exercise-2-FITS.ipynb

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     "source": [
      "# Exercise 2: Working with FITS images\n",
      "\n",
      "Managing FITS files is a big deal if you're an observer. Is the interface in Julia any good for this?\n",
      "\n",
      "Task:\n",
      "\n",
      "* load in a FITS image\n",
      "* display it on the screen using matplotlib's imshow\n",
      "* get the following information from the image: \n",
      "    * minimum pixel value using `minimum` function\n",
      "    * position of brightest pixel(s)\n",
      "* set all pixels with negative values to zero\n",
      "* write it back\n",
      "    \n",
      "[The fits file used](data/fits-image.fits) was downloaded from [NASA's sample FITS page](http:https://fits.gsfc.nasa.gov/fits_samples.html) (the [HST WFPC II file](http:https://fits.gsfc.nasa.gov/samples/WFPC2ASSNu5780205bx.fits))."
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     "source": [
      "# Using Python"
     ]
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    {
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     "input": [
      "# set up plotting\n",
      "using PyPlot\n",
      "cmap = get_cmap(\"gist_stern\")"
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     "language": "python",
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       "text": [
        "INFO: Loading help data...\n"
       ]
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y=\"0.0mm\" width=\"0.5mm\" height=\"5mm\" fill=\"#FFFFFF\" stroke=\"none\" /></svg>"
       ],
       "text": [
        "ColorMap \"gist_stern\""
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load in astropy\n",
      "using PyCall\n",
      "@pyimport astropy.io.fits as pyf"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load in the data\n",
      "hdulist = pyf.open(\"data/fits-image.fits\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "1-element Array{Any,1}:\n",
        " PyObject <astropy.io.fits.hdu.image.PrimaryHDU object at 0x7f320200f4d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# first and only HDU\n",
      "hdu = hdulist[1]\n",
      "header = hdu[\"header\"]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "PyObject SIMPLE  =                    T / file does conform to FITS standard             \n",
        "BITPIX  =                  -32 / number of bits per data pixel                  \n",
        "NAXIS   =                    2 / number of data axes                            \n",
        "NAXIS1  =                  100 / length of data axis 1                          \n",
        "NAXIS2  =                  100 / length of data axis 2                          \n",
        "EXTEND  =                    T / FITS dataset 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  = 'NOAO-IRAF FITS Image Kernel December 2001' / FITS file originator    \n",
        "DATE    = '2003-07-24T18:28:58' / Date FITS file was generated                  \n",
        "IRAF-TLM= '10:28:58 (24/07/2003)' / Time of last modification                   \n",
        "OBJECT  = 'U5780205B[1/4]'     / Name of the object observed                    \n",
        "CRVAL1  =      182.62570646887                                                  \n",
        "CRVAL2  =       39.41233768771                                                  \n",
        "CRPIX1  = -2.135000000000000E+02                                                \n",
        "CRPIX2  = -2.040000000000000E+02                                                \n",
        "CD1_1   =          2.756050E-5                                                  \n",
        "CD1_2   =         -2.082210E-6                                                  \n",
        "CD2_1   =         -2.080210E-6                                                  \n",
        "CD2_2   =         -2.758710E-5                                                  \n",
        "MIR_REVR=                    T                                                  \n",
        "ORIENTAT=             -174.798                                                  \n",
        "FILLCNT =                    0                                                  \n",
        "ERRCNT  =                    0                                                  \n",
        "FPKTTIME=         51229.799079                                                  \n",
        "LPKTTIME=         51229.799246                                                  \n",
        "CTYPE1  = 'RA---TAN'                                                            \n",
        "CTYPE2  = 'DEC--TAN'                                                            \n",
        "DETECTOR=                    4                                                  \n",
        "DEZERO  =             311.7072                                                  \n",
        "BIASEVEN=             311.7648                                                  \n",
        "BIASODD =             311.6496                                                  \n",
        "GOODMIN =            -3.289304                                                  \n",
        "GOODMAX =              3420.24                                                  \n",
        "DATAMEAN=            0.7084697                                                  \n",
