K-Corr1.ipynb.json

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##Imports\n",
    "#import pylab\n",
    "#import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from __future__ import division\n",
    "from scipy.integrate import simps\n",
    "from pysynphot import observation\n",
    "from pysynphot import spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import Filters Bandpasses\n",
    "filename='/Users/Emir/Astro/GROND/GROND_filtercurves.txt'\n",
    "filters=numpy.genfromtxt(filename,unpack=True,names=True)\n",
    "\n",
    "#For GROND: A\tgBand\trBand\tiBand\tzBand\tJBand\tHBand\tKBand\n",
    "names= filters.dtype.names[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Import Spectra\n",
    "specpath='/Users/Emir/Astro/SN1b-Thesis/Ogle-131/spec-OGLE-2014-131.ascii'\n",
    "sn=numpy.genfromtxt(specpath,usecols=(0,1), unpack=True, skiprows=0,names=('A','Flux'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "redshift=0.085\n",
    "LuminosityDistance=399e6\n",
    "ABmagdefinition=2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10e38b190>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dj0Zf3EOGQEEBFBVBcjL7qqoY51eq8LzumREjuG7bNmZs3cqKMWOw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\nUkopNBgopZRCg4FSSing/wO1FUnTgHOmdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b45190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def filters_plot(x,y,filterslist=filters):\n",
    "    for f in y:\n",
    "        plt.plot(filterslist[x],filterslist[f],label=f)\n",
    "    return    \n",
    "\n",
    "#Test Filter Bandpasses\n",
    "plt.figure()\n",
    "filters_plot('A',names)\n",
    "plt.plot(sn['A']/10,sn['Flux']/numpy.median(sn['Flux']))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning, 7 of 1200 bins contained negative fluxes; they have been set to zero.\n"
     ]
    }
   ],
   "source": [
    "#Resample Spectrum in to Filter Bandpass Wavelength Scale\n",
    "def rebin_spec(wave, specin, wavnew):\n",
    "    spec = spectrum.ArraySourceSpectrum(wave=wave, flux=specin)\n",
    "    f = np.ones(len(wave))\n",
    "    filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom')\n",
    "    obs = observation.Observation(spec, filt, binset=wavnew, force='taper')\n",
    " \n",
    "    return obs.binflux\n",
    "\n",
    "rebin=rebin_spec(sn['A'],sn['Flux'],(filters['A']*10.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need a 2nd Copy of the spectrum that is redshifted!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def z(wavelength,z=0.0):\n",
    "    return (wavelength/(1.0+z))\n",
    "\n",
    "sn_z=z(sn['A'],redshift)\n",
    "rebin_z=rebin_spec(sn_z,sn['Flux'],(filters['A']*10.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rebinned_bands=[]\n",
    "rebinned_bands_z=[]\n",
    "for name in names:\n",
    "    rebinned_bands.append(rebin*(filters[name]))\n",
    "    rebinned_bands_z.append(rebin_z*(filters[name]))\n",
    "    \n",
    "#Multiply New spectra by filter bandpasses\n",
    "rebinned_gband=rebin*(filters['gBand'])\n",
    "rebinned_rband=rebin*(filters['rBand'])\n",
    "rebinned_iband=rebin*(filters['iBand'])\n",
    "rebinned_zband=rebin*(filters['zBand'])\n",
    "rebinned_Jband=rebin*(filters['JBand'])\n",
    "\n",
    "#Multiply New redshifted spectra by filter bandpasses\n",
    "rebinned_gband_z=rebin_z*(filters['gBand'])\n",
