plot_Fig6_complhist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from astropy import table\n",
    "from astropy.table import Table\n",
    "from astropy.io import ascii\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "SAGA_DIR = os.environ['SAGA_DIR']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from palettable.colorbrewer.qualitative import Dark2_8\n",
    "if 'plt' in locals() and hasattr(plt, 'rcParams'):\n",
    "    plt.rcParams['lines.linewidth'] = 2.0\n",
    "    plt.rcParams['font.size'] = 17.0\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": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# NAMED HOSTS\n",
    "complete_hosts =[147100,166313,165536,61945,132339, 149781,33446,150887]\n",
    "host_ngcs   =['NGC 6181','NGC 5962','NGC 5750','NGC 7716','NGC 1015','PGC 068743','NGC 2543','NGC 7541']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gri_criteria(base):\n",
    "    gmag = base['g'] - base['EXTINCTION_G']\n",
    "    rmag = base['r'] - base['EXTINCTION_R']\n",
    "    imag = base['i'] - base['EXTINCTION_I']\n",
    "\n",
    "    gr = gmag - rmag\n",
    "    ri = rmag - imag\n",
    "\n",
    "    grerr = np.sqrt(base['g_err']**2 + base['r_err']**2)\n",
    "    rierr = np.sqrt(base['r_err']**2 + base['i_err']**2)\n",
    "    \n",
    "    cgmr = gr - 2.*grerr\n",
    "    crmi = ri - 2.*rierr\n",
    "\n",
    "    msk1 = cgmr < 0.85 \n",
    "    msk2 = crmi < 0.55\n",
    "\n",
    "    return msk1&msk2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "bn = np.arange(16,22,0.25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:87: RuntimeWarning: invalid value encountered in divide\n"
     ]
    },
    {
     "data": {
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Xvu/7/jMEW9420Jw5c/jpp58ybenp6TkGc4Dzzz8/0xK1GU0Bd911l7eG4/Dh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\n8Tpy5Eh++OEHLr74YoAC+TI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Jr6VdN2927zPOj2CemOheLUd+3MO6dSM7f/DXjwT7fxwZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113caf150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 7, 8\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1)\n",
    "\n",
    "# CREATE ODYESSY PLOT\n",
    "basefile  = os.path.join(SAGA_DIR, 'base_catalogs', 'base_sql_nsa147100.fits.gz')\n",
    "base = Table.read(basefile) \n",
    "\n",
    "\n",
    "# FIND GOOD OBJECTS IN BASE CATALOG\n",
    "m_rmv  = base['REMOVE'] == -1\n",
    "m_rvir = base['RHOST_KPC'] <= 300\n",
    "m_fib  = base['FIBERMAG_R'] <= 23\n",
    "base = base[m_rmv & m_rvir & m_fib]\n",
    "\n",
    "m_gal  = base['PHOT_SG'] == 'GALAXY'\n",
    "zgood  = base['ZQUALITY'] > 2\n",
    "\n",
    "#  DEFINE r-MAGNITUDE\n",
    "r   = base['r'] - base['EXTINCTION_R']\n",
    "m21 = r < 20.75\n",
    "gri = gri_criteria(base) \n",
    "\n",
    "\n",
    "# ALL GALAXIES\n",
    "N, x  = np.histogram(r[m_gal], bins=bn)\n",
    "N2,x2 = np.histogram(r[m_gal&zgood], bins=x)\n",
    "c_all=1.*N2/N\n",
