ModelDomainRegridder1D

class sherpa.models.regrid.ModelDomainRegridder1D(evaluation_space=None, name='regrid1d', **kwargs)

Bases: object

Allow 1D models to be evaluated on a different grid.

This class is not used directly in a model expression; instead it creates an instance that is used to evaluate the model.

Parameters:

• evaluation_space (object or None, optional)

• name (str, optional) – The default name is ‘regrid1d’.

• interp (callable, optional) – The interpolation function: it should accept three arrays: the output x values and the x, y values to interpolate, and return the interpolated y values. The default is to use sherpa.utils.akima.akima.

Examples

The “internal” model (gaussian plus constant) will be evaluated on the grid 0 to 10 (spacing of 0.5), and then linearly-interpolated onto the desired grid (1 to 8, spacing of 0.7). In this example there is no benefit to this approach - it is easier just to evaluate internal_mdl on the grid x - but it illustrates the approach.

>>> from sherpa.models import Gauss1D, Const1D
>>> internal_mdl = Gauss1D() + Const1D()
>>> eval_space = EvaluationSpace1D(np.arange(0, 10, 0.5))
>>> rmdl = ModelDomainRegridder1D(eval_space)
>>> mdl = rmdl.apply_to(internal_mdl)
>>> x = np.arange(1, 8, 0.7)
>>> y = mdl(x)

Attributes Summary

grid	The grid of the associated evaluation space.
method	Interpolate the data from the internal to requested grid.

Methods Summary

apply_to(model)	Evaluate a model on a different grid.
calc(pars, modelfunc, *args, **kwargs)	Evaluate and regrid a model
eval_integrated(pars, modelfunc, data_grid, ...)	Rebin the model onto a grid with low and high bins.
eval_non_integrated(pars, modelfunc, ...)	Interpolate the model.

Attributes Documentation

grid

The grid of the associated evaluation space.

method

Interpolate the data from the internal to requested grid.

This is only used for point grids, as integrated grids use a simple rebinning scheme. The default is sherpa.utils.akima.akima. The callable should accept (xout, xin, yin) arguments and interpolate the (xin, yin) data onto the xout grid, returning the interpolated data.

Methods Documentation

apply_to(model)

Evaluate a model on a different grid.

calc(pars, modelfunc, *args, **kwargs)

Evaluate and regrid a model

Evaluate the model on the internal grid and then convert it onto the desired grid either preserving flux (rebinning) or via interpolation.

Parameters:

• pars (sequence of numbers) – The parameter values of the model.

• modelfunc – The model to evaluate (the calc attribute of the model)

• args – The grid to interpolate the model onto. This must match the format of the grid attribute of the model - i.e. non-integrate (single array) or integrated (a pair of equal-sized arrays).

• kwargs – Keyword arguments for the model.

Notes

If the requested grid (i.e. that defined by args) does not overlap the stored grid (the grid attribute) then all values are set to 0. However, if the grids partially overlap then there will be extrapolation (depending on the method).

It is not clear yet whether the restriction on grid type (i.e. must match between the requested grid and the internal grid whether it is integrated or non-integrated) is too restrictive.

eval_integrated(pars, modelfunc, data_grid, eval_grid, **kwargs)

Rebin the model onto a grid with low and high bins.

Parameters:

• pars (list of numbers) – The parameters for the model.

• modelfunc (callable) – The model to evaluate. It is called as modelfunc(pars, lo, hi, **kwargs)

• data_grid ((sequence of numbers, sequence of numbers)) – The grid on which to return the values, as low and high edges.

• eval_grid ((sequence of numbers, sequence of numbers)) – The grid on which to evaluate the model, as low and high edges.

• kwargs – Any arguments to be sent to modelfunc.

Returns:

model – The model values matching the data_grid bins.

Return type:

ndarray

eval_non_integrated(pars, modelfunc, data_grid, eval_grid, **kwargs)

Interpolate the model.

Parameters:

• pars (list of numbers) – The parameters for the model.

• modelfunc (callable) – The model to evaluate. It is called as modelfunc(pars, x, **kwargs)

• data_grid (sequence of numbers) – The grid on which to return the values.

• eval_grid (sequence of numbers) – The grid on which to evaluate the model.

• kwargs – Any arguments to be sent to modelfunc.

Returns:

model – The model values matching the data_grid bins.

Return type:

ndarray