{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6ccc2d3e",
   "metadata": {},
   "source": [
    "# Comparing Stars Between Files using the `sdss_id`\n",
    "\n",
    "## Learning Goals\n",
    "By the end of this tutorial, you will:\n",
    "- Know how to identify the same objects from two different `astra` data products\n",
    "\n",
    "## Introduction\n",
    "Unique astronomical objects in SDSS-V are kept track of using the `sdss_id`. This identifier was created in order to combine the several crossmatch versions that have been completed during the ongoing survey and is a useful tool to compare the same objects from different files.\n",
    "\n",
    "Let's say we want to match results from two different pipelines: `LineForest` and `ASPCAP`. As an example problem, we will look at the iron abundances from stars observed with APOGEE and their H alpha equivalent width in BOSS.\n",
    "\n",
    "## Imports\n",
    "This tutorial requires three commonly used python packages\n",
    "- _numpy_ for doing large array operations\n",
    "- _matplotlib_ for plotting\n",
    "- _astropy_ for reading fits table\n",
    "\n",
    "Missing packages can be installed via `pip install numpy matplotlib astropy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34179f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from astropy.io import fits\n",
    "from astropy.table import Table\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter warnings (I only do this to make the notebook look nice)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110c2fdd-fdbe-49e5-8fb4-e25ea7b39e49",
   "metadata": {},
   "source": [
    "If running this notebook on your computer, two files need to be downloaded. Both can be found at https://data.sdss.org/sas/dr19/spectro/astra/0.6.0/summary/\n",
    "\n",
    "If you are running this notebook on SciServer, these files are already available at the path defined below. Otherwise you will need to set local path appropriately for your computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf1571",
   "metadata": {},
   "outputs": [],
   "source": [
    "LF_filename = 'astraAllStarLineForest-0.6.0.fits.gz'\n",
    "AC_filename = 'astraAllStarASPCAP-0.6.0.fits.gz'\n",
    "localpath = '/home/idies/workspace/sdss_sas/dr19/spectro/astra/0.6.0/summary/'\n",
    "\n",
    "# Open LineForest\n",
    "hdul_LF = fits.open(localpath + LF_filename)\n",
    "\n",
    "# Store the data\n",
    "boss_lineforest = hdul_LF[1].data\n",
    "\n",
    "\n",
    "# Do the same for ASPCAP\n",
    "hdul_AC = fits.open(localpath + AC_filename)\n",
    "apogee_aspcap = hdul_AC[2].data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08da7a5b",
   "metadata": {},
   "source": [
    "This might take a moment to open the large files. \n",
    "\n",
    "It is not easy to convert large astropy fits objects into more digestible data types because they use a lot of RAM (i.e. _astropy_ `Tables` or _pandas_ `DataFrames`). So, let's first find out what `sdss_ids` overlap between the two tables first and then convert later. A good tool for this is _numpy_'s `isin` routine, which returns a boolean array of whether the row is in another list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58103f08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 22844 LineForest rows in ASPCAP\n",
      "There are 24401 ASPCAP rows in LineForest\n"
     ]
    }
   ],
   "source": [
    "# Get sdss_ids\n",
    "boss_sdss_id = boss_lineforest[\"sdss_id\"]\n",
    "apog_sdss_id = apogee_aspcap[\"sdss_id\"]\n",
    "\n",
    "# Check if they are in the other catalog\n",
    "boss_in_apog = np.isin(boss_sdss_id, apog_sdss_id)\n",
    "apog_in_boss = np.isin(apog_sdss_id, boss_sdss_id)\n",
    "\n",
    "print(\"There are %s LineForest rows in ASPCAP\"%(np.sum(boss_in_apog)))\n",
    "print(\"There are %s ASPCAP rows in LineForest\"%(np.sum(apog_in_boss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c19c408b",
   "metadata": {},
   "source": [
    "Now we have the rows which overlap between the two catalogs. The numbers are different because there may be duplicated rows in one of the tables.\n",
    "\n",
    "Let's turn these into pandas DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74ba75b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can filter out the rows which don't match by passing it inside brackets\n",
    "boss_lineforest_match = boss_lineforest[boss_in_apog]\n",
    "apogee_aspcap_match = apogee_aspcap[apog_in_boss]\n",
    "\n",
    "# Turn the fits object into an astropy Table\n",
    "bl = Table(boss_lineforest_match)\n",
    "# Find the columns which are 1D arrays\n",
    "bl_names = [name for name in bl.colnames if len(bl[name].shape) <= 1]\n",
    "# Turn the Table into pandas DataFrame\n",
    "bl = bl[bl_names].to_pandas()\n",
