OrthoPolynomialBase¶

class
astropy.modeling.polynomial.
OrthoPolynomialBase
(x_degree, y_degree, x_domain=None, x_window=None, y_domain=None, y_window=None, n_models=None, model_set_axis=None, name=None, meta=None, **params)[source]¶ Bases:
astropy.modeling.polynomial.PolynomialBase
This is a base class for the 2D Chebyshev and Legendre models.
The polynomials implemented here require a maximum degree in x and y.
Parameters:  x_degree : int
degree in x
 y_degree : int
degree in y
 x_domain : list or None, optional
domain of the x independent variable
 x_window : list or None, optional
range of the x independent variable
 y_domain : list or None, optional
domain of the y independent variable
 y_window : list or None, optional
range of the y independent variable
 **params : dict
{keyword: value} pairs, representing {parameter_name: value}
Attributes Summary
inputs
outputs
Methods Summary
__call__
(self, x, y[, model_set_axis, …])Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated. evaluate
(self, x, y, \*coeffs)Evaluate the model on some input variables. get_num_coeff
(self)Determine how many coefficients are needed imhorner
(self, x, y, coeff)invlex_coeff
(self, coeffs)prepare_inputs
(self, x, y, \*\*kwargs)This method is used in __call__
to ensure that all the inputs to the model can be broadcast into compatible shapes (if one or both of them are input as arrays), particularly if there are more than one parameter sets.Attributes Documentation

inputs
= ('x', 'y')¶

outputs
= ('z',)¶
Methods Documentation

__call__
(self, x, y, model_set_axis=None, with_bounding_box=False, fill_value=nan, equivalencies=None, inputs_map=None)¶ Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.

get_num_coeff
(self)[source]¶ Determine how many coefficients are needed
Returns:  numc : int
number of coefficients

prepare_inputs
(self, x, y, **kwargs)[source]¶ This method is used in
__call__
to ensure that all the inputs to the model can be broadcast into compatible shapes (if one or both of them are input as arrays), particularly if there are more than one parameter sets. This also makes sure that (if applicable) the units of the input will be compatible with the evaluate method.