show_kernel

sherpa.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).
Raises:

sherpa.utils.err.IOErr – If outfile already exists and clobber is False.

See also

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

When outfile is None, the text is displayed via an external program to support paging of the information. The program used is determined by the PAGER environment variable. If PAGER is not found then ‘/usr/bin/more’ is used.

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.