    "\n",
    "# Do the same for ASPCAP\n",
    "aa = Table(apogee_aspcap_match)\n",
    "aa_names = [name for name in aa.colnames if len(aa[name].shape) <= 1]\n",
    "aa = aa[aa_names].to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a978e62",
   "metadata": {},
   "source": [
    "Now these should tables should be easier to work with! But note that removing the multi-dimensional data will get rid of the `SDSS5_TARGET_FLAGS` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7782824",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sdss_id</th>\n",
       "      <th>sdss4_apogee_id</th>\n",
       "      <th>gaia_dr2_source_id</th>\n",
       "      <th>gaia_dr3_source_id</th>\n",
       "      <th>tic_v8_id</th>\n",
       "      <th>healpix</th>\n",
       "      <th>lead</th>\n",
       "      <th>version_id</th>\n",
       "      <th>catalogid</th>\n",
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       "      <th>detection_stat_o_iii_5006</th>\n",
       "      <th>detection_raw_o_iii_5006</th>\n",
       "      <th>eqw_o_iii_4363</th>\n",
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       "      <th>detection_stat_o_iii_4363</th>\n",
       "      <th>detection_raw_o_iii_4363</th>\n",
       "      <th>eqw_li_i</th>\n",
       "      <th>abs_li_i</th>\n",
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       "      <th>detection_raw_li_i</th>\n",
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       "      <td>136949082</td>\n",
       "      <td>19993</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394824117131</td>\n",
       "      <td>4360324764</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>0.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2M11122553-0105412</td>\n",
       "      <td>3791857442582709504</td>\n",
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       "      <td>443586370</td>\n",
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       "      <td>4574381834</td>\n",
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       "      <td>3408645891905572096</td>\n",
       "      <td>68750781</td>\n",
       "      <td>63850</td>\n",
       "      <td>tic_v8</td>\n",
       "      <td>25</td>\n",
       "      <td>27021598058120469</td>\n",
       "      <td>4500358902</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22844 rows × 374 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sdss_id     sdss4_apogee_id   gaia_dr2_source_id   gaia_dr3_source_id  \\\n",
       "0      57986213  2M08202754+5039092   932171180709544960   932171180709544960   \n",
       "1      55054210  2M03400877+6322565   486950545777258496   486950545777258496   \n",
       "2      57304881  2M11351508+5235385   840242873232663040   840242873232663040   \n",
       "3      79731650  2M11122553-0105412  3791857442582709504  3791857442582709504   \n",
       "4      77372043                      3408645891905572096  3408645891905572096   \n",
       "...         ...                 ...                  ...                  ...   \n",
       "22839  60304544  2M14593880+4023228  1296900765557720448  1296900765557720448   \n",
       "22840  61651404  2M13544903+3945037  1497043222189724416  1497043222191647872   \n",
       "22841  88884090  2M15172043-0205307  4415089804623854080  4415089804623854080   \n",
       "22842  88884180  2M15172179-0154585  4415105369585406464  4415105369585406464   \n",
       "22843  60304616  2M14593515+4031479  1296914341950496128  1296914341950496128   \n",
       "\n",
       "       tic_v8_id  healpix             lead  version_id          catalogid  \\\n",
       "0      309663324    22407  gaia_dr3_source          31  63050394828054999   \n",
       "1      392377595    10556  gaia_dr3_source          31  63050394800032784   \n",
       "2      136949082    19993  gaia_dr3_source          31  63050394824117131   \n",
       "3      443586370    99823  gaia_dr3_source          31  63050395169824115   \n",
       "4       68750781    63850           tic_v8          25  27021598058120469   \n",
       "...          ...      ...              ...         ...                ...   \n",
       "22839   28347895    34368  gaia_dr3_source          31  63050394842772097   \n",
       "22840   23753266    35369  gaia_dr3_source          31  63050394850037390   \n",
       "22841   38947570   101958  gaia_dr3_source          31  63050395714873724   \n",
       "22842   38947519   101958  gaia_dr3_source          31  63050395714874342   \n",
       "22843   28347403    34368  gaia_dr3_source          31  63050394842772447   \n",
       "\n",
       "       catalogid21  ...  detection_stat_o_iii_5006  detection_raw_o_iii_5006  \\\n",
       "0       4358936662  ...                        NaN                       NaN   \n",
