UsingAXS_suberlak.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "\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",
    "    \n",
    "    \n",
    "#spark = pyspark.sql.SparkSession.builder \\\n",
    "#    .master(\"spark://epyc:7079\") \\\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()"
   ]
  },
  {
   "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:4043\">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 0x7fb40a42a710>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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='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='faraway4', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='faraway_set', 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='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='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_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_sdss_dr9_qso', database='default', description=None, tableType='MANAGED', isTemporary=False)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.catalog.listTables()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "| Catalog        | count         | count no dups | size compressed | uncompressed | table name       |\n",
    "|----------------+---------------+---------------+-----------------+--------------+------------------|\n",
    "| SDSS           | 768,618,139   | 709,543,959   | 66 GB           |              | sdss_zoned1am_hd |\n",
    "| Gaia           | 1,237,903,979 |               |                 |              | gaia_zoned1am_hd |\n",
    "| Gaia DR2       | 1,833,997,578 | 1,692,919,135 | 424 GB          |              | gaia_dr2_1am     |\n",
    "| AllWise        | 809,943,596   | 747,634,026   | 357 GB          |              | allwise_1am_dup  |\n",
    "| ZTF            | 15,569,370    |               | 4 GB            |              | ztf_1am          |\n",
    "| ZTF matchfiles | 3,266,237,701 | 2,927,622,797 | 1.23 TB         |              | ztf_1am_lc       |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load AXS catalogs with ZTF and SDSS\n",
    "\n",
    "( assuming that steps below have already been followed to load the QSO catalog into AXS ) ; `spark.catalog.listTables()` shows that all catalogs below exist. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axs.catalog import AxsCatalog\n",
    "from axs.axsframe import AxsFrame\n",
    "axs_catalog = AxsCatalog(spark)\n",
    "#ztf_alerts = axs_catalog.load(\"ztf_1am\")  # ztf alerts \n",
    "#ztf_lc = axs_catalog.load(\"ztf_1am_lc\") # ztf light curves .... \n",
    "#sdss = axs_catalog.load(\"sdss_zoned1am_hd\")\n",
    "#qso = axs_catalog.load(\"sdss_dr9_qso_s82\")\n",
    "exp = axs_catalog.load(\"ztf_exposures\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(expid='000476040344435975', filterid=2, diq=1.6597765684127808, infobits=0, magzp=26.03121566772461, magzprms=0.025604503229260445, magzpunc=7.669443220947869e-06, relphotsatmag=13.5, relphotsyserr=0.00957603007555008, relphotzp=0.000421752076363191, relphotzperr=0.012875262647867203)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axs import Constants\n",
    "axs_catalog.import_existing_table('ztf_exposures', path='', \n",
    "                                  num_buckets=500, zone_height=Constants.ONE_AMIN, \n",
    "                      import_into_spark=False,\n",
    "                      update_spark_bucketing=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "axs_catalog.import_existing_table('sdss_dr9_qso_s82', path='', \n",
    "                                  num_buckets=500, zone_height=Constants.ONE_AMIN, \n",
    "                      import_into_spark=False,\n",
    "                      update_spark_bucketing=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "axs_catalog.import_existing_table('ztf_fid3_sdss_stars_lt20_2asec', path='',                      num_buckets=500, zone_height=Constants.ONE_AMIN, \n",
    "                      import_into_spark=False,\n",
    "                      update_spark_bucketing=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps to save the SDSS S82 DR9 QSO catalog as AXS catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "AnalysisException",
     "evalue": "'Table `sdss_dr9_qso_s82` already exists.;'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m/epyc/opt/spark-axs/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\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[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m    327\u001b[0m                     \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m                     format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m    329\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o126.saveAsTable.\n: org.apache.spark.sql.AnalysisException: Table `sdss_dr9_qso_s82` already exists.;\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:418)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:403)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.lang.Thread.run(Thread.java:748)\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mAnalysisException\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-9604db8c6237>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mschema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStructType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0msdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreateDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0maxs_catalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_axs_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"sdss_dr9_qso_s82\"\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m500\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;31m# Constants.ONE_AMIN)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;31m#axs_catalog.drop_table(\"sdss_dr9_qso_s82\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/axs/catalog.py\u001b[0m in \u001b[0;36msave_axs_table\u001b[0;34m(self, df, tblname, repartition, calculate_zone, num_buckets, zone_height)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mnewdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"parquet\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0mbucketBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_buckets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msortBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ra\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtblname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         self._CatalogUtils.saveNewTable(tblname, num_buckets, AxsCatalog.ZONE_COLNAME, zone_height,\n\u001b[1;32m    180\u001b[0m                                                  \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRA_COLNAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDEC_COLNAME\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/readwriter.py\u001b[0m in \u001b[0;36msaveAsTable\u001b[0;34m(self, name, format, mode, partitionBy, **options)\u001b[0m\n\u001b[1;32m    771\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mformat\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[0m\n\u001b[1;32m    772\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 773\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    775\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0msince\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.4\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;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1255\u001b[0m         \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[0m\n\u001b[1;32m   1256\u001b[0m         return_value = get_return_value(\n\u001b[0;32m-> 1257\u001b[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\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/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m     67\u001b[0m                                              e.java_exception.getStackTrace()))\n\u001b[1;32m     68\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'org.apache.spark.sql.AnalysisException: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mAnalysisException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstackTrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'org.apache.spark.sql.catalyst.analysis'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mAnalysisException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstackTrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAnalysisException\u001b[0m: 'Table `sdss_dr9_qso_s82` already exists.;'"
     ]
    }
   ],
   "source": [
    "# Full lines ...\n",
    "import pyspark\n",
    "from axs.catalog import AxsCatalog\n",
    "from axs.axsframe import AxsFrame\n",
    "from astropy.table import Table\n",
    "from pyspark.sql.types import *\n",
    "from axs import Constants\n",
    "\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",
    "\n",
    "axs_catalog = AxsCatalog(spark)\n",
    "\n",
    "# Check how many SDSS S82 DR9 QSOs have ZTF matches ... \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)\n",