        "GPIXELS =               628289                                                  \n",
        "SOFTERRS=                    0                                                  \n",
        "CALIBDEF=                 1816                                                  \n",
        "STATICD =                    0                                                  \n",
        "ATODSAT =                   57                                                  \n",
        "DATALOST=                    0                                                  \n",
        "BADPIXEL=                    0                                                  \n",
        "OVERLAP =                    0                                                  \n",
        "PHOTMODE= 'WFPC2,4,A2D7,FR533P15,,CAL                      '                    \n",
        "PHOTFLAM=         8.566962E-19                                                  \n",
        "PHOTZPT =                -21.1                                                  \n",
        "PHOTPLAM=             5782.972                                                  \n",
        "PHOTBW  =             2070.218                                                  \n",
        "MEDIAN  =          -0.06821804                                                  \n",
        "MEDSHADO=          -0.03253492                                                  \n",
        "HISTWIDE=            0.9845759                                                  \n",
        "SKEWNESS=            -4.585337                                                  \n",
        "MEANC10 =            0.1359666                                                  \n",
        "MEANC25 =           0.04015671                                                  \n",
        "MEANC50 =            0.1129934                                                  \n",
        "MEANC100=             1.897491                                                  \n",
        "MEANC200=            0.9687142                                                  \n",
        "MEANC300=              1.05377                                                  \n",
        "BACKGRND=            0.3467301                                                  \n",
        "FILETYPE= 'SCI      '          / type of data found in data file                \n",
        "                                                                                \n",
        "TELESCOP= 'HST'                / telescope used to acquire data                 \n",
        "INSTRUME= 'WFPC2 '             / identifier for instrument used to acquire data \n",
        "EQUINOX =               2000.0 / equinox of celestial coord. system             \n",
        "                                                                                \n",
        "              / WFPC-II DATA DESCRIPTOR KEYWORDS                                \n",
        "                                                                                \n",
        "ROOTNAME= 'u5780205r            ' / rootname of the observation set             \n",
        "PROCTIME=   5.284443195602E+04 / Pipeline processing time (MJD)                 \n",
        "OPUS_VER= 'OPUS 14.3b        ' / OPUS software system version number            \n",
        "CAL_VER = '                        ' / CALWP2 code version                      \n",
        "                                                                                \n",
        "              / SCIENCE INSTRUMENT CONFIGURATION                                \n",
        "                                                                                \n",
        "MODE    = 'FULL'               / instr. mode: FULL (full res.), AREA (area int.)\n",
        "SERIALS = 'OFF'                / serial clocks: ON, OFF                         \n",
        "                                                                                \n",
        "              / IMAGE TYPE CHARACTERISTICS                                      \n",
        "                                                                                \n",
        "IMAGETYP= 'EXT               ' / DARK/BIAS/IFLAT/UFLAT/VFLAT/KSPOT/EXT/ECAL     \n",
        "CDBSFILE= 'NO                ' / GENERIC/BIAS/DARK/PREF/FLAT/MASK/ATOD/NO       \n",
        "PKTFMT  =                   96 / packet format code                             \n",
        "                                                                                \n",
        "              / FILTER CONFIGURATION                                            \n",
        "                                                                                \n",
        "FILTNAM1= 'FR533P15'           / first filter name                              \n",
        "FILTNAM2= '        '           / second filter name                             \n",
        "FILTER1 =                   69 / first filter number (0-48)                     \n",
        "FILTER2 =                    0 / second filter number (0-48)                    \n",
        "FILTROT =                 15.0 / partial filter rotation angle (degrees)        \n",
        "LRFWAVE =          4877.000000 / linear ramp filter wavelength                  \n",
        "                                                                                \n",