       "1       4323784458  ...                        NaN                       NaN   \n",
       "2       4360324764  ...                     1.0001                      0.72   \n",
       "3       4574381834  ...                     0.9998                      0.75   \n",
       "4       4500358902  ...                     0.9933                      0.72   \n",
       "...            ...  ...                        ...                       ...   \n",
       "22839   4350559881  ...                        NaN                       NaN   \n",
       "22840   4603684880  ...                        NaN                       NaN   \n",
       "22841   4776536612  ...                     0.9995                      0.78   \n",
       "22842   4776539852  ...                        NaN                       NaN   \n",
       "22843   4350560292  ...                        NaN                       NaN   \n",
       "\n",
       "       eqw_o_iii_4363  abs_o_iii_4363  detection_stat_o_iii_4363  \\\n",
       "0                 NaN             NaN                        NaN   \n",
       "1                 NaN             NaN                        NaN   \n",
       "2                 NaN             NaN                        NaN   \n",
       "3                 NaN             NaN                        NaN   \n",
       "4                 NaN             NaN                        NaN   \n",
       "...               ...             ...                        ...   \n",
       "22839             NaN             NaN                        NaN   \n",
       "22840             NaN             NaN                        NaN   \n",
       "22841             NaN             NaN                        NaN   \n",
       "22842             NaN             NaN                        NaN   \n",
       "22843             NaN             NaN                        NaN   \n",
       "\n",
       "       detection_raw_o_iii_4363  eqw_li_i  abs_li_i  detection_stat_li_i  \\\n",
       "0                           NaN       NaN       NaN                  NaN   \n",
       "1                           NaN       NaN       NaN                  NaN   \n",
       "2                           NaN       NaN       NaN                  NaN   \n",
       "3                           NaN       NaN       NaN                  NaN   \n",
       "4                           NaN       NaN       NaN                  NaN   \n",
       "...                         ...       ...       ...                  ...   \n",
       "22839                       NaN       NaN       NaN                  NaN   \n",
       "22840                       NaN    0.0243    4.5689               1.0016   \n",
       "22841                       NaN       NaN       NaN                  NaN   \n",
       "22842                       NaN       NaN       NaN                  NaN   \n",
       "22843                       NaN       NaN       NaN                  NaN   \n",
       "\n",
       "       detection_raw_li_i  \n",
       "0                     NaN  \n",
       "1                     NaN  \n",
       "2                     NaN  \n",
       "3                     NaN  \n",
       "4                     NaN  \n",
       "...                   ...  \n",
       "22839                 NaN  \n",
       "22840                0.91  \n",
       "22841                 NaN  \n",
       "22842                 NaN  \n",
       "22843                 NaN  \n",
       "\n",
       "[22844 rows x 374 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18da0d3",
   "metadata": {},
   "source": [
    "The next thing we can do is to combine both tables together using pandas' `merge` routine. In this routine, `left_on` and `right_on` tell `merge` which column to join the tables together on. The suffixes are added to columns which exist in both tables. You can make one of the suffixes an empty string (`\"\"`) because the meta information will be the same in both tables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a36c0c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sdss_id</th>\n",
       "      <th>sdss4_apogee_id</th>\n",
       "      <th>gaia_dr2_source_id</th>\n",
       "      <th>gaia_dr3_source_id</th>\n",
       "      <th>tic_v8_id</th>\n",
       "      <th>healpix</th>\n",
       "      <th>lead</th>\n",
       "      <th>version_id</th>\n",
       "      <th>catalogid</th>\n",
       "      <th>catalogid21</th>\n",
       "      <th>...</th>\n",
       "      <th>raw_si_h</th>\n",
       "      <th>raw_e_si_h</th>\n",
       "      <th>raw_s_h</th>\n",
       "      <th>raw_e_s_h</th>\n",
       "      <th>raw_ti_h</th>\n",
       "      <th>raw_e_ti_h</th>\n",
       "      <th>raw_ti_2_h</th>\n",
       "      <th>raw_e_ti_2_h</th>\n",
       "      <th>raw_v_h</th>\n",
