ASAS_SN 17_by_100_million.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06081d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "webbrowser.open('https://dr7.lamost.org/catalogue')    # Download LAMOST LRS General Catalog and LAMOST MRS General Catalog CSV files\n",
    "\n",
    "webbrowser.open('https://www.lamost.org/dr8/v2.0/catalogue') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eefaf6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to ASAS-SN Skypatrol!\n",
      "\n",
      "Current Deployment Version: 0.6.9 (28 April 2023)\n",
      "Please upgrade your client if not up to date.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from astroquery.gaia import Gaia\n",
    "from astroquery.simbad import Simbad\n",
    "\n",
    "import astropy.coordinates as coord\n",
    "import astropy.units as u\n",
    "\n",
    "import pyvo as vo\n",
    "\n",
    "from matplotlib import gridspec\n",
    "\n",
    "from specutils import Spectrum1D\n",
    "from pyvo.dal.ssa  import search, SSAService\n",
    "\n",
    "import requests\n",
    "from io import BytesIO\n",
    "\n",
    "import matplotlib.ticker as plticker\n",
    "\n",
    "from astropy.stats import sigma_clip\n",
    "\n",
    "import starcolorindexSpT\n",
    "\n",
    "from pyasassn.client import SkyPatrolClient\n",
    "client = SkyPatrolClient()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import webbrowser\n",
    "\n",
    "from colorama import Fore, Back, Style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d522d4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.0\n"
     ]
    }
   ],
   "source": [
    "from importlib.metadata import version\n",
    "\n",
    "import lightkurve as lk\n",
    "print(version('lightkurve'))\n",
    "\n",
    "from lightkurve import search_targetpixelfile\n",
    "from lightkurve import search_lightcurve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0678255",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas1.1.5\n",
      "numpy1.24.2\n",
      "scipy1.9.3\n",
      "matplotlib3.2.2\n",
      "astroquery0.4.7.dev8479\n",
      "lightkurve2.4.0\n",
      "\n",
      "\n",
      "Python 3.8.5\n",
      "\n",
      "\n",
      "Selected Jupyter core packages...\n",
      "IPython          : 8.12.0\n",
      "ipykernel        : 6.19.2\n",
      "ipywidgets       : 8.0.4\n",
      "jupyter_client   : 8.1.0\n",
      "jupyter_core     : 5.3.0\n",
      "jupyter_server   : 2.5.0\n",
      "jupyterlab       : not installed\n",
      "nbclient         : 0.5.13\n",
      "nbconvert        : 7.3.1\n",
      "nbformat         : 5.7.0\n",
      "notebook         : 6.5.3\n",
      "qtconsole        : 5.4.2\n",
      "traitlets        : 5.7.1\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import astroquery\n",
    "#import pyasassn\n",
    "\n",
    "print(\"pandas\" + str(pd.__version__))\n",
    "print(\"numpy\"  + str(np.__version__))\n",
    "print(\"scipy\"  + str(sp.__version__))\n",
    "\n",
    "print(\"matplotlib\" + str(matplotlib.__version__))\n",
    "\n",
    "print(\"astroquery\" + str(astroquery.__version__))\n",
    "\n",
    "print(\"lightkurve\" + str(lk.__version__))\n",
    "\n",
    "print(\"\\n\")\n",
    "!python --version\n",
    "\n",
    "print(\"\\n\")\n",
    "!jupyter --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ef11e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "webbrowser.open('https://www.aavso.org/vsx/index.php?view=about.vartypes')   # VARIABLE TYPES WITHIN MAIN GROUPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f52589e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import webbrowser\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/2207.01946.pdf')  # Cross-match of Gaia sources with variable objects from the literature\n",
    "\n",
    "webbrowser.open('https://gea.esac.esa.int/archive/documentation/GDR3/Gaia_archive/chap_datamodel/') # Data model \n",
    "\n",
    "webbrowser.open('https://gea.esac.esa.int/archive/documentation/GDR3/Gaia_archive/chap_datamodel/sec_dm_variability_tables/') # Variability tables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b22627a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import webbrowser\n",
    "\n",
    "webbrowser.open('https://en.wikipedia.org/wiki/List_of_astronomy_acronyms#C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b085aa57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import webbrowser\n",
    "\n",
    "webbrowser.open('https://www.zooniverse.org/projects/tharinduj/citizen-asas-sn/about/research')\n",
    "\n",
    "webbrowser.open('https://drive.google.com/drive/folders/1gxcIokRsw1eyPmbPZ0-C8blfRGItSOAu')\n",
    "\n",
    "webbrowser.open('https://asas-sn.osu.edu/variables')\n",
    "\n",
    "webbrowser.open('https://www.zooniverse.org/projects/tharinduj/citizen-asas-sn/about/research')\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/2207.01946.pdf')\n",
    "\n",
    "webbrowser.open('https://github.com/asas-sn/skypatrol')\n",
    "\n",
    "webbrowser.open('https://asas-sn.github.io/skypatrol/index.html')\n",
    "\n",
    "webbrowser.open('http://www.lamost.org/dr8/v2.0/catalogue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "915fb3d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e3db97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(HTML(data=\"\"\"\n",
    "<style>\n",
    "    div#notebook-container    { width: 95%; }\n",
    "    div#menubar-container     { width: 65%; }\n",
    "    div#maintoolbar-container { width: 99%; }\n",
    "</style>\n",
    "\"\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "866d2319",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ipython_default = plt.rcParams.copy()\n",
    "\n",
    "#plt.style.use('dark_background')\n",
    "\n",
    "# reset rcParams\n",
    "#plt.rcParams.update(Ipython_default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3dd153c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set the figure size to be wide in the horizontal direction\n",
    "#to accommodate the image cutouts alongside the spectra \n",
    "#fsize=[20,13]\n",
    "fsize=[50,18]\n",
    "#mpl.rcParams['axes.linewidth'] = 0.7\n",
    "FSIZE=18\n",
    "LWIDTH=0.5\n",
    "LABELSIZE=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce44779f",
   "metadata": {},
   "outputs": [],
   "source": [
    "make_canvas_flag = \"Y\"\n",
    "\n",
    "hydrogen_lines_flag = \"N\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe0f2054",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80bac16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_labels(xlabel = '', ylabel = '', fontsize = 12):\n",
    "    #plt.xlabel(xlabel,  fontsize = fontsize)\n",
    "    plt.xlabel(\"Wavelength [Angstroms]\",  fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    plt.xticks(fontsize = fontsize)\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2feb23e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_RVS_canvas(xlabel = '', ylabel = '', fontsize = 12):\n",
    "    #plt.xlabel(xlabel,  fontsize = fontsize)\n",
    "    plt.xlabel(\"Wavelength [Angstroms]\",  fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    plt.xticks(fontsize = fontsize)\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.grid()\n",
    "\n",
    "    yMin = float(xp['flux'].min())\n",
    "    #yRangeMin = yMin + 0.2\n",
    "    yRangeMin = 1.8 \n",
    "\n",
    "    yRangeMax = float(xp['flux'].max())\n",
    "    yRange = yRangeMax\n",
    "\n",
    "    plt.annotate('Ca II 8498', xy=[8498, yRangeMin ],  rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8542', xy=[8542, yRangeMin ], rotation=-90, color='red')\n",
    "    plt.annotate('Pa14 8600', xy=[8600, yRangeMin - 0.3 ], rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8662.1', xy=[8662, yRangeMin ],  rotation=-90, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3eeb69ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotter ==============================================\n",
    "def make_canvas(xlabel = '', ylabel = '', fontsize = 12):\n",
    "    #plt.xlabel(xlabel,  fontsize = fontsize)\n",
    "    plt.xlabel(\"Wavelength [Angstroms]\",  fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    plt.xticks(fontsize = fontsize)\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.xticks([3839, 3889, 3970, 4101, 4340, 4472, 4861, 5175, 5270, 5411, 5755, 5895, 6302,\n",
    "               6365, 6562, 6562, 6890, 7135, 7500, 7775, 8000, 8498, 8542, 8662, 9000, 9200])\n",
    "    \n",
    "    xLabels = (3839, 3889, 3970, 4101, 4340, 4472, 4861, 5175, 5270, 5411, 5755, 5895, 6302,\n",
    "               6365, 6562, 6562, 6890, 7135, 7500, 7775, 8000, 8498, 8542, 8662, 9000, 9200)\n",
    "\n",
    "    plt.xticks(xLabels, rotation=90)\n",
    "\n",
    "    yMin = float(xp['flux'].min())\n",
    "    #yRangeMin = yMin + 0.2\n",
    "    yRangeMin = 0.8 \n",
    "\n",
    "    yRangeMax = float(xp['flux'].max())\n",
    "    yRange = yRangeMax\n",
    "\n",
    "    plt.annotate('CN 3839', xy=[3839,   yRangeMin + 0.6],  rotation=-90)\n",
    "    plt.annotate('H-zeta 3889', xy=[3889, yRangeMin +  .20],  rotation=-90, color='red')\n",
    "    plt.annotate('H-epsilon 3970', xy=[3970, yRangeMin +  0.6],  rotation=-90, color='red')\n",
    "    plt.annotate('H-delta 4101', xy=[4101, yRangeMin +  .20],  rotation=-90, color='red')\n",
    "    plt.annotate('H-gamma 4340', xy=[4340, yRangeMin + .20],  rotation=-90, color='red')\n",
    "    plt.annotate('He I 4471.7', xy=[4472, yRangeMin + .20],  rotation=-90, color='orange')\n",
    "    plt.annotate('H-beta 4861', xy=[4861, yRangeMin + .20],  rotation=-90, color='red')\n",
    "    plt.annotate('Mg I 5175', xy=[5175, yRangeMin + .20],  rotation=-90)\n",
    "    plt.annotate('Fe I 5270', xy=[5270, yRangeMin +  .20],  rotation=-90)\n",
    "    plt.annotate('He II 5411', xy=[5411, yRangeMin +  .20],  rotation=-90, color='orange')\n",
    "    plt.annotate('N II 5755', xy=[5755, yRangeMin +  .20],  rotation=-90)\n",
    "    plt.annotate('D1 Na I 5895', xy=[5895, yRangeMin +  .20],  rotation=-90, color='blue')\n",
    "    plt.annotate('OI 6302', xy=[6302, yRangeMin +  .20],  rotation=-90, color='green')\n",
    "    plt.annotate('OI 6365.5', xy=[6365, yRangeMin +  .20],  rotation=-90, color='green')\n",
    "    plt.annotate('H-alpha 6562', xy=[6562, yRangeMin +  .20],  rotation=-90, color='red')\n",
    "    plt.annotate('CN 6890', xy=[6890, yRangeMin +  .20],  rotation=-90)\n",
    "    plt.annotate('Ar III 7135', xy=[7135, yRangeMin +  .20],  rotation=-90)\n",
    "    plt.annotate('O I 7775', xy=[7775, yRangeMin +  .20], rotation=-90, color='green')\n",
    "    plt.annotate('Ca II 8498', xy=[8498, yRangeMin +  .35],  rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8542', xy=[8542, yRangeMin +  .30], rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8662.1', xy=[8662, yRangeMin +  .30],  rotation=-90, color='red')\n",
    "\n",
    "\n",
    "def Hydrogen_lines(xlabel = '', ylabel = '', fontsize = 12):\n",
    "    #plt.xlabel(xlabel,  fontsize = fontsize)\n",
    "    plt.xlabel(\"Wavelength [Angstroms]\",  fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    plt.xticks(fontsize = fontsize)\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.xticks([3835, 3889, 3970, 4101, 4340, 4861, 6000, 6562, 7000, 8000, 9000, 10000])\n",
    "    \n",
    "    xLabels = (3835, 3889, 3970, 4101, 4340, 4861, 6000, 6562, 7000, 8000, 9000, 10000)\n",
    "\n",
    "    plt.xticks(xLabels, rotation=90)\n",
    "\n",
    "    yMin = float(xp['flux'].min())\n",
    "    #yRangeMin = yMin + 0.2\n",
    "    yRangeMin = 0.8 \n",
    "\n",
    "    yRangeMax = float(xp['flux'].max())\n",
    "    yRange = yRangeMax\n",
    "\n",
    "    plt.annotate('H12 3750', xy=[3750, yRangeMin +  .50],  rotation=-90, color='red')\n",
    "    plt.annotate('H11 3771', xy=[3771, yRangeMin -  .35],  rotation=-90, color='red')\n",
    "    plt.annotate('H10 3798', xy=[3798, yRangeMin +  .20],  rotation=-90, color='red')\n",
    "    plt.annotate('H9 3835', xy=[3835, yRangeMin -  .10],  rotation=-90, color='red')\n",
    "\n",
    "    plt.annotate('H8-zeta 3889', xy=[3889, yRangeMin +  .20],  rotation=-90, color='red')\n",
    "    plt.annotate('H-epsilon 3970', xy=[3970, yRangeMin +  0.6],  rotation=-90, color='red')\n",
    "    plt.annotate('H-delta 4101', xy=[4101, yRangeMin +  .20],  rotation=-90, color='red')\n",
    "    plt.annotate('H-gamma 4340', xy=[4340, yRangeMin + .20],  rotation=-90, color='red')\n",
    "    plt.annotate('H-beta 4861', xy=[4861, yRangeMin + .20],  rotation=-90, color='red')\n",
    "    plt.annotate('H-alpha 6562', xy=[6562, yRangeMin +  .20],  rotation=-90, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d97fb59f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_RVS_canvas1(xlabel = '', ylabel = '', fontsize = 12):     ### 8460 - 8580\n",
    "    plt.xlabel(\"Wavelength [Angstroms]\",  fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    plt.xticks(fontsize = fontsize)\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.grid()\n",
    "\n",
    "    yMin = float(xp['flux'].min())\n",
    "    yRangeMin = 1.8 \n",
    "\n",
    "    yRangeMax = float(xp['flux'].max())\n",
    "    yRange = yRangeMax\n",
    "\n",
    "    plt.annotate('Fe I 8470', xy=[8470, yRangeMin ],  rotation=-90, color='green')\n",
    "    plt.annotate('Mg I 8476', xy=[8476, yRangeMin - 0.3],  rotation=-90, color='blue')\n",
    "    plt.annotate('Fe I 8483', xy=[8483, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Ca II 8498', xy=[8498, yRangeMin ],  rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8502', xy=[8502, yRangeMin],  rotation=-90, color='red')\n",
    "    plt.annotate('Ce II 8514', xy=[8514, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Fe I 8516', xy=[8516, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Ti I 8520.6', xy=[8520.6, yRangeMin - 0.3],  rotation=-90, color='blue')\n",
    "    plt.annotate('Zr II 8527', xy=[8527, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Fe I 8529', xy=[8529, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Si I 8538', xy=[8538, yRangeMin - 0.3],  rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8542', xy=[8542, yRangeMin],  rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8546', xy=[8546, yRangeMin - 0.3],  rotation=-90, color='red')\n",
    "    plt.annotate('Cr I 8551', xy=[8551, yRangeMin  - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Si I 8559', xy=[8559, yRangeMin  - 0.3],  rotation=-90, color='red')\n",
    "    plt.annotate('Ti I 8572', xy=[8572, yRangeMin - 0.3],  rotation=-90, color='blue')\n",
    "    plt.annotate('Fe I 8574', xy=[8574, yRangeMin - 0.3],  rotation=-90, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85e545a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_RVS_canvas2(xlabel = '', ylabel = '', fontsize = 12):    ### 8580 - 8700\n",
    "    plt.xlabel(\"Wavelength [Angstroms]\",  fontsize = fontsize)\n",
    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
    "    plt.xticks(fontsize = fontsize)\n",
    "    plt.yticks(fontsize = fontsize)\n",
    "    plt.grid()\n",
    "\n",
    "    yMin = float(xp['flux'].min())\n",
    "    yRangeMin = 1.8 \n",
    "\n",
    "    yRangeMax = float(xp['flux'].max())\n",
    "    yRange = yRangeMax\n",
    "\n",
    "    plt.annotate('Fe I 8584', xy=[8584, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Fe II 8588', xy=[8588, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Nd II 8594', xy=[8594, yRangeMin - 0.3],  rotation=-90, color='blue')\n",
    "    plt.annotate('Fe I 8624', xy=[8624, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('N I 8631',  xy=[8631, yRangeMin  - 0.3 ],  rotation=-90, color='red')\n",
    "    plt.annotate('Ca I 8636',  xy=[8636, yRangeMin  - 0.3 ],  rotation=-90, color='red')\n",
    "    plt.annotate('Ni I 8639',  xy=[8639, yRangeMin  - 0.3 ],  rotation=-90, color='red')\n",
    "    plt.annotate('Cr I 8645', xy=[8645, yRangeMin  - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Ca II 8662', xy=[8662, yRangeMin],  rotation=-90, color='red')\n",
    "    plt.annotate('Ca II 8666', xy=[8666, yRangeMin],  rotation=-90, color='red')\n",
    "    plt.annotate('S I 8672',  xy=[8672, yRangeMin  - 0.3],  rotation=-90, color='red')\n",
    "    plt.annotate('Fe I 8677', xy=[8677, yRangeMin],  rotation=-90, color='green')\n",
    "    plt.annotate('N I 8685',  xy=[8685, yRangeMin  - 0.3],  rotation=-90, color='red')\n",
    "    plt.annotate('Si I 8688', xy=[8688, yRangeMin  - 0.3],  rotation=-90, color='red')\n",
    "    plt.annotate('Fe I 8691', xy=[8691, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('Ti I 8694', xy=[8694, yRangeMin - 0.3],  rotation=-90, color='green')\n",
    "    plt.annotate('S I 8697',  xy=[8697, yRangeMin  - 0.3 ],  rotation=-90, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b53a09df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotTarget4(target, i, targetstring, freqmin, freqmax):\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.style.use('dark_background');\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    import astropy.units as u\n",
    "\n",
    "    from lightkurve import search_targetpixelfile\n",
    "    \n",
    "\n",
    "    plt.rcParams.update({'figure.max_open_warning': 0})\n",
    "    \n",
    "\n",
    "\n",
    "    lc = lk.search_lightcurve(target)[i].download();\n",
    "\n",
    "    #lc = lc0.remove_nans().normalize().remove_outliers()\n",
    "    #lc = lc0.flatten()\n",
    "    #lc = lc0.remove_outliers()\n",
    "    #lc = lc0.remove_outliers(sigma=3)\n",
    "\n",
    "\n",
    "    if plot_pixelfile == 'Y':\n",
    "        plt.style.use('dark_background');\n",
    "        pixelfile = search_targetpixelfile(target)[i].download()\n",
    "        pixelfile.plot(frame=1);\n",
    "        pixel_lc = pixelfile.to_lightcurve(aperture_mask='all')\n",
    "        pixel_lc.plot()\n",
    "        sText = \"\\nPixelfile Lightcurve Plot\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText);\n",
    "\n",
    "\n",
    "    if plot_lightcurve  == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        lc.plot()\n",
    "        #lc.plot(label='Lightcurve Plot2')\n",
    "        sText = \"\\nLightcurve Plot\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText);\n",
    "\n",
    "        if plot_PDC_AP_Flux == 'Y':\n",
    "            plt.style.use('dark_background');\n",
    "            ax = lc.plot(column='pdcsap_flux', label='PDCSAP Flux', normalize=True)\n",
    "            lc.plot(column='sap_flux', label='SAP Flux', normalize=True, ax=ax);\n",
    "            plt.title(str(target) + \" (\" + str(i) + \")\"  + \"\\nCompare the PDCSAP and the SAP flux\");\n",
    "\n",
    "        #ax = lc.plot()\n",
    "        #ax.set_title(\"PDCSAP light curve of \" + str(target));\n",
    "    \n",
    "    #period = np.linspace(1, 20, 10000)\n",
    "    period = np.linspace(freqmin, freqmax, 10000)\n",
    "    bls = lc.to_periodogram(method='bls', period=period, frequency_factor=500);\n",
    "    #bls = lc.normalize().to_periodogram(method='bls', period=period, frequency_factor=500);\n",
    "    \n",
    "    period_array = bls.period\n",
    "    period_rounded = np.round(period_array, 1)\n",
    "    power_array = bls.power\n",
    "\n",
    "    # Save the highest peak as the planet candidate\n",
    "    #index = np.argmax(power_array.power)\n",
    "    #bls_period = period_array.period[index]\n",
    "\n",
    "\n",
    "    df_dict = {\n",
    "        'Period': period_rounded,\n",
    "        'Power': power_array\n",
    "        }\n",
    "    df = pd.DataFrame(df_dict)\n",
    "    df = df.sort_values(by=['Power'], ascending=False)\n",
    "    df = df.drop_duplicates(subset=['Period'], keep='first')\n",
    "    #print(df[0:10])\n",
    "\n",
    "\n",
    "    planet_period = bls.period_at_max_power\n",
    "    planet_t0 = bls.transit_time_at_max_power\n",
    "    planet_dur = bls.duration_at_max_power\n",
    "\n",
    "    if plot_period      == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        #print(\"Period Array: \" + str(period_array))\n",
    "        bls.plot()\n",
    "        #plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring) + \"\\n                    LightKurve Period:  \" + str(planet_period));\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring) + \"\\n                    LightKurve Period:   + {0:.4f} \".format(planet_period)+ \"\\nlc = lk.search_lightcurve(target)[i].download()\");\n",
    "        #plt.axvline(bls_period, color=\"C1\", lw=5, alpha=0.8)\n",
    "        # + \"\\nPeriod array: \" + str(period_array)  + \"\\nPower array: \" + str(power_array)\n",
    "        #print(df['Period'][0:10])\n",
    "\n",
    "\n",
    "    if plot_phase_fold  == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        ax = lc.fold(period=planet_period, epoch_time=planet_t0).scatter()\n",
    "        ax.set_xlim(-5, 5)\n",
    "        sText = \"\\nPhase-fold the light curve to confirm period and transit time (epoch) \\ncorrespond to a transit signal.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText);\n",
    "        plt.show();\n",
    "        \n",
    "\n",
    "    if plot_half_phase_fold == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        ax = lc.fold(period=0.5*planet_period, epoch_time=planet_t0).scatter()\n",
    "        ax.set_xlim(-5, 5)\n",
    "        sText1 = \"\\nThe above eclipses may not line up or look like the correct period of an eclipsing binary.\"  \n",
    "        sText2 = \"\\nThis is common for eclipsing binaries with deep secondary eclipses, we may have found a harmonic of the period.\" \n",
    "        sText3 = \"\\nLet's plot it again with half the period.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText1 + sText2 + sText3)\n",
    "        plt.show();\n",
    "\n",
    "    if plot_2phase_fold == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        ax = lc.fold(period=2*planet_period, epoch_time=planet_t0).scatter()\n",
    "        ax.set_xlim(-5, 5)\n",
    "        sText1 = \"\\nThe above eclipses may not line up or look like the correct period of an eclipsing binary.\"  \n",
    "        sText2 = \"\\nThis is common for eclipsing binaries with deep secondary eclipses, we may have found a harmonic of the period.\" \n",
    "        sText3 = \"\\nLet's plot it again with twice the period.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText1 + sText2 + sText3)\n",
    "        plt.show();\n",
    "\n",
    "    if plot_3phase_fold == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        ax = lc.fold(period=3*planet_period, epoch_time=planet_t0).scatter()\n",
    "        ax.set_xlim(-5, 5)\n",
    "        sText1 = \"\\nThe above eclipses may not line up or look like the correct period of an eclipsing binary.\"  \n",
    "        sText2 = \"\\nThis is common for eclipsing binaries with deep secondary eclipses, we may have found a harmonic of the period.\" \n",
    "        sText3 = \"\\nLet's plot it again with three times the period.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText1 + sText2 + sText3)\n",
    "        plt.show();\n",
    "\n",
    "    if plot_4phase_fold == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        ax = lc.fold(period=4*planet_period, epoch_time=planet_t0).scatter()\n",
    "        ax.set_xlim(-5, 5)\n",
    "        sText1 = \"\\nThe above eclipses may not line up or look like the correct period of an eclipsing binary.\"  \n",
    "        sText2 = \"\\nThis is common for eclipsing binaries with deep secondary eclipses, we may have found a harmonic of the period.\" \n",
    "        sText3 = \"\\nLet's plot it again with quadruple the period.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText1 + sText2 + sText3)\n",
    "        plt.show();\n",
    "        \"\"\"pg = lc.to_periodogram(minimum_period=0.9*u.day, maximum_period=1.2*u.day, oversample_factor=10)\n",
    "        pg.period_at_max_power\n",
    "        lc.fold(period=4*pg.period_at_max_power, wrap_phase=0.2).scatter();\n",
    "        sText1 = \"\\nThe above eclipses may not line up or look like the correct period of an eclipsing binary.\"  \n",
    "        sText2 = \"\\nThis is common for eclipsing binaries with deep secondary eclipses, we may have found a harmonic of the period.\" \n",
    "        sText3 = \"\\nLet's plot it again with quadruple the period.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\"  + sText1 + sText2 + sText3)\n",
    "        plt.show();\"\"\"\n",
    "\n",
    "    \n",
    "    if plot_periodogram == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        pg = lc.to_periodogram(oversample_factor=1)\n",
    "        pg.plot()\n",
    "        sText = \"\\nPlot the periodogram in the same way that we plot the original light curve.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring)   + sText)\n",
    "        plt.show()\n",
    "\n",
    "        pg.plot(view='period', scale='log')\n",
    "        sText = \"\\nLet's plot it in period space, so that we can see what period the oscillation is occurring at.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring) + sText)\n",
    "        plt.show()\n",
    "\n",
    "        lc.fold(period=pg.period_at_max_power).scatter()\n",
    "        sText = \"\\nLet's use the period_at_max_power to fold the light curve\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring) + sText)\n",
    "        plt.show()\n",
    "\n",
    "        lc.fold(period=4*pg.period_at_max_power, wrap_phase=0.2).scatter()\n",
    "        sText = \"\\nIf the eclipses do not line up, try plotting it again with quadruple the period.\"\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring) + sText)\n",
    "        plt.show()\n",
    "\n",
    "        pg = lc.to_periodogram(minimum_period=0.9*u.day, maximum_period=1.2*u.day, oversample_factor=10)\n",
    "        pg.period_at_max_power  \n",
    "        sText =\"\\nLet's try to get a more precise period by increasing the number of points in our periodogram \\nusing the `oversample_factor` parameter and by constraining the range of the period value:\"\n",
    "        #sText =\"\\nLet's try to get a more precise period by increasing the number of points in our periodogram \\nusing the `oversample_factor` parameter.\"\n",
    "        lc.fold(period=pg.period_at_max_power, wrap_phase=0.2).scatter()\n",
    "        #lc.fold(period=pg.period_at_max_power).scatter()\n",
    "        plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring)  + sText)\n",
    "        plt.show()\n",
    "\n",
    "    plt.figure(figsize=(13.5, 1))\n",
    "    plt.axhline(y=0.5, color='r', linestyle='-')\n",
    "    plt.axis('off')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b11cc78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotTarget4B(target, i, targetstring, freqmin, freqmax, target1):\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.style.use('dark_background');\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    import astropy.units as u\n",
    "    \n",
    "\n",
    "    plt.rcParams.update({'figure.max_open_warning': 0})\n",
    "    \n",
    "\n",
    "    #print(Fore.RED + \"PlotTarget4B: i = \" + str(i))\n",
    "    print(Fore.RED + \"PlotTarget4B: target = \" + str(target))\n",
    "    print(Style.RESET_ALL)\n",
    "\n",
    "    lc0 = lk.search_lightcurve(target)[i].download();\n",
    "    \n",
    "    #print(\"PlotTarget4B: lc0 = \" + str(lc0))\n",
    "    \n",
    "    #print(Fore.RED + \"PlotTarget4B: lc0 length = \" + str(len(lc0)))\n",
    "    print(Style.RESET_ALL)\n",
    "\n",
    "    lc = lc0.remove_nans().normalize().remove_outliers()\n",
    "\n",
    "    \n",
    "    #lc = lc0.flatten()\n",
    "    #lc = lc0.remove_outliers()\n",
    "    #lc = lc0.remove_outliers(sigma=3)\n",
    "\n",
    "\n",
    "    if plot_lightcurve  == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        lc.plot()\n",
    "        #lc.plot(label='Lightcurve Plot2')\n",
    "        sText = \"\\nLightcurve Plot\"\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\"  + sText);\n",
    "       \n",
    "    \n",
    "    #period = np.linspace(1, 20, 10000)\n",
    "    period = np.linspace(freqmin, freqmax, 10000)\n",
    "    bls = lc.to_periodogram(method='bls', period=period, frequency_factor=500);\n",
    "    #bls = lc.normalize().to_periodogram(method='bls', period=period, frequency_factor=500);\n",
    "    \n",
    "    period_array = bls.period\n",
    "    period_rounded = np.round(period_array, 1)\n",
    "    power_array = bls.power\n",
    "\n",
    "    # Save the highest peak as the planet candidate\n",
    "    #index = np.argmax(power_array.power)\n",
    "    #bls_period = period_array.period[index]\n",
    "\n",
    "\n",
    "    df_dict = {\n",
    "        'Period': period_rounded,\n",
    "        'Power': power_array\n",
    "        }\n",
    "    df = pd.DataFrame(df_dict)\n",
    "    df = df.sort_values(by=['Power'], ascending=False)\n",
    "    df = df.drop_duplicates(subset=['Period'], keep='first')\n",
    "    #print(df[0:10])\n",
    "\n",
    "\n",
    "    planet_period = bls.period_at_max_power\n",
    "    planet_t0 = bls.transit_time_at_max_power\n",
    "    planet_dur = bls.duration_at_max_power\n",
    "\n",
    "    if plot_period      == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        #print(\"Period Array: \" + str(period_array))\n",
    "        bls.plot()\n",
    "        #plt.title(str(target) + \" (\" + str(i) + \")\" + str(targetstring) + \"\\n                    LightKurve Period:  \" + str(planet_period));\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\" + str(targetstring) + \"\\n                    LightKurve Period:   + {0:.4f} \".format(planet_period) + \"\\nlc0.remove_nans().normalize().remove_outliers()\");\n",
    "        #plt.axvline(bls_period, color=\"C1\", lw=5, alpha=0.8)\n",
    "        # + \"\\nPeriod array: \" + str(period_array)  + \"\\nPower array: \" + str(power_array)\n",
    "        #print(df['Period'][0:10])\n",
    "\n",
    "\n",
    "    if plot_phase_fold  == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        ax = lc.fold(period=planet_period, epoch_time=planet_t0).scatter()\n",
    "        ax.set_xlim(-5, 5)\n",
    "        sText = \"\\nPhase-fold the light curve to confirm period and transit time (epoch) \\ncorrespond to a transit signal.\"\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\"  + sText);\n",
    "        plt.show();\n",
    "\n",
    "    if plot_4phase_fold == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        #plt.figure(figsize=(13, 10))\n",
    "        pg = lc.to_periodogram(minimum_period=0.9*u.day, maximum_period=1.2*u.day, oversample_factor=10)\n",
    "        pg.period_at_max_power\n",
    "        lc.fold(period=4*pg.period_at_max_power, wrap_phase=0.2).scatter();\n",
    "        sText1 = \"\\nThe above eclipses may not line up or look like the correct period of an eclipsing binary.\"  \n",
    "        sText2 = \"\\nThis is common for eclipsing binaries with deep secondary eclipses, we may have found a harmonic of the period.\" \n",
    "        sText3 = \"\\nLet's plot it again with quadruple the period.\"\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\"  + sText1 + sText2 + sText3)\n",
    "        plt.show();\n",
    "\n",
    "    \n",
    "    if plot_periodogram == \"Y\":\n",
    "        plt.style.use('dark_background');\n",
    "        pg = lc.to_periodogram(oversample_factor=1)\n",
    "        pg.plot()\n",
    "        sText = \"\\nPlot the periodogram in the same way that we plot the original light curve.\"\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\" + str(targetstring)   + sText)\n",
    "        plt.show()\n",
    "\n",
    "        pg.plot(view='period', scale='log')\n",
    "        sText = \"\\nLet's plot it in period space, so that we can see what period the oscillation is occurring at.\"\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\" + str(targetstring) + sText)\n",
    "        plt.show()\n",
    "\n",
    "        lc.fold(period=pg.period_at_max_power).scatter()\n",
    "        sText = \"\\nLet's use the period_at_max_power to fold the light curve\"\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\" + str(targetstring) + sText)\n",
    "        plt.show()\n",
    "\n",
    "        lc.fold(period=4*pg.period_at_max_power, wrap_phase=0.2).scatter()\n",
    "        sText = \"\\nIf the eclipses do not line up, try plotting it again with quadruple the period.\"\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\" + str(targetstring) + sText)\n",
    "        plt.show()\n",
    "\n",
    "        pg = lc.to_periodogram(minimum_period=0.9*u.day, maximum_period=1.2*u.day, oversample_factor=10)\n",
    "        pg.period_at_max_power  \n",
    "        sText =\"\\nLet's try to get a more precise period by increasing the number of points in our periodogram \\nusing the `oversample_factor` parameter and by constraining the range of the period value:\"\n",
    "        #sText =\"\\nLet's try to get a more precise period by increasing the number of points in our periodogram \\nusing the `oversample_factor` parameter.\"\n",
    "        lc.fold(period=pg.period_at_max_power, wrap_phase=0.2).scatter()\n",
    "        #lc.fold(period=pg.period_at_max_power).scatter()\n",
    "        plt.title(str(target1) + \" (\" + str(i) + \")\" + str(targetstring)  + sText)\n",
    "        plt.show()\n",
    "\n",
    "    plt.figure(figsize=(13.5, 1))\n",
    "    plt.axhline(y=0.5, color='r', linestyle='-')\n",
    "    plt.axis('off')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32f667e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotTarget5(target, i, targetstring, freqmin, freqmax):\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.style.use('dark_background');\n",
    "\n",
    "    # https://gallery.exoplanet.codes/tutorials/tess/\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    import astropy.units as u\n",
    "\n",
    "    import numpy as np\n",
    "    import lightkurve as lk\n",
    "    \n",
    "    from astropy.io import fits\n",
    "    \n",
    "    plt.rcParams.update({'figure.max_open_warning': 0})\n",
    "    \n",
    "\n",
    "    lc_file = lk.search_lightcurve(target)[i].download();\n",
    "\n",
    "    lc = lc_file.remove_nans().normalize().remove_outliers()\n",
    "\n",
    "    #lc = lc_file\n",
    "\n",
    "    time = lc.time.value\n",
    "    flux = lc.flux\n",
    "    m = lc.quality == 0\n",
    "    with fits.open(lc_file.filename) as hdu:\n",
    "        hdr = hdu[1].header\n",
    "\n",
    "    texp = hdr[\"FRAMETIM\"] * hdr[\"NUM_FRM\"]\n",
    "    texp /= 60.0 * 60.0 * 24.0\n",
    "\n",
    "    ref_time = 0.5 * (np.min(time) + np.max(time))\n",
    "    x = np.ascontiguousarray(time[m] - ref_time, dtype=np.float64)\n",
    "    y = np.ascontiguousarray(1e3 * (flux[m] - 1.0), dtype=np.float64)\n",
    "\n",
    "    \"\"\"plt.plot(x, y, \".k\")\n",
    "    plt.xlabel(\"time [days]\")\n",
    "    plt.ylabel(\"relative flux [ppt]\")\n",
    "    _ = plt.xlim(x.min(), x.max())\n",
    "\n",
    "    plt.close()\"\"\"\n",
    "\n",
    "\n",
    "    # Transit search\n",
    "\n",
    "    # Now, let’s use the box least squares periodogram from AstroPy \n",
    "    # (Note: you’ll need AstroPy v3.1 or more recent to use this feature) to estimate the period, phase, and depth of the transit.\n",
    "\n",
    "    from astropy.timeseries import BoxLeastSquares\n",
    "\n",
    "    #period_grid = np.exp(np.linspace(np.log(1), np.log(15), 50000))\n",
    "    period_grid = np.linspace(freqmin, freqmax, 50000)\n",
    "\n",
    "    bls = BoxLeastSquares(x, y)\n",
    "    bls_power = bls.power(period_grid, 0.1, oversample=20)\n",
    "\n",
    "    # Save the highest peak as the planet candidate\n",
    "    index = np.argmax(bls_power.power)\n",
    "    bls_period = bls_power.period[index]\n",
    "    bls_t0 = bls_power.transit_time[index]\n",
    "    bls_depth = bls_power.depth[index]\n",
    "    transit_mask = bls.transit_mask(x, bls_period, 0.2, bls_t0)\n",
    "\n",
    "\n",
    "\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(13, 10))\n",
    "\n",
    "\n",
    "\n",
    "    # Plot the periodogram\n",
    "    ax = axes[0]\n",
    "    #ax.axvline(np.log10(bls_period), color=\"C1\", lw=5, alpha=0.8)\n",
    "    ax.axvline(bls_period, color=\"C1\", lw=5, alpha=0.8)\n",
    "    #ax.plot(np.log10(bls_power.period), bls_power.power, \"k\")\n",
    "    ax.plot(bls_power.period, bls_power.power, \"k\")\n",
    "    ax.annotate(\n",
    "        \"period = {0:.4f} d\".format(bls_period),\n",
    "        (0, 1),\n",
    "        xycoords=\"axes fraction\",\n",
    "        xytext=(5, -5),\n",
    "        textcoords=\"offset points\",\n",
    "        va=\"top\",\n",
    "        ha=\"left\",\n",
    "        fontsize=12,\n",
    "    )\n",
    "    ax.set_ylabel(\"bls power\")\n",
    "    ax.set_yticks([])\n",
    "    #ax.set_xlim(np.log10(period_grid.min()), np.log10(period_grid.max()))\n",
    "    ax.set_xlim(period_grid.min(), period_grid.max())\n",
    "    #ax.set_xlabel(\"log10(period)\")\n",
    "    ax.set_xlabel(\"Period\")\n",
    "    sText = \"\\nPlot the periodogram\"\n",
    "    ax.set_title(str(target) + \" (\" + str(i) + \")\"  + sText);\n",
    "    #plt.axis('off')\n",
    "    #plt.show()\n",
    "\n",
    "    # Plot the folded transit\n",
    "    ax = axes[1]\n",
    "    x_fold = (x - bls_t0 + 0.5 * bls_period) % bls_period - 0.5 * bls_period\n",
    "    m = np.abs(x_fold) < 0.4\n",
    "    ax.plot(x_fold[m], y[m], \".k\")\n",
    "    ax.annotate(\n",
    "        \"Plot the folded transit\",\n",
    "        (0, 1),\n",
    "        xycoords=\"axes fraction\",\n",
    "        xytext=(5, -5),\n",
    "        textcoords=\"offset points\",\n",
    "        va=\"top\",\n",
    "        ha=\"left\",\n",
    "        fontsize=12,\n",
    "    )\n",
    "    sText = \"\\nPlot the folded transit \\n Transit Time : \" + str(bls_t0)\n",
    "    #ax.set_title(str(target) + \" (\" + str(i) + \")\"  + sText);\n",
    "    #plt.axis('off')\n",
    "\n",
    "    # Overplot the phase binned light curve\n",
    "    bins = np.linspace(-0.41, 0.41, 32)\n",
    "    denom, _ = np.histogram(x_fold, bins)\n",
    "    num, _ = np.histogram(x_fold, bins, weights=y)\n",
    "    denom[num == 0] = 1.0\n",
    "    ax.plot(0.5 * (bins[1:] + bins[:-1]), num / denom, color=\"C1\")\n",
    "\n",
    "    ax.set_xlim(-0.3, 0.3)\n",
    "    ax.set_ylabel(\"de-trended flux [ppt]\")\n",
    "    _ = ax.set_xlabel(\"time since transit\")\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(13.5, 1))\n",
    "    plt.axhline(y=0.5, color='r', linestyle='-')\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "138c6294",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e266caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_By_PseudoColor(target1, target2, target3, target4, target5, target6, target7, target8, target9, target10,\n",
    "                s1, s2, s3, s4, s5, s6, s7, s8, s9, s10):\n",
    " \n",
    "    sptype_len_list = [target1, target2, target3, target4, target5, target6, target7, target8, target9, target10]\n",
    "    sptype_list = [str(s1), str(s2), str(s3), str(s4), str(s5), str(s6), str(s7), str(s8), str(s9), str(s10)]\n",
    "\n",
    "    with plt.style.context('dark_background'):\n",
    "    \n",
    "        fig, axs = plt.subplots(1,10, figsize=(50,6))\n",
    "        for df2, label, ax in zip(sptype_len_list, sptype_list, axs.reshape(-1)):\n",
