2010jl.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#2010jl\n",
      "\n",
      "Summary: \n",
      "Use confirm_spectrum to figure out if the right source is being extracted. If not, find the location of the source and extract using run_calcos.\n",
      "Use sum_spectra to sum individual observations into a single spectrum"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Visit Info:\n",
      "\n",
      "* 2010jl is visit 03\n",
      "* Obs 01 - 09 and A - C are FUV\n",
      "* Obs D - K are NUV"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Quick look at the TA image\n",
      "* TA is of an of an offset star --> confirmation image is of star not target"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Quick look at data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy.io import fits\n",
      "import numpy as np\n",
      "from matplotlib import pyplot\n",
      "import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Confirm Extraction Location\n",
      "* looks good for FUV\n",
      "* wrong for NUV\n",
      "\n",
      "###There is an offset between the NUV and FUV. We should confirm that it is expected"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def confirm_spectrum_location(rootname, spec_filename = None, saveloc = None):\n",
      "    img = fits.getdata(os.path.join('2010jl_data/', '{}_flt.fits'.format(rootname)), 1)\n",
      "    if spec_filename:\n",
      "        spec = fits.getdata(os.path.join('2010jl_data/', spec_filename), 1)\n",
      "    else:\n",
      "        spec = fits.getdata(os.path.join('2010jl_data/', '{}_x1d.fits'.format(rootname)), 1)\n",
      "    fig = pyplot.figure()\n",
      "    ax1 = fig.add_subplot(1, 1, 1)\n",
      "    im1 = ax1.imshow(img, interpolation = 'none', cmap = 'bone', vmin = 0, vmax = 1.5)\n",
      "    ax1.plot(np.sum(img, axis = 1), np.arange(1024), color = 'lime')\n",
      "    ax1.plot(np.arange(1024), spec['extrlocy'][0], 'r', lw = 2)\n",
      "    ax1.plot(np.arange(1024), spec['extrlocy'][0]+spec['extrsize'][0]/2.0, '--', color = 'y', lw = 4)\n",
      "    ax1.plot(np.arange(1024), spec['extrlocy'][0]-spec['extrsize'][0]/2.0, '--', color = 'y', lw = 4)\n",
      "    ax1.set_title(rootname)\n",
      "    if saveloc:\n",
      "        pyplot.savefig(os.path.join(saveloc, rootname+'spec_extract_loc.pdf'))\n",
      "        pyplot.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Double check extraction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = ['ocdd03010_x1d.fits', 'ocdd03020_x1d.fits', 'ocdd03030_x1d.fits', 'ocdd03040_x1d.fits', \n",
      "'ocdd03050_x1d.fits', 'ocdd03060_x1d_loc381.fits', 'ocdd03070_x1d.fits', 'ocdd03080_x1d_loc461.fits', 'ocdd03090_x1d_loc344.fits', \n",
      "'ocdd030a0_x1d_loc388.fits', 'ocdd030b0_x1d_loc425.fits', 'ocdd030c0_x1d_loc473.fits']\n",
      "for ifile in flist:\n",
      "    confirm_spectrum_location(ifile.split('_')[0], spec_filename = ifile, saveloc = 'confirm_spec_xtrac_loc/')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "confirm_spectrum_location('ocdd030a0')\n",
      "#confirm_spectrum_location('ocdd030g0')\n",
      "confirm_spectrum_location('ocdd030g0', spec_filename = 'ocdd030g0_x1d.fits')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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4nhfsz+8WJpHKZyaC9Yjt3tlJfOYvR4kbJk4K5Hfr+BniptHTIn+x03xvb28X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\ng4ODg4ODw6aF/w8MIgj7W6z6nwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115f39e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nqfT62bZnNz/Zt599+46NyOgKRIJVUIyzLMs1UMgg7YyMGRrUzH6Sk6fqRa0A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\nCwsLCwsLiwvh/wHJvFQ+bWUv+wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1131fcc50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.median(fits.getdata('2010jl_data/ocdd030g0_flt.fits'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "-0.39966476"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Check OTFR extraction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###FUV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "flist = glob.glob('*flt.fits')\n",
      "for ifile in flist[:12]:\n",
      "    confirm_spectrum_location(ifile.split('_')[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "0 ocdd03010_flt.fits FUV-MAMA\n",
      "1 ocdd03020_flt.fits FUV-MAMA\n",
      "2 ocdd03030_flt.fits FUV-MAMA   \n",
      "3 ocdd03040_flt.fits FUV-MAMA  \n",
      "4 ocdd03050_flt.fits FUV-MAMA   \n",
      "5 ocdd03060_flt.fits FUV-MAMA  381\n",
      "6 ocdd03070_flt.fits FUV-MAMA  \n",
      "7 ocdd03080_flt.fits FUV-MAMA  461\n",
      "8 ocdd03090_flt.fits FUV-MAMA   344\n",
      "9 ocdd030a0_flt.fits FUV-MAMA   388\n",
      "10 ocdd030b0_flt.fits FUV-MAMA 425\n",
      "11 ocdd030c0_flt.fits FUV-MAMA 473\n",
      "12 ocdd030d0_flt.fits NUV-MAMA\n",
      "13 ocdd030e0_flt.fits NUV-MAMA\n",
      "14 ocdd030f0_flt.fits NUV-MAMA\n",
      "15 ocdd030g0_flt.fits NUV-MAMA\n",
      "16 ocdd030h0_flt.fits NUV-MAMA\n",
      "17 ocdd030i0_flt.fits NUV-MAMA\n",
      "18 ocdd030j0_flt.fits NUV-MAMA\n",
      "19 ocdd030k0_flt.fits NUV-MAMA\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###NUV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = glob.glob('*flt.fits')\n",
      "for ifile in flist[12:]:\n",
      "    confirm_spectrum_location(ifile.split('_')[0])\n",
      "    print ifile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "ocdd030d0_flt.fits 470\n",
      "ocdd030e0_flt.fits 506\n",
      "ocdd030f0_flt.fits 555\n",
      "ocdd030g0_flt.fits 590\n",
      "ocdd030h0_flt.fits 470\n",
      "ocdd030i0_flt.fits 510\n",
      "ocdd030j0_flt.fits 547\n",
      "ocdd030k0_flt.fits 600\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Extract FUV Spectrum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "extrlocy = 381\n",
      "import pyraf\n",
      "from pyraf import iraf\n",
      "from iraf import stsdas,hst_calib,stis,x1d as x1d\n",
      "import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = [('ocdd03060_flt.fits', 381), ('ocdd03080_flt.fits', 461), ('ocdd03090_flt.fits', 344), ('ocdd030a0_flt.fits', 388), ('ocdd030b0_flt.fits', 425), ('ocdd030c0_flt.fits', 473)]\n",
      "for tup_list in flist:\n",
      "    input_file, extrlocy = tup_list\n",
      "    run_calcos(input_file, extrlocy)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd03060_flt.fits' 'ocdd03060_x1d_loc381.fits' -t -v -back -cte -disp -hel -flux -c 381.0 -r 3 -x 25.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    20-Mar-2014 18:36:26 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014079183626-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd03060_flt.fits\n",
        "Output   ocdd03060_x1d_loc381.fits\n",
        "Rootname ocdd03060\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G140L\n",
        "DETECTOR FUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$n7p10323o_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14504o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 18:36:36 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 18:36:36 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 2.13936 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014079183636-I--------------- X1DCORR complete: OCDD03060 ---------------------\n",
        "         Spectrum extracted at y position = 383.139\n",
        "         Extraction box height = 25\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16110o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 04/07/1997 04/07/1997\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$h1v1541eo_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014079183636-I--------------- DISPCORR complete: OCDD03060 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014079183636-I--------------- HELCORR complete: OCDD03060 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816445o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q541740to_pct.fits\n",
        "TDSTAB   oref$u7c1846ho_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  No exact extraction box height match in PCTAB.\n",
        "Warning  Using entry for height 21\n",
        "FLUXCORR COMPLETE\n",
        "2014079183639-I--------------- FLUXCORR complete: OCDD03060 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 18:36:39 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.0447484 degree.\n",
        "Imset 1  End 18:36:39 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      20-Mar-2014 18:36:39 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014079183639-I--------------- CALSTIS-6 completed: OCDD03060 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd03080_flt.fits' 'ocdd03080_x1d_loc461.fits' -t -v -back -cte -disp -hel -flux -c 461.0 -r 3 -x 25.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    20-Mar-2014 18:36:40 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014079183640-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd03080_flt.fits\n",
        "Output   ocdd03080_x1d_loc461.fits\n",
        "Rootname ocdd03080\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G140L\n",
        "DETECTOR FUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$n7p10323o_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14504o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 18:36:40 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 18:36:40 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 1.73042 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014079183640-I--------------- X1DCORR complete: OCDD03080 ---------------------\n",
        "         Spectrum extracted at y position = 462.73\n",
        "         Extraction box height = 25\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16110o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 04/07/1997 04/07/1997\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$h1v1541eo_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014079183640-I--------------- DISPCORR complete: OCDD03080 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014079183640-I--------------- HELCORR complete: OCDD03080 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816445o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q541740to_pct.fits\n",
        "TDSTAB   oref$u7c1846ho_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  No exact extraction box height match in PCTAB.\n",
        "Warning  Using entry for height 21\n",
        "FLUXCORR COMPLETE\n",
        "2014079183640-I--------------- FLUXCORR complete: OCDD03080 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 18:36:40 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.0447488 degree.\n",
        "Imset 1  End 18:36:40 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      20-Mar-2014 18:36:40 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014079183640-I--------------- CALSTIS-6 completed: OCDD03080 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd03090_flt.fits' 'ocdd03090_x1d_loc344.fits' -t -v -back -cte -disp -hel -flux -c 344.0 -r 3 -x 25.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    20-Mar-2014 18:36:41 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014079183641-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd03090_flt.fits\n",
        "Output   ocdd03090_x1d_loc344.fits\n",
        "Rootname ocdd03090\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G140L\n",
        "DETECTOR FUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$n7p10323o_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14504o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 18:36:41 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 18:36:41 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 0.957413 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014079183641-I--------------- X1DCORR complete: OCDD03090 ---------------------\n",
        "         Spectrum extracted at y position = 344.957\n",
        "         Extraction box height = 25\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16110o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 04/07/1997 04/07/1997\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$h1v1541eo_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014079183641-I--------------- DISPCORR complete: OCDD03090 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014079183641-I--------------- HELCORR complete: OCDD03090 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816445o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q541740to_pct.fits\n",
        "TDSTAB   oref$u7c1846ho_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  No exact extraction box height match in PCTAB.\n",
        "Warning  Using entry for height 21\n",
        "FLUXCORR COMPLETE\n",
        "2014079183641-I--------------- FLUXCORR complete: OCDD03090 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 18:36:41 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.0447488 degree.\n",
        "Imset 1  End 18:36:41 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      20-Mar-2014 18:36:41 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014079183641-I--------------- CALSTIS-6 completed: OCDD03090 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030a0_flt.fits' 'ocdd030a0_x1d_loc388.fits' -t -v -back -cte -disp -hel -flux -c 388.0 -r 3 -x 25.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    20-Mar-2014 18:36:41 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014079183641-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030a0_flt.fits\n",
