plot_Fig12_nsat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "%matplotlib inline\n",
    "\n",
    "from astropy import table\n",
    "from astropy.table import Table\n",
    "from astropy.io import ascii\n",
    "from tabulate import tabulate\n",
    "import pyspherematch as sm\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord, FK4, FK5\n",
    "\n",
    "SAGA_DIR = os.environ['SAGA_DIR']\n",
    "SAGA_DROPBOX= os.environ['SAGA_DROPBOX']\n",
    "\n",
    "#import pandas as pd\n",
    "from scipy.stats import spearmanr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from palettable.colorbrewer.qualitative import Dark2_8\n",
    "if 'plt' in locals() and hasattr(plt, 'rcParams'):\n",
    "    plt.rcParams['lines.linewidth'] = 2.5\n",
    "    plt.rcParams['font.size'] = 16.0\n",
    "    plt.rcParams['font.weight'] = 'medium'\n",
    "    plt.rcParams['font.family'] = 'serif'\n",
    "    plt.rcParams['axes.prop_cycle'] = plt.cycler(color=Dark2_8.mpl_colors)\n",
    "    plt.rcParams['legend.fontsize'] = 'medium'\n",
    "    plt.rcParams['legend.frameon'] = False\n",
    "    plt.rcParams['figure.dpi'] = 100\n",
    "    plt.rcParams['figure.figsize'] = 7, 6\n",
    "    plt.rcParams['xtick.major.size'] = 6\n",
    "    plt.rcParams['xtick.minor.size'] = 4\n",
    "    plt.rcParams['ytick.major.size'] = 6\n",
    "    plt.rcParams['ytick.minor.size'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# READ SUBMASTER LIST \n",
    "master = Table.read(SAGA_DROPBOX+'hosts/submaster.ecsv.gz', format='ascii.ecsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33446\n",
      "61945\n",
      "132339\n",
      "147100\n",
      "149781\n",
      "150887\n",
      "165536\n",
      "166313\n"
     ]
    }
   ],
   "source": [
    "file = SAGA_DIR + '/data/sats.txt'\n",
    "sats = ascii.read(file)\n",
    "\n",
    "hid = np.in1d(master['NSAID'],sats['NSAID'])\n",
    "hosts = master[hid]\n",
    "for obj in hosts:\n",
    "    print obj['NSAID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nsa = Table.read (SAGA_DIR+'/cats/nsa_v0_1_3.fits')\n",
    "nids = np.in1d(nsa['NSAID'],sats['NSAID'])\n",
    "mass = nsa['MASS'][nids]\n",
    "log_SM = np.log10(mass/0.7**2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.101733037454\n",
      "0.385120643556\n",
      "(-0.11904761904761905, 0.77888572605237971)\n",
      "(-0.11904761904761905, 0.77888572605237971)\n",
      "    M_g     \n",
      "------------\n",
      "0.7175047579\n",
      "0.7915435751\n",
      "0.8173822771\n",
      "0.6644220353\n",
      " 0.816135892\n",
      "0.7189708983\n",
      "0.7478537619\n",
      "0.6828352152\n",
      "    M_g     \n",
      "------------\n",
      "0.7175047579\n",
      "0.7915435751\n",
      "0.8173822771\n",
      "0.6644220353\n",
      " 0.816135892\n",
      "0.7189708983\n",
      "0.7478537619\n",
      "0.6828352152\n",
      "0.778885726052\n",
      "0.385120643556\n",
      "0.20703125\n",
      "0.18272075054\n"
     ]
    },
    {
     "data": {
