{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Marvin Results\n",
    "\n",
    "This tutorial explores some basics of how to handle results of your Marvin Query.  Much of this information can also be found in the [Marvin Results documentation](https://sdss-marvin.readthedocs.io/en/latest/query/results.html).\n",
    "\n",
    "Table of Contents:\n",
    "- [Performing a Query](#query)<br>\n",
    "- [Retrieving Results](#retrieve)<br>\n",
    "- [Formatting Results](#format)<br>\n",
    "- [Quickly Plotting Results](#plot)<br>\n",
    "- [Downloading Results](#download)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='query'></a>\n",
    "## Performing a Query\n",
    "Our first step is to generate a query.  Let's perform a simple metadata query to look for all galaxies with a redshift < 0.1.  Let's also return the absolute magnitude g-r color and the Elliptical Petrosian half-light radius.  This step assumes familiarity with Marvin Queries.  To learn how to write queries, please see the [Marvin Query documentation](https://sdss-marvin.readthedocs.io/en/latest/query/query.html) or the [Marvin Query Tutorial](https://sdss-marvin.readthedocs.io/en/latest/tutorials/notebooks/marvin_queries.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: No release version set. Setting default to DR15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Brian/anaconda2/envs/marvindev/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/Users/Brian/anaconda2/envs/marvindev/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/Users/Brian/anaconda2/envs/marvindev/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/Users/Brian/anaconda2/envs/marvindev/lib/python3.5/site-packages/marvin/tools/query.py:236: MarvinUserWarning: No local database found. Cannot perform queries.\n",
      "  warnings.warn('No local database found. Cannot perform queries.', MarvinUserWarning)\n"
     ]
    }
   ],
   "source": [
    "# set up and run the query\n",
    "from marvin.tools.query import Query\n",
    "q = Query(search_filter='nsa.z < 0.1', return_params=['absmag_g_r', 'nsa.elpetro_th50_r'])\n",
    "r = q.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marvin Results(query=nsa.z < 0.1, totalcount=4275, count=100, mode=remote)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# repr the results\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our query runs and indicates a total count of 4275 results.  By default, queries that return more than 1000 rows will be automatically paginated into sets (or chunks) of 100 rows, indicated by ``count=100``.  The number of rows queries return can be changed using the ``limit`` keyword argument to ``Qeuery``.  The results are stored in the ``results`` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ResultSet(set=1.0/43, index=0:100, count_in_set=100, total=4275)>\n",
       "[ResultRow(mangaid='1-109056', plateifu='8077-6103', elpetro_absmag_g_r=1.11703872680664, elpetro_th50_r=3.709, z=0.0473019),\n",
       " ResultRow(mangaid='1-109073', plateifu='8078-12704', elpetro_absmag_g_r=1.05978775024414, elpetro_th50_r=7.54724, z=0.0435882),\n",
       " ResultRow(mangaid='1-109081', plateifu='8077-12705', elpetro_absmag_g_r=1.23031806945801, elpetro_th50_r=9.60665, z=0.0438592),\n",
       " ResultRow(mangaid='1-109112', plateifu='8078-1901', elpetro_absmag_g_r=1.5319881439209, elpetro_th50_r=2.25541, z=0.0249702),\n",
       " ResultRow(mangaid='1-109152', plateifu='8154-12702', elpetro_absmag_g_r=1.5439453125, elpetro_th50_r=16.1263, z=0.0281736),\n",
       " ResultRow(mangaid='1-109167', plateifu='8078-1902', elpetro_absmag_g_r=1.65657424926758, elpetro_th50_r=1.91322, z=0.0246344),\n",
       " ResultRow(mangaid='1-109234', plateifu='9193-1901', elpetro_absmag_g_r=1.52570343017578, elpetro_th50_r=2.16528, z=0.0409309),\n",
       " ResultRow(mangaid='1-109244', plateifu='8080-12702', elpetro_absmag_g_r=1.52271270751953, elpetro_th50_r=15.379, z=0.0267911),\n",
       " ResultRow(mangaid='1-109250', plateifu='8080-1902', elpetro_absmag_g_r=0.780342102050781, elpetro_th50_r=2.15977, z=0.0383731),\n",
       " ResultRow(mangaid='1-109270', plateifu='9193-12703', elpetro_absmag_g_r=1.28193092346191, elpetro_th50_r=2.7424, z=0.0252266),\n",
       " ResultRow(mangaid='1-109284', plateifu='8080-6103', elpetro_absmag_g_r=1.13128662109375, elpetro_th50_r=3.61366, z=0.0686101),\n",
       " ResultRow(mangaid='1-109356', plateifu='8081-3703', elpetro_absmag_g_r=1.01610565185547, elpetro_th50_r=4.40028, z=0.0241782),\n",
       " ResultRow(mangaid='1-109378', plateifu='8084-12703', elpetro_absmag_g_r=1.15368461608887, elpetro_th50_r=9.6417, z=0.0391104),\n",
       " ResultRow(mangaid='1-109392', plateifu='8085-6101', elpetro_absmag_g_r=1.57899284362793, elpetro_th50_r=6.78671, z=0.0514803),\n",
       " ResultRow(mangaid='1-109394', plateifu='8082-9102', elpetro_absmag_g_r=1.26038932800293, elpetro_th50_r=7.17137, z=0.0361073),\n",
       " ResultRow(mangaid='1-109428', plateifu='8155-3704', elpetro_absmag_g_r=1.65565872192383, elpetro_th50_r=1.6106, z=0.0476594),\n",
       " ResultRow(mangaid='1-109521', plateifu='8156-6101', elpetro_absmag_g_r=1.79952621459961, elpetro_th50_r=5.32495, z=0.0490484),\n",
       " ResultRow(mangaid='1-113208', plateifu='8618-3701', elpetro_absmag_g_r=1.48788070678711, elpetro_th50_r=2.7162, z=0.0699044),\n",
       " ResultRow(mangaid='1-113219', plateifu='7815-9102', elpetro_absmag_g_r=0.543312072753906, elpetro_th50_r=5.15853, z=0.0408897),\n",
       " ResultRow(mangaid='1-113242', plateifu='7972-6101', elpetro_absmag_g_r=1.90381813049316, elpetro_th50_r=6.17146, z=0.0437197),\n",
       " ResultRow(mangaid='1-113265', plateifu='7972-12703', elpetro_absmag_g_r=1.04117393493652, elpetro_th50_r=8.04141, z=0.0437398),\n",
       " ResultRow(mangaid='1-113315', plateifu='7972-3702', elpetro_absmag_g_r=1.66540908813477, elpetro_th50_r=3.02008, z=0.0622162),\n",
       " ResultRow(mangaid='1-113322', plateifu='7972-12701', elpetro_absmag_g_r=2.22256088256836, elpetro_th50_r=6.59968, z=0.0431628),\n",
       " ResultRow(mangaid='1-113343', plateifu='7972-1901', elpetro_absmag_g_r=1.6228084564209, elpetro_th50_r=1.47839, z=0.0433949),\n",
