Polynomial2D

class astropy.modeling.polynomial.Polynomial2D(degree, x_domain=[-1, 1], y_domain=[-1, 1], x_window=[-1, 1], y_window=[-1, 1], n_models=None, model_set_axis=None, **params)

Bases: astropy.modeling.polynomial.PolynomialModel

2D Polynomial model.

Represents a general polynomial of degree n:

Parameters:

degree : int

highest power of the polynomial, the number of terms is degree+1

x_domain : list or None

domain of the x independent variable

y_domain : list or None

domain of the y independent variable

x_window : list or None

range of the x independent variable

y_window : list or None

range of the y independent variable

param_dim : int

number of parameter sets

**params : dict

keyword: value pairs, representing parameter_name: value

Other Parameters:  

fixed : a dict

A dictionary {parameter_name: boolean} of parameters to not be varied during fitting. True means the parameter is held fixed. Alternatively the fixed property of a parameter may be used.

tied : dict

A dictionary {parameter_name: callable} of parameters which are linked to some other parameter. The dictionary values are callables providing the linking relationship. Alternatively the tied property of a parameter may be used.

bounds : dict

A dictionary {parameter_name: boolean} of lower and upper bounds of parameters. Keys are parameter names. Values are a list of length 2 giving the desired range for the parameter. Alternatively the min and max properties of a parameter may be used.

eqcons : list

A list of functions of length n such that eqcons[j](x0,*args) == 0.0 in a successfully optimized problem.

ineqcons : list

A list of functions of length n such that ieqcons[j](x0,*args) >= 0.0 is a successfully optimized problem.

Attributes Summary

input_names	list() -> new empty list
n_inputs	int(x=0) -> int or long

Methods Summary

evaluate(x, y, *coeffs)
fit_deriv(x, y, *params)	Computes the Vandermonde matrix.
invlex_coeff(coeffs)
multivariate_horner(x, y, coeffs)	Multivariate Horner’s scheme
prepare_inputs(x, y, **kwargs)

Attributes Documentation

input_names = []

n_inputs = 2

Methods Documentation

evaluate(x, y, *coeffs)

fit_deriv(x, y, *params)

Computes the Vandermonde matrix.

Parameters:

x : ndarray

input

y : ndarray

input

params : throw away parameter

parameter list returned by non-linear fitters

Returns:

result : ndarray

The Vandermonde matrix

invlex_coeff(coeffs)

multivariate_horner(x, y, coeffs)

Multivariate Horner’s scheme

Parameters:

x, y : array

coeff : array of coefficients in inverse lexical order

prepare_inputs(x, y, **kwargs)