    "axs_catalog.save_axs_table(sdf,\"sdss_dr9_qso_s82\",  True, True, 500 ) # Constants.ONE_AMIN)\n",
    "\n",
    "#axs_catalog.drop_table(\"sdss_dr9_qso_s82\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps to crossmatch ZTF matchfiles (LC) to SDSS S82  DB9 QSO\n",
    "\n",
    "Need to limit the range of ZTF to ra,dec covered by S82 - otherwise crossmatch becomes very slow ... much slower than  the crossmatch of full SDSS catalog to ZTF LC !  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "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'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select ZTF light curves that have over 1 obs , and that have  $\\|{ \\delta }\\| < 1.3$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import abs as sparkabs  # need to use the spark abs \n",
    "from axs import Constants\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": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.6 ms, sys: 5.29 ms, total: 18.9 ms\n",
      "Wall time: 1min 33s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "364618"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "ztf_lc_select.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "print('Crossmatching  SDSS S82 QSO  to ZTF Lightcurves  ...')\n",
    "fname = \"ztf_lc_sdss_qso_dr9_2asec\"\n",
    "crossmatch = ztf_lc_select.crossmatch(qso, r=2*Constants.ONE_ASEC).\\\n",
    "    save_axs_table(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Table ztf_lc_sdss_qso_dr9_2asec not found in AXS catalog",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-deb7f8d1213c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mqso_ztf\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0maxs_catalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ztf_lc_sdss_qso_dr9_2asec\"\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/catalog.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, table_name)\u001b[0m\n\u001b[1;32m     48\u001b[0m         \"\"\"\n\u001b[1;32m     49\u001b[0m         \u001b[0;31m# if table_name not in AxsCatalog._AXS_TABLES:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/axs/catalog.py\u001b[0m in \u001b[0;36mtable_info\u001b[0;34m(self, table_name)\u001b[0m\n\u001b[1;32m    110\u001b[0m                     \u001b[0;34m'bucket_col'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetBucketCol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ra_col'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetRaCol\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    111\u001b[0m                     'dec_col': x.getDecCol(), 'has_lightcurves': x.isLightcurves(), 'lc_columns': x.getLcColumns()}\n\u001b[0;32m--> 112\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Table %s not found in AXS catalog\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlist_tables\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[0;31mAttributeError\u001b[0m: Table ztf_lc_sdss_qso_dr9_2asec not found in AXS catalog"
     ]
    }
   ],
   "source": [
    "qso_ztf= axs_catalog.load(\"ztf_lc_sdss_qso_dr9_2asec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qso_ztf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps to crossmatch SDSS to ZTF LC, to obtain offsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (can skip) Inspect ZTF lightcurves \n",
    ": Show the first row of ZTF light curves database... BTW, 1am means its zoned into patches 1 arcmin wide. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(matchid='000377120321326', ra=227.4253907, dec=-9.6100735, zone=4823, dup=0, astrometricrms=1.480942858968073e-07, bestastrometricrms=1.6221133420573738e-07, bestchisq=0.2913026809692383, bestcon=1.0, bestlineartrend=-0.00032020866638049483, bestmagrms=0.013065037317574024, bestmaxmag=17.100509643554688, bestmaxslope=0.0006273130420595407, bestmeanmag=17.0830020904541, bestmedianabsdev=0.012224197387695312, bestmedianmag=17.083158493041992, bestminmag=17.06770133972168, bestnmedianbufferrange=1, bestnpairposslope=3, bestprobnonqso=0.0, bestprobqso=0.0, bestskewness=0.04900272563099861, bestsmallkurtosis=-5.769548416137695, beststetsonj=-0.7886717915534973, beststetsonk=0.8206236362457275, bestvonneumannratio=0.8191483020782471, bestweightedmagrms=0.013745675794780254, bestweightedmeanmag=17.08048439025879, chisq=0.24118776619434357, con=1.0, lineartrend=-0.00017615032265894115, magrms=0.012040390633046627, maxmag=17.100509643554688, maxslope=0.11018139868974686, meanmag=17.082378387451172, medianabsdev=0.011322021484375, medianmag=17.080839157104492, minmag=17.06770133972168, nbestobs=6, ngoodobs=7, nmedianbufferrange=1, nobs=7, npairposslope=3, probnonqso=0.0, probqso=0.0, refchi=0.0, refmag=0.0, refmagerr=0.0, refsharp=0.0, refsnr=0.0, skewness=0.1802356094121933, smallkurtosis=-5.0761542320251465, stetsonj=-0.81882643699646, stetsonk=0.7800307273864746, uncalibmeanmag=0.0, vonneumannratio=0.8093398809432983, weightedmagrms=0.012759239412844181, weightedmeanmag=17.08028221130371, is_transient=True, bestnabovemeanbystd=[0, 0, 0], bestnbelowmeanbystd=[2, 0, 0], bestnconsecabovemeanbystd=[0, 0, 0], bestnconsecbelowmeanbystd=[0, 0, 0], bestnconsecfrommeanbystd=[0, 0, 0], nabovemeanbystd=[1, 0, 0], nbelowmeanbystd=[2, 0, 0], nconsecabovemeanbystd=[0, 0, 0], nconsecbelowmeanbystd=[0, 0, 0], nconsecfrommeanbystd=[0, 0, 0], bestpercentiles=[17.069517135620117, 17.0686092376709, 17.068220138549805, 17.068063735961914, 17.06797981262207, 17.067928314208984, 17.067852020263672, 17.06783676147461, 17.067821502685547, 17.067811965942383, 17.06780242919922, 17.06779670715332], percentiles=[17.071340560913086, 17.068790435791016, 17.068323135375977, 17.06813621520996, 17.068037033081055, 17.067974090576172, 17.067882537841797, 17.06786346435547, 17.067846298217773, 17.067832946777344, 17.067821502685547, 17.06781578063965], bestperiodsearch=[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], periodsearch=[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], filterid=[2, 2], catflags=[0, 0], chi=[0.7400000095367432, 1.003000020980835], declc=[-9.6100814, -9.610067], expid=[47729274, 46532184], hjd=[2458231.7983044707, 2458219.8269698024], mag=[17.085477828979492, 17.093965530395508], magerr=[0.030965270474553108, 0.04260929673910141], mjd=[58231.2927431, 58219.3218403], programid=[2, 2], psfflux=[4296.302734375, 2693.982177734375], psffluxerr=[117.12841033935547, 100.9868392944336], psfmag=[17.08242416381836, 17.08732032775879], psfmagerr=[0.029999999329447746, 0.04100000113248825], ralc=[227.4253863, 227.4253978], relphotflags=[0, 0], sharp=[0.012000000104308128, -0.04899999871850014], sid=[4719, 3707], snr=[36.68000030517578, 26.68000030517578], xpos=[3057.77587890625, 3057.712890625], ypos=[2719.758056640625, 2658.87890625])]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ztf_lc.head(1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so per row there are both aggregate values (summary stats over multiple epochs), and epochal values. Display again only a couple to see what they are : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(zone=4823, matchid='000377120321326', expid=[47729274, 46532184], mjd=[58231.2927431, 58219.3218403], mag=[17.085477828979492, 17.093965530395508], filterid=[2, 2], nobs=7)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ztf_lc['zone','matchid' , 'expid', 'mjd', 'mag', 'filterid', 'nobs'].head(1)\n",
    "#, 'ra','dec', , 'bestmedianmag',, 'bestminmag', 'bestmaxmag','nobs', 'nbestobs', "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+--------------------+--------------------+--------------------+--------+----+\n",
      "|        matchid|               expid|                 mjd|                 mag|filterid|nobs|\n",
      "+---------------+--------------------+--------------------+--------------------+--------+----+\n",
      "|000377120321326|[47729274, 46532184]|[58231.2927431, 5...|[17.0854778289794...|  [2, 2]|   7|\n",
      "+---------------+--------------------+--------------------+--------------------+--------+----+\n",
      "only showing top 1 row\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# another way to show the table is .show() method : \n",
    "# ztf_lc['matchid' , 'expid', 'mjd', 'mag', 'filterid', 'nobs'].show(1)\n",
    "#, 'ra','dec', , 'bestmedianmag',, 'bestminmag', 'bestmaxmag','nobs', 'nbestobs', "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So ztf_1am_lc contains some scalar ( matchid, ra, dec, zone, dup[licate], astrometricrms, bestastrometricrms, bestmedianmag, bestmeanmag, medianmag, bestmag, etc.  ) and some vector quantities (mag, magerr, mjd, pfflux, psfmag, fid ... ).  \n",
    "\n",