    "            scatter_2D(df2['bp_rp'], df2['AbsoluteMagnitude'], ax=ax, \n",
    "                    c=df2['bp_rp'], cmap='RdYlBu',)\n",
    "\n",
    "            ax.set(xlim=(df2['bp_rp'].min(),df2['bp_rp'].max()), ylim=(15,-10))\n",
    "            \n",
    "            ax.set_title(' {}: {} Stars'.format(label, len(df2)), fontsize = 20)\n",
    "            ax.set_xlabel('Color index $(bp_rp)$', fontsize = 18)\n",
    "            ax.set_ylabel('M', fontsize = 18)\n",
    "        #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44a1a479",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_By_PseudoColor5(target1, target2, target3, target4, target5, \n",
    "                s1, s2, s3, s4, s5, ):\n",
    " \n",
    "    sptype_len_list = [target1, target2, target3, target4, target5]\n",
    "    sptype_list = [str(s1), str(s2), str(s3), str(s4), str(s5)]\n",
    "\n",
    "    with plt.style.context('dark_background'):\n",
    "    \n",
    "        fig, axs = plt.subplots(1,5, figsize=(50,6))\n",
    "        for df2, label, ax in zip(sptype_len_list, sptype_list, axs.reshape(-1)):\n",
    "            scatter_2D(df2['bp_rp'], df2['AbsoluteMagnitude'], ax=ax, \n",
    "                    c=df2['bp_rp'], cmap='RdYlBu',)\n",
    "\n",
    "            ax.set(xlim=(df2['bp_rp'].min(),df2['bp_rp'].max()), ylim=(15,-10))\n",
    "            \n",
    "            ax.set_title(' {}: {} Stars'.format(label, len(df2)), fontsize = 20)\n",
    "            ax.set_xlabel('Color index $(bp_rp)$', fontsize = 18)\n",
    "            ax.set_ylabel('M', fontsize = 18)\n",
    "        #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78503f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_2D(x, y, fig=None, ax=None, c='k', s=0.1, *args, **kwargs):\n",
    "    \n",
    "    if (fig or ax) is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    plot = ax.scatter(x, y, c=c, s=s, *args, **kwargs)\n",
    "    \n",
    "    #axes styling\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.tick_params(left=True, bottom=True,\n",
    "                   labelleft=True, labelbottom=True)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6aff4216",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_spectrum_s(name,axes,title): \n",
    "    #query the SSA service\n",
    "    url = \"https://datacentral.org.au/vo/ssa/query\"\n",
    "    service = SSAService(url)\n",
    "    custom = {}\n",
    "    custom['TARGETNAME'] = name\n",
    "    custom['COLLECTION'] = 'galah_dr3'\n",
    "    results = service.search(**custom)\n",
    "    df = results.votable.get_first_table().to_table(use_names_over_ids=True).to_pandas()\n",
    "    #df.to_csv(\"df_\" + str(name) + \".csv\")\n",
    "    filters = ['B','V','R','I']\n",
    "    colours = [\"#085dea\",\"#1e8c09\",\"#cf0000\",\"#640418\"]\n",
    "\n",
    "    #go through each filter and plot its spectrum (if available)\n",
    "    for idx in range(0,4):\n",
    "        filt = filters[idx]\n",
    "        ax = axes[idx]\n",
    "        ax.tick_params(axis='x', labelrotation=90, labelsize=LABELSIZE)\n",
    "        ax.clear()\n",
    "        #show the title in the first position only\n",
    "        if(idx == 0):\n",
    "            ax.set_title(title)\n",
    "            #plt.xticks(rotation=90)\n",
    "        #select only the spectrum of the filter of interest\n",
    "        subset = df[(df['band_name'] == filt)].reset_index()\n",
    "        #give preference to using the continuum normalised spectra \n",
    "        if(subset[subset['dataproduct_subtype'].isin(['normalised'])].shape[0] > 0):\n",
    "            subset = subset[(subset['dataproduct_subtype'] == \"normalised\")].reset_index()\n",
    "        #only proceed if we have the filter of interest available in the results \n",
    "        if(subset.shape[0] > 0):\n",
    "            #add RESPONSEFORMAT=fits here to ensure we get fits format back\n",
    "            url= subset.loc[0,'access_url'] + \"&RESPONSEFORMAT=fits\"\n",
    "            #load the spectrum\n",
    "            spec = Spectrum1D.read(BytesIO(requests.get(url).content),format='wcs1d-fits')\n",
    "            #print(spec)\n",
    "            #df_spec = pd.DataFrame(spec)\n",
    "            #df_spec.to_csv(\"df_spec_\" + str(idx))\n",
    "            exptime = subset.loc[0,'t_exptime']\n",
    "            ax.tick_params(axis='both', which='major')\n",
    "            ax.tick_params(axis='both', which='minor')\n",
    "            #plt.xticks(rotation=90)\n",
    "\n",
    "            loc = plticker.MultipleLocator(base=2.0)\n",
    "            ax.xaxis.grid(True)\n",
    "            #ax.xaxis.set_tick_params(labeltop=True)\n",
    "            \n",
    "            ax.xaxis.set_major_locator(loc)\n",
    "            #plot label at last position (caveat: no check if spectra are missing)\n",
    "            if(idx == 3): \n",
    "                ax.set_xlabel(\"Wavelength ($\\mathrm{\\AA}$)\",labelpad=10)\n",
    "            #plot the spectrum \n",
    "            ax.plot(spec.wavelength,spec.flux,linewidth=LWIDTH,color=colours[idx])\n",
    "            #print(spec.wavelength)\n",
    "            #print(spec.flux)\n",
    "            #adjust the y-scale to best fit the spectrum with some sigma clipping\n",
    "            nspec = len(spec.spectral_axis)\n",
    "            clipped = sigma_clip(spec[int(0+0.01*nspec):int(nspec-0.01*nspec)].flux,masked=False,sigma=15)\n",
    "            ymin = min(clipped).value\n",
    "            ymax = max(clipped).value \n",
    "            xmin = spec.wavelength[0].value\n",
    "            xmax = spec.wavelength[nspec-1].value\n",
    "            #add a 1% buffer either side of the x-range\n",
    "            dx=0.01*(xmax-xmin)\n",
    "            ax.set_xlim(xmin-dx,xmax+dx)\n",
    "            #add a 1% buffer either side of the y-range\n",
    "            dy=0.03*(ymax-ymin)\n",
    "            ax.set_ylim(ymin-dy,ymax+dy)\n",
    "\n",
    "        fsize = 4\n",
    "        rsize = -90\n",
    "\n",
    "        blue_offset =     0.5  # 0.85\n",
    "        green_offset =    0.90  # 0.90\n",
    "        red_offset =      0.80  # 0.92\n",
    "        infrared_offset = 0.85  # 0.85\n",
    "\n",
    "\n",
    "        if(idx == 0):\n",
    "            # Blue 4712 - 4903\n",
    "            ax.annotate(xy=[4713.15, ymax - ((ymax - ymin)/2)], s='He_I 4713', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4715, ymax - ((ymax - ymin)/2)], s='Ni_I 4715', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4716, ymax - ((ymax - ymin)/2)], s='La 4716', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4720, ymax - ((ymax - ymin)/2)], s='Ti 4720', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4722, ymax - ((ymax - ymin)/2)], s='Zn 4722', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4737, ymax - ((ymax - ymin)/2)], s='C2 4737', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4740.12, ymax - ((ymax - ymin)/2)], s='Ar_IV 4740', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4738, ymax - ((ymax - ymin)/2)], s='C2 4738', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4750, ymax - ((ymax - ymin)/2)], s='SiC2 4750', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4754, ymax - ((ymax - ymin)/2)], s='Mn_I 4754', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4760, ymax - ((ymax - ymin)/2)], s='TiO 4760', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4761, ymax - ((ymax - ymin)/2)], s='TiO 4761', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4762, ymax - ((ymax - ymin)/2)], s='TiO 4762', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4763, ymax - ((ymax - ymin)/2)], s='TiO 4763', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4766, ymax - ((ymax - ymin)/2)], s='Mn_I 4766', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4767.3, ymax - ((ymax - ymin)/2)], s='Ti_II 4767', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4768, ymax - ((ymax - ymin)/2)], s='Fe_I 4768', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4780, ymax - ((ymax - ymin)/2)], s='MgH 4780', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4784, ymax - ((ymax - ymin)/2)], s='Mn_I 4784', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4792, ymax - ((ymax - ymin)/2)], s='ZrO 4792', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4793, ymax - ((ymax - ymin)/2)], s='ZrO 4793', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4800, ymax - ((ymax - ymin)/2)], s='Hb 4800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4804, ymax - ((ymax - ymin)/2)], s='TiO 4804', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4807, ymax - ((ymax - ymin)/2)], s='Ti_II 4807', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4811, ymax - ((ymax - ymin)/2)], s='Fe_II 4811', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4820, ymax - ((ymax - ymin)/2)], s='Ti 4820', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4824.13, ymax - ((ymax - ymin)/2)], s='Cr_II 4824', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4827, ymax - ((ymax - ymin)/2)], s='ZrO 4827', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4828, ymax - ((ymax - ymin)/2)], s='Zr 4828', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4832, ymax - ((ymax - ymin)/2)], s='SiC2 4832', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4837, ymax - ((ymax - ymin)/2)], s='Sm 4837', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4840, ymax - ((ymax - ymin)/2)], s='Fe_I 4840', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4847, ymax - ((ymax - ymin)/2)], s='TiO 4847', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4848, ymax - ((ymax - ymin)/2)], s='Cr_II 4848', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4849, ymax - ((ymax - ymin)/2)], s='Ti 4849', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4851, ymax - ((ymax - ymin)/2)], s='SiC2 4851', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4854, ymax - ((ymax - ymin)/2)], s='Sm 4854', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4855, ymax - ((ymax - ymin)/2)], s='Ni_I 4855', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4861.33, ymax - ((ymax - ymin)/2)], s='He_I 4861', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4862.68, ymax - ((ymax - ymin)/2)], s='H-beta 4862', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4866, ymax - ((ymax - ymin)/2)], s='Ti 4866', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4867, ymax - ((ymax - ymin)/2)], s='SiC2 4867', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4869, ymax - ((ymax - ymin)/2)], s='Ti 4869', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4874, ymax - ((ymax - ymin)/2)], s='Ti 4874', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4884, ymax - ((ymax - ymin)/2)], s='Y 4884', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4887, ymax - ((ymax - ymin)/2)], s='Fe_II 4887', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4890, ymax - ((ymax - ymin)/2)], s='Fe_II 4890', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4893.37, ymax - ((ymax - ymin)/2)], s='Fe_VII 4893', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4900, ymax - ((ymax - ymin)/2)], s='Co 4900', color='green', rotation=rsize, fontsize=fsize)\n",
    "\n",
    "            ax.annotate(xy=[4730, ymin + 0.1], s='Carbon 4737/36/[4719 - 4755]', color='red', fontsize=fsize)\n",
    "\n",
    "        if(idx == 1):\n",
    "            # Green 5649 - 5873\n",
    "            ax.annotate(xy=[5647, ymax - ((ymax - ymin)/2)], s='Co 5647', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5663, ymax - ((ymax - ymin)/2)], s='Y 5663', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5668, ymax - ((ymax - ymin)/2)], s='TiO 5668', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5677.00, ymax - ((ymax - ymin)/2)], s='Fe_VI 5677', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5681, ymax - ((ymax - ymin)/2)], s='Zr 5681', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5689, ymax - ((ymax - ymin)/2)], s='Ti 5689', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5695.92, ymax - ((ymax - ymin)/2)], s='C_III 5695', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5696, ymax - ((ymax - ymin)/2)], s='C_III 5696', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5699, ymax - ((ymax - ymin)/2)], s='Ru 5699', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5700, ymax - ((ymax - ymin)/2)], s='Cu 5700', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5711, ymax - ((ymax - ymin)/2)], s='Mg 5711', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5712, ymax - ((ymax - ymin)/2)], s='Fe 5712', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5715, ymax - ((ymax - ymin)/2)], s='Fe 5715', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5716, ymax - ((ymax - ymin)/2)], s='Ti 5716', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5718, ymax - ((ymax - ymin)/2)], s='ZrO 5718', color='red', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[5720, ymax - ((ymax - ymin)/2)], s='Ti 5720', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5720.70, ymax - ((ymax - ymin)/2)], s='Fe_VII 5720', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5724, ymax - ((ymax - ymin)/2)], s='ZrO 5724', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5727, ymax - ((ymax - ymin)/2)], s='V_I 5727', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5729, ymax - ((ymax - ymin)/2)], s='Y 5729', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5739, ymax - ((ymax - ymin)/2)], s='Si_III 5739', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5741, ymax - ((ymax - ymin)/2)], s='Nd 5741', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5748, ymax - ((ymax - ymin)/2)], s='ZrO 5748', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5749, ymax - ((ymax - ymin)/2)], s='CN 5749', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5751, ymax - ((ymax - ymin)/2)], s='Mo 5751', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5753, ymax - ((ymax - ymin)/2)], s='Fe_I 5753', color='red', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[5754, ymax - ((ymax - ymin)/2)], s='ZrO 5754', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5754.59, ymax - ((ymax - ymin)/2)], s='N_II 5754', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5755, ymax - ((ymax - ymin)/2)], s='N_II 5755', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5760, ymax - ((ymax - ymin)/2)], s='TiO 5760', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5770, ymax - ((ymax - ymin)/2)], s='Hg 5770', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5782, ymax - ((ymax - ymin)/2)], s='Cu 5782', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5790, ymax - ((ymax - ymin)/2)], s='Hg 5790', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5791, ymax - ((ymax - ymin)/2)], s='Cr 5791', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5800, ymax - ((ymax - ymin)/2)], s='Na 5800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5800, ymax - ((ymax - ymin)/2)], s='Na 5800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5801.33, ymax - ((ymax - ymin)/2)], s='C_IV 5801', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5802, ymax - ((ymax - ymin)/2)], s='C_IV 5802', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5803, ymax - ((ymax - ymin)/2)], s='C_IV 5803', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5804, ymax - ((ymax - ymin)/2)], s='C_IV 5804', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5805, ymax - ((ymax - ymin)/2)], s='C_IV 5805', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5806, ymax - ((ymax - ymin)/2)], s='C_IV 5806', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5807, ymax - ((ymax - ymin)/2)], s='C_IV 5807', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5808, ymax - ((ymax - ymin)/2)], s='C_IV 5808', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5809, ymax - ((ymax - ymin)/2)], s='C_IV 5809', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5810, ymax - ((ymax - ymin)/2)], s='C_IV 5810', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5811.98, ymax - ((ymax - ymin)/2)], s='C_IV 5811', color='red', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[5812, ymax - ((ymax - ymin)/2)], s='C_IV 5812', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5814, ymax - ((ymax - ymin)/2)], s='TiO 5814', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5819, ymax - ((ymax - ymin)/2)], s='Eu 5819', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5839, ymax - ((ymax - ymin)/2)], s='ZrO 5839', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5842, ymax - ((ymax - ymin)/2)], s='Nd 5842', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5847, ymax - ((ymax - ymin)/2)], s='TiO 5847', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5849, ymax - ((ymax - ymin)/2)], s='ZrO 5849', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5853, ymax - ((ymax - ymin)/2)], s='Ba_II 5853', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5857, ymax - ((ymax - ymin)/2)], s='Ca_I 5857', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5858, ymax - ((ymax - ymin)/2)], s='Mo 5858', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5860, ymax - ((ymax - ymin)/2)], s='ZrO 5860', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5861, ymax - ((ymax - ymin)/2)], s='Telluric 5861', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5862, ymax - ((ymax - ymin)/2)], s='Telluric 5862', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5863, ymax - ((ymax - ymin)/2)], s='Telluric 5863', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5864, ymax - ((ymax - ymin)/2)], s='Telluric 5864', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5865, ymax - ((ymax - ymin)/2)], s='Telluric 5865', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5866, ymax - ((ymax - ymin)/2)], s='Telluric 5866', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5867, ymax - ((ymax - ymin)/2)], s='Telluric 5867', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5868, ymax - ((ymax - ymin)/2)], s='Telluric 5868', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5869, ymax - ((ymax - ymin)/2)], s='Telluric 5869', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5870, ymax - ((ymax - ymin)/2)], s='ZrO 5870', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5871, ymax - ((ymax - ymin)/2)], s='Telluric 5871', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5872, ymax - ((ymax - ymin)/2)], s='Telluric 5872', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5873, ymax - ((ymax - ymin)/2)], s='Telluric 5873', color='red', rotation=rsize, fontsize=fsize)\n",
    "\n",
    "\n",
    "\n",
    "        if(idx == 2):\n",
    "            # Red 6476 - 6739\n",
    "            ax.annotate(xy=[6478, ymax - ((ymax - ymin)/2)], s='TiO 6478', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6481, ymax - ((ymax - ymin)/2)], s='ZrO 6481', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6490, ymax - ((ymax - ymin)/2)], s='Co 6490', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6493, ymax - ((ymax - ymin)/2)], s='Ca_I 6493', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6494, ymax - ((ymax - ymin)/2)], s='ZrO 6494', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6495, ymax - ((ymax - ymin)/2)], s='Fe_I 6495', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6496, ymax - ((ymax - ymin)/2)], s='Ba_II 6496', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6502, ymax - ((ymax - ymin)/2)], s='CN 6502', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6512, ymax - ((ymax - ymin)/2)], s='Fe_II 6512', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6513, ymax - ((ymax - ymin)/2)], s='Fe_II 6513', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6514, ymax - ((ymax - ymin)/2)], s='Fe_II 6514', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6515, ymax - ((ymax - ymin)/2)], s='Fe_II 6515', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6516.081, ymax - ((ymax - ymin)/2)], s='Fe_II 6516', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6517, ymax - ((ymax - ymin)/2)], s='Fe_II 6517', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6518, ymax - ((ymax - ymin)/2)], s='Fe_II 6518', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6529.03, ymax - ((ymax - ymin)/2)], s='N_I 6529', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6532, ymax - ((ymax - ymin)/2)], s='VO 6532', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6548.05, ymax - ((ymax - ymin)/2)], s='N_II 6548', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6549.86, ymax - ((ymax - ymin)/2)], s='N_II 6549', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6550, ymax - ((ymax - ymin)/2)], s='Sr 6550', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6551, ymax - ((ymax - ymin)/2)], s='Co 6551', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6560, ymax - ((ymax - ymin)/2)], s='He_II 6560', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6562.82, ymax - ((ymax - ymin)/2)], s='H-alpha 6564', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6569, ymax - ((ymax - ymin)/2)], s='TiO 6569', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6572, ymax - ((ymax - ymin)/2)], s='Ca_I 6572', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6578, ymax - ((ymax - ymin)/2)], s='C_II 6578', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6579, ymax - ((ymax - ymin)/2)], s='C_II 6579', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6580, ymax - ((ymax - ymin)/2)], s='C_II 6580', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6581, ymax - ((ymax - ymin)/2)], s='C_II 6581', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6582, ymax - ((ymax - ymin)/2)], s='C_II 6582', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6583.46, ymax - ((ymax - ymin)/2)], s='N_II 6583', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6584, ymax - ((ymax - ymin)/2)], s='N_II 6584', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6585.27, ymax - ((ymax - ymin)/2)], s='N_II 6585', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6586, ymax - ((ymax - ymin)/2)], s='Ni 6586', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6588, ymax - ((ymax - ymin)/2)], s='C 6588', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6599, ymax - ((ymax - ymin)/2)], s='Ti 6599', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6600, ymax - ((ymax - ymin)/2)], s='Ha 6600', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6619, ymax - ((ymax - ymin)/2)], s='Mo 6619', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6632, ymax - ((ymax - ymin)/2)], s='Co 6632', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6645, ymax - ((ymax - ymin)/2)], s='Eu 6645', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6651, ymax - ((ymax - ymin)/2)], s='TiO 6651', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6656, ymax - ((ymax - ymin)/2)], s='CN 6656', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6678.15, ymax - ((ymax - ymin)/2)], s='He_I 6678', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6679, ymax - ((ymax - ymin)/2)], s='Co 6679', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6680, ymax - ((ymax - ymin)/2)], s='He_I 6680', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6681, ymax - ((ymax - ymin)/2)], s='TiO 6681', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6708, ymax - ((ymax - ymin)/2)], s='Li 6708', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6713, ymax - ((ymax - ymin)/2)], s='Si_II 6713', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6715, ymax - ((ymax - ymin)/2)], s='TiO 6715', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6716.44, ymax - ((ymax - ymin)/2)], s='S_II 6716', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6717, ymax - ((ymax - ymin)/2)], s='Ti 6717', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6718.29, ymax - ((ymax - ymin)/2)], s='S_II 6718', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6719, ymax - ((ymax - ymin)/2)], s='S_II 6719', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6720, ymax - ((ymax - ymin)/2)], s='S_II 6720', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6721, ymax - ((ymax - ymin)/2)], s='S_II 6721', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6722, ymax - ((ymax - ymin)/2)], s='S_II 6722', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6723, ymax - ((ymax - ymin)/2)], s='S_II 6723', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6724, ymax - ((ymax - ymin)/2)], s='S_II 6724', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6725, ymax - ((ymax - ymin)/2)], s='S_II 6725', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6726, ymax - ((ymax - ymin)/2)], s='S_II 6726', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6727, ymax - ((ymax - ymin)/2)], s='S_II 6727', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6728, ymax - ((ymax - ymin)/2)], s='S_II 6728', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6729, ymax - ((ymax - ymin)/2)], s='S_II 6729', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6730.81, ymax - ((ymax - ymin)/2)], s='S_II 6730', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[6731, ymax - ((ymax - ymin)/2)], s='S_II 6731', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6732.67, ymax - ((ymax - ymin)/2)], s='S_II 6732', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6733, ymax - ((ymax - ymin)/2)], s='S_II 6733', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6736, ymax - ((ymax - ymin)/2)], s='S_II 6736', color='blue', rotation=rsize, fontsize=fsize)\n",
    "\n",
    "        if(idx == 3):\n",
    "            # Infrared 7590 - 7890\n",
    "            ax.annotate(xy=[7590, ymax - ((ymax - ymin)/2)], s='Telluric 7590', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7591, ymax - ((ymax - ymin)/2)], s='Telluric 7591', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7592, ymax - ((ymax - ymin)/2)], s='Telluric 7592', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7593, ymax - ((ymax - ymin)/2)], s='Telluric 7593', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7594, ymax - ((ymax - ymin)/2)], s='O2_A_band 7594', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7595, ymax - ((ymax - ymin)/2)], s='Telluric 7595', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7596, ymax - ((ymax - ymin)/2)], s='O2_A_band 7596', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7598, ymax - ((ymax - ymin)/2)], s='O2_A_band 7598', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7600, ymax - ((ymax - ymin)/2)], s='O2_A_band 7600', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7602, ymax - ((ymax - ymin)/2)], s='O2_A_band 7602', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7604, ymax - ((ymax - ymin)/2)], s='O2_A_band 7604', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7605, ymax - ((ymax - ymin)/2)], s='Telluric 7605', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7606, ymax - ((ymax - ymin)/2)], s='O2_A_band 7606', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7608, ymax - ((ymax - ymin)/2)], s='O2_A_band 7608', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7610, ymax - ((ymax - ymin)/2)], s='O2_A_band 7610', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7611.00, ymax - ((ymax - ymin)/2)], s='S_XII 7611', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7612, ymax - ((ymax - ymin)/2)], s='O2_A_band 7612', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7614, ymax - ((ymax - ymin)/2)], s='O2_A_band 7614', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7615, ymax - ((ymax - ymin)/2)], s='Telluric 7615', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7616, ymax - ((ymax - ymin)/2)], s='O2_A_band 7616', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7618, ymax - ((ymax - ymin)/2)], s='O2_A_band 7618', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7621, ymax - ((ymax - ymin)/2)], s='O2_A_band 7621', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7625, ymax - ((ymax - ymin)/2)], s='Telluric 7625', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7635, ymax - ((ymax - ymin)/2)], s='Telluric 7635', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7645, ymax - ((ymax - ymin)/2)], s='Telluric 7645', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7665, ymax - ((ymax - ymin)/2)], s='Telluric 7665', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7675, ymax - ((ymax - ymin)/2)], s='Telluric 7675', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7685, ymax - ((ymax - ymin)/2)], s='Telluric 7685', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7695, ymax - ((ymax - ymin)/2)], s='Telluric 7695', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7698, ymax - ((ymax - ymin)/2)], s='Telluric 7698', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7699, ymax - ((ymax - ymin)/2)], s='CN 7699', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7700, ymax - ((ymax - ymin)/2)], s='Telluric 7700', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7713, ymax - ((ymax - ymin)/2)], s='Co 7713', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7751.06, ymax - ((ymax - ymin)/2)], s='Ar_III 7751', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7772, ymax - ((ymax - ymin)/2)], s='O_I 7772', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7774, ymax - ((ymax - ymin)/2)], s='O_I 7774', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7775, ymax - ((ymax - ymin)/2)], s='O_I 7775', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7800, ymax - ((ymax - ymin)/2)], s='Rb 7800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7816.136, ymax - ((ymax - ymin)/2)], s='He_I 7816', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7838, ymax - ((ymax - ymin)/2)], s='Co 7838', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7853, ymax - ((ymax - ymin)/2)], s='Ti 7853', color='red', rotation=rsize, fontsize=fsize)\n",
    "\n",
    "            #plt.xticks(rotation=90)\n",
    "        #else:\n",
    "            #print missing data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b71d014c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_spectrum_text(name,axes,title): \n",
    "    #query the SSA service\n",
    "    url = \"https://datacentral.org.au/vo/ssa/query\"\n",
    "    service = SSAService(url)\n",
    "    custom = {}\n",
    "    custom['TARGETNAME'] = name\n",
    "    custom['COLLECTION'] = 'galah_dr3'\n",
    "    results = service.search(**custom)\n",
    "    df = results.votable.get_first_table().to_table(use_names_over_ids=True).to_pandas()\n",
    "    #df.to_csv(\"df_\" + str(name) + \".csv\")\n",
    "    filters = ['B','V','R','I']\n",
    "    colours = [\"#085dea\",\"#1e8c09\",\"#cf0000\",\"#640418\"]\n",
    "\n",
    "    #go through each filter and plot its spectrum (if available)\n",
    "    for idx in range(0,4):\n",
    "        filt = filters[idx]\n",
    "        ax = axes[idx]\n",
    "        ax.tick_params(axis='x', labelrotation=90, labelsize=LABELSIZE)\n",
    "        ax.clear()\n",
    "        #show the title in the first position only\n",
    "        if(idx == 0):\n",
    "            ax.set_title(title)\n",
    "            #plt.xticks(rotation=90)\n",
    "        #select only the spectrum of the filter of interest\n",
    "        subset = df[(df['band_name'] == filt)].reset_index()\n",
    "        #give preference to using the continuum normalised spectra \n",
    "        if(subset[subset['dataproduct_subtype'].isin(['normalised'])].shape[0] > 0):\n",
    "            subset = subset[(subset['dataproduct_subtype'] == \"normalised\")].reset_index()\n",
    "        #only proceed if we have the filter of interest available in the results \n",
    "        if(subset.shape[0] > 0):\n",
    "            #add RESPONSEFORMAT=fits here to ensure we get fits format back\n",
    "            url= subset.loc[0,'access_url'] + \"&RESPONSEFORMAT=fits\"\n",
    "            #load the spectrum\n",
    "            spec = Spectrum1D.read(BytesIO(requests.get(url).content),format='wcs1d-fits')\n",
    "            #print(spec)\n",
    "            #df_spec = pd.DataFrame(spec)\n",
    "            #df_spec.to_csv(\"df_spec_\" + str(idx))\n",
    "            exptime = subset.loc[0,'t_exptime']\n",
    "            ax.tick_params(axis='both', which='major')\n",
    "            ax.tick_params(axis='both', which='minor')\n",
    "            #plt.xticks(rotation=90)\n",
    "\n",
    "            loc = plticker.MultipleLocator(base=2.0)\n",
    "            ax.xaxis.grid(True)\n",
    "            #ax.xaxis.set_tick_params(labeltop=True)\n",
    "            \n",
    "            ax.xaxis.set_major_locator(loc)\n",
    "            #plot label at last position (caveat: no check if spectra are missing)\n",
    "            if(idx == 3): \n",
    "                ax.set_xlabel(\"Wavelength ($\\mathrm{\\AA}$)\",labelpad=10)\n",
    "            #plot the spectrum \n",
    "            ax.plot(spec.wavelength,spec.flux,linewidth=LWIDTH,color=colours[idx])\n",
    "            #print(spec.wavelength)\n",
    "            #print(spec.flux)\n",
    "            #adjust the y-scale to best fit the spectrum with some sigma clipping\n",
    "            nspec = len(spec.spectral_axis)\n",
    "            clipped = sigma_clip(spec[int(0+0.01*nspec):int(nspec-0.01*nspec)].flux,masked=False,sigma=15)\n",
    "            ymin = min(clipped).value\n",
    "            ymax = max(clipped).value \n",
    "            xmin = spec.wavelength[0].value\n",
    "            xmax = spec.wavelength[nspec-1].value\n",
    "            #add a 1% buffer either side of the x-range\n",
    "            dx=0.01*(xmax-xmin)\n",
    "            ax.set_xlim(xmin-dx,xmax+dx)\n",
    "            #add a 1% buffer either side of the y-range\n",
    "            dy=0.03*(ymax-ymin)\n",
    "            ax.set_ylim(ymin-dy,ymax+dy)\n",
    "\n",
    "        fsize = 4\n",
    "        rsize = -90\n",
    "\n",
    "        blue_offset =     0.5  # 0.85\n",
    "        green_offset =    0.90  # 0.90\n",
    "        red_offset =      0.80  # 0.92\n",
    "        infrared_offset = 0.85  # 0.85\n",
    "\n",
    "\n",
    "        if(idx == 0):\n",
    "            # Blue 4712 - 4903\n",
    "            ax.annotate(xy=[4713.15, ymax - ((ymax - ymin)/2)], text='He_I 4713', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4715, ymax - ((ymax - ymin)/2)], text='Ni_I 4715', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4716, ymax - ((ymax - ymin)/2)], text='La 4716', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4720, ymax - ((ymax - ymin)/2)], text='Ti 4720', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4722, ymax - ((ymax - ymin)/2)], text='Zn 4722', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4737, ymax - ((ymax - ymin)/2)], text='C2 4737', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4740.12, ymax - ((ymax - ymin)/2)], text='Ar_IV 4740', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4738, ymax - ((ymax - ymin)/2)], text='C2 4738', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4750, ymax - ((ymax - ymin)/2)], text='SiC2 4750', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4754, ymax - ((ymax - ymin)/2)], text='Mn_I 4754', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4760, ymax - ((ymax - ymin)/2)], text='TiO 4760', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4761, ymax - ((ymax - ymin)/2)], text='TiO 4761', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4762, ymax - ((ymax - ymin)/2)], text='TiO 4762', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4763, ymax - ((ymax - ymin)/2)], text='TiO 4763', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4766, ymax - ((ymax - ymin)/2)], text='Mn_I 4766', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4767.3, ymax - ((ymax - ymin)/2)], text='Ti_II 4767', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4768, ymax - ((ymax - ymin)/2)], text='Fe_I 4768', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4780, ymax - ((ymax - ymin)/2)], text='MgH 4780', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4784, ymax - ((ymax - ymin)/2)], text='Mn_I 4784', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4792, ymax - ((ymax - ymin)/2)], text='ZrO 4792', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4793, ymax - ((ymax - ymin)/2)], text='ZrO 4793', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4800, ymax - ((ymax - ymin)/2)], text='Hb 4800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4804, ymax - ((ymax - ymin)/2)], text='TiO 4804', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4807, ymax - ((ymax - ymin)/2)], text='Ti_II 4807', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4811, ymax - ((ymax - ymin)/2)], text='Fe_II 4811', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4820, ymax - ((ymax - ymin)/2)], text='Ti 4820', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4824.13, ymax - ((ymax - ymin)/2)], text='Cr_II 4824', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4827, ymax - ((ymax - ymin)/2)], text='ZrO 4827', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4828, ymax - ((ymax - ymin)/2)], text='Zr 4828', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4832, ymax - ((ymax - ymin)/2)], text='SiC2 4832', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4837, ymax - ((ymax - ymin)/2)], text='Sm 4837', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4840, ymax - ((ymax - ymin)/2)], text='Fe_I 4840', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4847, ymax - ((ymax - ymin)/2)], text='TiO 4847', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4848, ymax - ((ymax - ymin)/2)], text='Cr_II 4848', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4849, ymax - ((ymax - ymin)/2)], text='Ti 4849', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4851, ymax - ((ymax - ymin)/2)], text='SiC2 4851', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4854, ymax - ((ymax - ymin)/2)], text='Sm 4854', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4855, ymax - ((ymax - ymin)/2)], text='Ni_I 4855', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4861.33, ymax - ((ymax - ymin)/2)], text='He_I 4861', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4862.68, ymax - ((ymax - ymin)/2)], text='H-beta 4862', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4866, ymax - ((ymax - ymin)/2)], text='Ti 4866', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4867, ymax - ((ymax - ymin)/2)], text='SiC2 4867', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4869, ymax - ((ymax - ymin)/2)], text='Ti 4869', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4874, ymax - ((ymax - ymin)/2)], text='Ti 4874', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4884, ymax - ((ymax - ymin)/2)], text='Y 4884', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4887, ymax - ((ymax - ymin)/2)], text='Fe_II 4887', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4890, ymax - ((ymax - ymin)/2)], text='Fe_II 4890', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4893.37, ymax - ((ymax - ymin)/2)], text='Fe_VII 4893', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[4900, ymax - ((ymax - ymin)/2)], text='Co 4900', color='green', rotation=rsize, fontsize=fsize)\n",
    "\n",
    "            ax.annotate(xy=[4730, ymin + 0.1], text='Carbon 4737/36/[4719 - 4755]', color='red', fontsize=fsize)\n",
    "\n",
    "        if(idx == 1):\n",
    "            # Green 5649 - 5873\n",
