checking_MaNGA_ppxf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this code will compare the results from LOGCUBE to a run of pPXF I do myself'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''this code will compare the results from LOGCUBE to a run of pPXF I do myself'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyfits\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import heapq\n",
    "import pyfits\n",
    "import matplotlib as plt\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "from decimal import *\n",
    "import numpy\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.integrate import quad\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from pylab import *\n",
    "from scipy import stats\n",
    "from scipy.stats import chisquare\n",
    "import matplotlib.patches as patches\n",
    "from astropy.io import fits\n",
    "\n",
    "\n",
    "from astropy.cosmology import WMAP9 as cosmo\n",
    "\n",
    "import pyfits\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import heapq\n",
    "import pyfits\n",
    "import matplotlib as plt\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "from decimal import *\n",
    "import numpy\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.integrate import quad\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.modeling import models, fitting\n",
    "from pylab import *\n",
    "from scipy import stats\n",
    "from scipy.stats import chisquare\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "import sys \n",
    "\n",
    "sys.path.append('/usr/local/lib/python2.7/site-packages/ppxf')\n",
    "sys.path\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import glob\n",
    "from time import clock\n",
    "from os import path\n",
    "\n",
    "from ppxf import ppxf\n",
    "import ppxf_util as util\n",
    "plt.clf()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file='../MaNGA_code/practice_logcube/manga-8459-3702-LOGCUBE-VOR10-GAU-MILESHC.fits'\n",
    "file='../MaNGA_code/practice_logcube/manga-8459-3702-LOGCUBE-VOR10-GAU-MILESHC.fits'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DAP VERSION 2.0.2\n",
      "REFF 4.0408902\n",
      "SN min in fit 1.0\n",
      "Input Vel 21648.163\n",
      "Input Sigma 100.0\n",
      "Number of Stellar Templates 597\n",
      "Type of Fitting stellar_kinematics\n",
      "PpXF?  ppxf\n",
      "Template lib MILESHC\n",
      "Bias value None\n",
      "Cleaned? False\n",
      "Moments 2\n",
      "Polynomial Degree 8\n",
      "Multiplicative Polynomial Degree -1\n"
     ]
    }
   ],
   "source": [
    "logcube=pyfits.open(file)\n",
    "#print(logcube[0].header)\n",
    "print('DAP VERSION', logcube[0].header['VERSDAP'])\n",
    "\n",
    "print('REFF', logcube[0].header['REFF'])\n",
    "#print('Binning?', logcube[0].header['BINTYPE'])\n",
    "print('SN min in fit', logcube[0].header['SCMINSN'])\n",
    "print('Input Vel', logcube[0].header['SCINPVEL'])\n",
    "print('Input Sigma', logcube[0].header['SCINPSIG'])\n",
    "print('Number of Stellar Templates', logcube[0].header['NSCMOD'])\n",
    "print('Type of Fitting', logcube[0].header['SCTYPE'])\n",
    "print('PpXF? ', logcube[0].header['SCMETH'])\n",
    "print('Template lib', logcube[0].header['PPXFTPLK'])\n",
    "print('Bias value', logcube[0].header['PPXFBIAS'])\n",
    "print('Cleaned?', logcube[0].header['PPXFCLN'])\n",
    "print('Moments', logcube[0].header['PPXFMOM'])\n",
    "print('Polynomial Degree', logcube[0].header['PPXFDEG'])\n",
    "print('Multiplicative Polynomial Degree', logcube[0].header['PPXFMDEG'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4563, 42, 42)\n"
     ]
    }
   ],
   "source": [
    "print(shape(logcube[6].data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MZp3md73ZT5C+om8aV/T1Xx4oN7AD1K8bx4grb2TlO99wsvk3kjK/4qZZyWzc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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ed8ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stellar_cont=logcube[6].data-logcube[7].data-logcube[8].data\n",
    "plt.plot(logcube[4].data,stellar_cont[:,20,20],lw=3, label='Stellar Continuum')\n",
    "plt.plot(logcube[4].data,logcube[6].data[:,20,20],lw=2, label='Model')\n",
    "plt.legend()\n",
    "#plt.xlim([5400,5600])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4563,)\n"
     ]
    }
   ],
   "source": [
    "xs=logcube[4].data\n",
    "ys=logcube[1].data[:,20,20]\n",
    "noise=logcube[2].data[:,20,20]\n",
    "mask=logcube[3].data[:,20,20]\n",
    "\n",
    "print(shape(mask))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "import sys \n",
    "\n",
    "sys.path.append('/usr/local/lib/python2.7/site-packages/ppxf')\n",
    "sys.path\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import glob\n",
    "from time import clock\n",
    "from os import path\n",
    "\n",
    "from ppxf import ppxf\n",
    "import ppxf_util as util\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x129b86da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Okd2cl9qoOo+ioRBXjx+htabD9IW0ig1n1QPjOJFfwj8+38hjF/W2GwgWBF9F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\nFwRBCDBE+AVBEAIMEX5BEIQA4/8BlXzqv7rnjisAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12979eeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x131041da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frac 1.00023028502 3704.92187579 3704.06888421\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:78: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "data": {
      "image/png": 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EE889h/7Az4EVZrY0mPe/QAqAu08A3iN0GWsmoUtZr49XMed3asHsey9SmllE\nJAbxvFrpM0LPfoi2jgO3xKuGcGamxiAiEiNFfUVEJIKag4iIRFBzEBGRCGoOIiISQc1BREQiqDmI\niEgENQcREYmg5iAiIhHUHEREJIKag4iIRFBzEBGRCGoOIiISQc1BREQiqDmIiEgENQcREYmg5iAi\nIhHUHEREJIKag4iIRFBzEBGRCGoOIiISQc1BREQiqDmIiEgENQcREYmg5iAiIhHUHEREJIKag4iI\nRIhbczCzSWaWa2Yry1g+wMx2mtnS4PVAvGoREZHyqRfHbb8IPANMibLOLHcfHMcaRETkKMRtz8Hd\nZwLb47V9ERGJn6o+59DPzJab2ftm1qWKaxERkUA8DysdyWIgxd33mNlA4E2gU2krmtloYDRASkpK\n5VUoIlJLVdmeg7vvcvc9wfR7QH0za1HGuhPdPc3d05KSkiq1ThGR2qjKmoOZnWJmFkz3DmrZVlX1\niIjIYXE7rGRmU4EBQAsz2wT8GqgP4O4TgGHAzWaWD+QBI9zd41WPiIjELm7Nwd2vOcLyZwhd6ioi\nIgmmqq9WEhGRBKTmICIiEdQcREQkgpqDiIhEUHMQEZEIag4iIhJBzUFERCKoOYiISAQ1BxERiaDm\nICIiEdQcREQkgpqDiIhEUHMQEZEIag4iIhKhKh8TelTWbt3L8GfnVnUZIiI1mvYcREQkglW3h6+l\npaX5woULq7oMEZFqxcwWuXtarOtrz0FERCKoOYiISAQ1BxERiaDmICIiEdQcREQkgpqDiIhEUHMQ\nEZEIag4iIhKh2oXgzGwrsL6URS2Abyu5nPJQfccm0euDxK9R9R2b6l7fae6eFOvGql1zKIuZLSxP\n+q+yqb5jk+j1QeLXqPqOTW2rT4eVREQkgpqDiIhEqEnNYWJVF3AEqu/YJHp9kPg1qr5jU6vqqzHn\nHEREpOLUpD0HERGpIAnbHMysoZktMLNlZrbKzP4vmP+gmeWY2dLgNTDsM/eaWaaZfWVml4bNP9vM\nVgTL/mxmVoF11jWzJWb2TvD+JDP7yMz+Ffw8McHqS5jxM7PsYLtLzWxhMC/Rxq+0GhNpDJub2d/N\n7Esz+8LM+ibSGJZRX0KMn5mdEVbDUjPbZWZ3JMr4RamvcsbP3RPyBRjQJJiuD8wHzgEeBO4sZf0z\ngWVAA6A9kAXUDZYtCD5rwPvA5RVY538DfwXeCd7/HrgnmL4HeCzB6kuY8QOygRYl5iXa+JVWYyKN\n4WRgVDBRdK+bAAAFBUlEQVR9HNA8kcawjPoSZvzCvrsu8A1wWiKNXxn1Vcr4Jeyeg4fsCd7WD17R\nTpAMBaa5+wF3XwdkAr3N7FSgqbvP89AoTQGurIgazSwZGAT8pUQdk4PpyWHflSj1laXS64tSR0KM\n31Go1BrNrBlwPvA8gLsfdPcdJMgYRqmvLFX5Z/wjIMvd15Mg4xelvrJUaH0J2xyg+JDIUiAX+Mjd\n5weL/tPMlpvZpLBdvjbAxrCPbwrmtQmmS86vCE8CvwQKw+a1cvfNwfQ3QKsEqw8SZ/wc+NjMFpnZ\n6GBeIo1fWTVCYoxhe2Ar8IKFDh3+xcwakzhjWFZ9kBjjF24EMDWYTpTxK6s+qITxS+jm4O4F7t4D\nSCbUAc8CxgMdgB7AZuAPVVGbmQ0Gct19UVnrBF26Si4Hi1JfQoxf4Nzgz/dy4BYzOz98YVWOX5jS\nakyUMawH9ALGu3tPYC+hwyDFqngMy6ovUcYPADM7DhgCvFZyWSL8N1hKfZUyfgndHIoEu6LpwGXu\nviVoGoXAc0DvYLUcoG3Yx5KDeTnBdMn5x6o/MMTMsoFpwEVm9jKwJdiNI/iZm0j1JdD44e45wc9c\n4I2glkQZvzJrTKAx3ARsCtuj/juhv4wTZQxLrS+Bxq/I5cBid98SvE+U8Su1vkobv6M9QRLvF5AE\nNA+mGwGzgMHAqWHr/BehY2wAXfj+yZi1lH0yZmAF1zqAwyd8H+f7J7N+n2D1JcT4AY2BE8Km5wCX\nJdL4RakxIcYw2O4s4Ixg+sFg/BJpDEurL2HGL9j2NOD6sPcJM35l1Fcp41dhfwFV9AvoBiwBlgMr\ngQeC+S8BK4L5b5cYqF8ROkP/FWFn44G0YBtZwDME4b8KrHUAh//yPRn4BPgX8DFwUoLVlxDjR2i3\neFnwWgX8KtHGL0qNCTGGwXZ7AAuDWt4ETkywMSytvkQav8bANqBZ2LxEGr/S6quU8VNCWkREIlSL\ncw4iIlK51BxERCSCmoOIiERQcxARkQhqDiIiEkHNQWolM9tz5LXKvc1sM2tRFd8tUtHUHEREJIKa\ng0jAzK4ws/nBTeI+NrNWwfwHzWyymc0ys/Vm9hMz+31wf/x/mln9sM38Mpi/wMw6Bp9vb2Zzg/mP\nhH1fEzP7xMwWB8uGBvMbm9m7FnqWyUozG16pAyGCmoNIuM+Aczx0k7hphO5oWyQVuIjQDdBeBtLd\nvSuQR+i26EV2BvOfIXRXXICnCN18riuhG6UV2Q9c5e69gAuBPwQPYbkM+Nrdu7v7WcA/K/j3FDki\nNQeRw5KBD8xsBXAXoXvVFHnf3Q8Rum1BXQ7/hb0CaBe23tSwn32D6f5h818KW9eAR81sOaHbNLQh\ndHvoFcAlZvaYmZ3n7jsr4HcTKRc1B5HDngaeCf6FfyPQMGzZAQAP3QnzkB++70whoVtTF/EYpotc\nS+gGk2d76LbgW4CG7r6G0N1VVwCPmNkDR/8riRwdNQeRw5px+FbG/3GU2xge9nNuMD2b0MNaINQQ\nwr8v190PmdmFhB4BiZm1Bva5+8uE7hDa6yhrETlq9Y68ikiNdLyZhT8d64+Ebin9mpl9B3xK6LbH\n5XVicJjoAHBNMO924K9mdjfwVti6rwD/CA5jLQS+DOZ3BR43s0LgEHDzUdQhckx0V1YREYmgw0oi\nIhJBzUFERCKoOYiISAQ1BxERiaDmICIiEdQcREQkgpqDiIhEUHMQEZEI/x9cIkWbfCsHIwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131104358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "velscale 