{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48f08fdc",
   "metadata": {},
   "source": [
    "======================== Import Packages =========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8cb36591",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,pdb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.table import Table, join, MaskedColumn\n",
    "from astropy import constants as const\n",
    "import csv\n",
    "import matplotlib as mpl\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from astroquery.vizier import Vizier\n",
    "import warnings\n",
    "from astropy.logger import AstropyWarning\n",
    "warnings.filterwarnings('ignore', category=AstropyWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84278d69",
   "metadata": {},
   "source": [
    "===================== Define Functions ==================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fddd834c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(catalog, join_key='Name', join_type='inner'):\n",
    "\n",
    "    \"\"\"\n",
    "    PURPOSE:    Get data from literature with Vizier\n",
    "\n",
    "    INPUT:      catalog = ctalog name on Vizier (str)\n",
    "                join_key = column header to join tables, if multiple (str; optional)\n",
    "                join_type = way to join tables, if multiple (str; optional)\n",
    "\n",
    "    OUTPUT:     t = data table (AstroPy Table)\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    ### GET FULL CATALOG (ALL COLUMNS, ALL ROWS)\n",
    "    viz = Vizier(catalog=catalog, columns=['**'])\n",
    "    viz.ROW_LIMIT = -1\n",
    "    tv = viz.get_catalogs(catalog)\n",
    "\n",
    "    ### IF MULTIPLE TABLES, JOIN THEN\n",
    "    for i, val in enumerate(tv.keys()):\n",
    "        if i == 0:\n",
    "            t = tv[val]\n",
    "        else:\n",
    "            tt = tv[val]\n",
    "            if join_key in tt.columns:\n",
    "                t = join(t, tt, join_type=join_type, keys=join_key)\n",
    "\n",
    "    return t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ba2729",
   "metadata": {},
   "source": [
    "============================= Code =================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa689d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### LOAD IN LUPUS DATA\n",
    "T = get_data(\"J/ApJ/828/46\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bb940db",
   "metadata": {},
   "outputs": [],
   "source": [
    "### REMOVE SOURCES WITH UNKNOWN STELLAR MASSES\n",
    "T = T[~T['Mass'].mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fab58e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE PLOT VARIABLES\n",
    "x_data = np.ma.log10(T['Mass'])\n",
    "x_err  = np.ma.array(0.434*(T['e_Mass'] / T['Mass']))\n",
    "y_data = np.ma.log10(T['Mgas'])\n",
    "y_max  = np.ma.log10(T['B_Mgas'])\n",
    "y_min  = np.ma.log10(T['b_Mgas'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b8cfd14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### SETUP PLOT\n",
    "mpl.rc('xtick', labelsize=15) \n",
    "mpl.rc('ytick', labelsize=15)\n",
    "mpl.rc('xtick.major', size=7, pad=7, width=1)\n",
    "mpl.rc('ytick.major', size=7, pad=7, width=1)\n",
    "mpl.rc('axes', linewidth=1)\n",
    "mpl.rc('lines', markersize=5)\n",
    "fig = plt.figure(figsize = (8, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlim([-1.3, 0.7])\n",
    "ax.set_ylim([-2.0, 2.0])\n",
    "ax.yaxis.set_major_locator(MaxNLocator(5, integer=True))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(4))\n",
    "ax.set_xlabel(r'$\\mathregular{log(M_{\\ast})}$' + ' ' + r'$\\mathregular{[M_{\\odot}]}$', fontsize=17)\n",
    "ax.set_ylabel(r'$\\mathregular{log(M_{gas})}$' + ' ' + r'$\\mathregular{[M_{Jup}]}$', fontsize=17)\n",
    "ax.tick_params(which='minor', axis='x', length=3, color='k', width=1)\n",
    "ax.tick_params(which='minor', axis='y', length=3, color='k', width=1)\n",
    "ax.minorticks_on()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb476d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "### PLOT DATA \n",
    "for i, val in enumerate(T['Name']):\n",
    "\n",
    "    ### PLOT NON-DETECTIONS\n",
    "    if T['l_Mgas'][i] == '<':\n",
    "        ax.errorbar(x_data[i], y_data[i], fmt='v', color='lightgray', ms=6, mec='black', mew=0.9, zorder=2)\n",
    "\n",
    "    ### PLOT DETECTIONS WITH ONLY UPPER LIMITS\n",
    "    elif T['b_Mgas'][i] is np.ma.masked:\n",
    "        ax.scatter(x_data[i], y_data[i], marker='o', facecolor='lightblue', s=60, edgecolor='darkslategray', zorder=3, linewidth=1)\n",
    "        ax.arrow(x_data[i], y_data[i], 0.0, -0.3, head_width=0.02, head_length=0.04, fc='lightgray', ec='lightgray', linewidth=1, zorder=1)\n",
    "        ax.errorbar(x_data[i], y_data[i], yerr=[[0], [y_max[i] - y_data[i]]], xerr=[x_err[i]],\n",
    "                    fmt='o', mfc='lightblue', ms=0, mec='black', mew=1, ecolor='lightgray', elinewidth=1, zorder=1, capsize=3)\n",
    "        \n",
    "    ### PLOT DETECTIONS WITH BOTH UPPER AND LOWER LIMITS\n",
    "    else:        \n",
    "        ax.errorbar(x_data[i], y_data[i], yerr=[[y_data[i] - y_min[i]], [y_max[i] - y_data[i]]], xerr=[x_err[i]],\n",
    "                    fmt='o', mfc='lightblue', ms=0, mec='black', mew=1, ecolor='lightgray', elinewidth=1, zorder=1, capsize=3)\n",
    "        ax.scatter(x_data[i], y_data[i], marker='o', facecolor='lightblue', s=60, edgecolor='darkslategray', zorder=3, linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6308a88b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SAVE FIGURE\n",
    "fig.savefig('../output/figure_07.png', bbox_inches='tight', dpi=100)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1751b7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}