    "            ax.annotate(xy=[5647, ymax - ((ymax - ymin)/2)], text='Co 5647', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5663, ymax - ((ymax - ymin)/2)], text='Y 5663', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5668, ymax - ((ymax - ymin)/2)], text='TiO 5668', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5677.00, ymax - ((ymax - ymin)/2)], text='Fe_VI 5677', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5681, ymax - ((ymax - ymin)/2)], text='Zr 5681', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5689, ymax - ((ymax - ymin)/2)], text='Ti 5689', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5695.92, ymax - ((ymax - ymin)/2)], text='C_III 5695', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5696, ymax - ((ymax - ymin)/2)], text='C_III 5696', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5699, ymax - ((ymax - ymin)/2)], text='Ru 5699', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5700, ymax - ((ymax - ymin)/2)], text='Cu 5700', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5711, ymax - ((ymax - ymin)/2)], text='Mg 5711', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5712, ymax - ((ymax - ymin)/2)], text='Fe 5712', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5715, ymax - ((ymax - ymin)/2)], text='Fe 5715', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5716, ymax - ((ymax - ymin)/2)], text='Ti 5716', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5718, ymax - ((ymax - ymin)/2)], text='ZrO 5718', color='red', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[5720, ymax - ((ymax - ymin)/2)], text='Ti 5720', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5720.70, ymax - ((ymax - ymin)/2)], text='Fe_VII 5720', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5724, ymax - ((ymax - ymin)/2)], text='ZrO 5724', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5727, ymax - ((ymax - ymin)/2)], text='V_I 5727', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5729, ymax - ((ymax - ymin)/2)], text='Y 5729', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5739, ymax - ((ymax - ymin)/2)], text='Si_III 5739', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5741, ymax - ((ymax - ymin)/2)], text='Nd 5741', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5748, ymax - ((ymax - ymin)/2)], text='ZrO 5748', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5749, ymax - ((ymax - ymin)/2)], text='CN 5749', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5751, ymax - ((ymax - ymin)/2)], text='Mo 5751', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5753, ymax - ((ymax - ymin)/2)], text='Fe_I 5753', color='red', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[5754, ymax - ((ymax - ymin)/2)], text='ZrO 5754', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5754.59, ymax - ((ymax - ymin)/2)], text='N_II 5754', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5755, ymax - ((ymax - ymin)/2)], text='N_II 5755', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5760, ymax - ((ymax - ymin)/2)], text='TiO 5760', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5770, ymax - ((ymax - ymin)/2)], text='Hg 5770', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5782, ymax - ((ymax - ymin)/2)], text='Cu 5782', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5790, ymax - ((ymax - ymin)/2)], text='Hg 5790', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5791, ymax - ((ymax - ymin)/2)], text='Cr 5791', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5800, ymax - ((ymax - ymin)/2)], text='Na 5800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5800, ymax - ((ymax - ymin)/2)], text='Na 5800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5801.33, ymax - ((ymax - ymin)/2)], text='C_IV 5801', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5802, ymax - ((ymax - ymin)/2)], text='C_IV 5802', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5803, ymax - ((ymax - ymin)/2)], text='C_IV 5803', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5804, ymax - ((ymax - ymin)/2)], text='C_IV 5804', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5805, ymax - ((ymax - ymin)/2)], text='C_IV 5805', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5806, ymax - ((ymax - ymin)/2)], text='C_IV 5806', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5807, ymax - ((ymax - ymin)/2)], text='C_IV 5807', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5808, ymax - ((ymax - ymin)/2)], text='C_IV 5808', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5809, ymax - ((ymax - ymin)/2)], text='C_IV 5809', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5810, ymax - ((ymax - ymin)/2)], text='C_IV 5810', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5811.98, ymax - ((ymax - ymin)/2)], text='C_IV 5811', color='red', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[5812, ymax - ((ymax - ymin)/2)], text='C_IV 5812', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5814, ymax - ((ymax - ymin)/2)], text='TiO 5814', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5819, ymax - ((ymax - ymin)/2)], text='Eu 5819', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5839, ymax - ((ymax - ymin)/2)], text='ZrO 5839', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5842, ymax - ((ymax - ymin)/2)], text='Nd 5842', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5847, ymax - ((ymax - ymin)/2)], text='TiO 5847', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5849, ymax - ((ymax - ymin)/2)], text='ZrO 5849', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5853, ymax - ((ymax - ymin)/2)], text='Ba_II 5853', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5857, ymax - ((ymax - ymin)/2)], text='Ca_I 5857', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5858, ymax - ((ymax - ymin)/2)], text='Mo 5858', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5860, ymax - ((ymax - ymin)/2)], text='ZrO 5860', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5861, ymax - ((ymax - ymin)/2)], text='Telluric 5861', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5862, ymax - ((ymax - ymin)/2)], text='Telluric 5862', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5863, ymax - ((ymax - ymin)/2)], text='Telluric 5863', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5864, ymax - ((ymax - ymin)/2)], text='Telluric 5864', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5865, ymax - ((ymax - ymin)/2)], text='Telluric 5865', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5866, ymax - ((ymax - ymin)/2)], text='Telluric 5866', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5867, ymax - ((ymax - ymin)/2)], text='Telluric 5867', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5868, ymax - ((ymax - ymin)/2)], text='Telluric 5868', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5869, ymax - ((ymax - ymin)/2)], text='Telluric 5869', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5870, ymax - ((ymax - ymin)/2)], text='ZrO 5870', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5871, ymax - ((ymax - ymin)/2)], text='Telluric 5871', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5872, ymax - ((ymax - ymin)/2)], text='Telluric 5872', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[5873, ymax - ((ymax - ymin)/2)], text='Telluric 5873', color='red', rotation=rsize, fontsize=fsize)\n",
    "\n",
    "\n",
    "\n",
    "        if(idx == 2):\n",
    "            # Red 6476 - 6739\n",
    "            ax.annotate(xy=[6478, ymax - ((ymax - ymin)/2)], text='TiO 6478', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6481, ymax - ((ymax - ymin)/2)], text='ZrO 6481', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6490, ymax - ((ymax - ymin)/2)], text='Co 6490', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6493, ymax - ((ymax - ymin)/2)], text='Ca_I 6493', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6494, ymax - ((ymax - ymin)/2)], text='ZrO 6494', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6495, ymax - ((ymax - ymin)/2)], text='Fe_I 6495', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6496, ymax - ((ymax - ymin)/2)], text='Ba_II 6496', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6502, ymax - ((ymax - ymin)/2)], text='CN 6502', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6512, ymax - ((ymax - ymin)/2)], text='Fe_II 6512', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6513, ymax - ((ymax - ymin)/2)], text='Fe_II 6513', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6514, ymax - ((ymax - ymin)/2)], text='Fe_II 6514', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6515, ymax - ((ymax - ymin)/2)], text='Fe_II 6515', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6516.081, ymax - ((ymax - ymin)/2)], text='Fe_II 6516', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6517, ymax - ((ymax - ymin)/2)], text='Fe_II 6517', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6518, ymax - ((ymax - ymin)/2)], text='Fe_II 6518', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6529.03, ymax - ((ymax - ymin)/2)], text='N_I 6529', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6532, ymax - ((ymax - ymin)/2)], text='VO 6532', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6548.05, ymax - ((ymax - ymin)/2)], text='N_II 6548', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6549.86, ymax - ((ymax - ymin)/2)], text='N_II 6549', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6550, ymax - ((ymax - ymin)/2)], text='Sr 6550', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6551, ymax - ((ymax - ymin)/2)], text='Co 6551', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6560, ymax - ((ymax - ymin)/2)], text='He_II 6560', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6562.82, ymax - ((ymax - ymin)/2)], text='H-alpha 6564', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6569, ymax - ((ymax - ymin)/2)], text='TiO 6569', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6572, ymax - ((ymax - ymin)/2)], text='Ca_I 6572', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6578, ymax - ((ymax - ymin)/2)], text='C_II 6578', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6579, ymax - ((ymax - ymin)/2)], text='C_II 6579', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6580, ymax - ((ymax - ymin)/2)], text='C_II 6580', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6581, ymax - ((ymax - ymin)/2)], text='C_II 6581', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6582, ymax - ((ymax - ymin)/2)], text='C_II 6582', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6583.46, ymax - ((ymax - ymin)/2)], text='N_II 6583', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6584, ymax - ((ymax - ymin)/2)], text='N_II 6584', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6585.27, ymax - ((ymax - ymin)/2)], text='N_II 6585', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6586, ymax - ((ymax - ymin)/2)], text='Ni 6586', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6588, ymax - ((ymax - ymin)/2)], text='C 6588', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6599, ymax - ((ymax - ymin)/2)], text='Ti 6599', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6600, ymax - ((ymax - ymin)/2)], text='Ha 6600', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6619, ymax - ((ymax - ymin)/2)], text='Mo 6619', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6632, ymax - ((ymax - ymin)/2)], text='Co 6632', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6645, ymax - ((ymax - ymin)/2)], text='Eu 6645', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6651, ymax - ((ymax - ymin)/2)], text='TiO 6651', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6656, ymax - ((ymax - ymin)/2)], text='CN 6656', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6678.15, ymax - ((ymax - ymin)/2)], text='He_I 6678', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6679, ymax - ((ymax - ymin)/2)], text='Co 6679', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6680, ymax - ((ymax - ymin)/2)], text='He_I 6680', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6681, ymax - ((ymax - ymin)/2)], text='TiO 6681', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6708, ymax - ((ymax - ymin)/2)], text='Li 6708', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6713, ymax - ((ymax - ymin)/2)], text='Si_II 6713', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6715, ymax - ((ymax - ymin)/2)], text='TiO 6715', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6716.44, ymax - ((ymax - ymin)/2)], text='S_II 6716', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6717, ymax - ((ymax - ymin)/2)], text='Ti 6717', color='green', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6718.29, ymax - ((ymax - ymin)/2)], text='S_II 6718', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6719, ymax - ((ymax - ymin)/2)], text='S_II 6719', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6720, ymax - ((ymax - ymin)/2)], text='S_II 6720', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6721, ymax - ((ymax - ymin)/2)], text='S_II 6721', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6722, ymax - ((ymax - ymin)/2)], text='S_II 6722', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6723, ymax - ((ymax - ymin)/2)], text='S_II 6723', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6724, ymax - ((ymax - ymin)/2)], text='S_II 6724', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6725, ymax - ((ymax - ymin)/2)], text='S_II 6725', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6726, ymax - ((ymax - ymin)/2)], text='S_II 6726', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6727, ymax - ((ymax - ymin)/2)], text='S_II 6727', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6728, ymax - ((ymax - ymin)/2)], text='S_II 6728', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6729, ymax - ((ymax - ymin)/2)], text='S_II 6729', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6730.81, ymax - ((ymax - ymin)/2)], text='S_II 6730', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            #ax.annotate(xy=[6731, ymax - ((ymax - ymin)/2)], text='S_II 6731', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6732.67, ymax - ((ymax - ymin)/2)], text='S_II 6732', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6733, ymax - ((ymax - ymin)/2)], text='S_II 6733', color='blue', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[6736, ymax - ((ymax - ymin)/2)], text='S_II 6736', color='blue', rotation=rsize, fontsize=fsize)\n",
    "\n",
    "        if(idx == 3):\n",
    "            # Infrared 7590 - 7890\n",
    "            ax.annotate(xy=[7590, ymax - ((ymax - ymin)/2)], text='Telluric 7590', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7591, ymax - ((ymax - ymin)/2)], text='Telluric 7591', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7592, ymax - ((ymax - ymin)/2)], text='Telluric 7592', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7593, ymax - ((ymax - ymin)/2)], text='Telluric 7593', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7594, ymax - ((ymax - ymin)/2)], text='O2_A_band 7594', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7595, ymax - ((ymax - ymin)/2)], text='Telluric 7595', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7596, ymax - ((ymax - ymin)/2)], text='O2_A_band 7596', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7598, ymax - ((ymax - ymin)/2)], text='O2_A_band 7598', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7600, ymax - ((ymax - ymin)/2)], text='O2_A_band 7600', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7602, ymax - ((ymax - ymin)/2)], text='O2_A_band 7602', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7604, ymax - ((ymax - ymin)/2)], text='O2_A_band 7604', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7605, ymax - ((ymax - ymin)/2)], text='Telluric 7605', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7606, ymax - ((ymax - ymin)/2)], text='O2_A_band 7606', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7608, ymax - ((ymax - ymin)/2)], text='O2_A_band 7608', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7610, ymax - ((ymax - ymin)/2)], text='O2_A_band 7610', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7611.00, ymax - ((ymax - ymin)/2)], text='S_XII 7611', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7612, ymax - ((ymax - ymin)/2)], text='O2_A_band 7612', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7614, ymax - ((ymax - ymin)/2)], text='O2_A_band 7614', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7615, ymax - ((ymax - ymin)/2)], text='Telluric 7615', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7616, ymax - ((ymax - ymin)/2)], text='O2_A_band 7616', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7618, ymax - ((ymax - ymin)/2)], text='O2_A_band 7618', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7621, ymax - ((ymax - ymin)/2)], text='O2_A_band 7621', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7625, ymax - ((ymax - ymin)/2)], text='Telluric 7625', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7635, ymax - ((ymax - ymin)/2)], text='Telluric 7635', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7645, ymax - ((ymax - ymin)/2)], text='Telluric 7645', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7665, ymax - ((ymax - ymin)/2)], text='Telluric 7665', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7675, ymax - ((ymax - ymin)/2)], text='Telluric 7675', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7685, ymax - ((ymax - ymin)/2)], text='Telluric 7685', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7695, ymax - ((ymax - ymin)/2)], text='Telluric 7695', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7698, ymax - ((ymax - ymin)/2)], text='Telluric 7698', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7699, ymax - ((ymax - ymin)/2)], text='CN 7699', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7700, ymax - ((ymax - ymin)/2)], text='Telluric 7700', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7713, ymax - ((ymax - ymin)/2)], text='Co 7713', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7751.06, ymax - ((ymax - ymin)/2)], text='Ar_III 7751', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7772, ymax - ((ymax - ymin)/2)], text='O_I 7772', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7774, ymax - ((ymax - ymin)/2)], text='O_I 7774', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7775, ymax - ((ymax - ymin)/2)], text='O_I 7775', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7800, ymax - ((ymax - ymin)/2)], text='Rb 7800', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7816.136, ymax - ((ymax - ymin)/2)], text='He_I 7816', color='aqua', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7838, ymax - ((ymax - ymin)/2)], text='Co 7838', color='red', rotation=rsize, fontsize=fsize)\n",
    "            ax.annotate(xy=[7853, ymax - ((ymax - ymin)/2)], text='Ti 7853', color='red', rotation=rsize, fontsize=fsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "712d7114",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_Spectra_Source_Id(df):\n",
    "    #print(\"Plot_Spectra_Source_Id: df parameter: \" + str(df))\n",
    "    plt.rcParams.update(Ipython_default)\n",
    "    plt.clf()\n",
    "\n",
    "    for idx in range(0, df.shape[0]):\n",
    "        target = str(df['sobject_id'].iloc[idx])\n",
    "        source_id = str(int(df['source_id'].iloc[idx]))\n",
    "        subclass = str(df['SpT2'].iloc[idx])\n",
    "        feh = df['fe_h'].iloc[idx]\n",
    "        logg = df['logg'].iloc[idx]\n",
    "        teff = str(df['teff'].iloc[idx])\n",
    "        ra = str(df['ra'].iloc[idx])\n",
    "        dec = str(df['dec'].iloc[idx])\n",
    "        star_type = str(df['Star_Type'].iloc[idx])\n",
    "        if pd.isna(df['rv_galah'].iloc[idx]):\n",
    "            rv = 0\n",
    "        else:\n",
    "            rv = str(int(df['rv_galah'].iloc[idx]))\n",
    "\n",
    "        #setup the plot using gridspec\n",
    "        gs = gridspec.GridSpec\n",
    "        fig = plt.figure(figsize=fsize)\n",
    "\n",
    "        gs = gridspec.GridSpec(4,2)\n",
    "        #B\n",
    "        axB = fig.add_subplot(gs[0,1])\n",
    "        #V\n",
    "        axV = fig.add_subplot(gs[1,1])\n",
    "        #R\n",
    "        axR = fig.add_subplot(gs[2,1])\n",
    "        #I\n",
    "        axI = fig.add_subplot(gs[3,1])\n",
    "\n",
    "        #rtcode = show_spectrum_text(target,[axB,axV,axR,axI],\"GAIA DR3 Source_ID: [\" + str(source_id) + \"]\" + \n",
    "        rtcode = show_spectrum_s(target,[axB,axV,axR,axI],\"GAIA DR3 Source_ID: [\" + str(source_id) + \"]\" + \n",
    "            \"\\nStar_Type: [\" + str(star_type) + \"]    Subclass: [\" + str(subclass[:-1]) + \"]\" + \n",
    "            \"    Fe/H: [\" + str(round(feh,2))   + \"]\" +\n",
    "            \"    LogG: [\" + str(round(logg,2))  + \"]\" +\n",
    "            \"    Teff: [\" + str(teff[:-4])      + \"]\" +\n",
    "            \"    RV: [\" + str(rv)      + \"]\" +\n",
    "            \"\\nRa: \" + str(ra) + \n",
    "            \"    Dec: \" + str(dec))\n",
    "\n",
    "        #plt.savefig(\"./Images/\" + str(subclass[:-1]) + \"_\" + str(star_type) + \"_LogG[\" + str(round(logg,2))  + \"]\" + \"_FeH[\" + str(round(feh,2)) + \"]\" + \"_Ra[\" + str(ra) + \"]\" + \"_Dec[\" + str(dec) + \"]\" + \".png\")\n",
    "        if rtcode == 1:\n",
    "            plt.close()\n",
    "        else:\n",
    "            plt.show()\n",
    "\n",
    "    print(\"Done!\")\n",
    "    plt.style.use('dark_background');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6350c48f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_LightKurve(TIC, plot_count, target_period, target1):\n",
    "     import matplotlib.pyplot as plt\n",
    "     plt.style.use('dark_background');\n",
    "\n",
    "     tic_target = \"tic \" + str(TIC) \n",
    "\n",
    "     #target_period = ''\n",
    "\n",
    "     search_result = lk.search_lightcurve(tic_target)\n",
    "\n",
    "     #print(search_result)\n",
    "     #print(\"\\n\")\n",
    "\n",
    "     plot_pixelfile       = \"N\"\n",
    "     plot_lightcurve      = \"Y\"\n",
    "     plot_period          = \"Y\"\n",
    "     plot_phase_fold      = \"Y\"\n",
    "     plot_half_phase_fold = \"NN\"\n",
    "     plot_2phase_fold     = \"NN\"\n",
    "     plot_3phase_fold     = \"NN\"\n",
    "     plot_4phase_fold     = \"Y\"\n",
    "     plot_PDC_AP_Flux     = \"NN\"\n",
    "     plot_periodogram     = \"Y\" # Large output\n",
    "\n",
    "     # Change range to fit requirement\n",
    "\n",
    "     for i in range(0, plot_count):\n",
    "          if i <= len(search_result) -1:\n",
    "               try:\n",
    "                    freqmin = 0.4\n",
    "                    freqmax = 15\n",
    "                    #Target_Name = '\\n[Kepler-8 b]  Period: 3.52 days'\n",
    "                    #Target_Name = '\\nRA: ' + str(RA) + '  ' + 'DEC: ' + str(DEC)  + '\\n' + str(txt)  + '  Period: 3.90 days'\n",
    "                    Target_Name =  '\\nSpectral Type: ' + str(sp_txt) + ' ' + str(txt)  + '  Period: ' + str(target_period) + ' days'\n",
    "                    #Target_Name = '\\n' + \"[TOI-125 d]\"  + '  NASA Reported Period: 19.98  days'\n",
    "                    #PlotTarget4(target1, i, Target_Name, freqmin, freqmax)\n",
    "\n",
    "                    #PlotTarget4B(target1, i, Target_Name, freqmin, freqmax)\n",
    "                    PlotTarget4B(tic_target, i, Target_Name, freqmin, freqmax, target1)\n",
    "\n",
    "                    #PlotTarget5(target1, i, Target_Name, freqmin, freqmax)  # KeyError: \"Keyword 'FRAMETIM' not found.\"\n",
    "               except Exception as e:\n",
    "                    print(\"LightKurve error: \" + str(e))\n",
    "                    print(\"i: \" + str(i))\n",
    "                    print(\"TIC: \" + str(TIC))\n",
    "                    print(\"plot_count: \" + str(plot_count))\n",
    "                    print(\"target_period: \" + str(target_period))\n",
    "                    print(\"target1: \" + str(target1))\n",
    "                    pass\n",
    "\n",
    "     print(\"\\nDone!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1c583e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f7efced3",
   "metadata": {},
   "source": [
    "https://www.aavso.org/vsx/index.php?view=about.vartypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1483d2b",
   "metadata": {},
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def defaultvalue():\n",
    "        return \"Unknown\"\n",
    "\n",
    "vType = defaultdict(defaultvalue)\n",
    "\n",
    "vType['AHB1'] = 'AHB1: Above Horizontal Branch variables of subtype 1, also known as XX Vir variables. \\nPulsating variables with periods between 0.7 and 3 d. showing highly asymmetric light curves (rise durations <25%) \\nof large amplitude and low metallicity ([Fe/H] <-1), crossing the instability strip from the blue to the red on their way \\nto the AGB after helium core exhaustion. They have low masses and higher luminosities (Mv = +0.2 to -1.4; ~1 mag. brighter) \\nthan the RR Lyrae stars of the same period. There is controversy in the literature about their nomenclature. \\nThey were originally called RRD but now that designation is used for the double-mode RR Lyrae stars. \\nThey were also called \"short period type II cepheids\" and have been classified among the CWB variables but their light curves \\nare different from the BL Her stars (which show humps in their light curves and longer rise durations) and are very similar \\nto the light curves of RRAB-type stars with periods around 0.5 d. AHB1 stars occupy the same region in the HR-diagram \\nas the anomalous cepheids (ACEP) but their light curves are different (1994A&A...283..111S). \\nSome researchers (OGLE team) have explained this saying that the AHB1 variables are fundamental mode ACEP while the other ACEP \\nwith more symmetrical light curves and smaller amplitudes are supposed to be first overtone ACEP. \\nHowever, AHB1 and ACEP show different evolutionary paths and masses. ACEP are more massive (1.3 - 2.2 solar masses), \\nthey are not post-HB stars and appear predominantly in the dwarf spheroidal galaxies, but are almost absent in \\nGalactic globular clusters, while the AHB1 stars are absent in dwarf spheroidals but are present in globular clusters \\n(2006ARA&A..44...93S). Examples: XX Vir, BF Ser'\n",
    "\n",
    "vType['AM'] = 'AM Herculis-type variables; close binary systems consisting of a dK-dM type dwarf and a superstrong magnetic white dwarf \\nprimary, in which the magnetic field of the primary not only prevents the formation of an accretion disk \\nbut also synchronizes the primarys rotation with its orbital period. \\nThey are characterized by variable linear and circular polarization of light. \\nThe total range of light variations may reach 4-5 mag. in V. Also known as polars.'\n",
    "\n",
    "vType['BE+LERI'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nBE: GCVS-type for Be stars that show variability but no light outbursts (GCAS variables). Most of them may be LERI variables.] \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d. '\n",
    "vType['BE+LERI:'] = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nA plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nBE: GCVS-type for Be stars that show variability but no light outbursts (GCAS variables). Most of them may be LERI variables.  \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d. ]'\n",
    "\n",
    "vType['BY']  = 'BY Draconis-type variables, which are emission-line dwarfs of dKe-dMe spectral type showing quasi-periodic \\nlight changes with periods from a fraction of a day to 120 days and amplitudes from several hundredths to 0.5 mag. in V.\\n The light variability is caused by axial rotation of a star with a variable degree of non-uniformity of the surface \\nbrightness (spots) and chromospheric activity. Some of these stars also show flares similar to those of UV Ceti stars, \\nand in those cases they also belong to the latter type and are simultaneously considered eruptive variables.'\n",
    "\n",
    "vType['BY+UV'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nBY Draconis-type variables, which are emission-line dwarfs of dKe-dMe spectral type showing quasi-periodic \\nlight changes with periods from a fraction of a day to 120 days and amplitudes from several hundredths to 0.5 mag. in V.\\n The light variability is caused by axial rotation of a star with a variable degree of non-uniformity of the surface \\nbrightness (spots) and chromospheric activity. Some of these stars also show flares similar to those of UV Ceti stars, \\nand in those cases they also belong to the latter type and are simultaneously considered eruptive variables.\\nUV: [UV Ceti flare stars have spectral types K or M]'\n",
    "\n",
    "vType['CEP'] = 'CEP: Cepheids. Radially pulsating, high luminosity (classes Ib-II) variables with periods in the range of 1-135 days and amplitudes \\nfrom several hundredths to 2 mag. in V (in the B band, the amplitudes are greater). Spectral type at maximum light is F; at minimum, \\nthe types are G-K. The longer the period of light variation, the later is the spectral type. \\nThe maximum of the surface-layer expansion velocity almost coinciding with maximum light. \\nThere are several subtypes (see DCEP, DCEP(B), DCEPS, DCEPS(B), CWA, CWB and ACEP). Some DCEP and CW stars are quite often called Cepheids \\nbecause it is often impossible to discriminate between them on the basis of the light curves for periods in the range 3 - 10 days. \\nHowever, these are distinct groups of entirely different objects in different evolutionary stages. One of the significant spectral differences \\nbetween W Virginis stars and Cepheids is the presence, during a certain phase interval, of hydrogen-line emission in the former \\nand of Ca II H and K emission in the latter.'\n",
    "\n",
    "vType['CV']  = 'CV: [cataclysmic variables - novae and dwarf novae in main sequence, white dwarf ]'\n",
    "vType['CV:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nCV: [cataclysmic variables - novae and dwarf novae in main sequence, white dwarf ]'\n",
    "vType['CV+E']  = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nCV+E: [cataclysmic variables - novae and dwarf novae in main sequence, white dwarf ] \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion. \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d. '\n",
    "\n",
    "vType['CWA']  = 'CWA: [W Virginis type variables with P > 8 d \\n \"https://asas-sn.osu.edu/atlas/CWA\"]'\n",
    "vType['CWB']  = 'CWB: [W Virginis type variables with P < 8 d (BL Herculis variables) \\n \"https://asas-sn.osu.edu/atlas/CWB\"]'\n",
    "\n",
    "vType['DCEP']  = 'DCEP: [Fundamental Mode Classical Cepheids: δ Cepheids or classical Cepheids,\\ntend to be brighter than the Type II Cepheids \\n \"https://asas-sn.osu.edu/atlas/DCEP\". \\nComparatively young objects that have left the main sequence and evolved into the instability strip of the Hertzsprung-Russell (H-R) diagram, \\nthey obey the well-known Cepheid period-luminosity relation and belong to the young disk population. DCEP stars are present in open clusters. \\nThey display a certain relation between the shapes of their light curves and their periods. They are fundamental mode pulsators.]'\n",
    "vType['DCEPS']  = 'DCEPS: [These are δ Cep variables having light amplitudes <0.5 mag. in V (<0.7 mag. in B) \\nand almost symmetrical light curves (M-m approx. 0.4 - 0.5 periods); as a rule, their periods do not exceed 7 days. \\nFirst Overtone Cepheid variables \\n \"https://asas-sn.osu.edu/atlas/DCEPS\"]'\n",
    "\n",
    "vType['DQ']  = 'DQ: [DQ white dwarfs variables with C and H rich atmospheres.]\\nDQ Herculis type. Magnetic cataclysmic variables with a red dwarf secondary and a white dwarf primary component that generates a magnetic field \\nweaker than the field associated with AM Herculis stars and that is not strong enough to synchronize the orbits of the rotating white dwarf \\nwith the orbital period of the system. Also known as intermediate polars (IP).'\n",
    "vType['DQ:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nDQ: [DQ white dwarfs variables with C and H rich atmospheres.]'\n",
    "\n",
    "vType['DSCT']  = 'DSCT: [Low Amplitude δ Scuti variables are pulsating variables similar to δ Cepheids. Periods (from 0.01 to 0.2 days). Their spectral type is between A0 and F5. Usually, lie on the instability strip] \\n \"https://asas-sn.osu.edu/atlas/DSCT\"'\n",
    "vType['DSCT:']  = 'DSCT: [A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nLow Amplitude δ Scuti variables are pulsating variables similar to δ Cepheids. Periods (from 0.01 to 0.2 days). Their spectral type is between A0 and F5. Usually, lie on the instability strip] \\n \"https://asas-sn.osu.edu/atlas/DSCT\"'\n",
    "\n",
    "vType['DYPer']  = 'DYPer: [CSTAR]'\n",
    "\n",
    "vType['EA']  = 'EA: [Detached Algol-type binaries. \\nEA Algol (β Persei) type systems may not have a secondary minimum.] \\n \"https://asas-sn.osu.edu/atlas/EA\" \\nβ Persei-type (Algol) eclipsing systems. \\nBinaries with spherical or slightly ellipsoidal components. \\nIt is possible to specify, for their light curves, the moments of the beginning and end of the eclipses. \\nBetween eclipses the light remains almost constant or varies insignificantly because of reflection effects, \\nslight ellipsoidality of the components, or physical variations. \\nSecondary minima may be absent. \\nAn extremely wide range of periods is observed, from 0.2 to ≥10000 days. \\nLight amplitudes are also quite different and may reach several magnitudes.'\n",
    "vType['EB']  = 'EB: [Algol-type or β Lyrae-type binaries. \\nEB (β-Lyrae) type systems possess light curves that transition smoothly between from the eclipse \\nto the out-of-eclipse phase, but have minima of significantly different depths.\\nThe majority of such eclipsing binaries have periods larger than half a day. \"https://asas-sn.osu.edu/atlas/EB\"]'\n",
    "vType['EW']  = 'EW: [W Ursae Majoris type binaries. \\nEW (W UMa) type systems have light curves with minima of similar depths and transition smoothly from the eclipse \\nto the out-of-eclipse phase.\\nComposed of two stars of similar spectral type between A and K with most of them being F or G. \\nThey have short periods, typically between 0.25 and 1 day.\\n \"https://asas-sn.osu.edu/atlas/EW\"]'\n",
    "\n",
    "vType['EW!EA'] = 'A pipe character ( | ) between two different types signifies a logical OR; the classification is uncertain and all possible types are indicated. \\nEW: [W Ursae Majoris type binaries. \\nEW (W UMa) type systems have light curves with minima of similar depths and transition smoothly from the eclipse \\nto the out-of-eclipse phase.\\nComposed of two stars of similar spectral type between A and K with most of them being F or G. \\nThey have short periods, typically between 0.25 and 1 day.\\n \"https://asas-sn.osu.edu/atlas/EW\"] \\nEA: [Detached Algol-type binaries. \\nEA Algol (β Persei) type systems may not have a secondary minimum.] \\n \"https://asas-sn.osu.edu/atlas/EA\"'\n",
    "\n",
    "vType['ELL']  = 'ELL : [ELL: Ellipsoidal variables are close binaries whose light curves do not contain an eclipse \\nbut their variability is due to the distortion of their shape from the mutual gravitational fields, \\nthus the observed light varies because of varying projected surface towards the observer.]'\n",
    "\n",
    "vType['EP']  = 'Stars showing planetary transits. Prototype: V376 Peg.'\n",
    "\n",
    "vType['GCAS']  = 'GCAS: [Gamma CASsiopeiae, a class of eruptive variable stars. Rapidly rotating early type stars. \\nGCAS variables to be eruptive irregular variables of early spectral types (O9-A0 III-Ve) with mass outflow from their equatorial zones  ]'\n",
    "vType['GCAS:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nGCAS: [Gamma CASsiopeiae, a class of eruptive variable stars. Rapidly rotating early type stars. \\nGCAS variables to be eruptive irregular variables of early spectral types (O9-A0 III-Ve) with mass outflow from their equatorial zones  ]'\n",
    "\n",
    "vType['GCAS+LERI'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nGCAS: [Gamma CASsiopeiae, a class of eruptive variable stars. Rapidly rotating early type stars. \\nGCAS variables to be eruptive irregular variables of early spectral types (O9-A0 III-Ve) with mass outflow from their equatorial zones. \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d.  ]'\n",
    "\n",
    "vType['HADS']  = 'HADS: [High amplitude δ Scuti type variables]\\n \"https://asas-sn.osu.edu/atlas/HADS\"'\n",
    "\n",
    "vType['HB']  = 'HB: [HB: Heartbeat variables are binary star systems with eccentric orbits that cause \\nboth variations of stellar shapes and vibrations induced by such changes.]'\n",
    "\n",
    "vType['HMXB']  = 'High Mass X-ray Binaries. \\nSystems with a massive star (usually an O or B star, a Be star or a blue supergiant) \\nand a compact object (generally a neutron star, a black hole or a white dwarf). \\nA fraction of the stellar wind of the normal star is captured by the compact object and produces X-rays \\nas it falls onto it or onto an accretion disk thats surrounding it. \\nIn X-ray binaries, the subtypes indicate which kind of behavior the binary displays, \\nlike X-ray bursts (XB), large amplitude outbursts also in the visual (XN), reflection effect (XR) \\nor it may also inform about the objects nature, e.g.: if the compact object is a pulsar (XP).'\n",
    "\n",
    "vType['L']  = 'L: [Red irregular variables]'\n",
    "\n",
    "vType['LMXB']  = 'LMXB: Low Mass X-ray Binaries. \\nSystems where one of the components is either a black hole or a neutron star. \\nThe other, donor, component usually fills its Roche lobe and therefore transfers mass to the compact object. \\nThe donor can be a normal dwarf, a white dwarf, or an evolved star (red giant). \\nX-rays are emitted as the mass falls onto the compact object or onto an accretion disk thats surrounding it. \\nThe X-ray emission is incident upon the atmosphere of the cooler companion of the compact object and is reradiated \\nin the form of optical high-temperature radiation (reflection effect), \\nthus making that area of the cooler companions surface an earlier spectral type. \\nThese effects lead to quite a peculiar complex character of optical variability in such systems. \\nIn X-ray binaries, the subtypes indicate which kind of behavior the binary displays, \\nlike X-ray bursts (XB), large amplitude outbursts also in the visual (XN), reflection effect (XR) \\nor it may also inform about the objects nature, e.g.: if the compact object is a pulsar (XP)'\n",
    "\n",
    "vType['LSP']  = 'LSP : [LSP: Long secondary period variables are luminous red giants stars which have secondary period an order of magnitude longer than their primary. Their periods range from 200 to 1500 days.]'\n",
    "\n",
    "vType['M']  = 'M: [M (or Mira, o Ceti) variables are late type stars with periods between 80 and 1000 days.] \\n \"https://asas-sn.osu.edu/atlas/M\" \\nο (omicron) Ceti-type (Mira) variables. These are long-period variable giants with characteristic late-type emission spectra (Me, Ce, Se) \\nand light amplitudes from 2.5 to 11 mag. in V. Their periodicity is well pronounced, and the periods lie in the range between 80 and 1000 days. \\nInfrared amplitudes are usually less than in the visible and may be <2.5 mag. For example, in the K band they usually do not exceed 0.9 mag.'\n",
    "vType['M:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nM: [M (or Mira, o Ceti) variables are late type stars with periods between 80 and 1000 days.] \\n \"https://asas-sn.osu.edu/atlas/M\" \\nο (omicron) Ceti-type (Mira) variables. These are long-period variable giants with characteristic late-type emission spectra (Me, Ce, Se) \\nand light amplitudes from 2.5 to 11 mag. in V. Their periodicity is well pronounced, and the periods lie in the range between 80 and 1000 days. \\nInfrared amplitudes are usually less than in the visible and may be <2.5 mag. For example, in the K band they usually do not exceed 0.9 mag.'\n",
