# show_kernel¶

sherpa.astro.ui.show_kernel(id=None, outfile=None, clobber=False)

Display any kernel applied to a data set.

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.

Parameters: id (int or str, optional) – The data set. If not given then all data sets are displayed. outfile (str, optional) – 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 (bool, optional) – 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). sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

image_kernel()
Plot the 2D kernel applied to a data set.
list_data_ids()
List the identifiers for the loaded data sets.
load_psf()
Create a PSF model.
plot_kernel()
Plot the 1D kernel applied to a data set.
set_psf()
Add a PSF model to a data set.
show_all()
Report the current state of the Sherpa session.
show_psf()
Display any PSF model applied to a data set.

Notes

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.