Generaloptions:-h, --help
Gives general summary about the command line options.
--long-help, --help-long
Gives a detailed list of command line options.
--wiki-help, --help-wiki, --mediawiki-help, --help-mediawiki
Gives a detailed list of command line options in Mediawiki format.
--version, --version-short, --short-version
Gives some version information about the program.
-r, --reference, --input-reference <image file>
Name of the reference FITS image file.
-i, --input <image file>
Name of the input FITS image file (required only for kernel fitting).
-k, --kernel, --kernels <kernel set>
List of kernel bases used for fitting convolution kernel.See also "Kernel specifications" below
for the format of this <kernel set> argument.
--input-kernel-list <file>
Name of the file containing kernel bases. The kernel bases in this file should have no associated
coefficients if convolution fitting is done, otherwise the kernel basis file must contain the
convolution coefficients
--output-kernel-list <file>
Name of the file where the coefficients for the kernel bases are stored after convolution kernel
fitting
-o, --output, --output-convolved <image file>
Name of the output file which is the reference image convolved with the kernel solution (which can
either be a previously fitted and now read from a file or the result of the current fit)
--output-subtracted <image file>
The difference between the input image and the convolved reference image.
-a, --add-to <image file>
This optionally specified file is added to the convolved image
-M, --input-mask <fits>
Input mask file to co-add to output image mask.
-n, --iterations <iterations>
Use an iterative fit with the rejection of the outlier pixels.The maximum number of iterations
should be specified with this command line argument
-s, --rejection-level <sigma>
Rejection level in standard deviation units.
Kernelspecifications(eachseparatedwithasemi-colon,;"):"
i/<spatial order>
identity kernel (a.k.a. "flux term") with the specified order of polynomial spatial variations
b/<spatial order>
constant offset kernel (a.k.a. "background term") with the specified order of polynomial spatial
variations
d=<size>/<spatial order>
discrete kernel with the half-size of <size> and the specified order of polynomial spatial
variations
g=<size>,<sigma>,<order>/<spatial order>
Gaussian kernel with the half-size of <size>, standard deviation of <sigma> and Hermite basis
order of <order>, with the specified order of polynomial spatial variations