MIRIWFSSForwardDispersion

class stdatamodels.jwst.transforms.MIRIWFSSForwardDispersion(orders, lmodels=None, xmodels=None, ymodels=None, theta=None, sampling=10)

Bases: _WFSSForwardGrismDispersion

Calculate the wavelengths of the dispersed MIRI WFSS data.

The dispersion polynomial is relative to the input x,y pixels in the direct image for a given wavelength. This transform uses a generic method for both MIRI and NIRISS. For MIRI the theta parameter = 0,

Initialize the model.

Parameters:

orderslist[int]

List of spectral orders corresponding to the dispersion models given by the lmodels, xmodels, and ymodels parameters. For MIRI WFSS we only have order = 1, so the orders is expected to equal [1,]

lmodelslist[astropy.modeling.polynomial.Polynomial1D]

The forward dispersion polynomial model, such that wavelength = lmodel(t) computes the wavelength from the trace parameter.

xmodelslist[list[astropy.modeling.polynomial.Polynomial2D]]

The models encoding the x-position of the spectral trace. Because the shape of the trace depends on the direct-image x0, y0 position, this takes the form dx = C0(x0, y0) + C1(x0, y0) * t + C2(x0, y0) * t^2 + C3(x0,y0) * t^3. The inner list corresponds to the 2-D polynomials (C0, C1, C2, C3). The outer list corresponds to the different spectral orders.

ymodelslist[list[astropy.modeling.polynomial.Polynomial2D]]

The models encoding the y-position of the spectral trace. Because the shape of the trace depends on the direct-image x0, y0 position, this takes the form dy = C0(x0, y0) + C1(x0, y0) * t + C2(x0, y0) * t^2 + C3(x0,y0) * t^3. The inner list corresponds to the 2-D polynomials (C0, C1, C2, C3). The outer list corresponds to the different spectral orders.

thetafloat

Set = 0 for MIRI.

samplingint, optional

Number of sampling points in t to use; these will be linearly interpolated.