MIRIWFSSBackwardDispersion

class stdatamodels.jwst.transforms.MIRIWFSSBackwardDispersion(orders, lmodels=None, xmodels=None, ymodels=None)

Bases: _BackwardGrismDispersionBase

Calculate the dispersion extent of MIRI WFSS pixels.

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 inverse dispersion polynomial trace models, such that t = lmodel(wavelength) 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 + 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.

Methods Summary

evaluate(x0, y0, wavelength, order)

Transform from the direct image plane to the dispersed plane.

Methods Documentation

evaluate(x0, y0, wavelength, order)

Transform from the direct image plane to the dispersed plane.

Parameters:

Returns:

x, yfloat or np.ndarray: The x = (dx + x0), y = (dy + y0) values in the dispersed plane.
x0, y0float or np.ndarray: Source object x-center, y-center in the direct image.
orderint: Output spectral order, same as input