plot_kernel

sherpa.astro.ui.plot_kernel(id=None, replot=False, overplot=False, clearwindow=True, **kwargs)

Plot the 1D kernel applied to a data set.

The plot_psf function shows the full PSF, from which the kernel is derived.

Parameters
  • id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id.

  • replot (bool, optional) – Set to True to use the values calculated by the last call to plot_kernel. The default is False.

  • overplot (bool, optional) – If True then add the data to an exsiting plot, otherwise create a new plot. The default is False.

  • clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

Raises

sherpa.utils.err.IdentifierErr – If a PSF model has not been created for the data set.

See also

get_kernel_plot()

Return the data used by plot_kernel.

get_default_id()

Return the default data set identifier.

plot()

Create one or more plot types.

plot_psf()

Plot the 1D PSF model applied to a data set.

set_psf()

Add a PSF model to a data set.

set_xlinear()

New plots will display a linear X axis.

set_xlog()

New plots will display a logarithmically-scaled X axis.

set_ylinear()

New plots will display a linear Y axis.

set_ylog()

New plots will display a logarithmically-scaled Y axis.

Examples

Create a model (a step function) that is convolved by a gaussian, and display the kernel overplotted on the PSF:

>>> dataspace1d(1, 10, step=1, dstype=Data1D)
>>> set_model(steplo1d.stp)
>>> stp.xcut = 4.4
>>> load_psf('psf1', gauss1d.gline)
>>> set_psf('psf1')
>>> gline.fwhm = 1.2
>>> plot_psf()
>>> plot_kernel(overplot=True)