      "image/png": 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34PNLgH8CnYPP+xP4hPHjRO9bsG0JgcN5EDjlsxS40o/bDtgCDAl5fiOB6xye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\nH7CEjHvH8p+eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff1df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(6,8), sharex=True)\n",
    "plt.rcParams['font.size'] = 13.0\n",
    "\n",
    "MWsats = 5\n",
    "M31sats = 9\n",
    "\n",
    "ns = sats['Ncompl']\n",
    "np.append(ns,MWsats)\n",
    "\n",
    "ylim = 11.5\n",
    "yl = 10.25\n",
    "\n",
    "# MK\n",
    "ax = plt.subplot2grid((3,2), (0, 0))\n",
    "ax.set_ylim(0, ylim)\n",
    "ax.plot(hosts['M_K'],sats['Nsat'],'ko')\n",
    "ax.plot(hosts['M_K'],sats['Ncompl'],'ko',markerfacecolor='none')\n",
    "ax.plot([-24],[MWsats],'y*',color='#ff8c00',markersize=11)\n",
    "ax.plot([-24.51-0.188],[M31sats],'*',color='#ba55d3',markersize=11) # hammer 2007\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(0.5))\n",
    "ax.set_ylabel('N$_{\\mathrm{sats}}$')\n",
    "ax.set_xlabel('$M_K$')\n",
    "hp = hosts['M_K']\n",
    "np.ma.append(hp,-24)\n",
    "sr = spearmanr(hp,ns)\n",
    "t = '$M_K$: ($r_s$={:04.2f}, p={:04.2f})'.format(sr[0],sr[1])\n",
    "ax.text(-24.4,yl,t,fontsize=8)\n",
    "print sr[1]\n",
    "\n",
    "\n",
    "# Mr\n",
    "ax = plt.subplot2grid((3,2), (0, 1))\n",
    "ax.set_ylim(0, ylim)\n",
    "ax.set_xlim(-22.15,-20.8)\n",
    "ax.plot(hosts['M_r'],sats['Nsat'],'ko')\n",
    "ax.plot(hosts['M_r'],sats['Ncompl'],'ko',markerfacecolor='none')\n",
    "#ax.plot([-21. + 5.*np.log10(0.7)],[MWsats],'y*',color='#ff8c00',markersize=11)\n",
    "ax.plot([-20.7 + 5.*np.log10(0.7)],[MWsats],'y*',color='#ff8c00',markersize=11)\n",
    "ax.plot([-22.0],[M31sats],'*',color='#ba55d3',markersize=11) # hammer 2007\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(0.5))\n",
    "ax.set_xlabel('$M_r$')\n",
    "ax.get_yaxis().set_ticklabels([])\n",
    "hp = hosts['M_r']\n",
    "np.ma.append(hp,-20.7+ 5.*np.log10(0.7))\n",
    "sr = spearmanr(hp,ns)\n",
    "#sr = spearmanr(hosts['M_r'],sats['Ncompl'])\n",
    "t = '$M_r$: ($r_s$={:04.2f}, p={:04.2f})'.format(sr[0],sr[1])\n",
    "ax.text(-21.85,yl,t,fontsize=8)\n",
    "print sr[1]\n",
    "\n",
    "\n",
    "# COLORS\n",
    "# g-r\n",
    "gr =  hosts['M_g'] - hosts['M_r']\n",
    "ax = plt.subplot2grid((3,2), (1, 0))\n",
    "ax.plot(gr,sats['Nsat'],'ko')\n",
    "ax.plot(gr,sats['Ncompl'],'ko',markerfacecolor='none')\n",
    "ax.plot([0.68],[MWsats],'y*',color='#ff8c00',markersize=11)\n",
    "ax.plot([0.708],[M31sats],'y*',color='#ba55d3',markersize=11)\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(0.05))\n",
    "ax.set_ylim(0, ylim)\n",
    "ax.set_xlabel('$(g-r)_o$')\n",
    "ax.set_ylabel('N$_{\\mathrm{sats}}$')\n",
    "hp = gr\n",
    "np.ma.append(hp,0.68)\n",
    "sr = spearmanr(hp,ns)\n",
    "print sr\n",
    "sr = spearmanr(gr,sats['Ncompl'])\n",
    "print sr\n",
    "print hp\n",
    "print gr\n",
    "t = '(g-r): ($r_s$={:04.2f}, p={:04.2f})'.format(sr[0],sr[1])\n",
    "ax.text(0.705,yl,t,fontsize=8)\n",
    "print sr[1]\n",
    "\n",
    "\n",
    "# u-r\n",
    "ur =  hosts['M_u'] - hosts['M_r']\n",
    "ax = plt.subplot2grid((3,2), (1, 1))\n",
