qlf_reionisation_seed_formation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import os.path\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy import wcs\n",
    "from astropy.nddata import Cutout2D\n",
    "from astropy import coordinates\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.wcs import WCS\n",
    "import matplotlib.gridspec as gridspec\n",
    "import aplpy\n",
    "import pandas as pd\n",
    "from photutils import SkyCircularAperture\n",
    "from photutils import aperture_photometry\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path = '/Users/justin/Documents/Master_Thesis/data/catalogues/chandra_7ms/big_table.fits'\n",
    "big_table = fits.open(path)[1].data\n",
    "\n",
    "#path = '/Users/justin/Documents/Master_Thesis/data/catalogues/cutouts/something.cat'\n",
    "#cat = pd.read_csv(path, delim_whitespace=True, header=None, names=['whatever'], skiprows=None)\n",
    "\n",
    "#begin analysis\n",
    "mbh=10e6 #solar masses\n",
    "er=0.01\n",
    "z=5.\n",
    "kcorr=25.\n",
    "gamma=1.8\n",
    "nh=10e22 #cm-2\n",
    "flsoft=6.4*10e-18 #erg cm-2 s-1\n",
    "flhard=2.7*10e-17 #erg cm-2 s-1\n",
    "\n",
    "#go from mbh to bolometric luminonity using er: formula\n",
    "\n",
    "#go from bolometric luminosity to X-ray luminosity using kcorr (see Vasudevan & Fabian 2009, Treister 2011)\n",
    "\n",
    "#transform from rest-frame X-ray luminosity to flux at a certain redshift and photon index (using formula given by Benny that includes k corrections)\n",
    "\n",
    "#use nh to calculate extinction in soft band (use XSPEC?)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "527 -20.5262908906 -19.8019984436 -21.3516723914 4.996 4.502 5.833\n",
      "-20.53^{+0.72}_{-0.83}\n",
      "622 -20.9943628752 5.75\n",
      "640 -21.8265499846 -20.6945243298 -22.8708085224 6.031 5.071 7.503\n",
      "-21.83^{+1.13}_{-1.04}\n",
      "714 -20.9795458423 -20.6799297275 -21.7879410633 5.177 4.991 6.026\n",
      "-20.98^{+0.30}_{-0.81}\n",
      "-21.1107955725 0.458966404986 0.519105357418\n"
     ]
    }
   ],
   "source": [
    "#calculate m1450 from template\n",
    "path = '/Users/justin/Documents/Master_Thesis/data/catalogues/chandra_7ms/tbl04.fits'\n",
    "cat = fits.open(path)[1].data\n",
    "xid=[527,622,640,714]\n",
    "z_grid=[3.,3.5,4.,4.5,5.,5.5,6.,6.5,7.,7.5,8.]\n",
    "d_l=[25417.2,30575.2,35842.1,41200.5,46637.4,52142.5,57707.7,63326.5,68993.5,74704.0,80454.2] #Mpc luminosity distance based on redshift (Ned Wright cosm. calc.)\n",
    "m_mean=0.\n",
    "m_var_l=0.\n",
    "m_var_u=0.\n",
    "for i in range(len(xid)):\n",
    "    if xid[i] == 622:\n",
    "        path_sed = '/Users/justin/Documents/Master_Thesis/softwares/eazy/inputs/OUTPUT_zspec/photz_' + str(xid[i]) + '.temp_sed'\n",
    "        zphot=5.75\n",
    "        cat_sed = pd.read_csv(path_sed, delim_whitespace=True, header=None, names=['Wavelength', 'Flux'], skiprows=[0,1])\n",
    "        w1450=1450.*(zphot+1) #angstrom\n",
    "        f1450=np.interp(w1450,cat_sed['Wavelength'],cat_sed['Flux']) #Jansky\n",
    "        #convert from Jansky to AB absolute magnitude (defined as flux from 10 parsec - 32.6 ly)\n",
    "        m1450=-2.5*np.log10(f1450*((np.interp(zphot,z_grid,d_l)*1000000./10.)**2)/3631.)\n",
    "        print(xid[i], m1450, zphot)\n",
    "    else:\n",
    "        path_sed = '/Users/justin/Documents/Master_Thesis/softwares/eazy/inputs/OUTPUT/photz_' + str(xid[i]) + '.temp_sed'\n",
    "        path_le = '/Users/justin/Documents/Master_Thesis/softwares/eazy/inputs/OUTPUT_zspec/photz_' + str(xid[i]) + '.temp_sed'\n",
    "        path_ue = '/Users/justin/Documents/Master_Thesis/softwares/eazy/inputs/OUTPUT_zspec/photz_' + str(xid[i]+1) + '.temp_sed'\n",
    "        zphot=big_table.field('Phot_z')[xid[i]-1]\n",
    "        zphot_l=big_table.field('Phot_z_l1')[xid[i]-1]\n",
    "        zphot_u=big_table.field('Phot_z_u1')[xid[i]-1]\n",
    "        cat_sed = pd.read_csv(path_sed, delim_whitespace=True, header=None, names=['Wavelength', 'Flux'], skiprows=[0,1])\n",
    "        cat_le = pd.read_csv(path_sed, delim_whitespace=True, header=None, names=['Wavelength', 'Flux'], skiprows=[0,1])\n",
    "        cat_ue = pd.read_csv(path_sed, delim_whitespace=True, header=None, names=['Wavelength', 'Flux'], skiprows=[0,1])\n",
    "    \n",
    "        w1450=1450.*(zphot+1) #angstrom\n",
    "        w1450_l=1450.*(zphot_l+1)\n",
    "        w1450_u=1450.*(zphot_u+1)\n",
    "        f1450=np.interp(w1450,cat_sed['Wavelength'],cat_sed['Flux']) #Jansky\n",
    "        f1450_l=np.interp(w1450_l,cat_le['Wavelength'],cat_le['Flux'])\n",
    "        f1450_u=np.interp(w1450_u,cat_ue['Wavelength'],cat_ue['Flux'])\n",
    "        #convert from Jansky to AB absolute magnitude (defined as flux from 10 parsec - 32.6 ly)\n",
    "        m1450=-2.5*np.log10(f1450*((np.interp(zphot,z_grid,d_l)*1000000./10.)**2)/3631.)\n",
    "        m1450_l=-2.5*np.log10(f1450_l*((np.interp(zphot_l,z_grid,d_l)*1000000./10.)**2)/3631.)\n",
    "        m1450_u=-2.5*np.log10(f1450_u*((np.interp(zphot_u,z_grid,d_l)*1000000./10.)**2)/3631.)\n",
    "        print(xid[i], m1450, m1450_l, m1450_u, zphot, zphot_l, zphot_u)\n",
    "        print('%.2f^{+%.2f}_{-%.2f}' % (m1450, m1450_l-m1450, m1450-m1450_u))\n",
    "        m_mean+=m1450\n",
    "        m_var_l+=(m1450-m1450_l)**2\n",
    "        m_var_u+=(m1450_u-m1450)**2\n",
    "m_mean=m_mean/3.\n",
    "m_var_l=(m_var_l**0.5)/3.\n",
    "m_var_u=(m_var_u**0.5)/3.\n",
    "print(m_mean, m_var_l, m_var_u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.43740618431 -5.91452743902 -5.65183909472 0.295053706105 0.341352740152 0.761953896871 0.518513939878\n",
      "2.50182261461 2.78431978601 1.103e+10\n"
     ]
    },
    {
     "data": {