        "Output   ocdd030a0_x1d_loc388.fits\n",
        "Rootname ocdd030a0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G140L\n",
        "DETECTOR FUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$n7p10323o_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14504o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 18:36:42 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 18:36:42 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 0.108838 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014079183642-I--------------- X1DCORR complete: OCDD030A0 ---------------------\n",
        "         Spectrum extracted at y position = 388.109\n",
        "         Extraction box height = 25\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16110o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 04/07/1997 04/07/1997\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$h1v1541eo_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014079183642-I--------------- DISPCORR complete: OCDD030A0 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014079183642-I--------------- HELCORR complete: OCDD030A0 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816445o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q541740to_pct.fits\n",
        "TDSTAB   oref$u7c1846ho_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  No exact extraction box height match in PCTAB.\n",
        "Warning  Using entry for height 21\n",
        "FLUXCORR COMPLETE\n",
        "2014079183642-I--------------- FLUXCORR complete: OCDD030A0 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 18:36:42 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.0447489 degree.\n",
        "Imset 1  End 18:36:42 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      20-Mar-2014 18:36:42 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014079183642-I--------------- CALSTIS-6 completed: OCDD030A0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030b0_flt.fits' 'ocdd030b0_x1d_loc425.fits' -t -v -back -cte -disp -hel -flux -c 425.0 -r 3 -x 25.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    20-Mar-2014 18:36:42 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014079183642-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030b0_flt.fits\n",
        "Output   ocdd030b0_x1d_loc425.fits\n",
        "Rootname ocdd030b0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G140L\n",
        "DETECTOR FUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$n7p10323o_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14504o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 18:36:42 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 18:36:42 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 2.2396 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014079183642-I--------------- X1DCORR complete: OCDD030B0 ---------------------\n",
        "         Spectrum extracted at y position = 427.24\n",
        "         Extraction box height = 25\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16110o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 04/07/1997 04/07/1997\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$h1v1541eo_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014079183642-I--------------- DISPCORR complete: OCDD030B0 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014079183642-I--------------- HELCORR complete: OCDD030B0 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816445o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q541740to_pct.fits\n",
        "TDSTAB   oref$u7c1846ho_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  No exact extraction box height match in PCTAB.\n",
        "Warning  Using entry for height 21\n",
        "FLUXCORR COMPLETE\n",
        "2014079183643-I--------------- FLUXCORR complete: OCDD030B0 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 18:36:43 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.044749 degree.\n",
        "Imset 1  End 18:36:43 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      20-Mar-2014 18:36:43 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014079183643-I--------------- CALSTIS-6 completed: OCDD030B0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030c0_flt.fits' 'ocdd030c0_x1d_loc473.fits' -t -v -back -cte -disp -hel -flux -c 473.0 -r 3 -x 25.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    20-Mar-2014 18:36:43 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014079183643-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030c0_flt.fits\n",
        "Output   ocdd030c0_x1d_loc473.fits\n",
        "Rootname ocdd030c0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G140L\n",
        "DETECTOR FUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$n7p10323o_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14504o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 18:36:43 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 18:36:43 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 2.0039 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014079183643-I--------------- X1DCORR complete: OCDD030C0 ---------------------\n",
        "         Spectrum extracted at y position = 475.004\n",
        "         Extraction box height = 25\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16110o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 04/07/1997 04/07/1997\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$h1v1541eo_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014079183643-I--------------- DISPCORR complete: OCDD030C0 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014079183643-I--------------- HELCORR complete: OCDD030C0 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816445o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q541740to_pct.fits\n",
        "TDSTAB   oref$u7c1846ho_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  No exact extraction box height match in PCTAB.\n",
        "Warning  Using entry for height 21\n",
        "FLUXCORR COMPLETE\n",
        "2014079183643-I--------------- FLUXCORR complete: OCDD030C0 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 18:36:43 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.0447493 degree.\n",
        "Imset 1  End 18:36:43 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      20-Mar-2014 18:36:43 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014079183643-I--------------- CALSTIS-6 completed: OCDD030C0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def run_calcos(input_file, extrlocy, maxsrch = 3, extrsize = 25):\n",
      "    output_file = '{}_x1d_loc{}.fits'.format(input_file.split('_')[0], extrlocy)\n",
      "    os.environ['oref'] = '/grp/hst/cdbs/oref/'\n",
      "    x1d.unlearn()\n",
      "    x1d(input = input_file, output = output_file, a2center = extrlocy, maxsrch = maxsrch, extrsize = extrsize)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 209
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###NUV "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = [('ocdd030d0_flt.fits', 470), ('ocdd030e0_flt.fits', 506), ('ocdd030f0_flt.fits', 555), ('ocdd030g0_flt.fits', 590), ('ocdd030h0_flt.fits', 470), ('ocdd030i0_flt.fits', 510),\n",
      "         ('ocdd030j0_flt.fits', 547), ('ocdd030k0_flt.fits', 600)]\n",
      "for tup_list in flist:\n",
      "    input_file, extrlocy = tup_list\n",
      "    run_calcos(input_file, extrlocy, maxsrch = 10, extrsize = 21)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030d0_flt.fits' 'ocdd030d0_x1d_loc470.fits' -t -v -back -cte -disp -hel -flux -c 470.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:54:43 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235443-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030d0_flt.fits\n",
        "Output   ocdd030d0_x1d_loc470.fits\n",
        "Rootname ocdd030d0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G230L\n",
        "DETECTOR NUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$l3m1437to_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14502o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 23:54:52 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 23:54:53 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: -0.225052 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014089235453-I--------------- X1DCORR complete: OCDD030D0 ---------------------\n",
        "         Spectrum extracted at y position = 469.775\n",
        "         Extraction box height = 21\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16111o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 23/01/1999 23/01/1999\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$l3m1437ro_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014089235453-I--------------- DISPCORR complete: OCDD030D0 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014089235453-I--------------- HELCORR complete: OCDD030D0 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816448o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q5417413o_pct.fits\n",
        "TDSTAB   oref$ub920085o_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR COMPLETE\n",
        "2014089235454-I--------------- FLUXCORR complete: OCDD030D0 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 23:54:54 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.00314255 degree.\n",
        "Imset 1  End 23:54:54 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      30-Mar-2014 23:54:54 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014089235454-I--------------- CALSTIS-6 completed: OCDD030D0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030e0_flt.fits' 'ocdd030e0_x1d_loc506.fits' -t -v -back -cte -disp -hel -flux -c 506.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:54:54 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235454-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030e0_flt.fits\n",
        "Output   ocdd030e0_x1d_loc506.fits\n",
        "Rootname ocdd030e0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G230L\n",
        "DETECTOR NUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$l3m1437to_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14502o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 23:55:03 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 23:55:04 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 6.9177 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014089235504-I--------------- X1DCORR complete: OCDD030E0 ---------------------\n",
        "         Spectrum extracted at y position = 512.918\n",
        "         Extraction box height = 21\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16111o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 23/01/1999 23/01/1999\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$l3m1437ro_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014089235504-I--------------- DISPCORR complete: OCDD030E0 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014089235504-I--------------- HELCORR complete: OCDD030E0 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816448o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q5417413o_pct.fits\n",
        "TDSTAB   oref$ub920085o_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR COMPLETE\n",
        "2014089235505-I--------------- FLUXCORR complete: OCDD030E0 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 23:55:05 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.00314257 degree.\n",
        "Imset 1  End 23:55:05 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      30-Mar-2014 23:55:05 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014089235505-I--------------- CALSTIS-6 completed: OCDD030E0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030f0_flt.fits' 'ocdd030f0_x1d_loc555.fits' -t -v -back -cte -disp -hel -flux -c 555.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:55:05 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235505-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030f0_flt.fits\n",
        "Output   ocdd030f0_x1d_loc555.fits\n",
        "Rootname ocdd030f0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G230L\n",
        "DETECTOR NUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$l3m1437to_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14502o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 23:55:08 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 23:55:08 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  Cross correlation to locate spectrum failed.\n",
        "ERROR    Cannot extract.\n",
        "Warning  No rows were written; no table created.\n",
        "         trace was rotated by = -0.00314266 degree.\n",
        "Imset 1  End 23:55:08 EDT\n",
        "\n",
        "End      30-Mar-2014 23:55:08 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014089235508-I--------------- CALSTIS-6 completed: OCDD030F0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030g0_flt.fits' 'ocdd030g0_x1d_loc590.fits' -t -v -back -cte -disp -hel -flux -c 590.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:55:08 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235508-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030g0_flt.fits\n",
        "Output   ocdd030g0_x1d_loc590.fits\n",
        "Rootname ocdd030g0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G230L\n",
