newcodeantara.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import astropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5590.  5770.  5445.  5422.  5222.  5645.  5070.  5148.  4466.  4572.\n",
      "  5809.  5485.  4338.  4381.  4261.  4369.  4793.  5096.  5270. 12443.\n",
      " 12741. 11755. 11561.  5605.  5323.  5603. 14329. 14802.  2596.  2422.\n",
      "  6188.  6077.  6200.  5638.  4557.  4661.  4089.  3995.  4039.  5763.\n",
      "  5704.  3355.  3477.  5267.  5338.  7917.  7882.  4366.  4339.  3162.\n",
      "  3086.  2330.  2479.  2984.  3033. 10778. 11394. 11040. 11260.  2488.\n",
      "  2618.  6056.  5100.  6858.  8318.  8438.  8490.  8591.  3065.  3005.\n",
      "  3016.  5988.  5971.  7533.  7382.  6886.  7532.  7243.  7118.  7538.\n",
      "  7814.  3653.  3771.  6169.  6227.  3015.  6896.  2676.  2602.  3267.\n",
      "  3208.  3164.  3259.  3079.  4820.  3325.  4696.  6310.  6230.  3998.\n",
      "  3816.  3790.  4031.  4013.  4944.  4806.  4761.  6623.  6608.  6158.\n",
      "  5998.  6329.  6367.  6492.  6979.  2360.  2490.  6974.  7384.  2282.\n",
      "  2317.  2830.  2802. 11599. 11788.  2874.  2838.  4661.  4728.  2667.\n",
      "  2656.  5601.  5715.  6704.  6541.  6759.  4838.  4856.  5787.  5533.\n",
      "  6230.  5780. 10610. 10659. 10892. 10586. 10230.  9977. 12427. 12728.\n",
      " 10302. 11225. 10150.  9520.  4944.  4798.  2926.  3064.  4608.  4982.\n",
      "  4095.  4208.  5089.  5286.  8365.  8085.  5441.  6322.  4979.  4846.\n",
      "  6985.  6931.  6017.  5965.  2888.  2840.  6225.  3069.  6357.  6032.\n",
      "  3052.  3108.  5603.  4349.  4669.  5621.  5154.  5085.  4550.  4809.]\n"
     ]
    }
   ],
   "source": [
    "cz=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    cz_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[1])\n",
    "    cz = np.append(cz, cz_temp)\n",
    "\n",
    "    \n",
    "print(cz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.6  7.6 31.9 31.9  9.8  9.8 29.5 29.5 17.3 17.3 51.2 51.2 27.2 15.5\n",
      " 15.5 37.5 37.5 48.5 48.5 24.2 24.2 35.7 35.7 34.1 13.6 13.6 39.  39.\n",
      " 39.8 39.8 42.2 42.2 49.5 49.5 50.8 50.8 25.4 20.2 20.2 47.5 47.5 17.5\n",
      " 17.5 10.7 10.7 30.4 30.4 40.3 40.3  4.2  4.2  5.3  5.3 32.6 50.2 10.6\n",
      " 10.6 30.9 31.4 48.4 48.4 16.6  7.3  7.3 41.9 41.9  6.1  6.1 42.9 42.2\n",
      " 42.2 21.8 21.8 11.2 11.2 15.9 15.9 29.7 29.7 26.5 26.5 10.1 10.1 12.\n",
      " 12.  31.9 53.8 10.7 10.7 12.6 12.6  5.2  5.2 17.1 41.8 17.1 41.8 27.3\n",
      " 27.3 54.5 54.5 29.4 17.  17.  29.8 31.5 29.8 12.1 12.1 32.7 32.7 30.5\n",
      " 30.5 19.4 19.4 11.1 11.1 25.2 25.2  8.2  8.2 19.7 19.7 42.4 42.4 13.5\n",
      " 13.5 46.4 46.4 30.  30.  29.3 29.3 34.2 20.  20.  29.  29.  18.4 18.2\n",
      " 57.2 52.7 14.6 14.6 38.3 38.3 13.  13.  18.6 18.6 76.2 76.2 47.8 47.8\n",
      " 15.6 15.6 20.1 20.1 42.4 42.4 50.3 50.3 21.4 21.4 17.3 17.3 24.3 24.3\n",
      " 11.3 11.3  5.1  5.1  8.6  8.6 26.1 26.1 46.  52.8 18.5 18.5 13.6 13.6\n",
