4MLLPjfb1Zh1Jj46+LK98fdStWgapqWrQEXf7YTjn/BQYFOT85NvapynZemx5+0P9Fq8/rusXbNTFT63WmCe+cHgQnCisdPjLbsuxIm0+VmibXKzZK05OGmfKa/Vfb5xtZmuv3XrZzkzlWmr04c4snXQw0dKLq8+Gjuq6Rr229qiu/fP6Nn12Oiq/rFYvfBNwvDnJtNx0LecaOPt4243b3PQhOW4ibNmXqqVf/D1ZHyef1s2vtW2C81TrNvjMoiqXfRJ2ZLR/7NTWW1VcWadfvp9sG5HzooOQ3NLK/c5/ubrTT8BRCG/P6+uO6Wi+dx23m+esuG3RVq09VKBn/pPWbq8EZ53E39ly0uXzHE1DXlnX+M3ooa0a//RXenGV/ec/3oFm7M5Q7WB4sjsWrjnq8vyX70XI8YY71507/rqjQ+/hBxUj3oWRRYsWKT4+XhEREUpISNCmTa6Hn27YsEEJCQmKiIjQOeecozfeeMOrwvrCL99v23HTmY5MYnbxU1/p5TVHtWRThu1X/34nQcLZKInWDueVO+yH4an/7HM8kiE4KMjtqsjZy1M9ek9Xv/if/neaw3v1/NrFvAYvO6gd+Wi3fbV380Ht7ETcsgOuo6r0lifqic+t0Z+9bDN2R3PHzHIP5ys5kG1RamapV+9ZUlmnaa9u0kP/SGnzWH2j4XIoa2eY/PxaVdU1KLOoShOfW6Or/7ROe1x8Fndmus0vq9Flf0jSlwfybDUUi9e3Hx7th4U2dZyta7A6DKmteTISrtmfVh92uK+TT5XoseWpKvCwA7Un92bxhLM+GpPmf+30fGa24x0YnVdW3fGRTR3lTofqjm57f2im8bjPyPLlyzV79mwtWrRIV111ld58801NnTpVaWlpGj16dJv1T5w4oRtvvFH333+/3n//fW3ZskUPPPCAhgwZop/85Ced8iE6oqSqc5o03PGXFh3ALh8zQLtOOm6Hd/Rr1pEfL/L+ZknN9maV6sOdjtuwcy01TqsI/c1f1hzRo9efZ7esdZVy8wHnbPu6M7/EgqQjSkrL78BQas9UePA+lqo63fLa5nY7oy100qz1nT8kuXxerYd3QvXGlc99rfpGw+tfs6295OZoj9Zuf3ObhkZGaE16vu1z9+8T6nAIvK/86LXNtot/RW2D/jpjgtvPbT03iK+VdOF28VSOm/dxcaR5qOzeeYl6f/spr2q9/NH6w/Y/eP1htuwgw8Mu5xMnTtRll12mxYsX25ZdcMEFuvXWWzV//vw26//f//2fPv/8c6Wnn21mmDVrlvbu3att29oOiXOkrKxM0dHRslgsioqK8qS4LhmGobG//dKU6Ya9cfL5myQ1DctsOTeKL7kKTf7q/inx+uW139KdS3bYDf187scX6y9rjnRoMrCuMjQyXPdeFa8XXMya62/GjYhyexiiWQb3C+u0PlVdKW5QH71+x2U6f1ikQoKDbB0riypqnc4tAbhrxQOTddnoAT55bXev3x6Fkbq6OvXp00cff/yxfvzjH9uWP/roo0pNTdWGDW07KV199dX6zne+o5dfftm27JNPPtHtt9+uqqoqhYaGdtqH8dRbG4/bOgEFgpDgIMX2j2jTQQ5AzxIZ3svjJjzAlVvGx+qeq8Z0eihx9/rtUZ+RwsJCNTY2KiYmxm55TEyM8vIcd/rKy8tzuH5DQ4MKCx33waitrVVZWZndny+sPuib+6H4SqPVIIgAIIig032+N0fZJeZdX7yaZ6T12Ov2xmM7Wt/R8mbz58/X008/7U3RPLL