    "\n",
    "vType['MISC']  = 'Miscellaneous variable stars. Usually red variables (L, SR) \\nor other types of irregular stars I, BE) that can\"t be classified \\nmore specifically by the automatic analysis made by the surveys.'\n",
    "\n",
    "vType['NL+E'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nNL+E: Nova-like stars. Cataclysmic variables where the mass transfer rate is above a certain limit and their accretion disks are stable \\nbecause they are nearly fully ionized to their outer (tidal cut off) boundary and this condition suppresses dwarf nova outbursts. \\nAlso known as UX (UX Ursae Majoris stars). \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.'\n",
    "\n",
    "vType['NL/VY+E'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nNL/VY+E: Anti-dwarf novae. VY Sculptoris stars. They are cataclysmic binary systems with a hot (35,000-65,000 K) and luminous white dwarf \\nthat occasionally undergo fadings of more than 1 magnitude (up to several magnitudes) due to a low rate of mass transfer. \\nThese fadings might last from days to years. At maximum they vary up to 1 magnitude. They show no outbursts even though at minimum they fall \\ninto the dwarf novae instability strip. This could be caused by the possible magnetic nature of the white dwarf. \\nOrbital periods usually range between 0.12 and 0.18 d. Examples: VY Scl, MV Lyr. \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.'\n",
    "\n",
    "vType['PPN']  = 'PPN : [PPN: Protoplanetary nebulae with yellow supergiant post-AGB stars, \\nexhibiting variability that resembles the SRD variables.]'\n",
    "\n",
    "vType['PULS']  = 'Pulsating variables of unspecified type.'\n",
    "\n",
    "vType['PVTELI']  = 'PVTELI: [PVTEL: PV Telescopii are supergiants of several spectral types with hydrogen deficiency. They are divided into 3 sub-classes of different period ranges, from 0.5 to 100 days]'\n",
    "\n",
    "vType['R']  = 'R: [R: close binaries that exhibit strong reflection in their light curves \\n(re-radiation of light of the hotter star from the surface of the cooler one). \\nMost of these stars fall in the region between the main sequence and the white dwarfs; \\nsome are found in the extreme horizontal branch too.]'\n",
    "\n",
    "vType['RCB']  = 'RCB: [RCoronae Borealis stars, diverse spectral types, post- AGB stars]'\n",
    "vType['RCB:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nRCB: [RCoronae Borealis stars, diverse spectral types, post- AGB stars]'\n",
    "\n",
    "vType['ROT']  = 'ROT: [Spotted Variables with rotational modulation. \\nLight curves showing evidence of rotational modulation were classified as a generic rotational variable (ROT). \\n \"https://asas-sn.osu.edu/atlas/ROT\"]'\n",
    "vType['ROT:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nROT: [Spotted Variables with rotational modulation. Light curves showing evidence of rotational modulation were classified as a generic rotational variable (ROT). \\n \"https://asas-sn.osu.edu/atlas/ROT\"]'\n",
    "\n",
    "vType['RRAB']  = 'RRAB: [RR Lyrae variables with asymmetric light curves (steep ascending branches), periods from 0.3 to 1.0 days, \\nand amplitudes from 0.5 to 2 mag. in V. They are fundamental mode pulsators. \\n \"https://asas-sn.osu.edu/atlas/RRAB\"]'\n",
    "vType['RRAB:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nRRAB: [RR Lyrae variables with asymmetric light curves (steep ascending branches), periods from 0.3 to 1.0 days, \\nand amplitudes from 0.5 to 2 mag. in V. They are fundamental mode pulsators. \\n \"https://asas-sn.osu.edu/atlas/RRAB\"]'\n",
    "\n",
    "\n",
    "vType['RRC']  = 'RRC: [First Overtone RR Lyrae variables. \\nThey have symmetric and sinusoidal light curves and shorter periods than RRAB stars. \\n \"https://asas-sn.osu.edu/atlas/RRC\"]'\n",
    "vType['RRD']  = 'RRD: [Double Mode RR Lyrae variables. \\nThey are double mode pulsators, which occupy a well defined region at GBP − GRP ∼ 0.5 mag \\n \"https://asas-sn.osu.edu/atlas/RRD\"]'\n",
    "\n",
    "vType['RS']  = 'RS Canum Venaticorum-type binary systems. The primaries are usually giants from late F to late K spectral type. \\nA significant property of these systems is the presence in their spectra of strong Ca II H and K emission lines of variable intensity, \\nindicating increased chromospheric activity of the solar type. These systems are also characterized by the presence of radio and X-ray emission. \\nTheir light curves look like sine waves outside eclipses, with amplitudes and positions changing slowly with time. \\nThe presence of this wave (often called a distortion wave) is explained by differential rotation of the star, its surface being covered \\nwith groups of spots; the period of the rotation of a spot group is usually close to the period of orbital motion but still differs from it, \\nwhich is the reason for the slow change (migration) of the phases of the distortion wave minimum and maximum in the mean light curve in the case \\nof the eclipsing binaries (E/RS). The variability of the waves amplitude (which may be up to 0.5 mag. in V) is explained by the existence \\nof a long-period stellar activity cycle similar to the 11-year solar activity cycle, during which the number and total area of spots \\non the stars surface vary. Small amplitude flares are often observed too.'\n",
    "\n",
    "vType['RV']  = 'RV Tauri variables are luminous variable stars that have distinctive light variations with alternating deep and shallow minima. \\n RV Tau variables exhibit changes in luminosity which are tied to radial pulsations of their surfaces. \\nTheir changes in brightness are also correlated with changes in their spectral type. \\nWhile at their brightest, the stars have spectral types F or G. \\nAt their dimmest, their spectral types change to K or M. \\n \"https://asas-sn.osu.edu/atlas/RVA\" \\n \"https://en.wikipedia.org/wiki/RV_Tauri_variable\"'\n",
    "vType['RVA']  = 'RVA: [RV Tauri variables (Subtype A). \\nRV Tauri variables are radially pulsating supergiants that change their spectral type along with their magnitude. \\nTheir spectral type span from F–G class to K–M, depending on their phase. \\nTheir periods are longer than 30 days, with typical values between 40–50 days. \\n \"https://asas-sn.osu.edu/atlas/RVA\"]'\n",
    "vType['RVB']  = 'RVB: RV Tauri variables that periodically (with periods from 600 to 1500 days and amplitudes up to 2 mag in V) vary in mean magnitude. \\nExamples: DF Cyg, RV Tau.'\n",
    "\n",
    "vType['SDOR']  = 'SDOR : [S Doradus stars (or Luminous Blue Variables) are evolved stars characterised by \\nlarge amplitude variations of Bpec to Fpec spectral type ]'\n",
    "vType['SDOR:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nSDOR : [S Doradus stars (or Luminous Blue Variables) are evolved stars characterised by \\nlarge amplitude variations of Bpec to Fpec spectral type ]'\n",
    "\n",
    "vType['SR']  = '[SR: Semi-regular variables, which are giants or supergiants of intermediate and late spectral types showing noticeable periodicity \\nin their light changes, accompanied or sometimes interrupted by various irregularities. Periods lie in the range from 20 to >2000 days,\\n while the shapes of the light curves are rather different and variable, and the amplitudes may be from several hundredths \\nto several magnitudes (usually 1-2 mag. in V). \\n \"https://asas-sn.osu.edu/atlas/SR\"]'\n",
    "vType['SRD']  = 'SRD : [SRD: They are giants and supergiants of types earlier than SRA, SRB, and SRC, with variability periods from 40 to 1100 days.]'\n",
    "\n",
    "vType['SXARI']  = 'SXARI: [SXARI: SX Arietis are B-type chemical peculiar stars with strong magnetic fields and periods of about 1 day.]'\n",
    "vType['SXARI:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nSXARI: [SXARI: SX Arietis are B-type chemical peculiar stars with strong magnetic fields and periods of about 1 day.]'\n",
    "\n",
    "vType['SXPHE']  = 'SXPHE: [SXPHE: SX Phoenicis are considered similar to δScuti stars that are sub-dwarfs with periods typically in the lower part of the DSCT range.]'\n",
    "\n",
    "vType['UG']  = 'UG: [U Geminorum, a class of cataclysmic variable stars (also known as dwarf novae)]'\n",
    "\n",
    "vType['UGER']  = 'UGER: ER Ursae Majoris-type subclass of UGSU dwarf novae. \\nThese stars typically spend a third of their time in super-outburst with a super-cycle of 20-90 days. \\nOutside of super-outburst they typically pack in a rapid succession of normal outbursts. \\nThe period given in VSX is usually the orbital period. \\nOutburst cycles are given between parentheses. \\nAmplitudes are smaller (around 3 mag.) than in other dwarf novae due to a higher mass transfer rate.'\n",
    "\n",
    "vType['UGSS']  = 'UGSS: [UG SS Cygni, a subclass of UG-type stars]'\n",
    "\n",
    "vType['UGSU']  = 'UGSU: [UG SU Ursae Majoris, a subclass of UG-type stars]'\n",
    "vType['UGSU+E']  = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nUGSU: [UG SU Ursae Majoris, a subclass of UG-type stars. \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.]'\n",
    "\n",
    "vType['UGZ']  = 'UGZ: Z Camelopardalis-type stars. \\nThese also show cyclic outbursts, differing from UGSS variables by the fact that sometimes after an outburst \\nthey do not return to the original brightness, \\nbut during several cycles retain a magnitude between maximum and minimum. \\nThe values of cycles are from 10 to 40 days, while light amplitudes are from 2 to 5 mag. in V. \\nThe period given in VSX is usually the orbital period. \\nOutburst cycles are given between parentheses.'\n",
    "\n",
    "vType['UV']  = 'UV: [UV Ceti flare stars have spectral types K or M]'\n",
    "vType['UV:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nUV: [UV Ceti flare stars have spectral types K or M]'\n",
    "\n",
    "vType['V361HYA']  = 'V361HYA: [Concentrated in the extreme horizontal branch. V361 Hya stars) can be redder than the main clump,\\nbut they follow the blue horizontal branch (BHB). Most of V361 Hya stars are hotter (with effective temperatures \\nin 28000–35000 K) than V1093 Her (23000–30000K), so the two populations are not distinct and overlap ]'\n",
    "vType['V838MON']  = 'V838MON: Luminous red transients of the V838 Monocerotis type \\nthat become redder in outburst and are thought to be the result of the merger of a contact binary. \\nAfter the outburst they get hidden in a dusty disk. \\nThe amplitudes are 9-13 mag. Also known as Luminous Red Novae (LNR).Examples: V838 Mon, V1309 Sco, V4332 Sgr.'\n",
    "vType['V1093HER']  = 'V1093HER: [Concentrated in the extreme horizontal branch. They follow the blue horizontal branch (BHB). \\nMost of V361 Hya stars are hotter (with effective temperatures in 28000–35000 K) than V1093 Her (23000–30000K), \\nso the two populations are not distinct and overlap ]'\n",
    "\n",
    "vType['VAR']  = 'VAR: [Variable star of unspecified type]'\n",
    "\n",
    "vType['E/WR']  = 'WR: [Wolf–Rayet, a type of hot, luminous star with strong stellar winds. \\nWR: Wolf-Rayet is a group of massive stars that present broad emission lines.\\nThey have high temperatures and luminosities and considered as descendants of O-type stars. \\nTheir variability is not periodic \\n \"https://en.wikipedia.org/wiki/Wolf–Rayet_star\" \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.]'\n",
    "vType['WR'] = 'WR: Eruptive Wolf-Rayet variables. Stars with broad emission features of He I and He II as well as C II-C IV, O II-O IV, and N III-N V. \\nThey display irregular light changes with amplitudes up to 0.1 mag. in V, which are probably caused by physical processes, \\nin particular, by non-stable mass outflow from their atmospheres.'\n",
    "vType['WR:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nWR: [Wolf–Rayet, a type of hot, luminous star with strong stellar winds. \\nWR: Wolf-Rayet is a group of massive stars that present broad emission lines.\\nThey have high temperatures and luminosities and considered as descendants of O-type stars. \\nTheir variability is not periodic \\n \"https://en.wikipedia.org/wiki/Wolf–Rayet_star\"]'\n",
    "\n",
    "vType['YSO']  = 'YSO: [Young Stellar Objects]'\n",
    "\n",
    "vType['ZAND']  = 'ZAND: [Z Andromedae stars, binary variables stars composed of a giant and a white dwarf. \\nThey display irregular variability with large amplitudes]'\n",
    "\n",
    "vType['ZZ']  = 'ZZ: [ZZ: These are generic ZZCeti without detailed class]'\n",
    "\n",
    "vType['ZZA']  = 'ZZA: [ZZA: ZZA (or DAV) are classical ZZ Ceti stars with DA spectral type with H atmospheres. ]'\n",
    "\n",
    "vType['ZZB']  = 'ZZB: [V777HER: The V777 Herculis (or ZZB, DBV) are stars with He-rich atmospheres and their periods \\nrange between 100 and 1400 s, grouping in the WD sequence between the warmer GWVIR and the cooler ZZA.]'\n",
    "\n",
    "vType['ZZLep']  = 'ZZLep: [ZZLEP: ZZ Leporis stars are central stars of planetary nebulae that exhibit \\nphotometric variations. They are O-type stars with periods that range from hours to days.]'\n",
    "\n",
    "vType['ZZO']  = 'ZZO: [GWVIR: GW Virginis (ZZO,DOV,PG1159) stars are a subtype of ZZ Ceti \\nwith absorption lines of HeII and CIV, and it’s the hottest known type of pulsating WD and pre-white dwarfs.]'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e515e54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4026fc97",
   "metadata": {},
   "source": [
    "<img width=\"800\" src=\"asas_image01.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70794a28",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2032aace",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cfc8aba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://vizier.cds.unistra.fr/viz-bin/VizieR-3?-source=J/A%2bA/635/A193')\n",
    "\n",
    "# https://vizier.cds.unistra.fr/viz-bin/VizieR?-source=J/A%2BA/621/A38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f8b5d370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41087 rows\n",
      "8.2 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>source_id</th>\n",
       "      <th>SpTypeSim</th>\n",
       "      <th>SpTypeSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>HS0258+3003</td>\n",
       "      <td>--</td>\n",
       "      <td>sdO</td>\n",
       "      <td>sdO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>GALEXJ06580+5614</td>\n",
       "      <td>1000059052992002688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sdB+MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9540</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000429687195499520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9483</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000497070938042496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9619</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000519267329142144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name            source_id SpTypeSim SpTypeSD\n",
       "495        HS0258+3003                   --       sdO      sdO\n",
       "1147  GALEXJ06580+5614  1000059052992002688       NaN   sdB+MS\n",
       "9540               NaN  1000429687195499520       NaN      NaN\n",
       "9483               NaN  1000497070938042496       NaN      NaN\n",
       "9619               NaN  1000519267329142144       NaN      NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hot_subluminous_all = pd.read_csv('df_hot_subluminous_all_export.csv')\n",
    "df_hot_subluminous_all = df_hot_subluminous_all.sort_values('source_id')\n",
    "print('{:d} rows'.format(len(df_hot_subluminous_all)))\n",
    "print('{:.1f} MB'.format(df_hot_subluminous_all.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_hot_subluminous_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e4ca0b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1c73737f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "962137 rows\n",
      "15.4 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1237645941824356481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      objid\n",
       "0       1237645879562862699\n",
       "515164  1237645941286502503\n",
       "514979  1237645941286568042\n",
       "1       1237645941291614227\n",
       "1110    1237645941824356481"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SDSS Stars with spectra\n",
    "\n",
    "\"\"\"\n",
    "df_sdss1 = pd.read_csv('StarsWithSpectraSDSS_195064.csv')\n",
    "df_sdss2 = pd.read_csv('StarsWithSpectraSegue1_207411.csv')\n",
    "df_sdss3 = pd.read_csv('StarsWithSpectraSegue2_111024.csv')\n",
    "df_sdss4 = pd.read_csv('StarsWithSpectraBoss_301597.csv')\n",
    "df_sdss5 = pd.read_csv('StarsWithSpectraEboss_147066.csv')\n",
    "\n",
    "df_sdss_all = pd.concat([df_sdss1,  df_sdss2, df_sdss3, df_sdss4, df_sdss5], ignore_index=True, sort=False)\n",
    "\n",
    "df_sdss_all= df_sdss_all.drop_duplicates(subset='objid', keep=\"first\")\n",
    "\n",
    "df_sdss_all = df_sdss_all[['objid']]\n",
    "\n",
    "df_sdss_all.to_csv('df_sdss_all_export.csv', index=False)\n",
    "\"\"\";\n",
    "\n",
    "df_sdss_all = pd.read_csv('df_sdss_all_export.csv')\n",
    "df_sdss_all = df_sdss_all.sort_values('objid')\n",
    "print('{:d} rows'.format(len(df_sdss_all)))\n",
    "print('{:.1f} MB'.format(df_sdss_all.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_sdss_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3147014",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "71990e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "448663 rows\n",
      "7.2 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>1237645941286502503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>1237645941286568042</td>\n",
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       "      <th>1110</th>\n",
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       "      <th>1111</th>\n",
       "      <td>1237645942898229471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1237645942904455343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    objid\n",
       "1665  1237645941286502503\n",
       "1480  1237645941286568042\n",
       "1110  1237645942898229350\n",
       "1111  1237645942898229471\n",
       "370   1237645942904455343"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_sdss_boss_all = pd.concat([df_sdss4, df_sdss5], ignore_index=True, sort=False)\n",
    "\n",
    "df_sdss_boss_all= df_sdss_boss_all.drop_duplicates(subset='objid', keep=\"first\")\n",
    "\n",
    "df_sdss_boss_all = df_sdss_boss_all[['objid']]\n",
    "\n",
    "df_sdss_boss_all.to_csv('df_sdss_boss_all_export.csv', index=False)\n",
    "\"\"\";\n",
    "\n",
    "df_sdss_boss_all = pd.read_csv('df_sdss_boss_all_export.csv')\n",
    "df_sdss_boss_all = df_sdss_boss_all.sort_values('objid')\n",
    "print('{:d} rows'.format(len(df_sdss_boss_all)))\n",
    "print('{:.1f} MB'.format(df_sdss_boss_all.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_sdss_boss_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "473d5921",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67f1a3c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "825263 rows\n",
      "19.8 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>original_ext_source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168422</th>\n",
       "      <td>2538157343578122752</td>\n",
       "      <td>1237645879562862699</td>\n",
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       "    <tr>\n",
       "      <th>62542</th>\n",
       "      <td>3262676721065431168</td>\n",
       "      <td>1237645941291614227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80290</th>\n",
       "      <td>2498461645215995264</td>\n",
       "      <td>1237645941824356481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117069</th>\n",
       "      <td>2498717831425504896</td>\n",
       "      <td>1237645942898229350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114720</th>\n",
       "      <td>2498705285825681536</td>\n",
       "      <td>1237645942898229471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  source_id  original_ext_source_id\n",
       "168422  2538157343578122752     1237645879562862699\n",
       "62542   3262676721065431168     1237645941291614227\n",
       "80290   2498461645215995264     1237645941824356481\n",
       "117069  2498717831425504896     1237645942898229350\n",
       "114720  2498705285825681536     1237645942898229471"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_gaia_sdss_neighbors = pd.read_csv('df_gaia_sdss_neighbors2_825263.csv')\n",
    "\n",
    "df_gaia_sdss_neighbors = df_gaia_sdss_neighbors[['source_id', 'original_ext_source_id']]\n",
    "\n",
    "df_gaia_sdss_all_neighbors = df_gaia_sdss_neighbors[df_gaia_sdss_neighbors['original_ext_source_id'].astype(str).isin(df_sdss_all['objid'].astype(str))]\n",
    "\n",
    "df_gaia_sdss_all_neighbors.to_csv('df_gaia_sdss_all_neighbors_export.csv', index=False)\n",
    "\"\"\"\n",
    "\n",
    "df_gaia_sdss_all_neighbors = pd.read_csv('df_gaia_sdss_all_neighbors_export.csv')\n",
    "df_gaia_sdss_all_neighbors = df_gaia_sdss_all_neighbors.sort_values('original_ext_source_id')\n",
    "print('{:d} rows'.format(len(df_gaia_sdss_all_neighbors)))\n",
    "print('{:.1f} MB'.format(df_gaia_sdss_all_neighbors.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_gaia_sdss_all_neighbors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aacbcd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b203a1be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315507 rows\n",
      "7.6 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>original_ext_source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30731</th>\n",
       "      <td>2498717831425504896</td>\n",
       "      <td>1237645942898229350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30182</th>\n",
       "      <td>2498705285825681536</td>\n",
       "      <td>1237645942898229471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31700</th>\n",
       "      <td>3263851136923447168</td>\n",
       "      <td>1237645942904455343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31626</th>\n",
       "      <td>3263676791315224064</td>\n",
       "      <td>1237645942904914039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35774</th>\n",
       "      <td>2501245570002658048</td>\n",
       "      <td>1237645943433396364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 source_id  original_ext_source_id\n",
       "30731  2498717831425504896     1237645942898229350\n",
       "30182  2498705285825681536     1237645942898229471\n",
       "31700  3263851136923447168     1237645942904455343\n",
       "31626  3263676791315224064     1237645942904914039\n",
       "35774  2501245570002658048     1237645943433396364"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\"\"\"df_gaia_sdss_neighbors = pd.read_csv('df_gaia_sdss_neighbors2_825263.csv')\n",
    "\n",
    "df_gaia_sdss_neighbors = df_gaia_sdss_neighbors[['source_id', 'original_ext_source_id']]\n",
    "\n",
    "df_gaia_sdss_boss_neighbors = df_gaia_sdss_neighbors[df_gaia_sdss_neighbors['original_ext_source_id'].astype(str).isin(df_sdss_boss_all['objid'].astype(str))]\n",
    "\n",
    "df_gaia_sdss_boss_neighbors.to_csv('df_gaia_sdss_boss_neighbors_export.csv', index=False)\"\"\";\n",
    "\n",
    "\n",
    "df_gaia_sdss_boss_neighbors = pd.read_csv('df_gaia_sdss_boss_neighbors_export.csv')\n",
    "df_gaia_sdss_boss_neighbors = df_gaia_sdss_boss_neighbors.sort_values('original_ext_source_id')\n",
    "print('{:d} rows'.format(len(df_gaia_sdss_boss_neighbors)))\n",
    "print('{:.1f} MB'.format(df_gaia_sdss_boss_neighbors.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_gaia_sdss_boss_neighbors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50e6d0d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5b3a24ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "427977 rows\n",
      "59.5 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sobject_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>fe_h</th>\n",
       "      <th>logg</th>\n",
       "      <th>teff</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>rv_galah</th>\n",
       "      <th>Star_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136156</th>\n",
       "      <td>160109002001142</td>\n",
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       "      <td>0.088830</td>\n",
       "      <td>4.265983</td>\n",
       "      <td>5653.2295</td>\n",
       "      <td>58.337147</td>\n",
       "      <td>11.537596</td>\n",
       "      <td>5.100546</td>\n",
       "      <td>main_sequence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136152</th>\n",
       "      <td>160109002001138</td>\n",
       "      <td>36735031378673152</td>\n",
       "      <td>-1.139942</td>\n",
       "      <td>4.112586</td>\n",
       "      <td>5259.7340</td>\n",
       "      <td>58.410739</td>\n",
       "      <td>11.531741</td>\n",
       "      <td>-51.292385</td>\n",
       "      <td>turnoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124013</th>\n",
       "      <td>151110003701135</td>\n",
       "      <td>36735306256577280</td>\n",
       "      <td>-0.186722</td>\n",
       "      <td>2.363157</td>\n",
       "      <td>4773.7144</td>\n",
       "      <td>58.473628</td>\n",
       "      <td>11.551758</td>\n",
       "      <td>24.607561</td>\n",
       "      <td>red_clump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124015</th>\n",
       "      <td>151110003701139</td>\n",
       "      <td>36735886075071616</td>\n",
       "      <td>-0.145903</td>\n",
       "      <td>2.912731</td>\n",
       "      <td>4871.8105</td>\n",
       "      <td>58.409546</td>\n",
       "      <td>11.560023</td>\n",
       "      <td>-14.902564</td>\n",
       "      <td>red_giants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124014</th>\n",
       "      <td>151110003701137</td>\n",
       "      <td>36736504550361984</td>\n",
       "      <td>-0.233572</td>\n",
       "      <td>3.990228</td>\n",
       "      <td>5885.7705</td>\n",
       "      <td>58.473711</td>\n",
       "      <td>11.604339</td>\n",
       "      <td>-29.974653</td>\n",
       "      <td>turnoff</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sobject_id          source_id      fe_h      logg       teff  \\\n",
       "136156  160109002001142  36734515982599808  0.088830  4.265983  5653.2295   \n",
       "136152  160109002001138  36735031378673152 -1.139942  4.112586  5259.7340   \n",
       "124013  151110003701135  36735306256577280 -0.186722  2.363157  4773.7144   \n",
       "124015  151110003701139  36735886075071616 -0.145903  2.912731  4871.8105   \n",
       "124014  151110003701137  36736504550361984 -0.233572  3.990228  5885.7705   \n",
       "\n",
       "               ra        dec   rv_galah      Star_Type  \n",
       "136156  58.337147  11.537596   5.100546  main_sequence  \n",
       "136152  58.410739  11.531741 -51.292385        turnoff  \n",
       "124013  58.473628  11.551758  24.607561      red_clump  \n",
       "124015  58.409546  11.560023 -14.902564     red_giants  \n",
       "124014  58.473711  11.604339 -29.974653        turnoff  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    " #GALAH DR3 Version 1 \n",
    "# https://cloud.datacentral.org.au/teamdata/GALAH/public/GALAH_DR3/old_version_1/\n",
    "\n",
    "df_GALAH = pd.read_csv('GALAH_DR3_main_allspec_v1.csv')\n",
    "\n",
    "df_GALAH['source_id'] = df_GALAH['source_id'].astype('Int64')\n",
    "\n",
    "df_GALAH = df_GALAH[(df_GALAH.flag_sp == 0) & (df_GALAH.flag_fe_h == 0)]\n",
    "\n",
    "conditions = [\n",
    "    (df_GALAH['logg'].astype(float) > 4.20),\n",
    "    ((df_GALAH['logg'].astype(float) <= 4.20) & (df_GALAH['logg'].astype(float) > 3.90)),\n",
    "    ((df_GALAH['logg'].astype(float) <= 3.90) & (df_GALAH['logg'].astype(float) > 3.60)),\n",
    "    ((df_GALAH['logg'].astype(float) < 2.55) & (df_GALAH['logg'].astype(float) > 2.35)),\n",
    "    (df_GALAH['logg'].astype(float) <= 3.60),\n",
    "    (df_GALAH['logg'].isna())\n",
    "]\n",
    "\n",
    "values = ['main_sequence', 'turnoff', 'subgiants', 'red_clump', 'red_giants', 'null']\n",
    "\n",
    "df_GALAH['Star_Type'] = np.select(conditions, values)\n",
    "\n",
    "df_GALAH= df_GALAH[['sobject_id', 'source_id', 'fe_h', 'logg', 'teff', 'ra', 'dec', 'rv_galah', 'Star_Type']]\n",
    "\n",
    "df_GALAH.to_csv('GALAH_DR3_main_allspec_v1_export.csv', index=False)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "df_GALAH = pd.read_csv('GALAH_DR3_main_allspec_v1_export.csv')\n",
    "df_GALAH = df_GALAH[df_GALAH['source_id'] > 0]\n",
    "df_GALAH['source_id'] = df_GALAH['source_id'].astype(int)\n",
    "df_GALAH = df_GALAH.sort_values('source_id')\n",
    "print('{:d} rows'.format(len(df_GALAH)))\n",
    "print('{:.1f} MB'.format(df_GALAH.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_GALAH.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3920921a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ad2e5bc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34228085 rows\n",
      "2533.2 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>spectraltype_esphs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23552907</th>\n",
       "      <td>38655544960</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5750171</th>\n",
       "      <td>2851858288640</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23552913</th>\n",
       "      <td>3332894779520</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23552914</th>\n",
       "      <td>3371550165888</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13844204</th>\n",
       "      <td>4711579935744</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              source_id spectraltype_esphs\n",
       "23552907    38655544960                  K\n",
       "5750171   2851858288640                  F\n",
       "23552913  3332894779520                  K\n",
       "23552914  3371550165888                  K\n",
       "13844204  4711579935744                  G"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_mergedA = pd.concat([df_O, df_B, df_A, df_F, df_G, df_K, df_M, df_CSTAR,\n",
    "    df_beStar, df_HerbigStar, df_RedDwarfEmStar, df_rrlyrae, df_TTauri, df_wN,\n",
    "    df_Very_Metal_Poor, df_Extremely_Metal_Poor, df_Ultra_Metal_Poor, df_Unknown,\n",
    "    df_K_RVS, df_G_RVS, df_F_RVS,\n",
    "    df_gaia_eclipsing_binary, df_gaia_cepheid, df_gaia_epoch_radial_velocity, df_gaia_long_period_variable,\n",
    "    df_gaia_ms_oscillator, df_gaia_planetary_transit, df_gaia_rotation_modulation, \n",
    "    df_gaia_rrlyrae, df_gaia_short_timescale], ignore_index=True, sort=False)\n",
    "\n",
    "df_mergedA = df_mergedA[['source_id', 'spectraltype_esphs']]\n",
    "\n",
    "df_mergedA = df_mergedA.drop_duplicates(subset='source_id',keep='first')\n",
    "\n",
    "df_mergedA.to_csv('df_mergedA_export.csv', index=False)\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "df_mergedA = pd.read_csv('df_mergedA_export.csv')\n",
    "df_mergedA = df_mergedA.sort_values('source_id')\n",
    "print('{:d} rows'.format(len(df_mergedA)))\n",
    "print('{:.1f} MB'.format(df_mergedA.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_mergedA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3a7cb77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68c272db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7180133 rows\n",
      "1142.3 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obsid</th>\n",
       "      <th>designation</th>\n",
       "      <th>subclass</th>\n",
       "      <th>gaia_source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>215109</td>\n",
       "      <td>J030001.01+000115.4</td>\n",
       "      <td>G8</td>\n",
       "      <td>34361129088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3657331</th>\n",
       "      <td>300702165</td>\n",
       "      <td>J030001.08+000110.8</td>\n",
       "      <td>K4</td>\n",
       "      <td>38655544960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4228839</th>\n",
       "      <td>367412199</td>\n",
       "      <td>J030011.58+000253.9</td>\n",
       "      <td>G9</td>\n",
       "      <td>549755818112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205366</th>\n",
       "      <td>18112179</td>\n",
       "      <td>J025958.38+000434.8</td>\n",
       "      <td>G8</td>\n",
       "      <td>1275606125952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3657329</th>\n",
       "      <td>300702156</td>\n",
       "      <td>J025952.58+000504.0</td>\n",
       "      <td>K4</td>\n",
       "      <td>1340029955712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             obsid          designation subclass  gaia_source_id\n",
       "3080        215109  J030001.01+000115.4       G8     34361129088\n",
       "3657331  300702165  J030001.08+000110.8       K4     38655544960\n",
       "4228839  367412199  J030011.58+000253.9       G9    549755818112\n",
       "205366    18112179  J025958.38+000434.8       G8   1275606125952\n",
       "3657329  300702156  J025952.58+000504.0       K4   1340029955712"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_lamost_LRS_stars = pd.read_csv('dr8_v2.0_LRS_catalogue.csv')\n",
    "\n",
    "print('{:d} rows'.format(len(df_lamost_LRS_stars)))\n",
    "print('{:.1f} MB'.format(df_lamost_LRS_stars.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "\n",
    "df_lamost_LRS_stars = df_lamost_LRS_stars[df_lamost_LRS_stars['gaia_source_id'].astype(str) != '-9999']\n",
    "\n",
    "df_lamost_LRS_stars = df_lamost_LRS_stars[['obsid','designation', 'class', 'subclass','gaia_source_id']]\n",
    "\n",
    "df_lamost_LRS_stars= df_lamost_LRS_stars.drop_duplicates(subset='gaia_source_id', keep=\"first\")\n",
    "\n",
    "df_lamost_LRS_stars['gaia_source_id'] = df_lamost_LRS_stars['gaia_source_id'].astype('Int64')\n",
    "\n",
    "unique_class = np.unique(df_lamost_LRS_stars['class'].astype(str))\n",
    "unique_class\n",
    "\n",
    "for vartype in unique_class:\n",
    "    v = df_lamost_LRS_stars[df_lamost_LRS_stars['class'] == vartype]\n",
    "    print(str(vartype) + \"  count: \"  + str(v.shape[0]))\n",
    "\n",
    "df_lamost_LRS_stars = df_lamost_LRS_stars[df_lamost_LRS_stars['class'] == 'STAR']\n",
    "\n",
    "print('{:d} rows'.format(len(df_lamost_LRS_stars)))\n",
    "print('{:.1f} MB'.format(df_lamost_LRS_stars.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "\n",
    "df_lamost_LRS_stars.to_csv('df_lamost_LRS_stars_export.csv', index=False)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "df_lamost_LRS_stars = pd.read_csv('df_lamost_LRS_stars_export.csv')\n",
    "df_lamost_LRS_stars = df_lamost_LRS_stars[['obsid', 'designation', 'subclass', 'gaia_source_id']]\n",
    "df_lamost_LRS_stars = df_lamost_LRS_stars.sort_values('gaia_source_id')\n",
    "print('{:d} rows'.format(len(df_lamost_LRS_stars)))\n",
    "print('{:.1f} MB'.format(df_lamost_LRS_stars.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_lamost_LRS_stars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2b18ecb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "77b0958a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "504324 rows\n",
      "88.3 MB\n"
     ]
    },
    {
     "data": {
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       "\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>mobsid</th>\n",
       "      <th>obsid</th>\n",
       "      <th>designation</th>\n",
       "      <th>gaia_source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22374</th>\n",
       "      <td>59130210983564814B</td>\n",
       "      <td>591302109</td>\n",
       "      <td>J031646.18+100209.4</td>\n",
       "      <td>14527439116480896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22660</th>\n",
       "      <td>59130211183564841B</td>\n",
       "      <td>591302111</td>\n",
       "      <td>J031715.27+100832.6</td>\n",
       "      <td>14553170265024384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22664</th>\n",
       "      <td>59130211083564814R</td>\n",
       "      <td>591302110</td>\n",
       "      <td>J031720.62+101309.8</td>\n",
       "      <td>14554166697435648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22742</th>\n",
       "      <td>59130212583564841B</td>\n",
       "      <td>591302125</td>\n",
       "      <td>J031732.61+101716.9</td>\n",
       "      <td>14557057210975872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22366</th>\n",
       "      <td>59130200583564814B</td>\n",
       "      <td>591302005</td>\n",
       "      <td>J031741.83+102100.7</td>\n",
       "      <td>14563345043095552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mobsid      obsid          designation     gaia_source_id\n",
       "22374  59130210983564814B  591302109  J031646.18+100209.4  14527439116480896\n",
       "22660  59130211183564841B  591302111  J031715.27+100832.6  14553170265024384\n",
       "22664  59130211083564814R  591302110  J031720.62+101309.8  14554166697435648\n",
       "22742  59130212583564841B  591302125  J031732.61+101716.9  14557057210975872\n",
       "22366  59130200583564814B  591302005  J031741.83+102100.7  14563345043095552"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_lamost_MRS_stars = pd.read_csv('dr8_v2.0_MRS_catalogue.csv')\n",
    "\n",
    "print('{:d} rows'.format(len(df_lamost_MRS_stars)))\n",
    "print('{:.1f} MB'.format(df_lamost_MRS_stars.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "\n",
    "df_lamost_MRS_stars = df_lamost_MRS_stars[df_lamost_MRS_stars['gaia_source_id'].astype(str) != '-9999']\n",
    "\n",
    "df_lamost_MRS_stars = df_lamost_MRS_stars[['mobsid','obsid','designation', 'gaia_source_id']]\n",
    "\n",
    "df_lamost_MRS_stars= df_lamost_MRS_stars.drop_duplicates(subset='gaia_source_id', keep=\"first\")\n",
    "\n",
    "df_lamost_MRS_stars['gaia_source_id'] = df_lamost_MRS_stars['gaia_source_id'].astype('Int64')\n",
    "\n",
    "df_lamost_MRS_stars.to_csv('df_lamost_MRS_stars_export.csv', index=False)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "df_lamost_MRS_stars = pd.read_csv('df_lamost_MRS_stars_export.csv')\n",
    "df_lamost_MRS_stars = df_lamost_MRS_stars.sort_values('gaia_source_id')\n",
    "print('{:d} rows'.format(len(df_lamost_MRS_stars)))\n",
    "print('{:.1f} MB'.format(df_lamost_MRS_stars.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_lamost_MRS_stars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99b25feb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "aabcf13c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "680179 rows\n",
      "247.0 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>asassn_name</th>\n",
       "      <th>other_names</th>\n",
       "      <th>period</th>\n",
       "      <th>variable_type</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43287</th>\n",
       "      <td>112811</td>\n",
       "      <td>ASASSN-V J030022.84+002836.8</td>\n",
       "      <td>ASAS J030023+0028.7</td>\n",
       "      <td>765.214121</td>\n",
       "      <td>VAR</td>\n",
       "      <td>30343944744320</td>\n",
       "      <td>TIC 347553421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375463</th>\n",
       "      <td>112945</td>\n",
       "      <td>ASASSN-V J030002.00+010340.4</td>\n",
       "      <td>CSS_J030002.0+010340</td>\n",
       "      <td>0.385857</td>\n",