    "rebinned_rband_z=rebin_z*(filters['rBand'])\n",
    "rebinned_iband_z=rebin_z*(filters['iBand'])\n",
    "rebinned_zband_z=rebin_z*(filters['zBand'])\n",
    "rebinned_Jband_z=rebin_z*(filters['JBand'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10e5a7fd0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SMlOq3e1Mfj4RZTuyoUFqFNeazZLvSXi82NhYz+5XaECSZlxYb5LzL21migyI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\nhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e5a7110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i,name in enumerate(names):\n",
    "    plt.plot(filters['A']*10,rebinned_bands[i],label=str(name)+' spec')\n",
    "plt.yscale('log')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11083f090>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WltYcKOpYvKigvJyyWnqYKKVaJg0UrUTld3PlIOsGqV6iaNuB9LyTlyi25OYS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\nGa4kxg0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11083f610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i,name in enumerate(names):\n",
    "    plt.plot(filters['A']*10,rebinned_bands_z[i],label=str(name)+' spec')\n",
    "plt.yscale('log')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'zBand': 4.5391227565656917e-18, 'gBand': 1.9226202851457163e-21, 'HBand': 0.0, 'rBand': 5.5893811168653355e-18, 'iBand': 9.0949174390115679e-18, 'JBand': 0.0}\n",
      "{'zBand': 1.5123345561126846e-18, 'gBand': 6.789970059372933e-22, 'HBand': 0.0, 'rBand': 8.1595829036405756e-18, 'iBand': 7.2715946546568702e-18, 'JBand': 0.0}\n"
     ]
    }
   ],
   "source": [
    "#Obtain Photometry by integrating using Simpson's rule integrator:\n",
    "I_list=[]\n",
    "I_list_z=[]\n",
    "for i,name in enumerate(names):\n",
    "    I_list.append(simps(rebinned_bands[i],(filters['A']*10.0))/simps(filters[name],(filters['A']*10.0)))\n",
    "    I_list_z.append(simps(rebinned_bands_z[i],(filters['A']*10.0))/simps(filters[name],(filters['A']*10.0)))\n",
    "    \n",
    "I_mean_flux=dict(zip(names,I_list))\n",
    "I_mean_flux_z=dict(zip(names,I_list_z))\n",
    "\n",
    "    \n",
    "print I_mean_flux\n",
    "print I_mean_flux_z                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'zBand': 8947.1247600079678, 'gBand': 4528.2550293305194, 'HBand': 16375.063481284889, 'rBand': 6159.984462554552, 'iBand': 7621.7239398495867, 'JBand': 12406.776208044283}\n"
     ]
    }
   ],
   "source": [
    "#Calculate Lambda Pivot\n",
    "\n",
    "def calc_lambda_pivot(lam,R):\n",
    "    lambdapivotsq=simps(R,lam)/simps(R,1.0/lam)\n",
    "    return (lambdapivotsq*-1.0)**(1.0/2)\n",
    "\n",
    "pivotlist=[]\n",
    "for name in names:\n",
    "    pivotlist.append(calc_lambda_pivot((filters['A']*10.0),filters[name]))\n",
    "lambdapivotlist=dict(zip(names,pivotlist)) \n",
    "\n",
    "print lambdapivotlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'zBand': 1.2136274234550904e-05, 'gBand': 1.3167451980836855e-09, 'HBand': 0.0, 'rBand': 7.0838510961028142e-06, 'iBand': 1.7646218677245223e-05, 'JBand': 0.0}\n",
      "{'zBand': 4.0435361394055139e-06, 'gBand': 4.6502476541454414e-10, 'HBand': 0.0, 'rBand': 1.0341264817546109e-05, 'iBand': 1.4108555714641863e-05, 'JBand': 0.0}\n"
     ]
    }
   ],
   "source": [
    "#Get Fv_r in Jy.\n",
    "def Fnu(Fl,lpivot):\n",
    "    return (3.34e4)*((lpivot)**2)*Fl\n",