       " ResultRow(mangaid='1-113346', plateifu='7972-12702', elpetro_absmag_g_r=0.943971633911133, elpetro_th50_r=7.12185, z=0.0431389),\n",
       " ResultRow(mangaid='1-113368', plateifu='7972-6103', elpetro_absmag_g_r=1.61076927185059, elpetro_th50_r=5.52075, z=0.0431534),\n",
       " ResultRow(mangaid='1-113375', plateifu='7972-12704', elpetro_absmag_g_r=0.757579803466797, elpetro_th50_r=8.96766, z=0.028215),\n",
       " ResultRow(mangaid='1-113375', plateifu='7815-9101', elpetro_absmag_g_r=0.757579803466797, elpetro_th50_r=8.96766, z=0.028215),\n",
       " ResultRow(mangaid='1-113379', plateifu='7972-3701', elpetro_absmag_g_r=1.09770011901855, elpetro_th50_r=6.08388, z=0.0171611),\n",
       " ResultRow(mangaid='1-113379', plateifu='7815-6101', elpetro_absmag_g_r=1.09770011901855, elpetro_th50_r=6.08388, z=0.0171611),\n",
       " ResultRow(mangaid='1-113403', plateifu='7815-12703', elpetro_absmag_g_r=0.745466232299805, elpetro_th50_r=4.26406, z=0.0715126),\n",
       " ResultRow(mangaid='1-113404', plateifu='7972-6104', elpetro_absmag_g_r=1.0419807434082, elpetro_th50_r=3.55168, z=0.0422713),\n",
       " ResultRow(mangaid='1-113405', plateifu='7972-3704', elpetro_absmag_g_r=1.27382469177246, elpetro_th50_r=2.43736, z=0.0424723),\n",
       " ResultRow(mangaid='1-113418', plateifu='7815-12704', elpetro_absmag_g_r=1.44098854064941, elpetro_th50_r=7.31411, z=0.0430806),\n",
       " ResultRow(mangaid='1-113469', plateifu='7972-12705', elpetro_absmag_g_r=0.847789764404297, elpetro_th50_r=6.08098, z=0.0394617),\n",
       " ResultRow(mangaid='1-113469', plateifu='7815-12702', elpetro_absmag_g_r=0.847789764404297, elpetro_th50_r=6.08098, z=0.0394617),\n",
       " ResultRow(mangaid='1-113518', plateifu='7972-6102', elpetro_absmag_g_r=0.380338668823242, elpetro_th50_r=6.41199, z=0.0299975),\n",
       " ResultRow(mangaid='1-113520', plateifu='7815-1901', elpetro_absmag_g_r=1.7510347366333, elpetro_th50_r=2.98524, z=0.0167652),\n",
       " ResultRow(mangaid='1-113525', plateifu='8618-6103', elpetro_absmag_g_r=1.57906627655029, elpetro_th50_r=7.97363, z=0.0169457),\n",
       " ResultRow(mangaid='1-113525', plateifu='7815-1902', elpetro_absmag_g_r=1.57906627655029, elpetro_th50_r=7.97363, z=0.0169457),\n",
       " ResultRow(mangaid='1-113525', plateifu='7972-9102', elpetro_absmag_g_r=1.57906627655029, elpetro_th50_r=7.97363, z=0.0169457),\n",
       " ResultRow(mangaid='1-113539', plateifu='8618-12701', elpetro_absmag_g_r=1.26716613769531, elpetro_th50_r=16.6994, z=0.0177002),\n",
       " ResultRow(mangaid='1-113540', plateifu='7815-3702', elpetro_absmag_g_r=0.952407836914062, elpetro_th50_r=3.77495, z=0.0293823),\n",
       " ResultRow(mangaid='1-113551', plateifu='7972-3703', elpetro_absmag_g_r=1.46421432495117, elpetro_th50_r=2.83342, z=0.0588507),\n",
       " ResultRow(mangaid='1-113567', plateifu='7815-12701', elpetro_absmag_g_r=1.41732978820801, elpetro_th50_r=9.66008, z=0.0167432),\n",
       " ResultRow(mangaid='1-113567', plateifu='8618-1902', elpetro_absmag_g_r=1.41732978820801, elpetro_th50_r=9.66008, z=0.0167432),\n",
       " ResultRow(mangaid='1-113585', plateifu='7815-3703', elpetro_absmag_g_r=1.68158912658691, elpetro_th50_r=4.03009, z=0.070276),\n",
       " ResultRow(mangaid='1-113587', plateifu='8618-12704', elpetro_absmag_g_r=1.02355575561523, elpetro_th50_r=5.08098, z=0.0704926),\n",
       " ResultRow(mangaid='1-113647', plateifu='8618-6104', elpetro_absmag_g_r=1.78754997253418, elpetro_th50_r=3.97352, z=0.0738563),\n",
       " ResultRow(mangaid='1-113651', plateifu='7815-3704', elpetro_absmag_g_r=1.4986743927002, elpetro_th50_r=2.96539, z=0.0708847),\n",
       " ResultRow(mangaid='1-113654', plateifu='8618-9102', elpetro_absmag_g_r=1.10831832885742, elpetro_th50_r=4.88881, z=0.0430694),\n",
       " ResultRow(mangaid='1-113663', plateifu='8618-3703', elpetro_absmag_g_r=2.80322933197021, elpetro_th50_r=3.05035, z=0.0316328),\n",
       " ResultRow(mangaid='1-113672', plateifu='8618-3704', elpetro_absmag_g_r=1.25676536560059, elpetro_th50_r=2.30303, z=0.0702278),\n",
       " ResultRow(mangaid='1-113698', plateifu='8618-1901', elpetro_absmag_g_r=0.995195388793945, elpetro_th50_r=3.06745, z=0.0167445),\n",
       " ResultRow(mangaid='1-113700', plateifu='8618-12703', elpetro_absmag_g_r=0.61408805847168, elpetro_th50_r=4.6722, z=0.0378372),\n",
       " ResultRow(mangaid='1-113712', plateifu='7815-6104', elpetro_absmag_g_r=0.69244384765625, elpetro_th50_r=4.57927, z=0.0806967),\n",
       " ResultRow(mangaid='1-114073', plateifu='7975-12705', elpetro_absmag_g_r=0.751516342163086, elpetro_th50_r=8.94859, z=0.0402895),\n",
       " ResultRow(mangaid='1-114082', plateifu='7975-3701', elpetro_absmag_g_r=1.44381332397461, elpetro_th50_r=4.73158, z=0.0402683),\n",
       " ResultRow(mangaid='1-114121', plateifu='7975-12701', elpetro_absmag_g_r=1.43171119689941, elpetro_th50_r=6.238, z=0.0879313),\n",
       " ResultRow(mangaid='1-114128', plateifu='7975-6101', elpetro_absmag_g_r=1.86342239379883, elpetro_th50_r=3.28624, z=0.077875),\n",
       " ResultRow(mangaid='1-114129', plateifu='7975-12702', elpetro_absmag_g_r=2.19032287597656, elpetro_th50_r=10.6624, z=0.0774097),\n",
       " ResultRow(mangaid='1-114145', plateifu='7975-6102', elpetro_absmag_g_r=1.41496467590332, elpetro_th50_r=6.64386, z=0.0341885),\n",
       " ResultRow(mangaid='1-114171', plateifu='7975-3702', elpetro_absmag_g_r=1.70641708374023, elpetro_th50_r=4.32828, z=0.0881405),\n",
       " ResultRow(mangaid='1-114303', plateifu='7975-1901', elpetro_absmag_g_r=0.658689498901367, elpetro_th50_r=9.02474, z=0.0220107),\n",
       " ResultRow(mangaid='1-114306', plateifu='7975-9101', elpetro_absmag_g_r=0.99525260925293, elpetro_th50_r=7.50072, z=0.0636505),\n",
       " ResultRow(mangaid='1-114325', plateifu='7975-12703', elpetro_absmag_g_r=1.34337997436523, elpetro_th50_r=15.3995, z=0.0288791),\n",
       " ResultRow(mangaid='1-114334', plateifu='7975-1902', elpetro_absmag_g_r=1.43183898925781, elpetro_th50_r=3.08396, z=0.0222473),\n",
       " ResultRow(mangaid='1-114454', plateifu='7975-12704', elpetro_absmag_g_r=1.29723358154297, elpetro_th50_r=5.00931, z=0.0888606),\n",
