plot_Fig5_colorcolor_NOAO.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "from astropy import table\n",
    "from astropy.table import Table\n",
    "from astropy.io import ascii\n",
    "\n",
    "from astroML.plotting import scatter_contour\n",
    "\n",
    "\n",
    "SAGA_DIR = os.environ['SAGA_DIR']\n",
    "SAGA_DROPBOX= os.environ['SAGA_DROPBOX']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# READ SPECTRA\n",
    "file = SAGA_DROPBOX+'/data/saga_spectra_clean.fits.gz'\n",
    "allspec = Table.read(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# FIND GOOD OBJECTS \n",
    "m_rmv  = allspec['REMOVE'] == -1\n",
    "m_fib  = allspec['FIBERMAG_R'] <= 23\n",
    "m_gal  = allspec['PHOT_SG'] == 'GALAXY'\n",
    "\n",
    "m_boss = allspec['survey'] != 'boss'\n",
    "\n",
    "spec=allspec[m_rmv & m_fib & m_gal &m_boss]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161\n",
      "378\n"
     ]
    }
   ],
   "source": [
    "# EXPLORE BRIGHT AND FAINT GALAXIES SEPARATELY\n",
    "m_ff1 = (spec['r'] - spec['EXTINCTION_R']) >19.\n",
    "m_ff2 = (spec['r'] - spec['EXTINCTION_R']) <=20.75\n",
    "m_ff=m_ff1 & m_ff2\n",
    "\n",
    "m_f1 = (spec['r'] - spec['EXTINCTION_R']) >17.7\n",
    "m_f2 = (spec['r'] - spec['EXTINCTION_R']) <=20.75\n",
    "m_f  = m_f1 & m_f2 \n",
    "m_b1  = (spec['r'] - spec['EXTINCTION_R']) < 17.7\n",
    "m_b2  = (spec['r'] - spec['EXTINCTION_R']) > 15\n",
    "m_b=m_b1& m_b2\n",
    "\n",
    "sats = spec['SATS'] == 1\n",
    "lowz = (spec['SPEC_Z'] > 0.005) & (spec['SPEC_Z'] < 0.015)\n",
    "\n",
    "print np.sum(sats)\n",
    "print np.sum(lowz)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MAGNITUDES\n",
    "gmag = spec['g'] - spec['EXTINCTION_G']\n",
    "rmag = spec['r'] - spec['EXTINCTION_R']\n",
    "imag = spec['i'] - spec['EXTINCTION_I']\n",
    "\n",
    "# COLORS\n",
    "gr = gmag - rmag\n",
    "ri = rmag - imag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SET PLOT LIMITS\n",
    "xl = [-0.3,1.3]\n",
    "yl=[-0.75,0.9]\n",
    "\n",
    "xl1 = gr > xl[0]\n",
    "xl2 = gr < xl[1]\n",
    "yl1 = ri > yl[0]\n",
    "yl2 = ri < yl[1]\n",
    "lims = xl1&xl2&yl1&yl2\n",
    "\n",
    "tgr = 0.85\n",
    "tri = 0.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11abbf5d0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XzymtYfYRWsb3Pq1XkHyfJji2gIS4mGIRGB6+npUr3asgxL2H7dvdvvDhRQtziUWZjfPo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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab8fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1)\n",
    "\n",
    "scatter_contour(gr[m_f&lims], ri[m_f&lims], ax=ax1,\n",
    "                threshold=30, log_counts=True, \n",
    "                histogram2d_args=dict(bins=40),\n",
    "                plot_args=dict(marker=',', linestyle='none', color='black'),\n",
    "                contour_args=dict(cmap=plt.cm.bone))\n",
    "\n",
    "ax1.plot(gr[m_b & lowz], ri[m_b & lowz],'bs',zorder=3,label = 'bright: 0.005$<$z$<$0.015')\n",
    "ax1.plot(gr[m_f & lowz], ri[m_f & lowz],'rs',zorder=3,label = 'faint: 0.005$<$z$<$0.015')\n",
    "#ax1.plot(gr[m_ff & lowz], ri[m_ff & lowz],'gs',zorder=3,label = 'faint: 0.005$<$z$<$0.015')\n",
    "\n",
    "ax1.set_xlim(xl)\n",
    "ax1.set_ylim(yl)\n",
    "#ax1.text(xl[0]+0.02,yl[1]-0.2,'Bright Galaxies: r $<$ 17.7',fontsize=11)\n",
    "\n",
    "ax1.axvline(tgr, c='w')\n",
    "ax1.axvline(tgr, c='k', ls=':')\n",
    "ax1.axhline(tri, c='w')\n",
    "ax1.axhline(tri, c='k', ls=':')\n",
    "\n",
    "# NEW LIMITS\n",
    "sgr=0.5\n",
    "sri=0.24\n",
    "ax1.plot([-10,sgr],[sri,sri], c='y',zorder=4)\n",
    "ax1.plot([sgr,sgr],[-10,0.25], c='y',zorder=4)\n",
    "\n",
    "#ax1.axhline(0.3, c='r')\n",
    "\n",
    "ax1.set_xlabel('$(g-r)_o$')\n",
    "ax1.set_ylabel('$(r-i)_o$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "m= (gr > sgr) & (ri > sri) & (spec['SATS'] ==1) & (spec['r']-spec['EXTINCTION_R'] > 17.7)& (spec['r']-spec['EXTINCTION_R'] < 20.75)\n",
    "print np.sum(m)\n",
    "\n",
    "#m=spec['OBJID'] == 1237662662147638034\n",
    "#print spec['RA'][m],spec['DEC'][m]\n",
    "\n"
   ]
  },
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