        "DETECTOR NUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$l3m1437to_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14502o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 23:55:12 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 23:55:13 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  Cross correlation to locate spectrum failed.\n",
        "ERROR    Cannot extract.\n",
        "Warning  No rows were written; no table created.\n",
        "         trace was rotated by = -0.00314267 degree.\n",
        "Imset 1  End 23:55:13 EDT\n",
        "\n",
        "End      30-Mar-2014 23:55:13 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014089235513-I--------------- CALSTIS-6 completed: OCDD030G0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030h0_flt.fits' 'ocdd030h0_x1d_loc470.fits' -t -v -back -cte -disp -hel -flux -c 470.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:55:13 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235513-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030h0_flt.fits\n",
        "Output   ocdd030h0_x1d_loc470.fits\n",
        "Rootname ocdd030h0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G230L\n",
        "DETECTOR NUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$l3m1437to_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14502o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 23:55:24 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 23:55:24 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  Cross correlation to locate spectrum failed.\n",
        "ERROR    Cannot extract.\n",
        "Warning  No rows were written; no table created.\n",
        "         trace was rotated by = -0.0031427 degree.\n",
        "Imset 1  End 23:55:24 EDT\n",
        "\n",
        "End      30-Mar-2014 23:55:24 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014089235524-I--------------- CALSTIS-6 completed: OCDD030H0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030i0_flt.fits' 'ocdd030i0_x1d_loc510.fits' -t -v -back -cte -disp -hel -flux -c 510.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:55:24 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235524-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "ERROR    Output file `ocdd030i0_x1d_loc510.fits' already exists.\n",
        "Error processing ocdd030i0_flt.fits.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030j0_flt.fits' 'ocdd030j0_x1d_loc547.fits' -t -v -back -cte -disp -hel -flux -c 547.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:55:24 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235524-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030j0_flt.fits\n",
        "Output   ocdd030j0_x1d_loc547.fits\n",
        "Rootname ocdd030j0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G230L\n",
        "DETECTOR NUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$l3m1437to_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14502o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 23:55:27 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 23:55:28 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Cross correlation offset: 6.22894 pixels\n",
        "BACKCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "BACKCORR COMPLETE\n",
        "X1DCORR  COMPLETE\n",
        "2014089235528-I--------------- X1DCORR complete: OCDD030J0 ---------------------\n",
        "         Spectrum extracted at y position = 553.229\n",
        "         Extraction box height = 21\n",
        "         Background box 1 height = 5 offset -300 from A2CENTER\n",
        "         Background box 2 height = 5 offset 300 from A2CENTER\n",
        "\n",
        "DISPCORR PERFORM\n",
        "DISPTAB  oref$m7p16111o_dsp.fits\n",
        "DISPTAB  PEDIGREE=INFLIGHT 23/01/1999 23/01/1999\n",
        "DISPTAB  DESCRIP =INFLIGHT Cal. Disp. Coeffs\n",
        "APDESTAB oref$obm1723ro_apd.fits\n",
        "APDESTAB PEDIGREE=INFLIGHT 01/03/1997 01/03/1997\n",
        "APDESTAB DESCRIP =Microscope Meas./Hartig Post-launch Offsets\n",
        "INANGTAB oref$l3m1437ro_iac.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Delta = 0.060224 arcsec.\n",
        "DISPCORR COMPLETE\n",
        "2014089235528-I--------------- DISPCORR complete: OCDD030J0 --------------------\n",
        "\n",
        "HELCORR  PERFORM\n",
        "HELCORR  COMPLETE\n",
        "2014089235528-I--------------- HELCORR complete: OCDD030J0 ---------------------\n",
        "\n",
        "PHOTTAB  oref$vb816448o_pht.fits\n",
        "APERTAB  oref$n7p1032ao_apt.fits\n",
        "APERTAB  PEDIGREE=MODEL\n",
        "APERTAB  DESCRIP =Bohlin/Hartig TIM Models Nov. 1998\n",
        "PCTAB    oref$q5417413o_pct.fits\n",
        "TDSTAB   oref$ub920085o_tds.fits\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FLUXCORR COMPLETE\n",
        "2014089235529-I--------------- FLUXCORR complete: OCDD030J0 --------------------\n",
        "\n",
        "SGEOCORR OMIT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         Row 1 written to disk.\n",
        "Order 1  End 23:55:29 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         trace was rotated by = -0.00314279 degree.\n",
        "Imset 1  End 23:55:29 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Warning  Keyword `XTRACALG' is being added to header.\n",
        "End      30-Mar-2014 23:55:29 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014089235529-I--------------- CALSTIS-6 completed: OCDD030J0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "!/Users/bostroem/Ureka/variants/common/iraf/stsci_iraf/stsdas/bin/cs6.e 'ocdd030k0_flt.fits' 'ocdd030k0_x1d_loc600.fits' -t -v -back -cte -disp -hel -flux -c 600.0 -r 10 -x 21.0 -bm -bo 3 -a unweighted \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "*** CALSTIS-6 -- Version 2.40 (23-May-2012) ***\n",
        "Begin    30-Mar-2014 23:55:29 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014089235529-I--------------- CALSTIS-6 started -------------------------------\n",
        "\n",
        "Warning  Grating-aperture throughput correction table (GACTAB) was not found,\n",
        "         and no gac corrections will be applied\n",
        "Input    ocdd030k0_flt.fits\n",
        "Output   ocdd030k0_x1d_loc600.fits\n",
        "Rootname ocdd030k0\n",
        "OBSMODE  ACCUM\n",
        "APERTURE 52X0.5\n",
        "OPT_ELEM G230L\n",
        "DETECTOR NUV-MAMA\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "XTRACTAB oref$l3m1437to_1dx.fits\n",
        "XTRACTAB PEDIGREE=INFLIGHT 29/05/97\n",
        "XTRACTAB DESCRIP =Analysis from prop. 7064\n",
        "SPTRCTAB oref$q8l14502o_1dt.fits\n",
        "\n",
        "Imset 1  Begin 23:55:33 EDT\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "         Input read into memory.\n",
        "Order 1  Begin 23:55:34 EDT\n",
        "X1DCORR  PERFORM\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning  Cross correlation to locate spectrum failed.\n",
        "ERROR    Cannot extract.\n",
        "Warning  No rows were written; no table created.\n",
        "         trace was rotated by = -0.0031428 degree.\n",
        "Imset 1  End 23:55:34 EDT\n",
        "\n",
        "End      30-Mar-2014 23:55:34 EDT\n",
        "\n",
        "*** CALSTIS-6 complete ***\n",
        "2014089235534-I--------------- CALSTIS-6 completed: OCDD030K0 ------------------\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 210
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print flist\n",
      "print glob.glob('*0k0*x1d*.fits')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[('ocdd030d0_flt.fits', 470), ('ocdd030e0_flt.fits', 506), ('ocdd030f0_flt.fits', 555), ('ocdd030g0_flt.fits', 590), ('ocdd030h0_flt.fits', 470), ('ocdd030i0_flt.fits', 510), ('ocdd030j0_flt.fits', 547), ('ocdd030k0_flt.fits', 600)]\n",
        "['ocdd030k0_x1d.fits']\n"
       ]
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = ['ocdd030d0_x1d_loc470.fits', 'ocdd030e0_x1d_loc506.fits', 'ocdd030i0_x1d_loc510.fits', 'ocdd030j0_x1d_loc547.fits']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Plot Spectrum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from stsci.convolve import boxcar\n",
      "def plot_flux(filename, new_fig = True):\n",
      "    spec = fits.getdata(filename, 1)\n",
      "    if new_fig:\n",
      "        fig2 = pyplot.figure()\n",
      "        ax2 = fig2.add_subplot(1, 1, 1)\n",
      "        ax2.plot(spec['wavelength'][0], boxcar(spec['flux'][0], (33,)))\n",
      "    else:\n",
      "        pyplot.plot(spec['wavelength'][0], boxcar(spec['flux'][0], (33,)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = ['ocdd03010_x1d.fits', 'ocdd03020_x1d.fits', 'ocdd03030_x1d.fits', 'ocdd03040_x1d.fits', \n",
      "'ocdd03050_x1d.fits', 'ocdd03060_x1d_loc381.fits', 'ocdd03070_x1d.fits', 'ocdd03080_x1d_loc461.fits', 'ocdd03090_x1d_loc344.fits', \n",
      "'ocdd030a0_x1d_loc388.fits', 'ocdd030b0_x1d_loc425.fits', 'ocdd030c0_x1d_loc473.fits']\n",
      "for ifile in flist:\n",
      "    plot_flux(ifile, new_fig = False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 133
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Check spectrum Extraction Location"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i, ifile in enumerate(flist):\n",
      "    print i, fits.getdata(ifile, 1)['A2center'][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "342.434\n",
        "1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "383.27\n",
        "2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "423.589\n",
        "3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "465.221\n",
        "4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "341.634\n",
        "5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "383.139\n",
        "6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "424.342\n",
        "7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "462.73\n",
        "8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "344.957\n",
        "9 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "388.109\n",
        "10 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "427.24\n",
        "11 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "475.004\n"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Sum dithers to make a combined spectrum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "#flist = glob.glob('*x1d.fits')\n",
      "for i, ifile in enumerate(flist):\n",
      "    print i, ifile, fits.getval(ifile, 'detector', 0), fits.getval(ifile, 'texpstrt', 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy.constants import c\n",
      "def sum_spectra(flist, output_filename = None, smooth = 3):\n",
      "    ref_tbdata = fits.getdata(flist[0], 1)\n",
      "    ref_wl = ref_tbdata['wavelength'][0]\n",
      "    ref_flux = ref_tbdata['flux'][0]\n",
      "    sum_flux = ref_flux.copy()\n",
      "    sum_exptime = 0\n",
      "    for ifile in flist:\n",
      "        exptime = fits.getval(ifile, 'texptime', 0)\n",
      "        tbdata = fits.getdata(ifile, 1)\n",
      "        #z = (wl_obs/wl_rest) -1 --> wl_rest = wl_obs/(z+1); z = v/c\n",
      "        sum_flux += np.interp(ref_wl, tbdata['wavelength'][0], tbdata['flux'][0]*exptime)\n",
      "        sum_exptime += exptime\n",
      "        \n",
      "    sum_flux = sum_flux/sum_exptime\n",
      "    pyplot.figure()\n",
      "    pyplot.plot(ref_wl/((3.6E5/c.value)+1.0), boxcar(sum_flux, (smooth,)))\n",
      "    if output_filename:\n",
      "        col1 = fits.Column(name = 'wavelength', format = 'D', array = ref_wl)\n",
      "        col2 = fits.Column(name = 'flux', format = 'E', array = sum_flux)\n",
      "        tbhdu = fits.new_table(fits.ColDefs([col1, col2]))\n",
      "        tbhdu.writeto(output_filename, clobber = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###FUV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = ['ocdd03010_x1d.fits', 'ocdd03020_x1d.fits', 'ocdd03030_x1d.fits', 'ocdd03040_x1d.fits', \n",
      "'ocdd03050_x1d.fits', 'ocdd03060_x1d_loc381.fits', 'ocdd03070_x1d.fits', 'ocdd03080_x1d_loc461.fits', 'ocdd03090_x1d_loc344.fits', \n",
      "'ocdd030a0_x1d_loc388.fits', 'ocdd030b0_x1d_loc425.fits', 'ocdd030c0_x1d_loc473.fits']\n",
      "sum_spectra(flist, 'fuv_spectrum.fits')\n",
      "line_list = [1302, 1304, 1309, 1393, 1403, 1483, 1487, 1549, 1551, 1661, 1666, 1747, 1753]\n",
      "for iline in line_list:\n",
      "    pyplot.axvline(iline, color = 'r')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "WARNING: Overwriting existing file 'fuv_spectrum.fits'. [astropy.io.fits.file]\n"
       ]
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy.io import fits\n",
      "from matplotlib import pyplot\n",
      "from stsci.convolve import boxcar\n",
      "flist = ['ocdd03010_x1d.fits', 'ocdd03020_x1d.fits', 'ocdd03030_x1d.fits', 'ocdd03040_x1d.fits', \n",
      "'ocdd03050_x1d.fits', 'ocdd03060_x1d_loc381.fits', 'ocdd03070_x1d.fits', 'ocdd03080_x1d_loc461.fits', 'ocdd03090_x1d_loc344.fits', \n",
      "'ocdd030a0_x1d_loc388.fits', 'ocdd030b0_x1d_loc425.fits', 'ocdd030c0_x1d_loc473.fits']\n",
      "fig1 = pyplot.figure()\n",
      "ax1 = fig1.add_subplot(1, 1, 1)\n",
      "fig2 = pyplot.figure()\n",
      "ax2 = fig2.add_subplot(1, 1, 1)\n",
      "fig3 = pyplot.figure()\n",
      "ax3 = fig3.add_subplot(1, 1, 1)\n",
      "for ifile in flist:\n",
      "    tbdata = fits.getdata(ifile, 1)\n",
      "    ax1.plot(tbdata['wavelength'][0], boxcar(tbdata['gross'][0], (9,)))\n",