    "We assume that whenever there is a vector of length= 3, it corresponds to filter-averages, in order of [g,r,i] (eg. nbelowmeanbystd, nabovemeanbystd, etc) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (can skip) Inspect SDSS database "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(index=29749, RUN=6361, CAMCOL=1, FIELD=110, ID=565, OBJC_TYPE=3, OBJC_FLAGS=268435968, OBJC_FLAGS2=0, RA=5.241996635503199, DEC=-16.26120978208088, RESOLVE_STATUS=17, uExt=0.13187265396118164, gExt=0.0970306396484375, rExt=0.07037471234798431, iExt=0.05336301401257515, zExt=0.03783504292368889, u=0.8058638572692871, g=1.0961277484893799, r=2.518923759460449, i=3.779599905014038, z=5.626303672790527, uErr=10.498696327209473, gErr=79.64900207519531, rErr=38.9918212890625, iErr=18.439966201782227, zErr=1.252055048942566, uCalib=1, gCalib=1, rCalib=1, iCalib=1, zCalib=1, obj_type=1, obj_index=344036178871, zone=4424, dup=0)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdss.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note  : the OBJC_TYPE corresponds to \n",
    "http://skyserver.sdss.org/dr14/en/help/browser/browser.aspx#&&history=enum+PhotoType+E\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ext =  PSF - aperture  # in fluxes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot one million Galaxies : OBJC_TYPE == 3 . (ensure that we are only using nonnegative fluxes : u,g,r,i,z are fluxes! ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "\n",
    "# select only galaxies,  \n",
    "# ensure non-negative fluxes ...\n",
    "sdsst = sdss.select(['zone', 'u','g','r','OBJC_TYPE', 'obj_type',\n",
    "                    'uExt', 'gExt']).\\\n",
    "    where(sdss['OBJC_TYPE'] == 3).\\\n",
    "    where((sdss['u'] > 0) &(sdss['g'] > 0)&(sdss['r'] > 0) ).\\\n",
    "    limit(1000000).toPandas()\n",
    "    \n",
    "# convert to mags ...\n",
    "u =  -2.5 * np.log10(sdsst['u']) + 22.5\n",
    "g =  -2.5 * np.log10(sdsst['g']) + 22.5\n",
    "r =  -2.5 * np.log10(sdsst['r']) + 22.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>OBJC_TYPE</th>\n",
       "      <th>obj_type</th>\n",
       "      <th>uExt</th>\n",
       "      <th>gExt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4424</td>\n",
       "      <td>0.805864</td>\n",
       "      <td>1.096128</td>\n",
       "      <td>2.518924</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.131873</td>\n",
       "      <td>0.097031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4424</td>\n",
       "      <td>0.214486</td>\n",
       "      <td>0.604853</td>\n",
       "      <td>0.924245</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.133380</td>\n",
       "      <td>0.098140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4424</td>\n",
       "      <td>1.273877</td>\n",
       "      <td>1.266236</td>\n",
       "      <td>2.032461</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.134188</td>\n",
       "      <td>0.098734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zone         u         g         r  OBJC_TYPE  obj_type      uExt      gExt\n",
       "0  4424  0.805864  1.096128  2.518924          3         1  0.131873  0.097031\n",
       "1  4424  0.214486  0.604853  0.924245          3         1  0.133380  0.098140\n",
       "2  4424  1.273877  1.266236  2.032461          3         1  0.134188  0.098734"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first few rows... \n",
    "sdsst[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hexbin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa488679400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig,ax = plt.subplots(1,1,figsize=(7,6))\n",
    "img = ax.hexbin(u-g , g-r , extent=(-0.5,3,-0.5, 1.6))\n",
    "ax.set_title('SDSS 1 mln Galaxies')\n",
    "ax.set_xlabel('SDSS u-g')\n",
    "ax.set_ylabel('SDSS g-r')\n",
    "plt.colorbar(img, label='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 1 million of Stars  : OBJC_TYPE == 6 . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.3 s, sys: 2 s, total: 21.3 s\n",
      "Wall time: 8.35 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# select stars,  \n",
    "# ensure non-negative fluxes ...\n",
    "sdsst = sdss.select(['u','g','r','OBJC_TYPE', 'obj_type',\n",
    "                    'uExt', 'gExt']).\\\n",
    "    where(sdss['OBJC_TYPE'] == 6).\\\n",
    "    where((sdss['u'] > 0) &(sdss['g'] > 0)&(sdss['r'] > 0) ).\\\n",
    "    limit(1000000).toPandas()\n",
    "\n",
    "# convert to mags ...\n",
    "u =  -2.5 * np.log10(sdsst['u']) + 22.5\n",
    "g =  -2.5 * np.log10(sdsst['g']) + 22.5\n",
    "r =  -2.5 * np.log10(sdsst['r']) + 22.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa489f93a20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig,ax = plt.subplots(1,1,figsize=(7,6))\n",
    "img = ax.hexbin(u-g , g-r , extent=(-0.5,3,-0.5, 1.6))\n",
    "ax.set_title('SDSS 1 mln Stars')\n",
    "ax.set_xlabel('SDSS u-g')\n",
    "ax.set_ylabel('SDSS g-r')\n",
    "plt.colorbar(img, label='count')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks pretty!  Exactly what I'd expect from a stellar locus.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So in summary, to select stars, with non-negative fluxes, we want : \n",
    "\n",
    "    * OBJC_TYPE == 6 \n",
    "    * u > 0 , g > 0 , r > 0 , i>0,  z>0 (fluxes)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (can skip) Select subset of the SDSS table for crossmatching "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are billions of stars in the SDSS catalog - we may want to skip the very faint ones. Also, since there may be some junk at faint end, need to select stars brighter than eg. 22.5 mag in u ... To do that need to use  a pyspark function  `sparklog10`.  So first find out how many there are  (this is done by only applying `.where().count()`,  instead of `.select().where()` )  : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98735687\n",
      "CPU times: user 6.18 ms, sys: 1.02 ms, total: 7.19 ms\n",
      "Wall time: 5.92 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# first count : do  .where().count()\n",
    "# limit at  g < 20 magnitude \n",
    "\n",
    "from pyspark.sql.functions import log10 as sparklog10\n",
    "limitMag = 20 \n",
    "a = sdss.where((sdss['OBJC_TYPE'] == 6) & (sdss['u'] > 0) &(sdss['g'] > 0)&(sdss['r'] > 0)&\\\n",
    "          ((-2.5 * sparklog10(sdss['g']) + 22.5) < limitMag)).count()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.57 ms, sys: 6.21 ms, total: 13.8 ms\n",
      "Wall time: 688 ms\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# now do the  selection  :  .select().where()\n",
    "from pyspark.sql.functions import log10 as sparklog10\n",
    "limitMag = 20 \n",
    "sdss_sel = sdss.select(['zone', 'RA', 'DEC', 'u','g','r','i',\n",
    "                        'uErr','gErr', 'rErr', 'iErr', 'dup']).\\\n",
    "    where((sdss['OBJC_TYPE'] == 6) & (sdss['u'] > 0) &(sdss['g'] > 0)&(sdss['r'] > 0)&\\\n",
    "    ((-2.5 * sparklog10(sdss['g']) + 22.5) < limitMag))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (can skip) Inspect further ZTF ... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First,  show with Pandas what the ZTF matchfiles look like : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this average doesn't work, I don't know why ...\n",
    "#from pyspark.sql.functions import first, avg\n",
    "#ztf_lct = ztf_lc.select(['matchid', 'ra', 'dec', 'zone', 'dup', \n",
    "#               'bestmagrms', 'bestmeanmag', 'bestmedianmag',\n",
    "#               'medianmag','meanmag',\n",
    "#'nbestobs', 'ngoodobs', 'nobs','bestmedianmag', 'filterid', 'mag','mjd']).\\\n",
    "#  where(ztf_lc['ngoodobs']>100).groupBy(ztf_lc.matchid).\\\n",
    "#    agg(avg(\"filterid\").alias(\"fid\")).\\\n",
    "#    limit(1000).toPandas()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Either way, turns out that each row in ztf_1am_lc  corresponds to a different filter,  and different filters have different matchids. Select objects that have lots of obs to illustrate the point : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ztf_lct = ztf_lc.select(['matchid', 'ra', 'dec', 'zone', 'dup', \n",
    "               'bestmagrms', 'bestmeanmag', 'bestmedianmag',\n",
    "               'medianmag','meanmag',\n",
    "'nbestobs', 'ngoodobs', 'nobs','bestmedianmag', 'filterid', 'mag','mjd']).\\\n",
    "  where(ztf_lc['ngoodobs']>100).\\\n",
    "    limit(1000).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ztf_lct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, each row of ztf_1am_lc  has a bunch of aggregate informations, and some column arrays. Each row corresponds to a different filterid  (as we see above,  filterid is the same for all obs in a given row ... ).\n",
    "\n",
    "Thus, we need to first select rows that have obs from filterid=1,  then =2,  etc.  The majority of obs have either fid 1 or 2. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out how many rows we are talking about ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%time\n",
    "#ztf_lc.where((ztf_lc['ngoodobs']>500) & (array_contains(ztf_lc['filterid'], 1))).count()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select subset of ZTF to crossmatch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to select each filter individually \n",
    "# First filterid = 1,  then =2,  then=3  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First , count how many ZTF objects have over 10 obs in filterid 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48301066\n",
