proj2-buildTables-All-ZeroCheck.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import numpy as np\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.io import readsav\n",
    "\n",
    "import re\n",
    "\n",
    "from astropy.cosmology import FlatLambdaCDM\n",
    "from astropy import units as u\n",
    "import astropy \n",
    "\n",
    "from matplotlib.pylab import *\n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib.ticker import NullFormatter\n",
    "\n",
    "rcParams['figure.figsize'] = (10,8)\n",
    "rcParams['font.size'] = 22\n",
    "\n",
    "import sys\n",
    "sys.path.append('/Users/earnric/Google Drive/ASU/Codes/PythonCode/modules')\n",
    "import loadfilt as lf\n",
    "import lymanAbs as lyA\n",
    "import buildFluxTables as bft\n",
    "\n",
    "import linecache\n",
    "import os\n",
    "import subprocess\n",
    "import glob\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def log_formatter(x,pos):\n",
    "    return format(np.log10(x), '.0f')\n",
    "logfmt  = FuncFormatter(log_formatter)\n",
    "\n",
    "def log_GHzFormatter(x,pos):\n",
    "    return format(np.log10(x)-9.0, '.0f')\n",
    "logGHzfmt  = FuncFormatter(log_GHzFormatter)\n",
    "\n",
    "def GHzFormatter(x,pos):\n",
    "    return format(x/1e9, '.2e')\n",
    "GHzfmt  = FuncFormatter(GHzFormatter)\n",
    "\n",
    "def div_formatter(x,pos):\n",
    "    return format(x/1000, '.1f')\n",
    "divfmt  = FuncFormatter(div_formatter)\n",
    "\n",
    "nullfmt = NullFormatter()\n",
    "labelsize = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Setup the cosmology used in the runs\n",
    "# This provides comoving distance, lumin distance, etc... \n",
    "# THis is Seth's cosmology\n",
    "cosmo = FlatLambdaCDM(H0=73.2, Om0=0.24, Ob0=0.0449,name='myCosmo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$6560.4677 \\; \\mathrm{Mpc}$"
      ],
      "text/plain": [
       "<Quantity 6560.467651039068 Mpc>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosmo.luminosity_distance(z=1.0)\n",
    "# Matches Ned's calc: The luminosity distance DL is 6560.5 Mpc or 21.398 Gly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11046bf28>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiIiIlKmNOxXckIVpZXBzHSuJafUxySX1L8k19THJNfUxyTX1Mck19THUmeW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\n4a0SK6z8002ilC8l/6SSqPJPRIqBrrj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lVQAgObkCjRrdRvfuK2jW7DFSU2uzY8ePzJt3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5zDsmXLog6x4qn4JyKVRFeiEnH39e7+orvf4u6/TtxuTWxbH3V8IiKpNOw3m+TfRC+NEo7kMN/N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\nOlmJwAL2DAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11043ec18>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 323,
       "width": 639
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "ax = plt.axes()\n",
    "lamRange = np.logspace(1.95,5.7,5500)\n",
    "ax.plot(lamRange,lyA.lyTauC(2.0)(lamRange),'k-',\n",
    "        lamRange,lyA.lyTauC(2.5)(lamRange),'y-',\n",
    "        lamRange,lyA.lyTauC(3.5)(lamRange),'g-',\n",
    "        lamRange,lyA.lyTauC(4.5)(lamRange),'k-')\n",
    "ax.set_xlim([1200,8500])\n",
    "ax.set_ylim([1e-9,1.1])\n",
    "ax.set_ylim([0,1.1])\n",
    "#ax.set_yscale('log')\n",
    "ax.set_ylabel(r'$e^{-\\tau}$', size=labelsize)\n",
    "ax.set_xlabel(r'$\\lambda_{obs}\\; [\\AA/1000]$', size=labelsize)\n",
    "ax.grid(b=True, which='major', color='b', linestyle='--')\n",
    "ax.xaxis.set_major_formatter(divfmt)\n",
    "ax.set_title(r'Ly-$\\alpha$ & line absorp',size=labelsize)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load SB99 data... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# SB99 format:     TIME [YR]    WAVELENGTH [A]   LOG TOTAL  LOG STELLAR  LOG NEBULAR  [ERG/SEC/A]\n",
    "# REMEMBER, SB99 data is for a population of 1e6 M_sun\n",
    "SB99Path = '/Users/earnric/OneDrive/STARBURST99-runs/' # Home computer dir... \n",
    "SB99Dirs = ['padova0004/','padova004/','padova008/','padova02/']\n",
    "Zs       = [0.0004, 0.004, 0.008, 0.02]\n",
    "SB99FilePat = 'padova*.spectrum1'\n",
    "\n",
    "# SB99 format:     TIME [YR]    WAVELENGTH [A]   LOG TOTAL  LOG STELLAR  LOG NEBULAR  [ERG/SEC/A]\n",
    "for i, (Z, SB99Dir) in enumerate(zip(Zs,SB99Dirs)):\n",
    "    SB99FilePattern = SB99Path + SB99Dir + SB99FilePat \n",
    "    SB99Files   = glob.glob(SB99FilePattern)  # All the files in the dir... should be one!\n",
    "    if len(SB99Files) != 1:\n",
    "        print('Error: too many files in an SB99 dir! - ',SB99Path + SB99Dir)\n",
    "        sys.exit()\n",
    "    SB99Data    = np.loadtxt(SB99Files[0],skiprows=6)\n",
    "    if i == 0:\n",
    "        SB990004 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n",
    "    elif i == 1:\n",
    "        SB99004 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n",
    "    elif i == 2:\n",
    "        SB99008 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n",
    "    elif i == 3:\n",
    "        SB9902 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n",
    "    # We now have:\n",
    "    # [[log age, waveln, flux], [], ...]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.        ,  6.30319606,  6.60314437,  6.77887447,  6.90363252,\n",
       "        7.00043408,  7.07954301,  7.14643814,  7.20439133,  7.25551371,\n",
       "        7.30124709,  7.34262004,  7.38039216,  7.41514035,  7.44731311,\n",
       "        7.477266  ,  7.50528567,  7.53160663,  7.55642312,  7.57989787])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(SB9902[:,0])[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jwstFilters   = lf.loadJWSTFilters(suppress=True)\n",
    "hubbleFilters = lf.loadHubbleFilters(suppress=True)\n",
    "jhkFilters    = lf.loadJHKFilters(suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogAge, redshift, f115w, f150w, f430m, f090w, f360m, f140m, f444w, f410m, f182m, f200w, f162m, f460m, f335m, f070w, f480m, f277w, f356w, f210m, f250m, f300m, F125W_WFC3, F105W_WFC3, F275W_WFC3, F775W_ACS, F098M_WFC3, F850LP_ACS, F435W_ACS, F160W_WFC3, F606W_ACS, F336W_WFC3, F225W_WFC3, H, Ks, J\n",
      "working SB990004 at z=2.0\n",
      "log age=4.0000\n",
      "log age=6.3032\n",
      "log age=6.6031\n",
      "log age=6.7789\n",
      "log age=6.9036\n",
      "log age=7.0004\n",
      "log age=7.0795\n",
      "log age=7.1464\n",
      "log age=7.2044\n",
      "log age=7.2555\n",
      "log age=7.3012\n",