       " ResultRow(mangaid='1-114465', plateifu='7975-6104', elpetro_absmag_g_r=1.21394157409668, elpetro_th50_r=4.04488, z=0.0788547),\n",
       " ResultRow(mangaid='1-114500', plateifu='7975-9102', elpetro_absmag_g_r=1.14164924621582, elpetro_th50_r=7.54684, z=0.0220849),\n",
       " ResultRow(mangaid='1-114502', plateifu='7975-6103', elpetro_absmag_g_r=1.4673023223877, elpetro_th50_r=3.43077, z=0.0798058),\n",
       " ResultRow(mangaid='1-114532', plateifu='7975-3703', elpetro_absmag_g_r=1.73165702819824, elpetro_th50_r=2.77167, z=0.0902261),\n",
       " ResultRow(mangaid='1-114928', plateifu='7977-3702', elpetro_absmag_g_r=1.65719413757324, elpetro_th50_r=4.63049, z=0.0273478),\n",
       " ResultRow(mangaid='1-114955', plateifu='7977-12701', elpetro_absmag_g_r=1.01249313354492, elpetro_th50_r=5.03401, z=0.0922799),\n",
       " ResultRow(mangaid='1-114956', plateifu='7977-3704', elpetro_absmag_g_r=1.3456974029541, elpetro_th50_r=3.93806, z=0.0270248),\n",
       " ResultRow(mangaid='1-114980', plateifu='7977-9102', elpetro_absmag_g_r=1.14808464050293, elpetro_th50_r=4.93319, z=0.0630915),\n",
       " ResultRow(mangaid='1-114998', plateifu='7977-6102', elpetro_absmag_g_r=2.77035713195801, elpetro_th50_r=6.30899, z=0.0614042),\n",
       " ResultRow(mangaid='1-115062', plateifu='7977-1901', elpetro_absmag_g_r=1.65952682495117, elpetro_th50_r=1.29971, z=0.0260569),\n",
       " ResultRow(mangaid='1-115085', plateifu='7977-6103', elpetro_absmag_g_r=0.912630081176758, elpetro_th50_r=4.95689, z=0.0349304),\n",
       " ResultRow(mangaid='1-115097', plateifu='7977-3701', elpetro_absmag_g_r=1.49947357177734, elpetro_th50_r=4.65282, z=0.0274473),\n",
       " ResultRow(mangaid='1-115128', plateifu='7977-1902', elpetro_absmag_g_r=1.1044979095459, elpetro_th50_r=7.78621, z=0.0358116),\n",
       " ResultRow(mangaid='1-115162', plateifu='7977-12703', elpetro_absmag_g_r=1.13131713867188, elpetro_th50_r=7.3267, z=0.0738627),\n",
       " ResultRow(mangaid='1-115320', plateifu='7977-3703', elpetro_absmag_g_r=0.99519157409668, elpetro_th50_r=4.72487, z=0.0275274),\n",
       " ResultRow(mangaid='1-120935', plateifu='8144-12703', elpetro_absmag_g_r=1.19215965270996, elpetro_th50_r=8.77348, z=0.0389089),\n",
       " ResultRow(mangaid='1-120941', plateifu='8144-9101', elpetro_absmag_g_r=1.37554359436035, elpetro_th50_r=7.13642, z=0.0157875),\n",
       " ResultRow(mangaid='1-120943', plateifu='8144-6102', elpetro_absmag_g_r=1.49009323120117, elpetro_th50_r=8.04322, z=0.0393096),\n",
       " ResultRow(mangaid='1-120955', plateifu='8144-12702', elpetro_absmag_g_r=1.05715751647949, elpetro_th50_r=8.80421, z=0.0512278),\n",
       " ResultRow(mangaid='1-120967', plateifu='8144-12704', elpetro_absmag_g_r=1.39360427856445, elpetro_th50_r=5.12678, z=0.0889617),\n",
       " ResultRow(mangaid='1-121023', plateifu='8144-6103', elpetro_absmag_g_r=1.77910804748535, elpetro_th50_r=3.76035, z=0.0617402),\n",
       " ResultRow(mangaid='1-121035', plateifu='8144-1901', elpetro_absmag_g_r=1.74846458435059, elpetro_th50_r=2.75435, z=0.0268051),\n",
       " ResultRow(mangaid='1-121075', plateifu='8144-3702', elpetro_absmag_g_r=1.42728805541992, elpetro_th50_r=2.65939, z=0.0978535),\n",
       " ResultRow(mangaid='1-121151', plateifu='8139-12701', elpetro_absmag_g_r=0.917177200317383, elpetro_th50_r=9.73124, z=0.0155876),\n",
       " ResultRow(mangaid='1-121202', plateifu='8139-1902', elpetro_absmag_g_r=1.09575843811035, elpetro_th50_r=2.09932, z=0.0406385),\n",
       " ResultRow(mangaid='1-121206', plateifu='8139-9101', elpetro_absmag_g_r=0.632560729980469, elpetro_th50_r=8.30522, z=0.0191816),\n",
       " ResultRow(mangaid='1-121209', plateifu='8139-6101', elpetro_absmag_g_r=1.92057991027832, elpetro_th50_r=5.64209, z=0.027711),\n",
       " ResultRow(mangaid='1-121218', plateifu='8139-3703', elpetro_absmag_g_r=0.984956741333008, elpetro_th50_r=7.58118, z=0.0440361),\n",
       " ResultRow(mangaid='1-121235', plateifu='8936-3702', elpetro_absmag_g_r=1.61528015136719, elpetro_th50_r=3.35647, z=0.0609332),\n",
       " ResultRow(mangaid='1-121236', plateifu='8139-6104', elpetro_absmag_g_r=1.68639945983887, elpetro_th50_r=7.48554, z=0.0607858),\n",
       " ResultRow(mangaid='1-121277', plateifu='8977-6101', elpetro_absmag_g_r=2.23527336120605, elpetro_th50_r=3.7656, z=0.0541615),\n",
       " ResultRow(mangaid='1-121330', plateifu='8977-12702', elpetro_absmag_g_r=0.697284698486328, elpetro_th50_r=9.80444, z=0.0174976)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the results\n",
    "r.results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A ``ResultSet`` contains a list of tuple rows with some default parameters like ``mangaid`` and ``plateifu``, plus any parameters used in the ``Query`` ``search_filter`` or requested with the ``return_params`` keyword.  The redshift, g-r color, and half-light radius has been returned.  We can look at all the columns available using the ``columns`` attribute.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ParameterGroup name=Columns, n_parameters=5>\n",
       " [<QueryParameter full=cube.mangaid, name=mangaid, short=mangaid, remote=mangaid, display=Manga-ID>,\n",
       " <QueryParameter full=cube.plateifu, name=plateifu, short=plateifu, remote=plateifu, display=Plate-IFU>,\n",
       " <QueryParameter full=nsa.elpetro_absmag_g_r, name=elpetro_absmag_g_r, short=absmag_g_r, remote=elpetro_absmag_g_r, display=Absmag g-r>,\n",
       " <QueryParameter full=nsa.elpetro_th50_r, name=elpetro_th50_r, short=th50_r, remote=elpetro_th50_r, display=r-band half-light radius>,\n",
       " <QueryParameter full=nsa.z, name=z, short=z, remote=z, display=Redshift>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the columns returned by your results\n",
    "r.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='retrieve'></a>\n",
    "## Retrieving Results\n",
    "There are several options for handling paginated results.  To page through the sets of results without extending the results, use ``getNext`` and ``getPrevious``.  These methods simply page through.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Retrieving next 100, from 100 to 200\n"
     ]
    }
   ],
   "source": [
