proyecto_AAPE.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "proyecto_AAPE.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/astrodatos/proyecto_aape/blob/master/proyecto_AAPE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwlngFjUBMYF",
        "colab_type": "code",
        "outputId": "c614ab01-22e3-4f46-905c-44d9ef1a323a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JN1F3I-oCkIP",
        "colab_type": "code",
        "outputId": "f1af1e94-a2c3-49a7-aafd-7433106b5e13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "from astropy.io import fits\n",
        "from astropy.wcs import WCS\n",
        "\n",
        "filename = ('/content/gdrive/My Drive/Colab Notebooks/dr9q_fit.fits')\n",
        "hdu_fits = fits.open(filename)\n",
        "hdu = fits.open(filename)[1]\n",
        "header = hdu.header\n",
        "wcs = WCS(header)\n",
        "print (wcs)\n",
        "\n",
        "data=hdu.data\n",
        "print (data)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WCS Keywords\n",
            "\n",
            "Number of WCS axes: 2\n",
            "CTYPE : ''  ''  \n",
            "CRVAL : 0.0  0.0  \n",
            "CRPIX : 0.0  0.0  \n",
            "PC1_1 PC1_2  : 1.0  0.0  \n",
            "PC2_1 PC2_2  : 0.0  1.0  \n",
            "CDELT : 1.0  1.0  \n",
            "NAXIS : 686  87822\n",
            "[('000000.47-002703.9', 1.97831659e-03, -0.45108829, 4216, 310, 55477, 0.24995296, 0.25 , -1.        , -1.00000000e+00, -1.        , -1.        , -1.00000000e+00, -1.        , -1.        ,    0.        , -1.,   0.        , -1., -3.00000000e+05, -1.,   0.        , -1.,    0.        , -1.,  0.        , -1., -300000.        , -1.,   0.        , -1.,     0.        , -1.,  0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 0.00000000e+00, -1.,   0.        , -1.,    0.        , -1.,   0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 2298.67000834, -1., 64.27742969, -1.,  1.64958607e+02, -1., -14.65182438, -1., 2.23529114, -1., -16.11066829, -1., 254.741708, -1., 26.34479015, -1., -15.04412284, -1., -5.4489311e+01, -1., -13.12042921, -1., -12.90400265, -1., -12.7601818 , -1., [ 3.32646096, -0.37591011], [0.07121186, 0.0307967 ], 1.14984235, 1, 1.08966604, 1, 306,  69,   0,   0)\n",
            " ('000001.93-001427.4', 8.06669135e-03, -0.24097079, 4216, 312, 55477, 2.16453075, 2.163,  2.16007701,  6.53816260e+03, 29.02243739,  0.87297049,  5.14380975e+03, 37.58693278,  1.40144574, 6848.38565979, -1.,  34.81942578, -1.,  1.46327338e+03, -1., -14.36527166, -1., 5498.15814182, -1.,  8.844851  , -1.,     442.00827118, -1., -15.14877666, -1., 14119.02569616, -1.,  2.86674265, -1., -2.70112559e+02, -1., -15.61598716, -1., 2.47488924e+01, -1., -14.69621891, -1., 4060.20905512, -1.,  24.31090498, -1., -8.76369287e+01, -1., -15.05100106, -1.,    0.        , -1.,  0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 0.        , -1.,   0.        , -1.,   0.      , -1.,  0.        , -1.,   0.        , -1., -3.0000000e+05, -1., -12.65899028, -1., -13.02106513, -1., -13.26167289, -1., [ 1.00346977, -2.04408281], [0.01584002, 0.02517791], 0.97130166, 1, 0.94227645, 1,   0, 151, 332, 269)\n",
            " ('000003.17+011510.6', 1.32289990e-02,  1.25296711, 4296, 364, 55499, 2.35428119, 2.354,  2.35223261,  6.49238436e+03, 32.81777038,  0.87281569,  6.75670115e+03, 52.074875  ,  3.94385402, 5570.60548231, -1.,  41.74732628, -1.,  2.55910927e+02, -1., -14.66586957, -1., 9907.70187743, -1., 33.02961838, -1.,    -502.73772883, -1., -14.99253223, -1., 14119.02569616, -1., 14.36684345, -1.,  9.98308892e+02, -1., -15.31972777, -1., 0.00000000e+00, -1.,   0.        , -1., 7216.34340406, -1.,  37.34312595, -1.,  9.30036612e+02, -1., -15.34153557, -1.,    0.        , -1.,  0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 0.        , -1.,   0.        , -1.,   0.      , -1.,  0.        , -1.,   0.        , -1., -3.0000000e+05, -1., -13.01066629, -1., -13.51384112, -1., -13.84821333, -1., [ 0.30439545, -2.45096018], [0.01483779, 0.06792301], 0.86800839, 1, 1.00600674, 1,   0, 159, 278, 281)\n",
            " ...\n",
            " ('235957.06-002243.7', 3.59987755e+02, -0.37883175, 4216, 314, 55477, 3.70247722, 3.703,  3.68855527,  3.34558827e+03, 66.91817799,  2.56292613, -1.00000000e+00, -1.        , -1.        , 3578.68937731, -1.,  59.20526931, -1.,  7.57074349e+02, -1., -14.65557897, -1., 1649.13140773, -1.,  9.24304542, -1.,     514.45658954, -1., -15.5544808 , -1.,     0.        , -1.,  0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 6.31390187e-16, -1., -31.71573526, -1.,    0.        , -1.,   0.        , -1., -3.00000000e+05, -1.,   0.        , -1.,    0.        , -1.,  0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 0.        , -1.,   0.        , -1.,   0.      , -1.,  0.        , -1.,   0.        , -1., -3.0000000e+05, -1., -13.23811183, -1., -13.24317426, -1., -13.24653838, -1., [ 0.40492798, -1.01459809], [0.15240483, 0.51293367], 1.23451336, 1, 0.85804887, 1,   0,   0, 176, 186)\n",
            " ('235958.62+002551.4', 3.59994273e+02,  0.43094775, 4216, 734, 55477, 3.0647893 , 3.066,  3.06256524,  2.68937139e+03, 52.74361613,  4.02669454, -1.00000000e+00, -1.        , -1.        , 2045.52072082, -1., 106.7713175 , -1.,  9.40496580e+00, -1., -14.73050492, -1., 6362.80587496, -1., 38.29215826, -1.,    -758.74507746, -1., -15.2964244 , -1.,  8134.48656635, -1.,  6.67421263, -1.,  9.98308892e+02, -1., -16.03623874, -1., 0.00000000e+00, -1.,   0.        , -1.,    0.        , -1.,   0.        , -1., -3.00000000e+05, -1.,   0.        , -1.,    0.        , -1.,  0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 0.        , -1.,   0.        , -1.,   0.      , -1.,  0.        , -1.,   0.        , -1., -3.0000000e+05, -1., -13.55017604, -1., -13.658931  , -1., -13.73120139, -1., [ 0.17984969, -1.31360695], [0.02445154, 0.20023001], 0.95016265, 1, 1.12346181, 1,   0,   0, 154, 228)\n",
            " ('235959.1+024906.7', 3.59996270e+02,  2.81855191, 4277, 138, 55506, 2.3982718 , 2.398,  2.37460517,  5.32961522e+03, 34.0841442 ,  0.84192181, -1.00000000e+00, -1.        , -1.        , 5738.6525794 , -1.,  37.55075538, -1.,  1.49517281e+03, -1., -14.87882778, -1., 2414.40939766, -1., 18.8683295 , -1.,     608.0235605 , -1., -15.44862883, -1.,  5801.25250012, -1.,  8.96820384, -1., -9.98308892e+02, -1., -15.7436346 , -1., 5.73136826e+00, -1., -15.96171604, -1., 1756.52137914, -1., 205.20169888, -1.,  3.01917799e+02, -1., -14.90316607, -1.,    0.        , -1.,  0.        , -1., -3.00000000e+05, -1.,   0.        , -1., 0.        , -1.,   0.        , -1.,   0.      , -1.,  0.        , -1.,   0.        , -1., -3.0000000e+05, -1., -13.15351751, -1., -13.82835638, -1., -14.27680361, -1., [ 0.1456345 , -2.94597236], [0.01453748, 0.1336987 ], 0.68420145, 1, 0.84059411, 5,   0, 157, 281, 279)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHKySxtTRc0c",
        "colab_type": "code",
        "outputId": "3496a8f4-8abb-4d16-fc62-570c32730f25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from astropy.table import Table\n",
        "import pandas as pd\n",
        "\n",
        "table=Table.read(hdu_fits)\n",
        "#table.colnames\n",
        "table\n",
        "#table['SDSS_NAME', 'PLATE']"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<i>Table length=87822</i>\n",
              "<table id=\"table140584720090504\" class=\"table-striped table-bordered table-condensed\">\n",
              "<thead><tr><th>SDSS_NAME</th><th>RA</th><th>DEC</th><th>PLATE</th><th>FIBER</th><th>MJD</th><th>Z_PIPE</th><th>Z_VI</th><th>Z_PCA</th><th>FWHM_CIV_FPG</th><th>REW_CIV_FPG</th><th>REW_CIV_ERR_FPG</th><th>FWHM_MGII_FPG</th><th>REW_MGII_FPG</th><th>REW_MGII_ERR_FPG</th><th>FWHM_CIV</th><th>FWHM_CIV_ERR</th><th>REW_CIV</th><th>REW_CIV_ERR</th><th>VOFF_CIV</th><th>VOFF_CIV_ERR</th><th>LOGF_CIV</th><th>LOGF_CIV_ERR</th><th>FWHM_CIII</th><th>FWHM_CIII_ERR</th><th>REW_CIII</th><th>REW_CIII_ERR</th><th>VOFF_CIII</th><th>VOFF_CIII_ERR</th><th>LOGF_CIII</th><th>LOGF_CIII_ERR</th><th>FWHM_ALIII</th><th>FWHM_ALIII_ERR</th><th>REW_ALIII</th><th>REW_ALIII_ERR</th><th>VOFF_ALIII</th><th>VOFF_ALIII_ERR</th><th>LOGF_ALIII</th><th>LOGF_ALIII_ERR</th><th>REW_SIIII</th><th>REW_SIIII_ERR</th><th>LOGF_SIIII</th><th>LOGF_SIIII_ERR</th><th>FWHM_MGII</th><th>FWHM_MGII_ERR</th><th>REW_MGII</th><th>REW_MGII_ERR</th><th>VOFF_MGII</th><th>VOFF_MGII_ERR</th><th>LOGF_MGII</th><th>LOGF_MGII_ERR</th><th>FWHM_BROAD_HB</th><th>FWHM_BROAD_HB_ERR</th><th>REW_BROAD_HB</th><th>REW_BROAD_HB_ERR</th><th>VOFF_BROAD_HB</th><th>VOFF_BROAD_HB_ERR</th><th>LOGF_BROAD_HB</th><th>LOGF_BROAD_HB_ERR</th><th>REW_NARROW_HB</th><th>REW_NARROW_HB_ERR</th><th>LOGF_NARROW_HB</th><th>LOGF_NARROW_HB_ERR</th><th>FWHM_OIII_5007</th><th>FWHM_OIII_5007_ERR</th><th>REW_OIII_5007</th><th>REW_OIII_5007_ERR</th><th>LOGF_OIII_5007</th><th>LOGF_OIII_5007_ERR</th><th>VOFF_OIII_5007</th><th>VOFF_OIII_5007_ERR</th><th>LOGF1350</th><th>LOGF1350_ERR</th><th>LOGF3000</th><th>LOGF3000_ERR</th><th>LOGF5100</th><th>LOGF5100_ERR</th><th>CONTI_FIT [2]</th><th>CONTI_FIT_ERR [2]</th><th>CONTI_REDCHI2</th><th>CONTI_STATUS</th><th>LINE_REDCHI2</th><th>LINE_STATUS</th><th>LINE_NPIX_HB</th><th>LINE_NPIX_MGII</th><th>LINE_NPIX_CIII</th><th>LINE_NPIX_CIV</th></tr></thead>\n",
              "<thead><tr><th>str18</th><th>float64</th><th>float64</th><th>int32</th><th>int32</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th></tr></thead>\n",
              "<tr><td>000000.47-002703.9</td><td>0.0019783165939202263</td><td>-0.45108829471677764</td><td>4216</td><td>310</td><td>55477</td><td>0.24995295703411102</td><td>0.25</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>2298.670008340593</td><td>-1.0</td><td>64.2774296887242</td><td>-1.0</td><td>164.95860656420774</td><td>-1.0</td><td>-14.65182438231868</td><td>-1.0</td><td>2.2352911356404155</td><td>-1.0</td><td>-16.110668291496495</td><td>-1.0</td><td>254.74170800292904</td><td>-1.0</td><td>26.344790150755056</td><td>-1.0</td><td>-15.04412284248246</td><td>-1.0</td><td>-54.48931101084666</td><td>-1.0</td><td>-13.120429210786082</td><td>-1.0</td><td>-12.904002646679636</td><td>-1.0</td><td>-12.760181804702583</td><td>-1.0</td><td>3.3264609558185136 .. -0.3759101100717362</td><td>0.07121186279526269 .. 0.03079669808147335</td><td>1.1498423548947068</td><td>1</td><td>1.089666039538195</td><td>1</td><td>306</td><td>69</td><td>0</td><td>0</td></tr>\n",
              "<tr><td>000001.93-001427.4</td><td>0.008066691350313349</td><td>-0.24097078525990853</td><td>4216</td><td>312</td><td>55477</td><td>2.1645307540893555</td><td>2.163</td><td>2.1600770125260818</td><td>6538.162600803452</td><td>29.02243738994315</td><td>0.8729704914045182</td><td>5143.809749376881</td><td>37.58693277814791</td><td>1.4014457383956558</td><td>6848.385659787697</td><td>-1.0</td><td>34.819425782128825</td><td>-1.0</td><td>1463.2733787650475</td><td>-1.0</td><td>-14.365271657331485</td><td>-1.0</td><td>5498.158141824284</td><td>-1.0</td><td>8.844850998221855</td><td>-1.0</td><td>442.0082711821357</td><td>-1.0</td><td>-15.148776656013794</td><td>-1.0</td><td>14119.02569616</td><td>-1.0</td><td>2.8667426458471046</td><td>-1.0</td><td>-270.1125592626993</td><td>-1.0</td><td>-15.615987159317584</td><td>-1.0</td><td>24.748892384141914</td><td>-1.0</td><td>-14.696218911079507</td><td>-1.0</td><td>4060.2090551234855</td><td>-1.0</td><td>24.310904980047667</td><td>-1.0</td><td>-87.63692867982165</td><td>-1.0</td><td>-15.051001058208485</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-12.658990276491146</td><td>-1.0</td><td>-13.021065130436128</td><td>-1.0</td><td>-13.261672888398198</td><td>-1.0</td><td>1.0034697711159482 .. -2.0440828124646395</td><td>0.015840020377284526 .. 0.025177912718590403</td><td>0.9713016643443894</td><td>1</td><td>0.9422764509582113</td><td>1</td><td>0</td><td>151</td><td>332</td><td>269</td></tr>\n",
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              "<tr><td>000009.27+020622</td><td>0.03865736392742747</td><td>2.106111925697201</td><td>4296</td><td>616</td><td>55499</td><td>1.4354486465454102</td><td>1.432</td><td>1.43395907262234</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>3857.235126647534</td><td>36.92174751403631</td><td>0.500509017919172</td><td>2303.015808245649</td><td>-1.0</td><td>61.136037917592574</td><td>-1.0</td><td>121.16430227325345</td><td>-1.0</td><td>-14.019072405422975</td><td>-1.0</td><td>6415.902481333355</td><td>-1.0</td><td>43.13124733236029</td><td>-1.0</td><td>1002.7832043691425</td><td>-1.0</td><td>-14.29541770583723</td><td>-1.0</td><td>5093.326543289084</td><td>-1.0</td><td>6.997252440526138</td><td>-1.0</td><td>639.3554859660477</td><td>-1.0</td><td>-15.069484901680504</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>3822.512309603849</td><td>-1.0</td><td>33.48258122180126</td><td>-1.0</td><td>228.92948448108066</td><td>-1.0</td><td>-14.639071121772703</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-12.591865733638436</td><td>-1.0</td><td>-12.729344404986849</td><td>-1.0</td><td>-12.820702406019896</td><td>-1.0</td><td>2.5524389526502573 .. -1.396434925738213</td><td>0.020826811833910355 .. 0.020516768864659078</td><td>0.9141201189380214</td><td>2</td><td>1.0889455706967968</td><td>5</td><td>0</td><td>281</td><td>343</td><td>262</td></tr>\n",
              "<tr><td>000013.03+044403.5</td><td>0.054310219507442525</td><td>4.734322876874042</td><td>4277</td><td>872</td><td>55506</td><td>2.620950222015381</td><td>2.624</td><td>2.6208277084362175</td><td>5420.626122563788</td><td>37.381521868502766</td><td>1.3516223139829466</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>5926.332850626705</td><td>-1.0</td><td>44.05689160345957</td><td>-1.0</td><td>636.5765184641133</td><td>-1.0</td><td>-14.85711908311641</td><td>-1.0</td><td>4534.647388215468</td><td>-1.0</td><td>46.68720780683568</td><td>-1.0</td><td>396.1339317415302</td><td>-1.0</td><td>-14.966410116104147</td><td>-1.0</td><td>14119.02569616</td><td>-1.0</td><td>16.113105223718378</td><td>-1.0</td><td>998.3088918000166</td><td>-1.0</td><td>-15.409649599110633</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>4614.223886785351</td><td>-1.0</td><td>60.402811474554916</td><td>-1.0</td><td>-300.6185746921612</td><td>-1.0</td><td>-15.101606422508821</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.283912874226617</td><td>-1.0</td><td>-13.449451585845008</td><td>-1.0</td><td>-13.559456182475968</td><td>-1.0</td><td>0.32704280944313197 .. -1.4773491495340527</td><td>0.01417287658066588 .. 0.08395264259675454</td><td>0.7704959919268723</td><td>1</td><td>0.7509428606794514</td><td>1</td><td>0</td><td>115</td><td>295</td><td>267</td></tr>\n",