      "log age=7.3426\n",
      "log age=7.3804\n",
      "log age=7.4151\n",
      "log age=7.4473\n",
      "log age=7.4773\n",
      "log age=7.5053\n",
      "log age=7.5316\n",
      "log age=7.5799\n",
      "log age=7.6234\n",
      "log age=7.6629\n",
      "log age=7.6991\n",
      "log age=7.7325\n",
      "log age=7.7635\n",
      "log age=7.7925\n",
      "log age=7.8196\n",
      "log age=7.8452\n",
      "log age=7.8809\n",
      "log age=7.9139\n",
      "log age=7.9445\n",
      "log age=7.9732\n",
      "log age=8.0000\n",
      "log age=8.0253\n",
      "log age=8.0569\n",
      "log age=8.0864\n",
      "log age=8.1140\n",
      "log age=8.1399\n",
      "log age=8.1703\n",
      "log age=8.1987\n",
      "log age=8.2253\n",
      "log age=8.2504\n",
      "log age=8.2788\n",
      "log age=8.3054\n",
      "log age=8.3304\n",
      "log age=8.3580\n",
      "log age=8.3838\n",
      "log age=8.4116\n",
      "log age=8.4378\n",
      "log age=8.4654\n",
      "log age=8.4914\n",
      "log age=8.5185\n",
      "log age=8.5441\n",
      "log age=8.5706\n",
      "log age=8.5977\n",
      "log age=8.6233\n",
      "log age=8.6493\n",
      "log age=8.6758\n",
      "log age=8.7024\n",
      "log age=8.7275\n",
      "log age=8.7528\n",
      "log age=8.7782\n",
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      "log age=8.0000\n",
      "log age=8.0253\n",
      "log age=8.0569\n",
      "log age=8.0864\n",
      "log age=8.1140\n",
      "log age=8.1399\n",
      "log age=8.1703\n",
      "log age=8.1987\n",
      "log age=8.2253\n",
      "log age=8.2504\n",
      "log age=8.2788\n",
      "log age=8.3054\n",
      "log age=8.3304\n",
      "log age=8.3580\n",
      "log age=8.3838\n",
      "log age=8.4116\n",
      "log age=8.4378\n",
      "log age=8.4654\n",
      "log age=8.4914\n",
      "log age=8.5185\n",
      "log age=8.5441\n",
      "log age=8.5706\n",
      "log age=8.5977\n",
      "log age=8.6233\n",
      "log age=8.6493\n",
      "log age=8.6758\n",
      "log age=8.7024\n",
      "log age=8.7275\n",
      "log age=8.7528\n",
      "log age=8.7782\n",
      "log age=8.8035\n",
      "log age=8.8287\n",
      "writing SB9902_9.5.gz\n",
      "working SB9902 at z=10.0\n",
      "log age=4.0000\n",
      "log age=6.3032\n",
      "log age=6.6031\n",
      "log age=6.7789\n",
      "log age=6.9036\n",
      "log age=7.0004\n",
      "log age=7.0795\n",
      "log age=7.1464\n",
      "log age=7.2044\n",
      "log age=7.2555\n",
      "log age=7.3012\n",
      "log age=7.3426\n",
      "log age=7.3804\n",
      "log age=7.4151\n",
      "log age=7.4473\n",
      "log age=7.4773\n",
      "log age=7.5053\n",
      "log age=7.5316\n",
      "log age=7.5799\n",
      "log age=7.6234\n",
      "log age=7.6629\n",
      "log age=7.6991\n",
      "log age=7.7325\n",
      "log age=7.7635\n",
      "log age=7.7925\n",
      "log age=7.8196\n",
      "log age=7.8452\n",
      "log age=7.8809\n",
      "log age=7.9139\n",
      "log age=7.9445\n",
      "log age=7.9732\n",
      "log age=8.0000\n",
      "log age=8.0253\n",
      "log age=8.0569\n",
      "log age=8.0864\n",
      "log age=8.1140\n",
      "log age=8.1399\n",
      "log age=8.1703\n",
      "log age=8.1987\n",
      "log age=8.2253\n",
      "log age=8.2504\n",
      "log age=8.2788\n",
      "log age=8.3054\n",
      "log age=8.3304\n",
      "log age=8.3580\n",
      "log age=8.3838\n",
      "log age=8.4116\n",
      "log age=8.4378\n",
      "log age=8.4654\n",
      "log age=8.4914\n",
      "log age=8.5185\n",
      "log age=8.5441\n",
      "log age=8.5706\n",
      "log age=8.5977\n",
      "log age=8.6233\n",
      "log age=8.6493\n",
      "log age=8.6758\n",
      "log age=8.7024\n",
      "log age=8.7275\n",
      "log age=8.7528\n",
      "log age=8.7782\n",
      "log age=8.8035\n",
      "log age=8.8287\n",
      "writing SB9902_10.0.gz\n",
      "working SB9902 at z=11.0\n",
      "log age=4.0000\n",
      "log age=6.3032\n",
      "log age=6.6031\n",
      "log age=6.7789\n",
      "log age=6.9036\n",
      "log age=7.0004\n",
      "log age=7.0795\n",
      "log age=7.1464\n",
      "log age=7.2044\n",
      "log age=7.2555\n",
      "log age=7.3012\n",
      "log age=7.3426\n",
      "log age=7.3804\n",
      "log age=7.4151\n",
      "log age=7.4473\n",
      "log age=7.4773\n",
      "log age=7.5053\n",
      "log age=7.5316\n",
      "log age=7.5799\n",
      "log age=7.6234\n",
      "log age=7.6629\n",
      "log age=7.6991\n",
      "log age=7.7325\n",
      "log age=7.7635\n",
      "log age=7.7925\n",
      "log age=7.8196\n",
      "log age=7.8452\n",
      "log age=7.8809\n",
      "log age=7.9139\n",
      "log age=7.9445\n",
      "log age=7.9732\n",
      "log age=8.0000\n",
      "log age=8.0253\n",
      "log age=8.0569\n",
      "log age=8.0864\n",
      "log age=8.1140\n",
      "log age=8.1399\n",
      "log age=8.1703\n",
      "log age=8.1987\n",
      "log age=8.2253\n",
      "log age=8.2504\n",
      "log age=8.2788\n",
      "log age=8.3054\n",
      "log age=8.3304\n",
      "log age=8.3580\n",
      "log age=8.3838\n",
      "log age=8.4116\n",
      "log age=8.4378\n",
      "log age=8.4654\n",
      "log age=8.4914\n",
      "log age=8.5185\n",
      "log age=8.5441\n",
      "log age=8.5706\n",
      "log age=8.5977\n",
      "log age=8.6233\n",
      "log age=8.6493\n",
      "log age=8.6758\n",
      "log age=8.7024\n",
      "log age=8.7275\n",
      "log age=8.7528\n",
      "log age=8.7782\n",
      "log age=8.8035\n",
      "log age=8.8287\n",
      "writing SB9902_11.0.gz\n",
      "working SB9902 at z=12.0\n",
      "log age=4.0000\n",
      "log age=6.3032\n",
      "log age=6.6031\n",
      "log age=6.7789\n",
      "log age=6.9036\n",
      "log age=7.0004\n",
      "log age=7.0795\n",
      "log age=7.1464\n",
      "log age=7.2044\n",
      "log age=7.2555\n",
      "log age=7.3012\n",
      "log age=7.3426\n",
      "log age=7.3804\n",
      "log age=7.4151\n",
      "log age=7.4473\n",
      "log age=7.4773\n",
      "log age=7.5053\n",
      "log age=7.5316\n",
      "log age=7.5799\n",
      "log age=7.6234\n",
      "log age=7.6629\n",
      "log age=7.6991\n",
      "log age=7.7325\n",
      "log age=7.7635\n",
      "log age=7.7925\n",
      "log age=7.8196\n",
      "log age=7.8452\n",
      "log age=7.8809\n",
      "log age=7.9139\n",
      "log age=7.9445\n",
      "log age=7.9732\n",
      "log age=8.0000\n",