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v8N1KJSTE+AJISUEXEUFI3Tocf8ufOK+m6MdNAX/rfDkWojz5tzTeCCYmJrJ622oC9wYy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\n7AF+VlYW9913H59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PZuzYsTRv3pyUlBQuX75M3bp1uXjxInFxcab9/vnnH44ePVqk0bFl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\nAAAAAMAi/QZ9I1KfNo5fpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b68358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#UV qlf determination\n",
    "#first point (se phot): -20.5 to -22.5 (3 sources), with z=5-6.5 bin\n",
    "# second point (manual phot): -20.5 to -22.5 (could choose -21.5) (1 source 714), with z= 5.0-6.5 bin\n",
    "#evt upper limit, no sources are high z (z=5 to 6.5 bin)\n",
    "\n",
    "n=[0,1,2,3]\n",
    "l=[0,0.173,0.708,1.367] #Poissonian 1-sigma Gaussian upper and lower limits for n counts (Gehrels 1986)\n",
    "u=[1.831,3.300,4.638,5.918]\n",
    "\n",
    "goods_s=110/3600 #deg2, only CANDELS region\n",
    "whole_sky=(360.**2)/np.pi #deg2\n",
    "sky_frac = goods_s/whole_sky\n",
    "\n",
    "vol_com_5 = 1967.314 #Gpc3 (note: comoving volume within redshift z, might want luminosity distance instead)\n",
    "vol_com_5_5 = 2162.529\n",
    "vol_com_6_5 = 2521.764 #Gpc3\n",
    "\n",
    "vol_tot = sky_frac * (vol_com_6_5 - vol_com_5) * (1000**3) #Mpc3\n",
    "vol_tot2= sky_frac * (vol_com_6_5 - vol_com_5) * (1000**3)\n",
    "vol_tot3= sky_frac * (vol_com_6_5 - vol_com_5) * (1000**3)\n",
    "\n",
    "density_tot = 3. / vol_tot / 2. #Mpc-3 Mag-1\n",
    "density_tot2= 1. / vol_tot2/ 2.\n",
    "density_tot3= 1.831 / vol_tot3/ 2.\n",
    "\n",
    "print(np.log10(density_tot), np.log10(density_tot2), np.log10(density_tot3), np.log10(5.918/3.), np.log10(3./1.367), np.log10(1./0.173), np.log10(3.300/1.))\n",
    "\n",
    "def lf_dp(m,beta=1.75,gamma=2.41,m_star=-22.58,log_phi_star=-6.62):\n",
    "    num=10.**log_phi_star\n",
    "    denom=10.**(0.4*(m_star-m)*(beta-1.))+10.**(0.4*(m_star-m)*(gamma-1.))\n",
    "    return np.log10(num/denom)\n",
    "def lf_sp(m, gamma=2.8,norm=1.):\n",
    "    num=norm\n",
    "    denom=10.**(0.4*(-m)*(gamma-1.))\n",
    "    return np.log10(num/denom)\n",
    "norm1 = density_tot*10.**(0.4*(21.5)*(2.8-1.))\n",
    "beta_zim2=1. + np.log10((9.93*(10**(-9))/density_tot2)-10**(2./5.*(-25.2+21.5)*(2.8-1.)))/(2./5.*(-25.2+21.5))\n",
    "beta_zimup=1. + np.log10((9.93*(10**(-9))/density_tot3)-10**(2./5.*(-25.2+21.5)*(2.8-1.)))/(2./5.*(-25.2+21.5))\n",
    "beta_var=2.15\n",
    "print(beta_zim2, beta_zimup, '%.3e' % norm1)\n",
    "m_grid=[]\n",
    "phi_parsa=[]\n",
    "phi_giallongo=[]\n",
    "phi_jiang=[]\n",
    "phi_jiang_u=[]\n",
    "phi_jiang_l=[]\n",
    "phi_zim1=[]\n",
    "phi_zim2=[]\n",
    "phi_zimup=[]\n",
    "phi_zimvar=[]\n",
    "for i in range(0,140):\n",
    "    m_grid.append(-30.+(i*0.1))\n",
    "    phi_parsa.append(lf_dp(m_grid[i]))\n",
    "    phi_giallongo.append(lf_dp(m_grid[i],beta=1.66,gamma=3.35,m_star=-23.4,log_phi_star=-5.8))\n",
    "    phi_jiang.append(lf_dp(m_grid[i],beta=1.9,gamma=2.8,m_star=-25.2,log_phi_star=np.log10(9.93*(10**(-9)))))\n",
    "    phi_jiang_u.append(lf_dp(m_grid[i],beta=1.32,gamma=2.8,m_star=-25.2,log_phi_star=np.log10(9.93*(10**(-9)))))\n",
    "    phi_jiang_l.append(lf_dp(m_grid[i],beta=2.3,gamma=2.8,m_star=-25.2,log_phi_star=np.log10(9.93*(10**(-9)))))\n",
    "    phi_zim1.append(lf_sp(m_grid[i],gamma=2.8, norm=norm1))\n",
    "    phi_zim2.append(lf_dp(m_grid[i],beta=beta_zim2,gamma=2.8,m_star=-25.2,log_phi_star=np.log10(9.93*(10**(-9)))))\n",
    "    phi_zimup.append(lf_dp(m_grid[i],beta=beta_zimup,gamma=2.8,m_star=-25.2,log_phi_star=np.log10(9.93*(10**(-9)))))\n",
    "    phi_zimvar.append(lf_dp(m_grid[i],beta=beta_var,gamma=2.8,m_star=-25.2,log_phi_star=np.log10(9.93*(10**(-9)))))\n",
    "\n",
    "\n",
    "    \n",
    "f=plt.figure(figsize=(16,8))\n",
    "ax=f.add_subplot(131)\n",
    "ax.plot(m_grid,phi_parsa,ls='--',lw=0.5,marker='',color='blue')\n",
    "ax.plot(m_grid,phi_giallongo,ls='--',lw=0.5,marker='',color='red')\n",
    "ax.plot(m_grid,phi_jiang,ls='-',lw=0.5,marker='',color='black')\n",
    "ax.plot(m_grid,phi_zim1,ls='-',lw=0.5,marker='',color='green')\n",
    "ax.fill_between(m_grid, phi_jiang_l, phi_jiang_u, facecolor='gray',alpha=0.1)\n",
    "ax.fill_between([-20.963-0.46,-20.963+0.46], -13, -3, facecolor='violet',lw=0,alpha=0.2)\n",
    "plt.gca().invert_xaxis()\n",
    "plt.axis([-18,-29.5,-11.5,-4])\n",
    "ax.set_aspect((11.5/7.5))\n",
    "#plt.title('$z\\simeq5.75$')\n",
    "plt.ylabel('log($\\phi$) [Mpc$^{-3}$ Mag$^{-1}$]', size=14)\n",
    "ax.errorbar(-21.5,np.log10(density_tot), xerr=1., yerr=[[np.log10(3./1.367)],[np.log10(5.918/3.)]], marker='o', ms=8, color='green',label='This Work\\n(SE Auto. Phot.)')\n",
    "#ax.arrow((m_bin1+m_bin2)/2.,np.log10(density_bin1), 0., -0.5, head_width=0.3, head_length=0.3, fc='k', ec='k', lw=0.6)\n",
    "#ax.arrow((m_bin2+m_bin3)/2.,np.log10(density_bin2), 0., -0.5, head_width=0.3, head_length=0.3, fc='k', ec='k', lw=0.6)\n",
    "ax.errorbar(-19.,-4.7, xerr=0.5, yerr=[[np.log10(1./0.173)],[np.log10(3.300/1.)]], marker='^', color='red',label='Giallongo+15')\n",
    "ax.errorbar(-20.,-4.7, xerr=0.5, yerr=[[np.log10(3./1.367)],[np.log10(5.918/3.)]], marker='^', color='red')\n",
    "ax.errorbar(-21,-5.7, xerr=0.5, yerr=[[np.log10(1./0.173)],[np.log10(3.300/1.)]], marker='^', color='red')\n",
    "ax.errorbar(-20,-5.99, xerr=0.5, yerr=0.66, marker='v', color='blue',label='Parsa+17')\n",
    "ax.errorbar(-24.83, np.log10(7.1*(10**(-9))), xerr=0.53, yerr=[[np.log10(7.*(10**(-9)))-np.log10(4.5*(10**(-9)))],[np.log10(10.*(10**(-9)))-np.log10(7.*(10**(-9)))]], marker='.', color='black', label='Jiang+16')\n",
