- sherpa.ui.load_user_model(func, modelname, filename=None, ncols=2, colkeys=None, dstype=<class 'sherpa.data.Data1D'>, sep=' ', comment='#')
Create a user-defined model.
Assign a name to a function; this name can then be used as any other name of a model component, either in a source expression - such as with
set_model- or to change a parameter value. The
add_user_parsfunction should be called after
load_user_modelto set up the parameter names and defaults.
func (func) – The function that evaluates the model.
modelname (str) – The name to use to refer to the model component.
filename (str, optional) – Set this to include data from this file in the model. The file should contain two columns, and the second column is stored in the
_yattribute of the model.
ncols (int, optional) – The number of columns to read in (the first
ncolscolumns in the file).
colkeys (array of str, optional) – An array of the column name to read in. The default is
dstype (data class to use, optional) – What type of data is to be used. Supported values include
sep (str, optional) – The separator character. The default is
comment (str, optional) – The comment character. The default is
load_user_modelfunction is designed to make it easy to add a model, but the interface is not the same as the existing models (such as having to call both
add_user_parsfor each new instance). The
add_modelfunction is used to add a model as a Python class, which is more work to set up, but then acts the same way as the existing models.
The function used for the model depends on the dimensions of the data. For a 1D model, the signature is:
def func1d(pars, x, xhi=None):
where, if xhi is not None, then the dataset is binned and the x argument is the low edge of each bin. The pars argument is the parameter array - the names, defaults, and limits can be set with
add_user_pars- and should not be changed. The return value is an array the same size as x.
For 2D models, the signature is:
def func2d(pars, x0, x1, x0hi=None, x1hi=None):
There is no way using this interface to indicate that the model is for 1D or 2D data.
The format for the input file, and how to control what columns to read, are described in the help for the
Create a two-parameter model of the form “y = mx + c”, where the intercept is the first parameter and the slope the second, set the parameter names and default values, then use it in a source expression:
>>> def func1d(pars, x, xhi=None): ... if xhi is not None: ... x = (x + xhi) / 2 ... return x * pars + pars ... >>> load_user_model(func1d, "myfunc") >>> add_user_pars(myfunc, ["c", "m"], [0, 1]) >>> set_source(myfunc + gauss1d.gline)