69.0297644781\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ej78K7L4qdyEi2w7EPtPf91vo07EpMycNjbl8PLjpNNsKdLTtd7DS3OSJVHaH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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1294da7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Jnf2X98IVM2bMIDY21mW+67Vr19KyZUuSk5NJTk7mqaeestWtXLmSnj17kpSU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1295be518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelength lims [3900, 4100]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129796780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w64mVhWJXfDYjfF35JS6Tu8f5tXqMTUcbP9CdYX1c+DzyrMmP3ZoSSgQQr7U+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\nFEKIy5W9vT2HDx8mNjaWTZs2sX79ep5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1296ce668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelength lims [4250, 4350]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130dbda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3pnKjmQtf+xNfDyfm3zukUcFBa80fe47ywapU/krNw9XRwCWxQZgsmrS8Uvbn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a2dc470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelength lims [5100, 5300]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130dbd7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RDBRLpwdSWl3Pqm2p7da9vyONOpOJ56+MwM/Fji/2tu1mfDC7jNHegwbs97G/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\nBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1299bb668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelength lims [5800, 5950]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1292898d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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du7j//vuHbZ3jdV8qpYaXiLQZY8IGM+8pSzAiYgUeAFYAZcBWEXnZGLOnxzTR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\nEJpglFJKBYQmGKWUUgHx/wEVEuQU8Ry02wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129261908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len gal 3109 len temp 3208\n"
     ]
    },
    {
     "data": {
      "image/png": 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9O+X17VQ0tFPd3El1Syf1bV7SYiOYkBbDhNRYLpuTRXZiVMjb0dDmZfX+eopr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129369518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "must improve chi2 by:  72.5534285889\n",
      "input vel 21648.163\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ppxf/ppxf.py:1299: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "  a = np.zeros((ncols, nrows))  # This array is used for the system solution\n",
      "/usr/local/lib/python2.7/site-packages/ppxf/ppxf.py:868: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "  for j in range(1, reg_dim - 1):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Fit:       Vel     sigma\n",
      " comp. 0:     20918       178\n",
      "chi2/DOF: 2.084\n",
      "Function evaluations: 23 Status: 1\n",
      "Nonzero Templates:  50  /  156\n",
      "Formal errors:\n",
      "     dV    dsigma   dh3      dh4\n",
      "     3.4     3.8\n",
      "Elapsed time in PPXF: 9.52 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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z53PixImcO3cu//vvP8bExFg0pvxMmTKFAPjPP/9w/PjxtLe3p7W1NT/55BNL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\nJgDXAyfYXp+D66SrVNOHgYslbQBOA85VmiBbJ01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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1297e9748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=ppxf_miles(xs,ys,noise,mask,1,'yes',0,'yes',8,-1,100,0,0, logcube[0].header['SCINPVEL'],  logcube[0].header['SCINPSIG'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wdisp 2891 lam_gal 2891\n",
      "velscale 69.1451441267\n",
      "header miles hc SIMPLE  =                    T / Written by IDL:  Tue Feb 10 09:53:13 2015      BITPIX  =                  -64 / Number of bits per data pixel                  NAXIS   =                    1 / Number of data axes                            NAXIS1  =                 4300 /                                                TPLLIB  = 'MILES   '           /Template library                                FWHM    =              2.50000 /FWHM of resolution element (angstroms)          CRPIX1  =                    1 /                                                CRVAL1  =        3540.50000000 /                                                CDELT1  =       0.900000000000 /                                                END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
      "must improve chi2 by:  72.7323861839\n",
      "input vel 18416.8261719\n",
      "Best Fit:       Vel     sigma\n",
      " comp. 0:     18529       133\n",
      "chi2/DOF: 2.438\n",
      "Function evaluations: 11 Status: 1\n",
      "Nonzero Templates:  10  /  984\n",
      "Formal errors:\n",
      "     dV    dsigma   dh3      dh4\n",
      "      24      30\n",
      "Elapsed time in PPXF: 11.70 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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NGsWkSZNo27YtoCgXy1TkilatuAE4DfxlcwYDvzuHxigzcHkOD72/kqZAdffu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\nf1J+EkQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e726470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=ppxf_int_J1354(10,'no',0,'yes',8,-1,0,0,0, c*np.log(1 + 0.06335),  logcube[0].header['SCINPSIG'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "redshift 0.0637347634308\n"
     ]
    }
   ],
   "source": [
    "c=2.99792458e5\n",
    "redshift=np.exp( 18523     /c)-1\n",
    "print('redshift', redshift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ppxf_miles(t,flux,noise,mask,vr, plot, bs, conv, dg, mdg, regularization, redo_noise, last_chi,input_vel, input_sig, mom ):\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "    c=2.99792458e5\n",
    "    input_z=np.exp( input_vel     /c)-1\n",
    "\n",
    "    if plot=='yes':\n",
    "        plt.plot(mask)\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "        \n",
    "    '''step 1: Mask with the Manga Mask'''\n",
    "    #flux = ma.masked_array(flux, mask=mask)\n",
    "    #t = ma.masked_array(t, mask=mask)\n",
    "    #noise = ma.masked_array(noise, mask=mask)\n",
    "    #print('wavelengths 1', t)\n",
    "    #plt.plot(t, flux)\n",
    "    #plt.show()\n",
    "    '''step 2: Mask with the other mask to restrict wavelengths'''\n",
    "    mask_2 =  (t < 7409) & (t > 3540) #&\n",
    "    \n",
    "    flux=flux[mask_2]\n",
    "    t=t[mask_2]\n",
    "    noise=noise[mask_2]\n",
    "   \n",
    "\n",
    "    \n",
    "    \n",
    "    var = 1/np.sqrt(noise)\n",
    "    var_scaled=var#/np.median(flux)\n",
    "    \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(2,1,1)\n",
    "        ax1.plot(var, label='Noise')\n",
    "        plt.legend()\n",
    "        ax2=fig.add_subplot(2,1,2)\n",
    "        ax2.plot(flux, label='flux')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    galaxy = flux   \n",
    "    if regularization!=0:\n",
    "        #TEMPLATES/=np.median(TEMPLATES))\n",
    "        galaxy/=np.median(galaxy)\n",
    "    #apparently we don't need the normalization\n",
    "    #/np.median(flux)   # Normalize spectrum to avoid numerical issues\n",
    "\n",
    "    \n",
    "    loglam_gal = np.log(t)\n",
    "    lam_gal = t\n",
    "    \n",
    "    lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "\n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, galaxy, label='Air')\n",
    "        \n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    if redo_noise == 0:\n",
    "        noise = var_scaled\n",
    "    else:\n",
    "        noise = var_scaled*np.sqrt(last_chi)\n",
    "\n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[100]/lam_gal[99]    # Constant lambda fraction per pixel\n",
    "    \n",
    "    print('frac', frac, lam_gal[100], lam_gal[99])\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    #wdisp = t['wdisp'][mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    #print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    print(type(lam_gal))\n",
    "    xs=np.linspace(0,len(lam_gal)-1,len(lam_gal))\n",
    "    \n",
    "    x_1=np.linspace(0,len(lam_gal)-2,len(lam_gal)-1)\n",
    "    fwhm_gal=[2.355*(lam_gal[x+1]-lam_gal[x]) for x in x_1]\n",
    "    fwhm_gal.append(fwhm_gal[len(x_1)-1])\n",
    "    fwhm_gal=np.array(fwhm_gal)\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.axhline(y=(lam_gal[-1]-lam_gal[0])/len(lam_gal))\n",
    "        plt.annotate('Mean FWHM = '+str(round(2.355*((lam_gal[-1]-lam_gal[0])/len(lam_gal)),1)),xy=(0.7,0.9),xycoords='axes fraction')\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()\n",
    "    \n",
    "    '''fwhm_gal = [(2.355*(lam_gal[x]))/2000 for x in xs] # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    #resolution is 2000\n",
    "    #\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()'''\n",
    "        \n",
    "        \n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    #lam_gal = lam_gal/(1+z)  # Compute approximate restframe wavelength\n",
