plot_model_component
- sherpa.ui.plot_model_component(id, model=None, replot=False, overplot=False, clearwindow=True, **kwargs)
Plot a component of the model for a data set.
This function evaluates and plots a component of the model expression for a data set, including any instrument response. Use
plot_source_component
to display without any response. For PHA data, the response model is automatically added by the routine unless the model contains a response.- 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
.model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string).
replot (bool, optional) – Set to
True
to use the values calculated by the last call toplot_model_component
. The default isFalse
.overplot (bool, optional) – If
True
then add the data to an existing plot, otherwise create a new plot. The default isFalse
.clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?
See also
get_model_component_plot
Return the data used to create the model-component plot.
get_default_id
Return the default data set identifier.
plot
Create one or more plot types.
plot_source_component
Plot a component of the source expression for a data set.
plot_model
Plot the model 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 function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the
model
parameter. If given two un-named arguments, then they are interpreted as theid
andmodel
parameters, respectively.The additional keyword arguments match the keywords of the dictionary returned by get_model_plot_prefs.
Examples
Overplot the
pl
component of the model expression for the default data set:>>> plot_model() >>> plot_model_component(pl, overplot=True)
Display the results for the ‘jet’ data set (data and model), and then overplot the
pl
component evaluated for the ‘jet’ and ‘core’ data sets:>>> plot_fit('jet') >>> plot_model_component('jet', pl, overplot=True) >>> plot_model_component('core', pl, overplot=True)
For PHA data sets the response is automatically added, but it can also be explicitly included, which will create the same plot:
>>> plot_model_component(pl) >>> rsp = get_response() >>> plot_model_component(rsp(pl))