      " 32.2 13.5 13.5 32.2 12.8 12.8 15.5 15.5]\n"
     ]
    }
   ],
   "source": [
    "d=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    d_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[2])\n",
    "    d = np.append(d, d_temp)\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "X = [d]\n",
    "Y = [cz]\n",
    "plt.title('CZ and Distance')\n",
    "plt.ylabel('CZ')\n",
    "plt.xlabel('Distance')\n",
    "plt.scatter(X,Y,label= 'star', color= 'purple', marker= '*', s=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[181. 181.  22.  22. 423. 423.  78.  78. 106. 106. 324. 324.  44.  44.\n",
      "  77. 154. 154. 174. 174. 298. 298. 194. 194.   2. 280.   2. 473. 473.\n",
      " 174. 174. 111. 111. 562. 562. 104. 104.  39.  44.  44.  59.  59. 122.\n",
      " 122.  72.  72.  35.  35.  27.  27.  76.  76. 149. 149.  48.  31. 262.\n",
      " 262. 220. 134. 130. 130. 802. 956. 802. 120. 120. 102. 102.  49.  11.\n",
      "  11.  17.  17. 151. 151. 646. 646. 125. 125. 276. 276. 117. 117.  58.\n",
      "  58. 336. 152.  74.  74.  59.  59.  85.  65.  85. 124.  65. 124.  80.\n",
      "  80. 183. 183. 223.  18.  18. 138.  46.  46.  15.  15. 160. 160.  38.\n",
      "  38. 406.  81. 130. 130. 409. 409.  35.  35.  28.  28. 189. 189.  37.\n",
      "  37.  67.  67.  11.  11. 113. 113.  55. 163.  55.  18.  18. 254. 254.\n",
      " 450. 450.  49.  49. 305. 305. 254. 254. 301. 301. 923. 923. 630. 630.\n",
      " 146. 146. 138. 138. 374. 374. 113. 113. 196. 196. 180. 180. 880. 880.\n",
      " 132. 132.  53.  53.  52.  52.  48.  48. 132. 738. 132. 193.  56.  56.\n",
      "  18. 321. 321.  18.  69.  69. 258. 258.]\n"
     ]
    }
   ],
   "source": [
    "vs=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    vs_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[3])\n",
    "    vs = np.append(vs, vs_temp)\n",
    "\n",
    "print(vs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -19.94  -20.14  -21.4   -21.17  -20.84  -21.56  -20.23  -20.66  -20.69\n",
      "  -21.1   -20.88  -20.59  -22.06  -21.61  -20.39  -20.95  -20.69  -21.66\n",
      "  -20.72  -22.24  -22.01  -21.24  -22.4   -21.32  -21.46  -21.41  -22.92\n",
      "  -23.3   -21.39  -21.72  -20.69  -22.09  -23.07  -20.89  -20.71  -19.78\n",
      "  -19.84  -21.54  -20.22  -20.07  -19.97  -18.65  -21.51  -20.23  -20.86\n",
      "  -20.68  -20.35  -20.95  -19.55  -19.73  -18.89  -20.3   -18.3   -20.46\n",
      "  -18.55  -22.36  -22.36  -21.89  -20.18  -19.34 -201.14  -21.26  -20.62\n",
      "  -23.88  -20.46  -21.39  -21.65  -20.82  -22.31  -19.45  -20.83  -21.01\n",
      "  -20.11  -20.95  -22.76  -20.53  -22.42  -20.77  -20.93  -21.75  -21.64\n",
      "  -20.06  -18.67  -21.47  -19.36  -18.87  -20.07  -20.87  -21.32  -19.8\n",
      "  -21.36  -19.73  -19.73  -20.89  -21.09  -21.32  -21.11  -20.02  -21.12\n",
      "  -20.34  -21.15  -21.88  -20.69  -21.2   -19.89  -21.22  -21.13  -21.8\n",
      "  -21.75  -21.38  -20.87  -20.18  -20.56  -20.91  -21.91  -18.77  -21.51\n",