      "    ax1.set_title('Gross')\n",
      "    ax2.plot(tbdata['wavelength'][0], boxcar(tbdata['background'][0], (9,)))\n",
      "    ax2.set_title('Background')\n",
      "    ax3.plot(tbdata['wavelength'][0], boxcar(tbdata['net'][0], (9,)))\n",
      "    ax3.set_title('Net')\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###NUV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = ['ocdd030d0_x1d_loc470.fits', 'ocdd030e0_x1d_loc506.fits', 'ocdd030i0_x1d_loc510.fits', 'ocdd030j0_x1d_loc547.fits']\n",
      "sum_spectra(flist, 'nuv_spectrum.fits')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tbdata_fuv = fits.getdata('fuv_spectrum.fits', 0)\n",
      "tbdata_nuv = fits.getdata('nuv_spectrum.fits', 0)\n",
      "pyplot.figure()\n",
      "pyplot.plot(tbdata_nuv['wavelength'], boxcar(tbdata_nuv['flux'], (9,)))\n",
      "pyplot.plot(tbdata_fuv['wavelength'], boxcar(tbdata_fuv['flux'], (9,)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 221,
       "text": [
        "[<matplotlib.lines.Line2D at 0x111de7fd0>]"
       ]
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.xlabel('Wavelength')\n",
      "pyplot.ylabel('Flux')\n",
      "pyplot.legend(['NUV, smoothed by 9 pixels', 'FUV smoothed by 9 pixels'])\n",
      "pyplot.title('2010JL initial extraction')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 222,
       "text": [
        "<matplotlib.text.Text at 0x11702f7d0>"
       ]
      }
     ],
     "prompt_number": 222
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###compare calstis versions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "import os\n",
      "flist = glob.glob('2010jl_data/*flt.fits')\n",
      "for ifile in flist:\n",
      "    filename = os.path.basename(ifile)\n",
      "    print fits.getval(ifile, 'cal_ver', 0), fits.getval(os.path.join('otfr_data2/', filename), 'cal_ver', 0)\n",
      "for ifile in flist:\n",
      "    filename = os.path.basename(ifile)\n",
      "    print fits.getval(ifile, 'opus_ver', 0), fits.getval(os.path.join('otfr_data2/', filename), 'opus_ver', 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) 3.3 (03-October-2013)\n",
        "3.3 (03-October-2013) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.3 (03-October-2013)\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " OPUS 2014_1a\n",
        "OPUS 2014_1a OPUS 2014_1a\n",
        "OPUS 2014_1a "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OPUS 2014_1a\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Cross correlate each column with STIS XD profile"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print fits.getval('otfr_data2/ocdd03050_flt.fits', 'orientat', 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "36.6144\n"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.ndimage.interpolation import rotate\n",
      "import time\n",
      "from matplotlib.backends.backend_pdf import PdfPages\n",
      "pp = PdfPages('xd_profile_match.pdf')\n",
      "\n",
      "wfpc2_img = fits.getdata('hst_08645_11_wfpc2_f300w_wf/hst_08645_11_wfpc2_f300w_wf_drz.fits', 1)\n",
      "rot_wfpc2_img = rotate(wfpc2_img, 36.6)\n",
      "\n",
      "xd_profile = np.sum(img[:, 500:524], axis = 1)\n",
      "#Remove occulation bar\n",
      "xd_profile[854:910] = np.median(xd_profile)\n",
      "#Bin to WFPC2 resolution\n",
      "fuv_plate_scale = 0.0246 #arcsec/pix\n",
      "wfpc2_platescale = 0.1 #arcsec/pix\n",
      "binning_factor = wfpc2_platescale/fuv_plate_scale\n",
      "binned_xd_profile = xd_profile[::int(binning_factor)]\n",
      "for indx in range(1,int(binning_factor)):\n",
      "    binned_xd_profile = binned_xd_profile + xd_profile[indx::int(binning_factor)] \n",
      "\n",
      "stis_slit_height_pix = 25.0/wfpc2_platescale\n",
      "\n",
      "#0.2 arcsec slit = 2 WFPC2 pixels\n",
      "columns = np.arange(1210, 1260, 2)\n",
      "corr = []\n",
      "offset = []\n",
      "wfpc2_y_start = 750\n",
      "#make WFPC2 xd profile 2 times actual profile to account for offsetting\n",
      "\n",
      "for column in columns:\n",
      "    #0.2 arcsec slit = 2 WFPC2 pixels\n",
      "    wfpc2_profile = np.sum(rot_wfpc2_img[wfpc2_y_start: wfpc2_y_start + 2*stis_slit_height_pix, column:column+2], axis = 1)\n",
      "    #Normalize the 2 XD profiles\n",
      "    #Correlate bigger array, smaller array apply offset to smaller array\n",
      "    correlation_array = np.correlate(wfpc2_profile/np.max(wfpc2_profile), binned_xd_profile/np.max(binned_xd_profile))\n",
      "    max_corr_indx = np.argmax(correlation_array)\n",
      "    max_corr = correlation_array[max_corr_indx]\n",
      "    corr.append(max_corr)\n",
      "    offset.append(max_corr_indx)\n",
      "    #Plot the XD profile for each column tested \n",
      "    fig_temp = pyplot.figure()\n",
      "    ax_temp = fig_temp.add_subplot(1,1,1)\n",
      "    ax_temp.plot(wfpc2_profile/max(wfpc2_profile), np.arange(len(wfpc2_profile)))\n",
      "    ax_temp.plot(binned_xd_profile/max(binned_xd_profile), np.arange(len(binned_xd_profile))+max_corr_indx)\n",
      "    ax_temp.legend(['Norm WFPC2 profile', 'Norm, binned, STIS profile'], loc = 'best')\n",
      "    ax_temp.set_title('WFPC2 Column {}'.format(column))\n",
      "    pp.savefig()\n",
      "    pyplot.close()\n",
      "pp.close()\n",
      "corr = np.array(corr)\n",
      "fig2 = pyplot.figure()\n",
      "ax2 = fig2.add_subplot(1,1,1)\n",
      "pyplot.plot(columns, corr)\n",
      "\n",
      "final_max_corr_indx = np.argmax(corr)\n",
      "fig3 = pyplot.figure()\n",
      "ax3 = fig3.add_subplot(1,1,1)\n",
      "ax3.plot(binned_xd_profile/binned_xd_profile.max(), np.arange(len(binned_xd_profile))+offset[final_max_corr_indx])\n",
      "best_col = rot_wfpc2_img[wfpc2_y_start : wfpc2_y_start+ 2.0*stis_slit_height_pix, columns[final_max_corr_indx]]\n",
      "ax3.plot(best_col/best_col.max(), range(len(best_col)))\n",
      "print np.max(corr), offset[final_max_corr_indx], columns[final_max_corr_indx]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "16.938 138 1230\n"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.ndimage.interpolation import rotate\n",
      "import time\n",
      "from matplotlib.backends.backend_pdf import PdfPages\n",
      "pp = PdfPages('xd_profile_match_reverse.pdf')\n",
      "\n",
      "wfpc2_img = fits.getdata('hst_08645_11_wfpc2_f300w_wf/hst_08645_11_wfpc2_f300w_wf_drz.fits', 1)\n",
      "rot_wfpc2_img = rotate(wfpc2_img, 36.6)\n",
      "\n",
      "xd_profile = np.sum(img[:, 500:524], axis = 1)\n",
      "#Remove occulation bar\n",
      "xd_profile[854:910] = np.median(xd_profile)\n",
      "#Bin to WFPC2 resolution\n",
      "fuv_plate_scale = 0.0246 #arcsec/pix\n",
      "wfpc2_platescale = 0.1 #arcsec/pix\n",
      "binning_factor = wfpc2_platescale/fuv_plate_scale\n",
      "binned_xd_profile = xd_profile[::int(binning_factor)]\n",
      "for indx in range(1,int(binning_factor)):\n",
      "    binned_xd_profile = binned_xd_profile + xd_profile[indx::int(binning_factor)] \n",
      "binned_xd_profile = binned_xd_profile[::-1] #reverse\n",
      "stis_slit_height_pix = 25.0/wfpc2_platescale\n",
      "\n",
      "#0.2 arcsec slit = 2 WFPC2 pixels\n",
      "columns = np.arange(1210, 1260, 2)\n",
      "corr = []\n",
      "offset = []\n",
      "wfpc2_y_start = 750\n",
      "#make WFPC2 xd profile 2 times actual profile to account for offsetting\n",
      "\n",
      "for column in columns:\n",
      "    #0.2 arcsec slit = 2 WFPC2 pixels\n",
      "    wfpc2_profile = np.sum(rot_wfpc2_img[wfpc2_y_start: wfpc2_y_start + 2*stis_slit_height_pix, column:column+2], axis = 1)\n",
      "    #Normalize the 2 XD profiles\n",
      "    #Correlate bigger array, smaller array\n",
      "    correlation_array = np.correlate(wfpc2_profile/np.max(wfpc2_profile), binned_xd_profile/np.max(binned_xd_profile))\n",
      "    max_corr_indx = np.argmax(correlation_array)\n",
      "    max_corr = correlation_array[max_corr_indx]\n",
      "    corr.append(max_corr)\n",
      "    offset.append(max_corr_indx)\n",
      "    #Plot the XD profile for each column tested \n",
      "    fig_temp = pyplot.figure()\n",
      "    ax_temp = fig_temp.add_subplot(1,1,1)\n",
      "    ax_temp.plot(wfpc2_profile/max(wfpc2_profile), np.arange(len(wfpc2_profile)))\n",
      "    ax_temp.plot(binned_xd_profile/max(binned_xd_profile), np.arange(len(binned_xd_profile))+max_corr_indx)\n",
      "    ax_temp.legend(['Norm WFPC2 profile', 'Norm, binned, STIS profile'], loc = 'best')\n",
      "    ax_temp.set_title('WFPC2 Column {}'.format(column))\n",
      "    pp.savefig()\n",
      "    pyplot.close()\n",
      "pp.close()\n",
      "corr = np.array(corr)\n",
      "fig2 = pyplot.figure()\n",
      "ax2 = fig2.add_subplot(1,1,1)\n",
      "pyplot.plot(columns, corr)\n",
      "\n",
      "final_max_corr_indx = np.argmax(corr)\n",
      "fig3 = pyplot.figure()\n",
      "ax3 = fig3.add_subplot(1,1,1)\n",
      "ax3.plot(binned_xd_profile/binned_xd_profile.max(), np.arange(len(binned_xd_profile))+offset[final_max_corr_indx])\n",
      "best_col = rot_wfpc2_img[wfpc2_y_start : wfpc2_y_start+ 2.0*stis_slit_height_pix, columns[final_max_corr_indx]]\n",
      "ax3.plot(best_col/best_col.max(), range(len(best_col)))\n",
      "print np.max(corr), offset[final_max_corr_indx], columns[final_max_corr_indx]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "16.4001 99 1242\n"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Conclusions about FUV Location\n",
      "\n",
      "* Best correlation is with WFPC2 columns 1230+1231 starting at row 888 (750+138)\n",
      "* The reversed spectrum returned worse results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "----------------------------------\n",
      "\n",
      "# Create Finder Image for FUV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "from scipy.ndimage.interpolation import rotate\n",
      "wfpc2_img = fits.getdata('hst_08645_11_wfpc2_f300w_wf/hst_08645_11_wfpc2_f300w_wf_drz.fits', 1)\n",
      "orientat = 36.6\n",
      "rot_wfpc2_img = rotate(wfpc2_img, orientat)\n",
      "\n",
      "fig = pyplot.figure(figsize = [20, 15])\n",
      "ax2 = fig.add_subplot(1,3,2)\n",
      "ax3 = fig.add_subplot(1, 3, 3)\n",
      "ax1 = fig.add_subplot(1, 3, 1)\n",
      "im = ax2.imshow(rot_wfpc2_img, interpolation = 'nearest')\n",
      "im.set_clim(0, 0.2)\n",
      "\n",
      "wfpc2_platescale = 0.1 #arcsec/pix\n",
      "stis_slit_height_pix = 25.0/wfpc2_platescale\n",
      "stis_slit_width_pix = 0.2/wfpc2_platescale\n",
      "#ax1.set_xlim(1234 - stis_slit_width_pix, 1234)\n",
      "ax2.set_ylim(888, 1138)\n",
      "ax2.set_xlim(1220,1250)\n",
      "ax2.axvspan(1220, 1230, color = 'k', alpha = 0.5)\n",
      "ax2.axvspan(1232, 1250, color = 'k', alpha = 0.5)\n",
      "im2 = ax1.imshow(rot_wfpc2_img, interpolation = 'nearest')\n",
      "im2.set_clim(0, 0.2)\n",
      "ax1.set_ylim(700, 1150)\n",
      "ax1.set_xlim(1100, 1300)\n",
      "ax1.plot([1220, 1220, 1250, 1250, 1220], [888, 1138, 1138, 888, 888], color = 'r')\n",
      "\n",
      "ax3.plot(np.sum(rot_wfpc2_img[890:1140, 1230:1232], axis = 1)/np.max(np.sum(rot_wfpc2_img[890:1140, 1230:1232], axis = 1)), np.arange(stis_slit_height_pix))\n",
      "ax4 = ax3.twinx()\n",
      "img = fits.getdata('otfr_data2/ocdd03010_flt.fits', 1)\n",
      "xd_profile = np.sum(img, axis = 1)\n",
      "ax4.plot(xd_profile/np.max(xd_profile), np.arange(len(xd_profile)), color = 'g')\n",
      "ax4.set_ylim(0, 1024)\n",
      "ax3.legend(['WFPC2'], loc = 1)\n",
      "ax4.legend(['STIS'], loc = 4)\n",
      "sn_loc = [1231.5, 975]\n",
      "#ax2.plot([sn_loc[0]], [sn_loc[1]], 'o', markerfacecolor = 'none', markeredgecolor = 'lime', markersize = 10, markeredgewidth = 2)\n",
      "north_dx = -50.0*sin(orientat*pi/180.0)\n",
      "north_dy = 50.0*cos(orientat*pi/180.0)\n",
      "east_dx = 50.0*sin((90.0-orientat)*pi/180.0)\n",
      "east_dy = 50.0*cos((90.0-orientat)*pi/180.0)\n",
      "arrow_center_x = 1150\n",
      "arrow_center_y = 1050\n",
      "ax1.arrow(arrow_center_x, arrow_center_y, north_dx, north_dy, color = 'w', width = 0.5, head_length = 12*0.5)\n",
      "ax1.arrow(arrow_center_x, arrow_center_y, east_dx, east_dy, color = 'w', width = 0.5, head_length = 12*0.5)\n",
      "ax1.text(arrow_center_x + north_dx, arrow_center_y + north_dy +10, 'N', color = 'w' )\n",
      "ax1.text(arrow_center_x + east_dx, arrow_center_y + east_dy +10, 'E', color = 'w' )\n",
      "ax1.set_title('WFPC2 Image of UGC 5189')\n",
      "ax2.set_title('STIS Slit position on WFPC2 Image')\n",
      "ax3.set_title('Normalized XD profiles from STIS and WFPC2')\n",
      "add_date_to_plot(ax3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime\n",
      "def add_date_to_plot(ax):\n",
      "    today = datetime.today()\n",
      "    xlims = ax.get_xlim()\n",
      "    ylims = ax.get_ylim()\n",
      "    ax.text(xlims[1], -.06*(ylims[1] - ylims[0])+ylims[0], '{}-{}-{}'.format(today.year, today.month, today.day))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Figure out Cross Correlation Behaviour here"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.zeros(21)\n",
      "x[10] = 1\n",
      "y = np.zeros(21)\n",
      "y[15] = 1\n",
      "\n",
      "print np.correlate(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.]\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Make one array larger than the other\n",
      "x = np.zeros(21)\n",
      "x[10] = 1\n",
      "y = np.zeros(41)\n",
      "y[15] = 1\n",