              "<tr><td>000014.07+012951.5</td><td>0.058653698857312975</td><td>1.497664480802927</td><td>4296</td><td>370</td><td>55499</td><td>3.22837495803833</td><td>3.228</td><td>3.225544643280438</td><td>7404.601862804548</td><td>30.658277811758968</td><td>1.2549131890327883</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>8132.118875219754</td><td>-1.0</td><td>48.93450434484109</td><td>-1.0</td><td>910.1625814233939</td><td>-1.0</td><td>-14.45711661063147</td><td>-1.0</td><td>6544.825075639525</td><td>-1.0</td><td>17.533813290657744</td><td>-1.0</td><td>-487.528553104192</td><td>-1.0</td><td>-15.049040338002785</td><td>-1.0</td><td>12860.990260216287</td><td>-1.0</td><td>15.708790655694823</td><td>-1.0</td><td>998.3088918000166</td><td>-1.0</td><td>-15.074710035933634</td><td>-1.0</td><td>8.703452156510796</td><td>-1.0</td><td>-15.343812447115951</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-12.924278963644834</td><td>-1.0</td><td>-13.124358111707332</td><td>-1.0</td><td>-13.25731568858892</td><td>-1.0</td><td>0.5920346718769223 .. -1.5769503110988345</td><td>0.020073577494157648 .. 0.0573246355589213</td><td>1.011073195290467</td><td>1</td><td>1.0053975742249948</td><td>1</td><td>0</td><td>0</td><td>170</td><td>252</td></tr>\n",
              "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
              "<tr><td>235944.64+003048.1</td><td>359.93602801269355</td><td>0.5133721261519022</td><td>4216</td><td>686</td><td>55477</td><td>2.7909657955169678</td><td>2.789</td><td>2.7916211760168217</td><td>3748.734370658232</td><td>41.34961978190519</td><td>1.4401138752120732</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>3631.4321034254735</td><td>-1.0</td><td>45.89106652770591</td><td>-1.0</td><td>413.20734651571223</td><td>-1.0</td><td>-14.815718997527151</td><td>-1.0</td><td>4956.422663710643</td><td>-1.0</td><td>20.69549481014987</td><td>-1.0</td><td>887.5517172286911</td><td>-1.0</td><td>-15.266891627976117</td><td>-1.0</td><td>2915.4692858457424</td><td>-1.0</td><td>1.9888876664814388</td><td>-1.0</td><td>-998.3088918000166</td><td>-1.0</td><td>-16.273498530079863</td><td>-1.0</td><td>2.3813459438805356e-15</td><td>-1.0</td><td>-31.204675654961274</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.277902686426348</td><td>-1.0</td><td>-13.336983907872211</td><td>-1.0</td><td>-13.376244850947561</td><td>-1.0</td><td>0.40471096534784745 .. -1.1703672242878698</td><td>0.023238355864158 .. 0.08371655820103512</td><td>0.8964925900650855</td><td>1</td><td>0.9532948213905872</td><td>1</td><td>0</td><td>39</td><td>216</td><td>269</td></tr>\n",
              "<tr><td>235944.94+022906.8</td><td>359.93726275636743</td><td>2.4852485675622273</td><td>4296</td><td>602</td><td>55499</td><td>2.253934621810913</td><td>2.258</td><td>2.2625522154841287</td><td>4917.340820255249</td><td>40.23495063832288</td><td>1.321868418885527</td><td>5141.78810370828</td><td>56.291955161074185</td><td>2.7115036257753142</td><td>6131.584108305951</td><td>-1.0</td><td>44.55988563773999</td><td>-1.0</td><td>628.7220053398864</td><td>-1.0</td><td>-14.561058653962197</td><td>-1.0</td><td>5932.702373214374</td><td>-1.0</td><td>25.25302895528049</td><td>-1.0</td><td>166.75734785469947</td><td>-1.0</td><td>-14.980824894014946</td><td>-1.0</td><td>6760.315669443074</td><td>-1.0</td><td>11.265642572633528</td><td>-1.0</td><td>-325.6399496377685</td><td>-1.0</td><td>-15.310294733295795</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>4535.485298613357</td><td>-1.0</td><td>65.02413423178211</td><td>-1.0</td><td>-402.90016268604785</td><td>-1.0</td><td>-14.886679518625376</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-12.968174939248986</td><td>-1.0</td><td>-13.278686240095444</td><td>-1.0</td><td>-13.485028732827363</td><td>-1.0</td><td>0.539241964703799 .. -1.8953936147664343</td><td>0.014558786008423634 .. 0.044695563609931266</td><td>0.8502863564369978</td><td>1</td><td>0.9477280741443967</td><td>1</td><td>0</td><td>161</td><td>312</td><td>277</td></tr>\n",
              "<tr><td>235945.2-005057.5</td><td>359.93834118517503</td><td>-0.8493217248990612</td><td>4216</td><td>336</td><td>55477</td><td>0.5764196515083313</td><td>0.58</td><td>0.5911199702199825</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>3176.4008622074675</td><td>-1.0</td><td>6.712100570448467</td><td>-1.0</td><td>-1955.6663259753984</td><td>-1.0</td><td>-14.815946476652675</td><td>-1.0</td><td>4654.213436361841</td><td>-1.0</td><td>68.49661195853415</td><td>-1.0</td><td>-644.5830495539407</td><td>-1.0</td><td>-13.866996397833208</td><td>-1.0</td><td>0.3829933604999041</td><td>-1.0</td><td>-16.11920715881954</td><td>-1.0</td><td>514.5250749480127</td><td>-1.0</td><td>1.9175340131747953</td><td>-1.0</td><td>-15.422876927583314</td><td>-1.0</td><td>88.48367744929948</td><td>-1.0</td><td>-12.432125486879896</td><td>-1.0</td><td>-12.172555734619614</td><td>-1.0</td><td>-12.000065169628302</td><td>-1.0</td><td>14.211868178445105 .. -0.25150196425448235</td><td>0.0663799224691446 .. 0.007846672830746061</td><td>21.824602474358265</td><td>1</td><td>1.5606338364520767</td><td>1</td><td>235</td><td>253</td><td>0</td><td>0</td></tr>\n",
              "<tr><td>235950.11+025229.3</td><td>359.9588213691294</td><td>2.874805689522851</td><td>4277</td><td>178</td><td>55506</td><td>2.250772714614868</td><td>2.244</td><td>2.2422100050956715</td><td>3921.3840160331147</td><td>63.6673667133807</td><td>3.3524901844006387</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>4378.910726769012</td><td>-1.0</td><td>74.76862447971504</td><td>-1.0</td><td>742.1691479188639</td><td>-1.0</td><td>-14.672064227287235</td><td>-1.0</td><td>nan</td><td>-1.0</td><td>7.343483346777082</td><td>-1.0</td><td>-1636.7365305165993</td><td>-1.0</td><td>-15.857069245973666</td><td>-1.0</td><td>4261.453953194878</td><td>-1.0</td><td>6.556800746874338</td><td>-1.0</td><td>-435.16497690989104</td><td>-1.0</td><td>-15.880715241500672</td><td>-1.0</td><td>20.370189639330334</td><td>-1.0</td><td>-15.403514844937186</td><td>-1.0</td><td>7264.039191389188</td><td>-1.0</td><td>48.66674068903875</td><td>-1.0</td><td>-1501.2093848890745</td><td>-1.0</td><td>-15.34828180668993</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.305022290747802</td><td>-1.0</td><td>-13.611070376068366</td><td>-1.0</td><td>-13.814446950926433</td><td>-1.0</td><td>0.25108557522520764 .. -1.8825234400825515</td><td>0.016891615094042023 .. 0.11030647484005958</td><td>0.7435698930516751</td><td>1</td><td>0.769901915239966</td><td>1</td><td>0</td><td>162</td><td>308</td><td>281</td></tr>\n",
              "<tr><td>235954.02+031518</td><td>359.9751127694846</td><td>3.2550098889114514</td><td>4277</td><td>142</td><td>55506</td><td>2.3715953826904297</td><td>2.371</td><td>2.3699142613883084</td><td>4986.539837257538</td><td>55.17293361743668</td><td>1.516369185772259</td><td>2091.168992046408</td><td>74.18704577127822</td><td>5.716454335535599</td><td>5693.724528973897</td><td>-1.0</td><td>53.572878114983276</td><td>-1.0</td><td>-126.17439299267757</td><td>-1.0</td><td>-14.647534926959448</td><td>-1.0</td><td>5579.002730273156</td><td>-1.0</td><td>48.66812054695297</td><td>-1.0</td><td>97.11355932804614</td><td>-1.0</td><td>-14.88702962051966</td><td>-1.0</td><td>5877.760129700198</td><td>-1.0</td><td>16.993016924863042</td><td>-1.0</td><td>-642.0392345713118</td><td>-1.0</td><td>-15.320430498344185</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>2346.7320150262526</td><td>-1.0</td><td>75.15174039165588</td><td>-1.0</td><td>-509.1303560773101</td><td>-1.0</td><td>-15.064134568959416</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.11497929453447</td><td>-1.0</td><td>-13.52735576828402</td><td>-1.0</td><td>-13.801390205373819</td><td>-1.0</td><td>0.2935536783142621 .. -2.189133086199091</td><td>0.022203650964485327 .. 0.09974439679089322</td><td>0.8510791810540801</td><td>1</td><td>0.932547084130999</td><td>1</td><td>0</td><td>171</td><td>283</td><td>280</td></tr>\n",
              "<tr><td>235954.98+015621.6</td><td>359.9790939770803</td><td>1.9393604323143134</td><td>4296</td><td>438</td><td>55499</td><td>1.8489781618118286</td><td>1.85</td><td>1.8458286036727234</td><td>3552.9336619431333</td><td>36.96646556289141</td><td>1.3970616391646469</td><td>4417.852057501288</td><td>65.06249025937649</td><td>11.307390800517998</td><td>3765.8681075953423</td><td>-1.0</td><td>55.065663429610545</td><td>-1.0</td><td>351.3973290149838</td><td>-1.0</td><td>-14.882308172500442</td><td>-1.0</td><td>6296.159883429478</td><td>-1.0</td><td>25.884578473321266</td><td>-1.0</td><td>-651.7206217018556</td><td>-1.0</td><td>-15.530663714697122</td><td>-1.0</td><td>4610.026063421241</td><td>-1.0</td><td>9.032846028147064</td><td>-1.0</td><td>585.0587566261353</td><td>-1.0</td><td>-15.94045930379572</td><td>-1.0</td><td>7.402316740665526</td><td>-1.0</td><td>-16.05482657361624</td><td>-1.0</td><td>3231.7075379449516</td><td>-1.0</td><td>62.03852025869326</td><td>-1.0</td><td>-111.87390179077181</td><td>-1.0</td><td>-15.726596131548773</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.286811662086471</td><td>-1.0</td><td>-14.14648837908034</td><td>-1.0</td><td>-14.717764967869682</td><td>-1.0</td><td>0.08350283990126897 .. -3.47897271713245</td><td>0.010428808311344549 .. 0.18139865464677465</td><td>0.8447028988754016</td><td>1</td><td>0.9351811738514519</td><td>1</td><td>0</td><td>158</td><td>331</td><td>269</td></tr>\n",
              "<tr><td>235955.76-011409.2</td><td>359.98234064248425</td><td>-1.235900606062801</td><td>4216</td><td>244</td><td>55477</td><td>2.712829351425171</td><td>2.713</td><td>2.7206427911663558</td><td>4068.3128839992446</td><td>46.89000089625033</td><td>1.42370009820949</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>4617.887243242448</td><td>-1.0</td><td>59.90935878847089</td><td>-1.0</td><td>336.2574027857459</td><td>-1.0</td><td>-13.960003598671019</td><td>-1.0</td><td>4197.440971248126</td><td>-1.0</td><td>22.492373858039436</td><td>-1.0</td><td>-110.3067020820788</td><td>-1.0</td><td>-14.573373819080524</td><td>-1.0</td><td>9269.778093775423</td><td>-1.0</td><td>3.850512652556652</td><td>-1.0</td><td>998.3088918000166</td><td>-1.0</td><td>-15.312225383426972</td><td>-1.0</td><td>7.499681409772054</td><td>-1.0</td><td>-15.039208753851977</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-12.485254375426633</td><td>-1.0</td><td>-12.852033064299201</td><td>-1.0</td><td>-13.0957666377202</td><td>-1.0</td><td>1.2622363773504164 .. -2.0576468397563827</td><td>0.023937625287041804 .. 0.026534435379052965</td><td>1.0531223322099077</td><td>1</td><td>1.0026167295169897</td><td>1</td><td>0</td><td>60</td><td>267</td><td>263</td></tr>\n",
              "<tr><td>235957.06-002243.7</td><td>359.9877546418122</td><td>-0.3788317528880652</td><td>4216</td><td>314</td><td>55477</td><td>3.702477216720581</td><td>3.703</td><td>3.6885552682354312</td><td>3345.588267310593</td><td>66.91817799193034</td><td>2.562926129763741</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>3578.6893773098377</td><td>-1.0</td><td>59.20526930581003</td><td>-1.0</td><td>757.0743488446698</td><td>-1.0</td><td>-14.655578969763418</td><td>-1.0</td><td>1649.131407731285</td><td>-1.0</td><td>9.243045423859895</td><td>-1.0</td><td>514.4565895416238</td><td>-1.0</td><td>-15.554480802364132</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>6.31390187428073e-16</td><td>-1.0</td><td>-31.71573526378395</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.238111828209272</td><td>-1.0</td><td>-13.243174262859814</td><td>-1.0</td><td>-13.246538376765612</td><td>-1.0</td><td>0.4049279756519595 .. -1.0145980891803632</td><td>0.15240483198314775 .. 0.5129336674369787</td><td>1.2345133586084083</td><td>1</td><td>0.8580488659723154</td><td>1</td><td>0</td><td>0</td><td>176</td><td>186</td></tr>\n",
              "<tr><td>235958.62+002551.4</td><td>359.99427316427636</td><td>0.43094774656769796</td><td>4216</td><td>734</td><td>55477</td><td>3.064789295196533</td><td>3.066</td><td>3.062565241303931</td><td>2689.371386147608</td><td>52.743616133949246</td><td>4.0266945395676705</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>2045.5207208232687</td><td>-1.0</td><td>106.7713174973272</td><td>-1.0</td><td>9.404965797122214</td><td>-1.0</td><td>-14.730504921308112</td><td>-1.0</td><td>6362.805874956171</td><td>-1.0</td><td>38.29215825521344</td><td>-1.0</td><td>-758.7450774575371</td><td>-1.0</td><td>-15.296424400026885</td><td>-1.0</td><td>8134.486566345074</td><td>-1.0</td><td>6.674212627552441</td><td>-1.0</td><td>998.3088918000166</td><td>-1.0</td><td>-16.03623873887053</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.550176035035419</td><td>-1.0</td><td>-13.658931002158303</td><td>-1.0</td><td>-13.731201386366347</td><td>-1.0</td><td>0.1798496947100541 .. -1.3136069536616135</td><td>0.024451543485543018 .. 0.20023001357763356</td><td>0.9501626535183652</td><td>1</td><td>1.1234618062672412</td><td>1</td><td>0</td><td>0</td><td>154</td><td>228</td></tr>\n",
              "<tr><td>235959.1+024906.7</td><td>359.9962697731987</td><td>2.818551913436165</td><td>4277</td><td>138</td><td>55506</td><td>2.3982717990875244</td><td>2.398</td><td>2.374605173353044</td><td>5329.615222699904</td><td>34.08414420475585</td><td>0.8419218136672436</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>5738.652579397983</td><td>-1.0</td><td>37.55075538179328</td><td>-1.0</td><td>1495.1728147267133</td><td>-1.0</td><td>-14.878827780912196</td><td>-1.0</td><td>2414.4093976599956</td><td>-1.0</td><td>18.868329499946267</td><td>-1.0</td><td>608.0235605027545</td><td>-1.0</td><td>-15.448628834906058</td><td>-1.0</td><td>5801.252500118422</td><td>-1.0</td><td>8.96820384487319</td><td>-1.0</td><td>-998.3088918000166</td><td>-1.0</td><td>-15.743634596765638</td><td>-1.0</td><td>5.731368262174471</td><td>-1.0</td><td>-15.96171604376317</td><td>-1.0</td><td>1756.5213791363253</td><td>-1.0</td><td>205.20169887968382</td><td>-1.0</td><td>301.91779872151676</td><td>-1.0</td><td>-14.903166065737095</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-300000.0</td><td>-1.0</td><td>-13.153517514506246</td><td>-1.0</td><td>-13.82835637771638</td><td>-1.0</td><td>-14.27680360925855</td><td>-1.0</td><td>0.14563449563641068 .. -2.9459723606432497</td><td>0.014537481009809063 .. 0.13369869917793095</td><td>0.6842014450569268</td><td>1</td><td>0.8405941115729154</td><td>5</td><td>0</td><td>157</td><td>281</td><td>279</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<Table length=87822>\n",