    "    #fwhm_gal = fwhm_gal/(1+z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    " \n",
    "    \n",
    "    file_dir = glob.glob('../ppxf/miles_models/Mun*.fits')\n",
    "    \n",
    "    #file_dir = glob.glob('../MaNGA_code/practice_logcube/DAP/data/spectral_templates/miles_cluster/*.fits')\n",
    "\n",
    "    \n",
    "\n",
    "    hdu = fits.open(file_dir[10])\n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    \n",
    "   \n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(ssp)\n",
    "        plt.title('Template')\n",
    "        plt.show()\n",
    "    \n",
    "    lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    sspNew_1 = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    \n",
    "    \n",
    "    lam_gal_plot = lam_gal/(1+input_z)\n",
    "    \n",
    "    lines=['Ca II H and K', 'G band','Mg b', 'Na I Doublet']\n",
    "    wavelength_limits=[[3900,4100],[4250,4350],[5100,5300],[5800,5950]]\n",
    "    \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        plt.title('Template vs Data')\n",
    "        ax1=fig.add_subplot(1,2,1)\n",
    "        ax1.plot(lam_temp,ssp, label='Template')\n",
    "        \n",
    "        plt.legend()\n",
    "\n",
    "        ax2=fig.add_subplot(1,2,2)\n",
    "        ax2.plot(lam_gal_plot, galaxy, label='Data')\n",
    "      \n",
    "\n",
    "        plt.legend()\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    if plot=='yes':\n",
    "        for i in range(len(lines)):\n",
    "            plt.clf()\n",
    "            fig=plt.figure()\n",
    "            plt.title(lines[i])\n",
    "            ax1=fig.add_subplot(2,1,1)\n",
    "            ax1.plot(lam_temp,ssp, label='Template')\n",
    "            print('wavelength lims', wavelength_limits[i])\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "            ax1.set_yticks([])\n",
    "            ax1.set_ylabel([])\n",
    "            ax1.yaxis.set_visible(False)\n",
    "            \n",
    "            plt.legend()\n",
    "\n",
    "            ax2=fig.add_subplot(2,1,2)\n",
    "            ax2.plot(lam_gal_plot, galaxy, label='Data')\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "        \n",
    "            ax2.xaxis.set_visible(False)\n",
    "            ax2.yaxis.set_visible(False)\n",
    "            ax2.set_yticks([])\n",
    "            ax2.set_ylabel([])\n",
    "            \n",
    "            ax2.set_xticks([])\n",
    "            ax2.set_xlabel([])\n",
    "\n",
    "            plt.legend()\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "    \n",
    "    print('len gal',len(lam_gal), 'len temp', len(sspNew_1))\n",
    "    templates = np.empty((sspNew_1.size, len(file_dir)))\n",
    "    \n",
    "    fwhm_tem = 2.5\n",
    "\n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels\n",
    "\n",
    "    for j, fname in enumerate(file_dir):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        if conv=='yes':\n",
    "            ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(211)\n",
    "        ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "        ax1.set_xlim([5850,5950])\n",
    "        ax1.set_ylim([0.5,2.0])\n",
    "        ax2=fig.add_subplot(212)\n",
    "        ax2.plot(lam_temp, ssp, label='Template')\n",
    "        ax2.set_xlim([5850,5950])\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, input_z)\n",
    "    print('must improve chi2 by: ', np.sqrt(2*len(goodpixels)))\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    vel = c*np.log(1 + input_z)   # Initial estimate of the galaxy velocity in km/s\n",
    "    print('input vel', vel)\n",
    "    start = [vel, input_sig]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    t = clock()\n",
    "    if regularization !=0:\n",
    "        templates/=np.median(templates)\n",
    "\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "              goodpixels=goodpixels, bias=bs, plot=True, moments=mom,\n",
    "              degree=dg,mdegree=mdg, vsyst=dv, velscale_ratio=vr, regul=regularization,\n",
    "              lam=lam_gal, clean=False)#was degree=12\n",
    "\n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "    \n",
    "    plt.plot(logcube[4].data,logcube[6].data[:,20,20],lw=1, label='Model')\n",
    "\n",
    "    plt.legend()\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    #print, 'Best-fitting redshift z:', (z + 1)*(1 + sol[0]/c) - 1\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    plt.xlim([6275,6400])\n",
    "    plt.ylim([0.8,1.5])\n",
    "    plt.show()\n",
    "    return lam_gal, galaxy, pp.error[0], pp.chi2\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.2941486656\n",
      "171.839357498\n",
      "NSA_vel 20902.2512663 NSA plus 20891.8055457\n",
      "my velocity 20919\n",
      "my sigma 106\n"
     ]
    }
   ],
   "source": [
    "'''checking parameters'''\n",
    "\n",
    "file='../MaNGA_code/practice_logcube/manga-8459-3702-MAPS-VOR10-GAU-MILESHC.fits'\n",
    "maps=pyfits.open(file)\n",
    "print(maps['STELLAR_VEL'].data[20,20])\n",
    "print(maps['STELLAR_SIGMA'].data[20,20])\n",
    "\n",
    "NSA_redshift=0.0722105#0.0723216\n",
    "\n",
    "#0.0722105 ± 0.000011744\n",
    "\n",
    "#vel balmer 21013.369140625\n",
    "#vel forbidden 20978.62109375\n",
    "#EBV_star 0.08089999854564667\n",
    "#vel stars 20968.185546875 4.110400199890137\n",
    "#sig stars 155.95849609375 4.435699939727783\n",
    "\n",
    "c=2.99792458e5\n",
    "NSA_vel=c*np.log(1 + NSA_redshift)\n",
    "#V*_corrected = V*_MPL-5 - 25.89\n",
    "\n",
    "print('NSA_vel',NSA_vel,'NSA plus', (maps['STELLAR_VEL'].data[20,20]-25.89)*(1+NSA_redshift)+(NSA_vel-25.89) )\n",
    "\n",
    "code_vel=20919\n",
    "code_sig=106\n",
    "\n",
    "print('my velocity', code_vel)\n",
    "print('my sigma', code_sig)\n",
    "\n",
    "\n",
    "#20813 from sdss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input vel 21648.163\n",
      "problem 0.1\n",
      "wdisp 2899 lam_gal 2899\n",
      "velscale 68.96649316\n",
      "fwhm_tem 2.119500000000214\n",
      "must improve chi2 by:  72.5534285889\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beckynevin/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:45: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/usr/local/lib/python2.7/site-packages/ppxf/ppxf.py:1299: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "  a = np.zeros((ncols, nrows))  # This array is used for the system solution\n",
      "/usr/local/lib/python2.7/site-packages/ppxf/ppxf.py:868: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "  for j in range(1, reg_dim - 1):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Fit:       Vel     sigma\n",
      " comp. 0:     20813       172\n",
      "chi2/DOF: 0.1726\n",
      "Function evaluations: 38 Status: 1\n",
      "Reddening E(B-V): 0\n",
      "Nonzero Templates:  34  /  49\n",
      "Formal errors:\n",
      "     dV    dsigma   dh3      dh4\n",
      "     5.4     6.1\n",
      "Elapsed time in PPXF: 4.62 s\n",
      "Best-fitting redshift z: 0.149504828644\n"
     ]
    },
    {
     "data": {
      "image/png": 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QfMlo2TLzvQcPWrJkCXQ6Hfr16wfs3g388IP5DOfRRz06H3F181riIPkTgPMl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\nMyviwGFmZkUcOMzMrIgDh5mZFfkPP/tbEMuQf7gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129b670b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=ppxf_miles_comparison('no',1,'no',0,'yes',8,-1,100,0,0, logcube[0].header['SCINPVEL'],  logcube[0].header['SCINPSIG'], 2,0.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "'''run ppxf on a random galaxy that is in MaNGA, sdss, and OSSY'''\n",
    "'''Luckily this ends up being the one from our code'''\n",
    "\n",
    "'''c=2.99792458e5#c*np.log(1 + redshift_J1354),\n",
    "y=ppxf_miles('no',1,'no',0,'yes',8,-1,100,0,0, 18493,  127, 2, 0.1)'''\n",
    "def ppxf_tremonti_log(file,lam_min, lam_max,pre_z,vr, plot, bs, conv, dg,mdg, regularization, redo_noise, last_chi, input_vel, input_sig, mom, reddening):\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "    \n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    #0.0723216\n",
    "    \n",
    "    hdu = fits.open(file)\n",
    "   \n",
    "    t = hdu['COADD'].data\n",
    "\n",
    "    c=2.99792458e5\n",
    "    \n",
    "    print('input vel', input_vel)\n",
    "    input_z = np.exp( input_vel     /c)-1\n",
    "    if pre_z=='yes':\n",
    "        z=0\n",
    "    else:\n",
    "        z=input_z\n",
    "    #z = 0.0633581002031   # SDSS redshift estimate\n",
    "    \n",
    "\n",
    "    # The SDSS wavelengths are in vacuum, while the MILES ones are in air.\n",
    "    # For a rigorous treatment, the SDSS vacuum wavelengths should be\n",
    "    # converted into air wavelengths and the spectra should be resampled.\n",
    "    # To avoid resampling, given that the wavelength dependence of the\n",
    "    # correction is very weak, I approximate it with a constant factor.\n",
