ZTF_SDSS_QSO_DR9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we ingest as an AXS Catalog the SDSS DR9 Quasar database,  and look for any ZTF observations for these objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "spark = pyspark.sql.SparkSession.builder \\\n",
    "    .config('spark.master', \"local[8]\")\\\n",
    "    .config(\"spark.driver.memory\", \"6g\") \\\n",
    "    .config(\"spark.executor.memory\", \"16g\") \\\n",
    "    .config(\"spark.cores.max\", \"15\") \\\n",
    "    .config(\"spark.executor.cores\", \"1\") \\\n",
    "    .enableHiveSupport() \\\n",
    "    .getOrCreate()\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See if everything is OK:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - hive</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://epyc.astro.washington.edu:4046\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.4.0-SNAPSHOT</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[8]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>pyspark-shell</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7fec3215fa58>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='_gatspy_ztf2', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='allwise_1am_dup', database='default', description=None, tableType='EXTERNAL', isTemporary=False),\n",
       " Table(name='allwise_500b_28e_10800z', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='asassn_variability_catalog', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='cesium_speedtest_ztfsample', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='cklein_flare', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='dambis_rrlyrae_metalicity', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='demo_result', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='faraway', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='faraway2', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='faraway3', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='faraway_labeled_training_dataset', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='faraway_set', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='faraway_training_dataset', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_500b_28e_10800z', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_dr2', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_dr2_1am_dup', database='default', description=None, tableType='EXTERNAL', isTemporary=False),\n",
       " Table(name='gaia_dr2_700lc_new', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_dr2_ebv', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_dr2_sfd', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_sdss_3asec', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_sdss_wise_1asec', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='gaia_zoned1am_hd', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='hi', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='jnk_ztf_test', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='jnk_ztf_test2', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='nemec_rrlyrae_metalicity', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='paula_listcvs', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_500b_28e_10800z', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_dr9_qso_s82', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_zoned1am_700lc', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_zoned1am_hd', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_zoned1am_hd_lc', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_zoned1am_hd_ps', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sesar_rrlyrae', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='test_ztf_rc00_20sec2', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='test_ztf_rc252', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='unwise_v1', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='unwise_v2', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf10', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf1000', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf10_assasn', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf10_assasn_best', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf10_assasn_cesium', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf10_assasn_cesium_best', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_1am', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_1am_lc', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_500b_28e_10800z', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_dec18', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_exposures', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_fid1_sdss_stars_lt20_2asec', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_fid2_sdss_stars_lt20_2asec', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_fid3_sdss_stars_lt20_2asec', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='ztf_mar19_all', database='default', description=None, tableType='EXTERNAL', isTemporary=False),\n",
