SIP

class astropy.modeling.polynomial.SIP(crpix, a_order, b_order, a_coeff={}, b_coeff={}, ap_order=None, bp_order=None, ap_coeff={}, bp_coeff={}, n_models=None, model_set_axis=None, name=None, meta=None)

Bases: Model

Simple Imaging Polynomial (SIP) model.

The SIP convention is used to represent distortions in FITS image headers. See [1] for a description of the SIP convention.

Parameters:

crpixpython:list or (2,) ndarray

CRPIX values

a_orderpython:int

SIP polynomial order for first axis

b_orderpython:int

SIP order for second axis

a_coeffpython:dict

SIP coefficients for first axis

b_coeffpython:dict

SIP coefficients for the second axis

ap_orderpython:int

order for the inverse transformation (AP coefficients)

bp_orderpython:int

order for the inverse transformation (BP coefficients)

ap_coeffpython:dict

coefficients for the inverse transform

bp_coeffpython:dict

coefficients for the inverse transform

References

[1]

David Shupe, et al, ADASS, ASP Conference Series, Vol. 347, 2005

Attributes Summary

n_inputs	The number of inputs.
n_outputs	The number of outputs.

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)	Evaluate the model on some input variables.

Attributes Documentation

n_inputs = 2

The number of inputs.

n_outputs = 2

The number of outputs.

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)

Evaluate the model on some input variables.