      "CPU times: user 72.2 ms, sys: 20.9 ms, total: 93.1 ms\n",
      "Wall time: 6min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# \n",
    "from pyspark.sql.functions import array_contains\n",
    "# Use this function to select only rows that have \n",
    "# a  given filterid \n",
    "\n",
    "a = ztf_lc.where((ztf_lc['ngoodobs']>10) & (array_contains(ztf_lc['filterid'], 1))).count()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensure that there are no columns with duplicate names between SDSS and ZTF :  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    ZTF : 'zone', 'dup', 'matchid', 'ra', 'dec', \n",
    "               'bestmagrms', 'bestmeanmag', 'bestmedianmag',\n",
    "               'medianmag','meanmag','nbestobs', 'ngoodobs'\n",
    "            \n",
    "    SDSS : 'zone','dup', 'RA', 'DEC', 'u','g','r','i',\n",
    "            'uErr','gErr', 'rErr', 'iErr', \n",
    "        \n",
    "( there aren't : `zone` and `dup` are required and are handled internally  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The actual ZTF-SDSS crossmatching "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###  Minimal working example : crossmatch ZTF LC to SDSS ####\n",
    "    \n",
    "from axs import Constants\n",
    "from pyspark.sql.functions import array_contains\n",
    "from pyspark.sql.functions import log10 as sparklog10\n",
    "from axs.catalog import AxsCatalog\n",
    "from axs.axsframe import AxsFrame\n",
    "import pyspark\n",
    "\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",
    "     \n",
    "axs_catalog = AxsCatalog(spark)\n",
    "\n",
    "ztf_lc = axs_catalog.load(\"ztf_1am_lc\")\n",
    "sdss = axs_catalog.load(\"sdss_zoned1am_hd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import array_contains\n",
    "from pyspark.sql.functions import log10 as sparklog10\n",
    "\n",
    "# select SDSS stars ... \n",
    "limitMag = 20 \n",
    "sdss_sel = sdss.select(['zone', 'RA', 'DEC', 'u','g','r','i',\n",
    "                        'uErr','gErr', 'rErr', 'iErr', 'dup']).\\\n",
    "    where((sdss['OBJC_TYPE'] == 6) & (sdss['u'] > 0) &(sdss['g'] > 0)&(sdss['r'] > 0)&\\\n",
    "    ((-2.5 * sparklog10(sdss['g']) + 22.5) < limitMag))\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crossmatching ZTF fid=3 to SDSS stars  g < 20.00...\n",
      "Saving to  ztf_fid3_sdss_stars_lt20_2asec\n"
     ]
    }
   ],
   "source": [
    "from axs import Constants\n",
    "# select ZTF obs in a given filter, with over 10 good obs ...\n",
    "fid = 3 \n",
    "ztf_lc_fid = ztf_lc.select(['zone', 'dup', 'matchid', 'ra', 'dec', \n",
    "               'bestmagrms', 'bestmeanmag', 'bestmedianmag',\n",
    "               'medianmag','meanmag','nbestobs', 'ngoodobs']).\\\n",
    "      where((ztf_lc['ngoodobs']>10) & (array_contains(ztf_lc['filterid'], fid)))\n",
    "        \n",
    "# crossmatch \n",
    "print('Crossmatching ZTF fid=%d to SDSS stars  g < %.2f...'%(fid, limitMag))\n",
    "fname = \"ztf_fid\"+str(fid)+\"_sdss_stars_lt\"+str(limitMag)+\"_2asec\"\n",
    "print('Saving to ', fname)\n",
    "    \n",
    "crossmatch = ztf_lc_fid.\\\n",
    "    crossmatch(sdss_sel, r=2*Constants.ONE_ASEC).\\\n",
    "    save_axs_table(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossmatch?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Crossmatch ZTF to SDSS  : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crossmatch ZTF fid=1 (2,3), i.e. \tZTF g, ZTF r, ZTF i,   to SDSS with matching radius of 2 arcsec : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crossmatching ZTF fid=1 to SDSS stars  g < 20.00...\n",
      "Saving to  ztf_fid1_sdss_stars_lt20_2asec\n"
     ]
    },
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o448.saveAsTable.\n: org.apache.spark.SparkException: Job aborted.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:196)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)\n\tat org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:503)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:217)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:176)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)\n\tat org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:670)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:670)\n\tat org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)\n\tat org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:670)\n\tat org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:468)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:447)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:403)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 15 in stage 1.0 failed 4 times, most recent failure: Lost task 15.3 in stage 1.0 (TID 60, 140.142.4.22, executor 13): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-axs/python/lib/pyspark.zip/pyspark/worker.py\", line 261, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:418)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:372)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:98)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:96)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:121)\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$7.apply(Executor.scala:367)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1348)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:373)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1879)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1867)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1866)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1866)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2100)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2049)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2038)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)\n\t... 35 more\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-axs/python/lib/pyspark.zip/pyspark/worker.py\", line 261, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:418)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:372)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:98)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:96)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:121)\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$7.apply(Executor.scala:367)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1348)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:373)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-axs/python/axs/axsframe.py\u001b[0m in \u001b[0;36msave_axs_table\u001b[0;34m(self, tblname, calculate_zone)\u001b[0m\n\u001b[1;32m    342\u001b[0m         \"\"\"\n\u001b[1;32m    343\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatalog\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m         \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_axs_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtblname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalculate_zone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_primitive_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoltype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mcol_names\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/axs/catalog.py\u001b[0m in \u001b[0;36msave_axs_table\u001b[0;34m(self, df, tblname, repartition, calculate_zone, num_buckets)\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0mnewdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"parquet\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0mbucketBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_buckets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msortBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ra\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m             \u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtblname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m         self._CatalogUtils.saveNewTable(tblname, num_buckets, AxsCatalog.ZONE_COLNAME,\n\u001b[1;32m    173\u001b[0m                                                                              \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRA_COLNAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDEC_COLNAME\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/readwriter.py\u001b[0m in \u001b[0;36msaveAsTable\u001b[0;34m(self, name, format, mode, partitionBy, **options)\u001b[0m\n\u001b[1;32m    771\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mformat\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[0m\n\u001b[1;32m    772\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 773\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    775\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0msince\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.4\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;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1255\u001b[0m         \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[0m\n\u001b[1;32m   1256\u001b[0m         return_value = get_return_value(\n\u001b[0;32m-> 1257\u001b[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\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/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\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[1;32m     62\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\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[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoString\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/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m    326\u001b[0m                 raise Py4JJavaError(\n\u001b[1;32m    327\u001b[0m                     \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m                     format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m    329\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 raise Py4JError(\n",