       "      <th>raw_e_v_h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57986213</td>\n",
       "      <td>2M08202754+5039092</td>\n",
       "      <td>932171180709544960</td>\n",
       "      <td>932171180709544960</td>\n",
       "      <td>309663324</td>\n",
       "      <td>22407</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394828054999</td>\n",
       "      <td>4358936662</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.100591</td>\n",
       "      <td>0.009673</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>0.009768</td>\n",
       "      <td>-0.058738</td>\n",
       "      <td>0.009695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049559</td>\n",
       "      <td>0.005419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55054210</td>\n",
       "      <td>2M03400877+6322565</td>\n",
       "      <td>486950545777258496</td>\n",
       "      <td>486950545777258496</td>\n",
       "      <td>392377595</td>\n",
       "      <td>10556</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394800032784</td>\n",
       "      <td>4323784458</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014422</td>\n",
       "      <td>0.003437</td>\n",
       "      <td>0.042457</td>\n",
       "      <td>0.003482</td>\n",
       "      <td>0.076718</td>\n",
       "      <td>0.003506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.597890</td>\n",
       "      <td>0.001641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57304881</td>\n",
       "      <td>2M11351508+5235385</td>\n",
       "      <td>840242873232663040</td>\n",
       "      <td>840242873232663040</td>\n",
       "      <td>136949082</td>\n",
       "      <td>19993</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394824117131</td>\n",
       "      <td>4360324764</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.136189</td>\n",
       "      <td>0.005951</td>\n",
       "      <td>-0.273327</td>\n",
       "      <td>0.005960</td>\n",
       "      <td>-0.221767</td>\n",
       "      <td>0.005957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.107470</td>\n",
       "      <td>0.002280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57304881</td>\n",
       "      <td>2M11351508+5235385</td>\n",
       "      <td>840242873232663040</td>\n",
       "      <td>840242873232663040</td>\n",
       "      <td>136949082</td>\n",
       "      <td>19993</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394824117131</td>\n",
       "      <td>4360324764</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.169689</td>\n",
       "      <td>0.006525</td>\n",
       "      <td>-0.098582</td>\n",
       "      <td>0.006561</td>\n",
       "      <td>-0.232714</td>\n",
       "      <td>0.006534</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.111020</td>\n",
       "      <td>0.002291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79731650</td>\n",
       "      <td>2M11122553-0105412</td>\n",
       "      <td>3791857442582709504</td>\n",
       "      <td>3791857442582709504</td>\n",
       "      <td>443586370</td>\n",
       "      <td>99823</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050395169824115</td>\n",
       "      <td>4574381834</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.585630</td>\n",
       "      <td>0.019964</td>\n",
       "      <td>-0.433420</td>\n",
       "      <td>0.019709</td>\n",
       "      <td>-0.540410</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.689840</td>\n",
       "      <td>0.006184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>24396</th>\n",
       "      <td>61651404</td>\n",
       "      <td>2M13544903+3945037</td>\n",
       "      <td>1497043222189724416</td>\n",
       "      <td>1497043222191647872</td>\n",
       "      <td>23753266</td>\n",
       "      <td>35369</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394850037390</td>\n",
       "      <td>4603684880</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.237625</td>\n",
       "      <td>0.003829</td>\n",
       "      <td>-0.267783</td>\n",
       "      <td>0.003846</td>\n",
       "      <td>-0.161970</td>\n",
       "      <td>0.003790</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.185250</td>\n",
       "      <td>0.001094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24397</th>\n",
       "      <td>61651404</td>\n",
       "      <td>2M13544903+3945037</td>\n",
       "      <td>1497043222189724416</td>\n",
       "      <td>1497043222191647872</td>\n",
       "      <td>23753266</td>\n",
       "      <td>35369</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394850037390</td>\n",
       "      <td>4603684880</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.201363</td>\n",
       "      <td>0.001790</td>\n",