    "# get the next set of results\n",
    "n = r.getNext()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ResultSet(set=2.0/43, index=100:200, count_in_set=100, total=4275)>\n",
       "[ResultRow(mangaid='1-121334', plateifu='8977-1902', elpetro_absmag_g_r=1.6729907989502, elpetro_th50_r=2.03886, z=0.0417555),\n",
       " ResultRow(mangaid='1-121375', plateifu='8977-3701', elpetro_absmag_g_r=1.73355865478516, elpetro_th50_r=2.59914, z=0.0828189),\n",
       " ResultRow(mangaid='1-121387', plateifu='8977-1901', elpetro_absmag_g_r=1.38675498962402, elpetro_th50_r=1.97636, z=0.0281629),\n",
       " ResultRow(mangaid='1-121413', plateifu='8977-12704', elpetro_absmag_g_r=0.746274948120117, elpetro_th50_r=8.3731, z=0.0158709),\n",
       " ResultRow(mangaid='1-121486', plateifu='8717-3704', elpetro_absmag_g_r=1.18856620788574, elpetro_th50_r=5.81742, z=0.0290077),\n",
       " ResultRow(mangaid='1-121523', plateifu='8977-12703', elpetro_absmag_g_r=2.02036094665527, elpetro_th50_r=5.31518, z=0.0752425),\n",
       " ResultRow(mangaid='1-121598', plateifu='8717-9102', elpetro_absmag_g_r=2.1969108581543, elpetro_th50_r=8.24715, z=0.0714115),\n",
       " ResultRow(mangaid='1-121607', plateifu='9485-1902', elpetro_absmag_g_r=1.71369934082031, elpetro_th50_r=3.44897, z=0.0405107),\n",
       " ResultRow(mangaid='1-121612', plateifu='9485-12702', elpetro_absmag_g_r=0.873281478881836, elpetro_th50_r=11.1178, z=0.0187191),\n",
       " ResultRow(mangaid='1-121697', plateifu='8717-6101', elpetro_absmag_g_r=1.64321327209473, elpetro_th50_r=3.93459, z=0.0456332),\n",
       " ResultRow(mangaid='1-121735', plateifu='8717-3703', elpetro_absmag_g_r=0.967935562133789, elpetro_th50_r=2.76035, z=0.0457813),\n",
       " ResultRow(mangaid='1-121773', plateifu='8717-12703', elpetro_absmag_g_r=0.82383918762207, elpetro_th50_r=12.1638, z=0.019982),\n",
       " ResultRow(mangaid='1-121784', plateifu='9181-3701', elpetro_absmag_g_r=1.23087501525879, elpetro_th50_r=2.91024, z=0.0402774),\n",
       " ResultRow(mangaid='1-121789', plateifu='9485-3701', elpetro_absmag_g_r=1.45931434631348, elpetro_th50_r=2.52096, z=0.0392324),\n",
       " ResultRow(mangaid='1-121803', plateifu='9181-1901', elpetro_absmag_g_r=1.66340065002441, elpetro_th50_r=2.15493, z=0.0408411),\n",
       " ResultRow(mangaid='1-121837', plateifu='9485-3702', elpetro_absmag_g_r=1.32039070129395, elpetro_th50_r=3.10665, z=0.0415041),\n",
       " ResultRow(mangaid='1-121851', plateifu='9485-6101', elpetro_absmag_g_r=1.6701717376709, elpetro_th50_r=5.74633, z=0.0719923),\n",
       " ResultRow(mangaid='1-121871', plateifu='9485-3703', elpetro_absmag_g_r=1.15477180480957, elpetro_th50_r=2.89156, z=0.0441646),\n",
       " ResultRow(mangaid='1-121911', plateifu='9183-12701', elpetro_absmag_g_r=1.38198661804199, elpetro_th50_r=2.09971, z=0.0211728),\n",
       " ResultRow(mangaid='1-121936', plateifu='9485-12703', elpetro_absmag_g_r=1.10766410827637, elpetro_th50_r=9.05546, z=0.0218013),\n",
       " ResultRow(mangaid='1-121947', plateifu='9485-6102', elpetro_absmag_g_r=1.85177803039551, elpetro_th50_r=4.76834, z=0.0891167),\n",
       " ResultRow(mangaid='1-121973', plateifu='9485-6104', elpetro_absmag_g_r=1.4388542175293, elpetro_th50_r=4.30801, z=0.0328443),\n",
       " ResultRow(mangaid='1-121992', plateifu='9183-1902', elpetro_absmag_g_r=1.55809211730957, elpetro_th50_r=1.70253, z=0.0280217),\n",
       " ResultRow(mangaid='1-121994', plateifu='9485-9102', elpetro_absmag_g_r=0.971985816955566, elpetro_th50_r=7.91071, z=0.0188169),\n",
       " ResultRow(mangaid='1-122002', plateifu='9485-1901', elpetro_absmag_g_r=1.04083061218262, elpetro_th50_r=2.24397, z=0.0712221),\n",
       " ResultRow(mangaid='1-122044', plateifu='9181-9102', elpetro_absmag_g_r=1.43736457824707, elpetro_th50_r=4.62257, z=0.0394439),\n",
       " ResultRow(mangaid='1-122054', plateifu='9485-12704', elpetro_absmag_g_r=1.55994606018066, elpetro_th50_r=1.83135, z=0.0422109),\n",
       " ResultRow(mangaid='1-122057', plateifu='9181-1902', elpetro_absmag_g_r=1.33530044555664, elpetro_th50_r=1.8542, z=0.0399474),\n",
       " ResultRow(mangaid='1-122059', plateifu='9183-12704', elpetro_absmag_g_r=1.40407371520996, elpetro_th50_r=8.17737, z=0.0762452),\n",
       " ResultRow(mangaid='1-122067', plateifu='9181-6104', elpetro_absmag_g_r=0.855253219604492, elpetro_th50_r=6.30572, z=0.0233687),\n",
       " ResultRow(mangaid='1-122088', plateifu='9486-12701', elpetro_absmag_g_r=0.916702270507812, elpetro_th50_r=23.9432, z=0.041528),\n",
       " ResultRow(mangaid='1-122094', plateifu='9183-9101', elpetro_absmag_g_r=1.50480842590332, elpetro_th50_r=7.58261, z=0.0355189),\n",
       " ResultRow(mangaid='1-122119', plateifu='9183-9102', elpetro_absmag_g_r=0.64360237121582, elpetro_th50_r=6.84965, z=0.0223205),\n",
       " ResultRow(mangaid='1-122141', plateifu='9183-3704', elpetro_absmag_g_r=1.48015403747559, elpetro_th50_r=5.15509, z=0.0404168),\n",
       " ResultRow(mangaid='1-122151', plateifu='9183-6102', elpetro_absmag_g_r=1.30054664611816, elpetro_th50_r=3.4436, z=0.0124344),\n",
       " ResultRow(mangaid='1-122284', plateifu='9183-6101', elpetro_absmag_g_r=1.33587646484375, elpetro_th50_r=3.25321, z=0.0224921),\n",
       " ResultRow(mangaid='1-122304', plateifu='9183-3703', elpetro_absmag_g_r=1.27674674987793, elpetro_th50_r=3.87237, z=0.0233453),\n",
       " ResultRow(mangaid='1-122329', plateifu='9486-6103', elpetro_absmag_g_r=2.3619499206543, elpetro_th50_r=6.43394, z=0.0982483),\n",
       " ResultRow(mangaid='1-122333', plateifu='9486-1901', elpetro_absmag_g_r=1.30059051513672, elpetro_th50_r=3.67772, z=0.0208727),\n",
       " ResultRow(mangaid='1-122334', plateifu='9486-6104', elpetro_absmag_g_r=1.59306716918945, elpetro_th50_r=3.71928, z=0.0636345),\n",
       " ResultRow(mangaid='1-122361', plateifu='9486-6102', elpetro_absmag_g_r=1.66129302978516, elpetro_th50_r=3.45415, z=0.063881),\n",
       " ResultRow(mangaid='1-122364', plateifu='9486-12703', elpetro_absmag_g_r=0.655183792114258, elpetro_th50_r=6.25982, z=0.0369295),\n",
       " ResultRow(mangaid='1-122412', plateifu='9486-12705', elpetro_absmag_g_r=1.55437088012695, elpetro_th50_r=3.98716, z=0.0260331),\n",