      "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o448.saveAsTable.\n: org.apache.spark.SparkException: Job aborted.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:196)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)\n\tat org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:503)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:217)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:176)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)\n\tat org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:670)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:670)\n\tat org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)\n\tat org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:670)\n\tat org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:468)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:447)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:403)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 15 in stage 1.0 failed 4 times, most recent failure: Lost task 15.3 in stage 1.0 (TID 60, 140.142.4.22, executor 13): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-axs/python/lib/pyspark.zip/pyspark/worker.py\", line 261, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:418)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:372)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:98)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:96)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:121)\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$7.apply(Executor.scala:367)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1348)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:373)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1879)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1867)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1866)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1866)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2100)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2049)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2038)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)\n\t... 35 more\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-axs/python/lib/pyspark.zip/pyspark/worker.py\", line 261, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:418)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:372)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:98)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:96)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:121)\n\tat org.apache.spark.executor.Executor$TaskRunner$$anonfun$7.apply(Executor.scala:367)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1348)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:373)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# we don't limit the extent, or express to Pandas , since \n",
    "# wer're trying to crossmatch within AXS framework  \n",
    "\n",
    "from axs import Constants\n",
    "from pyspark.sql.functions import array_contains\n",
    "\n",
    "# Loop over fid = 1,2,3 .... \n",
    "\n",
    "for fid in [1,2,3] : \n",
    "    ztf_lc_fid = ztf_lc.select(['zone', 'dup', 'matchid', 'ra', 'dec', \n",
    "               'bestmagrms', 'bestmeanmag', 'bestmedianmag',\n",
    "               'medianmag','meanmag','nbestobs', 'ngoodobs']).\\\n",
    "      where((ztf_lc['ngoodobs']>10) & (array_contains(ztf_lc['filterid'], fid)))\n",
    "        \n",
    "    print('Crossmatching ZTF fid=%d to SDSS stars  g < %.2f...'%(fid, limitMag))\n",
    "    fname = \"ztf_fid\"+str(fid)+\"_sdss_stars_lt\"+str(limitMag)+\"_2asec\"\n",
    "    print('Saving to ', fname)\n",
    "    \n",
    "    \n",
    "    #ztf_lc_fid = ztf_lc_fid.withColumnRenamed('zone', 'ztf_zone')\n",
    "    #ztf_lc_fid.withColumnRenamed('dup', 'ztf_dup')\n",
    "    \n",
    "    \n",
    "    crossmatch = ztf_lc_fid.\\\n",
    "        crossmatch(sdss_sel, r=2*Constants.ONE_ASEC).\\\n",
    "        save_axs_table(fname)\n",
    "    \n",
    "    \n",
    "    #crossmatch = ztf_lc_fid.withColumnRenamed(\"dup\", \"ztf_dup\").\\\n",
    "    #    withColumnRenamed(\"zone\", \"ztf_zone\").\\\n",
    "    #    crossmatch(sdss_sel, r=2*Constants.ONE_ASEC).\\\n",
    "    #    save_axs_table(fname)\n",
    "    \n",
    "    # withColumnRenamed(\"dup\", \"ztf_dup\").\\\n",
    "     #   withColumnRenamed(\"zone\", \"ztf_zone\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%time \n",
    "#c = ztf_lc.where(ztf_lc['ngoodobs']>1000).count()\n",
    "#print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No need to store before we can crossmatch to SDSS : \n",
    "#%%time \n",
    "#axs_catalog.save_axs_table(ztf_lc_fid, \"ztf_lc_fid_temp_table3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "AxsFrame[ztf_zone: bigint, dup: int, matchid: string, ra: double, dec: double, ztf_zone: bigint, dup: int, bestmagrms: double, bestmeanmag: double, bestmedianmag: double, medianmag: double, meanmag: double, nbestobs: bigint, ngoodobs: bigint]"
      ],
      "text/plain": [
       "AxsFrame[ztf_zone: bigint, dup: int, matchid: string, ra: double, dec: double, ztf_zone: bigint, dup: int, bestmagrms: double, bestmeanmag: double, bestmedianmag: double, medianmag: double, meanmag: double, nbestobs: bigint, ngoodobs: bigint]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.7 ms, sys: 2.16 ms, total: 15.8 ms\n",
      "Wall time: 168 ms\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "from axs import Constants\n",
    "crossmatch = ztf_lc_fid.crossmatch(sdss_sel, r=2*Constants.ONE_ASEC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossmatch.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossmatch.save_axs_table(\"ztf_fid1_sdss_stars_2asec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() missing 1 required positional argument: 'spark_session'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-68-7e2b602917c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatalog\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxsframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAxsFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0maxs_catalog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAxsCatalog\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      4\u001b[0m \u001b[0mztf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxs_catalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ztf_1am\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msdss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxs_catalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sdss_zoned1am_hd\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'spark_session'"
     ]
    }
   ],
   "source": [
    "from axs.catalog import AxsCatalog\n",
    "from axs.axsframe import AxsFrame\n",
    "axs_catalog = AxsCatalog()\n",
    "ztf = axs_catalog.load(\"ztf_1am\")\n",
    "sdss = axs_catalog.load(\"sdss_zoned1am_hd\")\n",
    "\n",
    "from pyspark.sql.functions import first, avg\n",
    "ztf_agg = ztf.select(ztf.zone, ztf.objectId, ztf.fid, \n",
    "                     ztf.classtar, ztf.magpsf, ztf.sigmapsf, \n",
    "                     ztf.ra, ztf.dec, ztf.jd).where((ztf.fid == \"1\") &\\\n",
    "                    (ztf.classtar > 0.5)).groupBy(ztf.objectId).\\\n",
    "                        agg(first(\"zone\").alias(\"zone\"), \n",
    "                        first(\"objectId\").alias(\"objectId2\"), \n",
    "                        first(\"fid\").alias(\"fid\"), \n",
    "                        first(\"classtar\").alias(\"classtar\"), \n",
    "                        avg(\"magpsf\").alias(\"avg_magpsf_g\"), \n",
    "                        avg(\"sigmapsf\").alias(\"avg_sigmapsf_g\"), \n",
    "                        avg(\"ra\").alias(\"ra\"), \n",
    "                        avg(\"dec\").alias(\"dec\"),\n",
    "                        avg(\"jd\").alias(\"avg_jd\"))\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['avg_jd', 'avg_magpsf_g', 'avg_sigmapsf_g', 'classtar', 'dec',\n",
       "        'fid', 'objectId', 'objectId2', 'ra', 'zone'], dtype='<U14'),\n",