    "    #\n",
    "    \n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(lam_min)) & (t['loglam'] < np.log10(lam_max))\n",
    "    \n",
    "    #mask_2 =  (t < 7409) & (t > 3540)\n",
    "    flux = t['flux']#[mask]\n",
    "    var = np.sqrt(1/abs(t['ivar']))\n",
    "    print('problem', var[1])\n",
    "    #var = np.full_like(flux, 0.5) \n",
    "    \n",
    "    \n",
    "    \n",
    "    var_scaled=var\n",
    "    \n",
    "    \n",
    "    galaxy = flux   \n",
    "    if regularization!=0:\n",
    "        #TEMPLATES/=np.median(TEMPLATES))\n",
    "        galaxy/=np.median(galaxy)\n",
    "        var_scaled/=np.median(galaxy)\n",
    "        # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam']#[mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    lam_gal_vac = lam_gal\n",
    "\n",
    "   \n",
    "    #lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "    #lam_gal = util.vac_to_air(lam_gal)\n",
    "    \n",
    "   \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, galaxy, label='Air')\n",
    "        plt.plot(lam_gal_vac, galaxy, label='Vacuum')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    if redo_noise == 0:\n",
    "        noise = var_scaled\n",
    "    else:\n",
    "        noise = var_scaled*np.sqrt(last_chi)\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal,noise, label='Sigma')\n",
    "        plt.legend()\n",
    "        plt.show()  \n",
    "    noise=np.array(noise)\n",
    "    for i in range(len(noise)):\n",
    "        if noise[i]<0:\n",
    "            print('problem var neg', noise[i])\n",
    "            noise[i]=1e6\n",
    "        if np.isinf(noise[i]):\n",
    "            print('problem isinifinite', noise[i])\n",
    "            noise[i]=1e6\n",
    "    \n",
    "        \n",
    "   \n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp']#[mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    if pre_z=='yes':\n",
    "        lam_gal = lam_gal/(1+input_z)  # Compute approximate restframe wavelength\n",
    "        fwhm_gal = fwhm_gal/(1+input_z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    " \n",
    " \n",
    "    file_dir = glob.glob('../MaNGA_code/practice_logcube/DAP/data/spectral_templates/miles_cluster/*.fits')\n",
    "\n",
    "    #file_dir = glob.glob('../ppxf/miles_models/Mun*.fits')\n",
    "    #file_dir = glob.glob('../ppxf/indo_us/IRAF/*.fits')\n",
    "\n",
    "    \n",
    "    file_dir = glob.glob('../gandalf_release_v1.5/SDSS_example/BC03_Tremonti04_templates/*.fits')\n",
    "    hdu = fits.open(file_dir[0])\n",
    "    \n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    \n",
    "    \n",
    "    #I think that the tremonti spectra are actually already in log space\n",
    "    lam_temp_log = (h2['CRVAL1']) + (h2['CD1_1'])*np.arange(h2['NAXIS1'])\n",
    "    \n",
    "    print('temp crval1', h2['CRVAL1'])\n",
    "    print('temp cd1_1', h2['CD1_1'])\n",
    "    \n",
    "    print('10** temp crval1', 10**h2['CRVAL1'])\n",
    "    print('10** temp cd1_1', 10**h2['CD1_1'])\n",
    "    \n",
    "    print('exp temp crval1', np.exp(h2['CRVAL1']))\n",
    "    print('exp temp cd1_1', np.exp(h2['CD1_1']))\n",
    "    lam_temp = 10**(lam_temp_log)\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    \n",
    "    print('min', np.min(lam_temp), 'max', np.max(lam_temp))\n",
    "    sspNew_1 = ssp#util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    templates = np.empty((sspNew_1.size, len(file_dir)))\n",
    "    \n",
    "    fwhm_tem = 10**(h2['CD1_1'])*2.355\n",
    "    print('fwhm_tem', fwhm_tem)\n",
    "\n",
    "    \n",
    "    lam_gal_plot = lam_gal/(1+input_z)\n",
    "    \n",
    "    lines=['Ca II H and K', 'G band','Mg b', 'Na I Doublet']\n",
    "    wavelength_limits=[[3900,4100],[4230,4270],[5100,5300],[5800,5950]]\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_temp,ssp, label='Template')\n",
    "        \n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    '''if plot=='yes':\n",
    "        for i in range(len(lines)):\n",
    "            plt.clf()\n",
    "            fig=plt.figure()\n",
    "            plt.title(lines[i])\n",
    "            ax1=fig.add_subplot(2,1,1)\n",
    "            ax1.plot(lam_temp,ssp, label='Template')\n",
    "            print('wavelength lims', wavelength_limits[i])\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "            ax1.set_yticks([])\n",
    "            ax1.set_ylabel([])\n",
    "            ax1.yaxis.set_visible(False)\n",
    "            \n",
    "            plt.legend()\n",
    "\n",
    "            ax2=fig.add_subplot(2,1,2)\n",
    "            ax2.plot(lam_gal_plot, galaxy, label='Data')\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "        \n",
    "            ax2.xaxis.set_visible(False)\n",
    "            ax2.yaxis.set_visible(False)\n",
    "            ax2.set_yticks([])\n",
    "            ax2.set_ylabel([])\n",
    "            \n",
    "            ax2.set_xticks([])\n",
    "            ax2.set_xlabel([])\n",
    "\n",
    "            plt.legend()\n",
    "            plt.tight_layout()\n",
    "            plt.show()'''\n",
    "    \n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/10**(h2['CD1_1']) # Sigma difference in pixels\n",
    "    \n",
    "    print('sigma kernel', sigma)\n",
    "    plt.clf()\n",
    "    for j, fname in enumerate(file_dir):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        h2 = hdu[0].header\n",
    "        lam_temp_log = (h2['CRVAL1']) + (h2['CD1_1'])*np.arange(h2['NAXIS1'])\n",
    "    \n",
    "        lam_temp = 10**(lam_temp_log)\n",
    "        lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    \n",
    "        if conv=='yes':\n",
    "            ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew=ssp#,logLam1, velscale_new = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)\n",
    "        plt.plot(lam_temp_log,sspNew/np.median(sspNew))\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    plt.show()\n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(211)\n",
    "        ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "        ax1.set_xlim([5850,5950])\n",
    "        ax1.set_ylim([0.5,2.0])\n",
    "        ax2=fig.add_subplot(212)\n",
    "        ax2.plot(lam_temp, ssp, label='Template')\n",
    "        ax2.set_xlim([5850,5950])\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    \n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, z)\n",
    "    print('must improve chi2 by: ', np.sqrt(2*len(goodpixels)))\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    \n",
    "    vel_start=c*np.log(1 + z)\n",
    "    \n",
    "    start = [vel_start, input_sig]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    \n",
    "    t = clock()\n",
    "    if regularization !=0:\n",
    "        templates/=np.median(templates)\n",
    "        \n",
    "        \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(2,1,1)\n",
    "        ax1.plot(noise, label='Noise')\n",
    "        plt.legend()\n",
    "        ax2=fig.add_subplot(2,1,2)\n",
    "        ax2.plot(galaxy, label='flux')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "    print('velscale', velscale)\n",
    "    \n",
    "    \n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "          goodpixels=goodpixels, bias=bs, plot=True, moments=mom,\n",
    "          degree=dg,mdegree=mdg, vsyst=dv, regul=regularization,\n",
    "          clean=False)#was degree=12\n",
    "\n",
    "    \n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    print('Best-fitting redshift z:', (input_z + 1)*(1 + pp.sol[0]/c) - 1)\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    #plt.xlim([1800,2200])\n",
    "    #plt.xlim([5800,5950])#This was for Na lines\n",
    "    #plt.xlim([3800,4100])\n",
    "    #plt.xlim([6030,6080])\n",
    "    #plt.ylim([0.8,1.2])\n",
    "    #plt.xlim([2180,2220])\n",
    "    plt.ylim([0,5])\n",
    "    plt.show()\n",
    "   \n",
    "    return lam_gal, galaxy, pp.error[0], pp.chi2\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input vel 18416\n",
      "problem 3.68277\n",
      "wdisp 3833 lam_gal 3833\n",
      "velscale 69.1451441267\n",
      "temp crval1 3.5314788818359375\n",
      "temp cd1_1 0.0001\n",
      "10** temp crval1 3399.9997243771677\n",
      "10** temp cd1_1 1.0002302850208247\n",
      "exp temp crval1 34.1744702652\n",
      "exp temp cd1_1 1.000100005\n",
      "min 3399.99972438 max 9197.69241143\n",
      "fwhm_tem 2.3555423212240423\n",
      "sigma kernel [ 0.          0.          0.         ...,  1.29821536  1.29945567\n",
      "  1.30069753]\n"
     ]
    },
    {
     "data": {
      "image/png": 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m6KZxZJ/bFwA76ChhGXItabtNbHiCW0YIbG9icw0pnIXUV5fGjdp4m7oRv6Va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\nrRVrnJPMKalMMk2h+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12961a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "must improve chi2 by:  84.7112743382\n",