       "      <td>-0.213379</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>-0.273590</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.157380</td>\n",
       "      <td>0.000519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24398</th>\n",
       "      <td>88884090</td>\n",
       "      <td>2M15172043-0205307</td>\n",
       "      <td>4415089804623854080</td>\n",
       "      <td>4415089804623854080</td>\n",
       "      <td>38947570</td>\n",
       "      <td>101958</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050395714873724</td>\n",
       "      <td>4776536612</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.121680</td>\n",
       "      <td>0.015180</td>\n",
       "      <td>-1.134650</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>-1.399291</td>\n",
       "      <td>0.015670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.348100</td>\n",
       "      <td>0.005723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24399</th>\n",
       "      <td>88884180</td>\n",
       "      <td>2M15172179-0154585</td>\n",
       "      <td>4415105369585406464</td>\n",
       "      <td>4415105369585406464</td>\n",
       "      <td>38947519</td>\n",
       "      <td>101958</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050395714874342</td>\n",
       "      <td>4776539852</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.421730</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>-0.284790</td>\n",
       "      <td>0.007704</td>\n",
       "      <td>-0.454750</td>\n",
       "      <td>0.007857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.149800</td>\n",
       "      <td>0.002112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24400</th>\n",
       "      <td>60304616</td>\n",
       "      <td>2M14593515+4031479</td>\n",
       "      <td>1296914341950496128</td>\n",
       "      <td>1296914341950496128</td>\n",
       "      <td>28347403</td>\n",
       "      <td>34368</td>\n",
       "      <td>gaia_dr3_source</td>\n",
       "      <td>31</td>\n",
       "      <td>63050394842772447</td>\n",
       "      <td>4350560292</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016145</td>\n",
       "      <td>0.001256</td>\n",
       "      <td>-0.037211</td>\n",
       "      <td>0.001253</td>\n",
       "      <td>-0.040279</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.218240</td>\n",
       "      <td>0.000624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24401 rows × 768 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sdss_id     sdss4_apogee_id   gaia_dr2_source_id   gaia_dr3_source_id  \\\n",
       "0      57986213  2M08202754+5039092   932171180709544960   932171180709544960   \n",
       "1      55054210  2M03400877+6322565   486950545777258496   486950545777258496   \n",
       "2      57304881  2M11351508+5235385   840242873232663040   840242873232663040   \n",
       "3      57304881  2M11351508+5235385   840242873232663040   840242873232663040   \n",
       "4      79731650  2M11122553-0105412  3791857442582709504  3791857442582709504   \n",
       "...         ...                 ...                  ...                  ...   \n",
       "24396  61651404  2M13544903+3945037  1497043222189724416  1497043222191647872   \n",
       "24397  61651404  2M13544903+3945037  1497043222189724416  1497043222191647872   \n",
       "24398  88884090  2M15172043-0205307  4415089804623854080  4415089804623854080   \n",
       "24399  88884180  2M15172179-0154585  4415105369585406464  4415105369585406464   \n",
       "24400  60304616  2M14593515+4031479  1296914341950496128  1296914341950496128   \n",
       "\n",
       "       tic_v8_id  healpix             lead  version_id          catalogid  \\\n",
       "0      309663324    22407  gaia_dr3_source          31  63050394828054999   \n",
       "1      392377595    10556  gaia_dr3_source          31  63050394800032784   \n",
       "2      136949082    19993  gaia_dr3_source          31  63050394824117131   \n",
       "3      136949082    19993  gaia_dr3_source          31  63050394824117131   \n",
       "4      443586370    99823  gaia_dr3_source          31  63050395169824115   \n",
       "...          ...      ...              ...         ...                ...   \n",
       "24396   23753266    35369  gaia_dr3_source          31  63050394850037390   \n",
       "24397   23753266    35369  gaia_dr3_source          31  63050394850037390   \n",
       "24398   38947570   101958  gaia_dr3_source          31  63050395714873724   \n",
       "24399   38947519   101958  gaia_dr3_source          31  63050395714874342   \n",
       "24400   28347403    34368  gaia_dr3_source          31  63050394842772447   \n",
       "\n",
       "       catalogid21  ...  raw_si_h  raw_e_si_h   raw_s_h  raw_e_s_h  raw_ti_h  \\\n",
