show_psf

sherpa.ui.show_psf(id: IdType | None = None, outfile=None, clobber=False) → None

Display any PSF model applied to a data set.

The PSF model represents the full model or data set that is applied to the source expression. The show_kernel function shows the filtered version.

Parameters:

idint, str, or None, optional

The data set. If not given then all data sets are displayed.

outfilestr, 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.

clobberbool, 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_psf

View the 2D PSF model applied to a data set.

list_data_ids

List the identifiers for the loaded data sets.

load_psf

Create a PSF model.

plot_psf

Plot the 1D PSF model 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_kernel

Display any kernel 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.