plot_fit_resid¶

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

Plot the fit results, and the residuals, for a data set.

This creates two plots - the first from plot_fit and the second from plot_resid - 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 previous values. 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 the data set does not exist or a source expression has not been set.

See also

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(): Plot the fit results for a data set.
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_data(): Plot the data values.
plot_model(): Plot the model for a data set.
plot_resid(): Plot the residuals (data - 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_resid are the same as the keywords of the dictionary returned by get_data_plot_prefs, and are applied to both plots.

Examples

Plot the results for the default data set:

>>> plot_fit_resid()

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

>>> set_xlog()
>>> plot_fit_resid('jet')
>>> plot_fit_resid('core', 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_data_plot_prefs. The following sets the data in both plots to be drawn in a blue color, have caps on the error bars, but to only draw the y error bars:

>>> plot_fit_resid(capsize=4, color='skyblue', xerrorbars=False)