DataSimulFit
- class sherpa.data.DataSimulFit(name: str, datasets: Sequence[Data], numcores: int = 1)[source] [edit on github]
Bases:
NoNewAttributesAfterInit
Store multiple data sets.
This class lets multiple data sets be treated as a single dataset by concatenation. That is, if two data sets have lengths n1 and n2 then they can be considered as a single data set of length n1 + n2.
- Parameters:
name (str) – The name for the collection of data.
datasets (sequence of Data objects) – The datasets to be stored; there must be at least one. They are assumed to behave as
sherpa.data.Data
objects, but there is no check for this condition.
See also
Examples
>>> d1 = Data1D('d1', [1, 2, 3], [10, 12, 15]) >>> d2 = Data1D('d2', [1, 2, 5, 7], [4, 15, 9, 24]) >>> dall = DataSimulFit('comb', (d1, d2)) >>> yvals, _, _ = dall.to_fit() >>> print(yvals) [10 12 15 4 15 9 24]
Methods Summary
eval_model_to_fit
(modelfuncs)to_fit
([staterrfunc])to_plot
([yfunc, staterrfunc])Methods Documentation
- eval_model_to_fit(modelfuncs: Sequence[Callable[[...], Sequence[float] | ndarray]]) ndarray [source] [edit on github]