- 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_psffunction shows the un-filtered version.
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
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
outfilealready exists and
Plot the 2D kernel applied to a data set.
List the identifiers for the loaded data sets.
Create a PSF model.
Plot the 1D kernel applied to a data set.
Add a PSF model to a data set.
Report the current state of the Sherpa session.
Display any PSF model applied to a data set.
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.