       "      <td>EW</td>\n",
       "      <td>138577120584320</td>\n",
       "      <td>TIC 347549043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131392</th>\n",
       "      <td>107642</td>\n",
       "      <td>ASASSN-V J030304.45+004809.4</td>\n",
       "      <td>SDSS J030304.48+004809.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ROT:</td>\n",
       "      <td>148026048510208</td>\n",
       "      <td>TIC 328233610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320099</th>\n",
       "      <td>107532</td>\n",
       "      <td>ASASSN-V J030313.07+005420.3</td>\n",
       "      <td>GI Cet</td>\n",
       "      <td>218.539724</td>\n",
       "      <td>M</td>\n",
       "      <td>154863636444160</td>\n",
       "      <td>TIC 328233635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647649</th>\n",
       "      <td>106901</td>\n",
       "      <td>ASASSN-V J030351.62+010109.5</td>\n",
       "      <td>ASAS J030352+0101.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>180736519432320</td>\n",
       "      <td>TIC 328254012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       source_id                   asassn_name               other_names  \\\n",
       "43287     112811  ASASSN-V J030022.84+002836.8       ASAS J030023+0028.7   \n",
       "375463    112945  ASASSN-V J030002.00+010340.4      CSS_J030002.0+010340   \n",
       "131392    107642  ASASSN-V J030304.45+004809.4  SDSS J030304.48+004809.4   \n",
       "320099    107532  ASASSN-V J030313.07+005420.3                    GI Cet   \n",
       "647649    106901  ASASSN-V J030351.62+010109.5       ASAS J030352+0101.2   \n",
       "\n",
       "            period variable_type   gaia_source_id         tic_id  \n",
       "43287   765.214121           VAR   30343944744320  TIC 347553421  \n",
       "375463    0.385857            EW  138577120584320  TIC 347549043  \n",
       "131392         NaN          ROT:  148026048510208  TIC 328233610  \n",
       "320099  218.539724             M  154863636444160  TIC 328233635  \n",
       "647649         NaN             L  180736519432320  TIC 328254012  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_asassn_stars = pd.read_csv('Stars 2023-04-28 20_14.csv')  # 687695\n",
    "\n",
    "df_asassn_stars['gaia_source_id'] = df_asassn_stars['edr3_source_id'].str.replace('EDR3 ','', regex=True)\n",
    "\n",
    "df_asassn_stars = df_asassn_stars[['source_id', 'asassn_name', 'other_names', 'period', 'variable_type', 'gaia_source_id', 'tic_id']]\n",
    "\n",
    "df_asassn_stars.to_csv('df_asassn_stars_export.csv', index=False)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "df_asassn_stars = pd.read_csv('df_asassn_stars_export.csv')\n",
    "df_asassn_stars = df_asassn_stars [df_asassn_stars ['gaia_source_id'] > 0]\n",
    "df_asassn_stars ['gaia_source_id'] = df_asassn_stars['gaia_source_id'].astype(int)\n",
    "df_asassn_stars = df_asassn_stars.sort_values('gaia_source_id')\n",
    "print('{:d} rows'.format(len(df_asassn_stars)))\n",
    "print('{:.1f} MB'.format(df_asassn_stars.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_asassn_stars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1c75958",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a3cc5e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220967 rows\n",
      "22.3 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asas_sn_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122123</th>\n",
       "      <td>2980</td>\n",
       "      <td>ASASSN-V J202758.57-792847.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183992</th>\n",
       "      <td>4998</td>\n",
       "      <td>ASASSN-V J083716.84-792249.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92175</th>\n",
       "      <td>5541</td>\n",
       "      <td>ASASSN-V J151144.32-793401.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21108</th>\n",
       "      <td>5556</td>\n",
       "      <td>ASASSN-V J075341.35-793019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96843</th>\n",
       "      <td>5764</td>\n",
       "      <td>ASASSN-V J132703.21-790437.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        asas_sn_id                          name\n",
       "122123        2980  ASASSN-V J202758.57-792847.3\n",
       "183992        4998  ASASSN-V J083716.84-792249.1\n",
       "92175         5541  ASASSN-V J151144.32-793401.7\n",
       "21108         5556  ASASSN-V J075341.35-793019.0\n",
       "96843         5764  ASASSN-V J132703.21-790437.7"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn_sn_name = pd.read_csv('df_asas_sn_aavsovsx_name_220967.csv')\n",
    "df_asassn_sn_name = df_asassn_sn_name.sort_values('asas_sn_id')\n",
    "df_asassn_sn_name = df_asassn_sn_name[['asas_sn_id', 'name']]\n",
    "print('{:d} rows'.format(len(df_asassn_sn_name)))\n",
    "print('{:.1f} MB'.format(df_asassn_sn_name.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_asassn_sn_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae49d95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "883e7080",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71357 rows\n",
      "23.8 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>asassn_name</th>\n",
       "      <th>other_names</th>\n",
       "      <th>period</th>\n",
       "      <th>variable_type</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9798</th>\n",
       "      <td>454688</td>\n",
       "      <td>ASASSN-V J031022.31+033554.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.17800</td>\n",
       "      <td>ROT</td>\n",
       "      <td>2582619670319744</td>\n",
       "      <td>TIC 328427616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71818</th>\n",
       "      <td>454696</td>\n",
       "      <td>ASASSN-V J030921.16+035003.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125.79000</td>\n",
       "      <td>SR</td>\n",
       "      <td>2648693447165184</td>\n",
       "      <td>TIC 328400154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47654</th>\n",
       "      <td>458036</td>\n",
       "      <td>ASASSN-V J030327.09+061950.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30754</td>\n",
       "      <td>EW</td>\n",
       "      <td>7017568540477440</td>\n",
       "      <td>TIC 365293920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10521</th>\n",
       "      <td>458022</td>\n",
       "      <td>ASASSN-V J030601.61+064752.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.49000</td>\n",
       "      <td>ROT</td>\n",
       "      <td>7434146008358272</td>\n",
       "      <td>TIC 365335164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32427</th>\n",
       "      <td>456120</td>\n",
       "      <td>ASASSN-V J031930.08+081444.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YSO</td>\n",
       "      <td>11009276785704960</td>\n",
       "      <td>TIC 399922229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      source_id                   asassn_name other_names     period  \\\n",
       "9798     454688  ASASSN-V J031022.31+033554.1         NaN   15.17800   \n",
       "71818    454696  ASASSN-V J030921.16+035003.2         NaN  125.79000   \n",
       "47654    458036  ASASSN-V J030327.09+061950.3         NaN    0.30754   \n",
       "10521    458022  ASASSN-V J030601.61+064752.8         NaN   90.49000   \n",
       "32427    456120  ASASSN-V J031930.08+081444.9         NaN        NaN   \n",
       "\n",
       "      variable_type     gaia_source_id         tic_id  \n",
       "9798            ROT   2582619670319744  TIC 328427616  \n",
       "71818            SR   2648693447165184  TIC 328400154  \n",
       "47654            EW   7017568540477440  TIC 365293920  \n",
       "10521           ROT   7434146008358272  TIC 365335164  \n",
       "32427           YSO  11009276785704960  TIC 399922229  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_asassn_stars_match = df_asassn_stars[df_asassn_stars['asassn_name'].astype(str).isin(df_asassn_sn_name['name'].astype(str))]  # 71,903\n",
    "\n",
    "df_asassn_stars_match.to_csv('df_asassn_stars_match_export.csv', index=False)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "df_asassn_stars_match = pd.read_csv('df_asassn_stars_match_export.csv')\n",
    "df_asassn_stars_match = df_asassn_stars_match[df_asassn_stars_match['gaia_source_id'] > 0]\n",
    "df_asassn_stars_match['gaia_source_id'] = df_asassn_stars_match['gaia_source_id'].astype(int)\n",
    "df_asassn_stars_match = df_asassn_stars_match.sort_values('gaia_source_id')\n",
    "print('{:d} rows'.format(len(df_asassn_stars_match)))\n",
    "print('{:.1f} MB'.format(df_asassn_stars_match.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_asassn_stars_match.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d64e8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fac38901",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98930410 rows\n",
      "2374.3 MB\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asas_sn_id</th>\n",
       "      <th>gaia_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>455267509223</td>\n",
       "      <td>4295806720</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>257699164104</td>\n",
       "      <td>38655544960</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>455267533319</td>\n",
       "      <td>549755818112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>455267517993</td>\n",
       "      <td>828929527040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>455267537667</td>\n",
       "      <td>1275606125952</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     asas_sn_id        gaia_id  tic_id\n",
       "0  455267509223     4295806720     NaN\n",
       "1  257699164104    38655544960     NaN\n",
       "2  455267533319   549755818112     NaN\n",
       "3  455267517993   828929527040     NaN\n",
       "4  455267537667  1275606125952     NaN"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#webbrowser.open('https://asas-sn.osu.edu/variables?&name=' + str(asassval))\n",
    "\n",
    "\"\"\"\n",
    "df_asassn_all1 = pd.read_csv('df_asassn_all_export.csv')\n",
    "\n",
    "df_asassn_all2 = pd.read_csv('df_asassn_all2_export.csv')\n",
    "\n",
    "df_asassn_all = pd.concat([df_asassn_all1, df_asassn_all2], ignore_index=True, sort=False)\n",
    "\n",
    "df_asassn_all = df_asassn_all.sort_values('gaia_id')\n",
    "\n",
    "df_asassn_all = df_asassn_all[['asas_sn_id', 'gaia_id', 'tic_id']]\n",
    "\n",
    "df_asassn_all = df_asassn_all.drop_duplicates(subset='gaia_id',keep='first')\n",
    "\n",
    "df_asassn_all.to_csv('df_asassn_all_100_million_export.csv', index=False)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "df_asassn_all = pd.read_csv('df_asassn_all_100_million_export.csv')    # 98,930,410 rows\n",
    "print('{:d} rows'.format(len(df_asassn_all)))\n",
    "print('{:.1f} MB'.format(df_asassn_all.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_asassn_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ba55935a",
   "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>asas_sn_id</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>455267509223</td>\n",
       "      <td>4295806720</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>257699164104</td>\n",
       "      <td>38655544960</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>455267533319</td>\n",
       "      <td>549755818112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>455267517993</td>\n",
       "      <td>828929527040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>455267537667</td>\n",
       "      <td>1275606125952</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     asas_sn_id  gaia_source_id  tic_id\n",
       "0  455267509223      4295806720     NaN\n",
       "1  257699164104     38655544960     NaN\n",
       "2  455267533319    549755818112     NaN\n",
       "3  455267517993    828929527040     NaN\n",
       "4  455267537667   1275606125952     NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn_all.rename({'gaia_id': 'gaia_source_id'}, axis=1, inplace=True)\n",
    "df_asassn_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "815976ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ba8c087f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "687695 rows\n",
      "829.3 MB\n"
     ]
    }
   ],
   "source": [
    "df_asassn_v_filter_variables = pd.read_csv('Stars 2023-04-28 20_14.csv')  # 687695\n",
    "print('{:d} rows'.format(len(df_asassn_v_filter_variables)))\n",
    "print('{:.1f} MB'.format(df_asassn_v_filter_variables.memory_usage(index=True,deep=True).sum()/1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4540664d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_asassn_v_filter_variables ['gaia_source_id'] = df_asassn_v_filter_variables ['edr3_source_id'].str.replace('EDR3 ','', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cfeb3d4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "687695 rows\n",
      "290.4 MB\n"
     ]
    }
   ],
   "source": [
    "df_asassn_v_filter_variables = df_asassn_v_filter_variables[['source_id', 'asassn_name', 'other_names', 'period', 'variable_type', 'gaia_source_id', 'tic_id']]\n",
    "print('{:d} rows'.format(len(df_asassn_v_filter_variables)))\n",
    "print('{:.1f} MB'.format(df_asassn_v_filter_variables.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "03f33c95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "445201 rows\n",
      "190.2 MB\n"
     ]
    }
   ],
   "source": [
    "df_asassn_v_filter_match = df_asassn_v_filter_variables[df_asassn_v_filter_variables['gaia_source_id'].astype(str).isin(df_mergedA['source_id'].astype(str))]  \n",
    "print('{:d} rows'.format(len(df_asassn_v_filter_match)))\n",
    "print('{:.1f} MB'.format(df_asassn_v_filter_match.memory_usage(index=True,deep=True).sum()/1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fd2d8b3e",
   "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>source_id</th>\n",
       "      <th>asassn_name</th>\n",
       "      <th>other_names</th>\n",
       "      <th>period</th>\n",
       "      <th>variable_type</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136101</td>\n",
       "      <td>ASASSN-V J221121.85-290944.6</td>\n",
       "      <td>ASAS J221122-2909.8</td>\n",
       "      <td>0.368478</td>\n",
       "      <td>EW</td>\n",
       "      <td>6615185839926932224</td>\n",
       "      <td>TIC 441157335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  source_id                   asassn_name          other_names    period  \\\n",
       "0    136101  ASASSN-V J221121.85-290944.6  ASAS J221122-2909.8  0.368478   \n",
       "\n",
       "  variable_type       gaia_source_id         tic_id  \n",
       "0            EW  6615185839926932224  TIC 441157335  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn_v_filter_match.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f86a6621",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"unique_v_filter_variable_type = np.unique(df_asassn_v_filter_match['variable_type'])  \"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04cc0206",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"for vartype in unique_v_filter_variable_type:\n",
    "    v = df_asassn_v_filter_variables[df_asassn_v_filter_variables['variable_type'] == vartype]\n",
    "    print(str(vartype) + \"  count: \"  + str(v.shape[0]))\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "498530bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4ccffeff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "378861 rows\n",
      "326.3 MB\n"
     ]
    }
   ],
   "source": [
    "df_asassn_blue_filter_variables = pd.read_csv('asassn_variables_x.csv')    # 378,861 rows\n",
    "print('{:d} rows'.format(len(df_asassn_blue_filter_variables)))\n",
    "print('{:.1f} MB'.format(df_asassn_blue_filter_variables.memory_usage(index=True,deep=True).sum()/1e6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "241c8cf4",
   "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>ID</th>\n",
       "      <th>ML_classification</th>\n",
       "      <th>EDR3_source_id</th>\n",
       "      <th>Period</th>\n",
       "      <th>EDR3_dist</th>\n",
       "      <th>parallax</th>\n",
       "      <th>TIC_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASASSN-V J004459.35-013020.5</td>\n",
       "      <td>EA</td>\n",
       "      <td>EDR3 2530574359774427904</td>\n",
       "      <td>1.9613</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>0.3839</td>\n",
       "      <td>TIC 213013694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ID ML_classification            EDR3_source_id  \\\n",
       "0  ASASSN-V J004459.35-013020.5                EA  EDR3 2530574359774427904   \n",
       "\n",
       "   Period  EDR3_dist  parallax         TIC_ID  \n",
       "0  1.9613     2099.0    0.3839  TIC 213013694  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn_blue_filter_variables = df_asassn_blue_filter_variables[['ID', 'ML_classification', 'EDR3_source_id','Period', 'EDR3_dist', 'parallax', 'TIC_ID']]\n",
    "df_asassn_blue_filter_variables.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4529c2bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ID</th>\n",
       "      <th>ML_classification</th>\n",
       "      <th>EDR3_source_id</th>\n",
       "      <th>Period</th>\n",
       "      <th>EDR3_dist</th>\n",
       "      <th>parallax</th>\n",
       "      <th>TIC_ID</th>\n",
       "      <th>gaia_source_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASASSN-V J004459.35-013020.5</td>\n",
       "      <td>EA</td>\n",
       "      <td>EDR3 2530574359774427904</td>\n",
       "      <td>1.9613</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>0.3839</td>\n",
       "      <td>TIC 213013694</td>\n",
       "      <td>2530574359774427904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ID ML_classification            EDR3_source_id  \\\n",
       "0  ASASSN-V J004459.35-013020.5                EA  EDR3 2530574359774427904   \n",
       "\n",
       "   Period  EDR3_dist  parallax         TIC_ID       gaia_source_id  \n",
       "0  1.9613     2099.0    0.3839  TIC 213013694  2530574359774427904  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn_blue_filter_variables['gaia_source_id'] = df_asassn_blue_filter_variables['EDR3_source_id'].str[5:]\n",
    "df_asassn_blue_filter_variables.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "70eba9f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>ID</th>\n",
       "      <th>ML_classification</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>Period</th>\n",
       "      <th>TIC_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASASSN-V J004459.35-013020.5</td>\n",
       "      <td>EA</td>\n",
       "      <td>2530574359774427904</td>\n",
       "      <td>1.9613</td>\n",
       "      <td>TIC 213013694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ID ML_classification       gaia_source_id  \\\n",
       "0  ASASSN-V J004459.35-013020.5                EA  2530574359774427904   \n",
       "\n",
       "   Period         TIC_ID  \n",
       "0  1.9613  TIC 213013694  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn_blue_filter_variables = df_asassn_blue_filter_variables[['ID', 'ML_classification', 'gaia_source_id','Period',  'TIC_ID']]\n",
    "df_asassn_blue_filter_variables.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8dd348f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "308778 rows\n",
      "94.4 MB\n"
     ]
    }
   ],
   "source": [
    "df_asassn_blue_filter_match = df_asassn_blue_filter_variables[df_asassn_blue_filter_variables['gaia_source_id'].astype(str).isin(df_mergedA['source_id'].astype(str))]  \n",
    "print('{:d} rows'.format(len(df_asassn_blue_filter_match)))\n",
    "print('{:.1f} MB'.format(df_asassn_blue_filter_match.memory_usage(index=True,deep=True).sum()/1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e67e2f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "227c2275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1260521 rows\n",
      "105.0 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asas_sn_id</th>\n",
       "      <th>period</th>\n",
       "      <th>variability_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1017520</th>\n",
       "      <td>421</td>\n",
       "      <td>0.307156</td>\n",
       "      <td>EW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541149</th>\n",
       "      <td>2546</td>\n",
       "      <td>0.381145</td>\n",
       "      <td>EW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309842</th>\n",
       "      <td>2980</td>\n",
       "      <td>3.557400</td>\n",
       "      <td>ROT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93026</th>\n",
       "      <td>3026</td>\n",
       "      <td>63.829788</td>\n",
       "      <td>MISC!SR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116300</th>\n",
       "      <td>3702</td>\n",
       "      <td>0.538100</td>\n",
       "      <td>RRAB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asas_sn_id     period variability_type\n",
       "1017520         421   0.307156               EW\n",
       "541149         2546   0.381145               EW\n",
       "309842         2980   3.557400              ROT\n",
       "93026          3026  63.829788          MISC!SR\n",
       "1116300        3702   0.538100             RRAB"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "df_asas_sn_aavsovsx_1 = pd.read_csv('df_asas_sn_aavsovsx_338821.csv')\n",
    "df_asas_sn_aavsovsx_2 = pd.read_csv('df_asas_sn_aavsovsx_1000000.csv')\n",
    "df_asas_sn_aavsovsx_3 = pd.read_csv('df_asas_sn_aavsovsx_variability_type_SRS_73915.csv')\n",
    "df_asas_sn_aavsovsx_4 = pd.read_csv('df_asas_sn_aavsovsx_variability_type_EW_83206.csv')\n",
    "df_asas_sn_aavsovsx_5 = pd.read_csv('df_asas_sn_aavsovsx_variability_type_RRAB_90096.csv')\n",
    "df_asas_sn_aavsovsx_6 = pd.read_csv('df_asas_sn_aavsovsx_variability_type_EC_94381.csv')\n",
    "df_asas_sn_aavsovsx_7 = pd.read_csv('df_asas_sn_aavsovsx_variability_type_VAR_75924.csv')\n",
    "df_asas_sn_aavsovsx_8 = pd.read_csv('df_asas_sn_aavsovsx_variability_type_SR_166101.csv')\n",
    "df_asas_sn_aavsovsx_9 = pd.read_csv('df_asas_sn_aavsovsx_variability_type_E_338129.csv')\n",
    "\n",
    "df_asassn_sn_aavsovsx_all = pd.concat([df_asas_sn_aavsovsx_1, df_asas_sn_aavsovsx_2, df_asas_sn_aavsovsx_3,\n",
    "        df_asas_sn_aavsovsx_4, df_asas_sn_aavsovsx_5, df_asas_sn_aavsovsx_6, df_asas_sn_aavsovsx_7, \n",
    "        df_asas_sn_aavsovsx_8, df_asas_sn_aavsovsx_9], ignore_index=True, sort=False)\n",
    "\n",
    "print('{:d} rows'.format(len(df_asassn_sn_aavsovsx_all)))\n",
    "print('{:.1f} MB'.format(df_asassn_sn_aavsovsx_all.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "\n",
    "df_asassn_sn_aavsovsx_all = df_asassn_sn_aavsovsx_all[['asas_sn_id', 'period', 'variability_type']]\n",
    "\n",
    "df_asassn_sn_aavsovsx_all = df_asassn_sn_aavsovsx_all.drop_duplicates(subset='asas_sn_id',keep='first')\n",
    "\n",
    "print('{:d} rows'.format(len(df_asassn_sn_aavsovsx_all)))\n",
    "print('{:.1f} MB'.format(df_asassn_sn_aavsovsx_all.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "\n",
    "df_asassn_sn_aavsovsx_all.to_csv('df_asassn_sn_aavsovsx_all_export.csv', index=False)\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "df_asassn_sn_aavsovsx_all = pd.read_csv('df_asassn_sn_aavsovsx_all_export.csv')\n",
    "df_asassn_sn_aavsovsx_all = df_asassn_sn_aavsovsx_all.sort_values('asas_sn_id')\n",
    "print('{:d} rows'.format(len(df_asassn_sn_aavsovsx_all)))\n",
    "print('{:.1f} MB'.format(df_asassn_sn_aavsovsx_all.memory_usage(index=True,deep=True).sum()/1e6))\n",
    "df_asassn_sn_aavsovsx_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1faedeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"unique_aavsovsx_variable_type = np.unique(df_asassn_sn_aavsovsx_all['variability_type'].astype(str))  \"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d01330",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"for vartype in unique_aavsovsx_variable_type:\n",
    "    v = df_asassn_sn_aavsovsx_all[df_asassn_sn_aavsovsx_all['variability_type'] == vartype]\n",
    "    print(str(vartype) + \"  count: \"  + str(v.shape[0]))\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21bc4b62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "030c134f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"unique_variable_type = sp.unique(df_asassn_4['variability_type'].astype(str))   # aavsovsx_502956\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7468b53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"for vartype in unique_variable_type:\n",
    "    v = df_asassn_4[df_asassn_4['variability_type'] == vartype]\n",
    "    print(str(vartype) + \"  count: \"  + str(v.shape[0]))\"\"\";"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ff2b0d84",
   "metadata": {},
   "source": [
    "https://www.aavso.org/vsx/index.php?view=about.vartypes\n",
    "\n",
    "CONVENTIONS USED IN THIS DOCUMENT\n",
    "\n",
    "A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\n",
    "\n",
    "A pipe character ( | ) between two different types signifies a logical OR; the classification is uncertain and all possible types are indicated. An example of this is ELL|DSCT, where the star may be an ellipsoidal binary system or a DSCT-type pulsating variable with half the given period.\n",
    "\n",
    "A plus character ( + ) signifies a logical AND; two different variability types are seen in the same star or system. An example of this would be ELL+DSCT, where one of the components of an ellipsoidal binary system is a DSCT-type pulsating variable.\n",
    "\n",
    "A slash character ( / ) indicates a subtype. In the case of binary systems (eclipsing, ellipsoidal or reflection variables) it is used to help describe either the physical properties of the system (E/PN or EA/RS), the luminosity class of the components (EA/DM), or the degree of filling of their inner Roche lobes (EA/SD). This is the GCVS classification system. In cataclysmic variables, slash characters are used to indicate some properties of the system, as in the degree of polarization (NA/DQ) or the nature of their components (UG/IBWD). In some cases, these subtypes also constitute an independent main variable star type, because the main type of variability they are usually associated with is not present. There are non-eclipsing RS CVn stars (RS), eruptive single Wolf-Rayet stars (WR) and cataclysmic binary subtypes showing variability but no outbursts (DQ, IBWD). In X-ray binaries, the subtypes indicate which kind of behavior the binary displays, as in X-ray bursts (XB), large amplitude outbursts also in the visual (XN), reflection effect (XR) or it may also indicate something about the object's nature, as in a compact object which is a pulsar (XP). Finally, the subtypes of NL (nova-like; NL/V and NL/VY) are shown as independent classes, since these subtypes do not apply to a class other than NL.\n",
    "\n",
    "Some variability classes show more than one type of variation, but they have been classified in the group that includes their more important type of variability. For example, SDOR stars show both eruptions and pulsations, but they have been included in the eruptive group only. The type definition explains their behavior. RCB stars also pulsate, but since their main feature is deep fadings, they are also included in the eruptive group only. The same is valid for the DYPer stars. DPV variables show periodic orbital variability, but they experience mass loss and what defines them is the presence of a long period lasting about 33 times the orbital period. They are therefore also included in the eruptive group only. The origin of variability of the λ Eri type variables (LERI) is not clear: they may be due to rotation or non-radial pulsation. They have been included in the class of rotating variables only.\n",
    "\n",
    "Survey types are classifications taken as they appear in the original published lists from the survey owners/analysts. They are included under a separate heading in this document because they are not actual types, per se, but are usually the result of less specific classification due to the automated nature of the analysis. These types should eventually be replaced by the conventional types.\n",
    "Variable type designations used in VSX but not currently used in the GCVS are highlighted in the main listings of this document by the accompanying  icon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5023c4ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e7bfdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_asassn_all = df_asassn_all[['asas_sn_id', 'gaia_source_id', 'tic_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bef4d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"print('{:d} rows'.format(len(df_asassn_all)))\n",
    "print('{:.1f} MB'.format(df_asassn_all.memory_usage(index=True,deep=True).sum()/1e6))\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d86b2f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"pd.set_option('display.max_columns', None)\n",
    "\n",
    "df_asassn_all.head()\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97dfbc4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_asassn_all['AbsoluteMagnitude'] = df_asassn_all['phot_g_mean_mag'] + 5*np.log10(df_asassn_all['parallax']) - 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c17012",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_asassn_all['gaia_source_id'] = df_asassn_all['gaia_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9403f8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9798      2582619670319744\n",
       "71818     2648693447165184\n",
       "47654     7017568540477440\n",
       "10521     7434146008358272\n",
       "32427    11009276785704960\n",
       "Name: gaia_source_id, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_asassn_stars_match = df_asassn_stars_match[df_asassn_stars_match['gaia_source_id'] > 0]\n",
    "\n",
    "df_asassn_stars_match['gaia_source_id'] = df_asassn_stars_match['gaia_source_id'].fillna(0)\n",
    "df_asassn_stars_match['gaia_source_id'] = df_asassn_stars_match['gaia_source_id'].astype(int)\n",
    "df_asassn_stars_match['gaia_source_id'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "673c02a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_asassn = \" \"\n",
    "\n",
    "All_flag                                = \"N\"\n",
    "Subluminous_candidates_flag             = 'N'\n",
    "GALAH_candidates_flag                   = 'N'\n",
    "SDSS_candidates_flag                    = 'N'\n",
    "SDSS_BOSS_candidates_flag               = 'N'\n",
    "aavsovsx_candidates_flag                = 'Y'\n",
    "asassn_name_candidates_flag             = 'N'   # https://asas-sn.osu.edu/variables with Phase plot web page\n",
    "asassn_blue_filter_flag                 = 'N'   # 299,831 rows\n",
    "asassn_v_filter_flag                    = 'N'   # 445,201 rows\n",
    "\n",
    "Subluminous_GALAH_SDSS_candidates_flag  = 'N'\n",
    "\n",
    "\n",
    "\n",
    "if Subluminous_candidates_flag  == 'Y':\n",
    "    df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_hot_subluminous_all['source_id'].astype(str))]  # 11,944\n",
    "       \n",
    "if GALAH_candidates_flag == 'Y':\n",
    "    df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_GALAH['source_id'].astype(str))]  # 39,970\n",
    "\n",
    "if SDSS_candidates_flag == 'Y':\n",
    "    df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_gaia_sdss_all_neighbors['source_id'].astype(str))]  # 285,205\n",
    "    \n",
    "if SDSS_BOSS_candidates_flag == 'Y':\n",
    "    df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_gaia_sdss_boss_neighbors['source_id'].astype(str))] # 285,205\n",
    "    \n",
    "if aavsovsx_candidates_flag == 'Y':\n",
    "    df_asassn = df_asassn_all[df_asassn_all['asas_sn_id'].astype(str).isin(df_asassn_sn_aavsovsx_all['asas_sn_id'].astype(str))]  # 395,489\n",
    "\n",
    "if asassn_name_candidates_flag == 'Y':    \n",
    "    df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_asassn_stars_match['gaia_source_id'].astype(str))]  # 11,619\n",
    "\n",
    "if asassn_blue_filter_flag == 'Y':    \n",
    "    df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_asassn_blue_filter_match['gaia_source_id'].astype(str))]  # 11,619\n",
    "\n",
    "if asassn_v_filter_flag == 'Y':    \n",
    "    df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_asassn_v_filter_match['gaia_source_id'].astype(str))]  # 11,619\n",
    "\n",
    "if Subluminous_GALAH_SDSS_candidates_flag  == 'Y':\n",
    "    df_asassn1 = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(df_GALAH['source_id'].astype(str))]  # 39,970\n",
    "    df_asassn = df_asassn1[df_asassn1['gaia_source_id'].astype(str).isin(df_gaia_sdss_all_neighbors['source_id'].astype(str))] # 285,205\n",
    "    \n",
    "if All_flag == \"Y\":\n",
    "  df_asassn = df_asassn_all\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(\"df_asassn: \" + str(len(df_asassn)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7679b690",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "62a8a64a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def defaultvalue():\n",
    "        return \"Unknown\"\n",
    "\n",
    "vType = defaultdict(defaultvalue)\n",
    "\n",
    "vType['AHB1'] = 'AHB1: Above Horizontal Branch variables of subtype 1, also known as XX Vir variables. \\nPulsating variables with periods between 0.7 and 3 d. showing highly asymmetric light curves (rise durations <25%) \\nof large amplitude and low metallicity ([Fe/H] <-1), crossing the instability strip from the blue to the red on their way \\nto the AGB after helium core exhaustion. They have low masses and higher luminosities (Mv = +0.2 to -1.4; ~1 mag. brighter) \\nthan the RR Lyrae stars of the same period. There is controversy in the literature about their nomenclature. \\nThey were originally called RRD but now that designation is used for the double-mode RR Lyrae stars. \\nThey were also called \"short period type II cepheids\" and have been classified among the CWB variables but their light curves \\nare different from the BL Her stars (which show humps in their light curves and longer rise durations) and are very similar \\nto the light curves of RRAB-type stars with periods around 0.5 d. AHB1 stars occupy the same region in the HR-diagram \\nas the anomalous cepheids (ACEP) but their light curves are different (1994A&A...283..111S). \\nSome researchers (OGLE team) have explained this saying that the AHB1 variables are fundamental mode ACEP while the other ACEP \\nwith more symmetrical light curves and smaller amplitudes are supposed to be first overtone ACEP. \\nHowever, AHB1 and ACEP show different evolutionary paths and masses. ACEP are more massive (1.3 - 2.2 solar masses), \\nthey are not post-HB stars and appear predominantly in the dwarf spheroidal galaxies, but are almost absent in \\nGalactic globular clusters, while the AHB1 stars are absent in dwarf spheroidals but are present in globular clusters \\n(2006ARA&A..44...93S). Examples: XX Vir, BF Ser'\n",
    "\n",
    "vType['AM'] = 'AM Herculis-type variables; close binary systems consisting of a dK-dM type dwarf and a superstrong magnetic white dwarf \\nprimary, in which the magnetic field of the primary not only prevents the formation of an accretion disk \\nbut also synchronizes the primarys rotation with its orbital period. \\nThey are characterized by variable linear and circular polarization of light. \\nThe total range of light variations may reach 4-5 mag. in V. Also known as polars.'\n",
    "\n",
    "vType['BE+LERI'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nBE: GCVS-type for Be stars that show variability but no light outbursts (GCAS variables). Most of them may be LERI variables.] \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d. '\n",
    "vType['BE+LERI:'] = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nA plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nBE: GCVS-type for Be stars that show variability but no light outbursts (GCAS variables). Most of them may be LERI variables.  \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d. ]'\n",
    "\n",
    "vType['BY']  = 'BY Draconis-type variables, which are emission-line dwarfs of dKe-dMe spectral type showing quasi-periodic \\nlight changes with periods from a fraction of a day to 120 days and amplitudes from several hundredths to 0.5 mag. in V.\\n The light variability is caused by axial rotation of a star with a variable degree of non-uniformity of the surface \\nbrightness (spots) and chromospheric activity. Some of these stars also show flares similar to those of UV Ceti stars, \\nand in those cases they also belong to the latter type and are simultaneously considered eruptive variables.'\n",
    "\n",
    "vType['BY+UV'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nBY Draconis-type variables, which are emission-line dwarfs of dKe-dMe spectral type showing quasi-periodic \\nlight changes with periods from a fraction of a day to 120 days and amplitudes from several hundredths to 0.5 mag. in V.\\n The light variability is caused by axial rotation of a star with a variable degree of non-uniformity of the surface \\nbrightness (spots) and chromospheric activity. Some of these stars also show flares similar to those of UV Ceti stars, \\nand in those cases they also belong to the latter type and are simultaneously considered eruptive variables.\\nUV: [UV Ceti flare stars have spectral types K or M]'\n",
    "\n",
    "vType['CEP'] = 'CEP: Cepheids. Radially pulsating, high luminosity (classes Ib-II) variables with periods in the range of 1-135 days and amplitudes \\nfrom several hundredths to 2 mag. in V (in the B band, the amplitudes are greater). Spectral type at maximum light is F; at minimum, \\nthe types are G-K. The longer the period of light variation, the later is the spectral type. \\nThe maximum of the surface-layer expansion velocity almost coinciding with maximum light. \\nThere are several subtypes (see DCEP, DCEP(B), DCEPS, DCEPS(B), CWA, CWB and ACEP). Some DCEP and CW stars are quite often called Cepheids \\nbecause it is often impossible to discriminate between them on the basis of the light curves for periods in the range 3 - 10 days. \\nHowever, these are distinct groups of entirely different objects in different evolutionary stages. One of the significant spectral differences \\nbetween W Virginis stars and Cepheids is the presence, during a certain phase interval, of hydrogen-line emission in the former \\nand of Ca II H and K emission in the latter.'\n",
    "\n",
    "vType['CV']  = 'CV: [cataclysmic variables - novae and dwarf novae in main sequence, white dwarf ]'\n",
    "vType['CV:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nCV: [cataclysmic variables - novae and dwarf novae in main sequence, white dwarf ]'\n",
    "vType['CV+E']  = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nCV+E: [cataclysmic variables - novae and dwarf novae in main sequence, white dwarf ] \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion. \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d. '\n",
    "\n",
    "vType['CWA']  = 'CWA: [W Virginis type variables with P > 8 d \\n \"https://asas-sn.osu.edu/atlas/CWA\"]'\n",
    "vType['CWB']  = 'CWB: [W Virginis type variables with P < 8 d (BL Herculis variables) \\n \"https://asas-sn.osu.edu/atlas/CWB\"]'\n",
    "\n",