    "ax.errorbar(-25.93,np.log10(3.16*(10**(-9))), xerr=0.57, yerr=[[np.log10(3.2*(10**(-9)))-np.log10(1.9*(10**(-9)))],[np.log10(4.3*(10**(-9)))-np.log10(3.2*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-26.45,np.log10(1.055*(10**(-9))), xerr=0.75, yerr=[[np.log10(1.05*(10**(-9)))-np.log10(0.71*(10**(-9)))],[np.log10(1.4*(10**(-9)))-np.log10(1.05*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-26.6,np.log10(0.527*(10**(-9))), xerr=0.4, yerr=[[np.log10(0.52*(10**(-9)))-np.log10(0.35*(10**(-9)))],[np.log10(0.7*(10**(-9)))-np.log10(0.52*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-27.2,np.log10(0.343*(10**(-9))), xerr=0.2, yerr=[[np.log10(0.34*(10**(-9)))-np.log10(0.21*(10**(-9)))],[np.log10(0.47*(10**(-9)))-np.log10(0.34*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-27.8,np.log10(0.136*(10**(-9))), xerr=0.4, yerr=[[np.log10(0.13*(10**(-9)))-np.log10(0.083*(10**(-9)))],[np.log10(0.185*(10**(-9)))-np.log10(0.13*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-28.7,np.log10(0.0151*(10**(-9))), xerr=0.5, yerr=[[np.log10(0.015*(10**(-9)))-np.log10(0.001*(10**(-9)))],[np.log10(0.03*(10**(-9)))-np.log10(0.015*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-24.7,np.log10(7.*(10**(-9))), yerr=[[np.log10(7.*(10**(-9)))-np.log10(5.*(10**(-9)))],[np.log10(9.*(10**(-9)))-np.log10(7.*(10**(-9)))]], marker='x', color='black',label=\"Willott+10\")\n",
    "ax.errorbar(-22.2,np.log10(43.8*(10**(-9))), yerr=[[np.log10(45.*(10**(-9)))-np.log10(0.0001*(10**(-9)))],[np.log10(90.*(10**(-9)))-np.log10(45.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-26.2,np.log10(1.*(10**(-9))), yerr=[[np.log10(1.*(10**(-9)))-np.log10(0.5*(10**(-9)))],[np.log10(1.5*(10**(-9)))-np.log10(1.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-23.1,np.log10(55.4*(10**(-9))), yerr=[[np.log10(55.*(10**(-9)))-np.log10(0.0001*(10**(-9)))],[np.log10(110.*(10**(-9)))-np.log10(55.*(10**(-9)))]], marker='p', color='black',label='Kashikawa+15')\n",
    "\n",
    "#Jiang (−25.1 0.17) is (7.5±3.8)×10−9, (−25.8 0.42) is (5.0 ± 2.1) × 10−9, (-26.75 0.15) 1.6 0.92 2.2 10-9, (-27.13 0.25) 0.42 0.6 0.25 10-9, (-27.63 0.25) 0.25 0.36 0.13 10-9\n",
    "plt.legend(numpoints=1, bbox_to_anchor=(1.,1.), fontsize=10, frameon=False)\n",
    "\n",
    "path = '/Users/justin/Documents/Master_Thesis/presentation/plots/UV_QLF1.pdf'\n",
    "plt.savefig(path, format='pdf', dpi=1000,bbox_inches='tight')\n",
    "\n",
    "ax=f.add_subplot(132)\n",
    "ax.plot(m_grid,phi_parsa,ls='--',lw=0.5,marker='',color='blue')\n",
    "ax.plot(m_grid,phi_giallongo,ls='--',lw=0.5,marker='',color='red')\n",
    "ax.plot(m_grid,phi_jiang,ls='-',lw=0.5,marker='',color='black')\n",
    "ax.plot(m_grid,phi_zim2,ls='-',lw=0.5,marker='',color='green')\n",
    "ax.fill_between(m_grid, phi_jiang_l, phi_jiang_u, facecolor='gray',alpha=0.1)\n",
    "ax.fill_between([-20.963-0.46,-20.963+0.46], -13, -3, facecolor='violet',lw=0,alpha=0.2)\n",
    "plt.gca().invert_xaxis()\n",
    "plt.axis([-18,-29.5,-11.5,-4])\n",
    "ax.set_aspect((11.5/7.5))\n",
    "#plt.title('$z\\simeq5.75$')\n",
    "plt.xlabel('M$_{1450}$', size=14)\n",
    "ax.errorbar(-21.5,np.log10(density_tot2), xerr=1., yerr=[[np.log10(1./0.173)],[np.log10(3.300/1.)]], marker='o', ms=8, color='green',label='This Work\\n(Manual Phot.)')\n",
    "#ax.arrow((m_bin1+m_bin2)/2.,np.log10(density_bin1), 0., -0.5, head_width=0.3, head_length=0.3, fc='k', ec='k', lw=0.6)\n",
    "#ax.arrow((m_bin2+m_bin3)/2.,np.log10(density_bin2), 0., -0.5, head_width=0.3, head_length=0.3, fc='k', ec='k', lw=0.6)\n",
    "ax.errorbar(-19.,-4.7, xerr=0.5, yerr=[[np.log10(1./0.173)],[np.log10(3.300/1.)]], marker='^', color='red')\n",
    "ax.errorbar(-20.,-4.7, xerr=0.5, yerr=[[np.log10(3./1.367)],[np.log10(5.918/3.)]], marker='^', color='red')\n",
    "ax.errorbar(-21,-5.7, xerr=0.5, yerr=[[np.log10(1./0.173)],[np.log10(3.300/1.)]], marker='^', color='red')\n",
    "ax.errorbar(-20,-5.99, xerr=0.5, yerr=0.66, marker='v', color='blue')\n",
    "ax.errorbar(-24.83, np.log10(7.1*(10**(-9))), xerr=0.53, yerr=[[np.log10(7.*(10**(-9)))-np.log10(4.5*(10**(-9)))],[np.log10(10.*(10**(-9)))-np.log10(7.*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-25.93,np.log10(3.16*(10**(-9))), xerr=0.57, yerr=[[np.log10(3.2*(10**(-9)))-np.log10(1.9*(10**(-9)))],[np.log10(4.3*(10**(-9)))-np.log10(3.2*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-26.45,np.log10(1.055*(10**(-9))), xerr=0.75, yerr=[[np.log10(1.05*(10**(-9)))-np.log10(0.71*(10**(-9)))],[np.log10(1.4*(10**(-9)))-np.log10(1.05*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-26.6,np.log10(0.527*(10**(-9))), xerr=0.4, yerr=[[np.log10(0.52*(10**(-9)))-np.log10(0.35*(10**(-9)))],[np.log10(0.7*(10**(-9)))-np.log10(0.52*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-27.2,np.log10(0.343*(10**(-9))), xerr=0.2, yerr=[[np.log10(0.34*(10**(-9)))-np.log10(0.21*(10**(-9)))],[np.log10(0.47*(10**(-9)))-np.log10(0.34*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-27.8,np.log10(0.136*(10**(-9))), xerr=0.4, yerr=[[np.log10(0.13*(10**(-9)))-np.log10(0.083*(10**(-9)))],[np.log10(0.185*(10**(-9)))-np.log10(0.13*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-28.7,np.log10(0.0151*(10**(-9))), xerr=0.5, yerr=[[np.log10(0.015*(10**(-9)))-np.log10(0.001*(10**(-9)))],[np.log10(0.03*(10**(-9)))-np.log10(0.015*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-24.7,np.log10(7.*(10**(-9))), yerr=[[np.log10(7.*(10**(-9)))-np.log10(5.*(10**(-9)))],[np.log10(9.*(10**(-9)))-np.log10(7.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-22.2,np.log10(43.8*(10**(-9))), yerr=[[np.log10(45.*(10**(-9)))-np.log10(0.0001*(10**(-9)))],[np.log10(90.*(10**(-9)))-np.log10(45.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-26.2,np.log10(1.*(10**(-9))), yerr=[[np.log10(1.*(10**(-9)))-np.log10(0.5*(10**(-9)))],[np.log10(1.5*(10**(-9)))-np.log10(1.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-23.1,np.log10(55.4*(10**(-9))), yerr=[[np.log10(55.*(10**(-9)))-np.log10(0.0001*(10**(-9)))],[np.log10(110.*(10**(-9)))-np.log10(55.*(10**(-9)))]], marker='p', color='black')\n",
    "plt.setp(ax.get_yticklabels(), visible=False)\n",
    "#Jiang (−25.1 0.17) is (7.5±3.8)×10−9, (−25.8 0.42) is (5.0 ± 2.1) × 10−9, (-26.75 0.15) 1.6 0.92 2.2 10-9, (-27.13 0.25) 0.42 0.6 0.25 10-9, (-27.63 0.25) 0.25 0.36 0.13 10-9\n",