       " ResultRow(mangaid='1-122586', plateifu='9486-1902', elpetro_absmag_g_r=1.61582374572754, elpetro_th50_r=3.69728, z=0.0990185),\n",
       " ResultRow(mangaid='1-124604', plateifu='8439-6103', elpetro_absmag_g_r=1.38611221313477, elpetro_th50_r=6.67132, z=0.0253001),\n",
       " ResultRow(mangaid='1-133922', plateifu='8486-6104', elpetro_absmag_g_r=1.51949119567871, elpetro_th50_r=5.85332, z=0.0174718),\n",
       " ResultRow(mangaid='1-133941', plateifu='8486-9102', elpetro_absmag_g_r=1.04214859008789, elpetro_th50_r=6.9901, z=0.0189045),\n",
       " ResultRow(mangaid='1-133945', plateifu='8486-3703', elpetro_absmag_g_r=1.70501899719238, elpetro_th50_r=4.45846, z=0.0183248),\n",
       " ResultRow(mangaid='1-133948', plateifu='8486-6103', elpetro_absmag_g_r=1.62374401092529, elpetro_th50_r=5.78962, z=0.0195194),\n",
       " ResultRow(mangaid='1-133976', plateifu='8486-9101', elpetro_absmag_g_r=1.26091766357422, elpetro_th50_r=7.11674, z=0.0182938),\n",
       " ResultRow(mangaid='1-133987', plateifu='8486-1902', elpetro_absmag_g_r=1.73217391967773, elpetro_th50_r=2.80256, z=0.0195435),\n",
       " ResultRow(mangaid='1-134004', plateifu='8486-1901', elpetro_absmag_g_r=1.27153015136719, elpetro_th50_r=2.74306, z=0.0185601),\n",
       " ResultRow(mangaid='1-134020', plateifu='8486-6102', elpetro_absmag_g_r=1.4318904876709, elpetro_th50_r=6.70244, z=0.0193267),\n",
       " ResultRow(mangaid='1-134209', plateifu='8549-9101', elpetro_absmag_g_r=1.46211814880371, elpetro_th50_r=7.43994, z=0.0545042),\n",
       " ResultRow(mangaid='1-134239', plateifu='8549-3703', elpetro_absmag_g_r=1.20720481872559, elpetro_th50_r=5.29078, z=0.0571086),\n",
       " ResultRow(mangaid='1-134248', plateifu='8549-3702', elpetro_absmag_g_r=1.04830741882324, elpetro_th50_r=4.19151, z=0.0212204),\n",
       " ResultRow(mangaid='1-134293', plateifu='8549-6103', elpetro_absmag_g_r=0.724908828735352, elpetro_th50_r=6.37536, z=0.0416784),\n",
       " ResultRow(mangaid='1-134503', plateifu='8555-1901', elpetro_absmag_g_r=1.38505744934082, elpetro_th50_r=2.05563, z=0.0371472),\n",
       " ResultRow(mangaid='1-134562', plateifu='8549-1902', elpetro_absmag_g_r=0.999540328979492, elpetro_th50_r=2.21746, z=0.0355137),\n",
       " ResultRow(mangaid='1-134597', plateifu='8549-12705', elpetro_absmag_g_r=1.32281875610352, elpetro_th50_r=9.29719, z=0.0441938),\n",
       " ResultRow(mangaid='1-134599', plateifu='8549-12704', elpetro_absmag_g_r=1.2156925201416, elpetro_th50_r=8.57387, z=0.019658),\n",
       " ResultRow(mangaid='1-134614', plateifu='8549-6102', elpetro_absmag_g_r=1.25503730773926, elpetro_th50_r=6.36671, z=0.0528277),\n",
       " ResultRow(mangaid='1-134620', plateifu='9031-12703', elpetro_absmag_g_r=1.27625465393066, elpetro_th50_r=6.19139, z=0.0546778),\n",
       " ResultRow(mangaid='1-134634', plateifu='8549-3704', elpetro_absmag_g_r=1.71317291259766, elpetro_th50_r=2.98447, z=0.0523251),\n",
       " ResultRow(mangaid='1-134695', plateifu='9029-6104', elpetro_absmag_g_r=1.37136077880859, elpetro_th50_r=5.64757, z=0.0304438),\n",
       " ResultRow(mangaid='1-134716', plateifu='8600-12705', elpetro_absmag_g_r=1.10339736938477, elpetro_th50_r=5.6302, z=0.0446535),\n",
       " ResultRow(mangaid='1-134737', plateifu='8600-3701', elpetro_absmag_g_r=0.759981155395508, elpetro_th50_r=7.45251, z=0.0272129),\n",
       " ResultRow(mangaid='1-134793', plateifu='8600-6104', elpetro_absmag_g_r=0.940788269042969, elpetro_th50_r=4.10349, z=0.0422411),\n",
       " ResultRow(mangaid='1-134848', plateifu='8555-12703', elpetro_absmag_g_r=1.4580078125, elpetro_th50_r=8.75956, z=0.0584495),\n",
       " ResultRow(mangaid='1-134924', plateifu='8555-9101', elpetro_absmag_g_r=1.76020240783691, elpetro_th50_r=7.2697, z=0.0319997),\n",
       " ResultRow(mangaid='1-134954', plateifu='8555-12705', elpetro_absmag_g_r=1.38137054443359, elpetro_th50_r=7.2771, z=0.0315232),\n",
       " ResultRow(mangaid='1-134964', plateifu='8555-3701', elpetro_absmag_g_r=1.5971508026123, elpetro_th50_r=4.08391, z=0.0462348),\n",
       " ResultRow(mangaid='1-135003', plateifu='8978-3704', elpetro_absmag_g_r=1.6054744720459, elpetro_th50_r=2.54767, z=0.047362),\n",
       " ResultRow(mangaid='1-135030', plateifu='8603-12704', elpetro_absmag_g_r=1.31695175170898, elpetro_th50_r=9.51023, z=0.0273289),\n",
       " ResultRow(mangaid='1-135044', plateifu='8978-9101', elpetro_absmag_g_r=1.29121780395508, elpetro_th50_r=7.6628, z=0.0303346),\n",
       " ResultRow(mangaid='1-135054', plateifu='8550-12703', elpetro_absmag_g_r=1.34156799316406, elpetro_th50_r=8.45056, z=0.0298122),\n",
       " ResultRow(mangaid='1-135055', plateifu='8601-6104', elpetro_absmag_g_r=1.68307113647461, elpetro_th50_r=6.24969, z=0.0300581),\n",
       " ResultRow(mangaid='1-135057', plateifu='8601-12703', elpetro_absmag_g_r=0.928314208984375, elpetro_th50_r=10.3366, z=0.0288518),\n",
       " ResultRow(mangaid='1-135058', plateifu='8603-6103', elpetro_absmag_g_r=1.1861629486084, elpetro_th50_r=5.48034, z=0.0270087),\n",
       " ResultRow(mangaid='1-135059', plateifu='9869-3703', elpetro_absmag_g_r=1.56065368652344, elpetro_th50_r=4.51742, z=0.0316739),\n",
       " ResultRow(mangaid='1-135072', plateifu='9029-12701', elpetro_absmag_g_r=1.53062629699707, elpetro_th50_r=15.7064, z=0.0324632),\n",
       " ResultRow(mangaid='1-135077', plateifu='8312-6104', elpetro_absmag_g_r=1.33458137512207, elpetro_th50_r=4.5256, z=0.0290664),\n",
       " ResultRow(mangaid='1-135078', plateifu='9029-12705', elpetro_absmag_g_r=0.914762496948242, elpetro_th50_r=11.8246, z=0.0295154),\n",
       " ResultRow(mangaid='1-135086', plateifu='9029-3704', elpetro_absmag_g_r=1.7380199432373, elpetro_th50_r=4.3875, z=0.0332592),\n",
       " ResultRow(mangaid='1-135091', plateifu='9029-3702', elpetro_absmag_g_r=1.69864463806152, elpetro_th50_r=5.19483, z=0.0317749),\n",
       " ResultRow(mangaid='1-135093', plateifu='9029-12702', elpetro_absmag_g_r=1.25051116943359, elpetro_th50_r=10.9635, z=0.0318872),\n",
       " ResultRow(mangaid='1-135095', plateifu='8312-3702', elpetro_absmag_g_r=1.44723129272461, elpetro_th50_r=5.08395, z=0.0332324),\n",