      "log age=8.0253\n",
      "log age=8.0569\n",
      "log age=8.0864\n",
      "log age=8.1140\n",
      "log age=8.1399\n",
      "log age=8.1703\n",
      "log age=8.1987\n",
      "log age=8.2253\n",
      "log age=8.2504\n",
      "log age=8.2788\n",
      "log age=8.3054\n",
      "log age=8.3304\n",
      "log age=8.3580\n",
      "log age=8.3838\n",
      "log age=8.4116\n",
      "log age=8.4378\n",
      "log age=8.4654\n",
      "log age=8.4914\n",
      "log age=8.5185\n",
      "log age=8.5441\n",
      "log age=8.5706\n",
      "log age=8.5977\n",
      "log age=8.6233\n",
      "log age=8.6493\n",
      "log age=8.6758\n",
      "log age=8.7024\n",
      "log age=8.7275\n",
      "log age=8.7528\n",
      "log age=8.7782\n",
      "log age=8.8035\n",
      "log age=8.8287\n",
      "writing SB9902_12.0.gz\n",
      "working SB9902 at z=13.0\n",
      "log age=4.0000\n",
      "log age=6.3032\n",
      "log age=6.6031\n",
      "log age=6.7789\n",
      "log age=6.9036\n",
      "log age=7.0004\n",
      "log age=7.0795\n",
      "log age=7.1464\n",
      "log age=7.2044\n",
      "log age=7.2555\n",
      "log age=7.3012\n",
      "log age=7.3426\n",
      "log age=7.3804\n",
      "log age=7.4151\n",
      "log age=7.4473\n",
      "log age=7.4773\n",
      "log age=7.5053\n",
      "log age=7.5316\n",
      "log age=7.5799\n",
      "log age=7.6234\n",
      "log age=7.6629\n",
      "log age=7.6991\n",
      "log age=7.7325\n",
      "log age=7.7635\n",
      "log age=7.7925\n",
      "log age=7.8196\n",
      "log age=7.8452\n",
      "log age=7.8809\n",
      "log age=7.9139\n",
      "log age=7.9445\n",
      "log age=7.9732\n",
      "log age=8.0000\n",
      "log age=8.0253\n",
      "log age=8.0569\n",
      "log age=8.0864\n",
      "log age=8.1140\n",
      "log age=8.1399\n",
      "log age=8.1703\n",
      "log age=8.1987\n",
      "log age=8.2253\n",
      "log age=8.2504\n",
      "log age=8.2788\n",
      "log age=8.3054\n",
      "log age=8.3304\n",
      "log age=8.3580\n",
      "log age=8.3838\n",
      "log age=8.4116\n",
      "log age=8.4378\n",
      "log age=8.4654\n",
      "log age=8.4914\n",
      "log age=8.5185\n",
      "log age=8.5441\n",
      "log age=8.5706\n",
      "log age=8.5977\n",
      "log age=8.6233\n",
      "log age=8.6493\n",
      "log age=8.6758\n",
      "log age=8.7024\n",
      "log age=8.7275\n",
      "log age=8.7528\n",
      "log age=8.7782\n",
      "log age=8.8035\n",
      "log age=8.8287\n",
      "writing SB9902_13.0.gz\n"
     ]
    }
   ],
   "source": [
    "redshifts    = [2.0,3.0,4.0,5.0,5.5,6.0,6.5,7.0,7.5,8.0,8.5,9.0,9.5,10.0,11.0,12.0,13.0]\n",
    "# redshifts    = [14.0,15.0,16.0]\n",
    "sb99List  = [SB990004, SB99004, SB99008, SB9902]\n",
    "sb99Names = [\"SB990004\", \"SB99004\", \"SB99008\", \"SB9902\"]\n",
    "# sb99List  = [SB9902]\n",
    "# sb99Names = [\"SB9902\"]\n",
    "hdr = 'LogAge, redshift, '\n",
    "hdr += ', '.join([jFilt for jFilt in jwstFilters])\n",
    "hdr += ', '\n",
    "hdr += ', '.join([hFilt for hFilt in hubbleFilters])\n",
    "hdr += ', '\n",
    "hdr += ', '.join([jFilt for jFilt in jhkFilters])\n",
    "fmtStr = '%.4f, %.1f, '\n",
    "fmtStr += ', '.join(['%.4e' for ii in arange(len(jwstFilters)+len(hubbleFilters)+len(jhkFilters))]) \n",
    "print(hdr)\n",
    "for sb99Data, fname in zip(sb99List,sb99Names):\n",
    "    ages = np.unique(sb99Data[:,0]) # Get the list of ages\n",
    "    # Get the log age, wavelength & Luminosity/s/Ang\n",
    "    allAges = sb99Data[:,0]\n",
    "    wavelns = sb99Data[:,1] * u.Angstrom\n",
    "    LperA   = sb99Data[:,2] * u.erg / u.second / u.angstrom\n",
    "    for z in redshifts:\n",
    "        prevAge = 0.0\n",
    "        outfile = []\n",
    "        print(\"working {} at z={:.1f}\".format(fname,z))\n",
    "        absorb = lyA.lyTauC(z) # Create a lyman forest absorption function for z... ***** NEW truncated absorption\n",
    "        for age in ages: # Once for each age group in the list\n",
    "            if age - prevAge < 0.025:\n",
    "                continue # We don't super-tight spacing in age... \n",
    "            print(\"log age={:.4f}\".format(age))\n",
    "            prevAge = age\n",
    "            ageCond = (allAges == age) # select records based on log age field... \n",
    "            # Convert to freq & Lumin/s/Hz\n",
    "            freq = (wavelns[ageCond][::-1]).to(u.Hz, equivalencies=u.spectral()) # Reverse array order\n",
    "            LperHz = (LperA[ageCond] * wavelns[ageCond]**2/astropy.constants.c).to(u.erg/u.s/u.Hz)[::-1] # Reverse array order...\n",
    "\n",
    "            rsWaveln, rsLperA  = lyA.rsSEDwavelen(wavelns[ageCond], LperA[ageCond], z) # Redshift SED in wavelen\n",
    "            rsFreq,   rsLperHz = lyA.rsSEDfreq(freq, LperHz, z)      # Redshift SED in freq\n",
    "            lyForFluxHz = (rsLperHz * absorb(rsWaveln[::-1]))\n",
    "            \n",
    "            # Need to extract only the data for one sed (at a single age)\n",
    "            # to compute the flux in the filters\n",
    "            rsWavelnOneAge    = rsWaveln\n",
    "            rsFreqOneAge      = rsFreq\n",
    "            lyForFluxHzOneAge = lyForFluxHz\n",
    "            # Create first part [log age, z, ...]\n",
    "            # Flux is in erg/s/Hz/cm^2\n",
    "            jwstNormFlux = np.array([np.trapz(jwstFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in jwstFilters])\n",
    "            jwstNormFlux[jwstNormFlux <= 0.0] = -1.0\n",
    "            jwstFlux = np.array([(np.trapz(jwstFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n",
    "                        for aFilt in jwstFilters])\n",
    "            jwstFlux[jwstFlux < 1e-90] = 0.0\n",
    "            jwstFlux   = np.divide(jwstFlux,jwstNormFlux).tolist()\n",
    "            \n",
    "            hubbNormFlux = np.array([np.trapz(hubbleFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in hubbleFilters])\n",
    "            hubbNormFlux[hubbNormFlux <= 0.0] = -1.0\n",
    "            hubbFlux = np.array([(np.trapz(hubbleFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n",
    "                        for aFilt in hubbleFilters])   \n",
    "            hubbFlux[hubbFlux < 1e-90] = 0.0\n",
    "            hubbFlux = np.divide(hubbFlux,hubbNormFlux).tolist()\n",
    "            \n",
    "            jhkNormFlux = np.array([np.trapz(jhkFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in jhkFilters])\n",