       " Table(name='ztf_nobs100', database='default', description=None, tableType='MANAGED', isTemporary=False)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Update 6/11/2019 : now there is ZTF DR1 table `ztf_mar19_all` which contains all light curves for the release..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Database(name='default', description='Default Hive database', locationUri='file:/epyc/projects/lsd2/pzwarehouse')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check where these tables physically live, do \n",
    "# spark.catalog.listDatabases()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to drop a table : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "axs_catalog.drop_table(\"ztf_sdss_dr9_qso\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "axs_catalog.drop_table(\"ztf_sdss_dr9_qso2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load AXS catalog with ZTF "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axs.catalog import AxsCatalog\n",
    "from axs.axsframe import AxsFrame\n",
    "axs_catalog = AxsCatalog(spark)\n",
    "ztf_lc = axs_catalog.load(\"ztf_mar19_all\")# axs_catalog.load(\"ztf_1am_lc\") # ztf light curves .... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bucket_col': 'zone',\n",
       " 'dec_col': 'dec',\n",
       " 'has_lightcurves': False,\n",
       " 'lc_columns': JavaObject id=o175,\n",
       " 'num_buckets': 500,\n",
       " 'ra_col': 'ra',\n",
       " 'table_id': 501,\n",
       " 'table_name': 'ztf_mar19_all',\n",
       " 'zone_height': 0.0166666666}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ztf_lc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-737ed12dc1d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mztf_lc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\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[0;32m/epyc/opt/spark-axs/python/axs/axsframe.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(self, *cols)\u001b[0m\n\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mcols\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--> 613\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcols\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    614\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    615\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\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[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/pyspark/sql/dataframe.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(self, *cols)\u001b[0m\n\u001b[1;32m   1170\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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   1171\u001b[0m             \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1172\u001b[0;31m         \u001b[0mjdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jseq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\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   1173\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql_ctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1253\u001b[0m             \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEND_COMMAND_PART\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1255\u001b[0;31m         \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\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   1256\u001b[0m         return_value = get_return_value(\n\u001b[1;32m   1257\u001b[0m             answer, self.gateway_client, self.target_id, self.name)\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/py4j/java_gateway.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, command, retry, binary)\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_connection\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    984\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--> 985\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\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    986\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbinary\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_connection_guard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/py4j/java_gateway.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, command)\u001b[0m\n\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\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-> 1152\u001b[0;31m             \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmart_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\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[0;36m1\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   1153\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Answer received: {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer\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   1154\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0manswer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRETURN_MESSAGE\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/epyc/opt/anaconda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\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[1;32m    585\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--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\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    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ztf_lc.