    "vType['DCEP']  = 'DCEP: [Fundamental Mode Classical Cepheids: δ Cepheids or classical Cepheids,\\ntend to be brighter than the Type II Cepheids \\n \"https://asas-sn.osu.edu/atlas/DCEP\". \\nComparatively young objects that have left the main sequence and evolved into the instability strip of the Hertzsprung-Russell (H-R) diagram, \\nthey obey the well-known Cepheid period-luminosity relation and belong to the young disk population. DCEP stars are present in open clusters. \\nThey display a certain relation between the shapes of their light curves and their periods. They are fundamental mode pulsators.]'\n",
    "vType['DCEPS']  = 'DCEPS: [These are δ Cep variables having light amplitudes <0.5 mag. in V (<0.7 mag. in B) \\nand almost symmetrical light curves (M-m approx. 0.4 - 0.5 periods); as a rule, their periods do not exceed 7 days. \\nFirst Overtone Cepheid variables \\n \"https://asas-sn.osu.edu/atlas/DCEPS\"]'\n",
    "\n",
    "vType['DQ']  = 'DQ: [DQ white dwarfs variables with C and H rich atmospheres.]\\nDQ Herculis type. Magnetic cataclysmic variables with a red dwarf secondary and a white dwarf primary component that generates a magnetic field \\nweaker than the field associated with AM Herculis stars and that is not strong enough to synchronize the orbits of the rotating white dwarf \\nwith the orbital period of the system. Also known as intermediate polars (IP).'\n",
    "vType['DQ:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nDQ: [DQ white dwarfs variables with C and H rich atmospheres.]'\n",
    "\n",
    "vType['DSCT']  = 'DSCT: [Low Amplitude δ Scuti variables are pulsating variables similar to δ Cepheids. Periods (from 0.01 to 0.2 days). Their spectral type is between A0 and F5. Usually, lie on the instability strip] \\n \"https://asas-sn.osu.edu/atlas/DSCT\"'\n",
    "vType['DSCT:']  = 'DSCT: [A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nLow Amplitude δ Scuti variables are pulsating variables similar to δ Cepheids. Periods (from 0.01 to 0.2 days). Their spectral type is between A0 and F5. Usually, lie on the instability strip] \\n \"https://asas-sn.osu.edu/atlas/DSCT\"'\n",
    "\n",
    "vType['DYPer']  = 'DYPer: [CSTAR]'\n",
    "\n",
    "vType['EA']  = 'EA: [Detached Algol-type binaries. \\nEA Algol (β Persei) type systems may not have a secondary minimum.] \\n \"https://asas-sn.osu.edu/atlas/EA\" \\nβ Persei-type (Algol) eclipsing systems. \\nBinaries with spherical or slightly ellipsoidal components. \\nIt is possible to specify, for their light curves, the moments of the beginning and end of the eclipses. \\nBetween eclipses the light remains almost constant or varies insignificantly because of reflection effects, \\nslight ellipsoidality of the components, or physical variations. \\nSecondary minima may be absent. \\nAn extremely wide range of periods is observed, from 0.2 to ≥10000 days. \\nLight amplitudes are also quite different and may reach several magnitudes.'\n",
    "vType['EB']  = 'EB: [Algol-type or β Lyrae-type binaries. \\nEB (β-Lyrae) type systems possess light curves that transition smoothly between from the eclipse \\nto the out-of-eclipse phase, but have minima of significantly different depths.\\nThe majority of such eclipsing binaries have periods larger than half a day. \"https://asas-sn.osu.edu/atlas/EB\"]'\n",
    "vType['EW']  = 'EW: [W Ursae Majoris type binaries. \\nEW (W UMa) type systems have light curves with minima of similar depths and transition smoothly from the eclipse \\nto the out-of-eclipse phase.\\nComposed of two stars of similar spectral type between A and K with most of them being F or G. \\nThey have short periods, typically between 0.25 and 1 day.\\n \"https://asas-sn.osu.edu/atlas/EW\"]'\n",
    "\n",
    "vType['EW!EA'] = 'A pipe character ( | ) between two different types signifies a logical OR; the classification is uncertain and all possible types are indicated. \\nEW: [W Ursae Majoris type binaries. \\nEW (W UMa) type systems have light curves with minima of similar depths and transition smoothly from the eclipse \\nto the out-of-eclipse phase.\\nComposed of two stars of similar spectral type between A and K with most of them being F or G. \\nThey have short periods, typically between 0.25 and 1 day.\\n \"https://asas-sn.osu.edu/atlas/EW\"] \\nEA: [Detached Algol-type binaries. \\nEA Algol (β Persei) type systems may not have a secondary minimum.] \\n \"https://asas-sn.osu.edu/atlas/EA\"'\n",
    "\n",
    "vType['ELL']  = 'ELL : [ELL: Ellipsoidal variables are close binaries whose light curves do not contain an eclipse \\nbut their variability is due to the distortion of their shape from the mutual gravitational fields, \\nthus the observed light varies because of varying projected surface towards the observer.]'\n",
    "\n",
    "vType['EP']  = 'Stars showing planetary transits. Prototype: V376 Peg.'\n",
    "\n",
    "vType['GCAS']  = 'GCAS: [Gamma CASsiopeiae, a class of eruptive variable stars. Rapidly rotating early type stars. \\nGCAS variables to be eruptive irregular variables of early spectral types (O9-A0 III-Ve) with mass outflow from their equatorial zones  ]'\n",
    "vType['GCAS:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nGCAS: [Gamma CASsiopeiae, a class of eruptive variable stars. Rapidly rotating early type stars. \\nGCAS variables to be eruptive irregular variables of early spectral types (O9-A0 III-Ve) with mass outflow from their equatorial zones  ]'\n",
    "\n",
    "vType['GCAS+LERI'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nGCAS: [Gamma CASsiopeiae, a class of eruptive variable stars. Rapidly rotating early type stars. \\nGCAS variables to be eruptive irregular variables of early spectral types (O9-A0 III-Ve) with mass outflow from their equatorial zones. \\nLERI: λ Eri type variables. Be stars with the light variation caused by rotational modulation or non-radial pulsations. \\nTheir light curves are usually double-waved and with changing amplitude. Periods in the order of 0.3 - 3 d.  ]'\n",
    "\n",
    "vType['HADS']  = 'HADS: [High amplitude δ Scuti type variables]\\n \"https://asas-sn.osu.edu/atlas/HADS\"'\n",
    "\n",
    "vType['HB']  = 'HB: [HB: Heartbeat variables are binary star systems with eccentric orbits that cause \\nboth variations of stellar shapes and vibrations induced by such changes.]'\n",
    "\n",
    "vType['HMXB']  = 'High Mass X-ray Binaries. \\nSystems with a massive star (usually an O or B star, a Be star or a blue supergiant) \\nand a compact object (generally a neutron star, a black hole or a white dwarf). \\nA fraction of the stellar wind of the normal star is captured by the compact object and produces X-rays \\nas it falls onto it or onto an accretion disk thats surrounding it. \\nIn X-ray binaries, the subtypes indicate which kind of behavior the binary displays, \\nlike X-ray bursts (XB), large amplitude outbursts also in the visual (XN), reflection effect (XR) \\nor it may also inform about the objects nature, e.g.: if the compact object is a pulsar (XP).'\n",
    "\n",
    "vType['L']  = 'L: [Red irregular variables]'\n",
    "\n",
    "vType['LMXB']  = 'LMXB: Low Mass X-ray Binaries. \\nSystems where one of the components is either a black hole or a neutron star. \\nThe other, donor, component usually fills its Roche lobe and therefore transfers mass to the compact object. \\nThe donor can be a normal dwarf, a white dwarf, or an evolved star (red giant). \\nX-rays are emitted as the mass falls onto the compact object or onto an accretion disk thats surrounding it. \\nThe X-ray emission is incident upon the atmosphere of the cooler companion of the compact object and is reradiated \\nin the form of optical high-temperature radiation (reflection effect), \\nthus making that area of the cooler companions surface an earlier spectral type. \\nThese effects lead to quite a peculiar complex character of optical variability in such systems. \\nIn X-ray binaries, the subtypes indicate which kind of behavior the binary displays, \\nlike X-ray bursts (XB), large amplitude outbursts also in the visual (XN), reflection effect (XR) \\nor it may also inform about the objects nature, e.g.: if the compact object is a pulsar (XP)'\n",
    "\n",
    "vType['LSP']  = 'LSP : [LSP: Long secondary period variables are luminous red giants stars which have secondary period an order of magnitude longer than their primary. Their periods range from 200 to 1500 days.]'\n",
    "\n",
    "vType['M']  = 'M: [M (or Mira, o Ceti) variables are late type stars with periods between 80 and 1000 days.] \\n \"https://asas-sn.osu.edu/atlas/M\" \\nο (omicron) Ceti-type (Mira) variables. These are long-period variable giants with characteristic late-type emission spectra (Me, Ce, Se) \\nand light amplitudes from 2.5 to 11 mag. in V. Their periodicity is well pronounced, and the periods lie in the range between 80 and 1000 days. \\nInfrared amplitudes are usually less than in the visible and may be <2.5 mag. For example, in the K band they usually do not exceed 0.9 mag.'\n",
    "vType['M:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nM: [M (or Mira, o Ceti) variables are late type stars with periods between 80 and 1000 days.] \\n \"https://asas-sn.osu.edu/atlas/M\" \\nο (omicron) Ceti-type (Mira) variables. These are long-period variable giants with characteristic late-type emission spectra (Me, Ce, Se) \\nand light amplitudes from 2.5 to 11 mag. in V. Their periodicity is well pronounced, and the periods lie in the range between 80 and 1000 days. \\nInfrared amplitudes are usually less than in the visible and may be <2.5 mag. For example, in the K band they usually do not exceed 0.9 mag.'\n",
    "\n",
    "vType['MISC']  = 'Miscellaneous variable stars. Usually red variables (L, SR) \\nor other types of irregular stars I, BE) that can\"t be classified \\nmore specifically by the automatic analysis made by the surveys.'\n",
    "\n",
    "vType['NL+E'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nNL+E: Nova-like stars. Cataclysmic variables where the mass transfer rate is above a certain limit and their accretion disks are stable \\nbecause they are nearly fully ionized to their outer (tidal cut off) boundary and this condition suppresses dwarf nova outbursts. \\nAlso known as UX (UX Ursae Majoris stars). \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.'\n",
    "\n",
    "vType['NL/VY+E'] = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nNL/VY+E: Anti-dwarf novae. VY Sculptoris stars. They are cataclysmic binary systems with a hot (35,000-65,000 K) and luminous white dwarf \\nthat occasionally undergo fadings of more than 1 magnitude (up to several magnitudes) due to a low rate of mass transfer. \\nThese fadings might last from days to years. At maximum they vary up to 1 magnitude. They show no outbursts even though at minimum they fall \\ninto the dwarf novae instability strip. This could be caused by the possible magnetic nature of the white dwarf. \\nOrbital periods usually range between 0.12 and 0.18 d. Examples: VY Scl, MV Lyr. \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.'\n",
    "\n",
    "vType['PPN']  = 'PPN : [PPN: Protoplanetary nebulae with yellow supergiant post-AGB stars, \\nexhibiting variability that resembles the SRD variables.]'\n",
    "\n",
    "vType['PULS']  = 'Pulsating variables of unspecified type.'\n",
    "\n",
    "vType['PVTELI']  = 'PVTELI: [PVTEL: PV Telescopii are supergiants of several spectral types with hydrogen deficiency. They are divided into 3 sub-classes of different period ranges, from 0.5 to 100 days]'\n",
    "\n",
    "vType['R']  = 'R: [R: close binaries that exhibit strong reflection in their light curves \\n(re-radiation of light of the hotter star from the surface of the cooler one). \\nMost of these stars fall in the region between the main sequence and the white dwarfs; \\nsome are found in the extreme horizontal branch too.]'\n",
    "\n",
    "vType['RCB']  = 'RCB: [RCoronae Borealis stars, diverse spectral types, post- AGB stars]'\n",
    "vType['RCB:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nRCB: [RCoronae Borealis stars, diverse spectral types, post- AGB stars]'\n",
    "\n",
    "vType['roAp']  = 'roAp: Rapidly oscillating Ap variables. \\nThese are pulsating variables oscillating in high-overtone, low-degree, non-radial pressure modes. \\nPulsation periods are in the range of 0.003-0.015 days (4-21 min.), \\nwhile amplitudes of light variation caused by the pulsation are about 0.01 mag. in V. \\nThe pulsational variations are superposed on those caused by rotation. \\nGCVS type ACVO.'\n",
    "\n",
    "vType['ROT']  = 'ROT: [Spotted Variables with rotational modulation. \\nLight curves showing evidence of rotational modulation were classified as a generic rotational variable (ROT). \\n \"https://asas-sn.osu.edu/atlas/ROT\"]'\n",
    "vType['ROT:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nROT: [Spotted Variables with rotational modulation. Light curves showing evidence of rotational modulation were classified as a generic rotational variable (ROT). \\n \"https://asas-sn.osu.edu/atlas/ROT\"]'\n",
    "\n",
    "vType['RRAB']  = 'RRAB: [RR Lyrae variables with asymmetric light curves (steep ascending branches), periods from 0.3 to 1.0 days, \\nand amplitudes from 0.5 to 2 mag. in V. They are fundamental mode pulsators. \\n \"https://asas-sn.osu.edu/atlas/RRAB\"]'\n",
    "vType['RRAB:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nRRAB: [RR Lyrae variables with asymmetric light curves (steep ascending branches), periods from 0.3 to 1.0 days, \\nand amplitudes from 0.5 to 2 mag. in V. They are fundamental mode pulsators. \\n \"https://asas-sn.osu.edu/atlas/RRAB\"]'\n",
    "\n",
    "\n",
    "vType['RRC']  = 'RRC: [First Overtone RR Lyrae variables. \\nThey have symmetric and sinusoidal light curves and shorter periods than RRAB stars. \\n \"https://asas-sn.osu.edu/atlas/RRC\"]'\n",
    "vType['RRD']  = 'RRD: [Double Mode RR Lyrae variables. \\nThey are double mode pulsators, which occupy a well defined region at GBP − GRP ∼ 0.5 mag \\n \"https://asas-sn.osu.edu/atlas/RRD\"]'\n",
    "\n",
    "vType['RS']  = 'RS Canum Venaticorum-type binary systems. The primaries are usually giants from late F to late K spectral type. \\nA significant property of these systems is the presence in their spectra of strong Ca II H and K emission lines of variable intensity, \\nindicating increased chromospheric activity of the solar type. These systems are also characterized by the presence of radio and X-ray emission. \\nTheir light curves look like sine waves outside eclipses, with amplitudes and positions changing slowly with time. \\nThe presence of this wave (often called a distortion wave) is explained by differential rotation of the star, its surface being covered \\nwith groups of spots; the period of the rotation of a spot group is usually close to the period of orbital motion but still differs from it, \\nwhich is the reason for the slow change (migration) of the phases of the distortion wave minimum and maximum in the mean light curve in the case \\nof the eclipsing binaries (E/RS). The variability of the waves amplitude (which may be up to 0.5 mag. in V) is explained by the existence \\nof a long-period stellar activity cycle similar to the 11-year solar activity cycle, during which the number and total area of spots \\non the stars surface vary. Small amplitude flares are often observed too.'\n",
    "\n",
    "vType['RV']  = 'RV Tauri variables are luminous variable stars that have distinctive light variations with alternating deep and shallow minima. \\n RV Tau variables exhibit changes in luminosity which are tied to radial pulsations of their surfaces. \\nTheir changes in brightness are also correlated with changes in their spectral type. \\nWhile at their brightest, the stars have spectral types F or G. \\nAt their dimmest, their spectral types change to K or M. \\n \"https://asas-sn.osu.edu/atlas/RVA\" \\n \"https://en.wikipedia.org/wiki/RV_Tauri_variable\"'\n",
    "vType['RVA']  = 'RVA: [RV Tauri variables (Subtype A). \\nRV Tauri variables are radially pulsating supergiants that change their spectral type along with their magnitude. \\nTheir spectral type span from F–G class to K–M, depending on their phase. \\nTheir periods are longer than 30 days, with typical values between 40–50 days. \\n \"https://asas-sn.osu.edu/atlas/RVA\"]'\n",
    "vType['RVB']  = 'RVB: RV Tauri variables that periodically (with periods from 600 to 1500 days and amplitudes up to 2 mag in V) vary in mean magnitude. \\nExamples: DF Cyg, RV Tau.'\n",
    "\n",
    "vType['SDOR']  = 'SDOR : [S Doradus stars (or Luminous Blue Variables) are evolved stars characterised by \\nlarge amplitude variations of Bpec to Fpec spectral type ]'\n",
    "vType['SDOR:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain. \\nSDOR : [S Doradus stars (or Luminous Blue Variables) are evolved stars characterised by \\nlarge amplitude variations of Bpec to Fpec spectral type ]'\n",
    "\n",
    "vType['SR']  = '[SR: Semi-regular variables, which are giants or supergiants of intermediate and late spectral types showing noticeable periodicity \\nin their light changes, accompanied or sometimes interrupted by various irregularities. Periods lie in the range from 20 to >2000 days,\\n while the shapes of the light curves are rather different and variable, and the amplitudes may be from several hundredths \\nto several magnitudes (usually 1-2 mag. in V). \\n \"https://asas-sn.osu.edu/atlas/SR\"]'\n",
    "vType['SRD']  = 'SRD : [SRD: They are giants and supergiants of types earlier than SRA, SRB, and SRC, with variability periods from 40 to 1100 days.]'\n",
    "\n",
    "vType['SXARI']  = 'SXARI: [SXARI: SX Arietis are B-type chemical peculiar stars with strong magnetic fields and periods of about 1 day.]'\n",
    "vType['SXARI:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nSXARI: [SXARI: SX Arietis are B-type chemical peculiar stars with strong magnetic fields and periods of about 1 day.]'\n",
    "\n",
    "vType['SXPHE']  = 'SXPHE: [SXPHE: SX Phoenicis are considered similar to δScuti stars that are sub-dwarfs with periods typically in the lower part of the DSCT range.]'\n",
    "\n",
    "vType['UG']  = 'UG: [U Geminorum, a class of cataclysmic variable stars (also known as dwarf novae)]'\n",
    "\n",
    "vType['UGER']  = 'UGER: ER Ursae Majoris-type subclass of UGSU dwarf novae. \\nThese stars typically spend a third of their time in super-outburst with a super-cycle of 20-90 days. \\nOutside of super-outburst they typically pack in a rapid succession of normal outbursts. \\nThe period given in VSX is usually the orbital period. \\nOutburst cycles are given between parentheses. \\nAmplitudes are smaller (around 3 mag.) than in other dwarf novae due to a higher mass transfer rate.'\n",
    "\n",
    "vType['UGSS']  = 'UGSS: [UG SS Cygni, a subclass of UG-type stars]'\n",
    "\n",
    "vType['UGSU']  = 'UGSU: [UG SU Ursae Majoris, a subclass of UG-type stars]'\n",
    "vType['UGSU+E']  = 'A plus character ( + ) signifies that the spectral types of the individual components in a multiple system are shown.\\nUGSU: [UG SU Ursae Majoris, a subclass of UG-type stars. \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.]'\n",
    "\n",
    "vType['UGZ']  = 'UGZ: Z Camelopardalis-type stars. \\nThese also show cyclic outbursts, differing from UGSS variables by the fact that sometimes after an outburst \\nthey do not return to the original brightness, \\nbut during several cycles retain a magnitude between maximum and minimum. \\nThe values of cycles are from 10 to 40 days, while light amplitudes are from 2 to 5 mag. in V. \\nThe period given in VSX is usually the orbital period. \\nOutburst cycles are given between parentheses.'\n",
    "\n",
    "vType['UV']  = 'UV: [UV Ceti flare stars have spectral types K or M]'\n",
    "vType['UV:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nUV: [UV Ceti flare stars have spectral types K or M]'\n",
    "\n",
    "vType['V361HYA']  = 'V361HYA: [Concentrated in the extreme horizontal branch. V361 Hya stars) can be redder than the main clump,\\nbut they follow the blue horizontal branch (BHB). Most of V361 Hya stars are hotter (with effective temperatures \\nin 28000–35000 K) than V1093 Her (23000–30000K), so the two populations are not distinct and overlap ]'\n",
    "vType['V838MON']  = 'V838MON: Luminous red transients of the V838 Monocerotis type \\nthat become redder in outburst and are thought to be the result of the merger of a contact binary. \\nAfter the outburst they get hidden in a dusty disk. \\nThe amplitudes are 9-13 mag. Also known as Luminous Red Novae (LNR).Examples: V838 Mon, V1309 Sco, V4332 Sgr.'\n",
    "vType['V1093HER']  = 'V1093HER: [Concentrated in the extreme horizontal branch. They follow the blue horizontal branch (BHB). \\nMost of V361 Hya stars are hotter (with effective temperatures in 28000–35000 K) than V1093 Her (23000–30000K), \\nso the two populations are not distinct and overlap ]'\n",
    "\n",
    "vType['VAR']  = 'VAR: [Variable star of unspecified type]'\n",
    "\n",
    "vType['E/WR']  = 'WR: [Wolf–Rayet, a type of hot, luminous star with strong stellar winds. \\nWR: Wolf-Rayet is a group of massive stars that present broad emission lines.\\nThey have high temperatures and luminosities and considered as descendants of O-type stars. \\nTheir variability is not periodic \\n \"https://en.wikipedia.org/wiki/Wolf–Rayet_star\" \\nE: Eclipsing binary systems. These are binary systems with orbital planes so close to the observers line of sight \\n(the inclination of the orbital plane to the plane orthogonal to the line of sight is close to 90 deg.) that the components \\nperiodically eclipse each other. Consequently, the observer finds changes of the apparent combined brightness of the system with \\nthe period coincident with that of the components orbital motion.]'\n",
    "vType['WR'] = 'WR: Eruptive Wolf-Rayet variables. Stars with broad emission features of He I and He II as well as C II-C IV, O II-O IV, and N III-N V. \\nThey display irregular light changes with amplitudes up to 0.1 mag. in V, which are probably caused by physical processes, \\nin particular, by non-stable mass outflow from their atmospheres.'\n",
    "vType['WR:']  = 'A colon ( : ) after the variability type -or any other field- means the value/classification is uncertain.\\nWR: [Wolf–Rayet, a type of hot, luminous star with strong stellar winds. \\nWR: Wolf-Rayet is a group of massive stars that present broad emission lines.\\nThey have high temperatures and luminosities and considered as descendants of O-type stars. \\nTheir variability is not periodic \\n \"https://en.wikipedia.org/wiki/Wolf–Rayet_star\"]'\n",
    "\n",
    "vType['YSO']  = 'YSO: [Young Stellar Objects]'\n",
    "\n",
    "vType['ZAND']  = 'ZAND: [Z Andromedae stars, binary variables stars composed of a giant and a white dwarf. \\nThey display irregular variability with large amplitudes]'\n",
    "\n",
    "vType['ZZ']  = 'ZZ: [ZZ: These are generic ZZCeti without detailed class]'\n",
    "\n",
    "vType['ZZA']  = 'ZZA: [ZZA: ZZA (or DAV) are classical ZZ Ceti stars with DA spectral type with H atmospheres. ]'\n",
    "\n",
    "vType['ZZB']  = 'ZZB: [V777HER: The V777 Herculis (or ZZB, DBV) are stars with He-rich atmospheres and their periods \\nrange between 100 and 1400 s, grouping in the WD sequence between the warmer GWVIR and the cooler ZZA.]'\n",
    "\n",
    "vType['ZZLep']  = 'ZZLep: [ZZLEP: ZZ Leporis stars are central stars of planetary nebulae that exhibit \\nphotometric variations. They are O-type stars with periods that range from hours to days.]'\n",
    "\n",
    "vType['ZZO']  = 'ZZO: [GWVIR: GW Virginis (ZZO,DOV,PG1159) stars are a subtype of ZZ Ceti \\nwith absorption lines of HeII and CIV, and it’s the hottest known type of pulsating WD and pre-white dwarfs.]'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2860c4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ASASSN_Variable_Type = vType['ROT']\n",
    "\n",
    "ASASSN_Variable_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73a702d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b204e9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_blue_filter_variable_type = np.unique(df_asassn_blue_filter_match['ML_classification'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c5f05af",
   "metadata": {},
   "outputs": [],
   "source": [
    "for vartype in unique_blue_filter_variable_type:\n",
    "    v = df_asassn_blue_filter_match[df_asassn_blue_filter_match['ML_classification'] == vartype]\n",
    "    print(str(vartype) + \"  count: \"  + str(v.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2b0426",
   "metadata": {},
   "source": [
    "https://www.aavso.org/vsx/index.php?view=about.vartypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9273e59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "vartype = 'CWA'\n",
    "blue_filter_match = df_asassn_blue_filter_match[df_asassn_blue_filter_match['ML_classification'] == vartype]\n",
    "df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(blue_filter_match['gaia_source_id'].astype(str))]  \n",
    "\n",
    "print(\"df_asassn: \" + str(len(df_asassn)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17d05daa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c2adf93",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_v_filter_variable_type = np.unique(df_asassn_v_filter_match['variable_type'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c291740",
   "metadata": {},
   "outputs": [],
   "source": [
    "for vartype in unique_v_filter_variable_type:\n",
    "    v = df_asassn_v_filter_variables[df_asassn_v_filter_variables['variable_type'] == vartype]\n",
    "    print(str(vartype) + \"  count: \"  + str(v.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11721569",
   "metadata": {},
   "source": [
    "https://www.aavso.org/vsx/index.php?view=about.vartypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "140bee71",
   "metadata": {},
   "outputs": [],
   "source": [
    "vartype = 'EA'\n",
    "v_filter_match = df_asassn_v_filter_match[df_asassn_v_filter_match['variable_type'] == vartype]\n",
    "df_asassn = df_asassn_all[df_asassn_all['gaia_source_id'].astype(str).isin(v_filter_match['gaia_source_id'].astype(str))]  \n",
    "\n",
    "print(\"df_asassn: \" + str(len(df_asassn)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e3d1abc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c5ca3798",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_aavsovsx_variable_type = np.unique(df_asassn_sn_aavsovsx_all['variability_type'].astype(str)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1e66fcac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*  count: 18\n",
      "ACEP  count: 43\n",
      "ACEP!CEP  count: 95\n",
      "ACEP+EA  count: 1\n",
      "ACEP:  count: 26\n",
      "ACV  count: 1359\n",
      "ACV!CW-FO  count: 3\n",
      "ACV!CW-FO!EC!ESD  count: 1\n",
      "ACV!CW-FU  count: 2\n",
      "ACV!CW-FU!CW-FO  count: 1\n",
      "ACV!DCEP-FO  count: 1\n",
      "ACV!DCEP-FO!EC  count: 1\n",
      "ACV!DCEP-FO!EC!ESD  count: 2\n",
      "ACV!DCEP-FU!EC!ESD  count: 2\n",
      "ACV!DCEP-FU!ESD!ED  count: 1\n",
      "ACV!EB  count: 3\n",
      "ACV!EC  count: 2\n",
      "ACV!EC!ESD  count: 14\n",
      "ACV!ELL  count: 2\n",
      "ACV!ESD  count: 1\n",
      "ACV!RRAB!CW-FO!EC!ESD!ED  count: 1\n",
      "ACV!RRAB!EC!ESD  count: 2\n",
      "ACV!SPB  count: 1\n",
      "ACV+DSCT:  count: 1\n",
      "ACV+ELL:  count: 1\n",
      "ACV+R  count: 1\n",
      "ACV+roAp  count: 1\n",
      "ACV:  count: 134\n",
      "ACV:+DSCT  count: 1\n",
      "ACVO  count: 1\n",
      "ACYG  count: 51\n",
      "ACYG:  count: 32\n",
      "AHB1  count: 207\n",
      "AHB1/BL  count: 1\n",
      "AHB1:  count: 23\n",
      "AM  count: 86\n",
      "AM!DQ  count: 1\n",
      "AM!DQ:  count: 1\n",
      "AM+E  count: 34\n",
      "AM+E:  count: 1\n",
      "AM+ELL  count: 1\n",
      "AM:  count: 7\n",
      "AM:+E  count: 3\n",
      "BCEP  count: 241\n",
      "BCEP!DSCT  count: 9\n",
      "BCEP!DSCT!EC!ESD  count: 1\n",
      "BCEP!DSCT!EC!ESD!ED  count: 1\n",
      "BCEP+EA  count: 1\n",
      "BCEP+ELL:  count: 2\n",
      "BCEP+SPB  count: 4\n",
      "BCEP+SPB:  count: 1\n",
      "BCEP:  count: 45\n",
      "BCEPS  count: 2\n",
      "BE  count: 168\n",
      "BE+BCEP:  count: 1\n",
      "BE+HMXB/XP  count: 1\n",
      "BE+LERI  count: 3\n",
      "BE+LERI:  count: 2\n",
      "BE+R:  count: 1\n",
      "BE:  count: 10\n",
      "BLAP  count: 14\n",
      "BXCIR  count: 2\n",
      "BY  count: 2983\n",
      "BY!EB  count: 1\n",
      "BY!EW  count: 1\n",
      "BY!LMXB!CV  count: 1\n",
      "BY+BY+UV  count: 3\n",
      "BY+E:  count: 1\n",
      "BY+UV  count: 410\n",
      "BY+UV:  count: 5\n",
      "BY+UVN  count: 10\n",
      "BY+ZZA/O:  count: 1\n",
      "BY:  count: 182\n",
      "BY:+UV  count: 1\n",
      "BY:+UV:  count: 2\n",
      "CBSS  count: 1\n",
      "CBSS+E  count: 1\n",
      "CBSS/V+E  count: 3\n",
      "CEP  count: 720\n",
      "CEP!ELL  count: 2\n",
      "CEP!LPV  count: 5\n",
      "CEP!PER  count: 1\n",
      "CEP!RV!PER  count: 1\n",
      "CEP:  count: 24\n",
      "CTTS/ROT  count: 91\n",
      "CTTS/ROT+CTTS  count: 1\n",
      "CTTS/ROT+UV  count: 1\n",
      "CTTS/ROT+UXOR  count: 1\n",
      "CTTS/ROT:  count: 3\n",
      "CV  count: 38\n",
      "CV!LMXB  count: 1\n",
      "CV!LMXB!RS:  count: 1\n",
      "CV!PULS  count: 10\n",
      "CV+E  count: 23\n",
      "CV+ZZ/GWLIB  count: 1\n",
      "CV:  count: 11\n",
      "CW  count: 2\n",
      "CW!ROT  count: 1\n",
      "CW-FO  count: 14\n",
      "CW-FO!ACV  count: 2\n",
      "CW-FO!ACV!EC!ESD  count: 1\n",
      "CW-FO!CW-FU  count: 3\n",
      "CW-FO!CW-FU!EC  count: 1\n",
      "CW-FO!CW-FU!EC!ESD  count: 3\n",
      "CW-FO!EC  count: 3\n",
      "CW-FO!EC!ESD  count: 2\n",
      "CW-FO!EC!ESD!ED  count: 1\n",
      "CW-FU  count: 64\n",
      "CW-FU!CW-FO  count: 6\n",
      "CW-FU!CW-FO!ACV!EC!ESD  count: 1\n",
      "CW-FU!CW-FO!EC!ESD  count: 3\n",
      "CW-FU!CW-FO!EC!ESD!ED  count: 1\n",
      "CW-FU!CW-FO!ESD!ED  count: 1\n",
      "CW-FU!EC  count: 11\n",
      "CW-FU!EC!ESD  count: 30\n",
      "CW-FU!EC!ESD!ED  count: 1\n",
      "CW-FU!EC!ESD!SR  count: 1\n",
      "CW-FU!ESD  count: 4\n",
      "CW-FU!ESD!ED  count: 1\n",
      "CW:  count: 2\n",
      "CWA  count: 676\n",
      "CWA:  count: 16\n",
      "CWB  count: 904\n",
      "CWB(B)  count: 4\n",
      "CWB+EA  count: 1\n",
      "CWB+HB  count: 1\n",
      "CWB:  count: 27\n",
      "DCEP  count: 2279\n",
      "DCEP!CV  count: 1\n",
      "DCEP!EA  count: 46\n",
      "DCEP!EW  count: 1\n",
      "DCEP!PULS  count: 9\n",
      "DCEP(B)  count: 58\n",
      "DCEP+DCEP  count: 1\n",
      "DCEP+EA  count: 1\n",
      "DCEP-FO  count: 33\n",
      "DCEP-FO!ACV  count: 2\n",
      "DCEP-FO!ACV!EC!ESD  count: 2\n",
      "DCEP-FO!DCEP-FU  count: 6\n",
      "DCEP-FO!DCEP-FU!EC!ESD  count: 5\n",
      "DCEP-FO!DCEP-FU!EC!ESD!ED  count: 1\n",
      "DCEP-FO!DCEP-FU!ESD  count: 1\n",
      "DCEP-FO!DCEP-FU!ESD!ELL  count: 1\n",
      "DCEP-FO!EC  count: 4\n",
      "DCEP-FO!EC!ELL  count: 1\n",
      "DCEP-FO!EC!ESD  count: 16\n",
      "DCEP-FO!EC!ESD!ELL  count: 2\n",
      "DCEP-FO!EC!SR  count: 1\n",
      "DCEP-FO!EC:  count: 1\n",
      "DCEP-FO!ESD  count: 1\n",
      "DCEP-FO!MISC  count: 1\n",
      "DCEP-FO!RRAB  count: 4\n",
      "DCEP-FO!RRAB!EC!ESD  count: 6\n",
      "DCEP-FO!RRAB:  count: 1\n",
      "DCEP-FO:  count: 1\n",
      "DCEP-FU  count: 108\n",
      "DCEP-FU!ACV  count: 1\n",
      "DCEP-FU!DCEP-FO  count: 6\n",
      "DCEP-FU!DCEP-FO!EC  count: 2\n",
      "DCEP-FU!DCEP-FO!EC!ESD  count: 4\n",
      "DCEP-FU!DCEP-FO!ESD  count: 1\n",
      "DCEP-FU!EC  count: 11\n",
      "DCEP-FU!EC!ESD  count: 33\n",
      "DCEP-FU!EC!ESD!ED  count: 3\n",
      "DCEP-FU!EC!ESD!ELL  count: 1\n",
      "DCEP-FU!EC!ESD!SR  count: 8\n",
      "DCEP-FU!EC!SR  count: 2\n",
      "DCEP-FU!ESD  count: 14\n",
      "DCEP-FU!ESD!ED  count: 6\n",
      "DCEP-FU!ESD!ED!MISC  count: 1\n",
      "DCEP-FU!ESD!ELL  count: 1\n",
      "DCEP-FU!ESD!SR  count: 1\n",
      "DCEP-FU!SR  count: 7\n",
      "DCEP-FU:  count: 11\n",
      "DCEP:  count: 43\n",
      "DCEPS  count: 675\n",
      "DCEPS(B)  count: 97\n",
      "DCEPS+DCEPS  count: 1\n",
      "DCEPS+EA  count: 1\n",
      "DCEPS:  count: 15\n",
      "DPV/EA  count: 13\n",
      "DPV/EB  count: 1\n",
      "DPV/ELL  count: 15\n",
      "DPV:/EA  count: 2\n",
      "DPV:/EB  count: 1\n",
      "DPV:/EB/GS  count: 1\n",
      "DQ  count: 50\n",
      "DQ!AM  count: 1\n",
      "DQ+E  count: 3\n",
      "DQ+E:  count: 1\n",
      "DQ+ELL  count: 1\n",
      "DQ+R  count: 1\n",
      "DQ:  count: 7\n",
      "DQ:+E  count: 1\n",
      "DSCT  count: 5813\n",
      "DSCT!BCEP  count: 7\n",
      "DSCT!BCEP!EC  count: 1\n",
      "DSCT!BCEP!EC!ESD  count: 4\n",
      "DSCT!BCEP!EC!ESD!ED  count: 2\n",
      "DSCT!BCEP!ESD!ED  count: 1\n",
      "DSCT!DSCTr  count: 9\n",
      "DSCT!DSCTr!EC  count: 4\n",
      "DSCT!DSCTr!EC!ESD  count: 7\n",
      "DSCT!E  count: 1\n",
      "DSCT!EC  count: 165\n",
      "DSCT!EC!ED  count: 2\n",
      "DSCT!EC!ELL  count: 2\n",
      "DSCT!EC!ESD  count: 128\n",
      "DSCT!EC!ESD!ED  count: 6\n",
      "DSCT!EC!ESD!ELL  count: 2\n",
      "DSCT!EC:  count: 2\n",
      "DSCT!ED  count: 1\n",
      "DSCT!ELL  count: 1\n",
      "DSCT!ESD  count: 3\n",
      "DSCT!ESD!ED  count: 3\n",
      "DSCT!EW  count: 9\n",
      "DSCT!R  count: 1\n",
      "DSCT!RRC  count: 1\n",
      "DSCT!RRC!BCEP!EC!ESD  count: 1\n",
      "DSCT!RRC!DSCTr!EC  count: 1\n",
      "DSCT!RRC!EC  count: 4\n",
      "DSCT!RRC!EC!ESD  count: 12\n",
      "DSCT!RRC!EC!ESD!ED  count: 1\n",
      "DSCT!RRc  count: 1\n",
      "DSCT+EA  count: 1\n",
      "DSCT+EP:  count: 1\n",
      "DSCT+GDOR  count: 205\n",
      "DSCT+NSIN ELL  count: 1\n",
      "DSCT:  count: 201\n",
      "DSCTC  count: 310\n",
      "DSCTC+E:  count: 1\n",
      "DSCTC+ELL  count: 2\n",
      "DSCTC:  count: 67\n",
      "DSCTr  count: 17\n",
      "DSCTr!DSCT  count: 6\n",
      "DSCTr!DSCT!EC  count: 3\n",
      "DSCTr!DSCT!EC!ESD  count: 6\n",
      "DSCTr!EC  count: 11\n",
      "DSCTr!EC!ESD  count: 10\n",
      "DSCTr!EC!ESD!ED  count: 1\n",
      "DSCTr!ESD  count: 1\n",
      "DSCTr!ESD!ED  count: 1\n",
      "DSCTr!RRC!DSCT!EC!ESD  count: 1\n",
      "DSCTr!RRC!EC!ESD  count: 1\n",
      "DWLYN  count: 1\n",
      "DYPer  count: 4\n",
      "DYPer:  count: 1\n",
      "E  count: 338128\n",
      "E+BE  count: 1\n",
      "E+E  count: 23\n",
      "E+EA  count: 2\n",
      "E+NL  count: 2\n",
      "E+PULS  count: 1\n",
      "E+PVTELI  count: 1\n",
      "E+R  count: 1\n",
      "E+RS:  count: 1\n",
      "E+SR  count: 72\n",
      "E+TTS  count: 1\n",
      "E+UV  count: 1\n",
      "E+YSO  count: 1\n",
      "E+ZZA  count: 1\n",
      "E/D  count: 2\n",
      "E/D/WR  count: 1\n",
      "E/D:  count: 1\n",
      "E/DM  count: 6\n",
      "E/DM:  count: 1\n",
      "E/DS  count: 2\n",
      "E/DS:  count: 1\n",
      "E/DW  count: 2\n",
      "E/GS  count: 1\n",
      "E/GS+GCAS  count: 1\n",
      "E/KE  count: 8\n",
      "E/KE:  count: 1\n",
      "E/KW  count: 1\n",
      "E/KW:  count: 6\n",
      "E/PSR  count: 1\n",
      "E/RS  count: 141\n",
      "E/RS:  count: 2\n",
      "E/SD  count: 62\n",
      "E/SD:  count: 41\n",
      "E/WD  count: 3\n",
      "E/WD+NL  count: 1\n",
      "E/WR  count: 3\n",
      "E/WR+E:  count: 1\n",
      "E:  count: 95\n",
      "E:+NL  count: 1\n",
      "E:+SXARI  count: 1\n",
      "E:+UV  count: 2\n",
      "E:/RS  count: 12\n",
      "E:/RS:  count: 1\n",
      "E:/WR  count: 2\n",
      "EA  count: 48663\n",
      "EA!EB  count: 605\n",
      "EA!ROT  count: 1\n",
      "EA+ACEP  count: 1\n",
      "EA+ACV  count: 3\n",
      "EA+ACYG  count: 1\n",
      "EA+BCEP  count: 7\n",
      "EA+BCEP:  count: 2\n",
      "EA+BE  count: 5\n",
      "EA+BY  count: 31\n",
      "EA+BY+BY  count: 1\n",
      "EA+BY+EP  count: 1\n",
      "EA+BY+UV  count: 10\n",
      "EA+CV:  count: 4\n",
      "EA+DSCT  count: 59\n",
      "EA+DSCT+EA+EA  count: 1\n",
      "EA+DSCT+GDOR  count: 1\n",
      "EA+DSCT:  count: 3\n",
      "EA+DSCTC  count: 3\n",
      "EA+DSCTC:  count: 1\n",
      "EA+E  count: 2\n",
      "EA+EA  count: 10\n",
      "EA+EA+UV  count: 1\n",
      "EA+EB  count: 1\n",
      "EA+EC  count: 1\n",
      "EA+ELL  count: 2\n",
      "EA+ELL/RS  count: 1\n",
      "EA+ELL:  count: 1\n",
      "EA+EP  count: 1\n",
      "EA+GCAS  count: 1\n",
      "EA+GDOR  count: 12\n",
      "EA+GDOR+DSCT  count: 1\n",
      "EA+HADS  count: 2\n",
      "EA+HB  count: 16\n",
      "EA+HB:  count: 1\n",
      "EA+INT  count: 1\n",
      "EA+NL  count: 2\n",
      "EA+PULS  count: 5\n",
      "EA+R  count: 1\n",
      "EA+ROT  count: 2\n",
      "EA+ROT+EA  count: 1\n",
      "EA+ROT+EP  count: 1\n",
      "EA+RRAB  count: 1\n",
      "EA+RRC  count: 1\n",
      "EA+SDOR  count: 1\n",
      "EA+SPB  count: 7\n",
      "EA+SPB:  count: 2\n",
      "EA+SR  count: 1\n",
      "EA+SRB  count: 2\n",
      "EA+SRD:  count: 1\n",
      "EA+TTS  count: 1\n",
      "EA+TTS/ROT  count: 1\n",
      "EA+UV  count: 10\n",
      "EA+UV+BY  count: 1\n",
      "EA+V1093HER  count: 1\n",
      "EA+ZAND+BE  count: 1\n",
      "EA+ZAND:  count: 3\n",
      "EA-BLEND  count: 1\n",
      "EA/AR  count: 7\n",
      "EA/AR/RS  count: 7\n",
      "EA/AR:  count: 4\n",
      "EA/AR:/RS  count: 2\n",
      "EA/D  count: 117\n",
      "EA/D+BY  count: 1\n",
      "EA/D/RS  count: 6\n",
      "EA/D/WR  count: 1\n",
      "EA/D:  count: 14\n",
      "EA/D:/PN  count: 1\n",
      "EA/D:/RS  count: 1\n",
      "EA/DM  count: 250\n",
      "EA/DM+ACV  count: 1\n",
      "EA/DM+DSCT  count: 3\n",
      "EA/DM+RS  count: 1\n",
      "EA/DM+UV  count: 2\n",
      "EA/DM/RS  count: 2\n",
      "EA/DM:  count: 32\n",
      "EA/DS  count: 121\n",
      "EA/DS/RS  count: 2\n",
      "EA/DS:  count: 28\n",
      "EA/DW  count: 6\n",
      "EA/DW+GDOR  count: 1\n",
      "EA/DW/RS:  count: 1\n",
      "EA/EL  count: 63\n",
      "EA/GS  count: 37\n",
      "EA/GS+L:  count: 1\n",
      "EA/GS+SR  count: 1\n",
      "EA/GS+SRC  count: 3\n",
      "EA/GS+SRD  count: 1\n",
      "EA/GS/D  count: 5\n",
      "EA/GS/RS  count: 3\n",
      "EA/GS/RS:  count: 1\n",
      "EA/GS/WR+SDOR  count: 1\n",
      "EA/GS:  count: 11\n",
      "EA/HW  count: 67\n",
      "EA/HW+V361HYA  count: 2\n",
      "EA/K  count: 1\n",
      "EA/K:  count: 1\n",
      "EA/KE  count: 65\n",
      "EA/KE:  count: 55\n",
      "EA/KW  count: 2\n",
      "EA/KW:  count: 1\n",
      "EA/PN  count: 1\n",
      "EA/RS  count: 291\n",
      "EA/RS+EA  count: 2\n",
      "EA/RS:  count: 17\n",
      "EA/SD  count: 913\n",
      "EA/SD+DSCT  count: 17\n",
      "EA/SD+SXPHE  count: 1\n",
      "EA/SD/RS  count: 2\n",
      "EA/SD/RS:  count: 1\n",
      "EA/SD:  count: 276\n",
      "EA/WD  count: 60\n",
      "EA/WD+BY  count: 1\n",
      "EA/WD+NL  count: 5\n",
      "EA/WD+R  count: 1\n",
      "EA/WD+UV  count: 1\n",
      "EA/WD+ZZ  count: 1\n",
      "EA/WD/RS  count: 1\n",
      "EA/WR  count: 5\n",
      "EA:  count: 287\n",
      "EA:+HB  count: 2\n",
      "EA:/KE:  count: 1\n",
      "EA:/RS  count: 2\n",
      "EB  count: 11268\n",
      "EB!EA  count: 7\n",
      "EB!EW  count: 3425\n",
      "EB!PULS  count: 12\n",
      "EB!ROT  count: 2\n",
      "EB+BE:  count: 1\n",
      "EB+DSCT  count: 2\n",
      "EB+EA  count: 1\n",
      "EB+ELL  count: 1\n",
      "EB+GCAS  count: 1\n",
      "EB+SDOR:  count: 2\n",
      "EB+UG  count: 1\n",
      "EB/AR  count: 3\n",
      "EB/AR/RS  count: 1\n",
      "EB/AR:  count: 2\n",
