NIRI Imaging

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    NIRI imaging data reduction thread


    SKYNAMIC: Dynamic sky generation front-end to NISKY and NIREDUCE. This task takes an input sequence of dithered on-source NIRI images and generates a running average sky for each frame.

    Syntax: skynamic @Images_K.list flat=Flat_J nskip=1 nsky=7 thresh=2 minpix=15 ngrow=10 agrow=10 interact-



    Cookbook from Mukremin Kilic for reducing NIRI data.

    I have found that this works very well, although I first nprepare and run, and then create the flats from these prepared and linearized files.


    To create flat-field images, I define a procedure in IRAF named nirical
    procedure nirical( name, m, n )

    char name { prompt=”File rootname, e.g. N20050820S0″ }
    int m { prompt=”First Image in the GCAL OPEN sequence, e.g. 141″}
    int n { prompt=”First Image in the GCAL CLOSED sequence, e.g. 151″}

    del offlist
    del nofflist
    del onlist
    del nonlist
    del ndarklist
    !awk ‘{print “n”$1}’ darklist > ndarklist
    print(“Remember to put the short dark exposures in darklist before you run
    this program”)

    for(i=m+1; i<=m+9; i+=1) {
    print(name//””+i, >> “onlist”)
    print(“n”//name//””+i, >> “nonlist”)
    for(i=n+1; i<=n+9; i+=1) {
    print(name//””+i, >> “offlist”)
    print(“n”//name//””+i, >> “nofflist”)


    niflat(“@nonlist”, lampsoff=”@nofflist”, darks=”@ndarklist”)


    and afterwards to create combined images, I first use Nprepare, and
    Andy’s linearity correction.
    eg nN20110202S02\*.fits (after making script executable)
    (currently have to first: source gempython.csh)

    After that, I use the following procedure
    (in this example seeing=5 pixels).

    procedure niril55( name, n, badpixname )

    char name { prompt=”File rootname, e.g. N20050820S0″ }
    int n { prompt=”First Image in the Dither5 sequence, e.g. 141″}
    char badpixname { prompt=”Flatfield Image, e.g. nN20050825S0494″ }


    del inlist
    del ninlist
    del bninlist
    del rbninlist
    print(“Ignoring the first image in the sequence, using linearized images”)

    for(i=n; i<=n+4; i+=1) {
    print(name//””+i, >> “inlist”)
    print(“ln”//name//””+i, >> “ninlist”)
    for(i=n+1; i<=n+4; i+=1) {
    print(“bln”//name//””+i, >> “bninlist”)
    print(“rbln”//name//””+i, >> “rbninlist”)

    nresidual (“@ninlist”, proptime=0.5)

    nisky (“@bninlist”)

    nireduce (“@bninlist”, skyimage=”bln”//name//””+n+1//”_sky.fits”,

    imcoadd (“@rbninlist”, thresho=20, fwhm=5, box=5, geofitgeom=”shift”,
    rotate-, fl_over+, fl_scale-, fl_fixpix+, fl_find+, fl_map+, fl_trn+,
    fl_med+, fl_add+, fl_avg+, badpix=badpixname//””, niter=1)


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