      "\n",
      "print np.correlate(x, y)\n",
      "print np.where(np.correlate(x, y) > 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.\n",
        "  0.  0.  0.]\n",
        "(array([15]),)\n"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Experiment with modes\n",
      "x = np.zeros(21)\n",
      "x[10] = 1\n",
      "y = np.zeros(41)\n",
      "y[15] = 1\n",
      "corr = np.correlate(x, y, mode = 'full')\n",
      "print corr\n",
      "print np.where(corr > 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.]\n",
        "(array([35]),)\n"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Experiment with modes\n",
      "x = np.zeros(21)\n",
      "x[10] = 1\n",
      "y = np.zeros(41)\n",
      "y[15] = 1\n",
      "corr = np.correlate(x, y, mode = 'same')\n",
      "print corr\n",
      "print np.where(corr > 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        "(array([35]),)\n"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#If the length of y changes does the location of the match change?\n",
      "x = np.zeros(21)\n",
      "x[10] = 1\n",
      "y = np.zeros(51)\n",
      "y[15] = 1\n",
      "corr = np.correlate(x, y, mode = 'same')\n",
      "print corr\n",
      "print np.where(corr > 0)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Trying to figure out a \n",
      "x = np.zeros(21)\n",
      "x[10] = 1\n",
      "y = np.zeros(51)\n",
      "y[15] = 1\n",
      "corr = np.correlate(x, y)\n",
      "print corr\n",
      "print np.argmax(corr)\n",
      "corr2 = np.correlate(y, x)\n",
      "print np.argmax(corr2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
        "25\n",
        "5\n"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.plot(np.arange(21)+15, x, '-')\n",
      "pyplot.plot(np.arange(51), y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "[<matplotlib.lines.Line2D at 0x14d95ba90>]"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Trying to figure out a \n",
      "x = np.zeros(21)\n",
      "x[10] = 1\n",
      "y = np.zeros(51)\n",
      "y[32] = 1\n",
      "corr = np.correlate(x, y)\n",
      "print corr\n",
      "print np.argmax(corr)\n",
      "corr2 = np.correlate(y, x)\n",
      "print np.argmax(corr2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        "8\n",
        "22\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Trying to figure out a \n",
      "x = np.zeros(51)\n",
      "x[10] = 1\n",
      "y = np.zeros(21)\n",
      "y[20] = 1\n",
      "corr = np.correlate(x, y, mode = 'full')\n",
      "print corr\n",
      "print np.argmax(corr)\n",
      "corr2 = np.correlate(y, x, mode = 'full')\n",
      "print corr2\n",
      "print np.argmax(corr2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        "10\n",
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        "60\n"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Trying to figure out a \n",
      "x = np.zeros(51)\n",
      "x[10] = 1\n",
      "y = np.zeros(21)\n",
      "y[8] = 1\n",
      "corr = np.correlate(x, y)\n",
      "print corr\n",
      "print np.argmax(corr)\n",
      "corr2 = np.correlate(y, x)\n",
      "print corr2\n",
      "print np.argmax(corr2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        "2\n",
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]\n",
        "28\n"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Correlate appears to be sensitivity to where the features occur in the smaller array"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Check alignment of the FUV dither\n",
      "\n",
      "8/29/2014\n",
      "\n",
      "I think it goes position 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "flist = glob.glob('2010jl_data/*flt.fits')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ifile in flist: print ifile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2010jl_data/ocdd03010_flt.fits\n",
        "2010jl_data/ocdd03020_flt.fits\n",
        "2010jl_data/ocdd03030_flt.fits\n",
        "2010jl_data/ocdd03040_flt.fits\n",
        "2010jl_data/ocdd03050_flt.fits\n",
        "2010jl_data/ocdd03060_flt.fits\n",
        "2010jl_data/ocdd03070_flt.fits\n",
        "2010jl_data/ocdd03080_flt.fits\n",
        "2010jl_data/ocdd03090_flt.fits\n",
        "2010jl_data/ocdd030a0_flt.fits\n",
        "2010jl_data/ocdd030b0_flt.fits\n",
        "2010jl_data/ocdd030c0_flt.fits\n",
        "2010jl_data/ocdd030d0_flt.fits\n",
        "2010jl_data/ocdd030e0_flt.fits\n",
        "2010jl_data/ocdd030f0_flt.fits\n",
        "2010jl_data/ocdd030g0_flt.fits\n",
        "2010jl_data/ocdd030h0_flt.fits\n",
        "2010jl_data/ocdd030i0_flt.fits\n",
        "2010jl_data/ocdd030j0_flt.fits\n",
        "2010jl_data/ocdd030k0_flt.fits\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Dither position 1 @ y = 340"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from matplotlib import pyplot\n",
      "import numpy as np\n",
      "import os\n",
      "fig = pyplot.figure(figsize = [10, 5])\n",
      "ax1 = fig.add_subplot(1,2,1)\n",
      "ax2 = fig.add_subplot(1,2,2)\n",
      "ax1.set_title('Cross Dispersion Profile for Dither position #1 2010JL')\n",
      "ax2.set_title('Zoom on SN position')\n",
      "\n",
      "for ifile in flist[::4]:\n",
      "    if fits.getval(ifile, 'opt_elem', 0) == 'G140L':\n",
      "        img = fits.getdata(ifile, 1)\n",
      "        ax1.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024), label = os.path.basename(ifile)[0:9])\n",
      "        ax1.axhline(340, color = 'k', ls = '--')\n",
      "        ax1.set_ylim(300,800)\n",
      "        ax1.set_xlim(0.5, 5.5)\n",
      "        ax1.legend(loc = 'best')\n",
      "        #Zoom in\n",
      "        ax2.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024))\n",
      "        ax2.axhline(340, color = 'k', ls = '--')\n",
      "        ax2.set_ylim(300,400)\n",
      "        ax2.set_xlim(0.5, 5.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Zvn27ZGZmyqRJk8Te3t7sXD9lyhQJDQ2V+Ph4iY6OllatWpmd60VEhg0bJsOH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22lA7jZuuoX7VvEu1veJceSQ441hxxLDiVlJVx2CH75vPK4InwxUEoOIeQQwgnCOUgPUojhU24m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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e5f0190>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Dither Position 2 @ y = 340 + 40.65"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = pyplot.figure(figsize = [10, 5])\n",
      "ax1 = fig.add_subplot(1,2,1)\n",
      "ax2 = fig.add_subplot(1,2,2)\n",
      "ax1.set_title('Cross Dispersion Profile for Dither position #2 2010JL')\n",
      "ax2.set_title('Zoom on SN location')\n",
      "\n",
      "for ifile in flist[1::4]:\n",
      "    if fits.getval(ifile, 'opt_elem', 0) == 'G140L':\n",
      "        img = fits.getdata(ifile, 1)\n",
      "        ax1.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024), label = os.path.basename(ifile)[0:9])\n",
      "        ax1.axhline(340 + 40.65, color = 'k', ls = '--')\n",
      "        ax1.set_ylim(300,800)\n",
      "        ax1.set_xlim(0.5, 6)\n",
      "        ax1.legend(loc = 'best')\n",
      "        #Zoom in\n",
      "        ax2.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024))\n",
      "        ax2.axhline(340 + 40.65, color = 'k', ls = '--')\n",
      "        ax2.set_ylim(340.65,440.65)\n",
      "        ax2.set_xlim(0.5, 6)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kfbXGWkM6GjmEkmM6TlWQZkXVeRbZsXiXsmuBmZ/3tdqmN5D1LdJ0HKa4nYaq1ePSvIis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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1100be210>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Dither Position #3 # y = 340 + 2*40.65"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = pyplot.figure(figsize = [10, 5])\n",
      "ax1 = fig.add_subplot(1,2,1)\n",
      "ax2 = fig.add_subplot(1,2,2)\n",
      "ax1.set_title('Cross Dispersion Profile for Dither position #3 2010JL')\n",
      "ax2.set_title('Zoom on SN Location')\n",
      "\n",
      "for ifile in flist[2::4]:\n",
      "    if fits.getval(ifile, 'opt_elem', 0) == 'G140L':\n",
      "        img = fits.getdata(ifile, 1)\n",
      "        ax1.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024), label = os.path.basename(ifile)[0:9])\n",
      "        ax1.axhline(340 + 2*40.65, color = 'k', ls = '--')\n",
      "        ax1.set_ylim(300,800)\n",
      "        ax1.set_xlim(0.5, 4.0)\n",
      "        ax1.legend(loc = 'best')\n",
      "        #Zoom in\n",
      "        ax2.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024))\n",
      "        ax2.axhline(340 + 2*40.65, color = 'k', ls = '--')\n",
      "        ax2.set_ylim(381.3,481.3)\n",
      "        ax2.set_xlim(0.5, 4.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ePZuXX34ZAI1Gg6dn5aVbPDw8KC4urnSuoKCAkpISFi9ezP3338/x48et8b1s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TAAAg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\nGJsmwgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108e55550>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Dither Position #4 @ y = 340 + 3*40.65"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = pyplot.figure(figsize = [10, 5])\n",
      "ax1 = fig.add_subplot(1,2,1)\n",
      "ax2 = fig.add_subplot(1,2,2)\n",
      "ax1.set_title('Cross Dispersion Profile for Dither position #4 2010JL')\n",
      "ax2.set_title('Zoom on SN Location')\n",
      "\n",
      "for ifile in flist[3::4]:\n",
      "    if fits.getval(ifile, 'opt_elem', 0) == 'G140L':\n",
      "        img = fits.getdata(ifile, 1)\n",
      "        ax1.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024), label = os.path.basename(ifile)[0:9])\n",
      "        ax1.axhline(340 + 3*40.65, color = 'k', ls = '--')\n",
      "        ax1.set_ylim(300,800)\n",
      "        ax1.set_xlim(0.5, 2)\n",
      "        ax1.legend(loc = 'best')\n",
      "        #Zoom in\n",
      "        ax2.plot(np.sum(img, axis = 1)/np.median(np.sum(img, axis = 1)), np.arange(1024))\n",
      "        ax2.axhline(340 + 3*40.65, color = 'k', ls = '--')\n",
      "        ax2.set_ylim(300+3*40.65,400+3*40.65)\n",
      "        ax2.set_xlim(0.5, 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sJkaEmPItU3QDSpT1T+pd9Tyy4hFKGktaiC63z02aNY00S1p4GjlvTSPVkkrx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\nCCGEEEIIIYQYG/4fEN4eoCjx14wAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fa783d0>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Check NUV Location"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = pyplot.figure(figsize = [5, 10])\n",
      "ax1 = fig.add_subplot(1,1,1)\n",
      "ax1.set_title('Cross Dispersion Profile for Dither position #4 2010JL')\n",
      "ax2.set_title('Zoom on SN Location')\n",
      "\n",
      "for ifile in flist[::4]:\n",
      "    if fits.getval(ifile, 'opt_elem', 0) == 'G230L':\n",
      "        img = fits.getdata(ifile, 1)\n",
      "        ax1.plot(np.sum(img[:,300:724], axis = 1), np.arange(1024), label = os.path.basename(ifile)[0:9])\n",
      "        ax1.set_ylim(300,800)\n",
      "        ax1.legend(loc = 'best')\n",
      "        #Zoom in\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UtpLGVNKYpLHiLEacQYtt4lAn8g0Cz8B3DXzHwHMMPEfHqxm4NQOnIqSwZxlj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usi53aRc7vJlrrIOF1k7C5Sdid5RwzpFRTqAvU0hppoDgtpCjfRGGqkKdTE3x/290yOTB6lpyDZ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\nB8Rko9rjPWfzAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11eb498d0>"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print ifile\n",
      "print np.mean(img), np.median(img), np.max(img)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2010jl_data/ocdd030h0_flt.fits\n",
        "-0.125337 -0.390576 9.08237\n"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Cross Correlate NUV and FUV XD profile and overplot to check location of SN"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fuv_img = fits.getdata('2010jl_otfr/ocdd03010_flt.fits', 1) #First FUV\n",
      "nuv_img = fits.getdata('2010jl_otfr/ocdd030d0_flt.fits', 1) #First NUV\n",
      "\n",
      "xd_profile_fuv = np.sum(fuv_img, axis = 1)\n",
      "xd_profile_nuv = np.sum(nuv_img, axis = 1)\n",
      "\n",
      "correlation_array = np.correlate(xd_profile_nuv[300:800], xd_profile_fuv)\n",
      "offset = np.argmax(correlation_array)\n",
      "\n",
      "pyplot.figure(figsize = [5, 10])\n",
      "pyplot.plot(xd_profile_nuv, np.arange(1024))\n",
      "pyplot.plot(xd_profile_fuv, np.arange(1024)+246, 'r')\n",
      "pyplot.ylim(300,800)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "(300, 800)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KjuYW2Jmy6kqRdfas934PA/DaCqbr5ebSuxwfT6dKczNFFGLAIApRcBnLl7Op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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1233f7d10>"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.plot(correlation_array)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1233bd150>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KT5cvutpo2VKmT1mwQL7YEmnZMunF1qGDVMd57Qu1pv78Z1mULLJLbnm5jHnp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\n5WdI9dQy4JfIl38KUp1lKQF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       "text": [
        "<matplotlib.figure.Figure at 0x121063c50>"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###I can't get this to work, not sure why, maybe they look too different"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Looking directly at the files to get NUV/FUV offset\n",