    "l_all = 'All Galaxies (N ={:d})'.format(np.sum(m_gal&m21))\n",
    "\n",
    "# ALL GALAXIES\n",
    "N, x  = np.histogram(r[m_gal&gri], bins=bn)\n",
    "N2,x2 = np.histogram(r[m_gal&zgood&gri], bins=x)\n",
    "c_gri=1.*N2/N\n",
    "l_gri = 'gri Galaxies (N={:d})'.format(np.sum(m_gal&gri&m21))\n",
    "\n",
    "\n",
    "# ALL STARS\n",
    "N, x  = np.histogram(r[~m_gal], bins=bn)\n",
    "N2,x2 = np.histogram(r[~m_gal&zgood], bins=x)\n",
    "c_star=1.*N2/N\n",
    "l_star = 'Stars (N={:d})'.format(np.sum(~m_gal&m21&zgood))\n",
    "\n",
    "\n",
    "bins = [(bn[i]+bn[i+1])/2. for i in range(len(bn)-1)]\n",
    "ax1.step(bins,c_all,color='k',where='mid',label=l_all)\n",
    "ax1.step(bins,c_gri,color='r',where='mid',label=l_gri)\n",
    "ax1.step(bins,c_star,color='b',where='mid',label=l_star)\n",
    "\n",
    "ax1.set_ylim(0,1.02)\n",
    "ax1.set_xlim(16.1,21.5)\n",
    "#ax1.axhline(1, c='k',ls=':')\n",
    "ax1.axvline(20.75, c='k',ls=':')\n",
    "ax1.legend(fontsize=11,frameon=True,numpoints = 1,loc=(0.03,0.2),title='NGC 6181')\n",
    "ax1.get_legend().get_title().set_fontsize('11')\n",
    "ax1.get_legend().get_title().set_position((-50, 0))\n",
    "ax1.set_xlabel('$r_o$')\n",
    "\n",
    "\n",
    "#########################################################\n",
    "\n",
    "clr = cm.rainbow(np.linspace(1, 0, len(complete_hosts)))\n",
    "for host,ngc,c in zip(complete_hosts,host_ngcs,clr):\n",
    "\n",
    "    \n",
    "    # READ BASE CATALOG\n",
    "    basefile  = os.path.join(SAGA_DIR, 'base_catalogs', 'base_sql_nsa{0}.fits.gz'.format(host))\n",
    "    base = Table.read(basefile) \n",
    "    \n",
    "    # FIND GOOD OBJECTS IN BASE CATALOG\n",
    "    m_rmv  = base['REMOVE'] == -1\n",
    "    m_rvir = base['RHOST_KPC'] <= 300\n",
    "    m_fib  = base['FIBERMAG_R'] <= 23\n",
    "    base = base[m_rmv & m_rvir & m_fib]\n",
    "\n",
    "    m_gal  = base['PHOT_SG'] == 'GALAXY'\n",
    "    zgood  = base['ZQUALITY'] > 2\n",
    "\n",
    "    #  DEFINE r-MAGNITUDE\n",
    "    r   = base['r'] - base['EXTINCTION_R']\n",
    "    m21 = r < 20.75\n",
    "    gri = gri_criteria(base) \n",
    "\n",
    "\n",
    "    # GRI GALAXIES\n",
    "    N, x  = np.histogram(r[m_gal&gri], bins=bn)\n",
    "    N2,x2 = np.histogram(r[m_gal&zgood&gri], bins=x)\n",
    "    c_gri=1.*N2/N\n",
    "    l = '{} (N={:d})'.format(ngc,np.sum(gri&m21&m_gal))\n",
    "\n",
    "\n",
    "\n",
    "    bins = [(bn[i]+bn[i+1])/2. for i in range(len(bn)-1)]\n",
    "    ax2.step(bins,c_gri,where='mid',label=l,color=c)\n",
    "    \n",
    "    \n",
    "ax2.legend(loc=3,fontsize=11,frameon=True,numpoints = 1)\n",
    "\n",
    "ax2.set_ylim(0,1.02)\n",
    "ax2.set_xlim(16.1,21.5)\n",
    "#ax2.axhline(1, c='k',ls=':')\n",
    "ax2.axvline(20.75, c='k',ls=':')\n",
    "ax2.set_xlabel('$r_o$')\n",
    "\n",
    "ax2.text(15.483, 1.3, 'Spectroscopic Completeness', va='center', rotation='vertical')\n",
    "\n",
    "plt.tight_layout(w_pad=0,h_pad=0)\n",