    "            jhkNormFlux[jhkNormFlux <= 0.0] = -1.0\n",
    "            jhkFlux = np.array([(np.trapz(jhkFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n",
    "                        for aFilt in jhkFilters])\n",
    "            jhkFlux[jhkFlux < 1e-90] = 0.0\n",
    "            jhkFlux = np.divide(jhkFlux,jhkNormFlux).tolist()\n",
    "\n",
    "            aLine = [age] + [z] + jwstFlux + hubbFlux + jhkFlux\n",
    "            outfile.append(aLine)\n",
    "        filename = fname + \"_\" + str(z) + \".gz\"\n",
    "        print('writing {}'.format(filename))\n",
    "        np.savetxt(filename, outfile, fmt=fmtStr, delimiter=', ', header=hdr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1009950.29412032,  1510080.15416415,  2010018.24711369])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(10,[6.0043,6.179,6.3032])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Schaerer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1001\n",
      "1001 [ 4.     6.004  6.303 ...,  8.999  9.     9.   ]\n",
      "1001\n",
      "1001 [ 4.     6.004  6.303 ...,  8.999  9.     9.   ]\n",
      "112\n",
      "112 [ 4.    4.05  4.1   4.15  4.2   4.25  4.3   4.35  4.4   4.45  4.5   4.55\n",
      "  4.6   4.65  4.7   4.75  4.8   4.85  4.9   4.95  5.    5.05  5.1   5.15\n",
      "  5.2   5.25  5.3   5.35  5.4   5.45  5.5   5.55  5.6   5.65  5.7   5.75\n",
      "  5.8   5.85  5.9   5.95  6.    6.05  6.1   6.15  6.2   6.25  6.3   6.35\n",
      "  6.4   6.45  6.5   6.55  6.6   6.65  6.7   6.75  6.8   6.85  6.9   6.95\n",
      "  7.    7.05  7.1   7.15  7.2   7.25  7.3   7.35  7.4   7.45  7.5   7.55\n",
      "  7.6   7.65  7.7   7.75  7.8   7.85  7.9   7.95  8.    8.05  8.1   8.15\n",
      "  8.2   8.25  8.3   8.35  8.4   8.45  8.5   8.55  8.6   8.65  8.7   8.75\n",
      "  8.8   8.85  8.9   8.95  9.    9.05  9.1   9.15  9.2   9.25  9.3   9.35\n",
      "  9.4   9.45  9.5   9.55]\n",
      "114\n",
      "114 [ 4.    4.05  4.1   4.15  4.2   4.25  4.3   4.35  4.4   4.45  4.5   4.55\n",
      "  4.6   4.65  4.7   4.75  4.8   4.85  4.9   4.95  5.    5.05  5.1   5.15\n",
      "  5.2   5.25  5.3   5.35  5.4   5.45  5.5   5.55  5.6   5.65  5.7   5.75\n",
      "  5.8   5.85  5.9   5.95  6.    6.05  6.1   6.15  6.2   6.25  6.3   6.35\n",
      "  6.4   6.45  6.5   6.55  6.6   6.65  6.7   6.75  6.8   6.85  6.9   6.95\n",
      "  7.    7.05  7.1   7.15  7.2   7.25  7.3   7.35  7.4   7.45  7.5   7.55\n",
      "  7.6   7.65  7.7   7.75  7.8   7.85  7.9   7.95  8.    8.05  8.1   8.15\n",
      "  8.2   8.25  8.3   8.35  8.4   8.45  8.5   8.55  8.6   8.65  8.7   8.75\n",
      "  8.8   8.85  8.9   8.95  9.    9.05  9.1   9.15  9.2   9.25  9.3   9.35\n",
      "  9.4   9.45  9.5   9.55  9.6   9.65]\n"
     ]
    }
   ],
   "source": [
    "# jwstFilters   = lf.loadJWSTFilters(suppress=True)\n",
    "# hubbleFilters = lf.loadHubbleFilters(suppress=True)\n",
    "# jhkFilters    = lf.loadJHKFilters(suppress=True)\n",
    "\n",
    "lamRange      = np.logspace(1.95,5.7,5500)\n",
    "\n",
    "schaererPath = '/Users/earnric/Research/Research-Observability/Software-Models/Schaerer/'\n",
    "schaererDirs = ['pop3_TA/','pop3_TE/','e-70_mar08/','e-50_mar08/']\n",
    "# schaererDirs = ['pop3_TAtest/']\n",
    "Zs           = [0.0, 0.0, 1.0e-7, 1.0e-5]\n",
    "schaererPopFilePattern  = 'pop3_ge0_log?_500_001_is5.[0-9]*' # is5 files have ages in step of 1 Myr\n",
    "schaererLowZFilePattern = 'e-?0_sal_100_001_is2.[0-9]*'      # is2 files have ages in step of 0.05 dex\n",
    "\n",
    "# Load the schaerer files... \n",
    "# Note that due to spacing (in age), there are sometimes two files that \n",
    "# are SEDS for the same time-stamp! Skip the second one (and third!)\n",
    "lastAge = 0.0\n",
    "for i, (Z, schaererDir) in enumerate(zip(Zs,schaererDirs)):\n",
    "    if schaererDir.startswith('pop3'):\n",
    "        schaererFilePattern = schaererPath + schaererDir + schaererPopFilePattern  # Pop III files, 1 Myr spacing\n",
    "    else:\n",
    "        schaererFilePattern = schaererPath + schaererDir + schaererLowZFilePattern # Low Z files, 0.05 dex spacing\n",
    "\n",
    "    schaererFiles   = glob.glob(schaererFilePattern)  # All the files in the dir... \n",
    "    schaererFiles   = [a for a in schaererFiles if not re.search('\\.[1-2][0-9][b]*$', a)] # remove .1? and .2? files\n",
    "    schaererAges    = [linecache.getline(file,13) for file in schaererFiles]    # Get the line with the (log) age... \n",
    "    schaererAges    = np.array([float(sa[30:]) for sa in schaererAges],dtype=float)         # Log age starts at position 30\n",
    "\n",
    "    schaererData    = np.array([np.loadtxt(file,skiprows=16) for file in schaererFiles])\n",
    "    ageSortIndxes   = schaererAges.argsort()          # Array of indices to sort things by age...\n",
    "    \n",
    "    schaererData    = schaererData[ageSortIndxes]\n",
    "    schaererAges    = schaererAges[ageSortIndxes]\n",
    "    print(len(schaererData))\n",
    "    print(len(schaererAges),schaererAges)\n",
    "\n",
    "    # Ignore data files with the same age! This occurs in the popIII dirs\n",
    "    # because the timestep is smaller than the age-resolution printed in the file\n",
    "    # Hence we get 2 files with different data but the same time stamp\n",
    "    lastAge = 0.0\n",
    "    schaererDataGood = []\n",
    "    schaererAgesGood = []\n",
    "    for ii,(sd,age) in enumerate(zip(schaererData,schaererAges)):\n",
    "        if age == lastAge:\n",
    "            # Remove it\n",
    "            continue\n",
    "        lastAge = age\n",
    "        schaererDataGood.append(sd)\n",
    "        schaererAgesGood.append(float(age))\n",
    "    \n",
    "    # The following builds an array of arrays (one for each age) with each array's entries:\n",
    "    # log age, Z, waveln, lum/A\n",
    "    allSchaererData = [np.insert(sed[:,[0,2]],[0],[[anAge] for ii in range(0,len(sed))], axis=1) \n",
    "        for anAge,sed in zip(schaererAgesGood, schaererDataGood)]\n",
    "    allSchaererData = np.array(allSchaererData).reshape(len(allSchaererData)*len(allSchaererData[0]),3)\n",
    "    if i == 0:\n",
    "        pop3TA = allSchaererData # may need a np.copy(...) here... ??\n",
    "    elif i == 1:\n",
    "        pop3TE = allSchaererData\n",
    "    elif i == 2:\n",
    "        Zem7 = allSchaererData\n",
    "    elif i == 3:\n",
    "        Zem5 = allSchaererData\n",