      "\n",
      "The Plate scales are almost the same\n",
      "\n",
      "FUV A2CENTER = 342.43\n",
      "\n",
      "NUV A2CENTER = 588.40\n",
      "\n",
      "FUV SHIFTA2 = 13.1 to 13.23"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fuv_spec = fits.getdata('2010jl_otfr/ocdd03010_x1d.fits', 1) #First FUV\n",
      "nuv_spec = fits.getdata('2010jl_otfr/ocdd030d0_x1d.fits', 1) #First NUV\n",
      "\n",
      "print fuv_spec['A2Center']\n",
      "print nuv_spec['A2Center']\n",
      "\n",
      "pyplot.figure(figsize = [5, 10])\n",
      "pyplot.suptitle('Comparison of the XD profiles of the NUV and FUV (shift = 232.8)')\n",
      "pyplot.xlabel('XD profile')\n",
      "pyplot.ylabel('Y Pixel (NUV)')\n",
      "pyplot.plot(xd_profile_nuv, np.arange(1024), label = 'NUV/ocdd030d0')\n",
      "pyplot.legend(loc = 4)\n",
      "#pyplot.ylim(400, 1000)\n",
      "pyplot.twiny()\n",
      "#This offset corresponds to the difference in A2CENTER between the files - the shifta2 from the FUV (NUV has no shift)\n",
      "fuv_shift = nuv_spec['A2Center'] - fuv_spec['A2Center'] - 13.15\n",
      "pyplot.plot(xd_profile_fuv, np.arange(1024)+fuv_shift, 'r', label = 'FUV shifted/ocdd03010')\n",
      "#pyplot.ylim(400, 1000)\n",
      "pyplot.xlim(-150, 350)\n",
      "\n",
      "print fuv_shift\n",
      "pyplot.legend(loc = 1)\n",
      "pyplot.savefig('2010jl_nuv_fuv_xd_profile_compare.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 342.43399048]\n",
        "[ 588.40783691]\n",
        "[ 232.82385254]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mT67feQsXqhArrZKW1OteLWMr8dRT8OGHMvhjyZLQznngAXjssejWS1HCpCUM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       "text": [
        "<matplotlib.figure.Figure at 0x12f147e50>"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Conclusion\n",
      "\n",
      "I belive the SN is the brightest source in the NUV even though the top XD source in the FUV triple is not observed in the NUV. but wait, what about manually aligning the profiles?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Try lining up the 3 humps:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fuv_spec = fits.getdata('2010jl_otfr/ocdd03010_x1d.fits', 1) #First FUV\n",
      "nuv_spec = fits.getdata('2010jl_otfr/ocdd030d0_x1d.fits', 1) #First NUV\n",
      "\n",
      "print fuv_spec['A2Center']\n",
      "print nuv_spec['A2Center']\n",
      "\n",
      "fig = pyplot.figure(figsize = [10, 10])\n",
      "ax1 = fig.add_subplot(1,2,1)\n",
      "ax2 = fig.add_subplot(1, 2, 2)\n",
      "fig.suptitle('Comparison of the XD profiles of the NUV and FUV (shift = 122.8)')\n",
      "ax1.set_xlabel('XD profile')\n",
      "ax1.set_ylabel('Y Pixel (NUV)')\n",
      "ax1.plot(xd_profile_nuv, np.arange(1024), label = 'NUV/ocdd030d0')\n",
      "ax1.legend(loc = 4)\n",
      "#pyplot.ylim(400, 1000)\n",
      "ax3 = ax1.twiny()\n",
      "fuv_shift = nuv_spec['A2Center'] - fuv_spec['A2Center'] - 13.15 - 112\n",
      "ax3.plot(xd_profile_fuv, np.arange(1024)+fuv_shift, 'r', label = 'FUV shifted/ocdd03010')\n",
      "#pyplot.ylim(400, 1000)\n",
      "ax3.set_xlim(-150, 350)\n",
      "print fuv_shift\n",
      "ax3.legend(loc = 1)\n",
      "\n",
      "\n",
      "ax2.set_xlabel('XD profile')\n",
      "ax2.set_ylabel('Y Pixel (NUV)')\n",
      "ax2.plot(xd_profile_nuv, np.arange(1024), label = 'NUV/ocdd030d0')\n",
      "ax2.set_ylim(350, 800)\n",
      "ax2.grid()\n",
      "ax4 = ax2.twiny()\n",
      "fuv_shift = nuv_spec['A2Center'] - fuv_spec['A2Center'] - 13.15 - 112\n",
      "ax4.plot(xd_profile_fuv, np.arange(1024)+fuv_shift, 'r', label = 'FUV shifted/ocdd03010')\n",
      "ax4.set_ylim(350, 800)\n",
      "ax4.set_xlim(-150, 300)\n",
      "pyplot.savefig('2010jl_nuv_fuv_xd_profile_compare_man_shift.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 342.43399048]\n",
        "[ 588.40783691]\n",
        "[ 120.82385254]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GjQylT+IgIrYFUt+UzKwr4jhrgEyFFRbmvLQlDfehdDXqeVO8hsVi4f3336e0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\nbJ08XYzR5AFjytRelPGmaJHNm1uerr+MN7mTP/scCw2BjVE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KyZMyyPvtX4hJCiM2lvoW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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1342d7350>"
       ]
      }
     ],
     "prompt_number": 202
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Conclusion\n",
      "\n",
      "It seems that the manual alignment is better, not only are the 3 FUV sources located in the NUV, a 4th source around y = 200 also lines up. I will go with this one for now."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Try to manually derive FUV and NUV separation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tbdata_nuv = fits.getdata('oref/q8l14502o_1dt.fits', 1)\n",
      "tbdata_fuv = fits.getdata('oref/q8l14504o_1dt.fits', 1)\n",
      "indx_nuv = np.where((tbdata_nuv['cenwave'] == 2376) & (tbdata_nuv['opt_elem'] == 'G230L') & (tbdata_nuv['sporder'] == 1))\n",
      "indx_fuv = np.where((tbdata_fuv['cenwave'] == 1425) & (tbdata_fuv['opt_elem'] == 'G140L') & (tbdata_fuv['sporder'] == 1))\n",
      "#print tbdata_nuv['A2center'][indx_nuv]\n",
      "#print tbdata_fuv['A2center'][indx_fuv]\n",
      "#print tbdata_nuv['A2displ'][indx_nuv]\n",
      "#print tbdata_fuv['A2displ'][indx_fuv]\n",
      "print tbdata_nuv.names\n",
      "print tbdata_fuv['A2CENTER'][indx_fuv] - tbdata_nuv['A2CENTER'][indx_nuv][:20]\n",
      "#print tbdata_fuv[indx_fuv]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['OPT_ELEM', 'CENWAVE', 'SPORDER', 'NELEM', 'A2DISPL', 'A1CENTER', 'A2CENTER', 'SNR_THRESH', 'MJD', 'DEGPERYR', 'PEDIGREE', 'DESCRIP']\n",
        "[ -65.08895111  -55.9401474   -72.53900146  -53.66950989  -63.9666748\n",
        "  -65.55480957  -52.53469849  -18.8243103   -20.34558105   15.92160034\n",
        "   48.03701782   75.10968018   73.47827148   93.07049561  106.13793945\n",
        "  118.62561035  138.33422852  153.70770264  164.33789062  183.9274292 ]\n"
       ]
      }
     ],
     "prompt_number": 157
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Check Orientation is the same for all observations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist = glob.glob('2010jl_otfr/*flt.fits')\n",
      "for ifile in flist:\n",
      "    print fits.getval(ifile, 'ORIENTAT', 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "fits.getheader(ifile.replace('flt', 'x1d'), 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 161,
       "text": [
        "SIMPLE  =                    T / file does conform to FITS standard             \n",
        "BITPIX  =                   16                                                  \n",
        "NAXIS   =                    0                                                  \n",
        "EXTEND  =                    T / FITS file may contain extensions               \n",
        "COMMENT   FITS (Flexible Image Transport System) format is defined in 'Astronomy\n",
        "COMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n",
        "ORIGIN  = 'HSTIO/CFITSIO March 2010'                                            \n",
        "DATE    = '2014-04-04' / date this file was written (yyyy-mm-dd)                \n",
        "NEXTEND =                    1 / Number of extensions                           \n",
        "FILENAME= 'ocdd030k0_x1d.fits' / name of file                                   \n",
        "FILETYPE= 'SCI      '          / type of data found in data file                \n",
        "                                                                                \n",
        "TELESCOP= 'HST'                / telescope used to acquire data                 \n",
        "INSTRUME= 'STIS  '             / identifier for instrument used to acquire data \n",
        "EQUINOX =               2000.0 / equinox of celestial coord. system             \n",
        "                                                                                \n",
        "              / DATA DESCRIPTION KEYWORDS                                       \n",
        "                                                                                \n",
        "ROOTNAME= 'ocdd030k0                         ' / rootname of the observation set\n",
        "PRIMESI = 'STIS  '             / instrument designated as prime                 \n",
        "                                                                                \n",
        "              / TARGET INFORMATION                                              \n",
        "                                                                                \n",
        "TARGNAME= 'SN-2010JL                     ' / proposer's target name             \n",
        "RA_TARG =   1.457222170833E+02 / right ascension of the target (deg) (J2000)    \n",
        "DEC_TARG=   9.495048333333E+00 / declination of the target (deg) (J2000)        \n",
        "                                                                                \n",
        "              / PROPOSAL INFORMATION                                            \n",
        "                                                                                \n",
        "PROPOSID=                13287 / PEP proposal identifier                        \n",
        "LINENUM = '03.006         '    / proposal logsheet line number                  \n",
        "PR_INV_L= 'Fox                           ' / last name of principal investigator\n",
        "PR_INV_F= 'Ori                 ' / first name of principal investigator         \n",
        "PR_INV_M= 'Dosovitz            ' / middle name / initial of principal investigat\n",
        "                                                                                \n",
        "              / SUMMARY EXPOSURE INFORMATION                                    \n",
        "                                                                                \n",
        "TDATEOBS= '2014-03-11'         / UT date of start of first exposure in file     \n",
        "TTIMEOBS= '20:50:28'           / UT start time of first exposure in file        \n",
        "TEXPSTRT=   5.672786838092E+04 / start time (MJD) of 1st exposure in file       \n",
        "TEXPEND =       56727.87860082 / end time (MJD) of last exposure in the file    \n",
        "TEXPTIME=                 883. / total exposure time (seconds)                  \n",
        "QUALCOM1= '                                                                    '\n",
        "QUALCOM2= '                                                                    '\n",
        "QUALCOM3= '                                                                    '\n",
        "QUALITY = '                                                                    '\n",
        "                                                                                \n",
        "              / TARGET OFFSETS (POSTARGS)                                       \n",
        "                                                                                \n",
        "POSTARG1=             0.000000 / POSTARG in axis 1 direction                    \n",
        "POSTARG2=             1.500000 / POSTARG in axis 2 direction                    \n",
        "                                                                                \n",
        "              / DIAGNOSTIC KEYWORDS                                             \n",
        "                                                                                \n",
        "OVERFLOW=                    0 / Number of science data overflows               \n",
        "OPUS_VER= 'OPUS 2014_1a      ' / OPUS software system version number            \n",
        "CAL_VER = '3.3 (03-October-2013)' / CALSTIS code version                        \n",
        "PROCTIME=   5.675177199074E+04 / Pipeline processing time (MJD)                 \n",
        "                                                                                \n",
        "              / SCIENCE INSTRUMENT CONFIGURATION                                \n",
        "                                                                                \n",
        "CFSTATUS= 'SUPPORTED  '        / configuration status (support., avail., eng.)  \n",
        "OBSTYPE = 'SPECTROSCOPIC '     / observation type - imaging or spectroscopic    \n",
        "OBSMODE = 'ACCUM     '         / operating mode                                 \n",
        "PHOTMODE= 'STIS 52X0.5 G230L NUVMAMA 2376 MJD#56727.8684' / observation con     \n",
        "SCLAMP  = 'NONE          '     / lamp status, NONE or name of lamp which is on  \n",
        "LAMPSET = '0.0   '             / spectral cal lamp current value (milliamps)    \n",
        "NRPTEXP =                    1 / number of repeat exposures in set: default 1   \n",
        "SUBARRAY=                    F / data from a subarray (T) or full frame (F)     \n",
        "DETECTOR= 'NUV-MAMA  '         / detector in use: NUV-MAMA, FUV-MAMA, or CCD    \n",
        "OPT_ELEM= 'G230L   '           / optical element in use                         \n",
        "APERTURE= '52X0.5          '   / aperture name                                  \n",
        "PROPAPER= '52X0.5          '   / proposed aperture name                         \n",
        "FILTER  = 'Clear             ' / filter in use                                  \n",