       " array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1]))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.unique(ztf_agg.columns, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now need to store before we can crossmatch to SDSS : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o472.saveAsTable.\n: org.apache.spark.SparkException: Job aborted.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:233)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)\n\tat org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:529)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:216)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:176)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)\n\tat org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)\n\tat org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:458)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:437)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 16.0 failed 4 times, most recent failure: Lost task 3.3 in stage 16.0 (TID 3549, 128.95.79.19, executor 4): org.apache.spark.SparkException: Task failed while writing rows.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:294)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:206)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:205)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: java.io.IOException: Mkdirs failed to create file:/epyc/projects/lsd2/pzwarehouse/ztf_agg_temp_table/_temporary/0/_temporary/attempt_20180822171747_0016_m_000003_3 (exists=false, cwd=file:/data/epyc/opt/spark-2.3.0-smj/work/app-20180822155624-0001/4)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:455)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:440)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:911)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:892)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:789)\n\tat org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:241)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:342)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:302)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:151)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.org$apache$spark$sql$execution$datasources$FileFormatWriter$DynamicPartitionWriteTask$$newOutputWriter(FileFormatWriter.scala:520)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:555)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:536)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:893)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1336)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.execute(FileFormatWriter.scala:536)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:278)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:276)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1357)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:281)\n\t... 8 more\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2030)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:203)\n\t... 33 more\nCaused by: org.apache.spark.SparkException: Task failed while writing rows.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:294)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:206)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:205)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\nCaused by: java.io.IOException: Mkdirs failed to create file:/epyc/projects/lsd2/pzwarehouse/ztf_agg_temp_table/_temporary/0/_temporary/attempt_20180822171747_0016_m_000003_3 (exists=false, cwd=file:/data/epyc/opt/spark-2.3.0-smj/work/app-20180822155624-0001/4)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:455)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:440)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:911)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:892)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:789)\n\tat org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:241)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:342)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:302)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:151)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.org$apache$spark$sql$execution$datasources$FileFormatWriter$DynamicPartitionWriteTask$$newOutputWriter(FileFormatWriter.scala:520)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:555)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:536)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:893)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1336)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.execute(FileFormatWriter.scala:536)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:278)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:276)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1357)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:281)\n\t... 8 more\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/axs/catalog.py\u001b[0m in \u001b[0;36msave_axs_table\u001b[0;34m(self, df, tblname, repartition, calculate_zone)\u001b[0m\n\u001b[1;32m     73\u001b[0m             \u001b[0mnewdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"parquet\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m                 \u001b[0mbucketBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mConstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNUM_BUCKETS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msortBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ra\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m                 \u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtblname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXS_TABLES\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtblname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/pyspark/sql/readwriter.py\u001b[0m in \u001b[0;36msaveAsTable\u001b[0;34m(self, name, format, mode, partitionBy, **options)\u001b[0m\n\u001b[1;32m    744\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mformat\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[0m\n\u001b[1;32m    745\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 746\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    747\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0msince\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/anaconda/lib/python3.6/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \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[0m\n\u001b[1;32m   1159\u001b[0m         return_value = get_return_value(\n\u001b[0;32m-> 1160\u001b[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1162\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\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[1;32m     62\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\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[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoString\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/site-packages/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m    318\u001b[0m                 raise Py4JJavaError(\n\u001b[1;32m    319\u001b[0m                     \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m                     format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 raise Py4JError(\n",
      "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o472.saveAsTable.\n: org.apache.spark.SparkException: Job aborted.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:233)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)\n\tat org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:529)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:216)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:176)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)\n\tat org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)\n\tat org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:458)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:437)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 16.0 failed 4 times, most recent failure: Lost task 3.3 in stage 16.0 (TID 3549, 128.95.79.19, executor 4): org.apache.spark.SparkException: Task failed while writing rows.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:294)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:206)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:205)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: java.io.IOException: Mkdirs failed to create file:/epyc/projects/lsd2/pzwarehouse/ztf_agg_temp_table/_temporary/0/_temporary/attempt_20180822171747_0016_m_000003_3 (exists=false, cwd=file:/data/epyc/opt/spark-2.3.0-smj/work/app-20180822155624-0001/4)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:455)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:440)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:911)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:892)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:789)\n\tat org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:241)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:342)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:302)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:151)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.org$apache$spark$sql$execution$datasources$FileFormatWriter$DynamicPartitionWriteTask$$newOutputWriter(FileFormatWriter.scala:520)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:555)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:536)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:893)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1336)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.execute(FileFormatWriter.scala:536)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:278)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:276)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1357)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:281)\n\t... 8 more\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2030)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:203)\n\t... 33 more\nCaused by: org.apache.spark.SparkException: Task failed while writing rows.