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the top few rows ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(matchid=10294422001559, ra=0.029200815384615383, dec=-16.263277997435896, nobs_avail=39, combined_matchids=[10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294421013987, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559, 10294422001559], mjd=[58429.2307176, 58378.3356597, 58426.3169329, 58323.4388194, 58366.3977315, 58338.4080093, 58351.418831, 58345.4153588, 58423.2403356, 58342.436956, 58369.3298611, 58372.4009954, 58432.2429861, 58363.3348495, 58390.3144097, 58360.3542824, 58472.1060532, 58426.2527778, 58323.4809606, 58354.4090509, 58378.3773264, 58354.3533333, 58338.4618403, 58429.2522917, 58363.4237731, 58348.4140394, 58351.3726736, 58372.371331, 58290.480544, 58375.3357755, 58432.2522222, 58320.4817477, 58332.4182523, 58342.4621875, 58390.2685764, 58369.3774769, 58335.4381366, 58382.3158681, 58366.3486574], programid=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], filterid=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], mag=[20.52320671081543, 20.821575164794922, 20.585926055908203, 20.539337158203125, 20.541175842285156, 20.734291076660156, 20.50154685974121, 20.308853149414062, 20.584842681884766, 20.639564514160156, 20.595897674560547, 20.529415130615234, 20.647716522216797, 20.641342163085938, 20.3294620513916, 19.837047576904297, 19.966659545898438, 20.30284309387207, 20.21855926513672, 20.41679573059082, 20.11280059814453, 20.279518127441406, 20.231952667236328, 20.239704132080078, 20.18246078491211, 20.159944534301758, 20.132511138916016, 20.278358459472656, 20.140403747558594, 20.097074508666992, 20.00757598876953, 20.230989456176758, 20.361095428466797, 19.957855224609375, 20.20929527282715, 20.179622650146484, 20.169395446777344, 20.071941375732422, 20.224212646484375], magerr=[0.14372503757476807, 0.16154265403747559, 0.14747047424316406, 0.14468836784362793, 0.1447981595993042, 0.15633034706115723, 0.1424316167831421, 0.13231007754802704, 0.147405743598938, 0.15067362785339355, 0.14806592464447021, 0.144095778465271, 0.1511603593826294, 0.15077972412109375, 0.1334926337003708, 0.10343597829341888, 0.11251258850097656, 0.13754260540008545, 0.13117675483226776, 0.1460341513156891, 0.12322573363780975, 0.13578322529792786, 0.13218875229358673, 0.13277462124824524, 0.1284523755311966, 0.12675748765468597, 0.12469904124736786, 0.13569556176662445, 0.12529046833515167, 0.12205412983894348, 0.11546806246042252, 0.13211596012115479, 0.14191249012947083, 0.11188191175460815, 0.1304769366979599, 0.12823839485645294, 0.12746837735176086, 0.12018980830907822, 0.13160386681556702], psfmag=[20.52320671081543, 20.821575164794922, 20.585926055908203, 20.539337158203125, 20.541175842285156, 20.734291076660156, 20.50154685974121, 20.308853149414062, 20.584842681884766, 20.639564514160156, 20.595897674560547, 20.529415130615234, 20.647716522216797, 20.641342163085938, 20.3294620513916, 19.837047576904297, 19.966659545898438, 20.30284309387207, 20.21855926513672, 20.41679573059082, 20.11280059814453, 20.279518127441406, 20.231952667236328, 20.239704132080078, 20.18246078491211, 20.159944534301758, 20.132511138916016, 20.278358459472656, 20.140403747558594, 20.097074508666992, 20.00757598876953, 20.230989456176758, 20.361095428466797, 19.957855224609375, 20.20929527282715, 20.179622650146484, 20.169395446777344, 20.071941375732422, 20.224212646484375], psfmagerr=[0.11400000005960464, 0.1770000010728836, 0.31299999356269836, 0.16300000250339508, 0.21299999952316284, 0.24500000476837158, 0.1550000011920929, 0.11900000274181366, 0.20200000703334808, 0.17499999701976776, 0.19099999964237213, 0.1899999976158142, 0.3269999921321869, 0.3569999933242798, 0.2759999930858612, 0.22499999403953552, 0.20200000703334808, 0.3199999928474426, 0.13500000536441803, 0.2849999964237213, 0.10700000077486038, 0.24300000071525574, 0.12399999797344208, 0.14399999380111694, 0.16699999570846558, 0.10499999672174454, 0.13699999451637268, 0.10899999737739563, 0.3009999990463257, 0.12600000202655792, 0.23899999260902405, 0.11699999868869781, 0.33000001311302185, 0.12600000202655792, 0.19499999284744263, 0.0989999994635582, 0.1809999942779541, 0.210999995470047, 0.11299999803304672], psfflux=[191.24563598632812, 147.75660705566406, 149.0281219482422, 179.8417205810547, 197.1156768798828, 144.69248962402344, 186.60198974609375, 204.89398193359375, 183.45167541503906, 139.1451416015625, 175.8006591796875, 191.78863525390625, 166.39573669433594, 165.9228057861328, 228.7760467529297, 381.1890869140625, 344.3011474609375, 189.44644165039062, 262.6701965332031, 221.56874084472656, 299.5089111328125, 251.5665283203125, 252.5135040283203, 263.90570068359375, 267.3955383300781, 271.4394226074219, 283.13763427734375, 260.7051696777344, 