contour

sherpa.ui.contour(*args, **kwargs)

Create a contour plot for an image data set.

Create one or more contour plots, depending on the arguments it is set: a plot type, followed by an optional data set identifier, 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. This is for 2D data sets.

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

Raises

sherpa.utils.err.DataErr – The data set does not support the requested plot type.

See also

contour_data

Contour the values of an image data set.

contour_fit

Contour the fit to a data set.

contour_fit_resid

Contour the fit and the residuals to a data set.

contour_kernel

Contour the kernel applied to the model of an image data set.

contour_model

Contour the values of the model, including any PSF.

contour_psf

Contour the PSF applied to the model of an image data set.

contour_ratio

Contour the ratio of data to model.

contour_resid

Contour the residuals of the fit.

contour_source

Contour the values of the model, without any PSF.

get_default_id

Return the default data set identifier.

sherpa.astro.ui.set_coord

Set the coordinate system to use for image analysis.

Notes

The supported plot types depend on the data set type, and include the following list. There are also individual functions, with contour_ prepended to the plot type, such as contour_data and the contour_fit_resid variant:

data

The data.

fit

Contours of the data and the source model.

fit_resid

Two plots: the first is the contours of the data and the source model and the second is the residuals.

kernel

The kernel.

model

The source model including any PSF convolution set by set_psf.

psf

The PSF.

ratio

Contours of the ratio image, formed by dividing the data by the model.

resid

Contours of the residual image, formed by subtracting the model from the data.

source

The source model (without any PSF convolution set by set_psf).

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

Examples

>>> contour('data')

>>> contour('data', 1, 'data', 2)

>>> contour('data', 'model')

>>> contour('data', 'model', 'fit', 'resid')

>>> contour('data', 'model', alpha=0.7)