       " ResultRow(mangaid='1-135101', plateifu='9869-3701', elpetro_absmag_g_r=1.41915702819824, elpetro_th50_r=5.17462, z=0.0294585),\n",
       " ResultRow(mangaid='1-135115', plateifu='9029-3701', elpetro_absmag_g_r=1.5458869934082, elpetro_th50_r=3.82893, z=0.0300883),\n",
       " ResultRow(mangaid='1-135117', plateifu='9029-6101', elpetro_absmag_g_r=1.53122138977051, elpetro_th50_r=5.67636, z=0.0333227),\n",
       " ResultRow(mangaid='1-135129', plateifu='8603-12705', elpetro_absmag_g_r=1.81981086730957, elpetro_th50_r=7.00506, z=0.0327969),\n",
       " ResultRow(mangaid='1-135133', plateifu='8603-12703', elpetro_absmag_g_r=1.36585807800293, elpetro_th50_r=8.37236, z=0.0299683),\n",
       " ResultRow(mangaid='1-135134', plateifu='8603-9101', elpetro_absmag_g_r=1.85215187072754, elpetro_th50_r=6.96944, z=0.030343),\n",
       " ResultRow(mangaid='1-135142', plateifu='9869-12704', elpetro_absmag_g_r=1.43869781494141, elpetro_th50_r=4.46846, z=0.0326757),\n",
       " ResultRow(mangaid='1-135152', plateifu='8312-6103', elpetro_absmag_g_r=0.762582778930664, elpetro_th50_r=5.63706, z=0.0301811),\n",
       " ResultRow(mangaid='1-135157', plateifu='8603-3702', elpetro_absmag_g_r=1.68464851379395, elpetro_th50_r=4.97032, z=0.0323713),\n",
       " ResultRow(mangaid='1-135193', plateifu='9869-1901', elpetro_absmag_g_r=1.84131813049316, elpetro_th50_r=2.23994, z=0.0308858),\n",
       " ResultRow(mangaid='1-135202', plateifu='8600-3703', elpetro_absmag_g_r=1.58297348022461, elpetro_th50_r=8.49454, z=0.0277109),\n",
       " ResultRow(mangaid='1-135207', plateifu='8555-1902', elpetro_absmag_g_r=1.51096343994141, elpetro_th50_r=3.59802, z=0.031485),\n",
       " ResultRow(mangaid='1-135227', plateifu='9029-6103', elpetro_absmag_g_r=1.5926513671875, elpetro_th50_r=5.6132, z=0.0304491)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at page 2\n",
    "r.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Retrieving previous 100, from 0 to 100\n"
     ]
    }
   ],
   "source": [
    "# get the previous set \n",
    "p = r.getPrevious()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extend your results and keep them, use the ``extendSet`` method.  By default, extending a set grabs the next page of 100 results (defined by ``r.chunk``) and appends to the existing set of results.  Rerunning ``extendSet`` continues to append results until you've retrieved them all. To avoid running ``extendSet`` multiple times, you can run use the ``loop`` method, which will loop over all pages appending the data until you've retrieved all the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Retrieving next 100, from 100 to 200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Marvin Results(query=nsa.z < 0.1, totalcount=4275, count=200, mode=remote)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extend the set by one page\n",
    "r.extendSet()\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have 200 results out of the 4275.  For results with a small number of total counts, you can attempt to retrieve all of the results with the ``getAll`` method.  Currently this method is limited to returning results containing 500,000 rows or rows with 25 columns.\n",
    "\n",
    "#### Getting all the results\n",
    "There are several options for getting all of the results.  \n",
    "- Use the ``getAll`` method to attempt to retrieve all the results in one request. \n",
    "- Use the ``loop`` method to loop over all the pages to extend/append the results together\n",
    "- Rerun the ``Query`` using a new ``limit`` to retrieve all the results. \n",
    "\n",
    "**Note:** A bug was recently found in ``getAll`` and might not work.  Instead we will rerun the query using a large limit to return all the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marvin Results(query=nsa.z < 0.1, totalcount=4275, count=4275, mode=remote)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get all the results\n",
    "# r.getAll()\n",
    "\n",
    "# rerun the query\n",
    "q = Query(search_filter='nsa.z < 0.1', return_params=['absmag_g_r', 'nsa.elpetro_th50_r'], limit=5000)\n",
    "r = q.run()\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have all the results.  We can extract columns of data by indexing the results list using the column name.  Let's extract the redshift and color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract individual columns of data\n",
    "redshift = r.results['nsa.z']\n",
    "color = r.results['absmag_g_r']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='format'></a>\n",
    "## Formatting Results\n",
    "You can convert the results to a variety of formats using the ``toXXX`` methods.  Common formats are **FITS**, **Astropy Table**, **Pandas Dataframe**, **JSON**, or **CSV**.  Only the FITS and CSV conversions will write the output to a file.  Astropy Tables and Pandas Dataframes have more options for writing out your dataset to a file.  Let's convert to Pandas Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>mangaid</th>\n",
       "      <th>plateifu</th>\n",
       "      <th>elpetro_absmag_g_r</th>\n",
       "      <th>elpetro_th50_r</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-109056</td>\n",
       "      <td>8077-6103</td>\n",
       "      <td>1.117039</td>\n",
       "      <td>3.70900</td>\n",
       "      <td>0.047302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-109073</td>\n",
       "      <td>8078-12704</td>\n",
       "      <td>1.059788</td>\n",
       "      <td>7.54724</td>\n",
       "      <td>0.043588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-109081</td>\n",
       "      <td>8077-12705</td>\n",
       "      <td>1.230318</td>\n",
       "      <td>9.60665</td>\n",
       "      <td>0.043859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-109112</td>\n",
       "      <td>8078-1901</td>\n",
       "      <td>1.531988</td>\n",
       "      <td>2.25541</td>\n",
       "      <td>0.024970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-109152</td>\n",
       "      <td>8154-12702</td>\n",
       "      <td>1.543945</td>\n",
       "      <td>16.12630</td>\n",
       "      <td>0.028174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mangaid    plateifu  elpetro_absmag_g_r  elpetro_th50_r         z\n",