      "velscale 69.1451441267\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ppxf/ppxf.py:1299: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "  a = np.zeros((ncols, nrows))  # This array is used for the system solution\n",
      "/usr/local/lib/python2.7/site-packages/ppxf/ppxf.py:868: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "  for j in range(1, reg_dim - 1):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Fit:       Vel     sigma\n",
      " comp. 0:     18399        75\n",
      "chi2/DOF: 0.02466\n",
      "Function evaluations: 9 Status: 1\n",
      "Nonzero Templates:  29  /  39\n",
      "Formal errors:\n",
      "     dV    dsigma   dh3      dh4\n",
      "      33      53\n",
      "Elapsed time in PPXF: 1.50 s\n",
      "Best-fitting redshift z: 0.128615135671\n"
     ]
    },
    {
     "data": {
      "image/png": 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orK+vZ/78+YbXBiE4FjhxbhUjF9Tw0sNVfIxyR9Sfr4AXgT/PLGJo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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129ca8ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#y=ppxf_tremonti_log('../spec-0941-52709-0466.fits',3490,9167,'no',1,'no',0,'yes',8,-1,100,0,0, 20963,  100, 2, 0.08)\n",
    "\n",
    "y=ppxf_tremonti_log('../spec-1777-53857-0076.fits',3490,9167,'no',1,'no',0,'yes',8,-1,100,0,0, 18416,  100, 2, 0.08)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y=ppxf_tremonti_log(3490,9167,'no',1,'no',0,'yes',8,-1,100,0,0, 20963,  100, 2, 0.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3282,)\n",
      "everything (8459, '3702', '8459-3702', '1-166919', 'v1_5_0', 'v1_5_0', 'v1_2_0', 'v5_5_23', 'v2_0', 'APOGEE-2&MaNGA', 'MaNGA dither', 146.709100143, 43.423842959600002, 176.80346978, 49.573335614500003, 146.70910000000001, 43.423842999999998, 0.0152958, 12, 10801.0, 1, 38.508200000000002, 68.248400000000004, 'ma128', 27, '6', 8957, 148.60705999999999, 43.236012000000002, 1.01762, 1.02241, 1.0478400000000001, 1.46065, 1.80497, 2.1418699999999999, 0.86625200000000002, 0.883799, 0.89207499999999995, 57093, 57096, 57096, 2.90401, 2.8277100000000002, 2.8156400000000001, 2.8031299999999999, 4288, 0, 0, 1, 'plateTargets-1.par', 6958, 'J094650.18+432525.8', 37, 37, 0, 143, 2964, 'v1_0_0', 176496, 0.072210499999999997, 0.0723216, array([-17.3546, -18.3471, -19.7321, -21.1649, -21.9362, -22.3022, -22.5646]), array([-17.8735, -18.5189, -19.8366, -21.1537, -21.8119, -22.1061, -22.3549]), array([   13.8206,   140.221 ,   296.063 ,  2437.26  ,  1894.6   ,\n",
      "        2444.37  ,   999.217 ]), 73104000000.0, 60497400000.0, 0.86695599999999995, 72.956400000000002, array([ 0.127805 ,  0.126109 ,  0.0794733,  0.0584757,  0.0424114,\n",
      "        0.0321593,  0.0228013]), 3.8842699999999999, 4.0408900000000001, array([   22.4717,    31.3284,   131.863 ,   474.858 ,   919.113 ,\n",
      "        1334.05  ,  1671.17  ]), array([ 0.0268891 ,  0.126686  ,  0.0809483 ,  0.557065  ,  0.0106435 ,\n",
      "        0.0698362 ,  0.00397291]), array([   25.4287,    42.9103,   124.067 ,   463.628 ,   906.917 ,\n",
      "        1317.36  ,  1653.14  ]), array([ 0.0257653 ,  0.115737  ,  0.087259  ,  0.532602  ,  0.0112132 ,\n",
      "        0.074614  ,  0.00427995]), 0.83583700000000005, 2.4426100000000002, 107.238, 4.4251899999999997, array([   15.5297,    35.9575,   109.695 ,   459.544 ,   997.886 ,\n",
      "        1538.29  ,  1959.89  ]), array([ 0.0861199,  0.120044 ,  0.327712 ,  0.683495 ,  0.0215476,\n",
      "        0.0968472,  0.0123962]))\n",
      "NSA redshift 0.0722105\n",
      "NSA redshift J094650.18+432525.8\n"
     ]
    }
   ],
   "source": [
    "file='../drpall_test.fits'\n",
    "drpall=pyfits.open(file)\n",
    "#print(drpall[1].header)\n",
    "#print(drpall[1].data[50][69])\n",
    "#print(drpall[1].data[51]['NSA_Z'])\n",
    "\n",
    "print(shape(drpall[1].data))\n",
    "for i in range(len(drpall[1].data)):\n",
    "    if str(drpall[1].data[i][0])=='8459' and drpall[1].data[i][1]=='3702':\n",
    "        print('everything', drpall[1].data[i])\n",
    "        print('NSA redshift',drpall[1].data[i]['NSA_REDSHIFT'])\n",
    "        print('NSA redshift',drpall[1].data[i]['IAUNAME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0526543\n"
     ]
    }
   ],
   "source": [
    "'''All of the MaNGA redshifts come from the NSA catalog:'''\n",
    "\n",
    "file='../nsa_v0_1_2.fits'\n",
    "nsa_cat=pyfits.open(file)\n",
    "\n",
    "#print(nsa_cat[1].header)\n",
    "print(nsa_cat[1].data[1]['Z'])#146.70908349497\n",
    "\n",
    "for i in range(len(nsa_cat[1].data)):\n",
    "    #if nsa_cat[1].data[i]['IAUNAME']=='J135429.05+132757.2':\n",
    "    #if nsa_cat[1].data[i]['PLATE']==941 and nsa_cat[1].data[i]['FIBERID']==466:\n",
    "    #    print(nsa_cat[1].data[i])\n",
    "    #if nsa_cat[1].data[i]['MJD']==52709:\n",
    "    #    print(nsa_cat[1].data[i])\n",
    "    #if nsa_cat[1].data[i]['PLUG_RA']=='146.70908':\n",
    "    #    print(nsa_cat[1].data[i])\n",
    "    if nsa_cat[1].data[i]['Z'] > 0.07:\n",
    "        print(nsa_cat[1].data[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "'''run ppxf on a random galaxy that is in MaNGA, sdss, and OSSY'''\n",
    "'''Luckily this ends up being the one from our code'''\n",
    "\n",
    "'''c=2.99792458e5#c*np.log(1 + redshift_J1354),\n",
    "y=ppxf_miles('no',1,'no',0,'yes',8,-1,100,0,0, 18493,  127, 2, 0.1)'''\n",
    "def ppxf_tremonti(lam_min, lam_max,pre_z,vr, plot, bs, conv, dg,mdg, regularization, redo_noise, last_chi, input_vel, input_sig, mom):\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "    \n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    #0.0723216\n",
    "    file = '../spec-0941-52709-0466.fits'\n",
    "    hdu = fits.open(file)\n",
    "   \n",
    "    t = hdu['COADD'].data\n",
    "\n",
    "    c=2.99792458e5\n",
    "    \n",
    "    print('input vel', input_vel)\n",
    "    input_z = np.exp( input_vel     /c)-1\n",
    "    if pre_z=='yes':\n",
    "        z=0\n",
    "    else:\n",
    "        z=input_z\n",
    "    #z = 0.0633581002031   # SDSS redshift estimate\n",
    "    \n",
    "\n",
    "    # The SDSS wavelengths are in vacuum, while the MILES ones are in air.\n",
    "    # For a rigorous treatment, the SDSS vacuum wavelengths should be\n",
    "    # converted into air wavelengths and the spectra should be resampled.\n",
    "    # To avoid resampling, given that the wavelength dependence of the\n",
    "    # correction is very weak, I approximate it with a constant factor.\n",
    "    #\n",
    "    \n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(lam_min)) & (t['loglam'] < np.log10(lam_max))\n",
    "    \n",
    "    #mask_2 =  (t < 7409) & (t > 3540)\n",
    "    flux = t['flux']#[mask]\n",
    "    var = np.sqrt(1/abs(t['ivar']))\n",
    "    print('problem', var[1])\n",
    "    #var = np.full_like(flux, 0.5) \n",
    "    \n",
    "    \n",
    "    \n",
    "    var_scaled=var\n",
    "    \n",
    "    \n",
    "    galaxy = flux   \n",
    "    if regularization!=0:\n",
    "        #TEMPLATES/=np.median(TEMPLATES))\n",
    "        galaxy/=np.median(galaxy)\n",
    "        var_scaled/=np.median(galaxy)\n",
    "        # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam']#[mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    lam_gal_vac = lam_gal\n",
    "\n",
    "   \n",
    "    lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "    #lam_gal = util.vac_to_air(lam_gal)\n",
    "    \n",
    "   \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, galaxy, label='Air')\n",
    "        plt.plot(lam_gal_vac, galaxy, label='Vacuum')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    if redo_noise == 0:\n",
    "        noise = var_scaled\n",
    "    else:\n",
    "        noise = var_scaled*np.sqrt(last_chi)\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal,noise, label='Sigma')\n",
    "        plt.legend()\n",
    "        plt.show()  \n",
    "    noise=np.array(noise)\n",
    "    for i in range(len(noise)):\n",
    "        if noise[i]<0:\n",
    "            print('problem var neg', noise[i])\n",
    "            noise[i]=1e6\n",
    "        if np.isinf(noise[i]):\n",
    "            print('problem isinifinite', noise[i])\n",
    "            noise[i]=1e6\n",
    "    \n",
    "        \n",
    "   \n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp']#[mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    if pre_z=='yes':\n",
    "        lam_gal = lam_gal/(1+input_z)  # Compute approximate restframe wavelength\n",
    "        fwhm_gal = fwhm_gal/(1+input_z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    " \n",
    " \n",
    "    file_dir = glob.glob('../MaNGA_code/practice_logcube/DAP/data/spectral_templates/miles_cluster/*.fits')\n",
    "\n",