       "0       4358936662  ... -0.100591    0.009673  0.002239   0.009768 -0.058738   \n",
       "1       4323784458  ... -0.014422    0.003437  0.042457   0.003482  0.076718   \n",
       "2       4360324764  ... -0.136189    0.005951 -0.273327   0.005960 -0.221767   \n",
       "3       4360324764  ... -0.169689    0.006525 -0.098582   0.006561 -0.232714   \n",
       "4       4574381834  ... -0.585630    0.019964 -0.433420   0.019709 -0.540410   \n",
       "...            ...  ...       ...         ...       ...        ...       ...   \n",
       "24396   4603684880  ... -0.237625    0.003829 -0.267783   0.003846 -0.161970   \n",
       "24397   4603684880  ... -0.201363    0.001790 -0.213379   0.001794 -0.273590   \n",
       "24398   4776536612  ... -1.121680    0.015180 -1.134650   0.015205 -1.399291   \n",
       "24399   4776539852  ... -0.421730    0.007830 -0.284790   0.007704 -0.454750   \n",
       "24400   4350560292  ... -0.016145    0.001256 -0.037211   0.001253 -0.040279   \n",
       "\n",
       "       raw_e_ti_h  raw_ti_2_h  raw_e_ti_2_h   raw_v_h  raw_e_v_h  \n",
       "0        0.009695         NaN           NaN -0.049559   0.005419  \n",
       "1        0.003506         NaN           NaN -0.597890   0.001641  \n",
       "2        0.005957         NaN           NaN -0.107470   0.002280  \n",
       "3        0.006534         NaN           NaN -0.111020   0.002291  \n",
       "4        0.019891         NaN           NaN -0.689840   0.006184  \n",
       "...           ...         ...           ...       ...        ...  \n",
       "24396    0.003790         NaN           NaN  0.185250   0.001094  \n",
       "24397    0.001812         NaN           NaN  0.157380   0.000519  \n",
       "24398    0.015670         NaN           NaN -1.348100   0.005723  \n",
       "24399    0.007857         NaN           NaN  0.149800   0.002112  \n",
       "24400    0.001252         NaN           NaN -0.218240   0.000624  \n",
       "\n",
       "[24401 rows x 768 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both = pd.merge(bl, aa, left_on=\"sdss_id\", right_on=\"sdss_id\", suffixes=[\"\", \"_aa\"])\n",
    "\n",
    "both"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15ca0a01",
   "metadata": {},
   "source": [
    "We now have a table with information from both ASPCAP and LineForest. Let's do some fun plotting to see what we've found "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2a550d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Figure\n",
    "fig = plt.figure(figsize=(8,8), facecolor=\"white\")\n",
    "\n",
    "# Make Histogram of H alpha\n",
    "ax0 = plt.subplot(221)\n",
    "ax0.hist(both[\"eqw_h_alpha\"], bins=np.arange(-20,20, 0.5),\n",
    "         color=\"black\", label=r\"$H\\alpha$ Emission\")\n",
    "ax0.set(xlabel=r\"$H \\alpha$ EQW\", ylabel=\"N stars\")\n",
    "\n",
    "# Make HR Diagram\n",
    "ax1 = plt.subplot(222)\n",
    "ax1.scatter(both[\"bp_mag\"]-both[\"rp_mag\"],\n",
    "            both[\"g_mag\"]+5*np.log10(both[\"plx\"]/100),\n",
    "            s=1, color=\"black\", alpha=0.1)\n",
    "ax1.set(xlabel=\"BP-RP\", ylabel=r\"$M_G$\", xlim=(-1,5), ylim=(16,-2))\n",
    "\n",
    "# Make Histogram of [Fe/H]\n",
    "ax2 = plt.subplot(223)\n",
    "ax2.hist(both[\"fe_h\"], bins=np.arange(-2.5,0.5, 0.1),\n",
    "         color=\"black\", label=r\"[Fe/H] abundance\")\n",
    "ax2.set(xlabel=r\"[Fe/H] abundance\", ylabel=\"N stars\")\n",
    "\n",
    "# Make HR Diagram\n",
    "ax3 = plt.subplot(224)\n",
    "ax3.scatter(both[\"fe_h\"], both[\"eqw_h_alpha\"],\n",
    "            s=1, color=\"black\", alpha=0.1)\n",
    "ax3.set(xlabel=r\"[Fe/H] abundance\", ylabel=r\"$H \\alpha$ EQW\", \n",
    "        xlim=(-2.5,0.5), ylim=(-10,10))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e919d0c",
   "metadata": {},
   "source": [
    "Unfortunately, there doesn't seem to be much of a correlation between H alpha emission and iron abundance. But, there are quite a few stars with both BOSS and APOGEE spectra!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d19d30",
   "metadata": {},
   "source": [
    "### About this notebook\n",
    "\n",
    "This notebook was created to illustrate how to find the same targets that exist in two different tables.\n",
    "\n",
    "**Authors**: Zach Way\n",
    "\n",
    "**Last Update**: 2024 December 10\n",
    "\n",
    "If you use `astropy`  for published research, please cite the\n",
    "authors. Follow this link for more information about citing `astropy`:\n",
    "\n",
    "* [Citing `astropy`](https://www.astropy.org/acknowledging.html)\n",
    "\n",
    "And of course please [cite](https://sdss.org/collaboration/citing-sdss/) SDSS when you use our data."
   ]
  }
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