    "    # We now have:\n",
    "    # [[log age, waveln, flux], [], ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogAge, redshift, f115w, f150w, f430m, f090w, f360m, f140m, f444w, f410m, f182m, f200w, f162m, f460m, f335m, f070w, f480m, f277w, f356w, f210m, f250m, f300m, F125W_WFC3, F105W_WFC3, F275W_WFC3, F775W_ACS, F098M_WFC3, F850LP_ACS, F435W_ACS, F160W_WFC3, F606W_ACS, F336W_WFC3, F225W_WFC3, H, Ks, J\n",
      "working pop3TA at z=2.0\n",
      "log age=4.0000\n",
      "log age=6.0040\n",
      "log age=6.3030\n",
      "log age=6.4790\n",
      "log age=6.6030\n",
      "log age=6.7000\n",
      "log age=6.7790\n",
      "log age=6.8460\n",
      "log age=6.9040\n",
      "log age=6.9550\n",
      "log age=7.0000\n",
      "log age=7.0420\n",
      "log age=7.0800\n",
      "log age=7.1140\n",
      "log age=7.1460\n",
      "log age=7.1760\n",
      "log age=7.2040\n",
      "log age=7.2310\n",
      "log age=7.2560\n",
      "log age=7.3010\n",
      "log age=7.3430\n",
      "log age=7.3800\n",
      "log age=7.4150\n",
      "log age=7.4470\n",
      "log age=7.4770\n",
      "log age=7.5050\n",
      "log age=7.5320\n",
      "log age=7.5680\n",
      "log age=7.6020\n",
      "log age=7.6340\n",
      "log age=7.6630\n",
      "log age=7.6900\n",
      "log age=7.7160\n",
      "log age=7.7480\n",
      "log age=7.7780\n",
      "log age=7.8060\n",
      "log age=7.8330\n",
      "log age=7.8630\n",
      "log age=7.8920\n",
      "log age=7.9190\n",
      "log age=7.9450\n",
      "log age=7.9730\n",
      "log age=8.0000\n",
      "log age=8.0250\n",
      "log age=8.0530\n",
      "log age=8.0790\n",
      "log age=8.1070\n",
      "log age=8.1340\n",
      "log age=8.1610\n",
      "log age=8.1880\n",
      "log age=8.2150\n",
      "log age=8.2410\n",
      "log age=8.2670\n",
      "log age=8.2920\n",
      "log age=8.3180\n",
      "log age=8.3440\n",
      "log age=8.3690\n",
      "log age=8.3940\n",
      "log age=8.4200\n",
      "log age=8.4460\n",
      "log age=8.4710\n",
      "log age=8.4960\n",
      "log age=8.5210\n",
      "log age=8.5470\n",
      "log age=8.5730\n",
      "log age=8.5980\n",
      "log age=8.6240\n",
      "log age=8.6500\n",
      "log age=8.6750\n",
      "log age=8.7010\n",
      "log age=8.7260\n",
      "log age=8.7520\n",
      "log age=8.7780\n",
      "log age=8.8030\n",
      "log age=8.8290\n",
      "log age=8.8550\n",
      "log age=8.8800\n",
      "log age=8.9060\n",
      "log age=8.9320\n",
      "log age=8.9570\n",
      "log age=8.9830\n",
      "writing pop3TA_2.0.gz\n",
      "working pop3TA at z=3.0\n",
      "log age=4.0000\n",
      "log age=6.0040\n",
      "log age=6.3030\n",
      "log age=6.4790\n",
      "log age=6.6030\n",
      "log age=6.7000\n",
      "log age=6.7790\n",
      "log age=6.8460\n",
      "log age=6.9040\n",
      "log age=6.9550\n",
      "log age=7.0000\n",
      "log age=7.0420\n",
      "log age=7.0800\n",
      "log age=7.1140\n",
      "log age=7.1460\n",
      "log age=7.1760\n",
      "log age=7.2040\n",
      "log age=7.2310\n",
      "log age=7.2560\n",
      "log age=7.3010\n",
      "log age=7.3430\n",
      "log age=7.3800\n",
      "log age=7.4150\n",
      "log age=7.4470\n",
      "log age=7.4770\n",
      "log age=7.5050\n",
      "log age=7.5320\n",
      "log age=7.5680\n",
      "log age=7.6020\n",
      "log age=7.6340\n",
      "log age=7.6630\n",
      "log age=7.6900\n",
      "log age=7.7160\n",
      "log age=7.7480\n",
      "log age=7.7780\n",
      "log age=7.8060\n",
      "log age=7.8330\n",
      "log age=7.8630\n",
      "log age=7.8920\n",
      "log age=7.9190\n",
      "log age=7.9450\n",
      "log age=7.9730\n",
      "log age=8.0000\n",
      "log age=8.0250\n",
      "log age=8.0530\n",
      "log age=8.0790\n",
      "log age=8.1070\n",
      "log age=8.1340\n",
      "log age=8.1610\n",
      "log age=8.1880\n",
      "log age=8.2150\n",
      "log age=8.2410\n",
      "log age=8.2670\n",
      "log age=8.2920\n",
      "log age=8.3180\n",
      "log age=8.3440\n",
      "log age=8.3690\n",
      "log age=8.3940\n",
      "log age=8.4200\n",
      "log age=8.4460\n",
      "log age=8.4710\n",
      "log age=8.4960\n",
      "log age=8.5210\n",
      "log age=8.5470\n",
      "log age=8.5730\n",
      "log age=8.5980\n",
      "log age=8.6240\n",
      "log age=8.6500\n",
      "log age=8.6750\n",
      "log age=8.7010\n",
      "log age=8.7260\n",
      "log age=8.7520\n",
      "log age=8.7780\n",
      "log age=8.8030\n",
      "log age=8.8290\n",
      "log age=8.8550\n",
      "log age=8.8800\n",
      "log age=8.9060\n",
      "log age=8.9320\n",
      "log age=8.9570\n",
      "log age=8.9830\n",
      "writing pop3TA_3.0.gz\n",
      "working pop3TA at z=4.0\n",
      "log age=4.0000\n",
      "log age=6.0040\n",
      "log age=6.3030\n",
      "log age=6.4790\n",
      "log age=6.6030\n",
      "log age=6.7000\n",
      "log age=6.7790\n",
      "log age=6.8460\n",
      "log age=6.9040\n",
      "log age=6.9550\n",
      "log age=7.0000\n",
      "log age=7.0420\n",
      "log age=7.0800\n",
      "log age=7.1140\n",
      "log age=7.1460\n",
      "log age=7.1760\n",
      "log age=7.2040\n",
      "log age=7.2310\n",
      "log age=7.2560\n",
      "log age=7.3010\n",
      "log age=7.3430\n",
      "log age=7.3800\n",
      "log age=7.4150\n",
      "log age=7.4470\n",
      "log age=7.4770\n",
      "log age=7.5050\n",
      "log age=7.5320\n",
      "log age=7.5680\n",
      "log age=7.6020\n",
      "log age=7.6340\n",
      "log age=7.6630\n",
      "log age=7.6900\n",
      "log age=7.7160\n",
      "log age=7.7480\n",
      "log age=7.7780\n",
      "log age=7.8060\n",
      "log age=7.8330\n",
      "log age=7.8630\n",
      "log age=7.8920\n",
      "log age=7.9190\n",
      "log age=7.9450\n",
      "log age=7.9730\n",
      "log age=8.0000\n",
      "log age=8.0250\n",
      "log age=8.0530\n",
      "log age=8.0790\n",
      "log age=8.1070\n",
      "log age=8.1340\n",
      "log age=8.1610\n",
      "log age=8.1880\n",
      "log age=8.2150\n",
      "log age=8.2410\n",
      "log age=8.2670\n",
      "log age=8.2920\n",
      "log age=8.3180\n",
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      "log age=8.4200\n",
      "log age=8.4460\n",
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      "log age=8.5730\n",
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      "log age=8.6240\n",