      "  -22.42  -20.6   -21.69  -21.46  -18.98  -18.14  -21.24  -21.44  -20.89\n",
      "  -19.39  -21.21  -20.16  -19.78  -19.7   -20.94  -19.72  -20.17  -21.25\n",
      "  -22.4   -22.15  -22.36  -21.73  -21.46  -21.7   -21.87  -22.24  -21.77\n",
      "  -21.58  -21.98  -21.8   -20.71  -22.82  -22.66  -22.04  -21.33  -22.44\n",
      "  -22.19  -20.24  -20.33  -19.31  -21.45  -20.29  -22.89  -19.63  -20.61\n",
      "  -21.61  -20.9   -21.12  -20.93  -21.14  -21.88  -21.81  -21.13  -21.13\n",
      "  -20.14  -19.63  -20.44  -20.63  -22.27  -20.42  -19.93  -21.1   -20.84\n",
      "  -20.26  -20.26  -19.97  -20.97  -23.06  -21.56  -21.72  -21.56  -22.11\n",
      "  -21.19]\n"
     ]
    }
   ],
   "source": [
    "absmag=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    absmag_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[4])\n",
    "    absmag = np.append(absmag, absmag_temp)\n",
    "    \n",
    "print(absmag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.75 0.92 1.39 1.51 1.38 1.6  1.36 0.94 0.63 0.63 1.62 1.56 1.56 1.5\n",
      " 0.96 1.63 0.96 1.45 1.27 1.4  1.36 0.98 1.52 1.32 0.85 0.93 0.99 1.77\n",
      " 1.28 1.56 0.87 1.25 1.43 0.66 1.67 1.08 1.12 1.18 0.9  0.83 0.89 0.79\n",
      " 1.48 0.66 0.77 0.69 0.91 1.29 0.7  0.55 0.56 1.51 0.99 1.34 0.84 1.72\n",
      " 1.72 1.3  0.98 1.07 2.31 1.52 1.61 1.34 1.18 1.58 1.35 1.7  1.93 1.24\n",
      " 1.5  1.58 0.73 1.41 0.   1.64 1.56 1.24 1.4  1.2  1.46 1.02 0.48 1.64\n",
      " 0.83 0.96 1.47 1.27 1.65 1.15 1.41 0.25 0.68 1.11 1.41 0.63 0.84 0.6\n",
      " 1.24 1.28 1.47 1.64 1.34 1.75 1.13 1.41 1.35 1.46 1.57 1.68 1.56 0.74\n",
      " 0.94 1.44 1.5  0.92 1.59 1.69 1.22 1.67 1.67 0.58 0.62 1.6  1.62 2.07\n",
      " 1.   1.48 1.39 1.4  0.89 0.97 0.89 1.3  1.4  1.63 1.53 1.63 1.69 1.68\n",
      " 1.38 1.62 1.02 2.24 1.2  0.71 1.03 1.07 1.68 1.81 1.6  1.26 1.77 1.62\n",
      " 1.28 1.23 0.84 1.39 1.4  1.72 0.85 1.38 1.48 1.33 1.39 0.96 1.31 1.45\n",
      " 1.47 1.08 0.69 0.63 0.82 1.16 1.41 1.45 1.08 0.98 1.2  1.05 1.37 0.8\n",
      " 0.94 0.76 1.36 1.42 1.52 1.62 1.9  2.08]\n"
     ]
    }
   ],
   "source": [
    "br=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    br_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[5])\n",
    "    br = np.append(br, br_temp)\n",
    "    \n",
    "print(br)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.301e+02 4.710e+01 0.000e+00 5.800e+00 9.000e+00 0.000e+00 0.000e+00\n",
      " 1.810e+01 9.680e+01 7.100e+01 1.100e+01 6.100e+00 1.350e+01 0.000e+00\n",
      " 1.040e+01 6.500e+00 3.690e+01 1.200e+01 1.150e+01 6.200e+00 2.750e+01\n",
      " 2.260e+01 1.060e+01 6.400e+00 5.820e+01 4.590e+01 1.790e+01 0.000e+00\n",
      " 7.790e+01 0.000e+00 1.760e+01 1.060e+01 0.000e+00 1.870e+01 0.000e+00\n",
      " 1.010e+01 2.300e+01 2.840e+01 2.000e+01 2.860e+01 2.610e+01 3.170e+01\n",
      " 0.000e+00 9.770e+01 7.180e+01 3.990e+01 8.630e+01 9.300e+00 2.990e+01\n",
      " 8.670e+01 1.002e+02 0.000e+00 2.170e+01 4.230e+01 2.080e+01 0.000e+00\n",
