load_conv
- sherpa.astro.ui.load_conv(modelname, filename_or_model, *args, **kwargs)
Load a 1D convolution model.
The convolution model can be defined either by a data set, read from a file, or an analytic model, using a Sherpa model instance. A source model can be convolved with this model by including
modelname
in theset_model
call, using the form:modelname(modelexpr)
- Parameters:
modelname (str) – The identifier for this PSF model.
filename_or_model (str or model instance) – This can be the name of an ASCII file or a Sherpa model component.
args – Arguments for
unpack_data
iffilename_or_model
is a file.kwargs – Keyword arguments for
unpack_data
iffilename_or_model
is a file.
See also
delete_psf
Delete the PSF model for a data set.
load_psf
Create a PSF model.
load_table_model
Load tabular data and use it as a model component.
set_full_model
Define the convolved model expression for a data set.
set_model
Set the source model expression for a data set.
set_psf
Add a PSF model to a data set.
Examples
Create a 1D data set, assign a box model - which is flat between the xlow and xhi values and zero elsewhere - and then display the model values. Then add in a convolution component by a gaussian and overplot the resulting source model with two different widths.
>>> dataspace1d(-10, 10, 0.5, id='tst', dstype=Data1D) >>> set_source('tst', box1d.bmdl) >>> bmdl.xlow = -2 >>> bmdl.xhi = 3 >>> plot_source('tst') >>> load_conv('conv', normgauss1d.gconv) >>> gconv.fwhm = 2 >>> set_source('tst', conv(bmdl)) >>> plot_source('tst', overplot=True) >>> gconv.fwhm = 5 >>> plot_source('tst', overplot=True)
Create a convolution component called “cmodel” which uses the data in the file “conv.dat”, which should have two columns (the X and Y values).
>>> load_conv('cmodel', 'conv.dat')