ECHMERGE-Merge echelle spectra into a single long spectrum

Description:

The program expects two input files, each of which may be 1-D or 2-D, but both of which must have the same number of pixels in X, must have a recognised wavelength unit as the units of X and must have (near enough) identical .X.DATA arrays. In practice this means that both must have been scrunched on to the same wavelength scale. (The details of this may change when SCRUNCH has been upgraded to write an output file with a 2-D .X.DATA array describing the discontinuous scrunched orders.)

It creates a 1-D output file which consists of all the orders from the input files. Where orders overlap a weighted sum of the overlapping orders is used. The formula used is:

in1weight(i) * in1(i) + in2weight(i) * in2(i) in1weight(i) + in2weight(i)

and the weights are simply the result of median smoothing the data that they weight. This means that more weight is given to stronger signal data, that data where one of the inputs is zero is set to the other of the inputs and that data where both of the inputs are equal is left unaltered. All of these are desirable qualities. There may be less desirable statistical consequences and it is not obvious that signal to noise ratio cannot be degraded although intuitively it will not be since on the assumption of Poisson statistics the weights are essentially just the inverse variances. At low signal, a cutoff applies since the major noise contribution will no longer be Poisson.

The output file can be the same as either of the two input files and the second input file can be given a blank name, in which case it is not required. Often the first run will use a single input file to create the output file and subsequent runs will add in more input files to the existing output file.

Parameters:

IMAGE

ECHMERGE merges one or two images that contain scrunched echelle orders. The images must have the same size in the X direction but each may be 1-D or 2-D.

IMAGE1

The name of the image that is to be merged with IMAGE. A blank name can be given.

BOX

The input and output data is smoothed (in workspace) in order to provide slowly varying data from which to derive weights. This is the total width of the median filter used. An even value is rounded down to the next odd number.

CUTOFF

If the ration (stronger signal)/(weaker signal) exceeds CUTOFF, only the stronger signal will be used. This is an attempt to prevent signal to noise degradation due to adding in of weak signal.

OUTPUT

The name of the resulting image. The output image is a single long spectrum containing the merged orders from IMAGE and IMAGE1.

Source comments:

 E C H M E R G E

 Program name:
    ECHMERGE

 Function:
    Merge scrunched echelle orders into a single long spectrum.

 Description:
    The program expects two input files, each of which may be 1-D or
    2-D, but both of which must have the same number of pixels in X,
    must have a recognised wavelength unit as the units of X and must
    have (near enough) identical .X.DATA arrays. In practice this
    means that both must have been scrunched on to the same wavelength
    scale. (The details of this may change when SCRUNCH has been
    upgraded to write an output file with a 2-D .X.DATA array
    describing the discontinuous scrunched orders.)

    It creates a 1-D output file which consists of all the orders from
    the input files. Where orders overlap a weighted sum of the
    overlapping orders is used. The formula used is:

                   in1weight(i) * in1(i) + in2weight(i) * in2(i)
                            in1weight(i) + in2weight(i)

    and the weights are simply the result of median smoothing the data
    that they weight. This means that more weight is given to stronger
    signal data, that data where one of the inputs is zero is set to
    the other of the inputs and that data where both of the inputs are
    equal is left unaltered. All of these are desirable qualities.
    There may be less desirable statistical consequences and it is not
    obvious that signal to noise ratio cannot be degraded although
    intuitively it will not be since on the assumption of Poisson
    statistics the weights are essentially just the inverse variances.
    At low signal, a cutoff applies since the major noise contribution
    will no longer be Poisson.

    The output file can be the same as either of the two input files
    and the second input file can be given a blank name, in which case
    it is not required. Often the first run will use a single input
    file to create the output file and subsequent runs will add in
    more input files to the existing output file.

 Parameters:

    (>) IMAGE    (File) The name of the first input image. This can be
                 1-D or 2-D and will normally be the output from
                 SCRUNCH.  However it can also be the results of a
                 previous run of this program.
    (>) IMAGE1   (File) The name of the second input image. This can
                 be 1-D or 2-D and will normally be the output from
                 SCRUNCH. However it can also be the results of a
                 previous run of this program. It must have the same X
                 size as IMAGE, must agree in X units (which must be
                 a recognised wavelength unit) and must more or less
                 agree in the contents of .X.DATA. If no second
                 input image is required, its name can be specified
                 as blank.
    (>) BOX      (Integer) The size of the box (in pixels) to be used
                 in calculating the medians.  Should be odd; if even,
                 BOX-1 will be used.
    (>) CUTOFF   (Real) The ratio of higher signal to lower signal at
                 which no contribution from the lower signal will be
                 taken.
    (<) OUTPUT   (File) The name of the output image. This will be
                 a 1-D image with the same size and X information as a
                 row of either of the input images.

 Language:
    FORTRAN

 External variables used:

    None

 Prior requirements:
    None

Authors:

William Lupton, AAO