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:294)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:206)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:205)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\nCaused by: java.io.IOException: Mkdirs failed to create file:/epyc/projects/lsd2/pzwarehouse/ztf_agg_temp_table/_temporary/0/_temporary/attempt_20180822171747_0016_m_000003_3 (exists=false, cwd=file:/data/epyc/opt/spark-2.3.0-smj/work/app-20180822155624-0001/4)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:455)\n\tat org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:440)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:911)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:892)\n\tat org.apache.hadoop.fs.FileSystem.create(FileSystem.java:789)\n\tat org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:241)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:342)\n\tat org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:302)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)\n\tat org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:151)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.org$apache$spark$sql$execution$datasources$FileFormatWriter$DynamicPartitionWriteTask$$newOutputWriter(FileFormatWriter.scala:520)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:555)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask$$anonfun$execute$5.apply(FileFormatWriter.scala:536)\n\tat scala.collection.Iterator$class.foreach(Iterator.scala:893)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1336)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$DynamicPartitionWriteTask.execute(FileFormatWriter.scala:536)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:278)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:276)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1357)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:281)\n\t... 8 more\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "axs_catalog.save_axs_table(ztf_agg, \"ztf_agg_temp_table\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ztf_agg_axs = axs_catalog.load(\"ztf_agg_temp_table\")\n",
    "ztf_agg_axs.crossmatch(sdss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load AXS tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdss = axs_catalog.load(\"sdss_zoned1am_hd\")\n",
    "ztf = axs_catalog.load(\"ztf_1am\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "768618139"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdss.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15569370"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ztf.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the first row : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(index=29749, RUN=6361, CAMCOL=1, FIELD=110, ID=565, OBJC_TYPE=3, OBJC_FLAGS=268435968, OBJC_FLAGS2=0, RA=5.241996635503199, DEC=-16.26120978208088, RESOLVE_STATUS=17, uExt=0.13187265396118164, gExt=0.0970306396484375, rExt=0.07037471234798431, iExt=0.05336301401257515, zExt=0.03783504292368889, u=0.8058638572692871, g=1.0961277484893799, r=2.518923759460449, i=3.779599905014038, z=5.626303672790527, uErr=10.498696327209473, gErr=79.64900207519531, rErr=38.9918212890625, iErr=18.439966201782227, zErr=1.252055048942566, uCalib=1, gCalib=1, rCalib=1, iCalib=1, zCalib=1, obj_type=1, obj_index=344036178871, zone=4424, dup=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdss.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-matching catalogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can cross-match two catalogs, using 2'' as the matching distance, and counting the results like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11844046"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from axs import Constants\n",
    "ztf.crossmatch(sdss, r=2*Constants.ONE_ASEC).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: If `r` is not specified, the default is `Constants.ONE_ASEC`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the results with ALL columns as a new table (takes about 30 minutes). When saving as a new table, Spark complains if there are duplicate column names, so we need to change them first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The tables `gaia_sdss_3asec` (cross-match of gaia and sdss with 3 arc-sec distance) and `gaia_sdss_wise_1asec` (cross-match of gaia, sdss and wise with 1 arc-sec distance) have already been calculated and are available for use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'AxsFrame' object has no attribute 'save_axs_table2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/pyspark/sql/dataframe.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1232\u001b[0m             raise AttributeError(\n\u001b[0;32m-> 1233\u001b[0;31m                 \"'%s' object has no attribute '%s'\" % (self.__class__.__name__, name))\n\u001b[0m\u001b[1;32m   1234\u001b[0m         \u001b[0mjc\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[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mColumn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'AxsFrame' object has no attribute 'save_axs_table2'"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "ztf.withColumnRenamed(\"obj_index\", \"ztf_obj_index\").withColumnRenamed(\"index\", \"ztf_index\").\\\n",
    "crossmatch(sdss, r=2*Constants.ONE_ASEC).\\\n",
    "save_axs_table2(\"ztf_sdss_2asec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['zone', 'dup'], dtype='<U11')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "np.asarray(ztf.columns)[np.in1d(ztf.columns,sdss.columns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[objectId: string, jd: double, fid: int, pid: bigint, diffmaglim: double, programid: int, candid: bigint, isdiffpos: string, tblid: int, nid: int, rcid: int, field: int, xpos: double, ypos: double, ra: double, dec: double, magpsf: double, sigmapsf: double, chipsf: double, magap: double, sigmagap: double, distnr: double, magnr: double, sigmagnr: double, chinr: double, sharpnr: double, sky: double, magdiff: double, fwhm: double, classtar: double, mindtoedge: double, magfromlim: double, seeratio: double, aimage: double, bimage: double, aimagerat: double, bimagerat: double, elong: double, nneg: int, nbad: int, rb: double, ssdistnr: double, ssmagnr: double, ssnamenr: string, sumrat: double, magapbig: double, sigmagapbig: double, ranr: double, decnr: double, sgmag1: double, srmag1: double, simag1: double, szmag1: double, sgscore1: double, distpsnr1: double, ndethist: int, ncovhist: int, jdstarthist: double, jdendhist: double, scorr: double, tooflag: int, objectidps1: bigint, objectidps2: bigint, sgmag2: double, srmag2: double, simag2: double, szmag2: double, sgscore2: double, distpsnr2: double, objectidps3: bigint, sgmag3: double, srmag3: double, simag3: double, szmag3: double, sgscore3: double, distpsnr3: double, nmtchps: int, rfid: bigint, jdstartref: double, jdendref: double, nframesref: int, zone: bigint, ztf_dup: int]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ztf.withColumnRenamed(\"zone\", \"ztf_zone\")\n",