              "    SDSS_NAME                RA          ... LINE_NPIX_CIII LINE_NPIX_CIV\n",
              "      str18               float64        ...     int32          int32    \n",
              "------------------ --------------------- ... -------------- -------------\n",
              "000000.47-002703.9 0.0019783165939202263 ...              0             0\n",
              "000001.93-001427.4  0.008066691350313349 ...            332           269\n",
              "000003.17+011510.6  0.013228998987813156 ...            278           281\n",
              "000004.61+035122.4   0.01922318383401489 ...            217           267\n",
              "000004.96-001642    0.020695853072012937 ...            212           268\n",
              "000005.02-003829     0.02092856146740729 ...            287           275\n",
              "000007.58+002943.2    0.0316175511158292 ...            307           281\n",
              "000009.27+020622     0.03865736392742747 ...            343           262\n",
              "000013.03+044403.5  0.054310219507442525 ...            295           267\n",
              "000014.07+012951.5  0.058653698857312975 ...            170           252\n",
              "               ...                   ... ...            ...           ...\n",
              "235944.64+003048.1    359.93602801269355 ...            216           269\n",
              "235944.94+022906.8    359.93726275636743 ...            312           277\n",
              "235945.2-005057.5     359.93834118517503 ...              0             0\n",
              "235950.11+025229.3     359.9588213691294 ...            308           281\n",
              "235954.02+031518       359.9751127694846 ...            283           280\n",
              "235954.98+015621.6     359.9790939770803 ...            331           269\n",
              "235955.76-011409.2    359.98234064248425 ...            267           263\n",
              "235957.06-002243.7     359.9877546418122 ...            176           186\n",
              "235958.62+002551.4    359.99427316427636 ...            154           228\n",
              "235959.1+024906.7      359.9962697731987 ...            281           279"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mls5FmO6FB3t",
        "colab_type": "code",
        "outputId": "bdc05b2a-1b41-4312-a017-d947ea9a1c60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        }
      },
      "source": [
        "QuasarTable= table['SDSS_NAME','Z_VI', 'LOGF_CIV','LOGF_CIV_ERR',\n",
        "                   'LOGF_CIII','LOGF_CIII_ERR','LOGF_ALIII','LOGF_ALIII_ERR',\n",
        "                   'LOGF_SIIII','LOGF_SIIII_ERR','LOGF_MGII','LOGF_MGII_ERR',\n",
        "                   'LOGF_BROAD_HB','LOGF_BROAD_HB_ERR','LOGF_NARROW_HB',\n",
        "                   'LOGF_NARROW_HB_ERR','LOGF_OIII_5007','LOGF_OIII_5007_ERR',\n",
        "                   'LOGF1350','LOGF1350_ERR','LOGF3000','LOGF3000_ERR','LOGF5100',\n",
        "                   'LOGF5100_ERR']\n",
        "QuasarTable['class']='QUASAR'\n",
        "QuasarTable"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<i>Table length=87822</i>\n",
              "<table id=\"table140584720230048\" class=\"table-striped table-bordered table-condensed\">\n",
              "<thead><tr><th>SDSS_NAME</th><th>Z_VI</th><th>LOGF_CIV</th><th>LOGF_CIV_ERR</th><th>LOGF_CIII</th><th>LOGF_CIII_ERR</th><th>LOGF_ALIII</th><th>LOGF_ALIII_ERR</th><th>LOGF_SIIII</th><th>LOGF_SIIII_ERR</th><th>LOGF_MGII</th><th>LOGF_MGII_ERR</th><th>LOGF_BROAD_HB</th><th>LOGF_BROAD_HB_ERR</th><th>LOGF_NARROW_HB</th><th>LOGF_NARROW_HB_ERR</th><th>LOGF_OIII_5007</th><th>LOGF_OIII_5007_ERR</th><th>LOGF1350</th><th>LOGF1350_ERR</th><th>LOGF3000</th><th>LOGF3000_ERR</th><th>LOGF5100</th><th>LOGF5100_ERR</th><th>class</th></tr></thead>\n",
              "<thead><tr><th>str18</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str6</th></tr></thead>\n",
              "<tr><td>000000.47-002703.9</td><td>0.25</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-14.65182438231868</td><td>-1.0</td><td>-16.110668291496495</td><td>-1.0</td><td>-15.04412284248246</td><td>-1.0</td><td>-13.120429210786082</td><td>-1.0</td><td>-12.904002646679636</td><td>-1.0</td><td>-12.760181804702583</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000001.93-001427.4</td><td>2.163</td><td>-14.365271657331485</td><td>-1.0</td><td>-15.148776656013794</td><td>-1.0</td><td>-15.615987159317584</td><td>-1.0</td><td>-14.696218911079507</td><td>-1.0</td><td>-15.051001058208485</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-12.658990276491146</td><td>-1.0</td><td>-13.021065130436128</td><td>-1.0</td><td>-13.261672888398198</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000003.17+011510.6</td><td>2.354</td><td>-14.665869568966045</td><td>-1.0</td><td>-14.992532227472173</td><td>-1.0</td><td>-15.319727770673868</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-15.341535565628329</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.01066628984442</td><td>-1.0</td><td>-13.513841123176414</td><td>-1.0</td><td>-13.848213331552273</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000004.61+035122.4</td><td>2.795</td><td>-14.668890299079425</td><td>-1.0</td><td>-15.211973046561669</td><td>-1.0</td><td>-15.79280866543717</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.592787490749963</td><td>-1.0</td><td>-13.661024457420178</td><td>-1.0</td><td>-13.70636962122199</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000004.96-001642</td><td>2.879</td><td>-14.931736862166844</td><td>-1.0</td><td>-15.242458029703393</td><td>-1.0</td><td>-15.81921541286709</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.639728082120913</td><td>-1.0</td><td>-13.839519170035096</td><td>-1.0</td><td>-13.972285323773956</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000005.02-003829</td><td>2.662</td><td>-14.737058325267366</td><td>-1.0</td><td>-15.052214167147367</td><td>-1.0</td><td>-15.312190374673959</td><td>-1.0</td><td>-15.847419215921606</td><td>-1.0</td><td>-14.202602873642363</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.337832439337294</td><td>-1.0</td><td>-13.657385727857172</td><td>-1.0</td><td>-13.869736846589813</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000007.58+002943.2</td><td>2.254</td><td>-14.254588412916753</td><td>-1.0</td><td>-14.97676990106867</td><td>-1.0</td><td>-16.171391997120104</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-14.704092143246513</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.13401466715044</td><td>-1.0</td><td>-13.178771900626616</td><td>-1.0</td><td>-13.208514196955552</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000009.27+020622</td><td>1.432</td><td>-14.019072405422975</td><td>-1.0</td><td>-14.29541770583723</td><td>-1.0</td><td>-15.069484901680504</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-14.639071121772703</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-12.591865733638436</td><td>-1.0</td><td>-12.729344404986849</td><td>-1.0</td><td>-12.820702406019896</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000013.03+044403.5</td><td>2.624</td><td>-14.85711908311641</td><td>-1.0</td><td>-14.966410116104147</td><td>-1.0</td><td>-15.409649599110633</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-15.101606422508821</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.283912874226617</td><td>-1.0</td><td>-13.449451585845008</td><td>-1.0</td><td>-13.559456182475968</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000014.07+012951.5</td><td>3.228</td><td>-14.45711661063147</td><td>-1.0</td><td>-15.049040338002785</td><td>-1.0</td><td>-15.074710035933634</td><td>-1.0</td><td>-15.343812447115951</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-12.924278963644834</td><td>-1.0</td><td>-13.124358111707332</td><td>-1.0</td><td>-13.25731568858892</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
              "<tr><td>235944.64+003048.1</td><td>2.789</td><td>-14.815718997527151</td><td>-1.0</td><td>-15.266891627976117</td><td>-1.0</td><td>-16.273498530079863</td><td>-1.0</td><td>-31.204675654961274</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.277902686426348</td><td>-1.0</td><td>-13.336983907872211</td><td>-1.0</td><td>-13.376244850947561</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235944.94+022906.8</td><td>2.258</td><td>-14.561058653962197</td><td>-1.0</td><td>-14.980824894014946</td><td>-1.0</td><td>-15.310294733295795</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-14.886679518625376</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-12.968174939248986</td><td>-1.0</td><td>-13.278686240095444</td><td>-1.0</td><td>-13.485028732827363</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235945.2-005057.5</td><td>0.58</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-14.815946476652675</td><td>-1.0</td><td>-13.866996397833208</td><td>-1.0</td><td>-16.11920715881954</td><td>-1.0</td><td>-15.422876927583314</td><td>-1.0</td><td>-12.432125486879896</td><td>-1.0</td><td>-12.172555734619614</td><td>-1.0</td><td>-12.000065169628302</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235950.11+025229.3</td><td>2.244</td><td>-14.672064227287235</td><td>-1.0</td><td>-15.857069245973666</td><td>-1.0</td><td>-15.880715241500672</td><td>-1.0</td><td>-15.403514844937186</td><td>-1.0</td><td>-15.34828180668993</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.305022290747802</td><td>-1.0</td><td>-13.611070376068366</td><td>-1.0</td><td>-13.814446950926433</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235954.02+031518</td><td>2.371</td><td>-14.647534926959448</td><td>-1.0</td><td>-14.88702962051966</td><td>-1.0</td><td>-15.320430498344185</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-15.064134568959416</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.11497929453447</td><td>-1.0</td><td>-13.52735576828402</td><td>-1.0</td><td>-13.801390205373819</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235954.98+015621.6</td><td>1.85</td><td>-14.882308172500442</td><td>-1.0</td><td>-15.530663714697122</td><td>-1.0</td><td>-15.94045930379572</td><td>-1.0</td><td>-16.05482657361624</td><td>-1.0</td><td>-15.726596131548773</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.286811662086471</td><td>-1.0</td><td>-14.14648837908034</td><td>-1.0</td><td>-14.717764967869682</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235955.76-011409.2</td><td>2.713</td><td>-13.960003598671019</td><td>-1.0</td><td>-14.573373819080524</td><td>-1.0</td><td>-15.312225383426972</td><td>-1.0</td><td>-15.039208753851977</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-12.485254375426633</td><td>-1.0</td><td>-12.852033064299201</td><td>-1.0</td><td>-13.0957666377202</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235957.06-002243.7</td><td>3.703</td><td>-14.655578969763418</td><td>-1.0</td><td>-15.554480802364132</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-31.71573526378395</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.238111828209272</td><td>-1.0</td><td>-13.243174262859814</td><td>-1.0</td><td>-13.246538376765612</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235958.62+002551.4</td><td>3.066</td><td>-14.730504921308112</td><td>-1.0</td><td>-15.296424400026885</td><td>-1.0</td><td>-16.03623873887053</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.550176035035419</td><td>-1.0</td><td>-13.658931002158303</td><td>-1.0</td><td>-13.731201386366347</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235959.1+024906.7</td><td>2.398</td><td>-14.878827780912196</td><td>-1.0</td><td>-15.448628834906058</td><td>-1.0</td><td>-15.743634596765638</td><td>-1.0</td><td>-15.96171604376317</td><td>-1.0</td><td>-14.903166065737095</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>0.0</td><td>-1.0</td><td>-13.153517514506246</td><td>-1.0</td><td>-13.82835637771638</td><td>-1.0</td><td>-14.27680360925855</td><td>-1.0</td><td>QUASAR</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<Table length=87822>\n",
              "    SDSS_NAME        Z_VI        LOGF_CIV      ... LOGF5100_ERR class \n",
              "      str18        float64       float64       ...   float64     str6 \n",
              "------------------ ------- ------------------- ... ------------ ------\n",
              "000000.47-002703.9    0.25                 0.0 ...         -1.0 QUASAR\n",
              "000001.93-001427.4   2.163 -14.365271657331485 ...         -1.0 QUASAR\n",
              "000003.17+011510.6   2.354 -14.665869568966045 ...         -1.0 QUASAR\n",
              "000004.61+035122.4   2.795 -14.668890299079425 ...         -1.0 QUASAR\n",
              "000004.96-001642     2.879 -14.931736862166844 ...         -1.0 QUASAR\n",
              "000005.02-003829     2.662 -14.737058325267366 ...         -1.0 QUASAR\n",
              "000007.58+002943.2   2.254 -14.254588412916753 ...         -1.0 QUASAR\n",
              "000009.27+020622     1.432 -14.019072405422975 ...         -1.0 QUASAR\n",
              "000013.03+044403.5   2.624  -14.85711908311641 ...         -1.0 QUASAR\n",
              "000014.07+012951.5   3.228  -14.45711661063147 ...         -1.0 QUASAR\n",
              "               ...     ...                 ... ...          ...    ...\n",
              "235944.64+003048.1   2.789 -14.815718997527151 ...         -1.0 QUASAR\n",
              "235944.94+022906.8   2.258 -14.561058653962197 ...         -1.0 QUASAR\n",
              "235945.2-005057.5     0.58                 0.0 ...         -1.0 QUASAR\n",
              "235950.11+025229.3   2.244 -14.672064227287235 ...         -1.0 QUASAR\n",
              "235954.02+031518     2.371 -14.647534926959448 ...         -1.0 QUASAR\n",
              "235954.98+015621.6    1.85 -14.882308172500442 ...         -1.0 QUASAR\n",
              "235955.76-011409.2   2.713 -13.960003598671019 ...         -1.0 QUASAR\n",
              "235957.06-002243.7   3.703 -14.655578969763418 ...         -1.0 QUASAR\n",
              "235958.62+002551.4   3.066 -14.730504921308112 ...         -1.0 QUASAR\n",
