August 2013


The test data files (*.fits) for Part III are in the 
IDL exercises data directory.  Copy those to your local directory.  

0) From a UNIX shell, start X-windows if it is not already running. 

   From the shell in an X-window, "cd" into your "IDL" directory.  Normally 
   that would be named "~/idl".  

   Choose a graphics display method for images

       For making image displays during these exercises, you can use
       either the "direct graphics" display techniques described
       in Part II or the GUI-based ATV program written by Aaron Barth.

       If you plan to use direct graphics, first check whether your
       computer display supports "true-color" graphics (for background,
       see the section titled "Setting a Visual Display Mode on Your
       Computer" in the IDL Guide).

              From the shell within an X-window, type: "xdpyinfo"
              If the response includes "TrueColor" or "24 planes" then your display
              is a 24-bit, true-color device.  If not, it is an 8-bit, pseudo-color

          After you start IDL, set the visual class of your display to
          pseudo-color or emulated pseudo-color as follows: 

              If you have an 8-bit display, type DEVICE,RETAIN=2,PSEUDO_COLOR=8

              If you have a 24-bit display, type DEVICE,RETAIN=2,TRUE_COLOR=24,DECOMPOSED=0

       If you plan to use ATV, check that it is in your IDL path.  
       If not download the software from this link and place it 
       in your IDL path.  ATV performance is independent of display
       bit depth.  One limitation of ATV is that only one display
       window can be opened at a time, although you can "blink"
       between 2 or 3 pre-loaded images.

       For plots, you should use the direct graphics techniques described
       in the IDL Guide (basic plotting commands are unaffected by the bit depth 
       of your display).

   Start IDL from the X-window. 

         On most UNIX systems, just type "idl".  This will start IDL
         running in the command-line mode using the window from which
         you called it.

   Before starting the exercises, give the IDL command:


       This will return control to the main program level if
       an error occurs in a called routine.


    The image file "mystery-image.fits" contains an encrypted,
    low-contrast, 2-D message.  See if you can determine what it is by
    using any of the tools you have tried so far: plotting, IMLIST,
    contrast adjustment, interactive value inspection, different color
    tables, histograms, flagging special values, and so forth. Once
    you find the message, develop a method for making it stand out
    (i.e. maximize the signal-to-noise) and make a hardcopy (from
    TVLASER or the File/WriteImage menu of ATV).


    Read in the two images of M87 (courtesy of Brian McNamara).
      These are roughly co-centered.  One was taken in the B band, the
      other in the I band.  

    Compare the general statistics for the two images: mean, min, max,
      variance.  One image contains some flagged bad pixels; which is

    Load a black-on-white color table.  (See the IDL Guide
      for a sample direct graphics script.  In ATV, invert the default 
      color table.) 

    Using a 0.3 power law display to preserve the dynamic range,
      display the images in two windows, with the [MIN,MAX] for each
      adjusted to show as much of the range of scientifically
      meaningful intensity values as possible.  The central density of
      the two displays should be roughly the same.  What differences
      between the two images are apparent?

    Now use the AstUseLib BLINK command to blink between the two
      windows (in ATV, use the BLINK menu).  Are differences easier to

    In direct graphics, "roll" a color table using the ICROLL routine
      from Part II in conjunction with one of the following standard
      IDL color tables: 11, 25, 31, or 33. Note how different color
      table settings affect the appearance of the images and highlight
      different features.


    Reload the black-on-white color table.

    M87 represents a modestly complicated scene with a diffuse
      background, a structured extended source (the famous nonthermal
      jet), and a system of unresolved globular star clusters, the
      brightest of which can be seen in the outer parts of the image.
      There are many image processing techniques available to extract
      information on the structure of a scene like this by enhancing
      lower contrast features.  Here, we'll illustrate two simple ones
      which suppress low-frequency structure (the diffuse background).

      Use the M87 I band image for these: 

      i)  Edge detection:  this is fast, if crude.  Results also 
          depend on the orientation of structures in the image.

            Simply display IMAGE - SHIFT(IMAGE,K,K) , where K is a
	    small integer (2 is a good starting value).  For best
	    appearance, adjust the display to show a small range of
	    values around zero (e.g. -3 to +3).  

            When properly adjusted, your display should readily show
	    both the jet (in the bright central parts of the galaxy)
	    and the faint globular clusters scattered throughout the
	    frame.  You can try various color tables to adjust the
	    appearance, although B&W may actually be the most useful.

     ii) Unsharp masking: this is a similar differencing technique,
	    except that the subtracted image is a smoothed version of
	    the original.  This removes low-frequency structure and is
	    especially useful where there is a bright background, as
	    in the case of M87.  The fastest approach with IDL is to
	    use the SMOOTH(IMAGE,K) function.  However, the smoothed
	    image would include the effects of compact structures
	    (e.g. the jet here, or stars in the case of a star
	    cluster).  A better, if somewhat slower, approach is to
	    use the MEDIAN(IMAGE,K) function, which is resistant to a
	    minority of bright pixels within the KxK smoothing box.

            Make displays for several values of K.  Good trial values
	    are in the range 9 to 21.  Displays are best for a small
	    positive range of values, e.g. 0 to 2.  


    Use the SKY routine to obtain an estimate of the sky background on
       the M87 I-band frame.  What is the result?  Does it agree with
       the approximate sky value you can infer by, for example, sampling
       blank regions of the frame with CURVAL or other techniques?
       Refer to the SKY header for an explanation of how the program

    Determine in which pixel the galaxy nucleus lies on the M87 I band
       frame as follows.  Adjust the display until you can easily see
       the starlike nucleus.  Measure an approximate location
       interactively with the cursor.  In direct graphics, you can use
       the AstUseLib CURVAL routine or the built-in CURSOR routine.
       Then, take the [xx,yy] location of the nucleus and use it as
       input to the AstUseLib CNTRD centroiding routine.  In ATV,
       place the cursor near the center of the nucleus and press "p".
       What are the centroid values for the nucleus?

