# image_model_component¶

sherpa.astro.ui.image_model_component(id, model=None, newframe=False, tile=False)

Display a component of the model in the image viewer.

This function evaluates and displays a component of the model expression for a data set, including any instrument response. Use image_source_component to exclude the response.

The image viewer is automatically started if it is not already open.

Parameters: id (int or str, optional) – The data set. If not given then the default identifier is used, as returned by get_default_id. model (str or sherpa.models.model.Model instance) – The component to display (the name, if a string). newframe (bool, optional) – Create a new frame for the data? If False, the default, then the data will be displayed in the current frame. tile (bool, optional) – Should the frames be tiles? If False, the default, then only a single frame is displayed. sherpa.utils.err.IdentifierErr – If the data set does not exist or a source expression has not been set.

get_model_component_image()
Return the data used by image_model_component.
image_close()
Close the image viewer.
image_fit()
Display the data, model, and residuals for a data set in the image viewer.
image_model()
Display the model for a data set in the image viewer.
image_open()
Open the image viewer.
image_source()
Display the source expression for a data set in the image viewer.
image_source_component()
Display a component of the source expression in the image viewer.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the model parameter. If given two un-named arguments, then they are interpreted as the id and model parameters, respectively.

Image visualization is optional, and provided by the DS9 application [1].

References

Examples

Display the full source model and then just the ‘gsrc’ component for the default data set:

>>> image_model()
>>> image_model_component(gsrc)


Display the ‘clus’ component of the model for the ‘img’ data set side by side without the with any instrument response (such as convolution with a PSF model):

>>> image_source_component('img', 'clus')
>>> image_model_component('img', 'clus', newframe=True,
...                       tile=True)