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 isFalse
. - overplot (bool, optional) – If
True
then add the data to an exsiting 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)?
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_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')