# NIRISSForwardRowGrismDispersion¶

class jwst.transforms.NIRISSForwardRowGrismDispersion(orders, lmodels=None, xmodels=None, ymodels=None, theta=0.0, name=None, meta=None)[source]

Bases: astropy.modeling.core.Model

This model calculates 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.

Parameters
• orders (list) – The list of orders which are available to the model

• xmodels (list[tuples]) – The list of tuple(models) for the polynomial model in x

• ymodels (list[tuples]) – The list of tuple(models) for the polynomial model in y

• lmodels (list) – The list of models for the polynomial model in l

• theta (float) – Angle [deg] - defines the NIRISS filter wheel position

Notes

Given the x,y, source location as known on the dispersed image, as well as order, it returns the tuple of x,y,wavelength,order.

This model needs to be generalized, at the moment it satisfies the 2t x 6(xy)th order polynomial currently used by NIRISS.

Attributes Summary

Methods Summary

 __call__(*inputs[, model_set_axis, …]) Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated. evaluate(x, y, x0, y0, order) Return the valid pixel(s) and wavelengths given center x,y and lam

Attributes Documentation

fittable = False
linear = False
n_inputs = 5
n_outputs = 4
standard_broadcasting = False

Methods Documentation

__call__(*inputs, model_set_axis=None, with_bounding_box=False, fill_value=nan, equivalencies=None, inputs_map=None, **new_inputs)

Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.

evaluate(x, y, x0, y0, order)[source]

Return the valid pixel(s) and wavelengths given center x,y and lam

Parameters
Returns

• x, y, lambda, order, theta, in the direct image for the pixel that was

• specified as input using the wavelength l and spectral order

Notes

There’s spatial dependence for NIRISS as well as dependence on the filter wheel rotation during the exposure.