              "235959.1+024906.7    2.398 -14.878827780912196 ...         -1.0 QUASAR"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlaotW_6OPuL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "outputId": "ad9d8a08-fe22-4415-a8b1-6b015146b70e"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from mpl_toolkits import mplot3d\n",
        "%matplotlib inline\n",
        "data_quasar=QuasarTable.to_pandas()\n",
        "sns.pairplot(data_quasar,hue='class' , diag_kind = 'hist')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-c09016b69467>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdata_quasar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mQuasarTable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pandas\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[0;32m----> 7\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_quasar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'class'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'hist'\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/python3.6/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[1;32m   2126\u001b[0m     \u001b[0;31m# Add a legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhue\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2128\u001b[0;31m         \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_legend\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[0m\u001b[1;32m   2129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2130\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36madd_legend\u001b[0;34m(self, legend_data, title, label_order, **kwargs)\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0;31m# Draw the plot to set the bounding boxes correctly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"get_renderer\"\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[0;32m---> 94\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;31m# Calculate and set the new width of the figure so the legend fits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1649\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1651\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2626\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2628\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2630\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    881\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\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    882\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    322\u001b[0m             renderer.draw_markers(\n\u001b[1;32m    323\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcombined_transform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\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[0;32m--> 324\u001b[0;31m                 mpath.Path(offsets), transOffset, tuple(facecolors[0]))\n\u001b[0m\u001b[1;32m    325\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             renderer.draw_path_collection(\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "text": [
            "Error in callback <function flush_figures at 0x7fdc829362f0> (for post_execute):\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mshow\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[0m\u001b[1;32m    114\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m             \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\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[0;32m---> 36\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[0;34m=\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[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(*objs, **kwargs)\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m</usr/local/lib/python3.6/dist-packages/decorator.py:decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0m\u001b[1;32m    242\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\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[0;32m--> 125\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \"\"\"\n\u001b[0;32m--> 510\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    511\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\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    512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    400\u001b[0m         \u001b[0mtoolbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoolbar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    403\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1649\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1651\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2626\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2628\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2630\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    881\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\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    882\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    322\u001b[0m             renderer.draw_markers(\n\u001b[1;32m    323\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcombined_transform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\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[0;32m--> 324\u001b[0;31m                 mpath.Path(offsets), transOffset, tuple(facecolors[0]))\n\u001b[0m\u001b[1;32m    325\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             renderer.draw_path_collection(\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Yazn3ss8nKS",
        "colab_type": "code",
        "outputId": "c8d54ce6-9a35-4e5f-da8f-e0ba29794801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "source": [
        "QuasarTable2 = table['SDSS_NAME', 'Z_VI', 'LOGF_CIV', 'LOGF_CIII', \n",
        "                    'LOGF_ALIII', 'LOGF_SIIII', 'LOGF_MGII','LOGF_BROAD_HB',\n",
        "                     'LOGF_NARROW_HB','LOGF_OIII_5007']\n",
        "\n",
        "QuasarTable2['class']='QUASAR'\n",
        "QuasarTable2"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<i>Table length=87822</i>\n",
              "<table id=\"table140584358947192\" class=\"table-striped table-bordered table-condensed\">\n",
              "<thead><tr><th>SDSS_NAME</th><th>Z_VI</th><th>LOGF_CIV</th><th>LOGF_CIII</th><th>LOGF_ALIII</th><th>LOGF_SIIII</th><th>LOGF_MGII</th><th>LOGF_BROAD_HB</th><th>LOGF_NARROW_HB</th><th>LOGF_OIII_5007</th><th>class</th></tr></thead>\n",
              "<thead><tr><th>str18</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str6</th></tr></thead>\n",
              "<tr><td>000000.47-002703.9</td><td>0.25</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-14.65182438231868</td><td>-16.110668291496495</td><td>-15.04412284248246</td><td>QUASAR</td></tr>\n",
              "<tr><td>000001.93-001427.4</td><td>2.163</td><td>-14.365271657331485</td><td>-15.148776656013794</td><td>-15.615987159317584</td><td>-14.696218911079507</td><td>-15.051001058208485</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000003.17+011510.6</td><td>2.354</td><td>-14.665869568966045</td><td>-14.992532227472173</td><td>-15.319727770673868</td><td>0.0</td><td>-15.341535565628329</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000004.61+035122.4</td><td>2.795</td><td>-14.668890299079425</td><td>-15.211973046561669</td><td>-15.79280866543717</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000004.96-001642</td><td>2.879</td><td>-14.931736862166844</td><td>-15.242458029703393</td><td>-15.81921541286709</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000005.02-003829</td><td>2.662</td><td>-14.737058325267366</td><td>-15.052214167147367</td><td>-15.312190374673959</td><td>-15.847419215921606</td><td>-14.202602873642363</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000007.58+002943.2</td><td>2.254</td><td>-14.254588412916753</td><td>-14.97676990106867</td><td>-16.171391997120104</td><td>0.0</td><td>-14.704092143246513</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000009.27+020622</td><td>1.432</td><td>-14.019072405422975</td><td>-14.29541770583723</td><td>-15.069484901680504</td><td>0.0</td><td>-14.639071121772703</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000013.03+044403.5</td><td>2.624</td><td>-14.85711908311641</td><td>-14.966410116104147</td><td>-15.409649599110633</td><td>0.0</td><td>-15.101606422508821</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>000014.07+012951.5</td><td>3.228</td><td>-14.45711661063147</td><td>-15.049040338002785</td><td>-15.074710035933634</td><td>-15.343812447115951</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
              "<tr><td>235944.64+003048.1</td><td>2.789</td><td>-14.815718997527151</td><td>-15.266891627976117</td><td>-16.273498530079863</td><td>-31.204675654961274</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235944.94+022906.8</td><td>2.258</td><td>-14.561058653962197</td><td>-14.980824894014946</td><td>-15.310294733295795</td><td>0.0</td><td>-14.886679518625376</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235945.2-005057.5</td><td>0.58</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-14.815946476652675</td><td>-13.866996397833208</td><td>-16.11920715881954</td><td>-15.422876927583314</td><td>QUASAR</td></tr>\n",
              "<tr><td>235950.11+025229.3</td><td>2.244</td><td>-14.672064227287235</td><td>-15.857069245973666</td><td>-15.880715241500672</td><td>-15.403514844937186</td><td>-15.34828180668993</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235954.02+031518</td><td>2.371</td><td>-14.647534926959448</td><td>-14.88702962051966</td><td>-15.320430498344185</td><td>0.0</td><td>-15.064134568959416</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235954.98+015621.6</td><td>1.85</td><td>-14.882308172500442</td><td>-15.530663714697122</td><td>-15.94045930379572</td><td>-16.05482657361624</td><td>-15.726596131548773</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235955.76-011409.2</td><td>2.713</td><td>-13.960003598671019</td><td>-14.573373819080524</td><td>-15.312225383426972</td><td>-15.039208753851977</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235957.06-002243.7</td><td>3.703</td><td>-14.655578969763418</td><td>-15.554480802364132</td><td>0.0</td><td>-31.71573526378395</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235958.62+002551.4</td><td>3.066</td><td>-14.730504921308112</td><td>-15.296424400026885</td><td>-16.03623873887053</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "<tr><td>235959.1+024906.7</td><td>2.398</td><td>-14.878827780912196</td><td>-15.448628834906058</td><td>-15.743634596765638</td><td>-15.96171604376317</td><td>-14.903166065737095</td><td>0.0</td><td>0.0</td><td>0.0</td><td>QUASAR</td></tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<Table length=87822>\n",
              "    SDSS_NAME        Z_VI        LOGF_CIV      ...    LOGF_OIII_5007   class \n",
              "      str18        float64       float64       ...       float64        str6 \n",
              "------------------ ------- ------------------- ... ------------------- ------\n",
              "000000.47-002703.9    0.25                 0.0 ...  -15.04412284248246 QUASAR\n",
              "000001.93-001427.4   2.163 -14.365271657331485 ...                 0.0 QUASAR\n",
              "000003.17+011510.6   2.354 -14.665869568966045 ...                 0.0 QUASAR\n",
              "000004.61+035122.4   2.795 -14.668890299079425 ...                 0.0 QUASAR\n",
              "000004.96-001642     2.879 -14.931736862166844 ...                 0.0 QUASAR\n",
              "000005.02-003829     2.662 -14.737058325267366 ...                 0.0 QUASAR\n",
              "000007.58+002943.2   2.254 -14.254588412916753 ...                 0.0 QUASAR\n",
              "000009.27+020622     1.432 -14.019072405422975 ...                 0.0 QUASAR\n",
              "000013.03+044403.5   2.624  -14.85711908311641 ...                 0.0 QUASAR\n",
              "000014.07+012951.5   3.228  -14.45711661063147 ...                 0.0 QUASAR\n",
              "               ...     ...                 ... ...                 ...    ...\n",
              "235944.64+003048.1   2.789 -14.815718997527151 ...                 0.0 QUASAR\n",
              "235944.94+022906.8   2.258 -14.561058653962197 ...                 0.0 QUASAR\n",
              "235945.2-005057.5     0.58                 0.0 ... -15.422876927583314 QUASAR\n",
              "235950.11+025229.3   2.244 -14.672064227287235 ...                 0.0 QUASAR\n",
              "235954.02+031518     2.371 -14.647534926959448 ...                 0.0 QUASAR\n",
              "235954.98+015621.6    1.85 -14.882308172500442 ...                 0.0 QUASAR\n",
              "235955.76-011409.2   2.713 -13.960003598671019 ...                 0.0 QUASAR\n",
              "235957.06-002243.7   3.703 -14.655578969763418 ...                 0.0 QUASAR\n",
              "235958.62+002551.4   3.066 -14.730504921308112 ...                 0.0 QUASAR\n",
              "235959.1+024906.7    2.398 -14.878827780912196 ...                 0.0 QUASAR"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8STV0VIC9wxH",
        "colab_type": "code",
        "outputId": "68249b94-3a2f-41d7-fb95-46e0c7c0a0db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from mpl_toolkits import mplot3d\n",
        "%matplotlib inline\n",
        "data_quasar=QuasarTable2.to_pandas()\n",