    "plt.legend(numpoints=1, bbox_to_anchor=(1.,1.), fontsize=10, frameon=False)\n",
    "\n",
    "path = '/Users/justin/Documents/Master_Thesis/presentation/plots/UV_QLF2.pdf'\n",
    "plt.savefig(path, format='pdf', dpi=1000,bbox_inches='tight')\n",
    "\n",
    "ax=f.add_subplot(133)\n",
    "ax.plot(m_grid,phi_parsa,ls='--',lw=0.5,marker='',color='blue')\n",
    "ax.plot(m_grid,phi_giallongo,ls='--',lw=0.5,marker='',color='red')\n",
    "ax.plot(m_grid,phi_jiang,ls='-',lw=0.5,marker='',color='black')\n",
    "ax.fill_between(m_grid, phi_jiang_l, phi_jiang_u, facecolor='gray',alpha=0.1)\n",
    "#-20.9634818906 0.46 -22.2094046415 -20.0957989327\n",
    "ax.fill_between([-20.963-0.46,-20.963+0.46], -13, -3, facecolor='violet',lw=0,alpha=0.2)\n",
    "plt.gca().invert_xaxis()\n",
    "plt.axis([-18,-29.5,-11.5,-4])\n",
    "ax.set_aspect((11.5/7.5))\n",
    "#plt.title('$z\\simeq5.75$')\n",
    "ax.errorbar(-21.5,np.log10(density_tot3), xerr=1., marker='', color='green', lw=0.8,label='This Work\\n(Upper Limit)')\n",
    "ax.arrow(-21.5,np.log10(density_tot3), 0., -1., head_width=0.4, head_length=0.4, fc='green', ec='green')\n",
    "#ax.arrow((m_bin2+m_bin3)/2.,np.log10(density_bin2), 0., -0.5, head_width=0.3, head_length=0.3, fc='k', ec='k', lw=0.6)\n",
    "ax.errorbar(-19.,-4.7, xerr=0.5, yerr=[[np.log10(1./0.173)],[np.log10(3.300/1.)]], marker='^', color='red')\n",
    "ax.errorbar(-20.,-4.7, xerr=0.5, yerr=[[np.log10(3./1.367)],[np.log10(5.918/3.)]], marker='^', color='red')\n",
    "ax.errorbar(-21,-5.7, xerr=0.5, yerr=[[np.log10(1./0.173)],[np.log10(3.300/1.)]], marker='^', color='red')\n",
    "ax.errorbar(-20,-5.99, xerr=0.5, yerr=0.66, marker='v', color='blue')\n",
    "ax.errorbar(-24.83, np.log10(7.1*(10**(-9))), xerr=0.53, yerr=[[np.log10(7.*(10**(-9)))-np.log10(4.5*(10**(-9)))],[np.log10(10.*(10**(-9)))-np.log10(7.*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-25.93,np.log10(3.16*(10**(-9))), xerr=0.57, yerr=[[np.log10(3.2*(10**(-9)))-np.log10(1.9*(10**(-9)))],[np.log10(4.3*(10**(-9)))-np.log10(3.2*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-26.45,np.log10(1.055*(10**(-9))), xerr=0.75, yerr=[[np.log10(1.05*(10**(-9)))-np.log10(0.71*(10**(-9)))],[np.log10(1.4*(10**(-9)))-np.log10(1.05*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-26.6,np.log10(0.527*(10**(-9))), xerr=0.4, yerr=[[np.log10(0.52*(10**(-9)))-np.log10(0.35*(10**(-9)))],[np.log10(0.7*(10**(-9)))-np.log10(0.52*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-27.2,np.log10(0.343*(10**(-9))), xerr=0.2, yerr=[[np.log10(0.34*(10**(-9)))-np.log10(0.21*(10**(-9)))],[np.log10(0.47*(10**(-9)))-np.log10(0.34*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-27.8,np.log10(0.136*(10**(-9))), xerr=0.4, yerr=[[np.log10(0.13*(10**(-9)))-np.log10(0.083*(10**(-9)))],[np.log10(0.185*(10**(-9)))-np.log10(0.13*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-28.7,np.log10(0.0151*(10**(-9))), xerr=0.5, yerr=[[np.log10(0.015*(10**(-9)))-np.log10(0.001*(10**(-9)))],[np.log10(0.03*(10**(-9)))-np.log10(0.015*(10**(-9)))]], marker='.', color='black')\n",
    "ax.errorbar(-24.7,np.log10(7.*(10**(-9))), yerr=[[np.log10(7.*(10**(-9)))-np.log10(5.*(10**(-9)))],[np.log10(9.*(10**(-9)))-np.log10(7.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-22.2,np.log10(43.8*(10**(-9))), yerr=[[np.log10(45.*(10**(-9)))-np.log10(0.0001*(10**(-9)))],[np.log10(90.*(10**(-9)))-np.log10(45.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-26.2,np.log10(1.*(10**(-9))), yerr=[[np.log10(1.*(10**(-9)))-np.log10(0.5*(10**(-9)))],[np.log10(1.5*(10**(-9)))-np.log10(1.*(10**(-9)))]], marker='x', color='black')\n",
    "ax.errorbar(-23.1,np.log10(55.4*(10**(-9))), yerr=[[np.log10(55.*(10**(-9)))-np.log10(0.0001*(10**(-9)))],[np.log10(110.*(10**(-9)))-np.log10(55.*(10**(-9)))]], marker='p', color='black')\n",
    "plt.setp(ax.get_yticklabels(), visible=False)\n",
    "#Jiang (−25.1 0.17) is (7.5±3.8)×10−9, (−25.8 0.42) is (5.0 ± 2.1) × 10−9, (-26.75 0.15) 1.6 0.92 2.2 10-9, (-27.13 0.25) 0.42 0.6 0.25 10-9, (-27.63 0.25) 0.25 0.36 0.13 10-9\n",
    "plt.legend(numpoints=1, bbox_to_anchor=(1.,1.), fontsize=10, frameon=False)\n",
    "plt.subplots_adjust(wspace = 0.)\n",
    "path = '/Users/justin/Documents/Master_Thesis/presentation/plots/UV_QLF3.pdf'\n",
    "plt.savefig(path, format='pdf', dpi=1000,bbox_inches='tight')\n",
    "#make M1450 bins:\n",
    "\n",
    "#need binning our sample: 1st option: make simple z=[5,6.5] bin, all 5 sources fall into it, possibly leave out 527\n",
    "#are these detections, or upper limit on number of detections?\n",
    "\n",
    "#then check M1450 using a redshift of 5.75 to get to rest-frame\n",
    "\n",
    "#get 5.75 QLF for far UV using M bins (-19,-20, etc...) and calculating space density\n",
    "\n",
    "#calculate ionising fraction: eq(1) in Parsa. only usable if get parameters of QLF fit. BUT\n",
    "#these parameters are going to be almost exactly the same as Giallongo.\n",
    "\n",
    "#if so then only get one upper limit for emissivity, which will agree widely with Giallongo\n",
    "\n",
    "#conclusion: will be difficult to argue against AGN reionising universe based on these results, if we incorporate\n",
    "#all 5 sources in the redshift bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrated number density of z=5-6.5 AGN per Mpc^3: (Jiang, Parsa, Giallongo, Our fit(1, 3 sources)) 5.47692180998e-07 8.78842587578e-07 1.19786228066e-05 3.66808819809e-06 1.19720184164e-05\n",
      "Expected number of z=5-6.5 AGN present in our probed volume: (Jiang, Parsa, Giallongo, Our fit(1, 3 sources)) 0.224923060479 0.360917996159 4.91931160508 1.506389268 4.91659935225\n"
     ]
    }
   ],
   "source": [
    "#fit qlf, integrate faint end from -20 to break:\n",
    "m_integral = np.linspace(-28., -20.5, 20)\n",
    "phi_integraljiang = np.interp(m_integral, m_grid, phi_jiang)\n",
    "phi_integralparsa = np.interp(m_integral, m_grid, phi_parsa)\n",