       "0  1-109056   8077-6103            1.117039         3.70900  0.047302\n",
       "1  1-109073  8078-12704            1.059788         7.54724  0.043588\n",
       "2  1-109081  8077-12705            1.230318         9.60665  0.043859\n",
       "3  1-109112   8078-1901            1.531988         2.25541  0.024970\n",
       "4  1-109152  8154-12702            1.543945        16.12630  0.028174"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the marvin results to a Pandas dataframe\n",
    "df = r.toDF()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also convert the data into Marvin objects using the ``convertToTool`` method.  This will attempt to convert each result row into its corresponding Marvin Object.  The default conversion is to a ``Cube`` object.  Converted objects are stored in the ``r.objects`` attribute.  Let's convert our results to cubes. **Depending on the number of results, this may take awhile.  Let's limit our conversion to 5.**  Once converted, we now have Marvin Tools at our disposal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting results to Marvin Cube objects\n"
     ]
    }
   ],
   "source": [
    "# convert the top 5 to cubes\n",
    "r.convertToTool('cube', limit=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Marvin Cube (plateifu='8077-6103', mode='remote', data_origin='api')>,\n",
       " <Marvin Cube (plateifu='8078-12704', mode='remote', data_origin='api')>,\n",
       " <Marvin Cube (plateifu='8077-12705', mode='remote', data_origin='api')>,\n",
       " <Marvin Cube (plateifu='8078-1901', mode='remote', data_origin='api')>,\n",
       " <Marvin Cube (plateifu='8154-12702', mode='remote', data_origin='api')>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the objects\n",
    "r.objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='plot'></a>\n",
    "## Quickly Plotting the Results\n",
    "You can quickly plot the full set of results using the ``plot`` method.  ``plot`` accepts two string column names and will attempt to create a scatter plot, a hex-binned plot, or a scatter-density plot, depending on the total number of results.  The ``plot`` method returns the matplotlib **Figure** and **Axes** objects, as well as a dictionary of histogram information for each column.  The ``Results.plot`` method uses the underlying [plot utility function](https://sdss-marvin.readthedocs.io/en/latest/tools/utils/plot-scatter.html).  The utility function offers up more custom plotting options. Let's plot g-r color versus redshift.  **Regardless of the number of results you currently have loaded, the ``plot`` method will automatically retrieve all the results before plotting.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a scatter plot\n",
    "fig, ax, histdata = r.plot('z', 'absmag_g_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, it will also plot histograms of the column as well.  This can be turned off by setting `with_hist=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make only a scatter plot\n",
    "fig, ax = r.plot('z', 'absmag_g_r', with_hist=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also quickly plot a histogram of a single column of data using the ``hist`` method, which uses an underlying [hist utility function](https://sdss-marvin.readthedocs.io/en/latest/tools/utils/plot-hist.html).    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qqioIgoDu3bvj1VdfBQAMGDAABQUFUKlUaNeuHdLS0sSKRkREfxCtBLp37w61Wt1o+ebNm5tcXyaT4R//+IdYcYiIqAm8Y5iISMJYAkREEsYSICKSMJYAEZGEmeU+ASKyXcGrCi0dgUTEmQARkYSxBIiIJIwlQEQkYSwBIiIJYwkQEUkYS4CISMJYAkREEsYSICKSMJYAEZGE8Y5hImqEdwlLB2cCREQSxhIgIpIwlgARkYSJ9ppATU0Nxo4di9raWhgMBkRFRSE5ORnnzp3DnDlzUF1dDX9/f7zxxhtwcHBAbW0t5s2bh59++gmurq5YvXo1Hn74YbHiERERRJwJODg4YNOmTdixYwfUajWKiopw9OhRrFy5EklJSdizZw+cnZ2xbds2AEBWVhacnZ2xZ88eJCUlYeXKlWJFIyKiP4hWAjKZDB06dAAA1NfXo76+HjKZDAcOHEBUVBQAID4+Hvn5+QCAffv2IT4+HgAQFRWFb7/9FoIgiBWPiIgg8iWiBoMBCQkJOHv2LMaMGQMfHx84OzvD3v7mw3p5eUGj0QAANBoNHnzwwZuh7O3h5OSEqqoquLm5NRhToXCEvb1clLxyuR1cXduLMrZYmNk8bDEzYNncf/ZxbfFY22LmW0QtAblcjpycHOh0OsyYMQNnzpz5y2Pq9TX3IFnTXF3bo7r6mmjji4GZzcMWMwMNc9957f/BlyNEfew/e7xs8VjbQmYPD6cml5vl6iBnZ2eEhobi6NGj0Ol0qK+vBwCUl5dDqVQCAJRKJS5evAjg5umjK1eu4IEHHjBHPCIiyRKtBCorK6HT6QAA169fx/79+9GlSxeEhoZi9+7dAIDs7GxERkYCACIjI5GdnQ0A2L17N/r27QuZTCZWPKL7UvCqwgZfRC0R7XSQVqtFSkoKDAYDBEHA0KFDMXDgQDz22GOYPXs2MjIy0KNHDyQmJgIARo0ahblz50KlUsHFxQWrV68WKxoREf1BtBLo3r071Gp1o+U+Pj7Gy0Jv5+joiDVr1ogVh4iImsA7homIJIwlQEQkYSwBIiIJYwkQEUkYS4CISMJYAkREEsaPlyQi3lgmYZwJEBFJGEuAiEjCWAJERBLGEiAikjCWABGRhLEEiIgkjCVARCRhvE+ASKJ4bwABnAkQEUkaZwJEJApzf7A9/TmizQQuXryI5557DtHR0Rg+fDg2bdoEAFi7di2efPJJxMbGIjY2FgUFBcZt1q9fD5VKhaioKBQVFYkVjYiI/iDaTEAulyMlJQX+/v7Q6/UYOXIk+vfvDwBISkrCpEmTGqx/6tQp5ObmIjc3FxqNBhMnTsTu3bshl8vFikhEJHmizQQ8PT3h7+8PAFAoFPD19YVGo7nr+vn5+Rg+fDgcHBzg4+ODTp06oaSkRKx4REQEM70mcP78eRw/fhwBAQH4/vvvsXXrVqjVavTs2RMpKSlwcXGBRqNBQECAcRulUtlsaRBRy/xSv7B0BLJyopfA1atXkZycjIULF0KhUODZZ5/F9OnTIZPJ8PbbbyM9PR0rVqwweTyFwhH29uKcIpLL7eDq2l6UscXCzOZhi5mtjanHzxaPtS1mvkXUEqirq0NycjJiYmIwZMgQAEDHjh2Nv09MTMS0adMA3PzLv7y83Pg7jUYDpVLZaEy9vka0vK6u7VFdfU208cXAzOZhi5mtjanHzxaPtS1k9vBwanK5aK8JCIKARYsWwdfXFxMnTjQu12q1xu/37t0LPz8/AEBkZCRyc3NRW1uLc+fOoaysDI8//rhY8YiICCLOBA4fPoycnBx07doVsbGxAIA5c+bgs88+Q2lpKQDA29sbS5cuBQD4+flh2LBhiI6Ohlwux+LFi3llEBGRyGSCIAiWDtEav/12RbSxbWFKdydmNg9byWzNbwVh6s1itnKsb2cLmc1+OoiIiKwfS4CISMJaXQKXL182ntMnIiLbZlIJPPfcc9Dr9aiurkZ8fDxSU1NbdW0/ERFZJ5OuDrpy5QoUCgWysrIQFxdnvPafiCzPml8MJutn0kzAYDBAq9Vi165deOqpp0SORERE5mJSCcyYMQOTJk3CI488gscffxznzp3Do48+KnI0IiISm0mngzw8PLBz507jzz4+PkhKShIrExERmYlJM4Fly5aZtIyIiGxLszOBI0eO4MiRI6isrMTGjRuNy/V6PQwGg+jhiIhIXM2WQF1dHa5duwaDwYCrV68alysUCqxZs0b0cEREJK5mSyAkJAQhISGIj4+Ht7e3uTIREZGZmPTCcG1tLVJTU3HhwgXU19cbl2/evFm0YEREJD6TSuCll17C6NGjkZiYCDs7vt0QEdH9wqQSsLe3x5gxY8TOQkREZmbSn/UDBw7E1q1bodVqUV1dbfwiIiLbZtJMIDs7GwDw3nvvGZfJZDLk5+eLk4qIiMzCpBLYt29fqwe+ePEi5s2bh4qKCshkMjzzzDOYMGECqqurMXv2bFy4cAHe3t7IyMiAi4sLBEHA8uXLUVBQgLZt2yI9PR3+/v6tflwiIjKdSSWgVqubXB4XF3fXbeRyOVJSUuDv7w+9Xo+RI0eif//+2L59O8LCwjBlyhRkZmYiMzMTc+fORWFhIcrKypCXl4djx45hyZIlyMrK+nN7RUREJjHpNYEffvjB+HXo0CGsXbu2xdmBp6en8S95hUIBX19faDQa5OfnG8sjLi4Oe/fuBQDjcplMhsDAQOh0Omi12r+yb0RE1AKTZgKpqakNftbpdJg9e7bJD3L+/