      "EB/D  count: 13\n",
      "EB/D/GS  count: 1\n",
      "EB/D:  count: 4\n",
      "EB/DM  count: 56\n",
      "EB/DM/WR  count: 1\n",
      "EB/DM:  count: 7\n",
      "EB/DS  count: 1\n",
      "EB/DW  count: 5\n",
      "EB/DW/RS  count: 1\n",
      "EB/GS  count: 10\n",
      "EB/GS+ACYG  count: 1\n",
      "EB/GS+LB  count: 1\n",
      "EB/GS/D  count: 1\n",
      "EB/GS/SD:  count: 1\n",
      "EB/GS:  count: 4\n",
      "EB/K  count: 2\n",
      "EB/K:  count: 1\n",
      "EB/KE  count: 157\n",
      "EB/KE:  count: 24\n",
      "EB/KW  count: 16\n",
      "EB/KW:  count: 5\n",
      "EB/RS  count: 16\n",
      "EB/SD  count: 46\n",
      "EB/SD:  count: 17\n",
      "EB/WR  count: 1\n",
      "EB:  count: 314\n",
      "EB:+SR  count: 1\n",
      "EB:+TTS/DIP  count: 1\n",
      "EB:/GS  count: 1\n",
      "EB:/GS:  count: 1\n",
      "EB:/KE  count: 1\n",
      "EB:/KE:  count: 1\n",
      "EB:/RS  count: 2\n",
      "EB:/SD  count: 1\n",
      "EC  count: 94381\n",
      "EC!ACV!ESD  count: 6\n",
      "EC!BCEP!DSCT!DSCTr!ESD  count: 4\n",
      "EC!CW-FO  count: 3\n",
      "EC!CW-FO!ACV!ESD  count: 6\n",
      "EC!CW-FO!ESD  count: 4\n",
      "EC!CW-FU  count: 3\n",
      "EC!CW-FU!CW-FO!ESD  count: 3\n",
      "EC!CW-FU!ESD  count: 8\n",
      "EC!CW-FU!ESD!SR  count: 2\n",
      "EC!DCEP-FO  count: 5\n",
      "EC!DCEP-FO!ESD  count: 39\n",
      "EC!DCEP-FO!ESD!MISC  count: 2\n",
      "EC!DCEP-FO!ESD:  count: 1\n",
      "EC!DCEP-FO!MISC  count: 1\n",
      "EC!DCEP-FU  count: 11\n",
      "EC!DCEP-FU!DCEP-FO  count: 1\n",
      "EC!DCEP-FU!DCEP-FO!ESD  count: 5\n",
      "EC!DCEP-FU!ESD  count: 31\n",
      "EC!DCEP-FU!ESD!SR  count: 3\n",
      "EC!DSCT  count: 143\n",
      "EC!DSCT!DSCTr  count: 14\n",
      "EC!DSCT!DSCTr!ESD  count: 16\n",
      "EC!DSCT!ELL  count: 1\n",
      "EC!DSCT!ESD  count: 157\n",
      "EC!DSCT!MISC  count: 2\n",
      "EC!DSCTr  count: 43\n",
      "EC!DSCTr!ESD  count: 27\n",
      "EC!ELL!SR  count: 1\n",
      "EC!ESD  count: 817\n",
      "EC!ESD!ELL  count: 1\n",
      "EC!ESD!ELL!SR  count: 1\n",
      "EC!ESD!MISC  count: 3\n",
      "EC!ESD!RS  count: 1\n",
      "EC!ESD!SR  count: 5\n",
      "EC!ESD:  count: 1\n",
      "EC!MISC  count: 1\n",
      "EC!RRAB!DCEP-FO!ESD  count: 4\n",
      "EC!RRAB!ESD  count: 6\n",
      "EC!RRAB!RRC!ESD  count: 3\n",
      "EC!RRC  count: 161\n",
      "EC!RRC!BCEP!DSCT!ESD  count: 3\n",
      "EC!RRC!DSCT  count: 33\n",
      "EC!RRC!DSCT!DSCTr  count: 2\n",
      "EC!RRC!DSCT!DSCTr!ESD  count: 4\n",
      "EC!RRC!DSCT!ESD  count: 26\n",
      "EC!RRC!DSCTr  count: 1\n",
      "EC!RRC!DSCTr!ESD  count: 1\n",
      "EC!RRC!ELL  count: 3\n",
      "EC!RRC!ESD  count: 187\n",
      "EC!RRC!ESD!ELL  count: 1\n",
      "EC!RRC!ESD!ELL!SR  count: 1\n",
      "EC!RRC!ESD:  count: 1\n",
      "EC!SR  count: 3\n",
      "EC+BY  count: 1\n",
      "EC+E  count: 3\n",
      "EC+EA  count: 5\n",
      "EC+EC  count: 1\n",
      "EC:  count: 11\n",
      "ECL!PULS!ROT  count: 3\n",
      "ED  count: 1231\n",
      "ED!ACV!ESD  count: 1\n",
      "ED!CW-FU!ACV!EC!ESD  count: 1\n",
      "ED!DCEP-FU  count: 1\n",
      "ED!DCEP-FU!ESD  count: 2\n",
      "ED!DSCT!EC!ESD  count: 1\n",
      "ED!DSCT!ESD  count: 1\n",
      "ED!ESD  count: 229\n",
      "ED!ESD!MISC  count: 1\n",
      "ED!ESD!SR  count: 1\n",
      "ED!ESD:  count: 1\n",
      "ED!MISC  count: 2\n",
      "ED:  count: 13\n",
      "ELL  count: 25438\n",
      "ELL!BY  count: 3\n",
      "ELL!EB  count: 1\n",
      "ELL!EW  count: 2\n",
      "ELL!ROT  count: 14\n",
      "ELL!ROT!GDOR+DSCT  count: 1\n",
      "ELL!SPB  count: 1\n",
      "ELL+BCEP  count: 1\n",
      "ELL+BCEP:  count: 1\n",
      "ELL+DSCT  count: 9\n",
      "ELL+DSCTC  count: 1\n",
      "ELL+EA/SD  count: 1\n",
      "ELL+HMXB  count: 1\n",
      "ELL+IMXB  count: 1\n",
      "ELL+L  count: 1\n",
      "ELL+LB  count: 1\n",
      "ELL+R  count: 1\n",
      "ELL+RRAB  count: 1\n",
      "ELL+RS  count: 1\n",
      "ELL+SPB+DSCT  count: 1\n",
      "ELL+SR  count: 508\n",
      "ELL+SR+ZAND:  count: 1\n",
      "ELL+SR:  count: 1\n",
      "ELL+UV  count: 2\n",
      "ELL+V361HYA  count: 1\n",
      "ELL+ZAND  count: 1\n",
      "ELL+ZAND:  count: 1\n",
      "ELL/DM  count: 2\n",
      "ELL/DW:  count: 1\n",
      "ELL/KE  count: 1\n",
      "ELL/PN  count: 2\n",
      "ELL/RS  count: 8\n",
      "ELL/RS/BY  count: 11\n",
      "ELL/WD  count: 2\n",
      "ELL/WR  count: 1\n",
      "ELL:  count: 125\n",
      "ELL:+DSCT  count: 1\n",
      "ELL:+DSCTC  count: 1\n",
      "ELL:+UV  count: 1\n",
      "ELL:+WR  count: 1\n",
      "EP  count: 379\n",
      "EP+BY  count: 9\n",
      "EP+BY+UV  count: 1\n",
      "EP+DSCT  count: 1\n",
      "EP+GDOR  count: 1\n",
      "EP+HB  count: 1\n",
      "EP+ROT  count: 4\n",
      "EP+ROT:  count: 1\n",
      "EP+UV  count: 1\n",
      "EP:  count: 173\n",
      "ESD  count: 455\n",
      "ESD!ACV!EC  count: 6\n",
      "ESD!ACV!ED  count: 1\n",
      "ESD!BCEP!DSCT  count: 1\n",
      "ESD!BCEP!DSCT!DSCTr!EC  count: 3\n",
      "ESD!BCEP!DSCT!EC  count: 3\n",
      "ESD!BCEP!DSCT!EC!ED  count: 1\n",
      "ESD!CW-FO!ACV!EC  count: 1\n",
      "ESD!CW-FO!EC  count: 6\n",
      "ESD!CW-FU!CW-FO!ACV!EC  count: 1\n",
      "ESD!CW-FU!CW-FO!ACV!EC!ED  count: 1\n",
      "ESD!CW-FU!CW-FO!EC  count: 4\n",
      "ESD!CW-FU!EC  count: 6\n",
      "ESD!CW-FU!EC!ED  count: 2\n",
      "ESD!CW-FU!EC!SR  count: 2\n",
      "ESD!CW-FU!ED  count: 1\n",
      "ESD!DCEP-FO  count: 4\n",
      "ESD!DCEP-FO!ACV!EC  count: 1\n",
      "ESD!DCEP-FO!EC  count: 10\n",
      "ESD!DCEP-FO!EC!MISC  count: 1\n",
      "ESD!DCEP-FU  count: 5\n",
      "ESD!DCEP-FU!ACV!ED  count: 2\n",
      "ESD!DCEP-FU!DCEP-FO  count: 1\n",
      "ESD!DCEP-FU!DCEP-FO!ACV!EC!ED  count: 1\n",
      "ESD!DCEP-FU!DCEP-FO!EC  count: 2\n",
      "ESD!DCEP-FU!DCEP-FO!EC!ED  count: 3\n",
      "ESD!DCEP-FU!EC  count: 14\n",
      "ESD!DCEP-FU!EC!ED  count: 1\n",
      "ESD!DCEP-FU!EC!SR  count: 2\n",
      "ESD!DCEP-FU!ED  count: 2\n",
      "ESD!DSCT  count: 3\n",
      "ESD!DSCT!DSCTr!EC  count: 23\n",
      "ESD!DSCT!DSCTr!EC!ED  count: 3\n",
      "ESD!DSCT!EC  count: 49\n",
      "ESD!DSCT!EC!ED  count: 2\n",
      "ESD!DSCT!EC!ELL  count: 1\n",
      "ESD!DSCT!ED  count: 4\n",
      "ESD!DSCTr!EC  count: 17\n",
      "ESD!DSCTr!EC!ED  count: 4\n",
      "ESD!EC  count: 556\n",
      "ESD!EC!ED  count: 37\n",
      "ESD!EC!ELL  count: 2\n",
      "ESD!EC!MISC  count: 2\n",
      "ESD!EC!SR  count: 4\n",
      "ESD!EC:  count: 1\n",
      "ESD!ECV!ED  count: 1\n",
      "ESD!ED  count: 433\n",
      "ESD!ED!MISC  count: 2\n",
      "ESD!ED!SR  count: 2\n",
      "ESD!ED:  count: 3\n",
      "ESD!RRAB!DCEP-FO!EC!ED  count: 5\n",
      "ESD!RRAB!EC  count: 3\n",
      "ESD!RRAB!EC!ED  count: 2\n",
      "ESD!RRAB!RRC!EC!ED  count: 2\n",
      "ESD!RRC!DSCT!DSCTr!EC  count: 1\n",
      "ESD!RRC!DSCT!EC  count: 1\n",
      "ESD!RRC!DSCT!EC!ED  count: 1\n",
      "ESD!RRC!EC  count: 60\n",
      "ESD!RRC!EC!ED  count: 2\n",
      "ESD!RRC!ED  count: 1\n",
      "ESD!SR  count: 3\n",
      "ESD!SR:  count: 1\n",
      "ESD:  count: 11\n",
      "EW  count: 83201\n",
      "EW!DSCT  count: 75\n",
      "EW!DSCT:  count: 5\n",
      "EW!EA  count: 2167\n",
      "EW!EB  count: 20\n",
      "EW!ELL  count: 10\n",
      "EW!NSIN ELL  count: 1\n",
      "EW!PULS  count: 5\n",
      "EW!PULS:  count: 1\n",
      "EW!RR  count: 1\n",
      "EW+BE  count: 1\n",
      "EW+BLEND  count: 1\n",
      "EW+DSCT  count: 1\n",
      "EW+EW  count: 2\n",
      "EW/D  count: 1\n",
      "EW/D:  count: 1\n",
      "EW/DM:  count: 2\n",
      "EW/DW  count: 13\n",
      "EW/DW/RS  count: 1\n",
      "EW/DW:  count: 2\n",
      "EW/K  count: 3\n",
      "EW/KE  count: 50\n",
      "EW/KE:  count: 9\n",
      "EW/KW  count: 289\n",
      "EW/KW:  count: 4\n",
      "EW/RS  count: 94\n",
      "EW/WTTS  count: 1\n",
      "EW:  count: 368\n",
      "EW:/KE  count: 1\n",
      "EW:/KE:  count: 2\n",
      "EW:/KW:  count: 4\n",
      "EW:/RS  count: 2\n",
      "EXOR/ROT  count: 1\n",
      "FKCOM  count: 6\n",
      "FKCOM:  count: 2\n",
      "GCAS  count: 15\n",
      "GCAS+ELL  count: 1\n",
      "GCAS+HMXB/XP  count: 3\n",
      "GCAS+LERI  count: 12\n",
      "GCAS+LERI!SPB  count: 1\n",
      "GCAS+LERI:  count: 1\n",
      "GCAS+R  count: 1\n",
      "GCAS:  count: 1\n",
      "GCAS:+E:  count: 1\n",
      "GDOR  count: 920\n",
      "GDOR!ROT  count: 1\n",
      "GDOR+DSCT  count: 40\n",
      "GDOR+DSCT+ELL+R  count: 1\n",
      "GDOR+DSCT:  count: 11\n",
      "GDOR:  count: 40\n",
      "Galaxy  count: 1\n",
      "HADS  count: 2585\n",
      "HADS(B)  count: 152\n",
      "HADS:  count: 33\n",
      "HB  count: 156\n",
      "HB+EA  count: 1\n",
      "HB+HMXB/XP  count: 1\n",
      "HB+SPB  count: 1\n",
      "HB:  count: 3\n",
      "HMXB  count: 11\n",
      "HMXB+E  count: 4\n",
      "HMXB+ELL  count: 1\n",
      "HMXB+GCAS  count: 2\n",
      "HMXB+IA  count: 1\n",
      "HMXB/BHXB  count: 1\n",
      "HMXB/BHXB+ELL  count: 1\n",
      "HMXB/BHXB/XN+ELL+E  count: 1\n",
      "HMXB/BHXB:  count: 1\n",
      "HMXB/BHXB:/XJ+E  count: 1\n",
      "HMXB/XN+PULS:  count: 1\n",
      "HMXB/XP  count: 12\n",
      "HMXB/XP+BE  count: 1\n",
      "HMXB/XP+E  count: 3\n",
      "HMXB/XP+ELL  count: 1\n",
      "HMXB/XP+GCAS  count: 4\n",
      "HMXB/XP+LERI  count: 1\n",
      "HMXB/XP+XN  count: 1\n",
      "HMXB/XPR  count: 1\n",
      "HMXB/XPR:+GCAS  count: 1\n",
      "HMXB/XR+GCAS  count: 1\n",
      "I  count: 65\n",
      "I:  count: 9\n",
      "IA  count: 2\n",
      "IA:  count: 1\n",
      "IB  count: 29\n",
      "IB:  count: 3\n",
      "IBWD  count: 11\n",
      "IBWD+ZZB:  count: 1\n",
      "IBWD:  count: 2\n",
      "IN  count: 4\n",
      "INA  count: 2\n",
      "INA:  count: 1\n",
      "INB  count: 257\n",
      "INB+BY  count: 1\n",
      "INB:  count: 8\n",
      "INSA  count: 1\n",
      "INSB  count: 2\n",
      "INST  count: 1\n",
      "INST+UV  count: 1\n",
      "INT  count: 20\n",
      "INT:  count: 1\n",
      "IS:  count: 1\n",
      "IS:!E:  count: 1\n",
      "ISB  count: 2\n",
      "L  count: 689\n",
      "L+EA  count: 1\n",
      "L:  count: 3347\n",
      "LB  count: 934\n",
      "LB!SR  count: 1\n",
      "LB:  count: 260\n",
      "LC  count: 13\n",
      "LC+E:  count: 1\n",
      "LC:  count: 3\n",
      "LERI  count: 10\n",
      "LERI+BE  count: 2\n",
      "LERI+GCAS  count: 2\n",
      "LERI:  count: 4\n",
      "LMXB  count: 6\n",
      "LMXB!CV  count: 1\n",
      "LMXB!CV+EA  count: 3\n",
      "LMXB!CV+ELL  count: 1\n",
      "LMXB+E  count: 3\n",
      "LMXB+ELL  count: 1\n",
      "LMXB+ZAND+SRS  count: 1\n",
      "LMXB/BHXB  count: 1\n",
      "LMXB/BHXB/XN  count: 11\n",
      "LMXB/BHXB/XN+E:  count: 1\n",
      "LMXB/BHXB/XN+ELL  count: 3\n",
      "LMXB/BHXB:  count: 5\n",
      "LMXB/BHXB:/XN  count: 5\n",
      "LMXB/R  count: 1\n",
      "LMXB/XB  count: 5\n",
      "LMXB/XB+E  count: 2\n",
      "LMXB/XBR  count: 2\n",
      "LMXB/XN+E  count: 1\n",
      "LMXB/XN+XB  count: 2\n",
      "LMXB/XP  count: 3\n",
      "LMXB/XP+E  count: 1\n",
      "LMXB/XP+XN  count: 1\n",
      "LMXB:  count: 1\n",
      "LMXB:+ELL:  count: 1\n",
      "LMXB:/XPR+E  count: 1\n",
      "LPB  count: 1\n",
      "LPV  count: 2435\n",
      "LPV!CEP  count: 8\n",
      "LPV!ELL  count: 8\n",
      "LPV!ROT  count: 4\n",
      "LPV!RS  count: 3\n",
      "LPV:  count: 311\n",
      "M  count: 19104\n",
      "M!CEP  count: 1\n",
      "M!DCEP-FU  count: 1\n",
      "M!DCEP-FU!SR  count: 1\n",
      "M!SR  count: 66\n",
      "M+SRD  count: 1\n",
      "M+ZAND  count: 6\n",
      "M:  count: 643\n",
      "MISC  count: 26864\n",
      "MISC!ACV  count: 2\n",
      "MISC!CW  count: 35\n",
      "MISC!DCEP  count: 57\n",
      "MISC!DCEP-FO  count: 16\n",
      "MISC!DCEP-FO!EC  count: 8\n",
      "MISC!DCEP-FO!EC!ESD  count: 21\n",
      "MISC!DCEP-FO!EC!ESD!ED  count: 3\n",
      "MISC!DCEP-FO!ESD  count: 2\n",
      "MISC!DCEP-FO!ESD!ED  count: 1\n",
      "MISC!DCEP-FU  count: 10\n",
      "MISC!DCEP-FU!EC  count: 2\n",
      "MISC!DCEP-FU!ESD  count: 1\n",
      "MISC!DCEP-FU!ESD!ED  count: 2\n",
      "MISC!DCEP-FU!SR  count: 1\n",
      "MISC!DSCT  count: 1\n",
      "MISC!EC  count: 29\n",
      "MISC!EC!ESD  count: 61\n",
      "MISC!EC!ESD!ED  count: 7\n",
      "MISC!ED  count: 20\n",
      "MISC!ESD  count: 19\n",
      "MISC!ESD!CW-FU!ECV!ED!SR  count: 1\n",
      "MISC!ESD!DCEP-FU!EC!ED  count: 1\n",
      "MISC!ESD!ED  count: 39\n",
      "MISC!L  count: 5\n",
      "MISC!LPV  count: 14\n",
      "MISC!RRAB  count: 13\n",
      "MISC!RRAB!DCEP-FO  count: 11\n",
      "MISC!RRAB!DCEP-FO!EC  count: 1\n",
      "MISC!RRAB!DCEP-FO!EC!ESD  count: 6\n",
      "MISC!RRAB!DCEP-FO!ED  count: 1\n",
      "MISC!RRAB!DCEP-FO!ESD  count: 1\n",
      "MISC!RRAB!DCEP-FO!ESD!ED  count: 4\n",
      "MISC!RRAB!ESD!ED  count: 5\n",
      "MISC!RV  count: 1\n",
      "MISC!SR  count: 3486\n",
      "MISC!SR!DCEP-FU  count: 1\n",
      "MISC!SR!EC!ESD  count: 1\n",
      "MISC:  count: 6\n",
      "Minor planet  count: 1\n",
      "N  count: 5\n",
      "N+CBSS:  count: 1\n",
      "N+E  count: 4\n",
      "N+E:  count: 1\n",
      "N+ELL  count: 1\n",
      "N+UG:+EA  count: 1\n",
      "N:  count: 1\n",
      "N:+UG+E  count: 1\n",
      "NA+E  count: 12\n",
      "NA+E:  count: 1\n",
      "NA+EA  count: 4\n",
      "NA+ELL  count: 1\n",
      "NA+NL/VY  count: 1\n",
      "NA+UG  count: 2\n",
      "NA+UG/DQ+E  count: 1\n",
      "NA+UG:  count: 1\n",
      "NA+UGZ/DQ:  count: 1\n",
      "NA+ZAND:+E:  count: 1\n",
      "NA/AM  count: 2\n",
      "NA/AM+E  count: 1\n",
      "NA/DQ  count: 4\n",
      "NA/DQ+E  count: 2\n",
      "NA/DQ+UG  count: 2\n",
      "NA/DQ:  count: 3\n",
      "NA/VY  count: 1\n",
      "NA/VY+E  count: 1\n",
      "NB  count: 10\n",
      "NB+E  count: 5\n",
      "NB+E:  count: 1\n",
      "NB+EA  count: 1\n",
      "NB+UG  count: 1\n",
      "NB/AM  count: 1\n",
      "NB/DQ  count: 1\n",
      "NB/DQ+EA  count: 1\n",
      "NB/DQ:  count: 1\n",
      "NB:  count: 2\n",
      "NC  count: 2\n",
      "NC+E  count: 1\n",
      "NC+EA+SR  count: 1\n",
      "NC+M  count: 1\n",
      "NC+SR  count: 1\n",
      "NL  count: 30\n",
      "NL!UGZ:  count: 1\n",
      "NL+E  count: 34\n",
      "NL+EA  count: 1\n",
      "NL+ZZ:  count: 2\n",
      "NL/V:  count: 1\n",
      "NL/VY  count: 27\n",
      "NL/VY+E  count: 9\n",
      "NL/VY:  count: 1\n",
      "NL:  count: 1\n",
      "NON-CV  count: 1\n",
      "NR  count: 2\n",
      "NR+E  count: 6\n",
      "NR+E:  count: 1\n",
      "NR+ELL  count: 1\n",
      "NR+ELL+SRB  count: 1\n",
      "NR+LB  count: 1\n",
      "NR+ZAND+M  count: 1\n",
      "NR:+ELL  count: 1\n",
      "NSIN  count: 26\n",
      "NSIN ELL  count: 22\n",
      "PER  count: 538\n",
      "PER!CEP  count: 1\n",
      "PER!EA  count: 1\n",
      "PPN  count: 20\n",
      "PPN:  count: 3\n",
      "PUL  count: 2\n",
      "PULS  count: 2000\n",
      "PULS!EW  count: 5\n",
      "PULS!ROT  count: 1\n",
      "PULS!RRC  count: 1\n",
      "PULS+ROT  count: 1\n",
      "PULS:  count: 176\n",
      "PULS:+GCAS:  count: 1\n",
      "PVTELI  count: 4\n",
      "PVTELII  count: 1\n",
      "PVTELIII  count: 3\n",
      "QSO  count: 1\n",
      "R  count: 116\n",
      "R+BE  count: 1\n",
      "R+E:  count: 1\n",
      "R+ELL  count: 1\n",
      "R+FF:  count: 1\n",
      "R+V361HYA  count: 7\n",
      "R/PN  count: 2\n",
      "R:  count: 9\n",
      "R:/PN  count: 2\n",
      "RCB  count: 33\n",
      "RCB:  count: 8\n",
      "ROT  count: 58363\n",
      "ROT!CV!LMXB  count: 1\n",
      "ROT!ELL  count: 1\n",
      "ROT+EA  count: 1\n",
      "ROT+PULS  count: 1\n",
      "ROT+SPB  count: 1\n",
      "ROT+UV  count: 172\n",
      "ROT/WD  count: 5\n",
      "ROT:  count: 146\n",
      "ROT:/WD  count: 1\n",
      "RR  count: 280\n",
      "RR!EW  count: 1\n",
      "RR/BL:  count: 3\n",
      "RR:  count: 37\n",
      "RRAB  count: 90036\n",
      "RRAB!CW-FO!EC!ESD  count: 1\n",
      "RRAB!DCEP-FO  count: 5\n",
      "RRAB!EC  count: 4\n",
      "RRAB!EC!ESD  count: 3\n",
      "RRAB!EC!ESD!ED  count: 2\n",
      "RRAB!ED  count: 1\n",
      "RRAB!ESD  count: 2\n",
      "RRAB!ESD!ED  count: 3\n",
      "RRAB!RRC  count: 2\n",
      "RRAB!RRC!EC  count: 1\n",
      "RRAB+RRAB  count: 1\n",
      "RRAB/BL  count: 1691\n",
      "RRAB/BL:  count: 73\n",
      "RRAB:  count: 53\n",
      "RRC  count: 34758\n",
      "RRC!BCEP!DSCT  count: 1\n",
      "RRC!DSCT  count: 3\n",
      "RRC!DSCT!DSCTr!EC!ESD  count: 1\n",
      "RRC!DSCT!EC  count: 11\n",
      "RRC!DSCT!EC!ESD  count: 2\n",
      "RRC!EC  count: 107\n",
      "RRC!EC!ELL  count: 1\n",
      "RRC!EC!ESD  count: 89\n",
      "RRC!EC!ESD!ED  count: 3\n",
      "RRC!EC!ESD!ELL  count: 1\n",
      "RRC!ELL  count: 1\n",
      "RRC!ESD  count: 1\n",
      "RRC!RRAB  count: 1\n",
      "RRC+RRC  count: 1\n",
      "RRC/BL  count: 98\n",
      "RRC/BL:  count: 8\n",
      "RRC:  count: 74\n",
      "RRD  count: 1415\n",
      "RRD:  count: 3\n",
      "RRc  count: 4\n",
      "RS  count: 5022\n",
      "RS!BY  count: 18\n",
      "RS!BY!ELL  count: 1\n",
      "RS!DSCT  count: 1\n",
      "RS+EP  count: 1\n",
      "RS+UV  count: 1\n",
      "RS:  count: 91\n",
      "RV  count: 24\n",
      "RV:  count: 12\n",
      "RVA  count: 295\n",
      "RVA:  count: 15\n",
      "RVB  count: 47\n",
      "RVB:  count: 1\n",
      "S  count: 164\n",
      "S:  count: 27\n",
      "SDOR  count: 2\n",
      "SDOR+HB:  count: 1\n",
      "SDOR:  count: 2\n",
      "SIN  count: 74\n",
      "SPB  count: 196\n",
      "SPB!LERI  count: 1\n",
      "SPB+BCEP  count: 3\n",
      "SPB:  count: 71\n",
      "SR  count: 166097\n",
      "SR!E  count: 1\n",
      "SR!EA  count: 21\n",
      "SR!EC  count: 8\n",
      "SR!EC!ESD  count: 175\n",
      "SR!EC!ESD!ED  count: 29\n",
      "SR!EC!ESD:  count: 1\n",
      "SR!EC:  count: 1\n",
      "SR!ESD  count: 44\n",
      "SR!ESD!ED  count: 74\n",
      "SR!ESD!ED:  count: 1\n",
      "SR!L  count: 650\n",
      "SR!SR:  count: 4\n",
      "SR+EA  count: 1\n",
      "SR+M:  count: 1\n",
      "SR+Microlens  count: 1\n",
      "SR+ZAND  count: 1\n",
      "SR+ZAND:  count: 1\n",
      "SR:  count: 720\n",
      "SR:+E  count: 1\n",
      "SRA  count: 1265\n",
      "SRA+EA  count: 1\n",
      "SRA+ZAND:  count: 1\n",
      "SRA:  count: 63\n",
      "SRA:!CWA:  count: 1\n",
      "SRB  count: 3177\n",
      "SRB:  count: 116\n",
      "SRC  count: 102\n",
      "SRC:  count: 5\n",
      "SRD  count: 233\n",
      "SRD:  count: 67\n",
      "SRS  count: 73915\n",
      "SRS:  count: 36508\n",
      "SXARI  count: 21\n",
      "SXARI+BE  count: 1\n",
      "SXARI/E  count: 5\n",
      "SXARI:  count: 9\n",
      "SXPHE  count: 106\n",
      "SXPHE(B)  count: 9\n",
      "SXPHE:  count: 2\n",
      "Sr  count: 1\n",
      "TTS  count: 5\n",
      "TTS+E  count: 1\n",
      "TTS/ROT  count: 980\n",
      "TTS/ROT+DIP  count: 1\n",
      "TTS/ROT+E  count: 1\n",
      "TTS/ROT+UV  count: 8\n",
      "TTS/ROT+UV:  count: 1\n",
      "TTS/ROT/DIP  count: 29\n",
      "TTS/ROT:  count: 59\n",
      "TTS:  count: 1\n",
      "UG  count: 91\n",
      "UG+E  count: 50\n",
      "UG+EA  count: 3\n",
      "UG+ELL  count: 2\n",
      "UG+ELL+ROT  count: 1\n",
      "UG+EW/WD  count: 1\n",
      "UG+NL/VY  count: 1\n",
      "UG+R  count: 1\n",
      "UG+ZZ  count: 1\n",
      "UG/DQ  count: 7\n",
      "UG/DQ+E  count: 4\n",
      "UG/IBWD  count: 9\n",
      "UG/IBWD+E  count: 1\n",
      "UG/IBWD:  count: 2\n",
      "UG:  count: 6\n",
      "UG:+E  count: 2\n",
      "UGER  count: 6\n",
      "UGER+E  count: 1\n",
      "UGSS  count: 59\n",
      "UGSS+E  count: 8\n",
      "UGSS+ELL  count: 1\n",
      "UGSS+EW  count: 1\n",
      "UGSS+ZZ  count: 1\n",
      "UGSS+ZZ:  count: 1\n",
      "UGSS/DQ:  count: 1\n",
      "UGSU  count: 241\n",
      "UGSU+E  count: 46\n",
      "UGSU+E/IBWD  count: 1\n",
      "UGSU+EA  count: 1\n",
      "UGSU+UGZ  count: 1\n",
      "UGSU/DQ+E  count: 1\n",
      "UGSU/IBWD  count: 13\n",
      "UGSU/IBWD+E  count: 1\n",
      "UGSU:  count: 13\n",
      "UGSU:+E  count: 2\n",
      "UGWZ  count: 78\n",
      "UGWZ+E  count: 3\n",
      "UGWZ+E+ZZ  count: 1\n",
      "UGWZ+E+ZZ/GWLIB  count: 2\n",
      "UGWZ+ZZ/GWLIB  count: 7\n",
      "UGWZ/DQ+E+ZZ/GWLIB  count: 1\n",
      "UGWZ/IBWD  count: 2\n",
      "UGWZ:  count: 3\n",
      "UGZ  count: 15\n",
      "UGZ!NL+E  count: 1\n",
      "UGZ+E  count: 4\n",
      "UGZ+N  count: 1\n",
      "UGZ+VY  count: 2\n",
      "UGZ/DQ:  count: 1\n",
      "UGZ/IW  count: 13\n",
      "UGZ/IW+E  count: 1\n",
      "UGZ/IW+VY  count: 1\n",
      "UGZ/IW:  count: 2\n",
      "UGZ:  count: 4\n",
      "UGZ:+E  count: 1\n",
      "UV  count: 9\n",
      "UV+BY  count: 16\n",
      "UV+BY:  count: 2\n",
      "UV+PULS  count: 1\n",
      "UV+ROT  count: 1\n",
      "UV:  count: 1\n",
      "UVN+BY  count: 1\n",
      "UVN+TTS/ROT  count: 11\n",
      "UXOR+DSCT  count: 2\n",
      "UXOR+TTS/ROT  count: 1\n",
      "UXOR/ROT  count: 3\n",
      "V1093HER  count: 4\n",
      "V1093HER+V361HYA  count: 1\n",
      "V1093Her  count: 1\n",
      "V361HYA  count: 28\n",
      "V838MON  count: 1\n",
      "VAR  count: 75917\n",
      "VAR:  count: 102\n",
      "VBD  count: 7\n",
      "WR  count: 13\n",
      "WR:  count: 1\n",
      "WTTS  count: 8\n",
      "WTTS+EP  count: 1\n",
      "WTTS/ROT  count: 126\n",
      "WTTS/ROT+UV  count: 2\n",
      "WTTS/ROT:  count: 3\n",
      "X  count: 1\n",
      "X+E  count: 1\n",
      "XB  count: 1\n",
      "XR:  count: 1\n",
      "YSO  count: 18\n",
      "YSO+E  count: 4\n",
      "YSO+E:  count: 1\n",
      "YSO/ROT  count: 31\n",
      "YSO/ROT:  count: 4\n",
      "ZAND  count: 45\n",
      "ZAND+E  count: 6\n",
      "ZAND+EA  count: 6\n",
      "ZAND+EA:  count: 1\n",
      "ZAND+ELL  count: 7\n",
      "ZAND+ELL+R  count: 1\n",
      "ZAND+ELL+SR  count: 1\n",
      "ZAND+ELL:  count: 1\n",
      "ZAND+LMXB:/XP  count: 1\n",
      "ZAND+M  count: 5\n",
      "ZAND+R  count: 7\n",
      "ZAND+R+ELL  count: 1\n",
      "ZAND+SR  count: 14\n",
      "ZAND+SR+E  count: 1\n",
      "ZAND+SRA  count: 3\n",
      "ZAND+SRB  count: 2\n",
      "ZAND+SRB+EA:  count: 1\n",
      "ZAND+X  count: 1\n",
      "ZAND:  count: 2\n",
      "ZAND:+SR  count: 1\n",
      "ZAND:+SRB  count: 1\n",
      "ZZ  count: 8\n",
      "ZZ/GWLIB  count: 1\n",
      "ZZA  count: 189\n",
      "ZZA/O  count: 3\n",
      "ZZA/O:  count: 1\n",
      "ZZB  count: 6\n",
      "ZZLep  count: 6\n",
      "ZZO  count: 3\n",
      "nan  count: 0\n",
      "roAm  count: 11\n",
      "roAm+ELL  count: 1\n",
      "roAp  count: 25\n",
      "roAp:  count: 1\n"
     ]
    }
   ],
   "source": [
    "for vartype in unique_aavsovsx_variable_type:\n",
    "    v = df_asassn_sn_aavsovsx_all[df_asassn_sn_aavsovsx_all['variability_type'] == vartype]\n",
    "    print(str(vartype) + \"  count: \"  + str(v.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ce770750",
   "metadata": {},
   "outputs": [],
   "source": [
    "vartype = 'roAp'\n",
    "\n",
    "aavsovsx_filter_match = df_asassn_sn_aavsovsx_all[df_asassn_sn_aavsovsx_all['variability_type'] == vartype]\n",
    "df_asassn = df_asassn_all[df_asassn_all['asas_sn_id'].astype(str).isin(aavsovsx_filter_match['asas_sn_id'].astype(str))]  # 395,489\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9a50a0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "df4beaaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn = df_asassn[df_asassn['gaia_source_id'].astype(str).isin(df_mergedA['source_id'].astype(str))]\n",
    "df_asassn.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "966c53c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>asas_sn_id</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3404004</th>\n",
       "      <td>85899828454</td>\n",
       "      <td>250663469745725184</td>\n",
       "      <td>266666382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8551701</th>\n",
       "      <td>51540280536</td>\n",
       "      <td>505502605482123648</td>\n",
       "      <td>445543326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10955738</th>\n",
       "      <td>231929439388</td>\n",
       "      <td>712924065301624832</td>\n",
       "      <td>302602874.0</td>\n",
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       "    <tr>\n",
       "      <th>13909594</th>\n",
       "      <td>85899970558</td>\n",
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       "    <tr>\n",
       "      <th>15454871</th>\n",
       "      <td>120260181144</td>\n",
       "      <td>1286927649634994432</td>\n",
       "      <td>286992225.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            asas_sn_id       gaia_source_id       tic_id\n",
       "3404004    85899828454   250663469745725184  266666382.0\n",
       "8551701    51540280536   505502605482123648  445543326.0\n",
       "10955738  231929439388   712924065301624832  302602874.0\n",
       "13909594   85899970558  1059387566556243584  147949646.0\n",
       "15454871  120260181144  1286927649634994432  286992225.0"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "14b5dd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_asassn['gaia_source_id'] = df_asassn['gaia_source_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "88fdda63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stars that have a LAMOST low resolution spectra [LRS]\n",
    "\n",
    "df_asassn_lrs1 = df_asassn[df_asassn['gaia_source_id'].astype(str).isin(df_lamost_LRS_stars['gaia_source_id'].astype(str))] \n",
    "df_asassn_lrs1.shape[0]   # 5,815,856"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "0ae24e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stars that have a Gaia DR3 XP spectra\n",
    "\n",
    "df_asassn_lrs = df_asassn_lrs1[df_asassn_lrs1['gaia_source_id'].astype(str).isin(df_mergedA['source_id'].astype(str))]\n",
    "df_asassn_lrs.shape[0]    # 2,878,434"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "69447d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stars that have a LAMOST medium resolution spectra [MRS]\n",
    "\n",
    "df_asassn_lrs_mrs = df_asassn_lrs[df_asassn_lrs['gaia_source_id'].astype(str).isin(df_lamost_MRS_stars['gaia_source_id'].astype(str))] \n",
    "df_asassn_lrs_mrs.shape[0]   # 220,362"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5c2c8080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>asas_sn_id</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3404004</th>\n",
       "      <td>85899828454</td>\n",
       "      <td>250663469745725184</td>\n",
       "      <td>266666382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8551701</th>\n",
       "      <td>51540280536</td>\n",
       "      <td>505502605482123648</td>\n",
       "      <td>445543326.0</td>\n",
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       "    <tr>\n",
       "      <th>10955738</th>\n",
       "      <td>231929439388</td>\n",
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       "      <td>302602874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13909594</th>\n",
       "      <td>85899970558</td>\n",
       "      <td>1059387566556243584</td>\n",
       "      <td>147949646.0</td>\n",
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       "    <tr>\n",
       "      <th>15454871</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            asas_sn_id       gaia_source_id       tic_id\n",
       "3404004    85899828454   250663469745725184  266666382.0\n",
       "8551701    51540280536   505502605482123648  445543326.0\n",
       "10955738  231929439388   712924065301624832  302602874.0\n",
       "13909594   85899970558  1059387566556243584  147949646.0\n",
       "15454871  120260181144  1286927649634994432  286992225.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_asassn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb3bc4ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f5c396c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 rows\n",
      "0.0 MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Process_all_flag = 'Z'\n",
    "\n",
    "df_file = \" \"\n",
    "\n",
    "#df_target = df_asassn[df_asassn['gaia_source_id'].astype(str).isin(df_mergedA['source_id'].astype(str))]\n",
    "\n",
    "\n",
    "\n",
    "#df_file = pd.read_csv('df_O_60108_small.csv')\n",
    "#df_file = pd.read_csv('df_B_812641.csv')\n",
    "#df_file = pd.read_csv('df_A_1717083_small.csv')\n",
    "#df_file = pd.read_csv('df_F_8596775_small.csv')\n",
    "#df_file = pd.read_csv('df_G_7343673_small.csv')\n",
    "#df_file = pd.read_csv('df_K_13814065_small.csv') \n",
    "#df_file = pd.read_csv('df_M_1735402_small.csv')\n",
    "#df_file = pd.read_csv('df_CSTAR_83028_small.csv')\n",
    "\n",
    "#df_file = pd.read_csv('df_beStar_9138_small.csv')\n",
    "#df_file = pd.read_csv('df_HerbigStar_1897_small.csv')\n",
    "#df_file = pd.read_csv('df_RedDwarfEmStar_454_small.csv')\n",
    "#df_file = pd.read_csv('df_rrlyrae_10532_small.csv')\n",
    "#df_file = pd.read_csv('df_TTauri_28399_small.csv')\n",
    "#df_file = pd.read_csv('df_wN_185_small.csv')\n",
    "\n",
    "#df_file = pd.read_csv('df_Very_Metal_Poor_75849_small.csv')\n",
    "#df_file = pd.read_csv('df_Extremely_Metal_Poor_28869_small.csv')\n",
    "#df_file = pd.read_csv('df_Ultra_Metal_Poor_11187_small.csv')\n",
    "\n",
    "#df_file = pd.read_csv('df_Unknown_4635_small.csv')\n",
    "\n",
    "#df_file = pd.read_csv(\"df_F_RVS_171155_small.csv\")\n",
    "#df_file = pd.read_csv(\"df_G_RVS_170669_small.csv\")\n",
    "#df_file = pd.read_csv(\"df_K_RVS_488696_small.csv\")\n",
    "\n",
    "#df_file = pd.read_csv('df_gaia_eclipsing_binary_146712_small.csv')\n",
    "#df_file= pd.read_csv('df_gaia_cepheid_3196_small.csv')\n",
    "#df_file = pd.read_csv('df_gaia_epoch_radial_velocity_42553_small.csv')\n",
    "#df_file = pd.read_csv('df_gaia_long_period_variable_537525_small.csv')\n",
    "#df_file = pd.read_csv('df_gaia_ms_oscillator_31060_small.csv')\n",
    "#df_file = pd.read_csv('df_gaia_planetary_transit_198_small.csv')\n",
    "#df_file = pd.read_csv('df_gaia_rotation_modulation_72336_small.csv')\n",
    "#df_file = pd.read_csv('df_gaia_rrlyrae_10532_small.csv')\n",
    "#df_file = pd.read_csv('df_gaia_short_timescale_177322_small.csv')\n",
    "\n",
    "\n",
    "if Process_all_flag == 'Y':\n",
    "    df_asassn_variables = df_asassn[df_asassn['gaia_source_id'].astype(str).isin(df_mergedA['source_id'].astype(str))]\n",
    "elif Process_all_flag == 'N':\n",
    "    df_asassn_variables  = df_asassn[df_asassn['gaia_source_id'].astype(str).isin(df_file['source_id'].astype(str))]\n",
    "else:\n",
    "    df_asassn_variables  = df_asassn\n",
    "\n",
    "print('{:d} rows'.format(len(df_asassn_variables)))\n",
    "print('{:.1f} MB'.format(df_asassn_variables.memory_usage(index=True,deep=True).sum()/1e6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "bf4bc9ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asas_sn_id</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
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       "  </thead>\n",
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       "      <td>286992225.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            asas_sn_id       gaia_source_id       tic_id\n",
       "3404004    85899828454   250663469745725184  266666382.0\n",
       "8551701    51540280536   505502605482123648  445543326.0\n",
       "10955738  231929439388   712924065301624832  302602874.0\n",
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       "15454871  120260181144  1286927649634994432  286992225.0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx = df_asassn_variables\n",
    "dfx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "df6254a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"xslice = 0\n",
    "\n",
    "xmax = df_asassn_variables.shape[0] - xslice\n",
    "\n",
    "if xmax > 5:\n",
    "    xmin = xmax - 5\n",
    "else:\n",
    "    xmin = 0\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "bb1b0ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asas_sn_id</th>\n",
       "      <th>gaia_source_id</th>\n",
       "      <th>tic_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3404004</th>\n",
       "      <td>85899828454</td>\n",
       "      <td>250663469745725184</td>\n",
       "      <td>266666382.0</td>\n",
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       "      <th>10955738</th>\n",
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       "      <td>302602874.0</td>\n",
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       "      <td>286992225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27750081</th>\n",
       "      <td>120259818226</td>\n",
       "      <td>2116863318617538944</td>\n",
       "      <td>123231021.0</td>\n",
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       "    <tr>\n",
       "      <th>31871554</th>\n",
       "      <td>515396298245</td>\n",
       "      <td>2401638960512043264</td>\n",
       "      <td>69855370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33762662</th>\n",
       "      <td>240519418343</td>\n",
       "      <td>2741721583476129664</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55384177</th>\n",
       "      <td>369368226783</td>\n",
       "      <td>4339177971358491264</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65851947</th>\n",
       "      <td>661425279714</td>\n",
       "      <td>5257642094689491072</td>\n",
       "      <td>469246567.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            asas_sn_id       gaia_source_id       tic_id\n",
       "3404004    85899828454   250663469745725184  266666382.0\n",
       "8551701    51540280536   505502605482123648  445543326.0\n",
       "10955738  231929439388   712924065301624832  302602874.0\n",
       "13909594   85899970558  1059387566556243584  147949646.0\n",
       "15454871  120260181144  1286927649634994432  286992225.0\n",
       "27750081  120259818226  2116863318617538944  123231021.0\n",
       "31871554  515396298245  2401638960512043264   69855370.0\n",
       "33762662  240519418343  2741721583476129664          NaN\n",
       "55384177  369368226783  4339177971358491264          NaN\n",
       "65851947  661425279714  5257642094689491072  469246567.0"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2878ee5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[250663469745725184,\n",
       " 505502605482123648,\n",
       " 712924065301624832,\n",
       " 1059387566556243584,\n",
       " 1286927649634994432,\n",
       " 2116863318617538944,\n",
       " 2401638960512043264,\n",
       " 2741721583476129664,\n",
       " 4339177971358491264,\n",
       " 5257642094689491072]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia_list = dfx['gaia_source_id'][0:10].to_list()\n",
    "gaia_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "261d63ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"gaia_list = dfx['gaia_source_id'][xmin:xmax].to_list()\n",
    "gaia_list\"\"\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c73875",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "986e47cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#gaia_list = [4066962566180518912]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7823bbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "62b29277",
   "metadata": {},
   "outputs": [],
   "source": [
    "#webbrowser.open(https://asas-sn.osu.edu/atlas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f1203ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cds_portal_flag    = 'N'\n",
    "simbad_portal_flag = 'N'\n",
    "sdss_portal_flag   = 'N'\n",
    "vizier_portal_flag = 'N'\n",
    "asas_sn_flag       = 'Y'\n",
    "\n",
    "show_lamost_LRS_flag = 'N'\n",
    "show_lamost_MRS_flag = 'N'\n",
    "show_sdss_flag       = 'N'\n",
    "show_galah_flag      = 'N'\n",
    "show_rvs_flag        = 'N'\n",
    "\n",
    "show_SkyPatrol_flag  = 'Y'\n",
    "Plot_LightKurve_flag = 'Y'\n",
    "plot_count           = 1\n",
    "\n",
    "plot_pixelfile       = \"N\"\n",
    "plot_lightcurve      = \"Y\"\n",
    "plot_period          = \"Y\"\n",
    "plot_phase_fold      = \"Y\"\n",
    "plot_half_phase_fold = \"NN\"\n",
    "plot_2phase_fold     = \"NN\"\n",
    "plot_3phase_fold     = \"NN\"\n",
    "plot_4phase_fold     = \"Y\"\n",
    "plot_PDC_AP_Flux     = \"NN\"\n",
    "plot_periodogram     = \"Y\" # Large output\n",
    "\n",
    "plt.style.use('dark_background');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "cc388077",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2d2bffba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gaia.logout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "950acdf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RETRIEVE A SET OF SOURCES WITH XP SAMPLED SPECTRA OBAFGKM & CSTAR\n",
    "\n",
    "plt.close(\"all\")\n",
    "\n",
    "# for idx in range(0, len(results)):\n",
    "#print(\"len(gaia_subluminous_list): \" + str(len(gaia_subluminous_list)))\n",
    "for idx in range(0, len(gaia_list)):\n",
    "    #print(\"idx: \" + str(idx))\n",
    "    source_id_target = gaia_list[idx]\n",
    "    #print(\"\\n\")\n",
    "    query = f\"SELECT TOP 10 s.*, p.* \\\n",
    "    FROM gaiadr3.gaia_source AS s \\\n",
    "    join gaiadr3.astrophysical_parameters AS p USING (source_id) \\\n",
    "    WHERE p.source_id = \" + str(source_id_target)\n",
    "\n",
    "    job     = Gaia.launch_job_async(query)\n",
    "    job_results = job.get_results()\n",
    "    target = job_results.to_pandas()\n",
    "    results = target['source_id'].values.tolist()\n",
    "\n",
    "    \n",
    "    query2 = f\"SELECT TOP 10 sdss.* \\\n",
    "    FROM gaiadr3.sdssdr13_best_neighbour AS sdss \\\n",
    "    WHERE sdss.source_id = \" + str(source_id_target)\n",
    "\n",
    "    job2     = Gaia.launch_job_async(query2)\n",
    "    job2_results = job2.get_results()\n",
    "    target2 = job2_results.to_pandas()\n",
    "    #print(job2_results)\n",
    "    if show_sdss_flag == 'Y':\n",
    "        if len(job2_results) > 0:\n",
    "            #print(job2_results)\n",
    "            print(Fore.RED + \"1.Gaia SDSS Match!\")\n",
    "            print(Style.RESET_ALL)\n",
    "            xRa = str(target['ra'].iloc[0])\n",
    "            yDec = str(target['dec'].iloc[0])\n",
    "            source_id = str(target['source_id'].iloc[0])\n",
    "            objid = str(target2['original_ext_source_id'].iloc[0])\n",
    "            sdss_des = df_sdss_all[df_sdss_all['objid'].astype(str) == str(objid)]\n",
    "            if len(sdss_des) > 0:\n",
    "                sdss_portal = 'https://skyserver.sdss.org/dr17/VisualTools/explore/summary?ra=' + str(xRa) + '&dec=' + str(yDec)\n",
    "                webbrowser.open(sdss_portal)\n",
    "                print(Fore.RED + \"2. SDSS Match Found!: \" + str(objid))\n",
    "                print(Style.RESET_ALL)\n",
    "            else:\n",
    "                print(Fore.RED + \"3. SDSS Match Not Found!: \" + str(objid))\n",
    "                print(Style.RESET_ALL)\n",
    "        else:\n",
    "            #print(job2_results)\n",
    "            print(Fore.RED + \"4. No Gaia SDSS Match!\")\n",
    "            print(Style.RESET_ALL)\n",
    "\n",
    "    \"\"\"df_GALAH_match = df_GALAH[df_GALAH['source_id'].isin(target['source_id'])]\n",
    "    if len(df_GALAH_match) > 0:\n",
    "        df_GALAH_match['SpT2'] = target.apply(lambda row: starcolorindexSpT.subclass[round(row.bp_rp, 2)], axis=1)\n",