        "sns.pairplot(data_quasar, hue='class', diag_kind = 'hist')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-1773b09b0bf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdata_quasar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mQuasarTable2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pandas\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[0;32m----> 7\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_quasar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'hist'\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/python3.6/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[1;32m   2126\u001b[0m     \u001b[0;31m# Add a legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhue\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2128\u001b[0;31m         \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_legend\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[0m\u001b[1;32m   2129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2130\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36madd_legend\u001b[0;34m(self, legend_data, title, label_order, **kwargs)\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0;31m# Draw the plot to set the bounding boxes correctly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"get_renderer\"\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[0;32m---> 94\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;31m# Calculate and set the new width of the figure so the legend fits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1649\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1651\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2626\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2628\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2630\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    881\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\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    882\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    322\u001b[0m             renderer.draw_markers(\n\u001b[1;32m    323\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcombined_transform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\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[0;32m--> 324\u001b[0;31m                 mpath.Path(offsets), transOffset, tuple(facecolors[0]))\n\u001b[0m\u001b[1;32m    325\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             renderer.draw_path_collection(\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "text": [
            "Error in callback <function flush_figures at 0x7fdc829362f0> (for post_execute):\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mshow\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[0m\u001b[1;32m    114\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m             \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\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[0;32m---> 36\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[0;34m=\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[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(*objs, **kwargs)\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m</usr/local/lib/python3.6/dist-packages/decorator.py:decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0m\u001b[1;32m    242\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\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[0;32m--> 125\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2047\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2048\u001b[0m                         \u001b[0mdryrun\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-> 2049\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   2050\u001b[0m                     \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2051\u001b[0m                     \u001b[0mbbox_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bbox_extra_artists\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \"\"\"\n\u001b[0;32m--> 510\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    511\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\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    512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    400\u001b[0m         \u001b[0mtoolbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoolbar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    403\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2626\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2628\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2630\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    881\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\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    882\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    322\u001b[0m             renderer.draw_markers(\n\u001b[1;32m    323\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcombined_transform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\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[0;32m--> 324\u001b[0;31m                 mpath.Path(offsets), transOffset, tuple(facecolors[0]))\n\u001b[0m\u001b[1;32m    325\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             renderer.draw_path_collection(\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-J254BWEDs9",
        "colab_type": "code",
        "outputId": "8835e3f5-9187-41f3-fdac-7b10939da1f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "data_quasar"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SDSS_NAME</th>\n",
              "      <th>Z_VI</th>\n",
              "      <th>LOGF_CIV</th>\n",
              "      <th>LOGF_CIII</th>\n",
              "      <th>LOGF_ALIII</th>\n",
              "      <th>LOGF_SIIII</th>\n",
              "      <th>LOGF_MGII</th>\n",
              "      <th>LOGF_BROAD_HB</th>\n",
              "      <th>LOGF_NARROW_HB</th>\n",
              "      <th>LOGF_OIII_5007</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>000000.47-002703.9</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>-14.651824</td>\n",
              "      <td>-16.110668</td>\n",
              "      <td>-15.044123</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000001.93-001427.4</td>\n",
              "      <td>2.163</td>\n",
              "      <td>-14.365272</td>\n",
              "      <td>-15.148777</td>\n",
              "      <td>-15.615987</td>\n",
              "      <td>-14.696219</td>\n",
              "      <td>-15.051001</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>000003.17+011510.6</td>\n",
              "      <td>2.354</td>\n",
              "      <td>-14.665870</td>\n",
              "      <td>-14.992532</td>\n",
              "      <td>-15.319728</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.341536</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>000004.61+035122.4</td>\n",
              "      <td>2.795</td>\n",
              "      <td>-14.668890</td>\n",
              "      <td>-15.211973</td>\n",
              "      <td>-15.792809</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000004.96-001642</td>\n",
              "      <td>2.879</td>\n",
              "      <td>-14.931737</td>\n",
              "      <td>-15.242458</td>\n",
              "      <td>-15.819215</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>000005.02-003829</td>\n",
              "      <td>2.662</td>\n",
              "      <td>-14.737058</td>\n",
              "      <td>-15.052214</td>\n",
              "      <td>-15.312190</td>\n",
              "      <td>-15.847419</td>\n",
              "      <td>-14.202603</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>000007.58+002943.2</td>\n",
              "      <td>2.254</td>\n",
              "      <td>-14.254588</td>\n",
              "      <td>-14.976770</td>\n",
              "      <td>-16.171392</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.704092</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>000009.27+020622</td>\n",
              "      <td>1.432</td>\n",
              "      <td>-14.019072</td>\n",
              "      <td>-14.295418</td>\n",
              "      <td>-15.069485</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.639071</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>000013.03+044403.5</td>\n",
              "      <td>2.624</td>\n",
              "      <td>-14.857119</td>\n",
              "      <td>-14.966410</td>\n",
              "      <td>-15.409650</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.101606</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>000014.07+012951.5</td>\n",
              "      <td>3.228</td>\n",
              "      <td>-14.457117</td>\n",
              "      <td>-15.049040</td>\n",
              "      <td>-15.074710</td>\n",
              "      <td>-15.343812</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>000015.17+004833.2</td>\n",
              "      <td>3.028</td>\n",
              "      <td>-14.198450</td>\n",
              "      <td>-14.747883</td>\n",
              "      <td>-15.899388</td>\n",
              "      <td>-15.836534</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>000016.49+022715.1</td>\n",
              "      <td>0.885</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.348638</td>\n",
              "      <td>-15.103369</td>\n",
              "      <td>-16.108961</td>\n",
              "      <td>-15.293289</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>000017.97+002426.9</td>\n",
              "      <td>2.281</td>\n",
              "      <td>-14.564166</td>\n",
              "      <td>-15.173194</td>\n",
              "      <td>-16.328882</td>\n",
              "      <td>-15.526432</td>\n",
              "      <td>-15.023266</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>000018.12+013637.1</td>\n",
              "      <td>2.400</td>\n",
              "      <td>-14.472973</td>\n",
              "      <td>-15.231678</td>\n",
              "      <td>-15.931496</td>\n",
              "      <td>-15.877745</td>\n",
              "      <td>-15.178575</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>000018.18+050803.6</td>\n",
              "      <td>2.227</td>\n",
              "      <td>-14.321572</td>\n",
              "      <td>-14.702217</td>\n",
              "      <td>-15.691818</td>\n",
              "      <td>-15.283966</td>\n",
              "      <td>-15.105398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>000018.64+033254.1</td>\n",
              "      <td>2.238</td>\n",
              "      <td>-14.371299</td>\n",
              "      <td>-14.850127</td>\n",
              "      <td>-15.245404</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.534885</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>000019.59+011504.9</td>\n",
              "      <td>1.302</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.168325</td>\n",
              "      <td>-17.469639</td>\n",
              "      <td>-31.421413</td>\n",
              "      <td>-14.938278</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>000020.44-005040.4</td>\n",
              "      <td>2.735</td>\n",
              "      <td>-14.370292</td>\n",
              "      <td>-14.849782</td>\n",
              "      <td>-15.450420</td>\n",
              "      <td>-16.405958</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>000020.59+032357.1</td>\n",
              "      <td>2.903</td>\n",
              "      <td>-14.523564</td>\n",
              "      <td>-15.810633</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-30.590839</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>000024.58+013823.6</td>\n",
              "      <td>2.401</td>\n",
              "      <td>-14.744036</td>\n",
              "      <td>-15.259463</td>\n",
              "      <td>-15.555151</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.148757</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>000026.27+022140.8</td>\n",
              "      <td>0.770</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.805334</td>\n",
              "      <td>-14.954644</td>\n",
              "      <td>-16.582483</td>\n",
              "      <td>-15.711293</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>000026.65-001147.6</td>\n",
              "      <td>2.314</td>\n",
              "      <td>-14.918437</td>\n",
              "      <td>-15.424063</td>\n",
              "      <td>-15.643425</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-16.066548</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>000026.75+020911.7</td>\n",
              "      <td>2.237</td>\n",
              "      <td>-14.294118</td>\n",
              "      <td>-14.829801</td>\n",
              "      <td>-15.490664</td>\n",
              "      <td>-15.263438</td>\n",
              "      <td>-14.881000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>000027.01+030715.5</td>\n",
              "      <td>2.343</td>\n",
              "      <td>-13.984467</td>\n",
              "      <td>-14.124638</td>\n",
              "      <td>-15.289569</td>\n",
              "      <td>-15.488623</td>\n",
              "      <td>-14.377969</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>000027.31+013126.1</td>\n",
              "      <td>2.589</td>\n",
              "      <td>-14.636703</td>\n",
              "      <td>-15.319518</td>\n",
              "      <td>-15.991553</td>\n",
              "      <td>-15.610863</td>\n",
              "      <td>-15.118730</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>000029.23+015122.7</td>\n",
              "      <td>0.852</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-13.481897</td>\n",
              "      <td>-13.119765</td>\n",
              "      <td>-15.045342</td>\n",
              "      <td>-14.940669</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>000029.99+004845.3</td>\n",
              "      <td>2.412</td>\n",
              "      <td>-14.870530</td>\n",
              "      <td>-15.291307</td>\n",
              "      <td>-16.347170</td>\n",
              "      <td>-16.574561</td>\n",
              "      <td>-15.550596</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>000030.78+015221.1</td>\n",
              "      <td>2.760</td>\n",
              "      <td>-15.369485</td>\n",
              "      <td>-15.831751</td>\n",
              "      <td>-15.894687</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>000031.27-010350.7</td>\n",
              "      <td>2.416</td>\n",
              "      <td>-14.634846</td>\n",
              "      <td>-15.102484</td>\n",
              "      <td>-15.979819</td>\n",
              "      <td>-15.841510</td>\n",
              "      <td>-15.142708</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>000031.84+014108.7</td>\n",
              "      <td>2.526</td>\n",
              "      <td>-14.548181</td>\n",
              "      <td>-15.292006</td>\n",
              "      <td>-15.531825</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.564593</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87792</th>\n",
              "      <td>235919.13+001027.1</td>\n",
              "      <td>2.922</td>\n",
              "      <td>-14.189280</td>\n",
              "      <td>-14.723163</td>\n",
              "      <td>-15.106097</td>\n",
              "      <td>-14.735928</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87793</th>\n",
              "      <td>235919.15-003121</td>\n",