278.9226379394531, 301.4784851074219, 324.9134216308594, 251.80294799804688, 220.73556518554688, 295.3309020996094, 270.6766662597656, 272.9713134765625, 279.1842346191406, 304.5993347167969, 271.9491271972656], psffluxerr=[20.133228302001953, 24.114946365356445, 42.98982238769531, 26.949684143066406, 38.688358306884766, 32.69032287597656, 26.673723220825195, 22.381521224975586, 34.08028793334961, 22.478809356689453, 30.95879554748535, 33.56233215332031, 50.14540481567383, 54.49580764770508, 58.240386962890625, 78.8543930053711, 64.02513122558594, 55.80082321166992, 32.53934097290039, 58.119808197021484, 29.48921012878418, 56.21074295043945, 28.908889770507812, 35.05013656616211, 41.20270919799805, 26.225521087646484, 35.59638977050781, 26.124874114990234, 77.37744903564453, 34.8755989074707, 71.40255737304688, 27.134532928466797, 67.02964782714844, 34.32763671875, 48.63890075683594, 24.814746856689453, 46.54201126098633, 59.279441833496094, 28.228466033935547], chi=[0.45500001311302185, 0.5740000009536743, 0.9190000295639038, 0.9940000176429749, 1.1089999675750732, 1.152999997138977, 1.3300000429153442, 0.414000004529953, 0.925000011920929, 0.8920000195503235, 1.0950000286102295, 0.7730000019073486, 1.281000018119812, 1.0859999656677246, 0.5569999814033508, 0.5440000295639038, 0.47999998927116394, 0.8980000019073486, 0.7799999713897705, 0.765999972820282, 0.5979999899864197, 0.5669999718666077, 0.4050000011920929, 1.0509999990463257, 0.4959999918937683, 0.6269999742507935, 1.2139999866485596, 0.328000009059906, 0.5920000076293945, 0.8920000195503235, 0.8880000114440918, 0.8059999942779541, 0.367000013589859, 0.8330000042915344, 0.6019999980926514, 0.453000009059906, 1.0750000476837158, 0.6909999847412109, 0.6430000066757202], catflags=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -32768, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], sharp=[-0.020999999716877937, -0.19499999284744263, -0.2709999978542328, -0.17900000512599945, -0.07100000232458115, 0.00800000037997961, 0.05999999865889549, -0.0430000014603138, -0.15399999916553497, -0.19499999284744263, 0.08699999749660492, -0.08399999886751175, -0.08900000154972076, -0.2639999985694885, -0.21299999952316284, -0.21899999678134918, -0.05000000074505806, 0.028999999165534973, 0.04500000178813934, 0.2160000056028366, 0.03400000184774399, -0.21799999475479126, -0.0949999988079071, -0.09700000286102295, -0.04699999839067459, -0.07400000095367432, -0.1469999998807907, 0.014000000432133675, -0.33500000834465027, -0.054999999701976776, -0.24899999797344208, 0.020999999716877937, -0.07900000363588333, -0.20000000298023224, -0.1550000011920929, -0.014000000432133675, -0.05900000035762787, 0.17499999701976776, -0.11400000005960464], xpos=[2146.785888671875, 2133.739990234375, 2154.0, 2111.368896484375, 2136.56298828125, 2121.302978515625, 2133.113037109375, 2126.491943359375, 2157.009033203125, 2127.056884765625, 2125.736083984375, 2143.489013671875, 2154.177001953125, 2120.31103515625, 2139.132080078125, 2121.820068359375, 2148.985107421875, 2151.136962890625, 2123.52294921875, 2132.888916015625, 2142.052001953125, 2118.492919921875, 2137.660888671875, 2166.76708984375, 2140.625, 2126.241943359375, 2116.972900390625, 2142.803955078125, 2114.5869140625, 2135.033935546875, 2162.447998046875, 2123.27294921875, 2069.04296875, 2133.52099609375, 2139.47607421875, 2141.9619140625, 2129.7109375, 2126.847900390625, 2125.658935546875], ypos=[667.2349853515625, 565.3690185546875, 653.9550170898438, 707.5440063476562, 568.968994140625, 797.9219970703125, 780.5250244140625, 625.843017578125, 680.177978515625, 710.5050048828125, 564.10400390625, 585.5670166015625, 684.218994140625, 564.8319702148438, 483.02301025390625, 568.0469970703125, 684.3270263671875, 662.4249877929688, 715.4310302734375, 613.625, 566.7620239257812, 604.7369995117188, 806.10400390625, 669.0599975585938, 568.6119995117188, 580.6270141601562, 576.1079711914062, 588.280029296875, 636.4429931640625, 568.8109741210938, 685.6610107421875, 642.708984375, 784.3610229492188, 714.9500122070312, 473.2919921875, 570.5449829101562, 704.4650268554688, 463.4700012207031, 565.6320190429688], zone=4424, dup=0)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ztf_lc.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So each row is a nested light curve. Cool! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ingest the SDSS S82 DR9 QSO catalog as AXS catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute the cell below only if `sdss_dr9_qso_s82` was not on the list of spark tables ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "File = 'DB_QSO_S82.dat'\n",
    "colnames = ['dbID', 'ra', 'dec', 'SDR5ID', 'M_i', 'M_i_corr', 'redshift',\n",
    "                   'mass_BH', 'Lbol', 'u', 'g', 'r', 'i', 'z', 'Au']\n",
    "table = Table.read(File, format='ascii', names = colnames)\n",
    "# make pandas df \n",
    "df = table.to_pandas()\n",
    "\n",
    "# make spark df \n",
    "fields = ['dbID', 'ra', 'dec', 'SDR5ID', 'M_i', 'M_i_corr', 'redshift',\n",
