plot

sherpa.ui.plot(*args, **kwargs)

Create one or more plot types.

The plot function creates one or more plots, depending on the arguments it is sent: a plot type, followed by optional identifiers, and this can be repeated. If no data set identifier is given for a plot type, the default identifier - as returned by get_default_id - is used.

Changed in version 4.15.0: A number of labels, such as “bkgfit”, are marked as deprecated and using them will cause a warning message to be displayed, indicating the new label to use.

Changed in version 4.12.2: Keyword arguments, such as alpha and ylog, can be sent to each plot.

Raises:: sherpa.utils.err.ArgumentErr – The label is invalid.

See also

get_default_id: Return the default data set identifier.
sherpa.astro.ui.set_analysis: Set the units used when fitting and displaying spectral data.
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 supported plot types depend on the data set type, and include the following list. There are also individual functions, with plot_ prepended to the plot type, such as plot_data. There are also several multiple-plot commands, such as plot_fit_ratio, plot_fit_resid, and plot_fit_delchi.

arf: The ARF for the data set (only for DataPHA data sets).
bkg: The background.
bkg_chisqr: The chi-squared statistic calculated for each bin when fitting the background.
bkg_delchi: The residuals for each bin, calculated as (data-model) divided by the error, for the background.
bkg_fit: The data (as points) and the convolved model (as a line), for the background data set.
bkg_model: The convolved background model.
bkg_ratio: The residuals for each bin, calculated as data/model, for the background data set.
bkg_resid: The residuals for each bin, calculated as (data-model), for the background data set.
bkg_source: The un-convolved background model.
chisqr: The chi-squared statistic calculated for each bin.
data: The data (which may be background subtracted).
delchi: The residuals for each bin, calculated as (data-model) divided by the error.
fit: The data (as points) and the convolved model (as a line).
kernel: The PSF kernel associated with the data set.
model: The convolved model.
model_component: Part of the full model expression (convolved).
model_components: Parts of the full model expression (convolved).
order: Plot the model for a selected response
psf: The unfiltered PSF kernel associated with the data set.
ratio: The residuals for each bin, calculated as data/model.
resid: The residuals for each bin, calculated as (data-model).
source: The un-convolved model.
source_component: Part of the full model expression (un-convolved).
source_components: Parts of the full model expression (un-convolved).

The plots can be specialized for a particular data type, such as the set_analysis command controlling the units used for PHA data sets.

See the documentation for the individual routines for information on how to configure the plots.

The plot capabilities depend on what plotting backend, if any, is installed. If there is none available, a warning message will be displayed when sherpa.ui or sherpa.astro.ui is imported, and the plot set of commands will not create any plots. The choice of back end is made by changing the options.plot_pkg setting in the Sherpa configuration file.

The keyword arguments are sent to each plot (so care must be taken to ensure they are valid for all plots).

Examples

Plot the data for the default data set. This is the same as plot_data.

>>> plot("data")

Plot the data for data set 2.

>>> plot("data", 2)

Plot the data and ARF for the default data set, in two seaparate plots.

>>> plot("data", "arf")

Plot the fit (data and model) for data sets 1 and 2, in two separate plots.

>>> plot("fit", 1, "fit", 2)

Plot the fit (data and model) for data sets “fit” and “jet”, in two separate plots.

>>> plot("fit", "nucleus", "fit", "jet")

Draw the data and model plots both with a log-scale for the y axis:

>>> plot("data", "model", ylog=True)

Plot the backgrounds for dataset 1 using the “up” and “down” components (in this case the background identifier):

>>> plot("bkg", 1, "up", "bkg", 1, "down")