        "APER_FOV= '52x0.5          '   / aperture field of view                         \n",
        "CENWAVE =                 2376 / central wavelength of spectrum                 \n",
        "REPELLER= 'ON '                / repeller voltage: applies only to fuv-mama     \n",
        "                                                                                \n",
        "              / MAMA OFFSETS                                                    \n",
        "                                                                                \n",
        "MOFFSET1=                    2 / axis 1 MAMA offset (low-res pixels)            \n",
        "MOFFSET2=                    7 / axis 2 MAMA offset (low-res pixels)            \n",
        "                                                                                \n",
        "              / LOCAL RATE CHECK IMAGE                                          \n",
        "                                                                                \n",
        "LRC_XSTS=                    F / Local Rate check image exists (T/F)            \n",
        "LRC_FAIL=                    F / Local Rate Check Failed (T/F)                  \n",
        "                                                                                \n",
        "              / READOUT DEFINITION PARAMETERS                                   \n",
        "                                                                                \n",
        "CENTERA1=                  513 / subarray axis1 center pt in unbinned dect. pix \n",
        "CENTERA2=                  513 / subarray axis2 center pt in unbinned dect. pix \n",
        "SIZAXIS1=                 1024 / subarray axis1 size in unbinned detector pixels\n",
        "SIZAXIS2=                 1024 / subarray axis2 size in unbinned detector pixels\n",
        "BINAXIS1=                    1 / axis1 data bin size in unbinned detector pixels\n",
        "BINAXIS2=                    1 / axis2 data bin size in unbinned detector pixels\n",
        "                                                                                \n",
        "              / CALIBRATION SWITCHES: PERFORM, OMIT, COMPLETE                   \n",
        "                                                                                \n",
        "DQICORR = 'COMPLETE'           / data quality initialization                    \n",
        "RPTCORR = 'OMIT    '           / add individual repeat observations             \n",
        "DOPPCORR= 'OMIT    '           / convolve ref. files with orbital Doppler shift \n",
        "LORSCORR= 'COMPLETE'           / Convert MAMA data to Lo-Res before processing  \n",
        "GLINCORR= 'COMPLETE'           / correct for global detector non-linearities    \n",
        "LFLGCORR= 'COMPLETE'           / flag pixels for local and global nonlinearities\n",
        "DARKCORR= 'COMPLETE'           / Subtract dark image                            \n",
        "FLATCORR= 'COMPLETE'           / flat field data                                \n",
        "STATFLAG=                    T / Calculate statistics?                          \n",
        "WAVECORR= 'COMPLETE'           / use wavecal to adjust wavelength zeropoint     \n",
        "X1DCORR = 'COMPLETE'           / Perform 1-D spectral extraction                \n",
        "BACKCORR= 'COMPLETE'           / subtract background (sky and interorder)       \n",
        "SGEOCORR= 'OMIT    '           / correct for small scale geometric distortions  \n",
        "HELCORR = 'COMPLETE'           / convert to heliocenttric wavelengths           \n",
        "DISPCORR= 'COMPLETE'           / apply 2-dimensional dispersion solutions       \n",
        "FLUXCORR= 'COMPLETE'           / convert to absolute flux units                 \n",
        "X2DCORR = 'PERFORM '           / rectify 2-D spectral image                     \n",
        "SC2DCORR= 'OMIT    '           / 2-D scattered light correction algorithm       \n",
        "                                                                                \n",
        "              / CALIBRATION REFERENCE FILES                                     \n",
        "                                                                                \n",
        "BPIXTAB = 'oref$v2s21472o_bpx.fits' / bad pixel table                           \n",
        "DARKFILE= 'oref$tcl1742bo_drk.fits' / dark image file name                      \n",
        "PFLTFILE= 'oref$mbj1658do_pfl.fits' / pixel to pixel flat field file name       \n",
        "DFLTFILE= 'N/A     '           / delta flat field file name                     \n",
        "LFLTFILE= 'oref$h5s1140ko_lfl.fits' / low order flat                            \n",
        "PHOTTAB = 'oref$vb816448o_pht.fits' / Photometric throughput table              \n",
        "IMPHTTAB= 'oref$y2i1649no_imp.fits' / Imaging photometric table                 \n",
        "APERTAB = 'oref$n7p1032ao_apt.fits' / relative aperture throughput table        \n",
        "MLINTAB = 'oref$j9r16559o_lin.fits' / MAMA linearity correction table           \n",
        "SDSTFILE= 'N/A     '           / small scale distortion image                   \n",
        "WAVECAL = 'ocdd030k0_wav.fits     ' / wavecal image file name                   \n",
        "APDESTAB= 'oref$obm1723ro_apd.fits' / aperture description table                \n",
        "SPTRCTAB= 'oref$q8l14502o_1dt.fits' / spectrum trace table                      \n",
        "DISPTAB = 'oref$m7p16111o_dsp.fits' / dispersion coefficient table              \n",
        "INANGTAB= 'oref$l3m1437ro_iac.fits' / incidence angle correction table          \n",
        "LAMPTAB = 'oref$l421050oo_lmp.fits' / template calibration lamp spectra table   \n",
        "SDCTAB  = 'oref$obm1723po_sdc.fits' / 2-D spatial distortion correction table   \n",
        "XTRACTAB= 'oref$l3m1437to_1dx.fits' / parameters for 1-D spectral extraction tab\n",
        "PCTAB   = 'oref$q5417413o_pct.fits' / Photometry correction table               \n",
        "MOFFTAB = 'oref$h4s1350ko_moc.fits' / MAMA offsets table                        \n",
        "WCPTAB  = 'oref$lag1815lo_wcp.fits' / wavecal parameters table                  \n",
        "CDSTAB  = 'oref$k8m0958bo_cds.fits' / Cross-Disperser Scattering table          \n",
        "ECHSCTAB= 'oref$k8m0958co_ech.fits' / Echelle Scattering table                  \n",
        "EXSTAB  = 'oref$k8m0958do_exs.fits' / Echelle Cross-Dispersion Scattering table \n",
        "RIPTAB  = 'oref$vb816447o_rip.fits' / Echelle Ripple table                      \n",
        "HALOTAB = 'oref$k8m0958eo_hal.fits' / Detector Halo table                       \n",
        "TELTAB  = 'oref$k8m0958ho_tel.fits' / Telescope Point Spread Function table     \n",
        "SRWTAB  = 'oref$k8m0958go_srw.fits' / Scattering Reference Wavelengths table    \n",
        "TDSTAB  = 'oref$ub920085o_tds.fits' / time-dependent sensitivity algorithm used \n",
        "TDCTAB  = 'oref$xaf1519so_tdc.fits' / Coefficient table for NUV MAMA dark scalin\n",
        "GACTAB  = '                       ' / grating-aperture correction table         \n",
        "                                                                                \n",
        "              / TARGET ACQUISITION DATASET IDENTIFIERS                          \n",
        "                                                                                \n",
        "ACQNAME = 'ocdd03LP% '         / rootname of acquisition exposure               \n",
        "ACQTYPE = '               '    / type of acquisition                            \n",
        "PEAKNAM1= '          '         / rootname of 1st peakup exposure                \n",
        "PEAKNAM2= '          '         / rootname of 2nd peakup exposure                \n",
        "                                                                                \n",
        "              / OTFR KEYWORDS                                                   \n",
        "                                                                                \n",
        "T_SGSTAR= '                  ' / OMS calculated guide star control              \n",
        "                                                                                \n",
        "              / PATTERN KEYWORDS                                                \n",
        "                                                                                \n",
        "PATTERN1= 'STIS-ALONG-SLIT         ' / primary pattern type                     \n",
        "P1_SHAPE= 'LINE              ' / primary pattern shape                          \n",
        "P1_PURPS= 'DITHER    '         / primary pattern purpose                        \n",
        "P1_NPTS =                    4 / number of points in primary pattern            \n",
        "P1_PSPAC=                  1.0 / point spacing for primary pattern (arc-sec)    \n",
        "P1_LSPAC=             0.000000 / line spacing for primary pattern (arc-sec)     \n",
        "P1_ANGLE=             0.000000 / angle between sides of parallelogram patt (deg)\n",
        "P1_FRAME= 'POS-TARG '          / coordinate frame of primary pattern            \n",
        "P1_ORINT=                 90.0 / orientation of pattern to coordinate frame (deg\n",
        "P1_CENTR= 'YES'                / center pattern relative to pointing (yes/no)   \n",
        "                                                                                \n",
        "              / ARCHIVE SEARCH KEYWORDS                                         \n",
        "                                                                                \n",
        "BANDWID =               1616.0 / bandwidth of the data                          \n",
        "SPECRES =                740.0 / approx. resolving power at central wavelength  \n",
        "CENTRWV =               2376.0 / central wavelength of the data                 \n",
        "MINWAVE =               1568.0 / minimum wavelength in spectrum                 \n",
        "MAXWAVE =               3184.0 / maximum wavelength in spectrum                 \n",
        "PLATESC =        2.4799967E-02 / plate scale (arcsec/pixel)                     \n",
        "                                                                                \n",
        "              / PAPER PRODUCT SUPPORT KEYWORDS                                  \n",
        "                                                                                \n",
        "PROPTTL1= 'Late-Time UV Spectroscopic Signatures from Circumstellar Interaction'\n",
        "PROPTTL2= ' in Type IIn Supernovae                                             '\n",
        "OBSET_ID= '03'                 / observation set id                             \n",
        "TARDESCR= 'STAR;SUPERNOVA;SUPERNOVA TYPE II                                    '\n",
        "MTFLAG  = ' '                  / moving target flag; T if it is a moving target \n",
        "PARALLAX=   0.000000000000E+00 / target parallax from proposal                  \n",
        "MU_RA   =   0.000000000000E+00 / target proper motion from proposal (degrees RA)\n",
        "MU_DEC  =   0.000000000000E+00 / target proper motion from proposal (deg. DEC)  \n",
        "MU_EPOCH= 'J2000  '            / epoch of proper motion from proposal           \n",
        "                                                                                \n",
        "              / ASSOCIATION KEYWORDS                                            \n",
        "                                                                                \n",
        "ASN_ID  = 'OCDD030K0 '         / unique identifier assigned to association      \n",
        "ASN_TAB = 'ocdd030k0_asn.fits     ' / name of the association table             \n",
        "                                                                                \n",
        "              / POINTING INFORMATION                                            \n",
        "                                                                                \n",
        "PA_V3   =           261.602509 / position angle of V3-axis of HST (deg)         \n",
        "HISTORY DQICORR complete ...                                                    \n",
        "HISTORY   values checked for saturation                                         \n",
        "HISTORY   DQ array initialized ...                                              \n",
        "HISTORY   reference table oref$v2s21472o_bpx.fits                               \n",
        "HISTORY Uncertainty array initialized.                                          \n",
        "HISTORY LORSCORR complete; image converted from high-res to low-res.            \n",
        "HISTORY GLINCORR complete ...                                                   \n",
        "HISTORY LFLGCORR complete ...                                                   \n",
        "HISTORY   reference table oref$j9r16559o_lin.fits                               \n",
        "HISTORY     GROUND                                                              \n",
        "HISTORY     T. Danks Gathered Info                                              \n",
        "HISTORY DARKCORR complete ...                                                   \n",
        "HISTORY   reference image oref$tcl1742bo_drk.fits                               \n",
        "HISTORY     INFLIGHT 06/01/2009                                                 \n",