4fy7T25tPaMWebEX3DtU1Y4fo7c0ndO9VY9RoNWQ1DP308tF68pP9GtQvXOfF9FNaTpnKqus1PLq3IiN66au0fN0yPlaJF8Vo9cE8xQ3sq1sujVVdg1X9wnvpX3tO61hBhW0c/oC+YSoor9G240UaGxOpkQN6a09mqcqq63WqqFI3Xjxcv73pQu0+Vay0nDLdcmmsktLyteVYU3CrrreqT2iIYvv3Vn5ZjYKDg7ThcIHKaho0PDpC3x4WqQuHR2lYdIQG9AnTR7uzdFFstIZGhis0JEiXjOyvoKCmKcijIkI1ODJMx/IrVFZTr7pGQ9/79hBdPmagzlTUKre0RlV1DTpTUasjeeW6Y2KcgtR0B2CrYWhoZITGjYjW9owiffe8wRrSL1w7ThQrSE2jL+IG9dWU8wbrYHaZLo8fqO0ZRUrNKtWI/r01oE+YjhVUaPXBPF0YG6U+YSEK7xWsKecNUXFlnRIvilGfsF7anlGknSeKNWZwX9U3WHXOkL6SpKq6RqXnlmnkgD46kG1RYUWthkZF6EC2RZnFVbp0VH81Wg2tPVSgq741SJHhoTpaUK5h0REaOaCPiivrFCTp8jEDFRnRS7H9e+tAjkWHcss1emAfhfUK1iUjo5WaVaqDOWW68pxBCu8VrDPltQoOkuIG91XyyRLVW6361pB+2p9t0e0TRmnniWKV1zSotqGpfGMG99WI/r21+mCeBvYNU2pWqQb3C9fvpl2o3qEhGh4dodLqejVaDa1Jy9eBHIv6hvVSQXmt9mSWaPK5g1VcWaufJIxU3MC+qqitV3hoiPqG9dLBHIvCezVtt1UH8xQRGqLymnpdPmagckqrNTw6Qtd+e6jScsu051SJIkJDNG5EtA7mWBQcFKSRA3prTXq+fnnNt5RTWq29p0t1zpB+mjpumDLOVKqoslbXjB2i0yXVyiyuUm1Do3736UHdcmmszhvaT3llNSooq1VEaLAO55Vr1MA+GhoZriP5FZo6bpiG9++tcwb3VWVdg/6xI1M3XTxcCXEDFBIcpORTJQoKkk4WVimsV7DiB/fVgL5hOl1cpf/sy9WIAb016ZxBunhEtIKDg3Qg26J/78tRbb1V9141xvb9Tz5VokF9w3WiqFLjYqN0NL9CE88ZqMKKWmUVVyuvrEblNfXKtdSoqrZR13x7iEYP7KN/783R0Mhwbc8o1s2XxkqGoS3HinTB8CiVVNXpnCF9lVNao5TMEhVW1Co4KEi/vPZc9e8TqmvHDtVHu7N0qrhKY2P6qbiyXpeMjNbIAb0V0StE/fuEaseJYlXUNGj0oD5ak56v6N6hunB4lEJDgpVfVqNrxg6RpbpeK/Zkq6K2QYMjwxUbHaFDeeX6V/JpxQ3qo8nnDlZkRC9bB8aC8hpNjB+kNen5uu78odqTWaqdJ4o0JDK86T4yI/tr0rmDdLygQrUNVk27ZLiq6ho1PDpCezJLNLhfuKrqGlVcWau1hwr07K0Xq9FqKCaq6bg9WlChIEnXjB2ipVtO6OqxQ/R1er6+Ti9QQtwARfUO1aC+YQoNCdaJwkrll9VoQtwA3XtVvDIKK3WsoFz7sy0a2CdMsf17KyI0RNdfGKOMMxVatO64Hvzet1RcVadlOzMVN6ivci3VGhcbreH9IzR6YB8N7BumsF7B+mfyafUJDdHPJo7W3iyLwnoF61RRpaZdEqua+kZ9sOOU+vcJ05ZjheoX3kuD+oXLajUUN6iPGq2Gbh4fqwaroS/35yqrpEoZZyo1elAfjRrQtN9HDOitE4WV+uFFwxQ3qI+q6xu16WihBvUNU3BQkKrrG1VWXa8r4gfplktjtfZQgapqG3TdBUP15f48BQdJRZV1ih/cdC6afO5g7TpZrMnnDtLW40Wqb7QqqneoLh3ZX4s3HFdVXYN+e9OFyjhTqV3fjJ58Z8sJhYYEKyYqQteMHaKQ4CAN6BuqjUcKNSQyXPllNQoNCVbfsBCNHtRX5w+L1OZjhZoYP1CFFXXaf7pUqVml+tkVozW4X7gaDUMF5bUa2CdMB3Isumz0AEWEBqtPWC8lpeVr5IDeKq2qU029VUOjwvXFvlwFBUlDIyPUKzhIlXUNunhEtC6M7byWB0/5ZTNNbW2tamvPtuuXlZVp1KhRnd5MAwAAfMcnzTRhYWFKSEhQUpJ9r/ukpCRNnjzZ4XMmTZrUZv2vvvpKEyZMcNpfJDw8XFFRUXZ/AACge/J4npE5c+ZoyZIlWrp0qdLT0/XYY48pMzNTs2bNkiTNnTtXM2bMsK0/a9YsnTp1SnPmzFF6erqWLl2qt99+W7/+9a8771MAAICA5XGfkenTp6uoqEjPPPOMcnNzNW7cOK1cuVJxcXGSpNzcXGVmnp23Ij4+XitXrtRjjz2m119/XbGxsXrllVf8Yo4RAABgPo/nGTGDr4b2AgAA3/FJnxEAAIDORhgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAEzl8XTwZmieJLasrMzkkgAAAHc1X7fbm+w9IMJIeXm5JGnUqFEmlwQAAHiqvLxc0dHRTh8PiHvTWK1W5eTkKDIyUkFBQZ32umVlZRo1apSysrK4543J2Bf+gf3gP9gX/oH90DGGYai8vFyxsbEKDnbeMyQgakaCg4M1cuRIn71+VFQUXzI/wb7wD+wH/8G+8A/sB++5qhFpRgdWAABgKsIIAAAwVY8OI+Hh4Zo3b57Cw8PNLkqPx77wD+wH/8G+8A/sh64REB1YAQBA99Wja0YAAID5CCMAAMBUhBEAAGAqwggAADBVjw4jixYtUnx8vCIiIpSQkKBNmzaZXaRu46mnnlJQUJDd37Bhw2yPG4ahp556SrGxserdu7euvfZaHTx40O41amtr9fDDD2vw4MHq27evbrnlFp0+fbqrP0rA2bhxo26++WbFxsYqKChIn376qd3jnbXtS0pKdNdddyk6OlrR0dG66667VFpa6uNPF1ja2xf33HNPm+PkyiuvtFuHfdFx8+fP1+WXX67IyEgNHTpUt956qw4fPmy3DseFuXpsGFm+fLlmz56tJ598UikpKZoyZYqmTp2qzMxMs4vWbVx00UXKzc21/e3fv9/22IsvvqgFCxbotdde065duzRs2DDdcMMNtvsQSdLs2bP1ySefaNmyZdq8ebMqKio0bdo0NTY2mvFxAkZlZaXGjx+v1157zeHjnbXt77jjDqWmpmrVqlVatWqVUlNTddddd/n88wWS9vaFJP3whz+0O05Wrlxp9zj7ouM2bNigBx98UNu3b1dSUpIaGhqUmJioyspK2zocFyYzeqgrrrjCmDVrlt2y888/33jiiSdMKlH3Mm/ePGP8+PEOH7NarcawYcOM559/3raspqbGiI6ONt544w3DMAyjtLTUCA0NNZYtW2ZbJzs72wgODjZWrVrl07J3J5KMTz75xPb/ztr2aWlphiRj+/bttnW2bdtmSDIOHTrk408VmFrvC8MwjLvvvtv40Y9+5PQ57AvfKCgoMCQZGzZsMAyD48If9Miakbq6OiUnJysxMdFueWJiorZu3WpSqbqfo0ePKjY2VvHx8frpT3+qjIwMSdKJEyeUl5dnt/3Dw8N1zTXX2LZ/cnKy6uvr7daJjY3VuHHj2Ecd0Fnbftu2bYqOjtbEiRNt61x55ZWKjo5m/3ho/fr1Gjp0qMaOHav7779fBQUFtsfYF75hsVgkSQMHDpTEceEPemQYKSwsVGNjo2JiYuyWx8TEKC8vz6RSdS8TJ07Ue++9p9WrV+uvf/2r8vLyNHnyZBUVFdm2savtn5eXp7CwMA0YMMDpOvBcZ237vLw8DR06tM3rDx06lP3jgalTp+qDDz7Q2rVr9dJLL2nXrl267rrrVFtbK4l94QuGYWjOnDn67ne/q3HjxkniuPAHAXHXXl8JCgqy+79hGG2WwTtTp061/fviiy/WpEmTdO655+pvf/ubrYOeN9uffdQ5OmPbO1qf/eOZ6dOn2/49btw4TZgwQXFxcfriiy902223OX0e+8J7Dz30kPbt26fNmze3eYzjwjw9smZk8ODBCgkJaZNUCwoK2iRjdI6+ffvq4osv1tGjR22jalxt/2HDhqmurk4lJSVO14HnOmvbDxs2TPn5+W1e/8yZM+yfDhg+fLji4uJ09OhRSeyLzvbwww/r888/17p16zRy5Ejbco4L8/XIMBIWFqaEhAQlJSXZLU9KStLkyZNNKlX3Vltbq/T0dA0fPlzx8fEaNmyY3favq6vThg0bbNs/ISFBoaGhduvk5ubqwIED7KMO6KxtP2nSJFksFu3cudO2zo4dO2SxWNg/HVBUVKSsrCwNHz5cEvuisxiGoYceekgrVqzQ2rVrFR8fb/c4x4UfMKXbrB9YtmyZERoaarz99ttGWlqaMXv2bKNv377GyZMnzS5at/CrX/3KWL9+vZGRkWFs377dmDZtmhEZGWnbvs8//7wRHR1trFixwti/f7/xs5/9zBg+fLhRVlZme41Zs2YZI0eONNasWWPs2bPHuO6664zx48cbDQ0NZn2sgFBeXm6kpKQYKSkphiRjwYIFRkpKinHq1CnDMDpv2//whz80LrnkEmPbtm3Gtm3bjIsvvtiYNm1al39ef+ZqX5SXlxu/+tWvjK1btxonTpww1q1bZ0yaNMkYMWIE+6KT/fKXvzSio6ON9evXG7m5uba/qqoq2zocF+bqsWHEMAzj9ddfN+Li4oywsDDjsssusw3zQsdNnz7dGD58uBEaGmrExsYat912m3Hw4EHb41ar1Zg3b54xbNgwIzw83Lj66quN/fv3271GdXW18dBDDxkDBw40evfubUybNs3IzMzs6o8ScNatW2dIavN39913G4bRedu+qKjIuPPOO43IyEgjMjLSuPPOO42SkpIu+pSBwdW+qKqqMhITE40hQ4YYoaGhxujRo4277767zXZmX3Sco30gyXjnnXds63BcmCvIMAyjq2tjAAAAmvXIPiMAAMB/EEYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYKr/D/gjpVnsuFx+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(op['index'], op['data'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "itsc-fcx",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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