    "        df_GALAH_match['SpT2_dereddened'] = target.apply(lambda row: starcolorindexSpT.subclass[round((row.bp_rp - row.ebpminrp_gspphot), 2)], axis=1)\n",
    "        Plot_Spectra_Source_Id(df_GALAH_match)\n",
    "    else:\n",
    "        print(\"\\nNo GALAH match found!\")\n",
    "        print(\"\\n\")\"\"\"\n",
    "\n",
    "    #if len(results) != 0:\n",
    "    #df_variable = df_hot_subluminous[df_hot_subluminous['source_id'] == source_id_target]\n",
    "    #SpTypeSim = str(df_variable['SpTypeSim'].iloc[0])\n",
    "    #SpTypeSD = str(df_variable['SpTypeSD'].iloc[0])\n",
    "    #SpName = str(df_variable['Name'].iloc[0])\n",
    "\n",
    "    #print(\"4A. bp_rp: \" + str(target.bp_rp))\n",
    "    if target.bp_rp.empty:\n",
    "        target['SpT2'] = 'Unknown'\n",
    "        target['SpT2_dereddened'] = 'Unknown'\n",
    "    else:\n",
    "        target['SpT2'] = target.apply(lambda row: starcolorindexSpT.subclass[round(row.bp_rp, 2)], axis=1)\n",
    "        target['SpT2_dereddened'] = target.apply(lambda row: starcolorindexSpT.subclass[round((row.bp_rp - row.ebpminrp_gspphot), 2)], axis=1)\n",
    "\n",
    "    conditions = [\n",
    "        (target['logg_gspphot'].astype(float) > 4.20),\n",
    "        ((target['logg_gspphot'].astype(float) <= 4.20) & (target['logg_gspphot'].astype(float) > 3.90)),\n",
    "        ((target['logg_gspphot'].astype(float) <= 3.90) & (target['logg_gspphot'].astype(float) > 3.60)),\n",
    "        ((target['logg_gspphot'].astype(float) < 2.55) & (target['logg_gspphot'].astype(float) > 2.35)),\n",
    "        (target['logg_gspphot'].astype(float) <= 3.60),\n",
    "        (target['logg_gspphot'].isna())\n",
    "    ]\n",
    "    values = ['main_sequence', 'turnoff', 'subgiants', 'red_clump', 'red_giants', 'unknown']\n",
    "    target['Star_Type'] = np.select(conditions, values)\n",
    "\n",
    "    des = df_lamost_LRS_stars[df_lamost_LRS_stars['gaia_source_id'].astype(str) == str(source_id_target)]\n",
    "    if len(des) > 0:\n",
    "        lamost_LRS_portal_flag = 'Y'\n",
    "        lamost_LRS_designation = des['designation'].iloc[0]\n",
    "        lamost_LRS_subclass = des['subclass'].iloc[0]\n",
    "        lamost_LRS_obsid = des['obsid'].iloc[0]\n",
    "        lamost_LRS_source_id = des['gaia_source_id'].iloc[0]\n",
    "    else:\n",
    "        lamost_LRS_portal_flag = 'N'\n",
    "\n",
    "    des2 = df_lamost_MRS_stars[df_lamost_MRS_stars['gaia_source_id'].astype(str) == str(source_id_target)]\n",
    "    if len(des2) > 0:\n",
    "        lamost_MRS_portal_flag = 'Y'\n",
    "        lamost_MRS_designation = des2['designation'].iloc[0]\n",
    "        lamost_MRS_mobsid = des2['mobsid'].iloc[0]\n",
    "        lamost_MRS_obsid = des2['obsid'].iloc[0]\n",
    "        lamost_MRS_source_id = des2['gaia_source_id'].iloc[0]\n",
    "    else:\n",
    "        lamost_MRS_portal_flag = 'N'\n",
    "\n",
    "    if show_lamost_LRS_flag == 'Y':\n",
    "        if lamost_LRS_portal_flag == 'Y':\n",
    "            lamost_portal = 'https://dr7.lamost.org/spectrum/view?obsid=' + str(lamost_LRS_obsid)\n",
    "            webbrowser.open(lamost_portal)     \n",
    "\n",
    "    if show_lamost_MRS_flag == 'Y':\n",
    "        if lamost_MRS_portal_flag == 'Y':\n",
    "            lamost_MRS_portal = 'https://dr7.lamost.org/v2.0/medspectrum/view?obsid=' + str(lamost_MRS_obsid)\n",
    "            webbrowser.open(lamost_MRS_portal)\n",
    "\n",
    "    xRa = str(target['ra'].iloc[0])\n",
    "    yDec = str(target['dec'].iloc[0])\n",
    "    source_id = str(target['source_id'].iloc[0])\n",
    " \n",
    "    print(Fore.YELLOW + \"\\n5A. \" + \"Ra: \" + str(xRa))                                                                    \n",
    "    print(\"5B. Dec: \" + str(yDec))\n",
    "\n",
    "    print(\"\\n5C. Source_Id: \" + str(source_id))\n",
    "\n",
    "    if cds_portal_flag == 'Y':\n",
    "        cds_portal = 'http://cdsportal.u-strasbg.fr/?target=' + str(xRa) + '%20' + str(yDec)\n",
    "        webbrowser.open(cds_portal) \n",
    "\n",
    "    if simbad_portal_flag == 'Y':\n",
    "        simbad_portal = 'http://simbad.cds.unistra.fr/simbad/sim-coo?Coord=' + str(xRa) + '+' + str(yDec) + \\\n",
    "                    '&CooFrame=FK5&CooEpoch=2000&CooEqui=2000&CooDefinedFrames=none&Radius=2&Radius.unit=arcmin&submit=submit+query'\n",
    "        webbrowser.open(simbad_portal)  \n",
    "\n",
    "    if sdss_portal_flag == 'Y':\n",
    "        sdss_portal = 'https://skyserver.sdss.org/dr17/VisualTools/explore/summary?ra=' + str(xRa) + '&dec=' + str(yDec)\n",
    "        webbrowser.open(sdss_portal)           \n",
    "\n",
    "    if vizier_portal_flag == 'Y':\n",
    "        vizier_portal = 'http://vizier.u-strasbg.fr/viz-bin/VizieR-S?Gaia%20EDR3%20' + str(source_id) \n",
    "        webbrowser.open(vizier_portal)\n",
    "        \n",
    "\n",
    "    # ====================================================================================\n",
    "                                                \n",
    "    result_table = Simbad.query_region(coord.SkyCoord(str(xRa) + \" \" + str(yDec), \n",
    "        unit=(u.deg, u.deg),frame='icrs'))\n",
    "\n",
    "    #print(\"6: Simbad query restult table: \" + str(result_table))                                                                          \n",
    "    if result_table is not None:\n",
    "        if len(result_table) > 0:\n",
    "            #print(\"7: \" + \"Ra: \" + str(xRa))                                                                     \n",
    "            #print(\"8: \" + \"Dec: \" + str(yDec))   \n",
    "            print(\"\\n\")                                                                \n",
    "\n",
    "            for i in range(0, len(result_table['MAIN_ID'])):\n",
    "                if i < 2:\n",
    "                    print(\"9. \" + str(result_table['MAIN_ID'][i]))\n",
    "\n",
    "            target1 = result_table['MAIN_ID'][0]\n",
    "\n",
    "            object = Simbad.query_object(target1)\n",
    "\n",
    "            tap_service = vo.dal.TAPService(\"http://simbad.u-strasbg.fr/simbad/sim-tap\")\n",
    "\n",
    "            tap_sql = \"SELECT main_id, sp_type, otype_txt, otype_longname \\\n",
    "                            FROM basic JOIN ident ON oidref = oid \\\n",
    "                                JOIN otypedef ON basic.otype = otypedef.otype \\\n",
    "                            WHERE id = '\" + str(target1) + \"';\"\n",
    "\n",
    "            tap_results = tap_service.search(tap_sql)\n",
    "            sptype = tap_results['sp_type']\n",
    "            txt = str(sptype).split(\"=\")\n",
    "            txt = str(txt).replace(\"['\", \"\")\n",
    "            sp_txt = str(txt).replace(\"']\", \"\")\n",
    "            #print(\"\\n\" + \"Simbad star subclass: \" + str(sp_txt))\n",
    "            print(Fore.GREEN + \"\\n10. Simbad star subclass: \" + str(sp_txt))\n",
    "            print(Style.RESET_ALL)\n",
    "    else:\n",
    "        print(Fore.RED + \"\\n10B. No Simbad Match\")\n",
    "        print(Style.RESET_ALL)\n",
    "\n",
    "    print(\"11. BP_RP star subclass: \" + str(target['SpT2'].iloc[0]))\n",
    "    print(\"12. BP_RP dereddened star subclass: \" + str(target['SpT2_dereddened'].iloc[0]))\n",
    "    print(Fore.GREEN + \"13. classlabel_espels: \" + str(target['classlabel_espels'].iloc[0])) \n",
    "    print(Fore.YELLOW + \"14. Star Type: \" + str(target['Star_Type'].iloc[0]))\n",
    "    print(Style.RESET_ALL) \n",
    "    #print(\"\\nSpTypeSim: \" +  str(SpTypeSim))\n",
    "    #print(\"SpTypeSD: \" +  str(SpTypeSD))\n",
    "    #print(\"SpName: \" +  str(SpName))\n",
    "    #print(\"\\n\")\n",
    "\n",
    "    TIC_ID_2 = 0\n",
    "    Period_2 = 0\n",
    "    variable_type1 = \" \"\n",
    "    variable_type2 = \" \"\n",
    "    variable_type3 = \" \"\n",
    "\n",
    "    df_v_filter = df_asassn_v_filter_match[df_asassn_v_filter_match['gaia_source_id'] == str(source_id_target)]\n",
    "    if len(df_v_filter) > 0:\n",
    "        asassval3 = df_v_filter['asassn_name'].values[0]\n",
    "        print(\"14A. v_filter [asassn_name asassval3]: \" + str(asassval3))\n",
    "        variable_type1 = df_v_filter['variable_type'].values[0]\n",
    "        print(Fore.RED + \"14A. v_filter [variable_type]: \" + str(variable_type1))\n",
    "        print(Style.RESET_ALL)\n",
    "        ASASSN_Variable_Type1 = vType[variable_type1]\n",
    "        print(Fore.YELLOW + \"\\n14A. v_filter [ASASSN_Variable_Type]: \" + str(ASASSN_Variable_Type1))\n",
    "        print(Style.RESET_ALL)\n",
    "        period = df_v_filter['period'].values[0]\n",
    "        print(\"14A. v_filter [Period]: \" + str(period))\n",
    "        tic_id = df_v_filter['tic_id'].values[0]\n",
    "        print(\"14A. v_filter [TIC_ID]: \" + str(tic_id[4:]))\n",
    "        if len(tic_id) > 0:\n",
    "            TIC_ID_2 = tic_id[4:]\n",
    "        if period > 0:\n",
    "            Period_2 = period\n",
    "        #webbrowser.open('https://asas-sn.osu.edu/variables?&name=' + str(asassval3))\n",
    "    else:\n",
    "        #print(\"14A. source_id_target: \" + str(source_id_target))\n",
    "        print(\"14A. No df_asassn_v_filter_match!\")\n",
    "\n",
    "    \n",
    "\n",
    "    df_blue_filter = df_asassn_blue_filter_match[df_asassn_blue_filter_match['gaia_source_id'] == str(source_id_target)]\n",
    "    if len(df_blue_filter) > 0:\n",
    "        asassval2 = df_blue_filter['ID'].values[0]\n",
    "        print(\"\\n14B. blue_filter [ID asassval2]: \" + str(asassval2))\n",
    "        variable_type2 = df_blue_filter['ML_classification'].values[0]\n",
    "        print(Fore.RED + \"14B. blue_filter [ML_classification]: \" + str(variable_type2))\n",
    "        print(Style.RESET_ALL)\n",
    "        if variable_type2 != variable_type1:\n",
    "            ASASSN_Variable_Type2 = vType[variable_type2]\n",
    "            print(Fore.YELLOW + \"\\n14B. Blue filter [ASASSN_Variable_Type]: \" + str(ASASSN_Variable_Type2))\n",
    "            print(Style.RESET_ALL)\n",
    "        period = df_blue_filter['Period'].values[0]\n",
    "        print(\"14B. blue_filter [Period]: \" + str(period))\n",
    "        tic_id = df_blue_filter['TIC_ID'].values[0]\n",
    "        print(\"14B. blue_filter [TIC_ID]: \" + str(tic_id[4:]))\n",
    "        if len(tic_id) > 0:\n",
    "            TIC_ID_2 = tic_id[4:]\n",
    "        if period > 0:\n",
    "            Period_2 = period\n",
    "        #webbrowser.open('https://asas-sn.osu.edu/variables?&name=' + str(asassval2))\n",
    "    else:\n",
    "        #print(\"14B. source_id_target: \" + str(source_id_target))\n",
    "        print(\"\\n14B. No df_asassn_blue_filter_match!\")\n",
    "\n",
    "    if asas_sn_flag == 'Y':\n",
    "        df_variable = df_asassn_variables[df_asassn_variables['gaia_source_id'] == source_id_target]\n",
    "        asassval = df_variable['asas_sn_id'].values[0]\n",
    "        df_aavs_name = df_asassn_sn_name[df_asassn_sn_name['asas_sn_id'] == asassval]\n",
    "        if len(df_aavs_name) > 0:\n",
    "            print(\"\\n15. df_aavs_name: \" + str(df_aavs_name ))\n",
    "            asassval1 = df_aavs_name['name'].values[0]\n",
    "            print(\"16. name asassval1: \" + str(asassval1))\n",
    "            webbrowser.open('https://asas-sn.osu.edu/variables?&name=' + str(asassval1))\n",
    "        \n",
    "        df_sublum = df_hot_subluminous_all[df_hot_subluminous_all['source_id'].astype(str) == str(source_id_target)]\n",
    "        if len(df_sublum) > 0:\n",
    "            SpTypeSim = df_sublum['SpTypeSim'].values[0]\n",
    "            SpTypeSD = df_sublum['SpTypeSD'].values[0]\n",
    "            SpName = df_sublum['Name'].values[0]\n",
    "            print(Fore.GREEN + \"\\n17. hot_subluminous SpTypeSim: \" + str(SpTypeSim))\n",
    "            print(Fore.GREEN + \"\\n18. hot_subluminous SpTypeSD: \" + str(SpTypeSD))\n",
    "            print(Fore.GREEN + \"\\n19. hot_subluminous Name: \" + str(SpName))\n",
    "            print(Style.RESET_ALL)\n",
    "        else:\n",
    "            #print(Fore.RED + \"No hot subluminous star match for source_id: \" + str(source_id_target))\n",
    "            print(Style.RESET_ALL)\n",
    "        df_aavsovsx = df_asassn_sn_aavsovsx_all[df_asassn_sn_aavsovsx_all['asas_sn_id'] == asassval]\n",
    "        period = 0\n",
    "        variability_type = \" \"\n",
    "        if df_aavsovsx.shape[0] > 0:\n",
    "            period = df_aavsovsx['period'].values[0]\n",
    "            print(Fore.GREEN + \"20. aavsovsx [Period]: \" + str(period))\n",
    "            variable_type3 = df_aavsovsx['variability_type'].values[0]\n",
    "            print(Fore.RED + \"\\n21. aavsovsx [Variability type]: \" + str(variable_type3))\n",
    "            if variable_type3 in (variable_type1, variable_type2):\n",
    "                print(Style.RESET_ALL)\n",
    "            else:\n",
    "                ASASSN_Variable_Type = vType[variable_type3]\n",
    "                print(Fore.YELLOW + \"\\n22. aavsovsx [ASASSN_Variable_Type]: \" + str(ASASSN_Variable_Type))\n",
    "                print(Style.RESET_ALL)\n",
    "        else:\n",
    "            print(Fore.RED + \"23. No aavsovsx star match for asas_sn_id: \" + str(asassval))\n",
    "            print(Style.RESET_ALL)\n",
    "        if np.isnan(df_variable['tic_id'].values[0]):\n",
    "            tic_id = TIC_ID_2\n",
    "        else:\n",
    "            tic_id = int(df_variable['tic_id'].values[0])\n",
    "        print(Fore.GREEN + \"24. asassval[asas_sn_id]: \" + str(asassval))\n",
    "        print(Fore.GREEN + \"\\n25. [tic_id]: \" + str(tic_id))\n",
    "        print(Style.RESET_ALL)\n",
    "        #webbrowser.open('http://asas-sn.ifa.hawaii.edu/skypatrol/objects/' + str(asassval))\n",
    "\n",
    "    if lamost_LRS_portal_flag == 'Y':\n",
    "        print(\"\\n26. lamost_LRS_designation: \"  + str(lamost_LRS_designation))\n",
    "        print(\"27. lamost_LRS_obsid: \"  + str(lamost_LRS_obsid))\n",
    "        #print(\"lamost_LRS_gaia_source_id: \"  + str(lamost_LRS_source_id))\n",
    "        print(\"28. lamost_LRS_subclass: \" + str(lamost_LRS_subclass) + '\\n' )\n",
    "    else:\n",
    "        print(Fore.RED + \"\\n29. No Lamost LRS Match found!\")\n",
    "        print(Style.RESET_ALL)\n",
    "\n",
    "    if lamost_MRS_portal_flag == 'Y':\n",
    "        print(\"30. lamost_MRS_designation: \"  + str(lamost_MRS_designation))\n",
    "        print(\"31. lamost_MRS_obsid: \"  + str(lamost_MRS_obsid))\n",
    "        #print(\"lamost_MRS_gaia_source_id: \"  + str(lamost_MRS_source_id))\n",
    "        print(\"32. lamost_MRS_mobsid: \" + str(lamost_MRS_mobsid) + '\\n' )\n",
    "    else:\n",
    "        print(Fore.RED + \"33. No Lamost MRS Match found!\")\n",
    "        print(Style.RESET_ALL)\n",
    "\n",
    "        # ====================================================================================\n",
    "\n",
    "    #print(\"\\nGaia XP match found for source_id: \" + str(source_id_target))\n",
    "    #print(\"\\n1: \" + str(results[0]) + ' has_xp_sampled: ' + str(target['has_xp_sampled'].iloc[0]))                        # 1st print statement\n",
    "    if target['has_xp_sampled'].iloc[0] == True:\n",
    "        #print(\"2A\")\n",
    "        datalink = Gaia.load_data(results[0], data_structure = 'INDIVIDUAL', retrieval_type = 'XP_SAMPLED')\n",
    "        outputs = [datalink[key][0] for key in datalink.keys()]\n",
    "\n",
    "        #print(\"2: \" + str(results[0]) + \" Number of stars with XP Spectra: \" + str(len(outputs)))                         # 2nd print statement\n",
    "        if outputs is not None:\n",
    "            #print(\"3: \" + \"outputs is not None\")                                                                            # 3rd print statement\n",
    "            if len(outputs) > 0:\n",
    "                xp      = outputs[0].to_table()                       \n",
    "                # Add flux-density columns ============================\n",
    "                xp['flux_jy']         = xp['flux'].to(u.Jansky, equivalencies = u.spectral_density(xp['wavelength'].value * xp['wavelength'].unit))\n",
    "                xp['flux_mjy']        = xp['flux_jy'].to(u.millijansky)\n",
    "                xp['flux_mjy'].format = '7.4f'\n",
    "                \n",
    "                # =========\n",
    "                \n",
    "                #display(xp)\n",
    "\n",
    "                # =========\n",
    "\n",
    "                fig       = plt.figure(figsize=[20,10])\n",
    "\n",
    "                yRangeMax = float(xp['flux'].max())\n",
    "\n",
    "                plt.plot(xp['wavelength']*10, xp['flux']/yRangeMax * 2, linewidth = 2, marker='o', markerfacecolor='red') \n",
    "                #plt.title('XP_SAMPLED Spectra Gaia DR3 Class '+ str(target['spectraltype_esphs'].iloc[0]) + \"    SpTypeSim: \" + str(SpTypeSim) +\n",
    "                plt.title('XP_SAMPLED Spectra Gaia DR3 Class '+ str(target['spectraltype_esphs'].iloc[0]) +\n",
    "                ' \\nLogG: ' + str(round(target['logg_gspphot'].iloc[0], 2)) + \n",
    "                '    Teff: ' + str(round(target['teff_gspphot'].iloc[0],0)) +\n",
    "                '    Mass: ' + str(round(target['mass_flame'].iloc[0],2)) +\n",
    "                '    Luminosity: ' + str(round(target['lum_flame'].iloc[0],2)) +\n",
    "                '    Radius: ' + str(round(target['radius_gspphot'].iloc[0],2)) +\n",
    "                '    Age: ' + str(round(target['age_flame'].iloc[0],2)) +\n",
    "                '    [M/H]: ' + str(round(target['mh_gspphot'].iloc[0],2)) +\n",
    "                '    [Alpha/Fe]: ' + str(round(target['alphafe_gspspec'].iloc[0],2)) +\n",
    "                '\\nRa: ' + str(target['ra'].iloc[0]) + \"    Dec: \" + str(target['dec'].iloc[0]) +\n",
    "                ' \\nSource_ID: ' + str(target['source_id'].iloc[0]), fontsize=15)\n",
    "\n",
    "                if make_canvas_flag == \"Y\":\n",
    "                    make_canvas(xlabel = f\"Wavelength [{xp['wavelength'].unit}]\", ylabel = f\"Flux [{xp['flux'].unit}]\", \n",
    "                                fontsize = 12)\n",
    "                #else:\n",
    "                    #make_labels()\n",
    "\n",
    "                if hydrogen_lines_flag == \"Y\":\n",
    "                    Hydrogen_lines(xlabel = f\"Wavelength [{xp['wavelength'].unit}]\", ylabel = f\"Flux [{xp['flux'].unit}]\", \n",
    "                                fontsize = 12)\n",
    "\n",
    "                plt.show()\n",
    "                \n",
    "            else:\n",
    "                print(\"\\n34: Gaia XP match not found for source_id: \" + str(source_id_target))\n",
    "        else:\n",
    "                print(\"\\n35: Gaia XP match not found for source_id: \" + str(source_id_target))\n",
    "    else:\n",
    "                print(\"\\n36: Gaia XP match not found for source_id: \" + str(source_id_target))\n",
    "\n",
    "    if show_SkyPatrol_flag  == 'Y':\n",
    "        asas_sn_id = int(asassval)\n",
    "        lcs = client.query_list([asas_sn_id],\n",
    "            catalog='master_list',id_col='asas_sn_id',download=True)\n",
    "        lightcurve = lcs[asas_sn_id]\n",
    "        #lightcurve.plot()\n",
    "\n",
    "        \"\"\"\n",
    "        Periodigram Utility\n",
    "        We have included a thin-wrapper for astropy’s lomb scargle periodagram utility. \n",
    "        Using the lomb_scargle function we can get the freqency and power spectrum of the light curve. \n",
    "        While ‘plot’ is set to True, the function will also produce a plot of the power spectrum\n",
    "        \"\"\";\n",
    "\n",
    "        # An astropy LombScargle object is also returned as ls\n",
    "        frequency, power, ls = lightcurve.lomb_scargle(plot=False)\n",
    "\n",
    "        \"\"\"\n",
    "        Finally, we can use the power spectrum to find the period of our target and generate a \n",
    "        phase folded lightcurve.\n",
    "        \"\"\";\n",
    "\n",
    "        # If plot is set we will also get a plot.\n",
    "        lightcurve.find_period(frequency, power, plot=True)\n",
    "\n",
    "    if show_rvs_flag == \"Y\":\n",
    "        #print(\"\\n\" + str(results[0]) + ' has_rvs: ' + str(target['has_rvs'].iloc[0]) + \"\\n\")\n",
    "        print(Fore.RED + \"37. \" + str(results[0]) + ' has_rvs: ' + str(target['has_rvs'].iloc[0]))\n",
    "        print(Style.RESET_ALL)\n",
    "        if target['has_rvs'].iloc[0] == True:\n",
    "            datalinkRVS = Gaia.load_data(results[0], data_structure = 'INDIVIDUAL', retrieval_type = 'RVS')\n",
    "            outputsRVS = [datalinkRVS[key][0] for key in datalinkRVS.keys()]\n",
    "            #print(\"datalinkRVS: \" + str(datalinkRVS))\n",
    "\n",
    "            if outputsRVS is not None:\n",
    "                if len(outputsRVS) >0:\n",
    "                    xpRVS      = outputsRVS[0].to_table()\n",
    "\n",
    "                    # =========\n",
    "                \n",
    "                    #display(xpRVS)\n",
    "\n",
    "                    # =========\n",
    "\n",
    "                    fig       = plt.figure(figsize=[20,10])\n",
    "\n",
    "                    yRangeMax = float(xpRVS['flux'].max())\n",
    "\n",
    "                    plt.plot(xpRVS['wavelength']*10, xpRVS['flux']/yRangeMax * 2, linewidth = 2, marker='.', markerfacecolor='red') \n",
    "                    plt.title('RVS Spectra Gaia DR3 Class '+ str(target['spectraltype_esphs'].iloc[0]) + \n",
    "                    ' \\nLogG: ' + str(round(target['logg_gspspec'].iloc[0], 2)) + \n",
    "                    '    Teff: ' + str(round(target['teff_gspspec'].iloc[0],0)) +\n",
    "                    '    Mass: ' + str(round(target['mass_flame'].iloc[0],2)) +\n",
    "                    '    Luminosity: ' + str(round(target['lum_flame'].iloc[0],2)) +\n",
    "                    '    Radius: ' + str(round(target['radius_gspphot'].iloc[0],2)) +\n",
    "                    '    Age: ' + str(round(target['age_flame'].iloc[0],2)) +\n",
    "                    '    [M/H]: ' + str(round(target['mh_gspspec'].iloc[0],2)) +\n",
    "                    '    [Alpha/Fe]: ' + str(round(target['alphafe_gspspec'].iloc[0],2)) +\n",
    "                    '\\nRa: ' + str(target['ra'].iloc[0]) + \"    Dec: \" + str(target['dec'].iloc[0]) +\n",
    "                    ' \\nSource_ID: ' + str(target['source_id'].iloc[0]), fontsize=15)\n",
    "\n",
    "                    if make_canvas_flag == \"Y\":\n",
    "                        #make_canvas(xlabel = f\"Wavelength [{xp['wavelength'].unit}]\", ylabel = f\"Flux [{xp['flux'].unit}]\", fontsize = 12)\n",
    "                        #make_labels()\n",
    "                        make_RVS_canvas()\n",
    "                        \n",
    "                    plt.show()\n",
    "\n",
    "\n",
    "                    fig       = plt.figure(figsize=[20,10])\n",
    "\n",
    "                    yRangeMax = float(xpRVS['flux'].max())\n",
    "\n",
    "                    plt.plot(xpRVS['wavelength']*10, xpRVS['flux']/yRangeMax * 2, linewidth = 2, marker='.', markerfacecolor='red') \n",
    "                    plt.title('RVS Spectra Gaia DR3 Class '+ str(target['spectraltype_esphs'].iloc[0]) + \n",
    "                    ' \\nLogG: ' + str(round(target['logg_gspspec'].iloc[0], 2)) + \n",
    "                    '    Teff: ' + str(round(target['teff_gspspec'].iloc[0],0)) +\n",
    "                    '    Mass: ' + str(round(target['mass_flame'].iloc[0],2)) +\n",
    "                    '    Luminosity: ' + str(round(target['lum_flame'].iloc[0],2)) +\n",
    "                    '    Radius: ' + str(round(target['radius_gspphot'].iloc[0],2)) +\n",
    "                    '    Age: ' + str(round(target['age_flame'].iloc[0],2)) +\n",
    "                    '    [M/H]: ' + str(round(target['mh_gspspec'].iloc[0],2)) +\n",
    "                    '    [Alpha/Fe]: ' + str(round(target['alphafe_gspspec'].iloc[0],2)) +\n",
    "                    '\\nRa: ' + str(target['ra'].iloc[0]) + \"    Dec: \" + str(target['dec'].iloc[0]) +\n",
    "                    ' \\nSource_ID: ' + str(target['source_id'].iloc[0]), fontsize=15)\n",
    "\n",
    "                    plt.xlim(8460, 8580)\n",
    "\n",
    "                    if make_canvas_flag == \"Y\":\n",
    "                        #make_canvas(xlabel = f\"Wavelength [{xp['wavelength'].unit}]\", ylabel = f\"Flux [{xp['flux'].unit}]\", fontsize = 12)\n",
    "                        #make_labels()\n",
    "                        make_RVS_canvas1()\n",
    "                        \n",
    "                    plt.show()\n",
    "\n",
    "\n",
    "                    fig       = plt.figure(figsize=[20,10])\n",
    "\n",
    "                    yRangeMax = float(xpRVS['flux'].max())\n",
    "\n",
    "                    plt.plot(xpRVS['wavelength']*10, xpRVS['flux']/yRangeMax * 2, linewidth = 2, marker='.', markerfacecolor='red') \n",
    "                    plt.title('RVS Spectra Gaia DR3 Class '+ str(target['spectraltype_esphs'].iloc[0]) + \n",
    "                    ' \\nLogG: ' + str(round(target['logg_gspspec'].iloc[0], 2)) + \n",
    "                    '    Teff: ' + str(round(target['teff_gspspec'].iloc[0],0)) +\n",
    "                    '    Mass: ' + str(round(target['mass_flame'].iloc[0],2)) +\n",
    "                    '    Luminosity: ' + str(round(target['lum_flame'].iloc[0],2)) +\n",
    "                    '    Radius: ' + str(round(target['radius_gspphot'].iloc[0],2)) +\n",
    "                    '    Age: ' + str(round(target['age_flame'].iloc[0],2)) +\n",
    "                    '    [M/H]: ' + str(round(target['mh_gspspec'].iloc[0],2)) +\n",
    "                    '    [Alpha/Fe]: ' + str(round(target['alphafe_gspspec'].iloc[0],2)) +\n",
    "                    '\\nRa: ' + str(target['ra'].iloc[0]) + \"    Dec: \" + str(target['dec'].iloc[0]) +\n",
    "                    ' \\nSource_ID: ' + str(target['source_id'].iloc[0]), fontsize=15)\n",
    "\n",
    "                    plt.xlim(8580, 8700)\n",
    "\n",
    "                    if make_canvas_flag == \"Y\":\n",
    "                        #make_canvas(xlabel = f\"Wavelength [{xp['wavelength'].unit}]\", ylabel = f\"Flux [{xp['flux'].unit}]\", fontsize = 12)\n",
    "                        #make_labels()\n",
    "                        make_RVS_canvas2()\n",
    "                        \n",
    "                    plt.show()\n",
    "                \n",
    "    if show_galah_flag == 'Y':\n",
    "        df_GALAH_match = df_GALAH[df_GALAH['source_id'].isin(target['source_id'])]\n",
    "        if len(df_GALAH_match) > 0:\n",
    "            df_GALAH_match['SpT2'] = target.apply(lambda row: starcolorindexSpT.subclass[round(row.bp_rp, 2)], axis=1)\n",
    "            df_GALAH_match['SpT2_dereddened'] = target.apply(lambda row: starcolorindexSpT.subclass[round((row.bp_rp - row.ebpminrp_gspphot), 2)], axis=1)\n",
    "            Plot_Spectra_Source_Id(df_GALAH_match)\n",
    "        else:\n",
    "            print(Fore.RED + \"38. No GALAH match found!\")\n",
    "            print(Style.RESET_ALL)\n",
    "            #print(\"\\n\")\n",
    "\n",
    "    if Plot_LightKurve_flag == \"Y\":\n",
    "        df_variable = df_asassn_variables[df_asassn_variables['gaia_source_id'] == source_id_target]\n",
    "        if np.isnan(df_variable['tic_id'].values[0]):\n",
    "            #tic_id = 0\n",
    "            print(\"38A. tic_id is NAN\")\n",
    "            tic_id = int(TIC_ID_2)\n",
    "        else:\n",
    "            tic_id = int(df_variable['tic_id'].values[0])\n",
    "            \"\"\"if len(tic_id) > 0:\n",
    "                TIC_ID_2 = tic_id\"\"\"\n",
    "        #print(\"38B. tic_id: \" + str(tic_id))\n",
    "        print(Fore.YELLOW + \"39. tic_id: \" + str(tic_id))\n",
    "        tic_target = \"tic \" + str(tic_id)\n",
    "        search_result = lk.search_lightcurve(tic_target)\n",
    "        #print(\"\\n\")\n",
    "        #print(search_result)\n",
    "        print(Fore.YELLOW + \"40. \" +str(search_result))\n",
    "        print(Style.RESET_ALL)\n",
    "        #print(\"\\n\")\n",
    "        #print(\"Length of lightcurve search_result: \" + str(len(search_result)))\n",
    "        if period == 0:\n",
    "            period = Period_2\n",
    "            print(Fore.RED +  \"41A. Resetting period to V or Blue filter period value!\")\n",
    "        #print(Fore.YELLOW + \"41. Length of lightcurve search_result: \" + str(len(search_result)))\n",
    "        print(Style.RESET_ALL)\n",
    "        if len(search_result) > 0:\n",
    "            #print(\"41B. tic_id: \" + str(tic_id))\n",
    "            Plot_LightKurve(tic_id, plot_count, period, target1)\n",
    "        else:\n",
    "            print(Fore.RED + \"42. LightKurve plot bypassed. Length of LightKurve search result equals zero.\")\n",
    "            print(Style.RESET_ALL)\n",
    "            print(\"\\n\")\n",
    "\n",
    "    \n",
    "    \n",
    "\n",
    "print(\"\\nDone!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbc17b29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "271f1114",
   "metadata": {},
   "source": [
    "https://en.wikipedia.org/wiki/Subdwarf_O_star\n",
    "\n",
    "https://en.wikipedia.org/wiki/Subdwarf_B_star\n",
    "\n",
    "\n",
    "\n",
    "http://www.astronomy.villanova.edu/lward/Seattle%20Poster%20Final.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c294d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Absortion lines\n",
    "\n",
    "\"\"\"\n",
    "3889:HeI\n",
    "3728.3:OII\n",
    "3934.78:CaK\n",
    "3969.59:CaH\n",
    "3971.19:He\n",
    "4072.3:SII\n",
    "4102.89:Hd\n",
    "4227.92:CaI\n",
    "4305.61:G\n",
    "4341.68:Hc\n",
    "4364.44:OIII\n",
    "4862.68:Hb\n",
    "4932.6:OIII\n",
    "4960.3:OIII\n",
    "5008.24:OIII\n",
    "5176.7:Mg\n",
    "5895.6:Na\n",
    "6302.05:OI\n",
    "6365.54:OI\n",
    "6529.03:NI\n",
    "6549.86:NII\n",
    "6564.61:Ha\n",
    "6585.27:NII\n",
    "6718.29:SII\n",
    "6732.67:SII\n",
    "8500.35:Ca\n",
    "8544.44:Ca\n",
    "8664.52:Ca\n",
    "\n",
    "\n",
    "3889:HeI\n",
    "3728.3:OII\n",
    "3934.78:CaK\n",
    "3969.59:CaH\n",
    "3971.19:He\n",
    "4072.3:SII\n",
    "4102.89:Hd\n",
    "4227.92:CaI\n",
    "4305.61:G\n",
    "4341.68:Hc\n",
    "4364.44:OIII\n",
    "4862.68:Hb\n",
    "4932.6:OIII\n",
    "4960.3:OIII\n",
    "5008.24:OIII\n",
    "5176.7:Mg\n",
    "5895.6:Na\n",
    "6302.05:OI\n",
    "6365.54:OI\n",
    "6529.03:NI\n",
    "6549.86:NII\n",
    "6564.61:Ha\n",
    "6585.27:NII\n",
    "6718.29:SII\n",
    "6732.67:SII\n",
    "8500.35:Ca\n",
    "8544.44:Ca\n",
    "8664.52:Ca\n",
    "\n",
    "\n",
    "3889:HeI\n",
    "3728.3:OII\n",
    "3934.78:CaK\n",
    "3969.59:CaH\n",
    "3971.19:He\n",
    "4072.3:SII\n",
    "4102.89:Hd\n",
    "4227.92:CaI\n",
    "4305.61:G\n",
    "4341.68:Hc\n",
    "4364.44:OIII\n",
    "4862.68:Hb\n",
    "4932.6:OIII\n",
    "4960.3:OIII\n",
    "5008.24:OIII\n",
    "5176.7:Mg\n",
    "5895.6:Na\n",
    "6302.05:OI\n",
    "6365.54:OI\n",
    "6529.03:NI\n",
    "6549.86:NII\n",
    "6564.61:Ha\n",
    "6585.27:NII\n",
    "6718.29:SII\n",
    "6732.67:SII\n",
    "8500.35:Ca\n",
    "8544.44:Ca\n",
    "8664.52:Ca\n",
    "\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a5d77a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Emission lines\n",
    "\n",
    "\"\"\"\n",
    "1400:SIV+OIV\n",
    "1549:CIV\n",
    "1640:HeII\n",
    "1909:CIII\n",
    "2326:CII\n",
    "2800:Mg\n",
    "3727:OII\n",
    "5876:HeI\n",
    "1033.3:OVI\n",
    "1239.42:NV\n",
    "1305.53:OI\n",
    "1335.52:CII\n",
    "3346.79:NeV\n",
    "3426.85:NeV\n",
    "3728.3:OII\n",
    "3869.7867:NeIII\n",
    "4102.89:Hdelta\n",
    "4341.6803:Hgamma\n",
    "4364.3782:OIII\n",
    "4862.6778:Hbeta\n",
    "4960.214:OIII\n",
    "5008.1666:OIII\n",
    "6301.9425:OI\n",
    "6549.7689:NII\n",
    "6564.6127:Halpha\n",
    "6585.1583:NII\n",
    "6718.1642:SII\n",
    "6732.5382:SII\n",
    "7137.637:ArIII\n",
    "\n",
    "\n",
    "1216:LyA\n",
    "1400:SIV+OIV\n",
    "1549:CIV\n",
    "1640:HeII\n",
    "1909:CIII\n",
    "2326:CII\n",
    "2800:Mg\n",
    "3727:OII\n",
    "5876:HeI\n",
    "1033.3:OVI\n",
    "1239.42:NV\n",
    "1305.53:OI\n",
    "1335.52:CII\n",
    "3346.79:NeV\n",
    "3426.85:NeV\n",
    "3728.3:OII\n",
    "3869.7867:NeIII\n",
    "4102.89:Hdelta\n",
    "4341.6803:Hgamma\n",
    "4364.3782:OIII\n",
    "4862.6778:Hbeta\n",
    "4960.214:OIII\n",
    "5008.1666:OIII\n",
    "6301.9425:OI\n",
    "6549.7689:NII\n",
    "6564.6127:Halpha\n",
    "6585.1583:NII\n",
    "6718.1642:SII\n",
    "6732.5382:SII\n",
    "7137.637:ArIII\n",
    "\n",
    "\n",
    "1216:LyA\n",
    "1400:SIV+OIV\n",
    "1549:CIV\n",
    "1640:HeII\n",
    "1909:CIII\n",
    "2326:CII\n",
    "2800:Mg\n",
    "3727:OII\n",
    "5876:HeI\n",
    "1033.3:OVI\n",
    "1239.42:NV\n",
    "1305.53:OI\n",
    "1335.52:CII\n",
    "3346.79:NeV\n",
    "3426.85:NeV\n",
    "3728.3:OII\n",
    "3869.7867:NeIII\n",
    "4102.89:Hdelta\n",
    "4341.6803:Hgamma\n",
    "4364.3782:OIII\n",
    "4862.6778:Hbeta\n",
    "4960.214:OIII\n",
    "5008.1666:OIII\n",
    "6301.9425:OI\n",
    "6549.7689:NII\n",
    "6564.6127:Halpha\n",
    "6585.1583:NII\n",
    "6718.1642:SII\n",
    "6732.5382:SII\n",
    "7137.637:ArIII\n",
    "\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d1f6c81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0033a9ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "lc0 = lk.search_lightcurve(\"OGLE BLG-ECL-129658\")[0].download();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31e3710",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a987fe22",
   "metadata": {},
   "source": [
    "ConnectionResetError: [Errno 54] Connection reset by peer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1db03816",
   "metadata": {},
   "source": [
    "HTTPError: Error 500:\n",
    "Code: 500, msg: Unable to create connection to database\n",
    "Source: [ajp-nio-127.0.0.1-8009-exec-169] Timeout: Pool empty. Unable to fetch a connection in 60 seconds, none available[size:15; busy:1; idle:0; lastwait:60000].\n",
    "\n",
    "Cannot process request: 'https://gea.esac.esa.int/data-server/data' (req: Reqid: jkarpins1686092790084, retrieval access: DIRECT, retrieval type: XP_SAMPLED, compression: null), for user: UwsJobOwner{id='jkarpins', name='Joseph Karpinski', mail='josephkarpinski@hotmail.com', authUsername='jkarpins', authGroups=[], pseudo='jkarpins', session='DAFBDE79F134AFB91DB788C9F75CC1BA', ip='71.115.217.107', roles=0, parameters=Owner parameters: 6},  due to: Code: 500, msg: Unable to create connection to database\n",
    "Source: [ajp-nio-127.0.0.1-8009-exec-169] Timeout: Pool empty. Unable to fetch a connection in 60 seconds, none available[size:15; busy:1; idle:0; lastwait:60000]."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "72507f8e",
   "metadata": {},
   "source": [
    "500 Error 500:\n",
    "Code: 500, msg: Unable to create connection to database\n",
    "Source: [ajp-nio-127.0.0.1-8009-exec-216] Timeout: Pool empty. Unable to fetch a connection in 60 seconds, none available[size:15; busy:1; idle:0; lastwait:60000].\n",
    "\n",
    "Cannot process request: 'https://gea.esac.esa.int/data-server/data' (req: Reqid: anonymous1686092360033, retrieval access: DIRECT, retrieval type: XP_SAMPLED, compression: null), for user: UwsJobOwner{id='anonymous', name='null', mail='null', authUsername='null', authGroups=[], pseudo='anonymous', session='081E6F644B4352FD4D22F9268146026B', ip='71.115.217.107', roles=0, parameters=Owner parameters: 6},  due to: Code: 500, msg: Unable to create connection to database\n",
    "Source: [ajp-nio-127.0.0.1-8009-exec-216] Timeout: Pool empty. Unable to fetch a connection in 60 seconds, none available[size:15; busy:1; idle:0; lastwait:60000]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61a079b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ac9f3c09",
   "metadata": {},
   "source": [
    "<img width=\"800\" src=\"screen_shot_1.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aad7acf4",
   "metadata": {},
   "source": [
    "<img width=\"800\" src=\"screen_shot_2.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e20241b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3775d79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faec66fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/1803.01001.pdf') # The ASAS-SN Catalog of Variable Stars I: The Serendipitous Survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95a4ccb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/1809.07329.pdf') # The ASAS-SN Catalog of Variable Stars II: Uniform Classification of 412,000 Known Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a622207",
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/1901.00009.pdf') # The ASAS-SN Catalog of Variable Stars III: Variables in the Southern TESS Continuous Viewing Zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f816b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/1907.10609.pdf') # The ASAS-SN Catalog of Variable Stars V: Variables in the Southern Hemisphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02829df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/2006.10057.pdf') # The ASAS-SN Catalog of Variable Stars IX: The Spectroscopic Properties of Galactic Variable Stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc79bb42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import webbrowser\n",
    "\n",
    "webbrowser.open('https://arxiv.org/pdf/2205.02239.pdf') # The ASAS-SN Catalog of Variable Stars X: Discovery of 116,000 New Variable Stars Using g-band Photometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4665ab9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e9b2115",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyasassn.client import SkyPatrolClient\n",
    "client = SkyPatrolClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c41c071",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT c2.name, c1.ra_deg, c1.dec_deg, c1.asas_sn_id, c1.gaia_id, c1.tic_id, c2.variability_type,  c2.period\n",
    "FROM stellar_main AS c1\n",
    "JOIN aavsovsx AS c2 USING(asas_sn_id)\n",
    "WHERE variability_type = 'YSO'\n",
    "\"\"\"\n",
    "\n",
    "#client.adql_query(query)\n",
    "\n",
    "results = client.adql_query(query)\n",
    "\n",
    "\n",
    "print(str(len(results)))\n",
    "#results\n",
    "\n",
    "\n",
    "# variability_type = 'CV'\n",
    "# variability_type = 'CV:'\n",
    "# variability_type = 'CV+E'\n",
    "# variability_type = 'EA'\n",
    "# variability_type = 'EB'\n",
    "# variability_type = 'EW'\n",
    "# variability_type = 'L'\n",
    "# variability_type = 'M'\n",
    "# variability_type = 'RRAB'\n",
    "# variability_type = 'RRC'\n",
    "# variability_type = 'SR'\n",
    "# variability_type = 'WR'\n",
    "# variability_type = 'YSO'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e07cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('asas_sn_yso_variables_' + str(len(results)) + '.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac46a96a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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