# plot_model¶

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

Plot the model for a data set.

This function plots the model for a data set, which includes any instrument response (e.g. a convolution created by set_psf).

Parameters: id (int or str, optional) – The data set that provides the data. 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_model. 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)?

get_model_plot()
Return the data used to create the model plot.
get_model_plot_prefs()
Return the preferences for plot_model.
get_default_id()
Return the default data set identifier.
plot()
Create one or more plot types.
plot_model_component()
Plot a component of the model for a data set.
plot_source()
Plot the source expression for 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.

Notes

The additional arguments supported by plot_model are the same as the keywords of the dictionary returned by get_model_plot_prefs.

Examples

Plot the convolved source model for the default data set:

>>> plot_model()


Overplot the model for data set 2 on data set 1:

>>> plot_model(1)
>>> plot_model(2, overplot=True)


Create the equivalent of plot_fit('jet'):

>>> plot_data('jet')
>>> plot_model('jet', overplot=True)


Additional arguments can be given that are passed to the plot backend: the supported arguments match the keywords of the dictionary returned by get_model_plot_prefs. The following plots the model using a log scale for both axes, and then overplots the model from data set 2 using a dashed line:

>>> plot_model(xlog=True, ylog=True)
>>> plot_model(2, overplot=True, linestyle='dashed')