    "plt.savefig('fig_completeness.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0 300 600 900]\n"
     ]
    }
   ],
   "source": [
    "# DEFINE RADIAL BINS FOR REFEREE REPORT\n",
    "bn = np.arange(0,1200,300)\n",
    "print bn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[150.0, 450.0, 750.0]\n",
      "[ 0.96923077  0.15947712         nan]\n",
      "[150.0, 450.0, 750.0]\n",
      "[ 0.97769231  0.30869383         nan]\n",
      "[150.0, 450.0, 750.0]\n",
      "[ 0.96824324  0.33671498         nan]\n",
      "[150.0, 450.0, 750.0]\n",
      "[ 0.93667007  0.1502947   0.14606742]\n",
      "[150.0, 450.0, 750.0]\n",
      "[ 0.9163722   0.09664623  0.05132193]\n",
      "[150.0, 450.0, 750.0]\n",
      "[ 0.89639115  0.06161695  0.00555042]\n",
      "[150.0, 450.0, 750.0]\n",
      "[ 0.85035629  0.05426696  0.03381643]\n",
      "[150.0, 450.0, 750.0]\n",
      "[ 0.81618497  0.01943463  0.00174216]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:30: RuntimeWarning: invalid value encountered in divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q3E8/0FdxJKPXDf/HW63KHvHeQABXNpRfTrrM/JGBdpS0BbBx474R8Tvg/nxs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117075050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ****** REDO AS A FUNCTION OF RADIUS\n",
    "plt.rcParams['figure.figsize'] = 7, 5\n",
    "\n",
    "\n",
    "clr = cm.rainbow(np.linspace(1, 0, len(complete_hosts)))\n",
    "for host,ngc,c in zip(complete_hosts,host_ngcs,clr):\n",
    "\n",
    "    \n",
    "    # READ BASE CATALOG\n",
    "    basefile  = os.path.join(SAGA_DIR, 'base_catalogs', 'base_sql_nsa{0}.fits.gz'.format(host))\n",
    "    base = Table.read(basefile) \n",
    "    \n",
    "    # FIND GOOD OBJECTS IN BASE CATALOG\n",
    "    m_rmv  = base['REMOVE'] == -1\n",
    "    m_fib  = base['FIBERMAG_R'] <= 23\n",
    "    m_gal  = base['PHOT_SG'] == 'GALAXY'\n",
    "    r   = base['r'] - base['EXTINCTION_R']\n",
    "    m21 = r < 20.75\n",
    "    \n",
    "    base = base[m_rmv & m_fib & m_gal & m21]\n",
    "    dist = base['RHOST_KPC']\n",
    "    \n",
    "    zgood  = base['ZQUALITY'] > 2\n",
    "    gri = gri_criteria(base) \n",
    "\n",
    "\n",
    "    # GRI GALAXIES\n",
    "    N, x  = np.histogram(dist[gri], bins=bn)\n",
    "    N2,x2 = np.histogram(dist[zgood&gri], bins=x)\n",
    "    c_gri=1.*N2/N\n",
    "#    l = '{} (N={:d})'.format(ngc,np.sum(gri&m_gal))\n",
    "\n",
    "\n",
    "\n",
    "    bins = [(bn[i]+bn[i+1])/2. for i in range(len(bn)-1)]\n",
    "    plt.step(bins,c_gri,where='mid',color=c)\n",
    "    print bins\n",
    "    print c_gri\n",
    "    \n",
    "plt.legend(loc=3,fontsize=11,frameon=True,numpoints = 1)\n",
    "\n",
    "plt.ylim(0,1.02)\n",
    "plt.xlim(0,800)\n",
    "\n",
    "plt.xlabel('Radius [kpc]')\n",
    "plt.ylabel('gri Completeness')\n",
    "\n",
    "\n",
    "plt.tight_layout(w_pad=0,h_pad=0)\n"
   ]
  },
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   "source": []
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