    "phi_integralgiallongo = np.interp(m_integral, m_grid, phi_giallongo)\n",
    "phi_integral1 = np.interp(m_integral, m_grid, phi_zim1)\n",
    "phi_integral2 = np.interp(m_integral, m_grid, phi_zim2)\n",
    "density_integraljiang = np.trapz(10**phi_integraljiang, m_integral)\n",
    "density_integralparsa = np.trapz(10**phi_integralparsa, m_integral)\n",
    "density_integralgiallongo = np.trapz(10**phi_integralgiallongo, m_integral)\n",
    "density_integral1 = np.trapz(10**phi_integral1, m_integral)\n",
    "density_integral2 = np.trapz(10**phi_integral2, m_integral)\n",
    "\n",
    "print('Integrated number density of z=5-6.5 AGN per Mpc^3: (Jiang, Parsa, Giallongo, Our fit(1, 3 sources))',density_integraljiang, density_integralparsa, density_integralgiallongo, density_integral2, density_integral1)\n",
    "print('Expected number of z=5-6.5 AGN present in our probed volume: (Jiang, Parsa, Giallongo, Our fit(1, 3 sources))', density_integraljiang*vol_tot, density_integralparsa*vol_tot, density_integralgiallongo*vol_tot, density_integral2*vol_tot, density_integral1*vol_tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all magnitudes are AB. Absolute assumes z=6.\n",
      "sensitivity limit based on faintest flux detected by Source Extractor:\n",
      "H abs  -21.8333032047\n",
      "H app  26.9728\n",
      "J app  26.93\n",
      "Y abs  -21.5889032047\n",
      "Y app  27.2172\n",
      "z abs  -21.3080032047\n",
      "z app  27.4981\n",
      "i app  27.5983\n",
      "v app  28.1427\n",
      "b app  28.561\n",
      "in order to get a taste of how sensitivity varies across the field: use background noise\n",
      "sensitivity limit based on background noise: 3 sigma\n",
      "Y min app  26.2283565239\n",
      "Y min abs  -22.5778123031\n",
      "Y avg app  27.1897526699\n",
      "Y avg abs  -21.6164161571\n",
      "Y max app  28.4662701486\n",
      "Y max abs  -20.3398986784\n",
      "z min app  26.2123521681\n",
      "z min abs  -22.5938166588\n",
      "z avg app  26.9081976023\n",
      "z avg abs  -21.8979712247\n",
      "z max app  27.7810530436\n",
      "z max abs  -21.0251157834\n",
      "sensitivity limit based on background noise (aperture diameter rescaled to 1.0 arcsec (2.5 arcsec for Spitzer)): 3 sigma\n",
      "Y min app  26.5967641855\n",
      "Y min abs  -22.2094046415\n",
      "Y avg app  27.8426869364\n",
      "Y avg abs  -20.9634818906\n",
      "Y std app  0.459971246003\n",
      "Y std abs  0.459971246003\n",
      "Y max app  28.7103698943\n",
      "Y max abs  -20.0957989327\n",
      "z min app  26.6718092956\n",
      "z min abs  -22.1343595314\n",
      "z avg app  27.55449491\n",
      "z avg abs  -21.251673917\n",
      "z max app  28.2952240182\n",
      "z max abs  -20.5109448088\n",
      "J min app  26.3347545207\n",
      "J min abs  -22.4714143063\n",
      "J avg app  27.6625534195\n",
      "J avg abs  -21.1436154075\n",
      "J max app  28.455627457\n",
      "J max abs  -20.35054137\n",
      "H min app  25.9778836655\n",
      "H min abs  -22.8282851615\n",
      "H avg app  27.3779298217\n",
      "H avg abs  -21.4282390053\n",
      "H max app  28.201966605\n",
      "H max abs  -20.604202222\n",
      "i min app  26.8231862865\n",
      "i min abs  -21.9829825405\n",
      "i avg app  27.7485781635\n",
      "i avg abs  -21.0575906635\n",
      "i max app  28.4638899372\n",
      "i max abs  -20.3422788898\n",
      "V min app  27.6570821878\n",
      "V min abs  -21.1490866392\n",
      "V avg app  28.3171122618\n",
      "V avg abs  -20.4890565652\n",
      "V max app  29.0698086223\n",
      "V max abs  -19.7363602047\n",
      "B min app  26.6231157205\n",
      "B min abs  -22.1830531064\n",
      "B avg app  27.9697783149\n",
      "B avg abs  -20.8363905121\n",
      "B max app  28.6063751533\n",
      "B max abs  -20.1997936737\n",
      "K min app  24.6118653647\n",
      "K min abs  -24.1943034623\n",
      "K avg app  26.4424989916\n",
      "K avg abs  -22.3636698354\n",
      "K max app  27.2187702322\n",
      "K max abs  -21.5873985948\n",
      "3.6 min app  23.6951710712\n",
      "3.6 min abs  -25.1109977558\n",
      "3.6 avg app  26.2098476556\n",
      "3.6 avg abs  -22.5963211714\n",
      "3.6 max app  27.7104432563\n",
      "3.6 max abs  -21.0957255707\n",
      "4.5 min app  24.039447375\n",
      "4.5 min abs  -24.766721452\n",
      "4.5 avg app  26.0908766414\n",
      "4.5 avg abs  -22.7152921856\n",
      "4.5 max app  27.4438032179\n",
      "4.5 max abs  -21.3623656091\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:23: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:24: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:25: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:26: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:27: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:28: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:30: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:31: RuntimeWarning: divide by zero encountered in log10\n",
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      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:34: RuntimeWarning: divide by zero encountered in log10\n",
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      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:43: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:44: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:45: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:47: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:48: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:50: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:51: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:52: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:54: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:55: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:56: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:58: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:59: RuntimeWarning: divide by zero encountered in log10\n",
