plot_data

Context: plotting

Synopsis

Plot the data values.

Syntax

plot_data(id: int | str | Sequence[int | str] | None = None, replot:
bool = False, overplot: bool = False, clearwindow: bool = True,
**kwargs)

No return value.

Description

Examples

Example 1

Plot the data from the default data set:

>>> plot_data()

Example 2

Plot the data from data set 1:

>>> plot_data(1)

Example 3

Plot the data from data set labelled "jet" and then overplot the "core" data set. The `set_xlog` command is used to select a logarithmic scale for the X axis.

>>> set_xlog("data")
>>> plot_data("jet")
>>> plot_data("core", overplot=True)

Example 4

Draw both datasets on the same plot, with a linear X axis, both drawn in green but with the opacity of the "core" dataset set to 0.5:

>>> plot_data(["jet", "core"], xlog=False, color="green", alpha=[1, 0.5])

Example 5

Label each data set (the behaviour depends on the selected back end):

>>> plot_data(["jet", "core"], label=["Data: jet", "Data: core"])

Example 6

The following example requires that the Matplotlib backend is selected, and uses a Matplotlib function to create a subplot (in this case one filling the bottom half of the plot area) and then calls `plot_data` with the `clearwindow` argument set to `False` to use this subplot. If the `clearwindow` argument had not been used then the plot area would have been cleared and the plot would have filled the area.

>>> plt.subplot(2, 1, 2)
>>> plot_data(clearwindow=False)

Example 7

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` . Examples include (for the Matplotlib backend): adding a "cap" to the error bars:

>>> plot_data(capsize=4)

changing the symbol to a square:

>>> plot_data(marker='s')

using a dotted line to connect the points:

>>> plot_data(linestyle='dotted')

and plotting multiple data sets on the same plot, using a log scale for the Y axis, setting the alpha transparency for each plot, and explicitly setting the colors of the last two datasets:

>>> plot_data(ylog=True, alpha=0.7)
>>> plot_data(2, overplot=True, alpha=0.7, color='brown')
>>> plot_data(3, overplot=True, alpha=0.7, color='purple')

Example 8

Set the labels used for the X and Y axes for the data. In this example the matplotlib backend is used and so the LaTeX support is used to display an Angstrom symbol as part of the X axis label. Note that the labels will be retained for other plots, including other plot types such as plot_model() or plot_fit_resid().

>>> d = get_data()
>>> d.set_xlabel(r"x axis [$\AA$]")
>>> d.set_ylabel("y axis")
>>> plot_data()

PARAMETERS

The parameters for this function are:

Parameter	Type information	Definition
id	int, str, sequence of int or str, or None, optional	The data set that provides the data. 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_data` . 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_data` are the same as the keywords of the dictionary returned by `get_data_plot_prefs` .

Changes in CIAO

Changed in CIAO 4.18

Multiple data sets can be displayed by using a list of identifiers. Per-plot options can now be given by using a list of values.

Bugs

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