sherpa.astro.ui.load_table_model(modelname, filename, method=<function linear_interp>, *args, **kwargs)

Load tabular or image data and use it as a model component.

Note

Deprecated in Sherpa 4.9 The new load_xstable_model routine should be used for loading XSPEC table model files. Support for these files will be removed from load_table_model in the next release.

A table model is defined on a grid of points which is interpolated onto the independent axis of the data set. The model will have at least one parameter (the amplitude, or scaling factor to multiply the data by), but may have more (if X-Spec table models are used).

Parameters: modelname (str) – The identifier for this table model. filename (str) – The name of the file containing the data, which should contain two columns, which are the x and y values for the data, or be an image. method (func) – The interpolation method to use to map the input data onto the coordinate grid of the data set. Linear, nearest-neighbor, and polynomial schemes are provided in the sherpa.utils module. args – Arguments for reading in the data. kwargs – Keyword arguments for reading in the data.

load_conv()
load_psf()
Create a PSF model
load_template_model()
Load a set of templates and use it as a model component.
load_xstable_model()
set_model()
Set the source model expression for a data set.
set_full_model()
Define the convolved model expression for a data set.

Notes

Examples of interpolation schemes provided by sherpa.utils are: linear_interp, nearest_interp, neville, and neville2d.

Examples

Load in the data from filt.fits and use it to multiply the source model (a power law and a gaussian). Allow the amplitude for the table model to vary between 1 and 1e6, starting at 1e3.

>>> load_table_model('filt', 'filt.fits')
>>> set_source(filt * (powlaw1d.pl + gauss1d.gline))
>>> set_par(filt.ampl, 1e3, min=1, max=1e6)


Load in an image (“broad.img”) and use the pixel values as a model component for data set “img”:

>>> load_table_model('emap', 'broad.img')
>>> set_source('img', emap * gauss2d)