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      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:73: RuntimeWarning: divide by zero encountered in log10\n",
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      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:77: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:78: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:79: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:80: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:82: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:83: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:84: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:85: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:86: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:87: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:89: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:90: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:91: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:92: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:93: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:94: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:96: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:97: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:98: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:99: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:100: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:101: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:103: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:104: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:105: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:106: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:107: RuntimeWarning: divide by zero encountered in log10\n",
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:108: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    }
   ],
   "source": [
    "#flux limits for each band, in order to know which magnitude bin to choose\n",
    "path = '/Users/justin/Documents/Master_Thesis/data/catalogues/chandra_7ms/big_table.fits'\n",
    "big_table = fits.open(path)[1].data\n",
    "print('all magnitudes are AB. Absolute assumes z=6.')\n",
    "print('sensitivity limit based on faintest flux detected by Source Extractor:')\n",
    "print('H abs ', -2.5*np.log10(min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_JanskyAper')==0, big_table.field('CANDELS_160_JanskyAper'))))*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.))\n",
    "print('H app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_MagAper')==0, big_table.field('CANDELS_160_MagAper')))))\n",
    "print('J app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_125_MagAper')==0, big_table.field('CANDELS_125_MagAper')))))\n",
    "print('Y abs ', -2.5*np.log10(min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_JanskyAper')==0, big_table.field('CANDELS_105_JanskyAper'))))*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.))\n",
    "print('Y app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, big_table.field('CANDELS_105_MagAper')))))\n",
    "print('z abs ', -2.5*np.log10(min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_JanskyAper')==0, big_table.field('GOODS_z_JanskyAper'))))*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.))\n",
    "print('z app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, big_table.field('GOODS_z_MagAper')))))\n",
    "print('i app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_i_MagAper')==0, big_table.field('GOODS_i_MagAper')))))\n",
    "print('v app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_v_MagAper')==0, big_table.field('GOODS_v_MagAper')))))\n",
    "print('b app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_b_MagAper')==0, big_table.field('GOODS_b_MagAper')))))\n",
    "\n",
    "print('in order to get a taste of how sensitivity varies across the field: use background noise')\n",
    "sigmalimit=3.\n",
    "aper=1.\n",
    "aper_spitzer = 2.5\n",
    "\n",
    "print('sensitivity limit based on background noise: %1d sigma' % sigmalimit)\n",
    "print('Y min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit/3631.)))))\n",
    "print('Y min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('Y avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit/3631.)))))\n",
    "print('Y avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('Y max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit/3631.)))))\n",
    "print('Y max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('z min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit/3631.)))))\n",
    "print('z min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('z avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit/3631.)))))\n",
    "print('z avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('z max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit/3631.)))))\n",
    "print('z max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('sensitivity limit based on background noise (aperture diameter rescaled to %1.1f arcsec (%1.1f arcsec for Spitzer)): %1d sigma' % (aper, aper_spitzer, sigmalimit))\n",