        "              / INSTRUMENT STATUS USED IN DATA PROCESSING                       \n",
        "                                                                                \n",
        "UCH1CJTM=             -88.2569 / TEC cold junction #1 temperature (Celsius)     \n",
        "UCH2CJTM=             -88.6697 / TEC cold junction #2 temperature (Celsius)     \n",
        "UCH3CJTM=             -88.3028 / TEC cold junction #3 temperature (Celsius)     \n",
        "UCH4CJTM=             -88.7671 / TEC cold junction #4 temperature (Celsius)     \n",
        "UBAY3TMP=              13.2302 / bay 3 A1 temperature (deg C)                   \n",
        "KSPOTS  = 'OFF'                / Status of Kelsall spot lamps: ON, OFF          \n",
        "SHUTTER = 'A'                  / Shutter in place at beginning of the exposure  \n",
        "ATODGAIN=                  7.0 / Analog to Digital Gain (Electrons/DN)          \n",
        "                                                                                \n",
        "              / RSDP CONTROL KEYWORDS                                           \n",
        "                                                                                \n",
        "MASKCORR= 'COMPLETE'           / Do mask correction: PERFORM, OMIT, COMPLETE    \n",
        "ATODCORR= 'COMPLETE'           / Do A-to-D correction: PERFORM, OMIT, COMPLETE  \n",
        "BLEVCORR= 'COMPLETE'           / Do bias level correction                       \n",
        "BIASCORR= 'COMPLETE'           / Do bias correction: PERFORM, OMIT, COMPLETE    \n",
        "DARKCORR= 'COMPLETE'           / Do dark correction: PERFORM, OMIT, COMPLETE    \n",
        "FLATCORR= 'SKIPPED '           / Do flat field correction                       \n",
        "SHADCORR= 'OMIT    '           / Do shaded shutter correction                   \n",
        "DOSATMAP= 'OMIT    '           / Output saturated pixel map                     \n",
        "DOPHOTOM= 'COMPLETE'           / Fill photometry keywords                       \n",
        "DOHISTOS= 'OMIT    '           / Make histograms: PERFORM, OMIT, COMPLETE       \n",
        "OUTDTYPE= 'REAL  '             / Output image datatype: REAL, LONG, SHORT       \n",
        "                                                                                \n",
        "              / CALIBRATION REFERENCE FILES                                     \n",
        "                                                                                \n",
        "MASKFILE= 'uref$f8213081u.r0h     ' / name of the input DQF of known bad pixels \n",
        "ATODFILE= 'uref$dbu1405iu.r1h'      / name of the A-to-D conversion file        \n",
        "BLEVFILE= 'ucal$u5780205r.x0h     ' / Engineering file with extended register da\n",
        "BLEVDFIL= 'ucal$u5780205r.q1h     ' / Engineering file DQF                      \n",
        "BIASFILE= 'uref$j9a1612mu.r2h'      / name of the bias frame reference file     \n",
        "BIASDFIL= 'uref$j9a1612mu.b2h'      / name of the bias frame reference DQF      \n",
        "DARKFILE= 'uref$j2g1549cu.r3h'      / name of the dark reference file           \n",
        "DARKDFIL= 'uref$j2g1549cu.b3h'      / name of the dark reference DQF            \n",
        "FLATFILE= 'uref$f4i1559cu.r4h'      / name of the flat field reference file     \n",
        "FLATDFIL= 'uref$f4i1559cu.b4h'      / name of the flat field reference DQF      \n",
        "SHADFILE= 'uref$e371355eu.r5h'      / name of the reference file for shutter sha\n",
        "PHOTTAB = '                       ' / name of the photometry calibration table  \n",
        "GRAPHTAB= 'mtab$n3d14531m_tmg.fits' / the HST graph table                       \n",
        "COMPTAB = 'mtab$n431303km_tmc.fits' / the HST components table                  \n",
        "                                                                                \n",
        "              / DEFAULT KEYWORDS SET BY STSCI                                   \n",
        "                                                                                \n",
        "SATURATE=                 4095 / Data value at which saturation occurs          \n",
        "USCALE  =                  1.0 / Scale factor for output image                  \n",
        "UZERO   =                  0.0 / Zero point for output image                    \n",
        "                                                                                \n",
        "              / READOUT DURATION INFORMATION                                    \n",
        "                                                                                \n",
        "READTIME=                  464 / Length of time for CCD readout in clock ticks  \n",
        "                                                                                \n",
        "              / PLANETARY SCIENCE KEYWORDS                                      \n",
        "                                                                                \n",
        "PA_V3   =            49.936909 / position angle of V3-axis of HST (deg)         \n",
        "RA_SUN  =   3.337194516616E+02 / right ascension of the sun (deg)               \n",
