NIRISSForwardRowGrismDispersion

class stdatamodels.jwst.transforms.NIRISSForwardRowGrismDispersion(orders, lmodels=None, xmodels=None, ymodels=None, theta=0.0, sampling=10)

Bases: _WFSSForwardGrismDispersion

Calculate the wavelengths of vertically dispersed NIRISS grism data.

The dispersion polynomial is relative to the input x,y pixels in the direct image for a given wavelength.

Initialize the model.

Parameters:
orderslist[int]

List of spectral orders corresponding to the dispersion models given by the lmodels, xmodels, and ymodels parameters.

lmodelslist[astropy.modeling.core.Model]

The forward dispersion polynomial models, one per order, such that wavelength = lmodel(t) computes the trace parameter from the wavelength.

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. The inner list corresponds to the 2-D polynomials (C0, C1, C2). 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. The inner list corresponds to the 2-D polynomials (C0, C1, C2). The outer list corresponds to the different spectral orders.

thetafloat

Angle [deg] - defines the NIRISS filter wheel position

samplingint, optional

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