plot_fit_resid

Context: plotting

Synopsis

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

Syntax

plot_fit_resid(id=None, replot=False, overplot=False, clearwindow=True,
**kwargs)

Description

This creates two plots - the first from `plot_fit` and the second from `plot_resid` - for a data set.

Examples

Example 1

Plot the results for the default data set:

>>> plot_fit_resid()

Example 2

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)

Example 3

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)

PARAMETERS

The parameters for this function are:

Parameter	Type information	Definition
id	int, str, or None, 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 existing 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)?

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.

For the residual plot, the ylog setting is ignored, and the Y axis is drawn using a linear scale.

Changes in CIAO

Changed in CIAO 4.13

The overplot option now works.

Changed in CIAO 4.12

The Y axis of the residual plot is now always drawn using a linear scale.

Bugs

See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.