Last modified: December 2020

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AHELP for CIAO 4.13 Sherpa v1


Context: psfs


Display any kernel applied to a data set.


show_kernel(id=None, outfile=None, clobber=False)

id - int or str, optional
outfile - str, optional
clobber - bool, optional


The kernel represents the subset of the PSF model that is used to fit the data. The `show_psf` function shows the un-filtered version.


The parameters for this function are:

Parameter Definition
id The data set. If not given then all data sets are displayed.
outfile If not given the results are displayed to the screen, otherwise it is taken to be the name of the file to write the results to.
clobber If `outfile` is not none , then this flag controls whether an existing file can be overwritten ( True ) or if it raises an exception ( False , the default setting).


The point spread function (PSF) is defined by the full (unfiltered) PSF image or model expression evaluated over the full range of the dataset; both types of PSFs are established with `load_psf` . The kernel is the subsection of the PSF image or model which is used to convolve the data: this is changed using `set_psf` . While the kernel and PSF might be congruent, defining a smaller kernel helps speed the convolution process by restricting the number of points within the PSF that must be evaluated.


See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.

See Also

contour_kernel, contour_psf, delete_psf, get_psf, get_psf_contour, get_psf_image, get_psf_plot, image_kernel, image_psf, load_conv, load_psf, plot_kernel, plot_psf, set_psf, show_psf