    "    #file_dir = glob.glob('../ppxf/miles_models/Mun*.fits')\n",
    "    #file_dir = glob.glob('../ppxf/indo_us/IRAF/*.fits')\n",
    "\n",
    "    \n",
    "    file_dir = glob.glob('../gandalf_release_v1.5/SDSS_example/BC03_Tremonti04_templates/*.fits')\n",
    "    hdu = fits.open(file_dir[0])\n",
    "    \n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    \n",
    "    \n",
    "    #I think that the tremonti spectra are actually already in log space\n",
    "    lam_temp_log = (h2['CRVAL1']) + (h2['CD1_1'])*np.arange(h2['NAXIS1'])\n",
    "    \n",
    "    print('temp crval1', h2['CRVAL1'])\n",
    "    print('temp cd1_1', h2['CD1_1'])\n",
    "    \n",
    "    print('10** temp crval1', 10**h2['CRVAL1'])\n",
    "    print('10** temp cd1_1', 10**h2['CD1_1'])\n",
    "    \n",
    "    print('exp temp crval1', np.exp(h2['CRVAL1']))\n",
    "    print('exp temp cd1_1', np.exp(h2['CD1_1']))\n",
    "    lam_temp = 10**(lam_temp_log)\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    \n",
    "    print('min', np.min(lam_temp), 'max', np.max(lam_temp))\n",
    "    sspNew_1 = ssp#util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    templates = np.empty((sspNew_1.size, len(file_dir)))\n",
    "    \n",
    "    fwhm_tem = 10**(h2['CD1_1'])*2.355\n",
    "    print('fwhm_tem', fwhm_tem)\n",
    "\n",
    "    \n",
    "    lam_gal_plot = lam_gal/(1+input_z)\n",
    "    \n",
    "    lines=['Ca II H and K', 'G band','Mg b', 'Na I Doublet']\n",
    "    wavelength_limits=[[3900,4100],[4230,4270],[5100,5300],[5800,5950]]\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_temp,ssp, label='Template')\n",
    "        \n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    '''if plot=='yes':\n",
    "        for i in range(len(lines)):\n",
    "            plt.clf()\n",
    "            fig=plt.figure()\n",
    "            plt.title(lines[i])\n",
    "            ax1=fig.add_subplot(2,1,1)\n",
    "            ax1.plot(lam_temp,ssp, label='Template')\n",
    "            print('wavelength lims', wavelength_limits[i])\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "            ax1.set_yticks([])\n",
    "            ax1.set_ylabel([])\n",
    "            ax1.yaxis.set_visible(False)\n",
    "            \n",
    "            plt.legend()\n",
    "\n",
    "            ax2=fig.add_subplot(2,1,2)\n",
    "            ax2.plot(lam_gal_plot, galaxy, label='Data')\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "        \n",
    "            ax2.xaxis.set_visible(False)\n",
    "            ax2.yaxis.set_visible(False)\n",
    "            ax2.set_yticks([])\n",
    "            ax2.set_ylabel([])\n",
    "            \n",
    "            ax2.set_xticks([])\n",
    "            ax2.set_xlabel([])\n",
    "\n",
    "            plt.legend()\n",
    "            plt.tight_layout()\n",
    "            plt.show()'''\n",
    "    \n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/10**(h2['CD1_1']) # Sigma difference in pixels\n",
    "    \n",
    "    print('sigma kernel', sigma)\n",
    "    plt.clf()\n",
    "    for j, fname in enumerate(file_dir):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        h2 = hdu[0].header\n",
    "        lam_temp_log = (h2['CRVAL1']) + (h2['CD1_1'])*np.arange(h2['NAXIS1'])\n",
    "    \n",
    "        lam_temp = 10**(lam_temp_log)\n",
    "        lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    \n",
    "        if conv=='yes':\n",
    "            ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew,logLam1, velscale_new = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)\n",
    "        plt.plot(logLam1,sspNew/np.median(sspNew))\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    plt.show()\n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(211)\n",
    "        ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "        ax1.set_xlim([5850,5950])\n",
    "        ax1.set_ylim([0.5,2.0])\n",
    "        ax2=fig.add_subplot(212)\n",
    "        ax2.plot(lam_temp, ssp, label='Template')\n",
    "        ax2.set_xlim([5850,5950])\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    \n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, z)\n",
    "    print('must improve chi2 by: ', np.sqrt(2*len(goodpixels)))\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    \n",
    "    vel_start=c*np.log(1 + z)\n",
    "    \n",
    "    start = [vel_start, input_sig]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    \n",
    "    t = clock()\n",
    "    if regularization !=0:\n",
    "        templates/=np.median(templates)\n",
    "        \n",
    "        \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(2,1,1)\n",
    "        ax1.plot(noise, label='Noise')\n",
    "        plt.legend()\n",
    "        ax2=fig.add_subplot(2,1,2)\n",
    "        ax2.plot(galaxy, label='flux')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "    print('velscale', velscale)\n",
    "    \n",
    "    \n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "          goodpixels=goodpixels, bias=bs, plot=True, moments=mom,\n",
    "          degree=dg,mdegree=mdg, vsyst=dv,\n",
    "          clean=False)#was degree=12\n",
    "\n",
    "    \n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    print('Best-fitting redshift z:', (input_z + 1)*(1 + pp.sol[0]/c) - 1)\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    #plt.xlim([1800,2200])\n",
    "    #plt.xlim([5800,5950])#This was for Na lines\n",
    "    #plt.xlim([3800,4100])\n",
    "    #plt.xlim([6030,6080])\n",
    "    #plt.ylim([0.8,1.2])\n",
    "    #plt.xlim([5000,6000])\n",
    "    plt.ylim([0,120])\n",
    "    plt.show()\n",
    "   \n",
    "    return lam_gal, galaxy, pp.error[0], pp.chi2\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "'''run ppxf on a random galaxy that is in MaNGA, sdss, and OSSY'''\n",
    "'''Luckily this ends up being the one from our code'''\n",
    "\n",
    "'''c=2.99792458e5#c*np.log(1 + redshift_J1354),\n",
    "y=ppxf_miles('no',1,'no',0,'yes',8,-1,100,0,0, 18493,  127, 2, 0.1)'''\n",
    "def ppxf_miles_comparison(pre_z,vr, plot, bs, conv, dg,mdg, regularization, redo_noise, last_chi, input_vel, input_sig, mom, reddening):\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "    \n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    #0.0723216\n",
    "    file = '../spec-0941-52709-0466.fits'\n",
    "    hdu = fits.open(file)\n",
    "   \n",
    "    t = hdu['COADD'].data\n",
    "\n",
    "    c=2.99792458e5\n",
    "    \n",
    "    print('input vel', input_vel)\n",
    "    input_z = np.exp( input_vel     /c)-1\n",
    "    if pre_z=='yes':\n",
    "        z=0\n",
    "    else:\n",
    "        z=input_z\n",
    "    #z = 0.0633581002031   # SDSS redshift estimate\n",
    "    \n",
    "\n",
    "    # The SDSS wavelengths are in vacuum, while the MILES ones are in air.\n",
    "    # For a rigorous treatment, the SDSS vacuum wavelengths should be\n",
    "    # converted into air wavelengths and the spectra should be resampled.\n",
    "    # To avoid resampling, given that the wavelength dependence of the\n",
    "    # correction is very weak, I approximate it with a constant factor.\n",
    "    #\n",
    "    \n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(3540)) & (t['loglam'] < np.log10(7409))\n",
    "    \n",
    "    #mask_2 =  (t < 7409) & (t > 3540)\n",
    "    flux = t['flux'][mask]\n",
    "    var = np.sqrt(1/abs(t['ivar'][mask]))\n",
    "    var = np.full_like(flux, 0.1) \n",
    "    print('problem', var[1])\n",
    "    var_scaled=var\n",
    "    \n",
    "    \n",
    "    galaxy = flux   \n",
    "    if regularization!=0:\n",
    "        #TEMPLATES/=np.median(TEMPLATES))\n",
    "        galaxy/=np.median(galaxy)\n",
    "        # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam'][mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    lam_gal_vac = lam_gal\n",
    "\n",
    "   \n",
    "    lam_gal *= np.median(util.vac_to_air(lam_gal)/lam_gal)\n",
    "    #lam_gal = util.vac_to_air(lam_gal)\n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, galaxy, label='Air')\n",
    "        plt.plot(lam_gal_vac, galaxy, label='Vacuum')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    if redo_noise == 0:\n",
    "        noise = var_scaled\n",
    "    else:\n",
    "        noise = var_scaled*np.sqrt(last_chi)\n",
    "        \n",
    "        \n",
    "   \n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp'][mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    print('wdisp',len(wdisp), 'lam_gal', len(lam_gal))\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    \n",
    "    if plot=='yes':\n",
    "        plt.plot(lam_gal, fwhm_gal)\n",
    "        plt.xlabel('Lambdas')\n",
    "        plt.ylabel('Interp FWHM')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "    print('velscale', velscale)\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    if pre_z=='yes':\n",