    "print('Y min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('Y min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('Y avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('Y avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('Y std app ', np.std(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('Y std abs ', np.std(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('Y max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('Y max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_105_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_105_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('z min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('z min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('z avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('z avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('z max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('z max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_z_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_z_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('J min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_125_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_125_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('J min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_125_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_125_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('J avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_125_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_125_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('J avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_125_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_125_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('J max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_125_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_125_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('J max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_125_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_125_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('H min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_160_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('H min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_160_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('H avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_160_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('H avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_160_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('H max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_160_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('H max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('CANDELS_160_MagAper')==0, -2.5*np.log10(big_table.field('CANDELS_160_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('i min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_i_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_i_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('i min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_i_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_i_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('i avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_i_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_i_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('i avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_i_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_i_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('i max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_i_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_i_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('i max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_i_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_i_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('V min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_v_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_v_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('V min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_v_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_v_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('V avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_v_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_v_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('V avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_v_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_v_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('V max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_v_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_v_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('V max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_v_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_v_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('B min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_b_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_b_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('B min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_b_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_b_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('B avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_b_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_b_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('B avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_b_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_b_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('B max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_b_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_b_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('B max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('GOODS_b_MagAper')==0, -2.5*np.log10(big_table.field('GOODS_b_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('K min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('HUGS_MagAper')==0, -2.5*np.log10(big_table.field('HUGS_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('K min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('HUGS_MagAper')==0, -2.5*np.log10(big_table.field('HUGS_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('K avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('HUGS_MagAper')==0, -2.5*np.log10(big_table.field('HUGS_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('K avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('HUGS_MagAper')==0, -2.5*np.log10(big_table.field('HUGS_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('K max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('HUGS_MagAper')==0, -2.5*np.log10(big_table.field('HUGS_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)/3631.)))))\n",