    "                   'mass_BH', 'Lbol', 'u', 'g', 'r', 'i', 'z', 'Au']\n",
    "\n",
    "field_types = [IntegerType(),DoubleType(),DoubleType(),IntegerType(),\n",
    "               DoubleType(),DoubleType(),DoubleType(),DoubleType(),DoubleType(),\n",
    "               DoubleType(),DoubleType(),DoubleType(),DoubleType(),DoubleType(),\n",
    "               DoubleType()]\n",
    "\n",
    "fields = [StructField(field_name, type_class, True) for field_name, \n",
    "          type_class in zip(fields, field_types)]\n",
    "schema = StructType(fields)\n",
    "sdf = spark.createDataFrame(df,schema=schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axs import Constants\n",
    "axs_catalog.save_axs_table(sdf,\"sdss_dr9_qso_s82\",  True, True, 500 ,Constants.ONE_AMIN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the steps above have been done,  I can read in the results : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "qso = axs_catalog.load(\"sdss_dr9_qso_s82\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(dbID=133470, ra=0.237056, dec=-1.069386, SDR5ID=40, M_i=-26.287, M_i_corr=-26.913, redshift=2.1065, mass_BH=0.0, Lbol=0.0, u=20.502, g=20.186, r=19.632, i=19.279, z=18.842, Au=0.213, zone=5335, dup=0)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qso.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps to crossmatch ZTF matchfiles (LC) to SDSS S82  DB9 QSO\n",
    "\n",
    "S82 is a narrow strip in dec. Illustrate the extent :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'dec')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Illustrate the extent of S82 : \n",
    "# it's a narrow strip in dec \n",
    "table = qso.select(['ra','dec']).limit(10000).toPandas()\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "plt.scatter(table['ra'], table['dec'], s=0.1)\n",
    "plt.xlabel('ra')\n",
    "plt.ylabel('dec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed up the process, we limit the range of ZTF to ra,dec covered by S82 : $\\|{ \\delta }\\| < 1.3$. Also,  select ZTF light curves that have more than 1 good epoch : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['matchid',\n",
       " 'ra',\n",
       " 'dec',\n",
       " 'nobs_avail',\n",
       " 'combined_matchids',\n",
       " 'mjd',\n",
       " 'programid',\n",
       " 'filterid',\n",
       " 'mag',\n",
       " 'magerr',\n",
       " 'psfmag',\n",
       " 'psfmagerr',\n",
       " 'psfflux',\n",
       " 'psffluxerr',\n",
       " 'chi',\n",
       " 'catflags',\n",
       " 'sharp',\n",
       " 'xpos',\n",
       " 'ypos',\n",
       " 'zone',\n",
       " 'dup']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ztf_lc.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import abs as sparkabs  # need to use the spark abs \n",
    "\n",
    "ztf_lc_select = ztf_lc.select(['zone', 'dup', 'matchid', 'ra', 'dec', \n",
    "              'mag', 'magerr', 'psfmag','psfmagerr', 'filterid', 'mjd','nobs_avail']).\\\n",
    "     where((ztf_lc['nobs_avail']>1) & (sparkabs(ztf_lc['dec']) < 1.3) )\n",
    "                               \n",
    "#ztf_lc_select = ztf_lc.select(['zone', 'dup', 'matchid', 'ra', 'dec', \n",
    "#               'bestmagrms', 'bestmeanmag', 'bestmedianmag',\n",
    "#               'medianmag','meanmag','nbestobs', 'ngoodobs']).\\\n",
    "#      where((ztf_lc['ngoodobs']>1) & (sparkabs(ztf_lc['dec']) < 1.3) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show how many rows this selection includes... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28.3 ms, sys: 9.51 ms, total: 37.8 ms\n",
      "Wall time: 3min 10s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "30638696"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "ztf_lc_select.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the crossmatch:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "( before the  crossmatch I had to remove the existing catalog because the connection stalled during the crossmatch ...) . `axs_catalog.drop_table()` wouldn't work because it didn't get fully ingested to axis. Needed to manually remove the temporary files. This was done by :\n",
    "\n",
    "    ssh suberlak@epyc.astro.washington.edu  \n",
    "    cd /epyc/projects/lsd2/pzwarehouse/\n",
    "    rm -rf ztf_dr1_s82_qso/\n",
    "    exit \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.72 s, sys: 984 ms, total: 3.7 s\n",
      "Wall time: 3h 43min 58s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from axs import Constants\n",
    "fname = \"ztf_dr1_s82_qso\"\n",
    "crossmatch = ztf_lc_select.crossmatch(qso, r=2*Constants.ONE_ASEC).\\\n",
    "    save_axs_table(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If all the above has already been executed, just \n",
    "# load the AXS catalog, move to pandas, to astropy,\n",
    "# and save as a text file to export locally and compare to SDSS, PS1 data ... \n",
    "from axs.catalog import AxsCatalog\n",
    "axs_catalog = AxsCatalog(spark)\n",
    "zq = axs_catalog.load(\"ztf_dr1_s82_qso\")\n",