    "        lam_gal = lam_gal/(1+input_z)  # Compute approximate restframe wavelength\n",
    "        fwhm_gal = fwhm_gal/(1+input_z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    " \n",
    " \n",
    "    file_dir = glob.glob('../MaNGA_code/practice_logcube/DAP/data/spectral_templates/miles_cluster/*.fits')\n",
    "\n",
    "    #file_dir = glob.glob('../ppxf/miles_models/Mun*.fits')\n",
    "    #file_dir = glob.glob('../ppxf/indo_us/IRAF/*.fits')\n",
    "\n",
    "    \n",
    "\n",
    "    hdu = fits.open(file_dir[0])\n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    \n",
    "    \n",
    "    \n",
    "    lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    sspNew_1 = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "    templates = np.empty((sspNew_1.size, len(file_dir)))\n",
    "    \n",
    "    fwhm_tem = h2['CDELT1']*2.355\n",
    "    print('fwhm_tem', fwhm_tem)\n",
    "\n",
    "    \n",
    "    lam_gal_plot = lam_gal/(1+input_z)\n",
    "    \n",
    "    lines=['Ca II H and K', 'G band','Mg b', 'Na I Doublet']\n",
    "    wavelength_limits=[[3900,4100],[4230,4270],[5100,5300],[5800,5950]]\n",
    "    \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        plt.title('Template vs Data')\n",
    "        ax1=fig.add_subplot(1,2,1)\n",
    "        ax1.plot(lam_temp,ssp, label='Template')\n",
    "        \n",
    "        plt.legend()\n",
    "\n",
    "        ax2=fig.add_subplot(1,2,2)\n",
    "        ax2.plot(lam_gal_plot, galaxy, label='Data')\n",
    "      \n",
    "\n",
    "        plt.legend()\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    if plot=='yes':\n",
    "        for i in range(len(lines)):\n",
    "            plt.clf()\n",
    "            fig=plt.figure()\n",
    "            plt.title(lines[i])\n",
    "            ax1=fig.add_subplot(2,1,1)\n",
    "            ax1.plot(lam_temp,ssp, label='Template')\n",
    "            print('wavelength lims', wavelength_limits[i])\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "            ax1.set_yticks([])\n",
    "            ax1.set_ylabel([])\n",
    "            ax1.yaxis.set_visible(False)\n",
    "            \n",
    "            plt.legend()\n",
    "\n",
    "            ax2=fig.add_subplot(2,1,2)\n",
    "            ax2.plot(lam_gal_plot, galaxy, label='Data')\n",
    "            plt.xlim(wavelength_limits[i])\n",
    "        \n",
    "            ax2.xaxis.set_visible(False)\n",
    "            ax2.yaxis.set_visible(False)\n",
    "            ax2.set_yticks([])\n",
    "            ax2.set_ylabel([])\n",
    "            \n",
    "            ax2.set_xticks([])\n",
    "            ax2.set_xlabel([])\n",
    "\n",
    "            plt.legend()\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "    \n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "    \n",
    "    \n",
    "    #print('fwhm_gal', fwhm_gal)\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels\n",
    "\n",
    "    for j, fname in enumerate(file_dir):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        if conv=='yes':\n",
    "            ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale/vr)[0]\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(211)\n",
    "        ax1.plot(lam_gal, galaxy, label='Gal')\n",
    "        ax1.set_xlim([5850,5950])\n",
    "        ax1.set_ylim([0.5,2.0])\n",
    "        ax2=fig.add_subplot(212)\n",
    "        ax2.plot(lam_temp, ssp, label='Template')\n",
    "        ax2.set_xlim([5850,5950])\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    \n",
    "    #z=0\n",
    "    '''goodpix also have to be adjusted for the redshift'''\n",
    "    \n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, z)\n",
    "    print('must improve chi2 by: ', np.sqrt(2*len(goodpixels)))\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    \n",
    "    vel_start=c*np.log(1 + z)\n",
    "    \n",
    "    start = [vel_start, input_sig]  # (km/s), starting guess for [V, sigma]\n",
    "    #start = [0,200]\n",
    "    \n",
    "    t = clock()\n",
    "    if regularization !=0:\n",
    "        templates/=np.median(templates)\n",
    "        \n",
    "        \n",
    "    if plot=='yes':\n",
    "        fig=plt.figure()\n",
    "        ax1=fig.add_subplot(2,1,1)\n",
    "        ax1.plot(noise, label='Noise')\n",
    "        plt.legend()\n",
    "        ax2=fig.add_subplot(2,1,2)\n",
    "        ax2.plot(galaxy, label='flux')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "    \n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "          goodpixels=goodpixels, bias=bs, plot=True, moments=mom,\n",
    "          degree=dg, vsyst=dv, velscale_ratio=vr, regul=regularization,\n",
    "          clean=False, lam=lam_gal,reddening=reddening)#was degree=12\n",
    "\n",
    "    \n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    print('Best-fitting redshift z:', (input_z + 1)*(1 + pp.sol[0]/c) - 1)\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "    #plt.xlim([1800,2200])\n",
    "    #plt.xlim([5800,5950])#This was for Na lines\n",
    "    #plt.xlim([3800,4100])\n",
    "    #plt.xlim([6030,6080])\n",
    "    #plt.ylim([0.8,1.2])\n",
    "    plt.xlim([6200,6400])\n",
    "    plt.show()\n",
    "    return lam_gal, galaxy, pp.error[0], pp.chi2\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Fit:       Vel     sigma\n",
      " comp. 0:     20894       181\n",
      "chi2/DOF: 0.04057\n",
      "Function evaluations: 14 Status: 1\n",
      "Reddening E(B-V): 0.0789\n",
      "Nonzero Templates:  7  /  156\n",
      "Formal errors:\n",
      "     dV    dsigma   dh3      dh4\n",
      "     4.4     4.8\n",
      "Elapsed time in PPXF: 4.24 s\n"
     ]
    },
    {
     "data": {
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GLwWaNG0K7dqpZXnjRuQGZXjK75rLmq5prAcixQJNnpDD5Ak57Ad45x3485+ZDZxlG3ca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\nSLG3mHbaKfGWOGQDmDx4sqUAveLlyvwrufukuy2F78btqN9h6cP4Tel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LnjJhWDFk/jVw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\n0Gg0Go2FNgoajUajsdBGQaPRaDQW/wczjm4hzi6UIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129cebeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/env python\n",
    "##############################################################################\n",
    "#\n",
    "# Usage example for the procedure PPXF, which implements the\n",
    "# Penalized Pixel-Fitting (pPXF) method originally described in\n",
    "# Cappellari M., & Emsellem E., 2004, PASP, 116, 138\n",
    "#     http://adsabs.harvard.edu/abs/2004PASP..116..138C\n",
    "# and upgraded in Cappellari M., 2017, MNRAS, 466, 798\n",
    "#     http://adsabs.harvard.edu/abs/2017MNRAS.466..798C\n",
    "#\n",
    "# The example also shows how to include a library of templates\n",
    "# and how to mask gas emission lines if present.\n",
    "# The example is specialized for a fit to a SDSS spectrum.\n",
    "#\n",
    "# MODIFICATION HISTORY:\n",
    "#   V1.0.0: Written by Michele Cappellari, Leiden 11 November 2003\n",
    "#   V1.1.0: Log rebin the galaxy spectrum. Show how to correct the velocity\n",
    "#       for the difference in starting wavelength of galaxy and templates.\n",
    "#       MC, Vicenza, 28 December 2004\n",
    "#   V1.1.1: Included explanation of correction for instrumental resolution.\n",
    "#       After feedback from David Valls-Gabaud. MC, Venezia, 27 June 2005\n",
    "#   V2.0.0: Included example routine to determine the goodPixels vector\n",
    "#       by masking known gas emission lines. MC, Oxford, 30 October 2008\n",
    "#   V2.0.1: Included instructions for high-redshift usage. Thanks to Paul Westoby\n",
    "#       for useful feedback on this issue. MC, Oxford, 27 November 2008\n",
    "#   V2.0.2: Included example for obtaining the best-fitting redshift.\n",
    "#       MC, Oxford, 14 April 2009\n",
    "#   V2.1.0: Bug fix: Force PSF_GAUSSIAN to produce a Gaussian with an odd\n",
    "#       number of elements centered on the middle one. Many thanks to\n",
    "#       Harald Kuntschner, Eric Emsellem, Anne-Marie Weijmans and\n",
    "#       Richard McDermid for reporting problems with small offsets\n",
    "#       in systemic velocity. MC, Oxford, 15 February 2010\n",
    "#   V2.1.1: Added normalization of galaxy spectrum to avoid numerical\n",
    "#       instabilities. After feedback from Andrea Cardullo.\n",
    "#       MC, Oxford, 17 March 2010\n",
    "#   V2.2.0: Perform templates convolution in linear wavelength.\n",
    "#       This is useful for spectra with large wavelength range.\n",
    "#       MC, Oxford, 25 March 2010\n",
    "#   V2.2.1: Updated for Coyote Graphics. MC, Oxford, 11 October 2011\n",
    "#   V2.3.0: Specialized for SDSS spectrum following requests from users.\n",
    "#       Renamed PPXF_KINEMATICS_EXAMPLE_SDSS. MC, Oxford, 12 January 2012\n",
    "#   V3.0.0: Translated from IDL into Python. MC, Oxford, 10 December 2013\n",