        "HISTORY     Avg of 72 post-SM4 dark frames from 11395, 11402 and 11857--------- \n",
        "HISTORY   reference table oref$xaf1519so_tdc.fits                               \n",
        "HISTORY FLATCORR complete ...                                                   \n",
        "HISTORY   reference image oref$mbj1658do_pfl.fits                               \n",
        "HISTORY     INFLIGHT 17/04/1998 17/05/2002                                      \n",
        "HISTORY     On-orbit NUV P-flat created from proposals 7647,8429,8863,8923      \n",
        "HISTORY   reference image oref$h5s1140ko_lfl.fits                               \n",
        "HISTORY     DUMMY                                                               \n",
        "HISTORY     Dummy file created by R. Katsanis                                   \n",
        "HISTORY WAVECORR complete ...                                                   \n",
        "HISTORY   wavecal = ocdd030k0_w2d_tmp.fits                                      \n",
        "HISTORY X1DCORR performed ...                                                   \n",
        "HISTORY   reference table oref$q8l14502o_1dt.fits                               \n",
        "HISTORY   reference table oref$l3m1437to_1dx.fits                               \n",
        "HISTORY BACKCORR performed ...                                                  \n",
        "HISTORY   reference table oref$l3m1437to_1dx.fits                               \n",
        "HISTORY DISPCORR performed ...                                                  \n",
        "HISTORY   reference table oref$m7p16111o_dsp.fits                               \n",
        "HISTORY   reference table oref$l3m1437ro_iac.fits                               \n",
        "HISTORY   reference table oref$obm1723ro_apd.fits                               \n",
        "HISTORY HELCORR performed (no reference file needed)                            \n",
        "HISTORY FLUXCORR performed ...                                                  \n",
        "HISTORY   reference table oref$vb816448o_pht.fits                               \n",
        "HISTORY   reference table oref$q5417413o_pct.fits                               \n",
        "HISTORY   reference table oref$n7p1032ao_apt.fits                               \n",
        "HISTORY   reference table oref$ub920085o_tds.fits                               \n",
        "XTRACALG= 'UNWEIGHTED'         / extraction algorithm                           \n",
        "HISTORY Heliocentric correction = 12.8017 km/s                                  "
       ]
      }
     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print fits.getval(ifile, 'sdqflags', 1)\n",
      "print ifile\n",
      "tbdata = fits.getdata(ifile.replace('flt', 'x1d'), 1)\n",
      "print tbdata.names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "31743\n",
        "2010jl_otfr/ocdd030k0_flt.fits\n",
        "['SPORDER', 'NELEM', 'WAVELENGTH', 'GROSS', 'BACKGROUND', 'NET', 'FLUX', 'ERROR', 'NET_ERROR', 'DQ', 'A2CENTER', 'EXTRSIZE', 'MAXSRCH', 'BK1SIZE', 'BK2SIZE', 'BK1OFFST', 'BK2OFFST', 'EXTRLOCY', 'OFFSET']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Check shifts for all files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ifile in flist:\n",
      "    print fits.getval(ifile, 'shifta2', 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "13.1013502291\n",
        "13.107913715"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.1135436404"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.145236431"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.1515387477"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.1635163956"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.1731290202"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.2223110311"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.2319791748"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.2375692019"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.2375692019"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13.2375692019"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0\n",
        "0.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0\n",
        "0.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "flist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 209,
       "text": [
        "['2010jl_otfr/ocdd03010_flt.fits',\n",
        " '2010jl_otfr/ocdd03020_flt.fits',\n",
        " '2010jl_otfr/ocdd03030_flt.fits',\n",
        " '2010jl_otfr/ocdd03040_flt.fits',\n",
        " '2010jl_otfr/ocdd03050_flt.fits',\n",
        " '2010jl_otfr/ocdd03060_flt.fits',\n",
        " '2010jl_otfr/ocdd03070_flt.fits',\n",
        " '2010jl_otfr/ocdd03080_flt.fits',\n",
        " '2010jl_otfr/ocdd03090_flt.fits',\n",
        " '2010jl_otfr/ocdd030a0_flt.fits',\n",
        " '2010jl_otfr/ocdd030b0_flt.fits',\n",
        " '2010jl_otfr/ocdd030c0_flt.fits',\n",
        " '2010jl_otfr/ocdd030d0_flt.fits',\n",
        " '2010jl_otfr/ocdd030e0_flt.fits',\n",
        " '2010jl_otfr/ocdd030f0_flt.fits',\n",
        " '2010jl_otfr/ocdd030g0_flt.fits',\n",
        " '2010jl_otfr/ocdd030h0_flt.fits',\n",
        " '2010jl_otfr/ocdd030i0_flt.fits',\n",
        " '2010jl_otfr/ocdd030j0_flt.fits',\n",
        " '2010jl_otfr/ocdd030k0_flt.fits']"
       ]
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(tbdata['dq'][0][:10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 212,
       "text": [
        "10"
       ]
      }
     ],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(tbdata['dq'][0][-10:])\n",
      "8192*4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 218,
       "text": [
        "32768"
       ]
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dq_flags = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]\n",
      "not_sdqflag = 0\n",
      "for flag in dq_flags:\n",
      "    if 31743&flag == 0:\n",
      "        not_sdqflag+=flag\n",
      "        \n",
      "print not_sdqflag\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "33792\n"
       ]
      }
     ],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "33792&4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 221,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "--------------------------"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Find SN 2010jl by stepping across detector in the XD direction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "from stistools.x1d import x1d\n",
      "import shutil\n",
      "import glob\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The following tasks in the stistools package can be run with TEAL:\n",
        "   basic2d      calstis     ocrreject     wavecal        x1d          x2d\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import helper_functions\n",
      "repo_path = helper_functions.get_repository_path()\n",
      "os.environ['oref'] = os.path.join(repo_path, 'oref'+'/')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start_pix = 0\n",
      "end_pix = 1024\n",
      "step = 11 #Pixels\n",
      "\n",
      "input_dataset = 'ocdd030e0'\n",
      "\n",
      "repo_path = helper_functions.get_repository_path()\n",
      "output_dir = os.path.join(repo_path, 'find_2010jl_step_xd')\n",
      "\n",
      "if not os.path.exists(os.path.join(repo_path, 'find_2010jl_step_xd')):\n",
      "    output_dir = os.path.join(repo_path, 'find_2010jl_step_xd')\n",
      "    os.makedirs(output_dir)\n",
      "else:\n",
      "    flist = glob.glob(os.path.join(repo_path, 'find_2010jl_step_xd/*'))\n",
      "    for ifile in flist:\n",
      "        os.remove(ifile)\n",
      "    \n",
      "\n",
      "\n",
      "input_list = ['{}_flt.fits'.format(input_dataset), \n",
      "              '{}_wav.fits'.format(input_dataset),\n",
      "              '{}_spt.fits'.format(input_dataset)]\n",
      "\n",
      "for input_file in input_list:\n",
      "    shutil.copyfile(os.path.join('2010jl_otfr', input_file), os.path.join(output_dir,input_file))\n",
      "\n",
      "for extraction_loc in np.arange(start_pix, end_pix, step):\n",
      "\n",
      "    x1d(input = os.path.join(output_dir, '{}_flt.fits'.format(input_dataset)),\n",
      "\t\t\ta2center = extraction_loc,\n",
      "\t\t\textrsize = step,\n",
      "\t\t\tmaxsrch = 0,\n",
      "\t\t\ttrailer = os.path.join(output_dir, input_dataset+'_trl.txt'))\n",
      "    flist = glob.glob(os.path.join(output_dir, '{}????.fits'.format(input_dataset)))\n",
      "    \n",
      "    if extraction_loc < 10:\n",
      "        four_digit_extraction_loc = '000{}'.format(extraction_loc)\n",
      "    elif extraction_loc < 100 and extraction_loc > 9:\n",
      "        four_digit_extraction_loc = '00{}'.format(extraction_loc)\n",
      "    elif extraction_loc < 1000 and extraction_loc > 99:\n",
      "        four_digit_extraction_loc = '0{}'.format(extraction_loc)\n",
      "    else:\n",
      "        four_digit_extraction_loc = '{}'.format(extraction_loc)\n",
      "    \n",
      "    for ifile in flist:\n",
      "       if os.path.basename(ifile) not in input_list:\n",
      "            shutil.move(ifile, ifile.replace('.fits', '_loc{}.fits'.format(four_digit_extraction_loc)))\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Look for MgII Line at 2800"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot\n",
      "from astropy.io import fits\n",
      "import os\n",
      "import numpy as np\n",
      "import glob\n",
      "from astropy.convolution import convolve, Box1DKernel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: AstropyDeprecationWarning: Config parameter 'auto_colname' in section [table.column] of the file '/Users/abostroem/.astropy/config/astropy.cfg' is deprecated. Use 'auto_colname' in section [table] instead. [astropy.config.configuration]\n",
        "WARNING:astropy:AstropyDeprecationWarning: Config parameter 'auto_colname' in section [table.column] of the file '/Users/abostroem/.astropy/config/astropy.cfg' is deprecated. Use 'auto_colname' in section [table] instead.\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import helper_functions\n",
      "import time\n",
      "from matplotlib.backends.backend_pdf import PdfPages\n",
      "\n",
      "def deredshift_wavelength(wavelength, redshift):\n",
      "\tderedshift_wl = wavelength/(redshift + 1.0)\n",
      "\treturn deredshift_wl\n",
      "\n",
      "line_list = [1661, 1666, 1747, 1753, 1815, 1883, 1906, 1909, 2322, 2329]\n",
      "repo_path = helper_functions.get_repository_path()\n",
      "output_dir = os.path.join(repo_path, 'find_2010jl_step_xd')\n",
      "pp = PdfPages(os.path.join(output_dir, 'find_2010jl_in_xd.pdf'))\n",
      "flist = glob.glob(os.path.join(output_dir, '*x1d*.fits'))\n",
      "redshift = 0.0107 #http://www.astronomy.ohio-state.edu/~stoll/stoll2011.pdf\n",
      "for ifile in flist:\n",
      "    tbdata =  fits.getdata(ifile, 1)\n",
      "    deredshift_wl = deredshift_wavelength(tbdata['wavelength'][0], redshift)\n",
      "    mg_indx = np.where((deredshift_wl>1650) & (deredshift_wl<3000))\n",
      "    smoothed_signal = convolve(tbdata['flux'][0][mg_indx], Box1DKernel(15))\n",
      "    pyplot.plot(deredshift_wl[mg_indx], smoothed_signal)\n",
      "    pyplot.axvline(2796, color = 'grey')\n",
      "    pyplot.title(os.path.basename(ifile))\n",
      "    pyplot.xlabel('Wavelength (deredshifted)')\n",
      "    pyplot.ylabel('Flux')\n",
      "    for line in line_list: pyplot.axvline(line, color = 'k', ls = ':')\n",
      "    pp.savefig()\n",
      "    pyplot.clf()\n",
      "    \n",
      "    \n",
      "pp.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x106b2b2d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Plot in Gross Counts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import helper_functions\n",
      "import time\n",
      "from matplotlib.backends.backend_pdf import PdfPages\n",
      "\n",
      "def deredshift_wavelength(wavelength, redshift):\n",
      "\tderedshift_wl = wavelength/(redshift + 1.0)\n",
      "\treturn deredshift_wl\n",
      "\n",
      "line_list = [1661, 1666, 1747, 1753, 1815, 1883, 1906, 1909, 2322, 2329]\n",
      "repo_path = helper_functions.get_repository_path()\n",
      "output_dir = os.path.join(repo_path, 'find_2010jl_step_xd')\n",
      "pp = PdfPages(os.path.join(output_dir, 'find_2010jl_in_xd_gross.pdf'))\n",
      "flist = glob.glob(os.path.join(output_dir, '*x1d*.fits'))\n",
      "redshift = 0.0107 #http://www.astronomy.ohio-state.edu/~stoll/stoll2011.pdf\n",
      "for ifile in flist:\n",
      "    tbdata =  fits.getdata(ifile, 1)\n",
      "    deredshift_wl = deredshift_wavelength(tbdata['wavelength'][0], redshift)\n",
      "    mg_indx = np.where((deredshift_wl>1650) & (deredshift_wl<3000))\n",
      "    smoothed_signal = convolve(tbdata['gross'][0][mg_indx], Box1DKernel(15))\n",
      "    pyplot.plot(deredshift_wl[mg_indx], smoothed_signal)\n",
      "    pyplot.axvline(2796, color = 'grey')\n",
      "    pyplot.title(os.path.basename(ifile))\n",
      "    pyplot.xlabel('Wavelength (deredshifted)')\n",
      "    pyplot.ylabel('Gross')\n",
      "    for line in line_list: pyplot.axvline(line, color = 'k', ls = ':')\n",
      "    pp.savefig()\n",
      "    pyplot.clf()\n",
      "    \n",
      "    \n",
      "pp.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x106b18850>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}