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Last modified: December 2013

URL: http://cxc.harvard.edu/sherpa/ahelp/plot_kernel.html
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AHELP for CIAO 4.9 Sherpa v1

plot_kernel

Context: psfs

Synopsis

Plot the PSF kernel

Syntax

plot_kernel( [id], [replot=False, overplot=False] )

Description

The plot_kernel command plots the PSF kernel associated with a dataset. To plot the full, unfiltered PSF, use the plot_psf command.

  • id - the id of the dataset to use; if not given, uses the default dataset id (id=1 by default, see "ahelp get_default_id")
  • replot - should the cached arrays be used in the plot? False (default)=do the calculation, True=redisplay the existing values
  • overplot - should the new plot be overlaid in the plotting window? False (default)=clear the window, True=overplot on any existing plots

The plot is displayed in a ChIPS plotting window. If there is no plotting window open, one is created. If a plotting window exists, the overplot parameter value determines whether the new plot is overlaid on any existing plots in the window or if the window is cleared before the plot is drawn.

ChIPS commands may be used within Sherpa to modify plot characteristics and create hardcopies; refer to the ChIPS website for information.

Plotting Multiple Objects

To plot multiple Sherpa objects - e.g. data, model, fit, residuals - in a single window, use the plot command.

What is the difference between the PSF and the kernel?

The point spread function (PSF) is defined by the full (unfiltered) PSF image loaded into Sherpa or the PSF model expression evaluated over the full range of the dataset; both types of PSFs are established with the load_psf() command. The kernel is the subsection of the PSF image or model which is used to convolve the data. This subsection is created from the PSF when the size and center of the kernel are defined by the command 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 Sherpa must evaluate.

Example 1

sherpa> plot_kernel()

Plot the PSF for the default dataset.

Example 2

sherpa> plot_kernel("src", overplot=True)

Plot the PSF kernel for the "src" dataset, overlaying on any existing plots.

See Also

contrib
get_data_prof, get_data_prof_prefs, get_delchi_prof, get_delchi_prof_prefs, get_fit_prof, get_model_prof, get_model_prof_prefs, get_resid_prof, get_resid_prof_prefs, get_source_prof, get_source_prof_prefs, plot_chart_spectrum, prof_data, prof_delchi, prof_fit, prof_fit_delchi, prof_fit_resid, prof_model, prof_resid, prof_source
data
get_arf_plot, get_bkg_plot
modeling
normal_sample, t_sample, uniform_sample
plotting
get_energy_flux_hist, get_lrt_plot, get_lrt_results, get_photon_flux_hist, get_pvalue_plot, get_pvalue_results, get_split_plot, plot, plot_arf, plot_bkg, plot_cdf, plot_chisqr, plot_data, plot_delchi, plot_energy_flux, plot_fit, plot_model, plot_model_component, plot_order, plot_pdf, plot_photon_flux, plot_pvalue, plot_ratio, plot_resid, plot_scatter, plot_source, plot_source_component, plot_trace, set_xlinear, set_xlog, set_ylinear, set_ylog
psfs
contour_kernel, contour_psf, delete_psf, get_kernel, get_psf, image_kernel, image_psf, load_conv, load_psf, plot_psf, set_psf, show_kernel, show_psf
statistics
get_chisqr_plot, get_delchi_plot
visualization
contour_resid

Last modified: December 2013
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