    "    #return (Fl*(lpivot**2.0)/(3e8))\n",
    "\n",
    "    \n",
    "Fv={}    \n",
    "for key in lambdapivotlist:\n",
    "    Fv[key]=Fnu(I_mean_flux[key],lambdapivotlist[key])\n",
    "print Fv\n",
    "\n",
    "\n",
    "##REDSHIFTED\n",
    "Fv_z={}    \n",
    "for key in lambdapivotlist:\n",
    "    Fv_z[key]=Fnu(I_mean_flux_z[key],lambdapivotlist[key])\n",
    "print Fv_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'zBand': 21.310213453314233, 'gBand': 11.39875435811004, 'HBand': -inf, 'rBand': 20.725673559153453, 'iBand': 21.716629142079505, 'JBand': -inf}\n",
      "{'zBand': 20.116903322508175, 'gBand': 10.268690205861851, 'HBand': -inf, 'rBand': 21.13643414904362, 'iBand': 21.473706393835624, 'JBand': -inf}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Emir/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/IPython/kernel/__main__.py:14: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    }
   ],
   "source": [
    "#Calculate AB Mag\n",
    "#Regular\n",
    "def ABmag(fv):\n",
    "    return (-5.0/2)*numpy.log10(fv)+8.90\n",
    "\n",
    "#SDSS\n",
    "def ABmag_sdss(fv):\n",
    "    return -2.5*numpy.log10(fv)-48.60\n",
    "\n",
    "#GROND\n",
    "#THIS NEEDS TO BE IN JANSKIES i.e. 1Jy is 1e-23 of cgs spectral irradience w/r/t freq.\n",
    "#Divide by 1e-23 before input.\n",
    "def ABmag_Grond(fv):\n",
    "    return -1*((2.5*(29-numpy.log10(fv)))-48.6)\n",
    "\n",
    "#Set ABmag conversion function\n",
    "ABmagfunctionlist=[ABmag,ABmag_sdss,ABmag_Grond]\n",
    "ABmagfunction=ABmagfunctionlist[ABmagdefinition]\n",
    "#ABmagfunction=ABmag_sdss\n",
    "#ABmagfunction=ABmag\n",
    "\n",
    "abmags_sn={}\n",
    "for key in Fv:\n",
    "    abmags_sn[key]=ABmagfunction(Fv[str(key)]/1e-23) #Conversion\n",
    "print abmags_sn\n",
    "\n",
    "##REDSHIFTED\n",
    "abmags_sn_z={}\n",
    "for key in Fv_z:\n",
    "    abmags_sn_z[key]=ABmagfunction(Fv_z[str(key)]/1e-23) #Conversion\n",
    "print abmags_sn_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zBand Kcorr is 1.19331013081\n",
      "gBand Kcorr is 1.13006415225\n",
      "HBand Kcorr is nan\n",
      "rBand Kcorr is -0.41076058989\n",
      "iBand Kcorr is 0.242922748244\n",
      "JBand Kcorr is nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Emir/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/IPython/kernel/__main__.py:13: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "#Calculate Distance Modulus Given Luminosity distance\n",
    "def distance_mod(DL):\n",
    "    return -5.0*(numpy.log10(DL)-1)\n",
    "\n",
    "\n",
    "#DISTANCE MODULUS\n",
    "DM=LuminosityDistance\n",
    "for key in abmags_sn:\n",
    "    #Calculate Absolute AB Mag of synphot, using DM of 399.9 Mpc from NED\n",
    "    Abs_mag_key=abmags_sn[key]+distance_mod(DM)\n",
    "    #Calculate Absolute AB mag of r-band photometry:\n",
    "    Abs_mag_key_z=abmags_sn_z[key]+distance_mod(DM)\n",
    "    print str(key)+' Kcorr is', Abs_mag_key-Abs_mag_key_z\n",
    "    \n",
    "#print 'Peak mag is', 20.219-5*(numpy.log10(399.9e6)-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DISTANCE MODULUS IS** **-38.005** assuming 2015 plank H0 of 67.8, 0.3Omega M, and 0.7Omega Lambda in a flat universe to get DL of 399.9Mpc."
   ]
  }
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