              "      <td>0.836</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.833339</td>\n",
              "      <td>-15.788549</td>\n",
              "      <td>-15.889940</td>\n",
              "      <td>-15.441966</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87794</th>\n",
              "      <td>235920.13-021607.9</td>\n",
              "      <td>2.611</td>\n",
              "      <td>-15.104336</td>\n",
              "      <td>-15.580091</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.711475</td>\n",
              "      <td>-14.836421</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87795</th>\n",
              "      <td>235920.2-001334.8</td>\n",
              "      <td>0.741</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.349213</td>\n",
              "      <td>-15.743959</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-16.337864</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87796</th>\n",
              "      <td>235921.15+004858.8</td>\n",
              "      <td>3.063</td>\n",
              "      <td>-15.181121</td>\n",
              "      <td>-15.803694</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87797</th>\n",
              "      <td>235923.01+011319.3</td>\n",
              "      <td>3.553</td>\n",
              "      <td>-14.907150</td>\n",
              "      <td>-14.597860</td>\n",
              "      <td>-32.039206</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87798</th>\n",
              "      <td>235923.21+025439.6</td>\n",
              "      <td>2.531</td>\n",
              "      <td>-13.796802</td>\n",
              "      <td>-14.026279</td>\n",
              "      <td>-15.286768</td>\n",
              "      <td>-14.790754</td>\n",
              "      <td>-14.437488</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87799</th>\n",
              "      <td>235925.23-004634.4</td>\n",
              "      <td>1.776</td>\n",
              "      <td>-14.852847</td>\n",
              "      <td>-15.338852</td>\n",
              "      <td>-16.854342</td>\n",
              "      <td>-15.722886</td>\n",
              "      <td>-15.401342</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87800</th>\n",
              "      <td>235927.16-000204.9</td>\n",
              "      <td>2.368</td>\n",
              "      <td>-14.111936</td>\n",
              "      <td>-14.893077</td>\n",
              "      <td>-14.845479</td>\n",
              "      <td>-15.017135</td>\n",
              "      <td>-14.743466</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87801</th>\n",
              "      <td>235928.07+042531.6</td>\n",
              "      <td>2.261</td>\n",
              "      <td>-14.193205</td>\n",
              "      <td>-15.013705</td>\n",
              "      <td>-15.514231</td>\n",
              "      <td>-15.468155</td>\n",
              "      <td>-14.779040</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87802</th>\n",
              "      <td>235928.1+022421.8</td>\n",
              "      <td>1.058</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-13.840549</td>\n",
              "      <td>-15.006239</td>\n",
              "      <td>-12.835784</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87803</th>\n",
              "      <td>235929.03-002918.5</td>\n",
              "      <td>2.195</td>\n",
              "      <td>-14.608356</td>\n",
              "      <td>-15.250904</td>\n",
              "      <td>-15.967147</td>\n",
              "      <td>-15.447465</td>\n",
              "      <td>-15.268091</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87804</th>\n",
              "      <td>235931.16+034323.6</td>\n",
              "      <td>1.546</td>\n",
              "      <td>-14.319398</td>\n",
              "      <td>-14.860855</td>\n",
              "      <td>-15.455981</td>\n",
              "      <td>-15.571055</td>\n",
              "      <td>-14.979004</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87805</th>\n",
              "      <td>235932.89-000000.2</td>\n",
              "      <td>2.762</td>\n",
              "      <td>-14.605369</td>\n",
              "      <td>-15.270674</td>\n",
              "      <td>-15.803473</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87806</th>\n",
              "      <td>235934.63-002202.5</td>\n",
              "      <td>2.233</td>\n",
              "      <td>-15.007834</td>\n",
              "      <td>-15.577095</td>\n",
              "      <td>-16.579470</td>\n",
              "      <td>-15.780650</td>\n",
              "      <td>-16.030207</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87807</th>\n",
              "      <td>235936.2-001156.7</td>\n",
              "      <td>1.049</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.906271</td>\n",
              "      <td>-16.229641</td>\n",
              "      <td>-16.047630</td>\n",
              "      <td>-15.473576</td>\n",
              "      <td>-14.822056</td>\n",
              "      <td>-15.554475</td>\n",
              "      <td>-15.618147</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87808</th>\n",
              "      <td>235937.64-004251</td>\n",
              "      <td>1.714</td>\n",
              "      <td>-15.300053</td>\n",
              "      <td>-15.727678</td>\n",
              "      <td>-15.642368</td>\n",
              "      <td>-15.442326</td>\n",
              "      <td>-15.618670</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87809</th>\n",
              "      <td>235938.4-003448.4</td>\n",
              "      <td>1.206</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.225639</td>\n",
              "      <td>-15.026107</td>\n",
              "      <td>-15.002220</td>\n",
              "      <td>-13.843695</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87810</th>\n",
              "      <td>235943.43+002008.8</td>\n",
              "      <td>2.578</td>\n",
              "      <td>-14.723900</td>\n",
              "      <td>-15.442295</td>\n",
              "      <td>-15.941582</td>\n",
              "      <td>-15.679358</td>\n",
              "      <td>-15.378678</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87811</th>\n",
              "      <td>235944.03+035922.3</td>\n",
              "      <td>2.975</td>\n",
              "      <td>-14.985885</td>\n",
              "      <td>-15.271020</td>\n",
              "      <td>-15.933995</td>\n",
              "      <td>-16.023696</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87812</th>\n",
              "      <td>235944.64+003048.1</td>\n",
              "      <td>2.789</td>\n",
              "      <td>-14.815719</td>\n",
              "      <td>-15.266892</td>\n",
              "      <td>-16.273499</td>\n",
              "      <td>-31.204676</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87813</th>\n",
              "      <td>235944.94+022906.8</td>\n",
              "      <td>2.258</td>\n",
              "      <td>-14.561059</td>\n",
              "      <td>-14.980825</td>\n",
              "      <td>-15.310295</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.886680</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87814</th>\n",
              "      <td>235945.2-005057.5</td>\n",
              "      <td>0.580</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14.815946</td>\n",
              "      <td>-13.866996</td>\n",
              "      <td>-16.119207</td>\n",
              "      <td>-15.422877</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87815</th>\n",
              "      <td>235950.11+025229.3</td>\n",
              "      <td>2.244</td>\n",
              "      <td>-14.672064</td>\n",
              "      <td>-15.857069</td>\n",
              "      <td>-15.880715</td>\n",
              "      <td>-15.403515</td>\n",
              "      <td>-15.348282</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87816</th>\n",
              "      <td>235954.02+031518</td>\n",
              "      <td>2.371</td>\n",
              "      <td>-14.647535</td>\n",
              "      <td>-14.887030</td>\n",
              "      <td>-15.320430</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-15.064135</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87817</th>\n",
              "      <td>235954.98+015621.6</td>\n",
              "      <td>1.850</td>\n",
              "      <td>-14.882308</td>\n",
              "      <td>-15.530664</td>\n",
              "      <td>-15.940459</td>\n",
              "      <td>-16.054827</td>\n",
              "      <td>-15.726596</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87818</th>\n",
              "      <td>235955.76-011409.2</td>\n",
              "      <td>2.713</td>\n",
              "      <td>-13.960004</td>\n",
              "      <td>-14.573374</td>\n",
              "      <td>-15.312225</td>\n",
              "      <td>-15.039209</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87819</th>\n",
              "      <td>235957.06-002243.7</td>\n",
              "      <td>3.703</td>\n",
              "      <td>-14.655579</td>\n",
              "      <td>-15.554481</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-31.715735</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87820</th>\n",
              "      <td>235958.62+002551.4</td>\n",
              "      <td>3.066</td>\n",
              "      <td>-14.730505</td>\n",
              "      <td>-15.296424</td>\n",
              "      <td>-16.036239</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87821</th>\n",
              "      <td>235959.1+024906.7</td>\n",
              "      <td>2.398</td>\n",
              "      <td>-14.878828</td>\n",
              "      <td>-15.448629</td>\n",
              "      <td>-15.743635</td>\n",
              "      <td>-15.961716</td>\n",
              "      <td>-14.903166</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>QUASAR</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>87822 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                SDSS_NAME   Z_VI  ...  LOGF_OIII_5007   class\n",
              "0      000000.47-002703.9  0.250  ...      -15.044123  QUASAR\n",
              "1      000001.93-001427.4  2.163  ...        0.000000  QUASAR\n",
              "2      000003.17+011510.6  2.354  ...        0.000000  QUASAR\n",
              "3      000004.61+035122.4  2.795  ...        0.000000  QUASAR\n",
              "4      000004.96-001642    2.879  ...        0.000000  QUASAR\n",
              "5      000005.02-003829    2.662  ...        0.000000  QUASAR\n",
              "6      000007.58+002943.2  2.254  ...        0.000000  QUASAR\n",
              "7      000009.27+020622    1.432  ...        0.000000  QUASAR\n",
              "8      000013.03+044403.5  2.624  ...        0.000000  QUASAR\n",
              "9      000014.07+012951.5  3.228  ...        0.000000  QUASAR\n",
              "10     000015.17+004833.2  3.028  ...        0.000000  QUASAR\n",
              "11     000016.49+022715.1  0.885  ...      -15.293289  QUASAR\n",
              "12     000017.97+002426.9  2.281  ...        0.000000  QUASAR\n",
              "13     000018.12+013637.1  2.400  ...        0.000000  QUASAR\n",
              "14     000018.18+050803.6  2.227  ...        0.000000  QUASAR\n",
              "15     000018.64+033254.1  2.238  ...        0.000000  QUASAR\n",
              "16     000019.59+011504.9  1.302  ...        0.000000  QUASAR\n",
              "17     000020.44-005040.4  2.735  ...        0.000000  QUASAR\n",
              "18     000020.59+032357.1  2.903  ...        0.000000  QUASAR\n",
              "19     000024.58+013823.6  2.401  ...        0.000000  QUASAR\n",
              "20     000026.27+022140.8  0.770  ...      -15.711293  QUASAR\n",
              "21     000026.65-001147.6  2.314  ...        0.000000  QUASAR\n",
              "22     000026.75+020911.7  2.237  ...        0.000000  QUASAR\n",
              "23     000027.01+030715.5  2.343  ...        0.000000  QUASAR\n",
              "24     000027.31+013126.1  2.589  ...        0.000000  QUASAR\n",
              "25     000029.23+015122.7  0.852  ...      -14.940669  QUASAR\n",
              "26     000029.99+004845.3  2.412  ...        0.000000  QUASAR\n",
              "27     000030.78+015221.1  2.760  ...        0.000000  QUASAR\n",
              "28     000031.27-010350.7  2.416  ...        0.000000  QUASAR\n",
              "29     000031.84+014108.7  2.526  ...        0.000000  QUASAR\n",
              "...                   ...    ...  ...             ...     ...\n",
              "87792  235919.13+001027.1  2.922  ...        0.000000  QUASAR\n",
              "87793  235919.15-003121    0.836  ...      -15.441966  QUASAR\n",
              "87794  235920.13-021607.9  2.611  ...        0.000000  QUASAR\n",
              "87795  235920.2-001334.8   0.741  ...      -16.337864  QUASAR\n",
              "87796  235921.15+004858.8  3.063  ...        0.000000  QUASAR\n",
              "87797  235923.01+011319.3  3.553  ...        0.000000  QUASAR\n",
              "87798  235923.21+025439.6  2.531  ...        0.000000  QUASAR\n",
              "87799  235925.23-004634.4  1.776  ...        0.000000  QUASAR\n",
              "87800  235927.16-000204.9  2.368  ...        0.000000  QUASAR\n",
              "87801  235928.07+042531.6  2.261  ...        0.000000  QUASAR\n",
              "87802  235928.1+022421.8   1.058  ...        0.000000  QUASAR\n",
              "87803  235929.03-002918.5  2.195  ...        0.000000  QUASAR\n",
              "87804  235931.16+034323.6  1.546  ...        0.000000  QUASAR\n",
              "87805  235932.89-000000.2  2.762  ...        0.000000  QUASAR\n",
              "87806  235934.63-002202.5  2.233  ...        0.000000  QUASAR\n",
              "87807  235936.2-001156.7   1.049  ...      -15.618147  QUASAR\n",
              "87808  235937.64-004251    1.714  ...        0.000000  QUASAR\n",
              "87809  235938.4-003448.4   1.206  ...        0.000000  QUASAR\n",
              "87810  235943.43+002008.8  2.578  ...        0.000000  QUASAR\n",
              "87811  235944.03+035922.3  2.975  ...        0.000000  QUASAR\n",
              "87812  235944.64+003048.1  2.789  ...        0.000000  QUASAR\n",