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      "log age=8.8290\n",
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      "log age=8.8800\n",
      "log age=8.9060\n",
      "log age=8.9320\n",
      "log age=8.9570\n",
      "log age=8.9830\n",
      "writing pop3TA_4.0.gz\n",
      "working pop3TA at z=5.0\n",
      "log age=4.0000\n",
      "log age=6.0040\n",
      "log age=6.3030\n",
      "log age=6.4790\n",
      "log age=6.6030\n",
      "log age=6.7000\n",
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      "log age=7.0000\n",
      "log age=7.0420\n",
      "log age=7.0800\n",
      "log age=7.1140\n",
      "log age=7.1460\n",
      "log age=7.1760\n",
      "log age=7.2040\n",
      "log age=7.2310\n",
      "log age=7.2560\n",
      "log age=7.3010\n",
      "log age=7.3430\n",
      "log age=7.3800\n",
      "log age=7.4150\n",
      "log age=7.4470\n",
      "log age=7.4770\n",
      "log age=7.5050\n",
      "log age=7.5320\n",
      "log age=7.5680\n",
      "log age=7.6020\n",
      "log age=7.6340\n",
      "log age=7.6630\n",
      "log age=7.6900\n",
      "log age=7.7160\n",
      "log age=7.7480\n",
      "log age=7.7780\n",
      "log age=7.8060\n",
      "log age=7.8330\n",
      "log age=7.8630\n",
      "log age=7.8920\n",
      "log age=7.9190\n",
      "log age=7.9450\n",
      "log age=7.9730\n",
      "log age=8.0000\n",
      "log age=8.0250\n",
      "log age=8.0530\n",
      "log age=8.0790\n",
      "log age=8.1070\n",
      "log age=8.1340\n",
      "log age=8.1610\n",
      "log age=8.1880\n",
      "log age=8.2150\n",
      "log age=8.2410\n",
      "log age=8.2670\n",
      "log age=8.2920\n",
      "log age=8.3180\n",
      "log age=8.3440\n",
      "log age=8.3690\n",
      "log age=8.3940\n",
      "log age=8.4200\n",
      "log age=8.4460\n",
      "log age=8.4710\n",
      "log age=8.4960\n",
      "log age=8.5210\n",
      "log age=8.5470\n",
      "log age=8.5730\n",
      "log age=8.5980\n",
      "log age=8.6240\n",
      "log age=8.6500\n",
      "log age=8.6750\n",
      "log age=8.7010\n",
      "log age=8.7260\n",
      "log age=8.7520\n",
      "log age=8.7780\n",
      "log age=8.8030\n",
      "log age=8.8290\n",
      "log age=8.8550\n",
      "log age=8.8800\n",
      "log age=8.9060\n",
      "log age=8.9320\n",
      "log age=8.9570\n",
      "log age=8.9830\n",
      "writing pop3TA_5.0.gz\n",
      "working pop3TA at z=5.5\n",
      "log age=4.0000\n",
      "log age=6.0040\n",
      "log age=6.3030\n",
      "log age=6.4790\n",
      "log age=6.6030\n",
      "log age=6.7000\n",
      "log age=6.7790\n",
      "log age=6.8460\n",
      "log age=6.9040\n",
      "log age=6.9550\n",
      "log age=7.0000\n",
      "log age=7.0420\n",
      "log age=7.0800\n",
      "log age=7.1140\n",
      "log age=7.1460\n",
      "log age=7.1760\n",
      "log age=7.2040\n",
      "log age=7.2310\n",
      "log age=7.2560\n",
      "log age=7.3010\n",
      "log age=7.3430\n",
      "log age=7.3800\n",
      "log age=7.4150\n",
      "log age=7.4470\n",
      "log age=7.4770\n",
      "log age=7.5050\n",
      "log age=7.5320\n",
      "log age=7.5680\n",
      "log age=7.6020\n",
      "log age=7.6340\n",
      "log age=7.6630\n",
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      "log age=7.7160\n",
      "log age=7.7480\n",
      "log age=7.7780\n",
      "log age=7.8060\n",
      "log age=7.8330\n",
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      "log age=7.8920\n",
      "log age=7.9190\n",
      "log age=7.9450\n",
      "log age=7.9730\n",
      "log age=8.0000\n",
      "log age=8.0250\n",
      "log age=8.0530\n",
      "log age=8.0790\n",
      "log age=8.1070\n",
      "log age=8.1340\n",
      "log age=8.1610\n",
      "log age=8.1880\n",
      "log age=8.2150\n",
      "log age=8.2410\n",
      "log age=8.2670\n",
      "log age=8.2920\n",
      "log age=8.3180\n",
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      "log age=8.3690\n",
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      "log age=8.5210\n",
      "log age=8.5470\n",
      "log age=8.5730\n",
      "log age=8.5980\n",
      "log age=8.6240\n",
      "log age=8.6500\n",
      "log age=8.6750\n",
      "log age=8.7010\n",
      "log age=8.7260\n",
      "log age=8.7520\n",
      "log age=8.7780\n",
      "log age=8.8030\n",
      "log age=8.8290\n",
      "log age=8.8550\n",
      "log age=8.8800\n",
      "log age=8.9060\n",
      "log age=8.9320\n",
      "log age=8.9570\n",
      "log age=8.9830\n",
      "writing pop3TA_5.5.gz\n",
      "working pop3TA at z=6.0\n",
      "log age=4.0000\n",
      "log age=6.0040\n",
      "log age=6.3030\n",
      "log age=6.4790\n",
      "log age=6.6030\n",
      "log age=6.7000\n",
      "log age=6.7790\n",
      "log age=6.8460\n",
      "log age=6.9040\n",
      "log age=6.9550\n",
      "log age=7.0000\n",
      "log age=7.0420\n",
      "log age=7.0800\n",
      "log age=7.1140\n",
      "log age=7.1460\n",
      "log age=7.1760\n",
      "log age=7.2040\n",
      "log age=7.2310\n",
      "log age=7.2560\n",
      "log age=7.3010\n",
      "log age=7.3430\n",
      "log age=7.3800\n",
      "log age=7.4150\n",
      "log age=7.4470\n",
      "log age=7.4770\n",
      "log age=7.5050\n",
      "log age=7.5320\n",
      "log age=7.5680\n",
      "log age=7.6020\n",
      "log age=7.6340\n",
      "log age=7.6630\n",
      "log age=7.6900\n",
      "log age=7.7160\n",
      "log age=7.7480\n",
      "log age=7.7780\n",
      "log age=7.8060\n",
      "log age=7.8330\n",
      "log age=7.8630\n",
      "log age=7.8920\n",
      "log age=7.9190\n",
      "log age=7.9450\n",
      "log age=7.9730\n",
      "log age=8.0000\n",
      "log age=8.0250\n",
      "log age=8.0530\n",
      "log age=8.0790\n",
      "log age=8.1070\n",
      "log age=8.1340\n",
      "log age=8.1610\n",
      "log age=8.1880\n",
      "log age=8.2150\n",
      "log age=8.2410\n",
      "log age=8.2670\n",
      "log age=8.2920\n",
      "log age=8.3180\n",
      "log age=8.3440\n",
      "log age=8.3690\n",
      "log age=8.3940\n",
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      "working Zem5 at z=9.5\n",
      "log age=4.0000\n",
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      "log age=8.9500\n",
      "log age=9.0000\n",
      "writing Zem5_9.5.gz\n",
      "working Zem5 at z=10.0\n",
      "log age=4.0000\n",
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      "log age=6.8000\n",
