# plot_fit¶

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

Plot the fit results (data, model) for a data set.

This function creates a plot containing the data and the model (including any instrument response) for a data set.

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_fit. 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)? sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

get_fit_plot()
Return the data used to create the fit plot.
get_default_id()
Return the default data set identifier.
plot()
Create one or more plot types.
plot_fit_delchi()
Plot the fit results, and the residuals, for a data set.
plot_fit_ratio()
Plot the fit results, and the ratio of data to model, for a data set.
plot_fit_resid()
Plot the fit results, and the residuals, for a data set.
plot_data()
Plot the data values.
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 additional arguments supported by plot_fit are the same as the keywords of the dictionary returned by get_data_plot_prefs.

Examples

Plot the fit results for the default data set:

>>> plot_fit()


Overplot the ‘core’ results on those from the ‘jet’ data set, using a logarithmic scale for the X axis:

>>> set_xlog()
>>> plot_fit('jet')
>>> plot_fit('core', overplot=True)


Keyword arguments can be given to override the plot preferences; for example the following sets the y axis to a log scale, but only for this plot:

>>> plot_fit(ylog=True)


The color can be changed for both the data and model using (note that the keyword name and supported values depends on the plot backend; this example assumes that Matplotlib is being used):

>>> plot_fit(color='orange')