      " 0.000e+00 1.290e+01 2.160e+01 3.320e+01 1.320e+01 6.200e+00 0.000e+00\n",
      " 0.000e+00 1.410e+01 0.000e+00 2.510e+01 2.980e+01 8.000e+00 1.020e+01\n",
      " 0.000e+00 1.350e+01 4.330e+01 1.090e+01 9.999e+03 7.100e+00 0.000e+00\n",
      " 5.140e+01 6.300e+00 1.120e+01 1.030e+01 3.260e+01 5.890e+01 4.500e+01\n",
      " 0.000e+00 1.280e+01 0.000e+00 3.730e+01 0.000e+00 1.210e+01 1.590e+01\n",
      " 3.470e+01 1.523e+02 2.260e+01 8.600e+00 7.930e+01 4.730e+01 2.140e+01\n",
      " 7.600e+00 3.400e+01 0.000e+00 0.000e+00 0.000e+00 8.900e+00 1.160e+01\n",
      " 5.000e+00 1.540e+01 1.440e+01 1.440e+01 0.000e+00 0.000e+00 3.950e+01\n",
      " 4.730e+01 5.400e+00 0.000e+00 2.870e+01 0.000e+00 0.000e+00 8.000e+00\n",
      " 0.000e+00 0.000e+00 6.200e-01 3.070e+01 6.400e+00 5.500e+00 0.000e+00\n",
      " 7.100e+00 0.000e+00 1.370e+01 4.210e+01 2.710e+01 3.060e+01 3.480e+01\n",
      " 1.500e+01 1.290e+01 8.200e+00 0.000e+00 0.000e+00 4.100e+00 9.999e+03\n",
      " 4.100e+00 0.000e+00 1.120e+01 7.500e+00 2.390e+01 5.470e+01 4.420e+01\n",
      " 2.390e+01 3.800e+00 6.500e+00 0.000e+00 3.250e+01 4.400e+00 0.000e+00\n",
      " 1.440e+01 1.360e+01 1.040e+01 0.000e+00 0.000e+00 0.000e+00 3.490e+01\n",
      " 2.970e+01 0.000e+00 1.440e+01 0.000e+00 3.120e+01 1.460e+01 0.000e+00\n",
      " 1.480e+01 1.540e+01 7.150e+01 5.740e+01 2.390e+01 1.990e+01 1.760e+01\n",
      " 1.390e+01 1.480e+01 9.600e+00 1.990e+01 1.750e+01 0.000e+00 2.210e+01\n",
      " 2.120e+01 5.270e+01 4.640e+01 5.360e+01 0.000e+00 0.000e+00 1.440e+01\n",
      " 1.010e+01]\n"
     ]
    }
   ],
   "source": [
    "ha=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    ha_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[6])\n",
    "    ha = np.append(ha, ha_temp)\n",
    "    \n",
    "print(ha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.07   4.82   0.9999 0.9999 2.88   0.9999 0.9999 2.92   5.77   3.57\n",
      " 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 4.74   0.9999 0.9999 0.9999\n",
      " 0.9999 4.17   0.9999 0.9999 3.85   4.95   0.9999 0.9999 5.89   0.9999\n",
      " 2.88   0.9999 0.9999 3.25   0.9999 0.9999 0.9999 5.97   2.88   3.54\n",
      " 3.25   2.88   0.9999 4.81   3.91   3.51   3.48   0.9999 2.88   3.79\n",
      " 2.88   0.9999 2.95   6.52   2.88   0.9999 0.9999 0.9999 3.39   4.98\n",
      " 0.9999 0.9999 0.9999 0.9999 2.88   0.9999 0.9999 0.9999 0.9999 0.9999\n",
      " 0.9999 0.9999 2.99   0.9999 0.9999 0.9999 0.9999 6.1    0.9999 0.9999\n",
      " 0.9999 3.65   3.6    3.79   0.9999 0.9999 0.9999 7.     0.9999 0.9999\n",
      " 3.71   2.88   3.12   3.36   0.9999 3.85   4.2    2.88   0.9999 4.49\n",
      " 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999\n",
      " 0.9999 2.88   3.8    0.9999 0.9999 2.88   0.9999 0.9999 0.9999 0.9999\n",
      " 0.9999 4.58   3.64   0.9999 0.9999 0.9999 0.9999 0.9999 2.88   5.41\n",
      " 2.88   3.06   3.96   0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999\n",