    "ztf.withColumnRenamed(\"dup\", \"ztf_dup\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o855.saveAsTable.\n: org.apache.spark.SparkException: Job aborted.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:233)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)\n\tat org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:529)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:216)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:176)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)\n\tat org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)\n\tat org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:458)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:437)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 17.0 failed 4 times, most recent failure: Lost task 3.3 in stage 17.0 (TID 1526, 128.95.79.19, executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-2.3.0-smj/python/pyspark/worker.py\", line 216, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:300)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:164)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:114)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:254)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2030)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:203)\n\t... 33 more\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-2.3.0-smj/python/pyspark/worker.py\", line 216, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:300)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:164)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:114)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:254)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/axs/axsframe.py\u001b[0m in \u001b[0;36msave_axs_table\u001b[0;34m(self, tblname, calculate_zone)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave_axs_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtblname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalculate_zone\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatalog\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m         \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_axs_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtblname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalculate_zone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;31m#####################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/axs/catalog.py\u001b[0m in \u001b[0;36msave_axs_table\u001b[0;34m(self, df, tblname, repartition, calculate_zone)\u001b[0m\n\u001b[1;32m     73\u001b[0m             \u001b[0mnewdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"parquet\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m                 \u001b[0mbucketBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mConstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNUM_BUCKETS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msortBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zone\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ra\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m                 \u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtblname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0mAxsCatalog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXS_TABLES\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtblname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/pyspark/sql/readwriter.py\u001b[0m in \u001b[0;36msaveAsTable\u001b[0;34m(self, name, format, mode, partitionBy, **options)\u001b[0m\n\u001b[1;32m    744\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mformat\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[0m\n\u001b[1;32m    745\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 746\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveAsTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    747\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0msince\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/anaconda/lib/python3.6/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \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[0m\n\u001b[1;32m   1159\u001b[0m         return_value = get_return_value(\n\u001b[0;32m-> 1160\u001b[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1162\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/epyc/opt/spark-2.3.0-smj/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\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[1;32m     62\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\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[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoString\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/site-packages/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m    318\u001b[0m                 raise Py4JJavaError(\n\u001b[1;32m    319\u001b[0m                     \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m                     format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 raise Py4JError(\n",
      "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o855.saveAsTable.\n: org.apache.spark.SparkException: Job aborted.\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:233)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)\n\tat org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:529)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:216)\n\tat org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:176)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)\n\tat org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)\n\tat org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)\n\tat org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)\n\tat org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:656)\n\tat org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:458)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:437)\n\tat org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:748)\nCaused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 17.0 failed 4 times, most recent failure: Lost task 3.3 in stage 17.0 (TID 1526, 128.95.79.19, executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-2.3.0-smj/python/pyspark/worker.py\", line 216, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:300)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:164)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:114)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:254)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat java.lang.Thread.run(Thread.java:748)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2030)\n\tat org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:203)\n\t... 33 more\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/epyc/opt/spark-2.3.0-smj/python/pyspark/worker.py\", line 216, in main\n    (\"%d.%d\" % sys.version_info[:2], version))\nException: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.\n\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:300)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:164)\n\tat org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:114)\n\tat org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:254)\n\tat org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:90)\n\tat org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)\n\tat org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:288)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:109)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\t... 1 more\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "ztf.withColumnRenamed(\"obj_index\", \"ztf_obj_index\").\\\n",
    "withColumnRenamed(\"index\", \"ztf_index\").\\\n",
    "withColumnRenamed(\"field\", \"ztf_field\").\\\n",
    "crossmatch(sdss, r=2*Constants.ONE_ASEC).\\\n",
    "save_axs_table(\"ztf_sdss_2asec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2836"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = gaia.region(ra1=40, dec1=42, ra2=40.5, dec2=42.5)\n",
    "region.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also calculate a histogram from the cross-matched data. The arguments are:\n",
    "- the column (expression) to compute it on\n",
    "- the resulting column name\n",
    "- number of bins\n",
    "\n",
    "Should take about 3.5 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+\n",
      "| bin|count|\n",
      "+----+-----+\n",
      "| 148| 5037|\n",
      "|2122|   26|\n",
      "| 307| 1319|\n",
      "| 496|  530|\n",
      "| 229| 2258|\n",
      "|1342|   72|\n",
      "| 766|  200|\n",
      "|1088|  105|\n",
      "|2366|   29|\n",
      "|1545|   55|\n",
      "| 833|  168|\n",
      "|1591|   41|\n",
      "|6357|    3|\n",
      "|6454|    1|\n",
      "|2983|   14|\n",
      "|4101|    7|\n",
      "|7638|    2|\n",
      "|3918|    4|\n",
      "|6685|    2|\n",
      "|4916|    4|\n",
      "+----+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "hist = crossmatch.histogram(crossmatch.g - crossmatch.phot_g_mean_mag,\n",
    "                       \"magdiff\", 100)\n",
    "hist.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using light-curve data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is still under development and the currently loaded catalogs do not have light-curve columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Table(name='allwise_1am_dup', database='default', description=None, tableType='EXTERNAL', isTemporary=False),\n",
       " Table(name='demo_result', 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', database='default', description=None, tableType='MANAGED', 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='gaia_zoned1am_hd_ps', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='regiontestscala', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_test', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_test2', 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_700lc_new', database='default', description=None, tableType='MANAGED', isTemporary=False),\n",
       " Table(name='sdss_zoned1am_700lc_noc', 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)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.catalog.listTables()"
   ]
  }
 ],
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