    "zqt = zq.toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "zqt1 = Table.from_pandas(zqt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "zqt1.write('ztf_dr1_s82_qso_lcs.tbl', format='ipac')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "I scp that to my local machine :\n",
    "    \n",
    "    cd ~/GradResearch/Paper2_SDSS_PTF_PS1/raw_data/\n",
    "    scp suberlak@epyc.astro.washington.edu:/epyc/users/suberlak/ztf_dr1_s82_qso_lcs.tbl .\n",
    "        \n",
    "I can read it in with \n",
    "       \n",
    "    from astropy.table import Table\n",
    "    ztf = Table.read('ztf_dr1_s82_qso_lcs.tbl', format='ipac')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "( Before ZTF DR1 the problem was : no actual overlap ... ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = ztf_lc_select.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zone</th>\n",
       "      <th>dup</th>\n",
       "      <th>matchid</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>bestmagrms</th>\n",
       "      <th>bestmeanmag</th>\n",
       "      <th>bestmedianmag</th>\n",
       "      <th>medianmag</th>\n",
       "      <th>meanmag</th>\n",
       "      <th>nbestobs</th>\n",
       "      <th>ngoodobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5335</td>\n",
       "      <td>0</td>\n",
       "      <td>0004241202216748</td>\n",
       "      <td>195.684502</td>\n",
       "      <td>-1.077257</td>\n",
       "      <td>0.104484</td>\n",
       "      <td>20.637121</td>\n",
       "      <td>20.637121</td>\n",
       "      <td>20.637121</td>\n",
       "      <td>20.637121</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5335</td>\n",
       "      <td>0</td>\n",
       "      <td>0004241202206664</td>\n",
       "      <td>195.688365</td>\n",
       "      <td>-1.068434</td>\n",
       "      <td>0.021098</td>\n",
       "      <td>16.971289</td>\n",
       "      <td>16.977032</td>\n",
       "      <td>16.976179</td>\n",
       "      <td>16.970150</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5335</td>\n",
       "      <td>0</td>\n",
       "      <td>0004241202201518</td>\n",
       "      <td>195.690204</td>\n",
       "      <td>-1.079686</td>\n",
       "      <td>0.109264</td>\n",
       "      <td>19.722658</td>\n",
       "      <td>19.702240</td>\n",
       "      <td>19.714390</td>\n",
       "      <td>19.731030</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5335</td>\n",
       "      <td>1</td>\n",
       "      <td>0004241202206655</td>\n",
       "      <td>195.692898</td>\n",
       "      <td>-1.066118</td>\n",
       "      <td>0.166418</td>\n",
       "      <td>19.209494</td>\n",
       "      <td>19.247002</td>\n",
       "      <td>19.246765</td>\n",
       "      <td>19.214813</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5335</td>\n",
       "      <td>0</td>\n",
       "      <td>0004241202201469</td>\n",
       "      <td>195.706372</td>\n",
       "      <td>-1.071385</td>\n",
       "      <td>0.186656</td>\n",
       "      <td>20.317308</td>\n",
       "      <td>20.231001</td>\n",
       "      <td>20.260391</td>\n",
       "      <td>20.315016</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zone  dup           matchid          ra       dec  bestmagrms  bestmeanmag  \\\n",
       "0  5335    0  0004241202216748  195.684502 -1.077257    0.104484    20.637121   \n",
       "1  5335    0  0004241202206664  195.688365 -1.068434    0.021098    16.971289   \n",
       "2  5335    0  0004241202201518  195.690204 -1.079686    0.109264    19.722658   \n",
       "3  5335    1  0004241202206655  195.692898 -1.066118    0.166418    19.209494   \n",
       "4  5335    0  0004241202201469  195.706372 -1.071385    0.186656    20.317308   \n",
       "\n",
       "   bestmedianmag  medianmag    meanmag  nbestobs  ngoodobs  \n",
       "0      20.637121  20.637121  20.637121         2         2  \n",
       "1      16.977032  16.976179  16.970150        21        24  \n",
       "2      19.702240  19.714390  19.731030        19        21  \n",
       "3      19.247002  19.246765  19.214813        19        22  \n",
       "4      20.231001  20.260391  20.315016        11        12  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "\n",
    "File = 'DB_QSO_S82.dat'\n",
    "colnames = ['dbID', 'ra', 'dec', 'SDR5ID', 'M_i', 'M_i_corr', 'redshift',\n",
    "                   'mass_BH', 'Lbol', 'u', 'g', 'r', 'i', 'z', 'Au']\n",
    "dbqso = Table.read(File, format='ascii', names = colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "plt.scatter(dbqso['ra'], dbqso['dec'], s=0.1, label='sdss')\n",
    "plt.scatter(t['ra'], t['dec'], s=0.01, label='ztf')\n",
    "plt.xlabel('ra')\n",
    "plt.ylabel('dec')\n",
    "lgnd = plt.legend()\n",
    "lgnd.legendHandles[0]._sizes = [30]\n",
    "lgnd.legendHandles[1]._sizes = [30]\n",
    "plt.savefig('ztf_1am_lc_vs_sdss_dr9_qso_s82_190301.png', bbox_inches='tight', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3-AXS Spark",
   "language": "python",
   "name": "spark-smj"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}