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      "log age=6.9000\n",
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      "log age=7.0000\n",
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      "log age=7.9000\n",
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      "log age=8.2500\n",
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      "log age=8.5500\n",
      "log age=8.6000\n",
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      "log age=8.7000\n",
      "log age=8.7500\n",
      "log age=8.8000\n",
      "log age=8.8500\n",
      "log age=8.9000\n",
      "log age=8.9500\n",
      "log age=9.0000\n",
      "writing Zem5_10.0.gz\n",
      "working Zem5 at z=11.0\n",
      "log age=4.0000\n",
      "log age=4.0500\n",
      "log age=4.1000\n",
      "log age=4.1500\n",
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      "log age=4.8000\n",
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      "log age=4.9500\n",
      "log age=5.0000\n",
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      "log age=5.8000\n",
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      "log age=5.9500\n",
      "log age=6.0000\n",
      "log age=6.0500\n",
      "log age=6.1000\n",
      "log age=6.1500\n",
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      "log age=6.2500\n",
      "log age=6.3000\n",
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      "log age=6.4000\n",
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      "log age=6.5000\n",
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      "log age=6.6000\n",
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      "log age=6.7000\n",
      "log age=6.7500\n",
      "log age=6.8000\n",
      "log age=6.8500\n",
      "log age=6.9000\n",
      "log age=6.9500\n",
      "log age=7.0000\n",
      "log age=7.0500\n",
      "log age=7.1000\n",
      "log age=7.1500\n",
      "log age=7.2000\n",
      "log age=7.2500\n",
      "log age=7.3000\n",
      "log age=7.3500\n",
      "log age=7.4000\n",
      "log age=7.4500\n",
      "log age=7.5000\n",
      "log age=7.5500\n",
      "log age=7.6000\n",
      "log age=7.6500\n",
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      "log age=7.7500\n",
      "log age=7.8000\n",
      "log age=7.8500\n",
      "log age=7.9000\n",
      "log age=7.9500\n",
      "log age=8.0000\n",
      "log age=8.0500\n",
      "log age=8.1000\n",
      "log age=8.1500\n",
      "log age=8.2000\n",
      "log age=8.2500\n",
      "log age=8.3000\n",
      "log age=8.3500\n",
      "log age=8.4000\n",
      "log age=8.4500\n",
      "log age=8.5000\n",
      "log age=8.5500\n",
      "log age=8.6000\n",
      "log age=8.6500\n",
      "log age=8.7000\n",
      "log age=8.7500\n",
      "log age=8.8000\n",
      "log age=8.8500\n",
      "log age=8.9000\n",
      "log age=8.9500\n",
      "log age=9.0000\n",
      "writing Zem5_11.0.gz\n",
      "working Zem5 at z=12.0\n",
      "log age=4.0000\n",
      "log age=4.0500\n",
      "log age=4.1000\n",
      "log age=4.1500\n",
      "log age=4.2000\n",
      "log age=4.2500\n",
      "log age=4.3000\n",
      "log age=4.3500\n",
      "log age=4.4000\n",
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      "log age=4.5000\n",
      "log age=4.5500\n",
      "log age=4.6000\n",
      "log age=4.6500\n",
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      "log age=4.7500\n",
      "log age=4.8000\n",
      "log age=4.8500\n",
      "log age=4.9000\n",
      "log age=4.9500\n",
      "log age=5.0000\n",
      "log age=5.0500\n",
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      "log age=5.8000\n",
      "log age=5.8500\n",
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      "log age=6.0000\n",
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      "log age=6.1000\n",
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      "log age=6.3000\n",
      "log age=6.3500\n",
      "log age=6.4000\n",
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      "log age=6.5000\n",
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      "log age=6.6000\n",
      "log age=6.6500\n",
      "log age=6.7000\n",
      "log age=6.7500\n",
      "log age=6.8000\n",
      "log age=6.8500\n",
      "log age=6.9000\n",
      "log age=6.9500\n",
      "log age=7.0000\n",
      "log age=7.0500\n",
      "log age=7.1000\n",
      "log age=7.1500\n",
      "log age=7.2000\n",
      "log age=7.2500\n",
      "log age=7.3000\n",
      "log age=7.3500\n",
      "log age=7.4000\n",
      "log age=7.4500\n",
      "log age=7.5000\n",
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      "log age=7.6000\n",
      "log age=7.6500\n",
      "log age=7.7000\n",
      "log age=7.7500\n",
      "log age=7.8000\n",
      "log age=7.8500\n",
      "log age=7.9000\n",
      "log age=7.9500\n",
      "log age=8.0000\n",
      "log age=8.0500\n",
      "log age=8.1000\n",
      "log age=8.1500\n",
      "log age=8.2000\n",
      "log age=8.2500\n",
      "log age=8.3000\n",
      "log age=8.3500\n",
      "log age=8.4000\n",
      "log age=8.4500\n",
      "log age=8.5000\n",
      "log age=8.5500\n",
      "log age=8.6000\n",
      "log age=8.6500\n",
      "log age=8.7000\n",
      "log age=8.7500\n",
      "log age=8.8000\n",
      "log age=8.8500\n",
      "log age=8.9000\n",
      "log age=8.9500\n",
      "log age=9.0000\n",
      "writing Zem5_12.0.gz\n",
      "working Zem5 at z=13.0\n",
      "log age=4.0000\n",
      "log age=4.0500\n",
      "log age=4.1000\n",
      "log age=4.1500\n",
      "log age=4.2000\n",
      "log age=4.2500\n",
      "log age=4.3000\n",
      "log age=4.3500\n",
      "log age=4.4000\n",
      "log age=4.4500\n",
      "log age=4.5000\n",
      "log age=4.5500\n",
      "log age=4.6000\n",
      "log age=4.6500\n",
      "log age=4.7000\n",
      "log age=4.7500\n",
      "log age=4.8000\n",
      "log age=4.8500\n",
      "log age=4.9000\n",
      "log age=4.9500\n",
      "log age=5.0000\n",
      "log age=5.0500\n",
      "log age=5.1000\n",
      "log age=5.1500\n",
      "log age=5.2000\n",
      "log age=5.2500\n",
      "log age=5.3000\n",
      "log age=5.3500\n",
      "log age=5.4000\n",
      "log age=5.4500\n",
      "log age=5.5000\n",
      "log age=5.5500\n",
      "log age=5.6000\n",
      "log age=5.6500\n",
      "log age=5.7000\n",
      "log age=5.7500\n",
      "log age=5.8000\n",
      "log age=5.8500\n",