HkcP34cAQEBqKiogKenJ4Cbb0xXUVEBANBoNPDy8jJu4+XlBY1GY1yXiIjuPZNK4E7t2rXD+fPnTVr36tWrSE5OxsKFC6FQKBr8TiaTQSaTteqxFQpH2NvLW7WNqeRyO7i6thdlbLEws3nYYmZrY+rxs8VjbYuZbzGpBKZNm2b8/saNGzh9+jSGDRvW4nZ1dXXGTyEbMmQIAMDd3R1arRaenp7QarVwc3MDACiVSpSXlxu3LS8vh1KpbDSmXl9jSuQ/xdW1Paqrr4k2vhiY2TxsMbO1MfX42eKxtoXMHh5OTS43qQSef/554/dyuRze3t4NTt00RRAELFq0CL6+vpg4caJxeWRkJNRqNaZMmQK1Wo1BgwYZl2/ZsgXDhw/HsWPH4OTkxFNBREQiM6kEQkJCcOnSJfzwww8AYNKnih0+fBg5OTno2rUrYmNjAQBz5szBlClTMGvWLGzbtg0PPfQQMjIyAAADBgxAQUEBVCoV2rVrh7S0tD+5S0REZCqZIAhCSyt9/vnnePPNNxESEgJBEHDo0CHMmzcPQ4cONUfGBn777YpoY9vClO5OzGwe1pzZVj5o/uDLESatZ83H+m5sIfNfOh3073//G9u2bYO7uzsAoLKyEklJSRYpASIiundMuk9AEARjAQCAq6srTJhAEBGRlTNpJhAeHo5JkyZh+PDhAG6eHoqIMG1qR0RE1qvZEvjf//6HS5cuYf78+cjLy8Phw4cBAIGBgXj66afNEpCIiMTT7OmgtLQ04w1eQ4YMwYIFC7BgwQKoVCpevUNEdB9otgQuXbqEbt26NVrerVs3XLhwQbRQRERkHs2WwJUrd78c8/r16/c8DBERmVezJdCzZ0988sknjZZnZWXxvf6JiO4Dzb4wvHDhQrz44ovYuXOn8Un/xx9/RF1dHd555x2zBCQiIvE0WwIdO3bERx99hAMHDuDkyZMAbr69Q1hYmFnCERGRuEy6T6Bv377o27ev2FmIyAS28jYRZBtMumOYiIjuTywBIiIJYwkQEUkYS4CISMJYAkREEsYSICKSMJYAEZGEiVYCCxYsQFhYGEaMGGFctnbtWjz55JOIjY1FbGwsCgoKjL9bv349VCoVoqKiUFRUJFYsIiK6jUk3i/0ZCQkJGDduHObPn99geVJSEiZNmtRg2alTp5Cbm4vc3FxoNBpMnDgRu3fvhlwuFyseERFBxJlAcHAwXFxcTFo3Pz8fw4cPh4ODA3x8fNCpUyeUlJSIFY2IiP4g2kzgbrZu3Qq1Wo2ePXsiJSUFLi4u0Gg0CAgIMK6jVCqh0Wia3F6hcIS9vTgzBLncDq6u7UUZWyzMbB62mNnamHr8bPFY22LmW8xaAs8++yymT58OmUyGt99+G+np6VixYkWrxtDra0RKd/N/0urqa6KNLwZmNg9bzGxtTD1+tnisbSGzh4dTk8vNenVQx44dIZfLYWdnh8TERPzwww8Abv7lX15eblxPo9FAqVSaMxoRkSSZtQS0Wq3x+71798LPzw8AEBkZidzcXNTW1uLcuXMoKyvD448/bs5oRESSJNrpoDlz5qC4uBhVVVWIiIjAzJkzUVxcjNLSUgCAt7c3li5dCgDw8/PDsGHDEB0dDblcjsWLF/PKICIiM5AJgiBYOkRr/Pbb3T/3+K+yhfN6d2Jm87CmzLb6eQIHX44waT1rOtamsoXMVvGaABERWReWABGRhLEEiIgkjCVARCRhLAEiIgljCRARSRhLgIhIwlgCREQSxhIgIpIwlgARkYSxBIiIJIwlQEQkYSwBIiIJYwkQEUkYS4CISMJYAkREEsYSICKSMNFKYMGCBQgLC8OIESOMy6qrqzFx4kQMGTIEEydOxOXLlwEAgiBg2bJlUKlUiImJwU8//SRWLCKykOBVhcYvsh6ilUBCQgI2bNjQYFlmZibCwsKQl5eHsLAwZGZmAgAKCwtRVlaGvLw8vPbaa1iyZIlYsYiI6DailUBwcDBcXFwaLMvPz0dcXBwAIC4uDnv37m2wXCaTITAwEDqdDlqtVqxoRET0B7O+JlBRUQFPT08AgIeHByoqKgAAGo0GXl5exvW8vLyg0WjMGY2ISJLsLfXAMpkMMpms1dspFI6wt5eLkAiQy+3g6tpelLHFwszmYYuZrVlzx9IWj7UtZr7FrCXg7u4OrVYLT09PaLVauLm5AQCUSiXKy8uN65WXl0OpVDY5hl5fI1o+V9f2qK6+Jtr4YmBm87DFzNasuWNpi8faFjJ7eDg1udysp4MiIyOhVqsBAGq1GoMGDWqwXBAEHD16FE5OTsbTRkTEK2tIPKLNBObMmYPi4mJUVVUhIiICM2fOxJQpUzBr1ixs27YNDz30EDIyMgAAAwYMQEFBAVQqFdq1a4e0tDSxYhER0W1EK4G33nqryeWbNm1qtEwmk+Ef//iHWFGIiOgueMcwEZGEsQSIiCSMJUBEJGEsASIiCWMJEBFJGEuAiEjCWAJERBLGEiAikjCWABGRhLEEiIgkjCVARCRhLAEiIgljCRARSRhLgIhIwlgCREQSxhIgIpIwlgARkYSZ9YPmb4mMjESHDh1gZ2cHuVyO7du3o7q6GrNnz8aFCxfg7e2NjIwMuLi4WCIeEZFkWGwmsGnTJuTk5GD79u0AgMzMTISFhSEvLw9hYWHIzMy0VDQiIsmwmtNB+fn5iIuLAwDExcVh7969Fk5ERHT/s1gJTJo0CQkJCfj4448BABUVFfD09AQAeHh4oKKiwlLRiIgkwyKvCXz44YdQKpWoqKjAxIkT4evr2+D3MpkMMpmsyW0VCkfY28tFySWX28HVtb0oY4uFmc3DFjNbs+BVhQ1+PvnaUOP3tnisbTHzLRYpAaVSCQBwd3eHSqVCSUkJ3N3dodVq4enpCa1WCzc3tya31etrRMvl6toe1dXXRBtfDMxsHraY2Zbcfmxt8VjbQmYPD6cml5v9dNC1a9eg1+uN33/zzTfw8/NDZGQk1Go1AECtVmPQoEHmjkZEJDlmnwlUVFRgxowZAACDwYARI0YgIiICvXr1wqxZs7Bt2zY89NBDyMjIMHc0IiLJMXsJ+Pj4YMeOHY2WP/DAA9i0aZO54xBZpTvPmROJxWouESUiIvNjCRARSRhLgIhIwlgCREQSxhIgIpIwlgARkYSxBIiIJIwlQEQkYSwBIiIJs8gbyBFRY1K+S/j2fb/9HUVJfJwJEBFJGEuAiEjCWAJERBLGEiAikjCWABGRhLEEiIgkjJeIEpHNuPMy2oMvR1goyf2DJUBkIVK+L6A5fqlfWDqCpFhdCRQWFmL58uW4ceMGEhMTMWXKFEtHIrpn+MRP1saqSsBgMGDp0qXYuHEjlEolRo0ahcjISDz22GOWjkYSd+dfp6ao0wGbAAAEcUlEQVSehuCTPlk7qyqBkpISdOrUCT4+PgCA4cOHIz8/nyUgIbc/adrK+V4+0VuOqce+Nf8vNTemrfw/2RoyQRAES4e45YsvvkBRURGWL18OAFCr1SgpKcHixYstnIyI6P7ES0SJiCTMqkpAqVSivLzc+LNGo4FSqbRgIiKi+5tVlUCvXr1QVlaGc+fOoba2Frm5uYiMjLR0LCKi+5ZVvTBsb2+PxYsXY/LkyTAYDBg5ciT8/PzMmiEjIwP5+fmws7ODu7s7VqxYYfWzkddffx1ffvkl2rRpg0ceeQQrVqyAs7OzpWM1a9euXXjnnXdw+vRpZGVloVevXpaOdFe2eNnyggUL8NVXX8Hd3R2fffaZpeO06OLFi5g3bx4qKiogk8nwzDPPYMKECZaO1aKamhqMHTsWtbW1MBgMiIqKQnJysqVjtY5ADVy5csX4/aZNm4TU1FQLpjFNUVGRUFdXJwiCILzxxhvCG2+8YeFELTt16pRw+vRpYdy4cUJJSYml49xVfX29MGjQIOHs2bNCTU2NEBMTI5w8edLSsVpUXFws/Pjjj8Lw4cMtHcUkGo1G+PHHHwVBuPlvcMiQITZxnG/cuCHo9XpBEAShtrZWGDVqlHDkyBELp2odqzodZA0UCoXx+99//x0ymcyCaUwTHh4Oe/ubk7rAwMAGr6tYqy5dusDX19fSMVp0+2XLDg4OxsuWrV1wcDBcXFwsHcNknp6e8Pf3B3Dz36Cvry80Go2FU7VMJpOhQ4cOAID6+nrU19fbxHPG7azqdJC1WL16NdRqNZycnLB582ZLx2mVTz/9FMOGDbN0jPuGRqOBl5eX8WelUomSkhILJrr/nT9/HsePH0dAQIClo5jEYDAgISEBZ8+exZgxY2wm9y2SLIGkpCRcunSp0fJZs2Zh8ODBmD17NmbPno3169djy5YtVnGOr6XMALBu3TrI5XI8/fTT5o7XJFMyE93u6tWrSE5OxsKFCxvMyq2ZXC5HTk4OdDodZsyYgRMnTqBr166WjmUySZbA+++/b9J6MTExmDJlilWUQEuZt2/fjq+++grvv/++1UxHTT3O1oyXLZtPXV0dkpOTERMTgyFDhlg6Tqs5OzsjNDQURUVFNlUCfE3gDmVlZcbv8/PzbeK8dWFhITZs2IB169ahXbt2lo5zX+Fly+YhCAIWLVoEX19fTJw40dJxTFZZWQmdTgcAuH79Ovbv328Tzxm3s6q3jbAGM2fOxC+//AKZTAZvb2+8+uqrVv+Xn0qlQm1tLVxdXQEAAQEBWLp0qYVTNW/Pnj147bXXUFlZCWdnZ/To0QPvvfeepWM1qaCgAGlpacbLll944QVLR2rRnDlzUFxcjKqqKri7u2PmzJlITEy0dKy7OnToEMaOHYuuXbvCzu7m36Zz5szBgAEDLJyseaWlpUhJSYHBYIAgCBg6dChefPFFS8dqFZYAEZGE8XQQEZGEsQSIiCSMJUBEJGEsASIiCWMJEBFJGEuAiEjCWAJERBLGEiAikrD/B6G7q5tiCFRPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histdata, fig, ax = r.hist('absmag_g_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='download'></a>\n",
    "## Downloading Results\n",
    "You can download the raw files from your results using the ``download`` method.  This uses the ``downloadList`` utility function under the hood.  By default this will download the DRP cubes for each target row.  It accepts any keyword arguments as ``downloadList``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the DRP datacube files from the results \n",
    "# r.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Resoures\n",
    "- Get a refresher on [Queries](https://sdss-marvin.readthedocs.io/en/latest/query/query.html)\n",
    "- See more details of what you can do with [Results](https://sdss-marvin.readthedocs.io/en/latest/query/results.html#using-results)\n",
    "- See the [Results API](https://sdss-marvin.readthedocs.io/en/latest/reference/queries.html#marvin-results-ref) for a look at the Marvin Results object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:marvindev]",
   "language": "python",
   "name": "conda-env-marvindev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "nbsphinx": {
   "allow_errors": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}