    "ax.plot(ur,sats['Nsat'],'ko')\n",
    "ax.plot(ur,sats['Ncompl'],'ko',markerfacecolor='none')\n",
    "ax.plot([2.043],[MWsats],'y*',color='#ff8c00',markersize=11)\n",
    "ax.plot([2.32],[M31sats],'y*',color='#ba55d3',markersize=11)\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(0.25))\n",
    "ax.set_ylim(0, ylim)\n",
    "ax.set_xlabel('$(u-r)_o$')\n",
    "ax.get_yaxis().set_ticklabels([])\n",
    "hp = ur\n",
    "np.ma.append(hp,2.043)\n",
    "sr = spearmanr(hp,ns)\n",
    "#sr = spearmanr(ur,sats['Ncompl'])\n",
    "t = '(u-r): ($r_s$={:04.2f}, p={:04.2f})'.format(sr[0],sr[1])\n",
    "ax.text(2.1,yl,t,fontsize=8)\n",
    "print sr[1]\n",
    "\n",
    "\n",
    "# DERIVED PROPERTIES\n",
    "# MSTAR\n",
    "# 6.08 +/- 1.14 x 10^10\n",
    "ax = plt.subplot2grid((3,2), (2, 0))\n",
    "ax.plot(log_SM,sats['Nsat'],'ko',label='_nolabel_')\n",
    "ax.plot(log_SM,sats['Ncompl'],'ko',markerfacecolor='none',label='_nolabel_')\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(0.25))\n",
    "ax.plot([np.log10(6.08e10)],[MWsats],'y*',color='#ff8c00',markersize=11,label='MW')\n",
    "ax.plot([np.log10(10.5e10)],[M31sats],'y*',color='#ba55d3',markersize=11,label='M31')\n",
    "ax.set_ylim(0, ylim)\n",
    "ax.set_xlim(10.4, 11.1)\n",
    "ax.set_xlabel('log[M$_{\\mathrm{star}}$]')\n",
    "ax.set_ylabel('N$_{\\mathrm{sats}}$')\n",
    "hp = log_SM\n",
    "np.ma.append(hp,np.log10(6.08e10))\n",
    "sr = spearmanr(hp,ns)\n",
    "#sr = spearmanr(log_SM,sats['Ncompl'])\n",
    "t = 'log[M$_{{star}}$] : ($r_s$={:04.2f}, p={:04.2f})'.format(sr[0],sr[1])\n",
    "ax.text(10.55,yl,t,fontsize=8)\n",
    "print sr[1]\n",
    "ax.legend(fontsize=10,loc=4,numpoints = 1,frameon=True)\n",
    "\n",
    "# SFR\n",
    "# 1.65 +/- 0.19 msun/year\n",
    "ax = plt.subplot2grid((3,2), (2, 1))\n",
    "ax.plot(sats['logSFR'],sats['Nsat'],'ko')\n",
    "ax.plot(sats['logSFR'],sats['Ncompl'],'ko',markerfacecolor='none')\n",
    "ax.set_ylim(0, ylim)\n",
    "ax.set_xlabel('log[SFR]')\n",
    "ax.set_xlim(-1.28,1.6)\n",
    "ax.get_yaxis().set_ticklabels([])\n",
    "ax.plot([np.log10(1.65)],[MWsats],'y*',color='#ff8c00',markersize=11)\n",
    "ax.plot([np.log10(0.7)],[M31sats],'y*',color='#ba55d3',markersize=11)\n",
    "hp = sats['logSFR']\n",
    "np.ma.append(hp,-1.28)\n",
    "sr = spearmanr(hp,ns)\n",
    "#sr = spearmanr(sats['logSFR'],sats['Ncompl'])\n",
    "t = 'log[SFR] : ($r_s$={:04.2f}, p={:04.2f})'.format(sr[0],sr[1])\n",
    "ax.text(-0.57,yl,t,fontsize=8)\n",
    "print sr[1]\n",
    "\n",
    "plt.tight_layout(w_pad=0)\n",
    "plt.savefig('fig_nsats.pdf')\n",
    "\n",
    "\n"
   ]
  },
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   },
   "source": [
    "There are 9 M31 satellites.\n",
    "M33 -19.14\n",
    "M32 -16.73\n",
    "IC 10 -15.3\n",
    "\n",
    "NGC 205 -16.78\n",
    "NGC 147 -14.95\n",
    "NGC 185 -15.05\n",
    "\n",
    "And XXXII -12.55\n",
    "Andromeda II -12.87\n",
    "Andromeda VII -13.51\n",
    "\n",
    "\n",
    "Andromeda I  -12.16\n",
    "And XXXI  -12.0\n",
    "Andromeda VI  -11.77\n"
   ]
  }
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