        "DEC_SUN =  -1.086675160382E+01 / declination of the sun (deg)                   \n",
        "EQNX_SUN=               2000.0 / equinox of the sun                             \n",
        "MTFLAG  =                    F / moving target flag; T if it is a moving target \n",
        "EQRADTRG=             0.000000 / equatorial radius of target (km)               \n",
        "FLATNTRG=             0.000000 / flattening of target                           \n",
        "NPDECTRG=             0.000000 / north pole declination of target (deg)         \n",
        "NPRATRG =             0.000000 / north pole right ascension of target (deg)     \n",
        "ROTRTTRG=             0.000000 / rotation rate of target                        \n",
        "LONGPMER=             0.000000 / longitude of prime meridian (deg)              \n",
        "EPLONGPM=             0.000000 / epoch of longitude of prime meridian (sec)     \n",
        "SURFLATD=             0.000000 / surface feature latitude (deg)                 \n",
        "SURFLONG=             0.000000 / surface feature longitude (deg)                \n",
        "SURFALTD=             0.000000 / surface feature altitude (km)                  \n",
        "                                                                                \n",
        "              / PODPS FILL VALUES                                               \n",
        "                                                                                \n",
        "PODPSFF =                    0 / 0=(no  podps fill); 1=(podps fill present)     \n",
        "STDCFFF =                    0 / 0=(no st dcf fill); 1=(st dcf fill present)    \n",
        "STDCFFP = '0x5569'             / st dcf fill pattern (hex)                      \n",
        "RSDPFILL=                 -100 / bad data fill value for calibrated images      \n",
        "                                                                                \n",
        "              / EXPOSURE TIME AND RELATED INFORMATION                           \n",
        "                                                                                \n",
        "UEXPODUR=                  300 / commanded duration of exposure (sec)           \n",
        "NSHUTA17=                    1 / Number of AP17 shutter B closes                \n",
        "DARKTIME=   3.000000000000E+02 / Dark time (seconds)                            \n",
        "UEXPOTIM=                16880 / Major frame pulse time preceding exposure start\n",
        "PSTRTIME= '1999.051:19:08:37 ' / predicted obs. start time (yyyy.ddd:hh:mm:ss)  \n",
        "PSTPTIME= '1999.051:19:16:37 ' / predicted obs. stop time (yyyy.ddd:hh:mm:ss)   \n",
        "                                                                                \n",
        "              / EXPOSURE INFORMATION                                            \n",
        "                                                                                \n",
        "SUNANGLE=           141.618347 / angle between sun and V1 axis                  \n",
        "MOONANGL=           126.698997 / angle between moon and V1 axis                 \n",
        "SUN_ALT =           -31.523479 / altitude of the sun above Earth's limb         \n",
        "FGSLOCK = 'FINE              ' / commanded FGS lock (FINE,COARSE,GYROS,UNKNOWN) \n",
        "                                                                                \n",
        "DATE-OBS= '1999-02-20'         / UT date of start of observation (yyyy-mm-dd)   \n",
        "TIME-OBS= '19:03:13'           / UT time of start of observation (hh:mm:ss)     \n",
        "EXPSTART=   5.122979390428E+04 / exposure start time (Modified Julian Date)     \n",
        "EXPEND  =   5.122979737650E+04 / exposure end time (Modified Julian Date)       \n",
        "EXPTIME =                 300. / exposure duration (seconds)--calculated        \n",
        "EXPFLAG = 'NORMAL       '      / Exposure interruption indicator                \n",
        "                                                                                \n",
        "              / TARGET & PROPOSAL ID                                            \n",
        "TARGNAME= 'NGC4151                       ' / proposer's target name             \n",
        "RA_TARG =   1.826355000000E+02 / right ascension of the target (deg) (J2000)    \n",
        "DEC_TARG=   3.940576666667E+01 / declination of the target (deg) (J2000)        \n",
        "ECL_LONG=           164.096619 / ecliptic longitude of the target (deg) (J2000) \n",
        "ECL_LAT =            36.623709 / ecliptic latitude of the target (deg) (J2000)  \n",
        "GAL_LONG=           155.079532 / galactic longitude of the target (deg) (J2000) \n",
        "GAL_LAT =            75.062679 / galactic latitude of the target (deg) (J2000)  \n",
        "                                                                                \n",
        "PROPOSID=                 8019 / PEP proposal identifier                        \n",
        "PEP_EXPO= '02-030         '    / PEP exposure identifier including sequence     \n",
        "LINENUM = '02.030         '    / PEP proposal line number                       \n",
        "SEQLINE = '               '    / PEP line number of defined sequence            \n",