      " 0.9999 0.9999 0.9999 0.9999 5.49   3.48   4.52   2.88   0.9999 0.9999\n",
      " 0.9999 6.86   0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999\n",
      " 4.55   7.22   0.9999 0.9999 0.9999 3.7    4.42   0.9999 0.9999 0.9999\n",
      " 5.02   3.18   3.93   2.88   3.53   0.9999 3.57   2.88   4.15   2.88\n",
      " 0.9999 3.06   3.18   3.96   6.4    8.22   0.9999 0.9999 0.9999 0.9999]\n"
     ]
    }
   ],
   "source": [
    "ba=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    ba_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[7])\n",
    "    ba = np.append(ba, ba_temp)\n",
    "    \n",
    "print(ba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.9999 221.5      0.9999 372.6    347.9      0.9999   0.9999   0.9999\n",
      "   0.9999 161.7    339.     342.1    448.5      0.9999   0.9999 227.8\n",
      "   0.9999 355.2    278.5    433.8    460.8    377.5      0.9999 335.7\n",
      " 312.9      0.9999   0.9999   0.9999 391.5      0.9999 229.4    368.9\n",
      "   0.9999 232.1      0.9999   0.9999 177.5      0.9999   0.9999   0.9999\n",
      " 274.9      0.9999   0.9999   0.9999 290.5      0.9999   0.9999   0.9999\n",
      "   0.9999 198.8     62.6    432.8      0.9999   0.9999   0.9999   0.9999\n",
      "   0.9999   0.9999   0.9999   0.9999   0.9999 427.8      0.9999   0.9999\n",
      "   0.9999   0.9999   0.9999   0.9999   0.9999 227.9      0.9999   0.9999\n",
      "   0.9999 399.       0.9999 296.8      0.9999   0.9999   0.9999   0.9999\n",
      " 441.5    274.5      0.9999   0.9999   0.9999 195.       0.9999 314.9\n",
      " 404.5    224.1      0.9999 126.1    117.6      0.9999 340.9     61.5\n",
      " 258.5     73.6    348.7    288.2    332.       0.9999   0.9999 359.2\n",
      "   0.9999   0.9999 247.5    411.       0.9999   0.9999   0.9999   0.9999\n",
      "   0.9999 211.3      0.9999 210.2    392.1    638.7      0.9999   0.9999\n",
      "   0.9999  42.1      0.9999   0.9999 404.1    444.8     55.4    349.\n",
      " 291.3    242.6    204.3    248.6    189.2    191.8    328.6      0.9999\n",
      " 439.2    488.1    540.9      0.9999 540.9      0.9999   0.9999   0.9999\n",
      "   0.9999   0.9999   0.9999   0.9999   0.9999 512.1      0.9999   0.9999\n",
      " 639.5      0.9999 243.3      0.9999   0.9999   0.9999   0.9999 520.9\n",
      " 152.4    352.8      0.9999 307.7      0.9999   0.9999 326.4      0.9999\n",
      " 322.1    149.6      0.9999   0.9999   0.9999 237.4    279.     434.\n",
      " 204.2      0.9999 279.6    229.9      0.9999 201.9    129.7      0.9999\n",
      " 696.4      0.9999   0.9999 250.1    545.     430.    ]\n"
     ]
    }
   ],
   "source": [
    "cv=[]\n",
    "with open('Barton_Table_New_float.csv') as csv_file:\n",
    "    cv_temp = np.genfromtxt(csv_file, dtype=float, delimiter=',', filling_values=9999, usecols=[8])\n",
    "    cv = np.append(cv, cv_temp)\n",
    "    \n",
    "print(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  }
 ],
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