              "87813  235944.94+022906.8  2.258  ...        0.000000  QUASAR\n",
              "87814  235945.2-005057.5   0.580  ...      -15.422877  QUASAR\n",
              "87815  235950.11+025229.3  2.244  ...        0.000000  QUASAR\n",
              "87816  235954.02+031518    2.371  ...        0.000000  QUASAR\n",
              "87817  235954.98+015621.6  1.850  ...        0.000000  QUASAR\n",
              "87818  235955.76-011409.2  2.713  ...        0.000000  QUASAR\n",
              "87819  235957.06-002243.7  3.703  ...        0.000000  QUASAR\n",
              "87820  235958.62+002551.4  3.066  ...        0.000000  QUASAR\n",
              "87821  235959.1+024906.7   2.398  ...        0.000000  QUASAR\n",
              "\n",
              "[87822 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSlTAl9lSAse",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(24,24), ncols=3, nrows=3)\n",
        "\n",
        "redshift = QuasarTable2['Z_VI']\n",
        "civ_flux = QuasarTable2['LOGF_CIV']\n",
        "ciii_flux = QuasarTable2['LOGF_CIII']\n",
        "aliii_flux = QuasarTable2['LOGF_ALIII']\n",
        "siiii_flux = QuasarTable2['LOGF_SIIII']\n",
        "mgii_flux = QuasarTable2['LOGF_MGII']\n",
        "bhbeta_flux = QuasarTable2['LOGF_BROAD_HB']\n",
        "nhbeta_flux = QuasarTable2['LOGF_NARROW_HB']\n",
        "oiii_flux = QuasarTable2['LOGF_OIII_5007']\n",
        "\n",
        "n = 'Cantidad de cuásares'\n",
        "\n",
        "labels = [redshift, civ_flux, ciii_flux, aliii_flux, siiii_flux, mgii_flux, bhbeta_flux, nhbeta_flux, oiii_flux]\n",
        "labels_s = ['Redshift', 'C IV flux $( \\mathrm{erg/s/cm^2} )$', 'C III flux $( \\mathrm{erg/s/cm^2} )$', 'AL III flux $( \\mathrm{erg/s/cm^2} )$', 'S IIII flux $( \\mathrm{erg/s/cm^2} )$',\n",
        "            'MG II flux $( \\mathrm{erg/s/cm^2} )$', 'H BETA BROAD flux $( \\mathrm{erg/s/cm^2} )$', 'H BETA NARROW flux $( \\mathrm{erg/s/cm^2} )$', 'O III flux $( \\mathrm{erg/s/cm^2} )$']\n",
        "\n",
        "# REDSHIFT\n",
        "ax[0,0].hist(labels[0], color='r', label=labels_s[0], alpha=0.5, bins=50)\n",
        "ax[0,0].set_title('Redshift según cantidad de cuásares')\n",
        "ax[0,0].set_xlabel(labels_s[0])\n",
        "ax[0,0].set_ylabel(n)\n",
        "ax[0,0].legend(loc='best')\n",
        "\n",
        "# C IV FLUX\n",
        "ax[1,0].hist(labels[1], color='r', label=labels_s[1], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[1,0].set_title('Cantidad de cuásares según emisión de C IV en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[1,0].set_xlabel(labels_s[1])\n",
        "ax[1,0].set_ylabel(n)\n",
        "ax[1,0].legend(loc='best')\n",
        "\n",
        "# C III FLUX\n",
        "ax[2,0].hist(labels[2], color='r', label=labels_s[2], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[2,0].set_title('Cantidad de cuásares según emisión de C III en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[2,0].set_xlabel(labels_s[2])\n",
        "ax[2,0].set_ylabel(n)\n",
        "ax[2,0].legend(loc='best')\n",
        "\n",
        "# AL III FLUX\n",
        "ax[0,1].hist(labels[3], color='r', label=labels_s[3], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[0,1].set_title('Cantidad de cuásares según emisión de AL III en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[0,1].set_xlabel(labels_s[3])\n",
        "ax[0,1].set_ylabel(n)\n",
        "ax[0,1].legend(loc='best')\n",
        "\n",
        "# S IIII FLUX\n",
        "ax[1,1].hist(labels[4], color='r', label=labels_s[4], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[1,1].set_title('Cantidad de cuásares según emisión de S IIII en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[1,1].set_xlabel(labels_s[4])\n",
        "ax[1,1].set_ylabel(n)\n",
        "ax[1,1].legend(loc='best')\n",
        "\n",
        "# MG II FLUX\n",
        "ax[2,1].hist(labels[5], color='r', label=labels_s[5], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[2,1].set_title('Cantidad de cuásares según emisión de MG II en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[2,1].set_xlabel(labels_s[5])\n",
        "ax[2,1].set_ylabel(n)\n",
        "ax[2,1].legend(loc='best')\n",
        "\n",
        "# BROAD H BETA FLUX\n",
        "ax[0,2].hist(labels[6], color='r', label=labels_s[6], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[0,2].set_title('Cantidad de cuásares según emisión de H BETA (BROAD) en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[0,2].set_xlabel(labels_s[6])\n",
        "ax[0,2].set_ylabel(n)\n",
        "ax[0,2].legend(loc='best')\n",
        "\n",
        "# NARROW H BETA FLUX\n",
        "ax[1,2].hist(labels[7], color='r', label=labels_s[7], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[1,2].set_title('Cantidad de cuásares según emisión de H BETA (NARROW) en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[1,2].set_xlabel(labels_s[7])\n",
        "ax[1,2].set_ylabel(n)\n",
        "ax[1,2].legend(loc='best')\n",
        "\n",
        "# O III FLUX\n",
        "ax[2,2].hist(labels[8], color='r', label=labels_s[8], alpha=0.5, bins=50, range=(-20,-10))\n",
        "ax[2,2].set_title('Cantidad de cuásares según emisión de O III en $ \\mathrm{erg/s/cm^2} $')\n",
        "ax[2,2].set_xlabel(labels_s[8])\n",
        "ax[2,2].set_ylabel(n)\n",
        "ax[2,2].legend(loc='best')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxnDB12TAQLJ",
        "colab_type": "code",
        "outputId": "0ebb7138-ab5e-4433-eb3e-5a704027e080",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#Filtración de Datos:\n",
        "import numpy as np\n",
        "QuasarTable3=table['LOGF_CIV', 'LOGF_CIII', 'LOGF_ALIII', 'LOGF_SIIII', 'LOGF_MGII']\n",
        "QuasarTable3['class']='QUASAR'\n",
        "data_quasar2=QuasarTable3.to_pandas()\n",
        "sinCIV=data_quasar2[data_quasar2.LOGF_CIV != 0].shape[0]\n",
        "sinCIII= data_quasar2[data_quasar2.LOGF_CIII != 0].shape[0]\n",
        "sinALIII= data_quasar2[data_quasar2.LOGF_ALIII != 0].shape[0]\n",
        "sinSIII= data_quasar2[data_quasar2.LOGF_SIIII != 0].shape[0]\n",
        "sinMGII= data_quasar2[data_quasar2.LOGF_MGII != 0].shape[0]\n",
        "data_quasar2= data_quasar2[data_quasar2.LOGF_CIV != 0] #34361\n",
        "data_quasar2= data_quasar2[data_quasar2.LOGF_CIII != 0] #32070\n",
        "data_quasar2= data_quasar2[data_quasar2.LOGF_ALIII != 0] #34899\n",
        "data_quasar2= data_quasar2[data_quasar2.LOGF_SIIII != 0] #44605\n",
        "#data_quasar2= data_quasar2[data_quasar2.LOGF_MGII != 0] #45924\n",
        "data_quasar2.shape[0]"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "45924"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTXSDnx2JYHR",
        "colab_type": "code",
        "outputId": "4202cb04-3622-48e4-ca1d-1f21e9d727bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sns.pairplot(data_quasar2, hue='class', diag_kind = 'hist')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fdc56932278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zH1DTGKa2KXRYG0t3ndr6kNpA6nskE3zNl3m/NQTj1/CeaSPta7Hq1V3c3bIxsm5+OT/5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\n3ZRwOxN39AtWVsDrUnH/W1vgd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yZg0cWj4HWpeHL9dntanvWa57oyZxPY9ifR1pSxK5Zv\nwC0vbLKTYY1BLe4zect5w+1p79aJtmHKpLHYEtYBCfjdCppDJh7/63Zc8+SnCGkm3q2sQdiQdmwV\n5+dgQF93h8d5t7IGUspjir9jWe8rlWI/m0DHsWosl0OFKaX9Ofa7nWhq1bBk5lhMKS3CstnlWL1g\nIlbNm4D9LRFcsXwDdjcGE75vtYEwAiE97rbmsG63wZGGfcCh3mOZpEp+d2MIt6/ejOnjjscNz1XA\nMIF/1LeiMDe6yUuyRJP12IYp4VQVBLVoBcq9r3+JsUMKOvxNouNRZ/vVTOx/Ey0ab63NZsVvZy7o\nxi4yHdFNTCktsteyKcx148HLR2NHfSvue+srOwF065QRHS4m3blmM+66sBQep4Lfv78VIU3i7te+\niFt3a0ppUcZUY8WK7csvLy+GIhQEQgb0toRabAwnO08M6yYMU6Kfz4lp5SV2P/HA21X2ciSJig/u\nXLMZ08pLkONSURMIY/YTn0AiOobJxP42W3RrYkgI8Z4Q4osEXxcf4s/2ADheSjkGwE8APC+E6JPk\n8ecJITYKITbW1tYmfcDDHTwTJQ4WrKzAPT84FR//7NwOWfhEj2edeBT63YjoxhEvYOhQBUr6eTF3\n8lDc+PwmPPr+3yEEMPepT3Hhw/+LmY9/DLdTsQ/qs/7wCW57KTr169eXj8bz10/A8f2iCRzrpOHc\nh/5id2qaYSZNyFjZ9eqGIKSUqG0O4763vsIz147H2p+chWevHQ9A4v5pZSjwufDw2q0dqpaWzirH\nqk++RU0gfMj/41QVPNauImrx9IMnbdZte5tC9qDyWKXb1cDOxi1ROumOuE3UlxblulGcH12kftns\nctxw9jDc8NxnCfudwrb7xrLWdHjh+glYd9vZdvI8dj21TB6AtK9uUUT0JEZVolf/D4Q0FOa60Rox\nEr42qhB4bN02KEJgX1MYEV3aySBrwWnrNQQy96q0pTNxe6gEZfvBcmwyZNPORvzi9UoAQH+/GxHD\nxJIP/o6rJw1FSDu4RpOUErUZdCGi/esRO2XMWi/wwctG49TjcrF0VjmK873Y0xRNyIY0Iy5hW9cc\nThqLeTkuFPZxY9M/DkAzDi583hzWUZzvhSdmrRpFEfA6Hd1yQnEkCZae0pm4PZLPZoHPBacq7PdL\nVQR+/j9fYGAfF245bwQWvVGJ6Us/wtf7mnHzC9GqrPaJP+DgRVCrf7Bus8Z+y2aXw52Gawwd6j1O\n9vm3Eppq27SiiG7irf/bg5vOHQ6HkrjayjClPSb2e1S4HQJOVUFTUMO7lTUdYirTj0exOjtGSLZo\n/HF9PXb8Hu6CrmlKVNUEMKOtknPuU5/ipnOHQzNN3HzeCMx54hOc125h6TvXbMbAvolnPOxvieCq\nFR/j3coa+4KA9bs712zGvReNyqjjnsWK7TEleZg18QRc1ba74N6mkL1EiSXZ5701El3r0Tod+9Wb\nW6KJpeaD08qSJZUKfC60Rgz7HHDhc59hX1Oow1gu1f0tHdStvbeU8ntSylEJvl47xN+EpZT1bd9X\nANgGYESS+y6XUo6TUo4rLCxM2o7DHTyTJQ72NIWwv1XrsO6D9XgvzZ+I1Qsm4u6ppXjwnWiZ9R0X\njMQVyzcc8ZQlvW1RwBP7+7Bq3gTc8r3hHUr67ntrCx6dcTCpUtscRn+/Cz956XN894EP8E8PrMO2\nmuaEWVtVER1K3mOnoFkVO3uboqXx1lzS8379F5zz0F9QuSeApz/cjr5eJ2qbw/aUAusqaX+/C5ed\ncQJO6OeNa2P7/7N1X7O9Q87qBRPx0rwJKMx12x2MtY7AQ+9+HXdycizS7WpgZ+OWKJ10R9wm6puP\n6+vFM9eOtxPcNW1XtWKnMq2aNwHPXjseEd3o0K89OmMsHn5vK3Y3BvHjVX+Dy6HE9S+ZPgBpX91i\nmLLt5EbBslnlCGomnt+wA8fleTq8Nstml+Nv/6jHTecOR3VDK/rnuhDUdLuE3xoYLp1VjiL/wbn+\nmXhV2tKZuE100mglHdsPlpeu2xb3utY2h+F3q6gJhJDnjV5RffrD7djdtnbOYzPH4rEPtkE3MmML\nb6Dj69G+Mm/TzkY0BTVcufxjBEIaFl08Cn63A25Vgduhxk03+9WbW1Cc3zEWf3P5aCgCCP5/9u48\nPsry3v//67pny2QhCSFhCwoooBFRDCJoF1pbqxXlWHBHK7YCta1dXfrzS7WH2qNSj611AW2rdWmV\naj1abW3VVj11qQVRj0VRwQXcCCGBLJPZ7uv3xyxkIEiAJLO9n4/HPGAmmZlrJp/7vq/7c1/X54rE\nue7xN9InfPXVQVo7o1x7yiF4PeZj29VX23MuJj93p3/b25ElI6tKGTW4hLu+egQ+T2K/GI6RsepY\n99FdS59Y22N/7r6V6zOK0y+b28jBIwdx9/xpVJV6ieZgrH/c37imzM+yZMIMtm3/qYRmKtnzwZYQ\nxx08nPPveoH2cJSbtnvOTclk0JVfOpjrHn+dSMySyA9b/t/9r2Rc+Eg9J9+PR931NmZTReO7SyUr\nd3Ve1r3Wa/cp56mkg9dxdpjid/F9L7Nwxn5saAlRkqyJt/17b+xWOyd1QaD7fWv3fvZCNqRi+4Kj\nM88pU8Wiu2/f961cv0NML5kziVGDg3gceHdzom7eRcdOwOcxGcmdnSX/B5f5qS7zZYzYDsfcHqel\n5+P3W4hybmyzMaYW2GytjRtjxgLjgHV7+7qpg2dPdrYCQHNHhJufWsvlJ07EWptRHNRxDMMGlbAl\nFE3XG7j1nMN7XA2nNwW1Sv0OHWFL3Fp83sSy8D0Nmf7mZ8exeNZERg8pxdrEkLzuWVufx+k5E18V\nxO8x/H7BdLpiLm9v6siYgpZKbN369FtcdOwBLJ41MX0A29ASYukTa7nkuANY8pfX0oULF9yxMt3x\nbe6IcPx1/wASw86v/NLBjKwu7fF9rjnlEBY/tJplcxvTJ23Xnz6ZylI/b2/q4Mo/v0ZTe5hlcxsp\nC+x9YmhPV/8Rkf7X0765vMTL2b9+OeOqdSo5lNrvLJ41kXFDyxk7xMf1p08mHHNpDUW5/MF/09Qe\n5uiGoelk0uJZE9mvrpygL/8Lz29/vLrlqXV8+/PjiMZc4tYypDzAsv99m+ffbuWiYydwx7lTiVtL\n0OfBcaB0ZDU3PbGWUw4fRXN7hG/f82J6fz+4zMfyBdOpKw/gzcGr/v1l+0LgPq9DwGtYNreRnz/+\nekbR7ab2MEG/h5+deihVpT58HoeuaJyv/KbbakdzGxlc5ksUn31iLc+sa+bbn+/x+lZO2v77AHbo\nI6WSRfe/8B4LZ+zH5o4IW7uiDKnwc9Pf16aLQq9a30okWXMmFYsfbuniJ396jZ+ddihNbWGa2sPp\nESdL5zbS1hXlJ396jevPmAxlO29XXy4k8XF9xELhOIbKYCCxKlZrFzfNbSS23ZLt2+9v73ruHW4/\ndyqbOyI0d0TSdcmGlPl58sIZeB2Dz2MIxyweB37z9Nt8+cgxWfyUO7ezv7HjGIZVBRIXOSsCVAV9\nlPodRg1OnBjfu+JdbprbyC8ef50ffPFANrSEuOS+V1h21mTunj+NuGuJxFxueWody1duSL/upcc3\nUOr3MP/2VTS1h7nl7CmMqAz2S/zmk6Dfw5I5kzKK/S+ZMymjDtuuVmbbeeKo5/o1qcLSjgM3ndnI\n1+5amfHeVz+yJuN9upe3yOdRXY5jGFldQnmyOHxK6pzO6zEZhbjveOZtFs1s4IBhFVgLfq/B40BL\nZ5SrH1mTrjt0+7lT+c0zb3HXV4/AAk1bw1xz8iF87/cvZRwHQ5F4eiVU2PbdpvpyAE9f/Jmi2wZy\nWTaXqz8J+AVQCzxsjHnRWvsF4FPAfxpjooALLLTWbu7PtvSUOLhq9iQeWPUeXz5yDKcsezYjoZDK\nbG7fSYn3ME+5t1OWOsJxbnnqLb7z+XF0dMX5qK3n1ckGBX28v6WL797zEqvWt/KPi2ewbG5jukBg\nKmu7/fMcY9jQ0kXMdXnitY84Y9porjhpIqV+Lz/5U2Kjra8OcsHR46kp81Hq84Ah/dqr1rfyq3+s\nY9HMgzCG9MHQ4xiue+wNzvvU2IzOxNxfPc9TF80A4JLjDqA1FE0niPweh0UzG6gsTSyTXFHiI+5a\nnnztQ45uGM61pyaKIg4qScyFrQxu/23tnv7sTIpI3+s+KmbpE2t3WA1ryZxJDB1UwojkzmFzZ5Rv\nJKc/pE4ur3v8dSAxomNYZQn1VcGC2Oa3P161hiK0dEYJReLE3cTKWfXViSnHp9/yTyBxDLh7/jTW\nbuzgmr++TlN7mK/N2I/vLk904varLaM8uTy73+sUxPe0u3o6aSwb6uOyExLHvN+cOxUD+L0O//nH\nf/PX1RvTv3dMQx33zJ9GLHlM9HsNX7rx2Yx47D4CKx90/z42d4R3OJGrrQhwTEMdsyaP5Oxuq43d\nM38az6xr5qTDRqZPtCuDvoyr+7CtFs3NTyWSSDXlPm6bNxW/1/Bea4jaCn+PJ2PFkMDpb1VBPx9s\n6eKhVRs468gxGX3G1Ii41N/6mXXNnPepMQwu8+PzOPzgiwcClg+3dmGMobrUT1tXnA+2hLj92bf5\nyifGYvIwp2xIxNXCO1dSWx5gycmJldZK/R5OO2JfygJefvDFA/F7tpV0WNvUiddxGFZZwrzb/tVj\nfLd3RfnvUw8l6HOoqyhR/JKIv6GDSlg8ayKlfg+dkThDB5VQFdx2sXZXF3R3njhyeny8M5IYXfz1\nu1Yxrq6cW885PLmvdmjpiGSMKk4lAVP38/1C8qASP6GIm/G9pM7pLjvhoB32zc+sa+bu+dN446N2\nDhoxiLVN7Rn1hDa0hPAmi7Zf/uArVAX9nP+Z/fF7Tfo42NQW5sEXNzDzkJEZ321PSbh8TboVKmNt\n7q0esCemTJliV6xYscfPd93Ege791hDNHRGWPrGWhTP2Sy/TmVJfHdzpCKCmtjAn3fh0r3+/p+fe\nes7hbAlFueLhV7nsxIb0ChzdM+qp7GuqE3b7M2/xyfF1DK8Ksrk9gmttRtY2NVJn1fpW7l04Hb/X\n4cnXNnLcpBGEY3H8Hof2cIyNbWHuW7mey044iFNvfo4jx9bw/WMn0N4VoysaT544xPA4Hs65NdER\nXHZWI4sfWk1teYDvf2FCxsnbHV+ZSntXLL08fX11YjnPO599h2fWNfOzUw9lztJnM76Hpy6cQZnf\ngzUQjrr4vZ5sHEQH7Kxob+NW+s7oSx7e7ee8feXx/dCSPVYwcbv9vnTyqCouOHoc+9WW4XEMQb+H\nquC25K7rWpo7IunEb3XQR0soWrCJ4NTnDUVjeIzh1JufY9HMBvweh989/w5f+cTYjGPAjWcexvV/\ne4O/rt6YTlTc/szbLF+5gfrqIL87b1p6+djtL4AMgJyO241bu1i3qYPvJ7/PUxrrmTt934wlfW+a\n20jQ5xD0efB5HAI+aO9yiSWLz+b7CCzXtbzd3IFroSMcozLoI+AzRGKWM3/5z4w+z+8XTCfmutz6\n9FvpJcBrywNcdOyEjMTSTXMbGVLuw3WhNOAQjlrmLM1Mph0wtCKXv7ecjttdicVcPmzrwuPApvZo\nxrLrt55zOAGfg2sT5QWstTjGEI27VJR4ueHvbzLvqDEMqywh4HUSozU7o+k+5A9POIj66tI+bW9/\ne6+lk03tYSpKfGzuiFAZ9LElFMXnMVQGE/WZWjujDC5PjGy/8N5tcd091rvHdzQaZWhlKT6PoT0c\nZ3RNWS4chwakAbuK2e2P2T0doz/ud7Zfhj01VXp8bTlvburIfHxuI8OqAmzpjGUksbvPllg4Yz8O\nGFaBYwwBnwGbiPdC6T9s3BqiqX3b9LvU539vcwcHjqzK2P5vmtvIyrc2cdjoGkr9Hq5+5LWMCyH1\n1UH+cP6RRGMuR13194z3mTyqiv8+9RA2bk3Ultu/royAz0M05uLzOrR3Zf4NdqOvkd9/gDyixFA3\n2+9o7l04fYfEBSSGvY3s4aDX046qt0Gfem4k5vLh1i4WP7SaJXMm0RV1KfV7aA1FWfrEWprawyya\n2cDih1azdG4j1aW+9IY5eVQVC2fsx4jKEgaX+QnHXAzw4dYurn4kMVrnd+dNY2soSmnAQ6nPw7ub\nO/nO8pcykk+Dy3xE4vC15JWTy09sYHNHlOGVASpL/URiLp9e8kT6PVMJodryABccPY7RQ8oAS3tX\nDMcYqkp9xFybWPr3ycSw+tQV/e13NotnTQRg/NByXGsZWVWajR1yXnf4ZM8oMdR7/R23e7MvLSYf\nbQnRldwfTx5VxWUnNhCKxLn16bc4e/roxBRir4Pf6xCNuYRjLhvbwniM4eRlz6Y70z9/bMd9cW8u\naPSRnI7bVFLko61d6cTGgk+O5qwjxxB3E8tXL3/+Hf7jsFEFHZ+ua2kNRfigtYufP/46844aw8iq\nIJ9K9gVSJo+qYsnJk1i/OcTwygBlgcSJttdjqAwmRgd7HcOj//6AifXVjKwuIRq3/Pih1dmMwT2R\n03HbG7GYy3tbQvz2ubc5Y9pogIx+2q3zDqe9K5YuRl1fHeS2eYfj9zpYC17H0BGJce5tKzL6kOPq\nyhlSUdLn7e1PTW1hLr3/Zb752XFsao+wb00pb2xs576V6/neMRMIx1zqKgL4PIbNHYlCuqV+D44x\nDK/a9llT8f2HlRu45rE3+Nv3Po3rWs657V+5Es85kRjqCztLHO3q8VA0ztqN7Vz3+BsZU5zunj8N\nx5i8T+T35P3WEMZYXBe6Yi7vNndy3eNv0NQe5g9fm0Y0TnrEq2stsbhNT4O+66tHpC8AdO+LNXdE\nehwMsXzB9B3Kr6T0JiG4E4V5YM1BOVdjKJu2n3JkkgXSdjbHdVfP352gTz33w61d3PD3xOo7Po+T\nngrQ3bi6chbPmkhVqZfXP2rfof7GMQ11fOOz4zKuaKZGG13wu1Xp0Ua/O28aP/nTaxnzS69+ZA2X\nHHcA963cwG3zpuLzGEq8TnrFlUjMpSVZyb57QdjFsyayT00p7zZ3UuZ3iMYtZQEvcddy+zNvMWfK\nKPxeDwtn7Me3PjcOn8dwwdHjWf1BW7qN155yCH6vk5yznnjPQu1ki8jOafpn73TF3PT04VXrW/nt\nc+/yzaP35wdfPJC4a9nYFqbE52TM8Qd48sIZPHnhDPweB49Dxgk5ZHfVxlzjOIbRNWVUlfq4Z/40\n4hZKfA6Dg/70qLSvfmr/go9PxzEMLgtQFfTz45MOJpRcqWb7PlJtxbYpF1tCMdq64tRWBGjpjPDv\n97ey9Im1/PcphzC8uoybn1rLj06cSDgaUwxmgdfrMLIyyImT6znzl//kyLE1zP/0fnz9s/vzzaPH\nYbEMqizh7vnTCEddMLD8+Xf4/EHD+fY9L6YvHHafEpSoVZb15Mduqynz853PT+DaR9dw9vTR+DwO\n961cz5ePHMMfVq7n1Kn7EorGaW6P8+CLG5gzZR88yb7q/ckkUMr95x/JNY+9kZhO5jF8K3nxVfHc\ntz6uZtTHPe66lo5wbIdFKUZUFsZ08554HUMknqiFddWfX+Xs6aP56SmH8G5zJwvuWEVthZ9Lj2+g\nqS1R723pE2vTfQYD/Pa8I9i4NczwyhKGJ7+nnU33Gzao5GOL4OdAclQ+hhJD2+ketK5rd7to8d4E\nfaqgdergdOEXDugxMWWASNylqS2SXsK5+xDWS49vyBjenSoWduWXDs6YI5pamj5VACz1+q2hKM+s\na2Z2Yz2/+sc6fjRrIqFonPWbQ9RXlzCssoSb5jamhx42tYfxex2u+vOrXHD0eD7Y0sVdz73LiYeO\nYHhlkLOPHIPHMUTiLj6Pw/utXZT4HCpLfdw9fxpuMkvtOIamtjABn0dJIZEipw7Ernkdw7WPrkkX\n+j26YSin3/LPHY4Zi2Y2pPfz9dVB/B4Hx7FsaotQnVxBqrcXQIpRKinSvRAyUJTx6TiGuooSXNey\npSvMsrMaM6YnbN//gMwYrK8O8vrG9vSo582dkfRzFYMDz+t1OHDYIP5w/pF0huO8takjPZLglrOn\nUD+0FNe1bGwPY7CcOLmenz/2OotmNlBT5mdw8gaGkjwu7p+6GHHFSZOIxOIE/R6+87kJXPvYGmY3\njiIUjVMV9HHWnckyCv/7NrAttlNSK1ylpjBd//ib6YuxiufcUIwXnqLxxAySaDyeHhX35eSUrpTV\nH7SxeNbEHc4JvR6Hyx98hXlHjSHo96S/p2L8HouBEkMfIxtBn3rPy084CBfLjWceljHy56rZk/ju\n8kTh6VvPOTxj6fiqoI/OSJyY23MR7O5Lv9dXJ5aC757gqa9OLPXsGFg0s4Er//waq9a3ctkJlrFD\nyqkM+onE4rjW4mCTo5Z8VJT4CHgN589IFB8797ZV6aKFN555GD6vj9c/aGdIeWLZwrpBAZrawrzb\n3MmgoI8h5X6shUjMZXCZn2EVO882i4hIQl15gAuOHs8fX9zAb796BK6lx31/6mJG6mQl6rq0tEWo\nKPFRVx7Qqo2y2xzHUF1aQoXfn4g9EvVodtb/SK0KdMMZh+H1GG6bN5WKgIeFd75AbYW1Lk3wAAAg\nAElEQVQ/ndxUDA68dLKvLDHS+/ozJu+wCu+IqkSh/9pyl0UzDyISc3EMfLglTE25n9E1WZn236e2\nvxiRGh0Xjrq41u50gZnu+9cbzzyM6lIft55zOJG4m67jpnjOLcV24cnv9fD2pg78XoPXcRhbW9Zj\nLO9bU5pO0qdqvQV9htOn7rtDgXAovu+xGCgxtAvZCHrHMfi8Hn751JvMnT6aO74yFWvBGPivP72a\nvvowpNzPTWcextfueiF9Je7GMw8j8DFV+VP/v+XsKQwdVEJteYDlC6YTjbt4HcOPtlttJXWVo/v3\nEIu5eB2HqlI/UTcxF7W1M0pLZ5Tqcj8/PfkQDNAZiVPq99DWFSMSd/nF397gkuMOpLLUi9cpSc/F\n9jrOTuejiohIz3w+D+Nry/jykWMwDrjujsuK11cHqQz6uHfhdGorAtSUedgSchk1uDRdwFtX/WRP\neb0O9dWlbOkK09bl5d3mzh5jsG5QgEUzG7jh728w76gx1FY4BAMmnYSoDvoUg1nWm/6u1+swsiqY\nrhMyekhZwf6tUgkzgEgkxqbOaI+xXVXq55750+iMxLE20R/+xd/e4LITDuKpiz5Dic9hSJlGwUv2\n1JT5aeuK0taVKBJfXuLtMZbfbw2xeNZERg8pwzFQ5vewJRTnoJGDFMNFQsWnc1Sq+Oq1jyaGsdaU\n+RlZVYLHcQhF40TjllAkRlfUZVhlCY4BYwzGgAM0d0SY3214981nNVJT5icctzs9SO1JwdddrQaU\np6sD5X1RSdl9Kj7de4rb3JLaD3scywetYRZ0G3mxdG4jg8t8WAs1QT8lJTl3PUhxWyBiMZfNnRE2\ntoUzRv9ce8ohlAa8lAW8iRqCjmFwqR+fL6+n1ihui0w0Guf1pvaMqZPLzmqktsxPKObiGMOm9i5u\nemIt3/n8hFwtRl8wxadl96QWEAhF4jiOYXNH5iply+Y2UlnqJRRxuXfFu5w4uZ5hlQGqgzmREMp6\nA4qFEkM5bHcr7vfmuXv6nkVGHb4ipMRQ7yluc5frWja1h+mKxfEYg2PA4+R0MX/FbYFxXcumjjDh\nqIvHJEZeROIuJT5PIV11VtwWoVRsdyVj2+dJjHj3eZ3E6ruxnF/iXIkhATLP9wB8HkM0nsgJ5GAM\n50xDCt0uLx0aY4ZZaz8ciMZIpt2tuL+7v9OXzxMRkexzHEPdoPxaKloKS/cpOCKFRLEthULne9IT\nZ9e/wovGmMeMMV8xxlT1e4tERERERERERGRA9CYxNBJYAnwCWGOMecAYc5oxJti/TRMRERERERER\nkf60y8SQtTZurf2LtXYeMAr4NTALeMsYc1d/N1BERERERERERPpHb0YMpVlrI8Bq4FVgK3BgfzRK\nRERERERERET6X68SQ8aYUcaYC40xLwAPJZ93orX2sH5tnYiIiIiIiIiI9JverEr2DIk6Q78HzrPW\nruz3VomIiIiIiIiISL/bZWIIuAT4X2ut7e/GiIiIiIiIiIjIwOlNYuhkYI4xpscfWmsv6NMWiYiI\niIiIiIjIgOhNYmhFv7dCREREREREREQGXG8SQ/cAFdbapu4PGmNqgbZ+aZWIiIiIiIiIiPS73qxK\ndh3wyR4e/wRwbd82R0REREREREREBkpvEkON1to/bP+gtfZ+4FN93yQRERERERERERkIvUkMle7l\n80VEREREREREJAf1JrGz0RgzdfsHjTGHA009/L6IiIiIiIiIiOSB3hSfvhBYboy5DViZfGwKcDZw\n2p6+sTFmCXACEAHWAvOsta3Jn/0A+AoQBy6w1v5lT99HRERERERERER6tssRQ9ba54GpgAHOSd4M\ncIS19p978d6PAhOttZOA14EfABhjGkgknA4CjgVuNMZ49uJ9RERERERERESkB70ZMYS1diNw2cf9\njjHmPmvt7N6+sbX2r93uPgfMSf5/FnC3tTYMvGWMeZNEYurZ3r62iIiIiIiIiIjsWl8Wjx67F889\nF/hz8v8jgfXdfrYh+ZiIiIiIiIiIiPShvkwM2e0fMMY8Zox5pYfbrG6/cykQA+7a3Tc0xsw3xqww\nxqxoalIdbMkPilvJR4pbyUeKW8lHilvJN4pZkfzXr8vNW2s/Z62d2MPtAQBjzDnATOBMa20qsfQe\nMKrby9QnH+vp9W+21k6x1k6pra3tx08i0ncUt5KPFLeSjxS3ko8Ut5JvFLMi+a8vE0Nmt37ZmGOB\ni4ATrbWd3X70IHCaMSZgjBkDjAOe77tmioiIiIiIiIgI9KL4tDFmH2vtu714rYt3872vBwLAo8YY\ngOestQuttf82xiwHVpOYYvZ1a218N19bRERERERERER2oTerkv0PcBh8/Mpj260ytkvW2v0/5mdX\nAFfszuuJiIiIiIiIiMju6c1Usu5TxPZm5TEREREREREREckhvUkM2Z38X0RERERERERE8lhvppId\nYozZSmLkUDD5f5L3rbV2UL+1TkRERERERERE+s0uE0PWWs9ANERERERERERERAZWb0YMZTDGjARS\nyaL3rbWxvm2SiIiIiIiIiIgMhN4sV/8DwGet/c/kQ88CWwAf8Bvgv/qveSIiIiIiIiIi0l96U3z6\nZOCabvebrbUHAwcBx/dLq0REREREREREpN/1JjGEtbaj292fJx+LA8H+aJSIiIiIiIiIiPS/3iSG\nyo0xvtQda+1tAMaYAKAVyURERERERERE8lRvEkP3AsuMMaWpB4wxZcDS5M9ERERERERERCQP9SYx\ntAjYCLxrjFlpjHkBeBv4KPkzERERERERERHJQ7tclSxZS+gSY8yPgP2TD79prQ31a8tERERERERE\nRKRf7TIxBGCMqQO+TmIlMoB/G2NusNZu7LeWiYiIiIiIiIhIv9rlVDJjzFHAv5J3b0/eAJ5P/kxE\nRERERERERPJQb0YMXQP8h7V2VbfHHjTG3A8sA47ol5aJiIiIiIiIiEi/6k3x6UHbJYUAsNa+CFT0\nfZNERERERERERGQg9CYxZIwx1T08OLiXzxcRERERERERkRzUm8TOtcBfjTGfNsZUJG8zgD8DP+vX\n1omIiIiIiIiISL/pzXL1Nxtj3gcWk1iVzAKrgR9ba//Yz+0TEREREREREZF+0qvl6q21DwEPbf+4\nMebb1lqNGhIRERERERERyUN7WyPou33SChERERERERERGXB7mxgyfdIKEREREREREREZcHubGLJ7\n+kRjzBJjzGvGmJeNMfcbY6qSj482xoSMMS8mb0v3so0iIiIiIiIiItKDXdYYMsa00XMCyADBvXjv\nR4EfWGtjxpirgB8AFyd/ttZae+hevLaIiIiIiIiIiOxCb1Ylq+iPN7bW/rXb3eeAOf3xPiIiIiIi\nIiIi0rO9nUrWV84F/tzt/hhjzCpjzJPGmE9mq1EiIiIiIiIiIoWsV8vV7yljzGPAsB5+dKm19oHk\n71wKxIC7kj/7ANjHWttsjGkE/scYc5C1dmsPrz8fmA+wzz779MdHEOlzilvJR4pbyUeKW8lHilvJ\nN4pZkfzXryOGrLWfs9ZO7OGWSgqdA8wEzrTW2uRzwtba5uT/VwJrgfE7ef2brbVTrLVTamtr+/Oj\niPQZxa3kI8Wt5CPFreQjxa3kG8WsSP7L2lQyY8yxwEXAidbazm6P1xpjPMn/jwXGAeuy00oRERER\nERERkcLVr1PJduF6IAA8aowBeM5auxD4FPCfxpgo4AILrbWbs9dMEREREREREZHClLXEkLV2/508\nfh9w3wA3R0RERERERESk6OTKqmQiIiIiIiIiIjLAlBgSERERERERESlSSgyJiIiIiIiIiBQpJYZE\nRERERERERIqUEkMiIiIiIiIiIkVKiSERERERERERkSKlxJCIiIiIiIiISJFSYkhEREREREREpEgp\nMSQiIiIiIiIiUqSUGBIRERERERERKVJKDImIiIiIiIiIFCklhkREREREREREipQSQyIiIiIiIiIi\nRUqJIRERERERERGRIqXEkIiIiIiIiIhIkVJiSERERERERESkSCkxJCIiIiIiIiJSpJQYEhERERER\nEREpUkoMiYiIiIiIiIgUKSWGRERERERERESKlDfbDZD+4bqW5o4IkVgcv9dDddBHSyiavl9T5sdx\nTLabKZLXRl/y8G4/5+0rj++HlohkSh0DXNclbsFaq32/ZIXrWlpDEUKROHFrKfF5GFIWUBxKQVB8\nS6FQLIsSQwXIdS1rPmrjvNtXsKElRH11kKVzG7nu8df56+qN1FcHueXsKUwYWqGNXUSkwKSOAdc+\nuoYvHzmGi+97OX0s0L5fBpLrWt5u7uCjrV1ceK/iUAqL4lsKhWJZIItTyYwxi40xLxtjXjTG/NUY\nMyL5uDHGXGeMeTP588Oy1cZ81dwRSSeFADa0hFh450pmN45K3z/v9hU0d0Sy2UwREekHqWPA7MZR\n6aQQaN8vA6+5I8I7zZ3pEw1QHErhUHxLoVAsC2S3xtASa+0ka+2hwEPAD5OPHweMS97mAzdlqX15\nKxKLpzfqlA0tIaqCvoz7kVh8oJsmIiL9LHUMqAr6ejwWaN8vAyUSi1Pq9ygOpSApvqVQKJYFspgY\nstZu7Xa3DLDJ/88CbrcJzwFVxpjhA97APOb3eqivDmY8Vl8dpDUUzbjv93oGumkiItLPUseA1lC0\nx2OB9v0yUPxeD52RuOJQCpLiWwqFYlkgy6uSGWOuMMasB85k24ihkcD6br+2IfmY9FJNmZ9bzp6S\n3rhTNYbuW7k+ff+Ws6dQU+bPZjNFRKQfpI4B961cz1WzJ2UcC7Tvl4FUU+Zn35pSlsxRHErhUXxL\noVAsC4Cx1u76t/b0xY15DBjWw48utdY+0O33fgCUWGsvM8Y8BFxprf1H8mePAxdba1f08PrzSUw3\nY5999ml85513+uNj5CWtSrZX+vWLUdzmpj1ZYWxP9OOqZIpbScujVckUtwUuc6UbKPE5hbDSjeJW\ngLyL735rlGI2/+VwLGe9AcWiXxNDvW6EMfsAf7LWTjTGLAOesNb+LvmzNcAMa+0HH/caU6ZMsStW\n7JA7EtkTA7YDUtzmDiWGek9xK31IcSv5SHEr+WhA4lYxK31MiaEBks1VycZ1uzsLeC35/weBs5Or\nk00DtuwqKSQiIiIiIiIiIrvPm8X3vtIYMwFwgXeAhcnH/wR8EXgT6ATmZad5IiIiIiIiIiKFLWuJ\nIWvt7J08boGvD3BzRERERERERESKTjZHDEk/274AdQ4WHRURkX6mY4HkCsWiFCrFthQCxXFxU2Ko\nQLmuZc1HbZx3+wo2tITSSw5OGFqhDVxEpEjoWCC5QrEohUqxLYVAcSxZKz4t/au5I5LesAE2tIQ4\n7/YVNHdEstwyEREZKDoWSK5QLEqhUmxLIVAcixJDBSoSi6c37JQNLSEisXiWWiQiIgNNxwLJFYpF\nKVSKbSkEimNRYqhA+b0e6quDGY/VVwfxez1ZapGIiAw0HQskVygWpVAptqUQKI5FiaECVVPm55az\np6Q38NQ80Zoyf5ZbJiIiA0XHAskVikUpVIptKQSKY1Hx6QLlOIYJQyu4//yjVFleRKRI6VgguUKx\nKIVKsS2FQHEsSgwVMMcx1FYEst0MERHJIh0LJFcoFqVQKbalECiOi5umkomIiIiIiIiIFCklhkRE\nREREREREipQSQyIiIiIiIiIiRUqJIRERERERERGRIqXEkIiIiIiIiIhIkTLW2my3oU8YY5qAd7Ld\njm6GAJuy3Yg9oHbDJmvtsX30Wh+rD+M2X/9ue6KYPiv0/vPmY9z2l3yNkXxtN+x524sxbvPt76z2\n7qgY47Y3FCv9a2/bOyBxm0cxm29//5R8bTfsWdsHbH9b7AomMZRrjDErrLVTst2O3aV256di+vzF\n9Fmh+D5vX8jX7yxf2w353faBlm/fldorvZVv373aW9zy9fvM13ZDfre9GGgqmYiIiIiIiIhIkVJi\nSERERERERESkSCkx1H9uznYD9pDanZ+K6fMX02eF4vu8fSFfv7N8bTfkd9sHWr59V2qv9Fa+ffdq\nb3HL1+8zX9sN+d32gqcaQyIiIiIiIiIiRUojhkREREREREREipQSQyIiIiIiIiIiRUqJIRERERER\nERGRIqXEkIiIiIiIiIhIkVJiSERERERERESkSCkxJCIiIiIiIiJSpJQYEhEREREREREpUkoMiYiI\niIiIiIgUKSWGRERERERERESKlBJDIiIiIiIiIiJFSokhEREREREREZEipcSQiIiIiIiIiEiRUmJI\nRERERERERKRIKTEkIiIiIiIiIlKklBgSERERERERESlSBZMYOvbYYy2gm259cRswilvd+vA2YBS3\nuvXhbcAobnXrw9uAUdzq1oe3AaGY1a2PbzJACiYxtGnTpmw3QWS3KW4lHyluJR8pbiUfKW4l3yhm\nRfJTwSSGRERERERERERk9ygxJCIiIiIiIiJSpJQYEhEREREREREpUkoMiYiIiIiIiIgUKSWGRERE\nRERERESKlDfbDZDi5bqW5o4IkVgcv9dDTZkfxzHZbpZIVmm7EBHZfdp3Si5TfEqxUKznLyWGJCtc\n17LmozbOu30FG1pC1FcHueXsKUwYWqGdhxQtbRciIrtP+07JZYpPKRaK9fymqWSSFc0dkfROA2BD\nS4jzbl9Bc0ckyy0TyR5tFyIiu0/7Tsllik8pFor1/KbEkGRFJBZP7zRSNrSEiMTiWWqRSPZpuxAR\n2X3ad0ouU3xKsVCs57esTSUzxpwMXA4cCEy11q7o9rMfAF8B4sAF1tq/ZKWR0m/8Xg/11cGMnUd9\ndRC/15PFVolkl7YL2V2jL3l4t5/z9pXH90NLRLJH+07JZYpPKRaK9fyWzRFDrwBfAp7q/qAxpgE4\nDTgIOBa40RijaCowNWV+bj93Kreeczj3zJ/Grecczu3nTqWmzJ/tpol8LNe1NLWFea+lk6a2MK5r\n++y1a8r83HL2FOqrgwDpudnaLkREdpTaH0dicX771SM4pqEO0L5T+s+e9AF0bJd8tbvxrljPb1kb\nMWStfRXAmB0KUc0C7rbWhoG3jDFvAlOBZwe2hdLfwjGXRQ+8klGcTCSX9XdRPccxTBhawf3nH6XV\nHEREPkZP++NlZzWyeNZEHMfRvlP63J72AXRsl3y0J/GuWM9vuVhjaCSwvtv9DcnHpICoOJnko4GI\nW8cx1FYEGFldSm1FQAdTEZEe9LQ/XnDHShzH0b5T+sXe9AF0bJd8s6fxrljPX/2aGDLGPGaMeaWH\n26w+ev35xpgVxpgVTU1NffGSMkCKuTiZ4jZ/KW4Vt5JfFLeFq5D3x4rb3FTIMbe3FLOFR/FefPp1\nKpm19nN78LT3gFHd7tcnH+vp9W8GbgaYMmVK3xX6kH63fXGyyaOquODoccRtYi5rIQ87VNzmr1Tc\n1pYHWDhjP6qCPjojcYL+wi+DpriVfKS4zV+ua2nuiOx0OkIhFzlV3OamXIu5XW0jA0kxm/+2j6eg\nP7fiXfpfLk4lexA4zRgTMMaMAcYBz2e5TdLHuhcnmzyqiouOncCiB17hU1c/wUk3Ps2aj9r6tKiv\nSF9IFU2/6NgJLH5oNafe/ByLHniFj7b2bRFqEZFilqptcdKNT3PUVX/vsV+gIqcy0HIp5nqzjYj0\nVk/x9NHWMLefOzUn4l0GRjaXqz8J+AVQCzxsjHnRWvsFa+2/jTHLgdVADPi6tVZj1grM9sXJTr35\nuR3msN5//lHUVgSy3FKRbRzHUF7i5exfv6x4FRHpJzurbdF9P6sipzLQcinmerONiPTWzuLpD+cf\nmRPxLgMjm6uS3Q/cv5OfXQFcMbAtkj21p0NZU8XJ3mvp1BxWyQuua+mKas61iEhv7Gn/oLe1LVL9\nCJGB0lPMZWNKl+q/SF/aWTxFYy4jq0uz1CoZaFlLDElh2Jvlu1MHUkBzWCXnpWL9wy1dPcarMQbX\ntbqSIiLC3vUPdlbLRftZyTV7E+d7w+d11HeWPrOzeHKM4b2WTo0WKhK5WGNI8sieLmXYfS7rN367\niiVzJmkOq+S0VKxf9/gbXDU7M16vmj2Jyx98RfP7RUSS9mZp755quWg/K7lob+J8T7mupb0rpr6z\n9BmvY3aIpyVzJrGhJaQaVkVEI4Zkr+zpUNbuB9INLSGufmQNi2dNZL+6coI+ZaUl96RifUNLiJ/+\nZQ2LZjZQFfRRVxHgu8tfYtX6VlZ/0Kb5/SIi7N1Ul1Qtl+ULpvN+a4jmjgg//csa7Wcl52RjSldz\nR4Szf/08teWBdF+kMxJn6KCA+s6yR0KROFc/sq1v2xqKcvUja7jkuAMA1bAqFkoMyV7Z06U7tz+Q\nrlrfyrzb/sXTF39GOxzJSd1jfdX6VhbcsZL66iCLZjawan0roPn9IiIpe7u0t+MYrLXMWfpsxuPa\nz0ouycYS9t0vVC24Y2X68acv/gyU9dvbSgHzez00tYcz4qm+OkhrKJq+r31v4dNUshzjupamtjDv\ntXTS1Jb7S2Dv6dKdqQNpd5obLbls+1g/pqGO28+dSl1FgGVnNTJ5VJViWEQkaWf9g+qgr9f9HPUV\nJNel4vyYhjqWndXIvQun89uvHkF10Ndv76ntQvpaT/vrJXMmsfSJtenf2T7G8u2cVXZNI4ZySLYK\n2O2NPV26M7UD2v6zam605Kruse66LpuSQ7lT8btkziSGDipRDIuI0HP/oDro442m9l73c9RXkFzn\nOIZxteV863PjWXDHygHpv2u7kL62/f7a53Vo74rR1B4Gdrzwn4/nrLJrxtrCyO5NmTLFrlixItvN\n2CtNbWFOuvHpHYajFup8zmws79lLA9aIQojbYrSzbfUP5x9JXUVJtpqluC1Coy95eLef8/aVx/dD\nS/aY4raI7Ek/J0f7CopbSctG/30Pt4sBiVvFbGH4uBgb4JjP+g6/WGjEUA7pywJ2uzpgfNzP+6IT\n5rqWTe1hwBKNW2KuxesY6soDeDxOxusPrwzmQidPZKexH4u5bGwPE427+ByD12MyCvQtfWItq9a3\n0hV1ea+lE2MMxljiLvgcg8/rUBXMiZMZEZGs2Vk/x5LYd6b6CoOCDkGvH6/XwXEMNWX+9L65uSOS\nK8mhgpajCbmc47qWUDSWEdenNNZz3qfG0hmJ8V5LnKqgh9ZQnLi1lPg8DCnb+yLRjmMK8qKx9K/u\n23XQ7yHmWqIxF2MMfo/BtWCtJepaXNfi8zh4HYPF0hqKEI25xNzEed1AF12X/qfEUA7pqwJ2Oxve\nN662nK3hKF2ROFHXYi18sCXErU+/xXc+P4EJQysAenzu8MoAHeHeHdRc17Lmwzb+Z9V6jj9kJOff\n9UL6tW6a20hV0Mvpt/xTQw8lp+xsu9l/SBlrNraz8M7EEPFjGur45tHjWfzQ6vTvXTV7Er955i3W\nbmxn3m3/ynjsvE+Oxe91aC2JMrqmTHEuIkXL53V26Ocs+ORoPtoa4Wt3rszoK9RXQwWJZISmLAys\nj+tHtoSiGVMDu98vtuRR6nv6cEtXOq5Paaxn7vR9mXfbvzLiORqN8qWl/1T8StZ0365rywNcdOwE\nLrz35XTf9vtfmEAoEqczEk8/Xl8d5JqTD+FX/1jHRcceQFfUZeGdK1k0s2HAi65L/9NUshzS04F4\n2VmNjKgM0Blx01fSfB5DNG4p8TsYDKHItgP05lCEUCTOuqYO/vx/H3DSYSMZVR3Ekljdo8TnIRrf\n9lqlfodQxCXqWjyOwe9xeKe5kxKfw6CgDwO4Frwew5bOKC2dEUp8HkZUleD1OOnMsc8xlPg9VAX9\ntIYivLR+C/vXlXP6Lc/tsNO4bd5UPvffT2Y8lhp6mCNXqDREvAjtbFjsPfOncerN2+J42VmN6aQQ\nwORRVfz4PyYypNxP1LXEk9uDccC6EIom5mp/tCXM6CFl/XmFT3FbhDSVrPcUt9mROq67rosF2sMx\nNrVHEv0IY9L9D69jcK3lneZObn36LS474SCMMfg8Ti5Osy/ouG1qC3Pp/S8zu3EUIypLKPF56IzE\nGVLuJ24tHV1xyku8eJxEH7G5PcwNf38zfZGxkBMe3fupxhh+8/Q6jj14ODXlASIxi89jWNfUQdPW\nEEeOqyXuWjzG4Pc6YC048Kun1vHVT+2fjfgt+qlkOXKekRXd+7nb92WXndWI35NYk+p3z7/DvKPG\nMGxQojSCz+vgmMS27nMMxkBrKEpLR5Tv/f6lgUjYF8cfKAdoxFCOqSn3cc/8aenEjWMgHLO0dkYZ\nFEz8ubqiLh7HEItbIjGXUDSGz2P4qK0r/bzDRw/ioOEVRFyLYxLP6YjEibmWK//8Kn9dvZH66iC3\nzjucjq4Y4ZhLZyTOqMFB9q0J4vcaNrSEWXjnSmrLA1xw9DjGDCmjqtSHz2PY2hWjqa0zI6O87KzG\n5HBE2L+uHNdaFs1sSE+zAagtD1Dic3jywhnpJFfcQjgW553mDryOoTUUZUNLiPtWri+KTobkhp1N\ncTCGjGljY4eUsmhmA3UVAWrK/FSWeojGoCMSp6ktTHNHhPtWrudbR49ncLkPn8fhrmff5oRD63Fd\nNzsfTkQkC1IXvP7nhfWcOnVfQtE4Aa+DxxgiMZf32yPUlgdwjMECXsfhwOHl/PCEBoyBoM/QGem7\nafayc90TeHHXcslxB9LcHiEcc/lacuT3MQ11XHp8AxVBD9ZCNJ6YalJbEeDyEyfy5kdbGFIeKNgp\nTq5rebu5g3eaOxlSEaC61Me8T4zFMbC1K8b6zSHqq0uYNGoQXZFyYq7FMYbNnRFK/R4qAl4iUcv8\nT4+lUC7M55NiL5jcvZ9bFfRl7FdHVJZQGfThOPCjWRNpbo9w1q+fp7Y8wMXHHcA+g4MYIBxLnIM+\n80YTR+w3hCu/dDCjBpdS6vcWVZKtUCkxlENCkTBNWyMs6DacetlZjQS8Dj9//HW+fOQYLr5vWyJm\n6dxGqkt9DCrxpg9Ipf7EVZ396srY0hnlF397Y4fnXTV7Ek1tEcbVlWOtpaUzyvp8K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KgmNrHcBT7x7Ajy4a1mofEO9FwVR1tMHbYmn9rU9/gPkXDDbjN57XIPy4/cEzO7Hnm3r4Ypy3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1cbY64C/ga4Zi9ffz7J5WaMHj16X5vbL7muZd2WZi5avopNjWFGlge5e+5Uxg8rzemBuTcd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/1QY5LN9HUcBDjs/krEVr2n1P/OZAVV0IRymKAh68bkPquohvzbKdRCy2vjm1rrI+MZY+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\nB69LI+I4/HjkYZx4VDF3vRpbHNK1WK0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OVliytXEb76zuL02+pWK/lQ1BendL7y1j21sw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\n",
            "text/plain": [
              "<Figure size 1158x1080 with 42 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjByS1QmcnWv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Identificamos las clases existentes y creamos un mapeo\n",
        "clases = data_quasar2['class'].unique()\n",
        "mapa = {}\n",
        "for clase, i in enumerate(clases):\n",
        "    mapa[i] = clase\n",
        "    \n",
        "# Separamos en conjunto de atributos en un Dataframe X\n",
        "X = np.array(data_df.iloc[:,:-1])\n",
        "\n",
        "# Separamos el conjunto de clases, y reemplazamos los string por un valor numérico\n",
        "# En este caso la clase galaxia se asocia a un 0, estrellas a un 1\n",
        "y = np.array(data_df['class'].map(mapa))\n",
        "\n",
        "# Guardamos la semilla para separar aleatoriamente los datos\n",
        "rs = 1851\n",
        "\n",
        "# Separamos el conjunto de entrenamiento y prueba usando scikit-learn y la semilla anterior\n",
        "# Escogemos un conjunto de entrenamiento del 70% de las muestras y 30% de test\n",
        "train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.3, random_state = rs)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}