      "log age=5.9000\n",
      "log age=5.9500\n",
      "log age=6.0000\n",
      "log age=6.0500\n",
      "log age=6.1000\n",
      "log age=6.1500\n",
      "log age=6.2000\n",
      "log age=6.2500\n",
      "log age=6.3000\n",
      "log age=6.3500\n",
      "log age=6.4000\n",
      "log age=6.4500\n",
      "log age=6.5000\n",
      "log age=6.5500\n",
      "log age=6.6000\n",
      "log age=6.6500\n",
      "log age=6.7000\n",
      "log age=6.7500\n",
      "log age=6.8000\n",
      "log age=6.8500\n",
      "log age=6.9000\n",
      "log age=6.9500\n",
      "log age=7.0000\n",
      "log age=7.0500\n",
      "log age=7.1000\n",
      "log age=7.1500\n",
      "log age=7.2000\n",
      "log age=7.2500\n",
      "log age=7.3000\n",
      "log age=7.3500\n",
      "log age=7.4000\n",
      "log age=7.4500\n",
      "log age=7.5000\n",
      "log age=7.5500\n",
      "log age=7.6000\n",
      "log age=7.6500\n",
      "log age=7.7000\n",
      "log age=7.7500\n",
      "log age=7.8000\n",
      "log age=7.8500\n",
      "log age=7.9000\n",
      "log age=7.9500\n",
      "log age=8.0000\n",
      "log age=8.0500\n",
      "log age=8.1000\n",
      "log age=8.1500\n",
      "log age=8.2000\n",
      "log age=8.2500\n",
      "log age=8.3000\n",
      "log age=8.3500\n",
      "log age=8.4000\n",
      "log age=8.4500\n",
      "log age=8.5000\n",
      "log age=8.5500\n",
      "log age=8.6000\n",
      "log age=8.6500\n",
      "log age=8.7000\n",
      "log age=8.7500\n",
      "log age=8.8000\n",
      "log age=8.8500\n",
      "log age=8.9000\n",
      "log age=8.9500\n",
      "log age=9.0000\n",
      "writing Zem5_13.0.gz\n"
     ]
    }
   ],
   "source": [
    "redshifts    = [2.0,3.0,4.0,5.0,5.5,6.0,6.5,7.0,7.5,8.0,8.5,9.0,9.5,10.0,11.0,12.0,13.0]\n",
    "# redshifts    = [14.0,15.0,16.0]\n",
    "schaererList = [pop3TA, pop3TE, Zem7, Zem5]\n",
    "schaererNames = [\"pop3TA\", \"pop3TE\", \"Zem7\", \"Zem5\"]\n",
    "# schaererList = [pop3TA]\n",
    "# schaererNames = [\"pop3TA_TEST\"]\n",
    "hdr = 'LogAge, redshift, '\n",
    "hdr += ', '.join([jFilt for jFilt in jwstFilters])\n",
    "hdr += ', '\n",
    "hdr += ', '.join([hFilt for hFilt in hubbleFilters])\n",
    "hdr += ', '\n",
    "hdr += ', '.join([jFilt for jFilt in jhkFilters])\n",
    "fmtStr = '%.4f, %.1f, '\n",
    "fmtStr += ', '.join(['%.4e' for ii in arange(len(jwstFilters)+len(hubbleFilters)+len(jhkFilters))]) \n",
    "print(hdr)\n",
    "for schaererData, fname in zip(schaererList,schaererNames):\n",
    "    ages = np.unique(schaererData[:,0]) # Get the list of ages\n",
    "    ages = ages[ages <= 9.01] # We don't need flux for stars older than 1.02 Gyr\n",
    "    # Get the log age, wavelength & Luminosity/s/Ang\n",
    "    allAges = schaererData[:,0]\n",
    "    wavelns = schaererData[:,1] * u.Angstrom\n",
    "    LperA   = schaererData[:,2] * u.erg / u.second / u.angstrom\n",
    "    for z in redshifts:\n",
    "        prevAge = 0.0\n",
    "        outfile = []\n",
    "        print(\"working {} at z={:.1f}\".format(fname,z))\n",
    "        absorb = lyA.lyTauC(z) # Create a lyman forest absorption function for z... # NEW Truncated absorption\n",
    "        for age in ages: # Once for each age group in the list\n",
    "            if age - prevAge < 0.025:\n",
    "                continue # We don't super-tight spacing in age... \n",
    "            print(\"log age={:.4f}\".format(age))\n",
    "            prevAge = age\n",
    "            ageCond = (allAges == age) # select records based on log age field... \n",
    "            # Convert to freq & Lumin/s/Hz\n",
    "            freq = (wavelns[ageCond][::-1]).to(u.Hz, equivalencies=u.spectral()) # Reverse array order\n",
    "            LperHz = (LperA[ageCond] * wavelns[ageCond]**2/astropy.constants.c).to(u.erg/u.s/u.Hz)[::-1] # Reverse array order...\n",
    "\n",
    "            rsWaveln, rsLperA  = lyA.rsSEDwavelen(wavelns[ageCond], LperA[ageCond], z) # Redshift SED in wavelen\n",
    "            rsFreq,   rsLperHz = lyA.rsSEDfreq(freq, LperHz, z)      # Redshift SED in freq\n",
    "            lyForFluxHz = (rsLperHz * absorb(rsWaveln[::-1]))\n",
    "            \n",
    "            # Need to extract only the data for one sed (at a single age)\n",
    "            # to compute the flux in the filters\n",
    "            rsWavelnOneAge    = rsWaveln\n",
    "            rsFreqOneAge      = rsFreq\n",
    "            lyForFluxHzOneAge = lyForFluxHz\n",
    "            # Create first part [log age, z, ...]\n",
    "            # Flux is in erg/s/Hz/cm^2\n",
    "            jwstNormFlux = np.array([np.trapz(jwstFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in jwstFilters])\n",
    "            jwstNormFlux[jwstNormFlux <= 0.0] = -1.0\n",
    "            jwstFlux = np.array([(np.trapz(jwstFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n",
    "                        for aFilt in jwstFilters])\n",
    "            jwstFlux[jwstFlux < 1e-90] = 0.0\n",
    "            jwstFlux   = np.divide(jwstFlux,jwstNormFlux).tolist()\n",
    "            \n",
    "            hubbNormFlux = np.array([np.trapz(hubbleFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in hubbleFilters])\n",
    "            hubbNormFlux[hubbNormFlux <= 0.0] = -1.0\n",
    "            hubbFlux = np.array([(np.trapz(hubbleFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n",
    "                        for aFilt in hubbleFilters])   \n",
    "            hubbFlux[hubbFlux < 1e-90] = 0.0\n",
    "            hubbFlux = np.divide(hubbFlux,hubbNormFlux).tolist()\n",
    "            \n",
    "            jhkNormFlux = np.array([np.trapz(jhkFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in jhkFilters])\n",
    "            jhkNormFlux[jhkNormFlux <= 0.0] = -1.0\n",
    "            jhkFlux = np.array([(np.trapz(jhkFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n",
    "                        for aFilt in jhkFilters])\n",
    "            jhkFlux[jhkFlux < 1e-90] = 0.0\n",
    "            jhkFlux = np.divide(jhkFlux,jhkNormFlux).tolist()\n",
    "\n",
    "            aLine = [age] + [z] + jwstFlux + hubbFlux + jhkFlux\n",
    "            outfile.append(aLine)\n",
    "        filename = fname + \"_\" + str(z) + \".gz\"\n",
    "        print('writing {}'.format(filename))\n",
    "        np.savetxt(filename, outfile, fmt=fmtStr, delimiter=', ', header=hdr)"
   ]
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