    "#   V3.0.1: Uses MILES models library. MC, Oxford 11 December 2013\n",
    "#   V3.0.2: Support both Python 2.6/2.7 and Python 3.x. MC, Oxford, 25 May 2014\n",
    "#   V3.0.3: Explicitly sort template files as glob() output may not be sorted.\n",
    "#       Thanks to Marina Trevisan for reporting problems under Linux.\n",
    "#       MC, Sydney, 4 February 2015\n",
    "#   V3.0.4: Use redshift in determine_goodpixels. MC, Oxford, 5 May 2015\n",
    "#   V3.1.0: Illustrate how to deal with variable instrumental resolution.\n",
    "#       Use example galaxy spectrum from SDSS DR12. MC, Oxford, 12 October 2015\n",
    "#   V3.1.1: Support both Pyfits and Astropy to read FITS files.\n",
    "#       MC, Oxford, 22 October 2015\n",
    "#   V3.1.2: Illustrates how to show the wavelength in the plot.\n",
    "#       MC, Oxford, 18 May 2016\n",
    "#   V3.1.3: Make files paths relative to this file, to run the example from\n",
    "#       any directory. MC, Oxford, 18 January 2017\n",
    "#\n",
    "##############################################################################\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import glob\n",
    "from time import clock\n",
    "from os import path\n",
    "\n",
    "from ppxf import ppxf\n",
    "import ppxf_util as util\n",
    "\n",
    "def ppxf_example_kinematics_sdss():\n",
    "\n",
    "    file_dir = '../ppxf'  # path of this procedure\n",
    "\n",
    "    # Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/\n",
    "    # The spectrum is *already* log rebinned by the SDSS DR12\n",
    "    # pipeline and log_rebin should not be used in this case.\n",
    "    #0.0723216\n",
    "    file = '../spec-0941-52709-0466.fits'\n",
    "    hdu = fits.open(file)\n",
    "    t = hdu['COADD'].data\n",
    "    z = 0.0723216   # SDSS redshift estimate\n",
    "\n",
    "    # Only use the wavelength range in common between galaxy and stellar library.\n",
    "    mask = (t['loglam'] > np.log10(3540)) & (t['loglam'] < np.log10(7409))\n",
    "    flux = t['flux'][mask]\n",
    "    galaxy = flux/np.median(flux)   # Normalize spectrum to avoid numerical issues\n",
    "    loglam_gal = t['loglam'][mask]\n",
    "    lam_gal = 10**loglam_gal\n",
    "    noise = np.full_like(galaxy, 0.166)       # Assume constant noise per pixel here\n",
    "\n",
    "    c = 299792.458                  # speed of light in km/s\n",
    "    frac = lam_gal[1]/lam_gal[0]    # Constant lambda fraction per pixel\n",
    "    dlam_gal = (frac - 1)*lam_gal   # Size of every pixel in Angstrom\n",
    "    wdisp = t['wdisp'][mask]        # Intrinsic dispersion of every pixel, in pixels units\n",
    "    fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms\n",
    "    velscale = np.log(frac)*c       # Constant velocity scale in km/s per pixel\n",
    "\n",
    "    # If the galaxy is at a significant redshift (z > 0.03), one would need to apply\n",
    "    # a large velocity shift in PPXF to match the template to the galaxy spectrum.\n",
    "    # This would require a large initial value for the velocity (V > 1e4 km/s)\n",
    "    # in the input parameter START = [V,sig]. This can cause PPXF to stop!\n",
    "    # The solution consists of bringing the galaxy spectrum roughly to the\n",
    "    # rest-frame wavelength, before calling PPXF. In practice there is no\n",
    "    # need to modify the spectrum in any way, given that a red shift\n",
    "    # corresponds to a linear shift of the log-rebinned spectrum.\n",
    "    # One just needs to compute the wavelength range in the rest-frame\n",
    "    # and adjust the instrumental resolution of the galaxy observations.\n",
    "    # This is done with the following three commented lines:\n",
    "    #\n",
    "    # lam_gal = lam_gal/(1+z)  # Compute approximate restframe wavelength\n",
    "    # fwhm_gal = fwhm_gal/(1+z)   # Adjust resolution in Angstrom\n",
    "\n",
    "    # Read the list of filenames from the Single Stellar Population library\n",
    "    # by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset\n",
    "    # of the library is included for this example with permission\n",
    "    vazdekis = glob.glob(file_dir + '/miles_models/Mun1.30Z*.fits')\n",
    "    fwhm_tem = 2.51 # Vazdekis+10 spectra have a constant resolution FWHM of 2.51A.\n",
    "\n",
    "    # Extract the wavelength range and logarithmically rebin one spectrum\n",
    "    # to the same velocity scale of the SDSS galaxy spectrum, to determine\n",
    "    # the size needed for the array which will contain the template spectra.\n",
    "    #\n",
    "    hdu = fits.open(vazdekis[0])\n",
    "    ssp = hdu[0].data\n",
    "    h2 = hdu[0].header\n",
    "    lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])\n",
    "    lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]\n",
    "    sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale)[0]\n",
    "    templates = np.empty((sspNew.size, len(vazdekis)))\n",
    "\n",
    "    # Interpolates the galaxy spectral resolution at the location of every pixel\n",
    "    # of the templates. Outside the range of the galaxy spectrum the resolution\n",
    "    # will be extrapolated, but this is irrelevant as those pixels cannot be\n",
    "    # used in the fit anyway.\n",
    "    fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)\n",
    "\n",
    "    # Convolve the whole Vazdekis library of spectral templates\n",
    "    # with the quadratic difference between the SDSS and the\n",
    "    # Vazdekis instrumental resolution. Logarithmically rebin\n",
    "    # and store each template as a column in the array TEMPLATES.\n",
    "\n",
    "    # Quadratic sigma difference in pixels Vazdekis --> SDSS\n",
    "    # The formula below is rigorously valid if the shapes of the\n",
    "    # instrumental spectral profiles are well approximated by Gaussians.\n",
    "    #\n",
    "    # In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.\n",
    "    # In principle it should never happen and a higher resolution template should be used.\n",
    "    #\n",
    "    fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))\n",
    "    sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels\n",
    "\n",
    "    for j, fname in enumerate(vazdekis):\n",
    "        hdu = fits.open(fname)\n",
    "        ssp = hdu[0].data\n",
    "        ssp = util.gaussian_filter1d(ssp, sigma)  # perform convolution with variable sigma\n",
    "        sspNew = util.log_rebin(lamRange_temp, ssp, velscale=velscale)[0]\n",
    "        templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates\n",
    "\n",
    "    # The galaxy and the template spectra do not have the same starting wavelength.\n",
    "    # For this reason an extra velocity shift DV has to be applied to the template\n",
    "    # to fit the galaxy spectrum. We remove this artificial shift by using the\n",
    "    # keyword VSYST in the call to PPXF below, so that all velocities are\n",
    "    # measured with respect to DV. This assume the redshift is negligible.\n",
    "    # In the case of a high-redshift galaxy one should de-redshift its\n",
    "    # wavelength to the rest frame before using the line below (see above).\n",
    "    #\n",
    "    c = 299792.458\n",
    "    dv = np.log(lam_temp[0]/lam_gal[0])*c    # km/s\n",
    "    goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, z)\n",
    "\n",
    "    # Here the actual fit starts. The best fit is plotted on the screen.\n",
    "    # Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.\n",
    "    #\n",
    "    vel = c*np.log(1 + z)   # Initial estimate of the galaxy velocity in km/s\n",
    "    start = [vel, 200.]  # (km/s), starting guess for [V, sigma]\n",
    "    t = clock()\n",
    "\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "              goodpixels=goodpixels, plot=True, moments=2,\n",
    "              degree=8, mdegree=-1, vsyst=dv, clean=False, lam=lam_gal, reddening=0.08)\n",
    "    \n",
    "\n",
    "    print(\"Formal errors:\")\n",
    "    print(\"     dV    dsigma   dh3      dh4\")\n",
    "    print(\"\".join(\"%8.2g\" % f for f in pp.error*np.sqrt(pp.chi2)))\n",
    "\n",
    "    print('Elapsed time in PPXF: %.2f s' % (clock() - t))\n",
    "    plt.show()\n",
    "    # If the galaxy is at significant redshift z and the wavelength has been\n",
    "    # de-redshifted with the three lines \"z = 1.23...\" near the beginning of\n",
    "    # this procedure, the best-fitting redshift is now given by the following\n",
    "    # commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34):\n",
    "    #\n",
    "    #print, 'Best-fitting redshift z:', (z + 1)*(1 + sol[0]/c) - 1\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    ppxf_example_kinematics_sdss()\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.pause(1)\n",
    "\n"
   ]
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