    "print('K max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('HUGS_MagAper')==0, -2.5*np.log10(big_table.field('HUGS_JanskyErrAper')*sigmalimit*((aper/big_table.field('Aperture'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('3.6 min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch1_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch1_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)/3631.)))))\n",
    "print('3.6 min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch1_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch1_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('3.6 avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch1_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch1_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)/3631.)))))\n",
    "print('3.6 avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch1_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch1_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('3.6 max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch1_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch1_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)/3631.)))))\n",
    "print('3.6 max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch1_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch1_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n",
    "print('4.5 min app ', min(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch2_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch2_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)/3631.)))))\n",
    "print('4.5 min abs ', min(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch2_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch2_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('4.5 avg app ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch2_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch2_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)/3631.)))))\n",
    "print('4.5 avg abs ', np.mean(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch2_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch2_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "print('4.5 max app ', max(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch2_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch2_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)/3631.)))))\n",
    "print('4.5 max abs ', max(np.ma.compressed(np.ma.masked_where(big_table.field('Spitzer_ch2_MagAper')==0, -2.5*np.log10(big_table.field('Spitzer_ch2_JanskyErrAper')*sigmalimit*((aper_spitzer/big_table.field('Spitzer_Aper_Flag'))**2)*((np.interp(6.,z_grid,d_l)*1000000./10.)**2)/3631.)))))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e912 0.3592, 4.0588 gamma-12 0.0163, 0.1845\n",
      "required alpha upper limit 2.15, phi required upper value -6.35, actual phi upper value from survey -5.65, required survey field area: 543.99 arcmin^2, 0.151 deg^2\n",
      "-19.046448095667287 AB abs,  29.75955190433271 AB app,  4.531119198748547e-09 Jansky 4.531119198748547 nanoJansky\n"
     ]
    }
   ],
   "source": [
    "#calculate reionisation epsilon912 and Gamma-12 based on Jiang fit + alpha based on upper limit.\n",
    "#what criteria do we want? Calverley 11: gamma-12=0.1445 at z=6. Wyithe Bolton 2010: 0.18 gamma-12 at z=6.\n",
    "#average of two measurements: 0.16225 -> means alpha=2.15 at least.\n",
    "f_esc = 1.\n",
    "m_integration = np.linspace(-28,-18,100)\n",
    "phi_integration_var = np.interp(m_integration,m_grid,phi_zimvar)\n",
    "phi_integration_up = np.interp(m_integration,m_grid,phi_zimup)\n",
    "whole_integral_var = []\n",
    "whole_integral_up = []\n",
    "for i in range(len(m_integration)):\n",
    "    whole_integral_var.append((10**phi_integration_var[i])*((1200/1450)**0.44)*((912/1200)**1.57)*3631*4*3.14159*((1.7806736e+29*10/57707.7/1000000)**2)*(10**(-m_integration[i]/2.5)))\n",
    "    whole_integral_up.append((10**phi_integration_up[i])*((1200/1450)**0.44)*((912/1200)**1.57)*3631*4*3.14159*((1.7806736e+29*10/57707.7/1000000)**2)*(10**(-m_integration[i]/2.5)))\n",
    "e_var = f_esc*np.trapz(whole_integral_var, m_integration)\n",
    "e_up = f_esc*np.trapz(whole_integral_up, m_integration)\n",
    "e24_var = e_var*1e-47\n",
    "e24_up = e_up*1e-47\n",
    "gamma12_var = e24_var*0.6*(((1+6)/4.5)**(3-5.4))/(3+1.57)\n",
    "gamma12_up = e24_up*0.6*(((1+6)/4.5)**(3-5.4))/(3+1.57)\n",
    "print('e912 %.4f, %.4f gamma-12 %.4f, %.4f' % (e24_var, e24_up, gamma12_var, gamma12_up))\n",
    "\n",
    "phi_newup = np.interp(-21.5,m_grid,phi_zimvar)\n",
    "min_volume = 1.831/2./(10**phi_newup)\n",
    "min_skyfrac = min_volume/((vol_com_6_5 - vol_com_5) * (1000**3))\n",
    "min_area_deg = min_skyfrac*whole_sky\n",
    "min_area_arcmin = min_area_deg*3600\n",
    "print('required alpha upper limit %.2f, phi required upper value %.2f, actual phi upper value from survey %.2f, required survey field area: %.2f arcmin^2, %.3f deg^2'% (beta_var, phi_newup, np.log10(density_tot3), min_area_arcmin, min_area_deg))\n",
    "\n",
    "mnewlow = np.interp(np.log10(density_tot3),phi_zimvar,m_grid) + 1.\n",
    "print(mnewlow, 'AB abs, ', mnewlow+48.806, 'AB app, ', 3631.*(10**((-mnewlow-48.806)/2.5)), 'Jansky', 1e9*3631.*(10**((-mnewlow-48.806)/2.5)), 'nanoJansky')\n",
    "#CDF-S: 0\"11 deg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#seed formation constraints, look at Treister in detail\n",
    "#will need to calculate XLF at z=5.75, then convolve with ERDF etc. to get BH mass distribution. we then want the\n",
    "#total mass for all AGN at z=5.75, which will give a data point/upper limit for the BH total mass evolution with z plot"
   ]
  }
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