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_spaceobject or None, optional

namestr, optional

The default name is ‘regrid1d’.

interpcallable(), 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.

Attributes:

grid

The grid of the associated evaluation space.

method

Interpolate the data from the internal to requested grid.

Methods

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.

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:

parssequence 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:

parslist of numbers

The parameters for the model.

modelfunccallable()

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:

modelndarray

The model values matching the data_grid bins.

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

Interpolate the model.

Parameters:

parslist of numbers

The parameters for the model.

modelfunccallable()

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

data_gridsequence of numbers

The grid on which to return the values.

eval_gridsequence of numbers

The grid on which to evaluate the model.

kwargs

Any arguments to be sent to modelfunc.

Returns:

modelndarray

The model values matching the data_grid bins.