    Do aperture photometry on the M87 I-band frame as follows:

       Read the information header for the AstUseLib routine DIST_CIRCLE,
         and be sure you understand how it works.  You may want to try
         using it on a small test array and plotting slices across the
       Devise a method, similar to that given as a sample script in the
         DIST_CIRCLE header, to obtain the integrated flux of the M87 image
         within a circular aperture (centered on the galaxy nucleus).

       Measure the total fluxes within circular apertures of 10, 20,
         30, and 40 pixel radii.

    Make up a simple table (printed to your screen and your journal file) of
       the aperture radius, total flux in aperture, and net flux in aperture
       after an appropriate correction for the sky background is made.

       By what percentage did the sky correction reduce the final flux in
       the 40 pixel aperture?

    To illustrate the general shape of the galaxy brightness profile
       and emphasize any asymmetries, make a "pixel plot" of the
       galaxy's surface brightness as follows:

       Extract a subarray of 71x71 pixels centered on the nuclear pixel
         you identified above.  Call this FLX.

       Use DIST_CIRCLE to make a matching array containing the distance of
         each pixel from the center.  Call this RAD.

       Now open a plotting window and make a plot of the radius of
	 each pixel in the subarray vs its flux by typing
	 PLOT,RAD,FLX,PSYM=5.  (Setting PSYM = 5 plots points as open
	 triangles; alternatively, use PSYM = 1 for plus signs.)  Most
	 data points should fall in a compact distribution showing
	 that the source is highly symmetrical.  Have you reached the
	 sky background level at the edge of the subarray?  Is there
	 evidence of an important asymmetry?  [A linear-log plot using
	 PLOT_IO can also be useful, but use WHERE to exclude any
	 negative flux points.]

       Test the sensitivity of this technique to asymmetries by 
         re-extracting the subarray but with the center offset by a
         couple of pixels from the true nuclear cenrtroid.

       In ATV, you can see a similar radial plot by using the "p"
          function and pressing the "Show radial profile" button on
          the pop-up menu.

     Another way to visualize the source structure is to make a
       surface plot with the SURFACE routine.  Open a plotting window
       and give the command SURFACE,FLX.  You can explore the shape of
       the surface by viewing it from other angles
       (e.g. SURFACE,FLX,AX=15,AZ=70) or inverting it (SURFACE,-FLX).

       In ATV, you can quickly make nice surface plots by pressing
       the "s" key.  


    Read in the image of the globular cluster M13 from the file

    Display the image.  The center of the cluster is very crowded but
       is not saturated in this exposure.  Find a set of display
       parameters which retains information in the center while still
       showing the stars in the outer part of the cluster.  

    Extract a 100x100 subarray of the image near the cluster center.
       Open a plotting window, and make a SURFACE plot of the
       subarray.  Explore several regions at different radii.  How
       far from the center do you have to be to avoid blended point-spread

       The ATV "s" feature is more convenient for exploring the
       overlap problem, since it automatically extracts a subarray
       of the main image.  

    Extract a 400x400 subarray of the image in its upper left hand
       quadrant, and use this for the remainder of exercise (5).

    The AstUseLib FIND routine is part of the DAOPHOT photometry package
       written by Peter Stetson.  This version has been converted to IDL
       from the original FORTRAN.  FIND locates stellar objects within
       a given image.  It is efficient and employs only a few parameters,
       the most important of which is HMIN, the minimum flux of a candidate
       source above the background for a threshold detection.

       Apply FIND to the subarray.  Use an HMIN value of 120 and a
	 convolution FWHM of 4 pixels.  Leave other parameters at their
	 default values.  In the initial call to FIND, be sure to
	 specify output variables to store the [x,y] and flux values
	 produced by the routine.

       Display the subarray on your terminal and use the AstUseLib
	 TVBOX (or ATVPLOT) routine to place small boxes around all
	 the stars chosen by FIND. (11 pixels is a good boxsize; use
         the /DEVICE keyword in TVBOX.

    APER (in AstUseLib) is the next routine used in a standard DAOPHOT
       reduction.  It obtains circular aperture photometry of stars
       selected by FIND.  It uses a small inner aperture to measure
       each star and a slightly larger annulus to measure the local

       Use APER to obtain brightnesses for the sources located in the
	 FIND run above.  Reasonable values for this image are 5
	 pixels for the "first" star aperture radius (press the return
	 key when you are asked for "another aperture radius") and 6
	 and 9 pixels for the sky aperture.  Set the number of "photons
	 per ADU" equal to 1.  The output will be expressed in
	 magnitudes with an arbitrary zero point.  Be sure to store
	 the magnitude values.  [Note for non-astronomers: "magnitudes"
         are related to fluxes measured in power per unit area by
         m = -2.5*alog10(f) + const. ]

       Make a histogram of the cluster luminosity function you obtained.

       Note:  the standard procedure with DAOPHOT to use a measuring
         aperture sufficient to include a significant portion but not
         all of the light from the point spread function.  Extrapolation
         to the "integrated" light of the source is then determined from
         a PSF derived from well-exposed but isolated stellar images.

END OF IDL EXERCISES Part III Part II 2D arrays and image displays. Part I scalars, vectors, plotting. IDL Tutorial Home Page

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