        "SEQNAME = '               '    / PEP define/use sequence name                   \n",
        "HISTORY   MASKFILE=uref$f8213081u.r0h  MASKCORR=COMPLETED                       \n",
        "HISTORY   PEDIGREE=INFLIGHT 01/01/1994 - 15/05/1995                             \n",
        "HISTORY   DESCRIP=STATIC MASK - INCLUDES CHARGE TRANSFER TRAPS                  \n",
        "HISTORY   BIASFILE=uref$j9a1612mu.r2h  BIASCORR=COMPLETED                       \n",
        "HISTORY   PEDIGREE=INFLIGHT 29/08/98 - 21/08/99                                 \n",
        "HISTORY   DESCRIP=not significantly different from j6e16008u.                   \n",
        "HISTORY   DARKFILE=uref$j2g1549cu.r3h  DARKCORR=COMPLETED                       \n",
        "HISTORY   PEDIGREE=INFLIGHT 16/02/1999 - 16/02/1999                             \n",
        "HISTORY   DESCRIP=Pipeline dark: 120 frame superdark with hotpixels from        \n",
        "HISTORY   16/02/99                                                              \n",
        "HISTORY   FLATFILE=uref$f4i1559cu.r4h  FLATCORR=SKIPPED                         \n",
        "HISTORY   PEDIGREE=DUMMY  18/04/1995                                            \n",
        "HISTORY   DESCRIP=All pixels set to value of 1. Not flat-fielded.               \n",
        "HISTORY   PC1: bias jump level ~0.100 DN.                                       \n",
        "HISTORY   The following throughput tables were used:                            \n",
        "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
        "HISTORY   crwfpc2comp$wfpc2_dqepc1_005_syn.fits,                                \n",
        "HISTORY   crwfpc2comp$wfpc2_a2d7pc1_004_syn.fits,                               \n",
        "HISTORY   crwfpc2comp$wfpc2_flatpc1_003_syn.fits                                \n",
        "HISTORY   The following throughput tables were used:                            \n",
        "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
        "HISTORY   crwfpc2comp$wfpc2_dqewfc2_005_syn.fits,                               \n",
        "HISTORY   crwfpc2comp$wfpc2_a2d7wf2_004_syn.fits,                               \n",
        "HISTORY   crwfpc2comp$wfpc2_flatwf2_003_syn.fits                                \n",
        "HISTORY   The following throughput tables were used:                            \n",
        "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
        "HISTORY   crwfpc2comp$wfpc2_dqewfc3_005_syn.fits,                               \n",
        "HISTORY   crwfpc2comp$wfpc2_a2d7wf3_004_syn.fits,                               \n",
        "HISTORY   crwfpc2comp$wfpc2_flatwf3_003_syn.fits                                \n",
        "HISTORY   The following throughput tables were used:                            \n",
        "HISTORY   crotacomp$hst_ota_007_syn.fits, crwfpc2comp$wfpc2_optics_006_syn.fits,\n",
        "HISTORY   crwfpc2comp$wfpc2_dqewfc4_005_syn.fits,                               \n",
        "HISTORY   crwfpc2comp$wfpc2_a2d7wf4_004_syn.fits,                               \n",
        "HISTORY   crwfpc2comp$wfpc2_flatwf4_003_syn.fits                                \n",
        "HISTORY   Ran task WARMPIX, version 1.1 (Sep 28, 1999) at Thu 10:24:40          \n",
        "HISTORY   24-Jul-2003, rej_thresh=0.10000, fix_thresh=0.00300,                  \n",
        "HISTORY   var_thresh=0.00300,                                                   \n",
        "HISTORY   fix_dqval=1024, rej_val=INDEF                                         \n",
        "SKYSUB1 =                   0. / Sky value for chip 1                           \n",
        "PYS_VERS=                  1.8 / PyStack.py: Version                            \n",
        "IRAF_I  = 'IRAF V2.12.1 July 2002 release:2.12'                                 \n",
        "STAC_I  = '$RCSfile: imstack_imshift_imcomb.cl,v $ $Revision: 1.1 $'            \n",
        "PYS_ITER=                    5 / PyStack.py: Number of iterations               \n",
        "PYS_HEXP=                    2 / PyStack.py: Value of exponent                  \n",
        "SKYSUB2 =                 0.01 / Sky value for chip 2                           \n",
        "SKYSUB3 =                -0.08 / Sky value for chip 3                           \n",
        "SKYSUB4 =                -0.03 / Sky value for chip 4                           \n",
        "NCOMBINE=                    2 / Number of exposures for this association       \n",
        "HISTORY   Ran task WMOSAIC, version 2.1 (Jun 1995), at Thu 10:28:58 24-Jul-2003 \n",
        "COMMENT                                                                         \n",
        "COMMENT                                                                         "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imdata = hdu[:data]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "100x100 Array{Float32,2}:\n",
        "  1.03158     1.08554     0.511125    \u2026  7.40503    6.80991    5.86466  \n",
        "  0.476544    0.369005    0.524518       7.08262    7.12883    5.27363  \n",
        "  1.26589     0.799219    0.218222       7.61593    9.10855    6.91347  \n",
        "  1.18256     0.878865    0.204786       8.80005    8.40067    7.88117  \n",
        "  0.927722    0.752777    0.584217       8.84981    8.04905    7.60208  \n",
        "  0.482523    0.127957    0.27255     \u2026  8.67788    7.67705    8.29861  \n",
        "  0.406336    0.394538    0.268648       8.63436    6.78155    8.92089  \n",
        "  0.940592    0.52642     0.691145       7.9926     6.79646    8.01739  \n",
        "  1.27879     0.915581    0.52578        7.55549    6.94876    7.38695  \n",
        "  0.697374    0.52315     0.314522       7.16162    6.69554    6.81852  \n",
        " -0.134354    0.515386    0.738036    \u2026  7.27472    6.81387    6.00003  \n",
        " -0.0580475   0.665356    0.614793       7.2126     5.88157    4.65218  \n",
        "  0.720517    0.68849     0.855741       8.33559    5.1659     4.01831  \n",
        "  \u22ee                                   \u22f1                                 \n",
        "  1.00261     0.63835     0.377774       1.09655    0.759311   0.635015 \n",
        "  0.547952    0.700689    0.269449       0.575319  -0.0219589  0.783337 \n",
        "  0.66705     0.420551    0.0238154   \u2026  0.684515   0.597013   0.83292  \n",
        "  0.573887    0.39462     0.49563        0.600988   1.09629    0.58552  \n",
        "  0.296847    0.0363498   0.250984       0.868481   0.936388   0.250051 \n",
        "  0.50536     0.322373   -0.10824        0.83425    0.0869685  0.116338 \n",
        " -0.197808   -0.171628   -0.0166526      0.827145   0.898985   0.418266 \n",
        "  0.279576   -0.739037    0.00439202  \u2026  0.253484   0.553173   0.279392 \n",
        "  1.05891     0.347345   -0.164634       0.251521   0.54144    0.456686 \n",
        "  0.0482905   0.171759   -0.0425407      0.368141   0.481697   0.0305117\n",
        " -0.0420618   0.59643    -0.387436       0.441201   0.498471   0.0837238\n",
        " -0.0112627  -0.0142497   0.0181858      0.584082   0.412454   0.4871   "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imshow(imdata, cmap=cmap, interpolation=\"bicubic\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7f320200f510>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "PyObject <matplotlib.image.AxesImage object at 0x7f3201f7b3d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get the minimum pixel value\n",
      "minimum(imdata)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "-1.8990651f0"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get the position of the brightest pixel(s)\n",
      "brightest = maximum(imdata)\n",
      "find(x -> x==brightest, imdata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "1-element Array{Int64,1}:\n",
        " 8310"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# the find command gives single indexes: turn it into coords using ind2sub\n",
      "ind2sub(size(imdata), 8310)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "(10,84)"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# find all the negative pixels\n",
      "negatives = find(x -> x<0, imdata)\n",
      "\n",
      "# copy the array and set those pixels to 0\n",
      "newimdata = copy(imdata)\n",
      "newimdata[negatives] = 0\n",
      "\n",
      "# write back to file\n",
      "pyf.writeto(\"data/new-image.fits\", newimdata, header, clobber=true)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: Overwriting existing file 'data/new-image.fits'. [astropy.io.fits.file]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# With FITSIO in Julia"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using FITSIO\n",
      "hdulist = FITS(\"data/fits-image.fits\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "file: data/fits-image.fits\n",
        "mode: r\n",
        "extnum exttype         extname\n",
        "1      image_hdu       \n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "image = hdulist[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "file: data/fits-image.fits\n",
        "extension: 1\n",
        "type: IMAGE\n",
        "image info:\n",
        "  bitpix: -32\n",
        "  size: (100,100)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imdata = read(image)\n",
      "imshow(imdata, cmap=cmap, interpolation=\"bicubic\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7f320eeca450>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "PyObject <matplotlib.image.AxesImage object at 0x7f32019fab10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Performance comparison"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function read_data_with_python()\n",
      "    hdulist = pyf.open(\"data/fits-image.fits\")\n",
      "    image = hdulist[1]\n",
      "    data = image[:data]\n",
      "end\n",
      "\n",
      "function read_data_with_fitsio()\n",
      "    hdulist = FITS(\"data/fits-image.fits\")\n",
      "    image = hdulist[1]\n",
      "    data = read(image)\n",
      "end\n",
      "\n",
      "read_data_with_python()\n",
      "read_data_with_fitsio();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@time read_data_with_python()\n",
      "@time read_data_with_fitsio();"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "elapsed time: 0."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "015030285 seconds (530944 bytes allocated)\n",
        "elapsed time: 0.000598368 seconds (42008 bytes allocated)\n"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}